dtw.ipynb 116 KB
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{
 "cells": [
  {
   "cell_type": "code",
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   "execution_count": 14,
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   "metadata": {},
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   "outputs": [],
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   "source": [
    "from dtw import dtw\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
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    "import matplotlib.pyplot as plt\n",
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    "from itertools import product\n",
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    "from sklearn.model_selection import train_test_split, GroupShuffleSplit"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 11,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "NUMBER_OF_MFCCS = 13\n",
    "CLIP_SIZE = 1290"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
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   "metadata": {},
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   "outputs": [],
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   "source": [
    "from audio_classification.dtwclassifier.dtw import dtw\n",
    "from audio_classification.dtwclassifier.distances import euclid\n",
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    "from audio_classification.dtwclassifier.ranges import default_range, itakura_range"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 7,
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   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>1</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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      ],
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       "      0     1     2     3     4     5     6     7     8     9     10    11  \\\n",
       "0      1  None  None  None  None  None  None  None  None  None  None  None   \n",
       "1      1     1     1  None  None  None  None  None  None  None  None  None   \n",
       "2   None     1     1     1     1  None  None  None  None  None  None  None   \n",
       "3   None     1     1     1     1     1     1  None  None  None  None  None   \n",
       "4   None  None     1     1     1     1     1     1     1  None  None  None   \n",
       "5   None  None     1     1     1     1     1     1     1     1     1  None   \n",
       "6   None  None  None     1     1     1     1     1     1     1     1     1   \n",
       "7   None  None  None     1     1     1     1     1     1     1     1     1   \n",
       "8   None  None  None  None     1     1     1     1     1     1     1     1   \n",
       "9   None  None  None  None     1     1     1     1     1     1     1     1   \n",
       "10  None  None  None  None  None     1     1     1     1     1     1     1   \n",
       "11  None  None  None  None  None     1     1     1     1     1     1     1   \n",
       "12  None  None  None  None  None  None     1     1     1     1     1     1   \n",
       "13  None  None  None  None  None  None  None     1     1     1     1     1   \n",
       "14  None  None  None  None  None  None  None  None  None     1     1     1   \n",
       "15  None  None  None  None  None  None  None  None  None  None  None     1   \n",
       "16  None  None  None  None  None  None  None  None  None  None  None  None   \n",
       "17  None  None  None  None  None  None  None  None  None  None  None  None   \n",
       "18  None  None  None  None  None  None  None  None  None  None  None  None   \n",
       "19  None  None  None  None  None  None  None  None  None  None  None  None   \n",
       "\n",
       "      12    13    14    15    16    17    18    19  \n",
       "0   None  None  None  None  None  None  None  None  \n",
       "1   None  None  None  None  None  None  None  None  \n",
       "2   None  None  None  None  None  None  None  None  \n",
       "3   None  None  None  None  None  None  None  None  \n",
       "4   None  None  None  None  None  None  None  None  \n",
       "5   None  None  None  None  None  None  None  None  \n",
       "6      1  None  None  None  None  None  None  None  \n",
       "7      1     1  None  None  None  None  None  None  \n",
       "8      1     1     1  None  None  None  None  None  \n",
       "9      1     1     1  None  None  None  None  None  \n",
       "10     1     1     1     1  None  None  None  None  \n",
       "11     1     1     1     1  None  None  None  None  \n",
       "12     1     1     1     1     1  None  None  None  \n",
       "13     1     1     1     1     1  None  None  None  \n",
       "14     1     1     1     1     1     1  None  None  \n",
       "15     1     1     1     1     1     1  None  None  \n",
       "16  None     1     1     1     1     1     1  None  \n",
       "17  None  None  None     1     1     1     1  None  \n",
       "18  None  None  None  None  None     1     1     1  \n",
       "19  None  None  None  None  None  None  None     1  "
      ]
     },
598
     "execution_count": 7,
599 600 601 602 603 604 605 606 607 608 609 610 611 612 613
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 20\n",
    "m = 20\n",
    "\n",
    "res = itakura_range(n, m, 2)\n",
    "array = np.array([[None]*n]*m)\n",
    "for (i, j) in res:\n",
    "    array[i, j] = 1\n",
    "pd.DataFrame(array)"
   ]
  },
614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cached features found\n"
     ]
    }
   ],
   "source": [
    "from audio_classification.preprocess import preprocess\n",
    "data, similarity = preprocess(NUMBER_OF_MFCCS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('preprocessed_nmfcc_13/data.csv')\n",
    "\n",
    "X = df[['file']]\n",
    "y = df[['label']]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=666)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, random_state=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 5, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 5, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 5, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 10, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 10, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 10, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 15, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 15, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 15, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 20, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 20, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 20, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 25, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 25, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 25, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 30, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 30, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 30, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 35, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 35, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 35, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 40, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 40, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MEAN', 'n_compared_songs': 40, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 5, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 5, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 5, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 10, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 10, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 10, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 15, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 15, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 15, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 20, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 20, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 20, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 25, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 25, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 25, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 30, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 30, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 30, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 35, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 35, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 35, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 40, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 40, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function manhattan at 0x7fb1335f6200>, 'mode': 'MIN', 'n_compared_songs': 40, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function euclid at 0x7fb15a283b90>, 'mode': 'MEAN', 'n_compared_songs': 5, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function euclid at 0x7fb15a283b90>, 'mode': 'MEAN', 'n_compared_songs': 5, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function euclid at 0x7fb15a283b90>, 'mode': 'MEAN', 'n_compared_songs': 5, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function euclid at 0x7fb15a283b90>, 'mode': 'MEAN', 'n_compared_songs': 10, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function euclid at 0x7fb15a283b90>, 'mode': 'MEAN', 'n_compared_songs': 10, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function euclid at 0x7fb15a283b90>, 'mode': 'MEAN', 'n_compared_songs': 10, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function euclid at 0x7fb15a283b90>, 'mode': 'MEAN', 'n_compared_songs': 15, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function euclid at 0x7fb15a283b90>, 'mode': 'MEAN', 'n_compared_songs': 15, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dist_fn': <function euclid at 0x7fb15a283b90>, 'mode': 'MEAN', 'n_compared_songs': 15, 'range_fn': <function default_range at 0x7fb15a283b00>}\n",
      "{'dist_fn': <function euclid at 0x7fb15a283b90>, 'mode': 'MEAN', 'n_compared_songs': 20, 'range_fn': <function sakoe_chiba_range at 0x7fb15a283ef0>}\n",
      "{'dist_fn': <function euclid at 0x7fb15a283b90>, 'mode': 'MEAN', 'n_compared_songs': 20, 'range_fn': <function itakura_range at 0x7fb15a28f050>}\n",
      "{'dist_fn': <function euclid at 0x7fb15a283b90>, 'mode': 'MEAN', 'n_compared_songs': 20, 'range_fn': <function default_range at 0x7fb15a283b00>}\n"
722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "from audio_classification.dtwclassifier.dtwclassifier import DTWClassifier\n",
    "from audio_classification.dtwclassifier.distances import euclid, minkowski\n",
    "from audio_classification.dtwclassifier.ranges import sakoe_chiba_range, itakura_range, default_range\n",
    "\n",
    "def manhattan(v1, v2):\n",
    "    return minkowski(v1, v2, 1)\n",
    "\n",
    "params = ParameterGrid({\n",
    "    'mode': [DTWClassifier.MODE_MEAN, DTWClassifier.MODE_MIN],\n",
    "    'n_compared_songs': range(5, 40 + 1, 5),\n",
    "    'dist_fn': [manhattan, euclid],\n",
    "    'range_fn': [sakoe_chiba_range, itakura_range, default_range]\n",
    "})\n",
    "\n",
    "res = []\n",
    "for param in params:\n",
    "    print(param)\n",
    "    cls = DTWClassifier(param['mode'], param['n_compared_songs'], param['dist_fn'], param['range_fn'])\n",
    "    cls.fit(X_train, y_train)\n",
    "    resdf = cls.classify_multiple(X_valid)\n",
    "    res.append({\n",
    "        'accurancy': accuracy_score(y_valid, resdf),\n",
    "        'n_compared_songs': param['n_compared_songs'],\n",
    "        'params': {'mode': param['mode'], 'range_fn': str(param['range_fn'].__name__), 'dist_fn': str(param['dist_fn'].__name__)}\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(res)\n",
    "df['params'] = str(df['params'])\n",
    "pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ytm0bPXv2ZPbs2QA88cQTPPHEE+zYsYPmzZtf8liKoiiVJR26sf43v8AIWLJy0DOy0NPPoqeewnHkBI6DR3HsTUH/40/0/YfQDxxBTzmGfjgV/dhJ5MkzyIwsyCsAhzRmxd1djb7axcF4yaDc3RXh6mwETk4WVeBNqVHCYjEuEHm4QW4e+qHj6AePop/NQhYtFVWUG4HKB63DkpKSLhkgODs7M2DAAABCQkJwcXHBarUSEhJirkVfv349jz32GAAdO3akVatW7N27l7Vr15qBTmhoKKGhoQD8+uuvJCcn0717dwAKCwvp2rXrRcf+9NNPK/067rrrLubNm8dvv/3GJ598Uuqx8lLl58+fz7BhwwBj1n7s2LE8/fTT5uMWi4VJkyYxffp0Bg4cWOmxlCUuLs7MErDZbEycOJGkpCQsFosZ5AFER0eb70dYWBgpKSl4enrSpk0bWrduDRjr1Itnb5cvX87SpUvNYDQ/P5/Dhw8TGBhYqXHdddddAERGRpZZWyAqKqrC96FLly6MHTsWm83GnXfeaQbuCxYsYNasWdjtdlJTU0lOTjY/A3fccQfPPvusGVyvX7+eb775BoBevXpx+vRpzp07d1mvb+XKlYwfP97MtvDz8yvzdX777bdlvo5hw4ahaRrt27enTZs27Nmzh9atW5f7XpUlOjrafJ8A3n//fRYtWgQYF4r27dtH/fr1cXZ2NrMmIiMjWbFiBQAbN25k8eLFANxzzz0VXiBSFOXGJqU8XyXdbjcLt8ni1HSboxIV1EtUUVcV1JXrnFnMDpB2O/LEKWSaQNTzQvh4IYoeU5TrlQrcq9FNN93EkSNHzNtHjx4ttX77atxyyy3s3buXrVu3lruN1Wo109U0TcPFxcX82W63X9FxpZT07duXhISEK3p+WYYPH05kZCRjxoy5KIOgPAkJCZw4cYIvv/wSgOPHj7Nv3z7at29vbjN69GimT59Op06drmp8Hh4e5s8zZsygcePGbNu2DV3XcXV1NR8rPr9gXDio6BxLKfnmm28ICAi4onEVH68yxypPz549Wbt2LT/88APx8fH83//9Hz169ODtt99m8+bN1KtXj/j4ePLz883ndO/enf/+97/cc889l0yHvNrXV6wyr/PCcQghLvlelaXk+7xmzRpWrlzJxo0bcXd3JzY21jwHJX+vrubcK4pyY5AOh9nKTBbaIb8AmV8AhYVIigJyUGvFFeUyCCcn8HRC6joyIxt5JhPc3NDq+4CHm7qIpVyX1Ke6GnXp0oV9+/bx559/UlhYyLx586qsoNb69et54IEHSq29vRI9evQwg9+9e/dy+PBhAgIC6NmzJ1999RUAO3fuNFOlY2Ji2LBhg5mCnpOTU+FMZkVatWrFtGnTmDBhQqW237t3L9nZ2Rw7doyUlBRSUlKYPHnyRRcTrFYrTz31VLlV2BctWsTkyZMva6yZmZk0bdoUTdP44osvzGr+5QkICODgwYPmjHjJlP7+/fvzwQcfmOu0fv/998saS1U4dOgQjRs35sEHH2TcuHFs3bqVc+fO4eHhgY+PD2lpafz444+lnvPqq69Sr149Hn30UaD0Z2jNmjU0aNAAb2/vy3p9ffv25ZNPPjGD4DNnzlzW6/j666/RdZ0DBw5w8OBBAgICLvu9KikzM5N69erh7u7Onj17+PXXXyt8TkxMjJl5MG/evMsav6IodZuU0khnz8s31pSfPIPjcKqRvr73kJHWe+QE8uRpZE6eEaS7u51PU1drxRXlighNM5Z2eHqAw4F+JA1932H09Axkoa2mh6coVUoF7tXIycmJf/7zn/Tv35/AwECGDRtGcHBwle0/ICDgsgOcC02YMAFd1wkJCWH48OHMnTsXFxcXHnnkEbKzswkMDOSll14iMjISMIqazZ0712xN1rVrV7NgWEnlrXEvz8MPP0zbtm0vuv/CNe7PP/88CQkJDB48uNR2Q4YMKTML4IEHHih3RvTAgQN4e3tXeoxgnK/PP/+czp07s2fPnlKztGVxc3Pjo48+YsCAAURGRuLl5WUWj3vxxRex2WyEhoYSHBzMiy++eFljqcil1rgXW7NmDZ07dyY8PJz58+fzxBNPmLc7duzIPffcYy6LKOm9994jLy+PZ599lqlTp5KYmEhoaCjPP/88n3/++WW/vnHjxtGyZUtCQ0Pp3LmzedGoslq2bEl0dDQDBw5k5syZuLq6XvZ7VdKAAQOw2+0EBgby/PPPExMTU+Fz3n33Xd555x1CQ0PZv39/qSKBxUsQil9r8e/GzJkzL2pnqChK7SUdOrKgEJmda7RBO34Kx8Gj6H/8iWP/YfRDRWvKM84ZafDFRd1KBueuzsa6cRWUK3WEtDvQj6Xh2LwT25JV2L78Hsdv25Hnsmt6aKUYxezcwcUZmX7GKMJ49IRRpFEVs1OuA+J6+SBHRUXJCwPF3bt3V3q9cF20YMECFi5cWKfbwdWkUaNGMWPGDLPCenXJzs7G09MTKSWPPvoo7du356mnnqrWYyrXXm5uLm5ubgghmDdvHgkJCVfVReJ6//5SlNpKSmmuO8dmR+YXIgsKoaDQCMaL15kLARYLOFlAzY4r1xGZl488fsq4CHX8JPLkaaMdIEA9b4SrCzLVKLYrWjZFC26L1rYlwlq7VuBKKaHAZvwuW50Q9X0RXh4Ip0t3QFKUmiSESJRSRpX1WO36DVMuS58+ffj0008JCwsjMTGxwlZsSmn/+c9/rslxZs+ezeeff05hYSHh4eE8/PDD1+S4yrWVmJjIxIkTkVLi6+vLnDlzanpIiqJcgtmvvKh9mrH23AjQpZRGIC4lWDQjQLc6IVyca3rYilKlpJSQkYV+3AjS9WMn4ew540GLhmhUHy0sEO2mhoimjRDuRr0YmZmFI/kAevIBHD+ux+FsRQvwRwtqh2jaoFZcyDKK2TkDzkabxBPpyBPpiHreCF9VzE6pe9SMu6LUIj/99BPPPfdcqftat25tVjev62r69e3YsYPRo0eXus/FxYXffvvtmhz/cqjvL0W5elJKcDiKKrTbjTT3vIJyZs+1otlzlcauXL+kw2G0Bzx2sihYPwV5RQVoXZ0RTRuh3dQI0awhonGDCmenpZTIo2nou/aj7ztsXAyr540lqC1aUFuEp/s1eFWVJ6WE/ALje8HNFeHnYyxjUcXslFriUjPuKnBXFEWphdT3l6JUntR1s7WatNkgr2j2vLAQqcvzVdm1EsG5Rf2hrlz/ZH6BkfZeNKMuT5w2glYAXy+0ZkaQrjVrBH4+V3XRShba0PemoCcfQB47CUIgWjVFCypKpa9lKeqy0Gak0ls0RH0fhLcnwtla08NSbnAqVV5RFEVRlDqtwtnzkpyK1p67uaKp2XPlBiGlhMzs80H68VPI0xnGg5ow0t47dygK1hshPNyq9PjC2YqlU3ssndojM86dT6Vftg6Hi7ORSh/cDtG4fq3IahHOVnC2Gi3lTmegnzqL8HRH8/MxZuO1mh+jopSkLjdXs7Fjx9KoUaOr7iVeFl3XGTRoECEhIWa/+Pj4eNasWVPlx4qPj2fhwoUVblPRsefOncvUqVMBmDp1KkIIs7UcGJW5hRBm1W1/f39CQkLMqvKPP/64ua3dbqdhw4Y8//zzpY4RGxtLVNT5C1VbtmwhNjYWMKqox8fHX3KMKSkp5va1yaRJkwgODmbSpEnlblPZsfv7+5s/d+vWzXxuZaq5r1mzhkGDBlW4XV1W8vwoinJtSb2ocntOHnpGFvqJdByHjqHvTTEqt6ccQz+ahjydCYU2cHIqXbnd0x3h6oJwcqoVwYGiVBfp0I3fj63J2L5bg23WQmz/WoTjpw3oe1PAywNLtzCchvbDOmEk1pG343RrF7T2rao8aL+Q8PXGqVs41rF34XRXH7TWN6HvOoA9YRn2fy/FsWWn0RqxFjBayrkZ56SgEP3QcfSDR9DPnkOW05lIUWqCmnGvZvHx8UycOJH77ruvyve9Y8cOTp48yY4dO6p839dKSEgI8+bNY8qUKYDRj/vClnmrV6+mQYMGFz13xYoVdOjQga+//prp06eX+gPt5MmT/PjjjwwcOLDKxmq323FyqrlfmVmzZnHmzJkqL0L4yy+/AOcD93vuuadK91/S1ZxDKSVSSjS1Dk1RrguyZOX2Ahsyv8BYe1py9lxgFIazqNlzRZEFhcjUU+badJmabvwOAXh7orVsYsyk39TIqKBeC35fhKYhWjVDa9UM2avQSKXfdQDHuq041v+O8G+GJbgdonXzGk+lF0KAi7PRTs7uQKalI09gFLLz9TZqANSCc6rcuNRfwNWsZ8+e+Pn5Vcu+MzIyaNSoUan7fHx8cHY2qt76+/szefJkwsLCiIqKYuvWrfTv35+2bduavaOllEyaNIlOnToREhLC/PnzzfsnTpxIQEAAffr04eTJk+YxEhMTufXWW4mMjKR///6kpqZedOzyuLm54enpad6+8847zZZZBw4cwMfHp8wgvSwJCQk88cQTtGzZko0bN5Z6bNKkSUybNu2i5zg7O5fqr10Wi8Vivmdz584lLi6OXr160bt3b7Kzs+nduzcRERGEhISYY09JSSEwMJAHH3yQ4OBg+vXrR16ecSV58+bNhIaGEhYWZp5rAIfDwaRJk+jSpQuhoaF88skn5Y4pLi6O7OxsIiMjmT9/PvHx8Tz++ON069aNNm3amNkQJcd+KSVb4BW/H88//zzr1q0jLCyMGTNmkJKSQo8ePYiIiCAiIsIM8EvavHkz4eHhHDhwgNjYWDNTIj093Zy1ruw5LEtKSgoBAQHcd999dOrUiSNHjvDII48QFRVFcHAwL7/8srmtv78/L7/8srnfPXv2AHDq1Cn69u1LcHAw48aNo1WrVqSnpwNGZ4Ho6GjCwsJ4+OGHcRSt+6vuFoGKcqOQukQW2i6aPXf88SeO/YfQ/yyePT9rBO1Olgv6nhfNnlvV7LlyY5FSIs9l49hzEPvPv2L74jtsH83Dvuhn9E07ocCG1qkdltt7Yn1wKM4P3IXTwB5YOgegNahXK39fhIszlpAOWEcMxDrmDrSoYOTJM9i//x+22V9jX70JPe10rei5LpwsCA938HBDZuWg/3kUPeU4+rlsoyOFotQANeNehzkcjotmH997771St1u2bElSUhJPPfUU8fHxbNiwgfz8fDp16sT48eP59ttvSUpKYtu2baSnp9OlSxd69uzJxo0b+eOPP0hOTiYtLY2goCDGjh2LzWbjscceY8mSJTRs2JD58+fzwgsvMGfOnFLHfumll4iKiiIuLq7UeIYPH17qtre3Ny1atGDnzp0sWbKE4cOH869//avUNrfddps5yzxmzBieeuop8vPzWblyJZ988gkZGRkkJCSYKd8AXbt2ZdGiRaxevRovLy/z/m7dupXariwtWrTg22+/NW9v3bqV7du34+fnh91uZ9GiRXh7e5Oenk5MTIz5Gvft20dCQgKzZ89m2LBhfPPNN4waNYr777+f2bNn07Vr11Jp/Z999hk+Pj5s3ryZgoICunfvTr9+/WjduvVFY1q6dCmenp4kJSUB8OOPP5Kamsr69evZs2cPcXFxDB06tNTYjx8/zrhx41i2bNlF+9u8efNF973++uu8/fbbfP/994DRl3zFihW4urqyb98+Ro4cSckCkL/88ov5WWjZsuUlz2llzmF5f2Ts27ePzz//nJiYGACmTZuGn58fDoeD3r17s337dkJDQwFo0KABW7du5aOPPuLtt9/m008/5ZVXXqFXr15MnjyZ//73v3z22WeAUfxt/vz5bNiwAavVyoQJE/jyyy+57777yjw/iqKUr8LZ8+JfbzV7rihlkrqOPHXWXJuuHz8J2bnGg85WRJMGWLp2NmbUmzSo80XUhJ8PTrdEILuFIQ+nou86gL5jL3rSHkQDX7TgdmgdWyPcqzelv8JxCgFuRS3wbDbksTSkphnV6L09VYtI5ZpSgXsdlpSURPPmzS+5TXFQGRISQnZ2Nl5eXnh5eeHi4kJGRgbr169n5MiRWCwWGjduzK233srmzZtZu3ateX+zZs3o1asXAH/88Qc7d+6kb9++gHHxoGnTphcd99VXX6306xgxYgTz5s3jp59+4ueff74ocC8rVf7777/ntttuw83NjSFDhvD3v/+dd999t1Qa+ZQpU3jttdd44403Kj2WsvTt29ecxZZS8re//Y21a9eiaRrHjh0jLS0NMNqahYWFARAZGUlKSgoZGRlkZWXRtWtXAO655x4zMF6+fDnbt283Z8szMzPZt29fmYF7We688040TSMoKMgcQ0nNmjUrM2ivLJvNxsSJE0lKSsJisbB3717zsd27d/PQQw+xfPlymjVrVuG+KnMOmzRpUuZzW7VqZQbtAAsWLGDWrFnY7XZSU1NJTk42A/e77roLMM5/8QWM9evXm+3mBgwYQL169QD4+eefSUxMpEuXLgDk5eVdlMGiKMp5Upfng3O73eh3nlcIBQXg0M+3VtOEEZw7WdQftYpSDllouyDt/dT5C12e7kUt2Yr+a+B73bYrE5qG8L8Jzf8mZH4B+h9GVXrH/7bgWJeIaN0cS1BbI5W+hjtBCKsVrEXF7M5koqdnIDzcjGJ27m6qmJ1S7VTgXkcVB4AVFYNzcXEBQNM08+fi2/YrKLghpSQ4OPii1PSrMWjQICZNmkRUVBTe3t6Vek5CQgLr168307FPnz7NqlWrzAsKAL169WLKlCn8+uuvVzU+Dw8P8+cvv/ySU6dOkZiYiNVqxd/fn/x8o/9pyfNrsVjMVPnySCn54IMP6N+//xWNq+TxqiOtbMaMGTRu3Jht27ah6zqurq7mY02bNiU/P5/ff//dDNydnJzQdSN9rPicFKvsOSxLyef++eefvP3222zevJl69eoRHx9f6rnF58RisVT4+ZZSMmbMGKZPn17RqVCUG4p0OM63VissqtyeX2gUgoMLZs81cHG+boMKRakqMivnfJB+/CTy1Fko+rdbNKxn9Dxv1gitWUOEt2cFe7s+CVcXLJ0DsHQOQE/PQE8+gL77IPYDR4wsnY6t0YLbojWsniWolR6nphmBOkbdAf3ICeNCZX1fhLcHogbrISnXN/UvbR311Vdf8dZbb/Huu+9e1X569OjB/PnzcTgcnDp1irVr1xIdHU3Pnj3N+1NTU1m9ejUAAQEBnDp1ygzcbTYbu3btuqoxuLu788Ybb/DCCy9Uavtz586xbt06Dh8+TEpKCikpKXz44YckJCRctO2UKVN48803y9zPpk2bLrtoYGZmJo0aNcJqtbJ69WoOHTp0ye19fX3x8vLit99+A2DevHnmY/379+fjjz/GZjP+GN67dy85OTmXNZ6q5OXlRVZWlnk7MzOTpk2bomkaX3zxhbn+G4zX9cMPPzB58mTz4pG/vz+JiYkAl+xAcLnnsKRz587h4eGBj48PaWlp/PjjjxU+p3v37ixYsAAwshzOnj0LQO/evVm4cKFZv+HMmTNljqV3794cO3as0mNUlLrAXHuem488l41+8jSOQ6k49qag7z1kVG4/cgJ58izkFRj9zz3czq87N9eeW1XQrigXkLqOfuosjm17sP+4jsLPvsH26Tc4lq1D37kfXJzRokNwuqsP1gkjsI76K069bsbSsfUNG7RfSGvgi1PPSKwPDsHpjl6I5o3Rt/2B/T/fY/vyexy/70bmlX/R/1oRLs4IT3dwckKmnUbfdxjH8ZPIvPxasVZfub6oS0LVbOTIkaxZs4b09HSaN2/OK6+8wgMPPFAl+w4ICOC77767qn0MHjyYjRs30rlzZ4QQvPnmmzRp0oTBgwezatUqgoKCaNmypZnq7ezszMKFC3n88cfJzMzEbrfz5JNPXlQJvrw17uUZMWJEuY+VXOMeGhpK79696dWrV6kZ5zvuuINnn32WgoKCUs+9/fbbyy00dvjwYdzcLm/t1L333stf//pXQkJCiIqKomPHjhU+57PPPuPBBx9E0zRuvfVWszjeuHHjSElJISIiAiklDRs2ZPHixZc1nku51Br3soSGhmKxWOjcuTPx8fFMmDCBIUOG8O9//5sBAwaUmvkGaNy4Md9//z0DBw5kzpw5PPPMMwwbNoxZs2bxl7/8pdzjXMk5LNa5c2fCw8Pp2LEjLVq0oHv37hU+5+WXX2bkyJF88cUXdO3alSZNmuDl5UWDBg147bXX6NevH7quY7Va+fDDD2nVqpX5XF3X2b9/f5kF/0p+xpcuXcqWLVt49dVXL/u8K0p1utTsuRQgiv+utGhG33M1e64ol03abMgTp5HHT6IfO2mkvRdnqHi4GX3Tw4MQzRoiGvrVeMp3XSI0DdGmOVqb5si8fCOVftd+HGs241ibiGhTnEp/U41+dwknC3i6G8F6di56ZhY4OxvLHDw91HuuVAlxvVwNioqKkiULZ4GxDjcwMLCGRlT9Nm3axGOPPcavv/5aK6uH1naTJk1i9OjR5vro6pKdnW1Wbn/99ddJTU29qIigUn0KCgqwWCw4OTmxceNGHnnkEbPIX0V27tzJnDlzeOedd6p3kGW43r+/lKpjrj0vKhAn8wuQ+YVGcbiS1Y+FMILzohR39e+GolwZmZN3Pkg/fhJ56gzoRWnv9X2L1qY3RLupEXh7qt+1aqCfOouevB9995+Qlw/urmiBbdCC2qE18K3p4QEgbUYtEIQwitn5qGJ2SsWEEIlSyqgyH1OBe91VWFjIPffcw759+/j+++9p0aJFTQ9JKcP8+fOZPn06drudVq1aMXfuXNVu7Brat28fw4YNQ9d1nJ2d+eijj8yCdLXZ9f79pVw+6XAYwbnNjiy0Q36BUb29sBBJGbPnFouaPVeUqySlRJ7OLKr2ftKo9p6ZbTxosRgV3m8qWpvetCHC1eXSO1SqlHToyJRjOHbtR/55FHSJaFzfWAsf0LpWvB9S16GgAGnXjWJ29X1VMTulXCpwV5Q6YseOHYwePbrUfS4uLuYa+evV6dOn6d2790X3//zzz9SvX78GRlTz1PfXjUlKac6cY7Mj8wvPt1a7cPa8qHK7mj1XlKoj7fbzae9FxeQoKDQedHc9X0CuWSNEIz9EiW42Ss2Sufnoew6i7zqATD8LFg2tTQu04HaIVk1rxYVMWVBoLF9ysiDq+yC8PBFWtXJZOe9SgXu1flKEEAOA9wAL8KmU8vULHh8PPAo4gGzgISllctFjocAngDegA12klDVfhUJRqlFISEil07ivJ/Xr178hX7dy45IO/XxwfuHsuSzqHSylMXtuUWvPFaW6yNz8EkH6SWTaGSjqkIKfD1r7lkXBeiPw9VIXyWox4e6KJSIIS0QQ+snTRm/4PX+i7ztk1BoIbIMluB3Cz6fmxujiDC7OSIcDefKs8Xnz9kSr5w1uLurzpVxStf0VIISwAB8CA4EgYKQQIuiCzb6SUoZIKcOAN4F3ip7rBPwHGC+lDAZiAVt1jbW6HDlyhNtuu42goCCCg4OrfF2zrusMGjSIkJAQjhw5AkB8fHyFLeKuRHx8/CUrhVf22HPnzmXq1KkATJ06FSEE+/fvNx9/9913EUJQnD3h7+9PSEgIYWFhhIWF8fjjj5vb2u12GjZsyPPPP1/qGLGxsURFnb9QtWXLFmJjYwFYs2YN8fHxlxxjSkqKuX1tMmnSJIKDg5k0aVK521R27MVt9C40depU3n777Us+99SpU9x8882Eh4ezbt26Co91oblz5zJx4kQAFi9eTHJy8mXvo7pNnTqVuXPn1vQwlDpOSom02Y3qwuey0U+ewXHkBI79h9H3pqD/ebSocvtpZE6eMYvu7obm6Y4oruDu5opwVpXbFaUqSCmRZzJx7NyHffkGCucuxvbJAuzfrUFP2gNCoEUE4hR3G9bxw3AecwdOfbsZwV49bxVU1SFao/o43RaN9aGhOA26FdG4PnpiMrbPl2CbtwzH9r1GLZAaIiwWhIcbeLhBbp7RyePgUfTMLGNZlKKUoTpn3KOB/VLKgwBCiHnAHYD5V7qU8lyJ7T2A4rz9fsB2KeW2ou1OV+M4q42TkxP/+Mc/iIiIICsri8jISPr27UtQ0IXXL67Mjh07OHnyJDt27KiS/dWEkJAQ5s2bx5QpUwD4+uuvL6pQv3r1aho0aHDRc1esWEGHDh34+uuvmT59eql/UE+ePMmPP/7IwIEDq2ysdrsdpxrszTlr1izOnDljVtivKT///DMhISF8+umnV72vxYsXM2jQoEr/TtT0e6AoZTFnz+0OZKGtaPa80Jg91+XFs+dWJ1WgSFGuAWl3GBfGSlZ7zyvqPuPqYhSQC25npL03rm9UBleuK8JiQbRvhda+FTInz0yld/z8K441m9HaFaXSt2hSIxdIhRDg6gKuLsYyjeOnkEIYF4p8PGvFGn2l9qjOT+hNwJESt48W3VeKEOJRIcQBjBn34unUDoAUQvwkhNgqhHi2GsdZbZo2bUpERARg9MgODAys0n7QGRkZNGrUqNR9Pj4+ODsbfxD6+/szefJkwsLCiIqKYuvWrfTv35+2bdsyc+ZMwLj6PGnSJDp16kRISAjz58837584cSIBAQH06dPH7HUNkJiYyK233kpkZCT9+/cnNTX1omOXx83NzaywDnDnnXeyZMkSAA4cOICPj0+ZQXpZEhISeOKJJ2jZsqXZV77YpEmTmDZt2kXPcXZ2NtuxlcdisZjtv+bOnUtcXBy9evWid+/eZGdn07t3byIiIggJCTHHnpKSQmBgIA8++CDBwcH069ePvLw8ADZv3kxoaChhYWHmuQZwOBxMmjSJLl26EBoayieffFLumOLi4sjOziYyMpL58+cTHx/P448/Trdu3WjTpo2ZDVFy7JdSsjjetGnT6NChA7fccgt//PGHef+BAwcYMGAAkZGR9OjRgz179pCUlMSzzz7LkiVLCAsLIy8vj0ceeYSoqCiCg4N5+eWXzef7+/uTnp4OlM56KPbLL7+wdOlSJk2aRFhYGAcOHChzrLGxsTz55JNERUXx3nvv8d1335kz/n369CEtLQ0wZsnHjh1LbGwsbdq04f333zf38fe//52AgABuueUWRo4caWYVlPUaATw9PS+7VaByfZNSGn9U5RUgs3LQ089ePHt+OBWZdhqZnQtIcHNVs+eKcg3JvHz0A0ewr0vENv+/2D5KwD7/vzjWbUWeyURr3RxL365Yx9yBdfwwrHf0wtKlE9pNjVTQfhmkrtfJHuXCww1LZDBOo/+K08jb0Tq1Q085jv3bldg++xb7ht+RZ89VvKPqGp+Tk9ET3s0FmXEOx8GjOA4dR2bnGgXulBtejU9dSSk/BD4UQtwDTAHGYIzrFqALkAv8XLRQ/+eSzxVCPAQ8BNCyZctrOu7LlZKSwu+//87NN99cZft0OBxoF/wBeGE6fsuWLUlKSuKpp54iPj6eDRs2kJ+fT6dOnRg/fjzffvstSUlJbNu2jfT0dLp06ULPnj3ZuHEjf/zxB8nJyaSlpREUFMTYsWOx2Ww89thjLFmyhIYNGzJ//nxeeOEF5syZU+rY5fVxHz58eKnb3t7etGjRgp07d7JkyRKGDx/Ov/71r1LblOzjPmbMGJ566iny8/NZuXIln3zyCRkZGSQkJNCtWzfzOV27dmXRokWsXr0aLy8v8/5u3bqV2q4sLVq04NtvvzVvb926le3bt+Pn54fdbmfRokV4e3uTnp5OTEyM+Rr37dtHQkICs2fPZtiwYXzzzTeMGjWK+++/n9mzZ9O1a9dSaf2fffYZPj4+bN68mYKCArp3706/fv1o3br1RWNaunQpnp6e5jrwH3/8kdTUVNavX8+ePXuIi4tj6NChpcZ+qX7imzdvBoyLMPPmzSMpKQm73U5ERASRkZEAPPTQQ8ycOZP27dvz22+/MWHCBFatWsWrr77Kli1b+Oc//wkYgb+fnx8Oh4PevXuzffv2SrXY69atG3FxcQwaNIihQ4dectvCwkJz+cTZs2fNFoiffvopb775Jv/4xz8A2LNnD6tXryYrK4uAgACz9ds333zDtm3bsNlslXqNzzzzTIXjV65PUtfP9z232SDvgtnz4g21osrtTmr2XFFqgpQSMrPQj51CHk9DP34KzmQaD2oaorEfWlhHo4d604ZGWrJyxaSUUGADmw00ARIkEpys4GKtU8sIhBCIJg3QmjRA9oxCHjyCY9cB9M070TftMDIxgtqhdWhVI9/vQtOMqvOALLShHzlhFCH180V4eyCcrdd8TErtUJ2B+zGgZH+y5kX3lWce8HHRz0eBtVLKdAAhxDIgAigVuEspZwGzwKgqXzXDrnrZ2dkMGTKEd999F29v7yrbb1JSEs2bN7/kNsVBZUhICNnZ2Xh5eeHl5YWLiwsZGRmsX7+ekSNHYrFYaNy4MbfeeiubN29m7dq15v3NmjWjV69eAPzxxx/s3LmTvn37AsbFg6ZNm1503FdffbXSr2PEiBHMmzePn376iZ9//vmiwL2sVPnvv/+e2267DTc3N4YMGcLf//533n333VJp5FOmTOG1117jjTfeqPRYytK3b19zFltKyd/+9jfWrl2LpmkcO3bMnPFt3bo1YWFhAERGRpKSkkJGRgZZWVl07doVgHvuuYfvv/8egOXLl7N9+3ZztjwzM5N9+/aVGbiX5c4770TTNIKCgswxlNSsWbMyg/aS1q1bx+DBg3F3dwfOf16ys7P55ZdfuPvuu81tCwoKytzHggULmDVrFna7ndTUVJKTkysVuF+Okhd8jh49yvDhw0lNTaWwsLDU+frLX/6Ci4sLLi4uNGrUiLS0NDZs2MAdd9yBq6srrq6u/PWvf73s16hcX6SU4HCArajveUEhMq/AqBxts5fe2KmocrubK1od+sNUUa43RjGvM8jjp8xCcuQW1Sx2cTaCrcA2Rtp7k/oItayqSkibzQjYAbzc0Zo2AHdXo299Xj56ZjZk5xrfq04WqGNZRcLJgujgj9bBH5mdi777II7kAzhWbsSxZhNau1ZowW2NVPoa+DdAOFvB2Wq0vUs/i37qDMLLHc3PVxWzuwFV57faZqC9EKI1RsA+Arin5AZCiPZSyn1FN/8CFP/8E/CsEMIdKARuBWZU41irjc1mY8iQIdx7773cddddVbbf4gCwomJwLi7G2hhN08yfi2/b7fbynlYuKSXBwcEXpaZfjUGDBjFp0iSioqIqfWEjISGB9evXm0XWTp8+zapVq8wLCgC9evViypQp/Prrr1c1Pg8PD/PnL7/8klOnTpGYmIjVasXf35/8fOMPh5Ln12KxmKny5ZFS8sEHH9C/f/8rGlfJ41V1ypqu6/j6+lZY6f3PP//k7bffZvPmzdSrV4/4+HjzfDg5OaEXpXYV33elSr4Hjz32GP/3f/9HXFwca9asMYsdwsXvwaU+45V9jUrdVXr2vKhye0EB5BcipTzf91zNnitKrSPzC5Gp54N0eSLdaJUIRhXuVs2MIL1ZI6Otlgpgqox0OKCgEOnQjSU+zYyMhVIXQzTAywOLl4dR5yMvH3kuB5mVY3z3WrQ61w1DeLobSyeigpEn0o2q9Hv/RN9zELw90ALbYglqi/D1qnhnVT02i2YUspMS8grQDx036qXUr4fwcldLPW4Q1fbbJKW0AxMxgvDdwAIp5S4hxKtCiOL86YlCiF1CiCTg/zDS5JFSnsWoML8ZSAK2Sil/qK6xVhcpJQ888ACBgYH83//9X5Xu+6uvvuKtt97i3Xffvar99OjRg/nz5+NwODh16hRr164lOjqanj17mvenpqayevVqAAICAjh16pQZuNtsNnbt2nVVY3B3d+eNN97ghRdeqNT2586dY926dRw+fJiUlBRSUlL48MMPSUhIuGjbKVOm8Oabb5a5n02bNnHfffdd1lgzMzNp1KgRVquV1atXc+jQoUtu7+vri5eXl9mHfd68eeZj/fv35+OPP8ZmM65k7927l5ycnMsaz9Xq2bMnixcvJi8vj6ysLL777jvAWMLQunVrvv76a8D4LG/btu2i5587dw4PDw98fHxIS0vjxx9/NB/z9/cnMTERgG+++abM43t5eZGVlXVZY87MzOSmm4xyGZ9//nmF23fv3p3vvvuO/Px8srOzzYyHyr7Gf/7zn+ayAKVu0E9nGGvP/0gxqvQeTkWeOIXMygFH0dpzD3dj3bmnO8K9aO25pe78gako1xMpJTIzC8fug9h//hXbv5di+3ge9sU/o2/eCTY7WkgHnP5yK9YHh+L8wF04DbgFS2gHtAa+KmivAlLXjQ4Y2blgsyPq18PStgWW1jeh+XhdMoNBWDSEpztas4Zo7VuitWyK8PE0gv/sHGRuXp2qlC6EQGvaEKc+MVgfuhvLwB6Iet7ov23H9q9F2Bb8hGPXfqMYaQ2MTbi6GGvhLRbkiVPo+w+jp6UbLUWV61q15hFJKZcByy6476USPz9xief+B6MlXJ21YcMGvvjiC7OdGcD/+3//j9tvv71K9h8QEGAGWldq8ODBbNy4kc6dOyOE4M0336RJkyYMHjyYVatWERQURMuWLc1Ub2dnZxYuXMjjjz9OZmYmdrudJ5988qJK8OWtcS/PiBEjyn2s5Br30NBQevfuTa9evUrNrt5xxx08++yzF6U633777aUKsZV0+PDhyy5Adu+99/LXv/6VkJAQoqKi6NixY4XP+eyzz3jwwQfRNI1bb73VLI43btw4UlJSiIiIQEpJw4YNWbx48WWN51Iutca9WEREBMOHD6dz5840atSILl26mI99+eWXPPLII7z22mvYbDZGjBhB586dSz2/c+fOhIeH07FjR1q0aEH37t3Nx15++WUeeOABXnzxxXJb1I0YMYIHH3yQ999/n4ULF9K2bdsKX9fUqVO5++67qVevHr169eLPP/+85PZdunQhLi6O0NBQGjduTEhIiPkeVOY17tmzp9TrKrZlyxZmzpxpVtcPCwszZ+/HjRvH+PHjS7UlVK4NPb2oL66Hq5o9V5RaSuo68tRZYyb9mNFDnZyiLDVnK6JpQywd/BE3FaW9W9Wa3uogpYRCGxTaQRNGFXMfL6PivnZlF0OEZswMCw83ZMP654P3zGxkXr7R9tLFpc7MEAsnJywdW2Pp2NooTLr7II5d+3Es/wXH6k1o7YtS6W9qfM0vIAknC3i6G79PGdnIM5nGhen6vsZ7UIeyHZTKEXWxKmRZoqKiZHHxqmK7d+8mMDCwhkZU/TZt2sRjjz1mFupSLs+kSZMYPXp0la/HvlB2drZZSf/1118nNTX1oiKCSvUqfg9yc3Pp2bMns2bNMjs+VGTQoEF8++23FXZMqGrX+/dXddDTM5Bpp8FT/cGiKLWJLCg0Uo+PlUh7L64n4eWB1qyhEaQ3a4So76t+f6uZtNuNJUMYlda1et5GMbRqzDoyitsVGrPvZ7OQhYUIhFHYro5dmJFSGrUWkvej7z1kXPzw8cQS1BYtsK2RbVBTYyssqklg0YwlJN6eqphdHVNUkL3MmR9VuaMOCwsLo0WLFoSFhfH999/TokWLip+kmN56661rcpwffviB6dOnY7fbadWqFXPnzr0mx1XOe+ihh0hOTiY/P58xY8ZUOmgHzNR6pXbTz2QgT6qgXVFqA5mVY6xNLw7U0zOMtblCIBrWM2YomzUyKr57eVS4P+XqSV2H/ALQdXB2Nqqqe7hds6CuuF+5cHUBP1/jYk5uPjIzCz071+jY4Ww11m3X8skoIQTipkZoNzVCxnZB338Efdd+HBu34di4DdGiCVpQW7T2rRDWaxtqXVzM7qxRzK6eD7i71vpzq1yamnFXlFpkx44djB49utR9Li4u5hr569mjjz7Khg0bSt33xBNPcP/999fQiGqW+v6qPP1MBvLEaZUaqCg1QOo6Mj3DCNCPF6W9Z+UaD1qdEE0aGEFOcVs2Nft3zRTPcmN3GC3y/LyNCyUuzrUqgJOFNmN9fWb2+SUTVidj2UQtGmdF5Lls9OSDOJL3Q2Y2OFvROrRCC2pnFPirgdditvGz24uK2fkivDzqzFKFG9GlZtxV4K4oilILqe+vytHPZCJT09VMu6JcI9JmQ6amm0G6TE03UoUBPNzQilPemzVCNKynfi9rgCy0nX9PvD3RfL3AzfWK161fS9LuKGozlwXZeUamRh1rMyelNGo37NqPvu+QsSzE18tIpQ9qW2NZJtLuMLIuAOHrhajnbWRAKLWKSpVXFEVRrjv6WRW0K0p1k9m5RUH6KSP1/dQZI5gCRANftI6tzbR3vD3q1Azp9UTaHVBQYKxIcHNFNPNFeNS9NmHCyVKizZzDaH12LhuycpC6rBNt5oQQiOaN0Zo3Rt4Wjb7vEHryARy/JOH4JQnRsilacDu0di0uWa2/ysdVXMxOSqNt39lzRvq8n4/xWVGdVWo99Q5Vo/z8fKKjo+ncuTPBwcG8/PLLVbr/rKwsunfvTlhYGI6iNhuxsbGkpKRU6XGK93thRkNZ21R07KlTp5prvOPj43F3dy/VDuzJJ59ECEF6ejpg9OIOCwsz/3v99dfNbdPT07FarcycObPUMfz9/RkyZIh5e+HChcTHxwMwd+7cUn2/y7JmzRpz+9pk5MiRhIaGMmPGjHK3qezY/f39r2gM/v7+5ntzLZR3vKVLl5qfhfj4eBYuXHjNxqTUDvrZc8jUUypoV5QqJKVETz+LY9sf2H9cT+Fn32KbvRD7D2vRd+wFZye0Lp1wGtwb6yMjsI6Ow6l3DJbANkZFchW0X1PnW7jlgMOBaORntHDzb1bUwq1uBe0XEhaL0Vu9WSOjentxm7n8QuOCUh1oMyecrViC22G9uz/W+wej3RyKzDiH48d12GZ9jX3lr+ipp7iWGdBCCOPijpcH6Dry6EmjpdypMzXS4k6pPDXjXo1cXFxYtWoVnp6e2Gw2brnlFgYOHEhMTEyV7H/VqlXcdNNNLFiwoEr2VxPatWvHkiVLGDVqFLqum6+pmJubm9li60Jff/01MTExJCQkMH78+FKPJSYmkpycTFBQUJWN1W6343QNr4yWdOLECTZv3sz+/ftr5Pi1TVxcXKVbDV6umnyflcrRz2YhU0+Ch7sK2hXlKkibHZmWblTIPnbSuBhWUGg86O5qzKSHdyxKe/dTM3K1gNnCzVbUws3XC+HtBa61a916Vbu4zVyBEbxnZiPzCkATRtG9WnyxQvh64dQtDNm1M/LICfTkA+i7DxgXxfx8iqrStzF6tF+rMVmtYLUaF4HOZKKnZxidBur71pnlFTcS9Q1cjYQQZhswm82GzWar0i/VjIwMGjVqVOo+Pz8/s+e5p6cnkyZNIjg4mD59+rBp0yZiY2Np06YNS5cuBYysgPvvv5+QkBDCw8NZvXo1AHl5eYwYMYLAwEAGDx5MXl6eeYzly5fTtWtXIiIiuPvuu8nOzr7o2OXx9PQs1Tt9xIgRzJ8/HzBmi7t3717poCkhIYF//OMfHDt2jKNHj5Z67Omnn2batGkXPcfNzc18T8rj7Oxs9vmeOnUqo0ePpnv37owePZqUlBR69OhBREQEERER/PLLL+bYY2NjGTp0KB07duTee+81r54uW7aMjh07EhkZyeOPP86gQYMAyMnJYezYsURHRxMeHs6SJUvKHVO/fv04duwYYWFhrFu3jtjYWJ577jmio6Pp0KED69atu2jsl1Lc2z41NZWePXsSFhZGp06dzP088sgjREVFlZspkpeXx8CBA5k9ezZnzpzhzjvvJDQ0lJiYGLZv337Zr8/hcPDMM8/QqVMnQkND+eCDD8zHPvjgAyIiIggJCWHPnj2AkTkxceJEc5uVK1cSFRVFhw4dzCrw5b1XZVmzZg09evQgLi7OvNhz5513EhkZSXBwMLNmzTK39fT05IUXXqBz587ExMSQlpYGwIEDB4iJiSEkJIQpU6ZU+DlTroyekWUUv1JBu6JcNpmTh77vEPb/bcGWsAzbRwnYv16OY8PvcC4brX0rLP27Y71/MNaH7sb611gsEUFoTRqooL2GSZsdmZ1rFG9zdUZr0cSYhW7cAOHmcl0H7RcSmjFjrDX0Q2vbAq31TYj69UDX0bNzkDl5yOJ2g7WQEAKtZVOcBtyC9aG7sfTtinB1wbF+K7ZPv8G26Gccf6QYyx+u1Zg0DeFuXBih0IZ+6Dj6wSNGHRl77T2XNxo1rVTNHA4HkZGR7N+/n0cffZSbb765SvetXfCH67fffmv+nJOTQ69evXjrrbcYPHgwU6ZMYcWKFSQnJzNmzBji4uL48MMPEUKwY8cO9uzZQ79+/di7dy8ff/wx7u7u7N69m+3bt5vts9LT03nttddYuXIlHh4evPHGG7zzzju89NJLpY49btw4xo8fT1RU6doKzzzzTKnbHTp0YOnSpZw9e5aEhARGjRrFjz/+aD6el5dHWFiYeXvy5MkMHz6cI0eOkJqaSnR0NMOGDWP+/Pk8/fTT5nbDhg3jo48+umiGevjw4RWe127dutGtWzfzdnJyMuvXr8fNzY3c3FxWrFiBq6sr+/btY+TIkeYSgt9//51du3bRrFkzunfvzoYNG4iKiuLhhx9m7dq1tG7dmpEjR5r7nTZtGr169WLOnDlkZGQQHR1Nnz598PC4uGjJ0qVLGTRoUKnsA7vdzqZNm1i2bBmvvPIKK1euLDX2LVu2MHPmTD799NOL9rd582YAvvrqK/r3788LL7yAw+EgNzfXHJufnx8Oh4PevXuzfft2s999dnY2I0aM4L777uO+++7jscceIzw8nMWLF7Nq1Sruu+8+kpKSLuv1zZo1i5SUFJKSknBycuLMmTPmYw0aNGDr1q189NFHvP3222W+npSUFDZt2sSBAwe47bbb2L9/P40aNSr3vSrL1q1b2blzJ61btwZgzpw5+Pn5kZeXR5cuXRgyZAj169cnJyeHmJgYpk2bxrPPPsvs2bOZMmUKTzzxBE888QQjR468aPmGUjX0jCz0YycRqnq8olRISglnMo216cXV3jOKlqZZNKMdWGSwUe29WUNVpKoWkg4dCgpAl8a67iYNEV5u13RddG1Xqs1cg6I2czl1p82ccHHG0qk9lk7tkWfP4Ug+YKyHX7YWh4szWsfWRkG7xvWvyfiFEODiDC7OSLsDefI0Mu00wscoZne9Z3bUduo3v5pZLBaSkpLIyMhg8ODB7Ny5k06dOlXJvpOSkmjevHm5jzs7OzNgwAAAQkJCcHFxwWq1EhISYq5FX79+PY899hgAHTt2pFWrVuzdu5e1a9fy+OOPAxAaGmoGbb/++ivJycl0794dgMLCQrp27XrRscsKrspz1113MW/ePH777Tc++eSTUo+Vlyo/f/58hg0bBhiz9mPHji0VuFssFiZNmsT06dMZOHBgpcdSlri4ODNLwGazMXHiRJKSkrBYLOzdu9fcLjo62nw/wsLCSElJwdPTkzZt2pjB4MiRI83Z2+XLl7N06VLefvttwMh+OHz4cKUrid91110AREZGlllbICoqqsL3oUuXLowdOxabzcadd95pXiRZsGABs2bNwm63k5qaSnJysvkZuOOOO3j22We59957AeMz9M033wDQq1cvTp8+zblz5y7r9a1cuZLx48eb2RZ+fn5lvs6SF4dKGjZsGJqm0b59e9q0acOePXto3bp1ue9VWaKjo833CeD9999n0aJFABw5coR9+/ZRv359nJ2dzayJyMhIVqxYAcDGjRtZvHgxAPfcc89FF6mUq1M80y483NTMn6KUQdodyLTT56u9Hz8J+UVp724uRtp7SAcj7b2RX61OKb6RlWrhZtHOt+9yca7podUJwsXZOFd+3ufbzGVkQU4eEmp1mzlRzxun7uHnU+l37UffuR992x+I+r5Gb/jANsas+LUYj5MFnIqK2WXnIDPOGRdJ6vsgPD3Uv8U1QAXu14ivry+33XYb//3vf6skcL/lllvYu3cvW7duLXcbq/X8F5Omabi4uJg/268w7UVKSd++fUlISLii55dl+PDhREZGMmbMmIsyCMqTkJDAiRMn+PLLLwE4fvw4+/bto3379uY2o0ePZvr06Vd9vkvOEM+YMYPGjRuzbds2dF3H1dXVfKz4/IJx4aCicyyl5JtvviEgIOCKxlV8vMocqzw9e/Zk7dq1/PDDD8THx/N///d/9OjRg7fffpvNmzdTr1494uPjyc/PN5/TvXt3/vvf/3LPPfdc8h++q319xSrzOi8chxDiku9VWUq+z2vWrGHlypVs3LgRd3d3YmNjzXNQ8vfqas69Unn6uWz042kId1X1VlGKybz882vTj59EnjwNDt14sJ43WtuWiGYNjWrv9bxrZaCiGKSUxpr1wkIQGsLbE+HrZQRJao3xFRPOVoSzFXy8jHTvvAL0jCzIzjUaI1gtta6nPRSlrbdqhtaqGTK/EH1vCnryfhzrEnGs34rwvwlLcFtEm+aICpaoVsl4hAA3428oabMZ3zeaZlSj9/ZUF5WuIfUXUDU6deoUGRkZgJHyvWLFCjp27Fgl+16/fj0PPPBAqbW3V6JHjx5m8Lt3714OHz5MQEAAPXv25KuvvgJg586d5rrlmJgYNmzYYKag5+TkVDiTWZFWrVoxbdo0JkyYUKnt9+7dS3Z2NseOHSMlJYWUlBQmT5580cUEq9XKU089VW4V9kWLFjF58uTLGmtmZiZNmzZF0zS++OILs5p/eQICAjh48KA5I168nh+gf//+fPDBB+Za+N9///2yxlIVDh06ROPGjXnwwQcZN24cW7du5dy5c3h4eODj40NaWlqppQsAr776KvXq1ePRRx8FSn+G1qxZQ4MGDfD29r6s19e3b18++eQTMwgumSpfGV9//TW6rnPgwAEOHjxIQEDAZb9XJWVmZlKvXj3c3d3Zs2cPv/76a4XPiYmJMTMP5s2bd1njV8qnn8tGP5pmrL1TQbtyg5JSGmm0u/ZjX/ELhXMXY5u5APvS1ei/7wZAC+uIU1ws1oeH4Rx/J079umHp1N7447qWBSaKQdodRoG1nDywOqE1b4LWviVas4YId1UYrCoJJyeElweWFk3QOrRCa9kEPNwgL994D/Lykbpe08O8iHB1xhLaAeuI27GOuQMtMgh58jT27/+HbdZC7Ks3oZ88fc2q0gurFeHpAa4uyDOZOA4cwXE41TiH+rWrjH+jUjPu1Sg1NZUxY8bgcDjQdZ1hw4aZKbZVISAgoMIWbRWZMGECjzzyCCEhITg5OTF37lxcXFx45JFHuP/++wkMDCQwMJDIyEjAKGo2d+5cRo4cSUFBAQCvvfYaHTp0KLXf8ta4l+fhhx8u8/4L17gPGDAANzc3Bg8eXGq7IUOGMHz4cF566aVS9z/wwAO89tprZe77wIEDeHt7V2p8xSZMmMCQIUP497//zYABA8pcr12Sm5sbH330kbltly5dzMdefPFFnnzySUJDQ9F1ndatW5uF1arCpda4F1uzZg1vvfUWVqsVT09P/v3vf9O6dWvCw8Pp2LEjLVq0MJdFlPTee+8xduxYnn32WaZOncrYsWMJDQ3F3d2dzz///LJf37hx49i7dy+hoaFYrVYefPDBUsXnKtKyZUuio6M5d+4cM2fOxNXV9bLfq5IGDBjAzJkzCQwMJCAgoFKdIN59911GjRrFtGnTGDBgQKkigWFhYeaSj5K/G8Vr4S/siqAYzgftrtdkVkFRagvpcCBPnjHS3o+dRB4/BXlFmU8uzkbae1BbI+29SX215rkOkboO+QVGdoSLM6JxAzRPN2NmWLkmhMXoZ27xdDfej7wCo695ZrZxWyvqFV/LLhYLPx+cekQiu4cjD6WiJ+9H37EXPWkPokE9tOC2aB3bINwvnWFYJWPRNHB3QwCyoBD9yAlwshhp9F6eCKv6TqoO4lr2DaxOUVFR8sIgdvfu3ZVeL1wXLViwgIULF9bpdnA1adSoUcyYMcOssF5dsrOz8fT0RErJo48+Svv27Xnqqaeq9ZjKtZebm4ubmxtCCObNm0dCQsIlK+lX5Hr//qqInpWDfuSECtqVG4LMLzDS3o8bQbo8kQ7FWUI+XsYM7E2NjLR3NYNe55Rat65piHreCG+PWpmmfSOTujTazGXlGuu5HXqtbzMn8wvQ//gTfdcBZNppo0Vg6+ZGKr1/82t68UE6HJBnTOrh44lWz9tY7qE+45dFCJEopSxz5lNdDqnD+vTpw6effkpYWBiJiYkVtmJTSvvPf/5zTY4ze/ZsPv/8cwoLCwkPDy83u0Cp2xITE5k4cSJSSnx9fZkzZ05ND6nO0rNyjJl2NxW0K9cnmZePPJqGfvQE8kga8nSG8YAmEI3qo3XuUFTtvdE1K0SlVD1ps0GBzbjh5WEEMm4uqitGLSU0Yy23cHNFNqwHBYVGCnhGFjK/AATGxZZalOEiXF2wdO6IpXNH9PSzRb3hD2I/cATcXNECW6MFtUNrWK/6x1KUySClhJw89Mws46JHA1+Ep7v697wKqBl3RalFfvrpJ5577rlS97Vu3dqsbl7X1fTr27FjB6NHjy51n4uLC7/99ts1Of7luFG/v8yg3dWl1s5wKMrlknn5yGMn0Y+cQB49gUzPMB5wckLc1BDtpsZFae8NVIppHSftDmPWVpdGxlA9b4SHu/o+q8OklFBoM/rDnz2HLLSZbeZq4xIH6dCRh47j2LUfefAo6DqikZ9Rlb5ja4Rb9afSm2Ox2Y1sEyGMehs+qphdRS41464Cd0VRlFroRvz+0rNz0Q+nGjPt6o9cpQ6T+QVFM+ppyCMnkOlnjQecLMb69BZNEM0bG72Z1SxUnSd1/XwqvLMV4euN8HJXAcp1ShbajF7x57KQuflGEF9L28zJvHz0PX+iJx9AnjxjLNVo0xxLcDuEf7Nrlv1h/I4UIB06wt0Nrb6vsUZeFWC8yKUCd5Wrcw04HA7Cw8OrtDAdQFZWFt27dycsLMysmB0bG1tmT++rFRsbW2EhvMoce+rUqcydOxeA+Ph43N3dycrKMh9/8sknEUKQnp4OGO22wsLCzP9ef/11c9v09HSsVqtZ4KuYv78/Q4YMMW8vXLiQ+Ph4AObOncvUqVMvOcY1a9aY29cmI0eOJDQ0tNwq+VD5sfv7+wNGG72hQ4cCkJSUxLJlyyp87ty5cy+rcFxdVHx+lGtH5uSpoF2ps2R+AfqBw9jXbMb2n++wfTwf+3dr0HfsBTcXLN3CcBo2AOuEEViH9MUSHWKkwqugvc6SUiILCpFZuZBfgPDxRGt9E1rbFmgNfFXQfh0Tzla0el5YWjXD0s5ou4iz1egVn52LzC+8ZlXeKyLcXLGEB2K9dxBOowahdQ5AHkvDvmQVttkLsa/dcn6pTnWOQ9MQbm5onh5gs6MfTkXffwj9TIYxK69UisrHugbee+89AgMDOXfuXJXud9WqVdx00011ujhdu3btWLJkCaNGjULXdfM1FXNzczOrcV/o66+/JiYmhoSEhIuqcicmJpKcnExQUFCVjdVut+NUQ+uaTpw4webNm802fFWlWbNmLFy4EDAC9y1btnD77bdX6TFKuppzWJPnX6leMicPhwralTpE5hcijxWtUT+aZsxkAVgsiGYNsXQLK5pRb6A+09cZaS9K/ZWAhxtakwbGuuhaVoFcuTaE1Qnh41XUK94BefnomdlFveIlOFmMmfhaUNdAa+iHFuuH7BGB/PMYjuQD6L/vRk9MRjSujxbcDi3AH+HqUq3jEC7O4OJc1D3jLHraGYS35/kaELUsa6E2qflP0XXu6NGj/PDDD4wbN67K952RkUGjRo1K3efn52cWqfP09GTSpEkEBwfTp08fNm3aRGxsLG3atGHp0qUA5Ofnc//99xMSEkJ4eDirV68GjDZsI0aMIDAwkMGDB5OXl2ceY/ny5XTt2pWIiAjuvvtusrOzLzp2eTw9PXFzO19oZ8SIEWZv8zVr1tC9e/dKB2cJCQn84x//4NixYxw9erTUY08//TTTpk276Dlubm54enpecr/Ozs5mK6+pU6cyevRounfvzujRo0lJSaFHjx5EREQQERHBL7/8Yo49NjaWoUOH0rFjR+69917zauuyZcvo2LEjkZGRPP7442bmRU5ODmPHjiU6Oprw8PBLViDv168fx44dIywsjHXr1hEbG8tzzz1HdHQ0HTp0YN26dReN/VKKK+mnpKTQqVMnCgsLeemll5g/fz5hYWHMnz+fTZs20bVrV8LDw+nWrRt//PHHRfv54Ycf6Nq1K+np6fj7+5uZElu2bCE2NvayzmFZ1qxZQ48ePYiLizMvwtx5551ERkYSHBzMrFmzzG09PT154YUX6Ny5MzExMaSlpQFG27+YmBhCQkKYMmVKqff/rbfeokuXLoSGhvLyyy9fdH6U6idz842g3bX2Vu1VFJlfiH7gCPb/bcH25ffYPp5n9FDf9ge4OGPp2hmnu/sbM+pD+2G5OdRYt64+09cF6dCRuXnI7BzQJaJxQ7R2LbG0bFpUdEv9Oa2AcLIYveKbN0Zr3wqtZVOEhzvkFxW5y81HOmq+V7ywWNDatcQadxvWB4diuTUKHA4cq37DNutr7D/8Dz3lWLX3tRcWC8LDzSjAmZuHnnIM/eBR9Iwso0K9chE1fVXNnnzySd58881S6eBVxeFwoF1wBe/bb781f87JyaFXr1689dZbDB48mClTprBixQqSk5MZM2YMcXFxfPjhhwgh2LFjB3v27KFfv37s3buXjz/+GHd3d3bv3s327duJiIgAjPT01157jZUrV+Lh4cEbb7zBO++8w0svvVTq2OX1cX/mmWdK3e7QoQNLly7l7NmzJCQkMGrUKH788Ufz8Qv7uE+ePJnhw4dz5MgRUlNTiY6OZtiwYcyfP5+nn37a3G7YsGF89NFHF81QDx8+vMLz2q1bN7p162beTk5OZv369bi5uZGbm8uKFStwdXVl3759jBw50lxC8Pvvv7Nr1y6aNWtG9+7d2bBhA1FRUTz88MOsXbuW1q1bM3LkSHO/06ZNo1evXsyZM4eMjAyio6Pp06dPmf3Gly5dyqBBg0plH9jtdjZt2sSyZct45ZVXWLlyZamxX6qP++bNm0vddnZ25tVXX2XLli3885//BODcuXOsW7cOJycnVq5cyd/+9je++eYb8zmLFi3inXfeYdmyZdSrd+lqpZU9h2XZunUrO3fupHXr1gDMmTMHPz8/8vLy6NKlC0OGDKF+/frk5OQQExPDtGnTePbZZ5k9ezZTpkzhiSee4IknnmDkyJGlllUsX76cffv2sWnTJqSUxMXFsXbtWnr27HnR+VGqh8zNRz90vChoV/8cKbWHLCg0iskVV30/dQakBIuGaNoQS0xnRIsmRjE5FZxfl8wWbja7UZvAzwfh5VHts5HK9UFYNCgKSqVe/3zwnnHOCEpFUa/4Gv7+EO5uWCKC0MIDkSfPGFXp9xxE33vIyCgJaoslqC3Cr+JJoSsegxDg6gKuLki7HZl6CnlCIOp5IXy81O9cCeovpWr0/fff06hRIyIjI1mzZk2V7z8pKYnmzZuX+7izszMDBgwAICQkBBcXF6xWKyEhIeZa9PXr1/PYY48B0LFjR1q1asXevXtZu3Ytjz/+OAChoaGEhoYC8Ouvv5KcnEz37t0BKCwspGvXrhcdu6xgsTx33XUX8+bN47fffuOTTz4p9Vh5qfLz589n2LBhgDFrP3bs2FKBu8ViYdKkSUyfPp2BAwdWeixliYuLM7MEbDYbEydOJCkpCYvFwt69e83toqOjzfcjLCyMlJQUPD09adOmjRl0jhw50pwlXr58OUuXLuXtt98GjOyHw4cPV7og2V133QVAZGRkmbUFoqKiLut9uFBmZiZjxoxh3759CCGw2WzmY6tWrWLLli0sX74cb2/vCvdV2XNYlujoaPP8Abz//vtmFfojR46wb98+6tevj7Ozs5nNEBkZyYoVKwDYuHEjixcvBuCee+4xLx4tX76c5cuXEx4eDkB2djb79u2jZ8+elTk9ylWSefnoh1NBBe1KLSALCpHHi6u+F6W+lwjUtZtDjIJyTRrW+B/aSvWShTYoLDQCK28PNB8vIxVeFdFSrpDQNHB3RbgXtZnLL0TmFLeZywcharzNnBDCSJdvXB/ZIxL551Ecu/ajb9mFvnmn8T0Y1NZIpa/G+g3CyQk8nZC6jszIRj+daRSza+BrnMNasOSgJqm/lqrRhg0bWLp0KcuWLSM/P59z584xatSoKukffsstt7B37162bt1a7jZW6/nqlpqm4eLiYv5st19ZIQgpJX379iUhIeGKnl+W4cOHExkZyZgxYy7KIChPQkICJ06c4MsvvwSMImv79u2jffv25jajR49m+vTpdOrU6arGV3IGfMaMGTRu3Jht27ah6zqurudbahSfXzAuHFR0jqWUfPPNNwQEBFzRuIqPV5ljXYkXX3yR2267jUWLFpGSkmKmvgO0bduWgwcPsnfvXjOrwsnJCb0orSo/P7/Uvip7DstS8rlr1qxh5cqVbNy4EXd3d2JjY81jlfy8V/b8T548mYcffriCM6FUNZmXj55yvMb/UFFuXLLQVro9W8lAvUlDtOiiQL1pA/UZvQEYLdyMgmLC3RXRsLGRwquKBypVTAhhrON2c0HW9zU+d7l5yLNZ6Nm5RoV6FyvCWnNt5oSTBdG+FVr7VsjsXKMq/a79OH7+FceazWjtW6IFtTWyjqopkDYvdmB8X+tHThgV8ev7Irw9amUbvmvhxr5sUc2mT5/O0aNHSUlJYd68efTq1atKgnYwZsofeOCBUmt8r0SPHj3M4Hfv3r0cPnyYgIAAevbsyVdffQXAzp072b59OwAxMTFs2LDBTEHPycmpcMa0Iq1atWLatGlMmDChUtvv3buX7Oxsjh07RkpKCikpKUyePPmiiwlWq5Wnnnqq3CrsixYtYvLkyZc11szMTJo2bYqmaXzxxRdmNf/yBAQEcPDgQXNGvHg9P0D//v354IMPzLXwv//++2WNpap5eXmVWtKRmZlpFgos7gRQrFWrVnzzzTfcd9997Nq1CzAqsScmJgKUSqm/0OWewwufW69ePdzd3dmzZw+//vprhc+JiYkxxzNv3jzz/v79+zNnzhyzRsOxY8c4efLkRc/v2LFjpcenVEzmFZwP2lW/auUakYU29JRj2NclYktYhu2jedgX/4z++25wsqBFh+A0pK+xRn1Yf5y6hRmBuwrar1tS1401x9k54HAgGvlhadsCS6tmaN6eKmhXqp0QAuHqgubna3z22rZANGkAmoaenYvMzjEuMtZghXrh6Y4lKhin++JwGnk7WnBb9D+PYf92JbY532Lf8Dsyo2qLb180BmcrwtPdKGiXfgbHgSM4jp5A5uTVmur914oK3OuwgIAAzpw5c1X7mDBhArquExISwvDhw5k7dy4uLi488sgjZGdnExgYyEsvvURkZCRgFO2aO3eu2Zqsa9eu7Nmz56L9jhs3rsL2cSU9/PDDtG3b9qL7i9e4F//3/PPPk5CQwODBg0ttN2TIkDKzAB544IFyZ14PHDhQqTTvkiZMmMDnn39O586d2bNnT5nr0Utyc3Pjo48+YsCAAURGRuLl5WUWj3vxxRex2WyEhoYSHBzMiy++eFljqciWLVsuqyjibbfdRnJyslmc7tlnn2Xy5MmEh4eXeQ47duzIl19+yd13382BAwd4+eWXeeKJJ4iKirpkkcLLPYclDRgwALvdTmBgIM8//zwxMTEVPufdd9/lnXfeITQ0lP3795vnv1+/ftxzzz107dqVkJAQhg4delEtivT09HL/USj5GZ85c6a5fv5yz/uNROYVoB86XjSboAIipfqYgfr6rdjmFQXqi35G37obNIEW3alEoD7ACNRbNlWB+nVOSml0BMjKgfxCRD1vNP/mRgs3P58bdhZPqR2MNnPeWPxvKmoz18joD59jFEaUBTXXZk4IgdakAU69Y7A+dDeW23si6vuib96J7V+Lsc3/L46d+4ylJtU1BouG8HA3itnlFRgt5Q4cQT+bZWTN3ADE9XKlIioqSl4YKO7evbvS64XrogULFrBw4cI63Q6uJo0aNYoZM2ZUewXx7OxsPD09kVLy6KOP0r59e5566qlqPaZyXm5uLm5ubgghmDdvHgkJCZes4F/S999/z8GDB816D9fS9fb9JfOLZtqdnWo0BVC5PkmbDXn81Pk16mnpoEvQhNGSrUUTtOaNEc0aqs/fDUjabFBQFFB4uaP5eqv1skqdcWGbOWpRmzmZnYuefABH8gE4ew6cnIxU+uB2RkvMam7tJu0OyC8Acf0UsxNCJEopo8p8TAXuddeZM2cYMWIEJ0+eJDExscJWbErNmDFjBp9//jmFhYWEh4cze/Zs3N3da3pYN4x169YxceJEpJT4+voyZ84c2rVrV9PDqtD19P1lBu1WJzWjpVQJM1A/moY8cuLiQL15YyPVXQXqNyzpKFq3rusIN1dEPW9j3brKqlDqMOnQIS8feS7HyBzRdbAUVaivwSBeSolMTUdP3o/+RwoU2sDbEy2ojVGV3ser2o9PXgHoDqOYpJ+P0aqxDl6cU4G7otQRP/30E88991yp+1q3bm1WUb9e7dixg9GjR5e6z8XFhd9++62GRlTzrpfvL5lflB7vpIJ25cpJm9EiyCwmd+K08QerEIgm9RHNmxQVk2uoPmc3MKnrRgs3h260cKvng/Byr9Yq2IpSU6SuQ16BkUafmW187rWiCvU1OJkn7Xb0/YfRdx1AHk4FMC6mBrVDa9+y2r+jZWFRho1FQ9T3QXh71ql/F1TgriiKUsdcD99fMt9Yg4bFUqf+0VRqnrTbi2bUi/qon0g/H6g3rl9iRr2R+mzd4KSUxuxeod3IuPD1Qnh7GpW7qzlNV1FqC6lLI8OkOIi32UDTwLlme8XLc9nouw8aqfQZWWB1QuvQCi2oHeKmRtX6OyodOuTnIyVoDeqhNaxXbceqSpcK3FW+kKIoilLlZEGhCtqVSpN2u5Fmac6opxuzR8W9hSMC0Zo3Mf7QU58nBeMzQ34hEhAebmiN64O7G8JS91JjFeVqCa1Em7kG9S5oM5ePQNRINxfh7Ynl5lC06BDk8ZPouw6g701B33UAfDyxBLVDC2pjXGyr6mNbNPBwB5sdmZsH1I3A/VJU4K4oiqJUKVlQaKTHa5oKspQySbvDSH03Z9RPnQ/UG/mhhQWitWhszKirNGeliDGDVgBSN2YSmzRA83RXXSoUpQQhBLi6GEXa/HyNavS5+ciMEr3ina1G3ZlrlJUihEDc1BjtpsbI27qg7ztsFLXbmIRjY5JRQDS4HVq7lur3+RKq9cwIIQYA7wEW4FMp5esXPD4eeBRwANnAQ1LKZCGEP7Ab+KNo01+llOOrc6yKoijK1TNn2jVNBVyKSdodyBOnkEfSjGA9tUSg3tAPLazj+Rl19blRSpDSSAHG7jC+V+r7ILw8jNlDlQqvKBUSLs7G92o9b6MvfF6+kU6fk4cEo+Wcs/XaBfFWK5agtliC2iIzs82q9I7/rsfhbDVS6YPbGTVL1O94KdUWuAshLMCHQF/gKLBZCLFUSplcYrOvpJQzi7aPA94BBhQ9dkBKGVZd41MURVGqlhm0C6GCrxucEainI4+eMCq/Hz8FDqPPrjGjXhSoN2uEcFWfFeVistBmrF0H8PFE8/EyqkVr6g95RblSwtlqZML5eBnLTfIK0DOzIDvPWCdvtVzTi2LCxxNL185oMaHIY2lGKv2eFPSd+8HXC0twO7TANsbFOqVaZ9yjgf1SyoMAQoh5wB2AGbhLKc+V2N4DuD4q5SmKotxgZKFNBe03sPOBetGM+oWBeucORTPqjVWgrpRL2otauElptHBr5ovwcK/R4lqKcr0STk7g5YTFy8Non5hXgH4uG7JyjCD+GraZE0IY3UGaN0HeFo2+7xD6rgM4NvyOY8PviFZNjVT6ti1u6JaO1fnKbwKOlLh9FLj5wo2EEI8C/wc4A71KPNRaCPE7cA6YIqVcV41jVRRFUa6QGbSjgvYbhXQ4kCdOGzPqR4pS3+1FgXrDemihHYyq7zc1MtZZKko5pK5DfqHRf9lqRTSqh+bpoepjKMo1JCwW8HTH4ul+vs1cllGhXurXts2ccLZiCW6HJbgdMiMLR/IBI51+2TocLla0Dq3RgtsimjS44VLpa/yShZTyQ+BDIcQ9wBRgDJAKtJRSnhZCRAKLhRDBF8zQI4R4CHgIoGXLltd45IqiKIoZtEupArTrmHQ4kGmnkUeKU99Plg7UQzqgNW+MaN5YfQ6UChnr1m1gtxvr1n09Ed5e4KrWrStKTROaBh5uCA83ZKP6UFCAzM5FZmQh8wqMIP4atZkTvl44dQtDdu1s/Puz6wD67gPoO/aCnw+WoLZGKr2ne7WPpTaozsD9GNCixO3mRfeVZx7wMYCUsgAoKPo5UQhxAOgAlGrULqWcBcwCo497lY1cqROWLl3Kli1bePXVVzl+/Djjxo1j2bJlANx+++18+umnNGvWrIZHqSjXLxW0X7+kQzcC9eIZ9eOnjCALEA3qoXVqf35G3c21hker1BXSZjcKzQF4uaP5NgB312uSiqsoyuUz2sy5Itxcz7eZy85DZmYhswtACKOwXTVXghdCIFo2RWvZFFkQjb73EPqu/TjWbzVS6f2bYQlqi2jT4rpeWiOkrJ54VwjhBOwFemME7JuBe6SUu0ps015Kua/o578CL0spo4QQDYEzUkqHEKINsA4IkVKeKe94UVFRcsuWUnE9u3fvJjAwsKpfmqIoSrWr7d9f0mY3gnZdV0H7dUA6dOTJ4hn1okDdVhyo+xatPSyaUVeBunIZpKNo3bpDN9at+3kjPNxu6HWqinI9kAWFyJx8I4jPL0AIwHpt28zJs+dw7NqPvvsgZOeCizNax9ZoQW0RjesjhDAuGFo0LK3qxmSeECJRShlV1mPV9q0ppbQLISYCP2G0g5sjpdwlhHgV2CKlXApMFEL0AWzAWYw0eYCewKtCCBugA+MvFbQriqIo144K2us+qRfPqBcVkzt28nygXt8XLbjt+WJy7ipQVy6PlNLot+7QwaIZnykvD1UDQ1GuI2abOb+iNnO5RhB/LdvMiXreON0SgewWVpRKvx995z70bX+Y/5aJdi2vm6r01Xq5U0q5DFh2wX0vlfj5iXKe9w3wTVWOxbboZ/TjJ6tyl2jNGmEd3LtK96koilKbmUG7w6FmXusQqevIk2fOr1E/lnY+UPfzQQsqCtSbN0K4u9XwaJW6SEpptG+z2UBoCG9PhK8XuLqoFm6Kcp0z28z5Gm3mZG4+MqMoiJcYQbxL9QXxQtMQrZqhtWqGzC9E/+NPo6Dd2kRYtxWtS6c6M+N+KSpPSVEURakUaS+eaVdBe21nBurFfdSPnTzfE9vPBy2wTdEa9cYIDxWoK1dO2h2QX2C0cPN0R2tcH9zdEBa1bl1RbkTCyQnh7QnenqXbzJ3LMS7wVXObOeHqjKVzAJbOAcjTGTh27kM0rFctx7rWbpjAvaZmxjMyMhg3bhw7d+5ECMGcOXPo2rUrw4cP548//jC38fX1JSkp6aqO9fe//50vv/yS6dOnM3jwYObOnUtKSgpTp069+hdSwty5c9myZQv//Oc/L7lNRcdOSUkhPj6eNWvWsGbNGm677TZmz57NuHHjAEhKSiI8PJy33nqLZ555hvj4eAYNGsTQoUOJjY0lOzub4roGW7Zs4ZlnnjH3NXfuXObOnVupY9cmkyZNYtmyZdx+++289dZbZW5T2bH7+/uTkpICQLdu3fjll19ISUnhl19+4Z577rnkc9esWcPbb7/N999/fyUvo04oeX7KExsby9y5c/H3978mY6rNjKD9hBG0u6qgvbaRuo48dfb8GvULA/WOrc+vU1eBunKVjBZuRanwLs6IJg3QPNxUCzdFUUop1WausfG9UbrNXFEQX00X+kR9Xyzdwo2LBdeBGyZwrylPPPEEAwYMYOHChRQWFpKbmwvA/PnzzW2efvppfHx8rvpY7777LocOHcLT0/Oq91UTOnXqxIIFC8zAPSEhgc6dO5e7/cmTJ/nxxx8ZOHBglY3BbrfjVIMFc2bNmsWZM2ewVHGfzF9++QUwgv6vvvqqwsD9alzNOZRSIqVEUxWGaxVpt6MfOQF2u5ppryXMQP3oCfQjRanvxYF6PW+0gNaIFo2N9HcVqCtVwGjhVmi0AdQ0RD0fhLeH8Ue3auGmKEoFhOWCNnP5xW3mzhkz86IoiL+Oq8JfLfXXcTXKzMxk7dq1PPDAAwA4Ozvj6+tbahspJQsWLGDkyJFXfbzCwsJSQbubm5t5Oz4+nkceeYSYmBjatGnDmjVrGDt2LIGBgcTHx5vPSUhIICQkhE6dOvHcc8+Z9//rX/+iQ4cOREdHs2HDBvP+U6dOMWTIELp06UKXLl3Mx0oeuzwWiwU/Pz/zdqtWrcjPzyctLQ0pJf/9738vGZRPmjSJadOmXXS/s7NzhRdCSh577ty5xMXF0atXL3r37k12dja9e/cmIiKCkJAQlixZAhhBb2BgIA8++CDBwcH069ePvLw8ADZv3kxoaChhYWFMmjSJTp06AeBwOJg0aRJdunQhNDSUTz75pNwxxcXFkZ2dTWRkJPPnzyc+Pp7HH3+cbt260aZNGxYuXFjmeStPw4YNzZ+L34vnn3+edevWERYWxowZM0hJSaFHjx5EREQQERFhBvglbd68mfDwcA4cOEBsbKyZ5ZCenm7ORFf2HJYlJSWFgIAA7rvvPjp16sSRI0d45JFHiIqKIjg4mJdfftnc1t/fn5dfftnc7549ewDjc9i3b1+Cg4MZN24crVq1Ij09HYD//Oc/REdHExYWxsMPP4zD4bjo/JTHz8+vyi+i1DXS7jCCdpsK2muS1HX0k6dxbE3GtmQVtpnzsX/1A461iciMc2gB/lgG9sD64FCc4+/EqU8MloDWKmhXrpostCGzcyEnD9zd0Fo2RWvfEq2RH8LVRQXtiqJcNqEJhLsrWiM/tPat0Fo3RzTwBYfDCOZzcpFFLUiV89SMezX6888/adiwIffffz/btm0jMjKS9957Dw+P85UN161bR+PGjWnfvv1VHUvX9Yv+8Rw+fHip22fPnmXjxo0sXbqUuLg4NmzYwKeffkqXLl1ISkqiUaNGPPfccyQmJlKvXj369evH4sWLufnmm3n55ZdJTEzEx8eH2267jfDwcMDIKHjqqae45ZZbOHz4MP3792f37t2ljl2y33pJLVq04Ntvvy1139ChQ/n6668JDw8nIiICF5fyK1Z37dqVRYsWsXr1ary8vMz7u3XrRrdu3S55vi489tatW9m+fTt+fn7Y7XYWLVqEt7c36enpxMTEEBcXB8C+fftISEhg9uzZDBs2jG+++YZRo0Zx//33M3v2bLp27crzzz9v7vezzz7Dx8eHzZs3U1BQQPfu3enXrx+tW7e+aExLly7F09PTXDLx448/kpqayvr169mzZw9xcXEMHTq01Ngv7F9f0ubNmy+67/XXXy+VAp+bm8uKFStwdXVl3759jBw5kpJtFX/55Rcee+wxlixZQsuWLS95TitzDsv7A2/fvn18/vnnxMTEADBt2jT8/PxwOBz07t2b7du3ExoaCkCDBg3YunUrH330EW+//Taffvopr7zyCr169WLy5Mn897//5bPPPgOMlmrz589nw4YNWK1WJkyYwJdffsl9991X5vm50IWfzxuNtDvQj54Amw3hpgLAa0lKWWJGvSj1vbj/ta8XWnv/8zPqnu41O1jluiPtDigoQEoQbi6IZg0RHu5qJkxRlConhDCKWLq6IOv7QqENmZOHPHsOPTsXAUZhO6taiqMC92pkt9vZunUrH3zwATfffDNPPPEEr7/+On//+9/NbRISEqpktj0pKYnmzZtfcpu//vWvCCEICQmhcePGhISEABAcHExKSgqHDh0iNjbWnIm89957Wbt2LUCp+4cPH87evXsBWLlyJcnJyeYxzp07R3Z2dqnZ9ri4ODPwrciwYcMYPnw4e/bsYeTIkWXOAJc0ZcoUXnvtNd54441K7b88ffv2NWexpZT87W9/Y+3atWiaxrFjx0hLSwOgdevWhIWFARAZGUlKSgoZGRlkZWXRtWtXAO655x4zMF6+fDnbt283Z8szMzPZt29fmYF7We688040TSMoKMgcQ0nNmjUrM2ivLJvNxsSJE0lKSsJisZjvKxhB70MPPcTy5ctp1qziSpyVOYdNmjQp87mtWrUyg3aABQsWMGvWLOx2O6mpqSQnJ5uB+1133QUY5784sF6/fj2LFi0CYMCAAdSrZxQh+fnnn0lMTKRLly4A5OXl0ahRo8s6RzcqM2gvKFRVxq8BKSUy/SzySFF7tqNp5wN1Hy+09i3Pr1G/TtraKLWL1PXzqfDOVkQjPzRPD7VuXVGUa0YIYaTLuziDn4+R8ZOTjzyXdT6IvwZt5morFbhXo+bNm9O8eXNuvvlmwJhNfv31183H7XY73377LYmJiVd1nH/+859MmjSJd99995LbFc9ea5pWaiZb0zTsdjvWK7iSpes6v/76K65VVKyqSZMmWK1WVqxYwXvvvVdh4N6rVy+mTJnCr7/+elXHLZkF8eWXX3Lq1CkSExOxWq34+/uTn58PUOq8WSwWM1W+PFJKPvjgA/r3739F4yp5PCnlFe3jUmbMmEHjxo3Ztm0buq6Xeh+bNm1Kfn4+v//+uxm4Ozk5oes6gHlOilX2HJal5HP//PNP3n77bTZv3ky9evWIj48v9dzic2KxWLBXkEYlpWTMmDFMnz69olOhlCAdKmivbkagnnG+6vvRE5BfHKh7qkBduSbOt3CzgyYQvl5GNWiVAq8oSi1gtpmr54W02ZG5ecjM7KJe8RKcrNXaZq62UWvcq1GTJk1o0aKFWT3+559/JigoyHx85cqVdOzYscKZ8ooUz5iWtd77ckRHR/O///2P9PR0HA4HCQkJ3Hrrrdx8883873//4/Tp09hsNr7++mvzOf369eODDz4wb19tZXyAV199lTfeeKPSa4unTJnCm2++WeZjmzZt4r777rus42dmZtKoUSOsViurV6/m0KFDl9ze19cXLy8vfvvtNwDmzZtnPta/f38+/vhjbDajaNTevXvJycm5rPFUJS8vL7KysszbmZmZNG3aFE3T+OKLL8z132C8rh9++IHJkyebFez9/f3NC03FWQRludxzWNK5c+fw8PDAx8eHtLQ0fvzxxwqf0717dxYsWAAYWQ5nz54FoHfv3ixcuJCTJ08CcObMmTLH0rt3b44dO1bpMV7PjKA9DQpsKmivQlJK9PSzOH7fje27NdhmLsD+n+9wrNmMPHkGrW0LLP27Y33gLpzH3oVT325YAtuooF2pFtJmP79u3cUZrUUTtHat0Bo3QLi53jB/BCuKUncIqxOajxeWlk2NdfHNm4CbC+TmG+vi8wuMzKHrmJpxr2YffPAB9957L4WFhbRp04Z//etf5mPz5s2rkjR5gICAAM6cOXNV+2jatCmvv/46t912G1JK/vKXv3DHHXcAMHXqVLp27Yqvr6+ZKg7w/vvv8+ijjxIaGordbqdnz57MnDmz1H7LW+NenorWp1/o9ttvL7fQ2OHDh3G7zLW59957L3/9618JCQkhKiqKjh07Vviczz77jAcffBBN07j11lvN4njjxo0jJSWFiIgIpJQ0bNiQxYsXX9Z4LuVSa9zLEhoaisVioXPnzsTHxzNhwgSGDBnCv//9bwYMGFBq5hugcePGfP/99wwcOJA5c+bwzDPPMGzYMGbNmsVf/vKXco9zJeewWOfOnQkPD6djx460aNGC7t27V/icl19+mZEjR/LFF1/QtWtXmjRpgpeXFw0aNOC1116jX79+6LqO1Wrlww8/pFWrVuZzdV1n//79FRb8GzduHOPHjycqKsr8jI8fP54tW7Ywc+ZMPv3000q/xtrKDNrzClRRs6skpUSeziyxRt04rwB4e6K1aY5oUTSj7l03O4EodYt06FBQALo00kybNER4uSFqsJOKoijKlRBOFvDywOLlYXy35RcYLeaycozg3aKBc/W1maspojrSb2tCVFSULFlUC4w1uoGBgTU0omvP29ubo0eP4u3tXdNDqTUmTZrE6NGjzfXR1aXkuv7XX3+d1NRU3nvvvWo9pnJeQUEBFosFJycnNm7cyCOPPFLp7I+dO3cyZ84c3nnnneod5GW61t9f0qEb6fEqaL8iUko4k4l+pDj1PQ3yipZ4eHmgtWiCaF5UTM5HBerKtVGqhZtFQ/j5ILw8jPWjiqIo1xmp65BfiMzKNlLqHQ6QgIcbllYV12uqDYQQiVLKqLIeU5dZryPPPPMMt9xyC6+88gqDBw+u6eHUCm+99dY1Oc4PP/zA9OnTsdvttGrVirlz516T4yqGw4cPM2zYMHRdx9nZmdmzZ1f6uZ06dap1Qfu1Jh06+jE10345zED9aBqyKFg/H6i7o/k3Oz+j7uN16Z0pShWThTYoLAShIbw9jYtFbq4ITaXAK4py/RKaBu6uCHdXZMP6UFCIzMmt6WFVGTXjrijX2I4dOxg9enSp+1xcXMw18ter06dP07t374vu//nnn6lfv34NjKh2u1bfX2bQnpOn2opdgpQSzp4z0t6PGpXfyS0K1D3dS82o4+Op1ggr15y0O4x0UUC4uyLqeSM83BCVrBejKIqi1Dw1464otUhISEiVFPGra+rXr39Dvu7aTOo6+vGTkKuC9guZgXpRxXf9SBrkFnWR8HRHa9nM7KOuAnWlphSnhaLrxrr1xvXRPN1VCzdFUZTrkArcFUVRbkBS19GPnYScXISHCtqllJCRVTSjXpT6nlMUqHu4obVsYrRna9EYfLxUoK7UGGPdug3sdtA0Y2bd28Pofaw+l4qiKNet66vUXi2UkZHB0KFD6dixI4GBgWzcuBEw2qbFxMQQFhZGVFQUmzZtuupjPfTQQwQFBZkp11OnTq2WtdZTp07l7bffrnCbio69Zs0a4uPjAZg7dy5CCFauXGk+vnjxYoQQZtux2NhYipdD+Pv7M2TIEHPbhQsXltrX1KlTK33s2mTkyJGEhoYyY8aMcrep7Nj9/f3LvD8+Pv6SrdwA9uzZQ1hYGOHh4Rw4cKDCY12o5Gdk7ty5HD9+/LL3Ud3i4+PNNnflqa7foZpmzLSfguwbN2iXUiLPnsOxYy/2H9dhm70Q29zFOH7+Ff1oGlrzxlj6xGCNvxPrg0NxGtgDS0h7hK+3Co6UGiFttvMt3Nxc0Fo2RWvfEq2RH0L1XVcURbnuqRn3avbEE08wYMAAFi5cSGFhIbm5RoGEZ599lpdffpmBAweybNkynn322QqDiEvJzMzku+++49ixY2ha3bweExISwrx58+jTpw8ACQkJdO7cudztExMTSU5OJigoqMrGYLfbcaqh1jgnTpxg8+bN7N+/v0aOX9LixYsZOnQoU6ZMuep9zZ07l06dOtGsWeWqeTocDixqTWa1MYP2rGyE543TI1xKCZnZpWfUs4sK1ri7obVoXDSj3gR81Yy6UjtIR9G6dV0a69abNUR4uButkBRFUZQbSt2M8OqIzMxM1q5dywMPPACAs7Mzvr6+AAghOHfunLldZYOa8mRkZFC/fv1SQbunp6fZwzw2NpannnqKqKgoAgMD2bx5M3fddRft27cvFZy98847dOrUiU6dOvHuu++a90+bNo0OHTpwyy238Mcff5j3HzhwgAEDBhAZGUmPHj3Ys2fPRccuj7Ozs9nvHKBHjx5s2rQJm81GdnY2+/fvL9Uz/kJPP/0006ZNu+h+Nzc3szVbZY49depURo8eTffu3Rk9ejQpKSn06NGDiIgIIiIi+OWXXwBjpjs2NtbMoLj33nspLu64bNkyOnbsSGRkJI8//jiDBg0CICcnh7FjxxIdHU14eDhLliwpd0z9+vXj2LFjhIWFsW7dOmJjY3nuueeIjo6mQ4cOrFu3rszzVp7i3vZSSiZOnEhAQAB9+vTh5MmT5jaJiYnceuutREZG0r9/f1JTU1m2bBnvvvsuH3/8MbfddhsAd955J5GRkQQHBzNr1izz+SXPc8msh5L3bdmyhXvvvZewsDDy8vLKHKu/vz/PPfccERERfP3118yePZsuXbrQuXNnhgwZYl7wio+P5/HHH6dbt260adPGzBzQdZ0JEybQsWNH+vbty+23324+VtZrBPDx8cHZ+dItkSrzOa5LpC7RU09BVs51H7RLKZEZWTh27sP+43psn36D7V+LcKzciH44Fa1ZIyy9bsY65g6sDw3F6faeWEI7GGnHKmhXapDUdWRevjG7bncgGvhhadsCi/9NaD5eKmhXFEW5QakZ92r0559/0rBhQ+6//362bdtGZGQk7733Hh4eHrz77rv079+fZ555Bl3XzeDwSjkcjotm2p955plSt52dndmyZQvvvfced9xxB4mJifj5+dG2bVueeuopUlJS+Ne//sVvv/2GlJKbb76ZW2+9FV3XmTdvHklJSdjtdiIiIoiMjASM9PyZM2fSvn17fvvtNyZMmMCqVatKHXvmzJkAjB8/vtR4unXrRrdu3czbQgj69OnDTz/9RGZmJnFxcfz555/lvuZhw4bx0UcfXTRDPXz48ArP14XHTk5OZv369bi5uZGbm8uKFStwdXVl3759jBw50kzR//3339m1axfNmjWje/fubNiwgaioKB5++GHWrl1L69atGTlypLnfadOm0atXL+bMmUNGRgbR0dH06dMHD4+Lg6alS5cyaNCgUgXc7HY7mzZtYtmyZbzyyiusXLmy1Ni3bNnCzJkz+fTTTy/a3+bNmwFYtGgRf/zxB8nJyaSlpREUFMTYsWOx2Ww89thjLFmyhIYNGzJ//nxeeOEF5syZw/jx4/H09DTfxzlz5uDn50deXh5dunRhyJAhlaoEP3ToUP75z3/y9ttvExVVZoFMU/369dm6dStgVKB/8MEHAZgyZQqfffYZjz32GACpqamsX7+ePXv2EBcXx9ChQ/n2229JSUkhOTmZkydPEhgYWOFrfO+99yoc/4W/Q3WZEbSfhHM513UhOpmRheO37ehHUiGreEbd1WjL1txoz4afjwrOlVpFSgmFNii0gyYQvl5GCzeVAq8oiqIUUYF7NbLb7WzdupUPPviAm2++mSeeeILXX3+dv//973z88cfMmDGDIUOGsGDBAh544IFS67svV1JSEs2bN7/kNnFxcYCRkh4cHEzTpk0BaNOmDUeOHGH9+vUMHjzYDCrvuusu1q1bh67rDB48GHd391L7yc7O5pdffuHuu+82j1FQUHDRcS8M2C9lxIgRvP/++2RmZvKPf/yD//f//l+521osFiZNmsT06dMZOHBgpY9Rlri4OHNm1WazMXHiRJKSkrBYLOzdu9fcLjo62jzPYWFhpKSk4OnpSZs2bWjdujVgrFMvnpVevnw5S5cuNdd75+fnc/jw4Uq3+brrrrsAiIyMJCUl5aLHo6KiygzaS1q7di0jR47EYrHQrFkzevXqBcAff/zBzp076du3L2Bc/Cn+TFzo/fffZ9GiRQAcOXKEffv2VXkLt5IXXHbu3MmUKVPIyMggOzub/v37m4/deeedaJpGUFAQaWlpAKxfv567774bTdNo0qSJmSlwOa/xeiZ1iX4iHTKzEV7X70y7fiId++JV4HCgtWqG6FJU9V0F6kotJe12o8+wBOHhhtakgdFv3aISIhVFUZTSVOBejZo3b07z5s25+eabAWP28fXXXwfg888/N2f87r77bsaNG3fFx3nuued4//33Kyw45uLiAoCmaebPxbftdvtlH1fXdXx9fau0xVd0dDQ7duzA3d2dDh06VLj96NGjmT59Op06dbqq45acAZ8xYwaNGzdm27Zt6LqOq6ur+VjJ82axWCo8b1JKvvnmGwICAq5oXMXHq8yxLpeUkuDgYLNgYnnWrFnDypUr2bhxI+7u7sTGxpKfb/SvLhkMFd93pUq+B/Hx8SxevJjOnTszd+7cUvUfSr4HxUsVylPZ13g9Ox+0n4PreKZd//Mo9u/Xgrsr1mH9EX4VLydRlJogHToUFBS1cHNGNG5gtHCzqj/JFEVRlPKpS7rVqEmTJrRo0cJcE/7zzz+bhdSaNWvG//73PwBWrVpF+/btr/g4b7zxBosXL+aNN964qvH26NGDxYsXk5ubS05ODosWLaJHjx707NmTxYsXk5eXR1ZWFt999x0A3t7etG7dmq+//howgqRt27Zd1RgAXn/99UvOtJdktVp56qmnyq3CvmjRIiZPnnxZx8/MzKRp06ZomsYXX3yBw+G45PYBAQEcPHjQnBGfP3+++Vj//v354IMPzADz999/v6yxVIWePXsyf/58HA4HqamprF692hz3qVOnzKDWZrOxa9eui56fmZlJvXr1cHd3Z8+ePfz666/mY40bN2b37t3oum7OyF/Iy8uLrKysyxpzVlYWTZs2xWaz8eWXX1a4fffu3fnmm2/QdZ20tDQz0K/sa5w8eXK546/LpC7R09Ih4xx4uF+3s86OnfuwL1mN8PPGOmKgCtqVWkdKicwvMNatFxYi/HzQWjfH0rYFWj1vFbQriqIoFVKBezX74IMPuPfeewkNDSUpKYm//e1vAMyePZunn36azp0787e//a1Uwa8rERAQwJkzZ65qHxEREcTHxxMdHc3NN9/MuHHjCA8PJyIiguHDh9O5c2cGDhxIly5dzOd8+eWXfPbZZ3Tu3Jng4OAyi6/NnDnTXOdeGQMHDjRTnSvjgQceKHc2+sCBA3h7e1d6XwATJkzg888/p3PnzuzZs6fM9eglubm58dFHH5lF+ry8vMzicS+++CI2m43Q0FCCg4N58cUXL2ssFdmyZUuF2RqDBw+mffv2BAUFcd9999G1a1fAqHmwcOFCnnvuOTp37kxYWFiZtRYGDBiA3W4nMDCQ559/npiYGPOx119/nUGDBtGtW7dyU9Dj4+MZP378JYvTXejvf/87N998M927d6djx44Vbj9kyBCaN29OUFAQo0aNIiIiwiw+V5nXuGPHDpo0aXLJY5T8HF943i9VRLGmSFkUtJ81Ztqvx6BdSonj1204VmxEtGyC0939ER7XTzFBpe6ThUUt3HLzwcPNaOHWrhVaQ6OFm6IoiqJUlqgo1bSuiIqKksUFxIrt3r270muJ67qTJ08SHh7OoUOHaqydWW00atQoZsyYYVZYry7Z2dl4enoipeTRRx+lffv2PPXUU9V6TKW04vfg9OnTREdHs2HDhgqD8WL9+/fnp59+quYRXp6r+f6S8v+3d+fxUVZn/8c/557sG2Hfl4BsJiEBAogrioIrbih1q2jdpbXWUvV5qKKtj7b1aevWWq2K7eMPdxGrVkDcQSFA2DdRXICyhQDZM3Of3x93GBNIQgJJJjP5vl8vXmbuuZczS+Jcc65zXRa7fRc2fz8kxUdm0O66BN7/AnfVRpzBffGdcbzWBUuLYP2BynXrlS3c2qZgEuMxanMpIiKHYYxZYq2tsaKzIrwI0alTJyZMmMCwYcN4+umng+vqW7v/+7//a5brPP300zz//POUl5czdOhQbrzxxma5rvzg3HPPpaCggPLycn7961/XO2gHWlzQfjSstdgdu7H5ETzTXlGB/+2PsV9vwRmZie/47Ih8nBI+rOtCaTm4AYiOxnRq561bj4kO9dBERCRCaMZdpJm999573HnnndW2paWlReQa64NdeOGFh7T4+93vfletarx4juTvlxe052N3F0Ru0F5cgn/WfOyOfHynjsSXdWSFH0WOlrUWyiqgogJ8PkxqEiYlGeJiIvJ3T0REmp5m3EVakPHjx7faQLU1fDkRKq0iaC/YR8Xr70NhMVHnnYLTr1eohyStkK2o8AJ2gOQEnK4dICEO42iphoiINB39X6aJFRQUMHHiRAYNGsTgwYOD1a2XL1/O6NGjyczM5LzzzmPfvn1Hfa3f/OY3DBo0KBgczZgxg+nTpx/1eQ82Y8YMpkyZcth9DnftzZs3M2bMGMBrOWaMqdaTPC8vD2NMsAf65MmTgy3vxowZQ07OD19G5ebmVjvX5MmT633tlmTq1Kmkp6czderUWvep79j79OlzRGMYM2YMB2evNKXarpebm8vPfvYzAKZPnx58H8ihrLXYnZEdtLv/2UXFi/+GsnKiJo5T0C7NygYC2OIS3MIiMA6mW0ec/r3w9eiCSUpQ0C4iIk1OM+5N7LbbbuPMM8/k1Vdfpby8nOLiYgCuu+46Hn74YU455RSeffZZ/vCHP/Cb3/zmqK715z//mW+++YakpKTGGHqzy8jI4OWXXw5W6545cyZZWVm17r9jxw7effddzjrrrEYbg9/vD2lxv6eeeor8/Hx8KmJETk5OtS9nGpO1FmstTgR82LbWYnftwe7aA0mJkRm0f/U9/rcre7RfOFbt3qRZWNeFsnIIuBDlw7Rvi5OcgImNCfXQRESkFQr/T60t2N69e/n444/5yU9+Anjtt1JTUwHYsGEDJ598MgBnnHEGr7322lFfr7y8vFrQHh8fH7w9efJkbr75Zo477jj69u3Lhx9+yLXXXsvgwYOrzU7PnDmTzMxMMjIyqq3Dfu655xgwYECwWvcBO3fu5OKLL2bEiBGMGDEieF/Va9fG5/PRrl274O3evXtTWlrK9u3bsdby73//u86gfOrUqTzwwAOHbI+JiQm2Y6vPtWfMmMGECRM47bTTGDt2LIWFhYwdO5Zhw4aRmZkZbHG3efNmBg8ezPXXX096ejrjxo0LtjdbvHgxQ4YMITs7m6lTp5KRkQFAIBBg6tSpjBgxgiFDhvC3v/2t1jFNmDCBwsJChg8fzksvvcTkyZP52c9+xvHHH0/fvn2D2QYHP2+1OVBJv6ioiHPOOYesrCwyMjKCfebvv/9+RowYQUZGBjfccAMH17twXZfJkyczbdo0SktLueaaa8jMzGTo0KHBXvANeXzgrWfPzMwkKyuLu+66K7j9lVdeYeTIkQwYMIBPPvkE8DInzj333OA+B7JU+vfvz9NPPw1Q62tVk82bNzNw4EB+/OMfk5GRwXfffcfNN99MTk4O6enp3HvvvcF9+/Tpw7333hs877p16wDv/X7GGWeQnp7OddddR+/evdm1axfgFUIcOXIk2dnZ3HjjjQQCgTqfi8YQDNp3Rm7QHli1Ef9s9WiX5mGtxZaVY/cXQ0kZJiUJp3c3nH69cDqkKmgXEZGQUeDehL7++ms6duzINddcw9ChQ7nuuusoKioCqNbz/JVXXuG77747qmu5rnvIh/ZJkybxy1/+Mnh7z549LFy4kD/96U9MmDCB22+/ndWrV7Ny5Ury8vLYunUrd955J/PnzycvL4/Fixcza9Ystm3bxr333stnn33Gp59+ypo1a4LnvO2227j99ttZvHgxr732WnC2vOq1Z8+ezT333HPImHv27Mnrr79ebdvEiRN55ZVXWLBgAcOGDSM2tvY+t6NHjyYmJiYYRB5w/PHH88gjj9T5fB187aVLl/Lqq6/y0UcfERcXxxtvvMHSpUv54IMPuOOOO4JB7caNG7n11ltZvXo1qampwS9crrnmGv72t7+Rl5dXbbb8mWeeoU2bNixevJjFixfz9NNPH1Kc7YDZs2cTHx9PXl4ekyZNAmDbtm18+umn/Otf/woGulXHvnXrVs4+++waz7d48WIA/v3vf9OtWzeWL1/OqlWrOPPMMwGYMmUKixcvZtWqVZSUlPCvf/0reKzf7+eKK66gf//+/Pa3v+WJJ57AGMPKlSuZOXMmV199NaWlpQ16fO+++y5vvvkmX3zxBcuXL+dXv/pVtestWrSIP//5z9x33301Hr9ixQrmz5/PwoULuf/++9m6dWudr1VNNm7cyC233MLq1avp3bs3DzzwALm5uaxYsYKPPvqIFStWBPft0KEDS5cu5eabbw6m6d93332cdtpprF69mokTJ/Ltt98CXiG5l156ic8++yz4HnjhhRdqHUdj+SFoj7z0eGstgYXq0S7Nw/r92MJibFEJxETj9OyM0783TpcOXks3J7J+v0REJPwocG9Cfr8/+MF/2bJlJCYm8tBDDwHw7LPP8pe//IXhw4ezf/9+YmKO7lv8vLw8evToUec+5513HsYYMjMz6dy5M5mZmTiOQ3p6Ops3b2bx4sWMGTOGjh07EhUVxRVXXMHHH3/MF198EdweExMTDCoB5s2bx5QpU8jOzmbChAns27ePwsLCatedMGEC999/f70ex6WXXsorr7zCzJkzueyyyw67/7Rp0/jtb39br3PX5YwzzgjOYltr+a//+i+GDBnC6aefzpYtW9i+fTvgVX/Pzs4GYPjw4WzevJmCggL279/P6NGjAbj88suD550zZw7/+Mc/yM7OZtSoUezevZuNGzfWe1wXXHABjuNw7LHHBsdQVbdu3XjnnXfqPEdmZiZz587lzjvv5JNPPglmI3zwwQeMGjWKzMxM5s+fz+rVq4PH3HjjjWRkZPDf//3fAHz66adceeWVAAwaNIjevXuzYcOGBj2+efPmcc0115CQkABQLWvgoosuAn54Tmty/vnnEx8fT4cOHTj11FNZtGhRna9VTXr37s1xxx0XvP3yyy8zbNgwhg4dyurVq6t9KVXTmD799FN+9KMfAXDmmWfStm1bAN5//32WLFnCiBEjyM7O5v333+err76qdRyNwd21B7sjQoN21yUwbyGBz5fjDO5L1Plj1VZLGp11XWxxCbawCFyL6dIB3zG98PXqiklOxPj0EUlERFqOJl3Ma4w5E3gE8AF/t9Y+dND9NwG3AgGgELjBWrumyv29gDXAdGtt2FWm6tGjBz169Aj2VJ84cWIwcB80aBBz5swBvLT5t99++4iv8/jjjzN16lT+/Oc/17nfgdlrx3GqzWQ7joPf7yc6uuEfjF3X5fPPPycuLq7Bx9akS5cuREdHM3fuXB555BEWLFhQ5/6nnXYa06ZN4/PPPz+q6yYmJgZ/fuGFF9i5cydLliwhOjqaPn36UFpaClDtefP5fMFU+dpYa3nssceOuIp81esdaevGAQMGsHTpUt555x2mTZvG2LFj+dWvfsUtt9xCbm4uPXv2ZPr06cHHCF7WwoEZ7Lpe26N9fAcceJw+nw+/31/jPgcHp8aYOl+rmlR9nb/++msefvhhFi9eTNu2bZk8eXK1Y+szpgOstVx99dU8+OCDdT/QRuLu2oPdng/JERi0q0e7NCGvhVs5+APgOJh2bTDJiRCrFm4iItKyNdnXycYYH/AEcBZwLHCZMebYg3b7f9baTGttNvB74I8H3f9H4N2mGmNT69KlCz179mT9+vWANyt37LHeU7Bjxw7AC3x/+9vfctNNNx3xdaZMmUJeXl6N670bYuTIkXz00Ufs2rWLQCDAzJkzOeWUUxg1ahQfffQRu3fvpqKigldeeSV4zLhx43jssceCt/Py8o5qDOCtvf7d735X7wJt06ZN4/e//32N9y1atIgf//jHDbr+3r176dSpE9HR0XzwwQd88803de6fmppKcnIyX3zxBQAvvvhi8L7x48fz17/+lYoKr3XQhg0bgsslmsvWrVtJSEjgyiuvZOrUqSxdujQYoHbo0IHCwsLg+vkDfvKTn3D22Wdz6aWX4vf7Oemkk4Kp3xs2bODbb79l4MCBDXp8Z5xxBs8991ywQGN+fn6DHsebb75JaWkpu3fv5sMPP2TEiBENfq2q2rdvH4mJibRp04bt27fz7ruH/1Nzwgkn8PLLLwNeNsWePXsAGDt2LK+++mrw9zo/P79BY2kId1cBdvtuSIqPuEDDFpfgf2UOdvNWfGOPI+qEoRH3GCU0bHkFtrAYikogMR6nV1cvFb5jO0xcrN5nIiLS4jXljPtI4Etr7VcAxpgXgfPxZtABsNZW7YGWCASnFI0xFwBfA80b5TSyxx57jCuuuILy8nL69u3Lc889B3hF4J544gnAS8m95pprjuo6AwcObHAgdLCuXbvy0EMPceqpp2Kt5ZxzzuH8888HvHZco0ePJjU1NZgqDvDoo49y6623MmTIEPx+PyeffDJPPvlktfPOnj2b3NzceqfLH3/88Q0a99lnnx0sxHawb7/9lvj4hq2LveKKKzjvvPPIzMwkJyeHQYMGHfaYZ555huuvvx7HcTjllFOC6ejXXXcdmzdvZtiwYVhr6dixI7NmzWrQeOqydetWrrvuujrT5VeuXMnUqVNxHIfo6Gj++te/kpqayvXXX09GRgZdunRhxIgRhxz3i1/8gr1793LVVVfxzDPPcOutt5KZmUlUVBQzZswgNja2QY/vzDPPJC8vj5ycHGJiYjj77LP5n//5n3o/1iFDhnDqqaeya9cufv3rX9OtW7cjeq0OyMrKYujQoQwaNIiePXtywgknHPaYe++9l8suu4x//vOfjB49mi5dupCcnEyHDh347W9/y7hx43Bdl+joaJ544gl69+7Nddddx0033UROTk7wd+Omm24iNzeXJ598sloLxMNx8wuwO3Z76fERUBG/qmCP9qJios4bg9OvZ6iHJGHO+gNQVoa1YOLjMN1SMYkJmCh17RARkfBjjjT99rAnNmYicKa19rrK21cBo6y1Uw7a71bgF0AMcJq1dqMxJgmYC5wB/BIoPFyqfE5Ojj24F/TatWsZPHhwYz2kFi8lJYXvv/+elJSUUA+lxZg6dSpXXXUVQ4YMadLrFBYWBqvoP/TQQ2zbtu2wBfIk/JSVleHz+YiKimLhwoXcfPPNjZJlUpOD/365+QXY/+yGxPiIC9rd/+zCP2s+WEvU+afhdKv5iziRw/mhhVsAoqMx7VIwSYmqkSAiImHBGLPEWltjP+SQ93G31j4BPGGMuRyYBlwNTAf+ZK0trCt9zRhzA3ADQK9evZp+sC3cL3/5S0488UTuu+8+LrzwwlAPp0X4wx/+0CzXefvtt3nwwQfx+/307t2bGTNmNMt1pXl9++23XHrppbiuS0xMTLAtXVNz8/dit1Wmx0da0F61R/tFp2Pa6otHaRhrLZRXQIUfHINJTcakJEOc1q2LiEjkaMoZ99F4ReXGV96+G8BaW2P1JmOMA+yx1rYxxnwCHMiTTAVc4B5r7eO1XU8z7hIuVq5cyVVXXVVtW2xsbHCNfLgL9ePbvXs3Y8eOPWT7+++/T/v27ZtlDI3hwN8vd89e7NZdERm0B1ZuJPD+55iObYm6YKzavUmD2Aq/N7sOkJyAk5oCCXER93siIiKtR6hm3BcD/Y0xacAW4EfA5VV3MMb0t9Ye6B11DrARwFp7UpV9puOlytcatIuEk8zMzCZLr24JQv342rdvHzHPr7tnH3bbzohb026tJfD5ctzPV2B6dyPq3FOUyiz1YgMulJWBayEuFtOlIyY5HhMV8gRCERGRJtVk/6ez1vqNMVOA9/DawT1rrV1tjLkfyLXWzgamGGNOByqAPXhp8iIirZ51Xey2HZAYYUG76xJ4/3PcVV/iHNsP3+mj1S9b6mSthdIyCLjgczDtU70+67ExoR6aiIhIs2nST0vW2nestQOstf2stQ9UbrunMmjHWnubtTbdWpttrT3VWru6hnOEZQ93gPXr15OdnR38l5KSEuy1np+fzxlnnEH//v0544wzgm2ljsYNN9zAscceG0xJnj59epOstZ4+fToPP1z3S1Kfa3/44YdMnjwZgBkzZmCMYd68ecH7Z82ahTEm2KpszJgxHFgO0adPHy6++OLgvq+++mq1c02fPr3e125JLrvsMoYMGcKf/vSnWvep79j79OkDeJXnJ06cCHjt+uqqQH/AjBkzmDJlymH3C2cHnp+j3acpWH/AK64VaUF7RQX+2R94QfuoTHzjjlfQLjWy1la2cCuC4lJMUiJO7244x/TG6dBWQbuIiLQ6+sTUhAYOHEheXh55eXksWbKEhISEYNG4hx56iLFjx7Jx40bGjh3LQw89dFTX2rt3L2+99RarVq1i1KhRjTH8ZpeZmVmtB/rMmTPJysqqdf8lS5awZs2aWu8/En6/v1HP1xD/+c9/WLx4MStWrOD2229vtPN269Yt+OVHfQP3o3E0z2Eon/+WwvoDUFEBxomsoP3gHu3Hq0e7HMr6A9jCYmxRCURH4fTogtO/F063jpiEOIyj94yIiLRO9fpUaIwJn4pOLdT7779Pv3796N27NwBvvvkmV1/trQy4+uqrj7q3d0FBAe3bt8ep8kE/KSkp2MN8zJgx3H777eTk5DB48GAWL17MRRddRP/+/Zk2bVrwmD/+8Y9kZGSQkZERzA4AeOCBBxgwYAAnnngi69evD27ftGkTZ555JsOHD+ekk05i3bp1h1y7NjExMcF+5wAnnXQSixYtoqKigsLCQr788stqPeMPdscdd/DAAw8csj0+Pj7Ymq0+154+fTpXXXUVJ5xwAldddRWbN2/mpJNOYtiwYQwbNowFCxYA3kz3mDFjmDhxIoMGDeKKK67gQHHHd955h0GDBjF8+HB+9rOfce655wJQVFTEtddey8iRIxk6dChvvvlmrWMaN24cW7ZsITs7m08++YQxY8Zw5513MnLkSAYMGMAnn3xS4/NWmwO97Tdv3kxGRgbl5eXcc889vPTSS2RnZ/PSSy+xaNEiRo8ezdChQzn++OOrvbYHvP3224wePZpdu3bRp08fdu3aBUBubi5jxoxp0HNYkw8//JCTTjqJCRMmcOyxxwJwwQUXMHz4cNLT03nqqaeC+yYlJfHf//3fZGVlcdxxx7F9+3bAex8ed9xxZGZmMm3atGqv/x/+8AdGjBjBkCFDuPfeew95furzHDaXYNAeQQE7VPZof/Hf2N0FRJ03Bt+QAaEekrQg1nWxxSXY/UXgupjOHfD164mvV1cvJd6nvusiIiL1XeP+uTEmD3gOeNc2VSn6CPbiiy9y2WWXBW9v376drl27AtClS5dgAHKkAoFAtaAdvPZwVcXExJCbm8sjjzzC+eefz5IlS2jXrh39+vXj9ttvZ/PmzTz33HN88cUXWGsZNWoUp5xyCq7r8uKLL5KXl4ff72fYsGEMHz4c8NLzn3zySfr3788XX3zBLbfcwvz586td+8knnwTgpptuqjae448/nuOPPz542xjD6aefznvvvcfevXuZMGECX3/9da2P+dJLL+Uvf/kLX375ZbXtkyZNOuzzdfC116xZw6effkp8fDzFxcXMnTuXuLg4Nm7cyGWXXRZM0V+2bBmrV6+mW7dunHDCCXz22Wfk5ORw44038vHHH5OWllbtdX7ggQc47bTTePbZZykoKGDkyJGcfvrpJCYmHjKm2bNnc+6551YrrOb3+1m0aBHvvPMO9913H/Pmzas29tzcXJ588kn+/ve/H3K+xYsXV7sdExPD/fffT25uLo8/7tV63LdvH5988glRUVHMmzeP//qv/+K1114LHvPGG2/wxz/+kXfeeYe2bdvW+ZzW9zmsydKlS1m1ahVpaWkAPPvss7Rr146SkhJGjBjBxRdfTPv27SkqKuK4447jgQce4Fe/+hVPP/0006ZN47bbbuO2227jsssuC77fAObMmcPGjRtZtGgR1lomTJjAxx9/zMknn3zI81OT+uzTWGygStAeQTPR1Xq0TxyH01U92qVy3XpZOfgD4DiYtm0wKYkQqxZuIiIiNalv4D4AOB24FnjUGPMyMMNau6HJRhZBysvLmT17Ng8+WGMnPIwxR/1BJS8vjx49etS5z4QJEwAvJT09PT34xUHfvn357rvv+PTTT7nwwguDQeVFF13EJ598guu6XHjhhSQkJFQ7T2FhIQsWLOCSSy4JXqOsrOyQ6x4csNflRz/6EY8++ih79+7lf//3f/mf//mfWvf1+XxMnTqVBx98kLPOOqve16jJhAkTghkCFRUVTJkyhby8PHw+Hxs2/PA2HzlyZPB5zs7OZvPmzSQlJdG3b99g0HnZZZcFZ4nnzJnD7NmzgzUBSktL+fbbb+vdpvCiiy4CYPjw4WzevPmQ+3NycmoM2utr7969XH311WzcuBFjDBUVFcH75s+fT25uLnPmzCEl5fC9tev7HNZk5MiRwecP4NFHH+WNN94A4LvvvmPjxo20b9+emJiYYDbD8OHDmTt3LgALFy4MZq1cfvnlwS+O5syZw5w5cxg6dCjgvWc3btzIySefXJ+np9nYQMDrQx1pQbt6tMtBbEUFlFX+nUlOxGmbAvGxEbUsREREpCnUK3CvnGGfC8w1xpwK/B9wizFmOXCXtXZhE44x7L377rsMGzaMzp07B7d17tyZbdu20bVrV7Zt20anTp2O+Px33nknjz76aHAdc21iY2MBcBwn+POB20eytth1XVJTUxu19dbIkSNZuXIlCQkJDBhw+HTaq666igcffJCMjIyjum7VGfA//elPdO7cmeXLl+O6LnFxccH7qj5vPp/vsM+btZbXXnuNgQMHHtG4DlyvPtc6Er/+9a859dRTeeONN9i8eXMw9R2gX79+fPXVV2zYsIGcHK+dZFRUFK7rAt6XEFXV9zmsSdVjP/zwQ+bNm8fChQtJSEhgzJgxwWtFR0cHv+Sq7/N/9913c+ONNx7mmQidSA3aAys3EHj/C0zHdkRdcJp6tLdi1h+AsjKsa7116t06YhITMFFKgRcREamveq9xN8bcZozJBX4J/BToANwB/L8mHF9EmDlzZrX0afBmJ59//nkAnn/+ec4///wjPv/vfvc7Zs2axe9+97ujGudJJ53ErFmzKC4upqioiDfeeIOTTjqJk08+mVmzZlFSUsL+/ft56623AEhJSSEtLY1XXnkF8IKk5cuXH9UYwCvcV9dMe1XR0dHcfvvttVZhf+ONN7j77rsbdP29e/fStWtXHMfhn//8J4FAoM79Bw4cyFdffRWcEX/ppZeC940fP57HHnssuBZ+2bJlDRpLY0tOTmb//v3B23v37qV79+4Ah3QB6N27N6+99ho//vGPWb3aa/jQp08flixZAlAtpf5gDX0ODz62bdu2JCQksG7dOj7//PPDHnPccccFx1O1wOH48eN59tlnKSwsBGDLli3s2LHjkOMHDRpU7/E1pkgM2q21+BfkEZj3OaZ3V6IuGaegvRWyrostKfXWrQcCmI7tvHXrfbrjtElW0C4iItJA9c1NWwikABdYa8+x1r5urfVba3OBJw9zbKtWVFTE3LlzgynPB9x1113MnTuX/v37M2/ePO66666jus7AgQPJz88/qnMMGzaMyZMnM3LkSEaNGsV1113H0KFDGTZsGJMmTSIrK4uzzjqLESNGBI954YUXeOaZZ8jKyiI9Pb3G4mtPPvlktXXHh3PWWWdx6qmn1nv/n/zkJ7XOvG7atKlead5V3XLLLTz//PNkZWWxbt26GtejVxUfH89f/vKXYJG+5OTkYPG4X//611RUVDBkyBDS09P59a9/3aCxHE5ubi7XXXddvfc/9dRTWbNmTbA43a9+9Svuvvtuhg4dWuNzOGjQIF544QUuueQSNm3axL333sttt91GTk4OvjoKRjX0OazqzDPPxO/3M3jwYO666y6OO+64wx7z5z//mT/+8Y8MGTKEL7/8Mvj8jxs3jssvv5zRo0eTmZnJxIkTq31xAbBr1y7qU7bj7LPPZuvWrQDcc889zJ49G/BqE9xzzz31fnwHeEG7P7KC9oBLYO5C3C9W4Bzbj6gJp2FiokM9LGkm1lpsWTl2fzGUlmHaJOGkdcfp1xOnfapauImIiBwFU58PrMYY09IL0uXk5NiDi1+tXbu23muJw92OHTsYOnQo33zzDVFR9S1dEPmuvPJK/vSnPzV5dfDCwkKSkpKw1nLrrbfSv3//Rm3pJnUrLi4mPj4eYwwvvvgiM2fOrLOCf1X/+te/+Oqrr/jZz37WxKP8wQ9Bu6k1aF+7fj2DUjs025iOli2vwP/2x9jNW3BGDcE3OktFxloJW+GH8nKwQGI8Trs2EB+H8WnduoiISEMYY5ZYa3Nquq++Ed4cY8wl1tqCyhO2BV601o5vpDHKUerUqRMTJkxg2LBhPP3002Hby72x/d///V+zXOfpp5/m+eefp7y8nKFDh7boNdWRaMmSJUyZMgVrLampqTz77LP1PvZAsbvmEpHp8cUl+GfNx+7Ixzf2OLV7awVswIWyMnBdiInBdO6ISYrHROuLYxERkaZQ3xn3PGtt9kHblllrhzbVwBqqtc+4S/h47733uPPOO6ttS0tLC1ZRj1QrV67kqquuqrYtNjaWL774IkQjan424Hozk/UI2sNlxt0W7KPi9fehqJios0/G6dcz1EOSJhJs4Vbhhygfpl0bTFICJi728AeLiIjIYTXGjHvAGNPLWvtt5Ql74yXFiUgDjR8/nvHjW1+ySmZmZqN2IAg3XtAeWTPtXo/29wHUoz2C2fIK7wsn40BKIk6bZC8V3omM97GIiEg4qO8CtP8GPjXG/NMY83/Ax0DDSnW3QuvXryc7Ozv4LyUlhT//+c8AvPLKK6Snp+M4DgdnChyp3/zmNwwaNCg4cztjxgymT5/eKOeuasaMGUyZMuWw+xzu2lXbj3344YcYY6r1JM/Ly8MYE+yBPnny5GDLuzFjxgRblIFXpK3quSZPnlzva7ckU6dOJT09nalTp9a6T33H3qdPnxq3T58+Pfic1mbnzp2MGjWKoUOH8sknnxz2Wger+h6ZNWsWa9asafA5mtr06dMPqaR/sMb6HfohaK99TXu4cb/6Hv8rcyAmmuhJZylojzDWH8AWleAWFnuz69074/Tvha9bJ0xivIJ2ERGRZlbfPu7/NsYMAw6Ud/65tXZX0w0rMgwcODA4wxgIBOjevTsXXnghABkZGbz++uuNuhb6z3/+M9988w1JSUmNds7mlJGRwcsvvxyskj5z5kyysrJq3X/Hjh28++67nHXWWY02Br/fH9Lifk899RT5+fl1VmxvDu+//z6ZmZnVvkg5UrNmzeLcc8/l2GOPrdf+oX4NGlskBu3q0R6ZrOtCabm3bj06CtOpHU5SgjoDiIiItAANKfkaC+QD+4BjjTEnN82QItP7779Pv3796N27NwCDBw9m4MCBjXqN8vLyakF7fHx88PbkyZO5+eabOe644+jbty8ffvgh1157LYMHD642Oz1z5kwyMzPJyMiotg77ueeeY8CAAYwcOZLPPvssuH3nzp1cfPHFjBgxghEjRgTvq3rt2vh8Ptq1axe83bt3b0pLS9m+fTvWWv7973/XGZRPnTqVBx544JDtMTExwXZg9bn2jBkzmDBhAqeddhpjx46lsLCQsWPHMmzYMDIzM4PVyTdv3szgwYO5/vrrSU9PZ9y4cZSUlACwePFihgwZQnZ2NlOnTiUjIwPwvrCZOnUqI0aMYMiQIfztb3+rdUwTJkygsLCQ4cOH89JLLzF58mR+9rOfcfzxx9O3b99gtsHBz1ttqlbSf+CBBxgwYAAnnngi69evD27ftGlTsI3dSSedxLp168jLy+NXv/oVb775JtnZ2ZSUlHDzzTeTk5NDeno69957b/D4Pn36sGuX9x1e1ayHAxYsWMDs2bOZOnUq2dnZbNq0qcaxjhkzhp///Ofk5OTwyCOP8NZbbwVn/E8//XS2b98OeLPk1157LWPGjKFv3748+uijwXP85je/YeDAgZx44olcdtllwayCmh4jQFJSEvHxdQec9Xkf18W6kRW0V+/R3k092iOAtRZbWo4tLIbSckzbFJw+lS3c2rVR0C4iItJC1GtayxjzO2ASsBpwKzdbvJR5qYcXX3yRyy67rMnO77ruIa2XJk2aVO32nj17WLhwIbNnz2bChAl89tln/P3vf2fEiBHk5eXRqVMn7rzzTpYsWULbtm0ZN24cs2bNYtSoUdx7770sWbKENm3acOqppzJ0qFeX8LbbbuP222/nxBNP5Ntvv2X8+PGsXbu22rVnz55Nbm4u999/f7Xx9OzZk9dff73atokTJ/LKK68E+8fHxtZe9Gj06NG88cYbfPDBByQnJwe3H3/88Rx//PF1Pl8HX3vp0qWsWLGCdu3a4ff7eeONN0hJSWHXrl0cd9xxTJgwAYCNGzcyc+ZMnn76aS699FJee+01rrzySq655hqefvppRo8ezV133RU87zPPPEObNm1YvHgxZWVlnHDCCYwbN460tLRDxjR79mySkpKCWRrvvvsu27Zt49NPP2XdunVMmDCBiRMnVhv71q1bue6663jnnXcOOd/ixYsBr+L6iy++SF5eHn6/n2HDhjF8+HAAbrjhBp588kn69+/PF198wS233ML8+fO5//77yc3N5fHHHwe8wL9du3YEAgHGjh3LihUrGDJkSJ3P8YHXYsKECZx77rlMnDixzn3Ly8uDy0b27NnD559/Hlw+8fvf/57//d//BWDdunV88MEH7N+/n4EDB3LzzTeTl5fHa6+9xvLly6moqKjXY/zlL3952PEf/DvUENZ1oSyCgvaAS+D9hbirN+Gk98M3drTafYUxW1HhvT8BkhNxUjtAQhzG0WsqIiLSEtU3H/UCYKC1tqwJxxKxysvLmT17Ng8++GCTXSMvL48ePXrUuc95552HMYbMzEw6d+5MZmYmAOnp6WzevJlvvvmGMWPGBGdqr7jiCj7+2Ptupur2SZMmsWHDBgDmzZtXbf3yvn37gj3ND5gwYUIw8D2cSy+9lEmTJrFu3Touu+wyFixYUOf+06ZN47e//S2/+93v6nX+2pxxxhnBWWxrLf/1X//Fxx9/jOM4bNmyJTjjm5aWRnZ2NgDDhw9n8+bNFBQUsH//fkaPHg3A5Zdfzr/+9S8A5syZw4oVK4Kz5Xv37mXjxo01Bu41ueCCC3Ach2OPPTY4hqq6detWY9Be1SeffMKFF15IQkICQPC1KCwsZMGCBVxyySXBfcvKav4Vf/nll3nqqafw+/1s27aNNWvW1Ctwb4iqQfL333/PpEmT2LZtG+Xl5dWer3POOYfY2FhiY2Pp1KkT27dv57PPPuP8888nLi6OuLg4zjvvvAY/xsYUcUG7erRHBBsIQFk51nUx8XGYbh0xiQmYqNAuzREREZHDq2/g/hUQDShwPwLvvvsuw4YNo3Pnzk1y/scff5ypU6cGC9/V5sDsteM41WayHcfB7/cTHd3wlEjXdfn888+Ji4tr8LE16dKlC9HR0cydO5dHHnnksIH7aaedxrRp0/j888+P6rqJiYnBn1944QV27tzJkiVLiI6Opk+fPpSWlgJUe958Pl8wVb421loee+yxI64iX/V69Wnd2BCu65KamnrYSu9ff/01Dz/8MIsXL6Zt27ZMnjw5+HxERUXhul4SzoFtR6rqa/DTn/6UX/ziF0yYMIEPP/ywWoG4g18Dv99f6znr+xgbU8QF7UWVPdp3qkd7OPLej+UQcL0ic+3b4iQnYGJjQj00ERERaYD65sQVA3nGmL8ZYx498K8pBxZJZs6c2aRp8lOmTCEvL6/G9d4NMXLkSD766CN27dpFIBBg5syZnHLKKYwaNYqPPvqI3bt3U1FRwSuvvBI8Zty4cTz22GPB240RIN1///387ne/q3eBtmnTpvH73/++xvsWLVrEj3/84wZdf+/evXTq1Ino6Gg++OADvvnmmzr3T01NJTk5OdiP/MUXXwzeN378eP76179SUeGlpG7YsIGioqIGjedonXzyycyaNYuSkhL279/PW2+9BUBKSgppaWnB19Nay/Llyw85ft++fSQmJtKmTRu2b9/Ou+++G7yvT58+LFmyBIDXXnutxusnJyezf//+Bo157969dO/eHYDnn3/+sPufcMIJvPXWW5SWllJYWBjMeKjvY3z88ceDywKORsQF7Xv2UfHSu9j8AqLOG6OgPUxYa7Fl5dj9xVBShklJwunTDeeYXjgdUhW0i4iIhKH6Bu6zgd8AC4AlVf7JYRQVFTF37lwuuuiiatvfeOMNevTowcKFCznnnHOOuq/3wIEDyc/PP6pzdO3alYceeohTTz2VrKwshg8fzvnnn0/Xrl2ZPn06o0eP5oQTTmDw4MHBYx599FFyc3MZMmQIxx57LE8++eQh5509ezb33HNPvcdx/PHHc8EFF9R7/7PPPrtaIbaqvv3228MWIDvYFVdcQW5uLpmZmfzjH/9g0KBBhz3mmWee4frrryc7O5uioqJgcbzrrruOY489lmHDhpGRkcGNN95Y5wxxQ23dupWzzz67zn2GDRvGpEmTyMrK4qyzzmLEiBHB+1544QWeeeYZsrKySE9PDxbiqyorK4uhQ4cyaNAgLr/8ck444YTgfffeey+33XYbOTk5tX7R8qMf/Yg//OEPDB06tNbidAebPn06l1xyCcOHD6dDhw6H3X/EiBFMmDCBIUOGcNZZZ5GZmRl8DerzGNetW0f79u3rvEbV9/HBz/vZZ5/Nlu+/94J2ExlBu7ttJxUvvQvlFV6P9n49Qz0kOQzr92MLi7FFJRATjdOzM07/3jhdOnip8RHwvhQREWmtTGOn34ZKTk6OPbgf+tq1a6sFmZEuJSWF77//npSUlFAPpcWYOnUqV111VaOvxz5Y1XX9Dz30ENu2beORRx5p0mtKdQdeg+LiYk4++WSeeuophg0bVq9jzz33XF5//XViYo5sJjJYPR7jzbY3grXr1zMo9fBfWjQF96vv8b/9ESTGE33h6Zi2+pvSUtmAC6VlYF2IicG0a4NJSsBER05LRRERkdbCGLPEWptT0331rSrfH3gQOBYILma21vZtlBFKo/jlL3/JiSeeyH333RfsF9/a/eEPf2iW67z99ts8+OCD+P1+evfuzYwZM5rluvKDG264gTVr1lBaWsrVV19d76AdCKbWH4mmCNpDKbBiA4H5X2A6tSPqfPVob4mstd66dX8AfA6mfRtMciImrvYuHCIiIhLe6jXjboz5FLgX+BNwHnAN4Fhr65//3MQ04y7hYuXKlVx11VXVtsXGxgbXyEeyW2+9lc8++6zatttuu41rrrkmRCM6Ok0ZtDf3jLu1lsDC5bhfrMD06U7UOSerh3cLY8srKt9vQJsknDbJEB+HiYAvjERERKQRZtyBeGvt+8YYY639BphujFkCtJjAXSRcZGZmNmuV85bkiSeeCPUQGs0PQTthP9NevUf7MfjGHqce7S2E9Ve2cLMWkxCH6dgWkxiPqWfxThEREYkM9f1kVmaMcYCNxpgpxpgLgaTDHSRgjOGOO+4I3n744YertbaS2k2fPp2HH364yc6fl5dXrQf6kVxv0qRJZGdnk52dTZ8+fYI93ufOncvw4cPJzMxk+PDhzJ8/v0Hn3bx5MxkZGTWOM5T69OnDrl27Dtn+5JNP8o9//KPB5ysoKOAvf/lLYwytWVUP2sM7wLXlFfhnz/eC9lFD8J0xWkF7iFnXxRaXYguLIBDAdGqLr19PfL274aQkKWgXERFpheo7434bkAD8DK+6/KnA1U01qEgSGxvL66+/zt13312v6tgtWSAQqHeLtnCQl5dHbm7uYauy1+Wll14K/nzHHXcEK5l36NCBt956i27durFq1SrGjx/Pli1bQjbOpnbTTTcd0XEHAvdbbrmlkUfUdCIqaK/ao/304/Blqt1bqHjr1ivA7wfHwaQmYVKSIS5G1eBFRETk8DPuxhgfMMlaW2it/d5ae4219mJr7efNML6wFxUVxQ033MCf/vSnQ+576623GDVqFEOHDuX0009n+/btgDfze/XVV3PSSSfRu3dvXn/9dX71q1+RmZnJmWeeGewJvmTJEk455RSGDx/O+PHj2bZtW51jmTFjBueffz5jxoyhf//+3HfffcH7LrjgAoYPH056ejpPPfVUcHtSUhJ33HEHWVlZLFy4kPvvv58RI0aQkZHBDTfcwIEaCWPGjOH2228nJyeHwYMHs3jxYi666CL69+/PtGnTAK813jnnnENWVhYZGRnVgt6G+MMf/sCIESMYMmQI9957L+DNUA8ePJjrr7+e9PR0xo0bR0lJCQCLFy9myJAhZGdnM3XqVDIyMigvL+eee+7hpZdeIjs7OziWNWvWMGbMGPr27cujjz5a7zFZa3n55Ze57LLLABg6dCjdunUDID09nZKSEsrKyuo8x5IlS8jKyiIrKyuYUl7TOPv378/OnTsBcF2XY445hp07dzJ58mRuuukmcnJyGDBgQLDgWiAQYOrUqcHn7G9/+9thH8/777/P0KFDyczM5Nprr6029t///vdkZmYycuRIvvzyS6B6tsKmTZs488wzGT58OCeddBLr1q0DYPv27Vx44YXBx7hgwQLuuusuNm3aFHxtWrqICtr37KPixcoe7RNOVdAeIrbCa+FGUQnEx+D07ILTvxdO5w6Y+FgF7SIiIgLUI3C31gaAE5thLBHr1ltv5YUXXmDv3r3Vtp944ol8/vnnLFu2jB/96Ef8/ve/D963adMm5s+fz+zZs7nyyis59dRTWblyJfHx8bz99ttUVFTw05/+lFdffZUlS5Zw7bXX8t///d+Al7ZcUz91gEWLFvHaa6+xYsUKXnnlFQ4U9Hv22WdZsmQJubm5PProo+zevRvwgu1Ro0axfPlyTjzxRKZMmcLixYtZtWoVJSUl1apxx8TEkJuby0033cT555/PE088wapVq5gxYwa7d+/m3//+N926dWP58uWsWrWKM888s8HP5Zw5c9i4cSOLFi0iLy+PJUuW8PHHHwOwceNGbr31VlavXk1qaiqvvfYaANdccw1/+9vfyMvLC2YMxMTEcP/99zNp0iTy8vKYNGkS4PXzfu+991i0aBH33Xdf8EuSs88+m61bt9Y6rk8++YTOnTvTv3//Q+577bXXGDZsGLGxdVd8vuaaa3jsscdYvnx5cFtN47zyyit54YUXAJg3bx5ZWVnBPvabN29m0aJFvP3229x0002UlpbyzDPP0KZNGxYvXszixYt5+umn+frrrwGCqf1VlZaWMnnyZF566SVWrlyJ3+/nr3/9a/D+Nm3asHLlSqZMmcLPf/7zQ46/4YYbeOyxx1iyZAkPP/xwcDb9Zz/7GaeccgrLly9n6dKlpKen89BDD9GvXz/y8vKarQPAkYqkoN3dtpOKF9+Fisoe7X17hHpIrYoNBLDFJbj7i8AYTLeOOP174evRxWvlFubvLxEREWl89U2VX2aMmQ28AhQd2Gitfb1JRhVhUlJS+PGPf8yjjz5KfPwPrZW+//57Jk2axLZt2ygvLyctLS1431lnnUV0dDSZmZkEAoFgkJuZmcnmzZtZv349q1at4owzzgC8WdWuXbsCdactn3HGGbRv3x6Aiy66iE8//ZScnBweffRR3njjDQC+++47Nm7cSPv27fH5fFx88cXB4z/44AN+//vfU1xcTH5+Punp6Zx33nkATJgwITjG9PT04Hj69u3Ld999R2ZmJnfccQd33nkn5557LieddFKDn8s5c+YwZ84chg4dCni9uzdu3EivXr1IS0sLBqLDhw9n8+bNFBQUsH//fkaPHg3A5ZdfXmfrr3POOYfY2FhiY2Pp1KkT27dvp0ePHoddYz5z5szgbHtVq1ev5s4772TOnDl1Hl9QUEBBQQEnn3wyAFdddRXvvvtujftee+21nH/++fz85z/n2WefrVaR/dJLL8VxHPr370/fvn1Zt24dc+bMYcWKFbz66qsA7N27l40bN5KWllZjkbz169eTlpbGgAHeDOzVV1/NE088EQzSDzzOyy67jNtvv73asYWFhSxYsIBLLrkkuO3AbP38+fOD6+B9Ph9t2rRhz549dT4vLUVEBe2bvsP/zseQmED0hWPVo72ZWGu9fusBF6J8mPapOMmJmNiYUA9NREREwkB9A/c4YDdwWpVtFlDgXk8///nPGTZsWLUg66c//Sm/+MUvmDBhAh9++GG1onUHZmcdxyE6OjqYLuk4Dn6/H2st6enpLFy4sEHjODjt0hjDhx9+yLx581i4cCEJCQmMGTOG0tJSAOLi4oKz1KWlpdxyyy3k5ubSs2dPpk+fHtzv4DFXnV0+MOYBAwawdOlS3nnnHaZNm8bYsWO5556GNSaw1nL33Xdz4403Vtu+efPmatf0+XzBVPmGOPgcfr//sMf4/X5ef/11lixZUm37999/z4UXXsg//vEP+vXr1+Cx1KZnz5507tyZ+fPns2jRouDsO9T8+lpreeyxxxg/fnyjXL/qNQ6+nuu6pKamRlTV/EgK2qv1aL/gNEyCerQ3JWut996p8Hsz622SMG2SIS5WLdxERESkQer1KbRyXfvB/65t6sFFknbt2nHppZfyzDPPBLft3buX7t27A/D888836HwDBw5k586dwcC9oqKC1atXH/a4uXPnkp+fT0lJCbNmzeKEE05g7969tG3bloSEBNatW8fnn9dcvuBAkN6hQwcKCwuDM7j1tXXrVhISErjyyiuZOnUqS5cuBeDuu+8OzvYfzvjx43n22WcpLCwEYMuWLezYsaPW/VNTU0lOTg72SH/xxReD9yUnJ7N///4GPYaazJs3j0GDBtGjxw/pxgUFBZxzzjk89NBDnHDCCdX2//GPf8yiRYsOGWdqaiqffvopQLVgvKZxXnfddVx55ZVccskl1QoGvvLKK7iuy6ZNm/jqq68YOHAg48eP569//Wsw7X/Dhg0UFRVRm4EDB7J58+bg+vV//vOfnHLKKcH7D9QDeOmll4KZDAekpKSQlpbGK6+8AniBy4HU/7FjxwZT7gOBAHv37m2016CpWNdCeeWXN2EctFtr8S/II/D+55g+3YiaOE5BexOy/gC2sBhbVAIx0Tg9OuP0743TtaPX0k1Bu4iIiDRQvT6JGmOeM8Y8e/C/ph5cpLnjjjuqtdKaPn06l1xyCcOHD29wxfmYmBheffVV7rzzTrKyssjOzmbBggVA3WvcR44cycUXX8yQIUO4+OKLycnJ4cwzz8Tv9zN48GDuuusujjvuuBqPTU1N5frrrycjI4Px48czYsSIBo155cqVjBw5kuzsbO67775g0bqVK1fSpUuXGo/57W9/S48ePYL/xo0bx+WXX87o0aPJzMxk4sSJhw38nnnmGa6//nqys7MpKioKVn4/9dRTWbNmTbXidLWpa437iy++eEia/OOPP86XX37J/fffH2wXd+ALhhUrVgQL11X13HPPceutt5KdnR0s+lfbOCdMmEBhYWG1DA6AXr16MXLkSM466yyefPJJ4uLiuO666zj22GMZNmwYGRkZ3HjjjcFMgprWuMfFxfHcc89xySWXkJmZieM41ZZf7NmzhyFDhvDII49UK7p4YPb9hRde4JlnniErK4v09HTefPNNAB555BE++OCDYIu8NWvW0L59e0444QQyMjJaXHE6L2ivAGx4B+0Bl8DcBbhfrMDJOIaoCadiYqJDPayI47VwK8HuLwLXxXTp4LVw69UVk5yoFnsiIiJyVEzVAKHWnYy5uMrNOOBCYKu19meHOe5M4BHAB/zdWvvQQfffBNwKBIBC4AZr7RpjzEjgQGlzA0y31tY5JZuTk2MPFFo7YO3atQwePPhwD6/ZWdf1Fho4plkrBs+YMYPc3Fwef/zxZrtmfYwfP5733nuvyc5fWFhIUlISAA899BDbtm3jkUceabLr1WXfvn385Cc/Cc5IH6nc3Fxuv/12Pvnkk+C2yZMnc+655zJx4sSjHWaD/fSnPz1kKUg4awlB+9r16xmUenQtJG15Bf63P8Ju3opz3BB8x2WpSnkj8lq4lYM/4LVwa5uCSUmEWLVwExERkYYzxiyx1ubUdF+91rhba1876IQzgU8Pc1Ef8ARwBvA9sNgYM9tau6bKbv/PWvtk5f4TgD8CZwKrgBxrrd8Y0xVYbox5y1p7+AXH4cB1vfRbx2B9PvA53vrHVvpBrymDdoC3336bBx98EL/fT+/evZkxY0aTXq8uKSkpRx20P/TQQ/z1r3+tlk4fSr/+9a/54osvqtVoCGfWWqiIgJn2aj3aR+PLPLTjgRwZW17xQ92DlCSc1GSIVwq8iIiINJ16zbgfcpAxA4G3rbXH1LHPaLyZ8vGVt+8GsNY+WMv+lwE/ttaeddD2NOBzoHtdgXtYzbj7/VARAMeAtd4/YyDK583ahHGwIBLOgsXEbOiD9qOZcbd79lHx+jwoLiXqnJPV7q0RWH8Aysq8P9fxsd7semICJsp3+INFRERE6uGoZ9yNMfvxkrsP+A9w52EO6w58V+X298CoGs59K/ALIIYqVeuNMaOAZ4HewFVHOtturW25M9nGeP/ACxQqAoAf6zhQORPfYscuEmFaUtBureVIf/PdbTvxz5oPBqIuGYfT5ejS7Vsz67peKnwgANHRmE7tcJISVSNAREREml19U+WTm2oA1tongCeMMZcD04CrK7d/AaQbYwYDzxtj3rXWllY91hhzA3ADeEW5DhYXF8fu3btp3759yw+AjQFf1SDeDxVgnSoz8S39MYiEqZYWtO/ek0+sr77dOn9QrUf7RWMxqerR3lDVWrg5BpOajElJ8lq46W+wiIiIhEh9Z9wvBOZba/dW3k4FxlhrZ9Vx2BagZ5XbPSq31eZF4K8Hb7TWrjXGFAIZQO5B9z1FZRG7nJycQ3L+e/Towffff8/OnTvruGzzs64LAfeH2fY6d67ysByDMU7lTH3TjU+kVbEWGwh4OUUtIDAzQKwviu6JDfu+VD3aj46t8Huz6wDJCV6mQkKcli6JiIhIi1DfKZ17q1Z1t9YWGGPuBWbVccxioH/lGvUtwI+Ay6vuYIzpb63dWHnzHGBj5fY04LvK4nS9gUHA5nqONSg6Opq0tLSGHtbk3PwC7I49mMT6f7BW9WKRxmcDAdzv/gNl5WEb6FprCSzMw/1iJSatO1HnnIyJVip3fdiAC2Vl4Frvb2mXjpjkeExUw7MdRERERJpSfT+d1DTlUOexlUH3FOA9vHZwz1prVxtj7gdyrbWzgSnGmNOBCmAPlWnywInAXcaYCsAFbrHW7jr0Kq2HMQbiYoHKfsF79mJ3F0B0FKZdZZGk2JjQDlIkjERE0B5wCcxbiLtmE07GMfjGHqcZ4sOo9iWoz8G0T/X6rOvvp4iIiLRg9e3j/ixQgNfeDbze6+2stZObbGQNVFNV+ZbqSGbcaxOsdOxaTEJcZaVjzRiJ1MUGArjfb4fSckxCXKiHc0RseQX+f32E/WYrvuOycI4bouybOngt3MrBOJiUJExqsrduXS3cREREpIU46qrywE+BXwMv4a0EnYsXvEuImSgfRCVgAFtRgd260yv/nxiP0zYFEuIxPs3AiRxgA64XtJeUNcqXZ6Hg9Wh/H7tzD74zRuPLUI/2mlh/AErLvPIFifGYjm29//rUwk1ERETCS32ryhcBdzXxWOQomehoiI4OVkV2v9vuFbRLScK0SYL4OM0uSatmAy7uljAP2qv2aJ9wqnq018CWlnmp8DHRmM7tcZIS1MJNREREwlp9q8rPBS6x1hZU3m4LvGitHd+EY5MjZIyB2BiIjcFaiy0qxhbsA59PRe2k1QoG7cWlYRu0/9Cj3ahHew2stVBUAglxOD26QJz+zomIiEhkqG+qfIcDQTuAtXaPMaZT0wxJGlOwqF1cLDagonbSOtmAi7t1BxSVYJISQj2cI6Ie7XWz/gCUlHrF5jq2VZE+ERERiSj1DdxdY0wva+23AMaYPnhr3SWMGJ8DldWzrT+A3ZGP6+5WUTuJaNY9ELQXh23QHli+nsAHi9SjvRa2tBwCAUzPLjjJiaEejoiIiEijq2+U9t/Ap8aYjwADnATc0GSjkianonbSGljXxd2yAwrDM2i31hJYkIe7SD3aa2KtheISiI3B6dVF2UMiIiISsepbnO7fxpgcvGB9GTALKGnCcUkzUlE7iUTeTPvO8A3aq/Vo749v7Cilf1dhAwGvXkG7FEzH9vqiUURERCJafYvTXQfcBvQA8oDjgIXAaU02Mml2tRa1i6osapesonYSHoJB+/5CTFL4pU5X69E+OgtnlHq0V2XLyqHCj+nWCSc1OdTDEREREWly9U2Vvw0YAXxurT3VGDMI+J+mG5aE2iFF7fL3YncV/FDULilR7ZWkRbKuxd22E/YXhWfQrh7ttfJS40shOgonrTsmLjbUQxIRERFpFvUN3EuttaXGGIwxsdbadcaYgU06MmkxDi1qtwd3ez4mPlZF7aRF8YL2HbCvKDzT4/P3UvHG+16P9vNPxUlTj/YDrOt6rd7aJON0aY/x+UI9JBEREZFmU99o63tjTCre2va5xpg9wDdNNShpubyidvGHFrVLSvCK2sXHaa2phIR1Le5/dsHeQm9ZR5hxt+7E/6Z6tNfElldgyytwunbEpCZr2YCIiIi0OvUtTndh5Y/TjTEfAG2AfzfZqCQsVCtqV1aO++1/VNROQsK6Fnf7Lti7D8Jwpt3d9C3+tz+B5ASiLzwdo3XbQba4BHw+fH26Y+KVGi8iIiKtU4Pzm621HzXFQCR8VStq57rYwiIVtZNmEwza93hBe7i9z4I92ju3J+r80zAJcaEeUosQTI1PTvRm2qOUGi8iIiKtlxYmS6MyjgPxXuARLGq3cw/ERKuonTQ6ay12R3gG7erRXjtb4ceWluF0ao9p3yasXlcRERGRpqDAXZqMitpJU7LWYrfvwubvD7+gXT3aa2VLS8HipcYr+0BEREQEUODe7Gx5Be7ar6BtG8InzDh6Kmonjcmbac/H5ofhTHt5Bf5/fYj9Zhu+0dk4ozLDavxNxVoLRcWQEI/TrRMmWv97EhERETlAn4yambtiA/7X5kFiPL6hg3Ey+7e6XsS1FrVrk4RJUVE7qVswaN9dEH5Bu3q018j6A1BSiunYFtO+rX7/RURERA6iwL2ZOcMGE+WvIPBpHoFPlxL4fDnO4H74hg7CtE8N9fCa1SFF7fYXYQv2g89RUTupUVgH7dV6tJ+Gk9Y91ENqEWxpGQRcTM8uOGHYxk9ERESkOShwb2bGcXAG9MGktsEWl+LmrcVd8yXuyg2YXl3xDRuM6dM9rAKSxlBrUbvYGC+IT0pQUbtWzlqL3RmeQbvXo/19cBz1aK/kpcaXQHwsTu9O+v0WERERqYMC9xByOrbFOeN47InDcFduJJC3Dv+s+dA2BV/2IJxj+7XKD7OHFrXLx92+W0XtWjFrLXbXHuyuPZCUGF5B+5ff4n9HPdqrsoEAFJdi2qd66fEqzCciIiJSJ0U/LYCJj8M3MhNneDruxm9wl60l8MEiAp8t8ypOZw/EtGmdH/ZrLWqXnICTqqJ2rUEwaN8ZfkG7erQfypaVQ4Uf06MzTkpSqIcjIiIiEhYUuLcgxufgG5SGb1Aa7raduMvWeqn0y9Zi+vbw0ui7dw6rwKUxVStqV3pQUbs2yRAXq6JWEcjuKqgM2sMnPb56j/YeRJ1zUqvv0W6theISiInB6dsDExsT6iGJiIiIhA0F7i2U07UjTteO2MJiAsvXe9XoN32H6dgWZ+hgnIFp3mx0K6Sidq2Hu2sPdkc+JIdR0B5wCcxdgLv2K/Vor2QDrhe0t03B6dReWTIiIiIiDaTAvYUzSQlEnTAUOyoTd+3XXhr9nAUEPlmKM2QAviEDMEkJoR5myBxS1G63itpFCnfXHuz2MAva1aP9ELa8Alte4fVmb5PU6p8PERERkSOhwD1MmKgofJn9cTKOwX73HwLL1uJ+sQJ38SqcAb29WfhWXqna+BxIVFG7SODuKvBm2pPiwybQs4XF+N+c7/VoH3c8vvRjQj2kkLPFpeBz8KV1x8TFhno4IiIiImFLUUyYMcZgenXF6dUVW7CPQN563NVf4q77GtO1oxfA9+/V6lNzqxW1K1dRu3Di5hdgd+yGxPiweR97PdrnQUmZerQD1nW9Vm9tknA6d2i1y3pEREREGosC9zBmUlOIGjMCOzoLd/UmAnnrCLzzMYGkBHxZA3Ey+2PiVcXaxERDjIrahQM3vwC7bbc30x4mQbu7dQf+N+d7Pdonqke7rajAlpbjdOngZbqEScaEiIiISEumwD0CmNgYfMMG42QPxH69hcCytQQ+W0bgixU4g/viZA/G6ZAa6mGGnIratWxu/t7wC9rVo70aW1ICpjI1Xl8aioiIiDQaBe4RxDgOpl9PnH49cXft8drJrfkKd+VGTK+u+IYOxqR1V2BKLUXtdu2BGBW1CwV3z17stl1hFbQHlq8j8MFi9WjnQGp8qbcUpWsH1ZIQERERaWT6dBWhnA5tcc44HnviMNyVGwnkrfPSeVOT8WUPwkk/RoFppdqL2sVh2iarqF0Tc/fsx27b6fVpD4Og3VpL4LNluItXYfr2IOrskzHRrff9Yf1+bEkZTqd2mHapWnYiIiIi0gRa76fNVsLEx+EbmYkzPB33y2+8dnIfLiawIA8n4xh8WYNafXpvVSpq17y8oH07JIZJ0B4IEJi70OvRntkf32mtu0e7LSkFwNe7G6byyy8RERERaXxNGrgbY84EHgF8wN+ttQ8ddP9NwK1AACgEbrDWrjHGnAE8BMQA5cBUa+38phxrpDM+B9/ANHwD03C37fTS6PPW4S5di+nX00uj79FZafRVHFLU7rsDRe2SMSlJKmp3lNyC/bhbd2DCJWgvr8D/1ofYb7fhOz4bZ2Tr7dFurfWqxifE4XTtqOwdERERkSbWZIG7McYHPAGcAXwPLDbGzLbWrqmy2/+z1j5Zuf8E4I/AmcAu4Dxr7VZjTAbwHtC6+ys1IqdrR5yuHbGFxQSWr8ddsQH/pu8wHdriDB2EM6iv2jdVcUhRu32F2D37VNTuKLgF+3G37PCWIYRBBoN6tP/A+gNQUoLp2A7TPjUsvnQRERERCXdNOeM+EvjSWvsVgDHmReB8IBi4W2v3Vdk/EbysZGvtsirbVwPxxphYa21ZE4631TFJCUSdMBQ7KhN33ddeGv3chQQ+XYozZAC+IQMxSQmhHmaLckhRu3wVtWsob6Z9uzfTHg5Be9Ue7RechtOn9X6HaEvLIRDA9OyKk5wY6uGIiIiItBpNGbh3B76rcvt7YNTBOxljbgV+gZcWf1oN57kYWKqgvemYqCh8Gf1x0o/Bfr+dwNI1uF+sxF28CmdAH5yhg1t9b+qaGJ8DCVWK2m3ffVBRuwRlLhzE3VfoBe0J4RG0u1t24J9d2aP9kvE4nduHekghYa2F4hKIjcHp1QUTGxPqIYmIiIi0KiEvTmetfQJ4whhzOTANuPrAfcaYdOB3wLiajjXG3ADcANCrV6+mH2yEM8ZgenbB6dkFW7CfQN463NVf4q77GtO1o5dGf0zvsAi4mpuJ8nlV0VFRu9q4+wpxv9+OSQiP9Hj1aPfYQACKSzHtUjAd24fFayciIiISaYy1tmlObMxoYLq1dnzl7bsBrLUP1rK/A+yx1rapvN0DmA9cY6397HDXy8nJsbm5uY01/Cbl5hdgd+wJiyrMtqwcd80mAsvWwd79kJSAL2sgTmZ/THzr7VtdH9ZaKKsAv7/VF7X7IWiPw/hafhZCIG8dgQ8WYbp0IOqC01rte92WlUOFH9OtI06b1vnFhYiIiEhzMcYssdbm1HRfU864Lwb6G2PSgC3Aj4DLDxpYf2vtxsqb5wAbK7enAm8Dd9UnaJemY2Jj8A0djJM9CPv1FgJL1xD4bBmBL1bgDOrrpdF3SA31MFskYwzExQCtu6idu78obIJ29Wj3eKnxpRAdhZPWHRMXG+ohiYiIiLRqTfaJ1FrrN8ZMwasI7wOetdauNsbcD+Raa2cDU4wxpwMVwB5+SJOfAhwD3GOMuady2zhr7Y6mGq/UzRiD6dsDp28P3F17cJetw137Fe6qjZheXb12cmndIz4IPVKttahdMGiPD4OgvVqP9gH4ThvZKiumW9f1Wr2lpuB0btfiXzcRERGR1qDJUuWbm1Llm58tKcVduZHA8vVQWAypyfiyB+GkHxORQWhTsP4AlJZhIeKK2gWD9rjYFv94bFk5/n991Op7tNvyCmx5BU6XDpjU5Fb5HIiIiIiESqhS5SXCmfg4fCMzcYan4375De6ydQQ+XExgQR5O+jH4sge12oJe9RWpRe3cwmLc7/7jfRnR0oP2wmL8s97H7ipo1T3abXEJ+Hz4+nTHxCs1XkRERKQlUeAuR834HHwD0/ANTMP9zy7cZWtxl6/DXbYW06+nl0bfo7Nm7w7DxERDTLS3vrikHHf/f8KyqJ0tKsH9dlt4BO3q0f5DanxKkjfT3sJfMxEREZHWSIG7NCqnSwecs07CnjScwPL1uCs34N/0HaZDW6+d3KA0TJTednWps6hduzaYpIQWW9TOFpUQCJOgXT3awVb4saVlOF3aY9q2aZHvKRERERFR4C5NxCQlEHXCUOyoTNx1X3tp9HMXEvh0qVf4K2ugF4BKnQ4pare7ALszv7KoXRtMUnyLqSdgi0u9mfa4mJYftB/o0Z6SSPSFYzGtsNWZLS0Fi5can9A6292JiIiIhAsF7tKkTFQUvoz+OOnHYL/fTmDZWtxFK3FzV+H0740z7FicLh1CPcywYHwOJHgFDa3fj92+C3c7Xpu11NAWtbPFpbjfbIW4mBafURHs0d61A1Hnt74e7dZar5hkUgJO146tst2diIiISLjRJ7ZQiI4Ga71iUPFxrSI91RiD6dkFp2cXbMF+AsvX4a76Enf9ZkzXDl4/+GN6h2UhtlAwUVGQFFV7UbuEuGZrZWZLvJl2Ylt20G6tJfDpUtzc1a22R7v1B7DFpTid2mLatw2LmgkiIiIionZwIWPLK7B79uLm7/OC1bjYVhHAV2XLK3BXf0kgbx0U7IekBHxZA3Ey+7e6WdDGYK2Fsgrw+5utqJ0tKcXdvNUL2ltwEGwDAQJzFuKu+wpnyAB8p7a+Hu22tAxcF9OtE05yYqiHIyIiIiIHqasdnAL3ELPlFdjdBbgF+1tvAG8t9ustBJatxX67DXw+nMFp3ix8h7ahHl5Ysq4LZeUQcKsVtTNxjdfmy5aU4W7eArHRmOiWsc6+JtV6tJ8wFGdERqv6HbPWelXj42NxunVqMTURRERERKQ69XFvwUxMNKZrR0y7Nri7C7AF+yHK16oCeGMMpm8PnL49cHcV4OatxV3zFe6qLzE9u3jt5Pr2aDXPR2OoXtQu0OhF7WxpmbemvaUH7Qd6tO9unT3arT8AxaWYDqmYjm1bXZaBiIiISKTQjHsLY8vKcXftwe4t9FKPW2jbr6ZmS0pxV24ksHy9V0irTTK+oYNwju2HiY0J9fDClvX7obQcy5EXtbOlZV56fHRUi569tbsLqHjjfSgtI+rcU1pdj3ZbVg4Vfkz3TjgpSaEejoiIiIgchlLlw5AtLcPduQcKiyAqqlFTnMOJDbi4X36Lu2wtdttOiInGST8GX/YgTGrra+HVmGx5BZRXeDfqWdQuXIJ2d8sO/G/OB59D1AVjW1WPdmstFJdATAxO9076oktEREQkTChVPgyZuFh8Pbt464h37cHuL4KY6Fb3Idz4HHwD++Ab2Af3P7twl63FXb4Od9laTN8eXhp9zy6tMivhaJmYaIiJ9gK9knLc/f+ps6hdMD2+pQftrbhHuw243nr2dik4ndqrS4OIiIhIhNCMe5iwJaW4O/K9mbTo1hfAV2ULiwmsWI+7YgOUlGE6pOJkD8YZnNai25GFg9qK2gFeyzefr0UH7T/0aO9I1PmntqruBLa8AltegdO1I46yUURERETCjlLlI4gtLsXdme/NqsXGtOggqqlZfwB3/ddeGv3OPRAX67X6yhoYDDblyNlAwAviXResgZioFvuFUbUe7f16EnXWSS26PV1js0UlEOXD6dG51S6rEREREQl3SpWPICYhDqdXV6gM4O3+olYbwJsoH770Y3CO7Yf9fjuBZWtxF63EzV2F07+3106ua8dQDzNsGZ8PEuIBLzBuqcsRvB7tC3DXfd3qerRbtzI1vk0STpcO3msmIiIiIhFHgXsYMsZAYjxOQjcoKsHdURnAx8W06NZcTcUYg+nZBadnF2zBfgLL1+Gu+hJ3/WZMlw5eAN+/t9b7HoUWG7SXleN/60Psd/9pdT3abUUFtqzCC9jbprSaxy0iIiLSGilVPgJYa70Afvtur0p4bEyrShOuiS2vwF2zicCytVCwH5IS8GUNxMns36rWPUeyaj3aTz8eX3q/UA+p2diSEjCOlxqv97OIiIhIRFCqfIQzxkBSAk5CPLawCLtzD7aw2JuBb6XF2kxMNL7sQThZA7FfbyGwbC2Bz5YR+HwFzuA0bxa+Q9tQD1OOULUe7ReMxendLdRDahZeanyp176va4dW+/stIiIi0troU18EMY7BpCRhkxK9AH5HPrakGOJjMVGtc+2rMQbTtwdO3x7Y3QUElq3DXbsJd9WXmJ5dvHZyad1bzZroSOBu2Y7/zQ+8Hu2XjG81Pdqt348tKfPavLVrU61Vn4iIiIhENqXKRzDrWuz+QuyOfPD7IS6u1QbwVdnSMtyVGwnkrYPCYmiThC97ME56vxZbNV087sZv8L/7CaQktaoe7bakFACne2dMYnyIRyMiIiIiTUHt4Fo567rYfUXYnfngD3gz8Ko+7T0vX35LYOla7LadEBONc2w/fEMHYVJTQj08OUhr7NF+oH4FCXE43Tq1+toVIiIiIpFMa9xbOeM4mNRkbHIidl8hdmc+NlAG8XGtutK6cRzMgD44A/rg/mcX7rJ1uCs24Oatw/Tt4aXR9+yiat0hdkiP9rNPahVru60/ACUlmI7tMO3bKjVeREREpBXTjHsrZAMudu9+7K49EHBbfQBflS0sJrBiA+6KDVBSimmf6hWyG5zWKoLFlqZaj/asgfjGjGgV9QhsaRkEXEz3TjjJiaEejoiIiIg0A6XKS41sIOAF8Dv3gGshIa5VBEX1Yf0B3PVf4y5b6z0/cbE4mf3xZQ3EKJBqFq2xR3swNT4+1kuNj4kO9ZBEREREpJkoVV5qZHw+TLtUbEoytsCbgbfgrYFv5QG8ifLhSz8G59h+2C3bvWr0uatxl6zG6d/bm4Xv2jHUw4xY1Xq0jz8B37GR36PdBgJQXIppl4Lp1L7V/w6KiIiIyA8UuAsmyofpkIpNTcYW7PP6wBujAJ7KdnI9uuD06ILdu59A3nrc1Rtx12/GdOmAM3QQTv/eKvbXiFpjj3ZbVg7+gJca30oq5YuIiIhI/SlVXg5h/X7snn3YXQXgGG8NfISnKDeELa/AXbPJaye3Zx8kxuPLGoiTOQCTEPmVzpuS+/12/LM/AJ+PqAtPw+kU2T3arbVQXArRUTg9OqsdoYiIiEgrplR5aRATFYXp2A6bmoLN3+v9UwAfZGKi8WUPwskaiN28hcCytQQW5BH4YgXO4L5eGn2HtqEeZtip3qP9dEybpFAPqUlZ1/XWs6em4HRup6wNEREREamVAneplYmOwnRuj22bgt2zFzd/H/gciItVAE9lGn1aD5y0HtjdBd46+LWbcFd9ienZxesHn9aj1S83qI/AsrUEPlzcanq02/IKbFk5TtdOmNQk/T6JiIiISJ2UKi/1ZssrsLsLcAv2e+3jFMAfwpaW4a7cSGD5OthfDG2SvNn59GOUBl0Day2BT5biLmk9PdptcQn4fDjdO2PiY0M9HBERERFpIdQOThqVLSvH3V2ALdiPifIpgK+BdV3sl98SWLYWu3Wnt4Y5/Rh82YMwbVNCPbwWobX1aA+mxqck4XTp4P3uiIiIiIhUCtkad2PMmcAjgA/4u7X2oYPuvwm4FQgAhcAN1to1xpj2wKvACGCGtXZKU45TGsbExuDr1gnbPhV31x7s3kKIjsLEafbwAOM4mAF9cAb0wd2+G3fZWtwVG3Dz1mHSenhp9L26ttovPFpbj3Zb4ceWluF0aY9p2yaiH6uIiIiINL4mm3E3xviADcAZwPfAYuAya+2aKvukWGv3Vf48AbjFWnumMSYRGApkABn1Cdw14x46trQMd+ceKCyCKAXwtbGFxQRWbMBducHr190+1WsnN6gvJjqy08OrsoXF+N94H5tfgO+M4yO+R7stKQVjvNR4dR0QERERkVqEasZ9JPCltfarykG8CJwPBAP3A0F7pUTAVm4vAj41xhzThOOTRmLiYvH17IItKfNm4PcXQUy01nQfxCQlEHV8NnZkJu76zbjL1hCY9zmBT5fiZA7AlzUQk5wY6mE2qdbUo91aC4XFkJSA07Vjq/pyRkREREQaV1N+kuwOfFfl9vfAqIN3MsbcCvwCiAFOa8LxSBMz8QcC+FLcHfnYwiKIVgB/MBPlw5feD+fYvtgtOwgsW4ubuxo3dzVO/944QwdjunaIuHTqaj3aLx0f0T3arT+ALS7F6dQO0z4V40TWaykiIiIizSvkU0DW2ieAJ4wxlwPTgKvre6wx5gbgBoBevXo1zQClwUx8HL7e3bDFpbg7870Z+NgYTEx0qIfWohhjMD064/TojN1bSGD5OtxVG3E3bMZ0bo8zbDBO/94R0d+7NfVot6Vl4Lr4+nTDJMaHejgiIiIiEgGasoTzFqBnlds9KrfV5kXggoZcwFr7lLU2x1qb07Fjx4aPUJqUSYjD6dXVS4f2Odj9RdjyilAPq0UybZKIOjmH6Osm4jttJLa8gsC7n1LxzOsEvliBLS4N9RCPWGDZWvz/+gjTqT3Rk86M2KDdWostLPY6CKT1UNAuIiIiIo2mKWfcFwP9jTFpeAH7j4DLq+5gjOlvrd1YefMcYCMSUYwxkBiPk9ANikq8FPr9RRAXg4nWDPzBTEw0vqxBOEMGYjdvJbBsLYEFeQS+WIEzqC/O0ME4HduGepj10pp6tFt/wCs42CEV07FtRLe1ExEREZHm12Sfoq21fmPMFOA9vHZwz1prVxtj7gdyrbWzgSnGmNOBCmAPVdLkjTGbgRQgxhhzATCuakV6CS/GGK9IV2K8F8Bv3+3NTsbGqGhXDYwxmLTuOGndsbsLCOStw13zFe7qLzE9OuMbOhjTt0eLDRCtP0Bgzme46zdHfI92W1oOfj+mZ2eclMjMJhARERGR0GqydnDNTe3gwot1LbawCLtzD5RXeDPwETob21hsaRnuqo0E8tbD/iJIScI3dBBO+jEtqgBgtR7tJw7DyUmPuEJ7UFk1vrgEYmJwundqUa+BiIiIiISfutrBKXCXkAoG8DvyodwP8bGYqPAvxtaUrOtiv/yOwLK12K07vDXV6cfgyx6EaZsS2rFV7dE+7gR8g/uGdDxNxQZcKCqBdik4ndpjfJGZTSAiIiIizSdUfdxFDss4BpOShE1K8IrX7cjHlpZCXJwC+FoYx8EM6I0zoDfu9t24y9birtiAm7cOk9bdS6Pv1bXZZ7m9Hu3zoKwionu02/IKKK/AdO+Ek5oc6uGIiIiISCugGXdpUazrYvcVYXfmgz/gzcBHQDu0pmaLSgisWI+7YoNXJK19Kk72IJzBfZulhkC1Hu0XjsXp1K7JrxkKtqjEy3Do3gkTFxvq4YiIiIhIBFGqvIQdG3Cx+wq9AD7gQnyc0pHrwfoDuOs34+at9ZYfxMXgZPT30uiTE5vkmu6Gb/D/+xNok0z0BWMjst2bdStT49sk4XTpoC+TRERERKTRKVVewo7xOZi2KdiUJOze/dhde7x1xQrg62SifPjS++Ec2xe7ZQeBZWtxl6zBXbIGp38vnKGDMV07NloafWDZWgIfLsZ060jU+adF5Cy0rajAllXgdO2ASU2JyEJ7IiIiItKyKXCXFs34HEy7Ntg2lQH8zj1Y10JCXMS2F2sMxhhMj844PTpj9xYSWL4Od9WXuBu+wXRu7/WDH9D7iGeOvR7tS3CXrMEc04uos06MyK4AtqQEjIOvTzdMfFyohyMiIiIirZRS5SWsWH8AW+DNwAPeGngF8PViyytw135FYNla2LMPEuPxDRmIM6Q/JiG+/udpBT3avdT4UkhO8GbaI/BLCRERERFpWZQqLxHDRPkwHVKxqcnYgn3eDLwxCuDrwcRE48saiDNkAPabrV6a+8I8AotW4AxK82bhO9ZdVK419Gi3fj+2pMxr89auDcaJrMcnIiIiIuFHgbuEJS+Ab+sF8Hv2YXcVYB3jrYGPsECysRljMH264/Tpjs3f662DX/MV7upNmB6dvXZyfXsc8kWI16N9HjZ/H74zT4zIHu22tBQs+Hp3wyTWPwtBRERERKQpKVVeIoKt8GPz92Lz94IC+AazpWW4q74kkLcO9hdBShK+7EE4GcdgYmNwdxXgn1XZo/3cUyKuR7u1FoqKISEep1unZmmhJyIiIiJSldrBSathyyuwe/bi5u/zqs/HxSqAbwDruthN3xFYtha7ZYfXs3xgH9yN30KUj6gLIq9Hu/UHoKQE07Edpn1bpcaLiIiISEhojbu0GiYmGtO5A6ZtG+zuAtyC/aAAvt6M42D698bp3xt3+27cZWtx137l9Wi/cCwmJbJ6tNvSMgi4mJ5dcZqoz72IiIiIyNFS4C4RycREe/3K27XB3V2ALdgPUT4F8A3gdG6Pc+aJ2DEjIMoXUZXVvdT4EoiPxendCRMTHeohiYiIiIjUKnI+iYvUwMTG4OvWCds+FXfXHuzeQoiOwsTFhnpoYSPSnisbCEBxKaZ9KqZjW3UjEBEREZEWT4G7tAomNgZf985eAL9zD7awCKIUwLc2tqwc/AFMj844EZb2LyIiIiKRS4G7tComLhZfzy7YkjJvBn5/EcREY2JjQj00aULWWigu9YrtpXXX6y0iIiIiYUWBu7RKJv5AAF+KuyPfm4GPVgAfiWzAheISSE3B6dze6zYgIiIiIhJGFLhLq2bi4/D17oYtLsXdme/NwMfGqFhZhLDlFdiycpyunTCpSSpMKCIiIiJhSYG7CGAS4nB6dQUF8BHDFpeAz4evbw/VMhARERGRsKbAXaSSMQYS43ESukFRiZdCv78I4mIw0Qrgw4V1Xa/VW0oSTpcOmChfqIckIiIiInJUFLiLHMQYA0kJOInxXgC/fTe2sNibgY/Wr0xLZiv82NIynC7tMW3bKDVeRERERCKCohCRWgQD+IR4bGERduceL4CPi8FE6VenpbElpWAMvj7dMQlxoR6OiIiIiEijUfQhchjGMZiUJGxSohfA78jHlhRDfKzSsFsAay0UFntfsnTrqC9VRERERCTi6BOuSD39EMAnYPdXBvClpRAXpwA+RKzfjy0pw+nUDtMuFeMoNV5EREREIo8Cd5EGMo6DaZOMTU7E7ivC7szHlpZ5M/A+BfDNxZaWgevi690Nkxgf6uGIiIiIiDQZBe4iR8g4Dib1QABf6AXwgTKIj8P4nFAPL2JZa72q8QlxOF07qmWfiIiIiEQ8Be4iR8n4HEzbFGxKEnbvfuyuPdiAqwC+CVh/AEpKMe1TMR3bYhw9vyIiIiIS+RS4izQS43Mw7dpg21QG8Dv3YF0LCXEKMBuBLS2HQADTozNOSlKohyMiIiIi0mwUuIs0MuPzYdqlYlOSsQWVM/DgrYFXAN9g1looLoHYGJxeXTCxMaEekoiIiIhIs1LgLtJETJQP0yEVm5qMLdjnzcAbowC+AWzAhaISTPsUTMf2WnogIiIiIq2SAneRJuYF8G29AH7PPuyuAqxjvDXwRu3LamPLyqHCj+neCSc1OdTDEREREREJmSadvjLGnGmMWW+M+dIYc1cN999kjFlpjMkzxnxqjDm2yn13Vx633hgzvinHKdIcTFQUTsd2OMf0wqSmQFEJtrjESwWXamxRCRiDk9ZdQbuIiIiItHpNNuNujPEBTwBnAN8Di40xs621a6rs9v+stU9W7j8B+CNwZmUA/yMgHegGzDPGDLDWBppqvCLNxURHYTq3x7ZNwe7Zi5u/D3wOxMW2+hl463qp8bRJwunSAePzhXpIIiIiIiIh15Qz7iOBL621X1lry4EXgfOr7mCt3VflZiJwYOrxfOBFa22ZtfZr4MvK84lEDBMTjdO5A75+PTHJidiiEmxJaaudgbflFdjiUkzXDjjdOiloFxERERGp1JRr3LsD31W5/T0w6uCdjDG3Ar8AYoDTqhz7+UHHdm+aYYqElomJxnTtiGnXBnd3AbZgP0T5WtUMvC0uAcfB16c7Jj421MMREREREWlRQl6i2Vr7hLW2H3AnMK0hxxpjbjDG5Bpjcnfu3Nk0AxRpJiY2Bl+3Tvj69YTEeGxhMba0LNTDalLWdbH7iyAhHidNQbuIiIiISE2aMnDfAvSscrtH5bbavAhc0JBjrbVPWWtzrLU5HTt2PLrRirQQJjYGX/fO+Pr2gLhYbGFRRAbwtsKPLSrBdGqP06MzJkpNLkREREREatKUgftioL8xJs0YE4NXbG521R2MMf2r3DwH2Fj582zgR8aYWGNMGtAfWNSEYxVpcUxcLL6eXXD6VAbw+4u8FmkRwJaWgt+Pr3c3nA6prWZJgIiIiIjIkWiyKS5rrd8YMwV4D/ABz1prVxtj7gdyrbWzgSnGmNOBCmAPcHXlsauNMS8DawA/cKsqyktrZeK9AN6WlOLuyMcWFkF0NCY2JtRDazBrLRQVe6nx3TphojXLLiIiIiJyOCZSKljn5OTY3NzcUA9DpMnZ4lLcnfle27TYGExMdKiHVC/WH4CSUkzHtpj2bTGOZtlFRERERA4wxiyx1ubUdJ+mu0TCjEmIw+nVFSoDeLu/qMUH8La0DAIupmcXnOTEUA9HRERERCSsKHAXCUPGGEiMx0noBkUlXgr9/iKIi8FEt5wA3kuNL4X4GJzenVr0lwsiIiIiIi2VAneRMGaMgaQEnMR4L4DfvrsygI8N+fpxGwhAcSmmfaqXHu+EvPukiIiIiEhYUuAuEgGCAXxCvNc+bucebGGxNwMfgjZrtqwcKvyYHp1xUpKa/foiIiIiIpFEgbtIBDGOwaQkYZMSvQB+Rz62pBjiYzFRvia/vpca7xXNc/r2CMvK9yIiIiIiLY0Cd5EI9EMAn+D1f9+R7/VOj4trsgDeBlwoLoG2KTid2mN8So0XEREREWkMCtxFIphxHEybZGxyInZfEXZnvlfhPT4W42u8AN6WV2DLK3C6dsKkJnmp+yIiIiIi0igUuIu0AsZxMKkHAvhCL4APlEF83FHPjNviUvA5+NK6Y+JiG2nEIiIiIiJygAJ3kVbE+BxM2xRsShJ2737srj1Y1/VS6BsYwFvX9dazt0nC6dyhWdbQi4iIiIi0RgrcRVoh43Mw7dpg21QG8Dv3YF0LCXH1attmKyqwpeU4XTpg2qYoNV5EREREpAkpcBdpxYzPh2mXik1JxhZUzsCDtwa+lgDelpSCMV5qfHxcs45XRERERKQ1UuAuIpgoH6ZDKjY1GVuwz5uBN6ZaAO+lxpdCcgJO1w4h6Q8vIiIiItIa6ZO3iAR5AXxbL4Dfsw+7qwDrGIiO8lLjO7XDtEvFOEqNFxERERFpLgrcReQQJioK07EdNjUFm78Xu68QX+9umMT4UA9NRERERKTVUeAuIrUy0VGYzu2hc/tQD0VEREREpNU6ugbOIiIiIiIiItKkFLiLiIiIiIiItGAK3EVERERERERaMAXuIiIiIiIiIi2YAncRERERERGRFkyBu4iIiIiIiEgLpsBdREREREREpAVT4C4iIiIiIiLSgilwFxEREREREWnBFLiLiIiIiIiItGAK3EVERERERERaMAXuIiIiIiIiIi2YAncRERERERGRFsxYa0M9hkZhjNkJfBPqcdRTB2BXqAch0gB6z0o40ftVwo3esxJO9H6VcBNO79ne1tqONd0RMYF7ODHG5Fprc0I9DpH60ntWwonerxJu9J6VcKL3q4SbSHnPKlVeREREREREpAVT4C4iIiIiIiLSgilwD42nQj0AkQbSe1bCid6vEm70npVwoverhJuIeM9qjbuIiIiIiIhIC6YZdxEREREREZEWTIF7MzPGbDbGrDTG5BljckM9HpGDGWOeNcbsMMasqrKtnTFmrjFmY+V/24ZyjCIH1PJ+nW6M2VL5dzbPGHN2KMcocoAxpqcx5gNjzBpjzGpjzG2V2/U3VlqkOt6z+jsrLY4xJs4Ys8gYs7zy/Xpf5fY0Y8wXxpgvjTEvGWNiQj3WI6FU+WZmjNkM5Fhrw6WXoLQyxpiTgULgH9bajMptvwfyrbUPGWPuAtpaa+8M5ThFoNb363Sg0Fr7cCjHJnIwY0xXoKu1dqkxJhlYAlwATEZ/Y6UFquM9eyn6OystjDHGAInW2kJjTDTwKXAb8AvgdWvti8aYJ4Hl1tq/hnKsR0Iz7iJSjbX2YyD/oM3nA89X/vw83v+0RUKulverSItkrd1mrV1a+fN+YC3QHf2NlRaqjvesSItjPYWVN6Mr/1ngNODVyu1h+zdWgXvzs8AcY8wSY8wNoR6MSD11ttZuq/z5P0DnUA5GpB6mGGNWVKbSK+1YWhxjTB9gKPAF+hsrYeCg9yzo76y0QMYYnzEmD9gBzAU2AQXWWn/lLt8Tpl8+KXBvfidaa4cBZwG3VqZ5ioQN662v0Robacn+CvQDsoFtwP+GdDQiBzHGJAGvAT+31u6rep/+xkpLVMN7Vn9npUWy1gastdlAD2AkMCi0I2o8CtybmbV2S+V/dwBv4L2hRFq67ZXr3A6sd9sR4vGI1Mpau73yf9wu8DT6OystSOW6y9eAF6y1r1du1t9YabFqes/q76y0dNbaAuADYDSQaoyJqryrB7AlVOM6Ggrcm5ExJrGysAfGmERgHLCq7qNEWoTZwNWVP18NvBnCsYjU6UAAVOlC9HdWWojKwknPAGuttX+scpf+xkqLVNt7Vn9npSUyxnQ0xqRW/hwPnIFXl+EDYGLlbmH7N1ZV5ZuRMaYv3iw7QBTw/6y1D4RwSCKHMMbMBMYAHYDtwL3ALOBloBfwDXCptVYFwSTkanm/jsFL37TAZuDGKuuHRULGGHMi8AmwEnArN/8X3pph/Y2VFqeO9+xl6O+stDDGmCF4xed8eBPUL1tr76+MwV4E2gHLgCuttWWhG+mRUeAuIiIiIiIi0oIpVV5ERERERESkBVPgLiIiIiIiItKCKXAXERERERERacEUuIuIiIiIiIi0YArcRURERERERFowBe4iIiIiIiIiLZgCdxEREWl0xpgZxpiJoR6HiIhIJFDgLiIiIvVijIkK9RhERERaIwXuIiIizcgY08cYs9YY87QxZrUxZo4xJr6WfY8xxswzxiw3xiw1xvQznj8YY1YZY1YaYyZV7jvGGPORMeZNY8xXxpiHjDFXGGMWVe7Xr3K/GcaYJ40xucaYDcaYc6uM65PK6yw1xhxf5byfGGNmA2uMMb7K6y82xqwwxtxYuZ8xxjxujFlvjJkHdDrM8/CQMWZN5TkerjKG+ZXb3jfG9Koy5keNMQsqH9vEyu2OMeYvxph1xpi5xph3qtx3yPlFRETClb45FxERaX79gcustdcbY14GLgb+r4b9XgAesta+YYyJw/vC/SIgG8gCOgCLjTEfV+6fBQwG8oGvgL9ba0caY24Dfgr8vHK/PsBIoB/wgTHmGGAHcIa1ttQY0x+YCeRU7j8MyLDWfm2MuQHYa60dYYyJBT4zxswBhgIDgWOBzsAa4NmaHrwxpj1wITDIWmuNMamVdz0GPG+tfd4Ycy3wKHBB5X1dgROBQcBs4NXK56JP5TU7AWuBZ+s4v4iISFjSjLuIiEjz+9pam1f58xK84LMaY0wy0N1a+waAtbbUWluMF7zOtNYGrLXbgY+AEZWHLbbWbrPWlgGbgDmV21cedI2XrbWutXYjXoA/CIgGnjbGrARewQuGD1hkrf268udxwI+NMXnAF0B7vC8iTq4yrq3A/Doe/16gFHjGGHMRUFy5fTTw/yp//mflYz1gVuWY1+B9MUDl/a9Ubv8P8MFhzi8iIhKWFLiLiIg0v7IqPwdovAy4qud1q9x2D7qGPeg4C9wObMebtc8BYqrcX1TlZwP81FqbXfkvzVo7hwaw1vrxZvxfBc4F/l2Pw6o+NtME5xcREWmxFLiLiIi0QNba/cD3xpgLAIwxscaYBOATYFLlWvOOeDPdixp4+ksq14f3A/oC64E2wDZrrQtcBfhqOfY94GZjTHTluAYYYxKBj6uMqytwam0XN8YkAW2ste/gfWGQVXnXAuBHlT9fUflY6/IZcHHlY+kMjDnM+UVERMKS1riLiIi0XFcBfzPG3A9UAJcAb+CllC/Hmyn/lbX2P8aYQQ0477d4wX4KcFPluva/AK8ZY36MN0NdVMuxf8dLu19qjDHATrx16G8Ap+Gtbf8WWFjH9ZOBNyvX7RvgF5Xbfwo8Z4yZWnneaw7zOF4DxlZe8ztgKV6afG3nFxERCUvG2oOz5URERCRSGWNmAP+y1r4a6rE0BmNMkrW2sLIg3SLghMr17iIiIhFDM+4iIiISzv5VWTU+BviNgnYREYlEmnEXEREJMWPME8AJB21+xFr7XCjG05iMMW8AaQdtvtNa+14oxiMiIhKOFLiLiIiIiIiItGCqKi8iIiIiIiLSgilwFxEREREREWnBFLiLiIiIiIiItGAK3EVERERERERaMAXuIiIiIiIiIi3Y/wchoxG3tY+jagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "dtw_accurancy = df\n",
    "fig, axs = plt.subplots(figsize=(17,6))\n",
    "ax = sns.lineplot(data=dtw_accurancy, x='n_compared_songs', y=\"accurancy\", hue=\"params\", palette=\"husl\")\n",
    "\n",
    "plt.savefig(f\"graphs/dtw-accurancy.pdf\", format=\"pdf\", bbox_inches=\"tight\")"
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
822
}