KIMS-Urine-df-reset-Copy4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data Formatting and Pre-processing(Binning, MInMax Scaling) of Raman Spectrum\n",
    "## Convert dataset into Tidy Format for CIMCB\n",
    "# Python notebook for Urine Raman spectra (Panc Cancer) Data Set\n",
    "# https://www.kaggle.com/kritikaaggarwal/complete-analysis-and-prediction-on-tumors\n",
    "# https://github.com/EBjerrum/Deep-Chemometrics/blob/master/Deep_Chemometrics_with_data_augmentation.py.ipynb\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "\n",
    "import sklearn as skl\n",
    "from scipy.io import loadmat\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics \n",
    "\n",
    "from sklearn.cross_decomposition import PLSRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn import svm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1902, 80)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the data\n",
    "df_i = pd.read_excel('./Prostate_0331_rawdata.xlsx')\n",
    "df_i.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['wavenumber', 'Pros 2', 'Pros 3', 'Pros 4', 'Pros 5', 'Pros 6',\n",
       "       'Pros 11', 'Pros 12', 'Pros 24', 'Pros 25', 'Pros 26', 'Pros 27',\n",
       "       'Pros 28', 'Pros 32', 'Pros 33', 'Pros 34', 'Pros 35', 'Pros 36',\n",
       "       'Pros 39', 'Pros 40', 'Pros 19', 'Pros 20', 'Pros 21', 'Pros 22',\n",
       "       'Pros 23', 'Pros 29', 'Pros 30', 'Pros 31', 'Pros 37', 'Pros 38',\n",
       "       'Pros 1', 'Pros 7', 'Pros 8', 'Pros 9', 'Pros 10', 'Pros 14', 'Pros 15',\n",
       "       'Pros 16', 'Pros 17', 'Pros 18', 'Normal 41', 'Normal 45', 'Normal 46',\n",
       "       'Normal 28', 'Normal 30', 'Normal 31', 'Normal 51', 'Normal 52',\n",
       "       'Normal 1', 'Normal 2', 'Normal 3', 'Normal 4', 'Normal 5', 'Normal 6',\n",
       "       'Normal 7', 'Normal 8', 'Normal 9', 'Normal 10', 'Normal 11',\n",
       "       'Normal 12', 'Normal 13', 'Normal 14', 'Normal 15', 'Normal 16',\n",
       "       'Normal 18', 'Normal 19', 'Normal 20', 'Normal 21', 'Normal 22',\n",
       "       'Normal 23', 'Normal 24', 'Normal 25', 'Normal 26', 'Normal 27',\n",
       "       'Normal 28.1', 'Normal 29', 'Normal 30.1', 'Normal 31.1', 'Normal 17',\n",
       "       'Normal 32'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_i.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wavenumber</th>\n",
       "      <th>Pros 2</th>\n",
       "      <th>Pros 3</th>\n",
       "      <th>Pros 4</th>\n",
       "      <th>Pros 5</th>\n",
       "      <th>Pros 6</th>\n",
       "      <th>Pros 11</th>\n",
       "      <th>Pros 12</th>\n",
       "      <th>Pros 24</th>\n",
       "      <th>Pros 25</th>\n",
       "      <th>...</th>\n",
       "      <th>Normal 24</th>\n",
       "      <th>Normal 25</th>\n",
       "      <th>Normal 26</th>\n",
       "      <th>Normal 27</th>\n",
       "      <th>Normal 28.1</th>\n",
       "      <th>Normal 29</th>\n",
       "      <th>Normal 30.1</th>\n",
       "      <th>Normal 31.1</th>\n",
       "      <th>Normal 17</th>\n",
       "      <th>Normal 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PN</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>P</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>400</td>\n",
       "      <td>291.167</td>\n",
       "      <td>160.067</td>\n",
       "      <td>360.333</td>\n",
       "      <td>251.633</td>\n",
       "      <td>237.8</td>\n",
       "      <td>154.833</td>\n",
       "      <td>357.333</td>\n",
       "      <td>236</td>\n",
       "      <td>319.167</td>\n",
       "      <td>...</td>\n",
       "      <td>642.133</td>\n",
       "      <td>543.767</td>\n",
       "      <td>344.033</td>\n",
       "      <td>457.967</td>\n",
       "      <td>648.067</td>\n",
       "      <td>152.067</td>\n",
       "      <td>747.033</td>\n",
       "      <td>1042.83</td>\n",
       "      <td>827.9</td>\n",
       "      <td>553.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>401</td>\n",
       "      <td>289.767</td>\n",
       "      <td>163</td>\n",
       "      <td>360.067</td>\n",
       "      <td>260.333</td>\n",
       "      <td>244.3</td>\n",
       "      <td>153.967</td>\n",
       "      <td>356.367</td>\n",
       "      <td>233.5</td>\n",
       "      <td>319.1</td>\n",
       "      <td>...</td>\n",
       "      <td>643.067</td>\n",
       "      <td>549.867</td>\n",
       "      <td>345.5</td>\n",
       "      <td>447.1</td>\n",
       "      <td>653.367</td>\n",
       "      <td>158.433</td>\n",
       "      <td>756.367</td>\n",
       "      <td>1039.8</td>\n",
       "      <td>829.067</td>\n",
       "      <td>556.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>402</td>\n",
       "      <td>286.933</td>\n",
       "      <td>164.767</td>\n",
       "      <td>360.467</td>\n",
       "      <td>260.167</td>\n",
       "      <td>249.1</td>\n",
       "      <td>155.767</td>\n",
       "      <td>345.033</td>\n",
       "      <td>227.833</td>\n",
       "      <td>320.367</td>\n",
       "      <td>...</td>\n",
       "      <td>612.9</td>\n",
       "      <td>568.133</td>\n",
       "      <td>339.867</td>\n",
       "      <td>428.1</td>\n",
       "      <td>646.467</td>\n",
       "      <td>155.267</td>\n",
       "      <td>745.033</td>\n",
       "      <td>1011.4</td>\n",
       "      <td>813.067</td>\n",
       "      <td>555.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>403</td>\n",
       "      <td>283.667</td>\n",
       "      <td>166.233</td>\n",
       "      <td>361.1</td>\n",
       "      <td>257.3</td>\n",
       "      <td>253.367</td>\n",
       "      <td>158.267</td>\n",
       "      <td>330.7</td>\n",
       "      <td>221.3</td>\n",
       "      <td>322.133</td>\n",
       "      <td>...</td>\n",
       "      <td>573.667</td>\n",
       "      <td>589.967</td>\n",
       "      <td>332.267</td>\n",
       "      <td>406.8</td>\n",
       "      <td>635.9</td>\n",
       "      <td>149.2</td>\n",
       "      <td>727.433</td>\n",
       "      <td>975.567</td>\n",
       "      <td>791.933</td>\n",
       "      <td>553.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>2296</td>\n",
       "      <td>81.3667</td>\n",
       "      <td>57.3667</td>\n",
       "      <td>79.7667</td>\n",
       "      <td>72.8</td>\n",
       "      <td>59.4</td>\n",
       "      <td>55.3667</td>\n",
       "      <td>62.0333</td>\n",
       "      <td>66.7</td>\n",
       "      <td>73.7667</td>\n",
       "      <td>...</td>\n",
       "      <td>101.8</td>\n",
       "      <td>134.6</td>\n",
       "      <td>70.6</td>\n",
       "      <td>134.533</td>\n",
       "      <td>92.4</td>\n",
       "      <td>60</td>\n",
       "      <td>107.733</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>114.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>2297</td>\n",
       "      <td>81.4333</td>\n",
       "      <td>54.2667</td>\n",
       "      <td>80.3667</td>\n",
       "      <td>74.1333</td>\n",
       "      <td>68.6</td>\n",
       "      <td>53.6</td>\n",
       "      <td>59.6667</td>\n",
       "      <td>71</td>\n",
       "      <td>70.3</td>\n",
       "      <td>...</td>\n",
       "      <td>103.067</td>\n",
       "      <td>133.6</td>\n",
       "      <td>70.6</td>\n",
       "      <td>136.3</td>\n",
       "      <td>92.4</td>\n",
       "      <td>55.2</td>\n",
       "      <td>103.5</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>114.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>2298</td>\n",
       "      <td>78.6333</td>\n",
       "      <td>49.2</td>\n",
       "      <td>80.6667</td>\n",
       "      <td>70.9</td>\n",
       "      <td>74.5</td>\n",
       "      <td>51.2667</td>\n",
       "      <td>64.3333</td>\n",
       "      <td>67.2</td>\n",
       "      <td>67.7667</td>\n",
       "      <td>...</td>\n",
       "      <td>98.4667</td>\n",
       "      <td>130.8</td>\n",
       "      <td>78.7667</td>\n",
       "      <td>128.467</td>\n",
       "      <td>97.7333</td>\n",
       "      <td>54.6</td>\n",
       "      <td>93.2</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>121.833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>2299</td>\n",
       "      <td>78.6333</td>\n",
       "      <td>50.2667</td>\n",
       "      <td>79.7667</td>\n",
       "      <td>70.9</td>\n",
       "      <td>74.5333</td>\n",
       "      <td>49.7</td>\n",
       "      <td>66.9</td>\n",
       "      <td>59.8667</td>\n",
       "      <td>67.7667</td>\n",
       "      <td>...</td>\n",
       "      <td>98.4667</td>\n",
       "      <td>127.433</td>\n",
       "      <td>85.5333</td>\n",
       "      <td>136.733</td>\n",
       "      <td>106</td>\n",
       "      <td>53.2333</td>\n",
       "      <td>93.2</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>2300</td>\n",
       "      <td>78.6333</td>\n",
       "      <td>54.9333</td>\n",
       "      <td>79.7667</td>\n",
       "      <td>70.9</td>\n",
       "      <td>68.1667</td>\n",
       "      <td>51.8333</td>\n",
       "      <td>60.0333</td>\n",
       "      <td>60.9667</td>\n",
       "      <td>67.7667</td>\n",
       "      <td>...</td>\n",
       "      <td>98.4667</td>\n",
       "      <td>127.433</td>\n",
       "      <td>83.0667</td>\n",
       "      <td>133.3</td>\n",
       "      <td>109.8</td>\n",
       "      <td>49.3333</td>\n",
       "      <td>104.433</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>114.533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1902 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     wavenumber   Pros 2   Pros 3   Pros 4   Pros 5   Pros 6  Pros 11  \\\n",
       "0            PN        P        P        P        P        P        P   \n",
       "1           400  291.167  160.067  360.333  251.633    237.8  154.833   \n",
       "2           401  289.767      163  360.067  260.333    244.3  153.967   \n",
       "3           402  286.933  164.767  360.467  260.167    249.1  155.767   \n",
       "4           403  283.667  166.233    361.1    257.3  253.367  158.267   \n",
       "...         ...      ...      ...      ...      ...      ...      ...   \n",
       "1897       2296  81.3667  57.3667  79.7667     72.8     59.4  55.3667   \n",
       "1898       2297  81.4333  54.2667  80.3667  74.1333     68.6     53.6   \n",
       "1899       2298  78.6333     49.2  80.6667     70.9     74.5  51.2667   \n",
       "1900       2299  78.6333  50.2667  79.7667     70.9  74.5333     49.7   \n",
       "1901       2300  78.6333  54.9333  79.7667     70.9  68.1667  51.8333   \n",
       "\n",
       "      Pros 12  Pros 24  Pros 25  ... Normal 24 Normal 25 Normal 26 Normal 27  \\\n",
       "0           P        P        P  ...         N         N         N         N   \n",
       "1     357.333      236  319.167  ...   642.133   543.767   344.033   457.967   \n",
       "2     356.367    233.5    319.1  ...   643.067   549.867     345.5     447.1   \n",
       "3     345.033  227.833  320.367  ...     612.9   568.133   339.867     428.1   \n",
       "4       330.7    221.3  322.133  ...   573.667   589.967   332.267     406.8   \n",
       "...       ...      ...      ...  ...       ...       ...       ...       ...   \n",
       "1897  62.0333     66.7  73.7667  ...     101.8     134.6      70.6   134.533   \n",
       "1898  59.6667       71     70.3  ...   103.067     133.6      70.6     136.3   \n",
       "1899  64.3333     67.2  67.7667  ...   98.4667     130.8   78.7667   128.467   \n",
       "1900     66.9  59.8667  67.7667  ...   98.4667   127.433   85.5333   136.733   \n",
       "1901  60.0333  60.9667  67.7667  ...   98.4667   127.433   83.0667     133.3   \n",
       "\n",
       "     Normal 28.1 Normal 29 Normal 30.1 Normal 31.1 Normal 17 Normal 32  \n",
       "0              N         N           N           N         N         N  \n",
       "1        648.067   152.067     747.033     1042.83     827.9     553.4  \n",
       "2        653.367   158.433     756.367      1039.8   829.067   556.133  \n",
       "3        646.467   155.267     745.033      1011.4   813.067     555.2  \n",
       "4          635.9     149.2     727.433     975.567   791.933   553.233  \n",
       "...          ...       ...         ...         ...       ...       ...  \n",
       "1897        92.4        60     107.733       178.2   152.167   114.533  \n",
       "1898        92.4      55.2       103.5       178.2   152.167   114.533  \n",
       "1899     97.7333      54.6        93.2       178.2   152.167   121.833  \n",
       "1900         106   53.2333        93.2       178.2   152.167       126  \n",
       "1901       109.8   49.3333     104.433       178.2   152.167   114.533  \n",
       "\n",
       "[1902 rows x 80 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pros 2</th>\n",
       "      <th>Pros 3</th>\n",
       "      <th>Pros 4</th>\n",
       "      <th>Pros 5</th>\n",
       "      <th>Pros 6</th>\n",
       "      <th>Pros 11</th>\n",
       "      <th>Pros 12</th>\n",
       "      <th>Pros 24</th>\n",
       "      <th>Pros 25</th>\n",
       "      <th>Pros 26</th>\n",
       "      <th>...</th>\n",
       "      <th>Normal 24</th>\n",
       "      <th>Normal 25</th>\n",
       "      <th>Normal 26</th>\n",
       "      <th>Normal 27</th>\n",
       "      <th>Normal 28.1</th>\n",
       "      <th>Normal 29</th>\n",
       "      <th>Normal 30.1</th>\n",
       "      <th>Normal 31.1</th>\n",
       "      <th>Normal 17</th>\n",
       "      <th>Normal 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>291.167</td>\n",
       "      <td>160.067</td>\n",
       "      <td>360.333</td>\n",
       "      <td>251.633</td>\n",
       "      <td>237.8</td>\n",
       "      <td>154.833</td>\n",
       "      <td>357.333</td>\n",
       "      <td>236</td>\n",
       "      <td>319.167</td>\n",
       "      <td>267.833</td>\n",
       "      <td>...</td>\n",
       "      <td>642.133</td>\n",
       "      <td>543.767</td>\n",
       "      <td>344.033</td>\n",
       "      <td>457.967</td>\n",
       "      <td>648.067</td>\n",
       "      <td>152.067</td>\n",
       "      <td>747.033</td>\n",
       "      <td>1042.83</td>\n",
       "      <td>827.9</td>\n",
       "      <td>553.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>289.767</td>\n",
       "      <td>163</td>\n",
       "      <td>360.067</td>\n",
       "      <td>260.333</td>\n",
       "      <td>244.3</td>\n",
       "      <td>153.967</td>\n",
       "      <td>356.367</td>\n",
       "      <td>233.5</td>\n",
       "      <td>319.1</td>\n",
       "      <td>269.5</td>\n",
       "      <td>...</td>\n",
       "      <td>643.067</td>\n",
       "      <td>549.867</td>\n",
       "      <td>345.5</td>\n",
       "      <td>447.1</td>\n",
       "      <td>653.367</td>\n",
       "      <td>158.433</td>\n",
       "      <td>756.367</td>\n",
       "      <td>1039.8</td>\n",
       "      <td>829.067</td>\n",
       "      <td>556.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>286.933</td>\n",
       "      <td>164.767</td>\n",
       "      <td>360.467</td>\n",
       "      <td>260.167</td>\n",
       "      <td>249.1</td>\n",
       "      <td>155.767</td>\n",
       "      <td>345.033</td>\n",
       "      <td>227.833</td>\n",
       "      <td>320.367</td>\n",
       "      <td>271.5</td>\n",
       "      <td>...</td>\n",
       "      <td>612.9</td>\n",
       "      <td>568.133</td>\n",
       "      <td>339.867</td>\n",
       "      <td>428.1</td>\n",
       "      <td>646.467</td>\n",
       "      <td>155.267</td>\n",
       "      <td>745.033</td>\n",
       "      <td>1011.4</td>\n",
       "      <td>813.067</td>\n",
       "      <td>555.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>283.667</td>\n",
       "      <td>166.233</td>\n",
       "      <td>361.1</td>\n",
       "      <td>257.3</td>\n",
       "      <td>253.367</td>\n",
       "      <td>158.267</td>\n",
       "      <td>330.7</td>\n",
       "      <td>221.3</td>\n",
       "      <td>322.133</td>\n",
       "      <td>273.567</td>\n",
       "      <td>...</td>\n",
       "      <td>573.667</td>\n",
       "      <td>589.967</td>\n",
       "      <td>332.267</td>\n",
       "      <td>406.8</td>\n",
       "      <td>635.9</td>\n",
       "      <td>149.2</td>\n",
       "      <td>727.433</td>\n",
       "      <td>975.567</td>\n",
       "      <td>791.933</td>\n",
       "      <td>553.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>280.4</td>\n",
       "      <td>167.533</td>\n",
       "      <td>361.967</td>\n",
       "      <td>253.7</td>\n",
       "      <td>256.967</td>\n",
       "      <td>160.767</td>\n",
       "      <td>318.533</td>\n",
       "      <td>215.7</td>\n",
       "      <td>323.467</td>\n",
       "      <td>275.433</td>\n",
       "      <td>...</td>\n",
       "      <td>538.8</td>\n",
       "      <td>609.767</td>\n",
       "      <td>326.233</td>\n",
       "      <td>388.167</td>\n",
       "      <td>625.5</td>\n",
       "      <td>143.133</td>\n",
       "      <td>710.5</td>\n",
       "      <td>942.567</td>\n",
       "      <td>771.5</td>\n",
       "      <td>551.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>81.3667</td>\n",
       "      <td>57.3667</td>\n",
       "      <td>79.7667</td>\n",
       "      <td>72.8</td>\n",
       "      <td>59.4</td>\n",
       "      <td>55.3667</td>\n",
       "      <td>62.0333</td>\n",
       "      <td>66.7</td>\n",
       "      <td>73.7667</td>\n",
       "      <td>67.1667</td>\n",
       "      <td>...</td>\n",
       "      <td>101.8</td>\n",
       "      <td>134.6</td>\n",
       "      <td>70.6</td>\n",
       "      <td>134.533</td>\n",
       "      <td>92.4</td>\n",
       "      <td>60</td>\n",
       "      <td>107.733</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>114.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>81.4333</td>\n",
       "      <td>54.2667</td>\n",
       "      <td>80.3667</td>\n",
       "      <td>74.1333</td>\n",
       "      <td>68.6</td>\n",
       "      <td>53.6</td>\n",
       "      <td>59.6667</td>\n",
       "      <td>71</td>\n",
       "      <td>70.3</td>\n",
       "      <td>69.4667</td>\n",
       "      <td>...</td>\n",
       "      <td>103.067</td>\n",
       "      <td>133.6</td>\n",
       "      <td>70.6</td>\n",
       "      <td>136.3</td>\n",
       "      <td>92.4</td>\n",
       "      <td>55.2</td>\n",
       "      <td>103.5</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>114.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>78.6333</td>\n",
       "      <td>49.2</td>\n",
       "      <td>80.6667</td>\n",
       "      <td>70.9</td>\n",
       "      <td>74.5</td>\n",
       "      <td>51.2667</td>\n",
       "      <td>64.3333</td>\n",
       "      <td>67.2</td>\n",
       "      <td>67.7667</td>\n",
       "      <td>67.2333</td>\n",
       "      <td>...</td>\n",
       "      <td>98.4667</td>\n",
       "      <td>130.8</td>\n",
       "      <td>78.7667</td>\n",
       "      <td>128.467</td>\n",
       "      <td>97.7333</td>\n",
       "      <td>54.6</td>\n",
       "      <td>93.2</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>121.833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>78.6333</td>\n",
       "      <td>50.2667</td>\n",
       "      <td>79.7667</td>\n",
       "      <td>70.9</td>\n",
       "      <td>74.5333</td>\n",
       "      <td>49.7</td>\n",
       "      <td>66.9</td>\n",
       "      <td>59.8667</td>\n",
       "      <td>67.7667</td>\n",
       "      <td>64.0667</td>\n",
       "      <td>...</td>\n",
       "      <td>98.4667</td>\n",
       "      <td>127.433</td>\n",
       "      <td>85.5333</td>\n",
       "      <td>136.733</td>\n",
       "      <td>106</td>\n",
       "      <td>53.2333</td>\n",
       "      <td>93.2</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>78.6333</td>\n",
       "      <td>54.9333</td>\n",
       "      <td>79.7667</td>\n",
       "      <td>70.9</td>\n",
       "      <td>68.1667</td>\n",
       "      <td>51.8333</td>\n",
       "      <td>60.0333</td>\n",
       "      <td>60.9667</td>\n",
       "      <td>67.7667</td>\n",
       "      <td>64.0667</td>\n",
       "      <td>...</td>\n",
       "      <td>98.4667</td>\n",
       "      <td>127.433</td>\n",
       "      <td>83.0667</td>\n",
       "      <td>133.3</td>\n",
       "      <td>109.8</td>\n",
       "      <td>49.3333</td>\n",
       "      <td>104.433</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>114.533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1901 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pros 2   Pros 3   Pros 4   Pros 5   Pros 6  Pros 11  Pros 12  Pros 24  \\\n",
       "1     291.167  160.067  360.333  251.633    237.8  154.833  357.333      236   \n",
       "2     289.767      163  360.067  260.333    244.3  153.967  356.367    233.5   \n",
       "3     286.933  164.767  360.467  260.167    249.1  155.767  345.033  227.833   \n",
       "4     283.667  166.233    361.1    257.3  253.367  158.267    330.7    221.3   \n",
       "5       280.4  167.533  361.967    253.7  256.967  160.767  318.533    215.7   \n",
       "...       ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "1897  81.3667  57.3667  79.7667     72.8     59.4  55.3667  62.0333     66.7   \n",
       "1898  81.4333  54.2667  80.3667  74.1333     68.6     53.6  59.6667       71   \n",
       "1899  78.6333     49.2  80.6667     70.9     74.5  51.2667  64.3333     67.2   \n",
       "1900  78.6333  50.2667  79.7667     70.9  74.5333     49.7     66.9  59.8667   \n",
       "1901  78.6333  54.9333  79.7667     70.9  68.1667  51.8333  60.0333  60.9667   \n",
       "\n",
       "      Pros 25  Pros 26  ... Normal 24 Normal 25 Normal 26 Normal 27  \\\n",
       "1     319.167  267.833  ...   642.133   543.767   344.033   457.967   \n",
       "2       319.1    269.5  ...   643.067   549.867     345.5     447.1   \n",
       "3     320.367    271.5  ...     612.9   568.133   339.867     428.1   \n",
       "4     322.133  273.567  ...   573.667   589.967   332.267     406.8   \n",
       "5     323.467  275.433  ...     538.8   609.767   326.233   388.167   \n",
       "...       ...      ...  ...       ...       ...       ...       ...   \n",
       "1897  73.7667  67.1667  ...     101.8     134.6      70.6   134.533   \n",
       "1898     70.3  69.4667  ...   103.067     133.6      70.6     136.3   \n",
       "1899  67.7667  67.2333  ...   98.4667     130.8   78.7667   128.467   \n",
       "1900  67.7667  64.0667  ...   98.4667   127.433   85.5333   136.733   \n",
       "1901  67.7667  64.0667  ...   98.4667   127.433   83.0667     133.3   \n",
       "\n",
       "     Normal 28.1 Normal 29 Normal 30.1 Normal 31.1 Normal 17 Normal 32  \n",
       "1        648.067   152.067     747.033     1042.83     827.9     553.4  \n",
       "2        653.367   158.433     756.367      1039.8   829.067   556.133  \n",
       "3        646.467   155.267     745.033      1011.4   813.067     555.2  \n",
       "4          635.9     149.2     727.433     975.567   791.933   553.233  \n",
       "5          625.5   143.133       710.5     942.567     771.5   551.033  \n",
       "...          ...       ...         ...         ...       ...       ...  \n",
       "1897        92.4        60     107.733       178.2   152.167   114.533  \n",
       "1898        92.4      55.2       103.5       178.2   152.167   114.533  \n",
       "1899     97.7333      54.6        93.2       178.2   152.167   121.833  \n",
       "1900         106   53.2333        93.2       178.2   152.167       126  \n",
       "1901       109.8   49.3333     104.433       178.2   152.167   114.533  \n",
       "\n",
       "[1901 rows x 79 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_i.iloc[0, 1:] # store the output label\n",
    "# crop only the intensity values before scaling and data augmentation\n",
    "df_j = df_i.drop([0,])\n",
    "wv = df_j['wavenumber'] # store the wavenumber\n",
    "df_k = df_j.drop(['wavenumber'],axis = 1)\n",
    "df_k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_P = df_k.filter(like = 'Pro', axis = 1)\n",
    "df_N = df_k.filter(like = 'Nor', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAABUVklEQVR4nO29d3wc1bn//3m2qMsqVrPlJmODy8WAMcYXTIvpl4DDL7nBFzAQiEMJgZifweQmlEt43RBiCCUUh4AJAQKBUG5iiukEg7Fpxt1ylWR1ydJKq9W28/1j54zPzM5s0e5qZ1fn/Xrppd0zszNn2mee85znPIcYY5BIJBLJyMCW7gpIJBKJZPiQoi+RSCQjCCn6EolEMoKQoi+RSCQjCCn6EolEMoJwpLsCkaioqGCTJk1KdzUkEokko/jiiy86GGOVRsssLfqTJk3Chg0b0l0NiUQiySiIaJ/ZMunekUgkkhGEFH2JRCIZQUjRl0gkkhGEFH2JRCIZQUjRl0gkkhGEFH2JRCIZQUjRl0gkkhGEFH1J0gkGg+jq6oJM2y2RWA9LD86SZCadnZ1obW0FEaGsrCzd1ZFIJALS0pckHZ/PBwAIBAJprolEItETVfSJ6EkiaiOiTbry64loGxFtJqLfCuW3ElE9EW0norOE8rOVsnoiWp7cw5BIJBJJLMTi3lkF4GEAf+YFRHQagAsAHMUYGySiKqV8BoCLAMwEMBbAO0R0uPKzPwA4A0AjgPVE9DpjbEuyDkRiPaSlL5FYj6iizxj7iIgm6YqvAfAbxtigsk6bUn4BgL8q5XuIqB7AXGVZPWNsNwAQ0V+VdaXoZyFc7KXoSyTWY6g+/cMBnERE64joQyI6TimvBdAgrNeolJmVh0FES4hoAxFtaG9vH2L1JOlEir5EYl2GKvoOAOUA5gFYBuBFIqJkVIgxtpIxNocxNqey0jAdtMTiBINBzX+JRGIdhhqy2Qjg7ywUiP05EQUBVABoAjBeWG+cUoYI5ZIsg8fnS0tfIrEeQ7X0XwVwGgAoHbU5ADoAvA7gIiLKJaI6AFMBfA5gPYCpRFRHRDkIdfa+nmDdJRZFir5EYl2iWvpE9DyAUwFUEFEjgNsBPAngSSWM0wvgMsXq30xELyLUQesHcB1jLKBs56cA3gJgB/AkY2xzCo5HYgG46Ev3jkRiPWKJ3llksugSk/XvBnC3QflqAKvjqp0kI5GiL5FYFzkiV5J0uOjL3DsSifWQoi9JOtLSl0isixR9SdIRLXxp7Usk1kKKviTpiEIvrX2JxFpI0ZckHcYYbLbQrSVFXyKxFlL0JUmFMQbGGOx2u/pdIpFYByn6kpTARV9a+hKJtZCiL0kq3LKX7h2JxJpI0ZckFS760r0jkVgTKfqSpKIXfWnpSyTWQoq+JKlI945EYm2k6EuSil70pXtHIrEWUvQlSUWKvkRibaToZwCtra2or6/PCAGVoi+RWJuhzpwlGUb4XMHBYFDtILUqetGXSCTWIuqTSURPElGbMmGKftlNRMSIqEL5TkT0IBHVE9FGIpotrHsZEe1U/i5L7mGMDDKhU1Ra+hKJtYnFHFsF4Gx9IRGNB3AmgP1C8TkITZE4FcASAI8q65YjNOPW8QDmAridiMoSqfhIJBOmH5SiL5FYm6iizxj7CECXwaL7AdwMQHyqLwDwZxbiMwClRDQGwFkA1jDGuhhj3QDWwOBFIomMFH2JRJIoQ3K8EtEFAJoYY9/oFtUCaBC+NyplZuVG215CRBuIaAP3ZUtCSPeORCJJlLhFn4gKAPwCwG3Jrw7AGFvJGJvDGJtTWVmZil1kHEQEILMsfV5nKfoSibUYiqV/GIA6AN8Q0V4A4wB8SUQ1AJoAjBfWHaeUmZVLYoBbzZkm+kQkRV8isRhxiz5j7FvGWBVjbBJjbBJCrprZjLEWAK8DWKxE8cwD0MMYawbwFoAziahM6cA9UymTxEAmpTSQoi+RWJtYQjafB/ApgCOIqJGIroyw+moAuwHUA/gjgGsBgDHWBeAuAOuVv/9RyiRxkGmiL5FIrEfUwVmMsUVRlk8SPjMA15ms9ySAJ+OsnwSHhDQTrGZp6Usk1kYOm8wAuHBmmqUvRV8isR5S9DMAKfoSiSRZSNHPADLJvcNfTFL0JRJrIkXf4jDGMsrS50jRl0isiRT9DCITBFS6dyQSayNF3+KIopkJlj5jTA3XlKIvkVgPKfoWRxTNTBBQUfT5d4lEYh2k6FucTLf0JRKJtZCib3FEoc8EqzkYDEr3jkRiYaToW5xMs/QBSNGXSCyMFP0MwW63Z4SAyo5cicTaSNG3OOKkJJlg6UvRl0isTdSEa5L0Iop+JggoF/19+/YhEAhkRJ0lkpGEFP0MQZyJyspRMbw14nK5AABOpzOd1ZFIJDqke8fiZNqcs/r6Wb2+EslIQ4q+xck00deTafWVSLKdWGbOepKI2ohok1B2LxFtI6KNRPQKEZUKy24lonoi2k5EZwnlZytl9US0POlHkqVk2kxUUuSN8Xg8OHjwYLqrIZHEZOmvAnC2rmwNgH9jjM0CsAPArQBARDMAXARgpvKbR4jITkR2AH8AcA6AGQAWKetKYiRTLH3p3jFm165daGxslOdDknaiij5j7CMAXbqytxljfuXrZwDGKZ8vAPBXxtggY2wPQnPlzlX+6hljuxljXgB/VdaVREHv3rF62KYUfWP4efD7/VHWlEhSSzJ8+j8C8IbyuRZAg7CsUSkzKw+DiJYQ0QYi2tDe3p6E6mU2mebe0b+UpOhrkaIvSTcJiT4R/TcAP4Bnk1MdgDG2kjE2hzE2p7KyMlmbzXgyyb0jrX1zpOhL0s2Q4/SJ6HIA5wFYwA491U0AxgurjVPKEKFcEoFMj94BrD+2YDgJBALproJkhDMkS5+IzgZwM4DzGWNuYdHrAC4iolwiqgMwFcDnANYDmEpEdUSUg1Bn7+uJVX1koHfvWF30paUfGWnpS9JNVEufiJ4HcCqACiJqBHA7QtE6uQDWKGL0GWPsasbYZiJ6EcAWhNw+1zHGAsp2fgrgLQB2AE8yxjan4Hiyjkyz9I3qZ/U6pxrx+KWlL0k3UUWfMbbIoPhPEda/G8DdBuWrAayOq3aSMKwuoEaW/khHPB8+ny+NNZFI5Ihcy8MFo7m5WfPdqnDR5y0TXjaSEa17j8eTxppIJFL0LU8m+cd53RhjmkRrVq7zcMDDWG02m7T0JWlHin6GYWUBFUXfbreHlY9UuKXvdDoRDAZH/PmQpBcp+hYnkyx9DmMMDodD830kw4/f6XTKPg9J2pGib3EySfRFS9+ofKTC3Tv8RSgjeCTpRIq+xdELppVz74h17e3tNSwfifBrxvs5pOhL0okU/QzDygJqVjcr13k44MfPLX0rv7gl2Y8UfYuTqZZ+LOUjBX7NeOe2la+hJPuRom9xskH0Rzr8vHDRl+dJkk6k6GcYmSgYmVjnZCItfYmVkKJvcTIpEka6d4zRi/5IPx+S9CJF3+Jk0qQkUvSN4dfQ7XZrvksk6UCKfoZhZcGQom8MT6fc1BSaQmKknw9JepGib3EyKaZbir4x+mto5Re3JPuRom9xpE8/88kkF50k+4kq+kT0JBG1EdEmoayciNYQ0U7lf5lSTkT0IBHVE9FGIpot/OYyZf2dRHRZag4n+8gkwZCib4y09CVWIhZLfxWAs3VlywG8yxibCuBd5TsAnIPQFIlTASwB8CgQekkgNOPW8QDmAridvygkkckk0ZcYk0mtNUn2E1X0GWMfAejSFV8A4Gnl89MAFgrlf2YhPgNQSkRjAJwFYA1jrIsx1g1gDcJfJBIDMkkwpKVvjHxxS6zEUH361YyxZuVzC4Bq5XMtgAZhvUalzKw8DCJaQkQbiGhDe3v7EKuXPUjRz3wyaVS1JPtJuCOXhe7opD3VjLGVjLE5jLE5lZWVydpsxiJFP/PJpGsoyX6GKvqtitsGyv82pbwJwHhhvXFKmVm5JAqZJBBS9I2Roi+xEkMV/dcB8AicywC8JpQvVqJ45gHoUdxAbwE4k4jKlA7cM5UySRQyyR8sRT825PmQpBNHtBWI6HkApwKoIKJGhKJwfgPgRSK6EsA+AP+prL4awLkA6gG4AVwBAIyxLiK6C8B6Zb3/YYzpO4clMZCJgpGJdU4m0tKXWImoos8YW2SyaIHBugzAdSbbeRLAk3HVTpJRAiEt/cjY7XYEAoGMGmUtyT6iir4kvWSSlThcou/3+2Gz2WCzpW9AucfjgcfjARGhpKQkpt84HA4EAgFLX0NJ9iNF3+JI0Q/f1rZt22C32zF9+vSkbTde6uvr1c+FhYXqVIh6xGN3OBwYHByUIZuStCJz72QYI130uWvEShZzf3+/6TK96AMyTl+SXqToS5LGcIg+T1MMAD6fL2nbTQSPx2O6zEj0rfKykoxMpOhnGFYWjOEWfZfLlbTtJkKkl4947E6nM6xMIhlupOhnGJkoGKkS/YMHDyZtu4kQKRpHWvoSqyFFX5I0Rop7J55cOvyFQERS9CWWQIq+JGmYiVkyOy5FqzpdIZvxRFTxYycidWJ0iSSdSNHPMKwc+TGc0TtA+s4F36/NZkNhYWHEeoiiL4Z1Smtfki6k6EuSxnCIviiw6RJOXocxY8bAbrdHrAdfRkSalgkv7+vrQ3d3dwprK5FokYOzJEljOETYSqJvs9lARDFZ+kBI+MVym82GvXv3AgBKSkrSOsJYMnKQd1mGkYlugWS6YUR3SbrOBXcx8VQQkeohduTqRV/EKmMOJNmPFH1J0hiuNAyA1moebriQHzhwIKqlz+vLWwWcYDCIzs7OsG1KJKlGunckSWM4RT+dHdo8bJRb57FG7+jLm5ubw9aTSFKNtPQtTKa5coajvlY4J6IrhucAivbC46LP/+tFPlmiPzAwoBnLIJHokaIvSRrDaemnE6/Xq37m9YlX9Lkwl5eXA0iOe4cxhl27dmkygEokehISfSL6ORFtJqJNRPQ8EeURUR0RrSOieiJ6gYhylHVzle/1yvJJSTmCLMYKAhcPI1n0zSx1MdIHOCT6fBt5eXkRfx8PfBvS0pdEYsiiT0S1AH4GYA5j7N8A2AFcBOAeAPczxqYA6AZwpfKTKwF0K+X3K+tJImAFgYuHdLh3hvscMcY0WTW50EYbjcxFn/9PhejLCCBJLCTq3nEAyCciB4ACAM0AvgPgJWX50wAWKp8vUL5DWb6A0hmCkQFki+in0tIf7g5Qv9+vqQPfv1k9xOgd4JClzwU6Nzc34u/jQYq+JBaGLPqMsSYAvwOwHyGx7wHwBYCDjDHevmwEUKt8rgXQoPzWr6w/Wr9dIlpCRBuIaEN7e/tQqydJA+mw9Ic71FHvOolm6Ytx+sAh8ff5fLDZbLDb7bDZbEkRfenWkcRCIu6dMoSs9zoAYwEUAjg70QoxxlYyxuYwxuZUVlYmurmMJpr1aDViSUeQ7H0Mt6Wvf8nw79GuFU+2xkXf7/fD6XSira0NRJSUlxe39GUDWhKJRNw7pwPYwxhrZ4z5APwdwIkAShV3DwCMA9CkfG4CMB4AlOUlADohMSWbRD9VDLfoi8eYk5MTVfT1Pn0u/sFgEMFgUBX9ZLp3IoWQSiSJiP5+APOIqEDxzS8AsAXA+wC+r6xzGYDXlM+vK9+hLH+PyTszIpk2YCcdlv5wu3fEa5KXlxd3yKZR0rVUuHcy7d6RDB+J+PTXIdQh+yWAb5VtrQRwC4ClRFSPkM/+T8pP/gRgtFK+FMDyBOo9IjATtEx8V6aqzukU/fz8fMNyETP3DhCegC1RBgYGkro9SXaSUBoGxtjtAG7XFe8GMNdgXQ+AHySyv5FGNrl3stXS58Rq6Ys59UUSPY5AICAtfUlMyBG5FibTHtx0iP5wR6yIPno+0blYrkcfsmk2kUqi13pwcFDzXSZwk5ghRd/CmAmBVR/o4RB9PcP9YjQT/VgtfaMXRTI6cvWi39fXl9D2JNmLFH0LYybuVm0BcIHr7e3FjTfeiJ07d4YtSzbpEn0eX2+WQI2j9+mLlr4Y459s0Xe73QltT5K9SNG3MGZCacVBOGJdP//8c7z77rt45JFHUr7fdIg+n/pQnPc2VveOaOmL6yQaZimmhgC0+YEkEhEp+hYm2ihPq8IFZ926dWpZtln6eh99tOMz68gVXTuJHAs3BHhEkdfrtWyHvyS9SNG3MJnk3hEFpr+/HwDgcrnQ29sbtjwZ+4hUlkpE9w5wSPyjWfr61Mqc4uJi9Toncixc9IuKitQyl8s15O1Jshcp+hYmkzpyRcES/cm7d+8OW56MfXDSlYZBnzUzmuj7/X60traGXTtRpBM5Fr5dcXt6l49EAsjpEi1NtHS9VsJM9FtbW8OWJ2MfkcpSSTAYBGMsLK1CtHrs27dP7Q/Izc1VO15zcnI0206kTkBo7AAf4SvdOxIjpKVvYTLJpy/WVRwZyq3NbLH0uRslXvcOX+71elFYWKgu59sR140X8X6w2Wzqi8SKxoEk/UjRtzDZ4N5JpugbnY90+fT1ln6s18Tn86miPGrUKE1ahqGKtPgiIiJ1+9LSlxghRd/CRMvcaCX0ol9WVgYg9aI/nOdCjKfXW/qxHp/f71d/W11drbH0h3os/IWjHwtgxftEkn6k6FsYUUiSnZwr2ZiJvujqSeY+0oG4/1jdO3pE0Q8EAkmx9Lnoc7GXoi+JhBR9CyO6DETRzwT3TnFxMRwOR1a5d8TzzoVbzI8f6zZE0Rev61CPRUznsGnTpqiTtUtGNlL0LYy+g45jxYdZL/oFBQXIz89Xo1SSIc5GL7t0ib4+ZDOeyCLe+uGiH++Lw6xe/D8fHGdF40CSfqToW5hMdu8UFBQgLy9PFbhk1Dndxy3mt4nFvRMtjYY+5j9R0df3N6TbHSaxJgmJPhGVEtFLRLSNiLYS0b8TUTkRrSGincr/MmVdIqIHiaieiDYS0ezkHEL2Ij60RjMuWQkz0U91yOZwnove3l715cuFNVLCNX3duK+diz6f3jBZos8tfF4nK94nkvSTqKX/AIA3GWPTABwFYCtCM2K9yxibCuBdHJoh6xwAU5W/JQAeTXDfIwpREKz4MOvj9PWinypLfzjPhc/nU4XbaCYsfV3033mytUAggJycHFWk440A0qN340ifviQSQxZ9IioBcDKU6RAZY17G2EEAFwB4WlntaQALlc8XAPgzC/EZQhOojxnq/kca3CoErCn6IsNp6Q8nfr9fk2ET0Iq+Hr3o8vh5j8eDnJycpFv6+v2m+3xJrEkiln4dgHYATxHRV0T0BBEVAqhmjDUr67QAqFY+1wJoEH7fqJRJsgAxx4zH40F+fn7SRT+dHZOMMY3ocyJ1sOu/8wyYPp8PdrtddfMkaunr98O3K0VfYkQiou8AMBvAo4yxYwD0QzfZOQvddXHdeUS0hIg2ENGG9vb2BKqXvVjxYeZ14h23hYWFSe/ITedxB4PBsI5SILJ7J1JyNSJKWkcu/x1/qfBZs6x4n0jSTyKi3wigkTHGk6a/hNBLoJW7bZT/bcryJgDjhd+PU8o0MMZWMsbmMMbmVFZWJlC9zCbTHlheX56Cgbt3khmymc5zIrrXRNGPFFWl/+50OjFq1CgAh0b3BoPBhC19/exc+nKJRGTIos8YawHQQERHKEULAGwB8DqAy5SyywC8pnx+HcBiJYpnHoAewQ0k0RHN6rPaA20m+qnuyB0OGGM4ePCg+tlM9PXXRF9fIlI7c/kyv9+fNEtfH+JrtXtEYg0STa18PYBniSgHwG4AVyD0InmRiK4EsA/AfyrrrgZwLoB6AG5lXYkJ0fzXwWAwzLIbDrq7u9Hd3Y2JEycaZog0E/1MtvQ7OjrQ0dGh1kF06RCRKrDRRN9ms4UNxBJH5Q5V9Pl+9SO4pehLjEhI9BljXwOYY7BogcG6DMB1iexvJBFNANIl+p2dnfB4POjv71ddFcDwiH66LH1xUJbReY9F9PmLQi/wjY2N6nlM1NI3C2nVz9YlGdlk7Yhcv99v6Thl7jIwm+Tcqu4dXl/9xNv6jly9Tz+TO3J53QsKCjS5czhm7hnxuz6un1vlg4ODYSNq40W09Pk4AjlAS2JGVoq+3+/Htm3b0Nxs3S6Dnp4eNDY2qjNL6REFYPPmzXjllVdMlw8XjLGw0Z/iMiDc0vf7/fD5fEkJt0zXSzwQCKCgoAATJ04EEN5haiawRpY+/68/f0a/jxVR9PPy8tTPiWxTkr1k5XSJ/CHs7e0FYwylpaWacDkrwB96s9TDomAsW7YMDQ0NmDNnDsaPDwVApeNh9vv9mnh8ETPRB0KDkYqLixPev9kxp9qF4ff7kZOTE5a3ns97G4voG43g5aRyBK0UfYmerLT07XY7ysvLVRdKU1NYZGja4aI5ODho+GCKljFvsXz11VdqWTqsXtE61Ys+rw8XfT44CwgdYyqjd1J9LrhLRx9X39HRge7ublPRjpSVU4T/PhWd3VZ2cUrSQ1aKPhCKieY3vBhjbRXEUZNGrg/xYS0oKAAANDQcGtCcDguOi35+fn7YOY1k6Q8MDKS0IzeVwsavj8PhCLP0zXLeGNXLZrOBMaaZSlK/3lCOQ5+UL1qdJJKsFX0z94NVEB/OSKIfDAbR398PAJoWSzot/YKCAo2rBzh0DG63G06nE06nE7m5uQDMWzPxko45g3lUjt1uDxuRa/bi4+hF3+12Qxxlzt1CiQxg07uQzFpgEgknK0V/cHAQnZ2dYWVWQnwYI8392tvbq4paV1eXujxdln5OTg4cDodmvlixPjzZGnAoLYDH40lpnH4qRV+cdFxv6fOXoJl7Ry/I+s5bvh3x3oz3PEUTfbPoMMnIJStFn496FOHx4lZBHOQTydIXhZ6PChWXDyfct20005Po0+ein2z3TjpEX5x/VhR9xphq6Zv55MXz4/V6w/qWjAQ53usqrm/UmS1nz5LoyUrRN7rRrWbxBIPBsCH5InrrvrKyEt3d3ZrfJ5v9+/ejra3NdHkwGNRkmTRyUYmin2z3TjpEn1vnTqdT05Hr8/nCxF5/TcT6ulyumPYX73mKZeS2RCKSlaLvdDqhT9ZmRdHnA2kiWfo9PT0AgEmTJmlEP9nuHcYYent7I4o+b50YWfqie6ewsBAANCGbqXRHpVL03W43iAi5ublqcjQiUn3zeXl5ph2xkY65qqrKsDxekRbva6OABWnpS/RkpegDCIsLt7LoR/Lp8zS548ePx8DAQFITmInEcn54PLyRpS+KPvfl8/9mYxHixUxEU2nNDgwMID8/HzabDX6/X33h9fb2wm63G6aiiFRfcfYsI+I9FqNroK+/RCKStaLPBZVjJYuHd4JyQY9k6fPIndra0HwzYrbHZCJGlUQbBBWrT19076SSVLt3+HH4/X44HA4Eg0EEAgGMHj1ac5/FYulzv7tZH1O81zXajGq9vb1xbU+S/WSt6OuHyltN9IFDdYpk6XPRr66u1nxPZstlcHBQ02EcyQq12WyGln6kjtxUd6Kn6toyZbYsLuw+nw9Op1MVWqfTqbnPYomR59ctlpHYsSBGBPGXMh+1LZEYkZVpGPx+P1paWjRlVhL9SCM39ev09fXB6XSirKxM/Q4kX/RFxAnARYws/UAgoA48AsxDNsXfD4VI1y9V/QX8HHOXjN/v14h+Z2dnREvbqF7R5q+N91j0YaBEpOn7kUj0ZKWlT0Sa8EbAWlEMsYg+L+vv70dhYaEqpKmw9PXCYTRqFAj36ft8PmzduhUtLS2Gou90OkFESemHiDSqOlUvdL5P7tLh/TC83OPxGLZ2YiFZ/RP6lw5jTDUMou1LMjLJStG32+1hlqqVRF//EEbz6RcWFqoJ47ggJ1Po9K4GPmGIHi76XPj5y6Kzs1PtpxgYGFBFn4g0OfUTeVFF6hdI1bUV3TjiIC2zF1A80TtmxPubWDvgJRJOwqJPRHYi+oqI/qF8ryOidURUT0QvKLNqgYhyle/1yvJJie47Ejk5OZrvVhL9aJa+OCFHX18fCgsL1TDIVFj6ehGL5tMHtBN78zpzceeiD4RcPMkQfX7cRqTK0hfdO/yzw+GA1+s1dX8lSjz3aazjH4bSerDS85IOGGPo7+/PyhdmMiz9GwBsFb7fA+B+xtgUAN0ArlTKrwTQrZTfr6yXMsyE1O/3o76+Pq1+T33dIg3fd7vdKCoqCnPvBIPBpN2QohgTkerKEOH7IiL09fWBiMJEXEy2xsnNzU2K6Edy7yQy41Skc+jz+dQ+DHFkrlmfRzIs/XiOxWhMRbSMrbHsf8eOHdi7d2/Mv8lG2tvbsWfPHlNXZyaTkOgT0TgA/wHgCeU7AfgOgJeUVZ4GsFD5fIHyHcryBZSiJOgDAwOG7gC/34+enh54PB5NtMpwo49miST6ektfvAmTJfpGQ/nNJkkJBoPYu3cvAoGAaV8A78AFkDT3jl64tmzZorqlhmLpM8awZcsWHDhwwHQdLu7iC46LvtGtm8pRx0aI5z/SoxTPi4Rbt9kodrHS3d2tvlCNJrvJdBK19H8P4GYA/K4aDeAgY4w/3Y0AapXPtQAaAEBZ3qOsr4GIlhDRBiLaIMaOx4NR7h0gJDrJnLN1qHCRMpt8QxQxPsI1NzcXdrtd4+ZIVhPcaGSt3rLWR50YnT8jSz9Zoi/Wcfv27fjhD3+IFStWDHl7/Pgitfh4iCaAMJ++UcjlcLt39BOhJ2ObYrqIkeriEWezs1LUX7IYsugT0XkA2hhjXySxPmCMrWSMzWGMzdGnUogVh8Nh2Pz2+Xzqm9vj8WD//v1pEf943Dvc0iciFBQUaEQ/WXXn2xHjzvWtEf3YAo4Ypx5N9BN5gMTfbtiwAQBQX18PYGjiFM2S5X0U4sCsaPPODpelzzvNxRZHMkSfp+LgZKPgxYJ4vrLxHCRi6Z8I4Hwi2gvgrwi5dR4AUEpEXHHHAeCpBZsAjAcAZXkJAG3+4yRiNEMRn6+V09vbm5bmm1E8t1jG8+0Ah6J3AKCwsDDplr64b1HA9R2nRknF7HY76urq1O/RRJ//1sg1FA1xv5s2bQJg3qKLRn9/PxobGwGEj9zm8M5M7qrig7RSPV4glmva2tqKLVu2aK6dWK+hRq75/X74/X71+mWj4EXD6/WqiQWB7GztDFn0GWO3MsbGMcYmAbgIwHuMsYsBvA/g+8pqlwF4Tfn8uvIdyvL3WIrMbK/Xa9jx5/V64fV6NeKWjpTLRm4OI9EPBAIYGBhQwzULCwuT7tMXb2pRLMz6HfRTAHJLGDj0otB35PL+Ff7bffv2YceOHXHVX1x3165dADDkfhnRNWNmIYvhmgDCZs+KVsehEovQmoXUchIRfQBhk6uPJPjzddhhh2kyq2YTqYjTvwXAUiKqR8hn/yel/E8ARivlSwEsT8G+VYweQP6wiKKUjqkUjUZl6sMfbTabKqKipS8OvEmGFSK+gHieeH19xLqK63OLiL9EjSx9MWRTHMAFxHfuxWPlVrroj483QsXos4h+NC7376da9GPZBr8fOHqR17eiYj03/Jj5izwbBS8aYpSW0fST2UBS0jAwxj4A8IHyeTeAuQbreAD8IBn7i0akDsOCggKMGTMGTqcTXV1dacm+aTYC1+l0grHQnKxEZCj6yfbpi3URpwTkrgO9H1sUanH6wEAgoIq5KEp5eXmqZa0PBfX5fGHjKaLR09MDl8sFm82m8T+LGTCjwX3h5eXl6sAyvcUvjsZlyoQpRUVFMYvAUK9NLNvXbztSnxAQewQKP2Yp+lAnC5LunQyhoKDAtNk+adIk5OTkYOzYsWm7qJEmTRFFN5rox1t3xhiampo0KSpEERctfbFO/Lf6fepfDFzczdw7jDFNKG2slr6RlT99+nQMDg6q24vnXPh8PuTl5akWspFAizH6/GUVT3PfaD3uNolErIOtjPqszIhV9PWW/lCj5zIZbnDxezobX3xZKfqRBt2ID4vNZrOc6Is3GXflcNFPNHrH7Xaju7sbjY2Nar4cLgiim0ZfJ7M6A6FORX5O3W43HA6HpoNVdO8Eg0FNX0GsD5SR6M+cORPAodTB8TycXq8XTqdTrbfZ9eCCv2/fPgDakbmAcSewmXtMXBaJWO5HxpjqfojlN7GIfk9PD9ra2kBE6nFlY4x6NILBoDqfsdvtTov7N9VkpehHGrIvkmrR93q9aGhoiBqiCUQWfbEjNxFLX/xtR0eHJprJbrerIhJpkhQ9nZ2dqlXEJ1ARhYNH7/BWgSj6sdZffPB45+3kyZMBxC/6fX198Hq9KCgoiDpHsc1mg8vlUt1WeXl5mpaKuA2Omejv2bMHf//738P2o/99LOeEGzWxjm2M5sL0+/1oaGgAEEpfQkTqJETZmIYgEvxlb/Q8ZgtZKfqxPgxElNKburm5GT09Pejq6sL27dvV5nKklLviQ88HyvDZmXj0TqQBUpHQx6aL7hHR0jea0UvclyhUPJMm3z537fDfihOp8NhyXhZvVAlw6JyMGzcOgDbSKRa6urrgcDhQXl5uOBkMh3em8/OTn5+P3NxcTThtTU2NqWjr63P99ddj+fLlaqtB3I/R7yPB14nVxRMt3YQ4Z8OECRMAHGpdZqNPOxI8VXg2ij0nK0VfDCM0IhgMwu/3p9zS59vu6OiAz+dDa2tr1JS64s3GrVhR9AOBwJD82EB4c503YQFoJkfhYmjm3nE4HOocr6LFaST64kQqwWBQ41oZiqXvcrmQm5uL0aNDg7njtfQHBgaQl5enOV4jS5hb+oODg8jJyVFbFiLcKhYx6vsQXUSffvqp4fpm343Q96XEQqTzw1tfFRUV6rMTq+so2+DunVhauZlKVoq+zWZDR0eHaf6dhoYGbNu2DUBqb2qjpn5vb29ESz+S6OuTrsUb566PlhFbDeKE50aiL+7L4XCoYu73+9W6i6LP9yNOpMIt/URFv7i4WD0n8Yg+Pwd9fX0YGBhQhU1vfQOHRJ9Pl8gYw/79+8PW0/eDGIW2ir9bv3591DpGWz6UZHuRzg+/JuJLJJLrK5vh7h2j1mW2kJWi39DQgNNOOw3XXXdd2LLdu3erF5E/QKnC6ME062/g64rLe3t7kZubq1pf+qRr8dTd7/erLguO6F838umLN75e9Pk6jDE1asdI9HndxVQM8Yq+/gEsLi5GSUkJgPhEX1zHLFOmuC4Rqeu1tLRoQkT5RD36VoKRpc9HEE+ZMgVffPFFxLpGOyfi9sUok2jEIvrBYFC9/yK5vrIZfl7FMSDpCOtOJVkp+vxGXbduHRYuXKjJpGiWPTIVGG3bbBQpr7MoxB0dHSgvL1e/63Pqx1N3bi2Lv/F4PKolbrfb1Vw2XEjMRJ/75/XLRNEXO3L1x8VFfyjhj319fSguLlY7t7kQxyJOnZ2Hsn4Eg0Hk5OSguLjY0Dfu8/ngcrnUUdFG1623t9dU9MU6r1+/HgUFBViyZAk6Ozuxbt26qHU1Q+zPiadDN9L54aLf0dGBPXv2YOfOnYZ9TCOBQCCAQCCguV+zLYopK0W/qqoKX375JZYvX47du3fjlVdeMV030Zva7XabpnKIZ9tG7p2GhgbNJNdc9HlUTzyiz29cvcuLi74ofEaiLx7L4OCgYZNXnDWLtwb08+QCh0Q/1vobWfp2ux3FxcVqx2os1pg4PoGf5/z8/LBBY0D4y1GPPjkZR38dGWP44IMPcMopp2DBggUoLCzEe++9ZzooTZ+HyWz7+s+cb775BkuXLtV0OIv10cOjmZxOp3pfDQ4OqobFSHLv8Jeo/vzrz2Wmk5WiD4SE5eKLL8bEiRNVC9aIREV/9+7dhtvn/utY4cIj/qapqQm1tbXqd717J54H0igVMHAozYDRaFYzSx8wtn70lj4Rhbl3+D717h0ewx+pvwMIiT638keNGhWXpS/CI6n0qZMTRW8hu1wudHV1YebMmcjJycGMGTOwZcsWFBUVmY4gjkf0xQi0rq4uXHPNNVizZg3eeecd09+J8MlS9K4u3rIZSZa+WetGduRmAOJFqqurw+7du03XizVaorm5Oa7kbAcOHIjrZunv79dYFH6/H52dnWqUDJCYe8ftdoe5Mex2u3pMRi4O8aWif8EYiX5/f79q2XNL38i9w18I/OHyer3YsmUL6uvrDVsQ4r65pQ9oRT9en77f78fg4KAqdvoJxoeKXji4b5i76Y499lhs374dvb29pllCI+2fdzoXFBSAMaZ5caxatUo1CPQdxtHE2+FwGAY+jCRLnx+r3kCSop8BiA/TYYcdhv379xta3Vz0o11ULsDiFHJer1fzkPBt7N+/H5s2bYq7xz8QCGiyJ3Z1dYExBnFOAb3oGz2Qfr9fk5SN183ohRUIBNQXjWjpGbma9KJh5MsWLX3eMawXfR4lJLp3ovlPxX1znz4Qn6Vv1Gnv9XrV+hnlkY81l4+IPp2GXvRPOOEEeL1evPPOO6ZpGSIdCz8//HqJdfziiy9w1FFH4fTTT8fGjRtj2iZ/VkpKSgzvp3RkoU0XYgoUkUAgkFUtnqwUfVGQJk+erBlxKKJ/QM3gy8Xt7tu3Dzt37tSsI/p5h2IhiS8R/gKoqKhQy7hbI5JPf+/evdi7d2+YPz7ay018KfLPestYRL+tvr4+MMbUUEqesEov+lysRPeOuG+j/fB68BftUETf6GXCI4n0KatjcfWYdZ7y3/L68M7jsrIyAMCMGTNARNixY4ep6JtdJ7Gcb5/3DXi9XmzduhWzZs3CrFmz0NDQoOm4jjTZfXl5ueFyno5gpBDpmTVqBWUqWSn6oqXPB9WIAs0xGz2px8i3rb8J9u3bl1RrwEj0+bB/3oow2h8XV/FhNbNgRMQmLf8svijEfRkJHvcBc4vWbrfD6XSqwsa3yS1T0b0jurX0rRSjeGnRpy+G30bCKAc933ZOTo7mfBlFOukx64g1E31+HXNzczF27Fg89NBDuPPOOw23YXYfieW8tcePf/PmzfD5fDj66KNx1FFHAQC+/fbbmLbJxyPoSXVIs9WIpYWVDWSl6IuW45QpU2C327F9+/aw9SKFpTHG4HK54Pf7NU3/nTt3qpN4iLjd7qTG83KREt07NpsNhYWFpkJnliEzlgdXL7b6CB7x2OIRfW6V8+2LYwHEUE8OH8RldBz6XETFxcWacxHJGjOKwBBz5vMmvNvtjvhS5Zh10otROwDUJGZi6C2/pg8//DA++uijsG3EIvr6a8+nkDzmmGPU1sTWrVsBaF+w+u3xsRviPS5mH822GPVIRDpWs0CITCQrRb+oqEgVptzcXNTV1akjcI0weiDcbrc6w5PYTPZ6vaY3QDKtAR5dwtMNcEaNGqWKXyQ/uyiA0UTfyM2gz7QYLVTQSPQdDgdycnKQl5enigrfHhd9I2vaLCkbF2P+IikuLobH41G32dLSYnh8RiGZfHvNzc3qsfb392P37t0xzchldk71os/HWoh9JnfccQeuv/56OBwO3HfffWHbiJaqw4h169bh8MMPR3l5OfLy8lBeXq5O8G0m+tx4sdvtqpFRVlaG6upqtR5+vz+rrNxIRBL9bBqVm8jE6OOJ6H0i2kJEm4noBqW8nIjWENFO5X+ZUk5E9CAR1RPRRiKanayD0COmFwBCudeNLH2OWYcoEF/IWrJFv6SkJCyPUFFRkamlL960ouUezX0VKVeRmAs/Elwoue+aiFSrXnTD8PMZaQ5SsxmxjERfLDd7GZt1Rg4ODqKzs1Otg1E6Bn1UU7SBUHrRb29v10RgAaHggiVLluDnP/85du3apaaL5sRi6Yv4/X589dVXmDv30NxFVVVVquiL9RK3xa+teL7FkdmcbPJnR0Iv+uK1zqYUy4lY+n4ANzHGZgCYB+A6IpqB0DSI7zLGpgJ4F4emRTwHwFTlbwmARxPYd0T0oXDTpk1DW1ub6dyiRg/TUDpikzWIo6WlBdu2bUNNTU3YMtGloa+3OPhIvIGjHYtRagh9J2u0l59o6XPR4NZtcXGxpoNbTAdh9DIxGwKvd+/wTuNo7phoqbbF3+lfgEapk3nZ9OnTwyJ89C7D9vZ2Tb+MyKmnngoAeP/998P2YYRReW5uLtrb2+H1ejFlyhS1vKqqCm1tbWH14ohRQOJLkeetEhkpLp5Io/WHku/IqiQyMXozY+xL5bMLwFYAtQAuAPC0strTABYqny8A8GcW4jMApUQ0Zqj7j4RoZQLA0UcfDSAU0maE3pLx+XxDutH1qYsj4fV6NW4jzvbt23HGGWfgm2++MRR90dLXP8iiWIrLovkjzbJM8mWxhLV2d3ejqKgIOTk5YYnbRNEHQuc7WkIvo4ipaJa+mehHuy6RJkYxq5/NZsNnn32Gb775RlMupkkAQum1x4wxvs0nTJiA6dOn44033tCUx2PpDw4Oorm5GQA090tVVZVm5iv9b/k9r7/2Docj7H4ZKaIf7TnJlk7tpPj0iWgSgGMArANQzRhrVha1AKhWPtcCEOMmG5Uy/baWENEGItow1Ona9D7M6dOno6CgwDTDYVtbGwYGBtS49e3bt8c8EctQufXWW3Hqqadqmo09PT34/ve/r37nD7HYzBR9+qIQNzY2mnaADsXtJOaQicXC6erqUv35+hhyMbQSCLlbook+L48WvSOWm23PzD3BJ3sRz4/+ukeyuufPn49FixaFRUrx8FW3242DBw+aij4AnHPOOfj2229V4QbMRdbsXPHfivupqqpCd3c3vF6vJuyVYyZwRu6rbHJtmMFz7kRbJxtIWPSJqAjAywBuZIxpkpGw0BMTV5uIMbaSMTaHMTZHjFyJB71f0ul04phjjsGGDRvg9/vx9ddfh93Iu3btwv79+9V4/lSL/ttvvw0A+PLLL9UyfepeowE4Zj590bUDaG/QRHyysQ5M6erqUv35Yiw+oHVJAaGXkFF+H5GWlpawiCiXywUiUgepcdGPlhHR7GFljCEnJyfuAUiBQACXX365+v2TTz7RbJMP4jMSYz3HHHMMAISN+TDbrxG8A1u09HlnLJ/DQb/Nnp6eMFeW3W7XvHw4I8HSj8UwkqIPgIicCAn+s4yxvyvFrdxto/znjsUmAOOFn49TylKC3kKbO3cudu3ahd/85je49NJL8etf/zrsN6kU+vvuuw8/+clP0NXVpfH9i4LBs4E+/fTTOOecc1RhEV9gxcXF6Ovri0mIBwYGNLNjDYV4RJ9b+jyGnYu/2JELhEa/RprABAiJUlNTk+Zc8UgY/hLkHaRii1CfTmHTpk0RH9ZAIBC3qL3wwgvYsGEDFi5ciJKSEo17RjxXXEDHjh1rui0+A5g4eNDMsjarZ3NzM0aNGqWZkJ7nbGpqalLrJY4a9/l8YUJXUFBgeK2l6IcY8aJPIVPtTwC2MsbEuLPXAVymfL4MwGtC+WIlimcegB7BDZRUjFwS5557Lux2O1544YVQZV5/XZNyOZW4XC489dRTWLt2Lf785z9r9vvRRx+BMYb169dj7dq1AEJjC37729+qlpt4Q44aNUodQwBEzh/U1NSkEZP9+/fj5JNPxooVK2Kuu8fjielmNxJ9bs1zS19MH22W50RE/7Jqb2/XjFsoKipCQUGBJkqFC5SRSyOWfcTCc889h1mzZuHOO+/EqaeeirVr14b58oFDL/FIlv7o0aORn5+vieCJFKVjREtLS9g++MvEaLvc1aa/b8xyAY0E0Y/FhTXiRR/AiQAuBfAdIvpa+TsXwG8AnEFEOwGcrnwHgNUAdgOoB/BHANcmsO+I6AcaAaGmL4+WmD17NogI1113HYbabxArBw8exAknnAAgJH5/+9vf1ARw3//+97Fr1y6sXbsWP/rRj/D3v/8dJSUlqtvCCB63zzuB/X6/qXvC4/Folq1btw7d3d1YtWoVHnrooZijjYzOp0gwGMTBgwdV0efiwUW/pKQEwWBQY+3zh0x0zUTDKBKmurpaI/q8E37z5s2GqTcS5cUXX8SePXtwxhlnwGazYdasWejp6QkLuwSAzz77DJWVlWEhmyJEhIkTJ2LPnj1qmZG4uFwu0+tlJPrV1dVwOByqpQ9EFm9x5jQ9Xq9Xs51sRFr6McAY+xdjjBhjsxhjRyt/qxljnYyxBYyxqYyx0xljXcr6jDF2HWPsMMbYkYyxDck7jLC6AQgPt7vwwgsBAN/5znfw4x//GPX19XjqqadSVQ0AwMcffwwgZL0/+OCD6O3txcqVKwEAP/nJTzB69GhcffXV6vpiKmUjuOhx0T9w4IDhCGEjNm/eDCDUjF+5ciXuuusuzfKWlhbDVoPo9vL5fPjnP/8ZNsNXIBAI68jVH5MojPE+QEyZrlCcXwAID03s7e1VX1LJdNf19fXh7rvvVs/ZGWecAQA48sgjARyaHUs8f7t27cJRRx0VdQLzKVOmaNJzG1n6+/bti9iRy334HLvdjrFjx4a5jczEjQkzoBnR3d1tiZDFWDpch4J4XhhjePnll8PGbYx40bcypaWlmDFjhiZuGQBOPvlkPPPMM7j44otxzTXXYMqUKZrMmZHYtGmTaYrmSHz88ceoqKjAyy+/jGOPPRbTp0/H7t27kZeXh+rqatx7773qtHc33HADbr/99ojb01v6+pGCHo9HFXeRNWvWYPXq1Zg3bx5ee+01nHnmmXj77bfR3d0Nj8eDq6++GmeccQYuueSSsN+Klv5zzz2H5cuX41e/+hXa29uxcuVKLF26FMAhN4Ze9CdOnAhAO/gpXpfBm2++if7+/rAJyqurqzWiPzAwkPTRkwcPHsQtt9yCv/71rwCAZ599Vn2RTZkyBXl5eWGhm0C4O8qMKVOmoLW11TBZX09Pj6kLqrOzEw899BB6e3tRV1cXtry2tlZjoTc2NmLHjh2G22KMRW3RGYleIhko29raUF9fj02bNqn1jPRiAoAdO3ao6SWM8Pl8hvXs7u6G3+837MNxu92aY//www9xxx13YOHChZqIv2yJYspK0QdCVn5OTk5YYqyjjz5aFaVJkybF5AJoaWnBokWLcMEFF+D000/HV199FVMd/H4//vWvf+HEE0+EzWYDEWHRokUAQuJMRDjuuOPwzjvv4C9/+QuuuuoqzJgxI+I2uaVvNNDM7XZj8eLFuOiiizTH1dLSgqVLl2JgYABz585FTU0NrrjiCjDG8NJLL+Ef//iH2qG8ceNGTUSRyMDAgCp877//Pn7605/ioYcewvr16zF79myceOKJAMJFf/z48SAijeiLwlxfX48TTjgB1157rakL45lnngFwaEATp6amBm1tbWGhr0bwkaux4PV60dPTg4GBASxdulTNkXPbbbdh1qxZ6npOpxNHHnlk2HY9Hg9cLldMoj916lQAUK19cZ6BhoYGjeuH4/P5cPnll6utxrPOOitsHb3oJ+qb5+MdvF4vuru70dvbi23bthkmM+R4vV7Tl0lbW5vqfuRuvu3bt0fcnpGgi5MPbd++XX2x8f4u7p7av38/tm7dGpaSRYx8a2howP333w8gNNDwnnvuUZfp7yvGGHbu3IkDBw6oRlh/f39YJJ3VyFrR55h1TgGhh8LMpSHy1ltvqZ9bW1tx6623wufzobe3F8888wyuu+46PP/882oTHwjd7CtXroTL5cIpp5yilp999tkoLy/HD3/4Q7WsqqpKIySRGDVqFJxOp8a65fztb39TrSAxwyLvIL7iiitw2WWhPnb+cnnwwQdx5513Ytq0afj8889RVVWF5cuXG1rLV199NRobG3HyySfD7/djy5Yt6rKHHnpIzeGjd2fwzJKieIkCtHbtWrhcLnz88cd4/PHHw/Z74MABfPvtt/jpT38aNmBt0qRJCAQCMb28f/e732Hx4sV44YUXsH37ds11v+uuu/Doo4+iq6sLixcvxg9+8APMnz8fc+fOxfr165GTk4OHHnoIP/jBD8K2O2vWLOzYsUNjkfO+olhEn18L/uIIBALYu3ev2hGsF+uPP/4Y3/3ud7F3714QEX71q18ZjvrlsfrJslB51M+OHTtUEWWMwefzYXBwUB2V7XK50NbWhq6uLuzbtw979+7ViLXb7Ta8f/nvefK8gYEBtLa2Yt++faivr9e4McVrt3v3bk2aFb6vbdu2Ye/everxi4P0fD4fWlpa4PP5NNu65ZZbsHv3bjz88MNYtGgRtm/frhpY+hcOn4inq6sLzc3N6O/vx549e9DY2KheM96K4L8VW0ZerzcteY0c0VfJbHJzc019u2PGjIHH48E777yDyspKdeRuS0sLdu/ejRNOOAE9PT144okncOSRR6pC2tTUhNmzQ6mDeFrejz76CLm5udiwYQOam5tx55134pNPPkFNTQ1OOukkdZ/5+fl49913o/p5zbDZbJgwYUKYW4oxhk8//RQFBQXwer3YsWMHzj33XHR0dODxxx/HmDFj8POf/1ztXLXZbLjhhhvwwAMPAAAuu+wy5OfnY9myZVi2bBn++Mc/qm4bIHSDfvPNNzj33HPxs5/9TLV8n3rqKRQWFqqdz2a5aSZPnozdu3fD4/GEJXgTXQ5GA+hWrlwJp9OJ888/Xy3j5527e/bs2RPm+hF55JFH8OyzzwKAGq57zjnnYNGiRdi4cSNefPFFACF3kd5qJyKsXr06zG/O+bd/+zf4/X5s27ZNTWscj+hXVFRgypQpWL9+Pa688kp1gJcR77zzDn7+858DAK666irccMMNEbcLhNxARqO7jdi2bRumTJkS1lrjmKUyqa+vB2MMeXl5hjmMduzYoQ7SM/ONi1Ftkax9IGR8+f1+1NTUqK0F8eU2ODiIQCCA/v5+Q4Ng//79GBgY0BxPMBjEzp07cckll+CUU05BZWUlHnjgAXz66af47ne/CyDUEuD9V/ogENGo0bcm8vLyUFNTo7a8xGlcS0tL1ePwer1wuVyorq42nW8hUbJe9M3yngMhKxGAKm6vvfYaJk+ejCVLlmDPnj24+uqr8d5776G3txe33347br311rCb0ev1orCwEP39/RgcHMTTTz+Nxx9/HC6XC+PHj8eqVavCLp7ZAxUrkydPDvPN3n777fjkk09w3nnnYceOHeryf/7znzhw4ACeeeaZMEG+6qqrsHDhQmzbtk11zZx99tl4//338eyzz+Kss87CzJkzARzqSDz55JNRW1uL5557DnV1deroWI7ZjTp58mR8/PHHOO644/D444+rEU3BYBAbNmzASSedhKOOOgoPP/wwuru7UVZWBq/Xi40bN2L9+vU45ZRTNBEqZWVlaG1tVX3Zt912G0488UTD/b/xxht49NFQqqc5c+agqqoKXq8Xb7zxRlgKBLFP5e6778b8+fPR3d1tKvhASPSBUL/PUEQfAI477ji8+uqr8Pl8pq3TtrY2VfBvuukmzQAxI0RXYDTR9/v9eO+993DTTTdhwoQJ+N73vodLL700bACXUeoQ4JDlbdbvFQgE4orUigYXa9GVIlr74nNq5NYy6rTm7iZ+T02bNg1lZWVYu3atKvrcxROt/0OPx+PRGGpix/3BgwfR29sbllHWKLdTMhjR7p158+bh2msPRY4+//zzquADwGOPPYa+vj7cd999OOKII/DCCy/gyy+/xMKFCzXb+f3vf6+Kx+9+9zv4/X5cddVVePHFFyOG68WKfht1dXVoaGjQNA1feeUVACHr9fDDD8eOHTvQ19eHjz/+GIcddpjaitFTUVGB+fPna14IS5cuxahRo3DNNdeorSQ+0TbvHD/yyCPDBB8wf6Eddthh6mcxwdgXX3yBpqYmnHvuuWqWSJ4b/re//S2uuOIK7N+/X2PF5+fnq/vmA5J6e3s1Av7UU0/hjDPOwDnnnIObb74ZQMi9tXLlStxzzz1YsWKFZptnnHGGGhk0c+ZMXHvttaorTqy7EdXV1aisrFTde8FgEP/617/UZbFw/PHHY2BgQOOW08OtxNmzZxt2uOvhLxwj65y7ZdxuN9rb23H++efjpptuUgeSPfDAA7j33ntjqns2wBhTW4DTp08HEGoNz5s3D59++mnK8+4YbT9SOvhEyHpLP5JVbbfbcc011+DCCy/E6aefrnZScsaNG4fXX39dfXHw/xMmTAAAfO9730NOTg6OPfZYOJ1OlJSUoKenB8888wyOOOKIpB1DQUEBxowZo47wnDx5MoLBIPbt24epU6eqCcwWL16Mk08+GQ0NDfjHP/6Bf//3fweAqBahnurqavzyl7/EjTfeiPXr1+PAgQN45JFHUFtbq3Y6mqE/33xaxJNOOkltEX388cd44okncOGFF+LVV19FUVERFixYAIfDgfz8fLzyyiv4+uuv1YF0ADQd3MXFxZqX+YMPPoif/exneOmll7Bw4UJ89NFHuO+++0BEqgX6+9//HgsWLNDUa/Hixbjjjjvw1FNPYc6cOQBClmpNTY1mdGs0iAgzZ85URf/xxx/Hq6++itraWpSUlMS0jTlz5oCIsG7dOtV1KNLT06Nair/85S9jai1yS5/7zw8ePIj169djxYoVpnH3N998MxYsWIClS5fihRdewPjx49V+oGzm1VdfxYcffohLLrlEbbkBwPz58/HGG29gw4YNmtTVw0GqQmSzXvQj5YrnVFdXw+l0qj7BE088Eb/+9a/hdDoNWwoXX3wxxowZg7POOkuzfOXKldi+fXvCgm+32zFt2jRs2bJFTeVbXl6uij63trdt24apU6di7969CAaDqivmvPPOw4MPPqh2XIliFyvz58+H3W7HTTfdBL/fjyOPPBL33ntv1L4IM9GvqKjAG2+8gSeffBKrVq3CAw88gHXr1qn9BPn5+QCAa6+9FitWrFDHN5x11lmYNm2apjN89OjRmnqcdtppOOuss/DWW2/h8ssvx86dOzF58mS89NJL+J//+R9s3LgR8+bNC6vrhRdeiLlz52pi/yP1C0TiyCOPxAcffIDu7m68/PLLAEKd27FSUlKC6dOn47XXXsOll16qtmRWr16tBgzw3EyRRviKVFVVIT8/X225XnrppYYhyoWFhTj//POxfPly9bwuW7YMa9aswe9+9zu88soreOaZZ9Ssppyuri7cfffdsNvt+N///V9DV8T69esxY8YMbNq0CQ6HA8ceeyyAUL+Z3W5Hf38/PvnkE1x00UWw2+1qrqCamho12srr9eKwww7DunXr8NFHH2HZsmUad1UwGERHRwcqKyuxbNkyTJgwAddff72m9epyuZCfn4/W1lYMDg6ivLwca9euRU1NDXp7e3Hbbbdh9uzZWLZsmeZ3Z555JlasWIE//vGPhqIfCARgt9vx9ddfY/z48Rg9ejR6enrUwXQzZ85ET08PiouLh9SPxxiLOodDvJAVBlyYMWfOHMab+okgRtWYceONN+KDDz7Aa6+9psaVp4OcnBwcfvjhAA7Ve+rUqcjNzcWOHTvg9XoRDAZx2mmnweFw4IQTTkBzczPWrVuHV199VXVF8M7olpYWfO973xvSjXPPPffgL3/5C4BQyKSZi0hk0qRJGrfP3r17wyZ0eeCBB9RBcQ6HA6+88oravwIA//d//4df/OIXAIAnnngCxx9/vGYfog+d09XVhVtuuQWfffYZqqqqsGrVqrCBXKlk8+bNuOiii1BeXo6uri7ccMMNuOqqq+LaxieffIKrr74aJSUl+OUvfwmn04kbb7xRs8706dPVTmcjKisrNR2M//Vf/4Vvv/1WMwDstttuQ3Fxseq7NjNS1q5di5/85CcAwlu9n332GX784x9r1p89ezYefPBBvP322+jo6MBjjz0W5rbIy8vDlClTsHXr1oiDnfh5NCMnJwfTpk3Dxo0bDZcXFxdj9OjRKCsriylMd/To0fjb3/5m2AezatUqrFixAitXrsScOXMQCATw1ltv4fPPP8frr78ecbu8dcuZPn06Jk+ejLKyMpx00kmYO3cuGhsb8eGHH+Koo47Czp07UVVVBZfLBYfDgRtvvHFIfYBE9AVjbI7hspEg+h0dHaZT6XE8Hg86OzujjohNNVVVVaoPnwvmtGnT4HA4EAgE1JDMP/zhD3jsscfU311yySW4+eabk24VvPnmm+js7MTFF18cdV273a76Qznt7e2aNAmc9957D7fccgtuueUWTTppTlNTE95++20sXrw4zILkor9ly5YwUdmyZQtqamo0c9IOB4wx/PjHP8a6desAAI8++ijmz5+vLhddTZG47bbb1P4Zjt1uR35+PlatWoWamhpTlxG/d8Rz/uabb2LZsmXqOmvWrIk5kgcIdVjeddddWL16NYqLi1FUVKTJxMldgGLiQDNqampMn8P/+I//wBdffIHS0lJTX/aYMWNw8sknY+PGjdizZ48mxUhFRYWm76K8vBxHHHEE9u3bp4kK4iO4CwsL8cMf/hD79+9HVVUVrrrqKtNO94GBAZx33nloa2vDqFGjYLPZ1A7kiooKHDx4EH6/X73G06ZNw7Zt21BYWIgjjjgC+/fvN416isTs2bPx+eefD6kzd8SLPhAuEHa7Pe5h1aJfPdnk5uZicHBQYynzvDqi5SwO91+7di38fj8OO+wwNcFWNPgLxShOeijwCWsCgQBKSkrCrOtgMKiJ5xfx+/1xWzHii6WxsdFSA2E6Ojrw9ttvY3BwEJdeeql6bHV1dWHZVc1wu924//77sWfPHqxbtw4zZ87EqlWrMDg4GLV/YMaMGaoLQWwFffXVV2htbcWJJ54Y5qKJBcYYfvGLX+DNN9+E3+9XO/+XLFmiXm/GGB555BFs2rQJdXV1KC0txfnnn4/CwkK8//77OPPMM5GXl6d58RGRoesjEAio91VfXx+KiorU+Xp5S8Pn86kTvsTS/2IUKhwPu3btwo9+9CN4vV5MmzYNxx13HBYuXIiamhoQkZoewsydzBhDW1ub6prs6OjAc889h/fffx9Tp07FrFmz4Pf71dxRxx9/POrq6nDiiSdKS3+otLa2qs3empoaja8zFvSddUOBzyo1MDCAyspKMMZUC6Curg75+flR/X5tbW0RBVvsm9AjvlAGBwejxkLHwowZM9DR0aHe0Eb+5ubmZtNQv3gpLS1VX3BiyydeHA7HsGSPHDt2LMrLy9XW5rhx4wyTsxkRCATUkdzRyM/P10QZifd7MkmFj9lKlJWVYezYsWCMobGxUTP5TzqYOXPmkM53JNHP+o5cTlVVlToJdkVFhcbqt9lsqKmp0TQDebTIwMAAbDabamHk5+djYGBAMxtUbW0tnE5nxDw+ubm5GD9+POx2O9xuN0pKSlQhLC4uVicGieU4XC6XaXKs3NxcFBcXo6urS3UJ5efnw+l0aloMubm5mDp1Ktra2lQLlLc2OGZ+VX4OHA4HbDabat2YjYmorq6G2+2OOh1dLJSWlqqf7XY7JkyYEDb5DIeIUFBQgJKSEvh8PtjtdowaNUqt58DAADwejzrpTnNzs3r8U6dORVNTE9xuN2w2G2pra9VRnPHAJ5YpLy9HUVER8vLyUFRUpIZLRmo5RmrW19bWwm63w+VygTEWFtZbWVmpmfQ9WWSz4AMh3z7PhRVLEEiqScX5HjGiT0SYMmWKZkQqz/NeUlKiWmNerxcTJ05ULW69GE+cOBEejweFhYVqYrPCwkLDJlhxcbEaXig2q3kzne8jnvBAICR8ZgLqcDhQUFCArq4u+P1+VFZWmsaK5+bmYsyYMejt7VUjJrxerzopx9ixY9VBZ/w4+bnxeDzqi3DUqFGYNGmS6XHYbDaUlZWpdeaiy8cZlJWVoaqqCg0NDWrEkfgCmjJlCtrb29HT0xO2DzN3xbhx41BUVBSxaZyfn69GDQGhHEG8szM3NxeVlZVoa2tDbW2t6ppwu93o7e1FVVVVxGRodrsddXV1mvuNuxccDgccDgdycnLgdrtjTnENhF6gpaWlmnNvhM1mQ0lJiemAKIfDgZqamrBWR15eHjweDwoKCjRpC+x2u+Hk4ENxkyYLo33zaDEgvBM1GsXFxRqh553ieXl5CAaD8Hq9w3a8xcXFKQtEGDGiD4RboqNHj1ZFHwgNIAoEAhFH8TocDtVinjBhArxer7q+vjOpuLg4Yocit1pF6zUWjCwQ3knGJxYBQi+TaIPDHA4HjjjiCNjtdhCRKszcQp08ebKanyQnJ0d9UYn+USIyHKglIlqtpaWl6tR8eXl5auf55MmT4ff70dPTg/Lycni9XvT39yMvLw9jx45FVVVVmPvLyBLKzc2N+5zy34nwl7a4r/Hjx8PlcqGoqAgVFRVqf4XerTZlypSIAwOB0Dmpra3ViP7YsWPVCWf0gjV69OiwcNVI6Pc/btw4tLe3o6CgAGPHjgURqS0Ol8sFp9OJiooKNDY2hk0mbyR0eXl5KC8vjzgZ0eTJk3Hw4EF0dXVh/PjxaG1tNcw3U1lZCZfLhQkTJsDhcKgzpwEhIa+srAwLCNDXafLkycjPz0d/fz/27t2LkpISDAwMIBgMorS0VNP/o88HVVZWFhbEYbPZMHbsWM3xGZ2H0tJSuN1ueL3esPmg+XmKd0rO6urqIadqicaw+/SJ6GwADwCwA3iCMfYbs3WT6dM3gs+ulGhaBD1cuMrKylJy4YLBIJqbm9UcLVVVVSgvL1dj+oGQz97pdA5p/8FgMOn1DgaD6OzsRF5entp66unpQWlpacJN2J6eHjDGUFxcjJaWFpSXl2ss+Hjo6uoKa5lForW1Ffn5+SguLkZ3dzcGBgZQU1MTV8RFa2sr3G632vIiIrjdbnR0dKj1CAaDalrtWBkcHERbWxv8fj/KyspMX4SMMXR2dqqWbnNzM/r6+pCbmwsiQk5OjppGoaamBj6fD+Xl5Wp5c3MzioqKYLfbcfDgQfh8PhARKisrUVRUBI/Ho6ay4NlHeeu4q6sLhYWFhp3UPE12ZWWlGhlDROjo6IDT6URvby88Hg8qKipAROrx8cyaOTk5cLlc8Hg8qKyshM/nQ39/P0aNGqW6cNra2lBQUGB6vXkHbHd3N5xOJ/Ly8tTABS7Mdrsdfr8fra2tqKysVFvB+fn5asuId/a2trZiYGAAJSUlsNvtat2CwSDa2tpUV2NVVVVCz4VlOnKJyA5gB4AzADQCWA9gEWPMMLwj1aIvkUgk2Ugk0R/u3DtzAdQzxnYzxrwA/grggmGug0QikYxYhlv0awGIeU4blTIVIlpCRBuIaEOq56+VSCSSkYblsmwyxlYyxuYwxubEmpZWIpFIJLEx3KLfBECMQxqnlEkkEolkGBhu0V8PYCoR1RFRDoCLAETOWCSRSCSSpDGscfqMMT8R/RTAWwiFbD7JGNs8nHWQSCSSkcywD85ijK0GsHq49yuRSCQSC3bkSiQSiSR1WDrLJhG1A9iXxipUAIg/EfbwIeuXGLJ+iSHrlxiprN9Exphh+KOlRT/dENEGs1FtVkDWLzFk/RJD1i8x0lU/6d6RSCSSEYQUfYlEIhlBSNGPzMp0VyAKsn6JIeuXGLJ+iZGW+kmfvkQikYwgpKUvkUgkIwgp+hKJRDKCGNGiT0R7iehbIvqaiDYoZeVEtIaIdir/y5RyIqIHiaieiDYS0ewU1+0IpV78r5eIbiSiO4ioSSg/V/jNrUr9thPRWSmo05NE1EZEm4SyuM8XEV2mrL+TiC5Lcf3uJaJtSh1eIaJSpXwSEQ0I5/Ex4TfHKvdFvXIMSZmd2qR+cV9PIjpbKasnouXJqFuE+r0g1G0vEX2tlKfj/I0noveJaAsRbSaiG5RyS9yDEepnmXsQQGg6sJH6B2AvgApd2W8BLFc+Lwdwj/L5XABvACAA8wCsG8Z62gG0AJgI4A4A/7/BOjMAfAMgF0AdgF0A7Emux8kAZgPYNNTzBaAcwG7lf5nyuSyF9TsTgEP5fI9Qv0nierrtfK7UmZRjOCeF9Yvreip/uwBMBpCjrDMjVfXTLV8B4LY0nr8xAGYrn4sRmoVvhlXuwQj1s8w9yBgb2Za+CRcAeFr5/DSAhUL5n1mIzwCUEtGYYarTAgC7GGORRidfAOCvjLFBxtgeAPUIzVSWNBhjHwHoMthvPOfrLABrGGNdjLFuAGsAnJ2q+jHG3maM+ZWvnyGUztsUpY6jGGOfsdDT92fhmJJevwiYXc+UzT4XqX6KpfmfAJ6PtI0Un79mxtiXymcXgK0ITcJkiXvQrH5WugeBEe7eAcAAvE1EXxDREqWsmjHWrHxuAVCtfI4661cKuQjah+2nSlPxSd6UTWP94j1f6TyPP0LIauLUEdFXRPQhEZ2klNUqdRrO+sVzPdN1/k4C0MoY2ymUpe38EdEkAMcAWAcL3oO6+omk/R4c6aI/nzE2G8A5AK4jopPFhcpbNq0xrRSad+B8AH9Tih4FcBiAowE0I9TktgRWOF9mENF/A/ADeFYpagYwgTF2DIClAJ4jolFpqJplr6eORdAaHmk7f0RUBOBlADcyxnrFZVa4B83qZ5V7cESLPmOsSfnfBuAVhJrOrdxto/xvU1ZP16xf5wD4kjHWqtS1lTEWYIwFAfwRh1w46apfvOdr2OtJRJcDOA/AxYooQHGbdCqfv0DIT364Uhex+Z3S+g3heqbj/DkAXAjgBaHeaTl/RORESFCfZYz9XSm2zD1oUj9L3YMjVvSJqJCIivlnhDpbNiE0kxfvzb8MwGvK59cBLFYiAuYB6BGalKlEY2Hp+hG+h1Cdef0uIqJcIqoDMBWhzqBUE+/5egvAmURUprgyzlTKUgIRnQ3gZgDnM8bcQnklEdmVz5MROl+7lTr2EtE8xY+9WDimVNQv3uuZjtnnTgewjTGmuhzScf6U7f0JwFbG2H3CIkvcg2b1s9w9mKwe4Uz7Qyj64RvlbzOA/1bKRwN4F8BOAO8AKFfKCcAfEHobfwtgzjDUsRBAJ4ASoewZZf8bEbqpxwjL/lup33Yksbdf2P7zCDVJfQj5Ga8cyvlCyK9Zr/xdkeL61SPkv/1a+XtMWff/U6771wC+BPBdYTtzEBLfXQAehjJyPUX1i/t6IhSVskNZ9t+pPH9K+SoAV+vWTcf5m4+Q62ajcD3Ptco9GKF+lrkHGWMyDYNEIpGMJEase0cikUhGIlL0JRKJZAQhRV8ikUhGEFL0JRKJZAQhRV8ikUhGEFL0JRKJZAQhRV8ikUhGEP8PmkmVQmR9dQQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "_ = plt.plot(wv, df_P, color='lightgray')\n",
    "_ = plt.plot(wv, df_P.mean(axis=1), color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "%matplotlib inline\n",
    "_ = plt.plot(wv, df_N, color='lightgray')\n",
    "_ = plt.plot(wv, df_N.mean(axis=1), color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ramsn shift range selection\n",
    "wv_c = wv[(wv >= 450) & (wv <= 2250)]\n",
    "df_c = df_k[(df_j['wavenumber'] >= 450) & (df_j['wavenumber'] <= 2250) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_c.index = wv_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pros 2</th>\n",
       "      <th>Pros 3</th>\n",
       "      <th>Pros 4</th>\n",
       "      <th>Pros 5</th>\n",
       "      <th>Pros 6</th>\n",
       "      <th>Pros 11</th>\n",
       "      <th>Pros 12</th>\n",
       "      <th>Pros 24</th>\n",
       "      <th>Pros 25</th>\n",
       "      <th>Pros 26</th>\n",
       "      <th>...</th>\n",
       "      <th>Normal 24</th>\n",
       "      <th>Normal 25</th>\n",
       "      <th>Normal 26</th>\n",
       "      <th>Normal 27</th>\n",
       "      <th>Normal 28.1</th>\n",
       "      <th>Normal 29</th>\n",
       "      <th>Normal 30.1</th>\n",
       "      <th>Normal 31.1</th>\n",
       "      <th>Normal 17</th>\n",
       "      <th>Normal 32</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wavenumber</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>153.167</td>\n",
       "      <td>119.1</td>\n",
       "      <td>159.267</td>\n",
       "      <td>216.633</td>\n",
       "      <td>161.9</td>\n",
       "      <td>103.633</td>\n",
       "      <td>194.533</td>\n",
       "      <td>127.5</td>\n",
       "      <td>132.733</td>\n",
       "      <td>220.267</td>\n",
       "      <td>...</td>\n",
       "      <td>257.067</td>\n",
       "      <td>303</td>\n",
       "      <td>242.7</td>\n",
       "      <td>144.267</td>\n",
       "      <td>434.567</td>\n",
       "      <td>192.367</td>\n",
       "      <td>559.733</td>\n",
       "      <td>284.7</td>\n",
       "      <td>325.433</td>\n",
       "      <td>145.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>157.7</td>\n",
       "      <td>111.567</td>\n",
       "      <td>167.633</td>\n",
       "      <td>213.333</td>\n",
       "      <td>157.2</td>\n",
       "      <td>101.9</td>\n",
       "      <td>196.433</td>\n",
       "      <td>126.433</td>\n",
       "      <td>133.333</td>\n",
       "      <td>203.667</td>\n",
       "      <td>...</td>\n",
       "      <td>266.167</td>\n",
       "      <td>297.9</td>\n",
       "      <td>236.367</td>\n",
       "      <td>140.2</td>\n",
       "      <td>440.6</td>\n",
       "      <td>191.333</td>\n",
       "      <td>554.767</td>\n",
       "      <td>260.667</td>\n",
       "      <td>330.133</td>\n",
       "      <td>152.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>162</td>\n",
       "      <td>103.667</td>\n",
       "      <td>176.667</td>\n",
       "      <td>209.867</td>\n",
       "      <td>152.467</td>\n",
       "      <td>100.2</td>\n",
       "      <td>198.833</td>\n",
       "      <td>125.267</td>\n",
       "      <td>134.4</td>\n",
       "      <td>186.4</td>\n",
       "      <td>...</td>\n",
       "      <td>277.8</td>\n",
       "      <td>293.9</td>\n",
       "      <td>229.967</td>\n",
       "      <td>136.333</td>\n",
       "      <td>447.067</td>\n",
       "      <td>189.733</td>\n",
       "      <td>549.2</td>\n",
       "      <td>237.933</td>\n",
       "      <td>336.533</td>\n",
       "      <td>161.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>166.267</td>\n",
       "      <td>96.5667</td>\n",
       "      <td>182.1</td>\n",
       "      <td>208</td>\n",
       "      <td>150.3</td>\n",
       "      <td>99.4667</td>\n",
       "      <td>200.967</td>\n",
       "      <td>122.367</td>\n",
       "      <td>137.9</td>\n",
       "      <td>171.667</td>\n",
       "      <td>...</td>\n",
       "      <td>289.4</td>\n",
       "      <td>288.367</td>\n",
       "      <td>225.267</td>\n",
       "      <td>131.7</td>\n",
       "      <td>452.333</td>\n",
       "      <td>188.133</td>\n",
       "      <td>543.367</td>\n",
       "      <td>223.167</td>\n",
       "      <td>339.1</td>\n",
       "      <td>167.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>170.3</td>\n",
       "      <td>92</td>\n",
       "      <td>176.8</td>\n",
       "      <td>210.667</td>\n",
       "      <td>156</td>\n",
       "      <td>101.7</td>\n",
       "      <td>202.3</td>\n",
       "      <td>114.5</td>\n",
       "      <td>148.433</td>\n",
       "      <td>164.767</td>\n",
       "      <td>...</td>\n",
       "      <td>300.733</td>\n",
       "      <td>278.367</td>\n",
       "      <td>225.633</td>\n",
       "      <td>124.833</td>\n",
       "      <td>453.867</td>\n",
       "      <td>186.7</td>\n",
       "      <td>536.6</td>\n",
       "      <td>231.867</td>\n",
       "      <td>330.367</td>\n",
       "      <td>164.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2246</th>\n",
       "      <td>84.1333</td>\n",
       "      <td>55.2333</td>\n",
       "      <td>82.7333</td>\n",
       "      <td>70.9</td>\n",
       "      <td>61.8667</td>\n",
       "      <td>50.3333</td>\n",
       "      <td>75.2</td>\n",
       "      <td>59.9667</td>\n",
       "      <td>70.9667</td>\n",
       "      <td>65.5333</td>\n",
       "      <td>...</td>\n",
       "      <td>112.467</td>\n",
       "      <td>127.433</td>\n",
       "      <td>87.9667</td>\n",
       "      <td>120.967</td>\n",
       "      <td>92.4</td>\n",
       "      <td>60.7</td>\n",
       "      <td>103.633</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>124.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2247</th>\n",
       "      <td>82.6</td>\n",
       "      <td>49.2</td>\n",
       "      <td>83.0333</td>\n",
       "      <td>70.9</td>\n",
       "      <td>52.0667</td>\n",
       "      <td>55.2</td>\n",
       "      <td>72.4333</td>\n",
       "      <td>63.8</td>\n",
       "      <td>67.7667</td>\n",
       "      <td>64.0667</td>\n",
       "      <td>...</td>\n",
       "      <td>105.033</td>\n",
       "      <td>127.433</td>\n",
       "      <td>86.8</td>\n",
       "      <td>120.967</td>\n",
       "      <td>92.4</td>\n",
       "      <td>63.3</td>\n",
       "      <td>111.533</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>132.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2248</th>\n",
       "      <td>78.6333</td>\n",
       "      <td>50.4</td>\n",
       "      <td>88.9</td>\n",
       "      <td>74.5</td>\n",
       "      <td>52.0667</td>\n",
       "      <td>57.5</td>\n",
       "      <td>72.7</td>\n",
       "      <td>63.8</td>\n",
       "      <td>67.7667</td>\n",
       "      <td>67.2667</td>\n",
       "      <td>...</td>\n",
       "      <td>98.4667</td>\n",
       "      <td>127.433</td>\n",
       "      <td>79.3</td>\n",
       "      <td>120.967</td>\n",
       "      <td>92.4</td>\n",
       "      <td>49.3333</td>\n",
       "      <td>108.567</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>136.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>78.6333</td>\n",
       "      <td>54.8667</td>\n",
       "      <td>100.167</td>\n",
       "      <td>80.1333</td>\n",
       "      <td>56.2333</td>\n",
       "      <td>57</td>\n",
       "      <td>69.5</td>\n",
       "      <td>61.8</td>\n",
       "      <td>67.7667</td>\n",
       "      <td>73.2333</td>\n",
       "      <td>...</td>\n",
       "      <td>102.9</td>\n",
       "      <td>127.433</td>\n",
       "      <td>81.4333</td>\n",
       "      <td>120.967</td>\n",
       "      <td>92.4</td>\n",
       "      <td>49.3333</td>\n",
       "      <td>106.467</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>138.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>79.2333</td>\n",
       "      <td>56.9</td>\n",
       "      <td>95.8</td>\n",
       "      <td>80.2</td>\n",
       "      <td>53.6</td>\n",
       "      <td>51.8</td>\n",
       "      <td>69.0333</td>\n",
       "      <td>62.1333</td>\n",
       "      <td>67.7667</td>\n",
       "      <td>69.1</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0333</td>\n",
       "      <td>127.433</td>\n",
       "      <td>78.3667</td>\n",
       "      <td>120.967</td>\n",
       "      <td>92.4</td>\n",
       "      <td>50.9</td>\n",
       "      <td>105.1</td>\n",
       "      <td>178.2</td>\n",
       "      <td>152.167</td>\n",
       "      <td>129.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1801 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pros 2   Pros 3   Pros 4   Pros 5   Pros 6  Pros 11  Pros 12  \\\n",
       "wavenumber                                                                  \n",
       "450         153.167    119.1  159.267  216.633    161.9  103.633  194.533   \n",
       "451           157.7  111.567  167.633  213.333    157.2    101.9  196.433   \n",
       "452             162  103.667  176.667  209.867  152.467    100.2  198.833   \n",
       "453         166.267  96.5667    182.1      208    150.3  99.4667  200.967   \n",
       "454           170.3       92    176.8  210.667      156    101.7    202.3   \n",
       "...             ...      ...      ...      ...      ...      ...      ...   \n",
       "2246        84.1333  55.2333  82.7333     70.9  61.8667  50.3333     75.2   \n",
       "2247           82.6     49.2  83.0333     70.9  52.0667     55.2  72.4333   \n",
       "2248        78.6333     50.4     88.9     74.5  52.0667     57.5     72.7   \n",
       "2249        78.6333  54.8667  100.167  80.1333  56.2333       57     69.5   \n",
       "2250        79.2333     56.9     95.8     80.2     53.6     51.8  69.0333   \n",
       "\n",
       "            Pros 24  Pros 25  Pros 26  ... Normal 24 Normal 25 Normal 26  \\\n",
       "wavenumber                             ...                                 \n",
       "450           127.5  132.733  220.267  ...   257.067       303     242.7   \n",
       "451         126.433  133.333  203.667  ...   266.167     297.9   236.367   \n",
       "452         125.267    134.4    186.4  ...     277.8     293.9   229.967   \n",
       "453         122.367    137.9  171.667  ...     289.4   288.367   225.267   \n",
       "454           114.5  148.433  164.767  ...   300.733   278.367   225.633   \n",
       "...             ...      ...      ...  ...       ...       ...       ...   \n",
       "2246        59.9667  70.9667  65.5333  ...   112.467   127.433   87.9667   \n",
       "2247           63.8  67.7667  64.0667  ...   105.033   127.433      86.8   \n",
       "2248           63.8  67.7667  67.2667  ...   98.4667   127.433      79.3   \n",
       "2249           61.8  67.7667  73.2333  ...     102.9   127.433   81.4333   \n",
       "2250        62.1333  67.7667     69.1  ...   99.0333   127.433   78.3667   \n",
       "\n",
       "           Normal 27 Normal 28.1 Normal 29 Normal 30.1 Normal 31.1 Normal 17  \\\n",
       "wavenumber                                                                     \n",
       "450          144.267     434.567   192.367     559.733       284.7   325.433   \n",
       "451            140.2       440.6   191.333     554.767     260.667   330.133   \n",
       "452          136.333     447.067   189.733       549.2     237.933   336.533   \n",
       "453            131.7     452.333   188.133     543.367     223.167     339.1   \n",
       "454          124.833     453.867     186.7       536.6     231.867   330.367   \n",
       "...              ...         ...       ...         ...         ...       ...   \n",
       "2246         120.967        92.4      60.7     103.633       178.2   152.167   \n",
       "2247         120.967        92.4      63.3     111.533       178.2   152.167   \n",
       "2248         120.967        92.4   49.3333     108.567       178.2   152.167   \n",
       "2249         120.967        92.4   49.3333     106.467       178.2   152.167   \n",
       "2250         120.967        92.4      50.9       105.1       178.2   152.167   \n",
       "\n",
       "           Normal 32  \n",
       "wavenumber            \n",
       "450            145.4  \n",
       "451          152.533  \n",
       "452          161.467  \n",
       "453          167.533  \n",
       "454          164.633  \n",
       "...              ...  \n",
       "2246         124.667  \n",
       "2247           132.6  \n",
       "2248         136.633  \n",
       "2249         138.133  \n",
       "2250           129.3  \n",
       "\n",
       "[1801 rows x 79 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "wv_c = wv_c.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1801,)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv_c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_d = df_c.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_e = df_d.values.T.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bining (bucketing) for smoothing and reducing the number of variables\n",
    "# The original data were measured at every 1 cm-1. But the spectral resolution of CBEx is 14~16 cm-1\n",
    "# So, the data averaging within 2 cm-1 is still effective 1800 data set (450~2150 cm-1) down to 900 data set for every 2 cm-1\n",
    "\n",
    "from scipy.stats import binned_statistic\n",
    "df_binned, bin_edges, misc = binned_statistic(wv_c, df_e, statistic=\"mean\", bins=900)\n",
    "wv_binned = (bin_edges[:-1]+bin_edges[1:])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[155.433335  , 164.133335  , 172.283335  , ...,  86.3       ,\n",
       "         83.366665  ,  78.83333   ],\n",
       "       [115.333335  , 100.11667   ,  90.45      , ...,  68.383335  ,\n",
       "         52.216665  ,  54.05555667],\n",
       "       [163.45      , 179.383335  , 171.083335  , ...,  83.416665  ,\n",
       "         82.88333   ,  94.95555667],\n",
       "       ...,\n",
       "       [272.683335  , 230.55      , 242.9       , ..., 178.2       ,\n",
       "        178.2       , 178.2       ],\n",
       "       [327.78333   , 337.816665  , 322.75      , ..., 153.25      ,\n",
       "        152.16667   , 152.16667   ],\n",
       "       [148.966665  , 164.5       , 160.68333   , ..., 118.383335  ,\n",
       "        128.633335  , 134.68888667]])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_binned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>890</th>\n",
       "      <th>891</th>\n",
       "      <th>892</th>\n",
       "      <th>893</th>\n",
       "      <th>894</th>\n",
       "      <th>895</th>\n",
       "      <th>896</th>\n",
       "      <th>897</th>\n",
       "      <th>898</th>\n",
       "      <th>899</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>155.433335</td>\n",
       "      <td>164.133335</td>\n",
       "      <td>172.283335</td>\n",
       "      <td>177.200000</td>\n",
       "      <td>147.516665</td>\n",
       "      <td>98.366670</td>\n",
       "      <td>95.833335</td>\n",
       "      <td>124.483330</td>\n",
       "      <td>137.083335</td>\n",
       "      <td>136.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>78.816665</td>\n",
       "      <td>82.700000</td>\n",
       "      <td>88.016665</td>\n",
       "      <td>88.866670</td>\n",
       "      <td>91.500000</td>\n",
       "      <td>82.350000</td>\n",
       "      <td>78.63333</td>\n",
       "      <td>86.300000</td>\n",
       "      <td>83.366665</td>\n",
       "      <td>78.833330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>115.333335</td>\n",
       "      <td>100.116670</td>\n",
       "      <td>90.450000</td>\n",
       "      <td>84.866670</td>\n",
       "      <td>82.383335</td>\n",
       "      <td>82.316665</td>\n",
       "      <td>87.083335</td>\n",
       "      <td>94.366670</td>\n",
       "      <td>91.733335</td>\n",
       "      <td>82.016665</td>\n",
       "      <td>...</td>\n",
       "      <td>78.233335</td>\n",
       "      <td>66.933330</td>\n",
       "      <td>56.300000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>60.250000</td>\n",
       "      <td>55.550000</td>\n",
       "      <td>55.80000</td>\n",
       "      <td>68.383335</td>\n",
       "      <td>52.216665</td>\n",
       "      <td>54.055557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>163.450000</td>\n",
       "      <td>179.383335</td>\n",
       "      <td>171.083335</td>\n",
       "      <td>146.783330</td>\n",
       "      <td>125.516665</td>\n",
       "      <td>108.333335</td>\n",
       "      <td>111.516665</td>\n",
       "      <td>128.283335</td>\n",
       "      <td>134.900000</td>\n",
       "      <td>138.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>79.766670</td>\n",
       "      <td>79.766670</td>\n",
       "      <td>81.366670</td>\n",
       "      <td>90.933335</td>\n",
       "      <td>92.766670</td>\n",
       "      <td>79.766670</td>\n",
       "      <td>80.55000</td>\n",
       "      <td>83.416665</td>\n",
       "      <td>82.883330</td>\n",
       "      <td>94.955557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>214.983330</td>\n",
       "      <td>208.933335</td>\n",
       "      <td>213.300000</td>\n",
       "      <td>222.550000</td>\n",
       "      <td>207.950000</td>\n",
       "      <td>178.283330</td>\n",
       "      <td>163.116670</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>161.733330</td>\n",
       "      <td>168.383335</td>\n",
       "      <td>...</td>\n",
       "      <td>79.266670</td>\n",
       "      <td>76.533335</td>\n",
       "      <td>76.116665</td>\n",
       "      <td>73.850000</td>\n",
       "      <td>86.283335</td>\n",
       "      <td>78.733335</td>\n",
       "      <td>74.75000</td>\n",
       "      <td>70.900000</td>\n",
       "      <td>70.900000</td>\n",
       "      <td>78.277777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>159.550000</td>\n",
       "      <td>151.383335</td>\n",
       "      <td>161.033335</td>\n",
       "      <td>180.666665</td>\n",
       "      <td>179.650000</td>\n",
       "      <td>164.266670</td>\n",
       "      <td>148.233335</td>\n",
       "      <td>132.600000</td>\n",
       "      <td>121.183335</td>\n",
       "      <td>115.850000</td>\n",
       "      <td>...</td>\n",
       "      <td>56.416665</td>\n",
       "      <td>59.716665</td>\n",
       "      <td>57.416670</td>\n",
       "      <td>59.566665</td>\n",
       "      <td>58.416670</td>\n",
       "      <td>58.266665</td>\n",
       "      <td>67.80000</td>\n",
       "      <td>77.666670</td>\n",
       "      <td>56.966670</td>\n",
       "      <td>53.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>191.850000</td>\n",
       "      <td>188.933330</td>\n",
       "      <td>186.016665</td>\n",
       "      <td>183.733335</td>\n",
       "      <td>185.716670</td>\n",
       "      <td>188.800000</td>\n",
       "      <td>170.350000</td>\n",
       "      <td>138.566665</td>\n",
       "      <td>125.683335</td>\n",
       "      <td>127.366670</td>\n",
       "      <td>...</td>\n",
       "      <td>66.016665</td>\n",
       "      <td>67.333335</td>\n",
       "      <td>57.400000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>56.900000</td>\n",
       "      <td>50.300000</td>\n",
       "      <td>49.33333</td>\n",
       "      <td>56.716670</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>49.855553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>557.250000</td>\n",
       "      <td>546.283335</td>\n",
       "      <td>532.983335</td>\n",
       "      <td>517.200000</td>\n",
       "      <td>487.166665</td>\n",
       "      <td>448.450000</td>\n",
       "      <td>420.383335</td>\n",
       "      <td>401.266670</td>\n",
       "      <td>399.016665</td>\n",
       "      <td>401.566665</td>\n",
       "      <td>...</td>\n",
       "      <td>114.316670</td>\n",
       "      <td>117.833335</td>\n",
       "      <td>93.200000</td>\n",
       "      <td>93.200000</td>\n",
       "      <td>93.200000</td>\n",
       "      <td>94.916665</td>\n",
       "      <td>93.20000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>107.583330</td>\n",
       "      <td>106.711113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>272.683335</td>\n",
       "      <td>230.550000</td>\n",
       "      <td>242.900000</td>\n",
       "      <td>284.966670</td>\n",
       "      <td>260.233335</td>\n",
       "      <td>193.083335</td>\n",
       "      <td>168.833330</td>\n",
       "      <td>175.866665</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>178.20000</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>178.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>327.783330</td>\n",
       "      <td>337.816665</td>\n",
       "      <td>322.750000</td>\n",
       "      <td>289.133335</td>\n",
       "      <td>238.933335</td>\n",
       "      <td>182.733335</td>\n",
       "      <td>184.400000</td>\n",
       "      <td>221.266665</td>\n",
       "      <td>208.300000</td>\n",
       "      <td>160.783335</td>\n",
       "      <td>...</td>\n",
       "      <td>152.166670</td>\n",
       "      <td>152.166670</td>\n",
       "      <td>154.933330</td>\n",
       "      <td>152.166670</td>\n",
       "      <td>152.616670</td>\n",
       "      <td>152.516670</td>\n",
       "      <td>152.16667</td>\n",
       "      <td>153.250000</td>\n",
       "      <td>152.166670</td>\n",
       "      <td>152.166670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>148.966665</td>\n",
       "      <td>164.500000</td>\n",
       "      <td>160.683330</td>\n",
       "      <td>144.083335</td>\n",
       "      <td>134.183330</td>\n",
       "      <td>130.300000</td>\n",
       "      <td>140.033335</td>\n",
       "      <td>156.516665</td>\n",
       "      <td>148.716670</td>\n",
       "      <td>119.433330</td>\n",
       "      <td>...</td>\n",
       "      <td>119.533335</td>\n",
       "      <td>114.883330</td>\n",
       "      <td>124.300000</td>\n",
       "      <td>130.516670</td>\n",
       "      <td>114.533330</td>\n",
       "      <td>114.533330</td>\n",
       "      <td>116.85000</td>\n",
       "      <td>118.383335</td>\n",
       "      <td>128.633335</td>\n",
       "      <td>134.688887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 900 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0           1           2           3           4           5    \\\n",
       "0   155.433335  164.133335  172.283335  177.200000  147.516665   98.366670   \n",
       "1   115.333335  100.116670   90.450000   84.866670   82.383335   82.316665   \n",
       "2   163.450000  179.383335  171.083335  146.783330  125.516665  108.333335   \n",
       "3   214.983330  208.933335  213.300000  222.550000  207.950000  178.283330   \n",
       "4   159.550000  151.383335  161.033335  180.666665  179.650000  164.266670   \n",
       "..         ...         ...         ...         ...         ...         ...   \n",
       "74  191.850000  188.933330  186.016665  183.733335  185.716670  188.800000   \n",
       "75  557.250000  546.283335  532.983335  517.200000  487.166665  448.450000   \n",
       "76  272.683335  230.550000  242.900000  284.966670  260.233335  193.083335   \n",
       "77  327.783330  337.816665  322.750000  289.133335  238.933335  182.733335   \n",
       "78  148.966665  164.500000  160.683330  144.083335  134.183330  130.300000   \n",
       "\n",
       "           6           7           8           9    ...         890  \\\n",
       "0    95.833335  124.483330  137.083335  136.400000  ...   78.816665   \n",
       "1    87.083335   94.366670   91.733335   82.016665  ...   78.233335   \n",
       "2   111.516665  128.283335  134.900000  138.750000  ...   79.766670   \n",
       "3   163.116670  159.000000  161.733330  168.383335  ...   79.266670   \n",
       "4   148.233335  132.600000  121.183335  115.850000  ...   56.416665   \n",
       "..         ...         ...         ...         ...  ...         ...   \n",
       "74  170.350000  138.566665  125.683335  127.366670  ...   66.016665   \n",
       "75  420.383335  401.266670  399.016665  401.566665  ...  114.316670   \n",
       "76  168.833330  175.866665  178.200000  178.200000  ...  178.200000   \n",
       "77  184.400000  221.266665  208.300000  160.783335  ...  152.166670   \n",
       "78  140.033335  156.516665  148.716670  119.433330  ...  119.533335   \n",
       "\n",
       "           891         892         893         894         895        896  \\\n",
       "0    82.700000   88.016665   88.866670   91.500000   82.350000   78.63333   \n",
       "1    66.933330   56.300000   58.000000   60.250000   55.550000   55.80000   \n",
       "2    79.766670   81.366670   90.933335   92.766670   79.766670   80.55000   \n",
       "3    76.533335   76.116665   73.850000   86.283335   78.733335   74.75000   \n",
       "4    59.716665   57.416670   59.566665   58.416670   58.266665   67.80000   \n",
       "..         ...         ...         ...         ...         ...        ...   \n",
       "74   67.333335   57.400000   60.000000   56.900000   50.300000   49.33333   \n",
       "75  117.833335   93.200000   93.200000   93.200000   94.916665   93.20000   \n",
       "76  178.200000  178.200000  178.200000  178.200000  178.200000  178.20000   \n",
       "77  152.166670  154.933330  152.166670  152.616670  152.516670  152.16667   \n",
       "78  114.883330  124.300000  130.516670  114.533330  114.533330  116.85000   \n",
       "\n",
       "           897         898         899  \n",
       "0    86.300000   83.366665   78.833330  \n",
       "1    68.383335   52.216665   54.055557  \n",
       "2    83.416665   82.883330   94.955557  \n",
       "3    70.900000   70.900000   78.277777  \n",
       "4    77.666670   56.966670   53.966667  \n",
       "..         ...         ...         ...  \n",
       "74   56.716670   62.000000   49.855553  \n",
       "75   94.500000  107.583330  106.711113  \n",
       "76  178.200000  178.200000  178.200000  \n",
       "77  153.250000  152.166670  152.166670  \n",
       "78  118.383335  128.633335  134.688887  \n",
       "\n",
       "[79 rows x 900 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df_binned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pros 2</th>\n",
       "      <th>Pros 3</th>\n",
       "      <th>Pros 4</th>\n",
       "      <th>Pros 5</th>\n",
       "      <th>Pros 6</th>\n",
       "      <th>Pros 11</th>\n",
       "      <th>Pros 12</th>\n",
       "      <th>Pros 24</th>\n",
       "      <th>Pros 25</th>\n",
       "      <th>Pros 26</th>\n",
       "      <th>...</th>\n",
       "      <th>Normal 24</th>\n",
       "      <th>Normal 25</th>\n",
       "      <th>Normal 26</th>\n",
       "      <th>Normal 27</th>\n",
       "      <th>Normal 28.1</th>\n",
       "      <th>Normal 29</th>\n",
       "      <th>Normal 30.1</th>\n",
       "      <th>Normal 31.1</th>\n",
       "      <th>Normal 17</th>\n",
       "      <th>Normal 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>451.0</th>\n",
       "      <td>155.433335</td>\n",
       "      <td>115.333335</td>\n",
       "      <td>163.450000</td>\n",
       "      <td>214.983330</td>\n",
       "      <td>159.550000</td>\n",
       "      <td>102.766665</td>\n",
       "      <td>195.483330</td>\n",
       "      <td>126.966665</td>\n",
       "      <td>133.03333</td>\n",
       "      <td>211.966670</td>\n",
       "      <td>...</td>\n",
       "      <td>261.616670</td>\n",
       "      <td>300.450000</td>\n",
       "      <td>239.533335</td>\n",
       "      <td>142.233335</td>\n",
       "      <td>437.583335</td>\n",
       "      <td>191.850000</td>\n",
       "      <td>557.250000</td>\n",
       "      <td>272.683335</td>\n",
       "      <td>327.783330</td>\n",
       "      <td>148.966665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453.0</th>\n",
       "      <td>164.133335</td>\n",
       "      <td>100.116670</td>\n",
       "      <td>179.383335</td>\n",
       "      <td>208.933335</td>\n",
       "      <td>151.383335</td>\n",
       "      <td>99.833335</td>\n",
       "      <td>199.900000</td>\n",
       "      <td>123.816670</td>\n",
       "      <td>136.15000</td>\n",
       "      <td>179.033335</td>\n",
       "      <td>...</td>\n",
       "      <td>283.600000</td>\n",
       "      <td>291.133335</td>\n",
       "      <td>227.616670</td>\n",
       "      <td>134.016665</td>\n",
       "      <td>449.700000</td>\n",
       "      <td>188.933330</td>\n",
       "      <td>546.283335</td>\n",
       "      <td>230.550000</td>\n",
       "      <td>337.816665</td>\n",
       "      <td>164.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455.0</th>\n",
       "      <td>172.283335</td>\n",
       "      <td>90.450000</td>\n",
       "      <td>171.083335</td>\n",
       "      <td>213.300000</td>\n",
       "      <td>161.033335</td>\n",
       "      <td>103.683335</td>\n",
       "      <td>202.666665</td>\n",
       "      <td>109.066665</td>\n",
       "      <td>155.73333</td>\n",
       "      <td>163.483335</td>\n",
       "      <td>...</td>\n",
       "      <td>306.316665</td>\n",
       "      <td>272.100000</td>\n",
       "      <td>227.250000</td>\n",
       "      <td>120.716665</td>\n",
       "      <td>453.633335</td>\n",
       "      <td>186.016665</td>\n",
       "      <td>532.983335</td>\n",
       "      <td>242.900000</td>\n",
       "      <td>322.750000</td>\n",
       "      <td>160.683330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457.0</th>\n",
       "      <td>177.200000</td>\n",
       "      <td>84.866670</td>\n",
       "      <td>146.783330</td>\n",
       "      <td>222.550000</td>\n",
       "      <td>180.666665</td>\n",
       "      <td>110.516665</td>\n",
       "      <td>203.066665</td>\n",
       "      <td>87.550000</td>\n",
       "      <td>181.10000</td>\n",
       "      <td>159.650000</td>\n",
       "      <td>...</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>245.966670</td>\n",
       "      <td>232.916665</td>\n",
       "      <td>105.083335</td>\n",
       "      <td>449.433335</td>\n",
       "      <td>183.733335</td>\n",
       "      <td>517.200000</td>\n",
       "      <td>284.966670</td>\n",
       "      <td>289.133335</td>\n",
       "      <td>144.083335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459.0</th>\n",
       "      <td>147.516665</td>\n",
       "      <td>82.383335</td>\n",
       "      <td>125.516665</td>\n",
       "      <td>207.950000</td>\n",
       "      <td>179.650000</td>\n",
       "      <td>99.016670</td>\n",
       "      <td>190.400000</td>\n",
       "      <td>76.700000</td>\n",
       "      <td>148.30000</td>\n",
       "      <td>157.083330</td>\n",
       "      <td>...</td>\n",
       "      <td>286.233335</td>\n",
       "      <td>215.683335</td>\n",
       "      <td>219.500000</td>\n",
       "      <td>103.083335</td>\n",
       "      <td>413.833330</td>\n",
       "      <td>185.716670</td>\n",
       "      <td>487.166665</td>\n",
       "      <td>260.233335</td>\n",
       "      <td>238.933335</td>\n",
       "      <td>134.183330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2241.0</th>\n",
       "      <td>82.350000</td>\n",
       "      <td>55.550000</td>\n",
       "      <td>79.766670</td>\n",
       "      <td>78.733335</td>\n",
       "      <td>58.266665</td>\n",
       "      <td>51.916670</td>\n",
       "      <td>71.850000</td>\n",
       "      <td>70.183330</td>\n",
       "      <td>73.65000</td>\n",
       "      <td>64.066670</td>\n",
       "      <td>...</td>\n",
       "      <td>98.466670</td>\n",
       "      <td>131.800000</td>\n",
       "      <td>70.600000</td>\n",
       "      <td>134.450000</td>\n",
       "      <td>93.466665</td>\n",
       "      <td>50.300000</td>\n",
       "      <td>94.916665</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>152.516670</td>\n",
       "      <td>114.533330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2243.0</th>\n",
       "      <td>78.633330</td>\n",
       "      <td>55.800000</td>\n",
       "      <td>80.550000</td>\n",
       "      <td>74.750000</td>\n",
       "      <td>67.800000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>59.666670</td>\n",
       "      <td>66.083335</td>\n",
       "      <td>74.30000</td>\n",
       "      <td>66.766665</td>\n",
       "      <td>...</td>\n",
       "      <td>98.750000</td>\n",
       "      <td>127.433330</td>\n",
       "      <td>70.600000</td>\n",
       "      <td>141.566665</td>\n",
       "      <td>92.400000</td>\n",
       "      <td>49.333330</td>\n",
       "      <td>93.200000</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>152.166670</td>\n",
       "      <td>116.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245.0</th>\n",
       "      <td>86.300000</td>\n",
       "      <td>68.383335</td>\n",
       "      <td>83.416665</td>\n",
       "      <td>70.900000</td>\n",
       "      <td>77.666670</td>\n",
       "      <td>46.116665</td>\n",
       "      <td>73.283330</td>\n",
       "      <td>64.066665</td>\n",
       "      <td>71.11667</td>\n",
       "      <td>66.016665</td>\n",
       "      <td>...</td>\n",
       "      <td>111.483335</td>\n",
       "      <td>127.433330</td>\n",
       "      <td>82.283335</td>\n",
       "      <td>136.516665</td>\n",
       "      <td>92.400000</td>\n",
       "      <td>56.716670</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>153.250000</td>\n",
       "      <td>118.383335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2247.0</th>\n",
       "      <td>83.366665</td>\n",
       "      <td>52.216665</td>\n",
       "      <td>82.883330</td>\n",
       "      <td>70.900000</td>\n",
       "      <td>56.966670</td>\n",
       "      <td>52.766665</td>\n",
       "      <td>73.816665</td>\n",
       "      <td>61.883335</td>\n",
       "      <td>69.36667</td>\n",
       "      <td>64.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>108.750000</td>\n",
       "      <td>127.433330</td>\n",
       "      <td>87.383335</td>\n",
       "      <td>120.966670</td>\n",
       "      <td>92.400000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>107.583330</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>152.166670</td>\n",
       "      <td>128.633335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249.0</th>\n",
       "      <td>78.833330</td>\n",
       "      <td>54.055557</td>\n",
       "      <td>94.955557</td>\n",
       "      <td>78.277777</td>\n",
       "      <td>53.966667</td>\n",
       "      <td>55.433333</td>\n",
       "      <td>70.411110</td>\n",
       "      <td>62.577777</td>\n",
       "      <td>67.76667</td>\n",
       "      <td>69.866667</td>\n",
       "      <td>...</td>\n",
       "      <td>100.133333</td>\n",
       "      <td>127.433330</td>\n",
       "      <td>79.700000</td>\n",
       "      <td>120.966670</td>\n",
       "      <td>92.400000</td>\n",
       "      <td>49.855553</td>\n",
       "      <td>106.711113</td>\n",
       "      <td>178.200000</td>\n",
       "      <td>152.166670</td>\n",
       "      <td>134.688887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pros 2      Pros 3      Pros 4      Pros 5      Pros 6  \\\n",
       "451.0   155.433335  115.333335  163.450000  214.983330  159.550000   \n",
       "453.0   164.133335  100.116670  179.383335  208.933335  151.383335   \n",
       "455.0   172.283335   90.450000  171.083335  213.300000  161.033335   \n",
       "457.0   177.200000   84.866670  146.783330  222.550000  180.666665   \n",
       "459.0   147.516665   82.383335  125.516665  207.950000  179.650000   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "2241.0   82.350000   55.550000   79.766670   78.733335   58.266665   \n",
       "2243.0   78.633330   55.800000   80.550000   74.750000   67.800000   \n",
       "2245.0   86.300000   68.383335   83.416665   70.900000   77.666670   \n",
       "2247.0   83.366665   52.216665   82.883330   70.900000   56.966670   \n",
       "2249.0   78.833330   54.055557   94.955557   78.277777   53.966667   \n",
       "\n",
       "           Pros 11     Pros 12     Pros 24    Pros 25     Pros 26  ...  \\\n",
       "451.0   102.766665  195.483330  126.966665  133.03333  211.966670  ...   \n",
       "453.0    99.833335  199.900000  123.816670  136.15000  179.033335  ...   \n",
       "455.0   103.683335  202.666665  109.066665  155.73333  163.483335  ...   \n",
       "457.0   110.516665  203.066665   87.550000  181.10000  159.650000  ...   \n",
       "459.0    99.016670  190.400000   76.700000  148.30000  157.083330  ...   \n",
       "...            ...         ...         ...        ...         ...  ...   \n",
       "2241.0   51.916670   71.850000   70.183330   73.65000   64.066670  ...   \n",
       "2243.0   47.500000   59.666670   66.083335   74.30000   66.766665  ...   \n",
       "2245.0   46.116665   73.283330   64.066665   71.11667   66.016665  ...   \n",
       "2247.0   52.766665   73.816665   61.883335   69.36667   64.800000  ...   \n",
       "2249.0   55.433333   70.411110   62.577777   67.76667   69.866667  ...   \n",
       "\n",
       "         Normal 24   Normal 25   Normal 26   Normal 27  Normal 28.1  \\\n",
       "451.0   261.616670  300.450000  239.533335  142.233335   437.583335   \n",
       "453.0   283.600000  291.133335  227.616670  134.016665   449.700000   \n",
       "455.0   306.316665  272.100000  227.250000  120.716665   453.633335   \n",
       "457.0   324.000000  245.966670  232.916665  105.083335   449.433335   \n",
       "459.0   286.233335  215.683335  219.500000  103.083335   413.833330   \n",
       "...            ...         ...         ...         ...          ...   \n",
       "2241.0   98.466670  131.800000   70.600000  134.450000    93.466665   \n",
       "2243.0   98.750000  127.433330   70.600000  141.566665    92.400000   \n",
       "2245.0  111.483335  127.433330   82.283335  136.516665    92.400000   \n",
       "2247.0  108.750000  127.433330   87.383335  120.966670    92.400000   \n",
       "2249.0  100.133333  127.433330   79.700000  120.966670    92.400000   \n",
       "\n",
       "         Normal 29  Normal 30.1  Normal 31.1   Normal 17   Normal 32  \n",
       "451.0   191.850000   557.250000   272.683335  327.783330  148.966665  \n",
       "453.0   188.933330   546.283335   230.550000  337.816665  164.500000  \n",
       "455.0   186.016665   532.983335   242.900000  322.750000  160.683330  \n",
       "457.0   183.733335   517.200000   284.966670  289.133335  144.083335  \n",
       "459.0   185.716670   487.166665   260.233335  238.933335  134.183330  \n",
       "...            ...          ...          ...         ...         ...  \n",
       "2241.0   50.300000    94.916665   178.200000  152.516670  114.533330  \n",
       "2243.0   49.333330    93.200000   178.200000  152.166670  116.850000  \n",
       "2245.0   56.716670    94.500000   178.200000  153.250000  118.383335  \n",
       "2247.0   62.000000   107.583330   178.200000  152.166670  128.633335  \n",
       "2249.0   49.855553   106.711113   178.200000  152.166670  134.688887  \n",
       "\n",
       "[900 rows x 79 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_b = pd.DataFrame(df_binned.T, columns = df_c.columns, index = wv_binned)\n",
    "df_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalization by MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(df_b)\n",
    "df_scaled = scaler.transform(df_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_s = pd.DataFrame(df_scaled, columns = df_b.columns, index = df_b.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pros 2</th>\n",
       "      <th>Pros 3</th>\n",
       "      <th>Pros 4</th>\n",
       "      <th>Pros 5</th>\n",
       "      <th>Pros 6</th>\n",
       "      <th>Pros 11</th>\n",
       "      <th>Pros 12</th>\n",
       "      <th>Pros 24</th>\n",
       "      <th>Pros 25</th>\n",
       "      <th>Pros 26</th>\n",
       "      <th>...</th>\n",
       "      <th>Normal 24</th>\n",
       "      <th>Normal 25</th>\n",
       "      <th>Normal 26</th>\n",
       "      <th>Normal 27</th>\n",
       "      <th>Normal 28.1</th>\n",
       "      <th>Normal 29</th>\n",
       "      <th>Normal 30.1</th>\n",
       "      <th>Normal 31.1</th>\n",
       "      <th>Normal 17</th>\n",
       "      <th>Normal 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>451.0</th>\n",
       "      <td>0.131714</td>\n",
       "      <td>0.117202</td>\n",
       "      <td>0.173539</td>\n",
       "      <td>0.240172</td>\n",
       "      <td>0.157539</td>\n",
       "      <td>0.085757</td>\n",
       "      <td>0.234963</td>\n",
       "      <td>0.142828</td>\n",
       "      <td>0.138503</td>\n",
       "      <td>0.260510</td>\n",
       "      <td>...</td>\n",
       "      <td>0.352059</td>\n",
       "      <td>0.320956</td>\n",
       "      <td>0.329241</td>\n",
       "      <td>0.100854</td>\n",
       "      <td>0.955569</td>\n",
       "      <td>0.167947</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.243808</td>\n",
       "      <td>0.385716</td>\n",
       "      <td>0.129279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453.0</th>\n",
       "      <td>0.146634</td>\n",
       "      <td>0.090235</td>\n",
       "      <td>0.206581</td>\n",
       "      <td>0.230088</td>\n",
       "      <td>0.145569</td>\n",
       "      <td>0.081430</td>\n",
       "      <td>0.242604</td>\n",
       "      <td>0.136428</td>\n",
       "      <td>0.144415</td>\n",
       "      <td>0.202501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.399496</td>\n",
       "      <td>0.303673</td>\n",
       "      <td>0.306016</td>\n",
       "      <td>0.079687</td>\n",
       "      <td>0.989111</td>\n",
       "      <td>0.164509</td>\n",
       "      <td>0.976367</td>\n",
       "      <td>0.144892</td>\n",
       "      <td>0.407753</td>\n",
       "      <td>0.187598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455.0</th>\n",
       "      <td>0.160612</td>\n",
       "      <td>0.073104</td>\n",
       "      <td>0.189369</td>\n",
       "      <td>0.237366</td>\n",
       "      <td>0.159713</td>\n",
       "      <td>0.087109</td>\n",
       "      <td>0.247391</td>\n",
       "      <td>0.106461</td>\n",
       "      <td>0.181562</td>\n",
       "      <td>0.175112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448516</td>\n",
       "      <td>0.268365</td>\n",
       "      <td>0.305301</td>\n",
       "      <td>0.045425</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.161072</td>\n",
       "      <td>0.947707</td>\n",
       "      <td>0.173886</td>\n",
       "      <td>0.374661</td>\n",
       "      <td>0.173268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457.0</th>\n",
       "      <td>0.169044</td>\n",
       "      <td>0.063209</td>\n",
       "      <td>0.138976</td>\n",
       "      <td>0.252785</td>\n",
       "      <td>0.188489</td>\n",
       "      <td>0.097190</td>\n",
       "      <td>0.248083</td>\n",
       "      <td>0.062745</td>\n",
       "      <td>0.229680</td>\n",
       "      <td>0.168360</td>\n",
       "      <td>...</td>\n",
       "      <td>0.486675</td>\n",
       "      <td>0.219886</td>\n",
       "      <td>0.316345</td>\n",
       "      <td>0.005152</td>\n",
       "      <td>0.988373</td>\n",
       "      <td>0.158382</td>\n",
       "      <td>0.913695</td>\n",
       "      <td>0.272645</td>\n",
       "      <td>0.300827</td>\n",
       "      <td>0.110944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459.0</th>\n",
       "      <td>0.118136</td>\n",
       "      <td>0.058808</td>\n",
       "      <td>0.094874</td>\n",
       "      <td>0.228448</td>\n",
       "      <td>0.186999</td>\n",
       "      <td>0.080225</td>\n",
       "      <td>0.226169</td>\n",
       "      <td>0.040702</td>\n",
       "      <td>0.167462</td>\n",
       "      <td>0.163839</td>\n",
       "      <td>...</td>\n",
       "      <td>0.405179</td>\n",
       "      <td>0.163709</td>\n",
       "      <td>0.290197</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.889822</td>\n",
       "      <td>0.160719</td>\n",
       "      <td>0.848975</td>\n",
       "      <td>0.214579</td>\n",
       "      <td>0.190570</td>\n",
       "      <td>0.073775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2241.0</th>\n",
       "      <td>0.006374</td>\n",
       "      <td>0.011254</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013057</td>\n",
       "      <td>0.009087</td>\n",
       "      <td>0.010744</td>\n",
       "      <td>0.021077</td>\n",
       "      <td>0.027462</td>\n",
       "      <td>0.025861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080804</td>\n",
       "      <td>0.002953</td>\n",
       "      <td>0.001139</td>\n",
       "      <td>0.003699</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2243.0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011697</td>\n",
       "      <td>0.001624</td>\n",
       "      <td>0.006418</td>\n",
       "      <td>0.023060</td>\n",
       "      <td>0.004229</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019132</td>\n",
       "      <td>0.027094</td>\n",
       "      <td>0.004756</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099137</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245.0</th>\n",
       "      <td>0.013148</td>\n",
       "      <td>0.033997</td>\n",
       "      <td>0.007569</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037522</td>\n",
       "      <td>0.002188</td>\n",
       "      <td>0.023557</td>\n",
       "      <td>0.015035</td>\n",
       "      <td>0.021055</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028088</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022770</td>\n",
       "      <td>0.086128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008701</td>\n",
       "      <td>0.002801</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.002379</td>\n",
       "      <td>0.014455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2247.0</th>\n",
       "      <td>0.008118</td>\n",
       "      <td>0.005346</td>\n",
       "      <td>0.006463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007182</td>\n",
       "      <td>0.011998</td>\n",
       "      <td>0.024480</td>\n",
       "      <td>0.010599</td>\n",
       "      <td>0.017736</td>\n",
       "      <td>0.001292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022190</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032710</td>\n",
       "      <td>0.046069</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014927</td>\n",
       "      <td>0.030995</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249.0</th>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.008605</td>\n",
       "      <td>0.031498</td>\n",
       "      <td>0.012298</td>\n",
       "      <td>0.002785</td>\n",
       "      <td>0.015932</td>\n",
       "      <td>0.018588</td>\n",
       "      <td>0.012010</td>\n",
       "      <td>0.014701</td>\n",
       "      <td>0.010216</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003596</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017735</td>\n",
       "      <td>0.046069</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000615</td>\n",
       "      <td>0.029116</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.075673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Pros 2    Pros 3    Pros 4    Pros 5    Pros 6   Pros 11   Pros 12  \\\n",
       "451.0   0.131714  0.117202  0.173539  0.240172  0.157539  0.085757  0.234963   \n",
       "453.0   0.146634  0.090235  0.206581  0.230088  0.145569  0.081430  0.242604   \n",
       "455.0   0.160612  0.073104  0.189369  0.237366  0.159713  0.087109  0.247391   \n",
       "457.0   0.169044  0.063209  0.138976  0.252785  0.188489  0.097190  0.248083   \n",
       "459.0   0.118136  0.058808  0.094874  0.228448  0.186999  0.080225  0.226169   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "2241.0  0.006374  0.011254  0.000000  0.013057  0.009087  0.010744  0.021077   \n",
       "2243.0  0.000000  0.011697  0.001624  0.006418  0.023060  0.004229  0.000000   \n",
       "2245.0  0.013148  0.033997  0.007569  0.000000  0.037522  0.002188  0.023557   \n",
       "2247.0  0.008118  0.005346  0.006463  0.000000  0.007182  0.011998  0.024480   \n",
       "2249.0  0.000343  0.008605  0.031498  0.012298  0.002785  0.015932  0.018588   \n",
       "\n",
       "         Pros 24   Pros 25   Pros 26  ...  Normal 24  Normal 25  Normal 26  \\\n",
       "451.0   0.142828  0.138503  0.260510  ...   0.352059   0.320956   0.329241   \n",
       "453.0   0.136428  0.144415  0.202501  ...   0.399496   0.303673   0.306016   \n",
       "455.0   0.106461  0.181562  0.175112  ...   0.448516   0.268365   0.305301   \n",
       "457.0   0.062745  0.229680  0.168360  ...   0.486675   0.219886   0.316345   \n",
       "459.0   0.040702  0.167462  0.163839  ...   0.405179   0.163709   0.290197   \n",
       "...          ...       ...       ...  ...        ...        ...        ...   \n",
       "2241.0  0.027462  0.025861  0.000000  ...   0.000000   0.008100   0.000000   \n",
       "2243.0  0.019132  0.027094  0.004756  ...   0.000611   0.000000   0.000000   \n",
       "2245.0  0.015035  0.021055  0.003435  ...   0.028088   0.000000   0.022770   \n",
       "2247.0  0.010599  0.017736  0.001292  ...   0.022190   0.000000   0.032710   \n",
       "2249.0  0.012010  0.014701  0.010216  ...   0.003596   0.000000   0.017735   \n",
       "\n",
       "        Normal 27  Normal 28.1  Normal 29  Normal 30.1  Normal 31.1  \\\n",
       "451.0    0.100854     0.955569   0.167947     1.000000     0.243808   \n",
       "453.0    0.079687     0.989111   0.164509     0.976367     0.144892   \n",
       "455.0    0.045425     1.000000   0.161072     0.947707     0.173886   \n",
       "457.0    0.005152     0.988373   0.158382     0.913695     0.272645   \n",
       "459.0    0.000000     0.889822   0.160719     0.848975     0.214579   \n",
       "...           ...          ...        ...          ...          ...   \n",
       "2241.0   0.080804     0.002953   0.001139     0.003699     0.021990   \n",
       "2243.0   0.099137     0.000000   0.000000     0.000000     0.021990   \n",
       "2245.0   0.086128     0.000000   0.008701     0.002801     0.021990   \n",
       "2247.0   0.046069     0.000000   0.014927     0.030995     0.021990   \n",
       "2249.0   0.046069     0.000000   0.000615     0.029116     0.021990   \n",
       "\n",
       "        Normal 17  Normal 32  \n",
       "451.0    0.385716   0.129279  \n",
       "453.0    0.407753   0.187598  \n",
       "455.0    0.374661   0.173268  \n",
       "457.0    0.300827   0.110944  \n",
       "459.0    0.190570   0.073775  \n",
       "...           ...        ...  \n",
       "2241.0   0.000769   0.000000  \n",
       "2243.0   0.000000   0.008698  \n",
       "2245.0   0.002379   0.014455  \n",
       "2247.0   0.000000   0.052938  \n",
       "2249.0   0.000000   0.075673  \n",
       "\n",
       "[900 rows x 79 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "%matplotlib inline\n",
    "_ = plt.plot(wv_binned, df_s, color='lightgray')\n",
    "_ = plt.plot(wv_binned, df_s.mean(axis=1), color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the dataframe into Tidy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_s.T\n",
    "df.columns = wv_binned.astype(int)\n",
    "df.columns = df.columns.astype(str) + 'cm-1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>451cm-1</th>\n",
       "      <th>453cm-1</th>\n",
       "      <th>455cm-1</th>\n",
       "      <th>457cm-1</th>\n",
       "      <th>459cm-1</th>\n",
       "      <th>461cm-1</th>\n",
       "      <th>463cm-1</th>\n",
       "      <th>465cm-1</th>\n",
       "      <th>467cm-1</th>\n",
       "      <th>469cm-1</th>\n",
       "      <th>...</th>\n",
       "      <th>2231cm-1</th>\n",
       "      <th>2233cm-1</th>\n",
       "      <th>2235cm-1</th>\n",
       "      <th>2237cm-1</th>\n",
       "      <th>2239cm-1</th>\n",
       "      <th>2241cm-1</th>\n",
       "      <th>2243cm-1</th>\n",
       "      <th>2245cm-1</th>\n",
       "      <th>2247cm-1</th>\n",
       "      <th>2249cm-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pros 2</th>\n",
       "      <td>0.131714</td>\n",
       "      <td>0.146634</td>\n",
       "      <td>0.160612</td>\n",
       "      <td>0.169044</td>\n",
       "      <td>0.118136</td>\n",
       "      <td>0.033843</td>\n",
       "      <td>0.029498</td>\n",
       "      <td>0.078634</td>\n",
       "      <td>0.100243</td>\n",
       "      <td>0.099071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.006974</td>\n",
       "      <td>0.016093</td>\n",
       "      <td>0.017550</td>\n",
       "      <td>0.022067</td>\n",
       "      <td>0.006374</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013148</td>\n",
       "      <td>0.008118</td>\n",
       "      <td>0.000343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pros 3</th>\n",
       "      <td>0.117202</td>\n",
       "      <td>0.090235</td>\n",
       "      <td>0.073104</td>\n",
       "      <td>0.063209</td>\n",
       "      <td>0.058808</td>\n",
       "      <td>0.058690</td>\n",
       "      <td>0.067137</td>\n",
       "      <td>0.080045</td>\n",
       "      <td>0.075378</td>\n",
       "      <td>0.058158</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051453</td>\n",
       "      <td>0.031427</td>\n",
       "      <td>0.012583</td>\n",
       "      <td>0.015595</td>\n",
       "      <td>0.019583</td>\n",
       "      <td>0.011254</td>\n",
       "      <td>0.011697</td>\n",
       "      <td>0.033997</td>\n",
       "      <td>0.005346</td>\n",
       "      <td>0.008605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pros 4</th>\n",
       "      <td>0.173539</td>\n",
       "      <td>0.206581</td>\n",
       "      <td>0.189369</td>\n",
       "      <td>0.138976</td>\n",
       "      <td>0.094874</td>\n",
       "      <td>0.059240</td>\n",
       "      <td>0.065842</td>\n",
       "      <td>0.100612</td>\n",
       "      <td>0.114333</td>\n",
       "      <td>0.122317</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003318</td>\n",
       "      <td>0.023157</td>\n",
       "      <td>0.026959</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001624</td>\n",
       "      <td>0.007569</td>\n",
       "      <td>0.006463</td>\n",
       "      <td>0.031498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pros 5</th>\n",
       "      <td>0.240172</td>\n",
       "      <td>0.230088</td>\n",
       "      <td>0.237366</td>\n",
       "      <td>0.252785</td>\n",
       "      <td>0.228448</td>\n",
       "      <td>0.178997</td>\n",
       "      <td>0.153716</td>\n",
       "      <td>0.146854</td>\n",
       "      <td>0.151410</td>\n",
       "      <td>0.162495</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013946</td>\n",
       "      <td>0.009390</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.004917</td>\n",
       "      <td>0.025642</td>\n",
       "      <td>0.013057</td>\n",
       "      <td>0.006418</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pros 6</th>\n",
       "      <td>0.157539</td>\n",
       "      <td>0.145569</td>\n",
       "      <td>0.159713</td>\n",
       "      <td>0.188489</td>\n",
       "      <td>0.186999</td>\n",
       "      <td>0.164452</td>\n",
       "      <td>0.140952</td>\n",
       "      <td>0.118038</td>\n",
       "      <td>0.101304</td>\n",
       "      <td>0.093487</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006376</td>\n",
       "      <td>0.011213</td>\n",
       "      <td>0.007842</td>\n",
       "      <td>0.010993</td>\n",
       "      <td>0.009307</td>\n",
       "      <td>0.009087</td>\n",
       "      <td>0.023060</td>\n",
       "      <td>0.037522</td>\n",
       "      <td>0.007182</td>\n",
       "      <td>0.002785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal 29</th>\n",
       "      <td>0.167947</td>\n",
       "      <td>0.164509</td>\n",
       "      <td>0.161072</td>\n",
       "      <td>0.158382</td>\n",
       "      <td>0.160719</td>\n",
       "      <td>0.164352</td>\n",
       "      <td>0.142610</td>\n",
       "      <td>0.105156</td>\n",
       "      <td>0.089973</td>\n",
       "      <td>0.091957</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019660</td>\n",
       "      <td>0.021212</td>\n",
       "      <td>0.009506</td>\n",
       "      <td>0.012570</td>\n",
       "      <td>0.008917</td>\n",
       "      <td>0.001139</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008701</td>\n",
       "      <td>0.014927</td>\n",
       "      <td>0.000615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal 30.1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976367</td>\n",
       "      <td>0.947707</td>\n",
       "      <td>0.913695</td>\n",
       "      <td>0.848975</td>\n",
       "      <td>0.765543</td>\n",
       "      <td>0.705061</td>\n",
       "      <td>0.663865</td>\n",
       "      <td>0.659017</td>\n",
       "      <td>0.664512</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045505</td>\n",
       "      <td>0.053083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003699</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002801</td>\n",
       "      <td>0.030995</td>\n",
       "      <td>0.029116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal 31.1</th>\n",
       "      <td>0.243808</td>\n",
       "      <td>0.144892</td>\n",
       "      <td>0.173886</td>\n",
       "      <td>0.272645</td>\n",
       "      <td>0.214579</td>\n",
       "      <td>0.056932</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016512</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal 17</th>\n",
       "      <td>0.385716</td>\n",
       "      <td>0.407753</td>\n",
       "      <td>0.374661</td>\n",
       "      <td>0.300827</td>\n",
       "      <td>0.190570</td>\n",
       "      <td>0.067135</td>\n",
       "      <td>0.070796</td>\n",
       "      <td>0.151768</td>\n",
       "      <td>0.123289</td>\n",
       "      <td>0.018925</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002379</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal 32</th>\n",
       "      <td>0.129279</td>\n",
       "      <td>0.187598</td>\n",
       "      <td>0.173268</td>\n",
       "      <td>0.110944</td>\n",
       "      <td>0.073775</td>\n",
       "      <td>0.059195</td>\n",
       "      <td>0.095739</td>\n",
       "      <td>0.157625</td>\n",
       "      <td>0.128340</td>\n",
       "      <td>0.018397</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018772</td>\n",
       "      <td>0.001314</td>\n",
       "      <td>0.036669</td>\n",
       "      <td>0.060009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008698</td>\n",
       "      <td>0.014455</td>\n",
       "      <td>0.052938</td>\n",
       "      <td>0.075673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 900 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              451cm-1   453cm-1   455cm-1   457cm-1   459cm-1   461cm-1  \\\n",
       "Pros 2       0.131714  0.146634  0.160612  0.169044  0.118136  0.033843   \n",
       "Pros 3       0.117202  0.090235  0.073104  0.063209  0.058808  0.058690   \n",
       "Pros 4       0.173539  0.206581  0.189369  0.138976  0.094874  0.059240   \n",
       "Pros 5       0.240172  0.230088  0.237366  0.252785  0.228448  0.178997   \n",
       "Pros 6       0.157539  0.145569  0.159713  0.188489  0.186999  0.164452   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "Normal 29    0.167947  0.164509  0.161072  0.158382  0.160719  0.164352   \n",
       "Normal 30.1  1.000000  0.976367  0.947707  0.913695  0.848975  0.765543   \n",
       "Normal 31.1  0.243808  0.144892  0.173886  0.272645  0.214579  0.056932   \n",
       "Normal 17    0.385716  0.407753  0.374661  0.300827  0.190570  0.067135   \n",
       "Normal 32    0.129279  0.187598  0.173268  0.110944  0.073775  0.059195   \n",
       "\n",
       "              463cm-1   465cm-1   467cm-1   469cm-1  ...  2231cm-1  2233cm-1  \\\n",
       "Pros 2       0.029498  0.078634  0.100243  0.099071  ...  0.000314  0.006974   \n",
       "Pros 3       0.067137  0.080045  0.075378  0.058158  ...  0.051453  0.031427   \n",
       "Pros 4       0.065842  0.100612  0.114333  0.122317  ...  0.000000  0.000000   \n",
       "Pros 5       0.153716  0.146854  0.151410  0.162495  ...  0.013946  0.009390   \n",
       "Pros 6       0.140952  0.118038  0.101304  0.093487  ...  0.006376  0.011213   \n",
       "...               ...       ...       ...       ...  ...       ...       ...   \n",
       "Normal 29    0.142610  0.105156  0.089973  0.091957  ...  0.019660  0.021212   \n",
       "Normal 30.1  0.705061  0.663865  0.659017  0.664512  ...  0.045505  0.053083   \n",
       "Normal 31.1  0.000000  0.016512  0.021990  0.021990  ...  0.021990  0.021990   \n",
       "Normal 17    0.070796  0.151768  0.123289  0.018925  ...  0.000000  0.000000   \n",
       "Normal 32    0.095739  0.157625  0.128340  0.018397  ...  0.018772  0.001314   \n",
       "\n",
       "             2235cm-1  2237cm-1  2239cm-1  2241cm-1  2243cm-1  2245cm-1  \\\n",
       "Pros 2       0.016093  0.017550  0.022067  0.006374  0.000000  0.013148   \n",
       "Pros 3       0.012583  0.015595  0.019583  0.011254  0.011697  0.033997   \n",
       "Pros 4       0.003318  0.023157  0.026959  0.000000  0.001624  0.007569   \n",
       "Pros 5       0.008696  0.004917  0.025642  0.013057  0.006418  0.000000   \n",
       "Pros 6       0.007842  0.010993  0.009307  0.009087  0.023060  0.037522   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "Normal 29    0.009506  0.012570  0.008917  0.001139  0.000000  0.008701   \n",
       "Normal 30.1  0.000000  0.000000  0.000000  0.003699  0.000000  0.002801   \n",
       "Normal 31.1  0.021990  0.021990  0.021990  0.021990  0.021990  0.021990   \n",
       "Normal 17    0.006077  0.000000  0.000988  0.000769  0.000000  0.002379   \n",
       "Normal 32    0.036669  0.060009  0.000000  0.000000  0.008698  0.014455   \n",
       "\n",
       "             2247cm-1  2249cm-1  \n",
       "Pros 2       0.008118  0.000343  \n",
       "Pros 3       0.005346  0.008605  \n",
       "Pros 4       0.006463  0.031498  \n",
       "Pros 5       0.000000  0.012298  \n",
       "Pros 6       0.007182  0.002785  \n",
       "...               ...       ...  \n",
       "Normal 29    0.014927  0.000615  \n",
       "Normal 30.1  0.030995  0.029116  \n",
       "Normal 31.1  0.021990  0.021990  \n",
       "Normal 17    0.000000  0.000000  \n",
       "Normal 32    0.052938  0.075673  \n",
       "\n",
       "[79 rows x 900 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>451cm-1</th>\n",
       "      <th>453cm-1</th>\n",
       "      <th>455cm-1</th>\n",
       "      <th>457cm-1</th>\n",
       "      <th>459cm-1</th>\n",
       "      <th>461cm-1</th>\n",
       "      <th>463cm-1</th>\n",
       "      <th>465cm-1</th>\n",
       "      <th>467cm-1</th>\n",
       "      <th>...</th>\n",
       "      <th>2231cm-1</th>\n",
       "      <th>2233cm-1</th>\n",
       "      <th>2235cm-1</th>\n",
       "      <th>2237cm-1</th>\n",
       "      <th>2239cm-1</th>\n",
       "      <th>2241cm-1</th>\n",
       "      <th>2243cm-1</th>\n",
       "      <th>2245cm-1</th>\n",
       "      <th>2247cm-1</th>\n",
       "      <th>2249cm-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pros 2</th>\n",
       "      <td>P</td>\n",
       "      <td>0.131714</td>\n",
       "      <td>0.146634</td>\n",
       "      <td>0.160612</td>\n",
       "      <td>0.169044</td>\n",
       "      <td>0.118136</td>\n",
       "      <td>0.033843</td>\n",
       "      <td>0.029498</td>\n",
       "      <td>0.078634</td>\n",
       "      <td>0.100243</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.006974</td>\n",
       "      <td>0.016093</td>\n",
       "      <td>0.017550</td>\n",
       "      <td>0.022067</td>\n",
       "      <td>0.006374</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013148</td>\n",
       "      <td>0.008118</td>\n",
       "      <td>0.000343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pros 3</th>\n",
       "      <td>P</td>\n",
       "      <td>0.117202</td>\n",
       "      <td>0.090235</td>\n",
       "      <td>0.073104</td>\n",
       "      <td>0.063209</td>\n",
       "      <td>0.058808</td>\n",
       "      <td>0.058690</td>\n",
       "      <td>0.067137</td>\n",
       "      <td>0.080045</td>\n",
       "      <td>0.075378</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051453</td>\n",
       "      <td>0.031427</td>\n",
       "      <td>0.012583</td>\n",
       "      <td>0.015595</td>\n",
       "      <td>0.019583</td>\n",
       "      <td>0.011254</td>\n",
       "      <td>0.011697</td>\n",
       "      <td>0.033997</td>\n",
       "      <td>0.005346</td>\n",
       "      <td>0.008605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pros 4</th>\n",
       "      <td>P</td>\n",
       "      <td>0.173539</td>\n",
       "      <td>0.206581</td>\n",
       "      <td>0.189369</td>\n",
       "      <td>0.138976</td>\n",
       "      <td>0.094874</td>\n",
       "      <td>0.059240</td>\n",
       "      <td>0.065842</td>\n",
       "      <td>0.100612</td>\n",
       "      <td>0.114333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003318</td>\n",
       "      <td>0.023157</td>\n",
       "      <td>0.026959</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001624</td>\n",
       "      <td>0.007569</td>\n",
       "      <td>0.006463</td>\n",
       "      <td>0.031498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pros 5</th>\n",
       "      <td>P</td>\n",
       "      <td>0.240172</td>\n",
       "      <td>0.230088</td>\n",
       "      <td>0.237366</td>\n",
       "      <td>0.252785</td>\n",
       "      <td>0.228448</td>\n",
       "      <td>0.178997</td>\n",
       "      <td>0.153716</td>\n",
       "      <td>0.146854</td>\n",
       "      <td>0.151410</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013946</td>\n",
       "      <td>0.009390</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.004917</td>\n",
       "      <td>0.025642</td>\n",
       "      <td>0.013057</td>\n",
       "      <td>0.006418</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pros 6</th>\n",
       "      <td>P</td>\n",
       "      <td>0.157539</td>\n",
       "      <td>0.145569</td>\n",
       "      <td>0.159713</td>\n",
       "      <td>0.188489</td>\n",
       "      <td>0.186999</td>\n",
       "      <td>0.164452</td>\n",
       "      <td>0.140952</td>\n",
       "      <td>0.118038</td>\n",
       "      <td>0.101304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006376</td>\n",
       "      <td>0.011213</td>\n",
       "      <td>0.007842</td>\n",
       "      <td>0.010993</td>\n",
       "      <td>0.009307</td>\n",
       "      <td>0.009087</td>\n",
       "      <td>0.023060</td>\n",
       "      <td>0.037522</td>\n",
       "      <td>0.007182</td>\n",
       "      <td>0.002785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal 29</th>\n",
       "      <td>N</td>\n",
       "      <td>0.167947</td>\n",
       "      <td>0.164509</td>\n",
       "      <td>0.161072</td>\n",
       "      <td>0.158382</td>\n",
       "      <td>0.160719</td>\n",
       "      <td>0.164352</td>\n",
       "      <td>0.142610</td>\n",
       "      <td>0.105156</td>\n",
       "      <td>0.089973</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019660</td>\n",
       "      <td>0.021212</td>\n",
       "      <td>0.009506</td>\n",
       "      <td>0.012570</td>\n",
       "      <td>0.008917</td>\n",
       "      <td>0.001139</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008701</td>\n",
       "      <td>0.014927</td>\n",
       "      <td>0.000615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal 30.1</th>\n",
       "      <td>N</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976367</td>\n",
       "      <td>0.947707</td>\n",
       "      <td>0.913695</td>\n",
       "      <td>0.848975</td>\n",
       "      <td>0.765543</td>\n",
       "      <td>0.705061</td>\n",
       "      <td>0.663865</td>\n",
       "      <td>0.659017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045505</td>\n",
       "      <td>0.053083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003699</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002801</td>\n",
       "      <td>0.030995</td>\n",
       "      <td>0.029116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal 31.1</th>\n",
       "      <td>N</td>\n",
       "      <td>0.243808</td>\n",
       "      <td>0.144892</td>\n",
       "      <td>0.173886</td>\n",
       "      <td>0.272645</td>\n",
       "      <td>0.214579</td>\n",
       "      <td>0.056932</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016512</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal 17</th>\n",
       "      <td>N</td>\n",
       "      <td>0.385716</td>\n",
       "      <td>0.407753</td>\n",
       "      <td>0.374661</td>\n",
       "      <td>0.300827</td>\n",
       "      <td>0.190570</td>\n",
       "      <td>0.067135</td>\n",
       "      <td>0.070796</td>\n",
       "      <td>0.151768</td>\n",
       "      <td>0.123289</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002379</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal 32</th>\n",
       "      <td>N</td>\n",
       "      <td>0.129279</td>\n",
       "      <td>0.187598</td>\n",
       "      <td>0.173268</td>\n",
       "      <td>0.110944</td>\n",
       "      <td>0.073775</td>\n",
       "      <td>0.059195</td>\n",
       "      <td>0.095739</td>\n",
       "      <td>0.157625</td>\n",
       "      <td>0.128340</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018772</td>\n",
       "      <td>0.001314</td>\n",
       "      <td>0.036669</td>\n",
       "      <td>0.060009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008698</td>\n",
       "      <td>0.014455</td>\n",
       "      <td>0.052938</td>\n",
       "      <td>0.075673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 901 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Class   451cm-1   453cm-1   455cm-1   457cm-1   459cm-1   461cm-1  \\\n",
       "Pros 2          P  0.131714  0.146634  0.160612  0.169044  0.118136  0.033843   \n",
       "Pros 3          P  0.117202  0.090235  0.073104  0.063209  0.058808  0.058690   \n",
       "Pros 4          P  0.173539  0.206581  0.189369  0.138976  0.094874  0.059240   \n",
       "Pros 5          P  0.240172  0.230088  0.237366  0.252785  0.228448  0.178997   \n",
       "Pros 6          P  0.157539  0.145569  0.159713  0.188489  0.186999  0.164452   \n",
       "...           ...       ...       ...       ...       ...       ...       ...   \n",
       "Normal 29       N  0.167947  0.164509  0.161072  0.158382  0.160719  0.164352   \n",
       "Normal 30.1     N  1.000000  0.976367  0.947707  0.913695  0.848975  0.765543   \n",
       "Normal 31.1     N  0.243808  0.144892  0.173886  0.272645  0.214579  0.056932   \n",
       "Normal 17       N  0.385716  0.407753  0.374661  0.300827  0.190570  0.067135   \n",
       "Normal 32       N  0.129279  0.187598  0.173268  0.110944  0.073775  0.059195   \n",
       "\n",
       "              463cm-1   465cm-1   467cm-1  ...  2231cm-1  2233cm-1  2235cm-1  \\\n",
       "Pros 2       0.029498  0.078634  0.100243  ...  0.000314  0.006974  0.016093   \n",
       "Pros 3       0.067137  0.080045  0.075378  ...  0.051453  0.031427  0.012583   \n",
       "Pros 4       0.065842  0.100612  0.114333  ...  0.000000  0.000000  0.003318   \n",
       "Pros 5       0.153716  0.146854  0.151410  ...  0.013946  0.009390  0.008696   \n",
       "Pros 6       0.140952  0.118038  0.101304  ...  0.006376  0.011213  0.007842   \n",
       "...               ...       ...       ...  ...       ...       ...       ...   \n",
       "Normal 29    0.142610  0.105156  0.089973  ...  0.019660  0.021212  0.009506   \n",
       "Normal 30.1  0.705061  0.663865  0.659017  ...  0.045505  0.053083  0.000000   \n",
       "Normal 31.1  0.000000  0.016512  0.021990  ...  0.021990  0.021990  0.021990   \n",
       "Normal 17    0.070796  0.151768  0.123289  ...  0.000000  0.000000  0.006077   \n",
       "Normal 32    0.095739  0.157625  0.128340  ...  0.018772  0.001314  0.036669   \n",
       "\n",
       "             2237cm-1  2239cm-1  2241cm-1  2243cm-1  2245cm-1  2247cm-1  \\\n",
       "Pros 2       0.017550  0.022067  0.006374  0.000000  0.013148  0.008118   \n",
       "Pros 3       0.015595  0.019583  0.011254  0.011697  0.033997  0.005346   \n",
       "Pros 4       0.023157  0.026959  0.000000  0.001624  0.007569  0.006463   \n",
       "Pros 5       0.004917  0.025642  0.013057  0.006418  0.000000  0.000000   \n",
       "Pros 6       0.010993  0.009307  0.009087  0.023060  0.037522  0.007182   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "Normal 29    0.012570  0.008917  0.001139  0.000000  0.008701  0.014927   \n",
       "Normal 30.1  0.000000  0.000000  0.003699  0.000000  0.002801  0.030995   \n",
       "Normal 31.1  0.021990  0.021990  0.021990  0.021990  0.021990  0.021990   \n",
       "Normal 17    0.000000  0.000988  0.000769  0.000000  0.002379  0.000000   \n",
       "Normal 32    0.060009  0.000000  0.000000  0.008698  0.014455  0.052938   \n",
       "\n",
       "             2249cm-1  \n",
       "Pros 2       0.000343  \n",
       "Pros 3       0.008605  \n",
       "Pros 4       0.031498  \n",
       "Pros 5       0.012298  \n",
       "Pros 6       0.002785  \n",
       "...               ...  \n",
       "Normal 29    0.000615  \n",
       "Normal 30.1  0.029116  \n",
       "Normal 31.1  0.021990  \n",
       "Normal 17    0.000000  \n",
       "Normal 32    0.075673  \n",
       "\n",
       "[79 rows x 901 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.insert(0,'Class', y)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(inplace = True)\n",
    "df.index = df.index +1\n",
    "df.rename(columns = {'index': 'SampleID'}, inplace = True)\n",
    "df.index.name = 'Idx'\n",
    "df.insert(1,'SampleType','Sample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SampleID</th>\n",
       "      <th>SampleType</th>\n",
       "      <th>Class</th>\n",
       "      <th>451cm-1</th>\n",
       "      <th>453cm-1</th>\n",
       "      <th>455cm-1</th>\n",
       "      <th>457cm-1</th>\n",
       "      <th>459cm-1</th>\n",
       "      <th>461cm-1</th>\n",
       "      <th>463cm-1</th>\n",
       "      <th>...</th>\n",
       "      <th>2231cm-1</th>\n",
       "      <th>2233cm-1</th>\n",
       "      <th>2235cm-1</th>\n",
       "      <th>2237cm-1</th>\n",
       "      <th>2239cm-1</th>\n",
       "      <th>2241cm-1</th>\n",
       "      <th>2243cm-1</th>\n",
       "      <th>2245cm-1</th>\n",
       "      <th>2247cm-1</th>\n",
       "      <th>2249cm-1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idx</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pros 2</td>\n",
       "      <td>Sample</td>\n",
       "      <td>P</td>\n",
       "      <td>0.131714</td>\n",
       "      <td>0.146634</td>\n",
       "      <td>0.160612</td>\n",
       "      <td>0.169044</td>\n",
       "      <td>0.118136</td>\n",
       "      <td>0.033843</td>\n",
       "      <td>0.029498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.006974</td>\n",
       "      <td>0.016093</td>\n",
       "      <td>0.017550</td>\n",
       "      <td>0.022067</td>\n",
       "      <td>0.006374</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013148</td>\n",
       "      <td>0.008118</td>\n",
       "      <td>0.000343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pros 3</td>\n",
       "      <td>Sample</td>\n",
       "      <td>P</td>\n",
       "      <td>0.117202</td>\n",
       "      <td>0.090235</td>\n",
       "      <td>0.073104</td>\n",
       "      <td>0.063209</td>\n",
       "      <td>0.058808</td>\n",
       "      <td>0.058690</td>\n",
       "      <td>0.067137</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051453</td>\n",
       "      <td>0.031427</td>\n",
       "      <td>0.012583</td>\n",
       "      <td>0.015595</td>\n",
       "      <td>0.019583</td>\n",
       "      <td>0.011254</td>\n",
       "      <td>0.011697</td>\n",
       "      <td>0.033997</td>\n",
       "      <td>0.005346</td>\n",
       "      <td>0.008605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pros 4</td>\n",
       "      <td>Sample</td>\n",
       "      <td>P</td>\n",
       "      <td>0.173539</td>\n",
       "      <td>0.206581</td>\n",
       "      <td>0.189369</td>\n",
       "      <td>0.138976</td>\n",
       "      <td>0.094874</td>\n",
       "      <td>0.059240</td>\n",
       "      <td>0.065842</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003318</td>\n",
       "      <td>0.023157</td>\n",
       "      <td>0.026959</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001624</td>\n",
       "      <td>0.007569</td>\n",
       "      <td>0.006463</td>\n",
       "      <td>0.031498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pros 5</td>\n",
       "      <td>Sample</td>\n",
       "      <td>P</td>\n",
       "      <td>0.240172</td>\n",
       "      <td>0.230088</td>\n",
       "      <td>0.237366</td>\n",
       "      <td>0.252785</td>\n",
       "      <td>0.228448</td>\n",
       "      <td>0.178997</td>\n",
       "      <td>0.153716</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013946</td>\n",
       "      <td>0.009390</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.004917</td>\n",
       "      <td>0.025642</td>\n",
       "      <td>0.013057</td>\n",
       "      <td>0.006418</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pros 6</td>\n",
       "      <td>Sample</td>\n",
       "      <td>P</td>\n",
       "      <td>0.157539</td>\n",
       "      <td>0.145569</td>\n",
       "      <td>0.159713</td>\n",
       "      <td>0.188489</td>\n",
       "      <td>0.186999</td>\n",
       "      <td>0.164452</td>\n",
       "      <td>0.140952</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006376</td>\n",
       "      <td>0.011213</td>\n",
       "      <td>0.007842</td>\n",
       "      <td>0.010993</td>\n",
       "      <td>0.009307</td>\n",
       "      <td>0.009087</td>\n",
       "      <td>0.023060</td>\n",
       "      <td>0.037522</td>\n",
       "      <td>0.007182</td>\n",
       "      <td>0.002785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Normal 29</td>\n",
       "      <td>Sample</td>\n",
       "      <td>N</td>\n",
       "      <td>0.167947</td>\n",
       "      <td>0.164509</td>\n",
       "      <td>0.161072</td>\n",
       "      <td>0.158382</td>\n",
       "      <td>0.160719</td>\n",
       "      <td>0.164352</td>\n",
       "      <td>0.142610</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019660</td>\n",
       "      <td>0.021212</td>\n",
       "      <td>0.009506</td>\n",
       "      <td>0.012570</td>\n",
       "      <td>0.008917</td>\n",
       "      <td>0.001139</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008701</td>\n",
       "      <td>0.014927</td>\n",
       "      <td>0.000615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Normal 30.1</td>\n",
       "      <td>Sample</td>\n",
       "      <td>N</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976367</td>\n",
       "      <td>0.947707</td>\n",
       "      <td>0.913695</td>\n",
       "      <td>0.848975</td>\n",
       "      <td>0.765543</td>\n",
       "      <td>0.705061</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045505</td>\n",
       "      <td>0.053083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003699</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002801</td>\n",
       "      <td>0.030995</td>\n",
       "      <td>0.029116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Normal 31.1</td>\n",
       "      <td>Sample</td>\n",
       "      <td>N</td>\n",
       "      <td>0.243808</td>\n",
       "      <td>0.144892</td>\n",
       "      <td>0.173886</td>\n",
       "      <td>0.272645</td>\n",
       "      <td>0.214579</td>\n",
       "      <td>0.056932</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.021990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Normal 17</td>\n",
       "      <td>Sample</td>\n",
       "      <td>N</td>\n",
       "      <td>0.385716</td>\n",
       "      <td>0.407753</td>\n",
       "      <td>0.374661</td>\n",
       "      <td>0.300827</td>\n",
       "      <td>0.190570</td>\n",
       "      <td>0.067135</td>\n",
       "      <td>0.070796</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002379</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Normal 32</td>\n",
       "      <td>Sample</td>\n",
       "      <td>N</td>\n",
       "      <td>0.129279</td>\n",
       "      <td>0.187598</td>\n",
       "      <td>0.173268</td>\n",
       "      <td>0.110944</td>\n",
       "      <td>0.073775</td>\n",
       "      <td>0.059195</td>\n",
       "      <td>0.095739</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018772</td>\n",
       "      <td>0.001314</td>\n",
       "      <td>0.036669</td>\n",
       "      <td>0.060009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008698</td>\n",
       "      <td>0.014455</td>\n",
       "      <td>0.052938</td>\n",
       "      <td>0.075673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 903 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SampleID SampleType Class   451cm-1   453cm-1   455cm-1   457cm-1  \\\n",
       "Idx                                                                         \n",
       "1         Pros 2     Sample     P  0.131714  0.146634  0.160612  0.169044   \n",
       "2         Pros 3     Sample     P  0.117202  0.090235  0.073104  0.063209   \n",
       "3         Pros 4     Sample     P  0.173539  0.206581  0.189369  0.138976   \n",
       "4         Pros 5     Sample     P  0.240172  0.230088  0.237366  0.252785   \n",
       "5         Pros 6     Sample     P  0.157539  0.145569  0.159713  0.188489   \n",
       "..           ...        ...   ...       ...       ...       ...       ...   \n",
       "75     Normal 29     Sample     N  0.167947  0.164509  0.161072  0.158382   \n",
       "76   Normal 30.1     Sample     N  1.000000  0.976367  0.947707  0.913695   \n",
       "77   Normal 31.1     Sample     N  0.243808  0.144892  0.173886  0.272645   \n",
       "78     Normal 17     Sample     N  0.385716  0.407753  0.374661  0.300827   \n",
       "79     Normal 32     Sample     N  0.129279  0.187598  0.173268  0.110944   \n",
       "\n",
       "      459cm-1   461cm-1   463cm-1  ...  2231cm-1  2233cm-1  2235cm-1  \\\n",
       "Idx                                ...                                 \n",
       "1    0.118136  0.033843  0.029498  ...  0.000314  0.006974  0.016093   \n",
       "2    0.058808  0.058690  0.067137  ...  0.051453  0.031427  0.012583   \n",
       "3    0.094874  0.059240  0.065842  ...  0.000000  0.000000  0.003318   \n",
       "4    0.228448  0.178997  0.153716  ...  0.013946  0.009390  0.008696   \n",
       "5    0.186999  0.164452  0.140952  ...  0.006376  0.011213  0.007842   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "75   0.160719  0.164352  0.142610  ...  0.019660  0.021212  0.009506   \n",
       "76   0.848975  0.765543  0.705061  ...  0.045505  0.053083  0.000000   \n",
       "77   0.214579  0.056932  0.000000  ...  0.021990  0.021990  0.021990   \n",
       "78   0.190570  0.067135  0.070796  ...  0.000000  0.000000  0.006077   \n",
       "79   0.073775  0.059195  0.095739  ...  0.018772  0.001314  0.036669   \n",
       "\n",
       "     2237cm-1  2239cm-1  2241cm-1  2243cm-1  2245cm-1  2247cm-1  2249cm-1  \n",
       "Idx                                                                        \n",
       "1    0.017550  0.022067  0.006374  0.000000  0.013148  0.008118  0.000343  \n",
       "2    0.015595  0.019583  0.011254  0.011697  0.033997  0.005346  0.008605  \n",
       "3    0.023157  0.026959  0.000000  0.001624  0.007569  0.006463  0.031498  \n",
       "4    0.004917  0.025642  0.013057  0.006418  0.000000  0.000000  0.012298  \n",
       "5    0.010993  0.009307  0.009087  0.023060  0.037522  0.007182  0.002785  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "75   0.012570  0.008917  0.001139  0.000000  0.008701  0.014927  0.000615  \n",
       "76   0.000000  0.000000  0.003699  0.000000  0.002801  0.030995  0.029116  \n",
       "77   0.021990  0.021990  0.021990  0.021990  0.021990  0.021990  0.021990  \n",
       "78   0.000000  0.000988  0.000769  0.000000  0.002379  0.000000  0.000000  \n",
       "79   0.060009  0.000000  0.000000  0.008698  0.014455  0.052938  0.075673  \n",
       "\n",
       "[79 rows x 903 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['451cm-1', '453cm-1', '455cm-1', '457cm-1', '459cm-1', '461cm-1',\n",
       "       '463cm-1', '465cm-1', '467cm-1', '469cm-1',\n",
       "       ...\n",
       "       '2231cm-1', '2233cm-1', '2235cm-1', '2237cm-1', '2239cm-1', '2241cm-1',\n",
       "       '2243cm-1', '2245cm-1', '2247cm-1', '2249cm-1'],\n",
       "      dtype='object', length=900)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wavenumber = df.columns[3:]\n",
    "wavenumber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_peak = pd.DataFrame()\n",
    "df_peak['Name'] = wavenumber\n",
    "df_peak['Label'] = wavenumber\n",
    "df_peak['Perc_missing'] = 0.\n",
    "df_peak['QC_RSD'] = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Label</th>\n",
       "      <th>Perc_missing</th>\n",
       "      <th>QC_RSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>451cm-1</td>\n",
       "      <td>451cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>453cm-1</td>\n",
       "      <td>453cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>455cm-1</td>\n",
       "      <td>455cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>457cm-1</td>\n",
       "      <td>457cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>459cm-1</td>\n",
       "      <td>459cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>2241cm-1</td>\n",
       "      <td>2241cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>2243cm-1</td>\n",
       "      <td>2243cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>2245cm-1</td>\n",
       "      <td>2245cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>2247cm-1</td>\n",
       "      <td>2247cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>2249cm-1</td>\n",
       "      <td>2249cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name     Label  Perc_missing  QC_RSD\n",
       "0     451cm-1   451cm-1           0.0     0.0\n",
       "1     453cm-1   453cm-1           0.0     0.0\n",
       "2     455cm-1   455cm-1           0.0     0.0\n",
       "3     457cm-1   457cm-1           0.0     0.0\n",
       "4     459cm-1   459cm-1           0.0     0.0\n",
       "..        ...       ...           ...     ...\n",
       "895  2241cm-1  2241cm-1           0.0     0.0\n",
       "896  2243cm-1  2243cm-1           0.0     0.0\n",
       "897  2245cm-1  2245cm-1           0.0     0.0\n",
       "898  2247cm-1  2247cm-1           0.0     0.0\n",
       "899  2249cm-1  2249cm-1           0.0     0.0\n",
       "\n",
       "[900 rows x 4 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_peak.index.rename = 'Idx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Label</th>\n",
       "      <th>Perc_missing</th>\n",
       "      <th>QC_RSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>451cm-1</td>\n",
       "      <td>451cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>453cm-1</td>\n",
       "      <td>453cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>455cm-1</td>\n",
       "      <td>455cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>457cm-1</td>\n",
       "      <td>457cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>459cm-1</td>\n",
       "      <td>459cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>2241cm-1</td>\n",
       "      <td>2241cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>2243cm-1</td>\n",
       "      <td>2243cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>2245cm-1</td>\n",
       "      <td>2245cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>2247cm-1</td>\n",
       "      <td>2247cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>2249cm-1</td>\n",
       "      <td>2249cm-1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name     Label  Perc_missing  QC_RSD\n",
       "1     451cm-1   451cm-1           0.0     0.0\n",
       "2     453cm-1   453cm-1           0.0     0.0\n",
       "3     455cm-1   455cm-1           0.0     0.0\n",
       "4     457cm-1   457cm-1           0.0     0.0\n",
       "5     459cm-1   459cm-1           0.0     0.0\n",
       "..        ...       ...           ...     ...\n",
       "896  2241cm-1  2241cm-1           0.0     0.0\n",
       "897  2243cm-1  2243cm-1           0.0     0.0\n",
       "898  2245cm-1  2245cm-1           0.0     0.0\n",
       "899  2247cm-1  2247cm-1           0.0     0.0\n",
       "900  2249cm-1  2249cm-1           0.0     0.0\n",
       "\n",
       "[900 rows x 4 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_peak.index = df_peak.index + 1\n",
    "df_peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write the dataset to excel file\n",
    "with pd.ExcelWriter('./Pros2_Tidy_binned.xlsx') as writer:\n",
    "    df.to_excel(writer,sheet_name=\"Data\")\n",
    "    df_peak.to_excel(writer,sheet_name=\"Peak\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "## End of data setup to Tidy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc9b76375c0>"
      ]
     },
     "execution_count": 62,
     "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": [
    "df_j = df_i.drop([0])\n",
    "plt.scatter(df_j['wavenumber'], df_j['pro 19'], marker=\"+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How to reduce the dataset without loosing the key features\n",
    "# Python spectroscopic data reduction module\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ChemUtils import GlobalStandardScaler \n",
    "xscaler = GlobalStandardScaler() #It works on a global scale, not per feature as is otherwise the norm for scikit-learn scalers.\n",
    "\n",
    "df_s = xscaler.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "%matplotlib inline\n",
    "_ = plt.plot(df_j['wavenumber'], df_s.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 1901)"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Augmentation for all Spectra (X10)\n",
    "X = df_s[0:1]\n",
    "#Repeating the spectrum 10x\n",
    "X = np.repeat(X, repeats=10, axis=0)\n",
    "#Augment (Large pertubations for illustration)\n",
    "X_aug = dataaugment(X, betashift = 0.5, slopeshift = 0.5,multishift = 0.5)\n",
    "    \n",
    "plt.plot(df_j['wavenumber'], X_aug.T)\n",
    "_= plt.plot(df_j['wavenumber'], X.T, lw=3, c='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "390\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": [
    "# data augmentation\n",
    "# The idea is to simulate the expected form of irrelevant noise (here baseline offset and slope and overall spectrum intensity)\n",
    "from ChemUtils import dataaugment\n",
    "\n",
    "#Repeating the spectrum 10x\n",
    "X = np.repeat(df_s, repeats=10, axis=0)\n",
    "#Augment (with 40% for offset and multiplication, 20% of slopeshift)\n",
    "X_aug = dataaugment(X, betashift = 0.4, slopeshift = 0.2,multishift = 0.4)\n",
    "y_aug = np.repeat(y, repeats=10)\n",
    "    \n",
    "print(len(X_aug))\n",
    "_= plt.plot(df_j['wavenumber'], X_aug.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(X_aug)\n",
    "df_x = pd.DataFrame(X_aug) # convert to pd.DataFrame\n",
    "df_y = y_aug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>1891</th>\n",
       "      <th>1892</th>\n",
       "      <th>1893</th>\n",
       "      <th>1894</th>\n",
       "      <th>1895</th>\n",
       "      <th>1896</th>\n",
       "      <th>1897</th>\n",
       "      <th>1898</th>\n",
       "      <th>1899</th>\n",
       "      <th>1900</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.543335</td>\n",
       "      <td>0.542685</td>\n",
       "      <td>0.494366</td>\n",
       "      <td>0.432208</td>\n",
       "      <td>0.376201</td>\n",
       "      <td>0.351168</td>\n",
       "      <td>0.365894</td>\n",
       "      <td>0.392923</td>\n",
       "      <td>0.42039</td>\n",
       "      <td>0.445661</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.542851</td>\n",
       "      <td>-0.553386</td>\n",
       "      <td>-0.576443</td>\n",
       "      <td>-0.472749</td>\n",
       "      <td>-0.411013</td>\n",
       "      <td>-0.444833</td>\n",
       "      <td>-0.505014</td>\n",
       "      <td>-0.536638</td>\n",
       "      <td>-0.535531</td>\n",
       "      <td>-0.524538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.534722</td>\n",
       "      <td>0.534027</td>\n",
       "      <td>0.489793</td>\n",
       "      <td>0.432918</td>\n",
       "      <td>0.381661</td>\n",
       "      <td>0.358695</td>\n",
       "      <td>0.372045</td>\n",
       "      <td>0.396631</td>\n",
       "      <td>0.421619</td>\n",
       "      <td>0.4446</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.648388</td>\n",
       "      <td>-0.658111</td>\n",
       "      <td>-0.679272</td>\n",
       "      <td>-0.584661</td>\n",
       "      <td>-0.528374</td>\n",
       "      <td>-0.559365</td>\n",
       "      <td>-0.614435</td>\n",
       "      <td>-0.64342</td>\n",
       "      <td>-0.64251</td>\n",
       "      <td>-0.63257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.965621</td>\n",
       "      <td>0.964979</td>\n",
       "      <td>0.914744</td>\n",
       "      <td>0.85011</td>\n",
       "      <td>0.791876</td>\n",
       "      <td>0.765866</td>\n",
       "      <td>0.781222</td>\n",
       "      <td>0.809376</td>\n",
       "      <td>0.837987</td>\n",
       "      <td>0.864312</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0996452</td>\n",
       "      <td>-0.110572</td>\n",
       "      <td>-0.134525</td>\n",
       "      <td>-0.0266104</td>\n",
       "      <td>0.0376528</td>\n",
       "      <td>0.00250114</td>\n",
       "      <td>-0.0600754</td>\n",
       "      <td>-0.0929417</td>\n",
       "      <td>-0.0917555</td>\n",
       "      <td>-0.080285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.753673</td>\n",
       "      <td>0.752969</td>\n",
       "      <td>0.703173</td>\n",
       "      <td>0.639126</td>\n",
       "      <td>0.581413</td>\n",
       "      <td>0.555597</td>\n",
       "      <td>0.570728</td>\n",
       "      <td>0.598528</td>\n",
       "      <td>0.62678</td>\n",
       "      <td>0.65277</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.431184</td>\n",
       "      <td>-0.442068</td>\n",
       "      <td>-0.465848</td>\n",
       "      <td>-0.359096</td>\n",
       "      <td>-0.295553</td>\n",
       "      <td>-0.330417</td>\n",
       "      <td>-0.392428</td>\n",
       "      <td>-0.425031</td>\n",
       "      <td>-0.423925</td>\n",
       "      <td>-0.41264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.64544</td>\n",
       "      <td>0.644728</td>\n",
       "      <td>0.587494</td>\n",
       "      <td>0.513851</td>\n",
       "      <td>0.447501</td>\n",
       "      <td>0.417876</td>\n",
       "      <td>0.435396</td>\n",
       "      <td>0.467502</td>\n",
       "      <td>0.500129</td>\n",
       "      <td>0.530151</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.532619</td>\n",
       "      <td>-0.545052</td>\n",
       "      <td>-0.572333</td>\n",
       "      <td>-0.449324</td>\n",
       "      <td>-0.376065</td>\n",
       "      <td>-0.416108</td>\n",
       "      <td>-0.487407</td>\n",
       "      <td>-0.524845</td>\n",
       "      <td>-0.523474</td>\n",
       "      <td>-0.510383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.616838</td>\n",
       "      <td>0.616166</td>\n",
       "      <td>0.574312</td>\n",
       "      <td>0.520502</td>\n",
       "      <td>0.472006</td>\n",
       "      <td>0.450269</td>\n",
       "      <td>0.462882</td>\n",
       "      <td>0.486122</td>\n",
       "      <td>0.509743</td>\n",
       "      <td>0.531465</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.529401</td>\n",
       "      <td>-0.538613</td>\n",
       "      <td>-0.558642</td>\n",
       "      <td>-0.469168</td>\n",
       "      <td>-0.415942</td>\n",
       "      <td>-0.44527</td>\n",
       "      <td>-0.497372</td>\n",
       "      <td>-0.524803</td>\n",
       "      <td>-0.523956</td>\n",
       "      <td>-0.514569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.671104</td>\n",
       "      <td>0.670495</td>\n",
       "      <td>0.620006</td>\n",
       "      <td>0.555035</td>\n",
       "      <td>0.496501</td>\n",
       "      <td>0.470378</td>\n",
       "      <td>0.48586</td>\n",
       "      <td>0.514214</td>\n",
       "      <td>0.543028</td>\n",
       "      <td>0.569544</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.32966</td>\n",
       "      <td>-0.340612</td>\n",
       "      <td>-0.364667</td>\n",
       "      <td>-0.25609</td>\n",
       "      <td>-0.191416</td>\n",
       "      <td>-0.226735</td>\n",
       "      <td>-0.289636</td>\n",
       "      <td>-0.322656</td>\n",
       "      <td>-0.321425</td>\n",
       "      <td>-0.309851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.261043</td>\n",
       "      <td>0.260589</td>\n",
       "      <td>0.231489</td>\n",
       "      <td>0.194071</td>\n",
       "      <td>0.16035</td>\n",
       "      <td>0.145243</td>\n",
       "      <td>0.154031</td>\n",
       "      <td>0.170212</td>\n",
       "      <td>0.186656</td>\n",
       "      <td>0.201781</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.510014</td>\n",
       "      <td>-0.516408</td>\n",
       "      <td>-0.530327</td>\n",
       "      <td>-0.468073</td>\n",
       "      <td>-0.431034</td>\n",
       "      <td>-0.451421</td>\n",
       "      <td>-0.487651</td>\n",
       "      <td>-0.506718</td>\n",
       "      <td>-0.506115</td>\n",
       "      <td>-0.499572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.544763</td>\n",
       "      <td>0.543954</td>\n",
       "      <td>0.48864</td>\n",
       "      <td>0.417501</td>\n",
       "      <td>0.353396</td>\n",
       "      <td>0.324706</td>\n",
       "      <td>0.341478</td>\n",
       "      <td>0.372317</td>\n",
       "      <td>0.403657</td>\n",
       "      <td>0.432486</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.822511</td>\n",
       "      <td>-0.834624</td>\n",
       "      <td>-0.861053</td>\n",
       "      <td>-0.742555</td>\n",
       "      <td>-0.672031</td>\n",
       "      <td>-0.710768</td>\n",
       "      <td>-0.779646</td>\n",
       "      <td>-0.815871</td>\n",
       "      <td>-0.814671</td>\n",
       "      <td>-0.802169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.14838</td>\n",
       "      <td>0.148128</td>\n",
       "      <td>0.122752</td>\n",
       "      <td>0.0900817</td>\n",
       "      <td>0.0606532</td>\n",
       "      <td>0.04755</td>\n",
       "      <td>0.0554035</td>\n",
       "      <td>0.0697407</td>\n",
       "      <td>0.0843095</td>\n",
       "      <td>0.0977205</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.251414</td>\n",
       "      <td>-0.256876</td>\n",
       "      <td>-0.268937</td>\n",
       "      <td>-0.214191</td>\n",
       "      <td>-0.181561</td>\n",
       "      <td>-0.199295</td>\n",
       "      <td>-0.230923</td>\n",
       "      <td>-0.2475</td>\n",
       "      <td>-0.246825</td>\n",
       "      <td>-0.24094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.603251</td>\n",
       "      <td>0.60227</td>\n",
       "      <td>0.543835</td>\n",
       "      <td>0.468543</td>\n",
       "      <td>0.401323</td>\n",
       "      <td>0.37019</td>\n",
       "      <td>0.387015</td>\n",
       "      <td>0.419034</td>\n",
       "      <td>0.44583</td>\n",
       "      <td>0.447698</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.407098</td>\n",
       "      <td>-0.451763</td>\n",
       "      <td>-0.494055</td>\n",
       "      <td>-0.494086</td>\n",
       "      <td>-0.48842</td>\n",
       "      <td>-0.469221</td>\n",
       "      <td>-0.460468</td>\n",
       "      <td>-0.491601</td>\n",
       "      <td>-0.494244</td>\n",
       "      <td>-0.48834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.538224</td>\n",
       "      <td>0.53741</td>\n",
       "      <td>0.482571</td>\n",
       "      <td>0.411883</td>\n",
       "      <td>0.348784</td>\n",
       "      <td>0.319619</td>\n",
       "      <td>0.335548</td>\n",
       "      <td>0.365765</td>\n",
       "      <td>0.391071</td>\n",
       "      <td>0.392936</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.205807</td>\n",
       "      <td>-0.247698</td>\n",
       "      <td>-0.287355</td>\n",
       "      <td>-0.287276</td>\n",
       "      <td>-0.281839</td>\n",
       "      <td>-0.263677</td>\n",
       "      <td>-0.255338</td>\n",
       "      <td>-0.284504</td>\n",
       "      <td>-0.28688</td>\n",
       "      <td>-0.28122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.73157</td>\n",
       "      <td>0.730971</td>\n",
       "      <td>0.691185</td>\n",
       "      <td>0.639903</td>\n",
       "      <td>0.594126</td>\n",
       "      <td>0.572961</td>\n",
       "      <td>0.584507</td>\n",
       "      <td>0.606415</td>\n",
       "      <td>0.624761</td>\n",
       "      <td>0.626105</td>\n",
       "      <td>...</td>\n",
       "      <td>0.174699</td>\n",
       "      <td>0.144305</td>\n",
       "      <td>0.11553</td>\n",
       "      <td>0.115579</td>\n",
       "      <td>0.119513</td>\n",
       "      <td>0.132678</td>\n",
       "      <td>0.138717</td>\n",
       "      <td>0.117553</td>\n",
       "      <td>0.11582</td>\n",
       "      <td>0.119917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.228553</td>\n",
       "      <td>0.227874</td>\n",
       "      <td>0.179946</td>\n",
       "      <td>0.118156</td>\n",
       "      <td>0.0630036</td>\n",
       "      <td>0.0375288</td>\n",
       "      <td>0.0514937</td>\n",
       "      <td>0.0779543</td>\n",
       "      <td>0.10012</td>\n",
       "      <td>0.101784</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.359496</td>\n",
       "      <td>-0.3961</td>\n",
       "      <td>-0.430751</td>\n",
       "      <td>-0.430649</td>\n",
       "      <td>-0.42586</td>\n",
       "      <td>-0.409943</td>\n",
       "      <td>-0.402617</td>\n",
       "      <td>-0.428091</td>\n",
       "      <td>-0.430137</td>\n",
       "      <td>-0.425153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.420988</td>\n",
       "      <td>0.420301</td>\n",
       "      <td>0.380738</td>\n",
       "      <td>0.32977</td>\n",
       "      <td>0.284263</td>\n",
       "      <td>0.263174</td>\n",
       "      <td>0.274536</td>\n",
       "      <td>0.296179</td>\n",
       "      <td>0.314287</td>\n",
       "      <td>0.315528</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.306028</td>\n",
       "      <td>-0.336274</td>\n",
       "      <td>-0.364913</td>\n",
       "      <td>-0.364957</td>\n",
       "      <td>-0.361146</td>\n",
       "      <td>-0.348178</td>\n",
       "      <td>-0.342279</td>\n",
       "      <td>-0.363367</td>\n",
       "      <td>-0.365179</td>\n",
       "      <td>-0.361207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.313897</td>\n",
       "      <td>0.313104</td>\n",
       "      <td>0.264324</td>\n",
       "      <td>0.201466</td>\n",
       "      <td>0.14535</td>\n",
       "      <td>0.119374</td>\n",
       "      <td>0.133453</td>\n",
       "      <td>0.160223</td>\n",
       "      <td>0.18263</td>\n",
       "      <td>0.184216</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.479674</td>\n",
       "      <td>-0.516953</td>\n",
       "      <td>-0.552248</td>\n",
       "      <td>-0.552248</td>\n",
       "      <td>-0.547489</td>\n",
       "      <td>-0.531427</td>\n",
       "      <td>-0.52409</td>\n",
       "      <td>-0.550066</td>\n",
       "      <td>-0.552247</td>\n",
       "      <td>-0.547289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.270864</td>\n",
       "      <td>0.269873</td>\n",
       "      <td>0.204872</td>\n",
       "      <td>0.12109</td>\n",
       "      <td>0.0463021</td>\n",
       "      <td>0.0117188</td>\n",
       "      <td>0.0305661</td>\n",
       "      <td>0.0663417</td>\n",
       "      <td>0.0962982</td>\n",
       "      <td>0.0984814</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.661151</td>\n",
       "      <td>-0.710811</td>\n",
       "      <td>-0.757826</td>\n",
       "      <td>-0.757759</td>\n",
       "      <td>-0.751344</td>\n",
       "      <td>-0.729852</td>\n",
       "      <td>-0.719998</td>\n",
       "      <td>-0.754581</td>\n",
       "      <td>-0.757423</td>\n",
       "      <td>-0.750743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.128288</td>\n",
       "      <td>0.127514</td>\n",
       "      <td>0.0788055</td>\n",
       "      <td>0.0160327</td>\n",
       "      <td>-0.0400052</td>\n",
       "      <td>-0.0659348</td>\n",
       "      <td>-0.0518519</td>\n",
       "      <td>-0.0250917</td>\n",
       "      <td>-0.00268934</td>\n",
       "      <td>-0.0010856</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.628657</td>\n",
       "      <td>-0.665877</td>\n",
       "      <td>-0.701116</td>\n",
       "      <td>-0.701097</td>\n",
       "      <td>-0.696324</td>\n",
       "      <td>-0.68026</td>\n",
       "      <td>-0.672912</td>\n",
       "      <td>-0.698842</td>\n",
       "      <td>-0.701002</td>\n",
       "      <td>-0.69603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.702004</td>\n",
       "      <td>0.701331</td>\n",
       "      <td>0.653974</td>\n",
       "      <td>0.592921</td>\n",
       "      <td>0.538427</td>\n",
       "      <td>0.513255</td>\n",
       "      <td>0.527049</td>\n",
       "      <td>0.553189</td>\n",
       "      <td>0.575085</td>\n",
       "      <td>0.576726</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114827</td>\n",
       "      <td>0.0786587</td>\n",
       "      <td>0.0444197</td>\n",
       "      <td>0.0445176</td>\n",
       "      <td>0.0492453</td>\n",
       "      <td>0.0649685</td>\n",
       "      <td>0.0722039</td>\n",
       "      <td>0.0470313</td>\n",
       "      <td>0.0450074</td>\n",
       "      <td>0.0499279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0311791</td>\n",
       "      <td>0.0306254</td>\n",
       "      <td>0.000498915</td>\n",
       "      <td>-0.0383039</td>\n",
       "      <td>-0.0729517</td>\n",
       "      <td>-0.0890251</td>\n",
       "      <td>-0.0804137</td>\n",
       "      <td>-0.0639815</td>\n",
       "      <td>-0.0502377</td>\n",
       "      <td>-0.049325</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.580919</td>\n",
       "      <td>-0.603958</td>\n",
       "      <td>-0.625775</td>\n",
       "      <td>-0.625839</td>\n",
       "      <td>-0.622972</td>\n",
       "      <td>-0.613138</td>\n",
       "      <td>-0.608682</td>\n",
       "      <td>-0.624755</td>\n",
       "      <td>-0.626164</td>\n",
       "      <td>-0.623174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.58201</td>\n",
       "      <td>1.55693</td>\n",
       "      <td>1.46631</td>\n",
       "      <td>1.35663</td>\n",
       "      <td>1.25516</td>\n",
       "      <td>1.19234</td>\n",
       "      <td>1.17944</td>\n",
       "      <td>1.18229</td>\n",
       "      <td>1.18607</td>\n",
       "      <td>1.18998</td>\n",
       "      <td>...</td>\n",
       "      <td>0.188565</td>\n",
       "      <td>0.188637</td>\n",
       "      <td>0.188709</td>\n",
       "      <td>0.188782</td>\n",
       "      <td>0.188854</td>\n",
       "      <td>0.188926</td>\n",
       "      <td>0.188999</td>\n",
       "      <td>0.219654</td>\n",
       "      <td>0.238526</td>\n",
       "      <td>0.197954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.38697</td>\n",
       "      <td>1.35465</td>\n",
       "      <td>1.238</td>\n",
       "      <td>1.09683</td>\n",
       "      <td>0.966223</td>\n",
       "      <td>0.885354</td>\n",
       "      <td>0.868706</td>\n",
       "      <td>0.872329</td>\n",
       "      <td>0.877145</td>\n",
       "      <td>0.882132</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.494603</td>\n",
       "      <td>-0.494557</td>\n",
       "      <td>-0.494511</td>\n",
       "      <td>-0.494465</td>\n",
       "      <td>-0.494418</td>\n",
       "      <td>-0.494372</td>\n",
       "      <td>-0.494326</td>\n",
       "      <td>-0.45493</td>\n",
       "      <td>-0.430694</td>\n",
       "      <td>-0.482945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.35704</td>\n",
       "      <td>2.31725</td>\n",
       "      <td>2.17381</td>\n",
       "      <td>2.00022</td>\n",
       "      <td>1.83962</td>\n",
       "      <td>1.74015</td>\n",
       "      <td>1.71963</td>\n",
       "      <td>1.72402</td>\n",
       "      <td>1.72987</td>\n",
       "      <td>1.73594</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0758583</td>\n",
       "      <td>-0.0758651</td>\n",
       "      <td>-0.0758718</td>\n",
       "      <td>-0.0758786</td>\n",
       "      <td>-0.0758854</td>\n",
       "      <td>-0.0758922</td>\n",
       "      <td>-0.0758989</td>\n",
       "      <td>-0.0275377</td>\n",
       "      <td>0.00218829</td>\n",
       "      <td>-0.0620998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.47882</td>\n",
       "      <td>2.43169</td>\n",
       "      <td>2.26203</td>\n",
       "      <td>2.05672</td>\n",
       "      <td>1.86675</td>\n",
       "      <td>1.74908</td>\n",
       "      <td>1.72473</td>\n",
       "      <td>1.72984</td>\n",
       "      <td>1.73668</td>\n",
       "      <td>1.74378</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.547616</td>\n",
       "      <td>-0.547703</td>\n",
       "      <td>-0.547791</td>\n",
       "      <td>-0.547878</td>\n",
       "      <td>-0.547965</td>\n",
       "      <td>-0.548053</td>\n",
       "      <td>-0.54814</td>\n",
       "      <td>-0.491042</td>\n",
       "      <td>-0.455976</td>\n",
       "      <td>-0.532063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.56228</td>\n",
       "      <td>2.51578</td>\n",
       "      <td>2.34792</td>\n",
       "      <td>2.14476</td>\n",
       "      <td>1.95679</td>\n",
       "      <td>1.84042</td>\n",
       "      <td>1.81646</td>\n",
       "      <td>1.82168</td>\n",
       "      <td>1.82861</td>\n",
       "      <td>1.83579</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.140453</td>\n",
       "      <td>-0.140384</td>\n",
       "      <td>-0.140315</td>\n",
       "      <td>-0.140245</td>\n",
       "      <td>-0.140176</td>\n",
       "      <td>-0.140107</td>\n",
       "      <td>-0.140038</td>\n",
       "      <td>-0.0833392</td>\n",
       "      <td>-0.0484587</td>\n",
       "      <td>-0.123651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.43916</td>\n",
       "      <td>1.41478</td>\n",
       "      <td>1.32691</td>\n",
       "      <td>1.22059</td>\n",
       "      <td>1.12221</td>\n",
       "      <td>1.06127</td>\n",
       "      <td>1.04869</td>\n",
       "      <td>1.05136</td>\n",
       "      <td>1.05492</td>\n",
       "      <td>1.05862</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.086069</td>\n",
       "      <td>-0.0860918</td>\n",
       "      <td>-0.0861146</td>\n",
       "      <td>-0.0861374</td>\n",
       "      <td>-0.0861602</td>\n",
       "      <td>-0.086183</td>\n",
       "      <td>-0.0862058</td>\n",
       "      <td>-0.0566069</td>\n",
       "      <td>-0.0384206</td>\n",
       "      <td>-0.0778109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.0396</td>\n",
       "      <td>2.00611</td>\n",
       "      <td>1.88523</td>\n",
       "      <td>1.73892</td>\n",
       "      <td>1.60356</td>\n",
       "      <td>1.51975</td>\n",
       "      <td>1.50251</td>\n",
       "      <td>1.50627</td>\n",
       "      <td>1.51128</td>\n",
       "      <td>1.51645</td>\n",
       "      <td>...</td>\n",
       "      <td>0.108446</td>\n",
       "      <td>0.108504</td>\n",
       "      <td>0.108562</td>\n",
       "      <td>0.10862</td>\n",
       "      <td>0.108678</td>\n",
       "      <td>0.108736</td>\n",
       "      <td>0.108794</td>\n",
       "      <td>0.149636</td>\n",
       "      <td>0.174765</td>\n",
       "      <td>0.12062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2.08198</td>\n",
       "      <td>2.04933</td>\n",
       "      <td>1.93168</td>\n",
       "      <td>1.78932</td>\n",
       "      <td>1.65759</td>\n",
       "      <td>1.57601</td>\n",
       "      <td>1.55916</td>\n",
       "      <td>1.56274</td>\n",
       "      <td>1.56752</td>\n",
       "      <td>1.57247</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0482152</td>\n",
       "      <td>0.0481892</td>\n",
       "      <td>0.0481632</td>\n",
       "      <td>0.0481372</td>\n",
       "      <td>0.0481111</td>\n",
       "      <td>0.0480851</td>\n",
       "      <td>0.0480591</td>\n",
       "      <td>0.0876964</td>\n",
       "      <td>0.112052</td>\n",
       "      <td>0.0593134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.2031</td>\n",
       "      <td>2.165</td>\n",
       "      <td>2.02756</td>\n",
       "      <td>1.86122</td>\n",
       "      <td>1.70733</td>\n",
       "      <td>1.61203</td>\n",
       "      <td>1.59239</td>\n",
       "      <td>1.59664</td>\n",
       "      <td>1.60229</td>\n",
       "      <td>1.60813</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0620236</td>\n",
       "      <td>-0.0619948</td>\n",
       "      <td>-0.0619661</td>\n",
       "      <td>-0.0619373</td>\n",
       "      <td>-0.0619086</td>\n",
       "      <td>-0.0618798</td>\n",
       "      <td>-0.0618511</td>\n",
       "      <td>-0.0154645</td>\n",
       "      <td>0.0130612</td>\n",
       "      <td>-0.0485197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.1652</td>\n",
       "      <td>1.13531</td>\n",
       "      <td>1.02729</td>\n",
       "      <td>0.896528</td>\n",
       "      <td>0.775557</td>\n",
       "      <td>0.700679</td>\n",
       "      <td>0.685312</td>\n",
       "      <td>0.68873</td>\n",
       "      <td>0.693253</td>\n",
       "      <td>0.697934</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.464367</td>\n",
       "      <td>-0.464264</td>\n",
       "      <td>-0.464161</td>\n",
       "      <td>-0.464058</td>\n",
       "      <td>-0.463955</td>\n",
       "      <td>-0.463852</td>\n",
       "      <td>-0.463749</td>\n",
       "      <td>-0.427181</td>\n",
       "      <td>-0.404663</td>\n",
       "      <td>-0.453021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>0.637152</td>\n",
       "      <td>0.635023</td>\n",
       "      <td>0.585838</td>\n",
       "      <td>0.522781</td>\n",
       "      <td>0.464816</td>\n",
       "      <td>0.43073</td>\n",
       "      <td>0.427373</td>\n",
       "      <td>0.433672</td>\n",
       "      <td>0.440499</td>\n",
       "      <td>0.448554</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.364496</td>\n",
       "      <td>-0.329224</td>\n",
       "      <td>-0.315901</td>\n",
       "      <td>-0.331725</td>\n",
       "      <td>-0.344389</td>\n",
       "      <td>-0.35477</td>\n",
       "      <td>-0.369189</td>\n",
       "      <td>-0.376585</td>\n",
       "      <td>-0.351147</td>\n",
       "      <td>-0.302004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>0.379064</td>\n",
       "      <td>0.376815</td>\n",
       "      <td>0.324817</td>\n",
       "      <td>0.258154</td>\n",
       "      <td>0.196875</td>\n",
       "      <td>0.160841</td>\n",
       "      <td>0.157293</td>\n",
       "      <td>0.163954</td>\n",
       "      <td>0.171173</td>\n",
       "      <td>0.17969</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.677257</td>\n",
       "      <td>-0.639967</td>\n",
       "      <td>-0.62588</td>\n",
       "      <td>-0.642608</td>\n",
       "      <td>-0.655994</td>\n",
       "      <td>-0.666968</td>\n",
       "      <td>-0.682211</td>\n",
       "      <td>-0.690029</td>\n",
       "      <td>-0.663133</td>\n",
       "      <td>-0.611178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>0.542896</td>\n",
       "      <td>0.539968</td>\n",
       "      <td>0.472073</td>\n",
       "      <td>0.385028</td>\n",
       "      <td>0.305013</td>\n",
       "      <td>0.257965</td>\n",
       "      <td>0.25334</td>\n",
       "      <td>0.262048</td>\n",
       "      <td>0.271483</td>\n",
       "      <td>0.282614</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.820586</td>\n",
       "      <td>-0.77188</td>\n",
       "      <td>-0.753476</td>\n",
       "      <td>-0.775313</td>\n",
       "      <td>-0.792786</td>\n",
       "      <td>-0.807107</td>\n",
       "      <td>-0.827004</td>\n",
       "      <td>-0.837205</td>\n",
       "      <td>-0.802075</td>\n",
       "      <td>-0.734219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>0.658899</td>\n",
       "      <td>0.656636</td>\n",
       "      <td>0.602539</td>\n",
       "      <td>0.533163</td>\n",
       "      <td>0.469396</td>\n",
       "      <td>0.431932</td>\n",
       "      <td>0.428315</td>\n",
       "      <td>0.435336</td>\n",
       "      <td>0.442936</td>\n",
       "      <td>0.451891</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.290742</td>\n",
       "      <td>-0.251809</td>\n",
       "      <td>-0.237052</td>\n",
       "      <td>-0.254401</td>\n",
       "      <td>-0.268269</td>\n",
       "      <td>-0.279622</td>\n",
       "      <td>-0.295424</td>\n",
       "      <td>-0.30349</td>\n",
       "      <td>-0.275388</td>\n",
       "      <td>-0.221176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>0.819787</td>\n",
       "      <td>0.816973</td>\n",
       "      <td>0.751204</td>\n",
       "      <td>0.666878</td>\n",
       "      <td>0.589364</td>\n",
       "      <td>0.543796</td>\n",
       "      <td>0.539338</td>\n",
       "      <td>0.547799</td>\n",
       "      <td>0.556965</td>\n",
       "      <td>0.567775</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.457596</td>\n",
       "      <td>-0.410375</td>\n",
       "      <td>-0.392518</td>\n",
       "      <td>-0.413655</td>\n",
       "      <td>-0.430564</td>\n",
       "      <td>-0.444419</td>\n",
       "      <td>-0.463676</td>\n",
       "      <td>-0.473538</td>\n",
       "      <td>-0.439472</td>\n",
       "      <td>-0.373694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>0.557359</td>\n",
       "      <td>0.555354</td>\n",
       "      <td>0.506445</td>\n",
       "      <td>0.44371</td>\n",
       "      <td>0.386051</td>\n",
       "      <td>0.352194</td>\n",
       "      <td>0.348964</td>\n",
       "      <td>0.355359</td>\n",
       "      <td>0.36228</td>\n",
       "      <td>0.370425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.220819</td>\n",
       "      <td>-0.185547</td>\n",
       "      <td>-0.17215</td>\n",
       "      <td>-0.187806</td>\n",
       "      <td>-0.200312</td>\n",
       "      <td>-0.210543</td>\n",
       "      <td>-0.224799</td>\n",
       "      <td>-0.232054</td>\n",
       "      <td>-0.206582</td>\n",
       "      <td>-0.157484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>0.951496</td>\n",
       "      <td>0.948585</td>\n",
       "      <td>0.882182</td>\n",
       "      <td>0.797063</td>\n",
       "      <td>0.718815</td>\n",
       "      <td>0.672786</td>\n",
       "      <td>0.668217</td>\n",
       "      <td>0.676678</td>\n",
       "      <td>0.68585</td>\n",
       "      <td>0.69668</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.473541</td>\n",
       "      <td>-0.425989</td>\n",
       "      <td>-0.408052</td>\n",
       "      <td>-0.429442</td>\n",
       "      <td>-0.446567</td>\n",
       "      <td>-0.460613</td>\n",
       "      <td>-0.480107</td>\n",
       "      <td>-0.490125</td>\n",
       "      <td>-0.455841</td>\n",
       "      <td>-0.389573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>0.610781</td>\n",
       "      <td>0.608135</td>\n",
       "      <td>0.547061</td>\n",
       "      <td>0.468763</td>\n",
       "      <td>0.396788</td>\n",
       "      <td>0.354463</td>\n",
       "      <td>0.350291</td>\n",
       "      <td>0.35811</td>\n",
       "      <td>0.366583</td>\n",
       "      <td>0.376583</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.63868</td>\n",
       "      <td>-0.594888</td>\n",
       "      <td>-0.578348</td>\n",
       "      <td>-0.597999</td>\n",
       "      <td>-0.613726</td>\n",
       "      <td>-0.626619</td>\n",
       "      <td>-0.644526</td>\n",
       "      <td>-0.653712</td>\n",
       "      <td>-0.622129</td>\n",
       "      <td>-0.561115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>0.518232</td>\n",
       "      <td>0.515307</td>\n",
       "      <td>0.444785</td>\n",
       "      <td>0.354336</td>\n",
       "      <td>0.271202</td>\n",
       "      <td>0.222371</td>\n",
       "      <td>0.21768</td>\n",
       "      <td>0.226862</td>\n",
       "      <td>0.2368</td>\n",
       "      <td>0.248504</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.670676</td>\n",
       "      <td>-0.619877</td>\n",
       "      <td>-0.600606</td>\n",
       "      <td>-0.623206</td>\n",
       "      <td>-0.641265</td>\n",
       "      <td>-0.656045</td>\n",
       "      <td>-0.676626</td>\n",
       "      <td>-0.687118</td>\n",
       "      <td>-0.650444</td>\n",
       "      <td>-0.579718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>0.362946</td>\n",
       "      <td>0.360851</td>\n",
       "      <td>0.310224</td>\n",
       "      <td>0.245291</td>\n",
       "      <td>0.18561</td>\n",
       "      <td>0.150557</td>\n",
       "      <td>0.147195</td>\n",
       "      <td>0.153792</td>\n",
       "      <td>0.160933</td>\n",
       "      <td>0.169342</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.480196</td>\n",
       "      <td>-0.443719</td>\n",
       "      <td>-0.429878</td>\n",
       "      <td>-0.446097</td>\n",
       "      <td>-0.459057</td>\n",
       "      <td>-0.469663</td>\n",
       "      <td>-0.484434</td>\n",
       "      <td>-0.491962</td>\n",
       "      <td>-0.465625</td>\n",
       "      <td>-0.414842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>2.10999</td>\n",
       "      <td>2.15104</td>\n",
       "      <td>2.21537</td>\n",
       "      <td>2.28698</td>\n",
       "      <td>2.36816</td>\n",
       "      <td>2.4876</td>\n",
       "      <td>2.65631</td>\n",
       "      <td>2.8402</td>\n",
       "      <td>3.01557</td>\n",
       "      <td>3.14727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0884464</td>\n",
       "      <td>-0.0501031</td>\n",
       "      <td>-0.0290176</td>\n",
       "      <td>-0.0278931</td>\n",
       "      <td>-0.0579576</td>\n",
       "      <td>-0.0828239</td>\n",
       "      <td>-0.102492</td>\n",
       "      <td>-0.0797432</td>\n",
       "      <td>-0.0359938</td>\n",
       "      <td>0.0418556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>1.36405</td>\n",
       "      <td>1.38804</td>\n",
       "      <td>1.42566</td>\n",
       "      <td>1.46754</td>\n",
       "      <td>1.51502</td>\n",
       "      <td>1.58489</td>\n",
       "      <td>1.6836</td>\n",
       "      <td>1.7912</td>\n",
       "      <td>1.8938</td>\n",
       "      <td>1.97085</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0213874</td>\n",
       "      <td>0.0437984</td>\n",
       "      <td>0.0561091</td>\n",
       "      <td>0.0567376</td>\n",
       "      <td>0.0391126</td>\n",
       "      <td>0.0245299</td>\n",
       "      <td>0.0129895</td>\n",
       "      <td>0.0262737</td>\n",
       "      <td>0.0518486</td>\n",
       "      <td>0.0973806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>1.84386</td>\n",
       "      <td>1.88814</td>\n",
       "      <td>1.95758</td>\n",
       "      <td>2.03488</td>\n",
       "      <td>2.12252</td>\n",
       "      <td>2.2515</td>\n",
       "      <td>2.43372</td>\n",
       "      <td>2.63234</td>\n",
       "      <td>2.82175</td>\n",
       "      <td>2.96398</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.664156</td>\n",
       "      <td>-0.622797</td>\n",
       "      <td>-0.600084</td>\n",
       "      <td>-0.598939</td>\n",
       "      <td>-0.631494</td>\n",
       "      <td>-0.658431</td>\n",
       "      <td>-0.679752</td>\n",
       "      <td>-0.655243</td>\n",
       "      <td>-0.608043</td>\n",
       "      <td>-0.523998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>1.94359</td>\n",
       "      <td>1.9815</td>\n",
       "      <td>2.04097</td>\n",
       "      <td>2.10718</td>\n",
       "      <td>2.18223</td>\n",
       "      <td>2.29269</td>\n",
       "      <td>2.44876</td>\n",
       "      <td>2.61887</td>\n",
       "      <td>2.78109</td>\n",
       "      <td>2.90291</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.220185</td>\n",
       "      <td>-0.184769</td>\n",
       "      <td>-0.165324</td>\n",
       "      <td>-0.164352</td>\n",
       "      <td>-0.192243</td>\n",
       "      <td>-0.215323</td>\n",
       "      <td>-0.233593</td>\n",
       "      <td>-0.212609</td>\n",
       "      <td>-0.17219</td>\n",
       "      <td>-0.100213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>1.59771</td>\n",
       "      <td>1.64166</td>\n",
       "      <td>1.71055</td>\n",
       "      <td>1.78724</td>\n",
       "      <td>1.87418</td>\n",
       "      <td>2.00209</td>\n",
       "      <td>2.1828</td>\n",
       "      <td>2.37976</td>\n",
       "      <td>2.56759</td>\n",
       "      <td>2.70865</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.794463</td>\n",
       "      <td>-0.75341</td>\n",
       "      <td>-0.730843</td>\n",
       "      <td>-0.729658</td>\n",
       "      <td>-0.761882</td>\n",
       "      <td>-0.788539</td>\n",
       "      <td>-0.809627</td>\n",
       "      <td>-0.785278</td>\n",
       "      <td>-0.738434</td>\n",
       "      <td>-0.655062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>1.50157</td>\n",
       "      <td>1.53099</td>\n",
       "      <td>1.57714</td>\n",
       "      <td>1.62851</td>\n",
       "      <td>1.68676</td>\n",
       "      <td>1.77248</td>\n",
       "      <td>1.89361</td>\n",
       "      <td>2.02565</td>\n",
       "      <td>2.15156</td>\n",
       "      <td>2.2461</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.209977</td>\n",
       "      <td>-0.182503</td>\n",
       "      <td>-0.167425</td>\n",
       "      <td>-0.166687</td>\n",
       "      <td>-0.188355</td>\n",
       "      <td>-0.206288</td>\n",
       "      <td>-0.220487</td>\n",
       "      <td>-0.204215</td>\n",
       "      <td>-0.172856</td>\n",
       "      <td>-0.117001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>2.16039</td>\n",
       "      <td>2.21267</td>\n",
       "      <td>2.29472</td>\n",
       "      <td>2.38606</td>\n",
       "      <td>2.48962</td>\n",
       "      <td>2.64208</td>\n",
       "      <td>2.8575</td>\n",
       "      <td>3.09232</td>\n",
       "      <td>3.31625</td>\n",
       "      <td>3.48438</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.960993</td>\n",
       "      <td>-0.912161</td>\n",
       "      <td>-0.885382</td>\n",
       "      <td>-0.88411</td>\n",
       "      <td>-0.922693</td>\n",
       "      <td>-0.954633</td>\n",
       "      <td>-0.979931</td>\n",
       "      <td>-0.951027</td>\n",
       "      <td>-0.895287</td>\n",
       "      <td>-0.795972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>1.48352</td>\n",
       "      <td>1.51944</td>\n",
       "      <td>1.57584</td>\n",
       "      <td>1.63864</td>\n",
       "      <td>1.70985</td>\n",
       "      <td>1.81471</td>\n",
       "      <td>1.9629</td>\n",
       "      <td>2.12444</td>\n",
       "      <td>2.27848</td>\n",
       "      <td>2.39412</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.778398</td>\n",
       "      <td>-0.744853</td>\n",
       "      <td>-0.726485</td>\n",
       "      <td>-0.72567</td>\n",
       "      <td>-0.752282</td>\n",
       "      <td>-0.774323</td>\n",
       "      <td>-0.791792</td>\n",
       "      <td>-0.771961</td>\n",
       "      <td>-0.733662</td>\n",
       "      <td>-0.665377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>2.01633</td>\n",
       "      <td>2.06116</td>\n",
       "      <td>2.13148</td>\n",
       "      <td>2.20977</td>\n",
       "      <td>2.29853</td>\n",
       "      <td>2.42916</td>\n",
       "      <td>2.61372</td>\n",
       "      <td>2.8149</td>\n",
       "      <td>3.00675</td>\n",
       "      <td>3.15081</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.562632</td>\n",
       "      <td>-0.520756</td>\n",
       "      <td>-0.497769</td>\n",
       "      <td>-0.49663</td>\n",
       "      <td>-0.529627</td>\n",
       "      <td>-0.556935</td>\n",
       "      <td>-0.578553</td>\n",
       "      <td>-0.553745</td>\n",
       "      <td>-0.505953</td>\n",
       "      <td>-0.420838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>1.55386</td>\n",
       "      <td>1.59219</td>\n",
       "      <td>1.65226</td>\n",
       "      <td>1.71912</td>\n",
       "      <td>1.79492</td>\n",
       "      <td>1.90644</td>\n",
       "      <td>2.06396</td>\n",
       "      <td>2.23566</td>\n",
       "      <td>2.3994</td>\n",
       "      <td>2.52237</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.49309</td>\n",
       "      <td>-0.457286</td>\n",
       "      <td>-0.437597</td>\n",
       "      <td>-0.436544</td>\n",
       "      <td>-0.464611</td>\n",
       "      <td>-0.487825</td>\n",
       "      <td>-0.506186</td>\n",
       "      <td>-0.484943</td>\n",
       "      <td>-0.444092</td>\n",
       "      <td>-0.371403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>-0.0443184</td>\n",
       "      <td>-0.0440506</td>\n",
       "      <td>-0.0403975</td>\n",
       "      <td>-0.0356562</td>\n",
       "      <td>-0.0322448</td>\n",
       "      <td>-0.0340324</td>\n",
       "      <td>-0.0428326</td>\n",
       "      <td>-0.0533255</td>\n",
       "      <td>-0.0612793</td>\n",
       "      <td>-0.055208</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.592728</td>\n",
       "      <td>-0.574808</td>\n",
       "      <td>-0.568495</td>\n",
       "      <td>-0.581768</td>\n",
       "      <td>-0.596251</td>\n",
       "      <td>-0.595137</td>\n",
       "      <td>-0.594144</td>\n",
       "      <td>-0.557121</td>\n",
       "      <td>-0.518284</td>\n",
       "      <td>-0.52249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>0.631199</td>\n",
       "      <td>0.631752</td>\n",
       "      <td>0.639786</td>\n",
       "      <td>0.650225</td>\n",
       "      <td>0.657724</td>\n",
       "      <td>0.653735</td>\n",
       "      <td>0.63425</td>\n",
       "      <td>0.611024</td>\n",
       "      <td>0.593409</td>\n",
       "      <td>0.606786</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.654145</td>\n",
       "      <td>-0.614584</td>\n",
       "      <td>-0.600672</td>\n",
       "      <td>-0.630043</td>\n",
       "      <td>-0.662086</td>\n",
       "      <td>-0.659663</td>\n",
       "      <td>-0.657507</td>\n",
       "      <td>-0.575732</td>\n",
       "      <td>-0.48995</td>\n",
       "      <td>-0.499282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>0.46326</td>\n",
       "      <td>0.463597</td>\n",
       "      <td>0.468654</td>\n",
       "      <td>0.475228</td>\n",
       "      <td>0.479947</td>\n",
       "      <td>0.477419</td>\n",
       "      <td>0.465114</td>\n",
       "      <td>0.450449</td>\n",
       "      <td>0.439323</td>\n",
       "      <td>0.447752</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.369907</td>\n",
       "      <td>-0.344959</td>\n",
       "      <td>-0.336194</td>\n",
       "      <td>-0.354736</td>\n",
       "      <td>-0.374964</td>\n",
       "      <td>-0.373447</td>\n",
       "      <td>-0.372098</td>\n",
       "      <td>-0.320518</td>\n",
       "      <td>-0.266409</td>\n",
       "      <td>-0.272309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>0.00219245</td>\n",
       "      <td>0.00244612</td>\n",
       "      <td>0.00734612</td>\n",
       "      <td>0.0137396</td>\n",
       "      <td>0.0183077</td>\n",
       "      <td>0.0157405</td>\n",
       "      <td>0.00354873</td>\n",
       "      <td>-0.0109661</td>\n",
       "      <td>-0.0219962</td>\n",
       "      <td>-0.0137774</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.965692</td>\n",
       "      <td>-0.941212</td>\n",
       "      <td>-0.932661</td>\n",
       "      <td>-0.950992</td>\n",
       "      <td>-0.970983</td>\n",
       "      <td>-0.969568</td>\n",
       "      <td>-0.968319</td>\n",
       "      <td>-0.917619</td>\n",
       "      <td>-0.864431</td>\n",
       "      <td>-0.870317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>0.262973</td>\n",
       "      <td>0.263403</td>\n",
       "      <td>0.270637</td>\n",
       "      <td>0.280059</td>\n",
       "      <td>0.286807</td>\n",
       "      <td>0.283105</td>\n",
       "      <td>0.265309</td>\n",
       "      <td>0.244109</td>\n",
       "      <td>0.228014</td>\n",
       "      <td>0.240109</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.04441</td>\n",
       "      <td>-1.0085</td>\n",
       "      <td>-0.995916</td>\n",
       "      <td>-1.0227</td>\n",
       "      <td>-1.05192</td>\n",
       "      <td>-1.04979</td>\n",
       "      <td>-1.0479</td>\n",
       "      <td>-0.973596</td>\n",
       "      <td>-0.895643</td>\n",
       "      <td>-0.904205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>0.628603</td>\n",
       "      <td>0.62896</td>\n",
       "      <td>0.63355</td>\n",
       "      <td>0.6395</td>\n",
       "      <td>0.643788</td>\n",
       "      <td>0.641574</td>\n",
       "      <td>0.630592</td>\n",
       "      <td>0.617493</td>\n",
       "      <td>0.607569</td>\n",
       "      <td>0.615183</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0158937</td>\n",
       "      <td>0.00653628</td>\n",
       "      <td>0.0144523</td>\n",
       "      <td>-0.002124</td>\n",
       "      <td>-0.0202121</td>\n",
       "      <td>-0.0187971</td>\n",
       "      <td>-0.0175333</td>\n",
       "      <td>0.0287841</td>\n",
       "      <td>0.0773693</td>\n",
       "      <td>0.0721321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>0.422706</td>\n",
       "      <td>0.423053</td>\n",
       "      <td>0.429119</td>\n",
       "      <td>0.437023</td>\n",
       "      <td>0.442681</td>\n",
       "      <td>0.439556</td>\n",
       "      <td>0.424586</td>\n",
       "      <td>0.406756</td>\n",
       "      <td>0.393216</td>\n",
       "      <td>0.403366</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.702219</td>\n",
       "      <td>-0.672054</td>\n",
       "      <td>-0.661494</td>\n",
       "      <td>-0.684022</td>\n",
       "      <td>-0.708591</td>\n",
       "      <td>-0.706814</td>\n",
       "      <td>-0.705241</td>\n",
       "      <td>-0.642806</td>\n",
       "      <td>-0.577308</td>\n",
       "      <td>-0.584517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>0.324082</td>\n",
       "      <td>0.324439</td>\n",
       "      <td>0.329835</td>\n",
       "      <td>0.336851</td>\n",
       "      <td>0.341887</td>\n",
       "      <td>0.339185</td>\n",
       "      <td>0.326044</td>\n",
       "      <td>0.310384</td>\n",
       "      <td>0.298503</td>\n",
       "      <td>0.307499</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5708</td>\n",
       "      <td>-0.544168</td>\n",
       "      <td>-0.534812</td>\n",
       "      <td>-0.554612</td>\n",
       "      <td>-0.576211</td>\n",
       "      <td>-0.574594</td>\n",
       "      <td>-0.573157</td>\n",
       "      <td>-0.51809</td>\n",
       "      <td>-0.460322</td>\n",
       "      <td>-0.466624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>0.499991</td>\n",
       "      <td>0.500482</td>\n",
       "      <td>0.507896</td>\n",
       "      <td>0.517535</td>\n",
       "      <td>0.524455</td>\n",
       "      <td>0.520743</td>\n",
       "      <td>0.502692</td>\n",
       "      <td>0.481179</td>\n",
       "      <td>0.464858</td>\n",
       "      <td>0.477217</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.72774</td>\n",
       "      <td>-0.691151</td>\n",
       "      <td>-0.678298</td>\n",
       "      <td>-0.705497</td>\n",
       "      <td>-0.735169</td>\n",
       "      <td>-0.732947</td>\n",
       "      <td>-0.730972</td>\n",
       "      <td>-0.655321</td>\n",
       "      <td>-0.575961</td>\n",
       "      <td>-0.584617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>0.193507</td>\n",
       "      <td>0.194137</td>\n",
       "      <td>0.202206</td>\n",
       "      <td>0.212666</td>\n",
       "      <td>0.220204</td>\n",
       "      <td>0.216316</td>\n",
       "      <td>0.197018</td>\n",
       "      <td>0.173999</td>\n",
       "      <td>0.15656</td>\n",
       "      <td>0.169943</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.934459</td>\n",
       "      <td>-0.895036</td>\n",
       "      <td>-0.881122</td>\n",
       "      <td>-0.910252</td>\n",
       "      <td>-0.942039</td>\n",
       "      <td>-0.939549</td>\n",
       "      <td>-0.937326</td>\n",
       "      <td>-0.855921</td>\n",
       "      <td>-0.770531</td>\n",
       "      <td>-0.779733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>390 rows × 1901 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0           1            2          3          4          5     \\\n",
       "0      0.543335    0.542685     0.494366   0.432208   0.376201   0.351168   \n",
       "1      0.534722    0.534027     0.489793   0.432918   0.381661   0.358695   \n",
       "2      0.965621    0.964979     0.914744    0.85011   0.791876   0.765866   \n",
       "3      0.753673    0.752969     0.703173   0.639126   0.581413   0.555597   \n",
       "4       0.64544    0.644728     0.587494   0.513851   0.447501   0.417876   \n",
       "5      0.616838    0.616166     0.574312   0.520502   0.472006   0.450269   \n",
       "6      0.671104    0.670495     0.620006   0.555035   0.496501   0.470378   \n",
       "7      0.261043    0.260589     0.231489   0.194071    0.16035   0.145243   \n",
       "8      0.544763    0.543954      0.48864   0.417501   0.353396   0.324706   \n",
       "9       0.14838    0.148128     0.122752  0.0900817  0.0606532    0.04755   \n",
       "10     0.603251     0.60227     0.543835   0.468543   0.401323    0.37019   \n",
       "11     0.538224     0.53741     0.482571   0.411883   0.348784   0.319619   \n",
       "12      0.73157    0.730971     0.691185   0.639903   0.594126   0.572961   \n",
       "13     0.228553    0.227874     0.179946   0.118156  0.0630036  0.0375288   \n",
       "14     0.420988    0.420301     0.380738    0.32977   0.284263   0.263174   \n",
       "15     0.313897    0.313104     0.264324   0.201466    0.14535   0.119374   \n",
       "16     0.270864    0.269873     0.204872    0.12109  0.0463021  0.0117188   \n",
       "17     0.128288    0.127514    0.0788055  0.0160327 -0.0400052 -0.0659348   \n",
       "18     0.702004    0.701331     0.653974   0.592921   0.538427   0.513255   \n",
       "19    0.0311791   0.0306254  0.000498915 -0.0383039 -0.0729517 -0.0890251   \n",
       "20      1.58201     1.55693      1.46631    1.35663    1.25516    1.19234   \n",
       "21      1.38697     1.35465        1.238    1.09683   0.966223   0.885354   \n",
       "22      2.35704     2.31725      2.17381    2.00022    1.83962    1.74015   \n",
       "23      2.47882     2.43169      2.26203    2.05672    1.86675    1.74908   \n",
       "24      2.56228     2.51578      2.34792    2.14476    1.95679    1.84042   \n",
       "25      1.43916     1.41478      1.32691    1.22059    1.12221    1.06127   \n",
       "26       2.0396     2.00611      1.88523    1.73892    1.60356    1.51975   \n",
       "27      2.08198     2.04933      1.93168    1.78932    1.65759    1.57601   \n",
       "28       2.2031       2.165      2.02756    1.86122    1.70733    1.61203   \n",
       "29       1.1652     1.13531      1.02729   0.896528   0.775557   0.700679   \n",
       "..          ...         ...          ...        ...        ...        ...   \n",
       "360    0.637152    0.635023     0.585838   0.522781   0.464816    0.43073   \n",
       "361    0.379064    0.376815     0.324817   0.258154   0.196875   0.160841   \n",
       "362    0.542896    0.539968     0.472073   0.385028   0.305013   0.257965   \n",
       "363    0.658899    0.656636     0.602539   0.533163   0.469396   0.431932   \n",
       "364    0.819787    0.816973     0.751204   0.666878   0.589364   0.543796   \n",
       "365    0.557359    0.555354     0.506445    0.44371   0.386051   0.352194   \n",
       "366    0.951496    0.948585     0.882182   0.797063   0.718815   0.672786   \n",
       "367    0.610781    0.608135     0.547061   0.468763   0.396788   0.354463   \n",
       "368    0.518232    0.515307     0.444785   0.354336   0.271202   0.222371   \n",
       "369    0.362946    0.360851     0.310224   0.245291    0.18561   0.150557   \n",
       "370     2.10999     2.15104      2.21537    2.28698    2.36816     2.4876   \n",
       "371     1.36405     1.38804      1.42566    1.46754    1.51502    1.58489   \n",
       "372     1.84386     1.88814      1.95758    2.03488    2.12252     2.2515   \n",
       "373     1.94359      1.9815      2.04097    2.10718    2.18223    2.29269   \n",
       "374     1.59771     1.64166      1.71055    1.78724    1.87418    2.00209   \n",
       "375     1.50157     1.53099      1.57714    1.62851    1.68676    1.77248   \n",
       "376     2.16039     2.21267      2.29472    2.38606    2.48962    2.64208   \n",
       "377     1.48352     1.51944      1.57584    1.63864    1.70985    1.81471   \n",
       "378     2.01633     2.06116      2.13148    2.20977    2.29853    2.42916   \n",
       "379     1.55386     1.59219      1.65226    1.71912    1.79492    1.90644   \n",
       "380  -0.0443184  -0.0440506   -0.0403975 -0.0356562 -0.0322448 -0.0340324   \n",
       "381    0.631199    0.631752     0.639786   0.650225   0.657724   0.653735   \n",
       "382     0.46326    0.463597     0.468654   0.475228   0.479947   0.477419   \n",
       "383  0.00219245  0.00244612   0.00734612  0.0137396  0.0183077  0.0157405   \n",
       "384    0.262973    0.263403     0.270637   0.280059   0.286807   0.283105   \n",
       "385    0.628603     0.62896      0.63355     0.6395   0.643788   0.641574   \n",
       "386    0.422706    0.423053     0.429119   0.437023   0.442681   0.439556   \n",
       "387    0.324082    0.324439     0.329835   0.336851   0.341887   0.339185   \n",
       "388    0.499991    0.500482     0.507896   0.517535   0.524455   0.520743   \n",
       "389    0.193507    0.194137     0.202206   0.212666   0.220204   0.216316   \n",
       "\n",
       "           6          7           8          9     ...       1891        1892  \\\n",
       "0      0.365894   0.392923     0.42039   0.445661  ...  -0.542851   -0.553386   \n",
       "1      0.372045   0.396631    0.421619     0.4446  ...  -0.648388   -0.658111   \n",
       "2      0.781222   0.809376    0.837987   0.864312  ... -0.0996452   -0.110572   \n",
       "3      0.570728   0.598528     0.62678    0.65277  ...  -0.431184   -0.442068   \n",
       "4      0.435396   0.467502    0.500129   0.530151  ...  -0.532619   -0.545052   \n",
       "5      0.462882   0.486122    0.509743   0.531465  ...  -0.529401   -0.538613   \n",
       "6       0.48586   0.514214    0.543028   0.569544  ...   -0.32966   -0.340612   \n",
       "7      0.154031   0.170212    0.186656   0.201781  ...  -0.510014   -0.516408   \n",
       "8      0.341478   0.372317    0.403657   0.432486  ...  -0.822511   -0.834624   \n",
       "9     0.0554035  0.0697407   0.0843095  0.0977205  ...  -0.251414   -0.256876   \n",
       "10     0.387015   0.419034     0.44583   0.447698  ...  -0.407098   -0.451763   \n",
       "11     0.335548   0.365765    0.391071   0.392936  ...  -0.205807   -0.247698   \n",
       "12     0.584507   0.606415    0.624761   0.626105  ...   0.174699    0.144305   \n",
       "13    0.0514937  0.0779543     0.10012   0.101784  ...  -0.359496     -0.3961   \n",
       "14     0.274536   0.296179    0.314287   0.315528  ...  -0.306028   -0.336274   \n",
       "15     0.133453   0.160223     0.18263   0.184216  ...  -0.479674   -0.516953   \n",
       "16    0.0305661  0.0663417   0.0962982  0.0984814  ...  -0.661151   -0.710811   \n",
       "17   -0.0518519 -0.0250917 -0.00268934 -0.0010856  ...  -0.628657   -0.665877   \n",
       "18     0.527049   0.553189    0.575085   0.576726  ...   0.114827   0.0786587   \n",
       "19   -0.0804137 -0.0639815  -0.0502377  -0.049325  ...  -0.580919   -0.603958   \n",
       "20      1.17944    1.18229     1.18607    1.18998  ...   0.188565    0.188637   \n",
       "21     0.868706   0.872329    0.877145   0.882132  ...  -0.494603   -0.494557   \n",
       "22      1.71963    1.72402     1.72987    1.73594  ... -0.0758583  -0.0758651   \n",
       "23      1.72473    1.72984     1.73668    1.74378  ...  -0.547616   -0.547703   \n",
       "24      1.81646    1.82168     1.82861    1.83579  ...  -0.140453   -0.140384   \n",
       "25      1.04869    1.05136     1.05492    1.05862  ...  -0.086069  -0.0860918   \n",
       "26      1.50251    1.50627     1.51128    1.51645  ...   0.108446    0.108504   \n",
       "27      1.55916    1.56274     1.56752    1.57247  ...  0.0482152   0.0481892   \n",
       "28      1.59239    1.59664     1.60229    1.60813  ... -0.0620236  -0.0619948   \n",
       "29     0.685312    0.68873    0.693253   0.697934  ...  -0.464367   -0.464264   \n",
       "..          ...        ...         ...        ...  ...        ...         ...   \n",
       "360    0.427373   0.433672    0.440499   0.448554  ...  -0.364496   -0.329224   \n",
       "361    0.157293   0.163954    0.171173    0.17969  ...  -0.677257   -0.639967   \n",
       "362     0.25334   0.262048    0.271483   0.282614  ...  -0.820586    -0.77188   \n",
       "363    0.428315   0.435336    0.442936   0.451891  ...  -0.290742   -0.251809   \n",
       "364    0.539338   0.547799    0.556965   0.567775  ...  -0.457596   -0.410375   \n",
       "365    0.348964   0.355359     0.36228   0.370425  ...  -0.220819   -0.185547   \n",
       "366    0.668217   0.676678     0.68585    0.69668  ...  -0.473541   -0.425989   \n",
       "367    0.350291    0.35811    0.366583   0.376583  ...   -0.63868   -0.594888   \n",
       "368     0.21768   0.226862      0.2368   0.248504  ...  -0.670676   -0.619877   \n",
       "369    0.147195   0.153792    0.160933   0.169342  ...  -0.480196   -0.443719   \n",
       "370     2.65631     2.8402     3.01557    3.14727  ... -0.0884464  -0.0501031   \n",
       "371      1.6836     1.7912      1.8938    1.97085  ...  0.0213874   0.0437984   \n",
       "372     2.43372    2.63234     2.82175    2.96398  ...  -0.664156   -0.622797   \n",
       "373     2.44876    2.61887     2.78109    2.90291  ...  -0.220185   -0.184769   \n",
       "374      2.1828    2.37976     2.56759    2.70865  ...  -0.794463    -0.75341   \n",
       "375     1.89361    2.02565     2.15156     2.2461  ...  -0.209977   -0.182503   \n",
       "376      2.8575    3.09232     3.31625    3.48438  ...  -0.960993   -0.912161   \n",
       "377      1.9629    2.12444     2.27848    2.39412  ...  -0.778398   -0.744853   \n",
       "378     2.61372     2.8149     3.00675    3.15081  ...  -0.562632   -0.520756   \n",
       "379     2.06396    2.23566      2.3994    2.52237  ...   -0.49309   -0.457286   \n",
       "380  -0.0428326 -0.0533255  -0.0612793  -0.055208  ...  -0.592728   -0.574808   \n",
       "381     0.63425   0.611024    0.593409   0.606786  ...  -0.654145   -0.614584   \n",
       "382    0.465114   0.450449    0.439323   0.447752  ...  -0.369907   -0.344959   \n",
       "383  0.00354873 -0.0109661  -0.0219962 -0.0137774  ...  -0.965692   -0.941212   \n",
       "384    0.265309   0.244109    0.228014   0.240109  ...   -1.04441     -1.0085   \n",
       "385    0.630592   0.617493    0.607569   0.615183  ... -0.0158937  0.00653628   \n",
       "386    0.424586   0.406756    0.393216   0.403366  ...  -0.702219   -0.672054   \n",
       "387    0.326044   0.310384    0.298503   0.307499  ...    -0.5708   -0.544168   \n",
       "388    0.502692   0.481179    0.464858   0.477217  ...   -0.72774   -0.691151   \n",
       "389    0.197018   0.173999     0.15656   0.169943  ...  -0.934459   -0.895036   \n",
       "\n",
       "          1893       1894       1895        1896       1897       1898  \\\n",
       "0    -0.576443  -0.472749  -0.411013   -0.444833  -0.505014  -0.536638   \n",
       "1    -0.679272  -0.584661  -0.528374   -0.559365  -0.614435   -0.64342   \n",
       "2    -0.134525 -0.0266104  0.0376528  0.00250114 -0.0600754 -0.0929417   \n",
       "3    -0.465848  -0.359096  -0.295553   -0.330417  -0.392428  -0.425031   \n",
       "4    -0.572333  -0.449324  -0.376065   -0.416108  -0.487407  -0.524845   \n",
       "5    -0.558642  -0.469168  -0.415942    -0.44527  -0.497372  -0.524803   \n",
       "6    -0.364667   -0.25609  -0.191416   -0.226735  -0.289636  -0.322656   \n",
       "7    -0.530327  -0.468073  -0.431034   -0.451421  -0.487651  -0.506718   \n",
       "8    -0.861053  -0.742555  -0.672031   -0.710768  -0.779646  -0.815871   \n",
       "9    -0.268937  -0.214191  -0.181561   -0.199295  -0.230923    -0.2475   \n",
       "10   -0.494055  -0.494086   -0.48842   -0.469221  -0.460468  -0.491601   \n",
       "11   -0.287355  -0.287276  -0.281839   -0.263677  -0.255338  -0.284504   \n",
       "12     0.11553   0.115579   0.119513    0.132678   0.138717   0.117553   \n",
       "13   -0.430751  -0.430649   -0.42586   -0.409943  -0.402617  -0.428091   \n",
       "14   -0.364913  -0.364957  -0.361146   -0.348178  -0.342279  -0.363367   \n",
       "15   -0.552248  -0.552248  -0.547489   -0.531427   -0.52409  -0.550066   \n",
       "16   -0.757826  -0.757759  -0.751344   -0.729852  -0.719998  -0.754581   \n",
       "17   -0.701116  -0.701097  -0.696324    -0.68026  -0.672912  -0.698842   \n",
       "18   0.0444197  0.0445176  0.0492453   0.0649685  0.0722039  0.0470313   \n",
       "19   -0.625775  -0.625839  -0.622972   -0.613138  -0.608682  -0.624755   \n",
       "20    0.188709   0.188782   0.188854    0.188926   0.188999   0.219654   \n",
       "21   -0.494511  -0.494465  -0.494418   -0.494372  -0.494326   -0.45493   \n",
       "22  -0.0758718 -0.0758786 -0.0758854  -0.0758922 -0.0758989 -0.0275377   \n",
       "23   -0.547791  -0.547878  -0.547965   -0.548053   -0.54814  -0.491042   \n",
       "24   -0.140315  -0.140245  -0.140176   -0.140107  -0.140038 -0.0833392   \n",
       "25  -0.0861146 -0.0861374 -0.0861602   -0.086183 -0.0862058 -0.0566069   \n",
       "26    0.108562    0.10862   0.108678    0.108736   0.108794   0.149636   \n",
       "27   0.0481632  0.0481372  0.0481111   0.0480851  0.0480591  0.0876964   \n",
       "28  -0.0619661 -0.0619373 -0.0619086  -0.0618798 -0.0618511 -0.0154645   \n",
       "29   -0.464161  -0.464058  -0.463955   -0.463852  -0.463749  -0.427181   \n",
       "..         ...        ...        ...         ...        ...        ...   \n",
       "360  -0.315901  -0.331725  -0.344389    -0.35477  -0.369189  -0.376585   \n",
       "361   -0.62588  -0.642608  -0.655994   -0.666968  -0.682211  -0.690029   \n",
       "362  -0.753476  -0.775313  -0.792786   -0.807107  -0.827004  -0.837205   \n",
       "363  -0.237052  -0.254401  -0.268269   -0.279622  -0.295424   -0.30349   \n",
       "364  -0.392518  -0.413655  -0.430564   -0.444419  -0.463676  -0.473538   \n",
       "365   -0.17215  -0.187806  -0.200312   -0.210543  -0.224799  -0.232054   \n",
       "366  -0.408052  -0.429442  -0.446567   -0.460613  -0.480107  -0.490125   \n",
       "367  -0.578348  -0.597999  -0.613726   -0.626619  -0.644526  -0.653712   \n",
       "368  -0.600606  -0.623206  -0.641265   -0.656045  -0.676626  -0.687118   \n",
       "369  -0.429878  -0.446097  -0.459057   -0.469663  -0.484434  -0.491962   \n",
       "370 -0.0290176 -0.0278931 -0.0579576  -0.0828239  -0.102492 -0.0797432   \n",
       "371  0.0561091  0.0567376  0.0391126   0.0245299  0.0129895  0.0262737   \n",
       "372  -0.600084  -0.598939  -0.631494   -0.658431  -0.679752  -0.655243   \n",
       "373  -0.165324  -0.164352  -0.192243   -0.215323  -0.233593  -0.212609   \n",
       "374  -0.730843  -0.729658  -0.761882   -0.788539  -0.809627  -0.785278   \n",
       "375  -0.167425  -0.166687  -0.188355   -0.206288  -0.220487  -0.204215   \n",
       "376  -0.885382   -0.88411  -0.922693   -0.954633  -0.979931  -0.951027   \n",
       "377  -0.726485   -0.72567  -0.752282   -0.774323  -0.791792  -0.771961   \n",
       "378  -0.497769   -0.49663  -0.529627   -0.556935  -0.578553  -0.553745   \n",
       "379  -0.437597  -0.436544  -0.464611   -0.487825  -0.506186  -0.484943   \n",
       "380  -0.568495  -0.581768  -0.596251   -0.595137  -0.594144  -0.557121   \n",
       "381  -0.600672  -0.630043  -0.662086   -0.659663  -0.657507  -0.575732   \n",
       "382  -0.336194  -0.354736  -0.374964   -0.373447  -0.372098  -0.320518   \n",
       "383  -0.932661  -0.950992  -0.970983   -0.969568  -0.968319  -0.917619   \n",
       "384  -0.995916    -1.0227   -1.05192    -1.04979    -1.0479  -0.973596   \n",
       "385  0.0144523  -0.002124 -0.0202121  -0.0187971 -0.0175333  0.0287841   \n",
       "386  -0.661494  -0.684022  -0.708591   -0.706814  -0.705241  -0.642806   \n",
       "387  -0.534812  -0.554612  -0.576211   -0.574594  -0.573157   -0.51809   \n",
       "388  -0.678298  -0.705497  -0.735169   -0.732947  -0.730972  -0.655321   \n",
       "389  -0.881122  -0.910252  -0.942039   -0.939549  -0.937326  -0.855921   \n",
       "\n",
       "           1899       1900  \n",
       "0     -0.535531  -0.524538  \n",
       "1      -0.64251   -0.63257  \n",
       "2    -0.0917555  -0.080285  \n",
       "3     -0.423925   -0.41264  \n",
       "4     -0.523474  -0.510383  \n",
       "5     -0.523956  -0.514569  \n",
       "6     -0.321425  -0.309851  \n",
       "7     -0.506115  -0.499572  \n",
       "8     -0.814671  -0.802169  \n",
       "9     -0.246825   -0.24094  \n",
       "10    -0.494244   -0.48834  \n",
       "11     -0.28688   -0.28122  \n",
       "12      0.11582   0.119917  \n",
       "13    -0.430137  -0.425153  \n",
       "14    -0.365179  -0.361207  \n",
       "15    -0.552247  -0.547289  \n",
       "16    -0.757423  -0.750743  \n",
       "17    -0.701002   -0.69603  \n",
       "18    0.0450074  0.0499279  \n",
       "19    -0.626164  -0.623174  \n",
       "20     0.238526   0.197954  \n",
       "21    -0.430694  -0.482945  \n",
       "22   0.00218829 -0.0620998  \n",
       "23    -0.455976  -0.532063  \n",
       "24   -0.0484587  -0.123651  \n",
       "25   -0.0384206 -0.0778109  \n",
       "26     0.174765    0.12062  \n",
       "27     0.112052  0.0593134  \n",
       "28    0.0130612 -0.0485197  \n",
       "29    -0.404663  -0.453021  \n",
       "..          ...        ...  \n",
       "360   -0.351147  -0.302004  \n",
       "361   -0.663133  -0.611178  \n",
       "362   -0.802075  -0.734219  \n",
       "363   -0.275388  -0.221176  \n",
       "364   -0.439472  -0.373694  \n",
       "365   -0.206582  -0.157484  \n",
       "366   -0.455841  -0.389573  \n",
       "367   -0.622129  -0.561115  \n",
       "368   -0.650444  -0.579718  \n",
       "369   -0.465625  -0.414842  \n",
       "370  -0.0359938  0.0418556  \n",
       "371   0.0518486  0.0973806  \n",
       "372   -0.608043  -0.523998  \n",
       "373    -0.17219  -0.100213  \n",
       "374   -0.738434  -0.655062  \n",
       "375   -0.172856  -0.117001  \n",
       "376   -0.895287  -0.795972  \n",
       "377   -0.733662  -0.665377  \n",
       "378   -0.505953  -0.420838  \n",
       "379   -0.444092  -0.371403  \n",
       "380   -0.518284   -0.52249  \n",
       "381    -0.48995  -0.499282  \n",
       "382   -0.266409  -0.272309  \n",
       "383   -0.864431  -0.870317  \n",
       "384   -0.895643  -0.904205  \n",
       "385   0.0773693  0.0721321  \n",
       "386   -0.577308  -0.584517  \n",
       "387   -0.460322  -0.466624  \n",
       "388   -0.575961  -0.584617  \n",
       "389   -0.770531  -0.779733  \n",
       "\n",
       "[390 rows x 1901 columns]"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n 41      N\n",
       "n 41      N\n",
       "n 41      N\n",
       "n 41      N\n",
       "n 41      N\n",
       "n 41      N\n",
       "n 41      N\n",
       "n 41      N\n",
       "n 41      N\n",
       "n 41      N\n",
       "n 45      N\n",
       "n 45      N\n",
       "n 45      N\n",
       "n 45      N\n",
       "n 45      N\n",
       "n 45      N\n",
       "n 45      N\n",
       "n 45      N\n",
       "n 45      N\n",
       "n 45      N\n",
       "n 46      N\n",
       "n 46      N\n",
       "n 46      N\n",
       "n 46      N\n",
       "n 46      N\n",
       "n 46      N\n",
       "n 46      N\n",
       "n 46      N\n",
       "n 46      N\n",
       "n 46      N\n",
       "         ..\n",
       "pan 28    P\n",
       "pan 28    P\n",
       "pan 28    P\n",
       "pan 28    P\n",
       "pan 28    P\n",
       "pan 28    P\n",
       "pan 28    P\n",
       "pan 28    P\n",
       "pan 28    P\n",
       "pan 28    P\n",
       "pan 31    P\n",
       "pan 31    P\n",
       "pan 31    P\n",
       "pan 31    P\n",
       "pan 31    P\n",
       "pan 31    P\n",
       "pan 31    P\n",
       "pan 31    P\n",
       "pan 31    P\n",
       "pan 31    P\n",
       "pan 35    P\n",
       "pan 35    P\n",
       "pan 35    P\n",
       "pan 35    P\n",
       "pan 35    P\n",
       "pan 35    P\n",
       "pan 35    P\n",
       "pan 35    P\n",
       "pan 35    P\n",
       "pan 35    P\n",
       "Name: 0, Length: 390, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(y_aug)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ee816fdcc0>"
      ]
     },
     "execution_count": 384,
     "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": [
    "df_y.value_counts().plot(kind =\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ee816eea20>"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.value_counts().plot(kind =\"pie\",autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classification Analysis\n",
    "# split the data now\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(df_x,df_y,test_size = 0.3,random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification without scaling\n",
      "DECISION TREE CLASSIFIER::  0.9316239316239316\n",
      "FORSEST TREE CLASSIFICATION::  0.9401709401709402\n",
      "SUPPORT VECTOR MACHINE::  0.8632478632478633\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aslan\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K NEAREST NEIGHBOURS::  0.9230769230769231\n",
      "NAIVE BAYES ALGORITHM::  0.6495726495726496\n",
      "LOGISTIC REGRESSION::  1.0\n",
      "PLS-DA::  0.9487179487179487\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aslan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# FITTING & TESTING THE CLASSIFICATION MODELS (without scaling the data)\n",
    "print('Classification without scaling')\n",
    "#decision tree\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(xtrain,ytrain)\n",
    "ypred = dt.predict(xtest)\n",
    "print('DECISION TREE CLASSIFIER:: ',metrics.accuracy_score(ypred,ytest))\n",
    "\n",
    "#random forest\n",
    "model=RandomForestClassifier(n_estimators=100)\n",
    "model.fit(xtrain,ytrain)# now fit our model for traiing data\n",
    "prediction=model.predict(xtest)# predict for the test data\n",
    "print('FORSEST TREE CLASSIFICATION:: ',metrics.accuracy_score(prediction,ytest))\n",
    "\n",
    "#SVM\n",
    "model = svm.SVC()\n",
    "model.fit(xtrain,ytrain)# now fit our model for traiing data\n",
    "prediction=model.predict(xtest)# predict for the test data\n",
    "metrics.accuracy_score(prediction,ytest)\n",
    "print('SUPPORT VECTOR MACHINE:: ',metrics.accuracy_score(prediction,ytest))\n",
    "\n",
    "#knn\n",
    "knn = KNeighborsClassifier(n_neighbors = 5)\n",
    "knn.fit(xtrain,ytrain)\n",
    "ypred = knn.predict(xtest)\n",
    "print('K NEAREST NEIGHBOURS:: ',metrics.accuracy_score(ypred,ytest))\n",
    "\n",
    "#naive bayes\n",
    "NB = GaussianNB()\n",
    "NB.fit(xtrain,ytrain)\n",
    "ypred = NB.predict(xtest)\n",
    "print('NAIVE BAYES ALGORITHM:: ',metrics.accuracy_score(ypred,ytest))\n",
    "\n",
    "#logistic regression\n",
    "LR = LogisticRegression()\n",
    "LR.fit(xtrain,ytrain)\n",
    "ypred = LR.predict(xtest)\n",
    "print('LOGISTIC REGRESSION:: ',metrics.accuracy_score(ypred,ytest))\n",
    "\n",
    "#PLS Discriminant Analysis\n",
    "binary_ytrain = (ytrain == 'N').astype('uint8') # assign 'P' to 0, 'N' to 1\n",
    "binary_ytest = (ytest == 'N').astype('uint8')\n",
    "PLS = PLSRegression(n_components=5)\n",
    "PLS.fit(xtrain,binary_ytrain)\n",
    "ypred = PLS.predict(xtest) # these won't generally be integer numbers\n",
    "binary_ypred = (PLS.predict(xtest) > 0.5).astype('uint8')\n",
    "print('PLS-DA:: ',metrics.accuracy_score(binary_ypred,binary_ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification after scaling\n",
      "DECISION TREE CLASSIFIER::  0.9316239316239316\n",
      "FORSEST TREE CLASSIFICATION::  0.9316239316239316\n",
      "SUPPORT VECTOR MACHINE::  0.9316239316239316\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aslan\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K NEAREST NEIGHBOURS::  0.8974358974358975\n",
      "NAIVE BAYES ALGORITHM::  0.6495726495726496\n",
      "LOGISTIC REGRESSION::  1.0\n",
      "PLS-DA::  0.9487179487179487\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aslan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# FITTING & TESTING THE CLASSIFICATION MODELS AFTER SCALING\n",
    "sc = StandardScaler()\n",
    "df_x = sc.fit_transform(df_x)\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(df_x,df_y,test_size = 0.3,random_state = 1)\n",
    "\n",
    "print('Classification after scaling')\n",
    "\n",
    "#decision tree\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(xtrain,ytrain)\n",
    "ypred = dt.predict(xtest)\n",
    "print('DECISION TREE CLASSIFIER:: ',metrics.accuracy_score(ypred,ytest))\n",
    "\n",
    "#random forest\n",
    "model=RandomForestClassifier(n_estimators=100)\n",
    "model.fit(xtrain,ytrain)# now fit our model for traiing data\n",
    "prediction=model.predict(xtest)# predict for the test data\n",
    "print('FORSEST TREE CLASSIFICATION:: ',metrics.accuracy_score(prediction,ytest))\n",
    "\n",
    "\n",
    "#SVM\n",
    "model = svm.SVC()\n",
    "model.fit(xtrain,ytrain)# now fit our model for traiing data\n",
    "prediction=model.predict(xtest)# predict for the test data\n",
    "metrics.accuracy_score(prediction,ytest)\n",
    "print('SUPPORT VECTOR MACHINE:: ',metrics.accuracy_score(prediction,ytest))\n",
    "\n",
    "#knn\n",
    "knn = KNeighborsClassifier(n_neighbors = 5)\n",
    "knn.fit(xtrain,ytrain)\n",
    "ypred = knn.predict(xtest)\n",
    "print('K NEAREST NEIGHBOURS:: ',metrics.accuracy_score(ypred,ytest))\n",
    "\n",
    "#naive bayes\n",
    "NB = GaussianNB()\n",
    "NB.fit(xtrain,ytrain)\n",
    "ypred = NB.predict(xtest)\n",
    "print('NAIVE BAYES ALGORITHM:: ',metrics.accuracy_score(ypred,ytest))\n",
    "\n",
    "#logistic regression\n",
    "LR = LogisticRegression()\n",
    "LR.fit(xtrain,ytrain)\n",
    "ypred = LR.predict(xtest)\n",
    "print('LOGISTIC REGRESSION:: ',metrics.accuracy_score(ypred,ytest))\n",
    "\n",
    "#PLS Discriminant Analysis\n",
    "binary_ytrain = (ytrain == 'N').astype('uint8') # assign 'P' to 0, 'N' to 1\n",
    "binary_ytest = (ytest == 'N').astype('uint8')\n",
    "PLS = PLSRegression(n_components=5)\n",
    "PLS.fit(xtrain,binary_ytrain)\n",
    "ypred = PLS.predict(xtest) # these won't generally be integer numbers\n",
    "binary_ypred = (PLS.predict(xtest) > 0.5).astype('uint8')\n",
    "print('PLS-DA:: ',metrics.accuracy_score(binary_ypred,binary_ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUPPORT VECTOR MACHINE::  0.9316239316239316\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aslan\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[53,  2],\n",
       "       [ 6, 56]], dtype=int64)"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SVM\n",
    "model = svm.SVC()\n",
    "model.fit(xtrain,ytrain)# now fit our model for traiing data\n",
    "prediction=model.predict(xtest)# predict for the test data\n",
    "metrics.accuracy_score(prediction,ytest)\n",
    "print('SUPPORT VECTOR MACHINE:: ',metrics.accuracy_score(prediction,ytest))\n",
    "metrics.confusion_matrix(ytest,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOGISTIC REGRESSION::  1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aslan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[55,  0],\n",
       "       [ 0, 62]], dtype=int64)"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#logistic regression\n",
    "LR = LogisticRegression()\n",
    "LR.fit(xtrain,ytrain)\n",
    "ypred = LR.predict(xtest)\n",
    "print('LOGISTIC REGRESSION:: ',metrics.accuracy_score(ypred,ytest))\n",
    "metrics.confusion_matrix(ytest,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PLS-DA::  0.9487179487179487\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[56,  6],\n",
       "       [ 0, 55]], dtype=int64)"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PLS Discriminant Analysis\n",
    "binary_ytrain = (ytrain == 'N').astype('uint8') # assign 'P' to 0, 'N' to 1\n",
    "binary_ytest = (ytest == 'N').astype('uint8')\n",
    "PLS = PLSRegression(n_components=5)\n",
    "PLS.fit(xtrain,binary_ytrain)\n",
    "ypred = PLS.predict(xtest) # these won't generally be integer numbers\n",
    "binary_ypred = (PLS.predict(xtest) > 0.5).astype('uint8')\n",
    "print('PLS-DA:: ',metrics.accuracy_score(binary_ypred,binary_ytest))\n",
    "metrics.confusion_matrix(binary_ytest,binary_ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convolutional Neural Network algorithm\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": false,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}