tdatda_makeit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.manifold import MDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>PPE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>numPulses</th>\n",
       "      <th>numPeriodsPulses</th>\n",
       "      <th>meanPeriodPulses</th>\n",
       "      <th>stdDevPeriodPulses</th>\n",
       "      <th>locPctJitter</th>\n",
       "      <th>...</th>\n",
       "      <th>tqwt_kurtosisValue_dec_28</th>\n",
       "      <th>tqwt_kurtosisValue_dec_29</th>\n",
       "      <th>tqwt_kurtosisValue_dec_30</th>\n",
       "      <th>tqwt_kurtosisValue_dec_31</th>\n",
       "      <th>tqwt_kurtosisValue_dec_32</th>\n",
       "      <th>tqwt_kurtosisValue_dec_33</th>\n",
       "      <th>tqwt_kurtosisValue_dec_34</th>\n",
       "      <th>tqwt_kurtosisValue_dec_35</th>\n",
       "      <th>tqwt_kurtosisValue_dec_36</th>\n",
       "      <th>class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>3.8686</td>\n",
       "      <td>4.2105</td>\n",
       "      <td>5.1221</td>\n",
       "      <td>4.4625</td>\n",
       "      <td>2.6202</td>\n",
       "      <td>3.0004</td>\n",
       "      <td>18.9405</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76686</td>\n",
       "      <td>0.69481</td>\n",
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       "      <td>234</td>\n",
       "      <td>233</td>\n",
       "      <td>0.008258</td>\n",
       "      <td>0.000073</td>\n",
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       "      <td>11.0261</td>\n",
       "      <td>9.5082</td>\n",
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       "      <td>6.3431</td>\n",
       "      <td>45.1780</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.85083</td>\n",
       "      <td>0.67604</td>\n",
       "      <td>0.58982</td>\n",
       "      <td>232</td>\n",
       "      <td>231</td>\n",
       "      <td>0.008340</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.00176</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5643</td>\n",
       "      <td>2.3308</td>\n",
       "      <td>9.4959</td>\n",
       "      <td>10.7458</td>\n",
       "      <td>11.0177</td>\n",
       "      <td>4.8066</td>\n",
       "      <td>2.9199</td>\n",
       "      <td>3.1495</td>\n",
       "      <td>4.7666</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.41121</td>\n",
       "      <td>0.79672</td>\n",
       "      <td>0.59257</td>\n",
       "      <td>178</td>\n",
       "      <td>177</td>\n",
       "      <td>0.010858</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>0.00419</td>\n",
       "      <td>...</td>\n",
       "      <td>3.7805</td>\n",
       "      <td>3.5664</td>\n",
       "      <td>5.2558</td>\n",
       "      <td>14.0403</td>\n",
       "      <td>4.2235</td>\n",
       "      <td>4.6857</td>\n",
       "      <td>4.8460</td>\n",
       "      <td>6.2650</td>\n",
       "      <td>4.0603</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.32790</td>\n",
       "      <td>0.79782</td>\n",
       "      <td>0.53028</td>\n",
       "      <td>236</td>\n",
       "      <td>235</td>\n",
       "      <td>0.008162</td>\n",
       "      <td>0.002669</td>\n",
       "      <td>0.00535</td>\n",
       "      <td>...</td>\n",
       "      <td>6.1727</td>\n",
       "      <td>5.8416</td>\n",
       "      <td>6.0805</td>\n",
       "      <td>5.7621</td>\n",
       "      <td>7.7817</td>\n",
       "      <td>11.6891</td>\n",
       "      <td>8.2103</td>\n",
       "      <td>5.0559</td>\n",
       "      <td>6.1164</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 755 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  gender      PPE      DFA     RPDE  numPulses  numPeriodsPulses  \\\n",
       "0   0       1  0.85247  0.71826  0.57227        240               239   \n",
       "1   0       1  0.76686  0.69481  0.53966        234               233   \n",
       "2   0       1  0.85083  0.67604  0.58982        232               231   \n",
       "3   1       0  0.41121  0.79672  0.59257        178               177   \n",
       "4   1       0  0.32790  0.79782  0.53028        236               235   \n",
       "\n",
       "   meanPeriodPulses  stdDevPeriodPulses  locPctJitter  ...  \\\n",
       "0          0.008064            0.000087       0.00218  ...   \n",
       "1          0.008258            0.000073       0.00195  ...   \n",
       "2          0.008340            0.000060       0.00176  ...   \n",
       "3          0.010858            0.000183       0.00419  ...   \n",
       "4          0.008162            0.002669       0.00535  ...   \n",
       "\n",
       "   tqwt_kurtosisValue_dec_28  tqwt_kurtosisValue_dec_29  \\\n",
       "0                     1.5620                     2.6445   \n",
       "1                     1.5589                     3.6107   \n",
       "2                     1.5643                     2.3308   \n",
       "3                     3.7805                     3.5664   \n",
       "4                     6.1727                     5.8416   \n",
       "\n",
       "   tqwt_kurtosisValue_dec_30  tqwt_kurtosisValue_dec_31  \\\n",
       "0                     3.8686                     4.2105   \n",
       "1                    23.5155                    14.1962   \n",
       "2                     9.4959                    10.7458   \n",
       "3                     5.2558                    14.0403   \n",
       "4                     6.0805                     5.7621   \n",
       "\n",
       "   tqwt_kurtosisValue_dec_32  tqwt_kurtosisValue_dec_33  \\\n",
       "0                     5.1221                     4.4625   \n",
       "1                    11.0261                     9.5082   \n",
       "2                    11.0177                     4.8066   \n",
       "3                     4.2235                     4.6857   \n",
       "4                     7.7817                    11.6891   \n",
       "\n",
       "   tqwt_kurtosisValue_dec_34  tqwt_kurtosisValue_dec_35  \\\n",
       "0                     2.6202                     3.0004   \n",
       "1                     6.5245                     6.3431   \n",
       "2                     2.9199                     3.1495   \n",
       "3                     4.8460                     6.2650   \n",
       "4                     8.2103                     5.0559   \n",
       "\n",
       "   tqwt_kurtosisValue_dec_36  class  \n",
       "0                    18.9405      1  \n",
       "1                    45.1780      1  \n",
       "2                     4.7666      1  \n",
       "3                     4.0603      1  \n",
       "4                     6.1164      1  \n",
       "\n",
       "[5 rows x 755 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawdata = pd.read_csv('./data/parkinson_disease_speech.csv',header=1)\n",
    "rawdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PPE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>numPulses</th>\n",
       "      <th>numPeriodsPulses</th>\n",
       "      <th>meanPeriodPulses</th>\n",
       "      <th>stdDevPeriodPulses</th>\n",
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       "      <th>locAbsJitter</th>\n",
       "      <th>rapJitter</th>\n",
       "      <th>...</th>\n",
       "      <th>tqwt_kurtosisValue_dec_27</th>\n",
       "      <th>tqwt_kurtosisValue_dec_28</th>\n",
       "      <th>tqwt_kurtosisValue_dec_29</th>\n",
       "      <th>tqwt_kurtosisValue_dec_30</th>\n",
       "      <th>tqwt_kurtosisValue_dec_31</th>\n",
       "      <th>tqwt_kurtosisValue_dec_32</th>\n",
       "      <th>tqwt_kurtosisValue_dec_33</th>\n",
       "      <th>tqwt_kurtosisValue_dec_34</th>\n",
       "      <th>tqwt_kurtosisValue_dec_35</th>\n",
       "      <th>tqwt_kurtosisValue_dec_36</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.85247</td>\n",
       "      <td>0.71826</td>\n",
       "      <td>0.57227</td>\n",
       "      <td>240</td>\n",
       "      <td>239</td>\n",
       "      <td>0.008064</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.00218</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.00067</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5466</td>\n",
       "      <td>1.5620</td>\n",
       "      <td>2.6445</td>\n",
       "      <td>3.8686</td>\n",
       "      <td>4.2105</td>\n",
       "      <td>5.1221</td>\n",
       "      <td>4.4625</td>\n",
       "      <td>2.6202</td>\n",
       "      <td>3.0004</td>\n",
       "      <td>18.9405</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.76686</td>\n",
       "      <td>0.69481</td>\n",
       "      <td>0.53966</td>\n",
       "      <td>234</td>\n",
       "      <td>233</td>\n",
       "      <td>0.008258</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.00195</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.00052</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5530</td>\n",
       "      <td>1.5589</td>\n",
       "      <td>3.6107</td>\n",
       "      <td>23.5155</td>\n",
       "      <td>14.1962</td>\n",
       "      <td>11.0261</td>\n",
       "      <td>9.5082</td>\n",
       "      <td>6.5245</td>\n",
       "      <td>6.3431</td>\n",
       "      <td>45.1780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.85083</td>\n",
       "      <td>0.67604</td>\n",
       "      <td>0.58982</td>\n",
       "      <td>232</td>\n",
       "      <td>231</td>\n",
       "      <td>0.008340</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.00176</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.00057</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5399</td>\n",
       "      <td>1.5643</td>\n",
       "      <td>2.3308</td>\n",
       "      <td>9.4959</td>\n",
       "      <td>10.7458</td>\n",
       "      <td>11.0177</td>\n",
       "      <td>4.8066</td>\n",
       "      <td>2.9199</td>\n",
       "      <td>3.1495</td>\n",
       "      <td>4.7666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.41121</td>\n",
       "      <td>0.79672</td>\n",
       "      <td>0.59257</td>\n",
       "      <td>178</td>\n",
       "      <td>177</td>\n",
       "      <td>0.010858</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>0.00419</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.00149</td>\n",
       "      <td>...</td>\n",
       "      <td>6.9761</td>\n",
       "      <td>3.7805</td>\n",
       "      <td>3.5664</td>\n",
       "      <td>5.2558</td>\n",
       "      <td>14.0403</td>\n",
       "      <td>4.2235</td>\n",
       "      <td>4.6857</td>\n",
       "      <td>4.8460</td>\n",
       "      <td>6.2650</td>\n",
       "      <td>4.0603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.32790</td>\n",
       "      <td>0.79782</td>\n",
       "      <td>0.53028</td>\n",
       "      <td>236</td>\n",
       "      <td>235</td>\n",
       "      <td>0.008162</td>\n",
       "      <td>0.002669</td>\n",
       "      <td>0.00535</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.00166</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8832</td>\n",
       "      <td>6.1727</td>\n",
       "      <td>5.8416</td>\n",
       "      <td>6.0805</td>\n",
       "      <td>5.7621</td>\n",
       "      <td>7.7817</td>\n",
       "      <td>11.6891</td>\n",
       "      <td>8.2103</td>\n",
       "      <td>5.0559</td>\n",
       "      <td>6.1164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 752 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PPE      DFA     RPDE  numPulses  numPeriodsPulses  meanPeriodPulses  \\\n",
       "0  0.85247  0.71826  0.57227        240               239          0.008064   \n",
       "1  0.76686  0.69481  0.53966        234               233          0.008258   \n",
       "2  0.85083  0.67604  0.58982        232               231          0.008340   \n",
       "3  0.41121  0.79672  0.59257        178               177          0.010858   \n",
       "4  0.32790  0.79782  0.53028        236               235          0.008162   \n",
       "\n",
       "   stdDevPeriodPulses  locPctJitter  locAbsJitter  rapJitter  ...  \\\n",
       "0            0.000087       0.00218      0.000018    0.00067  ...   \n",
       "1            0.000073       0.00195      0.000016    0.00052  ...   \n",
       "2            0.000060       0.00176      0.000015    0.00057  ...   \n",
       "3            0.000183       0.00419      0.000046    0.00149  ...   \n",
       "4            0.002669       0.00535      0.000044    0.00166  ...   \n",
       "\n",
       "   tqwt_kurtosisValue_dec_27  tqwt_kurtosisValue_dec_28  \\\n",
       "0                     1.5466                     1.5620   \n",
       "1                     1.5530                     1.5589   \n",
       "2                     1.5399                     1.5643   \n",
       "3                     6.9761                     3.7805   \n",
       "4                     7.8832                     6.1727   \n",
       "\n",
       "   tqwt_kurtosisValue_dec_29  tqwt_kurtosisValue_dec_30  \\\n",
       "0                     2.6445                     3.8686   \n",
       "1                     3.6107                    23.5155   \n",
       "2                     2.3308                     9.4959   \n",
       "3                     3.5664                     5.2558   \n",
       "4                     5.8416                     6.0805   \n",
       "\n",
       "   tqwt_kurtosisValue_dec_31  tqwt_kurtosisValue_dec_32  \\\n",
       "0                     4.2105                     5.1221   \n",
       "1                    14.1962                    11.0261   \n",
       "2                    10.7458                    11.0177   \n",
       "3                    14.0403                     4.2235   \n",
       "4                     5.7621                     7.7817   \n",
       "\n",
       "   tqwt_kurtosisValue_dec_33  tqwt_kurtosisValue_dec_34  \\\n",
       "0                     4.4625                     2.6202   \n",
       "1                     9.5082                     6.5245   \n",
       "2                     4.8066                     2.9199   \n",
       "3                     4.6857                     4.8460   \n",
       "4                    11.6891                     8.2103   \n",
       "\n",
       "   tqwt_kurtosisValue_dec_35  tqwt_kurtosisValue_dec_36  \n",
       "0                     3.0004                    18.9405  \n",
       "1                     6.3431                    45.1780  \n",
       "2                     3.1495                     4.7666  \n",
       "3                     6.2650                     4.0603  \n",
       "4                     5.0559                     6.1164  \n",
       "\n",
       "[5 rows x 752 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = rawdata.iloc[:,2:-1]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
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       "10     1\n",
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       "16     1\n",
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       "18     1\n",
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       "730    1\n",
       "731    1\n",
       "732    0\n",
       "733    0\n",
       "734    0\n",
       "735    0\n",
       "736    0\n",
       "737    0\n",
       "738    0\n",
       "739    0\n",
       "740    0\n",
       "741    1\n",
       "742    1\n",
       "743    1\n",
       "744    1\n",
       "745    1\n",
       "746    1\n",
       "747    1\n",
       "748    1\n",
       "749    1\n",
       "750    0\n",
       "751    0\n",
       "752    0\n",
       "753    0\n",
       "754    0\n",
       "755    0\n",
       "Name: class, Length: 756, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label = rawdata['class']\n",
    "label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.sparse import diags\n",
    "from skfeature.utility.construct_W import construct_W\n",
    "\n",
    "\n",
    "def lap_score(X, **kwargs):\n",
    "    \"\"\"\n",
    "    This function implements the laplacian score feature selection, steps are as follows:\n",
    "    1. Construct the affinity matrix W if it is not specified\n",
    "    2. For the r-th feature, we define fr = X(:,r), D = diag(W*ones), ones = [1,...,1]', L = D - W\n",
    "    3. Let fr_hat = fr - (fr'*D*ones)*ones/(ones'*D*ones)\n",
    "    4. Laplacian score for the r-th feature is score = (fr_hat'*L*fr_hat)/(fr_hat'*D*fr_hat)\n",
    "\n",
    "    Input\n",
    "    -----\n",
    "    X: {numpy array}, shape (n_samples, n_features)\n",
    "        input data\n",
    "    kwargs: {dictionary}\n",
    "        W: {sparse matrix}, shape (n_samples, n_samples)\n",
    "            input affinity matrix\n",
    "\n",
    "    Output\n",
    "    ------\n",
    "    score: {numpy array}, shape (n_features,)\n",
    "        laplacian score for each feature\n",
    "\n",
    "    Reference\n",
    "    ---------\n",
    "    He, Xiaofei et al. \"Laplacian Score for Feature Selection.\" NIPS 2005.\n",
    "    \"\"\"\n",
    "\n",
    "    # if 'W' is not specified, use the default W\n",
    "    if 'W' not in kwargs.keys():\n",
    "        W = construct_W(X)\n",
    "    # construct the affinity matrix W\n",
    "    W = kwargs['W']\n",
    "    # build the diagonal D matrix from affinity matrix W\n",
    "    D = np.array(W.sum(axis=1))\n",
    "    L = W\n",
    "    tmp = np.dot(np.transpose(D), X)\n",
    "    D = diags(np.transpose(D), [0])\n",
    "    Xt = np.transpose(X)\n",
    "    t1 = np.transpose(np.dot(Xt, D.todense()))\n",
    "    t2 = np.transpose(np.dot(Xt, L.todense()))\n",
    "    # compute the numerator of Lr\n",
    "    D_prime = np.array(np.sum(np.multiply(t1, X), 0)) - np.multiply(tmp, tmp)/D.sum()\n",
    "    # compute the denominator of Lr\n",
    "    L_prime = np.array(np.sum(np.multiply(t2, X), 0)) - np.multiply(tmp, tmp)/D.sum()\n",
    "    # avoid the denominator of Lr to be 0\n",
    "    D_prime[D_prime < 1e-12] = 10000\n",
    "\n",
    "    # compute laplacian score for all features\n",
    "    score = 1 - np.array(np.multiply(L_prime, 1/D_prime))[0, :]\n",
    "    return np.transpose(score)\n",
    "\n",
    "\n",
    "def feature_ranking(score):\n",
    "    \"\"\"\n",
    "    Rank features in ascending order according to their laplacian scores, the smaller the laplacian score is, the more\n",
    "    important the feature is\n",
    "    \"\"\"\n",
    "    idx = np.argsort(score, 0)\n",
    "    return idx\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PPE                           0.131453\n",
       "DFA                           0.072777\n",
       "RPDE                          0.094006\n",
       "numPulses                     0.037126\n",
       "numPeriodsPulses              0.037204\n",
       "meanPeriodPulses              0.059586\n",
       "stdDevPeriodPulses            0.177133\n",
       "locPctJitter                  0.062519\n",
       "locAbsJitter                  0.062797\n",
       "rapJitter                     0.051840\n",
       "ppq5Jitter                    0.047089\n",
       "ddpJitter                     0.051852\n",
       "locShimmer                    0.106539\n",
       "locDbShimmer                  0.108772\n",
       "apq3Shimmer                   0.120668\n",
       "apq5Shimmer                   0.105978\n",
       "apq11Shimmer                  0.126318\n",
       "ddaShimmer                    0.120668\n",
       "meanAutoCorrHarmonicity       0.047373\n",
       "meanNoiseToHarmHarmonicity    0.039930\n",
       "meanHarmToNoiseHarmonicity    0.084059\n",
       "minIntensity                  0.033735\n",
       "maxIntensity                  0.048149\n",
       "meanIntensity                 0.038274\n",
       "f1                            0.088434\n",
       "f2                            0.117828\n",
       "f3                            0.163546\n",
       "f4                            0.162248\n",
       "b1                            0.201480\n",
       "b2                            0.265286\n",
       "                                ...   \n",
       "tqwt_kurtosisValue_dec_7      0.103954\n",
       "tqwt_kurtosisValue_dec_8      0.060120\n",
       "tqwt_kurtosisValue_dec_9      0.040404\n",
       "tqwt_kurtosisValue_dec_10     0.076966\n",
       "tqwt_kurtosisValue_dec_11     0.130710\n",
       "tqwt_kurtosisValue_dec_12     0.151650\n",
       "tqwt_kurtosisValue_dec_13     0.101527\n",
       "tqwt_kurtosisValue_dec_14     0.028817\n",
       "tqwt_kurtosisValue_dec_15     0.065865\n",
       "tqwt_kurtosisValue_dec_16     0.118154\n",
       "tqwt_kurtosisValue_dec_17     0.092093\n",
       "tqwt_kurtosisValue_dec_18     0.099594\n",
       "tqwt_kurtosisValue_dec_19     0.092835\n",
       "tqwt_kurtosisValue_dec_20     0.080475\n",
       "tqwt_kurtosisValue_dec_21     0.071630\n",
       "tqwt_kurtosisValue_dec_22     0.101868\n",
       "tqwt_kurtosisValue_dec_23     0.028447\n",
       "tqwt_kurtosisValue_dec_24     0.034783\n",
       "tqwt_kurtosisValue_dec_25     0.056259\n",
       "tqwt_kurtosisValue_dec_26     0.085323\n",
       "tqwt_kurtosisValue_dec_27     0.101074\n",
       "tqwt_kurtosisValue_dec_28     0.114386\n",
       "tqwt_kurtosisValue_dec_29     0.116327\n",
       "tqwt_kurtosisValue_dec_30     0.103475\n",
       "tqwt_kurtosisValue_dec_31     0.080457\n",
       "tqwt_kurtosisValue_dec_32     0.069079\n",
       "tqwt_kurtosisValue_dec_33     0.077204\n",
       "tqwt_kurtosisValue_dec_34     0.105587\n",
       "tqwt_kurtosisValue_dec_35     0.113183\n",
       "tqwt_kurtosisValue_dec_36     0.110552\n",
       "Length: 752, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_scaled = (data-data.min())/(data.max()-data.min())\n",
    "kwargs_W = {\"metric\":\"euclidean\",\"neighbor_mode\":\"knn\",\"weight_mode\":\"heat_kernel\",\"k\":5,'t':1}\n",
    "W = construct_W(data_scaled, **kwargs_W)\n",
    "score = pd.Series(lap_score(data_scaled,W=W),index=data.columns)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tdaSelection(data,lens,r,is_min_best=True):\n",
    "    data_scaled = StandardScaler().fit_transform(data)\n",
    "    clusters = {}\n",
    "    bins = np.histogram(lens,bins=r)[1]\n",
    "    bins[-1] = np.inf\n",
    "    for i,f in zip(np.digitize(lens,bins),lens.index):\n",
    "        if (i-1 not in clusters.keys()):\n",
    "            clusters[i-1] = [f]\n",
    "        else:\n",
    "            clusters[i-1].append(f)\n",
    "    dist = pd.DataFrame(np.arccos(abs(np.corrcoef(data_scaled.T))),index=data.columns,columns=data.columns)\n",
    "    distmat = pd.DataFrame(np.zeros((len(clusters.keys()),len(clusters.keys()))),index=clusters.keys(),columns=clusters.keys())\n",
    "    for i in np.arange(len(distmat)):\n",
    "        for j in np.arange(i):\n",
    "            distmat.iloc[i,j] = dist.loc[clusters[distmat.index[i]],clusters[distmat.index[j]]].min().min()\n",
    "    distmat += distmat.T\n",
    "            \n",
    "    initial_cluster = max(clusters.keys()) if (is_min_best==True) else 0\n",
    "    initial_feature = lens[clusters[initial_cluster]].idxmin() if (is_min_best==True) else lens[clusters[initial_cluster]].idxmax()\n",
    "    \n",
    "    used_clusters = [initial_cluster]\n",
    "    selected_features = [initial_feature]\n",
    "    \n",
    "    for k in clusters.keys():\n",
    "        if k==initial_cluster:\n",
    "            continue\n",
    "        else:\n",
    "            new_cluster = distmat[used_clusters].min(axis=1).idxmax()\n",
    "        used_clusters += [new_cluster]\n",
    "        new_feature = lens[clusters[new_cluster]].idxmin() if (is_min_best==True) else lens[clusters[new_cluster]].idxmax()\n",
    "        selected_features.append(new_feature)\n",
    "        \n",
    "    return selected_features,clusters,used_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "752"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi\n"
     ]
    }
   ],
   "source": [
    "print('hi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-17-f0295a700048>, line 8)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-17-f0295a700048>\"\u001b[0;36m, line \u001b[0;32m8\u001b[0m\n\u001b[0;31m    clusters[i] = score_[]\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "def tdaSelection2(data,lens,r,scroe,is_min_best=True, group_bin_num = 10):\n",
    "    '''\n",
    "    group bin method\n",
    "        data : \n",
    "        lens : \n",
    "        r : 원하는 cluster 갯수\n",
    "        is_min_best : \n",
    "        \n",
    "    '''\n",
    "    data_scaled = StandardScaler().fit_transform(data)\n",
    "    clusters = {}\n",
    "    score_ = score.sort_values()\n",
    "    \n",
    "    \n",
    "    len_feature = len(data.T)\n",
    "    iter_num = int(len_feature / group_bin_num)\n",
    "    temp_cnt = 0\n",
    "    group_bin_output = {}\n",
    "    for i in range(iter_num):\n",
    "        temp_add_list = \n",
    "        group_bin_output{str(i) :}\n",
    "        # iter len_feature / group_bin_num\n",
    "        \n",
    "        \n",
    "        \n",
    "            \n",
    "            \n",
    "    dist = pd.DataFrame(np.arccos(abs(np.corrcoef(data_scaled.T))),index=data.columns,columns=data.columns)\n",
    "    distmat = pd.DataFrame(np.zeros((len(clusters.keys()),len(clusters.keys()))),index=clusters.keys(),columns=clusters.keys())\n",
    "    for i in np.arange(len(distmat)):\n",
    "        for j in np.arange(i):\n",
    "            distmat.iloc[i,j] = dist.loc[clusters[distmat.index[i]],clusters[distmat.index[j]]].min().min()\n",
    "    distmat += distmat.T\n",
    "            \n",
    "    initial_cluster = max(clusters.keys()) if (is_min_best==True) else 0\n",
    "    initial_feature = lens[clusters[initial_cluster]].idxmin() if (is_min_best==True) else lens[clusters[initial_cluster]].idxmax()\n",
    "    \n",
    "    used_clusters = [initial_cluster]\n",
    "    selected_features = [initial_feature]\n",
    "    \n",
    "    for k in clusters.keys():\n",
    "        if k==initial_cluster:\n",
    "            continue\n",
    "        else:\n",
    "            new_cluster = distmat[used_clusters].min(axis=1).idxmax()\n",
    "        used_clusters += [new_cluster]\n",
    "        new_feature = lens[clusters[new_cluster]].idxmin() if (is_min_best==True) else lens[clusters[new_cluster]].idxmax()\n",
    "        selected_features.append(new_feature)\n",
    "        \n",
    "    return selected_features,clusters,used_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jaeminpark/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/Users/jaeminpark/anaconda3/lib/python3.7/site-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "selected_features,clusters,used_clusters = tdaSelection(data,score,700)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        0.12809548, 0.12859426, 0.12909305, 0.12959183, 0.13009061,\n",
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       "        0.17797377, 0.17847255, 0.17897133, 0.17947011, 0.1799689 ,\n",
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       "        0.18794942, 0.18844821, 0.18894699, 0.18944577, 0.18994455,\n",
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       "        0.19293725, 0.19343603, 0.19393482, 0.1944336 , 0.19493238,\n",
       "        0.19543117, 0.19592995, 0.19642873, 0.19692751, 0.1974263 ,\n",
       "        0.19792508, 0.19842386, 0.19892265, 0.19942143, 0.19992021,\n",
       "        0.20041899, 0.20091778, 0.20141656, 0.20191534, 0.20241413,\n",
       "        0.20291291, 0.20341169, 0.20391047, 0.20440926, 0.20490804,\n",
       "        0.20540682, 0.2059056 , 0.20640439, 0.20690317, 0.20740195,\n",
       "        0.20790074, 0.20839952, 0.2088983 , 0.20939708, 0.20989587,\n",
       "        0.21039465, 0.21089343, 0.21139222, 0.211891  , 0.21238978,\n",
       "        0.21288856, 0.21338735, 0.21388613, 0.21438491, 0.2148837 ,\n",
       "        0.21538248, 0.21588126, 0.21638004, 0.21687883, 0.21737761,\n",
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       "        0.22535814, 0.22585692, 0.2263557 , 0.22685448, 0.22735327,\n",
       "        0.22785205, 0.22835083, 0.22884962, 0.2293484 , 0.22984718,\n",
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       "        0.23533379, 0.23583258, 0.23633136, 0.23683014, 0.23732892,\n",
       "        0.23782771, 0.23832649, 0.23882527, 0.23932406, 0.23982284,\n",
       "        0.24032162, 0.2408204 , 0.24131919, 0.24181797, 0.24231675,\n",
       "        0.24281554, 0.24331432, 0.2438131 , 0.24431188, 0.24481067,\n",
       "        0.24530945, 0.24580823, 0.24630702, 0.2468058 , 0.24730458,\n",
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       "        0.26027293, 0.26077172, 0.2612705 , 0.26176928, 0.26226807,\n",
       "        0.26276685, 0.26326563, 0.26376441, 0.2642632 , 0.26476198,\n",
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       "        0.27274251, 0.27324129, 0.27374007, 0.27423885, 0.27473764,\n",
       "        0.27523642, 0.2757352 , 0.27623399, 0.27673277, 0.27723155,\n",
       "        0.27773033, 0.27822912, 0.2787279 , 0.27922668, 0.27972547,\n",
       "        0.28022425, 0.28072303, 0.28122181, 0.2817206 , 0.28221938,\n",
       "        0.28271816, 0.28321695, 0.28371573, 0.28421451, 0.28471329,\n",
       "        0.28521208, 0.28571086, 0.28620964, 0.28670843, 0.28720721,\n",
       "        0.28770599, 0.28820477, 0.28870356, 0.28920234, 0.28970112,\n",
       "        0.29019991, 0.29069869, 0.29119747, 0.29169625, 0.29219504,\n",
       "        0.29269382, 0.2931926 , 0.29369139, 0.29419017, 0.29468895,\n",
       "        0.29518773, 0.29568652, 0.2961853 , 0.29668408, 0.29718287,\n",
       "        0.29768165, 0.29818043, 0.29867921, 0.299178  , 0.29967678,\n",
       "        0.30017556, 0.30067434, 0.30117313, 0.30167191, 0.30217069,\n",
       "        0.30266948, 0.30316826, 0.30366704, 0.30416582, 0.30466461,\n",
       "        0.30516339, 0.30566217, 0.30616096, 0.30665974, 0.30715852,\n",
       "        0.3076573 , 0.30815609, 0.30865487, 0.30915365, 0.30965244,\n",
       "        0.31015122, 0.31065   , 0.31114878, 0.31164757, 0.31214635,\n",
       "        0.31264513, 0.31314392, 0.3136427 , 0.31414148, 0.31464026,\n",
       "        0.31513905, 0.31563783, 0.31613661, 0.3166354 , 0.31713418,\n",
       "        0.31763296, 0.31813174, 0.31863053, 0.31912931, 0.31962809,\n",
       "        0.32012688, 0.32062566, 0.32112444, 0.32162322, 0.32212201,\n",
       "        0.32262079, 0.32311957, 0.32361836, 0.32411714, 0.32461592,\n",
       "        0.3251147 , 0.32561349, 0.32611227, 0.32661105, 0.32710984,\n",
       "        0.32760862, 0.3281074 , 0.32860618, 0.32910497, 0.32960375,\n",
       "        0.33010253, 0.33060132, 0.3311001 , 0.33159888, 0.33209766,\n",
       "        0.33259645, 0.33309523, 0.33359401, 0.3340928 , 0.33459158,\n",
       "        0.33509036, 0.33558914, 0.33608793, 0.33658671, 0.33708549,\n",
       "        0.33758428, 0.33808306, 0.33858184, 0.33908062, 0.33957941,\n",
       "        0.34007819, 0.34057697, 0.34107576, 0.34157454, 0.34207332,\n",
       "        0.3425721 , 0.34307089, 0.34356967, 0.34406845, 0.34456724,\n",
       "        0.34506602, 0.3455648 , 0.34606358, 0.34656237, 0.34706115,\n",
       "        0.34755993, 0.34805871, 0.3485575 , 0.34905628, 0.34955506,\n",
       "        0.35005385]),\n",
       " <a list of 700 Patch objects>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(score,bins=700)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(752, 752)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.13878765,  1.0019906 ],\n",
       "       [-0.24198329, -0.19787974],\n",
       "       [-0.30647468,  0.43541337],\n",
       "       ...,\n",
       "       [ 0.13788553, -1.05062301],\n",
       "       [ 1.05399444,  0.01584695],\n",
       "       [ 0.16468557, -0.80424967]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist = np.arccos(abs(np.corrcoef(data.T)))\n",
    "print(dist.shape)\n",
    "mds = MDS(dissimilarity='precomputed').fit_transform(dist)\n",
    "mds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1a18c0f780>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mds[:,0],mds[:,1],c=score,cmap=plt.cm.jet)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PPE                           0.373391\n",
       "DFA                           0.206009\n",
       "RPDE                          0.266094\n",
       "numPulses                     0.103004\n",
       "numPeriodsPulses              0.103004\n",
       "meanPeriodPulses              0.167382\n",
       "stdDevPeriodPulses            0.505007\n",
       "locPctJitter                  0.175966\n",
       "locAbsJitter                  0.177396\n",
       "rapJitter                     0.145923\n",
       "ppq5Jitter                    0.131617\n",
       "ddpJitter                     0.145923\n",
       "locShimmer                    0.301860\n",
       "locDbShimmer                  0.309013\n",
       "apq3Shimmer                   0.343348\n",
       "apq5Shimmer                   0.300429\n",
       "apq11Shimmer                  0.359084\n",
       "ddaShimmer                    0.343348\n",
       "meanAutoCorrHarmonicity       0.133047\n",
       "meanNoiseToHarmHarmonicity    0.111588\n",
       "meanHarmToNoiseHarmonicity    0.237482\n",
       "minIntensity                  0.092990\n",
       "maxIntensity                  0.134478\n",
       "meanIntensity                 0.105866\n",
       "f1                            0.250358\n",
       "f2                            0.334764\n",
       "f3                            0.466381\n",
       "f4                            0.462089\n",
       "b1                            0.575107\n",
       "b2                            0.758226\n",
       "                                ...   \n",
       "tqwt_kurtosisValue_dec_7      0.294707\n",
       "tqwt_kurtosisValue_dec_8      0.168813\n",
       "tqwt_kurtosisValue_dec_9      0.113019\n",
       "tqwt_kurtosisValue_dec_10     0.217454\n",
       "tqwt_kurtosisValue_dec_11     0.371960\n",
       "tqwt_kurtosisValue_dec_12     0.432046\n",
       "tqwt_kurtosisValue_dec_13     0.287554\n",
       "tqwt_kurtosisValue_dec_14     0.078684\n",
       "tqwt_kurtosisValue_dec_15     0.185980\n",
       "tqwt_kurtosisValue_dec_16     0.336195\n",
       "tqwt_kurtosisValue_dec_17     0.260372\n",
       "tqwt_kurtosisValue_dec_18     0.281831\n",
       "tqwt_kurtosisValue_dec_19     0.263233\n",
       "tqwt_kurtosisValue_dec_20     0.227468\n",
       "tqwt_kurtosisValue_dec_21     0.201717\n",
       "tqwt_kurtosisValue_dec_22     0.288984\n",
       "tqwt_kurtosisValue_dec_23     0.078684\n",
       "tqwt_kurtosisValue_dec_24     0.095851\n",
       "tqwt_kurtosisValue_dec_25     0.157368\n",
       "tqwt_kurtosisValue_dec_26     0.241774\n",
       "tqwt_kurtosisValue_dec_27     0.286123\n",
       "tqwt_kurtosisValue_dec_28     0.324750\n",
       "tqwt_kurtosisValue_dec_29     0.330472\n",
       "tqwt_kurtosisValue_dec_30     0.293276\n",
       "tqwt_kurtosisValue_dec_31     0.227468\n",
       "tqwt_kurtosisValue_dec_32     0.194564\n",
       "tqwt_kurtosisValue_dec_33     0.217454\n",
       "tqwt_kurtosisValue_dec_34     0.298999\n",
       "tqwt_kurtosisValue_dec_35     0.321888\n",
       "tqwt_kurtosisValue_dec_36     0.313305\n",
       "Length: 752, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_label = pd.Series(np.zeros(len(data.columns)),index=data.columns)\n",
    "for k,v in clusters.items():\n",
    "    cluster_label[v] = k\n",
    "cluster_label /= cluster_label.max()\n",
    "cluster_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x10b6878d0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mds[:,0],mds[:,1],c=cluster_label,cmap=plt.cm.jet)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_label = pd.Series(np.zeros(len(data.columns)),index=data.columns)\n",
    "for i,f in enumerate(selected_features[:20]):\n",
    "    selected_label[f] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sZL/99isa6k5wVgPPeiJwJ/BQju37AJu4rx2Av7j/L6JAqKrxwpxGCAxHrPYmrOrUoXXHF5j4ikPjeJyWhyC4BZQcZ36eVg2amoV34tCFUwd1h6Oud6mh3aHsXKg70jVisdzHdgVrMAx4MqMwREK7wqB3TDELfghsZfjSXZ7iVIg9hVH7c9/TqCnmqZ3WNodY5RDevaDlqcYg+Sben0+ivdAHIHIYNN9J7kQiQAniW6ftfFQq8Iplp9MQKR/MZtspeCgVtjSmuOHYCxi+1xEcduYyiD+BJj4CuxLHaXTZN+3HTZkyhbPPPpvq6mrefvttgsEgkUiEvfbaiwkTJqzWioAmwdh/gw29sjJVfV1EhuTZ5SDgIbdJ5AwRqRKRNbtqvV6Egaa/QetPNtobWKBptPxirNKjzfbokzk407mQBHs2xGZDbApKGHBMmCQvXM2xVk898SIkXia/UcqHAIT3QUqOhsDWnhV8IgETIsgD1RQkXkaTHyL+ddHo42TfONRIndpfg3/D7i813+fbqahHSk9Ek+9A8gOMce8cNhGQIITbRZY0tJPHmsHngz1Ovgzi0yH6GZ3j5YGg8vlM5chzbiTdEMfvTxv1QLsZFm+PJhZA0sFpvo/X3tuYo48+mrPOOguAESNGsM466/D5559z1VVXMXLkSD755BMqK7273/zU0Zpg7K9YVbeRtYH5Hf7+zn0vw1iLyGnAaQDrrbfeKlpa/4aqovUnZmsqN92IBjZDgtuA/QMrpq/hHmvPz79bFlaEkucH/8ZI5XV548Sa+gqNToT0fAiNQEqOQqx2Gpo6zYZlYX9vvGci5Lx5iM87jFEAxCpD/UMh3Vk3xQ/hPTP3lSBUP2A0QFIfo/iMWFV6tnvIULclWEn7MckZ7k0z3mksnylTjz9BZ0MdaxGm3DOQjbaMM3hIM0/+uwmAHxamaWxymDy1ge8XJAgEhM//ewu//vV3bLrppowdOzZDb2SPPfZgp512Yuutt+aBBx7gvPPO69Fn9FOAXeRZewaCsgudVe8F7gXYdttt+2/LhlWJ1Mcm/JD1cSXQ6D+Q4DZIcBuX3bACYQhg5Sn1BqDsdybpmP7KJNcihyClx+Q31IlX0fpzMDcFG1IfGK3qAU+BNkPyv6bUO/0N7Qm91s/ASxc1AP78xSX5IJXXo3VHu08WCSACViVSdmH7mjUF+M1TQnBrCG5tfvylR5ucAIpYNdmDtzFFOsOG+DNZvHDHgQ/eKOPvtwxm7FmLWd6cYuypCzP2af17h21CvPtBAw0NUT766CN23jmT8XL88cczceJEhg0bxn//u/qmklorGPsrVpWx/g5Yt8Pf6wA/rKK5f5RQpw6c5ai9DEMXy9qj/QIP7wUNl6zK5RWIkKtwdwVWZH/gxIKPVHXQhsvI9DQT4Nho3THu04RF/htUxN3uB/xI1U05k4iqabTlbmh52MT+g9sg5ZchgXbjLoHNYOCLaPQxSH9tQjeRAxGrBCf2LDTdBM5CkCq07AyIHI+kZ0LqE7DWhPCeuW9Oge1znEPYs4DHsmCjLeOAsHRBgDUHhrAXZFM0DztlAcvqbE4YW8EJY2uwBudXVFzdW445/ZgNsqpW9jRwnBiMABqK8WpvqNOAU3cKungXdOk+0HAGRsy/M8IglTiLd0cX/ZwV96pzoQQku/qtIEgIal9xDXU3YX8Pjtd5p8Geh/Fs852zH8rHQfgAKD0JqZ2KhHbLubc2XAbN94LWmbGT76B1Y1H7+4z9xKrBKjsdq+oGrNIjEKvEVEQ2XALOAkzRSj003QZL90TrT0ObJqCNl6FLdkXT33jOL4FNILy3WzTTigj4NyHXZZpOGcP65rQqUknB6erBKE/x0KxZs/jiiy/4xS/a8wNddahpxSeffML+++9PZWUl5eXlbL/99rz//vtdLKb/wQg5/cS1QURkErAbUCsi3wFX4vZBUtW7gWkY2t4cDHWvcBdrNYPWn+XKj+ZLGLoVhIl/07sSo15I9FwVTxthyS5o1c1Ip7hul7DKyB2W6eqcLQhs7iZgj+56mfYi03vRq0VYy9+McFO+45tvJztnEAfnuw47pU1Mffn5SO3j7rzfGwW/xDvgGwglJyOhXdDoo4YXHzkIKTkcXXaUy8du/zwcJ8jM1zYmFHFIxCx+9+uNufTueay9QZJYwub511oQq2P8OoGU7sB+B0V55ZVXePjhh9l///1Za621mD17Ntdeey3rrbceJ5xwQtscXXWoAfjwww8ZOXIkBx10EI8++igA7733HrHYynIeVh4UIdWPy82l0Malqxrbbrutzpw5s6+XsUqh6fno0n3Jza6wIPAL8K3jbVwKguXKkhaitObH3DRW9DcSRmofzylf2gq1l6LRByH+Noax8hU9iqPLAGTAvxD/ul3vC2jiHXT5Wd5PMIHhWAMey3u8s2g4mf0b8yGADHwdSKJLD3Dplu6NWSJQeg5W2cmZ60vPR+uONPtqCvAZhozUcPzWX7FwfntoZcDgFC12C1MXv+g5+9dff01jYyPnn38+H3/8McuXL2fAgAHsvffeXHfddRlyqY7jtCUiW4trXn311YzxRowYwYYbbsg///nPAs9/5UBE3lfVfApkXWLIlmV6xeNbd70jcPKwt1Z4vu6i/5IKV0c4S0xFXU5PNohU3YrWn07PDDWYx/RCDIsF1vrgFCKJ6hae5ORop9CWSUhlbg9V7YXo0oNc49XTcwMIQc3fCjbUAPjXx1vD2wf+TdvXGH8Obb7b9IAMbIOUn2d0SXwbeLBEckEAB22+x41Fd3iC0hg034GWHpVR+CP+dWHgK6Z9mr3AKPhpA7r8HHbZv4Kn/jqQVNIY1WULA0AVR617IA+9+zltIWhrIDLwzbaY9EsvvdTlSrvqUPPZZ5/x7rvvcttttxV47v0d0q+LYvpvNH01QNZTjX9ojlJmF+JDU59A+osCZ/D6ek1krqBjrTDeyc3OsMlbTIPtakbnhjbf5Xq2K2KoAfEh3eRRi28tUyZOqNOWIFJ6EgBOy0NowzhIf2puqokXjP50+n9I+UVki1v58fzs/EMQ30BIvovnZyY+k7zs/LYEkPCeSOmxSHA4GnsWNMqR5yxm4NopwiXmOw0EHcIlNuP+/C0ZuUKnAe+Gvz3Hu+++C5jS9eHDh+P3+9loo434619zNTPu31DAVqugV1+gaKz7AJr6DGfZWHTRpjiLtsZpvA7VpKlKLDsTb6H9MJSeCS1/o2tjGwH/ZlB1B1TdT88eoNKQ/h+9Q+eLQHBk/l0Sb5Df4Bc4T9k5iHQ2ul1Dqm6FyBiM0RXwb4bUTET8G5iGAM23dXoicUBjaPOdSGhnpPpOlxYYAGsdKPuDSQ5KK5c6AlKBVE4wf/pydGfRpNEy6XLBRpmwrNLh7he/4DfX/MAeh9Zx+FlLuP/1L9hqh87VrBZKBE28hrY8gBN/ESfxnuGx9zAUunChoQYed9xxHH300bzwwgvsvffenHLKKUybNq1HY/Y1fvIJxiLaYcrCvzTFF4HNswTsTfzxqHY6lkYhOgm1f0Cq78Qq+w3q3xBtus0tUrHBGgClZyElR6Kxf+WZPWLoZCWHQXg0OEvQ5RfTcyMYMyp4rcUcGqVHxtu3BhI5OOMtTX9runzb843KnlUJzvc5BigA/k2RsjORDlWB3YFICKm8Eq24Akhnfm+dC5La4EDS8JIl9EvwDUEbL4fkf6D5GgjuBqUnQXou4lsbwvuZEndASk9Dk++RmZgMGkEs36CsmbLWG/k1Gp8KGiMUUUYfWcfoI+sweX2Pp7PAL2DZr1FnkVsYZAOCtqoE1txr1tgNOC795JRTTmHcuHEAjBo1is8//5zrr7+efffN3XGoP0KRYvOB1QWanmviyc4SzEOLDypvQjroT2h0okd8NAGJ11D7e3PB+LcwlDNtMHQzV9EOQIM7QewHsg1wBFnjvTYer9o/oEsOALzob92Aby2kZiIkZxgVu+YJrtEuxBsTCGyPVN+VUa2nibfQ+t9gjEra9aoDmLBBd8rmfea4yhuw8rQi6w7M59ypQ4w1wLA5PJdgDJw6zeiyw0AbaCvLT74K9lykdnqWpomEdkArroKmPwJpM35oV6TyxsLWGdwWLTkeWh5oXQigENzO1TDpdFNtYxh1PA8F4mDPRetOhNrnWPZDHQ1Lm1hvs7UJBLM75XREa9uwUaMyO/7svvvuP8o4tgKpn7o2SBFuUUXdsW6hSrsh0+XnQe1UxO+Wz6c+w9vTVTQ12yivNVyMubBSpjIxuANU3WX0pMvOROPT3URc6zgRKD8Pku/hoIYpEp+OYUmuCFwR/fizENoLCY5EU59C/EmyjaqFMXKWW9adgtAopGpChpfqpOZA/RlkMl7idK9c3g++jYw3Ht6r+7TAbkKsCjS8D8T/Tea6w0jZmeaf8amux9rRSKbBWYxG/26+LymD8L6Iz4Q5rJJfo5H9jeduVWWU0XeEOk3mhiYCwZFtIl5W+QVoZIxJPEoJhPdCl+6P99NPPvqlQ+PSpVx7+Dg+f/d7fAEfIsKZt53A3ifmFrrabLPNPN9X1S6Tk/0TUtSz/qnAKK99YChWgeGZ3lLyLTem2dnjTKOxR90kFBDYwlWR6/yomoSm68BeQobh0qhJRsWfg8i+iG9NqH3asAmS74BvMFiDoGkCKgEKU94rFA7EHkNjT4BcDaWnQWIant6vfzhS/SdTGm/PNyyK9P/QpQei9jyw1oCSI6D5Lnom/GQZY+db34ggpT51HcVP0ea/wIB/dL9jTDcgldcajY/4sxghphCU/x4J7QKApufgWaSjMWi6CcUGAtB0C1T9CQkbb1QkAK5sqxec2HRz83a1uFEbrbwZK2Jaj4l/PfAf2z6dVWGqKLuJq05ci9kffEs65UDc/DbvPPuvrL3xmmw10tso77TTTlRXV/PSSy8xenR7K7SXXnqJ4cOHex7Tn6H07wrGorEuEE5sKjReRtvjppRC9b1IYHN3By/9DjAVd+097qT0RDQ6Gc+4or3Ie3KNovFn2rqOi29NpHK82RR/Hl1+EZDMQT8rBCXuelrX1PqDdTvBkDSn1vxnvJ8KIkjlFUaD2jcYApubRNbyc2m78Tg/QPPt9Cxh6Ueq7kDCe+K0TIKmG9rHVYAoWn8WMnB6D8YuDCIhpOoG1PkDmv7KrCmjFH1zVEo8SsPdkAjQepPShvMhNAMRr/Zo7VB7ETSMI0PqFaDhdzj+oYivCrGqMw8qOREarybzxhEAqXDZINmf/4Jvgnz5ccgY6g6ItkT548VXceg52/H9d/NobIoxefJkAPbdd19KSkq44oorGDduHFVVVWy33XZMmTKF119/nddeey3vufVXFD3rHzk0PRcaLiXT420xcb5Bb5g4cWBbvGU0S0zyyYX41kYrLoHG8WR7qK0dvL3grSmhLRNZsVLzENT801RDxqYarxU/2F967JvD0Hqo2WnTLWSHNnrILAnv1x7qiD1KLvlTTX/bHm5aCVCnzghLpT4C8aMIWn45VskhpjtO0+0uR771e/USkwKwzNNSaNf8E8Zz3XySsGwfFB/q3xCpvLHNaZDIIWj6C4hOMt+lpiCwGVRcDfUnuyX8mZ9fY30EfzBAMm4TCDoEQkq0Sdj7xO+5+IH3ePSdR9v2PeywwwBTXDNkyBDOO+88HMfhz3/+M+PHj2fYsGFMnjyZkSO7YP/0Q6hKr3rWIrI3pumKD7hfVW/otH094EGgyt3n96qak0ZTNNZ5oIl30abrXDaE10WXgsRbEB5ldJQjYyD+eAeKVxj8GxhmRgdIYCuThfeMKVtkGTWJIB6dXQDTaqrHCEFoZ6zg5hDcHMovMEPWnZ7DWAfctXmxDbbK/DuHBka3ISVIqMOFn4uHLsIKc7TB0PTiL6KpWcbwh/dvixFr/W/cEFa6vXCpcTzqXx8J/gIGTEabboDES4APrOo8srMFeHAaI1d+g9b+lukvjLDVwBcRqwYRQSouRUtPN79b3xptlaM68AWITzfNCZIvtumjD926hd9c/Q2Iza4HNmBZSmOdn7JKm99d11EcKgih3bCq78xYzQUXXMAFF1zQ9fn0c5gEY++Um4vpIfd/wF4YIbv3RORpVe1YPXU58C9V/YuIbI6R5RiSa8yXQTnEAAAgAElEQVT+G6DpY2jyA7T+VEh/Tm7mg+Nm/w2k4gqk8gYI7AD+raD8AmTApGylNf9m4BtE9scfgfLfmxCLlGI4vyGIHAHBXbyXEN6DLPZClxCTOCw5Bqm6I3tr5MAO/OCOG3xm7W3b/EDYeHadz9G3TjfXlAOqpiNNKyL7k128gnnM93Uds9b0HJy6k3AWboWzaARO059Rl+mhTgO69AC04VKI3o82Xo8uGWVYPulvTJw8y3gm0Ja/mSX4BmFV3Yq1xkdYa/wXKb/c+3NE29qX5UVoNwr6bjWNRp/IeEt8Awz/u0OJv0gEiRwCkjAdh9wCKSHNHmPqGHXwcoIhxR+AmjXSBMOdf/dJSLyKOg38NCG9WRSzPTBHVf+nqkngEUwTlo5QoML9dyVdKJEWjXUOeIvzdN4phaptsvUY2peE98Ea8Hes2ilYpSd4xiVFBKm+3xg0KTGJM0JQfg5W6XGmLLjiGqTiEqR2KlbFJTmlK6X0JEMtazNgrfvleGiSCFJxPdYa72NVXOwt2RneG4I7uoZGMCGYMFI5wdx8Kq+HyCFQejJS+wwS3it7mvILyK7qC0Hgl1n7djqy0982tNzf4XxPAP/GHYxgyHjfVbd1Ke+p9kJ02eFuM98EaB203Ic2XGy2N90B9ne0P/HEQBvR5ePapQCyRzUl4F4I7Qbh/TCfg9/9fxip+lNBhTsS2AxKDsNIveY7tzjYhT3JqCZNuKtTkteyjJHuGpaphuwCn332GXvssQclJSWstdZaXHHFFdh2d2iZqx4mwSgFvTCidTM7vE7rNFyuhisdMR44xhW/mwacnW99xTBIB2jqM0OLw4LU54Ud1PRHtHE8Wn4pVumRBc8l/vWg9gVIf4La9RDYGstn2imJVep6kAWMY9VA7TOmE3jiTdOaq+Q4xKpGY48afWY6hGV8G3Q5togPqu6C5Lto4nVDK4scaBKIYFpxdVV8EvqlYak4HRv4piE9p4sz8vDmYv8y1ESMd8iAxyDxEpqYAb41kcivTQl3VyO3PEDnnoQQh/jzqL3QZbp0DrMopD9HrbVzc609WChz5szh5ptvZsaMGcya9Skjd9qYV567HMJ7ezcfyAEpvxzCo9HYUybklXiDLDaNlCDBwgSITBJ6RYxmHG24EqpuQXwDPPeor69nzz33ZPPNN+epp55i7ty5XHjhhTiOw7XXXrsCc698dKM6cWkXQk6FNFw5EpioqhNEZEfg7yKypap6JneKxtqF0zgBog9i4p5GbMcbgvnYXPZEawy16Xo0+DMksEU3Jl1oNDESrwOCEx6NVPwhO8PfBcSqRMp+A2W/yXy//CI0OMIYcqfRcHxLxuTtztJ2rIhpoxUa0a21aOor08oqPdctDur4+7RBcz3p5UrGkZW8FPFDeDTSKRfQJTwpk5hEXHoO+R40xSpHI4dAzENdLj4Vx/49VgfjlSUv6lsDKTkq61B16k3Sz7e2uUl2nlcEgtsjQdOcwKk7wa2abP1MAmDVmARnARCrDPWtZ3pR9hSpd9H6E2DA055PM3fffTexWIzHH3+ciooK9tprLxobGxk/fjzjxo2joqIie8x+gF6uYCyk4crJwN4AqvqOmMfwWmAxHiiGQQBNfeEa6taihlyyoBEI7Ws4tllIGh3iQufUGLpsDCRepa2pavzf6LIj0G41v80PCY3Eqr4ba8A/sUqP6ZIu1lOopnHqz0GXHYo2Xg3RB+geSyUEPi8BJsuN3fYC/Jvi6Z9o0vC3I78mOx5uGYU9q4zc6oO2W+jTjgMOOID58+fz2GOPscUW2Tfw9iYTI02cfMnOaPyFLk9Bqu+F0lNN5xlrAEQORwZM6db3KpXXYEIyrTeH7ibV0iZxmvrYc+v06dMZPXp0hlE+4ogjiMVi/Z7S52AV9CoA7wGbiMgGYryjIzBNWDriW2APABHZDPOlLMk14GpvrNWpQ5vvw7tQww/ierlSaUSWwnuRUyfCWV74xLHp4LTgVfFmYqo/Lmh0knvjiWOMdHduOALlFxsxJSmlnaYYBqsGKR/XK2uU0hNdamJHhCC0I+JfFyn7raG5SQmGm1wK1iCk6iaza75mu+lPMxJvXVXwaf0ZpqiJJBADpw5dfiGa6qrtVhCr/GysQa9hDXoHq/LK7j+JBbc3DRAiv4bA1hDoSQGLuJop2Zg9ezabbrppxnvrrbceJSUlzJ49uwdzrRqoQsqxCnp1PZamgbOA54DPMayPT0XkahE50N3tQuBUEfkImAScoHlUtVbbMIiqos23udoKrVSozvAjZWdDyZFtj6jq1LlNUTujBAn/qvD501/gSd3TFKTnupKdPyJE/0nPOqz7oeR4t6sLUPu8ibWn57g9Dg9tEz/qLjT9reFE+9aAwLYmT1D9INp4pUvHDJjGvRWmf6VIBGoeNUJM6c+M7kdoVFu5vIT3QePP5D4Pp9GUwHe5rq9dZkl2Fau2/BWpmtCj8+0OxO0sD6DRx9HUbLolT6ApU43bAbvttlub53zVVVdx1VVXtW17++23qa6upr6+d2VaexMmDNJ7/qvLmZ7W6b0rOvz7M2DnzsflwmprrEn82w195Ct9Vgj/KiOWKFYNWnauW83XmqyKGI+sGzFUCQzzrngTv2E7/OiQh+Mspeb/bQm61s/cAilFSts7o4hvIFJ21gqtRNVBGy+F2LPm8wQTMqj5OxIcjtQ+aVgR+LMFlkQgtIN5tY2XNp3VCYE12Luc2yrJLXvaeX3peXg/eTiQeBOn5ZG2RryrBOHR0HR9boaqJ7J10e+66y4aGxvZZZddOPvssxk7dixXXHEFH3zwAdtttx2q2u8b8vbnCsbVNgyiLRPJ3TElZF4VV3vKVVplpyI1f4PwgcbzqrwKqXkwSw41L8L7Gq5zRrwwANZaECz4Ztt/EN4HzypLWRMqbzNUxUHvmySoNcjQFUN7mXirrwD95m5Ao4+ZMBMJo5WiLWB/hy4/p31ZEswy1J5jJT9CF+9s1BQbznWLkEJkJvvDUH6VZ4IwazxVaL6DnPFvrTfJ6qV7o3bO8GWvQqxSpOZhl6fu/vatdcimXnZE2uQmOmCzTddih1/UUFNTQ1VVFdtssw0zZ85kzJgx+P1+GhoaqKrqYfPlVYBuUvdWOVZfzzonVzQIpacgJUfnNSIS3BYJ9rwFm0gYBjyGNl7jxnotQ4mruLTNiKjahskR/Ye5sYT2RMrP6hb1a2VD1TZqgKWnmwSZs8h9WgiC+JHqW011H655KzvTbbCwEhH7B9nJTQdSn6P2IsS3RkHDqMbQ+hPJ7lcZgshhRkHRtw5SejILvxvMs/c9zOJvlrLNnj9j1JE7E4p4JKJTHxTAxIiBk0SbJ5giq16COi2QeA7sZUZKNTC8zdOVwDBk4HTU/gFQI4uQ/ABtvCpHyzI1srmqQNJ0h48/B+Jn0w2jfD5rKtOnb019fT1HHnkk8+fPp6WlJSuW3b/Qu2GQ3sbqa6zDe0DLfLI9nDRIraFDrWSIbzBS/X85t2vDxRB/gTbDE3sETbwEtc+2lUD3BVTVNA5ouROcZai1JpRfhNQ+DfFn0eR/wFrH0ARbudmrdIG5Yq/ZGiZ5EX8F79hAAhJvG53uwKbMfP4jxh9yIXbKJp1K884zM/nXLU/z5xnXUVpRAjhgL8JZeohJQhckuGVD/CWoNGEdoKAngVzQ1Cy07nhXvyYJBCC0I1TdaaiQLqRDKEeCP4fqv6JLdswxqpFSdRquMoaaBGiCvXcPc8tfPsC2b2Lttddm5MiRTJgwgUgkwq67dqGH0sco9mDsh5DSUzpV/rXCgeab0MYrvA5bZdD0t+4F0NFDTINTbyRL+xAa/Ts03+RqdwPOAmi4FI2/5naqOQkp+03fGGowFZheZdpWhemKUii0EW9xLsD5Dq07gnTif9x47B0kognSKROTj7ckWDRvMVNum2ri58mZhuqWngXOdxTcuUfBqT8DXbQFumgLnLrTTPFON6GqaH1r9/aoO38MEu+g0Sn5D059QO5q2BpU4xB7mo65n9OPqyQYUJ54+m1GjBjBfffdx/jx47ngggv6LccaWtkgvoJefYHV11hb1UjtVFejofPdNAaxp9BcZcSrAqmP8b5I4m6z1b6Bibne6RHvj0PD+ejSX6N1h6NLRrptq1Y9pPRUU8lJa4fwAEbG9abueafBHcmbddME335wF/FYtqcci8WYeP9DTH7kWn74YRlLlqWYPLWJyVObiEYLVR9sgMTLmESeDcnX0WWH4CQ/NEayUKS/wrtZbgzij+U/1qoml+IjugRdfmnW29VVPn53ZjW2Dc888wxXXnkl559/fgY7pD+itSimGLPuhxCrwr0UPS5ICRh6lW/NVbwqF761QNRjaQHwr98XK3KRMB6nJ1qrOgGNGiGs2hd7PYHYFcSqdEvwn4bUOyauHBmL+LvhVQPiXx8tOQKij+DNGrIJB77AsbO9xSQJ3vpuOq8c9Wzbe2NPNV7x3P8MYUiJjzYnIfBzU+3pLCN/yzTHKOXVHYuKhZZdiFV6XAFn4pDtkLjwrmxuR+Dn4KsBu3MXHDOuE38ex3bwd7Ikb/4nzsYbhPhyzsKcHXD6I4phkD6GpuejiTdQl8Sv6Tlo4nXUXmwq1zzvWbYR0l+V60z+F6fuFJwle6ItfwcZQHZ1mR+JFK5B0vsIud5WAVAbjT25cpeTAyIRrNKxWFW3Y5X/zkjYqm2+9+gjXRaftI1TfilUXof3pWIxeKONWGfomlhW5kVeXVbD8w++itP8IPaCrbAXbNL2GrJuwBTeVNyErPEZ1oBJSO0LUHYRhSkoJsyTTdMENFFARaB/qCsW1hlhI8iVByIWUj3RaMp4fAaWJEHTJDrcyxoabf79SpQjDi53e23+OFBkg/QhVBOmB2LiTVeIPeFym+OGf6tJCO2FMYgd44h+Y8T93dD5WNG1xl82a20tLLG/A0JGkjT9BWCBVYtU3YT4e0d+1HHigA/LKpxyKCJo2XnQ+Ee6LoJJmIrMfgC1F5iu8s7ytji0hkYgVf+Xl3IpIkjkAJzEKxB/kc7dyKX0NMY/PoDfjRpPU32zEeFL24w6Yhf2PPaXJvzQfKuHs+xDInu1hWVEBHwDUQkWmIAEiKFN97jKjaXg38yTxyxiQdUdaP3J7rnHzTH+nyElh+edQdUx3n7Vn6HueNBsOmEyYTH/qyBDt44jAk9MbyGRUI44uBRSs1Z6U4jeRJEN0kfQppuNoXaz1OZN90JovXgSL0PkcEg879L5HAj+Aqm8dZUR+FXVUKQyDIEDxMCqRAa9adZvrdEra3LiL0PDhbT2a3SoAErAciA02qUH5vaerZKxOISg5U+mFZk12FD2OjNrpAQpRLd5FUCXX+BKmXZ4lE/MQFseQMo6q1t2OE4dkyMIbG1+O4nXgLRJVJaPB/tb1qi4nonvRvn4vR2oq9+GLXbakrU2cp/KpAaq7zctzrQFUJAapPr/TMVkRwS26oahdpF+33DAcUzCvPo+xJ+tsSLBbWDgK4atYy9BgttBcKe8vydNvGtakGnUZN9EyCW4ZfnczcCjTzYxfIsggQCcMW4B7364J7M+ncvIkSN59dVXM4676667ePbZZ5kxYwZ1dXW88sor7Lbbbt37DHoJqkK6aKxXPVQVYo/RdXPWGCTfQAa+bvoESkm3tRZWGNrsKtR5IPVhr67HSc+F5Wd0erfRvBwMPTD5iqEHdjYmHWCVHAwlB7ePW3+2qx7YsUvO0N4TYVoBqFPvJmw7x1zjRn41h7FWZzlad7TRwFAHxDJyqNX3YfkG4jReg8Ymg8bwAT/f9ivwv40M+FfGOBLcFga+AekvAR/4N84wkmovdBsgv2VCTE49hXe90XYeuB1D646Dga95K/hZVVBydEFRWbUXo8tPy0wke4TSYy3CQzevQVVtmvWHJWhsTvPym1GuHjeAT79IMv2lBkbsuAvJlDeD4qGHHkJEGD16NJMmTSpgZSsXfRXiKAQ/WWNtfsQFZsy1yX0M7awNvoogreL0HpSuHvK9VRMQf96EU/ybQuiX5gJuuK6LI1OmaCL2LORqJeYBqbodYlNc5cEUhA9CSo8pqKpvpUNT5E6w5TaK2ng1pOfR9sSgGM2S5jtM26zov8h0BuJm//i/IXJgxlgiFgSyC0LUXoQuPdA1uK3ff8BUflpBVyypQKofarz35H8Mh3oFoLGnciQfI+DfhGR0Ht/NSfPP2wfyxtQq1t4ozkEnLaWm2kdivmkF5jhhDhpzJVbZ6YwZM4alS5dmjfb2229jWRazZs3qc2PdGrPur/jJGmsRCw38zAj55IVvhUWTjMCWU5BOtBdEAmjJYRB9jMxQSARKT+/+etLz0bqx7uNr3NwMfOtCzSSw/1fACDE0NROhG8ZafFByeJcx0L6A+Aah1gDDB8+A3y2Tz4aqujz3bLEl4s9A8BeYRsGdj4yiiddNa7QCoM33djLUuHMuh8q/Q/3xeDc9yHFzB9BuqD/mgrMI76dSB4kcTLDmKB4+fwLvv/IR1QObmTBlLpZPaRcbFKyyE7DK8v9+u1InXNUoGus+glSMN4+x6lLK8GE4q61Nad2WUGXn9Wh8dZajyy+E5Bvmb0qh8hqsAru8ZKy1/GJUoxB7xtAG1YbSk5DIYd1fV8PF4NTR9tivUUh/jTbfYbzspLe0ZTtC4BvS7Xn7K9RpzNFY2IaSo3MdRU6ZV027BVVeF7bf7a9ZIJJv4210LdOUIFeDYN965ubTme+uKQj8ovD5c6GN/dHZuxYIbotlWfzhoQ34aPpkAsEUFQPS+DIeohTik9Hy81ao8nJVopebD/Q6euVTFJG9ReQLEZkjIr/32H6CiCwRkQ/d1ym9MW+X6wpsgQyYCiVHQmA7KDkWqh80ug6B7aH0VGTgdKQHXGpVRZce1maoDVqg4QKclu4/zokEkYprYOCbSM2jyKAZWOXndjuhqE4LpD4k+yJLmkqzissLWIwfiRTuVfcFVBVNvmdoeMn3yCMDDPFp7dmvDASR5Jueh4hYblFM50vEbYaQ0aOyI/xIZGzB55GTHqppJLAxBEeQXZQSgfKLXIPaQWxJIuYG352bhdfUGoOmP+HZLSm0KxLYFLW/R5quYutd6tli++ZOhtqF0+Kymn48cJCCXn2BFfasC2y5DvCoqq6Y9mVP1udfB+lsoFYwngdAaiY433pva7oaLTmiYEOridfQxmvB/hakDC09GSntnAQsFPl1Li3/2jjWum5vRK8dhiDVE1Z5IUt3oE4TWncs2PPaE3++IUYC1Uv7OqceRxJNvAnBkZ4FM1JxFbrsMDf3EQMiYJUiFZcbPY2avxsmhrMYY9QtUyVZQNGSOs1oy92Qmk02w8ICCaENl0PkULMt+S7mydCBsnORwFZoybHmxpz6EqwKpPRopDcSuvHnQJIeP6Vge8I4/m9yt75rhQ1WmakEthe3M7L6KVQhXUBjgb5Cb4RB2lquA4hIa8v1wqoOfqxIf0tuw2ijTROg/NwuZVM1+R5afzZtsWptguZ7UKcFqbio28sSqwwNDHc1HTpeTIG2RrlSfr5RScvQHQlBaE+s6tu6PWd3oKmv0NgUc56hPYEwpN4CqUIi++fUE9HU52j8RbeydJYpoc5I/H2FNl1nOq93PtY/NNdqIPEimngR9f8cah7Esto9VfGvBwNfMsm29Jfg39ys0Sp1t28Itc+75dwxCGxekEyuagqtO8JNXna8iXQIO2gjJF+D1H9M4Yo931APJQDNt6DNN5vPTgTwIZV/QwJbdTl3QbB/yA6vAJBCWrW8NUHXxjqMNt4A8elo6jtIpXCWHY5U32eqTPsh+nMYpDeMtVfLdS9y7aEi8kvgS+B8Vc1y7dx27qeBaQPUrxHIZQBcRB9E018gNffl3EWdehPzziouiUH072j52T3qmSiVN7oJxpiJV0uJacjqxuYlsr+Rwmz5P8Aycc7wXp6GrjfhRB91i2lSgA2xx3Fz8EDQxNSr/oSER+GkZkP96Z2SgsYwecd4U4bB0ukc1GmGhj/QZZux9Aew7FC0dmrGE5FYZUhprri2W8yS57egqiapq0nwDzWJ2MSLbnigs7fvw1QwdvBANWYkclu97wzvtKXNX9C6U2HQmxkKej1GYEsTUslqjBEx2wBCo6D5bvJ9rrbdhC8+FfN9mSQ8qU/Rhkvzqk32Ffp7zLo3jHUhLdefASapakJEzgAeBHbPOkj1XuBegG233bZbfStWNSSwFUo50JRjjwSk/oMmP0KC2T3u1GlGlx7s3XUEjMdkL4Fu6llAq0f4inmctb8zVZChXTNodFbZaWjpsZCeD76BBXO5VZOQfA9TPLRdwTcTdRqg8VoyGQYdL3RjuLThAjTwPCw7mGzPTclPY8veprEnydajzgH7fya8FdyusP27gKbnmHJre5H5PiUMlbeiyQ+yDaFZALmb8nZ1OSQgORO62Y0eDH0QTYBvXbeb+i6meXH6K9q/r5CJkQdHAiCBzdCSI13dlFh7zUwH+Hxe31cKEq+aJ0f3CaU/QX/ixrrLluuquqzDn/cBN/bCvH2P2qdg6a/ITaGyDXXQy1hHH83BUOiAFUgUiYQhclAX+0QyvEJNvGX6Uqa/Bv+GSNn5SGinDtvfQZefRbvhcEwXmPCorheUnOGW+HcVtxTXEy5Uma4VljEynZH6kMK7rNumeKYXjLVqEl12DEbtzhXk0ihaf6bpTk6Y7Ceq1nhpd88dQHKELvKsMf2dqaxMf2mOt2qg6mZT3VjzsImpt2q7RA5GSs/IYHZYFZeg4X3Q6CSc5ifx+TNvKNGow7SXTZXsDwvTNDY5TJ7aBPjZ74h6SstKmTlzJvPmzWP+fPOg/dprr7F06VKGDBnCttv2vLlHT9GfhZx6w1i3tVwHvse0XD+q4w4isqaqtj7PHojp9vujh+VfBx30Dlp/vom7dvZ+JAC+Nbx7zyXfJre2RtAwVcSj08hKgsZfMRdu65pSH5kO3NV3IKHdUKcBXX5GlkHQ5efCwBfydl9RJ4rGXy3cmKS/KXDVrVzjAKBgz8dpvt8k2lqrL/0bYjTLC0luBcEqrItMl0i8TnuPzo6wwYnhyTKRctfj7kEiTlPdusmo2obW6iyi7ebg/IDWnwK1z5nGGOUXQPkFeceRoOmOvvjL6ayxTpyOtOkfFtltSoOtaP37f3uk2KAM7rzzTh588MG27ePHjwfg+OOPZ+LEiQWfT29AtX/HrFc49Vlgy/VzRORTt+X6OcAJKzpvf4FYlUj1LS6NqxO0BV0+Dl00DGfRDjgtE9spZr61yfnxlxyHlP52pa3ZC9p0Pdk3j7hJEIFbIOIFB+LP5tjmeph1YyE+lcI8Rh8Ety5gPyAwEqz1aQuP2HNNdeGyo9o60EvkcMiK4+a4IKUMwnsVNndXcJbi3bggCbEHzXoRzG8gAP4tkAGPuTH3EoznHaJrf0rM8eXjutc9KPm2SWJ2/k7URqOTCx8HE7efdOcviTYJdtoYPVXYcP0AiW83Ib1gC1dxcDPshT/DSbzLBhtsAMDEieaa6Pxa1YbaPRNsxyro1RfolVlVdZqqDlXVjVT1j+57V6jq0+6/L1HVLVR1uKqOUtXZvTFvf4FYNa6M5DpkX1yuN6n10HQb2nKPOabkWLL5sz7wb4KUX7RKu0CbJFgOb9ae5+7UlKOSLmWKTnIh/qxhMnh6i60JwyBQYgqUqu+CglgwPggMc2P+HdcVNz0OEy+aGXy1SM0/XQVFH+CH0O5QcQtINeYS8BvFugGTeu9pJm9/zqT7Ukx7rdFYtVNMrsH+hgxDLmG8ZVOtDi8fNN2CJl4tfH32YrzLyZNuN5vuYa+TzuSFyYNwHDc8L2BZprJV/BuCf0sI7Q2RY9DGa3GWHopGJ7e1LOsvUJWCXn2Bn3QF46qEBIejNc/AknwqczFouRctPcUIApWdA8130dYJJLAVUvWnVWqowZU9tQYYb7AzrAHm/8FdgD+RnQALI3nK9TXxeo5kWsj0bQzuaASMpALCvzLsC8CpvAsa8mkh+8Eqx9NL1iiaeAtxS8klsBlS+4QpGBJ/u0EuOdBtk+VDfAPzzNV9iH9jNDza5SPnC2skIPG8Ya2kPoTme8lgieTMK3Y0cq7ka/25MOitwjzs4HA8n3R6qJQ4fLct2GzjlqwmBD6/DenZQCmkP3XfdVt+NM6B5FtI1cqlixaK/q4N0n8Z4D9CiDOfLrP2mkKT76NLdjXUOcEcU36xEaFfweqzHqP0TNrbYLUi4r4PEhhmEpYZ4Z4SQ+EK/Dz3uNZgPH0C8SP+jZDAJkjpCUjJIRlGxorsiQyaCSXjQDahndYWxBj6SxD/RkafIwtBd95OU1qlWZ6z+Ab3uqFuG7vyxsKEuMRvGg9HH6bwZKjXOBYU6F2Lf2MI7UHmd+7G7MP79mh6vz8XkwXAlYfNuD5iEH8JTX3Ro/l6Hdoewunq1Rcoeta9Cd8gclOvOmD5OWT1xGu6GQ3taC6iPoCUHGPKjFvuMYwNCUHpmUgH7QypuBpCu6OxxwEbiRwEob3yPglIyVg0+g8yQxVi4sPB/DQzsSqQilOg4hTTQDjxAuAznHDf2iYuLWGyW2H5kJJDe/Ap9A7UXoC2PGSYJc6iAg5Io/HnTWhixWYGkt4JbQ9I1QQ0+gjE/gkah/B+yIoktoM7uPIL3bRmqfdNSKsf4KfOBvlJw8Rz5wNW1x1anCa8xW86IoX3Y3EKjf4LqchuQLoqICJI2Wlo6Ukm8SQVWQUWIgLhUYVR9VqP8Q+BqtuMuBS2iZP61kSq7+6WfKr41wP/yZ3WEzAUs/rfgL2wjcsslRN6pPfSXai9COLTUKcZCY2EwHBIf+6KhyUp6MYNgA3Nt2N+N3nU9LpcUAJtuAEafo/6hiDll+T9rkR8puAnT9GPJj9EY09jbs77QWC7nDcCqbisQ3l+gXrc4gOrf0gbqJtg7K8oGus80NQsQ02zlwCK+uffTT8AACAASURBVNZBqu5AApt479/we7r2Kvw5WAK2aZjaxxDxg/RMQzvnmOE9IPSOiV1KBHwb9VpcXvwbQe2/TVJRE+1VgisZGn/RdJ/BAVJoy/0Q/pUpIXc78ORZNZm/Ezdn0bbNB11VXGah9VJ25VHteea3W30P0kMtHKfpVmh5kFYKosaehMghSOWVnvuLfwOonY5G/2meKuwlYH+V/1zURv1D+40/21chjkLQf28jfQx1GkzXDXs+htKWAPt/aN3RqEdTA9WYW4DR1bedqwovjIT2WOF191eIBJDAVkinLim9M7Yp6NCWB9BFW+Ms3BKn/izj+a4EqMbQht9hfhetrI6YafaQ/jjPkT5MjLiL85cB3ViNDwI7YOL5nX9XcbT59m6M1Q5Nfw0tD2Bi6A5t5xibgqZm5TxOfAOxys/FqvkrUnOvSRzn9QnTsPRANPFWj9bZ2+jPbJDV3lir/QNO4zU4Sw/DabgUTc81G+LPenjACqQg/oLHSK396bpCCm+DnkT93h57Efmh6qDLjnK53AkgCYmX0GVjPG+sK4zEDLwvnRj5fwN+CO9HhqxpFtQ0a85K9mLm9P8CpNb8W6qgfBxU3kLO0Fv66zxz5UHiNXL+TuMvFzSE+AbDgCfJb2bSQBxtuBD1fOJcdTDJw6Kx7pfQ9Bx06f4QnQTpjyD2BLrsEDQ506V0eWTm1btjt0gYAoWWx3pdWI5J7hXRfSTfcgWfOnqWtuGGx6f3/nz5xPR962CKWbyQgOQr5M9p+KH8DJeW2CmcI25D3EFvIWt8jAx6F6v0RMRX41H40zpcdvPcgiAhvM2DL29vzqxh7K+hkA5KGnfL3g3mzJnD6aefzvDhw/H5fFlNdJPJJIcffjgbbrghkUgEYLiITBeRFeq84KgU9OoLrN7GuvF6N77YepHboDG04UrTDdqrKlECptO1B6TqJrwLGApE8p2eH7s6Iz0HT71qjaKpL7PfX1EER+DpdUoJlP8egtvnPtZpIr+xFhPWqPkHWb8lXQ71J5i9JNgWThIJ5KBehpGy8/OeSk6EfpVjg6LRh3AWbo6zZB80/koXAxXoLau6BUAGn376KdOmTWPo0KEMHZqtamjbNiLCJZdcwrPPPgvwDab082UR6eEdqn9T91ZrY01yJp4XnT0XDWwH/k3IfGQNG05xYJusQzT9rdG0qLrfPcbXfkyhH7MmcBpvxGn+K47jVUhShCf8G+bw3kqQlRBaEgkhVXdgjGMEE5MNmybBoT2Q6nvJ8oozlnVcntEtRJcjyZlkh1RSYM8xyoCd11R6quke8//snXWcXOX1xr/njs+6JAGCBA0FWmixYiUUzw8pVty9SCGUUqxAIMFJocVDkOIJ0OBFghctGqxQgkNkszY7Pvf8/njvzo7cOzu72Qgpz+ezn2Rnr7wzc++55z3vc57HGgZY4FsNaboGCQ28wQVAfC1OeSUMUgPUkDc/sGfT296vHSeiqee8DxTcgKpkBnzLFFnJ7bzzznz11VdMnTqVtddeu2zzSCTCPffcw5FHHsmvf/1rMCurvTWm31T7PguhCLZtVfWzOPC/zQaxah1RnVIETFmj+e9oz62QeADwQXRPJLp/0QKZ2t1ox3GQfssEDE0Zdw9ruLmxAusaMff4HfRL5dIuiN9k/h+7BLvhSqzIDkP1bpdYaPYLR8p1jcE1qAQ3dz7vr+ibJVlg1UDE3RB3QSGhLWD4s0YzRXsguLlpHAI0X9N2ySqtBqz6P2KnnnOYEqUH9oHViGbew70Ml4PMx2WiTSKC1BwANQcs6FvrG2pkOzT0EqSeM/XkrtMpf08ptOsSZNiWrscQiaD1l0Knm257AQIbFN1XgzTS7XFOMjjnagbMEF+k+N/OrKMHUr7YE4LIboaDKmGs2qOxhj2ONewRUx8syeC083RIvwmkTI2UtJGV9A3HapyEVXMIUnsCFTMtVyh0nrRwFsiWEKgdw55/EDpvJ5Ohzd0Ku/OcAetFiPiQlrsgvD2mdOAz+t0t0wZUXx0oxGpCovsgNYfnAzXgGAt43FrOrMzw6UuvvQhEDzUUzowHq0T84B+1gCOvHmLVIZGdnHN68L9zn1U8hhXZFhn2T7BGeW+UnF5ZY8YDqko2mwWTeF6CeZoM3AQVnA7GoVtg7M+b1tnmtyLygSN0d2el4/1PB2upOdKxugoaeUpCENq86sYUtWOQeobyBoAEdF2A3f57NP36wJoEimAbS6mlFNp1luuDTuMVr1lXiNWM1TgJGTETGfEBVtP1i6QxxhWBtXG/tSJ5j0QJbQYNlzvqi2Kuv9pjIXoQOm83yLq54vnNDCK4qcvfFjKylVrCc+aha8/33EJ8yyKNE/AMORKE9GsDHtbFF19MIBAAWBc4GBirqtVq7JZDq/zpBwXetDsCawH7ishaJdusDpwObKaqawMnVTrm/3QZRMSHNExEa0+G7H/BvwLiG1n1/pop8AEsQwJSjxsltJoDGZygPP0bFPxAoZqA5FO4Pujitwx6Or+oRbBcxxBYBw2u76yJ9M6M/OBrgcj/5bezIttCZFtHztWPiGDHbnAWvV2ul8BGSNMVRQYAbtDsp2jPzeaaDq6PRA+uqDmjmobkk2j6DfAth0R2NzXrwvfkG4kSxDPpSL+Ctu1ptLA9fCgluCHq/6lhXpWPwn1Bvx8ccsghbLPNNmy44YafAp8CD4vIr1wMu6vCENLyqvGmPRK4WtVoT6hqRb2B/+lg3QvxDYPB1Eo7z+tnA6eRoOdWyrvWqkRk94Hv80OAJvD8PAYxHV7SIE3XoT03QHwqkIbQ9kjd711t0IqCW+YtXOUIpAap2QfpRxhKU6+iHUc57JgcZN4zrkQt95uW/dLt7Rg6f19j70YcCKE9V0PTLcZYoBfBX5qs3v4Gz+8t97U5VvM9rl2kas/vk9wtQwgNbIja9oDq1cssswzLLLMMQCewM/A+8Ceg0iquKxSw7aqDdauIFK703uDYEvaiGm/aNQBE5CVMnfRcVX3c64T/02WQBYFtZ8Gu8uEtAfCt2c9GLjXt8O5Yi0uFbyFA1TYLsmobLWlXdxkLQpst8rENNUSCWLXHYw1/Dmv4y1gN51bnc+lfDXf6pw2+yn6cqop2nek8CHsXAjOgMbT7Uvd9em5yXNZ72UcpQ3nsHNdnlEHvusCdjsJihYCW+dhpqHE71y24uQVlMzD50j35TdNh7BDYh2N+cSrv/2vgSnyOEcp7wOCoewqoVPcD81R1g4KfG0qOVo03rR9YHRgD7AtMFpFGr+H9GKwHAc1+DnO3H8AeNtSd1s82OcwidjP4VoWGK7AaLxr0GJckqCp2z9/RORv3/cRvhbrzMdS33gdVEKQWqa1sJbU0Q6L7mYd7EQLgWw38a7nuk4d2Qe47lz/Y3hz+fNdnCXLzHHZNwdh8y2C13A3Rw/BeME95U/lSL+BWRpl0yso8eO2/SXQn+d7+irvfmsLPN/sZoVCY0aNH8+GHHxY9OLwgZtryC2CQbZtDyrPu15vW2Wa6qmZUdRbwMSZ4u+LHMsgAodn/ovN2pnplNAGrBQltUkURJA3WCKxhS9eioibuhe7L6HPN6YTuScaKqvU+tGeKaYsOro9ED1p8mt5LAMS3LDTdajLk7GeAQOjXSMMF/dfjJYxn1it1Hjt5NXGpZ+eh1B5lvlPtdj+e5aFt4htRYEBg0D7Xz3MP1ZJJmfspQ5omhrOirM7wNZtY5qct3HnnnTQ2NjJtmrEbGzt2LNOnT+exxx5jhx12YLnllgNoBp4ElgWu8HhT/WPouHv9etMC/8Bk1LeISCumLOJJrfkxWA8Q2nUh1QXqiJHstFqRppvQOV4dYSWwF72XsNrdppZoLVe2sDQkiF1NOWc4AT1XG3PbholDf84fMCS4LtL6sPleJFi1vrRIyHGn+SfFGWwEag523ym6N3RfTjEH2gL/qkbbw+08VhPa8iDM257yTNmHeKyzSM3haKrYKPq7L0IEgkrGSe6Xd5oPE9rD0+8+Bg6Dsb29nb322guAWbNmMXr0aG6//XbGjRtHe3s7mBrx68AGqlr8RKgaQ6f7oapZEen1pvUBU3q9aYE3HMvDfwLbicgHmKn1qarqKb35YxlkoMi8Wd12VivSfBvS+iSa+RQWgE20sKCq2F2XonM2RecfjM7dErvjJFQH4a5dCS5aKub1eVVNb/9XIVbdgI0ApH680zAT7qOjRnZGPLomJbofhDYlb9ArNWYm2HhlxfNY/pFIy92mY1JqjJmE1CCNV3jqvktwQ6g/y+mINO9ruZWTZNLlAbLGX8dlh12NqnL55ZcTiUSwbRtVZdSoUfziF7/gkUce4fvvvyeVSgG8p6p7Dz5QOxgi6h5QjTetquo4VV1LVX+qqndXOt5Sl1mbmz8NBBcOjUsaQGPVbIgEfmZoUZ0DcSofdPPVgKHxuyBxO2ZRyQnQyadRmYA0jB+6E/lWMnrTZa+vsERQ7ZYqSAipPdbIH1jNSGCdimUlkYBhrmTeNz0Bue+dhcz+8zgJrAPDXnAaeNIQWK+saawUVvS32OFdYN6vwU7R2JJj6z3aeeaBRlKJvjp4IORnlxO358UXX+Sqq67i2GOPXfjXioJWzwZZ5FhqMmuziHUnOmcTdPa66NxNseP3DP2J6k6sbrvI7qimTIPDQDjW9e7C7gsF8ckuq/Mpoz7oJow0SEjdnyjv1gtDbX+Lrj9iIND0O+iczdD2Y6B7InScjKZfrXLnJMSugsQ90H0xOm8sdtcl/e4mYiHB9ZDgRp6BWnPfo5lPHD45iPYU0TNPvPhr9j5uDvVNWXx+Za1N1uDJzDTWWG81tthiC7bccksuvdSdzTL0kCp/Fj2WmszaLGJdTL42ardB10RsAljRoeMqW5HdsFOvQfK+yhtmZ6Kz16N6x48INFxmGiUWFewOjz/kQJNo5l2ngzKHhHeC4CaDym4kvBU0XY12X25q476VkLqT8918P2LBoZpC2w8rX/TrPBMNrGNcXDz3zZgAXzpjjN+BhjYz3ZaDGZM9H20/ATLvOBKufrT+PCT866LtfD7Yf9wc9h83B3yjsIZN4OA39yAej/Paa68xfvx4jj/+eK655ppBjWNgg174pxgslppgTewqXBexYlfCEAZrAKvxQuzcOJi7M1DaYhs0nVipp6n6m7dWxhr+zyEdY1UIrA/p5ykbpzUC7b4Oknc4rfKKJh81hqoNEwZ1KgltYcSPfsTCQepZ3GdwWTRxP1J3ive+6ddwTyoSaGJqVcFaNYvGroX47aYDM7Ce6b7NzQKy5CVsO08H/x0Q2gpS5ppP9Fh8/HaEuoYcq6xv+kZ+8QujobL55pvT2trKwQcfzCmnnMKqq67a71gWCD8G64ULVQV7rvsfq3GXHgQs3zBY5hXsXA4St5lpXc0RkLjbPCCq/dajf8aqHzqltGqgycfQ2NWQ+4a+aZ3t/BuC2mOg6wKK+Lcah8TDaOS3SHDdRTrehQnNfmEeRJpBwtsigZ8M/lh2HJKPoJmPkcAa5uFm1ZRsEzOzlcw74F8Nie7Zb1diVbC7cb/mshVmUL2ooAZpx7C7zjfuSBKB6AFIdL+yDkXtPNMxenCYHhkvnY+0aY6RRkB4+NYmbhg/Ep/fxs4Jrcu+x4R/fsdyq/TpuvQG7lmzZi3cYN3bFLOEYqkI1iKC0kjeLLQQ/XR9LSgsnw9qD83/bmffo6IUZCEihy/yQG333AGxS0pq1T6wVoDAmkjtMZD+N+p646fQ1DNLTbC2e+6A7oswWaWN9kxGowdi1Z+a30aTz6DdF0PuC9NuXXsiVnSPsmNp7jujjWH3AHE0ETWO5S3TEN9yzjaz0bbdwY5hZoEhtOd6aL7LBPcFQfCXuBsxR5FQP270gQ099o1A5j2nocYJ6N2XoZm3kcbL81tpbg4kH8W1uaYMtkkScnN4//UIN4wfSSph0bt89u0sizN2HM/NH12TL7m99JLxZ1x5Ze9SzlBhSSYnLRULjEZQycNReoi74TQ3G038A7vnTuz4A2j6rTz9THPfQfbL6g4U3AaprzA1XQhQzULsCpdFxRz4R2E1XY0Efuo0V7h1qPkQqXF5fcmAJv+J3fZb7LlbY3eeV9EwV3PfQ/eFmACTxcwskhD/e94QVlPPO+72n2Hc57+DrvEmyJcer2u8407f27YdB3s+WqAfo92XgD2fvnJdyrSCd1Wn8lgJ4l/emBoUScJGILgeKvXYbftiz14Pe+722PEHi/e1aqBhAmYR2MnfJGosyjRBceZtjIE1+3nfS7nPq7PuAnqVLbEamD6llXSyOJP9d+5FXvviVSb/9WaeeOIJzjnnHE455RT23nvvhV8CAbClup/FgKUis9bEVNzrdWFX4ZzBwo5dDbHrMDe3yUSUEPhXROsvh/aDTMtvvwgijRMRL9+8hQV7HqjHlLewsyy8HXS5NapYEBm7UIa2oLBj10DsevKBMHGPKW+0PoL4Wst3SD2L+wMpjSYfN8p5Zc0imOPHrkKj+xar36Wep/watCH9PKpqssTUM5TXhhUy76OaLLpWNfMfNPEAaA8S3taYG/SzuGvVn4qGNjXCTZpAIjujvuVh/iF97yM3C7rOxtZ2rIJGGSuyExr4GZq4H+xOJLw12nO3h0GCHzIz+3S1fSviaquGUGzCEACrAYkeAL6V6Jh7XVkTSj1NfJ2Zxe9PO55gKMgqq6zChRdeyDHHHFPxvQ8VZAnOrJeKYG1kRN2mcRaaedtIn/pXW6BTaPp1iN1A+VQvZdqCO47BU9qyUHFPjMC8WJ56LQsPVlPfOMr+1icNK1YzNF6JdpxE3hxWs1A/YUASsoOFmamkMFz5/id/ascgdi3F300WtAeN34LU/cFlL8tDCLE3wOAIHLmdsNvU8KW2+Hiu8BUEWa/ss+CcgN1zp1OeyQA5NPkgBLeAxiv7/TykhL2hhYE6j94Hzv5FCYP4V0Tq+iSVNf0KpAOU17QVCrTCxbcMGhrjCDgVnisEdX8wJRK7HUJbITVHIlYjGt6JTXZ5lo/e+px0Utho62622KmDZKKRZx5Ygwn/vJOahkU8ixtAw8viwFIRrCW8NZp60txARYhD/Da052bUvzLSdAPiqvTWPzR+L9616BzYpRotvYhAaAxkZ4LVhEQPg/DCsZrqF5rFnSMqUHt88SvhrWD4vyD9oqlnhjZHrPqFP8TkM2j3+cYKTcJoZH9D8yuZhajmADHBK/uxET8q67xMQ+olEzBKEdoaus53GUHA0BQB/CsUOW7nITXl2svhsY4oUmFgCxR/19E9oOc2ih8qfqP94ZQR1J5fUJ7pfbNxSL9ggmG4n/pzKTIfub+uGVO2qXA/SGQfNP73ktmYD6xl0Nw8dO4Opv7sHwW1vzc+ir33iX9NpP5cJPhzqCnvnhQRxh43gUdv/QOH/vFVfr55N5Eam1wOdty/C7/vDuCogb3XBUZeUW+JxFJRsya0DfjXptzdGSfbNTb32n704M+hcSo/dr3I8jZSfxrWsKexWqYhkbGLrWtPEw97/MUPUlPW+i1WDRLe3ox5UQTq9L+dGvHXgG0+8/jf0a4L+rbJfok9/1B09tro7HWw2080pSiv8o6HoL34WqD+Akzbs9NqTQhqj88v9hn1v9J2b2ebkgxX6s8A/8rO+Zy2bf8opP7Mvm1qT3AkRiNA1GzrXxlpKHhopF52OMmlH04cTXpKHXvDo/XbCIxVnt2Jf3mkabIz6woBQQhsYOzwOk91avkp87DsGIcEN0VGvG2celqnm0BdAZGaMNe8tCsb/jpBpMbMSH0+8PszJvPPVdTiXzgYwnbzocaQZNYisgNwJaYIOFlVLyr5ewi4DVgfaAP2VtXPh+Lc5vh+aL4Fkg+hiUcg+5ELlS8H2c/Q7GeIf+BytxLeEU2/6LI458Aa4VCkCrPvAAQ3yLMBFjuy7+M+O8hC+4EoATS8I1J/FmI1LOrRGTph2fiSkLgPdbJjbfstaAem3GRD6inIfgK+0ZBz8S3MvIfaMcSqLfuTFd0VDW0KqSdNsA/9GvEXsIcCa5nAWZSxe+hK52ZDYGOQFlMiCI9FQpsXBXWRMNJym1nAzHwE/pXKjGLx1AIRyH2LJp8xsxwPN5ayvWpPRNuPp/hzjUB0/6p0RyS4IQybAfb3ppXdasaesyVu35N2X4IV3oqB+I0G5DkIuHfLaup5JLpn1ccaEgzS0GlRYIEz62q8xoDDgXZVXQ2YBFy8oOctH0cAieyO1XwTWO5qYYh/8DZZ4R0cs9PS7N0CiSKNk6DhQoc/GgGCENoCabxqcOdbGPCvUcIW6EVvupCG5KPo/AMHLbCkqmj6DTRxP+o1BfdC1kOGWPxgz0YTDzkPy8I7KmsCSWAV3Gc2WbTrHE9/QPENM7zhmoOLAzWgsb86TUGFyEDX2UWmvnb8PkPbS9wBmZch9Tj0XI9X96oE1jH86uCG5bOs0Oau+5iFyLeNKcDcLdHsfz22KzlX6FfQMNHQDvGbbL7m4IpNMpr9DI1dj8ZuNBoj5Ez2nHwMO/Wm+bzdkBuEWJnU4B6G0hC7btEaRg/MfGCRYygy62q8xnYFznX+Pw34m4iILizJtfBWEPsPZYuBmgP/4JoeRPzQNBlSzxqWQW424IfgT5HovohvObNeFd7e1PGs+uqcQRYhJLKrCUCk8E4hMpD70nS1hUpdiCpD7fno/IOcMgagigY3QJqu7VfgBzCZbOpbyuaZmgPfck792GVmo2nIflq+HwBZM+NKPorWX4gV/U31byj1PK4B1+42ND7fSLO42XUeRZmmxg1bIvkwRHar/nyY7JvG69COozE11Ax9MqSO4JbG0fmHotHDEP9ICI1xzbRVM5CaAel/g+8nIPON9Gl4B1fbLehl1fQynsQ0eEnE/O6sE5jM2UUm2ENSteL7jexuBMXcZnz2bDR+D+Il77oQsLSzQarxGstv4+i8dgItwLzCjUTkKJxVhRVXLPeLqxYSPQiN32eoavmAHTFi91b/ppx5qlXpccUH4a2R8Nbe5xa/md4ugRCrDlqmol1nQ/pVPAtwakPuv5R/jZWhnb2C+QU3cvo1NHZNEcugbL/cN5B+C4JjnABZulAoYHcggbVNswmlC8mZfty3AXLQdSYa2qT6RWarwaMD1gapNcJJsStx1zdPoIlHEY9grdlPITnDlD3C2xdpR0toYxj2L0g/h3ZPcvEtVJPdxi5Fxbjr0HJ3EVPHeCvuDdmvKXrA5Waiqceh8QokvE35mGLXURY4y2h5PkzoKHzfFoQH8CDsHWNyOt4T/BQkHvbW4l4YWIKD9VAsMFbjNVbNNqjqDb2eZsOGDcLAtvdkVj3SOh1qj4XAuhDaBmm+Eatm/4r72fEHseeMQWePxp7zK+z4AwM6r6Zfx24/Hrttb+zY9SbrWsIg/hWxmm9FRrwPdefguigrFvg93YXyUM2i8fuw2/bHnneAw10uDVwp6Lkeu/uvZZ+HqmJ3noPO3QHtPBtiE3EvHSTRronGGdyqxb0mWqFlumAbnbcbds9tpkGoP0QPpfzzCUBoMzRxHzr/QEi/hKcZheVOPbO7LkPn7YbG/oJ2X4bO3dalUSWKhHd0ygTe7wftAXsu2lHMeNHY1ZD9gvKZiAJJtPMsh1FT8JfEE1T3OeYoz/Ns6JmMpt2cy8uhmjEPk57bKH/4FsC1bPe/iaEI1tV6ja0AIIaD1UC5AtKQQqx6rNrfIU1TkMgeoNmKN6ideBi6zuqj4NnfQ9c5VQdsu+cOdP4RkHrCOFTH/oa27WrcPpZAiPiQyC5gRSm+DALgG2VW/StAVdH2Y0znXuZ1yHqJAWFe77kBbdsDLVygTT4EiX9gMukeh7nj9h0ppJ5DJIK03AehbfGeFPZzSes80zLdcXKF95Y2M7PEIw61LYAR8g9D4GdQdzp0X4HJQL1SsQgS2bv82Ol3IP53+jonU+an60zUbT0lvBPl8rKlsCHzLurIjqrdYz5bF7/DvoEkyjwWGRBLya21PInGqnTUSs1wtGkqSfFGkJp9BzCmBYdodT+LA0MRrPNeY2IKk/sAD5Zs8yDQO5fZE5ix0OrVBbDj9xgXlM5T0Y7jjNZv5j33jWNu3WpJiE3q/zzZz6H7AoqzmBTk5qDx8tbkJQEmIN0D9Lp2WEAYIrsizX/vn16YfgUyb+BaQ3ZFCnLfowUPP43fPoD9TXAW3wispqsg6qWpUs1yfhJSz6GZ8tKJyfgORLvHQ+ZFpwThg9B2SOt0rJa7kMwHFdqrfUAQao9AQpuUHz/5EO6BzufuCh7dD/yrAlWU73JzsOcfhs7Z0NudJ48cpb6MEt6e6pgcfjxDR+YD99dLoOm3XfoiikYDkd9AaIeqjjckUJbodvMFDtaO/Xuv19iHwL29XmMisouz2U1Ai4h8CowD/rSg5+13XJkPoWsCvfoLaA9oOzr/MHdhfRdX6E9npTnmlHdYd9118fl8jBkzpvw8dge07QXkOPnsufiW/YRTz+ulDaYcqdQlC71ZMbErwf4SEzADDi94vKlt93eM9KsVbjavyyphmjt6YXvoubgiiR3/R/43CW3hyaGuDuI4nJSe5jFDqyuiaCbNYqE4XHPPdQ+BwEbIsKexak8YxJj68hdNzsCeuy3MWc8sZod3gPDu4P8Zria3/tWg43fmIdqvR6jfoZQW+22KfxVzjkqQKPhG4jmz0e6qZpOGeVOpxBFAao5Y9D0JSzDPekiaYqrwGkuq6l6qupqqbtTLHFmY0PhU3KdYOdPVVgqXNur3P07z2Iwka6yxBmus4a6KpvG7QON88HGKm+/uor6u5CO1XHQpFjcy7xiGQNFMImVYICUPF7W70OynZRQqw3Rx4+lGjE62V4aWehp77vaGLxzeAfcWbIvyZY6socz1Zm7BzZzzDBJiuXbvafIpXLN98aOp58yDPrgp7u8vhNSfWrSAaaiMb6I9t6LJJyC0Pe6fW850ugKaetG0Nih/UQAAIABJREFU+ue+ANSUbpKPQWB1pHmKEVjKZ9oR8xCJHuD0FvQXqAPgXwtp9ChX+Lz49RYEfm4e5q2PQGhLj+3U6Fr3h/BO7s0/vZAA1ensDC2W9jLIkgntwnVKrDk080HeYiiP2nGU1gZ33q6FLz65l6lTp7L22mu7nyf9KpDhpLPncuIRjTQ1FH6kQSS6CFeyq0XmXVzryxpH08YQWDWN3XGaKSO17YXO2Rg7dkPftuGdwE2nQnzQeK2h2nldXrlZplPRnkvxglavnvbJuAfxFNptjFxFLKTpepDBNO9YJsAFXUT1rSb3cWscus5AZ//CsGkarzaceqkhbwBb8zuQxj4VRk2j7Yeg7Yeh3ZeinadB50kQ3gVzrTklE0JQf0Ge6qn5enghEhC7BqQGaX3IOMJHD0Xq/4gMm2EebdpfCSgEdeOwWqd5amib190++zAS3RuJ7GJomIH13T8nFOI3m4dxBYhVjzTfAeKl5S1VLXIPOZb2zHpJhIS39ZgmJyB+Izrnl04WZWBF/s80tfiWByywRmI1XoAV3bXyiXyjmPZwDx9+kua0E0p41dGDkNAvF/i9LAhUc2jum2Imhm+EyVzKEHaCLKbFO/kYkHZKSAmzaJp4CADxtSCN1xUHLKsVGi5GUg9DZB8zNXfNIsF0Jt5D8ZVvQfCXjgWVR4ZY4Cko4ofo/hXOUQjBBKEg+NdBmu8oF9C3uwwNzpO8ZANpQyeL34QMf8k0PdWdAf5VoGcSOm9rdPa6plGmZ7KhJGqc/Odot0H2Q6TlPqT2JKTuD8iwJ4uvszKqXu8QUqDdiASRyFis+tOR6P5GCiDgkUwUIeXMqCogvLN54JZCBELb9f0aXAc8FS1zaNe5/TZWSWBNaH0Q11mKb3R13PyhxhIcrJcKISdXhLY2tL3025RNa51aq3aMg9bpeX86K/J/hh42ACStvTj13IlMPKOVmmjvs0/AtxZW/R/73V9zc5126eUR/+C55W6w49Ohe4K5ycmh4e2RhguMpZKEKdM7cRgiqsY0t3whLIl2nQfh7RAJmQW04f8yDSBiocmXoWMcipisW22oORF6/oI7Jaz0qs9B+mW0/gy8mSUJVBOIQ+mS2mNM/TzzAd6LlUGIHm5alyXk6vat2U/Rtn2cz6Q/38wMpF40M4Pg5jB3G7ALmRVJ6DodpIXyDNmG7Ifga0FqPbRqfKOM8FcpJARSh53rgu5LDaNCQhA9EKk5EILrQ/oNl3MWHqM4wKrdgcaug+QTYEUgcgA0/A06T+77HCSMNF5T3LIf2AD8ow3zyQ32PNBO8zCvAEm9gBKg7DPPzkSzsyp6Rw41FmeJoxostcHadBzeBMnHjXJY5j3Ks7UsGr8XqR+8y/ZFl97DsiNX54DfNmM0KwR8I5GWmyvup2qjXeeYoCgh0DQa2ADq/4z4RlTVvFPx+KlXoOvPFAWw5BOoZgybovkuU4rIOssHvmWQxisQqwnNzXM9pjlwl3nINU4yC7fSgATXMwu6PVeTD/C9F33PVYY54SW0VArxO8qlNbgbSgQoXGATCUPznZB5A+250/H1C2Cy4ByEtkfqTjHi/IVvQxVST+XXHMh9P8Aaac5Qz+w2sL9230S9pA0EKvG8a4+Fjt9TfL2GoPY4EwDn/bpvcVeB2IVo8mFovgN6bjTKdzqH8odhBIn8tm94dtw41+RmAxnzkXVfBOEdkOEvO+UyHwR+WjYLERFovgWdvTngtqBoVbUArOmXcX24iA8ybxtxrEWJxcT0qAZLbbAGJ2BHdgKcwFh2g2S9dQ6qwKxZs7jsssuYMWMG32WWZ801R9PTkyFjbdVvq7nGb4XEg5jpsbMQmnkJ2nZE8aPh/0Mazhu0eYL2XEd5ppmC1AzUno/4RyGt0x03lSxYy/WtvFvNpvnE9rBpSs1AZ2+A6eSLonWnONrPbgu6Pghu6QjvF47Hwn1O6TOLvbXHGVusomMGIbJHmVyqiEBwQyS4oXnQpJ42YwttVdQZWPT5dF8E8btdPqNqYYN/VfNQ9JwX25R3+gHWcNfsHhxKW+cfKW/ptiE+DU3/252Fk50J6Vex6k6AuhPQ9Nto++HkBa80BzWHFJXlNPEA5Nood4J5FGqPQ4KVF3BFImjdqY6ka+HnGDIU0GrKGL6RmIdr6cNcjDjaIsaPmfXiRnB990xGIkjBqvZn737BY5OfJtbRw6a/2YhNd90An8+bd/qnP/2JHXfckTXXXJMjjjiCaLSGnp446XSajo4OGhoavKlHPbfiHiicumjyUZQU0vgXADT3Ldp1PqReMKvo4V2RulPzU1NV22hySI2hZOU8sj0JQG6eCcjg2notYqF1ZztTYberN0d+2qppQ5EMbuy+rQCBTU2WlZwOZEx5oPZEiF3qBJ7ehbEI1J5qbvKaw9DcV5C4Lz/zIDTGSJFWgPhaIVrejFIIzX0L8TupzjPQC1HEakYDlfwoo7h+x/Zc7Nw8rBIHG9Uc2nGsqW2XIWMkSXNeAk5qaulhcz1LcD0Y/pLhbtsxCG1Srv6YftV9fPhNVl1FWU6iexu5gPgtDoMjYyQZ6s/ud1+z/2+NgW5RsLZM+SS4GNZ7fgzWixfiG4lG94bEVPr4s2FTGwwbm6qHb3iS606+hUwqg20rLz7wGj/ZZHUufPRMfH73gP3xxx/zzjvvcP/99xe9fv3113P99dfz1VdfsfzyZvqtmkVj10D8dlAvJ+pCpCD5lKMWF0Tn7U5eGlTTRjY08wG03GusozpPd3jLOTSwnllw6tWFLoJd1U1oRcZiJx+qkieedGqXbjojOcj+G5KPk78htQeSD0DzNKNOl34NfMsitUfnH54iFtJwHlrnlGp8y3tmyQNG+nUzza76xvRRXFP1my5GwPKPxPatATkXk4LAL0x3p5u70NwtsRv+ghXZtu/lzEzKVf4K0etA7zXMEt60hCG8vff2/hUg5ZL5i1Y0JSg+h0DN/mhwQ7Pm4V+zjL9d+QD1UDfOaJL0Prj9qyNNf63KJWhI8WPNesmA1J0JwQ3RnjtMsAiPRWr2QyRIrKOHa0+6hXSyb8qd7Eny4cuf8MJ9rzBmbxeKFzB58mQ6Ozs5+uij2WGHHaitreXiiy9m991354QTTqBQ30S7zjbty9U6n4OTBc9B029QLg2ahtx/TK2y88zi42beBHtFTNNB4X4RqD2x6tKK1J3pNL/00G9kc633hqDmSFNHLQpYxgxCcp8gjZXVcsVqhqAXvasPdvJFyH2BBDcCCRr/xPSrRmC/5nAksld+lqPkKHeVqQDfT8D+3DwkJQy1JyDRvtovLdNg/gGQ7e2OdUSNpMl0QboiA53j0OAMxNd7nVTTfenHfbFWkOh+nntp6kWTwdrzTeYbPdA4wfTcWrKlZeRU+5EbAFBNoR2nmOxdgqApNLI71J/jqerXt6+iPX8zVnm9Gbl/DWi4CCuwGCh7+YEtvlP3h6UmWNuJh01HXu578K+C1P2xyItORIy6mUum8e5zH+AP+kiXxNGeWIxrJl3HPN93fPPNN3R1dTFt2jQAxo4dywYbbMDVV1+N3+9n0qRJ3HGHaS0fOXJkUbej5uZBwkurwcLzJlXbKPjFPUomCsTvofzmzRoJz4aJpv6YeQesYSZzDVffviv+5aHlXrTrItNaLnVgd7mPxe19hXdCfMuiHrxlTb3g+n0MBHZ6JszfPz8mzTv2OPXwXDt0TUCzXyD1p2Jnv4bOs+mf8VGA3H9g+FsIMZD6skBkWWFonWa+59w34F/J+AymXkCTd7rXmMGMM/lYn+1V4Ke4dicWwT1QUzfek01kx26C2FXkv7fYJ2jiPqeVuzhT//Rzi8tuGs6rr63HzJkz2WKLLXj22WeLtuno6GDcuHH844G7SKfTbL5xmKsmDGO1lYOQmI76VkBqj6z8NlKPQ2wyedlXMKYh3RdB8039fAYLD7IEmw8sFcHajt8LXReQzy6zH6Lth6PBTZG6k02mYHcYCyWXRY9QNOj6RM1Imqmv3s7UvW7Pv7bXXnsBZnGxrq6Os88+m9tvv51AoMJNlvuir+5aCqvJmCVkP8YEkN6BRKD2WLOI4x9NX5ZcABEn43ZrcMkY6yUsE6jr/lD08KoW4l8NaZ6c/93uuRu6/1zFnrZ539bWDo2v9O+BBe7uVE3B/H0ofgi6LVomjBdn7dFOoK6SmZJHGhEQqbxoLL5WKKxDBzdz6KOv484bz1DY1i7ih8ar0PZDqC7L9pm+gJbpWB7sIbW7nYXawpmE0WkhfjOl1877HyV47LF/8stNxpBOu4ss7b333sycOZNJ41toqLeZ+Jf5bLvXN7zzzIrU1yWg51o0vHVFRybtmUz5Qz8D6VfNArhH087/Mn7wTTGqWqCAVggb0i+ibXuic7dC5++DztmkSF+iF+uOWRt/sHza1hBt4r0XPzRTtpKfUaNGceaZZ7LxxhszduzYov0mTpxYfCDfiu6BGh+EtsRqfQBpfdg0HVgt4F8DaZiAVXsMABLZ3QT7MnW8lZzsyK0pJOv8pMH+Bm0/1lW4aKCQyHZULfbjHw2hX+HeEeczaogLACPpWUm1rQASMLXvTKWmEI96sDWqahutoqOJhTTdCNEjPY4dcD6fwpdGU30OlYPcbCT3pbkuU6+iPbegyRl9CpOZ93AXncri9pDfeTs/X8w80LNr9+WXX+aJJ57gtttu4sC9atll+1qm37Ycc9ty3Hi7UwrTGDpvV+yui8r27xu6h+im+MHurPy2FyaW4KaYH3ywNt11lfixinG56DELe11/NvSnAvgDfiY8cga1TTVE6yJEasMEwgH2P3N31tlszeKjqZJOZZg5cyZTpkzhnHPOoaOjg46ODuJxM93t6Ggn3vk8mnjITL99w5yFntJacRBC2zmdbi8j9ediDX8Zq/VhJLJTfiux6pGWqQ7jwsK4Zo816ng1ezvGp/3d4Gk0PqWfbaqANBmPwX63CyA1h5huu+bbwFrOMEKkxpQSGq8ss9EaMOxy8S1PaMY4mVTyHQzvRXlQDSEN1cwk3CESxKo/GSL7UqTNLBFDQwyUOBfZbVTUzCg7gQ/NzETbdkPbD0W7J+YVJu35x5iaslavq25Z/jxTyA1vv/02fr+fMWO2BZ/hQI8Y5udna4V49KlCFksK4neh6dfcDxT6Fe7XbNDd43JRoEpdkMW1CPnDL4NI1OnGq/aCTKE9U8o4pGtutDr3fHsjbz75LvGuOOv9eh2alyme9j5209PcfNbddMztpLt2PplMhk02KZfBXGGFFTlsv2ZuvGJF0Cwa3hHqJ5hyTMJpwvCvZTLjjpNQspiv4mK04QqsyDZlxxT/SkjzrY73nxRTAlunG6ZJcoYpOeTm4N45t+D6WSIC9Reg7cdistocps7aO56smRnUj0ccxxwJjIZhz5jOPU1DYO1BZaplYwn+3KmHV6GJEdoU8S1jWEE9N7hs40Mazkaj+0HsCmMT5l8DqT0OCVai51U51vpzILwtmjTqwRLeFYLl1w6+FanI+CiFZo3pQ7ZQmjRnGnLSMwYx0oCrDncvkskkfr8fn8+HNoxH248ATRIKCh9+UjrLSaKJB8yibwmk9nfGrV17MNeRAGFncXIxhqUfFxgXInKfV98dB5hFp+KMTLNfoLG/4k+/zkabjEBqjkHCxYH6yb8/x9W/v5lU3NT+gp1Rfhnehl2O255NdjIr548//jgXX3wxD9++PKus5Ovjyyb/CYFfmPbz+j+agJt+HW0/ir6g6kxbO09BQ/9CPFxG3OhMYjUj9WdB/VnGB3HOr1z2DCyYSl3h+UKbQut9aM8UY3Ib3ACJHuRkZE7wTj6APe/PZkEytKUJeoFSH+UFRGAD8K0JOTcNZcFc3mI68urPM6/WjkOTL0Duw4JtLWi4wrTQB9eCghr9UEFEILRZv+sGIkG09g/QfQl9Nd0KDySpcdfBHvAAa8w56saDCHbnaWjqZcgG0OyX+cXL1VZbjWQyyXvvvcdPf7ohNE8j/v1pzPz4U7pjpWMsv9fyp/ONgNZH0PjNkHrZUDNrDh+SB+MC4cdgvfCgXecwsOaGIBTcMJr9Am3brY/jaX+HdpyM1p2KVdMncH/L2XfnAzVAUEIEUyHevus/nH6ZsVSa9ZnRR97ilyFqawqDasI4X9fsA5iAayen495ma0H6xcr82AoQq9lkj/FpFN3sEkFqDh3UMV3P41/NKL+VwYfddWFxd2BiKpp6ElofGVITYRExBsU9bsFaIXIIUn98XkfE7GNB6z/Q9CuQfASsEUj0t56+jKo5E3Cshqp0vocCVs3+qH8kGrvetIIHVjdcdLdmGasJch1VHrmUL+5AWqDhciT0c8h8gM7bA8PS6AI7h7btAs13I4E12X777Vl55ZU5+uijmXLjROp0IqePf43OLptAwGVGkPnY29PU14rUnQqL5mPtF8KSzQb5QdesezNUbziZVR4BsOqQ6CF9x4hdXdJFB0aO8vIik4J537gviMz/rr1PXaxShl9G3/JeqdDMTOy23xofyPaT0AGWL6TuTKg7zUyppRFCOyAt91dvFLsA0FwbxO+geKU/C3Y32nO7126DR/Ih778l7jRNOSUQEazQJlgNF2DVneD5udiJx4xE7Lz/M4vT7ScsMl9NCY3BarkLa/gMpH6ix7VlmQXJqlk1btebBQ0XYoU3NcyjrnMo5uZjaJbdEwAIBoPcfffdzJ49m5+ssxXL//RJZn2R4sC96hnR6rLwrJ39rCktQRjimrWI7CAiH4vIpyLiabgiInuKiIpIRXL7DzpY90113eCH2j9A/ZXOdHkURPdDWh4s7rBKv4H7FFOLWraXXcX9hh6x0rB81nDIYePIzd68JKsGCDpC+wUjD++MqxmopkwreuZto1uSetx4Fw4gYItYWDX7YQ17CmvEa1hNfxmUop+qYsenYs8Zg/392tjzfoOmXq28U/ZDj0W8lONiMsSo2ODTg84/qF+pTjdo+i3o/JOp/ZIA0pB6psy7UdNvYs/7Dfb3o7Fnb4jdfVWZEe2CQnwtEN4RtwVqqTkK6s6k/zp3OSMkkxZuu3RZ9hk1hV0bD2bCvlcw54vP3XdP96nrbbTRRnzy/m188OKa/OfllXhu+grMmZdl4/XdvgvL/TpfUjFEbBAxZPyrgR2BtYB9RaSsDigidcCJQD831g88WIuII9TkRk1S6PkrxC5AGi7EGvYEVv2Z+W4xzX2L3XmukXJ0g2aLVsWPuGh/w8cuQCga5IiL+hzTRQRpuATDie59iESMol3NUcXHD24C4V0xN6DD8CDg7FdYHrFBE2j3VRU/Cy9o+t/Y8w81Abf9d0Ydr9p941MMf93+FshA9gO0/XDs+P3l5g298C3jnQUuKPvDBVJzCBUDVe4zU0IYILTnBsrLVGkj4Zoz4l92bDI6f19ncU9NFtkz2cjIDjGkYQJE9yF/vfhWQZpuQAJrYUV2hNo/UXQNWSsCtZhrMWj43iVB8/wjV2LqtU20z0kQ74rz/LRXOWHH1Yl1uoSFkhKQ6DxGr+pj1VFBPvkszdMvJDhs3/qSnULQa1bwQ8HQUfc2Aj5V1c/UTNHvBtzE8c8HLqGK1uYfdLAGkLqzwP8TzEVZyDDImRqfPRftOK5oH81+jc7bBRL34v4ZhSC8DWL1afFuvtvGnHHHSaz4k+UJhPysMHo5TrvtxKJWdNUc2qs7QRakHiL7G2cPq9jRRESwGsZD0/WO4L+NqSm6jcd25Qdr6hXstv2w52yOPf9INFOsgazJZ9D5h0L6JRNwU0+jbfsYh+1+oJqB2NWUNy6kjWPKnE2wE4+X7Sf+1Ty4wsGi8tOQIbyz43buAc05DUcDRO5rXO9KCUJuNnZsihGiKtsmCYkHjDfnEMJQAM9ARryNjHgXa9jjRQp6Vu2hyIg3kJa7kWFPYQ1/Chnxqrn2hr+C1BxB4UPti/+EeOuFOtLJvtKFnbOJxYRzzxamPdzNt99nmduWY9rDKaY9sU6emnr++edz7z++4ZmXOvjr5A423/lr9t61lm237F0Udxx/QmOQ+sHTHhcHBlAGaRWRNwp+SrIxRgKFIudfO6/1nUvk58AKqvpwNWP7wS8wilVrxIwy7xrfOvubki0Usl+i2a/yvF6N/ZVsppvLr21jyl1dfPlNlmEtPvbcqZYrxo+E8LYmkynBprtuyKa7bug5Fu06DxL/IB9wtQsSt0N0Fwis6b5T7ArTTVaoZOeGEhEjO/EkdJ7Sd670HLTtVWi+zehLq6Ld51Mc/BVIoN0XIS13eZ8LjIaEp+aybd5b5x9R/0plXGFpusHwe9OvAT6wapD6CcYZZIghIkjT37C7roD4jZR9huIH/6gBHVM1Df51DX2v9HiaMa7wyfvwTLEkYIK9VVl4fzAwbCD3TFUk4rSs9/4eyIt2aWgzCoP1Z+9H8PmLx7/R1l1sue9XbH/4RzC17/W9j/wSmMyszc9k1KhRtLW1Me6665g37ztWWM7PKcc2Mu6YwoVjHzLsacQ3HLV7sLsvhMR0wDb9AXUnlyUvSwyqr5jNU9VKNWYvuyHzR/NFTgIOqfaEP/hgDU45JLgu6lkbS6HzD0Sjv0VqDoP0Kxx20nfMeDHO2eNaWHO1AF99m+XD/+Sg+V6s4MApZmp3QuJ+yjvq0mjsWqTpyvJ9sv+FzMf0b3IaQWp+17efKnQXtNfnkUS7L3YCcQpy37ofLvN+P+fDsAxE+rl402j89rIHm1hNSPMUoxhoxwwtayErqEntUWjiHlOKyK9B+M3im5vXYgk09z0am2wYIr0mEqVrGRIxhrepR6j4wWgatYaBppeYEoBIEJpuMDMtkiw7KlVk2bjDfm0cO/4bwlEl912BkFLkIKyGs4qO9Ze//IVJkyahczdzLyP6RppArTY6/wDjhNR7XySmGsOB1oeHhGs/pNAhZYN8DRTW/ZYHCm/IOmAd4FlnzWsZ4EER2UVV33A74FIRrPOI7ASxaymn8qkpA8SuQ1PP8vgzGe6Z3s1bT63IWqMLF8OCiL/c5bwq5L4xU+SytnK7bBpu5zog95XRlZYA7rKY4tQYfVD7ByS8VcHbiYM9x30cve7fBOmz7ipBFboLIkE0ehj0TMFbuMn2fiBgaITVnGso0DvD0s4zjegU4kzDz+9fAS77Odq2h9NYVRqEfSB1RsK15khjBKten0fvYBpg7hgUQYObIQ0TPM0GFhY0N9so6qVfBf+qUHsiVnB9tOlaaD+G0eslWGG1FLM+DGPbwhFnfUc46vIAStwFJcFa0++gibtAmgC3NR+fSSjSL0NuFsUJTAbs2ZB6ylk0XcIwdDzr14HVRWRl4BtgHyAvi6iqnUCexiMizwJ/8ArUsJQFa4kejCYfg+xXgJvSmZHmvPke2Gqz2rJATWirsumZarbPDTywnnc24BtZgV5lpv92rgPm7we5TwtH7bJPEKKHGBlO37KAjSb+gaZeM1PryL6GceEWiB0hIRELjRwA8dsozsAjUOPh/VcCqT0BlRDErsfdYisMQbcGnMUD8a+EtNzuUC6tqjvhtPsyj0ANkAP/qlhO2UiTz/ZzND/ovL5jpV9E23ZHQ9uAdiKhbYz640Ls0tPsf9F5u9EnbPYeJKdjN/4VCW2DWk2IPZsL7/6MK09dnv9+ECYQ9EopM0U8abvnTqOMRxrPRp3ct+aeyX7kkrxgqICZD5AlMFgPVSu5qmZF5Hjgn5jFqCmq+r6IjAfeUNUHB3rMpStYWzXQch8k/+l017lM9zXOa2/MY+exG3DCGe/x96ntZLPK9tusxN+u/T0jC0pvmv432v47+tphLWi80rULTawGNLKHUwopDI4hpOZY89+23V38+pTiDrWAacCoPdz4IdrtxnjA7q1rA/HJ4FvDqNoVzSIi0HsuQOpOQrUHEtPMoqeq0XaO7tPfR2n2F8sY0tYcjXZfahp78lll0Ji+Rveq6liLEgMuPaQrWXNhZkG9qKSLEtrZZIxFZa2cmQUl7gRAU88YHnrzLQutRKKl+ubmVeg4CUbMhKYpMP9A6hq7OevGOaRTSiDo9f6DfTrgdsyx8OqvCc1pMw9t4iQVpWW+COIbWnPoIcMQdjCq6qPAoyWvua64quqY/o73g2eDlEIkiER2RmoOwtgqlSLK97O7ufWON3jnP2tw1x1/Y8qU63jzXZvd99g/z8lVu9vRPWg3rBKNYcxif+dpKCv1Z0Pt0aYRBQv86yDNtyCB0WjmI5fFzwL41zVaIdH9kJbp+U4/7Z7kUOdKFrpyn0DgZ0DIlEukxvjmFSjZifixGs5Bhr9smmJGvIJVd6K31ZgHRMRYiDVcbFrWfauZoN/yj2LH6yqgue+wO8/Gnrs1dts+aLIaJ5qFDKuUclYIgcA6fb+mvVT7gqak1Z/Wt8bNmkHykQEOsn+opsw6QeZtjy2yaPwxY5Sg8zFJSIpgzWgkuKn7LjWH9f0/86bzHqtAYjoa3MKUkIrCjJjyXHis156LD9XS9n4UchpihLYHmQjaK0TvQPyoCqrK9OkP0dJiGmSWG7kmW265JTNmzGDrrbeG5BMmEy2F2uZGqzkYzbWh8TvMRexfFYkejFV7nDF7LYUXFcwcFGm+2T3wJR732E8h9zky4jVT+/aNKMvU1O5Euy9xLLUwNcK6Uwe1Em/MG3YYkHkBmABC9mPzAJOgoUxqD5CF3Fdoxzi07vdYhUFhUSN6KHRfhnugDSO1vy/43YMOKwHDI5eoe3mqCAk08TAS2W1w4y2BahrtugASD2CulQqrZLFzKO4otCH7DkT2x0gdvER+thfZH6tunHMOdcx63cphrqNCcl9Cyz1o52lO8xnGKb3hYk/tm8UJYejKIAsDS22wFisKzXeiHb+H3Fd88m6I5x5eDiuyDfV141l9tVaarD9jd9Yhkb3ZfPPNCQaDfPDBByZYawfuWskpNPkE2nMj2HPJswbSr6GJadB0MxL8RflugZ8427pxd2vMj+viN7e9AAAgAElEQVQb8XvHeLvT0LVcmk1Us2jb3s4U3qmlJx4wFmGtD3vWTDXXBunnMFrLYxZID8OO3+ewVsTU86W2L1DnkYDYlWh03yINj4UNtTsg8xH4hiPR/dHc5xC/14yVFGatYQOk/k/F1MTw/0HsU8qDtt8E/cR9kEvRrxNN+nXzWUvEMCp8I5BK8q2V3kvnWc4DuQqNHK/W78T9WMu8Y0oddrtp5CrIorX7Moj/nerTyhxILeJbFmm+DbXjgC6RQboQPwbrxQQJrI4Me5QpZ9zA/Vc+SzqVRXgRVWHuV99B6klA0MRj2DXHo6pYljNlC26C+XhK622WM83sXUzs/XazoFm08yxk2KOUQnwjzbQw/Xz5QOvO8i5NRPdwJD1drqLAz7zffOpZs+pe5IrSuxL/LITLZViNC8wEp6lHQHOekq39QdNvQtd5FAU1T99Dn5FvDZSL3Q81VBWNTYKemx32TgYCP0GarjMzoux/wbcc4vNgBYU2hdhlJS9axjvQiqIt09DOP0PqiX5GkkbnHwi5L53fBa05Fqk9dkBlKrU7jHVbvyYMFvg3g+wLnuMBh1VTMsNTu9OxlvOypbMovk98xvTWv3z+FfFwslnisAQH66WuZl2Kz9//ivuvfI5UIoPaim0rLfayfP5dD++9adHbKPL8kxPJZDKsu66RaJTAWo7yXWG215v5VBBsyn3uKfYjTddD5FBMy7Bg1M4mYUV39zyc1B4HPjcD0TBSf7r3OLIfu0/HNe7a0afZWSZQkzLbaA+QNKaudrv3eTxgjFirVEPUzAJbfFWN5CNO4ElhXOaTkHkP7RhnpGaDG3oHanCs0kqz5oDT2GToitL4V/CtSuXby3ZYQWl6a8f0XIl2X4kdux67e5KhyPWna5KbU6GOXAfWSobF1HgVNPwRd/YRRjvHA5p+E8+HgW8NiB6IqdnXmjKQbwWk6ZrK415S8WPNevHhX9NfJ5spvrlGsgpf8Sl7Hf0Nl06ooztmc/qENrb+VSObbdSXVUjDxRDaypQ3yBmvxOSjVA5CgpuNkmrWqW+/aBgF4Z2Mfm8/GYdIGFofMgan8dvMFDWwPlJ3vGnt9oJvJad+WlJjlKj5W+n4Eg/hLp8pkHwaontWHGcZ7O+p7qoOQnCjIVcE1FwbGrsMkk8CPojsitSeZFhCZTzpbL4sUSTyVXbMbyD7hctfUpC4G2qMToyIQNP16PyDMKpz1dd5iV+DkU3IovFbzEJc/UTvbNu/AkXdLXlYEB6D1Xh5/hUBbP9PSowKHDRUsOBKe1J/wTcMq/50tOYwQ9ezhkFg3QEvYi8RGICi3uLAAmXWItIsIk+KyCfOv65ixSKSE5G3nZ8B8wsXBP6AD7GKLxy/BNg4uAW1ET/7HfM9J5wxl19vHuGe65vR9oPR7OdmQ02A1WjaY5tuQSJjqWy5FHQ4tCULfbk56NztoHui6ebKzYKeG9D5+3oLIhVARLCie2K1Pog1/AVHRa9CoAYIb2cynaKv2DK18fB2Ljv0ur6UQJWB6YU7CG2Juzekry8DIwjBzZDG8u7OBYFqEm3b07Q4a5dh9MTvNEa06qHZIX7vem7+wBk8b5mS71H8KyLDZiBNN4B/He/9XJEB1Fx/yccg/S/PLUUihoFUNAM0jAtxWeiW5tsdLRVnPDIMmm7DqiT6n3N7QDkImo5r8Y1AwtsiwfV+mIG6F0twZr2gZZA/AU+r6urA087vbkio6nrOzy4LeM4B4Vd7bYJllV88NVYtj9+zLN2frUbbR6ty85XL0NToA82gPTdjx6caLeOO402r+tytUWs5R+/BrSMuZDKK+vFFr2quDZ071uFXF37LKch+gnaeYxyohxgiQaTlXqf27jM/wU2Qlqmu/F4JbY273KhCcMuiV2w7jt31F+y2A7E7x2Pb5UFOogc43YuF54pA7clGWKhlGjL8eazm6wdM/+sXycdMgC6qo6Yh+x/wr4nXhFKtfrwlfSt56H2EHPXHYohYpqzSdD3uNNIqoPG8FRhgWrgz/0Gzn+ZLJFJzLNSfC75VTPdkcEuk+V5Xd3GxarGarkZGvIsMfxNrxEtYBYJQrvCPxl2PJIiEthjc+1pCIXZ1P4sDC1oG2RUY4/z/VuBZ4LQFPOaQYplRwznuqsO4+sQpWJZZ7LJzwomXfEXrsm6aHFnIvOXQoJJ98VXj0LYT0ADWCIcJAviWN8p64U1ds12N34x7959zruQDaOppaJk6KM3pSjAr8TfnTRQqNmEE1oPwLpB4ELMoKEAQao8pWiiys1/AvLHk6/aZVyFxB3bTHVihPl0bseqNN2TPLZCaAVazMdANjTEb9DczWABo+j2Pen0O/D8z36/dQdFMQjPQfgja/HfPLlURgcYr0PbDzbFIOWWlFZGoN/VQfMPQSqyefuF33tdbaMeJptauClYLNP0NCayFRHeDaPVUQJGga7muF5p6Ae252Rj4BjfGlGYK69YBCKyFFHLQlwIsyWWQBQ3WI1T1OwBV/U5EvAQQwiLyBibVuUhV/7GA5x0Qxh6xDRtvE+OV+ychpPjldl00tnqJJwUwHVduCyoKdICdMHXWxsuN/kUlpP5FZRpXDrQT7TwLDf7cULAkDNH9QZohcRvYnRDaBqk52ATBAaKaTjljhDseIrugiUdBAkhkV6SUoTH/KMoXWBU6joIRbxYf02pE6k6CupMGPOYFgn8VQ4krrU2LHwn+BI3cDfNK+eIZyHxo1iQibrLDziGC66MNl0LX5aBthpFTf17/bIcFCdbhnUwna/thxfVv+2tTFx/2vOv5Nfup08n7GQTXR6IHV9QoMXX+qx2FvIL2++x/TeOQtYrTFewz6nn151Q1fC9bryUOi7HEUQ36DdYi8hRGEaoUZw7gPCuq6rcisgowQ0TeU9X/upzrKOAogBVXHNoss3nZEey4f1c/iz1i2mN9w0v0O0qRMp1s9vz+hYp8y0J2ZuVtsCHzimmu6X1IdJ2LuXKcQJ/9DE0+AC0PDn3ZwIGIoAQNNzv7EZr9GGpPQJy6pKoN9iz3nTWG5r6pzKRYRJDIrmjsSswMoffu85tMNLg5knrWKDRqKWsngSYfRyoEazs+HbrOpk+a9mVo2w1tnY74lvMeVGR34wBUVP+3jElAYDXHY9GjZm63Q+IR3CzKjLv5ExD5TcFLX6LJJyB2FebBmjOMl/g90HK/6wxO7S607TeOil7pedJgd5vu2pp7MLorlSuoqorGb4XYdaDzUd9KSN0ZxYJkSyKW4GDdb81aVbdR1XVcfqYDs0VkWQDnX1cpOFX91vn3M/6/vfMOk6us/vjn3OkzW5NNgVACISRIicGAVOkgkRoFRCCUEKQK/FBAepMSRBAFpIOAQOgBQgelGSAgUoxAUJAS0rbv9Lnn98d7t8zOnbLJkt0k9/M88+zu3Hfe+97ZmXPf97znfI9xlUws0u4mVZ2kqpOGDRu2jJdUhOAWuJcX8gM1QMhsdg2daaI+yiE+k1RRrllsGoXlmIrRczafJf9Lk4LcEjQ+s8K++o6m3zKxv+mXjZ5F+u9o41FoyiU23LWD/i1ntayIVY0Mvd+4dvABfvO/HXKvUeCTGO7fSgEpvnJRzbpI02bNjar9utJjqjrRpK1LFCMREDPx3EPvxqq/npLhoNnPUXsx7tmTaeOqANSOYzdOQ5dMdgojJOn+DGXMONt6x4g71xa/v9A1lEcKUn9DxF+R3K123AhtV2PS2oHc52jzyWjqtbKvHSg6Mxj7qwZjf7O8bpBZwOHA5c7Px3o3cCJE4qqaEpEGYFtMGZsViogf6m9z/I1xTNJH2kQt+McioR9AYCLYCyBZQeEGVfDnh8Bp9nMzQ7LqIfQDs8kX3BytuRjaLsK4PBIUGooimY0FJE1CS9W3k5qtrZfiqpHdegky7Fmj5OcbA7mCRZFjfPq/bNeyIv71kaH3o5oAfPmuoOAk42oqWGWFSotc9cwGzT8AZYyQSBiG/MX4y7PzzF5HcLtu+VZfp4vBhfDOiL0IjbulsgeMXgugrRcYSdSiCTK2WQm4kX6DsiGpVmXhlaoZ6LiRwvT9JNp+jasQ2mBB7ME7tV5eY305MFNEpgH/Aw4AcKr0HquqRwMbATeKiI2ZyV+uqi6Bnt8+EhgPw16GzDto8hWI3w6pv0HqebOZEt7VKRFWjoDRCPabzRVVNV+UxMOY8DinmseQPyOBcVjRfdHIZDO7UD+0nGrC99R22oadJXC5IgRWnuqbnfkA2v5kIh/CuyGRnyyfi6RY+avcZ6jmjGGpvxmW7EG+0bKg7vpB6Zd0S2HvvnEfRZeWuGah+hQk6LroM1h1FK2e4yuf1GOKZGxuHoDa7djxR0zJNt/47lqOedQjdjMa2Ab8G0PmfbpvqBEIbQ2BCWYTuZJMxmLyAb51MKuQYjPrkFPvsgLs1uLvU2dY7GBkZfdZl0JVlwK7uDw/Fzja+f11YNPebQYKER/q3xgSR5M/k0hA8nkIW7h/YDsNkc8sr2PHGYWz3Gdo9mOnnJfTnwJ0oE0/h2EvGdU6CYB/AzOHHvqw+YJm54N/DGqNgCV7U95YB5HYYQDYLeeZRIxOMnPR9lth2Kwuxb4+Yw1xL2og1V0zQMu/Fvbwd6DjBuO37xS2962YIgP9hn8sDJkJ2fkIGQhuUfZ9E6setYaa1VdvgpWHsKndjmbeg+bTnZt0EhMaFzAx6J2uAwCa0KbDTLJJ7dVmVp54BJPo8xMkeoDZa7BL6Et3EYHo4e7XFju0O/krD8usQqrP6tq7KItVa7Iq3eQF/OtV1scAsSpHg6ycpN8o4l9NQOZTzAKg9/EgRI+B+E2mGkvzMUAWlZizNHVT6Gs2X65AfpkwM8OahAY2Q9uvh/h9zvmCdN0U/KPNzDv3hZOIo1B9ARLYBDv193xDbU4GuhDtuBmpPr2Pb4hD7Bhou4r85WsYYtPymllWaMVHeCwjmlsA6XeczcUtEbGw4zOh7UrHmCga2b87pLBUX3a7exkrcDSxT3Y/1vl6tY0KYvxuCmtuOjNiiTnumV6Gzl4MTUchQ+5CYi4Gt9MNlXPbAPZjVPT2RaKHuY5N/OtD/Q1oy5km+gjbaLXETkBCWxo3ToWI+NHYcdD+R3p/lqTq/yruZ0DwjPXgQrv0GFzIfUj+2+K4NCL7OAVZU+R9kcpFl7hVyuh8afNpxg3Ttay1zAbUkLuwnJA5zX4O2gL+8d1+144/FT9l4klYRmMt0cNQbYGOW5wPrUL0UCR2bMnXqaq5KWkaAhsPitp6pmDwpc6N0O/sHtWiseMdDZQeRiTxKApI7UXunXViL3VmjC5+62z5zWZtu8a43kpZBPsrin8t02jbb5Gh9xQcERE0eji0Xdijf8fFVnM+EtoO8ZXetJfQtsZNmPsKrGj5sNRSfcWONhOZjuvN++ZbD6n5dV5F9sGIN7MedJSaJSjd/lgfBLZGan6Jxu+jvLJZb6yiSnKa/Z/ZLMybQdnG4CVf6Hqd9NrENM1KpEUvo8wmmC+8VJ2Exo6B3ELwDSsrW6qZf6FNx5kbCgL4oO4qJLRDydd96ySfciRPnZurYjZ32y6mcCMtaeRjq88sHS/tWwNXjXNM35r9FPGPcT+stlmVlZ26ORWJilHkpqDZ/0H7FYX9BzbC6lOyjECPJKhlRUSQ2M8g9rPyjQcTg9hYr/Kqe26YGUYlRi0HutAo8NlLKe8T7CQAhJHa3xafZWY/wV0tLV2i0odDeHcopp7WDyL+IiGjbVHOUGsKbTzc+HA7lfq0FW06ybgfBhCN301hNIJN8YgHy2zUliL3DUV1x7Gdm28RMm9T/vPjh8AkSlqMIrHcGr/TZcZvQ+ZDNPufMuf1AOisbj5Y081XS2ONfyMTOlXJ5TtypxLelXyxHMP8/6Y59lcLmbjL5wRGfcLOP0lBbDrS8GRXAsCCBQs48sgjGTVqFFVVVUycOJF7Zr5bZMc8AP4NSw7J6G6MpMBg+ychkYPKX1N/kfor7puiOTTxcN4zareg2f90pb5/W2j6XezGI0yInCtFKp1LoGRomh2/H13yI0wx3CLntku4xOwmSn7eJAq+0Ujd1UjdNUXGGUGqTnJ/feYTXP8X4u+hme2OasZsepaTY13FWdXjrFdKRASG3II2Hu3UN/S5ZLMB+CHkBLuEf2QKnWY+oeeM7cOP0jz1Ypzvbx4jnVsD/KOxemy+2bbNPvvsw9KlS5kxYwYjR47kwQcf5LDDf0nkzh+w/+5LyXOvSKAr4qPo+K1qI5vacbfRZ5YIxI7CiqzgunZ2U5GN2owpNQZGAa/lLFMmTfyAoFWnYcUO7dehaO4bNP4X6LiVkgkmEnHG3DO7MQxVp5WonrMYWi+hrPpgKb2TwObkF0bueWw7pPp4I30rAr5d0GFvQOuZkHrZjFOqoPqXiKtiIhD8bn4GbNfgMybyxe26NI22XeG4i7JmAlBz3uDPMvw2GcQ3rNXSWAMmLbphtokttltQbYfmUzGzkywmjCoGzuaaSNCUCYs/CqnZzvEAe0/+mn33Wx+JTeeAg89lyZL8mdfHH3/M3LlzefSendl750tAguw0aT/eeGMCM59oYP99t4bEE+ac/o2Q2osqStkWq8Z8wauP7++3pnKCW+C6ZJdoV+KDKTn1HJDu3mxtuxL1jXRWK8uP3fY7U/mFtPt4APNR9yO1M8C3tqkWk/knWCORqhNKjyX1komHL/c9DhbfPBNfg9kL6LiVbqNvgW8UMuT6gmgLy1cD9debBBPtAKkpmTko0UPNzUqzdN8QwhDe1VQpshvNxqFvna4anNp8qlON3bkw+ysjFDXkLiT43TIXu2ribTAOUkQEAuPN74A2PIY2/wqy79MVydE4BR1yB+LfwGQkxg6E2IFdfZRzpKRTxnjXRj4GHHGhxEPUxZagbIhVezla8xsgV5Hg0mBC/GPQyN4mAqVrtRExbqbQTibULfk0hRuzCbTjT/1irDX1Snfll1JEDkFiB3fJhkp9iYiaApwyZyWbjCsba25Vn4IGJhh/ut1qChBHDy4ZFicSAHGTZQXNLYHkE6i9FAluDUMehPYZRv9aYkYMLHokdvOZJitXgqBpNHogRKc7Ze16k0Lb/4gMuaX09a6KrMpJMascua/Nxh9KV7klO4EunYbWX4uQM9WZJWCKnab+CiIQ2qloQsXG67/H9zePcMGVS7nxt8MZMczHw7OX8Nqbi5j9uFF+MwknRXypZdDMR0YvRFuQ8C4Q2q3ocr5P/ea+MjoSqVe6vvgSO6qgb6n5DQS3QxP3mZtbeB8k+hOTfJRrKn5duYXLPUYAjd9LYeWXXkgtVm1fdMd6Ed7JEdZyw5kdD72zoq4kvFO/uBk09Xe0+VjHpZM2WhxgkmeqzzbZrCLYrTO6Mxs7VzbxhyDzWfHOs/OWe3wrKwO1eVgJnrHugcbvoTCCQEEXQONhRpMYCw1PgcS9Tqq4gJ6P1l7q2qdk5/HkPWuw/xFfM35bU3EjEIBbr1mHnbcvLldZCXb8QWi9CHNjsdHU8+C/B4bcvlyxzmo3okummKQe1GTZtf8OTb9ZMOMSEYhMNlV0euMb6cQl935Pra4KI8tN2ZJZEYgdnf8SzRrhpY67gTgEN0eqzzZyBC6INQStPsNU+ulaSymEdkFiR63wMlZ2+l1omobrhqK9GFovMHUzY9Mh8RcK9V4STnRKEWQly0btRwazsV49o0GKUSp+maTZhNRWSNyBid1NONmLKWg5yzW91vaNY+pJi1naZHPvjSN54aFRnDy9nun/9wVPv/DVMg9V7XbHUCfp8lFq3GhHJJ9c5n5NN39xjGDPNaEN6Zex4w9U3I9IAKrPID+KxgKJIFW/WK4xdhH6Ee5qigBhiE5FYtO7nlFVdOkB0HEd0AJkIP0GuvQgNPtFQQ+qNnbLOdB2uRPDrqa475D7seqvXeFlrDTzPjQeSmlpggx0XG9ErIquOlIU/foX0w9Z1VHMBmMljwHAM9Y9Ce+JW3heb9z/V2k08wG9v0RPvljP7OfbePj2NTlwn2p23CbKFeeuyX6T1+GMs0rLarqeOzMPu/l0Y3Bc43YTpnhAX/rUlBGqt536hKWqWbfNMAkeFWJFD0Dq/wiBLcAaZQoFD30E6SeNCInuZ3zk0pnM4gNCUHUWMuJNrJrT8jbmtOPuIup2SSPU3wuN3wuJxzFuhA4gY2avHX/ol/G7oapo6hXsphON5GniMSPPCo7EaQXhj2oj9pISESo+isZ9Z/5pNiRXQ7zQvZUEif7EiNnk/uPMSCqVLsW0s5dCpjGvSvZHH39FNBph7PgdjCaJBCG8HxO/H+DxZy7r0/g0+RLafDKdbo+i9EF5z+64A9qvwbhzMmh4V7DWBV4tMoiEEXvydWt+a24JZOZi6v9t2S376SCh7fulVp9q0qm8k4HgNka3WoIw5C5IPmfKo1lDkeiBxQsKx28t1juk5xq9Zd8oxD/aaX8nha6xDKReRe32b6UQhLbNMO4LZ1as6blGvKn+VqNLU1kv4BsG4f2g/Sq6Py+dq4BS4Y1hUx0muBq6Q7wNxpUDkRAMvd/ssCdfAHymfmDFaeYKmkM7bkRqzgJg3XXXJR5P8MniXzNu3Liulu+8cxCjR4+ueGyqNtp6Nu4C9D2JVJwYY6qJXJ2/VE4+TblMO7XjaNsZJqRNs0DCfMHVBgmgockmBts/Eoke5F6bMrfUMfB1EJzUZeBVFdJvoulXzaZteC/ENxxNvY42n0CXsdEMWnMJVnRf424p5jcvOHGJ4sS5j50ah2k0OBGpu75I/D2YjMcOoH+NtWa/cISeeilCpt9GG6dTSmsmb2zRg9GOW4zOS56hjlG8JmjnINJO0tjqRWdSzGDFM9a9EAlCZAoSmQKA3Xw6JJ+hc3aV6BCal/oZMixLMKwkEjazXzQf/q+/ydLaZvPgg/ci1eszec+d2HPPPVlnnXXYb7/9OO+88xg2bBhPPvkkM2fO5LrrKnODqKbQtqu6KoK4EwZsqDoGCX2/dH92q8kwbL/exadZxsUhUWg80DFUPQvOOgZAE5B0FAHTgsbvQWOnIFXTu3y7dvv10H6Dk27vJHwMuQN8o41BTs8BjaOEoO33aN2V0PKrwrG2mrqVfSo0HNgc0n8rclC7jXn6HbT1XFOcIvEoBSqM1lCwlm+D2JX0HNy9kynIFFnt5OGD6BFGdmDxzuQbfcW46UpZpCCEtkd66KavNqiu0sUHVnmk9nII7UDHwj/z6T/n8+x9dTz/YD3f26GNC+/4L4uW5jho+jd5rzlo2gfAwXz65hhGj16f55+6irPOv5/TTjuN1tZWxowZw5/+9CeOOeaYsudXVZNpmfkHpb9kOai7Diu8o/M62/hg47cayUv/eDPbt0agS3+CEd0vE/LmOqDmvjQGstDxW7TjarTqNPCPh/YbMRu0nfrfcbRxGlSdAqm/9xiXc7z5JNxDAHNo4jGoOrHkJp+m5qDxP5vZfmCCqeaTl8HoRtok8zQ8CcmX6JYt9QFBpPbSb2dj0aqpLAHHjehxSPXJpqJP8nnUVVM6SdFMSoDIfkjNOctw8lWEwWurPWNdDhELIj8itObunDdhGvE2Y0jeerGG99+IsfGWHeQW9EzndcL5Omdiuc8YM/RiZt77UHE/aiky70D2Pcq7YjImHb7TWLdd5SynHcOX/cAYxMB3MAp5KzpGKWfqAvrG4R4e2Qzxu1yOOcddox+yEL8b7fgjKlUQPdxkI/bwmdsdt0Lbtd39Zj40VV383zH+X99aTvUVN9eAmmryw54yETLpN41+R+zwruSafie0A8u27y9IYHT3ZqpVh/v/2AL/pk7iV6/j1edjxQ5ZhnOvOgxmN4gXDVIhgWCAk66bRiga7JpRXXnyWP73STVKGKQao7bXw1B3omnXSIOKyHxAxYVos6Yiu9odEP8zhYYvUaH627eFQu6TIseW8aOoTXS5LzpuQVu7NanVboW2a8h/H5JGtyS4BdbwF7GG/rm4gfSNBKlDrHqsqhOwhtyJVXvhchlqtdvQ1N/Q9FzXqBqRMDLkdhPrXFThzw0LQrt1n8f/XVx1t1GoOhciR5KvPBmE7HsmPnt1RQFbK3sMAN7Mug/seugOrLXhmjz8+9ks/mIJW+w5kTU23wMr9jXYi4yRbP21yyZWDvogU6mZD9DEk5iY3iFOYkm5mbVAYJwThvdvljUj8tunyI1HsxCbCi3nUZl7xq1eYBISD6HV/2f0LzL/xL28VNJohjsVV6T6NDT9quMaStPl6qi5pF9dHXbHvdB2GV2Vf6QK6m9FAvkqixLYDIa/hrZe7CS1VED08LzIFMn8HcVHYdSHD9p+aXRC8o6lIfE4mn4HGmYPigISA8Ignll7xrqPjN9yLGfdY8o3qZpEEY2/ZOrOhXZ0TYwxFajzhXFUs87O/01m9uwfi8SOQRNPONVEOr9InW6VcmGEfiNItXASZuZcIjSrJIE+vNYyD996jgxnluIFV8sQ+SkS3gdNvw6Jpyge9eI38fDpN8F2SVmXoFPppLaEK0DyCtyKf21oeMr4tdNvg389JHYk+Mag6bcg/Za5aYYnG7fIMqCZ942hJtn9b9QOtOlIGPZyQbijZuZB4v4Kew8hVb32PzL/xF0vJQu5z4r0kzVly1IvOZrpqx/96QYRkR8Cv8fc/W9R1ct7Hf8/TK3aLLAYOEpVPy/Wn2eslxHVrCmIm3nbZA7ih447jFHOvEu3j9kUHO2sDK3puWjrhT0qiTtGOPsJmnyaQt9sz7Ary5zHty4Et4PU445OstOuVApxRYQh8mNIzKQygx2E4S9jWXVGDznxsIlmkIiRRC0nrtQLEUFqr0CjR6CtlzqSnz3HEYbYkVjVp2I3nQypZygwxprpDjvzb2K0MnJf9GoXKqhFKL5hSPVp3d1oBm062vn/OqGJbVdA/e3LpEin8Xtx3XfQOKTnQuj72Ol/mgo3dpOThVrqxueU7JLCQyYAACAASURBVMKG2qsKS3BZa2ISvOJ9HShkPwZWU2PdTy4OMXff64DdgC+Bt0Rklqr2DJT/BzBJVeMichwwAygad+sZ62Ul+bSJE+4KJ3OkVTNv0u27BgKbm8gB30g084nZ5Mtb5nd+OGxK+5LVGIyhs7H8RkJV7ZPQxdtTWh/DzV1QBKmGxEPdYweMUXBbTpsxieNPF6sKiU01rgxA7Sa05VJIOUbbWsNsbBaLW+4xs5TARjDkVrTlDBORISGzYgnvjlSdaNpUHY+m/9ornC8C0R93zX5FBOpvQ5umOxXJfcb/X30mEpxY8q3Qjr84YXTOdXcmqDSf6MyE++hjt5tw/f8qaOIJM8ayMfQAAqHdkfAegIVaVRCfid1xE4R+gMSmGhdQ+IdmJt9X2+MUQVgt6V/VvS2B+ar6HwARuQ/YF+gy1qr6Uo/2c4CSIu+esV5GNPm4S4xyJz0MW/ZfZgkNaPtV9HW2mY8PyX0JjrEm9TxldQp8o83GU/z28udWt9JlQfCNgtynLi8IOK6GQsSqR+qvBK7ses5e+lNntuxC5Mf5r5cgUnc1mvvGuFh86yK+7kouEhgH9bejrZcYlTipgdgRSCzfHdDp4iA7z2i/BDYrXWcR0Oxn0H45rjc5bTerosBGJfvojYR3My6egs9MApKPUHnilXF5SGBT7Pj90HQm5v+qkP03Gp+J1pwGqffANway71L5hrJl/OirqwsEkMp1PxpEZG6Pv29S1Zt6/D0K6Ck28yVQKgFiGvBUqRN6xnqZKVV0tweqRtsh+Qxk3mL5bt2KigV2I0i9IzxVStDHB8EJSPVxaOZ1Ey2iKYrPtN3GljRfYIIUGpSUmX06hQbKIVXHoU0nUnDTCGyBFXAPaxTfyLzU9rxjwc2Rhoddj+W1EzEhixWiLadTejWyDJuO4b2g4x5Hl6Sn8bTpUyHm6tORwKYm9b71UvJn4ynQhdByNn3fs7AguD1Se/FKp6ver1QeKLVEVUtJR7p9SFy//CJyKDAJ2KHUCT1jvYxI9ECXZbgbGeNayM5nuddYmoPGI1DEZNBVHU/pun5hJHacKXw7ZCak/45m3jXZg64GwsLVSFnVpjRUgQBSBm29ABnmJmLvMpzQDmj12UYgX53NyNAuSN0V3ZeYeg3tuN1ka4Z27l7WryDU7nDirosg1WVrZLp3nHaiTZY1bNIPkQOQyP5o8gVTzbyoka/UUAtEDjNJRVZ1wSbn6kgfZtbl+BJYu8ffawFfF5xPZFfgbGAHVdfohC48Y72MSGhbNHKoE89sYQyxm8/R72zYlJoBd/q4Ffcvmt853llyDOODbf2NiUBJ/63HTUMwM+rtkZpfIf51zbNiQWhbJLQtNgEjEZp3owkbv3Fv/7dEkMgUU0fRjdwX2I3HOyncAqFdkZpzu4SsemPFfopGpzgRG/VIDzeK3XEbtP2e7kSe+UZYq+HxolEYmluKdtxkCkFY9UjsSMefu2yYOOPis2qp+2NJf7VqzqxeJJIX9qet50Juft8HJFVm0zS0AwQmoYu2cf5PyxF5A4Afai7Aih5YvunqQv/6rN8CxorIesBXwE+Bn/VsICITgRuBH6rqonIdesZ6ObBqfoVGD4b066hUGw2JzJweRjBkohHsQp3kLmLHQnAHx6jm0PY/Gi0STZrZc2g78E/k0ZmncMGVC/no0wxrjvBx4lF1nHrsUEzq8++MUdM0EtkfwnuWnCVJbDpqDYWOG0yoln8TpOZ0sONo888dP3jajD+4A4QnQ9sMsL9x6U0h/SJdM8bUs2jj+9DwdNFYXZEg9JJIVbsd2q4m30WSArsRjd+NVBXWmlS7CV26D9jNmCK9/0Wb56FVH2MVqwLe8/WpOU6JrRanxNaPnRqJRYgcggQnuPelWZM1mviLmUX71oDqc5HwTsZlkXyOvlmCAFSdAf51jc8+t9AU0CWznAbFcvo+zjPUBfSfNoiqZkXkROAZzA79bar6oYhcBMxV1VmYDZ0q4AHnxv4/Vd2nWJ8yWMvPT5o0SefOnVu+4SBCNQfJ2WjiIWMAsh9jjJjbDMiC0K5Y9X8s2++rL93ED3b5OUceXMMBe1fx5jtJLr66kRnnNnDy8TtiNVReEKDsNdhNpgyU3QLBbbqqoNgddzjGtOdsvPOG0Ov6JIbUzkDCu1Epmn4TbTrWPVokMAFraOE12m1/gI6bKNw4DSHDXy3pPrHbbyqyugjjrn8i0PAUVpHsRbvlXEg8Rv7qyslG9K+PLtqO4u6JkIkLtxeDDAX/GMh+BtqI+fz4ndcub+ZpCKp/hUT2W+Z48cGKiLxdxodclprqUbrlxMoKUL/wyjnLfb6+4s2s+xERH0T2hvCP0MU/oPiXM2iWydWnV9TvxZfdx3ZbRrn5KhMNsfuOMZpabC6+upHjjp9Q6VZnRYhVbwqt9n4+OhXN/gcS9/V4VnEPR0s4Pvryxlo1DXYrKrXO0t4Fa4T78+lXcY1wkaDR/Qht7X5Ouwnar6XQ55t0/Mpu+Apjmbv6azN60y79afv1SP0tZpM057bC8iH1f+qqBm+3XevImvYcR1/cHT7M17pzEpYxz/k3RKrPRELFK7Cv9iiDuqyXZ6y/DXJflygRFoTYMUj0Z0iPLDo3VNOQ/YR3//k+xx+9FbCEzpntbjtUcc1Nzcx5b2N23LlfR++KiIXaS8iP2y7yyZaImR2WQNVG26+F+B1m41RCpniBZsg3TmHESQsvwLemk4DUa3WoGfCVkC9Nv0Nnpe8iF9CrTz8Et8jzr+dhLywuCZD9r/Fd11xikqjovD4fEIAhf0GCm5hha8Jxw1QSb+2CbxxSdykQNDkAVoMp5rw6R3f0lUHqaQDPWH87WDGKzob862JVl68/aMdnQdsFgJJMNBK0FkHsREgaF0uoalPgS/798cIVYqxV1WzilZ3l+U1ceah0BW/tuB46bqfLraIpIA0y0sR7i9+cq+rXSHAL1z4keoRTJKKncfObWWSpm4VVCyVLkykQ6tbw8I1Caq8q3tw3CnexLQGrHu24w1TQaXjYRLpk/wPB70FoL0g+iN1ysolXD01mmcICOwltjwQ2Nb8HxpVu6+HO4LXVy2esReQA4AJgI2BLVXV1MpfLkV/VEKseDW5pynjlRYFEIJo/S1RVyH2O0atYBxFBM+9B6zl0GqENRgd4653PIPUE1rBnAZg77wrgYRobB0OtvM4sRyC0m4kGKSEEpGo7M8jeYY9JsPxI/SyzaRgYZ8IOiyDBCaaqfOv5gG3cKIHNkPoy9REDmzs+4mKp2AKEoep4JDCxbPVykQgamwYdt1GQnZr9xKmbaEF4N6T2SmeV0oQu2avH5ugXkPmEPsVc9ybxF7T6Vyu0gO+qhtiD1w+yvDPrD4ApmPATVyrMkV/lkLqrTGp57j+YNOcMRPZHIgd0tdHMh6aMVG6xecI3HOquRTvupKcv9piptZxw5iJuvuMfHDD1Dd76RzNXXWVmej7fiomNFRE0tAukXiD/BmSElay6EjPP3mgJ33BuUXf9wwqwInuh4T0g+1+wakwSTRlELBhyB7rkQEyF84IBAq0QfxAZdmRF45CqX6DWcCPMlVuKWYFkyJv1p56H5BMQ2QeN3+O4ynruayyj+6Nr2AmMa8qLl14mimzBDBaWS89aVeep6kdlmnXlyKtqGujMkV+lEWsIVsMjyJD7kNqrkGHPY9Ve0DXrUbsdbTzM2XRKmkfuf2jjVEe+sns9dtTBNfx8ai0nnPkVQ0dsxZQpUzjvvPMAGDGiyObbt3FNNec7Gs8xTGpyzLgIas7uY0cRE5boRqDvCSciASSwYUWGuus1/vVg+BsQnUrRZLPc/9Bcfh6DZv6N3ToDu+USNP0WndFUIoIVOxhr2EvI0HuND7ugywQan2l+T73O8kkPuOAf6yW2LAeCIlrZYyBYET7rinPkReQY4BiAddbpQ129QYwENnLXkUjOdvdzapaP3rEYPdYiFDa3eZ9P+MOlw7no9KF8ucDHeuvG+PfXZrm91Vbld/dVbdA2E1Iny/4vF18DNDxjJDSzn5qsxtCOfe5TRNDqX0PLrykIdaswQkbtRsh+Af616dz07B2toZo1ldilzlUPxLIsqDkHO/VyEdlQyfsfmaozv8e4KhRNPGCif2ouznc9aJbivmdnVeIb5eikFJnKWSPMIzuP4uGfXY0x+tvnl2jjUREr8wajiDwPuE1ZzlbVxyo4R8U58o4Qyk1g4qwr6HvlxV6M27L3xUdC3Hxhimue8FHXoITC5m3IZqC+zkd9HUAHN1x3KdtstSHj1mtE7XhRcSI7/rApp2W3ggTR6BFI1Ul9V41zEAn0i9CPFfkRasXQtmvN6sK/IVJ9GhLcvOTrVLNo6/kmplkCGHlaAB/qX9/Edwe+Y0SO2q50oktsNLIvUnOee2REZJ/uupA98Q3vklvV3DdO1ZleVccTj0Nkf7Nh2ElgY0xWai8kAuH9za+xI4xeTDHXh70IGfYiIJB6Dk0+Z64l8w7YbUDOZDdatRDYBIkdgwTGl3zvPCpgZTbWqrrrcp6johz51Y7ARPPl1fxNrruuHEnjIosTdt+QKccsZus9Wnl1TpL77lVOOTtBW7vNfY+28exf47z8WK2R7NQs6v+OUcaTEEQOQqp+Dqm/QesFdBkEzUDHbSiKVJ+ywi+5NxLaEQnt2KfXaPv1xkCS7hUql4Xsx2jjoWjNBSYVv6chTMxCAam9pHAcsWlo8kWzv6BxOlPvpe7q7hlz6q+4ew2TaPJZpIexFvFD3TVo83FO1EnKSI8GJiDRKaZNYGO09kpo+QWucxeJAn5z/vCeSHhPc/2q5uYmoTwVQo9+YJD7rFeEG6RsjvxqSXBr8G/siAZ1GpUwSxaYmV9bs587Z6zBnTPWoFWb+Dfv8MpRzViWsN33w7wyay023SjYnfGXdaRHFei4Gc2+D7kFFM7cEhC/A606YeUs3RS/i5IbcZqFtqtc2iQh8RhafVbBKkQkAkMfgNRf0fRcxLcmRPbOj6uWIO6LRMvcIHshoW2g4Xk08TjYS00ySnDbvBWNFdkDO3c6tPeesYchephrVIeIgH/VcBEORgZzNMhybTCKyP4i8iWwNfCkiDzjPL+miMwGkyMPdObIzwNmqmpv+bbVDhExqchVp4BvQ6PkVn0qa49ft6BtjdSz58jtaZm/Ac2fjOGJu0ex6UaFBqKbJKTmQO5L98OadU/rXhkoO+5kj+o5vbGKpJKb7FMJ74JVcwYSO6wwASa0C27Trvse7WDSD26lqqqKUaNGMXXqVL7+2iwcxTcMq+oorJpfIaHtXV1PEjvK2eQMOxu3IYhMQapOLnOdHv2PGjdIJY8BYHmjQR5R1bVUNaSqI1R1D+f5r1V1co92s1V1Q1Udo6q/Wd5BryqIBM2XedgTWA1PYMWOZPqMwwhF8v2qoYjF0ectpU/hs2KBVSSLT2ImW7AHqins9luxl+yFvWRv7I4/o67VsQeYwCalj0vUyZ50ebMkUPw9KYNYtUjdNRgdc2NUZz0T55DjvmDrLaI8+vBNXHHFFbz88svstdde2BXO0ETEGPPhf0eGzESG/92JGvKiOlY4yqprrD36n+/tNoGLZp3Jht9bn3BVmNEbr82v7zmNXY48HwITQCr0U2rGyQrsbbSCEDsyb5anaqONh0P77434VPYjaPst2nQsg03oS6rPwdQWdDNmjjGuvcTsB+RdewSqfrl80TDhnZHhr0HVdEC595FWNt80xB8uamfnCTM45KDNuPbaa/nHP/7BRx+Vi2jt1bcVQwJj8yqUewwAdoWPAcBLNx+EbL7Lpmz+1hUFz3eq2NlLDnTKNZUiA3bnPm6n1oVj4Nqvw04+bbSZ/WsZQaTsv8n38yaNvkTmnfxIhwFGghOg4WG0/SanGIJlklBEjMxp1UmIVYsOmYm2X2OqfPtGIrHjkfAuBf098MAD3HXXXbz99tu0tDQzbuxwTjt5Pw6eegHYTWjbFU4R4CrjR45Ng9SrQJpMxqa2xinYoHG05ULq6oz7YrDd5DwqY6BiqCvBm1mvhEjtb3ANDStK5wcwh4kRTkF2Htp4mJlVp98piEoxL8tA5h9le58/fz4///nPmTBhAj6fjx133NG83G5Hs5+hmkZVufTSS1l77bWJRCL8YNsNeefFbbEXTsBevAt2/IGKDZz4x2DVXWFcRw2zsEa8hjX8Vayac7pkUSWwIVb99VjDX8Ma+pCroQb43e9+R1VVFVdd9kMevWM4O27VziFHXsMfLtsIXbqviSnXDiPW1P5HtPVsRzwKjjy4llfeSPDnma20tuX4+KN3OOecc9hpp534zncqLyPmMYgYxG4Qb2a9EiKBsWjNpdB6HiaKYFnWZQr2IjQ9p8v4FJ4oaIonlOHDDz9k9uzZbLXVVqTTJmHEbvm1CbETU+Xm8j+N4eLLn2TGZb9g3JoPcvWNX7H7Tz7lvb+uy8jhCWi9BLUXdlUvX1E8/vjjDK39Gl16KBBg5+3q+XphkqtvXMyJ03q7JJKQeALjhmnjR7vGuO2aEUw/bRFHnmy+wNtssyazZs1aodfg0U+oQm4VjQbxGDis6L7IiDkQ3oe+zbJ7kjHJIOkiFcc1h0odmplXcta7995788UXX/DAAw+w8cYbG+H8xJOYWOg4yUQbV1z1CGf+cgonHPwiu/7Ax8yb10BEuO62zuiMBLTfbKqqrEAaGhrQ+KP0FFCauEmYRUuKZAxKyJTYIsxLr8U5/oxF/OLoOl54aD3+cttPaWxsZP/99yeXW56SWx4Dhjez9vg2EIlAzTloZq4jBpXC3H/7MDvIvE1xpbcUNB+LEgDfCKi/GfGPNlEiyefQ9OtgjUCiPwHWcF6TM6XC6A4tfH1uktY2mwP2/JTOtOlY1GKv3WM8/WKci8/svCDLaIEXqcYCoLmFaNvvjHtCwhD9KRKbvpwx4/lVWF5/K8F3NnTXgLZzKe7/wyjS7Vtw1aP3svcedVx+ziijG133Wzbf5r+MHz+exx57jClTpizHmDwGhEHss/aM9UqOWDUwdBYav99kLPpGQGgP6PiD0U0WH2gAaMU9y79UIV8wxjXniEwdjjbMhsZDIfdfx88dQDtuhvobTLUTl2ovH81P4/PB2NH50SkbjQ0y87G27oaaLRpap5pCM/Og6VjQFjMmBdpvQJPPmpqSVo0p6lBE/7oYEp6MJmeBJnjhlTiznunglqsLo26yGR/vzwlz54VzsHw+5mXSfH/TyciwPyA+c7MaN24ckUiETz/9tE9j8BgEKNBPNRi/DTxjvQogVhVSNQ2qpnU/GdkVzX4JJFFrLVi8NQWVywkZQaHcfyki19IDBW01hXOzn9IdOZIBMmjzqWj1OU518PywuqZmm6qYhS+wliMZa6irtYgnlHRaCQYDRhgq8SgamZwnymR33GGy/DRL4SogBdlOtV1Bky+iVSdhVR1d5np6ENwKwnvy2cePcejx37DPHjGOOKjGuY4QkMS2ffxtVi2/P32kcW1mc4SJ8tRjb9HWXEWNIyI4b948EokEo0ePrvz8HoMEpXRRioHF81mvwoh/LcS/AZYVhpqLMAkdnf/ysJmF117ZLXlavkdIvYxrurc2Q+u5kPkAMxvP708EyH1ET/3mzhWnSfZRyH6Its1AF++Epl43bZLPQfvVziy+nDC/YnzfvzeqfIBmv8RuOhb7m02wF07EbrmQjpYlPH37S9x72SP8828mmbYxfRw/OmQB64zyc9d1nbplOXOtUsNNlx3BjJNGkUp034jWYn0W5P7H0VOn8/zzz3PPPfew3377MXr0aCZPnozHSoZiNhgreQwA3sx6NcGK7I36x6DxP0NuIYR2QCIHIFYMHToLbf8jJB+lpL9b0+CrI9n+Nc/eP4TXZtcyZpMER5+zAMsHpkpKjt6KOPV1Fm3tNrmc4vN1u0FaWm2iEYtAIEL3DSBpJjjNv4Dhf0c7biS/AnkFiB/Sb6PB76NLf+y4TWzQNPPfnMXpB3xALhcincwQDAfYYIvRzGn/E+l0nMcfXotYtOeNxgZNsskWH/OoJWiPZfLabEAoHOLtD+ay776PU1dXx3bbbcdll11GLBbr25g9Bgeez9pjMCCB7yC1hRXVxL8WUnc5mjkUbT7VGPMuYaHO6W8EotNIpdfg5L3uYsHnflIJH9/bsQ3bxjHW7ozbIEguB/P/m2HcBt0bd/+en2b8BgHchZlsNP02ZJdBoFE70MTTRuBfW+m8cajCJceMoqOVruuLtyd45O1b6PA388qstRje4PaVSLLZtjbBcJBUvFtwSURY1z+Wmf/6G5FYf9aY9xgwPGPtsTIggU2g4VnIfYmSNRuWyadAapHYIUhoB569/ikWfB4mlTAf6nDULmmoAbaZFKam2uLBx9s5+1Tji47HbZ54toPph9a6v0iz0HwSaJv78XKknnR+6Z7hL/3GT+Oi/I/8R/yDr1oXMuPcETQ255jzdvcsfuImIUIhCwhRM+J7HHT6MO67/BHEEizLQlU5/6FfeYZ6lWHgwvIqwTPWHnkYCc61TcyGfz2IHZF3/PVH3+oy1ACvPVXLtnsv5aU5xqh+/U2W1jabB58wf0/eOUY0anHGifVccnUj9XUW4zYIcvWNTdi2cuK04Zg4cRdJ0+WKuS505wwZkSUQtPP8zktZCMDpFy8saP/pm6MZvbYFEkQiB3HYeUPZ/fAdeevpdwlFg2yzzyRitZ67Y5VBgUEskeoZa48+UdtQg0j3BOTdV6t47rEwh5zzSV67g6Z/A8Cnb67P6CiccVI9tg2X/6GJpU05Jm0W4pn7RzFi1A/BEki+gNlADNJZNqu/FXNEYMtd2njx4e5Ik+1kMqFokBv/2sEaa31KQbWYwGZIzWWIz4R7jFh3GHv9fLd+HZfHIMKbWXusKuxzwg95fdZbpOKdkRnCHeeN44qDN+KX168HEkaiU5DAZkYTJPUGJJ9Bkg9x1ilDOOuU/DqJ2J9jDXkYzbxnfMxWLZp4AjJv9fvYReCQUxcy57labDtKMp4iEgsz9nvrM2yT0yFzA8Rnmhl9cGuoPhMrsF6/j8NjsDK40809Y+3RJzbZdjxHX34ot5xxN76AD7WVuuE1/OzCc7Dq1shrKxJEwtuj/jVM0olb0dcu4aXNILAZAGo3Q+Y9ylf/FiAE4R9B8kmnvWI+1u7JPmuNSXP324288tLxLF3QxMbbjmfizps45bN+CdW/rPzN8Fi1UCMXPFjxjLVHn9nvxD3ZbeoOzJvzCdX1MTacNMa1BFUn4t8A9a1takT2dG1IBIlOLWwf/Rkav9up+NJpdP2msIBETQECqQKrHonsjwTGo5mfoR23m+o4gW3A/spUkKd3AYUIsTV/weTpy1ta1GOVxMtg9FjViNVEmbT7hIrbS/2NaNORTlV3y8ivRqch4Z0K21q1MPQxtP0GSL3gpJEfAZH9i94UJLApUve7vOfszNHQfq2jP50CqYOqU7CinmaHRxE8n7XH6o741zJhgZn3wF4Kwe/mpZQXtPc1ILXnAucu8zmtwDiov85RDEwC4ZIrAI/VHFUvGsTDA5ywwGDls/F+PS+RFX5ej5UQb2bt4eHhMdhRdBDrkHvG2sPDwwM8iVQPDw+PlYZBHLrnSaR6eHh4YCbWamtFj0oQkR+KyEciMl9EznQ5HhKR+53jb4jI6FL9ecbaw8PDA5z6inZljzKIiA+4DtgT+A5wsIj0Lnk/DWhS1Q2Aq4ErSvXpGWsPDw8PB83lKnpUwJbAfFX9j6qmgfuAfXu12Re40/n9QWAXKRFbOmh91m+//fYSEfl8oMfhQgOwZKAHsYyszGMHb/wDzWAe/7rL20EbTc88rw82VNg8LCJze/x9k6re1OPvUcAXPf7+Evh+rz662qhqVkRagKEUeY8HrbFW1WEDPQY3RGSuqk4a6HEsCyvz2MEb/0Czso+/HKr6w37szm2G3NvZXUmbLjw3iIeHh0f/8yWwdo+/1wJ6lz3qaiMifqAWaCzWoWesPTw8PPqft4CxIrKeiASBnwKzerWZBRzu/P4T4EXV4imUg9YNMoi5qXyTQcvKPHbwxj/QrOzjX2E4PugTgWcAH3Cbqn4oIhcBc1V1FnArcJeIzMfMqH9aqk8pYcg9PDw8PAYJnhvEw8PDYyXAM9YeHh4eKwGesS6DiBwgIh+KiC0iRcOWyqWWDgQiMkREnhORT5yf9UXa5UTkXefRexNkhdPfabormgrGf4SILO7xnh89EON0Q0RuE5FFIvJBkeMiItc61/aeiGy+ose4uuIZ6/J8AEwBXi7WoMLU0oHgTOAFVR0LvOD87UZCVb/rPPZZccMr5NtI012R9OGzcH+P9/yWFTrI0twBlIo33hMY6zyOAW5YAWPywDPWZVHVear6UZlmlaSWDgQ901nvBPYbwLFUSr+n6a5gButnoSJU9WVKxPpiruXPapgD1InIGiXae/QTnrHuH9xSS0cN0Fh6MkJVFwA4P4cXaRcWkbkiMkdEBtqgV/Je5qXpAp1puoOBSj8LP3bcCA+KyNouxwcrg/WzvsrjxVkDIvI8MNLl0Nmq+lglXbg8t0JiIkuNvQ/drKOqX4vI+sCLIvK+qn7aPyPsM/2epruCqWRsjwP3qmpKRI7FrBJ2/tZH1j8M5vd+lcYz1oCq7rqcXVSSWvqtUGrsIrJQRNZQ1QXOUnVRkT6+dn7+R0T+CkwEBspY9yVN98tK0nRXMGXHr6pLe/x5M4PI514BA/ZZX93x3CD9QyWppQNBz3TWw4GCVYKI1ItIyPm9AdgW+NcKG2Eh/Z6mu4IpO/5ePt59gHkrcHzLyyxgqhMVshXQ0ulq8/iWUVXvUeIB7I+ZTaSAhcAzzvNrArN7tJsMfIyZkZ490ON2xjQUEwXyifNziPP8JOAW5/dtgPeBfzo/pw2CcRe8l8BFwD7O72HgAWA+8Caw/kCPuY/jvwz40HnPXwLGD/SYe4z927l3pwAAAGVJREFUXmABkHE+99OAY4FjneOCiXb51Pm8TBroMa8uDy/d3MPDw2MlwHODeHh4eKwEeMbaw8PDYyXAM9YeHh4eKwGesfbw8PBYCfCMtYeHh8dKgGesPTw8PFYCPGPt4eHhsRLw/7g9qoL10dZ7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mds[:,0],mds[:,1],c=selected_label==0)\n",
    "plt.colorbar()\n",
    "\n",
    "for i,f in enumerate(selected_features[:20]):\n",
    "    r = np.where(data.columns==f)[0]\n",
    "    plt.text(mds[r,0],mds[r,1],i,fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}