end-to-end-heart-disease-classification.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "e5c4ab3d",
   "metadata": {},
   "source": [
    "# Predicting heeart disease using ml\n",
    "\n",
    "this notebook looks into using various Python-bases ml and data science libraries in an attempt to buil a mchine learning model capable of predicting wether or not someone has heart disease based on their medical attributes\n",
    "\n",
    "\n",
    "approach:\n",
    "1. Problem def\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling\n",
    "6. Experimentation\n",
    "\n",
    "## 1.Problem Definition\n",
    "> Given clinical parameters about a patient predect whether a heart disease\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "> if 95% accuracy during the proof of concept ,the project will be pursued\n",
    "\n",
    "## 4. Features\n",
    "get different information about each of the features in your data.\n",
    "> maybe a subject expert "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0d810be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create data dictionary\n",
    "\n",
    "metadata= {'age':'age in years',\n",
    "          'sex':'male=1,female=0',\n",
    "           'cp':'chest pain type',\n",
    "          'trestbps':'resting blooad pressure',\n",
    "          'chol':'cholestoral in mg/dl',\n",
    "          'fbs':'fastig blood sugar 1= true,0=false',\n",
    "          'restecg':'resting electrocardiographic',\n",
    "          'thalach':'maximum heart rate achieved',\n",
    "          'exang':'exersice induced angina 1=yes 0=no',\n",
    "          'oldpeack':'ST depression induced by exercise ST segment',\n",
    "          'slope':'the slope of the peak exercise ST segment',\n",
    "          'ca':'number of major vessels (0-3) colored by flourosoby',\n",
    "          'thal':'3=normal 6=fixed defect 7=reversable defect',\n",
    "          'target':'1 oder 0'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c5027fb",
   "metadata": {},
   "source": [
    "## Preparing the tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7cf4215a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import everything\n",
    "# Regular EDA and plotting libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Model from Scikit-Learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model Evaluations\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, plot_roc_curve\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e813637",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f212193",
   "metadata": {},
   "outputs": [
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       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df = pd.read_csv('Data\\heart-disease.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a24c1b3",
   "metadata": {},
   "source": [
    "## Data Exploration (EDA)\n",
    "The Goal to find more about the data and become a subject matter expert on the dataset\n",
    "\n",
    "1. What question trying to solve?\n",
    "2. What kind of data do we have and how do we treat different types?\n",
    "3. What's missing from the data and how do to deal with it?\n",
    "4. Where are the outliers and why should care about them?\n",
    "5. How can I add, change or remove features to get more out of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16d710e0",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
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     "execution_count": 4,
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "37636db6",
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       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "898e69c0",
   "metadata": {},
   "source": [
    "##### Check for target inbalance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87db0c31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d417b1ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['target'].value_counts().plot(kind='bar',color=['salmon','lightblue']);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9778d620",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "93add96c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "81a90927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58cce0a1",
   "metadata": {},
   "source": [
    "### Heart Disease Freguency according to Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0de5411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea964951",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cp</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>41</td>\n",
       "      <td>69</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cp        0   1   2   3\n",
       "target                 \n",
       "0       104   9  18   7\n",
       "1        39  41  69  16"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.target,df.cp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "57e3b866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target to sex\n",
    "pd.crosstab(df.target,df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5223fdcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44.927536231884055, 75.0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*93/207,(72/96)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cdaf6b65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.0% chance to have heart-disease(to be classified as heart-disease based on the given data set)\n"
     ]
    }
   ],
   "source": [
    "print(f\"{100*(75+45)/200}% chance to have heart-disease(to be classified as heart-disease based on the given data set)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99ca0e69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot crosstab\n",
    "pd.crosstab(df.target,df.sex).plot(kind='bar',\n",
    "                                  figsize=(10,6),\n",
    "                                  color=['salmon','lightblue']);\n",
    "plt.title(\"Heart Disease Freq for Sex\")\n",
    "plt.xlabel('0 = No Disease,1 = Disease')\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend(['Female','Male']);\n",
    "plt.xticks(rotation=0);\n",
    "plt.yticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b4a8a38f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.cp,df.target).plot(kind='bar',\n",
    "                                 figsize=(6,8),\n",
    "                                 color=['salmon','lightblue','gold','green']);\n",
    "\n",
    "plt.title(\"Heart Disease Freq for CP\")\n",
    "plt.xlabel('0 = No Disease,1 = Disease')\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend();\n",
    "plt.xticks(rotation=0);\n",
    "plt.yticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d869f448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8983094d",
   "metadata": {},
   "source": [
    "### Age vs Max Heart Rate for Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dc939ccd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### create a figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Scatter with +ve examples\n",
    "plt.scatter(df.age[df.target==1],\n",
    "           df.thalach[df.target==1],\n",
    "           c=\"salmon\");\n",
    "\n",
    "# Scatter with -ve examples\n",
    "plt.scatter(df.age[df.target==0],\n",
    "           df.thalach[df.target==0]);\n",
    "\n",
    "# make plot more comm\n",
    "plt.title(\"Heart Disease in function of age and Max Heart Rate\")\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Max Heart rate')\n",
    "plt.legend(['1','0']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1f488004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of the age with a histogram\n",
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a3da0759",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f7851e19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m7Wnbd4eFB/HZrgRjA51BjXnr8OffuiqeqLaRhp2KBafoZk7RLir6/tgEwNb39wvz5do/1jP8t3W4OZq43Mi+v4btfrZfZmctLSV18zaCu3U6X6nOytl2/Z+/OY9W0U1oFX3iuo9u/drx7pyJjH/tdr777G87pBQXktpemO4KtASOnxh9AxAP3KmUGqC1fqRyYa31dOD46EM//NLi8xC1qiPJGYSHnJhuDAv2JSklC2cnR8IrnXoTFuLH0dSz/xS2Ltb+tpwNf9uuiQhrHkF2uqViWXZ6Nl5+XrX+XV7+5nOqXxcb/1jG1vm2/CHNIsittP7cDAuevlU/MXXz8qQ4rxBrWRkOJlONZbYvM/ZULIDUwhKC3E98+hjk5kJ6DadURXm782zXKB5eFk92ie0UiG5BZo7mF2Eptj1enJhBe38v/jqUZkx4bJ/IBVY6RSPAzZmM4ur5mzRwZ3y7pkzcsL3eT+GozNnHh5LMzIrHJRYLTmZzretbS0vZM20a/t2749vJuDfTVb8uZ+2ftvbfsEUElrQT/YYl3XLW+1/S/qP88O4c7nz5bjzsfCD2/ewV/DLPlr112whSki0Vy1JTLAQEVs2+a2ciiYfS+c8VLwNQVHSMG4a/zLw/nyEwyHzG+vbi7GOmOPPEdi/OysKllm3nXOqeL+lFJVVOrwpwPfW++3i7pjy1/sLYdxf9tILlv68BoHGLhhWnxwBkpVkw+3ufouaprf93M90NPBULILWgat8f6O5SZWbquChvd57pEsUjy2vo+8sfLz6SQXs/L/42qO939fWhqFL7LcrMwsVcdbu7+pgpyjipjI+54nHatni8GkXg4m3M/gowf94K/i2/pqNpy4ZkVJo9ykjLxucUbef7z+eTY8nj8Sdvq3F5645N+filOeRY8vAqv+nBpeZCvlbDKLXdAlHAQK31B1rrD4DLgFbAdcCQ09a0kz8WbOKmG2yf4nXrGEVObgHJqRY2bN1HVGQwjRoG4ORkYsRVPfljwUZDMnW/qg/3fzSB+z+aQKue7djy73q01hzekYCrhysNfGvfkbfs0fac6tdF5yv6VlxM3qxHe+IWrUNrzZGdB3Bxd602wFBKEdG+GTtXbgEg9t91NOvermJ5UX4hh+P20qxHO4y0PTOXCE83Qj1ccHRQDI4IYNnRzCplgtxdeKNXK55fu5tDeUUVzycXFNPOrwEuJtuu0TXImwM5BYbm35mdS5iHG8FuLjgqxcCQAFalVM0f6OrMlE4teXXrHhLzi07xm+qHZ+PGFKWmUpSejrW0lMz16/HpULvT8bTWHJg1C7eQkGoXrdtbr6v78Oi0CTw6bQJterVj0wLb/ndwRwJuHm54+dV+/8tKzWLWlC8YNeFmAsID7ZjaZsToGL7+4Qm+/uEJ+g5sy1+/2rLHbk3A09Ot2ilXMX3b8NeSKfw8fxI/z5+Eq6sT8/58BoA+A9qcsb69eEU2pjAllcI0W9tJXbcBv+jatZ1zqXu+nLzvDggJYFVq9X33hY7l+27BhbHvDrwupuJi8o592rF6/ga01uyLT8DNwxXzWQ7AC/IK2bV1H9Exbe2UuGbbs3Jp6OlGqLtt+w9pGMDyk/t+Nxde79WK59dV7/vb+lbq+wO9Scg1ru/3jmxEfkoqBeXtN2ntBgI7Vj2VLbBje46sXIPWmqy9+3F0c8O10kAlac16QnsYew3s0BtieGOm7WLyrn3bsuzvjWit2R13EHcPV3z8q7edf39dw7a1u3h4yi04OJw4DE1OTK+Yidu/K5HSY6U08DZuJk0Yr7YzIWGAB3D85D4PIFRrXaaUKj51tbqb+cGD9OnZCn+fBuxd+yEvvvMDTk62uP/7eiF/L9rM0AHRxC+fSkFhMXeP/xSwXWz56HMz+O2rpzCZHJj53RJ27E483arsonnX1uxev51373gRJ1dnrn/0popls56bxrWPjMbLz5vVvyxlxff/kpeVy0f3vU7zrq259pHRp61vhKZdWrN/QzyfjpuCk4szwx8eU7Fs7gvTGPbgaBr4eTPgtqv55Y0ZLPv6D4KahNN+SI+KcrtXbyOyY0ucT3NOtD2UaXhj0z7e79sWk4JfD6SwP6eA65sGA/DjvmTuat0QbxcnnuzUFIBSrbl14VbiM/P4NzGDrwdHU6Y1u7Ly+Wl/sqH5rRrej9/PG93a4AD8lZhKQl4hV0XY8v92KJmxzSLwcnbikbZNKl7zPSttFzE+G92caF9vvJ0dmTugCzP2HOLPxFTD8iuTicajR7Nr6lS01UpA7964h4aSsnQpAEH9+lGSnU3cyy9TVlSEUoqkhQtpP3kyhYmJpK9Zg1tYGLFTpgDQ8LrrMLczdiDbsltrdq7bweu3vYSzizMjxo+uWPb5M5/yn8dG4e3nzYqflrL0+0XkZubyzt1v0LJba0Y8NoqFX8+nICefnz6wTR47mEw8/NHjhmTv3ac1q5bt4IbhL+Pq6sxzL42qWPbIvdN5ZvJIAgJPPag4XX17UyYTUWNGEfvue2irleCY3niEhXJ0ia3thPa3tZ2NL75CWWERKEXiwn/p+uILOLq51VjXSFYNH2zfz+td2+CgbPvuwbxCrmxo23d/P5zMLVG2fffhNif23ftW2fbdZzo0p0P5vjtnQBdm7jnEXwbuuwDterQids0Onr7pFZxdnLh94om2P3XCdG6bMBKzvzcLf1jG/DmLyc7M5YU73qJdj1bcNsF2Dv/m5bG06doCFzfj+/43N9v6fgcFvx3v+5uU9/37y/t+5xN9f5lVc+u/J/r+ry4r7/stxvb9DiYTrW8Zxfo3P0BbrYT37UWD8FAOLVoGQMTAvgR0aEvatjiWPjEJk4sz7e8ae+K1F5eQHreTNreNOdUq7K5jr1ZsXr2Dh0e8irOrE/c+c6LvePXxz7h74o34BnjzvzfnERDkw7Pj3gdsp2D9544hrF28jWV/b8DkaMLZ2YlHXrzlrE9JExcXdarzVasUUupO4FlgCbazQ/sCrwCzgRe01k+cprp2ixh9msUXtsJDs5m7/+I9L/HGJpfz5e759R2jzm5vPpSuc1fUd4w6W39jDAP+XFnfMepk8fDejC0fOFyMZvXrxy8H/6rvGHV2TaNhWEr+rO8YdWZ2Hs64FUvqO0adTY/pz6C/Ls59999hvVme/Ed9x6izPsFX0O37i7ffXzcihkfWLKrvGHU2tcdAtmT8Xt8x6iza78qLYuTi1/whw65ky9j9/gW5TWo1E6K1/lwp9RdwC7AT+AdI1FrnA6cbgAghhBBCCCFEFbUahCil7gIeBsKBLUAPYDUw0G7JhBBCCCGEuATJhem1vzD9YaArcFBrPQDoCBh3uyAhhBBCCCHEJaO2F6YXaa2Lyr/h0kVrvVMp1cKuyYQQQgghhLgE1eW7py41tR2EJCqlzMDPwAKlVBZw1F6hhBBCCCGEEJeu2l6Yfl35f19QSi0GvIGL95ZRQgghhBBC1BO5JqT2MyEVtNYX7z07hRBCCCGEEPXurAchQgghhBBCiLqTmZDa3x1LCCGEEEIIIc4LmQkRQgghhBDCQDITIjMhQgghhBBCCIPJTIgQQgghhBCGknkA2QJCCCGEEEIIQ8lMiBBCCCGEEAaSa0JAaa3tvQ67r0AIIYQQQghA1XeA2ghp84xhx8dJ8S9fkNvEkJmQufsv3i9Xv7HJ5bhFjK7vGHVWeGg203b8U98x6uyeVkOYvnN+fceos3Eth7I+7Y/6jlEnXQOuYMK6RfUdo87e6Dbwom87H2+/ePfd+1oPYdTiZfUdo87mDOjLuBVL6jtGnUyP6c978Rdv23m4zRBuW3bxfi/yjL79GPTXyvqOUWf/DuvNnuzf6ztGnTXzvrK+I4haktOxhBBCCCGEMJCcjiUXpgshhBBCCCEMJjMhQgghhBBCGEjJPIBsASGEEEIIIYSxZCZECCGEEEIIA8k1ITITIoQQQgghhDCYzIQIIYQQQghhIKUuyK/uMJTMhAghhBBCCCEMJTMhQgghhBBCGEiuCZGZECGEEEIIIYTBZCZECCGEEEIIA8n3hMhMiBBCCCGEEMJgtRqEKKXuPOmxSSn1vH0iCSGEEEIIcelSysGwnwtVbU/HGqSUugG4E/ADvgSW2i1VOa01f077kd3rt+Pk4sT1j48hNKphtXJrfl3G6p+XkpmUzsQ5L+Ph7XlW9e1h2pt3M2xQR9IycugyeEKNZd6efCtDB0RTUFjCuMc/YUtcAgCD+3XgrRfGYjI5MGPOYt76+FdDMlemtWbJ/+ZxYGM8Ti7ODHnoZoKaVt922Snp/PnWDIryCghsEs7lj4zF5OTI4dg9/PrqdLwD/QCI6tmBHiOHGZp/8WfzOLBxO44uzlz+8JhT5M/g9zdP5B/+6C2YnGy7xeHYPSz+/EespWW4eXkw8pWHDc3/1Xs/sWX1DlxcnRn39GgiW4RXK/fx5K/Zv/Mwjo4mmrSK4I4JI3B0NNW6vr2kbIsn9qu5YNVE9O9N86uGVlmutSb2q7mkbo3H5OJMx3FjMTeOAOCfR5/B0dUV5eCAMjnQf8pThuWunO9ibT9aa5Z+Po+EjfE4ujgz5MGbCTzFvvvX2yeyD314bEV2gOQ9B5k78W2GPX47zXp1NCQ7QE58HIlz56CtVvx69yH48qr9RlFyEgdnzqDw8CFCrr6WoCEntS2rlV2vvoST2UzT+x8yLPdxmbFx7J09F62thPSJIWL45VWWFyQls/OLGeQdOkzkddfQ8PIhta5rBK01Kz6fx8FNtvYz6IGbCaih/eSkpPPPOzMozivAPzKcy8rbz4F121g7+w+UUjiYHIi54wZCWjU1LH9WXBwJc75DW60E9YkhbFjV9lOYlMTeGTPJP3SIiGuvJXSobfsXZ2ay94svOJadA0oR1LcvIZcNMiw3QFd/M/e3aoKDgj8TU5iz/0iV5YNCAxgVGWZ7HWVlTI3fx/7cAgDGt4uiR4APlpJj3LVii6G5j9NaM/3tn9mwyva+88ikUUS1rP6+8+ZzX7N3RyImRxPN2zTkgads71v5eYW8Nelb0pKzsJZZue7m/gy+qls9vBJhlFoNj7TWNwEzgVjgT+ARrfV4ewYD2LN+OxlH03jk82e55qFR/Pbh9zWWi2jdhNtevQ9zoG+d6tvDV98v5Zqxr51y+dAB0TRtHEzbvo/ywMTPeP9l22STg4Ni6ku3c82tr9Nx0HhGXN2Lls3CjIpdIWHjdixJqdz+ySQuu28Ui6Z9V2O55TN/pdPVA7j9k0m4eLoTt3B1xbKw1k25eepEbp460dABCMCBjdvJSkrjjmnPMfj+kSz8ZG6N5ZbN/IXOV/fnzmnP4erpTmx5/qK8AhZOm8u1z/yX2z58mqsm3GFkfLau2UHy4XTenvM0dz4xghlv/VBjuV5DOvHmtxN5ddYTlBQfY8lva86qvj1oq5VtM+fQ84kHGPj6JI6sXk/OkaQqZVK3xpOfksqgtybT4Y6b2Prl7CrLez/9KANefqZeBiBwcbefhE3bsRxN5daPJzHo3lEs+rTmfXflrF/peNUAbvt4Ei4e7sT/e2LftZZZWTnrFyKiWxkVG7C1ncOzv6XpAw/T6vkpZK1fR+HRo1XKmNw9CB85isDLhtT4O9IWLcQ1OMSIuNVoq5U938ym3aMP0vXFF0hdu578k/I7ergTddMoGg4dfNZ1jXBo03ayk1IZ89Ek+t8ziqXTa24/q7/6lQ5XDWDMR7a+f0d5+wlv14KR70xk5DsTGXD/GBZ//K1h2bXVyoFvv6XVww8RPWUy6evWU1Bt+3sQOWoUoUOqbn/l4ECjESOIfnEK7Z5+iuTFi6vVtScH4KE2TXhqQzx3LN/MwJAAGnm6VSmTVFDEo2tj+e/KLXy99zCPtY2qWDY/MZWnNmw3LG9NNqzaydHD6Uyf9xQPPDWCj1+fV2O5/pd3Ztr3T/LR7PGUFB/jn5/XAvDH9yuJiAziw2/H8+q0+/j8vV85dqzUyJdgKJkJqf3pWM2Ah4F5QAJwi1LK3Y65ANixJo7oQV1RStGwVWMK8wrJzcyuVi40KhyfIL8617eHlet2kmnJO+XyK4d05tt5ywFYt3kv3l7uBAea6Rodxb6EZBIOpXLsWBnf/7aaK4d0MSRzZfvWxdKqfzeUUoS0iKQ4v5C8k7ad1prDsbtp1isagNYDurNv7TbDs9Zk37pYWg+w5Q89Tf5D2/bQvHc0AG0GdmPvmlgAdi7bSLOeHfAKsA1s3c0NDM2/cXkcMZd3QSlFVNvG5OcVkpWeU61cdM/WKKVQStG0dQSZqdlnVd8esvYl4BEUgEdgAA6OjoT16ELyxq1VyiRt2krDmB4opfCNasKxggKKLMbsm7VxMbef/etiaTWg6r6bf5b77tY/lxLVMxr38llloxQkHMAlMACXAFvb8enalextW6qUcfLywqNxJMpkqla/JCuT7NhY/HrHGJS4qpz9B3ALDMStPH9gty5kbK7a9p29vPCKbFwtf23qGuHAulhalPf9wS0iKTlF+zkSu5umPaMBaDmgOwfW2dqPk5tLxZewlRaXAMZ9IVvegQO4BgTiWr4N/bt2JWtL1W3o5OWFZw3b39lsxrNRIwBMrq64hYRQYrEYFZ2W5gYcyS8iqbCYUq1ZnJRGr5M+WN1uySWvtKzi/wGuzhXLYrNyyKnnA/a1y+IYOLwzSilatmtEfm4hmTW873Tt3arifat56wjSUy22BUpRWFCM1prCgmIaeLljMl24B9Di3NX2r/sbMElrfTfQD9gDrLdbqnI5GRa8/c0Vj739vclJr/2ByrnWt6fQYF8SkzIqHh9JziQ02JfQYB8Sj1Z6PimDsCAfw/PlZVpo4H9ivZ5+5moHYUW5+bh4uOFQ3pk3OKlM0q4DfPXIq/w05WPSD1X9JNze8jKyaVDpb9/A30xeRtX8hbn5uFbKX/k1Zh1NpSivgO+eeZ+vHnuD+EXrDMsOkJWeg1+gueKxb6CZrNO03dLSMlbM30D7Hi3rVP98Ksqy4OZ7ou24+fpQlGU5Y5nCTFsZhWL16++z5LlXSFi03IjI1VzM7Scvw4Kn39ntu57+ZvLLX19ehoV9a7bRbqjxB/IlWRacfU4ceDmbfTh2Uts5nSNzvyPs+v9APX3yV2Kx4FKpXbv4+FBcywPZc6l7PuVnWvCs1Pd7+JmrDUKKcvNxrtR+PPxOtB+A/Wu28u2DL/LHy9MY+MAYY4JzfBtWaj8+ZootWWf9e4rS08k/fAjPyMjzGe+0/F2dSSsqqXicVlSCv6vLKcsPaxjEujSLAclqLyM1G/8gc8Vjv0BvMlJP/761+K+NdOppe9+6ckRvDiekMHb4ZB646S3GPXYtDg4yCLmU1faakG5a6xwArbUG3lZKnfJCBaXUOGAcwKeffor5soi6pdM1/nLj6tuRquHTIa11xSdIVZ83ItGZV3pyMn2aYIFNw7lz+hSc3Vw4sCGe3179jNs/mXSeQ55aTdmqbdrTlLGWWUndd5gRLz7AsZJjzJ7wLiEtGuMbFmiHtNXVmP805We8/QMtOzShZYcmdap/PtXYLk7e+KcpEjNpPG4+Zoqzc1j1+vt4hgbj37LZ+Q96Ghd3+6lp455Uosa/ke2fpZ/Po/fYq3Gol08gz5z9VLK3bcWxgRfujRqRu2vX+Y1VW+fSV9dHP1+D07WNSoVOW6ZJjw406dGBo/F7WTf7d65+4cHzmvGUauz3zq7nKysqYvcn02g8ciSObm5nrmBHp3qPjfb1Zlh4EI+Uz7xeKM72kOvj1+fRpmMT2na0vW9tWrOLJs3CeOXje0lKzOC5Bz6lTXQT3D1d7RO4nsktems/CHFTSr0LhGmtL1dKtQZ6YpsRqUZrPR2Yfvzh3P1/1zrQ2t+Ws+Fv27mlYc0jyE63VCzLTs/Gy8+r1r/Ly998TvXt6UhyBuEhJ04hCwv2JSklC2cnR8JDKz0f4sfR1LP/JKcutvy5jLh/VgEQ1CyC3PQT683LsODh612lvJuXJ8X5hVjLynAwmcjNsOBZXsbF/UTnHdmlDYs+nUthTh5uXvY7vWPzH8uIXWBrO8FREeRW+tvnptecv6hS/rwMCx4+tjKefmbcvDxwcnXBydWF8DZNSUs4YteDyAXzVrC4/JqOJq0aknF8ihrITLVg9veusd6PX8wn15LPHS+PqHjON8C71vXPN9usxom2U5iZhau56rpdfc3Vy/iYbfXL/3Xx9iKkSzSWfQmGDEIu5vaz9c9lxC0o33ejIsjLqLrvevqcft/Nq/T6Uvcd4q+3ZwBQlJtHwsbtOJgcaNq9g12yV+bs40NJVmbF4xJLFk5mc63q5u/bR/a2LeTExWItPUZZYREJX/yPxnfcZae01Tn7mCmu1K6Ls7JwqWX+c6l7rmL/Wsb28vYTGBVBXqW+P79Suz7O1cuTkkrtJ7+G9weA0DZRZH+Qbve+/zhnHx+KMyu1nywLzmexDa2lpez6ZBr+3bvj16mTHRKeWnpRSZXTqwJcnckoLqlWrkkDdx5v15Sn1m+v99OvAH7/fgXzy6/paNa6IekploplGanZ+AbU/L7z7WfzycnK44Gnbqt4buHv6/nP2IG202Ab+hMU6svhg6m0aFPHD7LFBa+2w7AZwHzg+NV+u4FH7JCH7lf14f6PJnD/RxNo1bMdW/5dbzt/eUcCrh6uNKihozuVlj3anlN9e/pjwSZuuqEPAN06RpGTW0ByqoUNW/cRFRlMo4YBODmZGHFVT/5YsNGQTNHD+1ZcSN60e3t2LFmH1pqkXQdw9nCtGGAcp5SiYbtm7Fm1BYDti9fStFs7APKzcio+xUnenYDWGtcGHnbN3/GKvoyd+iRjpz5JVI/2bF9sy3901wFcTpE/ol0zdq+05Y9ftI6o7rb8Ud3bcWT7fqxlZRwrLiFp90H8woPsmn/wDTG8MmM8r8wYT+c+7Vjx9wa01uyNS8Dd0xUf/+oD6MW/rSF23S7uf+HmKtPWnWLa1qq+PZibNCI/OZX81HSspaUcWbOB4E7tq5QJ7tSewyvWoLUmc+9+nNzdcDV7U1pUzLHCIgBKi4pJjd1Bg4ahhuS+mNtPh+F9GfPuRMa8W77vLj6x77q4u1Y7QFRKEd626r7bpHzfvf3Tydwx3fYT1TOaAXffaMgABMC9UWOKU1MpTk/DWlpK1vr1eLev3bpDr7uetq+9SZtXXqPxneNo0LKFoQMQAK/IxhSmpFKYZmv7qes24Bddu/znUvdctRvWt+Ji8shu7dlV3vcn7zqA8ynaT1jbZuxbvQWAnYvXEtnV1n6yk9Iq+v60fYexlpbZve8/zrNxY4pSUykq34bp69fj06F221Brzb6Zs3ALCal20boRdmbnEubhRrCbC45KMSAkgFWpmVXKBLo680LHlry6dQ+JBUWGZ6zJlSNi+OCbx/ngm8fp2a8ti/7ciNaanbEHcfd0xbeG9535P69h05pdPPHSLVXetwKCzGxdb/tsOysjl8RDqQSH+Varf8lQDsb9XKBqOxPir7Weq5R6CkBrXaqUKrNjLgCad23N7vXbefeOF3Fydeb6R2+qWDbruWlc+8hovPy8Wf3LUlZ8/y95Wbl8dN/rNO/ammsfGX3a+vY284MH6dOzFf4+Ddi79kNefOcHnMpvf/m/rxfy96LNDB0QTfzyqRQUFnP3+E8BKCuz8uhzM/jtq6cwmRyY+d0SduxONCz3cZGd25CwcTtf3jMFRxcnhjx0c8Wyn6Z8wuAHbsLT15uYsdfw59tfsvKb3wlsEk6bwT0B2LNqM1v/XoGDyQFHZ2eGj7+txlPN7Je/Nfs3xPP5PVNwcnFm6IMnzkv+cco0htw/Gk8/b/rcejV/vDWDld/8QWCTcNoO7gGAX8NgGndsxcyHXkM5ONBucA/8GxlzMAwQ3bMVW1fv4PGRr+Ds6sS4p0dXLHtz/HTumjgSH39vvnzrB/yDfHjh7vcB6NqvHdfdPvS09e3NwWSi/dhRrH7zA7TVSkTfXniFh3Lg32UARA7qS1CHtqRsiWPh+EmYnJ3p+N+xABTn5LBuqm1f0FYrYT27EtS+jWHZj7uY20/j8n135r22fXfwgyf23Z9f/ITL7j+x7/719pes/vZ3AiLDaXNZT0PynY4ymQgfeRP73p+Ktmr8evXGLTSM9GVLAPDv259j2dnsevUlyoqKUEqRtmghrZ6fgqmeT50BW/6oMaOIffc9tNVKcExvPMJCObrEdkf70P79KMnOZuOLr1BWWARKkbjwX7q++AKObm411jVao85tOLRpO9/cZ2s/Ax840X5+f+kTBtx3Ex6+3vS45RoWvPMla8vbT6vy9rNv9RZ2LV2Hg8mEo7MTQx6/3bC+X5lMRN40mh1Tp6K1lcDevXEPCyW5fPsHl2//2JdepqzItv2TFi6kw5TJFCQmkr5mDe5hYWydPAWAiOuvw6ddO0OyWzV8sH0/r3dtg4OCvxJTOZhXyJUNgwH4/XAyt0RF4OXsxMNtbKcvlWm4b5XtwvtnOjSng6833s6OzBnQhZl7DvFXYqoh2Y/r0rsVG1bt4L/Xv4qLqxOPPDeqYtnzj3zGQ8/ciF+ANx+9Po/AYB/G32l73+o1oB2j7xrCqDsHM3XKHO4f/SZaw+0PXIm32dibYwhjqdOd119RSKklwA3AAq11J6VUD+B1rXW/WqzjrE7HutDc2ORy3CKMO4A73woPzWbajn/qO0ad3dNqCNN3zq/vGHU2ruVQ1qf9Ud8x6qRrwBVMWLeovmPU2RvdBl70befj7Rfvvntf6yGMWrysvmPU2ZwBfRm3Ykl9x6iT6TH9eS/+4m07D7cZwm3L7P5VZHYzo28/Bv21sr5j1Nm/w3qzJ/v3+o5RZ828r7wwLv49g6adpxp2Jdi+jY9ckNuktjMhjwG/Ak2VUiuBAOA/dkslhBBCCCGEuGTVdhDSFBgGNMQ2I9L9LOoKIYQQQgghyhl5ivqFqrZXqzxXfoteH+AybHe++sRuqYQQQgghhBCXrNoOQo5fhH4FME1r/QvgfJryQgghhBBCiBooHAz7OWMWpS5XSu1SSu1VSk2sYbm3Uuo3pdRWpVS8Uur287ENajsIOaKU+hS4EfhTKeVyFnWFEEIIIYQQFxillAn4CNtlF62B0eXfB1jZ/cB2rXUHoD+2Ly0/58mI2l7XcSNwOfCW1tqilAoBnjjXlQshhBBCCPH/jbpwvr+jG7BXa70fQCk1B7gG2F6pjAYaKNuFLJ5AJnDO35ZZq0GI1roA+LHS4yQg6VxXLoQQQgghhLAfpdQ4YFylp6ZrraeX/z8MOFxpWSK2G1BV9iG2u+QeBRoAI7XW1nPNJXe4EkIIIYQQwkgG3h2rfMAx/RSLawpy8neYDAW2AAOx3TF3gVJqeflNq+rsgpkLEkIIIYQQQhgqEdtXcBwXjm3Go7LbgR+1zV7gANDyXFcsgxAhhBBCCCGM5GDgz+mtB5oppSLLLzYfhe3Uq8oOAYMAlFJBQAtgf11edmVyOpYQQgghhBD/D2mtS5VSDwDzARPwhdY6Xil1T/nyacCLwAylVCy207ee1Fqnn+u6ZRAihBBCCCHE/1Na6z+BP096blql/x8Fhpzv9cogRAghhBBCCCMZeGH6hUquCRFCCCGEEEIYSmZChBBCCCGEMJLMhKC0PvlWwOed3VcghBBCCCEENX/vxQWnea9phh0f7151zwW5TQyZCfly93wjVmMXtzcfyrQd/9R3jDq7p9UQ3CJG13eMOis8NJsJ6xbVd4w6e6PbQGbuuTjb/63NhjJuxZL6jlFn02P68+T6f+s7Rp293nUQnWcvr+8YdbZxdB8sJX+eueAFyuw8nIVHLs78l4UNZ+zSpfUdo85m9evH6tQ/6jtGnfUMvIKlSRdn2wHoFzKcRm9evO+7B58YWN8RakcuiJBNIIQQQgghhDCWXBMihBBCCCGEgbRcEyIzIUIIIYQQQghjyUyIEEIIIYQQRpKJEJkJEUIIIYQQQhhLZkKEEEIIIYQwkoNMhchMiBBCCCGEEMJQMhMihBBCCCGEkeTuWDITIoQQQgghhDCWzIQIIYQQQghhJJkIkZkQIYQQQgghhLFkECKEEEIIIYQwlJyOJYQQQgghhJHkFr0X9iBEa83C6fPYt3E7Ti7OXPHwGIKjGlYrZ0nO4Jc3Z1CUW0BQ03CueuwWTE6OrP3xX+KXbADAWmYlIzGZh75+BbcGHoZkX/K/eRzYGI+TizNDHrqZoKbVs2enpPPnWzMoyisgsEk4lz8yFpOTI4dj9/Drq9PxDvQDIKpnB3qMHGb33MdNe/Nuhg3qSFpGDl0GT6ixzNuTb2XogGgKCksY9/gnbIlLAGBwvw689cJYTCYHZsxZzFsf/2pY7uNStsUT+9VcsGoi+vem+VVDqyzXWhP71VxSt8ZjcnGm47ixmBtHAPDPo8/g6OqKcnBAmRzoP+Upw/NrrVkwfR77NmzH0cWZqx45ddv/+Y0ZFOYWEBwVztXlbb8ov5Bf35pFTloWVquV7tcNpMPgHoblz4yNY+/suWhtJaRPDBHDL6+yXGvNvtnfkREbh8nZmRZ33EaDRrbtn7jgX5KWrQA0IX1jCB98mWG5j0vZGk/sV9+jrZpG/XvR/Oqa2s/3pGyJx+TiRKdxYzFHRpxYbrWy5LnXcPUx03P8fYZm7xniw/hOTTApxc/7kpmxI7HK8n5hvtzbvjFWrSmzat7etJ8t6Tm1qmsErTXvvPYTq5bvwNXViedeGk3L1tXb/kuT5rAj/jBoTcPGAUx66Sbc3V3IyS7gpUlzOHI4HWcXJ56dMoqmzUIMzf/9hz8Rv3YHzq5O3DJhNBHNq+df8tNyFs9bRvrRdF7/6UU8vT0B2L1lL58+9zl+wb4ARPdpz/CxQ6vVtxdLXBwHv/sObbUSGBND6LCq7zuFSUnsnzmT/EOHaHjttYQMGQJAcWYm+774gmM5OSilCOzbl+BBgwzLfZzWmm/e+4lta3bg7OLMXU+PpnGL8GrlFs5bzj/fLyP1SAYf/DaFBmbb9i/IK+TTF78hMyWLsjIrw0YNoM8V3QzN/90HPxG7xtZ+bps4mkY1tJ9FPy7n3x+WkXY0nbd/frEiP8CuzXv57sOfKCsrw9Pbkyfee8CQ7P0a+/L8oGaYlGLOtiQ+WXewxnLtgxvw85guPPBbHH/uTsPF5MDc0Z1wNikcHRR/7k7j3ZUHDMks6letByFKKWegJaCBXVrrErulKrd/43ayjqZx96fPcXRXAvM/mcutbz9erdySGb/Q9Zr+tO7bmb8/+o6tC1bTaXgful8/iO7X2zrBPetiWf/LEkMGIAAJG7djSUrl9k8mkbw7gUXTvmP0m+OrlVs+81c6XT2AFn06s/CTOcQtXE2HYX0ACGvdlGufvceQvCf76vulTJs5n/+9W/MB1NAB0TRtHEzbvo/SrWMU7798J32veQ4HB8XUl27nijGvcCQpgxW/vczvCzayc88Rw7Jrq5VtM+fQ68mHcPP1Yemk1wju1B6vsBMHIqlb48lPSWXQW5PJ2neArV/Opt/kJyuW9376UVwaeNb06w2xb8N2Mo+mcc90W9v/++O53PZO9ba/qLztt+nXmb8+/I4tC1bTeXgfNv6xHP+IYG58/m7ys3P59O6Xadu/CyYn+3/uoK1W9nwzm/aPP4KLjw+bXnwVv+j2eISGVpTJjI2jICWVbq+8SO7+A+z56hs6PfsU+YlHSFq2gk7PPoWDo4lt776Pb/t2uAcF2T135fxbZ35H74kP4eZrZsmk1wnuXLX9pGyNJy85lcvefoGsfQlsnTGHfpNPDNb3/b2YBqHBHCssMiw32D5Ym9i5KfctjiOlsJivhkSz9EgmB3IKKsqsS7Gw9K9NAESZ3Xm9dytu+GNjreoaYdXyHRw+mMYPfzxN3LaDvPHSD3zx7aPVyj0y4Vo8PV0BmPrGz3z/7XJuvesyZvxvIc1bhvLGe3eQsD+FN1+Zx0f/M24gGL92B2lH0njhq6dJ2HGQOVN/YMLH1fM3bRtJu55tmProh9WWRbVrwr2v/NeIuFVoq5WEb7+l5aOP4uzjQ/wrr2Du0AH3Svuuo4cHjUaNImvz5ip1lYMDjUaMwKNRI8qKioh76SW8WrWqUtcI29bsICUxnddnP82+7QeZ9fYPTJr+SLVyzdpF0qFXG1576KMqz//740rCGgfx6Ot3kZOVx1NjXqXnkE44GtB3AsSt3UFKYhovffM0B7Yf5Jt3f+DpT6q3n6h2kbTv2Ya3H6nafgpyC/l26g889Mbd+AX5kJOVa0huBwUvDm7BmLmbSc4t5tdburBwXxp7MgqqlXuqbxTLEjIqnisuszL6u80UHCvD0UHxw+hOLNmfweakHEOy1xu5RW/trglRSl0B7APeBz4E9iql7P6x/J41sbQd2A2lFGEtIynOLyQvM7tKGa01B7ftoWXvaADaDerGnjWx1X7XjqWbaN23s70jV9i3LpZW/W3ZQ1qcOvvh2N006xUNQOsB3dm3dpthGU9n5bqdZFryTrn8yiGd+XbecgDWbd6Lt5c7wYFmukZHsS8hmYRDqRw7Vsb3v63myiFdjIoNQNa+BDyCAvAIDMDB0ZGwHl1I3ri1SpmkTVtpGNMDpRS+UU04VlBAkSX7FL/ReLvXxtKuUtsvOk3bbxUTDdja/u7VtravgJLCYrTWHCsswa2BOw4mYy4By9l/ALfAQNwCbNs/sFsXMjZX3f4ZW7YS3Mu2/b2aNqG0oJBiSzYFScl4NY3E5OKMMpkwt2hO+qYthuQ+LmtfAp5BAXgE+uPg6Eh4j87V2k/yxm1ExHQvbz+RHMsvoCjL9vcpzMgieUscjfr3NjQ3QBvfBhzOK+JIfhGlVs0/h9LoH+5bpUxhqbXi/24mE1rXvq4Rli2OY9jVXVFK0a5DY3JzC0lPq75vHh+AaK0pLj6GKn9DP7AvmS7dmwPQuEkQSUcyyUg35kAMYNuqOLoPtuWPbN2YwrxCsjOq52/YLLxituNCkXfgAK6BgbiW77u+XbuStbVq23fy8sKzcWOUyVTleWezGY9GjQAwubriGhLCMYvFqOgVNq+Io/flXVBKEdWmMQV5hVjSqx/MNmoeTkBI9e2vFBQV2PrO4sJiPLyM6zsBtqyMo+dQW/tp0sbWfiw1tJ+IZuH415B/3b8b6dinPX5BPgB4+TSwe2aA6BAvErIKOJxdxDGr5redqQyOCqhW7rZO4fy1J5X0gmNVni84VgaAo4PCyeSANiS1qG+13bPeBgZorftrrfsBA4B37RfLJjcjmwb+5orHDfzM5J60Mxbm5OPi6YZDeYdYU5ljRSXs37SDFr062DtyhbxMCw38fSoee/qZqx1EFuXm4+JRNXvlMkm7DvDVI6/y05SPST+UZEzwWgoN9iUx6cQnGUeSMwkN9iU02IfEo5WeT8ogLMinpl9hN0VZFtx8T6zTzdeHoizLGcsUZtrKKBSrX3+fJc+9QsKi5UZEriYvIxuvWrR910rtx8v/RJnOV/Yl/XAy7499js8eeJXB425AORjzRlpiseBSadu6+PhQfNLBSHGWBRdf30plzJRYsnAPCyV79x6O5eVRVlxCxrZYijMzDcl9XOFJbcPV14fCrOzqZfxOLmMBIPbrH2g7+rp6+ZQr0N2FlILiiscpBSUEuLlUKzcg3I95V3TmvX5tmLx291nVtbe01GyCgs0VjwODzKSl1vwBwZRnZzOs/yQSDqRy4022GeRmLcJYstD2YU587EGSk7JITbHYO3aF7PRszIHmisfmADOW9LP7gOPA9gReuetNPpr4KUcPGNf3l1gsOFfaL53NZo5lZZ317ylOT6fg0CE8IiPPZ7xayUrLwbfS9vcJMJN1Ftt/0A0xHD2YwiPXvsCzt73JTQ9dh4NBfSeAJS0bnwBzxWOfADOWGgbhp5JyOI2CvALeevhDXhr3Nqvnr7dDyuqCPV1Iyj3RfyTlFhPsWbX/CPJ0ZmizAL7eUv3MCAcFf97alU33x7A8IZMtl/osCNg+LTTq5wJV2/nFVK313kqP9wOppyqslBoHjAP49NNPcerfqI7xqo+Fq7+vn7nM3vVxhLWKNOxULICKjxcrOTm6rqHMcYFNw7lz+hSc3Vw4sCGe3179jNs/mXSeQ9adqqFVa60rPo2s+rwRiarmqObkXKcpEjNpPG4+Zoqzc1j1+vt4hgbj37LZ+Q96GjW/hpPKnKbt79+0g6Am4Yx55UGyktKZ/dxHNGzTBBd3NzukPUlt/t41NgqFR2gIDYcNZdvbUzG5uODZsGG1T1ztro75lVIkb47FxcsTc2QEadt3n/9sZ1DTe01NL2dxYgaLEzPoGODFve0bcd/iuFrXtbea+8Wa30UnvTSasjIrb7/6Iwv+3sxV13Vn7J2DeOe1n7j5P2/StFkIzVuGYXI07iCypvw19Yun0rBZOFNmT8LVzYW4NduZPukLXvjqmfMZ8dRq03eeQVlREbunTaPRyJE4uhnQ35yk5u1f+/pxa3cRERXGk+/dR+qRdN587FNadGiCm4freUx5ajX367V/AWVlVg7uSuSxd+6lpPgYr9//Hk1aNyKoYeD5jFkrJ7+W5wc257Wl+7DW0MysGobPXI+XiyPTr21Hc38PdqfnG5RU1JfaDkLilVJ/AnOxvS+NANYrpa4H0Fr/WLmw1no6MP34wy93z691oI1/LGPr/NUAhDSLIDfdUrEsN8OCp693lfJuXp4U5xViLSvDwWSqscz2ZcacirXlz2XE/bMKgKBmEeSmn/gEKS/DgkdN2fNrzl75YDGySxsWfTqXwpw83Lzq7zqFyo4kZxAe4lfxOCzYl6SULJydHAkPrfR8iB9HU8/+k7RzYZvVOLHOwswsXM1Vt72rr7l6GR+zrX75vy7eXoR0icayL8GQQciG35expbzthzaLIOektt/gpPbj7uVJUaX2k5N+ov1sW7iWnv8ZbDtdKDQAc5AfGYdTCW1R1w8Eas/Zx0xxpW1bnJWFi9lcpYyLr0+VGY7iLAvO5WVC+sQQ0icGgP3zfsLFx9iZNLeT2kZRZhZuPiftu74+FGZULeNq9ubIuk0kbYoleWs81mOllBYWsuHjL+ly3+2GZE8pKCbI/cSnj0HuzqQXFp+y/Oa0HMI93TA7O5513fPp+9kr+GWere23bhtBSrKlYllqioWAQK9T1jWZHLhsaDRfz1jMVdd1x9PTlUkvjQZsB6TXXf4ioWF+p6x/Piz9eQUr/7Dlb9QiAkvqifyWNAvefqfOf7LKB7tte7Tmu/d+IC87r+LCdXty9vGhpNJ+WWKx4HTSvns61tJS9kybhn/37vh26mSHhDVb+OMKlv62BoDIlg3JrLT9s9IsmP28T1GzuuV/ruOKmwehlCIoPICAEF+SDqbQpLX9+s7FP61g+e+29tO4ZQRZaZaKZVlpFrz9a99+fAK88fT2wMXNBRc3F5p1aMrhfUftPghJzismpMGJ/iOkgQspeVUvHW4f1IAPrmoDgK+bEwMi/Wynfu5NryiTU1zK6sNZ9I/0vfQHIXJ3rFqfjuUKpAD9gP5AGuADXAVceT4Ddb6iL3e8/yR3vP8kzXq0J27ROrTWHNl5ABd312oDDKUUEe2bsXPlFgBi/11Hs+7tKpYX5RdyOG4vzXq0w96ih/fl5qkTuXnqRJp2b8+OJbbsSbsO4OxRc/aG7ZqxZ5Ut+/bFa2nazZYzPyun4hOd5N0JaK1xNXIm5wz+WLCJm26wnf7QrWMUObkFJKda2LB1H1GRwTRqGICTk4kRV/XkjwUbDc1mbtKI/ORU8lPTsZaWcmTNBoI7ta9SJrhTew6vWIPWmsy9+3Fyd8PV7E1pUXHFxcSlRcWkxu6gQUNjLqzscmVf7vrgSe764Ema92xPbC3afqN2zdixYgtga/vNy9u5V4APCVt3AZCXlUNGYirmYPseiB3nFdmYwpRUCtNs2z913Qb8oqueCunXoQPJq2zbP2fffhzd3XApHyiW5Nim4YsyMknftJnA7l0NyX2cuUkj8iq1n8Q1G2toP+04tGJtefs5gKO7G64+3rQZeS2Xf/AKQ6e+RJf778C/dQvDBiAA2zNzadjAlVAPFxwdFEMiAliaWPV0tnDPEwe5LX08cHJQWEpKa1XXXkaMjuHrH57g6x+eoO/Atvz163rbHci2JuDp6YZ/QNW2r7Xm8KG0iv8vXxpPo0jbQVZuTiHHjpUC8Mu8NUR3blpx/Yi99Ls2hqc/e4KnP3uCDjFtWbvAlv/A9gTcPNzwPouD4OzME31/wo6DaK3x8DKm7/ds3Jii1FSK0m1tP3P9enw61O40Zq01B2bNwi0khJDBg+2ctKrLro/hxS/H8+KX4+nUpx0r/96A1pq98Qm4ebpiPouDeL8gH7ZvtM1iZmfmknQolYBQ+/adA66LYdLnTzDp8yeIjmnL6vm29rM/3tZ+zmYQFR3Tjr2x+ykrLaO4qIQD2w8SEmH/G3tsTcol0sedht6uODkormoZyIJKgwuAmM9WEzPd9vPn7jSeW7iLf/am4+vmhJeL7TNxF0cHYhr5sjfD2BtiiPpR25kQB+BhrbUFQCnlA7yttbbru2vTLq3ZvyGeT8dNwcnFmeEPj6lYNveFaQx7cDQN/LwZcNvV/PLGDJZ9/QdBTcJpP+TErUh3r95GZMeWOLsae25zZOc2JGzczpf3TMHRxYkhD91cseynKZ8w+IGb8PT1JmbsNfz59pes/OZ3ApuE02ZwTwD2rNrM1r9X4GBywNHZmeHjbzurKdlzNfODB+nTsxX+Pg3Yu/ZDXnznB5zK7w7yv68X8veizQwdEE388qkUFBZz9/hPAdtU8KPPzeC3r57CZHJg5ndL2LHb2Nt8OphMtB87itVvfoC2Wono2wuv8FAO/LsMgMhBfQnq0JaULXEsHD8Jk7MzHf87FoDinBzWTbW9Fm21EtazK0Ht2xiaH2xtf++GeD75r63tX/nIibb/3fPTGP5Qedu//Wp+fv1E2+9Q3vZjRl3O71O/5rP7X0VrGHj71bgb8EkqgDKZiBozith330NbrQTH9MYjLJSjS5YCENq/H77t25IZG8u6p54tv0XvrRX14z/+lNK8fJTJRLMxo3HyMHbw7WAy0f7Wkax640O01Uqjfj2rt5/otqRsjWfB48/j6OxMx3G3GJrxVMo0vLFhHx/2b4tJKX7Zn8L+nAJuiAoGYN7eZAY19OeKyEBKrZriMitPrdx52rpG692nNauW7eCG4S/j6urMcy+Nqlj2yL3TeWbySPz8GzDlmW/JzytGo2nWPJQJz40AIGF/Ci888w0mBwcimwbxzORRp1qVXbTp3pr4tTt44eaXcXZ15uYJJ9b/0cTpjBk/ErO/N4t/XMbCOYvIyczllbvepE33VowZP4rNS7ey/NeVmEwmnFycuOPZsYb1/cpkovHo0eyaOhVttRLQuzfuoaGkLLXtu0H9+lGSnU3cyy9TVlSEUoqkhQtpP3kyhYmJpK9Zg1tYGLFTpgDQ8LrrMLez/weAlXXo2Ypta3YwYdQruLg6cedToyuWvfPEdG5/ciQ+/t4s+GEZf367mOzMXJ677S3a92jFHRNHcvVtg/nfK7N59tY30BpuvOfKKre/tbd2PVoTt3YHz4x5GWcXZ2578kT7ef/J6Yx9wtZ+/p23jPmzbe1nyp1v0q57K8ZOGEVIoyDadGvJlDvfRClFzBU9CGti/1tUl2nNpIW7mfWfaEwOirmxR9mTkc+YDrYP8b7ZevSUdQM9nXlnWGscHBQOwO+7Ulm0P+OU5S8ZMhGCOt11CRWFlNqste54pudO4axOx7rQ3N58KNN2/FPfMersnlZDcIsYfeaCF6jCQ7OZsG5Rfceosze6DWTmnouz/d/abCjjViyp7xh1Nj2mP0+u/7e+Y9TZ610H0Xl2/dwY4XzYOLoPlpI/6ztGnZmdh7PwyMWZ/7Kw4YwtHzhcjGb168fq1D/qO0ad9Qy8gqVJF2fbAegXMpxGb16877sHnxh4URzeNxv2hWGX3e35644LcpvUeiZEKeWjtc4CUEr5nkVdIYQQQgghRDkt3xNS64HE28AqpdQP2C5MvxF42W6phBBCCCGEEJesWg1CtNazlFIbgIHYzmK7Xmu93a7JhBBCCCGEuBTJ3bFqf0pV+aBDBh5CCCGEEEKIcyLXdQghhBBCCGEkmQip9feECCGEEEIIIcR5ITMhQgghhBBCGEnujiUzIUIIIYQQQghjySBECCGEEEIIYSg5HUsIIYQQQggjyS16ZSZECCGEEEIIYSyZCRFCCCGEEMJIMhEiMyFCCCGEEEIIY8lMiBBCCCGEEEaSW/TKTIgQQgghhBDCWEprbe912H0FQgghhBBCcJFcbRF1w9eGHR/vnXfzBblNzup0LKWUD9BQa73tbOp1nbvirEJdSNbfGMP0nfPrO0adjWs5lAnrFtV3jDp7o9tA3CJG13eMOis8NJuxS5fWd4w6mdWvH1ctWF7fMerst8F9+Cnhr/qOUWfXNR7Gx9v/qe8YdXZf6yE8subi7Xum9hjI0PkX53vX/KEx/HLw4m371zQaxl0rltR3jDr7X0x/Hlt78bb9d7oP5J8jf9Z3jDobEja8viOIWjrjIEQptQS4urzsFiBNKbVUa/2YfaMJIYQQQghxCZILImq1Cby11jnA9cCXWuvOwGX2jSWEEEIIIYS4VNXmdCxHpVQIcCPwjJ3zCCGEEEIIcWmTu2PVaiZkCjAf2Ke1Xq+UagLssW8sIYQQQgghxKXqjDMhWuvvge8rPd4P3GDPUEIIIYQQQlyyZCLkzDMhSqkmSqnflFJpSqlUpdQvSqlII8IJIYQQQgghLj21OR3rW2AuEAKEYpsVmWPPUEIIIYQQQlyqtIMy7OdCVZtBiNJaf6W1Li3/+Rr5AkIhhBBCCCFEHdXm7liLlVITsc1+aGAk8IdSyhdAa51px3xCCCGEEEKIS0xtBiEjy/+9+6Tn78A2KGlyXhMJIYQQQghxKZNb9Nbq7lhyEboQQgghhBDivDnjIEQp5QrcB8Rgm/lYDkzTWhfZOZsQQgghhBCXHpkIqdXpWLOAXOCD8sejga+AEfYKJYQQQgghhLh01WYQ0kJr3aHS48VKqa32ClRZz2Azj0c3wUEpfjmQwsydiVWWXx4RwNiW4QAUlpbx2sZ97MnOB2B081CujQxCA3uzC5iybjclVuNu6qW1ZvFn8ziwcTuOLs5c/vAYgpo2rFYuOyWD39+cQVFeAYFNwhn+6C2YnGx/lsOxe1j8+Y9YS8tw8/Jg5CsPG5Y/ZVs8sV/NBasmon9vml81tMpyrTWxX80ldWs8JhdnOo4bi7lxBAD/PPoMjq6uKAcHlMmB/lOeMiz3cdPevJthgzqSlpFDl8ETaizz9uRbGTogmoLCEsY9/glb4hIAGNyvA2+9MBaTyYEZcxbz1se/GpjcxhIXx8HvvkNbrQTGxBA6bFiV5YVJSeyfOZP8Q4doeO21hAwZAkBxZib7vviCYzk5KKUI7NuX4EGDDM/fyc+H/7aw7bsLjiTzQ0LVfTfc3Y2H2zSnqZcnX+1N4KeDRyqWXRMRypCwYDSQkJfPe/G7OWbgvgu29v3bJz+ya90OnFydGPH4TYQ1q77/rvplOSt/WkpGUjrPzX0JD29PAFIPpfDDO99yZG8iQ2+9gr4jBhqafenn80jYGI+jizNDHryZwBr7nnT+evtE3zP04bGYnBxJjNvDb69OxyvQD4CoHh3oPnJYtfr2krYtnh3fzEVbNeH9etP0yup9z45v5pK2NR6TszPt/jsW78YR5CUls+XjzyvKFaSm0+z6K4kcamz77+Jv5p6WTTApxV+JKcw9ULXtN/Rw47G2zYjy8mTmnoP8kHCi7V8bEcqw8CCUgr8SU/jp4FFDs4Nt+/768Y/sXL8DJxcnbhx/E+E1tP2VvyxnxU9LyTiazvPfn2j7m/7dwJK5/wLg4ubCdQ+OILRpmGH5M2Pj2D97LlpbCe4TQ8Phl1dZXpCUzO4vZpB36DCNr7uG8MuH1LquvaVui2f717a237Bfb6JqeN/d/vWJ990O5W0f4Fh+Adu++JrcxKOAosNdt+DTzNhLdrXWzPvwJ+LX7sDZ1YmbJ4ymYfPqbWfpT8tZMm8Z6UfTefWnF/EsbzvHHdx5iLcfmMrtz42lY79og9LXgwv41rlGqc0gZLNSqofWeg2AUqo7sNK+sWx/mwmdmvLA0jhSCkuYeVk0y45mcCCnsKLM0fwi7l68jdxjZfQK9uHpLlHc/u9WAtycGRkVysj5mygus/JKzxYMiQjg94RUe8eucGDjdrKS0rhj2nMk7U5g4SdzGfPW49XKLZv5C52v7k/Lvp1Z8PF3xC5cTfSwPhTlFbBw2lxueOFevAJ8KbDkGpZdW61smzmHXk8+hJuvD0snvUZwp/Z4hYVUlEndGk9+SiqD3ppM1r4DbP1yNv0mP1mxvPfTj+LSwLOmX2+Ir75fyrSZ8/nfu/fVuHzogGiaNg6mbd9H6dYxivdfvpO+1zyHg4Ni6ku3c8WYVziSlMGK317m9wUb2bnnSI2/xx601UrCt9/S8tFHcfbxIf6VVzB36IB7aGhFGUcPDxqNGkXW5s1V6ioHBxqNGIFHo0aUFRUR99JLeLVqVaWuvTkA97RsynOb4sgoKuad7tGsTcvkcH5BRZncY6VM37WPHgF+Ver6ujhzVUQY963aSInVypPtWtI3KIB/k4zbdwF2rd9B+pE0xn/5DId3HuTnD77n/vcfq1auUZtIWnZvzfQJH1Z53t3LnavuvYHtq2KNilwhYdN2LEdTufXjSSTvTmDRp98x6o3x1cqtnPUrHa8aQIs+nfn3kznE/7ua9pf3ASC0VVOuefYeo6OjrVbiZ82h24SHcPX1YdULrxHYsT0NKvU9adviyU9Ope8bk7HsO0D8zNn0ev5JPEOCiXnxmYrfs+iRpwjuHG1ofgfg/lZNeWpDHOlFJXzQM5o1qRkcyj/xvpVzrJRPduynV2DVtt/I051h4UE8tGYrx7SVVzq3ZW1aJkcLjD3zeWd525/w5TMc2nmQn97/ngc/qN72G7eJpFX31nz6RNW27xvsxz1vPYh7A3d2rtvOvKnf1VjfHrTVyr5vZtP28Udw8fFhy4uv4hvdHo8qfac7TW8aRcbmLWdd197Z42fNoXt521/x/GsEdaqh7aek0v9NW9uPmzGb3i/Y3nfjv55LQLvWdH5wHNbSUsqKSwzJXdn2tTtIPZLGpK+eJmHHQb6b+gPjP360WrkmbSNp27MN7z/6YbVl1jIrv0z/jVZdWhoRWdSzU35PiFIqVim1DegOrFJKJSilDgCrgb72DtbGtwGH84o4kl9MqVWz4FAa/UKrdtrbMnLJPVYGQGxGDoFuzhXLHB0ULiYHTApcTSbSCo3dIfeti6X1gG4opQhtEUlxfiF5mdlVymitObRtD817RwPQZmA39q6xHbTsXLaRZj074BXgC4C7uYFh2bP2JeARFIBHYAAOjo6E9ehC8saqk19Jm7bSMKYHSil8o5pwrKCAIkv2KX6j8Vau20mmJe+Uy68c0plv5y0HYN3mvXh7uRMcaKZrdBT7EpJJOJTKsWNlfP/baq4c0sWo2ADkHTiAa2AgrgG27e/btStZW6tufycvLzwbN0aZTFWedzab8WjUCACTqyuuISEcs1iMig5AM+8GJBUUkVJYRKnWLEtOo3t5Oz4u+9gx9uTkUaqrz3A4KIWzgwMOClxMDmTWx5vp6lg6XdYVpRQRrRpTmF9ITkb19h0WFY5vsF+15z3NDWjYIgIHx9p8FdP5tX9dLK3K+56Q8r4nv4a+53Dsbpr1igag9YDu7Fu7zfCsJ7Pst/U97uV9T0j3LqRuqtr2UzdtJay3re/xiWpCaQ19T3r8TtwD/HHzr/63sacW3g04WlBEcmExpVqzJCmNnicNNrJLjrG7hrYf4eHGjuxciq1WrBq2ZWbTO9DY/ADbV8XSabCt7TeqQ9tv3CYS9wbuAES0akx2unHvC7n7bX2nW3nfGdCtC5mbq7YfZy8vGkRW7ztrU9eeLPsScA880fZDe3Qh5aS2n3JS2z/+vnussJDMXXtp2K83AA6Ojjh5uBuW/bjYVXF0K287ka0bU5hXSHYNbadhs3D8gn1r+A22WZLovh3w9Km/DzENo5RxPxeo071DXglcBVwORAL9gP7l/7/C3sEC3JxJKSiueJxSWExApUHGya5pEsyq5CwA0gpL+HrXEX67oit/XdWd/GOlrE2x2DtyFXkZ2TTwN1c8buBvJu+knbEwNx9XDzccyjtDTz9zxUAl62gqRXkFfPfM+3z12BvEL1pnWPaiLAtuvj4Vj918fSjKspyxTGGmrYxCsfr191ny3CskLFpuROSzFhrsS2JSRsXjI8mZhAb7EhrsQ+LRSs8nZRAW5FPTr7CbEosFZ98THbSz2cyxrKyz/j3F6ekUHDqER6SxN7jzc3EhvfjEvptRXIKfi0ut6mYWl/BTQiJf9OnGrL49yC8tY3N5uzJSTno25oATf3dvf3ONB2IXorwMC55+J7JX7leOK8rNx6Vy3+NvJr/S60vedYBvHn2Vn6d8TMahJGOCY+tXXCv1K66n6Htc/aqWKT6pTNLaDYT26GrPqDXyc3UmrehE208vKsbf9dTvW5Ul5BXQzsebBk6OuDg40DXAhwDX2u0351N2RtW2b/Y313ggWRvr/15Di66tzle0Myq2WHCp1H6cfXworuWHMOdS93woyrLg5neGtp9Z9X3X1deHokwLBanpOHt5su2zWSx/9mW2ff4VpZX6YKNY0rPxCTRXPDYHmM9qEGpJs7BtRSwxV/WyQzpxOkqpy5VSu5RSe8u/G7CmMv2VUluUUvFKqaXnY72nPB1La32wfKVfaa1vOSnIV8AtNVa0LR8HjAP49NNPwdz6rIPVNG471VnhnQO8uToyiP8usn2S18DJRN9QX675cz25JWW81qslwyIC+OtQ2lnnqCtdwye81QajpyljLbOSuu8wI158gGMlx5g94V1CWjTGNyzQDmlPjlXDlj45/GmKxEwaj5uPmeLsHFa9/j6eocH4t2x2/oOeA1VDC9Nao2r4xKCmzWFXtdn+Z1BWVMTuadNoNHIkjm5u5ylY7ZzNvnsyD0dHugf6cdeK9eSXljKxfUv6BwewJNm4fRdOkfcC/jSpqpraz0klamxjtn8CmoRz+/QpOLu5cGBjPL+99hm3fTzp/MesSY194tltd2tpKambt9FixLXnKVTt1dj2a9n4D+cXMvdAIq92aUtRaRkHcvMpM7zz4RTN5+zb/t4te1j/9xrufde4axlr3dGc77rnRU0B1JlLKNBlVnISDtPmlpH4NI0k/uu57PttPi3+c7Vdkp5KrY4dTmPeRz9z9bgrcTAZP4NcLy6QtxSllAn4CBgMJALrlVK/aq23VypjBj4GLtdaH1JKnZeD0dpcE9LmpLCOQOfTVdBaTwemH3/42dwVZx0stbCEIPcTnwIFubmQXsMpVVHe7jzbNYqHl8WTXVIKQLcgM0fzi7AU2x4vTsygvb+X3Qchm/9YRuyC1QAER0WQm26pWJabbsHD17tKeTcvT4ryC7GWleFgMpGXYcHDx1bG08+Mm5cHTq4uOLm6EN6mKWkJRwwZhNhmNU588l6YmYWruWp2V19z9TI+Zlv98n9dvL0I6RKNZV/CBTcIOZKcQXjIiVMJwoJ9SUrJwtnJkfBKp/2FhfhxNPXsZyHOhbOPDyWZmRWPSywWnMzmWte3lpayZ9o0/Lt3x7dTJzskPL304mL8K818+Lk4k1nLT+Wifc2kFBaRc+wYAKtSM2hl9jJkELL61+Ws+8u2/4Y3j8CSduLvnp1uwcvXy+4Z6mrrn8uIW7AKgKCoCPIyTmTPy7Dg6VO97ymu3PdU6p9c3E8MWiM7t2Hxp3MpzMnDzcv+p0fYPtk9kb0oMwuXk/seHzNFGSeVKe9zwHbevFejCFy8jf97pReVVJm98Hd1IeMsTiecfySF+UdSALi9WaMqsyr2tOrX5az909b2G7ao2vYt6Ra8/M5uWybtP8oP787hzpfvxsPL47xmPR0XHzPFldpPSVYWLrXsO8+l7vng6uND4Unt2vXk/fak993Kbd/V14xPU9usd0jXjuz9/R/7hwaW/byCVX/Y2k5EiwiyUi0VyyxpFrzPou0c2n2YGS/OAiAvO5/ta3fgYDLRIabdec0squkG7NVa7wdQSs0BrgG2VypzE/Cj1voQgNb6vFyoebprQp5SSuUC7ZVSOeU/uUAK8Mv5WPnpbM/MJcLTjVAPFxwdFIMjAlh2NLNKmSB3F97o1Yrn1+7mUN6Ji/eSC4pp59cAl/LRdNcgbw7kFGBvHa/oy9ipTzJ26pNE9WjP9sXr0FpzdNcBXDxc8TxpEKKUIqJdM3av3AJA/KJ1RHW37WxR3dtxZPt+rGVlHCsuIWn3QfzCg+z+GgDMTRqRn5xKfmo61tJSjqzZQHCn9lXKBHdqz+EVa9Bak7l3P07ubriavSktKuZYoe1vUVpUTGrsDho0NO6i6Nr6Y8EmbrrBdhFut45R5OQWkJxqYcPWfURFBtOoYQBOTiZGXNWTPxZsNDSbZ+PGFKWmUpRu2/6Z69fj06HDmSti+yTqwKxZuIWEEDJ4sJ2T1mxPTi6h7q4EubrgqBR9gwNYl5Z55opAWlExLb0b4OJg23c7+Jo5XOmiXnvqeXUfHv5kAg9/MoE2vdqxaeF623VbOxJwdXfDy8/7zL+knnQY3pcx705kzLsTadq9PTvK+56kXQdwcXet9gGIUorwts3Ys2oLANsXr6VJN1vfk5+VU/GJZvLuBLTWuDYw5kDSO7IR+SmpFKTZ2n7S2g0Edqza9wR2bM+Rlba+J2vvfhzd3Kp8SJK0Zj2hPYy9juu4XTm5hLm7EeRma/v9QwJYk1q7tg/g7ewEQICrC70D/ViSZMwMYK+r+/DotAk8Oq287S+wtf2DOxJw8zi7tp+VmsWsKV8wasLNBITb/0OzyhpENqYoJZWi8vaTtm4DvtG16zvPpe754N2kats/umYDQWdq++Xvu65mb1x9fchLSgYgPX4XDUKDDcnd99oYJn72BBM/e4L2MW1ZV952DmxPwNXDDe+zaDuTv32OybMnMXn2JKL7deDGh2+4tAcgDsqwH6XUOKXUhko/4yolCQMOV3qcWP5cZc0BH6XUEqXURqXU2POxCU53OtarwKtKqVe11obfY7VMwxub9vF+37aYFPx6IIX9OQVc39S2Y/24L5m7WjfE28WJJzs1BaBUa25duJX4zDz+Tczg68HRlGnNrqx8ftqfbGj+yM6t2b8hns/vmYKTizNDHxxTsezHKdMYcv9oPP286XPr1fzx1gxWfvMHgU3CaTu4BwB+DYNp3LEVMx96DeXgQLvBPfBvZMzBvIPJRPuxo1j95gdoq5WIvr3wCg/lwL/LbK9tUF+COrQlZUscC8dPwuTsTMf/2tpjcU4O66Z+Ctju9hHWsytB7duccl32MvODB+nTsxX+Pg3Yu/ZDXnznB5zKb338v68X8veizQwdEE388qkUFBZz93hb5rIyK48+N4PfvnoKk8mBmd8tYcfuxNOt6rxTJhONR49m19SpaKuVgN69cQ8NJWWp7RTMoH79KMnOJu7llykrKkIpRdLChbSfPJnCxETS16zBLSyM2ClTAGh43XWY2xnXkVs1TNu1j8md2uKgFAuPpnAov4DLw2377t+JyZidnXi3e0fcHU1YNVxdfkes3Tm5rExJZ2qPjpRpzf6cPP5ONO6ahONadGvNzvU7ePP2l3BycWbE46Mrln357Kfc8OgovPy8WfnzUpZ+v4i8zFym3vMGLbq15j+PjiI3M4cPHnyb4gLb32fFz0t5bPpTuHq42j17485tSNi4nZn3TsHRxYnBD95cseznFz/hsvtvwtPXm5ix1/DX21+y+tvfCYgMp81lPQHYu3oz2/5egYPJAUdnZ4Y9fttZnxJVVw4mE61vGcX68r4nvG8vGoSHcmiRre+JGNiXgA5tSdsWx9InJmFycab9XSfeC8uKS0iP20mb28acahV2ZdXw0Y59vNK5LQ4K/jmSwsH8Aq4ob/t/JCbj4+zEBz2jcXc0oTVc2yiUcSs2UVBWxqToljRwcqJMaz7csY+80jLDX0PLbq3ZuW4Hr9/2Es4uzowYf6Ltf/7Mp/znsVF4+3mz4idb28/NzOWdu9+gZbfWjHhsFAu/nk9BTj4/ffA9YPubPvxR9TtD2oMymWg6ZhRx776HtloJiumNR1goSUtsfWdIf1vfufnFVygrLAKlOLLwXzq/+AKObm411jWKg8lE27GjWPfGB2h9ou0fLG/7jQb2JbBDW9K2xrHkCdv7buW23+aWkWz55EusZWW4B/jT4b+nPGPebtp0b832tTuYcvPLOLk6c/OEURXLPpk4nZvGj8Tb35slPy7j3zmLyMnM5dW73qRN91bcNH7UaX6zOFcnnaF0stqcRX38LKhBgBuwWim1Rmu9+1xyqRrP4atcQKnewBatdb5S6magE/De8WtGakF3rcPpWBeK9TfGMH3n/PqOUWfjWg5lwrpF9R2jzt7oNhC3iNFnLniBKjw0m7FLz8v1W4ab1a8fVy24MG8sUBu/De7DTwl/1XeMOruu8TA+3m7MKRX2cF/rITyy5uLte6b2GMjQ+Rfne9f8oTH8cvDibfvXNBrGXSuW1HeMOvtfTH8eW3vxtv13ug/knyN/1neMOhsSNvwCudri9Jre+b1hVyLt+3zEKbeJUqon8ILWemj546egYjLieJmJgKvW+oXyx58Df2utvz+XXLW5+ucToEAp1QGYABzE9i3qQgghhBBCiIvXeqCZUipSKeUMjAJO/pbmX4A+SilHpZQ7tq/v2HGuK67NhemlWmutlLoG2wzI50qpW891xUIIIYQQQvx/pC+Q+RqtdalS6gFgPmACvtBaxyul7ilfPk1rvUMp9TewDbAC/9Nax53rumszCMktn5q5BdsoyAQ4neuKhRBCCCGEEPVLa/0n8OdJz0076fGbwJvnc721OR1rJFAM3KG1TsZ2xfx5DSGEEEIIIYT4/+OMMyFa62Sl1Dzg+Bc9pAM/2TWVEEIIIYQQlyqHC+R8rHp0xpkQpdR/gR+AT8ufCgN+tmMmIYQQQgghxCWsNteE3I/t2xTXAmit95yvr2sXQgghhBDi/x2Dvn/pQlaba0KKtdYlxx8opRyp/iUmQgghhBBCCFErtZkJWaqUehpwU0oNBu4DfrNvLCGEEEIIIS5Rck1IrWZCngTSgFjgbmy38HrWnqGEEEIIIYQQl67TzoQopRyAbVrrtsBnxkQSQgghhBDiElabaYBL3Gk3gdbaCmxVSkUYlEcIIYQQQghxiavNNSEhQLxSah2Qf/xJrfXVdkslhBBCCCHEpUrujlWrQYgncGWlxwp43T5xhBBCCCGEEJe62gxCHLXWSys/oZRys1MeIYQQQgghLm1ydyyU1jV/5YdS6l5st+NtAuyrtKgBsFJrfXMt1yHfKSKEEEIIIYxwURzdN3noZ8OOj/e/f+0FuU1ONxPyLfAX8CowsdLzuVrrzLNZyYA/V9Yh2oVh8fDerE/7o75j1FnXgCuYuWd+fceos1ubDWXs0qVnLniBmtWvH24Ro+s7Rp0UHprN5M0L6ztGnT3f8TIWHvmzvmPU2WVhw3lt64L6jlFnEzsM5pE1i+o7Rp1N7TGQ8WsvzvxvdR/IK1su3rbzdPRgHlq9uL5j1Nn7PQcwbsWS+o5RZ9Nj+vPtvr/rO0ad3dT08vqOUCtargk59SBEa50NZAMX5xGUEEIIIYQQ4oJUm2tChBBCCCGEEOeLfE+IbAIhhBBCCCGEsWQQIoQQQgghhDCUnI4lhBBCCCGEkeQWvTITIoQQQgghhDCWzIQIIYQQQghhJLlFr8yECCGEEEIIIYwlMyFCCCGEEEIYSa4JkZkQIYQQQgghhLFOOxOilIoFdE2LAK21bm+XVEIIIYQQQlyqZCLkjKdjXWlICiGEEEIIIcT/G6cdhGitDx7/v1IqCOha/nCd1jrVnsGEEEIIIYS4FGm5JqR2F6YrpW4E3gSWYJtA+kAp9YTW+gc7ZqOrv5kHWjfBpOCPwynM3n+kyvLLQgMY1SQMgMKyMqbG7WNfbgEAE9pF0SPQB0vJMe5YvsWeMWuktear935iy+oduLg6M+7p0US2CK9W7uPJX7N/52EcHU00aRXBHRNG4OhoqnV9e+ZfMH0e+zZsx9HFmaseGUNwVMNq5SzJGfz8xgwKcwsIjgrn6sduweTkSFF+Ib++NYuctCysVivdrxtIh8E9DMtviYvj4Hffoa1WAmNiCB02rMrywqQk9s+cSf6hQzS89lpChgwBoDgzk31ffMGxnByUUgT27UvwoEGG5QaY9ubdDBvUkbSMHLoMnlBjmbcn38rQAdEUFJYw7vFP2BKXAMDgfh1464WxmEwOzJizmLc+/tXA5Ccc3RLPxpk/oK1Wmg7sTZtrhlRZrrVm48zvObo5HkcXZ3rcewu+kREArJn2FUc2xeHq1YAr3nq2PuKjteb7D38ifu0OnF2duGXCaCKaV2//S35azuJ5y0g/ms7rP72Ip7cnALu37OXT5z7HL9gXgOg+7Rk+dqhh2dd++QOJ5ds25r5b8G9SPXtuajpLpn5JcV4BfpEN6fvgWEyOjliOJLPi46/JOJBIp1FX0u7qywzJfVzatnh2fDMXbdWE9+tN0yurbjetNTu+mUva1nhMzs60++9YvBtHkJeUzJaPP68oV5CaTrPrryRyqLH7b+q2eOK+tuWP6NebZldVzx//9VxStsZjcnEm+r9jMTe2tf2Fjz2Do6srysEB5eBA3ylPGZr9eL51M37gSHn76X3vLfidov0se+9E+4l5wNZ+alvfXtK2xbPz27loq5Xwvr1pcuXlVZZrrdn5zVzStsXZ2s9dt+LVOIL8pGS2fvy/inIFaelEXXcVjQ1sP5mxceydPRetrYT0iSFieNXsBUnJ7PxiBnmHDhN53TU0vHxIresaQWvN35/+yJ7123FyceLax8YQUsNxQ1ZyBvNem0lhXj4hTRty3fibMTk5UphbwK9TvyUzKR1HZyeueWQ0gY1DDX8dwji1vTD9GaCr1vpWrfVYoBvwnP1i2YI93KYJE9fHc9uyzQwKDaCRp1uVMkkFRTyyJpa7Vmzhq72HebxdVMWyvxNTeXL9dntGPK2ta3aQfDidt+c8zZ1PjGDGWzWP13oN6cSb307k1VlPUFJ8jCW/rTmr+vayb8N2Mo+mcc/05xj+wEj+/nhujeUWzfiFrtf0597PnsPVw50tC1YDsPGP5fhHBHPXhxMZ8+qD/Pv5z5QdKzUku7ZaSfj2W1o89BDtJ08mY/16Co4erVLG0cODRqNGETJ4cJXnlYMDjUaMoMOUKbR56ilSFi+uVtfevvp+KdeMfe2Uy4cOiKZp42Da9n2UByZ+xvsv3wmAg4Ni6ku3c82tr9Nx0HhGXN2Lls3CjIpdwWq1suGLuQyYeD9XvP0cB1duIDsxqUqZo1viyU1K46qpL9Dtvzex/n9zKpY16deDAU/db3TsKuLX7iDtSBovfPU0Nz12I3Om1rz/NW0byUNv3YtvkE+1ZVHtmvD0Z0/w9GdPGDYAAUjcvJ2c5DRueP95eo0bzepK27ayDV//QpsrBvCf95/HxcONPYts+66Lpwfdbx9B26sGGpb5OG21Ej9rDl0ef4A+r04iac16co9UbTtp2+LJT06l7xuTaXP7TcTPnA2AZ0gwMS8+Q8yLz9B78lOYXJwJ7hxteP7YWXPoPv4BBrw2iaM15E/dFk9eSioD35xMh9tvInbG7CrLez71KP1eeqZeBiAAR7ZsJzc5jevee56e/x3Nms9rbj8bv/mF1sMHcP17z+Ncqf3Utr49aKuVHV/NpvNjDxDzyvMkrV1P3pGq/Xf6tjgKUlLp8/oU2tw2hu2zvgXAIySYXi8+S68Xn6Xn5KcxOTsTZGD70VYre76ZTbtHH6Triy+QunY9+dXet9yJumkUDYcOPuu6Rti7YTuZR9J48H/PctVDo/jjw+9rLLfwi1/pcV1/Hvzfc7h6urHpH9txz/K5CwhqEsa9H0/kusdv5u9PfzQwfT1wUMb9XKBqOwhxOOn0q4yzqFsnLc0NOFpQRFJhMaVasygpjd5BvlXKxFtyySstA2B7Vi7+rs4Vy7Zl5ZBj0EFvTTYujyPm8i4opYhq25j8vEKy0nOqlYvu2RqlFEopmraOIDM1+6zq28vutbG0G9gNpRRhLSMpyi8kLzO7ShmtNQe37aFVTDQA7QZ1Y/fqWMA2XVZSWIzWmmOFJbg1cMfBZMzN2PIOHMA1MBDXgAAcHB3x7dqVrK1bq5Rx8vLCs3FjlMlU5XlnsxmPRo0AMLm64hoSwjGLxZDcx61ct5NMS94pl185pDPfzlsOwLrNe/H2cic40EzX6Cj2JSSTcCiVY8fK+P631Vw5pItRsStk7E3AMzgAzyB/TI6ONOrVmcQN26qUObJhG5F9u6OUwr9ZJCUFhRRm2dpXYKtmOHt4GJ67sm2r4ug+uCtKKSJbN6Ywr5DsjOxq5Ro2C6+Y7bhQHNqwjai+tn03sHkkJfmFFGRV33eT4nfTuEdHAKL6d+fgets+4ubdgICoRjictG8YwbI/AY+gANwDbftuSPcupG6quu+mbtpKWO8eKKXwiWpCaUEBRZaqry89fifuAf64+fsZGZ+sfQl4BAbgUZ4/tEcXkk/Kn7xpKw0r5T9WQ/76dHj9NpqUt5+A07Sf5PjdNCpvP037dedwefupTX17yd6fgHtQYKX205XUzVX7ntTN2wgt3/7mqCYcKyik+KTtn7F9J+6BxrafnP0HcAsMxK38fSuwWxcyNldtO85eXnhFVn/fqk1dI+xcE0f7QbZ+M7xlY4ryC8mt4bjhwLY9tI7pAECHy7qxq/y4If1QMk2imwPg3zAIS0omeVnGHfcI49X2qPBvpdR8pdRtSqnbgD+AP+0XC/xdnUktKql4nFZYgr+LyynLD28YxLo0iz0jnZWs9Bz8As0Vj30DzWSln7ojLi0tY8X8DbTv0bJO9c+3vIxsvPxPrL+Bn5nckw7CCnPycfVwqzhY8fI/UabzlX1JP5zM+2Of47MHXmXwuBtQDsYMQkosFpx9TxwYOpvNHMvKOuvfU5yeTsGhQ3hERp7PeOcsNNiXxKSMisdHkjMJDfYlNNiHxKOVnk/KIKyGT+jtrTDTgoffifW6+5opyLRUKVOQmY27n/m0ZepTdno25kr7nznAjOUs978D2xN45a43+Wjipxw9kHTmCudJQaYFD/8T29/Dr/q2Lc7Nx9n9xL7r7utDQWb9HwgXZVlw9T2R3dXXh6IsS/UyflXLFJ9UJmntBkJ7dMVoRVkW3PzOkD+z6mt08/WhqOLvo1jzxvssm/QKBxcvt3/gGhRknbT/1qL9eFRqP7Wpby9FWVlV24+PmaKT+v7ik9uYj7na3yh57QaCDW4/JRYLLpVyufj4UFzLD8DOpe75lJtuwTvAXPHYy9+b3PQzHzfkZFgACIoMZcdK26DxyK6DWFKzyDHwuMdwShn3c4Gq1TUhWusnlFI3AL2xfcg9XWv906nKK6XGAeMAPv30Uwhvc9bBatpkusa7BUO0rzfDGwbxUPlo+kKgdfWsp2sGM97+gZYdmtCyQ5M61T/falr/yQFq+nscb+v7N+0gqEk4Y155kKykdGY/9xEN2zTBxd2tWp3zrsbsZ7f1yoqK2D1tGo1GjsTRzYDMZ0HV0BK01qgaXmNNm6I+VM9WU9u5cDrKGve/s8jXsFk4U2ZPwtXNhbg125k+6Qte+OqZ8xnx1Gq8qfqZt/8F4Ry3O4C1tJTUzdtoMeLa8xTqbJzqjvZnUF4k5rnxuPqYKc7JYc3r7+MZEoxfy2bnNeGZ1Nx91qL9qLOpbyc1xqq67jO9t9naz1aa/efa85vtTM5ll7xAd2egWt9T8x5iKxNz42D+njaPaQ+8QVCjEEKahhl2BoWoH7X+xnSt9TxgXi3LTgemH384+8+VZx0sraiEwEqnVwW4OZNRXFKtXJMG7oxv15SJG7bX6+lXAAvmrWBx+TUdTVo1JCPVUrEsM9WC2d+7xno/fjGfXEs+d7w8ouI53wDvWtc/Xzb8vowt823n9YY2iyAn/cT6czMsNPCtun53L0+K8guxlpXhYDKRk27Bs7zMtoVr6fmfwSil8A0NwBzkR8bhVEJbNLLrawBw9vGhJDOz4nGJxYKT2Vzr+tbSUvZMm4Z/9+74dupkh4Tn5khyBuEhJ04TCAv2JSklC2cnR8JDKz0f4sfR1LOfATpXbr5m8jNOrLcg04Kbz0ltx9dMQfmnX6cqY7SlP69g5R+29t+oRQSWSvufJc2Ct59XrX+Xm4drxf/b9mjNd+/9QF52XsWF6+fbjr+XsvvfVQD4N21EfvqJ7Z+fYcH9pG3r0sCTkoIT+25BZhbuvvW7/aF85iDzRPaizCxczFVzufqYKco4qYyPueJx2rZ4vBpF4OJd+7/X+eLq40PhSdlcT9r2rr7mKq+xMDML1/L8x/918fIiuHM0lv0JhgxCds4/qf1U3n8zqu+bJ7ef/MysijbmcfL+X0N9e6nWfrIsVdrGqcq4Vnp/SN8WVy/tx9nHTHGlXMVZWbjU8n3rXOqeq3W/LWdTpeOG7EpnpOSkZ9PgpH7T3cuj2nFDAz9b+3Bxd+Wax8YAtsHie7dPwSfY2FMqDSXjq9ptAqXU9UqpPUqpbKVUjlIqVyll1xP1dmbnEubhRrCbC45KMTAkgFUpmVXKBLo6M6VTS17duofE/CJ7xqmVwTfE8MqM8bwyYzyd+7Rjxd8b0FqzNy4Bd09XfPyrd2qLf1tD7Lpd3P/CzThUOl2pU0zbWtU/n7pc2Ze7PniSuz54kuY92xO7aB1aa47sPICLu2vFAOM4pRSN2jVjx4otAMT+u47mPdoB4BXgQ8LWXQDkZeWQkZiK2aDOxLNxY4pSUylKT8daWkrm+vX4dOhQq7paaw7MmoVbSEi1i9YvFH8s2MRNN/QBoFvHKHJyC0hOtbBh6z6iIoNp1DAAJycTI67qyR8LNhqez69pI3KTU8lLTaestJSDqzYS1rldlTJhndtzYNlatNak7zmAk7tbvQ9C+l0bU3EheYeYtqxdsN7WHrYn4Obhhrdf7fNlZ+ZUfOKasOMgWms8vOx3nUury/txzZtPcc2bTxHRrT17l9n23dTdB3B2d6s2CFFKEdKmOQlrNgOwd8laIrrU/3fPekc2Ij8llYI0276btHYDgR2r5grs2J4jK9egtSZr734c3dxwrTRQSVqzntAexl8LBWBuUjX/0TUbCD4pf3DH9hyulN/J3Za/tLiY0kLb+1hpcTFpcTtoEG7MnYFaDu3H1W88xdVvPEVE1/bsL28/abtt+2ZN7Se4dXMOlreffUvX0rC8/TTs0u6M9e3FK7IRBVXaz/rq7Se6PUfLt79l734c3VyrDHST1mwgpB5O5fOKbExhSiqF5dlT123AL7p271vnUvdcdbuqD/d8OIF7PpxAy57t2Pavrd9M3JmAi4drtQ8vlVJEtm/G9hW2a1a2LlxHix5tASjKK6i4gc2m+atp1LYpLu6uiEtXbWdC3gCu0lrvsGeYyqwa3o/fzxvd2uAA/JWYSkJeIVdFBAPw26FkxjaLwMvZiUfa2k5hKtNwz0pbw342ujnRvt54Ozsyd0AXZuw5xJ+Jxn21SXTPVmxdvYPHR76Cs6sT454eXbHszfHTuWviSHz8vfnyrR/wD/LhhbvfB6Brv3Zcd/vQ09Y3QtMurdm7IZ5P/jsFJxdnrnxkTMWy756fxvCHRtPAz5sBt1/Nz6/PYNnXfxDUJJwOQ2y34Y0ZdTm/T/2az+5/Fa1h4O1X426nT4FPpkwmGo8eza6pU9FWKwG9e+MeGkrK0qUABPXrR0l2NnEvv0xZURFKKZIWLqT95MkUJiaSvmYNbmFhxE6ZAkDD667D3K7d6VZ5Xs384EH69GyFv08D9q79kBff+QEnJ9uu+r+vF/L3os0MHRBN/PKpFBQWc/f4TwEoK7Py6HMz+O2rpzCZHJj53RJ27E40LPdxDiYTXW6/kcWvfIS2WmkyoCfmhqHsWWA7x73Z4D6EdmzD0S3x/PbwC5hcnOlxz80V9Ve+/wUp2/dQnJvHT/c9Q/v/XEHTgb0MfQ1turcmfu0OXrj5ZZxdnbl5wqiKZR9NnM6Y8SMx+3uz+MdlLJyziJzMXF65603adG/FmPGj2Lx0K8t/XYnJZMLJxYk7nh1r2Ckp4R3bkLgpnnkPTcbk7ESf+05s239e/ZiYu2/C3ddMlzHXsGTql2ya8zt+kQ1pPrAnAAWWHH6b+AbHCm37xvY/l3DdO8/gbMCplA4mE61vGcX6Nz8ov8VqLxqEh3Jo0TIAIgb2JaBDW9K2xbH0iUmYXJxpf9fYivplxSWkx+2kzW1jTrUKu+dvO3YUa974AK2tNCzPn1Cev/HAvgR2aEvq1jgWPTEJk7Mz0eX5i7Nz2PCebV+2Wq2E9exKYPuzP5X5XIV1bEPi5nh+fHgyjs5O9L73RPtZ+OrH9CpvP53HXMPS975k83e/49u4Ic3K28/p6tubg8lEq5tHsvGt99FWK2F9euEZFsrh8u3fcGBf/Mvbz/IJz2FycabtnbdW1C8rLiEjfget66H9KJOJqDGjiH33PbTVSnBMbzzCQjm6xPa+Fdrf9r618cVXKCssAqVIXPgvXV98AUc3txrrGq1Z19bsWb+dD+58EScXZ6559KaKZd9MmsbVD9uOGy67/Sp+eH0mi2b9QUjTcDoOtbWdtMMp/Pz21ygHBwIigrn6YWOPe4TxVI3nR55cSKmVWuvedVyHHlCH07EuFIuH92Z92h/1HaPOugZcwcw98+s7Rp3d2mwoY8sHDxejWf364RZxcXakhYdmM3nzwvqOUWfPd7yMhUfsev8Mu7osbDivbV1Q3zHqbGKHwTyyZlF9x6izqT0GMn7txZn/re4DeWXLxdt2no4ezEOrF9d3jDp7v+cAxq1YUt8x6mx6TH++3fd3fceos5uaXn7hXGB4Go2f/9uwq3kSJl+Y2+S0MyFKqevL/7tBKfUd8DNQfHy51voSv4mzEEIIIYQQ4nw70+lYV5X/q4ECoPLXHmtABiFCCCGEEEKcjQv4SwSNctpBiNb6dgCl1EzgYa21pfyxD/C23dMJIYQQQgghLjm1vTC9/fEBCIDWOksp1dE+kYQQQgghhLiEyUxIre9S7FA++wGAUsqXs/iOESGEEEIIIYQ4rrYDibeBVUqpH7BdC3Ij8LLdUgkhhBBCCHGJ0gbdtv1CVqtBiNZ6llJqAzAQUMD1Wuvtdk0mhBBCCCGEuCTV+pSq8kGHDDyEEEIIIYQ4F7W9IOISJptACCGEEEIIYSi5uFwIIYQQQggjyTUhMhMihBBCCCGEMJbMhAghhBBCCGEk+Z4QmQkRQgghhBBCGEtmQoQQQgghhDCSzISgtNb2XofdVyCEEEIIIQS277O74DV6c5Fhx8cHnxh4QW4TQ2ZCxi5dasRq7GJWv35MWLeovmPU2RvdBjJuxZL6jlFn02P6c9WC5fUdo85+G9yHyZsX1neMOnm+42W4RYyu7xh1VnhoNl/tnV/fMerslqihvLZ1QX3HqLOJHQbz3MaLs+0DvNj5Ml66SPfdZztexvSdF2/bH9dyKKMWL6vvGHU2Z0Bf3ov/p75j1NnDbYbwbtzF2/c82nZwfUcQtSSnYwkhhBBCCGGkC3JuwlhyYboQQgghhBDCUDITIoQQQgghhIG0XJguMyFCCCGEEEIIY8lMiBBCCCGEEEZSMhMiMyFCCCGEEEIIQ8lMiBBCCCGEEEaSa0JkJkQIIYQQQghhLBmECCGEEEIIYSRl4M+Zoih1uVJql1Jqr1Jq4mnKdVVKlSml/nP2L7g6GYQIIYQQQgjx/5BSygR8BAwDWgOjlVKtT1HudWD++Vq3XBMihBBCCCGEgRwunGmAbsBerfV+AKXUHOAaYPtJ5R4E5gFdz9eKL5xNIIQQQgghhDivlFLjlFIbKv2Mq7Q4DDhc6XFi+XOV64cB1wHTzmcumQkRQgghhBDCQEZ+TYjWejow/VRRaqpy0uOpwJNa6zJ1HoOfcRCilOoNbNFa5yulbgY6Ae9prQ+etxRCCCGEEEIIoyUCDSs9DgeOnlSmCzCnfADiDwxXSpVqrX8+lxXXZibkE6CDUqoDMAH4HJgF9DuXFdeGJS6Og999h7ZaCYyJIXTYsCrLC5OS2D9zJvmHDtHw2msJGTIEgOLMTPZ98QXHcnJQShHYty/BgwbZO24VKdviif1qLlg1Ef170/yqoVWWa62J/WouqVvjMbk403HcWMyNIwD459FncHR1RTk4oEwO9J/ylKHZATJj49g7ey5aWwnpE0PE8Mur5d83+zsyYuMwOTvT4o7baNDIlj9xwb8kLVsBaEL6xhA++DLD83fy8+G/LZrgoBQLjiTzQ0JileXh7m483KY5Tb08+WpvAj8dPFKx7JqIUIaEBaOBhLx83ovfzTHryR8K2NfRLfFsnPkD2mql6cDetLlmSJXlWms2zvyeo5vjcXRxpse9t+Abadv+a6Z9xZFNcbh6NeCKt541NDfAtDfvZtigjqRl5NBl8IQay7w9+VaGDoimoLCEcY9/wpa4BAAG9+vAWy+MxWRyYMacxbz18a8GJj9Ba80/n85j74btOLk4c9WjYwiJalitXFZyBj+9PoPCvAJCmoZzzeO3YHJypCi/kF/emkV2WhbWMis9rh9I9OAehmVf++UPJJa3jZj7bsG/SfXsuanpLJn6JcV5BfhFNqTvg2MxOTpiOZLMio+/JuNAIp1GXUm7q43df5O2xrNllq3tRw7oTaurq7f9zbO+J3lLPCZnZ7rdcws+kRGUlRxj8ZR3KSstRZeVEd69I23/c6Wh2QGObIlnQ/m+GzWwN21r2HfXl++7Jhdnet17C37l++6qaV+RWL7vXl0P++7xfIs/m8eBjdtxdHHm8ofHENS0evvJTsng9zdnUJRXQGCTcIY/amv7AIdj97D48x+xlpbh5uXByFceNix/TnwciXPnoK1W/Hr3IfjyqscNRclJHJw5g8LDhwi5+lqChpz03my1suvVl3Aym2l6/0OG5a5Yv9as+HweBzfZ9t9BD9xMQA3bPyclnX/emUFxXgH+keFc9vBYTE6OHFi3jbWz/0AphYPJgZg7biCkVVPDsq/84gcObYrH0dmZAQ/eQkANfU9OSjoL3/2SotwCApo0ZOBDJ7Kvn/07ysGWvdft/zEse324gL4wfT3QTCkVCRwBRgE3VS6gtY48/n+l1Azg93MdgEDtrgkp1VprbBepvKe1fg9ocK4rPhNttZLw7be0eOj/2Lvv8Ciqvo3j35NNr5seQkgIhN577wKigpUHEMWOXR/LoyiiNBs2xAbYK0VQQbHRe68h9BIgkJ5set/z/rEhJCTgEtkN5P19rouLzM6Z7L2TM7NzZs6ceYLWkyaRunUruWcqNswcPTyIGDmSOgMHVnhdOTgQMXw4bSZPpsULL5C4cmWlZW2dfc/Xc+n2v8fo/+bLnN64lczT8RXKJO2OIScxiQFvT6LNvbez+8s5Feb3ePEp+r06vkYaINps5vD3c2j11ON0mjKRpM1byTlv/aVF7yU3MYnOr02h8Zg7OPzt9wDkxJ0mfs062r/0Ah0nTiB1dzS5iYl2ze8APNS0IRN3xvDohu30Dgmknod7hTJZRcXMPniUn89rnPi5ODM0vC5Pbd7FYxt3YEDROzjQjunBbDaz7Yv59Bv3KNe/M4ET67eREVex/pzZFUNWfDJDp0+k8wO3s/WzuWXzGvTpSr8XHrVr5vK+/XE1N45544LzB/drS8P6IbTs/RSPjfuUGa/eB4CDg2L61Hu48a43aTfgWYYP607TRnUv+Hts6ei2faSdSeaRTydw3eMj+OOj+VWWW/HlIrrc1JdHP52Aq6c7u/7eCMC239YSUC+EsR+O4843HmfZZ79QUlRsl+xxO/eRmZDMrTNeofvYUWwsVzfK2/bdIlpc34/bZryCi4cbh1dYsrt4etDlnuG0HNrfLnnLM5vN7PhyPr2ee5TBb03g5IbKdT9hVwzZCckMeXciHe+/ne1fWD6fg5MjfV56gsFvvMig118kYfc+Ug8ft3v+LV/Mp/+4Rxn6zgRi12/DdIFt98bpE+n6wO1sLvf3adinKwNqcNsFOL59H+nxydw7cwIDHx3Bsk+qrvtrvl5Eh2F9uW+mpe5HL7PUn/zsXJbNnM9N4x/g7g9fZOhz99otuzabOTXnBxo+9iTNXplM+tYt5J333WVw9yBsxEiCrhlU5e9IXrEM15A69ohbpZM79pERn8Toj16m70MjWT17XpXlNn67mDZD+zH6o5dx8XRn/3LL+g9r1YQR745jxLvj6PfoaFZ+/IOdsycz6sNX6PPwKNbOrnrfs+nbRbS+oR+3f/QKLp5uHCiXffi7LzD8nRfo+8gdrLZj9v/PtNbFwGNYRr3aD8zXWscopR5SSj1ky/e2phGSpZR6AbgTWFI6RJeTLUMBZB8/jmtQEK6BgTg4OuLXqRPpu3dXKOPk7Y1n/foog6HC685GIx4REQAYXF1xrVOHIpPJ1pHLpB+NxSM4EI8gS/a6XTuSsL1i9vgdu6nXsytKKfyiGlCUm0u+KcNuGS8m89hx3IKCcCtd90GdO5K6s2L+1F27Celuye/dsAHFuXkUmDLIjU/Au2EkBhdnlMGAsUljUnbssmv+Rj5exOfmk5iXT7HWrElIpkugX4UyGUVFHM7MplhXvsLhoBTODg44KHAxOJBWUGiv6ACkHonFMyQQz+AADI6ORHTvQNy2PRXKnN62h8jeXVBKEdAoksLcPPLSLfUnqFkjnD087Jq5vPVbDpBmyr7g/BsGdeCHhWsB2LLzCD7e7oQEGenUNoqjsQnEnkyiqKiEH3/dyA2DOtordgUHN0XTqn9nlFKENY0kPyePrLSK26fWmtg9h2nWsy0ArQd05uCmaMByhqswrwCtNYV5hbh5ueNgsM84ICe37SGqtyV7UONICnPyyE2vnD0+5hD1u7YDIKpvF05stWzjbj5eBEZF4HDeftUe0o7E4hl8ru6Hd+vAme3n1f3te6jfy1L3/RtFUlRa95VSOLm6AmAuKcFcYrZqfPzLKfVILF4hgXiV23ZPnbftntq2hwal225gaf6zf5/gZo1wqcFtF+Dolmia97PUn9AmkRTk5JFdRd0/uecwjXu0BaBF/84cKa37B9Zsp1G3NniX7nPdjTY/Z1kmN/Y4LkGBuJR+d/l26kTGnl0Vyjh5e+NRP7LScQNAYXoaGdHR+PfoaafElR3fEk2Tvpb1H9LEsv3mVLH+T0cfomG3tgA07deF41ss9czJzYWzffaLCwqx50YQu3UPjftYsgc3ttSdnCr2PWf2HqJBN8u+p3HfLhzfsrtS9qKCArtvv/+faa1/11o31lo31Fq/WvraTK11pRvRtdZ3a60XXI73taY71ggsl2Xu1VonKKXCgbcux5tfTKHJhLPfuQNHZ6ORnOOXflarICWF3JMn8YiM/OfCl0l+ugk3P9+yaTc/X9KPHv/HMnlpJlyNPigUG9+cAQrq9+tF/f697JYdLOvepVw2F19fMs9b9wXpJlzK/X1cfI0UmtJxrxvK8Z9/oSg7GwcnZ1L3RONVP8Ju2QH8XVxIKSgom04tKKSxt3VfhGkFhfwcG8cXvTpTaDazMzWdnWkmGyWtWl6aCQ//c+vf3c9IypHYCmVy0zJw9zdWKJObZsLN18dOKasvNMSPuPjUsunTCWmEhvgRGuJL3Jlyr8en0rltVE1EJCs1A+9AY9m0d4CRrNQMvPzOrd+8zBxcPdzKDta9SssAdLyhN/Mnz+b9OydQkJfPLc/fg7LTeIy5aSY8As7VHw9/S91wL1c3CrJycHY/l93dz5fctJo/CZKXbsLdv/x+0UjaeXU/Lz0DNz9jhTJ56Za6bzabWTb+DbITkmk4qA/+Ufbb70Ppui+X3+MC267Hedtu3nl/n5qUnZqBV4CxbNorwEh2agae5et+VsW67+lvLGuopJ9JoqS4hHnjZ1CYl0/7G/rSon9nu2QvTDfh7Fv+uMH3ko4bTs+fR91bbqMkP98W8aySk2bC87ztNyctA49y6z8/Kwfncuvfw99ITuq57ffYpt1s+n4xeRnZXD/epieyL5rd099ITqoJD98LZ/f0963QyDq+eTebv1tMXmYWQ160X/aacDlv8L5a/WMjpLTh8QPQWSk1FNiqtf7mYsuUDv01FmDWrFnQpMmlJ6viDPWldqAryc/n0MyZRIwYgaOb26VnqCZtTfaLFOn58rO4+RopyMhkw5sz8AwNIaBpo8sf9EKsuf2hqs+IwiO0DvWGDGbPO9MxuLjgWa9elWecbMmaYR4uxMPRkS5B/ty/bis5xcWMa92UviGBrEpIvpwRL1nlnVXlT3S17NBUFX8hrXWV+ausZvZQxRtX/gtcONyxHfsJbhDGHa8/Tnp8Ct+/9BHhLRvg4m6H/VCVm+Y/158rgjXZL7DvAXBwcGDQ6y9SmJPL+vdmk3HqDD71Qi97zEtizbq/grbdqr6/Kn+EC5cxl5hJOnqK4VMeo6iwiDnPvUedJvXxqxtkg7Tnq2rdWrdkxp7dOHp54x4RQdbBg5c31iWo+vihUqGLlmnQtQ0NurbhTMwRtsz5jWETH7+sGS+oyqr9z9tv+SKRXdoQ2cWSfeucJQy1V3ZRI6wZHet+4GVgBZZq/oFSarLW+osLLXPeUGB63erVlxzM2deXwrS0sulCkwkno9Hq5c3FxRyeOZOALl3wa9/+kt//37Bc1Ugvm85LS8fVWPEsl6ufsXIZX6Nl+dL/XXy8qdOxLaajsXZthDj7Gikol60gPR2X89a9i58vBeX+PgXpJpxLy9Tp1ZM6vSyXs48t/BkXX1/sKaWggAAXl7Jpfxdn0spdGbmYtn5GEvPyySwqAmBDUirNjN52bYS4+RnJST23/qu6wuHuZyQ31XTRMleq0wmphNXxL5uuG+JHfGI6zk6OhIWWe72OP2eS0qv6FTax7bc17PzT0je5TuNwMpNNZfMyU0x4+p/3N/D2JD8nD3NJCQ4GA1kpJrxKy+xeupnuwwdauluGBmIM9iflVBJ1m9jmquD+P1dzaPkGAAIaRpCTcm695aRWPsvu4uVJYe657Llp6bj71Xz9cfMzkptafr9YuV67lV45uFgZZw93gpo1In73Prs2QtzP23ZzLrDt5lxh2+7OJWuIXmqp+yFR4WSlmMrmZaWYKpyFB3A7r+5nlzvb7elvxM3bAydXF5xcXQhr0ZDk2NN2aYQ4+/pSmF7+uCHd6uOGnKNHydizi8y90ZiLiyjJyyf2i8+of+/9Nkp7TvQfa9i31LL9BkWFk33e9utxXv1w9faksNz6z0mt/DcCCG0RRcYHKeRlZuPm7WmT7Hv/WM3+ZZbsgVERFbJnp5oq7VfOz56dml7lVcDQFlGs/NC22WvaFXTuocZY0z/gf0C70j5gdwEdgOdtGws869cnPymJ/JQUzMXFpG3dim+bNlYtq7Xm+Dff4FanTqWb1u3B2CCCnIQkcpIs2U9v2kZI+9YVyoS0b82pdZvQWpN25BhO7m64Gn0ozi+gKM9yKbg4v4Ck6P142flMnndkffISk8hLtuRP2rIN/7YV171/mzYkbLDkzzx6DEd3N1xKG1qFmZkA5KemkbJjJ0FdLtvDNa1yODOLUHdXgl1dcFSK3iGBbElO++cFgeT8Apr6eOFS2nWmjZ+RUzl5toxbiX/DCLISkshOSqGkuJgTG7ZTt0OrCmXqdmjN8TWb0VqTcvg4Tu5uNX4gY60lS3dw+62WLoad20WRmZVLQpKJbbuPEhUZQkS9QJycDAwf2o0lS7fbLVfHG3rzwIfP88CHz9Oka2uiV2xBa03cgeO4erhW6IoFljN89Vs1Yv+6XQDsWb6Fxl0sfyfvIF+O77acTc1OzyTtdBK+If7YSrNr+3DjWy9w41svEN65NUfWWLInHTqOs7tbpS95pRR1WjQmdtNOAI6s2kx4x9ZV/Wq78msYQXa5un9y43ZCz6v7oR1aE7vWUvdTDx/Hyc1S9/MzsyjMyQWguLCQxL0H8Q4Ntmv+s9tuVrltt955+cM6tOZY6babXLrt1nRXrHbX92bM9OcZM/15orq2Zt9KS/05c/A4Lh6uFbpigaX+hLdqxKH1uwCIWbGFqNK6H9WlFaf3HcNcUkJRQSHxh07gH2afv4N7RH0KkpIoSEnGXFxM+tat+LS27rgh9OZbaPnGW7R47Q3q3zcWr6ZN7NIAAWg1pHfZzeSRnVtzcJVl/SccPI6zu2ulBoZSirotG3F04y4ADqzcTGQny/rPiE8uu5qSfPQU5uISXL1sd59RyyF9GP6O5WbyyM6tObTakj2xdN9zfgNKKUVoy8Yc22jZ9xxatZn6nVtXzn7sFCXFxTbNLmqeNfeExAFZ5aazqPhkRZtQBgP1R43i4PTpaLOZwB49cA8NJbH0qkpwnz4UZmSw99VXKcnPRylF/LJltJ40iby4OFI2bcKtbl2iJ08GoN7NN2Ns1epib3nZOBgMtB4zko1vfYA2mwnv3R3vsFCOL18DQOSA3gS3aUnirr0se/ZlDM7OtHtgDAAFmZlsmT4LsIz0UbdbJ4Jbt7BL7rOUwUDU6JFEv/c+2mwmpGcPPOqGcmaVZd2H9u2DX+uWpEVHs+WFl0qH6L2rbPmYj2dRnJ2DMhhoNHoUTna+0dKsYebBo0xq3xIHpVh2JpGTOblcGxYCwJ9xCRidnXivSzvcHQ2YNQwLr8sjG7ZzKDOL9YkpTO/ajhKtOZaZzZ/njW5jaw4GAx3v+Q8rX/sIbTbToF83jPVCObzUcjN3o4G9CG3XgjO7Yvj1yYkYXJzp+tAdZcuvn/EFifsOU5CVzc+PjKf1bdfTsH93u+X/+oPH6dWtGQG+XhzZ/CFT3l2AU+nQnZ99t4w/V+xkcL+2xKydTm5eAQ8+a6nvJSVmnprwFb9++wIGgwNfz1vF/kNxF3srm4nq1Jwj22L46P7JZUP0njXnlZnc8MQovPx96H/PMH6e9hWrvl1CSIMw2g62DMPba+S1LH7vO2Y98joA/e8ehruPfc7mhbVrQdyOGBY+MQmDsxO9HjlXN/5+/WN6Png77n5GOo6+kVXTv2TH3N/wj6xH4/7dAMg1ZfLruGkU5Vn2q/t+X8XN747H2Q5dyRwMBtrf/R/WvGGp+5F9u+ETFsqRZZa6H3VNL+q0bUH8rhh+f2oiji7OdHrQ8vnyTZls+eQbtNmM1pp6XdsT2t4++/zy+Tvf8x+Wl267UaXb7qHSbbfxwF7UbdeC07ti+OVJS/7u5bbdtaXbbn5WNgtLt91Gdtx2ASI7NOfYthg+f8hS9wc/fq7u/zR5JoMeHYWnvw+97hrGkre/Yv33SwhqEEbL0iGo/euFUL9dM75+4g2UgwOtBnYlIMI+J9KUwUDYiNs5OmM62qzx794Dt9C6pKxZBUBA774UZWRw8PWpZccNySuW0eyVyRjs2GX7YiI6tODkjn18/8hkHF2c6P/Yufrx29RP6PfI7Xj4+dD1zhtZ+u6XbP7hNwIjw2h2jWX7PbpxFwdXb8HBYMDR2YlBz9xjt6664e1bcHJHDHMenYSjixN9Hz2XfcnUj+n7yO14+BnpeseNLH3vS7bM+Y2AyHo0G2DJfmzTLg6t2oyDoyX7wKfvvWq6GVdHLf5oVlNV9j8ElFJPl/7YFmgFLMLS4+9GYIvW2to7hvSYanTHulJ806cPz21ZUdMxqm1a5/6MXbeqpmNU2+yefRla+gV+Nfp1YC8m7VxW0zGq5ZV21+AWPqqmY1Rb3sk5fHvkr5qOUW13Rg3mjd1LazpGtY1rM5AJ26/Oug8wpcM1TL1Kt92X2l3D7ANXb90f23QwI1euqekY1Ta3X2/ej/m7pmNU25MtBvHe3qt33/NUy4FXxeF9o1lr7HZz3uEHe1+R6+RiV0LaAUeAoVge137WIlsGEkIIIYQQojZT9hkw8Yp2sUZIB+Al4FbgA/vEEUIIIYQQQtR2F2uEzAT+BCKBbeVeV1i6ZTWwYS4hhBBCCCFqJbkn5CKjY2mtZ2itmwFfaq0blPsXqbWWBogQQgghhBCiWqx5WOHD9ggihBBCCCHE/wcOciXEqueECCGEEEIIIcRlY81zQoQQQgghhBCXidwTIldChBBCCCGEEHYmV0KEEEIIIYSwI7kSIldChBBCCCGEEHYmjRAhhBBCCCGEXUl3LCGEEEIIIexISX8suRIihBBCCCGEsC+5EiKEEEIIIYQdKbkMgNJa2/o9bP4GQgghhBBCAFdFP6dW36y12/Fx9JheV+Q6scuVkEUn/rDH29jEjRFDmH3gr5qOUW1jmw7m+a3LazpGtb3ZaQA/x1699efm+kNYdvr3mo5RLdfUvY5vj1y9df/OqMG4hY+q6RjVlndyzlW/7X556OqtP/c0HnzV7vvHNh3MQ+tX1nSMapvZo99VX3em7Vla0zGq7bnWA7l91eqajlFtP/TtU9MRrCK3hMg9IUIIIYQQQgg7k3tChBBCCCGEsCO5EmJlI0Qp1b6KlzOAE1rr4ssbSQghhBBCCFGbWXsl5GOgPbAHyw0/LUt/9ldKPaS1/ttG+YQQQgghhKhV5EqI9feExALttNYdtdYdgHbAXuAaYJqNsgkhhBBCCCFqIWuvhDTVWsecndBa71NKtdNaH5MnPgohhBBCCGE9Bzl8troRclAp9Qkwt3R6BHBIKeUCFNkkmRBCCCGEEKJWsrYRcjfwCPBfLPeErAOexdIA6WeLYEIIIYQQQtRG0pHI+kaIAzBda/0OgFLKALhorc1Atq3CCSGEEEIIIWofa29MXw64lZt2A5Zd/jhCCCGEEELUbkrZ79+VytpGiKvWuuyKR+nP7raJJIQQQgghhKjNrG2E5JR/YKFSqgOQZ5tIQgghhBBCiNrM2ntC/gv8qJQ6UzpdB8sIWUIIIYQQQohLoGSMXusaIVrrrUqppkATLKNjHdBay9C8QgghhBBCiEtmVSNEKeUOPA1EaK0fUEo1Uko10Vr/ZstwWmsWf/wTB7bux8nFif88ezthjepVKrd+0VrW/bya1DMpvPLjVDx8PAHYsXwbq+YvB8DFzYWbHx9OaMO6toxcIfvKTxdyfPs+HF2cufbJ0QQ3rJw9IzGV3976ivzsXIIahHHdU3dicLL8WU5FH2bl5z9hLi7BzduDEa89aZfsAIm7Y4j+9ke0WRPRtzuNhw2uMF9rTfS3P5K4KwaDixPtx47BGBl+br7ZzKoJb+Dqa6Tbs4/YLXf5fL9+8hMHt+zHydWJ4c/cTt0q6s6GRWtZ//NqUuNTmDD/XN1JOpnIgnd/4PSROAbfdT29h/e3e/4fP/yZmM37cXZ14s7nRhHeuHL+VT+vZeXCNaScSeHNn6fgWZr/0K4jzJrwOf4hfgC07dWa68YMrrS8LfP/PWshR7btw8nFmaFPjaZOVOX86Qmp/PzmV+Rl51KnYRg3PmOp//k5eSx6+xsyktMxl5jpekt/2g7sapfsM996kCED2pGcmknHgc9VWeadSXcxuF9bcvMKGfvMJ+zaGwvAwD5teHviGAwGB76au5K3P15sl8zl1YZtd9nshRzdbqk71z85mpAq6o4pIZVFb31FflYuwQ3DGPq0pe5s/mk5Mau2AWAuMZMal8AT372Gm5eH3fJfzfv+1OgYDv8wH7SZOr16EHH9tRXma605/MN80qL34uDsTLP77sIrwlJ/Ti1dTvya9WitCe3dk3qDBtgtd/l8V3v92fTlAk7tiMHRxZnej95JQIPK+bMSU1g5/UsKsnPxj6xHn8fHYHByxHQ6gTUffUfq8Tg6jrqBVsOusUtugIy9ezk1fx6YzQT07EnItUMqzM9PiCf2q6/JPXWS0BtvImTQoArztdnM/tdexdloJOqxx+2Wu6ZcyTeM24u194R8CRQC3Uqn44CpNklUzoGt+0k5ncxzX47n1v+O4OcZP1ZZrn6LSB5442F8g30rvO4X4s9Dbz/O07OeZ8Dtg1g4fZ6tI5c5vn0f6fHJ3DtzAgMfHcGyT+ZXWW7N14voMKwv982cgKunO9HLNgKQn53LspnzuWn8A9z94YsMfe5eu2XXZjO7v55Ht+ceY8C0CcRt2kbm6fgKZRJ3x5CdkMQ170yk7X2j2f3V3Arzj/65Eq/QELtlPt/B0rrz7JfjueXJEfzyQdV1J6JFJPe98TDG8+qOu7c7Qx++ld632rfxcVbM5v0kn05m4rcvcvvT/2Hu9AVVlmvYMpIn3n4Yv/PyA0S1asCLn/6PFz/9n10bIABHt+0j7Uwyj3w6geseH8EfH1Vd/1d8uYguN/Xl0U8t9X/X35b6v+23tQTUC2Hsh+O4843HWfbZL5QUFdsl+7c/rubGMW9ccP7gfm1pWD+Elr2f4rFxnzLj1fsAcHBQTJ96Dzfe9SbtBjzL8GHdadrIPic9zqoN2+6x7ftIP5PMg7MmcO2jI/jrAvvOVV8totONfXlwtqXu7F5qqTtdbhnAvTOe594Zz9Pnrhuo1zLKbgeQcPXv+w99N4c2Tz1G56mvkLh5Kzmnz1Qokxa9l7zEJLq8Ppkmd43m4Dc/AJAdd5r4Nevp8NI4Ok16idTd0eQmJtot+1lXe/2J27mPzPhkhn/wCj0fHMWGT+dWWW7r94tocUM/hn/wCi6ebhxaYcnv4ulBt3uH02qonU+cmc2cnPMDjR5/guYTJ5G2dSt5ZyrWHYO7B/VGjiR44MAqf0fS8uW4htSxR1xxhbC2EdJQaz2N0qeja63zsHTLsql9G6JpP7ATSikimtUnLyePzNSMSuXqRoXhF+Jf6fX6LSJx97IM4hXerD4ZKZWXtZWjW6Jp3q8zSilCm0RSkJNHdlrF99dac3LPYRr3aAtAi/6dObIpGoADa7bTqFsbvAMtZ7LdjV52y55+NBbP4EA8ggJwcHQkrGsHErbvrlAmYfsewnt2QSmFX1QkRTm55KdbPl9eajoJu/YS0beH3TKfb9/GaNpfY6k74dWoO55GL+o1CcfB0dpN5PLas2EvXUrrfmTz+uRl55FRRf56jcLKrnZcSQ5uiqZVf0v9D2saSX5OHllV1P/YPYdp1rMtAK0HdOZgaf1XCgrzCtBaU5hXiJuXOw4G+/wt1m85QJrpwo8/umFQB35YuBaALTuP4OPtTkiQkU5tozgam0DsySSKikr48deN3DCoo10yn1Ubtt3Dm6JpWVp36ja98L7zxJ7DNC3dd7Ya0JnDpXWnvP2rd9C8dwd7xC5zNe/7M4/F4hYUhFtQIA6OjgR36UTKrj0VyqTs3ENI964opfBp2IDi3DwKTBnkxifg3SASg4szDgYDxiaNSN6xy27Zz7ra68+JrXuI6mPJH9Q4ksKcPHLTK+c/s/cQkV3bARDVpwsntlq2czcfLwKjInBwNNg1d87x47gGBeESaKk7vh07Ydpdcd/j5O2NR/36KEPlbIXp6WRERxPQs6e9Itc4GaLX+kZIoVLKDdAASqmGQIHNUpXKSM3AGHjuDK8xwFjlgZg1tv65iSadml2uaP8oOzUDrwBj2bRXgJHs87LnZeXg6uGGQ+kG6elvLNtZpp9JIj87l3njZ/Dt09OIWbHFbtnz0k24+Z1b765+vuSdtxPMSzfh5n9+GRMA0d8toOWom2u05memVKw7PgHGKhshV6qMlAyMQcayaWOgEdMlNqKP74vltfvf4qNxszhzPP6fF7iMslIz8A40lk17BxjJOr/+Z1as/17lynS8oTcppxJ4/84JzH70dQaNvRXlUDMNwvOFhvgRF59aNn06IY3QED9CQ3yJO1Pu9fhU6lZxhcqWasO2m3X+vtO/6rrj4lmu7lRRpii/kGM79tOkexubZy7vat73F5jScS1Xf1x8jRSkp1csk27CpXwZPyMF6SY86oZiOnSYouxsSgoKSY3eS0FaxWXt4WqvP7lpJjzKbZ/u/kZy0kwVyhRk5eDsfi6/h78vOWk1+/1WZDLh5HvuhJizr5Eik/V//1Pz51H31ltRV/IRs7jsrB0dayLwJ1BPKfU90AO450KFlVJjgbEAs2bNInhw5f6MVtFV/O5qXIA5suswW//cxMPv2a9frdaVw1fati5SxlxiJunoKYZPeYyiwiLmPPcedZrUx69ukA3Snp/LmjJVZVck7IzGxdsTY2Q4yfsOXf5sVqryI1xFO7eq64/1+es1CmPynJdxdXNh76Z9zH75CyZ+O/5yRry4qvKfX+QiFe3Yjv0ENwjjjtcfJz0+he9f+ojwlg1wcXe74DL2UtU+SGtd5d+nitVgW7Vg263qQ1Retf9c5sjWvdRtFmnXrjRQC/f954WvartVCjxC6xA+ZDC73n4fg6sLnvXCaujEwdVefyq/dv4+p8o6ZqtAVquy8li1pGnPHpy8vPCIiCDr4MHLG+sKdhUdktiMtaNj/a2U2g50xVKrntRap1yk/Gxg9tnJRSf+sDrQhsVr2fy7pW9jvSbhmJLPtaRNKSa8/b2t/l0A8cfOsOC9udz36oN4eNt2Z7JzyRqiS/uVhkSFk5ViKpuXlWLCw8+nQnk3b0/yc/Iwl5TgYDCQnWrCw9dSxtPfiJu3B06uLji5uhDWoiHJsaft8kXk5mckr9wZrPy0dNx8z8vu50teasUyrkYfTm/ZQfyOaBJ2x2AuKqY4L49tH39Jx0cu2Ga9bDYuXsuWPyzrP6xxxbqTkWLC2+/S6o69rf5lHeuXWPJHNAnHlGQqm2dKNuFzCXXfzcO17OeWXZsz7/0FZGdkl924bgvbflvDzj8t+es0Dicz2VQ2LzPFhKd/xTrkfl79z0ox4VVaZvfSzXQfPtDSZSg0EGOwPymnkqjbJMJm+a11OiGVsDrnuvDVDfEjPjEdZydHwkLLvV7HnzNJ9j0TfLVuu9uXrGH3X6V1p9F5+85UE55V7DsLssvVnSrK7Ftjv640tWXf7+LrS365+lOQbsLFaKxQxtXXt8IVjoI0E86lZUJ79yC0t6Ur39GFv+DqW3FZW7na68++P1dzcNkGAAKiIsgpt33mpppwPy+bq7cnhbnn8uekplcqY29ORl+K0tPKpgvTTTidV3cuJOfoEUy7d5Oxdy/moiJK8vI4/vnnRN53n43SiiuFtaNjLddaDwCWVPHaZdV9WC+6D+sFwP7NMWxYtJa2fdtz8sAJ3Dzc8Pa3fkNLT0rnm8lfMPK5OwgMs/0OvN31vWl3fW8Ajm2LYeeSNTTt1Z74Q7G4eLhW2skppQhv1YhD63fRtHcHYlZsIapLKwCiurRixewFmEtKKCkuIf7QCToM62vzzwBgbBBBdkISOUkpuPkZidu0vdKBSEj7Vhxbupq63TqSfjQWR3c3XH19aDHiJlqMuAmA5H2HOPL7MrscxAB0G9aLbqV158DmGDYsXkubvu05deAEru6XVndqQp+betLnJkt/2L2bYlj9yzo69G9H7H5L3fe5hPwZaZl4+3qhlCJ2/wm01jZvhHe8oTcdb7DU/8NbYtj22xpa9GnP6YOxuHq44lVF/a/fqhH71+2iRZ8O7Fm+hcal9d87yJfjuw8S3rIh2emZpJ1OwreKe3dqwpKlO3jorkHMX7yBzu2iyMzKJSHJRHJqJlGRIUTUC+RMQhrDh3bj7ic+tGu2q3Xb7XB9bzqU7juPbI1hx29raNa7PWcOxuLifoF9Z+tGHFi/i+a9OxC9fAuNSusOQH5OHqf2HmHoM3faJX9t2fd7RUaQl5hEXnIKLr5GEjdvpcWDFQ8E/du25vTyVQR16UjmseM4urviYrR8vsLMTJy9vclPTSNl+07aj696dLnL7WqvP82v7UPza/sAcHL7Xvb/uYYGPTqQfDgWJ3c33H0r56/TojHHN+2kYY+OHFm9mfBOre2S9UI86tcnPymJgpQUnIxG0rdtJfK++61atu7Nt1D35lsAyDp4kMSlf/+/aIDIY0L+oRGilHIF3IEApZQv566teQOhNs5G087NObBlP2/ePRVnF2eGPzuqbN7n42dx29Mj8fH3Yd3Pq1n94wqy0rJ498FpNO3cnOFPj2TZd3+Rm5nDz6UjIzkYDDz50TO2jg1AZIfmHNsWw+cPTcbJxZnBj48um/fT5JkMenQUnv4+9LprGEve/or13y8hqEEYLUuHIfWvF0L9ds34+ok3UA4OtBrYlYAIm69ywLKeWt81gg3TPkSbzUT06YZ3WCjHl6+xfLYBvQlu25LE3TEsfeYVHJ2daTfWPjtrazXp3JwDW/fz1j1TcXJxZvgz5+rOly/N4tanRuLt78P6Xyx1Jzsti+kPTaNJ5+bc9tRIstIy+eDxdyjIzUcpxbpfVvP07BdwLXeFwZZadGlOzOb9TLzjVZxdnbnjuZFl8z4aN5vRz47AGODDyp/WsGzuCjLTsnjt/rdo0aUZo58dyc7Vu1m7eD0GgwEnFyfufWmMXfvaRnVqzpFtMXx0/+SyIXrPmvPKTG54YhRe/j70v2cYP0/7ilXfLiGkQRhtB1vqf6+R17L4ve+Y9cjrAPS/exjuNryKU97XHzxOr27NCPD14sjmD5ny7gKcSodO/ey7Zfy5YieD+7UlZu10cvMKePDZWQCUlJh5asJX/PrtCxgMDnw9bxX7D8XZJfNZtWHbbdjRsu+cNdZSd6578lzdmT9xJkMet9SdfncPY9G0r1jz3RKCG4TRetC5IZwPbdxDZLumOLu62D3/1b7vb3zHCHa/OwNtNlOnZ3c86oZyeqWl/tTt1xv/1i1J27OXTeMmYHB2pum9d5Utv/ej2RRlZ6MMBhrdMQonD/t2ZYKrv/7Ua9+CuJ0x/Pj4JBydnej16B1l8/567WN6PnQ7Hn5GOt1xIyvf+5Ltc37DP7IeTfpbBi/NTc9k0bhpFOVZvrv2LlnFre+Nx9nGXVmVwUD4yFEcfn862mwmoEcP3EJDSV69GoDAPn0oyshg/2uvUpJvyZa0fBktJk7C4Fbz3WxFzVBV9S0sm6nUk1ielh4KnOZcIyQT+FRrbc1pvkvqjnWluTFiCLMP/FXTMaptbNPBPL91eU3HqLY3Ow3g59irt/7cXH8Iy07/XtMxquWautfx7ZGrt+7fGTUYt/BR/1zwCpV3cs5Vv+1+eejqrT/3NB581e77xzYdzEPrV9Z0jGqb2aPfVV93pu1ZWtMxqu251gO5fdXqmo5RbT/07XNVXGPo8fM6u901uP7mnlfkOrnolRCt9fvA+0qpx7XWH9gpkxBCCCGEEKIWs3boigSllBeAUuolpdRPSqn2NswlhBBCCCFEraQc7PfvSmVttAla6yylVE9gMPA18IntYgkhhBBCCCFqK2sbISWl/18PfKK1XgQ42yaSEEIIIYQQtZc8Md36RshppdQs4D/A70opl0tYVgghhBBCCCHKWNuQ+A/wF3Ct1toE+AH/s1UoIYQQQgghRO1l7RPTc5VSSUBP4DBQXPq/EEIIIYQQ4hLY89ldVyqrroQopV4BngdeKH3JCfjOVqGEEEIIIYQQtZdVV0KAm4F2wA4ArfWZs0P2CiGEEEIIIawnF0KsvyekUFsera4BlFIetoskhBBCCCGEqM3+8UqIsnRa+610dCyjUuoB4F7gU1uHE0IIIYQQoraRKyFWNEK01lopdROWe0IygSbAy1rrpTbOJoQQQgghhKiFrL0nZCNg0lrLsLxCCCGEEEL8C3IlxPpGSD/gQaXUCSDn7Ita69Y2SSWEEEIIIYSotZTlfvN/KKRURFWva61PWPEe//wGQgghhBBC/HtXxTWGAX+st9vx8fIhPa7IdWLtwwqtaWxckKnw93+zeI0yOl/Hx/v+rukY1fZI80F0mLO2pmNU2/ZRva769f/G7qvz9qlxbQZetdnBkv/5rctrOka1vdlpAG7ho2o6RrXlnZxDWsHimo5RbX4uw0jIuzrzh7gNo8HHq2s6RrUde6QPO1KW1HSMamsfcD1zj/5Z0zGqbWTDawl/d2VNx6i2k0/3q+kIVx2l1LXA+4AB+Exr/cZ580djuTccIBt4WGu9+9++r7XdsYQQQgghhBCXgcMVcm1CKWUAPgIGAnHAVqXUYq31vnLFjgN9tNbpSqkhwGygy799b2ufEyKEEEIIIYSoXToDR7TWx7TWhcBc4MbyBbTWG7TW6aWTm4Cwy/HGVl0JUUp1B+qXL6+1/uZyBBBCCCGEEOL/Ewdlv1umlVJjgbHlXpqttZ5d+nNd4FS5eXFc/CrHfcAflyOXNQ8r/BZoCOwCSkpf1oA0QoQQQgghhLiClTY4Zl9gdlUdw6psISml+mFphPS8HLmsuRLSEWiurRlGSwghhBBCCHFRV8o9IViufNQrNx0GnDm/kFKqNfAZMERrnXo53tiae0L2AiGX482EEEIIIYQQV4ytQCOlVKRSyhkYCVQYGlApFQ78BNyptT50ud74gldClFK/Yrkc4wXsU0ptAQrOztdaD7tcIYQQQgghhPj/4koZGUprXayUegz4C8sQvV9orWOUUg+Vzp8JvAz4Ax8ry6Pei7XWHf/te1+sO9bb//aXCyGEEEIIIa5cWuvfgd/Pe21muZ/vB+6/3O97wUaI1no1gFIqEojXWueXTrsBwZc7iBBCCCGEEOL/B2uuBv0ImMtNl5S+JoQQQgghhLhEDkrb7d+VyppGiGPpw0sAKP3Z2XaRhBBCCCGEELWZNY2QZKVU2U3oSqkbgRTbRRJCCCGEEKL2clD2+3elsuY5IQ8B3yulPsTyQJNTwBibphJCCCGEEELUWv/YCNFaHwW6KqU8AaW1zrJ9rLL35t03fmbD2v24ujoxYeoomjavd8Hyb7+2kN9+2cKqLW9Wa/nLnX315wuJ3R6Do4szgx6/g6CGld87IzGFP975ivzsXIIahDH4yTEYnM79WRIOn2D+uHcY8sw9NOrezi7ZAbrV8eXZ9g0wKMUvRxP4an9chfl96vrxcOv6mLWmxKx5Z8cxdqVkWrWsPfzb9R+39zC/vj4b7yB/AKK6tqHLiCF2zb/5ywXE7bTk7/nInQQ0qJw/KymFVdO/pCA7F//IevR+fAwGR0dMpxNY9/F3pB6Po/3IG2g17Bq7Za8N+RN3xxD97Y9osyaib3caDxtcYb7WmuhvfyRxVwwGFyfajx2DMTL83HyzmVUT3sDV10i3Zx+xa/aZbz3IkAHtSE7NpOPA56os886kuxjcry25eYWMfeYTdu2NBWBgnza8PXEMBoMDX81dydsfL65yeVvSWvPem4vYsPaAZb89ZQRNmodVKvfqK/M5EBOH1prwiEBemjoCd3cXsrPymPjCHBITTJSUmLn9rj7ccFMnu+afMW0Rm9cdwMXViRcmj6Bxs8r535w4n4P7LPnrRQQybrIl/1n7957ikTEf8Mqbd9B3YGu75e9dz5eXe0bh4KCYvy+emTtPVZh/TX1/nu5SH7OGErNmyrojbEuw7Pvvbl2XEc3qoBTM2xfPl3tO2y33WVprvp7+M7s27sfZ1ZmHx48isknl9f/hxO84duAUBkcDDZuHc/9zw3F0NHD6RCKzXp3L8UNxjBh7HTfc3s/u+f+Y9ROHt+7DycWJm54eTWhU5X3n5l/XsOmX1aTFp/DcnFfx8PG8pOVtoU99Pyb2bYTBAeZGx/Px1pNVlmsd7MWiUR14dEkMvx9Opo6nC+8NaUaguzNaww/RZ/hip/2PG+ztShmityZZtQ6UUtcDjwBPKaVeVkq9bNtYFhvW7ufUiWQWLHmRca/8h2lTF1yw7P6Yk2Rl5VV7+cstdsc+TGeSuOvjlxnw8EhWzJpXZbn13yym3dB+3P3xy7h4uBOzfGPZPHOJmfXfLCK8bTN7xQYsl+7GdWjIE6tiuO337QyOCCTS271CmS2JJkb+sYPb/9zJpC2HmNClkdXL2sPlWP+hzRoy+r1xjH5vnF0bIABxO/eRmZDMrTNeofvYUWz8bG6V5bZ9t4gW1/fjthmv4OLhxuEVlvwunh50uWc4LYf2t2fsMldzfm02s/vreXR77jEGTJtA3KZtZJ6Or1AmcXcM2QlJXPPORNreN5rdX1X8fEf/XIlXaM084/XbH1dz45g3Ljh/cL+2NKwfQsveT/HYuE+Z8ep9ADg4KKZPvYcb73qTdgOeZfiw7jRtVNdesctsXHeAUydS+PG35xn38m1Mm/pTleX++79hfLvgab5b+AzBdYwsmLMegAVzNxDZMJhvFzzNR58/xIy3f6WoqNhu+TevO0DcyRS+X/w8z064jXdfrTr/Y88O44v5T/Plj88QFGLk57nry+aVlJiZ9f4SOnVrYq/YgGX/Pal3I+5ZEs3gOVsZ2iiIKN+K++8NcelcN287N8zfzvMrD/J6P0vGxn7ujGhWh5sX7uD6edvoH+FPfR83u+YH2LVxPwlxKbw370UeeG44n79d9fd+j0HteWfOOKZ9+z8KC4pY+esmADy93bnrqZu5YZR9Gx9nHd62j9TTyTzx2UsMfWIkv31Y9ThA4c0bMOa1RzAG+VVr+cvNQcHU/o256+fdDPhqC8OaBtPIr/J3v4OCF3o1ZPWJtLLXSrRm6uojDPh6CzfO2c6YtnWrXFbUPv/YCFFKzQRGAI9j6Y41HIiwcS4A1qzcy5BhnVBK0apNfbKy8khJzqhUrqTEzIx3fuXxp4dWa3lbOLYlmmb9OqOUok6TSApy8shJq/jeWmtORR+iUfe2ADTv14Wjm/eUzd/9+2qiurXFvfQMh7208PPiVHY+p3PyKTZr/j6ZTN+wiju6vOJzA6a5GQxobf2y9nA51n9NOrltD1G9LfmDGkdSmJNHbnrl/PExh6jf1XKFLKpvF05s3Q2Am48XgVEROBgMds8OV3f+9KOxeAYH4hEUgIOjI2FdO5CwfXeFMgnb9xDeswtKKfyiIinKySW/9PPlpaaTsGsvEX172D07wPotB0gzZV9w/g2DOvDDwrUAbNl5BB9vd0KCjHRqG8XR2ARiTyZRVFTCj79u5IZB//pZVJdszcoYhgztgFKKlm0iyM7KJyU5s1I5D09XwFKPCvKLKH2AFkopcnMK0FqTl1uIt487BoP9zjmuWxXD4Bss+Vu0tuRP/af8BefyA/w0Zz19BrTC18/DbrkB2gR5cyIjj1OZ+RSZNb8dSWJgpH+FMrnl9/2OBjSWnX9DX3d2JWaSX2ymRMPmMyYGRQbYNT/A9nV76XVtR5RSNGpZn9ysPNJTKq//dt2bo5RCKUVUs3DSkizbr4+vFw2bhWNwrJnz1Ac27aXtAMtxS72m9cnPySMrrfJxS52GYfgG+1d7+cutbYg3saY8TmZY6s6vBxIZ1LDy3/+etmH8cTiZ1Nyy8Y5Iyilkb5Jln5VTVMKR1BxCPF0qLVvbyD0h1l0J6a61HgOka60nAd0Au1zbS07KIDjEWDYdFGwkOanyxvTjnLX07tuCgECfai1vC9mpJjz9fcumPf2NZJ+3I8jPysHFw63sQMszwEhOakbZ8kc37aHV4J52yVtekLsLibkFZdOJuYUEulXeIfQL82fh9R14v08LJm0+dEnL2tq/Xf8ACQeP8/1Tr/PL5I9JPVnxTLit5aaZ8Ag4l9/D30humqlCmYKsHJzdz+V39/Ml1w5fNta4mvPnpZtw8zuX3dXPl7zzGlB56Sbc/M8vYwIg+rsFtBx1M6grc88fGuJHXHxq2fTphDRCQ/wIDfEl7ky51+NTqRvsW9WvsKnkpMwK++3AYJ8L7renTpjH9f0mcyI2meGjLI2+20Z1J/Z4EkMHTOGOW9/hqedvxMHBfgeUKUmZBFmZ//WX53HzgMmcPJ7MLSMt+ZMTM1i7ci/DhnezR9wKQjycic8+t/+Ozy4g2KPy/ntQpD9LR3Xi8+tb8vwKy77/UFounUN9MLo44uroQN8If+rUwIFkWnIm/kHGsmm/ICNpFzn5WFxcwtq/ttGmS1M7pPtnWSkmvAONZdPeAT5kpli/X/y3y1dXiKcLZ7Lyy6bjswsI9qr49w/2dGZwo0C+u0g3vTBvV1oEebEzoXLDUdQ+1uyZz/ZxylVKhQJFQOTFFlBKjVVKbVNKbZs9e3a1w2ld1djGFb/Yk5MyWP73bobf3qtay9tOFe993ltXma+0zOrPF9JjzDAc7HgG77wIFVS1JlfGpXLrku08s3YfD7eOuKRlbe/frf/ABmHcM3syo997gTbX9+HXNz69/BEvpsqqe/7avXLH/r6q81sTq4q6o5QiYWc0Lt6eFe4PudKoKrZSrXWFM/HnXrdHon9+06qyAbw0ZQS/Lp9A/cgglv1luVq1ef0hGjUJ5dflE/j6x6d457WfycnOr3J5W6hqv3Kh/C9MHsHCpROIiAxiRWn+D95azINPXmfXqzdlqohZVR34+3gqA+ds5cE/Yni6c30AjqbnMmvnKb4Z1pqvbmjFgdRsSmqgAlW9/i9c/ou3F9C0TQOatm1gw1TWq3rXaf1xy79dvrqq/O4/L8zEvo14fe1RzBeoFu5OBmYNbcmkVYfJLiy57BmvNEppu/27UlkzOtZvSikj8BawA0sd/+xiC2itZwNnWx/aVPj7xYpX8OOcdSxaaOkX3rxlOIkJprJ5SYkmAoO8K5Q/eCCOuJMp3Hb9qwDk5xdx63WvsvD38QQFG/9x+ctp9+9r2Lt0AwDBUeFkp6aXzctONeHpW/FKjZu3JwU5eZhLSnAwGMhOMeHhZymTdPQkf7zzleUzZWUTu30fDgYHGnZpY7P8ZyXmFhBc7gbJYHdnUvIKLlh+Z3ImYZ5uGJ0dL3nZy+lyrn8X93N9mSM7tGDlrPnkZWbj5m27rnH7/1zNoeWW/AENI8hJOZc/J9WE+3n5Xbw8Kcw9lz83LR13v4pl7Olqz3+Wm5+RvLRz2fPT0nE7v+74+ZKXWrGMq9GH01t2EL8jmoTdMZiLiinOy2Pbx1/S8ZF77Jb/n5xOSCWszrluHHVD/IhPTMfZyZGw0HKv1/HnTFJ6Vb/islswdz2LF24GoFmLehX228mJGQQEXni/bTA4MODaNnz/1WpuuKkTSxZt5c57+1m6o4QHEFrXj9jjSbRoZbuG4c9z1/PbT5b8TVrUI+kS8/cf3Ia5X6/mups6cXDfKSY//z0AGaYcNq07gMHgQK/+LW2W/6yE7MIKVy/qeLqQlHvh/ffW+AzCfVzxdXUkPb+Y+fsTmL8/AYBnu0SSkG2fff/fC9exYrHlno4GzeqRmmQqm5eWZMI3oOr9yoIv/iLLlMP9rw23R8wL2vzrWnb8ZTnuCW0UTmayqWxeZkoGXv7WH7d4Bxj/1fLVFZ9dQKiXa9l0HU8Xks77+7cK9uLD65oD4OfmRL9If0u37aMpODooZg1tyc/7E/nziDwF4v8La0bHmlL640Kl1G+Aq9baZtf2ho/qyfBRli5I69bEsOCHdQwa0o69e07g6elWqctVz94t+GPV5LLpvp2fZ+Hv4wHo1a/FPy5/ObW5rjdtrusNwPFte9n9+xoa9+xAwqFYXNxdyw5wz1JKEdayEYc37KJJrw7sW7mZBp1bAXDPrEll5f6e8S2RHVvapQECsC8ti3peroR6uJCUV8ig8EDGbzhYoUyYpytxpWcXm/p64OSgMBUWk2XFsrZyOdd/Tnom7kYvy9ntQ7ForXH1sm3/7GbX9qHZtX0AOLVjL/v/XENkjw4kH47F2d2t0kG8Uoo6LRoTu2knDXp05MiqzYR3tN8oOue72vOfZWwQQXZCEjlJKbj5GYnbtL1SIyKkfSuOLV1N3W4dST8ai6O7G66+PrQYcRMtRtwEQPK+Qxz5fdkV1QABWLJ0Bw/dNYj5izfQuV0UmVm5JCSZSE7NJCoyhIh6gZxJSGP40G7c/cSHdsl028ge3FbaHWn9mv0smLOegUPaErPnJB5erpUO4rXWxJ1KpV54AFpr1q3aR0T9QACCQ4xs23yEth0akJaaxYkTydQNq9x3/nK6eWQPbi7Nv3HNfn6at54B17ZlX/RJPDxd8a8i/+lTqYSV5t+wZh/hkZb8835/sazc6xPm0q13c7s0QAD2JGVS38eNMC9XEnMKuCEqiP8u3V+hTIS3KycyLfv+FgGeODk4kJ5vufHf382J1LwiQj1dGNwggFt/2mmX3INu7cmgWy3HDTs27OPvhevofk07jsScwN3TFd+AygfhKxZvYs/mg7w04yG7dterSpehvegy1NKb49CWGDb/upaWfdoTd/AErh6ueF3CyZmmXVr+q+Wra3dCFpFGN+p5u5KQXcDQpsE88XtMhTI9P99U9vM7g5uy/Fgqfx+1NDjeGtSUI2k5fLaj4mhstdmVfK+GvVywEaKUuuUi89BaVz3kx2XUo1dzNqzZz63XvYqrqzMTpo4sm/ffh2czftIIAoMuvHFdbHlbq9+hBbHb9/H1w5NxdHFi4ON3lM37ZconXPPo7Xj6+dBzzI388c6XbPzhNwIjw2hxjf37AZ+vRMO0bUf5sG9LDEqx6FgixzJzuTXKMtrPwiMJDKgXwPWRQRSbNQUlZl5Yf+Ciy9rbv13/RzbuZM+f63AwOODo7MyQZ+62yyXts8LatSBuRwwLn5iEwdmJXo+cy//36x/T88Hbcfcz0nH0jaya/iU75v6Gf2Q9Gve35M81ZfLruGkU5eWjlGLf76u4+d3xOLvbZ7Saqzm/g8FA67tGsGHah2izmYg+3fAOC+X48jUARA7oTXDbliTujmHpM6/g6OxMu7F32jyXtb7+4HF6dWtGgK8XRzZ/yJR3F+BUOuz3Z98t488VOxncry0xa6eTm1fAg8/OAiwDfDw14St+/fYFDAYHvp63iv2H7D9MZvdeTdmwdj/Dr38DF1dnXpryn7J5Tz/yOS9MvA3/AC+mvDSXnOwC0JqoJqE895LlK+ueB69h6oR5jL7lHdCaR/97HUZf+93g3bVXUzat28/tQy35x006l/+5Rz/nuVduwy/Ai9cnzCUnx5K/YeNQnh5/wa9cuynRMHHtEb4e2goHpfjxQAKH03O5vUUdAH6IiefahoHc3CSYYrMmv9jME3/vK1v+48EtMLo6UmzWvLLmMJkF9huV7Kx23Zqxa+N+/vuf13BxdeLBF0eVzXvzmdk8MG4EfoE+fP72AgKCfXl57AwAOvVpxa33DsaUmsn4+94jLycf5aD4Y/4a3vr+edw9XC/0lpdVo07NObR1H+/fNwUnF2dueur2snnfvTyTYU+Owtvfh02LVrN+wXKy07P45NE3adSxOTf+d9RFl7elEq2ZsPIQ397aBoNSzNsbz6HUXO5oHWrJvufMBZftFOrDrc1D2J+czR93WAbDmLb+GCuPp11wGVE7qKrvmwCl1JcXWU5rre+18j0uqTvWlcbofB0f7/u7pmNU2yPNB9FhztqajlFt20f1uurX/xu7l9Z0jGoZ12bgVZsdLPmf37q8pmNU25udBuAWPuqfC16h8k7OIa3A/s8ZuVz8XIaRkHd15g9xG0aDj1fXdIxqO/ZIH3akLKnpGNXWPuB65h79s6ZjVNvIhtcS/u7Kmo5RbSef7ndVXGMYuXKN3W7WmNuv9xW5Ti54JURrfWX1IRBCCCGEEELUCtbcmH72YYUtgLLrkVrryRdeQgghhBBCCFEVhyt41Cp7uaIfViiEEEIIIYSofa7ohxUKIYQQQgghah9rumOd/7DCVP7hYYVCCCGEEEKIqskQvTZ6WKEQQgghhBBCXMgV97BCIYQQQggharOafUTmlcHa0bG6A/XPli99WOE3NswlhBBCCCGEqKX+sRGilPoWaAjsAkpKX9aANEKEEEIIIYS4RHJPiHVXQjoCzfWFHq0uhBBCCCGEEJfAmkbIXiAEiLdxFiGEEEIIIWo9eVjhRRohSqlfsXS78gL2KaW2AAVn52uth9k+nhBCCCGEEKK2udiVkLexPCH9TeCmcq+ffU0IIYQQQghxieSekIs0QrTWqwGUUk5nfz5LKeVm62BCCCGEEEKI2uli3bEeBh4BGiil9pSb5QWst3UwIYQQQgghaiN5TgioCw16pZTyAXyB14Fx5WZlaa3TLuE95M4bIYQQQghhD1dFR6ex61bZ7fh4ds++V+Q6uVh3rAwgAxj1b99k7LpV//ZX1JjZPfsycuWamo5RbXP79cZU+HtNx6g2o/N1/HfTipqOUW3Tu/a/avNP79qfCduX1XSMapvS4Rq+PPRXTceotnsaDyatYHFNx6g2P5dhuIX/66+PGpN3cg4d566t6RjVsm1kL/abfqvpGNXWzHgDd69Z/c8Fr1Bf9e7DgD+u3g4jy4f0ILvo6vzeAvB06l/TEawio2PJ1SAhhBBCCCGEnVnznBAhhBBCCCHEZSKjY8mVECGEEEIIIYSdSSNECCGEEEIIYVfSHUsIIYQQQgg7ku5YciVECCGEEEIIYWdWXQlRSkVT+XkfGcA2YKrWOvVyBxNCCCGEEKI2kqsA1nfH+gMoAX4onR5Z+n8m8BUw9PLGEkIIIYQQQtRW1jZCemite5SbjlZKrdda91BK3WGLYEIIIYQQQtRG8rBC668GeSqlupydUEp1BjxLJ4sveyohhBBCCCFErWXtlZD7gS+UUp6AwtIN636llAfwuq3CCSGEEEIIUdvI6FhWNkK01luBVkopH0BprU3lZs+3RTAhhBBCCCFE7WTt6FguwK1AfcBRKUvzTWs92WbJhBBCCCGEqIVkdCzru2MtwjIk73agwHZxKkqL3suROfPR2kydXj0Jv+7aCvNz4xM48MVXZJ88ReTNN1Lv2kFWL2trmTF7iZs/F20249+jFyHXDqkwPz8hnhNff0XeqZPUGXYTwYMGV5ivzWYOvj4VJ6ORho8+Yc/olvfXmnff+JkNa/fj6urEhKmjaNq8XqVyU1+ey/6YU6A19eoH8vLU23F3dyEzI5epL8/l9KkUnF2ceGnySBo2qmO3/Ml7Ytj//Xy0WRPWpwcNbzhv/WrN/u/nk7w7BoOzM60eGINP/XCy4xPY9fHnZeVyk1JodMsNRA4eYLfstSF//O4Ydn2zAG02E9mvB82GDaowX2vNzm9+JGGXJX/nh+7ENzKcksIiVk5+j5LiYnRJCWFd2tHythvsmv1svmWzF3J0+z6cXJy5/snRhERVrv+mhFQWvfUV+Vm5BDcMY+jTd2JwcmTzT8uJWbUNAHOJmdS4BJ747jXcvDzskv29NxexYe0By7Y7ZQRNmodVKvfqK/M5EBOH1prwiEBemjoCd3cXsrPymPjCHBITTJSUmLn9rj7ccFMnm+c+a+ZbDzJkQDuSUzPpOPC5Ksu8M+kuBvdrS25eIWOf+YRde2MBGNinDW9PHIPB4MBXc1fy9seL7Zb7rG4hvjzbvgEOSvHLsQS+3h9XYf61EYHc1cxSl3KLS3hj2xEOm3IAGNk4lJsbhICCX44mMOfQGbvn11rz2bu/sH3DflxcnXliwkgaNq1cf959+TuO7I/D0dFAo+b1ePiF4Tg6GgCI3n6Ez99bRElxCd5GD16d+ajd8qfv3Uvs3Hlos5ngXj2pO6Tid29efDxHvvqanJMnCb/pJkIHW/ZNBWlpHPniC4oyMkEpgnv3ps419t1vdgow8mizBjgo+D0ukbnHTleYPyA0kJGRdS2fo6SE6TFHOZaVC8CzraLoGuiLqbCI+9ftsmvus7TWvPX6fNavjcHV1ZmJr46hWfPwC5af9to8Fv+8kXVbp1d4PSY6lrtHT+P1t+/nmkHtbZxa1CRrGyFhWmu7HsVrs5nD38+h9TP/xcXXlx1TXse/bWs8QkPLyjh6uBN1+0hSd+665GVtnf3UnB+IevIpnHx9Ofj6q/i0boNbufc3uHsQNmIkGbt2Vfk7klcswzWkDiX5eXbJfL4Na/dz6kQyC5a8yN49J5g2dQFf/PBUpXL/fe4mPD1dAZg+7Rd+/GEtd91/DV99tozGTUOZ9v69xB5L5K3XFvLRZ4/YJbs2m4n5Zi6dn3sCVz9fNkx8g6B2rfGqe64RlLwnhpyEJHpPm4Tp6HFivp5D91eex7NOCD2njC/7PSv++wIhHdraJXdtyW82m9nx5Xz6vPA4bv5Glr00jdD2rfAJO5c/YVcM2QnJDHl3ImlHYtn+xVyumfIcDk6O9HnpCZxcXTEXl7Bi0jvUadMC/0aRdv0Mx7bvI/1MMg/OmsCZg7H89cl87nrnmUrlVn21iE439qV57w78+dE8di/dSPvretHllgF0ucVyAHN4SzRbF62ySwMEYOO6A5w6kcKPvz1PzJ6TTJv6E5//UPlExn//NwyP0m33/bcWs2DOesbc158FczcQ2TCYtz+8l/S0bEYMm8bg69vh5GTt18W/8+2Pq5n59V989l7V+4vB/drSsH4ILXs/Red2Ucx49T563zgBBwfF9Kn3cP3o1zgdn8q6X1/lt6XbOXD4dJW/xxYcFDzfsSGPrtxLYl4B3wxsy5rTaRzPzC0rcyY7n7HL95BVVEz3Or6M7xTF3Ut309DHnZsbhDBm6S6KzWZm9GnJujNpnMrOt1t+gO0bDhB/KoVPFrzAob0nmTltIW998WSlcr0Hd+CpSaMBeHfCdyxdtJkht3YnOyuPWdN+4pX3HyAwxBdTWpbdsmuzmeM//EDzp57C2deX6Fdfw7dNG9wrHDd4EDlyJGm7dlZYVjk4EDF8OJ4REZTk57NnylR8mjersKwtOQBPtGjAc1tiSM4v5OPubdiYlMaJ7HPHAPG5+Ty1OZrs4hI6Bxh5umUUj23cA8BfcUksOhHP860b2SVvVdavjeHUySR++X0Se/cc5/Upc/hmzvNVlt239wRZ5baLs0pKzMx472e69Whu67g1Tu4Jsf5q0AalVCubJjlP5rHjuAUF4RYYiIOjI0GdO5K6c3eFMs7e3nhH1kcZDJe8rC3lxh7HJSgQl9L39+3UiYw9uyqUcfL2xqN+ZKXsAIXpaWRER+Pfo6edEle2ZuVehgzrhFKKVm3qk5WVR0pyRqVyZxsgWmsKCoo421Xv+NEEOnZpDED9BsHEn04jNcU+X0amY7F4BAfiHmRZ/3W6dCRpR8W/f9KO3dTt0RWlFL5RDSjOzSXfVPHzpcQcwD0wALcAf7vkPutqz592JBbP4EA8gwMwODoS3q0DZ7bvqVDm9PY91O/VBaUU/o0iKcrNIy89A6UUTq6WOmUuKcFcYrYMhWFnhzdF07J/Z5RS1G0aSUFOHtlpFdev1poTew7TtEdbAFoN6MzhTdGVftf+1Tto3ruDPWIDsGZlDEOGdkApRcs2EWRn5ZOSnFmpnEf5bTf/3LarlCI3pwCtNXm5hXj7uGMw2K/jwPotB0gzZV9w/g2DOvDDwrUAbNl5BB9vd0KCjHRqG8XR2ARiTyZRVFTCj79u5IZBHe0VG4AWfl6cysrndE4+xWbN3yeT6VPXr0KZPalZZBVZBpWMTskiyM0FgPre7kSnZlFQYqZEw47kDPqFBdg1P8CWNXvpO8RSf5q0iiAnK4+0lMr1p2OPZiilUErRqEU4qUkmANb8tYNu/VoRGOILgNHPy27Zs48fxzUwCNfS796ATp1I31Vx3+nk7Y1nFccNzkYjnhERABhcXXGrU4dCk8le0Wlq9OJ0Tj7xeQUUa83K+GS6B1WsO/tMWWQXl5T9HOjqXDYvOj2TzKKaHax09crdXD+sa+lxQwOys3JJruK4oaTEzPR3fuKJZ26pNG/eDysZMLAdvnasN6LmWPvN0hPYrpQ6qJTao5SKVkrt+cel/oVCkwkXP9+yaRdfXwqs3CH8m2Uvh8J0E86+53YezkZfitKtf//T8+dR95bbQNVcj8HkpAyCQ4xl00HBRpKTKu9MACa/NIchfV8m9ngS/7m9FwCNmtRl1TJLFYmJPkFCfDpJiSZbxwYgP92Ea7m/v6ufL/nnrf/8dBOu/hXLFJxXJn7zNkK72q8byllXe/68dBPu5bK5+RnJSzOdVyYDNz9jxTKl+c1mM3+/8BqLH3qe4FZN8Y+y71UQgKzUDLwCzuXz8jeSlVqx/udl5uDi6YZD6cFMVWWK8gs5tmM/Tbq3sXnms5KTMitsu4HBPhfcdqdOmMf1/SZzIjaZ4aMsj4K6bVR3Yo8nMXTAFO649R2eev5GHByunN7LoSF+xMWnlk2fTkgjNMSP0BBf4s6Uez0+lbrBvlX9CpsJcnMhMfdcj+WkvMKyRkZVbmwQzIb4dACOZuTQLtAbH2dHXAwO9KjjR7D7hZe1lbTkDAKCjWXT/kE+pFVxIHlWcXEJq/7YTruuTQE4czKZ7Mxcxj/8MU+PeY+Vv2+zdeQylu/+ct+9vkYKTOmX/HvyU1LIOXUSz0j77XsCXJ1Jzi8sm07OLyTA9cJ//yH1gtmSbLJDMuslJZoIDjm3zQUF+5Jcxff+vB9W0adfawIDfSotv3L5bm79T29bR70iKKXt9u9KZe03yxCgETAIy9PRb+AiT0lXSo1VSm1TSm2bPXt29ZL9m3VW4+u7igBWns3N2LMbRy9v3EvPyNQUrataiVV/iJenjmLJiklENghm6Z+WS9xj7htAZmYed9z2FvN/WEvjpnUxONrpQKaK7GfP8lrLXFxM0s49hHSugf6oV33+Kl47P/9F6peDgwODXn+RGz58lbSjsWScsn+/+Ko+ROU/wT+XObJ1L3WbRdqtKxZwSfXnpSkj+HX5BOpHBrHsL8sZ483rD9GoSSi/Lp/A1z8+xTuv/UyOnbsEXYyqYj+kta7yM1ZZzWypitV8oQgdgny4sUEIH+w+DkBsZh7fHIjjo76t+KBPSw6bciix+we49HU2a9pCmrdtQIt2DQDLPVBHD8Qx4d37mDjjAeZ/vpTTJ5NtkLQKVdX9S7yUWpKfz6FPZlJ/xAgc3dwuV7Jqqfp7GNr6+TAkLJhPD8baN9A/qCru+ZtlcpKJZX/vYMTtfSuVffvNH3niqZvseuVV1Cxrh+g9AaCUCgJcrSg/Gzjb+tDb1q265GDOvkYK0s6dwShIT8fFaLT5speDs68vhelpZdOFpnScrHz/nKNHydizi8y90ZiLiyjJyyf2i8+of+/9Nkp7zo9z1rFo4UYAmrcMJzHBVDYvKdFEYJD3BZc1GBy4ZnBbvvtqJUNv7oKnpysvTx0FWHakN187hdC69ukW5OrnS365v39+WjouxopnXFx9jeSnnlfG11g2nbwnBu+IcFx8LvyZbeVqz+/mZyS3XLa8NBNuvj6VypS/OlJVGWcPd4KaNSJ+9z586tm+X/b2JWvY/Zel/tdpFE5Wyrl8WakmPP3O+wzenhRk52EuKcHBYKiyzL419umKtWDuehYv3AxAsxb1Kmy7yYkZBARefNsdcG0bvv9qNTfc1Ikli7Zy5739UEpRLzyA0Lp+xB5PokWrC99gak+nE1IJq3NuX1I3xI/4xHScnRwJCy33eh1/ziRd+lnwfyMpt6DC1YsgN2eS8yqP5RLl486Ezo14YnUMGYXnutAsOpbIomOJADzSOoKk3MJKy9rC7z+u4+9FlvrTqHk9UsqdvU5NysDvvDPWZ8397C8y0rMZ9+bdZa/5B/ngZfTA1c0FVzcXmrdrQOzhM9QND7TlRwAs370FaeW+e9NNOF/Cd7+5uJiDn8wkoEsX/Nvb9wROSn5hhe5Vga7OpBZU/vs38HLnmVYNeWHrvhrvfgUwf84qfl6wHoDmLSNITDi3zSUlphMQZKxQ/sD+U8SdTOam614GID+/kBuHvMyiPyazP+YEL/zPMrCKKT2H9Wv3YjA40G9AW7t8FnuTe0KsvBKilBqmlDoMHAdWA7HAHzbMhXdkffISk8hLTrGc1d2yDf+21nVp+DfLXg7uEfUpSEqiICUZc3Ex6Vu34tPauvcPvfkWWr7xFi1ee4P6943Fq2kTuzRAAIaP6sl3C/7Hdwv+R+/+Lflj8Va01kTvjsXT042A876ItNacKj3DpbVm7eoYIiKDAMjKzKOodAe5aOEm2nZoWHb/iK35REaQk5hEbunfP37zNoLata5QJqhda06v34TWmvQjx3B0c8O13IF+/KathHa1b3/ys672/H4NI8hOSCI7KYWS4mJObtxOaIeKt5SFdmhN7NrNaK1JPXwcJzc33Hx9yM/MojDHcrNicWEhiXsP4h0abJfcHa7vzb0znufeGc/TqGtr9q7Ygtaa0weO4+LuWqmBoZQivHUjDqzfBUD08i006nLuc+bn5HFq7xEadbX97XS3jezBNz8+zTc/Pm3Zdn/djtaavbtP4OHlWqkRYtl2U8p+XrdqHxH1LQeJwSFGtm0+AkBaahYnTiRTN8y+9xVdzJKlO7j9Vku3z87tosjMyiUhycS23UeJigwhol4gTk4Ghg/txpKl2+2abV9aFvW8XAn1cMHRQTEoPJA1p9MqlAl2d+Gtns15eeNBTmZVHHjE18WprEz/sAD+OmGfKwjXDe/J9O+eYfp3z9Cld0tW/WGpPwejT+Dh6YpfQOVG7NJFm9i56SDPTLmzQne9zr1bsm/XMUqKSyjIL+RwzEnC6gfZ5XN41q9PflIS+aX7zpStW/FtY913r9aao19/g1udOoQOGmjjpJUdyMiirocbIW4uOCpFvzqBbEiqWHeCXJ2Z2K4pr+8+TFzulXF18j+j+jJn4XjmLBxP3/5tWLJ4U+lxwzE8Pd0qdbnq1acVf69+k9/+fpXf/n4VV1dnFv1hedrDr39NLXt9wKB2jHtpVK1tgAgLa4c7mQJ0BZZprdsppfoBo2wXC5TBQNTokUS/9z7abCakZw886oZyZtVqAEL79qEwI4PtU16jJC8flCJu2XI6TZmIo5tblcvaizIYCBtxO0dnTEebNf7de+AWWpeUNasACOjdl6KMDA6+PpWS/HyUUiSvWEazVyZjqOHLv2f16NWcDWv2c+t1lp3EhKkjy+b99+HZjJ80Av8ALyaP/4Gc7AI0mkaNQ3luwnAAYo8lMnH89xgcHIhsGMz4SSMv9FaXnYPBQPM7R7L1rQ/QZjNhvbvjFRbKyRVrAAjv35vANi1J3rOX1f97GYOLM63vH1O2fElBISl7D9Di7tF2y1zb8re/+z+seeMjyxC9fbvhExbKkWWWm4mjrulFnbYtiN8Vw+9PTcTRxZlOD94BQL4pky2ffIM2m9FaU69re0Lb23VMDAAadmzOsW0xzBo7GScXZ6578ty6nD9xJkMeH4WXvw/97h7Gomlfsea7JQQ3CKP1oK5l5Q5t3ENku6Y4X6Rfty1079WUDWv3M/z6N3BxdealKf8pm/f0I5/zwsTb8A/wYspLc8nJLgCtiWoSynMvWW4SvefBa5g6YR6jb3kHtObR/16H0dd+3cm+/uBxenVrRoCvF0c2f8iUdxeUjcz12XfL+HPFTgb3a0vM2unk5hXw4LOzAMvNrk9N+Ipfv30Bg8GBr+etYv+huIu91WVXouGt7Uf5oE9LDA6KxccSOZaZy60NQwBYeDSBB1qE4+PiyPMdo0qX0Yz5excA03o2w8fZiWKzmTe3Hy27gd2eOvRoxvYN+3no1tdxcXXiiQnn9t2T//spj43/D36BPnzy5kICQ3x5/v4ZAHTr24oR9w+iXmQw7bs25cnR7+DgoLhmWBciGtpneHZlMBB5+yj2T5+O1maCevTAvW4oCaXHDSGlxw3RU1+lJN9y3BC/bBltJk8iNy6OlE2bcK9bl92TLAfF4bfcjG8r++x/zBo+2HeMNzu1wEHBH3FJnMjO44Z6lrrz26kE7owKx9vZiSdbWLq+lWh4ZIOlG+X4No1p4+eDj7Mjc/t15OvDJ/kjLsku2c/q2bsl69fu5cYhL+Pq5szEKee+l554+EMmTLqDwPOujIj/39SF+hxWKKTUNq11R6XUbqCd1tqslNqite5sxXvosdXojnWlmN2zLyNXrqnpGNU2t19vTIW/13SMajM6X8d/N62o6RjVNr1r/6s2//Su/ZmwfVlNx6i2KR2u4ctDf9V0jGq7p/Fg0grs/5yLy8XPZRhu4TY9V2VTeSfn0HHu2pqOUS3bRvZiv+m3mo5Rbc2MN3D3mtU1HaPavurdhwF/rK/pGNW2fEgPsouuzu8tAE+n/ldFR6fx25bb7aavVzsOuCLXibVXQkxKKU9gDfC9UioJqPnOiEIIIYQQQoirjrWNkBuBfOApYDTgA0y2VSghhBBCCCFqK4creOhce7F2HLQIrXWJ1rpYa/211noGYP+O2kIIIYQQQoirnrWNkPlKqeeVhZtS6gPgdVsGE0IIIYQQojZyUPb7d6WythHSBagHbAC2AmeAHrYKJYQQQgghhKi9rL0npAjIA9ywPKzwuNbabLNUQgghhBBC1FJX8hUKe7H2SshWLI2QjkBPYJRSaoHNUgkhhBBCCCFqLWsbIQ8Ah4EXtdYJwOPALluFEkIIIYQQorYy2PHfP1FKXauUOqiUOqKUGlfFfKWUmlE6f49Sqn21P3g51jZC7sHyxPSzT57KwjJsrxBCCCGEEOIqpJQyAB8BQ4DmWHo7NT+v2BCgUem/scAnl+O9rb0npIvWur1SaieA1jpdKeV0OQIIIYQQQgjx/8kV9JyQzsARrfUxAKXUXCwXGvaVK3Mj8I3WWgOblFJGpVQdrXX8v3lja6+EFJW2lHRpwMCzPwshhBBCCCGuTEqpsUqpbeX+jS03uy5wqtx0XOlrXGKZS2btlZAZwM9AkFLqVeA24KV/++ZCCCGEEEL8f2PP0bG01rOB2ReYXVWS8y80WFPmklnVCNFaf6+U2g4MKA1yk9Z6/799cyGEEEIIIUSNicPyLMCzwrA8D/BSy1wya6+EoLU+ABz4t28ohBBCCCHE/2dX0HNCtgKNlFKRwGlgJHD7eWUWA4+V3i/SBcj4t/eDwCU0QoQQQgghhBC1h9a6WCn1GPAXlhF9v9BaxyilHiqdPxP4HbgOOALkYhk1919TlhvdbUpuYBdCCCGEEPZw5VxjuIhpe5ba7fj4udYDr8h1YpcrIQP+WG+Pt7GJ5UN6MHbdqpqOUW2ze/Zl2enfazpGtV1T9zoG/7WupmNU21+De/Ls5hU1HaNa3u7Sn6k7l9V0jGp7qd01zD7wV03HqLaxTQeTkLe4pmNUW4jbMDrOXVvTMapt28heuIWP+ueCV6C8k3OAQzUd419oTN8lV+9xw6rre1z1xz1ZRctrOka1eTkNqOkIVjFckc0C+7JqiF6llF8Vr0Ve/jhCCCGEEEKI2s7a54T8qpTyPjtR+iTFX20TSQghhBBCiNrLQdnv35XK2kbIa1gaIp5KqQ7Aj8AdtoslhBBCCCGEqK2sfU7IEqWUE/A34IXlOSGHbZpMCCGEEEKIWshBybhNF22EKKU+oOLoVt7AMeBxpRRa6ydsGU4IIYQQQghR+/zTlZBt501vt1UQIYQQQggh/j+4ku/VsJeLNkK01l/bK4gQQgghhBDi/wer7glRSjUCXgeaA65nX9daN7BRLiGEEEIIIWolQ00HuAJYOzrWl8AnQDHQD/gG+NZWoYQQQgghhBC1l7WNEDet9XJAaa1PaK0nAv1tF0sIIYQQQojaSZ4TYmV3LCBfKeUAHFZKPQacBoJsF0sIIYQQQghRW1nbCPkv4A48AUzB0iXrLhtlEkIIIYQQotaS54RY/7DCrQBKKa21vse2kYQQQgghhBC1mbWjY3UDPgc8gXClVBvgQa31I7YM1ynAyKPNGuCg4Pe4ROYeO11h/oDQQEZG1gUgr6SE6TFHOZaVC8CzraLoGuiLqbCI+9ftsmXMKqVF7+XInPlobaZOr56EX3dthfm58Qkc+OIrsk+eIvLmG6l37SCrl7UHrTU/fvgzMZv34+zqxJ3PjSK8cb1K5Vb9vJaVC9eQciaFN3+egqePJwCHdh1h1oTP8Q/xA6Btr9ZcN2aw3fJ3DDDyUNMGGJTij7hE5h+PqzC/nocbT7dsRJS3J18fPsGC2HN166bwUIaEBaMU/BGXyM8nztgt91lJe2LY+918tFkT3qcHjYZWXHdaa2K+m0/i7hgMLs60fWAMxvrhACx7ejyOrq4oBweUgwO9J79g9/ynd8Ww7esFaLOZqP49aHnjoArztdZs/fpHzuy05O/+8J34R1ryb5j5LXE79uLq7cWwt1+ye/az+VZ+upDj2/fh6OLMtU+OJrhh5fqfkZjKb299RX52LkENwrjuqTsxOFl2q6eiD7Py858wF5fg5u3BiNeetFv2GdMWsXndAVxcnXhh8ggaNwurVO7NifM5uC8OrTX1IgIZN3kE7u4uZfP37z3FI2M+4JU376DvwNZ2yQ7QLcSXZ9s3wEEpfjmWwNf7K26710YEclczy98it7iEN7Yd4bApB4CRjUO5uUEIKPjlaAJzDtl/25351oMMGdCO5NRMOg58rsoy70y6i8H92pKbV8jYZz5h195YAAb2acPbE8dgMDjw1dyVvP3xYjsmt9Ba8+qrs1m9ejuuri688caTtGgRVancuHHvsWXLXry8PAB4443/0qxZA7Kycvjf/97hzJlkSkpKuPfeW7j11mvslr9zoJHHmjfAoGDJqUR+OFrxuOGa0EBGNTx33PBe9FGOZuUS6OrMi20b4+fihFnDbycTWBgbb7fccHUf84Cl7rz9+o+sXxuDq6sTE18dQ9Pm4RcsP+21efz68ybWbn0PgFUrdjPzg19xcHDAYHDgmXG30bZ95bpXWxiu4Hs17MXa7ljTgcHAYgCt9W6lVG9bhQLLHfNPtGjAc1tiSM4v5OPubdiYlMaJ7LyyMvG5+Ty1OZrs4hI6Bxh5umUUj23cA8BfcUksOhHP860b2TJmlbTZzOHv59D6mf/i4uvLjimv49+2NR6hoWVlHD3cibp9JKk7d13ysvYQs3k/yaeTmfjti8TuP8Hc6Qt47uOnKpVr2DKSVt1aMP2pDyvNi2rVgIdfe8AecStwAB5t1pAXtu0lJb+QD7q1ZVNSKidzztWdzKJiPtl/jO5B/hWWjfB0Z0hYME9s2k2RNvNah5ZsTk7jTG6+3fJrs5nob+bS9bkncPPzZe0rbxDSvjVedeuUlUnaE0N2YhL935qE6ehxor+aQ6+Jz5fN7/bCU7h4edotc3lms5ktX8znmvGP4+5v5I8XpxHWoRXGsHP5z+yKISs+mRunTyTlSCybP5vLda9aDtga9ulKk8F9WP/RNzWSH+D49n2kxydz78wJxB+KZdkn8xn99jOVyq35ehEdhvWlae8OLP14HtHLNtJ2SC/ys3NZNnM+t058GO9AP3JNWXbLvnndAeJOpvD94ufZF32Sd1/9iZnfPVGp3GPPDsPD0zLi+odvL+bnuesZfa9lvJGSEjOz3l9Cp25N7JYbLDdQPt+xIY+u3EtiXgHfDGzLmtNpHM/MLStzJjufscv3kFVUTPc6vozvFMXdS3fT0MedmxuEMGbpLorNZmb0acm6M2mcyrbftgvw7Y+rmfn1X3z2XtXn6Ab3a0vD+iG07P0UndtFMePV++h94wQcHBTTp97D9aNf43R8Kut+fZXflm7nwOHTVf4eW1mzZjuxsWf4++9Z7N59kIkTP+HHH9+psuxzz93Ltdf2qPDa998voWHDcGbOfJm0tAyuvfYhhg7tg7Ozk82zOwBPtmjAs5stxw0ze7ZhfeJ5xw15+Ty5sfS4IdDIM62ieGTDHkq05uN9xzmcmYObwcDsnm3YlmKqsKyts1+txzxnrV8bw6mTSfz8+0T27onl9Slz+XpO1Q3xfXtPkJVZcd127tqEPv1ao5Ti8ME4xj37OQt/fcUe0UUNsXZ0LLTWp857qeQyZ6mgqdGL0zn5xOcVUKw1K+OT6R7kV6HMPlMW2cUlZT8HujqXzYtOzySzqNiWES8o89hx3IKCcAsMxMHRkaDOHUndubtCGWdvb7wj66MMhkte1h72bNhLl4GdUEoR2bw+edl5ZKRmVCpXr1FY2dWOK0UTHy/O5OaTUFp3VsUn0+28xkZGYRGHMrMp1hX7ZIZ7uLE/I4sCsxmzhj1pGfQ4b1lbSz8ai0dQIB5BljoQ2rUjCTsq1oGEHbup16MrSil8oxpQlJtLvqny36cmpB6JxSskEK/gAAyOjkR078CpbXsqlDm1bQ8NendBKUVgo0iKcvPITbfkD27WCBcPj5qIXubolmia9+uMUorQJpEU5OSRnVZx/WqtObnnMI17tAWgRf/OHNkUDcCBNdtp1K0N3oGWbcPd6GW37OtWxTD4hg4opWjROoLsrHxSkzMrlTvbANFaU1BQhFLnTsv9NGc9fQa0wtfPvn+HFn5enMrK53ROPsVmzd8nk+lTt+L+ZU9qFlml+/bolCyC3CxXb+p7uxOdmkVBiZkSDTuSM+gXFmDX/ADrtxwgzZR9wfk3DOrADwvXArBl5xF8vN0JCTLSqW0UR2MTiD2ZRFFRCT/+upEbBnW0V+wyy5dv4qab+qOUom3bpmRm5pCUlGb18kopcnJy0VqTk5OHj48Xjo72eSJCU6MXp3PPHTesOJNMj+CK9ScmvdxxQ3oWgW6W44a0giIOZ1quqOWVlHAiO5eAcscUdsl+lR7znLV65R6uG2bZr7dqE0lWVi4pyZW/l0pKzLz/zk88+czNFV53d3ct2w/l5RUiFwpqP2sbIaeUUt0BrZRyVko9C+y3YS4CXJ1Jzi8sm07OLyTA1eWC5YfUC2ZLssmWkaxWaDLh4udbNu3i60uByWTzZS+njJQMjEHGsmljoBFTyqUd5B7fF8tr97/FR+Nmcea4/S5r+7s6k5xfUDadkl9g9ZdJbHYurXx98HJyxMXBgU6BvgRepN7ZQn66CTf/c3XA1c+X/HRTxTJpJlzL1RM3P1/y086WUWyaNoM1L7/GiZVrbR/4PLlpJjzK5ffwM5JXlu1smQw8/I1l0+5VlKlJ2akZeAUYy6a9Aoxkn9cIz8vKwdXDDYfSEwme/sayhkr6mSTys3OZN34G3z49jZgVW+yWPSUpk6CQc9kDg31ITqp623395XncPGAyJ48nc8tIyxnt5MQM1q7cy7Dh3ewRt4IgNxcSc89tu0l5hWWNjKrc2CCYDfHpABzNyKFdoDc+zo64GBzoUcePYHf7brvWCA3xIy4+tWz6dEIaoSF+hIb4Enem3OvxqdQN9q3qV9hUYmIqISHnGm8hIf4kJqZWWfa9975l6NDHee21TyksLAJg9OjrOXo0jl697mLYsMcZP/4BHBysPt/5rwS6OpOcV/G44WL77+vDg9mSZKr0eoibC418PNl/kcbk5XY1H/OclZxoIiTkXJ0NDvYlKdFUqdz8H1bRu19rAgJ9Ks1buWwXtw6dxH8f+ZiXp9xpy7g1Tobotb471kPA+0BdLMPz/gU8eqHCSqmxwFiAWbNmQb0W/zKmhdZVjyTQ1s+HIWHB/Lf0LGSN+zcDHlwhgyVUta7Lnyn9J/UahTF5zsu4urmwd9M+Zr/8BRO/HX85I15QVSkvUHUqOZWTx/zjcbzesSX5xSUcz8qhxNqFL5uq3s+KdV9apOeEZ3H1NVKQmcmmN2fgWScE/6Y1d4neku38/FV8xkuoX7ZWdf2vVOiCZcwlZpKOnmL4lMcoKixiznPvUadJffzq2n5k80vZdl+YPMJyVvKNX1jx126uu6kTH7y1mAefvA6DwT4HjhVUEfNCW1+HIB9ubBDC/cstVwljM/P45kAcH/VtRW5xCYdNNbHt/jNVxYfUWlf5N6qJ+FW9Z1XZnn76LgIDfSkqKmbChA+ZPXsBjz02inXrdtKsWSTffPMqJ0/Gc889E+jYsQWenu52SF+ZvkANauvvw3X1gnl8Q8XjBjeDA5M6NOXDfcfILbZph49/dNUc85SyZt+TnGRi2d87mfXlf6v8Hf2uaUu/a9qyY9thZn74Kx9/Zp976UTNsHZ0rBRgtLW/VGs9G5h9dnLeH+svOVhKfmGFS42Brs6kFhRWKtfAy51nWjXkha37avxS5FnOvkYK0tLLpgvS03ExGm2+7L+1+pd1rF+yEYCIJuGYyp0hMiWb8PH3tvp3uXm4lv3csmtz5r2/gOyM7LIb120p5byzXwGuLlXWnQv563Qif51OBOCeRhEVrqrYg6uvL3mp5+pAflo6rr4Vzxi5+hnJL1dP8tLScfU1li5v+d/F25uQDm0xHYu1ayPE3c9ITrn8OWkm3M7LbyljKpvOraKMve1csobopZb6HxIVTlaKqWxeVooJD7+K+dy8PcnPycNcUoKDwUB2qgmP0s/g6W/EzdsDJ1cXnFxdCGvRkOTY0zZrhPw8dz2//bQZgCYt6pGUcC57cmIGAYEX3nYNBgf6D27D3K9Xc91NnTi47xSTn/8egAxTDpvWHcBgcKBX/5Y2yV5eUm5BhasXQW7OJOdV3v6ifNyZ0LkRT6yOIaPw3H5/0bFEFh2zbLuPtI4gKdf67d5eTiekElbnXBfPuiF+xCem4+zkSFhoudfr+HMmKb2qX3HZff/9EubP/wuAVq0akZCQUjYvISGVoKDKXW7Pvubs7MQtt1zDF1/8BMBPPy1j7NjbUEoRERFKWFgIx47F0bp1Y5t/juT8wrLuVWA5bkjJr/q44X+tGvL8eccNBqWY1KEpy04nszbB+i5ol8PVeswzf85qfllgOcZr3jKChIRzdTYxMZ3AoIr7zYP7TxF3Mpmbr7Pc65GfX8hNQ17hlz8mVSjXvmMj4k6lYErPxuhbM/c32tqVfIXCXqw61aWUaqCU+lUplayUSlJKLVJKNbBlsAMZWdT1cCPEzQVHpehXJ5AN5/VLDXJ1ZmK7pry++zBxdrxx+J94R9YnLzGJvOQUzMXFJG3Zhn/bNjZf9t/qc1NPXvz0f7z46f9o07Mlm5duRWvN8X2xuHm44eNv/UFiRlpm2VmR2P0n0Frj4W2f/uUHM7Oo6+5GcGnd6VsnkE2X0KfZp/QGykBXF3oE+bMqPtlWUatkbBBBTmISuaV14MymbYS0qzg6UUi71pxavwmtNelHjuHk7oar0YfiggKK8yzbQnFBAcl79+MVZt9BDfwbRpCVkERWUgolxcWc2LCdeh1aVSgT1qE1x9ZsRmtN8uHjOLm74V7DjZB21/dmzPTnGTP9eaK6tmbfyi1orTlz8DguHq54ntcIUUoR3qoRh9bvAiBmxRaiulg+Z1SXVpzedwxzSQlFBYXEHzqBf1iwzbLfPLIHn89/ms/nP02vfi3567ftlhHU9pzAw9MV//MaIVpr4k6mlP28Yc0+wiMDAZj3+4vM+8Pyr881rXjqxVvs0gAB2JeWRT0vV0I9XHB0UAwKD2TN6YrbbrC7C2/1bM7LGw9yMqvija2+Lk5lZfqHBfDXCftuu9ZYsnQHt9/aC4DO7aLIzMolIcnEtt1HiYoMIaJeIE5OBoYP7caSpdvtkmn06OtZtGgGixbN4JpruvLLLyvQWrNr1wG8vNyrbIScvU9Ea82yZZto1CgCgDp1Atm40XJ1KiUlnePH4wizYd0v72BGFmHljhv6hwayIbHyccOUDk15bfdh4nIqHjc81zqKk9l5/Hjc/qOqXa3HPP8Z1YcfFr7IDwtfpG//1vy+2LJfj959HE9Pt0pdrnr2acVfq9/g17+n8uvfU3F1dS5rgJw6mVR23HBg30mKiorxMdbs/YHCtqztjvUD8BFw9i6ikcAcoIstQgGYNXyw7xhvdmqBg4I/4pI4kZ3HDfVCAPjtVAJ3RoXj7ezEky0s7aESDY9ssOz8xrdpTBs/H3ycHZnbryNfHz7JH3FJtopbgTIYiBo9kuj33kebzYT07IFH3VDOrFoNQGjfPhRmZLB9ymuU5OWDUsQtW06nKRNxdHOrcll7a9GlOTGb9zPxjldxdnXmjudGls37aNxsRj87AmOADyt/WsOyuSvITMvitfvfokWXZox+diQ7V+9m7eL1GAwGnFycuPelMZfUnevfMGv4aP9RXuvQEgcFf59O5EROLteHWerOkrgEfJ2d+KBbW9wdDWgNN0WEMnbdDnJLSni5bVO8nJwo0ZoP9x8tuxHQXhwMBlqOGcmmaR+gtZl6vbvjFRZK7Io1ANTv35ugNi1J2r2XFf97GYOzM23vHwNAQUYm296fZVkPZjN1u3UiqPXl6Q55Kfk73/Mflr/2kWWI3n7dMNYL5dBSy/0pjQf2om67FpzeFcMvT07E0cWZ7g/dUbb82hlfkLjvMPlZ2Sx8ZDytb7ueRv272/UzRHZozrFtMXz+0GScXJwZ/Pi5C8E/TZ7JoEdH4envQ6+7hrHk7a9Y//0SghqE0XJgVwD864VQv10zvn7iDZSDA60GdiUgwj7bcddeTdm0bj+3D30DF1dnxk36T9m85x79nOdeuQ2/AC9enzCXnJwC0JqGjUN5evwtdsl3MSUa3tp+lA/6tMTgoFh8LJFjmbnc2tCy7S48msADLcLxcXHk+Y5Rpctoxvy9C4BpPZvh4+xEsdnMm9uPlt3Abk9ff/A4vbo1I8DXiyObP2TKuwtwKh22+bPvlvHnip0M7teWmLXTyc0r4MFnLdtrSYmZpyZ8xa/fvoDB4MDX81ax/1Dcxd7KJvr06cjq1dsYOHAsbm4uvFZuaOkHHpjI1KmPExzsz7PPvkN6egZaa5o2bcCkSZbRwB55ZAQvvDCdoUMfQ2vNs8/ejZ+ffU4wlGh4f+8x3up87rghNjuPYeGW+rP4ZAJ3NbIcNzxV7rjhwfW7aeXrxeCwII5m5vBZT8uJv08PnmRzsn2uRl3Nxzxn9ejdkvVrY7hpyCu4ujnzSrl7Op54+CMmTBpNYLl7Tc+3fOkufl+8GUdHAy6uTrz+9n12O26oCXIlBNSF+hxWKKTUZq11l/Ne26S17mrFe+gB1eiOdaVYPqQHY9etqukY1Ta7Z1+Wnf69pmNU2zV1r2PwX+tqOka1/TW4J89uXlHTMarl7S79mbpzWU3HqLaX2l3D7AN/1XSMahvbdDAJefZ/TsTlEuI2jI5z7T8wwuWybWQv3MJH1XSMask7OQc4VNMx/oXG9F1y9R43rLq+B1f7cU9W0fKajlFtXk4DrorD+2+P/GW3u77ujBp8Ra4Ta6+ErFRKjQPmYrlPcASwRCnlB6C1tm/nSSGEEEIIIa5SBnXlDZxhb9Y2QkaU/v/gea/fi6VRYtP7Q4QQQgghhBC1h7WjY0XaOogQQgghhBD/H9TAIOhXnIs2QpRSF71TUWv90+WNI4QQQgghhKjt/ulKyNDS/4OA7sDZO2z7AasAaYQIIYQQQghxCWR0rH9ohGit7wFQSv0GNNdax5dO18EyZK8QQgghhBBCXBJrb0yvf7YBUioRsP3jT4UQQgghhKhl5EqI9Y2QVUqpv7A8oFBjeVjhSpulEkIIIYQQQtRa1o6O9VjpTeq9Sl+arbX+2XaxhBBCCCGEqJ3kOSHWXwk5OxKW3IguhBBCCCGE+Ff+aYjeLCzdr1Tp/2WzAK219rZhNiGEEEIIIUQt9E+jY3md/Vkp1ZZz3bHWaK132zCXEEIIIYQQtZLcmG7lAxuVUk8A3wIBQCDwrVLqcVsGE0IIIYQQQtRO1t4Tcj/QVWudA6CUehPYCHxgq2BCCCGEEELURnIlBJTW/3x3vlIqGuiktc4vnXYFtmqtW1nxHnL7vxBCCCGEsIer4vD+15N/2O34eGj4kCtynVh7JeRLYLNS6uywvDcBn1v7JmsTllxirCtHr5DreT/m75qOUW1PthjEmNWrazpGtX3Tpw+LTvxR0zGq7caIIby2a2lNx6iWF9sOZPaBv2o6RrWNbTqYh9ZfvY8zmtmjHw0+vnq33WOP9GG/6beajlFtzYw3AIdqOkY1NcYtfFRNh6i2vJNz+PbI1bvvuTNqMAuO/1nTMarttshraf7FmpqOUW377u1d0xGsIldCrH9OyLtKqVVATywtzHu01jttGUwIIYQQQghRO13Kc0J2ADtsmEUIIYQQQohazyBXQqwbHUsIIYQQQgghLherr4QIIYQQQggh/j0HJeM2yZUQIYQQQgghhF3JlRAhhBBCCCHsSK4CyDoQQgghhBBC2JlcCRFCCCGEEMKO5DkhVl4JUUo1VkotV0rtLZ1urZR6ybbRhBBCCCGEELWRtd2xPgVeAIoAtNZ7gJG2CiWEEEIIIURtZVD2+3elsrYR4q613nLea8WXO4wQQgghhBCi9rO2EZKilGoIaACl1G1AvM1SCSGEEEIIIWota29MfxSYDTRVSp0GjgOjbZZKCCGEEEKIWkoeVmhlI0RrfQy4RinlAThorbNsG0sIIYQQQghRW1nVCFFK+QOvAD0BrZRaB0zWWqfaMpzWmjkzfiZ6836cXZy594VRRDQOq1RuxU9rWbpgDcmnU3lv0WS8jJ4A/DlnBZuX7QCgpMRM/IlE3ls0GU9vD1vGLsu+7vOFnNgRg6OLMwMeu4PAhvUqlctMTOHvd7+iIDuXgMgwrnlyDAYnR45v2cPmOUtQSuFgcKDnvbdSp1lDm+c+y7R3LyfmzUObzQT17EnokCEV5ufFx3Ps66/JOXmSejfdRJ1BgwAoSEvj6BdfUJSZiVKKoN69CRkwwG65z9Jas/jjnziwdT9OLk7859nbCWtUef2vX7SWdT+vJvVMCq/8OBUPH0vd2bF8G6vmLwfAxc2Fmx8fTmjDunbNv+WrBZzeaak/PR6+E/8GlfNnJaWw5v0vKcjOxT+yHj0fG4PB0dHq5W2Zf+WnCzm+fR+OLs5c++Rogquo/xmJqfz21lfkZ+cS1CCM6566E4OTZbd0KvowKz//CXNxCW7eHox47Um75U+NjuHwD/NBm6nTqwcR119bYb7WmsM/zCctei8Ozs40u+8uvCLCLbmXLid+zXq01oT27km9Qfat/73r+fJyzygcHBTz98Uzc+epCvOvqe/P013qY9ZQYtZMWXeEbQmZANzdui4jmtVBKZi3L54v95y2a3awrNvP3v2F7Rv24+LqzBMTRtKwaeX9/rsvf8eR/XE4Ohpo1LweD78wHEdHAwDR24/w+XuLKCkuwdvowaszH7Vr/ldfnc3q1dtxdXXhjTeepEWLqErlxo17jy1b9uLlZfk+euON/9KsWQOysnL43//e4cyZZEpKSrj33lu49dZr7JJ95lsPMmRAO5JTM+k48Lkqy7wz6S4G92tLbl4hY5/5hF17YwEY2KcNb08cg8HgwFdzV/L2x4vtkvl8Wmv+nrWQI9v24eTizNCnRlMnqvK+Jz0hlZ/f/Iq87FzqNAzjxmcs+578nDwWvf0NGcnpmEvMdL2lP20HdrVr/iWf/MTBrftwcnHi1mdGU7eK766Ni9ew4efVpMWn8OK8V8u+u6xd3hZ61vXlha4NMSjFgkMJfLan4r6nf7g/j7ePQGso1po3Nh9lR6Jl37N0eGdyikowa02x1vxn8U67ZK5JMkSv9feEzAWSgVuB20p/nmerUGdFb95PUlwKr33/ImOeHc537y6oslxUy0ieeedh/EN8K7x+7aj+vPL5s7zy+bPc8sD1NGnT0C4NEICTO/aREZ/E6I9epu9DI1k9u+rVtfHbxbQZ2o/RH72Mi6c7+5dvBCCsVRNGvDuOEe+Oo9+jo1n58Q92yQ2gzWZif/iBJk88QetJk0jdupXcM2cqlHH08CBi5EjqDBxY4XXl4EDE8OG0mTyZFi+8QOLKlZWWtYcDW/eTcjqZ574cz63/HcHPM36sslz9FpE88MbD+AZXrDt+If489PbjPD3reQbcPoiF021e3Ss4vWsfWQnJ3Pz+K3R7YBSbPp9bZbnt3y+i+XX9uOX9V3D2cOPwio2XtLytHN++j/T4ZO6dOYGBj45g2Sfzqyy35utFdBjWl/tmTsDV053oZZb8+dm5LJs5n5vGP8DdH77I0OfutVt2bTZz6Ls5tHnqMTpPfYXEzVvJOV2xDqdF7yUvMYkur0+myV2jOfiNZfvMjjtN/Jr1dHhpHJ0mvUTq7mhyExPtlt1BwaTejbhnSTSD52xlaKMgonzdK5TZEJfOdfO2c8P87Ty/8iCv92sCQGM/d0Y0q8PNC3dw/bxt9I/wp76Pm92yn7V9wwHiT6XwyYIXeGTccGZOW1hlud6DO/DR/Od5/4dnKSwoYumizQBkZ+Uxa9pPjH/7Xj6Y+xz/e22MPeOzZs12YmPP8Pffs5gy5VEmTvzkgmWfe+5eFi2awaJFM2jWrAEA33+/hIYNw1m8+AO+/fZ13nzzcwoLi+yS/dsfV3PjmDcuOH9wv7Y0rB9Cy95P8di4T5nx6n0AODgopk+9hxvvepN2A55l+LDuNG1kv5M25R3dto+0M8k88ukErnt8BH98VPW+Z8WXi+hyU18e/dSy79n1t2Xfs+23tQTUC2Hsh+O4843HWfbZL5QU2W8cnkNb95FyJpmnv3iJm54cyeIPq/7uimjegHtffwRjkF+1lr/cHBS81C2KB//ey9CftnFdg0AaGivuezadSefmX3Zwy6IdvLT2EJN7NK4w/+4/dnPLoh3/LxogwsLaRoif1nqK1vp46b+pgNGGuQDYtW4v3QZ3RClFwxb1yc3Ow5SaWalceOMwAur4VfEbztmyfAedB7SzVdRKjm+JpknfziilCGkSSWFOHjlpGRXKaK05HX2Iht3aAtC0XxeOb9kDgJObC0pZmsnFBYWA/ZrM2ceP4xoUhGtgIA6Ojvh16kT67t0Vyjh5e+NZvz7KYKjwurPRiEdEBAAGV1dc69ShyGSyV/Qy+zZE035gJ5RSRDSrJufVCAAAaTxJREFUT15OHpmpGZXK1Y0Kwy/Ev9Lr9VtE4u5l2YGGN6tPRkrlZW3p1NY9NOhtqT+BjS31Jze9cv1JiDlERFdLvW7Ypwuntu62enlbOrolmub9LO8f2iSSgpw8squo/yf3HKZxj7YAtOjfmSObogE4sGY7jbq1wTvQsl27G73slj3zWCxuQUG4BVnqf3CXTqTs2lOhTMrOPYR074pSCp+GDSjOzaPAlEFufALeDSIxuDjjYDBgbNKI5B277Ja9TZA3JzLyOJWZT5FZ89uRJAZGVqzfucXmsp/dHA1oy3gjNPR1Z1diJvnFZko0bD5jYlBkgN2yn7VlzV76DumAUoomrSLIycojLaXyfr9jj2YopVBK0ahFOKlJJgDW/LWDbv1aEVh6UsroZ7+6A7B8+SZuuqk/Sinatm1KZmYOSUlpVi+vlCInJxetNTk5efj4eJVd4bG19VsOkGbKvuD8GwZ14IeFawHYsvMIPt7uhAQZ6dQ2iqOxCcSeTKKoqIQff93IDYM62iXz+Q5uiqZVf8u+J6xpJPk5eWRVse+J3XOYZj3bAtB6QGcOlu57lILCvAK01hTmFeLm5Y6DwdpDpX9v/8a9tBtg+e4Kb1af/Oyqv7tCo8LwreK7y9rlL7dWAV6czMwjLsuy7/njWDL9wy+273Hg//sdEQ7Kfv+uVNZuWSuVUiOVUg6l//4DLLFlMABTSiZ+Qcayad9AI6bkS9+YCvIL2bvlAO37tL6M6S4uJ82EZ8C5s+se/sZKjZD8rBycPdxwKD2Q9/A3klNuZ3Fs025+eHwKS16dSf/H7DcOQKHJhLPfuUads9FIUXr6Jf+egpQUck+exCMy8nLGs0pGagbGwHPr3xhgJKOaO+Ktf26iSadmlyuaVXLTTXj4n8vv7m8kN81UoUxBVg7O7uXqj58vuaV1zJrlbSk7NQOvAGPZtFeAkezz1n9eVg6u5eq/p7+xrKGSfiaJ/Oxc5o2fwbdPTyNmxfkjhNtOgSkdV79z687F10jBefW/IN2ES/kyfkYK0k141A3FdOgwRdnZlPxfe/cdHkXVt3H8e7LpvTdq6L0oVToIiAJWpNj7Yy/wqKgoRcQudsXyiIWOBUFFepMqNaH3lpDeCyE57x+z6RsIITshvL8PVy52Z87s3jt75uzOnDOzOWdJ2BVJTuLFbzuVFerhTHR6TuH96PQcQjxcypQbEBHAkpEd+eaGVrywfD8A+xMz6RTug6+LI66ODvSuF0CYZ9ll7S0xLoXAEN/C+wHBPiSep90/dy6PlX/+S/suzQA4fTyO9NRMXn70M567+wNW/LHF3pFLOHMmgdDQop230NAAzpyxPXL5gw9+YMiQJ3njja8KezvuuOMGDh06SY8e9zB06JO8/PJDODiY9yX4fMJD/TkZXfRaTsUkEh7qT3ioHydPF5senUCtUr3LZklLSME7yLfwvnegL2ml257Ukm2PV7EyHQb3JP5EDB/eNY5pj09hwMO3okxc/6kJyfgUzx/kc1E7EZe6fGWFeLgQk1HU9sRk5BDs7lymXL96ASy8pQNfDGjFK2v2FU7XwNcDWzN3aHuGNQ21e15xeajo1bEeAZ4DfrTedwAylFLPAVpr7V28sFLqYeBhgC+//JLmQyvXLau1jf3kSuzR7fgnikatIkwbigUVzH6BMg26tKVBl7acjjrIppkLGTr+ySrNWC6buS5uxedlZ7P/iy+oN3w4jm7mD+mwdYhFVaLyHNx+gM1/beDRD8w7HwHKewvKVCAbhS5mefuxVf/Lxi+/TH5ePrGHTjBs0hPkns1l5vMfENa0Pv61gu2QtnQuG9NKhdc2CikFHuFh1B00kO3vfojF1QXPOrVN/QJjq4rbqgt/H0ng7yMJdAzz4blO9bnr950cSsrky20n+H5oGzJz89ibkE6erYXt7GKf8su359OiXQNatjeGM+Xn5XNo70kmfvofzubk8sIDH9OkVT1q1Q2yQ9qyKrrtPffcPQQF+ZGbe45x4z5h2rR5PPHESNau3Ubz5hF8//1kjh+P5r77xtGhQ0s8Pd3LPrDJbLWhWmubr68aqk65T1y25Sw/3OGtewhpUJs7pzxJUnQ8P73yKXVbNcDF3ZzPsUttu6ur7a/oMyw7lsCyYwlcHeLDU1fX54G/jB6oOxZuJy7rLP6uTnx9XWsOJ2fx7xlzRyCY7fI4tFC9Knp1rIvqz9ZaT8O4pC+AXhNT8U6T5b+sZc3CDQDUb1qHRGsXO0BSXDK+gT4XEwWAzcu20dmEoVi7/lzN7iX/ABDcqC7p8UVHQDMSkvHwK5nd1duTsxlZ5Ofl4WCxGGX8y76+8JaNSPk4nqzUdNy8Pe37IgBnPz/OJhYNHzibnIyTr2+Fl88/d44DX3xBYOfO+F91lR0S2vbPgjVs/MMY11unaV2S44rWf3J8Mt4B3uUtalP04dPM+2AWD0x+BA8TdmD3Ll7F/mVG/QlsWI+MhKL8mQnJuJWqPy5enpzNLFZ/EpNwt5bx8Pe94PJVbdui1exaYqz/0EZ1SYtPLpyXFl+2brt5e5JdrP6nF9tGPAN8cfP2wMnVBSdXF2q3bEjc0VOm7IS4+PmRXaz3IicpGZdS9d/Vz69ED0dOYjLO1jLhPbsR3rMbAIfm/4qrX8ll7Skm/WyJ3oswTxdiM3PKLb85OoW6Pq74uTqSlH2OOXtimLMnBoAxnSOISS9/2ar0x9y1/G09p6NxizrEn0kunJcQm4J/kO26O+vrxaQkpfPiW/cWTgsI9sHL1wNXNxdc3Vxo0b4BRw+ctutOyE8/LWLOnMUAtG7dmJiY+MJ5MTEJBAeXHS5cMM3Z2YlbbrmWb7/9GYCff17Kww/fZgwlrRdO7dqhHD58kjZtmpR5DLOdikmgdljREJtaof5En0nC2cmR2uHFpocFcDrWvB7ALQtXs+0vo+0Ja1KX1Ljkwnmp8cl4BpSsP+6l2p60+GS8rGV2LNnINcP6o5TCPzwI35AA4k/EUqtpPbvl37BgDZut+Ws3qUtK8fxxKXj5V/yzyyfQ95KWr6yYjBxCi/W6hnq4EJt5ttzy/55JoY6XG74ujiTnnCMuyyibmJ3LsmMJtAnyuuJ3QsRF7IgppYYqpd61/g22V6C+N3cvPJm8fY/WrF+8Ba01h6KO4ubhiu9FfpHMTM9i345DtOveyk6Ji7Qe1LPwZPKITm3Yt3KTMW5/3xGc3V3LfAlTSlGrVWMOrd8OwN4VG4no2BqAlOi4wqPJcYdOkH8uD1cvc3pyPOvXJzs2luz4ePLPnSNx82b82rat0LJaa458/z1uYWFlTlq3t2uG9uDZL57n2S+ep+U1rdm6ZDNaa47tOYqbhxveARX/Ep4Um8T3E79lxPN3ElTbhKPvQLOBvRj69liGvj2Wuh3bcHi1UX/i9h/Byd2tcAejgFKK0BZNOLbBOInv0KqN1OlgDDms06H1BZevau1v6MndU1/g7qkv0KhLG3avMJ7/9L4juHi44mmj/tdt3Zj967YDELV8E406G/W/UefWnNp9mPy8PHJzzhK9/xgBtUPsmr+AV0Q9ss7EkhVn1P8zGzcT2K7kUM6Adm2I+WcDWmtSDh3G0d0VF1/j9Z1NNc5fyE5IJP7fbQR37mhKboCdsanU93GjtpcrTg6KwY2CWXqk5FCget6uhbdbBnri5OBAUrZx4m2AmxMA4Z4uDGwQyIKDsabkvn5Yd6b+OJqpP46mc89WrPzzX7TW7Nt1DA9PV/wDy7b7S37bwLYN+xg96a4Sw5U69WzF7u2HyTuXR072WQ5EHad2fftuw3fccUPhCebXXtuFX39djtaa7dv34uXlbnMnpOA8Ea01S5duoHFj40tuWFgQ69cb53bFxydx5MhJaptU9y9k0ZKtjLq1BwCd2jciNS2TmNhktuw4RKOIUOrVCcLJycKwIV1ZtORf03J1GNyThz55gYc+eYGmXdqwa7nR9pzcewRXD1e8bLQ99Vs3Zs/a7QDsXLaJJta2xzvYjyM7jGFC6UmpJJ6KtXnuRVXqMrQHT372PE9+9jzNu7Zm2zLjs+v4nqO4eLhe1GdXsy6tLmn5yoqMT6Oejxu1PI22Z1CDIFYcL9n21PUqanuaB3ji5KBIzjmHm6MD7tbzntwcHbgm3JcDSRl2z1zdlDLv73JV0Uv0vgl0BH6yTnpaKdVda/2i3ZIBrbs0Z9eGPbw06g2cXZy478WRhfOmPj+Ne58fjm+gD0vnrWbxrBWkJKYx/v53ad2lOfc+PxyAbWt20bJjU1zczB3bXO/qlhzfupufHpuIo4sTfZ+4s3Dewtc/p89jo/Dw96HLXTey5P3/sXHGQoIiatP82q4AHFq/nX2rNuFgseDo7MSA0feZNpxGWSzUHzmSfVOnovPzCerWDffwcM6sWgVASK9enE1JIXLyZPKys1FKEb10KW0mTCDr5EniN2zArVYtdk2cCECdm2/Gt3VrU7IXaNapBXs37eGte1/H2cWZYWOK6s43L3/Jbc+NwCfAh7W/rGLV3OWkJabx/iNv06xTC4Y9N4KlPy4mMzWDXz42riziYLHw9KejTctfq31LTm6L4uenJ+Do7ES3R4vqz9Ipn3HNI6Nw9/fl6jtuZNWH/2Pb7IX4169D475dL7i8GSKubsHhLVF885+JOLk4M/DJonOafp74BQMeH4lngA897hnKone/Y91PiwhuUJtW1kthBtQJpX775kx/6k2UgwOt+3chsF64KdkdLBaa3DmcHe9/hM7PJ6z7NXjUCufUitUA1OrTk4A2rUjcGcmGF8dhcXam2f33FC4f+ek0ctPTURYLje8ciZOHecNA8zSMX3OQ6UNa46AUc/fGcCApk1EtwwCYERXNdQ2DuLlpCOfyNdnn8nnq792Fy382sCW+ro6cy9e8tvoAqTnmXRWowNXdmvPvP3v4z61TcHF14qlxIwrnTXzmK554+Xb8g3z4/K35BIX68cKDHwHQtXdrhj84gDoRIVzVpRlP3/EeDg6Ka4d2pl7DMNPy9+rVgVWrttC//8O4ubnwRrFLSz/00Hhef/1JQkICGDPmPZKSUtBa06xZAyZMeAyAxx4bztixUxky5Am01owZcy/+NnrI7WH6x0/So2tzAv28OLjxEya9Pw8n6yWzv/5xKX8t38bAPu2IWjOVzKwcHhnzJWBcAv/Zcd/x+w9jsVgcmD57JXv2nzQlc2mNOrbg4JYoPn1wYuElegvMfO0LBj81Eq8AH/reN5Rf3v6OlT8sIrRBbdoNNNqeHiOuY8EHP/LlY1MA6HvvUNx97D8CoUDTTi3Yv3k3798/CScXZ255blThvOnjvuDmZ0biHeDDP7+uYs28ZaQnpvHxo2/RpGMLbnl25HmXt6c8DZPXH+Srga1wUIpfDsRwMDmT4U2NbW/2vmj61w/kxkbWticvn9Er9wAQ4ObMR/1aAOCoFIsOx7L2lHk9aeL8lFL+GFfErQ8cBW7XWieVKlMH+B4IBfKBaVrrDy/42DbPXSgbYCfQTmudb71vAbZprStypvdFDce63PQIvYEPo/6u7hiV9nTLAdxt3Xmoib7v1Yvfjv1Z3TEq7cZ6g3hj+5LqjlEpL7Xrz7S9i6s7RqU93Gwg/1m3orpjVNoX3frQ4LOau+0efqwXe5IXVneMSmvuOxjYX90xKqkJbnVHXrjYZSrr+Ex+OFhz2567Gg1k3pG/qjtGpd0WcR0tvl1d3TEqbff9PS/jY/9FNsctMu3MqY5BN1R6nSil3gYStdZvKqVeBPy01i+UKhMGhGmttyqlvIB/gZu01rttPGShizkvxrfYbXMOywghhBBCCCGqy43AdOvt6cBNpQtoraO11lutt9OAPcAFr0pV0atjTQG2KaVWYFwEoScwtoLLCiGEEEIIIazMPFej+FVrraZZLyJVESFa62gwdjaUUuc9yU4pVR9oD2y80ANX9OpYM5VSKzHOC1HAC1rrmIosK4QQQgghhKgepa5aW4ZSainG+RylvXwxz6OU8gTmA89orcv+ymwp590JUUqVvr5qwZlm4Uqp8IKuFyGEEEIIIUTFXE6/E6K1vra8eUqpM0qpMGsvSBhg87KJSiknjB2Qn7TWP1fkeS/UE/KerazFbvetyJMIIYQQQgghapwFwD3Am9b/fytdQBmXb/0G2KO1fr+iD3zeHTGtdR+tdR/gc+BG6+0VQAowpsLxhRBCCCGEEDXNm0B/pdQBoL/1PkqpcKXUH9Yy3YC7gL5Kqe3Wv+sv9MAVPTH9Fa31HKVUd2uA9zB2TDpf5AsRQgghhBDi/zWlTLtC7yXRWicA/WxMPw1cb729FuOc8YtS0SFpedb/bwC+0Fr/Bjhf7JMJIYQQQgghREV3Qk4ppb4Ebgf+UEq5XMSyQgghhBBCCCtl4t/lqqI7ErcDi4HrtNbJgD/wX3uFEkIIIYQQQly5Kvo7IZnAz8XuRwPR9golhBBCCCHElcrMHyu8XMmQKiGEEEIIIYSpKnp1LCGEEEIIIUQVkI4Q6QkRQgghhBBCmExpbffrFNeMCyELIYQQQoiarkZ0MkQmLTTt+3Erv8GX5ToxZThWp7lrzXgau9g0rDv3rl5V3TEq7buevVgfu6i6Y1Ra1+AbeHDtyuqOUWlfd+/NU+tXVHeMSvmoax9GrFhd3TEqbVafnvxv/+LqjlFp9zUZyNb4mrvtXhV4Q41vO3svWlfdMSpl5Q3d+OFgza37dzUaiFvdkdUdo9Kyjs/ktuU1t+2c17cnSTkLqztGpfm5DK7uCKKC5JwQIYQQQgghTHRZdk2YTM4JEUIIIYQQQphKekKEEEIIIYQwkfxOiPSECCGEEEIIIUwmPSFCCCGEEEKYSDpCpCdECCGEEEIIYTLZCRFCCCGEEEKYSoZjCSGEEEIIYSIZjiU9IUIIIYQQQgiTSU+IEEIIIYQQJnKQrhDpCRFCCCGEEEKYS3pChBBCCCGEMJF0hEhPiBBCCCGEEMJk0hMihBBCCCGEiZTS1R2h2l3WOyFdQnwZ3b4BDkrx2+EzfL/vZIn5A+sGcXfT2gBkncvjra2HOJCSAcDIxuHcGBGCBg6mZDJp837O5pv3hidFRnJ01mx0fj4hPbpTa9CgEvOzoqM5+N10Mo4fp+5NNxE+cAAAOYmJHPz2W3JTUkEpQnr2JOzafqblLqC15qcPf2Hnhj04uzjz4EsjqW9d18Utnb+Gv+euJvZUAh//PhEvX08AMtOz+HLSTySeSSIvL59BI/rQ44ZOpuVP3BXJ4Zlz0Dqf0B7dqXP9dSXmZ0bHsP/b70g/foL6N99I7esGVHhZM8TtjGLvjDno/Hxq9+xGg8ElM2it2fvTHOJ2RmJxdqb1g/fgXb8uGdEx7Pjs68JymXHxNLp5CPUHmluHUqMiOTlnFjo/n4BuPQi9rmT9z46J5tj078g6cZywoTcRMmBgifk6P599U17HydeXho8/ZWZ04/m1Zum0+Rz6dzdOLs7c8PQdhDaqU6ZcckwCv73zHdlpmYQ0rM2Q5+7C4uTIxp+XEbVyCwD5efkknIzhqR/fwM3Lw5Ts06f+wvb1e3B2debRl0cSYWPb/WT8jxzeewKLo4WGLery4PPDcHS0cOrYGb6cPIsj+08y/OHrGTyqj90zF1fT285OQb480aIBFgWLTpxhxqFTJeZfGx7EyIa1jNeSl8cHuw5xKC2TIFdnXmrXBH8XJ/I1LDwew/yj0abn11rz95fzObjFqPtDnr2DMBt1PykmgV/e+o6s9EzCGtbmxtFG3c/OyOK3d78nJS6J/Lx8utzSl3b9u5iS/Yt3HmFQv/bEJaTSof/zNsu8N+EeBvZpR2bWWR4e/TnbI48C0L9XW94dfzcWiwPfzVrBu58tMCVzcWlRkZyeMwt0Pn7dehA8sGy7efL778g+cZyQoTcR1L+o3dz78os4uLqiHBTKwUKjsa+YHR+tNe+/9Svr1+zBxdWZcZNG0KxF2bZn8muz2RN1Aq2hbr0gxr0+And3F1JTM5n86mxOnkjAxcWRlycMp2HjMNNfhzDPZbsT4gA8f1VDnlgdSWzmWaZf2441pxM4kpZVWOZ0Rjb/WbmTtNw8uob6MfbqRty/fAdBrs4MbxzO8L+2kpOfzxtdmtK/ThCLjsWakl3n53NkxgxaPPsszn5+7Jr8Bn5t2+IeHl5YxtHDg4gRI0jcvq3EssrBgXrDhuFZrx552dnsnPQ6Pi2al1jWDDs37OHMyXjemvkSh3Yf4/v35vHqtGfKlGvcOoK217Tkzac+LTF92c/rqFU/hGffepDUpHTG3jGFrgOuwtHJ/lVO5+dz6KeZtBr9DC5+fmyfNAX/dm3wKLH+3Wk4agQJ27Zf9LJm5N/zw0w6/PdpXP39WD9hCsHt2+BZqyhD/M5IMs/E0uOtiaQcOsLu72fQ5dUX8QgL5ZpJrxQ+zspnXiTk6namZS943hMzZ9Do6Wdx8vNj35TJ+LRpi1uxdWhx96D28BGkbN9u8zHili/FNTSMvOwsm/Pt7fC/u0k6HccjX47j9L6jLP58Dve8N7pMuZXf/UbHG3vToufV/PXpbHYsWc9V1/eg8y396HyL8QX4wKZdbP5tpSk7IADb1+8h5mQ8H8x+iYNRx/jm3Xm8/tUzZcp1G3AVj792BwAfj/+RFb9voP/N3fD0dueeZ29my+pIU/IWV9PbTgfg6ZYNGLMxirjss3zRvS3rziRyLL2oHkdnZfP0+l2kn8ujU5Avo1s34rF/dpKnNZ/tPsKB1AzcLBamdW/LlvjkEsua4dCW3SSejuOxr8Zxat9R/vx0Dvd/ULbuL//fb3S+qTcte13NH5/MZvvf67n6hh5sWbiGwDqhDH/tETJS0vj84cm07t0Biwlt/w9zV/HF9MV8/cFjNucP7NOOhvVDadXzWTq1b8RHkx+g543jcHBQTH39Pm644w1ORSew9vfJLFzyL3sPnLL5OPag8/M5PWsGEU89i6OfH4fenIx3m7a4hhWr++4ehN8+gtQd220+RoNnR+Po6WVS4rLWr93LiWPxzF04lqidx3n79fl8O+PpMuWe+e+NeHi6AjD1nd+YN3Mtdz/Qj+lfLaNx03DemnofR4+c4d3JP/PJ14+a/TJMI+eEVPCcEKVUkFLqXaXUH0qp5QV/9gzW0t+Lk+nZnM7I4ZzW/H0ijp61AkqU2ZWQRlpuHgCRCakEuzsXzrMohYvFAYsCV4uF+Oyz9oxbQvqRI7gGBeMaFISDoyOBHTuStH1HiTJO3t54RtRHWSwlpjv7+uJZr57xGlxdcQsL42xyslnRC21bG0m36zqglKJRy/pkpmeRHJ9aply9JrUJCvMvM10pyM7MQWtNTlYOHt7uOFjMOQUp7fARXIODcbOu/6BOHUjcVnL9O3t742Vj/VdkWXtLOXwU95Bg3IONDGGdOxK7bWeJMrHbdhLerQtKKXwbNSA3M4uc5JQSZRJ278U9OBC3wJLbjb1lHj2CS3AQLtZ16NexIyk7t5co4+TtjUf9iDLrH+BsUiIpu3YR0K27SYnLOrBhF636dkIpRa1mEeRkZJGeWHL9aq05tvMAzbq1A6B1v04c2LCrzGPtWbWVFj2vNiM2AP+ujaSHddtt3Ko+mWlZJNnYdttf0wKllLGNN69LYqzx+nz8vGjYvC4WR/NPGazpbWczXy9OZWYTnWV8bi0/HUe3kJLtY1RSGunnjM+t3UlpBLkZn1uJObkcSDV68rPy8jiWnkmgqzNm27dhF62tdb92swiyM7JIs1H3j+48QPPu7QBo068T+6x1Xyk4m2W0/WezzuLmZV7bv27TXhKT08udP3jA1cyYvwaATdsO4uPtTmiwLx3bNeLQ0RiOHo8lNzePub+vZ/CADqZkLpB59AjOQUE4W+u+T4eOZXY2HL29cS+n3bwcrF4RyfVDrkYpRau29UhPyyI+rmzbU7ADorUmJzvXqDTAkcNn6NC5MQD1I0KIPp1EQkKaeS9AmK6iLcNPwB4gApgAHAU22ykTAEFuzpzJzCm8H5uZU9hY2zI0IpT10UkAxGWf5cd9p1gwuCN/DOlMeu45Np5JtmfcEs4mJ+PiX/TB4+znS05y0kU/TnZ8PBknjuMZEVGV8SokKS4V/2Dfwvt+Qb4kxaeUv0Ap/W7tzuljZ3jmpvG8cu87jHrqZhwczPkgyklOxsXfr/C+s58fORX8MnIpy1aV7KQkXItlcPXzJTupZP3JSUq2USa5RJmYjVsI7dLRrlltOZuUjLNfsfrv60duqWznc2rObGrdchuo6rtuRlpCCl6BvoX3vQJ8SUsoWf+zUjNw8XTDwfqFwFaZ3OyzHN66h6bXtLV75gKJcakEFNt2/YN9SYwrf9s9dy6PNYu30LZzMxPSnV9NbzuDXJ2Jyyo64BWXfZYgV5dyy99QN4RNscllpoe6udDYx5M95/lCbS9pCSl4B/kW3vcOtF33XT2K1f1iZToM7kn8iRg+vGsc0x6fwoCHb0WZ1PZfSHioPyejEwrvn4pJJDzUn/BQP06eLjY9OoFaIX62HsJuziUn41Ss3XTy8yP3Yj57FBz5aCoH3phE4prVVR+wAuJiUwgO9S28HxziQ1ys7bZn0rhZXN9nPMeOxnL7SOOAU+Mm4axcZuzMRu06Tkx0EnEmfnczm1Lm/V2uKtoyBGitvwFytdartNb3A+UO8lRKPayU2qKU2jJt2rRKBbO50so5pePqIB+GRoTwya6jAHg5WehVy5+bFm3m+t834eZo4bq6QZXKUSm6bFB1kR1vednZ7P/8C+oPH46jm1tVJaswbes1XMRLiNy4j7qNajH11/FM/HY0P079mayM7CpMeB6XcurP5XCemI0MpeuPrfeneJH8c+eI3baD0I7mHYEvcv5s55OycweOXt64W49oV5+K1P8Llzm4OZJazSNMG4oFF7/tfvvuPJq1bUCzdg3smKqCroC2szRdTqPSLsCH6+uE8OXeoyWmu1kcmHB1Mz7ZfZhMa4+JqWy+B6WKnKehPLx1DyENavP0D5N46OMX+OuLueRkVs+wytJs1SWtNcrGBmKribWrS/zMbTjmRRq/NI6IJ54mYdUKMg7sr8JwFWPzY6mc1zBu0ggWLnuN+hEhLF28HYC7H+hLWmoWdw17j7kz19KkWS0sl2mvj6gaFR2kmWv9P1opdQNwGih7tpGV1noaULD3ob+eu/aig8VmniXEvegIUrC7C3E2hlQ18nHn5Q6NeGZNFClnzwHQKcSX0xnZJFvvrziVQJsAb/46HnfROSrD2c+PnMTEwvtnk5Jx9vWt8PL5586x7/MvCOzcmYCrrrJDQtuW/ryWVb9vACCiWR0Six2hS4pLxjfAp8KPteaPTdxwZz+UUoTUDiIozJ/oY2do0ML+Xy5d/HzJSSw6eno2KQmXCq7/S1m2qrj6+5FdLEN2UjIufr4XLONaLGf8zki869XFxcfb3nHLcPbz42xSsfqfnIRTBddhxqFDpOzcTmrkLvLP5ZKXlc3Rb7+m/v0P2iltkX8XrWbH4vUAhDWuS1p8cuG8tIRkPP1L1n83b09y0rPIz8vDwWKxWWb3anOGYv09fy3LFxjbboPmdUgotu0mxibjF2h725337WLSkjN48I1hds9YETW17SwQl322RI99kKuzzaHADbzc+W/rhryweTepuecKp1uUYsLVzVh6Ko41MYlllrOXLQtXs+0va91vUpfUuOTCeanxyXiWavvdvT3JzihW9+OT8bKW2bFkI9cM649SCv/wIHxDAog/EUutptV9YAFOxSRQO6xoeGqtUH+izyTh7ORI7fBi08MCOB178T1wl8LRz4/cYu1mblISjj6+FV6+oI119PbGu117Mo8ewaNxkypOWda8WWv5bf5GAJq3rENsTHLhvNgzKQQGlf+9wWJx4Nrr2vHTdysYfFMnPDxdGTdpBGDsHN48aDLhtcoO975SXB79g9WrouvgdaWUDzAaGAN8DTxjr1BgjJWt4+lGuLsLjkoxoE4Qa06XbJRD3Fx465rmvLZpP8fTi46yx2Tm0MrfCxfrONSOwT4cTcu0Z9wSPOvXJzs2luy4ePLPnSN+82b82lZsOIbWmkPTv8ctLIzwAf3tnLSka2/pzqT/jWHS/8ZwVY/WrPtrC1prDkYdxc3TFd/Ain+hDQjxY/e/xpGYlMQ0oo/HEhRuzrkJXhH1yT5TtP7jNm3Bv13F1v+lLFtVvCPqkXkmlkxrhuiNmwlu36ZEmeB2bTi9bgNaa5IPHsbRzRUX36LGPnrDFsKqYSgWgHu9+uTExpITH0f+uXMkbd6MT5uKrcPwm2+h1Zvv0PKNN6n/wMN4NWtqyg4IwNU39OT+j17g/o9eoHGXNkQu34TWmlN7j+Di7lpmB0MpRd02jdm7bjsAu5ZtonHn1oXzszOyOBF5kMZdWmNvA27tzpvTx/Dm9DF06NmaNdZt90DkUdw9XfGzse0uX7CBnRv38eSEO00bKnkhNbXtLLAvJY3aHm6EuhmfW33Dg/jnTMnPrWBXZyZd3Yw3dhzgZKne4efbNOJ4ehZzj5w2MzYdBvfkoU9e4KFPXqBplzbsstb9k3uP4OrhipeNul+/dWP2rN0OwM5lm2hirfvewX4c2bEPgPSkVBJPxeIXau55aeVZtGQro27tAUCn9o1ITcskJjaZLTsO0SgilHp1gnBysjBsSFcWLfnX1GwF7eZZa7uZsmUz3hVsN/NzcsjLzi68nb5nN67htewZt9BtI7rzw9zR/DB3NL36tuKP3/9Fa03kjmN4erkSGFSy7dFac+J4fOHttSujqFc/GIC01CxyrTvlv83fSPurGhSePyKuTBXtCRkGrNVaRwJ9lFL+wLvA7/YKlqfhnW2H+KhnKxwU/H7kDIdTM7mlQSgAPx+O4cEWdfBxduKFqxoay+Rr7lm2g6jEdJadTOCHa9uRpzX7kjP45XCMvaKWoSwWIkaNZM/UqWidT3C3brjXCidm5SoAQnv34mxKCrten2w0HEoRvXQpbSdOIPPkSeI3bMC9Vi12TJgIQN1bbsavtf2/yBTXtmtzdm7Yw/Mj3sDF1YkHxo4snPf+f6dx3wvD8Qv0Ycm81fwxYwUpiWmMu/dd2nRpzv0vDmfovf35+o2ZvHLP22gNt/9ncOHle+1NWSw0vGMEkR98aFzms3s3PGqFE21d/2HW9b9t0hvkZRnr/9TSZVw9aTyObm42lzWTg8VC8zuH8++7H6Hz86nV4xo8a4VzYrkxzrdO354Etm1F3M5I1jw/DouLM60euKdw+bycsyRE7aHFvXeYmruAslioPXwUhz6ais7XBFzTDbfwWsSvXglAYM/e5KaksG/K6+RlZ6OUIm75Upq/NhHLZTB8BqBhhxYc3hLFlw9PxMnFmeufLlqXc8Z/waAnR+IV4EOfe4fy29vfsfrHRYQ0qE2bAUWjVPev30lE+2Y4n+ecAHto37U529fv4ZnbjW33kZeKtt23Rk/joReH4x/kwzfvziMwxI9XH/4IgI69WnPr/QNJTkjl5Qc+ICsjG+Wg+HPOat756QXcPez/ZaCmt515Gj6MPMw7nVrioODPk7EcTc9iaF3jc2vB8RjuaVwXb2cnnm3ZoHCZR9btoLWfFwNrB3MoNYOvuxtfPr/ad5yNceYekW/UsQUHt0Tx6YMTCy/RW2Dma18w+Cmj7ve9byi/vP0dK39YRGiD2rQbaNT9HiOuY8EHP/LlY1MA6HvvUNx9zGn7p3/8JD26NifQz4uDGz9h0vvzcLJelevrH5fy1/JtDOzTjqg1U8nMyuGRMV8CkJeXz7PjvuP3H8ZisTgwffZK9uw/eb6nqnLKYiF8xCiOfDwV8jV+13TDNbwWCdZ2M8Dabh5883XyrXU/fvlSmrw6kbz0dI59+RkAOj8P346d8WrZytT8ANf0aM4/a/Zw2w1TcHV14hVrrwbAs499xUvjbycg0IuJr8wkMz0braFR0zBeeOU2AI4eOcOEl2dicVDUbxjKyxNuN/01mOlyPlfDLMrm2PLShZTaprVuf6Fp5dCdKjEc63KxaVh37l29qrpjVNp3PXuxPnZRdceotK7BN/Dg2pXVHaPSvu7em6fWr6juGJXyUdc+jFhRPSc4VoVZfXryv/2LqztGpd3XZCBb42vutntV4A01vu3svWhddceolJU3dOOHgzW37t/VaCBudUdeuOBlKuv4TG5bXnPbznl9e5KUs7C6Y1San8vgGvH1/lj676adeVTPc8hluU4q2hPioJTy01onAVh7Qi7b3xgRQgghhBDicnVZ7hWYrKI7Eu8B/yil5mFcEuZ2YLLdUgkhhBBCCCGuWBXaCdFaf6+U2gL0xdh5u0VrvduuyYQQQgghhBBXpAoPqbLudMiOhxBCCCGEEJdATkyXyxQLIYQQQgghTCYnlwshhBBCCGEi6QiRnhAhhBBCCCGEyaQnRAghhBBCCBM5SFeI9IQIIYQQQgghzCU9IUIIIYQQQphIOkKkJ0QIIYQQQghhMukJEUIIIYQQwkRK6eqOUO2kJ0QIIYQQQghhKukJEUIIIYQQwkRyTggore3eHST9TUIIIYQQwgw14vv9mawFpn0/DnEbelmuE1N6Qp7ZsNyMp7GLqV360u/PddUdo9KWDerGqug/qjtGpfUKu57nNtbc+vN+5748vHZldceolGnde/Nh1N/VHaPSnm45gLd3LqnuGJX2fJv+zDr0V3XHqLQRDa+r8W1nTc2/bFA35h2puXXntojruG356uqOUWnz+vbEre7I6o5RaVnHZxKTtaC6Y1RaqNvQ6o5QIeqy3C0wl5wTIoQQQgghhDCVnBMihBBCCCGEiaQjRHpChBBCCCGEECaTnRAhhBBCCCGEqWQ4lhBCCCGEECaSXgBZB0IIIYQQQgiTSU+IEEIIIYQQJpJL9EpPiBBCCCGEEMJk0hMihBBCCCGEqaQrRHpChBBCCCGEEKaSnhAhhBBCCCFMpKQnpPydEKXULedbUGv9c9XHEUIIIYQQQlzpztcTMuQ88zQgOyFCCCGEEEJcJKVqxhkRSil/YDZQHzgK3K61TiqnrAXYApzSWg++0GOXuxOitb6vMmGFEEIIIYQQV4QXgWVa6zeVUi9a779QTtmngT2Ad0UeuELnhCilbgBaAq4F07TWEyuy7KWI2xnFnp/moPM1tXt1o+HggSXma63Z89Mc4nZEYXF2pvVDd+NTvy7p0TFs/+ybwnKZsfE0vmUwEQP72TtyoY6BvjzevAEOCv44eYZZh0+VmN8vPIgREbUAyMrLY2rUIQ6nZQIwpnUjugT5kXw2lwfXbjctc3Faa2Z//Au7NuzB2dWJe18cSb0mdcqUW/7zGpbNW03c6Xje+3USXr6ehfP2bTvI7E9+IS8vD08fT/774ROm5Y/dGcXuH426U6dXNxoNKVt3dv84h9gdUVhcnGlrrTsAuRmZ7Pz2R9JOngYUbR+8C7/GDUzLDpC4K5KDM+egdT5hPbpT9/rrSszPjI5h77ffkX78BBE330id6wZUeFkzaK1Z+818jm2NwtHFmX5P3ElQw7L1J/VMPH+//x056ZkERtTm2qfvxuLkyJFNO9k4cxFKKRwsDnS//1bCmjc0Nf+G/83jhDV/z8fvIrBB2fxpZ+JZMfV/5KRnEhBRh15PGvmTT8Ww+tMfSThykg4jB9N66LWmZv/zy585sHk3Ti5O3PTcHYQ3Kpt94++r2fDrKhKj43l+5mQ8fDwvanl7qeltZ03Pr7Vm0ec/s8/6/t86+g5qNS77/q9fsJp/fjHqz0uzS9afiixvL2lRkZyeMwt0Pn7dehA8cFCJ+dkx0Zz8/juyTxwnZOhNBPUv+mzY+/KLOLi6ohwUysFCo7GvmJYb4It3HmFQv/bEJaTSof/zNsu8N+EeBvZpR2bWWR4e/TnbI48C0L9XW94dfzcWiwPfzVrBu58tMDG5QWvNR2//xsa1e3FxdWLsxOE0aV67TLm3xs9h3+6TaK2pUy+IFycOx93dpXD+nsgTPHb3x7z21p307t/GzJdgshpzTsiNQG/r7enASmzshCilagM3AJOB5yrywBfsC1JKfQEMB57EWGPDgHoVefBLofPzifp+Fh1GP0GPKa8SvWEzaaeiS5SJ2xlFRkwsPd+eQMv7RhE1fSYAnmGhdJ/0Mt0nvUy3CWOxuDgTenU7e0cu5AA81bIBY7dEcf+abfQNC6Kep1uJMtGZ2Ty7cRcPrdvOjwdP8FyrRoXzFp+MZeyW3abltSVy4x7OnIzj9Z9e4q7Rt/PTB/NslmvUOoJn33uUgBC/EtMz07KYMXUej7/xIBO+e5FHxt9jRmygqO50GvMEvd58ldPl1Z0zsfR+ZwKt7xtF5HczC+dF/TiHoNYt6P3WeHpOfhnP8FDTshfkP/DTTFo/+yQdJ40nduNmMk6fLlHG0cOdRqNGUGdg/4te1gzHt+4mJTqWOz59ld7/GcGqabNtllv/wwLaDunDHZ++iounO3uWrQegduumDH//RYa//yJ9Hr+DFZ/NMDM+J7ftJjU6jmEfv0b3R0byz1ezbJbb/NNvtBzch2Efv4aLpxv7lxv5XTw96Hr/MFoP6WtmbAAObNlNwqk4nvr6FYY8NYKFn8y1Wa5uiwbc/cZj+Ab7V2p5e6jpbWdNzw+wf/Nu4k/H8dy3r3DT0yNYUM77X69FA+6fUrb+VHR5e9D5+ZyeNYOIJ56m8asTSdm8iezoUm2nuwfht48g8NoBNh+jwbOjafzya6bvgAD8MHcVN979ZrnzB/ZpR8P6obTq+SxPvPgVH01+AAAHB8XU1+/jxnveon2/MQwbeg3NGtcyK3ahjWv3cvJ4PD8teIEx427j/cm2R+0/MWYo3855jv/NHU1wqC+/zFpXOC8vL58vP1xEx65NzYr9/4JS6mGl1JZifw9fxOIhWutoAOv/weWUmwo8D+RX9IErMiDtGq313UCS1noC0BWw+2GN5MNH8QgJwj04CAdHR8I6dyB2644SZWK37qBWty4opfBr1IBzmZlkJ6eUKBMftRf3oEDcAgPsHblQM18vTmVkE52VwzmtWREdxzWlGurdyWmkn8srvB3k6lw4b1dSKqm550zLa8v2dZF0HdgRpRQNWtYnKz2L5ISUMuXqNq5NYJh/memblv1L+x5tCndOvP287J65QPKho7gHF9Wd8C4dOFOq7pwpVXdyrXUnNyuLxH0HqdOrGwAOjo44ebiblh0g9fAR3IKDcQsy8gd36kDCtpL5nb298Y6oj7JYLnpZMxzZtIumvTuhlCK0aQRnM7LISCxZf7TWnNq1n4Zd2wHQrE9njmzaCYCTmwvK+nOy53LOYvYRo2Obd9Kol5E/uImRPzOpbP7TkfuJ6NIegEa9OnNss7Gu3Xy8CGpUDwdHS5nHtre9GyJp18/Ydus0q092RhZpiWW33bCGtfELKdsuVnR5e6jpbWdNzw+wZ30k7a3vf93m9clOzyLVRtsf3qg2fqFl609Fl7eHzKNHcA4Kwtna/vl06Ejqju0lyjh6e+NeP6JM23k5WLdpL4nJ6eXOHzzgambMXwPApm0H8fF2JzTYl47tGnHoaAxHj8eSm5vH3N/XM3hAB7NiF1q7MoqBg69GKUXLNvVIT8smIS61TDkPT2NQjdaanJzcwrYe4OeZ6+jVrzV+/h6m5a4uysR/WutpWusOxf6mlcii1FKlVKSNvxsr9FqUGgzEaq3/vZh1UJHhWFnW/zOVUuFAAhBxMU9SGdlJybj6Fx1dd/X3I/nQkbJlAkqWyUlKxtXXp3Ba9MYthHfpaO+4JQS6OhOXfbbwflz2WZr7lv8lfFCdEDbFJZuQrOKS41LwC/ItvO8X5EtyXAq+AT7lL1TMmRNx5OXl8e7Tn5CdlUO/W3vSdaA570N2UjJuAReoO4nJuJWqX9mJySiLA87enuz86ntSj5/EJ6IuLe68HUcXF8xyNjkZl2LZXPz8SD1y5DxLVM2yVSkjMRnPwKIcHgG+ZCSm4OFfVH+y0zJw9nDDwfplwCPAl4xiX1YOb9jBhp8WkJWSzg0v/8e88EBmYjIexeqQe4AvGYnJuPsV5c9Jy8DZvXh+vzI7WtUhLT4Z72LbrnegD6nxKXj5V2zbvdTlL0VNbztren6A1IRkfIq//0E+pCak4F3Btv9Sl78U55KTcfIr2ulz8vMj82LaPwVHPpoKQECPXvj36FnFCS9NeKg/J6MTCu+fikkkPNSf8FA/Tp4uNj06gU7tGtl6CLuKj00lONS38H5QiA9xsSkEBJU9PWDKq7PZuHYv9RqE8PhzxnWQ4s6ksGZFJB9Me4S9USfMii0ArXW5Y4aVUmeUUmFa62ilVBgQa6NYN2CoUup6jFM3vJVSP2qt7zzf81akJ2ShUsoXeAfYinFmvO2xCUWBC7t9pk2bdr6i5dPa1uNe1EPknztH7LadhHa6qnIZqpC28XoA2vn7MKh2CF/tO2puoAvQXNr6z8vL59i+kzz55kM8/fYjLPr+b86csFVv7cHWulYXLqFA5+WTevQEdfv1pMfrL2NxceHQ74vtkrJctquK/ZetQjbre+nqc4EyDbq0ZdTH4xj0wkNsmrmwSvNdiO1opeqQrTbKXoEugu26XfFkl7p8VatpbWdpNS2/zbp/MfXnEpe/JDa/N1R88YZjXqTxS+OIeOJpElatIOPA/ioMd+ls/a6E1trm+i2n2tmVzTaxnDdg7MThzF8yjnoRwSxfbPQgf/zOAh55+noslppx1ahLp0z8uyQLgIIx9fcAv5UuoLUeq7WurbWuD4wAll9oBwQq0BOitZ5kvTlfKbUQcNVan/dwn7Wbp2DvQz+zYfmFnqYM48h00RXAshOTcPEteSTF1c+X7IRSZfx8C+/H7YzCu15dXHwqdJJ+lYnPPluiiz3I1ZmEnLNlyjXwcmd064aM3by72rvgAVb8spY1C40x7fWb1SWp2BG6pLhkfAIrvh79gnzw9PHAxc0FFzcXGrdtyIlDpwmpU95Qwqrj6udHVql64epXsu64+fuSVbp+WeuOq78vfg2Nzr6wju05uPBvu2cuztnPl5xi2XKSknDx9bX7spdq15+r2b3kHwCCG9UlPb4oR0ZCMh6l3gNXb0/OZmSRn5eHg8VilLFxtD28ZSNSPo4nKzUdN2/PMvOryu6/VrFvqZE/sFE9MorVocyEZNz9beTPLJ4/qUwZs2z8fQ1bFxvbbnjjuqQW23ZT41PwCqj4tusd6HtJy1+Kmtp2Fqip+TcsWMPmv6znYzWpS0rx9z8uBS//ir//PoG+l7T8pXD08yM3KbHwfm5SEo4+vhVe3snaVjp6e+Pdrj2ZR4/g0bhJFaesvFMxCdQOKxoCVyvUn+gzSTg7OVI7vNj0sABOx9q8gmqV+2XWOhb+vBGApi3rEBuTXDgv7kwKgTZ6QQpYLA70HdiWWdNXcf1NHdm3+wQTX/gJgJTkDDas3YvF4kCPvq3s+hrEBb0JzFFKPQAcxzg3HOvoqK+11tdX9oErtLuplLpGKTUK4wT1G5VSd1f2CSvKJ6IeGWdiyYyLJ//cOaI3biG4fcmrJAS3b8OpdRvQWpN08DCObm4lh2Jt2Ex4F/PHRe5NSaOWhxuhbi44KkWfsCD+iU0sUSbY1Znx7ZsxZccBTmZmm57Rlj43d+fVb/7Lq9/8l3bdW7F+8Wa01hyOOoqbh1uFh2IBtOvemoO7DpN3Lo+c7LMc2X2MsLohdkxfxKdBybpzesMWQi5Ud9yNuuPq64Orvx/p0TEAxEftw8vkE9O9I+qTdSaWLGv+2E1bCGjX1u7LXqrWg3oWnkwe0akN+1ZuQmtNzL4jOLu7ltnBUEpRq1VjDq3fDsDeFRuJ6NgagJTouMKjanGHTpB/Lg9XL/uOEW5xXS9ufncsN787lnod23BwlZE/dv8RnNzdSgzFKsgf1rIJRzZsA+Dgqo3U7Vg9V3LpPKQHj37yPI9+8jzNu7Zm+zJj2z2x9yiuHq4XNZSqWedWl7T8paipbWeBmpq/y9AePPnZ8zz5mVF/tlnf/+N7juLi4XpRQ6madWl1SctfCvd69cmJjeVsfBz5586RsmUz3m0q1v7l5+SQl51deDt9z25cw80/uft8Fi3ZyqhbewDQqX0jUtMyiYlNZsuOQzSKCKVenSCcnCwMG9KVRUsuamh+pd08ohvfzHmOb+Y8R48+rVi88F+01kTtPIaHp2uZoVhaa04ejy+8/c/q3dSNCAJg9h8vMftP46/Xta159qVbZAfkMqC1TtBa99NaN7b+n2idftrWDojWemVFfiMEKtATopT6AWgIbAfyCp4D+L6iL6AyHCwWWtw1gs3vfIzOz6d2z2vwqh3O8eWrAajbtydBbVsRtzOSVf99FYuLM20eLNo3yss5S3zkXlree4c9Y9qUr+Hj3Yd5q2NLHBT8eTKWY+lZDK5jfJldeCKGuxrVxdvZiadbGpd+zdPw2D9Gl+TLbZvQ1t8HH2dHZvXpwPQDx/nzpFlDmQytu7QgcuMeXr5jMs4uztz7wojCeR+9MI27/zsc30Afls1fzeKZy0lNTGPiA+/QunNz7n5+BGH1QmjZqRkTH3gHpRTdb+hCrQZhpmR3sFhodfcINr39MVoX1Z1j1rpTr29Pgtu2Im5HJCv/+yoW55J1p+Vdw9n++f/Iz8vDPSiQtg/dZUruAspiodEdI9j1wYfo/HxCu3fDo1Y4p1euAiC8dy/OpqTw76Q3yMvKBqU4uXQZHSeNx9HNzeayZqt3dUuOb93NT49NxNHFib5PFPXKLnz9c/o8NgoPfx+63HUjS97/HxtnLCQoojbNr+0KwKH129m3ahMOFguOzk4MGH2fqUOC6lzVkpPbopj75AQcnZ3o8XhR/sVvfEb3/4zCw9+XjnfeyIoP/se/MxcSEFGHpn2N/JlJqfz24tvkZmWjlCJy0Upu/eBlnN3dynvKKtO4Ywv2b97Nhw9MwsnFmZueHVU478dXv2Do0yPxDvBhw2+rWDdvGelJaXz++Fs07tCCG58Zed7l7a2mt501PT9A007G+//+/cb7f8tzRe//9HFfcPMzRv3559dVrJm3jPTEND5+9C2adGzBLc+OPO/y9qYsFsJHjOLIx1MhX+N3TTdcw2uRsHolAAE9e5ObksLBN18nP9toO+OXL6XJqxPJS0/n2JefAaDz8/Dt2BmvluZ+AZ7+8ZP06NqcQD8vDm78hEnvz8PJyfia9vWPS/lr+TYG9mlH1JqpZGbl8MiYLwFj+POz477j9x/GYrE4MH32SvbsP2lqdoAuPZqxYe0eRg15ExdXZ16ccHvhvOcf/4bnX7sN/0AvpoybRUZGDmhNwybhPPfyLaZnvRzUlB8rtCdV3njVwgJK7QFa6AsVLF+lhmNdLqZ26Uu/P9dduOBlatmgbqyK/qO6Y1Rar7DreW5jza0/73fuy8NrV1Z3jEqZ1r03H0aZOxStKj3dcgBv71xS3TEq7fk2/Zl16K/qjlFpIxpeV+Pbzpqaf9mgbsw7UnPrzm0R13Gb9aBRTTSvb0/c6o6s7hiVlnV8JjFZ5v/OSFUJdRt6OZyed0GpuUtMO3PH26n/ZblOKnJ1rEggFIi+UEEhhBBCCCHEhVyW+wWmKncnRCn1O8awKy9gt1JqE5BTMF9rPdT+8YQQQgghhBBXmvP1hLyLsZv2FnBTsekF04QQQgghhBAXydYll/+/KXcnRGu9CkAp5VRwu4BSyv5nVwohhBBCCCGuSOcbjvUo8BjQQCm1s9gsL6Bmnq0nhBBCCCFENZOekPMPx5oB/AlMAV4sNj2t4BrBQgghhBBCCHGxzjccKwVIAWrudeaEEEIIIYS47MjvhMgaEEIIIYQQQpiqIr8TIoQQQgghhKgiSsk5IdITIoQQQgghhDCV9IQIIYQQQghhKukJkZ4QIYQQQgghhKmkJ0QIIYQQQggTye+ESE+IEEIIIYQQwmRKa23v57D7EwghhBBCCEENOdki89wa074fuzv2uCzXiSnDsbYnLDTjaeyiXcBgDqTU3PyNfQZT753l1R2j0o79ty9/n/qjumNU2oBa1zPj0F/VHaNSRjW8jg8il1R3jEp7tlV/Rq1cVd0xKm1G717UfX9FdceotOPP9SE9t+a2PZ5OfUnLXVbdMSrFy6kfLb5dXd0xKm33/T1Jyqm5n7t+LoOJyVpQ3TEqLdRtKG51a+7vVGcdn1ndESpIBiPJGhBCCCGEEEKYSk5MF0IIIYQQwkRyYrr0hAghhBBCCCFMJj0hQgghhBBCmEgp6QmRnhAhhBBCCCGEqS64E6KUiqjINCGEEEIIIURFKBP/Lk8V6QmZb2PavKoOIoQQQgghhPj/odxzQpRSzYCWgI9S6pZis7wBV3sHE0IIIYQQ4kqk5IyI856Y3hQYDPgCQ4pNTwMesmMmIYQQQgghxBWs3J0QrfVvwG9Kqa5a6/UmZhJCCCGEEOIKdvmeq2GWivQFJSillimlIgGUUm2UUq/YOZcQQgghhBDiClWRnZCvgLFALoDWeicwwp6hhBBCCCGEuFIppUz7u1xVZCfEXWu9qdS0c/YII4QQQgghhLjyVeQX0+OVUg0BDaCUug2ItmsqK601333wK9vW78HF1ZlHXxlBg6a1y5T7aPyPHN57EovFQqMWdXjohWE4OlrYvDqSOV/9hXJQWCwO3PP0jTRr28CM6Gitmfber2z5x8j+zKsjaNSsbPZ3xv3IwT0nsThaaNKyDk+MNbJnpGfx7qsziItJIj8vn5vv7E3/IZ1MyQ7Qq74/r/VrjEUpZu2M5vNNx2yWaxPqxa93dOCJ3yP5Y38cLhYH5oy8CmeLwtFB8cf+OD5Yd8S03AW01sz/5BeiNu7B2dWJO58fSZ0mdcqUW/XLGlbOX0386Xim/DIJTx/PEvOP7T3Oe09M5b5xd9O+VzuT0hv5//ryZw5s3o2TixM3PXcHYY3K5k+KSWD+m9PJSs8grGEdbh5zJxYnR7LSMlkwdQaJ0fE4Ojtx4zMjCa4fbmr+dd/O4/jWKBydnenz5F0ENSibP/VMPEs/+B/ZaZkENahD36fuxuLkyJFNO9k8cyHKQeFgceCa+24jrHlD0/KnREZyYs5syM8nsHt3Qq8bVGJ+dkw0R7+bTuaJ44TfeBOhAwaUmK/z89nzxmScfX1p9MSTpuUGY9sd37sxFgeYtSuazzYft1muTYgXv428mscXRfHHgTjCPF34YFBzgtyd0Rpm7DrNt9tOmpodjLrzzpQ5rFsThaurM+Mn303zFnXLLf/2G7NZ8Mt61m6eWmJ61K6j3HvH20x590GuHXCVnVMX0Vrz7pS51vxOjJ98N80ukP/3XzawZvMHAKxcvoMvPv4dBwcHLBYHRr94G+2uamRWfLrX8mNsl4ZYlGLe/hi+3nmixPy+dQN48qp6aA3ntObNjYfYeiYVgCXDOpGRm0e+1pzTmtsXbDMtdwGtNe+/9Svr1xifveMmjaBZi7KfvZNfm82eqBNoDXXrBTHu9RG4u7uQmprJ5Fdnc/JEAi4ujrw8YTgNG4eZmv+jt39j49q9uLg6MXbicJo0L5v/rfFz2Lf7JFpr6tQL4sWJw3F3dymcvyfyBI/d/TGvvXUnvfu3MSX7F+88wqB+7YlLSKVD/+dtlnlvwj0M7NOOzKyzPDz6c7ZHHgWgf6+2vDv+biwWB76btYJ3P1tgSubqdfn2UJilIj0hjwNfAs2UUqeAZ4BH7RmqwPb1e4k5Gc+Hc8by0AvD+OYdWz9ZAj0GXM0HM1/g3R/HcDYnl+ULNgLQukNj3v5+NG9PH81/XhrOl1PmmBEbgC3/7OX0iXimzR/LE2OH8dlbtrP3vu5qvpj7Ap/ONLL//auRfdHcddSNCOGTGWOY8sVjfPPhAnJzzemAclAwqX9T7pm3g2u/3cjQ5sE0DnC3WW5sz0asPppQOC0nL5+Rs7cxaPpmBk3fTK/6/rQP8zYld3G7N+4h9lQcr/7wEiOeu53ZU23/tE2DVhE88e6j+If4lZmXn5fPb9N+p3mHZvaOW8bBLbtJPBXHk1+/wpCnRrDok7k2yy39dgFdbu7Nk1+Pw9XTja1/bwBgzZwlhDSoxaOfvcjNo+/kry9/NjE9HN+6m5ToOEZ+8hq9Hh3JmmmzbJbb8MNvtBnch1GfvoaLpxt7lxnXwKjduinD3h/LsPfG0vuxO1n12QzTsuv8fI7PnEHjJ5+ixfgJJG7eTNbp0yXKWNw9qDNiBCH9+9t8jNhly3ANNe+LSwEHBa/3bcI9v+yg33ebGNoshMb+5Wy7PRqy6lhi4bQ8rXl91UH6Td/EjTP/5e52tWwua2/r1kRx4ngsv/4xgVfGj2LKpJnllt0deYy01Mwy0/Py8vnog1/o2q2FPaPaVJD/lz/G8/L4O5gyyXbdh4L8WSWmderSlJk/v8yM+S/x6qQ7mfTaT/aOXMhBwStdG/HI35EM+XkL1zcIoqFvyTqw4XQSN/+6lVt+28ora/YzsVuTEvPv/XMHt/y2tVp2QADWr93LiWPxzF04lrGvDuPt121/9j7z3xv5cd4Yfpo/hpAwX+bNXAvA9K+W0bhpOD/NH8Ork0fywVu/mpgeNq7dy8nj8fy04AXGjLuN9yfbbrufGDOUb+c8x//mjiY41JdfZq0rnJeXl8+XHy6iY9emZsUG4Ie5q7jx7jfLnT+wTzsa1g+lVc9neeLFr/ho8gMAODgopr5+Hzfe8xbt+41h2NBraNa4llmxRTW64E6I1vqw1vpaIAhoprXurrU+avdkwOY1kfS87mqUUjRpVY+M9CyS4lPLlGt/TfPCcW+NWtQlMTYZAFd3l8KxcDlZZ8HEcXEbV0fS93oje7PW9chIyyLRRvaO3YqyN2lRl3hrdpQiKzMHrTVZmTl4ebtjsZhzTel2Yd4cTcrkREo2ufma3/fG0r9RUJly915Vmz8PxBKfmVtiemZuHgCODgoni4PRhWayXf9E0ql/R5RSRLSoT1Z6FikJKWXK1Wlcm4BQf5uPseqXNbTr2RZPP0+b8+1p74ZI2vQz8tduVp/sjCzSEkvm11pzZOcBWnRvC0Dbazuxb/0uAOKPx9CgnfHlILBOCMlnEklPKlv/7OXo5p006dUJpRQhTSLIycgiI6ls/tOR+2nQtT0ATXp35simHQA4uRVtu7k5OaYeMMo4cgTX4GBcgoJwcHTEr0NHknfsKFHGydsbj/r1URZLmeXPJiWRsmsXgd27mxW5ULtQb44mZ3G8cNs9w4CGgWXK3deuNn8eiCMh82zhtNiMs0TGpgOQkZvHwYQMQj1dyixrb6tW7OCGoV1QStG6bQPS0zKJiyu77ebl5TP1vZ95avQtZebNnrGCfv3b4+fvZUbkElat2Mn1Qztb80eQlpZJfDn5P3zvZ54efXOJ6e7uroV1PyvrrKnHSlsHenE8NYuTaUb9+fNwHH3rBpQok3kuv/C2m2P1tO/ns3pFJNcPMT57W7WtR3paFvFxZds+D0/j58601uRk5xZ+Pzhy+AwdOjcGoH5ECNGnk0hISDMt/9qVUQwcbORv2aYe6WnZJFwof05uiXH/P89cR69+rfHz9zAtN8C6TXtJTE4vd/7gAVczY/4aADZtO4iPtzuhwb50bNeIQ0djOHo8ltzcPOb+vp7BAzqYFVtUowt+q1VKPaeUeg54BHjIev8BpVQ7e4dLikshIMS38H5AkA+JNhrzAufO5bH6r39p26XoyPWmVbt4dsSbvDnmax59abg945aQEJtCYPHswT4kxJ4/+4o//+Wqrkb2wcO6ceLoGe6+fgJPjHqXh5+7CQcHc3ZCQj1diE7LKbwfnZZT5stIiKczAxsH8eP2U2WWd1Dwxz0d2fp4d9YcTWR7tHlffgskx6fgF+xbeN83yJeU+PLXf5nl45LZuXYX3YdcY4d0F5YWn4xPkG/hfe9AH9JK5c9KzcDVww0H6xdh70BfUhOSAQiJCGfPup0AnNp3jOTYJFIv4vVfqozEZDwDi3qXPAN8ybBmK5CdloFzsfyeAX5kFNvROrJxB7OenMSfb3xB78fvMCU3QG5yMk5+RTumzn6+5CYnVXj5E3NmU+vWW6vlZMBQTxdOp2UX3o9OzyHEq5xtd2fZbbdAbW9XWgZ7sS3G/G039kwyIaFFdSc4xI+4M8llys2esZJefdoQFORTZvkVy3Zw6+097R3VprgzyYQWyx8S4kesjfxzZqykZ582BJbKD7Bi6XZuHTKBZx77jFcn3WXPuCWEeLgQk1HU9sdk5BDs7lymXL96ASy8pQNfDGjFK2v2FU7XwNcDWzN3aHuGNQ01I3IZcbEpBIf6Ft4PDvEhrpzP3knjZnF9n/EcOxrL7SONgwaNm4SzcplxMCdq13FiopNs1j97iY9NLZE/6Dz5p7w6m5v7TeT4kThuGdENgLgzKaxZEcnQYV3NiHtRwkP9ORldNHLiVEwi4aH+hIf6cfJ0senRCdSyMTrhSqNwMO3vclWRZB2A/wC1rH8PA72Br5RSNgf9KaUeVkptUUptmTZtWqXDaRuHWM73uf7NO/Np3q4BzdsVnffRqVdrPpj1ImPevI/ZX/1V6SwXy9bRofNl/+yt+bRs34BW7Y3sWzfso0HjWnz/x2t89ONovnjnFzLTs8t/ADvTpV7Ra32b8OaqQ+TbeKH5Gq6fvpkuX/xDuzBvmgSaezQGjKNDZVzEl8L5n/7K0IcH42BS71OFlMpvs45Zj5t2v70/2emZfPHE22xasJqwhrXMfS02V3+p9W/jPSpeJKJzW0Z8PI6Bzz/M5pmLqjjg+dhesxWRvHMnTl5eeNSrV7WRKshWytKreXzvxkxZY3vbBXB3svDlkFZMWHmA9LN5VZ7xQiqy6cbFJrP0760MH9W7TNl335rLU8/eZFrPcWm22p7Sdd/Iv81mfoA+17Zj/u+v8e5Hj/DFJ7/bI6ZNFW0hlx1LYPDPW3hi6W6eurp+4fQ7Fm7ntgXbeOTvSEY2D+fqkLI7WPZ2MU3/uEkjWLjsNepHhLB08XYA7n6gL2mpWdw17D3mzlxLk2a1sNjo8bSXitSfAmMnDmf+knHUiwhm+WKjt/bjdxbwyNPXV1v9Px9lo4ZprW2+Plvvo7jyVOTE9ADgKq11OoBS6jVgHtAT+Bd4u/QCWutpQMHeh96esLDCgRbPX8sy6zkdDZvVIaHYEYiEuBT8Am03anO/WUxqcjqjX7jX5vwW7Rvy2euzSE1Ox9vXPsNrFs5dy2LrOR2NW9Qhvnj22BT8bRzxApjx1WJSk9J5Yuy9hdOWLtzMbXf3RSlFeJ1AQsL9OXEslqYtyz/BsarEpOcQVuzoaZiXC2fSz5Yo0ybEi4+HtATA382JPhEBnMvX/H0wvrBMas451p9IoneEP/vjM+yee/Wva/lnkXFOQd2mdUkqGNqG0bPhE1Dxc1OO7z/Bd5O+ByA9JYPdG/fgYLHQtnvrKs1c3Kbf17B1sZE/vHFdUuKSC+elxqfgVSq/u7cH2RlZ5Ofl4WCxkBqfjFeAUcdc3F258Tmj90BrzYf3TcQvtOSwiqoW+ecq9iz9B4CgRvVIjy/qPUhPSMbdv2T9d/X25Gyx/OkJSbj7ld1Gwls2YsUn8WSlpuPmbf+hcU6+fuQmFZ0rcTYpGSdf3wotm3HoIMk7dpASGUl+bi55WVkc+eYbIh54wE5pS4pOzyHcy7XwfpinC7HpOSXKtA7x4pPrjXMlSmy7h+JxdFB8OaQVv+w5w1/FtmV7mzNzJb/MM8a0t2hVjzMxRXUn9kwSgcV6NQH27jnByeNx3HT9qwBkZ5/lxkGv8tufE9kTdYyx//0GgOSkDNaticRicaBPv3Z2zL+KX4vljymW/8yZJIKCS9brfdb8N1//WmH+mwa9xq9/TihR7qoOjTl5Ip7kpHR8TRgWGpORQ6hHUdsf6uFCbObZcsv/eyaFOl5u+Lo4kpxzjrgso2xidi7LjiXQJsiLf8/Yvwd23qy1/Dbf+Oxt3rIOsTHJhfNiz6TY7G0qYLE4cO117fjpuxUMvqkTHp6ujJtk/AqB1pqbB00mvJbtIbtV5ZdZ61j4s5G/aan8cWdSCAwq/7PLYnGg78C2zJq+iutv6si+3SeY+IJxHlFKcgYb1u7FYnGgR99Wdn0NFXEqJoHaYUWfQ7VC/Yk+k4SzkyO1w4tNDwvgdGzFe59rLjkxvSI7IXWB4q1QLlBPa52llMopZ5lKG3hrdwbeanSLbl23m8Xz13FN//YciDqOu4crfoFlN8ZlCzawc+M+xn38aIkhSzEn4wmpFYBSisP7TnIu9xxePvY7Kj94WHcGDzOyb167m4Vz19FzQHv2RR7H3dMVfxvZF/+6ga0b9jH505LZg0J82bH5AK3aNyApIY2Tx2MJtXNDWGBHdBoRfu7U8XElJi2HIc2CeWrh7hJlun+1vvD2u4Oas/xQPH8fjMffzYlz+ZrUnHO4ODrQvZ4/n2+0fWWtqtbzpu70vMlY/5Ebolj961qu7tueo3uO4erhhk9AxY/KTZgxrvD2D2/NoFWXFnbdAQHoNKQHnYb0AGD/pig2/76GVr2u4tS+Y7h4uOJV6ku8UoqINo3ZvXYHrXpdxY6lm2jaxfigyU7PxMnFGYuTI1sXr6deq4a4uLuWec6q1GpQL1oN6gXAsX8jifxzNY26X03sgaM4u7vh4Vc2f3irJhxev41G3Tuwf+VG6ncyruKSEh2Hd2ggSiniDp8g79w5XL3M6VHzqF+f7NhYcuLjcfL1JWnLZiIeeLBCy9a6+RZq3Wyco5C2bx9nlvxt2g4IwI6YNCJ83ajj7UpMeg5DmoXw1B9RJcp0/2ZD4e33BjZj2eEE/j5k7HC8M6AZBxMz+HprySsi2dvtI3tz+8jeAKxZtYs5M1cycFAHIncewdPTrcyQqx69WvP3qrcK73fv+Ay//TkRgN8Xv144/bWXp9OjV2u77oAY+Xtx+0ij7q9dtYs5M1dZ8x/F09OtzJfg7r1as3hV0Qm8PTo+W7gDcuJ4LLXrBKGUYu/u4+TmnsPH15y6HxmfRj0fN2p5uhKbmcOgBkE8v3JviTJ1vVw5bh3y1zzAEycHRXLOOdwcHVAoMs/l4ebowDXhvny+3faV2arabSO6c9sIo+1ft3o3c2euo/+g9kTtPI6nl2uZL/Faa06eSKBO3UC01qxdGUW9+sEApKVm4ermhJOTI7/N30j7qxoUnn9hLzeP6MbN1uFU61fv4efZ6+h3XTt27zqOh6crATbynzqRQG1r/n9W76ZuhHHe5uw/XiosN2XcLLr2bHFZ7IAALFqylf/cM4A5C/6hU/tGpKZlEhObTFxCKo0iQqlXJ4jTMYkMG9KVe5/6pLrjChNUZCdkBrBBKfWb9f4QYKZSygPYXf5il679Nc3Ztn4PTw+bgrOrE4++XPQbiVNGf8UjL96Of5APX78zn6AQP155+CPAGIJ12/0D2LhiJ6v/2oLF0YKzsxPPTLrLtHHaHbo1Z8s/e3jolim4uDrxzLii7K898xVPvXw7AUE+fPrWfIJD/RjzgJH9mj6tGfngAEY80J+pE2fx+Mh30Brue2IwPnbqwSktT2teXbqf729rh8VBMWfXaQ4kZHBHW+MSrz/tOF3ussGezrw/qAUODsYoxIX7Yll+OKHc8vbSsnMLdm/cw8Q7J+Pk6sydzxet/89fnMaoMcPxCfRh5c+rWTZrOamJaUx58B1adm7OqDHV/1ucjTu24MDm3Xz8wCScXJy58dlRhfN+evULhj49Eq8AH669bwjz3prO8u8XEdawNu0HGuOA406c4df3fkQ5OBBUN5ShT480NX/dq1pyfGsUMx+fgKOLE70fv7Nw3qLXP6P3Y6Pw8Pely503suSD/7Fp5kICI+rQvJ+R//CG7exfuREHRwuOzk70f+5+07ZdZbFQd8RIDnw4FZ2fT2C3briFhxO3ahUAQb16kZuSwp43JpOXnY1SithlS2k5fgIWNzdTMpYnT2vGrdjPD7e2xaIUsyOj2Z+QyZ1tjG33x53lb7sdw324tUUoe+LS+fNO46TQt9cdZsWRxHKXsYfuPVuxbk0kNw56FVc3Z8ZPurtw3lOPfsK4CXcSVKpn5HLSrWcr1q2J4qZBr+Hq5sxrxc7peOrRTxk34Y7z5l+2ZDt/LNiIo6MFF1cnprz7gGl1P0/D5PUH+WpgKxyU4pcDMRxMzmR4U+NKb7P3RdO/fiA3NgrhXL4mOy+f0Sv3ABDg5sxH/YweNkelWHQ4lrWnzD+afU2P5vyzZg+33TAFV1cnXplU1J4/+9hXvDT+dgICvZj4ykwy07PRGho1DeOFV24D4OiRM0x4eSYWB0X9hqG8POF2U/N36dGMDWv3MGrIm7i4OvNised//vFveP612/AP9GLKuFlkZOSA1jRsEs5zL5e9QIPZpn/8JD26NifQz4uDGz9h0vvzcHIyvmZ+/eNS/lq+jYF92hG1ZiqZWTk8MuZLwLhIw7PjvuP3H8ZisTgwffZK9uw3//LgZrM1PO3/G2Vz7HzBTKPlqw0EA90x+o7Waq23XMRzXNRwrMtNu4DBHEipufkb+wym3jvLqztGpR37b1/+PvVHdceotAG1rmfGIfPORapKoxpexweRS6o7RqU926o/o1auqu4YlTajdy/qvr+iumNU2vHn+pCeW3PbHk+nvqTlLqvuGJXi5dSPFt+uru4Ylbb7/p4k5dTcz10/l8HEZNXc37kIdRuKW11zD1xVpazjM2vEt/tz+TtMO/PF0aHtZblOztsTorXWSqlftdZXY5z/IYQQQgghhLgE1XEFxctNRS6fsEEp1dHuSYQQQgghhBD/L1TknJA+wCNKqWNABsaQLK21bmPXZEIIIYQQQlyRLr/LKJutIjshg+yeQgghhBBCCPH/xgV3QrTWxwCUUsGAfa9TJ4QQQgghxBVOro5Vgb4gpdRQpdQB4AiwCjgK/GnnXEIIIYQQQogrVEUGpE0CugD7tdYRQD9gnV1TCSGEEEIIccVSJv5dniqyE5KrtU4AHJRSDlrrFUA7+8YSQgghhBBCXKkqcmJ6slLKE1gN/KSUigVy7RtLCCGEEEKIK5P8TkjFdkJ2AJnAs8AdgA/gac9QQgghhBBCiCtXhX4nRGudD+QD0wGUUjvtmkoIIYQQQghxxSp3J0Qp9SjwGNCw1E6HF3JiuhBCCCGEEJUkP1Z4vp6QGRiX4p0CvFhseprWOtGuqYQQQgghhBBXrHJ3QrTWKUAKMNK8OEIIIYQQQlzZ5McKpS9ICCGEEEIIYTKlta7uDJdEKfWw1npadeeoLMlfvWpy/pqcHSR/dZP81asm56/J2UHyV7eanl9UnSuhJ+Th6g5wiSR/9arJ+WtydpD81U3yV6+anL8mZwfJX91qen5RRa6EnRAhhBBCCCFEDSI7IUIIIYQQQghTXQk7ITV9XKHkr141OX9Nzg6Sv7pJ/upVk/PX5Owg+atbTc8vqkiNPzFdCCGEEEIIUbNcCT0hQgghhBBCiBpEdkKEEEIIIYQQppKdEFFjKKV8lVKPVdFjvVTsdn2lVGRVPK49KaW+U0rddhHlL4vXpZR6Sim1Ryn1k1JqTHXnuRRKqWeUUu7VnKFwO1BK9VZKLbzI5S+qHhVb7qKf6/8DpVR6OdMrtZ4v8Fz3KqU+qcrHPM9zrVRKdTDjuUSR6tq+L1ZVfh5f4HluUkq1sPfziOohOyGiJvEFyjR6SilLJR7rpQsXEVXkMeB64EB1BylNGS6mHXwGqNadEMrZDoQQVwRfasb27ctF5KxEW1vgJkB2Qq5Ql/1OiFLqV6XUv0qpKKXUw9ZpDyil9luP1HxVcGRIKRWklJqvlNps/etWvelBKeWhlFqklNqhlIpUSg1XSl2tlFplfV2LlVJhSikfpdQ+pVRT63IzlVIPVXf+4pRSdyuldlpfyw/WIy5fKKXWWN+PwXaO8CbQUCm13fr+rlBKzQB2KaUsSql3rNN3KqUesWYOU0qtti4TqZTqoZR6E3CzTvvJ+tiOSqnp1mXnFRztVkodVUq9pZTaZP1rZJ0+zPp4O5RSq+3xYkuvb+vknkqpf5RShwuOdlkb93eseXYppYbbI09lKKW+ABoAC4BngbZKqeVKqQMF9dvWe2TnTPWV0TPzGbAVGFes3kywlrG13T4FhAMrlFIrrOUGKKXWK6W2KqXmKqU8rdM7Wt+nHdZ646WUcldKzbE+z2yl1EZVuSPNhdsB8A7gaa2ze5XR26SsGV61vq5IpdS0guml1oXNMkqpRkqppdb8W5VSDa2L2HyuS6GUutO6jrYrpb5USnW2riNX6/sQpZRqpZTyVEots+bZpZS60bp8wfv5lbXs30opN+u8jtbHWl+wjVxi1ues6ypSKfVMqXlKKfWJUmq3UmoREFxsXnntiM3PLKVUJ2v92Wb9v6mNLDdYX1fgpbwm62OVqe+l5o+0rvNIpdRbxaanK6Xes74ny5RSQdbpDZVSfynjM26NUqrZpWa8FKrsZ9cQ6/a3zVrPQ6ozXylVtn2blVMp9cEFts2CtraOUmqc9bUsUcb3nDHWsmXqjFLqGmAo8I71eRqWm0bUTFrry/oP8Lf+7wZEArWAo4A/4ASsAT6xlpkBdLfergvsuQzy3wp8Vey+D/APEGS9Pxz41nq7P7AeGAH8Vd3ZS72OlsA+ILDgfQG+A/7C2JltDJwEXO2YoT4Qab3dG8gAIqz3HwZesd52AbYAEcBo4GXrdAvgZb2dXupxNdDNev9bYIz19tFiy98NLLTe3gXUst72NXF9z7Wu7xbAwWJ1bIn19YUAx4Gw4uurmuvOUSAQGA/ssG7LgcAJjC/1Nt8jO9ejfKALMADjcpHKul4XAj1tbbfFX4v1diCwGvCw3n8BeBVwBg4DHa3TvQFHYAzwpXVaK+Ac0KEKtoMUoLY1/3qK2kD/Ysv8AAyx3v4OuO0CZTYCN1tvu2L0/pT7XJfwXjQHfgecrPc/s25nrwPvAp8CY63zHAHvYuv+oPV9q29dl+2s8+YAd1pvRwLXWG+/eSnbA3A1xnbvAXgCUUB7rG0JcAtF22E4kFxsPR/Fdjti8zOroM5Yb18LzLfevhf4BLgZ47PPr4q2CVufUyuBDtbXchwIsr4Hy4GbrOU0cIf19qsUfRYvAxpbb3cGlttzm77Aa7PVlvpRdHXQB4H3qiufjbz1qaLt28Sc59s284Eu1nkdgO0YnwFeGL3jBZ+1NuuMWa9H/qrnz5HL31NKqZutt+sAdwGrtNaJAEqpuUAT6/xrgRbFDgh4K6W8tNZpZgYuZRfwrvXo0UIgCeMLyBJrTgsQDaC1XqKUGobxwdu2euKWqy8wT2sdD6C1TrTmn6O1zgcOKKUOA80wGhkzbNJaH7HeHgC0UUVjYX0wdow2A98qpZyAX7XW5WU7obVeZ739I/AUxpcggJnF/v/Aensd8J1Sag7wc1W8mFLKW9+/Wtf37mJH77oDM7XWecAZpdQqoCOw0w65LtVvWussIEsZvQmdqPh7VJWOaa03KKXexag726zTPTHqzRqKbbda6zU2HqMLxs7gOut744zxJaEpEK213gygtU4FUEp1Bz60TotUSlXV+7NJa33S+hzbMT741wJ9lFLPY+xA+GN8af691LJlyiilVmLsYP9izZptfezzPVdl9cP4cr/Z+vhuQCwwEaNeZGNsi2B8qXlDKdUT44tNLYydboAjxerNv0B9pZQvxg7tP9bpM4BL6a3tDvyitc4AUEr9DBTvtetJ0XZ4Wim1vNTyttoRm59ZGO3XdKVUY4wv+k7FHqcPxpe5AQV1qwqU+JzSWq8plqkjsFJrHQegjN7jnsCvGO/DbGu5H4GfldEbeA0wt9hjuFRRzsqw1Za2BmYrpcIwttsj53uAanYp27dZzrdtHtNab7De7k7RZwBKqd+t/19udUaY5LLeCVFK9cZopLtqrTOtH477MI6e2eJgLZtlSsAK0FrvV0pdjTEmfgrGkbIorXXX0mWVMV6yOZCF0aicNDPrBSiMD8PSSk8z84dnMordVsCTWuvFpQtZG8YbgB+UUu9orb+38Vjnex1lbmut/6OU6mx93O1KqXZa64TKvIhylLe+c0qVKf5/TVBmPWutV1fwPapKBXVHAVO01l+WLlB8u1VK/a21nli6CLBEaz2y1HJtsP3e2et9Kl4n8jCGFrpi9Cp00FqfUEqNx+jRKJ6zvDLny1nmuS4xuwKma63HlsoWirFD6GTNlAHcgXE0/mqtda5S6ihFr6l0Ljeqfn1X5PHO1/7ZalNsfmYppT4GVmitb1ZK1cfolShwGGOIYxOMHt9LVvpzSin1d/E4F/NQGK8pWWvdriqyVQFbbenHwPta6wXW7xnjTc50MSq1fZvsfNtm6c9pWy63OiNMcrmfE+IDJFl3QJphHHl0B3oppfyUUo4Y3cgF/gaeKLijlGpnZlhblFLhQKbW+keMI+udgSClVFfrfCelVEtr8WeBPcBIio4MXy6WAbcrpQIAlFL+1unDlFIO1rGaDTB2Eu0lDaML15bFwKMF60wp1UQZ45zrAbFa66+Ab4CrrOVzS63fugXvCcb6L350d3ix/9dbH7+h1nqj1vpVIB6jl64qlbe+bVkNDFfGeTFBGEcpN1VxnqpyozLG+gdgDDXYfJ73yAyLgftV0bkctZRSwTa224JMxevgBqCbKhrf766UagLsBcKVUh2t072sbdVa4HbrtBZA60pmPt92UKDgC0C89bXZulqOzTLWo+snlVI3WbO6KPtdEWwZcJtSKtj6XP7W+jANGAf8BBScg+CDUU9ylVJ9gHrne2CtdRKQppTqYp004hKzrgZusr7PHhQNiSo+f4R1OwzD6LEorkw7QvmfWT7AKevte0s9zjGMoV/fF/vsuCTnqe9gDM3rpZQKVMZFQEYCq6zzHCiqW6OAtdb6c0QZvfoF58pUZ8++rba0+Pq9p7qClaOqtm97K56zotvmWmCI9TPAE+PAExeoMxVZH6KGuqx7QjDON/iPMoYt7MP40D8FvIHRMJ4GdmOMmQSj2/5Ta3lHjA+F/5gdupTWGCdV5QO5wKMY45c/Ukr5YOScqpTKxRib2klrnaaMk51fAV6rptwlaK2jlFKTgVVKqTyKhq/sw/hACgH+UzB0w04ZEpRS65RxcmkWcKbY7K8xuqm3KqM/Nw7jqhq9gf9a1286xnhsML7k7FRKbQVextj5u0cp9SXGONXPiz22i1JqI8YHbsFR73eUMVRCYXzI7aji11re+rblF6CrNYMGntdax1iPoF5uNgGLMMa/T9Jan1ZK3YPt98jutNZ/K6WaA+uNakM6cCfQiLLbLRj15k+lVLTWuo9S6l5gplKqYOjAK9ajysOBj5VxgnQWRo/uZxhDbHZivJ87KWq7Libz+baDgjLJSqmvMIbZHMUY2nQxZe4CvlRKTbS+/mEXm7MitNa7lVKvAH8royc4F/gNOKe1nmH90vuPUqovxg7J70qpLRhDPvdW4CkeAL5SSmVg9CZc9PoulnWrUuo7inbwv9Zab1NFw0d+wRj6swvYT9EX9QK22pHyPrPexqgrz2Gcg1E6yz6l1B0Yw1eGaK0PVfZ1Wdn6nHrX+lzRSqmxwAqM9u4PrfVv1uUygJZKqX8x1m3BjtYdwOfW99YJmEUVt5EVVU5bOh5j3Z3C+F4RUR3ZbKmq7dveSuXcDDS70Laptd6slFqAUReOYfTkFWyT5dWZWRjb8FMY54Zcal0Xl5GCE7NqFKWUp9Y63Xp08ReME7t/qe5c/99YP5AXaq3nVXcWe1FGt3KHgvHEQlSW9Qu1k9Y6Wxk9h8uAJlrrs9Uc7YpV8Flhvf0iEKa1froachzlCmxHlFLpWmvP6s4hao5i39/cMXa6H9Zab63uXKJ6XO49IeUZr5S6FqNL8m+ME+SEEOJy5o5xeV8njCPKj8oOiN3dYD2K74hx5PXe6o0jxP9706zDUV0xzgeTHZD/x2pkT4gQQgghhBCi5rrcT0wXQgghhBBCXGFkJ0QIIYQQQghhKtkJEUIIIYQQQphKdkKEEEIIIYQQppKdECGEEEIIIYSp/g+HMRNadYTongAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Coreelation matrix more visual\n",
    "corr_mat = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "ax = sns.heatmap(corr_mat,\n",
    "                annot=True,\n",
    "                linewidths=0.5,\n",
    "                fmt='.2f',\n",
    "                cmap=\"YlGnBu\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dd9cc65a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'exersice induced angina 1=yes 0=no'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata['exang']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "650e40d2",
   "metadata": {},
   "source": [
    "## 5.Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "846daf82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into X and y\n",
    "X = df.drop('target',axis=1)\n",
    "y = df.target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fb748d3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                   y,\n",
    "                                                   test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fe4c231",
   "metadata": {},
   "source": [
    "1. Logistic Regression\n",
    "2. K-Nearest Neighbors Classifier\n",
    "3. RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9839fdc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {'Logistic Regression':LogisticRegression(),\n",
    "         'KNN':KNeighborsClassifier(),\n",
    "         'RandomForestClassifier':RandomForestClassifier()}\n",
    "\n",
    "# Create a fun to train and avaluate\n",
    "def fit_score(models,X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluate given dictionary of models and data\n",
    "    \"\"\"\n",
    "    np.random.seed(42)\n",
    "    scores = {}\n",
    "    for model_name,model in models.items():\n",
    "        model.fit(X_train,y_train)\n",
    "        scores[model_name] = model.score(X_test,y_test)\n",
    "        \n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d25b02ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiref\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "scores = fit_score(models,X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bf05d8ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(scores,index=[\"Accuracy\"])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2742fc62",
   "metadata": {},
   "source": [
    "Now we got a baseline model and we know a models first prediction arenot always what we should based our next steps off.\n",
    "\n",
    "* Hyperparameter tunung\n",
    "* Feture importance\n",
    "* Confusion matrix\n",
    "* Cross-val\n",
    "* Precision\n",
    "* Recall \n",
    "* F1 score\n",
    "* Classification report\n",
    "* ROC curve\n",
    "* AUC\n",
    "\n",
    "### Hyperparameter tuning by hand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5778806d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune KNN\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# a list of different values for n_neughbours\n",
    "\n",
    "neighbors = range(1, 21)\n",
    "\n",
    "# setup KNN\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "    knn.fit(X_train,y_train)\n",
    "    \n",
    "    train_scores.append(knn.score(X_train,y_train))\n",
    "    test_scores.append(knn.score(X_test,y_test))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "42097d51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Knn score on the test data: 75.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors,train_scores, label=\"Train score\")\n",
    "plt.plot(neighbors, test_scores, label=\"Test Scores\")\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.xticks(np.arange(1,21,1))\n",
    "plt.ylabel('Models Score')\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Max Knn score on the test data: {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4345f25",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning `RandomizedSearchCV` \n",
    "\n",
    "Tune:\n",
    "* LogisticRegression()\n",
    "* RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4978aff9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    }
   ],
   "source": [
    "# Create a hyperparameter Grid for logisticRegression\n",
    "log_reg_grid = {'C': np.logspace(-4, 4, 20),\n",
    "               'solver': ['liblinear']}\n",
    "\n",
    "# Create Hyperparameter grid for RandomForestClassifier\n",
    "\n",
    "rf_grid = {'n_estimators': np.arange(10,1000,50),\n",
    "          'max_depth':[None, 3, 5,10],\n",
    "          'min_samples_split': np.arange(2,20,2),\n",
    "          'min_samples_leaf':np.arange(10,30,2)}\n",
    "\n",
    "models = {\"Log\": LogisticRegression(),\n",
    "         'RFC': RandomForestClassifier()}\n",
    "# Hyperparameter grid are ready lets use them\n",
    "clf = {'log_RSCV': RandomizedSearchCV(models['Log'],\n",
    "                                      log_reg_grid,\n",
    "                                      random_state=42,\n",
    "                                      cv=5,\n",
    "                                      n_iter=20,\n",
    "                                     verbose=True).fit(X_train,y_train),\n",
    "      'RFC_RSCV': RandomizedSearchCV(models['RFC'],\n",
    "                                     rf_grid,\n",
    "                                     random_state=42,\n",
    "                                     cv=5,\n",
    "                                     n_iter=20,\n",
    "                                     verbose=True).fit(X_train,y_train)}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d834462c",
   "metadata": {},
   "source": [
    "### Evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "99122d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf['log_RSCV'].score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "930564d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf['RFC_RSCV'].score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a6c842",
   "metadata": {},
   "source": [
    "###  !!!!Something to do experiment with `RandomizedSearchCV`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f91277d",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning with gridSearchCV\n",
    "\n",
    "will try it with gridSerachCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f625049f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Different hyperParameter for our logisticregression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a6d327c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg_grid = {'C' : np.logspace(-4,4, 60),\n",
    "               'solver' :['liblinear']}\n",
    "\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                         log_reg_grid,\n",
    "                         cv=5)\n",
    "\n",
    "gs_log_reg.fit(X_train,y_train);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0e861adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.24537511066398168, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d320ec00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e8a99d0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.859504132231405\n"
     ]
    }
   ],
   "source": [
    "print(clf['log_RSCV'].score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb78d295",
   "metadata": {},
   "source": [
    "## Evaluating tuned ml classifier,beyond accuracy\n",
    "\n",
    "* ROC and AUC score\n",
    "* Confusuin Matrix\n",
    "* Classification report\n",
    "\n",
    ".. it would be great if cross validation were used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c38786cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "572ed4ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shiref\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x22b079cee50>"
      ]
     },
     "execution_count": 40,
     "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": [
    "\n",
    "plot_roc_curve(gs_log_reg,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4f58e94c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATIAAAEGCAYAAADmLRl+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVn0lEQVR4nO3de5AdZZ3G8e8zyZBALpAQko0YSMAQ5SIYYxRRDF4gsG4hIrsgKquygMqi623RtZCV2l1rUbwiKwoVVrkoAgJCEWLABWoRCDFCQqTkJuQiIQQklwEyc377x+nZHJLJnO7MufQ783yquqZPn3O6f5PAk/d9u/ttRQRmZinraHcBZmYD5SAzs+Q5yMwseQ4yM0ueg8zMkje83QXUGj2uM3bfc2S7y7ACnl8+ot0lWAFdlQ28HC9qIPs46ohR8ey6nlyfvf+Bl+ZHxNyBHC+PUgXZ7nuO5EvXzGx3GVbAdbP3bXcJVsBvN9ww4H2sXdfDPfNfneuznZMfnTDgA+ZQqiAzsxQEPVFpdxGv4CAzs0ICqFCuC+kdZGZWWAW3yMwsYUGw2V1LM0tZAD3uWppZ6jxGZmZJC6CnZLPmOMjMrLByjZA5yMysoCA8RmZmaYuAzeXKMQeZmRUlehjQ7ZoN5yAzs0ICqLhFZmapc4vMzJJWvSDWQWZmCQtgc5RrTlYHmZkVEoiekk0u7SAzs8Iq4a6lmSXMY2RmNgiIHo+RmVnKqjPEOsjMLGER4uUY1u4yXsFBZmaFVTxGZmYpqw72u2tpZknzYL+ZJc6D/WY2KPT4glgzS1kgNke5oqNc1ZhZ6Xmw38ySF6h0XctyxaqZJaFCR66lP5KmSLpd0nJJyyR9Ott+rqSVkpZkyzH16nGLzMwKiaBRl190A5+LiMWSxgD3S1qQvfetiPhG3h05yMyskOpg/8BvUYqI1cDqbH29pOXAnjuyL3ctzaywHjpyLcAESYtqltP62p+kqcAbgHuyTWdKekDSpZLG1avHLTIzKyRQkYkV10bErP4+IGk0cA3wmYh4QdJFwHlUT5CeB3wT+Fh/+3CQmVlhjbr8QlIn1RC7PCKuBYiIp2ve/xHwq3r7cZCZWSHV51oOPMgkCbgEWB4RF9Rsn5yNnwEcByytty8HmZkV1LAnjR8GfBh4UNKSbNuXgZMkHUI1M58ATq+3IweZmRVSfRxcQ85a3gV9JuLNRfflIDOzQiLUkK5lIznIzKwwz0dmZkmrzkdWrnstHWRmVpBniDWzxFUvv3CLzMwS1qh7LRvJQWZmhXnOfjNLWnUaH3ctzSxxHiMzs6RVZ79w19LMEla9RclBNmhtWt3BorPH8uLaDhBM+9supn+ki4e+P4rHrx7JiPEVAA74zEYmv+PlNldr29PREXz3miWsfXonzj3jgHaXU0JDrEUmaS7wHWAY8OOI+Hozj9duGgYHfXED4w7oZvNGcdvx45j01mpgTT9lE/t9rKvNFVoex35kFU8+ugu7jO5udymlVbYr+5sWq5KGARcCRwP7U52aY/9mHa8Mdp5YYdwB1f/4O0cFY/btoevpcv3LZf2bMOklZs9Zx/xfTGp3KaXVe9Yyz9Iqzfy/bDbwSEQ8FhEvA1cBxzbxeKWycWUHzy8fzviDq8H26OW7sODY8Sz6lzG8/Jdy/WtmW5z+5ce45PxpVCrtrqTcKtGRa2mVZh5pT+Cpmtcr6OMJKZJO630wwYbnNjexnNbp3ih+e9auHHz2BjpHB/ucuIm5tz7Lu69bx8g9Kjzwn6PbXaL1YfacdTy/rpNHlvnvpz+9c/bnWVqlmWNkff0Wsc2GiIuBiwH2PnDMNu+nprIZ7v70WKb8zYvseeRLAIycsOXXmnZCF/97xm5tqs76s//MF3jLO9fxpsPvo3NEhV1G9/CF8x/m/C/MaHdppRJA9xAa7F8BTKl5/WpgVROP13YRcP9XxjB2nx72+/stA/tdazrYeWK1r7JqwQjGTvcgchnNu2Aq8y6YCsBBs5/n+I+tdIhtx1A6a3kfMF3SNGAlcCLwwSYer+2eXdzJkzfszNj9uvn1cdVH8R3wmY2suGkkz/9hOAhG7dnDG85d3+ZKzQagxd3GPJoWZBHRLelMYD7Vyy8ujYhlzTpeGUx442aOX75mm+2+Ziw9D967Gw/eu1u7yyilITexYkTczA48SMDMym3ItMjMbHDyxIpmlrxAdFeGzmC/mQ1SQ2qMzMwGoXDX0swS5zEyMxsUHGRmlrRA9Hiw38xS58F+M0taeLDfzAaDcJCZWdrKd9N4uUbszCwJEcq19EfSFEm3S1ouaZmkT2fbx0taIOmP2c9x9epxkJlZIRHQU1GupY5u4HMR8TrgLcCnsud6nA0sjIjpwMLsdb8cZGZWWAXlWvoTEasjYnG2vh5YTnU6/GOBy7KPXQa8r149HiMzs0KCxg/2S5oKvAG4B5gUEauhGnaSJtb7voPMzAoqNNg/QdKimtcXZ8/p2LI3aTRwDfCZiHhBKh6SDjIzKyzyPyZobUTM2t6bkjqphtjlEXFttvlpSZOz1thkYNtpl7fiMTIzK6xBZy0FXAIsj4gLat66ATglWz8FuL5ePW6RmVkh1bOWDWkDHQZ8GHhQ0pJs25eBrwM/l/Rx4EnghHo7cpCZWWEFupb97CPuou/n3wK8q8i+HGRmVphvUTKzpAX1x79azUFmZoU1oGfZUA4yMysmIOrfftRSDjIzK8xdSzNLXiPOWjbSdoNM0vfopyscEWc1pSIzK7Vm3Gs5UP21yBb1856ZDVUBpBJkEXFZ7WtJoyJiY/NLMrOyK1vXsu59BpIOlfQQ1bmCkHSwpB80vTIzKykRlXxLq+S5YerbwFHAswAR8Xvg8CbWZGZlFzmXFsl11jIintpqjqCe5pRjZqUXaQ3293pK0luBkLQTcBZZN9PMhqjUxsiAM4BPUZ1LeyVwSPbazIYs5Vxao26LLCLWAie3oBYzS0Wl3QW8Up6zlvtIulHSM5LWSLpe0j6tKM7MSqj3OrI8S4vk6VpeAfwcmAy8CrgauLKZRZlZuUXkW1olT5ApIn4SEd3Z8lNKN9RnZi2VyuUXksZnq7dLOhu4imppfwfc1ILazKysErr84n6qwdVb8ek17wVwXrOKMrNyU8n6ZP3dazmtlYWYWSJCkOLEipIOBPYHRvZui4j/blZRZlZyqbTIekn6KjCHapDdDBwN3AU4yMyGqpIFWZ6zlh+g+oy5P0fER4GDgRFNrcrMyi2Vs5Y1uiKiIqlb0lhgDeALYs2GqpQmVqyxSNJuwI+onsncANzbzKLMrNySOWvZKyI+ma3+l6RbgLER8UBzyzKzUkslyCTN7O+9iFjcnJLMrOxSapF9s5/3Anhng2vhuWWdXPO6iY3erTXR/FV3trsEK2D2URsas6NUxsgi4ohWFmJmiWjxGck8/IBeMyvOQWZmqVPJJlZ0kJlZcSVrkeWZIVaSPiTpnOz1XpJmN780MysjRf6l7r6kS7OZp5fWbDtX0kpJS7LlmHr7yXOL0g+AQ4GTstfrgQtzfM/MBqvGTXU9D5jbx/ZvRcQh2XJzvZ3k6Vq+OSJmSvodQEQ8lz0WzsyGqgZ1LSPiDklTB7qfPC2yzZKGkZUuaQ9K9wwVM2ulAl3LCZIW1Syn5TzEmZIeyLqe4+p9OE+QfRe4Dpgo6d+oTuHz7zmLMbPBJqpnLfMswNqImFWzXJzjCBcB+1J9hu5q+r84H8h3r+Xlku6nOpWPgPdFhJ80bjaUNfGsZUQ83bsu6UfAr+p9J8/EinsBm4Aba7dFxJM7WKeZpa6JQSZpckSszl4eByzt7/OQb7D/JrY8hGQkMA14GDhgB+s0s8Q16qZxSVdSnYF6gqQVwFeBOZIOoZo7T/DKBx/1KU/X8qCtDjwzz47NzOqJiJP62HxJ0f0UvrI/IhZLelPR75nZIFKyK/vzjJF9tuZlBzATeKZpFZlZuUWa91qOqVnvpjpmdk1zyjGzJKTUIssuhB0dEV9oUT1mVnIioRliJQ2PiO7+prw2syEqlSCj+qSkmcASSTcAVwMbe9+MiGubXJuZlVHOmS1aKc8Y2XjgWapz9PdeTxaAg8xsqEposH9idsZyKVsCrFfJ8tjMWimlFtkwYDSvDLBeJfs1zKylSpYA/QXZ6oj4WssqMbM0JPYUpXI9uM7MSiOlruW7WlaFmaUllSCLiHWtLMTM0pHiLUpmZlskNkZmZrYNUb4BdAeZmRXnFpmZpS6ls5ZmZn1zkJlZ0hKdWNHM7JXcIjOz1HmMzMzS5yAzs9S5RWZmaQuSmljRzGwbST18xMxsuxxkZpY6RbmSzEFmZsV49gszGww8RmZmyfMtSmaWPrfIzCxpiT5p3MzslUoWZB3tLsDM0tJ7QWyepe6+pEslrZG0tGbbeEkLJP0x+zmu3n4cZGZWmCqRa8lhHjB3q21nAwsjYjqwMHvdLweZmRUTBZZ6u4q4A9j60ZPHApdl65cB76u3H4+RNUnniArfvPYROncKhg0P7rxpN37yjb9qd1m2lTUrOzn/03vx3JpO1BEc86FnOe7UtTy6bCTfO3sKXRs7mPTql/nnC//EqDElu+agjQpcfjFB0qKa1xdHxMV1vjMpIlYDRMRqSRPrHaRpQSbpUuC9wJqIOLBZxymrzS+JL56wLy9uGsaw4cEFv3yE+24bwx8Wj2p3aVZj2PDgtHNWMf31XWza0MGZc/dj5uHr+fbn9+IfzlnJ6w/dyPwrx/OLiyZyyhf/3O5yyyP/YP/aiJjVxEqA5nYt57Ft33cIES9uGgbA8M5gWGdQstvTDNh9UjfTX98FwC6jK0x5zUusXd3JikdHcNBbNgLwhsPXc9dNu7WxyvJp1GD/djwtaTJA9nNNvS80Lci20/cdUjo6gh8seJifPbCM390xmod/59ZYmf35qZ14dOnOvHbmJvae8SJ3zx8LwJ2/2o1nVnW2uboSCSAi37JjbgBOydZPAa6v94W2D/ZLOk3SIkmLNvNSu8tpqEpFfPI9Mzj5jfsz45BN7D2jq90l2XZ0bezgvFOncsbXVjJqTIXPXvAkN86bwKeO2o+uDR0M38nN6Vqq5Fvq7ke6ErgbmCFphaSPA18H3iPpj8B7stf9avtgfzbwdzHAWI0flP+1bHxhGL+/ezRvOmI9f3p453aXY1vp3gznnTqVd77/Od52zF8A2Gv6S/zHVY8BsOLREdyzcGw7SyyVRk6sGBEnbeetdxXZT9tbZIPVruO7GTW2B4CdRlaY+fYNPPXIyDZXZVuLgAs+txdTpr/E8ac/8//bn19b/Te+UoErvjOJ93742XaVWD55u5UtHBRue4tssBo/aTOf/86TdHRARwfcceOu3PNr/6teNsvuHcXCX4xn2uu6+MS7ZwDw0S+tYuXjI7hx3gQADjv6Lxx54pAe7t3GkLnXMuv7zqF6HckK4KsRcUmzjlc2jy/fmU8dOaPdZVgdB755I/NXLenjnfUcd+raVpeTjqESZP30fc0scUOmRWZmg1QAPeVKMgeZmRXmFpmZpa9kt6k4yMysMLfIzCxtfhycmaVOgDzYb2ap85PGzSxt7lqaWfpaex9lHg4yMyvMZy3NLH1ukZlZ0sJnLc1sMChXjjnIzKw4X35hZulzkJlZ0gIo2bOKHWRmVogIdy3NbBColKtJ5iAzs2LctTSzwcBdSzNLn4PMzNLmm8bNLHV+ipKZDQYeIzOz9DnIzCxpAVQcZGaWNA/2m9lg4CAzs6QF0FOuS/sdZGZWUEA0JsgkPQGsB3qA7oiYtSP7cZCZWXGN7VoeERFrB7IDB5mZFVPCs5Yd7S7AzBIUkW+BCZIW1Synbb0n4FZJ9/fxXm5ukZlZcfm7lmvrjHsdFhGrJE0EFkj6Q0TcUbQct8jMrJgI6OnJt9TdVazKfq4BrgNm70hJDjIzKy5/13K7JI2SNKZ3HTgSWLoj5bhraWbFNeas5STgOklQzaIrIuKWHdmRg8zMCoqGnLWMiMeAgwdej4PMzIoKiAZdENsoDjIzK863KJlZ0iL8ODgzGwQ8+4WZpS7cIjOztHliRTNLXQlvGneQmVkhAUSO249ayUFmZsVE4yZWbBQHmZkVFu5amlnyStYiU5To7IOkZ4A/tbuOJpgADGgqX2u5wfp3tndE7DGQHUi6heqfTx5rI2LuQI6XR6mCbLCStGhHH6pg7eG/s7R4PjIzS56DzMyS5yBrjYvbXYAV5r+zhHiMzMyS5xaZmSXPQWZmyXOQNZGkuZIelvSIpLPbXY/VJ+lSSWsk7dDTfKw9HGRNImkYcCFwNLA/cJKk/dtbleUwD2j6BZzWWA6y5pkNPBIRj0XEy8BVwLFtrsnqyJ5yva7ddVgxDrLm2RN4qub1imybmTWYg6x51Mc2X+ti1gQOsuZZAUypef1qYFWbajEb1BxkzXMfMF3SNEk7AScCN7S5JrNByUHWJBHRDZwJzAeWAz+PiGXtrcrqkXQlcDcwQ9IKSR9vd01Wn29RMrPkuUVmZslzkJlZ8hxkZpY8B5mZJc9BZmbJc5AlRFKPpCWSlkq6WtIuA9jXPEkfyNZ/3N8N7ZLmSHrrDhzjCUnbPG1ne9u3+syGgsc6V9Lni9Zog4ODLC1dEXFIRBwIvAycUftmNuNGYRFxakQ81M9H5gCFg8ysVRxk6boTeE3WWrpd0hXAg5KGSTpf0n2SHpB0OoCqvi/pIUk3ARN7dyTpN5JmZetzJS2W9HtJCyVNpRqY/5S1Bt8uaQ9J12THuE/SYdl3d5d0q6TfSfohfd9v+gqSfinpfknLJJ221XvfzGpZKGmPbNu+km7JvnOnpNc25E/T0hYRXhJZgA3Zz+HA9cAnqLaWNgLTsvdOA76SrY8AFgHTgPcDC4BhwKuA54EPZJ/7DTAL2IPqjB29+xqf/TwX+HxNHVcAb8vW9wKWZ+vfBc7J1v+a6k3yE/r4PZ7o3V5zjJ2BpcDu2esATs7WzwG+n60vBKZn628GbuurRi9Daxm+Y/FnbbKzpCXZ+p3AJVS7fPdGxOPZ9iOB1/eOfwG7AtOBw4ErI6IHWCXptj72/xbgjt59RcT25uV6N7C/9P8NrrGSxmTHeH/23ZskPZfjdzpL0nHZ+pSs1meBCvCzbPtPgWsljc5+36trjj0ixzFskHOQpaUrIg6p3ZD9D72xdhPwjxExf6vPHUP9aYSU4zNQHZI4NCK6+qgl9z1vkuZQDcVDI2KTpN8AI7fz8ciO+/zWfwZmHiMbfOYDn5DUCSBpP0mjgDuAE7MxtMnAEX18927gHZKmZd8dn21fD4yp+dytVG+IJ/vcIdnqHcDJ2bajgXF1at0VeC4LsddSbRH26gB6W5UfBO6KiBeAxyWdkB1Dkg6ucwwbAhxkg8+PgYeAxdkDNH5IteV9HfBH4EHgIuB/tv5iRDxDdYztWkm/Z0vX7kbguN7BfuAsYFZ2MuEhtpw9/VfgcEmLqXZxn6xT6y3AcEkPAOcBv615byNwgKT7gXcCX8u2nwx8PKtvGZ4+3PDsF2Y2CLhFZmbJc5CZWfIcZGaWPAeZmSXPQWZmyXOQmVnyHGRmlrz/A/rI7ePRNqHfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "cm = confusion_matrix(y_test, y_preds)\n",
    "disp = ConfusionMatrixDisplay(cm,display_labels=gs_log_reg.classes_)\n",
    "disp.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "288a1667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_preds))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13883612",
   "metadata": {},
   "source": [
    "### Evaluation metrics using cross-validation\n",
    "* Accuracy.\n",
    "* Recall.\n",
    "* Precision.\n",
    "* f1-score.\n",
    "\n",
    "\n",
    "\n",
    "using `cross_vak_score`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8ee65270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.24537511066398168, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3ed7b267",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(C=0.24537511066398168,\n",
    "                         solver= 'liblinear')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "df77f41c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_val_all(clf,X,y,scoring):\n",
    "    c = np.array([])\n",
    "    for score in scoring :\n",
    "        c = np.append(c,np.mean(cross_val_score(clf,X,y,cv=5,scoring=score)))\n",
    "        \n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a156dc6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = ['accuracy','precision','recall','f1']\n",
    "res = cross_val_all(clf,X,y,metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ea23afb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise cross-val metrics\n",
    "cv_metrics = pd.DataFrame({'Accuracy':res[0],\n",
    "                          'Precision':res[1],\n",
    "                          'Recall': res[2],\n",
    "                          'F1':res[3]},\n",
    "                         index=[0])\n",
    "\n",
    "cv_metrics.T.plot.bar(title='Cross val classification metrics',\n",
    "                     legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "874f8455",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "Feature importance is as asking \"Which features contributed most to the outcomes of the model and how did they contribute?\"\n",
    "\n",
    "Finding feature importance is different for each model. One way to find feature importance is to search for \"(MODEL NAME)feature importance\"\n",
    "\n",
    "Feature importance for `LogisticRegression`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "3f8b7843",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(C=0.24537511066398168,\n",
    "                         solver= 'liblinear')\n",
    "clf.fit(X_train,y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "20024af0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00390076, -0.9209317 ,  0.68039158, -0.01163103, -0.00171841,\n",
       "         0.05006529,  0.34309945,  0.02478829, -0.64120039, -0.5783471 ,\n",
       "         0.47899753, -0.65713109, -0.70801061]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6c7798c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.00390076270376665,\n",
       " 'sex': -0.920931695767178,\n",
       " 'cp': 0.6803915807850118,\n",
       " 'trestbps': -0.011631025800132429,\n",
       " 'chol': -0.0017184096885288756,\n",
       " 'fbs': 0.050065285751438086,\n",
       " 'restecg': 0.34309945289103694,\n",
       " 'thalach': 0.024788287812652417,\n",
       " 'exang': -0.6412003867245653,\n",
       " 'oldpeak': -0.5783470993278784,\n",
       " 'slope': 0.47899753438261666,\n",
       " 'ca': -0.6571310869662398,\n",
       " 'thal': -0.7080106067053217}"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match coefs of features to columns \n",
    "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "38aca4ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'feature Importance'}>"
      ]
     },
     "execution_count": 111,
     "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": [
    "feature_df = pd.DataFrame(feature_dict,index=[0])\n",
    "feature_df.T.plot.bar(title='feature Importance',legend=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a01a2c94",
   "metadata": {},
   "outputs": [],
   "source": [
    "   "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}