Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import re\n",
    "import peakutils\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "import scipy.signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "from scipy.sparse.linalg import spsolve\n",
    "\n",
    "def baseline_als(y, lam, p, niter=10):\n",
    "    \"\"\"Функция для сглаживания спектра методом Asymmetric Least Squares Smoothing\"\"\"\n",
    "    L = len(y)\n",
    "    D = sparse.diags([1,-2,1],[0,-1,-2], shape=(L,L-2))\n",
    "    w = np.ones(L)\n",
    "    for i in range(niter):\n",
    "        W = sparse.spdiags(w, 0, L, L)\n",
    "        Z = W + lam * D.dot(D.transpose())\n",
    "        z = spsolve(Z, w*y)\n",
    "        w = p * (y > z) + (1-p) * (y < z)\n",
    "    return z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"datasets/earLobe.csv\")\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_earLobe = pd.read_csv(\"datasets/earLobe.csv\")\n",
    "# df_innerArm = pd.read_csv(\"datasets/innerArm.csv\")\n",
    "# df_thumbNail = pd.read_csv(\"datasets/thumbNail.csv\")\n",
    "# df_vein = pd.read_csv(\"datasets/vein.csv\")\n",
    "# df = df_earLobe.append([df_innerArm.drop(0, axis=0), df_thumbNail.drop(0, axis=0), df_vein.drop(0, axis=0)])\n",
    "# df.index = range(0, 81)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "has_DM2 = df.pop('has_DM2')\n",
    "patientID = df.pop('patientID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</th>\n",
       "      <th>Var4</th>\n",
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       "      <th>Var3154</th>\n",
       "      <th>Var3155</th>\n",
       "      <th>Var3156</th>\n",
       "      <th>Var3157</th>\n",
       "      <th>Var3158</th>\n",
       "      <th>Var3159</th>\n",
       "      <th>Var3160</th>\n",
       "      <th>Var3161</th>\n",
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       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
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       "      <td>3151</td>\n",
       "      <td>3152</td>\n",
       "      <td>3153</td>\n",
       "      <td>3154</td>\n",
       "      <td>3155</td>\n",
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       "      <th>1</th>\n",
       "      <td>181.800000</td>\n",
       "      <td>181.800000</td>\n",
       "      <td>181.800000</td>\n",
       "      <td>181.800000</td>\n",
       "      <td>181.800000</td>\n",
       "      <td>181.800000</td>\n",
       "      <td>181.800000</td>\n",
       "      <td>181.800000</td>\n",
       "      <td>181.800000</td>\n",
       "      <td>181.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162.800000</td>\n",
       "      <td>162.800000</td>\n",
       "      <td>162.800000</td>\n",
       "      <td>162.800000</td>\n",
       "      <td>162.800000</td>\n",
       "      <td>162.800000</td>\n",
       "      <td>162.800000</td>\n",
       "      <td>162.800000</td>\n",
       "      <td>162.800000</td>\n",
       "      <td>162.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>107.400000</td>\n",
       "      <td>107.400000</td>\n",
       "      <td>107.400000</td>\n",
       "      <td>107.400000</td>\n",
       "      <td>107.400000</td>\n",
       "      <td>107.400000</td>\n",
       "      <td>107.400000</td>\n",
       "      <td>107.400000</td>\n",
       "      <td>107.400000</td>\n",
       "      <td>107.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>290.166667</td>\n",
       "      <td>290.166667</td>\n",
       "      <td>290.166667</td>\n",
       "      <td>290.166667</td>\n",
       "      <td>290.166667</td>\n",
       "      <td>290.166667</td>\n",
       "      <td>290.166667</td>\n",
       "      <td>290.166667</td>\n",
       "      <td>290.166667</td>\n",
       "      <td>290.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3160 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Var2        Var3        Var4        Var5        Var6        Var7  \\\n",
       "0    0.000000    1.000000    2.000000    3.000000    4.000000    5.000000   \n",
       "1  181.800000  181.800000  181.800000  181.800000  181.800000  181.800000   \n",
       "2  162.800000  162.800000  162.800000  162.800000  162.800000  162.800000   \n",
       "3  107.400000  107.400000  107.400000  107.400000  107.400000  107.400000   \n",
       "4  290.166667  290.166667  290.166667  290.166667  290.166667  290.166667   \n",
       "\n",
       "         Var8        Var9       Var10       Var11  ...  Var3152  Var3153  \\\n",
       "0    6.000000    7.000000    8.000000    9.000000  ...     3150     3151   \n",
       "1  181.800000  181.800000  181.800000  181.800000  ...        0        0   \n",
       "2  162.800000  162.800000  162.800000  162.800000  ...        0        0   \n",
       "3  107.400000  107.400000  107.400000  107.400000  ...        0        0   \n",
       "4  290.166667  290.166667  290.166667  290.166667  ...        0        0   \n",
       "\n",
       "   Var3154  Var3155  Var3156  Var3157  Var3158  Var3159  Var3160  Var3161  \n",
       "0     3152     3153     3154     3155     3156     3157     3158     3159  \n",
       "1        0        0        0        0        0        0        0        0  \n",
       "2        0        0        0        0        0        0        0        0  \n",
       "3        0        0        0        0        0        0        0        0  \n",
       "4        0        0        0        0        0        0        0        0  \n",
       "\n",
       "[5 rows x 3160 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "plt.plot(df.iloc[0], df.iloc[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Crop Data to 800-1800 cm^-1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "droped_columns = []\n",
    "for col in df.columns:\n",
    "    if int(re.findall(r'\\d+', col)[0]) <= 800 or int(re.findall(r'\\d+', col)[0]) >= 1800:\n",
    "        droped_columns.append(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(droped_columns, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Var801</th>\n",
       "      <th>Var802</th>\n",
       "      <th>Var803</th>\n",
       "      <th>Var804</th>\n",
       "      <th>Var805</th>\n",
       "      <th>Var806</th>\n",
       "      <th>Var807</th>\n",
       "      <th>Var808</th>\n",
       "      <th>Var809</th>\n",
       "      <th>Var810</th>\n",
       "      <th>...</th>\n",
       "      <th>Var1790</th>\n",
       "      <th>Var1791</th>\n",
       "      <th>Var1792</th>\n",
       "      <th>Var1793</th>\n",
       "      <th>Var1794</th>\n",
       "      <th>Var1795</th>\n",
       "      <th>Var1796</th>\n",
       "      <th>Var1797</th>\n",
       "      <th>Var1798</th>\n",
       "      <th>Var1799</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>799.000000</td>\n",
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       "      <td>801.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>803.000000</td>\n",
       "      <td>804.000000</td>\n",
       "      <td>805.000000</td>\n",
       "      <td>806.000000</td>\n",
       "      <td>807.000000</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1791.000000</td>\n",
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       "      <td>1795.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>187.887152</td>\n",
       "      <td>191.185864</td>\n",
       "      <td>195.943112</td>\n",
       "      <td>197.019885</td>\n",
       "      <td>194.774564</td>\n",
       "      <td>192.743616</td>\n",
       "      <td>191.215888</td>\n",
       "      <td>190.194934</td>\n",
       "      <td>195.590097</td>\n",
       "      <td>200.985260</td>\n",
       "      <td>...</td>\n",
       "      <td>51.245700</td>\n",
       "      <td>54.463985</td>\n",
       "      <td>56.393459</td>\n",
       "      <td>49.932257</td>\n",
       "      <td>47.622393</td>\n",
       "      <td>48.354281</td>\n",
       "      <td>51.604005</td>\n",
       "      <td>53.379442</td>\n",
       "      <td>51.303317</td>\n",
       "      <td>50.599999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>169.308038</td>\n",
       "      <td>175.024512</td>\n",
       "      <td>183.282382</td>\n",
       "      <td>189.843460</td>\n",
       "      <td>194.872977</td>\n",
       "      <td>195.259000</td>\n",
       "      <td>184.744665</td>\n",
       "      <td>175.342270</td>\n",
       "      <td>180.018082</td>\n",
       "      <td>184.693894</td>\n",
       "      <td>...</td>\n",
       "      <td>58.028577</td>\n",
       "      <td>56.525036</td>\n",
       "      <td>54.495767</td>\n",
       "      <td>52.179488</td>\n",
       "      <td>49.228832</td>\n",
       "      <td>45.813356</td>\n",
       "      <td>48.117433</td>\n",
       "      <td>51.241115</td>\n",
       "      <td>55.393367</td>\n",
       "      <td>55.345582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120.285950</td>\n",
       "      <td>120.814867</td>\n",
       "      <td>122.161259</td>\n",
       "      <td>124.735393</td>\n",
       "      <td>128.417717</td>\n",
       "      <td>129.147617</td>\n",
       "      <td>122.946858</td>\n",
       "      <td>117.390883</td>\n",
       "      <td>119.998546</td>\n",
       "      <td>122.606209</td>\n",
       "      <td>...</td>\n",
       "      <td>27.668530</td>\n",
       "      <td>35.821674</td>\n",
       "      <td>43.497690</td>\n",
       "      <td>39.352768</td>\n",
       "      <td>34.854444</td>\n",
       "      <td>30.097175</td>\n",
       "      <td>28.998998</td>\n",
       "      <td>29.130067</td>\n",
       "      <td>31.572569</td>\n",
       "      <td>35.551240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>305.211868</td>\n",
       "      <td>307.784993</td>\n",
       "      <td>313.469754</td>\n",
       "      <td>317.818830</td>\n",
       "      <td>320.962275</td>\n",
       "      <td>321.376920</td>\n",
       "      <td>315.385852</td>\n",
       "      <td>310.069199</td>\n",
       "      <td>313.291306</td>\n",
       "      <td>316.513413</td>\n",
       "      <td>...</td>\n",
       "      <td>65.438088</td>\n",
       "      <td>65.602856</td>\n",
       "      <td>66.066346</td>\n",
       "      <td>66.371121</td>\n",
       "      <td>67.086658</td>\n",
       "      <td>68.103169</td>\n",
       "      <td>66.164164</td>\n",
       "      <td>65.640090</td>\n",
       "      <td>68.896758</td>\n",
       "      <td>68.855319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 999 columns</p>\n",
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      ],
      "text/plain": [
       "       Var801      Var802      Var803      Var804      Var805      Var806  \\\n",
       "0  799.000000  800.000000  801.000000  802.000000  803.000000  804.000000   \n",
       "1  187.887152  191.185864  195.943112  197.019885  194.774564  192.743616   \n",
       "2  169.308038  175.024512  183.282382  189.843460  194.872977  195.259000   \n",
       "3  120.285950  120.814867  122.161259  124.735393  128.417717  129.147617   \n",
       "4  305.211868  307.784993  313.469754  317.818830  320.962275  321.376920   \n",
       "\n",
       "       Var807      Var808      Var809      Var810  ...      Var1790  \\\n",
       "0  805.000000  806.000000  807.000000  808.000000  ...  1788.000000   \n",
       "1  191.215888  190.194934  195.590097  200.985260  ...    51.245700   \n",
       "2  184.744665  175.342270  180.018082  184.693894  ...    58.028577   \n",
       "3  122.946858  117.390883  119.998546  122.606209  ...    27.668530   \n",
       "4  315.385852  310.069199  313.291306  316.513413  ...    65.438088   \n",
       "\n",
       "       Var1791      Var1792      Var1793      Var1794      Var1795  \\\n",
       "0  1789.000000  1790.000000  1791.000000  1792.000000  1793.000000   \n",
       "1    54.463985    56.393459    49.932257    47.622393    48.354281   \n",
       "2    56.525036    54.495767    52.179488    49.228832    45.813356   \n",
       "3    35.821674    43.497690    39.352768    34.854444    30.097175   \n",
       "4    65.602856    66.066346    66.371121    67.086658    68.103169   \n",
       "\n",
       "       Var1796      Var1797      Var1798      Var1799  \n",
       "0  1794.000000  1795.000000  1796.000000  1797.000000  \n",
       "1    51.604005    53.379442    51.303317    50.599999  \n",
       "2    48.117433    51.241115    55.393367    55.345582  \n",
       "3    28.998998    29.130067    31.572569    35.551240  \n",
       "4    66.164164    65.640090    68.896758    68.855319  \n",
       "\n",
       "[5 rows x 999 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QlRTHij3lANz98naAXgX6pkD7XwfGmLCqWEopFUoDfR8IHboJNTU/BYAZI9NoM4ay2mZu/8+mPvmZoQu2Glpawz5slFIqlOa66YXkOCu4JsR1rFkLcMa4TJ6+/kN8a/Ek8lKtsfu/vb3X2R9oPf5ZOBV1zc52baMWK1dKdU0DfS+8dtM53H/FqXjdnf9nFBFmjkrH43ZxxdzRzCkIz3m/6O43eGJ1Ia1tx76MICzQd5JjXymlgjTQ90J2chznT8vr0bE+j4t7PjszrG1veT1/fnM34777PA+9t++YfnZoqgUN9Eqpo9FAfwLlpPi5bM6osOInwRz2tz27ibLapg7n7Cuv45m1B/njG7uctrY2w29fbc86oUM3Sqmj0UB/gv38YyfxxrfO4cvnjmfxtFynvbXNMNtOndDWZnh+QxEtrW2cfdfrfO2xtfz8ha1OhaqapgBH6lv4yCnDnNdKKdUVDfQDwON2cdPiScwe3bFOrTGGN3eWcf3Da/j581vD9tU3W1MqqxusGTejM6xi59qjV0odjQb6AXTSiNQObbVNAQ5UWPlrVu8LX1QcfABbZQf64enWEFBdH6dWUErFFg30A2ju2EweXzqXqz5U4LRVNbRQeKQBgJqInnqHQG+P9Ucep5RSoTTQD7DTx2YyIr394WxVQwtVDVZAPxCRmTIY0D/Yb6VVyEmJw+sWnXWjlDoqDfSDQJyn/ddQ1dBCdYMVuFtaw+fX1zUHeH3bYX75kpVCITXeS1KcR8folVJHpevmB4FZo9Od7eqGFqobWzo97osPrSY/1e+8Tov3keT3UNPF8UopBdqjHxSmDUvlpRvnA8Ghm64Dd1FVo7Pt97pIi/excu8R3t5Z1uU5SqmhTQP9IBHsqf/t7b2sL6xidGZCt+eICGkJXg5WNnD5n1c48+yVUiqUBvpBIinOg8clzkrZ9ARfl8d+beEEXrb/AojztCdUC87WUUqpUBroBwkRITelffx9dsi4/Ts3Lwg79vLTRzEhNxkIL0/45o4yHa9XSnWgD2MHkazkOA5WNnDG2Ey+vWQyRVWNNLe2kZ/q579fOQu/10Vuip9kf3st2mvPGsM7u6yCJt99egNv7yzj950UKVdKDV09qRn7VxE5LCIbQ9puE5GDIrLW/rowZN8tIrJTRLaJyOL+uvBY5LXr0F4yYxg+j4vfXz6LP31uNiLC9OGpjM9JDgvyAAun5LL7ZxfyyVNHAPDchqJuf46O5Ss1tPRk6OYBYEkn7b82xsywv54HEJGpWEXDp9nn/EFEOq/KoToIFhzPSYk75vN+8fGT+cSpI3C7hOZAx4Im+8vrue3ZTRTc/Bxjbnme4qpGXtpUzI6SGowxPLvuEI0tWrxcqVjUbaA3xiwHKro7znYJ8JgxpskYswfYCczpxfUNKanxVm/d7zn2z0aXSzhzfCatbYaL73mLtohiJvPvWsYD7+x1Xu8tr2PpQ6s579fLWbGngq8++gF3vLAVpVTs6c3D2C+LyHp7aCf45HA4cCDkmEK7rQMRWSoiq0RkVWlpaS8uI3b85NLpfGHeGOaM6ZjVsiem5ltJ0rYW1/DS5mKnvbOhmtDCJUfsHDor9vT081wpFU2ON9DfC4wDZgBFwK+O9Q2MMfcbY2YbY2ZnZ2cf52XEltwUP7deNBVPF6UJuzMpL5nXbzqH9AQvy7a2f3iGlh0M+v4z7UXKNxdVA1BS3ciesjp2Hq6hpbWNZ9Ye1PF8pWLAcc26McaUBLdF5E/Af+2XB4GRIYeOsNvUCVKQlciU/BS2Flc7bfvttMefOHUET6wu7HDO/722E4Aj9c2c+8vXAfjGeRO5++XtxHncLJnes3KJSqnB6bi6jiKSH/Lyo0BwRs6zwGdEJE5ExgATgPd7d4nqWE3KS2Z7Sa0zTh8M9F+cP5adP73AOe62j0wNOy+0877dHtppaNGEaUpFu55Mr3wUeBeYJCKFInItcKeIbBCR9cC5wI0AxphNwD+BzcD/gBuMMTqV4wSbnJdMQ0urE+BLa6xatDkp/rBhoZNHpvHdCyd3+h7B1MctAR26USradTt0Y4y5rJPmvxzl+J8CP+3NRanemZSXAlgPZQuyEqmsb8HtElL84b/u3BQ/S+eP47ypeVTUNfPxe99x9gVz31fUt4/vN7a0cs0DK7nlgimdVsdSSg1OmgIhBk3MTUIEttl5c47UN5MW70VEwo7LTbbm64/JSmRqfkrYvj1ldQBU1rc4vfuNB6t4Z1c533tmI0qp6KGBPgYl+DyMzkhgc1EVYAXr1IT2FbXfvXAyNy6aGDaME+8Ln7sfnKlz3xu7OPm2F/nnqgM0tlgLsYIreAECrW089N4+mgI6QqfUYKWBPkadNSGLlzaXsGb/ESobmsOyYS6dP46vLZrQ4Zy8kKRq0L6Aq83Avz84SFmtNdbvDgn0z647xPf/vZH7Xt8ddm7wWKXUwNNAH6O+cd4kErxuHnt/P0fqWkiL93Z7zvnTcsNeJ8W1j+m/u7uc/6w7FLb/nZ1lTmrkXaW1Tvtf39rD7J+8wv7y8Jq3SqmBoYE+RmUk+jh7UjZv7Sijsr6ZtKPktw/6/oenctP5EwGI97qptitdTchJwhh4dethAJpb29heUsNn/7yCu1+26teWVDdysNIK+ve9sQton9YZaXdprTN9UynV/zTQx7APjcviUFUjh6oaSUvovkfvdbv4uJ0F83NnjKbWznUfOcOmrinAfyN69yv2VHDmHa+xrbiGw/Z0zpJqq+xhQ3Ors8L2cHUjC371Buf/ennvbk4p1WMa6GPYwik5znZWUs8yYuanxrPspnP4zpLJjM6wyhnOGJkWdsz2klrWFVZ1ev4dL2xxtt/bXc6Nj69l+m0v8sNnN7HxYBVzfvaqs78p0Eprm+HmJ9ez6VDn76eU6j0N9DEsPzWei08ZBsBpBendHN1uTFYiLpfw92tO59efPoWPzRrR4Zg3trfn0rn2rDHO9rJtpcR73bgE/rW6kKc/OEhrm+Hv7+5jb3ld2Hv8+uUdLPzV6zy28gBfffSDY709pVQPaaCPcb/85Ck8dO0cTh3d80AfNCozgY/OHEFSnIfNP+q6hsynTxvJ/74+j1H2XwDD0vyd1rzdfKg67PV9b+xir/3Atq5Jp2cq1V800Mc4n8fFvAnZHRZLHasEn1W8fHhavJNGedGUXH532Uwm5iYzOS+Fk+2x/JxkP8khq3CD1a/WHqjs8v2LqxtZX9j1fqXU8dNAr3ps/W3n8+KN88lMtHrrY7MTnaEhgAk5VsHy1Hgvfq+1AOvL547ni2ePA2DN/iPOsYun5bJkWnhWzN++sqNfr1+poUoDveqxBJ+HpDgPifb8+tSIufnD0toXXFXZUzPH5SQyPC0ewFlZC/CxWSO474pTndezR6eH5dVRSvUdDfTqmHnd1jBQckSStJmjrNk5l84cxjmTrGIypxVkEO9zO38FBAUXY73yjbP59w1nMjozkeKqxv6+dKWGpOMqPKKGNpc93h9ZfGp8TjJbf7wEv9fNOZNyuPz00YxItx7QDk+Ppzyk0lUwjcL4nCQAXt5czOGaJlrbTFiKBaVU72mPXh0zjx2IA20dc9UHx+b9XjfTh7cvtArO4w/29MdmJYadl5fip7XNUF7bRG1TgFue2sDhau3hK9UXNNCrY/bp00YBsChkQVZ3PjV7JMPT4vnOksnsveMiciISqOXarw8cqefO/23l0ff388j7+/vkeg9XN/KL/22lpbWt+4P70H1v7OKhd/ee0J+pVGd06EYds6nDUth7x0XHdM6S6XlHrT0bDPwfv/ddp62zwNzY0sqOktpjKnxy54vbeGJ1ITNGprF4WsdreHVLCb9ftpNHl84lzuPu5B2O3V0vbuX3y6ycP1ecUdAn76nU8dIevRoU0jvJxXOgoqFD2/UPr+Ej97zFxoM9T5kQHPHfF7EyN+jaB1exZn+lU2ylt1rbjBPkAaobW/rkfZU6Xj2pGftXETksIhtD2u4Ska0isl5EnhaRNLu9QEQaRGSt/XVff168ih2h2TXnFFgLsiKzX+4ureU1O4PmS5uKMcZQ3xygoq6Zm/61jg9C5umHCv5lsOlQNXvK6roMvHv7KNA3B8L/Eins5ANLqROpJz36B4AlEW0vA9ONMScD24FbQvbtMsbMsL+u65vLVLEutJ7t9z48hcvmjORARKD/05u78XtdZCX5+N1rOxlzy/NM/cGLzPrxyzyxupB7X7d60W/tKOOKv6yg0p6Xf8ietvn2zjLO/eXr/Pz5LWHvG5wu2lfTOzsE+iOal18NrG4DvTFmOVAR0faSMSZgv3wP6Jj1SqljEJqiIS/Vz8iMBMrrmqlrCjjta/ZVcua4LKZE1LcNqqhrxhjD7f/ZxJs7ylhu5+J/f4/1z7es1gr8O0rai6QYY5xpohV1fbNgq6nVyttzzZlWsrfyPnpfpY5XX4zRXwO8EPJ6jIh8ICJviMi8rk4SkaUiskpEVpWWlnZ1mBqCshLjnARpweGbQGsbu8tqmZCbTIKv8wemq/Yd4R/v7SPJ/utg5Z4KftNJWoUkv4fGFisY1ze3OtNEy/ooIAd79GOyrHvoqw8QpY5XrwK9iNwKBICH7aYiYJQxZibwDeAREem0+2WMud8YM9sYMzs7O7s3l6FixIfGZQLgcgnjsq2FVMFKVPsq6mlpNUzISeK2i6c550wbFv7P6/43d3PEDqyvbz/MlqJqRqTHc/npo5xj3tpRxuTv/4/n1hc5RVIAKmo7D8htbeaYgnUw0Cf7vcR73c71KDVQjjvQi8hVwIeBy41dPsgY02SMKbe3VwO7gIl9cJ1qCHjwmjls/bH1OGh8ThIJPjdPrjkIwLZiK+BPzE0mPzWe925ZCFgpkr+2cALfWjwJgMzEOEprmvB7XRyoaGDFngrmjMkgM6TwSrAH/+7uMqcKFkB5XRONLa2cecdrLLMf+gJ84e+rmPXjl2ntZIFYZ5rth78+j4uMRJ/m8FED7rjm0YvIEuDbwNnGmPqQ9mygwhjTKiJjgQnA7j65UhXzvG4X9sJavG4XS+eP5Tev7OBQZQPrCivxuV1MzLN6+nmpfjb/aDHxXrczvr/pUBXrDlRR19zKtxZP4j/rDrG1uIb8VD/JcR3/qZfWNDk9+km5yZTXNbOvvJ6DlQ38+L+bOXeytSAsWCu3oq6Z7OTuK3UFe/Q+t4v0RK/26NWA68n0ykeBd4FJIlIoItcC9wDJwMsR0yjnA+tFZC3wBHCdMaai0zdWqhvzJmQBsKWomnUHKpmSnxy2oCnB5wl7iJue4HMKlOem+Lnh3PGAlV4hMgEbwL7yeifNwuT8ZIqrGimvtQJ/Q0trh9kzh2t6NivHCfQeF+kJPirqdR69Gljd9uiNMZd10vyXLo59EniytxelFFhJ0gC2FtewobCKT5x69MldGSEZMrOT45g/IYu0BC9njM3kxU0lHY7fW15HSXUjcR4XU/JTeGbtIT775xUAFFU1Mv/OZWGplEtDxvOPJjTQZyT6OqwHUOpE05WxatBKjfeSl+Ln+Q1F1DW3ckpEkfJIw+y892A9pBUR5k3IxuN2OTNxgrKT42hsaWNrcQ05KXF8+OT8Du9XXN3Ipb9/23nd03KHTa0RPfouHvIqdaJooFeD2qmj09lk15rtLtCfZGfLHJkR72TLDIrMfBw8dsWeCkamJzAiPYFNt3ddFxes4ZyeCBujT/BR0xQ44QnVlAqlSc3UoPbDj0xlzf4jxPvcjMlMPOqx04alcO/ls5g2rGPCs2CO+zPHZzIlL4Ul0/N4bethmgNtzpz9xE4e2IZqjAj0j72/nzPHZzHSPj8oGOjjPNbDWIDK+pYePchVqj9ooFeDWk6Kn5dunI/X7cLVTUESEeGCkzoOwQDMHZPJN8+byOVzR5OR6At7sDoqsz1Q//Wq2dz85Iaw+fVBwUB/uLoRA9z81Aayk+NYeeuisONCx+iDOXwq63s2Y0ep/qBDN2rQS/a3Fxs/Xi6X8JWFE5wHtlmJ7UF3VEiPfMHkXN69ZWHYAqugLUU1HKioZ87PXuXzD64C2h/QNjS38tpW64Fvc9gYvdWjP6Izb9QA0h69GpJcLsHndtHc2sb0iKEet0tIT/B1OOfJNYU8/UESdb81AAAaYUlEQVQhABtC0iQbY/jSw6t5fVspr3xjfocxeoAjumhKDSAN9GrIev5rZ7G7tI6CrI5j/12NEnW2OHbMLc872/vK6yOGboJj9Bro1cDRoRs1ZI3PSeb8TipOAUwb3nUFqzGdfDAEfbC/MmLoJtijb9GZN2rAaKBXqhOLp+Xxq0+e0qH95BGp/PKTJ3d53j3LdjopD7wuFwk+Nz63i7te3MaEW1/QalNqQGigV6oLM0Z1nLf/zA1ncuroDOf1+7cuxOcJ/99of0U9XrfgcgkiQnZynJMQrSSkuMnBygbm37mM/eW6clb1Lw30SnUhKWRe/fcumsK8CVlhuXUAcpL9TM6zUjUEvxceacDnbv9fKyelfYbPVX9bSZmdT+fJ1YXsr6jnkff399s9KAUa6JXqUnABVU5yHJ+fN5aHrj3d2fffr5zFH+08OIk+67jgatvNRdXUNbcvrspP9TvbBysbeOjdfQBOZatulgco1Wsa6JXqQlKch+9dNIXHv3hGh33Th6ey2H6Q22ZH7MgiKEEXnTSsw/v+b2MRv35lOwARfyTQFGhl5+Ga3l6+Ug4N9EodxefnjT3qLBuAWy6cwvC0eBZNzXXaXvhaexXNi07OD8ujU93Ywp/e3OO8/v2yXfzyxW3O6+8+tZFFdy+nShdZqT6igV6pXpoxMo23b17AiPT2FbaRBcxD8+iU1TaHpVQGa7bOzsNW0fK3d5YB6Awd1Wc00CvVh/5w+Swe+cLpXe4DKK9toqqhYxC/6V/r2FtW5wzlBAN9W5uhrinQPxeshgQN9Er1oQtPyudD47K63HfG2Ewq6prDatUCnDU+i7UHKvnyo2sIDtlXN1jB/fb/bGLaD18k0NpGeW1TWAGU6sYWPnHvO2wpqmbZ1sNOhSylQmmgV+oEagq0smrfEfaV1/OFeWP4+KwRxHvd/OYzMwAoqW5ypnAerGzgQEU9D9qzdGqbAnz4/97itJ++grEfAL+/u4JV+45wwW/f5OoHVvLT57cAcOvTG3jHHgJSqkeBXkT+KiKHRWRjSFuGiLwsIjvs7+l2u4jI70Rkp4isF5FZ/XXxSkWbj85qL4eYnxrPrz51Clt+vISspDiuO3tcWE6cm/61jnl3LnNev7WzjCJ7wVVJtdVzr4jIoVNc1Uh9c4CHV+x3yiIq1dMe/QPAkoi2m4FXjTETgFft1wAXABPsr6XAvb2/TKViwxVzRzuVsuaMyQjbl5cSR0urcQqcR/ryIx8423N//io/fGYjhSH1aH0eFxV1zRTbHwaRK3bV0NWjfwnGmOVARUTzJcCD9vaDwKUh7X83lveANBHpvBqEUkPQX66czV+unM30iMRpeanxXZzRuQff3ceBI+0fCkum5VHd0EKxPf6f3E3FLDV09OYjP9cYU2RvFwPBScTDgQMhxxXabWFEZKmIrBKRVaWlpb24DKWiS1ZSHAun5HZoP2NcZoe2J647g0e/MLfL91q97winjk5n3Q/OJzs5jsqGFqrtGT0+j4vWNsNj7++nOdDGS5uKdfbOENUnf9sZ68lQJ5m6j3rO/caY2caY2dnZ2X1xGUpFtdR4L3+7+rSwtunDU8Ny5UTaX1HPmKxEUhO8pMV7qW9upaLOCvQCPLH6ADc/tYGfPLeZpQ+t5pv/XNeft6AGqd4E+pLgkIz9/bDdfhAYGXLcCLtNKdWNgpAC6DdfMBm/193tEMxIe6FWmr0Ia19FHWDV0C2ush7afrC/EoB3d5eHnXu42np4q2JbbwL9s8CV9vaVwDMh7Z+zZ9/MBapChniUUkdRkJnAjy+dzru3LOC6s8cBVs3coM0/WtzhnEl5SUB77ds/vrEbgKZAG4fsB7vB0odVDS3c/dI2Zv7oJQ7XNDLnZ69y2Z90dk6s6+n0ykeBd4FJIlIoItcCdwDnicgOYJH9GuB5YDewE/gTcH2fX7VSMUpEuGLuaPJDHsz6ve3/myb42nv3X1kwnqykOOaMscb2x2SG5+Qpq23iyTWFHX7G717byZH6Fl7ZbP0Rvu5A5VGv6WBlAwU3P8erW0qO/YbUoNCjx/LGmMu62LWwk2MNcENvLkop1S4yB77XLbS0Gr68YDzfOG+is39kRjyzR6ezat8R59hAZ0VubaEFzo9mjf1+j6880OlDZID7l+8iMc7D5aeP7tF7qhNL518pFSVykq2Hss/ccBZvbC8lzuMO2y8iTkrl7SU1XPDbNwH43BmjGZ+TxPC0eK77x2paWq3g39NUyMGcO13Nyz9S18zPnt8KwPwJ2YzMSOj0ODVwdEWFUlHgvVsW8so3zwZg6rAUvnTOuE6Pc7sEt0uYkp/CWeOtnDs5yXF87owCFk7J5ecfa693u3Jve89/f3k9D7y9h0BrG/e8toMdJTUYY1hfWMnWIusDwRWZON+2uaja2d6hefQHJe3RKxUF8kKqVB2r1IT2lMiXzhiGxyV8/fG1Ycd8/fEPWLO/ErdL+OVL2/nlS9u542MncfNTG5xjgrNzPvfX90lP8PLbz8wEcNIygFVGUQ0+2qNXKkY1tFjlDINDPgAet4tLZw7njLHhi7PW2NMvl+9oT4T2/t7wxfB1Tdb7Ld9eyjNrDzntoZk4NdAPThrolYpRzYE2AMZld6yQFbkwK2j59vZV6vvK68P21TcHnKyZYOXJByiqaiA13su47EQKj4SfowYHDfRKxahffeoUls4fy9ispA77/F53J2dYc++D1uxvH8MvyEygrrk1rGDK8xut5THFVU3kpfgZnp6gPfpBSgO9UjFqYm4y371wCi5X5w9Rv7JgPAALJ+eEtV8ywypmHtJ5Z3xOMvVNgbBefjCbZkl1I3mpfvJT/E7mTDW4aKBXaoj65vmT2HvHRdQ3t4a1nzE2k2ERD3+Hp/mpa25lZcS4fXOgjeLqRvJS/OSmxFFW20SgtQ01uGigV2qIO31seF78maPSmTosPIVyWoKPqoYWfvvqjrAx/9LaJspqm8hN9ZOb6qfNwJLfvskn73uHGx5Zc0KuX3VPp1cqNcR9ZcEELj5lGL96aTvPbShifE4SP/zIVGaNTuPiU4bhdglvbLMe0tY0BvjbVadxuKaJ6x9ew7biaoyB/FQ/2UnW7J6dh2ud9/724jpGZ3Z8GKxOLA30Sg1xbpcwNjuJuz99Cj++dDpulzAyI4HrzxnvHJOf1p5756QRqWy00yfc/p/NAOSl+MlO7phOubiqUQP9IKBDN0opAOI8bjISfZ3uCx2zj/O4GWPP5Ak+nM1N8ZMfcswT11mpGCrqwmvaqoGhgV4p1a3IlbnpCV7mTchyXg9L85OZ1N6jD+a7KbcDfVFVA7tKa1EDQ4dulFLdCubEnznKKmwuIjx07enUNwfYdKiaNDvNwpvfPhePW8hI9OFzu9hvFy8/4+evAbD3jouO+nOMMdz+n81cPGMYs0al99ftDDnao1dK9cg7Ny/g4c+fHtaW4PNwWkH7rJ2RGQnkp8bjdbuYkp/M39/d2+MsmQCNLW088M5ePn7vO3112YPGQE491UCvlOqRYWnxYYVPurNgci6NLW389LktTlt3ZQtr7eLl5pgqUA9+TYFWZv/kFb7/zMYB+fka6JVS/WJyfjIQXqf2H+/toylgLdDaebiGAxUd8+nEomDqiH+t6ljx60TQQK+U6hfp9rh9Y0sbw+3pmT97fis/+PcmapsCLLp7OfPuXBZ2TrBHH2uqG6z7OlrFr/503IFeRCaJyNqQr2oR+bqI3CYiB0PaL+zLC1ZKRYe0hPai5tOHpzjbj686wPQfvtjpOcFUyF3ZVVrLN/+5zsnMGS2CVboGynEHemPMNmPMDGPMDOBUoB542t796+A+Y8zzfXGhSqnoEhboI1IqRKprCrBybwV13QzdfOWRD3hyTWFYVatoUN0QpYE+wkJglzFmXx+9n1IqyqXFty++yk+L597LZ4UVQQl1/cNr+OR973ab5ri0tgmAQ5XRlQ65unFgh6T6KtB/Bng05PWXRWS9iPxVRDqdDCsiS0VklYisKi0t7ewQpVQUCy0mnpnk44KT8nnkC3P59pJJZNmLq4JlaN+wC57ssvPk+L2dhyaPnXI52gqcRH2PXkR8wMXAv+yme4FxwAygCPhVZ+cZY+43xsw2xszOzs7u7WUopQaxacOsMfrxOUlcf854nvvqWZw+JgO/J7wAyt/f3QtYaRY6U2c/rC2rDU+tUFpj9fTf31MxKGfuhI7Rtw7AA9m+6NFfAKwxxpQAGGNKjDGtxpg24E/AnD74GUqpKHTRSfmcMymbnOTwFAq5KX7OGJdJQ0trWOALbnb2sLW6scUZAgkGdoD1hZWc9tNX+PObu/nUH9/lG4+v64c76Z3grBsYmJlFfRHoLyNk2EZE8kP2fRQYmBUCSqkBd89nZ/LA1Z339ZLirMVXhyobwoZ5wCpsbiJWTe0prXO2QwP9njKr/ak1BwFYta+9OMpf3trD4yv3O68DrW38+c3dNLYcfXZPXwstwTgQwzi9CvQikgicBzwV0nyniGwQkfXAucCNvfkZSqnoJdJ5GUOARDvQz7tzGc0Ba679p2aP4KzxVrK0y/70Xtjxwbw5BZkJYYE+zv6QOGg/oK1rauXnL2zhnV1l/Pi/m/nOkxucY/+4fDc/eW4L972xqw/urudCh25qBuDBbK+Smhlj6oDMiLYrenVFSqkhIRjogxZOyeFHl0znrhe38tbOMt7bXcH+8npGZVqZMIP1aE8akcbbO8uc84Jz74O95oaWVv74xm7WH6hyjlm27TAPvrOX1+0CKmW17R8UJ0JoL75mAObU68pYpdSASPSFP3CN91qvQxdNzb9rmROUi6oaSfC5GZuVSEVdMy12grCuxrxDH8pe/beVTpAHaAkYXttawlNrCimp7v+C5tWNAWddQdT16JVS6nhFjstjj/J8fdEEEnxu/vC6NbyyvrCSBZNzKaluJC/VT06KNTWzvLaZvFR/p4F+TkHGURdVNQVaueaBVc7r7tIn91ZNQwsj0uOprG8ZkFWy2qNXSg2I3BRrJs7JI6xVs8GZNmkJPr69ZDLv3bIQgEOVVo+7qKohrDbt4RqrvbI+fKrla988m0VTc446u+Xfaw+FvW7r5ymP1Y0tTr6faJ11o5RSx2xibjKrvreIx5bO5ZOnjuDjs0aE7c9OjsPtEoqqrIesRVWN5IbUpr34nrc5VNnA1uKasHQLBZmJjMrovk5taNnEmqYA335iHXe8sLUvbq2DmsYA+anxzvaJpoFeKTVgspLiSPB5uOuTpzB9eHg+HLdLyE2OY0tRDdN/+CJFVY2My04KKzb+r1WFvLmjjMr69uEQl0vC6td+bNZwAE4ZkcrE3CQnwdqU/GTu/PjJAGworOKfqwq5741dfT71sq3N0BRoIy3Bi8/t0jF6pZQKlZ8Wz2tbDzuvTyvICOuJP7HmQKfnhda4vXB6Pr/4+Ml43Va/9if/3czGg9VkJMaREm+FwP/3lxXO8SXVjWEfJr3VYH9wxHvdJPk9OutGKaVCTc5Ldraf/+o85oyxyhZuuO18AA5UWMM6y246h5OGpzoPeLNCCpX7vW4nyAPO0I/f4yLF3z7kExR8JtBXgoE+wecm2e/RMXqllAr17cWTne0p+e1BPzkkQJ89MZsxWYk8ff2H2Hz7YsAa9gmK94WHueA+r8cV9j5fWTAewHkm0Fcamq1A7/dagV6HbpRSKkRqgpfffHoGbcZ0WGV70/kTeXbdIX7z6RkAeNyd91sjE6QtnpbH8h1l3LhoYthc+0n2Xw9FVX3To29rM9S3tIb06D0kxXmo1UCvlFLhLp05vNP2Ly+YwJcXTOj2/PiIhVkjMxL4+zVW/p2KuvYPj7wUP2kJ3mPu0ZfVNlFe2+x8UAR958n1PLmmkPuvmA0Eh268Herkngga6JVSMSk3JY6S6ibcR8m3k+xvD4Gp8V5ykuMorWniQEU9TYFWFt29HIDF03K55YIpjLbTMYT+dbHkN29SVtvE9p9cELYI7F+rrULgr2+3Hib7vW6S4wZmjF4DvVIqJv3uMzP5xf+2kp/m7/KY0Ie0+WnxZCXF8eKmEl7cVBJ2XGjbgsk5/PWq05x9wRQNVQ0tZCfHUVXfwr6KOrxuoaXVsL3EKqYSfBirY/RKKdVHTh+byVPXn9nj45PiPGFTN7sSnO4ZaG0Ley5Q1dBMdnIcVz/wPmv2VzrtwWRs8T43KfFeahpbOpzb33TWjVJqSPvdZTP55nkTAavXDZCe0HHaZXBWDlilD8ff+gLrC9sDelVDC+/uKg8L8hAS6L1uxuck0Wbgpc0lTrWsE0F79EqpIe3iU4Y52zeeN5H65lYuP320kw//M6eNZMHkHBZMzmFdYRXLt5fy0+c2A/BBSFAvrWniun+sCXvvjEQfFXVWLp54n5up+daq3OsfXtNhCKg/aaBXSilbfmo893x2FgBv37yA1HivUwkL4KsLxrN8e6kz7h46FXP1viNh7zUs1c/w9Hgn0Cf43CT6Epz9K/dWcKLo0I1SSnVieFp8WJAHa669J2QxVmilqvf3tgf6ry+awNM3nElxSK57v8cdNtUzuEL3RNBAr5RSPZTs97J4el6n+9YdqMTjEt6+eQFfWzjByrQZkorB5Qqf5rm7tI6Cm587IfVrex3oRWSvXSN2rYisstsyRORlEdlhf0/v/aUqpdTAu/3iafz8Yyc5r3/4kanO/PmTRqQyPC3emWd/z2dnceUZo/nxJdO6fL8TsYCqr3r05xpjZhhjZtuvbwZeNcZMAF61XyulVNTLSorjsjmjuPrMAnJT4rj6zDFO0ZQvzBsbduywtHhuv2Q6V5xR0OX77Smr68/LBfpv6OYS4EF7+0Hg0n76OUopNSB++JFprPjuorC2yRFpEDoTTL8Q9L+NxX16XZ3pi0BvgJdEZLWILLXbco0xRfZ2MZAbeZKILBWRVSKyqrS0NHK3UkpFnWF2ucCjmT8xm4tOygesurmfOHVEN2f0Xl8E+rOMMbOAC4AbRGR+6E5jjMH6MCCi/X5jzGxjzOzs7Ow+uAyllBoYS+ePxed24fe6uz+Y9hk3158zjg+Nz+rPSwP6INAbYw7a3w8DTwNzgBIRyQewvx/u+h2UUiq6fffCKWz/6QU9Pj6YEy1YIL2/9SrQi0iiiCQHt4HzgY3As8CV9mFXAs/05ucopVQsWTp/LF88e6xTz7a/9XZlbC7wtD2VyAM8Yoz5n4isBP4pItcC+4BP9fLnKKVUzMhPjeeWC6acsJ/Xq0BvjNkNnNJJezmwsDfvrZRSqm/oylillIpxGuiVUirGaaBXSqkYp4FeKaVinAZ6pZSKcRrolVIqxmmgV0qpGCdWKpoBvgiRUqyFVccrCyjro8uJFnrPQ4Pe89BwvPc82hjTbbKwQRHoe0tEVoXkwh8S9J6HBr3noaG/71mHbpRSKsZpoFdKqRgXK4H+/oG+gAGg9zw06D0PDf16zzExRq+UUqprsdKjV0op1YWoCPQicqOIbBKRjSLyqIj4RWSMiKwQkZ0i8riI+Oxj4+zXO+39BQN79T0jIn8VkcMisjGkLUNEXhaRHfb3dLtdROR39j2uF5FZIedcaR+/Q0Su7OxnDRZd3PNdIrLVvq+nRSQtZN8t9j1vE5HFIe1L7LadInLzib6PY9HZPYfs+6aIGBHJsl/H7O/Zbv+K/bveJCJ3hrTH5O9ZRGaIyHsistaulz3Hbu//37MxZlB/AcOBPUC8/fqfwFX298/YbfcBX7K3rwfus7c/Azw+0PfQw/ucD8wCNoa03QncbG/fDPzC3r4QeAEQYC6wwm7PAHbb39Pt7fSBvrdjvOfzAY+9/YuQe54KrAPigDHALsBtf+0CxgI++5ipA31vx3LPdvtI4EWs9SRZQ+D3fC7wChBnv86J9d8z8BJwQcjv9vUT9XuOih49VoGUeBHxAAlAEbAAeMLe/yBwqb19if0ae/9CkWCFxsHLGLMcqIhoDr2XyHv8u7G8B6TZtXkXAy8bYyqMMUeAl4El/X/1x6ezezbGvGSMCdgv3wNG2NuXAI8ZY5qMMXuAnVj1iecAO40xu40xzcBj9rGDUhe/Z4BfA98GQh+axezvGfgScIcxpsk+JlhXOpZ/zwZIsbdTgUP2dr//ngd9oDdW8fFfAvuxAnwVsBqoDAkIhVg9f+zvB+xzA/bxmSfymvtQrjGmyN4uxirdCCH3aAvef1ft0eoarJ4OxPA9i8glwEFjzLqIXTF7z8BEYJ49vPqGiJxmt8fyPX8duEtEDmDFtFvs9n6/50Ef6O1x6Uuw/owbBiQyiHsv/cVYf8sNmSlSInIrEAAeHuhr6U8ikgB8F/jBQF/LCebBGpKYC3wLq8b0oP/Lu5e+BNxojBkJ3Aj85UT94EEf6IFFwB5jTKkxpgV4CjgT68+bYM3bEcBBe/sg1ngn9v5UoPzEXnKfKbH/hMP+Hvzz1rlHW/D+u2qPKiJyFfBh4HL7Aw5i957HYXVi1onIXqzrXyMiecTuPYPVO33KHq54H2jDyvcSy/d8JVb8AvgX1nAUnIB7joZAvx+YKyIJ9if+QmAzsAz4hH3MlcAz9vaz9mvs/a+FBItoE3ovkff4Oftp/Vygyh7ieRE4X0TS7b+EzrfbooaILMEaq77YGFMfsutZ4DNizaoaA0wA3gdWAhPEmoXlw3oA/+yJvu7jZYzZYIzJMcYUGGMKsALgLGNMMTH8ewb+jfVAFhGZiPWAtYwY/T3bDgFn29sLgB32dv//ngf66XQPn2DfDmwFNgIPYT2RH4v1D2An1qdj8Om93369094/dqCvv4f3+CjWM4gWrP/Zr8V6tvCq/Q/iFSDDPlaA32PNQtgAzA55n2vse98JXD3Q93Uc97wTa1xyrf11X8jxt9r3vA179oLdfiGw3d5360Df17Hec8T+vbTPuonl37MP+If9//QaYEGs/56Bs7CeL64DVgCnnqjfs66MVUqpGBcNQzdKKaV6QQO9UkrFOA30SikV4zTQK6VUjNNAr5RSMU4DvVJKxTgN9EopFeM00CulVIz7/1FEpAPPUQVhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(df.iloc[0], df.iloc[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Удаляем cosmic rays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = list(df.columns)\n",
    "for row in range(1,21):\n",
    "    for n,value in enumerate(columns):\n",
    "        if df[columns[n]][row] < 0 or df[columns[n]][row] > 500:\n",
    "            df[columns[n]][row] = df[columns[n-1]][row]"
   ]
  },
  {
   "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": [
    "plt.plot(df.iloc[0], df.iloc[10]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Делаем коррекцию фона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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": [
    "# Baseline removal\n",
    "y1 = df.iloc[10].values\n",
    "baseline_values = peakutils.baseline(y1)\n",
    "\n",
    "plt.plot(y1);\n",
    "plt.plot(baseline_values);\n",
    "plt.plot(y1 - baseline_values);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Применяем коррекцию фону ко всему датасету\n",
    "\n",
    "for row in range(1,21):\n",
    "    baseline_values = peakutils.baseline(df.iloc[row].values)\n",
    "    df.iloc[row] = df.iloc[row].values - baseline_values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Сглаживание спектра"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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": [
    "# Smoothing with Asymmetric Least Squares\n",
    "p = 0.01\n",
    "lam = 10\n",
    "y1 = df.iloc[10].values\n",
    "y2 = baseline_als(y1, lam, p, niter=10)\n",
    "\n",
    "plt.plot(y1);\n",
    "plt.plot(y2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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": [
    "# Smoothing with a Savitzky-Golay filter\n",
    "y_noise = df.iloc[10].values\n",
    "y_sg = scipy.signal.savgol_filter(y_noise, 31, 5)\n",
    "plt.plot(y_noise);\n",
    "plt.plot(y_sg);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Применяем сглаживание ко всему датасету:\n",
    "\n",
    "for row in range(1,21):\n",
    "    #df.iloc[row] = baseline_als(df.iloc[row], lam, p, niter=10)\n",
    "    df.iloc[row] = scipy.signal.savgol_filter(df.iloc[row], 31, 5)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Разделяем данные на матрицу с признаками X и на столбец с целевой переменной (метками)\n",
    "И удаляем первую строку с частотами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = df.drop(0), has_DM2.drop(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Оценку качества модели (accuracy)  будем проводить методом отложенной выборки (hold-out set)\n",
    "При таком подходе мы оставляем какую-то долю обучающей выборки (как правило от 20% до 40%), обучаем модель на остальных данных (60-80% исходной выборки) и считаем некоторую метрику качества модели (например, самое простое – долю правильных ответов в задаче классификации) на отложенной выборке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_holdout, y_train, y_holdout = train_test_split(X, y, test_size=.3, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    0.55\n",
       "0.0    0.45\n",
       "Name: has_DM2, dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Точность предсказания должна быть лучше как минимум чем 55%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Centering data to zero-mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_holdout = scaler.transform(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)  \n",
    "X_train = pca.fit_transform(X_train)  \n",
    "X_holdout  = pca.transform(X_holdout)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.54452996, 0.1411542 , 0.08255622, 0.06990234, 0.03660153,\n",
       "       0.03018415, 0.02611233, 0.02046523, 0.01662045, 0.01143444])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}