notebooks/Model_eval-Aerial_cropped.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INPUT the RUN NAME: Options : (\"A new run name\" or \"Any Previous Run Name\"\n",
      " WHICH_RUN = sam_new\n",
      "INPUT: Image-type OPTIONS: (assessor, assessor_code, aerial, overlayed, aerial_cropped, streetside and ensemble \n",
      " IMAGE_TYPE = aerial_cropped\n"
     ]
    }
   ],
   "source": [
    "from __future__ import division, print_function, absolute_import\n",
    "import logging\n",
    "import time\n",
    "import tensorflow as tf\n",
    "import os\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)\n",
    "import numpy as np\n",
    "from config import pathDict\n",
    "from conv_net.train import Train\n",
    "from conv_net.test import Test\n",
    "from data_transformation.data_prep import get_intersecting_images_pin, DumpBatches\n",
    "from plot import Plot\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "\n",
    "which_net = 'resnet'\n",
    "batch_prepare = True\n",
    "train = True\n",
    "test = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from clean_directories import clean\n",
    "# clean(dict(overlayed='summary,batch'),which_vendor='google', which_model='resnet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PREPARE/DUMP BATCHES:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8339 8339\n",
      "16678 16678 16678 16678 1667816\n",
      "--------------- 226.42947602272034 seconds ------------------\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "max_batches = None\n",
    "\n",
    "if batch_prepare:\n",
    "    start_time = time.time()\n",
    "    \n",
    "    cmn_land_pins, cmn_house_pins = get_intersecting_images_pin(is_assessor=False, is_aerial=False,\n",
    "                                                                is_streetside=False, is_overlayed=False,\n",
    "                                                                is_aerial_cropped=True, equal_proportion=True)\n",
    "    print(len(cmn_land_pins), len(cmn_house_pins))\n",
    "    \n",
    "    tr_batch_size = 128\n",
    "    ts_batch_size = (len(cmn_land_pins) + len(cmn_house_pins))*5 // 100\n",
    "    cv_batch_size = (len(cmn_land_pins) + len(cmn_house_pins))*5 // 100\n",
    "\n",
    "    params = dict(\n",
    "            image_type='aerial_cropped',\n",
    "            img_in_shape=[400, 400, 3],\n",
    "            img_out_shape=[224, 224, 3],\n",
    "            img_resize_shape=[128, 128, 3],\n",
    "            img_crop_shape=[128, 128, 3],\n",
    "            tr_batch_size = tr_batch_size,\n",
    "            cv_batch_size = cv_batch_size,\n",
    "            ts_batch_size = ts_batch_size,\n",
    "            enable_rotation=True,\n",
    "            shuffle_seed=881,\n",
    "            get_stats=True,\n",
    "            max_batches=max_batches)\n",
    "    \n",
    "    obj_cb = DumpBatches(params)\n",
    "    obj_cb.dumpStratifiedBatches_balanced_class(cmn_land_pins, cmn_house_pins, is_cvalid_test=True)\n",
    "\n",
    "print ('--------------- %s seconds ------------------'%(time.time() - start_time))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EVALUATE MODELS:\n",
    "------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRAIN MODEL: Aerial Cropped\n",
    "------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# VARIATION 2: Dropouts after every layer: Learning is pretty slow but improves gradually.\n",
    "-----------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dumping/Retreiving Checkpoints to/from %s C:\\Users\\newline\\Documents\\ImageClassification\\data\\checkpoints\\sam_new\\aerial_cropped\\resnet\n",
      "Learning Rate: Initial:  0.0005\n",
      "Checkpoint latest at:  C:\\Users\\newline\\Documents\\ImageClassification\\data\\checkpoints\\sam_new\\aerial_cropped\\resnet\\resnet_epoch_7_batch_116\n"
     ]
    }
   ],
   "source": [
    "max_batches = 116\n",
    "start_time = time.time()\n",
    "if train:\n",
    "    tr_obj = Train(dict(pprocessor_inp_img_shape=[224,224,3],\n",
    "                        pprocessor_inp_crop_shape=[],\n",
    "                        model_inp_img_shape=[224,224,3],\n",
    "                        use_checkpoint=True,\n",
    "                        save_checkpoint=True,\n",
    "                        write_tensorboard_summary=False\n",
    "                        ),\n",
    "              device_type='gpu',\n",
    "              which_net='resnet')\n",
    "    (tr_loss_arr, tr_acc_arr, tr_precision_arr, tr_recall_arr, \n",
    "     cv_loss_arr, cv_acc_arr, cv_precision_arr, cv_recall_arr,\n",
    "     l_rate_arr)  = tr_obj.run(num_epochs=30, num_batches=max_batches, cv_num_batches=1, get_stats_at=10)# + 1)\n",
    "    \n",
    "print('----------%s seconds------------'%(str(time.time() - start_time)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Values: train_loss = 0.861143, train_acc = 0.856717, train_precision = 0.86338197178, train_recall = 0.849309040575, cv_loss = 0.550780010782, cv_acc = 0.848532938957, cv_precision = 0.856123632483, cv_recall = 0.839506980803\n"
     ]
    },
    {
     "data": {
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ECWjRogU6dOgAfX19aGhoKJQlCALGjRuH4OBgNG7cGP3798erV6/g4+OD0NBQHD58GLVr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fPn0z/MzOyhhx6iV69exRBl+eTo6MigQYPSOy9ERKR4Va1alWeffZaKFStSuXJlxo8fz+DB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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124fb623240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print ('Mean Values: train_loss = %s, train_acc = %s, train_precision = %s, train_recall = %s, cv_loss = %s, cv_acc = %s, cv_precision = %s, cv_recall = %s'%(\n",
    "    np.mean(tr_loss_arr), np.mean(tr_acc_arr), np.mean(tr_precision_arr), np.mean(tr_recall_arr), np.mean(cv_loss_arr), np.mean(cv_acc_arr), np.mean(cv_precision_arr), np.mean(cv_recall_arr)))\n",
    "oj = Plot(rows=1, columns=5, fig_size=(40,7))\n",
    "\n",
    "l_rate_df = pd.DataFrame(l_rate_arr, columns=['learning_rate'])\n",
    "oj.vizualize(data=l_rate_df, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Learning Rate Decay'})\n",
    "\n",
    "tr_loss_df = pd.DataFrame(tr_loss_arr, columns=['training_loss'])\n",
    "oj.vizualize(data=tr_loss_df, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Training Loss'})\n",
    "tr_data = pd.DataFrame(np.column_stack((tr_acc_arr, tr_precision_arr, tr_recall_arr)), columns=['accuracy', 'precision', 'recall'])\n",
    "oj.vizualize(data=tr_data, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Training Metrics'})\n",
    "\n",
    "cv_loss_df = pd.DataFrame(cv_loss_arr, columns=['crossvalidation_loss'])\n",
    "oj.vizualize(data=cv_loss_df, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Crossvalidation Loss'})\n",
    "cv_data = pd.DataFrame(np.column_stack((cv_acc_arr, cv_precision_arr, cv_recall_arr)), columns=['accuracy', 'precision', 'recall'])\n",
    "oj.vizualize(data=cv_data, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Cross-Validation Metrics'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n"
     ]
    }
   ],
   "source": [
    "which_data = 'cvalid'\n",
    "tsoj = Test(params=dict(pprocessor_inp_img_shape=[224,224,3],\n",
    "                        pprocessor_inp_crop_shape=[],\n",
    "                        model_inp_img_shape=[224, 224, 3]),\n",
    "                    device_type = 'gpu',\n",
    "                    which_net='resnet')\n",
    "fnl_tst_metric_stack = tsoj.run(use_checkpoint_for_run='sam_new',\n",
    "                                use_checkpoint_for_imageType='aerial_cropped',\n",
    "                                optional_batch_name=which_data,\n",
    "                                which_checkpoint='all',\n",
    "                                which_data=which_data,\n",
    "                                dump_stats=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checkpoint</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>test_precsion</th>\n",
       "      <th>test_recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>epoch_7_batch_89</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>epoch_7_batch_99</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>epoch_7_batch_109</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>epoch_7_batch_116</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>epoch_29_batch_89</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>epoch_29_batch_99</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>epoch_29_batch_109</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>epoch_29_batch_116</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>epoch_30_batch_9</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           checkpoint dataset_type  test_loss  test_acc  test_precsion  \\\n",
       "0    epoch_7_batch_89       cvalid      0.250     0.925          0.904   \n",
       "1    epoch_7_batch_99       cvalid      0.254     0.930          0.911   \n",
       "2   epoch_7_batch_109       cvalid      0.251     0.931          0.916   \n",
       "3   epoch_7_batch_116       cvalid      0.244     0.934          0.921   \n",
       "4   epoch_29_batch_89       cvalid      0.215     0.941          0.924   \n",
       "5   epoch_29_batch_99       cvalid      0.215     0.944          0.924   \n",
       "6  epoch_29_batch_109       cvalid      0.215     0.944          0.924   \n",
       "7  epoch_29_batch_116       cvalid      0.215     0.944          0.924   \n",
       "8    epoch_30_batch_9       cvalid      0.214     0.944          0.924   \n",
       "\n",
       "   test_recall  \n",
       "0        0.952  \n",
       "1        0.954  \n",
       "2        0.950  \n",
       "3        0.950  \n",
       "4        0.962  \n",
       "5        0.966  \n",
       "6        0.966  \n",
       "7        0.966  \n",
       "8        0.966  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_outcomes_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'cvalid_pred_outcomes.csv')\n",
    "prediction_metrics_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'cvalid_pred_metrics.csv')\n",
    "prediction_outcomes = pd.read_csv(prediction_outcomes_path)\n",
    "prediction_metrics = pd.read_csv(prediction_metrics_path)\n",
    "prediction_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n"
     ]
    }
   ],
   "source": [
    "which_data = 'test'\n",
    "tsoj = Test(params=dict(pprocessor_inp_img_shape=[224,224,3],\n",
    "                        pprocessor_inp_crop_shape=[],\n",
    "                        model_inp_img_shape=[224, 224, 3]),\n",
    "                    device_type = 'gpu',\n",
    "                    which_net='resnet')\n",
    "fnl_tst_metric_stack = tsoj.run(use_checkpoint_for_run='sam_new',\n",
    "                                use_checkpoint_for_imageType='aerial_cropped',\n",
    "                                optional_batch_name=which_data,\n",
    "                                which_checkpoint='all',\n",
    "                                which_data=which_data,\n",
    "                                dump_stats=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checkpoint</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>test_precsion</th>\n",
       "      <th>test_recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>epoch_7_batch_89</td>\n",
       "      <td>test</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>epoch_7_batch_99</td>\n",
       "      <td>test</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>epoch_7_batch_109</td>\n",
       "      <td>test</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>epoch_7_batch_116</td>\n",
       "      <td>test</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>epoch_29_batch_89</td>\n",
       "      <td>test</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>epoch_29_batch_99</td>\n",
       "      <td>test</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>epoch_29_batch_109</td>\n",
       "      <td>test</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>epoch_29_batch_116</td>\n",
       "      <td>test</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>epoch_30_batch_9</td>\n",
       "      <td>test</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           checkpoint dataset_type  test_loss  test_acc  test_precsion  \\\n",
       "0    epoch_7_batch_89         test      0.262     0.919          0.903   \n",
       "1    epoch_7_batch_99         test      0.268     0.913          0.896   \n",
       "2   epoch_7_batch_109         test      0.267     0.916          0.904   \n",
       "3   epoch_7_batch_116         test      0.260     0.917          0.906   \n",
       "4   epoch_29_batch_89         test      0.233     0.929          0.920   \n",
       "5   epoch_29_batch_99         test      0.233     0.929          0.918   \n",
       "6  epoch_29_batch_109         test      0.233     0.929          0.918   \n",
       "7  epoch_29_batch_116         test      0.232     0.928          0.918   \n",
       "8    epoch_30_batch_9         test      0.231     0.929          0.918   \n",
       "\n",
       "   test_recall  \n",
       "0        0.940  \n",
       "1        0.935  \n",
       "2        0.930  \n",
       "3        0.930  \n",
       "4        0.940  \n",
       "5        0.942  \n",
       "6        0.942  \n",
       "7        0.940  \n",
       "8        0.942  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_outcomes_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'test_pred_outcomes.csv')\n",
    "prediction_metrics_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'test_pred_metrics.csv')\n",
    "prediction_outcomes = pd.read_csv(prediction_outcomes_path)\n",
    "prediction_metrics = pd.read_csv(prediction_metrics_path)\n",
    "prediction_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# VARIATION 3:  Dropout removed from the residual_block_first and maintained accros the resudual_blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dumping/Retreiving Checkpoints to/from %s C:\\Users\\newline\\Documents\\ImageClassification\\data\\checkpoints\\sam_new\\aerial_cropped\\resnet\n",
      "Learning Rate: Initial:  0.0005\n",
      "Checkpoints not found, Hence starting at batch 0 and epoch 0........\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------3748.544000864029 seconds------------\n"
     ]
    }
   ],
   "source": [
    "max_batches = 70\n",
    "start_time = time.time()\n",
    "if train:\n",
    "    tr_obj = Train(dict(pprocessor_inp_img_shape=[224,224,3],\n",
    "                        pprocessor_inp_crop_shape=[],\n",
    "                        model_inp_img_shape=[224,224,3],\n",
    "                        learning_rate=0.0005,\n",
    "                        use_checkpoint=True,\n",
    "                        save_checkpoint=True,\n",
    "                        write_tensorboard_summary=False\n",
    "                        ),\n",
    "              device_type='gpu',\n",
    "              which_net='resnet')\n",
    "    (tr_loss_arr, tr_acc_arr, tr_precision_arr, tr_recall_arr, \n",
    "     cv_loss_arr, cv_acc_arr, cv_precision_arr, cv_recall_arr,\n",
    "     l_rate_arr)  = tr_obj.run(num_epochs=15, num_batches=max_batches, cv_num_batches=1, get_stats_at=10)# + 1)\n",
    "    \n",
    "print('----------%s seconds------------'%(str(time.time() - start_time)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Values: train_loss = 1.08237, train_acc = 0.845203, train_precision = 0.850863590186, train_recall = 0.8400369115, cv_loss = 0.677143497517, cv_acc = 0.835413030287, cv_precision = 0.83411625389, cv_recall = 0.842248400233\n"
     ]
    },
    {
     "data": {
      "image/png": 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nT5/m999/B0z7PfB0+br7f1qVKlUoWrSoQcwsWLAg77zzjl6abpszU35P5KRu\n5ubm6mSpJ+nSdHXz8vJiwYIFrF+/npCQEBo3bqx+1k8+xDc1nxBCPOlNi3nZqV+/PnZ2dgQHBzNh\nwgTMzc1RFIUdO3Zgb2+vvtSi4+DggIODA/fv3yc6OpoLFy6osTEuLi7HL06cOXMGe3t7vZdDICP+\nVK9encjISDXNzs4Ob29vUlNT1dh48eJFoqOj1diY09h8+vRpwPiYa9WqVSlcuLBBbLSxsaF06dJ6\naTnpaz/J09OTzZs3s3v3bnXSjW67M2OLXZw/f56kpCTS09ONPvtMTU0lLS2Nc+fOqdvoZcbU/i8Y\nf37auHFjGjduDGTcv729PXv27KFRo0Y0bNiQJk2a4ObmRtmyZbMtR7w4MqFI5Hu6gcybN2/y/fff\nZ5rv3r17QMaA59y5c1m/fj1JSUkAlClThrp16/L2229z8+ZNg3ON7dGp8/SEoCdnBmfn6XONnX/n\nzh0Ag7dSAYNglZ2iRYsydOhQvbTVq1czbdo0hg0bxpo1aww6UmfOnGH69OnqILClpSWVKlWiRo0a\nXLhwIdv7TExMBODnn3/ONI/us3leV65cAVDfrtGVrXt7NKuydTPOTdnz+8iRI8ycOZOoqCgg4/9H\n1apVcXBw4Nq1a3pt8t577+Hs7ExYWBj3799HURRCQ0Np0qTJM/0QFUKIN5GxfaCfN/5A1nHWFJmd\nryv77t27AJkOQuY0TmdH95vn6Qe2T5eXnJwMZLzF89577/Hzzz9z4sQJjh8/znfffUfFihWZPHmy\nukKFqfmEEEK8OBIL9Xl6ejJ79mz+/PNPdXC9QoUKBi/B6ORmv9PYZ/G0hIQEo330p/n6+lKiRAlW\nrlzJsWPHOHPmDEuWLKFMmTKMHTtWfVlFCCFeB+XKlePmzZtcvHgxyzGtxMREkpKSDGLA0+OrOe3f\nmPqdamo+MzMz2rVrx+LFi/nrr79wdnZm+/btWFpa6k1gfZax5KwkJCQYTCbSMbay388//8yCBQu4\nceMGkDG+6+TkRKVKlTh+/LhJvweeZEq7X7x4US/NlDHs3HD//n0KFixotLwiRYpgbW2t9xls2rSJ\nH374gT179hAYGEhgYCCWlpb4+PgwYcIEChYsaHI+IYR40psU8xISEvjpp58MyvT29lZX4zEzM6ND\nhw4sWrSIQ4cO0aBBA44ePcrVq1cZNGiQQR8yOTmZr7/+mg0bNqh1Llu2rBobdc81TZWQkGAwcVXH\nWGxcu3YtCxcuVGNwsWLFcHJyomLFipw8eTLXY2OpUqW4fv26Xtrz9rWf1KhRI2xtbdm1axfdu3cn\nJiaGmJgYg1WYdHTPM8+dO5cLbd3/AAAgAElEQVTlc3Fdfz0rpvZ/Ifvnp4ULF2bDhg0sWrSInTt3\nEhwcTHBwMBYWFnh6ehIQEJDrK/oL08iEIpHv6VajqVOnDmvWrMk2/6xZs1i7di1eXl707NkTrVbL\nW2+9BUDr1q1z3Al80XRf0LqA9iRjaTnVq1cv/v77b3799VeGDx/OqlWrsLCwUK/fr18/EhMTGTNm\nDA0bNuT999/HysqK48ePs23btmyvr/t8du/erbeMbm5LS0vjr7/+wtzcXJ1xqyt7xYoV2T4M1eU1\ntgRgamoqiqJgZWXFlStX6N+/PwULFmTatGm4uLhQoUIFLCws2LFjB7t37zY4v1OnTvz555+EhoaS\nnp5OSkqKbHcmhBBZyI348zLoltzNLB6bsqzss5SnG0h+mq5zp/tdY2ZmRpcuXejSpQu3b9/m999/\nZ9euXYSEhDBw4ED27t1L8eLFTc4nhBDi5cnvsdDLy4vZs2eza9cuatasSVhYGL169co0/8vqdz5Z\nXmb39vDhQ73tsT08PPDw8CAhIYFDhw6xd+9eAgMD8ff3p3Llymg0mhdeXyGEyA1NmjQhMjKS8PBw\nnJ2dM823fv16Zs+ezcCBAxkxYkSm+XLavwHTv1NNzdexY0cWL15MUFAQVapUYf/+/TRu3FhvhYTc\nHksuWrSoOhH2abpV9XSCgoKYPHkyWq2WyZMn4+DgoD5wnTx5MsePH89R2aDf7sb6effu3dNr85ep\ncOHCJCUlkZiYaPBQ99GjRyQnJ+t9NuXKlWPGjBmkp6fz999/c+DAATZv3sz69espUqQIo0ePzlE+\nIYTQeZNino2NjdFJJ66urnrbe3Xq1IlFixYRFBREgwYNMt3uDGDGjBmsX7+e1q1b06NHD73Y6Onp\nmeMJRTmJjdu2bSMgIIBq1aoREBBA9erV1dg4ceJEddeXnHjy8zE24SUhIeGFxkYrKyuaN29OcHAw\niYmJ7Ny5k0KFCmW6Laeuv+nj48PMmTNfWL2eLi+z56eQ8QIUZCyOMXHiRCZMmEB0dDQHDhxgy5Yt\nBAUFYWFhwddff/3C6ysMyfrIIt8rUqQIdnZ2nDt3Tp0J+6QtW7bw3XffcfnyZSAj2JQoUYJvv/2W\nevXqqUEgOTmZq1evArn7ZsXzcnBwADAaBJ+l02jMxIkTeeeddzh27BjLli1T0yMiIrh16xY9e/ak\nX79+VK1aVZ11GxMTA+i3lbHZt1qtNtP6X7hwgS+//JK9e/c+9z0EBwdz+/ZtGjZsqL7loytbt/z9\nk+7evcsXX3zBb7/9BqD++Dpx4oRB3qCgIJycnNiyZQu7d+8mKSmJYcOG0a1bNypVqqROwDLWJpCx\nRZqVlRWhoaGEhoZSpEgR3N3dn/uehRDiTZXT+JNXbG1tqVChAtHR0QZL2eoGKnNTtWrVANT91J92\n5MgRzMzMqFy5Mnfu3OG7777j119/BaBEiRK0b9+e+fPn4+PjQ1JSEqdOnTI5nxBCiJcrv8fCcuXK\nUa1aNXbv3k14eDj379/PdLszeHn9Th2NRsPVq1eNPkTu1KkTXl5epKSk8MMPP7BixQogY5Dcw8OD\nmTNnMnDgQB4/fsyff/6Za3USQogXrX379lhaWrJ69epMH/olJSWxceNGIONt+6y8//77FCxYkBMn\nThjdGuTIkSMAVK5c2eTv1Jx+91auXJlq1aqxd+9e9u7dS0pKit52Z5D7Y8m6Fc515+qkp6erW648\nWTbA119/TcuWLfVWbzh//nyOywbU1f6M9SsvXrzIzZs3qVKlSo6umVuyqtuxY8dQFIXKlSsDsGfP\nHqZMmcL9+/exsLDAycmJIUOGqC8cHzt2LEf5hBDiSW9SzHv33Xc5c+aMwZ+ntzGrWLEiTk5O7N69\nm9TUVHbt2kWdOnUoX768QX0DAwMpVaoU33zzjV5sfPjwocEuHqZwcHDgypUrBqsApaWlZRob586d\nS4sWLfRio66v/CRTVg3Kasw1JiaG+Ph4Nf68KJ6enqSmphIWFkZISAjNmjXTe1HlSZUqVcLS0lLd\nReVpy5cvZ+HCheoqvc+6cpKO7vmpsf72tm3bcHJyYtu2bRw6dIjp06er295Vq1aNjz/+mE2bNmFt\nbS0xNw/JhCIhyJghe/fuXebMmaO3b/S5c+eYOnUqy5cvVwNawYIFefTokTrjFzI6bF988YU6IUk3\no/JV0KJFC9566y1WrlxJXFycmv7vv/+ydOnSXCnD1taWyZMnA7BgwQK1HN2yjLdv39bLf/XqVXVG\nc1pamppeoEDGomlPtl+HDh2wsLDgm2++0RtsTUtLY9q0aSxbtsykZfeyEh0dzfTp07GwsGD48OFq\nuoeHB7a2tvz3v/8lNjZW75zZs2ezcuVKLl26BGTsG/7OO+/w22+/6f1ASUlJYcWKFZibm9OgQQO1\nTW7dumVQh5UrVxq0CWT8kHNzc+PAgQOEh4fTqlUrWUpXCCGykNP4k5d8fHy4f/++wX7VP/74Y66v\nemhnZ0e9evWIiopi3bp1esc2btxIZGQk9erVo2zZshQuXJiVK1cyb948gzirG7i2s7MzOZ8QQoiX\nS2JhxoBqXFwcixYt4r333lMHeY3Jab9T9/bks/b9O3TogKIozJkzh/T0dDU9KCiIixcv0qBBA6ys\nrNi2bRvffvutXl8e/rddt8RYIcTrpFy5cvTp04c7d+7w0UcfGayykJiYyKeffsqFCxdwc3Ojbt26\nWV7PysqKtm3bcuPGDebPn693bP/+/QQFBfHee+9Ru3Ztk79Tn+W7t0OHDsTFxbF06VIKFy5s8BJg\nbo8l61Z6mDVrlt55S5cuNRhvzGwccsuWLeqWqE/+HjAlvnXs2JECBQqwaNEivTZ6+PAhU6dOVfPk\nBR8fHyDjIXF8fLyaHh8fz1dffaVXt/Pnz7Nu3TqDvvHTn7Op+YQQ4klvaszLTqdOnbh9+zbLly8n\nPj6eTp06Gc2ni41PTrZKT09n2rRppKSk8PjxY71+Una8vb1RFIWZM2fqxbXFixcbrHake9Hm6b7y\npk2biIyMBPTjoLHnlk/T9Sd/+OEHdXEKyFiRZ/r06QCZtkVuadq0KdbW1qxatYro6OgsX6ixtrbG\ny8uLM2fOGGxn98cff/DVV1+xZcsWihYtCpjWBlmpV68epUuXZvPmzURHR6vpycnJ6vPTevXqcePG\nDVatWmVQp1u3bvHo0SOJuXlItjwTb7zFixerb80/rWfPnrRq1YqPP/6YgwcPsmrVKo4dO4arqysJ\nCQns3LmTpKQkZs+erW4d1r59e5YtW0bnzp1p2bIlaWlpHDx4kNjYWIoXL058fDx379412PM0r9jY\n2DBp0iT8/f3p3LkzHh4eWFhYEBISouYxN3/+uYVubm54enoSEhLClClTWLp0KS4uLtjb2/Pbb79x\n584dqlatyrVr19izZw8FCxbEzMxMb1C2TJkyAKxbt4579+7h5+dHhQoVGD16NLNmzaJdu3a4u7tT\nrFgx9u/fT0xMDG5ubgZv/mTm8OHD6iC1oig8fPiQf/75hz/++AOAgIAAHB0d1fxFixZl+vTpfPrp\np3h7e9OyZUtKly7N4cOHOXnyJDVr1qRfv35ARkCdMWMGn3zyCb6+vnh4eFCiRAnCwsK4cOEC48aN\no0yZMri5ufH111/z448/cv78ecqXL8/FixfVlYfA+L6k3t7eBAcHA3nXKRdCiNdFTuNPXurTpw87\nd+5k8eLFHDt2DEdHR06dOsXRo0cpWrRojrYnnTFjhtrRe9rw4cOpU6cOU6dOpWfPnkyZMoWQkBC0\nWi1nz54lPDyc0qVLM23aNCCjcz1s2DCmT59Ou3bt8PDwoFChQhw5coSTJ0/SsWNH3n//fQCT8wkh\nhHh58mssfJKXlxfffvstf/31F5988kmWeXPa79T1XcePH0+jRo3o3bt3jurWpUsXQkJC2LJli/p2\n7/Xr1wkJCcHe3p6RI0cCMGrUKAYPHoy3tzetWrWiWLFi/P3330RERODq6prtm8xCCPGqGTlyJLdv\n32bz5s20aNGC5s2bU758ea5fv054eDjx8fHUrl1bnfyRndGjRxMZGcmSJUs4cuQIzs7OxMXFsXfv\nXgoXLszs2bPVt+pN/U7N6Xdvu3btmD17NtHR0Xh7e1OoUCG947k9ltymTRuCg4PZuXMnsbGxNGjQ\ngHPnzhEREYG9vb36EBgyHnBu376dIUOG0LZtW2xtbTl58iSHDx+mRIkS3L592+jYbFbxrVy5cowZ\nM4YvvvhCHS+1sbFh//79xMXF0bZt2xfy0PT06dP4+fkZPVa+fHm++OIL6tatS9++fVm+fDkdOnTA\nzc0NgNDQUG7evEn//v3Vh/bdunVjw4YNzJkzh8OHD6PVarl9+zY7d+7ExsaGjz/+OEf5hBDiaW9i\nzMtO27ZtmTlzJgsXLsTa2prWrVsbzdehQwd++uknOnfujLu7O2lpaRw4cICLFy/y9ttvc+fOHe7d\nu2d0a01j2rdvT3BwMEFBQcTGxlKvXj3Onj3LkSNHsLOz01u5qEOHDgQHBzNw4EDatGmDra0tx48f\n5+jRo1nGxrFjx9K4cWOjW2lXrFhR7U96e3vTokULrK2t2b9/P5cvX6Z9+/a0a9cuJ02ZY9bW1jRp\n0oSQkBAKFSpEs2bNssw/btw4/vrrL2bMmKFuFf7vv/+ya9curKysmDFjhvr/SdcG27dvx8rKis6d\nO6uTjExhaWnJjBkzGDhwoPr89O233yY0NJRLly7x+eefU6pUKby8vFi5cqU6KcrJyYnExESCg4Mx\nNzdn6NChz95A4rnIhCLxxouNjTVYXUanRYsWABQqVIiVK1fy3//+lx07drB27VqKFClC7dq1+eST\nT3B1dVXPGTlyJIULF2br1q2sXbuW4sWLU6lSJSZOnEhMTAwzZsxg3759dO3a9aXcnynatm2LtbU1\nixYtYtu2bRQqVIi2bdtSp04dRo4cibW1da6UM3HiRH7//XcOHjzI1q1b6dChA8uXL2fOnDkcO3aM\no0eP8s4779ChQwcGDx7Mxx9/zNGjR3nw4AGFCxembt269OzZk99++401a9bQsGFDypQpQ9++fXn/\n/fdZtmwZISEhPH78mHLlyjF27Fh69uxpcuA6fPiw+vYNZMyCLlu2LB07dqR3795G31ht3bo1ZcuW\n5ccff+TAgQMkJSVhb2/PoEGD+PDDD9W9UQEaNmzIunXr+P7779m3bx9JSUlUrlyZL7/8Uu1IlylT\nhuXLlzN37lwiIiI4ePAgdnZ2+Pn58cknn+Dp6cmBAwdQFEVvGcEmTZpQuHBh3nrrLerUqfOsH5EQ\nQuQLNjY2OYo/ealgwYKsWLGCb775hl27dnHixAk0Gg2LFy/m22+/5dy5cyZf68k3PJ6m6whXqFCB\nX375hQULFhAWFsaRI0coXbo0fn5+DBw4UN32E8DPz48SJUqwcuVKduzYQVJSEhUqVGDcuHF6nWdT\n8wkhhHh58mssfFKlSpWoVKkSMTExeHl5ZZs/J/3OAQMGEBMTQ3h4OBcuXMjxhCLd26tLly5V+7+2\ntra0b9+eUaNGUaxYMSBjzGLp0qUsWbKE0NBQEhISsLOzY/DgwfTv3z9XXg4SQoiXycLCgpkzZ9K2\nbVt+/vlnoqOj2bdvHwUKFECr1TJ8+HC6du2KhYWFSdcrXrw4GzZsYNGiRQQHB7N69WqKFy9Op06d\nGDhwoN42K6Z+p+b0u7d06dI0aNCA8PBw2rdvb1DHFzGWPHfuXGrUqMGmTZtYt24dFSpU4Pvvv2fT\npk16E4qaN2/OvHnzWLJkCYGBgRQqVIhy5coxadIknJ2d8fb2Zt++fepDTlPjW+/evalQoQJLly4l\nJCQERVGoVKkSn3zyCV26dDH5PnIiMTFRb1z3SU+u/jR27FiqV6/OmjVrCAwMpECBAlSrVo1Jkybh\n6emp5itWrBirV6/mhx9+IDw8nIiICGxtbWnatClDhgxRt20zNZ8QQjztTYx52SlWrBhubm4EBwfT\noUMHdaGGp3366acUKVKEwMBANTZWrlyZyZMnEx0dzZdffsm+ffvUVflM8c0337Bs2TI1Nr7//vss\nXLiQtWvX6k0oatmyJXPmzGHp0qUEBgZibW1N+fLlmTJlCjVq1KBLly7s379fnQw1aNAgYmNjOXjw\nIJcuXcp0rFPXn/zvf/+rLg5QuXJlBg4c+MJi49N0iz7onidmpWTJkmzatIlFixaxe/du/vrrL4oX\nL467uzuDBg1StxGFjIm7Q4cOZdWqVaxZswaNRoOzs3OO6takSRPWrl3LwoULCQsLIykpiSpVqjB7\n9mz15R0rKyuWLFnC4sWL2bNnD6tXr6ZgwYI4OzszYMCAHJcpco+ZktONCIUQr5X79+/z4MEDSpcu\nbbDP5S+//ML48eOZN28ebdq0yaMaClOcP3+e1q1bM3DgQEaMGJHX1RFCCJFLLl++TPHixY3uae3m\n5oa1tTU7duzIg5oJIYQQL4fEQiGEEEIIIYQQQohXk7zKJMQbLjY2lqZNmzJ+/Hi99OTkZNasWUOB\nAgVwcXHJo9oJUyiKwsKFCzE3N6dz5855XR0hhBC5aNq0abi4uBjsVb5jxw6uXr1KvXr18qhmQggh\nxMshsVAIIYQQQgghhBDi1SRbngnxhnNwcMDR0ZHNmzdz+fJlHB0dSU5OJjQ0lCtXrjBy5Eh1/0vx\naklJScHHx4eEhASuX79O586dKVeuXF5XSwghRC7q3r07+/bto0uXLnh6evLWW28RExNDWFgYZcuW\nZciQIXldRSGEEOKFklgohLh+/Tpt2rRh6NCh9OnTR+/Y/fv3WbhwIbt27eLatWsULlwYFxcXhg4d\nanTreiGEEEIIIYQQuUe2PBMiH0hMTGT58uXs3LmTq1evYmlpiVarpVevXrRq1Sqvqyey4O3tTWxs\nLG5ubkyfPj3bfU+FEEK8fiIiIli2bBmnTp3i3r17lCpVCjc3NwYNGkSJEiXyunpCCCHECyexUIj8\n68GDB/Tt25fjx48zbtw4vQlFDx8+pEePHkRHR+Ps7IyzszP//vsvISEhWFhYsHz5cll1WwghhBBC\nCCFeIJlQJIQQQgghhBBCCCGEEOKlunLlCkOHDiUqKgrAYELR4sWL+frrr/Hz82PixIlq+uHDh+nT\npw+VKlUiMDDwZVdbCCGEEEIIIfIN87yugBBCCCGEEEIIIYQQQoj8Y8WKFbRv357o6Gjq169vNE9I\nSAhmZmaMGDFCL93V1RVXV1fOnj3L9evXX0Z1hRBCCCGEECJfKpDXFXiTHDt2LK+rIIQQIg/JUus5\nI3FTCCHyN4mbOSNxUwgh8q83MWauXLkSe3t7AgICuHDhAhEREQZ5unfvjoeHB7a2tgbHrKysgIwt\n054mMVMIIfK3NzFuvkgSN4UQIn/LLm7KhKJclhs/VE6fPk21atVyoTavL2mDDNIOGaQdMkg7vNpt\nIB2vZ/O8cfNV/j/xMkk7ZJB2yCDtIG2g8yq3g8TNZyNxM3dIO2SQdpA20JF2yPCqtsObGjMDAgJo\n2LAhFhYWXLhwwWierl27Gk2Pj4/n6NGj2NjY8O677xrNI2O0OSP3+ubJL/cJcq9vqme91zc1br5o\nEjdzJr/ca365T5B7fVPJvWbPlLgpW54JIYQQQgghhBBCCCGEeGmaNGmChYXFM507e/ZsHjx4QMeO\nHdWVioQQQgghhBBC5D5ZoUgIIYQQQgghhBBCCCHEK2/hwoVs3rwZe3t7Ro4cmWm+06dPP3dZycnJ\nuXKd14Hc65snv9wnyL2+qfLTvQohhBCvMplQJIQQQuRT169fp02bNgwdOpQ+ffqo6e7u7ly5ciXL\nc2fOnImPjw8Av//+O3379jWar2TJkoSHh+danYUQQgghhBBC5E/ffvstCxcu5K233uLHH3+kWLFi\nmebNja0NZIuEN1N+udf8cp8g9/qmki3PhBBCiFeDTCgSQggh8qEHDx4wdOhQ7t+/b3Csd+/eJCYm\nGqQnJyezbNkyrKysqFmzppp+5swZALp3706pUqX0zrGxscnlmgshhBCvjswm52bl7t27zJ8/n7Cw\nMG7fvk2lSpX46KOPaNOmzYutrBBCCPGaSk9PZ9KkSWzatIkSJUqwbNkyqlSpktfVEkIIIYQQQog3\nnkwoEkIIIfKZK1euMHToUKKioowez+yBaEBAAI8fP2b8+PF6g7e6CUWjR4+mSJEiuV5fIYQQ4lWU\n1eTczDx8+JB+/fpx6tQpWrduzTvvvENISAgjR44kPj6eXr16vcAaCyGEEK+flJQUhg0bRmhoKPb2\n9ixbtowKFSrkdbWEEEIIIYQQIl8wz+sKCCGEEOLlWbFiBe3btyc6Opr69eubfF5ERARr167F1dWV\n7t276x07c+YM9vb2MplICCFEvnHlyhX8/Pw4fvx4js5buXIlUVFRfP7558ybN4/PPvuMLVu2UKVK\nFebMmcPt27dfUI2FEEKI14+iKPj7+xMaGkqVKlVYt26dTCYSQgjxWkpLS2PFihW0adMGR0dHWrRo\nwYIFC0hNTTXp/NOnTzNgwADq1KlDnTp16N27N+Hh4UbzJiUl8c033+Dh4YGjoyNt2rRhzZo1KIqS\nm7ckhBAin5AJRUIIIUQ+snLlSuzt7Vm9ejUdO3Y06RxFUfjyyy8xNzfn888/1zuWnp5OTEwMGo3m\nRVRXCCGEeOU86+RcgLVr11KyZEl8fX3VNFtbWwYMGEBSUhKBgYG5XV0hhBDitbVq1SpCQkJ47733\nWLlyJWXKlMnrKgkhhBDPZOrUqcycOZO33nqL3r17U6ZMGebPn4+/v3+25x4+fBhfX1/CwsJwdXXF\n29ubGzdu8OGHH7J69Wq9vOnp6QwfPpwffviBihUr0rt3bwoUKMDUqVP56quvXtTtCSGEeIPJlmdC\nCCFEPhIQEEDDhg2xsLDgwoULJp2zbds2Tp06RceOHQ0mDsXGxvLo0SMKFSrE6NGjiYiIICEhgerV\nqzNw4ECaNm36Au5CCCGEyDu6ybkBAQFcuHCBiIgIk867dOkS169fx8vLCwsLC71j9erVA+DIkSOZ\nbj0qhBBC5CcpKSksXLgQAK1Wy5o1a4zm8/X1pVSpUi+zakIIIUSOREZGsn79ery8vPj2228xMzND\nURTGjh3Lli1bCA0Nxc3Nzei56enpjB8/nuTkZL799ltatWoFQHJyMh999BGzZs2icePG6gp+O3bs\nYN++ffTr148xY8YAMHz4cD766COWL19Op06d0Gq1L+W+hRBCvBlMXqHoeZfju3v3LlOnTsXd3R0n\nJyd8fHzYsWOH0bw5WY4vJ/XauHEjWq3W6J9u3boZ5A8LC6N79+44OzvToEEDxo8fL0vQCyGEeK01\nadLE4CFmdpYvXw5Av379DI6dOXMGgKCgIC5fvkz79u1p2bIlp06d4uOPP2bTpk3PX2khhBDiFRIQ\nEMCWLVuoXbt2js67dOkSAOXLlzc4VqpUKQoWLGjyZF8hhBDiTRcTE8OdO3cACAkJ4fvvvzf659at\nW3lcUyGEECJrukmxQ4YMwczMDAAzMzNGjRqFmZkZGzduzPTckydPEhcXR+PGjdXJRACFChVi1KhR\npKam6q1StGbNGgoUKMCAAQPUNEtLS0aMGIGiKDJWK4QQIsdMXqFo6tSprF+/HhcXF9zd3YmMjGT+\n/PmcOXOG+fPnZ3nuw4cP6devH6dOnaJ169a88847hISEMHLkSOLj4+nVq5eaV7cc3759+2jWrBle\nXl7s37+fqVOncvnyZXVG7bPUS/fQs3///hQsWFDvWNmyZfX+vW3bNvz9/SlXrhw9evTg2rVr/Prr\nrxw5coRffvmFokWLmtp0QgghxGvr6NGjREVF0bhxY6pWrWpwPDk5mfLly9O1a1c+/vhjNf3cuXN0\n796dadOm0bx5c0qWLGn0+qdPn36u+iUnJz/3Nd4E0g4ZpB0ySDtIG+hIO7wYTZo0eabz7t69C5Bp\nX9LW1pbExMRnrpcQQgjxuvLx8cHHx0cvrVq1aupYrhBCCPE6O3r0KG+//bbByu9lypShQoUKHDly\nJNNzL1++DECtWrUMjulWGoqMjAQyVvc7efIkVatWpVixYnp5HR0dsba2zrIsIYQQwhiTJhQ9z3J8\nkLEkfFRUFJMmTaJnz54ADBo0CF9fX+bMmUPr1q0pUaIEkLPl+HJarzNnzvDWW2/x6aefZnm/Dx48\nYNq0aZQrV44tW7Zga2sLQKNGjZgwYQI//PCDwcQmIYQQ4k3022+/AdC1a1ejxzt37kznzp0N0itX\nrswHH3zAggUL2L17N76+vkbPr1at2nPV7/Tp0899jTeBtEMGaYcM0g7SBjqvcjscO3Ysr6vw0qWl\npQFgZWVl9LiVlRVJSUlZXkMm4uYOaYcM0g7SBjrSDhmkHYQQQgiR21JSUvj3339xcnIyetze3p7Y\n2Fji4+MpXry4wXFd/zElJcXg2P379wG4cuWK+ndaWprRVXEtLCwoW7asrIorhBAix0yaUJTVcny/\n/fYbGzduzHJC0dq1aylZsqTew0RbW1sGDBiAv78/gYGB9OnTRy0rs+X4fH192bRpExMmTHimep09\ne9ZgBrAx27dv5+7duwwdOlSdTATQpUsX/vvf/7J582Y+/fTTHG8ZI4QQQrxOFEUhNDQUa2trmjVr\nluPzq1evDvzvTRohhBAiP9OtkmtsIFiXbmNjk+U1ZCJu7pB2yCDtIG2gI+2Q4VVth/w4CVcIIYR4\nU+hWqi1SpIjR47r0xMREoxOKHBwcAAgNDWX48OEUKPC/x7p79uwB/jexyJSyYmNjSUtL07uOEEII\nkRWTIsbzLMd36dIlrl+/jpeXl8EEnHr16gFw5MgR+vTpk+Pl+HJSr3///Ze7d++qqxtlRXeern5P\ncnV1Zf369fzzzz9Gt34RQggh3hRRUVHcvHkTT09PrK2tjeY5d+4cN27coEGDBurkXp1Hjx4BGGwz\nKoQQQuRHuj6ubrD3aVtRaNwAACAASURBVPfv31dX7hVCCCGEEEII8fozZaVa+N846tPs7e3x8vIi\nODiYkSNHMmrUKEqWLElYWBhz587F2tpafWklJ2U9PaEoN1ZpzE+rPeaXe80v9wlyr28qudfcke2E\nouddju/SpUsARpfYK1WqFAULFlSX2MvJcnw5rZduz+3U1FQGDx5MZGQkycnJ1K5dm+HDh+Po6Kie\nGxcXB0C5cuUMrvvuu+8CEBsbm6sTihQFAgLgp5/g7t0q5GTxI1tb6NcPPv8cnnqWK4QQQjyz48eP\nA1CnTp1M80yePJmjR4+yefNm9Y0ZHd2btDVq1Hgh9fvhB5g7tyIuLrBwIRj5GSKEEEK8MipUqAAY\nX7nvxo0bPHr0iIoVK77wevz89898ceAL3iv2HovbL8auiN0LL1MIkcfu3IEbN6BKFTA3N+0cRYHg\nYEhIAG9vsLTMPN/Dh1CoEDkazBLiFRYeDiNHwuPH77F8OdSsmdc1EkII8boqVKgQkPFs0hjdZKDM\nXuYEmD59Onfu3CEkJISQkBAgY2eXMWPGsGHDBnXLM91LnVmVZWZmZrSs3Fil8VVd7fFFyC/3ml/u\nE+ReX4rr12HAAPjnn4wf2x9++MKLlM81e6asiJvtKEJOluPL6vyiRYsaPW5ra6uea0pZSUlJpKWl\n5bheuglFP//8M8nJyfj4+NCoUSP++OMP/vOf/3DgwAH13Dt37mBlZaUG+qfrC5m/Vfqs9u7NmFB0\n4QLcvVuA27cx+c/FizB5Mhw8mKtVEkIIkc+dOnUKgJpZjJ62atUKgG+++UZ9CwYgMjKSDRs2UL58\neZo0aZLrdTt/HgYNgnPnCrF+Pcyfn+tFCCGEELnKzs4OOzs7jh07xuPHj/WOHT58GABnZ+cXWoe7\nj+7SZ0sf/r7xN9v/2c6UsCkvtDwh8r2kpIwfrbVqwaxZGZNvXrZDh6ByZahaFdq3N70OU6dC69bQ\nvTt06WI8T3o6dO2a8aabk1PGAFVe+PPPjDoOHpwxeUqI56Ao4OcHR47AsWM2DB6c1zUSQgjxOrO1\ntcXc3DzTZ4q6Z5iZPeuEjOerK1euZNmyZfj7+zN58mRCQkLw8/Pj5s2blCxZEsh+VdzExERsbGww\nN3WCuRBC5KZZs2DLFoiKgo8/hv+fDClefdmuUPS8y/GZcn5SUlKOy8ppvR4/foy9vT0jRoygQ4cO\nar7Dhw/Tp08fxo0bx549eyhYsCBpaWnPfL/PupRUdLQtYLgiUk7s3XuNkiXvPtc1XhX5aQmyrEg7\nZJB2yCDtIG3wsulWGXzvvfcyzePr60twcDD79++nU6dONG7cmGvXrrFnzx4s/4+9+46Pos7/OP7a\nhIQACQSkKAHBBggcRUTxELuncqCIItix0q1wJ7YTvVO8U/GnUkQ8OaqCYChSFASRHnoPLYWWAOll\nk82W3x+TLbMzsy2FhHyej0ce7Mx+p2wBst99z+cTEcGnn35aIT25x49XL48bB++9V+6HEUIIIcrV\n/fffz5QpU5g1axZPPfUUoEz2TpkyhaioKB544IEKPf7ilMUU29yfZb/Z8Q1T+06t0GMKUaPNmKGU\n1QTYvRtuuw169Kjccxg1CjIzldvLlsHq1XDXXf638/zlevFiOH0amntVNFuxAhYsUG7v36/8ku58\nvJXFaoV77oFz55Tl4mKYNq1yz0FcVPLzISnJvbxz54U7FyGEENVfZGQkzZs3161UC0oF24YNGxIb\nG+tzPyaTiZ49e9KzZ0/XulOnTpGVleW6MCUuLo6IiAjdY9lsNtLS0rjqqqvK8GiEEKIMEhLct+12\n2LUL4uIu3Pn4k58Pr7wC27fD008rt2sov9/wlbUcn7PEnnOc3vZ169ZVjQ2kHJ8z0BPoeQ0dOpSh\nQ4dqxt1www307duX+Ph4tm7dSq9evYiKigr58YZaNqtNG6XC0Pz5ygVeoWja9DKuvfay0DauYmpS\nCTJf5HlQyPOgkOehaj8HgZQFrG6ys7OJjIzUbWnqFBERwX//+1++/vprli5dyqxZs4iOjubuu+/m\npZdeqrDWLQa5XiGEEKLK+PLLLwEYNWqUa90LL7zAihUr+Ne//kVCQgItW7bkl19+4cSJE7zzzjs+\n/88tDzmWnArdvxDCi/c81CuvwObNlXsOnpO2AIsWBRYo8nb2rDZQ9Nln6uUpUyo/ULR2rTtMBPDt\ntxIoEmXiXbQh1HlaIYQQwqlbt24sWrSIpKQk1Vxpeno6KSkp3HbbbYbblpSU0Lt3b9q2bctXX32l\nuu/XX38F4OabbwagVq1adO7cmT179pCfn+/quAKwZ88ezGZzhVfFFUIIQ9leRVFycy/MeQTqyy+V\nz5eghJ9uu02pPlwD+a1rV9ZyfP5K7Hn+pxZMOb7yKBPo1L59ewBXard+/foUFxfrhqCcxwtkv8EI\nD4e5c5W/Sxs2HObcOfz+eLcW9Og0I4QQQvjVv39/EhMTGTx4sO79S5YsYe/evZhMJp/7iYyMZNSo\nUaxcuZJ9+/axefNmJkyYUGFhIgA/pySEEEJccF999ZVmwjc6OprZs2fz0EMPsW3bNubMmUP9+vX5\n7LPPeOKJJyr8nAqthRV+DCGEDzlVINQXaouL8HDtuqrwS3lR0YU+A3GR8X6rS6BICCFEWfXr1w+A\nCRMmuNpfOxwOPvvsMxwOBwMHDjTcNiIigksvvZR169aR4tFe9tChQ0yePJnGjRvTv39/1bEsFovr\nAhdQQkn/93//B8CAAQPK9bEJIUTAvD8PV1agyGqFPXvclXsD9eab6uW5c8vvnKoZvxWKylqOr3Xr\n1q5x3s6ePUtxcbHrC8dgyvEFe1779++nsLCQ7t27a8Y6qx05KyS1bt2aHTt2cPLkSa688krNfoEK\n+5I0OhoaNrRR2vLU71hP8gFXCCGEEEIIISpP//79VZO3nhITE3XXN27cmA8//LAiT8tQQUnBBTmu\nEKKUQTXssnI4HKxOWk2RtYje1/QmzOQjNBRqCEgviFQVAkUV0F5Z1GwSKBJCCFHe/vznP9O7d2+W\nLVvGwIEDufHGG9m5cyfbtm3jnnvuUVUo0qt0+/e//51BgwYxcOBA+vTpg8Vi4eeff8ZisTBlyhRV\nR5X+/fuzYMECpk+fzuHDh+nQoQN//PEHhw4d4tlnn6Vt27aV9riFEELlQgSKrFa49VbYuBEuuQR+\n/RUCqdSmV8WltJhNTRTQZUndunXj3LlzJHk2kMZdjq+Lj/JOzZs3p3nz5mzfvt2VvHXaunUrgKvE\nnrMc34EDBzSVh/TK8QVzXiNGjOCpp54iUyd95mxT07FjR9d+ARK8y0IDW7ZsISYmpkr0GfX+gCsV\nioQQQgghhBBCGJEKReKid/o07NgBXvNP5cpuhw8/hOuvh9dfD64Xb3kHiux22LGDtxa9xN0z76bv\n3L48s+iZ8j2GU1UMFCUnw8svh779+vVKm7b09MC3KSyEd9+FESOU4xspKYFt25RWcaJa0QsUORwX\n5lyEEEJcPP7973/z0ksvkZWVxf/+9z/Onz/PSy+9xCeffKKqDq9X6bZjx47MmTOHdu3asXjxYn79\n9VduvPFGfvjhB3r27KkaGx4ezrRp0xg8eDDHjh1jxowZ2Gw23n33XcaMGVMpj1UIITRsNm0gpxIq\n+NbbulUJEwFkZCif/wJx8KDOzuqV34lVMwEFispSjg/g/vvvJy0tjVmzZrnW5efnM2XKFKKionjg\ngQdUxwq0HF8w53Xvvfdit9uZMGECDo9PgcuXL2ft2rV0796dNm3aAHDXXXdRr149pk2bRrZHP78f\nf/yR5ORkBgwYQFioJaLLkfdFWBIoEkIIUVNc6O8uhBBCiOrIbDMb37luHaxaJd+aiqrJbIZXXoGe\nPeHrr/XH/PYbXHMNdOsGvXsH/l7+5Rdo0kQpA+0xb2Vo3Tp46y3Yvh0++wzmzQv8cZRnoMjhgD59\noFs3Ptrt/tJpxu4Z5Bb7uNIzkF+k9Z67qhYocjjgzjvh8OHQtl+0CHr1gmHDoHNnKAiwgtsrr8AH\nH8CkScrx9Z6rkhK4+Wbo3h3atlWCRaLa0HurV2RGUQghRM0QERHBiBEjWLVqFXv37mXlypWMGDGC\nyMhI1bjExETdaredOnVi+vTpbN26lU2bNjFp0iTat2+ve6zo6GjGjh3LunXr2LNnD0uXLuXxxx+v\nEt9rCiFqKL1qRJVQoSji9Gn1Cl8XhXjSKTpDVlaZz6e6CqgucFnL8b3wwgusWLGCf/3rXyQkJNCy\nZUt++eUXTpw4wTvvvEOjRo1cY4MpxxfMeQ0fPpx169Yxb948EhMT6datG0lJSaxdu5YmTZrw0Ucf\nucbGxsYyZswY3nvvPfr168d9991Heno6y5cvp3Xr1gwZMiToJ7oiSAleIYQQQgghhBCBchgFLN59\nV/mCHGDIkMCv2BKisnz7LZReaMbGjUpYo0MH9ZhnnlGqxwCsXKkEf2691f++hw6F8+eV288/D4MG\n+W6j5THfBcBTT8GTTwb2OMp6Jdjixcrf1RYt4NFHYfly3WF5xXnUr11ffx+BhID00hN6/35cyEDR\n1q1w/Hjo2z/6qPt2ejpMnQr33ut/u2++cd8+flwJC3Xvrh4zf75yfgDZ2TB6NKxdG/q5ikoXHq6e\nZ7XZtPOwQgghhBBCiADpVSOqhEBRmPcxAg0F6QWKdLpg1RQBx1HLUo4vOjqa2bNn89BDD7Ft2zbm\nzJlD/fr1+eyzz3jiiSdUY4MtxxfoedWvX5/vv/+ep59+mnPnzjFz5kz27dvHww8/zMKFC2nZsqVq\nv48++igTJkygUaNGzJ49m4SEBPr168fMmTOJjY0N9GmrUFKhSAghRE0lFYqEEEKIcuQME4FS/cXs\no5KREBeCd4jn7be1Y1JT1cu//RbYvpOS3LeLi5W2ab6UZdKzLBM3BQVK2GnbNoiPBx/Vwk2+flkO\n5Mp0vUpKeiGjC/lLua/J3ECeZ+9/5/QmjAORn69d9+OP6uXffw9t3+KCkYs4hRBCCCFEleRwwJIl\n8OWXSguv6sKjI5RLJQSKwr3brJUlUFSdnu9yFlCFInCX4xsxYoTPcXql+AAaN27Mhx9+GNCxnOX4\nxo4dW27nBUqo6M033+TNN98M6Dx69+5N7969Axp7IUigSAghhBBCCCFEmeiFBEpKoE6dyj8XIQJV\nkROP/gI3ZQnRlKXl2U8/BRz2M+HjHAM5f73z1EtUXMhAka9JsOJi31Wm9ITa7lHvOZAJumpPAkVC\nCCGEEKJKmjQJRo5Ubn/+OSQmBv/ZJ0B2h52krCSa1GtiXAE3UBeqQpF3oCiQKkPFxbBnj3a9VCgS\n1ZF8uBVCCCGEEEIIESgH2i/Mu0/tTtxr8NYduO+VUoBVls1u483Vb9L2q7a8sPgFzCXlW03ql2O/\n0HlKZ3p914v9Z/f7HZ9vyWdw/GDaftWW99a+h92hE1ALwty9c2k/sT1/mfkXUnNSjQcGUmUn1Pex\nv75GHsdOjoW7n4SrR9Xmg7+96nOzSd2h3TMFPPjDg5wrOBf8eVksuqvtOg/T+Xfd4XDwYS9oOxKe\n6A+5tdGECEtsJXy08yPafNmGUctGYbFZ9AMxPgJFa1tD1yFw03OwK22Xz4eReD6RW6ffyp/+rx1L\np78JZ8/6HG/IVzirqCigXTiA8Tcrz89jl20kz+KebD6Ze5J7Z93LtROvZfae2QDM3z+fDsOV1zzZ\nWbxc770ogaJqz/s7GZlzFUIIIYQQVcLMme7bx4+7Wy2XM7vDzl/n/JWrv7yaa768xu/nPL8qMlCU\nmmoY9tFUKMrO1r+wztOePfqfNyVQJKojqVAkhBCippLvOYUQQojysS19B6frw4e3KKEAIPRKHaLC\nrU1ey0frP+JwxmGm7ZzG3H1zA9+4pASmTYPJk3UDFza7jad+eoo96XtYn7qeV+c943eX03dN53+7\n/8fhjMOM+30cW05uCebhqOQW5zJ40WAOnj/Ir8d/5R9r/2E8OJBAUSD8TSTq8QgcvXM7rLoKjjW2\n8GHk56SdSNPdJKUBjPgrJF7iIP5QPJ9s/EQ9YPdu6N0b+vdXt2DzZPALsFXnqbDZlfTDrrRdvHUn\nHG4MszvB1G7AZ5+p2rotObyEmUdmciTzCF8lfMWiQ4uCannmAAb3g12XweaWMHLZSGWi9dln4Y47\nYOVK9/jt2xkz+UHWpaxjX3YiTx/8CEunDpCerv+YffFXoSgAuy+FsXcpz8/cBqnMOz7Pdd97a99j\n5bGVHDp/iGcXP8vJ3JM8Hf80B5oqr/k7t7ufg6DOTVQLchGnEEIIIYSokrw/L546VSGH2XRiEyuO\nrgDgbMFZJm6dWLYdVlTLs9deg1atoGVL+Plnzd2aCkV2O3iv82bUDlsCRaI6kqtlhBBCCCGEEEIE\nSq9Ckaetcc6BEijyKSdHKSt+AT6ED/t5mGr5ucXPBb7xCy8oP8OHQ506tHrsMdVE2b6z+0gvcAc7\nfs1IgD/+gO++g3/9S11JxmyG4cMZtXyU6hBjFg4N/HysVhg7Fv70Jxg5kvlvPahUxyk1fdd05YZe\nBZvyqlCkF/wwev9brXDwIBQUuFbN6uy+uygCJn86TnfTL25UL/9747/Vx3vkEVi+XGlr9uKL+sc3\neMw2nYdpO3oYsrMZu3qsav2Yv5TemDrVte7RBY+qxoxcPjKoCkWnYyAl1r1qw4kNMG6c8r5Zswb6\n9YP8fNi1C7p3Z4n9oGtsZl3YUOe8Mj5Y5RAocoWCSn2651PX7W93fuu6bbFZeGT+I5it7opgrtde\nKhRdlCRQJIQQQgghqhyzWfv5OJSLMwJwIveEajklJ6VsO6yICkUpKUrbN4DCQvjgA82QcL1j+AsG\nGQWKioqU49RAEiiqxrw/3Mp8hRBCCCGEEEKIULlaJ0mgyNjevdC2LbRrB3fdZfxBvKAApkyBefPK\n9fksKCnwP0iPwwH/+59qVd1du+A5dyDJ5tD5xvyWW5RKM2+/DT17uh/LnDlKpSMvxclH/Z+L2awE\nPlauhPHjYd8+mDgR29rf9MdPmqRdV06BIpPe66dXiaeoCG66Cdq3hzNnDPdns9uU52jePOX1L2WO\n8HESaWlw+LB7edUqg5MNokLR3XdBmzYUZBlMLnsEeDxDXIDSRi/QCkVhYbrH54sv3LeLipT3y+DB\nun8XimuhvJcKgnxvl0OgqNDX6+LFsx2ailQouihJoEgIIYQQQlQ5J09q16XpV8kNyrRp0Lw5XH89\nzJ0LDgeFJergTL4lv2zHqIgKRQkJ6s+Yhw5phmgqFAFkZfnfr5EaWqVIAkXVmLQ8E0IIUVNJyzMh\nhBAieI5Agy0SKDI2Zoz7CsC1ayE+Xn/cPffAsGEwcCC8+265Hd7ZyipoelcDghKQKm1/5jfPcfSo\nUnEG4PnndYeE5fu5Wu+LLyAmBpo1gz591Nsave30JgDLqeWZSS8loLdu5kzYts3v/sIwKeGrgQOV\n1z8Qgf59CyZQFAacO4c16Vhg+/YQUztGd4KpIFe/QpE9kN/LzWalrZuOEuf5f/114CcJ+qEnJ4vF\n+D4PAZ17qUKzwaSb3usnE3TVngSKhBBCCCFElZOaql1X1kDR+fMwapRy4cz27fDYY3DzzZiPqcM5\nhhdYBEpvTqKgoGy/aO/apT1Gvjr4FK43n+ArFJSfr1QmNiKBIlHdSMszIYQQQgghhBCBWJu8lj/S\n/vA5Zk8zmP0nOF94vpLOqhpauVK9PGeOdszBg7Bhg3v5n/8M+jB2h53FiYtZenipKghmd+hUifEj\ntziX2Vu/dbe081Y6waZ3saPG6dM+7/YMBe08s5PZe2aTZS69+q+wEF5+WZm88JpMTIpV3nu6vL/Z\nB5+BotMxMLMTHEL7Pv4t6Tfm7Z/nqsrjXaEoOwpmHV3I9tPbVettn/6HBdfC8qvBgRKCmd9ee2yT\nyQQffmh4bp52pe1i9p7ZZJa4r8rcEgdz/gSnk9XPc7G1mB8KtrC2tXY/uoGi0qBMQkzwk77RkdGa\nsM7+JjA5cTmJ5xOZvWc2206XhqsCCBQlx8Ksku0ca6h/f0k4rLgaFiz4AKvd4/UoKdGGdex2d6Uk\nH6GdwqI85u6dy4bUDaw8upIFBxawO203s/fMJj3fXbXJYXDu3lfjAuQVaCfdfrkK5p/+lRKbV7hJ\nAkXVngSKhBBCCCFElVMRgaJ9+1wXGbls3Ejh/32iWpVXXAGBIihblSLvQBHAqVOqxTC9/fuqULRj\nh351XqcaGiiq5X+IqKqk5ZkQQoiaSioUCSGEEIH7fPPnvLryVb/jvv+T8nPZnF4kvnREqVQiguc1\ngRWKIUuGMG3nNABGdh/Jl72/BAwCRTNnwvLlSlWkp55S/aJktVvp/k13DmccxvQ8zJsPDx/w2j4v\nD5o0wUQAlXKefBLOnjW82xkoWn5kOX3n9sXmsNGqQSsOjTxElGdbLw9HGkHXoVAQabBT76upwDBQ\nlF4POgyH7DoQZZvE+p/a0q3Pi3D4MJ9NeZrXGytBoTuvuJNVT61SBYqKw6HrEEhe/xphG8KIHxhP\n37Z9AXii+0m+v1oZ9/f1sK8p/NxGe3wTgf+Sev3U67E5bFweHcehWrC4LTz6sBJyufqT1uz/JJ/I\nKOVJ+cusv7AufR0Mhi+Wwait7v0YVShadk1ppSIjb7zB1E7aKj8xkTGqQNGmFnDbYLBk/JMxE5Vw\nXJgpjJ8G/sT9BoEiB2BCCYp1GQq5BTOpOwy2TdWOHXsnHG4MkM2ABY8xb8A8+PFHpR2f1aq0jktJ\ngXfeUTaIjYUBA+Cbb3QflgPoteE5duQm6t7ftF5TDo44SKPSsRr/93/cd/Q/0Fi92u7QTrrd8yRw\nYBx9N33N4g+TICpKuUMm6Ko9CRQJIYQQQogqJyVFu66sgaJ0/TbZ5nD1p6UyVyjSa3kGSqCoocHV\nJ/7oBYpOnlTa1ANYrYQX6lRRNgoUWa26rd1VMjKCO8eLhFQoqsak5ZkQQgghhBBCCH9m7pkZ1Pgz\nhelM2Talgs6mBvB1NVsArHarK0wE8FXCV67bNofOt9pPPQVz58Lgwe6WZKXm7Z/H4QwlyOMwwaMP\n6Ryw9Iq9qDSdyUk9r79ueJcJYP9+nvjpCde5puSk8N+d/zVMhL95p48wEQRVoWjcbUqYCKAozMbb\nC0bCnXfCHXe4wkQAq5NWc+DcAdVEyv+6QHLpPKbdYWf4suEAZH7/Hd9fbXaN+/hm/TARBDfJ5nx+\nUvNP8d+uMGiAu2LO0SYlfPreW4BSyWhdyjrXdi/1Vu/HqELRY3qvtaePP2bIkc80q6Mjo1XPy9/u\nBovX/JPdYWfQj4PAZKJE5+VxBpk+vhlySzM2hZEw5i/asYc9gjvzD8wno+A8PPOM8r4sLFTe384w\nESgT0QZhIoA1V2AYJgI4W3CWCb+PB/QrFGWOfYV1jbWhwKg846DgkgZpJC+e4V4hE3TVngSKhBBC\nCCFElVMRFYqMAkVenwGrXIWic+f0L+byLL1sdEy9KkOFhdC/P3z/ve/j1tAKRRIoqsak5ZkQQggh\nhBBCCH/yLfn+B3lZk7zG/yChbckEZftwXlKCZdN6w7v9tjwbPly1uPXUVtWyVSf84ZzAM9nLPqkQ\n5gC6dyfTrJ5k23hio2Gg6McOfnYaRKDIuw3ZimuAP/7QraqUkp2iqlD02xXq+0/mnoT//pe0l571\nc4JuJkdo02wbW2rXFSQoJeaPZx33ua1eFSJrGOREhXQq1K5VW1WhaH0r/XFmq1kJFPloufaj1+th\nFMTylHMmCfKD/zfLaX8T/2N+Xag8t3rVlQoiDDayW3zu8/hkj1Z33i0DRLUjgSIhhBBCCFHl6AWK\n0tPLdlGTQaCo0OtzUYm9xNU6PCTlHSjavVt/fSCBIu8KRVYr3HcfLFmiXt+8OQwZol4ngSJR3UjL\nMyGEEDWVtDwTQgghKlZ4mF7y5OJjLjGXbVJML1BUOplXEqadhPPJaoVevbDeebt2l6VBIr+BIq+2\nYrZAQkJ5pVca1ir7ax7mAMxmzXq7wx76N/JBtDzLqx3EfnftxGRxv/a6r9VzzwX1GoYF0fJMtZ3O\n28i5Ktzk+3UxankWqoiwiMAnmM6f91mhqK62o5r/46cZt9QrN6V/b/Vanhk9d3rPs6dzER5vvrwy\nXr0rLjgJFAkhhBBCiJB9/TW0bw8PPaRU0ikveoEiq7VsIRejCkU6n4PLVKXIV8szb8ePw/79vven\n1+4M1IEio2N6P19z5sC6dep1rVopFaBbeV1hE+BznZSVxMPzHuavc/7KzjM7A9qmKpNAUTUmLc+E\nEEIIIYQQQlQEfyGGi8H49eOp92E9mn/anLXJa7UDiopgxQo4cCC4HdtsbI2Dy1+Fem8pLb0CsmAB\nbNmiX/GlNBjkNyAUoZ71022R5q2gAABHmDYMo1fBxRe9YAyAAwcUFwe3M6cgKhQVe82ThPnKX735\nFnGvveZaNKpMY1ixRkeogaKDOlV1nO24/IX7jFqehSoiPAKHNcD0xO+/+6xQFEqgiAmfh7BRcJxv\nU72WZ0bveX+BotN2j759oV5lK6oMCRQJIYQQQoiQHD8Ow4bBwYOwcCH85z/ls1+HQz9QBGVrexZg\nhSKAPEsZAkWBVij65hu45hro2FGpwKx3EReULVDkXaFo7Vr18p/+BBs3Qps20KiR+r6MDP19enlh\nyQssOLiAZUeW8ciPj/i/OKyKk0BRNSYtz4QQQgghhBBCVIQwU/lPF2SaM3l43sNc9cVVfPjHhziM\nJoYqQaY5k7Grx+LAQYY5gyFLlTLWB84d4M/f/plrv7qWxQM6KWWvO3WCRYsC3neK+Qw3vgBpMcry\nR70gOTvZdf/+s/u56dubuHbitSw9vNS94aJFbGoBHdVdywB3MMhfQMjhNVEQUIWi0skEq06ZdGcw\nJPESuPlZaDtS21bMk1GgyO6wuwJFqQ3grqfg6pfguy6+Ty2vOI8nbPO58mV46w6lNViXodDpihX8\nnvy734dmD4MGhuGpAQAAIABJREFUbyjhLm8Tb4Br+hyhzSj4x23w25XaMZ2Hwm3P+D2MS6g5nu3N\ntes+6gVR/4yi79y+mvuevx8e7w9XvAyv3Kvd1leFor88CVe9BFO76d8ffyieNpse5Y6nITnW93n/\nqxf8+Xnt+kcfhrxIqBdC8S/76lWu17nzUNhQ2g5u1ZXK343uL8COy2BRW4h6G0zvuX+md4GXevs/\nRlYd6PsobNJpNTfmbv1t9IJTnrIjlRn37ORD9Lu/kPpjof0IWNPa//mIqkcCRUIIIYQQIiRbtqhD\nMAkJ+uOSk2HmTOXPQJw7Z3yRTgUEisw6hYLzivOUX4yHDFGCNg8+qA3nGAm0QtG//+1u4TZ5Mkyb\npr9deQaKDh3SnkPz0g/p3oGiACoUORwO1qW4Kx4dzTzKqdxTPrfJt+Tz7Y5vWX5k+QWdKzOi83YQ\n1YX3h9uVK5WwXCAaN4axY6Gvdl5KCCGEqPKk5ZkQQggROFMIMYeKaHk2OWEyCw4uAOCt397i/rb3\n07Fpx3I/TiC2nNyiWj6cobQKG/3LaDad3ATA4A6QvgwibDZ45BH9yTudiZ4P0uZp1i0/spxh3YcB\n8Povr7H55GblGPGDSRudRq2wWmC3M7SPO4jkyRkM8ndVm6PYgqmoSKngExkZWIUiZ6DIpi0nYwuD\nCDu8cRdsuFxZ9+wD0Ocw1NGpkmxYocjhrlD0wS2wujS886KfOYnpu6Yz27YTGsKHtyg/imyeX/I8\nh0ceVt7dc+YY7iM3Svnx9rPH/Mn7t+lvu+dS3+fnLVwnlAX+wyhGim36E8bfXue+ndxQe7+vCkW/\nXqX8OeyvxmOOFp3i6BUw7lbf5/e2QfWtn9vAd1313yP+2MJgSB/Y10xZHtIX9kyGZx6Akw1K1/WB\nY420Fame6RfYMY5covzoWWgQmNMr+e8pp3YYFBYyYWQ3FnVX1h1sovx9OTipLjpvQVGFSaBICCGE\nEEKE5MwZ9bJe4CY5Gbp0Uar2REbC7t3Qrp3v/aakGN9XmRWKVqyAqVOVFfHxYLHAkiWGVYQBZS7A\nKAzlGShyOODoUfX9Q4fCHXfAVVe515nN2hCQ0ymP4E4gLc8cDu2+PF+LEAJFmeZMSuzq+ZUTuSdo\n2UDnihaU+ZJbp9/KjjM7APjk7k94/c+v+z1OZZIKRdWYd4WiwkI4ciSwn02blNaNRhXGhBBCCCGE\nEELUXBVRoejtNW+rlv+x9h/lfoyyWn50uet2Vh3Y0qJ0wRJ4qZVvs3/TrLPaS5MV58+z8tgvrvUZ\n5gzXpBF2u2GAxeawgd3ut+JQmM0KdepAs2aQkBBYoKi0f7q1RCdQVBpMib/WvS6/tjtcpDl+AC3P\npnlUxrH6ya29tOIlw/uOZh5VJjSnTIEnnvC9o0oSbpA6KKnkDoK+KhQ52QMYM71r6Ofw8n2htTyz\nhrnDRAD7myoVppxhIoBtccrfz8rk7zXMjzTBtGkk1ilUrU9uCFkREieqbiRQJIQQQgghQnL6tHpZ\nL1D044/uL+gtFnj5Zf/7NWp3BqEHihwObaCocWNA/4KKvOI82LlTvXLZMv9t3XyFETwDRaXt2FXs\nduXzvtXjapX9+41/QT93TmlfD4FVKDp/Xr0cFQWXe0x4XOJ1JUoAgaIz+Wc0607knDAcvyd9j3te\nCJi0bZL7zv/8B+Li4N57Ne3WcotzK62akQSKqrG4uLJtX1KitHAUQgghhBBCCCE8VUSgyJsrZHMB\nmMqr3GGAkzeuYM+kSZr7nBWkHD7CQja7DT79VAnmBCI7G15+OaiWZyVW7ethFEyJMnjpTL4qFAUR\nygpUbnEuDNfpEXeBmAwqFFkrefbNV4WiyhRKyzO9ak6n6pf9XCpafoQdx44duq91XuSF+7dOhEYC\nRUIIIYQQIiSBBIq+/Va9/Msv2jHefAWKDKoM+ZWbq64cFBUFLZSrqvRanuVb8uHsWe0db70F69Zp\n1zsZBXuc5+BkFNbZvBnGj3cvG7U7c3K+BoFUKEpMVN/Xpo262pJ3hSKvUI+eM3naQFFqjvHr56yY\n7XQ86zgZhRmwYwf87W/K41m5Et59F4ASWwn3z72fBuMbcN3U68gyB9h2rgwkUFSNdepU9ovwdOYL\nhRBCiCpPWp4JIYQQFSvcVMklVSqLxcJlb76JqU8I/b/1PkAHGihyBnv+oa3K5Axv2X18Y21z2JSJ\nJC9GAR4ANm0KquWZTS9QZAK7zu9d0QZBkUAqFJWnnKKqVXbZZBCSq+yATyAViiqDUfDMl8qu5lRe\nCmuVcOakTfe1lkBR9SOBIiGEEEIIERLvQFFennYu4dpr0XBW1TFSERWKvINIzZpBA6U0rGHLs3Pn\ntHfYbDBokHEgyFeFIs/7fFX/+eQT9/PoL1B08qTyp1GgyPM18dXuDEJqeZaWr309TuQaVyg6mnlU\nsy7hdAL89JN65dKlAEzeNpklh5cAsCttF1O3T/V7TmVVRaYYRCjCwmDmTOXve2JiYD/du6v3IYEi\nIYQQ1ZEEioQQQojAhVKNJzysmn6r78+KFcTGx2MK8MPwydLKKFlR8L/NU9jcwvd4I1a7ce8n53Nt\n91ehSEeYA441hOldYHczmNkJfrvCY7v8PM02C6+FH9vDmWhlux3m48o5WrXnmFcbplyvPe7UbpAc\nq12/+1L4rot2vd1hp6SogO876j++UH2++XPO1y3ffZbF/NgNHGsIDmDZNfDAILjrKVh9ZeWex5I2\nlXs8Iwt15sn90atQVB0suWIvw5ttVrUHdMqsK5Nv1Y0EioQQQgghREjOaKvTaIItdXU+xG7f7nu/\nFREo8q425BEoMmx5phcoAmxpZ/h06jPcP/d+/rvzv+pWXDqBouwoeOk+eDB2JX+k/KGs9BXWycmB\no6XBG+9AUb166mVnoMhXkMn5mnhVKLJf05ZPP4V+/WD27NJ9R3g8GUVFYDar95WergSTSh+zbsuz\nYANFpxKUqkSeUlOxJx1nYsJE1eroyGjDfZcXnYJVorpp2lT5CURMjHpZPhALIYQQQgghhPBWGS3P\nLoiRI43ve+cdzSzJow9Dj5Nw+9OQvHoUpufgx3nQP8j24TabcaDAVaHIRws4o/ZwtjDoPAwKItXr\nJy2FYdvAvnQJdFDf99BA9XJ49n9YefxubDrHaPWq/vlM7g6z/6Rdf6o+PNtPu97hcDDw7CR+elh/\nf6GatnMay4fCkS+gThXIbGyMPkyXodDvEMzqfOHOY9INF+7YniwhzDpaqnGWcVEb7UQwwMODLFSt\nWlrCHwkUCSGEEEKIkHhXKAIlKNO4sXtZL+iyeTP07Gm835QU4/vKs0JRfeWqqqAqFAE/XQujixfD\nYVhyeAntm7SnR4seyp06lYLeuR2+uhHgNL/N7cPJV08S46/6z759Skuy3bvV6+++G+Lj3cv+KhSB\n+zXxqlA0fXM7Rq9Wbi9aBJGRJgZccon6Oc7MhLg45fbSpTBggBI0GjwYvvtOt+XZiRzjQNGxrGOa\ndQnJG2DbNs361Ssnq1qkRYRF8FD7h4wfZzm5SGcIhZFaXpM5UqFICCGEEEIIIYS3sMqYLti6NaD+\n80E7flz52bsXeveGBx6AI0eU+0qrNem2CvvnP3V312kYJDdUbjtM8PAjHncG2vKsNFCkN9qEck62\n5CTj7X20LvMOEwEM71O6XQDFqWzYefKnJ7EGOUGQGxX42PSzx/mp2E9Z8hCdqg/TrquQXYckv/aF\nDRNVd9W15ZkvwfxdEVWDBIqEEEIIUdUVWYuYv38+G1I3XOhTEU55eZCfr12flaVezs3Vjtm0yfe+\ny6FCkc1uY9GhRWxM26hUEPLR8sysc3GIrwpFa1p7LSetcS/oBKh+ucp9O7c4V2nxFUig6OhR9XMc\nGws33aQeF0igyPmaeFUomri6rWr5pZfAFuvV9sxzHuvjj93t6qZPh8RE0grK3vJs64nN6ipPpSYl\nzVMtP9T+IS6NvtRw3+VFKhTVMN4fiCVQJIQQQgghhBDCmx17xR/kTBpMnAjvvlt++/zsM3j9de36\nrCxYty6kvql5tdXLDs9dBBgostosgH5YwuawwcSJ2A8nau90jjmwL6DjaLYLMBd2Jv8M1mjjtmxl\ndTb1IMT4HxeqzS1g1NaK27+oPNW5QpG4eEigSAghhBBVmcPh4M4Zd7LxxEYAvu7zNS92e/ECn5XQ\nbXcGgQeKHA79OQuzWR3kCQ8Hu909H3H+PJSUqFtz6Xh84eP8sP8HAFJI4f10r2M1a+bap17Ls3wf\nFYoyvLq4ZZg9QjdegSK7CVK8WqgnZydrA0WxsepQ0L592tZw110HLVuq1wUaKLJYlIvRPCSiDhSl\npUESjbjac6XneXpVOGLHDs4Ua98HZwvOUmwtpnZBkdJLrV49GDAAc4SJU3mnNOPTbTmcrA8tPd4q\nqQ1gcZQ6WDai+wjjx1iOpEJRDSMVioQQQlwMQvguUAghhKixnBVwgmGzV9K3p//4R/nuTy9MBPDH\nH8qfYeU8DbJsmf5koBdnhaISncPbbuoBI0di9/Ey2cZ/FNLpBVKhyDXWR1u2srJW4L5BCXnl1vY/\nTlR9en9HhKhsEigSQgghKo7D4dCtvCECdzjjsCtMBDB1+9QLeDbCRa/dGQQWKDp92h2E8XbCq7pN\nXJy6hRrA2bM+Ty3LnMW8/e7qNt/u/NawQpHNBMV6FYryM9XBgnr1oEkTADLqqMeqAkVewZ70etr9\nJ2cna5+nW25RLTr278O2PUE95vrroUUL9bpTp3SPq5KZCceOYcH9i/5J4iggWjN0f5pXhSJnoKio\nSAlzedq3jzP5+sGyk7kn4cknYcQIpT3amDEczzquOxZga5x6+etuYPf4vNypWSd6tvTRJq8cycf0\nGkYCRUIIIYQQQggh/PHVYqvK8jcp7XG1X7lmk6+7TrmyzQebvTRQpFehqKQYwHegaMP6kE4t0ApF\nUMGBogqefbKb4Kd2FXsMUTkuxpZnovqRQJEQQghRMQosBfSe05uw98O4b/Z95Ft02kMJv84WqMMj\nqTk+2mGJyhNooEinBRhg3PbMu93Z5ZfDpV5trvy0PTuZexKHRxP29Px0HOle25QGiooM+lvl5Xm1\nrG/SBNq3B3QqFBUaVyhK9qpOBAYVim6+2XVzbkeI6Z9I09pfsrSNx5hu3bSBogAqFNkyM3hk5fNE\nvwl/eVJp8XYIZVLBZFKeCtdj4RL1xs7z1AuA7d1LWr7+a3Hi2A5YssS94ptvOJp+wPAcEzwCRcXh\n8E039f3Drx+OqZKuvJdAUQ0jgSIhhBBCCCGEEP4EXKHIZoM1a2Dv3nI9/rHMY/T6rhetP2/N9F3T\nA9vIrrRpW9cKOgyHjsPhj8s97vf4AGzSyR7d+HyoJ3uMg2/8HdM4E6Zx+pM54xMmcOX/Xcn/Ousc\n9wUwvQcN3zA+RDDBIKcF1wZXoehclrbMdnk5Xb/Cdg3ADx3hzTsr9hiicjw46EKfgRASKBJCCCEq\nysw9M1lxdAUAK46uYMGBBRf4jKqnPEueavl84XlKbBXXwloEyChQ5BmUcTiMqxxv3qy/PiVFvRxC\noMg75GJz2Cg871VJp1kzqF+fQoPOaXmFXsGopk2hQwcAMr0qFGWaPR6zV7An4EBRq1bQqhU2E7x8\nHxREQmaklRG9cUejrr8emjdXb3fmDBQX+6wmPSdzLfNzNlISDr9eBRNvcLc7+/hjpSuZ67HgVaEo\nozQspRMoKjy4h9xi/eOeOOAVGCsp4di+PwzP0bNC0W9XwLl67uX6tevzeKfHDbctbxIoqmG8A0Xy\ngVgIIUR1JC3PhBBCiIplsfmuuOPy0ENwxx3QuTNMn15ux//H2n+wPnU9KTkpDFk6hJwigyv4PNnt\nOIAhfeBAU9jfFIb18bi/pMTV8kzvV4mtLXRWBuiLw5/7HZOUncQr94W2/2CCQU7PPaBMuAVqkXlO\n8AepQio6tCSEqDkkUCSEEEKEZm3yWh5f+Dgfr/8Yq11b0WDYz8NUy2N+HVNZp3ZRyStWB4ocODRV\ni8QFcEa/1ZWqQlFxsTI3oacCKxTpteHKzdKvUGQ2ChR5z8s0aeIKFPlseRZqhaJGjaBDBzLrqMM0\nqbGQ1BBo2BCuuAJq11bCTU4OByQm6j+IUquLD6mWf7tCqVB0++0wejTceSc89phynyZQ5DxP71Z0\nwJmMFM06pxMp2gvxjh7fZjh++2Wllazvv1/znNVNfYCBD0YzebL/Yt3lQQJFNYxUKBJCCCGEEEKI\nmiWUEsgBtTw7eBAWLVJuOxzwzDNBH8fI7L3uy8EsNgsLDy7UH1hQAD/9BLt3g82G3QSHmrjv3t/U\n48o1i6XCUslTulfIbl1CqVCUEwUbLvc/zul444LgDyKEEBchCRQJIYQQwTtfdJ67ZtzFnL1zeGP1\nG0xKmOR3m6haUZVwZhcf7wpFoB8YERUsIQEefhhGjlSq1gTS8syo3RnAjh1K4Mibd6CoVavgA0V5\n2vdHTu459QpnoMio5ZlXi8J0u9LyzBIO+bXVY4NteXYq7xSWrPPqlY0aQceOZNXRjk9oDicv7YbV\nefWVd9uzffv0H4Rze9SvVUJzOFarDVOmuKeNnn5a+TOYQFFatPExU88f06w7mqVd55QbBYcvAcaN\n41y0ei4r93BDli2D4cNh/nzjY5YXCRTVMBIoEkIIIYQQQgjhT0Atzw4Y9HrPzFQu54qMVC7psuhX\nO3IEke3RDUXZbHDTTdC/P3TrBgsXKldveQ9zzny88YarVLhey7OqLJQKRUIIIUIjgSIhhBAieNvP\nbVddmLImeY3fbRrXbVyRp3TRyvcKdoC2pZUolZ8Pu3YpFWtOn1baYFksQZV1sdltrE9dT8KpBBzO\n7YqK4L77YMECmDgRxowJLFDkow0XFgvs3Kld773fFi3K3PIMINdudi9ERkJsLDRoYNjyLN9aqFqe\nuaIJm3I7aKoTgdLyzPlcOQJoeWZ32DlpMQgU6eQOE+Jg1sFurtAPcXHqAfv36z8IIC8SDkaoz+l8\nPbj+pWjatHGv69ULoqJ8BIp0Wp6diTE8LCfM2tfgmCNDtVwnV32s39o2hS5dSL6kiWr91YXu8z9y\nxPiY5UUCRTWM9wdiCRQJIYSojqTlmRBCCFGx7A67/0F6/yGPHw8jRsBvvyllvOfOhWXLynw+4aZw\n7cpFi2Bvaclomw0ef1w3UGR1znx8/bVhuKmqC6VCkRBCiNDI/KkQQggRvCJbkWq5sKTQYKSbBIpC\n493yDPQr0NR4O3cq1Xy6doV27ZTQSYMGSous8HBleepUv7sZtXwUvb7rxQ3TbuBff/xLWblnj1KV\nyGnRorIHigC2btWu824F1rixNlCUnu5zt3oVrHI8qwo1barM8fhqeWZX/x1PczRl0KgmZMQ11Iy1\nOWzkFCuViWyZ6gpFx2L1JziSHVnqFT4qFG2Ng21cz5w5sHgx/isUhbmPub25/gVubZ9QVxyqUwdu\nvRUyuEQ90Pm667U881Gh6ESEWbVcEgYp0eorF+7er05bzb+kOTYbHIhQB416FR4ElJfNFaqqQDIl\nVcNIhSIhhBAXAwkUCSGEEBUroJZnpRzAz9fA9C5Q+O5Y+P579YDRo3W386wStC5lHdN2TCPTnKk7\n9rstU0g8nwhAbnEu3+74lt8S5mnG6QWKplwP5+uq1xlNkAXqhw7KT2VVDkppAN9cVznHEkKImk4q\nFAkhhBDB8w4UFVnVyw6dijASKAqNtDwL0Mcfa8M4Tg6HEgAaPhzOndMfA5zIOcHkbZNdyx+t/wiL\nzaKtTpOZCUeP6u8kmECRTkhF8xgaNQq+5ZnO+yPXM1DUrJnyZ/36hhWK8kzqC7TO0YTUVEi7Sr/X\nuqvtmUfLM7sJTsTqV4dKruNu92YlnPETY6BdO7LraCdetl8G28I6A0rHueImfgJFHhWMEprrHp5d\n57Zr1t17b3AViny1PDtRX72cEqu+eMyU14wnk1NUY7Y1CmPyZDhaSz2p1bswgZ0TN5Kaqs1SVQQJ\nFNUw3oEi+UAshBA1V3p6Ot26dWP69Oma++bPn0/btm11fx555BHN+LVr1zJw4EC6du3KTTfdxJtv\nvklGRoZmnBBCCCEqn4ngUy8BVSgq9Z+e0OdxeKYf3P2kzgA/pcRn75nNrdNv5YUlL3D91Oux2rVX\nvqw5s5GuX3fl4LmD9JjWg+eXPM+dUT8wzStkoxcoevVeuG4IFHt8QfzXxwN4YD4MGqD8vNi3bPsJ\n1COPwIv3V86xhBCippNAkRBCCBE8i00dNvAOFOm16aoXUa9Cz+lipVehqDq1PJu6fSoNP25Imy/b\nsP20NsRRbjZu9D/GZnNXPtaxKHGRarmwpJCEUwm6YRJDnoGgnBzjcQBndIJhWV6Vexo2LJeWZzme\nrcScgaKYGMMLsEpMdtW8yjmUNlwJjst0x2eaM8HhICzP/ZjT64GllkGgyKM4TyaNeOttE6ez6pDR\nQhs8LIyElMZKxZ8TJ2DBFq9UzfHj6uUrr3Td3OrVHc0p4XSCZt0992gDRY4M5fW0p+pUKPLR8iy7\nDuRHupePeuWUmmXGcG2m+oNHfnQ+f/87ZHhdJNe4ELpMf4XaEYHP3ZWFBIpqGKlQJIQQAqCgoIBR\no0aRn6/9IAeQmKhUAHjhhRcYOXKk6mfAgAGqsUuXLmXIkCFkZGTw6KOP0qNHD3766ScGDRpErr/E\nvRBCCCGqJJs98G9P/363+/bGy2Gn17wWx48r7c8MPPHTE67bSdlJLDiwQHec2Wqmw6QOHDx/0LXu\nBa+QjVFrsBMNYF4H5faRRvpjQvFfqRokhPCjfu36/geJKkUuyBRCCCGC512hyFyibu+jVyGlxG78\nOVEYq84VirLMWby84mWyi7I5knmEN1a/UTEHSktTV/sxmZTQTL162vYHqamGu/np0E+adWuT18Kp\nU4Gfi48KRRne1W+8g0F2O2Rnq9eFECjSa4mnW6EoLIzCGJ0eY6XyPLZxBop+SdefZMnYtBrS0wnz\nmF86FBupOxa0gSK7HZYsgdQGTfQ3iNvmuvnfX/yU6bniCtfNBINA0bbT2zRzYe3aQd049eOzZ2SC\n2UxYpvaCel8VikBdpcg7UNQlM49mBV4bRKdTWAjUPa9a3bgQSEiA2bN9H7CcBBwoslqtTJ8+nd69\ne9OpUyfuvPNOJk6cSImPSUFP2dnZvP/++9xxxx107tyZ/v37s2zZMt2xZrOZzz//nLvvvptOnTrR\nu3dvZs+erVsOryznNWvWLNq2bcvChQs1923cuNGwMkPPnj0DesxVkQSKhBBCnDp1iieffJLdu3cb\njklMTCQ2NpbRo0czatQo1Y9noKigoIAPPviAli1bEh8fz9/+9jcmTJjABx98QGpqKpMnTzY8RllI\nyzMhhBCiYgVTochbcqzOyhUrAt5+T/oew/sc+K52pFehyGlX6XybryvGQuH7jIQQNd2MfjMu9CmI\nIEmFIiGEECJ4/lqe6QUa9KrTCv/0qj1VlwpFe9L3qN4bO87sqJgDJXhVm+nWTQnd5OfDmDHq+1LU\nbaacMs2Z/J78u2b9muQ1wVUoKihwX2TlFShKpK16rHeFopwcddXn+vWVL/sbNoQIj1JCeXnKcfQO\nbynQDaHleISDftndjPHjldM01/cRKPLIAzkDRSl19FM0Ge+PhTZtVOsOxhrv2ztQBBAfDwdpoDs+\nuq37NT7gaGe4X0DpC1arFmfrKa3G9BSUFHDo/CFm7ZnFi0teZNXxVZhMcPO90RSG1eKrG2D4X2F7\nYzMcOaK7jzNeT0U9deE2Tng8lGMN1ffdnJlOIzOEe07HReVArSL9QBHAG28Yvu7lKeBA0fvvv89H\nH31EbGwsTz31FM2aNeOLL77g9ddf97ttYWEhzz77LHPmzKFz5848/vjj5Obm8uqrrzJr1izVWJvN\nxssvv8zkyZO54ooreOqpp6hVqxbvv/8+//73v8vtvE6dOsWnn35qeL+zMsPAgQM1lRmeffZZv4+5\nqpJAkRBC1GzTp0+nb9++HDp0iB49ehiOO3z4MG28ftnT8/PPP5Odnc3gwYOJjnb/tvTwww9zxRVX\nsHDhQmwy+yqEEEJUOzZHAP9/GyR8dUM9bwR+1WGYKfRiyr4CRdGlEzm1yrkitFFVJCGE+OL273ig\n3QMX+jREkCRQJIQQQgTPu+WZ2aquUKQXeJEKRaHRrVCkE9iqio5lHVMtZ5ozKSwpNBhdBt6Bou7d\n3bcvv1x9n0GFop8P/6w7N7LxxEaKTxlXNdLlrFLk1fLMb6DIs10aKEEiUOZjAqxSZBQ282x5tmxH\nM8aOhdGjwRxdW3c8qFt2OQNFaXX1e6Rl1EUJOnk4EkSFIoDVq+FgUZTu+GbXbXXdPkNzUmlpuG8a\nNoSGDUlobjwEYNjPw3jypyf5Zsc33D3zbg6dP8Q995r4/Po6jOoNk7vDrYNhb8JS3e29L2Drdlq9\n7KxQdJ5LNBWKrs6EMAc08c4HNUiBSPffkQgbxBSXLpw+DT/84PtBlYOApr127NjBDz/8wD333MPs\n2bMZPXo0s2fPpl+/fqxcuZI1a9b43H7GjBns37+fd955hwkTJvC3v/2N+Ph4rrnmGj755BMyMtwl\noZYtW8bvv//Os88+y9SpUxk9ejQLFiygR48efPfdd66gT1nP691336Ww0PgfKOdxxowZo6nM8Nxz\nzwXytFVJEigSQoiabcaMGcTFxTFr1iweeEB/YjstLY3s7Gzatm2re7+nhNJfzG+88UbNfTfccAPZ\n2dkcMUhrl4VUKBJCCCECZwrhP86yVCjSDfUEcQ7hYeH+BwVz7FL1SueqI8r5i2FfxxRC1Gy3trr9\nQp9CtZCenk63bt2YPn267v3x8fH069ePLl26cMstt/DRRx9RUIFX4kqgSAghhAie3wpFei3PbBIo\nCkVesX7LM71OP1XN0cyjmnUnck7ojCwj70DRDTe4b7dqpb7PoEJRfGK87nqz1UxC8fHgzscZKPKq\nUHSMq7DPd4oDAAAgAElEQVR5xjWysqBYSYucyj3F2iO/Uuz5u2kjjxRKC682X8f1z+lMmv73M54t\nz9JRWp5NmQJ5UcahH2fLswLqUkg9AMx1zLpjM3SKESXFGk+gnIoBS+ljdQaKSkogPVJ/jiilaA/9\nH3H/O7OJmwz3ffR8LNaYhpp2Z+FeU19/pP6hWl56eCl33QU/t3UPLIqAe1I+0swF2Uxwrq563fXe\ngaIGcIIW/MrdHPMKFF1V+hbRtD1rul+12NhUD9Wh09OpaAEFimaX9l8bOXKkayLSZDLx2muvYTKZ\nmD9/vs/t58yZQ+PGjRk0aJBrXXR0NEOHDsVsNrNkyRLVsWrVqsXQoUNd6yIiInjllVdwOBz8+OOP\nZT6vBQsWsH79em655RbDc05MTCQuLo6YmHKuhX6ByQdiIYSo2caNG0d8fDzXXXed4RhnqLakpIQR\nI0Zw00030bVrV5577jn27FG3IDlR2oe4ZUtt+rtF6S+0SUlJ5XX6QgghhKgk3n3jg9q2dKZhaxx0\nHA5XvgyL47Ql2Re3g78+pt2+3MNMpaKsMOo+uOHFkHevyyaBIiGEgdoRtfwPquEKCgoYNWoU+fna\n/ycAvv76a/7+979jt9t54oknaNeuHdOnT+e5557DYrHoblNWMn8qhBBCBK/YVqxaNpdIhaKKoleh\nyGKzkF2UHfpOS0rg889h0CBYtKgMZ+ebd4UigNScIKv9+ONwlLlCkbnEzIqjxq3b19YJMsRRGihy\n5KgDRVk0dIV5XNLSWJeyjmsnXsvtW4bRbYg7aOOqUARwzTXq7fQu7P72W9Ie7at7Sjk6gSKLBdJK\nfASKSu9yVicCoG6G7tiMutp1J2KNf7G2h8HJ0go+zkARQH6U/jZWu5Vnxu6mfuk2mzHuyPG3j2LZ\nkdxIU6Go3yHDTQDlvRkbCwmt1EVqzoTnM7Wbe3kvHTlbT3kMTpcUukNCrv01gF10YYPpRk3Ls6tK\ni1FpAkVNvAJFTVvDnXcqCx07wjPP+H4Q5SCgQNG2bdto2LChpvVJs2bNaN26tas6gZ7U1FTXVSbh\nXp/GnNUMnNtbLBb27t1Lu3btaNBA3Q+vU6dO1KlTR3WsUM7r7NmzjB8/ngcffJCePXvqnrPNZuPY\nsWMBtXqpbrwrFI0f76ry5fenSxf4XdsqUgghRDXSq1cvzf/H3pyBou+//56ioiL69+9Pz5492bRp\nE4899hh//OFOaWdlZREZGUlUlLbspLMFmtHEsBBCCCGqLlVZ7+Ji2LoVzp833sCDM9Tz6j2wvykk\nNYQXup7SHbtM52O31R56KV1f4Z5NLeArbVHFMpOWZ0III7X8fPaq6U6dOsWTTz7J7t27de8/ffo0\nX3zxBV27dmXBggWMHj2aqVOnMnz4cHbu3Mm8efMq5LwkUCSEEEIEr9iuDhQFUqGoLJ/9ajK9CkWg\n/xwHpKAA+vWDV19V2if16wcHD5bhDI3pVijKLecKRcnJ4NEdiXr1oF0797J3haLUVCWE5GHV8VU+\nW7GtbRnkL4ilrcusmepAUS71SUPduqzgxHH6zOnjCo7tbworryq907NCkb9AUXExvPoqZ6L0/57p\nVSgCOFFofFGEs0KRKlBUxyBQpFOh6Eys+oKAGPU/G662Z56BIkcd46BcUvFWPvpIue2rQlEODThn\nj2WrV4WiIdt8f15zBiGj7Nrn5I274IzyFRjLuU/T7uzSfGip7nDHifpKoGjjZZdi8dhlo0JoWPpP\nZrOGXpWnvCsURTeFX39V5uh27tS2vqsAfi+TsVgspKWl0blzZ9374+LiSEpKIjMzk0aNGmnuTy1N\n9V3unfYDmjRpQu3atUlOTgaUD5FWq1V3bHh4OJdeeqlrbKjnNW7cOCIiInjjjTeIj9cvVZaUlERx\ncTFRUVGMGTOGzZs3k5ubS/v27Rk2bJjPykZVnXegqLjYVTnNr+xsePJJpfKbtJoRQoiLl91uJy4u\njldeeYX777/ftX7r1q0MHjyYsWPHsnr1amrXro3VaiUyUj+x7lxf7OM/moMhfjDJymoGqH/vCHVf\nF4OioqIa/fid5HlQyPMgz4GTPA+iLFxVgsxm5Uq+/fuhSRNYuxbat/e9bennxY0eH+3P1gl8orgs\n1ZF8VSj6/k8h7zbkYwoharaYehIoMjJ9+nS++OILioqK6NGjB5s3b9aM+eGHH7BarQwZMoSIiAjX\n+qFDhzJjxgzmz5/PE088Ue7nJoEiIYQQInjeFYpsDhtWu5VaYcoXk2fypOVZecm36F/AeybvDO2b\n+P68rnHuHPz1r9qKPmvWwO3l277X4XBwLFNboajcW55t3ape7tZN/QtegwZQv767/VhxMeEZ6lBM\n/CF1huDeq+9VVSza2BKKw6G20e+JjRq5QkSAq0JR8dkcIjyG5dCAM1wG7HSte3/Pl5oqVKnOOiye\nFYq8C6McPqxe3roV8vI0QRfXsT2uEfcMFGX6iI84KxSdpSnt2yu5M0edTN2xmV6BIrsJzsWqQ1o3\np8Jyj1yUM1CUhcfjjPIq8+Mh4XQC3w2BGTNg55auFBNJbSx8cSN83gPaZMC3iyA7L5YjsZGcr+fe\ntp4F2te7jVYNjpKSo9/27nTeaQosBeRFav+tyomC4X+F+fNhi/1GWkVHA+6/mzFFscQVFADubU80\ngMN1u3LzZwfY6VEd6YZTKCGO++6j2a3NYe/X7ju9KxTVbawENS65xPB5KW9+A0XZ2Urqy6j1l3N9\nXl6ebqDIuX19Z70pL9HR0eTl5QV8rKSkJKxWa0jntWzZMlatWsWECROIjY3V3Q7clRmWL1/Odddd\nR9++fUlPT2fVqlW8+OKL/POf/+Thhx823L4q8w5dBuvECSgsVMKcQgghLk5Dhw5VtR51uuGGG+jb\nty/x8fFs3bqVXr16ERUVRUmJ/gc/Z/n5OnV0ouilrr322pDOUedXjpD3dTE4ePBgjX78TvI8KOR5\nkOfAqSo/D9u3b7/Qp1CjmAg+8eIK9UyapISJQJlo/M9/4LvvSnesv9+yBmxU1ZGCdCHCPdLyTAhh\nRCoUGZsxYwZxcXGMGzeO5ORk3UCRs/p8d88WFUDt2rXp0qUL69evJy8vz3B+OFQSKBJCCCGCV2Qr\n0qwzl5iJqa38Py0tz8qHw+HQbXkG+s+xT4WFcPPN2iAKKJ//y1mGOYOc4hzN+nKvUOQdjrrhBu2Y\nyy+HfftcixFn1IG3n4/8rFp+rcdr7Du7j5O5JwEwR0BCnBKI0dWhA3h0enAGivQqFCmBIsWeZvDp\nOW3LOVc4J5gKRaVth9Ki9U/RWaHISriqIlBBhHEZ5nyPlmePPgpdu8KALRmYdcaer2sC3JWf0uuB\ntZb7F+tYM3RJ0w8UeZ4PdXwHisLDYepUuO662uywXQctNvPyfcr9SQ3hkz9D9spY9jRXV0fqdhpi\nHu5D97gNhoGiU3mnOJ132vD48dfCvU/AJbtiWBzeHVjjus8S9xca3NcYmORal9IAnnu+HfNzx6v2\n07vXcJg8HmJiaLbxE9jrvi+saSJ2j7GN6zY2PJ+K4jdQZLUqVxGGWn0gkO3NZnPQxwr2vLKysvjg\ngw+4/fbb6d27t+42TkVFRVx++eUMGDCAF1980bX+6NGjDBw4kA8++IDbbruNxo21L1h5XAFckVcS\nt2tn4s474/jtt2gcjtBmPffvTyQmxu5/YBnI1dQKeR4U8jwo5HmQ56AqaN++PfHx8Zw8qfziXL9+\nfYqLi7FYLJr/k52tzsp7clcIIYQQFc9VoWj0aPUd06e7A0VG25Y1UFRBFYoqilQoEsLt5c1QGAHf\ndCuf/bXIgVp2SG7of2xVFG6SQJGRcePG8ec//5nw8HBXRXpvqampNG7c2NVO21NcnNKvICkpiU6d\nOpXruUmgSAghhAied4UiUNqeOQNFeu24pEJR8MxWs/vzupegW55NmaIfJoKAW54HQ6/dGVRCoMgr\nnA4oFTgMAkV5xXmkF6S7liPDI7mt9W3c1vo2Zu2Z5Vq/trWPQFHHjrqBInuOccszuwle7As2tK9v\nRt3SG56BoquvVg86fhxKSsBZ2bM0UHTGIFCUUxooOktTHLhDRAURxpMcni3PWrSA3r0dlOzIQOeU\nOVmnDuCuSJTsVeuldbby48k5JieskbJPkw2itCE01/jsZBwOB506mXj9ddj075uY8qD6QoXvO4Jl\nZSzZMerYU4dzUP+tPtyVXYcfD/you//TeaddITIjq6+Eptc8x7m96vdZ6+ataD3ybzT5z2TORSvB\nqqIIyOm9kITN6vfow4+9DaXfozWr10x1n92k/neySgaKoqKUelehVh+oXbu2apze9nXr1lWN9XUs\nk8lEnTp1XEGhQM/rn//8J8XFxbz33nu64z099NBDPPTQQ5r1V199NU8//TQTJ05k1apVDBo0SDOm\nPK4ArugriVetUkKngbY6u/JKpd2Z01VXta3wKlpV+WrqyiTPg0KeB4U8D1X7ObiYKi3s37+fwsJC\nzVWg4A7qOv/Pbt26NTt27ODkyZNceeWVqrHO0NEVV1xR7ucorTeFEEKIimVz2Fh19Bfefh4e3Qt9\nDsPKq5UyzNeXjlmWs42TOqEBu8nzGrTgnY2fjccFekGxXIDv7qeWU3BCiIuFqSz/AHix+GojUA2E\nh0mgyEivXr38jsnOzqZFixa69zkvXHFeyFKeJFAkhBBCBE8vUGS2Kl/gW2wWzhdqAypSoSh4ecX6\n1YlAv62cT5s2Gd9XAYEivXZnUM4tz2w28P6uRi9QdPnlqsWI0+4qNKfyTqnui4uJIyI8gttaqQNF\na1rD2+t0ziEqCry/EykNFIXlqcMx7pZnMLcjbNH/1ZcMZwzDs+VZTAxcdhk4w1A2GyQnK5WLLBbY\nsAHAsOWZs0KRZ7szALOP9EieR4Wiri0gz5KH1a7f4j6zrvqDYZLXRSK+AkVtejRi/X7Isaifr5jI\nGErsJRRZlYpoRdYicotzaRDVgHffhVHft+OIV4YhLQYaxzYgoqn636hGZuDqq3nGdjlrU9ay4ugK\n+rbpyw/7f8BiU3ImFpuFPel7VNt1SlMCXqc8mnOdtZ2E9urgUecrLyO8aWPuaTiAWSXzXOvHb3lP\nNa5Hix5cFuOeBGtarym+VMlAUXR0NGFhYYYfzpztyoyqDzRooDT1M9o+Pz+fS0rTKf7G5uXlUbdu\nXcLCwoI6rzVr1rB06VLeffddLr30Ut3xgWrfXuk96fyStLqqW1f5CUREhHrZqv/vghBCiIvEiBEj\nSE9PZ8OGDZp2ps7gVMeOHQHo1q0bCxcuJCEhQRMo2rJlCzExMVx11VWVc+JCCCGE0GUKIYl74NwB\n7p59D7RQJrReKS0XHW6HNfM+YecPn/Nyp1PQV7ut3QS/tw79fOdcFnpp9eceCP24oXrzrso/phBV\nlQNCaLJozBIOYeUYUKpsobScFG5WqzXkqvllqW6cmXkJ4J7IT08/z8GD5d/2oyqpSRWha8pjrSmP\nE+SxXqxq0mO9WOi1PHN+6X+24KzuNkZBBGHMqN0ZQFpBkC3PDh0yvi/IQFFSVhKFJYV0aNrBcIyv\nCkUOhyOkuQuNQ4egoMC9fMkl0Lq1dpx3oMijQtGpXK9AUX2lMuZtrW9Trf+jlRLKSS++mmvweGzN\nm6uDPwCZmcpxzOoKRV1vqc+ZdUqYZFE7owdlUKEIlPCQZ7u2I0eUddu2QWmHKKOWZ/m1lRbu6Q4l\nUFSrlpIBMEcYfwDL86hq1KIFZBRmGI611jZjCYfI0nC+d4WiK3wEipp3bMRXw+C50dl4lqxpWKch\n4aZwkrKTXOvO5J+hQVQD6tUDXjsCXvuMy4Vho2txJrY5eLyto7reDigVqOY+NNe1PuF0AofOH1It\ne7otGd78AwY8orwHjLRprryu7w39kNlfzsdReumdzaG+WqFf237/z955h0dV5W/8MzOZzKQ3QiAh\nJBBa6NJBqYJUFVAsawPUlVWwrj/FtnZdRUTE3lFU7LrWdVVExUIRFYFQpEsq6T2Z+f0xyczcNpkJ\nk1D8fp4nD3PPOffcMzeT4Z573/O+iu2kSKXAS81RKSgKDQ0lOTnZUECzf/9+4uLiiI2N1a1Pb/gj\n1ds/NzeX6upqt3NBSkoKVqtVt219fT3Z2dnuh5KBjOuzzz4D4M477+TOO+/UtF24cCELFy5k+fLl\nDB06lB07dpCbm8vw4cM1X15qZ4a/AiGqT4kIigRBEI5vJk2axAsvvMDDDz/MnXfe6f6/8JNPPmHV\nqlUMHjyYbt26ATB+/Hjuvfdenn32WSZOnOi+HnjrrbfYvXs3c+fOxWw2ztwVBEEQBOHYot4Ml6y5\ngW19jWOwHSaYN60VB9WAE983cwRBaHlKwtpiq9V/YNQcai0uIeOxitVibbqRYIjdbm+2a/7huBur\n16PGxbUhM7P1b9y3JkezI3Sw+au817/K+wR5r8crzX2vx5OL/LFGo6OHN42CIiPnHIk8C5yyGmN3\nxoAciurrXeITIwIQFD234Tn+/uHfcTgdXD30ah6e9LBuu52F+g5FZTVlFFUVERcWhJzjn35Sbg8Z\noh93kKa8eeDLoSg5KhmAznGd6Rrfle2HXOet1gKfdIE/fj+Thdzvbl+f1gmLWlBUWAhOJ7ZqpaDo\nlDOjefm79lAPa5ON35auQxFAt26w2ssmads2mDLFHXdWZ4bcCON+S22QXeW6+J01y5WatsNqPAHz\ndihKSYGsUmNBEcCSsPP5v7JXcAI/q66x04ugoyrN7ECUa1FJ+17xnHc+pA0vZJTHFIo4exwRoREK\nQVF2WTY92vSgzlHH547XNWMoscGCBXD1l9EKQVG7GTN0x5wSlaIQFP10QPmZSimFpHL44iW4diIs\nG6r/3ttHugRFGfEZTOoyiU92fKLbbnoPlaAo4hgUFIHLfeD9999n165ditiSnJwc9uzZw5gxYwz3\nTU5OJjk5mfXr1+NwOBQPFX9q+KM+4YQTXIMJCaFfv378+uuvlJWVKfKxf/31VyorK91tAxnX+PHj\n3bna3mzcuJFvv/2Wk08+mczMTHebf/3rX6xbt4533nmHXr2USkq1M8NfAREUCYIg/LW4/PLLWb16\nNW+88QZZWVkMHDiQXbt2sWrVKhITE7nvvvvcbWNjY7n++uu5/fbbmT59OpMnTyYnJ4dPPvmE9PR0\nLrvsshYZo0SeCYIgCMKRY1uc76f7TiDrCDx3PRJxZ4IgKCkNT6SmrBYoDEp/teZj16GoX1I/Qi36\n7jqCf0RHR7td6NU05Zp/OEjkmSAIgiAEjp5DUWWtyyHlYJmBoEgizwLGZ+SZwXnWZc8eMHB6BCDP\nf3fGB9c8iMPpuk/w2NrHuGPsHUTbojXtjByKwOVSFBRB0bp1ym29uDPQOBSFeLn8/Fn6p6IuJcql\nHzCZTEzvMZ0H1zzornuvB2z9/SyG8iPj+IoarKzpfyVj4lXXqIWFUFVFiNPzkL2aUDpn2mjXvx15\nW2C3j7fv06HIm0aRWIOgKDcCnD6epRTbIKfKJWDp3RteeAEGPVBHgcFtn0aHosqIRKKi4FDuIePO\ngTvCFtC9+iDLT/2Wd3opP29pRWCvg/alnlg2hxk2J0JaP9fC9WqTcl4ZFxZHfJjyHDQK6f6T9R/2\nl2iNaEptEB5ZR2G10rooNq69pi14HKkaaRSQuesbNGFWBzz6CaRHDOHmfr9oYh/bRXoUVJcPvlxX\nUJTZJpPubboryhIjEjFhcjsaqTkSgiK/LAOmT3cpox5++GEcDtcnyOl0snjxYpxOJ2effbbP/U87\n7TSys7N55RWPhKysrIwnn3wSu93O6ad7PMmnT59OTU0Njz76qLustraWRx55BIBZs2YFPK7x48ez\nYMECzU9jVndjfWMm96RJkwBYsmQJdV7qmQ0bNvDGG2/QsWNHv3K+jxck8kwQBOGvRXR0NK+//joX\nXXQReXl5vPzyy2zatIkzzzyTd955h9TUVEX7c889l4cffpj4+HhWrFjB2rVrmT59Oi+//LKhg6Eg\nCIIgCMcv9UfInLDaryVTgvDX46bVTbcJHiZMQRQA1YSAxaC/Wb/DiL3BO1YjZ28KTj+vn6ldHSsE\nRnp6OgUFBVRVaR9QHjhwALPZTFpa8K3pRFAkCIIgCIGjfpgOHoei7DL9KC5xKAocn5FnZdks+2kZ\nPZb1YNabsyis9CHyV8ed9e+v3M7PB6d/F/bejj61jlrD37eRQxHAvuJ9fh2rSdQuZYMG6bdTOxT5\nijyL8ghMTlfFU33UDdoOacsEPmc0q+jJZu77/TStk1BhIRQr7XiKiSE5GXqNb89alS9KWoxyfIca\nHYq8BEXr1sGty12Cot8TYeQc6BuxnE+2fgjffQcYx525x2CHbFzCl27dwGYDc6SxEKA0FK6fAN//\n4wwueu8izblSU5FwgOlzC3inr/L7wV4Hwxu0PwOV+i3e6GanW6brglz9GY6zx7mdfxppFNI9tvYx\nw3GUVJdQVKUSFNn1n10lR/qwigKSVX+CV7QbxTdzvlF8TtJi0uia4BF7Te4ymfTYdE1fancigBBz\nCAnhCYbHP2odikaMGMGUKVP4+OOPOfvssxk6dCg///wz69atY+LEiQqHokYh0IIFC9xll156KZ9+\n+in33HMPa9euJTU1lf/+97/s27ePW2+9lXivD//MmTN5++23efHFF9m2bRu9evXim2++YevWrcyd\nO5fu3bs3a1yBcM455/DZZ5+xevVqpk+fzkknncTBgwf54osvsFqtPPTQQ4SobXuOY8ShSBAE4fhk\n5syZzJw5U7cuOjqam266iZtuusmvvqZMmcKUKVOCOTyfiEORIAiCIPiPidb9j7P6CDkFVf11pumC\nEBAnBunevD84IaiCIjB2KHriQ/g+FU79W9N9XLgRlvdvuh24BEUrg2BMHm4Nb7qR4JOBAwfy448/\nsm7dOk466SR3eXV1NRs3bqRLly4Kh/tgIYIiQRAEQQicaodWUFRZ1+BQZBDFVeeQB46B4suhqKiq\niAWfuJ7PZxVkkRaTxqJTFuk3VguKBg6ELVs8rkXV1ZgqKpocj9PppLymXFGmJ2QqqS4ht9w4Gnlf\niY9Jy/r18MYbMHw4TNcKMNzU1cEvvyjLBg7Ub9u+veuir+FCL6SwEMrLISJCE3nm7VjTJrcnSWWQ\n03AJWmqDU+/4lf9OTmE1owEo+AkcMXFKV5fCQpzFJYq7MyVE0749nDTezuc/2gDP39ApySN5pniP\ne7vIDvUm3FFqL74I8+ZBRnU37gIWTIFv0wDKmP7GTH6z1dKtDA42calcYoMcXA5FjWZHlU5jod//\nMlw/sJ/lvyznx/0/+j7AxGshbreiKMYWw8qvImlb7jrPp2fBh14mPf/p4eCeRNfrwqqmBUXZZdns\nK97HF7u+MBxGUVWR34IitUORpl71J2jvmsrglMFsuGwDD615iMKqQq4fcT1mk+cTYDFb+Megf3DD\n/25Q7KsnKAJX7Fl+hX7s4FHrUATwwAMPcOWVV1JYWMhLL71Efn4+V155JYsWLcLk9VRv2bJlLFu2\nTLFvZGQkK1as4IwzzmDdunW8+uqrREdHs3jxYs4//3xFW4vFwrPPPsvs2bPZuXMny5cvp76+nttu\nu43rr7++2eMKBKvVyvPPP8/8+fOpra3llVde4ccff2TChAm888479O3bt1n9HquIoEgQBEEQBEEQ\nBEHwl2snt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dxZvxx44iMIdWrnaW3KXXOgdmQTX6GcjzhTnISqkqt/y/lNsd0zD7oegh92\njGZm5kxFXUoJdChR7n/IZsFigU6dXNtqh6KwOv8dikyYyIjL0K3r1bYXEaGquZfN5spsa2DAQZfY\nqpHKukqNSK9RCNQ+sr3hOFJjUjXCG7WgKC4sDl8kRybrl0fplwcbQ4ciERQJRysiKBIEQRAEQRAE\nQXCxYsUKAObPn++OADKZTFx77bWYTCbefPNNn/v/9ttvxMTEMH685+F9REQE06ZNw+Fw8Ntvv/nY\nWxCODOP/ONIjCIwJnSe4X98x5g6YOBEuughLZNRh9201W3lqmmv1aTBcflqaS064RDeu7K6xdxEV\n6jofNouNh055yGc/upFnDUXmUN8ORQ983vQ4F36rX76s61UwapQrfuzSS8Gh9O/v5+czmviweE2Z\nxWwhyhbF2b3OdpddPfRqRRvvhwODDsBPz4C9iWdeJkxcM/wa/wYmHJWIoEgQBEEIJq05h8zKcim5\nuzdGWLUSviLPDpb6FhTVOY6Ph455FXlU11crylpEUFStFBT1b9efm0fejNVsJSMug+dPe54hyUMU\nbX468JOykyyV4t9AUBRySCkKWvLDEs5880yu/e+1DHt2GPWOesprDByKVJFncfY4Qi2hJIYnsmjC\nIgBSo40FRYVVhTi3GAiK9CLPVA5F6xlIRYW2mYLISIhXzhMOmJQCKVOpR1Bkp5LoGq/3ZbFAUhJW\ni1W5iMNcD5YadhToC4r65kC3AlhgPV0zpMF/gglX5FlipfIi1JSs/Vv5NedXTd+YzUQtfoy3Zr3F\n/SffT5QzlLBaWPwZxKi0f8U2E9274xYqqf+Ww2sh0k9BUVxYnNIJy/t9JQ/WLfeOPTPhEhU1dQzw\nLShKj03XOBCVVCuVVP44FAVSHmzUkW6NHClB0fHhISe0KCIoEgRBEARBEARBcLFu3Tri4uLo1k1p\n45yUlER6ejpr16412NNFbGwsu3btori4mJiYGHd5To7rRmNcnO9VUoLQWpzf93ySI9tza1Y7bkp8\nAtBZBdqKvHv2u3y/YAYPnNR02/+c+x/e3PwmcfY4pnT1rHg8XAHQU9OeYmjKUPq16we4BCktza1F\n8wjNX8+28rW83K/p9msvXcsv2b+wNX8rQzsM1axKbaRTXCc2ztvI17u/JrEmkb5JfX32q+dQ5Gg8\nnVarqq1SUNQjv+lxJ5eCU8dMbdrlS+Byr4JmKjsaxVPeNH4eXpn5CjN6zCA3O5f5J89XtJk/ZD49\nrr2XP8sOcuZm1w31Df9J5ocX72ZM+hjKenRmXTIkVsB/M6DtJVdx8YQbdAVMwrGDCIoEQRCEYNKa\nc8gjJigyijyrqySvIs/nvrWO48OhSO1OBC0TeaZ2KIoKjeLucXdz66hbsYXYABicMpgn1z/pbqNw\nKKqt1Tr8NH5emog8e23Ta+7XOwt38nP2z4YORerIs4UnLeS6EddR76jHanHNH3wJiuocdZRnbSJS\nr1I9focDfv5ZUTTr/gGwEjZtgvBwV1lVFRQXQ5K3wGfSJHj1VffmAdW0oabA40gziHXKyuRk94Vj\nhDWC4upiT521gm15xoIigIXpf+Ojot/Zmu9xjBrSkLgWSTmdKpUimKporbJHIygqtsEzj0OvXpiA\nG066gavW1GO98WYsTlirMtgpt9dz50LPtl7kWZRSJ2dIQlgC4dZw7CF2jWvZkJQh+jupMtz65sCH\nBl9f4dZwQi0u5VO7yHaG49ATFKmJtTUhKDKINmutyDNbiI1YeyxFVUqXMHEoEo5aVPeFyMmB/fv9\n+5EJtyAIgtASSOSZIAiCcCSoqakhOzubjmpb7AZSUlIoKSnh0CFja+9zzjmH+vp6rrvuOvbs2UNZ\nWRlvvfUW7777Lr169WLIEIObLILQyrw842X+PeEBIudfi2PWuUd6OEzvMZ2/+WngZQuxcX7f85na\nbaqus05zuWTAJW4xEbRO5NmdDz/BLROv5OINTbcF143ViwdczIOnPMiZPc/0KaLqHNeZOSfMISNa\n35ZejWF8okp9ER3l1G/XBH7tpXIo8hc98Vfj7y/EHMLZvc9mfMp43c/L+Dte5sJfXGIigMyHXmLO\nCXPoFNeJPrkwZyNM2wZLP4Fb+lxB+yjj1bLCsYEIigRBEIRg0dpzyKysLEwmExs2bGDGjBn079+f\nUaNGcc8991BaWmp4jMPFV+SZ2lFHzfESeaaOZoLWcSiKsjW4jjaIiUDrBrP2wFqcjdHBf/yhdI5o\n3x6io12vm4g8U8fXHao8RHmtjkNRcQ4F61YryhLCEzCbzG4xEeAz8gygcPcW/Qq1oGjnTijxiG8K\niWUXndi1C1audJVt2ACpqdCuHcye7ZWk/OCDMHs2te1cApU/VYKiimyPgGQqHykrR3lijjVxXqHl\nFDhdiwx+UwmK+jR8LOJTEnj+tOfdMczhtSbFvK9fxQHFfodM2r+zX3OVgqI+T78Pc+cqyuxJKVga\n3m+MShwUnlLH+ed7ttWRZ+G1+pFnNotNU9a4qEJvcYWhQ1F0NFx0kXuzb63xIrs4u6fO15wrPcYP\nQVET9XrRZmaTmaRI/SiylkAv9kwcioSjFrVD0bx5/u8bHw/vvAOjRwd3TIIgCIIgCIIgCK1NUcPN\ntKgo/dikxvLS0lLi4/XdKS644AIsFgv33nsvp5xyirv8xBNPZPHixVjUTzGDQDAFFcJfk4T4o+Mz\nZG6eTiV4x1eJc1rDoQgAp9NIyqPBarY23agZmEwmLGaLNpJi2DCN+iIy3AHKZw/Bo5mCIj1hld+O\nVePGwdNPw2efueLzTj7ZUzdhAnzekOmWlgZdujRrfMLRhfq/YnFrFwRBEJpLa88hs7KycDqdPPLI\nI0ycOJFBgwaxdu1ali9fzg8//MBrr71GZKSu58thYRR5VllXqXHUUXM8OxTllufidDqDOidXOwLp\nOXH2TOxJhDXCLfbJq8hjT/Ee0mPTYetWZePGuDPQFxQ5HGA243Q6yStXuk2V7txM+QFVf0AJVeRt\n3QBdPWUJYQmadh1j9IV2jRQWHkRXcpSdDWVlrsiyP/+ERYsU1RsYAA0zqB9/hDlz4NZbIb/BOfWl\nl2DWLJg6FZfL0AsvsGPLFjIvuogD0UrHsMK9HkHRND5UjmPaNPfLcGu4ss5azh90piAMDkR7FddD\n9wbzJlNsDMNT+7P+7+tZs28NE655lA4lHqel/pX7FV0WOJTirXpHPZtyNynK+rbTsbVN9MSQRasE\nRWUWpUhJ/bccVqfvUHRGzzN49bdXFWUJ4a7fcUJYAn+W/ukut4fY6d22t7aTRpYscQnb8vPpe+kM\n+GSqbrPGuDMIgkNRMyLPkiKSlNF2LUzbiLZkFSjjCUVQJBy1qAVFgXDokOtLevXqptsKgiAIgiAI\ngiAczdQ1PFEMbQyXV9FYXl1t7Ae9ceNGnn76aaxWK1OnTiUqKoo1a9awZs0aHnnkEW677TbDm41b\nthiszmuCqir91aKC4Avvz1t+vh+ZVS3Mli1b3Ksq/WnbUmPwJvvP7BY5jvqYUXl5foup/tjxB4ds\nxivc9aiqqmrynNXX1RMbGkt+leezEFFvYccdd1Cr2rdLXW3QLNHV47Lt3k1nVZsQUwh1Tt+Kj+1Z\n2zVlO3fspCTMs5LZ53k46STXDygewlivvZZ21dWYKivJvfZaqtQPaIRjEnEoEgRBEIJFa84hHQ4H\n0dHRZGZm8tRTT5HUkOvkcDi4/fbbWblyJY8++igLFy7UHONwr5937t2pW55flE9Ite+HjFuytlAa\n2XLuSS2B3nXjxl0bte3qqlj32zoircERcTmdTo1Aa98f+8g2a+clPWJ6sD5/vXt7+DPDibJGcUpe\nLEvMENKg0y9MSiLb6710i4zEUuYSLZkcDrJ+/BFHbCzlteVU1ys/p8U3XkNZO2Codqx/qIxmSnNL\n2eJUnrPo2miiQ6MpqVFGezVSaNctBmDXZ58R++abxL79NiaV+tslKHKxenUVv/22i1WrugGei7wH\nHiijc2ePCKyqqop9DguFYZ5+LJjZn+US+qWxm9787q5zWixsS0/H0XDuLA7VBaS1gh8ZyrokK+AR\nzWXmQWjDteWO3Fz3XGqYfRgxISsUXSQozYLIry1WfO52l+5WuIPF2+I5tPcQhSZlVJ29vJxODa9j\nVLeHiuvKFX1u27dNUW/kUNTL3ot24e3IrvB89kJqQtiyZQthzjBF2x4xPdixrYkI9QsvBFxRd6Hm\nUGoc2oPaHDb3WKsLjL8zTcUmcvb4dgerLKz0+b2nF+WXYE1osXsNeoQ5wjRluXtyKbLor97xZ17f\nXERQJDRJr16Ht//+/U23EQRBEIRAEKMFQRAE4Uhgt7vuZtXW6q+grKlx3fAIC9NO+gHKysq47LLL\ncDgcvPPOO3Tq1Mm93z//+U9effVVunTpwnnnnae7f2ZmZvPG/Y2Pu3CCoMPwqIGKz1vbnLaaNmN2\nwapOmuIWYU7/OWRmZpLlpzlNc/9WfBFji9H0m94lnYVrF7pvNnaJ78KOQ03cKA2QzMxMSE9n75M3\nA02LA4f2G+q/804DW7ZsafKcWa1W5p0wjzu+vsNdNu+kq+nitUreTYBOa5cOuBTm22DPMk2dZlzd\nu7tWrx5siFs49VRePeMCznrrLHeTJ6Y+wfyP51PvdN2pv/iEi+nVsxe8req7eyaJEZ7Vuv6cB50B\nwqRJALTUn8P69eubbiQEFREUCYIgCMGiNeeQZrOZN954Q9OH2Wzmhhtu4IMPPuCjjz7SFRQd7vVz\nfJm+u1KIPQRrhG8HzfTO6XRN6OqzzdGG3nVjxeYK3baxKbFBe38VtRU4nJ5Jkc1io2+vvrptR+8d\nrRAUZVdkk00220OhX3+4tCFaK65XL+K830vbti73nwa6JyRAt27sPKQVjZWGQrm+Vo5dKkHRgMwB\nZCZqP2f/ifkPi9YsIi0mjc35m/ly15fuukL9PwsAOj3zjMtBVIf/4pmjbN9up6wskwrVr+ebbyKx\n2TLp3LBaYcuWLawuUg66vSWW/QWuN6iOOzOdeCLdhw1zbyd8n6B0aQ0tpxo7HyR1Ajwinb5eOpcu\nAwYoXaEyM+GLL9ybkTUuR6PahmvTKkc16V3SCbO6Tszvmz0CJ4ATkk+gZ8+emvNBuMc9yV6n7LPW\nWUenrp2wh7i+q9bWKB2awmpd41Az6YRJrC9bzztb3nGXdW7XmczMTFJ/S+WnvJ/c5aMyRgX0HdPr\n2178nP2zpjwlIcXdj63QBl9qmgAwut9oUmNSMb1nwmkQrJ3ZKdPnmJxOJ2EfhlFZ53Fsymib0SL3\nGozouqsr/93/X/d2tC2afr11HKgaaNZ8Fv/mm8FaMCQcx1x9tStuMS0NUlKa/mmnchkzuE4SBEEQ\nBEEQBEE4poiMjMRsNlNWpl2pBC6bejC2s//iiy8oKiriggsucN8IBteq1H/9618AvPvuu0EetXC8\nMmYXZESkMq7TONJi0oLa993dlA8Z9FyzlnwKgw5ApPHCwCYZvRsyfJjpxFTB6LTR3DX2LgDMt97a\n/IMdJp+dr71ZHWYN4/nTnqdrfFdOTD2Rx6c87rOPEakjDOvmrx1kbNseFob5nnv9GmegYqJAuHrY\n1ZzW/TSibdFM6jKJG0+6Ub+hs2k7paldp9I5rjMndzqZW0bdAg4HTn8WDZjNsHIlnHgiTJkCS5Zw\nWvfTmDdwHumx6cwbOI85/efw5LQn6RzXmXGdxnHrqFsPL/JM+MshgiJBEAQhWBwtc8iIiAjS09PJ\ny8trEQdbw8iz2r925BlATrlvp5RAKK1Wnssom/7nBmBM+hjDug3tvTtR9aGKPSPPFXOWV6GMOwMo\nsUGZgaCoVnU9pRd5BjAqbRQfnPsBj055lDahypgpPYeibQnw5CDIWqedn5URwS3cxf8Y7y6rq4PH\ndaZpTic88YTXeGthY51y0PFVMe7XmrizqcpYLnXkWUiYK57su7bRinJvQRHRyjpGKOeLJrQuRQWV\nBe7Xv+b8quw7SV9c5h15ZkIbe1ZS7XGIqqhVHjDMwAi2Z2JPRnUcpShrFM51S+imKB+ZNlK/EwOM\n3od3TJnR3DnEHEJKdApmk5loW7RuG3VfephMJpKjkhVl6u2WJikiSbF9pOLOQByKBD8IC4PnnvO/\n/cGDrsjJRiRnXBAEQRAEQRCE44HQ0FCSk5PZb2DDun//fuLi4oiN1b8xkZ3tsoLOyMjQ1CUkJBAX\nF8fBRteNIGJCrP2CwVcvwtjZR3oUHr56CTjwg3sCbrojOL/nW7+GAVOVq970hBf9cmDta5H8aSoj\n5brmHWvVi65/Tbdr6xLLIfdBwLnKXWY552/w5F3NO1gzKbqhiBh7jGH9rF6zmNVrlqttlb71eCP3\njruX0emjmf76dN7Pel9RN2dvT941Gf/91w0dDJt9j7V3296+GxwGJkzE2mN5/5z3m278zDNw6qme\n7aeegoOXKZpcfMLFzMic4SnwQ4TkZuRI+PZb96YNeGLaE4omlwy4hEsGXOKzGxEUCUaEqO6Yi6BI\nEARBaC6tOYcsKSlhx44dxMXFKcRHjVRVVWE2m7FafTsGNQdvFw/FMeuqdKODvKmtP04ERSUGgqKy\nIAqKVOKsqFBjQdG0btOY1XMWb25+U1OncBWKVMWxqQVFDfHXeeX6gqJyPz9O8WH6LlaNVFfDx+/E\ngVdqTpGXoGgDJxAV/zNDL4GiMDA7YOOT0CfXVX8lj/AisylFKyJZuVL/mM8/D3fe6XoOfvCglQOq\n0xlR6BIJhVPOWL5SVk6bpmxrjVBsP7u8gvxv4J4/lAKdxvE6bXZM6ijEk0/WjDG+ErK9xlVQUUCH\n6A6AVlDUp20fzf6uNxAOERFQ7hI5xVRBgZf+qbiqmLYRLlditTgw/IQh7I61QMn3inJ7iJ2LB1zM\nq5te5acDPzEkZQjn9j4XgHmD5vHOlnfIKshicpfJzOgxg0AwEhTF2T0OUuHWcKJt0QoxFEBqdCoh\nZtfFfKw9luLqYt2+mhIUAaREp7Cz0OPMlRKV4qN18EmKPHoERTJzFoKOetItgiJBEAQh2EjkmSAI\ngnCkGDhwIHl5eezatUtRnpOTw549e+jfv7/hvgkJrhV56n0BiouLKSoqoo365p1w1GAOQO/QaiTo\nr/I8HBwmsIYr7wobitKeegrzrLP061oAs6314/usFv8fuPi6oQ9gMbtWvOrZrvfsaXLX66G+UXpU\nc8opcOmlrocRZ5wB557b9D5hYQZm9ILQ+ohDkSAIghBMWmsO+fvvv3Puuefy73//W9M2NzeX/fv3\nk5mZiSXAeFp/qKrTdz2qrKtsWlB0HDgU1TvqOVByQLcumA5F6nPpy6HIZDLxxqw3OHjdQZZMoRNZ\nygAAIABJREFUXKKoU4iA1A5FXm42gFtQlFueqzmGL4cib6Jt0Zp5VVUVVHppV1atgrI8ZeSYd+TZ\nfziVqye5xEQADjO8cILr9bemk3iUBZTaXXFwc+Yoj2+UpHPoELz+uuv13r2h5EQrH2jbs20AnMwX\n2PGy9enUyRVP5kVEqFJQZLGXc/U19VSn7FaUNzoUmWJ03HPatgXV90GCSqt3qNJj8butYJuizucC\nE6/fqy+HIrU4MGzkOMaMvFBR1ujUExkayfcXf0/e9XmsmbuGuDDX769jTEc2Xb6J/Ovz+ehvH/mc\n5+rhj6AIoH1ke02b9Nh092tfoiF/BEUdYzr63G5p1AImQ0fjVkAERULQEUGRIAiC0NKIoEgQBEE4\nUkyfPh2Ahx9+GIfDdbPK6XSyePFinE4nZ599tuG+Y8eOJSwsjFdeeYV9+zyrF+vr67n//vtxOp1M\nVdlmC0cPR6WgyGYLepdOtIIiQ1JSMC99NOhjaBwHlygdZiym4D/8aIrG1Y3+0NSNUl+OOHa77/en\njjdobfRi7wwJDYWnn3bFI7z1lvYhhV5/11xzmCMUhOAhgiJBEAQhmLTWHHLgwIEkJiayevVqfvrp\nJ3fbmpoa7rrrLmpraznvvPNa4i0aRp5V1VVprmPV0VB1jmP/IeLBsoPUO/UvGILqUKQ6l5GhkQYt\nPbSLbOeOomqkwpegyMihyCDyrNwPQZE67uzuu12GOenpLiERwI4dQKVKUOS1nuT9sJP4WJmkxVs9\nXf8+b5sOfx8MN8YRcelUzr/Q/8/UY4+5zFL37g0lP6pGURed6zpRunFnqvlMeIjyc11eU84fhX8o\nIsQSKqB9468wxsAFd8IExaavyDNvcRE0EcnVtq37ZYxKUOTt4qOOPAu3hjOr5yyFAOfF0190vzab\nzLQJb6OZC4eYQ0gITwhsHtmAkdNSo2CpET2BTTAFRef3Od+9uCrWHsu0btOa2CO4jOs0TiGaOq9P\ny3x/+4NEnglBR+2WaKT8FARBEARBEARBONYYMWIEU6ZM4eOPP+bss89m6NCh/Pzzz6xbt46JEycy\nZswYd9tHH3UJLRYsWAC4Vpfeeuut3HLLLZx++ulMnDiR6OhofvjhB7Zu3cqQIUOYPXv2EXhXgj+Y\nTxoJfHOkh+Fh9OgW6dZhAmuE8q6wLyFMS8VGOSwm153mAI/VM7FnUMcRiKCoKRpvRo7sOJIPsj5w\nl7druKk8tetUlq1d5i4fkTrC/bpTnDa2Qs3p3U8P0khbns5xnZUFHTrAqFFQtrrVxmAPaX3HK+HY\nQARFgiAIQjBprTlkaGgod911F/Pnz2fu3LlMmjSJ2NhY1qxZw86dO5k6dSozZ85skffoK/Ks3qH8\njzTOHqcQLRwPkWf7ivXjziC4DkWBRJ55oxZxtXbkWUK4R1D0449w662u17m5cPPN8N138McfQJVS\n5NHoULSXVH7tv17Tb3Q11JlCeH1wASS76stTPmaT9TnM5sto0O8pSEuDvXs9icvr18P27bBvn5Wi\nqHJF2+QSACdT+UjZyTStsETtUFRRW8Hveb8ryvrm4PH+jdZxKAKXoOjBB92baoeiggqXoMjpdFJY\nVaioUwtuFPhwKCqu8giK1OLAMGsYCeEJbLxsIx9t/4iB7QcytMNQ4+MEgaTIJNpGtNW4YmkciqKa\n71DkK9a8kYldJvLd3O/YcHADp/c43ff5bQHCrGFsnLeRtza/Rc/EnoxJH9Oqx/dGHIqEoCMORYIg\nCIIgCIIgHM888MADXHnllRQWFvLSSy+Rn5/PlVdeyaJFixSrr5YtW8ayZcsU+55xxhm88MILnHDC\nCXz++eesWLGCmpoarrrqKp577jlCQ/1Y3hcgzVkR9lchsrrpNo1Y7OFNN2olbslKgsWLW6RvpwlM\noarIMx+foZYSFDljYyEpSVHWlAOQ1Wzl6WlP+2xzz7h73K9Hthvps+2lAy4N+P1596+msa95g+Y1\n3JwGiwOWvwuYTNw86mb3TVJ7iJ2lk5a69x2cPJgIq/Im9etnvE6oxfWdER8Wz1VDrwporIFgGHvn\nJw9PfNj9enTaaH0b+y5dDusYTXH10Kvdry/sdyFh1jAfrYW/MiIoEgRBEIJNa80hx44dy4oVKxgx\nYgSrVq1i5cqVhISEcOutt2qOFUwMI89qtZFn8WHxiu3jIfJsX0krCYpUDkW+Is+8Uc8jvEVAL74d\nxeTJ8OSTDSIbA0FRbkXzI8+8HYquu05Zt2YNOBywaxdQpe9Q9LupB/WDn9X0uy8a/meeQGXyVkX5\nh3+8TU+DdSZnnOFax+DNxo0uh6LSaOX5TS11EEchKfzpKbTZdBf3aM5xbTn5FfmKsrQir41wg/sL\nJ52kcCI2ciiqrKukpt7jqGQ1WwkL8TG/8XYoUv25ekee6TkUAaTFpnH54MtbXEzUiN58US3oaW7k\nWWRopN8Lh4anDueKIVfQIbqDX+2DTduItlw++PIjKiYCcSgSWgARFAmCIAgtjTwXFQRBEI4kVquV\nK664giuuuMJnu6ysLN3yYcOGMWzYsJYYmhAAm5dBWB10urrptgDm8eNh22dBHcOA9gP4Lee3gG+i\n37niYItdEDlBYz1sKCax21tMUKRHU8facNkGerft7bPNTSNvYlDyIIqqiuhBD/q91U9R36dtH+4e\ndzcOp4PTup8W8BhvGnkTg5MHU1hVyNlvKeMrGh/gRIZG8su6QXxatI5euXBCNvDwDNpFtuO3f/zG\nl7u+ZFDyIDITMxX75l6fy6I1i9hXvI+bR91Memw6vdv2Zv3B9YzvPJ7EiERaisN9+HT1sKvpm9SX\n3PJcZvSYodvGScvmCi6euJhxncZR66g9ptychNZHBEWCIAhCsGnNOWT//v15+mnfIvtgY+RQVFlX\nicOptIlRiwKOd4citcvK4dBchyKNe47XdO/uRyLZCXz6KRQVwY09VIKiPJczkaFDkR+CoviGRRM/\n/uhyI1Kzf3+DQ5E68qxBH/NURjrEf649vh2etc6AeGUM91e7v+LsoUVs2qQVlJx4omsq/fXXnrJf\nf4U9e0Kp6FukaJteWkMiqvedkuLKq1ahcYGqKVcIdUAVNbZ9u6YPAMLCYORI+N//AGOHoqIq5Vjj\nwuJ8z9m8HIp8RZ6p/5Z9ipRakL5t+/K/P/6nKFMLhJobeeZP3JmgRARFQtDRm3Q7nfLwVxAEQRAE\nQRAEQTh6iKyB1JKm2zViHjkKtgV3DK+d8RrPrH+GRd8vCmi/lnSdcpoAlVOW7vE6dIDBgzGpbmoH\nbRw64hKLybdDUVNiokZOyTgFgC1btmjqQswhzRISeTMhYwKARlDkLYhqs/hJzp84EQoKYNw4t21+\nSnQKF/S7QLffcGs4t42+TVHWq20verXtdVjjbS3GdRrns76lBUUmk4lTu5/aoscQjg9EUCQIgiAI\ngaGOSWrE20EFXNfaaieX496hqKwFHYqCEHlWiqePhQth+D2JKPx3GiPPKvQFRf44FJXstFNWBnfc\noV+/bVuDoChMGUNVaIcdZPCfIfsN+/4oLhPidirK6hx1hPb6GPibpv3w4VCmNM1i40bYv99KXbhS\npNO5rII2KF2GNA5ODehFnqkFRYqosXJlvJqCCRPcgqJ4taCowaGosFIVd2ZvIo7Ly6FIHXn2Z+mf\nvL7pdVKiUrSCoiPk6tonqY+mTBN51kyHIhEUBY4IioSgYzK5Jt7ek+26Os0CR0EQBEEQBEEQBEE4\nYpgD1C6YLcGf1NbU17S4iCJQHCY0T9N1HYo++QTM5paLPHNqz0truCG15DEU53HgQNed8+xs6N4d\nzK3n9NQcDjfyTBCOJURQJAiCIAiBYRR5piYyNBKral5V5zj2Y05aK/JMHR8XjMizMiIVdVfd1YaN\n3gWNkWc6TksF4VDre80HAL98aqb93VohTyPffw+lpXAun/KaV3mRHS6OvRVH1zmGfVd1+BVCKzTl\neyPeRS0o6tQJ2reHPiqtyqpVUFPrBJuynw7VJVqHokR9V1i9yDN1ZLdCyHPNNbr9AC5B0Q03AMaR\nZ4VVKkFRWBOCIm+HItWf67+/+7fhbmoxWmvhV+RZlFJQFGIOITkq2b0tgqLgcXTfrRCOWdTiIYk9\nEwRBEARBEARBODKIEECfgAVFLSA08ffGe2sS4kBjMazrUNTb5QZ02OdlwgTdYluITVOmviHbErTk\nDVPNuYqPh549teqFo5CWdMVqJNTsx/JmQWgFRFAkCIIgCIFhFHmmJio0CqtZ+QDxeIg821u817Cu\nrKaMilqt4CUQ6h311NTXaCLPIkMjDfZQYhR55sBEBcr5z74qlQNPfj5Op1M38qxUO2XTJbGi3lBM\nBPDZZxBGBf+uul9RXhBmZnXnWjD5mLx3+kK3+If8TwgNV863R4xw/ZuZqbzeKy8HbEo3oahqiHcW\n++1QpHGBqtVGnkV3yHC9yMiAuXN1+wGgnyeWWx15dqjyEBBchyJfHKnIs56JPRUOxWaTWfMeU6JS\nFNsdYzoSYvZ46YigKHiIoEhoEUJU3lciKBIEQRCCicRoCoIgCIJwuAQqKGqOmMUeYjesi7PH0S+p\nn64Tjy9GpY0KeByBMP8nbZkvUVpzBUUn7QEiI2HxYt365097XlPmK/JscPLgZo1jycQliu0HJzzY\nrH78oTUcllqK1hAm/t+J/6fYvmXkLS1+TEHQQwRFgiAIghAYh+NQdFxEnhUrHYq8RQ1weLFnz254\nlsj7IklZnMKbm99U1Pkbeaael1ZZod7U6E6kvM4vIpZ6b/lAcTFl5YVU1wegQlGRXOl73x9+gBGs\noUNtIVav667aEAckZCnaqs8tnb7U7bO8tpz0cUqxUaOgyG53mcQqUAmKYqogjCo6oIpbO5zIs1vu\ngj174PffIS1Ntx/A5V57+umAjkNRRRAcigL4VR4phyJ7iJ1zep/j3p7WbZomfq1Hmx4KJ6O/9VY6\nUomgKHgcu3cyhKMataCo9ti/HhAEQRAEQRAEQRCOIwKVR+iJQboWQK+Yrob76N2oOilpML0Se7F8\nxnKsFmtAkWdDU4aydNJSv9sHQrd8ePxD6FCirfPlTuOvSKZfNlxf0oekiCQGJ/bn8ZMfgl9/dTsd\nedM+sj2nZJzi17GSo5IZnDyYx6c+7tc41Mw9YS7n9z2fLvFd+NfofzEkZUiz+vGHY1lQ1BpkxGew\nZOISusZ3ZUaPGVw17KojPSThL4oIigRBEAQhMCpr/XMoigyN1AhCjnWHouq6akWsmQkTPRN7Kto0\nN/bs54M/c9mHl1FVV0V+RT67i3Yr6v2NPDObzBphSIUVStHu78DCIeIVZbkHsjTtAqFbfbliO06l\nfamvhxQOYALi1B+lpF8VmyM7jlTWhxUZHjfhxPfcryMj3RodAPqqE7XsxYrNRhefLuxQtvM38qxG\nx6HIHgMdO4LND2un++6DNm00DkXuyLPWciiyHhmHIoDnTnuOJ6c+yWNTHmPlmSs19SaTia9nf83i\nUxbz8oyXuWPsHYp6Q0GRTQRFgRLSdBNBCBxxKBIEQRAEQRAEQRCOZoIRefb2Srj87jZQvF13n1h7\nLNll2Yqyb+bpWAD5yQ+X/NDsfX0xJz+V55c1rKq9Sivi8CWE8VckE92xKw9cv4EH1DcMdFg8cbFm\n5TLou0Rt+sempldj+iDKFsXLM15u9v6B0BqxYS1Fa439qmFXiZBIOOKIoEgQBEEQAsPvyDObNvKs\nznFsP0A8UHpAsd0+qj0pUSn8muMRwjTHoajeUc/fP/w7DqfDsI2/DkXgErx4R69VWKG0xrN/airs\na5gS5tOGRK+or7zsnQGMXMs5vYoYdQ+89x5ERcG110Lnzso27XDNm+OqINc7ya3dL4p2IzuO5Kvd\nX/l13J0hH/DQ4if59RcLc+ZAildCVp8+8PrrXo3VDkVGgiI/I8/0Yu6ibdF+jRtw5bLt2kX83l3w\npkf9dKjyEA6ng6IqpZCqSded5GSXK9KePcQEkLx+pCLPwBWDftmgy3y2ibXHcs3wawzrAikXjBFB\nkdAiiKBIEARBaEmO4WcxgiAIgtDqHMsihpYkGIIic0ICTouxoCbGFtNkv4FGnrUEzrFjYeBYCA+H\nM8/U1Acj8iwqrav2ZoEBRsfTO5ae8OhoRRyKBOHYQARFgiAIghAYAUWemY+vyDN13FlqdCpJkUmK\nMp8ORd9/D4WFMHGi4iLk8bWPs+7PdT6P7a9DEWgFL+WhUFbuUe5MmQKrV8OWLZBHIplsddfl5e3x\n+zh6tNmfx5DpMH26p6x9ezh40LPtFhSptWmRynM3Mk3lUOSD3PJchs39kWuvGaGp0zgU2ZQORY2i\nG38FRerIs/LacuodyovIgARFAJGRhPbsQ2RoJGU1ZQA4nA6Kq4q1kWdNORRZLLByJdx4IyHhpcB6\nv4ZwpCLPgoEIioKHCIqEFsGqup8ngiJBEAQhmMhzUUEQBEEQDhdTMARFt9+Bs+xVw31i7E0Lio4G\nnGYTzJ7drH39FhQFsHrWCItJ61CkfiBxNONLmHW0cyyPXRACRQRFgiAIghAYgUSeqRcEHC2RZ8VV\nxXy560u6t+muiSzzxb4SlaAoJpW24W0VZYYORYsXw3XXAeA8YyZfPXgFOw7toN5Rz81f3tzksQNy\nKFILXlSRZxkZEB/vEhTloxTN5Obv9fs4eiT8ka0p69bNQFDUhDZtYPuB2EPshiK24R2G8/3+793b\nW/K2MCLVH0GRKp6swaGoDQXKdgFEntXU1yj7DFRQ1EBCWIJbUASu2DONoMgf196hQ+Grr2Dj91g+\nGEm9s+mL3CMZeXa4iKAoeMjSKKFFUC86rD06rgcEQRAEQRAEQRAEAQjMoSgqNEpfUDRuvM/9+rZV\n36XU4iQ4DkWTu0xu9r7DOwz3We9LNOSvA9a4TuP8Ho9Rn3rlx5JDUZtw/dWsRyPqm+5Tu049QiMR\nhNZHBEWCIAiCEBh+R56FaiPPjgaHotr6WkY8P4KZb8yk1+O9uOHzGzTuMkYclkPR44+7X95c9A4n\nLz+Zyz68jMs/vpzSmtImjx0ZGtlkm0bUgpcKK5Th2b9LF4+D0H46KNrmFTRfUBRSD9E5RVBWpijv\n2lXZztChyIvI0Ehi7bGkx6br1ttD7AxOHqwoK64u1m2bmgox3ut/7CqHomqDQQQQeVZSrRIpNVdQ\nFJ6g2D5UeYjCygAdiryItcVy1VBXzHSoJZTl05cTagnVbXssOxRF26J1F8aIoChwRFAktAhqQdFH\nH8FbbzX98+67sH//kRmzIAiCIAiCIAiC8NfBX0GRCRPPnPqMrjuOxWzxGVk2oP0ARqeNdm8vm7xM\n08bfyLPrR1zvs37RKYvcN5Tj7HHM7T/XZ/vG95Mem85F/S/y2TbQ2Dz1zcjOcZ2Z3X92QH0YMX/w\nfPfreQPnHdUxYreNus39+qxeZ5EYob+a9Wjk0cmPEhbiWo3aJrwNN5504xEekSC0HiIoEgRBEAT/\ncTqdAUWehZiVDxCPBoeiNfvWsDlvs3v7gTUPMHnFZA6UHKC0upSK2grDfbMKshTbqdGpJEUoBUW5\n5bnaHZ1O2OcRIz07wHh8Rk5Ehxt5pnYoGjQIkpNhN+mKtnnFB2ku8ZW4JB37lMKrbt2U7fxxKOoY\n0xGTyWQoKMqIy9CIRdSinkZMJpVLkSryLDpAQZHaAaqspkwjCmuua29CmFJQlFee1zyHIi8emvgQ\nO6/cyb5r9nFBvwvol9RPt13jnPBYxGwyE2XVnnMRFAWORJ4JLYJaUHTVVf7va7PBM8+EkZkZ3DEJ\ngiAIxw8SeSYIgiAI/iNRRfo0dVYGJQ/in8P/Sae4TgxJGaJZeQpNx33VO+v57wX/5f2t79Mush0j\n00YGPM7z+57PeX3OY2LGRJ/teib2ZONlG1n751pOTD2RDtEdOKf3OZSX5PPSlw/zXtlaRfs3J7xJ\ndVQ1k7pMwh5i99l3oJ+hX+b9wsbsjdTU12A1W5nabarhisdAWTp5KZO7TsbpdDKl65Sg9NlS3D7m\ndkamjaS8ppxp3aYd6eEExID2A9g4byMbDm5gdNpozSprQTieEUGRIAiCIPhPraMWh9PhV9vI0EhN\nDFSdo64lhhUQeRV5mrLP//icDg97nHpGpY3i7bPe1riOfr3na8V2/3b9NVFSug5FpaVQ4zoXDhPk\nRWibAIzsOJKzep3Fgk8WaOoON/LM26EoIwPMZjj9dNjzRJqibW5lHhiMrykSGh2H9u7F+8GvkaAo\n1oegKDU6FYD0mHTd+oz4DI0LUHGVvkMRQJ8+8M03DRuqyLMYvXFYLBCrL0ZRO0CpxUT2EHuz3XX1\nHK8Ox6Gokc5xnd2vh6QMYe2fynsGoZZQLGbtwqpjiajQKEpqlb9bERQFjt+Corq6Ol555RXeeOMN\n9u/fT2JiIjNnzuTvf/87VmvTfwBFRUUsXbqUVatWUVBQQEZGBpdccglTpmhv/lRWVvLUU0/x0Ucf\nkZOTQ4cOHTjvvPP429/+plkVdzjjeuWVV7jrrru47777mDlzpqZ+1apVPPHEE2zbtg273c7YsWO5\n7rrrSEhI0OlN8Mbu+16kT6qr4e23Y7ngguCNRxAEQRAEQRAEQRC8MR/4E55bDlX6zisOp4Oze5/t\naa8XeWYy+4wsq3fUE2oJZVavWc0e59PTnibM6t+qwIz4DDLiM9zbEzImAPDqjndhs/LmYI/YHmT6\nuZInUIeiOHsc5/Q+J6B9FMfzIWAymUxHvZCoEZPJxPjOvmPxjma6JXSjW0K3phsKwnGGCIoEQRAE\nwX8qa/2LOwOXoKi0Wim0OBoiz8pqyppss3rPap5c9yS3jLrFXba7aDe7i3a7t20WG8NTh7O9YLti\n35wyHUFRrse1qEy19sJqtjK7/2w6x3Xm8sGXU1pdqi8oCsChSC/yrNGhqF07iGionj4dFj6Rrmib\nV2ssymmKhEZzp73K2DTvyDMbVcRRBPiOPHMLigwcirrEdSHGHqMoM3IoApVDkd0Ph6KEBJfqSger\nxUqIOcRQINfcuDOA9pHtFdsHSw9qHIoOVySjjoqDY9udqBFxKAoOfvtC33nnndx3333ExsZy4YUX\nkpSUxNKlS7nuuuua3LeiooK5c+fy6quv0q9fP8477zxKSkq45ppreOWVVxRt6+vrueqqq3jiiSfo\n1KkTF154ISEhIdx555088MADQRvXgQMHeOihhwzrP/zwQy677DIKCgo499xzGTZsGO+++y7nnHMO\nJSXGXz6CizFjDm//4uJjW/EoCIIgtCziUCQIgiAIwuFijo2DG24wrFff6DYUFPmILFOvTG0O6kiA\n5hCoIEizfyu7XB3ueAVBEA4HERQJgiAIgv/4G3cGLrcQtUtLa0eeZeVnMe/Defzrq3+5xVDlNeV+\n7bv9kFIo9PVupTvRsA7DsIfYdR1lNOR5XJFKVYKiNuFtePrUp7nxpBuJtkWTEp1C77a9NV0EMlf0\nFXnWpYunfMwYOBSpdCjKMxlHvjWFwqHIi4wMzz3+JDznx1fkWWqMb0GRrkNRtbEYShl5pnIo0hMU\nGcSdNaIWbXlzOIKidpHtFNsHyw5SVFWkKAs08kzN4BQdQZGfC5uOZqKt2vMugqLA8eubZsOGDaxc\nuZKJEyfyyCOPYDKZcDqd3Hjjjbz33nt89dVXjB071nD/5cuX8/vvv3Pbbbdx3nnnAXD55Zdzzjnn\nsGjRIiZPnux2/fn444/5+uuvmTt3Ljc03Ni76qqruOSSS3jhhReYPn063bt3P+xx3XbbbVRU6H8B\nlpeXc9ddd5Gamsp7771HZKTL8u3EE0/k5ptv5oknnnCPTdDn/vtd36s//+yKAW2K7Gz47jvPdl2d\n3DwUBEFoaXJycpgyZQoLFixg9uzZirqysjIef/xxPv/8cw4ePEhERAQDBw5kwYIFmpXsa9asYc6c\nObrHaNOmDd95f8ELgiAIgiAcJTQVV6aOAWuOQ5E/1v++9geCYjF+uIKg1hb4SEyfIAhHEhEUCYIg\nCIL/VNYF5lCkdlZpTYei2vpaxr88nv0l+wEoqCxg2ZRllNdqBUVpMWnklOcoBFPq+Kyvdn+l2B6T\nPgaAhLAEzCazez5YVFVEdV01thCbp7GXQ1GJDQV64pOTO53MptxNTb9JA9RiF+/IswyPyS2hoTB0\nSgJlb0QQieu85Ib58aAXiK6CElWCTXzjx+ODDyAqCqKj4ZJLsNlCSE+HXbs8cWdwmA5F8V00C358\nCYp6e2u0bMp2upFniYnGg8MVK2d0vGA6FO0t3ktFrUfjYDFZAoq/06N7QndNWXZZtk7LYwu986J2\nsRKaxi+HohUrVgAwf/58900kk8nEtddei8lk+n/27ju+6QL/4/grSdOZLjrooOwNIoiyVA6UOwSO\nociBCoocIsqBIiCOcxzOn3ogOHGciIgKiHoqKqKoeIqUJahQ9oaWVbqbjvz+SJPmu5K0TaGFz/Px\n4GG/M9+EWprknfeHpUuXej1+8eLFxMfHM2pUZd21zWZj4sSJFBYW8umnnypuKygoiIkTJ7rXWa1W\n7r77bhwOB8uWLavxdX344Yf8+OOP9O7dW3f7559/TnZ2NmPHjnWHiQCuv/56mjVrxvLlyymTZ5Je\nBQfDAw/A0qWwbJnvP488ojxeHl4hhKhd+fn5TJ48mbw8bZ1rQUEBN910E2+++SZxcXGMGTOGXr16\n8f333zNy5Eg2bNig2D8jIwOAkSNH8o9//EPxZ9y4cWfl/gghhBDCmLS96HOFVv7W4W+625/5s7Il\nWS/YYzFZvDcUlft+cuvt+E4NO/kMPvmjthuKejbq6f66RWwLEiMSa3R7QghxLkmgSAghhPBfVUee\nWc3KhiKjEVHVlX44nV8O/aK7bWvWVneYCGDVnlWAduTZI396hH137+PTGz5VrFeHRb7b951i2RUo\nspgtJIQrwydZ+VmKZc+GIn8CRbd2Vn6gt1PDTpp9vIkINh555tlQBNC9h4l9NAXAARxKjzuvAAAg\nAElEQVQ3Lt5RSNMZ8OMeebZli7Mh+I474NprobzcPfYsYA1FsdqGIm8jzyIjobVrwrM/I898NBSp\nW6AU56tJoChSGSjadmKbYjkmNKbGz/ktZgtxYXE1OkddpB55Zgu2BaQF+kLj16tS69evJzY2ltat\nlXPTGzZsSNOmTUlPTzc89sCBA2RmZtK1a1csqmdj3bt3B3Afb7fb2bp1K23btiU6WpkO69SpE2Fh\nYYrbqs51ZWVl8fTTT3Pttddy+eWX616z6zjX9Xnq1q0b2dnZ7Ny5U7NNVF+Q6v9daSgSQojac/jw\nYcaMGcOvv/6qu33RokVs376dMWPG8P777zNz5kzmzJnDW2+9RWlpKY8++qhif1egaMaMGUyePFnx\n5+9//3ut3Ad5X1QIIYQQRi5LuYyfxv3EM/2eIT60geF+rqDO01c/zZ+b/5k2cW1Ii0qjXXw7Zl4+\nk37N++nur17nrWGouiPPuqd2p3tqd94Y/Ea1jler7YaiVwa9wuVpl3NpyqUsGLag5gEm+WVPCHEO\nmVU/7h0O/xrYhRBCiAtRlUaehdTuyLNHVj9Ctze60ePNHsz8Wjtp5nDOYcWyK2yiHnlmC3aWTUSH\nROvuD7Avex/7z+x3L4dYQujRqId72efYsyo2FF2cdLE7VGQ2mbmnxz2afbzRG3nmaihSB4q6dIH9\nOMee5QVDsZ/5i0Z6gSK9vNlnn8Hzz7vDPFVtKEqMSNQ0CgeZg2gS00TTPqNulVJ78MGKMHk9Gnm2\n5/QexXJNx5256I3Vq+/UI89k3Fn1+AwU2e12jh07RuPGjXW3p6amkpOTw6lTp3S3H6iYiah3fEJC\nAiEhIezbtw9wvsFZWlqqu6/FYiEpKcm9b3Wv61//+hdWq5X77rtP9ziAgwcPApCWlqbZ1qhRIwD2\n7t1reLyoOqvy9wfKyuTFQyGEqA0LFixg8ODBbN++nR49eujus3LlSkwmE3fffbdifbdu3ejWrRs7\nduwgM7PyCVBGRgapqalERtasVlMIIYQQoqa+u+U71t22jp5pPZlx+QyO3/Qrjkfh08XafV0BoWax\nzVg5ZiXb/7GdA1MP8MekP3i639OaT60ZBYq88auhSCeQtHb8WtaOX8tlqZf5PN4fNQ3o+LqfFydd\nzI/jfiT9tnSuaHxFjW4LZOSZEOLcMpm0oSJpKRJCCCH0VXXkmbqhKFAjz8od5cxZO8e9/MK6FzRh\npcO5ykCRq5lI3VDkCoaoQyCe4RR1O1GPRj0UIZeGEapAUZ4qUFTFhiKAN4e8ycYJG9k5eSe3dL5F\ndx8jeiPPXA1FniPPADp3xt1QlOVnOxEYBIoKtOsAuO8+Lg/bCCgDRTF+NBSZTCZNS1HTmKYEmYO8\nhsD03Hwz7NgBDZucVKzXbSjyY+SZkUCOPFOPVo8NDUygaEDLAYrlBmHGH9CqL9QjzyRQVD0+A0XZ\n2dkAhm8Sutbn5uZ6PT4qSv9/FJvN5j7Wn9sqLCyktLS0Wte1YsUKVq1axT//+U9iYoy/YU6fPk1w\ncDChoaGaba4RaHojYkT1qRuKSkrkxUMhhKgNCxcuJDU1lUWLFjF06FDdfUaOHMnUqVMVYz9dgoOD\nAefINICysjJ2796taQsUQgghhDgXNMGXikoJS7l236qGbAwbiryNPKtmQ1Gg1bih6CwHfKShSAhx\nrsnYMyGEEMI/VR55VksNRVn5WeTaK98TLiwt5FShsnRC3VCUZ8/D4XCQX2LQUBRqHE4xGnfmEuiG\nInA+T+qS3IXmsc11t3ujN/LMqKEoJgbyGjgbitTjzsw6z61d0nTKgNwNRSYTeL7vXlLC4MWjiCBP\n2VBkEChqENZA0bLULKaZYnuLWGcqShMCK/beUATQvDnYTcqcQ7TeddRk5Flw9QNFUSFRhAWFGW4P\nVEjmjsvuID688j7e2+vegJz3XJKGosDwWVJWWuqcXel6A1HNtb64WC+q59/xhYWFVb6tql7X6dOn\neeyxx+jbty8DBw7UPcbzmqt7f7dt26a7viqKiooCcp765NChUKDyh39JieOCewz0XIjfC3rkcXCS\nx0Eeg0D417/+Ra9evbBYLO7WP7URI0borj916hTr168nPDxc0dhXXFxMaGgoM2bMYO3ateTk5NC+\nfXvuuOMOevfuXSv3Q95jEkIIIfx3IbW9GAaKAjCqplojz/xpKDoLc3QkoCOEEFVjsUCJx/ubEigS\nQggh9FVp5FlwpKYJNlANRQfPHNSsO1FwQhHsUTcUOXBQUFKgbSgKNmgoKjZuKFIHihLDExXLWflZ\niuXqNBT5Y9EiePllyKnIPlmt0L8/tBqhHXmWSySxsRCrU3AT1Kop/ALHVRmZxmdgn0Ehjm5D0R33\nQHBrGDQIfvkFrr/evS3i8E6etD5KUklloCiyGCyYKUOZXHKNO3NRNxS1bOBMRdmCbZgwuZ+nF5QU\nUFpeqvm+8+RwOMgtUQaKqtVQVEsjz0wmE0m2JPZm609PCtTIs6iQKL4f+z2vb3idVnGtmHjpxICc\n91xSNxQFqs3pQuMzUORq6Skp0f+BbrfbAQgL00/GhYSEKPbTOz48PFyxr7fbMplMhIWFuQM9/l7X\n448/TnFxMY8++qju/p5CQ0OrfX/btWvn8/y+bNu2LSDnqU/U3x7l5eYL7jHQcyF+L+iRx8FJHoe6\n/Rhs2LDhXF+CX6688spqH/vss8+Sn5/PDTfc4A7YZmRkAPDFF19wySWXMHjwYDIzM1m1ahUTJkzg\n8ccf53qPJwlCCCGEELXJKFBkDkBmx2KyaNeZLQxqNYiNRzfqHnNx0sU+z9srrRcvr3/Zvez6NGwg\n1TRU1jGxY4CuxD+tGrQ6q7cnhBBq0lAkhBBC+KemI89Ky0sDch0HzhzQrDtRcEKxfCT3iGafPHue\nYUNRWFAYQeYg9zXay+wUlRZxOP8w+8/sd+8fYgmhR6MeinNoGorUI8+8NBSpQxD+2rkTxozRrt+8\nGUZH6488U7cTgTNg81uvX5kUB0WqJEHLU8aBojS9QNHIW8H1fLJRI5gwAV57zb19QvT77C5pDBVZ\nLRMQE2TjZKnyZI2jGyuWu6V245X1r7iXXY+/yWQiKiRKEf7KKc7xOr6ruKxY8X1oLYNQvW9LHw1F\ntTXyDCA5Mtk4UBTAkEz7hPbMuWaO7x3riTbRbRTLnRp2OkdXUr/5DBTZbDbMZrPhiC/XSDGj0WPR\n0c46OKPj8/LyiIuL82vf3NxcwsPDMZvNVbqu1atX89lnn/Hwww+TlJSku7+nqKgoiouLsdvtmqYi\n1+0Z3V9RPVbl7w+UlsqnJ4UQoi55+eWXWb58OampqUydOtW9vqioiMaNGzNixAgmTJjgXr9r1y5G\njhzJY489Rp8+fYg3+GW7uo1Tx4/HA8pPBFzI7VXS3uUkj4OTPA7yGLjI4yDqkzYn48mIU77Y27NR\nT34+9LPu/l2Cm7DJvl+zXtPEU/E7iKkWG4ru7nE3T//4tOaTtZemXMrVza72ed6RHUfy+JrH2X5i\nOxaThfeGv1fzi1WpaUNRt9Ru9G7Smx/2/wDAm0PeDMRlub008CUmrZgEwF9a/IUOiR0Cen4hhKgq\ndaCoNDDvdQohhBDnHfXIM88AjpruyLNANRTl6DcUeVI3FEFFoMiuDBS5mmZMJhPRIdGcLDzp3pZT\nnMO6rHWK/Xs06kFoUKhiXcMIHyPP/GwoKiqCFSsgPBz69YMgL+/sr1plvO37VRHgMUzANfKsSwvt\nvq9vfJ0F0U9CN+22FqfB6GZSdQJFmiDPnDmwYIG7aSL0xGE6RCvf648NjeVknvJk6oaiUR1H8c3e\nb/hq11cMaDWAv3X4m3tbdGi0IlB0puiM10DRmSLlWLQou7PjSMPXyLMgLyPPahoosiUbbpPWHWPt\nYtvxUO+HeG3Da3RO6syU7lPO9SXVSz4DRcHBwaSkpHDo0CHd7YcOHSI2NpaYGP2Zc02bNnXvp5aV\nlUVxcTHNmjlHXaWmpmK1WnX3LSsr49ixY7Ro0aLK1/XVV18BMGvWLGbNmqXZ9/777+f+++9n4cKF\ndO/enaZNm7Jx40YOHTpE8+bNNecF3NcsAkP9D6AEioQQou6YO3cuL7/8MjExMcyfP98dAAYYPnw4\nw4cP1xzTsmVLbrnlFl566SVWrVrFqFGjdM9d3capxETturraXnU21OX2rrNJHgcneRzkMXCpy49D\nfWn2O1/U9XFXt22A9qe7MbXfCsX6z278jB8P/MjSP5ayaMsixbb/Td/G0j+WcsvHtyjWa0I/ERFw\n772YlzxT4+s0ChQ1CGvA4XsO8+G2D8kuyqZ5bHOsZisDWg3w67EPMgeRfls6X+36ilZxrWrlE3N6\n114VJpOJr8d8zYqdK0i2JdO9UfcAXZnTnZfdSZekLmTlZzGwlfcx9UIIcTZIQ5EQQgjhH/XIs9jQ\nWI4XHNfsF2IJwWqxahqKSspqd+SZp8M5+oEio5Fn4AyCeAaKzhSdYVfOLsX+VzbWTgXQNBR5Booc\nDr8CRdu3w7XXOv8L8MAD8MQTmpty27TJeNvBPeGKQJFr5FkrnXLYz3d+bnieZqedH9hx6DzVjSuE\n+Hw4UfHwhQaFEh+uCuGEh0PHjrDRo+X3jDLQExsRB3nKDxClRSsDRaFBobxz7Tu616gO7+QU6ySd\nPHiGjwCiSy2ATijO18izWmwoSrIZF6YEauTZ+WpW31nM6qvNhwj/+QwUAXTt2pVPPvmEvXv3KoI0\nmZmZ7N+/nz59+hgem5KSQkpKChs2bKC8vByzufJFrHXrnAnOLl26OC8mKIiLL76YLVu2kJeXh81W\nWfO9ZcsWCgsL3ftW5br69etHamqq5to2b97Mjz/+yNVXX027du3c+3Tt2pXly5eTnp6uCRT98ssv\nREZGuoNNIjDUDUXyJF0IIc69srIyHn74YZYtW0ZcXBz/+c9/aKX3DMNA+/btAf1QsRBCCCGES789\nENxRG3gJsYQwpM0QjuQe0QSKwqxhDG833HegCOD//g9L/7awZlyNrtMoUASQEJHAxEsnVvvctmAb\nw9trQ9qBUtORZwDBlmCGtR0WgKvR1zOtZ62dWwghqkoCRUIIIYR/1CPPYsP0A0WuMWJBZuVb02er\noaiwpJDTRac1++Tacw1HnoGz7cZTTnEOZ+zKAEqjqEaa82oaijxHnp05AyWV91svUPTf/8Lo0VAx\nkAeAuXNh1izt7yku6kBRUJBHy6Jd2Z6Tb4V8IrjmGu15DuUYv56fmA+RxZATqt0WYYe718I/K4p6\nJ102iWBLsHbHLl2UgSJPoaHERmibgNQNRd5Ehyj/ztSBITV14Ci6PBjdQFHFxCUjrmYrPdJQJOoz\nvz6iNmyY8wWjOXPmUF5eDjjnJ86ePRuHw8HIkSO9Hj9kyBCOHTvGokWVLwDm5eXx6quvEhoaytCh\nQxW3ZbfbeeGFF9zrSkpKmDt3LgAjRoyo8nX169ePyZMna/5ceeWViu2NGjVyL0dERPDGG2+QnZ3t\nvr1ly5axb98+RowYoQhGiZqThiIhhKhb7HY7kyZNYtmyZaSmprJ48WLatm2r2W/Xrl389NNPOBza\nCtDi4mIAQkJCNNtqqo4XLQghhBCiCswOSNB5/ctiNniVtIJe+49RE4+5pf+haCN65w5EUOdsqC/X\nKYQQdYX6tUoJFAkhhBD69BqK9LhCOuqRZ0bj0arKV6DoSO4R3eN0G4qsyoYiT2eKz5BjVwZQ9MZp\neW0oOq4MXKkDRRvXRjF0qDJMBJCfDzt26N4NSkpgyxbluvvuq/w6rET5fDbfaqJxsyB66nyuQ6/J\nySUxH6Ls+tvCS+DBNbDx4CDWjV/Hc395Tn9Hj/IQjaQkYsK0U5HUDUXe6IXAvNGMPEMnBBUe7vzj\nRbjVeHtkSKTXY32RhiJxLvnVUNSrVy8GDhzIihUrGDlyJN27d2fTpk2sX7+e/v37KxqKXEGgyZMn\nu9fddtttfPnllzzxxBOkp6eTlpbGypUrOXjwIA899BANGlT+oL3uuuv48MMPWbBgATt27KBDhw6s\nWbOG7du3M27cONq0aVOt66qKmJgYZsyYwaOPPsqwYcMYMGAAmZmZfPHFF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OkAACAASURB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nzggQe4cOFCsY41TcU4z6eEIhERcUm5E4pmzjQ+jqhY0XiTbswY58clpcvO2Nvi/fehXTsY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\nIiIiIiIiIrX/B6k7dEtjkmp8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x293d20ab898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print ('Mean Values: train_loss = %s, train_acc = %s, train_precision = %s, train_recall = %s, cv_loss = %s, cv_acc = %s, cv_precision = %s, cv_recall = %s'%(\n",
    "    np.mean(tr_loss_arr), np.mean(tr_acc_arr), np.mean(tr_precision_arr), np.mean(tr_recall_arr), np.mean(cv_loss_arr), np.mean(cv_acc_arr), np.mean(cv_precision_arr), np.mean(cv_recall_arr)))\n",
    "\n",
    "oj = Plot(rows=1, columns=5, fig_size=(40,7))\n",
    "\n",
    "l_rate_df = pd.DataFrame(l_rate_arr, columns=['learning_rate'])\n",
    "oj.vizualize(data=l_rate_df, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Learning Rate Decay'})\n",
    "\n",
    "tr_loss_df = pd.DataFrame(tr_loss_arr, columns=['training_loss'])\n",
    "oj.vizualize(data=tr_loss_df, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Training Loss'})\n",
    "tr_data = pd.DataFrame(np.column_stack((tr_acc_arr, tr_precision_arr, tr_recall_arr)), columns=['accuracy', 'precision', 'recall'])\n",
    "oj.vizualize(data=tr_data, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Training Metrics'})\n",
    "\n",
    "cv_loss_df = pd.DataFrame(cv_loss_arr, columns=['crossvalidation_loss'])\n",
    "oj.vizualize(data=cv_loss_df, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Crossvalidation Loss'})\n",
    "cv_data = pd.DataFrame(np.column_stack((cv_acc_arr, cv_precision_arr, cv_recall_arr)), columns=['accuracy', 'precision', 'recall'])\n",
    "oj.vizualize(data=cv_data, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Cross-Validation Metrics'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n"
     ]
    }
   ],
   "source": [
    "which_data = 'cvalid'\n",
    "tsoj = Test(params=dict(pprocessor_inp_img_shape=[224,224,3],\n",
    "                        pprocessor_inp_crop_shape=[],\n",
    "                        model_inp_img_shape=[224, 224, 3]),\n",
    "                    device_type = 'gpu',\n",
    "                    which_net='resnet')\n",
    "fnl_tst_metric_stack = tsoj.run(use_checkpoint_for_run='sam_new',\n",
    "                                use_checkpoint_for_imageType='aerial_cropped',\n",
    "                                optional_batch_name=which_data,\n",
    "                                which_checkpoint='all',\n",
    "                                dump_stats=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checkpoint</th>\n",
       "      <th>test_batch</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>test_precsion</th>\n",
       "      <th>test_recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>epoch_13_batch_69</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>epoch_13_batch_70</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>epoch_14_batch_9</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>epoch_14_batch_19</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.389</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>epoch_14_batch_29</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>epoch_14_batch_39</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.398</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>epoch_14_batch_49</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>epoch_14_batch_59</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>epoch_14_batch_69</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.378</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>epoch_14_batch_70</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          checkpoint test_batch  test_loss  test_acc  test_precsion  \\\n",
       "0  epoch_13_batch_69     cvalid      0.391     0.884          0.872   \n",
       "1  epoch_13_batch_70     cvalid      0.394     0.886          0.875   \n",
       "2   epoch_14_batch_9     cvalid      0.401     0.886          0.880   \n",
       "3  epoch_14_batch_19     cvalid      0.389     0.888          0.886   \n",
       "4  epoch_14_batch_29     cvalid      0.391     0.886          0.880   \n",
       "5  epoch_14_batch_39     cvalid      0.398     0.890          0.881   \n",
       "6  epoch_14_batch_49     cvalid      0.382     0.890          0.885   \n",
       "7  epoch_14_batch_59     cvalid      0.364     0.888          0.879   \n",
       "8  epoch_14_batch_69     cvalid      0.378     0.888          0.878   \n",
       "9  epoch_14_batch_70     cvalid      0.382     0.888          0.879   \n",
       "\n",
       "   test_recall  \n",
       "0        0.901  \n",
       "1        0.901  \n",
       "2        0.894  \n",
       "3        0.892  \n",
       "4        0.894  \n",
       "5        0.902  \n",
       "6        0.897  \n",
       "7        0.901  \n",
       "8        0.902  \n",
       "9        0.901  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_outcomes_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'pred_outcomes.csv')\n",
    "prediction_metrics_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'pred_metrics.csv')\n",
    "prediction_outcomes = pd.read_csv(prediction_outcomes_path)\n",
    "prediction_metrics = pd.read_csv(prediction_metrics_path)\n",
    "prediction_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n"
     ]
    }
   ],
   "source": [
    "which_data = 'test'\n",
    "tsoj = Test(params=dict(pprocessor_inp_img_shape=[224,224,3],\n",
    "                        pprocessor_inp_crop_shape=[],\n",
    "                        model_inp_img_shape=[224, 224, 3]),\n",
    "                    device_type = 'gpu',\n",
    "                    which_net='resnet')\n",
    "fnl_tst_metric_stack = tsoj.run(use_checkpoint_for_run='sam_new',\n",
    "                                use_checkpoint_for_imageType='aerial_cropped',\n",
    "                                optional_batch_name=which_data,\n",
    "                                which_checkpoint='all',\n",
    "                                dump_stats=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checkpoint</th>\n",
       "      <th>test_batch</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>test_precsion</th>\n",
       "      <th>test_recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>epoch_13_batch_69</td>\n",
       "      <td>test</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>epoch_13_batch_70</td>\n",
       "      <td>test</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>epoch_14_batch_9</td>\n",
       "      <td>test</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>epoch_14_batch_19</td>\n",
       "      <td>test</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>epoch_14_batch_29</td>\n",
       "      <td>test</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>epoch_14_batch_39</td>\n",
       "      <td>test</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>epoch_14_batch_49</td>\n",
       "      <td>test</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>epoch_14_batch_59</td>\n",
       "      <td>test</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>epoch_14_batch_69</td>\n",
       "      <td>test</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>epoch_14_batch_70</td>\n",
       "      <td>test</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          checkpoint test_batch  test_loss  test_acc  test_precsion  \\\n",
       "0  epoch_13_batch_69       test      0.309     0.906          0.892   \n",
       "1  epoch_13_batch_70       test      0.310     0.907          0.894   \n",
       "2   epoch_14_batch_9       test      0.312     0.908          0.898   \n",
       "3  epoch_14_batch_19       test      0.306     0.911          0.907   \n",
       "4  epoch_14_batch_29       test      0.306     0.912          0.904   \n",
       "5  epoch_14_batch_39       test      0.310     0.909          0.899   \n",
       "6  epoch_14_batch_49       test      0.305     0.910          0.902   \n",
       "7  epoch_14_batch_59       test      0.301     0.909          0.897   \n",
       "8  epoch_14_batch_69       test      0.308     0.909          0.897   \n",
       "9  epoch_14_batch_70       test      0.310     0.909          0.898   \n",
       "\n",
       "   test_recall  \n",
       "0        0.923  \n",
       "1        0.923  \n",
       "2        0.920  \n",
       "3        0.916  \n",
       "4        0.921  \n",
       "5        0.921  \n",
       "6        0.920  \n",
       "7        0.925  \n",
       "8        0.925  \n",
       "9        0.923  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_outcomes_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'pred_outcomes.csv')\n",
    "prediction_metrics_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'pred_metrics.csv')\n",
    "prediction_outcomes = pd.read_csv(prediction_outcomes_path)\n",
    "prediction_metrics = pd.read_csv(prediction_metrics_path)\n",
    "prediction_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VARIATION 4:  Different variation: \n",
    "### The input images are 224x224x3 where image is at the center 128x128x3 asn zero padded to make 224x224x3. Here we remove the padding and feed the Resnet with 128x128x3 images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dumping/Retreiving Checkpoints to/from %s C:\\Users\\newline\\Documents\\ImageClassification\\data\\checkpoints\\sam_new\\aerial_cropped\\resnet\n",
      "Learning Rate: Initial:  0.0005\n",
      "Checkpoints not found, Hence starting at batch 0 and epoch 0........\n",
      "----------2122.923999786377 seconds------------\n"
     ]
    }
   ],
   "source": [
    "max_batches = 70\n",
    "start_time = time.time()\n",
    "if train:\n",
    "    tr_obj = Train(dict(pprocessor_inp_img_shape=[224,224,3],\n",
    "                        pprocessor_inp_crop_shape=[128,128,3],\n",
    "                        model_inp_img_shape=[128,128,3],\n",
    "                        learning_rate=0.0005,\n",
    "                        use_checkpoint=True,\n",
    "                        save_checkpoint=True,\n",
    "                        write_tensorboard_summary=False\n",
    "                        ),\n",
    "              device_type='gpu',\n",
    "              which_net='resnet')\n",
    "    (tr_loss_arr, tr_acc_arr, tr_precision_arr, tr_recall_arr, \n",
    "     cv_loss_arr, cv_acc_arr, cv_precision_arr, cv_recall_arr,\n",
    "     l_rate_arr)  = tr_obj.run(num_epochs=15, num_batches=max_batches, cv_num_batches=1, get_stats_at=10)# + 1)\n",
    "    \n",
    "print('----------%s seconds------------'%(str(time.time() - start_time)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Values: train_loss = 0.784114, train_acc = 0.846013, train_precision = 0.857114713977, train_recall = 0.834537893247, cv_loss = 0.627970277766, cv_acc = 0.834322278202, cv_precision = 0.843314854052, cv_recall = 0.82414194299\n"
     ]
    },
    {
     "data": {
      "image/png": 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evXu4efMm/vjjDwCq3Q8ULl92/YV9/vnnqFKlikLM1NXVRd26deXSZNucqXI/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\nNy5eIFR6uromGc8Q5c38+fNp0KABW7duZc2aNbi6ujJ+/Hhef/11o6Wrly1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CCCFygp3q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QBAUlISNBoNAECj0WDZsmXQaDQ4fPiwLR/eU8vJyYGnpycSEhKMjkdGRsLPzw8///wz9Ho9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\n0Q0hRrIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x239fe40a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print ('Mean Values: train_loss = %s, train_acc = %s, train_precision = %s, train_recall = %s, cv_loss = %s, cv_acc = %s, cv_precision = %s, cv_recall = %s'%(\n",
    "    np.mean(tr_loss_arr), np.mean(tr_acc_arr), np.mean(tr_precision_arr), np.mean(tr_recall_arr), np.mean(cv_loss_arr), np.mean(cv_acc_arr), np.mean(cv_precision_arr), np.mean(cv_recall_arr)))\n",
    "\n",
    "oj = Plot(rows=1, columns=5, fig_size=(40,7))\n",
    "\n",
    "l_rate_df = pd.DataFrame(l_rate_arr, columns=['learning_rate'])\n",
    "oj.vizualize(data=l_rate_df, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Learning Rate Decay'})\n",
    "\n",
    "tr_loss_df = pd.DataFrame(tr_loss_arr, columns=['training_loss'])\n",
    "oj.vizualize(data=tr_loss_df, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Training Loss'})\n",
    "tr_data = pd.DataFrame(np.column_stack((tr_acc_arr, tr_precision_arr, tr_recall_arr)), columns=['accuracy', 'precision', 'recall'])\n",
    "oj.vizualize(data=tr_data, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Training Metrics'})\n",
    "\n",
    "cv_loss_df = pd.DataFrame(cv_loss_arr, columns=['crossvalidation_loss'])\n",
    "oj.vizualize(data=cv_loss_df, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Crossvalidation Loss'})\n",
    "cv_data = pd.DataFrame(np.column_stack((cv_acc_arr, cv_precision_arr, cv_recall_arr)), columns=['accuracy', 'precision', 'recall'])\n",
    "oj.vizualize(data=cv_data, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Cross-Validation Metrics'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n"
     ]
    }
   ],
   "source": [
    "which_data = 'cvalid'\n",
    "tsoj = Test(params=dict(pprocessor_inp_img_shape=[224,224,3],\n",
    "                        pprocessor_inp_crop_shape=[128,128,3],\n",
    "                        model_inp_img_shape=[128, 128, 3]),\n",
    "                    device_type = 'gpu',\n",
    "                    which_net='resnet')\n",
    "fnl_tst_metric_stack = tsoj.run(use_checkpoint_for_run='sam_new',\n",
    "                                use_checkpoint_for_imageType='aerial_cropped',\n",
    "                                optional_batch_name=which_data,\n",
    "                                which_checkpoint='all',\n",
    "                                dump_stats=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checkpoint</th>\n",
       "      <th>test_batch</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>test_precsion</th>\n",
       "      <th>test_recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>epoch_13_batch_69</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>epoch_13_batch_70</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>epoch_14_batch_9</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>epoch_14_batch_19</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>epoch_14_batch_29</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>epoch_14_batch_39</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.869</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>epoch_14_batch_49</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>epoch_14_batch_59</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>epoch_14_batch_69</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>epoch_14_batch_70</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          checkpoint test_batch  test_loss  test_acc  test_precsion  \\\n",
       "0  epoch_13_batch_69     cvalid      0.393     0.872          0.884   \n",
       "1  epoch_13_batch_70     cvalid      0.397     0.872          0.887   \n",
       "2   epoch_14_batch_9     cvalid      0.422     0.870          0.885   \n",
       "3  epoch_14_batch_19     cvalid      0.421     0.867          0.895   \n",
       "4  epoch_14_batch_29     cvalid      0.412     0.865          0.897   \n",
       "5  epoch_14_batch_39     cvalid      0.404     0.869          0.891   \n",
       "6  epoch_14_batch_49     cvalid      0.406     0.870          0.893   \n",
       "7  epoch_14_batch_59     cvalid      0.394     0.872          0.892   \n",
       "8  epoch_14_batch_69     cvalid      0.396     0.868          0.882   \n",
       "9  epoch_14_batch_70     cvalid      0.397     0.870          0.881   \n",
       "\n",
       "   test_recall  \n",
       "0        0.855  \n",
       "1        0.852  \n",
       "2        0.850  \n",
       "3        0.832  \n",
       "4        0.824  \n",
       "5        0.841  \n",
       "6        0.841  \n",
       "7        0.846  \n",
       "8        0.850  \n",
       "9        0.855  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_outcomes_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'pred_outcomes.csv')\n",
    "prediction_metrics_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'pred_metrics.csv')\n",
    "prediction_outcomes = pd.read_csv(prediction_outcomes_path)\n",
    "prediction_metrics = pd.read_csv(prediction_metrics_path)\n",
    "prediction_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n"
     ]
    }
   ],
   "source": [
    "which_data = 'test'\n",
    "tsoj = Test(params=dict(pprocessor_inp_img_shape=[224,224,3],\n",
    "                        pprocessor_inp_crop_shape=[128,128,3],\n",
    "                        model_inp_img_shape=[128, 128, 3]),\n",
    "                    device_type = 'gpu',\n",
    "                    which_net='resnet')\n",
    "fnl_tst_metric_stack = tsoj.run(use_checkpoint_for_run='sam_new',\n",
    "                                use_checkpoint_for_imageType='aerial_cropped',\n",
    "                                optional_batch_name=which_data,\n",
    "                                which_checkpoint='all',\n",
    "                                dump_stats=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checkpoint</th>\n",
       "      <th>test_batch</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>test_precsion</th>\n",
       "      <th>test_recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>epoch_13_batch_69</td>\n",
       "      <td>test</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>epoch_13_batch_70</td>\n",
       "      <td>test</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>epoch_14_batch_9</td>\n",
       "      <td>test</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>epoch_14_batch_19</td>\n",
       "      <td>test</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>epoch_14_batch_29</td>\n",
       "      <td>test</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>epoch_14_batch_39</td>\n",
       "      <td>test</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>epoch_14_batch_49</td>\n",
       "      <td>test</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>epoch_14_batch_59</td>\n",
       "      <td>test</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>epoch_14_batch_69</td>\n",
       "      <td>test</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>epoch_14_batch_70</td>\n",
       "      <td>test</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          checkpoint test_batch  test_loss  test_acc  test_precsion  \\\n",
       "0  epoch_13_batch_69       test      0.337     0.899          0.906   \n",
       "1  epoch_13_batch_70       test      0.338     0.895          0.904   \n",
       "2   epoch_14_batch_9       test      0.348     0.897          0.910   \n",
       "3  epoch_14_batch_19       test      0.353     0.894          0.920   \n",
       "4  epoch_14_batch_29       test      0.350     0.892          0.918   \n",
       "5  epoch_14_batch_39       test      0.347     0.899          0.913   \n",
       "6  epoch_14_batch_49       test      0.348     0.898          0.915   \n",
       "7  epoch_14_batch_59       test      0.341     0.897          0.910   \n",
       "8  epoch_14_batch_69       test      0.341     0.900          0.907   \n",
       "9  epoch_14_batch_70       test      0.341     0.900          0.906   \n",
       "\n",
       "   test_recall  \n",
       "0        0.890  \n",
       "1        0.885  \n",
       "2        0.881  \n",
       "3        0.864  \n",
       "4        0.860  \n",
       "5        0.881  \n",
       "6        0.878  \n",
       "7        0.881  \n",
       "8        0.890  \n",
       "9        0.892  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_outcomes_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'pred_outcomes.csv')\n",
    "prediction_metrics_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'pred_metrics.csv')\n",
    "prediction_outcomes = pd.read_csv(prediction_outcomes_path)\n",
    "prediction_metrics = pd.read_csv(prediction_metrics_path)\n",
    "prediction_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Variation 5: Variation 3 looked overall good, lets try with the batch where teh image is rotated by 90 degree it the height is greater than width.\n",
    "\n",
    "This may not help the model, but should be evaluated as a case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dumping/Retreiving Checkpoints to/from %s C:\\Users\\newline\\Documents\\ImageClassification\\data\\checkpoints\\sam_new\\aerial_cropped\\resnet\n",
      "Learning Rate: Initial:  0.0005\n",
      "Checkpoints not found, Hence starting at batch 0 and epoch 0........\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------4911.467549085617 seconds------------\n"
     ]
    }
   ],
   "source": [
    "max_batches = 70\n",
    "start_time = time.time()\n",
    "if train:\n",
    "    tr_obj = Train(dict(pprocessor_inp_img_shape=[224,224,3],\n",
    "                        pprocessor_inp_crop_shape=[],\n",
    "                        model_inp_img_shape=[224,224,3],\n",
    "                        learning_rate=0.0005,\n",
    "                        use_checkpoint=True,\n",
    "                        save_checkpoint=True,\n",
    "                        write_tensorboard_summary=False\n",
    "                        ),\n",
    "              device_type='gpu',\n",
    "              which_net='resnet')\n",
    "    (tr_loss_arr, tr_acc_arr, tr_precision_arr, tr_recall_arr, \n",
    "     cv_loss_arr, cv_acc_arr, cv_precision_arr, cv_recall_arr,\n",
    "     l_rate_arr)  = tr_obj.run(num_epochs=20, num_batches=max_batches, cv_num_batches=1, get_stats_at=10)# + 1)\n",
    "    \n",
    "print('----------%s seconds------------'%(str(time.time() - start_time)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Values: train_loss = 0.848651, train_acc = 0.858204, train_precision = 0.866375977295, train_recall = 0.850119084836, cv_loss = 0.537055360153, cv_acc = 0.849061957002, cv_precision = 0.85792766124, cv_recall = 0.838536212914\n"
     ]
    },
    {
     "data": {
      "image/png": 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PP/4AsvZd4MX2def/omrVqlGiRAmDfGlqaso777yjV6ZbViwr3yWyE5uxsbE6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TnMldbnaYxinhEj4NFHYf9+qUDou++sOnQpodmc8RzPr3JjKgv9C3q9Cn+8aBy7VtnKMtvLg9/p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NHDlSx48f17/+9S998sknWr16da2RZAAABLO63uC1p7KyUqNHj9aBAwesbuwCAAD/\nI0EIAFBfCxYs0NatW9WvXz8NHjxYWVlZWr58uXJycrR8+XKHxyUmJmrq1Kl2tx08eFDvv/++rr32\nWsu6iooKzZgxQ3v27NFNN92koUOH6v3339eCBQuUl5enOXPmeP3aXDYI2aQHGWSwNCrZGzFGgxAA\nBBYahAAvMhik3/++6uVMx45SXl7NMg1CABram2++qTNnziglJUWtWrWqtb2yslKJiYmaOXOmhg8f\nblm/d+9e3XPPPZo3b57++c9/KiIiotax2dnZdarp4sWOkmK88l6BoKSkJKjrl7iGQNEYrkFqHNfB\nNTRudb3Ba8+6det04MABH1UKAADqgwQhAEB9ZGVlaevWrRo6dKiWLVsmg8Egs9msuXPnaufOnUpP\nT9egQYPsHtuhQwdNmzat1voffvhBt99+u1q2bKklS5ZY1u/evVt79uxRamqqpRloxowZmjBhgtau\nXasRI0aoR48e3r1Ag0Fmg0GS2f72kHKrxVBjzVfNjBgDgMBHgxDgB+E2fw/RIASgoe3atUuSNGrU\nKLvbJ02apEmTJtVa379/f91+++3auXOn9u7dazdFqFevXnWqKSam9rq6vlcgyM7ODur6Ja4hUDSG\na5Aax3VwDc7t27fPJ+/bEOpzg9fWiRMntGzZMh9XDAAA6so2Qchsrnr5YkoLAKDx2bx5syRp6tSp\nluQcg8GgWbNmadeuXdq+fbvbnx+rPfHEEzp9+rSefPJJtWvXzupcoaGhVvdpw8LCNHPmTI0ePVo7\nduzQI4884oWrslYZ4qRByCZBKMRY03lrb8RYWIhnibwAAN8yut4FgLfRIATAnwoLC7V3714lJibq\nJz/5icfHX3nllZKkvEuj0LyAm7EAAH9xdoPXYDBo+/btbr2P2WzWI488onbt2ikpKclX5QIAgHow\nGGp//mTMGADAXZmZmYqLi1P37t2t1sfHxyspKUkZGRkevd+XX36pnTt3qm/fvhoxYoRlfVlZmT77\n7DP17NlTLVq0sDomOTlZUVFRHp/LXZVGJzdqjdYJQiGGmgYhEoQAIPDRIAT4QZhNwzQNQgAa0kcf\nfaTy8nL9/Oc/d7jPF1984fADZmlpqSTZHS8GAEAw8tYN3hdffFF79+7VggULFBkZ6YtSLQwOHuYE\nAACu2aYI0SAEAHBHWVmZTp8+rU6dOtndnpiYqB9++EEFBQVuv+fTTz8ts9msBx54wPLAiiSdOnVK\nJpPJ7rlCQkLUvn17HTt2zONrcEel7S9Kq5PbNAi5SBCiQQgAAgsNQoAfkCAEwJ8+/fRTSdK1117r\ncJ8pU6Zo3Lhxdj/MVo+Q6dOnj28KBACgAXnrBu8333yjp59+Wr/+9a91/fXX+6JUAADgJSEh1ssV\nFf6pAwAQXM6fPy9Jat68ud3t1esvXLjg1vvl5OToo48+Uu/evWt9jnTnXMXFxTKZTHa310ddE4Si\nwmgQAoBAF+rvAoCmiAYhAP6UnZ0tSU7Hi/3iF7/Q2rVrtWTJEi1YsMDy9Mqbb76p9957T9ddd12t\nlIX6YsQYAMAfPLnB26pVK4fvM3/+fEVHR2vOnDneLxIAAHgVCUIAgLqobsYJt/2S50fV66sT2F3Z\nsGGDJCk1NbVe5woNrf11b/U94LqodHaf1iZBSOaacxX/UFxr9zOnz9SrFk+UlJQ02Lm8gXp9i3p9\ni3p9y5f10iAE+IHt33Nr1kjvvmt/35AQ6X//t+oFAN5w4sQJRUZGKj4+3uE+kydP1vvvv69t27Yp\nJydH/fr1U25urt577z21bdtWCxcubMCKAQDwHW/c4N25c6fef/99LV++XLGxsR6dv6FvTgTizZBg\nu0njSGO4Dq4hcDSG6+AaEMhIEAIA1EX1KOny8nK728t+fBo8Kqp2ko69fXfv3q0WLVpo6NChtbZH\nRES4PJfBYHB4rl69ermswRFziJMBNDYJQuGh4ZZzdcjvIB2y3j2pY1K9avFEdnZ2g53LG6jXt6jX\nt6jXt+pTb/UUEEdoEAL8wPa7h3XrXB/z6qvSiBE+KQdAE3P+/Hm1b9/e6T6xsbF66aWXtGLFCr3z\nzjvauHGjWrZsqV//+teaPn262rVr5/W6SBACAPhDfW/wnj17VgsXLtSQIUPs3tR1paFvTgTizZBg\nu0njSGO4Dq4hcDSG6+AaHHN1wxa+R4IQAKAuYmJiZDQaVVhYaHd79WgxRwm1l/rPf/6joqIi/epX\nv1JYWFit7S1atJAkp+eKjo6W0faXmhc4HzFmPdIs1FjzVTMjxgAg8NEgBPiBG38b1rJjBw1CALwj\nKyvLrf1iY2P1+9//Xr///e99XBEAAP5T3xu8CxYsUEVFhebPn++zGu2hrxYAgLojQQgAUBfh4eFK\nSEhQXl6e3e15eXmKi4tTy5YtXb7Xnj17JEk///nP7W5PTExUWFiY3XNVVFTo9OnT6tKliwfVu89p\ng5DNiLEQY80v1eiw6Fq70yAEAIHF+22lAFz69a89P+bH7yUAAAAAeFF9b/D+4x//0IULF3TjjTeq\nR48eltehQ1W56j169NDgwYN9Vj8AAPAcCUIAgLrq16+fzpw5o9zcXKv1+fn5On78uK666iq33ufT\nTz+VwWDQtddea3d7aGio+vbtqy+//LLWAy0HDx5UcXGxrr766rpdhAvOE4RsGoQMNQ1CUaEkCAFA\noCNBCPCDu+6S2rWTPvrI8RNKR45ImzfXLJeWNkxtAOAvjBgDAPhLv379tGvXLuXm5qpz586W9dU3\neAcOHOjw2KlTp9pd/9JLL+ns2bOaOnWqW/Hy3hQdFq0BiQOUfiy9Qc8LAECwIEEIAFBXI0aM0K5d\nu7RkyRItXbpURqNRZrNZixcvltlsVkpKisv3MJlM+u9//6vLL79csbGxTs+VmZmptLQ0zZs3T1LV\neOxly5ZJkkaNGuWdi7JR1wQhRowBQOCjQQjwk5tvrno58q9/WTcIlZX5viYAAACgKarPDd5p06bZ\nXf/uu+/q7NmzDrf7Uv/E/vrX+H8p4a8J+qbwmwY/PwAAgY4EIQBAXd1www0aNmyYdu/erZSUFA0Y\nMED79+9XZmamhg4davWASVpamqTanxvz8/NVWlqqTp06OT3XyJEj9fLLL2vdunU6fPiwevfurQ8+\n+ECHDh1SamqqevTo4fXrkyRzHROE7I0YCwsJ81pdAID6o0EICFDhNk3VNAgBaOxIEAIA+Is3bvAG\nEoP4pQoAgDO2CUI0CAEAPLFo0SJ17dpVr776qtavX6+EhARNnz5dEydOlOGSm5wrVqyQVPvz4/nz\n5yVJ7du3d3qekJAQrVmzRmlpaXrzzTe1b98+derUSfPnz9ddd93l5auqUensRq3RZF2jkRFjABBM\naBACApRtgxAjxgAAAADfqe8N3kBioOsWAACnbBOEGDEGAPBEWFiYpkyZoilTpjjdLycnx+763r17\nO9xmKyYmRvPmzbOMGGsIHo0YMzBiDACCCQ1CQICKiLBeJkEIAAAA8J363uC1tWvXLm+U5ZDB7GQb\nCUIAADhFghAAAI457Zu1GTEWaqz5qtneiDEahAAgsBhd7wLAHxgxBqCpIewAAADvIEEIAADnSBAC\nAMC+c+ek7y96b8RYmDHMa7UBAOqPBCEgQNk2COXnS3/+s/Ttt63Vrl3t/ePipJEjJRcjawEgqJjN\nNA4BAOApEoQAAHCOBCEAAOxLT5dMThJrGTEGAMGNBiEgQNmOGDt3Tnr0UUmy0x30o7/8RcrOlqJr\npzgCQFCiQQgAAAAA4G0kCAEAYF9RkVTh7IaszYixSxOEGDEGAIGPEWNAgGrWzPNjTpyQ/vMf79cC\nAA3B7OzJFAAA4DZGjAEA4BwJQgAA2BcVJVU6+0jpLEHIzogxGoQAILDQIAQEqLg4adAgz4/74Qfv\n1wIA/kLTEAAA9jm7X8uIMQAAnCNBCAAA+6KinCcI/eKXJAgBQDBjxBgQwHbulDZvlvLyatadPXtW\nbdq0sSy/+mrVWLFqZWUNWCAAAACAgEOCEAAAzpEgBACAfZGRzhOE2saXS8drli9NEAoLCau1v711\nAAD/oUEICGCxsdLvfme9Ljv7jHr1qmkQOn7cukGotLSBigOABkCCEAAAniNBCAAA50gQAgDAPpcj\nxowmq8VQo/VXzVOum6KVGSslSalXpdbaDgDwL34qA0Eu3CadkQQhAI0JDUIAAHiOBCEAAJwjQQgA\nAPsiIyWzk4dOKg2OR4xJUtqtafp5l5+rorJC/9fz/3xSIwCg7mgQAoJcRIT1MglCAIIVzUAAAHhH\ndYKQWfxyBQDAHhKEAACwLypKqnDyzInZtkHIYN0gZDAYNLzHcF+UBgDwAqPrXQAEMtsEIRqEADQm\nNA0BAGCfwcnvSBKEAABwLizMeplEbgAAqkRGOh8x5ipBCAAQ2GgQAoKcbYIQNzQANCY0CAEA4Lnq\nBCGDk1h4AACasmbNrJcvXvRPHQAABJqoKBcNQnKeIAQACGw0CAFBzrZB6JFHqv6Ac/Tq3FnassU/\ntQIAAADwveoEIZ7kBADAvubNrZcvXPBPHQAABJqoKMnsdMSYyWqZz50AEFxoEAKCnO2IsYoKqaTE\n8evYMSk1VSoq8ku5AOAREoQAAPBcdXIQT3ICAGBfTIz1cmGhf+oAACDQuBoxVmGTIBRqDPVxRQAA\nb6JBCAhyXbt6fkxpqXT8uPdrAYD6oBkIAADvIEEIAADnSBACAMC+sDBGjAFAY0aDEBDkfvUrKSVF\nCvWwSbu01Df1AIA30TQEAIB9Tu7XkiAEAIALtg1CJAgBAFDFYHA+YqxSjBgDgGBG7hsQ5CIjpZde\nksrLJZPJ8X433ijt21ezTIMQgGBAgxAAAJ4jQQgAAOdsR4yRIAQAQA2zk3gJ2xFjPJgCAMGFBiGg\nkQgLq3o5EhlpvUyDEAAAANA4kSAEAIBzJAgBAOCY2eD4qU1TJQ1CABDMGDEGNBEREdbLNAgBCAYk\nCAEAUHckCAEAYB8JQgAAOOYsQajctkGIz50AEFRoEAKaCBqEAAQ6moEAAPAOy4gxnuQEAMAu2wQh\nGoQAALiEwfGm8goShAAgmDFiDGgiaBACEIxoGgIAwHOWEWM8yQkACFBnzpxRWlqa9uzZo++++04t\nWrTQ9ddfrxkzZqhjx44+P79tghAjxgAAqOFJglCoka+aASCYkCAENBGRkdbL48dLrVvbf7VpI916\nq3T6tH9qBYBqNAgBAGCfwcnvSBKEAACB7MyZMxo1apS2bt2qLl26aOzYsfrJT36iN954Q7/+9a91\n7Ngxn9dAghAAAI6ZnXzgNFWarJZ5MAUAggttnUATYZsgdPFi1cuRt96SHn1UWrPGt3UBAAAA8C4S\nhAAAgSwtLU3ffPON5s6dq3vvvdey/rXXXtNDDz2kJ598Us8884xPayBBCAAAx8xGxw1CjBgDgODm\ndoKQyWTSunXrNGzYMCUnJ+vmm2/WypUrVV5e7vpgSefPn9eCBQs0ePBg9e3bVyNHjtTu3bvt7ltc\nXKylS5dqyJAhSk5O1rBhw7R582aZ7cQIeFJXYWGhFi1apCFDhqhPnz4aMGCAJk+erOzsbHf/NQBB\nq3Nnz4/54gvv1wEAniBBCAAAz5EgBAAIZO+++65atWql8ePHW60fPny4OnXqpA8//FCVlZU+rYEE\nIQAAHDMbHG+zHTHGgykAEFzcbhBasGCBFi5cqJYtW2rcuHGKj4/X8uXL9eCDD7o8tqioSKmpqdqy\nZYv69u2rMWPG6IcfftADDzygTZs2We1bUVGhGTNmaPXq1ercubPGjRun0NBQLViwQIsWLapzXUVF\nRRozZoxeeOEFtW7dWmPHjtUNN9ygPXv2KCUlRfv27XP3XwUQlH73O+l//sezY0pLfVMLANhjrxmI\nBiEAADxHghAAIFBVVFTo/vvv19SpU2U01r41HR4ervLycrcfSq0rEoQAAHDCWYMQCUIAENTcGjGW\nlZWlrVu3aujQoVq2bJkMBoPMZrPmzp2rnTt3Kj09XYMGDXJ4/IYNG/TFF19o/vz5GjNmjCRp8uTJ\nGj16tJ5++mndeuutat26tSRp9+7d2rNnj1JTUzVnzhxJ0owZMzRhwgStXbtWI0aMUI8ePTyua9Om\nTTp06JDGjh2rRx991FLb3r17dc899+j//b//p9dff70O/wqB4NCunfTxx9L330smk/19srOlG2+s\nWaZBCAAAAAg+1QlCRoPbzwQBANAgQkJCaiUHVTty5IiOHj2qTp06KSIiwqd1kCAEAIBjZoOTEWMk\nCAFAUHPrbuHmzZslSVOnTrXcaDQYDJo1a5YMBoO2b9/u9PgtW7aoTZs2Gj16tGVdTEyMJk2apOLi\nYqvGnM2bNys0NFSTJk2yrAsLC9PMmTNlNpu1Y8eOOtX19ttvy2AwaObMmVa19e/fX/3799fhw4eV\nn5/vzr8OIKi1aCG1bm3/FR9vvW9JiX9qBIBqJAgBAGCfkwc6axKEeJITABAkKisr9dhjj6myslJ3\n3nmnz88XFSVdGmBUWir5OLQIAICgYTY6vilrqrR+Ap3PnQAQXNxKEMrMzFRcXJy6d+9utT4+Pl5J\nSUnKyMhweOyJEyeUn5+voUOHKiTE+pfEgAEDJEkZGRm65557VFZWps8++0w9e/ZUixYtrPZNTk5W\nVFSU1bk8qSslJUVDhgxRjG1+rKqiayXp4sWLzv41AI2e7cNZJAgBjdOSJUv0zDPP2N02bNgwLVmy\nxLK8c+dOrVu3TseOHVNsbKxuvfVWTZ8+Xc2aNWuocgEAgIeqH6C5s/edeufoO5b1A5MG+qkiAAAc\nM5vNmj9/vj7++GP16dPHYcJQdnZ2vc9VUlJieZ/o6O4qLKy5X71vX45atKis9zm86dJ6gwH1+hb1\n+hb1+law1dvUOU0QshkxFmp066tmAECAcPlTu6ysTKdPn1bfvn3tbk9MTFRubq4KCgrUqlWrWttP\nnDghSerUqVOtbW3btlVERISOHTsmSTp16pRMJpPdfUNCQtS+fXvLvp7WNWrUKLv7FRQUKDMzU9HR\n0erQoYPdfYCmggYhoGnIyclReHi47rvvvlrbunXrZvnnZ599VosXL1aPHj30m9/8RocPH9a6det0\n4MABbdiwwdJg60skCAEA4LnqBKGxyWO1KmOV9p/er1ZRrbT454v9XBkAANZMJpP+8Ic/6JVXXlHH\njh21atUqh581e/XqVe/zZWdnW96nRQupsLBmW2JiD3XsWO9TeNV6x6AbAAAgAElEQVSl9QYD6vUt\n6vUt6vWt+tS7b98+L1dTfyaTSZs2bdK2bduUl5entm3bauTIkbrvvvsUFhbm8vjS0lI9//zzev31\n1/X1118rPj5egwcP1tSpUxUbG2u17+zZs60msVxq4sSJmj17tleu6VKMGAOAxstlg9D58+clSc1t\nBzP/qHr9hQsX7DYIVR9v+wutWkxMjC78OOTZnXPl5ubKZDLVu65qTz31lC5evKi77rqrQb7oBAJZ\nZKT1cmGhtHWr4/1jYqT//d/ac9sBBLbDhw+ra9eumjZtmsN9vv76ay1fvlxXX321Nm7caPlgu2zZ\nMq1atUrbtm3Tb37zG6/WZa8ZiAYhAAA8V50gFBEaoY9/+7EO5B9QUssktWvWzs+VAQBQo7i4WDNm\nzNCePXuUlJSktWvXKj4+vsHObxs0/+MtagAAXFqwYIG2bt2qfv36afDgwcrKytLy5cuVk5Oj5cuX\nOz22vLxcEyZM0N69e9W/f3/dfPPN+uyzz7R+/Xp9+umn2rRpk9X3lTk5OWrTpo1Gjx5d67369evn\n9WuTJLMYMQYAjZXLBiGTqeoHvaPmmer1pQ6iRtw5vri42ONz1bcuSVq1apVeeeUVJSYm6oEHHnC4\nH9BU2CYIlZRIdv7mtNKtm5SRUfXUFYDAV1hYqFOnTql///5O99u6datMJpPuv/9+q6deJk2apA0b\nNmj79u1ebxACAADeUZ0gJFU1CfVPdP57HwCAhvb9999r4sSJOnDggK688kqtWbNGrVu3btAabB94\nuzRNCAAAR7KysrR161YNHTpUy5Ytk8FgkNls1ty5c7Vz506lp6dr0KBBDo/fsGGD9u7dq9/+9rd6\n+OGHLesXLFigzZs3a/fu3RoxYoSkqmai3NxcDRw40OnDnt5mNjjeZjtijAQhAAguLhuEIn+MFCkv\nL7e7vaysTJIUFRVld3vEjx0H1fvZOz46OtpqX2fnMhgMioqKsjT+1LWu6gSEli1b6tlnn1ULJ90N\n9Z2L2lhmqzaG6+AanDObJaOxpyornfz1Z+O//5XWrs3T0KGePWbF/xeBoTFcg9R4rqMhHDp0SJLU\no0cPp/tlZGRIkq677jqr9REREbrqqqv04Ycf6sKFCw6T/LyFBCEAAOxzkvhuSRACACAQlZaW6v77\n79eBAwfUv39/rV69WjG2cT4NgAQhAEBdbN68WZI0depUy2cvg8GgWbNmadeuXdq+fbvTBqHNmzfb\nDS5ITU1VUVGR5btSSTpy5IjKy8td3sv1No9GjJEgBABBxWWDUExMjIxGowodPEJRPR7M0ReE1Y03\njo4vLCy0PB3iat8LFy4oOjpaRqOxznVVVFRo/vz52rFjh1q3bq2//e1v6tatm933qFbfOa7BNgvW\nkcZwHVyDazffLL3zjmfHREV1kKcl8f9FYGgM1yD57joCcb51feXk5EiSzp07p3vvvVeff/65JOn6\n66/XzJkzdcUVV0iSTpw4oTZt2ti9SZuYmChJys3NVXJycgNVDgAA3HVpghAAAIFm8eLF2r9/v66+\n+mo9//zzlgdUGxoJQgCAusjMzFRcXJy6d+9utT4+Pl5JSUmWBy/t+eqrr3Tq1CmNHTvWKrVdkjp0\n6KAnn3zSal31vdwGbxDyZMQYCUIAEFRcNgiFh4crISFBeXl5drfn5eUpLi5OLVu2tLs9KSnJsp+t\nb7/9VqWlpercubOkqi8cw8LC7O5bUVGh06dPq0uXLnWuq6ysTNOnT1d6eroSExP1t7/9zVIfgCov\nvig9/bR09Kjjffbvr0oOquZkkh+AAFP9ofKFF17Q4MGDNWrUKOXk5Ogf//iH/v3vf2vjxo3q1auX\nzp8/rw4dOth9j+rmW0dNut5EghAAAAAANB5nzpyxJC9cccUVev755+3ud99991klKPgCCUIAAE+V\nlZXp9OnT6tu3r93tiYmJys3NVUFBgVq1alVr++HDhyVJ3bp10549e7R69WplZ2erefPmuu222zR9\n+nTL1BWp5l7usWPHNHr0aOXk5CgyMlIDBw7UzJkzFR8f74OrlMyGSrf3JUEIAIKLywYhSerXr592\n7dql3NxcSzOPJOXn5+v48eMaOHCgw2MTEhKUkJCgffv2qbKyUkaj0bJt7969kqSrr766qpjQUPXt\n21cHDx5UYWGhVWrBwYMHVVxcbNnX07rMZrMefPBBpaenq1u3bnrhhRd89osTCGatW0sLFzrf5+GH\npaeeqlkuKfFtTQC8JyQkRImJiVq4cKEGDBhgWf/aa6/poYce0u9//3u9+uqrMplMCg8Pt/se1etL\nHXQH1nXcW2FhR0nWd2hzcg7rzJmKOr2fvzWG0XdcQ2BoDNcgNY7r4BoQLEgQAgAEqgMHDqi8vGo0\nycsvv+xwv/Hjx/u8QYgEIQCAp86fPy/J8VSV6vUXLlyw2yD07bffSpLS09OVnp6um266SaNHj9be\nvXu1du1aHTx4UOvXr7ekC1U3CK1cuVJDhgzRVVddpQMHDuiVV17RRx99pG3btql9+/Zev85KJwlC\ntkKNbn3VDAAIEG791B4xYoR27dqlJUuWaOnSpTIajTKbzVq8eLHMZrNSUlKcHj98+HA988wz2rRp\nk8aNGyepKnXgmWeeUWRkpP7v//7P6lyZmZlKS0vTvHnzJEnl5eVatmyZJGnUqFF1qmvjxo16++23\ndfnll2vDhg12fzEDcI9t8jMNQkDw+OMf/2h3/fDhw7Vt2zZlZGTo6NGjioyMtNy0tVVWViZJioqK\nsru9ruPemjWrva579+5q27ZOb+d3jWGEH9cQGBrDNUiN4zq4Buca42jOYGUw0CAEAAhMt9xyi+XL\nTn+z/W6XBCEAgCsmU9V4rbo+WFlcXCypqkHoscce05133impaorKrFmz9NZbb2nLli0aP368JCky\nMlJJSUlasWKFunXrZnmf1atXa+nSpXr88ce1YsUKu+eqz8NBzkaM2co/nR8wDyIF20NR1Otb1Otb\n1OtbvqzXrQahG264QcOGDdPu3buVkpKiAQMGaP/+/crMzNTQoUOtknrS0tIkSdOmTbOsmzhxot56\n6y39+c9/VkZGhjp27Ki3335bJ0+e1B/+8AerZp2RI0fq5Zdf1rp163T48GH17t1bH3zwgQ4dOqTU\n1FSrOZvu1lVWVqZVq1ZJqprTWR1ja2v06NFqG6zfQgINyPYBLkaMAY3DlVdeqYyMDOXl5Sk2NlYX\nHNwdrV7v6EkZb2LEGAAA9jlrASJBCAAA12xHjJEgBABwJfLHp6fr+mBl9ZSVK6+80tIcJFWlvj/8\n8MN666239Oabb1oahFauXGn3fe6//37t2LFD6enpunjxoprZefKyPg8HmQ3u35TtmNgxYB6mCrYH\nu6jXt6jXt6jXt+pTr6uHON3OfVu0aJG6du2qV199VevXr1dCQoKmT5+uiRMnWj2dWN2pemmDUExM\njDZv3qzFixcrPT1dH3zwga644gotXrxYv/zlL63OExISojVr1igtLU1vvvmm9u3bp06dOmn+/Pm6\n66676lTXkSNHdO7cOUnS22+/rbffftvuNd5yyy00CAFusE0QOnlSOnDA8f6dOklxcb6tCYBrJpNJ\nX375pcxms9052SU/xoFFREQoKSlJGRkZKikpsXzwrXbq1CkZjUZdfvnlPq+ZBiEAADxHghAAAK6R\nIAQA8FRMTIyMRqMKHXSVunqwMubH7tQrr7yy1rbExETFxsbq5MmTLuswGo3q2bOn8vLydPr0aXXp\n0sXdS3CLJwlCIcYQr54bAOBbbjcIhYWFacqUKZoyZYrT/RxFxLZp00ZPPPGEW+eKiYnRvHnzLCPG\n6ltXr169Aia6FmgMbBuENm6sejliNEqrVkn33+/bugA4V1lZqbvvvlvR0dH6+OOPFRJS8+HNbDZr\n//79Cg0NVa9evdSvXz998sknyszM1M9+9jPLfqWlpfr000/VtWtXywdaAADQ8JzdriVBCAAA12w/\n0tIgBABwJTw8XAkJCcrLy7O7PS8vT3FxcWrZsqXd7UlJSZIcJxCZTCbFxsZKqhpHlpOTo8jISPXs\n2bPWvpc+7Oltlap0e98QAw1CABBMjP4uAEDwsW0QcqWyUpo/3ze1AHBfeHi4Bg0apO+//17PPfec\n1ba//e1vOnz4sG677TbFxsbq9ttvV0hIiFasWGGJxpWkZ555RoWFhUpJSfF6ffbSgkgQAgDAcyQI\nAQDgmm24AyPGAADu6Nevn86cOaPc3Fyr9fn5+Tp+/Liuuuoqh8cmJycrPDxcGRkZqqiosNp25MgR\nFRUVqUePHpKks2fPKiUlRQ899FCt9ykuLtaXX36pVq1aKTEx0QtXVcNsNpMgBACNGA1CADzWr5/n\nx3z7rWQyeb8WAJ6ZM2eO2rZtq6VLl+ree+/VX/7yF40dO1aLFi1Sly5dNHfuXEnSFVdcodTUVO3f\nv18jRozQU089pfvvv1+rVq3SNddcYzUjGwAANDyzkx4gEoQAAHCNBCEAQF2MGDFCkrRkyRJVVlYl\n7ZjNZi1evFhms9npg5XNmzfXrbfeqq+//trqAc7y8nI99dRTkqQ77rhDktSxY0f17t1bhw8f1muv\nvWbZ12w2669//asKCgp01113ef0BEU+agyQShAAg2Lg9YgwAql11lfTCC9Lf/ub85slnn1mnf5SU\n1L75AqBhdejQQS+//LKWLVum999/XxkZGWrXrp1SU1M1efJkq/nYDz74oC677DJt2bJFGzZsUNu2\nbXXPPfdo6tSpCg8Pb5B6SRACAMBzJAgBAOAaCUIAgLq44YYbNGzYMO3evVspKSkaMGCA9u/fr8zM\nTA0dOlQDBw607JuWliZJmjZtmmXdnDlz9Omnn2rp0qXau3evevbsqY8//ljZ2dkaNmyYbr75Zsu+\nCxYs0NixY/Xwww/r7bffVmJiojIzM/X555/ruuuu06RJk7x+fWYPb8iGGvmqGQCCCT+1AdRJamrV\ny5nWraWCgpplGoSAwBAfH68nnnjC5X4Gg0FjxozRmDFjGqAq+2gQAgDAcyQIAQDgGglCAIC6WrRo\nkbp27apXX31V69evV0JCgqZPn66JEydaPbCxYsUKSdYNQq1bt9bWrVu1cuVKvfPOO8rMzFRiYqIe\neugh3XvvvVbn6dOnj3bs2KHly5frP//5j9577z0lJiZazuWLhzgrzZUe7c+IMQAILjQIAfCZyEjr\n5ZIS/9QBAAAANDbOemhJEAIAwDUShAAAdRUWFqYpU6ZoypQpTvfLycmxuz4uLk6PPvqoHn30UZfn\n6tKli5YtW1anOuvC4wYhRowBQFAx+rsAAI0XDUIAPGEvLYgEIQAAPEeCEAAArpEgBABAbSQIAUDj\nRoIQAJ+xbRBas0a67LKqf87Pj1N8fM22sDDpppuk3r0brj4AAAAgWJmd9ACRIAQAgGskCAEAUBsJ\nQgDQuNEgBMBnbBuE/vKXS5fa19o/LExKT5d++lOflgUgiJAgBACA50gQAgDANdsEocLCqs+g9NkC\nAJoys9OB1rWRIAQAwYURYwB8xvZJLFfKy6WXXvJNLQCCEw1CAAB4jgQhAABcCwmRoqJqls1mqajI\nf/UAABAISBACgMaNBiEAPjN8uOfHnD/v/ToAAACAxoYeWgAA6s/24bYLF/xTBwAAgcLTBqFQI8Nq\nACCY8FMbgM888IB02WXS3r1Spc3flAUFBWrVqpW++kravbtmfXFxw9YIIHDYSwsiQQgAAPvMTkKC\nGDEGAIB7YmKkb7+tWS4s9F8tAAAEAo8ThBgxBgBBhQYhAD5jMEh33VX1spWdna9evVrp9ddpEAIA\nAAC8iRFjAAC4hwQhAACsMWIMABo3RowB8KtLZ71LUkmJf+oAEJhIEAIAwHMkCAEA4J6YGOtlEoQA\nAE0dCUIA0LiRIATAr2wbhC5ckAoKHO/frJkUEeHbmgAEDhqEAACwz9mvSBKEAABwDwlCAABYM3t4\nQ5YEIQAILjQIAfCryEjr5YwMqXVrx/tHREgPPSQ99phv6wIAAACCFQlCAAC4hwQhAACskSAEAI0b\nI8YA+JVtgpArpaXS449LJ0/6ph4A/mPv4RQShAAADcVkMmndunUaNmyYkpOTdfPNN2vlypUqLy93\n6/jPP/9ckydP1oABA9SnTx/dcsstevrpp1VUVOSTes1OeoBIEAIAwD22CUI//OCfOgAACBQeNwiR\nIAQAQYUGIQB+1bmz501CkpSb6/1aAAQeGoQAAA1lwYIFWrhwoVq2bKlx48YpPj5ey5cv14MPPujy\n2P/85z8aPXq03n//ff3sZz/T2LFj1bJlSz3//PMaN26cSktLvV6v0xFjJAgBAOCWNm2sl7Oy/FMH\nAACBwtMGoVAjw2oAIJjwUxuAX0VFSRs3Sn/4g3T6tOP9LlyQTKaa5eJi39cGAACApiErK0tbt27V\n0KFDtWzZMhkMBpnNZs2dO1c7d+5Uenq6Bg0a5PD4P/3pTzKbzXrxxReVnJwsSTKbzZo/f762bdum\nLVu26N57722oyyFBCAAANw0cKD31VM3yG29IK1dK/CoFADRVjBgDgMaNBCEAfnfHHdKXX0oFBY5f\nw4ZZH0ODENA0kCAEAGgImzdvliRNnTrV0lxjMBg0a9YsGQwGbd++3eGxX331lY4ePaqbb77Z0hxU\nffyUKVMkSe+//74Pq6+NBCEAANwzaJB1svXJk9Jnn/mvHgAA/M3sNK+2NkaMAUBwoUEIQFCwHUNG\ngxAAAAC8JTMzU3FxcerevbvV+vj4eCUlJSkjI8PhsTExMZo9e7buuOOOWtvCw8MlSUVFRd4tWJLZ\nSQ8QCUIAALgnKkoaMsR63Rtv+KcWAAACAQlCANC40SAEICjYNgidOSOdPev4ReoIEHzs/XfLf8sA\nAF8rKyvT6dOn1alTJ7vbExMT9cMPP6igoMDu9vbt22vixIm66aabam175513JEldu3b1XsFuIEEI\nAAD33Xab9fLrr/unDgAAAoHHDUIkCAFAUKFBCEBQsG0QmjFDatvW8atjR2nvXv/UCsB7aBACAPja\n+fPnJUnNmze3u716/YULFzx637Nnz2r58uWSpJSUlHpUaB8JQgAAeIftWPtPPpG+/dY/tQAA4G8k\nCAFA4xbq7wIAwB22DUKunDolPfKI9OND2wAAAIBdJpNJUs04MFvV60tLS91+zwsXLui+++7T2bNn\nNXbsWCUnJzvcNzs724NqL+Gkifa7s9/V/X39oKSkJKjqdaQxXAfXEDgaw3VwDQgWiYnSNddIWVlV\ny2az9Oab0vjx/q0LAAB/8LRBKNTIV80AEEz4qQ0gKPTu7fkxubnerwNAwyJBCADga5GRkZKk8vJy\nu9vLysokSVFudqwXFBRowoQJ+uKLLzRo0CDNnTvX6f69evXyoFr3tG3b1ifv6yvZ2dlBVa8jjeE6\nuIbA0Riug2twbN++fV5/T9TPbbfVNAhJ0j//SYMQAKBpYsQYADRujBgDEBR+8xvpd7+TEhKk1q3t\nv+LirI8pLvZPrQAAAAgeMTExMhqNKiwstLu9erSYoxFklzpx4oRSUlL0xRdfaPDgwVq+fLlCQ33z\nXI6zEWMAAMAzgwdbL9PDBQBoqswePrHJiDEACC4kCAEICuHh0qpVVS9HCgqqGoWqFRX5vi4A3mPv\nsycJQgAAXwsPD1dCQoLy8vLsbs/Ly1NcXJxatmzp9H2ys7P129/+Vt99951+9atf6fHHH/dZc5Ar\nBtE9BACAJ666ynr50CHp4kWpWTP/1AMAgL+QIAQAjRsJQgAaDdupDzQIAcGPBiEAQEPo16+fzpw5\no1ybGbX5+fk6fvy4rrL91tDG8ePHlZqaqu+++0733nuvFi5c6PPmIGcJQgYDDUIAAHiiRQupa9ea\n5cpK6eBB/9UDAIC/eNwgRIIQAAQVEoQANBqRkdbLZWXS0qWSo+9HoqKkX/xC6tTJ97UBAAAgcI0Y\nMUK7du3SkiVLtHTpUhmNRpnNZi1evFhms1kpKSkOj62srNSsWbNUUFCgcePGae7cuQ1Ss7MeWhKE\nAADw3DXXSF99VbOclSVdf73/6gEAwB88bRAyGsiiAIBgQoMQgEbDYKhq+ikurln3wAPOj4mLk/bv\nly6/3Le1AagbEoQAAA3hhhtu0LBhw7R7926lpKRowIAB2r9/vzIzMzV06FANHDjQsm9aWpokadq0\naZKkd999V59//rnCw8MVHR1t2X6pNm3a6K677mqQa5FIEAIAoC769ZO2batZzsryXy0AAPiLJw1C\njBcDgOBDgxCARiU21rpByJVz56TXX5emTvVdTQDqjgYhAEBDWbRokbp27apXX31V69evV0JCgqZP\nn66JEydaNdysWLFCUk2DUEZGhiSprKxMzzzzjN337tmzZ8M2CJEgBACAx665xnqZBiEAQFNkdppX\nay3UyNfMABBs+MkNoFG5/XZpzRrPjjl3zje1APAMzUAAAH8KCwvTlClTNGXKFKf75eTkWC0/8sgj\neuSRR3xZml1OR4yRIAQAgMeuvtp6+fPPpZKS2iPtAQBozDxKEDKSIAQAwYYGIQCNyooVVTd0bL63\nsZKZKf373zXLRUW+rwtA3WzZIj3+uL+rAAAguJAgBACA51q3rhpBf/x41bLJVNUkdO21/q0LAICG\nxIgxAGjcaBAC0KhEREiTJzvfZ+lSGoSAYPHnP9MgBACAPWYnPUA8xQkAQN1cc01Ng5BUNWaMBiEA\nQFNCghAANG40CAFocqKjrZe//db65o+ttm1rHwMAAAAEKqPB6O8SAAAIStdcI736as1yVpb/agEA\nBC6TyaRNmzZp27ZtysvLU9u2bTVy5Ejdd999CgsLc3l8aWmpnn/+eb3++uv6+uuvFR8fr8GDB2vq\n1KmKjY212re4uFjPPvus/v73vys/P18dOnTQmDFjdPfdd/tkvDQJQgDQuHHXEECTY9vs89JLUlKS\n41dcnLRyZQMXCQAAANQRDUIAANTNNddYL9MgBACwZ8GCBVq4cKFatmypcePGKT4+XsuXL9eDDz7o\n8tjy8nJNmDBBaWlpateuncaOHavLLrtM69ev14QJE1RWVmbZt6KiQjNmzNDq1avVuXNnjRs3TqGh\noVqwYIEWLVrkk2szm81u70uCEAAEH+4aAmhyPE0DKiuT5s6t+l8AvuPBZ08AAJo8pyPGeIoTAIA6\n6dvXevnQIT6rAgCsZWVlaevWrRo6dKg2b96s2bNna/PmzRoxYoT+8Y9/KD093enxGzZs0N69e/Xb\n3/5WGzdu1MMPP6yNGzdqzJgxOnDggHbv3m3Zd/fu3dqzZ49SU1P13HPPafbs2Xr55Zf1P//zP1q7\ndq1ycnK8fn0kCAFA40aDEIAmJznZ82MKC6WCAu/XAvjDmTNnNH/+fN10003q06ePfvrTn2r27Nk6\nefKk1X7bt29Xjx497L7uvPNOP1UPAAAkydl3lSQIAQBQNwkJUvPmNcsXLkhff+2/egAAgWfz5s2S\npKlTp1pGfBkMBs2aNUsGg0Hbt293eXxiYqIeeOABq/Wpqan61a9+pYiICKt9Q0NDNWnSJMu6sLAw\nzZw5U2azWTt27PDWZVl40iAUagz1+vkBAL7FT24ATU7XrtLzz0tpadL58473O3VKqqioWb540fe1\nAb525swZjRo1St98841++tOfatiwYcrNzdUbb7yhDz74QFu3blVSUpIkWZ5AmThxotUHU0lq3759\ng9VsNks+GKcNAECjRYMQAAB1YzBIPXtKGRk16w4dkhIT/VcTACCwZGZmKi4uTt27d7daHx8fr6Sk\nJGVc+kvExldffaVTp05p7NixCgsLs9rWoUMHPfnkk5blsrIyffbZZ+rZs6datGhhtW9ycrKioqKc\nnquuPEoQYsQYAAQdGoQANEkTJlS9nOnTR/rii5rloiLf1gQ0hLS0NH3zzTeaO3eu7r33Xsv61157\nTQ899JCefPJJPfPMM5KqGoRatmyp2bNn+6tcSZLJJNl8XgYAAE5wkxYAgLqz1yB0883+qwcAEDjK\nysp0+vRp9bWdSfmjxMRE5ebmqqCgQK1ataq1/fDhw5Kkbt26ac+ePVq9erWys7PVvHlz3XbbbZo+\nfbqio6MlSadOnZLJZFKnTp1qvU9ISIjat2+vY8eOee/ifsSIMQBo3HisEAAc+PHvcIuvvpKOHat5\nnToVZvnnkyeZSY/g8O6776pVq1YaP3681frhw4erU6dO+vDDD1VZWfUh8PDhw7WehPEHk8nfFQAA\nEFxIEAIAoO569rRePnTIP3UAAALP+R9HEjS/dB7lJarXX7hwwe72b7/9VpKUnp6u++67T7GxsRo9\nerTatm2rtWvXasKECSovL3f7XMXFxTJ5+eYpCUIA0LiRIAQADjRrZr08cqTtHl2tlpKSpLfflrp1\n82VVQN1VVFTo/vvvV2hoqIzG2l8choeHq7y8XOXl5Tp37pzOnz+vHj16+KFSa+XlUlSUv6sAACCw\nmJ2M36RBCACAurNtEMrO9k8dAIDAU92MEx4ebnd79frS0lK724uLiyVVNQg99thjuvPOOyVV3bed\nNWuW3nrrLW3ZskXjx4/36FyhobW/7s2u4y+w498cd3tfU7mpzufxhZKSkoCqxxXq9S3q9S3q9S1f\n1kuDEAA4YJsg5MqxY9KSJdKqVT4pB6i3kJCQWslB1Y4cOaKjR4+qU6dOioiIUE5OjiSpvLxcU6ZM\nUVZWlkpKSnTNNddoxowZSk5O9np9jlK4SBACAKA2Z+GVxLwDAFB3vXpZL5MgBACoFhkZKUmWlB9b\nZWVlkqQoB087Vj+0eeWVV1qag6Sq+7YPP/yw3nrrLb355psaP368IiIiXJ7LYDA4PFcv219obsoN\nzXV73+jI6Dqfxxeys7MDqh5XqNe3qNe3qNe36lPvvn37nG7nsUIAcOCqqzw/xgcjfwGfq6ys1GOP\nPabKykrLB9PqBqGXXnpJJSUlGjlypH7605/q448/1t133+FbOv0AACAASURBVK0PPvigwepz8BkY\nAAA4QIIQAAB116WLFHJJr+2pU5KDSTEAgCYmJiZGRqNRhYWFdrdXjxZzNBYsJiZGUlWDkK3ExETF\nxsbq5MmTkqQWLVpIktNzRUdH202Krw/bEWMhTiaO8XAKAAQfEoQAwIF586puAn3wgVRRUXt7eXmZ\nKirClZ9fs+7ixYarD/AGs9ms+fPn6+OPP1afPn0sCUOVlZVKTEzUzJkzNXz4cMv+e/fu1T333KN5\n8+bpn//8p+VJlkvVNfawqKiTpGa11mdn/1cFBcEXIxRskZX2cA2BoTFcg9Q4roNrQLCgQQgAEEzy\n8/M1bNgwTZs2Tffcc4+/y1F4eFWT0OHDNetycqRrr/VfTQCAwBAeHq6EhATl5eXZ3Z6Xl6e4uDi1\nbNnS7vakpCRJjlOBTCaTYmNjJVU1DIWFhdk9V0VFhU6fPq0uXbrU4Sqcs20QCq2UKhx8xAw18jUz\nAAQbfnIDgAMxMdK6dY63Z2cf0YULvTRgQM06GoQQTEwmk/7whz/olVdeUceOHbVq1SrL7OpJkyZp\n0qRJtY7p37+/br/9du3cuVN79+7VjTfeWGufusYeOhrr17lzN11+eZ3e0q+CLbLSHq4hMDSGa5Aa\nx3VwDc65iq+Fd5kNjrfRIAQACBYXL17UtGnTHKYj+EvPntYNQunpUliYlJwsGZz8DgYANH79+vXT\nrl27lJubq86dO1vW5+fn6/jx4xo4cKDDY5OTkxUeHq6MjAxVVFQo5JLIuiNHjqioqEjXX3+9JCk0\nNFR9+/bVwYMHVVhYaEkfkqSDBw+quLhYV199tdevz6MEISMJQgAQbLhrCAD10Mwm7CQvT0pLc/za\nuFH67jv/1Apcqri4WJMnT9Yrr7yipKQkbdiwQfHx8W4dWx2B6+hJGW9jxBgAAJ7hJi0AIBicOnVK\nY8eO1YEDB/xdSi09e1ovP/xw1Sj6kSMls9k/NQEAAsOIESMkSUuWLFFlZVX3jNls1uLFi2U2m5WS\nkuLw2ObNm+vWW2/V119/reeee86yvry8XE899ZQk6Y477rA6V1lZmdLS0qz2XbZsmSRp1KhR3ruw\nH5ltftGFOPm9x4gxAAg+JAgBQD3YNgjl50vTpzs/pksX6eBBx2kpgK99//33mjhxog4cOKArr7xS\na9asUevWra32+eKLL1RUVKTrrruu1vGlpaWSZHe8WH04uslqCr7pYgAA+Jyz7yZJEML/Z+/u42o+\n/z+Av87pPke6FSWTTIUvpoUxNxlri6+bNrfD2OZmSmZzfzfaz81oQu7ta5rbmCbGzGbuNyPMbYok\nQpEk0X3n98flnDp1zqnU6fb1fDw+D+d87s77c051XNfnfb0vIqLKbtOmTVixYgXS09PRvn17nD59\nuqJDUqGp4OKePcCZM1CpJk1ERDVLhw4d4OXlhQMHDmDgwIFo164dLly4gPDwcHh6eqpUEFIk9owf\nP165burUqfj333+xbNkynDlzBi4uLvj7778REREBLy8vvPPOO8p9vb29sXv3bmzatAlRUVFo3rw5\nTpw4gevXr+OTTz6Bs7NzmV+fuinGNOHgFCKiqoe9hkREpVCnTsmPiY4GTp0q+1iIiiMjIwNjxozB\nxYsX0bZtW2zevLlQchAA+Pj4YPjw4UhKSiq0TTGFTIsWLXQeL8AKQkREROpwijEiIqrKfvzxR9jb\n22PLli3o06dPRYdTSMEKQvkdP15+cRARUeW0ePFi+Pn54cmTJwgODkZiYiL8/PwQEBAASb65KFeu\nXImVK1eqHGtlZYWQkBAMGzYMt27dwpYtW5Ceno7JkycjICBAZV89PT18//33GDFiBKKjo/Hjjz8i\nJycHc+bMweTJk3VybSVKEGIFISKiKocVhIiISsHCAnj7beDkyZIdpybngqhcLF26FBcuXMAbb7yB\nDRs2wNjYWO1+7733Hn744QcEBgbC399f2bD99ddfcfToUbi7u6Np06blEjMrCBEREZUMO2mJiKiy\nmzdvHjp06AA9PT3cvn27osMpRFtBhpMnAR3dkyUioirCwMAAPj4+8PHx0bpfZGSk2vUWFhaYNWsW\nZs2aVeRryWQyTJ8+HdOnT3+lWEuqYIKQHisIERFVK0wQIiIqpbAwYMMGIC5O8z5//glcu5b3PDVV\n93ERFfTo0SNs3boVANC4cWNs2LBB7X6jR4/GuHHjcPz4cezcuRORkZFwc3NDTEwMjh49ChsbGyxc\nuLDc4maCEBERUcmwghAREVV2nTp1qugQtLKwAN54A7hwofC2U6fEFNkSLdX8iIiIqqpCCUJa5rfm\n4BQioqqHCUJERKVkaQlMnap9n/HjVROEUlK0Jz3o868z6cDFixeR9XK+rt27d2vc7+OPP4aZmRl2\n7NiBlStX4vfff8fmzZthbm6ODz/8EH5+fqhbt255hY2cnHJ7KSIioipDSx8tE4SIiKhaioiIKPU5\n0tPTi32er782wpIldZGZKcHZs7WU6x8/Bg4ciEbjxpmljqcoJYm3MmC8usV4dYvx6lZVi7cmK8kU\nY/pS3sggIqpq+JebiKgcyGSqz7/8UiyaNG4MbN4MdOig27ioZunevbvGsrbqmJmZYcaMGZgxY4YO\no8oj13CnkwlCREREJcMy70REVB25urqW+hwRERHFPo+rK9Cnj3j83nvAb7/lbXvwwAk9e5Y6nCKV\nJN7KgPHqFuPVLcarW6WJ99y5c2UcDWkjLzAchVOMERFVLxxWSERUDgomCBXl1i3giy90EwtRVZOr\npRFKRERUU8m1TGvCCkJERERl6+23VZ+fPFkxcRAREelaSSoIcYoxIqKqh72GRETlwMWl5MdERZV9\nHERVESsIERERFcYpxoiIiMoPE4SIiKimKFGCECsIERFVOew1JCIqB717A6NGAbVrA3p6mpf8nj3T\nPOUSUU3CBCEiIqKS4ShOIiKistW2LaCvn/c8OhqIj6+4eIiIiHSlYIKQnpZ7FGx7EhFVPUwQIiIq\nBwYGwPr1QEoKkJ2teTEyyjsmNxdIT6+4mIkqC04xRkREVDKsIERERFS2TE0BNzfVdadOVUwsRERE\numRqYKryvHaG5n1ZQYiIqOphryERUSUik6k+d3YGGjVSvzg7A1OmiMQioupAU8UsVhAiIiIqGSYI\nERERlb0OHVSfnz9fMXEQERHpUvfG3WFlYgUA0M8BBlzVvC8rCBERVT36Re9CRETlRSYDHj/Oe373\nrvb9lywBWrYEhg7VbVxEFYkJQkRERIXJJZq3cRQnERFR2WvdWvX5pUsVEwcREZEuWZta4+LYizi4\nbhLaLN0BqZYpxvSlvM1MRFTVcFghEVEl0qhRyY/5998yD4OoUuEUY0RERIVp6aOFkZ6Rlq1ERESV\ni7e3NyIjIzFixIiKDkWrli1VnzNBiIiIqit7M3t8atwBb8RDa4IQKwgREVU9TBAiIqpE/P0BC4uS\nHfPsmW5iIaosWEGIiIioZFrXa130TkRERFQirq6Afr5CCXfuAMnJFRcPERGRTklE2VotxWtZvZaI\nqApi7Tciokqkc2fg4UMgLk7zPnv2ABMn5j1nghBVd0wQIiIiKhkjfVYQIiIiKmtGRoCLC3DlSt66\ny5eBTp0qLiYiIiKdeZkgxApCRETVCysIERFVMvr6YqoxTYujo+r+27cDUqnmxc4O2LSpfK+B6FU4\nOalfzynGiIiICpNrG8ZJREREOlFwmrGLFysmDiIiIp2TilvIWhOEWEGIiKjKYYIQEVEVU7t24XVy\nueblwQNg7FggNbX8YyUqiWnTRMn2glhBiIiIiIiIiCqDgglCly5VTBxEREQ69zJBSMIKQkRE1QoT\nhIiIqhhn55Ifk5EB3L5d5qEQlSlXV+DaNaBnz6cq62/cqKCAiIiIKjEtfbRERESkI0wQIiKiGqM4\nU4yxghARUZXDBCEioirG3h5YsgSwsCjZcc+e6SYeorImLfC/kzlzgMzMiomFiIiIiIjUO3JEJPlL\nJOqXZs1cNW7Ttri6inMTVUYFE4T++Uf00ezcKQZnERERVRvFSBDSl+qXUzBEVNMU1d4s6aJon7K9\nCfAvNxFRFTRpEvDVV2IKMU3efRc4fDjv+d69QEyM6j737pnhwgXx5ejmBjRtqpt4iUpCqqbVuWcP\nMGBABQRDRERERERqjRmjm2qf16+Lc0dFqd+empqKmTNn4tmzZ3jy5An69++P5s2bY/78+ZDL5bC1\ntUVAQAAiIyMLrRs1ahTmzp0LJycnbN++HYmJiejXrx8+//xzmJubo3PnzmjVqhVWrlwJAHjy5AlW\nrFgBR0dHrF69Gn/88QdycnIwePBgSCQS3L59G1OnTkVOTg769u2L3bt3w9DQsOzfFKo07OwAS0sg\nKSlv3ZQp4l93dyAkBHB0rJjYiIiIypRiijEtu3CKMSLSlZrQ3kxPT8e3336rtr0ZHx+PvXv36qS9\nyQQhIqIqSpH1qomZmerzRYvU7WWvfCSVArt3A337lkl4RK+sYAUhALh8mQlCRERE+cm19dISEVVj\nsbGx6NmzJ959910kJCRg2LBhMDY2RmBgIJycnLB161ZER0dj9uzZhdZp8ujRI2Vn69atW7FkyRLY\n2trC398fBw8eRJcuXXD8+HHs2rULmZmZ+O677/DFF1/A29sbkyZNwokTJ9CuXTsmB9UAEgnQqpX6\nUcdnzwJt2gChoYCHR/nHRkREVKY4xRgR1UDl2d5cu3atxvaml5cXpk2bppP2ZrEThLKzs7Flyxbs\n3LkTcXFxsLGxgbe3N0aPHg0DA4Mij09OTsaKFStw9OhRPH78GE5OTvjss8/g5eVVaN+0tDSsW7cO\n+/fvR0JCAho0aICPPvoIQ4YMgaTA3fBXjevFixfo2bMnunfvjpkzZxb3bSAiqjLMzUu2f24usG4d\nE4So4unpFW51JidXQCBERESVmJY+WiKicrFuHeDjA0RElO15XV2BVas0b7e2tkZwcDAOHToEmUyG\n7OxsZV8jAHz00UcAoHZdfvJ8JXkbNGig7Gy1tbXF/PnzYWpqilu3bqFTp06IiYlBy5YtoaenBxMT\nE8yaNQsA4O7ujpMnTyI0NBTjxo0rk+unyq9lS83TEiQnAx9+KEY7W1qWb1xERERl6uUoTq0JQqwg\nREQ6UhPamwkJCWjTpo3a9mZERITO2ptqxuir5+/vj4ULF8Lc3BzDhw+Hra0tVqxYga+++qrIY1+8\neIFPPvkE27ZtQ6tWrfDRRx8hJSUFEydOxJYtW1T2zcnJwYQJE7BmzRo4Ojpi+PDh0NfXh7+/PxYv\nXlwmcWVnZ2PSpEm4f/9+cS+fiKjKeffdkh+TkFD2cRCVlLrKWOqqChERERERUcXx8ACuXRNTX6tb\nrl2L0LhN23LtmvbqKxs3bkTr1q0REBCA9957D3K5HHXr1sXt27cBAOvXr8fvv/+udp2hoSEePXoE\nALh27ZrynNJ8DY5Zs2ZhwYIFWLRoESwtLSGXy9G4cWNcu3YNubm5yMrKwsiRI5GZmYkBAwZg165d\nePz4MVxcXMr8PabK6Y03tG9PSgK++aZ8YiEiItKZl/8hk7CCEBFVgKLamyVvZ0ZUuvZm3bp1NbY3\ns7KydNbeLFYFofPnzyMkJASenp5Yvnw5JBIJ5HI5pk2bhj179uDIkSPw0PJO/vjjj7h69SrmzJmj\nzKAaN24cBg0ahICAALz//vuwsrICABw4cADHjh3DJ598gqlTpwIAJkyYgM8++ww//PAD+vbtC2dn\n51eOKzk5GV9++SVOnTpV8neLiKgKGTQIyM4Gfv8dyMpSv098/DMcOVJb+fzp03IKjkgLPTXtSiYI\nERERqdI0xZiJvkn5BkJEVM48PDwwd+5c7Nu3D+bm5tDT08PcuXMxY8YMSKVS2NjYYMSIEbC1tS20\nztDQEP7+/qhfvz7q1q2r9vx9+vTBgAEDYGZmBiMjIzx8+BCurq7o1KkTBg8ejNzcXAwePBiGhoZo\n1aoVYmNj1Y4Yperrgw+AefOAmBjA0FCMbr56FQgIyNtn2TJg0ybRH/Phh0BQEFC7tsZTEhERVT6v\nvYbHI0ZAunuTxl1YQYiIqpvybG9aW1trbG8aGBjA1dVVJ+3NYiUIbd26FQDg6+urnOJLIpHgyy+/\nRFhYGHbt2qU1QWjbtm2wtrbGoEGDlOtkMhnGjh2Lr776Cvv27cOIESOUr6Wvr4+xY8cq9zUwMMAX\nX3yBQYMG4aefflJOCVbSuH755RcsWLAAjx8/RseOHZkkRETV3tChYtHk5MkH6NQpr4fq1i2gTh3N\n+8tkwKefio4wdVVeiMqCVE3dWiYIERERFU1fqo+d/XdWdBhERDrVvn17HDx4sND6bdu2qTxv2bJl\noXVdunRBly5dCh27c2fe387p06dj+vTpAICIiAi4uroCAMaMGYMxY8aoHJebmwtTU1P06tXr1S6G\nqiSZDPj3X+Dvv4EWLQB7eyAtDdi5E7hzJ28/xVTZwcFipPKBA4C1tVinmHGAfStERFSZPZwyBeZz\nvwI2/Uftdn1psW4zExFVGeXZ3syvYHszIiJCZ+3NYt1uCw8Ph4WFBZo2baqy3tbWFo0aNcLZs2c1\nHnvnzh0kJCTAzc0NegVKArRr1w4AlMdnZmbi8uXLcHFxQZ0Cd6hbtmwJExMTldcqaVwhISEwNjbG\n2rVrCzXoiYhqIpkst9C6lBTNy/37okz2X39VQLBUo8m1lLIlIiIi4dLYS+jVlDepiYjKw927d9Gv\nXz/06dMHMpmsosOhcmZmBnh6iuQgADAxARYv1rz/2bNAx47A/v3AggXiOFtbYNIk0ddCRERUWUms\nrDVu4xRjRES6kZCQoLP2ZpEJQpmZmYiPj0fDhg3Vbre3t0dKSgqSkpLUbr/zctiEuuNtbGxgZGSk\nnJ/t3r17yM7OVruvnp4e6tWrp9z3VeIaN24cfv31V63VjoiIahJDQzkcHEp+3JUrZR8LkUJOTuEh\nlJmZFRAIERFRJaYud9bVxrXc4yAiqqkcHBwQFhaGIUOGVHQoVEkMGAB06KB5e1QU0KsXMHMm8OAB\n8OgR8N13gKMjMGECkJhYfrESEREVl1Si+VYypxgjItINW1tbnbU3i0wQSn5ZC7W2hkmSFeufPXum\n9XgzMzO122UymfLY4rxWWloasrOzXymut956C0ZGRmr3JyKqiSQSYMUKwNKyZMc9faqbeIgAILdw\nYSsmCBEREREREVGlJpEA27cDnToBDg7AuHHAO+8UfVxmpuibadIE6NlTTBW/ZQuQk6P7mImIiIqi\nNUGIFYSIiKqcIieHzM7OBgAYGhqq3a5Yn5GR8crHp6Wllfi1ShtXSURERJTq+PT09FKfozKoDtfB\na6g8qsN1VJdrcHaOwPHjwPPnmv+jv3GjFdatyyslumxZFnbt0pyxUadOLoYPT4K7+4syjVeT6vBZ\nkHZl8HVORERUrXD2TSIiosqnYUPg+PG85xkZIlFo48aij336FDhwQDzeuhXYtAmYM0cPriwQSERE\nFUiCwtXeFVhBiIio6ikyQcjY2BgAkJWVpXZ75ssh/SYmJmq3Kyr2ZGoY+p+ZmQlTU1OVfbW9lkQi\ngYmJiTLx51XjKgnXUrbCIiIiSn2OyqA6XAevofKoDtdRk67h9GnV5w8eGODBAwOtx5w6VRvx8UCd\nOqWJsHh09VmcO3euzM9Jr4YVhIiIiIiIiKiqMTIC/vc/kSQ0YwZw6BBgYgL4+YlkosWLgdhY9cce\nPgz8+29j7N8PtGtXvnETEREpsIIQEVH1UuQUYzKZDFKpFKmpqWq3K6bw0jTVV52Xd4Y1HZ+amgqZ\nTFasfZ89ewZTU1NIpdJSx0VERMXn4FDyY9LTgfPnyz4WqpmeP6/oCIiIiIiIiIhejZsb8NtvwKNH\nQFISsGiRSBq6fh1YskTz4KrHj/Xx7rtAcLBIMPrsMzGIKz0dGDMGkMkAD4+8JKPMTODePSAxkQNt\niIiobEgkrCBERFSdFJkgZGhoCDs7O8TFxandHhcXBwsLC5ibm6vd3qhRI+V+BT18+BAZGRlwdHQE\nANjb28PAwEDtvjk5OYiPj1fuW9q4iIio+N59V3RmlVRyctnHQjXTvn1ipCUREREJcs19tEREVAK+\nvr4at+3evRuXLl0qx2iourO2Bl4W7AcgHk+aBDx4AJw4AYSFAV27qh6TkgKMGAEsXCiqEXXsCLi7\nA+vXi8E0R48Cb78tEobMzYEGDQAbG8DUFOjRQ0xvxv4ZIiJ6VdoqCOlLi5yohoioxtLW1ly/fn2F\ntTWLTBACADc3Nzx69AgxMTEq6xMSEhAbG4vWrVtrPNbOzg52dnY4d+4ccnNzVbadOXMGAPDGG28A\nAPT19dGqVStcu3atUGWgS5cuIS0tTblvaeMiIqLiq1VLjFALDxcdT5oWT0/V4yZPBt56S/MyerQY\nOUdUHFOmVHQERERElYe8ogMgIjpyBHB1BSQStYtrs2Yat2ldXF3FucvJypUrNW774IMP0LJly3KL\nhWouExOR5NO7N/DHH9rbv7m5wJUrquvi4kTCUFpa3rqcHHGuTz8FbG2Bvn2BvXsBuZb/RMjlwLFj\nwNKlwPHjpbsmIqLqLDs7G5s2bYKXlxdatmyJd955B6tWrUJWVlaxjh88eDCcnZ3VLtu3b1fZd9Kk\nSRr3DQgI0MXlqeAUY0RUIYpob5Z0UbZPy7G9qa2tOXr06ApraxYrtbNv374ICwtDYGAgli1bBqlU\nCrlcjqVLl0Iul2PgwIFaj+/duzfWrl2LLVu2YPjw4QDENGJr166FsbEx+vTpo/Ja4eHhCAoKwvTp\n0wEAWVlZWL58OQCgf//+ZRYXEREVn75+0VWEdu8WJbMVoqPFosnp02K029atZRMjVW8XL4oOTj22\nO4mIiIiIKt6YMcCNG2V/3uvXxbmjojTuEhoaisOHDyM1NRVPnjyBj48PgoKC0KhRIxgaGmLevHmY\nOXMmnjx5AgCYNWsWnJ2dsWvXLmzfvh25ubl45513MH78eHTs2BGnTp3C1q1bsWfPHkilUrRp0wZT\np07F8uXLMWTIELz11luYMWMG7t69i5ycHIwcORJeXl4YNmwYXFxccOPGDaSmpmL58uWwt7cv+/eE\nahQ9PeDbb0V1IX//sjlnZqaoThQWBnTpAnz8sZiWzMREVI22thbTmG3apNqPs2CBSFY6fhzIyhLV\njQwNyyYmIqKqzN/fHyEhIXBzc0O3bt1w/vx5rFixApGRkVixYkWRx9+4cQOOjo7o2bNnoW0tWrRQ\neR4ZGQlra2sMGjSo0L5ur1L2v4Qk4BRjRFQBKqi9WV5tzWnTpsHLy0tjW3PmzJlwc3PTSVuzWAlC\nHTp0gJeXFw4cOICBAweiXbt2uHDhAsLDw+Hp6Ymu+eqeBgUFAQDGjx+vXDdq1CgcPHgQ8+fPx9mz\nZ+Hg4IBDhw7h7t27mD17NiwtLZX7ent7Y/fu3di0aROioqLQvHlznDhxAtevX8cnn3wCZ2fnV4qL\niIh0r2HDkh/z119lHwdVbSYmuRq3hYUB3t7lGAwREREREVVKL168wA8//ICkpCT0798fOTk5GDdu\nHJo1a4YlS5agffv2GDJkCG7fvo3p06dj5cqV2LBhA/bu3QtDQ0MsWrQIz58/V54vNDQUs2fPRuvW\nrbFt2zZkZ2crt4WEhMDCwgJLlixBamoqvL290b59ewBAy5YtMXPmTAQGBmL//v0YPXp0ub8XVD3N\nnQs8ffoQq1fXRVYW0KEDcO2a6nRh1tZAYqLqcbVriyQjTdOKHTsmFoVp0zTHMGOGWBTs7YEvvgBG\njgSsrEp8SURE1cL58+cREhICT09PLF++HBKJBHK5HNOmTcOePXtw5MgReHh4aDw+Li4Oz549wwcf\nfKByL1WdrKwsxMTEoGvXrkXuqyusIERENU11b2sWa4oxAFi8eDH8/Pzw5MkTBAcHIzExEX5+fggI\nCIBEkpc9unLlykLlkmQyGbZu3YoPPvgA4eHh2LZtG8zMzLB06VIMHTpUZV89PT18//33GDFiBKKj\no/Hjjz8iJycHc+bMweTJk185LiIi0r2PPgIaNy7ZMZo6rKjmGjZM87xzERHlGAgREVElJmdzl4gq\n2rp1ojx7WXN1Fecugru7O6RSKaytrWFmZoakpCQ4OjoCAKKiorB7924MGzYMs2fPRkpKCu7evYvX\nX38dxsbGkEqlmDFjBmrVqqU838KFC7Fjxw4MHToU9+/fhzzfPEzR0dFwd3cHIPo5nZyccPfuXQBA\ns2bNAAD16tVDRkZGmb0NRBIJMGbMY8TFAQ8fAqdOAWfPAp07iwSg/v1FtZ9FiwBLS8DZGVi5UiQM\nPXkCXL0KzJ4NNGlSdjHduyemk69XD+jRQyytWomYBg8GvvnGFpMmAUuWACdOAP/+K+IbOxY4eFDz\neWNjgd9/By5dAu7cAX79FVizRlwvEVFls/VlOXxfX1/lfUiJRIIvv/wSEokEu3bt0np8ZGQkAKgU\nRNAkOjoaWVlZxdpXV7QmCLGCEBHpSgW2N6t7W7NYFYQAwMDAAD4+PvDx8dG6n+KLrSBra2ssWLCg\nWK8lk8kwffp05RRjZRFXQe3atdMYKxERvZr69UV1vsuXAU3fVbm5wNtv5z1PThadXpqYmAADB4rv\na5axrhns7bOxfj2gLhn68GFg5szyj4mIiIiIiArw8BDlTDSIiIiAqy46dF+6evUqACAxMRGpqamw\nsrKCVCpuYDVu3Bi9e/fGf//7Xzx+/Bi7du1Cw4YNcevWLWRmZsLQ0BB+fn6Yma9xsXPnTsybNw9G\nRkb49NNPceHCBeU2JycnhIeHo0ePHkhNTUVUVBQaNGigs2sjyq9u3bzHTZqI6j9yeV5fytSpYhqw\ngn0rzZqJKcrmzQPCw0WyUP5p4TUxMhLnSk/XvE92NvDHH+q2WKpbCUD060yZIuKIjARevABSU0Ui\n0L59ml9r/nxg+nTRn5SbCxgY5G3LzQWkBe5bZ2cDd4Lu2gAAIABJREFUBw6Ifqnu3QELC7H++XPg\nu+/ENjc38b4QEb2K8PBwWFhYoGnTpirrbW1t0ahRI5wtIruxJAlCJdlXV7QVY2AFISLSmSLamyVV\nkvZpdW9rFjtBiIiIqDgMDIA2bbTvY2YGpKQU73xpacCmTcB774lEIaoZRo0ChgwBzM1F557CkSPA\nwoXAV18xYYyIiGo2edG7EBFVa4mJifj444/x7NkzfP3115g7d65y29ixYzFz5kzs3LkTqamp8PX1\nhaWlJUaNGoWhQ4dCIpHAw8MDtra2ymOcnZ3x4YcfwsLCAra2tmjVqpVy24ABAzB79mwMHjwYGRkZ\n8PX1hRXnV6IKVPBerbaBVxIJ4O4uqvLs3w+EhooKQ02aADduiKSh9HSRNDNypKgEdO4c0KsXkJmZ\ndw4jI+1JQ8WxeLFYSmLmTGDHDpFUlJMDtGghBqj9+y8QHw/07AmsWgW89pqoOjxsmIgfEP0GPXoA\ntrbiOu/dE+v/+QfYswdYuNBUZWB6/oSjlBRRBSkjQ0ynlpMj1llZiYFv5T15QVoakJnJEpJEFS0z\nMxPx8fEq/0/Iz97eHjExMUhKSoKlpfqkycjISEgkEpw/fx6zZs1CTEwMzMzM4OnpCT8/P9SuXVtl\nXwC4ffs2Bg0ahMjISBgbG6Nr16744osvVP4voyusIERENU15tDVDQ0MBVExbkwlCRERU7po1A06f\nLtkxZZgoTCWQnZ2NLVu2YOfOnYiLi4ONjQ28vb0xevRoGOQftqcDtWqJz73AYBzMmAGsWCE69Bo2\n1GkIRERUg5T2Oy85ORkrVqzA0aNH8fjxYzg5OeGzzz6Dl5eXTuLlFGNEVNO5u7tj0qRJyud//vmn\n8rGFhQVWr15d6Bhvb294e3urrDt16hQAoH///ujfv7/KtgkTJihHmX777beFzrd582bl48GDB7/C\nVRCVH4lEJP306qW6PitLVCTKPwinRw8xpVlAAGBjI6YIs7UF1q8Htmwp/+m/L1/Oe3zxolgU9u8H\njh8XSVCnTqlWtM7MFNvVuX8fGDmyIc6fF0lHS5YAcXFA375A+/bA11+LRCp1/vtfkbT06JFIXKpf\nH2jeXCQQ/fsvEBMjpkpLTMyrnN28OfDmm8BbbwGmpuI8SUnA7t0iWSknB/j0U+CDD/KSlORyMVBq\nyRKR4GRh0QRLlwIff/zq7yURlU5ycjIAqCTx5KdY/+zZM60JQnK5HMuXL4enpyfefPNNnD17Fj/+\n+CNOnz6N7du3QyaTKfcFgFWrVqFHjx5o3bo1Ll68iNDQUJw6dQo7d+5EvXr1yvoyVWhLENKX8jYz\nEVU/5dHWXLRokfKxurbm/Pnz4eTkBKDs25r8y01EROVu+XIxoisqqvjHnDwJrF2rftuDB+Z4803A\n05NVZcqav78/QkJC4Obmhm7duuH8+fNYsWIFIiMjsWLFCp2//muvqV8fHw84OYnONxMTnYdBREQ1\nQGm+8168eIFPPvkE165dw/vvv4/69evj0KFDmDhxIpKSkjB06NByugoiIiKiktGUB/3mmyIJJr8Z\nM8R0X5cvA1euAHXqiMShJ09Ewk1UVDxq166HK1eAv/8WU4i1bSumOIuP1x6Hq6uoIJyUJNr7UVEi\nuaY4nj0D8t23KbbcXAmWL1ddt22bWLTZtw9wcREJRfKXZR1r1RLTmBXF1FT0X8XHA2fPqlZN/u03\nwNkZ6NQJ0NcHDh4Ebt/O256UpI8RI0TSk5mZSEK6exd4+hRo3VpUU+rbF7C3F/vL5eL8is84MxO4\ndUvE/eQJ8J//iOtQkMvF+29sLK5HG7k8Lwmqfn2gXr3C070VV2ys+FkyNy96X3XTyulSVpZ4f62s\nyr9qFFVO2S9/aQ01dEIr1mfkz1bMJzc3F2ZmZnB1dcW6deuUFSZyc3Mxd+5chISEICgoCNOnTwcA\nGBsbo1GjRli5ciVef/115XnWrFmDZcuW4f/+7/+wcuVKta8VUQbZnOnp6bgecV3j9ntx9xCRU85Z\no1qkp6eXyXWXF8arW4xXt6prvPfv38fjx48r/Np0+f4yQYiIiMpd27ZihJVcy9wYwcGitLXCn39q\n6+ypDwAYMAAICSmzMGu88+fPIyQkBJ6enli+fDkkEgnkcjmmTZuGPXv24MiRI/Dw8NBpDNoSvrKz\n80bdAaIzat48wNsbsLbWaVhERFTNlPY778cff8TVq1cxZ84cfPTRRwCAcePGYdCgQQgICMD777/P\nqWiIiMpQwZGZRFR+JBKgZUuxFBQR8QSuroUrWcTFiT6bv/8Wz52cRFIJANjZico5PXqoJmBcvSqm\nm4+Le7U469YFHj5UXWdgIM75yy/a+6SK4+5d1efFSQ4CgBcvgJ9/1rw9MlIs2uzaVXjdgwdiGjk/\nP1HhSCIBDh0SU5M1bAjIZOK8+ROSAKBbN/F5/P03cPOmmEZOTw94913g//5PVG8+cQJo0wYYP14k\nLM2bBxw+rPr+GhqKKk6dOokKSv/+CxgaNsIHH4hEpJgYkaA0eDDQqJE4JjYWGDNGJEZJpcBHH4lE\nsS1bxPV8+ql4rV27xLrr18U0cfXqifN4eQGPH+dVaUpLE/vcuSOmzJs6VbXfSOHOHeDCBZEEp0im\nAsTPxJUrov/x6FFRqSo2VlR3at8e+N//RFV0hZwcEXfBxKEXL8Sxr79edP9UYqKIx9ERsLDQvq9C\nZiZw7BiQmGiCpk3F50Xlx9jYGACQlZWldnvmy7kZTTSMaJRKpdi5c6fa9VOnTsXevXuxf/9+ZYLQ\nqlWr1J5nzJgx+Omnn3DkyBE8f/4ctdRk9bnmn0PxFUVERGg9j+NrjnB1Kv3rlJWi4q1sGK9uMV7d\nqq7xVpZrKs37e04x364GTBAiIqIKo23kjY1Nyc+3axewbl3xRhxR0bZu3QoA8PX1heTlhyWRSPDl\nl18iLCwMu3bt0nmCEADMnw/MnFn0fvHxomNpzJjC2zp3FqMc//MfMforPl50OpmYiI4bIyPRcZac\nLKoSDRyY9zN4/77obFFMGavt51YuF9uTk0VH0avIzhavJ5GI0uiGhhylRkSka6X9ztu2bRusra0x\naNAg5TqZTIaxY8fiq6++wr59+zBixIgyjbmU97SIiIiIyk2DBmL6r9hYkQRRp07RxzRvLioVbd8u\nKtp07SqOO3tWtLldXcU5J09WTc6pX19MyTVkCHDunJgOTdG27tZNxPLHH8CoUarVedQxNhbHJCeL\nij5xcaICT2WWmwuEhamui43VvL+6AXk5OSLZ6Ndf89Zt3y4G892+LSpDFZSZKT6PlzNpvGSiMj0c\nIPp4Fi0SiT0BAXnnys0F8s3aCEBsX7dOVIjK78EDYOlSsWiyfz8QGgoEBop+npQU0bfyyy9ivaIS\nkZeXSFiKiwOOH38dSUnqz3f6tEiS6tZNJEbdvw8kJIh+pffeE0lZtWqJn88NG0SFJkND8fPp5ATs\n3Cn2t7ERyVqPH4vPRfEzaGAgEqT8/ETyXXq6SMx69EhUeWrZUvwMHjoETJgA3LgBAI0wYQLwzjui\nwpGtLdCvn/jdOXUK+Osv0d/VsaP4vYuPF9dtby8S817muUAuF9uio0UiV0aG6JcyNhbH1a4t3qun\nT4EzZ0TyXu3a4rrc3MTr16QkJZlMBqlUilR1vwgQU4sBmqcg06ZWrVpo1KgRIiIikJ6erkxGUkcq\nlcLFxQVxcXGIj49XTkOjK1KJFLny3ELr9aQ16MMnIqommCBERESVUseOItGnuOWkAdGgffiQCUJl\nJTw8HBYWFmjatKnKeltbWzRq1Ahnz54tlzgUiT29e7/6OY4fF0tx+fkVvY+Zmehg0qx8M82lUtHR\nU1Lu7uJ3x8FBjMILCxMdXULha2jUSHQS5eaKkXOKUvEWFqLjChAj32JiVI+rV0+1pHzbtqJz7tkz\n0fF165bouFJUYH7jDfFaBw6I17lwQfV8nTuL5C6pFGjRQnSGLV0qOqW6dhUdYseOAefP511D7dqF\nOxY//VRcz7FjInksLU1cW6tWolP0tddETHv3ilLr9vbi9WJiROeZIhFsxAgx2vLGDTGyMDlZdDxb\nWYlrO3RIdOhFRYkRlf/8I+J54w3ROQkAvXqJzsLGjcUow5s3ReebTGaH58/FSEYXF3Ft//wjRnQm\nJop9Dx/Oe49atxbxm5mJ8x06BDRpIkbkyuViRGVCgvh7mZAgPpv//Ee83vbt4hyjR4vP5/59Ec+1\na+I9cnYWCXS3b4v3qU4d0RmfmSk6RiMixEhLhRUrxHtVpw5w5oyd8uemQQPxepGR4rizZ4EOHURM\nDg6iw/TPP8V7162b+HlJTxcjPNPTxbSTVlbiPf3+e7HOwUF0sDZqJH43z58Xna6AuL4+fcT53dzy\nOs3fekt8pomJIo7ERPEZZWSIUbYWFiK2AwfEeT7+uC46dRKd2AYG4mf+3Dng/fdFZ+w//4j1Bgbi\nPJaW4jOLjxcV7m7cANq1E53QdeqI19DXFx3hdnbis5XLxe/C77+LTujOncV6CwtxI0PxGX3xhXgv\njY3F6yQmilG/b74pjldcR3y86Fju1Ut04Kam1sHx42JEb6tW4u/d228Xf7RqdVWa77w7d+4gISEB\nnp6e0CvQM96uXTsAwNmzZ8s8QYiIiIioKpFI8irHFJe5OfD556rrPD3zHrdsKfoKDhwQ7YNmzUTb\nR//lXY833xRLQd27iyQHP79EbN9ujawskZzRrRuwfLmo/tKjh2jPNGmSd1xysmhX5O9feP110S5I\nThbt0+bNxfLaayJZqU4d0X44f160ze7dU43F2RkYNEi0fbZtKzzYyMhI/F++f39gypRM3LmjpdSy\njl25UvpzPH8uKhEVV8E2fElcuSI+R01yc0XCUB7tt8syMlSTpgBxPbt3i6WgzEyREFUcWVnApk1i\nMTUVz/MXqJFI1Fe9evRIdSrAOXNEuy86uujXtLYW7cC4ONEX8qoaNxa/px4e4vP6/nvRls3KEr+L\nPXoAy5a9+vkrG0NDQ9jZ2SFOQ3mzuLg4WFhYwFxDB3VKSgpu3rwJCwsLODo6Ftqenp4OqVQKAwMD\npKWlITIyEsbGxnDJPx9gvn0BwMjIqBRXVDytbFvhQvyFQuv1JEwQIiKqapggRERElZK5uRjpEhys\nPUlozx5xg1vB2VncEJ88WSz0ajIzMxEfH49WrVqp3W5vb4+YmBgkJSXB0tJSp7FIJGIk1pMn4gZ6\naTotypL25KDy9yrJQYBIfgBEok9x5B9hGRWV91iRHAQUTg4CVJODAJH4UlD+6dkvXMhLeCmYHASo\ndsjmP1dOjuh0PXSo8DHqOhb/97/C6wDNZezv3SvcoQuITjyFgqM1FRTv8f794t/ERNX3SrVjMr+8\n4bV//SUWALh0Sf3e//4rlvxOnlSNsaA9e1Sfr1+vfr/Ll1FoBKg2qsl24joOHlS/r6Yy+9qnmMyj\n+FlWV0FVU9yaYtEkONgKwcGF1+/bV/xz/POPWIpLU8eupg7WdevUr1+5UvHITrnujz/EYmICfPdd\n4ZsvNUVpv/Pu3LkDAGjYsGGhbTY2NjAyMsLtooanvwI5q8sRERERwd5eVAMqKVNT4KuvHiEoSMwB\npZgNaNgwUdlXX82dE3NzMR3W+vWiLefpKRa5XFS1sbBQP6WVQm6uSJz491+xb+fOqlNcffutaDfG\nxIi+sNatRXUWxcxBjRvH4PhxZzx6JAZINGwo/pVIRFxbt2puJyrY24tBPVlZ6tslxsZiAEZxmJqK\npK/4eGisvEMl9+JF4XUlmRKvOMlBgOiTSEws/nk1uXVLex/sunXiZ7Vfv9K/VmXh5uaGsLAwxMTE\nqCT5JCQkIDY2Fl27dtV47NWrVzFixAh4eHhg7dq1KtsePnyIuLg4uLq6Qk9PD4mJiRg4cCCaNm2K\nfQU6HtLS0nDt2jVYWlrCPv8fEh2Z02UO+oUU/hBZQYiIqOphghAREVVarq6i7LA2t2+LTpD8Hj0C\npkwRDc/8I72o+JJfZmVpKoerWP/s2TOdJwgpmJuLTpLDh8UIQXUdJkREVHJpaeJ7c9Qo9TdCqrvS\nfucpjjczM1N7vEwmU5aZL0uGOUCaQZmfloiIiKhGUSQG5aft/8TGxoWrDkskqok+mkilonJqhw7q\nt9evLwZIaSKT5eKrr9Rva9VKJGn89ZeoZmNuLioPubqKhJHUVKBpU1F9VOHcOTFoIjtbVMJ1dxeJ\nSzt2iHPduydiatpUVJRVMDQUAxbytx/u3hX9NVeuAHXrimqtx47dx6VLdnj6VJxn167CfTm1awOL\nF4vXXb1aDBz68ENRcUlRPRUA+vYF5s4VlZl++UVM2fXwoTivlZX4DKRSkfx08aJqVR11rKzyKs4W\njKdLF1ENp0sXURE2LAzw8SlZlfPi0tcXn8nDhyU7TiIBatXKQWpq1UjOePKkoiMoW3379kVYWBgC\nAwOxbNkySKVSyOVyLF26FHK5HAMHDtR4rJubG2xsbHD8+HGcOXMGbdu2BSAGrnzzzTfIysrCRx99\nBABwcHBA8+bNcfXqVezduxe9X5ZXl8vl+O6775CUlAQfHx/lNNk6vWaXvhjVZhQ2nN+gsp4VhIiI\nqp4a2P1LRETahIaG4tatW5g0aVKZntfX1xcr88oXlJnmzQsnCAGAiclZxMTURpMmhcuvUtGys7MB\niLK56ijWZ+Qv+fJSREREqV47PT1d6zns7PIqsTx9KsXt24Zo2jQDV68aIydHgh07LPDbb+pv0hIR\n6YJEkoHatfciJaV/kfuamYUiJ6cOnj9/R+12C4v1SEtrj/T0lqWOy8oqCNnZ1nj6dHCR+8pkWbhx\n42apX7MqKs13XnGPT9NS/u5Vvzf7vmiMYONbyucDHtuX+ju4ohT13V9VVIfr4DVUHtXhOqrKNYwa\nNQqrVq3CmjVr0KlTJ7Rp00a5rapcAxERIJJGOnYUS34tWqjf381NLAUNHgwMGADcuSMqFOnpiWmj\n1qwRCTlz54pkovwcHMTU2/lZWz/FrFl5FUxnzgR8fUVF4DZtRLWmgQPzpjvOn1Mhl4vzHT8uqih5\neORtGzpULJrI5WJK7u++E1WGHR1FQpBcLqbi7tNHbA8PF4lPUqmYeltfPwbe3o6FEsSGDBHTZB08\nKBLK7OxEQpitrUhG2r1bTCctlYqqSl27iuSsgADxvpmZiXN4eQFPn4ppyaysRCJV06ZiiurQUGDj\nRhGToqpPkyYiQenSJfFZSCRiOu7OnYFvvgGk0ig8fuyK2FiRvPTTT3kVhwFR3UomE+fMyRGfHSAS\nv+LjVStRm5mJqckaNxbT4uXkiGSu5GRRjVkuFz8HzZqJKcDT0kQV3507xVRq2ri6Al9+qZsEq4rS\noUMHeHl54cCBAxg4cCDatWuHCxcuIDw8HJ6enioVhIKCggAA41/OrWdoaIhvvvkGvr6++OSTT/De\ne+/B3Nwcf/31F6Kjo9GzZ094e3srj/f398ewYcMwZcoUHDp0CPb29ggPD8eVK1fg7u6OsWPHltt1\nB3oG4ljsMUQ9FiXF9SR6eN3q9XJ7fSKiqqZbt2749ddf8fXXX8PLywudO3eu6JAAMEGIiKhSOnIE\nGDcOuH69bM/r4iJG4+RvVJcXXSQHAWJE08mTzxEeXkvZsLWyAtq33w0jIy8ATBB6FcbGxgCArPwT\nnueT+bL1b6JmqJ2rq2upXjsiIqJE52jfXvyr6MfP3yH1/Lno8IiLEx0RWVmic0QxoiwlRXSANGgg\nOjwePRKdNf/8A5w6JTpjjI3FSLvISDFtlakpMHWq6JB5+FCUEbexEb+3hw+LTrI7d4DMzBdwcDDF\nX3+J12nQQJwn/5R4ANCtG3DzpjjmtddEbHFxouPnxo3iv2/164tY8uvUCThxQjy2swPu39d8vKWl\n6DzKP0KvKG5uedM51aol3u+CFOXJGzTQPG1XWbCwyBuRphgJ2Lp14am2CmrRQoxw1Ebde2toWHQn\nWHGYm6vvJGvfXoziLGrqN3t79VOeqaOvLzrm4uIKj96zshKdmfmnistP3Xvg6gqU9/0yBwcRZ2xs\n+b6uOvnfez29R6hbd5fGBKGOHcXfFABIScnr6GvTRryv+d/bJ09Gqz3HG2+on24PEL+/KSniZ0Yb\nY2PgP/95jrNnaynXyWTi523hQoNS//0+p25+tyqgNN95AGBkZKSyn7rjTbXMNfGq7/v/+ezAjdXv\n4HSdZ3BLqYVFn22DYyk/w4pS0u/+yqo6XAevofKoCtdxJOYIxh0Yh+uJZdtwdbF2wWqv1fBwLJ+G\nq6GhIVxcXGBubg4HBweV911Xn0NV/c4koppDT0/0TyiMGvVqU7nl9/rrYpCfXC76PLSRSEQSj4FB\n6ftICx67Zk1eH2n+8ZEREekaq0fZ2IiEpoLatROLOkuWiOpIxSnu0r+/WORykbwjlYoEJEAkmFhY\nWKNNm9b488/D8PX1fRmv6FOaOHEiBg0ahIkT2+HMGdFP07Ej0PLlmJPIyEikpKTA3d0dEydOxLff\nfgup1BAJCWJqODs70aYsaRGaYcOG4eTJuTh+3AmnTon+i6ws0Rc1apRIftLXF+eXSKpXghAALF68\nGE2aNMHPP/+M4OBg2NnZwc/PD6NGjVKp6KPoE1ckCAGAh4cHtm7ditWrV+Po0aPIyMiAo6MjZs+e\njSFDhqgc36JFC/z0009YsWIFTp8+jaNHj8Le3l75WpoGquhCLcNa2D9kP4b9PAyxybGY3Xk2rE2t\ny+31iahmqS7tzcqICUJERJXQmDElSwworuvXxbmjoored/Pmzfjll18gkUjg5eWF4cOHIyoqCosW\nLUJubi5SUlIwa9YstGnTBh4eHmjcuDEaN26MZ8+ewdDQEPfu3cPDhw+xaNEiNG/eHB07dsSpU6cw\nbNgwuLi44MaNG0hNTcXy5cthb2+PVatW4Y8//oClpSXS0tIwYcIEtNPQwu7VqxcaNWoEQ0NDTJky\nBU2afIVmzQyRnJyMceN8UK9ePYwdewJLllxFkyZNcPHiRWzatAlSqRRubm5lXh2pOpLJZJBKpUhN\nTVW7XTFViqbpWCqLWi/vgavr0y84HXidOiKJRd224hgyRPV5RERspb+po862bXmPq8KNqaLwGiqP\n8riOpk118/0JiM7ssDD11zBr1locOHATzs4u6NChA168eIH58+djz549uHLlCp4/f45p05ywcOFC\nBAUFwdraGo0bN8aGDRvQtq0B4uLi4OXlhc8//xzTpk2Dl5cXEhMTcezYMaSnp+POnTsYNWoUvL29\ncenSJcybNw+1atWClZUVjIyMsEjDfJxBQYC1tRgBvGjRIpw7dw5paWnYtKk/Pv74Yxw6dAgbNmyA\nvr4+duywR5s2i3HhwgV8++230NfXh5mZGQICAiCTyXTzplYSpf3Oq1OnDgBoPD41NRVWVlZlEKmq\nBk3dcWpZCq5dvYpmzZuX+fmJiIoy5pcxuJFU9l+81xOvY8wvYxA1XnPDNTQ0FLt370Zubi6GDRuG\n4OBglfbe48ePMW3aNDx79gxyuRzffvstjI2NMXfuXGRkZCA5ORk+Pj7o3r17mcdPRETalSQRpTL0\nkZZGSZNuJJK8Sj/5ieo9rmjWTHubum1bseR36NAhWFtbw93dHYGBgcr19vbFmxpPG0tL4KuvoHHq\nu+rMwMAAPj4+8PHx0bpfZGSk2vWtW7fG+vXri/VaTk5OWL58eYlj1IUmlk3w96d/V3QYRFQDVFR7\nsya0NZkgREREhdy9exfnzp3Dtm3bIJFIMGLECLz99tu4efMmpk6dCmdnZ+zbtw+hoaFo06YNHjx4\ngNDQUFhYWGDatGmws7ODv78/du7ciZCQEPj7+6ucv2XLlpg5cyYCAwOxf/9+dO7cGSdOnMBPP/2E\nrKws/FfbhOsAXrx4gXHjxqFZs2b466+/0KdPHwwYMADnz59HUFAQfvjhB3Tq1AleXl4wNTVFUFAQ\ndu/eDRMTE0yePBmnTp1Cx4L1lkmFoaEh7OzsEKeh5EtcXBwsLCxgbm5ezpEREVU+Y8eORVRUFDp1\n6oSnT59i1qxZSE1NhZmZGX744Qfk5uaiZ8+eSChQQuz+/fvYu3cvMjMz0alTJ3z++ecq21NTU/G/\n//0Pt2/fxtixY+Ht7Y2vv/4aixcvxuuvv47AwMBC51TnyJEjiIuLw86dO3HlyhX4+/ujffv2+OWX\nXzBixAj07NkTe/bsQWpqKv744w/06NEDn376Kf7880+kpKRU+wSh0n7nNWrUSLlfQQ8fPlSOBtUV\niVSqs3MTEVVmZmZmWLhwIYYMGVKovXfkyBF069YNgwcPxt9//41Lly7B2toaI0eORLt27ZRtx8rc\naUtERBUrPT0d06dPx/3795GVlQVPT08cP34cubm58PPzw6NHjxAcHAxDQ0M0atQI/v7+iIuLw/Tp\n06Gvrw89PT0sXrwYBgYG+OKLLyCXy5GVlYV58+bhn3/+QUpKCnx9fZGZmYnevXtj7969CAoKUg4y\ncXISg0wU/vnnH+zYsQOBgYHYunUrNm/eDAcHBzx+/BiAaD/OnDkTz549w5MnT9C/f3+88847+Pnn\nn2FgYIDmzZvjiy++wK+//opHjx5h5syZyM7OhkQiwaxZs+Di4oJ3330Xbdq0QUxMDKysrBAUFAQ9\nPT2t71NKSgomT56M1NRU5OTkYMKECXjrrbcQGBiI06dPK9vD7dq1w9atW7Fnzx5IpVK0adMGU6dO\n1elnSERE9Cqqe1uTCUJERJXQunWAj0/ZT93i6gqsWlX0fleuXEF2djZGvJyr6enTp7hz5w7q1q2L\n1atXw9jYGM+fP1feMLSwsICFYsJw5E2VUa9ePZw/f77Q+Zs1a6bcnpiYiOjoaPznP/+Bnp4e9PT0\n0ELT5Oj5KG602djYYOPGjTh79iwkEgmyC8yvcufOHSQlJWH0aDFly/Pnz3H37t2i3wSCm5sbwsLC\nEBMTo3JjMyEhAbGxsSrzaRMRVQYV/f0J5H0zXLoTAAAgAElEQVQ/GRkZISkpCV9++SVMTU3x4sWL\nQlNYNW3aFPr6+tDX11dOc5Wfi4uYJrN+/frK6asePnyI119/HYD4O33gwIEiY4qOjsabb74JiUQC\nfX19tGrVCtHR0Zg+fTrWrVuH7du3o3HjxujevTvGjh2LtWvX4uOPP4atrS1aKurSV3Ol+c6zs7OD\nnZ0dzp07h9zcXEjzJeycOXMGAPDGG2/oLHYiooqyrtc6+BzwQURi2X7xulq7YpVX0V+8jo6OGtt7\nMTEx+PDDDwEAb731FgDgxo0bWLNmDX766Se1bUciIqp8KrKNt2PHDtjb2yMwMBBRUVH466+/YGZm\nhjVr1uDJkyeYM2cOfv75Z8hkMixYsAAhISGQSCRo3rw5pk2bhvDwcDx9+hT3799H7dq18d133+Hm\nzZtITU1Fnz59MGTIEPj4+ODw4cPw8PBAZmZmkYNMAFHh9Mcff8SSJUvg6uoKb28xjXVsbCx69uyJ\nd999FwkJCRg2bBiGDBmCfv36wdraWqVtt3jxYgwbNgzdu3dHREQEZsyYgdDQUNy9exfBwcGoX78+\nBg0ahMuXL6N169Za36c1a9agQ4cO+Pjjj5GQkIDBgwfjjz/+wJ49e7BlyxbY2toiNDQUgKjKMHv2\nbLRu3Rrbtm1DdnY29DXN60ZERDVaRbY3q3tbk9+8RESVkIcHcO1axb2+i4sL0tPT8f3330MikWDT\npk1o2rQpfHx8EBAQACcnJ6xYsQL37t0DAJUbYQBU5kkujiZNmmDz5s3Izc1FdnY2rhXj4hWvuXz5\ncnTt2hVDhw7F7t278fPPPytjkMvlaNCgAerXr4+NGzfCwMAAoaGh1WKqnvLQt29fhIWFITAwEMuW\nLYNUKoVcLsfSpUshl8sxcODAig6RiEiFrr8/NXVKS6VS5ObmKh8DwPHjx/HgwQMsW7YMSUlJ+P33\n3yGXy1WOK+r7Ut32evXq4ebNm8opNIvDyckJoaGhGDFiBLKzs3HhwgX069cPISEhGD9+PKysrDBn\nzhz8/vvveP78Ofr164epU6di3bp12LlzJ3x9fYv1OlVZab/zevfujbVr12LLli0YPnw4ADGCd+3a\ntTA2NkafPn3K4zKIiMqVh6MHrvlo/uLV9fSiUqlUY3vv1q1buHz5MlxcXHD27FkcPXoUsbGx6N+/\nP7p06aLSdiQiosqrIvtIb926hc6dOwMQgzuuXLmiHExw9+5dNGnSRDl40t3dHSdPnsSMGTOwYcMG\nfPbZZ6hduzYmTpyIzp074/bt2xg3bhz09fXx+eefo06dOnB1dcW5c+fw888/Y+rUqcUaZKKIq0mT\nJjAwMICBgYEy8cfa2hrBwcE4dOgQZDKZ1puT0dHRcHd3ByAGesbHxwMQg0Drv5zjrH79+sjIyCjy\nfYqOjlZWg7e1tYVMJkNSUhKWLl2KpUuXIjExEZ06dQIALFy4EBs3bkRAQABat25dqI1MRESkUFR7\ns6RK0j6t7m1NJggREVEhjo6OMDc3x+DBg5GZmYmWLVvC1tYWvXv3xrhx42BlZYV69erhyZMnZfJ6\nzs7O6NKlCwYMGAALCwsYGBgUe/TIe++9h4CAABw4cAD169dXxtSqVSsEBARg2bJlGDFiBIYNG4ac\nnBzY29vj/fffL5O4q7sOHTrAy8sLBw4cwMCBA9GuXTtcuHAB4eHh8PT0ZAUhIqKXrKyskJWVhfT0\ndOW6li1bYvXq1RgwYAAMDQ3h4OCAhw8flvq1vv76a8yYMQOmpqYwMDCAra1tkcd4eHjgzJkzGDhw\nIFJSUtCvXz80b94cCQkJGDlyJMzNzVGrVi107doVd+7cwbRp05TnLzhNaHVVku+8oKAgAMD48eOV\n60aNGoWDBw9i/vz5OHv2LBwcHHDo0CHcvXsXs2fPhqWlZXlfEhFRjWBpaam2vTd27FjMmDEDe/fu\nBQAsWLAAFy9exPz587Fu3TqVtiMREZE6Tk5OuHz5Mrp37467d+9i6dKl6Nu3LwCgQYMGiI6OxosX\nL2BqaoozZ87A0dERhw8fhpubG3x9ffHLL7/g+++/R+/evVG3bl1s3LgRFy5cwNKlS7F582YMGDAA\nwcHBSE9Ph5OTEw4fPlzkIBMAcHBwwM2bN5GRkYGcnBxERESgd+/e2LhxI1q3bo0hQ4bg9OnTOHbs\nGAAx8EQxoCX/tYWHh+Odd95BREQErK2tlfu+yvsUHh6OZs2aISEhASkpKTAzM8PBgweVAy569uwJ\nZ2dnnDhxAvPmzYORkRE+/fRTXLhwAW3bti3xaxIREeladW5rMkGIiIhUKMrSAsBnn32msm3kyJEY\nOXJkoWNOnTqlfLxo0SLl486dOytH2ij22bx5s3L74MGDAQCPHz+GmZkZfvrpJ2RmZqJnz57K0Srq\n/Pnnn8rHvXr1gpOTU6HM30GDBmHQoEEAREOVI/dfzeLFi9GkSRP8/PPPCA4Ohp2dHfz8/DBq1KhX\n6jQgIqqOjIyMEBYWprLOxsYGu3fvLrSvm5ub8nG7du2UjxXfk/m/R/OfX/Hdd/nyZaxduxaWlpYI\nDAyEgYGBxrjyJ7BMnToVgOpomW7duqFbt24qx1hYWCjLv9c0xf3OW7lyJQDV91cmk2Hr1q1YunQp\njhw5ghMnTqBx48ZYunQpevbsWe7XQkRU3eVvt/bp06dQe8/ExARr165VWefg4IBevXoVOpfiO1bd\ndzAREdVcgwYNwowZMzB06FDk5ORg5MiRyht+lpaWGD9+PIYPHw6pVIqGDRti0qRJSEhIwOTJkxEU\nFASpVIrp06fDzs4OEydORHBwMKRSKXx8fAAAbdu2xezZs/H5558DKP4gE0tLS0yYMAHTpk2DnZ0d\nTExMAIiBIXPnzsW+fftgbm4OPT09ZGZmokWLFli8eDGcnJyU55gyZQpmz56NjRs3Ijs7G/Pnz3/l\n92nMmDGYMWMGfvvtN6Snp8Pf3x+GhoaoU6cO+vTpgzp16qBjx46wsbGBs7MzPvzwQ1hYWMDW1hat\nWrV65dclIiLShZrQ1mSCEBERVTgLCwtcuXIFH3zwASQSCfr374/ExETlzcz83n//fQwZMqQCoqyZ\nDAwM4OPjo+y8ICKiimVlZYVPPvkEpqamqF27NhYtWgRfX188ffpUZT+ZTIY1a9ZUUJRVU3G/8yIj\nI9Wut7a2xoIFC3QRGhERERERlTMjIyN89913Grf/97//VU6tpdCwYUOEhIQU2nfTpk1qz/Hbb78p\nH5dkkImXlxccHR0LDZg8ePBgoeO7du2qrIiquFHZoEED/PDDD4X2zT8INDAwUG3MCvkHga5evbrQ\ndl9fX5XpqiMiItC/f3/0799f63mJiIhIt5ggREREFU4qlWLhwoWF1udvaBIREZGYWvO9995TWaeo\naENERERERERUVu7fv692AKe7uzv8/PwqICIiIiIqLSYIEREREREREREREREREZGSnZ0dB3ASERFV\nM9KKDoCIiIiIiIiIiIiIiIiIiIiIiHSHCUJERERERERERERERERERERERNUYE4SIiIiIiIiIiIiI\niIiIiIiIiKoxJggREREREREREREREREREREREVVjTBAiIiIiIiIiIiIiIiIiIiIiIqrGmCBERERE\nRERERERERERERERERPT/7d1/UNR1HsfxFyCanT9QNJtQA398VxpSFF38kZZBWY4aDNB2GOppF3cG\nzhVnipl66qh52eVdOVnjiZJ1pXJ1WU0el4GmkpDY3YmXcVSCvxjEEwT5tZ/7w9mVZRdi9fvZ72fh\n9Zhx5ub7/bR93kvf75Op7+12YD5CCGH0JlRWUFBg9BaIiMhAERERRm/Bq7CbRESdG7vpHnaTiKjz\nYjPdw2YSEXVu7KZ72E0ios6trW7yASEiIiIiIiIiIiIiIiIiIiIiog6MXzFGRERERERERERERERE\nRERERNSB8QEhIiIiIiIiIiIiIiIiIiIiIqIOjA8ISdTY2IiMjAxMnz4dI0eORFRUFF5//XU0NDQY\nuq/y8nKsWLEC999/P8LCwjBp0iT89re/xZkzZ5zWfvDBB4iJiUF4eDimTJmC9evX4+rVqy5f94sv\nvoDFYsHo0aMxYcIELFu2DBUVFbLHAQC89NJLMJlMyMvLczqn8gx/+9vfEB8fj1GjRuG+++7DokWL\nUFJS4lUzVFZWYuXKlZg8eTLCwsLw4IMPYuPGjaitrXVY5+714M7MN+PChQuIiIhARkbGLf/93XnP\njx8/jnnz5mHcuHEwm81YtGiRy2vvVmeorq7Gxo0b8dBDDyEsLAyRkZFYuHAhioqKvGaGlt5++22Y\nTCZkZWUpNQPpR8VusplqzcBuspvsJrtJ16nYTIDdVG0Gb+8mm2nsvZrdVGMG0kdn66an6NVImWS0\nUCZZ7dOLUY2TsV+ZLZOx35b07Jas/ebm5iIpKQljxoxBZGQkFixYgG+++caw/VL7sZtysJv6Ur2Z\nALtpdIeaYzcd8QEhiVavXo3169cjICAAc+bMwYABA/DHP/4RaWlphu2pvLwcCQkJeO+99zB06FAk\nJSXh3nvvxb59+xAfH4/vv//evnbr1q1YsmQJrFYrnnzySYwYMQIZGRlYsGAB6uvrHV533759SE5O\nRkVFBX7+859j/Pjx+Otf/4onnngCV65ckTrTN998gx07drg8p/IMf/jDH7B48WJUVVUhMTERZrMZ\n2dnZsFgsKC0t9YoZrl69isTERPzlL39BSEgIkpKScMcdd2Dbtm34xS9+gcbGRvtad64Hd2a+2X2n\npqaiurra5XlZ7/mxY8eQlJSE06dPIzY2FlFRUThw4ADi4+Mdfua3OkNNTQ1mz56Nbdu2ITAwEElJ\nSZg4cSJycnJgsVhQUFCg/AwtlZWVYdOmTa2eN2oG0pdq3WQz1ZqB3WQ32U12k25QrZkAu6naDN7e\nTTbT2Hs1u+mI3fR+nambnqJXI2WS0UKZZLVPz/0Z0TgZ+5XZMhn7bUnPbsna7/vvv49f/vKX+P77\n7xEXF4cHH3wQx44dQ2JiotN/7DTy931yjd3UH7upL9Wbadsju8lutne/Hu+mICkKCgqEpmkiNTVV\nWK1WIYQQVqtVPP/880LTNPH5558bsq8XX3xRaJom/vznPzsc//DDD4WmaSI5OVkIIURZWZm45557\nhMViEfX19fZ1r776qtA0TWRmZtqPVVdXC7PZLKKiokRVVZX9+O7du4WmaWLDhg3S5qmrqxMzZswQ\nmqYJTdPE0aNH7edUnuHEiRPCZDKJJ598UtTW1tqPf/rpp0LTNLF06VLlZxBCiG3btglN08TatWvt\nx6xWq0hLSxOapomsrCwhhHvXgzsz34zS0lIRGxtr/2dm+/btDudlvedWq1VMmzZNjB07Vpw7d85+\n/PDhw8JkMonU1FTdZti6davQNE2sWbPG4XheXp4IDQ0VM2bMUH6GlubPn29fu3fvXodzRs1A+lKx\nm2ymOjOwm+ymDbvJbpKazRSC3VRpho7QTTbTuHs1u8ludjSdqZueolcjZZLRQtlktE8vRjVO1n5l\ntUzWflvSq1uy9ltWViZGjhwpHn30UVFRUWE/XlhYKEaMGCGSkpI8ul9yD7upP3ZTfyo3Uwh20+j9\ntsRuOuMnCEmya9cuAEBKSgp8fHwAAD4+Pnjuuefg4+OD3bt3G7Kv7Oxs9O3bF3PnznU4PmvWLAwe\nPBiHDh2C1WrFe++9h8bGRiQnJ8Pf39++7le/+hV69OjhsP+PP/4Yly9fxrx589CjRw/78fj4eISE\nhCArKwtNTU1S5nnjjTdQUlKCiRMnOp1TeQbbPx+rV6/GbbfdZj/+yCOPwGKxYPDgwcrPAAD//Oc/\nAQBxcXH2Yz4+PkhISAAAFBYWOszbnuvBnZndlZGRgZkzZ+LUqVMYP368yzWy3vPDhw+jpKQE8fHx\nuPPOO+1rJ0yYgEmTJiE7OxuVlZW6zLB//374+PjgN7/5jcNxs9kMs9mMb7/9FhcuXFB6hub27t2L\nQ4cOYcqUKS7PGzED6U/FbrKZ6szAbrKbNuwmu0lqNhNgN1WaoSN0k8005l7NbjpjN71fZ+qmp+jV\nSJlktFA2Ge3Tg5GNk7VfWS2Ttd/m9OyWrP3u2bMH165dw/Lly9G3b1/78VGjRuGpp55CaGiox/ZL\n7mM39cdu6k/VZgLspgr7bY7ddI0PCEmSn5+PPn36QNM0h+MDBgxAcHAwjh075vE9NTU1ITk5GSkp\nKfD1df7Rd+3aFQ0NDWhoaLDvb9y4cQ5runXrhvDwcJw6dQpVVVUAYF8bGRnp9JpmsxmXL1/G6dOn\n9R4Hp06dwptvvonk5GQMGzbM6bzKM+Tm5kLTNISEhDidW716NX79618rPwMABAQEAADOnj3rcNwW\nAtuNzJ3rwZ2Z3bVz504EBQXh7bffxmOPPeZyjaz3vK21kZGRaGpqcvoYvpudwWKx4Nlnn3WIg03X\nrl0BwP49pqrOYHPx4kVs2LABsbGxmDRpkss1RsxA+lOtm2zmdarMwG5ex27CfozddI3d7BxUaybA\nbtqoMkNH6Cabacy9mt10xm56v87UTU/Qs5EyyWihbDLapwcjGydrv7JaJmu/Nnp3S9Z+c3Nz0bt3\nb5f/ITQtLQ3p6eke2y+5j93UF7sph6rNBNhNFfZrw262jg8ISVBfX4/z58/bn6hsKSgoCFeuXMGl\nS5c8ui8/Pz/MnTsXs2fPdjpXXFyM//73vxg8eDC6deuGH3/8Ef369XN5sQcFBQEASkpKAABnzpwB\nAAwaNMhp7cCBAx3W6qWpqQnLli3D3XffjeTkZJdrVJ2hoqICly5dwvDhw1FcXIyUlBSMHTsWERER\nWLRokX0vKs9gExcXB39/f6xfvx4FBQWora1FXl4eXn75ZfTs2RNxcXFuXw/uzOyu3/3ud/jggw8w\nZsyYVtfIes/bWmt73ebfk3srMyQkJLi8Li5duoT8/Hzcfvvt9v2pOkPztf7+/li6dGmra4yYgfSl\nYjfZzBuMnoHddJ6D3WQ321rLbnZsKjYTYDebM3qGjtJNNtOYezW76Yzd9G6drZuy6d1IWWS1UDYZ\n7dODkY2TtV9ZLZO13+Zr9ezWzfip/QohUFxcjCFDhqC8vBxLlizB+PHjER4ejgULFqCoqMij+yX3\nsJv6YjflUbWZALupwn6br2U3XeMDQhJcvnwZANCzZ0+X523HPfmUalusVivWrFkDq9WKxx9/HMD1\nGX5q/9XV1QCAyspKdO3a1eFj52xsNzfbWr1s27YNRUVFWLt2rf3pxJZUneHixYsArj/JmpCQgLKy\nMsTFxSEiIgKfffYZLBYLysrKlJ7BJiwsDNu3b8e1a9eQmJiI8PBwzJkzB35+fnj33XcxcOBAt68H\nd2Z21+TJk+Hn59fmGlnvue196NWrV6tr23NPaM8Mrfn973+Pq1ev4rHHHrNfNyrP8MknnyA7OxvL\nly+3PxHuihEzkL68qZtspudnYDddz8FuspstsZudgzc1E2A3AXbzZrGZxtyr2U1n7KZ362zdlE3v\nRsoiq4WyyWifHoxsnKz9tuZWWyZzvzK6JWO/VVVVqKmpQV1dHRISElBYWIgZM2bg/vvvx5EjR5CY\nmGj/aiBP7Jfcw27qi92UR9VmAuymKvtlN9vGB4QkaGxsBIBWb/i243V1dR7bU2uEEFixYgWOHDmC\nsLAw+/d3NjY2tnv/7qzVQ0lJCV577TUkJiZi9OjRra5TdYaamhoA1z8GLDo6Gnv27EF6ejrefPNN\nLF++HBUVFVi3bp3SM9hUVFTglVdeQXl5OaZOnYr58+fDbDbj7NmzWLFiBa5cueL29WDEHM3Jes8b\nGhocjrtaW19ffws7b9uWLVuQlZWFoKAgPPvss/bjqs5QWVmJNWvWYOrUqZg+fXqba1WdgdrPW7rJ\nZhozA7up3hzNsZvO+2I3SSZvaSbAbrKbt4bN9K57Nbupzs+CHHW2bsoko5GyyGqhbDLa5ykq/07R\nXnq0TBZZ3ZKhtrYWAHDy5EkMGTIEH374IZYvX47NmzfjT3/6E2pqarBixQpl9kuOVL3HuMJu6scb\nu+nNzQTYTXbzBqO6yQeEJLA9tWX7FwMt2f5lQPfu3T22J1caGxuxbNky7N69G4MGDcKWLVvs//Dc\ndttt7d6/O2tvlRACL7zwAgIDA/Hcc8+1uVbVGWzfkern54dly5Y5PDk4e/ZsDBo0CDk5OaitrVV2\nBpu0tDR8/fXXeOWVV/DGG29gyZIlyMzMRHp6Or7++mu8+OKLbl8PRszRnKz3vK33QfZcmzdvxubN\nmxEQEICtW7eid+/e9nOqzrB27VrU1dVh1apVP7lW1Rmo/byhm2ymcfdpdlO9OZpjN1vfF7tJMnhD\nMwF2s/m+2M2bw2Z6z72a3VTnZ0HOOls3ZZHVSFlktVA2Ge3zFJV/p2gPvVomi6xuyeDj42P/30uX\nLnX4hIOoqCiYzWacPHnS/pWbRu+XHKl6j2mJ3dSXN3bTm5sJsJvs5g1GdZMPCEnQo0cP+Pr6tvoR\nTraPK2vt48M8oba2FgsXLkRWVhaCg4Oxc+dODBgwwH6+V69erX6sWsv99+rVC3V1dS7/X1C290Cv\nWXft2oWCggKsWrUKP/vZz9pcq+oMttcJCgpy+lgzX19fmEwmNDQ04OzZs8rOAADnz5/HkSNHMG7c\nOKcnMOfNm4dhw4Zh//798Pf3d+t6cGdmGWS957aPGHf12jJ+PsD177h94YUXsGXLFgQGBmLHjh0Y\nPny4wxoVZzhw4AD27duHtLQ03HnnnT+5XsUZyD2qd5PNNLY37OZPz8Fu6oPdVOPnQG1TvZkAu2l0\nbzpCN9lM77hXs5vq/CyodZ2tm7LIaqQsslook6z2eYqqv1P8FL1bJoPMbslge21/f3+n9xIAQkND\nAQA//vgjAOP3S47YTX2wm3L36+3NBNhNdvMGo7rJB4Qk6Nq1K+666y6Ulpa6PF9aWoo+ffq0+Z13\nMv3vf//D3LlzkZOTg3vuuQfvvPMO7rrrLoc1wcHBqKiowLVr15z++rKyMvj6+uLuu++2rwXgcl7b\nsZCQEF32/tlnnwEAnn76aZhMJvufnTt3AgDmzJkDk8mE0tJSZWcYNGgQ/Pz8Wn3Cz/axd927d1d2\nBgA4d+4cAGDIkCEuzw8dOhRWqxUXL15063pwZ2YZZL3nnv751NfX45lnnsGePXsQFBSEd955ByNG\njHBap+IMtut89erVDtf5+vXrAQDp6ekwmUzIy8tTdgZyj8rdZDON7w276bw3dpPdbI7d7FxUbibA\nbho9A9Axuslmqn+vZjeNnYHar7N1UxZZjZRFVgtlktU+T1H1d4q2yGiZDDK7JUP37t1xxx13oKmp\nCVar1el88+sPMH6/5Ijd1Ae7KXe/3t5MgN1kN28wqpt8QEiSiIgIlJeXo6SkxOH4hQsX8MMPPyA8\nPNyQfdXV1SE5ORknTpyA2WxGZmYmAgMDndZFRETAarUiPz/f6a8vLCzEsGHD0KNHD/ta4Pr3U7aU\nl5eHnj17YujQobrsPzY2FikpKU5/Ro0a5XC+V69eys7QrVs3hIWF4dy5c/aPBLNpbGzEqVOnEBAQ\ngAEDBig7AwD069cPAJxmsPnhhx/g4+ODwMBAt64Hd2aWQdZ73tbar776Cr6+vhg5cqQuMwghkJaW\nhgMHDmD48OF499137dFoScUZoqOjXV7n9913H4DrH6uXkpKCoKAgZWcg96nYTTbT+BkAdrM5dvM6\ndtMRu9n5qNhMgN1UYQagY3STzVT7Xs1uGj8DuaczdVMWWY2URVYLZZLVPk9R9XeK1shqmQwyuyXL\n2LFjYbVaXe7h3//+N7p06dKudnpqv+RIxXsMwG7K5G3d9PZmAuwmu+nIkG4KkuLLL78UmqaJ1NRU\n0dTUJIQQwmq1iueff15omiY+//xzQ/a1bt06oWmasFgsora2ttV1xcXFIjQ0VFgsFlFXV2c//uqr\nrwpN00RmZqb9WGVlpRg9erSIjo4WlZWV9uO7d+8WmqaJDRs2yBmmmbVr1wpN08TRo0e9Ygbb6z71\n1FOivr7efnzr1q1C0zSxbt065WcQQojY2FhhMpnE3//+d4fj77//vtA0TSxYsEAI4d714M7Mt2Lv\n3r1C0zSxfft2h+Oy3vPGxkbxwAMPCLPZLM6cOWM/fvjwYWEymURqaqpuM+zYsUNomiYeeughUVFR\n0eZrqDqDK9u3bxeapom9e/cqNQPpQ8VuspnqzMBusps27Ca7SWo2Uwh2U6UZOkI32Uzj79Xsphoz\n0K3rTN30tFttpEwyWiibjPbpzdONk7VfWS2TtV9X9OiWrP0ePnxYaJomYmJiRFVVlf34xx9/LDRN\nE88884wh+6X2YTflYTf14w3NFILdZDfbt18jutnl1p5potZMnDgR06dPxyeffAKLxYLIyEgcP34c\n+fn5mDZtGh544AGP76m8vBy7du0CcP2j19566y2X655++mkMGTIE8+fPx1tvvYWYmBhMnToV3333\nHb744guMGTMGjz/+uH19QEAAFi9ejFWrViEmJgaPPvooLly4gE8//RTBwcFITk72yHwtqTxDXFwc\nDhw4gOzsbMTExGDKlCkoLi5GTk4OgoODkZKSovwMALBu3TokJSUhNTUVU6dORUhICP7zn//g4MGD\n6N+/P1auXAnAvevBnZllkPWe+/n5YeXKlVi4cCHi4uIwc+ZM1NTU4KOPPkKfPn2wePFiXfZfX1+P\nLVu2AABMJpP9mm/piSeeQP/+/ZWcwV0dYQZSr5tsplozsJvsJrupn44wQ2enWjMBdlO1GTpCN9lM\nNe/V7KZ3ztDZdaZuqkCV/cpooWwy2ucpKv9O0ZLMlqlAhf1OmDABSUlJyMzMxMyZM/Hwww/j/Pnz\n2L9/P/r164f09HSl9kuOVLzHsJvyeVs3vbmZALupEhX2a0Q3/VatWrVK0jydXlRUFLp06YLjx4/j\nyy+/hJ+fH+bMmYP09HR06eL5Z7OOHGIG6qcAAAKKSURBVDmCjz76CABQVFSEr776yuWf+fPno1u3\nbpgwYQL69u2Lf/3rX8jNzcW1a9cQFxeHNWvW4Pbbb3d47XvvvRdDhw5FUVERcnJyUFFRgYcffhgb\nN250+TF/ejt48CBOnDiB2NhYDBw40H5c1Rl8fHwwbdo09O7dGydPnsTBgwdRXV2NWbNm4eWXX0bv\n3r2VnwG4/lF+jzzyCK5cuYL8/HwcPXoUtbW1mDFjBjZt2uTw/a/uXA/uzHyzioqK8I9//AOTJ092\n+hhBWe95cHAwRo8eje+++w45OTkoKyvDpEmTsGnTppv6XlZXM3z77bfIyMgAABQXF7d6nc+aNQv9\n+/dXcobWFBYW4tChQ4iOjkZoaKjDOSNnIP2o1E02U60Z2E12k91sfYbWsJsdm0rNBNhN1WboCN1k\nM42/V7ObNxj9s6Bb15m66Ul6NFIWWS2USVb79GRE4/Ter+yW6b3f1ujVLVn7nTJlCoKCgnD69Gnk\n5uaivLwc0dHR2LRpk/1rXTy9X2o/dlMOdlM/3tBMgN1kN9u/X09300cIIXSbjIiIiIiIiIiIiIiI\niIiIiIiIlOJr9AaIiIiIiIiIiIiIiIiIiIiIiEgePiBERERERERERERERERERERERNSB8QEhIiIi\nIiIiIiIiIiIiIiIiIqIOjA8IERERERERERERERERERERERF1YHxAiIiIiIiIiIiIiIiIiIiIiIio\nA+MDQkREREREREREREREREREREREHRgfECIiIiIiIiIiIiIiIiIiIiIi6sD4gBARERERERERERER\nERERERERUQfGB4SIiIiIiIiIiIiIiIiIiIiIiDowPiBERERERERERERERERERERERNSB/R/y3Llh\n6hxwUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27a231cdb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print ('Mean Values: train_loss = %s, train_acc = %s, train_precision = %s, train_recall = %s, cv_loss = %s, cv_acc = %s, cv_precision = %s, cv_recall = %s'%(\n",
    "    np.mean(tr_loss_arr), np.mean(tr_acc_arr), np.mean(tr_precision_arr), np.mean(tr_recall_arr), np.mean(cv_loss_arr), np.mean(cv_acc_arr), np.mean(cv_precision_arr), np.mean(cv_recall_arr)))\n",
    "\n",
    "oj = Plot(rows=1, columns=5, fig_size=(40,7))\n",
    "\n",
    "l_rate_df = pd.DataFrame(l_rate_arr, columns=['learning_rate'])\n",
    "oj.vizualize(data=l_rate_df, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Learning Rate Decay'})\n",
    "\n",
    "tr_loss_df = pd.DataFrame(tr_loss_arr, columns=['training_loss'])\n",
    "oj.vizualize(data=tr_loss_df, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Training Loss'})\n",
    "tr_data = pd.DataFrame(np.column_stack((tr_acc_arr, tr_precision_arr, tr_recall_arr)), columns=['accuracy', 'precision', 'recall'])\n",
    "oj.vizualize(data=tr_data, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Training Metrics'})\n",
    "\n",
    "cv_loss_df = pd.DataFrame(cv_loss_arr, columns=['crossvalidation_loss'])\n",
    "oj.vizualize(data=cv_loss_df, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Crossvalidation Loss'})\n",
    "cv_data = pd.DataFrame(np.column_stack((cv_acc_arr, cv_precision_arr, cv_recall_arr)), columns=['accuracy', 'precision', 'recall'])\n",
    "oj.vizualize(data=cv_data, colX=None, colY=None, label_col=None, viz_type='line', params={'title':'Cross-Validation Metrics'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['cvalid']\n"
     ]
    }
   ],
   "source": [
    "which_data = 'cvalid'\n",
    "tsoj = Test(params=dict(pprocessor_inp_img_shape=[224,224,3],\n",
    "                        pprocessor_inp_crop_shape=[],\n",
    "                        model_inp_img_shape=[224,224,3]),\n",
    "                    device_type = 'gpu',\n",
    "                    which_net='resnet')\n",
    "fnl_tst_metric_stack = tsoj.run(use_checkpoint_for_run='sam_new',\n",
    "                                use_checkpoint_for_imageType='aerial_cropped',\n",
    "                                optional_batch_name=which_data,\n",
    "                                which_checkpoint='all',\n",
    "                                dump_stats=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checkpoint</th>\n",
       "      <th>test_batch</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>test_precsion</th>\n",
       "      <th>test_recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>epoch_19_batch_39</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>epoch_19_batch_49</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>epoch_19_batch_59</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>epoch_19_batch_69</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>epoch_19_batch_70</td>\n",
       "      <td>cvalid</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          checkpoint test_batch  test_loss  test_acc  test_precsion  \\\n",
       "0  epoch_19_batch_39     cvalid      0.419     0.885          0.878   \n",
       "1  epoch_19_batch_49     cvalid      0.405     0.891          0.894   \n",
       "2  epoch_19_batch_59     cvalid      0.391     0.887          0.885   \n",
       "3  epoch_19_batch_69     cvalid      0.394     0.887          0.884   \n",
       "4  epoch_19_batch_70     cvalid      0.396     0.887          0.885   \n",
       "\n",
       "   test_recall  \n",
       "0        0.894  \n",
       "1        0.887  \n",
       "2        0.888  \n",
       "3        0.890  \n",
       "4        0.888  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_outcomes_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'pred_outcomes.csv')\n",
    "prediction_metrics_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'pred_metrics.csv')\n",
    "prediction_outcomes = pd.read_csv(prediction_outcomes_path)\n",
    "prediction_metrics = pd.read_csv(prediction_metrics_path)\n",
    "prediction_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n",
      "Test Graphs: RESNET\n",
      "Learning Rate: Initial:  0.0005\n",
      "Batch path C:\\Users\\newline\\Documents\\ImageClassification\\data\\batch_data\\sam_new\\aerial_cropped, batch_names: ['test']\n"
     ]
    }
   ],
   "source": [
    "which_data = 'test'\n",
    "tsoj = Test(params=dict(pprocessor_inp_img_shape=[224,224,3],\n",
    "                        pprocessor_inp_crop_shape=[],\n",
    "                        model_inp_img_shape=[224,224,3]),\n",
    "                    device_type = 'gpu',\n",
    "                    which_net='resnet')\n",
    "fnl_tst_metric_stack = tsoj.run(use_checkpoint_for_run='sam_new',\n",
    "                                use_checkpoint_for_imageType='aerial_cropped',\n",
    "                                optional_batch_name=which_data,\n",
    "                                which_checkpoint='all',\n",
    "                                dump_stats=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checkpoint</th>\n",
       "      <th>test_batch</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>test_precsion</th>\n",
       "      <th>test_recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>epoch_19_batch_39</td>\n",
       "      <td>test</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>epoch_19_batch_49</td>\n",
       "      <td>test</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>epoch_19_batch_59</td>\n",
       "      <td>test</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>epoch_19_batch_69</td>\n",
       "      <td>test</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>epoch_19_batch_70</td>\n",
       "      <td>test</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          checkpoint test_batch  test_loss  test_acc  test_precsion  \\\n",
       "0  epoch_19_batch_39       test      0.346     0.909          0.902   \n",
       "1  epoch_19_batch_49       test      0.337     0.914          0.914   \n",
       "2  epoch_19_batch_59       test      0.330     0.910          0.905   \n",
       "3  epoch_19_batch_69       test      0.331     0.910          0.905   \n",
       "4  epoch_19_batch_70       test      0.332     0.910          0.905   \n",
       "\n",
       "   test_recall  \n",
       "0        0.918  \n",
       "1        0.913  \n",
       "2        0.916  \n",
       "3        0.916  \n",
       "4        0.916  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_outcomes_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'pred_outcomes.csv')\n",
    "prediction_metrics_path = os.path.join(pathDict['statistics_path'], 'prediction_stats', 'pred_metrics.csv')\n",
    "prediction_outcomes = pd.read_csv(prediction_outcomes_path)\n",
    "prediction_metrics = pd.read_csv(prediction_metrics_path)\n",
    "prediction_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROUGH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import argparse\n",
    "import imutils\n",
    "import cv2\n",
    "from scipy import misc\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "dir_in = '/Users/sam/All-Program/App-DataSet/HouseClassification/input_images/sam_new/aerial_cropped/house/20-20-311-043-0000.jpg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1131b8438>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SutqjlGK5XIbYPyW8UeRmZ8ZxpDcjWZKgNRjtyuhuHOhHi4odz2XoRmrtSvck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\nrGGIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1114bbdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_in = misc.imread(dir_in)\n",
    "plt.imshow(image_in)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x113472438>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zj2eyg/4btO+MoDfphTOUkdt6kLKG9YfRH/NkGWtReslWVzA5LYnJ1gIwRsk5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\nIxi1LvSfVYDqsG3UCUqZ3nPKTCRP86Hhy9ReZyRBeWQKkp3PT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EyWiupmoy1twm2DMRW9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\nuPx/VNNOOD9dELUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1133ee3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rotated = imutils.rotate_bound(image_in, 90)\n",
    "plt.imshow(rotated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for angle in np.arange(0, 360, 15):\n",
    "    \n",
    "    \n",
    "    cv2.imshow(\"Rotated (Problematic)\", rotated)\n",
    "\tcv2.waitKey(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 1024)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "X1  = np.random.random((100,512))\n",
    "X2 = np.random.random((100,512))\n",
    "X1_tf = tf.constant(X1)\n",
    "X2_tf = tf.constant(X2)\n",
    "X3 = tf.concat([X1_tf, X2_tf] , axis=-1)\n",
    "print (X3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}