data_analysis_free_text.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import nltk\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "import language_tool_python\n",
    "import scipy.stats as stats\n",
    "\n",
    "tool = language_tool_python.LanguageTool('en-US')\n",
    "is_bad_rule = lambda rule: rule.message == 'Possible spelling mistake found.' and len(rule.replacements) and rule.replacements[0][0].isupper()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data importation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_free = pd.read_csv('data/free_text_typing_dataset.csv', sep=';')\n",
    "df_freq = pd.read_csv('data/frequency_dataset.csv', sep=';')\n",
    "df_user = pd.read_csv('data/participants_information.csv', sep=';')\n",
    "\n",
    "df_free = df_free.rename(columns={'userid':'userId'})\n",
    "df_freq = df_freq.rename(columns={'User ID':'userId'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentences reconstruction and feature extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reconstruct users' sentences from the keycode in df_free\n",
    "\n",
    "sentence = ''\n",
    "uppercase = False\n",
    "uppercase_tmp = False\n",
    "i_start = 0\n",
    "i_end = 0\n",
    "text_free = pd.DataFrame(columns=['sentence', 'idx_start', 'idx_end'])\n",
    "\n",
    "df_free.keyCode = df_free.keyCode.astype(str)\n",
    "\n",
    "for i in range(len(df_free)):\n",
    "    if i > 0 and (df_free.userId[i] != df_free.userId[i-1] or df_free.emotionIndex[i] != df_free.emotionIndex[i-1]): #df_free._id[i][:-2] != df_free._id[i-1][:-2]:\n",
    "        i_end = i-1\n",
    "        if len(sentence) > 1 and sentence[0].islower() and sentence[1].isupper():\n",
    "            sentence = sentence.swapcase()\n",
    "        text_free = text_free.append(pd.DataFrame([[sentence, i_start, i_end]], columns=['sentence', 'idx_start', 'idx_end']), ignore_index = True)\n",
    "        i_start = i\n",
    "        sentence = ''\n",
    "        uppercase = False\n",
    "        uppercase_tmp = False\n",
    "    if df_free.keyCode[i] == '\\\\b':   # delete key\n",
    "        sentence = sentence[:max(0,len(sentence)-1)]\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if df_free.keyCode[i] == '\\\\u0014':   # caps lock key\n",
    "        uppercase = not uppercase\n",
    "        continue\n",
    "    if df_free.keyCode[i] == '\\\\u0010' and not uppercase:   # shift key\n",
    "        uppercase_tmp = True\n",
    "        continue\n",
    "    if df_free.keyCode[i] == '¼':     # comma key\n",
    "        sentence += ','\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if df_free.keyCode[i] == 'ß':     # exclamation mark key\n",
    "        sentence += '!'\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if df_free.keyCode[i] == '¾':   # dot key\n",
    "        sentence += '.'\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if df_free.keyCode[i] == '4    ':   # apostrophe key\n",
    "        sentence += \"'\"\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if df_free.keyCode[i] == '6    ':    # dash key\n",
    "        sentence += \"-\"\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if df_free.keyCode[i] == '¿':   # colon key\n",
    "        sentence += \":\"\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if uppercase:\n",
    "        sentence += df_free.keyCode[i].upper()\n",
    "        continue\n",
    "    if uppercase_tmp:\n",
    "        sentence += df_free.keyCode[i].upper()\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    sentence += df_free.keyCode[i]    \n",
    "\n",
    "text_free = text_free.append(pd.DataFrame([[sentence, i_start, i]], columns=['sentence', 'idx_start', 'idx_end']), ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>_id</th>\n",
       "      <th>userId</th>\n",
       "      <th>emotionIndex</th>\n",
       "      <th>index</th>\n",
       "      <th>keyCode</th>\n",
       "      <th>keyDown</th>\n",
       "      <th>keyUp</th>\n",
       "      <th>D1U1</th>\n",
       "      <th>D1U2</th>\n",
       "      <th>D1D2</th>\n",
       "      <th>U1D2</th>\n",
       "      <th>U1U2</th>\n",
       "      <th>answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5e1e2631d2dd163d472fd5ac</td>\n",
       "      <td>100</td>\n",
       "      <td>N</td>\n",
       "      <td>822</td>\n",
       "      <td>\\u0011</td>\n",
       "      <td>1.579030e+12</td>\n",
       "      <td>1.579030e+12</td>\n",
       "      <td>9.820000e+02</td>\n",
       "      <td>7.640000e+02</td>\n",
       "      <td>709.0</td>\n",
       "      <td>-2.730000e+02</td>\n",
       "      <td>-2.180000e+02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5e1e2631d2dd163d472fd5ad</td>\n",
       "      <td>100</td>\n",
       "      <td>N</td>\n",
       "      <td>823</td>\n",
       "      <td>v</td>\n",
       "      <td>1.579030e+12</td>\n",
       "      <td>1.579030e+12</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>-1.579030e+12</td>\n",
       "      <td>23415.0</td>\n",
       "      <td>2.336000e+04</td>\n",
       "      <td>-1.579030e+12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5e1e2631d2dd163d472fd5ae</td>\n",
       "      <td>100</td>\n",
       "      <td>N</td>\n",
       "      <td>824</td>\n",
       "      <td>\\b</td>\n",
       "      <td>1.579030e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.579030e+12</td>\n",
       "      <td>-1.579030e+12</td>\n",
       "      <td>607.0</td>\n",
       "      <td>1.579030e+12</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5e1e2631d2dd163d472fd5af</td>\n",
       "      <td>100</td>\n",
       "      <td>N</td>\n",
       "      <td>825</td>\n",
       "      <td>\\b</td>\n",
       "      <td>1.579030e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.579030e+12</td>\n",
       "      <td>-1.579030e+12</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.579030e+12</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5e1e2631d2dd163d472fd5b0</td>\n",
       "      <td>100</td>\n",
       "      <td>N</td>\n",
       "      <td>826</td>\n",
       "      <td>\\b</td>\n",
       "      <td>1.579030e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.579030e+12</td>\n",
       "      <td>-1.579030e+12</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.579030e+12</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id  userId emotionIndex  index keyCode       keyDown  \\\n",
       "0  5e1e2631d2dd163d472fd5ac     100            N    822  \\u0011  1.579030e+12   \n",
       "1  5e1e2631d2dd163d472fd5ad     100            N    823       v  1.579030e+12   \n",
       "2  5e1e2631d2dd163d472fd5ae     100            N    824      \\b  1.579030e+12   \n",
       "3  5e1e2631d2dd163d472fd5af     100            N    825      \\b  1.579030e+12   \n",
       "4  5e1e2631d2dd163d472fd5b0     100            N    826      \\b  1.579030e+12   \n",
       "\n",
       "          keyUp          D1U1          D1U2     D1D2          U1D2  \\\n",
       "0  1.579030e+12  9.820000e+02  7.640000e+02    709.0 -2.730000e+02   \n",
       "1  1.579030e+12  5.500000e+01 -1.579030e+12  23415.0  2.336000e+04   \n",
       "2           NaN -1.579030e+12 -1.579030e+12    607.0  1.579030e+12   \n",
       "3           NaN -1.579030e+12 -1.579030e+12     35.0  1.579030e+12   \n",
       "4           NaN -1.579030e+12 -1.579030e+12     42.0  1.579030e+12   \n",
       "\n",
       "           U1U2 answer  \n",
       "0 -2.180000e+02    NaN  \n",
       "1 -1.579030e+12    NaN  \n",
       "2  0.000000e+00    NaN  \n",
       "3  0.000000e+00    NaN  \n",
       "4  0.000000e+00    NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop features highly correlated  with other features\n",
    "df_free = df_free.drop(['D1U3','D1D3'], axis=1)\n",
    "df_free.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# functions to extract features from the df_free table\n",
    "def extract_editDistance_free(sentence):\n",
    "    matches = tool.check(sentence)\n",
    "    matches = [rule for rule in matches if not is_bad_rule(rule)]\n",
    "    correct = language_tool_python.utils.correct(sentence, matches)\n",
    "    return nltk.edit_distance(sentence, correct)\n",
    "\n",
    "def extract_nbKeystroke(val, start, end):\n",
    "    return val[end] - val[start] + 1\n",
    "\n",
    "def extract_mean(val, start, end):\n",
    "    return np.mean(val[start:end+1])\n",
    "\n",
    "def extract_std(val, start, end):\n",
    "    return np.std(val[start:end+1])\n",
    "\n",
    "def extract_answer(val, start, end):\n",
    "    for i in range(start, end+1):\n",
    "        if val[i] != np.nan:\n",
    "            return val[i]\n",
    "    return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>idx_start</th>\n",
       "      <th>idx_end</th>\n",
       "      <th>userId</th>\n",
       "      <th>emotionIndex</th>\n",
       "      <th>sentence</th>\n",
       "      <th>editDistance</th>\n",
       "      <th>nbKeystroke</th>\n",
       "      <th>answer</th>\n",
       "      <th>D1U1_mean</th>\n",
       "      <th>D1U1_std</th>\n",
       "      <th>D1U2_mean</th>\n",
       "      <th>D1U2_std</th>\n",
       "      <th>D1D2_mean</th>\n",
       "      <th>D1D2_std</th>\n",
       "      <th>U1D2_mean</th>\n",
       "      <th>U1D2_std</th>\n",
       "      <th>U1U2_mean</th>\n",
       "      <th>U1U2_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>100</td>\n",
       "      <td>N</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129.055556</td>\n",
       "      <td>209.692492</td>\n",
       "      <td>354.823529</td>\n",
       "      <td>388.461102</td>\n",
       "      <td>237.190476</td>\n",
       "      <td>1925.711443</td>\n",
       "      <td>1531.588235</td>\n",
       "      <td>5470.427094</td>\n",
       "      <td>27.120567</td>\n",
       "      <td>155.153800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>148</td>\n",
       "      <td>204</td>\n",
       "      <td>100</td>\n",
       "      <td>H</td>\n",
       "      <td>\\u0011v                                       ...</td>\n",
       "      <td>54</td>\n",
       "      <td>57</td>\n",
       "      <td>R</td>\n",
       "      <td>623.666667</td>\n",
       "      <td>805.151884</td>\n",
       "      <td>807.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>169.339286</td>\n",
       "      <td>731.543458</td>\n",
       "      <td>2504.500000</td>\n",
       "      <td>2799.500000</td>\n",
       "      <td>-4.018519</td>\n",
       "      <td>29.255256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>205</td>\n",
       "      <td>329</td>\n",
       "      <td>113</td>\n",
       "      <td>N</td>\n",
       "      <td>i spent all the day at home , watching tv</td>\n",
       "      <td>2</td>\n",
       "      <td>125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.840426</td>\n",
       "      <td>17.108106</td>\n",
       "      <td>452.322581</td>\n",
       "      <td>768.536497</td>\n",
       "      <td>316.620968</td>\n",
       "      <td>696.210104</td>\n",
       "      <td>319.215054</td>\n",
       "      <td>779.491188</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>691.356686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>330</td>\n",
       "      <td>428</td>\n",
       "      <td>113</td>\n",
       "      <td>C</td>\n",
       "      <td>i love nature and calm places they are so rela...</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.518519</td>\n",
       "      <td>21.773305</td>\n",
       "      <td>471.662500</td>\n",
       "      <td>708.471258</td>\n",
       "      <td>389.540816</td>\n",
       "      <td>711.718088</td>\n",
       "      <td>371.462500</td>\n",
       "      <td>766.997114</td>\n",
       "      <td>341.119565</td>\n",
       "      <td>670.032800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>429</td>\n",
       "      <td>483</td>\n",
       "      <td>113</td>\n",
       "      <td>H</td>\n",
       "      <td>Babies are always cute</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>R</td>\n",
       "      <td>79.944444</td>\n",
       "      <td>16.943716</td>\n",
       "      <td>355.641509</td>\n",
       "      <td>234.051123</td>\n",
       "      <td>292.018519</td>\n",
       "      <td>263.993090</td>\n",
       "      <td>207.905660</td>\n",
       "      <td>260.560373</td>\n",
       "      <td>272.692308</td>\n",
       "      <td>231.421904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  idx_start idx_end  userId emotionIndex  \\\n",
       "0         0     147     100            N   \n",
       "1       148     204     100            H   \n",
       "2       205     329     113            N   \n",
       "3       330     428     113            C   \n",
       "4       429     483     113            H   \n",
       "\n",
       "                                            sentence  editDistance  \\\n",
       "0                                                                0   \n",
       "1  \\u0011v                                       ...            54   \n",
       "2         i spent all the day at home , watching tv              2   \n",
       "3  i love nature and calm places they are so rela...             1   \n",
       "4                            Babies are always cute              0   \n",
       "\n",
       "   nbKeystroke answer   D1U1_mean    D1U1_std   D1U2_mean    D1U2_std  \\\n",
       "0          148    NaN  129.055556  209.692492  354.823529  388.461102   \n",
       "1           57      R  623.666667  805.151884  807.000000  738.000000   \n",
       "2          125    NaN   81.840426   17.108106  452.322581  768.536497   \n",
       "3           99    NaN   80.518519   21.773305  471.662500  708.471258   \n",
       "4           55      R   79.944444   16.943716  355.641509  234.051123   \n",
       "\n",
       "    D1D2_mean     D1D2_std    U1D2_mean     U1D2_std   U1U2_mean    U1U2_std  \n",
       "0  237.190476  1925.711443  1531.588235  5470.427094   27.120567  155.153800  \n",
       "1  169.339286   731.543458  2504.500000  2799.500000   -4.018519   29.255256  \n",
       "2  316.620968   696.210104   319.215054   779.491188  288.000000  691.356686  \n",
       "3  389.540816   711.718088   371.462500   766.997114  341.119565  670.032800  \n",
       "4  292.018519   263.993090   207.905660   260.560373  272.692308  231.421904  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_features = ['D1U1','D1U2','D1D2','U1D2','U1U2']\n",
    "\n",
    "text_free['userId'] = df_free.userId[text_free.idx_start].values\n",
    "text_free['emotionIndex'] = df_free.emotionIndex[text_free.idx_start].values\n",
    "text_free = text_free.reindex(columns=['idx_start','idx_end','userId','emotionIndex','sentence'])\n",
    "text_free['editDistance'] = text_free.apply(lambda x: extract_editDistance_free(x['sentence']), axis=1)\n",
    "text_free['nbKeystroke'] = text_free.apply(lambda x: extract_nbKeystroke(df_free.index, x['idx_start'], x['idx_end']), axis=1)\n",
    "text_free['answer'] = text_free.apply(lambda x: extract_answer(df_free.answer, x['idx_start'], x['idx_end']), axis=1)\n",
    "\n",
    "for feat in key_features:\n",
    "    df_free[feat] = df_free[feat].apply(lambda x: np.nan if np.abs(x) > 1570000000000 else x)\n",
    "    text_free[feat+'_mean'] = text_free.apply(lambda x: extract_mean(df_free[feat], x['idx_start'], x['idx_end']), axis=1)\n",
    "    text_free[feat+'_std'] = text_free.apply(lambda x: extract_std(df_free[feat], x['idx_start'], x['idx_end']), axis=1)\n",
    "\n",
    "\n",
    "text_free.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## correlation matrix\n",
    "\n",
    "# Numeric columns of the dataset\n",
    "text_free.emotionIndex = text_free.emotionIndex.map({'N':0,'H':1,'C':2,'S':3,'A':4})\n",
    "numeric_col = ['emotionIndex','editDistance','nbKeystroke'] + [col for col in text_free.columns if '_mean' in col] + [col for col in text_free.columns if '_std' in col]\n",
    "\n",
    "# Correlation Matrix formation\n",
    "corr_matrix = text_free.loc[:, numeric_col].corr()\n",
    "text_free.emotionIndex = text_free.emotionIndex.map({0:'N',1:'H',2:'C',3:'S',4:'A'})\n",
    " \n",
    "#Using heatmap to visualize the correlation matrix\n",
    "plt.figure(figsize = (15,15))\n",
    "sn.heatmap(corr_matrix, annot=True)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join tables: text_free with df_freq_free"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter free-text experiments in df_freq\n",
    "df_freq_free = df_freq[df_freq.textIndex == 'FR'].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# correction of expections to align df_freq_free and text_free tables\n",
    "text_free.loc[43,'userId'] = 94\n",
    "tmp = text_free.loc[29]\n",
    "text_free.loc[29] = text_free.loc[30]\n",
    "text_free.loc[30] = tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alignment of df_freq_free and text_free tables\n",
    "text_free['text_index'] = -1\n",
    "df_freq_free['text_index'] = -1\n",
    "index = 0\n",
    "\n",
    "i = 0\n",
    "j = 0\n",
    "while i<len(df_freq_free) and j<len(text_free):\n",
    "    if df_freq_free.userId[i] == text_free.userId[j] and df_freq_free.emotionIndex[i] == text_free.emotionIndex[j]:\n",
    "        df_freq_free.loc[i,'text_index'] = index\n",
    "        text_free.loc[j,'text_index'] = index\n",
    "        index += 1\n",
    "        i += 1\n",
    "        j += 1\n",
    "    elif j != len(text_free)-1 and df_freq_free.userId[i] == text_free.userId[j+1] and df_freq_free.emotionIndex[i] == text_free.emotionIndex[j+1]:\n",
    "        df_freq_free.loc[i,'text_index'] = index\n",
    "        text_free.loc[j+1,'text_index'] = index\n",
    "        index += 1\n",
    "        i += 1\n",
    "        j += 2\n",
    "    elif i != len(df_freq_free)-1 and df_freq_free.userId[i+1] == text_free.userId[j] and df_freq_free.emotionIndex[i+1] == text_free.emotionIndex[j]:\n",
    "        df_freq_free.loc[i+1,'text_index'] = index\n",
    "        text_free.loc[j,'text_index'] = index\n",
    "        index += 1\n",
    "        i += 2\n",
    "        j += 1\n",
    "    else:\n",
    "        i += 1\n",
    "        j += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# correction of expections to align df_freq_free and df_user tables\n",
    "tmp = df_user.loc[10]\n",
    "df_user.loc[10] = df_user.loc[11]\n",
    "df_user.loc[11] = tmp\n",
    "tmp = df_user.loc[70]\n",
    "df_user.loc[70] = df_user.loc[71]\n",
    "df_user.loc[71] = tmp\n",
    "tmp = df_user.loc[91]\n",
    "df_user.loc[91] = df_user.loc[92]\n",
    "df_user.loc[92] = tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alignment of df_freq_free and df_user tables\n",
    "df_freq_free['user_index'] = -1\n",
    "index = 0\n",
    "\n",
    "j = 0\n",
    "for i in range(len(df_freq_free)):\n",
    "    if i in [20,38,58,68,129,139,150,154,173,225,227]: \n",
    "        j += 1\n",
    "    if j < len(df_user) and df_freq_free.userId[i] == df_user.userId[j]:\n",
    "        df_freq_free.loc[i,'user_index'] = j\n",
    "        i += 1\n",
    "    else:\n",
    "        if j < len(df_user)-1 and df_freq_free.userId[i] == df_user.userId[j+1]:\n",
    "            df_freq_free.loc[i,'user_index'] = j+1\n",
    "            i += 1\n",
    "            j += 1\n",
    "        else:\n",
    "            for k in range(1,7):\n",
    "                if df_freq_free.userId[i] == df_freq_free.userId[i-k]:\n",
    "                    df_freq_free.loc[i,'user_index'] = df_freq_free.loc[i-k,'user_index']\n",
    "                    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>userId</th>\n",
       "      <th>textIndex</th>\n",
       "      <th>emotionIndex</th>\n",
       "      <th>delFreq</th>\n",
       "      <th>leftFreq</th>\n",
       "      <th>TotTime</th>\n",
       "      <th>text_index</th>\n",
       "      <th>user_index</th>\n",
       "      <th>idx_start</th>\n",
       "      <th>idx_end</th>\n",
       "      <th>...</th>\n",
       "      <th>U1U2_mean</th>\n",
       "      <th>U1U2_std</th>\n",
       "      <th>typeWith</th>\n",
       "      <th>typistType</th>\n",
       "      <th>pcTimeAverage</th>\n",
       "      <th>ageRange</th>\n",
       "      <th>gender</th>\n",
       "      <th>status</th>\n",
       "      <th>degree</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>FR</td>\n",
       "      <td>N</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>...</td>\n",
       "      <td>27.120567</td>\n",
       "      <td>155.153800</td>\n",
       "      <td>2 hands</td>\n",
       "      <td>Touch Typist</td>\n",
       "      <td>More than 3 hours per day</td>\n",
       "      <td>16-19</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>College/University</td>\n",
       "      <td>Tunisia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>FR</td>\n",
       "      <td>H</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>204</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.018519</td>\n",
       "      <td>29.255256</td>\n",
       "      <td>2 hands</td>\n",
       "      <td>Touch Typist</td>\n",
       "      <td>More than 3 hours per day</td>\n",
       "      <td>16-19</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>College/University</td>\n",
       "      <td>Tunisia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>113</td>\n",
       "      <td>FR</td>\n",
       "      <td>N</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>205</td>\n",
       "      <td>329</td>\n",
       "      <td>...</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>691.356686</td>\n",
       "      <td>2 hands</td>\n",
       "      <td>Two Finger Typist</td>\n",
       "      <td>less than an hour per day</td>\n",
       "      <td>20-29</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>College/University</td>\n",
       "      <td>Tunisia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>113</td>\n",
       "      <td>FR</td>\n",
       "      <td>C</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>428</td>\n",
       "      <td>...</td>\n",
       "      <td>341.119565</td>\n",
       "      <td>670.032800</td>\n",
       "      <td>2 hands</td>\n",
       "      <td>Two Finger Typist</td>\n",
       "      <td>less than an hour per day</td>\n",
       "      <td>20-29</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>College/University</td>\n",
       "      <td>Tunisia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>113</td>\n",
       "      <td>FR</td>\n",
       "      <td>H</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>429</td>\n",
       "      <td>483</td>\n",
       "      <td>...</td>\n",
       "      <td>272.692308</td>\n",
       "      <td>231.421904</td>\n",
       "      <td>2 hands</td>\n",
       "      <td>Two Finger Typist</td>\n",
       "      <td>less than an hour per day</td>\n",
       "      <td>20-29</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>College/University</td>\n",
       "      <td>Tunisia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId textIndex emotionIndex  delFreq  leftFreq  TotTime  text_index  \\\n",
       "0     100        FR            N      145         0      NaN           0   \n",
       "1     100        FR            H        0         0      NaN           1   \n",
       "2     113        FR            N       44         0      NaN           2   \n",
       "3     113        FR            C       24         0      NaN           3   \n",
       "4     113        FR            H       15         0      NaN           4   \n",
       "\n",
       "   user_index idx_start idx_end  ...   U1U2_mean    U1U2_std  typeWith  \\\n",
       "0           0         0     147  ...   27.120567  155.153800   2 hands   \n",
       "1           0       148     204  ...   -4.018519   29.255256   2 hands   \n",
       "2           1       205     329  ...  288.000000  691.356686   2 hands   \n",
       "3           1       330     428  ...  341.119565  670.032800   2 hands   \n",
       "4           1       429     483  ...  272.692308  231.421904   2 hands   \n",
       "\n",
       "          typistType              pcTimeAverage  ageRange  gender   status  \\\n",
       "0       Touch Typist  More than 3 hours per day     16-19  Female  Student   \n",
       "1       Touch Typist  More than 3 hours per day     16-19  Female  Student   \n",
       "2  Two Finger Typist  less than an hour per day     20-29  Female  Student   \n",
       "3  Two Finger Typist  less than an hour per day     20-29  Female  Student   \n",
       "4  Two Finger Typist  less than an hour per day     20-29  Female  Student   \n",
       "\n",
       "               degree  country  \n",
       "0  College/University  Tunisia  \n",
       "1  College/University  Tunisia  \n",
       "2  College/University  Tunisia  \n",
       "3  College/University  Tunisia  \n",
       "4  College/University  Tunisia  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge all tables\n",
    "df_free_all = df_freq_free.join(text_free, on='text_index', how='left', rsuffix='_right')\n",
    "df_free_all = df_free_all.drop(['userId_right', 'emotionIndex_right', 'text_index_right'], axis=1)\n",
    "df_free_all = df_free_all.join(df_user.reset_index().rename(columns={'index':'user_index'}), on='user_index', how='left', rsuffix='_right')\n",
    "df_free_all = df_free_all.drop(['user_index_right','userId_right'], axis=1)\n",
    "df_free_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Useful functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import metrics, model_selection\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import xgboost as xgb\n",
    "import shap\n",
    "from tqdm import trange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Receiving a dictionary of performance metrics and printing them\n",
    "def show_metrics(performance_metrics):\n",
    "    for metric_name, metric in performance_metrics.items():\n",
    "        if metric_name.startswith(\"Confusion\"):\n",
    "            print(\"Confusion Matrix: \")\n",
    "            if len(metric) != 5:\n",
    "                print(pd.DataFrame(metric))\n",
    "            else:\n",
    "                print(pd.DataFrame(metric, \n",
    "                  index=list(label_dictionary.values()), \n",
    "                  columns=list(label_dictionary.values())))\n",
    "        else:\n",
    "            print(\"Metric : % s, Score : % 5.2f\" %(metric_name, metric))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting the Confusion Matrix as a heatmap\n",
    "label_dictionary = {1: 'N', 2: 'H', 3: 'C', 4: 'S', 5: 'A'}\n",
    "\n",
    "def plot_confusion_matrix(confusion_matrix):\n",
    "    if len(confusion_matrix) != 5:\n",
    "        df_cm = pd.DataFrame(confusion_matrix)\n",
    "    else:\n",
    "        df_cm = pd.DataFrame(confusion_matrix, \n",
    "                         index = [i for i in label_dictionary.values()],\n",
    "                         columns = [i for i in label_dictionary.values()])\n",
    "    plt.figure()\n",
    "    sn.set(font_scale=1.4) # for label size\n",
    "    sn.heatmap(df_cm, annot=True, annot_kws={\"size\": 16}, cmap=\"Blues\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computing the different Performance Metrics\n",
    "def compute_metrics(clf, dataX, dataY, show=False):\n",
    "    # Using Confusion Metrics to compute metrics\n",
    "    def performance_measures(y_true, y_pred):\n",
    "        cnf_matrix = metrics.confusion_matrix(y_true, y_pred, normalize='true')\n",
    "\n",
    "        FP = cnf_matrix.sum(axis=0) - np.diag(cnf_matrix) \n",
    "        FN = cnf_matrix.sum(axis=1) - np.diag(cnf_matrix)\n",
    "        TP = np.diag(cnf_matrix)\n",
    "        TN = cnf_matrix.sum() - (FP + FN + TP)\n",
    "\n",
    "        FP = FP.astype(float)\n",
    "        FN = FN.astype(float)\n",
    "        TP = TP.astype(float)\n",
    "        TN = TN.astype(float)\n",
    "\n",
    "        acc = np.mean((TP + TN) / (TP + FP + FN + TN))\n",
    "        TP_rate = np.mean(TP / (TP + FN))\n",
    "        FP_rate = np.mean(FP / (FP + TN))\n",
    "        precision = np.mean(TP / (TP + FP))\n",
    "        f_measure = 2 * precision * TP_rate / (precision + TP_rate)\n",
    "        f_micro = metrics.f1_score(y_true, y_pred, average='micro')\n",
    "        f_macro = metrics.f1_score(y_true, y_pred, average='macro')\n",
    "        \n",
    "        # Returning \n",
    "        return (acc, TP_rate, FP_rate, precision, f_measure, f_micro, f_macro,\n",
    "                np.round(cnf_matrix, 2))\n",
    "\n",
    "    acc, tp_rate, fp_rate, precision, f_measure, f_micro, f_macro, confusion_matrix = performance_measures(y_true=dataY, y_pred=clf.predict(dataX))\n",
    "    \n",
    "    \n",
    "    performance_metrics = {\"ACC\": acc, \"TP_Rate\": tp_rate, \"FP_Rate\": fp_rate,\n",
    "            \"Precision\": precision, \"F_Measure\": f_measure, \"F_Micro\": f_micro, \"F_Macro\": f_macro,\n",
    "            \"Confusion Matrix\": confusion_matrix} \n",
    "    if show:\n",
    "        show_metrics(performance_metrics)\n",
    "\n",
    "    return performance_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_free = df_free_all.loc[:,df_free_all.columns.difference(['user_index','answer','emotionIndex','userId','textIndex','TotTime','text_index','idx_start','idx_end','sentence'])]\n",
    "label_free = df_free_all.emotionIndex.map({'N':0,'H':1,'C':2,'S':3,'A':4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert categorical features into numerical features\n",
    "data_free.gender = (data_free.gender == 'Male')\n",
    "data_free.ageRange = data_free.ageRange.map({'16-19':1,'20-29':2,'30-39':3,'>=50':4})\n",
    "data_free.degree = data_free.degree.map({'High School':1, 'College/University':2})\n",
    "data_free.pcTimeAverage = data_free.pcTimeAverage.map({'less than an hour per day':1, 'between 1 hour and 3 hours per day':2, 'More than 3 hours per day':3})\n",
    "data_free.status = data_free.status.map({'Student':1, 'Professional':2})\n",
    "data_free.typeWith = data_free.typeWith.map({'1 hand':1, '2 hands':2})\n",
    "data_free = pd.get_dummies(data_free, columns=['country','typistType'])\n",
    "data_free = data_free.fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_free_filter = data_free\n",
    "#label_free = (label_free == 1)  ## for binary models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     235.000000\n",
       "mean      575.903368\n",
       "std       462.613308\n",
       "min        37.000000\n",
       "25%       371.776121\n",
       "50%       445.984375\n",
       "75%       573.752895\n",
       "max      4306.631579\n",
       "Name: D1U2_mean, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_free.D1U2_mean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "typeWith: F_onewayResult(statistic=1.5165383466752125, pvalue=0.210946439952144)\n",
      "gender: F_onewayResult(statistic=1.5264998018995204, pvalue=0.20835703916217677)\n",
      "ageRange: F_onewayResult(statistic=2.2679154334853315, pvalue=0.08138116258879237)\n",
      "degree: F_onewayResult(statistic=0.335322606831029, pvalue=0.7998118143253563)\n",
      "pcTimeAverage: F_onewayResult(statistic=2.829350253966248, pvalue=0.03923893758331836)\n",
      "status: F_onewayResult(statistic=4.2294065261630305, pvalue=0.006192676918584422)\n",
      "country: F_onewayResult(statistic=5.250976275102535, pvalue=0.0015985275696754205)\n",
      "typistType: F_onewayResult(statistic=4.4932240437313045, pvalue=0.004365349896636562)\n"
     ]
    }
   ],
   "source": [
    "# only for demographic features\n",
    "personal_col = ['typeWith','gender','ageRange','degree','pcTimeAverage','status','country','typistType']\n",
    "for col in personal_col:\n",
    "    anova = stats.f_oneway(data_free.loc[data_free.D1U2_mean < 372, col],\n",
    "               data_free.loc[(data_free.D1U2_mean >= 372) & (data_free.D1U2_mean < 446), col],\n",
    "               data_free.loc[(data_free.D1U2_mean >= 446) & (data_free.D1U2_mean < 574), col],\n",
    "               data_free.loc[data_free.D1U2_mean >= 574, col])\n",
    "    print(col + ': ' + str(anova))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-class models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define classification models\n",
    "classifiers_name = ['LogReg','RF','XGB','SVM','MLP']\n",
    "\n",
    "classifiers = [\n",
    "    LogisticRegression(multi_class='auto', max_iter=500, solver='newton-cg', class_weight={0:0.1,1:1,2:1,3:1,4:1}), \n",
    "    RandomForestClassifier(n_estimators=200, max_depth=5, class_weight={0:0.1,1:1,2:1,3:1,4:1}),\n",
    "    xgb.XGBClassifier(objective='multi:softmax', eval_metric='mlogloss',use_label_encoder=False),\n",
    "    SVC(kernel='rbf',decision_function_shape='ovr', probability=True),#, class_weight={0:0.1,1:1,2:1,3:1,4:1}),\n",
    "    MLPClassifier(alpha=1, max_iter=500)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogReg\n",
      "RF\n",
      "XGB\n",
      "SVM\n",
      "MLP\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'LogReg': [0.17880434782608695, 0.05634640447079467],\n",
       " 'RF': [0.22253623188405797, 0.05586864883926758],\n",
       " 'XGB': [0.43061594202898557, 0.06317270821171064],\n",
       " 'SVM': [0.4724637681159421, 0.013133745623472371],\n",
       " 'MLP': [0.30659420289855066, 0.11804704801499087]}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# micro-F1 scores with a 10-fold cross-validation method\n",
    "\n",
    "model_scores_micro = {}\n",
    "for i in range(len(classifiers)):\n",
    "    model_scores_micro[classifiers_name[i]] = []\n",
    "    print(classifiers_name[i])\n",
    "    for j in range(10):\n",
    "        model = classifiers[i]\n",
    "        scores = model_selection.cross_val_score(model, X=data_free_filter, y=label_free, cv=10, n_jobs=1, scoring='f1_micro')\n",
    "        model_scores_micro[classifiers_name[i]] += list(scores)\n",
    "    mean = np.nanmean(model_scores_micro[classifiers_name[i]])\n",
    "    std = np.nanstd(model_scores_micro[classifiers_name[i]])\n",
    "    model_scores_micro[classifiers_name[i]] = [mean,std]\n",
    "\n",
    "model_scores_micro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogReg\n",
      "RF\n",
      "XGB\n",
      "SVM\n",
      "MLP\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'LogReg': [0.15633832833832834, 0.05943443102262968],\n",
       " 'RF': [0.20239003251650314, 0.0695114642756447],\n",
       " 'XGB': [0.2570290208985861, 0.08040337120819359],\n",
       " 'SVM': [0.12832492997198877, 0.0024135452489030917],\n",
       " 'MLP': [0.18167041445158602, 0.07357986050258784]}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# macro-F1 scores with a 10-fold cross-validation method\n",
    "\n",
    "model_scores_macro = {}\n",
    "for i in range(len(classifiers)):\n",
    "    model_scores_macro[classifiers_name[i]] = []\n",
    "    print(classifiers_name[i])\n",
    "    for j in range(10):\n",
    "        model = classifiers[i]\n",
    "        scores = model_selection.cross_val_score(model, X=data_free_filter, y=label_free, cv=10, n_jobs=1, scoring='f1_macro')\n",
    "        model_scores_macro[classifiers_name[i]] += list(scores)\n",
    "    mean = np.nanmean(model_scores_macro[classifiers_name[i]])\n",
    "    std = np.nanstd(model_scores_macro[classifiers_name[i]])\n",
    "    model_scores_macro[classifiers_name[i]] = [mean,std]\n",
    "\n",
    "model_scores_macro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best multi-class model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric : ACC, Score :  0.75\n",
      "Metric : TP_Rate, Score :  0.37\n",
      "Metric : FP_Rate, Score :  0.16\n",
      "Metric : Precision, Score :  0.54\n",
      "Metric : F_Measure, Score :  0.44\n",
      "Metric : F_Micro, Score :  0.55\n",
      "Metric : F_Macro, Score :  0.37\n",
      "Confusion Matrix: \n",
      "      N     H     C     S     A\n",
      "N  0.78  0.04  0.11  0.00  0.07\n",
      "H  0.57  0.14  0.00  0.00  0.29\n",
      "C  0.50  0.00  0.25  0.25  0.00\n",
      "S  0.50  0.00  0.00  0.33  0.17\n",
      "A  0.67  0.00  0.00  0.00  0.33\n",
      "here 2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = 'XGB'\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(data_free_filter, label_free, test_size=0.2)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "model = classifiers[classifiers_name.index(best_model)]\n",
    "model.fit(X_train, Y_train)\n",
    "\n",
    "performance_metrics = compute_metrics(model, X_test, Y_test, show=True)\n",
    "plot_confusion_matrix(performance_metrics[\"Confusion Matrix\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class N:\n",
      "0.578 0.114\n",
      "0.568 0.114\n",
      "0.537 0.154\n",
      "\n",
      "\n",
      "Class H:\n",
      "0.805 0.051\n",
      "0.47 0.045\n",
      "0.05 0.1\n",
      "\n",
      "\n",
      "Class C:\n",
      "0.868 0.03\n",
      "0.534 0.114\n",
      "0.1 0.153\n",
      "\n",
      "\n",
      "Class S:\n",
      "0.838 0.024\n",
      "0.567 0.099\n",
      "0.225 0.211\n",
      "\n",
      "\n",
      "Class A:\n",
      "0.89 0.033\n",
      "0.574 0.136\n",
      "0.183 0.229\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## binary classification with XGBoost\n",
    "for emotion_index in range(5):\n",
    "    f1_micro = np.array([])\n",
    "    f1_macro = np.array([])\n",
    "    recall = np.array([])\n",
    "    \n",
    "    print(\"Class \" + label_dictionary[emotion_index+1] + \":\")\n",
    "    label_free_binary = (label_free == emotion_index).astype(int)\n",
    "    \n",
    "    for i in range(10):\n",
    "        model = xgb.XGBClassifier(objective='binary:logistic',eval_metric='mlogloss',use_label_encoder=False)\n",
    "        scores = model_selection.cross_validate(model, X=data_free_filter, y=label_free_binary, cv=10, n_jobs=1, scoring=['f1_micro','f1_macro','recall'])\n",
    "        \n",
    "        f1_micro = np.concatenate([f1_micro, scores['test_f1_micro']])\n",
    "        f1_macro = np.concatenate([f1_macro, scores['test_f1_macro']])\n",
    "        recall = np.concatenate([recall, scores['test_recall']])\n",
    "        \n",
    "    print(np.round(np.nanmean(f1_micro),3), np.round(np.nanstd(f1_micro),3))\n",
    "    print(np.round(np.nanmean(f1_macro),3), np.round(np.nanstd(f1_macro),3))\n",
    "    print(np.round(np.nanmean(recall),3), np.round(np.nanstd(recall),3))\n",
    "    \n",
    "    print()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class N:\n",
      "0.461 0.034\n",
      "0.331 0.038\n",
      "0.945 0.061\n",
      "\n",
      "\n",
      "Class H:\n",
      "0.778 0.064\n",
      "0.437 0.021\n",
      "0.0 0.0\n",
      "\n",
      "\n",
      "Class C:\n",
      "0.747 0.083\n",
      "0.469 0.072\n",
      "0.126 0.198\n",
      "\n",
      "\n",
      "Class S:\n",
      "0.808 0.091\n",
      "0.598 0.118\n",
      "0.418 0.353\n",
      "\n",
      "\n",
      "Class A:\n",
      "0.865 0.052\n",
      "0.563 0.104\n",
      "0.23 0.228\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## binary classification with Random Forest\n",
    "for emotion_index in range(5):\n",
    "    f1_micro = np.array([])\n",
    "    f1_macro = np.array([])\n",
    "    recall = np.array([])\n",
    "    \n",
    "    print(\"Class \" + label_dictionary[emotion_index+1] + \":\")\n",
    "    label_free_binary = (label_free == emotion_index).astype(int)\n",
    "    \n",
    "    for i in range(10):\n",
    "        model = RandomForestClassifier(n_estimators=200, max_depth=5, class_weight={0:0.1,1:1})\n",
    "        scores = model_selection.cross_validate(model, X=data_free_filter, y=label_free_binary, cv=10, n_jobs=1, scoring=['f1_micro','f1_macro','recall'])\n",
    "        \n",
    "        f1_micro = np.concatenate([f1_micro, scores['test_f1_micro']])\n",
    "        f1_macro = np.concatenate([f1_macro, scores['test_f1_macro']])\n",
    "        recall = np.concatenate([recall, scores['test_recall']])\n",
    "        \n",
    "    print(np.round(np.nanmean(f1_micro),3), np.round(np.nanstd(f1_micro),3))\n",
    "    print(np.round(np.nanmean(f1_macro),3), np.round(np.nanstd(f1_macro),3))\n",
    "    print(np.round(np.nanmean(recall),3), np.round(np.nanstd(recall),3))\n",
    "    \n",
    "    print()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class N:\n",
      "0.566 0.081\n",
      "0.483 0.106\n",
      "0.203 0.14\n",
      "\n",
      "\n",
      "Class H:\n",
      "0.847 0.018\n",
      "0.459 0.005\n",
      "0.0 0.0\n",
      "\n",
      "\n",
      "Class C:\n",
      "0.864 0.015\n",
      "0.463 0.005\n",
      "0.0 0.0\n",
      "\n",
      "\n",
      "Class S:\n",
      "0.864 0.015\n",
      "0.463 0.005\n",
      "0.0 0.0\n",
      "\n",
      "\n",
      "Class A:\n",
      "0.898 0.019\n",
      "0.473 0.005\n",
      "0.0 0.0\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## binary classification with SVM\n",
    "for emotion_index in range(5):\n",
    "    f1_micro = np.array([])\n",
    "    f1_macro = np.array([])\n",
    "    recall = np.array([])\n",
    "    \n",
    "    print(\"Class \" + label_dictionary[emotion_index+1] + \":\")\n",
    "    label_free_binary = (label_free == emotion_index).astype(int)\n",
    "    \n",
    "    for i in range(10):\n",
    "        model = SVC(kernel='rbf',decision_function_shape='ovr', probability=True)\n",
    "        scores = model_selection.cross_validate(model, X=data_free_filter, y=label_free_binary, cv=10, n_jobs=1, scoring=['f1_micro','f1_macro','recall'])\n",
    "        \n",
    "        f1_micro = np.concatenate([f1_micro, scores['test_f1_micro']])\n",
    "        f1_macro = np.concatenate([f1_macro, scores['test_f1_macro']])\n",
    "        recall = np.concatenate([recall, scores['test_recall']])\n",
    "        \n",
    "    print(np.round(np.nanmean(f1_micro),3), np.round(np.nanstd(f1_micro),3))\n",
    "    print(np.round(np.nanmean(f1_macro),3), np.round(np.nanstd(f1_macro),3))\n",
    "    print(np.round(np.nanmean(recall),3), np.round(np.nanstd(recall),3))\n",
    "    \n",
    "    print()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "data_free = pd.DataFrame(scaler.fit_transform(data_free), columns=data_free.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chi-squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ordered features by importance with their corresponding chi2 score\n",
      "['country' 'gender' 'typistType' 'pcTimeAverage' 'status' 'ageRange'\n",
      " 'typeWith' 'degree']\n",
      "[1.460e+01 2.849e+00 2.551e+00 2.298e+00 2.197e+00 6.049e-01 7.724e-02\n",
      " 6.320e-03]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "demo_features = np.array(['typeWith','gender','ageRange','degree','pcTimeAverage','status','country','typistType'])\n",
    "\n",
    "# Feature extraction\n",
    "test = SelectKBest(score_func=chi2, k=8)\n",
    "fit = test.fit(data_free.loc[:,demo_features].fillna(100).replace(-1,16), pd.cut(data_free.D1U2_mean.fillna(0),bins=[0,372,445,573,4306],labels=[1,2,3,4]).fillna(1))\n",
    "\n",
    "# Summarize scores\n",
    "np.set_printoptions(precision=3)\n",
    "print(\"Ordered features by importance with their corresponding chi2 score\")\n",
    "print(demo_features[np.argsort(fit.scores_)][::-1])\n",
    "print(fit.scores_[np.argsort(fit.scores_)][::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D1U2_mean        1.000000\n",
      "U1D2_mean        0.929312\n",
      "D1U2_std         0.897616\n",
      "U1D2_std         0.883536\n",
      "D1D2_mean        0.858418\n",
      "U1U2_mean        0.851738\n",
      "U1U2_std         0.825045\n",
      "D1D2_std         0.822423\n",
      "typeWith        -0.219643\n",
      "country          0.204102\n",
      "ageRange         0.173974\n",
      "D1U1_std         0.153070\n",
      "pcTimeAverage   -0.132658\n",
      "D1U1_mean        0.111385\n",
      "typistType       0.065730\n",
      "nbKeystroke     -0.062158\n",
      "leftFreq         0.056437\n",
      "status          -0.049902\n",
      "editDistance     0.041198\n",
      "delFreq          0.030497\n",
      "degree           0.023296\n",
      "gender           0.012504\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "importances = data_free.apply(lambda x: x.corr(data_free.D1U2_mean))\n",
    "indices = np.argsort(importances)\n",
    "indices_abs = np.argsort(np.abs(importances))[::-1]\n",
    "print(importances[indices_abs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.barh(range(len(indices)), importances[indices], color='g', align='center')\n",
    "plt.yticks(range(len(indices)), importances[indices].index.values)\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Information Gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D1D2_mean</th>\n",
       "      <td>5.459586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1U2_mean</th>\n",
       "      <td>5.459586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1D2_std</th>\n",
       "      <td>5.459586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1U1_mean</th>\n",
       "      <td>5.459586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U1D2_mean</th>\n",
       "      <td>5.453686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U1D2_std</th>\n",
       "      <td>5.453686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1U1_std</th>\n",
       "      <td>5.453686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1U2_std</th>\n",
       "      <td>5.453686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U1U2_std</th>\n",
       "      <td>5.445561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U1U2_mean</th>\n",
       "      <td>5.445561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbKeystroke</th>\n",
       "      <td>4.653259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delFreq</th>\n",
       "      <td>3.213674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>editDistance</th>\n",
       "      <td>2.350760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>1.243070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pcTimeAverage</th>\n",
       "      <td>0.991976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>0.669410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.629409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>typistType</th>\n",
       "      <td>0.621849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leftFreq</th>\n",
       "      <td>0.572740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ageRange</th>\n",
       "      <td>0.568589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>typeWith</th>\n",
       "      <td>0.175975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degree</th>\n",
       "      <td>0.068356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      1\n",
       "0                      \n",
       "D1D2_mean      5.459586\n",
       "D1U2_mean      5.459586\n",
       "D1D2_std       5.459586\n",
       "D1U1_mean      5.459586\n",
       "U1D2_mean      5.453686\n",
       "U1D2_std       5.453686\n",
       "D1U1_std       5.453686\n",
       "D1U2_std       5.453686\n",
       "U1U2_std       5.445561\n",
       "U1U2_mean      5.445561\n",
       "nbKeystroke    4.653259\n",
       "delFreq        3.213674\n",
       "editDistance   2.350760\n",
       "country        1.243070\n",
       "pcTimeAverage  0.991976\n",
       "status         0.669410\n",
       "gender         0.629409\n",
       "typistType     0.621849\n",
       "leftFreq       0.572740\n",
       "ageRange       0.568589\n",
       "typeWith       0.175975\n",
       "degree         0.068356"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from info_gain import info_gain \n",
    "\n",
    "importances_ig = []\n",
    "for col in data_free.columns:\n",
    "    importances_ig.append(info_gain.info_gain(data_free[col],data_free.D1U2_mean))\n",
    "indices_ig = np.argsort(importances_ig)[::-1]\n",
    "pd.concat([pd.Series(data_free.columns[indices_ig]), pd.Series(np.sort(importances_ig)[::-1])], axis=1).set_index(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mutual information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import mutual_info_regression\n",
    "\n",
    "mi = mutual_info_regression(data_free, data_free.D1U2_mean)\n",
    "mi = pd.Series(mi)\n",
    "mi.index = data_free.columns\n",
    "mi.sort_values(ascending=False)\n",
    "mi.sort_values(ascending=False).plot.bar(figsize=(10, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recursive Feature Elimination (RFE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Ranking: [ 9 15 11 21 13 14 12 16 10 17  5  7 19  8  6 22  2 18  4  3  1 20]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(max_iter=1000) \n",
    "rfe = RFE(model, 1)\n",
    "fit = rfe.fit(data_free, label_free)\n",
    "\n",
    "print(\"Feature Ranking: %s\"% fit.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['U1U2_mean',\n",
       " 'editDistance',\n",
       " 'ageRange',\n",
       " 'typeWith',\n",
       " 'degree',\n",
       " 'delFreq',\n",
       " 'country',\n",
       " 'gender',\n",
       " 'U1U2_std',\n",
       " 'leftFreq',\n",
       " 'D1U2_mean',\n",
       " 'U1D2_mean',\n",
       " 'pcTimeAverage',\n",
       " 'U1D2_std',\n",
       " 'D1U2_std',\n",
       " 'typistType',\n",
       " 'D1D2_std',\n",
       " 'nbKeystroke',\n",
       " 'D1U1_std',\n",
       " 'D1U1_mean',\n",
       " 'D1D2_mean',\n",
       " 'status']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data_free.columns[fit.ranking_ - 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SHAP values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x1231.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DF, based on which importance is checked\n",
    "X_importance = pd.DataFrame(data_free_filter, columns=data_free_filter.columns)\n",
    "\n",
    "# Explain model predictions using shap library:\n",
    "explainer = shap.TreeExplainer(model)\n",
    "shap_values = explainer.shap_values(X_importance)\n",
    "\n",
    "# Plot summary_plot\n",
    "shap.summary_plot(shap_values, X_importance, max_display=data_free.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RF feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric : ACC, Score :  0.72\n",
      "Metric : TP_Rate, Score :  0.36\n",
      "Metric : FP_Rate, Score :  0.17\n",
      "Metric : Precision, Score :  0.33\n",
      "Metric : F_Measure, Score :  0.34\n",
      "Metric : F_Micro, Score :  0.30\n",
      "Metric : F_Macro, Score :  0.28\n",
      "Confusion Matrix: \n",
      "      N    H     C     S     A\n",
      "N  0.25  0.2  0.17  2.00  0.57\n",
      "H  0.10  0.2  0.67  0.25  0.14\n",
      "C  0.00  0.2  0.17  0.75  0.00\n",
      "S  0.00  0.0  0.00  0.75  0.14\n",
      "A  0.00  0.2  0.17  0.25  0.43\n",
      "here 2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = 'RF'\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(data_free, label_free, test_size=0.2)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "model = classifiers[classifiers_name.index(best_model)]\n",
    "model.fit(X_train, Y_train)\n",
    "\n",
    "performance_metrics = compute_metrics(model, X_test, Y_test, show=True)\n",
    "plot_confusion_matrix(performance_metrics[\"Confusion Matrix\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxEAAAJFCAYAAACiD3ozAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzde1zP9//4/1slK6QcYuQ0rFdNRUgYk5xNZZEhcspkRsok58NCImnxXqKmZd5ok5TYhm3We9ZnDWNsWM7vESMjnQ+/P/x6ffd6d1ip9Iz79XLZ5dLr+Xw8H4/H83lnXveej4NWYWFhIUIIIYQQQghRTto13QEhhBBCCCFE7SJJhBBCCCGEEKJCJIkQQgghhBBCVIgkEUIIIYQQQogKkSRCCCGEEEIIUSGSRAghhBBCCCEqRJIIIYQQQgghRIXUqekOCPEiSkt7TEGBbNGiVE2aNODevfSa7oYohcRH+SRGyicxUj4lxEhbW4tGjeqXeE6SiBeUvb09//3vf9WfdXV1adSoEb1798bT05OWLVsWuyYhIQF/f3+OHz+ucVylUuHn54eLi0uxa8aNG0fbtm3x9/cH4M8//yQwMJDExEQeP36Mubk53t7edOvWrYrvUNkKCgoliVA4iY+ySXyUT2KkfBIj5VNyjCSJeIFNmjSJ6dOnA5Cdnc3Vq1fZuHEjY8aMITo6mhYtWqjLHjlyBF9fX4yMjJ66vfz8fGbOnElBQQHBwcEYGRkRFRXF1KlT2bdvHx06dKjsLdUaTZo0qOkuiH9gbGxQ010QZZD4KJ/ESPkkRspXWoyysvN49DDzGfdGkyQRLzB9fX2MjY3Vn1u1aoWlpSUjRowgMDCQDRs2kJ6ejp+fH/Hx8XTo0IG0tLSnbu/s2bOcOXOGuLg4TE1NAVi2bBmJiYkcOHAALy+vSt9TbTHN70vupNXsX34hhBBC1E5xgU48quE+yMRqocHQ0BBnZ2e++uorcnJyuHnzJrdu3SI6OpqBAwdWqu42bdqwdetWOnbsqD6mpaWFlpYWDx48KHc9KpWK2NhY3NzcsLKyYsCAARw9epRjx44xbNgwOnfuzIQJE7hx44b6mtTUVLy8vOjevTu2trZ4eHhw9epV9fmcnBzWrVuHvb09FhYW2NjYMGfOHO7duwfAzZs3UalUJCQk4OLigoWFBfb29uzZs6dSz0QIIYQQojaSJEIUY2ZmRlZWFlevXsXMzIzIyEjMzc0rXW/jxo2xs7NDW/v//bFLSEjg2rVr2NnZVaiuNWvW4OrqSlxcHB07dmT+/Pls3ryZtWvXEhkZyY0bN9i4cSMAGRkZTJw4kfz8fHbu3ElUVBSNGjVizJgxpKamAhAQEMDhw4dZu3YtX3zxBevWrSMpKYktW7ZotLtu3To8PDw4dOgQtra2rFixQiNZEUIIIYR4EchwJlFMw4YNAXj0qHpflCUnJ7N48WIGDx5M//79K3Sto6MjQ4YMAcDFxYVvvvkGb29vunTpAsCgQYM4ceIEAAcPHiQtLY3AwEB0dXUBWL16NUlJSezdu5fZs2djaWnJ4MGD6dGjBwAmJib06dOHixcvarQ7adIkBgwYAICPjw/79u3j559/pnXr1k/9HIQQQgghKqqm57RIEiGKKUoeipKJf1KnTh0KCgpKPFdYWEidOsX/mB0+fBgfHx9sbGzYsGFDhfv4yiuvqH+uV68eAG3btlUf09PTIycnB4Dz58+Tnp6uThCKZGdnk5KSAoCTkxPff/89GzZs4OrVq1y+fJkrV66ok5Ii7du3V/9sYPDkL29ubm6F+y+EEEIIURl371b/rAhtba1SF4ORJEIUc+7cOerVq0e7du3KVd7Q0JCHDx+WeC4tLQ1DQ0ONY9u2bSMwMBBHR0dWr16tfjtQESVdo6WlVWLZgoIC2rRpQ1hYWLFzRQnIsmXLOHz4MCNHjqR///68++67REREaCyDC1C3bt1idRQWKnf5NSGEEEKI6iBJhNCQnp5OTEwMw4cPL/eXe0tLS5KTk9XLxRZJTU3lxo0bWFpaqo+FhYURGBjIO++8w7x586q076UxNTUlJiaGBg0a0KRJEwDy8vLw9vZm6NCh9OrViz179rB+/XocHR3V112+fLnEpKEqhC8ZXC31CiGEEOL5l5WdV9NdkCTiRZaZmcndu3eB/ze056OPPkJbWxtPT89y1+Pu7s6kSZNYvXo1o0ePpl69eqSkpBAcHIxKpVKv6nT27FmCgoJwcnLCzc1N3TY8GX5UNDyoqjk6OhIWFsbs2bPx8fHBwMCA0NBQjh8/jqenJw0aNMDAwIBjx45hZWVFdnY2O3fu5Ny5c3Tq1Kla+nTvXrqiN5B50RkbGzyT18Ti6Uh8lE9ipHwSI+VTeowkiXiBRUZGEhkZCTzZM6J58+bY2dkRHBxMs2bNyl2PjY0NO3bsYOvWrbi5ufH48WOaNWvGwIEDmT17tnpORHx8PAUFBcTGxhIbG6tRh4ODw1PNjSgPAwMDdu7cSUBAAO7u7uTn59OpUyciIiLUG9wFBwfj7++Po6MjhoaG2Nra4u3tTWhoKI8fP66WfgkhhBBC1FZahTKgW4hnTt5EKJvSf/vzopP4KJ/ESPkkRsqnhBiVNbFa9okQQgghhBBCVIgMZxKK4eHhQVJSUpllIiMjsbKyekY9EkIIIYQQJZEkQpQqJCSE6Ohojh8/XmoZe3t7HBwc8PLy0ji+adMmPvroIxYuXMjkyZPL1d7KlSvJysoqs0yLFi1KPP748WNiYmKYMGFCudoqzcSJE2nevHm1zc8QQgghhHgeSBIhqlxQUBBbt25lxYoVjBs3rtzXNW/e/Knb3LZtGwcOHKh0EvGslDa+UChHTe8EKsom8VE+iZHyVXeMsrLzePQws1rbEDVHkghRpYKCgggLC2PNmjU4Ozs/s3Zr2/oA0/y+5E6a/I9VCCHE8ysu0AmZuv38konVLzCVSsXevXuZPHkyVlZW9OnTh82bNxcr99FHH9GzZ0+6du3KvHnzuH//fon1bdy4ke3bt7N+/fpiCURhYSHbtm1jwIABdO7cGScnJw4cOKA+P3LkSHx8fDSuSU5OxtzcnFu3bpGZmcnixYt5/fXXsbS0xMnJiS+++AJ4MuwqNDSU//73v6hUKpKSkggJCWHcuHHMmzePrl27smjRIgDOnDnDlClT6N69OzY2NsydO5dbt26VeD8FBQXMnz+fPn36cOnSJQAePXrE0qVL6dmzJ926dcPNzY2zZ8+W84kLIYQQQjwfJIl4wQUEBPDWW29x8OBBRo0aRUhICD/++KP6fGpqKt9//z0RERFs3bqVX375BV9f32L1bNy4ka1bt+Lq6sqIESOKnQ8KCmLXrl0sWbKEuLg43NzcWLFiBZ9++ikAo0eP5quvviIjI0N9TUxMDL1796ZFixYEBwdz4cIFwsLCSEhIoF+/fnh5eXHjxg2mTp3KpEmTePnll0lMTMTa2hqAkydPYmRkRGxsLO7u7pw9exZXV1datWrFv//9b7Zu3cqtW7dwdXUlPT1do78FBQUsWrSIpKQkoqKiePXVVyksLGT69OlcvXqV0NBQ9u7dS5cuXRg3bhznz5+vkngIIYQQQtQGMpzpBefk5ISTkxMAc+fOJSoqipMnT2JjYwOArq4uQUFBNG3aFIBly5YxdepUUlJS1Bu17du3j7/++ovu3bsTHR3N2LFjad++vbqNjIwMduzYQUBAAP379wegTZs2/Pe//yU8PBxXV1ccHBwICAjgyy+/ZOTIkWRlZXH48GE++OADAK5fv079+vVp3bo1DRs2xNPTk+7du2NoaEj9+vXR19dHR0cHY2NjjfubO3eueidsLy8v2rdvz6pVq9DS0gLgww8/ZODAgRw4cIDx48cDT96aLFmyhKSkJD799FNat24NwA8//MCpU6c4ceIEjRs3BsDb25uTJ0/yySef4O/vX8XREUIIIWo3mRtTOUp+fpJEvOD+/mVfS0uLBg0akJubqz7Wpk0bdQIB0LlzZwAuXbqkTiIePXrE1q1bsbCwwNHRES8vL6Kjo6lbty4Av//+O9nZ2SxYsICFCxeq68rLyyMnJ4esrCwMDQ0ZNGgQsbGxjBw5ki+//BIdHR0GDhwIwPTp0/Hw8KBXr15YWVnx+uuvM2LECBo2bFjqvRkZGakTCIALFy7Qs2dPdQIBTyZzt23blgsXLqiPffnll+Tm5tK2bVuNpOTcuXMADBgwQKOdnJwcsrOzS+2HEEII8aKq6c3SajOlbzYnScQLruiL/t/9fZKyjo6Oxrn8/Pxi102YMIFevXoBT4ZHubm5sXbtWpYvX65RX2BgIK+++mqpfRg9ejRTp04lNTWV2NhYRowYoT5nbW3Nt99+y3/+8x9OnDhBbGwsH330EVu3bqVPnz4l3ttLL71U6n39XUFBAbq6uurPxsbGbNq0CXd3dzZt2qQevlVQUIC+vj779+8v9R6EEEIIIV4EkkSIMl2/fp309HQaNHiShf70009oaWlpJAN/TzRsbGxwd3cnLCyM3r17M2jQINq3b0+dOnX4448/1G8WAKKiorh06RKrVq0CoGfPnrRs2ZJ9+/bxww8/aOw98eGHH9KtWzcGDBjAgAEDWLRoEcOHD+eLL76gT58+Gm8XSqNSqUhOTqawsFBdPjU1levXrzNp0iR1ua5du2JlZcWCBQtYsmQJgwcPpmvXrpiampKZmUlmZiZmZmbq8kuXLkWlUlVoednwJYPLXVYIIYSojbKy82q6C6IaSRIhypSTk8PcuXPx9vbmwYMH+Pn5MWLECPU8gZLMmTOH//znPyxevJhOnTrRsmVLxo4dy4cffkjDhg3p1q0bycnJBAQEMGPGDPV1WlpaODs7ExoaSocOHbCwsFCfu3btGgcOHOCDDz6gTZs2/Pzzz/zxxx/qSdT169fnr7/+4vLly5iYmJTYr6lTpzJ+/HiWLVuGm5sbjx49Yt26dTRr1ozhw4cXKz9q1Cji4uJYuHAhsbGx9O3bF3Nzc+bNm8eSJUto2bIlu3fv5vPPPyc8PLxCz/XevXQKCmrXsrQvEiW8Qhalk/gon8RI+SRGorJkdSZRJnNzczp16sSkSZOYM2cOffr0Ub85KI2uri7r168nOzsbb29v8vLy1DtXf/jhhwwbNox//etfzJkzh1mzZmlc6+zsTFZWFqNGjdI4vnLlSnr37s38+fMZMmQIwcHBeHt7q5eSHT58OC1atMDR0ZFvvvmmxH5ZWVkRERHB77//zqhRo5g5cyatW7fm3//+t8bcib/74IMPSE1NJSgoCB0dHSIiIrCyssLLywtHR0eSkpLYvHmzejiXEEIIIcSLQKuwtu3SJZ5rp06dws3NjePHj9OoUaOa7k61kTcRyia/oVM2iY/ySYyUT2KkfEqIkUysFoqXkpLCxYsXCQsLw8HB4blOIIQQQgghajsZziQU4dq1ayxcuJD69eszf/78mu6OEEIIIYQogyQRL6iJEyfy/vvvl3guKCgIe3v7YscTEhJ44403ih1XqVRER0eXWNe4ceM0drj+888/WbhwIX379qVr1664urry008/YW9vz+nTp9m5c6e8hRBCCCGEUDgZziTK5ciRI/j6+mJkZPTUdeTn5zNz5kwKCgoIDg7GyMiIqKgopk6dyr59+9Sb170IShtfKJRDybuEColPbSAxKl1Wdh6PHmbWdDeEqBRJIkSZ0tPT8fPzIz4+ng4dOpCWlvbUdZ09e5YzZ84QFxeHqakpAMuWLSMxMZEDBw5o7AvxvJvm9yV30uQfECGEeBHFBTohU5pFbSfDmUSZbt68ya1bt4iOjtbYKO5ptGnThq1bt9KxY0f1MS0tLbS0tHjw4EG561GpVMTGxuLm5oaVlRUDBgzg6NGjHDt2jGHDhtG5c2cmTJjAjRs31Nekpqbi5eVF9+7dsbW1xcPDg6tXr6rP5+TksG7dOuzt7bGwsMDGxoY5c+Zw79499XNQqVQkJCTg4uKChYUF9vb27Nmzp1LPRAghhBCiNpIkQpTJzMyMyMhIzM3NK11X48aNsbOzQ1v7//2xS0hI4Nq1a9jZ2VWorjVr1uDq6kpcXBwdO3Zk/vz5bN68mbVr1xIZGcmNGzfYuHEjABkZGUycOJH8/Hx27txJVFQUjRo1YsyYMaSmpgIQEBDA4cOHWbt2LV988QXr1q0jKSmJLVu2aLS7bt06PDw8OHToELa2tqxYsUIjWRFCCCGEeBHIcCZRY5KTk1m8eDGDBw+mf//+FbrW0dGRIUOGAODi4sI333yDt7c3Xbp0AWDQoEGcOHECgIMHD5KWlkZgYCC6uroArF69mqSkJPbu3cvs2bOxtLRk8ODB9OjRAwATExP69OnDxYsXNdqdNGkSAwYMAMDHx4d9+/bx888/l7mDtxBCCPG/lDBnRAl9EGVTcowkiXhB1alTh9L2GSwsLKROnfL/0ahTpw4FBQUVquvw4cP4+PhgY2PDhg0byt1WkVdeeUX9c7169QBo27at+pienh45OTkAnD9/nvT0dHWCUCQ7O5uUlBQAnJyc+P7779mwYQNXr17l8uXLXLlyRZ2UFGnfvr3656JdrnNzcyvcfyGEEC+2mt5ETAkbmYmyKSFGstmcKMbQ0JCHDx+WeO7BgwcYGhpWSV1paWnF6tq2bRuBgYE4OjqyevVq9duBiijpGi0trRLLFhQU0KZNG8LCwoqdK0pAli1bxuHDhxk5ciT9+/fn3XffJSIigv/+978a5evWrVusDtn0XQghhBAvGkkiXlCWlpZs376drKws9PT01McLCwv56aefsLW1rVBdycnJTJ8+XeN4amoqN27cwNLSUn0sLCyMwMBA3nnnHebNm1f5GykHU1NTYmJiaNCgAU2aNAEgLy8Pb29vhg4dSq9evdizZw/r16/H0dFRfd3ly5dLTBqqQviSwdVSrxBCCOXLys6r6S4IUWmSRLygnJ2d+fjjj5k1axbvvvsuL7/8Mrdv3yYqKorU1FSmTJlS7rrc3d2ZNGkSq1evZvTo0dSrV4+UlBSCg4NRqVTqVZ3Onj1LUFAQTk5OuLm5cffuXXUdenp66uFBVc3R0ZGwsDBmz56Nj48PBgYGhIaGcvz4cTw9PWnQoAEGBgYcO3YMKysrsrOz2blzJ+fOnaNTp07V0qd799IpKJA3GEqlhFfIonQSH+WTGAnx/JMk4gXVqFEj9uzZQ3BwMHPnzlUPO7KxsWHPnj0VmihsY2PDjh072Lp1K25ubjx+/JhmzZoxcOBAZs+erZ4TER8fT0FBAbGxscTGxmrU4eDg8FRzI8rDwMCAnTt3EhAQgLu7O/n5+XTq1ImIiAj1BnfBwcH4+/vj6OiIoaEhtra2eHt7ExoayuPHj6ulX0IIIYQQtZVWoQzoFuKZkzcRyia/RVU2iY/ySYyUT2KkfEqIUVkTq2WfCCGEEEIIIUSFyHAmoRgeHh4kJSWVWSYyMhIrK6tn1CMhhBBCCFESSSJeUPb29hrLl+rq6tKoUSN69+6Np6cnLVu2LHZNQkIC/v7+HD9+XOO4SqXCz88PFxeXYteMGzeOtm3b4u/vX+zc8uXLefz4sXouxMqVK8nKyiqz3y1atCjX/QkhhBBCiOojScQLbNKkSeplWbOzs7l69SobN25kzJgxREdHa3xhP3LkCL6+vhgZGVW63YKCAjZt2sTu3btxcHBQH2/evHml664tShtfKJRDybuEColPbSAxerKU66OHmTXdDSGqhSQRLzB9fX2MjY3Vn1u1aoWlpSUjRowgMDCQDRs2kJ6ejp+fH/Hx8XTo0IG0tLRKtZmSksLixYu5du1aiW87XhTT/L7kTpr8wyKEEM+zuEAnZOqyeF7JxGqhwdDQEGdnZ7766itycnK4efMmt27dIjo6Wr3fQ2X88MMPdOjQgfj4eFq1avVUdahUKmJjY3Fzc8PKyooBAwZw9OhRjh07xrBhw+jcuTMTJkzgxo0b6mtSU1Px8vKie/fu2Nra4uHhwdWrV9Xnc3JyWLduHfb29lhYWGBjY8OcOXO4d+8eADdv3kSlUpGQkICLiwsWFhbY29uzZ8+eSj0PIYQQQojaSN5EiGLMzMzIysri6tWrmJmZERkZCTwZ0lRZrq6ula4DYM2aNaxatYoPPviANWvWMH/+fNq1a8fatWsB8PT0ZOPGjQQFBZGRkcHEiRMxMzNj586daGtr8/HHHzNmzBji4uJo3rw5AQEBHD16FH9/f1q1asWFCxdYuHAhW7ZsYdmyZep2161bx7JlyzA1NeVf//oXK1asoHfv3hXaV0MIIcSLQ8nDupTcN/GEkmMkSYQopmHDhgA8eqTcl7COjo4MGTIEABcXF7755hu8vb3p0qULAIMGDeLEiRMAHDx4kLS0NAIDA9HV1QVg9erVJCUlsXfvXmbPno2lpSWDBw+mR48eAJiYmNCnTx8uXryo0e6kSZMYMGAAAD4+Puzbt4+ff/5ZkgghhBAlqul1/kujhD0IRNmUEKOy9omQJEIUU5Q8FCUT/6ROnToUFBSUeK6wsFC9Y3VVeuWVV9Q/16tXD4C2bduqj+np6ZGTkwPA+fPnSU9PVycIRbKzs0lJSQHAycmJ77//ng0bNnD16lUuX77MlStX1ElJkfbt26t/NjB48tuB3NzcqrsxIYQQQohaQJIIUcy5c+eoV68e7dq1K1d5Q0NDHj58WOK5tLQ0DA0Nq7B3TxS9Ufg7LS2tEssWFBTQpk0bwsLCip0rSkCWLVvG4cOHGTlyJP379+fdd98lIiJCYxlcgLp16xarQzZ9F0IIIcSLRpIIoSE9PZ2YmBiGDx9e4hf1klhaWpKcnKxeLrZIamoqN27cwNLSsjq6Wm6mpqbExMTQoEEDmjRpAkBeXh7e3t4MHTqUXr16sWfPHtavX4+jo6P6usuXL5eYNFSF8CWDq6VeIYQQypGVnVfTXRCi2kgS8QLLzMzk7t27wP8b2vPRRx+hra2Np6dnuetxd3dn0qRJrF69mtGjR1OvXj1SUlIIDg5GpVJVyapOleHo6EhYWBizZ8/Gx8cHAwMDQkNDOX78OJ6enjRo0AADAwOOHTuGlZUV2dnZ7Ny5k3PnztGpU6dq6dO9e+kUFMgbDKVSwjhUUTqJj/JJjIR4/kkS8QKLjIxUr7ykr69P8+bNsbOzIzg4mGbNmpW7HhsbG3bs2MHWrVtxc3Pj8ePHNGvWjIEDBzJ79uxqmRNREQYGBuzcuZOAgADc3d3Jz8+nU6dORERE0KFDBwCCg4Px9/fH0dERQ0NDbG1t8fb2JjQ0lMePH9do/4UQQgghlEarUAZ0C/HMyZsIZZPfoiqbxEf5JEbKJzFSPiXEqKzVmWSzOSGEEEIIIUSFyHAmoRgeHh4kJSWVWSYyMhIrK6tn1CMhhBBCCFESSSJEpdjb22ssg6qrq0ujRo3o3bs3np6etGzZstg1CQkJ+Pv7c/z4cY3jX3/9NV5eXgwbNqzYNXPnzsXExAQzMzMA/vzzTwIDA0lMTOTx48eYm5vj7e1Nt27dquzekpOTAejevXuJ5/Py8ujUqRNr167F2dm5ytoVQgghhFA6SSJEpU2aNEm9vGt2djZXr15l48aNjBkzhujoaFq0aKEue+TIEXx9fTEyMiqxriZNmmhsGldET0+PBg0aULduXfLz85k5cyYFBQUEBwdjZGREVFQUU6dOZd++ferJ0pWRn5+Pq6sra9euLTWJqIzSxhcK5TA2NqjpLogySHyU73mIUVZ2Ho8eZtZ0N4RQJEkiRKXp6+tjbGys/tyqVSssLS0ZMWIEgYGBbNiwgfT0dPz8/IiPj6dDhw6kpaU9dXtnz57lzJkzxMXFYWpqCjzZLC4xMZEDBw7g5eVV6Xuq7vUGpvl9yZ00+YdJCCGULC7QCZl6LETJZGK1qBaGhoY4Ozvz1VdfkZOTw82bN7l16xbR0dGV3jeiTZs2bN26lY4dO6qPaWlpoaWlxYMHD8pdz7fffouzszOdO3emZ8+eLFiwgL/++gtAvT/EwoULmThxIgC3b99m5syZWFtb069fPxISEip1H0IIIYQQtZW8iRDVxszMjKysLK5evYqZmZl6T4ojR45Uqt7GjRtjZ2encSwhIYFr166xcOHCctVx//593nvvPXx9fbGzs+P27dv4+Piwbt061qxZw/Hjx3njjTdYtGgRTk5O5OXl4e7uTv369YmKiiInJ4fly5dX6j6EEEIo3/MwLKs0z/O9PS+UHCNJIkS1adiwIQCPHlXvy+Dk5GQWL17M4MGD6d+/f7muSU1NJScnh5YtW2JiYoKJiQmhoaHk5+cDT+ZmwJON6oyMjPjuu++4dOkShw4don379gCsWbOG0aNHV89NCSGEUISaXqe/uihhDwJRNiXEqKx9IiSJENWmKHkoSib+SZ06dSgoKCjxXGFhYYk7Xx8+fBgfHx9sbGzYsGFDuftmbm7OiBEj8PDwwNjYmN69e2NnZ8egQYNKLH/x4kXq16+vTiAALCws0NXVLXebQgghhBDPC5kTIarNuXPnqFevHu3atStXeUNDQx4+fFjiubS0NAwNDTWObdu2jblz5zJ06FBCQ0N56aWXKtS/wMBADh06hLu7Ow8ePMDHx4cpU6aUOqn6f49raWlJEiGEEEKIF5K8iRDVIj09nZiYGIYPH17uL9qWlpYkJyerl4stkpqayo0bN7C0tFQfCwsLIzAwkHfeeYd58+ZVuH+nT58mISGBRYsW0b59eyZPnkxcXBzvv/8+d+7coWnTphrlzc3NycjI4MKFC6hUKgBSUlLIyMiocNsA4UsGP9V1Qgghnp2s7Lya7oIQiiVJhKi0zMxM7t69CzzZJyIlJYWPPvoIbW1tPD09y12Pu7s7kyZNYvXq1YwePZp69eqRkpJCcHAwKpVKvarT2bNnCQoKwsnJCTc3N3Xb8GQ/CQODf56E1KBBA3bt2oWuri4uLi7k5uZy8OBBWrduTdOmTdHR0UFPT4/ff/+de/fuYWtri4WFBb6+vixfvhwdHR1WrVqFtvbTvcy7dy+dgoLqXUZWPD0ljEMVpZP4KJ/ESIjnnyQRotIiIyPVKy/p6+vTvHlz7OzsCA4OplmzZuWux8bGhh07drB161bc3Nx4/PgxzZo1Y+DAgcyePVs9JyI+Pp6CggJiY2OJjY3VqMPBwaFccyM6duxISEgIW7ZsYdeuXWhra9OjRw/Cw8PR0dEB4J133mH79u18//337N+/n23btvHBBx8wdepU9PX1mTlzJtevXy/3/QkhhBBCPC+0Cqt7Vy0hRDHyJkLZ5LeoyibxUT6JkfJJjJRPCTEqa3UmmVgthBBCCCGEqBAZziSeOx4eHiQlJZVZJjIyEisrq2fUIyGEEEKI54skEaJSJk6cSPPmzUuchxAUFERcXBzHjh3TOJ6QkIC/vz/Hjx/XOK5SqfDz88PFxaVYXePGjaNt27b4+/sD8OeffxIYGEhiYiKPHz/G3Nwcb29vunXrxsqVK8nKyiqz3y1atPjHe0tOTgage/fuJZ7Py8ujU6dOrF27Fmdn53+sTwghhBDieSFJhHimjhw5gq+vL0ZGRk9dR35+PjNnzqSgoIDg4GCMjIyIiopi6tSp7Nu3jw4dOlS6n/n5+bi6urJ27dpSk4jKKG18oVAOY+N/XuVL1ByJj/LV1hhlZefx6GFmTXdDCMWTJEI8E+np6fj5+REfH0+HDh1IS0t76rrOnj3LmTNniIuLw9TUFIBly5aRmJjIgQMH8PLyqnR/q3u9gWl+X3InTf6REkIIpYkLdEKmGwvxz2RitXgmbt68ya1bt4iOjlbv9/C02rRpw9atW+nYsaP6mJaWFlpaWjx48KDc9Xz77bc4OzvTuXNnevbsyYIFC/jrr78A6NSpEwALFy5k4sSJANy+fZuZM2dibW1Nv379SEhIqNR9CCGEEELUVvImQjwTZmZm6r0kjhw5Uqm6GjdujJ2dncaxhIQErl27xsKFC8tVx/3793nvvffw9fXFzs6O27dv4+Pjw7p161izZg3Hjx/njTfeYNGiRTg5OZGXl4e7uzv169cnKiqKnJwcli9fXqn7EEIIoUy1dShWRb0o91mbKTlGkkSIWi85OZnFixczePBg+vfvX65rUlNTycnJoWXLlpiYmGBiYkJoaCj5+fkANGnSBAADAwOMjIz47rvvuHTpEocOHaJ9+/YArFmzhtGjR1fPTQkhhKgxNb02/7OghD0IRNmUEKOy9omQJEJUSp06dUqdP1BYWKjeZbq8dRUUFFSorsOHD+Pj44ONjU25dqouYm5uzogRI/Dw8MDY2JjevXtjZ2fHoEGDSix/8eJF6tevr04gACwsLNDV1S13m0IIIYQQzwuZEyEqxdDQkIcPH5Z47sGDBxgaGlZJXWlpacXq2rZtG3PnzmXo0KGEhoby0ksvlb/jQGBgIIcOHcLd3Z0HDx7g4+PDlClTykyK/k5LS0uSCCGEEEK8kORNhKgUS0tLtm/fTlZWFnp6eurjhYWF/PTTT9ja2laoruTkZKZPn65xPDU1lRs3bmBpaak+FhYWRmBgIO+88w7z5s2rcL9Pnz5NQkICixYton379kyePJm4uDjef/997ty5Q9OmTTXKm5ubk5GRwYULF1CpVACkpKSQkZFR4bYBwpcMfqrrhBBCVK+s7Lya7oIQtYIkEaJSnJ2d+fjjj5k1axbvvvsuL7/8Mrdv3yYqKorU1FSmTJlS7rrc3d2ZNGkSq1evZvTo0dSrV4+UlBSCg4NRqVTqVZ3Onj1LUFAQTk5OuLm5cffuXXUdenp6GBj88ySkBg0asGvXLnR1dXFxcSE3N5eDBw/SunVrmjZtio6ODnp6evz+++/cu3cPW1tbLCws8PX1Zfny5ejo6LBq1Sq0tZ/uZd69e+kUFFTvMrLi6SlhHKooncRH+SRGQjz/JIkQldKoUSP27NlDcHAwc+fOVQ87srGxYc+ePbRu3brcddnY2LBjxw62bt2Km5sbjx8/plmzZgwcOJDZs2er50TEx8dTUFBAbGwssbGxGnU4ODiUa25Ex44dCQkJYcuWLezatQttbW169OhBeHg4Ojo6ALzzzjts376d77//nv3797Nt2zY++OADpk6dir6+PjNnzuT69esVeFpCCCGEEM8HrcLq3lVLCFGMvIlQNvktqrJJfJRPYqR8EiPlU0KMylqdSSZWCyGEEEIIISpEkghRKRMnTuT9998v8VxQUBD29vbFjickJPDGG29oHLt58yYqlUrjP0tLS4YMGUJQUBCZmZka5f/8808WLlxI37596dq1K66urvz0008AeHh4YG1tXeZ/Z86c+cd7S05OJjk5udTzeXl5qFQq9u3b9491CSGEEEI8T2ROhHimjhw5gq+vL0ZGRiWe37RpE927d6ewsJCMjAxOnz5NQEAAp06dIiIigjp16pCfn8/MmTMpKCggODgYIyMjoqKimDp1Kvv27WPlypVkZWWV2Y8WLVqUeT4/Px9XV1fWrl1L9+7dn/Z2hRBCCCGeS5JEiGciPT0dPz8/4uPj6dChA2lpaSWWMzQ0xNjYWP25Xbt2tGvXjrfffpuYmBhcXFw4e/YsZ86cIS4uDlNTUwCWLVtGYmIiBw4cwMvLq9L9re6pQqWNLxTKYWz8z6t8iZoj8VE+pcUoKzuPRw8z/7mgEKJcJIkQz8TNmze5desW0dHRHDlyhOjo6HJf26VLF7p27Up8fDwuLi60adOGrVu30rFjR3UZLS0ttLS0ePDgQbnr/fbbbwkODiYlJQV9fX369evHokWLMDQ0pFOnTgAsXLiQmJgYoqKiuH37NitXruSHH36gYcOGT7U/RZFpfl9yJ03+MRNCiGclLtAJmUYsRNWRORHimTAzMyMyMhJzc/Onvv63334DoHHjxtjZ2Wns0ZCQkMC1a9ews7MrV33379/nvffeY9SoUSQkJLBlyxaSk5NZt24dAMePHwdg0aJFhISEkJeXh7u7O/fv3ycqKoqgoCC2bdv2VPcihBBCCFHbyZsIUSs0bNiQ9PT0Es8lJyezePFiBg8eTP/+/ctVX2pqKjk5ObRs2RITExNMTEwIDQ0lPz8fgCZNmgBgYGCAkZER3333HZcuXeLQoUO0b98egDVr1jB69OgquDshhBDPgtKGWNU0eR7Kp+QYSRIhKqVOnTqlzh8oLCxUbxBXWY8ePSpxJ+rDhw/j4+ODjY1NuTaZK2Jubs6IESPw8PDA2NiY3r17Y2dnx6BBg0osf/HiRerXr69OIAAsLCzQ1dWt+M0IIYSoETW95r6SKGEPAlE2JcRI9okQ1cbQ0JCHDx+WeO7BgwcYGhpWSTvnzp1Tz1Mosm3bNubOncvQoUMJDQ3lpZdeqlCdgYGBHDp0CHd3dx48eICPjw9TpkwpMyn6Oy0tLUkihBBCCPFCkiRCVIqlpSW//PJLsSVVCwsL+emnn7C0tKx0G2fOnOH06dM4Ojqqj4WFhbFhwwamT59OQEBAhb/Mnz59mjVr1tC+fXsmT55MWFgYa9eu5ccff+TOnTtoaWlplDc3NycjI4MLFy6oj6WkpJCRkVG5mxNCCCGEqIVkOJOoFGdnZz7++GNmzZrFu+++y8svv8zt27eJiooiNTWVKVOmVKi+v/76i7t37wLw+PFjTp8+zcaNG+nduzcODg4AnD17lqCgIJycnHBzc1OXB9DT0ytx2NP/atCgAbt27UJXVxcXFxdyc3M5ePAgrVu3pmnTpujo6KCnp8fvv//OvXv3sLW1xcLCAl9fX5YvX46Ojg6rVq3SmNxdEeFLBj/VdUIIIZ5OVnZeTXdBiOeKJBGiUho1asSePXsIDg5m7ty5pKWlYWhoiI2NDXv27KF169YVqm/u3Lnqnxs2bEirVq2YPHkyrq6u6i/s8fHxFBQUEBsbS2xsrMb1Dg4O5Zob0bFjR0JCQtiyZQu7du1CW1ubHj16EB4ejo6ODgDvvPMO27dv5/vvv2f//v1s27aNDz74gKlTp6Kvr8/MmTO5fv16he6vyL176RQUVO9eFOLpKWEcqiidxEf5JEZCPP+0Cqt7Vy0hRDGSRCibfAFSNomP8kmMlE9ipHxKiJFMrBZCCCGEEEJUGRnOJJ47Hh4eJCUllVkmMjISKyurZ9QjIYQQQojniyQRL6iJEyfSvHnzEucPBAUFERcXx7FjxzSOJyQk4O/vr97NGeDmzZsMGDBAo1zdunVp2bIlQ4cOxcPDA319ffW5P//8k8DAQBITE3n8+DHm5uZ4e3vTrVu3Kru3lStXFlst6n+1aNGiytoTQgghhHjRSBIhyuXIkSP4+vpiZGRU4vlNmzbRvXt3CgsLycjI4PTp0wQEBHDq1CkiIiKoU6cO+fn5zJw5k4KCAoKDgzEyMiIqKoqpU6eyb98+OnToUCV9bd68eZXUI4QQQgghSiZJhChTeno6fn5+xMfH06FDB9LS0kosZ2hoiLGxsfpzu3btaNeuHW+//TYxMTG4uLhw9uxZzpw5Q1xcHKampgAsW7aMxMREDhw4gJeX1zO5JyUobZKSUA5j439eKljUHImP8j1tjLKy83j0MLOKeyOEqGqSRIgy3bx5k1u3bhEdHc2RI0eIjo4u97VdunSha9euxMfH4+LiQps2bdi6dSsdO3ZUl9HS0kJLS4sHDx6Uu16VSkVAQACff/45p0+fxtjYmEWLFqGlpcX69ev5448/sLS0ZO3ateolZlNTU/H39+e7775DR0cHa2trfH19adeuHQA5OTkEBQXxxRdfcOfOHfT19enVqxfLly+nSZMm6mFbQUFBfPzxx/z66680a9aMGTNm8Pbbb5e770Wm+X3JnTT5R1IIIf5XXKATsmaQEMonqzOJMpmZmREZGYm5uflTX//bb78B0LhxY+zs7DQ2aEtISODatWvY2dlVqN41a9bg6upKXFwcHTt2ZP78+WzevJm1a9cSGRnJjRs32LhxIwAZGRlMnDiR/Px8du7cSVRUFI0aNWLMmDGkpqYCEBAQwOHDh1m7di1ffPEF69atIykpiS1btmi0u27dOjw8PDh06BC2trasWLGCGzduPNWzEUIIIYSoreRNhKhWDRs2JD09vcRzycnJLF68mMGDB9O/f/8K1evo6MiQIUMAcHFx4ZtvvsHb25suXboAMGjQIE6cOAHAwYMHSUtLIzAwEF1dXQBWr15NUlISe/fuZfbs2VhaWjJ48GB69OgBgImJCX369OHixYsa7U6aNEk9kdzHx4d9+/bx888/V3hTPSGEEKWT4WrPhjxn5VNyjCSJeEHVqVOH0vYZLCwspE6dqvmj8ejRIwwMiv8FOHz4MD4+PtjY2JRrh+n/9corr6h/rlevHgBt27ZVH9PT0yMnJweA8+fPk56erk4QimRnZ5OSkgKAk5MT33//PRs2bODq1atcvnyZK1euqJOSIu3bt1f/XHRfubm5Fe6/EEKI0tX0BlsvAiVsZCbKpoQYlbXZnCQRLyhDQ0MePnxY4rkHDx5gaGhYJe2cO3eOTp06aRzbtm0bgYGBODo6snr1avXbgYoo6RotLa0SyxYUFNCmTRvCwsKKnStKQJYtW8bhw4cZOXIk/fv359133yUiIoL//ve/GuXr1q1brA7Z9F0IIYQQLxpJIl5QlpaWbN++naysLPT09NTHCwsL+emnn7C1ta10G2fOnFEv9VokLCyMwMBA3nnnHebNm1fpNsrD1NSUmJgYGjRoQJMmTQDIy8vD29uboUOH0qtXL/bs2cP69etxdHRUX3f58uUSkwYhhBBCiBedJBEvKGdnZz7++GNmzZrFu+++y8svv8zt27eJiooiNTWVKVOmVKi+v/76i7t37wLw+PFjTp8+zcaNG+nduzcODg4AnD17lqCgIJycnHBzc1OXhyfDj0oa9lQVHB0dCQsLY/bs2fj4+GBgYEBoaCjHjx/H09OTBg0aYGBgwLFjx7CysiI7O5udO3eW+BalqoQvGVwt9QohRG2XlZ1X010QQpSDJBEvqEaNGrFnzx6Cg4OZO3cuaWlpGBoaYmNjw549eyo8UXju3Lnqnxs2bEirVq2YPHkyrq6u6tWY4uPjKSgoIDY2ltjYWI3rHRwcnmpuRHkYGBiwc+dOAgICcHd3Jz8/n06dOhEREaHe4C44OBh/f38cHR0xNDTE1tYWb29vQkNDefz4cZX36d69dAoKZBiUUilhHKooncRH+SRGQjz/tAplQLcQz5wkEcomX4CUTeKjfBIj5ZMYKZ8SYlTWxGrZJ0IIIYQQQghRITKcSSiGh4cHSUlJZZaJjIzEysrqGfVICCGEEEKURJKIF5S9vb3G8qW6uro0atSI3r174+npScuWLYtdk5CQgL+/P8ePH1cfu3nzpnrztSJ169alZcuWDB06FA8PD/T19dXn/vzzTwIDA0lMTOTx48eYm5vj7e1Nt27dWLlyJVlZWWX2u0WLFk97y0IIIYQQoopIEvECmzRpEtOnTweebLx29epVNm7cyJgxY4iOjtb4wn7kyBF8fX0xMjIqsa5NmzbRvXt3CgsLycjIUC/teurUKSIiIqhTpw75+fnMnDmTgoICgoODMTIyIioqiqlTp7Jv3z71JGchhBBCCKFskkS8wPT19TE2NlZ/btWqFZaWlowYMYLAwEA2bNhAeno6fn5+xMfH06FDB9LS0kqsy9DQUKOudu3a0a5dO95++21iYmJwcXHh7NmznDlzhri4OExNTYEnm7wlJiZy4MABvLy8qveGFaS0SUpCOYyNq2fJYVE1JD7K9zQxysrO49HDzGrojRCiqkkSITQYGhri7OzMjh07yMnJ4ebNm9y6dYvo6GiOHDlCdHR0uevq0qULXbt2JT4+HhcXF9q0acPWrVvp2LGjuoyWlhZaWlo8ePCg3PWqVCoCAgL4/PPPOX36NMbGxixatAgtLS3Wr1/PH3/8gaWlJWvXrlUvVZuamoq/vz/fffcdOjo6WFtb4+vrS7t27QDIyckhKCiIL774gjt37qCvr0+vXr1Yvnw5TZo0UQ/bCgoK4uOPP+bXX3+lWbNmzJgxg7fffrvcfS8yze9L7qTJP5RCCPF3cYFOyHpBQtQOsjqTKMbMzIysrCyuXr2KmZkZkZGRmJubP3Vdv/32GwCNGzfGzs5OvW8EPJlnce3aNezs7CpU75o1a3B1dSUuLo6OHTsyf/58Nm/ezNq1a4mMjOTGjRts3LgRgIyMDCZOnEh+fj47d+4kKiqKRo0aMWbMGFJTUwEICAjg8OHDrF27li+++IJ169aRlJTEli1bNNpdt24dHh4eHDp0CFtbW1asWMGNGzee6tkIIYQQQtRW8iZCFNOwYUMAHj2q/O+DGjZsSHp6eonnkpOTWbx4MYMHD6Z///4VqtfR0ZEhQ4YA4OLiwjfffIO3tzddunQBYNCgQZw4cQKAgwcPkpaWRmBgILq6ugCsXr2apKQk9u7dy+zZs7G0tGTw4MH06NEDABMTE/r06cPFixc12p00aZJ6IrmPjw/79u3j559/rvDmfEIIIUomQ9WeHXnWyqfkGEkSIYopSh6KkonK1mVgUPwvwOHDh/Hx8cHGxuapdqp+5ZVX1D/Xq1cPgLZt26qP6enpkZOTA8D58+dJT09XJwhFsrOzSUlJAcDJyYnvv/+eDRs2cPXqVS5fvsyVK1fUSUmR9u3bq38uuq/c3NwK918IIUTJanpzrReFEjYyE2VTQozK2mxOkghRzLlz56hXr556vkBl6+rUqZPGsW3bthEYGIijoyOrV69Wvx2oiJKu0dLSKrFsQUEBbdq0ISwsrNi5ogRk2bJlHD58mJEjR9K/f3/effddIiIiNJbBhSfL1/4v2fRdCCGEEC8aSSKEhvT0dGJiYhg+fPhTfbn/uzNnzqiXei0SFhZGYGAg77zzDvPmzatsd8vF1NSUmJgYGjRoQJMmTQDIy8vD29uboUOH0qtXL/bs2cP69etxdHRUX3f58uUSkwYhhBBCiBedJBEvsMzMTO7evQv8v6E9H330Edra2nh6elaorr/++ktd1+PHjzl9+jQbN26kd+/eODg4AHD27FmCgoJwcnLCzc1NXR6eDD8qadhTVXB0dCQsLIzZs2fj4+ODgYEBoaGhHD9+HE9PTxo0aICBgQHHjh3DysqK7Oxsdu7cWeJblKoSvmRwtdQrhBC1WVZ2Xk13QQhRTpJEvMAiIyOJjIwEnuwZ0bx5c+zs7AgODqZZs2YVqmvu3Lnqnxs2bEirVq2YPHkyrq6u6tWY4uPjKSgoIDY2ltjYWI3rHRwcnmpuRHkYGBiwc+dOAgICcHd3Jz8/n06dOhEREaHe4C44OBh/f38cHR0xNDTE1tYWb29vQkNDefz4cZX36d69dAoKZBiUUilhHKooncRH+SRGQjz/tAplQLcQz5wkEcomX4CUTeKjfBIj5ZMYKZ8SYlTWxGrZJ0IIIYQQQghRITKcSSiGh4cHSUlJZZaJjIzEysrqGfVICCGEEEKURJIIUSn29vYay6Dq6urSqFEjevfujaenJy1btix2TUJCAv7+/hw/flzj+Ndff42XlxfDhg0rds3cuXMxMTHBzMys2Lnly5fz+PHjKp9TkZycDED37t1LPJ+Xl0enTp1Yu3Ytzs7OVdq2EEIIIYSSyXAmUWmTJk0iMTGRxMREDh8+zNq1a7l06RJjxozh1q1bGmWPHDmCr69vqXU1adKEtm3bFvtPT0+PBg0aaCy5WlBQwMaNG9m9e3eV31N+fj6urq5cv369yusWQgghhKjt5E2EqDR9fX2MjY3Vn1u1aoWlpSUjRowgMDCQDRs2kJ6ejp+fH/Hx8XTo0IG0tLRKtZmSksLixYu5du1aiW87Kqu61xsobZKSUA5j4+pZclhUDYmP8hkbG5CVncejh5k13RUhRDWQJEJUC0NDQ5ydndmxYwc5OTncvHmTW7duER0dzZEjR4iOjq5U/T/88AMdOnRgy5YtGsvLVsS3335LcHAwKSkp6Ovr069fPxYtWoShoaF6f4iFCxcSExNDVFQUt2/fZuXKlfzwww80bNiwUpvlTfP7kjtp8g+rEOL5FhfohKz/I8TzSZIIUW3MzMzIysri6tWrmJmZqfekOHLkSKXrdnV1rdT19+/f57333sPX1xc7Oztu376Nj48P69atY82aNRw/fpw33niDRYsW4eTkRF5eHu7u7tSvX5+oqChycnJYvnx5pe9DCCGEEKI2kiRCVJuGDRsC8OiR8n4PlZqaSk5ODi1btsTExAQTExNCQ0PJz88HnszNgCcb1RkZGfHdd99x6dIlDh06RPv27QFYs2YNo0ePrrF7EEKI2kCGnimXxEb5lBwjSSJEtSlKHoqSiX9Sp04dCgoKSjxXWFhInTpV98fV3NycESNG4OHhgbGxMb1798bOzo5BgwaVWP7ixYvUr19fnUAAWFhYoKurW2V9EkKI51FNb5YlSqaEjcxE2ZQQI9lsTtSIc+fOUa9ePdq1a1eu8oaGhjx8+LDEc2lpaRgaGlZh7yAwMJBDhw7h7u7OgwcP8PHxYcqUKaVOqv7f41paWpJECCGEEOKFJEmEqBbp6enExMQwfPjwcn/RtrS0VO/N8HepqancuHEDS0vLKuvf6dOnWbNmDe3bt2fy5MmEhYWxdu1afvzxR+7cuYOWlpZGeXNzczIyMrhw4YL6WEpKChkZGVXWJyGEEEKI2kKGM4lKy8zM5O7duwBkZ2eTkpLCRx99hLa2Np6enuWux93dnUmTJrF69WpGjx5NvXr1SElJITg4GJVKxcCBA6uszw0aNGDXrl3o6uri4uJCbm4uBw8epHXr1jRt2hQdHR309PT4/fffuXfvHra2tlhYWODr68vy5cvR0dFh1apVaGs/XR4evmRwld2LEEIoVVZ2Xk13QQhRTSSJEJUWGRmpXnlJX1+f5s2bY2dnR3BwMM2aNSt3PTY2NuzYsYOtW7fi5ubG48ePadasGQMHDmT27NlVOieiY8eOhISEsGXLFnbt2oW2tjY9evQgPDwcHR0dAN555x22b9/O999/z/79+9m2bRsffPABU6dORV9fn5kzZz71ZnT37qVTUFC9e1GIp6eEcaiidBIf5ZMYCfH80yqs7l21hBDFSBKhbPIFSNkkPsonMVI+iZHyKSFGMrFaCCGEEEIIUWVkOJN47nh4eJCUlFRmmcjISKysrJ5Rj4QQQgghni+SRIgKCQoKIi4ujmPHjgGgUqnw8/NTT07euXMnU6ZMASApKQk3NzeN6/X09GjTpg0jR45k0qRJ6nkORWW//PJL2rZt+4/9uHjxIn/88Qd2dnbFzq1cuZKsrKwyr2/RokV5blcIIYQQQpRAkghRKYmJiRgYPNlNcf/+/fj7+6uTiCJ79uzBxMSEwsJCHj16xPfff8/GjRv57bffWL9+PQDW1tYkJibSuHHjcrU7ffp0Ro8eXWIS0bx588rdlBBCCCGEKJMkEaJSjI2N/7FMo0aN1OWaNWtGhw4daNKkCV5eXrz11lv07t2bunXrlquuIrV9PYDSJikJ5TA2NqjpLogySHxqXlZ2Ho8eZtZ0N4QQNUSSiBfco0ePCAgI4KuvviI3N5dOnToxf/589cZue/bsYfv27aSmptKnTx9efvlljeuLhjPp6OiwZMkS9bG1a9diYmJSarvDhg0jICCA+Ph4evfuXWw405kzZ/D39+fXX39FR0cHW1tbFi9eTMuWLbG3tyc1NZXNmzcTExPDsWPHuHXrFuvXr+fEiRM8fPiQxo0bM2LECN5//310dHTYt28fmzdvZvbs2fzrX//i1q1bmJqasnjxYrp16wZAXl4eW7du5fPPP+fevXu88soreHp60r9/f+DJpnf+/v5899136OjoYG1tja+vb7l35P67aX5fcidN/vEVQtRecYFOyNo+Qry4ZHWmF1hhYSHTp0/n6tWrhIaGsnfvXrp06cK4ceM4f/488fHxrFy5kkmTJhEbG0vnzp3ZtWtXiXUNHz4cX19f4MkQp+HDh5fZtpaWFqampvz222/FzuXn5zNjxgxsbGw4cOAAkZGR3L59m4ULFwLw2WefYWxszNSpU/nss8+AJ5OpHzx4QEREBIcPH8bd3Z2IiAi++uordb137tzh3//+N+vXrycmJgYAX19f9VuNNWvW8Mknn/D+++8TFxfHwIEDee+99/j111/JyMhg4sSJ5Ofns3PnTqKiomjUqBFjxowhNTW1gk9eCCGEEKJ2kzcRL7AffviBU6dOceLECfVcBG9vb06ePMknn3zClStXGDp0KBMmTABgxowZnDx5kkuXLhWrS09PjwYNngzRKe+wJAMDA65evVrseHp6OmlpaTRr1gwTExNat27Npk2b+PPPPwFo3Lgx2tra1KtXj8aNG5OVlYWTkxNDhgxRv/2YNGkS27dv5+LFiwwdOhSA3Nxcli1bhoWFhfp+5syZw927d6lXrx579+5l4cKF6gTovffeo6CggIyMDA4ePEhaWhqBgYHo6uoCsHr1apKSkti7dy+zZ88u1z0LIcTzpKxhZTLkTPkkRsqn5BhJEvECO3fuHAADBgzQOJ6Tk0N2dja///67+gt4EWtr6xKTiKeRnp6unpT9d4aGhri7u/PBBx/w4Ycf0rNnT9544w2GDRtWYj16enpMmDCBw4cP8/HHH3Pt2jUuXLjAnTt3yM/P1yjbvn179c9Fbefm5nLlyhVyc3Pp3LmzRvk5c+YAEB8fT3p6Oj169NA4n52dTUpKSsVvXgghngOlbYSlhE2yRNkkRsqnhBiVtdmcJBEvsIKCAvT19dm/f3+xc3Xr1mX48OHFJjAXLclaWYWFhZw7dw57e/sSz7///vuMHz+eb7/9lhMnTuDn58e2bdvYv38/enp6GmUzMjKYMGECGRkZDBs2jLfeegtLS0v1G5T/va+S+lL0dkFLS6vE/hQUFNCmTRvCwsKKnatXr94/3q8QQgghxPNEkogXmKmpKZmZmWRmZmJmZqY+vnTpUlQqFWZmZpw6dUrjml9++aVK2j58+DB3797F0dGx2LnLly8TGRnJokWLGDduHOPGjePkyZPquRpdu3bV+LKfmJjIuXPnOH78uHp51wcPHnDv3r1y96dt27bo6upy9uxZOnXqpD4+ceJEevXqhampKTExMTRo0IAmTZoATyZie3t7M3To0H+cAyKEEEII8TyRJOIF1rdvX8zNzZk3bx5LliyhZcuW7N69m88//5zw8HCmTZvG7Nmz2b59OwMHDuTbb7/lyJEjNGvWrMT66tevD8CZM2fo0KGD+nhaWhr16tVT7xPxww8/EBQUxKhRo+jevXuxeoyMjIiPjycrK4t33nkHbW1t9u3bR8OGDenYsaO6ratXr5KamqpeMerAgQMMHz6cW7dusXHjRnJzc8nJySnXs9DX12fChAmEhITQtGlTTE1NiYuL49SpUyxcuJDWrVsTFhbG7Nmz8fHxwcDAgNDQUI4fP46np2eFnjtA+JLBFb5GCCGUJCs7r6a7IISoQZJEvMB0dHSIiIhg/fr1eHl5kZmZSYcOHdi8eTO9evUCICAggC1bthAcHIy1tTWTJ08mISGhxPr69OlD165dGT9+PPPmzeO1114D4O2331aXadSoEe3atcPHx4fRo0eXWE/jxo3Zvn07GzduZMyYMeTn52NlZUVERAQNGzYEYOrUqaxbt47ExEROnDjBwoUL2bFjByEhITRv3pzhw4fTokULzp49W+7n4e3tTZ06dVi5ciV//fUXr776KqGhoer72LlzJwEBAbi7u5Ofn0+nTp2IiIjQSJjK6969dAoKavdeF88zJYxDFaWT+AghRM3TKqztu3YJUQtJEqFs8iVV2SQ+yicxUj6JkfIpIUZlTayWfSKEEEIIIYQQFSJJhBBCCCGEEKJCJIkQlWJvb49KpVL/Z2FhQd++fVmwYAF//PFHidckJCTwxhtvaBy7efOmRj0qlQpLS0uGDBlCUFAQmZmZGuX//PNPFi5cSN++fenatSuurq789NNPVXpvycnJJCcnl3o+Ly8PlUrFvn37qrRdIYQQQgilkyRCVNqkSZNITEwkMTGRw4cPs3btWi5dusSYMWO4deuWRtkjR47g6+tbal2bNm0iMTGR7777jri4OGbOnEl0dDQzZswgL+/JSiD5+fnMnDmTixcvEhwczGeffYapqSlTp06tso3f8vPzcXV15fr161VSnxBCCCHE80RWZxKVpq+vj7Gxsfpzq1atsLS0ZMSIEQQGBrJhwwbS09Px8/MjPj6eDh06kJaWVmJdhoaGGnW1a9eOdu3a8fbbbxMTE4OLiwtnz57lzJkzxMXFYWpqCsCyZctITEzkwIEDeHl5Vfqeqnu9gdImKQnlMDYuvpu6UA6Jz9PJys7j0cPMfy4ohBD/QJIIUS0MDQ1xdnZmx44d5OTkcPPmTW7dukV0dDRHjhwhOjq63HV16dKFrl27Eh8fj4uLC23atGHr1q3qPSPgyU7TWlpaPHjwoNz1fvvttwQHB5OSkoK+vj79+vVj0aJFGBoaqjecW7hwITExMURFRXH79m1WrlzJDz/8QMOGDZk3b1652/pf0/y+5E6a/EMuhHi24gKdkPV4hBBVQYYziWpjZmZGVlYWV69exczMjMjISMzNzZ+6rt9++w14so+EnZ0d2tr/749vQkIC165dw87Orlz13b9/n/fee49Ro0aRkJDAli1bSE5OZt26dQAcP34cgEWLFhESEkJeXh7u7u7cv3+fqKgogoKC2LZt21PdixBCCCFEbSdvIkS1KdoY7tGjyv/eq2HDhqSnp5d4Ljk5mcWLFzN48GD69+9frvpSU1PJycmhZcuWmJiYYGJiQmhoKPn5+QA0adIEAAMDA4yMjPjuu++4dOkShw4don379gCsWbOm1A3zhBBCqZ7VUDAZcqZ8EiPlU3KMJIkQ1aYoeShKJipbl4FB8b9Ihw8fxsfHBxsbGzZs2FDu+szNzRkxYgQeHh4YGxvTu3dv7OzsGDRoUInlL168SP369dUJBICFhQW6uroVvxkhhKhBz2LzKiVskiXKJjFSPiXESDabEzXi3Llz1KtXj3bt2lVJXUXzFIps27aNuXPnMnToUEJDQ3nppZcqVGdgYCCHDh3C3d2dBw8e4OPjw5QpU0qdVP2/x7W0tCSJEEIIIcQLSd5EiGqRnp5OTEwMw4cPr/QX7TNnznD69GkCAgLUx8LCwggMDOSdd955qgnOp0+fJiEhgUWLFtG+fXsmT55MXFwc77//Pnfu3KFp06Ya5c3NzcnIyODChQuoVCoAUlJSyMjIeKp7Cl8y+KmuE0KIysjKzqvpLgghnhOSRIhKy8zM5O7duwBkZ2eTkpLCRx99hLa2Np6enhWq66+//lLX9fjxY06fPs3GjRvp3bs3Dg4OAJw9e5agoCCcnJxwc3NTlwfQ09MrcdjT/2rQoAG7du1CV1cXFxcXcnNzOXjwIK1bt6Zp06bo6Oigp6fH77//zr1797C1tcXCwgJfX1+WL1+Ojo4Oq1at0pjcXRH37qVTUFC9y8iKp6eEV8iidBIfIYSoeZJEiEqLjIwkMjISeLJnRPPmzbGzsyM4OJhmzZpVqK65c+eqf27YsCGtWrVi8uTJuLq6qr+wx8fHU1BQQGxsLLGxsRrXOzg4lGtuRMeOHQkJCWHLli3s2rULbW1tevToQXh4ODo6OgC88847bN++ne+//579+/ezbds2PvjgA6ZOnYq+vj4zZ86UzeiEEEII8ULSKqzuXbWEEMXImwhlk990K5vER/kkRsonMVI+JcRIJlYLIYQQQgghqowMZxLPHQ8PD5KSksosExkZiZWV1TPqkRBCCCHE80XeRAjFycvLQ6VSsW/fvnKVV6lUREdHA5CUlMTXX39NRkZGif9t27aN/fv3Y2ZmVp23IIQQQgjxXJM3EeK5tGfPHkxMTIodb9KkyVOvqFSVShtfKJRDybuEimcTn6zsPB49zKz2doQQojaSJEI8lxo1aoSxsXFNd6NU0/y+5E6afDkRQsniAp2QaadCCFGymv+VrHjh3b59m5kzZ2JtbU2/fv1ISEjQOP/111/j7OyMlZUVgwYNYtOmTeTk5Dx1ezdv3kSlUhEaGkqfPn2ws7Pj/v37PHr0iKVLl9KzZ0+6deuGm5sbZ8+e1bg2OjqawYMH07lzZ6ZPn054eDhvvPHGU/dFCCGEEKI2kjcRokbl5eXh7u5O/fr1iYqKIicnh+XLl6vPHz9+HE9PT3x9fenTpw/Xr1/ngw8+4MqVKwQHB1eq7ZiYGD7++GMyMzNp1KgR48aNQ1dXl9DQUAwMDIiNjWXcuHHs3buX1157jQMHDrB8+XIWLlxInz59OHToEMHBwRgZGVXyKQghhBBC1C6SRIgadeLECS5dusShQ4do3749AGvWrGH06NEAhIaGMnr0aMaPHw9AmzZtWLlyJZMmTeLmzZu0atWqxHqdnJzQ0tLSOLZx40b69++v/jx+/HheffVVdT9OnTrFiRMnaNy4MQDe3t6cPHmSTz75BH9/fz755BOGDh3KxIkTAXj33Xf55Zdf+OWXX6rwiQghlETmxjw9eXbKJzFSPiXHSJIIUaMuXrxI/fr11QkEgIWFBbq6ugCcP3+eM2fOEBMToz5ftD9iSkpKqUnERx99RMuWLTWO/e8cidatW6t/PnfuHAADBgzQKJOTk0N2djYAv//+O2+++abG+R49ekgSIcRzrKY3eqqtlLBJliibxEj5lBCjsjabkyRC1Lj/3TRdS0tLnUQUFBQwZcoU9ZuJvytr4nTLli1p27Ztme3q6empfy4oKEBfX5/9+/cXK1e3bl2NcqWdE0IIIYR4UUgSIWqUubk5GRkZXLhwAZVKBTx5w5CRkQHAq6++ypUrVzQSgh9//JEdO3awYsUK6tWrVyX9MDU1JTMzk8zMTI09JJYuXYpKpWLChAm89tprnDx5kmnTpqnP/+/E6/IKXzK40n0WQlSvrOy8mu6CEEIoliQRokbZ2tpiYWGBr68vy5cvR0dHh1WrVqn3cpg+fTqenp6EhIQwYsQIbt++zZIlSzAxManSJVz79u2Lubk58+bNY8mSJbRs2ZLdu3fz+eefEx4eDsCMGTPw8PAgLCyMwYMH85///IcDBw7QpEmTCrd37146BQWF/1xQ1AglvEIWpZP4CCFEzZMlXkWN0tHRYdu2bbRr146pU6fi4eGBk5MTDRs2BGDo0KFs2rSJo0eP4uDgwPz583n99dfZvHlzlfcjIiICKysrvLy8cHR0JCkpic2bN9OrVy8A+vXrx6ZNm4iJiWHEiBF88cUXODo6Vmk/hBBCCCFqA63C/x2QLoQot5CQEKKjozl+/HiFrpM3Ecomv+lWNomP8kmMlE9ipHxKiFFZE6vlTYQQQgghhBCiQiSJEEIIIYQQQlSIJBEvgH379qFSqcr8r6hMdHT0M+lTcnIyKpWKyZMnP5P2qsvs2bMrPJRJCCGEEKK2k9WZXgDDhw+nb9++6s+zZ8+mWbNmLF26VH3MwMCAvn37YmDwbHZG/Oyzz2jfvj0//PADV69epV27ds+kXaUobXyhUI7K7BKalZ3Ho4eZVdgbIYQQQlkkiXgB6OnpaWyspqurS926dYstkfr3MtUpPT2dw4cPs2TJEjZu3Mju3bvx9fV9Jm0rxTS/L7mTJl8yn1dxgU7IdEUhhBDPMxnOVEuoVCp27drF2LFjsbS0ZMSIERw5ckSjzIkTJxg3bhxdunShb9++rFu3jtzc3Aq1UTScydfXlwULFhAUFIStrS1du3ZlxYoVpKamMnPmTDp37oy9vT0HDhxQX19YWMi2bdsYMGAAnTt3xsnJSeN8kYSEBLKysujbty+DBg0iJiaG7Oxs9fkhQ4bg7++vcc2RI0fo1KkTf/75JwBff/01zs7OWFlZMWjQIDZt2kROTo7GvQQHB2Nvb0/v3r25cOECt27dwtvbm169etGpUyf1M8rPz1dfl5iYqK73zTff5LPPPkOlUnHz5k0AcnJyWL9+PX379sXa2poxY8aQmJhY7mcshBBCCPE8kCSiFlm/fj0ODg7ExsbSv39/3nvvPZKTkwH4+eefmTZtGhYWFnz++eesW7eOAwcOsHHjxqdu7+DBg/z111/s3bsXX19f/v3vf+Ps7MzAgQOJiYnBxsaGJUuW8PDhQwCCgoLYtWsXS5YsIS4uDjc3N1asWMGnn36qUe9nn31Gly5daN68OW+++SYPHjzg0KFD6vNvvfUWBw8epKCgQH3swIED9O3bl6ZNm3L8+HE8PT0ZPXo08fHxLF++nEOHDjF//nyNdnbt2sWmTZv417/+hampKR4eHjx48ICIiAgOHz6Mu7s7ERERfPXVVwD8+uuvzJgxA1tbW/bv38+sWbMICAjQqHPhwoV89913rF+/npiYGIYNG4aHhwfffPPNUz9nIYQQQojaRoYz1SIjR47E1dUVgHnz5pGUlMQnn3xC9+7diYyMxNzcnMWLFwPQoUMHPvjgA65fv/7U7dWvX5+lS5eio6ND27Zt2bBhAz179mTUqFEATJo0if3793P16lU6duzIjh07CAgIoH///gC0adOG//73v4SHh6v7/fvvv/Pzzz+zZMkSAGxsbHj55Zf597//zciRI9X3GRwcTFJSEr169eLhw4d8/fXX6oQoNDSU0aNHM378eHU7K1euZNKkSdy8eZNWrVoB4OTkhJWVFQBZWVk4OTkxZMgQTExM1P3fvn07Fy9eZOjQoezYsQMzMzMWLFgAQPv27fnzzz9ZvXo1ANeuXSM+Pp7PPvsMS0tLAKZMmcJvv/1GeHg4dnZ2T/2sxfOnMnMqxD+T56t8EiPlkxgpn5JjJElELWJjY6Px2dramm+//RaAixcvYmtrq3He3t6+Uu21adMGHR0d9Wc9PT3atGmj/vzSSy8BT4b4/P7772RnZ7NgwQIWLlyoLpOXl0dOTg5ZWVno6enx2Wefoa2tzdChQwHQ0tJi+PDhRERE8Ouvv2Jubs7LL79M7969iYuLo1evXhw6dIj69eurv6SfP3+eM2fOEBMTo26naM/ElJQUdRLRunVrjb5PmDCBw4cP8/HHH3Pt2jUuXLjAnTt31MOZzp8/X+wZ/v2Znz9/HgA3NzeNMrm5ueodtoUoUtMbBD3PlLABkyibxEj5JEbKp4QYlbXZnCQRtYiurq7G5/z8fPWX/Dp16qClpVWl7dWpU/yPh7Z2ySPgir7EBwYG8uqrrxY7X7duXXJzczlw4AAFBQX069ev2LX//ve/WbVqFQCjRo1i2bJlrFixgtjYWBwdHdX3X1BQwJQpUxg9enSxdv4+WfzvE8UzMjKYMGECGRkZDBs2jLfeegtLS0smTJigLqOjo0NZG7gXnfvkk0+KJQ2lPRchhBBCiOeRJBG1yNmzZxk0aJD686lTp3jttdeAJ8OXzp49q1F+z549REVFERcXV+UJxv9q3749derU4Y8//mDgwIHq41FRUVy6dIlVq1Zx5MgR7t27x5IlS4r9xn/p0qXExcXh4+NDgwYNGDhwICtWrCA6OpqTJ0+ybNkyddlXX32VK1eu0LZtW/WxH3/8kR07drBixQrq1atXrH+JiYmcO3eO48eP07x5cwAePHjAvXv31GXMzMz4+eefNa77++ei5OjOnTvq4UzwZC6ItrY2np6e5X5e4UsGl7usqH2ysvNqugtCCCFEtZIkohaJioqiY8eOWFpasnfvXi5cuICfnx8A7u7ujBo1io0bN/LWW2/x3//+l5CQEBwdHas9gYAn+0yMHTuWDz/8kIYNG9KtWzeSk5MJCAhgxowZAHz++ec0a9aMsWPHFnurMn36dGbNmkVsbCyurq7UrVuXN998k8DAQF577TXMzMw0ynp6ehISEsKIESO4ffs2S5YswcTEpNiytUVefvll4MkE7eHDh3Pr1i02btxIbm6uelWnqVOnMnLkSDZs2MCoUaNISUkhODgYeDLs6tVXX6V///6sWrWKwsJCVCoVX375JVu3bmXt2rUVel737qVTUFD6Ww9Rs5TwClkIIYRQMkkiapGxY8cSFRXFhQsXMDU1JTw8HHNzcwDMzc3517/+xYcffkhERARNmjRh1KhRzJ49+5n1b+HChTRq1IgPP/yQO3fu0KJFC+bMmYO7uzupqal89913vPvuu8USCHgyf6Ndu3bs3r1bPQnb2dmZXbt24ezsrFF26NChbNq0ia1bt7J161aMjIywt7fn/fffL7VvVlZWLFy4kB07dhASEkLz5s0ZPnw4LVq0UL/BMTU1ZfPmzWzcuJEdO3bwyiuv4OrqSkhIiLrPQUFBBAUFsWLFCv766y/atGnD6tWreeutt6rqMQohhBBCKJ5WYVmDwIViqFQq/Pz8cHFxqemuPLfOnDlDnTp11EPEAOLi4li0aBGnTp0qcY7I05I3EcombyKUTeKjfBIj5ZMYKZ8SYlTWxGqZDSrE/+/XX3/Fzc2No0eP8scff3DixAlCQkJ48803qzSBEEIIIYSo7eSbkRD/vzFjxnD37l3WrFlDamoqTZo04c0332TOnDk13TUhhBBCCEWR4UyiRlV0mNa5c+dYsGABV69exc7OjqCgIHbu3MmUKVMAuHnzJgMGDCj1+k8//ZTu3btXSd8rQ4YzKZsSXiGL0kl8lE9ipHwSI+VTQoxknwjx3NiyZQtaWlrEx8fToEED9u/fj7+/vzqJKLJp06YSkwUjI6Nn1NOylfYX8kWQlZ3Ho4eZNd0NIYQQQlSCJBGiVnnw4AGvvfYa7dq1K7OcoaFhqcu9KsE0vy+5k/ZifpGOC3RCfvclhBBC1G4ysVooytdff42zszNWVlYMGjSITZs2qfdxsLe356effmL//v2oVCr27dvHkiVLANSfy0ulUhEcHIy9vT29e/fmwoUL5OTksH79evr27Yu1tTVjxowhMTFR47ojR47g4OCAlZUVY8eOJTo6GpVKRV6ebC4mhBBCiBeHvIkQinH8+HE8PT3x9fWlT58+XL9+nQ8++IArV64QHBzMZ599xowZMzAxMWHx4sXo6enx119/4e/vT2JiIgYGBvz555/lbm/Xrl1s27aNgoICTE1Nef/997l06RLr16/n5Zdf5uuvv8bDw4PNmzdjZ2dHcnIy7733Hh4eHjg6OpKUlFThTeaEEEIIIZ4HkkQIxQgNDWX06NGMHz8egDZt2rBy5UomTZrEzZs3adWqFXXq1EFPT089VKlBgydzC/536JKHhwc6Ojoax95//331RnYATk5OWFlZAXDt2jXi4+P57LPPsLS0BGDKlCn89ttvhIeHY2dnR1RUFJ07d2bu3LkAtG/fnsuXL/PJJ59U/cN4zhkbG9R0F/5Rbejji0zio3wSI+WTGCmfkmMkSYRQjPPnz3PmzBliYmLUx4oWD0tJSaFVq1blrmvVqlVYW1trHGvcuLHG59atW2u0DeDm5qZRJjc3l4YNGwLw+++/06tXL43ztra2kkQ8hZpebeKfKGFFDFE6iY/ySYyUT2KkfEqIkazOJGqFgoICpkyZwujRo4udq+gk6WbNmtG2bdsyy+jp6al/LkpWPvnkE3XSUERb+/9NHSooKNA4p6urW6F+CSGEEEI8DySJEIrx6quvcuXKFY0v/z/++CM7duxgxYoV1KtXr1rbBrhz5456OBNAUFAQ2traeHp68tprr3Hq1CmN686ePftU7YUvGfz0na3lsrJlEroQQghR20kSIRRj+vTpeHp6EhISwogRI7h9+zZLlizBxMSk1DcR9evXB+DMmTN06NDhqdt+9dVX6d+/P6tWraKwsBCVSsWXX37J1q1b1ZOnp02bhouLC6tXr2bcuHGcP3+eiIiIp2pPNpsTQgghRG0mS7wKxRg6dCibNm3i6NGjODg4MH/+fF5//XU2b95c6jV9+vSha9eujB8/nr1791aq/aCgIIYMGcKKFSsYPnw4+/btY/Xq1bz11lsAmJmZER4ezk8//YSjoyMRERElDr0SQgghhHjeaRUWDQYXQlTYvn37WLhwIefOnaNOnfK/2JM3EcqmhMlsonQSH+WTGCmfxEj5lBCjsiZWy5sIIYQQQgghRIVIEiGEEEIIIYSoEJlYLRQpKSmp2J4NWlpa1KtXD1NTUzw9PYvt2VATnJ2dcXZ2ruluCCGEEEI8U5JECEXbs2cPJiYmwJM9Gv744w8CAwOZMWMGhw4dUp+rbUobX1jbZGXn8ehhZk13QwghhBDPmCQRQtEaNWqksbxr8+bNCQgIoH///hw9erTY24raYprfl9xJq/1fvuMCnZBpeUIIIcSLR+ZEiKdy8eJFZsyYgY2NDRYWFtjb27Nt2zb1+bi4OIYNG4alpSWjR48mMjISlUqlPv/o0SOWLl1Kz5496datG25ubuXeuO2ll14C0FgNKTo6GgcHB6ysrOjSpQvjxo3jzJkz6vP29vaEhYUxe/ZsrK2tsbW1xc/Pj7y8/7fxWWJiIs7OzlhZWfHmm2/y2WefoVKpuHnzJgA5OTmsX7+evn37Ym1tzZgxY0hMTHy6ByiEEEIIUYtJEiEqLDMzk6lTp2JgYMDu3buJj49n+PDhbNiwgbNnz/L111/j4+ODs7MzBw4cYNSoUQQGBqqvLywsZPr06Vy9epXQ0FD27t2r/uJ//vz5Mtu+e/cuq1atokGDBgwYMACAr776ipUrVzJt2jQOHTrEjh07yM3NZdGiRRrXhoSEYGNjw4EDB5gzZw5RUVHEx8cD8OuvvzJjxgxsbW3Zv38/s2bNIiAgQOP6hQsX8t1337F+/XpiYmIYNmwYHh4efPPNN1XwVIUQQgghag8ZziQqLDMzEzc3N8aPH0+DBk/G9s+ZM4dt27Zx8eJFYmJiGDRoENOnTwfglVde4erVq+zYsQOAH374gVOnTnHixAkaN24MgLe3NydPnuSTTz7B399f3ZaTkxNaWloA5OfnA2BjY8Onn35K8+bNATAyMsLPz4+RI0cCYGJiwpgxY1i6dCmFhYXq619//XX18CdXV1d2797NyZMnGTlyJDt27MDMzIwFCxYA0L59e/78809Wr14NwLVr14iPj+ezzz7D0tISgClTpvDbb78RHh6OnZ1dlT/n2sLY2KCmu1Atntf7el5IfJRPYqR8EiPlU3KMJIkQFda4cWPGjx/PwYMHOX/+PNevX+e3334Dnkx+PnfuHHPmzNG4xsbGRp1EnDt3DkD9JqFITk4O2dnZGsc++ugjWrZsSXp6OmFhYfz888/MnDkTMzMzjbobN27Mli1buHz5MteuXePChQvAk8SjaNjTK6+8olF3gwYNyM3NBeD8+fPY2toW63ORojck/zsHIzc3l4YNG5b1uJ57Nb0RTnVQwgY/onQSH+WTGCmfxEj5lBCjsjabkyRCVNjdu3d5++23adSoEQMGDKBPnz5YWlrSr18/4MlchbI2Qi8oKEBfX5/9+/cXO1e3bl2Nzy1btqRt27YAbNy4EXd3d9555x327dtHu3btgCfzL3x9fRkxYgTW1ta8/fbbXLp0iVWrVpVZN6Dup46OTpl9Ljr3ySefFEsatLVlVKAQQgghXiySRIgKi4+P5/79+xw+fFj9xbzoN/+FhYWYmZnx888/a1zz98+mpqZkZmaSmZmp8UZh6dKlqFQqJkyYUGK7Ojo6+Pv7M2LECHx9fdm1axfa2tqEhYXx1ltv4efnpy577NgxdX/K45/6/OqrrwJw584d9XAmgKCgILS1tfH09CxXO0XClwyuUHmlysrO++dCQgghhHjuSBIhKuzll18mKyuLhIQEbGxsuHLlCmvXrgWeDEmaPn06M2bM4OOPP8be3p6ffvqJqKgo9fV9+/bF3NycefPmsWTJElq2bMnu3bv5/PPPCQ8PL7Pt5s2b4+Pjw5IlS4iKimLSpEm0aNGC06dP88svv9CwYUOOHTvGzp071f3R1dX9x3uaOnUqI0eOZMOGDYwaNYqUlBSCg4OBJ5vcvfrqq/Tv359Vq1ZRWFiISqXiyy+/ZOvWrep7r4h799IpKChfgiOEEEIIoTQyDkNU2NChQ3F3d2f9+vUMHz6cNWvWMHr0aGxsbDh79ixvvPEGq1at4tNPP+XNN99k7969jB07Vv1lXkdHh4iICKysrPDy8sLR0ZGkpCQ2b95crl2oXVxc6NmzJ5s2beLmzZssXboUY2NjJk6ciIuLC19//bV6ZaXyLhtramrK5s2b+eabb3BwcCA4OBhXV1cAdb+DgoIYMmQIK1asYPjw4ezbt4/Vq1fz1ltvPc1jFEIIIYSotbQKyzveQ4hy+r//+z+aNm1K+/bt1cdCQ0P57LPPOHLkSA32rHRnzpyhTp06vPbaa+pjcXFxLFq0iFOnTmnsSVEV5E2EsilhMpsoncRH+SRGyicxUj4lxKisidXyJkJUucTERKZNm8YPP/zAH3/8wdGjR4mMjMTJyammu1aqX3/9FTc3N44ePcoff/zBiRMnCAkJ4c0336zyBEIIIYQQoraTb0eiyr333ntkZGTg4+PD/fv3adGiBZMnT8bd3b2mu1aqMWPGcPfuXdasWUNqaipNmjThzTffLLZUrRBCCCGEkOFMQgBP5jvExcWpV3WqbjKcSdmU8ApZlE7io3wSI+WTGCmfEmIk+0QIoTCl/YVUuqzsPB49zKzpbgghhBCihkkSIUQNmOb3JXfSat+X8bhAJ+T3VkIIIYSQidVCce7fv4+Xlxfdu3fH1taW9evXM3HiREJCQgD4+uuvcXZ2xsrKikGDBrFp0yZycnLU16tUKvbu3cvkyZOxsrKiT58+bN68WaONPXv2MGjQIKysrHj33Xd59Ejzq/GjR49YunQpPXv2pFu3bri5uWksFxsSEsK4ceOYN28eXbt2ZdGiRdX4RIQQQgghlEWSCKEoBQUFzJgxgytXrrBt2zYiIiI4c+YMP/74IwDHjx/H09OT0aNHEx8fz/Llyzl06BDz58/XqCcgIIC33nqLgwcPMmrUKEJCQtR1xMfHs3LlSiZNmkRsbCydO3dm165d6msLCwuZPn06V69eJTQ0lL1799KlSxfGjRvH+fPn1eVOnjyJkZERsbGxip40LoQQQghR1WQ4k1CU//u//+PMmTPEx8fz6quvAk8mPdvb2wNP9psYPXo048ePB6BNmzbqhODmzZu0atUKACcnJ/WSsnPnziUqKoqTJ09iY2NDVFQUQ4cOZcKECQDMmDGDkydPcunSJQB++OEHTp06xYkTJ2jcuDEA3t7enDx5kk8++QR/f391f+fOnYuBgcEzeDLKYWz8Ytzvi3KftZXER/kkRsonMVI+JcdIkgihKOfPn6d+/frqBAKgadOmvPLKK+rzZ86cISYmRn2+aIGxlJQUdRLx943utLS0aNCgAbm5uQBcvHiRoUOHarRrbW2tTiLOnTsHwIABAzTK5OTkkJ2drf5sZGT0wiUQQI2vFPEsKGFFDFE6iY/ySYyUT2KkfEqIkazOJGoNHR0dylp1uKCggClTpjB69Ohi54yNjdU/161bt9j5v9f7v238fUO5goIC9PX12b9/f7E6/l7vSy+9VGo/hRBCCCGeZ5JECEUxMzMjIyOD33//nY4dOwLw4MEDrl27BsCrr77KlStXaNu2rfqaH3/8kR07drBixQrq1atXrjZOnTqlceyXX35R/2xqakpmZiaZmZmYmZmpjy9duhSVSqUeBlUZ4UsGV7qOmpCVnVfTXRBCCCGEAkgSIRTF1taWzp07s2DBApYuXYqenh4bNmwgMzMTLS0tpk+fjqenJyEhIYwYMYLbt2+zZMkSTExMNN5ElGXatGnMnj2b7du3M3DgQL799luOHDlCs2bNAOjbty/m5ubMmzePJUuW0LJlS3bv3s3nn39OeHh4ldynbDYnhBBCiNpMVmcSihMSEsLLL7/M5MmTmTRpEpaWlrRs2RJdXV2GDh3Kpk2bOHr0KA4ODsyfP5/XX3+92BKuZRk4cCABAQF89tlnODg4cPToUSZPnqw+r6OjQ0REBFZWVnh5eeHo6EhSUhKbN2+mV69e1XDHQgghhBC1i1ZhWQPQhXjG7t+/z88//0yfPn3Q1dUFnkxotrW1Zfny5YwcObJmO1hF5E2EsilhMpsoncRH+SRGyicxUj4lxEgmVotao06dOnh5eTF27FjGjRtHbm4u4eHh1K1blzfeeKOmuyeEEEIIIZDhTEJhGjZsSGhoKKdPn2bkyJG8/fbb/Pnnn3zyySfqPRuEEEIIIUTNkjcRQnF69uzJ7t27K3xdcnIyAN27dy9X+cLCQmJiYujXrx9NmjSpcHtCCCGEEC8qSSLEcyE/Px9XV1fWrl1b7iTihx9+YOHChRw9erSae1dcaeMLlSwrO49HDzNruhtCCCGEUABJIsRz4WnWB6jJNQWm+X3JnbTa9YU8LtAJmYInhBBCCJA5EaKW+fbbb3F2dqZz58707NmTBQsW8Ndff9GpUycAFi5cyMSJEwG4ePEiM2bMwMbGBgsLC+zt7dm2bRsASUlJTJkyBYABAwYQEhJCUlISKpVKvbEdwLVr11CpVCQlJQFw79495syZg62tLVZWVowdO5b/+7//e5aPQAghhBCixkkSIWqN+/fv89577zFq1CgSEhLYsmULycnJrFu3juPHjwOwaNEiQkJCyMzMZOrUqRgYGLB7927i4+MZPnw4GzZs4OzZs1hbW7Np0yYAoqOjmTp1arn6sGLFCrKzs9m5cydxcXG88sorzJw5k8ePH1fXbQshhBBCKI4MZxK1RmpqKjk5ObRs2RITExNMTEwIDQ0lPz9fPTHawMAAIyMj7t+/j5ubG+PHj6dBgyfzD+bMmcO2bdu4ePEilpaWGBoaAtC4cWPq169frj5cv34dU1NTWrdujZ6eHosXL8bBwQEdHZ3quWmFMTY2qOkuPDMv0r3WRhIf5ZMYKZ/ESPmUHCNJIkStYW5uzogRI/Dw8MDY2JjevXtjZ2fHoEGDipVt3Lgx48eP5+DBg5w/f57r16/z22+/AVBQUPDUfXjvvfeYP38+X3zxBd26deP111/HwcEBPT29p66zNqnpTW+eFSVs8CNKJ/FRPomR8kmMlE8JMSprszkZziRqlcDAQA4dOoS7uzsPHjzAx8eHKVOmFJskfffuXRwdHdm7dy/Nmzdn/PjxxMTEVLi9vLw8jc+DBg3iu+++w9/fHxMTEyIjIxk6dCgXLlyo1H0JIYQQQtQm8iZC1BqnT58mISGBRYsW0b59eyZPnkxcXBzvv/8+9+/f1ygbHx/P/fv3OXz4MHXr1gVQf9EvSji0tLQ0rtHV1QUgPT1dfezvk6xzcnIIDAzEycmJ4cOHM3z4cLKysnj99df5+uuvUalU5b6X8CWDK3DnypCVnffPhYQQQgjxQpAkQtQaDRo0YNeuXejq6uLi4kJubi4HDx6kdevWNG3aFD09PX7//Xfu3bvHyy+/TFZWFgkJCdjY2HDlyhXWrl0LPEkGAPU8iF9//RVDQ0NMTU2pX78+W7duZd68eaSmprJp0yZ1slG3bl1+/vlnkpOTWbJkCcbGxhw/fpzHjx/TpUuXCt3LvXvpFBTU3BKzQgghhBCVIUmEqDU6duxISEgIW7ZsYdeuXWhra9OjRw/Cw8PR0dHhnXfeYfv27Xz//ffExMRw7tw51q9fT3p6OiYmJri4uHD06FHOnj0LgJmZGfb29nh5eTFu3DgWL17Mhg0bCAwM5M0336R9+/YsXLiQadOmqfsQHBzMunXrmDVrFg8fPuSVV14hICCAnj171tRjEUIIIYR45rQKa3LHLSFeUPImQtmUMJlNlE7io3wSI+WTGCmfEmIkE6uFEEIIIYQQVUaSCCGEEEIIIUSFyJwIUS6FhYXExMTQr18/9cZu1cHX17fUpVh79erFjh07qq1tIYQQQghRPpJEiHL54YcfWLhwIUePHq32tqysrPjXv/5V7HjRUq3Pg9LGF9a0rOw8Hj3MrOluCCGEEELhJIkQ5fIs59/XqVMHY2PjZ9ZeTZjm9yV30pT3ZT0u0AmZZieEEEKIfyJzImqJjIwM1qxZQ9++fenSpQtjx47l5MmTAKSkpPDee+/Rs2dPrK2tmT59OhcvXlRf6+vry7hx4zTqCwoKwt7eXv1ZpVKxd+9eJk+ejJWVFX369GHz5s0AJCUlMWXKFAAGDBhASEgISUlJqFQqwsPDsbW1xcHBgVmzZuHq6qrRzq1btzA3N+enn36qsmcREhLCuHHjmDdvHl27dmXRokUAREdH4+DggJWVFV26dGHcuHGcOXNGfZ29vT1hYWHMnj0ba2trbG1t8fPz09iV+vz580ydOhVra2t69erFokWLNDaf+/zzzxk2bBhWVlYMGzaMyMhICgoKquzehBBCCCFqA0kiagkvLy+OHDmCn58fsbGxmJqa4u7uzn//+1/Gjh1Lfn4+ERERfPrpp2hra+Pq6soff/xRoTYCAgJ46623OHjwIKNGjSIkJIQff/wRa2trNm3aBDz5oj516lT1NUeOHGHPnj2sW7eO0aNH89NPP3Hjxg31+ZiYGNq2bUu3bt2q5DkUOXnyJEZGRsTGxuLu7s5XX33FypUrmTZtGocOHWLHjh3k5uaqE4wiISEh2NjYcODAAebMmUNUVBTx8fEA3LhxA1dXVwwNDdm9ezcfffQRp06dUtdRdJ+zZs3i4MGDzJ07l23btrFhw4YqvTchhBBCCKWT4Uy1wOXLl/nmm2/YunUr/fr1A2DZsmXUr1+fdevWUbduXYKCgtDT0wNg06ZNDBw4kE8//ZT58+eXux0nJyecnJwAmDt3LlFRUZw8eRIbGxsMDQ0BaNy4sXqnZ4Bp06bRrl074MnbDGNjYw4cOMCsWbMAiI2NZdSoURW639OnT2Ntba1xzMDAgOPHj2scmzt3LgYGBgDcu3cPPz8/Ro4cCYCJiQljxoxh6dKlFBYWqnedfv3113FzcwPA1dWV3bt3c/LkSUaOHMnevXtp0KCB+pkCrF69msTERAoLC/nXv/7FjBkzGDFiBACtW7cmPT2dlStX4unpyUsvvVSh+1QqY2ODmu6CIshzUDaJj/JJjJRPYqR8So6RJBG1QNHQpM6dO6uP1alThwULFjB9+nQsLCzUCQSAvr4+FhYWXLhwoULttG/fXv2zlpYWDRo0IDc3t8xrWrdurf5ZR0eHt956S51EFL2VKPpiX17m5uYEBQVpHNPW1nxpZmRkpE4gAGxsbGjcuDFbtmzh8uXLXLt2TX3/+fn51Knz5I/6K6+8olHP3+/x4sWLdOrUSWMCd9euXenatSv379/n9u3bBAcHq4d5ARQUFJCdnc3Nmzfp0KFDhe5TqWp6YxslUMIGP6J0Eh/lkxgpn8RI+ZQQo7I2m5MkohYo+gJc9Nv0vyttwnNBQYH6upLk5+cXO1bS6kf/NKH678kLwKhRo9i6dSs///wzsbGx9O3bl2bNmpVZx/966aWXaNu27T+W+bu4uDh8fX0ZMWIE1tbWvP3221y6dIlVq1ZplCvrHuvUqVPicwHU8x4WLFhAnz59ip1v0aJFmf0VQgghhHieSBJRCxT9hvvs2bP07dsXePKldtiwYeTm5pKdnU1WVpb6C31GRga//PILLi4uAOjq6mpMDga4du1ahfpQUgJTkrZt29KjRw8OHTrEkSNHWL58eYXaeVphYWG89dZb+Pn5qY8dO3YMKP/KUh07dmT//v3k5eWpE7DvvvuORYsWkZCQQOPGjbl+/bpGgpOQkMBXX33FunXrKtTf8CWDK1T+WcnKzvvnQkIIIYR44UkSUQu88sorDB48GD8/P1asWEGLFi34+OOPSUtLIyYmBicnJ7y8vJg9ezYAH374Ibm5ubz99tvAkyE50dHR7Nu3jx49enDs2DESExNp1KhRuftQNA/i119/Vc+PKM2oUaNYvnw5+vr6GitAVacWLVpw+vRpfvnlFxo2bMixY8fYuXMnADk5Oejq6v5jHePHj+eTTz5h6dKlTJs2jb/++ou1a9fStWtXDAwMmD59Ohs3bqRVq1a88cYbXLp0iZUrV2Jvb1/hPSzu3UunoODZLZsrhBBCCFGVZHWmWmLt2rXY2tri5eXFW2+9xeXLlwkPD8fExISdO3eSk5ODq6srEydOBJ6sJGRiYgKAg4MDbm5urFu3DgcHB06ePMncuXMr1L6ZmRn29vZ4eXnx4Ycflll26NChaGtr4+joWK4v71Vh6dKlGBsbM3HiRFxcXPj6668JCAgAnrzBKY/mzZsTERHBjRs3cHZ2Zs6cOdja2rJ69WoApk6dysKFC/n0009588038fPzw8XFhZUrV1bbfQkhhBBCKJFW4bPcRUy8EG7fvo29vT379+/H1NS0prujSPImQtmUMJlNlE7io3wSI+WTGCmfEmIkE6vFM3Hr1i3OnDnD7t27sbGxkQRCCCGEEOI5JUmEqDJpaWn4+vrSunVrjWVQAVatWkVMTEyZ1/v7+zNkyJDq7KIQQgghhKgCMpxJVJnCwkJiYmLo168fTZo00Th3//59Hj0q+5Vc06ZNNTayK8t//vMftm3bxi+//EJ2djYmJiYMHTqU6dOnl7uO8vD19eXatWv8+9//rrI6QYYzKZ0SXiGL0kl8lE9ipHwSI+VTQozKGs4kSYSoMidOnGDy5MkcPXqUVq1aVVs733//Pe+88w6zZ89mwIABvPTSS5w9exZ/f386duxIRERElbX16NEj8vPzMTIyqrI6lSwrO49HDzNruhs1Tgn/4xalk/gon8RI+SRGyqeEGMmcCPFMPKt8dPfu3fTq1YsZM2aoj7Vu3Zq6desya9YsLly4gEqlqpK2/r4rdlWa5vcld9KU92U9LtAJ+SdFCCGEEP9Elnh9jmRkZLBmzRr69u1Lly5dGDt2LCdPngQgJSWF9957j549e2Jtbc306dO5ePGi+lpfX1/GjRunUV9QUJDGPg8qlYq9e/cyefJkrKys6NOnj3ruQ1JSElOmTAFgwIABhISEkJSUhEqlIjw8HFtbWxwcHJg1axaurq4a7dy6dQtzc3N++umnct2ntrY2Fy5c4I8//tA4/sYbb3Dw4EHatWunvidvb2/8/Pzo2rUrPXv2ZM2aNeTk5KivSU5Oxs3Nja5du2JhYcGwYcM05m78/bkU3c+3337LiBEjsLCwYOjQoRw5cqRc/RZCCCGEeF7Im4jniJeXF5cuXcLPz4927doRHh6Ou7s7cXFxjB07lu7du6uH+gQHB+Pq6kpsbCwtW7YsdxsBAQEsXbqUDz74gM8++4yQkBBsbW2xtrZm06ZNzJ07l+joaDp06MAvv/wCwJEjR9izZw8ZGRmkpqYyc+ZMbty4QevWrQGIiYmhbdu2dOvWrVx9mDJlCl9//TUDBw6ka9eu2NjYYGNjQ/fu3enYsaNG2S+//JJ+/fqxe/dubty4weLFi8nIyMDPz4/U1FSmTZvG+PHj+eCDD8jNzWX79u0sXbqU119/nWbNmpXY/vr161m8eDEtWrRg9erVLFiwgOPHj1fpXIyaZGxcPW9faht5Dsom8VE+iZHySYyUT8kxkiTiOXH58mW++eYbtm7dSr9+/QBYtmwZ9evXZ926ddStW5egoCD09PQA2LRpEwMHDuTTTz9l/vz55W7HyckJJycnAObOnUtUVBQnT57ExsZGvZN148aNNb5QT5s2Tf12QKVSYWxszIEDB5g1axYAsbGxjBo1qtx96Ny5MzExMezYsYNvvvmGH3/8EQAjIyN8fHw06jIwMGDDhg3o6+tjamrKnTt38PPzw8fHh5ycHGbPns3UqVPR1n7yUs7Dw4OYmBguX75cahIxe/ZsevXqBYCnpyejRo3i4sWLWFtbl/selKymx18qgRLGoYrSSXyUT2KkfBIj5VNCjGROxAugaGhS586d1cfq1KnDggULmD59OhYWFuoEAkBfXx8LCwsuXLhQoXbat2+v/llLS4sGDRqQm5tb5jVFbxwAdHR0eOutt9RJxE8//cSNGzcYOXJkhfuxatUqAK5cucKJEyfYtWsXixYtonnz5vTp0wcACwsL9PX11ddZW1uTl5fH5cuX6dKlC87OzkRFRXHx4kWuX7/Ob7/9BkBBQUG5nkHRnIl/egZCCCGEEM8TmRPxnKhT50k+qKWlVexcaROeCwoK1NeVJD8/v9ixunXrlrv+In9PXgBGjRrF1atX+fnnn4mNjaVv376l/tb/fxUNRSoaKgXwyiuvMH78eKKjozE2NubYsWPqc/97f0XJgba2Nr///jtDhw7l2LFjtG3blmnTphEeHv6PfXiaZyCEEEII8TyRNxHPiQ4dOgBw9uxZ+vbtCzz5wjxs2DByc3PJzs4mKytL/YU+IyODX375BRcXFwB0dXVJT0/XqPPatWsV6kNJCUxJ2rZtS48ePTh06BBHjhxh+fLl5W5DT0+P+Ph49STy/z330ksvaexR8euvv5KXl6dOJk6dOsVLL71Ehw4dCAoKwsjIiMjISHX5ogSkupOC8CWDq7X+p5WVnVfTXRBCCCFELSBJxHPilVdeYfDgwfj5+bFixQpatGjBxx9/TFpaGjExMTg5OeHl5cXs2bMB+PDDD8nNzeXtt98GoGvXrkRHR7Nv3z569OjBsWPHSExMpFGjRuXuQ9E8iF9//VU9P6I0o0aNYvny5ejr62usAPVPtLW18fHxYeHChQC4uLjQrFkzbt68yd69e8nOzlbfEzxZ+Wn58uVMnTqVy5cv8+GHHzJhwgTq16/Pyy+/zJ07d/j222/p2LEj586dw8/PD0BjBafqIJvNCSGEEKI2kyTiObJ27VoCAgLw8vIiOzsbCwsLwsPDMTExYefOnaxfvx5XV1e0tbWxsbFhz549mJiYAODg4MCvv/7KunXryMnJoV+/fsydO1fjt/T/xMzMDHt7e7y8vBg3bhwDBw4stezQoUNZuXIljo6O6OrqVug+nZ2dadq0KZGRkcycOZNHjx7RqFEj+vbty969e2natKm6rKWlJS+99BKjR4+mQYMGuLm5MXPmTADc3Ny4fPmyepJ1u3bt8Pb25sMPP+Ts2bP079+/Qv0SQgghhHhRyI7Vokbcvn0be3t79u/fj6mpabW04evry7Vr1/j3v/9dLfVXhryJUDYlrIghSifxUT6JkfJJjJRPCTGS1ZmEYty6dYszZ86we/dubGxsqi2BEEIIIYQQ1UeSCPFMpaWl4evrS+vWrdW7XRdZtWqVxm7RJfH392fIkCHV2UUhhBBCCPEPJIkQ/6iwsJCYmBj69eunsfLR03jttdc4depUseMhISF8+umnZV67fv169f4P5eHv71/icZVKhZ+fn3plqrLY29vj4OCAl5dXudsVQgghhHjeyZwI8Y9OnDjB5MmTOXr0KK1ataqWNh4/fkxGRob68+jRoxkyZAjTp09XHzM0NCxxj4aKunv3LgYGBsX2ryjJ/fv3eemllzR24H5eZGXn8ehhZk13Q5GUMA5VlE7io3wSI+WTGCmfEmIkcyJEpTyLPLN+/foaX9R1dHTQ19fH2Ni4ytuqSJ2NGzeu8vYBpvl9yZ20mv0CHxfohPzzIYQQQoinITtW1wJFG6v17duXLl26MHbsWE6ePAlASkoK7733Hj179sTa2prp06dz8eJF9bW+vr6MGzdOo76goCCNvRlUKhV79+5l8uTJWFlZ0adPH/V8haSkJKZMmQLAgAEDCAkJISkpCZVKRXh4OLa2tjg4ODBr1ixcXV012rl16xbm5ub89NNPVfIcbt68iUql4vvvv1cfy8vLQ6VSsW/fPuDJsKiJEyeybds23njjDSwtLZkwYQIpKSka9xsdHQ3AvXv3mDNnDra2tlhZWTF27Fj+7//+T13W3t6eoKAg4MnmfWFhYQwZMgQLCwu6du3KtGnTuHr1apXcnxBCCCFEbSFvImoBLy8vLl26hJ+fH+3atSM8PBx3d3fi4uIYO3Ys3bt3JyIiAoDg4GBcXV2JjY2lZcuW5W4jICCApUuX8sEHH/DZZ58REhKCra0t1tbWbNq0iblz5xIdHU2HDh345ZdfADhy5Ah79uwhIyOD1NRUZs6cyY0bN2jdujUAMTExtG3blm7dulX9QynDqVOn0NfXJywsjNzcXDw9PVm5ciWffPJJsbIrVqwgJyeHnTt3UrduXUJDQ5k5cybHjx8vNoTpk08+Ydu2baxbtw6VSsX169dZtmwZa9euZevWrc/q9qqUsbFBTXdBseTZKJvER/kkRsonMVI+JcdIkgiFu3z5Mt988w1bt26lX79+ACxbtoz69euzbt066tatS1BQkHp8/6ZNmxg4cCCffvop8+fPL3c7Tk5OODk5ATB37lyioqI4efIkNjY26t2nGzdurPHFetq0abRr1w548tt9Y2NjDhw4wKxZswCIjY1l1KhRlX4GFZWbm8u6devUu21PmDCBjRs3llj2+vXrmJqa0rp1a/T09Fi8eDEODg7o6OgUK9umTRv8/f3Vb3FMTEwYPnw4Bw4cqL6bqWY1PdZSqZQwDlWUTuKjfBIj5ZMYKZ8SYlTWnAgZzqRwRUOTOnfurD5Wp04dFixYQGZmJhYWFhoThPX19f8/9u49rufzf/z4o6OIoYnREkJsaYXKKW2hiCRlsjCiMXKImcwxayhMND5OMcXIoZMzMTMbGW3OsonIcviQfZAOevv90a/X11sH5fhmz/vt1u32fr8O13W9Xs9sr6vXdV1PLC0tSUlJKVc9DRo0UD5raWlRuXJl8vLySj2n8I0DFMxh8PDwUB6ojx49yuXLl+nRo0e52vE8VK9eXelAAFSpUqXEa/H392f37t3Y2dkxcOBA1q1bh7m5ebGTrp2cnKhRowbz589n9OjRuLu7s3z5cvLz81/YtQghhBBCaCLpRGg4Xd2Cl0VaWlpF9pU04VmlUinnFae4h97iVj160oTqxx+0PT09uXjxIseOHSM+Ph4HBwdq1qxZahnPqqzXUpJOnTrx888/M2vWLExMTFi1ahWdO3cuthO2dOlS+vXrx61bt2jVqhVBQUH4+vo+U/uFEEIIIV5HMpxJw5mbmwNw4sQJHBwcgIJOQpcuXcjLyyMnJ4fs7GzlgT4rK4uTJ08qORD09PS4e/euWplpaWnlakNxHZjimJmZYWdnx/bt20lMTGTq1KnlqudJ9PT0ANSu51kmNefm5jJ37lzc3d1xdXXF1dWV7Oxs2rZty48//oiFhYXa8YXzJT7//HNlW0RExFOtXhUxyfmp2/28ZOc8eNVNEEIIIcRrSjoRGq5+/fo4OzsTHBzMtGnTqF27NitXriQzM5PY2Fjc3d0JCAhgxIgRACxYsIC8vDx69+4NQPPmzdmwYQMxMTHY2dmxd+9eDhw4oDbc50kK50GcOXNGmR9REk9PT6ZOnUrFihXVVoB6HmrWrImpqSmrVq2iQYMGyqpVT5s7Ql9fn2PHjnHkyBEmTZqEsbEx+/fv5969e1hbWxc5vnbt2vz666907NgRHR0d4uPj2bVrF9WqVSt33Tdv3kWlkhQtQgghhHg9yXCm18DMmTOxt7cnICAADw8PUlNTiYiIwMTEhNWrV5Obm4uPjw/9+vUDIDo6GhMTEwDc3Nzo378/ISEhuLm5kZyczOjRo8tVf5MmTXByciIgIIAFCxaUemznzp3R1tame/fuypuD50VLS4vZs2eTk5NDjx49+OKLL+jXrx/vvPPOU5c5f/58zMzMGD58OJ07d2bt2rWEhobSqlWrIseGhoaSm5uLl5cXffv25dy5cwQFBXH79m0uX778LJcmhBBCCPFakYzV4rm6evUqTk5OxMXF0bhx41fdHI0lbyI0myasiCFKJvHRfBIjzScx0nyaECPJWC1euIyMDI4fP866deuwtbWVDoQQQgghxBtMOhHiucjMzCQwMBBTU1Ml23Wh6dOnExsbW+r5s2bNwsXF5UU2UQghhBBCPCfPNJzpypUr/PHHH3Tt2vW5NCY8PJwNGzawf//+Mh2/Z88ezMzMaNiwIf369ePw4cOlHl/e3AkvwuMr/jzOzs6OqKioF9qGwMDAEh/qW7duzffff1/uWJTm1q1b3LlT+uu4GjVqFMkQ/bj09HQ6dOhQ6jEeHh7MmjWr3G18VL9+/ahVqxZz5sx54rF5eXmsXr2agQMHlqsOGc6k2TThFbIomcRH80mMNJ/ESPNpQoxKG870TJ2ITz75RMni+zzcu3ePnJwcjIyMnnjspUuX6NSpE5GRkdjb23P79m0loVhGRga9evUiLCyMli1bKucYGxs/l3Y+ixs3biift23bxowZM9i/fz/a2gVz3PX09J5qtZ/yCAwM5Pz58yxatKjIPn19fapWrVquWLws+fn53Lp1S/m+YsUKNm/erNYhMjAwoEqVZ0sRf/v2bXR0dMpUzoYNG5g0aZJGdFDLIzvnAXf+d/9VN0NjacJ/uEXJJD6aT2Kk+SRGmk8TYvTC5kQ87znZhoaGT/xrdEl1P/rgnZOTA0DVqlU1ouPwqEfbU/iQ+vbbb5eaHO5F0NXVLfXelCcWL4uOjo5amytVqoS2tvZzj/GL7sQBDArexfXMV/cQv3muO/K/DiGEEEI8rade4rVfv34kJycTGxuLhYUFiYmJWFhYcOnSJbXjBgwYwKRJk4CCoTw//PAD3t7eNGvWjG7dupGYmKgcGx4eTvv27ZXvcXFxdO3alWbNmtGuXTuCg4PJzc0lPT0dZ+eCZF39+/cnMDDwie09e/YsFhYWHDx4UG37xIkTlaEoTk5O/Oc//2Hw4MFYWVnRsWNHoqOj1Y5PTk7Gx8cHKysrPvzwQ4KCgookc3sWI0aMYOjQocr31NRULCws1JZW3bx5M7a2tjx48ACVSkVkZCSurq40a9aMDh06sHTpUlQq1TO149FYpKenY2FhwbZt2+jVqxeWlpY4OTkVuTfff/89Tk5ONGvWjAEDBvDdd9+p5Yq4du0aAQEBtGzZEnt7e4YOHaqWLC4wMBB/f38GDRpE8+bNCQ8PL3e7Z8yYgZOTk1on8/79+7Ro0YKEhASSkpKU39dOnTphbW1N//79+fPPP5Xj+/XrxxdffAEUvP2YPXs2jo6OWFpa4uLiwtq1awGIiYlR+92OiYkpd3uFEEIIIV5HT/3n7/DwcPz8/DAxMWHixIlUr16dGjVqEB8fryQ+y8jIICkpiVGjRinnzZ49my+++IIZM2YQGxuLv78/q1evVht2BAUP/ZMmTWLOnDlYWVlx/vx5xo4dS/Xq1Rk6dCjR0dH07t2b8PBwWrdu/cT2NmnShPfff5/4+Hjl+Pv377Njxw6CgoKU4xYtWsSQIUP46quv2L9/P9OmTaNixYp0796ds2fPMmDAAIYOHco333zDzZs3CQkJwdfXl+jo6DJndi6Nk5MT06dPJzc3F319fX799Ve0tLQ4dOgQI0eOBGDfvn04Ojqiq6vLrFmzlCE1zZs358iRI0rbJkyY8MzteVRISAhTpkyhcePGLFq0iGnTptGmTRtMTU1Zs2YN3377LZMnT6Zly5bs2LGDBQsWULt2baAgk3a/fv1o0qQJq1evRltbm5UrV/Lxxx+zefNmatWqBcDu3bsZO3YskydPfqo8E15eXqxatYrDhw9jb28PwK5du9DS0sLZ2Zljx44BBZ2NqVOn8s477zB79mz69+/Pjh07iiTT++GHH9ixYwfz5s2jVq1a/Pjjj0ybNo1GjRrh6urKP//8w6xZszhw4MAzD6N62YyNX6/2vmxyfzSbxEfzSYw0n8RI82lyjJ66E1GtWjV0dXUxMDBQhpP06NGDhIQEpRMRFxdHvXr1sLGxUc7r0aMHPj4+AIwdO5akpCQiIyOLdCLS09PR0tLCxMSEOnXqUKdOHSIiIjA0NERHR0fJuFy1atUyP7x5eXkxe/ZsJaPy7t270dbWplOnTsoxrVu3xt/fH4AGDRpw7NgxVq1aRffu3YmIiKB169YMGzYMgHr16jF37lw6duyo9tD6LBwdHcnJySE5OZlWrVrxyy+/0KFDB3766Sfu37+Pvr4+P//8M9OnT+fu3busXr2asWPH4uHhAYCZmRn//PMP3377Lf7+/iXemz/++EMtLlAwvKq0idSffvqpMrH5yy+/JCYmhmPHjmFqakpERAQ+Pj706tULgM8//5xTp05x+vRpALZu3UpmZiZz585VOgfffPMNSUlJrF+/XvmdqVq1Kp999tlT37/GjRtjZWVFfHy8Eo/CN1oGBgbKcePGjcPR0RGAOXPm8OGHH7Jlyxbld7PQpUuXqFSpEu+++y41a9akb9++NGjQgHr16mFgYEDlygXjBDVt2FxZvOpxlppME8ahipJJfDSfxEjzSYw0nybEqLQ5Ec81Y7WnpyeXLl3i6NGjQMHDW8+ePdWOsbW1VftuY2PDuXPnipTl4OCAjY0NXl5edOjQgSlTpnDr1i3q16//1O1zc3NDpVKxe/duAGJjY+natSsVKlQoU/tOnz7NL7/8go2NjfLTvXt3AM6fP//U7XqUkZER1tbWHDhwgAcPHnD48GEGDx6Mrq4uR44cITk5mezsbBwcHEhNTSUvL69IB8zW1pa8vDxSU1NLrKdp06bExcWp/axZs6bUtjVo0ED5XNg5ycvLIzMzkytXrhTplDzartOnT3P37l3s7OyUe9eiRQuuXr2qdu/efffdJ9+kJ/Dy8mLnzp1kZ2eTkZHBoUOH8PT0VDvGzs5O+VytWjXq169f7ORoHx8f7t69i6OjIz179mTu3LlUq1aNGjVqPHM7hRBCCCFeV891Nm+DBg2Usec6OjpcvnyZHj16qB3z+BCV/Px8dHR0ipRVoUIFIiMjOX36NAcOHODAgQMMHToUNzc3QkNDn6p9VapUwdnZmYSEBOzt7Tl06BBjxowptX0qlUppn0qlwtXVleHDhxcp+3muYuTk5MSWLVtwcnJCV1eXDz74gBYtWnDo0CGg4G2JoaFhiRPbC7eXNlm7QoUKmJmZlatd+vr6xdZVWE9pE+1VKhV169Zl6dKlRfZVqlRJrV3PqmvXrsycOZO9e/eSnp6Oubk5VlZWasc8fm9K+j2sV68eu3bt4vDhw/z666/s27ePpUuXEhwcrLx1EUIIIYT4t3mmTkRxcwC8vLyYO3cuBgYGtG/fvsgwjxMnTqgNH/r999957733ipTz008/ceLECfz9/Xnvvff47LPPWLx4Md999x2hoaFPPf/Ay8uLQYMGsWnTJho2bEizZs2KtO9RycnJSvsaNWrEX3/9Rd26dZX6L1y4QEhICGPGjHluY+KdnJyYM2cO27Ztw97eHm1tbdq2bcvWrVvJzs5mwIABAJibm6Onp8eRI0fUruPw4cPo6+uXu5PwtKpUqYKJiQnHjh1TSxh3/Phx5XPjxo2JjY2lcuXKvP322wA8ePCAMWPG0LlzZ1xdXZ9beypXrkznzp3ZuXMnly9fLvI2DAri3K5dO6Agj0VaWhr9+vUrclxkZCRvv/02Xbt2pW3btowbNw5fX1+2b9/+TJ2IiEnOT33u85Cd8+CV1i+EEEKI19szdSIMDQ1JT0/nypUrmJiYANC5c2eCg4NZt25dscm6oqKilIf39evXk5KSQnBwcNGG6eqycOFCKleuTIcOHfjnn3/48ccflSEzhcuPpqSk0LhxY2WOxJPY2dlRu3ZtlixZQkBAQJH927dvx9raGgcHBxITE9m9ezf/+c9/APD19cXHx4epU6fSv39/7t27R1BQEFlZWdSrV69M9ZdFgwYNMDMzIzo6Wpkc3aZNG6XzVLjiUeXKlenTpw/fffcd1atXx8bGhqNHj7Jo0SL69OmjjNd/Gfz8/AgJCaFhw4a0aNGCvXv3sn37dmVidffu3Vm6dCkjRozgyy+/pEqVKixevJj9+/erTbx/Xry8vPD19UWlUuHu7l5k/9dff83XX3/NW2+9RWhoqNJReNzNmzdZuHAhBgYGNGnShNTUVE6fPq3MnSj8PTx+/Djm5uZlXhZXks0JIYQQ4nX2TJ2Ivn37MmHCBFxdXUlMTMTY2JhKlSrRpUsX9u7dy4cffljkHG9vb6KiopSH/4iICJo2bVrkuLZt2/LNN9+wcuVK5s2bp7zZGD9+PFCQW6F3797Mnj2bpKQkFi5cWKY2a2lp4eHhwaJFi5T5DI9yd3fnxx9/JDQ0FDMzM8LCwpTrsLa2Zvny5cyfP5+ePXtSqVIlWrduzfjx44sd6vMsnJycWLFiBW3atAEKlhA1MjLCzMxM+Us+FCyLamRkxIIFC7h+/TomJiYMHz683BmUn1WfPn34559/CAsLIzMzEzs7Ozw8PJT5MVWqVGH16tWEhoYyePBg8vPzef/991mxYgXm5ubPvT0tW7bknXfeoVGjRmr3q1Dv3r0JDAwkMzOTVq1asXr1arVhVYX8/f3Jz88nODiYGzduYGxsTO/evfn8888BaNeuHc2bN+eTTz5h7NixL/2+CyGEEEK8Cs+UsbokgwYNonHjxsoDfyELCwuNGEs+ZcoUbt++rZZ7AQoe3N3c3Ip9QyFKt3//fho2bEidOnWUbZMnT+bSpUusWrXqpbencPJ5aGgoH330kbI9KSmJ/v37s2vXrpc23Ks48iZCs2nCihiiZBIfzScx0nwSI82nCTF6YRmrH5eYmEhKSgpJSUlMnTr1eRb9XPzyyy+kpqYSGxvLihUrXnVz3ijx8fGcP3+eadOmYWxszG+//UZCQsJL/z34559/OHToEDt37sTIyEgteaEQQgghhHg+nmsnYvny5aSmpjJ16lTq1q37PIsu1ZUrV/jjjz+KHdP+qJiYGPbu3cuQIUOKLOX6qPDwcDZs2FBqzoRH7dmzh8qVKzN06FCys7OfmC26uKVEXzYLC4tS99vZ2REVFVXm8iZPnsysWbMYPnw4//vf/zAzM+Orr74qdlJzoX79+lGrVq1i584Ux8nJiStXrpS438TEhI0bNzJx4kSqVavGt99+W+yKS6V5mtibmZnRsGHDctUjhBBCCPE6eyHDmV62Tz75hLp16zJr1qznUt69e/fIyckp07Ktly5dolOnTnz//ffUqVOH//3vfzx4ULDyzfXr1xk5ciQTJ06kWbNm6Orq8s4772hEYrIbN24on7dt28aMGTPYv38/2toFqUP09PSoVq3aC21DeTsRt27dIj8/HyhY1WvEiBFER0crk/p1dHSeeandp4l9ZGRkuRMNynAmzaYJr5BFySQ+mk9ipPkkRppPE2L00oYzvSrPux9kaGhY5lV2CuvW1tYuMsY+PT0dgIYNGxZJxPaqPdqRKVya9u233y41t8Sr9uiDfdWqVQGoXr36c+2UPU3sn0ZJ/yBftOycB9z53/1XUrcQQggh3hya+8RYRv369SM5OZnk5GRiY2NZuHAhw4cPZ/fu3WpDqgYMGMC7775LcHAwFhYWTJ06lYSEBE6dOoWZmRmjR4+mY8eOQNEhLXFxcSxbtoxLly5RtWpVOnfuzJdffsn169dxdi5Y779///54eHg88W3I2bNncXd35/vvv6d169bK9okTJ/L333+zcuVKnJyc6NWrF0ePHuXw4cPUrFkTPz8/evfurRyfnJzM3LlzOXHiBEZGRnz00UeMHTv2uS3rqlKpWL16NevWrePy5cvUrFmT3r17M3jwYLS1tUlPT6dDhw6sXLlSWUHqwYMHvP/++8ycOVMZxnTw4EEWLFjAmTNnqFKlCt26dWPMmDFKUr+srCwmTpzIzp07UalUdOzYkSlTpjzVdSQmJmp07B81KHgX1zNf/sP85rnuyN+dhBBCCPGstF91A55VeHg4VlZWdOnShQMHDvDhhx9So0YN4uPjlWMyMjJISkrC09NT2TZ79mzc3NyIj4/no48+wt/fnyNHjhQp/+zZs0yaNIkRI0awc+dOZs6cSUJCAsuWLaN27dpER0cr7Zg4ceIT29ukSRPef/99tfbdv3+fHTt2qLVv0aJFWFtbExcXR9++fZk2bRoJCQlKmwYMGEDbtm1JSEhg7ty5nDp1Cl9f3+f2ViY0NJT58+fj5+fHli1bGDZsGIsXLyYkJKTMZRw7doxBgwZhaWnJpk2bCAkJISEhgW+//VY5Zs+ePdSoUYOYmBhmz57Njh07is1qXRaaHnshhBBCiDfFa/8molq1aujq6mJgYKAMa+nRowcJCQmMGDECKPhrcr169dSGFPXo0UNJGDZ27FiSkpKIjIykZcuWauWnp6ejpaWFiYkJderUoU6dOkRERGBoaIiOjo6S5K5q1aplzljt5eXF7NmzmTp1KhUrVmT37t1oa2urZfJu3bo1/v7+QEHyuWPHjrFq1Sq6d+9OREQErVu3ZtiwYQDUq1ePuXPn0rFjRw4fPlzu8fmPu3v3LqtXr2bs2LF4eHgAYGZmxj///MO3336rtOtJVq1aRdOmTZUHbHNzc77++msuXbqkHPP+++8rS+rWrVuXtm3bcvLkyadqt66ursbHXhMYG78+bX2V5D5pNomP5pMYaT6JkebT5Bi99p2I4nh6erJ8+XKOHj1KixYtiIuLw8vLS+2Yx1dnsrGx4aeffipSloODAzY2Nnh5efHuu+/Stm1bOnTogKWl5VO3z83NjZCQEHbv3k337t2JjY2la9euVKhQodT2JSYmAnD69GnS0tKKnWdx/vz5Z+5EpKamkpeXV+Sh2tbWlry8PFJTU4tN4Pa4c+fOFWlLYbbtQo9n+q5atapaJ6O8ND32muBVT9J6HWjCZDZRMomP5pMYaT6JkebThBi98ROrH9egQQNatGhBQkICOjo6XL58mR49eqgdUzgmv1B+fn6xy4FWqFCByMhITp8+zYEDBzhw4ABDhw7Fzc2N0NDQp2pflSpVcHZ2JiEhAXt7ew4dOsSYMWNKbZ9KpVLap1KpcHV1Zfjw4UXKftbViaDkCcOF20uafF24clIhXV1dtLS0Sq2rcDWostRfFpoeeyGEEEKIN8FrPycCKPZB1cvLi8TERLZv30779u2LrOBz4sQJte+///477733XpFyfvrpJ7777jvee+89PvvsMyIjIxk1ahTbtm0rse6y8PLy4tChQ2zatImGDRvSrFmzUtuXnJystK9Ro0b89ddf1K1bFzMzM8zMzFCpVMycOZOMjIynas+jzM3N0dPTKzJP4PDhw+jr62NmZqY8iN+9e1fZf/HixSLlPH4d0dHRdOvW7bmvqPUoTY+9EEIIIcTr7o14E2FoaEh6ejpXrlxRcgZ07tyZ4OBg1q1bV2wegqioKOXhff369aSkpBAcHFzkOF1dXRYuXEjlypXp0KED//zzDz/++KMylKhwOdCUlBQaN26sjJN/Ejs7O2rXrs2SJUuUOQGP2r59O9bW1jg4OJCYmMju3bv5z3/+A4Cvry8+Pj5MnTqV/v37c+/ePYKCgsjKyioyPOhpVK5cmT59+vDdd99RvXp1bGxsOHr0KIsWLaJPnz5UrlwZQ0NDTE1NWbVqFQ0aNCArK4sZM2agr6+vlDN48GA8PT359ttv8fDw4MqVK4SHh9O9e/cX+gCu6bEHiJjk/JRX92yycx68knqFEEII8WZ5IzoRffv2ZcKECbi6upKYmIixsTGVKlWiS5cu7N27lw8//LDIOd7e3kRFRSkPgBERETRt2rTIcW3btuWbb75h5cqVzJs3DwMDA9q3b8/48eOBgtwKvXv3Zvbs2SQlJbFw4cIytVlLSwsPDw8WLVpE9+7di+x3d3fnxx9/JDQ0FDMzM8LCwpTrsLa2Zvny5cyfP5+ePXtSqVIlWrduzfjx49Ue4p9FYGAgRkZGLFiwgOvXr2NiYsLw4cMZOHCg0v7Zs2fzzTff0KNHD+rUqcOoUaMICwtTymjatCmLFi1iwYIFrFixgrfffhtPT09l0vOLoumxB0k2J4QQQojX2xuRsbokgwYNonHjxspDXyELCwuCg4Pp1avXK2pZgSlTpnD79m0WLFigtt3JyQk3N7di31CIstH02EsnQrNpwmQ2UTKJj+aTGGk+iZHm04QY/esmVicmJpKSkkJSUhJTp0591c0p4pdffiE1NZXY2FhWrFjxqpvzRtH02AshhBBCvAneyE7E8uXLSU1NZerUqWqZizVFTEwMe/fuZciQIUWWG31W165do3PnzqUeU79+fWJiYp5rvZpC02MvhBBCCPEmeKOHM/0b5efnk56eXuoxenp61KlT5yW1CK5cucIff/xB165dX3hd/v7+/P3330U6SQMGDODgwYMsXLiQjh07KtsPHz5Mv3792LhxI/v27WPDhg3s378fKMhz8ffffyvzKp7nMDMZzqTZNOEVsiiZxEfzSYw0n8RI82lCjEobzvRGLPEq/o+Ojo6y7GtJPy+zAwEwbtw4fv7555dSV+vWrTl79iz37t1TtmVlZXHkyBFq166tdBAKHT58mGrVqvH+++/j6+tLXFycss/Pz6/IcrBCCCGEEOINHc4kNMvLfNnVpk0b8vPzOX78OK1btwbg0KFD6Ovr079/f1avXq12/JEjR2jTpg3a2toYGhoqy7a+6HaX1Kt/kbJzHnDnf/dfer1CCCGEePNIJ0K8UP369SM5OZnk5GRiY2MB2L17t9p8hQEDBvDuu+8SHByMhYUFU6dOJSEhgVOnTmFmZsbo0aPVhiD9+OOPhIeH89dff1GrVi26du3KsGHD0NfXp379+tSuXZujR48qnYiff/6ZVq1a4ejoSEhICOfPn8fc3Jy8vDyOHTvGxIkTAQgPD1eGMzk5OXHt2jW+++47YmNj2bt3LwA3b95k5MiR/Pzzz+jr6+Pm5kZgYGCJWbxLMih4F9czX+4D/ea57siLayGEEEI8DzKcSbxQ4eHhWFlZ0aVLF3766Sdq1KhBfHy8sj8jI4OkpCQ8PT2VbbNnz8bNzY34+Hg++ugj/P39lezZ+/fvZ9SoUXh5ebFlyxamTp3K9u3bGTdunHJ+69atSU5OVr4fOHAABwcHzM3NqV27tjK06tSpU2RlZdGuXbsi7d64cSPGxsb4+vqyceNGZXtMTAwtWrQgISGBcePGERUVxaZNm57fDRNCCCGEeA3ImwjxQlWrVg1dXV0MDAx455136NGjBwkJCUrCubi4OOrVq6dkgQbo0aMHPj4+AIwdO5akpCQiIyNp2bIlixcvxsvLi08++QSAunXrEhQUxKeffkp6ejrvvvsubdq0YerUqeTn53P58mUuXbqkdBTatWvHzz//zIABA/jtt99o2LAh77zzTpF2GxkZoa2tTaVKlTAyMlK2d+jQgU8//RQAU1NTIiMjOXnyJL17934xN/A5Mzau8qqb8NqQe6XZJD6aT2Kk+SRGmk+TYySdCPFSeXp6snz5co4ePUqLFi2Ii4vDy8tL7ZjHl721sbHhp59+AuD06dMcP35cGRoF/zd34fz587z77ru0bt2arKwsUlJSOHr0KPXq1cPU1BQo6EQEBgaSl5fHkSNHaNu2bbnaX69ePbXvVatWJTs7u1xlvEqvepWH14UmrIghSibx0XwSI80nMdJ8mhCjf12yOaG5GjRooAwH0tHR4fLly/To0UPtGD09PbXv+fn56OjoAKBSqRg4cGCRjgeAsbExADVq1KBRo0b8/vvv/PLLLzg4OCjHtGnThtzcXI4dO0ZycrLyRqOstLWLjgCUVZKFEEII8W8jcyLEC6elpaX23cvLi8TERLZv30779u2Vh/9Cjy+r+vvvv/Pee+8B0KhRIy5cuKC2ZO3169cJDQ1VW9a1TZs2Skfh0U7EW2+9hZWVFXFxcWRnZ2NnZ1fmdgshhBBCiALyJkK8cIaGhqSnp3PlyhVMTEzo3LkzwcHBrFu3jjlz5hQ5PioqioYNG9KsWTPWr19PSkoKwcHBQEHuhlGjRhEeHk63bt24evUqkyZNwsTERK0z0qZNG0aPHs2DBw+KdBTatWtHREQEzZs3p2LFiqW2++LFi1y7do1atWo9p7tRIGKS83Mtryyycx689DqFEEII8WaSToR44fr27cuECRNwdXUlMTERY2NjunTpwt69e5Vs0I/y9vYmKiqKlJQUGjduTEREBE2bNgWgc+fOhIWFsWTJEpYsWUK1atVwcnLiiy++UCvD1taWvLw8bG1ti3QU2rVrR3h4+BPnQ/j6+hISEsKBAwc4ePDgs92Ex0jGaiGEEEK8zrQeyoBu8QoMGjSIxo0bM378eLXtFhYWBAcH06tXr1fUspdDOhGaTRMms4mSSXw0n8RI80mMNJ8mxEgmVguNkZiYSEpKCklJSUydOvVVN0cIIYQQQjwF6USIl2r58uWkpqYydepUtazVQgghhBDi9fHUnYgrV67wxx9/0LVr1+fSkPDwcDZs2MD+/fvLdPyePXswMzOjYcOG9OvXj8OHD5d6fEpKyvNo5nNx7do1Fi9ezE8//cT169cxMjKiZcuWDBo0iPfff/+F1+/k5MSVK1eK3Tdq1CiGDRtGv379qFWrVrETn5/FunXrSt1fWpxiYmKYMGFCqefPnDmTnj17PlXbCpVnSNWtW7fYs2fPGz/8SgghhBDiUU89J+KTTz6hbt26zJo167k05N69e+Tk5KhlBy7JpUuX6NSpE5GRkdjb23P79m3y8vIAyMjIoFevXoSFhdGyZUvlnMeXEX1Vzpw5w8CBAzE3N2fo0KE0aNCAa9eusWbNGnbu3MnMmTNxc3N7oW1wcnKiY8eO+Pn5FdlnaGhIpUqVuH37Njo6OlSpojmZErOzs7lz5//GBo4YMYKaNWsyefJkZVuVKlUwMDB4pnpu3LhR5nLGjRvH1atXiYqKKlcdMidCs2nCOFRRMomP5pMYaT6JkebThBi9kDkRz3s+tqGhIYaGhk9Vd7Vq1ZTPOTk5QEEmYU3pOBR68OABo0aNolmzZixevFhJoGZiYkLz5s2VB2Jra2slw/KLUrFixVLvz6P3VFMYGBioPdjr6emhr6//3ONcnvKe9t9BSf8gyyM75wF3/nf/mcsRQgghhCivp0o2169fP5KTk4mNjcXCwoLExEQsLCy4dOmS2nEDBgxg0qRJQMEQkR9++AFvb2+aNWtGt27dSExMVI4NDw+nffv2yve4uDi6du1Ks2bNaNeuHcHBweTm5pKeno6zc8Ea+/379ycwMPCJ7T179iwWFhZFlumcOHEiAwcOBAr+Ov+f//yHwYMHY2VlRceOHYmOjlY7Pjk5GR8fH6ysrPjwww8JCgri7t27Zb5vP/30E2lpaYwePVrpQDxq1KhRaGlpKfXGxMTg5OREbGwsnTp1wtLSkp49e3L06FHlnNzcXGbPno2DgwM2NjZ8/PHHHDhwoMxtKkm/fv2UZVPL0o779+8zdepU7O3tad68ORMnTmTs2LFq8XnS/XNycmLWrFl07doVOzu7Mg9te1SPHj348ssv1bYdOXKEpk2bkpGRQXh4OH369OE///kP9vb2tGzZkvHjx6u94bCwsGDDhg0A3Lx5k5EjR2Jvb4+VlRXe3t7K0LnAwEA2b97M4cOHsbCwID09vcztHBS8C7ex8c/0Y1BBpjQJIYQQ4tV4qk5EeHg4VlZWdOnShQMHDvDhhx9So0YN4uPjlWMyMjJISkrC09NT2TZ79mzc3NyIj4/no48+wt/fnyNHjhQp/+zZs0yaNIkRI0YoQ3wSEhJYtmwZtWvXVh6yw8PDmThx4hPb26RJE95//3219t2/f58dO3aotW/RokVYW1sTFxdH3759mTZtGgkJCUqbBgwYQNu2bUlISGDu3LmcOnUKX1/fMv81+vfff6dSpUpK9uXHGRgY0Lx5c7WH8+vXr7N27Vpmz55NbGwsUPDwWljnhAkT+Pnnn5X9Xbp0YejQoezbt69MbSqrJ7Vj/Pjx/Pzzz3z77besW7eOu3fvsnXrVuX8st6/NWvWMGHCBFauXImtrW252+nl5cXu3bvJyspStsXGxtKmTRtq164NFGTE3r9/PytWrGDhwoUcPXqUkSNHFlvetGnTyMnJYfXq1WzevJn69evz+eefc+/ePSZOnIiLiws2NjYcOHBAKV8IIYQQ4k33VH/KrFatGrq6uhgYGChDP3r06EFCQgIjRowACt4k1KtXDxsbG+W8Hj164OPjA8DYsWNJSkoiMjJSbe4CQHp6OlpaWpiYmFCnTh3q1KlDREQEhoaG6OjoUL16daBgyFJZx+x7eXkxe/Zspk6dSsWKFdm9ezfa2tp06tRJOaZ169b4+/sD0KBBA44dO8aqVavo3r07ERERtG7dmmHDhgFQr1495s6dS8eOHTl8+DD29vZPbMM///zDW2+9hZaWVonHVKtWTe0v2nl5eUyZMgVLS0sAhgwZwsiRI7lx4wb3799ny5YtbNy4kWbNmgEwcOBAzp49S0RERLGJ3ApFREQQGRmptu2jjz7i22+/Lfb40tqRk5PDzp07WbJkiZLALTQ0VK0zVNb71759e9q1a1diu5/Ezc2N0NBQdu3aRY8ePcjOzmbHjh18/fXXaseFhYUpWainTJmCn58ff/75J40aNVI77tKlSzRu3BhTU1MMDAyYOHEibm5u6OjoYGhoiL6+Pnp6eq9s6JyxsebMWXnTyL3VbBIfzScx0nwSI82nyTF6buMhPD09Wb58OUePHqVFixbExcXh5eWldszjf1m2sbHhp59+KlJW4dAcLy8v3n33Xdq2bUuHDh2UB9in4ebmRkhICLt376Z79+7ExsbStWtXKlSoUGr7CodcnT59mrS0NLVOUaHz58+XqRNRvXr1Jw5/+t///ldkcnmDBg2Uz4Wdpry8PE6fPg0UDOt6VF5eHm+99Vap9fTq1YsBAwaobatUqVKp5zypHdbW1sr+ChUqYGVlpXwv6/171rkgVatWpVOnTsTHx9OjRw927dqFjo4OHTt2VI6pW7eu0oEAaN68OQDnzp0r0onw9/dn3Lhx7Ny5kxYtWtC2bVvc3NyeefL28/KqJ1y9qTRhMpsomcRH80mMNJ/ESPNpQoxeSrK5Bg0a0KJFCxISEtDR0eHy5cv06NFD7Rg9PT217/n5+cXODahQoQKRkZGcPn2aAwcOcODAAYYOHar8lflpVKlSBWdnZxISErC3t+fQoUOMGTOm1PapVCqlfSqVCldXV4YPH16k7LKsKAXQokULlixZwpkzZ2jatGmR/Tk5ORw7dqzIcqH6+vpFjn348KEyDCgyMrJIp0Fbu/SRam+99RZmZmZlaveT2lF4j0ob1lXW+/dop+5peXl54evry7Vr14iPj6dbt25qbdfVVf+1z8/PB4q/Z506deLnn3/m559/5tdff2XVqlUsXLiQdevWYWFh8cxtFUIIIYR4HT3VnAig2CE5Xl5eJCYmsn37dtq3b19kiMeJEyfUvv/+++/Fzg/46aef+O6773jvvff47LPPiIyMZNSoUWzbtq3EusvCy8uLQ4cOsWnTJho2bKgMASqpfcnJyUr7GjVqxF9//UXdunUxMzPDzMwMlUrFzJkzycjIKFP97dq1o3HjxsyZM0d5cH3UkiVLyMnJ4eOPPy5TeYV/Nb9+/brSJjMzM2JiYoiJiSlTGc+DhYUFWlpaHD9+XNn26BuKwrY+6/0rq1atWlGnTh1iYmI4dOhQkbwRly5d4p9//lG+//777wBFcnTk5uYyc+ZMLl++jKurK8HBwcowuB9//BF4+t9FIYQQQojX2VO/iTA0NCQ9PZ0rV65gYmICQOfOnQkODmbdunXFJimLiopSHt7Xr19PSkoKwcHBRRulq8vChQupXLkyHTp04J9//uHHH39UhsIULgWbkpJC48aNlTkST2JnZ0ft2rVZsmQJAQEBRfZv374da2trHBwcSExMZPfu3fznP/8BwNfXFx8fH6ZOnUr//v25d+8eQUFBZGVlUa9evTLVr6OjQ1hYGL6+vnz66acMHToUc3Nzbty4wfr164mLi+Obb74p8xuCRo0a8dFHHzF9+nQePnyIhYUFu3btYsmSJcycObNMZTwPpqamdOnSheDgYPT19alZsybLli0jIyNDech+HvevrLS0tOjZsyeLFy/G3Ny8yDC4+/fvM378eMaMGcPNmzeZPn06nTt3LpJBW19fn2PHjnHkyBEmTZqEsbEx+/fv5969e8rQLUNDQ65du8bly5epXbt2kbccJYmY5PzM15md8+CZyxBCCCGEeBpP/Saib9++pKam4urqyo0bN4CCMfVdunShUqVKxU7q9fb2JioqCnd3d3777TciIiKKHdbTtm1bvvnmGzZt2kS3bt0YNGgQdevWZd68eQC8/fbb9O7dm9mzZytLyJaFlpYWHh4e5Ofn07179yL73d3d+fHHH3FzcyMuLo6wsDDlOqytrVm+fDnnzp2jZ8+eDBkyBDMzM77//vtih/mUxNzcnNjYWKysrPj6669xcXFh2LBh3L9/n+joaNzd3ctcFsC8efNwcXFh2rRpuLq6EhMTwzfffIOHh0e5ynlWX3/9NS1atGDEiBF8/PHHGBgYYGNjowwRe173r6x69uxJdna22upbhWrWrImlpSU+Pj4EBATQoUOHEofJzZ8/HzMzM4YPH07nzp1Zu3YtoaGhtGrVCiiYW/LgwQNcXV3V3rw8yc2bd7lx484z/UiOCCGEEEK8Kk+dsbokgwYNonHjxowfP15tu4WFBcHBwUXG+79sU6ZM4fbt2yxYsEBtu5OTE25ubsW+oRCly8nJ4eeff6ZVq1ZUrvx/k29cXFzo3r17sfMgXrTff/+d/v37s3//frU3VeHh4WzYsOGpclA8T5KxWrNpwmQ2UTKJj+aTGGk+iZHm04QYvZSJ1YmJiaSkpJCUlMTUqVOfV7HPzS+//EJqaiqxsbGsWLHiVTfnjaKvr09QUBB2dnYMGzYMHR0dNm7cyN9//03nzp1falvOnz/PuXPnWLp0KW5ubmUe6iaEEEIIIcruuXUili9fTmpqKlOnTi0ytlwTxMTEsHfvXoYMGfJUScxKc+3atSc+LNevX/+lTnZ+mbS0tFi6dCmzZ8+md+/e5Ofn895777FixQrMzc1falvS0tKYMGEClpaWjBs37qXWLYQQQgjxb/HchzP9G+Xn56sliCuOnp4ederUeUktev4ePnxIbGwsjo6OvP322y+lPmdnZ9LT09mzZ88ru3cvahieDGfSbJrwClmUTOKj+SRGmk9ipPk0IUYvZTjTv5mOjk65cy68bg4dOsSECRPYs2fPS6nv8OHDXL16lTp16hAdHS1zVYQQQgghNIh0IkSZvOwXVhs3bsTa2hpLS0s2bdqEv79/kWSAr7OSevWlyc55ICsyCSGEEEIjSCfiNZGVlUVYWBjbt2/nzp07NGnShC+//JLmzZtz/vx55s2bx5EjR8jJyaFly5aMGzeOxo0bAxAYGEhaWhpr165Vyps3bx6bN29m7969QMGwna+//ppt27aRnJzMW2+9hbe3N/7+/iQlJTFw4EAAOnTogL+/P3Z2dvTv358vv/ySpUuXUrNmTerWrcvt27dZs2aNUk9GRgZOTk6sXr2aFi1alOla79y5w65du/j8889p3bo1K1asYPfu3bi6uirHBAYGkp+fT40aNYiLiyMrK4tWrVoxffp0atWqBRQklfv66685cuQIhoaG+Pr6snbtWj7//HMlAV1CQgIrVqzgwoULVK1alW7dujFq1KhiM2erVCqWL1/Opk2buHLlCvr6+tjY2DB58uRy57oYFLyL65nl6xBsnuuOvHgWQgghhCZ46jwR4uUKCAggMTGR4OBg4uPjady4MYMHD+bKlSt4e3uTn5/PihUrWLNmDdra2vj4+PD333+Xq47Q0FA8PDzYunUrnp6ehIeH89tvv2FjY0NYWBgAGzZswNfXVzknMTGR6OhoQkJC8PLy4ujRo1y+fFnZHxsbi5mZWZk7EABbtmwhOzubzp0788EHH/Duu++ybt26Isdt376d27dvs3r1ahYvXkxycrLSzvv37zNgwAAePHjADz/8QFhYGLGxsWpti4qK4quvvsLDw4P4+HgmT55MQkICY8eOLbZdkZGRLFu2jPHjx7Nz504WLlzIpUuXXmpiPyGEEEIITSBvIl4Dqamp7Nu3jyVLluDo6AgU5LswNDQkJCQEfX195s2bh4GBAQBhYWF07NiRNWvWlGuFInd3dyXZ3ejRo4mKiiI5ORlbW1uqVq0KgJGRkZIxHAryghT+Fd7CwgJjY2MSEhKU3BDx8fHFJnwrzaZNm3j//feVcrt168bixYs5f/682mpPhoaGTJ8+HT09PczNzenevTsHDhwAYNu2bfz3v/9l48aNGBkZATB79mzl+h4+fMiSJUvw9vbm008/BaBevXqoVCpGjhxZpC6AunXrMmvWLJycnAAwMTHB1dWVhISEcl3fszA2rvLS6vq3k3ut2SQ+mk9ipPkkRppPk2MknYjXwLlz5wD44IMPlG26urqMHz8ePz8/LC0tlQ4EQMWKFbG0tCQlJaVc9TRo0ED5rKWlReXKlcnLyyv1HFNTU+Wzjo4OHh4eSiei8K1Ejx49ytyGc+fOceLECbXOT2EnYt26dUycOFHZ/u6776rNk3i0vadPn8bMzEzpQAA0adKEKlUK/jHeunWLGzdu0LJlS7X6C5f/TUlJKdKJcHJy4tixY8yfP58LFy5w4cIF/vrrr5eyWlWhV71Kw7+FJqyIIUom8dF8EiPNJzHSfJoQo9JWZ5LhTK8BXd2Cvp6WllaRfSVNeFapVMp5xcnPzy+yTV9fv8zlF3q08wLg6enJxYsXOXbsGPHx8Tg4OFCzZs1Sy3jUxo0bAZg7dy7vvfce7733nvL2IC4ujuzs7FLbW0hHR6fUekq6rsLtxd27pUuX0q9fP27dukWrVq0ICgpSG9olhBBCCPFvIZ2I10DhX8RPnDihbFOpVLi4uJCamsrJkyfVHq6zsrI4efKkMrFaT0+Pu3fvqpWZlpZWrjYU14EpjpmZGXZ2dmzfvp3ExERlAnNZ5ObmkpCQQOvWrYmPjycuLk75GTt2LP/73//YunVrmcpq0qQJaWlpZGZmKttSU1O5c6egR1+jRg1q1KjBkSNH1M47fPgwAI0aNSpS5uLFi/n8888JCgrC29sba2trLl68+NJXrhJCCCGEeNVkONNroH79+jg7OxMcHMy0adOoXbs2K1euJDMzk9jYWNzd3QkICGDEiBEALFiwgLy8PHr37g1A8+bN2bBhAzExMdjZ2bF3714OHDhA9erVy9yGwnkQZ86cUeZHlMTT05OpU6dSsWJFZf5AWezdu5fMzEwGDhyodIAK1atXj1WrVrF27doyzbHo1q0b4eHhjBs3ji+++ILs7Gy+/vpr4P86RH5+fsyZMwdTU1M+/PBDzp07xzfffEPHjh2pX79+kTJr167Nr7/+SseOHdHR0SE+Pp5du3ZRrVq1Ml9joYhJzuU+JzvnQbnPEUIIIYR4EeRNxGti5syZ2NvbExAQgIeHB6mpqURERGBiYsLq1avJzc3Fx8eHfv36ARAdHY2JiQkAbm5u9O/fn5CQENzc3EhOTmb06NHlqr9JkyY4OTkREBDAggULSj22c+fOaGtr071793Lldti4cSN169alffv2Rfbp6+vTt29fTpw4wcmTJ59Ylr6+PsuXLycvL4+PP/6YESNGKG9FCts0YMAAJk2axNq1a+natSszZszA09OTb7/9ttgyQ0NDyc3NxcvLi759+3Lu3DmCgoK4ffu22qpPZXHz5l1u3LhTrh/JESGEEEIITaH1UMZiiOfs6tWrODk5ERcXV+SNwsuSnp7OxYsXadeunbLt2rVrtG/fnjVr1hSZUP2y3bx5F5VK/ulpKk2YzCZKJvHRfBIjzScx0nyaEKPSJlbLcCbx3GRkZHD8+HHWrVuHra3tK+tAAOTk5PDZZ58xduxYnJ2duXPnDmFhYdSrV09tlSshhBBCCFF+0okQz01mZiaBgYGYmpry3Xffqe2bPn06sbGxpZ4/a9YsXFxcnktbzM3N+fbbb1m8eDELFizAwMCA1q1bs3LlynINsRJCCCGEEEXJcKZ/iYcPHxIbG4ujo+NLyWtw+PBhVq9ezR9//MGtW7d46623eP/99/n4449LfENRo0YNtUR2bzIZzqTZNOEVsiiZxEfzSYw0n8RI82lCjCRPhODQoUNMmDCB+/df/OTciIgIBg4cSK1atQgPD2f37t0sXLiQGjVqEBAQQEZGBmZmZkV+/i0dCCGEEEKI150MZ/qXeFkvnI4fP87cuXOZMGGCslIUFCyPamNjQ05ODqGhocTExLyU9miqknr1JcnOeSCrMwkhhBBCY0gn4iXJysoiLCyM7du3c+fOHZo0acKXX35J8+bNOX/+PPPmzePIkSPk5OTQsmVLxo0bpwz7CQwMJC0tjbVr1yrlzZs3j82bN7N3714ALCws+Prrr9m2bRvJycm89dZbeHt74+/vT1JSEgMHDgSgQ4cO+Pv7Y2dnR//+/fnyyy9ZunQpNWvWpG7duty+fZs1a9Yo9WRkZODk5MTq1atp0aLFE69z9erVmJiY0Ldv32L3T5o0CW3t/3sBlpuby/z580lISODu3bs0atSIkSNHKqsqxcTE8N1339GpUyc2bdqElZUVQ4YMoX///kRGRhIUFMTly5dp0qQJoaGh7Ny5k9WrV3P//n2cnJz45ptvlMzWGzZsIDIykrS0NLS1tWnatCkTJkzAysoKACcnJ7y9vTlx4gQHDhxAX18fNzc3AgMDefjwIY6OjvTu3ZtRo0Yp7Y+Pj2f69On88ssvRbJ3l2ZQ8C6uZ5a9U7B5rjvy0lkIIYQQmkKGM70kAQEBJCYmEhwcTHx8PI0bN2bw4MFcuXIFb29v8vPzWbFiBWvWrEFbWxsfHx/+/vvvctURGhqKh4cHW7duxdPTk/DwcH777TdsbGwICwsDCh6kfX19lXMSExOJjo4mJCQELy8vjh49qpbzIDY2FjMzszJ1IKBgLkSrVq1KzHBtZGSklpxtwoQJ/Pzzz8yePZvY2Fi6dOnC0KFD2bdvn3LMlStX+Pvvv4mNjSUwMFDZPnPmTGbMmMH69eu5efMm3t7enDt3jlWrVjF9+nS2bdvGpk2bANi9ezdBQUEMGjSI7du38/3335OXl8dXX32l1r7w8HBsbW1JSEhg5MiRREVFsWXLFvT09HB3dychIUHtrU5sbCxdu3YtVwdCCCGEEOJ1J28iXoLU1FT27dvHkiVLcHR0BGDKlCkYGhoSEhKCvr4+8+bNUx5Ew8LC6NixI2vWrGHcuHFlrsfd3R13d3cARo8eTVRUFMnJydja2ipZpo2MjNTmHgwaNIh69eoBBW8zjI2NSUhIYPjw4UDBX9rLkiG60H//+1+MjIzUti1btoxFixapbUtISEClUrFlyxY2btxIs2bNABg4cCBnz54lIiKCDz/8UDl++PDhmJqaApCUlASAv78/1tbWAMr9Cg4OplKlSpibm7NkyRLOnTsHQLVq1QgODqZHjx4AmJiY8PHHHzN58mQePnyodHratm1L//79AfDx8WHdunUkJyfTo0cPvLy8WLFiBUePHqVly5ZkZGSQlJREQEBAme/PszA2rvJS6hEF5H5rNomP5pMYaT6JkebT5BhJJ+IlKHyQfTQ/ga6uLuPHj8fPzw9LS0u1v2RXrFgRS0tLUlJSylVPgwYNlM9aWlpUrlyZvLy8Us8pfDAH0NHRwcPDQ+lEFL6VKHzwLovq1atz+/ZttW0ff/wxzs7OABw7doxx48ahUqk4ffo0gPLQXigvL4+33npLbdu7775bpK769esrnytWrIixsTGVKlVSthkYGJCbmwuAra0tRkZGLFy4kNTUVNLS0pT7m5+fj66ubpEyAbV7aG5ujo2NDXFxcbRs2ZK4uDgaNGjw0vJOvOoVGv5NNGFFDFEyiY/mkxhpPomR5tOEGEmyuVes8AG1uCE+JU14VqlUynnFyc/PL7KtcOx/Wcov9PgwHE9PT5YsWcKxY8eIj4/HwcGBmjVrllrGo1q0aMHhw4fVtlWtWlV5E3L16tUibYuMjCzSaXh03kRx7QSK3J/Hz3nU5s2bCQwMpFu3btjY2NC7d2/+/PNPpk+frnbck+6hp6cnISEhTJkyhfj4eHr16lVinUIIIYQQbyqZE/ESmJubA3DixAllm0qlwsXFhdTUVE6ePEl2drayLysri5MnTyoTq/X09Lh7965amWlpaeVqQ0lzFB5nZmaGnZ0d27dvJzExkZ49e5arnv79+3Px4kXWr19f7P6MjAzlc6NGjQC4fv262lKvMTExz331pqVLl+Lh4UFISAh9+/bFzs5OmftRnpWrXF1dyc/P5/vvv+fy5cvK8DEhhBBCiH8TeRPxEtSvXx9nZ2eCg4OZNm0atWvXZuXKlWRmZhIbG4u7uzsBAQGMGDECgAULFpCXl0fv3r0BaN68ORs2bCAmJgY7Ozv27t3LgQMHqF69epnbUDgP4syZM8pbgZJ4enoydepUKlasiJOTU7mutXnz5gQGBjJ9+nROnjxJ9+7dqV27NhkZGSQkJLBx40aaNm1K9erVeeutt/joo4+YPn06Dx8+xMLCgl27drFkyRJmzpxZrnqfpHbt2vzxxx+cPHmSt956i71797J69WqgYIWosmaxNjQ0pEuXLixcuJD27dtTo0aNp2pPxCTnch2fnfPgqeoRQgghhHgR5E3ESzJz5kzs7e0JCAjAw8OD1NRUIiIiMDExYfXq1eTm5uLj46PkVoiOjsbExAQANzc3+vfvT0hICG5ubiQnJzN69Ohy1d+kSROcnJwICAhgwYIFpR7buXNntLW16d69e5kfrh/16aef8sMPP5Cdnc24ceNwcXFh+PDh/P3338ycOZONGzcqw5fmzZuHi4sL06ZNw9XVlZiYGL755hs8PDzKXW9pJk+ejLGxMf369aNXr178+OOPhIaGAupviMqiZ8+eZGdnl2vC+eNu3rzLjRt3yvwjOSKEEEIIoUm0Hr6sLGTitXH16lWcnJyIi4tThlSJ/7NlyxZmzZrFvn37Sp23UpqbN++iUsk/PU2lCZPZRMkkPppPYqT5JEaaTxNiJBOrRZlkZGRw/Phx1q1bh62trXQgHnPq1CkuXLjAt99+S58+fZ66AyGEEEII8bqTpyChyMzMJDAwEFNTU7777ju1fdOnTyc2NrbU82fNmoWLi8uLbOIr9ccffxAaGkq7du0YPHjwq26OEEIIIcQrI8OZ/gUePnxIbGwsjo6OvP32209Vxq1bt7hzp/RXahMmTODo0aPKd11dXWrWrEnXrl0ZOXJkscunFic9PZ0OHTqwcuVK2rRp81Tt1XQynEmzacIrZFEyiY/mkxhpPomR5tOEGMlwpn+5Q4cOMWHCBPbs2fPUZRgZGRXJRP04HR0dXFxcmDx5MlCw6lFKSgqTJk0iPz+f8ePHP3X9QgghhBBCc0gn4l/gZb5s0tfXx9jYWPluYmKCj48PkZGR0ol4REm9+kdl5zyQVZmEEEIIoZFkideXICsrixkzZuDg4IC1tTXe3t4kJycDcP78efz9/WnVqhU2Njb4+flx7tw55dzAwED69OmjVt68efPU8jdYWFiwfv16BgwYgJWVFe3atVPmNCQlJTFw4EAAOnToQHh4OElJSVhYWBAREYG9vT1ubm4MHz4cHx8ftXoyMjJo2rSp2hClp1GxYkW177m5uYSEhODk5ISlpSW2traMHDmSmzdvqh13/PhxevfujaWlJU5OTmzYsKHMZaSnp2NhYcGPP/5Iz549adasGV27duWPP/5gw4YNODk5YWNjw9ChQ7l9+7ZSbmJiIr169cLa2ppmzZrh4eHBTz/9pOy/ePEigwYNomXLltjY2DBw4EDOnj1b7nsyKHgXbmPjS/0xqCB9fCGEEEJoJulEvAQBAQEkJiYSHBxMfHw8jRs3ZvDgwVy5cgVvb2/y8/NZsWIFa9asQVtbGx8fH/7+++9y1REaGoqHhwdbt27F09OT8PBwfvvtN2xsbAgLCwNgw4YN+Pr6KuckJiYSHR1NSEgIXl5eHD16VMniDBAbG4uZmRktWrR46ms/f/480dHRfPzxx2pt3bFjBzNnzmTnzp2EhISQlJTEwoUL1c6NiopiyJAhbNu2DUdHRyZPnkxqamq5yvj666/54osviIuLw8DAgM8++4wtW7awaNEiwsPDOXz4MMuXLwfg5MmTjBgxAldXVzZv3sz69eupUaMGX375Jbm5uQCMGTOGWrVqsXHjRjZs2ICurq6SJFAIIYQQ4t9C/tT5gqWmprJv3z6WLFmCo6MjAFOmTMHQ0JCQkBD09fWZN28eBgYGAISFhdGxY0fWrFnDuHHjylyPu7s77u7uAIwePZqoqCiSk5OxtbVVMlQbGRkpmasBBg0aRL169YCCtxnGxsYkJCQwfPhwAOLj48udUG379u3K3Iu8vDzy8vIwMzNTkugBNGvWDGdnZ+zs7ICCIU/t2rVTewMDMHToUOWNy+jRo/nhhx84deoUDRo0KHMZ/fv3VyZnd+/enRkzZjBt2jTq168PgL29vXKOjo4OkyZNUnsjM2DAAHx9fbl27RqmpqZcunSJNm3aYGJigp6eHjNmzCA1NZX8/Hx0dHTKda/Kwti4ynMvU5SN3HvNJvHRfBIjzScx0nyaHCPpRLxghQ+oH3zwgbJNV1eX8ePH4+fnh6WlpdKBgIKhP5aWlqSkpJSrngYNGiiftbS0qFy5Mnl5eaWeY2pqqnzW0dHBw8ND6UQUvpXo0aNHudrh6OiozH148OABV69eZfHixfTq1Yu4uDiqV6+Ou7s7v/76K3PmzOHixYukpqZy4cIFrK2t1coq7OAASkcoOzsboMxlFHYWACpVqgSAmZmZss3AwIDMzEwAmjZtStWqVVm6dCmpqamkpaUpQ5VUKhVQ8FZpxowZ/PDDD9jZ2eHg4EDXrl1fSAcCeOWrMvxbacKKGKJkEh/NJzHSfBIjzacJMSptdSYZzvSCFSYk09LSKrKvpAnPKpWq1ERm+fn5RbYVt3zqkyZUP9p5AfD09OTixYscO3aM+Ph4HBwcqFmzZqllPK5SpUqYmZlhZmaGubk5bdu2Zc6cOVy9epVt27YBBW9iRo8eTW5uLh999BFz5syha9euRcoq7cG8rGXo6ekV2aatXfyv/eHDh3FxceH48eM0adIEf39/Zs+erXaMj48P+/fvZ9KkSVSpUoUFCxbQtWtXrl+/Xup9EUIIIYR4k8ibiBfM3NwcgBMnTuDg4AAUdBK6dOlCXl4eOTk5ZGdnKw/0WVlZnDx5kl69egEFD8F3795VKzMtLa1cbSiuA1McMzMz7Ozs2L59O4mJiUydOrVc9ZSksDOjUqnIzMwkOjqa2bNn0717d+WY1NTUMueReB5lFGfFihW0bNlSLdFeVFSUcg3//e9/WbRoEZ999hk9e/akZ8+eXLt2jfbt25OUlISbm1uZ64qY5PzEY7JzHpT/IoQQQgghXgLpRLxg9evXx9nZmeDgYKZNm0bt2rVZuXIlmZmZxMbG4u7uTkBAgDI5d8GCBeTl5dG7d28AmjdvzoYNG4iJicHOzo69e/dy4MABqlevXuY2FM6DOHPmjDIsqCSenp5MnTqVihUrqq0AVVa5ubncuHFD+X7t2jXmzZtHpUqVcHZ2pnLlylSpUoW9e/diZWVFTk4Oq1ev5tSpU7z//vtlquN5lFGc2rVrs3v3bo4cOcI777xDUlIS8+fPV66rWrVq7Nu3j0uXLjF27FgqV65MTEwMenp6WFpalqsuSTYnhBBCiNeZDGd6CWbOnIm9vT0BAQF4eHiQmppKREQEJiYmrF69mtzcXHx8fJTJx9HR0ZiYmADg5uZG//79CQkJwc3NjeTkZEaPHl2u+ps0aYKTkxMBAQEsWLCg1GM7d+6MtrY23bt3L3Yo0JPs3LmTdu3a0a5dOxwcHPD19UVLS4tVq1ZRq1Yt9PT0mD9/PufPn6d79+4MHjyY+/fvM2bMGC5cuMC9e/eeWMfzKKM4I0eOVJZ97dGjBxs2bGDGjBkYGBhw4sQJdHV1WbZsGdra2gwYMABXV1cOHDjA4sWL1eZeCCGEEEK86bQevsxMZELjXb16FScnJ+Li4mjcuPGrbs4bS95EaDZNmMwmSibx0XwSI80nMdJ8mhCj0iZWy3AmARQkljt+/Djr1q3D1tZWOhBCCCGEEKJE0okQQMFk5cDAQExNTdUmFgNMnz6d2NjYUs+fNWsWLi4uL7KJQgghhBBCQ0gnQgDw3nvv8fvvvxe7z9/fn08//bTYfQ8fPmTXrl289957L7J59OvXj8OHDyvfdXV1qVmzJl27dmXkyJFlXpUpPT2dDh06sHLlStq0aUNgYCBpaWmsXbv2RTVdCCGEEOKNI50I8URGRkYYGRkVu+/gwYPMnTsXV1fXF94OFxcXJk+eDBSslpSSksKkSZPIz89XEtyV18SJE4vNu/GiPT6+MDvnAXf+d/+lt0MIIYQQ4mlIJ0I8k5c5L19fXx9jY2Plu4mJCT4+PkRGRj51J6JKlVeTTn5Q8C6uZ/5fp2HzXHdkepsQQgghXheyxOtrLisrixkzZuDg4IC1tTXe3t4kJycDcP78efz9/WnVqhU2Njb4+flx7tw55dzAwED69OmjVt68efPU8kNYWFiwfv16BgwYgJWVFe3atVPmTCQlJTFw4EAAOnToQHh4OElJSVhYWBAREYG9vT1ubm4MHz4cHx8ftXoyMjJo2rQpR48efabrr1ixYpFtmzZtokuXLlhZWdGlSxdWrVqFSqUq9vxH70G/fv2wsLAo8lO49G55yxZCCCGEeFNJJ+I1FxAQQGJiIsHBwcTHx9O4cWMGDx7MlStX8Pb2Jj8/nxUrVrBmzRq0tbXx8fHh77//LlcdoaGheHh4sHXrVjw9PQkPD+e3337DxsaGsLAwADZs2ICvr69yTmJiItHR0YSEhODl5cXRo0e5fPmysj82NhYzMzNatGjx1Nd+/vx5oqOj+fjjj5VthXUOHz6crVu3Mnr0aJYtW8acOXOeWF54eDgHDhxQfiZOnIiuri6ff/75M5cthBBCCPEmkeFMr7HU1FT27dvHkiVLcHR0BGDKlCkYGhoSEhKCvr4+8+bNw8DAAICwsDA6duzImjVrGDduXJnrcXd3x93dHYDRo0cTFRVFcnIytra2SgZsIyMjJTM2wKBBg6hXrx5Q8DbD2NiYhIQEhg8fDkB8fDyenp7lut7t27ezZ88eAPLy8sjLy8PMzEztTcGiRYsYMmQI3bp1A8DU1JS7d+8SFBTEqFGjSi2/WrVqyuc//viDOXPmMHHiRNq0aVOmsitUqFCu63mcsfGrGVoliifx0GwSH80nMdJ8EiPNp8kxkk7Ea6xwaNIHH3ygbNPV1WX8+PH4+flhaWmpdCCgYOiPpaUlKSkp5aqnQYMGymctLS0qV65MXl5eqeeYmpoqn3V0dPDw8FA6EYVvJXr06FGudjg6OipzHx48eMDVq1dZvHgxvXr1Ii4ujocPH3L16lXmz5+vtkytSqUiJyeH9PT0Mj3op6enM2zYMD7++GM++eQTAG7duvXEss3Nzct1PY971QllxP/RhAQ/omQSH80nMdJ8EiPNpwkxkmRzbyhd3YLwaWlpFdlX0oRnlUqlnFec4lYqKm751CdNqH608wLg6enJkiVLOHbsGPHx8Tg4OFCzZs1Sy3hcpUqVMDMzU76bm5vTsGFD2rdvz7Zt25Q8FePHj6ddu3ZFzq9duzbXr18vtY47d+4wZMgQmjZtyoQJE5TthfMeSitbCCGEEOLfQjoRr7HCv3yfOHECBwcHoOBht0uXLuTl5ZGTk0N2drbyQJ+VlcXJkyfp1asXAHp6ety9e1etzLS0tHK1obgOTHHMzMyws7Nj+/btJCYmMnXq1HLVU5LCzoxKpeLtt9/GyMiIS5cuqXU2tm3bxu7duwkJCSm1rAcPHjBq1CgePnxIWFgYOjo6yr5nLftxEZOc1b5n5zwo1/lCCCGEEK+STKx+jdWvXx9nZ2eCg4M5ePAgFy9eJCgoiMzMTKKiosjJySEgIIDTp09z+vRpxowZQ15eHr179wagefPm/Pnnn8TExJCenk5kZCQHDhwoVxsK50GcOXOGO3dKf+Xm6enJ2rVrUalUaitAlVVubi43btxQfk6ePMnEiROpVKkSzs7OaGlp4efnx5o1a4iKiiItLY3ExESCgoIwMDB4YkK66dOnc+bMGebOnUtOTo5aXc9a9uNu3rzLjRt3lB/JESGEEEKI14m8iXjNzZw5k9DQUAICAsjJycHS0pKIiAhMTExYvXo1s2fPxsfHB21tbWxtbYmOjsbExAQANzc3zpw5Q0hICLm5uTg6OjJ69GhWrVpV5vqbNGmCk5MTAQEB9OnTh44dO5Z4bOfOnQkKCqJ79+7o6emV+1p37tzJzp07gYI3IG+99RZWVlasWrWKWrVqAeDr60uFChWIiooiJCSEGjVq0KtXL0aOHPnE8qOjowGKnauRkpLyTGULIYQQQrxJtB6+zGxh4l/t6tWrODk5ERcXR+PGjV91c16pmzfvolLJPz1NpQmT2UTJJD6aT2Kk+SRGmk8TYiQTq8UrlZGRwfHjx1m3bh22trb/+g6EEEIIIcTrTjoR4oXLzMwkMDAQU1NTteVRoWAeQmxsbKnnz5o1S1l5SQghhBBCvHoynEm8Urdu3XrihOxq1aphZ2fHzJkz6dmz50tq2Yslw5k0mya8QhYlk/hoPomR5pMYaT5NiJEMZxIay8jICCMjo1KPefDgzVv+9NF/kNk5D2R1JiGEEEK8VqQTIcQrMCh4F9czCzoOm+e6I38LEkIIIcTrRPJECI1z9epVPv/8c2xsbHB0dGTbtm1q+3/88Ud69uyJlZUVnTp1IiwsjNzcXGX/rVu3CAgIoGXLltjb2zN79mz69etHeHg4AOHh4fTp04exY8fSvHlzvvrqKwCSk5Px8fHBysqKDz/8kKCgILVkfLm5ucyePRsHBwdsbGz4+OOPy51XQwghhBDiTSCdCKFRHjx4wODBg7l16xZRUVHMmzePZcuWKfv379/PqFGj8PLyYsuWLUydOpXt27czbtw4oCBz9ZAhQ7hw4QLLli1jxYoVHD9+nN9++02tnuTkZKpVq0Z8fDyDBw/m7NmzDBgwgLZt25KQkMDcuXM5deoUvr6+SlbsCRMm8PPPPzN79mxiY2Pp0qULQ4cOZd++fS/t/gghhBBCaAIZziQ0ysGDB/nzzz/Zvn07DRo0AGDGjBl4eXkBsHjxYry8vPjkk08AqFu3LkFBQXz66aekp6eTnp7O8ePH2bJlC40aNQJg3rx5xWbIHj16NFWqVAFg3LhxtG7dmmHDhgFQr1495s6dS8eOHTl8+DDvvPMOW7ZsYePGjTRr1gyAgQMHcvbsWSIiIvjwww+f6bqNjas80/ni+ZOYaDaJj+aTGGk+iZHm0+QYSSdCaJRz585haGiodCAALC0tlQzXp0+f5vjx42rLwha+KTh//jznz5/H0NBQ6UAA1KhRg/r166vVU61aNaUDUVhuWloaNjY2Rdp0/vx5bt26BUD//v3V9uXl5fHWW2897eUqXvXqC0KdJqyIIUom8dF8EiPNJzHSfJoQI1mdSbxWHl91WEtLS+lEqFQqBg4cqLyZeJSxsTEXL14scn5xKlSooPZdpVLh6urK8OHDixxrZGTEzz//DEBkZGSRToO2towKFEIIIcS/izz9CI3StGlTsrKySElJUbadP3+erKwsABo1asSFCxcwMzNTfq5fv05oaCj37t2jSZMmZGVl8ddffynn3759m7S0tFLrbdSoEX/99Rd169ZVylWpVMycOZOMjAzlzcb169fV6o6JiSEmJqbc1xkxyZnNc93ZPNed7Jw3bwlbIYQQQrzZpBMhNIq9vT2WlpYEBgbyxx9/cOLECQIDA5W/9vv5+bF7927Cw8O5cOECBw8eJDAwkDt37mBsbIy9vT0ffPAB48eP548//uDs2bN88cUX3L9/Hy0trRLr9fX1JSUlhalTp/LXX39x7Ngxxo4dy8WLF6lXrx6NGjXio48+Yvr06SQmJnL58mUiIiJYsmQJdevWLfd13rx5lxs37nDjxh3JESGEEEKI1450IoRG0dHRYdmyZdSrVw9fX1+GDh2Ku7u7MoSoc+fOhIWFsWfPHtzc3Bg3bhxt27blu+++U8oIDw/nnXfeYcCAAXz66ac0a9aMOnXqKEOiimNtbc3y5cs5d+4cPXv2ZMiQIZiZmfH999+jr68PFEzQdnFxYdq0abi6uhITE8M333yDh4fHi70pQgghhBAaRuthWQaQC/GauHXrFseOHaNdu3ZKpyE3Nxd7e3umTp1Kjx49Xm0D/7+bN++iUsk/PU2lCZPZRMkkPppPYqT5JEaaTxNiJBOrxb+Grq4uAQEBeHt706dPH/Ly8oiIiEBfX5/27du/6uYJIYQQQrwRZDiTeKO89dZbLF68mD/++IMePXrQu3dv/vvf/xIZGYmRkdGrbp4QQgghxBtBOhH/Ug8fPiQmJoabN2++0Hr69euHhYWF8vP+++/z0UcfMWfOHHJzc8tcTnp6OhYWFvz6669PPLZVq1bMmTOHrKwswsPDWbZsGRYWFs9yGUIIIYQQ4hEynOlf6tChQ0yYMIE9e/a88LpcXFyYPHkyUDA/ISUlhUmTJpGfn8/48eNfSJ21a9fmwIEDVK1a9YWU/6weHV+YnfNAVmgSQgghxGtFOhH/Ui9zPr2+vj7GxsbKdxMTE3x8fIiMjHxhnQgdHR21OjXNoOBdXM8s6DhsnuuOTG0TQgghxOtEhjO9IllZWcyYMQMHBwesra3x9vYmOTkZKEiu5u/vT6tWrbCxscHPz49z584p5wYGBtKnTx+18ubNm4eTk5Py3cLCgvXr1zNgwACsrKxo166dsgxqUlISAwcOBKBDhw6Eh4eTlJSEhYUFERER2Nvb4+bmxvDhw/Hx8VGrJyMjg6ZNm3L06NFnuv6KFSsW2bZp0ya6dOmClZUVXbp0YdWqVahUqhLL+P7773FycqJZs2YMGDCA7777TrkHjw9/ys3NZdasWbRu3ZoWLVoQHBxMYGAgX3zxhXJPLCws1JLSpaWlYWFhQVJS0lO3UQghhBDiTSSdiFckICCAxMREgoODiY+Pp3HjxgwePJgrV67g7e1Nfn4+K1asYM2aNWhra+Pj48Pff/9drjpCQ0Px8PBg69ateHp6Eh4ezm+//YaNjQ1hYWEAbNiwAV9fX+WcxMREoqOjCQkJwcvLi6NHj3L58mVlf2xsLGZmZrRo0eKpr/38+fNER0fz8ccfK9sK6xw+fDhbt25l9OjRLFu2jDlz5hRbxpo1a/j222/5/PPPSUhIwN7enoULF5ZY5/Tp09m8eTMzZsxg3bp1XL9+na1bt5ar3eVtoxBCCCHEm0qGM70Cqamp7Nu3jyVLluDo6AjAlClTMDQ0JCQkBH19febNm4eBgQEAYWFhdOzYkTVr1jBu3Lgy1+Pu7o67uzsAo0ePJioqiuTkZGxtbZW5AkZGRhgaGirnDBo0iHr16gEFbzOMjY1JSEhg+PDhAMTHx+Pp6Vmu692+fbsy9yIvL4+8vDzMzMzo16+fcsyiRYsYMmQI3bp1A8DU1JS7d+8SFBTEqFGjipQZERGBj48PvXr1AuDzzz/n1KlTnD59usixd+7cITY2lsmTJ/PRRx8BBR2sjh07lus6ntTGChUqlKu8RxkbV3nqc8WLITHRbBIfzScx0nwSI82nyTGSTsQrUDg06YMPPlC26erqMn78ePz8/LC0tFQ6EFAw9MfS0pKUlJRy1dOgQQPls5aWFpUrVyYvL6/Uc0xNTZXPOjo6eHh4KJ2IwrcS5U3Y5ujoqMx9ePDgAVevXmXx4sX06tWLuLg4Hj58yNWrV5k/f75a5mmVSkVOTg7p6elqD+iZmZlcuXIFGxsbtXpatmxZbCfiwoULPHjwgGbNminbDAwMsLKyKvM13Lp164ltNDc3L3N5j3vVyWSEOk1I8CNKJvHRfBIjzScx0nyaECNJNqdhdHULbruWllaRfSVNeFapVMp5xcnPzy+yTV9fv8zlF3q08wLg6enJkiVLOHbsGPHx8Tg4OFCzZs1Sy3hcpUqVMDMzU76bm5vTsGFD2rdvz7Zt23BxcQFg/PjxtGvXrsj5tWvX5vr168r3wvtQ3snhj89dKO7+POrBgwdFzi2tjUIIIYQQ/xYyJ+IVKPyL9YkTJ5RtKpUKFxcXUlNTOXnyJNnZ2cq+rKwsTp48SePGjQHQ09Pj7t27amU+OiG4LIrrwBTHzMwMOzs7tm/fTmJiIj179ixXPSUp7ACoVCrefvttjIyMuHTpEmZmZsrPqVOnlLkbj6pSpQomJiYcO3ZMbfvx48eLrathw4ZUqFBBmbgOBZ2uU6dOKd/19PQA1O7ro/e0vG18kohJzmye687mue5k5zx48glCCCGEEBpEOhGvQP369XF2diY4OJiDBw9y8eJFgoKCyMzMJCoqipycHAICAjh9+jSnT59mzJgx5OXl0bt3bwCaN2/On3/+SUxMDOnp6URGRnLgwIFytaFwHsSZM2e4c6f0V2Wenp6sXbsWlUqltgJUWeXm5nLjxg3l5+TJk0ycOJFKlSrh7OyMlpYWfn5+rFmzhqioKNLS0khMTCQoKAgDA4Ni3xj4+fnxww8/EBMTQ1paGitXrmT79u3F1l+pUiX69+/PwoUL2bFjB6mpqQQFBXHp0iXlmMaNG2NoaMiSJUtIS0vj8OHDhIWFKZ2tp2ljaW7evMuNG3e4ceOO5IgQQgghxGtHhjO9IjNnziQ0NJSAgABycnKwtLQkIiICExMTVq9ezezZs/Hx8UFbWxtbW1uio6MxMTEBwM3NjTNnzhASEkJubi6Ojo6MHj2aVatWlbn+Jk2a4OTkREBAAH369Cl1knHnzp0JCgqie/fuyl/sy2Pnzp3s3LkTKHgYf+utt7CysmLVqlXUqlULAF9fXypUqEBUVBQhISHUqFGDXr16MXLkyGLL7NOnD//88w9hYWFkZmZiZ2eHh4dHiUvPjhkzBgMDA4KDg7l37x6urq5YW1sr+ytXrsycOXOYO3cuXbt2pUGDBkyYMIFBgwYpx5S3jUIIIYQQbyqthy8z65h4LV29ehUnJyfi4uKUIVWv2v79+2nYsCF16tRRtk2ePJlLly6VuTPVr18/atWq9UqWaL158y4qlfzT01SaMJlNlEzio/kkRppPYqT5NCFGMrFaPJWMjAyOHz/OunXrsLW11ZgOBBQsNXv+/HmmTZuGsbExv/32GwkJCUydOvVVN00IIYQQ4o0nnQhRoszMTAIDAzE1NVVb1hQKkrfFxsaWev6sWbOUlZeet8mTJzNr1iyGDx/O//73P8zMzPjqq6+e28RvIYQQQghRsteiE/Hw4UNiY2NxdHTk7bfffmH1BAYGkpaWxtq1a4vs27BhA5MmTSpzroakpCT69+/Prl27MDMz49atW+zZs0dJjvY00tLScHZ2JjIyEnt7+xKPO3LkCD4+PrRu3Zrvv//+qet77733+P333wGIiYmhU6dOnDp1Cl1dXfz9/fn0009LPf/XX3/FwsKixP1Dhw4lICDgqdoWFRXFr7/+yi+//PJU5wNERkYSGxvLzZs3X+jvlRBCCCHEm+a16EQcOnSICRMmKFmPXwc2NjYcOHAAIyMjoGAi9dWrV5+pE1FWGzdupEGDBhw6dIiLFy8qGaifJyMjI+XaSlI4yXn//v1oaxddCKxSpUpPXb+vry8+Pj5PfT682t+rwvGF2TkPZHUmIYQQQrx2XoslXl/Hud/6+voYGxujo6MDvLxruHv3Ljt27GDQoEEYGRmxbt26l1Jvad5++22MjY2L/BQuM/s0DA0Nn9iJeZJX+Xs1KHgXbmPjMajwWvTjhRBCCCHUPLETkZWVxYwZM3BwcMDa2hpvb2+1pF3nz5/H39+fVq1aYWNjg5+fH+fOnVP2BwYG0qdPH7Uy582bp5ZvwMLCgvXr1zNgwACsrKxo166dMgY/KSmJgQMHAtChQwfCw8NJSkrCwsKCiIgI7O3tcXNzY/jw4UX+Mp2RkUHTpk1LXPbzWfTr14+QkBC++uorWrZsSfPmzRk7dqySrKywjWlpaQQGBrJ582YOHz6MhYUF6enpAGzatIkuXbpgZWVFly5dWLVqlVpW5XPnztG/f3+sra1xdnbm8OHDT2zXtm3byM7OxsHBgU6dOhEbG0tOTo7aMRYWFsyfPx8nJyfatGlDSkoKGRkZjBkzhtatW/P+++/j4OBASEhIkUzYGzdupH379nzwwQcMGTKEy5cvP+utJCYmBicnJ7Zt24aTkxNWVlYMHDiQa9euMXPmTOzs7LC3t2fmzJnKOeHh4bRv3x6A9PR0LCwsWLx4Me3atePDDz/k1q1bnDt3jiFDhmBra4ulpSVOTk4sW7YMKP73Cgp+n/38/LCxsaFdu3aMHTuWGzduKPVevHiRQYMG0bJlS2xsbBg4cCBnz5595nsghBBCCPE6eWInIiAggMTERIKDg4mPj6dx48YMHjyY69ev8/fff+Pt7U1+fj4rVqxgzZo1aGtr4+Pjw99//12uhoSGhuLh4cHWrVvx9PQkPDyc3377DRsbGyUj8IYNG/D19VXOSUxMJDo6mpCQELy8vDh69KjaQ21sbCxmZma0aNGiXG0pq6ioKGrUqMHGjRuZMWMGO3fuLHYOwsSJE3FxcVGGONWuXVtp9/Dhw9m6dSujR49m2bJlynKjd+7cYcCAAVSqVIn169czZcoUFi5c+MQ2bdy4EWtra2rVqkXXrl25fft2sUnYfvjhB8LCwli0aBGNGzdm6NCh3L59mxUrVrBjxw4GDx7MihUr2L17t9p5a9asYd68eaxZs4bbt28zbNgwtY7P07p+/To//PADixYtYsWKFZw4cYLu3bvz8OFDoqOjGTZsGN9//z379+8vsYzY2FhWrlzJggULqFixIr6+vlSpUoV169axZcsWXF1dmTNnDidOnCj29+ratWt88sknmJqasnHjRpYsWcLdu3fp3bs3WVlZQEG+iVq1arFx40Y2bNiArq4uI0aMeObrF0IIIYR4nZQ6liI1NZV9+/axZMkSHB0dAZgyZQqGhob8888/xMfHo6+vz7x58zAwMAAgLCyMjh07smbNGsaNG1fmhri7u+Pu7g7A6NGjiYqKIjk5GVtbW6pWrQoUjMN/dAjMoEGDlPH+FhYWGBsbk5CQwPDhw4GCZUA9PT3L3Ibyql+/PmPGjAGgXr16xMXFKRORH1WlShX09fXR09PD2NgYgEWLFjFkyBC6desGgKmpKXfv3iUoKIhRo0axdetW7t27R0hICFWrVqVx48YEBgYyatSoEtvz119/cezYMSZNmgSAra0t77zzDmvXrqVHjx5qx7q7u2NlZQVAdnY27u7uuLi4KAntPv30U5YvX865c+fo3Lmzcl5oaChNmzYFICQkBBcXFw4cOKC8FSiOra1tsdt37NihJJvLy8tj4sSJNGnSBAB7e3tOnDhBYGAg2tra1K9fnwULFnDu3LkS6/rkk09o1KgRALdu3aJ///588sknVK5cMP9g5MiRLFu2jHPnztGsWbMiv1fLli2jZs2aTJkyRSkzLCyMVq1asWPHDnr27MmlS5do06YNJiYm6OnpMWPGDFJTU8nPz1eGrpWXsXGVpzpPvFgSF80m8dF8EiPNJzHSfJoco1I7EYXDkj744IP/O0FXl/HjxwOQkpKCpaWl0oEAqFixIpaWlmVexahQgwYNlM9aWlpUrlyZvLy8Us8xNTVVPuvo6ODh4aF0IgrfSjz+8FwaXV3dEsfJq1QqdHXVb1f9+vXVvleuXJlr1649sZ5bt25x9epV5s+fr7Z0qkqlIicnh/T0dM6dO0fdunWVB10omKxdmo0bN6Ktra089GtpaeHq6sqKFSs4c+aM8vAP6vfOwMCAvn37smPHDlauXElaWhopKSlcv35dbThTxYoV1cqoV68eVatW5c8//yy1E7Fp06ZiH7Br1Kih9v3R+1mxYkVMTU3VJmQbGBiQm5tbYj2PXpORkRGffPIJW7du5fTp01y6dEkZdlTSm5PTp09z/vz5Ivc5JyeH8+fPAwVv5mbMmMEPP/yAnZ0dDg4OdO3a9ak7EMArTyQjitKEBD+iZBIfzScx0nwSI82nCTF66mRzhQ/NWlpaxe4vzwP3ox4fZw8FE5HLWn6hRzsvAJ6enixZsoRjx44RHx+Pg4MDNWvWLLWMR1WtWpX//e9/xe67ffu22gN9SW0ui8KH2PHjx9OuXbsi+2vXrl3seXp6eiWWmZeXR0JCAiqVSnlrBP93D9euXcv06dOV7Y/eu6ysLPr27UtWVhZdunTBw8ODZs2a0bdvX7U6ivs9UKlUT7wPdevWLfX3odDjxxS3olNpHr2mGzdu0Lt3b6pXr06HDh1o164dzZo1U7s3j1OpVLRs2ZKvv/66yL4qVQr+EuDj40Pnzp356aefOHjwIAsWLGDRokXExsaW63dNCCGEEOJ1VupTmrm5OQAnTpxQtqlUKlxcXIiNjcXCwoKTJ0+SnZ2t7M/KyuLkyZNKdmM9PT1lsnGhtLS0cjWypE7M48zMzLCzs2P79u0kJiaWO/FYs2bNSEtLU5tIW+i3336jWbNm5SrvUY9ew9tvv42RkRGXLl3CzMxM+Tl16pQyTr9p06akpaVx69Yt5bxH4/C4H3/8kZs3bzJp0iTi4uKUn/j4eKytrdm8eXOROBQ6cOAAp06dYtWqVYwaNQpXV1eqVKnCzZs31Y7LyspSi11KSgp37txRhhBpki1btnDr1i3Wrl3LsGHD6NSpE//88w/wfx2rx3+vGjVqxIULF3jnnXeUmFSrVo0ZM2Zw7tw5/vvf/zJ9+nTy8vLo2bMns2fPJiEhgf/+978kJSWVq30Rk5zZPNed7JwHz+eChRBCCCFeolI7EfXr18fZ2Zng4GAOHjzIxYsXCQoKIjMzkzZt2vDJJ5+Qk5NDQEAAp0+f5vTp04wZM4a8vDx69+4NQPPmzfnzzz+JiYkhPT2dyMhIDhw4UK5GFs6DOHPmDHfulP5ax9PTk7Vr16JSqdRWgCoLJycn6tevz5AhQ/j111+5cuUKx48fZ/r06Rw6dIihQ4eWq7zHr+HatWtcvnyZ/Px8/Pz8WLNmDVFRUaSlpZGYmEhQUBAGBgbo6+vTpUsX3n77bcaOHcvZs2c5fPiw2upEj9u0aRM1a9bE29ubxo0bq/34+fmRlZVFfHx8see+8847ACQkJHDlyhWOHDnCsGHDyMvLUxs+pK2tTUBAAH/88Qd//PEHX375JS1atKBVq1alXvvNmze5ceNGkZ/bt2+X/0aW0TvvvEN2djbbtm3jypUrHDhwQJm/UnhNj/9effLJJ9y7d4+xY8dy5swZzp49y5gxYzhx4gSNGzemWrVq7Nu3j0mTJnHmzBkuX77MunXr0NPTw9LSslztu3nzLjdu3JEcEUIIIYR4LT1xjMnMmTMJDQ0lICCAnJwcLC0tiYiIUCbErl69mtmzZ+Pj44O2tja2trZER0crE3Td3Nw4c+YMISEh5Obm4ujoyOjRo1m1alWZG9mkSROcnJwICAigT58+dOzYscRjO3fuTFBQEN27dy91+E9x9PX1iYqKYsGCBUyaNInr169TuXJlrKysWL16NdbW1uUq71G9evVi//79uLq6smbNGnx9falQoQJRUVGEhIRQo0YNevXqxciRI4GC+RWRkZFMnz4db29vqlevjr+/P1999VWRsq9du8bPP//MsGHDir1mJycn6tWrx7p164pN0GZlZcWECRP4/vvvCQ8Pp1atWri6ulK7dm21tx9vvfUWHh4eDBs2jPv37+Po6MjkyZOfeO0lzZdo3rx5sdnBn4fOnTtz6tQpZs+ezd27dzExMaFXr17s2bNHuabHf68mTpzI6tWrmTt3Lp988gk6Ojo0b96cyMhIJSfFsmXLCAkJYcCAAWRlZdGkSRMWL15cZH6MEEIIIcSbTOvh65jJrRRXr17FycmJuLg4ZUiVEJrm5s27qFRv1D+9N4omTGYTJZP4aD6JkeaTGGk+TYjRU0+sfp1kZGRw/Phx1q1bh62trXQghBBCCCGEeEHemE5EZmYmgYGBmJqaqi2bCjB9+nRiY2NLPX/WrFm4uLi8yCYKIYQQQgjxRnjjhjMV59atW0+ckF2jRg21RHbixXr48CGxsbE4Ojry9ttvv7B6wsPD2bBhg5Lp+t69e8TGxhZZvrY0FhYWBAcH06tXr+fWLhnOpNk04RWyKJnER/NJjDSfxEjzaUKM/hXDmUpjZGSkTIwVmuHQoUNMmDCBPXv2vNR6ly1bRkJCQrk6ES9C5coG/E9WZhJCCCHEa6p82byEeE5e1QswTXnxVqHCv6L/LoQQQog3lHQi/qWysrKYMWMGDg4OWFtb4+3tTXJyMgDnz5/H39+fVq1aYWNjg5+fH+fOnVPODQwMpE+fPmrlzZs3Ty0vh4WFBevXr2fAgAFYWVnRrl07Za5KUlISAwcOBKBDhw6Eh4eTlJSEhYUFERER2Nvb4+bmxvDhw4ssSZuRkUHTpk05evRoua85PDycxYsXc+XKFSwsLEhKSkKlUrF06VJcXFywtLSkefPmDBo0iIsXLxY5/9atW1haWrJx40a17YsWLZL5NEIIIYT4V5FOxL9UQEAAiYmJBAcHEx8fT+PGjRk8eDBXrlzB29ub/Px8VqxYwZo1a9DW1sbHx4e///67XHWEhobi4eHB1q1b8fT0JDw8nN9++w0bGxslM/eGDRvw9fVVzklMTCQ6OpqQkBC8vLw4evQoly9fVvbHxsZiZmZGixYtyn3Nvr6+fPrpp7zzzjscOHAAGxsbIiMjWbZsGePHj2fnzp0sXLiQS5cuFZvYz8jICCcnpyJJ++Li4sqdHV0IIYQQ4nUmYyr+hVJTU9m3bx9LlizB0dERgClTpmBoaEhISAj6+vrMmzcPAwMDAMLCwujYsSNr1qxh3LhxZa7H3d0dd3d3AEaPHk1UVBTJycnY2tpStWpVoODB/NEJ7YMGDaJevXpAwdsMY2NjEhISGD58OADx8fF4eno+1XUbGhpSsWJFdHR0MDY2BqBu3brMmjVLeYtiYmKCq6srCQkJxZbh6enJkCFDuHLlCiYmJhw9epT09HR69OhR7vYYG1d5qusQL4fER7NJfDSfxEjzSYw0nybHSDoR/0KFQ5M++OADZZuuri7jx4/Hz88PS0tLpQMBULFiRSwtLUlJSSlXPQ0aNFA+a2lpUblyZfLy8ko9x9TUVPmso6ODh4eH0okofCvxNA/sJXFycuLYsWPMnz+fCxcucOHCBf76668SV4xycHCgVq1aJCQk8PnnnxMbG0vbtm2VDO7l8apXXBAl04QVMUTJJD6aT2Kk+SRGmk8TYlTa6kwynOlfSFe3oO+opaVVZF9JE49VKpVyXnHy8/OLbNPX1y9z+YUe7bxAwV/+L168yLFjx4iPj8fBwYGaNWuWWkZ5LF26lH79+nHr1i1atWpFUFCQ2vCqx2lraysdm9zcXLZv3/7Ub0aEEEIIIV5X0on4FzI3NwfgxIkTyjaVSoWLiwupqamcPHmS7OxsZV9WVhYnT55UsoDr6elx9+5dtTLT0tLK1YbiOjDFMTMzw87Oju3bt5OYmPjMcw8er3fx4sV8/vnnBAUF4e3tjbW1NRcvXiy1s+Pp6cmFCxf4/vvv0dXVVZtQXlY5OQ/KfY4QQgghhKaQTsS/UP369XF2diY4OJiDBw9y8eJFgoKCyMzMJCoqipycHAICAjh9+jSnT59mzJgx5OXl0bt3bwCaN2/On3/+SUxMDOnp6URGRnLgwIFytaFwHsSZM2eemAjQ09OTtWvXolKpnuqB/fF6//nnH1JTU8nJyaF27dr8+uuv/Pnnn6SmpjJv3jx27dpFbm5uiWWYmppib2/PwoULcXNzK/aNy5PcvZv95IOEEEIIITSUdCL+pWbOnIm9vT0BAQF4eHiQmppKREQEJiYmrF69mtzcXHx8fOjXrx8A0dHRmJiYAODm5kb//v0JCQnBzc2N5ORkRo8eXa76mzRpgpOTEwEBASxYsKDUYzt37oy2tjbdu3dHT0/vqa63kKurK7Vr16Z79+7s27eP0NBQcnNz8fLyom/fvpw7d46goCBu376ttirU43r27El2djZeXl7P1B4hhBBCiNeR1kNNyb4lRAmuXr2Kk5MTcXFxypCqV23JkiXs3LmTmJiYpzr/5s27qFTyT09TacJkNlEyiY/mkxhpPomR5tOEGJU2sVpWZxIaKyMjg+PHj7Nu3TpsbW01ogNx5MgR0tLSiIiI4KuvvnrVzRFCCCGEeCWkEyE0VmZmJoGBgZiamirZrgtNnz6d2NjYUs+fNWvWc88kvW/fPqKiotRyYAghhBBC/NvIcKZ/uYcPHxIbG4ujo2OJuRGeh379+nH48GHlu5aWFhUrVsTc3JwhQ4bQqVOncpV369YttQnZ165d4/Tp03z00UfKtho1aqglsnve0tPT6dChAytXrqRNmzblOleGM2k2TXiFLEom8dF8EiPNJzHSfJoQIxnOJEp06NAhJkyYwJ49e154XS4uLkyePBko6Lzcu3eP5cuXM3LkSKKjo7GysipzWUZGRhgZGSnfJ0yYQN26dRkwYMDzbrYQQgghhHiMrM70L/cyX0Tp6+tjbGyMsbExNWvWpH79+kydOhUDAwO2bNnyTGXLCzUhhBBCiJdHOhGvWFZWFjNmzMDBwQFra2u8vb1JTk4G4Pz58/j7+9OqVStsbGzw8/Pj3LlzyrmBgYH06dNHrbx58+ap5VKwsLBg/fr1DBgwACsrK9q1a6fML0hKSmLgwIEAdOjQgfDwcJKSkrCwsCAiIgJ7e3vc3NwYPnw4Pj4+avVkZGTQtGlTjh49+kzXX5gFuzDXgkqlIjIyEldXV5o1a0aHDh1YunQpKpUKKBhCZGFhweLFi2nXrh0ffvgh3bp1Izk5mdjYWCwsLICC4VNffPGFWl1ffPGFsmQtwKVLl/Dz88PGxoZ27dqxYsUKOnXqpKy4lJubS0hICE5OTlhaWmJra8vIkSO5efPmM12zEEIIIcTrTjoRr1hAQACJiYkEBwcTHx9P48aNGTx4MFeuXMHb25v8/HxWrFjBmjVr0NbWxsfHh7///rtcdYSGhuLh4cHWrVvx9PQkPDyc3377DRsbG8LCwgDYsGEDvr6+yjmJiYlER0cTEhKCl5cXR48eVcubEBsbi5mZGS1atHjqa8/MzGTGjBlkZ2fj7OystHX+/Pn4+fmxZcsWhg0bxuLFiwkJCVE7NzY2lpUrV7JgwQJWr16NlZUVXbp0KXPSu/v37zNgwAAePHjADz/8QFhYGLGxsWrXGBoayo4dO5g5cyY7d+4kJCSEpKQkFi5c+NTXLIQQQgjxJpA5Ea9Qamoq+/btY8mSJTg6OgIwZcoUDA0NCQkJQV9fn3nz5mFgYABAWFgYHTt2ZM2aNYwbN67M9Ty6ktDo0aOJiooiOTkZW1tbqlatChTMMXh0EvKgQYOoV68eUPA2w9jYmISEBIYPHw5AfHw8np6e5bre7du3K3Mv8vPzycnJoU6dOsyYMQMrKyvu3r3L6tWrGTt2LB4eHgCYmZnxzz//8O233+Lv76+U9cknn9CoUSPlu66uLgYGBhgbG5epLdu2beO///0vGzduVOZWzJ49W23FpWbNmuHs7IydnR0AJiYmtGvXTu1t0NMqaZKS0BzGxlVedRNEKSQ+mk9ipPkkRppPk2MknYhXqPBh9IMPPlC26erqMn78ePz8/LC0tFQ6EAAVK1bE0tKSlJSUctXToEED5bOWlhaVK1cmLy+v1HNMTU2Vzzo6Onh4eCidiMK3Ej169ChXOxwdHRk/fjwA2traVK5cmerVqyv7U1NTycvLo2XLlmrn2drakpeXR2pqqrKC1KPtexqnT5/GzMxMbXJ2kyZNqFLl//6xuru78+uvvzJnzhwuXrxIamoqFy5cwNra+pnqBlmdSdNpwooYomQSH80nMdJ8EiPNpwkxKm11JhnO9AoVzgfQ0tIqsq+kicIqlUo5rzj5+flFthXONyhL+YUe7bwAeHp6cvHiRY4dO0Z8fDwODg7UrFmz1DIeV6lSJczMzDAzM8PU1FStA1Famwq3P3rdj7evLB69Nzo6Ok88fsqUKYwePZrc3Fw++ugj5syZQ9euXctdrxBCCCHEm0Y6Ea+Qubk5ACdOnFC2qVQqXFxcSE1N5eTJk2RnZyv7srKyOHnypJK5WU9Pj7t376qVmZaWVq42FNeBKY6ZmRl2dnZs376dxMREevbsWa56ysLc3Bw9PT2OHDmitv3w4cPo6+tjZmZW4rmPX8eT7k2TJk1IS0sjMzNT2ZaamqrknsjMzCQ6OppJkybx1Vdf4enpyXvvvUdqaqqsBCWEEEKIfz3pRLxC9evXx9nZmeDgYA4ePMjFixcJCgoiMzOTqKgocnJyCAgI4PTp05w+fZoxY8aQl5dH7969AWjevDl//vknMTExpKenExkZWeaJxYUK50GcOXNGLXlbcTw9PVm7di0qlUptBajnpXLlyvTp04fvvvuOuLg40tLSiImJYdGiRfTp04fKlUueR2BoaEh6ejpXrlwBCu7NoUOH2Lt3L5cvX2bevHmcP39eOb5bt268/fbbjBs3jrNnz/LHH38o80wKh3xVqVKFvXv3cvHiRVJSUpg8eTKnTp0iNzf3uV+7EEIIIcTrRDoRr9jMmTOxt7cnICAADw8PUlNTiYiIwMTEhNWrV5Obm4uPj4+yNGl0dDQmJiYAuLm50b9/f0JCQnBzcyM5OZnRo0eXq/4mTZrg5OREQEAACxYsKPXYzp07o62tTffu3dHT03uq632SwMBABg8ezIIFC+jatStLlixh+PDhBAYGlnpe3759SU1NxdXVlRs3bjBgwABcXFwYN24cHh4eZGZmqiWi09fXZ/ny5eTl5fHxxx8zYsQI5e2Knp4eenp6zJ8/n/Pnz9O9e3cGDx7M/fv3GTNmDBcuXODevXsv5PqFEEIIIV4HWg9lbIYoo6tXr+Lk5ERcXJwypOp1lZ6ezsWLF2nXrp2y7dq1a7Rv3541a9YUmdz9vMnEas2mCZPZRMkkPppPYqT5JEaaTxNiVNrEalmdSTxRRkYGx48fZ926ddja2r72HQiAnJwcPvvsM8aOHYuzszN37twhLCyMevXqqa2WJYQQQgghipJOhHiizMxMAgMDMTU1VbJdF5o+fTqxsbGlnj9r1ixcXFxeZBPLzdzcnG+//ZbFixezYMECDAwMaN26NStXrnxhQ7WEEEIIId4UMpzpCR4+fEhsbCyOjo5KjoIXXZ+zszPp6ens2bOHOnXqPJdyLSwsCA4OplevXsXuDw8PZ8OGDezfv79c5d66davECdkPHz5k8+bNJCYmcvHiRbS1tWnUqBG9evUqsR0vU1paGs7OzkRGRmJvb09gYCBpaWmsXbsWQFkl6kUMbZLhTJpNE14hi5JJfDSfxEjzSYw0nybESPJEPINDhw4xYcIE7t+//1LqO3z4MFevXqVOnTpER0e/lDoBfH19iYuLK/d5RkZGSu6Hx3/i4+P54Ycf8PPzY/PmzWzcuJHOnTszbdo0Fi9e/Pwv4hlNnDiR//znP0BBTgkfHx8uXbr0ilslhBBCCKF5ZDjTE7zsFzUbN27E2toaS0tLNm3ahL+//0sZXmNoaKgs9/q8/PDDDwwePJhu3bop28zNzbl69SorV65k6NChz7W+Z/Votmp5QSeEEEIIUTKNfxORlZXFjBkzcHBwwNraGm9vb5KTkwE4f/48/v7+tGrVlDLoXwAAOaxJREFUChsbG/z8/Dh37pxybmBgIH369FErb968eWo5DiwsLFi/fj0DBgzAysqKdu3aKeP+k5KSGDhwIAAdOnQgPDycpKQkLCwsiIiIwN7eHjc3N4YPH46Pj49aPRkZGTRt2pSjR4+W+Vrv3LnDrl27aNu2LZ07d+bGjRvs3r1b7ZjAwED8/f0ZNGgQzZs3Jzw8HJVKxdKlS3FxccHS0pLmzZszaNAgLl68qHbuhQsX6NOnD5aWlnTu3Jlt27Yp+8LDw2nfvr3y/dq1awQEBNCyZUvs7e0ZOnRokfKeREdHh6SkJLKystS2Dxs2TO0tS0ZGBmPGjKF169a8//77ODg4EBISopZh+vjx4wwcOJCWLVtia2vL6NGjycjIUPb369ePL774Qq2eL774QlkaF+DcuXP0798fa2trnJ2dOXz4sNrxj/6+vP/++wBMmDBBKePOnTtMnjyZVq1a0aJFC/r376+WKFAIIYQQ4t9C4zsRAQEBJCYmEhwcTHx8PI0bN2bw4MFcuXIFb29v8vPzWbFiBWvWrEFbWxsfHx/+/vvvctURGhqKh4cHW7duxdPTk/DwcH777TdsbGwICwsDYMOGDfj6+irnJCYmEh0dTUhICF5eXhw9epTLly8r+2NjYzEzM6NFixZlbseWLVvIzs6mc+fOfPDBB7z77rusW7euyHG7d+/G3t6emJgYevbsSWRkJMuWLWP8+PHs3LmThQsXcunSJWbOnKl2XlRUFO7u7mzevJkuXbowZswYjh8/XqT8rKws+vXrR35+PqtXryYqKorq1avz8ccfc+3atTJfz9ChQ/n5559p27Ytw4YNY8WKFZw6dYqqVatSr149teNu377NihUr2LFjB4MHD2bFihVKB+rEiRP4+Pjw7rvvsnbtWpYsWUJGRgY+Pj5FslKX5M6dOwwYMIBKlSqxfv16pkyZwsKFC0s8vnBuyFdffUV4eDgPHz7Ez8+PixcvsnjxYtavX4+1tTV9+vTh9OnTZb4nQgghhBBvAo0ezpSamsq+fftYsmQJjo6OAEyZMgVDQ0NCQkLQ19dn3rx5GBgYABAWFkbHjh1Zs2aNkn24LNzd3XF3dwdg9OjRREVFkZycjK2tLVWrVgUKxv4/Otxn0KBByoOwhYUFxsbGJCQkMHz4cADi4+Px9PQs1/Vu2rSJ999/Xym3W7duLF68mPPnz2Nubq4cV7VqVT777DPle926dZk1a5byhsXExARXV1cSEhLUyvf29sbb2xuAUaNGcfDgQVauXMm8efPUjtu6dSuZmZnMnTtXGUr1zTffkJSUxPr16xkxYkSZrqd///40bNiQH374gYMHD7Jnzx6gYEjTjBkzsLa2Jjs7G3d3d1xcXJQkep9++inLly/n3LlzdO7cmRUrVtCgQQOmT5+OlpYWAAsWLKBjx44kJCTwySefPLEtW7du5d69e4SEhFC1alUaN25MYGAgo0aNKvb4wkn0VapUoVq1ahw8eJDff/+dgwcPYmRkBMCYMWNITk4mMjKSWbNmleme/F/5JWffFprB2LjKkw8Sr4zER/NJjDSfxEjzaXKMNLoTUTg06dF1+3V1dRk/fjx+fn5YWloqHQiAihUrYmlpSUpKSrnqadCggfJZS0uLypUrk5eXV+o5pqamymcdHR08PDyUTkThW4kePXqUuQ3nzp3jxIkTap2fwk7EunXrmDhxorL93XffVTvXycmJY8eOMX/+fC5cuMCFCxf466+/iqwmZWNjo/b9gw8+4JdffinSltOnT3P37l3s7OzUtufk5HD+/PkyXxNAmzZtaNOmDQ8ePOD06dPs37+f1atXM2jQIHbv3o2RkRF9+/Zlx44drFy5krS0NFJSUrh+/boynCklJYVWrVopHQiAWrVqYWZmVuZYnzt3jrp16yqdQih6P0pz6tQpoGBY26Nyc3PJyckpczmFZHUmzaYJK2KIkkl8NJ/ESPNJjDSfJsTotU02p6tb0LxHHx4LlTTxVaVSKecV59Fx9oX09fXLXH6hRzsvAJ6enixZsoRjx44RHx+Pg4MDNWvWLLWMR23cuBGAuXPn8u2336rti4uLY+zYsUqdFSpUUNu/dOlSvvvuOzw8PGjVqhUDBgxgz549xMfHqx2no6Oj9j0/P7/Ya1epVNStW5elS5cW2VepUqUyXc/Zs2f54YcfmDBhAhUrVkRXVxcrKyusrKxwcnLCw8ODw4cP0759e/r27UtWVhZdunTBw8ODZs2a0bdvX6Ws0mJd2qTz4mL9qPJMWFepVFSsWLHYFayKu4dCCCGEEG8yjZ4TUTiE59HJqyqVChcXF1JTUzl58iTZ2dnKvqysLE6ePKlkVNbT0ysyZj4tLa1cbSiuA1McMzMz7Ozs2L59O4mJifTs2bPMdeTm5pKQ8P/au/u4Hu/9geOvkhYyE7GdtJgpLN0pcaaTtU20VRKT5S4yLDflZsp91kY5Tm62c2ZOdig3uauYgy1mjklu2kaLSt8ilO1MOxtRqev3h0fXz3dCXyu+8n4+Hj0edd18br7v1a6363Ozg169epGSkkJycrL6NW3aNH799Vd27dp11/s/+eQTJkyYQGRkJAEBATg4OFBQUHDHw/fvx+5nZGTQqVOnO8qztramqKgIU1NTdblWCwsLli5dyrFjx2rVJwMDAxITE9m7d+8d56rfBrRu3ZpDhw7xww8/sHbtWqZMmYKXlxfNmzfn559/Vq+3sbHh+PHjWv25fPky58+fV9t/v1h36dKFc+fOceXKFfXYvSZF/z7u1tbWXL9+nevXr2stY/vPf/5THaYlhBBCCPGk0OskokOHDvTt25eoqCjS0tIoKCggMjKSkpIS4uPjKSsrIywsjKysLLKyspg6dSoVFRUMGTIEACcnJ3Jzc9m+fTsXLlxg3bp1HDp0SKc2VM+DOH369F03Vavm7+/Pxo0bqaqq0loB6n72799PSUkJQUFBWFtba32NGjWK1q1bqxug1eS5557j8OHD5ObmotFoiI2N5YsvvqC8vFzrurVr15KUlIRGo2HRokVkZ2drza2o5uPjQ8uWLZk0aRLfffcdeXl5REREcPDgQWxsbGrVJxsbGwYOHMiCBQv4xz/+QU5ODufPn2ffvn1MmjSJnj174uzszLPPPgvAjh07uHjxIsePH+fdd9+loqJCbf/o0aPRaDTMmzeP3NxcMjIymDx5Mm3atMHLywu4FesjR46wf/9+CgsLiY2N1Rp61b9/f1q1asW0adM4c+YMR48evWPi+e0aNWqEiYkJZ8+e5eeff8bNzY0uXbowbdo00tLSOHfuHNHR0Wzbtk1rvooQQgghxJNAr5MIgEWLFuHq6kpYWBh+fn5oNBri4uKwsLAgISGB8vJyAgMD1WU4ExMT1Qm63t7ejBgxgujoaLy9vcnIyCA0NFSn+jt37oyHhwdhYWGsWLHintf269cPQ0NDfHx8dBoqs3XrVp5//nmtJVarGRsbM2zYME6dOkVmZmaN98fExFBeXs6gQYMYNmwYOTk5REZG8ssvv2itGPXuu++SkJCAj48PaWlprFq1qsYH4ObNm5OQkECrVq0IDg5m0KBBFBUVsWbNGp0emKOiopg+fTr79+9n6NCheHl5ERMTQ58+fdTN5uzs7IiIiGD9+vX079+fiIgIXFxcePPNN9U3BXZ2dqxZs4azZ8/i7+/PhAkTsLS0ZOPGjereDqNGjcLT05MZM2bg5+dHSUkJo0aNUttiamrKunXrMDIyIiAgQJ1Xcy/vvPMOGzduZMyYMTRq1Ig1a9ZgZ2dHWFgYPj4+pKen89FHH9GrV69afyZCCCGEEA2BgSK7atWZ4uJiPDw8SE5OVodUCVETmVit3/RhMpu4O4mP/pMY6T+Jkf7Thxg9thOrHxdFRUWcPHmSTZs24eLiIgmEEEIIIYRo0CSJqAMlJSWEh4djaWmp7nZdbeHChSQlJd3z/sWLF+Pp6VmfTaxTPj4+WsOkapKamnrHErNCCCGEEKJhkOFM9ezKlSv3nZDdunVrrY3sHiZFUUhKSsLd3b3WD/2XLl267z4a7dq101pSdvjw4Rw9elT92cjIiDZt2vDGG28wefLkP7xMqo2NDVFRUQwePPgPlXO7mzdv8tJLL7Fo0SKdVtuqDRnOpN/04RWyuDuJj/6TGOk/iZH+04cYyXCmR8jMzEzd4VgfHTlyhIiICJ2WKf3Tn/70QHV5enoyd+5c4NayttnZ2cyZM4fKykpmzpz5QGUKIYQQQoiHT+9XZxL162G+iDI2Nsbc3Bxzc3MsLCzw8PAgMDCQ7du3P7Q2CCGEEEKIP06SiEestLSUDz/8EDc3NxwcHAgICCAjIwOAvLw8Jk6cSM+ePXF0dGTs2LHk5OSo94aHhzN06FCt8mJjY7X2qLCxsWHz5s2MGjUKOzs7evfurc7bSE9PJygoCIBXX32VlStXkp6ejo2NDXFxcbi6uuLt7U1ISAiBgYFa9RQVFdGlSxdOnDjxh/rfpEkTrZ/Ly8uJjo7Gw8MDW1tbXFxcmDx5srr53IULF7CxseGTTz6hd+/e9OnTR91ALj8/n6FDh2Jra0u/fv3497//rZZbVVXFp59+iqenJ7a2tjg5OTFmzBgKCgrUa4qLi5kwYQKOjo64u7tr3V/tq6++YuDAgdjZ2fH666+zbNmyO/bjEEIIIYRo6CSJeMTCwsJITU0lKiqKlJQUrK2tCQ4O5uLFiwQEBFBZWcmaNWtYv349hoaGBAYGcunSJZ3qiImJwc/Pj127duHv78/KlSs5duwYjo6OLFu2DIAtW7YwevRo9Z7U1FQSExOJjo5m0KBBnDhxQmsydVJSElZWVnTv3v2B+56Xl0diYiJvvfWWVlv37NnDokWL2Lt3L9HR0aSnp/Pxxx9r3ZuUlMRnn33GihUr1OFi8fHx+Pr6snPnTvr378/UqVM5efIkAOvWrWP16tXMnDmTvXv38vHHH3P+/Hl1w7mbN28SHBzMlStXiI+PJzY2ltWrV2vVefDgQaZMmcKgQYP4/PPPmT9/Prt372bGjBkP/BkIIYQQQjyOZE7EI6TRaDhw4ACrVq3C3d0dgHnz5tGsWTOio6MxNjYmNjYWExMTAJYtW8Zrr73G+vXrdXpw9fX1xdfXF4DQ0FDi4+PJyMjAxcWFFi1aALfmbtw+uXvMmDG0b98euPU2w9zcnB07dhASEgJASkoK/v7+OvV39+7d6tyLiooKKioqsLKyUjcKBOjWrRt9+/alR48eAFhYWNC7d2+tNzAAb7/9Np06ddI6FhAQQEBAAABTpkwhLS2Nzz77jNjYWJ5//nkWL16svqWxsLDAy8uLHTt2AJCWlkZubi67d+/mhRdeAODDDz9k0KBBavmffPIJgwYN4u233wbg+eefJzIykpEjR3LhwgXatWtX68/ibpOUhP4wN2/+qJsg7kHio/8kRvpPYqT/9DlGkkQ8QtUPxvb29uoxIyMjdTdlW1tbNYGAW0N/bG1tyc7O1qme6odiAAMDA0xNTe+7upKlpaX6faNGjfDz81OTiOq3EgMGDNCpHe7u7uoE6ps3b1JcXMwnn3zC4MGDSU5OpmXLlvj6+nL48GH++te/UlBQgEajIT8/HwcHh7u2r5qjo6PWz/b29nzzzTcAeHh48P3337N8+XLy8/PJz8/n7Nmz6opUOTk5NGvWTOuzsrW11dp5PCsri5MnT2ot2Vs9pyQvL0+nJEJWZ9Jv+rAihrg7iY/+kxjpP4mR/tOHGMnqTHrKyOjWx29gYHDHubtNeK6qqlLvq0llZeUdx2paPvV+E6pvT14A/P39WbVqFd9//z0pKSm4ubnRpk2be5bxe02bNsXKykr9uWPHjrz44ov85S9/4d///jeBgYHMmzePPXv2MGDAAF555RXeffdd1qxZw8WLF+/ZPkBrSVm49VlU9/3TTz/lo48+ws/Pj549ezJq1Cj27dtHSkqKev3vPxMDAwOtJKKqqoqgoCCttxPVzM3NdfgkhBBCCCEebzIn4hHq2LEjAKdOnVKPVVVV4enpiUajITMzkxs3bqjnSktLyczMVHfEbty4MVevXtUq89y5czq1oaYEpiZWVlb06NGD3bt3k5qaWmf7JlQ/uFdVVVFSUkJiYiJz5sxh1qxZ+Pv707VrVzQaTa1WkcrKytL6OSMjQx3y9MknnzBhwgQiIyMJCAjAwcGBgoICtdwuXbpQWlqq9ZYnLy+P0tJS9edOnTqRn5+PlZWV+vXjjz8SExPDtWvX/vBnIYQQQgjxuJAk4hHq0KEDffv2JSoqirS0NAoKCoiMjKSkpIT4+HjKysoICwsjKyuLrKwspk6dSkVFBUOGDAHAycmJ3Nxctm/fzoULF1i3bh2HDh3SqQ3V8yBOnz59303x/P392bhxI1VVVVorQNVWeXk5P/30k/qVmZnJ7Nmzadq0KX379sXU1JTmzZuzf/9+CgoKyM7OZu7cufzwww+1WgFp7dq1JCUlodFoWLRoEdnZ2bzzzjsAPPfccxw+fJjc3Fw0Gg2xsbF88cUXarmurq7Y2toSHh7Od999x6lTpwgPD8fQ8P9/RcaOHcuXX37JypUryc/PJy0tjfDwcH777Td5EyGEEEKIJ4okEY/YokWLcHV1JSwsDD8/PzQaDXFxcVhYWJCQkEB5eTmBgYHq5OPExEQsLCwA8Pb2ZsSIEURHR+Pt7U1GRgahoaE61d+5c2c8PDwICwtjxYoV97y2X79+GBoa4uPjozXMp7b27t1L79696d27N25ubowePRoDAwPWrl1L27Ztady4McuXLycvLw8fHx+Cg4O5fv06U6dOJT8//77/2v/uu++SkJCAj48PaWlprFq1Sn3bExMTQ3l5OYMGDWLYsGHk5OQQGRnJL7/8QmFhIY0aNWL16tW0b9+e0aNHM378eHx9fXn66ae1+r9s2TL27duHt7c3M2bM4OWXX1aXzBVCCCGEeFIYKA9ztzHxWCsuLsbDw4Pk5GR1SJV4MDKxWr/pw2Q2cXcSH/0nMdJ/EiP9pw8xkonV4g8pKiri5MmTbNq0CRcXF0kghBBCCCGecJJEiPsqKSkhPDwcS0vLO4buLFy4UGvJ05osXrwYT0/P+myiEEIIIYR4iCSJEPfVtWtXvv32WxRFISkpiWbNmqn7K0ycOJGRI0fe8/7WrVvrXOc333zD6tWryczMpKysDAsLC/r168fYsWO1NsW7HxsbG6Kiohg8eLDObRBCCCGEEDWTJELU2pEjR4iIiFB3nYZbO12bmZnVaT2HDx9m3LhxTJo0iTlz5vDUU09x6tQpFi9ezMmTJ1mzZk2tyzp06BDNm+vvbo9CCCGEEI8jSSJErT2sOfibNm2iV69ejBs3Tj1maWmJsbExISEhZGdnY2NjU6uyZOlVIYQQQoi6J0u8PkZKS0v58MMPcXNzw8HBgYCAADIyMoBbG6NNnDiRnj174ujoyNixY8nJyVHvDQ8PZ+jQoVrlxcbGau33YGNjw+bNmxk1ahR2dnb07t1bnQORnp5OUFAQAK+++iorV64kPT0dGxsb4uLicHV1xdvbm5CQEAIDA7XqKSoqokuXLpw4caJW/TQ0NCQ7O5tLly5pHf/LX/7Crl27aN++vU592rJlCwA///wzkydPxtXVFTs7OwICAjh69Kh67cmTJ3n77bdxdHTE2dmZkJAQrTZcvnyZsLAwnJ2dcXV1Zfz48RQUFNSqT0IIIYQQDYm8iXiMhIWFkZubS1RUFO3btycuLo7g4GB27txJQEAAzs7O6lCf5cuXExgYSEpKCn/6059qXUdMTAxz587l/fffZ+vWraxcuRJXV1ccHR1ZtmwZoaGhbNmyhY4dO5KZmQlAamoqiYmJlJaWcvnyZSZMmEBhYSGWlpYAJCUlYWVlRffu3WvVhqCgIL766itee+01nJyccHFxwcXFBWdnZ1588UUdP7X/t2DBAsrLy0lISMDY2FjdxfrgwYOYmJgwbtw43nrrLaKjo/n111+ZN28eERERrF27ltLSUoYPH07nzp1JSEjA0NCQzz77jLfeeoudO3fStm1bndpyt+XShP4wN5dhcPpM4qP/JEb6T2Kk//Q5RpJEPCY0Gg0HDhxg1apVuLu7AzBv3jyaNWtGdHQ0xsbGxMbGYmJiAsCyZct47bXXWL9+PTNmzKh1Pb6+vvj6+gIQGhpKfHw8GRkZuLi40KJFC+DWPIjbJzePGTNGfTtgY2ODubk5O3bsICQkBICUlBT8/f1r3QZ7e3uSkpL417/+xYEDBzh27BgAzzzzDO+9955OZd3u/PnzWFtbY2lpiYmJCbNnz8bb25tGjRpx9epVSkpKaNOmDRYWFlhaWrJs2TL++9//ArBr1y5KSkpYunSputHeBx98QHp6Ops3b2bSpEk6tUX2idBv+rA2t7g7iY/+kxjpP4mR/tOHGN1rnwgZzvSYqB6aZG9vrx4zMjJi5syZXL9+HVtbWzWBAGjSpAm2trZkZ2frVM8LL7ygfm9gYICpqSkVFRX3vKf6jQNAo0aN8PPzY8eOHQCcOHGCwsJCBgwYoHM7Fi5cyMGDB9mzZw/z58/H3NycWbNmcejQIZ3KqjZx4kS+/PJLevToQVBQEJs2baJjx46YmJjQokULgoODef/99+nVqxdTpkzh6NGj6tyLrKwsrl69So8ePXB0dMTR0ZHu3btTXFxMXl7eA7VHCCGEEOJxJUnEY8LI6NZLIwMDgzvO3W3Cc1VVlXpfTSorK+84ZmxsXOvyq92evAD4+/tTUFDA999/T0pKCm5ubrRp0+aeZVQrLS0lKipKHSoF0KFDB95++222bNmCubk5+/fvv+v9NfWp2uuvv85//vMfFi9ejIWFBWvXrqVfv35qojV9+nT2799PaGgoiqIQFRXFwIEDuXHjBlVVVTz//PMkJydrfe3evZvZs2fXqm9CCCGEEA2FJBGPiY4dOwJw6tQp9VhVVRWenp5oNBoyMzO5ceOGeq60tJTMzEx1d+nGjRtz9epVrTLPnTunUxtqSmBqYmVlRY8ePdi9ezepqakMHDiw1nWYmJjw+eefs2HDhhrPPfXUU+oeFbr0qby8nEWLFlFYWIiXlxdRUVF8+eWXGBoa8tVXX6HRaJg/fz6tWrVi6NChrFixgri4OPLz88nKysLa2pqioiJMTU2xsrLCysoKCwsLli5dqg63EkIIIYR4UkgS8Zjo0KEDffv2JSoqirS0NAoKCoiMjKSkpIT4+HjKysoICwsjKyuLrKwspk6dSkVFBUOGDAHAycmJ3Nxctm/fzoULF1i3bp3Ow4Kq50GcPn2a33679xg9f39/Nm7cSFVVldZqSfdjaGjIe++9x7Zt25g1axbffvstFy9eJD09nenTp1NWVvZAfTI2Nub7779n7ty5fPvtt1y4cIHt27dz7do1HBwceOaZZ/j888+ZN28eeXl55Ofns337dp5++mlefPFFfHx8aNmyJZMmTeK7774jLy+PiIgIDh48WOvlZoUQQgghGgpJIh4jixYtwtXVlbCwMPz8/NBoNMTFxWFhYUFCQgLl5eUEBgYyfPhwABITE7GwsADA29ubESNGEB0djbe3NxkZGYSGhupUf+fOnfHw8CAsLIwVK1bc89p+/fphaGiIj4+POhG5tgYOHMjq1avVlZ769u3LtGnTMDY2ZvPmzeoO2Lr2afny5VhZWRESEkK/fv3YuHEjMTEx9OzZEzMzM/75z39y6dIl3nrrLfz8/Dh//jxr1qzh6aefpnnz5iQkJNCqVSuCg4MZNGgQRUVFrFmzRn1LJIQQQgjxpDBQHtYOYuKJUlxcjIeHB8nJyeqQKvH/ZHUm/aYPK2KIu5P46D+Jkf6TGOk/fYjRvVZnkiVeRZ0qKiri5MmTbNq0CRcXF0kghBBCCCEaIEkiRJ0qKSkhPDwcS0tLdbfragsXLiQpKeme9y9evBhPT8/6bKIQQgghhPiDJIl4CBRFISkpCXd3d3VlofoQHh7OuXPn2Lhx4x3ntmzZwpw5c2q9b0R6ejojRozgiy++wMrKiitXrrBv3z4GDx58z/u6du3Kt99+W+M5X19f1q9fz5IlS7T2u7hd9XyHaoqisGHDBrZu3YpGo8HQ0JBOnToxePBgrbbUtn23y8nJ4dKlS/Tp06fW9wghhBBCCEkiHoojR44QERHBvn37HnVTas3R0ZFDhw5hZmYG3JrUXVxcrNND+u8988wzALRt2xYrK6ta3bNixQo2bdrE7NmzsbOzo6Kigq+//poFCxbw888/M378+Adu39ixYxk0aJAkEUIIIYQQOpIk4iF4HOeuGxsbY25urv78qPqwYcMGgoODefPNN9VjHTt2pLi4mM8++0xNIh6kfY9jXIQQQggh9METscRraWkpH374IW5ubjg4OBAQEEBGRgYAeXl5TJw4kZ49e+Lo6MjYsWPJyclR7w0PD2fo0KFa5cXGxmrtfWBjY8PmzZsZNWoUdnZ29O7dW50PkJ6eTlBQEACvvvoqK1euJD09HRsbG+Li4nB1dcXb25uQkBACAwO16ikqKqJLly6cOHGizj+T4cOHEx0dzaxZs3B2dsbJyYlp06apm7dVt/HcuXOEh4ezc+dOjh49io2NDRcuXABg27Zt9O/fHzs7O/r378/atWupqqpS68jJyWHEiBE4ODjQt29fjh49qnM7GzVqRHp6OqWlpVrH3333XRITEwFqbF95eTnR0dF4eHhga2uLi4sLkydP5ueffwbAw8ODy5cv89FHH6mxLC8vZ8mSJbi5ueHo6Mhbb72lte9EZWUlS5Yswd3dHVtbWzw9PWscOiaEEEII0dA9EW8iwsLCyM3NJSoqivbt2xMXF0dwcDA7d+4kICAAZ2dn1qxZA9zaSyAwMJCUlBT+9Kc/1bqOmJgY5s6dy/vvv8/WrVtZuXIlrq6uODo6smzZMkJDQ9myZQsdO3YkMzMTgNTUVBITEyktLVX3RCgsLMTS0hKApKQkrKys6N69e91/KEB8fDyjR49m69atnDlzhunTp9OhQwcmTpyodd3s2bMpLS3lxx9/ZOXKlZiZmZGYmMjSpUuZN28e9vb2ZGVl8f7773P58mXee+89fvvtNzWp2rx5Mz/++CNz5szRuY3jx4/ngw8+4OWXX6ZXr144Ozvj6upK165d1eFRNbVv0aJF7Nu3j8WLF9OuXTuys7OJiIjg448/Zt68eWzduhUfHx+8vb0ZO3YsABEREeTm5rJkyRKeffZZvvrqK8aPH89HH31Enz592LBhA3v27CE2Npa2bdvy1VdfsWDBAjp16oSzs7NO/brbcmlCf5ibN3/UTRD3IPHRfxIj/Scx0n/6HKMGn0RoNBoOHDjAqlWrcHd3B2DevHk0a9aM6OhojI2NiY2NxcTEBIBly5bx2muvsX79embMmFHrenx9ffH19QUgNDSU+Ph4MjIycHFxoUWLFgCYmZmpuz4DjBkzhvbt2wO33maYm5uzY8cOQkJCAEhJScHf3/8PfwZ306FDB6ZOnQpA+/btSU5OrnFSdPPmzTE2NqZx48bqEKe///3vjBs3Th1mZGlpydWrV4mMjGTKlCns2rWLa9euER0dTYsWLbC2tiY8PJwpU6bo1MYRI0bw4osvsmHDBtLS0tR5JR07duTDDz/EwcGhxvZ169aNvn370qNHDwAsLCzo3bu3+pbJzMwMQ0NDmjZtipmZGefOnePzzz9n69atdOvWDYCgoCDOnDlDXFwcffr04fz58zRt2pR27drRpk0bhg0bxgsvvKDGUBeyT4R+04e1ucXdSXz0n8RI/0mM9J8+xOiJ3iei+qHx9tWAjIyMmDlzJmPHjsXW1lZNIACaNGmCra1trVcxqvbCCy+o3xsYGGBqakpFRcU976l+4wC3hu34+fmpScSJEycoLCxkwIABtW6DkZHRXcf5V1VVYWSkHe4OHTpo/Wxqasrly5fvW8+VK1coLi5m+fLlWsu4VlVVUVZWxoULF8jJyeH5559XEyi4NVn7Qfz5z3/mz3/+Mzdv3iQrK4uDBw+SkJDAmDFj+PLLL9XJ37fz9fXl8OHD/PWvf6WgoACNRkN+fj4ODg411pGVlQXcSlpuV1FRwdNPPw1AYGAgqampuLu706VLF15++WX69+9/x4pSQgghhBANXYNPIqofnA0MDO44p8sD9+0qKyvvOGZsbFzr8qvdnrwA+Pv7s2rVKr7//ntSUlJwc3OjTZs29yzjdi1atODXX3+t8dwvv/yi9UB/tzbXRvW8h5kzZ9K7d+87zj/33HM13te4cWOd6jlz5gwbNmwgIiKCJk2aYGRkhJ2dHXZ2dnh4eODn58fRo0fp16/fHffOmzePPXv2MGDAAF555RXeffdd1qxZw8WLF2usqzpW69atU5OGaoaGt6YOtW/fni+++IKjR49y+PBhDhw4wKeffkpUVNQfWrVKCCGEEOJx0+AnVnfs2BGAU6dOqceqqqrw9PREo9GQmZnJjRs31HOlpaVkZmaqOy03btxYnWxc7dy5czq1oaYEpiZWVlb06NGD3bt3k5qaysCBA3Wqp1u3bpw7d46ffvrpjnPHjh1Th+k8iNv70KpVK8zMzDh//jxWVlbq1w8//MCyZcsA6NKlC+fOnePKlSvqfbfHoLZ1JiYmsnfv3jvOVSdE1W8Bbm9fSUkJiYmJzJkzh1mzZuHv70/Xrl3RaDRaid3t93Tq1AmAH3/8UatP27dvZ/v27cCtBOOLL77g5ZdfZsaMGezcuZOXX36Z3bt369QvIYQQQojHXYNPIjp06EDfvn2JiooiLS2NgoICIiMjKSkpIT4+nrKyMsLCwsjKyiIrK4upU6dSUVHBkCFDAHByciI3N5ft27dz4cIF1q1bp7ViT21Uz4M4ffo0v/1277Ft/v7+bNy4kaqqKq0VoGrDw8ODDh06MG7cOA4fPszFixc5efIkCxcu5MiRI+pyqA+iWbNmXL58mcLCQiorKxk7dizr168nPj6ec+fOkZqaSmRkJCYmJhgbG9O/f39atWrFtGnTOHPmDEePHmXRokU61WljY8PAgQNZsGAB//jHP8jJyeH8+fPs27ePSZMm0bNnT3VC8+3tMzU1pXnz5uzfv5+CggKys7OZO3cuP/zwA+Xl5Vp9Kigo4PLly3Tq1IlXXnmFhQsXkpqaSmFhIXFxcaxatYrnn38egJ9//pmFCxeyb98+Ll68yH/+8x+ysrIeeJiWEEIIIcTjqsEnEXBrIzJXV1fCwsLw8/NDo9EQFxeHhYUFCQkJlJeXExgYyPDhwwFITEzEwsICAG9vb0aMGEF0dDTe3t5kZGQQGhqqU/2dO3fGw8ODsLAwVqxYcc9r+/Xrh6GhIT4+PjoP/zE2NiY+Ph57e3vmzJmDp6cn77zzDhcuXCAhIeEPPewOHjyYmzdv4uXlRVZWFqNHjyYiIoL169fzxhtvqEN6IiMjgVvzK9atW4eRkREBAQHqHBRdRUVFMX36dPbv38/QoUPx8vIiJiaGPn368Mknn9TYvtOnT7N8+XLy8vLw8fEhODiY69evM3XqVPLz87l27RoAo0eP5uDBg/j4+FBVVUVsbCyenp4sWLAALy8vtm/fzgcffICfnx8AEydOZPDgwURFReHp6cm8efMYMmQIEyZMeODPVQghhBDicWSgyI5beqW4uBgPDw+Sk5PVIVWi4ZHVmfSbPqyIIe5O4qP/JEb6T2Kk//QhRk/06kyPi6KiIk6ePMmmTZtwcXGRBEIIIYQQQugtSSL0RElJCeHh4VhaWmotmwqwcOFCkpKS7nn/4sWL8fT0/MPtUBSFpKQk3N3dadWq1R8u7258fHwoLCzk5s2b3Lx5U13xydDQECMjI4yMjEhNTa3XNtzP4cOHCQoKYt++fbRr1+6RtUMIIYQQQt9IEqEnunbtWuNGb3BrLP7IkSPveX9d7VVw5MgRIiIi1E3d6ss//vEP5s+fz3fffcfYsWNxcHDAwMCAw4cPs27dOry9vdUdqYUQQgghhH6RJOIxYGZmVuOGavXhYU2ROXDgAIcPH2bLli289NJL6vHqvTGio6MZPny4uvSqEEIIIYTQH0/E6kyPk9LSUj788EPc3NxwcHAgICCAjIwMAPLy8pg4cSI9e/bE0dGRsWPHqjtyA4SHhzN06FCt8mJjY7WWirWxsWHz5s2MGjUKOzs7evfurQ6fSk9PJygoCIBXX32VlStXkp6ejo2NDXFxcbi6uuLt7U1ISAiBgYFa9RQVFdGlSxdOnDhRq35u2rSJPn36aCUQ1YYOHcq//vUvdWnV//3vf8ydOxc3NzdeeuklevXqxaxZsygtLVXbbWNjw9dff82bb76Jra0t/fr1IzU1VS3zfmUAHD9+nMGDB2NnZ8eAAQO0PtvaliGEEEII8SSQJELPhIWFkZqaSlRUFCkpKVhbWxMcHMzFixcJCAigsrKSNWvWsH79egwNDQkMDOTSpUs61RETE4Ofnx+7du3C39+flStXcuzYMRwdHdXN4rZs2cLo0aPVe1JTU0lMTCQ6OppBgwZx4sQJCgsL1fNJSUlYWVnRvXv3+9ZfVlZGTk4OTk5ONZ43MTGhZ8+ePPXUU8Ct5CgzM5OPPvqIvXv3EhERwY4dO9i4caPWfUuWLGH27Nl8/vnnWFpaMnPmTHU51/uVUVhYyOjRo+ncuTNJSUmMGzeOv//971rl17YdQgghhBANnQxn0iMajYYDBw6watUq3N3dAZg3bx7NmjUjOjoaY2NjYmNjMTExAWDZsmW89tprrF+/nhkzZtS6Hl9fX3x9fQEIDQ0lPj6ejIwMXFxc1J2gzczM1E3yAMaMGUP79u2BW28zzM3N2bFjByEhIQCkpKTg7+9fq/r/97//Af+/6/T9vPzyyzg7O9O5c2cA2rVrx4YNG+54UzBp0iR69eoFwJQpU/D39ycnJwdHR8f7lrF582bMzMyYP38+RkZGdOzYkUuXLhETE6NzO2rjbsulCf1hbt78UTdB3IPER/9JjPSfxEj/6XOMJInQI9UPo/b29uoxIyMjdaM2W1tbNYEAaNKkCba2tmRnZ+tUzwsvvKB+b2BggKmpKRUVFfe8x9LSUv2+UaNG+Pn5qUlE9VuJAQMG1Kr+Z555BgMDA0pKSmp1/dtvv83+/ftJTk6moKCAs2fPcuHChTtWTLq9X82b3/qlq+7X/crIycmhc+fOGBn9/6/E7zfnq207akP2idBv+rA2t7g7iY/+kxjpP4mR/tOHGN1rnwgZzqRHqh9gDQwM7jh3twnPVVVVWg++v1dZWXnHMWNj41qXX+325AXA39+fgoICvv/+e1JSUtQJ0bVhbGyMra2tOtfj965fv87IkSM5cOAAVVVVjBs3joULF2JkZET//v1ZtWpVjUOh7tYvXcq43e2f64OWIYQQQgjREMmbCD3SsWNHAE6dOoWbmxtw6+G1f//+VFRUUFZWxo0bN9QH+tLSUjIzMxk8eDAAjRs35urVq1plnjt3Tqc21JTA1MTKyooePXqwe/duUlNTmT9/vk71DBkyhPnz5/PDDz/cMbl648aNHDlyhFmzZnH69GkOHjzIhg0b1PkWFRUVnD9/nueee65WddWmjM6dO7Nt2zbKy8vVZCQzM1OnMoQQQgghnhTyJkKPdOjQgb59+xIVFUVaWhoFBQVERkZSUlJCfHw8ZWVlhIWFkZWVRVZWFlOnTqWiooIhQ4YA4OTkRG5uLtu3b+fChQusW7eOQ4cO6dSG6nkQp0+f5rff7v0Kzd/fn40bN1JVVaW1AlRtDBo0iL/85S8EBQWxfv168vPzOXPmDLGxsSxdupSQkBBsbGxo3bo1RkZG7Nmzh8LCQk6dOkVoaCg//fTTfYdgVatNGQEBAVy/fp1Zs2aRl5fH/v37tSZW10U7hBBCCCEaCkki9MyiRYtwdXUlLCwMPz8/NBoNcXFxWFhYkJCQQHl5OYGBgQwfPhyAxMRELCwsAPD29mbEiBFER0fj7e1NRkYGoaGhOtXfuXNnPDw8CAsLY8WKFfe8tl+/fhgaGuLj40Pjxo11qsfAwICPP/6YkJAQtm3bxqBBgxg+fDjp6eksWbKEyZMnA9C2bVsWL17M119/jZeXF1OmTKFt27aMGjWKzMzMWu1rUZsynnvuOdauXculS5fw8/NjyZIlTJgwQacyhBBCCCGeFAaKPP2IB1RcXIyHhwfJyclYW1s/6uY8VkpKrsnEaj3WqpUpP/989f4XikdC4qP/JEb6T2Kk//QhRoaGBrRs2azGc5JECJ0VFRVx8uRJNm3aRFVVFWvXrn3UTRJCCCGEEA+RTKwWOispKSE8PBxLS0t1t+tqCxcuJCkp6Z73L168GE9Pz/psohBCCCGEqEfyJkLUqStXrtx3Qnbr1q21NrITQgghhBCPF0kihBBCCCGEEDqR1ZmEEEIIIYQQOpEkQgghhBBCCKETSSKEEEIIIYQQOpEkQgghhBBCCKETSSKEEEIIIYQQOpEkQgghhBBCCKETSSKEeEBVVVWsWLECNzc3HBwcCA4OprCw8K7Xl5SUMG3aNFxcXOjRowfz58+ntLRU65rdu3fj5eWFnZ0dvr6+fPPNN/XdjQatPmK0fv16vLy8cHBwwNPTk9WrV1NZWVnfXWmw6iNG1crLy/H29mb69On11fwnQn3E6OTJkwQGBmJnZ4e7uzsrVqygqqqqvrvSYNVHjHbu3Mkbb7yBvb09Xl5ebNu2rb670aDpGqPb73vnnXeIjY2941xaWhoDBw7E3t4eT09Pdu7cWR9NvztFCPFAVq5cqbi6uir79+9XTp8+rQQHByuvv/66UlZWVuP1w4YNUwYOHKicOnVKOXLkiOLh4aHMmDFDPZ+Wlqa89NJLyr/+9S/l7NmzypIlSxRbW1vl7NmzD6tLDU5dx2jdunWKnZ2dsnXrVuXcuXPK559/rjg5OSkrVqx4WF1qcOo6Rrd7//33FWtra2XatGn12YUGr65jpNFoFHt7e2XWrFlKfn6+snfvXsXJyUlZtWrVw+pSg1PXMfrmm2+Url27Khs3blTOnz+vJCQkKJ07d1ZSU1MfVpcaHF1jpCiKUlZWpsycOVOxtrZW/va3v2mdO3v2rNKtWzflb3/7m5KXl6esWbNG6dq1q3L48OH67opKkgghHkBZWZni6OioJCQkqMd+++03xd7eXklOTr7j+oyMDMXa2lrJyclRjx0+fFixsbFRLl26pCiKoowePVqZNGmS1n1Dhw5VZs2aVU+9aNjqI0aenp7K4sWLte776KOPlN69e9dTLxq2+ohRtYMHDyp//vOflTfeeEOSiD+gPmI0c+ZMxc/PT6mqqlKvWblypfLOO+/UY08arvqIUWRkpOLn56d134ABA5T58+fXTycaOF1jpCiKcuLECeWNN95QXn31VcXZ2fmOJGLu3LnKwIEDtY5Nnz5dGTlyZJ23/25kOJMQD+DMmTNcu3aNnj17qsdMTU3p2rUrx44du+P648eP06pVKzp16qQe6969OwYGBhw7doyqqioyMjK0ygPo0aNHjeWJ+6vrGAEsWLCAt99+W+s+AwMDfv3113rqRcNWHzECuHLlChEREbz//vu0bNmyfjvRwNVHjA4dOoSXlxcGBgbqNRMnTmTVqlX12JOGqz5iZGZmRm5uLkeOHEFRFNLT08nLy8Pe3r7+O9QA6RojgK+//ho3NzeSk5Np3rz5HeePHz9e4zPDiRMnHtoQW6OHUosQDUxxcTEAbdu21Trepk0bioqK7rj+8uXLPPvss1rHjI2NadmyJcXFxfz666+Ulpbecc3dyhP3V9cxAu74g/3rr7+yYcMG3N3d67LpT4z6iBHA7NmzeeWVV/Dw8OCzzz6rh5Y/Oeo6RlevXuWnn36iRYsWzJo1i4MHD9K8eXP8/PwYM2YMjRo1qr/ONFD18Xs0atQoMjMzGTlyJI0aNaKyspLg4GD8/PzqqRcNm64xAggLC7tvmTU9M5SXl3PlyhXMzc3/QItrR5IIIR7A9evXgVt/eG9nbGxMeXl5jdf//trq68vKyrhx48Y9y1MURetf7cT91XWMfu/q1auMHz+eiooK3nvvvTpq9ZOlPmK0adMm8vLyWLp0aT20+MlT1zG6evUqADExMQwbNoxPP/2UM2fO8MEHH3Dt2rX7PjiJO9XH71FRURG//PILc+fOpXv37qSlpREbG0uHDh0YNGhQPfSiYdM1RrVx48aNGssDavx/Vn2QJEKIB2BiYgLcWv3l9l/i8vJymjZtWuP1Nf2hqL7+qaeeUn+u6bwkELqr6xjdrri4mPHjx1NUVERcXBzt2rWr49Y/Geo6RhqNhiVLlhAXF1fj/UJ3dR0jI6Nbjx09e/ZkypQpAHTt2pUrV66wcuVKpkyZgqGhjLTWRX38rZs8eTL9+/dn2LBhAHTp0oWSkhKWLFnCwIEDJUY60jVGtfHUU0/V+MwAPLS/f/JfgRAP4LnnngPgxx9/1Dr+448/3vG6EuDZZ5+949ry8nJKSkp49tlneeaZZ2jatGmtyxP3V9cxqnb69GkGDx6sDmWytbWth9Y/Geo6Rv/+97+5du0aQUFBODo64ujoyPHjx9m9ezeOjo5cunSp/jrTQNV1jFq2bMlTTz2FtbW11jWdOnXixo0b/PTTT3Xcg4avrmN05coVNBoN3bp107rG3t6eX375hV9++aVuO/AE0DVGtS2zpvKaNGlCixYtHqyhOpIkQogH0LlzZ0xNTTl69Kh67OrVq2RlZdGjR487rndxceGnn35Co9Gox44fPw6As7MzBgYGODk5aZUHkJ6eXmN54v7qOkYAZ8+eZeTIkbRq1YrExEQ6duxYz71o2Oo6RsOGDWPv3r0kJyerX7a2tri7u5OcnEybNm3qv1MNTF3HqFGjRjg5OXHq1Cmt+7KzszE1NcXMzKyeetJw1XWMWrRoQZMmTcjOzta6Lzc3l6efflpi9AB0jVFtODs71/jM0L1794c2t0iGMwnxAIyNjRk2bBixsbG0bt2adu3asXTpUtq2bYunpyeVlZVcuXKF5s2bY2Jigr29PU5OTkybNo3IyEiuX7/OvHnzGDBggPqvEEFBQbzzzjvqQ09SUhJZWVlERUU94t4+nuo6RoqiMG3aNIyNjdXx9rf/q+nDmMTW0NTH79EzzzyjVYeJiQlNmzbFysrqEfTw8VcfMZowYQJBQUEsX74cPz8/fvjhBz799FNGjBhB48aNH3GPHz/1EaMRI0awatUqnn32Wbp3786JEyf49NNPmThx4iPu7eNJ1xjVxvDhw/Hz8yMmJgZ/f3/+85//sGfPHlavXl3PvbnNQ1tMVogG5ubNm0pMTIzSs2dPxcHBQRk7dqxSWFioKIqiFBYWKtbW1sq2bdvU6//73/8qkyZNUhwcHBRXV1dl/vz5yo0bN7TKTEpKUl5//XWlW7duip+f30PdNKYhqssYnT59WrG2tr7rV0VFxSPp4+OuPn6Pbjds2DDZJ+IPqo8YHTx4UPHz81Neeuklxd3dXfn73/+uVFZWPtR+NSR1HaObN28qa9asUfr166fY29srb7zxhrJhwwatvT2EbnSN0e1eeeWVO/aJUBRF+frrr5U333xTsbW1Vfr166fs2rWrXvvwewaKoigPL2URQgghhBBCPO5kToQQQgghhBBCJ5JECCGEEEIIIXQiSYQQQgghhBBCJ5JECCGEEEIIIXQiSYQQQgghhBBCJ5JECCGEEEIIIXQiSYQQQgghhBBCJ5JECCGEEEIIIXQiSYQQQgghhBBCJ/8HdHMVZQPQzigAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "pd.Series(model.feature_importances_, index=data_free.columns).nlargest(data_free.shape[1])[::-1].plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### XGB feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric : ACC, Score :  0.80\n",
      "Metric : TP_Rate, Score :  0.40\n",
      "Metric : FP_Rate, Score :  0.16\n",
      "Metric : Precision, Score :  0.41\n",
      "Metric : F_Measure, Score :  0.41\n",
      "Metric : F_Micro, Score :  0.49\n",
      "Metric : F_Macro, Score :  0.37\n",
      "Confusion Matrix: \n",
      "      N     H     C    S     A\n",
      "N  0.67  0.25  0.00  1.0  0.25\n",
      "H  0.25  0.12  0.00  0.0  0.25\n",
      "C  0.17  0.00  0.17  0.2  0.00\n",
      "S  0.04  0.00  0.00  0.8  0.00\n",
      "A  0.04  0.12  0.17  0.0  0.25\n",
      "here 2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = 'XGB'\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(data_free, label_free, test_size=0.2)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "model = classifiers[classifiers_name.index(best_model)]\n",
    "model.fit(X_train, Y_train)\n",
    "\n",
    "performance_metrics = compute_metrics(model, X_test, Y_test, show=True)\n",
    "plot_confusion_matrix(performance_metrics[\"Confusion Matrix\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "pd.Series(model.feature_importances_, index=data_free.columns).nlargest(data_free.shape[1])[::-1].plot(kind='barh')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}