Dataset-Metadata.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "published-prime",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "wanted-insight",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'H:\\\\INVESTIGACION\\\\Datasets\\\\London Philarmonic Orchestra\\\\'\n",
    "set = 'test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "found-dodge",
   "metadata": {},
   "outputs": [],
   "source": [
    "chromatic_prec = ['agogo-bells', 'banana-shaker', 'bass-drum', 'bell-tree', 'cabasa', 'Chinese-hand-cymbals',\n",
    "                        'castanets', 'Chinese-cymbal', 'clash-cymbals', 'cowbell', 'djembe', 'djundjun', 'flexatone', 'guiro',\n",
    "                        'lemon-shaker',  'motor-horn',  'ratchet', 'sheeps-toenails', 'sizzle-cymbal', 'sleigh-bells', 'snare-drum',\n",
    "                        'spring-coil', 'squeaker', 'strawberry-shaker', 'surdo', 'suspended-cymbal', 'swanee-whistle',\n",
    "                        'tambourine', 'tam-tam', 'tenor-drum', 'Thai-gong', 'tom-toms', 'train-whistle', 'triangle',\n",
    "                        'vibraslap', 'washboard', 'whip', 'wind-chimes', 'woodblock']\n",
    "strings = ['violin', 'viola', 'cello', 'double-bass', 'banjo', 'guitar', 'mandolin']\n",
    "woodwinds = ['clarinet', 'bass-clarinet', 'saxophone', 'flute', 'oboe', 'bassoon']\n",
    "brass = ['english-horn', 'french-horn', 'trombone', 'trumpet', 'tuba']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "extreme-implementation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['banjo', 'bass-clarinet', 'chromatic-percussion', 'bassoon', 'cello', 'clarinet', 'contrabassoon', 'double-bass', 'english-horn', 'flute', 'french-horn', 'guitar', 'mandolin', 'oboe', 'saxophone', 'trombone', 'trumpet', 'tuba', 'viola', 'violin']\n",
      "['Gs3', 'Gs4', 'Gs5', '', 'Gs2', 'Gs6', 'Gs1', 'Gs7']\n",
      "['piano', 'forte', 'fortissimo', 'pianissimo', 'cresc-decresc', 'mezzo-forte', 'mezzo-piano', 'molto-pianissimo', 'crescendo']\n",
      "['normal', 'harmonic', 'rute', 'major-trill', 'minor-trill', 'roll', 'arco-normal', 'arco-col-legno-battuto', 'arco-harmonic', 'rhythm', 'fluttertonguing', 'tremolo', 'mute', 'scraped', 'shaken', 'struck-singly', 'rimshot', 'double-tonguing', 'triple-tonguing', 'vibrato', 'glissando', 'non-vibrato', 'arco-glissando', 'pizz-normal', 'snap-pizz', 'arco-martele', 'arco-spiccato', 'arco-staccato', 'arco-legato', 'arco-tenuto', 'arco-tremolo', 'arco-au-talon', 'arco-col-legno-tratto', 'con-sord', 'artificial-harmonic', 'molto-vibrato', 'arco-sul-ponticello', 'arco-sul-tasto', \"arco-punta-d'arco\", 'natural-harmonic', 'struck-together']\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "instruments = []\n",
    "instruments_all = []\n",
    "notes = []\n",
    "notes_all = []\n",
    "dynamics = []\n",
    "dynamics_all = []\n",
    "techniques = []\n",
    "techniques_all = []\n",
    "for (im_dirpath, im_dirnames, im_filenames) in os.walk(os.path.join(path, set)):\n",
    "    for name in im_filenames:\n",
    "        name_split = name.split('_')\n",
    "        \n",
    "        instrument = name_split[0]\n",
    "        if instrument in chromatic_prec:\n",
    "            instrument = 'chromatic-percussion'\n",
    "        instruments_all.append(instrument)\n",
    "        \n",
    "        note = name_split[1]\n",
    "        notes_all.append(note)\n",
    "        \n",
    "        dynamic = name_split[3]\n",
    "        dynamics_all.append(dynamic)\n",
    "        \n",
    "        technique = name_split[4].split('.')[0]\n",
    "        techniques_all.append(technique)\n",
    "        \n",
    "        if instrument not in instruments:\n",
    "            instruments.append(instrument)\n",
    "        if note not in notes:\n",
    "            notes.append(note)\n",
    "        if dynamic not in dynamics:\n",
    "            dynamics.append(dynamic)\n",
    "        if technique not in techniques:# and instrument in brass:\n",
    "            techniques.append(technique)\n",
    "\n",
    "print(instruments)        \n",
    "print(notes)\n",
    "print(dynamics)\n",
    "print(techniques)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "entire-holiday",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "prerequisite-enlargement",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_instr = [instruments_all.count(instr) for instr in instruments]   \n",
    "df_instr = pd.DataFrame(list(zip(instruments, count_instr)), columns=['instruments', 'count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "academic-establishment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instruments</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>banjo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bass-clarinet</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chromatic-percussion</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bassoon</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cello</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>clarinet</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>contrabassoon</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>double-bass</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>english-horn</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>flute</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>french-horn</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>guitar</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mandolin</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>oboe</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>saxophone</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>trombone</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>trumpet</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>tuba</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>viola</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>violin</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             instruments  count\n",
       "0                  banjo      4\n",
       "1          bass-clarinet     48\n",
       "2   chromatic-percussion     15\n",
       "3                bassoon     36\n",
       "4                  cello     44\n",
       "5               clarinet     42\n",
       "6          contrabassoon     35\n",
       "7            double-bass     43\n",
       "8           english-horn     35\n",
       "9                  flute     44\n",
       "10           french-horn     33\n",
       "11                guitar      5\n",
       "12              mandolin      4\n",
       "13                  oboe     29\n",
       "14             saxophone     37\n",
       "15              trombone     42\n",
       "16               trumpet     25\n",
       "17                  tuba     49\n",
       "18                 viola     49\n",
       "19                violin     75"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_instr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "loved-distinction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23de6f97130>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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GGGOMMcaYNsUGnzHGGGOMMca0KTb4jDHGGGOMMaZNscFnjDHGGGOMMW2KDT5jjDHGGGOMaVP61OCLiLUi4t2IeC8ijuzmvCUj4n8RsWlfpscYY4wxxhhjxiX6zOCLiP7AhcDawHzAVhExXxfnnQ482FdpMcYYY4wxxphxkb4c4VsKeK+U8n4p5TPg+8AGnZy3H3A78Ps+TIsxxhhjjDHGjHP0pcE3A/BB4/jD/O5LImIGYCPgkj5MhzHGGGOMMcaMk/SlwRedfFdajs8Fjiil/K/bG0XsHhEvRMQLf/jDH0ZV+owxxhhjjDGmrRnQh/f+EJipcTwj8HHLOUsA348IgKmAdSLi81LKnc2TSimXAZcBLLHEEq1GozHGGGOMMcaYTuhLg+95YM6ImBX4CNgS2Lp5Qill1ur/iLgGuKfV2DPGGGOMMcYY0zv6zOArpXweEfui1Tf7A1eVUt6MiD3zd8/bM8YYY4wxxpg+pC9H+Cil3Afc1/Jdp4ZeKWXHvkyLMcYYY4wxxoxr9OnG68YYY4wxxhhjxhw2+IwxxhhjjDGmTbHBZ4wxxhhjjDFtig0+Y4wxxhhjjGlTbPAZY4wxxhhjTJtig88YY4wxxhhj2hQbfMYYY4wxxhjTptjgM8YYY4wxxpg2xQafMcYYY4wxxrQpNviMMcYYY4wxpk2xwWeMMcYYY4wxbYoNPmOMMcYYY4xpU2zwGWOMMcYYY0ybYoPPGGOMMcYYY9oUG3zGGGOMMcYY06bY4DPGGGOMMcaYNsUGnzHGGGOMMca0KTb4jDHGGGOMMaZNscFnjDHGGGOMMW2KDT5jjDHGGGOMaVNs8BljjDHGGGNMm2KDzxhjjDHGGGPaFBt8xhhjjDHGGNOm2OAzxhhjjDHGmDbFBp8xxhhjjDHGtCk2+IwxxhhjjDGmTbHBZ4wxxhhjjDFtig0+Y4wxxhhjjGlTbPAZY4wxxhhjTJtig88YY4wxxhhj2hQbfMYYY4wxxhjTptjgM8YYY4wxxpg2xQafMcYYY4wxxrQpNviMMcYYY4wxpk2xwWeMMcYYY4wxbYoNPmOMMcYYY4xpU2zwGWOMMcYYY0ybYoPPGGOMMcYYY9oUG3zGGGOMMcYY06bY4DPGGGOMMcaYNsUGnzHGGGOMMca0KTb4jDHGGGOMMaZNscFnjDHGGGOMMW2KDT5jjDHGGGOMaVNs8BljjDHGGGNMm2KDzxhjjDHGGGPaFBt8xhhjjDHGGNOm2OAzxhhjjDHGmDbFBp8xxhhjjDHGtCk2+IwxxhhjjDGmTelTgy8i1oqIdyPivYg4spPft4mI1/LzdEQs3JfpMcYYY4wxxphxiT4z+CKiP3AhsDYwH7BVRMzXctovga+XUhYCTgYu66v0GGOMMcYYY8y4Rl+O8C0FvFdKeb+U8hnwfWCD5gmllKdLKX/Ow2eAGfswPcYYY4wxxhgzTtGXBt8MwAeN4w/zu67YBbi/D9NjjDHGGGOMMeMUA/rw3tHJd6XTEyNWRgbf8l38vjuwO8DMM888qtJnjDHGGGOMMW1NX47wfQjM1DieEfi49aSIWAi4AtiglPLHzm5USrmslLJEKWWJIUOG9ElijTHGGGOMMabd6EuD73lgzoiYNSIGAVsCdzVPiIiZgR8A25VSftaHaTHGGGOMMcaYcY4+C+kspXweEfsCDwL9gatKKW9GxJ75+yXAN4EpgYsiAuDzUsoSfZUmY4wxxhhjjBmX6Ms5fJRS7gPua/nuksb/uwK79mUajDHGGGOMMWZcpU83XjfGGGOMMcYYM+awwWeMMcYYY4wxbYoNPmOMMcYYY4xpU2zwGWOMMcYYY0ybYoPPGGOMMcYYY9oUG3zGGGOMMcYY06bY4DPGGGOMMcaYNsUGnzHGGGOMMca0KTb4jDHGGGOMMaZNscFnjDHGGGOMMW2KDT5jjDHGGGOMaVNs8BljjDHGGGNMm2KDzxhjjDHGGGPaFBt8xhhjjDHGGNOm2OAzxhhjjDHGmDbFBp8xxhhjjDHGtCk2+IwxxhhjjDGmTbHBZ4wxxhhjjDFtig0+Y4wxxhhjjGlTbPAZY4wxxhhjTJtig88YY4wxxhhj2hQbfMYYY4wxxhjTptjgM8YYY4wxxpg2xQafMcYYY4wxxrQpNviMMcYYY4wxpk2xwWeMMcYYY4wxbYoNPmOMMcYYY4xpU2zwGWOMMcYYY0ybYoPPGGOMMcYYY9oUG3zGGGOMMcYY06bY4DPGGGOMMcaYNsUGnzHGGGOMMca0KTb4jDHGGGOMMaZNscFnjDHGGGOMMW2KDT5jjDHGGGOMaVNs8BljjDHGGGNMm2KDzxhjjDHGGGPaFBt8xhhjjDHGGNOm2OAzxhhjjDHGmDbFBp8xxhhjjDHGtCk2+IwxxhhjjDGmTbHBZ4wxxhhjjDFtig0+Y4wxxhhjjGlTbPAZY4wxxhhjTJtig88YY4wxxhhj2hQbfMYYY4wxxhjTptjgM8YYY4wxxpg2xQafMcYYY4wxxrQpNviMMcYYY4wxpk2xwWeMMcYYY4wxbUqfGnwRsVZEvBsR70XEkZ38HhFxfv7+WkQs1pfpMcYYY4wxxphxiT4z+CKiP3AhsDYwH7BVRMzXctrawJz52R24uK/SY4wxxhhjjDHjGn05wrcU8F4p5f1SymfA94ENWs7ZALiuiGeAySJiuj5MkzHGGGOMMcaMM0QppW9uHLEpsFYpZdc83g5YupSyb+Oce4Bvl1KeyuMfAUeUUl5oudfuaAQQYG7g3T5JdN8xFfDpWHrN6HzW2HzN6HxWu10zOp81Nl8zOp81Nl8zOp/VbteMzmeNzdeMzmeNzdeMzmeNzdeMzme12zWj81lj8zWj+1ljkk9LKWsN820ppU8+wGbAFY3j7YDvtpxzL7B84/hHwOJ9laYx9QFeGFuvGdvT53wY+68Z29PnfHA+fFWuGdvT53xwPjgfvlrXjO3pa8d8GFs/fRnS+SEwU+N4RuDjXpxjjDHGGGOMMaYX9KXB9zwwZ0TMGhGDgC2Bu1rOuQvYPlfr/Brw11LKJ32YJmOMMcYYY4wZZxjQVzcupXweEfsCDwL9gatKKW9GxJ75+yXAfcA6wHvAv4Cd+io9Y5jLxuJrRuezxuZrRuez2u2a0fmssfma0fmssfma0fmsdrtmdD5rbL5mdD5rbL5mdD5rbL5mdD6r3a4Znc8am68Z3c8a6+izRVuMMcYYY4wxxoxZ+nTjdWOMMcYYY4wxYw4bfMaYrxQREWM6DcYYY4wxXxVs8H0FscJrRpSIWDwipsr/h6k/ETF+HzxzlNTTXNRpkog4BaA4Dv0rT0S47xkHiIgp+uCePZYrEbFSRMzWw3PHqToZEZNHxI4RMdeYTsuIMiJl1U76UkT0z79t806jkkrejGttuac4U75iRET/3ii8laDo4rfo7P+RJSKmiIgJe5umEW20I3LNqBYIUdO/9ftR+ZyRYB/gB539EBHjATtXilFELDwyD4qIfhER3dXT7sq+5bwBQAB/B9aNiN2rZwznuhGuP71J3+i4Z1d1qPl+fZHebtJTKR29yt+sG1/k/9NW3426FHb77H6d/d/Lew3T3kfiPqNsAbWq/Y2q+3X3nG5+6x8Rp6JVuCfp7rwReF4MT640zp08/90b2GV4Dq2WOjld9V1P09Z6r9F53UgwFFgeWKF69iiqz72qfz25pkpfKeWLiJi4Kqvu0lLVl9Bq8SNFVQeb9x8V9+zBOQMjYgNggvyqzw2bZh86MvVidMijiBgQEdsAl4PqR18+r/HcHukZY4seaIPvK0TI2PtfVrJTImL1HlzTD6CU8r88Htz4LfK3SiDuDKwyitI6DbAp2ppjxYhYtPWcRpqmb7m2fxFfRMR0lVI4PBrXzBURG0fEnHm/Do0tO4Gqc58jZPD0mhYlZMaImDoilqzSNDL3Hsl09WsIo11RWaxUSilNIVxK+Q9aJfdHEfE08LWR6UhKKV/kM74eEad1Ueeqsl+mO4UQuAb4PjL6jgX2jIhFunt+1clnXRihEYZme4mIiSJioYgY2Ez7iNLJO/fIoO5KwW3c74uIGBQREzfuPXln9xpV5LO/yL+TNb7vcX3JujFJRJwLnBoRA0dXO8k8myAiNsr/e2uED8w69r+ImDIi5s/ve1NHvgHsltev1Jv0NGm0v4UjYp2RUdY6o6l05/HAlt8j6+NrwHzA/N3cY5h+qZNzv2wH+V5fi4jtG/K9X8v5qwI75+EJwNeAYfqfJnnfmSLiGuCk6rvurukkncN1dHVz7ZfXhUYltxzRe/TwOU25/zLwDDAvsHieMklEDI6IBXsr7xr1b+msfzPls7u8X/TQkd2oL9sADwNHdSd7GobhpcC3RvhlOkljvtuASo9ovteI5tkIvPd/geWAayLiZeTA7TPDpqUPnQmYOL9fNyIm6+E9qnZb1YeJozYgR+XAwoBSyueoLg+IiI1H9TO6opFHs0TENhExaUva9o2I1Rs61y7D01/6Eht8YzHZXmaKiAcjYlAqFysCLwLTAe90c+3S0KFT3jQiXgUuiIhLWs5dMbSFxjLAyyOZ5kph/h3q7G9FXpfx8n2aHvY1Unh9LyI2qb7P9+wfEUcDjwPXZSffnUe54oh85szA/RGxeKtAzQY6bUTcAlwFnBIRy/fmXVPRWzg0knk5cDfwKHBGRFzcED6jra01FI8v8l0HZD04DbiwOq8lX6YHxgMeL6VcOqIdSUQcGhFH5v8TZAd7KvB2KeXvEbFCREzeUGpWj4iXgO3IzqRxr4haST0UWBWYHXgFeTYfAS6JiNk7S0u+88CIOA24OyIOjog1q3t3kf4vjag83hJ4HTgXGZ29UQBbHSo7RcRzwDdiON7mRodbImLViDgiIuZuud8hqH2ckfn/HHBaRGw+qutbo10XpDzfA/wgIp6LiPWA0k3edghDiojlgNOBfqWUXVKZ6ROaacp2sSZqp2tFxMFIRmzd2lF3c78qH/6bxzcA7wO3hrYc6ul9opG2B4ELQ86WHWIkwyAjYvyIuAK19bVQnejW4OnhfaeEDkr31hFxB7BSHldGXMm/NwP/BlYMOQCJ2hFW3WPbiHgH7dPb2TObhl6/iFgFtcdlgEcasq0pY39cSjk7IuYupbwFPAbsGA1FNYaNxPga8F3gN6WU3XqRN03FdsmQYjfcPG/KiIiYNCJ2Aw4Cfj+iaejBs/o10jhl5tc7wBfAOhHxI+Bp4F00Mnp8RMw6gs+IrH9nA+cDc6Nymrkz+dmoM/8LOUoPi4j1m/drOX+miHgMWBK4E9gaWLqb8+dAhsDvkMwZYRpl9KWzHbgCjRzPBZyf9XiiEe0j8p6DI+KbEbFrRMzYeG6rDP8lsAZwUynlpDxng4i4IyI2jDpCZ1QYOgvlO30L9TGPRsR7wLbAtyJihy7SSORoeqPdDk45eTO9dKZ08owJImLqvNfnETEBcDGwInBuRGyYzx7lRl+jH6v+Hoe2mJsB+Dy/q/JlIB1HHTcF/jCq09RTbPCNpWRnMU0p5QPgMzS6ATAHEjZ7Ap+EPAsDWq6dBVg+GwEhRfGAvOZQNIKzZ/4WqBIeBVxRSvlTL9NbKUJNr/lbwH+Ao4HnG96Q+SNiPmARYEfgDOCw/I7sZG4HpiylzA38BNgiz//yec3GnAJkAmAwsDLwY2AqYN5OOvfJkef33lLKikh52C0iFmrkSXfvOnFELIsa897IQ38m8CHwHHAMsCbwJHBcREzWV564zmh06otFxAWkMlVKOV/Jj32zHGaOiKNS2bkS2AxYI7ofceuK14CtQ8rq5/nZsZRyXUQMQXXgLPhy9PdY4MBSyt6llI+rm0TD45nK3G+RUX5bfi4BPgGmATYKGe6dhcOdiMJANwUWBg6KbrypjWdOkgJ8O2D5UsoqwGKhcJoekUpBh2eFRuO3AjYspZxaSvmsi2ub7WhIyPA8GbX7UyNi/dDIzd7AnKWUZVFo1pHAXsBPgXOQQjTSNNtOdtwDkeL0GSrjiVH5bEAn+7pGfDnaAxmGBPwNmBboTT3raboj0mjOOnIY8pCfiIyyt1Ge3oAMliN6cL9m2N9MEfFbYDHUbj4DtkeyYHhpa44U9AdmRI62yUspO42IDG6VbcmiwAellOWBPyKjb+JOzusxIUPk0EohjYhvItl9ZinlYeigEK9byVLgOmBBYNGImBk5Jyon2GZo790tSimXd/bcRrs8CrgJldVqpZS9gFeBk7Mv+DJqo5TyWUSsBZwV8qafgxxGK1eyvWFwzpFt7mMggFny+x6FjIciGGapyjIiDgS+B8yDRmO2iE7CSaPFOE62BvYA7iqlPDqqldWUKUNDyvclyAH4LWSUL4bk6lNo3+R/onq9SHf3bM2nfJ9pkOxdDjlDpkFtrXldazlMgfr8mYDzUs71a8kfUNv9SynlwFLK6WiPtJ2qPqvZd+T5swO3ICfBFBGxZvR8hGqWiDgRWC2PN6N2sp8MHI+cNY/nOTdFN1NYmu/dOF4c6Sr/zrSeFqkHldpxeXBELEiOaAKzRsSMEXEeqi9XoMisQ5p5MCJER0d8f2A9JOenR477t4G/oLo9P5IFk5ZhRziHAkdExAx5vC4y8n6OdKUtI2KnxnNGmNBaBHsDQ/J4c+BXwNTABUjnvHh4ZdGL53YYwc82PwWwEOrXzwD+LyImaMiic4A/RMR+KRP/XEr5aFS37Z5ig2/sZU/kwQIZJxuF4tU/R5vVP4yE9gPIWBnUaLS/Ab4DbBIKV5waCapnSil/QB7EU4Fvoo7pIODTPK9XNBShbdGoyq7I63Es6qQXiojxImKXfK9zUaN9vZRyP1J4dsxG+h9krFUK4eVImVwqMvSnYdQsF/KoToE6i3lRx3EqsEYp5QbkwaxGOTcspfw53/2FiPhxvvtfkbHTkxCL1YEbgf2RcTkr6tCmR2WzIOpMb0MG4Ah7jEeU6KiYR0R8Gwm/B4DtIuKc/Lny3G4O3Av8A3UiuyOh/ipwXN6nuxHVVoP7IeQdPhYpDOMDt0TERZmGvwGrZwf3WT5rx4g4MCK+GxHHpKCsjIO9gG2yUzkMjWjPB7yJRpcmRqFaKxaFc1B1+hExMTKCnkT1bErgkMa9v8ynxv+TRsTBpZS/ofoyFbWSfDpwQHQTdtYkdfnKY71fttvPkOKzVETsnYriuq15WXWgISP8AVQuGxeNOjyMQnm2Qp3MVBFxE1JS70Te332BU0opz/YkrV0REctkeqo82w64AzgPOTpWAg4upcwHDAI2QvWelnpRhXbdhzyve6Gyvxn4c9aHUeWV/pJsw5OHjIv10Wjtc6jdLgEcjjzC1wOXAktXaWklOhpoM4YiA7ZARsJnyKHyZ+AJNNrf7SIYDSX3UOrQrMWB8SNiw/yt2zl9WUeica+FQ44+kNNr/dBIyHxIGflxT+tv4xkDQ6PFM6GokhlQvQP1G1cBH0TE8qGRhiWRwbATcHBE7FBKeRG1o2OQDP8+sHnKq1mB+4EFImKziDg+ZJw32+UaEXE46pueQQZ1ZVQfhBw6c2W7mTc0ij5FKeUBVM/WKaX8E7ga2AbJaCJi5Yh4ATmi7kAGytXAfyJiibxfd/pR/+zzZkBOBJDMWxVYM2XWt5HxvWzrxY1y2yVl4GLIeHkBmDkixu+N4t4kWkLRU6ZchYyUK5GRsCgqr2WRnJwTydjdgcNKKXdEF9EI2S6qvnWVkAOxP+qDF0KG0LbA4qWUR1vevzLM1guNbO8P3FBK2R8ZUjtkPkwcEadHxCYhRX9i4K9RTwM5E8medfJ+M0TEQ8hoXB21+4WQwbczcBFynHU59y/qEOV/ABMih9+UqF8D9SsnIgPoOCQHFkA6VleOvIjODdgFgVOQLrcymlrxUV6zJvA8MBuwMeqnL0aG4WrAbaWUdVAbXxm1oy5H3rp53yo0tX8oCud/+Y6fAwsWTfnYE/g1cC0yMF8hR+vy9RaJiE1KKb9CDvzqPVdBhv/dpZRfA/sh3ajZt/Q0nVUf+Snqh6YKRYHNiMrpWqTX7Y+cN6elzB4ldk5D5/xaROwQCimfCjlKrgnpXA8BJ4ZCovcJ6aX7IR1+exR9Nuam+pRS/BlLPsgAj8bxR8hoAXkpL8//F0SNvj+wC3BSJT8b184E3IWE3FzAs8BEqJN9DnlcTkfhLnPneU+OQFqj+bz87hTUoS+AOpZ7kDJ4FTI0fpS/T4o63u8jBXUa1An9DnnjBgFrI4Nxtrz3pqhDXDOPByPB8zQa3XgWhSQ+BRzbSNNSqFOoQi2/lt8PQF66bfL49Lx27eGVTeb/R8iI2RYZORdmfj+Tz3k673kssEMf1plZu/h+3cyj9ZHi8wmwTP52NxLYiyBj4ReZzn6os/5l5ulNwLyd5UXj/5mBQfn/nKiDnTfLcGkkiOdCHdqlwB157rKZZ5uj0efrMq0LohHd76GO9D0kMLdGoxXXApMDP0Pe41fyeddmfdkWjfRehTrkNRtp/RpSGCbt5J2GZvmvj+rjhcD2jd8fbtar7tpF/t0LeAMpBpPnd5cgR8wByLP+EcO2oSWQMrYTagPvZRmdn2l4HjlH/g38CY1M7YRG069vPGs6FCHQmzq1OPIc90Md2vGZN1ei9vnbPD4hz78Std+989oZGm1suUz3SkgZfjbTOwFqlwcCE4yittCalzdnnTkt68nTyFi+Ao3Gr4fk2ATIALyx5fr+jf8nyjr2CYqSmBSF3f0f8J08Z+G891HDSdesmX/fQkrcXVnGWwDvN86bsAfvPCMyWF5EbW8uJH8fRAZ5dd7XkfIVw7tn45opgOUaxwcgB0rl2PoVUqAvR7L7h1nekXn9cD5zPWQQ75X18oYsh6FIgb0868HzSCaPD0yRz7wIyafp83hXFHo5cx6fj+TtyVnGNyClavO8/83AqnnuD5CBVymHi6M+9DNk4AxFcvDkEcij5ZEcWjaPbwG2yv8nRXJt407q02TIMXkTahf/II1z1NevPBLtYP2W40lRW54OKcP7ISfnn7KcnkN9wttIqT8XKfyHIIP26417zQDM0TieGinfv8x3PQi1lXfIvjXP2wDYs6XeHoL69K2z3B5qabvnIh3hBuS8vCXf4z5kAPXLc+9Dsm9+ZNQfms97AZi7yoP8Ow9yxI7XRd7NAaxUtdtGeayF+rp/Z95tieTeq6j+VX3rxJ3cs9lfzoWMp6XzeA/Uz/0E2Khx3pRZTrfn+z+e73N35tndqM6+g+r74HzvBzpLQydpmqyRJ/1Q3/39/GyMZPdpWa6bNdrir1H7+SZyzC+M2tCWSJecOsv2VtTup8tyW6/K80zjBa15001am7pXP6TnHZJ5fyOSR39GcmEyZCA/g3Tc4d6/h20qkE5zCmov+yN5tWLWqa2RHvINJM9/AZwNTJvXX4ocyZdkmndHMnmiUZG+nn48wjeWEB3j66uh6BORoAN5CxePiNVKKa8jIbEa8oRV4YKbpJd0aVQ5XwE2AT5AStqlyBP0HBoFXAIpLX8upVylZMQBPUxrybRW3sMJkFfnQKS4zIuU9ulQY1kECdPV0VD8H5AgWxt1+k/k+YshI/C3mc698xl3IAX+nxGxFFLK3ywKafsUCZ7JyY48FBN/AhLKUwO/LaWsUkp5JiIGF40KzQ6skK81eT7vd528b+VxHhCaf/SbzMt/og7g7nzHa5CwnBMp858jAfDp8PJ0RAmNCu2N8mfi9IRfGhmqW0q5F3mU9kMezgeBK0MhF3siw+dmlD+7llJOAQaUUt4GDkNOgAvzuANF3sDJQqN3P0BzGL5RSvk56kxPLwpZnAR1Tuej8j8LmCMUGvNaKWWfUsotqCObDhlI86MOfDpUxz9AwvRzlKeLojpzRN5vCBKsz6AyWQEZ2pcjT/Vr6V09GRmxp+W9iYi1ImL/fK0PUYexNzISnkej0kvk76cAw6w2Fh0XxqGkdEcdwV6llOPRqMGgUsqepZSDSynnIWfIw0g5qu61PFK0Kk/qA6gdXA/8F3UkA5HB8DJSNF9HxsN/85lzZR29O/Opx0Q9qvR6PnPbIm/qpKhc5imlbI7KdFJgmVDo3GxIWVwedeybh0L+tkXtaROkkJyNZNJOqL09gNr7iiOSzk7SPWco/Lea51WNQv0e1cd/IXnxz3yPO/K3amTm//K7aULbl7QuKLIwCpX9HCliG6LO+vR8n4XzPqui+vibrHMT531avbnzALeUUo5GBtIXwP+K5rz9JSIuiIi7UL1vvmfrXMh1Mw3PFY0Q3p3v9EfU3teJiNVCoV+XoPbdrWc501150v8EvB4RR+azrsn8u72Ucl++90+R4X4Kmlv7P2ol+EmkeD8AvITk+v+yTLYF/l1K2auUslsp5VzUfqdFSvE5ofnqh6O6Xc3XfR6V52Z5fDQysmdBoZ7boj7zm0jmzA+sHRr9/wUK358fyZYtkAPim6WUy4pGJ34CzJ3v25o3zbC3aSPibiRjfwd8N0eGXgJmiYhpSyl/RcZkNZ++WuBnHiQbHyCVf9QfDkSK/V/QyplDuimq7rgiFEUwWWiU9+p8zmdInnwblcNA1GePh5yXf8z3nxUZW3sgJ20VntcPGVRVu1gVyZ73SymzIoNgXSRfLwbWC809PocMsQ9FIk2LHNL7IYPlJiSXP416sZoXM81/zDI9GNWBVTP9GwPXR8Sj+U6bIyNjO+CyUsoPkbF0Zt5vvNAKz9cjWfW/pixvlO1kaHT8e8iB8RYqj6NRFND1yND5JZIZU6J+5o3QqOF10bIoR/aX/SJiO2RQLQzcG5qD/kuk+xwLPBOax3c+Mv5XRW1hY2Scv5DPH5BlsiIamT6zlPJ3JFOHIKdll4SmzJxCvbDRsqjtvYXaxrlolO5kZEidEhHzIt3tl0iHnDTL4c8pJx9DZbZHKeVD1P7Xoo58WIW6De8HbBqa89jtVJeI2B6NZJZsw4dkek9C/d+fUf/zvcyn/ZA8nwYZh0O7u383z+0sVLlynKyAHLFDgclKKe+UUm5MXWkmJGPOLKUcUjQlBaSv/AnJ4UuQfrUeozvKcnRal/4M14uwKPKSnUjtmXoRhaOBwn8eyv93RR3GGqjx74Ea1knIcDkLGVRXoXCaO5BC+wzqQN8BNm88eyASMI8jxaAn6T0CCfk58vgp1GkcCgzO77ZDHfJ+yMP7GBIEE6LRgwuQAbUoqvyL5/mLoYbzOOrIQaN1P8z8OQ55jh5FHpOZGulaGgnW6zNvVsn73I8a26NIwM6Q37+FBGD/bt51F6TI3IsUnzlQGN0bSLjsjTydByCF/Q2kDG3QB/Wk8myuhEaMzkQGxJaZxu8hxenQfM/9kILzBfCrvPawrBvT5PEsSCGfuotnNkePB2U+HpbH1wFv5f8TozkJ92VZP5LPPjV/3xQpXifkM6/J/DoOefZ3zN//BuzeqEPnonmRv0f1fLL87VjgD420zZFlv2y+/52ZJ0+hjiGQA2AS5PD4hNoLNwVSjvZChthFqKMf1IMyWRgpt9WI9ClIsTgv68ytqH3NhOrcU8homBIpr1MgT+m1wHWN+96KOrD1872eR0bw+kgBuAspZS/l7w/lORv2sm6N32hrP8vyGIoMiI/Jdoba6KvImXEaqv9fQ57Pf+f7TZXnDkFtddE8/jHw3fx/y6ose5nexTIfFkTyY6dM2xxZlu9lfu2Y5X8TUlovQYtTVHW4PzB/y73XRPLyGWS8jJd16nWkTA7NfP8EKUL3AvPltRsBxzXaxN7kqA2Sfx8gY3HXxvMGIO/4ScBO3bzzlPn3a1n2xzd+e7Yq+8yLM1Ed7MloYXMkYtKqPiDF7xQks3dHMv6MzIPn8l0+RkrMccjRAep33st0DM36sjRqg+cC38rzVkXt9HXqkY9vo5HlQDL1R1UakZH1CJJ562T9+oi6HQdy+OyIZMlzqK1dhPqUSTLvL6OWI5NRR9OsTmPUmY6ybzByoK2AFFGQrPgZqstzoeiAe5Fs/gXwRUt73gwpp2+hulUtYFS9X5XW+UagHfRv3GPFLKPvInm7CjJKfpv59lm++/2Zhhuz3nwXyeMLkRy6H/XLQce6sSj1iOaLwIn5/4TIMLs6j1dAdefEzKOJyLnc+fvZ+e4D83Mwak9D8/cH0ejaBHm8CfBCo7z2RnXwQeR43CbrxAqNc57IvJ4L9XdLtNb5lnebKsvsjczHozNP/o2coxOjtv4eqlfvI53k6szjKlKmWWfGR23hKeoIozORnJoO6W4foX7wr9RtZhNUrw5FOtKKyGk5T56/UObvrUgXvJHUG3vQvs/KvJ4F9UGbovb3DtJb/oLa1k5Ixlej0Rfmew4TCYWcV/chY3UmVJ92zHK/Jt9zkjx3uH1q475/Q/LmZTQifW2mp9LjTkHy5XdIP/gEOS5m6UV/0q/leN3M94kz3+/IdNxCrffOhZxzd+fvDyJnU3/UZ1Tt8gw0hWmU6IG9+YyxB4/rH1qMC2pja2XU2T2Chqq/hoR3FTb3GgqHGa9x7QNotGBoHu+Vjfk0JDAfA17K31ZChuDOeRz5vK26SeuSwEGN45VQZ3ZJCoArUSewKgrzWQoJ3o3QKNI3kdIxJVIIfoeU00OzQVWjEYMa73MiUmq3yfttiYT62XnOvMjTu2wjXZvldU+hDv8O4Jj8bdn8zIU69CuQYJ6eFiMnv2+G3wxGHfNr+b6f5Xssj5Sdk5FRch9SZJbsozozTDgWMuaeoQ5N3Sbfey3kHf9FpvEW1IE8hhSpKbNu/Cjz7LWsL81OYWE6hkTOSYaLIKNpKBLAF+f1p+dvu6DOazBSEN9HRsjq+fsrSME4HHWsjyGj5jEkFB/O9C6S55+GhPjsqBN6DikL/fMZ7wKb5LnVqGKlvFVG5FWo4++POsgD8/eLkUe4esdjMq3ToHq+WCd1o9mZj4c6tpeREf0zZLyNjzrROZBRcgzqFKZvlNWuef4dqJ1slen8b77D3Ei5/QMykgZmPvwQtbnvoro3D3UY0lwjWp+afzMtNyFj9ceos90NteGfAFc18mEd1LnNgzr17ZFRdRVyBk2U564GPJH/L4A6/xvIzr+XbaHqRCdEbfL0TP8lWX9ORHLkHBQ6eTSSGfdnvs2GFNKnkANjUeCXec8hyFn0OpJLuyBP8sHIkfIUUoqGZD3ZhQyfJOUGamvnI4/0M0je/BbJ94WRU2arxvucyHCM9Lz2GVR/r8zv9kcKzzx5vDlqi5M309NZn9PFM8ZDbesR1G6mRwrcRUgGR+bvX/K990NOuxuot5d4K9/vz6gfOhnV3f2ABxryuDIo9kEOoAeBdfP3pVEb2CCPn6V2AO2LjPx1SeckGqG4vFEvzkcG+w7IKJkA1e0Z8x2+g+rpSqh/eSyPBzTyolX5+3q++0FI8T6h8dsGSOZNhNr+dqTjM9/rnDzvJ9Tt4mc0DHukEB+V/w83LK+L8psAGXh3UDvhrkAyuBr5/UP+XynIQ1Fo8SPIiJqAdPx0Uv+2yHw6K8t0w8zfqt7PnPepyqofMrKr+rge0gVWQcbGE/n7YcjY+BXqU3ZGbfQxZNhUMupp1J4nQg6SB5C8rPLtdCSrqrDyXYE7O3mXiVqOF0ZyaWnkRD8VyeQ70ajXPZlXi6E2fw+1Q2dhNBo5Xcs9l0JtJ1A9vz/fqwoPfB7V4ePR6M/hyOG9LjJm7kcOvO+hCJRfZFlOhPqTMzIPViGdLMOTl5UcyLw/Dzgjv9su68iSqA/9JYocAOkIP6c2cKqyXjXL7zvUTqajgEvy/80yvTPm/4fQSb3qRrb3z2t/jwzuS7M+HICczD9D8u8CVC/nRHLh7K7acDfPDDr26/1Q+30H9QU35fevN8p9QNaZ3+bfN5GT4Ray/ud5g4GF8v/dRyRdo/oz2h84rn/oes7V1ik0lkPK44nUMc+3UnsT50cdyvr5f38kiJ9CgrFqjE+gTvoC5J2uGu8ESKD9Ghl6L6HOeopO0lQJ2SnIOYN5fAdS2qdDCsIRSAhvno3zLqQovoGE0QJIiTgKCbA3kJLydF5zeqal6uxvQp6l7+X5FyLheQHqbAYjoXcL8vZsk+/6MlLmls33vB91bgs33mkucrXHTt63KRSnRF6qpfN+Vcjfm8jg2wQN8Z+TQuBdJEC3RoKgx3NlelBnWhWPfZHyNDTz4lrUaVSjM9chIT0pMkDfJ+dhoE70T9Te8E1RRzNHJ889EinQy2eePY+MgMXyHW/M/Jgg86Ygj9v4qIM7IK+rFKyHUH1dMe/zYH7/I1T3H0aK9I3Ig/kYqp/35bmzoLq6DbUHvR8ylN5G3tlqHmfTEdAP1c/TM63rZT2aFwnod6kNpuNR/V6tk/wYZq4ZUnCOQsrP2ijc6bKWc3ZCdX7JxncTovpdjeRXbefvyED/JN//btT+78z32xt5gN9DCu1T+f/RrfWkm/oU+anky1BqQ37prB+H5vMezXLbPPPlDbRQDqgNTpNlukCmZZkspwtpzF3N8voBUujWHRVtAcm/y1EdfhQpFX9AdX5b5BV/MPPueaQwfRsZzDtnvViFWgG/IvP7eqRc/BHVtxVRPX4qy3tX5Gwa3JKnrUrVEXm/rfO7PTMfpkVy4nUkB5/N+0/XuH5Ay3tPke+4AWpfj6F6NzOSO82Rwstoac907ixqlStDkUw9Gimx91CPzB+InHQzZ378Dbg4fxuMjMNzUDu4BymMOyPF+V3U1qbP75vzpm9DcvprqG7f1EjPKfnMCfO978sy+3J+LXWfNBXq0w5A/cQ71CPKP6IeGVincf+9Uf15nMZ8sy7q3TbIkDkwj9dExlvVbiZDIcNnNq6p2tccaORmTWTYDGzI3qeR8XQPql9Ld1dmLTKtWd82Re30XCQ7j0YRHRvk+x2G+oEXM8+fRf3sxahOTph/F+iknVVz/65G/eccqI1UcxMfAI7I/6v590sjR9BSqH5e16grB6ERroHIcLkW9VmzZTkuieTMEOQIOJt6xPmizPdbUITG7qhdVKPKi6E2uzldODioR64Gona7N+o/9qSeO3oQkheb5vFcqL0+htr2Zll2Qzopl4lRv/la5tmjec3hSK+bA7WHk1Hb2AzN4Vwh821K5OS8I+vGjqiNvN0on4uA7UZAbgYynq7Ie8+H+p9/I6N9X9Smp0Ay/T5Un6dDjp63kSyrjLGV8/1XR7rQ65n3C6L+ao3MhwuBvXuYxqZzqirv/ZC8/hTV2z8hZ+fRSM98GcnZygjbOctoenqhgyH94DtINh3T+P4j5NB6COk856G281fqKJGL8n3nR23u1MzXt2nI5zH5GeMJGFc+2eD2RMJrEuT1vpRaUO2CFLd7yIUyUMc+AI2m3IEE1HLIy3sL8ipUnecVWQEfQJ3zIUhhOQUZK/9Cnq9V8vnzoM5gmS7S22x8g5B3swonXQJ5b/+cx2sjz8rT2eBnICd+5++bZNqezrQvjQzIjTM/FkJK1P15n/PQkPjfkQEwWeM+30Gjayci4bR1Nq5rMn+qkJhXUCd4NurIBmaDfRnYbzhldQBSHv6MOvl9kCL5CTJKlkEd16aZ9u8Da/VRnWl6nSbJPHsUdRZPZh5sgDzaeyJBVYX+7YA68uuQwjJpo64814NynzzPvRI4PL87GtW9uVFH/Wg+72XkcXst69lM+ff4vG4vpIBfiDrxLVEHcidwbZ5zWZbVQVkfHsr68Jssi1tJYwopBw+j+tw/68kbmaYvRyqRY+QZ5K19j3pBhXOBo/P/nZDy8+usXxO35MmU+d6VgrMmUihmabTTS1CHtFSmdzXUwT6faZsPGUUb5jVzU0/+/wZSeD9FbWRfNE/kkizj6/IddkDy4E/UI0oLkkpAD+tUf9SeD0Ze0SoM+GTqcMRXkELYH9W3yjm0BpJZpzfudwd1aN4+wPfy/6ouLoiM//nz78TNtPSyXfTLdH0XdbInIZn3G+RtvRIpT5siz/RmSOm/hjqUd1I0Cn5tluduSPn6J2pLUyOnwEX5WRSNJFyOlJu7kcHbuiDLvMiwXQZ5nG+ho+LwCJrnAlKQts18jXyv9RrnDqA2RhdAimwVKj9TlssU1CPDX+ulXKlGXyZD9XLmLNfHkfPlFGSof0ztODw0n1+l52jqxWt2o15B+k5U9/sjZernyLHRn9ynkzqyowq7q/rE1fL8nRrlPl6mZzdawsKQkXoHah83oTY+iLqtXZR5eCeqmysxrNE7sKWeHY7kw2SZF2dSy5ab8p7boNGW05E8mhQZnUehVQ7J375AivQp+ewJkLzYguEYnK31v/H/dGjO3XXUI+4/RwbEY6ht/zDL4k/IoXUpMv5+Qq20Lt1898b7T4wcfv8F9m38tj+S5UNQH/gfcoGfzPOdkfzeBbWDl9BqnSCHQSXXts+0fUwjhBW1wdOR8fEqGvnaAcnxQajNX4h0j68jQ7Yyvo9HI1ZdGXzVgj/T5d/bUFu8MtN5YJb3Syia5BAko09FfcgCqP4uQ8cFRaq/i9EYVUR95AFZTuchQ+Ut1Gfelu9/VebBO6g+XYxk9KVoLjXUjqvTUducsyd1JI8Xz2v3QfXzKNQ+Hs73HIR0nNdQWz8+y6dyTlTh6FUZb0zHyJjjqUexd0PydgCNqTYjUL9PoTawVkBt8DGkw1Zh7CehsM7XaYmMoIfTaFB72YY6SmBXpFeennXhKaSHDkD19E3k/P8R0ivPyzKZpHHPD5Acmwf145fSRxFfvfmM8QSMS5+svN9BncO9SNl6GQmY3ZDCu1aeuxwapauOq47/OOQB658N+IGshMcgwX0QErYvok5lyay4P0NK6lVImRtmRK+LNG+EOpEpkTCqQufOQGE9u2aD+DFSyquRowkyDb9GRuzVSLn6PrBU4/7vI6NicqRoTNP4bR2kvG+Zx5Pl+1+b73EIEnxXZ9r2RwrBjqSnFXUuXyClanIaIQUMq6xNjzrxK5FicB3qVJZDSsrHqHPbHnWqt+e7z9LH9WZIpuUu1BFMgDqac5Fi0R916n9BAvxNJNCvzHdan1x6vnHPXTrLg8bv1dy+NVGndxB1h3Yn8gquhjr1/yJlaFDWt5OQIrslUnB+iDrt1ZGQ/D/Uee6CFOdrUWe4HTLq3s1ynBt1iOcgr+jOyPCZNdNxZJb9pKgNnEsjPBcp97dTj959K8+ZDSnwN1Gv4DcFqZzlcVOpGog6gpORoP9p5u2LyJCZFCn21UqCT6H2MB3qPK5DbfxV1PZOQ57sV7PMXkSG+T9QXd0o8+nSzJ/TUBu6BNWFu8gQ7V7WpwGo0/oZ9SIRb6G2OAmqO39CDpYBmYYfNPJpKB1HBN/N95kq83Qr1FarOVx3kUZFXtNjQ49hjZOpkeF2D7WH/CmkZD2NlJb9Ufu9P58/P6qXh5HzChsy6mKkXHyA5MmTpBzL9D+YdWgeJI/3ynfuR8tKf6hNvJ1lXY3u7J3lt0Qefx054Gbs5F2nR0rNfCis69dIzm2D6tiLyNCr2uGtaLR6emTcNsP8e7L63ZAsryeRjBic+f1d1NY2zbx9Oct8dVRnN0Ty4H3kHPhRpvsXqF3tiBw1WzXKcE3UxndFjplBSHl7ljpsc3wk83+JIgUeR+1ufDqGWh6W36/SyNPDM12n5XebZF04KI/PozZIF0CKb7NONg29Kn+rkZwfIeN+M9R/V0rvdPndHdQOsfVRv7xvPuNl6oXLfpVp3zHz/WEa0SfDaxswTCj5t1GfdD/1CMfamd5/IuPo91lGf0UhnKei+v5TZGjuTM55a3nWhGh0qVo/4EpyZcU8niy/qxxPDwA/zf9XQ46XLRvnH47a7CRoZOmPqJ3dhOr2sWgxoOr8c5GDdVXU/uZE/Uo1/29i1M4OQ3XpZupVzAd28j4d2gbqay7Ncr0ZjW49jwyL/6G2vzKSjS+h+n9wpvcC6jD9qq6sg/qoAajuXdL4bX7kjDoCyZtnUR+0PKrjH2SZ/DbL8jkk11ZFjpW7kOG7GGpX83dXR+hYTyqDbeN8jzVQX1mNaq6f+bw/kmt3IHnwMtIXK2NoPmS0/inLYsPMh2pEdBpUF6dAMmpnulgJtSsZhZwfd6K6+W+kYx2DjLqXs5y+g2TSXZmGZhvu0doTjfNnRzKvcjh9CNyX/y+O6vceWV8mRDrmWZmmi/L752m0nyz3NUYkHaPzM8YTMK59UEfwE7QpdVWxzkUd/A7Z2G7Kxv+bPOckpEDuhgT6PEhBOxGNlnwHedLPRArKS1kRq1jq40nFOI+HUQY6ERQTUC/TW3kpt0YCZyDyev4eeU77I2P2EXK55LzfnUhJeQsp54OQwvwt1EkviAzfV5E3pxKQlbLUH4VsXEJtgKyDvOyX57UH5vcr5nnro472yrz20mykM7S8b1Ohr543Neq47kbCcDbqZfQPQcrjQ0gQ/ZLGMtN9WF92RYJu56w7P6VWOGbK8lgRdfKfUCtZcyCFuAq1uRZ1uoNb7t8aijYfUhDupY43Pwt1rNWCHT9EAniBrCc3U8+trEKuDqWe51V5t69Fgvq8LO+pkWL+u8zznyOF5DrUEU+ODLxqgY+psu5Unf6gfP/mqMhkWa+qUK/HyDAupHBeBRyQx0chpWnGbvKjqpOL53s92vjtW0hRWRnVxyPRqOYl1O17HlQX78/jGVFb3CHP+xsyYPZCCuFnma5FkKLzZObTWdQe+5EJiZw6y/ZiJId+j5w3j+XnWmRA/Ba17YPJOZJIWdsSKSSrNvL4YuCa/H9j1Eaq9tqlcjKcdLYaU1U5zI3a+H5Zb/6S6bwSydA/UW9TsxIyiipnWeu86WmREfdj6rmL+yHj5QJU/65DisBraIPw6tr1yZBV6lD08xt1rUrvbKj+708dcr0ttZJRjexVo0YnZrmcjIzUdZBcHYoUjRvz+69lec3cw/zs15Ku3ahD57elNiwHIOX0+CzHn6HRm6o+75F5OhQ5Of6T+bccavM3IEX2VuScWyTf5ZeoLzgPydFqztVhSMZO1EjrrpkHryHDssqrWfMZa2d+Po36u1+Ri0hQh5c/kvl1O5IHU6CIkUU6kX/Nfm8+1HfOlceTo/p9HOr3vo36gnkaeTqAWjFeIH9bADm7fkO9qMkewHvN+tfLtrEMcg5fm3XgSCQXzkR934n5/V+R7nAqcqbehvq1X5COrk7uPS8ZpogM04uQwTJTluHcjXO3Q7JqPOql8auRoBupZULkOT+jXvzpDCRTrsjyngjpCUdnml9Ccudv5DYTSP84mZTXqN7e00j3hs0yzXte0UjvbNTzwudD7XVb1C/+k9p4uQfJ3VkzD+5Gyv6ZqB8+Ie/1HJIDl2R6b8//Z8nyqEYQF813eQjJmY+p6/TFqP28nvf+NerjK8fFAlle+49gHflalsHxyBgbL+/9R2Rw/wg5RJZG/fnPqevw15Du1WwXR6GRt/3y+kGoH9kDtbnFaYwIDidtw8zdRgbVldQ675XIGH4A1dmXkLwqKIKiOT1iRLaaaR3F3q6qI6gf/1v+/01UVy9F/fMT+d7roz7odtQOqwGR3ZEu8DLp+B0bP/0wo5zONnpsLAFcDecPDW00/SJqPCuVUq5FQu0q1IBejogHkMfpuPztWtTo7kUVdF7k5V0AVcrxkOKzI1qWe37UCf+UXIq41JulRv5tbiw8YW678G/kefpaKeX1/O5G1Im8jpSpu1EneG4p5ceoEXwLdTqT5Hs9ixrEQqhjuhspaDcgD+fDyAjcgVwuv5Tyn9AmwUujhv434KCIOB0J6ouRIPtH5mP/UsqTebwwUkSeRcL87lLKoaWUj5rvXLRU8hQRcQ1wQUQcVEr5PfLGj5/PnQ8pDgsjofMt6pUnv15KuaSz8h8VhDarXgZ1pCsCN5dSLkDKyFIRsQnqGF9DgvhYpAD9Om/xS6Q0LRzapuMUFG7398Yzqu01vghtWLtyPuuYfNeVI2J9pFwsBRwW2rZjaaS0/RCN7l0CTBkRm2Xd+gsS4Hehzm8GckEEpKwuTB1G+WneawrUGe2CFLR9UH04CZgpIoYUbRHwQ2CeiFgjn71VKeXufJ+dqOdxXpSv+ThaJn2CUsov87tNMm8rZWaivP7LulHlUUnpn+30OWBgRKyQP9+APKbVZPoFkEF3eCnlmjznfeSRXDAiJkVe0xmQF/zsTMO0eZ918z5HU48kDUDKy+FISVq3aMuNHtG6jQTyjA8ppeyFFJ5PUd6/ihSRzVDHfRwyMqemXuHyOqTYv47CiKfKe34bLSO/CLV8myvz7c1MR/8RSPNkwCURsU0uZ74v8K2ImKaU8i5SVg5G7XBFNKI9J1IGngHWjYjrkXHxOvCvlHv/y10HFs9HDUQKfTW38NHMn+8hD/jhqHO/qJSyEHBLIz+fQps7P4uMJ5DsXa5xb0op76M6uRiSJ5RSbiilfNZsf2QdLNrGYzxURs9Try78zVLKqcgZcyiqO5eXUn4znLyMpsynXgZ8LqScX11KqfqH5ZDM/g8aPZk98+ZM6q0FLs30bYHa9JvIK/6TUspZSHbOhIyi/yK5NE2+wzWllAOQMrV1Lg9/dz5z80zvCsiYuwbJrw2QDJsDyfXJSyn3I0XsN9T7za4a2o5jSmSUrJb5tQQqyz+jCIdXmnnT6PeWDW0AvhBqz8flaX9FbXI11E4fRAbnt4D5I6KaF3VUaLP0N5CyfAEyTtYBdomIFTLv3o6I2UPbiPy2M12hmb6Wcpw/Io5Ejo7Zsix/n2kekmW0Rf7/d9RvroGU5tVQWwhyhcPWNhnaRqQqH5Cz91/IofYBmrd3RnV+KeV6JL+uQ33Gq2iLnsVQPZ0i37sgR9Y6md7flFIOL6Xcjhx2q6F2tz4a6Zoy3+NUtBXK2/nI36H6dXAez4S2Q+hfSnm7lHJnpqsgI+CfwOoRcWz2Y48Dp0XEuch5WvVHjyOZtXdoO5k5kQG0Xb7bJpmPxyNH5X+yXX8/0/L3UkoVnr0ykpk/yLzYB8mVCZGetidyBOyd71DNZXwDjaA/hWTYFhFxXP5+OWoPnRIdtwvpHxEHIyfTJZlHZ6NR1LmRfjMw3+v/UFvfBcn+9TL/nslzNg9tjQIygjdAbW0mVNe+ifryu1EbuKeU8t+u0pnpmw5tXbF8Hh+W6Z2I3IMzIq5GdfQnqN5Mikb8foe2itoY1RsyvaW7Z+ZzdgptlfLfiBgvtDUKyNiePSKq9vGHiLg/3+fvSO+ZHUV1DUJl9M/MhwOQPvItJE+nB75RSvl4eOkZY4xpi7PdPnQ+etaPjnOjtkajetWE4H2AUzq5bl3UIKsRthlQw/wbEownU3s/ZkQV8Nh83vr5/QVdpHMAw4atnYsabzVaM1HeY5PGeWuhRjgUdR7TZnrWRx68N6nDKVdDisPayBNyD7UHcFvUgayUx3cgYdgvr/sAdSL9kSL9LlJOz0cCrFI6zqYOy1sLKWudLVAwKfWoQyCh/krm/bTI27Vh/n4S8vBdixTKS1GnsH1n9+6DOjR5vudqqPP7iHqO4hqoc3g169HV1LHzZyJPXOVZnBYpo63LzTc9d4E8rG8iRfpV6oU4NkWCbQ5kWP4ty+H4vPZO6tG3U9FIyM2Zr/Mjw/wvqOP5Lqqjd6KRyGeRInI/9RLf12d9mQEJ/Jvz+x9Sz3uaCIXGNDdFnyLL7O4s50GoLi6L6t6VqKNbEXXEu+Y10+V7V6FhXbXdyhs7M/Vm4ZV3/0fUi5gMauYvtcd0auRt/QX11h2/yHTNlb9VI0PjUa+YugLqeHo8Ob/Zvrv4fkjm86pIeboOGaWnoPbzGPDXlvPvzffeCTmVDs3zNm2c8zzweB73em5e/h0fKVu35/tfj9rf85nGSbNeHZrnP4IUtLtQne+P2s7QlvsvhJStKgxssiyP3ZEc2QeNQi2C2tzt1KMdTfkdSP49CTzb+H4DVNcXyeMFkDIV5CqaXbz30UjxOCzTvxbwSuP3WVDd3q7K717k7XLIQXE0GrmoQoormTd/5uFNqN28hBTd/sgBcwu5MTRS3PfO/89CMmdoHm8D3NV47iSoTr9Mx1D+s4FL8//9kIydBMnYqxvnXZD1bRYaW0uQoeP5/7FIJn4dtet3UX+zA+pzNhxO3qya5d5ckfhn1H3TLMggvj6PV0Pt8yQkv/ZDcu3G/H1Z6rYwAXImnMJI9B1Zh6ZHsmF81D/8AxkInyKn3M+QTP0E9aXLIn3hI+S82JHcNqbl3ltXZZPX3EG9fcBaqM0vj3SGt+m48fpq1AvKzZB16uIsh8OQfOlP3bbXyLyrolTmQzK4OQI+oLoGjfC+3njefEhePpLvOsNw8u1QpK+cmfl2JTIczsm68RIy2jbM769Gfe6WqG4PzfSsherwwdT6Rv8s90uo57LuiuTCKvnb1chJcAeSNwORXvdD6i18DkUOj5eRIfg8ah/fJPumHtaRQ1FdXjqftREyZJ/M+06G+s87UJ35gjoUeBJaQkFRu34ctf+BqM4tjUL2f9w4d7hzh6n7xMqpcDOSR5dneV6G2uvB1FtdHJd5dwIy0HvbrwzJcj8VhZa/nnlcydN9kfE+ENXZ/1BP3XmLek7xZNQDLVPkNdU6AGNk1c0RzosxnYB2/CDF9pxsZDM3vh+KBPG0yCvwdgqLtzprNEjg3Y+E1cRI8FyHvJX3ZWM8OAXL2lkR70CC7DFa9pxpufcN1Ar11NTL9Uc2uFPyty2BnzSumxoZR7M3vrs33+Fw5AHZisZ8qDxnPqTUrNb47gTkMZwQddYPIsVjBeqQmhXIpYvz+GDUsR2QDfRs5O2pYtK/3G6i8ZyBSEk5K9N3AOo8p0XC7EEkFD/N8+dGivylKAzmVfpglSU6KpETZBqrMLmbqBXay1CnvTxSnu5Cwnh/ZDjchTrSOZDw3IgeKBfU85RezuPJkGJTGTDToE7oGST8rkMd7Xr5+3RoVG5ONNL8NBpZ/H6m6Q4kTI/P+nEmUrQfQh6741AdXiLfv1I278hPtdT8Knnv+fO4MqympA7pPTnPqSbkH4DaxcRZpy7Jcl6j8f4zovCVaPnu8MzP5ryPGZDit37WjQvzHT+gDpnZnMYcsbxuFjQavg1SnBZFTolfIM/vJMgoqOTAFUjR/ijTPrQX9WpdZJRWdWkH6jlk/bM8TkahZ4uh9n479WJR+zbkz2w0FvhBSmM14f96pMjejhwLCzTOGyHllo5toT/qpC+i3ti7mpv6PpKjLyM58MNMx3mobu6KZFhr+ObuSE4d0fhuybznrchouBopAgvS+bYGA5D82ZSO2+Rs0GgPx+Z35yHHR3M7m9Y5K9WcmZOQM+IsZFj1RzL8iMZzt6NFuWh9x67yHhmij6K6fglqF7OhOvl847zrMo+GInl4MvXS6EeTq+y23HuOzL9zkSH5g5b6U80h+jZpGDTk+J+R3L6ClK9ohPnLEHxUl3+J+raFMk2B+ouXUL91C7mABWpPGyC5cis9WDSC2rGyLgpJXgcppD9A7eMgNGKyc+O9Vs3fz8/vJs30rJR5ckM+/7G8vmmsdqkcdpK/x6NRhkHISH8cyZ7rUX+3P6rDP0f9xG2ZR6dmXuyYZT5+J/ViUVTfb0QOuBNQ37cfHY3u11F9HkTLsvpIhn+bWtYsjQyn9VCo7oPA8o16vB4yTO7NvD0D6Tg/oOOqt/2onS3vIj1kKmrHWGdzYJsO7MFZj6bN/Hq5Ub92QLLiLeQ4egj1ETdQT4OZgFwELo9nynMezfw+E438rJLvUukrMyOZtB5ycm6B9JsfUYemTolk2/kt6d8WhY6+RBcht13IkQVRn3IO9TzyNZCTaDBq588i/WFuNDK/VpZ9t9MDsqxuQU6BG6lXCX2CdAAN5/ph5vEhfeFW0jFE7ei5ELXzF5Gc/x9qh1MN7zmdyUA6yu5l85k/QH3LNzLPVs26dg/qI45EffMDWf5XI0dgtafsydSOndWot5rq84GAUfEZ4wn4qn8Ydm7EUtm4jspGfTcSrKsgw6SKzV4beS/XG879F0ZK9RtZwe5CBsjx+fsApGjej5TOGVFc9USd3CuoO+Bp877VsvBzoI7+NtRJ/ZLa4HiAXIkvj7+NOp7TUQd5PPKK/A4pExfl/yciwXllvvvuaKTqoMynKVKIbJb3vRh5tcbP3x9FQnh+1KFei4Tz9kjgzIyUmHuQ4TLMpqqN/9dAXq13qVcjHJzXbpHHPwO+nf9vhRSMSRnBycC9qEOLISXlZuoRszWQsBmAvI/vUG+1MAQplt9DStYpma8DkMDanGE796ZRMyEyxDZAxuHHpAGP6u21qPOZGQnIvyHhN3Hm8zep51FciOrjEGRwXYQMuMtRh/pO1p+/Io/imqjz+A2q0w8Au+W97kZzx77eSOty+V6nkwZJ47c5UVtbJdN6ER33NnuKelGB1jlh/ZBhV3myqxHNN6iXgj4GjbbujOrzZpkH5yIl6jtV+lF7+DFqn9VqdDvme38TdfLno5DbQ5CycDf1SMmSNFbqQ4bLCHkNqZWudbNubJN15v4sm/3y9+1Q2/8NMkqey/83aN4n/58q60Pl1Z4GGWFHIaXh2+T8rtZ61ot2MB5SWi5GsmqzTOe7+fsGqI2ehdr9o2j05MJ8z2Va7jdZo3xnzDI9HimuiyEF4BDUFh5Cda8aNWqd27U2UpBOQTKr2r9qK3Iz6MZ1C2SdaC4Q02HLhvy7FmoTVRqnyfffFjlQPqFW4IYrg7qqL1mXtkPOrjeQF3tS1H7+j3qj7GOQA20RVJdfRnX8I6S4V7KpPzIOXkftaP/Mmzsyb2Zpef4cSNZ/gNrxkkgZvhE5kw5D9fNwpFhfRS2TJ8/6djgyqH+FlLcFUds7vPGc5akVsGbedyjLTvJnyqxTdyKF7mok987NOnM7agerZz3ZG8mk45DMmSXvc2BeNzFyEB1PY85gV+XTRZp2yfLZi9wrNMvm76h/fjvz4pbM/7+Q87xQ/bsd2DyPO93TD9X9qh7PixycW+Rzn0ZG3veRnjJb5vmxWUZVHV4O+H3jnkORQ/ixvK7akuQw1B98A7XLQ5GsvBr1P7+lNioD9R8/RPrDakjOv0EjsqObsqwios5DhtdGqJ1Ve6EdheTw6VmGr+X3i+ZzB1Hrc1WE0Q7U+wZPmNdVkUyXIQPybHLbgPx+j0zDECQnbqGOvpmHhjOi8bwl6WJTciQfmouSVbrcMvl+azV+2wdNZwEZNW+ifqxpxHaYp9dNnm6d79ZhRHA41wSqw9Uo2qSZh5VTcUfkpKgMqeVRPzNt1q/dUBg7tOjYI/JBcn9NJEdOAt5ppO9YNPAycZbtf5AD4iHUjmfOa6+n1lHOYjh7Ho7NnzGegK/qh2E3YK4U4O3IPWfy+DTUQSxKx06oPx0nqnflrT0LzXH6K1JsrssG/AvUqfRHndEtpHDtKr2N/yfNv6ciT24lbC5F4XLrUS/IMhVS4NdChsQNSCH+G4qx/ivycE2FOoVqlc5lqEM9lkcdw4vIWNuKet+0XVGnOQPqLCrD9gY6rvK1GIrfJq97nXpvn+5WrRqMvOeLZMO9pfHbgtQrRE6ERpg+oZP91vqoDq2FOsFvIYE4GHWwxyLF8jzUcV6BOqeXUefVDym4lfA8P8txXYb1/jXraHOlu4epF5s4m7rzGpTPfAh5usfPsl4mf18ZKaU7IaO8MiieQB39bkhx2hQ5DTZEyt6+mf5DUYf7KBKup6A6vgT13laTZjrORB3pKdSbvm5MPRowEHUcladwz8ynhfN4XRoKYaPdVfX9BODNxm/HZj2ZhVxYgno+ybTUC2zMjzqJwfk+L6P6GUhBvgl1druiel6NPmxCx9Gyh8l5jqOgLrWODByHnDcH5PHXs3z6IW/58shQnhMpNI/SCLlrya/jkSJcLX5yE6qbrQb4yBh7MyDP9plIMZgcKYbXojZZKfK3IadNNQq9DlJcJ2+mAcmoV1HH/UMkW7ai3nS9Px0N2+nofHW/OZEhczFS0iZDxtBL1MulP4KMgxfpZs+prEvnofq8dH53HfXqgpH1qRrJu4jGfpL5XU9W39wSyZQqffujtngrMr6+hdrsJKh/egE5JYai9jchGiF/B41WDkYjA3/LfFon33eWvP/QzIMXkTPv59Qy9fwsh1mQbDg98+46pMRXWy4siEb8JkZt/E5ktLyN6uZ71OHct6L2eBBqQ2ugdvc2LSMWPcmvTuTkyllPxqMO1fsGMjybEQLL5vtt2/ju7eZxo1x71DZQ3XwKGbOL5XfTonD5P6I5db+h3mv1IdQf3oOcUhOjNr48ko8bUSvpE2cZVCNuB2ZdmDCPD6XeImc+1OZ3RkbXLsiwaS6QNTUyfC9B/c9LqG1ciPSgI1BdH4qcT/M2rt0FtduLkXF5AerndkNt7ko6buEyO930y5nGoait/xQZRy9QK/h/BH6e/x9D7iOI+qkOxlLjngNRn/JU1od/IaN/EqTPXIycUpcix8kZyCG1USMPL6Ne/OyxzupGT+pmlt13qZ3w5+bzq+kI55ERW3k8d+bDHVkmG/SmXTTOn58ejAjmuZUz4EAkw3ZEjsWbkExZIt/pYmoH+8RIh2jd13Bk+pQjUHs8Aukx8yK5Ug0wLIAM/J1R31+FbM+Xx8fn8bdQH/895KSdrrdpGtOfMZ6Ar/onBdEJwJ/yeFOkEFchKWsCLzbO/zKePY+7Wha/UlwOy8b8Q+Tl3Jl6xKPa8ypoLE87nPSeggy0ykD9iHoex+XIILsWjTZ9mI22H/X8nU2RQjFtnvsx6mx2zHtsiITcS0jJrza/3QV1Ql9DHX0VCjNJPrda/nkdpCy8TUcDeVMkcKbNPDmdVBa6edf9UtBclHk2RT6rEpqT5m835/O2YPQZe0OQ0rVqy/czZf7+ERnTOzR+OwKNqAxAwv9HKYTuIL2XjXNbDb9Vsp5WIzV7UxvQSyFBVhl9W6FOojKsDkOKX/989jeRQPw+8v7NjDq+3yMv2sdoxPGHyGM5V17zNlIov5P16WnU6V+FOtZJkRPi9qw/T6CObV6kdE6MnB2/pnZaDEEdyfbISLg009vVaEfrKl3vUm9RcV4eP0I917QftYex8qjOgDr3iVBH+D5SaPqjzuxppOR+L/9vrgL623z/H2d5TN1ZOntZp6ZAsmf9TOMdyGidIH+/By2wVJ0/KMvxJWCfbu47I1LiHkHt6fhM/0qVTOtler9UhJFT5gYkGx5GhtqhyJB+LevEINQZX42UzImRorcsdUjVFFkut1J79I8Fnsr/r0SyaJLhpT3r1KvUCtwSaERqY2RE3YPk0TTIo75lN/eaARkuJ2SZ3IscALMjA6laZfJ75Ea+Pci/icl20PjuuCybzZFytUum7WLUF12HjOZHqUcWp0Bt7eIs442oV9+9tVF/LqIRlpnfrZXpOJF6r9O/ojDJxcn5ko16tB8atZo433Vfcv4QkhmVE2+nvHalTMMTjTJ7kXr/2R1RW/wuXYxk9TAv+yNjq4pG2aHl95OR4jcbUvp3QbJwd+SkqEb1Z2+5bnj9e+t86p2RnK/CeBfJfP8OGt37F/Vql48ih9EEyGD+KbUyW91/I2SUHopk76VIXm2GDOYTGvVgLnJUrSWtMyPZfUMeD0DOrE8yjYegNvpYpuEeJBfnyHJpGsTV+9xCHfmxJfW0i7tprHJIz0a2F8r3qJwpf0F1/znqdr8uMux2zrK7Dsmbm1Bbaepk/ZCs2R31B1U4/JWZP+chGXM7cqJdidrWeGh0vjm/7XuoTc1LL1ZvREZJFWa6eebdnfm++yCDf1ukNzxHyuQ8f0HUrzYN596MkvVr/u3mvGjJx0FIptxGvb3Y4aQ+jJyQf0a63D3UW071Oo2N4yFIbszekp4dqPddPBL1IztknWgOBuyI+qHFsgzOpqVtfxU/YzwBX9VPVu4zs5HthxS5/bPhXUm9PP4USLh1OgTOsJ751k08q9DGs7Ox34aU4kmRUOuRtyGF2NXImGuGBWyLRgu/jUJs7kKTwX+ClJK/ZqN4FCl75yCFZ0UUxrZsNpxrkRf3NLSK0dZI4Xw5hVMVMnUn9YTtCalDFo+jViwGk0v3t7zDZUjg3sWwc6VaG/yMyCCZs5nXSCG5D3nqp0MGySq0zDkcDfVncdQZDM661LqJ8EHkKmuN7xZFBlAVJrMZaSh385yt0XyZpVHn/DLy/lUd9UAUKncx6ryOz+u+QcfNY3+GlN1vI2F6EApfWYPaQ/w48jLvhoyID5Ciezby3i5Gx/C3HZAheCJSRFZo1IvZUUd+NmpXt1N7Sq8nFxbK428hRWPSTE8V0lO1ofFphLkiZXPq/H9t4JP8/zBkfFaL+8xCLi/feNbBSLk5HXUg4yGP8YdZNntn3lyBOuQv8p7V/J9pkbzYbGTlT8vx0kgenNpI/wFo9LTKj2kyPXM1rluX4eyXlOcNoB4RnBApZsOMCA7nHsMYV9RLts+P5MurSEn8B5I9K2YdeIN6xGoG6hGy/tSjD49mXfk6uX0Gtbf5TSSr1sm6tFAP0rs4WmmyklcHUi81v2SW+X50sodb43iJLJtFgSca32+P2tI0+fd3aJTjJjoqvN2FIq4GnJr/b4Pa8veovf4rInm9FlLQX0COpDezbgygVuYWRQpOQe14KiQDriSXQM+y+nN+vzBSmK9EDqLb0UjI7c361ajzVyFDd9csqyGoTV5MbSyNj+rn9dTLoN+ERieb84eORc6lzhbU6ZXzIa+dL8t4POrR7GrLnjky/65Dbez9zNuZUP+4WovM6a7c1qYxEoza09yZvxchQ+j5vP+Ps35U850/RjL8oMyXj5Gc6kcdHteqdB+J+t8d8rv1UZvaDMnee/N+r1BHBKyXZbZRpm971L4mp54SUO2HugAygA5GESvXIgdRP9QXXEG9NcZVZB+Q+fwI9by8KTNdl7W+x3DKbR0kMz5Fo5+/RMbnx/l/FQ3wErXxtBbS36ZskUUrorY9EdIh7gKOzN8nQ/3gRyictqqnrzXuMTDf6TuZd3egtjlB8zk9fK+qLk1KPaXidHLUMn/bEjk8Zsu8fraLe/W6XfSiHc2f5TgLkrkP03Gqxc+pQz3PQzJk6V48Z3pyy5NOftsY+FFDrlQLqA3NMvsTcuI8htrWJqidVQuRrYBGrc/saXl9FT5jPAFfhU9nggcJ0muoQ82WQ56liZABczfqEN8nhehwntFpWCKNDdKRAD2Ket7PcBW1xrVTImFb7aVWNYAl0AIajyFP3hJIUT0QdUz3IcXr2HznvZDR9iQdQ4aeo14w4ULUeWyIhPpuyIPzC2pv4tTZ0NfpJK2RabgWCeB5My/3opvY8Ux/5dVfCHVk30UG573I2zVbvssvkGCeu6d5OLJ1piXf5yeNlMZv41HP2Rmcadyj5fpjUWcytJtnB+qUr8hyXYXay35Y3uMJ1AH+HI3YzI686Wcgx8C+eW5lhK+NlK4XkMe4muj/OHXI0MtIuTgDdbyPIqW5ucjK9XScD/aTTM+ldFxhtApvu5069PM7yOCYLtNdKf2noI51uS7yY3mkPG6adfEVVK+rdNyHRianzGf8CLXt15ASPl6WxwA0uvAgUg4HUo/oPYKM1y9Q57FzPvc+pBBVCzj0aCR+OGXbmTw6hsYCIZXsQG1sZ+o9prYnw1N78exB+ZxuRwS7awvIQJus8f93kazcM+vDOsggORzJzmuo51hcgORrq5NsfzT/4trGd++RiyTk8dk0DKERSPvZ1ItbLUUdInQvquvdyaOJs/4ugdrXjdQRD/Og9j8DUqIfIBc7qcq5B2mbFCm1v0YK+7TUC0VUbe77md5+yEh7FHmvv0s9YjYVasdHZx19hXrBmAuqe+fxvsiJcCvwg/xusiyzHaDDPNJ9kbJ7ceN+syCn4CGoPV2Y5TcDcgi9h0ZLjso8ORv1FecgBW021FYfIA2O4cncnrSpxv8TZLrX664MkKysQnGn7OlzUBvaEfXF1aJUC+f7fB31c6+hSIZpsyw+QE6DK5Aj5JdIid0bhRLO0cl7zJf3GID6xbtorASOjO4Ds158AxkS1SjzkUi275dl9yLSbf6NZEk18n0iqseBnA/XZ7rWQPrAhsiwP4daVq+MnDdVO9gbGfVV/VqKxqrgTbnRnRzMevUPZIwdgRT2Z5E8rvSUufK4chR8OXKF5Mrl+V5X5bVTI8foRdQLhp2AHLFvVWWPohE2b6RlZtRu7qWbhfN6WGcGI6f8LzONi6O+ploAZkbkFJgf1d0badmbkz40WFrqXGSdehvpalVEzJlItszSkA1f5P+96g+zzIagEdhq9HBA45njI1nS3K9vqvw8QB3ltQzq047O+vorJGeeQ46sTudTflU/YzwBX6UPUka2Q16CSVJwNUfLHqT2Ts2QFWbocO65PPKM3oMU7ObKhPcjATs5Et5PZGWds4t7dReeNBVSkls3ID8cKfC/zPdqXV1zQyQkD8zjIcgY+wn1iMLjNGLEUQf2X3Lxjbxmf+RlfBV1Iq+Q3uku0jsemvR8G52sksmwIRhnI4PjHKSQ9EeKw0Goc94TCebKK7lGV88exXVmCboYTUCe4SuyrFfI+rNapn0u1Mneh4zeeZDy+006rvxaKVgDWu49NfJGT9Z6XtbNx1DHtUnLdYMy3z8E3ugkzSchBWRA1oO7sy6sjhT0l1CHeSvDbnLcnA82cX53V9ahtfJes2RdvZfOQz+rhQn2QYrCB8jgGyYUl7pDnwQZejdknkyQ9fGKfO50aOSiMja3QopDNWl8c9Q+b6b2IL+C2uKz1HMu/4I63OrdZqdWigfSWKmvl3WpWeerhUGq0ZFjqLfnaM7V3BTV+0VG5tmN+/VoRLCT6yLT8lqW+Y7I+/ttZNB/iORftWrjT7OO/heFgU/amn/ICTE1Mmz3y7pVjQJtikYb1s17vkw3WyN0k+6ps4wr58Dmmb6VOmmD1d+TkfEzC/WiU7OikehvNq67j3pp9m3IOUYjkLZlkEx9sVHXj0cyoppzezRpnCPl9Lisq9Pm51jUtl5Ebf8e1DauRKMyiyNnyoZIno6fZbk/cvpVUwNOzLp/FPWI7PZolOM5Oo5orY8MjvmRAfBWpvtEMuSYeln5H+YzF818f5cWJ9goak/NNnM/KRepHZPVyNBiyBH5Jr3sQ1B/dC715vMTIeX0H6gvPTvf8+coquIPWY9mQTK7UIcZTtDyHoFk8YdIPt6GZPWOqJ1Vo9XbVe9UXZd/x0dydm7ULz2DInP2y/v9MctuJdR/nZxldTSSj3MjA+V25FR4NfPrFaRrzIXaefPZT9DFxuIMa+h1ORKUdemDzK/5Mx3P0jGkcQk6humPh2TE1MD9jfNuRKPg8+Y7NtvtDqgvPjmPt818bq7EOl5LmfTK6EJt83DkGKkW2Dk4n1/1M4/SMGxGx4fOnY7jI9lezVmfBsmcmbL+rkdtkHWIjBiJdBwLPNzJ9zOifvx16n2rf4VCis+hjowYHzkIqnnTK2e5LzI683O0lduYTsDY+qGj92dC5L16Eimhz6YgOJuOXuWj0VDxoq33ovOlwpfK+y6GBOVbSKGeDnncdmlcvwm5clkP0r5x/m0NL6pW1/wOGvI+NCv7x3S/uuZddIxvPgUpVFvlPWfuJA3ndHLNNtn4N6KTZZW7eJdJ6SaOn3rbhmPz+GAUdtG6WMc6yBM2R0+eOwrqz9SZpw9nGe9LbnpNvaLXEOQRuxGNKm3cuH4hFHLTNHp3a3nGAjT2KGr5bRVyOw3UqX3ZoeffzbLcF8nj1rltGyCFdAWkHFQe2elQh1rNWTgYKSa/Qor1YTSWvu8kXTOgzuthZByegRwa05DbkyAvfmehnycgZaMK0RlCrrjabLMtz5sdGctTIYPyB4082ZN6VcbL6LigyjpIUR0v03Mn6jzWzrK7LvP4IRTCfAv1Sl4DMp+2pYt9MEeiXg1Eht7L1GG402d5nkIdFjsl9YIYy4+OOt9IY6v3fZqs57ch7/9rmWfPI2XqB8iAeB3Jmx9nPfoOkkVz0dGTvDBqL49lmR5Hvafefo3zdkRG1u00tozoxfvsQWNxn+Z7thxXo/ebICfGE8hxUBlF8+W73oHazEnU/UxVrgM7uW+nYf+Ncr6TlHdIWfkNcr68knk6F2rDhyOF5pLMu38j2T4UGVYnIUVyvkx7pWDejAyc76IRw4VROz6X2gHTn9oI+CjL5YT87QzU11Rh1NOhtnwz6ksvR8bm15DzswqnXBHV8+oZ49Nx6f5REqaGRrgOpR4p2I3acXt+1sGqbNdB7WyEn53pvwbV3XuRDFwxy/CvyLlxbtaPV5H8eQsZz18gfWHXrFvDbDeB6ukZWc5VXt+InEHjU29zsg+qf5VRuy8y8qpRrB9n3XkHGRNnov58fjSSXhk6K1DrElOiPuDTrCsXIpla9cuLZd3YmHqF3EpPWYYWJzbDzoVcMuvGm8g5UC1YtgVylG7UKLv/IvkxYdavPfO3GWhEUmXeX5fvviGKbKnybZF81kTIQXE9HRedWQy1rcGtz+mu7fawnixAva/lOsjoni3LZC40gvgT1IYfQP1A08Ds0z3h6Ch/VkdGabXy+eko4uAKJO9/ixwV+6J2PssoSsO6WTerwYpqusfayEF8DpIpd1Gvcv8k0lc2QX1FpdMcSjeDD+30GeMJGNs+dJwPUAn5IdngK+/ETkhZ7Z+N/ljkxTo9K/qpjXsME4LVaBwDkaKyGXUowaUMZ6PY5r1bjtdDQvFupOBWysTm1Ktr/iUbxKdIoE9Px9U1v0Zjdc38OzXqIA5OYfMCw9kMupNrnifnMY2icqrmNlYe9EnpuG3DY8iInhl5rR8lR0P6ss40vtuZ2rN+K/XE7f1pKKX5++Qtx631pYPRS0cP5RvUo6+DWurv23TcoHw16vlciyBjYfOW51TKaX/qRTrOz3ytnrMf8NPGdfdQz8PoSShafzRi3dyIeXZqr3VPQj+n7a6NNX7bj3qltk2RArRIHq+OOoFqZKZysFShbt+n3o9yB+Rd/wdyWHwTjQS8iLzxlXE1EfKEH9WTvBhOPrXWg4mQ8vkTahlyMercZkTK/tuZttdpGBSj60MnoyZopOhOJDfeyLJ/EClUu1Er+N9EMmMfJJs2a7l3NZ9qf+D0/H8OpGjOQ71AxR7I+TbMJtO9fKcqLL0fLasz5+9rI5l7Mh2VwrWQ8nk1UoBnRTJ/JUYwlDyvW4uOowj75zM3Qkb0asiIWiHz8neZ97uidrwcWohiNqRUfjfza7a819WNtnA1UtA3QCGYUyLF/GfUy71vgdrjYkh2XIgMlnVRe34cORRnQSHVzT1Yl0b96KVIaZ4ejfheRW1QrJy/30NHRX2kFwnK42WRU+tCNNf5laxH21CHoDZHh1rbY3cRNZ31CcuS86Kz3p6CnEcLIFlyCqr3zyLD7UZkbF1Bztvv4llL5nlXZ369QT11YiGkW8yY+Xk3CtlcAI12nYEcu+cgmbsiMhg/zTLfBxmAJ+b9zgHubTx796xHM2c5f4TmV66BFOnjqdvt8dRbexwGXNrF+8yNjMXKUX4sMnoWRg7jc/Idps73Xb/l+vORYfs0Cq2s5jbORx06ezsyFKpFguZCxsGijfvcn+U0OY3+pvH76Z09ZyRlzWBkmH+fXMAk/18iy+WmPG/vzIMRjloYFZ/M+/OzbhyI9K2lGm270ie/g5xNk5OrzvbiWa0Or37Uq6xWoc2vIMN796x7q2S9vJW6P18zy2y3TPMrqN28Caw5JvJxtJfbmE7A2PpJwXdpVpxFgZfy+2puxPNIgM+GDIxDkCJ7Jp3MS8tr1kKG4ouoE56EesPpam7dE0jQdxq22bhXU7GvBOO1md41UAfWj2FX15wq//+IjqtrTpJpeonGXJLGMw5EG2GeSw8Nt95c08P7Dmb42za8Ri3MlxlVzx5Ouvak3vtoj8zfH6MOe8r8fpitOGjs+TOCz5sAdYa/pe5U50Gdz9rIqHsbKW63kWFtWVe2QZ36NUhwbog8zmuhzmUDZOBsTb0h+2+QIjcACckHUef7CL303NG5Q6RHoZ/N+t/yd10ayn7W/52QQXw6OfKMOoobqJdeXwh1ED/O44WR4TRbto8fZ57NnGn4RebjwmiEpMrjU3qTF93k0UpZvwegzvQh6oV7JkVttlpVdGXkTV14dNT5btJ8BPVKm+NnHbsF+AwpqEtlfbop360fUq5eyfxtyrdAysW9+W4XICdZ1W5ObpTpFllXj+2j92qmayJkZD9IvfrgtdRhtvMhB9zk2U6uoOMUgC8dN8N55vbIqXASCl+t+qDTs75tgLznv0Shgdcgw7naAH3SzJfXkWf7O6i/mhA5wtZGivRNSCH+FVKW7slymijL8wU04vAs6kvGp169dRvkiHia2oCfgXqrgCOz3GZtvNfcSPF+HvW1D2Td/nnWnTeQEXs8udnxSJRb0xExa9bBX9BwriLn5LHkyElLOXW5r+lwnrsydRjlcmRERtbj05Dc2DnL5pdo5cuFqKMH7kJ6Roew4ZZnXAb8unF8Rd6zUnTPAn6Y/8/TOO9S4DeN4wOR/jI4y/8HqP5+RL2Y1+SofVWjcwuh/n0N5JQ4Pt9lvKxzp1M78pZADokgpxq05ikdDfJqxHVTNFex6kM3QPJgzdbrG//vSWNLk7z3RFnmH6E6vzOSN02H03XIsLoCGXzdrtrd2XNGgYwZiBwfF6MpP+cgWTJF1pGVaMwtoxuH56j4dFL3l0c6alUHdkeRbedTT0maDo0sv0EPFsjq4rlNWTsB9YjcVMgxMnVVZ5BRfHxVFmhU9nhSVjWuO496QcU1kA4wylbKHts/YzwBY/rTSWVeGHVA12dlPSu/f57G3AEkLKv5IoFGmR5GAnoGOnYwE6YweQgpMyugznWDvPb3SMBPhzzhh9GJR6mTtE+EQir3Qp36fSkc7mTEV9f8Bgo16TRGPhvc1oxA3HVvrunhfaelZ9s2jDIjczh1ZlbqeZiVwNkj83ndxnkbMpw5nSPwzMNQx3oA6gQuze/PQcL3thSCr6H5HNVI1ZzI4XA89f5INyBlenXU6T+JvGArIePpZaToXQV8v/EuT9OYyzSK83i4oZ953jDhvtThadXqoasghXkQMph+jjrzW5ABPBF1qN2x2XYqZeMM6uXI10bhKo+gdrwr6pSrRQC+Rg/a7XDeu7ni4xTUoeTHIYOzX/5/HBlKjeapvsAobmc9TG9nyvD5yHBeOv9ekWV3JpJHVQTB95AxfiS1MjcxHRW3Bajn+q2ODJtP816V4bMgGt2oRhRHeI7hCL7zACRfds+6NUHWhZ+hdndc1rVFqbfOGb+n+dlyPHfm2Qx0nIe7X5b5qajND8l8/h8Kl3ws68WsWcdPQI6b+dCIT7UFy56oL5oKKemHonlQDyHFeKU87zLqkL+bUZ+1JJINFyDjbyZyLngj/Tdnu5iTDFlteb8pqOcZL4hCcGeh40jLItRK28jsyzUeGsl8Cxlfr5Bzd/L3/mik8V4kWxcZgXvPSWP+O+rLH0SOoQvQyPwKWU/fyfp8KYqyeBLJ1iPQaMhbSM6cR13HO+ynmuctksezofD8qu/ZDLWT5ZB+MQRFKAxA8n0rVGdnzbKurlsaGYfb5XWzI8N+H9TeqgXRtgNebaSnNULlVFQXx0Mj7fei+voWLdMSOqvz+d00KHy0WiH2VupR/cmp1x6YZjjl8qUxREeDdGIk96+kXoVzYjSKfU713QiU/yg1urJ8js+0/IV6i6RhpguNqmd29k4tx01j6oSs41XobuWI3CnL56Ks60N68dwZW47Xy7rzPGq/kc88t3HOGchB/WekVy+OjNK9kPOicupexXCi09r5M8YTMEZfvmWULP+eTO0ZrUJW1kad0etIKN+EOtRq+fNBKKxmk5Z7NlcMOoGOXrhDkXCdEoVcPoWMmE73cmqkr/JqL4E6jtOoFb/V6OXqmmO6LEaw3AYzgts29EWdaXy3ATLAqrKZECmq56KOdzHkdX+JbjaJ7+J5nU74TkFbzdeaN8t61nzWgZkP1byeozId86GOfyfUoWyU9XqSFJC7I2OouWz/YcAx+f8maC5JFerb13MFugr9XJmOi2bMhoT6/vn/zEhJXqLRBp9GIVP9kHK+cOP6GZDRW7WxPYD38v8pkVL7jTxeFhnMmyDF9ApGsgOhVsQqo2dQlscC1GHBB6AObXqkyH2n+Vway16PiQ/1CHMgR1O1aEi/rFOLI2fT9eTKatRbspzEsPvJzY0cKC+gcK5qhc2lkCPudaRMbodk2uGj6T0XRV7j8+i4tcdNSM5vhUa6ViW981W+dNdm6OggnBYpOYOQ4nsXcswdi9rxbUjBvx613RuQY21OpBhXYU0v5Gdt5Hx6EhnYW2Q5VQbyk+T+WHn/M5ExdD8yVCbM69ZFsuO0TMtq1CGZs1DPJ7wpy3u5zKth5ps13zvfc0vURs9ryYteLXjRms/I2L0YyYWqnW9Go6+glhUL5HsMHYHnHULH5fk3pw4RvxX1x4shI+WDzLv3UdtYCzlcH0Ny7Jn8f3GGnXe2NVJ8z0QOoGq7l8PpuOjITcjQrEb5FsnrTkT95MUozPNA4L7Gdfui9juUus7Okc/bp5FH16F+p8Mqjfl3OuRUWBg5G76DHN6rDCcPV0ftp1rI6NgqbWgk8RUyFDrzby+6CHNtpqeT70+hdsQsS2OhkfxuRCNt+mQVTNTu1keG76Gj45ndpGUT5GirjKkrUT/7LLVT4nEkm+amEcnUi+dUixmNj2TbHcghtRzSe7ZH/eBv0CDGiUgWnp7fXZXnvpj1+2zU/q5FsnmEdLB2+ozxBIzpD3UY5klIWTyDejRkkqwwN2TlmxcNAR/Yg/seiUJUjkId4pRoFKES0LOlYK0UulnofLSiU88REvCVQj8jo2B1za/Kh15u2zCK0/BNNDo2C+qsH0DKxHloRG1b1EkdkWk7qTd1s/H/SsgbPwVSkJ6noxf8ChRmWU3qvotcMAQZd7cj5bga9YoUli+iEJ2dU0g+QmPbD+SYuDSffRlS9vrEmO5BmVehy5VSshBSKH6GwkdOyDo9CxrduASNfI9HveFwpQDtihTh8dFIRbUZbNV5/Yp6sYh9aWytkm3rI+QRHjqS7zV5lk0lc5ZEndPRaLTjJqSQX0PHcMBDkfLYpTLdh2XRrJeDUAd7O/U+X/fTcb7WSVl3Bjbe6y26Vsi2p57rN0Xmx/bUoVcnIKV4VaQMjDZjlzrUfzCSu7Mj5efq/H0LFCmxF72Ya5bt8KW8x1XIcFsEOSKreWZ/zDw+CvVfeyLlfoGsPx/lPX6LRtgGZ74/0njOBMiJsAIyCremXh3169RbjjyGlKtvII/9T6lH+qqQzOeoQ+DuJ+dGZZnv0NqOO3nn5ZBRtPIoKqPWEeIZ8v/dMm8my+MZSWOkk+se60l66GicvoT6hH5IZjyTefMw9bYEuyKl9FNkRFfb3lQjbhuRm8rn9815Z8cjg3AupAC/gmTfDEgWPUo9ErRB3r+67jjqTdmfRAryFEjH+RG1cToTnWwQjgyxq+hhRAfqa95G/cvXh5NvU6LRmCdQf/kcddTEq9TTJM6gUYdHon5Ml3m3BIrMeg3Yq69lx0ikd4xs+I1k++bIyTU02/VVyEh+EzmALsy2fiGKKBrh/fQ6ee4k1CO7pwK/avy2M9Kxpsnnn4v0rR+jvvQYJJ83yvZ4Dur/1xqby3i0lemYTsAYfXlV3GeQ8r45MoQOQopUtXz/zkj4D7MUNOpsO4Q0ZWW9i5zEm0K4mlu2JfIcVsJsLzSaMNxQLKRYnE4dN700fbC65lflw3C2bRiFz2kNa1goO6YzkBL6CfLELZGfCfL7SxvXNOPtR9R7OB3qPJ9BDoPLUQd/NPBQnvM15DUuaGXCflnXLqEO+9kIhRg1jcRjkOJRhYMOQArJRdSjxvMh5eVxhuOhHQ1lXoW0zYCcNPsiw/+gqqyolw8fiBwCNyPjYkek+C6e7eTpPK9aifB5GqusoY7tV3QRipf5NNIjnEheVCNCj5PhtvnbkmjuykKN87dFCvIUjGZjr/V9kTK9MuqUV0POgrVSxjxI7Y0/kzokrx8ti6B08pzF6RjOtXPW5cXyeE6kpI3Unoa9zIOmkXM5Mkar8Mcfona6Qnf51siHZr8xPuqH3sl6Wjk2rshnnoba+amZt4dRLy0+ARqxORN53L+L5NT0WadWQKFqt6Ookj2QwnsbUorWrNoXMiCqVXAnRMZ1tbLtMG2BjiGZCyGZcjBdbADdXbvuLr96UU7TI2fAw0hubogcYRdSjyBUK4v+lHruUT80uvoaPXTmIMfRZVkuH2f5rZd5/D6SM++iUcDtUb+8UuP6U+h6D9GJ0LzMP1CH126W5bYqCkuv9gPcjpx72LiuWkBlVyQHX6BhhCOlfhsao4Mtz69G7aZEzowuF7Hp5NoladnHjI6G3kCktE9JKuNZN/8NfCuPN0L9+yCkT23fTNdI1I8qdPZlMuJpbP7Qx/P0unjmAKQP30pHY2rDzLfvIv3kLHo5Z7q1HJGcehYZcZMh2fYM6QRBsv801Ic3HY/HZbvaEkU4DEFRIE8yBhzUY+tnjCdgjL68FMfFkWft+0jBOwuNnt2LFKvbkQJ8Ih2Xt2+dYDx+4/95s7JeiYzFz1AoUv/87uw8b7jhPXm8Xwq9PdFo0XH5/NkYxatrftU+DGfbhpG8d2eK2qqog58ZebyfoTGPACnBtyHFYhB1+NBwQ5PoPFz0LeCJ/H8S5NWv5pU+g5S855DhX4Udjp8C7yTk3a3ScFLWzZ2yzvdHK1fdQe3gWAg5Aw4b02XbRR4tizqgB9EcsOuBB/O3gSj86C6kHEyKwoSWzDwZjEa9K4/+ollOm2S5vUm92fuhSAZs3V19GEXv9CZSFPfN9FxEvX3GvaiT3QYp/8/TYlCMgTJYCBl3zyNjtVqUYb2si4FG8a5HyvR99HAblsYzzqSeLzoo86TDXL8x+P5NI2dhZKgtT2NblfytqxHMZr8xZ6PtrQv8nVqxnxsZbO+gkO2fZr6viIyIFRr3WR05F2/Kej4zCg29DDkKJ6dejfMa1B+tkHVqXTTav062rduhQzjftl29Dx1DMn9KHcL6Dp0s/jWcfB2Vc6AOp16qvZpbPz1yTPyQeln/Kcn5wI1ru9wvk2GN0yFIDm+Yefw8MsrPRX3v80iPeBWt8DsPctY9Qb365K10MReNjvPOJs3vTqWO3NkZybTFu7luPNRn/ZB6JcV+SM73auXEXpRHa2jq19HI0NVIXkxBHYa6EjK4qzDuJ4H1+iBNS9FmG2v3Udl1ZUz9GDkWehNy3dX8wA2QE/cB6jmbm6dsqfSYrVBEwgA6ytJLsz1dlceLMZIb37fbZ4wnYEx/UFjHgymwp0FG3/aok/x+CoVNgPO7uH481KneSYZn5vfnUnd+F1CHjK1ArZi3Vvo16KhgLkw9WjMB8n68Tx+vrjmuf1qEyBQozKTaTPQ4FG72RDO/s5w2TSE4whsDtzxzEertETYA/t74bRk0cnU2Gn3bGHnbnkSjWZeg0ItFkQJ3C7B6XrsiUkBuy3eoFib5DmlE5vEOSBEcq0aAs60+nO81T7a5Y7LNVntKrQhc37hmAFJs7kSG3xPUy8pPjLzl12U5z40Mva3Q6MbVjODy+b14pwEoTPwXKJxqvWyrq+XvU6HO7VIaC0yMxjxvVXD3z/q2TKb3waz3lYH6Peol3Geii5GLHjx3GtR5V3V0QzqZ6zeG6mHrvLNz6ThyMdxR/Kx7lyAD+UbkzJsWGccXZf07FkVuVAurrNe4/lLk0FsbOWwCOf2ao48P5v2eIfuLljRMiRycd6KQ0avz+r8yAsYanYRkogiUPlW2UF+6YuN4dupVfY9C/fJT5KJBLe98eSf361JxbSnfAdThxfMjx8ZEKOxtCRQ1sTWSVTeg0dcj0YjbFnndamj0pEfLwWfdr5bkPzDryO75bqd01S6QcXhV1tdDqBdJejHr3QiNkneXR8O5rrMtEa5p/D4j8KPG8RtI/xqPHi545E/ffRjWmFp0VLVvOs4PXAqFbM6XdaRaGPFe4Dv5f6fyFcnP05HztE8X7vqqfsZ4Asb0B8XDP9U4fhMpwJOjTncHFI5RbXPQXPWpWq3o9BRkj1PPf7qEejPQ45DRtehw0rIZintfEHXirzAGVtccVz+Zl1M0jlfLcngw83tbpID/g47bApyGFNLx6GQhoBF4/hLIS38HChObHylTb1GHHu6Nwsd+iRSJLZACsi5SwifJ+ntM1t/VUGd/FJpfU4XW7Qy823juI3nN6Wi0YIoRSftoKp+Zkee3WnFrbWTMXYjCQC5Fnsdq0aXBSCE6CinD9+f7/ZM6XHAO1MF8O48H5Pu/kPcbLcoGMjTvQaOSJ2aahzR+75NR7E7SEc2/VT2mnge1CopYmD2P90OhPVXHvDgyCKccBWnZA3hrTNe7LtLW43lndD5yvwP1ptQXIYfdHEi+/xZNK9gn6/uyyEkzRZ4/WZ7zIJL9ze1pWkMsj0YG4zYtz++wCFijbE9AfU2PlTkYZguNUbIheg/q6AXIoFkJOdoeRX3kVGiE+Qky2iXP3xgZ2nPQ2B5iBJ+9EVpB82o06j5plt11KTPOR4bdI5m2Y6g30V4X+KKXz63mnS2Q5XQ3uWVDD657FTkRhyLDcY+e1NtRXGYT0fmWCNWc8qHIwDsN6TzX0jDm/RmzH0axMUX38wPfQYMdB1Ev2rMoXWx31nLf8bGu23X+jOkEjOkP8vhdirxOjyLjrAob2gQpg7PQeXjfLWiouVpValVkHA5ARtk1yIA7gVSYhpOWQMrrK8iAGJTC+U7aYHXNsfmTeb8n9cp1+6BQmWq1wU3Q6MUQFMJ1NzKsnkYja9M17tWtwkPnoVHj5/2r+TTvoLknRyPl4i/UI1BLp7B8i9wKAinl/ZHC/TAyDGeinsO3IFIQv5x7hhSVA/P/jZAHdv3e5N9oKqPJsz2u3/juxSyHVdHI30yN36ZBBt8CSCH7Ubapg4FnGuctRa2UTQrswnAUqT56v5eQE2nZTO9onzNJLtjQqCO7oVHhq5BzYSJk4F2Wv0+WeXwg9YI43e5dNQJp6Xau3xiuiz2ad0ZHg2ou6pGmPVG/8wgyHOdCCtVOqM/4GBkMMyNl5yd53SBkbF5Bbg7emg46hlieS/cjV/1RqGc173vHkciT0T3HaC40unYN9Ryfq5BRswEaFT0MhbFfne83e+P67vKl6djtl+VwQ957yZQ1jyBl9b5su4tlWX6CDM+5M33roRDLao7xl6H+I/i+1byzF4FVR/C6d/K60WroNdLQ1ZYI1cJzg1Af9T1yDrM/Y9eHUWhM0f38wNdQSH9kfe/TKJtx6TPGEzA2fLLCHU2LR4lhO/Vq4ZRN83g25EVcknpBiRup9zhagh54JVqesTDwX3JlK9podc2x8UNj5IR6JGxoHn9CvfLgUGToVeFq8yPDYYQ6UGSYbNI4XpR6WfBJ0EjxK1nPHsy/JyAD/36koFUr+e2IFJCzqDcr/ykyBk/KzvUm5DCYONN7CjBHPm8pZNT2agnlMVBW1UIWV2abq5aBP4RONk/N/HyERihVfj89WhRk8zH9Ti3p3QgtvjMJLavujqbnTw/8pXG8OlKmI+vOm8gYnRUpj9VeiNsh+fmVqEejOM96snH67MgxcxdyFM2HFux6ASk41VYPd2XeVgtyVYuybJRyYN285mgaXnaG7ac6HX2k6zmF8yGDfawNg2rNZ2Q4LYhG7V6ljqaZDPWPi6JRrdMyz3u8SjIdwzebi5S8h6IqAkVPvJ1lNjDl6ido65TLkMNmPKTUXkEjImQk82GYhVD68ro+KMfhbYkwWiIZ/Bk7PnQ9P/An2a5GifPQn8zvMZ2AsfFDJysiMezCKVU4znHZCVSe27nQSme9XhkIhTS05eqaY8OH3Oescdw0+u4GTsj/dwDeb/z2dTTxfRgjvrW+dFOvTkQrfG6KQt8eI733jbLfBXnAnkThh6ejUazK0XAtuVceCgd7GRmSU6LQxSEoLHS6POcy5DmdAHmcN6UOpdkLGYNj1QhKN3k4ATL6HkYGyNbdnNsPGcGnt+Tv0bQscjC2fMgRrTH4/MvIPZ+QofHdlD/P0dFoPgx4fkzn19j26UxhzTpXzd16C0VxVIs7HYscN68gh9565Hym7EemQaGDX6B5LEs27tvpQlD0waqXYzA/W+e5V3Opj6Re1OfqzMPKcfZdGgYeDaWxNW+G8+wTshz2Rc7d1dHcspXz99OzzLZDESDVHKM1kDF4YMqr0bIwylfhw1dsSwR/Rkud8GIro+nTD/MlIdZA+zp9kd8tHBH90LLIX0NG37LAshGxGVLE5wBWi4j+pZSfoU2dPx2JpJwGLBoRB0fEzGip8wlKKTeVUo4spfxmJO49ThMRq6CwxrMion9E7AY8EhGH5ymHAStFxFKllGuB30fEUfnbW2g07ZHG/QKgqi/dPDeKpNntyBDZAinWKwOfNZ7xLzQX72a0KMMP0IjWP4G5sy7OABwYEVug2PpvoVHhW1C47+ZIyfsk07Y78vpPjYzI9fMelFIuLqX8Y3jpH1sopfy7lHIBGuVcpJRyYzfnfoE6k4ki4sGIeAkZxdeVUl6EuvzGFkopl4/hsjgIOCoixkNbQqyCZM9SpZSbI2L6iJgV5evFAFknx2kiYn2AUsrneTwk/06C8nHeiPgpmjZwRCnlDRStUdAc64nR3OD1UXv/HwrjXhn4M1q8Zt1SyvPZT0VJWtNSSvlfPrtfHn8l2nZnNN8vInYA3o2IlZAc/XNETIucW+sAe0TEvCis/fG8Jkop/46Ifvn//1qf0Vp/s184FTnd9kSOvpNKKQ+jEcR7I2IZZKy8gsI950YyFrRi6/3AL1JevTQKsqItyD7pbDQCegOam37xmE2VGcMcjyLl1oqI8UopL5VSXhjTiWpHopP+YpwmjbiTqMPiZkWjMD9GKwmdRIZ/opCSvdBiK7Oh2PPPR1E6DkSC8bvAa6WUq0bFfcdVImJN5E3/PSrPZZH39SM0qnco2m/q4og4AY2O7RERSyHv04SllP9r3C86U7Z6mJbt0IjxCaWU+yJicaTkbYLmkB2JFpDZu5RyTV6zBlIGq33ljkFzU45H3uSLgXtLKTelAvNbNAfsjbz+CmSsfgTMV0p5tjdp/yqSRt0cqAxfrb7rbfm1OxGxFxqBOgwtjPFuKeX0iNgYyb8TSym3jsk0jm1ExAcoHPxZNHL3AVp18JyIuBU5W3Yvpbyb529W5WFEDEbhS18go28+1P7/AvyxlHJz4zn9OzNa2onqHVOO9UOy+VwUVn8umlM6AZJxj5ZSXoqIc1AY5y+AF3pqREREv4Zzd8JSyr+yPC5CdX831M9/gBbtOhCN/B2GQuL/iUaoSkTcgUZn30ILR/195HKifcl+9ZVSymdjOi1mzBMR4wP/K6X8d0ynpZ2xwddCKocXIG/dCWhC9k5oZakp0GIFN0TE/iiU45JSypV9kI4JUEjVrW4EI0dEbIKM9OPSwJoIddbXo4VwHo+ITdGKllchA+o+4OJSyo0RsUQp5YWeGglNJaLl+0GllM/SK30w2lz06lLK3yPiQhQWvHVETI0UmytKKY82FKBj0Qp0ZwCfVGnJ+z2G9uX6NL87GDkivonmb6wHbFhK+V0vs7EtyPYdX+VRj74mFe0PkAzsh9rO1Ej+HVNKeWIMJm+soWl8pYw5Ec1H+QmKCDkSjWT8CbXDc1GY5gkoVHNb4LdpLPRDRt9GwAHIcDxoXHJKdCM3b0ZzGq9Cxt/xyME1E1pd94aI+DoKF7ylYcD1VF5PiqZKTIFGnR5Dq21ujIzKK9C8yv9DET13oTLaDDnhtkejjBOjVX1/39s8MMaYvmKcD8VpJTuIy1B8+Z/TA/UDNGeoP3BshntsDJzSF8ZepuPfpZQbbez1nka43tJoHtd9qVT8E3mJ70YjuZRSbkOK2drAv1HI2r/ztxfyb4+Ur4bCsURELNT4vvJm/gWNGM+Gwn9ACuFfI2LCTMc7wOoRMWUae/ugcKI7SykfN4y9/sjL/BAKOa6e9R1yDz40ErjZuG7sgcrQxl73ZP5sBtxYSnm/lLIrGm1e3sZeTbbLARFxOlrB7tdoIZt70aJKV6GR/GeQPFkr/75eSlm1lPKl0ybzfAk0yn90KeXARhsfq8KORzWtYfERsVlE3BsRx0TETMixOjla0GwutGr20WhRlMPy2idKKd8vpXzRCGUdRl6nvGwefwMZ5T9DYZhboEWgPkIjrseVUs4iNwJHo31vo7I+B0WMLFdK+aKU8jcbe8aYsRWP8HVBhojMUErZPI9PQfH6A1HHc1HxXLqvBBHxEHBDKeW6iBhQ6nk2CyGv/PmllMdybshuaF7BqyPxvKmR02Ai5CF+Fq12OAFaxfVttNjPofndWaWU37bcYzoUgjobmm/3IlrC+g+dPC/QNgxLodDQP1CPTl83Lo0SmFFHRDyNDL1XxnRaxkYiYlHqzdMPQ6N2jwMrlVJ+HRGTo3l6/y6lHJLtdIJSyr/y+g7hmZ0cdzri1U60hFQORnOQlwGOAA4HPkdycGIUgbEf8FAp5cCIWBn4ZynluWo0r7tRvZZnzYhGDb+NtqKYOr//Olo59QO0cuAgajn+E+B91GccU0q5KiKWLKU8P8ozxhhjRjEe4esaL5zSPjwILJIhlZ9HxMD8/rcorHIvgFLK48AhI2LstXqMk28AD5dSVkee/w1R+NFWwF2llP1LKX/NdN3XNPYa3ulPkHJzKNoaYqdSyh868/angnMF2hNwf7SB7cOllGtt7JmRYAUbe90yGDkBj0bG3j/QSFC1ANRfUIjgeDly369ojlinC4iUNlpspafkiNzAiPgWilB4FEVdrIbmWQ9Goa/vIeP6U2COiJiolPJYKeW5vE9p/u3mWfNHxN1o2sYg5CD7eWjuPmikbya0GueTyCj8ETL0FkLhnr9C4aPY2DPGfFXwCF83hBdOaQsiYjm0Et7DpZQ7G9+viua3zYTK+Ze9NZAiYk/gT6WUWyJiDzRnblLgXbQq3x9TSflnL+/fo7lnOR/ln2UULR5kjOmciJgbrZA7EzL0ZkBbJSwGbFdKeXBcGKUbESJillLKrxvHG6Pw4beBk3OUbnMUWr8zWiVzMzRX76GImBKNmP6rF8+eBq3ueVkp5br8biAKGd0W7W3794h4As2/fKYRVrsisH0pZdfQSoL/6XUmGGPMGGDAmE7AWM6lKEbfC6d8tXkGeWcPjojP0UjYHmg/ulNLKd/u6Y1aQ4ZCS9Rfj7z532qcOjFSUu7N8zZEowG9MvjymcM1RnPk0BjTx5RS3g1t6/JFKeUvGSK+JpqfO2OeU4UQjtOGX0Nu7hMRj6ENyqdB/esWwFINuboi8FkafwVtOTN9RlP8JedOdrlaaWte5zy9fwC/QSOF/8gRvTnRcvA/Qnuu3hsRf0LzL18A+kXELGhF5IWR4xfAK0saY75yeITPjBPkCNk+aAGXGdE8t0NKKR/k78NVyDpTMiJiA7SQwFmpoEyI5t3tiuZ+XIzCLBdCXv83R+2bGWPGFBl+OQAt4nUAci4d7FDqmui4mum2aKP0F5Cz7Z4cUXu+lHJonjMfWgnz52iO86GlB/tydTZ/L0fw9kfzKteLiGOQLH4GhYyOh0bzhlLP3/5p4/r50EbqF3tUzxjzVcYGnxnniIgZSikf5f8j7HmPiG+i/Z6eQqu27Yvmg7yOVtk7EoV4rZ2/v19K+eaoewNjzNhChowfiBbyeqzxvfd6TCJiALmADZoPf38p5cT8bXbgZWCeUsrH+d1iwMwtIfjdLcjSNConQwu9HF9K+WdETA+cB9xTSrm2cc0QtIjWTmgE8JvAeKWUfcb1EVljTPthg8+MswyvU+8kfHMhFNbzLJrUfzowO9ooGeBNNN9kuVLKHnnNoJLbMXQXhmSM+WoyLq6uOSI0VjP9KXKGDUGrma5bSnknz7kYWKiUslwn1/dIbjaMyleAo9AWNt/MUNDV0WI6m6AR2SOAdYBLSynnZQTIksCMpZQfjNwbG2PM2IdX6TTjLMMx9vp14k0egpbxvgBYGe3FNLiU8kKGHM2ONuL9PCIG5T0+i8TGnjHtx7i4uuYIUq1mehyat/dvFA2xD2iEr5SyF/CniBjcuhJxD429RdG2CROhhVk2ALaJiNny+vfRisn7o/mDHwErllLOy2eUUspzNvaMMe2KF20xpkE1qpdLeE+B5nX8oJTyczTnYwvgj2ghn53ymn7Uc3huKKVc2rynw7qMaX9s6HXJ74Cp0OIozdVMl4+I29H2R8uXUtYbiWdURuVRaMuEv+ezDo2IffP57wDTo0V2zoEvt9X5wjLaGNPuOKTTGL5cye3pUsqf8ng15IGeEO3X9zDwANqDaf5qafGIOA2FeN4PfN709lsBNMYYSOdZczXTTYA7gSGllIca5/Uq7L2TLTJmRKsaf4bmV88E7FFK+UnjGs+xNMaMM3iEz4zzZAjRjCjUh4jYB83VW66U8tOI2ASN4D0InAFcEBE/RxsFfwCcX63gViksNvaMMeZL/gIMiIgtUVjl88CrjW0rKrnZq7D3LrbI2Ao4H5i6lPJqdW7ljLOxZ4wZl/AInxlniYgBJTcoj4hBwCHATaWUX0XEJ8CRpZRrI2IosD3Qv5RyfETMj/bberm5Kp8xxpjO6Wo101F4/+YWGZVReVCrUTmqn2uMMV8FbPCZcYo07BYspbyYx02j727gxVLKCRGxA1rWe7b87evAwWhVt/ta7unwTWOM6YbRsZppXxuVxhjzVcUGnxlniIhVgHnQ1gmrATsD2wD3lVLOiIh50PLhh5dSnouIZ4AfllJOyz2b5gGebWyz4DkgxhgzAvSlg8xbZBhjTOfY4DNtT0SsiTbi/T3wGFptcwK0NPfdwKFoJc6LI+IEYLpSyh4RsRTwDDBhKeX/GvezoWeMMWMpNvSMMaYjXrTFtDW54MrRwHGllPsiYiK0Qfr1wHmllMcjYjCwWhp4ZwP3RcTWpZQbI2KpUsr/NY08G3vGGDP2YmPPGGM64o3XTVvS2Lx3aeD0NPb6lVL+iSbz3w1sDlBKuQ34E7A22hT40vxLbqhuI88YY4wxxnwlscFn2pKGgbYIMH7+3y9/+wdwMTBdRKycvz0EzIX22LuhlHLHaEyuMcYYY4wxfYINPtPuPAgsEhGDSimfR8TA/P63wIfAXgCllMeBQ5r7NRljjDHGGPNVxwafaXeeAcYD1gEopfw3v18QLdryh4iYLefo/XYMpdEYY4wxxpg+wYu2mHbnGWAh4OCI+Bx4GtgD2BQ4tZTy7TGZOGOMMcYYY/oSb8tg2p5cwGUftIDLjMAfUPjmB/m7l/A2xhhjjDFtiQ0+M04RETOUUj7K/23oGWOMMcaYtsYGnxknsbFnjDHGGGPGBWzwGWOMMcYYY0yb4lU6jTHGGGOMMaZNscFnjDHGGGOMMW2KDT5jjDHGGGOMaVNs8BljjDHGGGNMm2KDzxhjTFsQEU/38roNI2K+UZ2e3hIRk0XE3mM6HcYYY9oDG3zGGGPaglLKsr28dEOgU4MvIgb0OkG9ZzLABp8xxphRgg0+Y4wxbUFE/CP/rhQRj0fEbRHxTkR8LyIif/t2RLwVEa9FxFkRsSywPnBmRLwSEbPntd+KiCeAAyLimojYtIvnPBERt0TEz/Le20TEcxHxekTMnucNiYjbI+L5/CyX358QEVfl896PiP3zEd8GZs/0nBkR00XEk3n8RkSsMNoy1RhjzFeeMeG5NMYYY/qaRYH5gY+BnwDLRcRbwEbAPKWUEhGTlVL+EhF3AfeUUm4DSNtwslLK1/P4mm6eszAwL/An4H3gilLKUhFxALAfcCBwHnBOKeWpiJgZeDCvAZgHWBkYDLwbERcDRwILlFIWyecfAjxYSjk1IvoDE4507hhjjBlnsMFnjDGmHXmulPIhQES8AgwFngH+D7giIu4F7unm+pt7+JznSymf5HN+ATyU37+ODDmA1YD50pAEmCQiBuf/95ZS/gP8JyJ+D0zT2TOAqyJiIHBnKeWVHqbNGGOMcUinMcaYtuQ/jf//BwwopXwOLAXcjubtPdDN9f9s/P852V9maOigLp7zReP4C2qnaj9gmVLKIvmZoZTy967S2ZqQUsqTwIrAR8D1EbF9N+k2xhhjOmCDzxhjzDhBREwMTFpKuQ+FWi6SP/0dhVR2xa+AxfP/DYCBI/joh4B9G+lYpOtTh01PRMwC/L6UcjlwJbDYCD7fGGPMOIxDOo0xxowrDAZ+GBHjAwEclN9/H7g8F03ZtJPrLs/rngN+RMfRv56wP3BhRLyG+t0ngT27OrmU8seI+ElEvAHcD7wBHBYR/wX+AXiEzxhjTI+JUsqYToMxxhhjjDHGmD7AIZ3GGGOMMcYY06bY4DPGGGOMMcaYNsUGnzHGGGOMMca0KTb4jDHGGGOMMaZNscFnjDHGGGOMMW2KDT5jjDHGGGOMaVNs8BljjDHGGGNMm2KDzxhjjDHGGGPalP8HsCLIjdqqeA8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.set(rc={'figure.figsize':(20 , 20)})\n",
    "g = sns.displot(data=df_instr, x='instruments', aspect=2.5)\n",
    "g.set_xticklabels(rotation=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "strange-creativity",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "spiritual-marina",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_notes = [notes_all.count(note) for note in notes]   \n",
    "df_note = pd.DataFrame(list(zip(notes, count_notes)), columns=['note', 'count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "radical-moore",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>note</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A3</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A4</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A5</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>As3</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>A7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>As7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>B7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>C8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>E8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   note  count\n",
       "0          128\n",
       "1    A3    298\n",
       "2    A4    272\n",
       "3    A5    203\n",
       "4   As3    330\n",
       "..  ...    ...\n",
       "74   A7      2\n",
       "75  As7      1\n",
       "76   B7      2\n",
       "77   C8      1\n",
       "78   E8      1\n",
       "\n",
       "[79 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_note"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "million-serial",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['', 'A3', 'A4', 'A5', 'As3', 'As4', 'As5', 'B3', 'B4', 'B5', 'C3', 'C4', 'C5', 'C6', 'Cs3', 'Cs4', 'Cs5', 'Cs6', 'D3', 'D4', 'D5', 'D6', 'Ds3', 'Ds4', 'Ds5', 'Ds6', 'E3', 'E4', 'E5', 'E6', 'F3', 'F4', 'F5', 'Fs3', 'Fs4', 'Fs5', 'A2', 'As2', 'B2', 'C2', 'Cs2', 'D2', 'Ds2', 'E2', 'F2', 'Fs2', 'G2', 'As1', 'B1', 'As6', 'C7', 'A6', 'B6', 'F6', 'Fs6', 'G3', 'A1', 'As0', 'B0', 'C1', 'Cs1', 'D1', 'Ds1', 'E1', 'F1', 'Fs1', 'G1', 'Cs7', 'D7', 'Ds7', 'E7', 'F7', 'G4', 'Fs7', 'A7', 'As7', 'B7', 'C8', 'E8']\n"
     ]
    }
   ],
   "source": [
    "print([note for note in notes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "metropolitan-brake",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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