random-forest-classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customer Churn Prediction ML/AI Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description\n",
    "\n",
    "Customer retention is one of the key metrics for businesses with a subscription/contract-based model. \n",
    "Customer churn is the percentage of customers that stopped using the company's product or service during a certain time frame. \n",
    "Predicting customer churn is a challenging but important business problem as the new customer acquisition is expensive. So by predicting the customers who are at a high risk of churning, the company can take actions to reduce the chance of these customer canceling their subscriptions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goals\n",
    "\n",
    "* Make RandomForestClassifier work and figure out how to optimize its predictive performance.\n",
    "* See if it performs better than commercial programs\n",
    "* Undertand why Random Forest works\n",
    "* Investigate the mechanisms of overfitting, high correlation, other pitfalls of ML algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "https://www.kaggle.com/datasets/blastchar/telco-customer-churn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"color:orange\">Initial Setup</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0.2\n"
     ]
    }
   ],
   "source": [
    "# Environment and sklearn package must be updated to handle at least sklearn version 1.0 for RocCurveDisplay to work\n",
    "# Using anaconda CMD.exe Prompt, run \"conda update --all\" to update\n",
    "import sklearn\n",
    "print(sklearn.__version__)\n",
    "del sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Imports & Settings\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "\n",
    "from sklearn.model_selection import train_test_split as split, RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import sklearn.metrics as skm\n",
    "\n",
    "# display all columns and rows:\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "from IPython.core.display import HTML\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style='color:orange'>Feature Selection</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing\n",
    "\n",
    "The dataset came with categorical data as well as some rows having missing features. Preprocessing is needed to convert this data into numerical for the random forest classifier.\n",
    "\n",
    "* Label Encoding (converting categorical to numerical)\n",
    "* Noticed that TotalCharges column has missing data\n",
    "* Options (replacing with mean value was chosen):\n",
    " * Replace with 0\n",
    " * Delete the rows with missing TotalCharges\n",
    " * Replace with the mean value of TotalCharges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dataset & Preprocessing\n",
    "\n",
    "# Dataset\n",
    "df_original = pd.read_csv('dataset/telco-customer-churn.csv', index_col=0)\n",
    "df = df_original.copy()\n",
    "\n",
    "# Label Encoding (converting categorical to numerical)\n",
    "categorical_columns = [\n",
    "    'gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines','InternetService',\n",
    "    'OnlineSecurity', 'OnlineBackup','DeviceProtection','TechSupport','StreamingTV',\n",
    "    'StreamingMovies','Contract','PaperlessBilling','PaymentMethod', 'Churn'\n",
    "]\n",
    "\n",
    "# convert and replace categorical columns with numerical data\n",
    "# encoding is done with categorical labels that sorted alphabetically so df = ['c','z','a'] will always encode to [1,2,0]\n",
    "for col in categorical_columns:\n",
    "    df[col] = df[col].astype('category')\n",
    "    df[col] = df[col].cat.codes\n",
    "\n",
    "# Dataset 1: where rows with blank TotalCharges ' ' are replaced with 0\n",
    "df_0 = df.copy()\n",
    "df_0['TotalCharges'].replace(\" \", 0, inplace=True)\n",
    "df_0['TotalCharges'] = df_0['TotalCharges'].astype(float)\n",
    "\n",
    "# Dataset 2: where rows with blank TotalCharges ' ' are deleted\n",
    "df_d = df.copy()\n",
    "df_d.drop(df_d[df_d.TotalCharges == \" \"].index, inplace=True)\n",
    "df_d['TotalCharges'] = df_d['TotalCharges'].astype(float)\n",
    "\n",
    "\n",
    "# Dataset 3: where rows with blank TotalCharges ' ' are replaced with the mean of TotalCharges column\n",
    "df_m = df.copy()\n",
    "total_charges_mean = (df_d['TotalCharges'].mean()) # mean from previously calculated dataset\n",
    "df_m['TotalCharges'].replace(\" \", total_charges_mean, inplace=True)\n",
    "df_m['TotalCharges'] = df_m['TotalCharges'].astype(float)\n",
    "\n",
    "\n",
    "# Using Dataset: 3\n",
    "df = df_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature ranking method 1 - Mean decrease in impurity\n",
    "\n",
    "TotalCharges, MonthlyCharges, tenure and Contract were identified as the features that lead to a bigger decrease in impurity when sectioning the nodes. This means that this will be the features to focus on  as we see a sharp \"elbow\" point with these features vs. the rest of the features that have lower MDI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df.drop(\"Churn\", axis=1)\n",
    "y = df[\"Churn\"]\n",
    "X_train, X_test, y_train, y_test = split(df.drop(\"Churn\", axis=1),  df[\"Churn\"], test_size=0.1)\n",
    "\n",
    "forest = RandomForestClassifier(random_state=0)\n",
    "forest.fit(X, y)\n",
    "feature_names = X.columns.values.tolist()\n",
    "importances = forest.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in forest.estimators_], axis=0)\n",
    "forest_importances_mdi = pd.Series(importances, index=feature_names)\n",
    "# print(forest_importances_mdi.sort_values(ascending=False))\n",
    "\n",
    "# Plot feature importance\n",
    "fig, ax = plt.subplots()\n",
    "forest_importances_mdi.plot.bar(yerr=std, ax=ax)\n",
    "ax.set_title(\"Feature importances using MDI (Mean decrease in impurity)\")\n",
    "ax.set_ylabel(\"Mean decrease in impurity\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature ranking method 2 - Permutation Importance\n",
    "\n",
    "Using a different metric we arrive to the same conclusion about the top features that are most important for this model. These features effect the model accuracy the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "result = permutation_importance(\n",
    "    forest, X, y, n_repeats=10, random_state=42, n_jobs=2\n",
    ")\n",
    "forest_importances_pi = pd.Series(result.importances_mean, index=feature_names)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "forest_importances_pi.plot.bar(yerr=result.importances_std, ax=ax)\n",
    "ax.set_title(\"Feature importances using permutation\")\n",
    "ax.set_ylabel(\"Mean accuracy decrease\")\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# print(forest_importances_pi.sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature-Selection by variance thresholds\n",
    "\n",
    "Using variance thresholds to select the features we see similar accuracy based on the different thresholds from 80% to 99% but noticeably the features used in the model range from 2 to all features. The middle ground would be using 4 or 5 features as they have very similar performances to using all the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<style type=\"text/css\">\n</style>\n<table id=\"T_dc9ff\">\n  <caption>Accuracy of a RF Classifier using columns within variance threshold</caption>\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_dc9ff_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n      <th id=\"T_dc9ff_level0_col1\" class=\"col_heading level0 col1\" >Threshold</th>\n      <th id=\"T_dc9ff_level0_col2\" class=\"col_heading level0 col2\" >K-features</th>\n      <th id=\"T_dc9ff_level0_col3\" class=\"col_heading level0 col3\" >Columns</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_dc9ff_level0_row0\" class=\"row_heading level0 row0\" >0.8</th>\n      <td id=\"T_dc9ff_row0_col0\" class=\"data row0 col0\" >0.740426</td>\n      <td id=\"T_dc9ff_row0_col1\" class=\"data row0 col1\" >0.160000</td>\n      <td id=\"T_dc9ff_row0_col2\" class=\"data row0 col2\" >2</td>\n      <td id=\"T_dc9ff_row0_col3\" class=\"data row0 col3\" >['MonthlyCharges', 'TotalCharges']</td>\n    </tr>\n    <tr>\n      <th id=\"T_dc9ff_level0_row1\" class=\"row_heading level0 row1\" >0.9</th>\n      <td id=\"T_dc9ff_row1_col0\" class=\"data row1 col0\" >0.751773</td>\n      <td id=\"T_dc9ff_row1_col1\" class=\"data row1 col1\" >0.090000</td>\n      <td id=\"T_dc9ff_row1_col2\" class=\"data row1 col2\" >3</td>\n      <td id=\"T_dc9ff_row1_col3\" class=\"data row1 col3\" >['tenure', 'MonthlyCharges', 'TotalCharges']</td>\n    </tr>\n    <tr>\n      <th id=\"T_dc9ff_level0_row2\" class=\"row_heading level0 row2\" >0.91</th>\n      <td id=\"T_dc9ff_row2_col0\" class=\"data row2 col0\" >0.751773</td>\n      <td id=\"T_dc9ff_row2_col1\" class=\"data row2 col1\" >0.082000</td>\n      <td id=\"T_dc9ff_row2_col2\" class=\"data row2 col2\" >3</td>\n      <td id=\"T_dc9ff_row2_col3\" class=\"data row2 col3\" >['tenure', 'MonthlyCharges', 'TotalCharges']</td>\n    </tr>\n    <tr>\n      <th id=\"T_dc9ff_level0_row3\" class=\"row_heading level0 row3\" >0.92</th>\n      <td id=\"T_dc9ff_row3_col0\" class=\"data row3 col0\" >0.744681</td>\n      <td id=\"T_dc9ff_row3_col1\" class=\"data row3 col1\" >0.074000</td>\n      <td id=\"T_dc9ff_row3_col2\" class=\"data row3 col2\" >4</td>\n      <td id=\"T_dc9ff_row3_col3\" class=\"data row3 col3\" >['tenure', 'Contract', 'MonthlyCharges', 'TotalCharges']</td>\n    </tr>\n    <tr>\n      <th id=\"T_dc9ff_level0_row4\" class=\"row_heading level0 row4\" >0.93</th>\n      <td id=\"T_dc9ff_row4_col0\" class=\"data row4 col0\" >0.744681</td>\n      <td id=\"T_dc9ff_row4_col1\" class=\"data row4 col1\" >0.065000</td>\n      <td id=\"T_dc9ff_row4_col2\" class=\"data row4 col2\" >4</td>\n      <td id=\"T_dc9ff_row4_col3\" class=\"data row4 col3\" >['tenure', 'Contract', 'MonthlyCharges', 'TotalCharges']</td>\n    </tr>\n    <tr>\n      <th id=\"T_dc9ff_level0_row5\" class=\"row_heading level0 row5\" >0.94</th>\n      <td id=\"T_dc9ff_row5_col0\" class=\"data row5 col0\" >0.744681</td>\n      <td id=\"T_dc9ff_row5_col1\" class=\"data row5 col1\" >0.056000</td>\n      <td id=\"T_dc9ff_row5_col2\" class=\"data row5 col2\" >4</td>\n      <td id=\"T_dc9ff_row5_col3\" class=\"data row5 col3\" >['tenure', 'Contract', 'MonthlyCharges', 'TotalCharges']</td>\n    </tr>\n    <tr>\n      <th id=\"T_dc9ff_level0_row6\" class=\"row_heading level0 row6\" >0.95</th>\n      <td id=\"T_dc9ff_row6_col0\" class=\"data row6 col0\" >0.768794</td>\n      <td id=\"T_dc9ff_row6_col1\" class=\"data row6 col1\" >0.048000</td>\n      <td id=\"T_dc9ff_row6_col2\" class=\"data row6 col2\" >5</td>\n      <td id=\"T_dc9ff_row6_col3\" class=\"data row6 col3\" >['tenure', 'Contract', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges']</td>\n    </tr>\n    <tr>\n      <th id=\"T_dc9ff_level0_row7\" class=\"row_heading level0 row7\" >0.96</th>\n      <td id=\"T_dc9ff_row7_col0\" class=\"data row7 col0\" >0.768794</td>\n      <td id=\"T_dc9ff_row7_col1\" class=\"data row7 col1\" >0.038000</td>\n      <td id=\"T_dc9ff_row7_col2\" class=\"data row7 col2\" >7</td>\n      <td id=\"T_dc9ff_row7_col3\" class=\"data row7 col3\" >['tenure', 'OnlineSecurity', 'TechSupport', 'Contract', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges']</td>\n    </tr>\n    <tr>\n      <th id=\"T_dc9ff_level0_row8\" class=\"row_heading level0 row8\" >0.97</th>\n      <td id=\"T_dc9ff_row8_col0\" class=\"data row8 col0\" >0.768794</td>\n      <td id=\"T_dc9ff_row8_col1\" class=\"data row8 col1\" >0.029000</td>\n      <td id=\"T_dc9ff_row8_col2\" class=\"data row8 col2\" >7</td>\n      <td id=\"T_dc9ff_row8_col3\" class=\"data row8 col3\" >['tenure', 'OnlineSecurity', 'TechSupport', 'Contract', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges']</td>\n    </tr>\n    <tr>\n      <th id=\"T_dc9ff_level0_row9\" class=\"row_heading level0 row9\" >0.98</th>\n      <td id=\"T_dc9ff_row9_col0\" class=\"data row9 col0\" >0.798582</td>\n      <td id=\"T_dc9ff_row9_col1\" class=\"data row9 col1\" >0.020000</td>\n      <td id=\"T_dc9ff_row9_col2\" class=\"data row9 col2\" >16</td>\n      <td id=\"T_dc9ff_row9_col3\" class=\"data row9 col3\" >['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges']</td>\n    </tr>\n    <tr>\n      <th id=\"T_dc9ff_level0_row10\" class=\"row_heading level0 row10\" >0.99</th>\n      <td id=\"T_dc9ff_row10_col0\" class=\"data row10 col0\" >0.797163</td>\n      <td id=\"T_dc9ff_row10_col1\" class=\"data row10 col1\" >0.010000</td>\n      <td id=\"T_dc9ff_row10_col2\" class=\"data row10 col2\" >18</td>\n      <td id=\"T_dc9ff_row10_col3\" class=\"data row10 col3\" >['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges']</td>\n    </tr>\n  </tbody>\n</table>\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate feature selection table\n",
    "# We want to remove all features that are either one or zero (on or off) in more than n percent of the samples\n",
    "threshold_percentiles = [0.8, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99]\n",
    "thresholds = [round((probability * (1 - probability)), 3) for probability in threshold_percentiles]\n",
    "threshold_dict = {\n",
    "    f'{threshold_percentiles[i]}': forest_importances_mdi[forest_importances_mdi > thresholds[i]].index.tolist()\n",
    "    for i in range(len(thresholds))\n",
    "}\n",
    "\n",
    "threshold_results = { key:[columns] for key, columns in threshold_dict.items()}\n",
    "\n",
    "for key, columns in threshold_dict.items():\n",
    "    # fit & train an RF classifier with certain columns that passed threshold\n",
    "    clf_i = RandomForestClassifier(random_state=0)\n",
    "    X_train_i = X_train[columns]\n",
    "    X_test_i = X_test[columns]\n",
    "    y_pred_i = clf_i.fit(X_train_i, y_train).predict(X_test_i)\n",
    "\n",
    "    threshold_results[key] = [skm.accuracy_score(y_test, y_pred_i), thresholds[threshold_percentiles.index(float(key))], len(columns), columns]\n",
    "\n",
    "feature_selection_results = pd.DataFrame.from_dict(threshold_results, orient='index', columns=['Accuracy', 'Threshold',  'K-features', 'Columns'])\n",
    "feature_selection_results = feature_selection_results.style.set_caption(\"Accuracy of a RF Classifier using columns within variance threshold\")\n",
    "\n",
    "# Show feature selection vs accuracy results table\n",
    "display(HTML(feature_selection_results.to_html()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style='color:orange'>Random Forest Algorithm</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RFC:\n",
    "    '''\n",
    "    Purpose:\n",
    "        This class performs the same function as sklearn's GridSearchCV, except it saves all the classifiers it trains\n",
    "        instead of just the \"best\" one as determined by some scoring function.  In this way, we can compare the effect of\n",
    "        different hyper parameter combinations on the performance of the forest.\n",
    "        \n",
    "        We measure performance with ROC AUC, which was proposed by Probst et. al. (2018) to be a better than the usual\n",
    "        accuracy measurement.\n",
    "\n",
    "    Initializing Parameters:\n",
    "      * hyperParameters = A dict whose keys correspond to the parameters of RandomForestClassifer and whose values are lists\n",
    "        of arguments for those parameters\n",
    "      * verbose = Whether to print the parameters used to train each forest\n",
    "      \n",
    "    Other notes:\n",
    "      * Research paper references throughout this Class are done in this manner:\n",
    "         - Breiman (2001) = B\n",
    "         - Probst  (2018) = P\n",
    "        Section and subsection numbers will be added after these aliases like\n",
    "        B2.1 to show that a comment refers to Breiman (2001) Section 2 Subsection 1\n",
    "    '''\n",
    "    def __init__(self, hyperParameters={}, verbose=True):\n",
    "        hyperParams = \\\n",
    "            self.__getDefaultParams__() if len(hyperParameters) == 0 else self.__validateParams__(hyperParameters)\n",
    "        self.params = self.__getParamCombos__(hyperParams)\n",
    "        self.verbose = verbose\n",
    "        self.classifiers = []\n",
    "    \n",
    "    def __getDefaultParams__(self):\n",
    "        # Supplies default parameters to test based on what our source papers suggest are generally good.\n",
    "        out = {}\n",
    "        \n",
    "        ''' B4\n",
    "            Suggests selecting more than 1 features to split a node with doesn't perform any better than selecting 1 so long\n",
    "            as we have a high number of trees.\n",
    "            \n",
    "            sklearn sets its default 'max_features'='sqrt', so this is added as one of our defaults to investigate why.  Did\n",
    "            they find that 'sqrt' performs better than Breiman's suggested 1?\n",
    "        '''\n",
    "        out['max_features'] = [1, 'sqrt']\n",
    "        \n",
    "        ''' P5.3\n",
    "            Suggests that the biggest performance gain is experienced by the first 100 trees; however, lower 'max_samples'\n",
    "            values, lower 'max_depth' values, and higher 'max_features' values reduce inter-tree correlation and therefore\n",
    "            increase the number of trees needed to reach convergence.\n",
    "        '''\n",
    "        out['max_depth'] = [None, 3]\n",
    "        out['n_estimators'] = [100, 200]\n",
    "        \n",
    "        # B3\n",
    "        # Suggests each bootstrap sample should ideally have 2/3s as many instances as the input.\n",
    "        out['max_samples'] = [float(2/3)]\n",
    "        \n",
    "        # These settings just make sklearn's RFC train faster or be more informative\n",
    "        out['oob_score'] = [True]\n",
    "        out['n_jobs'] = [-1]\n",
    "        return out\n",
    "\n",
    "    def __validateParams__(self, params):\n",
    "        ''' Removes any misspelled or non-existent-in-RandomForestClassifier parameter name keys from the dict passed in by\n",
    "            the caller.\n",
    "        '''\n",
    "        VALID = ['n_estimators', 'criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf',\n",
    "                 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'boostrap',\n",
    "                 'oob_score','n_jobs', 'random_state', 'verbose', 'warm_start', 'class_weight', 'ccp_alpha', 'max_samples']\n",
    "        invalidKeys = []\n",
    "        validatedParams = deepcopy(params)\n",
    "        for key in params:\n",
    "            if key not in VALID:\n",
    "                validatedParams.pop(key)\n",
    "                invalidKeys += [key]\n",
    "        if len(invalidKeys) > 0:\n",
    "            print(\"These are not valid RandomForestClassifier parameter names:\\n{}\\n\".format(invalidKeys) + \\\n",
    "                  \"They will not be tested\\n\")\n",
    "        return validatedParams if len(validatedParams) > 0 else self.__getDefaultParams__()\n",
    "    \n",
    "    def __getParamCombos__(self, dictParams):\n",
    "        ''' Assemble a dict for each unique combination of hyper parameters.  These dicts can be passed into a function to\n",
    "            supply keyword arguments\n",
    "        '''\n",
    "        combinations = []\n",
    "        for param, valList in dictParams.items():\n",
    "            if len(combinations) > 0:\n",
    "                for i in range(len(combinations)):\n",
    "                    base = combinations.pop(0)\n",
    "                    for val in valList:\n",
    "                        newCombo = deepcopy(base)\n",
    "                        newCombo[param] = val\n",
    "                        combinations.append(newCombo)\n",
    "            else:\n",
    "                for val in valList:\n",
    "                    combinations.append({param: val})\n",
    "        return combinations\n",
    "    \n",
    "    def train(self, trainX, trainY):\n",
    "        '''\n",
    "        Purpose:\n",
    "            Train an RFC for each unique combination of hyperparameters\n",
    "        Code references:\n",
    "          * Passing named arguments to a function with a dict of names,vals\n",
    "            https://stackoverflow.com/questions/334655/passing-a-dictionary-to-a-function-as-keyword-parameters\n",
    "        '''\n",
    "        if self.verbose:\n",
    "            print(\"Training {} classifiers...\".format(len(self.params)))\n",
    "        for dictParams in self.params:\n",
    "            if self.verbose:\n",
    "                print(\"\\t{}\".format(dictParams))\n",
    "            newRfc = RandomForestClassifier(**dictParams)\n",
    "            newRfc.fit(trainX, trainY)\n",
    "            self.classifiers.append(newRfc)        \n",
    "            \n",
    "    def showRocAuc(self, testX, testY):\n",
    "        # Plots the ROC curve for each classifier this class trained for visual comparison\n",
    "        if len(self.classifiers) == 0:\n",
    "            raise AttributeError(\"No classifiers have been trained yet.  You must call .train() to create \" + \\\n",
    "                                 \"some classifiers to display.\")\n",
    "        \n",
    "        fig = plt.figure(dpi=300)\n",
    "        axes = plt.gca()\n",
    "        for i in range(len(self.classifiers)):\n",
    "            skm.RocCurveDisplay.from_estimator(\n",
    "                self.classifiers[i],\n",
    "                testX,\n",
    "                testY,\n",
    "                pos_label=0,\n",
    "                name=\"RFC #{}\".format(i+1),\n",
    "                ax=axes)\n",
    "            \n",
    "    def computeRocAuc(self, testX, testY):\n",
    "        # Returns the AUC scores for each classifier this class has trained\n",
    "        if len(self.classifiers) == 0:\n",
    "            raise AttributeError(\"No classifiers have been trained yet.  You must call .train() to create \" + \\\n",
    "                                 \"some classifiers to display.\")\n",
    "        \n",
    "        scores = []\n",
    "        for clf in self.classifiers:\n",
    "            scores.append(skm.roc_auc_score(testY, clf.predict_proba(testX)[:, 1]))\n",
    "        return np.array(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style='color:orange'>Hyperparameter Performance Comparisons</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Random Search, Top Results\n",
    "We searched with RandomSearch with N = 1000 iterations and found the following results below.\n",
    "\n",
    "The top performing hyperparameters based on this table were\n",
    "\n",
    "```{'n_estimators': 118, 'min_samples_split': 11, 'min_samples_leaf': 6, 'max_samples': 0.6666666666666666, 'max_features': 'auto', 'max_depth': 23}```\n",
    "\n",
    "![Random Search Output](hyperparameter_search_results.png)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters {'n_estimators': 118, 'min_samples_split': 11, 'min_samples_leaf': 6, 'max_samples': 0.6666666666666666, 'max_features': 'auto', 'max_depth': 23} \n",
      "\n",
      "RandomSearch Results, Ranked by Accuracy\n",
      "Total Fit Time: 6080.18s\n",
      "N-Iterations: 1000\n"
     ]
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>rank_test_score</th>\n      <th>mean_test_score</th>\n      <th>mean_train_score</th>\n      <th>param_n_estimators</th>\n      <th>param_max_features</th>\n      <th>param_max_depth</th>\n      <th>param_min_samples_split</th>\n      <th>param_min_samples_leaf</th>\n      <th>mean_fit_time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>515</th>\n      <td>1</td>\n      <td>0.804039</td>\n      <td>0.849637</td>\n      <td>118</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.415864</td>\n    </tr>\n    <tr>\n      <th>167</th>\n      <td>2</td>\n      <td>0.803410</td>\n      <td>0.840468</td>\n      <td>167</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.473665</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>3</td>\n      <td>0.803253</td>\n      <td>0.839907</td>\n      <td>181</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.422525</td>\n    </tr>\n    <tr>\n      <th>615</th>\n      <td>4</td>\n      <td>0.803095</td>\n      <td>0.833070</td>\n      <td>134</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.466238</td>\n    </tr>\n    <tr>\n      <th>324</th>\n      <td>5</td>\n      <td>0.802938</td>\n      <td>0.832317</td>\n      <td>132</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.297292</td>\n    </tr>\n    <tr>\n      <th>745</th>\n      <td>6</td>\n      <td>0.802937</td>\n      <td>0.842081</td>\n      <td>157</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.555076</td>\n    </tr>\n    <tr>\n      <th>803</th>\n      <td>7</td>\n      <td>0.802937</td>\n      <td>0.846745</td>\n      <td>128</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.400588</td>\n    </tr>\n    <tr>\n      <th>222</th>\n      <td>8</td>\n      <td>0.802937</td>\n      <td>0.854195</td>\n      <td>189</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.507547</td>\n    </tr>\n    <tr>\n      <th>984</th>\n      <td>9</td>\n      <td>0.802936</td>\n      <td>0.848568</td>\n      <td>122</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.278673</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>10</td>\n      <td>0.802781</td>\n      <td>0.832527</td>\n      <td>163</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.407040</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>11</td>\n      <td>0.802779</td>\n      <td>0.839872</td>\n      <td>181</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.395996</td>\n    </tr>\n    <tr>\n      <th>933</th>\n      <td>12</td>\n      <td>0.802779</td>\n      <td>0.840013</td>\n      <td>104</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.263518</td>\n    </tr>\n    <tr>\n      <th>754</th>\n      <td>13</td>\n      <td>0.802622</td>\n      <td>0.840065</td>\n      <td>142</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.513931</td>\n    </tr>\n    <tr>\n      <th>255</th>\n      <td>14</td>\n      <td>0.802622</td>\n      <td>0.832772</td>\n      <td>167</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.416688</td>\n    </tr>\n    <tr>\n      <th>357</th>\n      <td>15</td>\n      <td>0.802620</td>\n      <td>0.832299</td>\n      <td>187</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.401079</td>\n    </tr>\n    <tr>\n      <th>432</th>\n      <td>16</td>\n      <td>0.802307</td>\n      <td>0.846394</td>\n      <td>110</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.405875</td>\n    </tr>\n    <tr>\n      <th>688</th>\n      <td>17</td>\n      <td>0.802305</td>\n      <td>0.884752</td>\n      <td>138</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.511424</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>18</td>\n      <td>0.802305</td>\n      <td>0.839083</td>\n      <td>118</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.302399</td>\n    </tr>\n    <tr>\n      <th>603</th>\n      <td>19</td>\n      <td>0.802305</td>\n      <td>0.832159</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.423499</td>\n    </tr>\n    <tr>\n      <th>219</th>\n      <td>20</td>\n      <td>0.802148</td>\n      <td>0.839609</td>\n      <td>165</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.431648</td>\n    </tr>\n    <tr>\n      <th>380</th>\n      <td>21</td>\n      <td>0.802147</td>\n      <td>0.839837</td>\n      <td>200</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.653262</td>\n    </tr>\n    <tr>\n      <th>470</th>\n      <td>22</td>\n      <td>0.802147</td>\n      <td>0.832895</td>\n      <td>142</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.535270</td>\n    </tr>\n    <tr>\n      <th>621</th>\n      <td>23</td>\n      <td>0.802147</td>\n      <td>0.841538</td>\n      <td>112</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.402779</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>24</td>\n      <td>0.802146</td>\n      <td>0.841082</td>\n      <td>134</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.325017</td>\n    </tr>\n    <tr>\n      <th>456</th>\n      <td>25</td>\n      <td>0.801991</td>\n      <td>0.849900</td>\n      <td>130</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.448413</td>\n    </tr>\n    <tr>\n      <th>239</th>\n      <td>26</td>\n      <td>0.801990</td>\n      <td>0.838733</td>\n      <td>132</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.339438</td>\n    </tr>\n    <tr>\n      <th>989</th>\n      <td>26</td>\n      <td>0.801990</td>\n      <td>0.832913</td>\n      <td>114</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.290309</td>\n    </tr>\n    <tr>\n      <th>497</th>\n      <td>28</td>\n      <td>0.801990</td>\n      <td>0.839539</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.375598</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>29</td>\n      <td>0.801990</td>\n      <td>0.832141</td>\n      <td>195</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.598046</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>30</td>\n      <td>0.801990</td>\n      <td>0.845728</td>\n      <td>144</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.413265</td>\n    </tr>\n    <tr>\n      <th>323</th>\n      <td>31</td>\n      <td>0.801989</td>\n      <td>0.841678</td>\n      <td>181</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.380225</td>\n    </tr>\n    <tr>\n      <th>710</th>\n      <td>32</td>\n      <td>0.801989</td>\n      <td>0.840714</td>\n      <td>102</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.375860</td>\n    </tr>\n    <tr>\n      <th>550</th>\n      <td>33</td>\n      <td>0.801988</td>\n      <td>0.846709</td>\n      <td>102</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.364365</td>\n    </tr>\n    <tr>\n      <th>430</th>\n      <td>34</td>\n      <td>0.801833</td>\n      <td>0.832895</td>\n      <td>167</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.584505</td>\n    </tr>\n    <tr>\n      <th>846</th>\n      <td>35</td>\n      <td>0.801833</td>\n      <td>0.849234</td>\n      <td>144</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.488930</td>\n    </tr>\n    <tr>\n      <th>271</th>\n      <td>36</td>\n      <td>0.801833</td>\n      <td>0.832439</td>\n      <td>169</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.414450</td>\n    </tr>\n    <tr>\n      <th>415</th>\n      <td>37</td>\n      <td>0.801833</td>\n      <td>0.842011</td>\n      <td>106</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.376860</td>\n    </tr>\n    <tr>\n      <th>654</th>\n      <td>38</td>\n      <td>0.801832</td>\n      <td>0.878879</td>\n      <td>112</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.421976</td>\n    </tr>\n    <tr>\n      <th>950</th>\n      <td>38</td>\n      <td>0.801832</td>\n      <td>0.840223</td>\n      <td>169</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.390221</td>\n    </tr>\n    <tr>\n      <th>385</th>\n      <td>38</td>\n      <td>0.801832</td>\n      <td>0.833088</td>\n      <td>163</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.399681</td>\n    </tr>\n    <tr>\n      <th>355</th>\n      <td>41</td>\n      <td>0.801832</td>\n      <td>0.840626</td>\n      <td>146</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.344825</td>\n    </tr>\n    <tr>\n      <th>705</th>\n      <td>42</td>\n      <td>0.801832</td>\n      <td>0.856825</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.478974</td>\n    </tr>\n    <tr>\n      <th>818</th>\n      <td>43</td>\n      <td>0.801675</td>\n      <td>0.857579</td>\n      <td>191</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.604603</td>\n    </tr>\n    <tr>\n      <th>871</th>\n      <td>43</td>\n      <td>0.801675</td>\n      <td>0.841029</td>\n      <td>177</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.569380</td>\n    </tr>\n    <tr>\n      <th>122</th>\n      <td>45</td>\n      <td>0.801674</td>\n      <td>0.850040</td>\n      <td>100</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.291107</td>\n    </tr>\n    <tr>\n      <th>954</th>\n      <td>46</td>\n      <td>0.801674</td>\n      <td>0.832439</td>\n      <td>179</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.367602</td>\n    </tr>\n    <tr>\n      <th>788</th>\n      <td>46</td>\n      <td>0.801674</td>\n      <td>0.840048</td>\n      <td>161</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.512806</td>\n    </tr>\n    <tr>\n      <th>291</th>\n      <td>48</td>\n      <td>0.801517</td>\n      <td>0.839802</td>\n      <td>171</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.405436</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>49</td>\n      <td>0.801516</td>\n      <td>0.839136</td>\n      <td>155</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.387895</td>\n    </tr>\n    <tr>\n      <th>944</th>\n      <td>50</td>\n      <td>0.801516</td>\n      <td>0.831703</td>\n      <td>161</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.378689</td>\n    </tr>\n    <tr>\n      <th>747</th>\n      <td>51</td>\n      <td>0.801515</td>\n      <td>0.866852</td>\n      <td>112</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.395971</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>51</td>\n      <td>0.801515</td>\n      <td>0.841380</td>\n      <td>118</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.278764</td>\n    </tr>\n    <tr>\n      <th>762</th>\n      <td>53</td>\n      <td>0.801515</td>\n      <td>0.860489</td>\n      <td>128</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.395661</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>54</td>\n      <td>0.801515</td>\n      <td>0.850163</td>\n      <td>126</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.426202</td>\n    </tr>\n    <tr>\n      <th>578</th>\n      <td>54</td>\n      <td>0.801515</td>\n      <td>0.841205</td>\n      <td>106</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.366995</td>\n    </tr>\n    <tr>\n      <th>508</th>\n      <td>56</td>\n      <td>0.801515</td>\n      <td>0.850391</td>\n      <td>132</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.471915</td>\n    </tr>\n    <tr>\n      <th>170</th>\n      <td>57</td>\n      <td>0.801360</td>\n      <td>0.840872</td>\n      <td>134</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.336658</td>\n    </tr>\n    <tr>\n      <th>524</th>\n      <td>57</td>\n      <td>0.801360</td>\n      <td>0.839960</td>\n      <td>122</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.446664</td>\n    </tr>\n    <tr>\n      <th>169</th>\n      <td>59</td>\n      <td>0.801359</td>\n      <td>0.841152</td>\n      <td>148</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.353373</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>60</td>\n      <td>0.801359</td>\n      <td>0.839048</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.284822</td>\n    </tr>\n    <tr>\n      <th>484</th>\n      <td>60</td>\n      <td>0.801359</td>\n      <td>0.839890</td>\n      <td>195</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.614157</td>\n    </tr>\n    <tr>\n      <th>999</th>\n      <td>62</td>\n      <td>0.801359</td>\n      <td>0.867010</td>\n      <td>167</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.408112</td>\n    </tr>\n    <tr>\n      <th>358</th>\n      <td>63</td>\n      <td>0.801359</td>\n      <td>0.849725</td>\n      <td>171</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.403413</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>63</td>\n      <td>0.801359</td>\n      <td>0.860086</td>\n      <td>153</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.416003</td>\n    </tr>\n    <tr>\n      <th>849</th>\n      <td>63</td>\n      <td>0.801359</td>\n      <td>0.860752</td>\n      <td>142</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.495589</td>\n    </tr>\n    <tr>\n      <th>729</th>\n      <td>63</td>\n      <td>0.801359</td>\n      <td>0.832334</td>\n      <td>159</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.562733</td>\n    </tr>\n    <tr>\n      <th>587</th>\n      <td>67</td>\n      <td>0.801359</td>\n      <td>0.840083</td>\n      <td>200</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.661092</td>\n    </tr>\n    <tr>\n      <th>199</th>\n      <td>68</td>\n      <td>0.801358</td>\n      <td>0.855615</td>\n      <td>112</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.281847</td>\n    </tr>\n    <tr>\n      <th>240</th>\n      <td>69</td>\n      <td>0.801358</td>\n      <td>0.867221</td>\n      <td>167</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.472580</td>\n    </tr>\n    <tr>\n      <th>295</th>\n      <td>69</td>\n      <td>0.801358</td>\n      <td>0.861576</td>\n      <td>140</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.332142</td>\n    </tr>\n    <tr>\n      <th>679</th>\n      <td>71</td>\n      <td>0.801357</td>\n      <td>0.848971</td>\n      <td>114</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.383545</td>\n    </tr>\n    <tr>\n      <th>401</th>\n      <td>72</td>\n      <td>0.801202</td>\n      <td>0.856316</td>\n      <td>126</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.349327</td>\n    </tr>\n    <tr>\n      <th>902</th>\n      <td>73</td>\n      <td>0.801202</td>\n      <td>0.833123</td>\n      <td>110</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.399098</td>\n    </tr>\n    <tr>\n      <th>532</th>\n      <td>74</td>\n      <td>0.801202</td>\n      <td>0.840556</td>\n      <td>169</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.601268</td>\n    </tr>\n    <tr>\n      <th>340</th>\n      <td>75</td>\n      <td>0.801202</td>\n      <td>0.849690</td>\n      <td>100</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.235831</td>\n    </tr>\n    <tr>\n      <th>471</th>\n      <td>75</td>\n      <td>0.801202</td>\n      <td>0.832246</td>\n      <td>165</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.571769</td>\n    </tr>\n    <tr>\n      <th>713</th>\n      <td>77</td>\n      <td>0.801202</td>\n      <td>0.849041</td>\n      <td>106</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.383118</td>\n    </tr>\n    <tr>\n      <th>957</th>\n      <td>78</td>\n      <td>0.801201</td>\n      <td>0.832632</td>\n      <td>159</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.372345</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>78</td>\n      <td>0.801201</td>\n      <td>0.839504</td>\n      <td>122</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.319911</td>\n    </tr>\n    <tr>\n      <th>280</th>\n      <td>80</td>\n      <td>0.801201</td>\n      <td>0.840293</td>\n      <td>102</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.271373</td>\n    </tr>\n    <tr>\n      <th>541</th>\n      <td>81</td>\n      <td>0.801201</td>\n      <td>0.832948</td>\n      <td>142</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.480565</td>\n    </tr>\n    <tr>\n      <th>284</th>\n      <td>82</td>\n      <td>0.801201</td>\n      <td>0.837734</td>\n      <td>132</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.309921</td>\n    </tr>\n    <tr>\n      <th>997</th>\n      <td>83</td>\n      <td>0.801201</td>\n      <td>0.849707</td>\n      <td>200</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.441294</td>\n    </tr>\n    <tr>\n      <th>689</th>\n      <td>84</td>\n      <td>0.801200</td>\n      <td>0.849918</td>\n      <td>155</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.571888</td>\n    </tr>\n    <tr>\n      <th>388</th>\n      <td>85</td>\n      <td>0.801200</td>\n      <td>0.845991</td>\n      <td>100</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.256419</td>\n    </tr>\n    <tr>\n      <th>563</th>\n      <td>85</td>\n      <td>0.801200</td>\n      <td>0.866888</td>\n      <td>104</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.386508</td>\n    </tr>\n    <tr>\n      <th>207</th>\n      <td>87</td>\n      <td>0.801045</td>\n      <td>0.841555</td>\n      <td>163</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.657528</td>\n    </tr>\n    <tr>\n      <th>850</th>\n      <td>87</td>\n      <td>0.801045</td>\n      <td>0.839469</td>\n      <td>112</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.383758</td>\n    </tr>\n    <tr>\n      <th>606</th>\n      <td>89</td>\n      <td>0.801044</td>\n      <td>0.850864</td>\n      <td>112</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.407113</td>\n    </tr>\n    <tr>\n      <th>419</th>\n      <td>90</td>\n      <td>0.801044</td>\n      <td>0.832334</td>\n      <td>163</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.575531</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>91</td>\n      <td>0.801044</td>\n      <td>0.841766</td>\n      <td>185</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.424761</td>\n    </tr>\n    <tr>\n      <th>394</th>\n      <td>91</td>\n      <td>0.801044</td>\n      <td>0.832895</td>\n      <td>195</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.464200</td>\n    </tr>\n    <tr>\n      <th>379</th>\n      <td>93</td>\n      <td>0.801043</td>\n      <td>0.884208</td>\n      <td>179</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.464703</td>\n    </tr>\n    <tr>\n      <th>422</th>\n      <td>94</td>\n      <td>0.801043</td>\n      <td>0.851934</td>\n      <td>148</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.536722</td>\n    </tr>\n    <tr>\n      <th>421</th>\n      <td>95</td>\n      <td>0.801043</td>\n      <td>0.850356</td>\n      <td>169</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.615174</td>\n    </tr>\n    <tr>\n      <th>674</th>\n      <td>95</td>\n      <td>0.801043</td>\n      <td>0.841555</td>\n      <td>181</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.570206</td>\n    </tr>\n    <tr>\n      <th>307</th>\n      <td>97</td>\n      <td>0.801043</td>\n      <td>0.848936</td>\n      <td>106</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.256241</td>\n    </tr>\n    <tr>\n      <th>439</th>\n      <td>98</td>\n      <td>0.801042</td>\n      <td>0.849602</td>\n      <td>136</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.497084</td>\n    </tr>\n    <tr>\n      <th>128</th>\n      <td>98</td>\n      <td>0.801042</td>\n      <td>0.845693</td>\n      <td>116</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.317970</td>\n    </tr>\n    <tr>\n      <th>793</th>\n      <td>98</td>\n      <td>0.801042</td>\n      <td>0.839609</td>\n      <td>167</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.546853</td>\n    </tr>\n    <tr>\n      <th>142</th>\n      <td>101</td>\n      <td>0.801042</td>\n      <td>0.851408</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.286337</td>\n    </tr>\n    <tr>\n      <th>546</th>\n      <td>102</td>\n      <td>0.801042</td>\n      <td>0.850268</td>\n      <td>165</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.612433</td>\n    </tr>\n    <tr>\n      <th>819</th>\n      <td>103</td>\n      <td>0.801041</td>\n      <td>0.865766</td>\n      <td>116</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.404521</td>\n    </tr>\n    <tr>\n      <th>389</th>\n      <td>104</td>\n      <td>0.800886</td>\n      <td>0.839574</td>\n      <td>140</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.387231</td>\n    </tr>\n    <tr>\n      <th>821</th>\n      <td>105</td>\n      <td>0.800886</td>\n      <td>0.841573</td>\n      <td>10</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.058503</td>\n    </tr>\n    <tr>\n      <th>925</th>\n      <td>105</td>\n      <td>0.800886</td>\n      <td>0.833088</td>\n      <td>197</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.432177</td>\n    </tr>\n    <tr>\n      <th>866</th>\n      <td>105</td>\n      <td>0.800886</td>\n      <td>0.848007</td>\n      <td>120</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.408575</td>\n    </tr>\n    <tr>\n      <th>770</th>\n      <td>108</td>\n      <td>0.800886</td>\n      <td>0.840539</td>\n      <td>148</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.471746</td>\n    </tr>\n    <tr>\n      <th>723</th>\n      <td>109</td>\n      <td>0.800886</td>\n      <td>0.850075</td>\n      <td>177</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.557546</td>\n    </tr>\n    <tr>\n      <th>241</th>\n      <td>110</td>\n      <td>0.800885</td>\n      <td>0.867659</td>\n      <td>185</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.457119</td>\n    </tr>\n    <tr>\n      <th>294</th>\n      <td>111</td>\n      <td>0.800885</td>\n      <td>0.857263</td>\n      <td>197</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.420002</td>\n    </tr>\n    <tr>\n      <th>647</th>\n      <td>111</td>\n      <td>0.800885</td>\n      <td>0.852898</td>\n      <td>136</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.472165</td>\n    </tr>\n    <tr>\n      <th>706</th>\n      <td>111</td>\n      <td>0.800885</td>\n      <td>0.841485</td>\n      <td>179</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.573796</td>\n    </tr>\n    <tr>\n      <th>548</th>\n      <td>114</td>\n      <td>0.800885</td>\n      <td>0.841415</td>\n      <td>193</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.635971</td>\n    </tr>\n    <tr>\n      <th>920</th>\n      <td>114</td>\n      <td>0.800885</td>\n      <td>0.839592</td>\n      <td>124</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.291901</td>\n    </tr>\n    <tr>\n      <th>644</th>\n      <td>114</td>\n      <td>0.800885</td>\n      <td>0.856141</td>\n      <td>132</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.457417</td>\n    </tr>\n    <tr>\n      <th>735</th>\n      <td>117</td>\n      <td>0.800885</td>\n      <td>0.839522</td>\n      <td>124</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.427702</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>117</td>\n      <td>0.800885</td>\n      <td>0.840889</td>\n      <td>126</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.343348</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>119</td>\n      <td>0.800885</td>\n      <td>0.861085</td>\n      <td>112</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.295221</td>\n    </tr>\n    <tr>\n      <th>740</th>\n      <td>119</td>\n      <td>0.800885</td>\n      <td>0.832089</td>\n      <td>148</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.500240</td>\n    </tr>\n    <tr>\n      <th>247</th>\n      <td>119</td>\n      <td>0.800885</td>\n      <td>0.841625</td>\n      <td>132</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.363292</td>\n    </tr>\n    <tr>\n      <th>407</th>\n      <td>122</td>\n      <td>0.800884</td>\n      <td>0.861821</td>\n      <td>153</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.600468</td>\n    </tr>\n    <tr>\n      <th>750</th>\n      <td>122</td>\n      <td>0.800884</td>\n      <td>0.855475</td>\n      <td>181</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.592612</td>\n    </tr>\n    <tr>\n      <th>913</th>\n      <td>124</td>\n      <td>0.800884</td>\n      <td>0.849935</td>\n      <td>161</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.757863</td>\n    </tr>\n    <tr>\n      <th>386</th>\n      <td>125</td>\n      <td>0.800884</td>\n      <td>0.849830</td>\n      <td>134</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.347583</td>\n    </tr>\n    <tr>\n      <th>619</th>\n      <td>125</td>\n      <td>0.800884</td>\n      <td>0.869710</td>\n      <td>165</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.612003</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>127</td>\n      <td>0.800884</td>\n      <td>0.855387</td>\n      <td>108</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.310923</td>\n    </tr>\n    <tr>\n      <th>495</th>\n      <td>128</td>\n      <td>0.800883</td>\n      <td>0.855580</td>\n      <td>130</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.410797</td>\n    </tr>\n    <tr>\n      <th>520</th>\n      <td>129</td>\n      <td>0.800729</td>\n      <td>0.856860</td>\n      <td>153</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.543478</td>\n    </tr>\n    <tr>\n      <th>459</th>\n      <td>130</td>\n      <td>0.800728</td>\n      <td>0.833053</td>\n      <td>132</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.507875</td>\n    </tr>\n    <tr>\n      <th>160</th>\n      <td>130</td>\n      <td>0.800728</td>\n      <td>0.832281</td>\n      <td>146</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.365445</td>\n    </tr>\n    <tr>\n      <th>746</th>\n      <td>132</td>\n      <td>0.800728</td>\n      <td>0.856457</td>\n      <td>183</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.626098</td>\n    </tr>\n    <tr>\n      <th>985</th>\n      <td>132</td>\n      <td>0.800728</td>\n      <td>0.839995</td>\n      <td>163</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.386322</td>\n    </tr>\n    <tr>\n      <th>571</th>\n      <td>134</td>\n      <td>0.800728</td>\n      <td>0.839364</td>\n      <td>126</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.424682</td>\n    </tr>\n    <tr>\n      <th>847</th>\n      <td>135</td>\n      <td>0.800728</td>\n      <td>0.839715</td>\n      <td>197</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.627276</td>\n    </tr>\n    <tr>\n      <th>702</th>\n      <td>136</td>\n      <td>0.800728</td>\n      <td>0.867133</td>\n      <td>120</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.425600</td>\n    </tr>\n    <tr>\n      <th>403</th>\n      <td>136</td>\n      <td>0.800728</td>\n      <td>0.832404</td>\n      <td>155</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.626631</td>\n    </tr>\n    <tr>\n      <th>837</th>\n      <td>136</td>\n      <td>0.800728</td>\n      <td>0.832246</td>\n      <td>193</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.593728</td>\n    </tr>\n    <tr>\n      <th>293</th>\n      <td>139</td>\n      <td>0.800727</td>\n      <td>0.832352</td>\n      <td>112</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.244714</td>\n    </tr>\n    <tr>\n      <th>171</th>\n      <td>139</td>\n      <td>0.800727</td>\n      <td>0.841976</td>\n      <td>146</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.390419</td>\n    </tr>\n    <tr>\n      <th>384</th>\n      <td>141</td>\n      <td>0.800727</td>\n      <td>0.861541</td>\n      <td>138</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.359481</td>\n    </tr>\n    <tr>\n      <th>651</th>\n      <td>142</td>\n      <td>0.800727</td>\n      <td>0.846639</td>\n      <td>144</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.533525</td>\n    </tr>\n    <tr>\n      <th>577</th>\n      <td>142</td>\n      <td>0.800727</td>\n      <td>0.884594</td>\n      <td>185</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.645254</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>144</td>\n      <td>0.800727</td>\n      <td>0.832316</td>\n      <td>132</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.309111</td>\n    </tr>\n    <tr>\n      <th>614</th>\n      <td>145</td>\n      <td>0.800727</td>\n      <td>0.839837</td>\n      <td>128</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.497656</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>145</td>\n      <td>0.800727</td>\n      <td>0.839101</td>\n      <td>140</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.447806</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>145</td>\n      <td>0.800727</td>\n      <td>0.839557</td>\n      <td>175</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.465686</td>\n    </tr>\n    <tr>\n      <th>228</th>\n      <td>148</td>\n      <td>0.800726</td>\n      <td>0.867589</td>\n      <td>179</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.451183</td>\n    </tr>\n    <tr>\n      <th>545</th>\n      <td>149</td>\n      <td>0.800726</td>\n      <td>0.841696</td>\n      <td>193</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.625676</td>\n    </tr>\n    <tr>\n      <th>801</th>\n      <td>150</td>\n      <td>0.800572</td>\n      <td>0.832667</td>\n      <td>120</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.369960</td>\n    </tr>\n    <tr>\n      <th>161</th>\n      <td>151</td>\n      <td>0.800571</td>\n      <td>0.832737</td>\n      <td>138</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.314363</td>\n    </tr>\n    <tr>\n      <th>917</th>\n      <td>152</td>\n      <td>0.800571</td>\n      <td>0.832474</td>\n      <td>185</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>14</td>\n      <td>8</td>\n      <td>1.680614</td>\n    </tr>\n    <tr>\n      <th>931</th>\n      <td>152</td>\n      <td>0.800571</td>\n      <td>0.840433</td>\n      <td>155</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.415509</td>\n    </tr>\n    <tr>\n      <th>947</th>\n      <td>154</td>\n      <td>0.800570</td>\n      <td>0.831791</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.291425</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>154</td>\n      <td>0.800570</td>\n      <td>0.861260</td>\n      <td>106</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.393953</td>\n    </tr>\n    <tr>\n      <th>557</th>\n      <td>154</td>\n      <td>0.800570</td>\n      <td>0.837997</td>\n      <td>163</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.579323</td>\n    </tr>\n    <tr>\n      <th>852</th>\n      <td>157</td>\n      <td>0.800570</td>\n      <td>0.857281</td>\n      <td>102</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.365345</td>\n    </tr>\n    <tr>\n      <th>776</th>\n      <td>158</td>\n      <td>0.800570</td>\n      <td>0.856702</td>\n      <td>173</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.564560</td>\n    </tr>\n    <tr>\n      <th>555</th>\n      <td>159</td>\n      <td>0.800570</td>\n      <td>0.842327</td>\n      <td>200</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.636767</td>\n    </tr>\n    <tr>\n      <th>857</th>\n      <td>160</td>\n      <td>0.800570</td>\n      <td>0.831931</td>\n      <td>197</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.609797</td>\n    </tr>\n    <tr>\n      <th>838</th>\n      <td>160</td>\n      <td>0.800570</td>\n      <td>0.832457</td>\n      <td>114</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.390071</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td>162</td>\n      <td>0.800569</td>\n      <td>0.841222</td>\n      <td>110</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.282917</td>\n    </tr>\n    <tr>\n      <th>624</th>\n      <td>162</td>\n      <td>0.800569</td>\n      <td>0.861593</td>\n      <td>136</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.495023</td>\n    </tr>\n    <tr>\n      <th>964</th>\n      <td>164</td>\n      <td>0.800569</td>\n      <td>0.840170</td>\n      <td>146</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.351526</td>\n    </tr>\n    <tr>\n      <th>238</th>\n      <td>165</td>\n      <td>0.800568</td>\n      <td>0.861155</td>\n      <td>157</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.415731</td>\n    </tr>\n    <tr>\n      <th>372</th>\n      <td>166</td>\n      <td>0.800568</td>\n      <td>0.884366</td>\n      <td>161</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.397328</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>167</td>\n      <td>0.800414</td>\n      <td>0.839820</td>\n      <td>128</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.331827</td>\n    </tr>\n    <tr>\n      <th>839</th>\n      <td>168</td>\n      <td>0.800414</td>\n      <td>0.841310</td>\n      <td>169</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.581877</td>\n    </tr>\n    <tr>\n      <th>772</th>\n      <td>169</td>\n      <td>0.800413</td>\n      <td>0.852354</td>\n      <td>100</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.330084</td>\n    </tr>\n    <tr>\n      <th>270</th>\n      <td>170</td>\n      <td>0.800413</td>\n      <td>0.832194</td>\n      <td>142</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.420983</td>\n    </tr>\n    <tr>\n      <th>972</th>\n      <td>171</td>\n      <td>0.800413</td>\n      <td>0.833140</td>\n      <td>187</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.399845</td>\n    </tr>\n    <tr>\n      <th>289</th>\n      <td>171</td>\n      <td>0.800413</td>\n      <td>0.839592</td>\n      <td>189</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.395730</td>\n    </tr>\n    <tr>\n      <th>224</th>\n      <td>173</td>\n      <td>0.800412</td>\n      <td>0.839399</td>\n      <td>108</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.369022</td>\n    </tr>\n    <tr>\n      <th>842</th>\n      <td>174</td>\n      <td>0.800412</td>\n      <td>0.841398</td>\n      <td>153</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.544207</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>175</td>\n      <td>0.800412</td>\n      <td>0.849970</td>\n      <td>179</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.420305</td>\n    </tr>\n    <tr>\n      <th>731</th>\n      <td>176</td>\n      <td>0.800412</td>\n      <td>0.867098</td>\n      <td>140</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.492684</td>\n    </tr>\n    <tr>\n      <th>774</th>\n      <td>176</td>\n      <td>0.800412</td>\n      <td>0.850391</td>\n      <td>171</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.555595</td>\n    </tr>\n    <tr>\n      <th>232</th>\n      <td>176</td>\n      <td>0.800412</td>\n      <td>0.839469</td>\n      <td>114</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.271421</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>176</td>\n      <td>0.800412</td>\n      <td>0.839697</td>\n      <td>153</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.425076</td>\n    </tr>\n    <tr>\n      <th>836</th>\n      <td>176</td>\n      <td>0.800412</td>\n      <td>0.849216</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.456393</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>181</td>\n      <td>0.800411</td>\n      <td>0.839750</td>\n      <td>124</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.321921</td>\n    </tr>\n    <tr>\n      <th>500</th>\n      <td>181</td>\n      <td>0.800411</td>\n      <td>0.860927</td>\n      <td>100</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.332115</td>\n    </tr>\n    <tr>\n      <th>194</th>\n      <td>183</td>\n      <td>0.800411</td>\n      <td>0.847025</td>\n      <td>153</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.470445</td>\n    </tr>\n    <tr>\n      <th>812</th>\n      <td>184</td>\n      <td>0.800411</td>\n      <td>0.861348</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.437872</td>\n    </tr>\n    <tr>\n      <th>476</th>\n      <td>185</td>\n      <td>0.800411</td>\n      <td>0.874917</td>\n      <td>157</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.653522</td>\n    </tr>\n    <tr>\n      <th>856</th>\n      <td>185</td>\n      <td>0.800411</td>\n      <td>0.861716</td>\n      <td>167</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.604387</td>\n    </tr>\n    <tr>\n      <th>376</th>\n      <td>187</td>\n      <td>0.800411</td>\n      <td>0.847972</td>\n      <td>169</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.392986</td>\n    </tr>\n    <tr>\n      <th>187</th>\n      <td>188</td>\n      <td>0.800255</td>\n      <td>0.848077</td>\n      <td>132</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.334651</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>188</td>\n      <td>0.800255</td>\n      <td>0.832562</td>\n      <td>130</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.326175</td>\n    </tr>\n    <tr>\n      <th>420</th>\n      <td>190</td>\n      <td>0.800255</td>\n      <td>0.857281</td>\n      <td>104</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.384285</td>\n    </tr>\n    <tr>\n      <th>609</th>\n      <td>191</td>\n      <td>0.800255</td>\n      <td>0.839750</td>\n      <td>161</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.576371</td>\n    </tr>\n    <tr>\n      <th>962</th>\n      <td>191</td>\n      <td>0.800255</td>\n      <td>0.833176</td>\n      <td>175</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.414849</td>\n    </tr>\n    <tr>\n      <th>206</th>\n      <td>191</td>\n      <td>0.800255</td>\n      <td>0.833158</td>\n      <td>161</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.417257</td>\n    </tr>\n    <tr>\n      <th>765</th>\n      <td>191</td>\n      <td>0.800255</td>\n      <td>0.840293</td>\n      <td>171</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.532106</td>\n    </tr>\n    <tr>\n      <th>334</th>\n      <td>191</td>\n      <td>0.800255</td>\n      <td>0.840381</td>\n      <td>153</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.354577</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>196</td>\n      <td>0.800255</td>\n      <td>0.853599</td>\n      <td>118</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.332587</td>\n    </tr>\n    <tr>\n      <th>226</th>\n      <td>196</td>\n      <td>0.800255</td>\n      <td>0.832071</td>\n      <td>193</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.446445</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>198</td>\n      <td>0.800255</td>\n      <td>0.839276</td>\n      <td>122</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.294350</td>\n    </tr>\n    <tr>\n      <th>450</th>\n      <td>198</td>\n      <td>0.800255</td>\n      <td>0.860191</td>\n      <td>130</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.480306</td>\n    </tr>\n    <tr>\n      <th>318</th>\n      <td>198</td>\n      <td>0.800255</td>\n      <td>0.831212</td>\n      <td>122</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.272652</td>\n    </tr>\n    <tr>\n      <th>733</th>\n      <td>198</td>\n      <td>0.800255</td>\n      <td>0.853038</td>\n      <td>161</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.598325</td>\n    </tr>\n    <tr>\n      <th>799</th>\n      <td>202</td>\n      <td>0.800254</td>\n      <td>0.849059</td>\n      <td>104</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.340628</td>\n    </tr>\n    <tr>\n      <th>794</th>\n      <td>203</td>\n      <td>0.800254</td>\n      <td>0.839820</td>\n      <td>134</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.402513</td>\n    </tr>\n    <tr>\n      <th>683</th>\n      <td>203</td>\n      <td>0.800254</td>\n      <td>0.831843</td>\n      <td>163</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.565962</td>\n    </tr>\n    <tr>\n      <th>498</th>\n      <td>203</td>\n      <td>0.800254</td>\n      <td>0.850058</td>\n      <td>138</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.436654</td>\n    </tr>\n    <tr>\n      <th>813</th>\n      <td>203</td>\n      <td>0.800254</td>\n      <td>0.852056</td>\n      <td>175</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.609649</td>\n    </tr>\n    <tr>\n      <th>332</th>\n      <td>203</td>\n      <td>0.800254</td>\n      <td>0.839364</td>\n      <td>169</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.386624</td>\n    </tr>\n    <tr>\n      <th>892</th>\n      <td>203</td>\n      <td>0.800254</td>\n      <td>0.867378</td>\n      <td>191</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.638979</td>\n    </tr>\n    <tr>\n      <th>715</th>\n      <td>209</td>\n      <td>0.800254</td>\n      <td>0.868308</td>\n      <td>100</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.365275</td>\n    </tr>\n    <tr>\n      <th>165</th>\n      <td>209</td>\n      <td>0.800254</td>\n      <td>0.850198</td>\n      <td>175</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.496732</td>\n    </tr>\n    <tr>\n      <th>144</th>\n      <td>209</td>\n      <td>0.800254</td>\n      <td>0.864433</td>\n      <td>195</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.459575</td>\n    </tr>\n    <tr>\n      <th>328</th>\n      <td>209</td>\n      <td>0.800254</td>\n      <td>0.848007</td>\n      <td>132</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.297230</td>\n    </tr>\n    <tr>\n      <th>393</th>\n      <td>213</td>\n      <td>0.800254</td>\n      <td>0.888819</td>\n      <td>171</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.598020</td>\n    </tr>\n    <tr>\n      <th>177</th>\n      <td>214</td>\n      <td>0.800254</td>\n      <td>0.849234</td>\n      <td>118</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.269247</td>\n    </tr>\n    <tr>\n      <th>748</th>\n      <td>214</td>\n      <td>0.800254</td>\n      <td>0.839574</td>\n      <td>195</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.631658</td>\n    </tr>\n    <tr>\n      <th>326</th>\n      <td>216</td>\n      <td>0.800253</td>\n      <td>0.866958</td>\n      <td>165</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.429271</td>\n    </tr>\n    <tr>\n      <th>248</th>\n      <td>217</td>\n      <td>0.800252</td>\n      <td>0.892711</td>\n      <td>120</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.377741</td>\n    </tr>\n    <tr>\n      <th>943</th>\n      <td>218</td>\n      <td>0.800252</td>\n      <td>0.849251</td>\n      <td>161</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.370379</td>\n    </tr>\n    <tr>\n      <th>566</th>\n      <td>219</td>\n      <td>0.800097</td>\n      <td>0.852197</td>\n      <td>163</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.593836</td>\n    </tr>\n    <tr>\n      <th>668</th>\n      <td>220</td>\n      <td>0.800097</td>\n      <td>0.839329</td>\n      <td>142</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.487653</td>\n    </tr>\n    <tr>\n      <th>310</th>\n      <td>221</td>\n      <td>0.800097</td>\n      <td>0.840539</td>\n      <td>128</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.289819</td>\n    </tr>\n    <tr>\n      <th>205</th>\n      <td>221</td>\n      <td>0.800097</td>\n      <td>0.839381</td>\n      <td>197</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.474876</td>\n    </tr>\n    <tr>\n      <th>364</th>\n      <td>221</td>\n      <td>0.800097</td>\n      <td>0.839767</td>\n      <td>191</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.393141</td>\n    </tr>\n    <tr>\n      <th>595</th>\n      <td>224</td>\n      <td>0.800096</td>\n      <td>0.856562</td>\n      <td>157</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.565129</td>\n    </tr>\n    <tr>\n      <th>712</th>\n      <td>225</td>\n      <td>0.800096</td>\n      <td>0.839154</td>\n      <td>148</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.521327</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>225</td>\n      <td>0.800096</td>\n      <td>0.849953</td>\n      <td>108</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.291747</td>\n    </tr>\n    <tr>\n      <th>680</th>\n      <td>225</td>\n      <td>0.800096</td>\n      <td>0.832667</td>\n      <td>112</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.367818</td>\n    </tr>\n    <tr>\n      <th>257</th>\n      <td>228</td>\n      <td>0.800096</td>\n      <td>0.842011</td>\n      <td>169</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.436487</td>\n    </tr>\n    <tr>\n      <th>811</th>\n      <td>228</td>\n      <td>0.800096</td>\n      <td>0.861979</td>\n      <td>191</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.627540</td>\n    </tr>\n    <tr>\n      <th>623</th>\n      <td>230</td>\n      <td>0.800096</td>\n      <td>0.847674</td>\n      <td>130</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.456262</td>\n    </tr>\n    <tr>\n      <th>660</th>\n      <td>231</td>\n      <td>0.800096</td>\n      <td>0.849514</td>\n      <td>132</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.454710</td>\n    </tr>\n    <tr>\n      <th>583</th>\n      <td>231</td>\n      <td>0.800096</td>\n      <td>0.849707</td>\n      <td>157</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.638574</td>\n    </tr>\n    <tr>\n      <th>755</th>\n      <td>233</td>\n      <td>0.800095</td>\n      <td>0.838575</td>\n      <td>106</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.498982</td>\n    </tr>\n    <tr>\n      <th>724</th>\n      <td>234</td>\n      <td>0.800095</td>\n      <td>0.866747</td>\n      <td>140</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.495683</td>\n    </tr>\n    <tr>\n      <th>278</th>\n      <td>234</td>\n      <td>0.800095</td>\n      <td>0.872392</td>\n      <td>159</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.520697</td>\n    </tr>\n    <tr>\n      <th>727</th>\n      <td>236</td>\n      <td>0.800095</td>\n      <td>0.852004</td>\n      <td>161</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.562642</td>\n    </tr>\n    <tr>\n      <th>781</th>\n      <td>237</td>\n      <td>0.800094</td>\n      <td>0.867150</td>\n      <td>148</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.496268</td>\n    </tr>\n    <tr>\n      <th>447</th>\n      <td>238</td>\n      <td>0.800093</td>\n      <td>0.884962</td>\n      <td>165</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.623504</td>\n    </tr>\n    <tr>\n      <th>967</th>\n      <td>239</td>\n      <td>0.799940</td>\n      <td>0.850899</td>\n      <td>114</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.259473</td>\n    </tr>\n    <tr>\n      <th>935</th>\n      <td>240</td>\n      <td>0.799939</td>\n      <td>0.848568</td>\n      <td>187</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.400130</td>\n    </tr>\n    <tr>\n      <th>909</th>\n      <td>241</td>\n      <td>0.799939</td>\n      <td>0.831966</td>\n      <td>114</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.370564</td>\n    </tr>\n    <tr>\n      <th>973</th>\n      <td>242</td>\n      <td>0.799939</td>\n      <td>0.832211</td>\n      <td>163</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.384575</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>242</td>\n      <td>0.799939</td>\n      <td>0.838119</td>\n      <td>148</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.407282</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>242</td>\n      <td>0.799939</td>\n      <td>0.839241</td>\n      <td>118</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.322919</td>\n    </tr>\n    <tr>\n      <th>628</th>\n      <td>245</td>\n      <td>0.799939</td>\n      <td>0.846955</td>\n      <td>116</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.391458</td>\n    </tr>\n    <tr>\n      <th>586</th>\n      <td>246</td>\n      <td>0.799939</td>\n      <td>0.838715</td>\n      <td>106</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.472123</td>\n    </tr>\n    <tr>\n      <th>395</th>\n      <td>247</td>\n      <td>0.799939</td>\n      <td>0.839084</td>\n      <td>128</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.295503</td>\n    </tr>\n    <tr>\n      <th>975</th>\n      <td>247</td>\n      <td>0.799939</td>\n      <td>0.866537</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.309979</td>\n    </tr>\n    <tr>\n      <th>670</th>\n      <td>249</td>\n      <td>0.799938</td>\n      <td>0.857211</td>\n      <td>118</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.407450</td>\n    </tr>\n    <tr>\n      <th>313</th>\n      <td>249</td>\n      <td>0.799938</td>\n      <td>0.855510</td>\n      <td>163</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.415241</td>\n    </tr>\n    <tr>\n      <th>343</th>\n      <td>251</td>\n      <td>0.799938</td>\n      <td>0.857684</td>\n      <td>185</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.382557</td>\n    </tr>\n    <tr>\n      <th>880</th>\n      <td>252</td>\n      <td>0.799938</td>\n      <td>0.862698</td>\n      <td>104</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.407444</td>\n    </tr>\n    <tr>\n      <th>742</th>\n      <td>253</td>\n      <td>0.799938</td>\n      <td>0.880386</td>\n      <td>155</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.556927</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>254</td>\n      <td>0.799937</td>\n      <td>0.861804</td>\n      <td>148</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.443235</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>254</td>\n      <td>0.799937</td>\n      <td>0.849567</td>\n      <td>163</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.420638</td>\n    </tr>\n    <tr>\n      <th>560</th>\n      <td>254</td>\n      <td>0.799937</td>\n      <td>0.872760</td>\n      <td>171</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.645851</td>\n    </tr>\n    <tr>\n      <th>320</th>\n      <td>254</td>\n      <td>0.799937</td>\n      <td>0.839609</td>\n      <td>161</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.363132</td>\n    </tr>\n    <tr>\n      <th>337</th>\n      <td>258</td>\n      <td>0.799937</td>\n      <td>0.887890</td>\n      <td>104</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.256887</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>259</td>\n      <td>0.799937</td>\n      <td>0.861085</td>\n      <td>134</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.381375</td>\n    </tr>\n    <tr>\n      <th>744</th>\n      <td>260</td>\n      <td>0.799782</td>\n      <td>0.908313</td>\n      <td>112</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.392792</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>261</td>\n      <td>0.799782</td>\n      <td>0.880036</td>\n      <td>126</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.344078</td>\n    </tr>\n    <tr>\n      <th>602</th>\n      <td>262</td>\n      <td>0.799782</td>\n      <td>0.839434</td>\n      <td>177</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.585625</td>\n    </tr>\n    <tr>\n      <th>979</th>\n      <td>263</td>\n      <td>0.799782</td>\n      <td>0.861435</td>\n      <td>128</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.305162</td>\n    </tr>\n    <tr>\n      <th>936</th>\n      <td>264</td>\n      <td>0.799781</td>\n      <td>0.852477</td>\n      <td>177</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.384602</td>\n    </tr>\n    <tr>\n      <th>831</th>\n      <td>265</td>\n      <td>0.799781</td>\n      <td>0.867343</td>\n      <td>161</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.570736</td>\n    </tr>\n    <tr>\n      <th>249</th>\n      <td>265</td>\n      <td>0.799781</td>\n      <td>0.832019</td>\n      <td>142</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.357288</td>\n    </tr>\n    <tr>\n      <th>260</th>\n      <td>267</td>\n      <td>0.799781</td>\n      <td>0.850391</td>\n      <td>159</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.407395</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>267</td>\n      <td>0.799781</td>\n      <td>0.848743</td>\n      <td>159</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.386416</td>\n    </tr>\n    <tr>\n      <th>948</th>\n      <td>267</td>\n      <td>0.799781</td>\n      <td>0.894744</td>\n      <td>151</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.376885</td>\n    </tr>\n    <tr>\n      <th>489</th>\n      <td>270</td>\n      <td>0.799781</td>\n      <td>0.908155</td>\n      <td>200</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.680343</td>\n    </tr>\n    <tr>\n      <th>590</th>\n      <td>270</td>\n      <td>0.799781</td>\n      <td>0.855720</td>\n      <td>146</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.525269</td>\n    </tr>\n    <tr>\n      <th>163</th>\n      <td>272</td>\n      <td>0.799780</td>\n      <td>0.832615</td>\n      <td>159</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.386023</td>\n    </tr>\n    <tr>\n      <th>690</th>\n      <td>273</td>\n      <td>0.799780</td>\n      <td>0.841064</td>\n      <td>140</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.476958</td>\n    </tr>\n    <tr>\n      <th>988</th>\n      <td>273</td>\n      <td>0.799780</td>\n      <td>0.848322</td>\n      <td>177</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.447126</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>273</td>\n      <td>0.799780</td>\n      <td>0.849584</td>\n      <td>193</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.469906</td>\n    </tr>\n    <tr>\n      <th>924</th>\n      <td>273</td>\n      <td>0.799780</td>\n      <td>0.849024</td>\n      <td>177</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.382218</td>\n    </tr>\n    <tr>\n      <th>616</th>\n      <td>277</td>\n      <td>0.799780</td>\n      <td>0.848287</td>\n      <td>159</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.568264</td>\n    </tr>\n    <tr>\n      <th>190</th>\n      <td>277</td>\n      <td>0.799780</td>\n      <td>0.849918</td>\n      <td>163</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.409275</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>277</td>\n      <td>0.799780</td>\n      <td>0.846674</td>\n      <td>185</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.603033</td>\n    </tr>\n    <tr>\n      <th>678</th>\n      <td>280</td>\n      <td>0.799780</td>\n      <td>0.855124</td>\n      <td>136</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.470427</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>281</td>\n      <td>0.799780</td>\n      <td>0.856860</td>\n      <td>159</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.463834</td>\n    </tr>\n    <tr>\n      <th>184</th>\n      <td>281</td>\n      <td>0.799780</td>\n      <td>0.873111</td>\n      <td>181</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.409836</td>\n    </tr>\n    <tr>\n      <th>753</th>\n      <td>281</td>\n      <td>0.799780</td>\n      <td>0.842099</td>\n      <td>165</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.568927</td>\n    </tr>\n    <tr>\n      <th>669</th>\n      <td>281</td>\n      <td>0.799780</td>\n      <td>0.849760</td>\n      <td>157</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.545965</td>\n    </tr>\n    <tr>\n      <th>521</th>\n      <td>285</td>\n      <td>0.799780</td>\n      <td>0.874566</td>\n      <td>171</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.620126</td>\n    </tr>\n    <tr>\n      <th>243</th>\n      <td>286</td>\n      <td>0.799779</td>\n      <td>0.884822</td>\n      <td>191</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.484263</td>\n    </tr>\n    <tr>\n      <th>627</th>\n      <td>287</td>\n      <td>0.799624</td>\n      <td>0.839872</td>\n      <td>187</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.599990</td>\n    </tr>\n    <tr>\n      <th>223</th>\n      <td>288</td>\n      <td>0.799624</td>\n      <td>0.849444</td>\n      <td>104</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.275059</td>\n    </tr>\n    <tr>\n      <th>193</th>\n      <td>289</td>\n      <td>0.799624</td>\n      <td>0.853897</td>\n      <td>197</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.429383</td>\n    </tr>\n    <tr>\n      <th>718</th>\n      <td>290</td>\n      <td>0.799624</td>\n      <td>0.857175</td>\n      <td>128</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.454129</td>\n    </tr>\n    <tr>\n      <th>980</th>\n      <td>291</td>\n      <td>0.799624</td>\n      <td>0.840205</td>\n      <td>165</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.382154</td>\n    </tr>\n    <tr>\n      <th>800</th>\n      <td>291</td>\n      <td>0.799624</td>\n      <td>0.833368</td>\n      <td>157</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.489731</td>\n    </tr>\n    <tr>\n      <th>656</th>\n      <td>293</td>\n      <td>0.799623</td>\n      <td>0.849620</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.423423</td>\n    </tr>\n    <tr>\n      <th>709</th>\n      <td>294</td>\n      <td>0.799623</td>\n      <td>0.902510</td>\n      <td>146</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.574301</td>\n    </tr>\n    <tr>\n      <th>611</th>\n      <td>295</td>\n      <td>0.799623</td>\n      <td>0.847463</td>\n      <td>169</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.671362</td>\n    </tr>\n    <tr>\n      <th>673</th>\n      <td>295</td>\n      <td>0.799623</td>\n      <td>0.841871</td>\n      <td>167</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.589904</td>\n    </tr>\n    <tr>\n      <th>722</th>\n      <td>297</td>\n      <td>0.799623</td>\n      <td>0.858578</td>\n      <td>195</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.634193</td>\n    </tr>\n    <tr>\n      <th>543</th>\n      <td>297</td>\n      <td>0.799623</td>\n      <td>0.868272</td>\n      <td>126</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.441998</td>\n    </tr>\n    <tr>\n      <th>810</th>\n      <td>297</td>\n      <td>0.799623</td>\n      <td>0.852232</td>\n      <td>132</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.503581</td>\n    </tr>\n    <tr>\n      <th>704</th>\n      <td>300</td>\n      <td>0.799623</td>\n      <td>0.884191</td>\n      <td>144</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.533026</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>301</td>\n      <td>0.799623</td>\n      <td>0.839680</td>\n      <td>138</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.336698</td>\n    </tr>\n    <tr>\n      <th>771</th>\n      <td>301</td>\n      <td>0.799623</td>\n      <td>0.832106</td>\n      <td>195</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.551988</td>\n    </tr>\n    <tr>\n      <th>466</th>\n      <td>303</td>\n      <td>0.799623</td>\n      <td>0.849918</td>\n      <td>179</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.569179</td>\n    </tr>\n    <tr>\n      <th>507</th>\n      <td>303</td>\n      <td>0.799623</td>\n      <td>0.873882</td>\n      <td>173</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.685805</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>303</td>\n      <td>0.799623</td>\n      <td>0.884489</td>\n      <td>151</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.400084</td>\n    </tr>\n    <tr>\n      <th>245</th>\n      <td>306</td>\n      <td>0.799623</td>\n      <td>0.861979</td>\n      <td>148</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.417185</td>\n    </tr>\n    <tr>\n      <th>265</th>\n      <td>306</td>\n      <td>0.799623</td>\n      <td>0.831931</td>\n      <td>118</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.319426</td>\n    </tr>\n    <tr>\n      <th>808</th>\n      <td>308</td>\n      <td>0.799622</td>\n      <td>0.849655</td>\n      <td>136</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.433290</td>\n    </tr>\n    <tr>\n      <th>201</th>\n      <td>308</td>\n      <td>0.799622</td>\n      <td>0.861891</td>\n      <td>146</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.357337</td>\n    </tr>\n    <tr>\n      <th>208</th>\n      <td>308</td>\n      <td>0.799622</td>\n      <td>0.849777</td>\n      <td>195</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.561589</td>\n    </tr>\n    <tr>\n      <th>998</th>\n      <td>311</td>\n      <td>0.799622</td>\n      <td>0.860857</td>\n      <td>169</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.439908</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>312</td>\n      <td>0.799622</td>\n      <td>0.855843</td>\n      <td>104</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.279476</td>\n    </tr>\n    <tr>\n      <th>914</th>\n      <td>313</td>\n      <td>0.799622</td>\n      <td>0.849357</td>\n      <td>140</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.492902</td>\n    </tr>\n    <tr>\n      <th>699</th>\n      <td>313</td>\n      <td>0.799622</td>\n      <td>0.850514</td>\n      <td>177</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.568158</td>\n    </tr>\n    <tr>\n      <th>561</th>\n      <td>313</td>\n      <td>0.799622</td>\n      <td>0.839048</td>\n      <td>138</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.476476</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>316</td>\n      <td>0.799622</td>\n      <td>0.859945</td>\n      <td>132</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.326359</td>\n    </tr>\n    <tr>\n      <th>828</th>\n      <td>317</td>\n      <td>0.799621</td>\n      <td>0.849444</td>\n      <td>155</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.529755</td>\n    </tr>\n    <tr>\n      <th>125</th>\n      <td>318</td>\n      <td>0.799467</td>\n      <td>0.839592</td>\n      <td>140</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.349321</td>\n    </tr>\n    <tr>\n      <th>230</th>\n      <td>319</td>\n      <td>0.799466</td>\n      <td>0.849024</td>\n      <td>142</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.366320</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>319</td>\n      <td>0.799466</td>\n      <td>0.857999</td>\n      <td>200</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.497219</td>\n    </tr>\n    <tr>\n      <th>437</th>\n      <td>321</td>\n      <td>0.799466</td>\n      <td>0.861541</td>\n      <td>151</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.550254</td>\n    </tr>\n    <tr>\n      <th>252</th>\n      <td>321</td>\n      <td>0.799466</td>\n      <td>0.849812</td>\n      <td>177</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.410174</td>\n    </tr>\n    <tr>\n      <th>544</th>\n      <td>321</td>\n      <td>0.799466</td>\n      <td>0.884699</td>\n      <td>177</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.612044</td>\n    </tr>\n    <tr>\n      <th>558</th>\n      <td>321</td>\n      <td>0.799466</td>\n      <td>0.841853</td>\n      <td>175</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.639284</td>\n    </tr>\n    <tr>\n      <th>438</th>\n      <td>325</td>\n      <td>0.799466</td>\n      <td>0.846289</td>\n      <td>110</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.404867</td>\n    </tr>\n    <tr>\n      <th>368</th>\n      <td>325</td>\n      <td>0.799466</td>\n      <td>0.868886</td>\n      <td>157</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.363083</td>\n    </tr>\n    <tr>\n      <th>777</th>\n      <td>325</td>\n      <td>0.799466</td>\n      <td>0.842239</td>\n      <td>167</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.549279</td>\n    </tr>\n    <tr>\n      <th>919</th>\n      <td>328</td>\n      <td>0.799465</td>\n      <td>0.844623</td>\n      <td>122</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.331389</td>\n    </tr>\n    <tr>\n      <th>349</th>\n      <td>328</td>\n      <td>0.799465</td>\n      <td>0.849339</td>\n      <td>146</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.335877</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>328</td>\n      <td>0.799465</td>\n      <td>0.861804</td>\n      <td>108</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.304723</td>\n    </tr>\n    <tr>\n      <th>574</th>\n      <td>331</td>\n      <td>0.799465</td>\n      <td>0.856527</td>\n      <td>134</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.481752</td>\n    </tr>\n    <tr>\n      <th>458</th>\n      <td>332</td>\n      <td>0.799465</td>\n      <td>0.861804</td>\n      <td>193</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.682067</td>\n    </tr>\n    <tr>\n      <th>528</th>\n      <td>332</td>\n      <td>0.799465</td>\n      <td>0.861996</td>\n      <td>197</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.697496</td>\n    </tr>\n    <tr>\n      <th>592</th>\n      <td>334</td>\n      <td>0.799465</td>\n      <td>0.867887</td>\n      <td>197</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.662378</td>\n    </tr>\n    <tr>\n      <th>429</th>\n      <td>335</td>\n      <td>0.799464</td>\n      <td>0.862294</td>\n      <td>120</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.433080</td>\n    </tr>\n    <tr>\n      <th>995</th>\n      <td>335</td>\n      <td>0.799464</td>\n      <td>0.867343</td>\n      <td>193</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.459908</td>\n    </tr>\n    <tr>\n      <th>236</th>\n      <td>335</td>\n      <td>0.799464</td>\n      <td>0.861313</td>\n      <td>148</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.407655</td>\n    </tr>\n    <tr>\n      <th>605</th>\n      <td>335</td>\n      <td>0.799464</td>\n      <td>0.861190</td>\n      <td>155</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.540221</td>\n    </tr>\n    <tr>\n      <th>869</th>\n      <td>339</td>\n      <td>0.799464</td>\n      <td>0.883910</td>\n      <td>155</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.576915</td>\n    </tr>\n    <tr>\n      <th>667</th>\n      <td>340</td>\n      <td>0.799464</td>\n      <td>0.867326</td>\n      <td>126</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.477539</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>340</td>\n      <td>0.799464</td>\n      <td>0.850198</td>\n      <td>179</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.419446</td>\n    </tr>\n    <tr>\n      <th>123</th>\n      <td>342</td>\n      <td>0.799464</td>\n      <td>0.851741</td>\n      <td>181</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.423601</td>\n    </tr>\n    <tr>\n      <th>797</th>\n      <td>343</td>\n      <td>0.799463</td>\n      <td>0.854756</td>\n      <td>126</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.385128</td>\n    </tr>\n    <tr>\n      <th>922</th>\n      <td>344</td>\n      <td>0.799463</td>\n      <td>0.840153</td>\n      <td>110</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.286026</td>\n    </tr>\n    <tr>\n      <th>499</th>\n      <td>345</td>\n      <td>0.799310</td>\n      <td>0.841643</td>\n      <td>177</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.487897</td>\n    </tr>\n    <tr>\n      <th>600</th>\n      <td>346</td>\n      <td>0.799310</td>\n      <td>0.909120</td>\n      <td>146</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.563608</td>\n    </tr>\n    <tr>\n      <th>687</th>\n      <td>347</td>\n      <td>0.799309</td>\n      <td>0.861488</td>\n      <td>116</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.419262</td>\n    </tr>\n    <tr>\n      <th>576</th>\n      <td>348</td>\n      <td>0.799309</td>\n      <td>0.861453</td>\n      <td>138</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.514914</td>\n    </tr>\n    <tr>\n      <th>414</th>\n      <td>349</td>\n      <td>0.799309</td>\n      <td>0.867291</td>\n      <td>161</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.613009</td>\n    </tr>\n    <tr>\n      <th>743</th>\n      <td>350</td>\n      <td>0.799309</td>\n      <td>0.848743</td>\n      <td>138</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.484306</td>\n    </tr>\n    <tr>\n      <th>598</th>\n      <td>351</td>\n      <td>0.799308</td>\n      <td>0.881351</td>\n      <td>148</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.580440</td>\n    </tr>\n    <tr>\n      <th>779</th>\n      <td>352</td>\n      <td>0.799308</td>\n      <td>0.861804</td>\n      <td>102</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.362918</td>\n    </tr>\n    <tr>\n      <th>178</th>\n      <td>353</td>\n      <td>0.799308</td>\n      <td>0.850128</td>\n      <td>153</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.375321</td>\n    </tr>\n    <tr>\n      <th>926</th>\n      <td>354</td>\n      <td>0.799308</td>\n      <td>0.841731</td>\n      <td>193</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.408952</td>\n    </tr>\n    <tr>\n      <th>449</th>\n      <td>354</td>\n      <td>0.799308</td>\n      <td>0.893885</td>\n      <td>128</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.465451</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>356</td>\n      <td>0.799308</td>\n      <td>0.849760</td>\n      <td>130</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.316476</td>\n    </tr>\n    <tr>\n      <th>136</th>\n      <td>356</td>\n      <td>0.799308</td>\n      <td>0.889117</td>\n      <td>200</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.522344</td>\n    </tr>\n    <tr>\n      <th>671</th>\n      <td>356</td>\n      <td>0.799308</td>\n      <td>0.862312</td>\n      <td>165</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.594540</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>356</td>\n      <td>0.799308</td>\n      <td>0.868167</td>\n      <td>161</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.398918</td>\n    </tr>\n    <tr>\n      <th>961</th>\n      <td>356</td>\n      <td>0.799308</td>\n      <td>0.842379</td>\n      <td>195</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.414921</td>\n    </tr>\n    <tr>\n      <th>938</th>\n      <td>356</td>\n      <td>0.799308</td>\n      <td>0.889134</td>\n      <td>148</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.421457</td>\n    </tr>\n    <tr>\n      <th>261</th>\n      <td>356</td>\n      <td>0.799308</td>\n      <td>0.849707</td>\n      <td>187</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.444307</td>\n    </tr>\n    <tr>\n      <th>829</th>\n      <td>356</td>\n      <td>0.799308</td>\n      <td>0.840889</td>\n      <td>165</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.580152</td>\n    </tr>\n    <tr>\n      <th>613</th>\n      <td>356</td>\n      <td>0.799308</td>\n      <td>0.832948</td>\n      <td>183</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.583215</td>\n    </tr>\n    <tr>\n      <th>579</th>\n      <td>365</td>\n      <td>0.799307</td>\n      <td>0.856176</td>\n      <td>155</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.566068</td>\n    </tr>\n    <tr>\n      <th>479</th>\n      <td>365</td>\n      <td>0.799307</td>\n      <td>0.864521</td>\n      <td>128</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.468295</td>\n    </tr>\n    <tr>\n      <th>268</th>\n      <td>365</td>\n      <td>0.799307</td>\n      <td>0.863679</td>\n      <td>175</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.472046</td>\n    </tr>\n    <tr>\n      <th>107</th>\n      <td>368</td>\n      <td>0.799307</td>\n      <td>0.850005</td>\n      <td>197</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.460581</td>\n    </tr>\n    <tr>\n      <th>805</th>\n      <td>368</td>\n      <td>0.799307</td>\n      <td>0.853073</td>\n      <td>132</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.419872</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>368</td>\n      <td>0.799307</td>\n      <td>0.881035</td>\n      <td>173</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.478465</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>371</td>\n      <td>0.799307</td>\n      <td>0.882350</td>\n      <td>144</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.392569</td>\n    </tr>\n    <tr>\n      <th>335</th>\n      <td>371</td>\n      <td>0.799307</td>\n      <td>0.849584</td>\n      <td>200</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.430018</td>\n    </tr>\n    <tr>\n      <th>974</th>\n      <td>371</td>\n      <td>0.799307</td>\n      <td>0.855475</td>\n      <td>151</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.365120</td>\n    </tr>\n    <tr>\n      <th>552</th>\n      <td>374</td>\n      <td>0.799307</td>\n      <td>0.849024</td>\n      <td>183</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.606523</td>\n    </tr>\n    <tr>\n      <th>406</th>\n      <td>375</td>\n      <td>0.799306</td>\n      <td>0.839189</td>\n      <td>163</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.620818</td>\n    </tr>\n    <tr>\n      <th>175</th>\n      <td>376</td>\n      <td>0.799306</td>\n      <td>0.864503</td>\n      <td>173</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.438039</td>\n    </tr>\n    <tr>\n      <th>113</th>\n      <td>377</td>\n      <td>0.799151</td>\n      <td>0.831230</td>\n      <td>102</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.267509</td>\n    </tr>\n    <tr>\n      <th>982</th>\n      <td>377</td>\n      <td>0.799151</td>\n      <td>0.839978</td>\n      <td>136</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.305361</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>379</td>\n      <td>0.799151</td>\n      <td>0.848147</td>\n      <td>136</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.328891</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>380</td>\n      <td>0.799150</td>\n      <td>0.849848</td>\n      <td>155</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.382977</td>\n    </tr>\n    <tr>\n      <th>362</th>\n      <td>381</td>\n      <td>0.799150</td>\n      <td>0.864784</td>\n      <td>189</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.401235</td>\n    </tr>\n    <tr>\n      <th>227</th>\n      <td>381</td>\n      <td>0.799150</td>\n      <td>0.889064</td>\n      <td>171</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.458562</td>\n    </tr>\n    <tr>\n      <th>581</th>\n      <td>383</td>\n      <td>0.799150</td>\n      <td>0.832474</td>\n      <td>157</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.572528</td>\n    </tr>\n    <tr>\n      <th>143</th>\n      <td>383</td>\n      <td>0.799150</td>\n      <td>0.851513</td>\n      <td>189</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.408467</td>\n    </tr>\n    <tr>\n      <th>759</th>\n      <td>385</td>\n      <td>0.799150</td>\n      <td>0.839785</td>\n      <td>148</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.467496</td>\n    </tr>\n    <tr>\n      <th>640</th>\n      <td>385</td>\n      <td>0.799150</td>\n      <td>0.846937</td>\n      <td>151</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.644553</td>\n    </tr>\n    <tr>\n      <th>327</th>\n      <td>387</td>\n      <td>0.799150</td>\n      <td>0.873374</td>\n      <td>112</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.279144</td>\n    </tr>\n    <tr>\n      <th>870</th>\n      <td>388</td>\n      <td>0.799150</td>\n      <td>0.850023</td>\n      <td>126</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.427358</td>\n    </tr>\n    <tr>\n      <th>204</th>\n      <td>389</td>\n      <td>0.799149</td>\n      <td>0.839241</td>\n      <td>106</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.260184</td>\n    </tr>\n    <tr>\n      <th>932</th>\n      <td>389</td>\n      <td>0.799149</td>\n      <td>0.849970</td>\n      <td>169</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.440228</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>391</td>\n      <td>0.799149</td>\n      <td>0.849602</td>\n      <td>134</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.405997</td>\n    </tr>\n    <tr>\n      <th>506</th>\n      <td>392</td>\n      <td>0.799149</td>\n      <td>0.832071</td>\n      <td>100</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.464954</td>\n    </tr>\n    <tr>\n      <th>482</th>\n      <td>392</td>\n      <td>0.799149</td>\n      <td>0.846762</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.440106</td>\n    </tr>\n    <tr>\n      <th>509</th>\n      <td>394</td>\n      <td>0.799149</td>\n      <td>0.839802</td>\n      <td>148</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.520226</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>394</td>\n      <td>0.799149</td>\n      <td>0.849216</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.285157</td>\n    </tr>\n    <tr>\n      <th>244</th>\n      <td>394</td>\n      <td>0.799149</td>\n      <td>0.861190</td>\n      <td>140</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.385250</td>\n    </tr>\n    <tr>\n      <th>297</th>\n      <td>397</td>\n      <td>0.799149</td>\n      <td>0.853406</td>\n      <td>189</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.417006</td>\n    </tr>\n    <tr>\n      <th>377</th>\n      <td>397</td>\n      <td>0.799149</td>\n      <td>0.853862</td>\n      <td>146</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.343556</td>\n    </tr>\n    <tr>\n      <th>666</th>\n      <td>397</td>\n      <td>0.799149</td>\n      <td>0.861172</td>\n      <td>197</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.686090</td>\n    </tr>\n    <tr>\n      <th>551</th>\n      <td>397</td>\n      <td>0.799149</td>\n      <td>0.877161</td>\n      <td>195</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.674399</td>\n    </tr>\n    <tr>\n      <th>356</th>\n      <td>401</td>\n      <td>0.799149</td>\n      <td>0.884892</td>\n      <td>146</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.342952</td>\n    </tr>\n    <tr>\n      <th>970</th>\n      <td>402</td>\n      <td>0.799148</td>\n      <td>0.849777</td>\n      <td>142</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.327972</td>\n    </tr>\n    <tr>\n      <th>872</th>\n      <td>402</td>\n      <td>0.799148</td>\n      <td>0.863785</td>\n      <td>144</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.511056</td>\n    </tr>\n    <tr>\n      <th>826</th>\n      <td>404</td>\n      <td>0.799148</td>\n      <td>0.861839</td>\n      <td>153</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.531125</td>\n    </tr>\n    <tr>\n      <th>822</th>\n      <td>405</td>\n      <td>0.798993</td>\n      <td>0.840837</td>\n      <td>134</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.465030</td>\n    </tr>\n    <tr>\n      <th>440</th>\n      <td>406</td>\n      <td>0.798993</td>\n      <td>0.889467</td>\n      <td>191</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.657384</td>\n    </tr>\n    <tr>\n      <th>875</th>\n      <td>407</td>\n      <td>0.798993</td>\n      <td>0.831387</td>\n      <td>193</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.607313</td>\n    </tr>\n    <tr>\n      <th>708</th>\n      <td>407</td>\n      <td>0.798993</td>\n      <td>0.892711</td>\n      <td>118</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.434176</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>409</td>\n      <td>0.798993</td>\n      <td>0.899478</td>\n      <td>148</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.389961</td>\n    </tr>\n    <tr>\n      <th>737</th>\n      <td>409</td>\n      <td>0.798993</td>\n      <td>0.849672</td>\n      <td>132</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.452836</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>411</td>\n      <td>0.798993</td>\n      <td>0.930192</td>\n      <td>120</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.346324</td>\n    </tr>\n    <tr>\n      <th>795</th>\n      <td>411</td>\n      <td>0.798993</td>\n      <td>0.841029</td>\n      <td>159</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.496778</td>\n    </tr>\n    <tr>\n      <th>612</th>\n      <td>411</td>\n      <td>0.798993</td>\n      <td>0.833614</td>\n      <td>10</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.058149</td>\n    </tr>\n    <tr>\n      <th>300</th>\n      <td>411</td>\n      <td>0.798993</td>\n      <td>0.832667</td>\n      <td>108</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.265179</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>415</td>\n      <td>0.798992</td>\n      <td>0.867571</td>\n      <td>110</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.309853</td>\n    </tr>\n    <tr>\n      <th>150</th>\n      <td>416</td>\n      <td>0.798992</td>\n      <td>0.864644</td>\n      <td>144</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.357078</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>416</td>\n      <td>0.798992</td>\n      <td>0.868939</td>\n      <td>120</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.313403</td>\n    </tr>\n    <tr>\n      <th>791</th>\n      <td>418</td>\n      <td>0.798992</td>\n      <td>0.869184</td>\n      <td>102</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.342505</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>418</td>\n      <td>0.798992</td>\n      <td>0.850251</td>\n      <td>153</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.379028</td>\n    </tr>\n    <tr>\n      <th>549</th>\n      <td>418</td>\n      <td>0.798992</td>\n      <td>0.847551</td>\n      <td>187</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.626304</td>\n    </tr>\n    <tr>\n      <th>457</th>\n      <td>421</td>\n      <td>0.798992</td>\n      <td>0.840013</td>\n      <td>151</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.645397</td>\n    </tr>\n    <tr>\n      <th>441</th>\n      <td>421</td>\n      <td>0.798992</td>\n      <td>0.850566</td>\n      <td>189</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.619360</td>\n    </tr>\n    <tr>\n      <th>804</th>\n      <td>421</td>\n      <td>0.798992</td>\n      <td>0.906683</td>\n      <td>185</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.586367</td>\n    </tr>\n    <tr>\n      <th>758</th>\n      <td>421</td>\n      <td>0.798992</td>\n      <td>0.877371</td>\n      <td>195</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.608628</td>\n    </tr>\n    <tr>\n      <th>884</th>\n      <td>421</td>\n      <td>0.798992</td>\n      <td>0.868886</td>\n      <td>183</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.606291</td>\n    </tr>\n    <tr>\n      <th>179</th>\n      <td>421</td>\n      <td>0.798992</td>\n      <td>0.838908</td>\n      <td>138</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.370165</td>\n    </tr>\n    <tr>\n      <th>346</th>\n      <td>421</td>\n      <td>0.798992</td>\n      <td>0.856334</td>\n      <td>116</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.261551</td>\n    </tr>\n    <tr>\n      <th>312</th>\n      <td>428</td>\n      <td>0.798992</td>\n      <td>0.842222</td>\n      <td>185</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.429198</td>\n    </tr>\n    <tr>\n      <th>625</th>\n      <td>429</td>\n      <td>0.798992</td>\n      <td>0.849392</td>\n      <td>169</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.610086</td>\n    </tr>\n    <tr>\n      <th>518</th>\n      <td>430</td>\n      <td>0.798991</td>\n      <td>0.875863</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.458928</td>\n    </tr>\n    <tr>\n      <th>322</th>\n      <td>430</td>\n      <td>0.798991</td>\n      <td>0.859945</td>\n      <td>200</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.426331</td>\n    </tr>\n    <tr>\n      <th>893</th>\n      <td>430</td>\n      <td>0.798991</td>\n      <td>0.882543</td>\n      <td>173</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.628544</td>\n    </tr>\n    <tr>\n      <th>301</th>\n      <td>430</td>\n      <td>0.798991</td>\n      <td>0.880684</td>\n      <td>169</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.410760</td>\n    </tr>\n    <tr>\n      <th>773</th>\n      <td>434</td>\n      <td>0.798991</td>\n      <td>0.881789</td>\n      <td>146</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.491449</td>\n    </tr>\n    <tr>\n      <th>299</th>\n      <td>435</td>\n      <td>0.798991</td>\n      <td>0.839837</td>\n      <td>163</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.385616</td>\n    </tr>\n    <tr>\n      <th>472</th>\n      <td>435</td>\n      <td>0.798991</td>\n      <td>0.873076</td>\n      <td>175</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.666864</td>\n    </tr>\n    <tr>\n      <th>381</th>\n      <td>435</td>\n      <td>0.798991</td>\n      <td>0.860822</td>\n      <td>108</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.645472</td>\n    </tr>\n    <tr>\n      <th>159</th>\n      <td>435</td>\n      <td>0.798991</td>\n      <td>0.876793</td>\n      <td>183</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.410918</td>\n    </tr>\n    <tr>\n      <th>664</th>\n      <td>439</td>\n      <td>0.798991</td>\n      <td>0.867641</td>\n      <td>114</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.400474</td>\n    </tr>\n    <tr>\n      <th>618</th>\n      <td>439</td>\n      <td>0.798991</td>\n      <td>0.855738</td>\n      <td>155</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.576935</td>\n    </tr>\n    <tr>\n      <th>641</th>\n      <td>439</td>\n      <td>0.798991</td>\n      <td>0.839276</td>\n      <td>171</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.598764</td>\n    </tr>\n    <tr>\n      <th>649</th>\n      <td>442</td>\n      <td>0.798991</td>\n      <td>0.856877</td>\n      <td>157</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.554151</td>\n    </tr>\n    <tr>\n      <th>361</th>\n      <td>443</td>\n      <td>0.798990</td>\n      <td>0.849883</td>\n      <td>175</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.407182</td>\n    </tr>\n    <tr>\n      <th>336</th>\n      <td>444</td>\n      <td>0.798990</td>\n      <td>0.849847</td>\n      <td>138</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.309980</td>\n    </tr>\n    <tr>\n      <th>119</th>\n      <td>445</td>\n      <td>0.798836</td>\n      <td>0.833088</td>\n      <td>189</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.439364</td>\n    </tr>\n    <tr>\n      <th>896</th>\n      <td>446</td>\n      <td>0.798835</td>\n      <td>0.837383</td>\n      <td>116</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.383346</td>\n    </tr>\n    <tr>\n      <th>139</th>\n      <td>447</td>\n      <td>0.798835</td>\n      <td>0.840276</td>\n      <td>179</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.401809</td>\n    </tr>\n    <tr>\n      <th>485</th>\n      <td>448</td>\n      <td>0.798835</td>\n      <td>0.841450</td>\n      <td>100</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.360010</td>\n    </tr>\n    <tr>\n      <th>537</th>\n      <td>449</td>\n      <td>0.798835</td>\n      <td>0.832948</td>\n      <td>136</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.443960</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>449</td>\n      <td>0.798835</td>\n      <td>0.833035</td>\n      <td>155</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.399104</td>\n    </tr>\n    <tr>\n      <th>971</th>\n      <td>451</td>\n      <td>0.798835</td>\n      <td>0.884191</td>\n      <td>175</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.462954</td>\n    </tr>\n    <tr>\n      <th>785</th>\n      <td>452</td>\n      <td>0.798835</td>\n      <td>0.833018</td>\n      <td>195</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.545954</td>\n    </tr>\n    <tr>\n      <th>510</th>\n      <td>453</td>\n      <td>0.798834</td>\n      <td>0.841590</td>\n      <td>140</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.518524</td>\n    </tr>\n    <tr>\n      <th>373</th>\n      <td>454</td>\n      <td>0.798834</td>\n      <td>0.885786</td>\n      <td>102</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.254812</td>\n    </tr>\n    <tr>\n      <th>285</th>\n      <td>454</td>\n      <td>0.798834</td>\n      <td>0.839154</td>\n      <td>177</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.388307</td>\n    </tr>\n    <tr>\n      <th>891</th>\n      <td>456</td>\n      <td>0.798834</td>\n      <td>0.895025</td>\n      <td>163</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.607879</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>456</td>\n      <td>0.798834</td>\n      <td>0.870008</td>\n      <td>185</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.470939</td>\n    </tr>\n    <tr>\n      <th>279</th>\n      <td>456</td>\n      <td>0.798834</td>\n      <td>0.849216</td>\n      <td>104</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.275177</td>\n    </tr>\n    <tr>\n      <th>383</th>\n      <td>459</td>\n      <td>0.798834</td>\n      <td>0.849339</td>\n      <td>106</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.286050</td>\n    </tr>\n    <tr>\n      <th>981</th>\n      <td>459</td>\n      <td>0.798834</td>\n      <td>0.869009</td>\n      <td>140</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.342055</td>\n    </tr>\n    <tr>\n      <th>769</th>\n      <td>459</td>\n      <td>0.798834</td>\n      <td>0.860804</td>\n      <td>102</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.338337</td>\n    </tr>\n    <tr>\n      <th>977</th>\n      <td>459</td>\n      <td>0.798834</td>\n      <td>0.850216</td>\n      <td>171</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.402253</td>\n    </tr>\n    <tr>\n      <th>570</th>\n      <td>463</td>\n      <td>0.798833</td>\n      <td>0.873129</td>\n      <td>110</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.405390</td>\n    </tr>\n    <tr>\n      <th>216</th>\n      <td>464</td>\n      <td>0.798833</td>\n      <td>0.884611</td>\n      <td>165</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.479062</td>\n    </tr>\n    <tr>\n      <th>835</th>\n      <td>465</td>\n      <td>0.798833</td>\n      <td>0.887942</td>\n      <td>122</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.425520</td>\n    </tr>\n    <tr>\n      <th>786</th>\n      <td>466</td>\n      <td>0.798833</td>\n      <td>0.861050</td>\n      <td>102</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.337144</td>\n    </tr>\n    <tr>\n      <th>404</th>\n      <td>466</td>\n      <td>0.798833</td>\n      <td>0.874373</td>\n      <td>118</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.590016</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>468</td>\n      <td>0.798833</td>\n      <td>0.898776</td>\n      <td>157</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.433800</td>\n    </tr>\n    <tr>\n      <th>513</th>\n      <td>468</td>\n      <td>0.798833</td>\n      <td>0.851758</td>\n      <td>153</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.536857</td>\n    </tr>\n    <tr>\n      <th>469</th>\n      <td>470</td>\n      <td>0.798832</td>\n      <td>0.864521</td>\n      <td>144</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.513045</td>\n    </tr>\n    <tr>\n      <th>345</th>\n      <td>471</td>\n      <td>0.798677</td>\n      <td>0.864731</td>\n      <td>175</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.423024</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>471</td>\n      <td>0.798677</td>\n      <td>0.842011</td>\n      <td>148</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.420650</td>\n    </tr>\n    <tr>\n      <th>197</th>\n      <td>473</td>\n      <td>0.798677</td>\n      <td>0.885172</td>\n      <td>197</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.477673</td>\n    </tr>\n    <tr>\n      <th>806</th>\n      <td>473</td>\n      <td>0.798677</td>\n      <td>0.849620</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.390246</td>\n    </tr>\n    <tr>\n      <th>882</th>\n      <td>475</td>\n      <td>0.798677</td>\n      <td>0.842064</td>\n      <td>142</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.486647</td>\n    </tr>\n    <tr>\n      <th>462</th>\n      <td>476</td>\n      <td>0.798676</td>\n      <td>0.867676</td>\n      <td>142</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.534122</td>\n    </tr>\n    <tr>\n      <th>186</th>\n      <td>476</td>\n      <td>0.798676</td>\n      <td>0.881193</td>\n      <td>167</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.431539</td>\n    </tr>\n    <tr>\n      <th>529</th>\n      <td>478</td>\n      <td>0.798676</td>\n      <td>0.861348</td>\n      <td>120</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.413881</td>\n    </tr>\n    <tr>\n      <th>861</th>\n      <td>479</td>\n      <td>0.798676</td>\n      <td>0.867116</td>\n      <td>161</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.579135</td>\n    </tr>\n    <tr>\n      <th>761</th>\n      <td>480</td>\n      <td>0.798676</td>\n      <td>0.867343</td>\n      <td>136</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.441052</td>\n    </tr>\n    <tr>\n      <th>978</th>\n      <td>480</td>\n      <td>0.798676</td>\n      <td>0.880369</td>\n      <td>173</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.422969</td>\n    </tr>\n    <tr>\n      <th>900</th>\n      <td>480</td>\n      <td>0.798676</td>\n      <td>0.857193</td>\n      <td>185</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.590036</td>\n    </tr>\n    <tr>\n      <th>663</th>\n      <td>483</td>\n      <td>0.798676</td>\n      <td>0.850023</td>\n      <td>138</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.478997</td>\n    </tr>\n    <tr>\n      <th>435</th>\n      <td>483</td>\n      <td>0.798676</td>\n      <td>0.851390</td>\n      <td>157</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.561410</td>\n    </tr>\n    <tr>\n      <th>630</th>\n      <td>485</td>\n      <td>0.798676</td>\n      <td>0.873216</td>\n      <td>161</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.609378</td>\n    </tr>\n    <tr>\n      <th>330</th>\n      <td>485</td>\n      <td>0.798676</td>\n      <td>0.880106</td>\n      <td>108</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.269921</td>\n    </tr>\n    <tr>\n      <th>531</th>\n      <td>485</td>\n      <td>0.798676</td>\n      <td>0.839978</td>\n      <td>140</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.485881</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>488</td>\n      <td>0.798675</td>\n      <td>0.849637</td>\n      <td>179</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.456300</td>\n    </tr>\n    <tr>\n      <th>519</th>\n      <td>489</td>\n      <td>0.798675</td>\n      <td>0.856071</td>\n      <td>104</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.403217</td>\n    </tr>\n    <tr>\n      <th>250</th>\n      <td>490</td>\n      <td>0.798675</td>\n      <td>0.890309</td>\n      <td>100</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.307769</td>\n    </tr>\n    <tr>\n      <th>741</th>\n      <td>491</td>\n      <td>0.798675</td>\n      <td>0.856983</td>\n      <td>181</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.581246</td>\n    </tr>\n    <tr>\n      <th>993</th>\n      <td>491</td>\n      <td>0.798675</td>\n      <td>0.873514</td>\n      <td>155</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.570242</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>493</td>\n      <td>0.798520</td>\n      <td>0.832018</td>\n      <td>114</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.265745</td>\n    </tr>\n    <tr>\n      <th>617</th>\n      <td>494</td>\n      <td>0.798520</td>\n      <td>0.831633</td>\n      <td>104</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.380508</td>\n    </tr>\n    <tr>\n      <th>152</th>\n      <td>495</td>\n      <td>0.798519</td>\n      <td>0.872673</td>\n      <td>173</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.443102</td>\n    </tr>\n    <tr>\n      <th>398</th>\n      <td>495</td>\n      <td>0.798519</td>\n      <td>0.880614</td>\n      <td>161</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.439467</td>\n    </tr>\n    <tr>\n      <th>720</th>\n      <td>497</td>\n      <td>0.798519</td>\n      <td>0.849935</td>\n      <td>187</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.606724</td>\n    </tr>\n    <tr>\n      <th>814</th>\n      <td>498</td>\n      <td>0.798519</td>\n      <td>0.861032</td>\n      <td>138</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.610309</td>\n    </tr>\n    <tr>\n      <th>732</th>\n      <td>498</td>\n      <td>0.798519</td>\n      <td>0.862277</td>\n      <td>197</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.681604</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>498</td>\n      <td>0.798519</td>\n      <td>0.839452</td>\n      <td>126</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.342667</td>\n    </tr>\n    <tr>\n      <th>864</th>\n      <td>498</td>\n      <td>0.798519</td>\n      <td>0.862119</td>\n      <td>175</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.640752</td>\n    </tr>\n    <tr>\n      <th>325</th>\n      <td>502</td>\n      <td>0.798519</td>\n      <td>0.840153</td>\n      <td>185</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.384017</td>\n    </tr>\n    <tr>\n      <th>717</th>\n      <td>503</td>\n      <td>0.798519</td>\n      <td>0.856720</td>\n      <td>148</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.516739</td>\n    </tr>\n    <tr>\n      <th>886</th>\n      <td>503</td>\n      <td>0.798519</td>\n      <td>0.850636</td>\n      <td>185</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.589631</td>\n    </tr>\n    <tr>\n      <th>787</th>\n      <td>503</td>\n      <td>0.798519</td>\n      <td>0.849900</td>\n      <td>183</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.535172</td>\n    </tr>\n    <tr>\n      <th>830</th>\n      <td>506</td>\n      <td>0.798518</td>\n      <td>0.861856</td>\n      <td>177</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.584819</td>\n    </tr>\n    <tr>\n      <th>711</th>\n      <td>506</td>\n      <td>0.798518</td>\n      <td>0.852267</td>\n      <td>165</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.584100</td>\n    </tr>\n    <tr>\n      <th>859</th>\n      <td>506</td>\n      <td>0.798518</td>\n      <td>0.860612</td>\n      <td>200</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.674074</td>\n    </tr>\n    <tr>\n      <th>276</th>\n      <td>506</td>\n      <td>0.798518</td>\n      <td>0.856755</td>\n      <td>146</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.358723</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>510</td>\n      <td>0.798518</td>\n      <td>0.839574</td>\n      <td>163</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.439062</td>\n    </tr>\n    <tr>\n      <th>582</th>\n      <td>511</td>\n      <td>0.798518</td>\n      <td>0.852302</td>\n      <td>179</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.572220</td>\n    </tr>\n    <tr>\n      <th>878</th>\n      <td>511</td>\n      <td>0.798518</td>\n      <td>0.869482</td>\n      <td>175</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.638819</td>\n    </tr>\n    <tr>\n      <th>565</th>\n      <td>511</td>\n      <td>0.798518</td>\n      <td>0.860559</td>\n      <td>185</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.583246</td>\n    </tr>\n    <tr>\n      <th>992</th>\n      <td>511</td>\n      <td>0.798518</td>\n      <td>0.849707</td>\n      <td>165</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.476083</td>\n    </tr>\n    <tr>\n      <th>588</th>\n      <td>511</td>\n      <td>0.798518</td>\n      <td>0.848988</td>\n      <td>140</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.505724</td>\n    </tr>\n    <tr>\n      <th>275</th>\n      <td>516</td>\n      <td>0.798518</td>\n      <td>0.849690</td>\n      <td>146</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.360422</td>\n    </tr>\n    <tr>\n      <th>304</th>\n      <td>516</td>\n      <td>0.798518</td>\n      <td>0.883419</td>\n      <td>106</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.271221</td>\n    </tr>\n    <tr>\n      <th>767</th>\n      <td>516</td>\n      <td>0.798518</td>\n      <td>0.862259</td>\n      <td>132</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.427803</td>\n    </tr>\n    <tr>\n      <th>503</th>\n      <td>519</td>\n      <td>0.798518</td>\n      <td>0.857123</td>\n      <td>159</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.706746</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>520</td>\n      <td>0.798517</td>\n      <td>0.862330</td>\n      <td>177</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.438137</td>\n    </tr>\n    <tr>\n      <th>633</th>\n      <td>521</td>\n      <td>0.798517</td>\n      <td>0.873532</td>\n      <td>187</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.633097</td>\n    </tr>\n    <tr>\n      <th>890</th>\n      <td>521</td>\n      <td>0.798517</td>\n      <td>0.874584</td>\n      <td>130</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.448162</td>\n    </tr>\n    <tr>\n      <th>939</th>\n      <td>523</td>\n      <td>0.798517</td>\n      <td>0.865888</td>\n      <td>120</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.311547</td>\n    </tr>\n    <tr>\n      <th>946</th>\n      <td>524</td>\n      <td>0.798362</td>\n      <td>0.841380</td>\n      <td>114</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.255128</td>\n    </tr>\n    <tr>\n      <th>790</th>\n      <td>525</td>\n      <td>0.798362</td>\n      <td>0.849304</td>\n      <td>148</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.458757</td>\n    </tr>\n    <tr>\n      <th>888</th>\n      <td>526</td>\n      <td>0.798362</td>\n      <td>0.866993</td>\n      <td>120</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.416873</td>\n    </tr>\n    <tr>\n      <th>402</th>\n      <td>527</td>\n      <td>0.798361</td>\n      <td>0.856369</td>\n      <td>126</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.356017</td>\n    </tr>\n    <tr>\n      <th>523</th>\n      <td>528</td>\n      <td>0.798361</td>\n      <td>0.841222</td>\n      <td>197</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.652058</td>\n    </tr>\n    <tr>\n      <th>693</th>\n      <td>529</td>\n      <td>0.798360</td>\n      <td>0.861523</td>\n      <td>122</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.488040</td>\n    </tr>\n    <tr>\n      <th>452</th>\n      <td>529</td>\n      <td>0.798360</td>\n      <td>0.850566</td>\n      <td>159</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.591166</td>\n    </tr>\n    <tr>\n      <th>121</th>\n      <td>531</td>\n      <td>0.798360</td>\n      <td>0.866449</td>\n      <td>138</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.369763</td>\n    </tr>\n    <tr>\n      <th>860</th>\n      <td>531</td>\n      <td>0.798360</td>\n      <td>0.841888</td>\n      <td>181</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.557056</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>531</td>\n      <td>0.798360</td>\n      <td>0.856474</td>\n      <td>161</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.394033</td>\n    </tr>\n    <tr>\n      <th>778</th>\n      <td>531</td>\n      <td>0.798360</td>\n      <td>0.850251</td>\n      <td>169</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.534732</td>\n    </tr>\n    <tr>\n      <th>642</th>\n      <td>535</td>\n      <td>0.798360</td>\n      <td>0.861874</td>\n      <td>200</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.661060</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>535</td>\n      <td>0.798360</td>\n      <td>0.859367</td>\n      <td>134</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.338511</td>\n    </tr>\n    <tr>\n      <th>937</th>\n      <td>537</td>\n      <td>0.798360</td>\n      <td>0.850040</td>\n      <td>191</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.407471</td>\n    </tr>\n    <tr>\n      <th>461</th>\n      <td>538</td>\n      <td>0.798360</td>\n      <td>0.839750</td>\n      <td>146</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.518483</td>\n    </tr>\n    <tr>\n      <th>682</th>\n      <td>539</td>\n      <td>0.798359</td>\n      <td>0.849655</td>\n      <td>157</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.552514</td>\n    </tr>\n    <tr>\n      <th>784</th>\n      <td>540</td>\n      <td>0.798204</td>\n      <td>0.839154</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.405492</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>541</td>\n      <td>0.798203</td>\n      <td>0.850461</td>\n      <td>153</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.382070</td>\n    </tr>\n    <tr>\n      <th>597</th>\n      <td>542</td>\n      <td>0.798203</td>\n      <td>0.866449</td>\n      <td>187</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.614109</td>\n    </tr>\n    <tr>\n      <th>645</th>\n      <td>542</td>\n      <td>0.798203</td>\n      <td>0.892746</td>\n      <td>142</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.516613</td>\n    </tr>\n    <tr>\n      <th>120</th>\n      <td>544</td>\n      <td>0.798203</td>\n      <td>0.849269</td>\n      <td>100</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.265747</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>544</td>\n      <td>0.798203</td>\n      <td>0.832159</td>\n      <td>142</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.346715</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>546</td>\n      <td>0.798203</td>\n      <td>0.861172</td>\n      <td>159</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.405504</td>\n    </tr>\n    <tr>\n      <th>763</th>\n      <td>546</td>\n      <td>0.798203</td>\n      <td>0.861120</td>\n      <td>165</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.543646</td>\n    </tr>\n    <tr>\n      <th>853</th>\n      <td>548</td>\n      <td>0.798203</td>\n      <td>0.883963</td>\n      <td>155</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.571116</td>\n    </tr>\n    <tr>\n      <th>413</th>\n      <td>548</td>\n      <td>0.798203</td>\n      <td>0.862312</td>\n      <td>148</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.554268</td>\n    </tr>\n    <tr>\n      <th>817</th>\n      <td>548</td>\n      <td>0.798203</td>\n      <td>0.877511</td>\n      <td>165</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.612456</td>\n    </tr>\n    <tr>\n      <th>684</th>\n      <td>551</td>\n      <td>0.798203</td>\n      <td>0.861961</td>\n      <td>130</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.471967</td>\n    </tr>\n    <tr>\n      <th>354</th>\n      <td>552</td>\n      <td>0.798202</td>\n      <td>0.848585</td>\n      <td>167</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.398036</td>\n    </tr>\n    <tr>\n      <th>658</th>\n      <td>553</td>\n      <td>0.798202</td>\n      <td>0.892202</td>\n      <td>104</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.401204</td>\n    </tr>\n    <tr>\n      <th>848</th>\n      <td>554</td>\n      <td>0.798202</td>\n      <td>0.886785</td>\n      <td>155</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.551680</td>\n    </tr>\n    <tr>\n      <th>448</th>\n      <td>555</td>\n      <td>0.798202</td>\n      <td>0.849900</td>\n      <td>165</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.608392</td>\n    </tr>\n    <tr>\n      <th>303</th>\n      <td>555</td>\n      <td>0.798202</td>\n      <td>0.849006</td>\n      <td>106</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.271091</td>\n    </tr>\n    <tr>\n      <th>263</th>\n      <td>555</td>\n      <td>0.798202</td>\n      <td>0.888486</td>\n      <td>134</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.347138</td>\n    </tr>\n    <tr>\n      <th>749</th>\n      <td>555</td>\n      <td>0.798202</td>\n      <td>0.869394</td>\n      <td>161</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.626183</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>555</td>\n      <td>0.798202</td>\n      <td>0.898987</td>\n      <td>177</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.899037</td>\n    </tr>\n    <tr>\n      <th>652</th>\n      <td>560</td>\n      <td>0.798202</td>\n      <td>0.872462</td>\n      <td>132</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.471050</td>\n    </tr>\n    <tr>\n      <th>188</th>\n      <td>561</td>\n      <td>0.798202</td>\n      <td>0.853564</td>\n      <td>175</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.450080</td>\n    </tr>\n    <tr>\n      <th>522</th>\n      <td>562</td>\n      <td>0.798201</td>\n      <td>0.861821</td>\n      <td>163</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.603809</td>\n    </tr>\n    <tr>\n      <th>911</th>\n      <td>563</td>\n      <td>0.798201</td>\n      <td>0.856650</td>\n      <td>138</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.530306</td>\n    </tr>\n    <tr>\n      <th>425</th>\n      <td>564</td>\n      <td>0.798046</td>\n      <td>0.915168</td>\n      <td>191</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.676978</td>\n    </tr>\n    <tr>\n      <th>168</th>\n      <td>565</td>\n      <td>0.798046</td>\n      <td>0.856018</td>\n      <td>175</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.471511</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>566</td>\n      <td>0.798046</td>\n      <td>0.892080</td>\n      <td>138</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.404601</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>566</td>\n      <td>0.798046</td>\n      <td>0.849549</td>\n      <td>165</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.422260</td>\n    </tr>\n    <tr>\n      <th>855</th>\n      <td>566</td>\n      <td>0.798046</td>\n      <td>0.884050</td>\n      <td>100</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.369523</td>\n    </tr>\n    <tr>\n      <th>940</th>\n      <td>569</td>\n      <td>0.798045</td>\n      <td>0.847989</td>\n      <td>124</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.291308</td>\n    </tr>\n    <tr>\n      <th>591</th>\n      <td>569</td>\n      <td>0.798045</td>\n      <td>0.878195</td>\n      <td>142</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.512701</td>\n    </tr>\n    <tr>\n      <th>865</th>\n      <td>571</td>\n      <td>0.798045</td>\n      <td>0.833298</td>\n      <td>179</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.556620</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>571</td>\n      <td>0.798045</td>\n      <td>0.911276</td>\n      <td>151</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.427889</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>573</td>\n      <td>0.798045</td>\n      <td>0.873619</td>\n      <td>167</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.449827</td>\n    </tr>\n    <tr>\n      <th>135</th>\n      <td>574</td>\n      <td>0.798045</td>\n      <td>0.847551</td>\n      <td>122</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.286895</td>\n    </tr>\n    <tr>\n      <th>129</th>\n      <td>575</td>\n      <td>0.798045</td>\n      <td>0.869465</td>\n      <td>183</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.472022</td>\n    </tr>\n    <tr>\n      <th>949</th>\n      <td>575</td>\n      <td>0.798045</td>\n      <td>0.886435</td>\n      <td>136</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.322505</td>\n    </tr>\n    <tr>\n      <th>879</th>\n      <td>577</td>\n      <td>0.798044</td>\n      <td>0.888258</td>\n      <td>171</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.633863</td>\n    </tr>\n    <tr>\n      <th>305</th>\n      <td>577</td>\n      <td>0.798044</td>\n      <td>0.887802</td>\n      <td>183</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.420562</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>579</td>\n      <td>0.798044</td>\n      <td>0.884085</td>\n      <td>108</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.291211</td>\n    </tr>\n    <tr>\n      <th>359</th>\n      <td>579</td>\n      <td>0.798044</td>\n      <td>0.866817</td>\n      <td>163</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.391537</td>\n    </tr>\n    <tr>\n      <th>968</th>\n      <td>581</td>\n      <td>0.798044</td>\n      <td>0.867361</td>\n      <td>195</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.498940</td>\n    </tr>\n    <tr>\n      <th>768</th>\n      <td>581</td>\n      <td>0.798044</td>\n      <td>0.889608</td>\n      <td>114</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.371886</td>\n    </tr>\n    <tr>\n      <th>553</th>\n      <td>583</td>\n      <td>0.798044</td>\n      <td>0.880807</td>\n      <td>191</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.652206</td>\n    </tr>\n    <tr>\n      <th>126</th>\n      <td>583</td>\n      <td>0.798044</td>\n      <td>0.857474</td>\n      <td>104</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.295357</td>\n    </tr>\n    <tr>\n      <th>292</th>\n      <td>583</td>\n      <td>0.798044</td>\n      <td>0.849024</td>\n      <td>100</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.241793</td>\n    </tr>\n    <tr>\n      <th>195</th>\n      <td>583</td>\n      <td>0.798044</td>\n      <td>0.888731</td>\n      <td>177</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.523111</td>\n    </tr>\n    <tr>\n      <th>719</th>\n      <td>587</td>\n      <td>0.797888</td>\n      <td>0.832755</td>\n      <td>144</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.484821</td>\n    </tr>\n    <tr>\n      <th>494</th>\n      <td>588</td>\n      <td>0.797888</td>\n      <td>0.911381</td>\n      <td>134</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.463859</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>589</td>\n      <td>0.797888</td>\n      <td>0.888468</td>\n      <td>146</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.393166</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>590</td>\n      <td>0.797887</td>\n      <td>0.869622</td>\n      <td>193</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.479474</td>\n    </tr>\n    <tr>\n      <th>298</th>\n      <td>591</td>\n      <td>0.797887</td>\n      <td>0.854002</td>\n      <td>151</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.354623</td>\n    </tr>\n    <tr>\n      <th>874</th>\n      <td>591</td>\n      <td>0.797887</td>\n      <td>0.856018</td>\n      <td>144</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.522201</td>\n    </tr>\n    <tr>\n      <th>525</th>\n      <td>593</td>\n      <td>0.797887</td>\n      <td>0.894096</td>\n      <td>108</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.411988</td>\n    </tr>\n    <tr>\n      <th>412</th>\n      <td>593</td>\n      <td>0.797887</td>\n      <td>0.885716</td>\n      <td>120</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.451210</td>\n    </tr>\n    <tr>\n      <th>274</th>\n      <td>595</td>\n      <td>0.797887</td>\n      <td>0.884524</td>\n      <td>197</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.483790</td>\n    </tr>\n    <tr>\n      <th>443</th>\n      <td>596</td>\n      <td>0.797887</td>\n      <td>0.856404</td>\n      <td>100</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.378135</td>\n    </tr>\n    <tr>\n      <th>832</th>\n      <td>596</td>\n      <td>0.797887</td>\n      <td>0.867378</td>\n      <td>179</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.609683</td>\n    </tr>\n    <tr>\n      <th>426</th>\n      <td>598</td>\n      <td>0.797887</td>\n      <td>0.861330</td>\n      <td>122</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.432813</td>\n    </tr>\n    <tr>\n      <th>446</th>\n      <td>599</td>\n      <td>0.797886</td>\n      <td>0.862277</td>\n      <td>132</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.481477</td>\n    </tr>\n    <tr>\n      <th>180</th>\n      <td>600</td>\n      <td>0.797886</td>\n      <td>0.869114</td>\n      <td>112</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.305877</td>\n    </tr>\n    <tr>\n      <th>816</th>\n      <td>600</td>\n      <td>0.797886</td>\n      <td>0.874005</td>\n      <td>185</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.618755</td>\n    </tr>\n    <tr>\n      <th>622</th>\n      <td>602</td>\n      <td>0.797886</td>\n      <td>0.884559</td>\n      <td>195</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.692178</td>\n    </tr>\n    <tr>\n      <th>400</th>\n      <td>603</td>\n      <td>0.797731</td>\n      <td>0.838733</td>\n      <td>197</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.580191</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>604</td>\n      <td>0.797731</td>\n      <td>0.881333</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.332770</td>\n    </tr>\n    <tr>\n      <th>281</th>\n      <td>604</td>\n      <td>0.797731</td>\n      <td>0.926177</td>\n      <td>185</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.894882</td>\n    </tr>\n    <tr>\n      <th>703</th>\n      <td>606</td>\n      <td>0.797730</td>\n      <td>0.849830</td>\n      <td>177</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.591987</td>\n    </tr>\n    <tr>\n      <th>465</th>\n      <td>606</td>\n      <td>0.797730</td>\n      <td>0.842940</td>\n      <td>10</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.055689</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>608</td>\n      <td>0.797730</td>\n      <td>0.911101</td>\n      <td>153</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.429883</td>\n    </tr>\n    <tr>\n      <th>287</th>\n      <td>608</td>\n      <td>0.797730</td>\n      <td>0.831808</td>\n      <td>140</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.329984</td>\n    </tr>\n    <tr>\n      <th>824</th>\n      <td>608</td>\n      <td>0.797730</td>\n      <td>0.912205</td>\n      <td>177</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.617533</td>\n    </tr>\n    <tr>\n      <th>601</th>\n      <td>608</td>\n      <td>0.797730</td>\n      <td>0.881105</td>\n      <td>100</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.371428</td>\n    </tr>\n    <tr>\n      <th>162</th>\n      <td>612</td>\n      <td>0.797730</td>\n      <td>0.859910</td>\n      <td>142</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.336232</td>\n    </tr>\n    <tr>\n      <th>153</th>\n      <td>612</td>\n      <td>0.797730</td>\n      <td>0.869114</td>\n      <td>108</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.283228</td>\n    </tr>\n    <tr>\n      <th>637</th>\n      <td>612</td>\n      <td>0.797730</td>\n      <td>0.862610</td>\n      <td>173</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.682051</td>\n    </tr>\n    <tr>\n      <th>635</th>\n      <td>615</td>\n      <td>0.797730</td>\n      <td>0.898513</td>\n      <td>157</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.591936</td>\n    </tr>\n    <tr>\n      <th>694</th>\n      <td>615</td>\n      <td>0.797730</td>\n      <td>0.848164</td>\n      <td>144</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.734614</td>\n    </tr>\n    <tr>\n      <th>382</th>\n      <td>617</td>\n      <td>0.797729</td>\n      <td>0.846604</td>\n      <td>193</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.777602</td>\n    </tr>\n    <tr>\n      <th>610</th>\n      <td>618</td>\n      <td>0.797729</td>\n      <td>0.862557</td>\n      <td>173</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.652032</td>\n    </tr>\n    <tr>\n      <th>575</th>\n      <td>618</td>\n      <td>0.797729</td>\n      <td>0.884559</td>\n      <td>179</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.599863</td>\n    </tr>\n    <tr>\n      <th>862</th>\n      <td>620</td>\n      <td>0.797729</td>\n      <td>0.874058</td>\n      <td>167</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.595404</td>\n    </tr>\n    <tr>\n      <th>375</th>\n      <td>621</td>\n      <td>0.797729</td>\n      <td>0.907121</td>\n      <td>128</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.307222</td>\n    </tr>\n    <tr>\n      <th>264</th>\n      <td>622</td>\n      <td>0.797729</td>\n      <td>0.839942</td>\n      <td>167</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.423406</td>\n    </tr>\n    <tr>\n      <th>833</th>\n      <td>623</td>\n      <td>0.797728</td>\n      <td>0.885891</td>\n      <td>130</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.458963</td>\n    </tr>\n    <tr>\n      <th>344</th>\n      <td>624</td>\n      <td>0.797728</td>\n      <td>0.892255</td>\n      <td>126</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.294264</td>\n    </tr>\n    <tr>\n      <th>505</th>\n      <td>624</td>\n      <td>0.797728</td>\n      <td>0.860436</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.644553</td>\n    </tr>\n    <tr>\n      <th>256</th>\n      <td>626</td>\n      <td>0.797573</td>\n      <td>0.882350</td>\n      <td>128</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.318998</td>\n    </tr>\n    <tr>\n      <th>517</th>\n      <td>627</td>\n      <td>0.797573</td>\n      <td>0.843764</td>\n      <td>10</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.055663</td>\n    </tr>\n    <tr>\n      <th>360</th>\n      <td>628</td>\n      <td>0.797572</td>\n      <td>0.847130</td>\n      <td>148</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.345736</td>\n    </tr>\n    <tr>\n      <th>259</th>\n      <td>629</td>\n      <td>0.797572</td>\n      <td>0.907279</td>\n      <td>183</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.442513</td>\n    </tr>\n    <tr>\n      <th>486</th>\n      <td>629</td>\n      <td>0.797572</td>\n      <td>0.856229</td>\n      <td>116</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.420285</td>\n    </tr>\n    <tr>\n      <th>390</th>\n      <td>631</td>\n      <td>0.797572</td>\n      <td>0.909085</td>\n      <td>169</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.502109</td>\n    </tr>\n    <tr>\n      <th>638</th>\n      <td>631</td>\n      <td>0.797572</td>\n      <td>0.841994</td>\n      <td>191</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.660217</td>\n    </tr>\n    <tr>\n      <th>927</th>\n      <td>633</td>\n      <td>0.797572</td>\n      <td>0.849865</td>\n      <td>169</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.418251</td>\n    </tr>\n    <tr>\n      <th>599</th>\n      <td>633</td>\n      <td>0.797572</td>\n      <td>0.845149</td>\n      <td>110</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.389726</td>\n    </tr>\n    <tr>\n      <th>851</th>\n      <td>633</td>\n      <td>0.797572</td>\n      <td>0.849953</td>\n      <td>189</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.601329</td>\n    </tr>\n    <tr>\n      <th>116</th>\n      <td>636</td>\n      <td>0.797572</td>\n      <td>0.859893</td>\n      <td>177</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.454772</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>636</td>\n      <td>0.797572</td>\n      <td>0.853073</td>\n      <td>106</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.278948</td>\n    </tr>\n    <tr>\n      <th>807</th>\n      <td>636</td>\n      <td>0.797572</td>\n      <td>0.867045</td>\n      <td>153</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.480887</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>636</td>\n      <td>0.797572</td>\n      <td>0.884927</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.370862</td>\n    </tr>\n    <tr>\n      <th>115</th>\n      <td>640</td>\n      <td>0.797571</td>\n      <td>0.839837</td>\n      <td>167</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.440090</td>\n    </tr>\n    <tr>\n      <th>251</th>\n      <td>641</td>\n      <td>0.797571</td>\n      <td>0.888871</td>\n      <td>120</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.304695</td>\n    </tr>\n    <tr>\n      <th>994</th>\n      <td>641</td>\n      <td>0.797571</td>\n      <td>0.855335</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.335494</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>643</td>\n      <td>0.797571</td>\n      <td>0.869658</td>\n      <td>187</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.471107</td>\n    </tr>\n    <tr>\n      <th>416</th>\n      <td>644</td>\n      <td>0.797571</td>\n      <td>0.868816</td>\n      <td>114</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.395849</td>\n    </tr>\n    <tr>\n      <th>648</th>\n      <td>644</td>\n      <td>0.797571</td>\n      <td>0.862014</td>\n      <td>136</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.488653</td>\n    </tr>\n    <tr>\n      <th>365</th>\n      <td>646</td>\n      <td>0.797571</td>\n      <td>0.868448</td>\n      <td>138</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.316308</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>647</td>\n      <td>0.797569</td>\n      <td>0.881456</td>\n      <td>173</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.460371</td>\n    </tr>\n    <tr>\n      <th>185</th>\n      <td>648</td>\n      <td>0.797416</td>\n      <td>0.841135</td>\n      <td>10</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.040062</td>\n    </tr>\n    <tr>\n      <th>840</th>\n      <td>649</td>\n      <td>0.797416</td>\n      <td>0.867291</td>\n      <td>165</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.588725</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>650</td>\n      <td>0.797415</td>\n      <td>0.894025</td>\n      <td>132</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.581067</td>\n    </tr>\n    <tr>\n      <th>798</th>\n      <td>651</td>\n      <td>0.797415</td>\n      <td>0.855948</td>\n      <td>136</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.442821</td>\n    </tr>\n    <tr>\n      <th>353</th>\n      <td>652</td>\n      <td>0.797415</td>\n      <td>0.872936</td>\n      <td>161</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.389311</td>\n    </tr>\n    <tr>\n      <th>923</th>\n      <td>653</td>\n      <td>0.797415</td>\n      <td>0.862172</td>\n      <td>200</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.457048</td>\n    </tr>\n    <tr>\n      <th>952</th>\n      <td>653</td>\n      <td>0.797415</td>\n      <td>0.888556</td>\n      <td>140</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.332960</td>\n    </tr>\n    <tr>\n      <th>246</th>\n      <td>653</td>\n      <td>0.797415</td>\n      <td>0.874040</td>\n      <td>169</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.431069</td>\n    </tr>\n    <tr>\n      <th>516</th>\n      <td>656</td>\n      <td>0.797414</td>\n      <td>0.857929</td>\n      <td>122</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.427248</td>\n    </tr>\n    <tr>\n      <th>333</th>\n      <td>657</td>\n      <td>0.797414</td>\n      <td>0.887890</td>\n      <td>197</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.450498</td>\n    </tr>\n    <tr>\n      <th>132</th>\n      <td>658</td>\n      <td>0.797414</td>\n      <td>0.860401</td>\n      <td>167</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.455004</td>\n    </tr>\n    <tr>\n      <th>899</th>\n      <td>659</td>\n      <td>0.797414</td>\n      <td>0.867080</td>\n      <td>122</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.421915</td>\n    </tr>\n    <tr>\n      <th>331</th>\n      <td>659</td>\n      <td>0.797414</td>\n      <td>0.857912</td>\n      <td>179</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.379432</td>\n    </tr>\n    <tr>\n      <th>983</th>\n      <td>659</td>\n      <td>0.797414</td>\n      <td>0.861646</td>\n      <td>112</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.261582</td>\n    </tr>\n    <tr>\n      <th>166</th>\n      <td>662</td>\n      <td>0.797413</td>\n      <td>0.873549</td>\n      <td>185</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.439709</td>\n    </tr>\n    <tr>\n      <th>272</th>\n      <td>663</td>\n      <td>0.797413</td>\n      <td>0.867431</td>\n      <td>134</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.345070</td>\n    </tr>\n    <tr>\n      <th>378</th>\n      <td>664</td>\n      <td>0.797413</td>\n      <td>0.861313</td>\n      <td>159</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.378119</td>\n    </tr>\n    <tr>\n      <th>473</th>\n      <td>665</td>\n      <td>0.797258</td>\n      <td>0.951965</td>\n      <td>132</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.516298</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>666</td>\n      <td>0.797257</td>\n      <td>0.893797</td>\n      <td>126</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.332791</td>\n    </tr>\n    <tr>\n      <th>907</th>\n      <td>667</td>\n      <td>0.797257</td>\n      <td>0.856544</td>\n      <td>122</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.431084</td>\n    </tr>\n    <tr>\n      <th>203</th>\n      <td>668</td>\n      <td>0.797257</td>\n      <td>0.839557</td>\n      <td>191</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.464985</td>\n    </tr>\n    <tr>\n      <th>986</th>\n      <td>668</td>\n      <td>0.797257</td>\n      <td>0.849707</td>\n      <td>179</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.373685</td>\n    </tr>\n    <tr>\n      <th>863</th>\n      <td>670</td>\n      <td>0.797257</td>\n      <td>0.884839</td>\n      <td>195</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.663777</td>\n    </tr>\n    <tr>\n      <th>716</th>\n      <td>670</td>\n      <td>0.797257</td>\n      <td>0.894096</td>\n      <td>130</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.495177</td>\n    </tr>\n    <tr>\n      <th>653</th>\n      <td>672</td>\n      <td>0.797257</td>\n      <td>0.960485</td>\n      <td>175</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.661057</td>\n    </tr>\n    <tr>\n      <th>431</th>\n      <td>672</td>\n      <td>0.797257</td>\n      <td>0.908050</td>\n      <td>195</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.690617</td>\n    </tr>\n    <tr>\n      <th>225</th>\n      <td>674</td>\n      <td>0.797256</td>\n      <td>0.876407</td>\n      <td>181</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.553655</td>\n    </tr>\n    <tr>\n      <th>626</th>\n      <td>674</td>\n      <td>0.797256</td>\n      <td>0.908120</td>\n      <td>144</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.523309</td>\n    </tr>\n    <tr>\n      <th>434</th>\n      <td>676</td>\n      <td>0.797256</td>\n      <td>0.885172</td>\n      <td>126</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.455517</td>\n    </tr>\n    <tr>\n      <th>490</th>\n      <td>676</td>\n      <td>0.797256</td>\n      <td>0.849760</td>\n      <td>106</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.425418</td>\n    </tr>\n    <tr>\n      <th>117</th>\n      <td>678</td>\n      <td>0.797256</td>\n      <td>0.860594</td>\n      <td>138</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.386406</td>\n    </tr>\n    <tr>\n      <th>941</th>\n      <td>679</td>\n      <td>0.797256</td>\n      <td>0.861681</td>\n      <td>138</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.318619</td>\n    </tr>\n    <tr>\n      <th>991</th>\n      <td>679</td>\n      <td>0.797256</td>\n      <td>0.861313</td>\n      <td>122</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.295287</td>\n    </tr>\n    <tr>\n      <th>367</th>\n      <td>681</td>\n      <td>0.797100</td>\n      <td>0.885155</td>\n      <td>126</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.302078</td>\n    </tr>\n    <tr>\n      <th>348</th>\n      <td>682</td>\n      <td>0.797100</td>\n      <td>0.884296</td>\n      <td>108</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.270894</td>\n    </tr>\n    <tr>\n      <th>442</th>\n      <td>683</td>\n      <td>0.797099</td>\n      <td>0.881596</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.465732</td>\n    </tr>\n    <tr>\n      <th>273</th>\n      <td>684</td>\n      <td>0.797099</td>\n      <td>0.861067</td>\n      <td>106</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.273872</td>\n    </tr>\n    <tr>\n      <th>736</th>\n      <td>684</td>\n      <td>0.797099</td>\n      <td>0.885383</td>\n      <td>195</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.691956</td>\n    </tr>\n    <tr>\n      <th>196</th>\n      <td>686</td>\n      <td>0.797099</td>\n      <td>0.889520</td>\n      <td>173</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.483170</td>\n    </tr>\n    <tr>\n      <th>350</th>\n      <td>687</td>\n      <td>0.797099</td>\n      <td>0.901704</td>\n      <td>187</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.421953</td>\n    </tr>\n    <tr>\n      <th>823</th>\n      <td>687</td>\n      <td>0.797099</td>\n      <td>0.867834</td>\n      <td>169</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.607871</td>\n    </tr>\n    <tr>\n      <th>266</th>\n      <td>689</td>\n      <td>0.797098</td>\n      <td>0.869184</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.303187</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>690</td>\n      <td>0.797098</td>\n      <td>0.868343</td>\n      <td>136</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.444449</td>\n    </tr>\n    <tr>\n      <th>681</th>\n      <td>691</td>\n      <td>0.797098</td>\n      <td>0.935696</td>\n      <td>126</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.470632</td>\n    </tr>\n    <tr>\n      <th>374</th>\n      <td>692</td>\n      <td>0.797098</td>\n      <td>0.855212</td>\n      <td>112</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.254113</td>\n    </tr>\n    <tr>\n      <th>539</th>\n      <td>693</td>\n      <td>0.797098</td>\n      <td>0.880912</td>\n      <td>134</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.492649</td>\n    </tr>\n    <tr>\n      <th>584</th>\n      <td>693</td>\n      <td>0.797098</td>\n      <td>0.876880</td>\n      <td>183</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.613413</td>\n    </tr>\n    <tr>\n      <th>951</th>\n      <td>693</td>\n      <td>0.797098</td>\n      <td>0.873076</td>\n      <td>193</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.424577</td>\n    </tr>\n    <tr>\n      <th>945</th>\n      <td>693</td>\n      <td>0.797098</td>\n      <td>0.911837</td>\n      <td>189</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.424766</td>\n    </tr>\n    <tr>\n      <th>906</th>\n      <td>697</td>\n      <td>0.797097</td>\n      <td>0.886242</td>\n      <td>114</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.414684</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>698</td>\n      <td>0.796942</td>\n      <td>0.853424</td>\n      <td>173</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.468753</td>\n    </tr>\n    <tr>\n      <th>564</th>\n      <td>699</td>\n      <td>0.796941</td>\n      <td>0.958890</td>\n      <td>110</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.430592</td>\n    </tr>\n    <tr>\n      <th>760</th>\n      <td>699</td>\n      <td>0.796941</td>\n      <td>0.880106</td>\n      <td>126</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.423820</td>\n    </tr>\n    <tr>\n      <th>783</th>\n      <td>701</td>\n      <td>0.796941</td>\n      <td>0.898899</td>\n      <td>163</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.541757</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>702</td>\n      <td>0.796941</td>\n      <td>0.911960</td>\n      <td>175</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.462163</td>\n    </tr>\n    <tr>\n      <th>580</th>\n      <td>702</td>\n      <td>0.796941</td>\n      <td>0.911539</td>\n      <td>169</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.650775</td>\n    </tr>\n    <tr>\n      <th>958</th>\n      <td>704</td>\n      <td>0.796940</td>\n      <td>0.861383</td>\n      <td>140</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.330407</td>\n    </tr>\n    <tr>\n      <th>672</th>\n      <td>704</td>\n      <td>0.796940</td>\n      <td>0.885576</td>\n      <td>142</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.509666</td>\n    </tr>\n    <tr>\n      <th>410</th>\n      <td>706</td>\n      <td>0.796939</td>\n      <td>0.876950</td>\n      <td>128</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.466614</td>\n    </tr>\n    <tr>\n      <th>568</th>\n      <td>707</td>\n      <td>0.796939</td>\n      <td>0.884576</td>\n      <td>161</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.585039</td>\n    </tr>\n    <tr>\n      <th>288</th>\n      <td>708</td>\n      <td>0.796785</td>\n      <td>0.886137</td>\n      <td>167</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.415869</td>\n    </tr>\n    <tr>\n      <th>934</th>\n      <td>709</td>\n      <td>0.796784</td>\n      <td>0.900407</td>\n      <td>195</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.461220</td>\n    </tr>\n    <tr>\n      <th>493</th>\n      <td>710</td>\n      <td>0.796784</td>\n      <td>0.909137</td>\n      <td>195</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.641421</td>\n    </tr>\n    <tr>\n      <th>854</th>\n      <td>711</td>\n      <td>0.796783</td>\n      <td>0.907349</td>\n      <td>177</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.633485</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>712</td>\n      <td>0.796783</td>\n      <td>0.849637</td>\n      <td>193</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.468632</td>\n    </tr>\n    <tr>\n      <th>661</th>\n      <td>713</td>\n      <td>0.796783</td>\n      <td>0.911346</td>\n      <td>191</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.661720</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>714</td>\n      <td>0.796783</td>\n      <td>0.893587</td>\n      <td>159</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.449347</td>\n    </tr>\n    <tr>\n      <th>290</th>\n      <td>715</td>\n      <td>0.796782</td>\n      <td>0.914449</td>\n      <td>112</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.267536</td>\n    </tr>\n    <tr>\n      <th>483</th>\n      <td>716</td>\n      <td>0.796782</td>\n      <td>0.885050</td>\n      <td>142</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.521544</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>716</td>\n      <td>0.796782</td>\n      <td>0.894078</td>\n      <td>165</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.520431</td>\n    </tr>\n    <tr>\n      <th>916</th>\n      <td>718</td>\n      <td>0.796782</td>\n      <td>0.879808</td>\n      <td>157</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>11</td>\n      <td>1</td>\n      <td>1.986007</td>\n    </tr>\n    <tr>\n      <th>369</th>\n      <td>719</td>\n      <td>0.796782</td>\n      <td>0.910715</td>\n      <td>112</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.273511</td>\n    </tr>\n    <tr>\n      <th>965</th>\n      <td>720</td>\n      <td>0.796782</td>\n      <td>0.856614</td>\n      <td>132</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.313414</td>\n    </tr>\n    <tr>\n      <th>845</th>\n      <td>721</td>\n      <td>0.796626</td>\n      <td>0.908155</td>\n      <td>122</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.450629</td>\n    </tr>\n    <tr>\n      <th>341</th>\n      <td>721</td>\n      <td>0.796626</td>\n      <td>0.848638</td>\n      <td>153</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.348635</td>\n    </tr>\n    <tr>\n      <th>308</th>\n      <td>721</td>\n      <td>0.796626</td>\n      <td>0.882700</td>\n      <td>146</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.345884</td>\n    </tr>\n    <tr>\n      <th>629</th>\n      <td>724</td>\n      <td>0.796625</td>\n      <td>0.872462</td>\n      <td>183</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.615802</td>\n    </tr>\n    <tr>\n      <th>221</th>\n      <td>724</td>\n      <td>0.796625</td>\n      <td>0.880246</td>\n      <td>161</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.493004</td>\n    </tr>\n    <tr>\n      <th>158</th>\n      <td>726</td>\n      <td>0.796625</td>\n      <td>0.902125</td>\n      <td>118</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.285282</td>\n    </tr>\n    <tr>\n      <th>728</th>\n      <td>727</td>\n      <td>0.796625</td>\n      <td>0.879562</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.477709</td>\n    </tr>\n    <tr>\n      <th>802</th>\n      <td>728</td>\n      <td>0.796625</td>\n      <td>0.887820</td>\n      <td>118</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.367152</td>\n    </tr>\n    <tr>\n      <th>347</th>\n      <td>729</td>\n      <td>0.796625</td>\n      <td>0.893622</td>\n      <td>118</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.281801</td>\n    </tr>\n    <tr>\n      <th>843</th>\n      <td>730</td>\n      <td>0.796624</td>\n      <td>0.877424</td>\n      <td>200</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.682278</td>\n    </tr>\n    <tr>\n      <th>915</th>\n      <td>730</td>\n      <td>0.796624</td>\n      <td>0.911591</td>\n      <td>118</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.425102</td>\n    </tr>\n    <tr>\n      <th>444</th>\n      <td>732</td>\n      <td>0.796624</td>\n      <td>0.885944</td>\n      <td>181</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.609918</td>\n    </tr>\n    <tr>\n      <th>990</th>\n      <td>733</td>\n      <td>0.796624</td>\n      <td>0.886347</td>\n      <td>200</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.563302</td>\n    </tr>\n    <tr>\n      <th>877</th>\n      <td>733</td>\n      <td>0.796624</td>\n      <td>0.908471</td>\n      <td>155</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.575008</td>\n    </tr>\n    <tr>\n      <th>314</th>\n      <td>735</td>\n      <td>0.796624</td>\n      <td>0.865292</td>\n      <td>116</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.343782</td>\n    </tr>\n    <tr>\n      <th>396</th>\n      <td>736</td>\n      <td>0.796623</td>\n      <td>0.857877</td>\n      <td>157</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.413549</td>\n    </tr>\n    <tr>\n      <th>218</th>\n      <td>737</td>\n      <td>0.796469</td>\n      <td>0.842029</td>\n      <td>179</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.415627</td>\n    </tr>\n    <tr>\n      <th>726</th>\n      <td>737</td>\n      <td>0.796469</td>\n      <td>0.911784</td>\n      <td>122</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.449498</td>\n    </tr>\n    <tr>\n      <th>607</th>\n      <td>739</td>\n      <td>0.796468</td>\n      <td>0.911223</td>\n      <td>179</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.638606</td>\n    </tr>\n    <tr>\n      <th>339</th>\n      <td>739</td>\n      <td>0.796468</td>\n      <td>0.931454</td>\n      <td>189</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.448022</td>\n    </tr>\n    <tr>\n      <th>154</th>\n      <td>741</td>\n      <td>0.796468</td>\n      <td>0.884085</td>\n      <td>102</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.264203</td>\n    </tr>\n    <tr>\n      <th>569</th>\n      <td>741</td>\n      <td>0.796468</td>\n      <td>0.911591</td>\n      <td>165</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.636514</td>\n    </tr>\n    <tr>\n      <th>686</th>\n      <td>743</td>\n      <td>0.796467</td>\n      <td>0.869868</td>\n      <td>112</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.387373</td>\n    </tr>\n    <tr>\n      <th>405</th>\n      <td>743</td>\n      <td>0.796467</td>\n      <td>0.873970</td>\n      <td>161</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.616722</td>\n    </tr>\n    <tr>\n      <th>411</th>\n      <td>745</td>\n      <td>0.796467</td>\n      <td>0.926317</td>\n      <td>128</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.527653</td>\n    </tr>\n    <tr>\n      <th>316</th>\n      <td>745</td>\n      <td>0.796467</td>\n      <td>0.926037</td>\n      <td>193</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.439460</td>\n    </tr>\n    <tr>\n      <th>477</th>\n      <td>747</td>\n      <td>0.796466</td>\n      <td>0.886592</td>\n      <td>177</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.654550</td>\n    </tr>\n    <tr>\n      <th>903</th>\n      <td>747</td>\n      <td>0.796466</td>\n      <td>0.889029</td>\n      <td>157</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.588125</td>\n    </tr>\n    <tr>\n      <th>827</th>\n      <td>749</td>\n      <td>0.796466</td>\n      <td>0.872883</td>\n      <td>110</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.413910</td>\n    </tr>\n    <tr>\n      <th>696</th>\n      <td>750</td>\n      <td>0.796311</td>\n      <td>0.899372</td>\n      <td>146</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.615793</td>\n    </tr>\n    <tr>\n      <th>676</th>\n      <td>750</td>\n      <td>0.796311</td>\n      <td>0.877161</td>\n      <td>136</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.480755</td>\n    </tr>\n    <tr>\n      <th>608</th>\n      <td>752</td>\n      <td>0.796310</td>\n      <td>0.987343</td>\n      <td>106</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.443902</td>\n    </tr>\n    <tr>\n      <th>514</th>\n      <td>753</td>\n      <td>0.796310</td>\n      <td>0.863890</td>\n      <td>185</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.607973</td>\n    </tr>\n    <tr>\n      <th>734</th>\n      <td>754</td>\n      <td>0.796310</td>\n      <td>0.890589</td>\n      <td>185</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.620214</td>\n    </tr>\n    <tr>\n      <th>306</th>\n      <td>755</td>\n      <td>0.796310</td>\n      <td>0.884717</td>\n      <td>138</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.325182</td>\n    </tr>\n    <tr>\n      <th>953</th>\n      <td>755</td>\n      <td>0.796310</td>\n      <td>0.908190</td>\n      <td>138</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.329911</td>\n    </tr>\n    <tr>\n      <th>533</th>\n      <td>755</td>\n      <td>0.796310</td>\n      <td>0.944830</td>\n      <td>200</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.731070</td>\n    </tr>\n    <tr>\n      <th>730</th>\n      <td>758</td>\n      <td>0.796309</td>\n      <td>0.853599</td>\n      <td>181</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.575059</td>\n    </tr>\n    <tr>\n      <th>572</th>\n      <td>758</td>\n      <td>0.796309</td>\n      <td>0.926423</td>\n      <td>128</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.468088</td>\n    </tr>\n    <tr>\n      <th>130</th>\n      <td>760</td>\n      <td>0.796309</td>\n      <td>0.881053</td>\n      <td>179</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.437356</td>\n    </tr>\n    <tr>\n      <th>234</th>\n      <td>761</td>\n      <td>0.796308</td>\n      <td>0.851864</td>\n      <td>10</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.043315</td>\n    </tr>\n    <tr>\n      <th>809</th>\n      <td>761</td>\n      <td>0.796308</td>\n      <td>0.861120</td>\n      <td>155</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.488143</td>\n    </tr>\n    <tr>\n      <th>536</th>\n      <td>763</td>\n      <td>0.796308</td>\n      <td>0.936889</td>\n      <td>161</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.635454</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>764</td>\n      <td>0.796153</td>\n      <td>0.880106</td>\n      <td>136</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.376444</td>\n    </tr>\n    <tr>\n      <th>929</th>\n      <td>765</td>\n      <td>0.796153</td>\n      <td>0.925248</td>\n      <td>104</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.286453</td>\n    </tr>\n    <tr>\n      <th>237</th>\n      <td>766</td>\n      <td>0.796152</td>\n      <td>0.884857</td>\n      <td>130</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>5</td>\n      <td>4</td>\n      <td>0.351280</td>\n    </tr>\n    <tr>\n      <th>164</th>\n      <td>766</td>\n      <td>0.796152</td>\n      <td>0.901651</td>\n      <td>118</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.286467</td>\n    </tr>\n    <tr>\n      <th>467</th>\n      <td>768</td>\n      <td>0.796152</td>\n      <td>0.861383</td>\n      <td>161</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.586582</td>\n    </tr>\n    <tr>\n      <th>417</th>\n      <td>769</td>\n      <td>0.796152</td>\n      <td>0.943550</td>\n      <td>100</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.426653</td>\n    </tr>\n    <tr>\n      <th>868</th>\n      <td>770</td>\n      <td>0.796151</td>\n      <td>0.873619</td>\n      <td>136</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.485422</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>771</td>\n      <td>0.796151</td>\n      <td>0.907156</td>\n      <td>181</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.474521</td>\n    </tr>\n    <tr>\n      <th>296</th>\n      <td>771</td>\n      <td>0.796151</td>\n      <td>0.880684</td>\n      <td>136</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.329335</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>773</td>\n      <td>0.796150</td>\n      <td>0.881684</td>\n      <td>112</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.294419</td>\n    </tr>\n    <tr>\n      <th>455</th>\n      <td>774</td>\n      <td>0.795995</td>\n      <td>0.908243</td>\n      <td>187</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.652910</td>\n    </tr>\n    <tr>\n      <th>650</th>\n      <td>775</td>\n      <td>0.795993</td>\n      <td>0.908103</td>\n      <td>126</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.459192</td>\n    </tr>\n    <tr>\n      <th>908</th>\n      <td>776</td>\n      <td>0.795836</td>\n      <td>0.898917</td>\n      <td>200</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.711218</td>\n    </tr>\n    <tr>\n      <th>697</th>\n      <td>777</td>\n      <td>0.795836</td>\n      <td>0.915361</td>\n      <td>155</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.579445</td>\n    </tr>\n    <tr>\n      <th>815</th>\n      <td>778</td>\n      <td>0.795836</td>\n      <td>0.861733</td>\n      <td>189</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.624491</td>\n    </tr>\n    <tr>\n      <th>282</th>\n      <td>778</td>\n      <td>0.795836</td>\n      <td>0.943866</td>\n      <td>110</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.434938</td>\n    </tr>\n    <tr>\n      <th>302</th>\n      <td>780</td>\n      <td>0.795836</td>\n      <td>0.865292</td>\n      <td>108</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.274722</td>\n    </tr>\n    <tr>\n      <th>311</th>\n      <td>781</td>\n      <td>0.795834</td>\n      <td>0.911118</td>\n      <td>104</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.267360</td>\n    </tr>\n    <tr>\n      <th>474</th>\n      <td>782</td>\n      <td>0.795679</td>\n      <td>0.865555</td>\n      <td>10</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.071635</td>\n    </tr>\n    <tr>\n      <th>427</th>\n      <td>783</td>\n      <td>0.795679</td>\n      <td>0.886750</td>\n      <td>181</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.636081</td>\n    </tr>\n    <tr>\n      <th>963</th>\n      <td>784</td>\n      <td>0.795679</td>\n      <td>0.864714</td>\n      <td>187</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.406523</td>\n    </tr>\n    <tr>\n      <th>956</th>\n      <td>785</td>\n      <td>0.795678</td>\n      <td>0.936520</td>\n      <td>171</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.426088</td>\n    </tr>\n    <tr>\n      <th>698</th>\n      <td>786</td>\n      <td>0.795678</td>\n      <td>0.880614</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.477775</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>787</td>\n      <td>0.795678</td>\n      <td>0.861085</td>\n      <td>104</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.302322</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>788</td>\n      <td>0.795677</td>\n      <td>0.893605</td>\n      <td>146</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.377949</td>\n    </tr>\n    <tr>\n      <th>881</th>\n      <td>789</td>\n      <td>0.795677</td>\n      <td>0.882630</td>\n      <td>10</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.061951</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>790</td>\n      <td>0.795522</td>\n      <td>0.893149</td>\n      <td>116</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.314977</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>791</td>\n      <td>0.795522</td>\n      <td>0.907244</td>\n      <td>146</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.362765</td>\n    </tr>\n    <tr>\n      <th>901</th>\n      <td>792</td>\n      <td>0.795522</td>\n      <td>0.879457</td>\n      <td>136</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.497471</td>\n    </tr>\n    <tr>\n      <th>231</th>\n      <td>792</td>\n      <td>0.795522</td>\n      <td>0.958382</td>\n      <td>138</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.371019</td>\n    </tr>\n    <tr>\n      <th>151</th>\n      <td>794</td>\n      <td>0.795521</td>\n      <td>0.944567</td>\n      <td>157</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.419904</td>\n    </tr>\n    <tr>\n      <th>209</th>\n      <td>795</td>\n      <td>0.795521</td>\n      <td>0.907051</td>\n      <td>110</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.336352</td>\n    </tr>\n    <tr>\n      <th>834</th>\n      <td>796</td>\n      <td>0.795520</td>\n      <td>0.885839</td>\n      <td>136</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.500034</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>797</td>\n      <td>0.795520</td>\n      <td>0.881999</td>\n      <td>200</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.454364</td>\n    </tr>\n    <tr>\n      <th>889</th>\n      <td>797</td>\n      <td>0.795520</td>\n      <td>0.963062</td>\n      <td>126</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.490019</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>799</td>\n      <td>0.795519</td>\n      <td>0.906928</td>\n      <td>167</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.471149</td>\n    </tr>\n    <tr>\n      <th>397</th>\n      <td>800</td>\n      <td>0.795364</td>\n      <td>0.911837</td>\n      <td>132</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.338768</td>\n    </tr>\n    <tr>\n      <th>512</th>\n      <td>801</td>\n      <td>0.795364</td>\n      <td>0.902545</td>\n      <td>153</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.646830</td>\n    </tr>\n    <tr>\n      <th>662</th>\n      <td>802</td>\n      <td>0.795363</td>\n      <td>0.880842</td>\n      <td>197</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.677530</td>\n    </tr>\n    <tr>\n      <th>756</th>\n      <td>803</td>\n      <td>0.795362</td>\n      <td>0.893745</td>\n      <td>181</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.593242</td>\n    </tr>\n    <tr>\n      <th>766</th>\n      <td>804</td>\n      <td>0.795362</td>\n      <td>0.897795</td>\n      <td>128</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.407808</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>805</td>\n      <td>0.795361</td>\n      <td>0.931156</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.357462</td>\n    </tr>\n    <tr>\n      <th>547</th>\n      <td>806</td>\n      <td>0.795206</td>\n      <td>0.926615</td>\n      <td>165</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.614476</td>\n    </tr>\n    <tr>\n      <th>480</th>\n      <td>806</td>\n      <td>0.795206</td>\n      <td>0.907612</td>\n      <td>138</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.526020</td>\n    </tr>\n    <tr>\n      <th>898</th>\n      <td>808</td>\n      <td>0.795206</td>\n      <td>0.914992</td>\n      <td>120</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.433983</td>\n    </tr>\n    <tr>\n      <th>692</th>\n      <td>809</td>\n      <td>0.795205</td>\n      <td>0.932173</td>\n      <td>197</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.702322</td>\n    </tr>\n    <tr>\n      <th>538</th>\n      <td>810</td>\n      <td>0.795204</td>\n      <td>0.943410</td>\n      <td>118</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.456428</td>\n    </tr>\n    <tr>\n      <th>887</th>\n      <td>811</td>\n      <td>0.795203</td>\n      <td>0.908085</td>\n      <td>122</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.445686</td>\n    </tr>\n    <tr>\n      <th>620</th>\n      <td>812</td>\n      <td>0.795048</td>\n      <td>0.893394</td>\n      <td>104</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.408223</td>\n    </tr>\n    <tr>\n      <th>329</th>\n      <td>813</td>\n      <td>0.795048</td>\n      <td>0.911714</td>\n      <td>197</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.441703</td>\n    </tr>\n    <tr>\n      <th>487</th>\n      <td>814</td>\n      <td>0.795047</td>\n      <td>0.929771</td>\n      <td>106</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.414671</td>\n    </tr>\n    <tr>\n      <th>134</th>\n      <td>815</td>\n      <td>0.795045</td>\n      <td>0.899688</td>\n      <td>163</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.423444</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>816</td>\n      <td>0.794889</td>\n      <td>0.925721</td>\n      <td>165</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.606760</td>\n    </tr>\n    <tr>\n      <th>319</th>\n      <td>816</td>\n      <td>0.794889</td>\n      <td>0.914992</td>\n      <td>136</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.325024</td>\n    </tr>\n    <tr>\n      <th>573</th>\n      <td>818</td>\n      <td>0.794889</td>\n      <td>0.888012</td>\n      <td>140</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.522790</td>\n    </tr>\n    <tr>\n      <th>912</th>\n      <td>819</td>\n      <td>0.794889</td>\n      <td>0.885681</td>\n      <td>116</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.431200</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>820</td>\n      <td>0.794733</td>\n      <td>0.901757</td>\n      <td>102</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.281356</td>\n    </tr>\n    <tr>\n      <th>277</th>\n      <td>821</td>\n      <td>0.794732</td>\n      <td>0.874636</td>\n      <td>136</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.454853</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>821</td>\n      <td>0.794732</td>\n      <td>0.936854</td>\n      <td>181</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.469549</td>\n    </tr>\n    <tr>\n      <th>283</th>\n      <td>823</td>\n      <td>0.794732</td>\n      <td>0.958294</td>\n      <td>122</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.314380</td>\n    </tr>\n    <tr>\n      <th>220</th>\n      <td>824</td>\n      <td>0.794732</td>\n      <td>0.890467</td>\n      <td>118</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.300448</td>\n    </tr>\n    <tr>\n      <th>258</th>\n      <td>825</td>\n      <td>0.794731</td>\n      <td>0.881561</td>\n      <td>102</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.287322</td>\n    </tr>\n    <tr>\n      <th>534</th>\n      <td>826</td>\n      <td>0.794575</td>\n      <td>0.907752</td>\n      <td>175</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.676216</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>827</td>\n      <td>0.794574</td>\n      <td>0.931401</td>\n      <td>193</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.521226</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>828</td>\n      <td>0.794573</td>\n      <td>0.930893</td>\n      <td>159</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.435157</td>\n    </tr>\n    <tr>\n      <th>511</th>\n      <td>829</td>\n      <td>0.794573</td>\n      <td>0.960888</td>\n      <td>181</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.677386</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>830</td>\n      <td>0.794572</td>\n      <td>0.892202</td>\n      <td>114</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.292098</td>\n    </tr>\n    <tr>\n      <th>200</th>\n      <td>831</td>\n      <td>0.794417</td>\n      <td>0.918919</td>\n      <td>116</td>\n      <td>log2</td>\n      <td>12</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.318125</td>\n    </tr>\n    <tr>\n      <th>639</th>\n      <td>832</td>\n      <td>0.794417</td>\n      <td>0.944725</td>\n      <td>161</td>\n      <td>log2</td>\n      <td>78</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.761968</td>\n    </tr>\n    <tr>\n      <th>496</th>\n      <td>833</td>\n      <td>0.794417</td>\n      <td>0.961484</td>\n      <td>171</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.581277</td>\n    </tr>\n    <tr>\n      <th>391</th>\n      <td>833</td>\n      <td>0.794417</td>\n      <td>0.926458</td>\n      <td>138</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.454969</td>\n    </tr>\n    <tr>\n      <th>955</th>\n      <td>835</td>\n      <td>0.794416</td>\n      <td>0.944269</td>\n      <td>146</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.387926</td>\n    </tr>\n    <tr>\n      <th>371</th>\n      <td>836</td>\n      <td>0.794415</td>\n      <td>0.963290</td>\n      <td>200</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.482379</td>\n    </tr>\n    <tr>\n      <th>559</th>\n      <td>837</td>\n      <td>0.794415</td>\n      <td>0.887679</td>\n      <td>163</td>\n      <td>sqrt</td>\n      <td>67</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.599702</td>\n    </tr>\n    <tr>\n      <th>370</th>\n      <td>838</td>\n      <td>0.794261</td>\n      <td>0.841170</td>\n      <td>10</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.041395</td>\n    </tr>\n    <tr>\n      <th>604</th>\n      <td>839</td>\n      <td>0.794259</td>\n      <td>0.944813</td>\n      <td>183</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.702588</td>\n    </tr>\n    <tr>\n      <th>677</th>\n      <td>840</td>\n      <td>0.794259</td>\n      <td>0.962747</td>\n      <td>138</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.527467</td>\n    </tr>\n    <tr>\n      <th>921</th>\n      <td>841</td>\n      <td>0.794258</td>\n      <td>0.930297</td>\n      <td>102</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.285669</td>\n    </tr>\n    <tr>\n      <th>885</th>\n      <td>842</td>\n      <td>0.794257</td>\n      <td>0.963904</td>\n      <td>195</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.739066</td>\n    </tr>\n    <tr>\n      <th>700</th>\n      <td>843</td>\n      <td>0.794257</td>\n      <td>0.884804</td>\n      <td>116</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.495752</td>\n    </tr>\n    <tr>\n      <th>589</th>\n      <td>844</td>\n      <td>0.794102</td>\n      <td>0.929736</td>\n      <td>104</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.411554</td>\n    </tr>\n    <tr>\n      <th>502</th>\n      <td>845</td>\n      <td>0.794099</td>\n      <td>0.988324</td>\n      <td>136</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.540097</td>\n    </tr>\n    <tr>\n      <th>172</th>\n      <td>846</td>\n      <td>0.793945</td>\n      <td>0.936766</td>\n      <td>144</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.394693</td>\n    </tr>\n    <tr>\n      <th>366</th>\n      <td>847</td>\n      <td>0.793944</td>\n      <td>0.930297</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.315480</td>\n    </tr>\n    <tr>\n      <th>987</th>\n      <td>848</td>\n      <td>0.793943</td>\n      <td>0.944550</td>\n      <td>140</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.380070</td>\n    </tr>\n    <tr>\n      <th>894</th>\n      <td>849</td>\n      <td>0.793942</td>\n      <td>0.930104</td>\n      <td>138</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.514649</td>\n    </tr>\n    <tr>\n      <th>418</th>\n      <td>850</td>\n      <td>0.793785</td>\n      <td>0.930227</td>\n      <td>102</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.416768</td>\n    </tr>\n    <tr>\n      <th>481</th>\n      <td>850</td>\n      <td>0.793785</td>\n      <td>0.911591</td>\n      <td>181</td>\n      <td>log2</td>\n      <td>23</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.654036</td>\n    </tr>\n    <tr>\n      <th>701</th>\n      <td>852</td>\n      <td>0.793784</td>\n      <td>0.962940</td>\n      <td>153</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.611967</td>\n    </tr>\n    <tr>\n      <th>910</th>\n      <td>853</td>\n      <td>0.793627</td>\n      <td>0.959241</td>\n      <td>136</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.570730</td>\n    </tr>\n    <tr>\n      <th>433</th>\n      <td>854</td>\n      <td>0.793468</td>\n      <td>0.936591</td>\n      <td>161</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.634695</td>\n    </tr>\n    <tr>\n      <th>191</th>\n      <td>855</td>\n      <td>0.793313</td>\n      <td>0.905876</td>\n      <td>195</td>\n      <td>auto</td>\n      <td>12</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.470515</td>\n    </tr>\n    <tr>\n      <th>478</th>\n      <td>856</td>\n      <td>0.793310</td>\n      <td>0.931086</td>\n      <td>169</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.688725</td>\n    </tr>\n    <tr>\n      <th>789</th>\n      <td>857</td>\n      <td>0.793309</td>\n      <td>0.871621</td>\n      <td>10</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.058587</td>\n    </tr>\n    <tr>\n      <th>556</th>\n      <td>858</td>\n      <td>0.793155</td>\n      <td>0.899495</td>\n      <td>124</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.441968</td>\n    </tr>\n    <tr>\n      <th>454</th>\n      <td>859</td>\n      <td>0.793154</td>\n      <td>0.963010</td>\n      <td>136</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.546087</td>\n    </tr>\n    <tr>\n      <th>309</th>\n      <td>860</td>\n      <td>0.793154</td>\n      <td>0.990148</td>\n      <td>163</td>\n      <td>log2</td>\n      <td>34</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.430029</td>\n    </tr>\n    <tr>\n      <th>504</th>\n      <td>861</td>\n      <td>0.793154</td>\n      <td>0.957400</td>\n      <td>100</td>\n      <td>auto</td>\n      <td>None</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.476627</td>\n    </tr>\n    <tr>\n      <th>93</th>\n      <td>862</td>\n      <td>0.792997</td>\n      <td>0.989306</td>\n      <td>163</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.445677</td>\n    </tr>\n    <tr>\n      <th>342</th>\n      <td>863</td>\n      <td>0.792996</td>\n      <td>0.943796</td>\n      <td>173</td>\n      <td>auto</td>\n      <td>67</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.444496</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>864</td>\n      <td>0.792995</td>\n      <td>0.962869</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.355203</td>\n    </tr>\n    <tr>\n      <th>408</th>\n      <td>865</td>\n      <td>0.792838</td>\n      <td>0.958785</td>\n      <td>124</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.485159</td>\n    </tr>\n    <tr>\n      <th>883</th>\n      <td>866</td>\n      <td>0.792837</td>\n      <td>0.853511</td>\n      <td>10</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.059738</td>\n    </tr>\n    <tr>\n      <th>897</th>\n      <td>867</td>\n      <td>0.792681</td>\n      <td>0.989955</td>\n      <td>167</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.657058</td>\n    </tr>\n    <tr>\n      <th>657</th>\n      <td>868</td>\n      <td>0.792680</td>\n      <td>0.963693</td>\n      <td>136</td>\n      <td>auto</td>\n      <td>45</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.518324</td>\n    </tr>\n    <tr>\n      <th>181</th>\n      <td>869</td>\n      <td>0.792522</td>\n      <td>0.987571</td>\n      <td>110</td>\n      <td>auto</td>\n      <td>89</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.317450</td>\n    </tr>\n    <tr>\n      <th>352</th>\n      <td>870</td>\n      <td>0.792521</td>\n      <td>0.998089</td>\n      <td>144</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.391435</td>\n    </tr>\n    <tr>\n      <th>858</th>\n      <td>871</td>\n      <td>0.792367</td>\n      <td>0.987693</td>\n      <td>120</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.456350</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>872</td>\n      <td>0.792208</td>\n      <td>0.929824</td>\n      <td>185</td>\n      <td>sqrt</td>\n      <td>12</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.472567</td>\n    </tr>\n    <tr>\n      <th>691</th>\n      <td>873</td>\n      <td>0.792207</td>\n      <td>0.962887</td>\n      <td>118</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.481808</td>\n    </tr>\n    <tr>\n      <th>928</th>\n      <td>874</td>\n      <td>0.792207</td>\n      <td>0.960327</td>\n      <td>169</td>\n      <td>log2</td>\n      <td>56</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.466934</td>\n    </tr>\n    <tr>\n      <th>267</th>\n      <td>875</td>\n      <td>0.792206</td>\n      <td>0.998089</td>\n      <td>140</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.414389</td>\n    </tr>\n    <tr>\n      <th>155</th>\n      <td>876</td>\n      <td>0.791891</td>\n      <td>0.998089</td>\n      <td>153</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.418469</td>\n    </tr>\n    <tr>\n      <th>157</th>\n      <td>877</td>\n      <td>0.791732</td>\n      <td>0.847078</td>\n      <td>10</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.041116</td>\n    </tr>\n    <tr>\n      <th>895</th>\n      <td>878</td>\n      <td>0.791575</td>\n      <td>0.874321</td>\n      <td>10</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.062838</td>\n    </tr>\n    <tr>\n      <th>905</th>\n      <td>878</td>\n      <td>0.791575</td>\n      <td>0.926265</td>\n      <td>148</td>\n      <td>log2</td>\n      <td>45</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.563436</td>\n    </tr>\n    <tr>\n      <th>757</th>\n      <td>880</td>\n      <td>0.791417</td>\n      <td>0.963869</td>\n      <td>197</td>\n      <td>auto</td>\n      <td>100</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.692883</td>\n    </tr>\n    <tr>\n      <th>242</th>\n      <td>881</td>\n      <td>0.790944</td>\n      <td>0.987606</td>\n      <td>108</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.345731</td>\n    </tr>\n    <tr>\n      <th>387</th>\n      <td>882</td>\n      <td>0.790787</td>\n      <td>0.890747</td>\n      <td>10</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.048111</td>\n    </tr>\n    <tr>\n      <th>752</th>\n      <td>883</td>\n      <td>0.790787</td>\n      <td>0.962869</td>\n      <td>151</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.687387</td>\n    </tr>\n    <tr>\n      <th>262</th>\n      <td>884</td>\n      <td>0.790628</td>\n      <td>0.998072</td>\n      <td>122</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.351860</td>\n    </tr>\n    <tr>\n      <th>399</th>\n      <td>884</td>\n      <td>0.790628</td>\n      <td>0.990901</td>\n      <td>191</td>\n      <td>sqrt</td>\n      <td>23</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.648758</td>\n    </tr>\n    <tr>\n      <th>492</th>\n      <td>886</td>\n      <td>0.790315</td>\n      <td>0.959205</td>\n      <td>144</td>\n      <td>log2</td>\n      <td>100</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.496768</td>\n    </tr>\n    <tr>\n      <th>198</th>\n      <td>887</td>\n      <td>0.790156</td>\n      <td>0.998089</td>\n      <td>187</td>\n      <td>log2</td>\n      <td>89</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.550664</td>\n    </tr>\n    <tr>\n      <th>463</th>\n      <td>888</td>\n      <td>0.790155</td>\n      <td>0.998089</td>\n      <td>165</td>\n      <td>sqrt</td>\n      <td>78</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.704430</td>\n    </tr>\n    <tr>\n      <th>253</th>\n      <td>889</td>\n      <td>0.789681</td>\n      <td>0.989972</td>\n      <td>159</td>\n      <td>log2</td>\n      <td>67</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.450720</td>\n    </tr>\n    <tr>\n      <th>725</th>\n      <td>890</td>\n      <td>0.789524</td>\n      <td>0.998089</td>\n      <td>183</td>\n      <td>log2</td>\n      <td>None</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.739780</td>\n    </tr>\n    <tr>\n      <th>535</th>\n      <td>891</td>\n      <td>0.789366</td>\n      <td>0.890011</td>\n      <td>10</td>\n      <td>auto</td>\n      <td>56</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.058756</td>\n    </tr>\n    <tr>\n      <th>643</th>\n      <td>892</td>\n      <td>0.789209</td>\n      <td>0.998089</td>\n      <td>116</td>\n      <td>auto</td>\n      <td>23</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.495502</td>\n    </tr>\n    <tr>\n      <th>217</th>\n      <td>893</td>\n      <td>0.789209</td>\n      <td>0.998089</td>\n      <td>197</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.611449</td>\n    </tr>\n    <tr>\n      <th>133</th>\n      <td>894</td>\n      <td>0.788898</td>\n      <td>0.869956</td>\n      <td>10</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.056077</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>895</td>\n      <td>0.788893</td>\n      <td>0.998089</td>\n      <td>116</td>\n      <td>auto</td>\n      <td>34</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.354493</td>\n    </tr>\n    <tr>\n      <th>428</th>\n      <td>896</td>\n      <td>0.788104</td>\n      <td>0.998089</td>\n      <td>181</td>\n      <td>sqrt</td>\n      <td>34</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.755583</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>897</td>\n      <td>0.788104</td>\n      <td>0.998089</td>\n      <td>157</td>\n      <td>sqrt</td>\n      <td>None</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.687496</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>898</td>\n      <td>0.787946</td>\n      <td>0.998089</td>\n      <td>173</td>\n      <td>sqrt</td>\n      <td>89</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.510682</td>\n    </tr>\n    <tr>\n      <th>585</th>\n      <td>899</td>\n      <td>0.787157</td>\n      <td>0.998089</td>\n      <td>195</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.879223</td>\n    </tr>\n    <tr>\n      <th>685</th>\n      <td>900</td>\n      <td>0.787003</td>\n      <td>0.942814</td>\n      <td>10</td>\n      <td>auto</td>\n      <td>78</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.064936</td>\n    </tr>\n    <tr>\n      <th>488</th>\n      <td>901</td>\n      <td>0.786057</td>\n      <td>0.926756</td>\n      <td>10</td>\n      <td>sqrt</td>\n      <td>100</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.060169</td>\n    </tr>\n    <tr>\n      <th>501</th>\n      <td>902</td>\n      <td>0.786053</td>\n      <td>0.942446</td>\n      <td>10</td>\n      <td>sqrt</td>\n      <td>56</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.056240</td>\n    </tr>\n    <tr>\n      <th>468</th>\n      <td>903</td>\n      <td>0.781478</td>\n      <td>0.980926</td>\n      <td>10</td>\n      <td>sqrt</td>\n      <td>45</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.060833</td>\n    </tr>\n    <tr>\n      <th>942</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>10</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.036779</td>\n    </tr>\n    <tr>\n      <th>792</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>10</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.046188</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>197</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.475526</td>\n    </tr>\n    <tr>\n      <th>675</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>161</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.459970</td>\n    </tr>\n    <tr>\n      <th>137</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>106</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.219095</td>\n    </tr>\n    <tr>\n      <th>114</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>161</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.330699</td>\n    </tr>\n    <tr>\n      <th>665</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>153</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.431328</td>\n    </tr>\n    <tr>\n      <th>567</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>122</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.315076</td>\n    </tr>\n    <tr>\n      <th>751</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.362904</td>\n    </tr>\n    <tr>\n      <th>286</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>191</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.317702</td>\n    </tr>\n    <tr>\n      <th>960</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>159</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.313804</td>\n    </tr>\n    <tr>\n      <th>464</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>114</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.311569</td>\n    </tr>\n    <tr>\n      <th>996</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>134</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.289953</td>\n    </tr>\n    <tr>\n      <th>631</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>132</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.346242</td>\n    </tr>\n    <tr>\n      <th>632</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>114</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.301589</td>\n    </tr>\n    <tr>\n      <th>127</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>100</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.226936</td>\n    </tr>\n    <tr>\n      <th>634</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>200</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.494733</td>\n    </tr>\n    <tr>\n      <th>636</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>120</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.320924</td>\n    </tr>\n    <tr>\n      <th>269</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>144</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.294290</td>\n    </tr>\n    <tr>\n      <th>131</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>142</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.307826</td>\n    </tr>\n    <tr>\n      <th>254</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>112</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.217552</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>153</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.319417</td>\n    </tr>\n    <tr>\n      <th>959</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>167</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.324666</td>\n    </tr>\n    <tr>\n      <th>460</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>191</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.476500</td>\n    </tr>\n    <tr>\n      <th>596</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>181</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.459696</td>\n    </tr>\n    <tr>\n      <th>118</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>108</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.229955</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.323510</td>\n    </tr>\n    <tr>\n      <th>594</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>106</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.322446</td>\n    </tr>\n    <tr>\n      <th>655</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>124</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.315502</td>\n    </tr>\n    <tr>\n      <th>969</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>130</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.244605</td>\n    </tr>\n    <tr>\n      <th>593</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>173</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.506990</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>151</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.328133</td>\n    </tr>\n    <tr>\n      <th>966</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>185</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.298790</td>\n    </tr>\n    <tr>\n      <th>780</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>189</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.409419</td>\n    </tr>\n    <tr>\n      <th>659</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>169</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.478918</td>\n    </tr>\n    <tr>\n      <th>976</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>171</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.342395</td>\n    </tr>\n    <tr>\n      <th>315</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>163</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.323833</td>\n    </tr>\n    <tr>\n      <th>445</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>118</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.300540</td>\n    </tr>\n    <tr>\n      <th>317</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>132</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.243599</td>\n    </tr>\n    <tr>\n      <th>363</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>126</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.244486</td>\n    </tr>\n    <tr>\n      <th>764</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>157</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.405705</td>\n    </tr>\n    <tr>\n      <th>721</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>146</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.404828</td>\n    </tr>\n    <tr>\n      <th>876</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>120</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.320718</td>\n    </tr>\n    <tr>\n      <th>873</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>120</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.338880</td>\n    </tr>\n    <tr>\n      <th>215</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>118</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.244297</td>\n    </tr>\n    <tr>\n      <th>530</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>112</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.302596</td>\n    </tr>\n    <tr>\n      <th>527</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>140</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.385217</td>\n    </tr>\n    <tr>\n      <th>526</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>118</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.321282</td>\n    </tr>\n    <tr>\n      <th>867</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>197</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.495217</td>\n    </tr>\n    <tr>\n      <th>424</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>161</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.460648</td>\n    </tr>\n    <tr>\n      <th>423</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>10</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.047624</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>118</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.227337</td>\n    </tr>\n    <tr>\n      <th>202</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>153</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.329344</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>179</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.317105</td>\n    </tr>\n    <tr>\n      <th>392</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>159</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.428795</td>\n    </tr>\n    <tr>\n      <th>739</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>116</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.308208</td>\n    </tr>\n    <tr>\n      <th>189</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>10</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>14</td>\n      <td>1</td>\n      <td>0.035316</td>\n    </tr>\n    <tr>\n      <th>820</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>155</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.437196</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>155</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.347664</td>\n    </tr>\n    <tr>\n      <th>844</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>159</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.452616</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>163</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.351628</td>\n    </tr>\n    <tr>\n      <th>841</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>138</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.379154</td>\n    </tr>\n    <tr>\n      <th>825</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>195</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.485695</td>\n    </tr>\n    <tr>\n      <th>491</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>155</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.406817</td>\n    </tr>\n    <tr>\n      <th>409</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>118</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.303522</td>\n    </tr>\n    <tr>\n      <th>738</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>132</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>11</td>\n      <td>6</td>\n      <td>0.356722</td>\n    </tr>\n    <tr>\n      <th>562</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>157</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.465840</td>\n    </tr>\n    <tr>\n      <th>714</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>142</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>2</td>\n      <td>8</td>\n      <td>0.402625</td>\n    </tr>\n    <tr>\n      <th>351</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>138</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>8</td>\n      <td>6</td>\n      <td>0.255048</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>120</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.246078</td>\n    </tr>\n    <tr>\n      <th>321</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>132</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.243786</td>\n    </tr>\n    <tr>\n      <th>453</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>142</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.390281</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>112</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>14</td>\n      <td>2</td>\n      <td>0.241137</td>\n    </tr>\n    <tr>\n      <th>233</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>173</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.352703</td>\n    </tr>\n    <tr>\n      <th>451</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>136</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.373533</td>\n    </tr>\n    <tr>\n      <th>930</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>187</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.314089</td>\n    </tr>\n    <tr>\n      <th>796</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>189</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.423632</td>\n    </tr>\n    <tr>\n      <th>182</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>165</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0.372712</td>\n    </tr>\n    <tr>\n      <th>554</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>146</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.414472</td>\n    </tr>\n    <tr>\n      <th>229</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>104</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.228152</td>\n    </tr>\n    <tr>\n      <th>775</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>140</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.370282</td>\n    </tr>\n    <tr>\n      <th>782</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>142</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>14</td>\n      <td>4</td>\n      <td>0.360708</td>\n    </tr>\n    <tr>\n      <th>918</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>116</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>14</td>\n      <td>6</td>\n      <td>0.281547</td>\n    </tr>\n    <tr>\n      <th>695</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>155</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>11</td>\n      <td>1</td>\n      <td>0.450444</td>\n    </tr>\n    <tr>\n      <th>183</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>173</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.367116</td>\n    </tr>\n    <tr>\n      <th>192</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>112</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>11</td>\n      <td>4</td>\n      <td>0.218687</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>193</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.335800</td>\n    </tr>\n    <tr>\n      <th>156</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>195</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>14</td>\n      <td>8</td>\n      <td>0.340179</td>\n    </tr>\n    <tr>\n      <th>904</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>102</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.320058</td>\n    </tr>\n    <tr>\n      <th>542</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>110</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.349132</td>\n    </tr>\n    <tr>\n      <th>475</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>193</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.595374</td>\n    </tr>\n    <tr>\n      <th>176</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>157</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>11</td>\n      <td>2</td>\n      <td>0.321982</td>\n    </tr>\n    <tr>\n      <th>707</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>114</td>\n      <td>sqrt</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0.306185</td>\n    </tr>\n    <tr>\n      <th>540</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>124</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>8</td>\n      <td>4</td>\n      <td>0.358546</td>\n    </tr>\n    <tr>\n      <th>436</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>159</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>11</td>\n      <td>8</td>\n      <td>0.483538</td>\n    </tr>\n    <tr>\n      <th>338</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>132</td>\n      <td>auto</td>\n      <td>1</td>\n      <td>5</td>\n      <td>6</td>\n      <td>0.239564</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>904</td>\n      <td>0.734459</td>\n      <td>0.734459</td>\n      <td>146</td>\n      <td>log2</td>\n      <td>1</td>\n      <td>8</td>\n      <td>2</td>\n      <td>0.348739</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Optimal Hyper-parameter Search using RandomSearch\n",
    "parameters = {\n",
    "    'n_estimators':[int(x) for x in np.linspace(start = 100, stop = 200, num = 50)],\n",
    "    'max_depth': [int(x) for x in np.linspace(1, 100, 10)],\n",
    "    'max_features': ['auto', 'sqrt','log2'],\n",
    "    'min_samples_split': [2, 5, 8, 11, 14],\n",
    "    'min_samples_leaf': [1, 2, 4, 6, 8],\n",
    "    'max_samples': [float(2/3), 0.5, None],\n",
    "}\n",
    "parameters['max_depth'].extend([None])\n",
    "parameters['n_estimators'].extend([10])\n",
    "\n",
    "# Classifier\n",
    "rf_clf = RandomForestClassifier(oob_score=True, n_jobs=-1)\n",
    "\n",
    "# N-iterations\n",
    "n_iterations = 1000\n",
    "\n",
    "# 10-fold cross validation\n",
    "k_fold = 10\n",
    "\n",
    "# Columns to display\n",
    "df_display_columns = ['rank_test_score', 'mean_test_score', 'mean_train_score', 'param_n_estimators', 'param_max_features', 'param_max_depth', 'param_min_samples_split', 'param_min_samples_leaf', 'mean_fit_time']\n",
    "\n",
    "# Randomized Search\n",
    "clf_rs = RandomizedSearchCV(estimator=rf_clf, param_distributions=parameters, cv=k_fold, return_train_score=True, scoring='accuracy', n_iter=n_iterations)\n",
    "start = time.time()\n",
    "search = clf_rs.fit(X_train, y_train)\n",
    "stop = time.time()\n",
    "df_rs = pd.DataFrame(clf_rs.cv_results_)\n",
    "\n",
    "print('Best Parameters', search.best_params_, '\\n')\n",
    "print('RandomSearch Results, Ranked by Accuracy')\n",
    "print(f'Total Fit Time: {round(stop - start, 2)}s')\n",
    "print(f'N-Iterations: {n_iterations}')\n",
    "display(HTML(df_rs[df_display_columns].sort_values('rank_test_score').to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testNaPreprocessing(itersPerDataset):\n",
    "    ''' Tests the effects of how we choose to treat missing values in the dataset.  It averages the ROC AUC scores each\n",
    "        dataset was able to achieve with a constant set of RFC hyperparameters but without a constant random seed.  \n",
    "    '''\n",
    "    dfs = (df_0, df_d, df_m)\n",
    "    names = (\n",
    "        'Replaced w/ 0\\'s\\t',\n",
    "        'Removed instances',\n",
    "        'Replaced w/ mean'\n",
    "    )\n",
    "    avgAuc = []\n",
    "    for df in dfs:\n",
    "        summ = np.zeros(1)\n",
    "        for j in range(itersPerDataset):\n",
    "            rfc = RFC(\n",
    "                {\n",
    "                    'max_features': [1],\n",
    "                    'max_depth': [None],\n",
    "                    'n_estimators': [100],\n",
    "                    'max_samples': [float(2/3)],\n",
    "                    'oob_score': [True],\n",
    "                    'n_jobs': [-1]\n",
    "                },\n",
    "                verbose = False\n",
    "            )\n",
    "            xTr, xTe, yTr, yTe = split(\n",
    "                df[['tenure', 'Contract','PaymentMethod',  'MonthlyCharges', 'TotalCharges']],\n",
    "                df[\"Churn\"],\n",
    "                test_size=0.1)\n",
    "            rfc.train(xTr, yTr)\n",
    "            summ += rfc.computeRocAuc(xTe, yTe)\n",
    "        avgAuc += [summ / itersPerDataset]\n",
    "    for i in range(len(dfs)):\n",
    "        print('{}\\t{}'.format(names[i], avgAuc[i]))\n",
    "\n",
    "testNaPreprocessing(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After training 100 forests with each preprocessed dataset, no statistically significant difference was found.  This suggests that, for this churn dataset, it doesn't matter what we do with our missing values.\n",
    "\n",
    "This is likely because the number of instances with missing values is a very small fraction of the total number of instances in the dataset.  What we do with this small fraction barely affects the dataset overall.\n",
    "\n",
    "The slight differences in scores exhibited above can be attributed to random luck.  There is an aspect of randomness in what instances end up in the training sample during the train_test_split and in how each Random Forest Classifier is trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testMaxDepth(iters, maxDepth=[1,2,None], valueNames=['1:\\t','2:\\t','Unlimited:']):\n",
    "    ''' This function tests the effects of maximum depth for each tree in a forest by averaging the scores of _iters_\n",
    "        forests trained with each max_depth setting\n",
    "    '''\n",
    "    xTr, xTe, yTr, yTe = split(\n",
    "        df_m[['tenure', 'Contract','PaymentMethod',  'MonthlyCharges', 'TotalCharges']],\n",
    "        df_m[\"Churn\"],\n",
    "        test_size=0.1)\n",
    "    avgAuc = np.zeros(len(maxDepth))\n",
    "    for i in range(iters):\n",
    "        rfc = RFC(\n",
    "            {\n",
    "                'max_features': [1],\n",
    "                'max_depth': maxDepth,\n",
    "                'n_estimators': [100],\n",
    "                'max_samples': [float(2/3)],\n",
    "                'oob_score': [True],\n",
    "                'n_jobs': [-1]\n",
    "            },\n",
    "            verbose = False\n",
    "        )\n",
    "        rfc.train(xTr, yTr)\n",
    "        avgAuc += rfc.computeRocAuc(xTe, yTe)\n",
    "    avgAuc = avgAuc / iters\n",
    "    for i in range(len(maxDepth)):\n",
    "        print('{}\\t{:.0%}'.format(valueNames[i], avgAuc[i]))\n",
    "        \n",
    "testMaxDepth(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the average results of 100 iterations, we can see examples of underfitting, overfitting, and fitting well enough.\n",
    "\n",
    "When trees are limited to only 1 level, it predictably leads to lower scores due to underfitting the training data.  Whether an individual tree predicts correctly is very much left up to chance, depending on whether the algorithm happens to select the most informative feature for the instances bagged for that tree or not.\n",
    "\n",
    "When the trees are not limited at all, we see that the algorithm is free to overfit the training data.  Allowing further node splitting until all leaves are pure leads to lower scores on average, as seen by \"Unlimited:...\" above.\n",
    "\n",
    "From prior testing not shown, we have observed that any small values between the two extremes above tends to perform better on average.  Higher values achieve roughly similar scores, and even higher values trend towards overfitting.  As such, we conclude that this churn dataset only needs a maximum depth of 2 to achieve optimum performance.  This is because we are able to train our forests faster with this smaller max depth and still achieve relatively high scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testMaxFeatures(iters, maxFeatures=[1, 3, None], valueNames=['1:', '3:', 'M:']):\n",
    "    ''' Tests the effects of max features for each tree in a forest by averaging the scores of _iters_ forests trained with\n",
    "        each max_features setting\n",
    "    '''\n",
    "    xTr, xTe, yTr, yTe = split(\n",
    "        df_m[['tenure', 'Contract','PaymentMethod',  'MonthlyCharges', 'TotalCharges']],\n",
    "        df_m[\"Churn\"],\n",
    "        test_size=0.1)\n",
    "    avgAuc = np.zeros(len(maxFeatures))\n",
    "    for i in range(iters):\n",
    "        rfc = RFC(\n",
    "            {\n",
    "                'max_features': maxFeatures,\n",
    "                'max_depth': [2],\n",
    "                'n_estimators': [100],\n",
    "                'max_samples': [float(2/3)],\n",
    "                'oob_score': [True],\n",
    "                'n_jobs': [-1]\n",
    "            },\n",
    "            verbose = False\n",
    "        )\n",
    "        rfc.train(xTr, yTr)\n",
    "        avgAuc += rfc.computeRocAuc(xTe, yTe)\n",
    "    avgAuc = avgAuc / iters\n",
    "    for i in range(len(maxFeatures)):\n",
    "        print('{}\\t{:.1%}'.format(valueNames[i], avgAuc[i]))\n",
    "        \n",
    "testMaxFeatures(100, [1,2,3,4,5], ['1:','2:','3:','4:','5:'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "max_features controls how many features are chosen at random to decide each node split on.  Higher values of this parameter will result in less inter-tree correlation, meaning each tree's path to a classification will be less likely to use the same feature selections as another tree in the forest.\n",
    "\n",
    "After testing for all possible values of this hyper parameter for our post-feature selection dataset, it appears that max_features=2 performs better on average but not by a significantly large margin, so the differences between different values for this hyper parameter are not significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testMaxSamples(iters, maxSamples=[0.5,float(2/3),None], valueNames=['0.50:\\t','0.66:\\t','Len(X):\\t']):\n",
    "    ''' Tests the effects of max sample size for each tree in a forest by averaging the scores of _iters_ forests trained\n",
    "        with each max_samples setting\n",
    "    '''\n",
    "    xTr, xTe, yTr, yTe = split(\n",
    "        df_m[['tenure', 'Contract','PaymentMethod',  'MonthlyCharges', 'TotalCharges']],\n",
    "        df_m[\"Churn\"],\n",
    "        test_size=0.1)\n",
    "    avgAuc = np.zeros(len(maxSamples))\n",
    "    for i in range(iters):\n",
    "        rfc = RFC(\n",
    "            {\n",
    "                'max_features': [2],\n",
    "                'max_depth': [2],\n",
    "                'n_estimators': [100],\n",
    "                'max_samples': maxSamples,\n",
    "                'oob_score': [True],\n",
    "                'n_jobs': [-1]\n",
    "            },\n",
    "            verbose = False\n",
    "        )\n",
    "        rfc.train(xTr, yTr)\n",
    "        avgAuc += rfc.computeRocAuc(xTe, yTe)\n",
    "    avgAuc = avgAuc / iters\n",
    "    for i in range(len(maxSamples)):\n",
    "        print('{}\\t{:.1%}'.format(valueNames[i], avgAuc[i]))\n",
    "        \n",
    "testMaxSamples(100, [0.1, 0.2, 0.3, 0.4, 0.5], ['0.1:','0.2:','0.3:','0.4:','0.5:'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "max_samples controls how many instances are added to a bootstrap sample to train a tree on in the forest.  In Breiman's paper, he suggested that 2/3s the length of the input dataset tends to perform the best; however, interestingly, our initial tests showed no significant difference in performance between this size, 1/2 the length of the input, and the length of the input.  \n",
    "\n",
    "Seeing this, we tested how low we could take this hyper parameter while still maintaining the same level of average performance.  A smaller sample size would result in quicker training times and would be advantageous.  Notably, even taking max sample size down to just 1/10 the length of the input still resulted in the same performance on average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style='color:orange'>Hyperparameter Confusion Matrix and Classifiation Report</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "We found the model to have an accuracy of 79% and a very high precision for class 0, which would be when the customer did not churn. We can see that we don't have the same support for each class, and the class 0 having much more support was correlated with much higher overall scores which is expected."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.83      0.87       574\n",
      "           1       0.48      0.68      0.56       131\n",
      "\n",
      "    accuracy                           0.80       705\n",
      "   macro avg       0.70      0.76      0.72       705\n",
      "weighted avg       0.84      0.80      0.81       705\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Metrics\n",
    "\n",
    "## based on optimal hyper-parameters\n",
    "best_params = {'n_estimators': 118, 'min_samples_split': 11, 'min_samples_leaf': 6, 'max_samples': float(2/3), 'max_features': 'auto', 'max_depth': 23}\n",
    "default_params = { 'oob_score': True, 'n_jobs': -1 }\n",
    "best_params.update(default_params)\n",
    "\n",
    "rfc = RandomForestClassifier(**best_params)\n",
    "rfc.fit(X_train,y_train)\n",
    "\n",
    "# Get predictions\n",
    "y_pred = rfc.predict(X_test)\n",
    "\n",
    "# Confusion Matrix\n",
    "cf = skm.confusion_matrix(y_pred, y_test, normalize='all')\n",
    "df_cm = pd.DataFrame(cf, index = ['Churn', 'Not Churn'],\n",
    "                     columns = ['Churn', 'Not Churn'])\n",
    "\n",
    "ax = sns.heatmap(df_cm, fmt='.2%', annot=True, cmap='Blues')\n",
    "ax.set_xlabel('\\nPredicted Values')\n",
    "ax.set_ylabel('Actual Values ')\n",
    "ax.set_title('Customer Churn Confusion Matrix with labels\\n\\n')\n",
    "\n",
    "# Classification Report\n",
    "print(skm.classification_report(y_pred, y_test))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style='color:orange'>Comparision with other ML algorithms</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have selected a black box algorithm provided by a commercial ML/AI package. Salesforce Einstein Discovery is a cloud-based analytics tool that makes it easier for business user to create ML models without writing any code. If we dig a little deeper, the technique used is Generalized Linear Model (GLM). It doesn't specify what specific implemenation but it provides AUC and other metrics to show the performance of the model.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you create a model, it shows the features selected based on the importance. Similar to Mean decrease in impurity and feature permutation, the cloud platform selects the most important and relevant features automatically. "
   ]
  },
  {
   "attachments": {
    "feature_selection.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAOXCAYAAABG1aURAAAMbWlDQ1BJQ0MgUHJvZmlsZQAASImVVwdYU8kWnluSkJDQAghICb0jUgNICaEFkF4EGyEJJJQYE4KKvSwquHYRxYquiii2lWYBsSuLYu+LBRVlXdTFhsqbkICu+8r3zvfNvX/OnPlPuTO59wCg+YErkeShWgDkiwukCeHBjDFp6QzSU4ADU6AFXIEVlyeTsOLiogGUwfvf5d0NgCjuV50VXP+c/6+iwxfIeAAg4yDO5Mt4+RA3A4Bv4EmkBQAQFXrLKQUSBZ4Dsa4UBgjxagXOVuJdCpypxEcHbJIS2BBfBkCNyuVKswHQuAf1jEJeNuTR+Ayxq5gvEgOg6QRxAE/I5UOsiN0pP3+SApdDbAftJRDDeAAz8zvO7L/xZw7xc7nZQ1iZ14CohYhkkjzutP+zNP9b8vPkgz5s4KAKpREJivxhDW/lTopSYCrE3eLMmFhFrSH+IOIr6w4AShHKI5KV9qgxT8aG9QP6ELvyuSFREBtDHCbOi4lW6TOzRGEciOFuQaeKCjhJEBtAvEggC01U2WyRTkpQ+UJrs6Rslkp/jisd8Kvw9UCem8xS8b8RCjgqfkyjSJiUCjEFYqtCUUoMxBoQu8hyE6NUNqOKhOyYQRupPEERvxXECQJxeLCSHyvMkoYlqOxL8mWD+WJbhCJOjAofLBAmRSjrg53icQfih7lglwViVvIgj0A2JnowF74gJFSZO/ZcIE5OVPF8kBQEJyjX4hRJXpzKHrcQ5IUr9BYQe8gKE1Vr8ZQCuDmV/HiWpCAuSRknXpTDjYxTxoMvB9GADUIAA8jhyASTQA4QtXXXdcNfypkwwAVSkA0EwFmlGVyROjAjhtdEUAT+gEgAZEPrggdmBaAQ6r8MaZVXZ5A1MFs4sCIXPIU4H0SBPPhbPrBKPOQtBTyBGtE/vHPh4MF48+BQzP97/aD2m4YFNdEqjXzQI0Nz0JIYSgwhRhDDiPa4ER6A++HR8BoEhxvOxH0G8/hmT3hKaCc8IlwndBBuTxTNk/4Q5WjQAfnDVLXI/L4WuA3k9MSDcX/IDplxfdwIOOMe0A8LD4SePaGWrYpbURXGD9x/y+C7p6GyI7uSUfIwchDZ7seVGg4ankMsilp/Xx9lrJlD9WYPzfzon/1d9fnwHvWjJbYIO4SdxU5g57GjWB1gYE1YPdaKHVPgod31ZGB3DXpLGIgnF/KI/uFv8MkqKilzrXbtcv2snCsQTC1QHDz2JMk0qShbWMBgwbeDgMER81ycGG6ubm4AKN41yr+vt/ED7xBEv/Wbbv7vAPg39ff3H/mmi2wC4IA3PP4N33R2TAC01QE418CTSwuVOlxxIcB/CU140gzhu8wS2MF83IAX8ANBIBREgliQBNLABBi9EO5zKZgCZoC5oBiUguVgDVgPNoNtYBfYCw6COnAUnABnwEVwGVwHd+Hu6QQvQQ94B/oQBCEhNISOGCJmiDXiiLghTCQACUWikQQkDclAshExIkdmIPORUmQlsh7ZilQhB5AG5ARyHmlHbiMPkS7kDfIJxVAqqouaoDboCJSJstAoNAkdj2ajk9EidAG6FC1HK9E9aC16Ar2IXkc70JdoLwYwdUwfM8ecMSbGxmKxdCwLk2KzsBKsDKvEarBG+JyvYh1YN/YRJ+J0nIE7wx0cgSfjPHwyPgtfgq/Hd+G1+Cn8Kv4Q78G/EmgEY4IjwZfAIYwhZBOmEIoJZYQdhMOE0/AsdRLeEYlEfaIt0RuexTRiDnE6cQlxI3EfsZnYTnxM7CWRSIYkR5I/KZbEJRWQiknrSHtITaQrpE7SBzV1NTM1N7UwtXQ1sdo8tTK13WrH1a6oPVPrI2uRrcm+5FgynzyNvIy8ndxIvkTuJPdRtCm2FH9KEiWHMpdSTqmhnKbco7xVV1e3UPdRj1cXqc9RL1ffr35O/aH6R6oO1YHKpo6jyqlLqTupzdTb1Lc0Gs2GFkRLpxXQltKqaCdpD2gfNOgaLhocDb7GbI0KjVqNKxqvNMma1poszQmaRZplmoc0L2l2a5G1bLTYWlytWVoVWg1aN7V6tenaI7VjtfO1l2jv1j6v/VyHpGOjE6rD11mgs03npM5jOka3pLPpPPp8+nb6aXqnLlHXVpejm6NbqrtXt023R09Hz0MvRW+qXoXeMb0OfUzfRp+jn6e/TP+g/g39T8NMhrGGCYYtHlYz7Mqw9wbDDYIMBAYlBvsMrht8MmQYhhrmGq4wrDO8b4QbORjFG00x2mR02qh7uO5wv+G84SXDDw6/Y4waOxgnGE833mbcatxrYmoSbiIxWWdy0qTbVN80yDTHdLXpcdMuM7pZgJnIbLVZk9kLhh6DxchjlDNOMXrMjc0jzOXmW83bzPssbC2SLeZZ7LO4b0mxZFpmWa62bLHssTKzGm01w6ra6o412ZppLbRea33W+r2NrU2qzUKbOpvntga2HNsi22rbe3Y0u0C7yXaVdtfsifZM+1z7jfaXHVAHTwehQ4XDJUfU0ctR5LjRsd2J4OTjJHaqdLrpTHVmORc6Vzs/dNF3iXaZ51Ln8mqE1Yj0EStGnB3x1dXTNc91u+vdkTojI0fOG9k48o2bgxvPrcLtmjvNPcx9tnu9+2sPRw+BxyaPW550z9GeCz1bPL94eXtJvWq8urytvDO8N3jfZOoy45hLmOd8CD7BPrN9jvp89PXyLfA96Punn7Nfrt9uv+ejbEcJRm0f9djfwp/rv9W/I4ARkBGwJaAj0DyQG1gZ+CjIMogftCPoGcuelcPaw3oV7BosDT4c/J7ty57Jbg7BQsJDSkLaQnVCk0PXhz4IswjLDqsO6wn3DJ8e3hxBiIiKWBFxk2PC4XGqOD2R3pEzI09FUaMSo9ZHPYp2iJZGN45GR0eOXjX6Xox1jDimLhbEcmJXxd6Ps42bHHcknhgfF18R/zRhZMKMhLOJ9MSJibsT3yUFJy1LuptslyxPbknRTBmXUpXyPjUkdWVqx5gRY2aOuZhmlCZKq08npaek70jvHRs6ds3YznGe44rH3RhvO37q+PMTjCbkTTg2UXMid+KhDEJGasbujM/cWG4ltzeTk7khs4fH5q3lveQH8VfzuwT+gpWCZ1n+WSuznmf7Z6/K7hIGCsuE3SK2aL3odU5Ezuac97mxuTtz+/NS8/blq+Vn5DeIdcS54lOTTCdNndQucZQUSzom+05eM7lHGiXdIUNk42X1Bbrwo75Vbif/Sf6wMKCwovDDlJQph6ZqTxVPbZ3mMG3xtGdFYUW/TMen86a3zDCfMXfGw5msmVtnIbMyZ7XMtpy9YHbnnPA5u+ZS5ubO/W2e67yV8/6anzq/cYHJgjkLHv8U/lN1sUaxtPjmQr+Fmxfhi0SL2ha7L163+GsJv+RCqWtpWennJbwlF34e+XP5z/1Ls5a2LfNatmk5cbl4+Y0VgSt2rdReWbTy8arRq2pXM1aXrP5rzcQ158s8yjavpayVr+0ojy6vX2e1bvm6z+uF669XBFfs22C8YfGG9xv5G69sCtpUs9lkc+nmT1tEW25tDd9aW2lTWbaNuK1w29PtKdvP/sL8pWqH0Y7SHV92ind27ErYdarKu6pqt/HuZdVotby6a8+4PZf3huytr3Gu2bpPf1/pfrBfvv/FgYwDNw5GHWw5xDxU86v1rxsO0w+X1CK102p76oR1HfVp9e0NkQ0tjX6Nh4+4HNl51PxoxTG9Y8uOU44vON7fVNTU2yxp7j6RfeJxy8SWuyfHnLx2Kv5U2+mo0+fOhJ05eZZ1tumc/7mj533PN1xgXqi76HWxttWz9fBvnr8dbvNqq73kfan+ss/lxvZR7cevBF45cTXk6plrnGsXr8dcb7+RfOPWzXE3O27xbz2/nXf79Z3CO31359wj3Cu5r3W/7IHxg8rf7X/f1+HVcexhyMPWR4mP7j7mPX75RPbkc+eCp7SnZc/MnlU9d3t+tCus6/KLsS86X0pe9nUX/6H9x4ZXdq9+/TPoz9aeMT2dr6Wv+98seWv4dudfHn+19Mb1PniX/67vfckHww+7PjI/nv2U+ulZ35TPpM/lX+y/NH6N+nqvP7+/X8KVcgc+BTA40KwsAN7sBICWBgAd9m2UscpecEAQZf86gMB/wsp+cUC8AKiB3+/x3fDr5iYA+7fD9gvya8JeNY4GQJIPQN3dh4ZKZFnubkouKuxTCA/6+9/Cno20CoAvy/v7+yr7+79sg8HC3rFZrOxBFUKEPcOWuC+Z+Zng34iyP/0uxx/vQBGBB/jx/i9ZvpEW5PKA6AAAAJZlWElmTU0AKgAAAAgABQESAAMAAAABAAEAAAEaAAUAAAABAAAASgEbAAUAAAABAAAAUgEoAAMAAAABAAIAAIdpAAQAAAABAAAAWgAAAAAAAACQAAAAAQAAAJAAAAABAAOShgAHAAAAEgAAAISgAgAEAAAAAQAAAvKgAwAEAAAAAQAAA5cAAAAAQVNDSUkAAABTY3JlZW5zaG903BLwyQAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAAthpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IlhNUCBDb3JlIDYuMC4wIj4KICAgPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4KICAgICAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgICAgICAgICAgeG1sbnM6ZXhpZj0iaHR0cDovL25zLmFkb2JlLmNvbS9leGlmLzEuMC8iCiAgICAgICAgICAgIHhtbG5zOnRpZmY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vdGlmZi8xLjAvIj4KICAgICAgICAgPGV4aWY6UGl4ZWxYRGltZW5zaW9uPjc1NDwvZXhpZjpQaXhlbFhEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlVzZXJDb21tZW50PlNjcmVlbnNob3Q8L2V4aWY6VXNlckNvbW1lbnQ+CiAgICAgICAgIDxleGlmOlBpeGVsWURpbWVuc2lvbj4xMTMyPC9leGlmOlBpeGVsWURpbWVuc2lvbj4KICAgICAgICAgPHRpZmY6UmVzb2x1dGlvblVuaXQ+MjwvdGlmZjpSZXNvbHV0aW9uVW5pdD4KICAgICAgICAgPHRpZmY6WVJlc29sdXRpb24+MTQ0PC90aWZmOllSZXNvbHV0aW9uPgogICAgICAgICA8dGlmZjpYUmVzb2x1dGlvbj4xNDQ8L3RpZmY6WFJlc29sdXRpb24+CiAgICAgICAgIDx0aWZmOk9yaWVudGF0aW9uPjE8L3RpZmY6T3JpZW50YXRpb24+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgqV0qn6AABAAElEQVR4AeydBZgkt9FAZcaYmZlix8wYM8Uxs2N2Yo5jZowZYsaYmZmZIWZmdhJDYmb47yl/rXv7pndndmduZ32vvm+uSVKrX99KpVKpeoifBkhSJCABCUhAAhKQgAQkIIGWEnjvvfdy+d99+20aeuih0zDDDJO3Qw01VBpyyCHTEEMM0dD9h2wotYklIAEJSEACEpCABCQggbYgoCLfFq/BSkhAAhKQgAQkIAEJSKAxAiryjfEytQQkIAEJSEACEpCABNqCgIp8W7wGKyEBCUhAAhKQgAQkIIHGCKjIN8bL1BKQgAQkIAEJSEACEmgLAirybfEarIQEJCABCUhAAhKQgAQaI6Ai3xgvU0tAAhKQgAQkIAEJSKAtCKjIt8VrsBISkIAEJCABCUhAAhJojICKfGO8TC0BCUhAAhKQgAQkIIG2IKAi3xavwUpIQAISkIAEJCABCUigMQIq8o3xMrUEJCABCUhAAhKQgATagoCKfFu8BishAQlIQAISkIAEJCCBxgioyDfGy9QSkIAEJCABCUhAAhJoCwIq8m3xGqyEBCQgAQlIQAISkIAEGiOgIt8YL1NLQAISkIAEJCABCUigLQioyLfFa7ASEpCABCQgAQlIQAISaIyAinxjvEwtAQlIQAISkIAEJCCBtiAwdFvUog8q8cEHH6SzzjorjTDCCGm99dZLo4wySh/UwltKQAISkIAEJCCB1hB49dVX0y233NJROMdTTjllmmKKKfK5JZdcsuOaO/2TwBA/DZD+WfXe1XqTTTZJ1113XS5ko402Sn/96197V+CA3HvvvXe64447cjmzzjprOvroo+su8/XXX0/rr79+R/pjjjkmzTLLLB3H7khAAhKQgAQkIIHuCITyzrYeWWKJJZIKfT2kmpPmvffeywV99+23aeihh07DDDNM3g411FBpyCGHTEMMMURDN2q6Rf6rr75KCy64YPrmm29yRX71q1+le++9N1euVs123XXXdO2113Zc+tvf/pYWW2yxjuNW7bz00ksdRRf3O072YOfdd99NL7/8cs451lhjNVQCvCIvGb/88suG8jea+LnnnktrrLFGzjbiiCOmyy+/PE044YQ1i3nkkUcSg52QG264IU000URx6FYCEpCABOogUGx3SX744YenpZZaqo6cJpFAfQRuvvnmThb4enJhseenQl8PrfZL03QfeVxVZpxxxvThhx/mH5bmxx57rOaTMxmAVTzSfvHFF2m++earmbbZJ3feeec09thjZ+V1m222aXbxbV/e999/38H9rbfeSltssUX64Ycfatb7u+++60jLu/rxxx9rpvOkBCQgAQlUEyi2u7SlX3/9dXXiwfDKLrvsko1EGIrmn3/+wZBA7x65J0p88Y4o85Sh9C8CTVfkefwVVlihE4Xbb7+903EcPP300wlf9ZDFF188+6zHcSu3yy23XB5gPPzww3kGoZX36g9lw4HZEEUCEpCABCTQFwQwEmFQ4qfBqLE30FslPu6mMh8k+s+2JYo80zPDDjtsB4XbbrutY7+4Uz7/u9/9rni50/4///nP9PHHH3c618jBp59+mr4d4I9UFHyT8EfqTrCaYLWuslh3l5/rzDY0czkCz/Paa68lts2So446KqHQ91Z6y4sG/J133qnkzf+Djz76qO5qYgXD7Ykf+4oEJCCB/kQA10v6wCrhGm6tPRHyNTozQH/GbDuzCj3t12r1yT2pP3l6qx98/vnn6V//+ldDt6cvoZ9qZMABK4yXsGuUeVeVa5YSH/dQmQ8S/WPbvRbbg+fAL/63v/1tR86y5T0uFC31uORgkS8KvvPLL798mmaaadJss82Wpp9++sQiUqLMvPLKK8WkeR9f/Jlnnrnjh+J27rnn5kUc0003XVpooYU68my44YYd6XCzKct//vOf9Je//CXNM888eYX33HPPnbcMUliIWk/jheJ/2mmnpTnnnDNNNdVU+X648ZQHMOV7Vx1zz1NOOSXXadppp81Tj2yp48knn1xXnarK5jz1xcWmJ4ODnvBaZ511Ot7BHnvskdcFbLXVVmmmmWbKzFhVv9dee3U0eDfddFN2veL/Ae5bCyywQPr73/9e+Uisfdhss83y6vw55pgj8aNMFjo3a11E5c29IAEJSKABAih2xf7ryiuvzOum1lxzzUT/RR84wwwz5D6NYlEkjzvuuI5r9JPLLrtsuv/++we6K7OtUfbSSy+d+4rzzz8/t6dEMJl88snz/nnnnTdQ3uKJM888My288MK5P8MNlraaPminnXZK77//fjFp3q+3Tz7ooINy/S699NKOMt5+++2OOtPnlqUZ+gEKKx4EoVvAGVZVRjsGLoccckhaddVV83PTt/P8K664YoJNldCn0pdxn9/85jeZNcy5N3XordRbBu+aXz1CmfUulq2nPNO0jkDTF7tGVfkPiuKFoIASzWX11VePy+m///1vJ9/5slvNlltumRdgdmT4/x1GzfywHKPU0qiEYLUoNib33XdfQkmPEXNsSY9FN9J+8sknUUTeUvbGG2+crQ3FC5T/zDPP5N+TTz6ZTjjhhDTccMMVk3Ta33///bOCHScZiV9yySXpsssuSzSYiyyySFyqa7vvvvt2Ki8yvfnmm2mfffZJrIQmTW+Ewc/222+fTj311LqL6SkvlP/iO2DgdNVVV3XclxkU6kGjyiBsgw026LjGDo0MAwCE91UULB40tkXXLa7j78+6jIceeihdffXVuQMr5nNfAhKQQF8QoJ+M9pD7s3/YYYflmdeoD/3mjjvumEYfffSOPjCuodg//vjj2dB18cUXp9lnnz0upc8++6yjbGaiTzzxxET/VBTazB122CFHz4hACMXrBx54YDr22GOLp/I+ZZ9zzjm5PldccUWuWySqt09G0S0+O/lp9+McFvOiNEM/YO0ehrWiZRxdAEUdzuW+lDrChQXLRaFu9Cf80A8OPvjgzDDSUD7Gx1qz3QSSwKiILsMgrCdSr097cSHrSSedVJeSjjJfr+Lfk7qbpzkEWmKRp2pl95qi9Z3rKPZFxbroVnPXXXd1UuKx7qPkMTgYeeSRyZ6txgcccEDer/qHRol70HBNMskkdfvfUy4jb2S88cZLf/jDHxKWYqwhIddff326884743CgLQ0qf5y47mA9HmmkkTrSUKfNN988YXGoV7C8YHVHCE3EHz3hLldZZZUcuojz3A/3mJ5IcbYCSwczGfVKM3ihVHNfrOZYjorhl4j3v/XWWyei61BPLE9FKfv2//vf/05YsUKJ593T8PN/COsJwvtdbbXVupyuLt7DfQlIQAKDkgAhkTFa0eZhvS0KbRlGDgI20D/ST4UQ8Yy+oEooE6UcwQLNoKAoGL+eeuqp4qkcSrmoxLMYFeWUmepwT33xxRdzu4tiXyVVfTJ9JOvWaKtDaO85x496hjRLP6BPQDdgZhcvgqKcccYZnfoG3I+YQQ4lnmemryJk9GSTTdaRFQNd0TLP4IpZ4VDiuQ/lYICKPpcBy5/+9Kd0zz33dJTTyE691nhccUMihnwcV221yFeRaa/zLVPky+41KL3F6aqiYl92q8Htgek/fqxiZwoQKwSKbFhgwfjss88mrLpVgjKIAowbDqPlrhTvKAPlb8wxx+y4P2EZGaHvvvvueYah2GDefffdkW2gLaNwptFoEPlDe/7559Omm27akQ4/73qVZRoRRvkhNLS4lPDHz/NRvxBi19N4NCp77rlndmOKfEwDRjjMshtR8bhZvPi/gcJ+zTXXpBtvvLGTtYhrww8/fB78XXTRRYmGnMFVCEo5sxIhsGFNAzLBBBPkMvl/w/8hBguhzDP7cOGFF0Y2txKQgATahsBoo42W2zraPGaXcTMNwTo811xzpX/84x+5f3zggQeym0tc53yVYEhiAPDggw/mNhXX1/32268jOVb0MBpxkv6HMJkhuJOQ99BDD024/8SggOv0d8VZ1cgT26o+GeUWN9TiDPs444yTz3F+5ZVXjiKyW2Qz9AOU2UcffTTPkjPDTh1CmLnFuh5Cn1Nkil5CX0W/DHuMQiEwCcG1KBRtYoWj5MOSQQTvNfJxv1ruQ1FO1bZeazz5UcpJzzbqVFVu8bzKfJFGe+63TJHncbGghzBlxR8NQkNSVKrLbjUovIyI+W277bZRRN4WY8yjUKIgVwmKGxbrcH8pWnmr8tDAxb3ZFi0hjN6LDU2MzqvKwmLCoADhjxjluGhN7qruxTJpHGMQxLMwO1CUtdZaKzfMnKMRZoDTEzniiCM6YsnTeDNQKC8QLpfbLF74WhbfLW5YxffFjE3RWsN7Lcobb7zRcchsSAgKPx1CCDM6xZj4VaFRI71bCUhAAn1BANfA+F4HbWHRNZX60E9GUAkMHUXXDNwsUQ6rhFnMSSedNF/mIzSUteiii3YkL/Zt9D9F4xD+7OQJwSqNRT2k2P7Gudj2pE+OvLFtln6w3XbbdXzRHWMi/WhRwhjEuSeeeKLjEu8ERbwoKPbrrrtu/jFzH+46RRbzzjtv9o0v5iuGvuYe6EatElxksMo3osRTl0bTt6r+lltNoGU+8twy3GtCGcQKjxWB0W8x6kjRraZYVdLwh0B6VqUzZVdWfovW4WJe9pky66nQcL3wwgv5D5gpQwYiWEHC759yu7r3uOOO2/EJ5KgDAwGePxZalp8l0pW3xcYARb04QIq04UbCMQMm3FMalVFGGSX7/WP9YOBAY44fJdaP7qS3vMp+eMzoMPiJ/zsTTzxxpyowgKglNIT8fwnBYlK2WhSV9+J+5HErAQlIoK8JlNvEMcYYo1OV6m0TO2UacIBbCC4xZUHRjJlyZrEZCNAGF9tIjotuLlEGinxYsIvp43pse9MnRxmx7a1+UHSVpUy+pM7zxQCIvjak2Kfw/OFOFNeZ+WU9Q1mKfTcz+LgJFSUMdJzD1x7doBbfYp7iftFdpni+vF/0j+davT7y5XI8bk8CLVXkw70mlF+itTByLUZtKbvVgAmlkIUmTKn1Rsq+f/WWxciYUT+hpXoqRUt+sYyiP12EQ0TB70rKCn9XDSXlxEChqzKrrjHQ+POf/5ywziO8g+Kgq1a+ZvAqWt/jHl0NlCJNbCMtTMs+ml3xwjUL15xGv8Qb93UrAQlIoBUEym1itHFV9ypfLx9HPoxMWPDLUuyb6INZw4X7CQatEBTWojU+zhcHFbhkoqDWStfTPjnuw7ZZ+kHMZkTZ1JdfKPJxnm24mbJf9QV0rpWlJ313I4p8T91eeK89zVt+Ro/7nkDXGmQT6leMXsOInYWIMeqn+LJbDedY2Bh+ZoyQsR7gl0cjwKgV37xWCVZo6hx/zDRcLCZiGhKfRRaY4nbSnVQNAornaVC7U+K5T3lhSnHRUdQDy3W4EFUNIiJtd1umHFl4Ewt0iERQJc3iVVV+o+fHH3/83EnF1CY+iLGoKMoKS1N0lLGAOq67lYAEJPBLJUAkGPqLsiJb7JuwONP3IcX+h/6bAUK0ncGoGOOevrKWEh9pe7vtC/2A2RGMREjxWbt7FtgFVwYAGDLLUuy7Ca/diFCvehRy3GNIW57lqede9ZRfTzmmaR2BlivyZfcawmIVp6nKbjVMZ7HYMYSFNPieheD/3UpFnnuHEo8vHAtZiso2g5Bi/aJe5S1/vPzBo1iG0AAWrcPlqb1IV94W/7hROvEPLzek5Ty9OaYRPv744/Mgqxyas1xus3iVy+3pMe8Kf3vCeiEsOMbXVJGABCQggf+FdcTlo+xeU1zMiXU+rPbErw/BQMJ6pLKxqGi1L/ZXka8n26LbSeTvK/0AV9UIboGrbVlwuyUwRsyCrL322tmwBovI19d9EQp5TxT5nuQp8/G4tQRautiVqod7TTwGEVZiQUcttxqm88KaSp5ilBiOGQi0UopuKbhbFJV4FhCxCr1e4eNKMSggD5Z0Fg6F1KvI47sXijszEmeffXYUkbdMNRJVhwU4/IjQ01thEFPL569cbjN5lcvu6XGx4yEcWHkwwkJrFgzDigXIigQkIIHBiQCzrsV2kVlXvq8RgtIZUg4HzKx0UYjeUhwE9EaRZwY+BGMYoTKL0lf6QXHNGRHSzhwQfaYoRKLZdddd02677Zaj0cXseLEvIvJQsf8nP1b+6Ivoj7qKwle8X2/3G1HOizMyvb2v+VtDoOUWeaqN1T385Iuj7FpuNfwHI9JL+GXzh4EVnsgj+NYXG5tWIMFHPMJn4fvNH9ciiyyS/QVRoFGk6xVCdKGsY/lgFB9TbOQnrnytj27UKptFqHw04vTTT8+XUdpxU2JxEmEsL7jggo6FRqQlqkAzhPeGZYHwn1XSTF5V92j0PPGN+fAWjSJWCBbvrrTSSjkCBAuBCXMZAyziMSsSkIAEBhcCGIWwqqOc0jdhoCp+KR23GtrQEPqUDTbYIEdz4xxuGizaJMoNhpxiVBMs+bWCMURZ3W2LfvpYt5dccskcKY6AC9yzr/QD3DMZ3ITPOwYgLO18pZVZe5T0kAgpyfGCCy6Yv8jLTDwuuYS4JDoOi4P5ABdr0OKbNXyhvbygOcqs2uLxUI/rS3mxKxw5V3x3VffwfPsTGCSKPH+M+ONFBJLAUnar4TyNDEprxK1lBF6MoY5bCV9GbZUss8wyOTZ7+MExXcYPYXaAz1LX+gR2uT4o2URW4UNHxcW9kY5471NNNVUcdrvlo0tYUHh2BkPEoC/HoceaQchLGt5mCVFrsPBXNRbN4tWs+lIOjRSDG1xqWPiKHz+/shBKFMuUIgEJSGBwIYC7J+6quKiG20fx2XfaaadOYZa5xsepaEsvvfTSnBTFtOgmykn6R2bMqyKK5Yzd/IPCTr3CYEY0Nu5JnVHk+0o/wLOAb46suOKKWQHHEHTDDTfkX/GRllpqqU7fOMHrgFlhjEkMAlDaa61xYz0CX4pvVOjr6pFa6ThXjyKP/qa0N4GWu9bw+GX3Gs7VcqvhPLL99tvnqDXF1eGjjjpq/hgU01etFBoMPhpEmKyYHuN+jKxppCL2bj11OPLII/NX3YqLimjscFkph6HqrjwaMGL/Mg1XXvmP+w8NIAOG8sLO7srt7jpf1yNUVXHKs5inmbyK5fZ2H0sJMwkMqMrCO2DtBQOhoutUOZ3HEpCABH6JBPg+yz777JPoVxHWRRFQAktzMbZ5PHv0Pyw0jW+jxDXyYt1F4S5Gr4nrjWzJz4eSKA/XVu6LhL8++32lH+AVQN2wqk899dQddaOOkw2YicAoxKw5fWZRCJIRg4CiLkAa0vLl11tvvbXTerpi/u72aynp5Two7IRhLv7qUeJ5D0r7ExhiwPTVT+1cTVbJY8lHqS/Hbm11vfE9Z/oLxY/BSE+Fcvi4BI0RZfX2OaI8og8Q+YbGrx0U0mbx6innqny4aeHWxEwGi7TKA6GqfJ6XgAQk8EsgwMxqWHyx/sbHGen+aRtR6OudyaWdZ6acvpl+kf6n3ryNsuReSFX/1pf6AW6tzFSj1Nf7/F988UXWBVgcywcO0QdisNIom0hPHTC2tULqWSfXivv+0svEnQ35boBuy/9tDKVsGRSjHzb6f6LtFflf+gv1+SQgAQlIQAKtJFClyLfynpY96Ahgaa/Hwt5IjbDG61bTCLH60zZbkR8krjX1P54pJSABCUhAAhKQgATqJYDC3Uw3GJX4esm3RzoV+fZ4D9ZCAhKQgAQkIAEJ9IhAs5R5lfge4e/TTIMkak2fPqE3l4AEJCABCQzGBFigGYEQikEcBmMkv8hHD1eYnrrZqMT3z/8W+sj3z/dmrSUgAQlIQAISkEBNAo34zRP5BiW+ngg4NW/myYYINNtHXkW+IfwmloAEJCABCUhAAv2DAAo98tprr+UoO0VlXeW9b96hinzfcPeuEpCABCQgAQlIQAIS6BWBZivyLnbt1eswswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVARX5XuEzswQkIAEJSEACEpCABPqGgIp833D3rhKQgAQkIAEJSEACEugVgaF7lXswyvz111+njz76KA055JBp/PHHb8mTf/LJJ+nzzz9PI444Yhp99NFbcg8LlYAEJCABCUhAAhL4ZRBoiSJ/7733ps8++6xhQnPMMUcae+yxG843KDLccccdaaONNkrjjTdeevzxx7u85XPPPZeuv/769NZbb6X//ve/aYYZZkizzTZbmnXWWdM444xTmffwww9Pp512Wlp55ZXT8ccfX5nOCxKQgAQk0L8JfPzxx+nhhx9OjzzySHr55ZdzPzH33HOnOeecMxtzunu6b7/9Nj377LPpiSeeSK+++mqafPLJ029+85v061//uq78VeVTp+uuuy73X8MOO2yaaaaZ0nzzzZf7sHKen376Kd14443l010eL7roomm44YbrlObtt99Ot9xyS+J+iy22WF3GsnfffTc99dRTuU+dffbZO5XngQQGJwItUeT33Xff9MwzzzTM8bzzzkv8kfdUvv/++w4lG+V5pJFG6mlRPcr33nvvpe222y7dfffdnfLfeuutHcebbbZZ2nPPPdPQQ7cEfcd93JGABCQggfYkcOedd2bD0FdffdVRwZtuuinvTzHFFOnCCy9ME088cce18s4//vGPnP+DDz4oX0qjjTZaOvXUU9MCCyww0LWuTtB/0j/dcMMNnZJdffXV+XjDDTdMe+21Vxp++OE7rv/www+5Hh0n6th57LHHOinq5557btpxxx3TEEMMkRgYIBi0lltuucrSqCuGtaeffjpdfvnllekG9YXZt+3c9w/q+3u//xF49OiFBisULdEmsTzXcg158MEH03fffZemnnrqbNkukx5jjDHKpxo6/vTTT9MKK6yQ82AlmHnmmRvK35vEzz//fFp77bXTv/71r1zM/PPPn/gxw/DCCy+kRx99NFtOTjnllGxFYdvb5+1Nfc0rAQlIQAKDngCK8VZbbZX7wt/+9rfZAj3hhBMmFNyLL744vfbaa2n55ZdPl112WZpqqqkGquCVV16Ztt1224RFHoPVwgsvnK3xr7/+emKAQF+01lprpb/97W9plVVWGSh/rROUtemmm6abb745G5mwilMulvO77rorW8vPOOOMxACC2eYwRKF8k647oW+OmeyhhhqqI/mLL76Y9thjjzxbffbZZ+dnWnPNNbNBjP57ookm6khb3Dn66KOzNZ6BxzzzzFO85L4EBjsCLVHkDz300Jog+cN8//330xZbbJH4Y/2lCIMTGkGUeBqpM888My2++OIDPd7555+fdt9993TfffelzTffPF100UUDpfGEBCQgAQn8Mglgwd5pp52yEv/HP/4x7bPPPh0PuvTSS+d+caWVVsr95FlnnZX233//juvs/Pjjj4kZbxTvTTbZJO29994dSjXXd9555/SnP/0pu7scd9xxdSvyKNMo8fRfuHWGQYwyMVA98MADeYsFHAv6BhtswKWcntmD7uSggw7Kivy8887byb0Ud5pvvvkmW9fHGmusXMx6662XLf8MStZdd92BiqYODFIY5Oy2224DXfeEBAY3AkatacIbp+HDRxHZZZddairxXKNBDN933G9eeeUVTisSkIAEJDAYEMCfnaAGI488cnaxLD8ybjXrrLNOPn377beXL6d77rknG4yYzS0r8STGgr7lllvmfMwE40fenWAtv+SSS3Ky7bffvpMSH3lRwA888MB8yFquRtbAESTi73//e86LC01RqCMyyyyzdJyOfaz1ZWEAs/XWW2cXnGOOOWYgX/tyeo8lMDgQaIlFvhng7r///oTPPH/o//nPf7KLCgt5sATMOOOMnW7BQiEaGf7IQ7BajDrqqPmQBm+yySaLS3lLg3jNNdfk8vFtn2aaafKint///vd5urJT4m4OsFAgSyyxREcjWpVlmWWWyfVnDQGW+wMOOKAqacJnEosFi494FhYDM+Xa1cIeplex5MCNwcUEE0yQn2uRRRapOcBgQS58EBpbOhp8Dpk1+PLLL9NDDz3UqX4sZKbeNLLMREw77bRp/fXXz2sbbrvttmytoTNiHUAtwb2K/NSPDoSpU9ZFUEYtd6wog2nnq666Kj355JO5c8Iaw7QyVqlWRRGKe7uVgAQk0AwCuL2wKJW2vOhiUiwb11QEFxvaYKKYheDSgrGI/ircW+JabKeccsrYzX0nbjtdCQtmicqGLLnkkpVJl1pqqezHjmJOH0GbXY9gvPriiy/yglkGBEX597//nQ+L6wFi/5///Gcxad4/5JBDct+Da1FwGiiRJyQwmBFoS0Ue//FQLuN94LbClNoFF1yQF8KgEIeg6JdXzjMVGMIC1BCUT6YR8ccrCpYLItOwyIaFQvUuuqVOYfXAkoLPYFfCde5NIzXCCCNUJuU5//KXv3S6jn8ifooMcPC/L8s555yTuRUXUL3zzjt5IICSzpRukQX5UaaDHcr5GmuskUNgcm2YYYZh0yEMEOhEivLmm2/mKVl8FSeddNJcVlUDC1cWSxUFDkRtwM2ISAm11g2Qb7/99ksscEKwOvFcTL3CAn9JOhlFAhKQQDsTwFWklrtIsc64nyLjjjtuJyWec7H2iv0qib5vlFFGyRFsqtLF+Q8//DDv0t5PP/30cXqgLW1zLEalza5HkedZ6LOQsjWecxh9MA4xwAlLPPtIcUDCMfc86aSTsgFnhx124JQiAQkMINB2rjUs5Am/Qazv+JVjJUYRxW8PH0H8y7FSh9AAYMFnEU7I6aefns9xfrrppovTeTERijTx4Hfddde8Qp9FOMcee2xaaKGFsgWEBqpWNICOQgo7LGINKd4nztXaYkEmHGVVo4kVHIWZhg8lG+s9ii4WaPwJqR9hLYuC9Z08KPGrr756fk5Cc6Hc43OIsHYBZblKSMfMBX6ZLMi69NJLO5KiNDMAQvDlRLnmvTDgIHoAxyzOqhL8L3mvDGTw4bziiivyO0QJh8Mbb7yRZ1uKsyqUBQuUf/IxuGOwwbMSWYHZE6apsc5guVIkIAEJ9HcCtI1ILWNNd89G24jRA8FNhn6uO4kZboxc5K8SvnES8tJLL8Vul1v6Vaz9RNCptSg1+sxYCEthsc9sbwjtO+08sxiUWTUbEendSmBwItBWFnks29tss00e9aPsFa3yyy67bMIKT2ODEveHP/whW+ixImChZboSy3wILiWcKwvKJw0kSmAouKRZddVVs+sJC3JRJrFqFBf8lMuJ41D4CXU5ySSTxOlebeFw2GGHdbLcMMjAhYepSaYpaeyKswZYNbhGRCDcjGJmgEW3/Jg54NlxKaoK64VFHcW/OJXLg7BAi/eBRZwoCwyqooPAaoTbDguVqhY5M5vCAmcGYSxOwscxhGlUno33i8WFRUzMHITEwAOLO3UIYfDGIIAyee+43jQabi3KcisBCUigHQhgsMGtkRCPGJq6EwwpGG/wWcftkH6Atpm+k1nSeoR+ku+j0E5jsKEPqSXFsMoYULoTZltj5ruWNZ78K664YmJR7lFHHZUXwWKoOvnkk/NCVgxGIbig4jYKEwxaigQk8DOBtlLkcR1BUUehDOvvz1VNWTlFecMFhoaEDy81GmISBblKiL+LnzvKI0plPYp8TEvisxjKc1X59Z7HHz4WPBXzYMnngyE0qDTaRUUeK31XU52rrbZaVuR5riohJFpZiSct1hd483w0uqHEF8vBZYcZkVrfD+C9MviAUVGJj/x0IuRHgS/75OOPiYRbTeRhywAONyxFAhKQQH8nQNtM1BkExbcq9GLxOTHaRB/EeZRy1idhXGlESE/0GRRm2ulySEmMQEVXT9rz7oS+GqPYggsumOaaa66ayVkXheLOLDvrnRCegRne6Ivo784csK6KtWGxkJd0rI1jppe6YKzCpbNZfTDlKxLoLwTaSpFHOUVQzqsWAv3qV7/KFgMszKRvVJGvejFYdrFqRAMV26r0cZ4v0SHFace41tMtz1TVIDHTgPAlvHoEn0aeBUsH0tVz8UXBWkIjjkw2wO2mlg975KGhraXIx3sNH8hIX9yGlYX81Dmen1kGFlbhYkQjzkdJcEuqNZgolue+BCQggf5CAGszhhjaaYw4zGDWI8xS4naCwsy6IWaScXVkgeuRRx6ZZzvrKQd3ShRjXEWpB4tembmmH6bMCE2Joo+RjX64K6EuuMUiVdb4yM/CXwYx+MbTnxZ941nDhZEHow1RakIvuPbaa3PIZ1xs+GHRD2NQlOtWAoMLgbZU5MNvruolcB1FHh/wnghKMA0B7hiUg9UXBTcW8jRSZsS+pSGmMQ3FvpEyymmjzPJ5jqP8WnXlGfCvjI9PMbXJAAPXmHqk+MW+YnqiGiBFn8Xi9djn0+C1JBR5PnYS/p/ldMzEIDTcbwzwl8cqg9CpMZBgUSsKPT9mLBh04GrFrAmh3BQJSEAC/ZEA7TRBBlj3xIwwkVnqlaKFmjwMBLDSM1OJQk4Iy2hLuyqTNhTFmxDJ9B9EdOMXgtsoFnb6TKSrCGNcR7GmTcdtsspARLqi1Fozxvooosrh98/CWARezA7gAkQkM9xaGbxQP+5Xyxe/eB/3JfBLI9BWijxKHMIfZlcSils9fnrlcnDLYRov8tIgYeVGOeR36623lrN0eRwWclw/cEGJhUNdZmrBRZRfrNURl5dpSVxWsG4QvQDfx54K5SARTaGqHHwsa0lxYW6Vsk8+BkN0OsWFq1jmiVuMQo//KCE5uQ/viR/Wob/+9a/5S4a17u05CUhAAu1K4OOPP85tF8YlFF7cTMLq3JM6Y7nGPx4LN+uoUG5Zd1SP0E9g4MIyj689ZfBlcmZLMZgQZS0+/sTaqCqhL8Jog3Rnja8qg/O09bT58803X4fbDeeZIWD2HFfQGFAQGIPwx/QJKvJQUgYnAm2lyONSgmtFrQ9BFF9KXCeufCPCQIFFsijdLG5FCSw3SPhps+CzXsH/D2UTCzmW63oUeRohfjTY5fvXe99yOgYnKPEwoeEuWzeIBkSanki4L7EmAet+VUcTlvfyPWDCey1/ybCcrqtj/B/5HXzwwYmQlyx4JjIRHSDRGRhssBBXkYAEJNAfCGCwwEBBf0Yby6xjVyGJ630m3A5xi0GRj9nUevOSDks3PwxDZYm+txwPvpjuiCOOyH0sfva4zfREMP4wCMBoR38WrpaUFXUoumrGfhiyenJP80igvxIYsp0qHgpjxJGtqltcj/S10tVyPcHKgBKPtRqfwFpKdKMNAQpkxE2nAQtLf606cY56ER4Sf3LSN0OI2hJuRqzqLyvx3KPR5yrWC840pIQRiygExevso8QTbaGWxHvCjakZwmJofEMJLco+TCMWfjPKtwwJSEACrSSA2wmKMq4qWLyxPHfnd059cH1BSceQ0ZXErCZrv7oT6kKQAX5drfXieqyXog61hK+Vh/tkb6zxhFImIhwhi+MDUXG/WNwbM8WcZ+0WbqdxLdK6lcDgQKAtFXkaiyrFjKlHRusoljEKjxdVjC1Lg1KW+CPHdaeW5YOwj+EDWM7b1fGf//znfBnrMNN9tQYRkZ9PYfN81L8YTjGu92SLIh8Ndi3/ehrqKp713I8pVyxHCD6LTL9GFBmeleeJmY5a5cV7YqqWXy1hoMHgCv/Q4Ee9scbgb1krvjHvO/wv610HUOvenpOABCQwqAjQVuPbTjQWIsTQJ+DWWY/QlvLFV6K6VAntJl8ER8KIUkyLO09RaEcJC0woyIMOOqh4qWOf9j7CYRI5rcpFEuMUbTGzo119gbyj4Bo7zB7zHZPFFluso98pJuOr3kgxsALuQKxRi2vF9O5L4JdOoK0UedxCUBhR5Ignf9ddd3UojMTKZaEjq+YRRuwomEXhOBpEPk4Un3+ONCyEQRjpn3DCCXkxTlyjUcAPsCcKIQuUfve73+Wi8NHDEhHx5aN8XGkICxZKP3HTY/FOpOnplsYrfPWJ5R5rDSiPwctaa63VMR3Z03vQwMOPBn3TTTfNVn9mFugoWHTKFGiETivfg86HxVx0MCj8MTMS6fi4FwuzeCdwi2lUvhHw4IMP5vj05ItQlJGPa/hRIsE/rrmVgAQk0I4EaCdjISlfPaWdC3fL8jYMJvEchBHGdQY/cfzCy20iC0FXWWWV7HKIvzzujCEMIJZffvmshOOiGEJ7G/0SX+9mkBDGFNIwE4sBh5lw0qL01xKMMSw+RXr65VX6bAYM9OOsjaolEQyjaHQjxDES12rl85wEfqkE2spHHshYZFlUiUVhzTXXzAtfiZaCos2IG6EBQ9GvJTRU+BqyuJMwhcTipbFk+nKyAeETUdYZ7aPwHn/88dktBus9Cyj5oBB+fXxko1Hha3MMAoilzv2xsmBtYbEQizjx647GkcEKCzSbKcwE7L777pkb1hLcfbC88GzjjDNObojji389uS9WGxp4Gln4MQXLx6UQmLLAiggJVUKjTCPNe6EzYlYEFyDClMUiWSw4+wyYSi0K/u9Y/LFCMdDj2Ri4cIw7EUxXWmmlPN1czOe+BCQggXYjwCJQPuwXUuWiEtfLHwYkGADujVj0MWIQFhL3Qn70M2+99VbOSr9Dv1eMNIYiHl8ip4/DGBbCRwJpl+m3UNqZ+Z5pppmy8YV2NgxTRMSpiglPXWmP+b4JfW9PhAEA/RZ9c9F1plhWxKWHA2E2UfqpF8asmDkuph+U+48e/T9j4aC8p/eSQFtZ5HkdLKSkEeGrdPyBElKRkTdKPAop1u6ulGAaIXwPyYsFgoataNU46aST8qeeaegoG+swVg3CV/HF0qJ7TiP/PbB+oOgyEMHSTn0ZfBAlh1X8WDJYQEQDhVJL+mYKzww3GnqeFxcWphuZ4mQmg49J9VaY8aD+PBcDFhR6OgfuSyMaA5WwqBfvFwOBjTfeOPuCwh4rCko874JBCJ1cRCSKvLjOwJCv/OFDip89Ppgs4sL6wlQulvyevre4j1sJSEACrSZQtqD35H64nOAjT6x3XCnpX5i9pq+jnWd2kutl1xaMWcyqMgNAf1cUrPzEaUcZp+/EzRR3TCLBoMQT/ey0007LRrRivtinTwi//Z76xjO4YEYbY9zKK68cRQ+0pa2nH2ewgC4QwQ7ofyOKzUCZPCGBXzCBIQYoXz+16/OhkBJDlsaPhamM0Bv5EBBWY5T5sgtOPC/WYNKg/DZTsQYpCi4+89QdBR5LcnypLu7fqi0WDZ6N2L9Vz96Ke2NNR6lfb7318oxH1T14r3QU1JMZkzHHHLMq6UDnmTpmTQBMI6b+QIk8IQEJSGAwIYAxhDVEzFTWCuBQxoCrTNU3Q0hL/4V1nwAGGGUwTNF/VUUrK5c/KI9ZL4fbbbiWDsp7ey8J9JQAei3y3QCDLwNTBtds+RtDx61lDO3qXm2tyHdVca8NOgI07Fh5aNzZFv0roxZ0JqwVwCefKV0s6IoEJCABCUhAAhKQwM8Emq3It52P/M+P6l67EGB0yGJVYuyzGIrFtPgp4kOJNYR4xSeeeGJW4rHesNZAkYAEJCABCUhAAhJoLQEt8q3l+4spHXeY4447Lh155JGdov0UH5CFwnwafFC68xTv774EJCABCUhAAhJoZwLNtsiryLfz227DuvFVPSIlsJAWVxvWLfDlVqzzKPLt6EfZhhitkgQkIAEJSEACgyEBFfnB8KX7yBKQgAQkIAEJSEAC/Z9AsxX5tgs/2f9fkU8gAQlIQAISkIAEJCCB1hNQkW89Y+8gAQlIQAISkIAEJCCBphNQkW86UguUgAQkIAEJSEACEpBA6wmoyLeesXeQgAQkIAEJSEACEpBA0wmoyDcdqQVKQAISkIAEJCABCUig9QRU5FvP2DtIQAISkIAEJCABCUig6QRU5JuO1AIlIAEJSEACEpCABCTQegIq8q1n7B0kIAEJSEACEpCABCTQdAIq8k1HaoESkIAEJCABCUhAAhJoPQEV+dYz9g4SkIAEJCABCUhAAhJoOgEV+aYjtUAJSEACEpCABCQgAQm0noCKfOsZewcJSEACEpCABCQgAQk0nYCKfNORWqAEJCABCUhAAhKQgARaT0BFvvWMvYMEJCABCUhAAhKQgASaTkBFvulILVACEpCABCQgAQlIQAKtJ6Ai33rG3kECEpCABCQgAQlIQAJNJ6Ai33SkFigBCUhAAhKQgAQkIIHWE1CRbz1j7yABCUhAAhKQgAQkIIGmE1CRbzpSC5SABCQgAQlIQAISkEDrCajIt56xd5CABCQgAQlIQAISkEDTCajINx2pBUpAAhKQgAQkIAEJSKD1BFTkW8/YO0hAAhKQgAQkIAEJSKDpBFTkm47UAiUgAQlIQAISkIAEJNB6AiryrWfsHSQgAQlIQAISkIAEJNB0AiryTUdqgRKQgAQkIAEJSEACEmg9ARX51jP2DhKQgAQkIAEJSEACEmg6ARX5piO1QAlIQAISkIAEJCABCbSegIp86xl7BwlIQAISkIAEJCABCTSdgIp805FaoAQkIAEJSEACEpCABFpPQEW+9Yy9gwQkIAEJSEACEpCABJpOQEW+6UgtUAISkIAEJCABCUhAAq0noCLfesbeQQISkIAEJCABCUhAAk0noCLfdKQWKAEJSEACEpCABCQggdYTUJFvPWPvIAEJSEACEpCABCQggaYTUJFvOlILlIAEJCABCUhAAhKQQOsJqMi3nrF3kIAEJCABCUhAAhKQQNMJqMg3HakFSkACEpCABCQgAQlIoPUEVORbz9g7SEACEpCABCQgAQlIoOkEVOSbjtQCJSABCUhAAhKQgAQk0HoCKvKtZ+wdJCABCUhAAhKQgAQk0HQCKvJNR2qBEpCABCQgAQlIQAISaD0BFfnWM/YOEpCABCQgAQlIQAISaDqBoZte4mBe4Pvvv5++++67NNpoo6WRRhqpIRqffPJJ+vzzz9OII46YRh999Ibytjpxb56r1XWzfAlIQAISkIAEJDA4EmiZIv/444+nf/3rX5npEksskYYeuv5b/fOf/0xPPPFEzjvTTDOliSaaqN+8m7XWWis999xz6fDDD0/rrLNOQ/Umz2mnnZZWXnnldPzxxzeUt57EP/30U7rjjjvS/fffn95+++30/fffp1lmmSXNNttsaeaZZ04jjzxyZTG9ea7KQr0gAQlIYDAm8Omnn6b77rsvE6AdHnfccStpfPzxx+nhhx9OjzzySHr55ZfTDDPMkOaee+4055xzZuNPZcZuLmB4uvjiixN9Nn3vJJNMkuadd970u9/9Lg0xxBDd5P7f5dtuuy3deeed6fXXX09jjTVWmnHGGdPaa6/dbb0wXt1+++3pvffeS4suumiafvrpu73f119/nfsxdAp0C0UCgzuB+rXrBkmdeOKJ6Zprrsm5UE6XW265uks44ogj0nnnnZfTs0+D0A7y3//+N73yyitpqKGGyspvO9Sp3jqgvO+88865/sU8119/fT4cbrjhEqxXWWWV4mX3JSABCUigBQRQyLfccstsVKH4M888My211FI174SSvNFGG6Wvvvqq4/pNN92U96eYYop04YUXpoknnrjjWr07GJ023njj9MYbb3TKQl0wJp111llpvPHG63StePDZZ5/l/Pfcc0/xdLrooovScccdl0444YQ033zzdboWB88++2xafvnlE4r5kEMOmQ444IBcFtuu5K9//Ws2eO20005tp8jPvu3dXVW9X1x79OiF+kU9reTPBFqmyP98i5Qbg3oVeRqGK664opi9bfaxnGy66abZyvDqq6+2Tb26q8iVV16Zttlmm+zyM8www+TGk5kOlPennnoqPfjgg+nNN99MW221VT7ea6+98mClu3K9LgEJSEACjRH44Ycf0tFHH52OPPLIxH53cvXVV+e2Gcv5b3/727TYYoulCSecMD322GPZkv7aa6/lNv2yyy5LU001VXfFdVx/9913s5Hs3//+d1bWt9hiizTppJPmGWUUcPoGjGhXXXVV+tWvftWRL3aY0d1www3zjMKwww6b/vjHP6bZZ589z8SfcsopiXptsMEGuT//9a9/Hdny9ptvvsl96fDDD594PgYjO+ywQ/r73/+e5p9//rTMMst0Sh8H9MGkYSZ56623jtNuJTBYExgkivy9996b/6j5Y+1OLrnkkvTll192l8zrdRKgMd12222zEo9lBaWexroodBB77713OuOMMxIN8BhjjJHzFNO4LwEJSEACvSPwzjvvZCs8LjIYVXbdddd0yCGH5Pa5Vsko+lieaaNRlPfZZ5+OZEsvvXRac80100orrZRYw4T1fP/99++43t0Olm+U+FFHHTXdcsst2SWGPEsuuWSibNxWnn/++WyZ32WXXQYq7oILLuhwCzr77LPTwgsv3JFm9dVXT4ssskh666238jOirBcFazxuOLhsYlRCmJ2gf7r22mtrKvKsH/vzn/+cGDQcc8wxDbnrFu/tvgR+aQQGSdQafLNpZOoRGgSleQRogL/99tvc6KGkl5V47kSHcuCBB+ZpTY55V1hbFAlIQAISaB6B9ddfP/u5Tz755FlhRXntSlgrhh8565f23HPPgZJiHIu1WPia1yso0aFcY9nGr70o0003XVayOYcFHF/+ovz4449ZmeYcvu1FJZ5zI4wwQlbg2ceFKNYBcIy88MILeYtlPYR7YqGPa3E+thibGAgx+Jl66qnjtFsJDPYEWm6R//3vf5+n5vCZ4w+QP9QqwcXjxRdfTGOPPXaiocNqUSUomjREl19+efbvw3eQ6cYFF1wwrbvuumn88ccfKCvWARoDhMYJdxIW+XBfFtvQKGKNoLHFZy8EX3/uE4t3mRZkShGhwWIaspZQx3POOSc98MADeZqSZ5prrrkS1grqWo/gZsRzTjDBBAnfwCo59thj81Qr05KbbLJJTsbzhe/ibrvtlhdFVeXnPO43rE1gwdPNN9+cll122ZrJe/pc1AWWNNTwnmaaabI1hv8jLNwqC1YXFmCx6ApfyjMH+G0yu8OUL5YZpm1DqBNTy/zw9yRiEAuumI1gupl3RIfCM6222mqRrWMb/5+YESI/g08GPSw85sdgp5YwSKJeTHNTL+5Lh8QUOPkUCUhAAkEAyzrtP4aTeqKaYRGn35hjjjkq3R1nnXXWXDyzr8xmE/WsO3nooYcSyjiztNFflPPg6kJ7iCX8mWee6eTrjkLND9ljjz3KWfMx7Tpr5WgX6QPpm0KYCUCKfv0sXqXfpv8pC4tpzz///Ly4F/dWRQIS+JlAyxV5fONQ2lCi8LVbY401fr57aS+s9n/4wx+ycl263HFIA7TZZpulG264oeMcO3EfykFhpAEsClaFG2+8MZ+iEULRCuWckyj6LCqiXBYPhTLPAtfIRzqmO+O4qjFGEfzLX/6SG0LyICjWlE/ZTCHWGmz8L+XP/6KExr0233zzmhF86BxYWMTz0UmERD6Ow2oT12ptxxlnnKzAs06BwVQt6clzUT8a+/JsCz6aRNFhMfSpp56aLTvFe6Ics6Br2mmnTQxEYgE0abBShVAnrEowLQoDBv7PocRTFjymnHLKYpK8T36mrWPhbyTgfd199925DtQx/j/Edf4PsVCMjgphyhfFnk7v0ksvzRY3fGFr+ZdGGW4lIIHBh8Chhx6a5plnnrofGKMUv64EtxqEiDf1KPGkRelHGCCwVqqW0B9gbKF9Y01YcdFq5McNsyrSDBFvyBP5i/cIN1sGKhg9EAYh9MExMIn0ROvZfvvt88CH9rTcDkc6txIYXAn8bHZuIYGwnIaiXutWH374YbruuuuyC0h3SidKHcr2KKOMkv0HyYf1nqgrNAIfffRRnhakzCpB4aUBQrlE8UKhjHpiOWb0H0K0AKK+hEWcWQWO+WEpqCUo1kx1ElKSdI8++mieiqTho7Gq16qA/yAWXiSiAJXvd9ddd2Ulntjziy++eMdl7olgzYdVPcKUJWHQipaSYr6ePBezHnCmAWZWhneHpZ1ZhIUWWig34MyCfPDBB8VbdeyjFDP4wXLETArKPX6hIYcddlhW4imfAV6EUsMaRPlY7xmMVQm+oijx+Irig8r/BX777bdf5kZ9a0VS2G677XInRehOfEyZrn766aezFYqpcPKdfvrpVbf1vAQkMJgRaESJrxdNBIcoWry7yxvtYXehnaMfiPRRbhz3ND/GGYR+IASFHyNZXIvz9PdY8JlNr+UaGuncSmBwJdByizxgcYsgEgp/tCg6sbilCB3FGcst0W26slRjdWVAwGgf14tiuC5W2JMf9xisqShauLbUEhogygmXiTHHHDMddNBBCSsxShkuGmEJQcHjxwIdBIWxbO0v34NBBHXFvSMElw4s+FhxYVHvNCj5WMSEIo9Vviyh4K+44oodz0OaUIyrLCblcuo57slz4edJJ8NU63rrrddxm1VXXTUPPFCEsWQz/brCCit0XI8dZk1Q4IvvOq7x/+moo47Kh4TXxD0oBB783+M9MtipJQwKsNjz/4DFvsRPDmEAxY+BJYMCfEEXWGCBfPk///lPri8HWImi88HXNN4D5fWn6Ebx3G4lIIH+QQCXVfzPMS5hJKlXol0KRb0qX1yP9JEujuN6nC9v4zoWfGY+Iy497SVGJ4wdrN1i5hlDGc9B/xhC38ZAZZFFFunUd8R1txKQwACddFBAwOUgYsHXssrjKnPuuefmqoRVvKpeKHsISmAtxQ6FGwstgu87jUctwSIeSnzxOsomgnW1N4KvflGJj7KIBsB9eWaU0HoEFyBi16P8h19i5GPwE/GEy77fMSMRFv3I05ttT56L94FVvajERx34Am581AP3q1qCol/rXZM21lEwWCkq8VEOfpcnnXRS5XRsKPgo60UlPvLzvBE6Fb/SEFx7InQc/vVlIQ/PzGBTkYAEJNBsArSXGC+QHXfcsabbZa170vdEjKD7jAAAQABJREFU/9bdWq2wuIfiHuWFa029+TFaFd1YUeiZkaVtx9JO+8t1ZtXD8IQhimAN9OmE6gzBlQjDH7OnzIizZk2RwOBMYJAo8gBGicOSzei6vAIeFxS+NMqoPCyeVS/lySefzJeIV1sl4WPHIp1yAxR5aEBqSTRMYc2ulaaec8XV+MX0MIgZB565HmFBEgolEtb3yIciilKJ73c8d1xjAIXAoVnSzOeiQ6HuX3zxRa5ebMt15cuFVRJfAMYdqEoYLNTyjSd9/H+qei7SxELc8IXnHDMy8aEULP7M/GClVyQgAQm0mgCKOO6IKLEowcSAr1fog8KXno8xdSXxAapIH2njuN785CuvJ6Ndxi2W9UtsMWwVAwQQdpM2FbfG6DMJREBfiM88yjxtL88fRpWon1sJDE4EBpkizxQbrgmMzFkJX5Sw0tMwdSVYn/kSHdKVlZnFhaGQF5WvYtnlcFtxLZTfOO7ptqp8yot7VM0W1LpnLGItK/JxXLbGU0bUgQVFzZIos1Z5XT0XgxbcU5gJwU8UX3ysPbzHOwcsAO5Kuop0FO5O4dpSVU75gySkw5Ie+fGj59PktX5EmEDK/5dQ3lmXgCWJTocZGBZuYWEqp80F+I8EJCCBXhIgqgtBI/jSOLOZxKFvVGKxaXmGt1xOGJvK4R7DMFJvfoInVK3TQknHEMMAI4R1TgQn4MNQzL6HsAYKPYBrRLjDYo9rEdZ9RQKDK4Gf/3IGAYFwmylGL6EhwCJPGMdQVquqgnUg3BjKo/tyHhYbIsXoJuU0/emYBo0BStG9Bha41TBNWUuRDysGDV5fCgtH6XBYPMrHPpiRYQEuEROKi3N7UsewikfkhqoyitO6kYZBZUzLMn2Lsl/rR/QGrPIxOIz8KO64b+E6NPfcc+eF2kTKwecTVyD8/elsFQlIQALNIEAEFz6ihILNTOXJJ59cGZayq/uFIh6KelXaUNQjfaSL457mj3JqbYk+R8x81q0R5SeENhyrPSGcmVGn3yMQBQMAwiUrEhhcCQySxa4BF2slFs+XXnopLxTEJxmrJi4WTKlVjdgjP9cnm2yy7N+HclrLB520KLjhw/eb3/wmsvfrLVZp4qkznYgy/Kc//Skv4GSggjsSinFZFllkkcRiKNLQ4MbCo3K64jF+9Sw8ZaCEcttbQWknnCjvBMsKYSgJk1YUrNlVi5KL6Wrt06AzUOhqvUHVegT+P/H/kShC+LKzSLpRoQymd/kxzYzPPs/CIi58WHEp430pEpCABHpDAMMDbiT0fbR7hOPFANYTwY0V6U4Rp21EQnHPBwP+ifwo+sVFrHE9tlX543p5S1kEqaDfIMBBcQY4DFJFN0iMW9QNnaKrepTv47EEfkkEBqlFnpEzSh2COw1TZCimSFjr80EX/4Rve1fuIoTGomwWOlYp+13com0vxYxFfJGvK7caHmKxxRbrWNBL1KDuhHUBuL2w/gDluBnCB5xQ4vGpJPJOWYnnHlVf8qvn/tGoYxmvcmfh/1qV/338f+pqIFBPPUjDYItwl8TEJ+woQgjQ7mYLckL/kYAEJFBBgP6MjxDiT87sIAaa3nyfIhRz2uewupdv/Y9//KMjbG8o7pEm8jNLHv1RXIst3yPB9x0p54805S2z9Xy7A8Ne+YOEEbwhZmEjLzPPtO/hzx/n3UpgcCEwSBV5oDItiC81cbsJz8cfJy4W9SrcoXhhjYjRfvFlMSo/+OCD8yn8r6s+dlHMU+9+RLnBMsL036AWphSJo4t7DYud8BNEQY6oKuX60NDHV/tIW/UFWvLBDV9wGkR817H+N0Oi8cXCX8t6RLhPOqeeCooz/3dwkWGNBd8ECKHz44u8TNMyqKslMRDAJzPqWk6H+xKDkAsuuKDjEt8aIOxlRFvquPD/OxEBh+lfF2KV6XgsAQnUS4AZxS233DIruHygiTVmjcyW4o5TloUXXjiXwexrlY89rpAISnu5f+Z7KBGAgf4WY01Zjj/++PxNF/rNqj6qmIeFrNwTRT3WJRWvx2Cg2MZznWOU+ViAW8zjvgQGBwKDXJGnAUBJRMliUSBSrzWetLgw0KjQOBFvNha/cg1FDIULxYvBwr777svppknRNQUrLwr9oBSUwlj4wxfzcJnBd76r9QJ8Zhv3EYQ4+ViLwy886k7M/TXXXDN/dIlzxKon3GUzBEUbwdrPQIL3HkIDjB95bxRdBgdYcWj88aHEF5/oPawZYCBHB4hlp2pggusLFi4Ghfzfevnll6N6eXDDh6j46it1Lw4KuS/+m4R9YwqYgVAIlqHw7WSQGmsV4rpbCUhAAvUSIMRkzL5i/EIxxtpd61dWqFGMad/4gF6xjcLIQ7uG8M0Uvp1SFD5kF+GA+UJ5cSFqpCNyDIICzmCAAUcIxhnWCiHMJBf7zkhT3JJ32223zX0qoSZrDVQYxFAPDFkhGLSIbNNV8ItI61YCv1QCtc2ULX5aFHcaDyQU+3pvySJWrPF86AdFEPcR3DVYFU8EEhorFF5WsRc/KV1v+V2loyHhhz8ePtUoxSjJ3UVd6arMRq+hoBJrN6TWIte4xhYrBayJ44+SiosN+YlCgDKK32G4fsTgp5GBVfFetfZZ04CyzvQryi1WGhRt3J9QvPHvxzpUtHbXKqercyjKuGjxBUBcbCiXH64uxJbH5zI6nXI5dGhY1fn/RChKBh78f2JhK6FLY7E009rF0Ggo6PFc+P3T+TBjgoULFx8GlSzWauQjLeW6eSwBCQzeBJgpLM76dff1VhbeY5BA6AtpF9nSLrJuLFxiuL711ltnZR03Slxe6S9pr+lHIywvho5iu0e+EBb4M8hAieeL3wwGiD5DVB1mLDHQMINej0GNRbusL2INAGvpagn9FYYZ+nYMKLAgH7OttPHtII8e/T/DVTvUxToMPgQGuUUetEVXGhTMCFtYL3aiiOAjiJUV6wSfb0app8HC+sAHgFCymi00GFgqsPqiJGJ5rfIvbPa9ozxca1AYEazQMb0Z12ttcZVhwSVKLbF7UU7xf6SxRYnH0kwEBPwZm6nER114H1hbGGzhuoNf5kcffZQjDmDNrnJ7ifz1bPmICN8owBLEF3XpVJitQZHmXYW1iEFeWRgI0GFiuWcmgv9PlAMnyqX+fHWwLHQiDIqIKc8MEe5LRGDi/yFTybfeemvNj0yVy/FYAhKQQC0CtJM9Fdo6XCvpI4kOVlTiKZN297TTTsshgUlz//33Z8UfJR6DGaEdw021qg6E7WWmF4McRiEMMhi2aAMJkclAoqsZY8rFMMZ9MIp1p/QzcGBWGuMUM8e01dy/q2+NVNXd8xL4pRAYYsAf3M8+Af3wqUKZZosVFSvooBKUN5REfoNSaCBZELTVVlul3XffvaFbE1kFBZdoBViPCbfILEMzlOl6KsLAhw9UofwWXVXqydubNESkYUErsexXXHHFyqJwl6KOuABhnequE4qC4IoFn/9/5cVYkcatBCQggUFNgLapuz4KhRhLPNZ0FGqivdVyb6mqOwYaZiJxdSHSDP1KOVxvVd6enMc9lD4s/OZ7UoZ5JNBXBGKN5XcDdDB0LwbSbDEk4j5Wy+DYVV37vSLf1cP9Eq9hSWe2gRfPdOng7n9Nx0PMdqZx8QddZZVVBnrtfP2WmR+EzopZCUUCEpCABCQgAQkMagLNVuT7xEd+UEPr7/djARMzDvgQ8rlqJlGI/jO4K/G8VxjgK4k7DYuycLHCT5M1ACywve2223KoU1xrWPClEt/f/xqsvwQkIAEJSEACQUCLfJBo4y2+i0VL80wzzZR9D4sfy2jj6re8anw9lYWuKPNVghsS/vK1oi9U5fG8BCQgAQlIQAISaCYBLfLNpNlPysJXkY808fVWfA8322yzmjHZ+8njNL2ao48+evZ9J4TmQw89lBdPEbUG/0lClcKOSDmKBCQgAQlIQAIS+CUR0CL/S3qbPosEJCABCUhAAhKQQNsSaLZFvk/CT7YtXSsmAQlIQAISkIAEJCCBfkJARb6fvCirKQEJSEACEpCABCQggSIBFfkiDfclIAEJSEACEpCABCTQTwioyPeTF2U1JSABCUhAAhKQgAQkUCSgIl+k4b4EJCABCUhAAhKQgAT6CQEV+X7yoqymBCQgAQlIQAISkIAEigRU5Is03JeABCQgAQlIQAISkEA/IaAi309elNWUgAQkIAEJSEACEpBAkYCKfJGG+xKQgAQkIAEJSEACEugnBFTk+8mLspoSkIAEJCABCUhAAhIoElCRL9JwXwISkIAEJCABCUhAAv2EgIp8P3lRVlMCEpCABCQgAQlIQAJFAiryRRruS0ACEpCABCQgAQlIoJ8QUJHvJy/KakpAAhKQgAQkIAEJSKBIQEW+SMN9CUhAAhKQgAQkIAEJ9BMCKvL95EVZTQlIQAISkIAEJCABCRQJqMgXabgvAQlIQAISkIAEJCCBfkJARb6fvCirKQEJSEACEpCABCQggSIBFfkiDfclIAEJSEACEpCABCTQTwioyPeTF2U1JSABCUhAAhKQgAQkUCSgIl+k4b4EJCABCUhAAhKQgAT6CQEV+X7yoqymBCQgAQlIQAISkIAEigRU5Is03JeABCQgAQlIQAISkEA/IaAi309elNWUgAQkIAEJSEACEpBAkYCKfJGG+xKQgAQkIAEJSEACEugnBFTk+8mLspoSkIAEJCABCUhAAhIoElCRL9JwXwISkIAEJCABCUhAAv2EgIp8P3lRVlMCEpCABCQgAQlIQAJFAiryRRruS0ACEpCABCQgAQlIoJ8QUJHvJy/KakpAAhKQgAQkIAEJSKBIQEW+SMN9CUhAAhKQgAQkIAEJ9BMCKvL95EVZTQlIQAISkIAEJCABCRQJqMgXabgvAQlIQAISkIAEJCCBfkJARb6fvCirKQEJSEACEpCABCQggSIBFfkiDfclIAEJSEACEpCABCTQTwioyPeTF2U1JSABCUhAAhKQgAQkUCSgIl+k4b4EJCABCUhAAhKQgAT6CQEV+X7yoqymBCQgAQlIQAISkIAEigRU5Is03JeABCQgAQlIQAISkEA/IaAi309elNWUgAQkIAEJSEACEpBAkYCKfJGG+xKQgAQkIAEJSEACEugnBFTk+8mLspoSkIAEJCABCUhAAhIoElCRL9JwXwISkIAEJCABCUhAAv2EwND9pJ4tq+aPP/6Y/vnPf+byxx133DT00IM9kpaxtmAJSEACEpCABCQggeYRaInWeu+996bPPvus4VrOMcccaeyxx244X28yUE/ui9x///1p8skn73Fxzz77bHrqqafS008/nd599900/fTTp9/85jdpttlmS+ONN16PyzWjBCQgAQn88gh8+umn6b777ssPRj+BMakraTR9VVmNlvPKK6+kyy+/PL3++uvp22+/zf3aLLPMkhZYYIE01FBDVd0mfffdd7lfffzxx9Ncc82Vf/UYy+6888701Vdfpfnnnz+NMsooleV7QQISSKklivy+++6bnnnmmYb5nnfeeWnRRRdtOF9fZ/jyyy/Ttttum6699tpOVbn55pvz8bDDDpsOPPDAtM4663S67kH3BL7//vtEJ4DMMMMMaaSRRuo+kykkIAEJtDmBRx55JG255Zbp7bffzjU988wz01JLLVVZ60bTVxXUSDm0v7vssku64IILErPXIddff33enXfeedNJJ52UxhlnnLjUsf3888/T0ksvnV599dU05JBD5vwzzzxzuvrqqxN9YpXccMMNaaONNkoLLbRQzl+Vrh3Pz77t3e1YrS7r9OjRC3V53YvtT6Alivyss86aRh999IGe/sEHH8wj9KmnnrqmhXqMMcYYKE+7n8AtBwX9+eefz43VkksumWaaaaY01lhj5QaM2Ynnnnsu7bDDDtlSf/DBB7f7I7VV/bAcrbDCCrlON954Y6IjUCQgAQn0VwI//PBDOvroo9ORRx6Z2O9OGk1fVV6j5aC4b7XVVumqq65KQwwxRFpppZXSfPPNl0YYYYTcl2F4e+CBB9Liiy+ebrnlloFmE3bdddfcBx5zzDFpxRVXTAxU9tprr3TAAQek/fbbr2Y1P/roo7TjjjtmK/xRRx2V71szoSclIIEOAi1R5A899NCOGxR3UMLef//9tMUWW6Q111yzeKnf7qOYo8QzCPn73/+e5plnnk7PQuN52GGH5Yb7rLPOSptsskmaaqqpOqXxQAISkIAEfvkE3nnnnWyFf/jhh9MwwwyTUHYPOeSQbOCq9fSNpq9VBud6Ug4KPD8Eq3sYVDheZZVVstUc5f69997L1/fee28uZWEQcN1116VJJ500rbbaavkcfd8RRxyRrrnmmkpFfqeddkoo83/729/SBBNM8P+luZGABLoiYNSarujUcY1pQISBSVmJ5zz+gzvvvHOabrrpOEwXX3xx3vqPBCQgAQkMXgTWX3/9hBLPWixcMXGt6UoaTV9VVqPlhAGK8pZYYolOSnzcY5JJJskKOUYslHZcaULeeuut7OOOH30IVn2Mef/617/SJ598Eqc7tpdddlnCZYdZ7TXWWKPjvDsSkEDXBFpike/6lvVdxQ2HqbgXXngh4V4x0UQTZf95GqRabjtRKj59jPhpFFiYg9B48Ft99dW7zEvaf//73+nCCy/MU4ZvvvlmXtQz55xzpvXWWy8NN9xwJOmQr7/+uqPxwp2oSmjAsGhQn3LdmWrER3LDDTfMPoG1ythtt91yZJ3NNtss4ZMYgg8+vovTTDNNtuxgGcGSgU/5f//730RDi8WERrGWTyKdCP79TGWyMBdml156aZ4O/dWvfpWZ/fGPf+wYhMR9y1sWCTPNyrv6z3/+kxcss8h3gw02SDPOOGM5eT4u3nu00UZL5557bsINCebsDz/88HldAQurQlh7Meqoo+ZDrD+TTTZZXHIrAQlIoO0JsPiTfog1U/Ws92k0fRWARsshaAP9FbMG+++/f1Wxabnllsu/cgL6UWTiiSfudCmOcUmNtpwEHO++++65fzz88MM75fFAAhLomkBbKvKnnnpq9qUrVp0/dBbpXHTRRXn0X8ufnum8zTfffKBFp6+99lq64oor0umnn54HByittYQINhtvvHF2lYnrb7zxRl6cc9ddd6UzzjijU3hKlE38/V966aXEKvvll18+sg20nXbaaRO/sqC84pqD1aNK7r777qxc02gWhbrhN85UJAOftdZaKzG4CCFyDj6Mt956a35uBhRFwa+RZ0bhxh3qpptuKl7O9UK558dgppaccsopqTilShosLkTuYZBx2mmnpWWWWWagrHFvZjK23nrrvI4gEvEMDDB4tqLwLCHbbbdd7LqVgAQk0C8I0M7Wmrmtqnyj6ZtVDv0S8utf/zq7xxTL/emnn7r1XZ9iiilyFvq2osRasnJ0uO233z5b6U8++eRBHrmuWD/3JdAfCbSdaw1W5n322Sc3FH/605+yAs5UJIuDUMBRXlE8i5baAI91m+nKEUccMf3lL3/JCj/WYhbWsACV6b7f/e53WfGNPMUtFm+UcwYSTzzxRLYQUw7hslCGa/n+E34LwSKNxRhL+KAWFHb8D2l0WUD16KOPZuWbQQ2uPTCtVfeoJ9ah22+/PS9suvLKK/OACasIVnUsOZTNQKospOVdIbyT888/P3NjrQD+lDGw4v1VCVYYrDf4RjJI4/0xw8CULO8uoiOQn4EY5/iFq1JVuZ6XgAQk0G4EGlHiqXuj6auet9Fy6P8Q2mIECz2RZOaee+48E8pML/7uBHKoJYSRxthGOSj+CH0J0ezwmy/ObjMDe8cdd+QFsUU//Frlek4CEhiYQFtZ5LHkshAWBRB3Eiy1IUzJEY5q2WWXzYomLiQofyG406BAIiizv//97+NS2nTTTfOCm4UXXjgvtqXhIFxkWVDisSLHlB8xfXE7wZ+PssNKUcyHIkpjhkUc9xks0NQTBZ/49LPPPnuOZlPM0+x9XGqwcKBIR8xdFgoRYWDMMcfMUQIYCC222GIdMfOLdaCRPuigg7IyHueJxIMlnZkCymdAA5sQBg/bbLNNbqQZcBWt8rwj8tJws4bgD3/4Q7bQM01bFnwxeXdlCw3pOIerTgjPVCtdXHcrAQlIQAK9JxDtLoo8/RqRZmirEWZ26e/4HXvssemcc85JCy644EA3xX0StxwithG1hn4XAxyRcEIwrmEMoq+lD1IkIIHGCbSVRf4f//hH+uKLL7IVoKjEx2PxUaVwqXjooYfidN6Gkk2DUVTiIxF+2FjaUUhRbmsJymso8cXrUR4KOz74RcH6jzUeZRUlmutYt5kFwLrARz5QclGWWykMbEKJL96HxhTrNlYRGt5awsADi3pZsKgQUQHBqs4AK4R3haKOdWWPPfaI0x1bGnsGDwyOGAhVWW5WXXVVlfMOau5IQAIS6HsCMbPMRw5RxnELRaF/8skns1GGvpQZ22+++Saxbq3o9hi1Z0aY9VmXXHJJXheA6yaz3muvvXZOQn+CQY0+nxlg+mgk3CrpN1mzFXXJF/1HAhIYiEBbKfI0EkhxpXu5xnwUCGGKLqbsOI6pQL4eVyVcw8K+7rrr1kxSdd8Ig0WjRfjMsqDMo/CirJ944onZfSfy4DaCHzkf+jj++OPLWZtyjKW7q8W2zAogVR/pii/b1qpMXKNxZa1BSLwrFhFXfdmPBbOsIUAifeSPbZXvfVx3KwEJSEACg5ZAuK6ytgyXTdZJYdDiw08YwlgPhoKOyypfYK21IBZjDgYmjDi4YeIfj/tpCLPXGJdY20UseoT74rZD8AdmgDHoMZMei2cjr1sJSOBnAm2pyBOikYgrtX6xoJRINvjLI/zxx6KaUPTzhQb/4SNOtaQY8aU4eCinxe+PGQEUd/zUaaSwKsRX75iexM+72cLColpuK3Gf8CdnAWotqVr8S1qsJPHZ8GL+UMyj7Frlci6uV81IYLFXJCABCUigfQgUZ6b33HPPDmt5sYbMAMdsLH0DlvVaMvLII2ffej4kFfLKK6/kyD1Eoysq96zXoq/g+ywEkQiLfK0Z+ijLrQQGdwJtpcgXp9CwAtT6YTWnkWGLlRjBUh4uL8XGoq9fLm4n+I/fc889HZZpGqZmCw1lVxJhzopxfovpe5KfgRQSZRfLK+5H2bXiBhfTuS8BCUhAAu1BoBgVrqtZU2ZsCQZB/9tVUIPiU+Frz/oqDHB8vZWZ2xACM9CHx6z5/PPPn/UAXGejv4+0biUggf8RaKvFrsQcx/2D2OUsgKlXaAiwSr/66qt5Gg/fvXYSLBc0XFgVsFwQXrFsiS76n5frji96V/Liiy9mN6NyeMnIw3WkaraC+O+LLLJITlP+h0ESsd2RYn5canhXUXY5XxzH9XZ7J1E/txKQgAQk0JkA7TVrvZCuZnuZhaYvw0hUr/vLcccdl791QqjniPrGfegX6WtwBS26a2K0YwYYC32V+yv5FQkMrgTayiKPcogUXTjqfTHxB87inCphRM8qeaKwNEOuvvrq7PvOYp/uBKsFgjViyCF/xh5TmMSCryVYOrqrL40oH5WqEhR1BH/GWlK1EJW0L7/8ckedy4o818Olif1aEtfj3dZKU++5rtya6i3DdBKQgAQk0DWBYhSarvpjrsVML24y3QnGH8JWEn2MiG9FYUYegxZBLYoy/vjj58MPP/yweNp9CUjg/wn8rFG2AZJQxvnwE79aglLKwhoWlxYVu8hLCMawAhfzk5YFNMTBPeyww4qXerxPWQwcmA7kg1BdCYuGEGYOij738eXTxx57rGZ2QjOG21DNBP9/stZiIy4R3ee2227LqaoU+auuuqpmVB2Y4bOITDXVVHnKMx8M+CcUcxYZlz/cFGn4uAeNMzMF8X7iWr3bGACRHr9KRQISkIAEqgl8/PHH1RfrvELo4rCWsyi12NdGEZyLNV98sbyrQBPkYWaZKDUo68ccc0yn/oTrKPC4apaDMsQxfZAiAQkMTKDtFHnCVfEHTzhH/OKKSiw+eFi/TzjhhPTBBx9kBTEeCZ86puSwuvPhCqbiovEhJi4xavlCKkolfuvNEBaBLrroorkoYtUTa75YXy6gyHINZR9hvyihXPOlUxTqojA4YLFslctMpGVq87rrrsvPGPF/aSwZDOGmRJ34IEitL6xSBtOYTHMymAgXH6wfuDfxoQ5mEKhHUZh6JVwnjHEb4su38exEMbj88stz7GHy7LLLLjVDYxbLq9rHLSlmLYicUO/0bVV5npeABCTwSyVA2GNmTon8Ev1fT5+VNp+2n3YX6zluliH4t7MIlsg1CF9mLRqoIl1xS4hJZn8JSxnR0IrX6ecIc/nGgCAWsV6OZ6Bfwm+e4BeKBCQwMIH/+XsMfL7PzvDHjrKGErvaaqvlETpRVd55553EB6MQFHaUzKKgzJ599tk5LBZhEpdeeukcbYVQWbh30CDQKPGhKRqLZgnThHzEikEHK/hpSLG6Y12gQcLnL+7N9bIbDlFuaAzJzwADCzihK8nH11SJu4tFHf//KmEwQCx46oL/IYuE4RWN4YQTTpg/VFW0bhfLYnbjr3/9a1puueVyaDGmMplpiI4AZjFgKecjHCeDkDXXXDO/K9hiQaGhR4hPj6LfGyFSEbH6+T9BXH6mcM8444xOPvu9Kd+8EpCABPo7AdprZqTZEjGNfnDKKafs8WMxW7zzzjvnr4LT3lJ2fCGWmV582hEUfgxBXQlR3Ai/TBSz4occy3kYEGCUozz6DmLP41pK31l0SS3na5fjR49eqF2qYj0GIwJtZZGHO8omH5vgD5lFrIS04uNDKPF89hnLAOEpIxpK8V2x0v6iiy7KISBZoMOAAAsAlgLirPNlOT6Q1EyhThdeeGH+UBUWehRY3H9QOlHkaXxovLg3loiyUDcaST4exfQkPvw0wijhfPwqwnuV85WPaQBxGSKEJn6LocQTg5cGuOojWJRDvZkNwGKPRR9FnM4AnvCuYoYlH/cZBhtYzXlXWE9gQMhNYvYzQOit7LXXXtktinswYwCjsP73tmzzS0ACEvglEMCivckmm+TFqcRl740SHzwwwuAWSgQ2LPLMvPJDiadPwQjU3Qw3aSmHvpAvwXa1eBaDEQMD3CiZTcZIhEKPsUyRgARqExhigML2U+1LfX8WZe3dd99N+Pxhhe1KGS3XFvcOrNIofvjWFVfBl9M28xiXFKzZWNOxTjObUI5Q09X94qNLTCNWWdAjP/HqafQID8bC2xCYoZBTRrilxLXils9vf/bZZ3nwg0UfgRvWfwZKMO+uDlEe7wrLCYt2GRgwI9EKCwoLq3intb5iG3VxKwEJSGBwJYDi3EifUw8n1AQMKBiJcF+lb8Anvt7+oZ57FNNwP5R5FsW26h7F+7kvgUFJIAKYfDfA6Mn/bwa3bNFT0Zu6c6cu17XtXGuKFeTBsATwa1TwqYuvijaatzfpsYhjBe+p4JbTW8GVhl9PBG6xALeR/Lyr+IBXI/kaTVtrJqbRMkwvAQlI4JdKoNlKPJxQLHraF/eEM/fri/67J3U1jwT6mkDbudb0NRDvLwEJSEACEpCABCQggf5AQEW+P7wl6ygBCUhAAhKQgAQkIIESgbZ2rSnV1cMSAVxgWNzU07BchPhkARP+jooEJCABCUhAAhKQQP8i0NaLXfsXSmsrAQlIQAISkIAEJCCBagLNXuyqa001a69IQAISkIAEJCABCUigbQmoyLftq7FiEpCABCQgAQlIQAISqCagIl/NxisSkIAEJCABCUhAAhJoWwIq8m37aqyYBCQgAQlIQAISkIAEqgmoyFez8YoEJCABCUhAAhKQgATaloCKfNu+GismAQlIQAISkIAEJCCBagIq8tVsvCIBCUhAAhKQgAQkIIG2JaAi37avxopJQAISkIAEJCABCUigmoCKfDUbr0hAAhKQgAQkIAEJSKBtCajIt+2rsWISkIAEJCABCUhAAhKoJqAiX83GKxKQgAQkIAEJSEACEmhbAirybftqrJgEJCABCUhAAhKQgASqCajIV7PxigQkIAEJSEACEpCABNqWgIp8274aKyYBCUhAAhKQgAQkIIFqAiry1Wy8IgEJSEACEpCABCQggbYloCLftq/GiklAAhKQgAQkIAEJSKCagIp8NRuvSEACEpCABCQgAQlIoG0JqMi37auxYhKQgAQkIAEJSEACEqgmoCJfzcYrEpCABCQgAQlIQAISaFsCKvJt+2qsmAQkIAEJSEACEpCABKoJqMhXs/GKBCQgAQlIQAISkIAE2paAinzbvhorJgEJSEACEpCABCQggWoCKvLVbLwiAQlIQAISkIAEJCCBtiWgIt+2r8aKSUACEpCABCQgAQlIoJqAinw1G69IQAISkIAEJCABCUigbQmoyLftq7FiEpCABCQgAQlIQAISqCagIl/NxisSkIAEJCABCUhAAhJoWwIq8m37aqyYBCQgAQlIQAISkIAEqgmoyFez8YoEJCABCUhAAhKQgATaloCKfNu+GismAQlIQAISkIAEJCCBagIq8tVsvCIBCUhAAhKQgAQkIIG2JaAi37avxopJQAISkIAEJCABCUigmoCKfDUbr0hAAhKQgAQkIAEJSKBtCajIt+2rsWISkIAEJCABCUhAAhKoJqAiX83GKxKQgAQkIAEJSEACEmhbAirybftqrJgEJCABCUhAAhKQgASqCajIV7PxigQkIAEJSEACEpCABNqWgIp8274aKyYBCUhAAhKQgAQkIIFqAiry1Wy8IgEJSEACEpCABCQggbYloCLftq/GiklAAhKQgAQkIAEJSKCagIp8NRuvSEACEpCABCQgAQlIoG0JqMi37auxYhKQgAQkIAEJSEACEqgmoCJfzcYrEpCABCQgAQlIQAISaFsCQw+qmr3//vvp8ssvT6+++mr65z//mSaZZJI022yz5d/kk0+ehhhiiEFVFe8jAQlIQAIS6HMCn376abrvvvtyPegPxx133C7rdNttt6U777wzvf7662msscZKM844Y1p77bXTiCOO2GW+7i42Uo+ffvopvfbaa+nJJ59MTz31VBp99NHTzDPPnH/sdyXfffdduv/++9Pjjz+e5pprrvwbeuju1RCe+auvvkrzzz9/GmWUUbq6hdckMNgRGGLAH+VPrXzqb7/9Nu25557pggsuSPwR1xL+OE855ZQ0xhhj1LrcZ+feeuut9O9//zuNNtpoaeqpp+6zevT0xv29/j19bvNJQAISaHcCjzzySNpyyy3T22+/nat65plnpqWWWqpmtT/77LO08cYbp3vuuWeg6yj/J5xwQppvvvkGulbPiUbqgRFuo402Sk888cRARWOM22677dIOO+xQ0zD3+eefp6WXXjob84Yccsj0448/ZuX/6quvTsMOO+xA5cWJG264Id9zoYUWShdeeGHNsiNtX29n3/buvq5Ct/d/9OiFuk1jgtYSeO+99/INvhugHzOQHWaYYfJ2qKGGSvxtNGrYbqlrDaP81VdfPZ199tlZiZ9iiinSNttskw477LC07bbbpoUXXjg/ABYJGrBnnnmmtfQaLP2kk05KK6ywQtpvv/0azNkeyft7/duDorWQgAQk0DwCP/zwQzryyCPTSiut1KHEd1X6999/nzbccMOsxKPwbr311gml/+CDD070qRibNthgg/Tss892VcxA1xqtx9NPP537aZT4McccM62zzjq5DrvssktaYoklEjZBnou6Uuey7LrrrlmJP+aYY9Ibb7yR+1Ws+gcccEA5acfxRx99lHbcccdshT/qqKMaVnA6CnJHAr9gAt3PafXi4ffaa6/00EMP5RJQ3Hfaaac82igW+eijj6Y//vGP6Z133kmrrrpqeuyxx3o9TVgs330JSEACEpBAOxCgn8MK//DDD2cjFsrtIYccUjlbTZ2ZzQ73G4xiGMBCMJQtssgiidlXysK6XY/0pB7HHXdc+uCDD9J0002XzjvvvDTBBBN0uhWus/TzN910U3afwYIegvX9uuuuS5NOOmlabbXV8ulNNtkkHXHEEemaa66pNJahM6DM/+1vfxvoflG2WwkM7gRaZpFHgb/ooosy38UWWyztvPPOAynxXJx99tnzH/Lwww+fPvnkk3TZZZcN7u/E55eABCQggV8ggfXXXz8r8awLu/baa7NS39VjogBjwUYWXXTRTko850YYYYSswLOPi0wo/Bx3JY3WAxfZ22+/PReJsl5W4rmw8sorp3nmmSenufHGG/M2/mGggY/7LLPMEqeydR3f+n/961+57++48P876ALXX399WnLJJdMaa6xRvuyxBCTw/wRaZpE/99xz8y0mnHDCxEi+K5+f8ccfP/3hD3/IfvJnnHFGWm+99QZ6QUzVYW1g1M+0HI0CZS+44IJp3XXXTZRRlptvvjlbM6aZZprc2LHIhobhgQceyBYQ/N65b9HCQRm4/jz33HP5xzHTf0wXIjRENGQhWFe+/PLLPP2HLz3Pfe+996Y333wz788000yRNOEjeNppp+UFQi+88EJuhLnOIiemKfGPqhIGRlhjyMeAZ4YZZsj+hcsvv3yadtppO2VrpP6dMnogAQlIQAItI8A6MazoBx54YBpppJG6vQ+Wc37IHnvsUTP973//+3TiiSfmfoW+jTVn3Umj9cA3nv4LYYFtlfz617/O/d/LL7/cKQnuP8jEE0/c6XwcU/6oo47acY3j3XffPS+kPfzwwzvOuyMBCQxMoCWKPEr3Lbfcku+23HLL5cWiA9+68xkWyNAgIeQvrmTHKrHZZpslFr0UhQUDWCHOOuusbNXHylEUFH4sA0zNseqdAULRd4/GBsV+++23zwt0Ii/TnijjIUwnhoUBv8Ki8JwsRFpzzTWz7yIDgJCvv/46drMVBqU/GuW4QHpmLohGgE87FpayoPyzYLgo7777bmZ8/PHH52lHfPlDGql/5HErAQlIQAKtJXDooYd2WK3ruRPRYRACQUw//fQ1s2AkY6ErEWSIClePNFoPFG4MVR9//HG2+k811VQ1bxMzAmVlH19+5Pnnn++Uj2MW95X7bvpkDFYnn3xyGnvssTvl8UACEuhMoCWuNTQ+/BEi+NPVI7/61a86wlEWlXjy7rbbblmJJ+wUPnP42qGs4l8366yzZkV9rbXWSh9++GHNWxGqC388rOpXXXVVtrRj3Z933nlz+qOPPjq9+OKLHXmPPfbY7OO3yiqr5HM0kljz+WHtriVYD7A6UD8Uc6ZNmQlAGDyEEj/HHHOk008/Pa8FIA2r/LFEMHtQa9EPafbee+9cDs+IxZ91BMxyEAGAmQnWGDAQCOlJ/SOvWwlIQAISaA2BcD2pt/RXXnklJ51ooom6zBKW7UjfZeIBFxutB8o2PvgIi2zL0XPo4zA2EbCCQcfmm2+e08Y/KOOcZ6FsBMpjVoD0+M0PN9xwkTT3cXfccUdaccUVc7CJjgvuSEACNQm0xCKPBTukyooQ17vbXnnlldnijtUBX8FieC7i52Lxx4cOVxaU4nPOOWegIlHwcYdhdX3InHPOmRfszD333HkBD4o9q+OR8cYbL28jXi0xessWg5yg8A+Wehbt1EpHzFwaK1gwaIhYu7gDsUbgm2++yeHDyo0jMw4MAJiRwKeRBjSEQQY/lHtmGwjfyVoEpCf1j3LdSkACEpBAexAIC3so6lW1iusY0VCUu3JlrSqju/O4odK/0U8yA41LJ5Z3rPS4n/KtGJRvDFJEtSkLfdn++++fZ79Jh1EK3/utttqqIym+9Pvss0+Op3/QQQd1nHdHAhKoJtASRT4s4zQmZf/t6qrUvoLPH0JEm6ISH6mxZmMlZzHMgw8+WLMRox5bbLFFZOnY4sZC2Kzzzz8/f2Cj40IPdqhfLSWeohg0XHrppZWlsoqfOMC4+jCTEb6CuA3R0LEWgAawlhDuiwYRiwkWDuKRKhKQgAQk0P8JhGsNfUBXEhZ71muxeLTWmrGu8td7DcMX8exRtnGLKbrK4D6DC2wtJZ7ysdK/9NJL6ZJLLsl9LlZ40mOQQzBYYXD74osvspsprjwIz3T33XfntW2sKcNgFcawnMB/JDCYE2iJIh8fd8AywB9lLb/verkz0kewXFcJ7jUIi3GwYJT99yabbLLKr8FFA1mcRai6T1fnUdZ7IvjRF/324RWKfHx0g+erUtBpsGMmoSf3N48EJCABCbQngfhia3G9Va2a4mIZUs8i2kjbyJY48gSWwPKOIo2baFjk8c+nv1p22WWzy+epp57aaZ0b98GgRhhJLPbEvP/Nb37TSTdgLRjGOGaZF1988Vw1DFnE26d83G+ZecZNh7Vp3X0Ft5FnM60E+jOBlijyfDo6hBE7kWV6IliYY/FoV772/IGjkLMAlD/4siJfrE+5HjHoKJ9v9Jjwmd0Jvv1En8HHnYW4fDCLZ6ySUOSJUKNIQAISkMDgRWDKKafMD1wOklCmEF+HRckNl9Bymt4cs/4L1xqU+EUGxK0nMEMYnKJcAkSwXovAEPjTV60nG3nkkROW/aLg208kH2YW9t13345LnKNPx60U91IW00ZgiYsvvrgjnTsSGJwJtGSxazHGLOESeypYGcJa3Z2VgcYBiUW2Pb1nK/JhZd90003zglssFTRMPA+DEwY5VdZ8phSR3sxotOJ5LFMCEpCABFpPIBT5UNSr7hiKfqSvStfT87if4rLD+is+BlVW4il3gQUWSHx9FcHdE6W/HmF9GV98x/pOfgxzIQSBoP9jJgAhtGaEuIz+MdK6lcDgSqAlijzW8WhQWJVej/BHzOJOfvjKIVgWcItBilFl8onCPyj74UvIdF27CX7sRJ+h8eMrfdQVyzyNFFYFphRrCR/LQGJWolYaz0lAAhKQwC+TQMwuo6hHtJdaT8oiUST63VppenOO/gphTRnrsaqEb7KE4YnIcvUIEdgICLHRRhvlwUDkwZ2IIBa47xS/scK3XGCBv70iAQmkVP0X2Us6yyyzTC7hiiuuyK4k3RXH1Bl+8CxaLa64D2W2uKimXBbTcrioELayHL+2nPb/2DsLMFmKq2EXluDuwZ3gFggQQnCCO8EtCRacoMElWHAJ7u6uwZ2Lu7tLcIf/vvXn7Nfb2zM7a3fm7r7neeZOT3dVd/Xbd6tOnTrndHd+1+tAGzkfqbQQliZZliyn16y1ahFvwcOfsJZgzaATp8Mr57iPOj1tf5zHbwlIQAISGHIEQjFndZrMalXCe0xw20RC8a8q15N9XAPpbMWbdoa7KK6jnQmGPtJIkyiCFM5F+eSTT7JRL7KwxbEI5I2kGrHfbwkMVAJ9psgT2c4SGX/UuJXwR1lLCFAltzqy6aabViryLOeF1aF4HpTUSMuIq0oxH22xXHe2I8C00dy8ta4RHU4tX/1aHXQo8vHSqKrzk5ITf0MChIoTIMr2Vvurrus+CUhAAhLoWwLkXo8YM8a5cDUtXpWXAvLSQ/p70jGXhfSQPZUwkPG+Eq5VSy655JK2Nhbfal5VHt2ALDWswDOOhSU/yqLA44JaXtWP3301aYnr+y2BoYVAnyny/BHyIicEdxkCVcqWZTolfOlIK0kudeqQirEo+MZFZDyTg6KbCQoyaRlvuOGGRNBqMUimeI7ubkduXqzdXKOWxbuz8y+00EK5CK/RDl9GdnDPtJlJSpUQ5MokCNlpp53yS7FwQUJYdmQCgNsOQhqv8pJnb7U/X8B/JCABCUhgiBPgLacICRIOOuigNtdT9pE4gUwuyOqrr56iz887Bv+zzz77JMYRMr/0ZGUWAxtGMuK9eG8LbjPF8zEekZFmjz32yJdm5bkzRf7QQw/N4zlpKcmAUxYMU6Sv5r7DEMg1uWeU/skmm6xcxd8SGJAE+iRrTZDcYIMNckpIIs/JiU5KKZYK+QMkCp686bEMR8AnkfDlLDIEsaLoLrvssnlmTg5Z0k4Rnc/EgD9s/uB5mylvYO1Nob2kykLh5l5IucXbVEN5bvRavFEWf3iChbhPOicsDaTzwhrBS6KwTFQJ+XqZCLF0ig8h9XhjLPceSj1ssMiXpbfaXz6vvyUgAQlIYMgQYMUVQw5KPP7kpF6cc8450zvvvJPfsIqBCRfUsiGLsZEgVb5J60hsVrjqdLXluL7wwkPGIIxRK6ywQo5hY5KAxZ+xPAxdK6+8ckJJryeDBg1KrCSwis7b0GsJkxiMeRjxGIMxqDEe7r777h0MV7XOMST3Dzry/xvthuQ1vZYE+swiH2h5axsBnkSbI7jR4DOOZR0lHn83ItYvvfTSVMx2E/X5Hn/88dMFF1yQlltuubx8yCSA5TU6KDoSJgDLL798sUqvbBNoe9ppp+UVAfzasQqEm0xXLoDvP9ZzOl+WPwncxarA+VmRwHpRS7Cy0+FhtSBYFosIgUGsZqDQ81psMuFUuRT1Vvtrtc39EpCABCTQ9wS22WabxJtOcbVh/GBM5Y3ejIHElaGwY+QpCgauTTbZJI85YUQrHu/qNhZ2DFKkf0Sxx2+eCQIxXhjgGOdoI+NV2U2meC2s94z5jG0Y4MIFtFgmthdZZJG05557JtxbSW3JJAaFvuoFj1HHbwkMNALDDO4Ifh5SN42PO1YB8r2T2YaOodZb4Gq1iWAaLAJ8c46u1q913s72Y20g0IdMOuVg1c7qFo9j3cd6wYpCV19ogfKONYJ24B9Yr7MsXpPt3mp/+bz+loAEJCCBIUMAQw7pi1955ZVEzBWpGBkH6wmKcyPvOal3jqpjKPIkocDAxHhUzCxTVb4n+1BTUOaZQPRk/O1JG6wrgd4igB6HfD/YVZr/z0xm+eZviAluOd6xs+sOUUW+s8Z4XAISkIAEJCABCUhAAv2VQG8r8n3uWtNfH4T3JQEJSEACEpCABCQggWYSUJFvJn2vLQEJSEACEpCABCQggW4SUJHvJjirSUACEpCABCQgAQlIoJkEVOSbSd9rS0ACEpCABCQgAQlIoJsEVOS7Cc5qEpCABCQgAQlIQAISaCYBFflm0vfaEpCABCQgAQlIQAIS6CYBFflugrOaBCQgAQlIQAISkIAEmklARb6Z9L22BCQgAQlIQAISkIAEuklARb6b4KwmAQlIQAISkIAEJCCBZhJQkW8mfa8tAQlIQAISkIAEJCCBbhJQke8mOKtJQAISkIAEJCABCUigmQRU5JtJ32tLQAISkIAEJCABCUigmwRU5LsJzmoSkIAEJCABCUhAAhJoJgEV+WbS99oSkIAEJCABCUhAAhLoJgEV+W6Cs5oEJCABCUhAAhKQgASaSUBFvpn0vbYEJCABCUhAAhKQgAS6SUBFvpvgrCYBCUhAAhKQgAQkIIFmElCRbyZ9ry0BCUhAAhKQgAQkIIFuElCR7yY4q0lAAhKQgAQkIAEJSKCZBFTkm0nfa0tAAhKQgAQkIAEJSKCbBFTkuwnOahKQgAQkIAEJSEACEmgmARX5ZtL32hKQgAQkIAEJSEACEugmARX5boKzmgQkIAEJSEACEpCABJpJQEW+mfS9tgQkIAEJSEACEpCABLpJQEW+m+CsJgEJSEACEpCABCQggWYSUJFvJn2vLQEJSEACEpCABCQggW4SUJHvJjirSUACEpCABCQgAQlIoJkEVOSbSd9rS0ACEpCABCQgAQlIoJsEVOS7Cc5qEpCABCQgAQlIQAISaCYBFflm0vfaEpCABCQgAQlIQAIS6CYBFflugrOaBCQgAQlIQAISkIAEmklARb6Z9L22BCQgAQlIQAISkIAEuklARb6b4KwmAQlIQAISkIAEJCCBZhJQkW8mfa8tAQlIQAISkIAEJCCBbhJQke8mOKtJQAISkIAEJCABCUigmQRU5JtJ32tLQAISkIAEJCABCUigmwRU5LsJzmoSkIAEJCABCUhAAhJoJgEV+WbS99oSkIAEJCABCUhAAhLoJgEV+W6Cs5oEJCABCUhAAhKQgASaSUBFvpn0vbYEJCABCUhAAhKQgAS6SUBFvpvgrCYBCUhAAhKQgAQkIIFmElCRbyZ9ry0BCUhAAhKQgAQkIIFuElCR7yY4q0lAAhKQgAQkIAEJSKCZBFTkm0nfa0tAAhKQgAQkIAEJSKCbBFTkuwnOahKQgAQkIAEJSEACEmgmARX5ZtL32hKQgAQkIAEJSEACEugmARX5boKzmgQkIAEJSEACEpCABJpJQEW+mfS9dpcI/Pzzz+nee+9NH3/8cZfqWVgCEpCABCQgAQn0RwLD99VNPfLII+ndd99Nk0wySZpllln66jKetw6B//73v4nn8Pjjj6cnnngijTrqqGm22WZLs846a/4efvg+e/x1WtX9QwcddFA68sgj0xhjjJHva6SRRur+yawpAQlIoIkEMEy8/PLL6bHHHst99FhjjZX7Zfpotrsr33//fbr++uvTHXfckd555500wQQTpJlnnjktuuiiabLJJmvotJzjwgsvzP0s56Deb3/727TccsulYYYZpuY5uCfGmttuuy1NPfXU6fe//30ed2pW+N8B6rz55ptpxhlnTFNMMUVnxT0uAQkUCPSZJnf88cenq666Kq2xxhrpiCOOKFyyZ5uffPJJevHFF9Nwww2X5pxzzp6dbCis/eSTT6avv/46TTrppGnCCSeseQd0pJtuumn69NNP25W54IIL8u955pknnXTSSbmTb1eghX+88cYbuXVffPFFYpKiIt/CD8umSUACNQmgHG+00Ubp0Ucf7VAGRXnbbbdNO+ywQ12luUPFwTs472qrrZZeeumlDofpL/fee++07rrrdjhW3PH000+njTfeOL366qvF3en0009Pxx57bDrjjDNqjj1bbrlluvTSS9Owww6bfvrpp2x0ueGGG9Lkk0/e7lzFHyjwK6+8cu7PGbeGZplr6ztaqvmDjlyopdpjY/qGQJ8p8n3T3JTuvvvu9Oc//zmNPPLIlZ1VX123Vc5LR/ncc8+lPfbYI2222WaVzTrxxBNzh01HinVj4YUXTtNPP3367LPP0rPPPpuuueaa9OCDD6YlllginXvuuWmmmWaqPE+r7dxtt93S+OOPn+aee+400UQTtVrzbI8EJCCBTglgfV577bXTBx98kMYZZ5y01FJL5VVrjBODBg1KN910U/rXv/6VnnrqqXTyySenRldO33777bTKKqtkBZxVS877u9/9Lr333nvZQn7nnXemv//97+n5559P++67b2U733rrrbTWWmvlOhiKNt9886yEo9wfd9xxeeWA41dccUUabbTR2p3jvPPOy0r8+uuvn/baa69szV9nnXXSJptsklDmUe7LggV/m222SRhnjjnmmDT22GOXi/hbAhLohMBQp8h3cj8D/vArr7yS9tlnn2wNYTXkwAMP7GC5RpnHGkRZOk9WT4YGmXjiidOee+45NDTVNkpAAhKoJECfixI/wwwzpHPOOSfRrxUFi/bWW2+dld977rknLbRQ51ZVFOI111wzK/HjjjtuVqinnXbattOikGNN32+//dKpp56alXXcWMrCcRR/JgJMKDgXgtGHicHiiy+ennnmmXyunXfeuV11VuCRrbbaKo044ojZFYe24+bz2muvpSmnnLJdeX7QFoxzq6++elpyySU7HHeHBCTQOYGOU+TO61iihQnQ+f74449phBFGyAp9lfsJA8iuu+6a7+K6667LlvoWviWbJgEJSKBfEPjuu+/Sf/7zn3wvKOtlJZ4DuJnMN998uQxKcCNy++23pxdeeCEXxYpfVOKj/hZbbJFWXXXVbOTBxaYsGHauvPLKvPtvf/tbmxIf5Rg3/vSnP+Wfp5xySodxAwPReOON1+6eZp999lyeY2UhPmD//ffP5WutEJTr+FsCEuhIYIhb5F9//fU2qyqdATN1gmruu+++xNLgVFNNlWf/LM8Vl+KY7WOpIIAW+fbbb9OGG26Yt1FWWfYrC+fEr49OBLcSAm8XWWSRxLmrgoluvPHGxPLgdNNNl3bZZZfcqbHvgQceSJwHEaAAAEAASURBVPiUY9FAyuWwmlx77bU5owpBQnSi6623Xg70Kbcpfv/www/5/BdddFG2omBRwY+QTpwPinhR8FnEVYalTwRfd9qFYIkJawaWHgSOo48+et6u+mfppZdOp512Wj5EW6qEjp1n9NBDD2UrDZ00cQkslU4zzTQdqpSfLf6fPDMsLl999VW6//7702WXXZbvm+CpqsEkTsrSMkvQuAXxvBCCt2hzvbpc48wzz8zPnPiAX//61zmAbNlll83uRXH+8jf/P/D9vPnmm3PQFYHB/D/gObI8rUhAAhLoKQF82HEjQQhArSW4O951111tynmtcrEflxxkzDHHzGNV7C9/Y1W/+OKLUyj+RYWfvpMxBpca+vgqwW+fMYt7IF5r/vnnz8Wox9hDIoWiEMuFcN9FwdiE5Z54L/r0emNVsZ7bEpBARwJDXJFHYQorQwS5hHJO81AGCXjBUnz++ee3KfMEuEY9ytERxO9RRhmFXe2EQE78yItCZ4JvOEowfuJlfzyCezjnRx99lC655JKEP3pIcVmwWI62EjxUVIaxjKDYb7/99jlgKc4R3yjtf/3rX3OZ2Mc3kxqUVfwJsaoUJzK0i84yBD95Psi8884bu9s6UtrAxKjK4kNhgoXp1GsJAwP+lkyYQnhOKNc8FwYDfNWLUny2tA3Xnhi0YmLCBIB74d7w8a8K2EXpZ/mZTh4fyxCCXamLcl4lMPvHP/7R7hATH1YpmIQRdL388su3O84PAqiXWWaZ7GpUPIgv6dVXX51XNojLUCQgAQn0hACKLco2/vAYOKoMIpyfY0g9ZT8X+N8/H374Yd5CkS6OG8UybJMRJ4SxsKjIYyFH6Nd/+ctfRrF238QoYeAgExoBtaHIc03isej3GaeiDbjhIGSwKQqGN8YYjDRktlEkIIHuE2iqaw1+cfjpYUFldn/rrbemDTbYIN8NgTkEYobg043lm6U4BB88fvO55ZZbolj+xmJOsA3R/2RuwQqM9ZrUhVwPRZzrsMxZJXRQWB5QdFH+UB7LkwLqYbHGcsHKAME/BASxNEmaLoTrhbKdd/zvH/wQUfTxQ6Sd3DcffNuxTDCJoUxRsM5wrzGhYJIR90/wUQhKPUoznekKK6yQVzqYOHRFUJjhgxIPA1Y1Hn744fyN5Z/9HGfSVUuY3NCxs2QKExR/hFSkLNHSPiZTVcLzRIln0MAi34igcIf/PMu/Z599dm4zEwLugfMxeSr/X+H/APfCs2SCcPjhh+f/K5dffnlbMDHPiMmVIgEJSKAnBFBwWe1F/vnPfybGuaJgEMIYwXiIoalWQoNiHbYjYUEozuXj8Tvcb/iNoaIoGMsQVq7rSVjZo3yUJaHCl19+2W4VgfTHCMdCaOMhhxySx4eqcTXK+S0BCTRGYPjGivVNKToM3BnCWksEP8GZYUXFKh4WWZRePkTyI3SIodQWW4fVmMAeFEX8wPH1C6EDIvjmj3/8Y7bMo6QTxV8WXjjEOcrW3XI5rCD4ORaDfnDBIYAJhZqlRpTYHXfcsa0qCiHWCO6ZJcVQ+imAgsuHjAYEoOIGtOCCC+a6ca+/+MUv8m86+diXd/zvH5TfE044IU9gWPFYaaWV0q9+9auszGJpwYJCmXrCpIl7I+8wVm6s9wiZYthH+1g5YGkUhbdKcBM666yzcnah8nHSo6Hg4y6Fy1BZImgKF6O4drlM8TcrD/h/8syx8DBAhrCqwAflntUTMvpwDyFMDJnk8X8Rt6pgw/8VniEuNgw6/H8g3zO/FQlIQALdJYC7Hn034wJukSi5WN6x0tPHvP/++2nFFVfMxhzGxEZkgQUWyMUYc5gE1LLkFw0Z5dTEkbIyFPVa143jUT7KYShhfOO+WI3G4s7KAv1vrLwyUWHcYEUdQxfZ5xQJSKBnBJpqkcddIZT44m1gSUawknZV8OfGKsDyX1GJj/PQoZCjF8EnsEpI9xVlqo7HPiz+KHhlwWef6H6kfA/4JiIow0UlPu8c/A/+2Lh5ILXalw/W+YeJCpOEOeaYI5diYoSvO9Yd9rESgntMuL0UT4VbEQMBwkSnrEjD5oADDsjHsdIXXYryzv/9w4pBrU6ajp3zsrRLhoSiYDnHTx2hnY0I58GyzoSlVtAUPvfbbbdd+s1vfpOIYwiJ54FlKJT4OMY3ddiPuw9uRYoEJCCBnhLASBBGDCzU+J3jAogST3zTX/7yl5yastHrMDGIXO0YYiKWqlifVUqyxIQwToZgBImxin60noTFvqzIY8Si/8XSzwTlsMMOy4aogw8+uO107GN8QemnL0ZQ6hnrGFdwiY12tFVyQwISqEugqRb5or9esZXRkUTgZvFYZ9tYNJCIlq8qHz7WdCi4naCQFwX3m0Ysr7iO1ArSqXUPjbYPSzd+iN0VrM58WErFrQU3HM6HwstyLp+jjz46HyvmZI9r0llH6rFyGxg0mKygdDMIVb25l069lvCmQVZGcCfCvYaBJyTcarAoVaVHi3LF73ipCpOUqokhZbnH4soI+1DOY6m5s/8vDLCwqZp8cS5FAhKQQCMEMAiw0kyfQtIFVkrDIk8fQ3+GMQaXQBTbRvPI46LKKiaukSQzIGUk/RXGEizj9LeMuXPNNVc27BTzwLPCjeEFK/0333xT9zbo95EqQw1uitwbLjWME8UxhPvC1REjW3EVm5VvDE+cD7dNXBlZHW0k7WbdhnpQAgOEQFMV+eIfeZF3uI8U9zW6HYoymXDwja+SsMgSnIm/PB1OUfC/b0RqtZ+6VfeA9Tpcg3gJRlmxjGtG+0Kpjv3d+SaYKXwyQ4mnk0SBJriJJVyU55i4BD/ccuq9zjvaSPkqRb4zhljbGVhwoykq8uFW06g1HiahyMcErVFOTOSwRCGxNF1VN+61N55H1fndJwEJDAwCKNW41qDELzw4/gc3SFxGi0I8FBZrYrPou3Hta0RQkLHs03diBMPFk08IbpUYb8KdtJy5jZUAFHD6/noSb9guBsoWyzPxKBtyUNBxqUFoQ4yPxLOhxDMBwdWGdpOQgJVuOBAYrEhAAvUJNFWRr9+07h0lA0lIBADF7+I3y3co8Fhlh5RwLTo0hM67PIGIduDzzSShKgd8lOnON51nWOrxX6dDJ2AVH3H88ZGu8mvUWlRuL9YmLEJcm0EN95Vwq+Gc+PY3KvEMu8qrq/daHnAbbZ/lJCABCUCABA7EceHiiZId2V2KdIiLIuieeB/cYTD4VLn9FevENquYKON8sMKzYso4Q9pgLPRIBLmyMloUMstQLxT14rHidij65Uw0xTLlbWLfWP3EVbGYohJFHiFhBP0+K6fLLbdcjhHDdTPGpfL5/C0BCfwfgX6nyLNEGT54LNG1kuCGg5Ub5fmoo45q61ib0UayypCSkZSXRUU+gqSwspM9p68Eiz2WFwYzVgfoyHlRCko5A0691Y5ym1guxlpO1qCuSNwrrlWs4BSXmrtyHstKQAISaIRAxD0RQ1WlxMc5SMkY7ov0z7wHo1EJi3jZKk59/OJDES+7CUYqzM4U+chW1qgiz/tccBFiTCnHnpHVjfZGX0wbw82R97+oyENEkUB9Ak0Ndq3ftO4dDb/7Vg1M7K32VaWUZB+520kRGZaOehTpQJHiuaJ9dKLhUlLvHD05Fu4z4U4T32S16YpExx9uS1V1CahiAGLiwjZCHAMTBu6fyZ8iAQlIoC8JfP755/n05Ywx5WuyOhn9Ly6gnQlJCpgkEPhfT1Co6e9wWSm7IoZijktLKPvlc5FMItJOhuJfLlP8zcQBN1LGGlxqYsyJMmRHY6Wz6IoZMVuRGz/K+i0BCVQTGOoU+QhmxHKLC0pZQqmjQ6vVqaGkEl1/0EEHtVNiy+fqi9/RPizAtToqUnjRPnzZyxL3H51p8TiWZfwcsU7TadYTJjqRdaAYVErnzAu2GETo9KuEzpkMA7Sx6hlU1anaR9YCAoYZgFCyydpApx5LwFV1qvYFUyzyvOyrSlgBIVMEaSiLwc0xccFXNRT8cn1SVnKvBAwrEpCABLpLICzPxCWhfNcSUi9HRrByDBL7yxnH2IcRh1XOWu/nwNKOHzrCCmh5RYBVAPpfYqkYG6uEd50gKP1xL1XlYh9v78Z4ghtnMZd8HGe8gUPxpZBhVGlkohDn8VsCA5nAUKfIRw5bHho56MM/Oh4iSh0dGoooQUVYF6JDpAzLlPgeksudwJqiUhfn6MtvXFqwhKC4Et0fWVO4JpYS0kIS6ET7qt6uF/dPoCj+j2VBUUWwnGDZjrf1RTmuQRAVjBDe/BrpLvlN5x4v3eKlVDAupiljMCA4lYkCE47ID0zd7siqq66aA05JFcqzJPg2AqEaPR88482rO+20U3YJYjBCyMBALn/STyKkdSsOYLxrAJcaVjCoW0yHyX3zkik+KPP1gn/zyf1HAhKQQB0CvKCQfp2+BYMF4xF9cgj9FWl/40VJBMQWFXmSM5DlBgME/W8I/u7R95MRJtLqxnHeak6wKecnyJV2lIV+kLEHYSKBYaUopK4M4xi+7sV+tFgutnlvB7FYuPjUerEV701B8IcPYexC4ljs91sCEqgmMNT5yBOZz4eAHaysWI1RsOg0Qg499NCskLEPZRYLM1Znlgtj5k8Krr2a4ENPZ0kAEz6PZHwhxRadMG4eWMhjyRWLCZH8ZaEevuvcB/6DKNK8fCNenEUgEVxQSpnEkI2FQCmsG0xuUP7DmoMCzFtbw8of10LJx9JO/l8Ghd133z2/OZAlXiYgWK55UQm56TvrzOOctb55PuQWZkBDwt2mVvla+3mWtDnSWfLM+X+Cu00o9bCLwS7OAwPug7z++OvzgRWTCZ4HwcnkvGfiEvmTo67fEpCABLpCgMDTyNTFeMQ7U4idoh/ihVAYdmJlkP6fsawoKNdhbCBFMW6UIfifE6zKuEI/hwWcPpCVXzJ7YSghOxnjT620yRhUUNYxFGEII9MNq6b0o5HRDGNU1dgU7eCbsYL24OfPKkCtcQKjEAHApKBkcoOhiPtabLHF2t6DUjxvq28POnKhVm+i7euHBIY6izw+dlgGCBbCrw5fwrI/H2VQ8HnhBoozHQSzfJTf8cYbL+222245uDFSLg7p54oPIK415ApGSaRjxiKBEs+EAzePsIqX20YHynIlij+rCdxTKOZRFuX42muvzZYblHSywuAWQgdNWSwyKMwMKGHhj7rxTSfMMirKKysadOJk+uF8dPBYsKuWSqN+o99MwnB5QViuJbtCd4SBguBdLD8sD/PMGdRoO4MZAwX/J6pWOXgJFysPcW3clnDToe4f/vCHzImVAkUCEpBATwlgYaf/5KVJKPb4zRMQissnBgSMTGR5oT8rZ+JizECxxsc9VlWjPRhsWH0klztjIOfjN30/SjxGI1IyRz8X9Yrf1ONt3vGyRuqiaNP/M15i2Cm+ObtYt7jNGMvYRL9bKzsb5RkLeYs5hhdWDMjWQ9YeDCdDerW82H63JTA0ERhm8LLe/63rDU0t/19bsWKg0BeDZYq3gTLGW+4oh1La6Cuvi+foy206WCYiWMvpoOnQGhUszaGY1+r0uH9WL1BMmdQwiOBO06jw34OJRkyC6HhrWVcaPWdfl+Oesc4zMcK6Xh4M612flJT8f6EOk4zyakW9uh6TgAQk0FUCKPKslGKAoL8qv027fD7efUEfV88FMVZf6fcx3JDznfiprgj9PpZ43HLoC1ntpY19Jaz2MkGptVrQV9f1vBIY0gQitvD7wTock2f0DL7520e/qqXP1WrnUK/I17ox90tAAhKQgAQkIAEJSKCVCPS2Ij/Uuda00sOwLRKQgAQkIAEJSEACEmgWARX5ZpH3uhKQgAQkIAEJSEACEugBARX5HsCzqgQkIAEJSEACEpCABJpFQEW+WeS9rgQkIAEJSEACEpCABHpAQEW+B/CsKgEJSEACEpCABCQggWYRUJFvFnmvKwEJSEACEpCABCQggR4QUJHvATyrSkACEpCABCQgAQlIoFkEVOSbRd7rSkACEpCABCQgAQlIoAcEVOR7AM+qEpCABCQgAQlIQAISaBYBFflmkfe6EpCABCQgAQlIQAIS6AEBFfkewLOqBCQgAQlIQAISkIAEmkVARb5Z5L2uBCQgAQlIQAISkIAEekBARb4H8KwqAQlIQAISkIAEJCCBZhFQkW8Wea8rAQlIQAISkIAEJCCBHhBQke8BPKtKQAISkIAEJCABCUigWQRU5JtF3utKQAISkIAEJCABCUigBwRU5HsAz6oSkIAEJCABCUhAAhJoFgEV+WaR97oSkIAEJCABCUhAAhLoAQEV+R7As6oEJCABCUhAAhKQgASaRUBFvlnkva4EJCABCUhAAhKQgAR6QEBFvgfwrCoBCUhAAhKQgAQkIIFmEVCRbxZ5rysBCUhAAhKQgAQkIIEeEFCR7wE8q0pAAhKQgAQkIAEJSKBZBFTkm0Xe60pAAhKQgAQkIAEJSKAHBFTkewDPqhKQgAQkIAEJSEACEmgWARX5ZpH3uhKQgAQkIAEJSEACEugBARX5HsCzqgQkIAEJSEACEpCABJpFQEW+WeS9rgQkIAEJSEACEpCABHpAQEW+B/CsKgEJSEACEpCABCQggWYRUJFvFnmvKwEJSEACEpCABCQggR4QUJHvATyrSkACEpCABCQgAQlIoFkEVOSbRd7rSkACEpCABCQgAQlIoAcEVOR7AM+qEpCABCQgAQlIQAISaBYBFflmkfe6EpCABCQgAQlIQAIS6AEBFfkewLOqBCQgAQlIQAISkIAEmkVARb5Z5L2uBCQgAQlIQAISkIAEekBARb4H8KwqAQlIQAISkIAEJCCBZhFQkW8Wea8rAQlIQAISkIAEJCCBHhBQke8BPKtKQAISkIAEJCABCUigWQRU5JtF3utKQAISkIAEJCABCUigBwRU5HsAz6oSkIAEJCABCUhAAhJoFgEV+WaR97oSkIAEJCABCUhAAhLoAQEV+R7As6oEJCABCUhAAhKQgASaRUBFvlnkva4EJCABCUhAAhKQgAR6QEBFvgfwrCoBCUhAAhKQgAQkIIFmEVCRbxZ5rysBCUhAAhKQgAQkIIEeEFCR7wG8ZlT95ptv0u23356+++67Zlzea0pAAhKQgAQkIAEJtAiB4YdEO5566qn0+OOPpyeeeCK99dZbacYZZ0yzzjprmnPOOdOEE044JJrQb66x+uqrpwcffDD9/ve/T+eff37T7+uZZ55Jr776apfbMdVUU6Xpp58+vffee+nhhx/O9RdbbLE0wggjdHquH374Id1000253Oyzz54mmmiiTutYQAISkEArEfj555/Tyy+/nB577LE8Po411lhpttlmyx+2uyP33HNP+vTTTzut+utf/zpNPvnknZajwI8//pj7W9qLzDzzzGnSSSfN21X/vPHGG7n8L37xi7Tooos21D+jF6AjjD/++GmuueaqOq37JCCBGgT6VJH/6quv0tZbb52uvvrqdpe/8cYb82/+0A844IC09tprtzseP5588sn09ddf505DhT8lOtI333wz44nvYNWsbyYTJ554Ypcvv8UWW6Tdd989DTfccOkvf/lLQjk/7bTT0lJLLdXpuW677ba00UYbpWGHHTYNGjSo0/IWkIAEJNBKBN55553chz366KMdmjXMMMOkbbfdNu2www6J7a7IjjvumCcHndVh3N1www07K5aPn3DCCWm//fZrK3vIIYekddZZp+13cePss89OtIF2h+J/8sknp2WWWaZYrN02fT/9OYa+Sy+9tN2xoe3HXFvf0TJNHnTkQi3TFhvStwT6TJGno0JBx2KLwrXEEkukWWaZJY077rjppZdeSnfddVd6+umnc2fFH/A///nPDne65ZZbpueeey7tscceabPNNutwfKDtoHM86aST0uWXX56wzLeCYFn/3e9+16Epzz77bPrggw+yhQXLe1mmmGKKvIv/DwsvvHC6+eab02WXXdaQIk85hOs6wcso/EcCEhhKCDDeMTbSP44zzji5z2Ns/O9//5sNE6w2/utf/0qsZKMEDz9848M050QWWmihupOAX/3qVw3RevHFFxOKO2M4q6XffvttzXqM1Rhn5phjjnTmmWdm988111wzT0pYaZhkkkkq6x555JHZGo9BZ7755qss404JSKA2gcZ7iNrnqDyCYo4SP/bYY6dTTjmlwx8oy3V0EPwRn3HGGWmTTTZJ00wzTeW53Pl/BFh2bKWlx/XXXz/xKQsTLyYcuAAdddRR5cPtfjMpQZFnpeaLL75Io446arvjxR+s0Fx//fV5V6tMZortc1sCEpBAPQLHHHNMVuJnmGGGdM4556SJJ564XXGs0qxk33DDDQlXGZTyRoT4qc8//zyNMsoo6YILLmikSt0yP/30U1bCUd5Rsu+4446EgaaWMAGhLNZ1DDTIuuuumw1xrKJWWfGZ1BxxxBF57N91111rndr9EpBAHQJ9Fux63XXX5csyI6+aZeNSsdNOOyU6M+TCCy/M3/4z8AgsueSSaYwxxkgMRKGk16LA4IbLFsr+H//4x1rF3C8BCUig5QiQpOA///lPbhfKelmJ58DKK6/cNmZ21h8WbzCs8RNMMEFxd7e3Tz/99PTQQw8lVk933nnnTs8TSj5xSyGxjbW+LLD429/+ll1wMPb88pe/LBfxtwQk0ACBPrHIo5BhWUVYZqsluIrgf/fKK6+kYnDPxhtvnLAGEACDYF144IEH8jYTAxQ/5PXXX0977rln3sbqj78h1oy77747K3v3339/Phb/cB3K0TkRZDneeOPlgNvOVgO4F5Y4CcahsxpppJGymxDBuiyRMikpCz7gKJz4C9KxnnXWWdmi8e677yYCjbbZZpscNEQ9llTpyGjvhx9+mAOBF1hggWzpLvtI4lqDlWbxxRdPa621Vttlyyxee+21PDm677770ttvv51wgcG9Ces5y6RVgq/iJZdckj8EsGLZIbCJAYfVkuOOOy4H2qJAr7baalWn6NY+YiWWX375zAi3mVVXXbXmebDyI8stt1waccQRa5bzgAQkIIFWI4DLaYyN9K21ZKaZZsrupy+88EKtIh32hyLfG+6GjL340SOHHnpoHvM6XLC0gzEVKQbCxjb3XZaDDjoou84yvtTTE8r1/C0BCbQn0CeKPArWtNNOm55//vnEktqyyy7b/qqFX/hPl32osUKgyIcwm48Z/bzzzhu702effdZmweX4Gmus0dZJlrOfEBS5yiqrtPPxQ6lmaY+AzYsvvjjNPffcbeeODSYQKOXl4FL8+5lg3HLLLXkygnJfFJYZWeZkORFrBplmQphQYFnGjxBrB3w+/vjjOJwnKAQIM2koxw4wWYFP2d+wyIK2YtXh/kJQ9HkWrJRwv2VlnsAkrCOhKEc92nDFFVdkJZ7sMlx76qmnjsO99s3EICY7H330UfYdLZ+cbAxhzerNiUT5Ov6WgAQk0BcEUGzHHHPMbLzB4FTLnZRjSD1lv9y+3lTkcXP58ssvs6EKo1IjgrGI2DdcasMSzzZSHjMYDzHiYdQiqFeRgAS6T6DaNNv987XVXHDBBfM2PoB77713+uSTT9qOdbZBZ4DVecopp8xFCXrlN5+iFbp4HnzxUIr33XffdOWVV2bFPI6TDmuDDTbISjxZUVgyRCnlG+s+fn0cR9ktChbqUOJR8k899dRcDyWbrAK4g+DXXYzoL9Zn+x//+EfOyIKSikUf5Z+JC+fmHOutt15bHAFpyK699tq0wgor5NMQO3DnnXeWT9npb3zHSfHJRIHMP7feemu+PypyvnPPPbfDOYhXQIlHwccfElenRx55JB1//PHZR5MVBAKf+krmmWee/LzhctVVV1Vehv3ff/99tvhUuWtVVnKnBCQggRYhQP+6yy675NZgpCn37/R/jBn028SXdSXJQyjypHBke5999skJAXBfxch19NFHtxnE6uG45ppr8rjGeUg00aiEmyzjRkhsF411kc2OlWza1JVg3jiv3xKQwP8R6BOLPKffbbfdclYaXDuYeeOaQtAOCj5KMQGbZatwNCsUeFwuEDq02Bdlyt/kxEVZHnnkkcuHcvANLivktKUd4QpD/nH24R5DIM9WW23VziJNJ8R5UYoJyg33H+rRfiYAuJuUO+NyA1CcscIguPNg/cdiwVIkHTeBnrEcSudJMBQWf5ZVaUNVVpjyNYq/sdYzCYhVCTIjHHjggdlViZUC3GeKgUesShx++OH5FMQtwCFkxRVXzCsGlOdFVH0pWNkPPvjgnL2GiVVZIlsNrjdll6NyWX9LQAISaEUCGG8Yz3C7xFUUJRfLOy6WGHPef//9RL+LgYi+u1GhHoLvOekeMWCFML7xwU0GAw9JCKoEtx8yzyAYxUYfffSqYpX7aDNjF2MJ4xjj47///e+86lBMK8x9sSrNhAaLvCIBCfSMQJ9Z5FGoscbTadEZoLDiFoGVAH9o/Mvxb8dK3RuC1b5KicdNA+sGQnR8KPFxTawB4QuIlZ52hmAlRumm4wslPo7xHe4dKNy1XsKx0kortSnxUZeI/ujAFl544TYlPo7TpshMQwqyrsqf//znNiW+WDcs/XSiRYn4AyYsRSU+ytAeJmO1Jl5RrqffoaCz7BrxEXFOfCyZFCLBPY75LQEJSGBoIoCLKLFgCO4nF110UX6JEso4LiqsinZFiec8YZFn5RhXT1acccVkcsDKKgY0lHyMJPfeey9VOgiGFFwyF1lkkTxOdyhQZwdjJIo7QavEnbGazT0Q1xVjM5MJ2sX4xvEQxtBjjz02G3IYhyMHfRz3WwISqE2gzxR5LskfLwEtKOt0JAQoRpQ+1mheJIRrC3/APRWU7iqJiQJW6kiJVS6HdYSOD7eN8Okrlyn/JqC3qPTjT1gl4StYPoZbDlJcciyWicwDWDW6KuTsrZLIHRwdfpSJl5IwuaolrCiU/Rxrle3ufvxHGeDoxMP6HufCXYq4CQajzlZnoo7fEpCABFqNACugjFe4rTAOkLgAN0te0ISCS6ICEgrwuzjGdHYf0a+z8k3sFmMr4wjWcazlxEZxXcauvfbaq8PpMHgxCSDGjRXc7gj9M4YYjHbhIhsuN8RxcZ8o+iR3CKMarqq0GVcj9ARWE3D1VCQggcYI9JlrTfHy/OHSkfBB6KgIuuSPFgsES20o0uSf7a7UymCCNQIhAHSyySareXqUeITyvJyjKPgMklEGS8Grg7O50CFF+WK5qu1aKbXC4tAXLiK1JizhqlRuZ1j9a00qojyZFLqSRSHqdeUb/34s7yjyrLKExBv/zB0fRPyWgASGNgIYsFilZtxjNZaVzjDqxL2gAP/1r3/NiQVwP2lUqSVAlYQFGDqq/M7JQsaK+NJLL51fNoUbTbyzg/GIpAy83wWXn3pjZbSz3jeru2Vh4kIGNdrAqgPCSut2222Xk2OQVIE2ogfgyopybyxUmaK/JdCRQJ9a5Dte7v/vwe980003zb7lZLdBcGHpCykG2aKI1vrQmWI9L3aAWNlxU2GZkOVBrPt0NFgY8FuvtQrQF/fRl+cM//zwsax1rWIWnFplerqfDD5MyogRIOsR8vLLL2f2TETCPain17G+BCQggSFNgHgp+lH6XFxPy0o87SGOLGKWzj777Kz0N9JOlGNWY+v5tWOkQnlHYScNc8h5552X3yrLJKArAbZRv7NvsrSR6GH++efP42mUx8WH7G64VeKaQx+P6w+rr8SOKRKQQOcEhohFvlYz6HDwycaKwHIjS361LOu1ztHZ/kjfRQcWL6nqrE4c5zXZLPvR2WI5oYMtKvoovrXcWOIcQ8M390BmG55BLaFjrXe8Vr2u7h9ttNHysjIWeD5YicLNhqXieoNUV69leQlIQAJDkkC82wR3mnoxRwSjskrNm6yJYaqXwrkr7cedhfEMa3wxDileyIjhi362SjCoIKwQ4AvPajI+750J58TKzwSCOLXiKnSklS66oMZ2vGCqs/N7XAIDnUCfWOTxZ6Yz4OVDnUkoxlgIanVs4YbS2bmqjoeiTafQqDtMnAflFmEplGXQaGsc7y8dTXScuLRETEHcY3yTBadWHECU6a3vCGaNnPahyMf+3rqO55GABCQwJAlgfUZqJUeItqDAx3iFK2dnwviJbznuKEVLe7keriyhwBffRRJjLJlzGNeqPgTKIhiwSEXcqJslxhj89/HLjxdERbvIJofEqjDbZKnDMh/H2KdIQAK1CfSJRZ6Axc033zwrhbcNfgkRSnAtCSWNZcGyD3ekT+xJ/nJeuIE7DEoo7jG0qywcwyePzpBMAhGQGx1JLZ9zJiz9Qej8Wbkg2InJF2k8YyWDwYT87eQ2ZiLTleCr7rLBbYmOnVgKMhy89NJLOVD5D3/4Q3dPaT0JSEACTSdAv0qsFcGotV58RyNJERx9bTlmi/2sXod/O+WxtDPWYVk/7bTTKl9uSDkCXhFWPouuoaw+xxtnc4GKf3AzJaUlb2LFz74RwRjDOBlpnst14oVYjD3TTTddPswEgUlDHCvX8bcEJNCeQJ9Y5ImUJ30Vwh//Kaec0tYpxeVZbuMYL1SKcnEsvmP2jmW80WwyUTe+sfLvv//++SdBtWXLMh0TwTW8mAI/vqJlAAUXISi3+GZXMsnwkit8HPuDsIRLik3uHf9Nln15ZTYWcOIBSBNGFgWyDg0JYVDizbQI7yNA+F1eEckH/EcCEpDAUEKA2DASIGA8WmKJJbLbTFjDuQUUdNxP4kVMGMGKijzJFsgMw0oz41VR8DNHcEnE+o1hKgTXSDLSkF4SoU+PlJD8JiMZ56z3icQNWPKjHHVrCYG9BOviykP++iqJjDZMbkJiRSGOxX6/JSCBagJ9YpHnUocddli2fhOBzwsmIlIdZZHOCGsrHRiKNser3HDwC8SvPfLaUnf77bdv9zKj6ttqv5e32hEtTyfGMh/XI+iVJUve5kqHR75bJhxF9x7SfzHR4PpYL8jqgnUfX3E6Rqz4WCf6g/CSKwKxyHyAiw33zIeYBeIYSBsG+yElTCJ42RacEd1qhhR5ryMBCfQVAYJJWeHEeIRxiOB94n54rwhuLVijQwHHeFFWgHmhHwoygrW76M/OSibvZuFFTviwE1yKQYaVbvr0cOfhLd1DYtzaYYcd8j2RXrpoICuypc2/+c1vsiGJ9Mgo/bzXhVVxXtQ4tMmgI/+/8W9oa7ftHboJ9JkizxtMWcZDoUdBpPMJvzuQYXUlYw2Wh1ouE3RkuLeQbx5FHMWys+W/Wo8DRZSlSM5FBxppKVFU6TDo2MKlJs5BTl+WBVFgUd4jMId2MzGJlzpF+aH9m5RhuDrhR8kEh0kLlhos9kgo1cVgpb66Z6wx4e5Du8LVp6+u53klIAEJDAkCWNgxEDGGEPyKYQtFG6GvZVzBur7B4OwtZWFlFMs6Sj8GqrJg8Z9iiimyRR5jWcR5UQ5XFd7QTWrLvhay4JB1BmNcrK5WXZNVVhJJkCmHlXMmMdw/AbVVL2GsOof7JDDQCQwz2Cr+85CAgEJOvnKURCzbKGddyVCDzxxKPH/cPVEkuV0mFUwKmGxgiS5a4WuxwJ0Gawl14mVNtcr21/0sBTOhwdUo3gnQX+/V+5KABCQwJAgQAIvrKNZolG2MXPUEgwp+8uWYsnIdxtxBgwblssSt1Yr1Ktdr1m/cbQnyLRvUmtUeryuBviKAYRr5frBey2SWeFC++dtHH+2qjjvEFPm+AuJ5e06AyRVLtFhDsBKtssoqHU56++23p7XWWivvZ0LGm14VCUhAAhKQgAQkIIHGCfS2It9nrjWN35Ilm02AVYkFFlgg+1zylj0yCMw555zZ9Ym0YWRYIEgYS9BKK62kEt/sB+b1JSABCUhAAhKQwGACWuT9b5AJsKxJoGvkbq/CsuWWW+YsBI24IlXVd58EJCABCUhAAhIYyAR62yKvIj+Q/zdV3DsuNARgPf/88zmOAJ9NAk0J/CUDgiIBCUhAAhKQgAQk0D0CKvLd42YtCUhAAhKQgAQkIAEJNJVAbyvyffJCqKYS8uISkIAEJCABCUhAAhIYAARU5AfAQ/YWJSABCUhAAhKQgAT6HwEV+f73TL0jCUhAAhKQgAQkIIEBQEBFfgA8ZG9RAhKQgAQkIAEJSKD/EVCR73/P1DuSgAQkIAEJSEACEhgABFTkB8BD9hYlIAEJSEACEpCABPofARX5/vdMvSMJSEACEpCABCQggQFAQEV+ADxkb1ECEpCABCQgAQlIoP8RUJHvf8/UO5KABCQgAQlIQAISGAAEVOQHwEP2FiUgAQlIQAISkIAE+h8BFfn+90y9IwlIQAISkIAEJCCBAUBARX4APGRvUQISkIAEJCABCUig/xFQke9/z9Q7koAEJCABCUhAAhIYAARU5AfAQ/YWJSABCUhAAhKQgAT6HwEV+f73TL0jCUhAAhKQgAQkIIEBQEBFfgA8ZG9RAhKQgAQkIAEJSKD/EVCR73/P1DuSgAQkIAEJSEACEhgABFTkB8BD9hYlIAEJSEACEpCABPofARX5/vdMvSMJSEACEpCABCQggQFAQEV+ADxkb1ECEpCABCQgAQlIoP8RUJHvf8/UO5KABCQgAQlIQAISGAAEVOQHwEP2FiUgAQlIQAISkIAE+h8BFfn+90y9IwlIQAISkIAEJCCBAUBARX4APGRvUQISkIAEJCABCUig/xFQke9/z9Q7koAEJCABCUhAAhIYAARU5AfAQ/YWJSABCUhAAhKQgAT6HwEV+f73TL0jCUhAAhKQgAQkIIEBQEBFfgA8ZG9RAhKQgAQkIAEJSKD/EVCR73/P1DuSgAQkIAEJSEACEhgABFTkB8BD9hYlIAEJSEACEpCABPofARX5/vdMvSMJSEACEpCABCQggQFAQEV+ADxkb1ECEpCABCQgAQlIoP8RUJHvf8/UO5KABCQgAQlIQAISGAAEVOQHwEP2FiUgAQlIQAISkIAE+h8BFfn+90y9IwlIQAISkIAEJCCBAUBARX4APGRvUQISkIAEJCABCUig/xFQke9/z9Q7koAEJCABCUhAAhIYAARU5AfAQ/YWJSABCUhAAhKQgAT6HwEV+f73TL0jCUhAAhKQgAQkIIEBQEBFfgA8ZG9RAhKQgAQkIAEJSKD/EVCR73/P1DuSgAQkIAEJSEACEhgABFTkB8BD9hYlIAEJSEACEpCABPofARX5/vdMvSMJSEACEpCABCQggQFAQEV+ADxkb1ECEpCABCQgAQlIoP8RGL7/3ZJ3JAEJSEACEmh9Ai+++GK69NJL0yuvvJK+++67NOuss6bZZ589Lbjggmm44Ybr1g18//336frrr0933HFHeuedd9IEE0yQZp555rToooumySabrKFz/vDDD7ldjzzySHrjjTfSpJNOmts1//zz5+16J6H8TTfdlH7xi1/ka0400UT1iudjb731Vnr88cfT+OOPn+aaa65Oy1tAAhL4PwJ9psjTEdAJIL/+9a/TKKOM8n9XdUsCEpCABCQwQAkwPu68887pvPPOSz/99FMbhWuvvTZv//a3v00nnHBCVmzbDjawgeK+2mqrpZdeeqlD6ZFGGintvffead111+1wrLjjgQceSFtuuWVW4Iv72R5xxBHTAQcckP70pz+VD+XfZ599dtpxxx3TMMMMk37++ee87+STT07LLLNMZXl2wmKjjTZKTzzxRJ481Cw4FByYa+s7hngrBx250BC/phdsLQJ9psh/9tlnafnll893i3Vgttlma607tzUSkIAEJCCBIUwAxR1F+YorrsgK70orrZSwdKNoo8yec8456d57702LLbZYtmxjUW9E3n777bTKKqukV199NY0xxhhpqaWWSr/73e/Se++9l2677bZ05513pr///e/p+eefT/vuu2/lKR9++OG09tprpy+++CL96le/ykr/lFNOmTj3jTfemNu13XbbpTfffDMr7MWTPPfcc2n33XdPc8wxRzrzzDPzCsOaa66Ztt122zz+TzLJJMXibdtHHnlktsb/5S9/SfPNN1/bfjckIIHGCOgj3xgnS0lAAhKQgAR6TAAFng+C1f24445L66yzTlbC99prr3TLLbekiSeeOH3wwQf5eCMXxPqN0owSP+6446arrroqHXHEEfmcm2++ebrwwguzks25Tj311PTMM890OO2PP/6Yttlmm6zEY3hD8d96662zQW7TTTdNl1xySVbKqUibP/zww3bnwJ3m22+/zdZ12sA9YP3//PPP80SiXeH//WDiQjunmWaatOuuu1YVcZ8EJNAJARX5TgB5WAISkIAEJNAbBFCWDznkkHyqxRdfvG3Vunhu/Nj32WefNPbYY6drrrkmK9bF41Xbt99+e3rhhRfyIVxZpp122g7Ftthii7TqqqtmVx5cbMqCrz7nwC3mn//8Z14hKJZh//bbb5/mnnvuNPLII6fzzz+/eDg9++yz+Tc+/iGxjbW+LMQE/O1vf8suOEcddVT65S9/WS7ibwlIoAECve5aQ0eADx1/pCF0Giz1IXvuuWeaYoop8nb8Q6DPKaeckh566KG8DDjeeOOlOeecM22yySZ5ph7l4vv111/P5+E39V577bVscbjvvvvyEuBUU02VllhiibT++uunYYdtP1cZNGhQOuaYY3Inedhhh8Up233jI3j88cdn/8SDDjqo7Vj5uo8++mj26bv77rvTV199le6///62smzgXnTGGWekm2++OS9FjjrqqGm66aZL6623Xl7ybFfYHxKQgAQk0K8JENDJeDfCCCPUdG8BAD7l9fzKy5AY15AxxxwzzTPPPOXDbb9xt7n44otTKP5Fhf/yyy/P5dZaa60c2NpWqbBBAC7W/irBhQchMDYktvHdLwtjKwo+Vn/ccRQJSKB7BHpdkf/4449zxHyxOfj7heAvVxQ6IPz6WJILeffdd7OvIDN+Oh0sAEVBQcbvHsFXb+WVV07UCUHhxifwuuuuy1aDojJPZ0PdepH0+ANSZvLJJ49T5u/idemA1lhjjTZrCR1zUT755JPcEdNpFwX/xKuvvjpbXP785z8XD7ktAQlIQAL9mMBdd92V726mmWbqML7gHoPVuzsSbi5kvSmOd+VzFWPVHnzwwTbLPQGnYYhaeuml21VrtF0Y0Lg/3HbCEh8uPFNPPXW7c3Jt3IpIhLHDDju0O+YPCUigawTam6u7VreyNH/A99xzT4roewrhk8c+PjPMMENbPdJUbbDBBlmJx1Jw+umnJ4Jt+F5yySXzfo6jmNeS1VdfPc0444w5uObJJ59Mt956az4n5fHxO/fcc2tV7dF+fP9YWSBo6Morr8wTjjghqxG0GyWejurwww9PWPmxeGy22Wa5GL6QN9xwQ1TxWwISkIAE+jkBVnERVmYRLPRkbJl33nnzeELgKyvFTz/9dD7e6D9MDJBQnGvVC/cbjmNUCmH/l19+mX/SNsYwfNdZFcBqT0pILOcXXXRRu9X2qM93jO2RrY59sT399NPzMwur15wL6/7RRx+dhh++1+2JcSm/JTAgCPT6XxB+bkS5Y5kPIeiFfWWhA8OSQH5b/Poiby7WcvYRPU8u3K222iorweX6/CYSHveVsIiPM8446cADD0zkpSX4hgAdAol6W7DWn3XWWdlXsHzu/fffPyvutI30YuTGRVhmpMPGxQY/SYKQHnvssfy7fA5/S0ACEpBA/yIQ4yLKMhbp/fbbL+E3j2CNxz2UDwou4wtZZxqRBRZYIBcjQBaDFnnjq4RA2pBPP/00NtvGa3zfyf++yCKLtKWwpF0o+QTM8sFwhXEuxtw4yYorrpjdVjFcMeaxyv7vf/87u8diqAvhnjFy7bLLLtnQFfv9loAEukeg1y3yjTbjo48+yh0O5Zn5hxIf9Zml42uPYKVn6a9KcE8pdyiUW2GFFXLxsmtL1Tm6s4/0YXR6VYL/IbLHHnu0KfHFcqTvoqPDMkHUviIBCUhAAv2fAC6XyFNPPZVXc7FUo9Bj0GEsOOmkk/JLoVCCifEquqXWo4OhLFxBMZBhyCoLOd5RwEPCAs/vaBfve8Fw9vLLL+dvgm3JhIOrKeMWbjvEfJEqsjwmjzXWWFlxx5hHfBvBtRjWuKcYKzHMseKOhZ/jIawIHHvssenggw/O433koI/jfktAArUJ9LpFvval2h9hSRHBak2qqiqhcyK37tdff52XDGeZZZYOxYo+f8WD5MBFsFD0hdQKKEI5j+XL8BOsuj4uN++//35eWuXlH4oEJCABCfRvApEE4rLLLkuMZ1i4CVANWXbZZdNCCy2Us8ug2OO6WXRTjXJV3+RuJ14Ml1X83En4wNhCXBgJGXA7ZbxEiSZJxGijjdZ2mmgX4yVGNtx7ii99oh4fMuoQ54Zij1KO5b4oxLPh/46LD5b9om88MWbURdEnS00Y74gZwyCH8Y4PFn3KkfNekYAEOifQNEUeCwRCsGq910bzummE8lWKfK1JAJ1IXwpvuKsSljXjTX2x3FlVLu4rJjRVZdwnAQlIQAL9h0Bkb+OO/vGPf7RT4uMuRx999JzznWQKKPNYzht5MzruOviwEzeGQs6LpfiE8NIpXHZCQcaCHlJsFzndi0p8lOGbNnFOlHVWC8qKfJQlbq0srFCTSILUmgTGImSzwdKPHz659blPVhR4SRQTGl8QVabobwl0JNA0RT6W8mhSBOp0bF7KvnRY5oeWgJiu3lexA626f/dJQAISkED/IEBu+JBaq7ocx7LNmIf7CokS/vCHP0S1ut8o0ASY8sEKj2Wc8ZN0zljokQhyLb4xttgu3GHqCVZ+FHmSVzQqJHa44IIL8htscbsJYTLAC6NwVY2JBYki8OXHhUdFPkj5LYHaBJqmyEcwDlZ20kQOaanngxfW8u60Ke6LACGWTYvLl905n3UkIAEJSKB/ECA95H/+8598M1WxXXGXuJ+w6vvFF19k15jY38g3EwAmCVUTBaz7rIIjRZdOJgC0h7FvwQUXrHuZMD5F3vi6hQcfxLi144475qQOxMMVU2zGi6KKbqixHS+Y6uz8HpfAQCcwRIJdq5Tm8G3nj7UninNXH2B0QvgBVrWL8/GCqe4Kvvm4+3Bu3GwUCUhAAhKQAASKWWjqJTrgGEo8QhxZZ8J4Rh54LOX1hMBTxib88onTCmHSEO9rIblEPQlLfCPt4jw777xzdvXZa6+92r0simOR/37CCSfkZxZWB3CNjWOx328JSKCaQJ8p8kVXmBdffLHD1fHDwx8OJZ7OpUqwHpC5hoAffOt6Q8JiznWrFG2WMmu9ua7R68ckhWwEkVqsXPfEE0/M9xWdYvm4vyUgAQlIoH8RwE89LN6kXK4yJrEvssvgbvKb3/ymHQTGqFDy4wD78F9ffvnlE5lmqoQgWHzPkQ033LDDi6N22mmnfIwxiaw6VYKrToxZvOulM+HdKaSrjHTS5fLoAUhxLCZZBMG3caxcx98SkEB7An2myBOwE9ZvcrmXl+FIY0W+dYS8suSCL6bDotMh6IXgHPzrijP29rfQtV+0KV4bTbBR5PXlLPjqEXhTNfHoylV23XXX7FJz4403JjrH4r1zj3vuuWf+oMzXC/TtyjUtKwEJSEACrU+A/p/xj3Fxt912a/dWcxRYxiWCVpHtt98+W6fjrkgFieUcY1HxhYL4u0eAKhbwSIEc9QgqJa3kN998k33RN9100zjU9s07Tsh2Q5a4CLRtOzh4A2WbQFqO43e/1lprFQ932GbcI1c8Y+6hhx7a4Tg74iVSxVWAhx56KJeNY5UV3SkBCbQRGGbw7P/ntl+9vMGrlyNqno6LpbjTTjut3ZIeqabIHYtgxSfwlTRVvM0VazaBN3R4xTfD0aEsvvjiuQ4Kf9H6n3cO/oeOgbfScd1yTl18FAmowSpPflvOjUUDSwC+iQTeMMkgLy8v5whp5LpRlrfK8kKrcBvCusBy4UsvvZQ7blJvHXPMMYmXaCgSkIAEJDBwCJB+kXGPMY4xJ4I6cY9B2UZQ+MsKN6vXZH9BGDuOP/74vM0/pDNeb731coY3/NAZ18hkg4sKb5QlNTIvIyTolODXKmE85QWKERA7xRRTJHzWyRoX72QhpeTFF1/cqXGNt58TsEp+eNJiVgnj7iqrrJKDc0PpZ+zF1Ye6EQBbVdd9EhhaCYSHyfeDJ+7or8Sn8I1eiM5ajCNp5B77zCLPxelwWMJjRk5KRpRz/nCLQr5Y0lGh5HMsOgxujE4Jq3ZRiS/W7e42KbNQovER5MUbRPjjk0jAz/nnn9+QT2Jn18YXklWG6DCx8vPabe6RDAS476jEd0bR4xKQgAT6HwGs4+SSx1jEGIQFnQ9KPMargw46qIMSD4U//vGPCeUaH3es5kXhJYO4sWCkQikg/ozfuMKgxJPOkWvGmFSsG9usVjPmbrzxxlmhYAUA9xiUeJQLznHppZd2qsTzRnMUcfLi11LiuSbtxAWVNqHAswLB6jsTFpX4eCp+S6A+gT61yBcvjU8fyjwuN1XCwgBLce+++24ab7zx0kQTTZQ7kqqyvbmPdtFJMZHoq46DqH1WBXi5Fa409bIV9Oa9eS4JSEACEmhdAox7GLgwJKFsMw7hE4+CW0sYRzEI1XtXCivB+LNjPGJcI0975G6vdd7yftxAqY91ngBUVsv70hWUcRK3nYknnrjcFH9LoF8R6G2L/BBT5PvVU/BmJCABCUhAAhKQgAQk0EUCva3I96lrTRfvzeISkIAEJCABCUhAAhKQQIMEVOQbBGUxCUhAAhKQgAQkIAEJtBIBFflWehq2RQISkIAEJCABCUhAAg0SUJFvEJTFJCABCUhAAhKQgAQk0EoEVORb6WnYFglIQAISkIAEJCABCTRIQEW+QVAWk4AEJCABCUhAAhKQQCsRUJFvpadhWyQgAQlIQAISkIAEJNAgARX5BkFZTAISkIAEJCABCUhAAq1EQEW+lZ6GbZGABCQgAQlIQAISkECDBFTkGwRlMQlIQAISkIAEJCABCbQSARX5VnoatkUCEpCABCQgAQlIQAINElCRbxCUxSQgAQlIQAISkIAEJNBKBFTkW+lp2BYJSEACEpCABCQgAQk0SEBFvkFQFpOABCQgAQlIQAISkEArEVCRb6WnYVskIAEJSEACEpCABCTQIAEV+QZBWUwCEpCABCQgAQlIQAKtREBFvpWehm2RgAQkIAEJSEACEpBAgwRU5BsEZTEJSEACEpCABCQgAQm0EgEV+VZ6GrZFAhKQgAQkIAEJSEACDRJQkW8QlMUkIAEJSEACEpCABCTQSgRU5FvpadgWCUhAAhKQgAQkIAEJNEhARb5BUBaTgAQkIAEJSEACEpBAKxFQkW+lp2FbJCABCUhAAhKQgAQk0CABFfkGQVlMAhKQgAQkIAEJSEACrURARb6VnoZtkYAEJCABCUhAAhKQQIMEVOQbBGUxCUhAAhKQgAQkIAEJtBIBFflWehq2RQISkIAEJCABCUhAAg0SUJFvEJTFJCABCUhAAhKQgAQk0EoEVORb6WnYFglIQAISkIAEJCABCTRIQEW+QVAWk4AEJCABCUhAAhKQQCsRUJFvpadhWyQgAQlIQAISkIAEJNAgARX5BkFZTAISkIAEJCABCUhAAq1EQEW+lZ6GbZGABCQgAQlIQAISkECDBFTkGwRlMQlIQAISkIAEJCABCbQSARX5VnoatkUCEpCABCQgAQlIQAINElCRbxCUxSQgAQlIQAISkIAEJNBKBFTkW+lp2BYJSEACEpCABCQgAQk0SEBFvkFQFpOABCQgAQlIQAISkEArEVCRb6WnYVskIAEJSEACEpCABCTQIAEV+QZBWUwCEpCABCQgAQlIQAKtREBFvpWehm2RgAQkIAEJSEACEpBAgwRU5BsEZTEJSEACEpCABCQgAQm0EgEV+VZ6GrZFAhKQgAQkIAEJSEACDRJQkW8QlMUkIAEJSECnp/+WAABAAElEQVQCEpCABCTQSgRU5FvpadgWCUhAAhKQgAQkIAEJNEhARb5BUBaTgAQkIAEJSEACEpBAKxHo94r8Tz/9lN566638+eGHH9qx/+abb9Ltt9+evvvuu3b7/dE4gXp8Gz+LJSUgAQlIQAISkIAEukpg+K5W6G75r776Kl1++eXp2WefTW+88Ub6+eef069+9as0/fTTpxVXXDGNPvro3T113Xqff/55mnvuuXOZe+65J0055ZRt5VdfffX04IMPpt///vfp/PPPb9vfrI3XX3893XbbbemJJ55IiyyySLtmDDfccGnaaadNU0wxRRpmmGHaHWvmj3p8m9kury0BCUig1Qm8+OKL6dJLL02vvPJKNijNOuusafbZZ08LLrhgos/vrjDGPvDAA/nz8ccfp7nmmivNM888+dyNnPeTTz7JY+Lzzz+fPvzwwzTDDDPkur/73e/qjtWM64xfjGNTTz11HltHHXXUTm+DOm+++WaaccYZ8xjXaQULSEACbQT6XJHHYnvEEUekf//73+mzzz5ru3BxY++9904bbLBB2mWXXdLww/d5k/Kl6XDoOJD4zj+a+M9//vOfzIAmnH322ZUtoVNcaKGF0uGHH163Q62s7E4JSEACEmg6AVaHd95553TeeeclxsiQa6+9Nm/+9re/TSeccEIaf/zx41DD34wNBx98cLvyV199df695JJL5vOOOOKI7Y4Xf5xxxhlpv/32S1988UXb7ptvvjlvY3w78cQT05xzztl2rLix5ZZb5onJsMMOm+9rjDHGSDfccEOafPLJi8XabTP+rrzyymmkkUbKE4B2B4eyH3NtfccQafGgIxcaItfxIkMHgT7VmnFd2XzzzdN1112XaYwzzjjZ+o5VnD/0V199NV155ZXp3XffTccdd1x68skn08knn5xGG220PqeHVfukk07KqwRY5ltNllhiiXZNorN7+umnc+dKZ//cc89li8kkk0zSrpw/JCABCUigdQmguKPwXnHFFXl1daWVVkrzzz9/VmSxTJ9zzjnp3nvvTYsttli66aab0gQTTNDQzWCc2muvvbKijUGMcW2++ebLlv377rsvjxco1ey/4IIL8vXKJz799NPbjElY8Jdbbrl8/RdeeCFdfPHFecxmBf3MM89MCy+8cLvqTEpYXVh//fVzOx555JG0zjrrpE022SQr84z5ZaHN22yzTR7XjjnmmDT22GOXi/hbAhLohECfKvL//Oc/25T4VVddNR1yyCGpbAnYfffdExb5U045Jd1xxx1pjz32yNbmTtrdK4dZbuTTajLppJMmrCJleeeddzLPPffcM7300kvp1FNPzbzK5fwtAQlIQAKtSQAFng+C1X355Zdva+gqq6ySNtpoo4Ry//bbb+fj9PeNCIYwrOUo8Zx3mWWWaauGxfsPf/hD+vOf/5zdSXF9WXrppduOs4E7DuMvwgr5AQcc0M6Nc7PNNksbb7xxtpoztpcV+auuuirX3WqrrfI4z6oCq8fXX399eu2119q5teaCg/9hDLv77rvz5ILVAkUCEug6gY5T5K6fo7LGY489li3eHERZPuywwzoo8RwbYYQRsiKPnzqCrzp/2EpHAhNNNFHu5OnoEQJ1FQlIQAISGDoI/Pjjj9mgRWsXX3zxdkp83MFkk02W9tlnn2ydvuaaa9q5uESZqm+UcwTluajE552D/0Fxj3gx3DjL8q9//St9//332QK/2267tVPiKTvyyCOnQw89NE044YRZ6ccHvyjEv4033nhp4oknbtuNvz/CsbK8/PLLaf/998/l99133/Jhf0tAAg0S6DOL/FlnnZV95PDxw9r+i1/8omaTCL7Bh54ZOTN3rNELLLBAW3mCQMMqwbkoc+GFFyaWC7FaTDXVVAlXFJb0qpbv2k5U2sC1hgBYOtS11lqr7eiNN96YfRenm266vMxIGdxZWO6koyPodL311suBPG2VShv4QOI2dNFFF+XlSJYQ8RPEMsKHCUx3ZY455shVsaDUkjvvvDNhIaEDhRH3Mssss6QVVlgh/frXv65VLdFu6l1yySU5AIuCs802W/6wJDvWWGPVrFt1gEAprDxff/11WnTRRfNSK+UGDRqUYimVSV6VMFAcf/zx2U/0oIMOaitS/v/w7bff5nJMbGDCUjQTQ6xPuHMpEpCABFqBwOOPP577Vfr/esoriniVMl7vHnBVxW0Vl5xawthx//3354DUYhn6/fCjp7+uFaCKjzwuM2XBXeiDDz5IBOsWhdVlhNXkojChwXLPuHDaaacZ71WE47YEukigTxR5/khZTkP++Mc/NuTjR1AMvndHHnlkuuWWWxL+9eGGQ5BsnC8CY/CrD4lsL/jiY9FvVJl/9NFH83nLfuZ0iFzvo48+ysuI6667blZw43r4C6LYb7/99mmHHXaI3W3fKO1//etfc5m2nYM3mIDgPoSfIrEAjbazeA62Q4GvcgtiooG7Ej6MRSEF56233pqvywSmnBWHsnTGLJ9Ghx71sZxcdtlleRkUH0oyCzQi8MOlCn/+pZZaKq2xxhpt1d57773MmFWGWsIEhOdQDpQq/n94//338/8b2IYwaPBs8dekvn6XQcZvCUigmQTuuuuufPmZZpqpQ7/GuNGTjGS1DCLF+6W/RDB+FYUVdIJbuT59dVEaaRdj2RRTTJH7esaRGNueeeaZfCoy2BSFmDiMORjfYjW+eNxtCUigcQJ94lpDKi2UOIQOq1GJsqSqJLCzSrAKo0iiqOITiHKKPx+CFfrcc8/N273xD/dBoM6GG26YfRppE1Z2fP8QJh0oqWUh4h9Fn8nJXnvtldtIO1kuJc0mEw7KdEfwjWf1Aj9ILBplYaUCNnSkZAHiWlhQjj766LzkCls6T6wnZcESgxLPEup2222XWNZlNYJ2Y81nwkTwUzzbcv3ib1KerbbaapkPqwBMHnqyClE8d3Gbe+E9ALQdizxLxiwLsxLEhOcvf/lLu0lYsa7bEpCABIYkAQwMCCukCBZ6XCXnnXferAjjG49CXmv8y5W6+c9///vfPBZRneDaokS7sLjT/6Pw77jjjtmvHkMKhh/6WMaxWkIq6S+//DJh6AoJ6z3HQlDuiZdD8eecigQk0DMCfWKRj1k/TZt55pkbbmGxbJWiyYmwnuN6E0ohrhMHHnhgfuETEf64hBAp3xuCW8jWW2+d04TF+YjkJ6sAHS9tRLGnwwvB2o61gfaxZBhKP8fJxctn7bXXzq4gdI7kCy4LCihBQCGscKA8M2kgDRhKKm4pxXNHWTpk3JJQnllJCMEyzpIrbjIovrgJFYOscKfBbQnBV5L6IbiooJRjOeHZkhoTLrWEHMSUp8Nm4sX5GsldXOt89fbDhLYz0QhhosfzIXCMeAvcc/72t7/FYb8lIAEJNIUABg4ERZ6AVAw69O8I1nDcRflgeME9lbztvSW4p9I341r5pz/9qd1pi+3CeMPk4tNPP81laBd9OR8MMqTNrOr/WYVm/GM8ZLUaizv9L/0wfvUILjwYoLhnDGFMGhQJSKBnBPpEkQ8lHKswimujwsyfXLL4zcU5ynVRKkOJLx5D8USRx4reW0IHRvrMstBG/Oqx/pevFwGoKOtVijYdM76PvBwLX8UqRZ7rYVWuEtyNsJaTgaBKsHTUkjHHHDO3G0s7L8IqKvKx5It7U1GJj3NRl06c+6vnd47VB+UdixITCXzb4dhXQuqyohIf12HCxWoKSjyTFhX5IOO3BCTQLAIo0shTTz2VV3kZH1FsGSswdtBXocRjqWe1EaNR1TjS1fazistqLWMnGWfKhpVoF66ZMeZRjvGJ4FXGC9JLMm6xn/NEuWgLfS5+/xxfc80186ow9Ys57VltYCUdF87f/OY3uSpK/UMPPZRdagmWxeCEr78iAQk0RqBPFPlf/vKX+er4yuHKEb7unTUJSzEfpFZwLBblKmFJEKk1Aaiq09m+KQYv/dV642yt6+FriES0ftU1sIjQIdJZ1xI6uqLgv4jvPhZo/PJ33XXX/KItlmIbEZ4Fb2Fl6ROJ76gbS6vRucb+4jfH6h3HgoNVho6aCRcuOX0tDB61JDI00B5FAhKQQLMJxPhGzBEGCJRrjCQhyy67bHaBZAWVnPIoxrhp9kQw3NAXY1BhdbSqz4x24Z5Ke0jSUDSQkAkHIxQWdAxYpKYk4UM5KBY3V1bEcalBGR933HHbms4Yw0oyqxFY9UP+8Y9/5NVrzk3iAtxRmTRwTUUCEuicQJ8o8syqQ7A8NLo8yNJdLDPWeqNdsWOIa/BdS/Evlunqdq1r1boey4bcL4KluOhyk3f+7x+sHkgtRZ5I/1q+g1wDiwfuOyxvkqGl7O+Iaw6+7g8//HAeDHDLQXEnaKlK6MRhj9TLaFNVt7ivmJe4nsJfrNPT7XqBt7EaxOSO4NpGX6zS0zZZXwISkEAVAeKmQlBgi0p87Md4RMICkgOgzNN3jzLKKHG4S9+stGI5x5DDKi8ThCoptgtlvKjER3kmArgCoeQzhmGlr1oZJn6rPFlAQY+YLlYcYrwmQxwuqGRyw9WGvpqVYtpM26v4RHv8loAE/j+BPgl2LeaR7UrQTijBNK14jqHlYbH6QIeF0DESvFv1YZKCwhxW/a7cH50knTxv7KMz5Q19RSEYCbcfLDAo82R4gSXW6VppyWgzEwQEt6HeECYyVbmDe+PccQ4Glno+lsXBr/i68ajvtwQkIIEhSaCYQaus7BbbQX9NX0+/XM7XXixXb5vVYRI1YKhhhZS3ydaSYrsYW2oJ40OsiuNL36gQx0YQLMp8MUUlijxCO7lfsphFQgUMUYoEJNA5gT5R5PljDGtoLatzVdPCLQUFN6L6q8q16j4sKbzMAznqqKNyxhiyxpQ/3CcpNstpHrtyX7HKgVUkBKWd5U5cXLC8sJTJRAq/doJyCZ4qBsBGvdFGG60tHVlXJl5RP76JC8D9h44aKxKdcwRMRZnyd61VAsrFykW5Tvym7vPPPx8/O3xHRiEGH30uO+BxhwQkMIQJFJXYqlivaA7uqeGSympiV+XFF1/M70bBgIGrS2dujsV2MR7Uk7DeN9ougneJr8LKv+2227Y7NX00Cnwx0UW4pfa1IahdQ/whgaGYQJ8o8vCIl1nwKmpyp3cmKKRhXY66ndVpxeNhrWBJtC8FpR0puv+wFIkFBys1vpVVriS1OsfoPIurIuX2s+JACkryu1fJFltskRiAyLhDQCxKPb7+LOuWJQYD3H5qKfPF3PDl+vG73sSj6C4UeY2jnt8SkIAEhjSBMMBw3XpjBMdiFbH8npPO2kz/jFsOmWhIPFAMNq1Vd84552xz30HxriW4vsYKQSPtwqDD6izKOi41fBeFzHCMBTFp4Vi8W4RjigQk0DmBPlPkSV/Fch1/+JtuumnbS4yqmsQLfMgwggUWawDlh1YJhZggplodESm6ULQJ6OmOkGEACztSDAiK6+FSUuUiw0uhai1XRrsJZApLdrFtKNtY2EnrWC8zDnVYUYlc97j64NNflrDA8MyrglGZkJBWsjNh5SMmNcWyTBBoA1Ll71ks67YEJCCBIUGAeKbIVMZLAauMGOyL9MO8Sbscb0TfGEp+uc2hvKPMk4Hs8MMPbyhrGGMGhhgEg1pVn8oxxjWSJiC8ib0z2XvvvfOLEP/+97+nYi75qDfNNNPk1MrFFzzGeMAxRQIS6JxAnynyKPHx0iMUT1w6iIgvC7N/lv4i9zxBnjEjL5cdGn5zn/i/Y7nmvoovx6CD5s2zZHYhWDWy+5Tvi466/CElJ6kuCTQiGIiOmoCh4lv4QqknYIjzF11T6BypF8HE5WvSVt4Ui9WdSRjuPzHIMDjg48jKCn7pjUy0yGUfAbtYYspKOVaYeH03QV+Rx5h2MVCQYpPl4c6EAQCrfzENKG+ipY1MEFmxMPVkZxQ9LgEJDCkC5HNnhZB3nhCAGnFVXB9/dvpD+nmEfOwRGMpvVjnxn2flF4NQUVDuyQ/PSwMxptBnYxGnP636lFdK6TPJ94674lprrdXuxX+MBbQ3EjjwxvZYfS62obh92223ZXdOYgHKWdiiXLjgFg1MpKJE4liU9VsCEqgm0H6dq7pMt/eSGvGbb75JO+20U7byssyHks7roVEI6ZTefPPNfH7y2uLHh0I5NAsrCrwwiTRiKMMo17i40LHSwYbPONZtIvWrBAU0lNyq4+xDQcVqE5Zt9k0xOF0myjrWepZTjz322DTHHHNkhRiFF0sQL3WqWglgaZM3wtJuFGEmCLQbFxlcVOjIGXxIe1llWeH6ZSHACj/9Sy+9NC+vYmEpZpnBUk+GBPLp09lzXiYwTH6Y5PBm2v3337982na/eYkVkycsXfjBMziFSw4+qBwfGgOn292kPyQggX5DgD6bMZE+mowtrIJGgCl9IWMmgsK/8cYbt7tv3pUSvumkMC5axUmCEDFprL4W+9p2J/nfD17WxFgcwiouFnwUel7mhN88H8Yu8tuHsQUlPlY7o275G4s+/vCck2w0tVwbMRpx/0xemHSQcY37IjEDY9fQJoOONGXm0PbM+kN7+8wiH3CwEKBY4veOso6SSgeCPzdKPH/gdEbk1eWPuj8IkxWWIOnwuGc6XiwOKPF0rrzRrzMFtcyBiQ+KKhxZprz++us7pPiiDucmLSUpQOkY4YybCWxRass+isXrsIrCsipLsijBtBsfdCxCdKpMUGL5tViv3jYvAGHgCkt/TGSow5ttySvMCgZWKXIP4xsKI1YuGvHBxOcU1lwDq3wo8QRLs3RdXpau11aPSUACEhgSBMjewpjHSxDp+0hIwAclHuMJL9KrWvlkTMFgQ1pG/OCLQj/fU1l44YUT7pD0qxhFMMSQhx4lHuMKL6kKt8l612KlAeMRCnq9RAOMlby0D9cemDCRwODEKi5jniIBCXROYJjBltbq5OKd1+1yCXy4sbaiwHNZFLWpp566Miizyydv0QoosNwvbi50wMWUiH3dZK7LcisdaS03nlptwJWH+nTmWNLLbwKsVa+7+2knijj/J/ALrSe4CZFiE8GCE5MTXLhwaWIlgY8DQT2KHpOABJpNgHGQPgsDBmMF/R/Gh+jTqtpHn8zKZdHlpqpcT/cxXtMu3DhRuHG9jCQFPT13VX04MEGp9RLGqjruk8DQSIC/KeT7wa50/K1jOOUbPQvjdld1lyGqyA+NwG1z6xGopci3XkttkQQkIAEJSEACEvg/Ar2tyPe5a83/Nd0tCUhAAhKQgAQkIAEJSKC3CKjI9xZJzyMBCUhAAhKQgAQkIIEhSKBPs9YMwfvwUgOIAMFgvHcAqZUNYQDh8FYlIAEJSEACEhigBPSRH6AP3tuWgAQkIAEJSEACEhiyBPSRH7K8vZoEJCABCUhAAhKQgARakoA+8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQIq8i35WGyUBCQgAQlIQAISkIAE6hNQka/Px6MSkIAEJCABCUhAAhJoSQLDt2SrbJQE6hD45ptv0kcffZSGHXbYNNFEE9Up2ZxDH3/8cfr666/TaKONlkYfffTmNMKrSkACEpCABCTQ7wn0mSL/yCOPpNtvvz2hdM0222ztQI4xxhhpzjnnTCOOOGK7/f4YsgR4Ru+++26+6OKLL56GH77x/w7vvPNOevTRR3PdWWaZJU0yySRDrPG33npr2mijjdKEE06YuIdWk7///e/pmmuuSVtssUXafffdW615tkcCEmgigQ8++CA99NBDnbZghBFGSIsttlin5aoKfPbZZ+nuu+/OhxhrJ5hggg7Fnn322fTKK6902F/eQT87xxxzlHenn3/+OT3xxBPptttuS1NPPXX6/e9/n0YdddQO5co7qPPmm2+mGWecMU0xxRTlw/6WgAS6SKBxza2LJz7++OPTVVddVbMWnRQKPgrZSiutVLOcB7pH4PXXX0/vvfdeGnPMMdO0005beZLiMzr55JPTMsssU1muaudhhx2WzjnnnHyI7bXWWquqWLf2Pfnkk9miPemkk2ZlvVsnsZIEJCCBFiRw1113pc0337zTlo099tjpqaee6rRcucCDDz6YjQhvvPFGPnT66aenJZdcslwsnXLKKenss8/usL+8Y9lll00nnXRSeXfacsst06WXXppXRn/66aeEge6GG25Ik08+eYeysQMFfuWVV04jjTRSngDE/v7yPdfWd/T6rQw6cqFeP6cn7F8E+kyRL2JaZJFF2n7yB//2229nSwBWiUGDBmWlrTcVwbaLDeCNE044IZ122mnZonPWWWd1SuKMM85oWJH//PPP02WXXdbpObtbgAHiueeeS3vssUfabLPNunsa60lAAhJoOQJY5BEU3noW6a665f3444/pyCOPTP/6178S251JtGPWWWdNY401Vs3iM800U4dj5513Xlbi119//bTXXnvlldF11lknbbLJJlmZx+2xLFjwt9lmm/TFF1+kY445JjFRUSQggZ4T6HNFfo011khHHHFEh5bi47znnnumSy65JO2www556W/RRRftUM4dQ4YAVqKXX345TTXVVJ1e8KKLLkpfffVVp+UsIAEJSEAC7QmEAr3BBhukTTfdtP3Bbv7C0o0r3wMPPJBY7d5ll13SQQcdlL7//vuaZ4x2HHLIIQllvisSq+1bbbVVdpH97W9/mxZaaKF0/fXXp9deey1NOeWUHU536qmnZnef1VdfvXKFoEMFd0hAAg0R6DhtbqhazwuNM8446aijjkrzzDNP9rW7+uqre35Sz9BtAlhLsMo3ImeeeWYjxSwjAQlIQAIlAu+//37eU+W3Xira8E8s4yjxKNCMpSj1nUlP2oF//XjjjZcmnnjitsvMPvvseZtjZcFItP/+++fy++67b/mwvyUggR4Q6HOLfL22sfyGxR6fvnvvvbdDUZbg8N1+/PHHE50DfnUEVhK8s/baa6fhhhuurQ6uHldeeWXuKOgwasnRRx+dHn744bTAAgvkZUDK3XjjjYmlwummmy5bMm6++eYcrHj//fcnJhwEHNExRjDoLbfcklBmcf/g+Nxzz519xKeffvpal82uRPgk4k6E7zqdIPfBUuQ000zToV65Tffcc0+69tprMyesLPi9r7feejnAqFgZ68rTTz+dP+x/7LHH0oYbbpiL0NFuvfXWxeJ5e4UVVkhXXHFFuuCCC/L91wtCvu+++/J9034GDQaPWvLDDz/kZ4IF/9VXX80TNpaT8ZHkg+WoKBtvvHHC9eqtt97Ku2lPnH/NNdesacUheIrnT9tYKeB5YB1afvnli6fvsA1T/Pz5v0WmGe4JyxSWsplnnrlD+djBfV1++eXpwgsvzM+VJXD+X+ISVPUso57fEpCABMISThBpbwljApbuAw44II0yyigNnZZ2MIaOO+64DZWPQvTR1C1b8YlpQkiEUBTcfLDck8kLd8+uugwVz+W2BCTQkUBTFXmaEz6CLMcRaR9/5ChwKM8sGRYFJRUFD2UaP3CUewQFimU9BL/qqiwqdHb45nEdOr0QlEzq4u5DsA6KLxZqhKh+lG8y8Fx88cXZHYjJRQjtZmLAMQJ/qpR54gBWWWWV9O2330a1nC0GBfT888/PdVE+i1JsE1kB1l133YQCGfLCCy9kxX777bfPrkmxH264yYTQ4QaXWn6T+EASt8CECoWeyVUtCas9kwgU51oCv7/+9a+5jcUy8LrjjjsyZzgWfSlpJ4NECBMlPsi8884bu9t9ExjLpIBJXwh1UNBhyABSJSeeeGJ+lsVjZPDhmTCpo21LL7108XDbNllpKFMU/l/iJsZEUZGABCRQi0BfKPIHH3xwmm+++WpdssN+xkDGIyYTRYNYh4IVO+izGbfpZ+mvow9/5plncmky2BTluOOOy7FwrBqQ2UaRgAR6l0DTXGviNlAgEfKBhxKPwhpKPAouvnUoyywZbrvttjk6Hov1fvvtF6fJFtEZZpgh/w7/vbaD/9tAGacDI7CnKq0Xih8W65122in78t15551ZSR5mmGGyJRwFFwWPtt100025TXRS5AvHoouCVxYyB2DhpdNcaqmlEhkEuBe+ySTAfo6TZaZKmEhgtWdygZKNwsjKAz6JCMFNoezyG0USSzMTB2T++efPv9mHtb6W0AYkFPX8o/TPhx9+mFcqWJlgRaSe8GxYQSCTAcFQpIzks88+++TnfN1117V7fpyLCQjtDP9KLNz85lMVDP3pp5/mdiyxxBJ5cgcb/l+QZQE58MADKzMjYE2nTQj3fe655+ZUmqyYYMVncGIyGKsBueD//qFMKPFMZtjmeR577LH5mWy33XZtqyHFem5LQAISgEAo8uOPP37urzAqsaJHn77jjjvmfr5o9GmEWleUeM5HX47Qhu+++y5nsGE8YgxlnKL/ZvyrJRisvvzyy4RBKSRSAReNWSj3jDso/iQvUCQggd4nMHzvn7JrZwylOxRTatMh4IJBnlkU1YioR9mfa665svKLAl3uaFZbbbWE/x3nrMp2EtdaccUVO7h1cF06WOqjOIdg8UZJZzKBoomiv/POO8fhnDoT6zPKPdZcrN5FCwfpNek0CeRlEhDHuBf2oRBjocZyjIJZFuqWr0lcARZnrNS0GcWeAQCJ5dqYFI088shtinH53MXfKL90tLDnPhhYyoLCy6rG/2vvTOCtq8YHvBqUIUPmRH2GDJGhKM2fmRRSKJJUijQYMiSpEEUkGRMiU0gDkaE0IFOmFFIqRYPmTBXd/3nW33tad397n7PPved83zn3Pu/vd+45Z++111772eeu9a73fde7SVPZ60FMeDW4P4TO4Eot7y0DBS+um/SXZDTaYIMN8qlCgV9uueXyd7IaxLZqW/iOqxarOV4WJlsIvxWs7UzUUOy5ZwsXLsz7+EPYDqy5Zyw0Y8F1yCabbJLr4xqZaKCowyJCgIjzjAnATjvtlPbff/84NHsFYEiYUuTX7+70gwQkIIEOAYwEeH7plz/+8Y9PM67Q12NMIiXkk570pGxUib5n2PAiPh5DC0aiU045pXsKwjF5YZxgXME4URW8rfTz7GeMxOtM3noMSDEGYZCjr2VMZBznmhUJSGD4BJaYRR5lCXcglm2EBxKFoKgSqkIceijxsY93FHYEawBW2RBCLFCUUUarITmcj44HiePjuHin0ySEpSqlO5BY7apE2AeKJcpeCB02oR8ImXtCiY/9WLaJaUSw6pahM1EGBbUu5zAhRcGszUM9or6md5TnsHrXWeUZgCLncFjvm+rC84GgrJdKfJTfcMMNu6kuWYcwG2HCFkp81MP3iI9nQCqFMCl+C0wU6x7WxLEMOqwT4LfFZCCE0CPu0SqrrJL22Wef2Nx9hyETGEUCEpBAHYFrr7029yGs5TnkkEOyMYEw0fPPPz97O8ngxriA9xJjQd2YUFfvoNvCK4AxjLEH7yWez/POOy97GbfccstcJdb0uj6NMRqjF+UZE3mWCAYZxvQQtjH+ofSvvfbaeTNKPX0+4x656YcxdsX5fJfAfCUwcos88eyEnoTwj0w4DQpWPFWUzgsreRvhSbFl54Z7D6sCgiUAJfHUTkx51SqPcoliRvxe3VPqOB4PwPLLL8/HaRL1o6gt6LgIq1JmHyhdoizSRYjXb1pQhMUZpZxJAG7IqiWc84V1vXrelVdeOW+KTrm6f9DvTGKwwrBwFOt8eV4sNoQJsRYhLOhN9YfyHFkM6sqtvvrq2QMRjOrK9NuGsh3hVNWykU0hHooS+6NtPIysOrGKMvxeWUyMNZ7y8WTieDgL32PhcxwT79xP7nW4rmO77xKQgASir6bvwcPJeBVC8gNe9D0Yb1gzhEIfBpsoN4z3aAdjJiGQpYcVDyYvHiaIF5nkEeSIL8cD2oBBh+0YzqLfi7bhlcRTSgKJ0oONAQQvLdZ5xko8nIQnkpxAkYAEZkZg5Io8zaIzqBPi8whj2W233ep2dzPHYDHA5Uh8O9bUXkK8YZ0iH2E1TdZ46qxT4tkeC1+rll/29ZJQGvEOYMVtkrgmylcV+aYJAHVFCEpTvYNuJ+sAoS5k7SHTDFlkQsJKz4KlXsIkKxReHv4RIT/VY+KaZ6PIM9A0KdTBJu5dnD/uSdMEIMqxH0W+bB/fkX7HMiGshn1Fvb5LQALzlwB9LAo6xhuU3DrBm0hGLCz1JBQYhSJPeCRhqmTqKpX4sj0o2RjieAAg3si657zQ/2KdLwUFPZIMsGYr+mLWL6HE4znH68lkgmtl0kIIJP25IgEJDE5gsSjy1RAG/mFZnFNd3R7Nx8qOEhi55Ql5obPBysqxWOXpWOqEmGksqhFegzUc5ZKwGhTxXop8XX2z2YYbNaTu6XixD/ciFo0mpTTKLY53rCwo8oQ1hSLPRASLPINPme2nrj24jMMrgSeD66oTvDJMUqhzcQqTQaRfirYVVlghlytDtyIzTuzLBWr+9Ku75hA3SUAC84AAfUN4+HpdLiGJKPKzDT1sOgde5NKTXFcOrwFKOn0/7ahT5OuOI0yHsFdi68sUlSjyCDH5jHWM6ZtttlleK4WxrnwCfF29bpOABOoJjFyRJ9MLC0EHER4xjRKPIkiKSUI5SiWXhTpNnSHhFnQOuC2pgwWNEVZDPRFyMUh7Zlo2cpFjZWfx5CQIi6zwHhD7SG5/BpSjjjoqL9LCklJ1r1avif0cTxYeHvhFRplxEn43xG2WmX7q2hf7y4GIcKBBjq2r120SkIAE+hHg+SRIPFOjX/lR7R+0HXgQiH1nzCPDXCn0qYzjMS6yL8IveZaHinxJy88SaE9giS127dVE4gIRsoYQq1cq8Wyve3Ic20PCakw2F6RNWE0cO8z3mGzQ3gglGWb9beqqhpb0O4acwHBHCKeh3UyKEKz1bSSuO0JR2hxTV2bQttfVUd0WbYucx9X98T32R3m2xwBULoCN8vGOR4Jc+YoEJCCBKgGeG0I8eD/jVnic656HUq1zJt+JW6cdKN29ZJB2hCed8ZqQmuq4zbohjHPlAwcjrMc1Rb3ugvsk0JvAWCry8U/dFB8eCnrTpbFCnqwkhNcQtkJMIotriAtcnMLCUFypKMNNHSadHyv4yQAQOfWH0cZIW0Y2hEFl6623znGNLIIippH7QT7/UGT71RdWFuI8415WjyHUiWuOnOzl/tm0vayn7nMo5izG4ndRJ6SFIyyKUKy4FsqFdZ5jYVMnLAwjw48iAQlIoEqA53rQJ5NQoMxwVpYjdjzSQdaFs1x33XVl8Rl9JuED4S+k6i2TR5SVEU7D2jSkrh15R/GHdLwYMXieSplLPoowHpLJLZJcsD2yurFPkYAEZkZgLBX5WMFOnvEyjSSx13QW5FDvJShgkT6LjpM4Z2LnF3fsMtZtFDuEB2xg4UZxDyGjCnnmsV6g2Eb+3dg/m/d4XDYdK3WTLaitkL+d8CQmIJFnva01nnOQ/YYwFMJryGpQPjQEKztWKVKSkdasboFxtB3PTFjG27a9XzmUcdJi0g4WZBF2FQMZmYN4Oi/3CsFqVYYSMUGMySBrOFhUHccSe08YGM8bWNy/s37X7H4JSGA8CBB2SNw5/Q9hp5EYIFqHkotHmXes1fSfpfBAPfrWzTffvJuEodzf9jP9GOuTGF8x3FQnBzwML1Ixk3450kc21U9fSAgm11b3DBeOiyQBxMOHkA4YiX2x3XcJSKA9gZHHyLdvym0lWQzDwhg6MzoGZvcoR4RqYO1kxTsPSeolLGolj23I4lzkGufknc4aSzv5dVEMWfjLwlcUPxRdFGziEHliaDzqujx+pp95IBIKKZMflHDy8fPEPtYftBGOOeaYY3LRUOzbHEcZFhuTc54HJJElhokZC6tIl3nBBRd0c/9zn4m7rwrHsaaA+0/cJBMcHjpSHdSqx7X9ftBBByXWWfAMA3Ig89viN4Z1iKccIlx/ZF6IepkgEvePl4fwGgZArpVF2xyLUs8CYdp94oknxmG+S0ACEugSwICBkoyRgjVE9D1kuiIEk20o+fSV9L+lV5rthDnyTiw6Fv2mhBHdkzV8YME+ijf9FRlj8Dxi5OC8hNNEbD5995Gdp5D3EsYy4uGZGDA2N41jGK1oPykoMWhhyOIhiIxVTSmhe513HPeddahpNMfxvsz1No2lRZ60WITPkFOXMAsWyTCLX9DJqY6CSNx8PyG0JqwIkV++3zGj2k8nhyUlMuig3KIMcm3EozNpqXNFzqY9sCIshnAYYhUJFWkKc6k7TxlKw4OiIoVYXdm6bViTCK3haalkP7jiiivyPcQ7wqCF9Tq8FdXjUe7xvDCooDyjGEfGmGrZmXynPYTP8MAVYjYZVPh9ocSTEpWUmU1tI0SLjD5YtBiwSM1GqA2Tgf322y+/ZtImj5GABOYHAfq1448/Pns98UiivOMJxDhAP4LxgtAbxrBS6AtJ18y4gfI7UyU+6lx//fVzQgjGWYwQKPAo1ijxGF7wXNLXlTHtcWz5vvfee+c+GgW9KUsZ5RkT8LJzjRhJeCAWCSjwSHNtigQkMDMCS3Vm91MzO3TxHIVFmdAM8t32S5dVbRHW8NNPPz3tuuuuic5mSQuoUWhRTCN/b5P1YphtxeqPAk2YSHUB0jDP01QXC0Bx4RKqs6AzwaAjbyso1yjxeBRG0dkzgOExIXaT3xeTvrb3BCWe0CWs8oQDtT2u7bVbTgISmNsE6H9Q5HlhzMHwgqGhl5B+uZ9y3ev4un300RgkLrvssmxA66WQ1x0/6Da80aSSLkMXB63D8hKYVAKxHvKWjn6DTsbknHf+99EjBtV1xl6Rn+mNIvaOh00ACDdkrI6faX0eJwEJSEACEpCABCQggdkQGLYiP5Yx8jMFhHWDBYss1CE+HAs4ccwq8TMl6nESkIAEJCABCUhAAuNKYE4p8ijwW2yxRZc1D6XYc889u9/9IAEJSEACEpCABCQggblCYE4p8ixcZKEsT28lMwyLGVlJr0hAAhKQgAQkIAEJSGCuEZizMfJz7UZ5PRKQgAQkIAEJSEACk01g2DHyY5l+crJvka2XgAQkIAEJSEACEpDA6AmoyI+esWeQgAQkIAEJSEACEpDA0AmoyA8dqRVKQAISkIAEJCABCUhg9ARU5EfP2DNIQAISkIAEJCABCUhg6ARU5IeO1AolIAEJSEACEpCABCQwegIq8qNn7BkkIAEJSEACEpCABCQwdAIq8kNHaoUSkIAEJCABCUhAAhIYPQEV+dEz9gwSkIAEJCABCUhAAhIYOgEV+aEjtUIJSEACEpCABCQgAQmMnoCK/OgZewYJSEACEpCABCQgAQkMnYCK/NCRWqEEJCABCUhAAhKQgARGT0BFfvSMPYMEJCABCUhAAhKQgASGTkBFfuhIrVACEpCABCQgAQlIQAKjJ6AiP3rGnkECEpCABCQgAQlIQAJDJ6AiP3SkVigBCUhAAhKQgAQkIIHRE1CRHz1jzyABCUhAAhKQgAQkIIGhE1CRHzpSK5SABCQgAQlIQAISkMDoCajIj56xZ5CABCQgAQlIQAISkMDQCajIDx2pFUpAAhKQgAQkIAEJSGD0BFTkR8/YM0hAAhKQgAQkIAEJSGDoBFTkh47UCiUgAQlIQAISkIAEJDB6Airyo2fsGSQgAQlIQAISkIAEJDB0AiryQ0dqhRKQgAQkIAEJSEACEhg9ARX50TP2DBKQgAQkIAEJSEACEhg6ARX5oSO1QglIQAISkIAEJCABCYyegIr86Bl7BglIQAISkIAEJCABCQydgIr80JFaoQQkIAEJSEACEpCABEZPQEV+9Iw9gwQkIAEJSEACEpCABIZOQEV+6EitUAISkIAEJCABCUhAAqMnoCI/esaeQQISkIAEJCABCUhAAkMnoCI/dKRWKAEJSEACEpCABCQggdETUJEfPWPPIAEJSEACEpCABCQggaETUJEfOlIrlIAEJCABCUhAAhKQwOgJqMiPnrFnkIAEJCABCUhAAhKQwNAJqMgPHakVSkACEpCABCQgAQlIYPQEVORHz9gzSEACEpCABCQgAQlIYOgEVOSHjtQKJSABCUhAAhKQgAQkMHoCKvKjZ+wZJCABCUhAAhKQgAQkMHQCKvJDR2qFEpCABCQgAQlIQAISGD0BFfnRM/YMEpCABCQgAQlIQAISGDoBFfmhI7VCCUhAAhKQgAQkIAEJjJ6AivzoGXsGCUhAAhKQgAQkIAEJDJ2AinwN0iuvvDL95Cc/qdnjprlIwPs9F++q1yQBCUhAAhKY+wSWHdUl/vKXv0yXX375ItUvvfTS6R73uEdaaaWV8ovv4yTXX399WnfdddM///nPtNdee6Xdd999iTbv73//ezrttNPSt771rfTkJz853eEOd+i2B3YPe9jD0oIFC7rb/DAYgXG734O13tISkMCkErj11lvTBRdckH7961+ns88+O9397ndPj3nMY9Iaa6yRx8jZXBd1f//7308333xzHh8e8YhHNFY3NTWV/vSnP+V2/OY3v0krrrhibgdt4XMvueWWW9KPfvSjxHi/9tpr59eyy/ZXK0499dT0r3/9K62//vrpLne5S69TuE8CEuhDoP9/XJ8KmnZ/9KMfTV//+tebduft973vfdMLXvCCtN1226X73e9+Pcsurp3XXHNN7mA436WXXrq4Ttt4nr/+9a9pxx13zPuPOeaY2nJw3GCDDdI73vGOdLe73a22zKg3/va3v83cHvCAByTaMy5y7bXXpvPPPz8ts8wyac0111ykWeN2vxdpoBskIIE5R+CKK65I22+/ffrFL36xyLVhoHnb296Wdt5550X2tdlw2WWXpd122y398Ic/zMV32mmntP/++9ceSlna8atf/WqR/UsttVR67Wtfm/bcc8/E56pgZHrmM5+ZJyO0mckDyv8JJ5yQlltuuWrx7neMUpxzo402ysd3d8yBD2vtcfrAV3HWoRsNfIwHSKAksMx+HSk3DOszSvx5552X7nOf+2QFatVVV028Vl555XS7290uW7yxhv70pz9NRx99dMJi8KAHPWhYp59xPVggaMeCjpWbzvBOd7rTjOsaxoFXX311OvLII3NVD33oQ3NH+cAHPjDxute97pUtLoSGnHvuudly/5znPCfd/va3H8apB6rjhS98YfrQhz6U7/cTnvCEgY4dZeGTTz45vfjFL07HHXdc2mOPPRY51bjd70Ua6AYJSGBOEfj973+fNt988/THP/4xGz2e//znp6222io97nGPy2PjRRddlLBY/+1vf0tPe9rTBrp2lGT6O+oOWWuttdKTnvSk+Np9xwtAO7DG4yWnHS95yUvS4x//+Oz5xVtw5plnJow0m266aap6z1//+tfnycIHP/jB9LGPfSxb77/0pS+lf/zjH7Xn48SMZ7QPHeCLX/zinLPGH37SxV2+bT/s/KxV2xa13BwhcOONN+YrufW//83/Vxga+f/ixaS5buLc69JHZpGPky5cuDB94AMfiK/dd2bzxx57bHrnO9+ZUOhf+tKX5n/sjTfeuFtmSX2gc+M1bvL5z38+3f/+91+kWSeddFJ64xvfmDtcOslvfvObi5RxQzOBcb3fzS12jwQkMKkEGA8JO33iE5+YPvnJT+aQmvJaDjvssPSud70rfe5zn8v9Okp2PyFMZd99901HHXVULrrllltmSzkhL02C4YXJwsMf/vDE2FL1in/ta1/Lxo9vf/vbOXwGC3oI1vcTTzwxG+fwqiN4jt/3vvdlT/zb3/72KDrtnXEKZR4G1fNNK+gXCUigNYGlW5cccsEVVlghK+9Ym3HDEaf3mte8Jiv1Qz7VnK8O9+ahhx6ar5OO+y9/+cucv2YvUAISkMCkEcASh2KMEApJXHxVXvWqV6U73vGOOVSFOPc28u53vzsr8YyrH/7whxOTgV6x58TOn3LKKblqPJV1SjUWeiYbCMaiUv785z/nUMrHPvax3c1YEQmtYZKCca4qhIZiZHr605+eXvSiF1V3+10CEpghgZFb5Pu1i4WlxNO/4hWvyB0AYTbE9NXJj3/84xxmgmvyhhtuyNZpFoC+7GUvm7YohwU4r371qxPvhMfUxUZTP65DPALIIYcc0o0vx2VI7HTTsXRSX/jCF9J3vvOdHEcfi4NwYWKdwE3SJG2voen4pu24TolNpxPFHYpFBqHDxVKDYP0hFhJLC/GTLOitZue57rrr8rV997vfzdfGwqVVVlklu1a32GKLPMDkyv73Z4cddsgDTkweuH+ESyG4i5/xjGf8r+T/v/3nP//JMZRf+cpXEi5kJnCEXDFo8MLl2iSDcCe0i+uMBdc33XRTevnLX56rZsHwRz7yke5p+t3vaDP10WasX4SIbbjhhmmbbbbJi7a7lf3vA78NXMeEQ7FomgVhDGLcG36Xq622Wtp2223TOHigqm33uwQkMBoCjCsYrFDUH/WoR9WeJPpcxjms120ExZzQHPo1wkL7CbHxeMWRpnaw75GPfGT6wQ9+MC1Uh+3E+COsiSolvlP/Xe961+4uvu+99955nD744IO72/0gAQnMnsASV+S5hE022SRnikG5JJa5TpH/xCc+kRcAlZdM5/Czn/0sx9jj5gvrBsogihvKFHHkTYo8oT1YGrAqlItEsVSgABL3XRU64s022yzHFcY+FsUSb4grlDoPP/zwaZ1YlBvkGuKYQd7pwGk3bQlFnglPWFP+8Ic/ZEtIdOBVpZlBg2u78MILp50W5fX0009PKN9f/vKXp8XgUzdu1hDOwQtZZ511YnN+R2lnAVc19Ofiiy/O9WOpOuKIIxaJxeTgQbmzwDWum+P/24lFi+/VdQ+97jfXxu+R2NNSWITMb+8zn/lMdiWzZqEUmHE+mBLvSugYE4IQYljhwCSCxWSKBCQw9wlgtKhbq1NeOX0d68sQDF1thP4eo1SbjDHUh8LNmIfhhnH3IQ95SO1pYsFsVdmP9Wy/+93vph3Hd+J8q/0h/RyGmI9//ON5TJ52kF8kIIFZEVhioTXVVuOSQwgNweJZCgo5a3Jx3b3yla/MyjJWX8JJWCSL0rRdJ/MNVomQUMJRwFDi6iSy6kTZujLlNtqFFZXFQSjNxAFiaeUcrO7HIo7Cu/XWW5eH5c8zuYZFKumzAeUSISVlnaBM0m5cumQW+OpXv9othnWehU4o8ViLidHk2r73ve+lffbZJ18biitu35In1hrKRce966675u9sI16/FAYalFcsNdxP3Ma84IgbGI7hISmPmwl3siLQhgMOOCBXxQJgvvNiAWxbectb3pLbRfuI72TCyG+PWFAsYCjq3O+rrrqqtkp4EjuKN+D444/Pi5JhHwM0v+GY+NRW4EYJSGDeEMDIgicYA8KznvWsntbyEgoLVNsq8RyHso2nEDnwwAPTGWeckT/HH4wO9PssdMVARr9fCgYytuPhxUCD4GmkPJOV5ZdfvlscAxf9/POe97xEMgZFAhIYLoGxsMhzSY9+9KO7V4bbDoUTwcK8yy675I4NpYpOLgSrAgtwsOijZLKABmULYbU/CiMKFuEMpGcsBYsHL6zSbRe2onCeddZZWekktCaUV+rFqk94C50VkxHaE9lbZnoNZXv7fSYX8SWXXJKLhZJYPYYOlsVQuHWrgmJNHYQJcW3lolrcq8RKcm1YmfE4RMceDCLdGJ17bCvPgbUdty+8P/3pT3cVWcqw2IoXEwnCrAiXKu/XTLhz73mdc845uRl1VqKyfXWf8Q5hcWcCSWaGMkyIScqzn/3sHO+JR4GJXCw0K+vi94cF7s1vfnN3M78LFpfhsWCxGYr9G97whu5+P0hAAvODAN46DEMowfRVWMDpEzAuvfe97631Tg6LDEYp+mr6HsIgMQBhecdKz1hANjT6fIwrdQtuCV/FKIRHkXIo7BjTMOaEENrJ2EL2OuL4FQlIYPgExsYiXyrydGQhP//5z3M6K2KNSyU+9mMFR4lCynhvFEs6FyQs7/nL//7ENhT+MqymLFP9jPUZeetb31qrrKKgYWF93etel+PP4/iZXkMc3+8dKwiTHSwjD37wg2vbRh10sHVKPPuY7CDkLy6V+Lyx84fwJM6BYNUeVHioFYKyXjfRIN4cxRgp7yPfZ8qdY2cjwQS3danER51MFBhsEdY+hGUq9vPOJCC4lduJ04/UctVQprKcnyUggblLgHU0xI6j7BK6yGJY+mCs4WEcGeXVY0xgnRNCWAxtYH0USjzhM4QV1inxlMeYw6JVjmHigbGG8uGJxauAEYN0lMTFxziL9xeDEGu38ArzrA9FAhKYOYFlZ37ocI+MuG1qLd1yWAaQcnV83lD8WX311fM3FFqUKZQnhM4FiyohG1gDsMqGhCLfNqyGDpbFsQhPsGuSiE0v98/mGsp66NxLJZs2Yc3hoSKEu+ChII9vk4SHoLof9tSDsGC3SWKtAU//G1TaMsAKXtY/G+6DtrFaPtrciwnhNQgM+X1UY03xLDVlj2DBLFJOXPMG/0hAAvOCAPnZ6TNQevHcEqpCP09oI6EvJIEYlbCWisX6KO14YgnPCYs8fTBtwdtNVjTWd1VDdxhn8YJjscebgDGufPI4650wcBB6+NSnPjVfBhZ7PODUf+c73zl7dwnTYfKA1V6RgAQGJzA2inwoTVwC/9ghsZ1FliwkrRPckggLO4mXj9AOFE8s1PFgCx4HjbDQkLhkwkCe8pSn5G39/tDxMEnASkKdg8hsrqE8TyzWLLfxmQ4WJZ20Y6WiXy3X9KAoOnQGEsJegl31WL4T/oLQ8bPQeKWVVsrf+/0h3jJCXMjY0BRGEvexVORnw71fu3rtpy08ZAuJ664rz2CEQk7WHtpaVeTvec971h2Wty0Oi1vjyd0hAQkscQIkF+BVCtmxdt9992yZjwc1lfuHC73rIwAAQABJREFU8ZnwVUJr6MsXLlyYH+iEh7EUPKEkJ2DcYVIR3seyDJ9JeVlNbECyASYjjEflU2XZRj9JXD7Z5gglIqwHbztjvCIBCQxOYGwU+VDeUDZLRb50uxGr3SSEJ6CE4rYrhXSQdBpY4EORD2s86Q6rVoby2PJz1Ivy1Su9ZHlMfJ7tNUQ9LOgtFXU8DHgjsKRUM7HEMW3emQAhWFN68aDDDuGYtoo87MgihDBYNE0WWKyL4ltadWbDPdo6k3cW2EaWmX5sg0td7uSZnNtjJCCB+UuAcQnjCk9LZezi+7CFdVB4AAhNZb1O6a2Oc7FOibTMKNzEv2OAufe97x27G9/xDjMRwfrO8Rg7Qkj6QP+OJwBhTI4Ul/T1TaGfcbzvEpDAogTGQpHHGkwcOYKFvFQmcfURMoNlgDjCQQVF/j3veU/OloI1gA4rFPm2YTWcM7LqEELBolJyq7eV2V5DnIfFRaUiH9tn+x7rE1DOe1naI7sKnW3V8tyrDYSWwIuFTywa5YEgbWU23Nueo64cbSYshgki1809rBOU/QhLCo515dwmAQlIoC0BkjigyDPW0C83hee1ra9aLtYhsU6nTomP8jznAsUbwwbZuggF6ic8MZaED8Tel0kL/v3vfycSAxCqWBrDCJvFa03yiV4htP3O634JzFcCtwWNL0ECxARGPtpqBplQ5LBQzER4Yh2zfuKQidfD5ceDNlihv8Yaa7SuEktEWKAjTKTuYKzvKKzlgzxmew115xnmNq4rLC1xH+rqj31YUMqOuCxbt+CT/TNlMBvuZbtm8jnaHNddVwe/J8JwmHw2Kft1x7lNAhKYfwQIT2E8wqjUS8IT2avMbPax9gjp50VEgY+Qx/Dc9jovRjdS8+J1ZRFvKYyNGO3wApQS42pTCt+yrJ8lIIFFCSxxRR7X26c+9ancskglWTYzZuikc+RVJyjmpME66KCDajOHhOUdS/xMrPFxzmgL7s66jhblnXhD4gXj8dccG8fN5hqiDaN6D6WVTjg67vJcdMJYiJBY4Fnuj4dLodjWSTAgDrKpwybrAfeRTA6lxLGDcqeOaBf3K/Lsl3X3+hxMcD0zOasKkxbahBBHXy7Srpb1uwQkIAEyduHBIwVvKNN1VHheBULK4Ko1nvSQs5UwOvBMjdLoVK33mGOO6YYY9jN8MW6QpQZlHc9rGSJJvSjwhCmi7JcS3wfx8pbH+1kC853AyBV5/rnpsOJFJ0SoArmzSUVI2AuC5ZwV8JFxJm4MShwprqiHxTkswInYZcrg7iOGjxzlWN2rx1OGlfd0IHSOpMrCmrzFFluwayDBc8ACWVyALM4plTsW2ZLekcVDdFilZ2EY1zBQQ2dQmBz9xK+TAYen8BFiE8LAw4O4cPMS2lOXTjEezc2DP+os2DyMinh+mBEfyYLjEBRisu0QPsV9rCrEM+VO/dEuPpPBqG4Cxr46oZ0MePxmcRPH4lfKMhlh0sHkg3UT5YKuurrcJgEJSGC99dbLfSghmqTbDSU2yBB+wrNQIrEDseal8EwN+lHGlybvZ1m+6TP9Of0sqSEJdWQcLeujHYzHpMJEMFD1U+RJMUkfSVpK1m1VhbEZTzhjZawb45yMOSj9g4SrVuv2uwTmM4GRx8izAp9XL8ESz4OAUJLrhA6CVfandh6eQcw7SjlPdL300kvzgh2OIe6uKYaemG46TazBKPs8uGkmqa5wF372s59NpJjkCaW8yG+P+zEexoQyTC75Ms6f9s32GqhjlIJFGSsRGQSY7PCqXht5gLGW17EjdpI0nyyg4oFOTGaYEMSiJhY8sWCKcsRDcs+ph4wvZBUKFy9PQK0u7poNd66BF5MvrESkUWPA4LfUT1jEijWeNjPgsn6DNrMYm/AqBiEGp8MOOywxQCsSkIAEehEgHp1xiH4OgxYx6oQPouBiGaefwlCFJ5Hwm8jJTp30NyxS5Z0wUQwsg2ZQi7bRp+Kd5gnYjKPPfe5zs+WfSQKGCwwt8QRv+mPGr17CgxLJmsY4Eg9lrCsfYwKGEZI3YAjBU8qzWXrF6tfVNY7bzjp0o3Fslm2a4wRGbpGv8kPxoeNiYSAWdpTho48+ulGJ53iUYhQw/vlRCLEisDgWpRGlilg8OsfIHlI9J98jvIbPTAZmKkwYUO4i3RYdL0o8CjwTBFJ1oaRWZRjXUK1z2N9x+/J00sg3H9fGPePaYNzk/qSzxyqNYk557g1Wp1KIhaQOPCR4RZicYY1BiWdiRujOAQccUB7S/Twb7oRuMWCSEYlJFwNXW+G3yu+TFHEMrrQZpZ7BlEGPNvvY8bY0LScBCaBE4x3GS8lnjEtnnHFGtmZjpGL8wGASRpAgRr+644475n6IvOwzVeKjPizsZJHBeEM78JozQSBUFS8jfS7PX0FBr4bJRB28Y73Hc4AijlEjwhnLMvEZIw8PgiIEEw8s+eNR6Ou8vHGM7xKQQG8CS3UUkqneRcZrL9YKcnZjNSDMo+mpc4uj1SigocSXIRz9zj1O19DUVvii8DIBgXOvSVK1DtKOocTzkBEGnzohxIX6CZla0MkOwwDWVmbKnfq5LhT6ppz6vdoQkwDembAsyd9er3a6TwISmBwCLCIlHBGjFAp1U58ZV4TiPJP+K45vekeRpx0YpTDYNCU0aDp+kO2oHSjzXG/Vez1IPZaVwCQSiPV6t3R0JX7/TH5553+OCXG/PqB6zROnyFcvwO8SkIAEJCABCUhAAhKYBALDVuQXe2jNJEC2jRKQgAQkIAEJSEACEhh3Airy436HbJ8EJCABCUhAAhKQgARqCKjI10BxkwQkIAEJSEACEpCABMadgIr8uN8h2ycBCUhAAhKQgAQkIIEaAiryNVDcJAEJSEACEpCABCQggXEnoCI/7nfI9klAAhKQgAQkIAEJSKCGgIp8DRQ3SUACEpCABCQgAQlIYNwJqMiP+x2yfRKQgAQkIAEJSEACEqghoCJfA8VNEpCABCQgAQlIQAISGHcCKvLjfodsnwQkIAEJSEACEpCABGoIqMjXQHGTBCQgAQlIQAISkIAExp2Aivy43yHbJwEJSEACEpCABCQggRoCKvI1UNwkAQlIQAISkIAEJCCBcSegIj/ud8j2SUACEpCABCQgAQlIoIaAinwNFDdJQAISkIAEJCABCUhg3AmoyI/7HbJ9EpCABCQgAQlIQAISqCGgIl8DxU0SkIAEJCABCUhAAhIYdwIq8uN+h2yfBCQgAQlIQAISkIAEagioyNdAcZMEJCABCUhAAhKQgATGnYCK/LjfIdsnAQlIQAISkIAEJCCBGgIq8jVQ3CQBCUhAAhKQgAQkIIFxJ6AiP+53yPZJQAISkIAEJCABCUighoCKfA0UN0lAAhKQgAQkIAEJSGDcCajIj/sdsn0SkIAEJCABCUhAAhKoIaAiXwPFTRKQgAQkIAEJSEACEhh3Airy436HbJ8EJCABCUhAAhKQgARqCKjI10BxkwQkIAEJSEACEpCABMadgIr8uN8h2ycBCUhAAhKQgAQkIIEaAiryNVDcJAEJSEACEpCABCQggXEnoCI/7nfI9klAAhKQgAQkIAEJSKCGgIp8DRQ3SUACEpCABCQgAQlIYNwJqMiP+x2yfRKQgAQkIAEJSEACEqghoCJfA8VNEpCABCQgAQlIQAISGHcCKvLjfodsnwQkIAEJSEACEpCABGoIqMjXQHGTBCQgAQlIQAISkIAExp2Aivy43yHbJwEJSEACEpCABCQggRoCKvI1UNwkAQlIQAISkIAEJCCBcSegIj/ud8j2SUACEpCABCQgAQlIoIaAinwNFDdJQAISkIAEJCABCUhg3AmoyI/7HbJ9EpCABCQgAQlIQAISqCGgIl8DxU0SkIAEJCABCUhAAhIYdwIq8uN+h2yfBCQgAQlIQAISkIAEagioyNdAcZMEJCABCUhAAhKQgATGnYCK/LjfIdsnAQlIQAISkIAEJCCBGgIq8jVQ3CQBCUhAAhKQgAQkIIFxJ6AiP+53yPZJQAISkIAEJCABCUighoCKfA0UN0lAAhKQgAQkIAEJSGDcCajIj/sdqrTv3//+dzrttNPSzTffXNnj12EQmJqaSmeeeWa65pprhlGddUhAAhKQgAQkIIGREVh2ZDUXFZ9zzjnpN7/5TTr77LPTX/7yl/SIRzwiPfrRj05rrrlmuu9971uU9GM/Ai984QvTz372s7TxxhunL33pS/2KL5b9f/7znxP3GOGe3uc+92l93ltuuSV973vfy+Xvd7/7pcc85jGtjx1FwYMOOigdeuih6a53vWv65S9/me5whzuM4jTWKQEJSCCdfPLJ6dRTT00XXnhhuuc975ke9ahHpRe/+MXpjne8Y2s6V111VR4T2h5A3YwfCIaLk046qe2hudyTn/zktPzyy3ePoQ7Gdq7jwQ9+cK57hRVW6O5v+sAxl156adYHFixY0FTM7RKQQB8CI1Xk//nPf6Y99tgjfeMb35jWjO985zv5+3LLLZfe9a53pZe85CXT9seX3/72t+lf//pXesADHqDC34FCh0nHh8R7/rKE/5xyyilpr732yq3Yfvvt0wEHHNC6RV//+tfTq1/96lx+iy22SB/60IdaHzuKgpdcckmu9u9//3u67rrrVORHAdk6JTDPCdx4441phx12SGecccY0EkcffXTuAz/ykY+k9dZbb9q+pi/nnntuot9tKw984APTj370o1z8v//970DHctAvfvGLtNJKK3VPt+uuu6avfe1raemll0633nprNoJ8+9vfTquuumq3TPUD49fzn//83L8yAZgrstYep3cv5axDN+p+9oMERklgZIr8ZZddlhX03/3ud/kf/OlPf3paY401stXhggsuSD/4wQ8SHdCee+6ZZ/MHHnjgItdJB/GHP/whve1tb0uvetWrFtk/3zYstdRS6ROf+EQ67rjjEpb5cZSvfvWrae+9925tUfrMZz4zVpdB2+9973unxz/+8dMGq7FqpI2RgAQmlsB//vOf9PKXvzz98Ic/TBizdt5557TWWmulyy+/PB1++OHpT3/6U9puu+3Ssccemx75yEf2vc673/3uXQt7r8J4TrH8o3CHMKaEdT621b3fcMMN2UPJvmWWWaZb5Itf/GJW4l/2spel/fbbL5fZZptt0o477phQ5stzxUEYpF7zmtckjCUYbmi/IgEJzJzAyBR5FHOUeP5JP/nJT6YnPvGJ01qJJeC9731vDmNAmeMf/yEPeci0Mn5ZlAAdPq9xFTp8rDN05v3k97//ffrpT3/ar9hi3U94z7777rtYz+nJJCCB+UMA5RclHvnsZz87TZHGQLNw4cKE0o2X84QTTugLhnCcNmGWz3nOc7Ii/7znPa9bJ0p5m2Pf/e53ZyV93XXXzYaOqACPKrL77run29/+9on9G220UQ7XufjiixPW/6p86lOfytfPtT7jGc+o7va7BCQwIIHbpuYDHtiv+Le+9a1cZKuttlpEiWcHHcib3vSm9PCHPzyX+/KXv5zf/TP5BNpa2duWm3wiXoEEJCCBlENPPvjBD2YUxJpXreGsyYkwRdZChcI/W3bf//73cxw9a3922mmngaq7+uqrszGOg97whjdMOxZjzL3uda+EASTksY99bP7IvqrgbSD0kvLveMc7qrv9LgEJzIDASCzyZFbBbYY87nGPa2wWbr2Pfexj2Uqw4oordssRO0isHQtjEeIGw3LLxCBm8VgtwnqK1f9Xv/pVtgbT+RGf/5Of/KRbJx9wK1Lu5z//ebriiityB8TizH7eAK7liCOOyAt26ZzobAkT4lji+0tXY5yQuG/aQMfH4s+jjjoqnX766dl9uvrqq2fXIpYUhFhsOnfay8IlFgKvv/76CXcljEohtIb4xqc97Wl5UVTsq7LAGsLk6Mc//nH661//mh70oAclwpuos87dST24fI855pj8uuiii9Kd7nSnvPiKdQ54S4jbZHDZZJNN0gte8II4dfedNsOHtQ3EUcKnSf7xj3/k87B/0003XWQdRfU4BgAUf85P2BYDEgurXvSiF2UWJSdc0liyVllllbT//vtXq+p+f//735/DurCAwQXhHn3605/ueSxMjzzyyHyteCDuf//7JwZl6ih/x90T/e8DTI4//vj061//Ov+2Ycpvgd9fGXNaPc7vEpDA3CBAbHisb3rrW99ae1HPfe5z00c/+tE83pBBi351tsIifuQVr3hFustd7jJQdR/+8IcT/TUx+1jcQxij//a3v+XxKrbxzpo2hH66FLzwWO5Z90YfO2g7yrr8LAEJ3EZgJIo8LrbVVlstnXfeeYmFLChqTfKwhz0s8SqFVfR0EiHEyfNC1llnndicUKJixT37UepiAnG7292uW44PZ511VmIx5U033dTdTkwiK+dxLRLbTVx0VZhAoJRH5xv7ie9ngkHWASYj1ewm3/3udxMLmggxefOb3zwtqwATCuIHcasuWLAg8ynTHaKUs0AYpbi6doDJCteM8lhKySIWEnF9IdTJvcBTwvVWlXniFnfbbbccfx/H8E4bUD5R4lFEOTcKdJ3AnOsl6wtKdy9FnvAb+DAwkMWouiC6rB/lHTcsE8QQro17/s1vfjNbmEqFHQWZdnKNrK2oy4zEJIv4TAaVMgyIxa4ci4JdJ0ykWLNRCgMWbeT3cOKJJ9bGfHLc29/+9jxZ4liyPnCfuCef//znM7OYoJZ1+1kCEpg7BDBIIISc0u/VCUYJlGYyvbGebLbCWIPxAOMHivwgcuWVV2alm2Oq1nj6V8Yv+mHG6xhTCKlFquMEYwjjMAaPqiciH+AfCUhgRgRGFlqzwQYb5AahpKBkXXvtta0byEJYrM4RX8eiV77zIjVXnbz0pS/NnQruOqyxKOYhKGfbdRYPocQ/85nPzNZUlNIjO1ZVlCe2sx9ltxQs1KHEo+QT28dxKJ2vfe1rc8dIBp53vvOd5WHTPu+zzz5ZecMiT8eMssfEhbqpY9ttt+2uI6CzRTHFIoOgDFezGkyrvOELSi+DBBMFrOO4Vbk+hPq+8IUv5M/lH9YrsIiWzhjXK9Z80i9iGSLmkcVJ559/fnlI7WfuAx4KlH88DU0CeyTalb/U/PnjH/+YGaHEL+xYzrEOcQ/wHHBvll122bxAjMlUCN4SQrYYXFCs64QJGEo8C1upt41wr1nQxUD7yle+Mi9GY6LHxAXeeDG4nmqOfzwtKP8chweJgY8BmkkV9/r666/P2Z2YXCgSkMDcJRB9aNUQU73isGpH+er+tt8x0Bx88MG5OItqB7WCH3bYYdmAwnheXedGpYxlWOvpp0MYN5DSQIdyzxizoKP4Vw0hcZzvEpDADAl0/tFHIp1/7qnOopqpjjU0vzod11RHCZ/qzMqnOsrPVMfN1ve8nVl7PpZj6qRjTe/W30llNcU56+SpT31qLtcJg5nqKNDTinTymE91FN+8v6NUTdtHOztW/KmOcjrVsZhP28eXjoU1H7fhhhsusq/jkcj7Ohbnqc4kZtr+jjtyauWVV877O1kJpjoW3Wn7aRN1wq6jJE7bt8suu+TtHbfstO0lC9rcUSan7ecL10Gd3JdSOhOMvL3ufJSjPR1vR7dMZ7JUHj7VcZPmfZ2wp7y9Y3HJ3zvK9bRy8aVjvc77OyFEue73ve99+XtHMY8i3feOWznv63h1aq+pk+Uh7++Epkx1FPzucR2FP2+v3tMo0LFM5f0dxTw25ffPfe5zeXsnVGbadu5Rx8KU93XCoKbt4wv7O2FkeX/HjT1tf2cyl7d3QmimbedLZ4IyRVu4Z51J1iL73SABCcwdAh3vbO4LOuGjPS+qYzTI5TohkVMdg0TPsr12dhaj5no6SvVUx2vbq+gi+zohmVOd8MR8fMcYsch+NjBGMrZvttlmUx3v4lRdX874wRjciYufaqqntvIJ3Ljm7qdNxWsCm2+TFxOBTtj4FK+LLrxw6tJLLpm64vLLp66+6qqp6zq64g3XXz91Y+d/dZDXyCzyPHQCazwWZ6wAWKDJN054AavnCbvYt2OdxEo9DMFqX/cQDRbqYJVGPvCBDywSz45Fl1z2CJZe2hnyhCc8IVv2sWzXxT5HnDjWCKyqdbL55punu93tbtN28eCPCN3AGtxRoKftp02RmSYetDStQJ8vuE+roUUcEpZ+QntKwaqMYFUmhrEqtAeLd7hOq/ur38PKjkeh839R3Z09DWxkfQF1Nwnxl+FaxhJfd01cK/eJ8xC7HkIYFZ4BQl5YD1EKlvh4CBXeizbCugosTw996ENzCFL1GO4hHhYEC3wp/AaR8rcV+wmxIeUcHqTwYsU+3yUggblFIEJrOoacnhcWFnu8dGWIZM+DKjvxSIY1Hg/ine9850qJ3l/xNOJd7BiV0tprr11bmL4XLzhhtKxf6yjyuR97z3ve0y3PNsZgPAJRD/Hy9JOMvYQdVsek7sF+kIAE+hIYmSLPmVGsWWSDsk6IRmfW3l3djnKFAkNoC0rabIUOpU5iokDHiAJdJ4TwEOPesRzklJl1ZarbCPUoFTOUvDqJFfzVfcQrIqX7sSwTT0ctY/rL/b0+Nz0dNQYPFORSiLtHesW0MxmpxjyWdZSfiX/EhUrnXA0NIsSKlGUo2YTh9BJCjRDuGwtXmyTaHeUpBz9CgurCayKshsXGTXGq1XNF3U33k/IxOWPQKicwsUCM2HvCgZgU0C5FAhKYXwTC2MT40UswNoSQdGAmQngjYXz03SyoH0RYvxMhmNXY+Go9GG7o8whppZ/8yle+0jWqMbawFgkDCGvFQgg5JQ0myScIvcWIQaIBRQISGJzASBX5aA5WR/5pUdxZ7ILlFGs88ckIMebEn89GWGBbJ6GA0TGhDNa9UDqj44zyZV3EWRPXx6JdlD/qQPknc0w/KR9lXZYNRa/MtlLun83npgkLDx+pk7D6N00q4pg2DyehLNeEJwbBKl8KC22ZnJBBp1+mlrgXkaK0rKf8HPtj0hb7wtoeuY5je3yP/bG913u0hbUDdb8htsWibhYeEy8fgueBF8IiXya0KP1MZBgsY4F2lPddAhKYmwTCGMJ41EviCdOkdhw0rp16sXiX1vgVVlih1+kW2XfIIYdkwxbGkCYjWXkQnlXKlWMP/Xx4eIm1j/GHtUZkreHJrkw08JqSjrITNtpzXVV5Pj9LQAK3EVgsivxtp/v/Tzy6GVcf1lqy2yDl4tT/LzWcv+UiWxTRphcWcqytZagHVnZCN7Bm4P5DUcQ6guKIu7FNBzecqxhtLRHaQ4aCXjKIixc3K5MYOu04jskLi34RrDj9BIUY6WeRikGqGt7EwmbcyYQOxbVFWA33mbCnttLmd8TvJ35H5cJVJjYMqixkJmMDvGkr4T2vf/3rc/o2HhKjSEACc5tAKPKhqDddbSj6Ub6pXNN2kgEQxkNIKOmcBxGMEPFcl37W+F718hApwk5R5kmpHMKYgPB0W/phDDoYNwhBJLxVkYAEBiPQHKA8WD0zKo2lgX9y0h6SBhJ3Y5NlfUYn6BwUudrJZBIPqWpbF3nGyVCDckaMOO6/UtFHOWwKY2l7jnEoxzWQ2YZ70CSEgvTaXz2OAYSYfAYE1kqgsOI6JdyGnPZMhPpJsMVq00tifzlYUJ7fEusxOD9eFQYO1mmgZOMRKK1HvepnH7+jiPPcb7/9+hWv3c8zFXiRUpQ8//we8UQxqMMHBf9JT3pS7bFulIAEJp9APL0cRR3DRpNHNjKozUSRJ+STsQvBYBaGjrb0iGmnDkIk61Iyt6kHrzvGL8bdWDsUx9FfM47G2Mz2CFkk3THP5FAkIIH2BEZikSdWjth3rI/9JBRjXIFNiykjDKVfXXX7QxmkgyAGfhBBuUUIE2FRarQ16qDOuSDRidL5VsNT4voIkWlaBxBlqu9hde9kgskDQ5lysmkAK+uIe8egVlq4yzJ8jrzFUb7cH+EzEU4T77FQuSzb63PUPchkpld94ZUipSqf+Y0TQ69IQAJzl0Ao5ngGGSfrhOdrRNrcUPzryjVtI3wRQwG56ge1xpPukgfqITO1xjNOkK6Y8ZKQmuq4yUMPMY6VRrsIs2SfIgEJDEZgJIo8D20i7hoXGg+86SXRaWCljRi6KB9ZSmaTS5eOkNAMlHgsBHVCx8PqeVbf8xTUkOhUmiy3TR1xHD8p78RBYh0hppHJF5bnELgR183ipGqHHGWa3rE+YyUntIaYSB6SxaJiHtzVRvhNEBqDNyCeTFg9jjUX4arlfFUhSwJrIMiQwISANjCIYJEfRGKyQzwnrzphYsdviLbG5BN+ZEsi5jQy8JTHwjRCtJjMKhKQwNwlgHId3kg8c2XChLhqkj8QZsL49+xnPzs2d997PZ8j+hsK8zC8fmGJ3Ur/9wFrPP0QnsHInFYt0+87i1eZSLzxjW+sTebAmMz1Rcgl9cWYM5OJS7/2uF8Cc53ASBR5MoaEe4wYc1amVzssYo7ZF0oYn6sSD8XAMh5W12qZft+x8h9wwAG5GItqq5Zlwhq23377bDngCXgRL84BKLgIGXciZpHvKLx0VoRszAVBuSbFJtdO58oiXpRirNasByDTyiabbJLjGAe93vDKsFiYAYK49LaLt7DaM8HinYXSTMSwViHURfYX1i8weD3lKU/Jr7r2bbnllnkyQAgXln0WXlcnjXXHldtQ5JmAcC48NDy0rPxNE4fPtfL0QrIChceBwRhPB+nYOC5SUUbd7ON3hxAnqkhAAnObAGF0CLHoTPrLDFbEiNPXIXgTYwzMGzp/SN/MQnn60TAWxD7eGZM6+anTPe5xjzyulfv6fcYQQaYbZM899+xXvHY/hjvWQWGcYCJRJ5GcoIyHpy9HYl/dcW6TgATqCYwsRp6ZPavQUXg6Dy/KHRAWVpRFOjBm7HREKNrsD4WvbCZZQIgjRrlkYsCxdILbbLNNWazvZxQwLO0oU6TA4nwsemUxJVZalEI6PiYcZXgPMdVMNDg/HRNZXbBwEF5B50ue3T322KPv+SehAK5NMqi85S1vyYon18wL9yfrGIhzjAFokOthwGHwiYWoEW7Ttg6UcNrBZIzJAHXhPSDWPupEyWbwa/IYMCHh9xj58iPcpm0bohwLVkmbymBFnfwWSF/JJI82Ilix9qvE0MONNGwsPsNDwW8PyxPfCWXi/wBOnYdfxal8l4AE5igBPNZvetObshJPaka8hKTQ7TxULieAYDwilA9jUSn0E/TRvGMAoP+IUB3KYWBiTEJQoiPVZd7Q4g9PXqVuxtpI6dvisG4RxlPGCQxDtKMcS7uFOh8wnHEdeHnxhmNM46ninYdGZQNSWXbSPp916P8b/yat3bZ3sgmMxCIPEtJmEavHPzYWeh4swYwfJQhFnn9yZt/ETzfN3ElPRWdG/nMsnChLM03VRztQAsknjyWVdIIog1hMsZSisFfTL6KUET5Dp0Y5FulgRVjQCdWg3Qs7cfNzSVBKCXXiGulYGWDOPffctNdee2WFPixHYW1uc+1lKA0cWfw0qPCwLyzzxJJz71CKUeKpu/O04Hwveg1apIZk8EQY+GYySHEsEwW8AsSdEvLDIIQlid8lv/e99947L+6tLi5jEohXKbLoMBGEM+Fn/A8wycCS3zQR4dyKBCQwdwgQQ05WF0JtGFfIWsXYiCKN4QlFtxoWQ7+LB5KxCKW3VOIhg1eVvohQUIxQgwihLZEMYqax8fR/nB8FnfTMTYLRCC8314eRiLBDEkkQTz/I2NJUv9slMN8ILNXpOBZ99OYIKBBvjuKC1QGFGaWxXOzS75RMBFDiyYYym392LheraihfdCpNloOyTVg7SKWFwhYPayr3z4fPxJWjhNIJE56yuIV7R6YgfkPEuTPBGzREZlhtZkKBC5t4VSaHeHTaCu2/5pprcurVJdX+tm21nAQkMDoCGAPwymFUQgHHWxcP7ms66yiyuzWda9Tb8YjzwKq24Zajbo/1S2BxEIi1mLd09FoMeEzOeedBmeijg+q4i02RXxxwPMfMCKBYkmUIly5eiy222GKRik477bRs/WYHEzI6X0UCEpCABCQgAQlIoD2BYSvyI4uRb39JllzSBPBKEJ9NOM3rXve6nEGA8BMe1sXCzZNPPjkvEia0hlhulfglfcc8vwQkIAEJSEACEkhJi7y/gkyALEIsdEWZbxJi1YmXbxOK1FSH2yUgAQlIQAISkMB8JTBsi7yK/Hz9JTVcNyE05Fw/77zz8joCsquQJYaFv3V52huqcbMEJCABCUhAAhKQQIWAinwFiF8lIAEJSEACEpCABCQwCQSGrciPLP3kJMC0jRKQgAQkIAEJSEACEphUAiryk3rnbLcEJCABCUhAAhKQwLwmoCI/r2+/Fy8BCUhAAhKQgAQkMKkEVOQn9c7ZbglIQAISkIAEJCCBeU1ARX5e334vXgISkIAEJCABCUhgUgmoyE/qnbPdEpCABCQgAQlIQALzmoCK/Ly+/V68BCQgAQlIQAISkMCkElCRn9Q7Z7slIAEJSEACEpCABOY1ARX5eX37vXgJSEACEpCABCQggUkloCI/qXfOdktAAhKQgAQkIAEJzGsCKvLz+vZ78RKQgAQkIAEJSEACk0pARX5S75ztloAEJCABCUhAAhKY1wRU5Of17ffiJSABCUhAAhKQgAQmlYCK/KTeOdstAQlIQAISkIAEJDCvCajIz+vb78VLQAISkIAEJCABCUwqARX5Sb1ztlsCEpCABCQgAQlIYF4TUJGf17ffi5eABCQgAQlIQAISmFQCKvKTeudstwQkIAEJSEACEpDAvCagIj+vb78XLwEJSEACEpCABCQwqQRU5Cf1ztluCUhAAhKQgAQkIIF5TUBFfl7ffi9eAhKQgAQkIAEJSGBSCajIT+qds90SkIAEJCABCUhAAvOagIr8vL79XrwEJCABCUhAAhKQwKQSUJGf1DtnuyUgAQlIQAISkIAE5jUBFfl5ffu9eAlIQAISkIAEJCCBSSWgIj+pd852S0ACEpCABCQgAQnMawIq8vP69nvxEpCABCQgAQlIQAKTSkBFflLvnO2WgAQkIAEJSEACEpjXBFTk5/Xt9+IlIAEJSEACEpCABCaVgIr8pN452y0BCUhAAhKQgAQkMK8JqMjP69vvxUtAAhKQgAQkIAEJTCoBFflJvXO2WwISkIAEJCABCUhgXhNQkZ/Xt9+Ll4AEJCABCUhAAhKYVAIq8pN652y3BCQgAQlIQAISkMC8JqAiP69vvxcvAQlIQAISkIAEJDCpBFTkJ/XO2W4JSEACEpCABCQggXlNQEV+Xt9+L14CEpCABCQgAQlIYFIJqMhP6p2z3RKQgAQkIAEJSEAC85qAivy8vv1evAQkIAEJSEACEpDApBJQkZ/UO2e7JSABCUhAAhKQgATmNQEV+Xl9+714CUhAAhKQgAQkIIFJJaAiP6l3znZLQAISkIAEJCABCcxrAnNekb/11lvTX/7yl/z6z3/+M+1m//vf/06nnXZauvnmm6dt98t4EZiamkpnnnlmuuaaa8arYbZGAhKQgAQkIAEJLEECyy6uc//zn/9Mxx13XPr973+fLrnkkoRytvLKK6eHPexh6XnPe166y13uMpKm3Hjjjenxj398rvtHP/pReuADH9g9zwtf+ML0s5/9LG288cbpS1/6Unf7OHy49NJL0y9/+ct09tlnpz/84Q/pAQ94QHrMYx6THvvYx6bVVlttHJq42Npw0EEHpUMPPTTd9a53zUzucIc7LLZzeyIJSEACoyJw8sknp1NPPTVdeOGF6Z73vGd61KMelV784henO97xjkM75X//+9/03e9+N4+5VMo5GE/qhPGZtvST+973vulxj3vcIsUY1xmzuKYHP/jBeWxdYYUVFilX3cAxjHmPeMQj0oIFC6q7/S4BCfQgMHJFHov4Bz7wgfTxj3883XDDDbVN2X///dN2222X9tprr7TssiNvUm4DHQ4dBxLv+csY/DnssMPSgQcemGBXJ9tuu2165zvfmW53u9vV7Z5z25j4IX//+9/Tddddl0pF/tprr03nn39+WmaZZdKaa645567dC5KABOYeAQxMO+ywQzrjjDOmXdzRRx+dPvShD6WPfOQjab311pu2b6ZfPvaxj+XxIo5/73vfm7bZZpv4Ou39k5/8ZPrc5z43bVvdl0033TR94hOfWGTXrrvumr72ta+lpZdeOo9fGF++/e1vp1VXXXWRsrGB8ff5z39+7teZAEyyrLXH6emsQzea5Euw7RNIYKRaM6Eru+yyS/rWt76V0dzjHvfI1nes4vyjX3TRRemEE05Il19+ee64fvvb36Yjjjgi3fnOdx45yqWWWip3RHgJsMyPgxD68+pXvzozoT14EtZZZ51soaCz+9WvfpVDgT772c+mc845J33xi19cLKyWNJu999473fve9848VlpppWnN+eEPf5he8YpXZAvWBRdcMG2fXyQgAQmMGwH6+Ze//OWJvmu55ZZLO++8c1prrbXyOHj44YenP/3pT9mwdeyxx6ZHPvKRs2o+Rg4Ud8ZbDD833XRTz/r+9re/5f2PfvSj04orrthYtq5djEco8S972cvSfvvtl72nTBh23HHHrMzThqpgUHvNa16TjTRMYO5+97tXi/hdAhLoQ2CkijxW5VDit9xyy9yh3P72t5/WpLe+9a0JizyWgNNPPz297W1vS4cccsi0MqP6QufJa1zkmGOO6Srx7373u3NnXm0bPJkcnXXWWTlU6aUvfWm1yJz7fr/73S/tu+++c+66vCAJSGD+EUDhRYlHMMoQ2hmCUWnhwoXpz3/+c/ZQY+iaqeDRfe1rX5uV95122imPr4TO9JJQ5FH+UeYHka9//eu5+O67754Y59ddd9200UYbpZNOOildfPHF08Jao95PfepTmQXX/YxnPCM2+y4BCQxAYNEp8gAH9yr661//uut6Q1l+3/vel/+5q8dgJUCRj86MWPXo5Kpl5/r3mPQQB79dJ9SoTp71rGelrbfeOu/CDatIQAISkMBkEEC5/uAHP5gb++QnP7k77kXrCRskxBRh/dZsxsIjjzwy/fznP88e3Te/+c1xip7vV155Zd5/n/vcp2e5up1MEu51r3slDC8hjGVI3QQCz8MBBxyQy7/jHe+IQ3yXgAQGJDAyi/xRRx2VY+QIicDajguxSYhvJoaeGTkz98985jNp/fXX7xbHOhEWWeqizJe//OX04x//OP31r39ND3rQg9LTn/707NKrc991K6p8IMaPBbBPe9rT8gKj2P2d73wnh6089KEPzZ0qZb75zW/mzCm33HJLXmxKnHpMPuK48h33KdaUr3zlKzmECBcicYLEAvKqi28Pa0jdIqKybqwsWDoQ6iVMqCqwoSOnA2Vtwv3vf//EwIHbs5fLlHZjWcE7EIueWGTLC6tJ9Vg8A+ESZbJWJz/96U/TRz/60Rwew8LVkOp9JXQI1yyDF4ujf/KTn+SieGo+/elPp1VWWSVP+thIGylLWBaCyxh3NcJgSIwp94pQJd7xYjzhCU/I+6t/iMHHE4RgiWLRmSIBCUhg2AQIkYw1WXij6+S5z31u7i9/85vf5DGnHAvrytdtI1Pbu971rrzr4IMPnrauqK58bGMMYjwetA9kgsKxVSt+LKq97LLL4hT5nQW4WO7/9a9/5b59VMkupp3ULxKYowRGosjzT4o7Ddlkk01Sm9k9i2LIXkN2ElbyE18fYTgoolFfLIwJBY5zoBCySAaLNhb9tso8iiP1ouSWQuw+26+++upcL+ErKLghf/zjH7Ni//rXvz7tueeesbn7jnJN3CPKfylMQFBKWfzDWoBqO+kEf/GLX2RFlvM1LfzF6vHMZz6zrHraZyYooZjGDjpSLDxY8U888cTaWEQ641e96lXpG9/4RhyW37GcEK+JG5TJAZkFQq644orMqhq7Hvt5Z7IFz+qCp/K+kpnnRS96UY6V5JhyooOizfGrr746u7IQ+8m2kPI3d6c73SlvjphQJmZ3u9vdGhV5JlxR/6ADWJzfdwlIQAL9CNCXIsSCl/1oeRyGGRa6osjPdN3PW97ylvSPf/wjveQlL5lmFCvPU/1Mf4xBhIw0KPODCGPZgk62GfpxxpEY2373u9/lashgUwqGFoxAGJZ6GcTKY/wsAQnUExhJaA2WXJRgpG5RTH1TbiuLNfbcc8+tLYZVmA6Q2EIWx37/+99P2/0vDIUMAF/4whdqj5vJRq6DhTpYeo8//vjcJpQ+Yv8QJh10XFUhowxKPJMTFv3QRl5vf/vbc5pNJhyUqcoGG2yQN5133nm5Aw6LeLVcr+8orZyTweCVr3xlVsCxiNNWuDFJgVdd7nyUf5R4Up+97nWvywo/3gjavcYaa+QJ02abbda9t73aMeg+JksMBLhYYfzVr361ZxXbb7999qbgmkWY9NFWXkwEQ/i9IDAvJ2Oxn/eI7Yyy5T4/S0ACEhgWAQwQSNV4VK0/LNlRvrq/13cMNYwDeMOrBp1ex1111VV5N8cxPuD9xkv+8Ic/PBuOGLOqWXbK+kglzeQBQ1cIKZQR9oWg3OP5pL8fpH1xvO8SkMB0AiOxyEecHaciZ21bKctGmEn1WDpAQm/CYksmHBaG4kokVy4hIU2ptap19ftOx7bHHnukMr6Q8IzPf/7zOZsMbUTpfMMb3tCtCms71gbaRzhIKP0UoEPkhZWEUBNCXUJ5Z/+zn/3srHyTLgzLPVYZrPSU47xPfOITe+YXxktBCAkWESwyu+22G9VmYWAgHAcPCZZ5UoK+8Y1vjN1ZmaXjRt7//vcn3LshZIV5wQtekC0n3FvSk8FlmIK1nnCstvmTmSTxInsPggWofEZAtI2wKcpdf/31mSksS8Gbw3oOLFCbb755ucvPEpCABIZKICzsoag3VR77seA3hU/WHUuK3gjZwSgySMhKjNv0lxivTjnllO4p6CN5ffjDH87jHYaequCFZvxjPMRbjcWdMMktttgiW/kpjzGFkBo8qBiX2vb31XP5XQISuI3ASCzyoYSjXKG4thWUucgRHnVUj0WpDCW+3BeK50ys2GU95Wes2ijGVaGNKIhI9Xw8KRZBWS+V+Lyx82fDDTfMCjvfIwY89vHOWgBeYbHBvYrSTX1YxYn5/t73vpc7wvI4PrOwCYsIsf2lEh/lcJkSX49Uz/2DH/wgbye8KVjmDf/7Q2gKITt04Eyehi3kHx5Fp87aDK4JCct72fYII8K9iyVKkYAEJDAqAhFaw8MQe0n0/3inyzDSXsew7z3veU8uj8HiOc95Tr/i0/bHmIvVnRBPDGR4OPEQk2mHzHMI1nSMVVXB2MTkgfJbbbVVTnCBoYo2hbCOCk86Sv/aa6+dN6PUMx4R088YUx1T41jfJSCBegIjUeSXX375fDYsw3REbQV3XoR8NC2OZdFlnUTHGJ1RXZlBt+H6a7JoNJ0PqwUSq/Xrzhmx3ijpdUJIDOEw4V3ALcmkCJYs8CQMhZAfFnGWMsi56Uyx9ISwXgCJzjW2l+/sw9oyLI9HWXfTQtSyzEw/401A6sJr8KggUSZ/8Y8EJCCBERAIYwVrwHoJi0BDYs1PfG96p09nHRNhhijhg0qMnRh8sMYTgomXk+e6LFy4MPGgQsYdhJBGYuqrwjG0g36V8YhkD3HNjDEkRsDQVHq599lnn2xswSNMBjuUfzzSigQk0I7ASBR5FmOGROhDfO/1Tuwcs3OkyTratBixSfHvdb5++5rOxXF158NtGNfLQy7IslL3imwCTYo89ceCJ6wfLOSlXtKWRVYAFmeyMLWUUOTJ6FN3XrbxRD6ETph4eYTJUyxKiklG3rEY/8TC5lGckvSnZDaK8Jo4B4toYcZA1WvxcJT3XQISkMBsCMSiz8hc01RXPM2asbTJmFQei1EG5ZjxM8aecn+bz4R2Mq4QY9+UvID1V/SXGOkI0awTkjRgmCnHTxbRElKDMCGI8ZNYfkJQyeTGejPqJH0lnnCe4q1IQAL9CYxEkS/zyDYtWq1rWijB7CvrqCs7jtuwmMeT84gzZKFv3YtJCgpzWPXbXAuhLViNWURLp4fQ6RITGXLttdfGx9rz0hY8BbSN9/CW0OZYCBqhTd2K5siHWMhahtfEZxbwjnIiMUcQehkSkMAsCYQiH4p6U3Wh6Ef5pnKxndAXYtKxoFcNPFGm3zvZ5fB49xp7WUsU3tNqeGav+vEQsAgWZT6MUZRHkUeIyWcCwAQiEioQ3qNIQAL9CYxksSv/jMTGk8O8l9W52rywKKPg4n6bNMFygtWbBZRYz8ltP2yhI917771ziA3WFzrvSN/FYuGIP8Ry0lawsGCxZiEWE6+yo21bRxmmUz2mGgJU3b84vhPfSQ57wmvwcjBohCIfSv7iaIfnkIAE5i+BhzzkIfniUdR7LWJlDEHaKvJ4YRGMOU1PSI34fPo/ntuC13cmISyxRooEE22EZ5oQ+84ar1ijFcdhhacvLhNdRFgq+kM1OUEc57sEJHAbgZFY5KkeNx1C2sY2nUXkOOeYOJbPkyYRw3/22WcP1HTCXOiAefVb3NSU43em56ah0XmWXpHqBWDBZ4AhL3wI1n2EdKNNyjz585e0MDnkwSoRXoNFjJhNJl691gUs6XZ7fglIYO4QCMWcGPhYn1O9uhtvvDGHt7A9FP9qmer36HsJR0EBrnvF+jOy05DWskwTSX2E5pDZDKW7l0RITSzI7VWW5AuE+qCsE1LDeylkhmMMKT2iEdYT6TDL8n6WgAQWJTAyRZ483zz0Aqsxizd7uRJ5WFEs3sQ6TPlJlVCIsZA0dUSk6GJ1P+7QEBbWsgAKDwYLlnrJcccdl3ezALb0XMS56Wijs63WQwfPubFOR+dPmTiWPPx1ufEpi/tznXXWyRbtqDcsKVjd8QZUhZCdsHxX9w3je2QwYpJRTjDq6g7LO+2JbDVY6rFMKRKQgARGTYAxkcxlyIEHHtgNaSzPS4pHDCP0bXVGrbrYcVIGE2rZ6xUpLUkdHOXK8/JEcZT7ww8/vLZdlCWcJtZWPeUpTykPr/3M4lUMOaQ6LnPJR2EmKlxrabyKcaTtJCbq8l0C85XAyBR5Oqx46BHuPjKt1D1MArcbWVAihy0PiIgZ+STeFK6T+Hcs11xXafVAGebJs6TeIn1XZPeJ69x6663zRzryvfbaa5GsACjFpKJEEUdYuFqyQhnn6ago1dtuu20ipWTEvlOeTDg8SY9zk6GgVGBpK4tCUYiZhBHmFIr+Nddck7Mg4FnhmHKihTUlBgiyD1A2BMsS6Spn8lCTqKPfe5ybcjxfIOL+644jhz4ZFLgHPOQKiZRqdeXdJgEJSGDYBMixjqAQY1Bh4WgIceEo0giGh7J/Yxv9FuMLz7yI/pntWPrxyPZ6xXiDJT3KcWwIkwbWSBH2w1hUnTAwfjC+IYRz9vNkkqSBZ4MQU98Utx/pqct4eNIoI7Evf/GPBCTQSGC6n6ux2Mx20NlgZX7Tm96Urbx0TCiexGOjENKRxaIewkXopEaR2nBmrZ/ZUXgUeGASSjbKMK5KFhER2kEMOqEdCNbtWLQaZ8IrgXeCjvzII4/MT6+lI1/QsdajeDMpiHhzcr2j1Ffl4IMPTldccUXOdMPiWFKX8URXOIfVA4V9v0oMPa5NnpZLu4mlJIsL7SYekow2DBp4AHjQVNWygmVpu07aMaw1dNrsZwJBexk8mJTEE1ir7Z3tdzwSvMhdzLoE3MKEyzCIVAUWDFakRENoa91DpKrH+V0CEpDAsAjg1WRMRIknHSMPMlxzzTVz34+xCy82ijbW7FLog/GY8o4BjH46QnXKcjP9vMIKK2TFe4cddshGIAxDrJdi7MLDGzHxjGmMT72ErGjEwzMx4MFPjB11gtGIa8IIRBgOnns8zk996lMTHoJJk7MO3WjSmmx75wCB+v+uIV4YM3tiAVGgUNZRVHnaG9ZilEv+wYkLP/bYY7MleIinXmJVMVkhtAYLMNeMYo3FASUepZont9YptsQP0nkfccQRebJDh41bkodMsQgVJR5rCotdqaOMK4yLpQ6UWTpjJhV0jlg4UOJJZcaxtI1Ouyp4UY4++uic0xe3Lu3mvKQKo1NlgsIDqarCgiQGJCxFZMDhsdysEeBasX63iaWs1tn2O9dLKBIP6IIHsacxOayro8wXH6E2deXcJgEJSGBUBIgbJ5MLfS6hjIRZYnygz8erinJbzR+P8QtjD30ziu4wlfi4TtYREXbIxAJjDAo8ijVKPIYdHkyIwadu7Ik6eGecYcxBQe9lLGGs5CnnXCsZbQ455JCcR554+tJjXNbtZwlIYDqBpTodx21PBZq+b+jfiBnHSouixWlR8OiM6CDmqhDqwfWihGNZr3bOva4bJRxFmtAULPPEo5OGsq3QEdMB4yKFdWQbaHN8KMS4fYlVbFpgW62LdJg8mY/zrbjiitXdI//OtTLINA00DEw88RBPAesR2uRoHnmjPYEEJDAvCdDH0w/RZ5J3nRTBWMB7CV7upv6t13GD7mPsIiEAxjcU+14K+aB115UnHJXxzT65jo7b5hKBWM93S+cZPhgjmZzzjp6FcXvQSexiVeTn0o3wWiaTAB4iLF+ENsWDuSbzSmy1BCQgAQlIQAKTRmDYivxIY+QnDa7tnZsE8C4QJkRIEUo8s9/ddtttbl6sVyUBCUhAAhKQwLwhoCI/b271/L1Q4jrPPPPMDADXFesTymw/85eMVy4BCUhAAhKQwCQTUJGf5Ltn21sRYNEtMWdk0yErz3rrrdfqOAtJQAISkIAEJCCBcSZgjPw43x3bJgEJSEACEpCABCQwZwgMO0Z+5Okn5wx5L0QCEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEkvaht4AAAEbSURBVJBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjREBFfoxuhk2RgAQkIAEJSEACEpBAWwIq8m1JWU4CEpCABCQgAQlIQAJjROD/AMoHmJzK3I71AAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![feature_selection.png](attachment:feature_selection.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model performance"
   ]
  },
  {
   "attachments": {
    "roc_auc.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![roc_auc.png](attachment:roc_auc.png)"
   ]
  },
  {
   "attachments": {
    "confusion_matrix.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![confusion_matrix.png](attachment:confusion_matrix.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}