Heart-Disease-Machine_learning.ipynb

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    "# Predicting Heart Disease using Machine Learning\n",
    "\n",
    "This notebook will introduce some foundation machine learning and data science concepts by exploring the problem of heart disease **classification**.\n",
    "\n",
    "It is intended to be an end-to-end example of what a data science and machine learning **proof of concept** might look like.\n",
    "\n",
    "## What is classification?\n",
    "\n",
    "Classification involves deciding whether a sample is part of one class or another (**single-class classification**). If there are multiple class options, it's referred to as **multi-class classification**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Problem Definition\n",
    "In our case, the problem we will be exploring is **binary classification** (a sample can only be one of two things). \n",
    "\n",
    "This is because we're going to be using a number of differnet **features** (pieces of information) about a person to predict whether they have heart disease or not.\n",
    "\n",
    "In a statement,\n",
    "\n",
    "> Given clinical parameters about a patient, can we predict whether or not they have heart disease?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heart Disease Data Dictionary\n",
    "\n",
    "A data dictionary describes the data you're dealing with. Not all datasets come with them so this is where you may have to do your research or ask a **subject matter expert** (someone who knows about the data) for more.\n",
    "\n",
    "The following are the features we'll use to predict our target variable (heart disease or no heart disease).\n",
    "\n",
    "1. age - age in years \n",
    "2. sex - (1 = male; 0 = female) \n",
    "3. cp - chest pain type \n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "4. trestbps - resting blood pressure (in mm Hg on admission to the hospital)\n",
    "    * anything above 130-140 is typically cause for concern\n",
    "5. chol - serum cholestoral in mg/dl \n",
    "    * serum = LDL + HDL + .2 * triglycerides\n",
    "    * above 200 is cause for concern\n",
    "6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) \n",
    "    * '>126' mg/dL signals diabetes\n",
    "7. restecg - resting electrocardiographic results\n",
    "    * 0: Nothing to note\n",
    "    * 1: ST-T Wave abnormality\n",
    "        - can range from mild symptoms to severe problems\n",
    "        - signals non-normal heart beat\n",
    "    * 2: Possible or definite left ventricular hypertrophy\n",
    "        - Enlarged heart's main pumping chamber\n",
    "8. thalach - maximum heart rate achieved \n",
    "9. exang - exercise induced angina (1 = yes; 0 = no) \n",
    "10. oldpeak - ST depression induced by exercise relative to rest \n",
    "    * looks at stress of heart during excercise\n",
    "    * unhealthy heart will stress more\n",
    "11. slope - the slope of the peak exercise ST segment\n",
    "    * 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "    * 1: Flatsloping: minimal change (typical healthy heart)\n",
    "    * 2: Downslopins: signs of unhealthy heart\n",
    "12. ca - number of major vessels (0-3) colored by flourosopy \n",
    "    * colored vessel means the doctor can see the blood passing through\n",
    "    * the more blood movement the better (no clots)\n",
    "13. thal - thalium stress result\n",
    "    * 1,3: normal\n",
    "    * 6: fixed defect: used to be defect but ok now\n",
    "    * 7: reversable defect: no proper blood movement when excercising \n",
    "14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)\n",
    "\n",
    "**Note:** No personal identifiable information (PPI) can be found in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "from sklearn.model_selection import train_test_split , cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV , GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix , classification_report\n",
    "from sklearn.metrics import precision_score , recall_score , f1_score\n",
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('E:/zero to mastery/data/heart-disease.csv')\n",
    "df.shape #rows and cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>0</th>\n",
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       "      <td>145</td>\n",
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       "      <td>1</td>\n",
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       "      <td>150</td>\n",
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       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
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       "      <th>2</th>\n",
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       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Data exploration and becoming SME On dataset\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <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",
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       "      <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",
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       "    <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many of each class are\n",
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SGvQ/4eRIdSHbT7sAAAAASUVORK5CYII=\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']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### heart disease frequency according to sex\n",
    "\n",
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare target col with sex\n",
    "pd.crosstab(df.target , df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 plot of cross tab\n",
    "pd.crosstab(df.target , df.sex).plot(kind='bar',\n",
    "                                    figsize = (10,6),\n",
    "                                    color=['salmon' , 'lightblue'])\n",
    "plt.title('Heart Disease Frequency 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",
    "           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age vs Max heart rate for heart disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "# crate another figure\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Scatter with positive examples\n",
    "plt.scatter(df.age[df.target == 1],\n",
    "           df.thalach[df.target==1],\n",
    "           c = \"salmon\")\n",
    "\n",
    "## Scatter with positive examples\n",
    "plt.scatter(df.age[df.target == 0],\n",
    "           df.thalach[df.target==0],\n",
    "           c = \"lightblue\")\n",
    "\n",
    "# Add some help or info\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(['Disease' , 'No Disease']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "# CHeck the distribution of the age column wiht a histogram\n",
    "df.age.hist(grid=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heart disease frequency per chest pain type\n",
    "cp - chest pain type \n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</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>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "cp             \n",
       "0       104  39\n",
       "1         9  41\n",
       "2        18  69\n",
       "3         7  16"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp , df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kB90BA9kysqw7Ry6NRjpWSpJUSp8+ffjMZz7DFVdcwRe+8AUAdtllF66//nqOOOIIxo4dy2677dbhdu666y7GjBnDhAkT3jF9wYIF/P3vf2ffffdlp512YtNNNwVgr732YvTo0Xzta18DYMqUKQwdOpR58+ax/vrrA7VPci4yY8YMNt54Y0466SRmzJjBww8/zF577cWBBx7IqaeeSr9+/XjppZeYP38+G2644VKdE/uQSZKkpjvttNN48cUXF49fcsklXHXVVQwZMoRrr72Wiy++uNX1brjhBoYOHcpmm23Gv/3bv3HzzTe/q4Vs/vz57L///gwZMoSPfvSjiz9EcMkllzBp0iSGDBnC4MGDueyyywA444wzOPPMM9l1113f0UH/hhtuYOutt2bo0KFMmzaNI488ksGDB3P++eez1157MWTIED75yU8ya9aspT4f0dq91+XFsGHDctKkSaXLAGwhkyR1bVOnTn1XcFHnae18R8TkzBzW2vK2kEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCOi2QRcSVETE7Ih6pm9YnIm6LiOnVz7Xq5p0ZEU9ExOMRsXdn1SVJksro0aMHQ4cOZauttmLbbbflBz/4AW+99RYAkyZNavj5lSuizvym/quB0cBP6qaNAsZn5ncjYlQ1/vWIGAwcBmwFrAfcHhGbZebSPz5dkiS9yy2PL/2XmdY7aPP+HS6zyiqrMGXKFABmz57N5z73OebNm8e5557LsGHDGDas1a/o6hY6rYUsM+8GXmox+UDgmmr4GmBE3fTrM/P1zHwKeALYobNqkyRJZfXr148xY8YwevRoMpM777xz8UPA77rrLoYOHcrQoUPZbrvtmD9/PgDf+973GD58OEOGDOHss89evK0RI0aw/fbbs9VWWzFmzBgA3nzzTY4++mi23nprttlmm8Xf1v/kk0+yzz77sP322/ORj3yEadOmNfnIW9fsZ1mum5mzADJzVkQsejz6+sAf6pabWU17l4gYCYwE+NCHPtSJpUqSpM608cYb89ZbbzF79ux3TL/gggu49NJL2XXXXVmwYAG9e/dm3LhxTJ8+nYkTJ5KZHHDAAdx9993svvvuXHnllfTp04dXX32V4cOHc/DBB/P000/z3HPP8cgjtZ5TL7/8MgAjR47ksssuY9CgQdx///18+ctf5o477mj6sbfUVR4u3toj3Vt9plNmjgHGQO3RSZ1ZlCRJ6lytPcJx11135atf/SqHH344Bx10EAMGDGDcuHGMGzeO7bbbDqg9QHz69OnsvvvuXHLJJdx6660APPvss0yfPp3NN9+cGTNmcOKJJ7Lffvux1157sWDBAu69914OOeSQxft6/fXXm3OgHWh2IHshIvpXrWP9gUWReCawQd1yA4Dnm1ybJElqohkzZtCjRw/69evH1KlTF08fNWoU++23H7/5zW/YaaeduP3228lMzjzzTI4//vh3bOPOO+/k9ttv57777mPVVVdljz324LXXXmOttdbioYce4re//S2XXnopN954IxdddBFrrrnm4n5sXUmzv/bil8BR1fBRwC/qph8WEStHxEbAIGBik2uTJElNMmfOHL74xS9ywgknEPHOG2VPPvkk22yzDV//+tcZNmwY06ZNY++99+bKK69kwYIFADz33HPMnj2befPmsdZaa7Hqqqsybdo0/vCHWg+oF198kbfeeouDDz6Yb33rWzzwwAOsscYabLTRRvzsZz8Daq1zDz30UHMPvA2d1kIWET8F9gDWiYiZwNnAd4EbI+JY4BngEIDMfDQibgQeA94AvuInLCVJWrG8+uqrDB06lIULF9KzZ0+OOOIIvvrVr75ruYsuuogJEybQo0cPBg8ezKc+9SlWXnllpk6dys477wzAaqutxnXXXcc+++zDZZddxpAhQ9h8883ZaaedgFpgO+aYYxZ/rcZ3vvMdAMaOHcuXvvQlzj//fBYuXMhhhx3Gtttu26Qz0LZo7d7t8mLYsGE5adKk0mUAsPDc00qXsFivs79fugRJUhczdepUttxyy9JldButne+ImJyZrX63h9/UL0mSVJiBTJIkqTADmSRJUmEGMkmSuonlud/48uS9nGcDmSRJ3UDv3r2ZO3euoayTZSZz586ld+/eS7ReV/mmfkmS1IkGDBjAzJkzmTNnTulSVni9e/dmwIABS7SOgUySpG6gV69ebLTRRqXLUBu8ZSlJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmFFAllEnBoRj0bEIxHx04joHRF9IuK2iJhe/VyrRG2SJEnN1vRAFhHrAycBwzJza6AHcBgwChifmYOA8dW4JEnSCq/ULcuewCoR0RNYFXgeOBC4ppp/DTCiUG2SJElN1fRAlpnPARcAzwCzgHmZOQ5YNzNnVcvMAvo1uzZJkqQSStyyXItaa9hGwHrA+yPi80uw/siImBQRk+bMmdNZZUqSJDVNiVuWnwCeysw5mbkQuAXYBXghIvoDVD9nt7ZyZo7JzGGZOaxv375NK1qSJKmzlAhkzwA7RcSqERHAnsBU4JfAUdUyRwG/KFCbJElS0/Vs9g4z8/6IuAl4AHgDeBAYA6wG3BgRx1ILbYc0uzZJkqQSmh7IADLzbODsFpNfp9ZaJkmS1K34Tf2SJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqrGfpAiRJy4dbHp9VuoTFDtq8f+kSpGXKFjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklRYkUAWEWtGxE0RMS0ipkbEzhHRJyJui4jp1c+1StQmSZLUbKVayC4G/icztwC2BaYCo4DxmTkIGF+NS5IkrfCaHsgiYg1gd+AKgMz8R2a+DBwIXFMtdg0wotm1SZIkldBhIIuI8Y1MWwIbA3OAqyLiwYi4PCLeD6ybmbMAqp/9lmIfkiRJy402A1lE9I6IPsA6EbFW1cerT0QMBNZbin32BD4M/DAztwP+xhLcnoyIkRExKSImzZkzZynKkCRJ6hraayE7HpgMbFH9XPT6BXDpUuxzJjAzM++vxm+iFtBeiIj+ANXP2a2tnJljMnNYZg7r27fvUpQhSZLUNbQZyDLz4szcCDg9MzfOzI2q17aZOfq97jAz/wI8GxGbV5P2BB4DfgkcVU07ilrwkyRJWuH17GiBzPyPiNgFGFi/fGb+ZCn2eyIwNiLeB8wAjqEWDm+MiGOBZ4BDlmL7kiRJy40OA1lEXAtsAkwB3qwmJ/CeA1lmTgGGtTJrz/e6TUmSpOVVh4GMWnAanJnZ2cVIkiR1R418D9kjwAc7uxBJkqTuqpEWsnWAxyJiIvD6oomZeUCnVSVJktSNNBLIzunsIiRJkrqzRj5leVczCpEkSequGvmU5Xxqn6oEeB/QC/hbZq7RmYVJkiR1F420kK1ePx4RI4AdOq0iSZKkbqaRT1m+Q2b+HPh4J9QiSZLULTVyy/KgutGVqH0vmd9JJkmStIw08inLT9cNvwE8DRzYKdVIkiR1Q430ITumGYVIkiR1Vx32IYuIARFxa0TMjogXIuLmiBjQjOIkSZK6g0Y69V8F/BJYD1gf+FU1TZIkSctAI4Gsb2ZelZlvVK+rgb6dXJckSVK30UggezEiPh8RParX54G5nV2YJElSd9FIIPsC8BngL8As4J+raZIkSVoGGvmU5TPAAU2oRZIkqVtq5IthNwJOBAbWL5+ZhjRJkqRloJEvhv05cAW1T1e+1bnlSJIkdT+NBLLXMvOSTq9EkiSpm2okkF0cEWcD44DXF03MzAc6rSpJkqRupJFAtg1wBPBx3r5lmdW4JEmSllIjgeyfgI0z8x+dXYwkSVJ31Mj3kD0ErNnZhUiSJHVXjbSQrQtMi4g/8nYfsszMAzuvLEmSpO6jkUB2dt1wALsBn+2cciRJkrqfDm9ZZuZdwDxgP+BqYE/gss4tS5Ikqftos4UsIjYDDqPWGjYXuAGIzPxYk2qTJEnqFtq7ZTkN+B3w6cx8AiAiTm1KVZIkSd1Ie7csDwb+AkyIiB9HxJ7U+pBJkiRpGWozkGXmrZl5KLAFcCdwKrBuRPwwIvZqUn2SJEkrvEY69f8tM8dm5v7AAGAKMKrTK5MkSeomGvli2MUy86XM/FFm+tgkSZKkZWSJApkkSZKWPQOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhPUvtOCJ6AJOA5zJz/4joA9wADASeBj6TmX8tVZ/UXdzy+KzSJQBw0Ob9S5cgScWUbCE7GZhaNz4KGJ+Zg4Dx1bgkSdIKr0ggi4gBwH7A5XWTDwSuqYavAUY0uy5JkqQSSrWQXQScAbxVN23dzJwFUP3s19qKETEyIiZFxKQ5c+Z0fqWSJEmdrOmBLCL2B2Zn5uT3sn5mjsnMYZk5rG/fvsu4OkmSpOYr0al/V+CAiNgX6A2sERHXAS9ERP/MnBUR/YHZBWqTJElquqYHssw8EzgTICL2AE7PzM9HxPeAo4DvVj9/0ezaVhRd5VNz4CfnJElqRFf6HrLvAp+MiOnAJ6txSZKkFV6x7yEDyMw7gTur4bnAniXrkSRJKqErtZBJkiR1SwYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhTU9kEXEBhExISKmRsSjEXFyNb1PRNwWEdOrn2s1uzZJkqQSSrSQvQGclplbAjsBX4mIwcAoYHxmDgLGV+OSJEkrvKYHssyclZkPVMPzganA+sCBwDXVYtcAI5pdmyRJUglF+5BFxEBgO+B+YN3MnAW10Ab0a2OdkRExKSImzZkzp1mlSpIkdZpigSwiVgNuBk7JzFcaXS8zx2TmsMwc1rdv384rUJIkqUmKBLKI6EUtjI3NzFuqyS9ERP9qfn9gdonaJEmSmq3EpywDuAKYmpk/qJv1S+Coavgo4BfNrk2SJKmEngX2uStwBPCniJhSTfsG8F3gxog4FngGOKRAbZIkSU3X9ECWmb8Hoo3ZezazFkmSpK6gRAuZJKlBC889rUrjyD4AAAexSURBVHQJbzvs9NIVSCssH50kSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhfkpS0mStMzd8vis0iUAcNDm/UuX0BBbyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVFjP0gVI3dHCc08rXcLbDju9dAWS1O3ZQiZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmF+SlLSZJWEH6Ce/llC5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqbAuF8giYp+IeDwinoiIUaXrkSRJ6mxdKpBFRA/gUuBTwGDgsxExuGxVkiRJnatLBTJgB+CJzJyRmf8ArgcOLFyTJElSp+pqgWx94Nm68ZnVNEmSpBVWZGbpGhaLiEOAvTPzuGr8CGCHzDyxbpmRwMhqdHPg8aYX2vWtA7xYuggtN7xe1CivFS0Jr5d32zAz+7Y2o2ezK+nATGCDuvEBwPP1C2TmGGBMM4ta3kTEpMwcVroOLR+8XtQorxUtCa+XJdPVbln+ERgUERtFxPuAw4BfFq5JkiSpU3WpFrLMfCMiTgB+C/QArszMRwuXJUmS1Km6VCADyMzfAL8pXcdyzlu6WhJeL2qU14qWhNfLEuhSnfolSZK6o67Wh0ySJKnbMZCtYHz0lBoVEVdGxOyIeKR0LeraImKDiJgQEVMj4tGIOLl0TeqaIqJ3REyMiIeqa+Xc0jUtL7xluQKpHj31v8AnqX2FyB+Bz2bmY0ULU5cUEbsDC4CfZObWpetR1xUR/YH+mflARKwOTAZG+G+LWoqIAN6fmQsiohfwe+DkzPxD4dK6PFvIViw+ekoNy8y7gZdK16GuLzNnZeYD1fB8YCo+RUWtyJoF1Wiv6mXLTwMMZCsWHz0lqVNFxEBgO+D+spWoq4qIHhExBZgN3JaZXisNMJCtWKKVaf7PRNIyERGrATcDp2TmK6XrUdeUmW9m5lBqT9vZISLsEtEAA9mKpcNHT0nSe1H1B7oZGJuZt5SuR11fZr4M3AnsU7iU5YKBbMXio6ckLXNVR+0rgKmZ+YPS9ajrioi+EbFmNbwK8AlgWtmqlg8GshVIZr4BLHr01FTgRh89pbZExE+B+4DNI2JmRBxbuiZ1WbsCRwAfj4gp1Wvf0kWpS+oPTIiIh6k1EtyWmf9VuKblgl97IUmSVJgtZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUxScRGxWkT8KCKejIhHI+LuiNgxIgZGxCPLYPtDl+XXNETEbxZ911KDy4+IiMHLav+SVjwGMkldweXUHnQ+KDO3Ao4G1lmG2x8KLLNAlpn7Vt9C3qgRgIFMUpsMZJKKiohNgB2Bb2bmWwCZOSMzf10t0iMifly1nI2rvv2biNgkIv4nIiZHxO8iYotq+iER8UhEPFS1tL0POA84tPpC00Nb7P/oiPhFta3HI+Lsunk/r7b/aESMrJv+dESsU7XgTW2tvrpldwEOAL5X7X+TiHigbv6giJhct91/j4iJ1WvTanrfiLg5Iv5YvXZdRqdfUhdhIJNU2lbAlMx8s435g4BLq5azl4GDq+ljgBMzc3vgdOA/q+lnAXtn5rbAAZn5j2raDZk5NDNvaGUfOwCHU2tJOyQihlXTv1BtfxhwUkSsvQT1AZCZ91J7hNnXqv0/CcyLiKHVIscAV9et8kpm7gCMBi6qpl0MXJiZw6vtX97GuZK0nOpZugBJ6sBTmTmlGp4MDIyI1YBdgJ/VHrMIwMrVz3uAqyPiRqDRh2DflplzASLiFmA3YBK1EPZP1TIbUAtfczuqr4H9XQ4cExFfBQ6lFggX+Wndzwur4U8Ag+uOdY2IWD0z5zewL0nLAQOZpNIeBbaNiJUW3bJs4fW64TeBVai17r+cmUNbLpyZX4yIHYH9gCl1LVHtafkMuYyIPagFoZ0z8+8RcSfQu8H6OnIzcDZwBzB5URhspZZFwytVdbzawLYlLYe8ZSmpqOoW3iTg3KiagKp+VQe2s84rwFMRcUi1fETEttXwJpl5f2aeBbxIrWVrPrB6O2V8MiL6VP2/RlBrZfsA8NcqjG0B7LQUh/mO/Wfma8BvgR8CV7VY9tC6n/dVw+OAExYt0GDIlLQcMZBJ6gqOAz4IPBERfwJ+DDzfwTqHA8dGxEPUWtkWBbjvRcSfqq/LuBt4CJhA7Zbfuzr1V34PXAtMAW7OzEnA/wA9I+Jh4FvAH5bi+K4HvhYRD1YfYgAYS60FbFyLZVeOiPuBk4FTq2knAcMi4uGIeAz44lLUIqkLisyWLfWS1H1ExNHAsMw8oaNll/F+Twc+kJn/p27a01UtLzazFknl2YdMkposIm4FNgE+XroWSV2DLWSSJEmF2YdMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFfb/AST1Lx3FlfaQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the crosstab more visual\n",
    "pd.crosstab(df.cp , df.target).plot(kind='bar',\n",
    "                                   figsize = (10,6),\n",
    "                                   color = ['salmon' , ['lightblue']])\n",
    "\n",
    "plt.title('Heart disease frequency per chest pain type')\n",
    "plt.xlabel(' Chest pain type')\n",
    "plt.ylabel('Amount')\n",
    "plt.legend(['No Disease' , 'Disease'])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Make a correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xhLcJtcjpb3CRYeYibaJ83I9KAEzjfv8gT+5YEcHI5btxstUx3MLjvrmx80r/WKbx/HlS9x/Ev1una5Xqilzp8P/VrKW07BBGyw4V1/l069+Wjxa9wqR3H+KnL/+sg5TXB4WNRX+uV9a+CcJzSqk7y/7fELgf2KRpWiaAUmoJ0Kxs+U1Aq0qd0k0p1UDTtNzKL6hp2jzgwsxHe376hmse+kxyBsEBFYd4g/w9SUrJwt7OluBKpzsFBXhxNvXK90DXxq7lW9jzp+kamKBmIWSnV+yZyU7Pxs3L7bJfy81bf1X1a2Pvis1ErjblD2gaQm6l98/NMODqWXVPsZObK8V5hRhLS7HR6cyWid1s2dPfAFILS/Bzrtjr6ufkQLqZ09jC3Z35T9dwnt8cQ3aJ6bSTbn56zuYXYSg2Pd6QmEE7bzdWnUqzTHhMeyN9K50W4+NkT0ZxzfxhDZyZ1LYJr+yJtfppMxfYe3hQkplZ/rjEYMBOr79EjaqM589zdO5cvLt3x7OT5b7Ety/bwq6VprbfsHkIhrSKMcOQbrjivpd0/Cw/f7SIR95+HBcLbAAuWbiV35ea8rdqE0JKckXfTU0x4ONbNf/hQ4kknkrn/25+G4CionPcPfJtlq58DV8//d/Wryv2HnqKMyvWfXFWFg6X2X6upu61kl5UUuWUNh/Hi/fdF9s2YXLE9dF31/+6lS1/7ASgcfOG5ackAWSlGdB7u1+s6kVFrNtPdwud/gaQWlB13Pd1dqhyNO6CcHdnXusSzoQtZsb9sscbzmTQzsuNPy047jt6elBUqf0WZWbhoK+63h099BRlVCvjUdHG0w7G4NYoBAd3y/RXgNVLt7Ku7BqeJi0aklHpyFlGWjYeF2k7S75aTY4hjxdfftDs8lYdm/DZ9EXkGPJwK7vBhvjnsdrUTCk1ANOkpqemae2B/cDhS1SxKSvboewnqPrkx1JWrNnHvXeb9l526xhOTm4ByakG9kTGEx7qT6OGPtjZ6Rh1a09WrNlrkUzdb+3L05++xNOfvkTLnm05sC4CTdM4HZeAo4sjDTwv/0ukRY82V1W/Njrf3K/8xgVNe7Qjev1uNE3jzKETODg71pjcKKUIadeUQ9tMdyuKWrebpt3bli8vyi/kdPQxmvZoiyXFZuYS4upEoIsDtjaKISE+bD6bWaWMn7MD7/VqyRu7jnAqr6j8+eSCYtp6NcBBZ+qWXf3cOZFTYNH8h7JzCXJxwt/JAVulGBTgw/aUqvl9He2Z2qkFMyKPkphfdJFXsjzXxo0pSk2lKD0d4/nzZEZE4NH+8k5/1DSNE99+i1NAQI0bJNS1Xrf1ZeLcl5g49yVa92rLvjWmvncyLgEnFyfcvC6/72WlZvHt1K8Z89K/8An2rcPUFUaN7cP3P/+b73/+N/0GtWHVMlP+qMgEXF2dapzm1qdfa1ZtnMpvq6fw2+opODrasXTlawD0Hdj6b+vXFbfQxhSmpFKYZmo/qbv34NXh8trP1dS9Vqr33YEBPmxPrdl33+xY1ncLro++O+jOPuU3LujYty07Vu9B0zTiYxJwcnFEf4U7AAryCjkcGU+HPm3qKHFNsVm5NHR1ItDZtO6HNvRhS/Vx38mBmb1a8sbumuN+G89K476vOwm5lh333UMbkZ+SSkFZ+03atQffjlVPH/Tt2I4z23aiaRpZx45j6+SEY6VJUtLOCAJ7WPaa52F39+G9BaYbF3Tt14bNf+5F0zSORJ/E2cURD++abWfdsp0c3HWY56fej41NxSZwcmJ6+RHI44cTOX/uPA3cLXf2iCXJNUAm1jwC5A5kaZpWoJRqgem0ty+B/kopDyAX06luF65s/wt4BpgFoJTqoGlandyrc8F/n6Vvz5Z4ezTg2K5PmPbhz9jZmVbV/75fy5/r9zNsYAditsymoLCYxyd9AZgu7J34+nyWfzcZnc6GBT9tJO5I4qXeqk4069qKIxGxfPTwNOwc7blr4r3ly759fS53TBiLm5c7O37fxNYl68jLyuXTp2bSrGsr7pgw9pL1LaFJl1Yc3xPDF+OnYudgz8jn7ytftvjNuYx4diwNvNwZ+OBt/P7efDZ/vwK/sGDaDe1RXu7IjoOEdmyB/SXOga8LpRq8ty+eOf3aoFOw7EQKx3MKuKuJPwC/xCfzaKuGuDvY8XKnJgCc1zQeWBtJTGYe6xIz+H5IB0o1jcNZ+fx6PNmi+Y0azIk5znvdWmMDrEpMJSGvkFtDTPmXn0pmXNMQ3OztmNAmrPwzP7HNdMHsfzo0o4OnO+72tiwe2IX5R0+xMjHVItmVTkfjsWM5PHs2mtGIT+/eOAcGkrJpEwB+/ftTkp1N9NtvU1pUhFKKpLVraffWWxQmJpK+cydOQUFETZ0KQMM770Tf1rIT6BbdWnFodxwzH5yOvYM9oyaNLV/21Wtf8H8vjMHdy52tv25i05L15Gbm8uHj79GiWytGvTCGtd+vpiAnn1//a7p7nI1Ox/Ofvmix/L37tmL75jjuHvk2jo72vD59TPmyCU/O47W3RuPje/EJzaXq1zWl0xF+3xiiPvoYzWjEv09vXIICObvR1H4CB5jaz95p71BaWARKkbh2HV2nvYmtk5PZupZk1OC/sceZ2bU1NsrUd0/mFXJLQ1Pf/eN0MveHm/ru860r+u5T201997X2zWhf1ncXDezCgqOnWGWhvntB2x4tidoZx6v3voO9gx0PvVLR/me/NI8HXxqN3tudtT9vZvWiDWRn5vLmw+/TtkdLHnzJdM3G/i1RtO7aHAcny439pRrM2m8a920ULL8w7oeVjfvHy8Z9+4pxv9So8cC6inH/u5vKxn2D5cd9G52OVvePIWLWf9GMRoL79aJBcCCn1m8GIGRQP3zatyHtYDSb/j0FnYM97R4dV/H5i0tIjz5E6wfvu9hb1LmOvVqyf0ccz4+agb2jHU++VjF2zHjxSx5/5R48fdz536yl+Ph58J/xcwDTaW//9/BQdm04yOY/96Cz1WFvb8eEafdf8WmA4saiLnaOcJ2/sVIOwG9AEKYjPz7Am5hOeZsEnAXigExN015TSnkDnwItMU3cNmua9sTfvI3mFDL2b4pcnwpPmW4xufj4jXke6j1hw/nmyGprx6i1h5oNo+virdaOUWsR9/Rh4Mpt1o5RKxtGmu5vMq5s4nKj+bZ/fwB+P7nKyklq5/ZGIzCUrLR2jFrT249k/NaN1o5Ra/P6DGDwqhuz764bYeq7W5JXWDlJ7fT1vxmAbktuzLF/96g+AEzYud7KSWpndo9BHMj4w9oxaq2D1y1gsYsWa8+r2XMW3fDPODLnulwnVjsCpGlaMTCi+vNKqT2aps1TStkCv2I68oOmaemA3JZDCCGEEEIIUWvWvgmCOW8qpW4CHDFNfn6zch4hhBBCCCFueNfzdTmWdN1NgDRNm2TtDEIIIYQQQoh/putuAiSEEEIIIYS49mrzt+X+ieQ4mBBCCCGEEKLekCNAQgghhBBC1ANyDZCJrAUhhBBCCCFEvSETICGEEEIIIUS9IafACSGEEEIIUQ/IKXAmshaEEEIIIYQQ9YYcARJCCCGEEKIekCNAJrIWhBBCCCGEEPWGHAESQgghhBCiXpBjHwBK0zRrZ6hL/+gPJ4QQQgghrhvK2gH+jn+ryRbdNk6OnXFdrhM5AiSEEEIIIUQ9INcAmfzjJ0CLj/9p7Qi1ck/YcACcQsZaOUntFJ5ayNy4v6wdo9aeaDmUeYdWWztGrY1vMYyItBXWjlErXX1uBuCl3eutnKR23us2COCGbT/jWwzjs9gbt+8+1WooYzZstnaMWls0sB/jt260doxamddnAAAfx9yY7ef51kMBeHDzJisnqZ35/foDMHjVNisnqZ11I3pzNPsPa8eotabut1g7grgC//gJkBBCCCGEEEKOAF0ga0EIIYQQQghRb8gRICGEEEIIIeoBJcc+ADkCJIQQQgghhKhH5AiQEEIIIYQQ9YBcA2Qia0EIIYQQQghRb8gRICGEEEIIIeoBpa7Lv0tqcXIESAghhBBCCFFvyARICCGEEEIIUW/IKXBCCCGEEELUA3ITBBNZC0IIIYQQQoh6Q44ACSGEEEIIUQ/IH0I1kbUghBBCCCGEqDfkCJAZmqaxcu4vHImIxc7BjrtevI/A8IY1yu1ctpkdv20iMymdVxa9jYu76xXVrytzZz3OiMEdScvIocuQl8yW+eCtBxg2sAMFhSWMf/FzDkQnADCkf3vef3McOp0N8xdt4P3Pllks9wWaprHxf0s5sTcGOwd7hj73L/ya1Fx/2SnprHx/PkV5BfiGBTN8wjh0dracjjrKshnzcPf1AiC8Z3t6jB5h0fwbvlzKib2x2DrYM/z5+y6SP4M/ZlXkHznxfnR2pi55OuooG776BeP5UpzcXBj9zvMWzf/dx79yYEccDo72jH91LKHNg2uU++yt7zl+6DS2tjrCWobw8EujsLXVXXb9upByMIao7xaDUSNkQG+a3TqsxmeL+m4xqZEx6Bzs6Th+HPrGIQD8NfE1bB0dUTY2KJ0NA6ZOtkjm6vlu9Laz6aulJOyNwdbBnqHP/gvfi/TdVR9U5B/2/Ljy/ADJR0+y+JUPGPHiQzTt1dFi+XNioklcvAjNaMSrd1/8h1cdN4qSkzi5YD6Fp08RcNsd+A2t1r6MRg7PmI6dXk+Tp5+zWO4LMqOiObZwMZpmJKBvH0JGDq+yvCApmUNfzyfv1GlC77ydhsOHXnZdS9A0ja1fLeXkPlP7GfzMv/Ax035yUtL568P5FOcV4B0azE1l7efE7oPsWrgCpRQ2Ohv6PHw3AS2bWCR7VnQ0CYt+QjMa8evbh6ARVdtOYVISx+YvIP/UKULuuIPAYaZ1X5yZybGvv+Zcdg4ohV+/fgTcNNgimSvr6q3n6ZZh2ChYmZjCouNnqiwfHOjDmNAg02cpLWV2TDzHcwsAmNQ2nB4+HhhKzvHo1gMWzw6mtjPvg9/Ys930vTNhyhjCW9T83pn1+vcci0tEZ6ujWeuGPDPZ9L2Vn1fI+1N+JC05C2OpkTv/NYAht3azwiepe3INkImsBTOORsSScTaNCV/9h9ufG8PyT5aYLRfSKowHZzyF3tezVvXryndLNnH7uHcvunzYwA40aexPm34TeeaVL5nz9iMA2NgoZk9/iNsfmEnHwZMYdVsvWjQNslTscgl7YzEkpfLQ51O46akxrJ/7k9lyWxYso9NtA3no8yk4uDoTvXZH+bKgVk341+xX+NfsVyw6+QE4sTeWrKQ0Hp77OkOeHs3azxebLbd5we90vm0Aj8x9HUdXZ6LK8hflFbB27mLueO0xHvzkVW596WFLxidyZxzJp9P5YNGrPPLvUcx//2ez5XoN7cSsH19hxrf/pqT4HBuX77yi+teaZjRycMEiev77GQbNnMKZHRHknEmqUiY1Mob8lFQGv/8W7R++l8hvFlZZ3vvViQx8+zWrTH7gxm87CftiMZxN5YHPpjD4yTGs/8J839327TI63jqQBz+bgoOLMzHrKvqusdTItm9/J6RDS0vFBkzt5/TCH2nyzPO0fGMqWRG7KTx7tkoZnbMLwaPH4HvTULOvkbZ+LY7+AZaIW4NmNHL0h4W0nfgsXae9SequCPKr5bd1cSb83jE0HDbkiutawql9sWQnpXLfp1MY8MQYNs0z3352fLeM9rcO5L5PTWN/XFn7CW7bnNEfvsLoD19h4NP3seGzHy2SWzMaOfHjj7R8/jk6TH2L9N0RFNRY9y6EjhlD4NCq617Z2NBo1Cg6TJtK21cnk7xhQ426dc0GeK51GJP3xPDwlv0MCvChkatTlTJJBUVM3BXFY9sO8P2x07zQJrx82erEVCbvibVo5ur2bD/E2dPpzFs6mWcmj+KzmUvNlhswvDNzl7zMpwsnUVJ8jr9+2wXAiiXbCAn145MfJzFj7lN89fEyzp07b8mPICxMJkBmxO2MpsPgriilaNiyMYV5heRmZtcoFxgejIefV63r15Vtuw+Raci76PJbhnbmx6VbANi9/xjubs74++rp2iGc+IRkEk6lcu5cKUuW7+CWoV0sFbtc/O4oWg7ohlKKgOahFOcXkldt/WmaxumoIzTt1QGAVgO7E7/roMWzmhO/O4pWA7Nz4MoAACAASURBVE35Ay+R/9TBozTrbcrfelA3ju2MAuDQ5r007dkeNx/TxNpZ38Ci+fduiabP8C4opQhv05j8vEKy0nNqlOvQsxVKKZRSNGkVQmZq9hXVv9ay4hNw8fPBxdcHG1tbgnp0IXlvZJUySfsiadinB0opPMPDOFdQQJHBcn3z79zobef47ihaDqzad/OvsO9GrtxEeM8OOJcdUbeUgoQTOPj64OBjaj8eXbuSfbDq3mw7NzdcGoeidLoa9UuyMsmOisKrdx9LRa4i5/gJnHx9cSrL79utCxn7q7Z/ezc33EIb18h/OXUt4cTuKJqXjf3+zUMpuUj7ORN1hCY9Te2nxcDunNhtaj92Tg7lf+TxfHEJYJk/+Jh34gSOPr44lq0/765dyTpQdf3ZubnhGlpz3dvr9bg2agSAztERp4AASgwGi+S+oIW+AWfyi0gqLOa8prEhKY1e1XbsxhpyyTtfWv5/H0f78mVRWTnkWHmysGtzNINGdkYpRYu2jcjPLSTTzPdO194ty7+3mrUKIT21bF0rRWFBMZqmUVhQTAM3Z3S6f+YmslI2Fv25Xlk1mVLKRSm1QikVqZSKVkqNVkp1VkptUkrtVUqtVkoFKKXclVKHlVLNy+otVEo9Vle5cjIMuHvryx+7e7uTk375G0lXW7+uBfp7kpiUUf74THImgf6eBPp7kHi20vNJGQT5eVg8X16mgQbeFe/r6qWvsRFYlJuPg4sTNmVfJg2qlUk6fILvJszg16mfkX6q6lGAupaXkU2DSr//Bt568jKq5i/MzcexUv7KnzHrbCpFeQX89NocvnvhPWLW77ZceCArPQcv34r8nr56si7Rfs+fL2Xr6j2069GiVvWvlaIsA06eFe3GydODoizD35YpzDSVUSh2zJzDxtffIWH9ljrPa86N3nbyMgy4el1Z33X11pNf9hnzMgzE7zxI22GWn0SUZBmw96jY6LPXe3Au6/I3RM8s/omgu/4PrPSFX2Iw4FCpbTt4eFB8mRvSV1P3WsrPNOBaaex38dLXmAAV5eZjX6n9uHhVtB+A4zsj+fHZaax4ey6DnrnPIrlN669S2/HQU2zIuuLXKUpPJ//0KVxDQ69lvL/l7WhPWlFJ+eO0ohK8HR0uWn5EQz92p1m+fVxKRmo23n4VY6eXrzsZqZf+3tqwai+depq+t24Z1ZvTCSmMG/kWz9z7PuNfuAMbm+t3411cPWtfAzQcOKtp2s0ASil3YBVwu6ZpaUqp0cDbmqY9rJR6BpivlPoY8NA07UtzL6iUGg+MB/jiiy/Q3xRy5ak0sy9sufp1TJnZK6ZpWvmes6rPWyLR379p9WTaJYL5NgnmkXlTsXdy4MSeGJbP+JKHPp9yjUNenLlsNVbtJcoYS42kxp9m1LRnOFdyjoUvfURA88Z4BvnWQdqazOa/RPn5H/xMi/ZhtGgfVqv614rZNlF9xV+iSJ8pk3Dy0FOcncP2mXNwDfTHu0XTax/0Em70tmN+BVcrYfb3ZPpn01dL6T3uNmyssuf177NfTPbBSGwbuOHcqBG5hw9f21iX62rGamuM82Zcqm1UKnTJMmE92hPWoz1nY46xe+Ef3Pbms9c0o1lmx7wrG/VKi4o48vlcGo8eja2T099XqGMX+47t4OnOiGA/JpQddb5eXOlm12czl9K6YxhtOpq+t/btPExY0yDe+exJkhIzeP2ZL2jdIQxnV8e6CWxFchc4E2tPgKKA95VSM4E/gCygDbCmbGNcByQBaJq2Rik1CvgUaH+xF9Q0bR4w78LDxcf/vKwgu5ZvYc+fpvOIg5qFkJ1esXcjOz0bNy+3y/5Qbt76q6pf184kZxAcUHHqXpC/J0kpWdjb2RIcWOn5AC/Opl75XqzaOLByM9F/bQfAr2kIuekV75uXYcDF071KeSc3V4rzCzGWlmKj05GbYcC1rIyDc8WXR2iX1qz/YjGFOXk4udXdKTX7V2wmao2p/fiHh5Bb6fefm24+f1Gl/HkZBlw8TGVcvfQ4ublg5+iAnaMDwa2bkJZwpk43Ytcs3cqGsmt4wlo2JCO1In9mqgG9t7vZer98vZpcQz4Pvz2q/DlPH/fLrn8tmY7mVLSbwswsHPVV39fRU1+zjIdpr6FT2b8O7m4EdOmAIT7BIhOgG73tRK7cTPSasr4bHkJeRtW+6+px6b6bV+kzpsafYtUH8wEoys0jYW8sNjobmnS/6JB/zdh7eFCSlVn+uMSQhZ1ef4kaFfLj48k+eICc6CiM589RWlhEwtf/o/HDj9ZV3BrsPfQUV2rbxVlZOFxm/qupe7WiVm0mtqz9+IaHkFdp7M+v1LYvcHRzpaRS+8k38/0AENg6nOz/ptf52A+mtlOcWantZBmwv4L1Zzx/nsOfz8W7e3e8OnWqi4iXlF5UUuWUNh9HezKKS2qUC2vgzIttmzA5Itbqp7wB/LFkK6vLruFp2qoh6SkVY2dGajaePua/d378cjU5WXk8M/nB8ufW/hHB/40bZDr9uKE3foGenD6ZSvPWtdiJLm4IVp0AaZp2RCnVGRgJzADWADGapvWsXlaZTiRsCRQCnkDitczS/da+dL+1LwCHd8ewa/kW2vbvROKhkzi6ONLAzAB7MS16tLmq+nVtxZp9PPHAUBYv2063juHk5BaQnGogLSOH8FB/GjX04WxyJqNu7cmDz31ikUwdRvajw8h+ABzfE03kys0079uZ5CMJ2Ls4lk9uLlBK0bBtU45uP0Dzvp2J3bCLJt3aApCflYOzvgFKKZKPJKBpGo4NXOo0f8eb+9Hx5gv5Y9i/YjMt+nYi6UgCDhfJH9K2KUe2HaBFv87ErN9NeHdT/vDubVk/72eMpaWUni8l6chJOt82oE7zD7m7D0PuNp12tH97LGuWbqXnTR2JjzmJs6sjHt41J/Ablu8kavdhJn/8RJVTBTr1aXNZ9a81fVgj8pNTyU9Nx8lTz5mde+j8VNWbAPh3aseJNRsJ6tGFrPgT2Dk74ah353yR6dxvOydHzhcVkxoVR/M7R9Z5Zrjx2077kf1oX9Z3T5T13WZ9TH3XwdmxxsapUorgNlX7blhZ333oi7fKy/015ztCu7SxyOQHwLlRY4pTUylOT8NO70FWRASNH7m8CUzgnXcReOddAOQePkzq2tUWnfwAuIU2pjAllcK0dBw89KTu3kPL8Y/Ued2r1XZEP9qOMLWfhD3RRK/aTHifzqQcScD+Iu0nqE1T4nccoGmfzhzasIvQrqb2k52Uhpu/N0op0uJPYzxfWudjP4Br48YUpaZSlJaOvYee9IgImj56eb9/TdOIX/AtTgEBNW6QYCmHsnMJcnHC38mB9KISBgb48HZk1SOZvo72vNmxBTMij5JYUGSVnNXdMqoPt4wyfW9FbI3ljyXb6De0I4ejT+Hs6oinme+d1b/tZN/Ow7z96ZNVvrd8/PRERhylTccwsjJySTyVin+QZ436/wjX8XU5lmTVCZBSKhDI1DTte6VUHqZT13yUUj01TduhlLIDmmmaFgNMBOKAV4Gvy8qcq4tczbq24khELB89PA07R3vumnhv+bJvX5/LHRPG4ublzo7fN7F1yTrysnL59KmZNOvaijsmjL1kfUtY8N9n6duzJd4eDTi26xOmffgzdmW3mP3f92v5c/1+hg3sQMyW2RQUFvP4pC8AKC01MvH1+Sz/bjI6nQ0LftpI3JFrOs+8LKGdW5OwN5ZvnpiKrYMdQ5/7V/myX6d+zpBn7sXV050+425n5QffsO2HP/ANC6b1ENO8+ej2/UT+uRUbnQ229vaMnPSg2dP76i5/K47vieGrJ6Zi52DPsGcrzkP/Zepchj49Flcvd/o+cBsr3p/Pth9W4BsWTJshPQDwauhP444tWfDcuygbG9oO6YF3o0CL5e/QsyWRO+J4cfQ72DvaMf7VseXLZk2ax6OvjMbD251v3v8Zbz8P3nx8DgBd+7flzoeGXbJ+XbLR6Wg3bgw7Zv0XzWgkpF8v3IIDObFuMwChg/vh174NKQeiWTtpCjp7ezo+Ng6A4pwcds829QPNaCSoZ1f82rW2SO7KbvS207is7y540tR3hzxb0Xd/m/Y5Nz1d0XdXffANO378A5/QYFrfVGOfl8UpnY7g0fcSP2c2mlHDq1dvnAKDSN+8EQDvfgM4l53N4RnTKS0qMm1kr19LyzemorsOTllSOh3h940h6qOP0YxG/Pv0xiUokLMbNwEQOKA/JdnZ7J32DqWFRaAUiWvX0XXam9g6OZmta2mNOrfm1L5YfnjK1H4GPVPRfv6Y/jkDn7oXF093etx/O2s+/IZdZe2nZVn7id9xgMObdmOj02Frb8fQFx+yyNivdDpC7x1L3OzZaJoR3969cQ4KJLls3fuXrfuo6W9TWmRa90lr19J+6lsUJCaSvnMnzkFBRL41FYCQu+7Eo23bOs99gVGD/8YeZ2bX1tgoWJWYysm8Qm5p6A/AH6eTuT88BDd7O55vbTplrFSDp7abbvTwWvtmtPd0x93elkUDu7Dg6ClWJaZaLD9Al94t2bM9jsfumoGDox0TXh9TvuyNCV/y3Gv34OXjzqczl+Lr78GkR0zfW70GtmXso0MZ88gQZk9dxNNjZ6Fp8NAzt+Cut+yNWIRlqUtdS1Hnb67UMGAWYATOAU8C54E5gDumCdpsYBPwO9BN07RcpdSHQK6maW/8zVtc9ilw15t7wkx/g8EpxDIbj9da4amFzI37y9oxau2JlkOZd2i1tWPU2vgWw4hIW2HtGLXS1edmAF7avd7KSWrnvW6DAG7Y9jO+xTA+i71x++5TrYYyZsNma8eotUUD+zF+60Zrx6iVeX0GAPBxzI3Zfp5vbbq9+YObN1k5Se3M79cfgMGrtlk5Se2sG9Gbo9l/WDtGrTV1vwUsdevBq9Ck82yLbvjH751wXa4Ta58Ctxowt5XQz8xz5X8UQtO0F+oslBBCCCGEEOIfy9o3QRBCCCGEEEJYgCUvCbieyZVQQgghhBBCiHpDJkBCCCGEEEIIi1NKDVdKHVZKHVNKvWJmubtSarlSKlIpFaOUeuhavK+cAieEEEIIIUQ9cD39IVSllA7T3/ccgunP20QopZZpmhZbqdjTQKymabcqpXyAw0qpHzRNq/nHqq7A9bMWhBBCCCGEEPVFN+CYpmnHyyY0i4Dbq5XRgAbKdPGSK5CJ6Y7RV0WOAAkhhBBCCFEPKAv/IVSl1HhMf+fzgnmaps0r+38QcLrSskSge7WX+ARYBpwFGgCjNU0zXm0umQAJIYQQQgghrrmyyc68iyw2d0u66n+naBhwABgENAHWKKW2aJqWczW55BQ4IYQQQggh6gOlLPtzaYlAw0qPgzEd6ansIeAXzeQYcAJocbWrQSZAQgghhBBCCEuLAJoqpUKVUvbAGEynu1V2ChgMoJTyA5oDx6/2jeUUOCGEEEIIIeqD6+jQh6Zp55VSzwCrAR3wtaZpMUqpJ8qWzwWmAfOVUlGYTpl7WdO09Kt9b5kACSGEEEIIISxO07SVwMpqz82t9P+zwNBr/b4yARJCCCGEEKI++PvrcuqF6+hAmBBCCCGEEELULaVp1e8294/yj/5wQgghhBDiunHdH15p1muuRbeNj2x/4rpcJ3IESAghhBBCCFFv/OOvAfrmyGprR6iVh5oNA2Bu3F9WTlI7T7QcilPIWGvHqLXCUwt5afd6a8eotfe6DWLB0Ruz7T/Q1NT2x2/daN0gtTSvzwAAXo5YZ90gtTSz62A6L9xi7Ri1tndsXwwlK/++4HVKbz+StWduzPw3BY0EYNymTVZOUjvf9u8PwI7UFVZOUjs9fW8GYFPSjdl++geMpNGsG/d79+S/B1k7wuWRQx+ArAYhhBBCCCFEPfKPPwIkhBBCCCGEAE3uAgfIESAhhBBCCCFEPSITICGEEEIIIUS9IafACSGEEEIIUR/IGXCAHAESQgghhBBC1CNyBEgIIYQQQoj6wEYOAYEcARJCCCGEEELUI3IESAghhBBCiPpAboMNyBEgIYQQQgghRD0iR4CEEEIIIYSoD+QAECBHgIQQQgghhBD1iBwBEkIIIYQQoj6Qu8ABMgEyS9M01s5bSvzeWOwc7Ln5+fvwD29Yo5whOYPfZ82nKLcAvybB3PrC/ejsbNn1yzpiNu4BwFhqJCMxmee+fwenBi4Wy7/xf0s5sTcGOwd7hj73L/ya1MyfnZLOyvfnU5RXgG9YMMMnjENnZ8vpqKMsmzEPd18vAMJ7tqfH6BEWyT531uOMGNyRtIwcugx5yWyZD956gGEDO1BQWML4Fz/nQHQCAEP6t+f9N8eh09kwf9EG3v9smUUyV5dyMIao7xaDUSNkQG+a3TqsynJN04j6bjGpkTHoHOzpOH4c+sYhAPw18TVsHR1RNjYonQ0Dpk62eH5N01gzbynxe2KxdbDn1gkXb/+/vTefwtwC/MODua2s/RflF7Ls/W/JScvCaDTS/c5BtB/SwyLZM6OiObZwMZpmJKBvH0JGDq/x2eIX/kRGVDQ6e3uaP/wgDRqZ1n3imnUkbd4KaAT060PwkJsskrmylMgYor5bgmbUaDSgF81uM9d2lpByIAadgx2dxo9DHxpSsdxoZOPr7+LooafnpKcsHZ+eAR5M6hSGTil+i09mflxileX9gzx5sl1jjJpGqVHjg33HOZCec1l1LUHTND5891e2b4nD0dGO16ePpUWrmm1/+pRFxMWcBk2jYWMfpky/F2dnB3KyC5g+ZRFnTqdj72DHf6aOoUnTAIvmX/LJr8TsisPe0Y77XxpLSLOa+Tf+uoUNSzeTfjadmb9Ow9XdFYAjB47xxetf4eXvCUCHvu0YOW5Yjfp1xRAdzcmffkIzGvHt04fAEVW/dwqTkji+YAH5p07R8I47CBg6FIDizEziv/6aczk5KKXw7dcP/8GDLZYbTOv+h49/5eDOOOwd7Hn01bE0bh5co9zapVv4a8lmUs9k8N/lU2mgN637grxCvpj2A5kpWZSWGhkxZiB9b+5m0fw//fdXonaa2s6Dr4ylkZm2s/6XLaz7eTNpZ9P54Ldp5fkBDu8/xk+f/EppaSmu7q78++NnLJa/f2NP3hjcFJ1SLDqYxOe7T5ot186/Ab/d14Vnlkez8kgaDjobFo/thL1OYWujWHkkjY+2nbBYbmE9MgEy4/jeWLLOpvH4F69z9nACqz9fzAMfvFij3Mb5v9P19gG06teZPz/9icg1O+g0si/d7xpM97tMg+/R3VFE/L7RYpMfgIS9sRiSUnno8ykkH0lg/dyfGDtrUo1yWxYso9NtA2netzNrP19E9NodtB/RF4CgVk244z9PWCzzBd8t2cTcBav530fmN96GDexAk8b+tOk3kW4dw5nz9iP0u/11bGwUs6c/xM33vcOZpAy2Ln+bP9bs5dDRMxbNrxmNHFywiF4vP4eTpwebpryLf6d2uAVVbASlRsaQn5LK4PffIiv+BJHfLKT/Wy+XL+/96kQcGriae3mLiN8TS+bZNJ6YZ2r/f362mAc/rNn+15e1/9b9O7Pqk584sGYHnUf2Ze+KLXiH+HPPG4+Tn53LF4+/TZsBXdDZ1e1woxmNHP1hIe1enICDhwf7ps3Aq0M7XAIDy8tkRkVTkJJKt3emkXv8BEe/+4FO/5lMfuIZkjZvpdN/JmNjq+PgR3PwbNcWZz+/Os1cPX/kgp/o/cpzOHnq2ThlJv6dq7adlMgY8pJTuemDN8mKTyBy/iL6v1WxoyD+zw00CPTnXGGRxXJfYKPglc5NeGpDNCmFxXw3tAObzmRyIqegvMzuFAObVu0DIFzvzMzeLbl7xd7LqmsJ27fEcfpkGj+veJXogyd5b/rPfP3jxBrlJrx0B66ujgDMfu83lvy4hQcevYn5/1tLsxaBvPfxwyQcT2HWO0v59H+Wm4jG7Ioj7Uwab373KglxJ1k0+2de+qxm/iZtQmnbszWzJ35SY1l42zCefOcxS8StQjMaSfjxR1pMnIi9hwcx77yDvn17nCv1X1sXFxqNGUPW/v1V6iobGxqNGoVLo0aUFhURPX06bi1bVqlb1w7ujCMlMZ2ZC18lPvYk337wM1PmTahRrmnbUNr3as27z31a5fl1v2wjqLEfE2c+Sk5WHpPvm0HPoZ2wreNx84LoXXGkJKYx/YdXORF7kh8++plXP6/ZdsLbhtKuZ2s+mFC17RTkFvLj7J957r3H8fLzICcr1yK5wTT2TBvSnPsW7yc5t5hl93dhbXwaRzMKapSb3C+czQkZ5c8VlxoZ+9N+Cs6VYmuj+HlsJzYez2B/Uo7F8luc3AUOkGuAzDq6M4o2g7qhlCKoRSjF+YXkZWZXKaNpGicPHqVF7w4AtB3cjaM7o2q8VtymfbTq19kiuS+I3x1FywGm/AHNL57/dNQRmvYy5W81sDvxuw5aNKc523YfItOQd9HltwztzI9LtwCwe/8x3N2c8ffV07VDOPEJySScSuXcuVKWLN/BLUO7WCp2uaz4BFz8fHDx9cHG1pagHl1I3htZpUzSvkga9umBUgrP8DDOFRRQZMi+yCta3pFdUbSt1P6LLtH+W/apaP9HdpjavwJKCovRNI1zhSU4NXDGRlf3Q03O8RM4+fri5GNa977dupCxv+q6zzgQiX8v07p3axLG+YJCig3ZFCQl49YkFJ2DPUqnQ9+8Gen7DtR55sqy4hNw9fPBxdcbG1tbgnt0rtF2kvceJKRP97K2E8q5/AKKsky/m8KMLJIPRNNoQG+L5r6gtWcDTucVcSa/iPNGjb9OpTEg2LNKmcLzxvL/O+l0aNrl17WEzRuiGXFbV5RStG3fmNzcQtLTavbNC5MfTdMoLj6HKtugOBGfTJfuzQBoHOZH0plMMtIttyF4cHs03YeY8oe2akxhXiHZGTXzN2waXH6U53qRd+IEjr6+OJb1X8+uXcmKrNr+7dzccG3cGKXTVXneXq/HpVEjAHSOjjgGBHDOYLBYdoD9W6PpPbwLSinCWzemIK8QQ3rNjehGzYLxCai57pWCogLTuFlcWIyLm2XGzQsObIum5zBT2wlrbWo7BjNtJ6RpMN5m8u9et5eOfdvh5ecBgJtHgzrPfEGHADcSsgo4nV3EOaPG8kOpDAn3qVHuwU7BrDqaSnrBuSrPF5wrBcDWRmGns0GzSGphbdftBEgpNU4pdVApFamU+k4pNV8pNVcptUUpdUQpdUtdvXduRjYNvPXljxt46cmtNhAU5uTj4OqETdlAbK7MuaISju+Lo3mv9nUV1ay8TAMNvD3KH7t66WtswBbl5uPgUjV/5TJJh0/w3YQZ/Dr1M9JPJVkm+GUI9PckMali782Z5EwC/T0J9Pcg8Wyl55MyCPLzMPcSdaooy4CTZ8X7Onl6UJRl+NsyhZmmMgrFjplz2Pj6OySs32KZ0NXkZWTjdhnt37FS+3HzrijT+ZZ+pJ9OZs641/nymRkMGX83yqbuh5oSgwGHSuvVwcOD4mobQcVZBhw8PSuV0VNiyMI5KJDsI0c5l5dHaXEJGQejKM7MrPPMlRVWaxeOnh4UZmXXLONVvYzpM0Z9/zNtxt5ptb17vs4OpBQUlz9OKSjBx8mhRrmBwV4svbkzH/dvzVu7jlxR3bqWlpqNn39F2/f105OWan7nxNT/LGTEgCkknEjlnntNR86bNg9i41rTjqSYqJMkJ2WRmmK5DfHs9Gz0vhX59T56DOlXtnPlRGwC7zw6i09f+YKzJyw39pcYDNhX6pv2ej3nsrKu+HWK09MpOHUKl9DQaxnvb2Wl5eBZad17+OjJuoJ1P/juPpw9mcKEO97kPw/O4t7n7sTGAuPmBYa0bDx8quY3mJn8X0zK6TQK8gp4//lPmD7+A3asjqiLmGb5uzqQlFsxfiTlFuPvWnX88HO1Z1hTH74/UPOsEBsFKx/oyr6n+7AlIZMD/+SjP2DaS2nJn+vUdXkKnFKqNfAa0FvTtHSllCfwIdAY6A80ATYopcI1TSuqVnc8MB7giy++wG5Ao1okqDn/r7lN8fdljkVEE9Qy1KKnvwGU71atpHp8zUyZC3ybBPPIvKnYOzlwYk8My2d8yUOfT7nGIWtHmelNmqaV74Gt+rwlEtXMUkP1bJco0mfKJJw89BRn57B95hxcA/3xbtH02ge9BPOfoVqZS7T/4/vi8AsL5r53niUrKZ2Fr39Kw9ZhODg71UHaKqEuo4y5QgqXwAAajhjGwQ9mo3NwwLVhwxp7metcLfMrpUjeH4WDmyv60BDSYo9c+2yXwdz3nLmPtCExgw2JGXT0cePJdo14akP0Zdeta+bHRfPf4FOmj6W01MgHM35hzZ/7ufXO7ox7ZDAfvvsr//q/WTRpGkCzFkHobC23EWsuv7mx8WIaNg1m6sIpODo5EL0zlnlTvubN7167lhEv7nLGzr9RWlTEkblzaTR6NLZOdTzeVGN+3V9+/ehdhwkJD+Llj58i9Uw6s174gubtw3BycbyGKS/O/Jh++R+gtNTIycOJvPDhk5QUn2Pm0x8T1qoRfg19r2XMy1b987wxqBnvborHaKaZGTUYuSACNwdb5t3RlmbeLhxJz7dQUmEt1+UECBgE/KxpWjqApmmZZR1xsaZpRuCoUuo40AKocp6KpmnzgHkXHn5zZPVlveHeFZuJXL0DgICmIeSmV+y1y80w4OrpXqW8k5srxXmFGEtLsdHpzJaJ3Wy5098OrNxM9F/bAfBrGkJuesWes7wMAy7m8uebz195QzW0S2vWf7GYwpw8nNysd13KBWeSMwgO8Cp/HOTvSVJKFvZ2tgQHVno+wIuzqVe+9/BqmY7mVLxvYWYWjvqq697RU1+zjIdpz5tT2b8O7m4EdOmAIT7BIhOgPX9s5kBZ+w9sGkJOtfbfoFr7cXZzpahS+8lJr2g/B9fuouf/DTGdphXog97Pi4zTqQQ2r83OiMtn76GnuNJ6Lc7KwkGvr1LGwdOjCjKKCAAAIABJREFUypGd4iwD9mVlAvr2IaBvHwCOL/0VBw/LHkF0qtYuijKzcPKo1m89PSjMqFrGUe/Omd37SNoXRXJkDMZz5zlfWMiez76hy1MPWSx/SkExfs4Ve139nO1JLyy+aPn9aTkEuzqht7e94rrX0pKFW/l9qantt2oTQkpyRdtPTTHg4+t20bo6nQ03DevA9/M3cOud3XF1dWTK9LGAaYP4zuHTCAzyumj9a2HTb1vZtsKUv1HzEAypFfkNaQbcvS6ev7rKG9tterTip49/Ji87r/wmCXXJ3sODkkp9s8RgwK5a/70U4/nzHJ07F+/u3fHs1KkuItaw9pet/8/efYdHVaUPHP+eTDKppPdKCCgQqnQJoQkoCugqS1Msq6i/tQuIoijYwV2xrCKuSlFRRLGvCNKREjoJvQRISC+kB5I5vz9mTAdCTCbBvJ/nyZPM3HNn3rnznnPvuefeE9b9sAWA8LYhZFbY9llp2bh7uV1o1Wo2/LyNG28fjFIKv2AffAI8STqZQqv2Dddurlm+kQ0/mnOnZdtQstIqx+/mXfvc8fBxw8XNGXtHe+wd7WnTOYLTx85YpQOUnFdMQIvy9iOghT0peecqlenk14J3RkQC4Olox8BwL/PltkfTy8rkFJew+XQWA8I9/9odIJkFDmi6l8Apaj4BWPW5ejtJ2O3GaO55+ynuefsp2vTuROzqbWitSTx4Ansnh2qdG6UUoZ3acHCTuf+177dttOnVsWx5UX4hp2OP0qZ3R6yhy/Bobp87jdvnTiOiVycOrDXHn3ToBEbnmuMP6diGI7+b49+/ZisRPc2x5mfllJ3NSj4cj9YaB2uPYl3ATyt3Mv5W8+UmPbu2Jie3gOTUbLbvOUbrcH/CQnywszMwekQfflq5w+rxubcKIz85lfzUdEwlJSRu2Y7/NZ0qlfG/phOnN25Ba03m0ePYOTni4O5GSVFx2c3rJUXFpO47QIsQ69zE2/2maO595ynufecprurTiX21yP+wjm04sLE8/6+y5Lqrjwfxew4BkJeVQ0ZCKu7+DXsQCOAa3pLClFQK08zbPnXbdry6VL781KtzZ5J/N2/7nGPHsXVyxN7SQT2XY77soSgjk/Sdu/Dt1aPBY67IvVUYeRVyJ2HLjhpypyOnNm615M4JbJ0ccfBwI3LMzVz/zisMm/sS3f95D97tr7Zq5wdgf2YuIS0cCHS2x9ZGMTTUh3UJlS8jDHYpP8Bu6+GMnY0i+1xJrdZtKKPHRfHpsil8umwK0YM68L/vY8yz7e2Jx8XFEW+fyrmvteb0qbSyvzesiyMs3HyQl5tTyPnzJQB89/UWunSLKLtfqKH0vzmKZz6cwjMfTqFzVAe2rjTHf2J/PI7OjrhdxkH42czytj/+wEm01ji7Wqftd2nZkqLUVIrSzfmfGRODR+faXT6utebEokU4BgQQMGRIA0da7rq/RfHiJ5N58ZPJXNOvI5t+2Y7WmqNx8Ti6OOB+GR0ILz8P9u8wj96ezcwl6VQqPoEN224OvCWKGR9NYcZHU+gS1YHNK8y5czzOnDuX04HrEtWRo/uOU1pSSnHROU7sP0lAqHUmkdmTlEu4hxMhbg7Y2ShGtPVlZYWODUDUh5uJmm/++flwGs+tOsSvR9PxdLTD1d48FmBva0NUmCdHM6w7+YpoHE11BOg3YLlS6k2tdYblEjiA0UqphUA40Ao41BBvHtG9Pce3x/HBpFnY2RsZ/uiEsmVLX5jHDQ+Po4WXGwPvGsl3sxew/tOf8GsVTKeh5VP9Ht68l/CubTE6WP869vBukcTv2M8nD8zC1t6OoY/cXrZs+az3GfLQeFw83YiaOIqf//UJmz77Ed9WwUQO6QPAkd93seeXjdgYbLA1Ghk++a7LGgr/Mxa+8zD9+rTD26MFR7e+y4v/XoadZRac/366il9W72LYwC7EbZhLQWEx90/+ADAPvz/+3AJ+WPw0BoMNC79cy4HD1p9G18ZgoNPEsWye8w7aZCI0+lpcgwM58dt6AMIHR+PXuQMpu2NZNXkGBqORrvdNBKA4J4dtc82fR5tMBPXpgV+nSKt/hoju7Tm6PY737zPn/02Plef/l8/PY/gjlvy/eyTfvl6e/50t+R819np+nPspH/7zVbSGQXePxMkKZ5CVwUDrCWPZ9+ZbaJMJ/6i+OAcFcmbtOgACB/THs1MHMvftY9vTz1qmwb6zbP249z6gJC8fZTDQZsI47Jyt2+m3MRjodOcYfp/9LtpkIqx/n+q506UDKXviWPnk89gajXSddIdVY7yYUg2ztx/j3QEdMCjFd8dTOJ5TwK2t/QH4+mgyg0O8uTHclxKTprjUxNObDl50XWvr2689v68/wK3DX8bBwchzL40tW/bYg/OZPnMMXt4tmDX9c/LzitFo2lwVyNTnRgMQfzyFF6Z/hsHGhvAIP6bPHHuht2oQkb3aE7f1AC/c/jJGByO3Ty1///9Mm8+EyWNw93ZjzTfrWfXFanIyc3nl3jlE9mrHhMlj2bVuDxu+34TBYMDO3o57np1otbZfGQy0HDeOQ3Pnok0mfPr2xSkwkJR15vrr178/586eJfbllyktKkIpRdKqVXSaOZPChATSt2zBMSiIfbNmARByyy24d7TOCUiAzn3asXfLAaaOfQV7Bzv+8fS4smX/njKfu58ag4e3GyuXrefnz9dwNjOX5+56g06923HPtDGMvGsI/31lCc/eORut4e8P3FRpiumG1rF3e2K3HmD6hJcx2hu566ny3Hn7qflMnGLOnd++Xs+KJebcmfWPOXTs1Y6JU8cSEOZHZM+2zPrHHJRSRN3Ym6BW1pkCvlRrZqw6zKLbumCwUSzdd4YjGflM6Gw+gfjZnjMXXNfXxci/b2iPjY3CBvjxUCqrj2dcsPxfggwAAaAudi9IY1JK3QlMAUqBP+a8zAK6A37AE1rrHy/xMrW+BK6pufsq8/9emHfg10aOpG4eaDcUx9Bxly7YRBWeWsLUbasbO4w6m91zEAuPXJm5f2cbc+5P2ri2cQOpo/lRAwB4Kua3xg2kjl7vMZhuSxpnAo76sGNcP7LP/dzYYdSZu3E4qxKvzPivCxoOwERLp+VKs6h/fwA2p/7UyJHUTR/fGwFYl3Rl5k//gOGEzbly97snpwyCK6B70eaGj6164H/kf/c0yW3SVEeA0FovBBb+8VgptQDYpLWuPjG9EEIIIYQQQtRCk+0ACSGEEEIIIeqPln+EClxBHSCt9V2NHYMQQgghhBDiynbFdICEEEIIIYQQf4JMgw003WmwhRBCCCGEEKLeyQiQEEIIIYQQzYEMAAEyAiSEEEIIIYRoRmQESAghhBBCiOZAZoEDZARICCGEEEII0YzICJAQQgghhBDNgcwCB8gIkBBCCCGEEKIZkREgIYQQQgghmgMZAAJkBEgIIYQQQgjRjMgIkBBCCCGEEM2BzAIHgNJaN3YMDekv/eGEEEIIIUST0eR7F63/ttiqx8ZHv7mjSW6Tv/wIUI+lGxs7hDqJ+XsUAPMPrmjkSOpmUtthTN22urHDqLPZPQfhGDquscOos8JTS5i4bl1jh1Eni/r3B2DEyg2NHEnd/DCkHwDL4//XyJHUzS0tb+C9/b82dhh19n/th/LYliu37ZnbexDDVlyZ+60Vw8z7re9OXpm5PyrsBgDu3bi2cQOpo/9GDQDgia1XZv7/u9cgfk38ubHDqLOhQcMbO4TakREgQO4BEkIIIYQQQjQj0gESQgghhBBCNBt/+UvghBBCCCGEEMjQh4VsBiGEEEIIIUSzISNAQgghhBBCNAcyCQIgI0BCCCGEEEKIZkRGgIQQQgghhGgOZAAIkBEgIYQQQgghRDMiI0BCCCGEEEI0A9pGhoBARoCEEEIIIYQQzYiMAAkhhBBCCNEcyCxwgIwACSGEEEIIIZoRGQESQgghhBCiOZABIEBGgIQQQgghhBDNiIwA1aCPvztPdmmFjVJ8dyKFhQcTKi2/PtSHiW2DASgsKeW1Hcc4cjYfgHFXBXJzuB8aOHq2gFnbDnPOpK0av9aaNR9+zYkd+7G1N3L9oxPwiwipVu5sSgY/zllAUV4Bvq2CGf74HRjszClxet8R1nz0DaaSUhxdnRnzyqNWiz9lbxz7Fi8FkyZ0QF+uGjGs0nKtNfsWLyV1TxwGeyNdJ03EvWUoAL8+Ph1bBweUjQ3KYMOAWU9bLW6AeXPu54bBXUnLyKH7kKk1lvnXzDsZNrALBYXnmPTk++yOjQdgSP/OvPHCRAwGGxZ8sYY33vveipGXy46N5eSXX6JNJnyjogi84YZKywuTkji+cCH5p04RcvPNBAwdCkBxZibHPv6Y8zk5KKXwjY7Gf/Bgq8Z+jZcH911trrsrE5NZFl+57gY7OfJo5FVEuLqw+Gg8y08mli0bFRrI0CB/NBCfl89bcYc53wh194f3v+HQtgPYOdgx+snxBLWpXnd//24Dm5avIyMpneeWvoSzmwsAqadSWPbvz0k8msCwO28kevQgq8e/7qOvid8Rh629kaEP345vjW1POv/7V3nbM+zRiRjsbEmIPcIPr87H1dcLgNa9O9NrzA3V1m8oaXvjOPDZUrRJE9y/LxE3VW97Dny2lLQ9cRiMRjreNxG3lqHkJSWz+72PysoVpKbT5m83ET7Muvnf3dudB9q2wqAU/0tIYemJyvkf4uzIEx3a0NrVhYVHTrIsvjz/bw4N5IZgP5SC/yWksPzkGavGDubt+/1733Aw5gB29nb8ffJ4gmvI/03fbWDj8nVknEnn+a/K83/nb9tZu/Q3AOwd7bnl4dEERgRZJfbMfbEcX7IUrU3494siZPj1lZYXJCVz+OMF5J06TctbRhF8/dBar2sNqXvj2P+pOfdD+veldQ373f2flu93O1tyH+B8fgF7P/6U3IQzgKLzvXfg0aaVVePXWvP1u8uJ23oAo4Mdt08dR8hV1XNn3fINrP16Peln0nl1+Yu4WHLnDycPnuJfD83l7ucm0rV/F2uFb10yCxxwiREgpZS7Uur/6uONlFLPVPi7pVIqtj5et77ZKJh6TQSPbojj7yt2MjTUh3BXx0plzuQXcf+avYz/dRcf7T/NM91bA+DjaGRM60AmrtrD2BW7sFEwNNTH6p/hxI79ZCWlcc+85xjyzzGsen9pjeXWL/yObiMH8I95z+Hg4sS+VZsBKMorYNW8pdw8/T7uevcZRky9x2qxa5OJvQu/oM+Uhxj0+gwSN8eQk5hUqUzqnjjyU1IZ/MZMOt8znj2fLKm0vO8zjzPw5elW7/wALP5qHaMmvnbB5cMGdiGipT8doh/noWkf8vbL/wDAxkYx96W7GXXn63QdPJnRI6+lbRvr7Lgr0iYT8Z9/ztWPPEKnmTPJiImh4EzlAyFbZ2fCxo4lYMiQSs8rGxvCRo+m86xZRD79NClr1lRbtyHZAA+0jeCFXXH88/cdRPv7EOLsVKlM7vkS5h86xvIqHSNPeyMjQoN4fOtuHtq8EwOKaD/r191DMQdIT0xj8ifT+dujY/j2na9qLBcWGc4/XnsQdz+PSs87uTox4sFbib7Vuh2fP8Tv3E/2mVTufG8Ggx8cy+oPvqyx3KZF39N1xEDuem8G9s5OxP22uWxZYLsIJrw5jQlvTrNq50ebTMQt+oLuTz5Ev1dnkLQlhtwqbU/a3jjyk1OJnj2TyLvHE7fQ3Pa4BPgT9eJ0ol6cTt+ZT2OwN+LfzboHTzbAP9tF8OyOOO7buJOBAT6EOlfed+WcL+H9A8f5+kRipefDXJy4IdiPR7bs4YHfd9HLx5NAJwcrRm920JL/Uz+Zzq2PjWH52zXnf8vIcO577UE8quS/p78XD7zxME988BSDxw/l67k151990yYTxz5bQuTjD9PtxRdI2xpDfrV204mI8WMJHjbkstdtaH/kfs/JD9H/tRmcuVDup6QyYM5MOt49ntgF5fvduE+X4tOxPQNef4Hol6fjEuhv1fgB9m89QGpiGjMWP8PYJ/7Ol3OX1ViuVYdwHnrjQTyr5A6AqdTEd/N/oF33tg0drmgCLnUJnDtQrQOklDLU4b2euXSRxhfp2YLTeUUk5hdTYtKsPJVG/0CvSmX2ZuSSe74UgH0ZOfg6GsuW2doo7A02GBQ4GAykFZ6zavwAx7bto/3AniilCLw6nOL8QvIyz1Yqo7Xm1N4jXNXXvJOOHNSTo1v2AXBw/Q7a9OmMq48nAE7uLawWe9axeJz9fHD29cHG1pag3t1J3rGnUpmknXsIieqNUgrP1q04X1BAUfbZC7yidW3adpDM7LwLLr9paDc+/3oDANt2HcXN1Ql/X3d6dGnNsfhk4k+lcv58KV/9sJmbhna3Vthl8k6cwMHXFwcf8/b37NGDrD2Vt7+dqysuLVuiDJWbAaO7O85hYQAYHBxwCAjgfHa21WJv49aCpIIiUgqLKNGa9clp9LLk8B/Onj/PkZw8SnT1kR0bpTDa2GCjwN5gQ2ax9evu/s37uOa6HiilCG3XksL8QnIyqud2UOtgPP29qj3v4t6CkKtDsbFtnKubj2/bRztL2xNgaXvya2h7Tu87TJtrzW1P+4G9OLZ1b2OEW0n2cXPb42RpewJ6dSd1Z+XcT925h6C+5rbHo3UrSmpoe9LjDuLk442jd/XvpyFd7daCMwVFJBcWU6I1a5PS6ONbOYaz585zuIb8D3V25MDZXIpNJkwa9maepa+vdeMH2P/7Pq4ZYs7/sDrkf8vIcJxamE96hLZrydl06+wXco+b201HS7vp07M7mbsq547R1ZUW4dXbzdqs29Cyj8Xj5Fue+4G9u5NSJfdTquT+H/vd84WFZB46Skj/vgDY2NpiV+XEkzXs+z2WnpbcCW/fksK8Qs7WkDshbYLx8ves4RXMo0Ndojvj4uFS4/K/DKWs+9NEXWov+RoQoZTarZSKUUqtUUp9DuxTShmUUnMsz+9VSt0PoJQKUEqtt6wTq5Tqp5R6DXC0PPeZ5bVtlVILLesuU0o5WdaPV0q9rpTaZvlpbXl+tOX19iil1jfQ9sDH0UhKQXHZ45TCYnwqdHCqGtXKn9+TswBIKzzHp4cS+eHGHvxvRC/yz5ewNcV6B4B/yMs4Swtv97LHLbzdyavSEBTm5uPg7IiNpTF28XIv6yRlnUmlKK+AL6e/zeInZhO3epvVYi/KysbRs/zMjKOnB0VZ2ZcsU5hpLqNQbH79bdY+9wrxqzdYJ+jLEOjvSUJSRtnjxORMAv09CfT3IOFMheeTMgiq4QxVQzuXnY3Rs3znYHR353xW1mW/TnF6OgWnTuEcHl6f4V2Ul7096cXldTej+Bxe9va1Wjez+BzL4xP4uF9PFkX3Jr+klF2Z1q+7Oelncfcp/97dvN1rPABsqvIysnHxKo+/Yrvyh6LcfOwrtj3e7uRX+IzJh07w2eOv8u2s98g4VfksdEMqysrGoUK74nCBtsfBq3KZ4iplkrZuJ7B3j4YNtgZeDkbSisrzP72oGG+HC++7KorPK6Cjhxst7Gyxt7Ghh48HPg61qzv16WxG5fx393av8SC2NmJ+2cLVPdrVV2gXVZydjX2F3DF6eFBcy5M/f2bd+lKUlY2j1yVyP7PyftfB04OizGwKUtMxurqw98NFbHj2ZfZ+tJiSCu2wtWSnn8XDt/y4x93H/bI6wNlp2ezduI+oEdc2RHjiIpRS1yulDimljiqlpl2gzABLHyJOKbWuPt73Uh2gacAxrXUXYArQE5iutW4P/AM4q7XuAfQA7lNKhQPjgRWWdToDu7XW04BCrXUXrfUEy2tfDczXWncCcqg80pSjte4JvAvMtTw3Aximte4MjLxQwEqpSUqp7Uqp7fPnz6/tdihfv4bnLnQXQDcfN0aG+/Hu3ngAWtgZiA70ZNTPMdzwwzYcbA3c0AiXwOkazm5X64RfpIyp1ETqsdP87bn7ufWF/2PL0hVkJqY2QKTV1RR7teAvUiRqxmQGvPQMfSY/xIlV60g/eKT+g/wTVA0ZprVG1XCWpKZN0eBqs/0vobSoiMPz5hE2Zgy2jo6XXqGeXE7drcrZ1pZevl7cuzGGO9dvxcFgwwD/Rqi7NT3ZhM+gVVdT/lQpUWOOmX/5tArm7vmzmPDm03S+sT8/vPZh/Yd4ITW2iZe37U0lJaTu2ot/z2vqK6paqzH/a1kBTucXsvREAq9278DL3SI5kZtPaWM0QDWmxuXn/9HdR4j5ZQvD7x1RD0HVwp/ZVI3RzldTUxDq0iUU6FITOfGnCR0cTb+XpmOwt+fYDysaJMqLqdWxw0V8/Z9vGTnpJmwMMjeYNVmuKPsPcAPQHhinlGpfpYw78B4wUmsdCYyuj/e+3EkQtmmtT1j+Hgp0UkrdZnnsBrQBYoCPlVJ2wLda690XeK3TWutNlr8/BR4B3rA8XlLh95uWvzcBC5RSS4FvLhSg1no+8EfPR3+4dGOtPxxAauE5/JzKz3z5OdqTXsNlbK3dnHi2R2seXR/H2XMlAPT0c+dMfhHZxebHaxIy6OTtyv9OpV1WDHWx66f17Ftpvo7ev3UouenlZ29y07Nx9nSrVN7R1YWi/EJMpaXYGAzkZWTj7GEu4+LljqOrM3YO9tg52BMcGUFafCKeQb4N/jnMoznlIw6FmVk4uFeO3cHTvXoZD/OZH0fLb3s3VwK6dyH7WDzebds0eNy1lZicQXBA+aUbQf6eJKVkYbSzJbjCpZZBAV6cSb38kZc/y+jhwbnMzLLH57KzsXN3v8galZlKSjgybx7evXrheY11DwLTi4vxrjDi42VvJLOWZyK7eLqTUlhEzvnzAPyemkE7d1fWJjd83d38/Qa2/c9cd4OvCiU7rfx7P5uejauna4PH8Gfs+Xk9sSt/B8CvdSh5GeXx52Vk4+JRve0prtj2VGif7J3KO8zh3SJZ88FSCnPycHRt+EtSzGe0y2MvyszCvmrb4+FOUUaVMh7l9SNtbxyuYaHYu1n/O0svOldp1MbbwZ6My7iMc0ViCisSUwC4u01YpdGkhvT79xvY+rM5/0Ourpz/2enZuHpd3rZMOn6GZW9+wT9evh9nV+d6jfVC7D3cKa6QO+eysrCvZbv5Z9atLw4eHhRWyWuHqvW2yn63Yu47eLrjEWEe7Q/o0ZWjP/5qhahh/bcb+f0nc+6EXh1KVmr5cU92WjZul5E7pw6fZsGLiwDIO5vP/q0HsDEY6BzVsX6Dbgqa1jm1nsBRrfVxAKXUF8AoYH+FMuOBb7TWpwC01vVyRv5yu7r5Ff5WwMOWUZ0uWutwrfWvWuv1QDSQCCxWSk28wGtV7a7ri/2ttX4AeBYIAXYrpRrkAuX9mbmEujgS6GyPrY1iSKgP689kVirj52TP7Gvb8fzWw5zKKyp7PrmgmI5eLbC3nEHo4efGiZyChgizmq43RjNx7lNMnPsUrXt3Yv+abWitOXPoBPbODrhU6QAppQjt2IbDm8z907jV22jdy1zRW/fqSOL+45hKSzlffI6kwyfxCvazyudwbxVGfnIq+anpmEpKSNyyHf9rOlUq439NJ05v3ILWmsyjx7FzcsTB3Y2SomLOF5q/j5KiYlL3HaBFSKBV4q6tn1buZPyt/QDo2bU1ObkFJKdms33PMVqH+xMW4oOdnYHRI/rw08odVo/PpWVLilJTKUo3b//MmBg8Oneu1bpaa04sWoRjQEC1CRKs4UhOLoFODvg52GOrFNH+PmxLy7z0ikBaUTFt3Vpgb2Ouu5093TmdX9iQ4ZbpM7Ifj74/lUffn0rktR3ZuSrGfI/egXgcnBxx9XK79Is0os7Do8smLYjo1YkDlrYn6dAJ7J0cqp18UUoR3KENR343tz3712ylVU9z25OflVN2Jjf5cDxaaxxaWOcg1i08jPyUVArSzLmftHU7vl0rtz2+XTuRuMnc9mQdPY6to2OlEzRJW2II7G39e/cADuXkEuTkiJ+jOf8HBPiwJbV2+Q/gZrQDwMfBnr6+XqxNavjOP8C1I/vx+LypPD7Pkv8rzfl/8kA8js6Xl/9ZqVksmvUxY6fejk9ww5+w+0OL8JYUpaRSZMmdtG3b8exSu3bzz6xbX9xaVc79M1u243ep3Lfsdx3c3XDw9CAvKRmA9LhDtLDSJAjRN0cx7cMpTPtwCp2iOrDNkjsn9sfj4OyI22XkzszPn2PmkhnMXDKDLv078/dHb/1rdn4aQcUrsyw/kyosDgJOV3icYHmuoqsAD6XUWqXUjov0Ky7LpUaAcoEL3QG/AnhQKbVaa31eKXUV5k6PN5Cotf5QKeUMXAMsAs4rpey01uct64cqpfporTcD44CKQzVjMN9/NAbYDKCUitBabwW2KqVGYO4IZVDPSjXM3nmMt6M7YFDw/YkUjucU8LcIc4X+5lgy97YPwc3ejqeuiQCgRGvuXLWHuMw8fkvI4NMhXSjVmkNZ+Sw/nlzfIV5SeLf2HN8ex0cPzMLO3siwhyeULftm1jyG/nMcLl5u9LtzJD+9sYBNn/2Eb6tgOgzpDYBXiD8tu7Zj4SOvoWxs6DikN95h1ulI2BgMdJo4ls1z3kGbTIRGX4trcCAnfjPf9hU+OBq/zh1I2R3LqskzMBiNdL3PXBeKc3LYNvcDwDyrTVCfHvh1irRK3H9Y+M7D9OvTDm+PFhzd+i4v/nsZdpapxf/76Sp+Wb2LYQO7ELdhLgWFxdw/2RxvaamJx59bwA+Ln8ZgsGHhl2s5cDjhYm/VIJTBQMtx4zg0dy7aZMKnb1+cAgNJWWe+5Navf3/OnT1L7MsvU1pUhFKKpFWr6DRzJoUJCaRv2YJjUBD7Zs0CIOSWW3DvaJ2diEnDvEPHmHlNB2yUYtWZFE7lF3B9sLnu/pKQjLvRjjd7dcXJ1oBJw8jQIP7v9x0czsllU0o6c3t3pVRrjufk8UuC9e4/+cPVPdtzMOYAc+5+CTt7I6OfHFe27JNnP+DWx8fi6uXGpm/Xse6r1eQzgJX2AAAgAElEQVRl5jL3gdlc3bM9tz0+ltzMHN55+F8UF5i/m43fruOJ+U/j4GydGb1adoskfsd+Fj44C1t7O4Y8fHvZsm9ffJ/r/jkeF083oiaO4n//+oTNn/+IT3gwkdf1AeDo5l3s/WUjNgYbbI1Gbnjyrsu+DK2ubAwG2t8xlhhL2xMcfS0tggM5tdrc9oQOisancwfS9saybsoMDPZGOt1bvh8uLT5HeuxBIu+acKG3aFAmDf85cIxXunXARsGviSmczC/gRkv+/5SQjIfRjnf6dMHJ1oDWcHNYIJM27qSgtJQZXdrSws6OUq1598Ax8kpKrf4Z2vZsz8FtB3j9rpcw2hsZPbk8/z+a/gG3PTEWNy83Ni43539uZi7/vn82bXu2Z/QTY1n16QoKcvJZbpk90cZg4NH/PNngcSuDgYgJY4l98y20yYRfVF+cgwJJWmtuNwMGmNvNXS++QmlhEShF4qrf6PbiC9g6Ota4rjXZGAx0mDiWbbPfQevy3D9pyf2wQdH4du5A2p5Y1k4x73cr5n7kHWPY/f4nmEpLcfLxpvN9d1g1foDIXu3Zv/UAs25/GTsHI7dPHVu27P1p8xk/eQxu3m6s/WY9v32xmpzMXF69dw6RvdoxfvLYi7zyX5CVp8GucmVWVbW5et0W6AYMBhyBzUqpLVrrw38mLlXjdZMVC5gnPegEFAIpWuubLM/bAC8BIywfIA242fIzBTgP5AETtdYnlFKvY753ZycwHfgZWA9cCxwB7tBaFyil4oFPgOGYR6jGaa2PKqW+wXyJnQJ+Ax7TlwoedI/LvASuqYj5exQA8w9a/1ra+jCp7TCmblvd2GHU2eyeg3AMHXfpgk1U4aklTFxXL/cJWt2i/v0BGLGy6U1iURs/DDGP8C2P/18jR1I3t7S8gff2W+cSlobwf+2H8tiWK7ftmdt7EMNWXJn7rRXDzPut705embk/Ksw87fq9G9c2biB19N+oAQA8sfXKzP9/9xrEr4k/N3YYdTY0aDg0tQvMahBx91Kr3nl27JO/X3CbKKX6AC9orYdZHj8NoLV+tUKZaYCD1voFy+OPgF+01jXPk19Ll7wHSGs9/gLPmzBPbV11euuFlp+q5Z8CnqrwVPuqZSr4j9Z6ZpX1/3apWIUQQgghhBAX0LT+EWoM0MYyiVoiMBbzPT8VfQe8q5SyBYxAL8rnB6izy50EQQghhBBCCCH+FK11iVLqIcy31RiAj7XWcUqpByzL52mtDyilfgH2Aibgv1rr2D/73k2uA6S1btnYMQghhBBCCPFXo5vUABBorX/GfFtMxefmVXk8B5hTn+8rE54LIYQQQgghmo0mNwIkhBBCCCGEaABN6x6gRiMjQEIIIYQQQohmQ0aAhBBCCCGEaA6s9L/VmjoZARJCCCGEEEI0GzICJIQQQgghRHMg9wABMgIkhBBCCCGEaEZkBEgIIYQQQojmQIY+ANkMQgghhBBCiGZEOkBCCCGEEEKIZkMugRNCCCGEEKI5kGmwAVBa68aOoSH9pT+cEEIIIYRoMpp876LVQ8utemx8/N1bmuQ2+cuPAA38eVNjh1Ana4b3BSAm7adGjqRuevjcyMIjKxo7jDq7s80wJq5b19hh1Nmi/v1xDB3X2GHUSeGpJQDM3LWqkSOpm+e7XgfAqsSfGzmSurkuaDiv7VnZ2GHU2bTOQ3hsy+rGDqPO5vYexOStV2b8b/QaBMAru6/M/HmmyxAAHtm8ppEjqZu3+wwEYNLGtY0bSB3NjxrA58d+aeww6mx8xPWNHULtyDTYgNwDJIQQQgghhGhG/vIjQEIIIYQQQgjQcg8QICNAQgghhBBCiGZERoCEEEIIIYRoDmToA5DNIIQQQgghhGhGZARICCGEEEKI5kBmgQNkBEgIIYQQQgjRjMgIkBBCCCGEEM2BzAIHyAiQEEIIIYQQohmRESAhhBBCCCGaA7kHCJARICGEEEIIIUQzIiNAQgghhBBCNAcyAATICJAQQgghhBCiGZEOkBBCCCGEEKLZkEvgatDD252H2rfCoOCn0yksOZ5Yafl1gT6MbRUEQGFpKXNjj3EstwCAqR1b09vXg+xz57lnw26rxw6gtWbxW8vZvfkA9g5GJj0zjvCrg6uVe2/mpxw/eBpbWwOt2oVyz9TR2Noaar1+Q8a/cv7XHNu+H1t7IyMem4B/65Bq5bKTM/h29gIKcwvwbx3MyCfuwGBnS1F+Id+/sYictCxMJhO9bhlE5yG9rRZ/dmwsJ7/8Em0y4RsVReANN1RaXpiUxPGFC8k/dYqQm28mYOhQAIozMzn28cecz8lBKYVvdDT+gwdbLW6AeXPu54bBXUnLyKH7kKk1lvnXzDsZNrALBYXnmPTk++yOjQdgSP/OvPHCRAwGGxZ8sYY33vveipGbndkdx46Fy9AmExGD+hI5amil5Vprdiz8ijO74rC1N9L7wTvwDA8FYMu8xSTujMXBtQU3vvGs1WP/I76v3l1O3NYDGB3suGPqOEKvqp77a5dvYM3X60k/k87ry1/Exc0FgMO7j/LBcx/h5e8JQJd+nRg+cZhV49/6yTISLNs36v/uwLtV9fhzU9NZO/cTivMK8AoPIfrhiRhsbclOTGbje5+ScSKBa8beRMeR11ktdoC0vXEc+Gwp2qQJ7t+XiJsqbzutNQc+W0ranjgMRiMd75uIW8tQ8pKS2f3eR2XlClLTafO3mwgfZt36m7o3jthPzfGH9u9LmxHV44/7dCkpe+Iw2Bvpct9E3Fua83/VE9OxdXBA2digbGyInvW0VWP/I75tC5aRaMmfvg/egdcF8mf9W+X5E/WQOX9qu35DSNsbx8HPl6JNJoKj+9LqpuurfbaDny0lbW+sOXfuvRPXlqHkJyWz573/lpUrSEun9S0jaGnl3MncF8vRJUvR2kRAvyhCh1eOvyApmYMfLyDv1GnCbxlFyPVDa72uNWit+eWDbzgSsx87eztufmICATUcN2QlZ/D1awspzMsnICKEWybfjsHOlsLcAr6f+zmZSenYGu0Y9dg4fFsGWv1zWIOWSRCAJjICpJRaoJS67TLKt1RKxTZELDbAo5GtmBYTx13rdzE40IcwF8dKZZIKinhsyz7u3bibxUdP82TH1mXLfklI5amY/Q0RWq3t2XKA5NPp/OuLZ/jHlNEseGNZjeWuHXoNcz6fxquLpnCu+Dxrf9hyWes3lGPb95N5Jo0H5j/H8IfG8Mt7S2sst3rBd/QYNYAHP3wOB2cndq/cDMCOnzbgHerPve9OY8KrD/PbR99Ser7EKrFrk4n4zz/n6kceodPMmWTExFBw5kylMrbOzoSNHUvAkCGVnlc2NoSNHk3nWbOIfPppUtasqbZuQ1v81TpGTXztgsuHDexCREt/OkQ/zkPTPuTtl/8BgI2NYu5LdzPqztfpOngyo0deS9s2QdYKGwCTycT2j5cycNo/ufFfz3Fy03bOJiRVKnNmdxy5SWmMmPsCPe8bT8x/vyhb1qp/bwY+/U+rxlxV3NYDpCWm8cLiZxj/xN/5Ym7NdS+iQziPvPEgnn4e1Za17tiKZz6cwjMfTrFq5wcgYdd+cpLTuPXt57l20jg2V9i+FW3/9DsibxzIbW8/j72zI0dWm+uuvYszve4eTYcRg6wZNmCuu3GLvqD7kw/R79UZJG2JITexcv6k7Y0jPzmV6Nkzibx7PHELlwDgEuBP1IvTiXpxOn1nPo3B3oh/ty5Wj3/foi/oNfkhBr42gzM1xJ+6N468lFQGzZlJ57vHs2/BkkrL+zz9OP1fmt4onR+AxN37yU1O45a3nqfPfePY8lHN+bPjs+9oP3wgf3vreYwV8qe269c3bTJxYPESuj3xEFGvPE/S1hjyEiu33el7YylISaXf67OIvGsC+xd9DoBzgD/Xvvgs1774LH1mPoPBaMSvEXLnyGdL6Pj4w/R48QVSt8aQX22/5UTr8WMJGTbkste1hqPb95OZmMbD/32WEY+M5ad3v6qx3KqPv6f3LQN4+L/P4eDiyM5fzcc9G5auxK9VEA++N41bnrydXz74xprhi0bQJDpATUlb9xacKSgiqbCYEq1ZnZRGXz/PSmXisnPJKykFYH9WLt4OxrJle7NyyLHSwfaF7NgQS9T13VFK0bpDS/LzCslKz6lWrkuf9iilUEoR0T6UzNSzl7V+Qzm8dR8dB/VEKUVQ23CK8gvJyzxbqYzWmpN7j9Auyryj6Di4J4c37wPM9/edKyxGa835wnM4tnDCxmCdVM87cQIHX18cfHywsbXFs0cPsvbsqVTGztUVl5YtUQZDpeeN7u44h4UBYHBwwCEggPPZ2VaJ+w+bth0kMzvvgstvGtqNz7/eAMC2XUdxc3XC39edHl1acyw+mfhTqZw/X8pXP2zmpqHdrRU2ABlH43Hx98HFzxuDrS1h13YjYfveSmUSt+8lPLoXSim824RzrqCQwixzbvm2a4PR2dmqMVe19/dYeg3pgVKK8PYtKcwr5GzG2WrlQtoEl43yNCWntu+ldbS57vpeFc65/EIKsqrX3aS4w7Ts3RWA1gN6cTLGXEcc3Vrg0zoMmyp1wxqyj8fj7OeDk6+57gb06k7qzsp1N3XnHoL69kYphUfrVpQUFFCUXfnzpccdxMnHG0dvL2uGT9axeJx9fXC2xB/YuzvJVeJP3rmHkArxn68h/sZ0OmYvrSz543OR/EmOO0yYJX8i+vfitCV/arN+Qzh7PB4nP98KudOD1F2V257UXXsJtGx799atOF9QSHGVbZ+x/yBOvtbPnZzjJ3D09cXRst/y7dmdjF2Vc8fo6oprePX9Vm3WtYaDW2LpNNjcdga3bUlRfiG5NRw3nNh7hPZRnQHofF1PDlmOG9JPJdOqy1UAeIf4kZ2SSV6W9Y57rMpGWfeniWqUDpBSaqJSaq9Sao9SarHl6Wil1O9KqeN/jAYpszlKqVil1D6l1JiGjs3bwUhq0bmyx2mF5/C2t79g+eEhfmxLs+5B6qVkpefg5ete9tjT152s9AvvBEpKStm4Yjuderet0/r1LS/jLK7e5e/fwsud3CoHgYU5+Tg4O5YdKLl6l5fpdlM06aeTeXvic3z40KsMmXQrysY6qX4uOxujZ/mBqdHdnfNZWZf9OsXp6RScOoVzeHh9hvenBfp7kpCUUfY4MTmTQH9PAv09SDhT4fmkDIJqGJ1oSIWZ2Th7lb+nk6c7BZmV62ZB5lmcvNwvWqYxnU0/i3uFuufu4072Zda9E/vjeeXeOfxn2gecOZF06RXqUUFmNs7e5d+Bs1f17Vucm4/RqbzuOnl6UJDZ+AfhRVnZOHiWx+7g6UFRVnb1Ml6VyxRXKZO0dTuBvXs0bLA1KMrKxtHrEvFnVv6Mjp4eFJV9P4ots99m/YxXOLlmgzVCrqYgq0odrkX+OFfIn9qs3xCKsrIq546HO0VV2v3iqvnl4V7t+0neuh3/Rsidc9nZ2FeIzd7Dg+Jannz7M+vWp9z0bNx8yttOV283ctMvfdyQk2GO1S88kAObzJ3WxEMnyU7NIseKxz3C+qx+D5BSKhKYDvTVWqcrpTyBfwMBQBTQFvgeWAb8DegCdAa8gRil1PpLvP4kYBLABx98AMGRlxdfDc9pdI1lu3i6MTzEj0csZxCaCq2rx3uxPviCfy2jbedWtO3cqk7r17ea3r9qADV9J8pS5vjOA/i1CmbCKw+TlZTOkuf+Q0hkK+ydHKutU+9qjP3ytl5pURGH580jbMwYbB2tEPNlUDVkgtYaVcNnrGlTWFv1uGrKm6ZzhqrGuncZ8YW0CWbWkhk4ONoTu2U/82d8zAuLp9dniBdX03dei++gSfiT2x7AVFJC6q69XD365vqK6jLUuPEvvZqlSNRzk3HwcKc4J4ctr7+NS4A/Xm3b1GuEl1Jz81mL/FGXs34DqDGkyu97qf2aOXf20Oa2RsidP1Mlm2h1Bqq1PTXXEHOZqL8P4Zd5XzPvodn4hQUQEBFktStHrK4J7fMaU2NMgjAIWKa1TgfQWmdaGqhvtdYmYL9Sys9SNgpYorUuBVKUUuuAHsDeGl4Xy+vNB+b/8XDJz5suK7i0onP4VrikzcfRSEbxuWrlWrVwYnLHCKZt39/ol7wBrPx6I2ss9/C0ahdCRmr5GZjM1Gzcvd1qXO+bj1eQm53PPS+PLnvO08et1uvXl+0/rmf3CvN13IFtQslJL3//3IxsWnhWfn8nVxeK8gsxlZZiYzCQk56Ni6XM3lVb6XPbEJRSeAb64O7nRcbpVAKvDmvQzwBg9PDgXGZm2eNz2dnYubtfZI3KTCUlHJk3D+9evfC85pqGCPFPSUzOIDig/PKMIH9PklKyMNrZEhxY4fkAL86kXv7I15/h6OlOfkb5exZkZuPoUSVvPN0pyMi+aBlrW/ftRjb9ZM79sKtDya5Q97LTsnHzcq31azk6O5T93aF3e758axl5Z/PKJkloCAd+Wcfh334HwDsijPz08u8gPyMbpyrb176FC+cKyutuQWYWTp6N+x2AZcQkszz2osws7N0rx+Xg4U5RRpUyHuX1O21vHK5hodi71f47qy8OHh4UVonNocq2d/B0r/QZCzOzcLDE/8dve1dX/Lt1Ift4vFU6QAdXVMmfinU4o3r9rJo/+ZlZZTnmXLUNqGH9hlAtd7KyK+XFhco4VNg3pO+NbbTcMXq4U1whtuKsLOxrud/6M+v+Wdt+2MDOCscNZytcjZOTfpYWVdpOJ1fnascNLbzM+WHv5MCoJyYA5s7qW3fPwsPfupciCutqjO6touaOeHGVMhV/W83Bs7kEOTvi72iPrVIMCvDh95TMSmV8HYzMuqYtr+45QkJ+kbVDrNGQW6N4ZcFkXlkwmW79OrLxl+1orTkaG4+TiwMe3tUb1TU/bGHftkP884Xbsalwidg1UR1qtX596n5TNPe+8xT3vvMUV/XpxL7V29Bak3jwBPZODmWdmz8opQjr2IYDG80z7e37bRtX9e4IgKuPB/F7DgGQl5VDRkIq7lZqyFxatqQoNZWi9HRMJSVkxsTg0blzrdbVWnNi0SIcAwKqTZDQVPy0cifjb+0HQM+urcnJLSA5NZvte47ROtyfsBAf7OwMjB7Rh59W7rBqbF4RYeQmp5KXmk5pSQknf99BULeOlcoEdevEifVb0VqTfuQEdk6Ojd4B6n9zVNmkBZ2jOrB1ZYw5F/bH4+jsiJtX7eM7m5lTdqY5/sBJtNY4uzbsfU3tru/PqDlPM2rO04T27MTR9ea6m3r4BEYnx2odIKUUAZFXEb9lFwBH124ltHunBo2xNtzCw8hPSaUgzVx3k7Zux7dr5bh8u3YicdMWtNZkHT2OraMjDhU6SUlbYgjsbd173/7g3qpy/Ge2bMe/Svz+XTtxukL8dk7m+EuKiykpNO/LSoqLSYs9QItg68yA1XZYf0bOfpqRs58mtEcnjlvyJ+2wuX7WlD/+7a/ipCV/jq3bSoglf0K6d7zk+g3BNTyMgkq5E1M9d7p04oxl22cfPY6to0OlDnbSlu0ENMLlbwCu4S0pTEml0BJ/6rbteHWp3X7rz6z7Z/Uc0Y8H3p3KA+9OpW2fjuz9zdx2JhyMx97ZodqJU6UU4Z3asH+j+R6lPau2cXXvDgAU5RWUTZa0c8VmwjpEYO/kwF+SjZV/mqjGGAH6DViulHpTa51huQTuQtYD9yulFgKeQDQwBWiwrDRpeDvuOLN7RmID/C8hlfi8QkaE+gPww6lkJrYJxdVox2MdzJeMlWp4YJO5Qj3b5Sq6eLrhZrRl6cDuLDhyip8TUhsq3Bp16dOOPZsP8OSYVzA62DHpmXFly+ZMns+908bg4e3GJ28sw9vPgxfufxuAHv07csvdwy66vjVEdG/P0e1xvH/fLOzsjdz02ISyZV8+P4/hj4yjhZcbA+8eybevL2D9pz/h1yqYzkPNU11Hjb2eH+d+yof/fBWtYdDdI3FqwDPgFSmDgZbjxnFo7ly0yYRP3744BQaSsm4dAH79+3Pu7FliX36Z0qIilFIkrVpFp5kzKUxIIH3LFhyDgtg3axYAIbfcgnvHjhd7y3q18J2H6denHd4eLTi69V1e/Pcy7OzMzcR/P13FL6t3MWxgF+I2zKWgsJj7J38AQGmpicefW8APi5/GYLBh4ZdrOXA4wWpxA9gYDHS/+++seeU/aJOJVgP74B4SyJGV5vsZ2gzpR2DXSM7sjuOHR1/AYG+k9wO3l62/6e2PSdl/hOLcPJb/33Q63XYjEYOutepniOzVnritB3jh9pcxOhi5ferYsmX/mTafCZPH4O7txppv1rPqi9XkZObyyr1ziOzVjgmTx7Jr3R42fL8Jg8GAnb0d9zw70aqX+AV3jSRhZxxfPzITg9GOfv9Xvn1/ffU9ou4fj5OnO90njGLt3E/Y+cWPeIWHcNWgPgAUZOfww7TZnC801439P6/lln9Px2iFy1dtDAba3zGWmDnvWKYyvpYWwYGcWm2+6jp0UDQ+nTuQtjeWdVNmYLA30uneiWXrlxafIz32IJF3TbjQWzR4/B0mjmXL7HfQ2kSIJf54S/wtB0Xj27kDqXtiWT1lBgajkS6W+IvP5rD9LXNdNplMBPXpgW+ny7t8vD4EdY0kYVcc3zw6E1ujHX0fLM+fVa++x7WW/Ok2YRTr3vqEXV/+iGfLENpY8udi6zckG4OBdrePYccbb6NNJoL6XYtLUCCnLds+ZFA03pbc2TD1OQz2Rjr8486y9UuLz5ERd4D2jZQ7ymCg9YSx7HvzLbTJhH9UX5yDAjmz1rzfChxg3m/tePEVSguLQCkSVv1GjxdfwNbRscZ1ra1Nj/YcidnPO/94ETt7I6MeH1+27LMZ8xj5qPm44bq7R7Ds9YWsXvQTARHBdB1mzp200yl8+69PUTY2+IT6M/JR6x73COtTNV6X2tBvqtSdmDsypcAuy9M/aq2XWZbnaa1dlHnPPRu4AfOo0Uta6y+VUi0t5Ttc4q30wMu8BK6pWDO8LwAxaT81ciR108PnRhYeWdHYYdTZnW2GMdHSabkSLerfH8fQK7MBLzxlnpp35q5VjRxJ3Tzf1fy/a1Yl/tzIkdTNdUHDeW3PysYOo86mdR7CY1tWN3YYdTa39yAmb70y43+jl3n68ld2X5n580wX88j7I5vXNHIkdfN2n4EATNq4tnEDqaP5UQP4/NgvjR1GnY2PuB4a4cqly9Xy+V+seuAfP/P6JrlNGuUfoWqtFwILL7LcxfJbY+4oTamyPB64VOdHCCGEEEIIISpplA6QEEIIIYQQwsqa8P/msaYmfHuSEEIIIYQQQtQvGQESQgghhBCiOZARIEBGgIQQQgghhBDNiIwACSGEEEII0QxoK/5rhKZMRoCEEEIIIYQQzYZ0gIQQQgghhBDNhlwCJ4QQQgghRHMgQx+AbAYhhBBCCCFEMyIjQEIIIYQQQjQHMgkCICNAQgghhBBCiGZERoCEEEIIIYRoDuQfoQIyAiSEEEIIIYRoRpTWurFjaEh/6Q8nhBBCCCGajCY/vBI2Z7VVj41PThnUJLfJX/4SuInr1jV2CHWyqH9/AKZuW93IkdTN7J6DmLRxbWOHUWfzowYwYuWGxg6jzn4Y0o+Zu1Y1dhh18nzX6wBwDB3XyJHUTeGpJQAsPrqikSOpmztaD+O1PSsbO4w6m9Z5CM/tuDJzH+DFbtfx0hVad5+11N35B6/M3J/UdhgAY9esb+RI6uaLgdEAvBX3ayNHUjePRg7lzdgrt+15vMOQxg5BXIa/fAdICCGEEEIIwRUwRmUdcg+QEEIIIYQQotmQESAhhBBCCCGaAS2zwAEyAiSEEEIIIYRoRmQESAghhBBCiOZAyQgQyAiQEEIIIYQQohmRESAhhBBCCCGaA7kHCJARICGEEEIIIUQzIh0gIYQQQgghhNUppa5XSh1SSh1VSk27SLkeSqlSpdRt9fG+0gESQgghhBCiOVBW/rlYKEoZgP8ANwDtgXFKqfYXKPc6sKKOn7oa6QAJIYQQQgghrK0ncFRrfVxrfQ74AhhVQ7mHga+B1Pp6Y5kEQQghhBBCiGbAxspDH0qpScCkCk/N11rPt/wdBJyusCwB6FVl/SDgFmAQ0KO+4pIOkBBCCCGEEKLeWTo78y+wuKaL5HSVx3OBp7TWpaoe/4eRdICEEEIIIYRoBprY/0FNAEIqPA4GzlQp0x34wtL58QaGK6VKtNbf/pk3lg5QDbJjYzn55ZdokwnfqCgCb7ih0vLCpCSOL1xI/qlThNx8MwFDhwJQnJnJsY8/5nxODkopfKOj8R882Orxp+yNY9/ipWDShA7oy1UjhlVarrVm3+KlpO6Jw2BvpOukibi3DAXg18enY+vggLKxQRlsGDDraavHn7kvlqNLlqK1iYB+UYQOv75a/MeWfEnGvlgMRiNX33MXLcLM8Ses/I2k9RsBTUB0FMFDrrN6/Nd4eXDf1a2wUYqVicksi0+otDzYyZFHI68iwtWFxUfjWX4ysWzZqNBAhgb5o4H4vHzeijvMeVPVkyEN68zuOHYsXIY2mYgY1JfIUUMrLddas2PhV5zZFYetvZHeD96BZ7h5+2+Zt5jEnbE4uLbgxjeetWrcAPPm3M8Ng7uSlpFD9yFTayzzr5l3MmxgFwoKzzHpyffZHRsPwJD+nXnjhYkYDDYs+GINb7z3vRUjN9Na8+sHX3N0+37s7I2MeHwCAa1DqpXLSs5g+esLKMwrICAimFFP3oHBzpai/EK+e2MRZ9OyMJWa6P23QXQZ0tuq8W/9ZBkJltyI+r878G5VPf7c1HTWzv2E4rwCvMJDiH54IgZbW7ITk9n43qdknEjgmrE30XGkdetv0p44di8y5374wL60G1k993ct+ork3XEYjEZ6PnAHHuGhlJ47z5pZb1JaUoIuLSW4V1c63HaTVWMHSNwdx3ZL3W09qC8daqi7MZa6a7A3cu2Dd+BlqZIbnBAAACAASURBVLu/z1tMgqXujmyEuvtHfGs+/JoTO/Zja2/k+kcn4BdRPX/OpmTw45wFFOUV4NsqmOGPm/Mf4PS+I6z56BtMJaU4ujoz5pVHrRJ7TlwsCUu/QJtMePXth//1lY8bipKTOLlwAYWnTxEw8mb8hlbZL5tMHHr1Jezc3Yn45yNWibnS+2vNxo++5uROc90d/NDt+NSw7XNS0vn13wsozivAOzyY6x6diMHOlhPb9rJ1yU8opbAx2BB1z60EtIuwavybPl7GqZ1x2BqNDHz4DnxqaHtyUtJZ9eYnFOUW4NMqhEGPlMcfs+RHlI05/mvvvs2q8TdjMUAbpVQ4kAiMBcZXLKC1Dv/jb6XUAuDHP9v5gSY0CYJS6hGl1AGl1GdKqcmNFYc2mYj//HOufuQROs2cSUZMDAVnKndGbZ3/n737Do+i2v84/j7ZJJueTQ9JSIGEFnqvgYCAKMXGpagoei8qiF7LT7GgiA07onIBG4hKF0Gx0UPvhJCEEiBAID3Z1E3d+f0xIXVBCMkmkfN6Hh/ZnTPZz06+c3bOnNmJPQHjx9Ns6NAqzwsLCwLGjqXT7NmEvvQSyVu31li3vilGI8eWLKfP/z3J4Pde49KeA2RfSqzSJiUymrzkFIZ8+AadHplI5LfLqizv9/IzhL/9SoMMfhSjkdM/LKPDM9Pp8eYsUvYdIK/aNsyIOk5+cgo933mTVpMe4PTSHwDIS7hEYsROur76Et1nzSQ9Mor85GSz5rcAHm/TkllHopm2+xBh3h40t7er0ianuIRFJ8+wttrAyFVrzSh/X57Zd5Qn9xxGgyDMy8OM6cFoNHLwm5WEz5jGnR/N5Pyug2QlVK2fy0ejyUlMZdTcWfT8z0QOfLW8fFmLgb0Jf2maWTNXtnTVdsZMmnPV5cPDO9My0Jv2Yc/w5Iwvmff2owBYWAjmvjWZMQ+9R5chzzN2dF/ahPiaK3a5MwdjyLicytQvZ3LH9HH8/sVKk+22fLuOXncNYtqXM7FxsOPoX3sAOPjrDtybezPl8xk8OGc6m776mdLiErPlTzgSQ3ZSKvfOe52+Uyawp1JtVHbw+3WE3hnOffNeR2tvy+ktan6tgz29Jo+l/ajBZst8hdFo5PC3KxnwwjSGfzCTC7tr1n7S0Whyk1IZ8fEsuv97Ioe+Ud+fhZUlA199iuFzXmbYuy+TFBlD+ulzZs+//5uVDJ4xjVEfzSR+10H0V9l3x8ydRe//TGRfpd9Py4G9GdKA+y7AuUMxZCam8siCmQydNo5N/zNd/xFL1tFt9CAeXaDWf9QmtX4KcvPZtGAld73yHx7+/GVGvfCIWXIrRiMXl/1Iyyefpu3rs8k8sB9Dtc8tjZ09fuPG43nbMJM/I3XLJmy8m5kjrkkXDseQlZjC/V+8xqDHx7N90QqT7fYsXU+nUeHc/8VraB3siN2sbnu/Dq0Z9/EMxn08g/Bp97N1/o/mjF+WP5UJn7/OwCcmsGOR6b5n79J1dBwZzsQvXkfrYMuJSvnHfvwSYz96iUFTH2C7mfObkxDm/e9aFEUpAZ5EvbtbLLBSUZRoIcTjQojH63M7NJoBEDAVuAM43ZAhcs+dw8bTExsPDywsLXHt0YPMyMgqbaycnHAIDERoNFWet9bpsA8IAEBjY4NNs2YU6/Vmyw6QeSYeey8P7D3V/L69u5N0qGr+xMORNO/fGyEErsEtKM7Pp0CfZdacV5N99hy2np7Ylm1/z57dST9SNX/60Ui8+6r5nVq2oCTfQKE+i/zEJJxaBqHRWiM0GnStW5F2+KhZ84c4O5KYX0CyoYASRSEiKZVeHq5V2mQVF3M6O5cSpebMjoUQWFtYYCFAq7Ego7DIXNEBSI+Lx8HbAwcvdzSWlgT07UbCwWNV2lw6eIygsF4IIXAPCaIo34AhU60fz7YhWNvbmzVzZbv2nyBDn3vV5SOHdePHNTsA2H8kDmcnO7w9dfToHMyZ+CTiL6RQXFzKql/2MHJYd3PFLndybxQdBvdECIFfmyAK8gzkZFTdNxVFIf7Yadr27wxAxyE9Obk3ClA/bIoMhSiKQpGhCFtHOyw05uvmLxw8RnCYmt+zVRBFeQbyM2vmT4w+RWDvLgAED+rF+QPqPm7r7IhHcAAW1fpWc8iIi8fBq6L2/ft04/KharV/6BiBA9TadwsJoris9oUQWNnYAGAsLcVYavzb27/WtfS4eBy9PXCstO9erLbvXjx4jBZl+65HWf4rvx+vtiFoG3DfBTizP4p24Wr9+LQOojDPQK6J+r9w7DSt+qn1Hzq4J3Fl9X8i4hAhfTrhVNbn2ukczZI7P/4cWk8PtGWfWy49epB1rOpnj5WTE/aBQTWOGwCKMjPIiorCrV9/s+Q15dz+KFoPUre9d2t1380zse0vRZ2iZR9127cJ78W5/WqNWdlqufL9jJLCIsy9A8QfOEargWp+r1Zq7eSZ6HsuHz9Fiz5q39NqUC/O7Y+skb+4sNDs+++tTFGU3xRFaaUoSktFUd4ue26BoigLTLR9WFGU1XXxuo3iEjghxAKgBbAe8AfWCyG2oF4X+L6iKF8KIZoBKwAn1NxPKIqyo66zFOn1WLtWHLBa63TknbvxM3mFaWnkX7iAfVDQ3zeuQwWZemxdXcof27q6kHnm3N+2MWTosdE5IxDseW8eCAgMH0Dg4AFmyw7q9tdWyqZ1cSG72vYvzNSjrfQ70rroKNJnYufrw7m1P1Ocm4uFlTXpx6JwDAwwW3YAN62WtMLC8sfphUW0crq+D+GMwiLWxifwzYCeFBmNHEnP5EiGeQfQhgw99m4V29/OVUdaXHyVNvkZWdi56aq0yc/QY+vibK6Ytebj7UpCYnr540tJGfh4u+Lj7ULC5UrPJ6bTs3Ow2fPlpGfh5FGxbZ3cdeSkZ+HoWrFtDdl52Njblg8SHMvaAHQfGcbK2Yv49MGZFBoKuOfFyQgz3vInP0OPvXtF/di7qbVhV6k2CnPysLaryG/n6kJ+RsOfgDFk6rFzq9wv6sioVvuGzCxsXXVV2hgy1do3Go1semUOuUmptBw2ELdg8/b9+dX2Xfur7Lv21fZdQ7XfT0PKTc/C0b0in6O7jtz0LBwq139O1fp3cNOVD5IyL6dQWlLKilfmUWQooOvIQYQO7lnvuYsy9Vi7VD5ucLmh44ZLK1fge899lBYU1Ee865KXoceh2r6bl5GFfaVtX5CTh3WlbW/vpiMvvWLfPbs3kr0/rMeQlcudr9Tryfsaqud3cNORl67H3uXq+R3cXKoM8s7ti2Tf9+sxZOcw4mXz5jenuryRQFPWKAZAiqI8LoS4HQhHnQq7G+gN2ANHhBAbgAnAn4qivF32B5HsTP2syrfbW7hwIbRufaNhTP3QG/oRpQUFnFqwgIBx47C0tb2x179JyvXkv0aT/q89j62LjsKsbHa/Nw8HH2/c24TUfdCruZ6vu5h6jwjsfZrRfMRwjn00F41Wi0Pz5ibPttWn67mdydXYW1rSy9ONf+88QF5JCTM6tmGQtwfbklLrMuINq9lZ1nxHTaVDFSZ+Q4qimMxvsszqm4kXrbn1rx7s7OFYvFr48cC708lMTOOHV7/Av30LtHZm6odM7pp/Xz+NwvVkv0rfA2BhYcGwd1+mKC+fXZ8sIuviZZyb+9R5zBtyPdu+Ee27pj6/ar6Fq7cxlhpJOXORsW8+SXFRMcte+IRmrQNx9fWsh7RVQtV86jo3a9axSCwdnbALCCDn5Mm6jXUDTB871Gh0zTYteneiRe9OXI6OY/+yXxk9a3qdZrwmk6X99/tv5SZBvToR1EvNf2DZBkaZM79kdo1iAGTCOkVRDIBBCLEV9Q8lHQC+EUJYAT8rimLy2qZqt9tTdm7ffkMvbO3iQlFGRvnjIr0eK53uGmtUZSwp4fSCBbj36oVr16439Np1QZ3NySx/bMjIxEZX9eyejauuZhsX9T3alv1f6+xEs+6d0Z+JN+sAyNpFR2GlbIWZmWirbX+tqwuFlX5HhZl6rMvaNBvQn2YD1MsIzq5Zi9bFBXNKKyzEXastf+ymtSaj0ozQtXR21ZFsKCC7uBiA3SnptNU5mXUAZOuqIy+9Yvubmtmxc9WRn66/ZpvG6lJSOn7N3Mof+3q7kpicibWVJX4+lZ5v5sbllExTP6LOHfw1giN/qNehN2vlT3ZqxbbNTtPj4FZt+zs5UJBnwFhaioVGQ06aHseyNpEb99F37FD18lYfD3RebqRdTMG3df3NhMb+sZ1Tm3cD4N4ygLy0iu2Wl15zdkHr6EBRfkX+/IxM7Fwbvn5sXXXkp1fuF2vWtW3ZjMm12ljb2+HZNoTEyBizDoDsqu27eVfZd/Ma2b57ZEMEURvV+vcO9icnrSJfTpq+ygwEgG21+s+tdJbfwU2HrZM9VjZarGy0+IW2JDX+Ur0PgKxdXCjKrHzckHndxw15Z86Qdewo2cejMJYUU2ooIP6brwh85N/1Fbdc1O8RxGxU913PYH9yq+279tVqw8bJgaJK2z4vvebvB8AnNJisz9IwZOdi6+RQb/mP/76d2E1qfo/ggCr5c9P1NfqV6vlz0zNNzn76hAaz9fP6z99QGtE5jwbVmL4DVFn1YbqiKEoEEIZ6l4ilQohJ9fHCDoGBFKSkUJCWhrGkhIwDB3Dp1Om61lUUhXPffYdts2Y1bpBgLroWAeQlpZCXoua/tPcg3l07Vmnj3bUjF3fuRVEUMuLOYmVni43OmZKCQooN6hR8SUEhKVGxOJr5DKZTUCCG5BQMqWr+lP0Hcetcdfu7depE0m41f/aZs1ja2aItG+QVZWcDUJCeQdrhI3j2qrO/mXVdTmfn4GNng5eNFkshCPP2YH9qxt+vCKQWFNLG2RFt2SVLnVx1XMwz1GfcGtxaBpCTlEJuShqlJSWc330I324dqrTx7daRcxH7UBSFtNPnsLKzbfCDqOu1YeNhJt6rXtbZs0sw2Tn5JKXoORh5huAgbwKae2BlpWHsqD5s2HjILJm6jwzjP5+/yH8+f5HWvTsStWU/iqKQcOIcNvY2VS5/A/WsZmCHEGJ3queAjm3eT6te6u/IydOFc5HqWeTczGwyLqXg4u1GfWp7+0DGfPASYz54Cf+eHYmLUPOnnDqHtZ1tjQMMIQTNQlsRv/cIAHHb9uHfvaOpH21Wri0DyK1U+xf2HMKnWu37dOtI/A619tNPn8PKVq39guwcivLyASgpKiL5+EmcfLzMmv/KvptTad9tXi2/X7eOnC3bd1PL9t2Gvvyty51hTJr7IpPmvkhw747EbFXr5/LJc2jtbapc/gZq/fh3COHULrX+o7fsJ7is/oN7deBSzFmMpaUUFxaReOo8bn71/3uwCwikMCWFwrRUjCUlZB44gHPH6ztu8Ln7HtrP+YDQd+YQ+OgUHNu0NsvgB6DDiLDyGxcE9ezIyW3qtk86eQ5rO5sagxshBL7tQzizR932J7buI6iHuu2zElPLZ5FSz1zEWFKKjWP9fqes/YiBjP1IvXFBUM+OnNqu5k8u63uqD+CEEPi0b8XZPWrfc2rbPgJ7dqyZ/+xFSktK6j2/1LAa6wzQGCHEu6iXwA0CZgghAoBLZd8Hsge6At/V9QsLjYbACRM4OXcuitGIR79+2Pn4kFw2k+Q1cCBFWVkcf/ttSgsKEEKQuGkTHd94A0NCAml792Lr60vU7NkANL/7bnQdOlzrJeuUhUZDx0nj2fPBZyhGI/5hfXHy8+Hc5ggAgoaE4dWpPclHj7Pp+dfQWFvT5T/qWLIwO5v9cxcC6l1tfPv0wKtjqNmyg7r9g+8fT9Qnn6IYjXj374e9rw+Xt6nb32fQQFw7ticjKor9L71adhvsh8rXj56/kJLcPIRGQ8j9E7Ay85d6jQosOHmGN7q2x0IINl1O5kJePrf7eQPwR0ISOmsrPunVBTtLDUYFRvv7MnX3IU5l57ArOY25vbtQqiiczc7lj2p3capvFhoN3Sf/i63vfIFiNNIivA+65j6c3qh+3S5k6AB8uoRy+Wg0vzw9C43Wmt6PP1C+/q5535Acc5rCnFzWTn2FjvfdScvBfc2Wf8ln0xnQpy3uLo7E7fucNz9ejVXZ7XG/+n4Tf2w5wvDwzkTvmEu+oZDHnlfrvbTUyDMzF/PL0pfQaCxYsmIbsacSrvVS9SK4RzviDkbzxb9nl98G+4plry9g5FMTcHRzZvDk0ax9fzHblm7Au4UfnYert7oeMP521n/yPQunvgvA4IdHY+dsvjOYfl1CSTgczZqn3kBjbcWAqRW18de78+n/2ETsXHV0v38M2+Z+y+Hlv+IW1JxWg/sAkK/P5pcZ71NsUPvWmN+2cffHr2Bthkv4LDQauj78LyLmqLUfNKgPzn4+xG1Saz/4tgE06xxK4tFofntmFpZaa3o8pr6/An02+//3HYrRiKIoNO/dFZ+u5uv3r+TvOflfbC7bd4PL9t1TZftuq6ED8O0SyqWj0fz8tJq/b6V9d0fZvluQk8uasn03xIz7LkBQt3acPRjN14+r9T98ekX9/zR7AcOmTcDBzZkBD41mw4eL2fXDBjxb+NG+7Fbvbs29CezSliVPzUFYWNBhaG/cA+r/JJ7QaPAbN5Ez8+aiGBXc+vbD1seXtIhtALiHDaI4K4uT775VftyQumUTbV+fjcbMl8lfTUC3UC4cjuGHqbOx1Fox+MmK2vj1rf8RPnUi9q7O9H5wDBs//pZ9P/6KR5AfbW9T990ze45ycvt+LDQaLK2tGPbcZLNeGu3fNZQLh6NZNu0NLLVWDJpWkX/DW/MZNHUi9q46ej8who2ffMv+Zb/iHtSctkPU/Gf3HuXUtn1YWKr5hz77SJO5tPtG/UPf1g0TJq/7bABCiHjUP3b0JOADtES9IcKVmyA8BPwfUAzkApMURfm7bxkqk27wErjG4ruBAwF4Yf+WBk5SO+/3HMyUndsaOkatLeo/iFEb6/weG2bzy9ABvHFkU0PHqJXXu6h/+8XWf0IDJ6kdwwX1tvJL4/5s4CS182DwcOZEbmzoGLU2o9NQZh5qmrUP8Ga323irie67r5btu4tONM3an9JG/ds847dGNHCS2lkeHgbAp9F/NXCS2nk6dBifHG+6fc8z7YdCE7h/XMjCCLMe+J9+LKxRbpNGMwOkKEpg2T9nXWX5EmCJufJIkiRJkiRJ0j+JaKxffjEzuRkkSZIkSZIkSbplyAGQJEmSJEmSJEm3jEZzCZwkSZIkSZIkSfVH3gRBJWeAJEmSJEmSJEm6ZcgZIEmSJEmSJEm6BVjIGSBAzgBJkiRJkiRJknQLkTNAkiRJkiRJknQLkN8BUskZIEmSJEmSJEmSbhlyBkiSJEmSJEmSbgFyBkglZ4AkSZIkSZIkSbplyBkgSZIkSZIkSboFCDkFBMgZIEmSJEmSJEmSbiFCUZSGzlCf/tFvTpIkSZIkSWo0Gv30Sofvdpj12Dhq0oBGuU3kDJAkSZIkSZIkSbeMf/x3gNad/72hI9TKmIARACw68WcDJ6mdKW2G8+KBzQ0do9be6zGEtfFNs3YA7g4cwaZLvzV0jFq5zfcOAJbGNc3afzB4OAC2/hMaOEntGC4sa/L77renmmbtAExuNbxJ9/sAj+/a2sBJamdBv3CAJls/k1up2//9YxsbOEntvNBxKBO3bW/oGLX246CBDR3husivAKnkDJAkSZIkSZIkSbeMf/wMkCRJkiRJkiRJcgboCjkDJEmSJEmSJEnSLUMOgCRJkiRJkiRJumXIS+AkSZIkSZIk6RYgL4FTyRkgSZIkSZIkSZJuGXIGSJIkSZIkSZJuARZyBgiQM0CSJEmSJEmSJN1C5AyQJEmSJEmSJN0C5HeAVHIGSJIkSZIkSZKkW4acAZIkSZIkSZKkW4CcAVLJGSBJkiRJkiRJkm4ZcgZIkiRJkiRJkm4BQt4GDpAzQJIkSZIkSZIk3ULkDJAJiqKwfv5PnDgQi5XWin89PxG/kOY12u1at4Oda7eTfjmN11e9hb2zAwCHNx9k28rNAGhttdw9fSw+LX3Nmn/rl2s4dygGS601tz99P14ta+bPSk7n1w8WU5Cbj2cLP+545kE0VmpJXIw6zdavf8JYUoqtkz3j3nnabPmTI6OJWroKxagQMKgvrUYPr7JcURSilq4i+Wg0Gq0VXadMQhfkX7HcaGTbzDnYuOjo8/xUs+WunO+X//3Eyf2xWNlYMfa5ifiaqJ/d63awa+120hPTmLmyon5SLiSz+uMfuRSXwPCH7iRs7GCz51/1+Vqi98VibWPFgy9MwL9Vzfzb1u5g65oI0i6n8d7aN3Eoy3/qaBwLZ36Nm7crAJ0HdOSOScNrrF9f2f9auIa4gzFYaa0Z9cz9NAuumT0zKZ217y3GkJtPs5Z+jHlOrf2CPAPrPvyOrNRMjKVGet8zmM5De5slO8CCDx5jxJAupKZn033oCybbfPTGQwwP70y+oYgpz/2Po8fjARg6sBMfzpqERmPB4uVb+XD+erPlvuKfsO9uWrSGM4fU+rnz6fvxNlE/+qR01n2wmIKcfLxa+jHqWbV+9v20mehtBwEwlhpJT0jiqe/fwdbR3mz5m3Lfnx4VzekfV4JipNmAfgTceXuV5YqicPrHlWREHcfC2pq2jz6EY4BaPxc3biYxYheKouAT1p/mw4aYLfeVbE29dvZ+u5qLh6Ox1FoTNu1B3FvUzJ+TnMbWud9SmJuPW1BzBk6fhMbKEv2lJCK++J70cwl0nzCSDqNvM0vuK7KOH+fiyhVgNOLevz/et4+osrwgKZH4xUvIv3gBnzF34T1sWJXlitFI7DtvY63TEfzkdHNGNzv5HSCVWWeAhBD/FULYmfM1a+PEgVjSLqXywrevcO9/x7F23iqT7QJDg/jPnCdw8XKp8ryrtxuPfzidZxe+yJCJw1gzd4U5Ypc7dyiGzMRUHlkwk6HTxrHpfytNtotYso5uowfx6IKZ2DjYEbVpDwAFuflsWrCSu175Dw9//jKjXnjEbNkVo5HIJSvo88KTDHl/Jgl7D5J9KbFKm+TIaHKTUrjto1l0fvR+Ihcvr7L8zB9bcfTxNlvm6k6W1c/z377CPU+P4+fPTNdPQGgQj855Al21+rFzsmPUE/cSdq95Bz5XRO+LJfVSKrOWvszEZ//F8rmrTbZr2T6Ipz58Atdq+QGCO7Tg5S//j5e//D+zDX4AzhyMIeNyKlO/nMkd08fx+xema3/Lt+voddcgpn2p1v7Rv9TaP/jrDtybezPl8xk8OGc6m776mdLiErPlX7pqO2Mmzbnq8uHhnWkZ6E37sGd4csaXzHv7UQAsLARz35rMmIfeo8uQ5xk7ui9tQsx30gX+Gfvu2UMxZF5O5bGFM7l92jj+vErfuW3xOnqMGcRji9T6idyo1k+ve4bwyLwXeWTeiwx8aCTN2web7QAWmn7ff+r7ZXR65kl6vvU6yfsOkHfpcpU2GVHHMSSn0Ovd2bR+6H5OfvcjALkJl0iM2EW3V2fQ441XSY+MIj852WzZoenXTsKRGLITUxn72ev0f2wCu79cbrLdgR/WEToynLGfvY7WwZZTW9T8Wgd7+jwylg6jzP+5pRiNXFj2IyHTn6LdrDfIOHAAw+WqtaOxs6f5+PF4DR1q8mekbN6MjXczc8SVGombGgAJ1Y38jP8CjX4AFLM7iq5DeyCEIKBtIIY8A9npWTXa+Qb74ertVuP5wNAg7BzVt+nfNpCstJrr1qcz+6NoF94TIQQ+rYMozDOQm1E1g6IoXDh2mlb9OgMQOrgncXujADgRcYiQPp1w8lDP4NvpHM2WPfNMPA5eHth7umNhaYlf724kHYqs0ibp0DH8+/dCCIFrcBDFefkUZKrvz5CeSdLR4wQM6me2zNXF7Imi621q/fjXon4cdI40b+2PhWXDXKF6bPdxepXVf1C7QAy5BrJM5G8e4lc+y9NYnNwbRYfBau37tQmiIM9Ajonajz92mrb91drvOKQnJ8tqXwgoMhSiKApFhiJsHe2w0Jjv97Br/wky9LlXXT5yWDd+XLMDgP1H4nB2ssPbU0ePzsGciU8i/kIKxcWlrPplDyOHdTdXbOCfse+e3htF+7L68W1z9b7z/LHTtCnrOzsM6cnpsvqpLHb7YdqFdTNL7iuact+ffTYeW09PbD09sLC0xKtXD9KOHqvSJu3IMbz79kYIgXPLFpTkGyjUZ5GfmIRTiyA0WmssNBp0rUNIPXzUbNmh6dfO+QPHCB6o5vdsFURRnoH8zJr5Lx8/RVDvLgAED+zF+QPqPm7r7IhHcAAWlhqz5gbIO3cOG09PtB5q7bh074E+smrfY+XkhH1gIEJTM19RZiZZUVG49+9vrsgNSgjz/tdY3fAnuxAiUAgRK4SYDxwGZgohDgghjgkh3ihrYy+E2CCEiBRCHBdCjBNCPAX4AFuFEFvL2g0TQuwRQhwWQqwSQjiUPd9DCLG7bP39QghHIYSdEGJl2eusEELsE0LUyyd8VnoWOo+Ks9o6d53JA8DrceCPvbTu0bauol2X3PQsHN115Y8d3XXkVstvyMnDxt4Wi7LOwMFNV95ZZ15OoSA3nxWvzGPps+8TvWW/2bIbMvXYulZsextXFwzVOmFDph5bt+pt9ABEfb+a9hPubtC9Ljutav04u+tMDoAaq6y0LHSeFfWj89Chv8FB/LmYeN759wd8MWMhl88l/v0KdSQnPQsnj4rsTu46cqrXfnbV2nes1Kb7yDDSLibx6YMzWTTtXYZNuRdh0Xi+Kunj7UpCYnr540tJGfh4u+Lj7ULC5UrPJ6bja2Jmrj79E/bdnOp9p5vp+tE6VKofE22KC4o4eziW1n071X/oSppy31+oz8SmUv1oXXQUZmZWbZOpR1u5jauOwkw99r4+6E+dpjg3l9LCItKjjlOYUXXd+tbUayc/Q499pX3Tzk1HXoa+SpvCnDys7Sry27u5kJfR8J9txXo9Vi4VYOEwLAAAIABJREFUJ+OsXXQU66//939x5Qp8770X0ZiP1qU6V9vvALUGJgM/A/cBPQEBrBdChAEewGVFUe4EEEI4K4qSJYR4FghXFCVNCOEOvArcpihKnhDiReBZIcQcYAUwTlGUA0IIJ8CAOnuUqShKRyFEe8Dk6R0hxBRgCsDChQvxGl7zGta/pZj4udz4jhF39DQH/tjLE5+Y7xpqUM/SVFdjv75GG2OpkZQzFxn75pMUFxWz7IVPaNY6EFdfz3pIWz3X9bQxlV2QdCQKrZMDuiB/UmNO1X2262TyLTShjtV0/Vx//uYhfsxe9ho2tlqO741h0WvfMGvpK3UZ8epMZa/e5BpFdvZwLF4t/Hjg3elkJqbxw6tf4N++BVo72zoOWjum+iFFUUz+fkxsivr1D9h3Tb2Jmpv279vEHTiOb9sgs17CBP/Avr9aeFP7rhBg79MM/xHDOfrhp2hstDg092uAExdNvXZqPle9vzFZX/UV6IaYLJ7rWlN/7BhWjo7YBwSQc/Jk3cZqpJrQ4Ui9qu0A6LyiKHuFEB8Cw4AjZc87ACHADuBDIcR7wK+Kouww8TN6A+2AXWUf3tbAHtTBVaKiKAcAFEXJBhBC9Ac+LXvuuBDimImfiaIoi4BFVx6uO//7db2h3et3sO839VrW5q390adWnD3Qp+lxcnO6rp9zReLZy6z+ZDmPvv0Y9k7135Ed2RBBVNm1xN7B/uSkVZy5yUnTY+/qXKW9rZMDBXkGjKWlWGg05KbrsXdR2zi46bB1ssfKRouVjRa/0Jakxl8yy4egrasOQ6UzdwUZmdi6VMvu6oIhvWobG50zl/YfJvFwFEmR0RiLSygxGDg4/1u6T51c77n3rN/B/t/V7e/Xqmr9ZKXpcXK9sfoxt+0/72TXBjV/QGt/9CkV9aNP1eN8A/Vva29T/u/2vdux4tPV5Gbllt8koa4d/DWCI3+o2Zu18ic7tSJ7dpoeB7eq9WNXrfZz0vQ4lrWJ3LiPvmOHqpdo+Xig83Ij7WIKvq0D6iX7jbqUlI5fs4rLJn29XUlMzsTayhI/n0rPN3Pjcop5z4A31X330IYIIv8sq5+Qan1nuh4HE31nYW6l+jHRJibCfJcw/VP6fq2LCwWV6qcwU49Wp6vSxsbFpcrMTmGGHuuyNj5h/fAJUy+fPLPmZ2xcqq5bH5p67cT8sZ2Tm3YD4B4cQF6lfTM/XY9dtWw2Tg4U5Vfkz0vPrNGmIVjpXCjOzCh/XJSpx0p3fb//vDNx6CMjyTp+HGNxMaUGA+e+/pqgRx+tr7hSI1HbAVBe2f8F8K6iKAurNxBCdAPuAN4VQvylKMrs6k2AjYqiTKi2XkduZjhfS31HD6Dv6AEAxO6LZve6HXQe1JULJ85ja2+Lk9v17+SZKZl8N/sbxr/wAB5+ZjhzBnS5M4wud4YBcPZgNEc2RNBmQFcST8Wjtbep0ckKIfDvEMKpXUdpE9aN6C37Ce7VAYDgXh3Ysmg1xtJSSktKSTx1nm6jB5nlfehaBJCblEJeShq2rjoS9h6qcRDk3bUDZzdux7dPdzLPxGNpZ4uNizOh4+4idNxdAKTGnCLut01mOYAC6DN6AH3K6ufEvmh2r99Bp0FduXjiPDZ2N1Y/DWHgXf0ZeJd6/fPxvdFs/3kn3QZ3IT5WrX/nG8iflZGNk4sjQgjiY8+jKEq9ngToPjKM7iPV2j+9P5qDv0YQOrArl07GY2Nvg6OJ2g/sEELszqOEDuzGsc37aVVW+06eLpyLPIl/+5bkZmaTcSkFFxPf02ooGzYe5vGHhrFy/W56dgkmOyefpBQ9qenZBAd5E9Dcg8tJGYwd1YeHn/rcrNma6r7b7c4wupX1nXEHojn8awRtw7py+WQ8Wrur9J0dQzix6yjtwroRtXk/IWX1A1CQZ+Di8ThGPfegWfL/U/p+x6AADMkpGFLT0LroSN53gNDHqh6EunXuyKXN2/Ds1Z3ss+ewtLNBq1PfX1F2NtZOThSkZ5B26AhdXzF9F8W61NRrp93tA2l3+0AALhw6TuwfEbTo143U0/FY2dli51Izf7PQVpzbe4SW/boTt30f/j06miXrtdgHBlKQkkJhWhpWOh2ZBw8Q9Oi/r2td37vvwffuewDIOXmS5I1/ycHPLeJmb4P9J/CmEOIHRVFyhRC+QHHZz81QFOV7IUQu8HBZ+xzAEUgD9gJfCCGCFUWJK7s7nB9wAvARQvQouwTOEfUSuJ3Av1C/Q9QO6EA9adOzHSf2x/Lew29hrbVm7PMVY7SvX1nIfc+Ox9nNmZ1rt7N91RZyMnL4+LH3adOzHWOfHc+m7/8kPzuPtWV3/7LQaHj6i+fqK24NQd3acfZgNF8/PhsrrTXDp99fvuyn2QsYNm0CDm7ODHhoNBs+XMyuHzbg2cKP9mW3+3Vr7k1gl7YseWoOwsKCDkN74x7gY5bsFhoNHR8ax+73P0cxGgkY2AcnPx/ObY5Q39uQMLw6tyc5MpqNz72OpbU1XaaY58PierXu2Y4TB2L5YPJbWGmtGftcRf18++pC7n1mPE5uzuz6Wa2f3Iwc5j7+Pq17tuO+Z8aTk5HNZ9M/ojC/ACEEO3/ezrOLXsKm0sxKfQrt1Y7ofbHMeuBtrG2seeCF8eXLvpixiPufH4fO3ZmtP0WwafkWsjNyeOffHxDaqy33Pz+eI9sj2bF+FxqNBiutFY+8Osls11YH92hH3MFovvj37PLbYF+x7PUFjHxqAo5uzgyePJq17y9m29INeLfwo/NwtfYHjL+d9Z98z8Kp7wIw+OHR2NXTzJUpSz6bzoA+bXF3cSRu3+e8+fFqrMpuT/zV95v4Y8sRhod3JnrHXPINhTz2vHruqbTUyDMzF/PL0pfQaCxYsmIbsacSzJYb/hn7bsvuat+5cIpaP3c8XVE/K2ctYMR0tX7CHx7NuvcXE/H9Brxa+NFxWMWt0k/tOUZQlzZY22jNnr+p9/2tHhhH5MfzUIxGmvXvi72vD5e2qvXjGx6GW8f2ZBw7zt4ZM9FYW9PmkYfK1z/+xSKKc3MRGg0hD0zAyt68l5A19dpp3jWUhCPRrJr+BpbWVgyY9kD5sj/fmU//xydi76qjxwNj2PrJtxxa9ituQc1pPbgPAPmZ2ayb8T7FBvVz6/iGbdz7yStYm+HyYaHR4D9+Aqc/nYtiNOLerx+2Pj6kbt8OgMfAgRRnZRH7ztuUFqj5UjZvInTWG2hsG8flzeYk/w6qSpi6pvOaKwgRiHpZW/uyx08DV4baucADQDDwAWBEHRA9oSjKQSHEdGAa6iVu4UKIwcB7wJW9/VVFUdYLIXoAnwG2qIOf21BnhZYArVAvuWsPjFcU5fQ14l73JXCNzZgA9R72i0782cBJamdKm+G8eGBzQ8eotfd6DGFtfNOsHYC7A0ew6dJvDR2jVm7zvQOApXFNs/YfDFZv+23rP+FvWjZOhgvLmvy+++2pplk7AJNbDW/S/T7A47u2NnCS2lnQLxygydbP5Fbq9n//2MYGTlI7L3QcysRt2xs6Rq39OGggNJavRV1D3592mvUborvv6d8ot8kNzwApihKPOvi48vhTyr6bU8kZ1Nmh6ut+hjqwufJ4C9DDRLsDqN8RKieE0AAPKIpSIIRoCWwGzt9ofkmSJEmSJEm6FcmbIKhu9hI4c7JDvfzNCnWE/YSiKEUNnEmSJEmSJEmSpCakyQyAFEXJAcz7l/0kSZIkSZIk6R9CNJ4/bdeg5GaQJEmSJEmSJOmW0WRmgCRJkiRJkiRJqj35HSCVnAGSJEmSJEmSJOmWIWeAJEmSJEmSJOkWYK6/y9fYyRkgSZIkSZIkSZJuGXIGSJIkSZIkSZJuAXICSCVngCRJkiRJkiRJumXIGSBJkiRJkiRJugXIGSCVnAGSJEmSJEmSJOmWIWeAJEmSJEmSJOkWIGeAVEJRlIbOUJ/+0W9OkiRJkiRJajQa/fAi/LddZj023npHv0a5TeQMkCRJkiRJkiTdAiwa5XDE/P7xAyB90W8NHaFWdNZ3ADA/5q8GTlI7U9sNo9uyHQ0do9YOTRjQZLc9qNt/TuTGho5RKzM6DQVo8vlfPLC5gZPUzns9hmDrP6GhY9Sa4cIyMgrXN3SMWnPVjibJ0DTze9uOBqDF/O0NnKR2zk4dCMDhtA0NnKR2urrfCcDyM380cJLaGd/ydvw/3trQMWrtwrPhDR2hSRJC3A58CmiArxRFmVNt+f3Ai2UPc4EnFEWJvNnXlTdBkCRJkiRJkiTJrIQQGuALYATQDpgghGhXrdk5YKCiKB2BN4FFdfHa//gZIEmSJEmSJEmSGt0lcD2BOEVRzgIIIZYDY4CYKw0URdldqf1ewK8uXljOAEmSJEmSJEmSVOeEEFOEEAcr/Tel0mJf4GKlxwllz13No8DvdZFLzgBJkiRJkiRJ0i3AQpj3BsmKoizi6petmZqPMhlQCBGOOgDqXxe55ABIkiRJkiRJkiRzSwCaV3rsB1yu3kgI0RH4ChihKEp6XbywHABJkiRJkiRJ0i2gkX0H6AAQIoQIAi4B44GJlRsIIfyBn4AHFUU5VVcvLAdAkiRJkiRJkiSZlaIoJUKIJ4E/UW+D/Y2iKNFCiMfLli8AXgPcgPlCCIASRVG63+xrywGQJEmSJEmSJN0CGtvdzxRF+Q34rdpzCyr9+9/Av+v6dRvbdpAkSZIkSZIkSao3cgZIkiRJkiRJkm4B5r4LXGMlZ4AkSZIkSZIkSbplyBkgSZIkSZIkSboFNLK7wDUYOQMkSZIkSZIkSdItQ84AmaAoCh/PWcvuHbHY2Fgx860JtGnX/KrtP3xnDb/+vJ9t+9+r1fp1TVEUtn+9hvhD0VhqrRk2/QE8W9Z8/azkNH7/aDEFufl4tvBj+NOT0FhVlETS6fOsnPERI56bTEjfLmbL36eZC893bYFGCH4+k8Ti2IQqywf6uvJEx0CMikKpUeGjw2c5mpZ9Xeuaw81u/4Tjp/nl3UU4eboBENy7E73GjTBr/n3fribhiJq//9QHcW9RM39OShrb5n5LYW4+bkHNCZs+CY2lJfpLSeyc/z3p5xLoOn4kHUbfJrNfp+TIaKKWrkIxKgQM6kur0cOrLFcUhailq0g+Go1Ga0XXKZPQBflXLDca2TZzDjYuOvo8P9Ws2QEWfPAYI4Z0ITU9m+5DXzDZ5qM3HmJ4eGfyDUVMee5/HD0eD8DQgZ34cNYkNBoLFi/fyofz15sxuUpRFD55bx27d5xQ++43x9G6nV+Ndm+/vpIT0QkoioJ/gAevvjUOOzstuTkGZr20jOQkPaWlRiY+NJCRd/Uwa/55769j384TaG2seGn2OFq1rZn/vVkrORmj5m8e4MGM2Wr+K2KPX2TqpM94/b0HGDS0o9nyhzV34bX+wVhYCFbGJLLgyMUqy28LdOPZXoEYFSg1Kry5M46DSWrf/3BHX8a1bYYQsCImkW+PXTJbblC3/ZK5azm6JxZrG2ueeGUCQa1rbvvPZ33P2RMX0VhqaNnOn3+/MBZLSw2Xziez8O3lnDuVwLgpdzByYrjZ8/++8CdOH4jBSmvFXc/ej09wzb5z3y8R7P15OxmJabyw7G3snR1uaP36MjDQlVmDQtBYwPKoROYfuGCyXUcvR9ZN6Ma0DdH8djqVZg5aPhnRFg87axQFfoy6zDdHzH/cYE5y5kN1Q9tBCKETQkwt+/cgIcSvN7j+YiHEfTeyTm1f62bs3hHLxfOprN7wMjNe/xfvv7X6qm1joy+Qk2Oo9fr1If5wDPrLKTw0/zWGPDGeLQtXmGy367v1dBkVzsPzX0Nrb0f05j3ly4ylRnZ9tw7/zm3NFRtQp2ZndGvJU9uiue+3QwwP8CDIya5Km/3Jesb/fpiJfxzhjf2nmNkr5LrXNYe62P4+bVty/yczuP+TGWYd/AAkHIkhOymVe+e9Tt8pE9jz1XKT7Q5+v47QO8O5b97raO1tOb1Fza91sKfX5LG0HzXYnLGBpp1dMRqJXLKCPi88yZD3Z5Kw9yDZlxKrtEmOjCY3KYXbPppF50fvJ3Jx1fd35o+tOPp4mzN2FUtXbWfMpDlXXT48vDMtA71pH/YMT874knlvPwqAhYVg7luTGfPQe3QZ8jxjR/elTYivuWKX27PzBBfPp7Hq1xeZ8dp9vP/WTybb/ff/RrN09bN8v+Y5vJrpWL1sFwCrl+8mqKUXS1c/yxdfP868D3+huLjEbPn37TxBwoU0flj/Is/PvI+P3zad/8nnR/PNymf5dtVzeHrrWLt8V/my0lIjCz/dQI8+rc0VG1D77zfCQpi8IYrhyw4wKsSTYJeq/ffuhEzuWHGIkSsP8eLWk7wbrmZs5WrHuLbNuHvNYe5ccZDBAW4EOtuaNf/RPbEkJaTxyYqX+c8LY/n6Q9Of+/2GdeWjZTN4f+n/UVRYzNZf9gLg4GTHQ8/czcgJ5h34XHH6YAzpl1J56qtXGfXUeH79fJXJdv7tWjDpnanoPF1rtX59sBDw1uBWPLQ2kiGL9zO6jRchrjU/+y0EvDSgJdvPZ5Q/V6oovLU9jiFL9jNm2SEmdfY1ua70z3OjA0EdYP7TimYWsfU4I0b3QAhBh06B5OQYSEvNqtGutNTIvI9+Yfqzo2q1fn05uz+KtuE9EULQrHUQhXkG8jKqvr6iKFyMOkVI384AtAvvxZl9x8qXR/62neA+nbErO7tjLqGujlzMLeBSXgElRoW/LqQyyK9qR2soMZb/21ajQVGuf11zqIvt35AuHDxGcJia37NVEEV5BvIza+ZPjD5FYG91ZjB4UC/OH4gEwNbZEY/gACw0Gpn9BmSeicfBywN7T3csLC3x692NpEORVdokHTqGf/9eCCFwDQ6iOC+fgrL3Z0jPJOnocQIG9TN79it27T9Bhj73qstHDuvGj2t2ALD/SBzOTnZ4e+ro0TmYM/FJxF9Iobi4lFW/7GHksJv+O3c3LGJrNCNGdUMIQftOAeTmFJCWml2jnb2DDaDWUmFBMWV/nA8hBPl5hSiKgiG/CCdnOzQa851v3bktmuEj1fyhHdX86X+Xv7AiP8BPy3YxcEgHXFztzZYboJOnE+ezDFzMLqDYqPBrXApDg9yqtMmv3PdbalBQO/+WLnYcTc6moMRIqQL7LusZFuRu1vyHdh5nwO3dEUIQ0j6Q/BwDmWk1t32Xvu0QQiCEILitPxkp6v7r7OJIy7b+aCwb5vz8ib3H6TxEPW5p3iaQgjwDORk1j1uatfTDxcut1uvXh87eTsTrDVzIUmvnlxPJDGtZ8/c/ubMfv59OJT2/qPy5lLwijqeofVZecSlx6Xl4O2hrrPtPYiHM+19jdaN72hygpRDiKPAB4CCEWC2EOCGE+EGU9aJCiNeEEAeEEMeFEItE5d61zNXaCCGChRCbhBCRQojDQoiWZauYfK36kJqShZe3rvyxp5eO1JSaO/KqZTsIGxSKu4dzrdavL7npehzcXMofO7jpyK3WERXk5KG1ty0/0HNw15GXnlW+/pm9x+gwvL/ZMl/haaclOb+w/HFyfhEetjU7o3A/N9bc2Y1PB4byxr5TN7RufbvZ7Q+QdPIcPzzzLj/Pnk/6haqzAPUtP0OPvXtFfns3HfkZ+iptCnPysLaryG/n6kK+mT7srqUpZzdk6rF1rchu4+qCodrgzZCpx9atehv1/UV9v5r2E+6G+usab5qPtysJienljy8lZeDj7YqPtwsJlys9n5iOr5eLqR9Rr1JTsqv03R5ezlftu9+auYI7w2dzPj6VsRPUQed9E/oSfy6FUUPe5IF7P+KZF8dgYWG+A9q0lGw8rzP/u6+t4O4hs7lwLpV7xqv5U5Oz2LH1OKPH9jFL3sq87a1JzK3ovxNzC/Gyr9l/DwtyY+OEHnx9Z3te3KL2/acy8unp44xOa4mNpQWDAtxoZuaD2IzUbNw8K7a9q6eOjGuc+CwpKWXHnwfp1KuNOeL9rZw0PU4eFfmd3J3JTrv+fvFm178Z3g5aLucUlD9OzC3Ey7Hq79/LwZrhIR58f41LI/2cbAj1dORIUs2Bq/TPc6M98wzgjKIonYH/A7oA/wXaAS2AK6ceP1cUpYeiKO0BW2CkiZ91tTY/AF8oitIJ6AtcOfq72mtVIYSYIoQ4KIQ4uGjRoht8eypFMXWP9KoHFakpWWz+K5KxEwfUav36ZeL1q728yYxlbbZ/vYZ+k0ZjYcYzl9UiVGFqa25NSOfeDYd4bkcMT3QMuKF169/NbX+PFn5MXjSb+z95iU53DuSXOV/WfcRrMVm+1bduI/07Av+07DXa1GwkhCDpSBRaJ4cq3wdqjISJvVRRFEydzzLZjda3q2xfU159cxy/bJ5JYJAnm/5UZ+r27TpFSGsfftk8kyWrnuGjd9aSl1tgcv36YKpfuVr+l2aPY83GmQQEebKlLP9nH6znsafvMOusVTkTMU3VwF/n0hm67ACP/R7Nsz0DATiTmc/CIxf5bnRHFo/swIn0XErNXECmt/3V23/z4WradGpBm84t6jHV9TPddV7/ccvNrn8zTH72Vws0a1AI7+44g/EqZWFnpWHhqPa8se00uUWldZ5Ranxu9iYI+xVFSQAomxUKBHYC4UKIFwA7wBWIBn6ptm6NNkKIbYCvoihrARRFKSj72dd6rSoURVkEXBn5KPqi367rjaxatpN1a9TvAbRr709yUsVZ45RkPR6eTlXanzyRQMKFNO67820ACgqKufeOt1nz2yt4eun+dv26FvlbBMc37gbAK9if3PTM8mW56XocXKrOUtk6OVCYZ8BYWoqFRkNumh57V7VNypkL/P7RYvV95eQSfygGC40FLXt1qtf3AJCcX4hXpS/jetlZk2YovGr7I6nZ+DnYorO2vOF161Jdbn+tXcW160HdQtm6cCWG7FxsnervcsTYP7ZzarOa371lAHlpFfnz0vXYVcuvdXSgKL8if35GJnauVduYS1POXpmtqw5DRkX2goxMbKvXjasLhvSqbWx0zlzaf5jEw1EkRUZjLC6hxGDg4Pxv6T51stnyX49LSen4Nau4fMbX25XE5EysrSzx86n0fDM3LqdkmvoRdW718l2sX7MPgLahzav03anJWbh7XL3v1mgsGHJ7J35YvJ2Rd/Vgw7oDPPhIuHoZkL87Pr6uxJ9LIbRD/Q1M1y7fxa8/qflbhzYn5QbzDx7eieVLtnPHXT04GXOR2S/+AECWPo+9O0+g0VgwYHD7est/RVJuUZVZm2YOWlLyr95/H0jMwt/ZBhcbSzILSlgZm8TK2CQAnu8VRFJu/ff9f63ZyZb16nd4WrRtTnpKxbbPSNHj4m66X1n9zZ/k6PP49ztj6z3jtez7ZQeH/1SPe3xC/MlOrcifnZaFo9v1H7c4uetuav2bkZhbiI+jTfnjZg5aUqr9/jt4OfL5He0AcLW1IjzITb1U/kwalhaChaPaszY2mT/i0sySuSEJ+YdQgZsfAFWusFLAUghhA8wHuiuKclEIMQuwqbzSNdpc63RBjde6yexVjJ3Qn7ET1Eu+dkZEs/rHnQwb0YXjx87j4GBb4zK3/mGh/L5tdvnjQT1fZM1vrwAwIDz0b9eva53uCKPTHWEAnDt4nMjfImjVvxtJp+LR2tmUH1xfIYTAr30Ip3cfpfWAbsRs3UeLnh0AmLzwjfJ2f81bSlD39mYZ/ADEZOTQ3NEGH3stKYYihvl78Mruk1Xa+DnYkFB2VrWNiz1WFgJ9UQk517FufanL7Z+XmY2dzlE9s38qHkVRsHGs3+vx294+kLa3DwTg4uHjxP4RQVC/bqSejsfazrbGIEIIQbPQVsTvPUKLft2J27YP/+7mu1tUZU05e2W6FgHkJqWQl5KGrauOhL2HagxgvLt24OzG7fj26U7mmXgs7WyxcXEmdNxdhI67C4DUmFPE/bap0Q1+ADZsPMzjDw1j5frd9OwSTHZOPkkpelLTswkO8iaguQeXkzIYO6oPDz/1uVky3Te+H/eVXQK2KyKW1ct2MXREZ6KPXcDe0abGAEJRFBIuptPc3x1FUdi5LYaAQA8AvLx1HNwXR+duLchIz+H8+VR8/Wp+X6Iu3T2+H3eX5d8TEctPK3Yx5PbOxERdwN7BBjcT+S9dTMevLP/uiBj8g9T8K357ubzduzOX0yesnVkGPwDHUrIJdLbFz9GG5LxCRgZ78t+NsVXaBDjZcD5b7ftD3R2wsrAgs0C9yYSbrRXphmJ8HLQMb+HOvT8dqffMw+7tz7B71eOGw7tj+GvNTvre1oW46PPYOdjg4l5zALBl/V6O7TvJq/MeN+vlkab0GjWAXqPUq1hO7Y9m3y87aD+wKwknz2Njb4PjDZwYatOr/U2tfzMik3II0tnS3MmGpNxCRrXx4qnfoqu06f/13vJ/fzS8DZvPpvPXGXWw88GwNsRl5PHV4ap3HZT+2W50EJEDOP5NmyuDnTQhhANwH1D9digm2yiKki2ESBBC3KUoys9CCC1g9m8j9xvQjt0Rsdx7x9vY2Fgz863x5cv++8QiXnljHB6eV9+xr7W+OQR2CyX+UAxLnpiNpdaKodMfKF/285v/47ZpE3Fwdab/pDH8/tG37PnxVzyC/Ai9zfzXfVdXqsD7B8/w+aD2aIRg3dlkzmbnc2+wemerNXFJDGnuzp1BnpQYFQpLjby068Q11zW3m93+cXuOcOyPnVhoLLC0tmbEcw+b7VICAL8uoSQcjmbNU2+gsbZiwNSK/H+9O5/+j03EzlVH9/vHsG3utxxe/ituQc1pNVjNn6/P5pcZ71NsKEAIQcxv27j741ewtqv/uzI15ewWGg0dHxrH7vc/RzEaCRjYByc/H85tjgAgaEgYXp3bkxwZzcbnXsfS2pouUx6s91w3Ysln0xnQpy3uLo7E7fucNz9ejVXZrfW/+n4Tf2w5wvDwzkTvmEu+oZDHnl8IqDeUeWbmYn5Z+hIajQVLVmwj9pT5b0Xbd0Abdu+eE/xiAAAgAElEQVSIZeydc9DaWPPqm/8qX/bs1K95adZ9uLk78uary8nLLQRFIbi1Dy+8eg8Akx+7jbdmruD+ez4CRWHaf+9A52K+mwn0HtCGvTtjmThKzT/jjYr8L0z7mhdevw9Xd0fenbmcvDw1f8tWPjz7yj1my3g1pQrM2hHHklEdsBCCVSeSOJ2Zz8TQZgD8GJ3I7S09uLu1FyVGhYISI0/9FVO+/vzhoehsLCkxKrwecZrsQvPdfQ+gS5+2HN0Ty3//9Q5aGysee3lC+bL3nlvEf2aMw9XDma8/XI27lwuvTZkHQI+BHbj3keHo07N55dFPMOQVICwEv6+M4IMfXsTO3uZqL1mnQnq049SBGD599E2stNbc9czE8mXfv7aA0U9PwMnNmb3rtrNr9WZyM3P437T3COnejjH/nXDN9etbqaIwc+splt7bCY0QrDieyKn0fB7o6KPmP3b5quv28HHm3nbexKbm8vsD6o1X3t91lq3nMq66TlPXmG9MYE7C9PdVrrGCED8CHQEDkKwoysiy5z8HDiqKslgI8RYwHogHLgLnFUWZJYRYDPyqKMrqa7QJARYC7kAxMBbwB5439Vp/E/e6L4FrbHTWdwAwP+avBk5SO1PbDaPbsh0NHaPWDk0Y0GS3Pajbf07kxoaOUSszOg0FaPL5XzywuYGT1M57PYZg6z/h7xs2UoYLy8goNP/fEKorrtrRJBmaZn5v29EAtJi/vYGT1M7ZqepM8uG0DQ2cpHa6ut8JwPIzfzRwktoZ3/J2/D/e2tAxau3Cs+Fg3i9818q/tkaY9Rq4leFhjXKb3PBlZIqimBzWK4ryZKV/vwq8aqLNw9fR5jT/z959h0dR7X8cf59seiG9EEJCSOgtSJfeBRvq9QJyxXJt144iigjSRbAgNsRywUITVFD0R++9k4ReQgik9972/P7YkGRJ8EIkGzDf1/PkSbJzZvPZzZmZc+acmYUrP4TjLLCpsr8lhBBCCCGE+N/kg1BN5H0QQgghhBBC1Bo39EYCQgghhBBCiJuTldwFDpARICGEEEIIIUQtIiNAQgghhBBC1AJyFzgTGQESQgghhBBC1BoyAiSEEEIIIUQtICMfJvI+CCGEEEIIIWoNGQESQgghhBCiFpBrgExkBEgIIYQQQghRa8gIkBBCCCGEELWAfA6QiYwACSGEEEIIIWoN6QAJIYQQQgghag2ZAieEEEIIIUQtIDdBMFFa/63nAv6tX5wQQgghhLhp3PTdiye2bbJo2/irbr1uyvdERoCEEEIIIYSoBeTaF5O/fQfoqW2bajpClczr1guAYRu31GyQKlrcuwdpBb/XdIwqc7MdzMu7NtR0jCqb3bnPLZt/duc+AIzfv66Gk1TNlHb9APjvydU1nKRqHms8kJT8lTUdo8o87O7BIXB4TceostzoRbRfvLWmY1TJvmHdATiW9lsNJ6maZm53AfDols01nKRq5vfoCUDfP7bXcJKqWT+oK1mFt+ZxC8DZpk9NRxDX4W/fARJCCCGEEELIbbAvk5EwIYQQQgghRK0hI0BCCCGEEELUAnIXOBMZARJCCCGEEELUGjICJIQQQgghRC0gI0AmMgIkhBBCCCGEqDVkBEgIIYQQQohaQEY+TOR9EEIIIYQQQtQaMgIkhBBCCCFELSCfA2QiI0BCCCGEEEKIWkNGgIQQQgghhKgF5C5wJjICJIQQQgghhKg1pAMkhBBCCCGEqDVkClwlUsIjOL1oKVobqdu9G4GD7zBbnhMbx/Fv5pMVfYHg++6l/h0DrnldS8iIjCBm6WK00Yhn1+743THIbHleXCznF8wn90I0de8Zgu+AgWbLtdHIiXemYuPmRshzL1oyuunva80HM35mx9Zj2NvbMH7qcJo2r1+h3NQJizkWeQG0pn4DbyZMfQhHRzsy0nOYOmExFy8kYWtnw1uThxHSqK7F8iceieTYD0vRRk1Az66E3HXF+6s1x35YSuLhSAy2trR6ciSuDQLJio3j0Gdfl5bLSUii0f13ETywr8Wy3+r5Yw9HcujbZWijkeDeXWl2zwCz5VprDn77I3GHTNk7PvMw7sGBFBcUsnHyhxQXFaGLiwno1JaW/7jLYrnL51s3bzln9h/Fxs6WO18agV9oxbqfFpfMilnzycvMwTckgLtfeRiDjTW7f1pP5KZ9ABiLjSTHxPHi99NxcHGyWP4P313Bjq3HTdvulKE0aR5Qody0t5dyPDIGrTWBQd68NXUojo52ZGXmMnHsIuLj0iguNvLQIz25a0gHi2SfO+tpBvVtS2JyBu37j6m0zPuTHmFg7zBycgt46tXPORQRBUD/nm14b+JIDAYr5i/eyHufrbRI5it18XNn9G0NsVKKX87GseBYjNnyO4K8eaSZqT7lFBUzY99pTqVlAzCssT/3NfQDBb+ciWPRyUsWz6+15qsPfmH/jmPY2dvy4vhhhDStWH8+mPA9p4/FYG1toFHz+vxn7INYWxsACN9/mq8/XEFxUTF13JyYNvc5i2RPjYggavEStNGIb/du1BtkftzNjY3l9PwFZEdHEzhkCP4DTfum/JQUTn/zDYXpGaAUvj16ULefZff5AB283HiuWUOsFPweE8/isxfNlvf192ZYcD3TaykuZnbkGc5m5gAwulUonb3dSSso5IlthyyeHUx1Z9Y7S9m+NRJ7e1smThtJs+aBVy0/c/oSVv68k217Z5s9HhkexaMjZvLOe0/Qb8Bt1R27RsjIh4l0gK6gjUZO/bCI1q++jJ27OwemvINnWGuc/P1Ly1g7ORL60DCSDx667nUtkf/CooWEvjQKG3d3TrwzDdfWbXAol8Hg6ETA0GGkH6p8R5W4YR32fnUpzsu1VGwzO7Ye48L5RJatepOII+eZOXUZ3ywcVaHcy2OG4OxsD8Dsmb/w48KtPPJEP+Z/tY7GTf2Z+dHjRJ2NZ9b05Xz61bMWya6NRiK/XUzHMS9i7+HOjokz8GnbGpd6ZR2wxCORZMcl0GPmJNLOnCNywSJuf/t1nOv60W3KuNLn2fDyWPzahVkk998hv9Fo5MB/l9Jz7As4eLqx7q2Z+N/WCteAsuxxhyLJiktk0AcTSTkdxf5vFtNvyhisbKzp+daL2NjbYywqZsOk96nbpgWejYItlh/g7P6jpF5K5OkvxnPpRBSrP1/KI++/WqHcpvkr6HBvL5r3aMf/fbqEw2t3ctvg7nS6vy+d7jc1nk7tCWfvik0W6/wA7Nx2nAvnk/jxt9eJPBLNzKk/8fXCiidRXn7tHpxKtt2PZq1k2aLtjPx3H5Yt3kFwiC/vffI4qSlZDL1nJgPvbIuNTfUfqr77cTNzF6zmqw8r31cM7B1GSAM/WvYYRce2ocyZ9m963DseKyvF7KmPceeI6VyMTWbbr9P4be1+jp+6WOnzVBcrBa+3D+G5jRHE5+bzbf8wtlxM4VxGTmmZS1l5PLX+CJmFRdxe151xHUJ5dO1hQlwdua+hHyPXHqLIaGROz5Zsu5TChaw8i76G/TuOE3shic+XjeVkRDRzZy5n1jcvVSjXY2A7Rk0aAcAH479n7YrdDHrgdrIyc/li5k+8/dGTePu5k5aSaZHc2mjk3MKFNB81Clt3d8KnTce9TRsczdoNTgQPG0bKoYNm6yorK4IefBDnoCCK8/I4MmUqrs2bma1b3ayAF1s0ZMyeSBLzCvjs9jbsTEjhfFZZGyA2J49Ru8PJKiqmo5cbr7QM5fmdRwBYHZPAivOxvN66kcUyX2n71kguRCfwy++TiDhyjnemLOLbRa9XWvZoxHkyy20XlxUXG5nz4c906dq8uuOKm4B0BK+QcfYcDj4+OHh7Y2VtjU/H9iQfPGxWxrZOHeoEN0AZDNe9bnXLiTqHnY83diUZ3Dt0IP2IeUfHpk4dnBoEV8gPUJCaQnp4OJ5du1kqcgVbNkYw6J4OKKVo1aYBmZm5JCWmVyh3ufOjtSY/vxClTFf2nTsTR/tOjQFo0NCX2IspJCdZ5kCYdjYKJ19vHH1M73/dTu1JOGBeBxIOHKZe184opXAPbUhRTg55aeavLynyOI7eXjh4eVok92W3cv6U01E4+3rj7OuFwdqawC7tuLT/iFmZi/uP0KB7J5RSeDYKpjAnl9zUdJRS2Nib6pOxuBhjsRFq4ELRU7vCadmnI0op6jUNJj87l6wU8/dWa835I6do2tXUuWzVtyOndoVXeK5jmw/QvEc7i+S+bMvGSAbd3Q6lFC3bBJGVmUdSYkaFck7lt928sm1XKUVOdj5aa3JzCqjj6ojBYJnD1PY9x0lJy7rq8rsGtGPh8q0A7Dl4Gtc6jvj5uNEhLJQzUXFERSdQWFjMj7/u5K4B7S2SubwWHi5cyMzjYnYeRUbNmuhEetbzMCtzJDmTzMIiAMKTMvFxsAOgQR1HwpMzyS82UqzhQGI6vQO8LP4a9myJoNcgU/1p0iqI7MxcUpIq1p/2XZuhlEIpRaMWgSQnpAGwZfUBuvRuhbefOwBuHi4WyZ117hz23j7Ylxx3vTp0IPWQ+X7Tpk4dnIMrthts3dxwDgoCwGBvj0PduhSkpVkk92VN3Vy4mJ1HbG4+RVqzMTaR233M687RtEyyiopLf/a2ty1dFp6aQUZJvaopmzce5s57Ope0GxqSlZlDYiXthuJiI7Pf/4kXX72/wrIlCzfSt39b3C1Ub2qKlbLs183qhh5ZlFL/UkrtUUodUkp9oZTqpJQ6opSyV0o5KaUilVItlVLOSqn1SqkDSqlwpdS9Jes3UEodU0p9WVJ2jVLKoWRZh5Ln2qmUmqWUiriR2S8rSEvDzsO99Hc7d3fyr3Fn9FfWvVEKUtOwdS/bcdm6uVOYeu0ZLi5dQr37/wGq5vrGiQnp+Pq5lf7u4+tGYkLFHRnA5LcWMajXBKLOJfDPh7oD0KhJPTatMzV8I8PPExebSkK8Zf4Pealp2JerA/Ye7uRd8f7npaZh72leJv+KMrG79+Hf2TJTf8q7lfPnpqbhWC6Xg4cbuSlpV5RJx8HDzbxMSXaj0ciasdNZ+czr+LZqimeoZUd/ADKT03HxKsvn4ulGZrJ53c/NyMbO2QGrkoZUZWUK8wo4e+AYTW5vU/2hy0lMyDDbdr19Xa+67U4dv4Q7e0/mfFQiDw7vCsA/ht9O1LkE7u47hX898D6jXr8XK6ub4zydv58HMbHJpb9fjEvB388Dfz93Yi6Vezw2mXq+7pU9RbXycbAjPie/9PeE3ILSDk5l7m3oy47YVADOpGfT1rsOrrbW2Bms6FrXA1/Hq69bXVIS0/HyLas/nj6upFTSiL2sqKiYTX/sp23npgBcik4kKyOHcf/5jFdGfsjG3/dVe2a4fOwvd9x1dyM/LfW6nycvKYnsC9E4B1t23+Nlb0tiXkHp74l5BXjZX/3/P6i+L3sSLdu2+V8S4tPw9Svb7nx83Ums5Li/ZOEmevZujbe3a4X1N64/zAP/7FHdUcVN4oYdWZRSzYChQFetdRhQDDQBVgJTgZnA91rrCCAPuE9rfRvQG3hfXT4FCI2AT7XWLYA04IGSx/8LPKO17lLy3FfL8ZRSap9Sat+8efOu/4X8lc+Huik+W6qSENfYA08/chhrlzo4lpyNqilaV/ZGVv4iJkwdzqoNkwhu6Mva/zNNLRj5775kZOTyr3/MYunCrTRuWg+DtYUaUZVkL6va18ZYVETCwSP4dayB+ce3cv5Kq80V2f+kbllZWTHgnTe565NppJyJIv2C5a+BqOxFVHz7/3eZ03sjqNcs2KLT34Drqj9vTRnKr+vH0yDYh3WrTWfLd28/SaMm/vy6fjwLfhzF+9N/JtvC07CuRlWyD9JaV/r6Kq1m1a2St/lqMdr5uHJvQz8+PnwOgKiMXL49HsOnvVrxcc+WnErLprgGXsT1/skvZi6neVhDWrRtCJiueztzPIbxH/ybiXOeZOnXa7kYnVgNSa9QWb2/ziHk4rw8Tn4+lwZDh2Lt4HCjklVZ5cdhCPNwZVCAL1+eiLJsoP+hsrhXbpqJCWmsW3OAoQ/1qlD2vXd/5MVRQyw24lyTlNIW/bpZ3ciJ1X2BdsDekgOCA5AATAb2Yur0XJ4MroDpSqkegBGoB/iWLDuntb48Z2s/0EAp5Qa4aK13lDy+EKj0CmWt9Tzgcs9H79u26bpehK27G/kpZWdu8lNTsXNz+5M1bsy6N4qtuzsFqSmlvxekpWJzjRmyz5wh/cghMiLCMRYVUpybR9Q3X9Hg8SeqK26pHxdtY8XynQA0bxlIfFzZmZuE+DS8fepcdV2DwYp+A8P4fv5G7r6vE87O9kyYOhww7cTvu2MK/vUsMxXL3sOdvHJ1IC8lFTs38zNN9u5u5CVfUca97H+UeCSSOkGB2Lle/TVXl1s5v4OHGznlcuWmpOHg7lqhTPlRocrK2Do54tOsEbGHj+Jav/rn4e9ftYXDq011v26jQDKTyvJlJqfh7HHFa6jjTH5WLsbiYqwMhkrLHN1iuelvyxZvZ+Xy3QA0a1HfbNtNjE/Hy/vPt92+d7Thh/mbuWtIB1at2MvDj/dGKUX9QC/863kQdS6BFq2ufjGzpVyMSyagbtl+pJ6fB7HxqdjaWBPgX+7xup5cSrj+s/9/VUJOvtmojY+DLYm5+RXKhbo6Mr5jI17cHEl6Qdm0pRVn41lxNh6AZ1sHkZBTUGHd6vD7j9tYs8JUfxo1r09SubP2yQnpeFxxpv6yxV+tJj01izfefbT0MU8fV1zcnLB3sMPewY7mbRsSdeoS9QK9q/U12Lq7k59S7ribmobtdRz7jUVFnPh8Ll6dOuF5m+VPfCXlFZhNafO2tyU5v+L/v6GLI6+2CmHs3qM1PuUNYOmiTfy8bDsAzVsGER9Xtt0lxKfi5WP+Pzh+7AIx0YkMGTwBgLy8Au4dNIEVf0zmWOR5xr5muolPWmo227dGYDBY0buvZa/DFZZzI7u6CligtQ4r+WqitZ4IeADOgAtgX1J2BOANtCsZLYovt6z8HrsYUyfNYrMI6wQ3IDc+gdzEJNOZ7D378Ay7tmkkf2XdG8UxqAH5CQnkJyViLCoide9eXFtfWwb/++6n5YxZtJg+gwb/fgqXpk0s0vkBeHB4N75f9hrfL3uNHn1a8sfKvWitCT8chbOzA15XHAS11lwoObOntWbr5kiCgn0AyMzIpbBk57xi+S7C2oWUXi9U3VyDg8iOTyCnpA7E7t6HT9vWZmV82rbm4vZdaK1JPX0WawcH7Mt1MmJ37cW/s+WvIYBbO79HSBBZcQlkJSRRXFRE9M79+LdrZVbGv11rorbuRmtN8qlz2Dg44ODuSl5GJgXZpotiiwoKiI84QR1/38r+zA3X7s4ePD7ndR6f8zqNOrcmYsMetNZcPH4OO0f7Cp0bpRSBrRtxfLvpPFH4+j006lT2OvOyc7kQcZpGnc1fe3X5x7CufPvjK3z74yumbffX/WitiTh8HicX+wodINO2m1T687ZNRwlqYGqg+vq5sW/3aQBSkjM5fz6RegGWvQ7ualatPcBDD5im2XZsG0pGZg5xCWnsO3yG0GA/gup7Y2Nj4MG7u7Bq7X6L5zuakkl9F3v8neywtlIMCPRmy8UUszK+jnbM6tacCTtPEJ1pfpMbdzub0jJ9ArxYfd4CIyfA4Ae7Mfv7V5n9/at06tGSTX+Y6s+J8PM4Odvj4VWxA712xS4O7jrBq1MeNpsi2bFHS44eOktxUTH5eQWciowmoIFPtb8G5wYNyEtIIK9kv5m0dy/uba7tuKu15syCb3GoWxf/Af2rOWnljqdnUs/JAT8HO6yVonddb3YkmNcdH3tbJrZtyjuHTxGTc3OMyv5zeC8WLR/HouXj6NWnDatW7ippN5zF2dmhwjS37j1bsWbzu/y2Zhq/rZmGvb0tK/6YDMCvq6eWPt53QFveeGv437bzI9cAmdzIEaD1wAql1Ida6wSllAemTs/HwHggGHgXeB5wBRK01oVKqd7An8650lqnKqUylVKdtda7gGE3MLcZZTAQOmIY4R9+hDYa8evWFad6/lzatBkA/149KUhPZ/+U6RTn5oFSxKxbT4cpE7F2cKh0XUtSBgMBQx/izJzZaKPG8/auOPjXI2nLJgC8evSiMD2dE+9MpTgvD6UUiRvW0eztyRhugmF3gK7dm7NjyzEeGGzaQY2fWvbvfvk/8xg3aSieXi5MHreQ7Kx8NJpGjf0ZM/5BAKLOxjNx3A8YrKwIDvFl3KRqqy4VWBkMNH94GHtnfYw2GgnocTsuAf5Eb9gCQGCfHni3aUnikQg2vzYBg50trZ8YWbp+cX4BSRHHafHoCItl/rvktzIYuO3Rf7Jlxqem22D36oJrgD+n15kuXA/t1526YS2IPRTJ76MmYm1nS4en/wVAXloGez7/Fm00orWmfufb8L/NMh2I8kLaN+fsvki+eGoyNna2DH6p7H1cOnEug14YjounK70fvYcVM+ez5ftV+DYMoPWAzqXlTu48QnDbptj+yRz+6nJ796bs2HqMB++cgZ29LW9N+Wfpslee/ZqxE/+Bp5cLU95aTHZWPmhNaBN/xrxluiD5saf7MXX8Ekbc/z5ozXMvD8bN3TLT+BZ8/ALduzTDy92F07s/YcoHy0rvPvfV9+v4vw0HGdg7jMits8nJzefp0V8ApouqR42fz6/fjcVgsGLBkk0cOxnzZ3+qWhRrmLX/DB/3bInBSrHybDxnM3J4IMQPgOVn4niyRSCudta83j60ZB3NyDWmjvTMbs1wtbWhyGjk3f1nSm+WYEntujZj/45jPPPAO9jZ2/Di+LJ99+SXv+T5cf/Ew9uVz99djrefO68/MQeALr1aMfSJAdQP9uW2zk15acT7WFkp+t3TiaCQ6v8IBGUwEPzQcI7Nno3WRny6dsWxnj9xJe0Gv5J2Q/jUaRTnmdoNsevW0WbyJHJiYkjatQvHevU4PMnUGA+8/z7cW1lu/2PU8PHRs7zboQVWCv6ISeB8Vi531TfVnd8uxPFwaCB1bG14qYVpumGxhmd3mKaujmvTmDYerrjaWrO4d3sWnIrmj5gEi+UH6NajJdu3RnDvoAnYO9gycUrZcenF/3zC+En/wtvHsjNyxM1NXW2eZ5WeTKmhwFhMI0uFwAogTGt9v1LKAOwoWX4E+BWwAQ4BXYHLN83/TWvdsuT5RgPOWuuJSqlOwJdANrAJ6KG17vo/IumnrnMK3M1iXrdeAAzbuKVmg1TR4t49SCv4vaZjVJmb7WBe3rWhpmNU2ezOfW7Z/LM79wFg/P51NZykaqa06wfAf0+uruEkVfNY44Gk5NfM59jcCB529+AQOLymY1RZbvQi2i/eWtMxqmTfMNMI2bG032o4SdU0czPNrH90y+YaTlI183v0BKDvH9trOEnVrB/UlazCW/O4BeBs0wdq5P6h12fcvvUWvTBnWvu+N+V7ckM/XEFrvQRYcpVlxUCncg91ucrTtCy3znvlHo/UWrcGUEq9AVjm9i5CCCGEEEKIv41b6YNQ71RKjcWU+TzwaM3GEUIIIYQQ4tZhdRPfmc2SbpkO0J+NLgkhhBBCCCHEtbhlOkBCCCGEEEKIqruZ78xmSX//T3wSQgghhBBCiBIyAiSEEEIIIUQtICNAJjICJIQQQgghhKg1pAMkhBBCCCGEsDil1B1KqRNKqdMlH3Nz5XKllJpTsvyIUuq2G/F3ZQqcEEIIIYQQtYChpgOUo5QyAJ8C/YEYYK9SaqXW+mi5YoOARiVfnYDPMf9c0SqRESAhhBBCCCGEpXUETmutz2qtC4DFwL1XlLkX+Fab7ALclFJ1/+oflhEgIYQQQgghagFLfxCqUuop4KlyD83TWs8r+bkecKHcshgqju5UVqYeEPtXckkHSAghhBBCCHHDlXR25l1lcWX3pLuyh3YtZa6bdICEEEIIIYSoBW6y22DHAPXL/R4AXKpCmesm1wAJIYQQQgghLG0v0EgpFayUsgWGASuvKLMSGFlyN7jOQLrW+i9NfwNQWlt2LqCF/a1fnBBCCCGEuGncXOMrlZh5ZK1F28ZjWvf/0/dEKTUYmI3pBnXfaK2nKaWeAdBaz1VKKeAT4A4gB3hMa73vr+b620+B6/vH9pqOUCXrB3UF4Kltm2o2SBXN69aLdRd/r+kYVdav3mAGrt5W0zGqbPXAbozevaGmY1TJe536ADD14LoaTlI1b7XtB8C846trOEnVPNV0IHG5V56Au3X4OdxD+8VbazpGle0b1h2HwOE1HaNKcqMXlfx0skZzVF1jAHqtujXbDZvuNLUbbuV2T2bh+pqOUWUuNn1rOsItSWv9O/D7FY/NLfezBp670X/3b98BEkIIIYQQQoDhph+jsgy5BkgIIYQQQghRa8gIkBBCCCGEELXATXYXuBojI0BCCCGEEEKIWkNGgIQQQgghhKgFrJTcIBlkBEgIIYQQQghRi8gIkBBCCCGEELWAXANkIiNAQgghhBBCiFpDOkBCCCGEEEKIWkOmwAkhhBBCCFELGGo6wE1CRoCEEEIIIYQQtYaMAAkhhBBCCFELyE0QTGQESAghhBBCCFFryAiQEEIIIYQQtYB8EKqJdIAq0cHLjeeaNcRKwe8x8Sw+e9FseV9/b4YF1wMgt7iY2ZFnOJuZA8DoVqF09nYnraCQJ7Ydsnh2gJTwCE4vWorWRup270bg4DvMlufExnH8m/lkRV8g+L57qX/HgGte1xK01vz4yc9E7j6Grb0ND48ZTmDj+hXKbfp5KxuXbyHpUhLv/jwFZ1dnAE4eOs0X47/G088DgLDurRk8cqDF8rf3cuOZpg0xKMUfMfEsPRdjtry+kwOvtGxEaB1nFpw6z7Kosvo1JNCfQQG+KAV/xMTz8/lLFst9WcKRSCK+X4o2agJ7dqXR3ebvndaayO+XEn84EoOdLWFPjsStQSAA614Zh7W9PcrKCmVlRY/JYy2a/eKhSPYtWIY2Ggnt05WW9x2fgdkAACAASURBVA4wW661Zu+CH7l00JT99v88jGewKfuOud8RcyAC+zou3PPeWxbNXT7fxi+Xc27/UaztbLnjpRH4hlSs++nxyfw2az55WTn4NAxg8KiHMdiYducXwk+x8eufMBYV41DHiaHTX7Jo/jkzV7B723Hs7G0YO3kojZsFVCj37sSlnDgag9aa+kHevDF5KI6OdqXLj0Vc4NmRH/P2u/+iV//WFsvfxc+d0bc1xEopfjkbx4Jj5tvuHUHePNLM9P/IKSpmxr7TnErLBmBYY3/ua+gHCn45E8eik5bddufOeppBfduSmJxB+/5jKi3z/qRHGNg7jJzcAp569XMORUQB0L9nG96bOBKDwYr5izfy3mcrLZi8jNaaadPmsXnzfuzt7Zgx4yVatAitUO6NNz5kz54IXFycAJgx42WaNWtIZmY2r732PpcuJVJcXMzjj9/PAw/0s0j2jt5uPN+8IQYFqy7Es/CMebuhn783w0PK2g0fhp/hTGYO3va2vBnWGA87G4wafouOY3lUrEUyl3ert3u01rz3zo9s3xqJvb0NE6eNpGnzwKuWnzl9Cb/+vIutez8EYNOGw8z9+FesrKwwGKx49Y1/EHZbxbon/j7+UgdIKZWltXau5PH5wG9a62V/5fmveM5HgfZa6+dv1HNWxgp4sUVDxuyJJDGvgM9ub8POhBTOZ+WWlonNyWPU7nCyiorp6OXGKy1DeX7nEQBWxySw4nwsr7duVJ0xr0objZz6YRGtX30ZO3d3Dkx5B8+w1jj5+5eWsXZyJPShYSQfPHTd61pC5O5jJF5MZOJ3bxJ17DyLZy9jzGejKpQLaRlMqy4tmD3qkwrLQls15D/Tn7REXDNWwHPNQhi7L4KkvAI+7hLGroRkorPL6k9GYRGfHzvL7T6eZusGOTsyKMCXF3cdplAbmd6uJbsTU7iUk2ex/NpoJPzbxXQe8yIOHu5sfXsGfre1xqVe3dIyCUciyYpPoM+sSaSdOUf4/EV0n/h66fIuY0dh51Jht1DtjEYje75ZSr9xL+Do6cYfb84koF0r3ALKsl86FElmbCL3zp5I0ukodn+1mMHTTI3FkJ6daTKwJ9s//dbi2S87t/8oqbGJPD53PLEno1j3+VJGvPdqhXJbFqyg3T29aNqjHWs/W0L4up2EDepOXlYO6+Yu5YGJ/6GOtwc5aZkWzb9723FiopP4YeXrHA2P5oNpPzH3+xcrlHt+9D04OdsD8Ml7K/l58XZGPN4HgOJiI198tIoOXZpYNLuVgtfbh/Dcxgjic/P5tn8YWy6mcC4jp7TMpaw8nlp/hMzCIm6v6864DqE8uvYwIa6O3NfQj5FrD1FkNDKnZ0u2XUrhQpbltt3vftzM3AWr+erDZytdPrB3GCEN/GjZYxQd24YyZ9q/6XHveKysFLOnPsadI6ZzMTaZbb9O47e1+zl+6mKlz1OdtmzZT1TUJdas+YLDh08wceLn/Pjj+5WWHTPmce64o6vZYz/8sIqQkEDmzp1ASko6d9zxDHff3RNbW5tqzW0FvNSiIaN3m9oNc7u1YXv8Fe2G3Dxe2lnSbvB249VWoTy74wjFWvPZ0XOcysjGwWBgXrc27EtKM1u3ut3q7R6A7VsjuRCdwM+/TyTiSBTvTFnMgkWVnwg4GnGezAzz97dj5yb07N0apRSnTsTwxuivWf7r25aIbnEGuQYIkGuAKmjq5sLF7Dxic/Mp0pqNsYnc7uNhVuZoWiZZRcWlP3vb25YuC0/NIKOwyKKZy8s4ew4HHx8cvL2xsrbGp2N7kg8eNitjW6cOdYIboAyG617XEo7siKBT/w4opQhu3oDcrFzSk9MrlKvfKKB0lOdm0cTVhUs5ecSV1J9NsYl0uaKjk15QyMmMLIq0+TB0oJMDx9IzyTcaMWo4kpJO1yvWrW6pZ6Jw8vHGycdUB/w7tyfugHkdiDtwmPpdO6OUwj20IYU5OeSlVfz/WFry6Shc/Lxx8fXCYG1N0O3tuLDviFmZC/uO0LBHJ5RSeDcKpjAnl5xUU3bfZo2wc3KqieilzuwJp3nvjiil8G8STH52Llkp5u+t1proI6do3DUMgBZ9OnJ6VzgAx7fsp1GXNtTxNm0Xjm4uFs2/bVMkA+9qh1KKFq2DyMrMIzkxo0K5y50frTX5+YUoVXZE/mnRdnr2bYW7h2X/Fy08XLiQmcfF7DyKjJo10Yn0rGe+fzmSnElmyf49PCkTHwfTqFWDOo6EJ2eSX2ykWMOBxHR6B3hZNP/2PcdJScu66vK7BrRj4fKtAOw5eBrXOo74+bjRISyUM1FxREUnUFhYzI+/7uSuAe0tFdvM+vW7GDKkD0opwsKakpGRTUJCyjWvr5QiOzsHrTXZ2bm4urpgbV39N/1t6ubCxZyydsOGS4l09TWvO5Gp5doNqZl4O5jaDSn5hZzKMI0i5hYXcz4rB69ybQpLuNXbPQCbNx5h8D2mfXurNsFkZuaQlFjxuFRcbOSj93/ipVfvM3vc0dG+dD+Um1uA9BH+/q65A6SUekUpFVHy9fIVy5RS6hOl1FGl1CrAp9yyKKXUu0qpPSVfoSWPeyulliul9pZ8dS15vKNSaodS6mDJ9wqnAZVSdyqldiqlbvgRxsvelsS8gtLfE/MK8LK3u2r5QfV92ZOYdqNjVFlBWhp2Hu6lv9u5u5Ofdm35/sq6N1J6UjpuPm6lv7t5u5GWdH0N7HNHo5j+xCw+feMLLp2z3HQCT3tbEvPyS39Pysu/5oNZVFYOrdxdcbGxxs7Kig7e7nj/Sd2rDnmpaTh4ltUBew938lLN60BeShr25eqJg4c7eSmXyyh2zZzDlgnTOb9xqyUil8pJScOpXHYnDzdyU9KuKJOOk2dZ3XKspExNykpOx8WrLJ+LlxtZV3T+czOzsXdywKrkBIazp1tpJyn1UgJ5WTksGTeH716ZSeSGPZYLDyQlZODjV5bf29eVxITKt913Jizhvr6TiT6XyP3DTGfyE+PT2boxgnse7GKRvOX5ONgRn1O27SbkFpR2cCpzb0NfdsSmAnAmPZu23nVwtbXGzmBF17oe+Dpadtv9X/z9PIiJTS79/WJcCv5+Hvj7uRNzqdzjscnU83Wv7CmqXXx8Mn5+ZYd1Pz9P4uOTKy374YffcffdLzB9+pcUFBQCMGLEnZw5E0P37o9wzz0vMG7ck1hZVf95Xm97WxJzzdsNf7bvvjPQlz0JFfc7fg52NHJ15tifdGSrw63e7gFIjE/Dz6+s3vr6upMQXzHj0oWb6NG7NV7erhWWbVx3iAfunsTLz37GhCkPV2fcGmWlLPt1s7qmKXBKqXbAY0AnQAG7lVKbyxW5D2gCtAJ8gaPAN+WWZ2itOyqlRgKzgbuAj4APtdbblFKBwGqgGXAc6KG1LlJK9QOmAw+Uy3If8AowWGudWknWp4CnAL744guo3+JaXuKf0rryC8bCPFwZFODLyyVnX28Kf+XatpvkurjK3u/yZ4j/l/qNApi8aAL2DnZE7DrKvAnfMPG7cTcy4lVVlvIq1aeCC9m5LD0XwzvtW5JXVMy5zGyKr3XlG6ayv3cN731JkW7jR2Pv7kZ+Rga73p2Dc10/PJvW3LQIKtSbSl7fddSt6lZ53a9Q6KpljMVGEs5c4MEpz1NYUMiiMR9St0kDPOr5VFinOlzPtjt28lDT2dgZv7Bh9WEGD+nAx7NW8vRLgzEYamByQiUxr7b1tfNx5d6Gfjyx3jQ6GpWRy7fHY/i0Vytyioo5lVYT2+6fU5W8QK11pf+fmope2d+tLN8rrzyCt7c7hYVFjB//CfPmLeP554ezbdtBmjUL5ttvpxEdHctjj42nffsWODs7WiC9OX2V2hPm6crg+r68sMO83eBgsGJSu6Z8cvQsOSUjLTXplmr3cG37nsSENNatOcgX/325QlmA3v3C6N0vjAP7TjH3k1/57CvLXT8pLO9arwHqBvystc4GUEr9BHQvt7wHsEhrXQxcUkptuGL9ReW+f1jycz+gebkKWkcp5QK4AguUUo0wHX/KT97tDbQHBmitK86rALTW84B5l39d8sf2a3yJJkl5BWZDu972tiTnF1Qo19DFkVdbhTB279EaH/otz9bdjfyUsn5hfmoqdm5uf7LGjVn3r9r8yza2r9oJQFCTQNLKnR1LS0zD1bPONT+Xg5N96c8tOzdnyUfLyErPKr1JQnVKuuLMn5e9XaX152pWX4xn9cV4AB5rFGQ2mmQJ9u7u5CaX1YG8lFTs3c3PlNl7uJFXrp7kpqRi7+5Wsr7pu12dOvi1CyPtbJTFOkCOHm5kl8uenZKGwxXZTWXK6lZOJWUs7eCqLYSvNdV9v9BAMpPK8mUmpeHkYZ7PoY4zedm5GIuLsTIYyEpOw6nkNTh7uuFQxwkbezts7O0IaBFCYtTFau0A/bx4O7/9tBuAJi3qkxBXlj8xPh0v76tvuwaDFX0GtmHxgs0MHtKBE0cvMPn1HwBIT8tm17bjGAxWdO/TstryX5aQk282auPjYEtibsXtL9TVkfEdG/Hi5kjSC8r2/SvOxrPirGnbfbZ1EAk5177dW8LFuGQC6pZNqa3n50FsfCq2NtYE+Jd7vK4nlxIqnFusNj/8sIqlS1cD0KpVI+LikkqXxcUl4+NTcZrz5cdsbW24//5+fPPNTwD89NM6nnrqHyilCAryJyDAj7NnY2jdunG1vobEvILSKW1gajck5VXebnitVQivX9FuMCjFpHZNWXcxka1x1z7l70a5Vds9Sxdt5pdlpjZe85ZBxMWV1dv4+FS8fcz3nSeOXSAmOpH7Bpuu7cnLK2DIoLf55Y9JZuVua9+ImAtJpKVm4eZu+etZq9vNPCpjSdd6mu1a3q4/O2ekK/nZCuiitQ4r+aqntc4EpgAbtdYtgbsB+3LrngVcgGrbmx1Pz6SekwN+DnZYK0Xvut7suGIOso+9LRPbNuWdw6eIseAF6teiTnADcuMTyE1MwlhURMKefXiGtan2df+qnkO68eaXr/Hml6/RpltLdq/di9aac0ejcHBywNXz2hup6SkZpWeDoo6dR2uNUx3LXE9wIiOTeo4O+JbUn151vdl1HXPYXUsu1vW2t6OrjyebYhOrK2ql3BoGkR2fQE5JHbi0ax9+bc3vwuXXtjUXtu9Ca03q6bPYODpg7+ZKUX4+Rbmm7aEoP5/EiGO4BFjuBhqeIUFkxiWQmZBEcVER53fsp367VmZlAtq15uyW3WitSTx1DhtHBxxruAPU9s4ejJz9OiNnv05o59Yc3bgHrTWXTpzDzske5ys6QEopAls14uR2001MIjfsIbST6XWGdmrFxaNnMRYXU5hfQOzJ83gG+FZr/vuGdeXrpa/w9dJX6N67Jat/22+6U+CR8zg52+N5RQdIa01MdFLpzzu2HCUw2BuAJb+/yZI/TF89+7Vi1Jv3W6TzA3A0JZP6Lvb4O9lhbaUYEOjNlovm266vox2zujVnws4TRGeaX0TtbmdTWqZPgBerz1t22/1fVq09wEMPmM5bdmwbSkZmDnEJaew7fIbQYD+C6ntjY2Pgwbu7sGrtfovlGjHiTlasmMOKFXPo168zv/yyAa01hw4dx8XFsdIO0OXrgrTWrFu3i0aNggCoW9ebnTtNo3JJSamcOxdDQDXXf4AT6ZkElGs39PH3Zkd8xXbDlHZNmX74FDHZ5u2GMa1Dic7K5cdzlr/rJ9y67Z5/Du/JwuVvsnD5m/Tq05rfV5r27eGHz+Hs7FBhmlu3nq1YvXkGv66Zyq9rpmJvb1va+bkQnVDabjh+NJrCwiJc3Wr2mlBRva51BGgLMF8pNQNTZ+g+4OErlj+tlPoW0/U/vYGF5ZYPBWaUfN9Z8tga4HlgFoBSKkxrfQjTCNDl2888ekWO88Bo4Gel1INa68hrzH/NjBo+PnqWdzu0wErBHzEJnM/K5a76fgD8diGOh0MDqWNrw0stGgJQrOHZHaad7rg2jWnj4YqrrTWLe7dnwalo/ohJuNExr0oZDISOGEb4hx+hjUb8unXFqZ4/lzaZZiz69+pJQXo6+6dMpzg3D5QiZt16OkyZiLWDQ6XrWlqLTs2J3H2Mif+ahq29Lf8aM6x02advzGPE6KG4ebmy8actrFu8gYyUTKY/MYsWnZoxYvQwDm4+zNaV2zEYDNjY2fD4WyOvawrdX2HU8OmxM0xv1xIrBWsuxnM+O4c7A0z1Z1VMHO62NnzcJQxHawNaw5Agf57adoCc4mImhDXFxcaGYq355NiZ0otOLcXKYKDlyGHsmvkxWhup3+N2XAL8idqwBYAGfXrg06YlCYcj2PDaBAy2toQ9MRKA/PQM9n30hel9MBqp16UDPq3/+hTU68ne8bF/sn76p6bbYPfuglt9f06uNV2L1Lh/d+q1bcHFQ5H88tJErO1suf2Zf5Wuv3XON8QfPUVeZhbLnx1H63/cSaM+t1ssP0Bwu+ac3RfJ189MxsbOloEvjChd9tPkuQx4bjjOnq50f+QeVr03n+0/rMKnYQAt+3cGwLO+Hw3aNmPBizNQVla06t8ZryDLbcOduzdl17ZjPHT3DOzsbXlj0j9Ll4157mvGvP0PPLxceGf8YrKz80FrQhr788q4+y2W8WqKNczaf4aPe7bEYKVYeTaesxk5PBBi2naXn4njyRaBuNpZ83r70JJ1NCPXmDqiM7s1w9XWhiKjkXf3nym9WYKlLPj4Bbp3aYaXuwund3/ClA+WYVNya/Svvl/H/204yMDeYURunU1Obj5PjzZtq8XFRkaNn8+v343FYLBiwZJNHDsZ82d/qtr07NmezZv30b//Uzg42DG93C3cn3xyIlOnvoCvryejR79Pamo6WmuaNm3IpEmmO989++xQxo6dzd13P4/WmtGjH8XDo/pPcBRr+CjiLLM6lrUborJyuSfQVHdWRsfxSCNTu2FUuXbD09sP08rdhYEBPpzJyOarbqYTjl+eiGZ3ouVG4W71dg9A1x4t2b41kiGD3sbewZa3y13D8+J/PmX8pBF4+1x9Rsv6tYf4feVurK0N2Nnb8M57/7ZYu8HSZATIRF1tnmeFgkq9Ajxe8utXWuvZl2+DrUy15GOgD3CypMz3WutlSqko4L/AYEyjPsO11qdLbmDwKabrfqyBLVrrZ5RSXYAFQCKwAXhYa92g/G2wlVJtgR+Au7XWZ/4ktu57nVPgbhbrB5kuCn5q26aaDVJF87r1Yt3F32s6RpX1qzeYgau31XSMKls9sBujd185E/XW8F4n0+2Qpx5cV8NJquattqbPHZl3fHUNJ6map5oOJC63Zj4H5kbwc7iH9ostewOOG2nfsO44BA6v6RhVkht9ebb7yT8td/MyTS7pterWbDdsutPUbriV2z2ZhetrOkaVudj0hWubMVWjvju92qJX+T0cOvCmfE+u+XOAtNYfAB9c8ZhzyXeNaTTnaj7VWptNstRaJ2EaEbry7+zEfIrb+JLH5wPzS34+CDS/1uxCCCGEEELUdgZ1c92gpabI5wAJIYQQQgghao1rHgGqKq11g+r+G0IIIYQQQghxLaq9AySEEEIIIYSoeTL1y0TeByGEEEIIIUStISNAQgghhBBC1AJyG2wTGQESQgghhBBC1BoyAiSEEEIIIUQtICNAJjICJIQQQgghhKg1ZARICCGEEEKIWkA+CNVERoCEEEIIIYQQtYaMAAkhhBBCCFELyDVAJjICJIQQQgghhKg1ZARICCGEEEKIWkBGgEyU1n/ri6H+1i9OCCGEEELcNG767sWv0X9YtG18d+Cgm/I9+duPAG2NW1XTEaqku9+dAHwUuaaGk1TNSy0GMHLz5pqOUWXf9uzJivN/1HSMKrs3aBDTD62t6RhV8mZYfwDmHV9dw0mq5qmmAwF4ZvvGGk5SNXO79qbhZ7futnv22Z4cS/utpmNUWTO3u4CTNR2jihoD4BA4vIZzVE1u9CIAvjt9a+57Hg417XuWnfu/Gk5SNf8IvoPm32yp6RhVdvTxHjUd4ZrICJCJXAMkhBBCCCGEqDX+9iNAQgghhBBCCDDICBAgI0BCCCGEEEKIWkQ6QEIIIYQQQohaQ6bACSGEEEIIUQtYKblBMsgIkBBCCCGEEKIWkREgIYQQQgghagEZ+TCR90EIIYQQQghRa8gIkBBCCCGEELWAfBCqiYwACSGEEEIIIWoNGQESQgghhBCiFpAPQjWRESAhhBBCCCFErSEjQEIIIYQQQtQC8jlAJjICJIQQQgghhKg1ZASoElprFs35mfDdx7C1s+XxscMJahxQodyGn7aydtkWEi8m8+GKybi4OQPwf4s2sHvdAQCKi43Eno/nwxWTca7jZLH8275ezvkDkVjb2dL3+X/hHVK/QrmM+CTWfDCf/KwcvIID6PfSSAw21pzbc4Tdi1ahlMLKYEW3xx+gbrMQi2QHSIuI4PySJWijEZ9u3fAfNMhseW5sLGcXLCA7Opr6Q4ZQd8AAAPJTUjjzzTcUZmSglMKnRw/8+va1WO7LtNas/Ownju89ho2dDf8c/RABjSq+/9tXbGXbz5tJvpTE2z9OxcnVVH8OrN/HpqXrAbBzsOO+Fx7EP6SeRfPvmb+MiwdN9afrfx7Gs2HF/JkJSWz56L/kZ+XgGVyfbs+PxGBtfc3rV1f2jV8u59z+o1jb2XLHSyPwraTup8cn89us+eRl5eDTMIDBox7GYGPaHV4IP8XGr3/CWFSMQx0nhk5/ySLZAZLDIzm1cCloI3W7dyXozjvMlmutObVwKSnhEVjZ2tLs34/gEhRoyr12PbFbtqO1xr9HN+oPsHzd71HfnQndQrGyUiw9GsvcgxfMlvdr4MkrnRpg1FBs1EzZdpp9cRkAPNq6HkOb1UUpWHI0lv8euWjx/FprvvrgF/bvOIadvS0vjh9GSNOK+/4PJnzP6WMxWFsbaNS8Pv8Z+yDW1gYAwvef5usPV1BcVEwdNyemzX3OovmnTZvH5s37sbe3Y8aMl2jRIrRCuTfe+JA9eyJwcTEdk2bMeJlmzRqSmZnNa6+9z6VLiRQXF/P44/fzwAP9LJJ97qynGdS3LYnJGbTvP6bSMu9PeoSBvcPIyS3gqVc/51BEFAD9e7bhvYkjMRismL94I+99ttIimcvTWrPmi+Wc3ncUGztb7h41grqhFfc9qXHJ/PzufHKzcqgbEsC9r5r2PXnZuax471vSE1MxFhvpfH8fwvp3tmj+VZ//xIm9R7Gxs+GBV0dQr5Lj1s6VW9jx82ZSYpN4c8m00uPWta5fXbrVc2ds5xAMSrHsZBxfHTHf9/QJ9OSF24LQGoq0ZsbuMxyIN+171j7YkezCYoxaU6Q1/1x50GK5a4LcBc7EoiNASqlNSqn2lvybVRG++xgJMUlM/+FNRo5+kO8/WFZpudCWwbz6/n/w9HM3e/yO4X14++vRvP31aO5/8k6atAmxWOcHIPrAUdJjExjx6QR6PTOMzfOWVFpu53craXN3b0Z8OgE7Z0eOrd8JQECrJgz94A2GfvAGvZ8bwcbPFlosuzYaiVq4kCYvvkjrSZNI3ruXnEuXzMpYOzkRNGwYdfv3N3tcWVkR9OCDtJk8mRZjxxK/cWOFdS3h+N5jJF1MZMx/x/HAy0P5ec6PlZZr0CKYJ2f8B3df8/rj4efJM++9wCtfvE7fhwawfHbl/7/qcvHQUTLjErnvo7fp8uRwdn29uNJy+39YQfPBvbn/o7exdXLg1Iad17V+dTi3/yipsYk8Pnc8/Z8byrrPl1ZabsuCFbS7pxf/njsee2dHwteZsudl5bBu7lKGjHuSRz95k7vHPG6x7Npo5OT3i2gz6nk6Tn2b+N17yb5oXn9TwiPIjU+g0zuTafLICE58a9o2s2IuErtlO+3eeoMOk94i+XA4OfHxFssOpoPqpB6NeGxVOAMX7eXuRj6EujualdkRk8rgJfu5a+l+Xt94gnd6NwGgsYcjQ5vV5b7lB7hzyT76BHnSwNXBovkB9u84TuyFJD5fNpZn33iQuTOXV1qux8B2fLr0dT5aOJqC/ELWrtgNQFZmLl/M/Ilx7z3Ox4vH8Nr0kZaMz5Yt+4mKusSaNV8wZcpzTJz4+VXLjhnzOCtWzGHFijk0a9YQgB9+WEVISCArV37Md9+9w7vvfk1BQaFFsn/342buHTnjqssH9g4jpIEfLXuM4vk3vmTOtH8DYGWlmD31Me595F3a9h3Ng/fcTtNGljthdNmZfUdJuZTIs1+OZ/ALQ/nj08r3PRv+u4JOQ3rx3Jemfc+hNaZ9z77ftuJV34+nPnmDh2e8wLqvfqG4sMhi+U/uPUrSpURe+eYthrw0jJWfVH7cCmrekMffeRY3H48qrV8drBS81SWUp9dEcPdP+xjc0JsQN/N9z65Lqdz3ywHuX3GAt7aeZHLXxmbLH/3jMPevOPC37/yIMjIFrhKHtkXQZWB7lFKEtGhATlYuackZFcoFNg7Aq65HJc9QZs/6A3Ts27a6olbq3J5wmvTqiFIKvybBFGTnkp2SblZGa83F8JOEdAkDoGnvTpzbcwQAGwc7lDKdIijKLwAsd7og69w57H18sPf2xsraGo8OHUg9fNisjE2dOjg3aIAyGMwet3VzwykoCACDvT32detSmJZmseyXHd0Rzm39O6CUIqhZA3Kzc8lITq9Qrl5oAB5+nhUeb9AiGEcX0847sFkD0pMqrludLuw9QsMepvrj3dhUf3JSK9afuMiTBHU21e2Qnp24sPfwNa9fXc7sCad5b9Pf9m8STH52LlmV1P3oI6do3NVU91v06cjpXeEAHN+yn0Zd2lDH27RdO7q5WCQ3QMbZKBx8fHDwMdV9304dSDp0xKxM0sEj+N3eGaUUriENKcrJJT8tnZzYOOo0DMZgZ4uVwYBbk0YkHjhksewAbXzqcD49lwsZeRQaNb+dTqB/sHn9zikylv7sYG1AY5qLHuLuyKH4DPKKjBRr2H0pjQHBW767LAAAIABJREFUXhbND7BnSwS9BrVDKUWTVkFkZ+aSklRx39++azOUUiilaNQikOQE035my+oDdOndCu+Sk2JuHparPwDr1+9iyJA+KKUIC2tKRkY2CQkp17y+Uors7By01mRn5+Lq6lI6slXdtu85Tkpa1lWX3zWgHQuXbwVgz8HTuNZxxM/HjQ5hoZyJiiMqOoHCwmJ+/HUndw2w/HnWE7vCadXHtO8JaBpMXnYumZXse6KOnKJZN9O+p3Xfjpwo2fcoBQW5+WitKcgtwMHFESuD5Zpox3ZG0Lav6bgV2KwBeVmVH7f8QwNwr+S4da3rV4dWXi5EZ+QSk2na9/xxNpE+gX+277GiNl8FY6Us+3WzqratSynlpJRapZQ6rJSKUEoNvWL5cKVUeMmyd8s9nqWUel8pdUAptV4p5V3yeIhS6v+UUvuVUluVUk2rK3taUgYePm6lv7t7u5GWeP0bcn5eARF7jnNbz9Y3Mt7/lJ2ShrNX2aiCk6dbhQ5QXmY2tk4OWJV0Ipw83cgut7M6u+swC1+Ywqppc+nz/AjLBAcK0tKw9SjrVNq6uVGYmnrdz5OflEROdDROwcE3Mt41SU9Ox8277P1383IjvYoHgr3/t4smHZrdqGjXJCc1DSfPsvyOnm7kpJh3JPMzs7F1LFd/PNzJKalj17J+dclKTsfFq2zbdfFyI+uK9z43Mxv7cnXf2dOttJOUeimBvKwcloybw3evzCRywx6L5Ab+n737jo+ieB84/plcyqXn0hshgdAJRbp0ELAAdmn2+rMioqAiFrDrV1Cs2EARELGDivSiVKUlhB5KIL33kGR+f1xIIQeEQC6Jed6vV15Jbmf2ntubndvZZ3aPgvQ0jJ7l283B5EHBGW2/IC0dh4plPD0oSEvHOSiQ9P0HOJWdTXFBISm7IylIvfD95mL4O9sTl11Q9n9cdgF+zg5Vyg0N82L5mG58fk17Jq/aD8D+1Fy6B7rj4WCL0daGAU29CHCpWre2pSZl4O1X3n68fN1JPUffX1RUzJrf/6FzT/PH0cljSWRn5jLlwQ954vYZrP5tW63HXFFCQgr+/uUDR39/LxISUiyWnTHja0aMeJRXX/20LMszbtw1HDoUS9++dzBy5KNMmXIfNjb14zxpoL8nsXHlr+VEfCqB/p4E+puIPVnh8bgUgs7IqltDVkoGbj7lbcfN24OsM/uezMp9j2uFMl2H9yP5eDzv3jaV2Q+/xtD7b0RZcdtnpqTjXjF+H/cLGsBcbP2L4efsQHxOed8Tn1OAr5N9lXKDm3qx5IaufDy0Pc+t31f2uAY+GxbBdyM7c3Mrf2uELOqB2rwG6ErgpNb6GgCllDvwYOnfgcAbQBcgDfhTKXWd1vonwBn4V2s9USn1PPAC8AgwG/g/rfUBpVQP4ENg0JlPqpS6H7gf4JNPPqHNyAtPhWtt4dxADUaxO/+OIrx9mFWnv0E14z9PmWY9O9KsZ0dORh1ky4IljHzx0Usa41lZjOvCNn5xfj77P/6YpqNGYeto/Wk0lk4tqRo0oIM7DrD1j008OMN616DA2d6CKg3IQqELqV87LLX9qqGfvUxJcQmJh45z8/RHOFV4igWTZhDQKhTPIN9aiPbMuCw8dkbw2kIhpcA5MICQq4ax4+13MRgdcGkSbNWDJ3MgVR+y1Bb+jEnhz5gUugW480T3UG77dReH0nL5ZPtxvhrZgdxTxexNyabYUuVadqFP+cmb39O2UzPadTZPISspLuHQ3limffB/FBacYvI9s2jZvilBIT61EG1V1d33nnjiDnx8TJw6VcTUqe8ze/ZiHnlkDBs2bKdNmzC++uoVjh2L4667ptK1aztcXJyqrtjKLPWhWmuLr68Omo7lfuXMIufIOxz+Nxq/ZsHc+tqjpMUl881zHxDSvhkOTtb5DLvYfrsu+/3qPsvKoymsPJpCFz93HusSyj1/mLNv45bsICmvEE+jHZ9dGcHh9Dz+SbDuzAtrqh+nNOpebQ6AdgNvl2Z3lmit11fYGboBa7TWSQBKqW+AfsBPQAlw+qKHecAPSikX4HLguwrrsHh6UGs9G/NgCUCvj19arWBX/biB9Us2ARDaqgmpieVnrNOS0vHwdq/WeiraunI7Paw0/W337+vYs/xvAHzDQ8hOLj/7m5OSjrOpcvxGNxcKc/IoKS7GxmAwl/Gs+hoD24WTMSuZvMxsHN1cavdFAPYmE4Wp5VM2CtPTsfPwOEeNykqKijjw8cd49+iB52WX1UaIFv39y3o2/2aey92kVQjpSeXbPz05HTcvtwtaX9zhkyyesZB7XnkAZysMoPcuW8v+leb24928KTkp5fHnpqTjeEb7cXB1oTC3QvtJTcOptIyzp8d5619K25euY/dy87b3Dw8hK7l8381KrtquHd1cyK/Q9rMr7B8uXh44ujljZ3TAzuhAcLvmJB05YZUBkIPJRH6FrE1BWjoOZ7R9o8lUKbNTkJqOfWmZwH69CezXG4BD3/+E0VT9/eZSiM8urJS1CXBxIDG34Kzlt8ZlEOJuxGS0JS2/iEXR8SyKjgfgyR5hxGefve6l9Nt3G/iz9BqeFm2bkJxQ3n5SEjPw9LHcdhd+toyMtGyefuPOsse8fN1x9XDG6OiA0dGBtp2bceTAyVodAH3zzVIWLVoGQEREC+Ljk8uWxcen4OtbdZr26cfs7e244YYr+OKLHwD44YcV3H//Tebpu00DCQ725/DhWDp0aFllHdZ2Ij6F4IDyaU1B/p7EJaRhb2dLcGCFxwO8OJlonezntiXr2P6Hue8JaBlCZlJ528lMTsfFq3LbcTqj78lKTse1tMzO5Zu5/OYhKKXwDPTBw8+L5OOJBLVqWmvxb/plPVtL4w9uGUJGxfiTMnD1rP7nlru3x0XVvxjxOQX4V8g2+zs7kJhbeNby/yRk0MTVEQ8HW9ILikjKM5dNzT/FyqMpdPBx/U8PgIRZrQ0Etdb7MWd4dgOvlWZzTruQ0wIac5zpWutOFX4u6bygQdf3KbtxQee+EWxctg2tNYeijuDobMTjAg9gc7Pz2LfzEJ36tL+UYZ5VxFX9ym5cENa9A/vWbDFfp7EvBnsnY5WDQKUUQe1bcGij+TqBvas3E9YtAoCMuKSyM+lJh45TUlSM0dU6WSyX0FDyExPJT06mpKiI1K1bMXXsWK26WmtivvoKx4CAKjdIqG2Xj+zLhI8nMeHjSbS7PIJ/l29Fa83R6CM4Ojvi5lX9AUBaYhpfTfuC0ZNuxSfYCpkHoPWw/ox88xlGvvkMId06cHiduf0k7Y/BzsmxbHBzmlIK/7YtObrJfMHoobWbadLVPNWzSdeI89a/lDpf04/bZ07m9pmTCe/ZgT2rzc99cl8MDs5GXCy0/ZCIFuz/y9z2o1ZtIbyHue2H94jgxJ7DlBQXc6qgkLj9R/EK9qu12CtyDWtKXkIieUnmtp+weSvenSpPn/Xq1IH4vzehtSbj0GFsnYw4eJhfX2Gm+VqV/JRUkv/Zjm+PblaJ+7RdiZmEujsS7GrEzkYxPNyXFTGVp181dTOW/d3O2wU7GxvS8s0Xens52gEQ6OLAsGbe/HIw0SpxX31zH2bOm8jMeRPp0a89a37/B601+3YfxdnFiKd31b5/+c+b2L5pHxOn31Zpilj3fu3Zs+MwxUXFFOQXciDqGMGhtbsPjxt3TdnNDK64oic//bQKrTU7duzF1dXJ4gDo9HVBWmtWrNhEixbmg+yAAB82bjRfy5ecnEZMTCzBVmr/57N0+b+MvbEvAN07h5OZlUt8Yjrbdh4iPMyfpk18sLMzcPOIXixd/o9VYuo6vB/3vT+Z+96fTKueHdi9ytz3xO6NwehsxNVC3xMa0YLoDea+Z9fKLbQs7XvcfE3E7DRPy8pOyyT1RKLFa20upZ4j+/Loh5N49MNJtOkVwfaV5s+tY9FHcHA2XtDnVuue7S+q/sWITM6iqbsjQS7mvueqZj6sPla57wlxLe972ni5YGejSC8owtHWBqfS69wcbW24PNCDA2k5Volb1K1aywCVTnNL1VrPU0plA3dWWLwZeFcp5Y15CtwYYFbpMhvgJmAhMBbYoLXOVErFKKVu1lp/p8xpoA5a68pXx18iET3bsHtTNM+OfRV7BzvuenpM2bKZk2Zz56RReHi7s2LxOpYtXE1GahYv3v02ET3bcOck86VO29fvpl23Vjg4Wn8ee9Mu7Tj27x6+eWgatg52DHrk1rJlS17+iIEPjcXZ052et13L8ne+ZPP8JfiEBdPmil4AHNq4g31rt2BjMGBrb8fQiXdZL5VtMBA6Zgz7Zs5El5Tg07s3ToGBJKxdC4Bf//4UZmQQ+corFOfno5QibsUKOrz0EnmxsSRv2oRjUBC7p00DoMn11+MREWGV2E9r3b0te7dE88adL2PvYM/NT5a3n8+nfMJNT4zG3cudDT+uZe13q8hKzeKdB96kdfe23PzEaFbMW0ZuZg4/zjLfRcfGYGD8BxOtFn9Q53bEbo/ih/EvYWtvR+8Hy9vPitc+5PIHxuLk6UGXcdey9t0v2f7tEjxDm9BiUK/z1q9tYV3acnhbFJ//3zTsHOwZ9mj59Ws/TPuYoQ+PwcXLnb53jGTp23P465ul+DYLpn3p7Wa9mvgT2rkNcx97HWVjQ8SQnng3DbRK7DYGAy1vHcXOd95Dl5QQ0OdynIMCObF6HQBBA/vh1aE9qbsi2fT0VAz29rS++46y+pEfzOZUdjbKYKDFrWOwc7bu1NtiDS+uP8jcERHYKMV3e+M5kJbL2HYBAMyPiuPK5j5c38qPohJNflEJj/25p6z+h8Pa4WG0pahE88K6A2QWWO8OWKd16d2Gf/6O5v9ufA0Hox2PTR1dtmza45/yyJRb8PRx56M3vsfH38Tke98DoNeACEbdO5QmYX5c1rM148f9DxsbxRUje9C0eYDV4u/fvytr125jyJD7cXR04NUKt3C/774XefnlR/Hz8+LJJ/9HWloGWmtat27GSy89BMBDD43imWdmMmLEI2itefLJO/G0MDOgNsyd9Sh9e7XB2+TKwc3vM/2dxdiV3pr+s3kr+GPVdoYN7ETU+pnk5hXwwJOfAOavmpgwdQ6/fv0MBoMNc79dQ/T+WKvEXFF4t7Yc3BbFB/dOK7sN9mkLXviY4Y+NwdXLnUF3jeTHN+ew5uul+DcLptMwc9/Td/SV/DJjHp889BoAg+4ciZN77c+6OK1V97bs37qHd+6ejp2DPTc8MbZs2dypH3P942Nw83Ln75/Wsn7xSrJTs5j14Bu07NaWGyaMOWf92las4ZWNB/l0WHtslOLHA/EcTM9lVCvzvvftvjiGhHpzbXhp31NcwsQ10QB4Odrz3uC2ANgqxdLDiWw4Yd3rJ63NSodzl4RSyhPzrLBQ4Ahwi9Y67YwyTYCvAH/Ms8hma63fPe+6LV4vcgkopYYBb5UGcwrz9T9vA09qrbcppcYCz2DOBv2mtZ5UWi8bmAFcDWQAo7TWSUqpMOAjIACwAxZqraedJ4xqT4Grb/r6XwPAu1F/1nEkNTO+3VBuLx20NERf9e/Pz0d/r+swauzaplfx6o7ldR1GjTzbyZy9m713WR1HUjP3tx4GwP/9tbqOI6mZj3sPpNmHDXffPfxQf6LTl9R1GDXWxmM4sL+uw6gh81Q5x5Ax5ylXP+UdWwDA1wcbZt9zW7i571kc80cdR1IzN4VdSdsv1tV1GDW25+5+YM3b5tbQlqSlVr1KrrvPNTXeJkqpNzEnU15XSj0NmLTWk88oEwAEaK3/VUq5Av8A12mt91hYZZlaywBprZcBZ/YiAyosnw9Y/IIZrfVUYOoZj8VgvrGCEEIIIYQQ4gLV+xFaZddSPnaYC6wBKg2AtNZxQFzp31lKqWggCDjnAEhuBiGEEEIIIYS45JRS9yultlX4uf8CqvuVDnBOD3TOeVGlUioU6Iz5Uptzqs27wNWI1tp6k16FEEIIIYRoJKx9DdAZd2euQim1AvP1O2eaciHPU3rH6O+Bx7XWVb/B+gz1bgAkhBBCCCGE+O/TWl9xtmVKqQSlVIDWOq70Wh+LtwdVStlhHvx8o7X+oTrPK1PghBBCCCGEaARsrPxzkX4BTt/u9A7g5zMLlN4Z+nMgWmv9TnVXLAMgIYQQQgghRH3zOjBEKXUAGFL6P0qpQKXUb6VlegO3AYOUUjtKf64+34plCpwQQgghhBCNgFJWvQv2RdFapwCDLTx+EvPX5aC13kANbm4nGSAhhBBCCCFEoyEZICGEEEIIIRqBBvY9QLVGMkBCCCGEEEKIRkMyQEIIIYQQQjQC1v4eoPpKMkBCCCGEEEKIRkMyQEIIIYQQQjQCkgAykwyQEEIIIYQQotFQWjec+4HXwH/6xQkhhBBCiHqj3idYdqUuseqxcQfP4fVym/znp8B1/25DXYdQI1tu7gPAnevW1nEkNTOnX382Ji6t6zBqrJfvNdy7YU1dh1Fjn/UZwGMbV9d1GDXyXq+BAIxeva6OI6mZhQP7AfDl/mV1HEnN3NVyGP8mN9x99zLvaxpsvwnmvnPA0r/qOowaWXNNbwC+Ptgw2/5t4cMAcAwZU8eR1EzesQUA3LSqYfadiwf1I61gSV2HUWMmh+F1HUK12NTL4Yj1yRQ4IYQQQgghRKPxn88ACSGEEEIIIRrAHD0rkQyQEEIIIYQQotGQDJAQQgghhBCNgHwRqplkgIQQQgghhBCNhmSAhBBCCCGEaAQkAWQmGSAhhBBCCCFEoyEZICGEEEIIIRoByQCZSQZICCGEEEII0WhIBkgIIYQQQohGwEZSQIBkgIQQQgghhBCNiGSAhBBCCCGEaAQkAWQmGSAhhBBCCCFEoyEZICGEEEIIIRoBpXRdh1AvyADIgp5+Hkzs3Awbpfj5cAJf7YuttHxYiA+3twoGIK+omDf+PcSBjBwAxrQI5NowPzRwMCOX6Vv3U1hi3caWFhnJkYXfoktK8Ovbh6Crrqq0PC8ujoNz5pJz7Bgh111H4LChABSkpnLwiy84lZEJSuHXrx8BVwy2auwAWmu+efdHdm2Kxt7BnnufHUNo6fauaMX36/nzu3Uknkhh1q/TcPVwASA3O49Ppn9DakIaxcUlXDV6IH2v6W61+FN3R3J4wSK0LsG/bx+aXH1lpeW5cfHs/2IO2ceOE3r9tQRfObTada0haVcUe+cvQpeUENyvN82GV45Ba83ebxaRtCsSg709EffegVtoCDlx8ez88LOycrlJyYRfP4LQYdZrQ5lRkcQuWoguKcGrd1/8r6zc9vPj4zg6dw55x48RMPI6/IYOq7Rcl5Sw77WXsfPwoPnDj1kt7rLn15oVs7/n0D97sHOw55rx4/APb1KlXHp8Cj+/NYf8rFz8mgcz4onbMNjZsvmHlUSt2QZASXEJKbHxPDbvVRxdna0W/9yZP7JjYzT2RnsenDKGMAv77vsvzuPw3uMYbA00bxvCvZNuxtbWwImjCXzyykJi9scy6v6rGT52oFXiPq2h953dfTx4pG0zDAqWHk9g/qETlZZfEejDmOZB5tdSXMyM3Yc4lJWLj9GeZzu1xNPBjhINS47F8/2ROKvHr7Xmz0++5+A2c/sfMWEcARbaf1p8Cj++MYe87FwCmgdz7URz+8/PyePnt78iIymNkuISet4wiE5Delol9o/feoCrBncmKSWTrkMmWSzzv5fuYNjATuTmFXL/xI/YEXkEgCH9O/L2i7djMNgwZ+Fq3v7wF6vEXFFWVCQnFy0EXYKpd198h1XtO2O/mkP+8WP4jbwOnyHlfefeKU9jYzSibBTKxkD4M89ZO3y01rzzxk9sXB+Ng9GeqdNH07pt1b7nlRe+JTrqOFpDSFMfpr48GicnBzIzc3nl+W+JPZ6Cg4MtU14aRfMWAVZ/HcJ6ZAB0Bhtg0mXNeWRdJIm5hcy9ohPrT6YQk5VXVuZkTj7/t2YXWaeK6eVv4pku4dy9aic+RntGtQhk1B//UlBSwqs9WzGkiQ9LjyZaLX5dUkLM/Pm0nTABe5OJ3a+8iqljR5wCA8vK2Do7EzZ6NKk7tleqq2xsaHrzzbg0bUpxfj67pr+Me9s2lepaw65N0STEJvPGgmc5tOcoX/1vMc/PfrxKuRYRYXS8vB2vP/ZBpcdX/vAXQaF+THjjXjLTsnlm3Gv0GnoZtna139x1SQmHvllA+4mP42AysWP6a3h26oBzpe3vRPOxo0nZvuOC61oj/uivF9D1qfEYPU1sfOk1fDt3wCWoPIbkXZHkJiTS941pZByKYc9X8+n5/NM4B/hz+fTnytaz5vGn8evSyaqxH18wn/DxE7Azmdj32iu4d+iIY4XtZ3ByJnjUaDJ27LC4jqRVKzD6B1Ccn2dxeW07/M8e0k4m8cAnUzm57wjLPlrEHf+bWKXcmjk/0+3aAbTt14U/PviWncs3ctnVfelxw2B63GA+8D6wZTdbf15jtcEPwI6N0cTHJjPj22c5GHWUz99ezMufVt13ew+9jIdfGAfArBfnsfrXTQy5vjcubk7cMeF6tq2LtFrMpzX0vtMGGN+uGU9ujiIpv5CP+3Tkr4RUjmaXt+W4vHzGb9xNdlEx3X08mBgRzkN/76JYaz7cE8OBzBwcDQZm9+nItuT0SnWt4dC2PaSeTOKhT6dyYt8Rfv9gEXfPqNr+V335Mz2uG0C7/l347f1v2fHnRrpc05dtS9bj3cSfUS88QE5GFh/d/woRA7pisELf//V3a/l47jI+m/GQxeXDBnaieag/7ftNoHvncN575R76XTsVGxvFzJfv4ppxr3IiLoUNv77CkuX/sPfACYvrqQ26pISTC+cT9tgEbE0mDr3+Cm4dOmIMqND2nZwJvGU0mTst953NJkzE1sXVWiFXsXHDXo4fTea7Jc8QtesYb778PV/MH1+l3ONPXYuzixGAmW/9zOIFG7j9nsHM/XQlLVoF8sbMuzgSk8Dbr/zA+589aO2XYRVyDZCZXAN0hnaersRm53Myp4AirfnzeBL9grwqldmdkkXWqWIAIlMy8XWyL1tmUAoHgw0GBUaDgeT8QqvGnx0Tg9HHF6OPDza2tnh360bajp2Vyti5ueESFooyGCo9bu/hgUvTpgAYjEYcAwIoTE+3Vuhltm+IpPeVXVFKEd4ulNzsPNKTM6uUa9oyGJ8AzyqPKwX5uQVorSnIK8DZzQkbg3WaetbhGIy+vjiWbn+f7l1J3V55+9u7ueEaVnX7V6dubcs4fAQnP1+cfM0xBPToRuL2XZXKJG7fRWDvniil8AhvxqncPArSMyqVSdmzFydfbxy9K+87tSn3SAwOvj44lG4/U7duZOyq/GFt5+aGc2hYlW0PUJiWSsbu3Xj17mOtkKs4sGk37Qd1RylFUOswCnLyyE6tvG211hzddYDWvc2Dy4jB3TmwaXeVdUWv/Ze2/bpYJe7T/tkQSd/SfbdF+1Bys/JIs7Dvdr68LUop8z7eJoTURPNrdDe50rxNCAZb6380NfS+s7WHKydy84nLM392rTqZRG+/yv1jVFoW2UXmz649aVn4OJo/u1ILTnEg0zyLIa+4mKPZuXgb7bG2fZt2E1Ha/oNbh5Gfk0eWhfZ/ZNcB2vQxt/8Og7uzr7T9KwWFeea+vzCvEEdX6/X9f23ZS2p69lmXDx/ahfnfrwdgy/aDuLs54e/rQbdO4Rw6Es+RY4mcOlXMd79uZPjQrlaJ+bTcIzHY+/hgX9r23bt2qzLQsXVzw+ksfWd9sG51JFeP6IJSivYdm5KdlUdyUtW+5/TgR2tNQf4pc6MBYg4n0LVHCwBCw/yIO5lGSkqW9V6AsLp6OwBSSt2ulNqllNqplPpaKTVCKbVZKbVdKbVCKeVXG8/r42hPQm5B2f+JuQVlHxKWjAzzZ2NcGgBJ+YXM23eCX4Z347cRPcg+VcTmBOt+CBamp+PgWf6hZ2/yoCA97YLXk5+cTM7xY7iEhV3K8KolLSkTT1+Psv9NPh6kJWeco0Zlg2/sw8mjCTx+3Ys8d+dbjH3semxsrNPUC9LTcfA0lf1vbzJRUM0DoYupe6nkp6VhrBCD0eRBflrl9lOQlm6hTOU44zdvw79nt9oN9gyFaenYmyq0fQ8Tp9Kqv/1OLPqWoBtuAlV33WJWSgau3uVt39XLg6yUym0/LzMHBxdHbEoPRCyVOZVfyOF/o2l1ecfaD7qC1KRMvCrsu56+HqQmnX3fLSoqZv2ybXTs0doa4Z1TQ+87fYz2JOWVn3BLyi/Ex+hw1vLXhPixJbHq/uHv6EALdxeiz3EwX1uyUjJw8ylvP27eltu/0blC+69QpuvwfiQfj+fd26Yy++HXGHr/jSgr9f3nE+jvSWxcStn/J+JTCfT3JNDfROzJCo/HpRDkZ7K0ilpTlJ6OXYW+085k4tSFfPYoiHlvJgdenU7q+nW1EOH5JSVm4Otf3nZ8/dxJSrTc90yfupCrB77I0SOJ3DLGfMKrRctA1qw0D6Sjdh8jPi6NJCsfvwnrqh89wxmUUu2AKcAgrXVHYDywAeipte4MLAQsTrJVSt2vlNqmlNo2e/bsGjy3hQfPcglPFx93Rob58f7uIwC42hnoH+TJdUu3cvWvW3C0NXBliM8Fx3BRdNVg1QUmPIvz89n/0ceEjhqFraPjpYqs2rSl13ABLyFy8z5CwoOY+dOLTPtiIvNm/kBeTv4ljPAcLuZyr/pwXaKFGM5sP5ben4pFSoqKSNy+E/9u1s0+nCX4asnYtRNbVzecSs/i153qtP3zlzm4NZKgNmFWnf4GF77vfvH2Ylp3bEbrTs1qMapq+g/0nWfSZ+lUOnm5c3UTPz7Ze6TS444GG17q0pr39xwmtzRTZFUW34Mzipyjozz8bzR+zYIZ//V07ps1mT8+/o6C3LqZznomS21Ja42ysINY6mJr1UV+5jZ/8mlaPDuVsEfGk7J2NTkH9l/C4KrH4sfSWV7D1OmjWbLyBUJOVSoNAAAgAElEQVTD/FixzJzpuv2eQWRl5nHbzf/juwUbaNk6CEM9zXZdLKWs+1Nf1ddrgAYBi7XWyQBa61SlVATwrVIqALAHYixV1FrPBk6PfPRn3224oCdOzC3Ez6n8rJmvkwNJFqaxhbs7MaVrOI+vjyKjsAiA7n4enMzJJ730/9UnUujg5cYfx5IuKIaLYW8yUZCaWvZ/YVo69h4e56hRWUlREfs++hjvHj3wuuyy2gjRohU/bGDtr5sACGvdhNQKZybTktLx8HKv9rrW/7aFa24djFIKv2AffAI8iTuaQLO2tX9w62DyoCC1/KxxYVoaDtXc/hdT91IxeprIrxBDflo6DiaP85YxVogzeVckbk1DcHB3q/2AK7A3mShMq9D209Owq+b2yzl0iIxdO8iM3E1J0SmK8/I58sVnhN59b22FW+afpevYuWwjAAEtQshKLm/7WSnpuHhWbvuObi4UZOdRUlyMjcFgscyeddab/vbn9xtY9Yt5323WpgkpFfbd1MR0TN6W993FXywjKz2He1+92Spxnk9D7TtPS8ovrDRbwcdob3EKdjNXJ56KaM7krXvIPFVU9rhBKV7q0poVJ5JYH59apV5t2bZkHdv/KG3/LUPITCpvP5nJ6bic0fc7ubmQn1Oh/Sen41paZufyzVx+8xCUUngG+uDh50Xy8USCWtX1iQ04EZ9CcED5lOAgf0/iEtKwt7MlOLDC4wFenEy88MzjxbA1mThVoe88lZaGrXv12/7pftbWzQ23Tp3JPRKDc4uWlzzOMy1euIGfv98MQJt2TUiML287iQkZePuc/bjBYLDhiis78c2c1Qy/rjvOLkamTh8NmAem11/1CoFBVafYi/+OepkBwnzS58zx/Czgfa11BPAAYKyNJ96TlkUTF0cCnRywVYqhTXxYf7Lyh4GfowNvXN6GF7bs51h2eWYhPreA9p6uOJTOOe7m686RrNzaCPOsXEJDyU9MJD8pmZKiIpK3bsXUsXrTYLTWHJr7FY4BAQQOHVLLkVZ2xQ19mP7lk0z/8kku6xvBX39sQ2vNwagjOLoY8fCu/sG0l5+JPf+Yz0BlpGYRdywRn0DrXIviGhZKfkL59k/asg3PTtXb/hdT91JxC2tKbkIiuaUxxG3eim/nDpXK+HbqwMm/NqG1Jv3gYWwdjTh4lH/QxG3aRoCVp78BODUNpSAxkYLkJEqKikjbuhX3DtXbfoHX30D719+i3auvE3rP/bi2bmWVwQ9Al2v6cfd7k7n7vcm06NmByFVb0FpzYm8MDk7GKoMbpRQhHVqw9y/zmcvdK7fQokdE2fL8nDyORx6kRc8IrGHojX14fe6TvD73Sbr2i2B96b57IPIITi5GTBb23VW/bGLX5n08+tKtVpueej4Nte88bV9GFsHOjvg7mj+7BgX68HdC5c8uX6M907u05tWdB4g9Iys+qUM4x7Lz+C7mpDXDpuvwftz3/mTue38yrXp2YHdp+4/dG4PR2YirhfYfGtGC6A3m9r9r5RZalrZ/N18TMTv3AZCdlknqiURM/ta7DvFcli7/l7E39gWge+dwMrNyiU9MZ9vOQ4SH+dO0iQ92dgZuHtGLpcv/sWpsp/vOwtK+M2PbVtyq2XeWFBRQnJ9f9nd29B6MgUG1GW6Zm0b34evvJvL1dxPpP6g9v/36D1prIncexcXViLdP5b5Ha83xY8llf29YE0XTUF8AsjLzOFV6QuDn7zfT+bJmZdcL/dfYWPmnvqqvGaCVwI9KqRla6xSllCfgDpy+LcodtfXExRre2n6I9/q1x0bBrzEJHM7M5YZm/gD8cDiee9s2wd3ejsmXNTfXKdHcsXInUanZrIxN4esrOlGsNfvSc/jxcHxthWqRMhgIGzuG6Jkz0boE3969cQoKJH7NWgD8B/SnMCOD3S+/Yu60lCJuxQo6TnuJ3NhYkjdtwikoiJ0vTQMg5IbrMUVY50DqtI692rBrUzSTRr+Kg9GOe54ZU7bsnadmc9fkUZi83Vm+eB2/zV9NRmoWU+98mw4923D306MYeecQPnt1Ac/d8SZawy3/N7zsFtm1TRkMNB83msgZ75pvpdunN85BgcSVbv+A0u2/ffqrFOeZt/+JFSvpMv1FbB0dLda1JhuDgTa3juKft99Dl5QQ1PdyXIICOb7KPK+7yaB+eHdsT9KuSNZPmorBwZ7295TvjsUFhaRERdP2znFWjRvM2z541FgOvTcTXaLxurw3joFBJK9bA4B3vwGcyshg32svU5yfj1KKpFUraPPCNAz1YLoSQPOubTm8LYpP7p+GnYM9V48v346LXvyYqx4dg6uXOwPvHMnPb85h3byl+DULpsPQ8lv97t+4i7DOrbE/x/UftaVzrzbs2BjN47eY990Hni3fd9+YOJv7nh6Fp487n7+9GG8/E8/f/x4A3fpHcOPdw0hPyWTKPTPIy8lH2Sh+X7SOt76ZjJNz7R+INPS+s1jDu5GHeat7O2wU/B6byJHsPEaGmD+7fjkWzx0tQnCzt2NCu2ZldR74aycRJleGBftyKDOHz/qYD3w/3XeMzUnWzUSEd2vLwW1RfHDvtLLbYJ+24IWPGf6Yuf0PumskP745hzVfL8W/WTCdhpnbf9/RV/LLjHl88tBrAAy6cyRO7tbp++fOepS+vdrgbXLl4Ob3mf7OYuxK7z732bwV/LFqO8MGdiJq/Uxy8wp44MlPACguLmHC1Dn8+vUzGAw2zP12DdH7Y8/1VJecMhgIHD2WmFkzoURjurw3xsAgUkr7Tq/SvvPg6y9TUtr2k1etoOXz0yjOzuboJx8CoEuK8ejWA9d27a0aP8Dlfdvw9/pobrrmNYxGO54rzeYATHjoU5598Ra8vF2Z9twCcrPz0RrCWwUw+bmbADgSk8BLUxZgsFGENvdnyku3WP01COtSFufz1wNKqTuAp4BiYDvwIzAD8yBoE9BNaz3gPKvR3S9wClx9seVm84V5d65bW8eR1Mycfv3ZmLi0rsOosV6+13DvhjV1HUaNfdZnAI9tXF3XYdTIe73M3/0yenXdXEx7sRYO7AfAl/uX1XEkNXNXy2H8m9xw993LvK9psP0mmPvOAUv/quswamTNNb0B+Ppgw2z7t4Wbv9vGMWTMeUrWT3nHFgBw06qG2XcuHtSPtIIldR1GjZkchkMDuMv00exfrXrg39RlRL3cJvU1A4TWei4w94yHf66LWIQQQgghhBD/DfV2ACSEEEIIIYS4dOplOqYO1Ofrk4QQQgghhBDikpIMkBBCCCGEEI1Aff5uHmuSDJAQQgghhBCi0ZAMkBBCCCGEEI2AJIDMJAMkhBBCCCGEaDQkAySEEEIIIUQjYCMpIEAyQEIIIYQQQohGRDJAQgghhBBCNAKSADKTDJAQQgghhBCi0ZABkBBCCCGEEKLRkClwQgghhBBCNAJK6boOoV5QWv+nN8R/+sUJIYQQQoh6o95fYhOf94tVj439HUfWy20iGSAhhBBCCCEagXo5GqkD//kB0OObVtV1CDUys+cgAAb//lcdR1IzK6/qzdq43+o6jBrrH3A1T2xumG0H4J0eg7h/w5q6DqNGZvcZAMC7UX/WbSA1NL7dUADe3LW8jiOpmUkdhrDw0B91HUaNjW5+ZYPtN8HcdzbU+Fde1RuAxTENs/3cFHal+feqdXUcSc0sHtQPAMeQMXUcSc3kHVtAfN4vdR1Gjfk7jqzrEMQF+M8PgIQQQgghhBCgJAUEyF3ghBBCCCGEEI2IZICEEEIIIYRoBCQBZCYZICGEEEIIIUSjIRkgIYQQQgghGgHJfJjJdhBCCCGEEEI0GpIBEkIIIYQQohGQu8CZSQZICCGEEEII0WhIBkgIIYQQQohGQVJAIBkgIYQQQgghRCMiGSAhhBBCCCEaASUZIEAyQEIIIYQQQohGRAZAQgghhBBCiHpFKeWplFqulDpQ+tt0jrIGpdR2pdSS6qxbBkBCCCGEEEI0AkrZWPXnIj0NrNRatwBWlv5/NuOB6OquWK4BsiBpVxTR3yxCl2iC+/em+fBhlZZrrYn+ZhFJO6Mw2NsTcd/tuIeGkB0Xz44PPy8rl5uYTIsbhhM2bLBV4+/m7cHDbZpho+C32AQWHj5RafngQB9GhwUBkFdczMyoQxzOygXgyYhwevqYSC88xb0bdlg17tO01nw760d2b4rG3mjHnU+PoWnLJlXKrfphPSsXryPpZDL/+2k6rh4uZcv2bT/It+//SHFxMS7uLjz17iNWiz9xVxR75pnbT5P+vQkfUbX97Jm3iMSdURgc7OlY2n4ATuXksuuLeWTFngQUHe+9DVOLZlaLHSB1dyQHFyxC6xIC+vYh5OorKy3PjYtn7xdzyD52nLDrr6XJlUOrXbe2aa3Z8Pn3HP03ClsHewY/cis+zau2ncyEZP58Zw4F2bl4hwVzxfjbMdjZErNlF5sXLEUphY3Bhj5330hAm+ZWjX/Tl4s5Xhp/v4dvw7tZ1fizEpJZPfNLCrJz8QprQv9HzfGnn4hn3QfzSImJpeuY4USMvMJqsZ+O//dPfuDA1j3YOdhx3RPjCAyvGv/mX9ex6ae1pMYlM2nBKzi7u1xQ/drS0PvOhh6/1pqlH/3AvtL3/8aJ4whqUfX93/jLOv7+0dx+nv22cvupTv3akBUVyclFC0GXYOrdF99hV1Vanh8fR+xXc8g/fgy/kdfhM6T8c2HvlKexMRpRNgplYyD8meesEnNFH7/1AFcN7kxSSiZdh0yyWOZ/L93BsIGdyM0r5P6JH7Ej8ggAQ/p35O0Xb8dgsGHOwtW8/eEvVozcTGvNe2/+zOYNe3Ew2vHMtFG0bBNcpdwbLy5i355YtNY0aerD09NG4eTkULY8OvI4D90+ixfeuJUBQzpY8yUIy64FBpT+PRdYA0w+s5BSKhi4BngFeKI6K66TDJBSykMp9VDp3wOqm66qUH+OUuqm2ohNl5QQ9dVCuk58hL6vPU/cpq1knYirVCZpVxQ58Yn0e/Ml2t01lqi5CwBwCfCnz/Qp9Jk+hd4vPYPBwR7/Lp1qI8yzsgEea9eMZ7ZFcff67QwK8KGpi2OlMnG5+UzYvJv7/trBvIPHeaJ9eNmyZbGJPLNtj1VjPlPk5mgSYpN4+ZtnuW3iLXwzY7HFcuERYUz434N4+VXOiOZm5TF/5mIefvVeXprzNA+8eIc1wgbK20/3Jx+h/+vPc/Js7SchkQFvvUTEXWOJnLOgbFnUvEX4RLRlwBsv0u+VKbgE+lst9tPxH/hmARETHqXb9BdJ3LyVnJMnK5WxdXYifOxomgwbcsF1a9uxf/eQEZfIuA+eZ8D/jWbt7G8tltv49S90HDGQcR88j4OLE9ErNwIQHNGKUe88zah3nmbgw+NY/eF8a4ZP7PY9ZMYlcfOsF+jzwBj+/nShxXJbv/mZdsMHcvOsF3BwcWT/KnP8Di7O9Lr7ZiJGDLJm2GUObNtDyokkHvvsOUY8Npol739nsVxI22bc/upDePh61qh+bWjofWdDjx9g/9Y9JJ9M4okvnuO68aP55Szvf9O2zbj7tartp7r1LzVdUsLJhfMJe2Q8LZ6fRsbWLeTHndFvOjkTeMtovK8YanEdzSZMpMWUF+pk8APw9Xdrufb218+6fNjATjQP9ad9vwk88vSnvPfKPQDY2ChmvnwX197xBp0HP8nNIy+ndYsga4VdZvOGvcQeS+abXybz5NSbeOeVHyyWe+TJkXyx6Am+/G4ivv4e/Ljwr7JlxcUlfPLuUrr1amWtsOuIsuqPUup+pdS2Cj/3X0CwflrrOIDS375nKTcTmASUVHfFdTUFzgN4qI6e+5zSDx/B2c8HJ18fbGxtCejRlcR/d1Yqk/jvToJ690QphSm8GUW5ueSnZ1Qqkxy1Fycfbxy9vawZPq09XDmRk09cXgFFWrM6LonLz/iQ2JOeRXZRcdnfPkb7smW70zLJPFVk1ZjPtOOvSHoN64ZSimbtQsnLziM9JaNKuZAWwXgHeFZ5fMvKf+jct0PZwMjN5FrrMZ+WfugITr7l7SewZ1cSzmg/CWe0n1Ol7edUXh6p+w7SpH9vAGxsbbFzdrJa7ACZh2Nw9PXF0cccv2/3rqRsrxy/vZsbbmGhKIPhguvWtpgtu2k1oDtKKfxbhVGYk0dOauW2o7XmxO79NO9lPjnRemAPYrbsAsDO0QFV+jXZRQWFWPv7Eo5u3UV4f3P8vi3N8eemVY3/ZOR+wnp2BiC8fw+ObjVvZ0d3V3zCm2Jja6iybmvYuymSToPN+26T1qHk5+SRlVp13w1oHozJr2rfWN36taGh950NPX6A6I2RdC59/0PahJKfnUemhb4/MDwYk3/V9lPd+pda7pEY7H18sC/t+9y7diNzZ+Usmq2bG06hYVX6zfriry17SU3PPuvy4UO7MP/79QBs2X4Qdzcn/H096NYpnENH4jlyLJFTp4r57teNDB/a1Vphl9mwJophw7uglKJdh6ZkZ+WTkpRZpZyzixEw96MFBafK+nuAHxb8Rf/BEZg8na0Wd2OgtZ6tte5a4Wd2xeVKqRVKqUgLP9dWZ/1KqeFAotb6nwuJq66mwL0ONFdK7QBOATlKqcVAe+Af4FattVZKPQ+MAByBv4EHtNa6NgPLT0vH6FmeUTB6mkg/FFO1jFflMgVp6Rg93Msei9u8jcCe3WozVIu8jfYk5ReW/Z+UX0gbj7MPAK5q4seWpHRrhFZt6UkZmHw8yv43+XiQnpSBh5f7OWqVSzieRHFxMW+Pf5/8vAIG39iPXsOs817kp6Xj6HWe9pOajuMZbSw/NR1lsMHezYVdn35F5rFY3MNCaHvrLdg6OGAthenpOFSIzcFkIjMm5hw1Lk3dSyUnNR0X7/IYnL08yEnNwNmzvO3kZ+Vg7+yITemBiLOXBzkVDpIOb9rJpm9+IS8jm2um/J/1ggdyU9NxrtB+nLw8yElNx8lUHn9BVg72ThXjN1UZ5NWVrOR03Crsu27e7mQmZ+DqWb1992LrX4yG3nc29PgBMlPSca/4/vu4k5mSgVs1+/6LrV9TRenp2JnKB5t2JhO5F9L3KYh5byYAXn3749m336UO8aIF+nsSG5dS9v+J+FQC/T0J9DcRe7LC43EpdO8UbmkVtSo5MRNf//L33sfPnaTEDLx83KqUfe35b9m8YS9Nm/nx8BMjAEhKyGD96khmzH6AvVHHrRZ3Xahvt8HWWp91rrZSKkEpFaC1jlNKBQCJFor1BkYqpa4GjICbUmqe1vrWcz1vXWWAngYOaa07AU8BnYHHgbZAM8wvBuB9rXU3rXV7zIOg4edbccVU2+zZs89XvCoL46uKZwiqo6SoiMTtu/DvftmFP38tONuYsZOnO1cF+/HpviPWDeg8NBf3HhQXl3B0XyyPvn4f4998gKVf/UnCcUv7TG2wtK3V+Uso0MUlZB45TsjgfvR9eQoGBwcO/bqsVqI8q4s5vVCrpyaqGYKltn5m0zlPmWY9OzJ21lSumnwfWxZc0Ozci2Y5tDPaj6U+qrYCukCW23b1o7vY+pdaQ+s7z9TQ4rfY/i+k/Vxk/RqzeNxQ/erNn3yaFs9OJeyR8aSsXU3Ogf2XMLhLw9JBs9ba4vat3dPUllnsF8/yJjwzbRTfL59K0zBfVi0zZ89nvfULD4y/GoNB7g1Wz/wCnL6O4Q7g5zMLaK2f0VoHa61DgdHAqvMNfqD+3ARhi9Y6FqA0KxQKbAAGKqUmAU6AJxAF/HquFZWm1k6PfPTjm1ZdUCDms/FpZf/np6bh4FH57JHR5EF+yhllTOVnHpJ2ReHWNAQH96pnHmpbcn5hpWkNPkZ7UgoKq5Rr5urExIjmPLN1T51PewBY/eMG1i8xX8cQ2jqEtApnJtOS0nH3rv62NPm44+LujIOjAw6ODrTo2Jzjh07i1+RsU0cvHaPJRN4ZbcNoqtx+HD09yDuzjZW2H6OnB6bmYQAEdOvMwSV/1nrMFdmbPCioEFtBWhoOHh7nqHFp6l6M3b+vY8/yvwHwDQ8hO7k8hpyUdJzP2P5GNxcKc/IoKS7GxmAwl7GQYQhsF07GrGTyMrNxdHOpsvxS2fPHWvatMMfvHd6UnArtJzclHSdPC/HnVow/rUoZa9r863r+XWbedwNbhJBZYd/NTM7A1av6+66bt8dF1b8YDbXvPK2hxr/pl/Vs/aP0GryWIWRUfP+TMnD1rP777+7tcVH1a8rWZOJUWmrZ/6fS0rB1r37fZ1faT9q6ueHWqTO5R2JwbtHyksd5MU7EpxAcUD7tMMjfk7iENOztbAkOrPB4gBcnE9MsreKS+3HhXyz5YTMArdo1ITG+/L1PSsjA20L25zSDwYZBwzqycO5arr6uG/v2HGfa5G8AyEjPYdOGvRgMNvQd1L52X0SdqC+nzKrldWCRUuoe4BhwM4BSKhD4TGt9dU1XXF+GugUV/i4GbJVSRuBD4CatdQTwKebUVq1yD2tKTkIiuUnJlBQVEbd5G76dK98JxLdzB078tQmtNWkHD2Pr6Fh5+tumrQT2tP4cWIC9GVkEOTvi7+iArVIMDPDh78TUSmV8jfa82Lk1r+08QGxufp3EeaaB1/fh+c+f4vnPn6JTn/ZsXLYVrTWHo47g6OxY7elvAJ36RHBw92GKi4opyC8kZs9RAkL8ajH6cu7NKrefk5u24Xe+9uNkbj9GD3eMniay4+IBSI7ah6uVb4LgFhZKXkIieaXxJ27ZhlenjrVe92JEXNWv7MYFYd07sG/NFrTWxO+Lwd7JWGVwo5QiqH0LDm00z9Hfu3ozYd0iAMiISyo7k5h06DglRcUYXWt3PnjbK/tz/dvPcP3bz9C0WwcOrjXHn7g/Bjsnx0rT307HH9CuJTGbtgNwcO1mQrrV3d2Keozoy4PvT+LB9yfRplcEO1aa993je49gdDZe0PS11j3aX1T9i9FQ+87TGmr8PUf25dEPJ/Hoh+b2s730/T8WfQQHZ+MFTV9r3bP9RdWvKaemoRQkJlKYnERJUREZ27bi1qF6fV9JQQHF+fllf2dH78EYaP2bCJzP0uX/MvbGvgB07xxOZlYu8YnpbNt5iPAwf5o28cHOzsDNI3qxdPkFXYpRY9eP7s3ni57g80VP0Hdge5Yt+QetNVG7juLsYqwy/U1rTeyx5LK//163h5AwHwC+/e1Zvv3d/NP/iggmPHvDf3Tw07BorVO01oO11i1Kf6eWPn7S0uBHa71Ga33e2WJQdxmgLOB8V6afHuwkK6VcgJsAy7cDu4RsDAba3jaarW/NQpeUENzvclyDAzm2ah0AIYP64dOxPUm7Iln71PMYHOzpcO/tZfWLCwpJjtxLuzvH1XaoFpVomLXnMG90a4eNgt9jEzmancfwJuYD6SXH47ktPAQ3ezvGtzPfXrlYw0N/m9PAUzq2pKOnO+72tiwc2JW5B47xe6y1po+ZRfRsS+TmaKaMewV7B3vunDy6bNl7k2dz+1Oj8PB2Z+X361i2YBWZqVlMu+ctInq04fZJowlo6ke77q2Zds9bKKXoc01PgpoFWCV2G4OB9rePZsubs9C6vP0cLW0/TQf1w7dje5J2RrLmqecx2FduP+1uG8WOj76kpLgYJx9vOt53m1XiPk0ZDISPG83uGe+iS0rw79Mb56BATq5ZC0DggP4UZmTwz/RXKc7LB6WIXbGSbtNfxNbR0WJda2rapR3H/t3DNw9Nw9bBjkGPlGfBl7z8EQMfGouzpzs9b7uW5e98yeb5S/AJC6bNFb0AOLRxB/vWbsHGYMDW3o6hE++y6hSsJpe1I3Z7FN89+hK29nb0fbg8/mWvfkif/xuLs6cH3W69ltUzvuSfBUvwCmtCq0Hm+HPTMvn56Tc5lZePUorIpWu4ccYU7J0cz/aUl1SLbm3Zv3UP794zHTsHe66bMLZs2bznP2bk+DG4ebmz6ee1/LV4JdlpWXz08Bu06NqWax8fc876ta2h950NPX6AVt3N7/87d5vf/xueKH//5079mOsfN7efv39ay/rFK8lOzWLWg2/Qsltbbpgw5pz1a5MyGAgcPZaYWTOhRGO6vDfGwCBS1q0BwKvfAE5lZHDw9ZcpyTf3m8mrVtDy+WkUZ2dz9JMPAdAlxXh064FrO+sfeM+d9Sh9e7XB2+TKwc3vM/2dxdjZmQ8RP5u3gj9WbWfYwE5ErZ9Jbl4BDzz5CWCecj5h6hx+/foZDAYb5n67huj9sVaPv2ff1mzaEM3YEa/jYLTn6ZduKVs26eHPmfTCTXh6u/La1IXk5BSA1jRvGcgTU26weqx17RJ8N89/gqrlewqc/YmVmg90APKAhNMjNqXU+8A2rfUcpdTLmOfzHQGOA0e11i8qpeYAS7TW5xsQXfAUuPpiZk/zbWwH//7XeUrWTyuv6s3auN/qOowa6x9wNU9sbphtB+CdHoO4f8Oaug6jRmb3GQDAu1HWnf53qYxvZ77N7Zu7ltdxJDUzqcMQFh76o67DqLHRza9ssP0mmPvOhhr/yqvMl+8ujmmY7eemMPP3lt1UesKqoVk8yHzzBMeQMXUcSc3kHVtAfJ71v0PoUvF3HAkNYH5Z5qnlVj3wd7MbUi+3SZ1dA6S1tnhqRmv9SIW/nwOq3BRfa31n7UUmhBBCCCHEf1G9HI9YneTBhBBCCCGEEI1GfbkLnBBCCCGEEKIW1bfvAaorkgESQgghhBBCNBqSARJCCCGEEKIRkAyQmWSAhBBCCCGEEI2GDICEEEIIIYQQjYZMgRNCCCGEEKJRkNwHyFYQQgghhBBCNCKSARJCCCGEEKIRUEpuggCSARJCCCGEEEI0IpIBEkIIIYQQolGQDBBIBkgIIYQQQgjRiCitdV3HUJv+0y9OCCGEEELUG/U+vZJbtN6qx8ZOtn3r5TaRDJAQQgghhBCi0fjPXwO0I2VJXYdQI528hgNwIKNhxt/CfThN31pV12HU2NGnBvHnid/qOowaGxp0NfMP/dEcAUUAACAASURBVFHXYdTI2OZXAjAjcnkdR1IzE9oPAWDsmrV1HEnNzB/Qn5B3Vtd1GDV27ImBZJ9quH2Pi90gsk6trOswasTVbjAAbb9YV8eR1Myeu/sBkFbQMD93TQ7m44b4vF/qOJKa8XcciWPImLoOo8byji2o6xCqSXIfIFtBCCGEEEII0Yj85zNAQgghhBBCCFD1/zIlq5AMkBBCCCGEEKLRkAyQEEIIIYQQjYBSkgECyQAJIYQQQgghGhHJAAkhhBBCCNEoSAYIJAMkhBBCCCGEaERkACSEEEIIIYRoNGQKnBBCCCGEEI2AktwHIBkgIYQQQgghRCMiGSAhhBBCCCEaBbkJAkgGSAghhBBCCNGISAZICCGEEEKIRkC+CNVMBkAWaK2ZM+Mntm+MxsFoz4PPjaZZq+Aq5d57cR6H98ZiMBgIb9uE+ybfjK2tga3rIln06R8oG4XBYMMd46+ldcdmVo1/9v9+Ytvf5vgff3404a2rxv/W1HkcjI7FYGugZbsmPPKMOf6c7Dzefn4+SfFplBSXcP2tAxgyorvV4u8f6skLg1tgUIqFu+L4aMtRi+U6+Lvy07iuPPJrJL/tT8LBYMOiMZdhb1DY2ih+25/EjL9irBb3aVprvn//R6I2R2NvtOPWSWNo0rJJlXJrf1zPmu/XkXwymdd+nI6Lu0ul5Uf3HuN/j8zkrqm307l/J2uFj9aaPz75gQNb92DnYMd1T4wjILxq/GnxKXz/+lzysnMIaN6E65+8FYOdLXlZufwycz6pccnY2ttx7eNj8A0NtFrsf32xmGP/RmFrb8/AR2/Dp1nV2DMTklkx40vys3LxadaEQY/djsHOlpgtu9i6YAnKRmFjsOHyu24ioE1zq8QOkBEZyfFF30JJCd59+uB/5VWVlufHx3Fkzlxyjx8j8Nrr8B86tNJyXVJC9KuvYO/hQfgjj1ot7tP6h3ry4oAWGGxg4e44Ptx6zGK5Dn6u/DymCw8vjeK3A0kEuDgw46o2+DjZozXM332SL7bHWjl6c/v5//bOO76KKv3/7yeFFBJSSEgA6R1CkyIlUgUWlqp0WXXX/erqKqvCqigdWUSwIyKoP7FQpaMISAmho6GFjpTQU0gnhJCc3x8zSW6SGwiQey+Y83697uuee+bMzGfOnDLnPM+ZO23KIraFH8LdvRTjJz9FvfqVC03/3v8WsnLZDrbu+ShP/KGDZ3jmyfeYMv2fPNb1YVvLzkEpxfQpi039royf/BR1b6N/1bKdhO/5EIDNG/cz69NVODk54ezsxIg3+9Pk4Zr2kk9oRT9GtaqBswg/Hr/MlwfO5dneqXJZXn64CkrBTaV4d9cfRFxJAmD9gJakZmSSpRQ3lWLgyr120w1G3n8wdTk7wo1+d8ykwdStX7DfnTxuIUcOnUMpqFwlkDHvDMbT042kpGtMHruQ8+ficHNz4e0Jg6hRq7xd9X/y3gp2bT2Km7sroyYOona9gvqnjl/EscPnUUpRqUogb04chKenW872I5HnePGpTxk3dRgdujSym/5Z056ne+emxMQl0bzL61bTvD/habp1bMK1tBs8N+Jz9kWeAaBL+8ZMH/8Uzs5OfLNgE9NnrrSbbo3jKDYXOBHxFZEXi+t4tzhPXxGpb8tz7NtxlMvnY/l40Sj+740BfDVtidV0j3Ztxofz32D69yO5kZ7BxpW7AGjYvBbvfTuC9+aO4F9vDeKLKYtsKbcAv20/ysVzscxeMoqXRg1g5lTr+jv8pRmzFr/BZ/MN/euWG/p/WryNytWCmDFvJFNmvchXH68kI+OmXbQ7CUzqUoenf9zPY1/vone9ctQq62k13ah2NdlyJi4nLj0ziyEL99J97h66z91D+6r+NC1fxi66LTm86wjRF2IY+91bDH5tIAs/+tFquuoh1Xhp+gv4B/kV2JaVmcWK2auo17yureUW4ORvh7l6IYaXvxxNr+GD+WnGYqvpfv16Ja36deDlL8fg7uVBxLqdAIQvWk9Q9Yq8MPNN+o0Yxi9fLLWb9qiIwyReimHIjHG0f2EI4bMXWE2387sVNOrZkaGfjcPNy4OjG3YA8FDDOgz4YBQD3h9FhxeHETZznt20q6wsoubPo9bLw6k/fgJX9+wh7eLFPGmcPUtTafBggrp0sXqM6A0bcA+230OTJU4C73SqzdPL9tP5m930rhtELf9C6u6jNQg7ezUnLlMp3gk7See5u+kz/3eealLR6r62Zlv4Ic5FRbP85wmMHj+UKZPmF5r2cORZkpOuFYjPzMzikw+X0bqtTbspq2TrX/bzeN4e/yRTJlkv/5CtPy1PXMtWdZi/9G3mLXmLsZOGMWncD7aWnIOTwOjWNXl+XSS9lv5Gj+qB1PDNWwZ2Xoyn3/IIHl8Rwejw40xsWzvP9mfW7OfxFRF2H/wA7Nh6lHNnY1m8ehSjxg7gvXes97uv/LcP3/84kh+WjCSovC8/zt8KwNw5G6hVpwI/LBnJ2MlD+HDqcnvKZ9fWo5yPiuWHlW8wckx/Pphsvd1+aWRvvl70Gv9v8QjKBfuybMG2nG2ZmVl88fFPtGhdx16yc/hucRh9nnq30O3dOjahRtVgQtq9yktvzuGTyc8C4OQkfPTO3+nz9FSadh7JgN5tqFuror1kOwix8+f+pDjXAPkCRR4AicHdnL8vYNOeZU94JO3+0gwRoXZIFVJT0oiPTSqQrmmbeogIIkLN+pW5Gp0AgLunW46JMT3tBtjZ3LhrSySdehj66zasQmpyGlet6G/RNld/7fqViTX1I0LatXSUUqRdS8e7jCfOzvZZLtakfBnOxF/jXOJ1MrIUq45G06VmYIF0zzz8EGtORBN7LSNP/LWMTABcnARXZyeUXVTn5eD2SFp2aYGIUK1+VdJS0kiMSyyQrlKthygb7G/1GGHLwmnSrjFefl5Wt9uSozsjadTZ0P9Q3apcT00j+Wpe/UopTh84Qf3QxgA0fqwlx3YcBCA26jLVmxgPJgGVgki4cpWU+ILlzxac2XOA2u1bIiIE1a5GemoaqfEFtV+MPE711k0BqN3hEU7v3g+Aq0du3c1IT7dr2516+jTu5crhFhiIk4sLfs1bkLB/f540rmXKULpqVcTZucD+N+LjSTx4kIDQUHtJzkOT4DKcSUgjKqfuXqFrjYAC6f7e5CHWnIgh7tqNnLjo1BtERqcAkJqRycm4VIK93Arsa2vCNu3nr71bISI0bFydlORrxMQUrLuZmVl89P5Sho94vMC2hfM20blLU/z8ve0hOQ9hmw7Qo/cjpv5qJCdfI7YQ/R+/v5T/jOiXJ97T0z2n/Kel3bDro0vDAG+iktI4n2yUnzWnYuhUuWyeNNduZuWEPVwc074XxpZNkfToZfS7IY2rkJKcRmxMwXavtJc7YLRD6dczcp4PTp+6QvNHagFQtVoQly7GExeXbDf9WzcfoltPQ3+DRlVISb5O3O30p2fkcadaOn8b7Ts3xM+/tN10Z7Nt91GuJqQUur1n12bMWxIOwO69J/Ep40lwOV9aNKnJH2cucyYqmoyMTBav2kHPrs3tJVvjQIrzqfZdoIaI7BORD0Vkg4hEiMhBEekDICJVReSIiMwEIoBKIjJGRI6KyHoRmS8iI820NUTkFxH5XUTCRaSuiLQBegPTzPPYxDclPiaRskG+Ob/LBvpw1Uonks3Nm5ls+eV3GrfKna3fHXaQVwe/y7sjv+SFtwbZQmahxEUnEmCpv5wPcdG31r9pze883NrQ33NAW86ducJTPSbw0tDpPPdaX5yc7DMACvZy41Jyes7vS8npBR6EgrxK0a1WIN/vu1BgfyeBn59uQcS/Qwk/c5V9l+zz4G1JQmwifuVy89830JfE2MLzv8D+MQkc2HqQ0F5tbCHvtiTHJuATmKu/TIAPyfn0pyWl4l7aAyfzQbxMgC9JccYAOqhaBY5sOwDAhWNnSYiOJ+kOrv9eSL2agFdArkXNq6wvqaaubK4np1LKQrtXWT9SLQZ4p3ftZ8HLk1jzv1l0+PeTdtENkJGQgKtf7oC4lJ8vGQnxRd7/3KKFVHziCYf5dwd7uXEx+XrO70sp6QR5F1J3DxSsu9k8VMadBuW82XvZ/nU3+koCQcG55adckB8xVxIKpFs4bzPtOzYiMNCnwP6bNuzniYHtbC3VKjFXEgi20B8U5Ee0Ff2L5m2mXcdGBOTTD7Dp13080WsCr7w4k7GT/mZLuXkIKu3G5dTctv9yajrlPEsVSNe5SllWP96cWV1DGB1+LCdeAV92a8ji3k0ZUCfYHpLzEBOdSLng3HazXJAPMYX0u5PGLKBHx/GcPRPNwCHGhEWt2hXYvMGYRDp0MIrLl+Ktlj1bERudlEd/4C30Txm7kH6dJxJ1OobHB7cFIOZKIuGbIuk9oLVd9N4pFYL9OX8p12PkwuWrVAj2p0KwH+cvWsRfiqOiFa+MPxOCk10/9yvFqexN4A+lVBPgv0A/pdTDQEfgfcntlesA3yqlmgKBwBNAU+BxwHLYPRt4WSnVDBgJzFRKbQdWAv9VSjVRSv2RX4SIPCciv4nIb7Nnz76rC1FWppVu9Uzx1bQl1GtSnXpNctf5tGzfkA8XvMnId//Owjm/3JWOu8XarNit9M+cuoQGTasT0tTQH7HzGNVrVeTbn8fxyfcjmDVtGddSrhd+ABuj8l3RuE61eTfsD7KsXGiWgh5z99Bq1naalC9D7QD7z0SpOy1A+Vjy2XJ6P9cTJztZ3YpEPv1Wy5g5Xxw6sAvXU64x66X32L1yC+VrVLTftVjN+nx5b+X+WCap9khjBn86hm6vP8ee+T8Vs8BbYT1Xi0LCgQO4entTukqV4pV0B1hTmj+rx3eoxZRw63UXwNPVmS96hTBh8wlSbmQWu8bbUZSqGxOdwK/rIhg0tEOBtNOnLmb4q33tZjHPj7W2J3/5N/TvtaofoONjTViyahzTP3meWTNW2UKmVYraQm44G0fPpb/x0q+HGd6sak78k6v30X/lXp5fF8mQehVoFlRwcGdL7qTZHzNpMKs3jKNqtSB+XbsPgKee7URyUhp/G/A+i+dvpXbdijhbsfTaiqKUnWxGTRzEkvVjqFKtHBvXGlbqT6et5Pn/9HBY2b8dYqWEKaWsXqO1e6n582GrlyAI8D8RaQdkARWBIHPbWaXUTjMcCqxQSqUBiMgq89sLaAMstiicRfKHUErNxhg8Aah9cauLJHjtkq1sMNfw1KhbiTiLmZe4mET8Aqw3pou/WktSQgoj3njG6vb6TWsw850FJCWkUMbXdu5MqxdvZa25hqdW/UrEWuqPTsTfykwfwLw5a0mKT+GlUc/kxP26eg/9n+qEiFChUgBBFfw5dzaaOg0KX0xbXFxOSae8xaxxeW83rqTcyJOmUZA3n/ZqAIC/hysdq5XlZpZi3cnYnDRJ6TfZcS6eDtX8OR6banPdW5ZvZftPxjqSynUqEx+dm/8JMQn4lC36WqSo4+f4ZtK3AKQkpnJ41xGcnJ1pHNqweEVbsHtVOBFrDf0ValUmMSZXf1JsIt759HuWKc311DSyMjNxcnYmKTYB77JGGXPzdKfPa4blRCnFx3+fiF9wXleW4iRyTRhHft0OQGDNKqTE5lpNUuIS8PTPW/bdy3hxw0J7Slw8nn4F60eFBjXZNCOWtKQUPMrY3hXR1dePjPjcdTE34hNw9fW9xR65pP5xkoT9+0mMjCQrI4PMtDROf/UV1Z591lZyC3ApJZ0K3u45v8t7uRGdkp4nTcMgb2b0MDyY89TdP2JxcRK+6BXCsiNX+MWiLtuaRfM3s+xHYx1D/ZAqXLmcW36ir8QTUC7vPTh65Bzno2Lo22MsANev36BP97GsWDORI4fOMuq/XwGQEJ/KtvBInJ2d6NjZdi8xWTQ/jOUW+i9b6L9yJZ7AcnnL9jFTf78e43L09+0+juVrJuRJ93DzWpw/F0tCfAq+dnDFvZyaTnDp3LY/uLQb0dduFJr+9yuJVPL2wNfNhYT0m8SkGWmvXs9gw9k4GgV68/sV21qef1ywlRVLjH63XoNKRF/ObTejryRatbBl4+zsxGN/acIP32yiZ9+WlPZyZ8ykwYDRbvbrPpkKFa27SBcXyxZsY/VSQ3+dfPpjriQSEFh4v+Xs7ESnbo1ZMDeMHn1bcOzwOSa+YawZS0xIZefWozg7O/FopxCbXkNRuXA5jofK5/ZDFYP9uXQlnlKuLjxUwSK+fFkuRhfd8v5gcv+uy7EnthoAPYlh3WmmlMoQkTNAds9o+TRa2F1wAhJMa5Jd6PZEKN2eMEzREdsOs3bJNtp0acqJQ1F4lnbHL6BgQ7Bh5U4O7DrGmE9fyOMidvl8LEEVyyIinDp2npsZN/H2sa0loueAUHoOMPTv2XqY1Yu30a5rU45FRuHp5Y6/Ff1rl+8kYucxJn+WV39gkC/795wgpGl14uOSOR8VTbCNG+Js9l9KppqfJ5V83LmcnE6vuuUYvvpwnjShc3bkhKd3r8fGP2JZdzIWfw9XbmYpktJv4ubiRGgVfz7fZf0NcsVNu76htOtr5H/kzkNsWb6VZp2acubIWdxLe+BTtuizkRPmjckJfzd1HiGt6tt08APQstejtOz1KADHdx9iz6pwQto/zIVjZ3Er7Y53vkGEiFCtUS0Ob91PSPuH2f/rbuq0Mjq66ynXcHUrhbOrCxFrd1AlpAZunu4FzllchHRvT0j39gCc/T2SyDVbqBnajOgTZyjl6UFpv4LaK4TU5tSOvdQMbc7xzbuo2tJ4W1HipRjKBAcgIsScOkfmzZu4e9vHili6alWuR0eTHhuLq68v8b/todqz/yzSvhX7PU7FfsZ6lORjx7iyfp1dBz8A+y8nU83Xg0pl3Lmckk6vukEM//lQnjShX+3MCb/frS4bTsWx7g9jsDOta11OXk3ly4i8b/6yNQOHdGDgkA4AhIcdZNH8zXTr3pzIA6fx8vIo4Ob2aPuGrAubmvM7tMUrrFgzEYBVa9/JiR/39lwebd/QpoMfQ397Bg4xyv/WsIMsmh9m6j+Dl5dHgYfw0PYNWRuWu1j80Rav5gx+zkVF81ClQESEo4ejyMi4iY+vfcp/ZGwyVXw8qOjlTvS1dLpXD+T1zUfzpKns7U6U6WZZr6wXrk5CQvpNPFycEIRrNzPxcHGiTQVfPt9n/Q2ExUn/waH0H2y0+9u2HGbx/G106d6UQwei8PJ2LzCAUEpx/lwclSoHoJRi6+ZDVKlaDoDkpDTcPVxxdXVhxZJdNH24es56G1vRb3Bb+pkubDu2HGHpwm10/ksTDh+MorSXO2Wt6L9wLo6HTP3btxymcjVjje7Cn9/KSTdlzAJat6t/3wx+AH5aH8G/nu7KopXbadm0JknJ17gcnUBMXBI1qwVTpVIgFy9fZUCv1jwzfIaj5WrsQHEOgJKB7FWfPkC0OfjpCBTml7EV+EJEppha/grMUUolichpERmglFpsus81Ukrtz3cem9C0TT327jjCfwZMoZS7Ky+8PThn25QRc3j+zYH4B/rw5bQlBAb5Mfq5TwDD7a3/P7qya9MBtvzyG84uzpQq5cork/5mV7/85m3r8dv2I/zf41Nwc3fllTG5+se9Mofhbw+kbKAPn01dQrlgP0Y+a+hv07EhQ/7ZlcHPduGjiQv495BpKAV/f6knPja0XlmSqRRjfz3Ot/2b4OwkLDp4kRNxqTzZ2HiN8g/7Lxa6bzmvUnzQvT5OTobX6epj0Ww8FVdoelvR4JH6HN51hInDJuPqXophr+fm/+dvzmboyEH4BPiweekWNizYSNLVZKb8cxoNHqnH0JGDb3Fk+1CrRX1O7DnMp89OwtWtFH1eHZqz7Yexs+j9nyF4l/Xhsb/34sepc9n47U+Ur/EQTbsZvt8x566w/P3vEScnAisH0/s/Q+ymvfLDDYiKOMT8f0/Axc2VDv8elrPtp3dm0uHFoZT296XVsD6s//D/sXv+agKqVaJeZ0P7qZ37OL55F04uzriUcqXLa/+wW90VZ2cqDx7CiY8/QmVlEdC2LR4VKhATFgZAYPv2ZCQmcuR/k8m8fh0RIXrDrzQYPwFnDw+7aLwVmUoxZtNxvnuiMc4iLIy8xPG4awxrZNTd7w8UXndbVPDhifrBHIlJYc0wwxP6vW2n2HT6aqH72ILQdiFsC4+kT/exuHuUYvykp3K2DX9hBmMmDCOwXNGsco6gbbsQtoUfom/3cbh7lGKcxRqe4S98xpgJT95S/4b1+/h55S5cXJxxc3dlyvRn7Vb+MxVM3nGSOd1CcBJh2YnLnEy4xqA6xlsNFx67RJeqAfSpGcTNLMX1zCxGbD4CQFmPUnzS2bAsuojw06lotl6w7yx+m0frsT38CP3/OgV3d1dGT8pty199cQ5vjR9I2QBvJo6ez7WU6ygFNeuU543R/QE4c/oKE96ej7OTULVGMG9PGGhX/a0ercvOrUcY2utd3NxL8abF+V//91e8Pq4//gHeTBmzgNTUdFCKGrUr8NrbBV8E4gjmfvoyj7auR4CfNyd3zWDSBz/i6mo84n75/a/8snEv3To24VD4R1xLS+f5kV8AxgtBXh3zDau+G4WzsxNzF27myHH7v4LfnlhzByyJiNX1Cnd7MJF5QCNgD1AXcAX2AW2B7D+0WK2UCrHYZzwwBDgLxACblVJzRKQa8DlQ3jzOAqXURBFpC8wB0oH+1tYBWVBkF7j7jSZlewJwIvHB1F/LpydVpm10tIy75ux/O7Huws+OlnHXdK3Yg3l/2HftWXExtMZfAPgwcr2Dldwdr4YYr6geujnMwUrujnkd2lP5g02OlnHXRL3WkZSMB7ft8XLtRHLGBkfLuCu8XTsDUP/rLQ5Wcncc/ofx8or49Aez3/VzM54bLqc9mP9jE+zRG4/K9pswK27SoubDA+BfdjNrv11XObk4Nb4v86RYXeCUUkNvn4r8NtHpSqnxIuIJbAHeN491GviLlXNsw8avwdZoNBqNRqPRaP5sOOpNofcbtloDdCfMNv/Y1B2Yq5SKcLQgjUaj0Wg0Go1G8+fE4QOgIlqNNBqNRqPRaDQajeaecfgASKPRaDQajUaj0diD+/O/muyNzgWNRqPRaDQajUZTYtAWII1Go9FoNBqNpgSgX4NtoC1AGo1Go9FoNBqNpsSgLUAajUaj0Wg0Gk2JQFuAQFuANBqNRqPRaDQaTQlCW4A0Go1Go9FoNJoSgP4jVANtAdJoNBqNRqPRaDQlBm0B0mg0Go1Go9FoSgTa9gE6FzQajUaj0Wg0Gk0JQluANBqNRqPRaDSaEoD+HyADUUo5WoMt+VNfnEaj0Wg0Go3mvuEBGF0ct/Ozce37Mk/+7AMgmyIizymlZjtax92i9TuOB1k7aP2ORut3HA+ydtD6HY3W71gedP2a4kOvAbo3nnO0gHtE63ccD7J20PodjdbvOB5k7aD1Oxqt37E86Po1xYQeAGk0Go1Go9FoNJoSgx4AaTQajUaj0Wg0mhKDHgDdGw+6H6nW7zgeZO2g9Tsard9xPMjaQet3NFq/Y3nQ9WuKCf0SBI1Go9FoNBqNRlNi0BYgjUaj0Wg0Go1GU2LQAyCNRqPRaDQajUZTYtADII2mCIiIr4i8WEzHessiXFVEIovjuLZCRL4Rkf53kP6+uSYRGS4iR0TkBxEZ6Wg994KIvCIing48f04dEJEOIrL6Dve/o3Jksd8dn6ukICIphcTfVV7f5lzPiMiM4jxmIefZLCLNbX0eTV4cVb/vhuLsj29znr4iUt/W59E4Bj0A0miKhi9QoMEVEee7ONZbt0+iKSZeBHoAJxwtJD9icCdt8CuAwwZAFFIHNBrNn4IHqX7fkda7aGuz6QvoAdCfFD0AugUislxEfheRQyLynBn3rIgcN2ep5mTPiIlIoIgsEZE95qetY9WDiJQWkZ9EZL+IRIrIIBFpJiJh5nWtFZHyIuIjIsdEpI6533wR+T9H67dERJ4SkQPmtXxnzjbNEpFw8370tLGEd4EaIrLPvL+bRGQecFBEnEVkmhl/QESeNzWXF5Et5j6RIvKoiLwLeJhxP5jHdhGRuea+P2bP8ovIGRGZKiK7zU9NM36Aebz9IrKluC80f16b0e1EZLuInMqe5TM7lWmmloMiMqi4tdwLIjILqA6sBF4FGovIRhE5kV2+rd0jG2uqKoZFaiYQAYyxKDcTzDTW6u1woAKwSUQ2mem6isgOEYkQkcUi4mXGtzDv1X6z3HiLiKeILDLPs1BEdsmdz7Ln1AFgGuBlltejYljYxDz/WPOaIkVkdnZ8vnywmkZEaorIr6b2CBGpYe5i9Vz3iogMM/Non4h8ISKPmHnkbt6HQyISIiJeIrLB1HRQRPqY+2ffzzlm2nUi4mFua2Eea0d2PblHra+Z+RUpIq/k2yYiMkNEDovIT0A5i22FtSNW+ywRaWmWn73mdx0rWv5qXlfAPV5TgbKeb/sQM78jRWSqRXyKiLxv3o8NIhJoxtcQkV/E6N/CRaTuvegrDqRg39XLrH97zbIe5GiNJsVWv+2pVUQ+vE3dzG5rK4nIGPN61ovxnDPSTFug3IhIG6A3MM08T41C1WgeTJRS+lPIB/A3vz2ASKAicAbwB1yBcGCGmWYeEGqGKwNH7gP9TwBzLH77ANuBQPP3IOBrM9wF2AEMBn5xtPZ819EAOAYEZN8X4BvgF4xBfC3gPOBuQw1VgUgz3AFIBaqZv58DRpthN+A3oBowAnjbjHcGvM1wSr7jKqCt+ftrYKQZPmOx/1PAajN8EKhohn3tlNeLzbyuD5y0KF/rzWsLAqKA8pZ55eiPmYcBwHhgv1mXA4BzGAMKq/fIxuUoC2gFdMV4JauYebsaaGet3lpeixkOALYApc3fbwBjgVLAKaCFGV8GcAFGAl+YcSHATaD5PdaBROAhU/sOcts/f4t9vgN6meFvgP63SbML6GeG3TEsXoWe6x7vRT1gFeBq/p5pPq7SgQAACM9JREFU1rN3gOnAZ8Aoc5sLUMYi70+a962qmZdNzG2LgGFmOBJoY4bfvZc6ATTDqPelAS/gENAUsy0BHie3LlYAEizy+gzW2xGrfVZ2mTHDjwFLzPAzwAygH0bf51cM98BaH7UZaG5eRxQQaOb/RqCvmU4BT5rhseT2wxuAWmb4EWCjLetzEa7PWnvqR+4beP8JvO9IjRZaq1JM9dvOWm9VN7OAVua25sA+jD7AG8MrILuvtVpu7HlN+mP/jwuaWzFcRPqZ4UrA34AwpdRVABFZDNQ2tz8G1LeYDCkjIt5KqWR7Cs7HQWC6OXO2GojHePhZb+p0Bi4BKKXWi8gAjE6/sWPkFkon4EelVCyAUuqqqX+RUioLOCEip4C6GA2cPditlDpthrsCjSTX/9kHY1C2B/haRFyB5UqpwrSdU0ptM8PfA8MxHsAA5lt8f2iGtwHfiMgiYGmxXE0uheX1cjOvD1vMWIYC85VSmcAVEQkDWgAHillTcbFCKZUGpIlhRWlJ0e9RcXJWKbVTRKZjlJ29ZrwXRrkJx6LeKqXCrRyjFcZgdJt5f0phPKTUAS4ppfYAKKWSAEQkFPjYjIsUkeK4R7uVUufN4+/DeODYCnQUkdcxBi/+GA/rq/LtWyCNiGzGGNgvM3VeN499q3PdC50xBhZ7zHN4ANHARIxycR2jLoLxQPU/EWmH8VBVEWPQD3Daotz8DlQVEV+MwfR2M34ecC9W6lBgmVIqFUBElgKW1sp25NbFiyKyMd/+1toRq30WRvs1V0RqYQw0XC2O0xHjQbJrdtm6R/L0UUqpcAs9LYDNSqkYADEs5u2A5Rj3YKGZ7ntgqRgW0DbAYotjuBWDxnvBWnvaEFgoIuUx6u3pWx3AgdxL/bYnt6qbZ5VSO81wKLl9ACKyyvy+H8uNxg7oAVAhiEgHjA6itVLqmtk5H8OYNbSGk5k2zT4Kb49S6riINMNYAzEFY4bwkFKqdf60YvjH1gPSMBq18/bUehsEoyPOT/44e/6pVapFWICXlVJr8ycyG+W/At+JyDSl1LdWjnWr6ygQVkr9S0QeMY+7T0SaKKXi7uYirFBYXqfnS2P5/aBQIJ+VUluKeI+Kk+yyI8AUpdQX+RNY1lsRWaeUmpg/CbBeKTUk336NsH7/bHGvLMtEJoYrpzuGJaW5UuqciIzHsORYaiwsza00FjjXvctHgLlKqVH59AVjDEZdTV2pwJMYlohmSqkMETlD7nXl1+ZB8ed3UY53q/bPWptitc8SkU+BTUqpfiJSFcMik80pDLfS2hiW7nsifx8lIusspdzJoTCuJ0Ep1eRedRUj1trTT4EPlFIrzeeM8fYWVUTuqn47gFvVzfz9tDXux3KjsQN6DVDh+ADx5uCnLsaMqyfQXkT8RMQFw3yfzTrgpewfIuLwyiQiFYBrSqnvMSwKjwCBItLa3O4qIg3M5K8CR4Ah5M6I3y9sAAaKSFkAEfE34weIiJPpm1sdY4BqK5IxzObWWAu8kJ1nIlJbDN/2KkC0UmoO8BXwsJk+I1/+Vs6+Jxj5bzmzPcjie4d5/BpKqV1KqbFALIZ1srgoLK+tsQUYJMYaqECM2dndxailuOkjxtqOshguHntucY/swVrgH5K7dqeiiJSzUm+zNVmWwZ1AW8ldz+EpIrWBo0AFEWlhxnubbdVWYKAZVx9oeBd6b1UHssl+8Ig1r8vaW6GspjEtCudFpK+p001s+9a7DUB/ESlnns/fLA+zgTHAD0D2uhMfjHKSISIdgSq3OrBSKh5IFpFWZtTge9S6Behr3ufS5LqhWW4fbNbF8hiWGksKtCMU3mf5ABfM8DP5jnMWw93uW4u+4665RVkHwx2yvYgEiPGymSFAmLnNidyyNRTYapaf02J4MmSvi3K0N4O19tQyf592lDArFFf9tgeWWotaN7cCvcw+wAtj0ovblJui5InmAUVbgArnF+BfYriKHMN44LgA/A+jYb4IHMbwkwXDVeIzM70LRof0L3uLzkdDjAV8WUAG8AKGv/onIuKDofMjEcnA8EVuqZRKFmNh/WhgnIN050EpdUhEJgNhIpJJrsvQMYwOMQj4V7bLjI00xInINjEWMqcBVyw2f4nhHhAhhg09BuPtMR2A/5r5m4Lhfw/GA9YBEYkA3sYYeD4tIl9g+CV/bnFsNxHZhdHhZ8/2TxPDPUUwOtj9xXidheW1NZYBrc3zK+B1pdRlc9b4fmQ38BPGeodJSqmLIvI01u+RzVFKrROResAO0/UiBRgG1KRgvQWj3KwRkUtKqY4i8gwwX0Sy3TVGmzPqg4BPxViMn4ZhyZ6J4dZ0AOOeHiC37Sqq3lvVgew0CSIyB8O16QyGK9mdpPkb8IWITDSvfcCdaLwTlFKHRWQ0sE4MC3gGsAK4qZSaZz50bxeRThiDoVUi8huGm+3RIpziWWCOiKRiWFHuKL/zaY0QkW/InWD4Uim1V3JddpZhuFsdBI6TO1DIxlo7Ulif9R5GWXkNY91Nfi3HRORJDJehXkqpP+72urDeR003z3NJREYBmzDaup+VUivM/VKBBiLyO0a+Zg/wngQ+N++rK7CAYmwf75RC2tPxGHl3AeO5opqj9FlSXPXbHuTTugeoe7u6qZTaIyIrMcrDWQwLZnadLKzcLMCow8Mx1gLdS1nX3GdkL8TTFBER8VJKpZizqsswXiKwzNG6Shrmw8BqpdSPjtZiK8Qw5TfP9h/XaO4W82HeVSl1XQyL6QagtlLqhoOl/WnJ7ivM8JtAeaXUfxyg4wx/snZERFKUUl6O1qF5sLB4fvPEGPA/p5SKcLQujWPQFqA7Z7yIPIZhCl6HsSBTo9Fo7mc8MV6h7Yoxm/6CHvzYnL+aFgwXjBnnZxwrR6Mp8cw2XYDdMdb/6cFPCUZbgDQajUaj0Wg0Gk2JQb8EQaPRaDQajUaj0ZQY9ABIo9FoNBqNRqPRlBj0AEij0Wg0Go1Go9GUGPQASKPRaDQajUaj0ZQY9ABIo9FoNBqNRqPRlBj+P+h8XAPHWe0ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's make out corr matrix a little prettier\n",
    "corr_matrix = df.corr()\n",
    "fig , ax = plt.subplots(figsize = (15,10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                annot = True,\n",
    "                linewidths=0.5,\n",
    "                fmt = \".2f\",\n",
    "                cmap = 'YlGnBu');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into X and y\n",
    "X = df.drop('target' , axis =1 )\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <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",
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       "      <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",
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       "      <th>301</th>\n",
       "      <td>57</td>\n",
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      ],
      "text/plain": [
       "     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  \n",
       "0        0   0     1  \n",
       "1        0   0     2  \n",
       "2        2   0     2  \n",
       "3        2   0     2  \n",
       "4        2   0     2  \n",
       "..     ...  ..   ...  \n",
       "298      1   0     3  \n",
       "299      1   0     3  \n",
       "300      1   2     3  \n",
       "301      1   1     3  \n",
       "302      1   1     2  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "298    0\n",
       "299    0\n",
       "300    0\n",
       "301    0\n",
       "302    0\n",
       "Name: target, Length: 303, dtype: int64"
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     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((242, 13), (61, 13), (242,), (61,))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Spliting the data into train and test\n",
    "np.random.seed(42)\n",
    "X_train , X_test , y_train , y_test = train_test_split(X , y , test_size = 0.2)\n",
    "\n",
    "# Shape of each\n",
    "X_train.shape , X_test.shape , y_train.shape , y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>160</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>270</th>\n",
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       "      <td>249</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "132   42    1   1       120   295    0        1      162      0      0.0   \n",
       "202   58    1   0       150   270    0        0      111      1      0.8   \n",
       "196   46    1   2       150   231    0        1      147      0      3.6   \n",
       "75    55    0   1       135   250    0        0      161      0      1.4   \n",
       "176   60    1   0       117   230    1        1      160      1      1.4   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "188   50    1   2       140   233    0        1      163      0      0.6   \n",
       "71    51    1   2        94   227    0        1      154      1      0.0   \n",
       "106   69    1   3       160   234    1        0      131      0      0.1   \n",
       "270   46    1   0       120   249    0        0      144      0      0.8   \n",
       "102   63    0   1       140   195    0        1      179      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "132      2   0     2  \n",
       "202      2   0     3  \n",
       "196      1   0     2  \n",
       "75       1   0     2  \n",
       "176      2   2     3  \n",
       "..     ...  ..   ...  \n",
       "188      1   1     3  \n",
       "71       2   1     3  \n",
       "106      1   1     2  \n",
       "270      2   0     3  \n",
       "102      2   2     2  \n",
       "\n",
       "[242 rows x 13 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choosing the machine learning model\n",
    "\n",
    "* RandomForestClassifier\n",
    "* Knearstmeighbours\n",
    "* RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {'Logitic Regression' : LogisticRegression(),\n",
    "          'KNN' : KNeighborsClassifier(),\n",
    "          'Random Forest' : RandomForestClassifier()   }\n",
    "\n",
    "# Create a function to fit and score models\n",
    "def fit_and_score(models , X_train , X_test , y_train , y_test):\n",
    "    '''\n",
    "    Fits and evaluated given machine learning models.\n",
    "    Models : a dict of different sci-kit learn machine learning models\n",
    "    X_train : Training data (no labels)\n",
    "    X_test : Testing data (No labels)\n",
    "    y_train : training data\n",
    "    y_test : test labels\n",
    "    '''\n",
    "    np.random.seed(42)\n",
    "    \n",
    "    #Make a dictionary to model scores\n",
    "    model_scores = {}\n",
    "    \n",
    "    # loop through models\n",
    "    for name,model in models.items():\n",
    "        #fit the model to the data\n",
    "        model.fit(X_train , y_train)\n",
    "        # Evaluate the model and append its score to model_scores\n",
    "        model_scores[name] = model.score(X_test , y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: 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",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logitic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_score = fit_and_score(models , X_train , X_test , y_train , y_test)\n",
    "model_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "model_compare = pd.DataFrame(model_score , index=['accuracy'])\n",
    "model_compare.T.plot.bar(rot = 0 , figsize = (10,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got a baseline model, The first prediction aren't always the one we should have our hopes on\n",
    "\n",
    "## Lets look at what we have got\n",
    "\n",
    "* Hyperparameter tuning \n",
    "* Feature inmportance \n",
    "* confusion matrix\n",
    "* cross validation\n",
    "* precision\n",
    "* recall\n",
    "* f1 score\n",
    "* classification report\n",
    "* ROC curve\n",
    "* Area under the curve (AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter tuning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score = []\n",
    "test_score = []\n",
    "\n",
    "# Create a list of different values for n_neighbours\n",
    "neighbors = range(1,21)\n",
    "\n",
    "# Set up knn \n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different n_neighbours\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors = i)\n",
    "    \n",
    "    # fit the algorithm\n",
    "    knn.fit(X_train , y_train)\n",
    "    \n",
    "    #Update the training scores list\n",
    "    train_score.append(knn.score(X_train ,y_train))\n",
    "    \n",
    "    # Update the test score list\n",
    "    test_score.append(knn.score(X_test , y_test))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.8099173553719008,\n",
       " 0.7727272727272727,\n",
       " 0.743801652892562,\n",
       " 0.7603305785123967,\n",
       " 0.7520661157024794,\n",
       " 0.743801652892562,\n",
       " 0.7231404958677686,\n",
       " 0.71900826446281,\n",
       " 0.6942148760330579,\n",
       " 0.7272727272727273,\n",
       " 0.6983471074380165,\n",
       " 0.6900826446280992,\n",
       " 0.6942148760330579,\n",
       " 0.6859504132231405,\n",
       " 0.6735537190082644,\n",
       " 0.6859504132231405,\n",
       " 0.6652892561983471,\n",
       " 0.6818181818181818,\n",
       " 0.6694214876033058]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6229508196721312,\n",
       " 0.639344262295082,\n",
       " 0.6557377049180327,\n",
       " 0.6721311475409836,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.7049180327868853,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.7540983606557377,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.6557377049180327]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum 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_score , label = \"Train score\")\n",
    "plt.plot(neighbors , test_score , label = \"Test score\")\n",
    "\n",
    "plt.xlabel(\"number of neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f'Maximum KNN score on the test data: {max(test_score)*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter tuning with RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create a hyperparameter grid for logistic regression\n",
    "\n",
    "log_reg_grid = {\"C\" : np.logspace( -4 , 4,20),\n",
    "               \"solver\" : ['liblinear']}\n",
    "\n",
    "# Create a hyperparameter grid for randomisedforestclassifier\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(1,20,2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got hyperparameter grids setup for each of our models , let's tune them\n",
    "using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.6s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=LogisticRegression(C=1.0, class_weight=None,\n",
       "                                                dual=False, fit_intercept=True,\n",
       "                                                intercept_scaling=1,\n",
       "                                                l1_ratio=None, max_iter=100,\n",
       "                                                multi_class='auto', n_jobs=None,\n",
       "                                                penalty='l2', random_state=None,\n",
       "                                                solver='lbfgs', tol=0.0001,\n",
       "                                                verbose=0, warm_start=False),\n",
       "                   iid='deprecated', n_iter=20, n_jobs=None,\n",
       "                   param_distributions={'C':...\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=True)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisticRegression\n",
    "np.random.seed(42)\n",
    "\n",
    "# setup random hyperparameter search for LogisiticRegression\n",
    "\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                                param_distributions=log_reg_grid,\n",
    "                                cv=5,\n",
    "                                n_iter = 20,\n",
    "                                verbose = True)\n",
    "\n",
    "# Fit hyperparameter search model for LogisticRegression\n",
    "\n",
    "rs_log_reg.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test , y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # Now we tune RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:  1.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=RandomForestClassifier(bootstrap=True,\n",
       "                                                    ccp_alpha=0.0,\n",
       "                                                    class_weight=None,\n",
       "                                                    criterion='gini',\n",
       "                                                    max_depth=None,\n",
       "                                                    max_features='auto',\n",
       "                                                    max_leaf_nodes=None,\n",
       "                                                    max_samples=None,\n",
       "                                                    min_impurity_decrease=0.0,\n",
       "                                                    min_impurity_split=None,\n",
       "                                                    min_samples_leaf=1,\n",
       "                                                    min_samples_split=2,\n",
       "                                                    min_weight_fraction_leaf=0.0,\n",
       "                                                    n_estimators=100,\n",
       "                                                    n_jobs...\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=True)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "#set up random hyperparameter search cv\n",
    "\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                          param_distributions=rf_grid,\n",
    "                          cv=5,\n",
    "                          n_iter = 20,\n",
    "                          verbose = True)\n",
    "\n",
    "# Fit hyperparameter search model for RFC\n",
    "rs_rf.fit(X_train , y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate thr Randomized search forest classifirer model\n",
    "rs_rf.score(X_test , y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning the Linear Regression model using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.6s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=100, multi_class='auto',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='lbfgs',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': array([1.00000000e-04, 1.8...\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         'solver': ['liblinear']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=True)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different different Hyperparameter grid\n",
    "\n",
    "log_reg_grid = {\"C\" : np.logspace(-4,4,30),\n",
    "             \"solver\" : ['liblinear']}\n",
    "\n",
    "# Set up grid for Logistic regression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                          param_grid=log_reg_grid,\n",
    "                          cv=5,\n",
    "                          verbose=True)\n",
    "\n",
    "# fit out grid hyperparameter\n",
    "gs_log_reg.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the best hypreparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.score(X_test , y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating out tuned machine learining classifier , beyond accuracy\n",
    "* Roc Curve and AUC curve\n",
    "* Confusion matrix\n",
    "* Classificatiion Report\n",
    "* Precision\n",
    "* Recal\n",
    "* F1 Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make prediciton with tuned model\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179    0\n",
       "228    0\n",
       "111    1\n",
       "246    0\n",
       "60     1\n",
       "      ..\n",
       "249    0\n",
       "104    1\n",
       "300    0\n",
       "193    0\n",
       "184    0\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.86725274, 0.13274726],\n",
       "       [0.24408505, 0.75591495],\n",
       "       [0.18547578, 0.81452422],\n",
       "       [0.94530777, 0.05469223],\n",
       "       [0.11546644, 0.88453356],\n",
       "       [0.12929703, 0.87070297],\n",
       "       [0.39487822, 0.60512178],\n",
       "       [0.99564019, 0.00435981],\n",
       "       [0.98623623, 0.01376377],\n",
       "       [0.43861542, 0.56138458],\n",
       "       [0.28279198, 0.71720802],\n",
       "       [0.88095856, 0.11904144],\n",
       "       [0.11269899, 0.88730101],\n",
       "       [0.93994962, 0.06005038],\n",
       "       [0.0324994 , 0.9675006 ],\n",
       "       [0.06818877, 0.93181123],\n",
       "       [0.03595298, 0.96404702],\n",
       "       [0.90547239, 0.09452761],\n",
       "       [0.98230236, 0.01769764],\n",
       "       [0.97353638, 0.02646362],\n",
       "       [0.28456862, 0.71543138],\n",
       "       [0.97272398, 0.02727602],\n",
       "       [0.85725328, 0.14274672],\n",
       "       [0.283392  , 0.716608  ],\n",
       "       [0.11801722, 0.88198278],\n",
       "       [0.30519409, 0.69480591],\n",
       "       [0.15765768, 0.84234232],\n",
       "       [0.30664443, 0.69335557],\n",
       "       [0.98169062, 0.01830938],\n",
       "       [0.1221763 , 0.8778237 ],\n",
       "       [0.9284924 , 0.0715076 ],\n",
       "       [0.93315846, 0.06684154],\n",
       "       [0.98489715, 0.01510285],\n",
       "       [0.85685508, 0.14314492],\n",
       "       [0.3977051 , 0.6022949 ],\n",
       "       [0.87359671, 0.12640329],\n",
       "       [0.3366498 , 0.6633502 ],\n",
       "       [0.14920918, 0.85079082],\n",
       "       [0.18101676, 0.81898324],\n",
       "       [0.15878454, 0.84121546],\n",
       "       [0.45484181, 0.54515819],\n",
       "       [0.20749172, 0.79250828],\n",
       "       [0.2218241 , 0.7781759 ],\n",
       "       [0.29461157, 0.70538843],\n",
       "       [0.16756787, 0.83243213],\n",
       "       [0.97887001, 0.02112999],\n",
       "       [0.26783628, 0.73216372],\n",
       "       [0.06765613, 0.93234387],\n",
       "       [0.89723325, 0.10276675],\n",
       "       [0.93559249, 0.06440751],\n",
       "       [0.8652935 , 0.1347065 ],\n",
       "       [0.96445443, 0.03554557],\n",
       "       [0.1955803 , 0.8044197 ],\n",
       "       [0.04687207, 0.95312793],\n",
       "       [0.68285362, 0.31714638],\n",
       "       [0.99690399, 0.00309601],\n",
       "       [0.9126528 , 0.0873472 ],\n",
       "       [0.06176426, 0.93823574],\n",
       "       [0.971862  , 0.028138  ],\n",
       "       [0.98690126, 0.01309874],\n",
       "       [0.93708249, 0.06291751]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds_proba = gs_log_reg.predict_proba(X_test)\n",
    "y_preds_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.13274726, 0.75591495, 0.81452422, 0.05469223, 0.88453356,\n",
       "       0.87070297, 0.60512178, 0.00435981, 0.01376377, 0.56138458,\n",
       "       0.71720802, 0.11904144, 0.88730101, 0.06005038, 0.9675006 ,\n",
       "       0.93181123, 0.96404702, 0.09452761, 0.01769764, 0.02646362,\n",
       "       0.71543138, 0.02727602, 0.14274672, 0.716608  , 0.88198278,\n",
       "       0.69480591, 0.84234232, 0.69335557, 0.01830938, 0.8778237 ,\n",
       "       0.0715076 , 0.06684154, 0.01510285, 0.14314492, 0.6022949 ,\n",
       "       0.12640329, 0.6633502 , 0.85079082, 0.81898324, 0.84121546,\n",
       "       0.54515819, 0.79250828, 0.7781759 , 0.70538843, 0.83243213,\n",
       "       0.02112999, 0.73216372, 0.93234387, 0.10276675, 0.06440751,\n",
       "       0.1347065 , 0.03554557, 0.8044197 , 0.95312793, 0.31714638,\n",
       "       0.00309601, 0.0873472 , 0.93823574, 0.028138  , 0.01309874,\n",
       "       0.06291751])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds_positive = y_preds_proba[: , 1]\n",
    "y_preds_positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr , tpr , threshold = roc_curve( y_test , y_preds_positive )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": [
    "def plot_roc_curve(fpr , tpr):\n",
    "    \"\"\"\n",
    "    Plots a roc curve given the flase positive rate (fpr) \n",
    "    and true positive rates (tpr) of a model\n",
    "    \"\"\"\n",
    "    plt.plot(fpr, tpr , color = 'cyan' , label = \"ROC\")\n",
    "    # plot line with no predictive power (baseline)\n",
    "    \n",
    "    #plt.plot([0,1] , [0,1] , color = 'darkblue' , linestyle = \"--\" , label = \"Guessing\")\n",
    "    plt.xlabel(\"False positive rate (fpr)\")\n",
    "    plt.ylabel(\"True positive rate (fpr)\")\n",
    "    plt.title(\"Grid SeachCV\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "plot_roc_curve(fpr , tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "# confusion matrix \n",
    "print(confusion_matrix(y_test , y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale= 1.5)\n",
    "\n",
    "def plot_conf_mat(y_test , y_preds):\n",
    "    '''\n",
    "    Plotting heatmap\n",
    "    '''\n",
    "    \n",
    "    fig , ax = plt.subplots(figsize = (3,3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test , y_preds),\n",
    "                    annot = True,\n",
    "                    cbar = False)\n",
    "    plt.xlabel('True label')\n",
    "    plt.ylabel('False label')\n",
    "    \n",
    "plot_conf_mat(y_test , y_preds)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": [
    "# classification report\n",
    "print(classification_report(y_test , y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate evaluation matrix using cross validation ,recall and f1-score of our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf = LogisticRegression(C = 0.20433597178569418,\n",
    "                        solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446994535519124"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross-validated accuracy\n",
    "cv_acc = cross_val_score(clf , X , y ,cv=5, scoring = 'accuracy')\n",
    "cv_acc\n",
    "np.mean(cv_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8207936507936507"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross validated precision\n",
    "cv_precision = cross_val_score(clf , X , y ,cv=5, scoring = 'precision')\n",
    "np.mean(cv_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212121212121213"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross validation recall\n",
    "cv_recall = cross_val_score(clf , X , y ,cv=5, scoring = 'recall')\n",
    "np.mean(cv_recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673007976269721"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_f1 = cross_val_score(clf , X , y ,cv=5, scoring = 'f1')\n",
    "np.mean(cv_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_metrics = pd.DataFrame({'Accuracy' : cv_acc,\n",
    "                          'Precision' : cv_precision,\n",
    "                          'Recall' : cv_recall,\n",
    "                          'F1' : cv_f1} )\n",
    "\n",
    "cv_metrics.T.plot.bar(title = \"Cross validation matrix\" , legend = False\n",
    "                     );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "feature_importance is another way of asking \" which feature contributed most\n",
    "to the outcomes of the model and how did they contribute\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding feature importance is different for each machine learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit an instance of Logistic regression\n",
    "\n",
    "gs_log_reg.best_params_\n",
    "\n",
    "clf = LogisticRegression(C = 0.20433597178569418, solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.fit(X_train , y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00316728, -0.86044651,  0.66067041, -0.01156993, -0.00166374,\n",
       "         0.04386107,  0.31275847,  0.02459361, -0.6041308 , -0.56862804,\n",
       "         0.45051628, -0.63609897, -0.67663373]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check ceoff \n",
    "# Mapping each column to a coefficient variable\n",
    "\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.0031672801993431563,\n",
       " 'sex': -0.8604465072345515,\n",
       " 'cp': 0.6606704082033799,\n",
       " 'trestbps': -0.01156993168080875,\n",
       " 'chol': -0.001663744504776871,\n",
       " 'fbs': 0.043861071652469864,\n",
       " 'restecg': 0.31275846822418324,\n",
       " 'thalach': 0.024593613737779126,\n",
       " 'exang': -0.6041308000615746,\n",
       " 'oldpeak': -0.5686280368396555,\n",
       " 'slope': 0.4505162797258308,\n",
       " 'ca': -0.6360989676086223,\n",
       " 'thal': -0.6766337263029825}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_dict = dict(zip(df.columns , list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21386d9e8c8>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_df = pd.DataFrame(feature_dict , index=[0])\n",
    "feature_df.T.plot.bar(figsize = (10,6) ,title = 'Feature importance ' , legend = False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'filename' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-77-e5b9bca514c8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpickle\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpkl\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'heart-disease-classification-model.sav'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mpkl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgs_log_reg\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'filename' is not defined"
     ]
    }
   ],
   "source": [
    "import pickle as pkl\n",
    "model = 'heart-disease-classification-model.sav'\n",
    "pkl.dump(gs_log_reg , open(filename , 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}