erc721__masters_and_cyberpunks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Masters of the Great Web and Cyberpunks. ERC721 Analysis"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from config import PROJECT_ID, INITIAL_TS, SNAPSHOT_TS, \\\n",
    "    ERC721_ANALYSIS_DATASET_NAME, ERC721_AMOUNT_TABLE_NAME, ERC721_ANALYSIS_DISTRIBUTION_TABLE_NAME, \\\n",
    "    ERC721_ROW_TRANSFERS_TABLE_NAME, ETHERSCAN_NFT_CSV_NAME, ERC721_NFT_TOKEN_TABLE_NAME, ERC721_TOKEN_TABLE_NAME, \\\n",
    "    MASTERS_AUDIENCE, CYBERPUNKS_AUDIENCE\n",
    "from src.utils_bigquery import drop_table, create_table, get_df, create_table_from_df\n",
    "from src.utils_charts import grade_boundaries_analysis\n",
    "from src.extractor_nft_token_list import extract_nft_tokens\n",
    "\n",
    "EXTRACT_NFT = False\n",
    "DROP_TABLES = True\n",
    "CREATE_TABLES = True"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "min_number_of_tokens = 0\n",
    "\n",
    "erc721_tokens_manual_grade_2_dict = {\n",
    "    'ENS': '0x57f1887a8bf19b14fc0df6fd9b2acc9af147ea85'}\n",
    "erc721_tokens_manual_grade_3_dict = {\n",
    "    'Gitcoin Kudos': '0x2aea4add166ebf38b63d09a75de1a7b94aa24163',\n",
    "    'LAND': '0xf87e31492faf9a91b02ee0deaad50d51d56d5d4d'}\n",
    "erc721_tokens_manual_cyberpunks_dict = {\n",
    "    'Unicorns': '0x89205a3a3b2a69de6dbf7f01ed13b2108b2c43e7',\n",
    "    'DRAGON': '0x960f401aed58668ef476ef02b2a2d43b83c261d8',\n",
    "    'Cryptopunks': '0xb47e3cd837ddf8e4c57f05d70ab865de6e193bbb'}\n",
    "\n",
    "nft_tokens_without_approvalforall_events = [\n",
    "    '0xdc76a2de1861ea49e8b41a1de1e461085e8f369f',\n",
    "    '0x7f556e211a3e4b57d005d3aa49a31306fa8bb34d',\n",
    "    '0x772da237fc93ded712e5823b497db5991cc6951e',\n",
    "    '0x9ab3ada106afdfae83f13428e40da70b3a22c50c',\n",
    "    '0x729cadcb048d96dacf4133d4418e57241da6a37a',\n",
    "    '0x79f75e9f93f89d33c20573dec03710c6d9ec538d',\n",
    "    '0x6ad0f855c97eb80665f2d0c7d8204895e052c373',\n",
    "    '0x07cdd617c53b07208b0371c93a02deb8d8d49c6e',\n",
    "    '0x06012c8cf97bead5deae237070f9587f8e7a266d',\n",
    "    '0x7fdcd2a1e52f10c28cb7732f46393e297ecadda1',\n",
    "    '0xd2f81cd7a20d60c0d558496c7169a20968389b40',\n",
    "    '0xf7a6e15dfd5cdd9ef12711bd757a9b6021abf643',\n",
    "    '0xdf5d68d54433661b1e5e90a547237ffb0adf6ec2',\n",
    "    '0x663e4229142a27f00bafb5d087e1e730648314c3',\n",
    "    '0x87d598064c736dd0c712d329afcfaa0ccc1921a1',\n",
    "    '0xa98ad92a642570b83b369c4eb70efefe638bc895',\n",
    "    '0x41a322b28d0ff354040e2cbc676f0320d8c8850d',\n",
    "    '0xefabe332d31c3982b76f8630a306c960169bd5b3',\n",
    "    '0x71c118b00759b0851785642541ceb0f4ceea0bd5',\n",
    "    '0xda9f43015749056182352e9dc6d3ee0b6293d80a',\n",
    "    '0xabc7e6c01237e8eef355bba2bf925a730b714d5f',\n",
    "    '0x1b5242794288b45831ce069c9934a29b89af0197',\n",
    "    '0x995020804986274763df9deb0296b754f2659ca1'\n",
    "]\n",
    "\n",
    "TRANSFER_256_EVENT_HASH = '0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef'\n",
    "TRANSFER_128_EVENT_HASH = '0x27772adc63db07aae765b71eb2b533064fa781bd57457e1b138592d8198d0959'\n",
    "TRANSFER_SINGLE_EVENT_HASH = '0xc3d58168c5ae7397731d063d5bbf3d657854427343f4c083240f7aacaa2d0f62'\n",
    "TRANSFER_BATCH_EVENT_HASH = '0x4a39dc06d4c0dbc64b70af90fd698a233a518aa5d07e595d983b8c0526c8f7fb'\n",
    "\n",
    "APPROVAL_FOR_ALL_EVENT_HASH = '0x17307eab39ab6107e8899845ad3d59bd9653f200f220920489ca2b5937696c31'\n",
    "\n",
    "MINT_EVENT_HASH = '0x0f6798a560793a54c3bcfe86a93cde1e73087d944c0ea20544137d4121396885'\n",
    "BURN_EVENT_HASH = '0xcc16f5dbb4873280815c1ee09dbd06736cffcc184412cf7a71a0fdb75d397ca5'\n",
    "\n",
    "ENS_NAMEREGISTERED_EVENT_HASH = '0xb3d987963d01b2f68493b4bdb130988f157ea43070d4ad840fee0466ed9370d9'\n",
    "LAND_TRANSFER_1_EVENT_HASH = '0x8988d59efc2c4547ef86c88f6543963bab0cea94f8e486e619c7c3a790db93be'\n",
    "LAND_TRANSFER_2_EVENT_HASH = '0xd5c97f2e041b2046be3b4337472f05720760a198f4d7d84980b7155eec7cca6f'\n",
    "CRYPTOPUNKS_ASSIGN_EVENT_HASH = '0x8a0e37b73a0d9c82e205d4d1a3ff3d0b57ce5f4d7bccf6bac03336dc101cb7ba'\n",
    "CK_BIRTH_EVENT_HASH = '0x0a5311bd2a6608f08a180df2ee7c5946819a649b204b554bb8e39825b2c50ad5'\n",
    "\n",
    "EVENTS = {\n",
    "    TRANSFER_256_EVENT_HASH: [1, 2, 3],\n",
    "    TRANSFER_128_EVENT_HASH: [1, 2, 3],\n",
    "    TRANSFER_SINGLE_EVENT_HASH: [2, 3, 4],\n",
    "    TRANSFER_BATCH_EVENT_HASH: [2, 3, 4],\n",
    "    MINT_EVENT_HASH: [0, 1, 2],\n",
    "    BURN_EVENT_HASH: [1, 0, 2],\n",
    "    ENS_NAMEREGISTERED_EVENT_HASH: [0, 2, 1],\n",
    "    LAND_TRANSFER_1_EVENT_HASH: [1, 2, 3],\n",
    "    LAND_TRANSFER_2_EVENT_HASH: [1, 2, 3],\n",
    "    CRYPTOPUNKS_ASSIGN_EVENT_HASH: [0, 1, 2],\n",
    "    CK_BIRTH_EVENT_HASH: [0, 1, 2]\n",
    "}\n",
    "EVENTS_HASHES = list(EVENTS.keys())\n",
    "\n",
    "erc721_tokens_manual_grade_2_tuple_str = str(tuple(erc721_tokens_manual_grade_2_dict.values())).replace(',)', ')')\n",
    "erc721_tokens_manual_grade_3_tuple_str = str(tuple(erc721_tokens_manual_grade_3_dict.values())).replace(',)', ')')\n",
    "erc721_tokens_manual_cyberpunks_tuple_str = str(tuple(erc721_tokens_manual_cyberpunks_dict.values())).replace(',)', ')')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Get Transfers"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table erc721_analysis:erc721_row_transfers has been deleted.\n",
      "Table erc721_analysis:erc721_row_transfers has been created and filled 57,546,765 rows.\n"
     ]
    }
   ],
   "source": [
    "query_1 = f'''\n",
    "WITH logs AS (\n",
    "    SELECT\n",
    "        address as token_address,\n",
    "        topics[SAFE_ORDINAL(1)] as event_hash,\n",
    "        topics,\n",
    "        transaction_hash,\n",
    "        block_number,\n",
    "        data\n",
    "    FROM `bigquery-public-data.crypto_ethereum.logs`\n",
    "    WHERE block_timestamp >= '{INITIAL_TS}'\n",
    "      AND block_timestamp <= '{SNAPSHOT_TS}'\n",
    "      AND topics[SAFE_ORDINAL(1)] IN {tuple(EVENTS_HASHES + [APPROVAL_FOR_ALL_EVENT_HASH])}\n",
    "),\n",
    "token_addresses AS (\n",
    "    SELECT\n",
    "        token_address\n",
    "    FROM (\n",
    "        SELECT\n",
    "            token_address,\n",
    "            ARRAY_AGG(DISTINCT event_hash) as event_hashes\n",
    "        FROM logs\n",
    "        GROUP BY token_address\n",
    "        HAVING '{APPROVAL_FOR_ALL_EVENT_HASH}' in UNNEST(event_hashes)\n",
    "           AND ARRAY_LENGTH(event_hashes) > 1\n",
    "    )\n",
    "    UNION ALL\n",
    "\n",
    "    SELECT token_address\n",
    "    FROM UNNEST ({list(erc721_tokens_manual_cyberpunks_dict.values()) + nft_tokens_without_approvalforall_events}) as token_address\n",
    "),\n",
    "token_transfers_row AS (\n",
    "    SELECT\n",
    "        token_address,\n",
    "        event_hash,\n",
    "        if(from_argument_number = 0, '0x0000000000000000000000000000000000000000', REPLACE(topics[SAFE_ORDINAL(from_argument_number + 1)], '0x000000000000000000000000', '0x')) as from_address,\n",
    "        if(to_argument_number = 0, '0x0000000000000000000000000000000000000000', REPLACE(topics[SAFE_ORDINAL(to_argument_number + 1)], '0x000000000000000000000000', '0x')) as to_address,\n",
    "        CASE\n",
    "            WHEN ARRAY_LENGTH(topics) >= id_argument_number + 1 THEN [topics[SAFE_ORDINAL(id_argument_number + 1)]]\n",
    "            WHEN event_hash='{TRANSFER_SINGLE_EVENT_HASH}' THEN [LEFT(data, 66)]\n",
    "            WHEN event_hash='{TRANSFER_BATCH_EVENT_HASH}' THEN\n",
    "                    (SELECT\n",
    "                         ARRAY_AGG(CONCAT('0x', SUBSTR(token_data.token_data, i * 64 + 1, 64)))\n",
    "                     FROM (\n",
    "                         SELECT SAFE.SUBSTR(data, 64*3+3, CAST((LENGTH(data) - 2 - 64 * 4)/2 AS INT64)) AS token_data) AS token_data\n",
    "                     CROSS JOIN\n",
    "                     UNNEST (GENERATE_ARRAY(0,CAST(LENGTH(token_data.token_data)/64 - 1 AS INT64))) AS i)\n",
    "            ELSE [data]\n",
    "        END AS token_ids,\n",
    "        CASE\n",
    "            WHEN event_hash='{TRANSFER_SINGLE_EVENT_HASH}' THEN [CAST(REPLACE(CONCAT('0x', RIGHT(data, 64)), '0x000000000000000000000000', '0x') AS FLOAT64)]\n",
    "            WHEN event_hash='{TRANSFER_BATCH_EVENT_HASH}' THEN\n",
    "                (SELECT\n",
    "                     ARRAY_AGG(CAST(REPLACE(CONCAT('0x', SUBSTR(token_data.token_data, i * 64 + 1, 64)), '0x000000000000000000000000', '0x') AS FLOAT64))\n",
    "                 FROM (\n",
    "                     SELECT SAFE.SUBSTR(data, 64*4+3 + CAST((LENGTH(data) - 2 - 64 * 4)/2 AS INT64), CAST((LENGTH(data) - 2 - 64 * 4)/2 AS INT64)) AS token_data) AS token_data\n",
    "                 CROSS JOIN\n",
    "                 UNNEST (GENERATE_ARRAY(0,CAST(LENGTH(token_data.token_data)/64 - 1 AS int64))) AS i)\n",
    "             ELSE [1.0]\n",
    "        END AS token_values,\n",
    "        data,\n",
    "        transaction_hash,\n",
    "        block_number\n",
    "    FROM logs\n",
    "    INNER JOIN token_addresses USING (token_address)\n",
    "    INNER JOIN (\n",
    "        SELECT\n",
    "            event_hash,\n",
    "            from_argument_number,\n",
    "            to_argument_number,\n",
    "            id_argument_number\n",
    "        FROM UNNEST([{''.join(f\"STRUCT('{k}' AS event_hash, {v[0]} AS from_argument_number, {v[1]} AS to_argument_number, {v[2]} AS id_argument_number), \" for k,v in EVENTS.items())[:-2]}])\n",
    "    )\n",
    "    USING (event_hash)\n",
    ")\n",
    "SELECT DISTINCT\n",
    "    token_address,\n",
    "    from_address,\n",
    "    to_address,\n",
    "    token_ids[SAFE_ORDINAL(id_ordinal)] as token_id,\n",
    "    token_values[SAFE_ORDINAL(id_ordinal)] as token_value,\n",
    "    transaction_hash,\n",
    "    block_number\n",
    "FROM token_transfers_row,\n",
    "UNNEST(GENERATE_ARRAY(1, array_length(token_ids))) as id_ordinal\n",
    "'''\n",
    "\n",
    "if DROP_TABLES:\n",
    "    drop_table(table_name=ERC721_ROW_TRANSFERS_TABLE_NAME,\n",
    "               dataset_name=ERC721_ANALYSIS_DATASET_NAME)\n",
    "\n",
    "if CREATE_TABLES:\n",
    "    create_table(query=query_1,\n",
    "                 table_name=ERC721_ROW_TRANSFERS_TABLE_NAME,\n",
    "                 dataset_name=ERC721_ANALYSIS_DATASET_NAME)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Get Balances"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table erc721_analysis:erc721_amounts has been deleted.\n",
      "Table erc721_analysis:erc721_amounts has been created and filled 6,055,104 rows.\n"
     ]
    }
   ],
   "source": [
    "query_2 = f'''\n",
    "WITH excluding_erc1155_tokens AS (\n",
    "    SELECT DISTINCT\n",
    "        CONCAT(token_address, token_id) as excluding_token\n",
    "    FROM `{PROJECT_ID}.{ERC721_ANALYSIS_DATASET_NAME}.{ERC721_ROW_TRANSFERS_TABLE_NAME}`\n",
    "    WHERE token_value > 1\n",
    "),\n",
    "token_transfers_without_excluding_erc1155 AS (\n",
    "    SELECT\n",
    "        token_transfers.token_address,\n",
    "        from_address,\n",
    "        to_address,\n",
    "        token_transfers.token_id\n",
    "        transaction_hash\n",
    "    FROM `{PROJECT_ID}.{ERC721_ANALYSIS_DATASET_NAME}.{ERC721_ROW_TRANSFERS_TABLE_NAME}` AS token_transfers\n",
    "    WHERE CONCAT(token_address, token_id) NOT IN (SELECT excluding_token FROM excluding_erc1155_tokens)\n",
    ")\n",
    "SELECT\n",
    "    token_address,\n",
    "    address,\n",
    "    sum(amount_change) as amount\n",
    "FROM (\n",
    "    SELECT\n",
    "        token_address,\n",
    "        from_address as address,\n",
    "        - 1 as amount_change\n",
    "    FROM token_transfers_without_excluding_erc1155\n",
    "\n",
    "    UNION ALL\n",
    "\n",
    "    SELECT\n",
    "        token_address,\n",
    "        to_address as address,\n",
    "        1 as amount_change\n",
    "    FROM token_transfers_without_excluding_erc1155)\n",
    "WHERE address != '0x0000000000000000000000000000000000000000'\n",
    "  AND token_address != address\n",
    "GROUP BY token_address, address\n",
    "ORDER BY amount\n",
    "'''\n",
    "\n",
    "if DROP_TABLES:\n",
    "    drop_table(table_name=ERC721_AMOUNT_TABLE_NAME,\n",
    "               dataset_name=ERC721_ANALYSIS_DATASET_NAME)\n",
    "\n",
    "if CREATE_TABLES:\n",
    "    create_table(query=query_2,\n",
    "                 table_name=ERC721_AMOUNT_TABLE_NAME,\n",
    "                 dataset_name=ERC721_ANALYSIS_DATASET_NAME)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### ERC721 Contracts List"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table erc721_analysis:nft_tokens has been deleted.\n",
      "Table erc721_analysis:nft_tokens has been created.\n"
     ]
    }
   ],
   "source": [
    "if EXTRACT_NFT:\n",
    "    extract_nft_tokens()\n",
    "\n",
    "if CREATE_TABLES:\n",
    "    nft_tokens_df = pd.read_csv(ETHERSCAN_NFT_CSV_NAME, index_col=0)\n",
    "    nft_tokens_df['token_address'] = nft_tokens_df['token_address'].map(lambda x: x.lower())\n",
    "    create_table_from_df(source_df=nft_tokens_df,\n",
    "                         table_name=ERC721_NFT_TOKEN_TABLE_NAME,\n",
    "                         dataset_name=ERC721_ANALYSIS_DATASET_NAME,\n",
    "                         drop_existing_table=DROP_TABLES)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table erc721_analysis:erc721_tokens has been deleted.\n",
      "Table erc721_analysis:erc721_tokens has been created and filled 11,685 rows.\n"
     ]
    }
   ],
   "source": [
    "query_3 = f'''\n",
    "WITH tokens AS  (\n",
    "    SELECT\n",
    "        token_address,\n",
    "        count(DISTINCT address) as number_of_owners\n",
    "    FROM `{PROJECT_ID}.{ERC721_ANALYSIS_DATASET_NAME}.{ERC721_AMOUNT_TABLE_NAME}`\n",
    "    WHERE amount > 0\n",
    "    GROUP BY token_address\n",
    "),\n",
    "manual_tokens AS (\n",
    "    SELECT\n",
    "        token.address AS token_address,\n",
    "        token.name AS token_name\n",
    "    FROM UNNEST(\n",
    "        [{''.join(f\"STRUCT('{k}' AS name,'{v}' AS address), \" for k,v in {**erc721_tokens_manual_grade_2_dict, **erc721_tokens_manual_grade_3_dict, **erc721_tokens_manual_cyberpunks_dict}.items())[:-2]}]\n",
    "        ) as token\n",
    ")\n",
    "SELECT\n",
    "    token_address as address,\n",
    "    if(manual_tokens.token_name is not null, manual_tokens.token_name, nft_tokens.token_name) as name,\n",
    "    number_of_owners\n",
    "FROM tokens\n",
    "LEFT JOIN `{PROJECT_ID}.{ERC721_ANALYSIS_DATASET_NAME}.{ERC721_NFT_TOKEN_TABLE_NAME}` as  nft_tokens\n",
    "USING (token_address)\n",
    "LEFT JOIN manual_tokens\n",
    "USING (token_address)\n",
    "'''\n",
    "\n",
    "if DROP_TABLES:\n",
    "    drop_table(table_name=ERC721_TOKEN_TABLE_NAME,\n",
    "               dataset_name=ERC721_ANALYSIS_DATASET_NAME)\n",
    "\n",
    "if CREATE_TABLES:\n",
    "    create_table(query=query_3,\n",
    "                 table_name=ERC721_TOKEN_TABLE_NAME,\n",
    "                 dataset_name=ERC721_ANALYSIS_DATASET_NAME)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Analysis of Grade Boundaries. Amount of ERC721 tokens"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "query_4 = f'''\n",
    "    SELECT\n",
    "        sum_amount,\n",
    "        count(address) as number_of_addresses\n",
    "    FROM (\n",
    "        SELECT\n",
    "            address,\n",
    "            count(distinct token_address) as number_of_tokens,\n",
    "            sum(amount) as sum_amount\n",
    "        FROM `{PROJECT_ID}.{ERC721_ANALYSIS_DATASET_NAME}.{ERC721_AMOUNT_TABLE_NAME}`\n",
    "        WHERE amount > 0\n",
    "          AND address != '0x0000000000000000000000000000000000000000'\n",
    "          AND address != token_address\n",
    "        GROUP BY address\n",
    "        HAVING sum_amount > {min_number_of_tokens})\n",
    "    GROUP BY sum_amount\n",
    "'''\n",
    "\n",
    "address_agg_by_sum_amount_of_tokens_df = get_df(query_4)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suggestion of Rules: \n",
      "\n"
     ]
    },
    {
     "data": {
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      "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>Grade</th>\n      <th>Rule</th>\n      <th>Addresses</th>\n      <th>Percentage of Addresses</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>1</td>\n      <td>0.0 &lt; Amount of ERC721 tokens &lt;= 12.0</td>\n      <td>1472280</td>\n      <td>88.9</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>12.0 &lt; Amount of ERC721 tokens &lt;= 160.0</td>\n      <td>167145</td>\n      <td>10.1</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>160.0 &lt; Amount of ERC721 tokens</td>\n      <td>16611</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
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     "data": {
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      "image/png": 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dpDvukE44QbrggkqPBgAAAAAAlANBI3TLNttIjz8urbeeP29vr+x4AAAAAABAaRE0QrdtsIH0ta958GjHHaU336z0iAAAAAAAQKkQNEKPbbuttOee0pZbSqNHSzU13iS7paXSIwMAAAAAAN1F0Ag9ZiadfrpUXe2BorY2ado0qbGx0iMDAAAAAADdRdAIJTNvXrze3i5NnSrdfLP0wQcVGxIAAAAAAOgmgkYombo6qSrzjaqqktZeW7rmGulnP/NtN90kTZ4shVC5MQIAAAAAgHQIGqFkmpqk+novU6uvlx55RLrjDuniiz1Q9MQT0t57S6NGSeed5+cQQAIAAAAAoG+qqfQAMHDU1nomUT5m0kUXSRde6GVr77zj23fZRVp+eWmvvTygVFvbe+MFAAAAAACFETRCrzKTxo+Pn996q3TPPZ6RdP/90r/+Jd1yizR8uLTTTtLQoRUbKgAAAAAAgxrlaaioESOkr3xFuuoqDxhJ0ptvSqedJq21lnTooV7C1tZW2XECAAAAADDYEDRCn/ONb3j/o+nTpSOO8OykE0+UPvMZ6fTTpcce89nZAAAAAABA+RA0Qp+11lrS7rv7+u9/L/3mN9KyZdL3v+/ZR//5j3TjjdL8+RUdJgAAAAAAAxJBI/QLNTXe4+j886WHHvIZ2pYu9aDRyJHSjjtK778vLVnCjGwAAAAAAJQCQSP0WxMnSrfdJr39tnTmmdIqq0h//rO04YbSscd6k+1PPqn0KAEAAAAA6J8IGqHfW245abfdvPfR8cdL990njRsn/e53Xrr24ote2jZ9eqVHCgAAAABA/0HQCAOKmVRXJ33nO9K990rrry9VVUmTJ0u77CKNHSs9/7z3Rlq8uNKjBQAAAACg7yJohAFv002lP/1Jeu016W9/k8aMkR5/XFp7bWm//aTLLvMSNwAAAAAAECNohEHDTPr0p6WVVvKm2i0t0le/Kj34oDRtmvTuu9KPfiTdfLM0frw3325o8OMAAAAAABhsaio9AKBSVl9dOuQQf0iebVRdLR12mM/CJnkwqbHRy9sAAAAAABhMyDQCMtZaSzr7bKm1Nd7W3u6BoxAqNy4AAAAAACqBoBGQo67Om2dLvhwyxGdna26u7LgAAAAAAOhNBI2AHE1NUn29l6rV10svvijtu683zV66tNKjAwAAAACgd9DTCMhRW9uxh9Emm0gnnujNsb/+denAA6U99qjM+AAAAAAA6A1kGgEpDRniM7B98YvSccdJBx8szZtX6VEBAAAAAFAevRo0MrMzzCzkPN7szTEAPbX33p6JNGqUdN993iS7ra3SowIAAAAAoLQqkWnULGndxGOzCowB6JEVVpDOPVc69FDpzjul7baTnn220qMCAAAAAKB0KtHTqDWEQHYRBoy99pLeflvac08vWfvFL6Rhwyo9KgAAAAAAeqYSmUa1ZvaGmb1iZjeZWW0FxgCUjJl05JFesrbuutLQodJrr3nZGgAAAAAA/VVvB42ekHSEpC9IOkbSOpIeNbPV8x1sZsea2dNm9vTll1/ee6MEumGNNaQf/MDX/+//pH32kV55pbJjAgAAAACgu3q1PC2E8O/kczN7XFKLpMMlXZjn+MslRdEi8jbQL5hJkyZJF14obb219PvfSwcdVOlRAQAAAADQNZUoT/ufEMJHkiZLGlvJcQClNnSo9MMfSk89JW2zjfTee9KDD1Z6VAAAAAAApFfRoJGZLSepXtK8So4DKJdRo6TaWqmlRfra16SjjpLefbfSowIAAAAAoHO9GjQys1+Z2efMbJSZbSvp75JWlHRNb44D6G1bby1NmSKNGCFtuaX0ySeVHhEAAAAAAMX1dqbRBpJulNQs6Z+SlkjaLoQwp5fHAfS64cOliy6SnnlGWn556de/9hnXAAAAAADoi3o1aBRCOCiEsF4IYWgIYf0QwpdDCFN6cwxApa25pi9XXFGaMEH60Y+kjz+u6JAAAAAAAOigoj2NgMHsG9+QXnpJmjtXuvTSSo8GAAAAAIBsBI2AClpnHen666VTTpEeeUT60pekV1+t9KgAAAAAACBoBPQJZtJnPiN96lPSFlt476PW1kqPCgAAAAAwmBE0AvqI5ZaTzjhDevRR6eWXpbY2ZlkDAAAAAFQOQSOgj9lkE+nPf5aGDZN23VVabTWppkZqaJBaWio9OgAAAADAYEHQCOjD3n9f+uADzzqaOlVqbKz0iAAAAAAAgwVBI6APmzlTCsHXQ5Cam6V995X+8hdK1wAAAAAA5UXQCOjD6uqkqsxPaVWVl64deaTPuLbhhh48AgAAAACgHAgaAX1YU5NUXy9VV/vy9tulL35R+ve/paeflrbbTpo/33sfXXst2UcAAAAAgNIhaAT0YbW10uTJUmurL2tr430jR0pjxkjDh0vf+pZ0003SBhtI//pXxYYLAAAAABhACBoB/dyQIdL++0t33ik9+6y07bbSK69IO+4oXXON9PHHlR4hAAAAAKA/ImgEDCAbbyytt573Ozr1VOnmm3393nsrPTIAAAAAQH9D0AgYgGpqpP32k+64w7OPPvMZ6cknpe23l66+muwjAAAAAEDnCBoBA9zGG0urriptuaX0ox9J//iHZx898YQUQqVHBwAAAADoqwgaAYNETY20774+I9vzz0uf+pT3QdpuO+nKK6VFi6SWFqmhwY9taPDnAAAAAIDBqabSAwDQ+zbc0JcTJ3q20eWXS9/7nrTaat5Eu71dmjZNamz0WdsAAAAAAIMPQSNgEKupkfbZxx+vvSaNHOkBI8mXzc0VHR4AAAAAoIIoTwMgSdpgA6muTqpK/FYIQXrjDam1tXLjAgAAAABUBkEjAP/T1CTV10vV1dL48dJjj0nrrSf95jfSNttIV1whffRRpUcJAAAAAOgNlKcB+J/a2vw9jL7zHQ8iXX65z8DW0iINGyYNHdr7YwQAAAAA9A4yjQB0qrpa2msv6ZZbpOnTpeHDpTPOkLbaygNJH35Y6RECAAAAAEqNoBGALll1VV/+/OfSOedIkyZJDQ3SsmXSokXeBwkAAAAA0P8RNALQLdXV0sSJ0j/+IU2dKg0ZIp1yivSZz0h//KO0cGGlRwgAAAAA6AmCRgB6bMUVfXnppdL550v33ittv71nHS1YIM2a5dlINTW+bGmp7HgBAAAAAJ2jETaAkqmqkvbYwx9Llkhm0rHHSrfeKi1d6kGkadOkxsb8DbcBAAAAAH0HmUYAymLYMF/eeKP3O4p6HbW3S83NlRsXAAAAACAdgkYAyqqqSqqv92X0fMgQ6RvfkGbOrOzYAAAAAACFETQCUHZNTR44qq725X//K625prTddtLZZ1d6dAAAAACAfOhpBKDsams79jDaZhvp+9+X3nlH+vhj6bDDPPtot928FxIAAAAAoLLINAJQMcOHe0BpyBBvjv3tb0tbbSU9+milRwYAAAAAIGgEoOKGDJEOP1x66SXpZz+TVl7ZM5Auu0xavLjSowMAAACAwanToJGZ7W9mV5nZE2Y2I/N4IrNt/14YI4BBoqpK2ndfadNNpQ8/lG6/XRo1SjrvPGnBgkqPDgAAAAAGl4JBIzNb1cwelvRPSbtIelfS45nHu5ImSPqnmT1iZqv2wlgBDCK1td5A+557pKlTpfnzpXnzpMcekxoapJoaX7a0VHqkAAAAADAwFWuE/WtJG0n6XAjhoXwHmNmOkv4i6VeSji798AAMdptuKl17ra//7W/SQQdJ7e3+fNo074WU22QbAAAAANBzxcrT9pX0vUIBI0kKITws6QeS9i/xuACggwMOyJ5Zrb1dam6Wliyp3JgAAAAAYKAqFjQaJun9FNf4QNLQkowGADpRV+e9jyRfrraatNFG0g9/KL3ySmXHBgAAAAADSbGg0WOSfmxmwwsdkNn3I0lMkA2gVzQ1SfX1UnW1Lx9/XHroIWnpUi9Va2vz3kdtbZUeKQAAAAD0b8V6Gp0s6QFJc8zsDkkvK848WlVSg6S9JbXJG2UDQNnV1ubvYXThhVIIXr529tk+89qxx0pHHy2ts07vjxMAAAAA+ruCmUYhhCmSNpd0jaTtJZ0r6Y+Zx7mSPivpWkmfDiHQhhZAxUX9ji65RLrlFmnOHOnLX/Zt8+Z5UAkAAAAAkE6xTCOFEOZJ+o6k75jZcvIMI0n6IITwSbkHBwDdtcUW0uWXxzOtnXCCN83+ylekm2+WZs70/khNTZ69BAAAAADIVjRolBRCWCxpXhnHAgAlFzXN/uc/vfdRY6O0cKFvmzbNn+crdwMAAACAwa5YI+xUzGwjMzusFIMBgHIxk3beWVq0KN7W3i5NnSp98EHFhgUAAAAAfVaPg0aStpZ0VQmuAwBlV1cXZx9VVUkjRkibbCLdemtlxwUAAAAAfU0pgkYA0G80NUn19VJ1tS+feUb673+lhgbpnXekq66SWlsrPUoAAAAAqLyCPY3M7P6U11izRGMBgLKrrS3cw2jGDA8aXXCBdN550r77xjOyAQAAAMBgUyzT6HOSaiUN6eRRXeYxAkCvGDvWs44uuED6+c+l116Tli3r2TVbWjyLqabGly0tpRkrAAAAAJRbsdnTZkp6LIRwRLELmNlXJP21lIMCgEoxk/beW9prL18/5RTPQDr3XGnTTdNdo73dz33vPelzn5Nef10KgdnaAAAAAPQvxTKNnpa0VYprBEkUcAAYUKKytHPPlXbdVdptN+n//i/OGhozRrrySunmm/2466+XdtnFt6+wgjR9umcqRQEjyYNJzc2VeT0AAAAA0FXFgkY3Sno8xTWeknRkaYYDAH3LsGHSySd7EOihhzxbqK1NmjVL+ta3pEcf9eM231z6yU+kO++U5s/3Wdo+/Wlp3Ljs2drq6ir1SgAAAACgawoGjUIIt4cQvt7ZBUIIc0MI15R2WADQt4wY4ZlD7e3xtsWLpd/8xtc33dSzkTbZxDONIsnZ2urq/DqUpwEAAADoD4plGgEAEurqup41FM3W1toqTZkinXCCl7s98EBZhwoAAAAAPVasEfb/mNlGRXa3S1oQQviwNEMCgL6pqckbWTc3e8Coqanr1zj0UGm99bw/0vPPS6uvXvJhAgAAAEBJpAoaSZotb3hdkJm1SPplCOFPPR0UAPRFUdZQT+26q/dGWnFF6cEHpZ12ihtvAwAAAEBfkbY87RuSXpX0kqQzJB0v6UxJL2e2/0hSs6Q/mtkRJR8lAAwwK64offyxdNJJ0je/6c21AQAAAKAvSRs02kTS0yGET4cQfh5CuDyEcFYIYXNJz0haJ4Swj6QbJH27XIMFgIFkhRV8RrapU6Uvf9mDSAAAAADQV6QNGh0q6YoC+66Q9LXM+t8kMaE0AKQ0YoT0739LO+wgzZ0rNTRINTW+bGmp9OgAAAAADGZpexoNl7RGgX1rSlops75QEkUWANAFQ4dK3/++NG6c9zqSfNnYWJoeSgAAAADQHWkzjf4r6Vwz+0xyo5ltJekcSf/JbBoraW7phgcAg8eMGfF6e7vP0gYAAAAAlZI2aHSipGWSnjSzV8zsCTN7RdITkpZI+mbmuJUkXVL6YQLAwFdXJ1VlfiubSSNHSm++KU2fXtFhAQAAABikUgWNQgivSKqXz5p2v6T3MstvSBqX2a8QwkUhhEvLNFYAGNCamqT6eqm62kvV7r5bev556bOflQ46SHrxxUqPEAAAAMBgkrankUIIyyRdnnkAAEqstrZjD6PaWm+I/cc/SvvvLz37rM+69tpr3vOoudkzlJqa/FgAAAAAKJW05WmSJDPb1MxONLPTzewEM2so18AAAG74cOnUU6WZM6VVVpG+8x3pU5+Spk6V2triptkAAAAAUEqpMo3MrEbS1ZIOlmSJXcHMbpB0RAiBWdMAoIyifkcXXeSZRyH4c5pmAwAAACiHtJlGP5N0oKSfSholafnM8qeSvppZAgB6wdCh3vsoCiJVVUmrrio9/nhlxwUAAABgYEkbNDpU0tkhhHNCCHNCCEsyy3MknS3psPINEQCQK9k0u75eOvlk6cADpb1fOk/PfTim0sMDAAAAMACkDRqtJ+nRAvsezezvMjP7kZkFM/t9d84HgMEqaprd2urLH/9YmjFD2mu1JzTl45EKwXsdtbRIDQ1STY0vW1oqPXIAAAAA/UXa2dPekPRZSffm2bdDZn+XmNl2ko6VxCTSAFACw4ZJJ65/iyTpldk/0YQJ0iefSB995H2PoobZuTO0AQAAAEA+aTONrpf048ysabVmtryZjTKzH0n6saTrunJTMxuRueZRkt7v0ogBAJ0aNcpnW/vwQw8YSTTMBgAAANA1aYNGZ0j6u6QzJc2Q9JGkmZLOyWz/eRfve7mkv4cQ/tPF8wAAKa20kjRuXNww28yzkZqa4kASAAAAABSSKmgUQmgNIRwiaTNJJ8lnSztJ0mYhhK+FEJalvaGZHSNpjKSfdGO8AIAuyG2Yfe650hlneH+jd9+t9OgAAAAA9GVpexpJkkIIkyVldcMws90lXRhC+FRn55tZnaRzJe2YJtBkZsfK+x7psssu07HHHtuV4QLAoBc1zE761rekJ56QVl9d+tOfpOnTPbg0c6ZUV+frtbWVGS8AAACAvqNLQaMCRkhqSHns9pLWkDTZzKJt1ZJ2NrNvSFoxhLAk2hFCuFxeyiZJoQRjBYBBz0zabjtf33576dRTpQUL/DnNsgEAAABE0vY0KpVb5CVun048npZ0U2Z9aS+PBwAGtU039dnVItEsa88/X7EhAQAAAOgjejVoFEL4IITwcvIhaZGk+ZnnZBMBQC+rq4ubZVdVSWutJe2zj/T5z0v33VfZsQEAAAConN7ONAIA9DG5zbIfeURqaZEOPVR6/HFfHzVKqqnxBtotLZUeMQAAAIDeULCnkZmlbYO6Tk8GEEKY0JPzAQA9k69ZtiQdfrgvx4+XZs/29alTPQtpypReGx4AAACACinWCHum0jWftpTHAQD6oenT4/UQvOeRJC1bJg0ZUpkxAQAAACi/YkGjI3ttFACAPquuzgNF7e3e86i+3kvUdtpJ+t73pD32kA48UGpu9mObmjx7CQAAAED/VjBoFEK4pjcHAgDom5qapMbGjkGh22+Xzj5bOvVUDyhFWUiNjfnL3QAAAAD0L8UyjQAAKNjzaIstpH/8wxtkR3Nftrd7cAkAAABA/8fsaQCAHqmr87K1pDPPlD75pDLjAQAAAFAaBI0AAD3S1OR9jqqrfaa1SZOkhQs9A+nOO31fTY3U0OC9kAAAAAD0D5SnAQB6JF/52m67+fLII6W33/b1qVPpdwQAAAD0J2QaAQDK5r334vUQvN/RjTdK114rPfmkZx+RhQQAAAD0TQSNAABlk+x3VFXlz5dbTrrtNmn77aUpU6S2tnjWtc60tBBoAgAAAHpLj4NGZrazmdWVYjAAgIEl2e+ovt6ff/GL0t//LpnFx7W3e+CoM42NXubWlUATAAAAgO4pRabRA5KmmNntZrZtCa4HABggon5Hra2+rK2N9yWzkMz88bOfScuW5b/WO+94wCgEf97e7uVuAAAAAMqjFEGjIyV9U9JCSX8rwfUAAINAMgtp3DjpoYekxx+Xrrqq47FLlkjbbCOtumocaJKktdfuvfECAAAAg02PZ08LIVyTWb20p9cCAAwe+WZd+/e/PZPouuukH/xAeustaY01pMcek555RvrgAy9Ja26WRo6U5s+XnntO2mKLSrwCAAAAYGCjETYAoM+oqvLMozPPlObN8xK0d97xQNFqq2WXu82cKf3hD9IPf1jpUQMAAAADU6qgkZntZ2ZHJp5vbGaPmdmHZvZ3M1upfEMEAAw2s2fH6yEU7l301a9Kt94qffKJN8cGAAAAUDppM41+ImnNxPMLJW0g6XJJO0s6o7TDAgAMZskm2VVV/ryQ5ZaTjjlGWmcdqaZGamiQWlp6Z5wAAADAQJY2aDRa0ouSZGbLS9pL0ndDCKdIOk3SF8szPADAYJRskl1f78+Leeop6d13Pdto2jQvZwMAAADQM2kbYS8n6ZPM+g6Z8+7OPG+WtF6JxwUAGMTyNckuZtaseL29vXA5GwAAAID00mYazZa0Y2Z9P0nPhBAWZJ6vJWlBvpMAAOgNueVsq68u7b+/9I9/eLkaZWsAAABA16UNGl0m6Qwze1rSCZL+nNi3vaQppR4YAABp5Zaz/ec/0h57SAcfLE2ZQtkaAAAA0B2pytNCCBeb2buStpP02xDCtYndwyVdXYaxAQCQSr5ytvHjpW99K37e3u6Bo9ZWzzwCAAAAUFzaTCOFEK4PIXwzJ2CkEMJxudsAAOgLkmVrZtKwYb7tl7+kbA0AAADoTOqgkbl9zexXZnaVmW2c2f45M6MRNgCgz0mWrY0bJ738snTNNdLFF3vWEWVrAAAAQGGpgkZmtqqkRyXdIukYSYdJWj2z+xhJPyzH4AAA6ImobK211Ze1tdKOO0pvveXlapIvp06Vrr+e7CMAAAAgKW2m0QWSNpT0WXmwyBL77pW0W4nHBQBA2eTOtrbuutL3vx9nH02dKu29tweOCCQBAABgsEobNNpP0o9DCI9JCjn75soDSgAA9Au5s6099FB29lEIHkDaZhvK2AAAADB4pZ0/ZiVJrxfYt5yyM48AAOjT8s22VlfngaH2ds8+2mQTacaM7DK25ubeHysAAABQKWkzjZol7VFg3+ckvVSa4QAAUBm52Ud33JFdxiZJG29cufEBAAAAvS1t0OhSSSeb2Y8lbZTZtoqZHSnpJEmXlGNwAAD0lnxNs5OBpA02kBYv9n30OQIAAMBgkKo8LYRwuZnVSjpT0lmZzfdIapf0yxDC9WUaHwAAFZNbxrZggbTDDt4oO+p71NjYsdQNAAAAGAjSZhophPBDSaMlHSfpJ5JOkFQXQvhxmcYGAECfMmKE9zUKmSkh2ts9cETmEQAAAAaitI2wJUkhhDmSrijTWAAA6POSDbPNvOdR9JzMIwAAAAwkqTKNzGwHM9sn8Xw1M7vRzF4ys1+ZWXX5hggAQN+R7HM0bpxnHSVnWJs6VXryyeLZRy0tZCcBAACg70tbnna+pM8knv9K0l6Spks6XtJpJR4XAAB9Um7D7OQMa1VV0iqrSAce6FlHbW1x9lFSY2Px/QAAAEBfkDZoNE7S05JkZkMkfUXSd0IIX5b0Y0mHlGd4AAD0bcnMo/p66emnpddey84+am6Oj1+61LORCu0HAAAA+oq0PY1WkrQws76NpBUl3Z55/qykjUo8LgAA+oXcGdak7L5HkrTcctL48R4cGjJEWn55afHieP/Ysb07ZgAAACCNtJlGr0vaPLO+p6SXQwhvZ56vKunjUg8MAID+Krfv0core8Covd0zjdZbL96/yirS+ut7qRoAAADQl6QNGt0o6Vwz+7uk70r6S2LflpJmlHpgAAD0V8m+R1OmSG+/HWcVhSC98kq8/623pA03pDE2AAAA+p60QaMzJP1C0jB5U+wLE/s2l/S30g4LAICBI7dZdl1dvG/oUJ9tbcYMGmMDAACgb0kVNAohtIUQzgkhNIYQzgohtCX27R9CuKh8QwQAoH/LbZbd1JS9Pypdk3w5dSqZRwAAAKi8VEEjM1vDzDbK2Xacmf3OzPYpz9AAABgYkuVqkyf786RkJpKZl7BNmxZnHk2cSBAJAAAAvS9tedqVkn4YPTGz0yX9QdIhkm41s6+WYWwAAAwKuY2zq6uzM49mzswOIlG+BgAAgN6QNmi0laT7Es+/IencEMLqki6RN8cGAADdkJuJlJt5JGUHkZqbKzNOAAAADC5pg0arSXpLksxsU0nrSLoms+8WSXX5TwMAAF2Vm3k0ZkwcRJI8kESpGgAAAMotbdDoPUkbZNZ3lfRGCGFG5vmQLlwHAAB0IjfzaNKkOIg0ZIhvp1QNAAAA5ZY22HOvpDPM7CRJp8iziyL1kuaUeFwAACAjGUSKytQkX582TRo/nswjAAAAlF7aoNH3Jb0q6TxJsySdmdj3NUkPl3hcAAAgj2S/o6oqzz6aOpXMIwAAAJReqqBRCOGtEMLnQwjDQwi7hhDeTezeXdK3yzM8AACQlOx3VF/fMfNoyhSyjgAAAFAaXepFZGZVZrapmX3OzFaUpBDCwhDC0vIMDwAAJBWbaS1C1hEAAABKIXXQyMxOlPSmpBcl3a/MjGlmdouZfas8wwMAAMUkM4+S2tu9bK2hwTOPxo71B1lIAAAASCtV0MjMjpF0sbwB9oGSLLH7IUlfLvnIAABAp5KZR+PHZ/c7kuJ+RzNn+oMsJAAAAKSVNtPou5J+HUI4VtK/cvZNUybrCAAAVE5uv6OqKimEjse1t0vNzb7e0hJnI5GBBAAAgKS0QaNRkiYV2LdI0iolGQ0AAOi2NP2OJMnM90mecTRtGhlIAAAA6Cht0OhdSSML7KuT9HpJRgMAAEommXk0Zow/qqul0aOlDz7w7KIpU+IZ2JIZSAAAAEDaoNHtkn5qZrWJbcHM1pD0HXmvIwAA0IckM49mzPBHa6sHi954w7OLcoVAqRoAAABc2qDRTyQtkfSypHslBUm/lTRVUpuks8oyOgAAUHIzZnTcVl0tDR3qQSNK1QAAACClDBqFEN6VtJWk8yQNkTRLUo2k30vaPoSwoGwjBAAAJZXsdVRV5bOutbZ6sChqnE2zbAAAAHQaNDKzoWZ2kaT6EMLPQwg7hhA2CSFsH0I4M4SwsBfGCQAASiR3lrWmJt+e2zi7vd0DRePGSVOnkoEEAAAw2HQaNAohLJV0nKTlyz8cAABQbrmzrNVmOhYmg0nJUrWlS/NnIAEAAGBgS9vT6DlJm5VzIAAAoLKSwaR8TbIjVVWegTR2rD8oWwMAABiY0gaNTpH0PTPbx8ysnAMCAACVlyxVM/PMo+pqacgQDyi1tUkzZ/qDsjUAAICBKW3Q6G+SVpd0q6RPzOxVM5ubeMwp3xABAEBvS5aqRT2NWlu9PK29vePxlK0BAAAMPDUpj7tPUijnQAAAQN8RlarlqqvzrKJ8gaO2Ni9Ta2qK+yTlamnxjKTmZr9WsWMBAABQWamCRiGEI8o8DgAA0A80NcVBn1GjfNvMmfH+KVM8M6mtLX9QqLExDjpFJW35glNdRTAKAACg9NKWpwEAAGQ1y54xwx/V1dnHLF1auM9Rc3OcpVTKkrbGRi+ho78SAABA6RTMNDKzw7pyoRDCtT0fDgAA6G8Klay1t3vmUU1NnP2zwgrSRx9JIVP0nqakLY3m5via9FcCAAAojWLlaVfnPI96GlmebZLUadDIzE6UdJykkZlNkyWdHUK4o7NzAQBA35QsWauujhtmR6Lsn112kVZdVdpgA88KipSiTK2uzgNUks/6VlfX/WsBAADAFStPG5V47CTpNUmXSZogaVxmebmkVyXtmPJ+r0n6gaQtJW0l6X5Jt5jZp7o+dAAA0BckS9amTo1nXUtqb5fmzvVsoNtvz95fisygpiZpjTU8YFRf788BAADQMxZC55OimdktkppDCD/Is++XksaGEL7YrQGYzZf0oxDCZZ0cOjBmb5swwZcPPFDJUQAYqPgdgz6koaFj2VoU1JHifdG2njbEnj9fGjlSWrBAMuv0cPQ3/H4DAKBcCv6fU9pG2LtJuqfAvrsz+7s2IrNqMztI0kqSHu3q+QAAoG9raooDRJEoqyjaV1Xl2UfTpnmQqaWle/f6z3+k44+XlltOmjev52MHAABA+qDREnk5WT5bS1qa9oZmtpmZfZS55h8lfTGE8FKBY481s6fN7OnLL7887S0AAEAfEJWtjR/vwSEp7jcU7YuCSlHT7HHjvHRt2DBvoJ02kDRtmrTyyn49mmADAACURrFG2Ek3SzrDzNok/U3SW5LWlnSgpJ9J+nMX7tks6dOSRkj6iqRrzGxCCOHl3ANDCJfL+yZJA6U8DQCAQSbZKDuaRS2SnPVMkpYuzV5OmSKNHu2Bp2IzrM2YIW2yiXTggT2bhQ0AAACxtEGjUyQNl3SepPMT24OkGzL7UwkhLJU0M/P0GTPbWtJ3JB2d9hoAAKD/iLKK8qmr69j3KJ/OZlhbaSVpiy2k3XaTPv64Z+MFAACAS1WeFkL4JITwf5LGSzpC0o8yy/EhhMNCCIt7OIZhPTgfAAD0U1Fvo+pqaejQuIwtV2czrJ11lrT77tKdd0pf+Up5xgoAADDYpM00kiSFEKZLmt7dm5nZ+ZLukPSqPHPpEEkTJO3d3WsCAID+K5mF1NLi2UTTpnk/o6WJjolmnpWUz7Jl0uGHS9df7yVq9DQCAAAojYJBIzPbqCsXCiHMTXHYOpL+klkukPSipD1DCJO6ci8AADDw5JaxJYNI7e3xDGu5vY1mz5Yee8wDS6NGSW+8IX3yibT88r3+EgAAAAaUYplGs9W15tPVnR0QQjiiC9cDAACDWBREamiQpk7NnmGttdWzkdrapPXXlzbK/FPX3LnSiitKw4fHTbdpjA0AANA9xYJGRykOGg2T9BNJC+Uzqb0lzxY6UF5m9vMyjhEAAAxinc2wNneuPxoafNsHH8SZScWaZwMAAKC4gkGjEMLV0bqZ/UbSs5K+GEL8v21mdpakW+QNsgEAAEquKzOsJY/prHk2AAAAiks1e5qkgyVdlgwYSVLm+R/lDa0BAABKriszrEnZ+9vaPAOppaX84wQAABho0gaNVpK0ZoF9a0lasTTDAQAAyBb1Nmpt9d5G9fUeGBo6NPu4qippzBjfnxSVqSW1tHgwqaaGoBIAAEAhaYNGD0g618y2Tm40s20knZPZDwAAUFZRAKmtTVqyRJo1Sxo/3rOQ6uulSZN8f3Vieo6ogXYyOBTNytbWlj+oBAAAgOKNsJNOknSvpMfN7FV5I+y1JW0o6ZXMfgAAgF4VBZFy5euDNGWKNHq0B5mS++h9BAAAkF+qTKMQwiuS6iV9Q9J9kt7LLI+TNC6EMLtcAwQAAOiqqA9SPlOmZAeTqqo8yBShdA0AAMClzTRSCGGZpD9lHgAAAH1WlIHU0ND5zGv19R5kikSla+3tcelavmwmAACAgS5tTyMAAIB+p1jGUeRf//IgU5RhlMxEonQNAAAMZqkzjcxsD0nHS6qTtFzO7hBCGF3KgQEAAPRUlHHU0uIZQ1OmxPvM/FFfL40bJy1d6o21k3JL1wAAAAaTVJlGZraXpH9LWkHe22iapLnyRtjtkh4s1wABAAB6KgoeJWdbGzJECsEfU6ZIM2f6elJVlWca0dsIAAAMRmnL006XdImkvTLPfxJCmCCpQVK1PKAEAADQp0XBo9ZWqa2tY5AoUlUlDR3q5WltbXFvIwAAgMEkbdCoXlKTPKsoKFPWFkKYLukMeVAJAACg36ir8+BQPvX1HixK9jaaMoUZ1QAAwOCSNmjULqk1hBAkvSNpo8S+NyTRzwgAAPQr+ZpkV1V5+drkyfl7GZF1BAAABpO0QaNmSSMz609LOtnM1jWzNSWdIml26YcGAABQPvn6HNXXezBJipe5OptRLZqFjawkAADQ36WdPe16SeMy6z+TdK+k1zLP2yQdUuJxAQAA9IooeJRv+/jxnlkUlalJnc+o1tgYnxNlJeW7PgAAQF+XKmgUQrgksf6MmW0m6Qvy2dTuDSFMKXgyAABAP9XUFAeBamqkpUuljTcunIUkeRZSshdSsawkAACAvixteVqWEMJrIYQrQgi/JWAEAAAGqigLqa1NWrJE+ta3pAULpE02kcaO9UduGVoyC6mzrCQAAIC+rFtBIwAAgMHozjul+fM9iDRzpj/a2nxmtXHjPHB0ww3SkCEeMNpkk+JZSQAAAH1Z2p5GAAAAg94rrxTet3SpNHq0NHSol6UNHSqddZZnKwEAAPRHZBoBAACklKbUbOlSzz5avFj69rfLPyYAAIByIWgEAACQUldLzebN835HNTWFeyABAAD0VQWDRmb2TzMbk1k/zMxW771hAQAA9D21tdL48d6vSJLMvAwtn6oq7200bVrHHkhTp3oPJAJIAACgLyuWabSfpNUy61dJGl3+4QAAAPRtTU1Sfb1UXe2Bn6lTpVmz4mDS0KG+r7ZWWrbM+xvlCiEuY5s2TWps9O0tLXFmEsEkAABQacWCRm9J2j6zbpJC+YcDAADQt9XWSpMnS62tvqytjbe1tUlLlvi+QhlIudrbpeZmX29s9JnYomDSxInFg0gEmQAAQDkVCxrdLOkiM2uTB4weN7O2Ao/W3hkuAABA/xAFgtKIGmwnz2lv93K2qLwtmZEUaWwsvh8AAKAnaors+46kRySNl/QzSVdLer0XxgQAANDv1dV5ICdfeVqupUs9S2j99aW5c7P3RecnM5Iizc3F9wMAAPREwaBRCCFI+pskmdkRki4OIbzQS+MCAADo15qaPPOnuVkaNcq3vfJKvD5zZnzszJnS6NHSeut5P6S2to7Xq6qKM5IidXXeUymE/PsBAAB6olim0f+EEEaVeyAAAAADSdTnqJCamo7BoTffzJ+ZZObNt5uasrc3NUmf+5z02mvShht23A8AANATxXoaZTGzdc3sV2b2lJnNyix/aWbrlHOAAAAAA1FdnWcHJUUBI7N42+qrS/fcEzfdTqqtlc46y9fPO6/jfgAAgJ5IFTQys00kvSDpW5I+kvRkZvltSc+b2diyjRAAAGAAamry7KFcQ4ZIG20UPx8+3MvWZs3KP1PakUdK06dLu+9e2vExMxsAAEibafQLSQskbRJC2CWEcHAIYRdJm2S2/6JcAwQAABiIovK1WbOkoUPj7W1t0rx5cRbSnDnS+PHSmDHSlCkdZ0q7/Xbp7belRYtKO77GRu+XxMxsAAAMXql6GknaRdI3QgizkxtDCHPM7AxJl5Z4XAAAAINCbW12b6P2dp9NLRJCx3OSM6VdcYVnJ9XUSDfeWLpxNTfH92ZmNgAABqe0mUZDJX1YYN+Hmf0AAADohmR/o6oqzzzK7XeU7xzJm2dvsYX0xhulH1OEmdkAABic0gaNnpf0TTPLOt7MTNIJmf0AAADohqi/UXW1LydNip8PLfBPc83N3mvo1VelLbcsfdCoqUlaeWUPGOWbuQ0AAAx8acvTzpJ0u6SpZvZXSfMkrSPpAEljJe1dnuEBAAAMfFF/o6ToeUuLNHGiNHOmPx8yRFq2LO41tPHG0vbbS8ccU9oxjRolLV7svZKWW6601wYAAP1DqkyjEMJdkvaRl6L9WNIlkn4in0FtnxDC3WUbIQAAwCBWWyvNmOH9hULw/kKR9nbplVek556T/vQnyazjY+zY7s18tmiR91Y6/vjSvRYAANC/pC1PUwjhrhDCVpKGS9pQ0vAQwjYhhEllGx0AAACy5OsttMsucSZSrpkzpXHjvFF2Q0P6ANL8+dKaa0o33JDdqBsAAAweqYNGkRDCxyGE10MIH5djQAAAACisO72Fli6Ny9kaG9Ods8oq0tlnezbTsGFdCzgBAICBoctBIwAAAFROba00fnz3zm1v98BRQ0PnmUfLLSf95jdSa2vXA04AAGBgIGgEAADQzzQ1Zc+qZuYNstNob5emTOk8EHTbbb4/eV5zc/fHDAAA+h+CRgAAAP1Mba00dapnHFVXe8+iadPiZtnRo7q6+HWKBYLmz5dGjJCqMv+3WFWVv58SAAAYuAgaAQAA9EO1tdLkyV4+NnmyP89VVxcHfQqprs5fojZ/vnTggdK663omU3199/opAQCA/qvToJGZDTWzZ81sj94YEAAAAEqjqcmDPcW0tsYlai0tcb+jSy6RPv1p6eabpW22KRyYAgAAA1enQaMQwlJJoyS1ln84AAAAKJUoG6lY4+yox1FLiwePpk71fkevvSadfLJnIr3ySuHzk4GmQo210xwDAAD6nrTlafdIItMIAACgH0pTVtbY6P2NQoi3LV0q7bSTN90uFPBpbOy8sXZjo+9jFjYAAPqXtEGj30k62Mx+ZWY7mtloM6tNPso5SAAAAHRfba1nG0X9jcw6HjNlirTWWh23L1vmWUeFAj7JRtqFGms3N/u+YscAAIC+J23Q6L+SNpT03cz6dEkzch4AAADoo6L+RtFsa2PGdDxm3rzi14hK2ZIZRyNHxvsLzbCW3Nabs7BRFgcAQM/UpDzuyLKOAgAAAGUV9TeKtLRIo0d371pRxtHkydLdd0v77CNNn+7BoHylcP/6VzyTW2/OwhaVxbW3Z48ZAACkkypoFEK4ptwDAQAAQO+JStaioEo+1dXSqFHSwoXS22/H26OMo5oaad11pV13lebM8bKzxkYPCiVnWlt/fV9ef7100EHle025KIsDAKBn0panSZLMrMrMNjWzz5nZiuUaFAAAAMovKlnLZ/x4qbVVmjFDevzxOPCTFM2ydu210scf+/MpU6SJE7OPW3FF6dhjpQ8+KPlLKCrKbpJ6tywOAICBInXQyMxOlPSmpBcl3S+pLrP9FjP7VnmGBwAAgHKJStZmzcrucTRmTHYJWXJGtTRmzsx+PmWKdPnlHjzqTU1Ncc+l3iyLAwBgoEgVNDKzYyRdLOkWSQdKSs658ZCkL5d8ZAAAAOgVtbWeURSCP2bMyC4va2yUXn+9a9ccOzZuPP3aa9Juu0n/93+lG3MatbXS3/8ufepTHhyrZb5fAAC6JG2m0Xcl/TqEcKykf+Xsm6ZM1hEAAAAGnu70Apo502dpa2mR3n/fgzbXXlv6sXVmyRLpxRd9CQAAuiZt0GiUpEkF9i2StEpJRgMAAIA+J9kbqCuWLvUspUWL/HHXXaUfW2dGjPDlhx/2/r0BAOjv0v7n/11JIwvsq5PUxYRlAAAA9BdRw+zq6o77qqu9pG38+PznNjdLRx0l3Xhj7zfCljzbad11yTQCAKA70gaNbpf0UzNLVoIHM1tD0nfkvY4AAAAwAEUNs1tbPTiUb0aypqbsZtqRtjYPLH3969Jbb3Xv/i0tUsNTV6nmv/epoSHulZTGww9L8+YRNAIAoDvSBo1+ImmJpJcl3SspSPqtpKmS2iSdVZbRAQAAoE9JZh0lZySLmmnPmiUNHZp9Tnu7B4wWLfLnLS1SQ4NUU6NUQaDGRmnaxxupTdWaNs2fp/XGG9Lqq0trrZX+HAAA4FIFjUII70raStJ5koZImiWpRtLvJW0fQlhQthECAACgz0hmHeWbkay21rOLcoXgpWoPPOAlY1Om+HHTpkkTJxYPIjU3S+3y2rj29q415l6yRNpqq46BLAAA0LnULQ1DCB+GEH4eQtgxhLBJCGH7EMKZIYSF5RwgAAAA+pe6IvPq7rKLN8iOtLf7TGtTp3oQacoUDyo98EAcSPJeSkFSdklcGu3t0qRJ0nPPdeeVAAAwuHVpHgwzW9nMdjCzA8xsezMbXq6BAQAAoH9qaup6Zk8I8frSpZ59FGUjLVsmDbVlqlZbVklcGocfLu20k7R4cdfGAwAAuhA0MrOfSnpV0kOS/irpEUmvmdlPyjQ2AAAA9EO1tZ45VGhGtTSS2UghSMtCja6tPzdvSVwxTz0lPfkkjbABAOiOVEEjMztT0hnyYNHnJW0maXdJN0s608zOKNP4AAAA0A9FvY9mzep68KiqKjtTKZqt7WvTTu/yOO6+WxoyRFp33S6fCgDAoJc20+gYSb8OIRwbQrg/hDA5szxG0kWSji3fEAEAANBfRcGj//wn/Tnt7dmZRvX10jBbWviEIpYskb73PWmzzbp1OgAAg1raoNEISZMK7Lsrsx8AAADIa8IEzziq6lJHTT9+8mRp4+XeluT9jbpiyRLpnHOk22/v2nkAACB90OgJSVsX2Ld1Zn+nzOxHZvaUmS00s3fMrMnMNk05BgAAAPRjTU2eNVRdLY0ZI623XufntLf7cto2h+mckX9Sa6s/b2mJZ1draPDn+RxzjPS5z0lvvZVujGmvCwDAYFAwaGRmVdFD0rckHWVmp5rZSDNbPrP8vqSjJJ2U8n4TJF0qaQdJu0pqlXSvma3Wo1cBAACAPi8qVWttlWbMkF5/Pe55VF1deMa1F16Qvj/rOF3/9u4aPtyDORMnerPttjafZW30aGns2I5BnpVWktZeO/3saY2N0rRpft1p0/w5AACDlYXk/KbJHWbtkpI7Led5cnt7CKGmyzc3W0nSAkn7hxA6mzw1/0D7mwkTfPnAA5UcBYCBit8xAPqxmhoP1uQX/a+gqaoqzkDKNX68B6YiRxwh3X+/dPrpnnXU1TFUV+t/2U0AAAxQVmhHsUDPWSp/oGa4PNvp/TLfBwAAAH1cXZ1n9+QPCMX/P1soYCR51lFLi2c1Sd7T6PzzpUMOST+GqVOlELyfUl1d6uEDADDgFAwahRDO6IX7XyzpeUmP5dtpZscqMzPbZZddpmOPZZI2AACAgaqpycvBpkzp2XUaG+NsoyVLpEmTpNNOk157zYNATU3xcc3N8bbaWl/us49vr6+PjwUAYDAqWJ5W9hubXSjpIEk7hhDStBikPA0AOsPvGAADQEtLoeBRUJEM+v9JlpT9+c/SySdLH30U7x8zxvsnRVlNVVUeIIoCTTNnSnffLR13nF8LAIABrlvladlXMBsn6SuSNpS0XM7uEEI4vAvXukgeMNolZcAIAAAAg0TUMLu6ungpWiFtbd4Ue9kyac6cjvtnzsy+dnu7ZxZF7r9fOvFE6cADpTXW6N5rAABgIEgVNDKzwyRdKf/nnbclLc05JHUWkJldLOmr8oDRtLTnAQAAYHCpr09kA6lN7YUn/u1g5szi+6urOza8jnohffihb/vgA4JGAIDBLe1/eU+XdKukNUMI64cQRuU8atNcxMwukXSkpEMkvW9m62QeK3Vv+AAAABiompo8cFRdLdWvMFdDrPA0ZsOHd+3aS3P+CbS11UvipOygUSm1tEgNDT5DW0ODPwcAoC9LGzRaR9KlIYQPeni/E+Qzpt0naV7i8b0eXhcAAAADTFSm1toqTd76SN292fc0dGj+Y3vaeyhZonbAAdIpp5Q+y6ix0TOn2tp8GQWpAADoq9IGjR6RNK6nNwshWIHHGT29NgAAAAa2Cau+oCVLpPHjvXm15Mt11pEWLOjeNc3i69TV+Xprq3Tnnd4wu5QZQc3NhfsoAQDQF6UNGp0k6VgzO9jMVjezqtxHOQcJAAAARJJla3V10hFHSOM6+efN8ePzb6+uzpS/1ft1JWnnnaWpU9NlBHWl5CwKSknZQSoAAPqqtLOnvSbpOUl/KbA/dOFaAAAAQLdFZWuS9yYaNky69VYvKSvUALtQVk9rpk1SSEzrEvU0kjrPCIpKztrb4wBTNLZcTU3S6NEeMEoGqQAA6KvSBnr+JJ/x7BZJ09Rx9jQAAACg10U9jt59V5oxw9fHjs0OHo0Z48dFwZ18mps94JMbyOksI6grJWe1malj7rlH2nXXwscBANBXpA0a7Sfp1BDCxeUcDAAAANAVUTnYMcdIv/61B30mTfIAUHOzB3yiQFC0TfLSs6T2dmnKFC9zS2Ydtbd7NlNLSxz0Saqr8/Ok9CVnG27YtdcIAEClpO1FtEjSlHIOBAAAAOiqqN9Qsjwsa9a1yf482jZ9urTiioWvtzRPPv3MmV5ONnZsx95FycykzkrOQpCeftqvAwBAf5A2aHSVpEPKORAAAACgq5LlYGlmJGtslBYu7Pp9li3z4FFuc+wo+8gsDlAV0tYmHX+89MADXb8/AACVkLY8bY6kg83sHkl3SXo/94AQwpWlHBgAAADQmbq6uFdRmvKwfEGlmpq4IbZZdnlaPrnBqT339AymzixYID31lPR+h/+TBgCgb0obNPpDZrmxpN3y7A+SCBoBAACgVzU15e9fVEiyB1EkChhJ0kYbeVDogQe8gXa+AFJVlTRqlJepNTdLm2wi3Xln52NdvNiX+UrgAADoi9IGjUaVdRQAAABAN0S9itJqavJm14UCN2+8Ie2wg193++2l55+XPv7Y90VZSO3t2bOzTZ3q14xmURszxptx55aqLVniy+WWSz9eAAAqKVVPoxDCnM4e5R4oAAAA0FO1tR7kGT9eqq7uuH/ZMum///Usossuk5ZfPt5XrGwtChhJHlCKeh4lrbyy9LvfSfvt1/3xAwDQm9I2wgYAAAAGhOTsauPH5z9m6lRpr72k997r3j2mTPESt4aGeMa1hQul9deXHn6420MHAKBXpQoamdkrZtZS7FHugQIAAACl1tQkDR3acXsI0quv9uzaEyd6k+5oxrXdd5e+9CVvht2ZlpbsgFML/7cNAKiAtD2N/itvdp20uqQdJH0k6f5SDgoAAADoDVG52ujRpb92sm9Se7s0Z07H7blaWry0Ldmse9o039aV3k0AAJRCqqBRCOGIfNvNbBVJd0m6t3RDAgAAAHpPba1nG/VkVrNVVvF+SIsW+fOqKs8Siq5p5tva26Vf/1o64ICOjbIlDw5Nm5a9rb3dZ2kDAKC39ainUQjhA0kXSPppSUYDAAAAVMCkSXGZ2tCh0sYbe6AnqapKWnddX4/2RcsPPogDRpIHhCZNivcPGeI9lCTvk5SvUbbkwaFkU+3ovnV13XpZAAD0SCkaYS+WtEEJrgMAAABUxIQJ0pIl3stoyRLp/vulceN8hrUomNTeLr35pq9HwaNCM6oNHerXHD7cn0cBo+g6U6Z07FfU0pJ/RreaGu+9BABAb+t20MjMaszs05LOkESFNQAAAAaM5AxrbW3x9ihItOKKxc+fMsWDQOedJz3yiAd+crW1eT+lceN8/7hxXuKW77h8pWwAAJRbqp5GZtaujo2wIwsl7V2yEQEAAAB9SF2d9xlqb/dSsQ02SDebWWOj9OST0n//mx14Sgoh7nuU7xhK0wAAlZR29rSz1DFotFjSHEn/DiEsKOmoAAAAgD6iqSluUF1TI82d6+VnhQJBkSlTpJVW8vUhQzo/Pp9VV6U0DQBQOWlnTzujzOMAAAAA+qSoVK2hIZ7ZLLeMLHe2tFz5ys5ymWX3SFp9dekzn4lL01paPHjV3OzZR01NlK0BAMqrFI2wAQAAgAEvObNZCB4oGj/em1fX1/tsaT2R21T7vfeku++Om2VH2U5tbb4sNANbpKXFz81tuA0AQFoFM43M7KdduVAI4ayeDwcAAADom3J7G9XXewZS0vjxXpZWSlGAKBm0am/351LhDKQoyNTeHl8jd7wAABRTrDztjBTnJ/89hKARAAAABqyot1EyOJPrkkukPfZIV46WVnu7z7KWm4lUXe1ZRNXVcVlcMjhUKMg00FHGBwClU6w8bUgnj60l3S3JJM0s7zABAACAyop6G7W2+jJfIOLEE31/ISNH+tIs/X2rqjoGjCS/T1tbdh+lZHCori6+z2Caha2rZXwAgMIKBo1CCG35HpJqJf1F0hOSxks6NrMEAAAABrXm5vwBnsgVV/iy2DG5agrUBkRZREnJ4FBTkzR2bNxzabDMwjZYM6wAoBxSN8I2sw3N7ApJkyXtKul7ksaGEK7IBJMAAACAQS2Z3ROpSvwf9267Saec0rVrFpqRLWImDRnSMThUWyv94Q/SiBGFM6MGosGaYQUA5dBp0MjM1jSziyVNl/Rlee+i2hDCb0IInfwnDAAAABg8mpo8gBOpqpJWW83XN9jAZ0R75RXpO9+RDj44+9ju2mgj76H09tsdg0NXXy3Nn+/rubOpPfDAwJxdrakpfs8HU4YVAJRDwaCRmY0ws3MltUg6WtLF8mDR2SGERb01QAAAAKC/qK31XjqR9nbp/fc9QPP4477vn/+UDjhA+vznPYuop7OtvfGGL996q+O+ZNZTbq+fiRPT9/7JDTj15QBTba30y196QG4wZVgBQDkUmz3tFUkj5M2uz5Y0T9KqZrZqvoNDCH34Px0AAABA76iri6e6N/PlhAme/fKf//gxu+8uffyx9KtfdV5+1plopraWFmncuHi9sTEOSNXUdAxmFWqgnU9jYzyDW3KGtr5qk02k44+v9CgAoP8rFjRaJbOcKGmPFNeq7vFoAAAAgH6uqSme8r26Og7OzJ8vHXSQr3/8sS+j4FKuqqr824v5xjeklVf2+1ZVxcEkKTtgJHkwK9mMu7PeP8kG3/2hufSQIZ7ZBQDomWJBoyN7bRQAAADAAFFbG2fh5M58Nn169vMoMJQbJKqvLxxQKuS11+L13CBRriFD8mcaNTR40Cu3pKuuLs5a6g/NpZ99VnryyUqPAgD6v4JBoxDCNb05EAAAAGCgqauLy7okzzxabjnpk088UFNV5QGaoUM9aFNXFwdtGhp63u+okHxBpWRvo2S2VF2ddMkl0n77SR991D+aSycDaACA7iuWaQQAAACgB5qavM9QlNWzbFl2hk9trTRpUv5mzU1N3qx65szSjmnbbaUPP8wOZkWijKOoaXZ7uy9PPFE67DDvFfTNb5Z2POWwyiqVHgEADAwFZ08DAAAA0DO5s6nl9hEaOrTw7F61tdKMGdLChaUd0wsveOAqN2AUaWvzDKeoNC4KJP3+99L995d2LOWy8cbSV75S6VEAQP9H0AgAAAAoo7o6DxDlSttQOjo33zW6Y/Fin10trWQPo/ffL80Yym2LLTr2kwIAdB1BIwAAAKCMmpq8D1B1tWcWJYNAaRpKv/mmL6PMn+oSzFnclQbbUQ+j0aOl3XeXxo712dfMfD0KQLW0eB+mmhpfdiUwVWozZ0o33VS5+wPAQEHQCAAAACijaDa11lbvIxQFkNI2lN533+zn+ZpYmxU+v7OMm+pqaeutpZVXzt6+0Ua+nDzZX8PGG0uXXZbdY2nmTO9/JPly6tTshtqV8thjlbs3AAwkBI0AAACAXpIMIEXBmM6kKWGL+hMtt1zHfa2t0siRhc+NehjlBo3mzvWlmTRsmLT++tLrr3c8f8oU6YYbfBmNI23pXbmUqpQPAAY7fp0CAAAAfVihnkj5LF6cf3uhpteRRYuKT1O/dKl03XWFM5q+9rWO29KU3pXL6NHSoYdW7v4AMFAQNAIAAAD6sGRPpDFjpCFD4n1pg0lz5pRmLF3phZSm9K5cNtss+30CAHQPQSMAAACgD0uWtM2Y4f2Cxo/3IFKa8rZSWmONdMeNH589tkJNssvVPHvFFaU77yzNtdC3mpwD6F0EjQAAAIB+JBlEGjq08+OrqryJdU8MGeIzpV1xRbrsptwso0JNssvVPPv666W33irNteCfy7RpfaPJOYDeRdAIAAAA6KeKNZseMiSepa2npVpm0v77S08+KR1ySOfH52ZANTfnb5JdaHtPFZtNDl3X3ByXJla6yTmA3kXQCAAAAOinijWbXrZMmj7ds5IKlRONGSOts07n91m6VHruOen886W//KX4sVHAJlnSVF0db6+q8nG3tHTMWho1qvOxpLHaatK4cZRVlUryexZ9fgAGB4JGAAAAQD/V1FS8RC0qI6qp6bhvzBhfpi3juvfedI2w11orvndUerZsWby/vt7H3diYvb2U6uqkffctX/nbYJMsN4w+PwCDA0EjAAAAoJ+qrfWgyPjx+XsNRWVEbW0d9w0dKr3ySlwelrTyyt0f09tve0ZPsvQsWv7wh575VFubv8TplVe6f9+kLbf0/kvlKn8bbKJyw2HD4s8PwOBA0AgAAADox6LG2Fde6c+j4FGyjKiurmNQado0LxvLZ+HC7o8nBGn06I6BqhCkX/1KGjnSS9XyBbJKVfZ0223Se+9RVlVKf/iD9Kc/VXoUAHobQSMAAABgADCT9t7by4eiBthRGVFTkz9Pam/3Gdi6K80sarlaW6U5c/LvGzOmdGVPr7/uy6Ymz8LKfT/QdRMmeAYXgMElT3UzAAAAgP7m44+lSZPy9wmKspEaGjzDqL3dgz5RIGnKlK7d6403pHfekTbfvOfjTpo40UvU6uo8wNPdMqhhw3xZW+uv7amnpK22Kt04B6O99/bg2/TplR4JgN5EphEAAAAwACxY0HnmUJRxlMy8aWqKm2KnUVPjPY8OPrhn4801c6Y/StG0esUVpe99L37+5ps9H99g19Linw+AwYWgEQAAANDPtbRIl13m68Wmlo8yjlpb44bGtbXSjBnSkCHp7tXaKt11V3mbSre3e4ZQoddRTEuLdNFF3j/JzLfNm1fa8Q1W+ZqmAxjYCBoBAAAA/VxjozR7tq93N0snX1lbIdXV0iabdP0eXdWd19HYKM2alb3tnHNKM57BbM89pQMPrPQoAPQ2gkYAAABAP9fbU8sfd5z0u9+V9x6SZxsVypxqafF9NTW+fOABX+brz/Taa2Uf6oDX1CRdf32lRwGgtxE0AgAAAPq5urp4NrPuTi0/fnx8DTN/PnRo/mPfflv61rfi52ed1fX7RTbcsPj+QplTjY3S1KlxD6SJE/15PqNGdX98cAce6O8xgMGFoBEAAADQz+VrcN1VW20lrbCCB4zWWcevcdVVhY9vbpa+8hXp5pultdbq/thffbX4/qi/UZRJFGUWTZuWnV21dGnhnjvJptiF5GYudaef0kD2r39J999f6VEA6G01lR4AAAAAgJ6JGlz3xPXXe9aO5DOj1dYW7ylUVyf9/e/++Ogj6aSTOp+9rScmTvS+SyF4wKi9vfjxK64o3XeftN12HgTrTGNjfN0ou6mn7+lAYkYjbGAwItMIAAAAwP9UV0u/+Y1n2xTqjbT22tnZTJttVt6AkZSdSdRZwEjykrT3309//ebm+LpRdtPYsV3LOBrI2UpjxkjjxlV6FAB6G0EjAAAAAKqu9mVbW5xtU12d3SspmjHtxhs9EykSzdzWmwr1W5KkNdeUXn7ZA1mHHy7ttFPn18vXB2rmzK7N4BZlK0V9lroz+1tfdcst0u23V3oUAHobQSMAAAAAmjEjDhxJHjhqbc3ulRTNnrX88tnnVqJsadKkwvsOPtiXNTXSt7+d7nqF+kB1ZSa63Gylcs9i15ueftr7VwEYXAgaAQAAANCLL3rZWTKzqL7e+/q0tvpyq608QLTddpUtvRo6VNpoIx9vLjPphht8ecop0pZbSg89FJeOVVdLw4bFJWQ33ODPR4/Of6/cDKRiJWjJY7s7i11f9fWvSz/6UaVHAaC3ETQCAAAAoH33ld54o/gsbEuX+va5c/OXXq20krTeeuUfa2ur3/+3v+24b9VVpffeixtmS57109goTZ0az7QWlZB97Wv+PJ8xYzq+B8VK0JqavJdSFHDrzix2fZVZpUcAoBKYPQ0AAADA/xSbMWzZMi+5mjcvf+nVqqt6QEkqb5AhalQdlaFFhg6VFizo2DD7pZf8+HzXycdMWn996U9/8qBQc7MHgyTvc5Q8P/k+1NZKV18tLV4s7bFH4fG3tMTXravz4FKyR1RfVFMjLVlS6VEA6G0EjQAAAACkEvU8qq72YMfUqR6gqarygM0hh8THrrJKdgCnHHKDPkuXenAj19lnd+26IUivvSZNnOhZTe3t2cGiSL4StMMOk+bMKf66o2ylqOF4Y2PxYF1fcNVV0mqrVXoUAHob5WkAAAAAdPnl0nXXFT8mChpVVXl2TNRTqL5e2nnnOBtH8udDhsTn/PSn2dfaZhtfljojqbW147Zly7p3raVLC2cjSflL0BYv7vy6/bFhdkNDpUcAoBIIGgEAAADQAQdIe+5Z/JiaGumKK6Rx47yc6uWXfUa1yZOlRYukBx6Ijz31VGnrreOAyDnnSOPHS7NmSS+8ID35pG+/4ILSv5buBqKGDct+PnRoecrsijXMLtZou5L22Ufaffe+Ny4A5dXrQSMz29nMbjOz180smNkRvT0GAAAAANnWX19ad93ix5h5s+soS2bpUu9jJEkTJmRno/zzn9Ijj3iDbSm7cXRNjbTOOtJZZ0nf+16cjVQqaUri8gWDkkGjESOkSZPia40Z0/H43EbYkvSzn/lrKibKTsrXMLtYo+1KmjPHl31tXADKqxKZRitJelnStyV9UoH7AwAAAMixaFG6Mq6DDvJsIckDQ3vt5evnnCNdeWV83H33+TJZehWVYtXU+AxnW27pWTZtbaV5DT21cGG8/sILHgiTPMA1Y4avJ4NN+UrLGhulk08ufp+o6fWll3qWVrIJdl8tXUuW6fWlcQEor14PGoUQ7gwhnBZC+LukIhXCAAAAAHpDstQoTelRlH0TBVBaWjxrZvbs+Pxdd5VOO82DQlWZvzqqqrzv0W67eYBqn308+FBVgX/KzpeNtMYa8frIkb48+GB/nVGD7eRY8zXCPugg6bOfjZ8XKzcbN67jGOrq4ve1L5WurbJKvJ7vdQMYmOhpBAAAAAxyyVKjNKVHuUGjxkbfFkJ8/vz5nq3T1OQlWNXVvpR8ZrKkYs2me1PU02nrreNtTU3Sm2/G2VDJrKiamo6NsJub41IuqXC52cknSxtu2HEMyev1pdK1hx7yjKh8JXUABq4+HTQys2PN7Gkze/ryyy+v9HAAAACAASlfCVkhq64az5q2zTbSppv68VEgKTp/xx2lBQs80DB5ss9qNnmy9Mor5XsdPXXTTb586ql426JFhXsktbVll5ZJHY8tVG72zDPSq692vGZtbZzV05dK18491zOb/vrXjuMCMHD16aBRCOHyEMJWIYStjj322EoPBwAAABiQckvIipUezZ8fN8z+zGekl17Kf/7ChR5wyXevQsx8xrJKlKtJPoNcruWWK3z82LEdt+2+e3bZWaGZ0h56KO6TlOsXv5AOOaTj9mKzrpXb3Xf7Mt97BGDg6tNBIwAAAADll1tCVqj0qKUl7u8zdqw3iDbzWdRqa7PPv+oq6bnn8t9r442zt0VlbiH4NStVrnbDDdnPzaRPikzds2RJx/5C11wjPf20r7e0ZAfO2tulKVM8MCZJxx3n548cKQ0Z4vczk374w/zBquTn0t7u73u5+xq1tPhn/d572dvyHVepfksAyoegEQAAADDI5ZaQFSo9inrotLVJM2dKb73lz1taPBCSPH/77aUddsh/r9mzPUD03e/6tlVXjfdH/Y7OPDOevSxt5lHUI2jnnfM3mS61OXM69hf6xjekE07w9cbG7P5GkWiWurY2f8yZ4+9dZP586fbbO56X+7m0tJS/r1Fjo3/WudvyHVepfksAyqemt29oZitJGpN5WiVpIzP7tKT5IYS5vT0eAAAAAOkU6qGTr7/OqFHS8OHFrzdtmi/nz8++liR96UvS8cdLa62VPvMoCjg9+KBnPZVbvv5Ct9wiffCBdPXVPes59Pbb6e5f7r5G+a5faFul+i0BKJ9KZBptJem5zGN5SWdm1s+qwFgAAAAApJTsXZSUr7/O3LnSrbcWv97220unndbxWpJ03XUeMDr5ZGmlldKNLwRpk038GmPGdNzfk15JUflYUvQ8+fqTjbDL0XMoWdrXG32N8l2/0LZ87weA/q3Xg0YhhAdCCJbncURvjwUAAABAesneR2PG+CNfH6SWFum223ymtGL9bd54w3sAJYNCtbXS2WdLH37ozz/8sHDW0Jprdtw2fXrhTJee9EpatqzjzGjJGeOmTPGgSdTDqKFBuuSS/NfKfT25z6PgVvTeJfsFLVwYH1es/5SUfd7Ysd47KeqbNHZsur5Dl1zi5yflu2dTU1weWFPj7//Ysf6gzxHQf9HTCAAAAEAqyd5HM2b4I18fpMZGad48D6oU62+z//5emvbRR/68qipuEh0Fkm66KTtQkrT99iV5WSUV9SaaNk068cSO+3//e2mDDbK3tbX56/3iF6Vhw+JgVPTeJfsFvf++7/v2t4v3n5Kyz5s5M7u/0syZ6foOnXhix2BbvnvW1nrzc8kDbNE9Z86kzxHQnxE0AgAAAFBSySyfYv1tmpuzZyeLjv3JT6SPP/ZtixZ1zPCJ3HZbacZbDtFr+fWvs7fffrv06qsdj//oI+lf//IZ2ZIZTM3N2f2CIssv3/kY8p2Xu7+n10had11f5vu86HME9E8EjQAAAACUVLL3UbH+NnfdFR+Te+zxx8clahEzafRoX19rrXh7T3oVlUv0WnbdNXu7mZeJdeUa+XpJ3Xtv5+d31lcoTd+hZK+izgwfLq24Yv599DkC+qc++OsVAAAAQH+W7H1UrO/OtttKW22VfWzUB2jTTT0IUVPjy+pqL+uaNcv3J2cX60mvonKprvbXsvfe2dv//W/PJiomCtJE711Tkzf4lryP01ZbealZdbWXs0XLqHfQAw/4MpqdLmoMnmyiPWZMx88l2QMp6kHU1JRdTjdmTHz93F5FCxd6ZtiGG8b3XHvtzr8HufKNA0Bl1HR+CAAAAACkF/U+6sxyy3lJU7LMrKEh+5jWVi/F+vDD/GVdfdWyZd4P6I03Ou57/fV010i+h88/7+/X5Zd776PI0qXZy2nTpIkT/X2Lgmkbb+z9p1ZZxZ/X10tTp3a8X9QDqb097kE0ebK0wgrxMTNnZl8/edzs2X7MZz8rjR8vnX66B7guvlj66lfTveZi4wDQ+8g0AgAAAFARQ4dKTz+dvS1f35t33+2d8ZRad3v45OsJNGyYL5MBo3za2z2AlMy+mjvXlwsW+PK88/Kfm+xflOxBNGNG9nHJ6+frVXTTTdINN/j6M89IDz9cfMxpxwGg9xE0AgAAAFAR663nGSlJ+frerLlm74yn1OrqpJVX7vp5Ztk9m6R4hrnORDPQRSVuVVVxaZvkZX+f/nT+c5P9i5I9iHJne4tmuMt3XNp+TcUUGgeA3kfQCAAAAEBFXHeddN992duamrwXTtKGG0r77ivtvHN2wELy59XV3iD7V7+K9w8Z4mVZ1dWdj6McjbRra/21PPRQx33rr1/83JVXlnbfPXtbW1v+Y2syDUeqqvxRXy9NmhQHq9rbpXPOiY9/+WXpqKPyX6upSVp9dV9P9iD60Y+yj5s0yZe5vYpGjPDywtGjpZ12io/PLTnsTKFxAOh9BI0AAAAA9LqWFumxx3w92ey4ttbLoUKIm14/84z30rnqKu/Fk8xm2Wgjn0ls0SLp1FM94DRrlpdQzZ7tvXciX/6ydMEFHceSWw624YbZTaO7+/pGj5Y237zjvs56Gi1Y4OVdI0dKY8d6YGiLLfIfG72+5ZcvPMvZCSdk75s7t2Oz6Qce8N5B773nx0SBmoYGPz9pl118ecUV3muottaf19RIzz7rQaojjvBtv/+9tNtu2efnu/fYsT5GMw8UJccRXb+QNI2zaa4NdI+FfAWzfVO/GWhREyb48oEHKjkKAAMVv2MADFT8fhtwGho8ABRCnCGT2+y4oUGaMiV+vsIKHkhJbpM8uyhqBJ3vWsmAyZprSu+8U9KX0qdUVXlgZNmy/L2RJH+/xoyJm01H5ySbZ0dlg9Ex+QwZEr/vknT33d4k+4c/9FK1E0+UDj/c+zDtv398XDSzW/LeyeskjR/feRPs3OsV+i51dgwwiBUIOZNpBAAAAKACmpvjoEahZse52z7+OP9xyYBDvmudemq8PpADRlLcCLtYbsCyZR2bTec2z25uzj6m0HWSohK688+Xrr/e16+9Vnr00ezj8t27kDRNsNM0zqa5NtA9BI0AAAAA9Lq6uriXUKFmx8ljkttyDR1a/FrJZtu5PZEGmqgRdjHjxmW/R7nNsyXf31kD6tz7jBgRr3/wQbz+ySfZx+U2ui423jRNsNM0zk7zfQPQEUEjAAAAAL2uqclLhHKbKRc6JvqDP7dR9pgx3pi52LWOPDJe/+Y3s4MUUSPpXFEj5kraeON45ri0s5JFjbCL2XTT+D2K3rNJk7L7ODU1xccU6pX0hz9kP199de8bJWXPuLbGGtnHNTXFAabOxpumCXZTk/d0iq5X6Ls0alTxYwB0RNAIAAAAQK+rrfWeMq2t2c2UCx2zZIkvk42yQ/D1CRM6v5YkfeYz0qc+FZdDvf669MQTfo0oyLHbbtL99/ssY/ncf7+09dadv77cRtrRbGZdMXu29weSpMcfT3fO5Mn+fuQGepIlYi+9FL9Hzz8fn/PHP8bH1NbGx1x5pS8ffjj7mlttlf18lVWkBx+Uvv996ZhjfNsBB0j77JN9XG2tz4aXHG/k2Wc7HtuZ2lpp++3j6xX6Lt1yS/FjAHREI+zeRhNHAOXE7xgAAxW/3wa1lhZpxx2lt96Ks0TS/tEfzWKWT22t9Npr+XvqDBnSsWdPfxA1tE7r17+W/vxnbxJdVZX/3E02kaZP90BU8s/Hqip/D5ctk+bMyd4e9Q+qrvb1IUO851GU7dPSIq24ogetamv9833kEQ+UffnL3iT944/9fi0tPrNbc7OXlUVZQsltX/iCdM89fo/c4yZO9Nn3IrNm+T3zXbccwaQ04y/XvYGUCjbCJmjU2/gfHgDlxO8YAAMVv98GteQsal2d+Sp3BjZ0lAzydGd/T0Szow0b5sG7l1/2bKPll/dA03775Z/5TMrettFGno321lsdj8v9/KN79taMamnGz2xuqLCCQaMCFbwAAAAA0DckZ7rq6sxXzJLVuc4CQuUKGEnx5zNqlK+vt570y196f6R1142PyTfzWXLb3LlxyWLucYXu2VszqqUZP99T9FX0NAIAAADQp/Vk5itmyeq5Qo2wSyH6fKKgyYcfStdeK110kfdGio7JnR0td/a3FVf0gFGx43LvmWbWtVLIHWu+8fM9RV9F0AgAAABAn5ZmprVi5yZnW5O87Eny5tcbbZT/vOiY7ho3TlpppTgosdxyPbteWtXVXTv+3HP9PTAr/JrXWceXJ5yQvd3M39tofyFm8Qx4Y8ZkzwSX+1m+/37H85ua4gyi6PNvapLWWiv+TnzmM74/dxa1pqaOM+FF92xq8u+AWXlnVMudqS4a17Bh3ftOA72JoBEAAACAPi3NTGvFzp0xw2fXevhhDz4sXeqBkocflvbfX9phBz924kRpyy39mCVLsq8zcaLPrBaZP7/4fadOlT76SDrkEH++eLF0xhnpx51WbpDooYfSnztmjDedvvde6cUXpWuuifd9//vS0KG+ftddvswNfF14ob+3jz8uff7z8fb99/fliy9KN93kfYr+8Q9p3jw//tln/ZiGhvizHDbMlyut5M28k6Jj1lor/vxraz3QsnChb9tsMz8mmtEtedzZZ/u2r341+3q1tT4z3AknlHdGtei6s2Zlj2viRP+eMZsb+jKCRgAAAAAGvMcek/773/j53LkevHjppXg6+jXWiKd8f+WV7PPXX9+zUpIOOqjz+0ZlR1ttJZ1ySv5jdt1VOuqozq+VTzLLJxkAkzzIUsjYsT6j2McfS9ts40GX2bPj/ePGSd/+tq9vuKEvL7ww+xrf+Y4v99nHZy6LPPOML9dbzzOZ7r3Xx3bzzb79P/+RbrlF+uCD+JyNN/Zle7t01lnSmWdK3/hGvP+735VOPTX7/ttuK91/v6/vsosH5VZbreNrjT7f7bbzIFbSyy9LV1zR8ZxyyC3zu+02DyYCfRlBIwAAAACDwoIF2c8XLoyzaYYNk774RWnttfOfe+WV0rvvxs8fftgDEN/7XvZxw4dnP99hB9/29NN+Tj6PPurXj6y0UuevJfL66/H6O+9k74sya/KZMcOXK6yQP3Dxs59Jv/mNr99xR8f9USBJ6tgo+9VXfdna6tlG0b2i+0RZXOutF58zd64vV1tNWrTI90VZWpLPgDZtWsdxRIGY1Vf3TKa//tUDYUm33+7LW2/1nklJL73UMausHN57r2PQUZKWLSv/vYGesBD6zUz2/WagRTFdLIBy4ncMgIGK32/ogZYWn2Z92TLvH3PJJdLuu3uww8yXNTWeMXPLLV5GtOee0vTpha9ZXS21taUfQ22tB6V+/esev5ySGjLE34OlS6XRo/21l8rIkdnZS93R2ftcVeXv7XvveT+ktdeWPvnEA0+jRvkxM2f6cqedvHxv442lOXPyX2/MGOlPf5KOOSY+b8wYadKk9CVkLS1SY6MHuWpqfPxmHjjcfHM/JgQf+/vvS6usku66he7T3OwZbU1NvVfmVsl7oywKtrsnaNTb+B8eAOXE7xgAAxW/39ADDQ2eqSL5H+o1NR4kybXyyp59NH68/8Ffyqnmq6u9vOr880t3zVKpqpJGjMjfhHogWWON7GyxQoYO7fj9GD/eew+lkfy+JY0ZE2ddSR4seuUVadVV0103332mTo0DUPX16cfYU5W8N8qiYNCI8jQAAAAAA1o0nbvkgaB8ASPJS5e+8AU/vpQBI8mzTXprBrWuam/3YNlA99576Y7L9/1Ifoc6U+jYZJ+s9nbp2GM9UNldzc3xrHLt7V0bY09V8t7oXQSNAAAAAAxodXWeDSH5cujQ+HnS8sv7TGHJ40vp0ENLf81SMOvYpHkgWnPNjjOz5RP1uUqKGpqnUejYqFxO8n5PF1zQs+Bk8j5VVV0bY09V8t7oXQSNAAAAAAxoTU1ePlNd7ctJk3wZBZCiaetPOknaYov4+FzJgEPuVPeStOKKhcew+uredLqcdt656+fU1HiwrLW19OPpaY+bjTZKF7wbM8YDQpI36F5vPf98ktslafvtPSMmmqmt0LUmTco+b8wY/06k1dTkn3dUCllVJa27rvSPf8THRMGi5AxyXRWNKSoP68oYe6qpyUv2op+p3rw3ehdBIwAAAAADWm2t91tpbfXlhAm+bGvzmbNaW32K+Q8/lJ57Lj7+tNOkn/88bsT87397SU4I0oMP+rbHH4/vs+222T1rpPi4997zBsuRz362+JiPPlp68snC+zfYwJs157tX0kknFb/P0qXeDLsc/vnP7Off/W728yOPlC66KHvb3nvH63PnSn/7W/5rX3JJvD5jhnTccb7+4IMexFhuOd/+059mX2/06OzPbLvt4vXjj/dzJkyQrrkm+/pdCYDV1now5YknvOH6L37hM9E1NMTHRKVdPWkxHI3p1lv9+9qbjahra71v0znn9P690bsIGgEAAAAY9IYO7ThzWFWVdPXVcelWsoQryjRK9r9ZddWOQYCoZ80Pf5idNbP77sXHs9JK0jbbFN7/9ts+O1muz38++/nvf1/8Pu+/72VTG25Y/LjumD8/+/no0dnPr7pKeuGF7G1PP539/Kab4vXVV4/Xk5lRIUh//7uvH3KIdP/9Pnua5LPgRfbZx5cffxxv23777PFE1lvPlyecoG55+22ffe+uu6S//lX66lezA4pRCVxP56XafHNv8F0pNL8e+AgaAQAAABj0XnhB2mST7GDOFltIm20WB4uSQZ/hwztuGzFCuu227OtGJWnnn59d0nbNNXEJ3Lhx2efsvLN08cXFx3vssfmzcLpaJvT669Kjj5ankXFuv54vfSleP+ooX+YGTd56K/t5MlCXbGS9cGHcvDuawSuf9deXvvUtX3/++Y77V1klDrQl7xUFnT796fzX7Yp8WUVm0umne3CwJyZPruysd/1nMnZ0F0EjAAAAAJCXLd17b/z8zDOlW27x9bPO8r40kfHjpRtu8EBR1P/myis7zkL24ovxerIUavZsado0X5861QMIkXxlZrl+/3vp1Vc7bl9nnc7PjURlaZdeKp18crpzivVtyj0md2awJ56I16+80gNyuVky++6b/Txfb6nqan//o9daVRWXteUGMf78Z+m3v/X1TTbxZbI31ZAh0j33+PpOO8XbP/nEl8nPpas23thL0qJspuTYPvzQS9bSvJ/FrLuuNGxYz67RE1HwFAOXhf4TGuw3Ay1qwgRfPvBAJUcBYKDidwyAgYrfbyijlpbs0qmoGfIxx3ipUwheCjZjRhxomTvX++qcf773z4nK1Kqq4gybYcM8KPL6655ZUlUV90cyI0ujHNZcUxo7VnrtNf+MkoYP92BNGoU+n5qa7NK4IUP8M62p8WVdnWd7/fGP0n77SQccIM2bl31sdbW0bFnhe6+3nmeovfJKPONatL5smTRnTvbxp58uHXGE1NjoGWPRGHL7DLW05D+m0PZcucdde60H75Zfvvh72dIiTZwozZzpz6Ofr+Q9omtPm9bxvUzbLynf65DSvTao4PyJBI16G//DA6Cc+B0DYKDi9xvKqKHBm/omjR8vHXSQB4N+/nMPIjz3XFyu9OKL3k9m7lyf5Qt9R25gpzdFM5kNHepZZy0t5b/n+ut7xtu0aR6wjMaQ22+ooSH/MYW258o9bsQIz7b7wheKj6/Qz1fyHslrR4qNpdB9cl+HlO61oXDQiPI0AAAAAINavn4+zc3SO+9Id9wRb0tmqUQ9dHKbPaPyKhUwkjw40dzs/ZNmz+6de86b5/eMAi7RGHIVOibNufmOe/996S9/6Xx8hcZS6NqRYmNJM77m5vSvDYURNAIAAAAwqNXV5d+2ww5xHxwpu9lytJ5sbg1UVcXfp2QPrHLaYIPs73ByDEl1ddlN3aNj0pyb7zgpXYllobEUGluasRS6T+7rS/vaUBhBIwAAAACDWlOT91mJjBnj2/72N58uXfImycmmxZts4rNuVVf7H+2S/1Ea9TxKXiv6Azv5RzENhMujWFPoznrvpJFvlrZkoKK+Pu6lc9ZZ+Y/tLNC43nr+PTLz70/UuHvMGG+unbTcctIFF/g9N9jAr50cQ1JTk7TWWr6ePCZ5bKFzk8clS7/SaGrycUain6/cY0aOzH5/io2l0H1WWSX73KYmb5he7H1BcQSNAAAAAAxqtbXe5DoEf8yYEW+LbLGFNG5c/HzxYunUU/0P3dde820vvug9kI47zp9/4Qt+jaiHynPPxefvt5/f6+9/l774xThjIzfbophbb0133Oc+l/6afUnuzGqFjB8fr+fOvpZ02mnZM9gVssUW2c9/9StfvvWWdPvtvj56tJegSdKf/uTLv/zFP+vaWmnnnf3x9a9nX+vhhz0QGZ2ba/58b5xeVyd95Sv+/YlmiZsxIy55u/9+bya9eLGXidXW+ixxzzwTjyFXba1/Z/fcM/uYaDluXOFzk8c9+aQfd/XV/p52prZW2mYbX582Lf75yj3mH/+Qfvc76dxzfVuxsRS6z9e+ln1u9Jpnzuz69eAIGgEAAABAHgcdJH3zm77+299Kb7wR75s3T9pjj7hfSn19HPB57DFf3nVX9vWSgYLo2MWLpX/9y9f33186/PCOjbULZXUsWdL5a1hvPem//+38uL7o3XfTHRc1WT7pJOnmmz04lywrjNx/v/T449Laaxe/Xu75L7zgWWVrreXZPBtvLM2a5ft2313acENfj4I7kp/f1hZnp4Xgs7qtvroHg266KfueW2/ty08+8eUXviBttZWvb7ZZdoZRTY104IE+I5kUz9z3pS9JP/1p8df2ySfeoDvXhht6OWZnNtrIX4MkfeYz0mqrdX6OFL+fyWy9XJdf7p/hllumu2Y+O+/s72/SKadI11/f/WsOdgSNAAAAACCPV1+Vnn46fv7WW/F6FPSJsoymTfPp3iXPOEqKyteSwZvLL/dlMvCz447S9tv7jGx//nP8x3O+AIiUrp9S7qxV5bTyyt0/97DDfJkbVNh//87PPfxw6dJLPbjym994ptD06YXHV6x5+Xe/68sRI+JtNTXSPfd4BtDbb8dT3ocg3Xuvz5B2zz3xzHqSdMUVnhV06aVxqdmMGXH5Ym4voKee8mUUAPrVr6Qrr/T13Myk1tY4oLbFFtJnPxvv6yyQePfd0iOPdNz+4IPSeecVP1fy7+bixb6+2Wbx+9WZSy/1ZVtb4WPef9+XY8f659gd//2vZ+/l+uCD7l0PBI0AAAAAIK9tt/UypEiydCxfGVkUNIpE5VVRP5eRI+N9UZBgjz3ibUuXSq+84utf/7r07LO+Hm3LNWpU0eFL8h4vxfr8SNJee3V+nTTyZbCkde21vly0KHv7Lbd0fu5dd0knnOBlgcVm89psM18uW1b4mAsv9OWnPhVv23pr/x58/HH2Z7jRRtKmm3rQ5sADPfss6b33/F7J+0VZTlFvocjOO/syCgTutZf01a/6+sUXSyefnH+8zz2XPiNLkjbfPP/34ac/jcvsillrLWnVVdPfL/K73/ky92cknxde8GBcd2y/vXTwwR23V3JGvf6OoBEAAAAA5PHyy3FwZ/nls7NPohKdYcPicrKZM30Z9cSJ/piPgj5RhoYUZxolMyBuvNH7FG25ZXYmykorxQ1+k/KVreU2Spa890wxL7xQfH/SP//pfXn6gl/8wpcff5zu+LSBiAMPjHsXSdLQoV6OFkL8mW64oX8mL7/s07i//37HDLN8TbPnzfMysPr6OANN8kwfKQ7ImMXnX3KJdM012dc56STp5z/37LSowfchh8QZW4Vst52XseW67jov7evM229L77wTP08ze5oUl2AWy/Lad18P+Lz4Yvb73xVR1lfSV7/qwSR0D0EjAAAAAMjj7rs9ICBJp5+ePYX6iBGeQbL88p5RIsXZDGPH+jI3aLDrrh3vMWtWHGR66SVp6tQ4wyjy1FP5y2uefdbHlRSVTuWeX8zrrxffn3T44dIdd6Q/vpx+8ANfJnvgFGsknu/9z2fWrOxSxGT2U5RN9OqrcclYsoQxMmaM9/yJGjNHPv7Yy8PGjYtLG5Oi8q077ogDJ8nATLR+6qnSkUd6AC+6zjnnxH2OCmlu9u9YPvmCXPksWODL7bbrepZasWy03XbzHmJRn7DuePbZjplX3/1u5+8LCiNoBAAAAAB57Lqrl6hJPktU1KRY8kDLkCFeelZd7eVLUWlR9IdxFDwqFshobY37Dn360x5oGDMm+5hCvYs+/NCzTToTlVyVwocfput9UwrJhtVRL6B8ol5RG23k/WxeeMEzgaKp6iOFyvxyzZmTnVX2/PPS3nt7b6mNNvLPeuxYD+Acckjcc2qFFeJzDjjAxxwFBO+7z5dVVf59uOii7HtGGUbRd+y003yGPckzzSIh+PmHHBL3Mnr7bV+OGiX95CfFX9u668bf6Xz70oiy3h57rPPMpkhUElesD9eVV3oWVu73vysOPbRjJtW223aebYfCCBoBAAAAQB4bbug9ayLJjJwoI+T11z1INGtWHNiIZk2LspSiAESyP080K9v778fNi/fe2wMFM2d6Jkk0M1Wy71FXpS0fqrTjjuu47a234sBIsT5Em20m/frXPiPZu+965tGrr3bsYzN5si+TDatzbb65XyOaEU3yz/iOOzyj7JNP/LOeMcP33X23H3/OOVJDQ3zOhRd60+g//tGfP/CAZ6VFn0duxs2HH/oyChpddZX00EO+ftNN0hNP+Hp7u1/jkUfirLKoV5PUeWDs73+XLrus4/a//S19MDD67ptJ3/52unOinlXFsoii8r6ddpKOPjrddXM1N/sseblys/eQHkEjAAAAAMjj3Xe9ZCySL2MoCkxUV0vrr5//OlHz3+SU7OPG+TIZaFh33TjA9OKLcf+XQn9op5k9rViWUyTZ7LtS8gUypPwzfeUaMcJnG9thB+nYYwvP0BWVUiVnIssV9XeKpruXPJg3bpz05pvZU8yvsop/R7bYwku+hg+P9y1Z4mVjUZ+rn//cA0JRgCf3u/L/7d153B3j/f/x1yebapSKIBGSiJA0lq+d2JIUXxrqa2stVVJFCW2VUr7aohSlqFq+KBWlWrvaQzTxC2KJSkksFUQQkUQSkZBEkuv3x+caM2fOnPs+505yr+/n4zGPOTNzzcx15lwzueeTa8n/Bu3bpzV/li71c0N5E7J11imt4VSfbK2lrGXLyjvyriTp4B1g2rTq9kmCbH36VE6TlNWXXirvKLxaZrDeeuXr6wo6St0stJTQM7SYjNZp8GCfjxnTlLkQkdZKzxgRaa30fJNG9vbbHoSYMcNf6CdP9hf5fv28Y+If/ciHde/YMX0hffppHwFtyJD0OKNHe1Om9df3Pmhuuind1qOHD6d+yCHeEfKWW6Y1ThqqQ4fyGjb77Qf33798x5XWz8ybxf32t+m6Dh3gkkvg9NPrD7yY+f2wZIkHt7IB1WwgL1tG89tq0auX1yrKBqLOOw8uvtgDdGutlTbdA89TNgDbty+MHAnPPlva91SPHh6kS+71Y49Ng3/JPnUFv1qoiuFlBY0am/7gEZGVSc8YEWmt9HyTRrbppmlfQ1nt2qUvvfkaQBtt5M2iFi9O13XqBK+/Xvklc/31vUbKgAHF5xORygYMSJsdgtf2mj+/tv0r3XfJvZ69n4vO2UpUDBqpeZqIiIiIiEhO0h9R3rJl/hJZ1GRsypTyF8zFi+tuIjZtGuyxR+XziUhl+fvms8+Wb/+s5F6vZZ/WSEEjERERERGRnH79iocgb9fOaw8VbdtwQ9+W1alT+SheWeuuC6NGVT6fiFTWr1/pcrbfsIbsn5Xc67Xs0xrpsSQiIiIiIpLzwAPQv7/3udK3r0/t2/u6kSN9nrxUJh1S33OPb8u+aI4c6U3QAA44oPQcPXqkQ6Qn50uaxKwoq6664o4lrVuHDnDWWeXrLr+8uv3N0oBqtgxX02F7Q/Tq5fdN1je/mX6urzPtvn19/7/+tXT96qv7PLnXi/ZpSxQ0EhERERERyenTx/stWbLER356803/PGmSd7E1aZJ34LtoUdqpb48evm3RIu84t08fX/7kE99+0EFwzDE+UlgI3pfRf/+3p0vOt3Rp2uHw6af7PBmKvGdP2GIL3zcEH5I+b6ONfFvHjr78+eew1161f//s6GHgnXV37pwu33xz7cdsrRqrL6rttqt9n2z/Pocckn5Oyl3W4YfD+eenyxtu6GXx5JPLj7vLLqXLX/mKn2vxYrjvPr8nLr7Yt2U7Zh86FAYNgiuv9BHpsp1uZ0cSTCQjDz7zTGn5u+oqbw6a/w6DBvn88stL74+hQ+Gyy7xmH8CFF/o93aePf+/NNvP1IaSd1Sf3+r77+vJZZ6X7tCUKGomIiIiIiKwAs2alnydM8BHYIA0azZnjL7JPPZWme+utNF3esmX+Erv55h4EmjoVtt8+3b7OOr496623fJ4d6er00/0FvRazZ5cuH3MMHH+8B0hCKB8iPmvDDWs7V0t3xRW1pb/xRp8XNUfs379yE6u11qrtPIMHp31vnXIKbLJJuu2xx8rL3fvvly6/847Pi/r1Oe+80uWFC2HuXP+clMEJE8r369QJnnwSXnoJXnstvRdOPBGuuQaOOqo0fTKq2YcfwoIF6fp8jajE6NE+z98Xp57qNZPOPNMDSEmQKJHUBgQfxbB373T5wQfT9W2RgkYiIiIiIiIrwCqrpJ+7dYOBA/1z0hH21lvDbrultSfAX6KT2hF5Zj69+qoHgQYMSF/kwT9X6mS7a9f089ln+wt6LTbYoHT5ttv85XnAAD/ns89W3jebx7agltG6wIMmUNyZ+pIlxf3oQO3NvF57Ld1n3jwYMqTu9JXKUjYAmVhlFTjppNJ1n3/u8+R6LFpUvt8HH/h85kyfJ7WQZs70POZrsK22WumxE0kgNq97d5+/+27p+vXXhwMPhIsu8nPmtyf5ARg7Fo48svzYH35YfM7WTkEjERERERGR5dShQ+lL/ccfw7hx/jlZ/8wz3kTmkUfSdB98kAYRsnbcEX7yE/88Z47PX30VnngiTbNwYeX8ZPtRytZsyqvU+fZPf1q6fO+9paNGJd+tuckGyxpLrTWA/vKXytsmT/YaZUXygZP6fPSRNxsDuOGG0t9syy3hq18tTf+tb5UuJ/0SZZuXJc47z4e3T2RrlyXrs83JEm+95bWHhgzx3+o3v/H1d9wBRx9dnv7vf/f5nnum3wVKawJlJc3mevQoXX/YYd4X0fTp3txz2rTS7R9/nH5+912/XonkXsimaUsUNBIREREREVlOS5ak/QhB6dDfSce677/vtRWyNRYq1VK58EL49FP/XKkGSP74WUnToAsv9HmvXsX7FtV2gfIX/mzTJiiuRQLw+OPF6+tTKdiTBM6qscce9Xd+vDLkr2G2xtmK9M9/1r5PtknXbbeln2+91aes4cNLl3fc0edLl/o826fS9Olp2YK0dtlrr8GwYV7WL700DTQmzSpnz/aOp//yF2/O+cor6TGy90xi8mSff/yxB0m32sr7Bpsypfj7Pvywz++7z+fnnuvzuXO9P6Ik7/mactnjzZhRGlRKmh++8ELxOVs7BY1ERERERESWU+fOpTU3ttjC+6eBNKiw667wwx96c7HELrsU9zc0ZEjal0pSGyjpxDeRBKnmzSvff5ttfN6/P3TpUt4cpz533FG6nO3zBSp3yrznnrWdJ5HtDyrrj3+s/hijRjVep9RZJ55YulwpoLa8sjV7qpUNaGVr6tx8M9x5Z2naX/+6dDmprdOli5ftbNCkKJDZu7f3fTVqlNcaGjsWTjjBtz3/vM979vTvMXCgB9eywZp8P0RZTz3lI5e99BLcfXfaL1Te1lv7fNddfZ7cawsXlh5/vfWKvyuU1xzbYguf5++BtkJBIxERERERkeW0YEFpDaJ334XXX/fPSX8pH3zgNSeS2hPgtR4q9Tc0b5733bL99t7PzUcflTbh2XBDeO654n1ffDH9fOONtdfAyQdfDjvMgyPjxnk+skObt3V/+1tt6fPBv7xKnYwnNX+qte++aaDkZz8rDU5eckl5vidOLF2+/Xafz5tXXgvoBz8oP99nn8HTT3stosce86Zn+dpRu+/uwaU11/S8JU0vwfsbyjc7S/pNmjev9L659dbi5nrJPZcf3S3J7ymnwN57+4hpWfvsk34+9dS0LyVIg7YHH1x+vrZAQSMREREREZEVINuBcefOabOx5KWzf39/mc0GDVZdFXbYofh47dr5y+3HH/sIVqutVjri1dy5pbWWKhk1ypvc1CLf19HUqV7bY9Agrz2S7xOmLcv2M1WNUaMqbzvllMpNBmvtcPvf/06bli1Z4s266lKpf6uivnwOOqi8WWQSNE2aVRb1ufXcc94k7aabvEwnzefuvttrqeXPlZwjGZktMXp0cZl+7z2f52tRnXQSnHyyN49btiwNLiUmTUo/33uvp00kTT1rra3XWihoJCIiIiIispy6di1tnhZCWhMiWT9tmte8yPbjMnt25dpCQ4bAQw+lTXiWLIExY9LtH30Ejz5avO9OO6Wfr766lm/ihg0rXb70Ug9CLF4M99zTsP51Wqt8p8v12Xzzytsuu6zy6HPZzqar8d57aWfWV15ZOmpf0Uhs/foVH6eoI+yzzoJbbkmXu3RJPyfBoqJR1+bPhx//2Gutde7sn8GDUD/8YRpwSiTlLN/ssWfP8kASpKOebbxx+fpXX4URI2CNNUpHS4PSYNWjj3pNqMTFF6d5b4sUNBIREREREVlOs2Z5QCUxeXL60py8oL/4ojfvevrpNF1SMyJv7Fgf4Sqr0mhpO+9cvu6ZZ3yeNE+q1AdRJUmfSIlswAG8WV2RhtZA+u53i9cfd1z1xzjjjPIRwRpDPqhS62hq1Uqai9Vi9uz0c3YY+Tvu8CZqWaefXro8eLDPk++XDTRNmAD/8z+l5wnBO9u+4QYfEfDaa+Gqq3x7Uotp6lRf/+STXsuoe/f0GOPHl+c/6bA6uRd69vQmbp06FQeNkr6Tkv6XDjjA548/ngZY77wz7S8ska15tGCBN1FL7p0HHvD52LHl52sLLNTV21Tz0mIyWqfkzsv+F4GIyIqiZ4yItFZ6vkkz9vbbXrPBzGtrXH219z30zjveee+iRWlwKOlA+Bvf8HTDhnmzlwED/OW0Tx8v5kOGVD5fr15eY+KYY3wIcxFpOsOHN6w2XzNTcYxGBY0am/7gEZGVSc8YEWmt9HyTZmzTTdOOo9u18yZBX3xR92hQ+XTt2nmfR5Mm+ahS2VpLRTp18hoglfq/EZHG03LCKhVVDBp1aMxciIiIiIiItDZvvJF+Xras/oBPUbply9LjVLN/NWlERJaX+jQSERERERFZDv36pX22tGvntYAqjUSVyKdr1y7tiDg7Clsl1ZxDRGR56TEjIiIiIiKyHB54wJuWtW/v85Ej0+W+fb0PokTHjsXp+vdPO9wdObLuwFGvXum+VqFRSaX1IrJiDR/e1DlYuZqkeZqZDQdOA7oDk4CTQwhttC9yERERERFpyfr08b6IsvLLlRSlGzzYO89uyL4iIitSo9c0MrNDgCuAC4CtgGeAR8ysZ2PnRUREREREREREijVF87RTgBEhhD+FEF4LIfwY+BA4oQnyIiIiIiIiIiIiBRo1aGRmnYBtgMdymx4DdmrMvIiIiIiIiIiISGWNXdOoK9Ae+Ci3/iOgWyPnRUREREREREREKmjWo6eZ2XFmNt7Mxl9//fVNnR0RERERERERkTajsUdPmwUsBdbNrV8XmJ5PHEK4HkiiRWHlZk1ERERERERERBKNWtMohLAYeBHYM7dpT3wUNRERERERERERaQYau6YRwGXALWb2PPA0cDywHnBtE+RFREREREREREQKNHrQKIRwu5mtBfwS6A5MBIaGEN5t7LyIiIiIiIiIiEixpqhpRAjhGuCapji3iIiIiIiIiIjUr1mPniYiIiIiIiIiIk1DQSMRERERERERESmjoJGIiIiIiIiIiJRR0EhERERERERERMooaCQiIiIiIiIiImUUNBIRERERERERkTIKGomIiIiIiIiISBkFjUREREREREREpIyFEJo6DyIiIiIiIiIi0syoppGIiIiIiIiIiJRR0EhERERERERERMooaCQiIiIiIiIiImUUNBIRERERERERkTIKGgFmdlxT50FaFpUZqZXKjNRC5UVqpTIjtVKZkVqpzEitVGZaBwWNnAqz1EplRmqlMiO1UHmRWqnMSK1UZqRWKjNSK5WZVkBBIxERERERERERKaOgkYiIiIiIiIiIlFHQyF3f1BmQFkdlRmqlMiO1UHmRWqnMSK1UZqRWKjNSK5WZVsBCCE2dBxERERERERERaWZU00hERERERERERMooaCQiIiIiIiIiImVafdDIzIab2TtmttDMXjSzXetJPyimW2hmb5vZ8Y2VV2keaikzZjbYzELB1L8x8yxNx8x2M7P7zeyD+NsPq2Kfzc3sSTP7PO73azOzRsiuNAO1lhkz613hObN3I2VZmpCZnWlmL5jZPDObaWYPmNlmVeyn50wb1ZAyo+dM22ZmJ5rZy7HMzDOzcWa2Tz376BnThtVaZvSMadladdDIzA4BrgAuALYCngEeMbOeFdJvCDwc020FXAhcaWYHNU6OpanVWmYyNgW6Z6Y3V2Y+pVlZDZgI/BT4vL7EZrY68DjwEbBd3O804JSVmEdpXmoqMxl7U/qc+eeKz5o0Q4OBa4CdgG8CS4BRZtal0g56zrR5g6mxzGToOdM2vQ/8Atga2Bb/3e8zsy2KEusZI9RYZjL0jGmBWnVH2Gb2HPByCOHYzLo3gbtCCGcWpP8dcGAIYePMuhuATUMIAxsjz9K0GlBmBgOjgbVDCLMaK5/SPJnZfOCkEMKIOtKcAPwOWDeE8Hlc90vgBGD90JofylKmyjLTG3gH2C6EML6RsibNlJmtBnwC7B9CeKBCGj1n5EtVlpne6DkjGWY2GzgzhHBdwTY9Y6RMPWWmN3rGtFittqaRmXUCtgEey216DP+flyIDC9KPBLY1s44rNofS3DSwzCTGm9mHZvaEmQ1ZKRmU1mIgMDb5IysaCawH9G6SHElLcY+ZzTCzp83s4KbOjDSZr+F/v82pI42eM5JVTZlJ6DnTxplZezM7FK8V+0yFZHrGyJeqLDMJPWNaoFYbNAK6Au3xapNZHwHdKuzTrUL6DvF40ro1pMx8iP+vykHAgcAbwBNWT99Z0qZVes4k20Ty5gM/B74LDAWeAG43syOaNFfSVK4AJgDj6kij54xkVVNm9Jxp42IfRfOBRcC1wAEhhFcqJNczRmotM3rGtGAdmjoDIi1ZCOENPFCUGBerX54GjG2STIlIqxKbvl6aWTXezLoCpwO3Nk2upCmY2WXALsAuIYSlTZ0faf6qLTN6zgj+9+yWwBrAwcDNZjY4hDCxSXMlzVnVZUbPmJatNdc0mgUsBdbNrV8XmF5hn+kV0i+Jx5PWrSFlpshzwMb1ppK2qtJzJtkmUg09Z9oYM7scOAz4Zgjh7XqS6zkjtZaZInrOtCEhhMUhhMkhhBdjP54TgJ9VSK5njNRaZoroGdNCtNqgUQhhMfAisGdu055Ubms5rkL68SGEL1ZsDqW5aWCZKbIl3mxNpMg4YFcz+0pm3Z7ANGBKk+RIWqIt0XOmzTCzK0hf/l+vYhc9Z9q4BpSZIlui50xb1g5YpcI2PWOkSF1lpsiW6BnTIrT25mmXAbeY2fPA08DxeAdt1wKY2V8AQghHxvTXAieZ2R+A64CdgWH4P7rSNtRUZszsZPwfx0lAJ+AIYH+8jyNpA+KoNH3jYjugp5ltCcwOIUw1swuB7UMIu8c0twFnAyPM7HxgE+AM4FyNNtI21FpmzOwo4AvgJWAZ8G3gRHyoW2nlzOxq4Pv4vy1zzCzpL2R+CGF+TKPnjHypIWVGz5m2zcwuAh4C3sM7Tj8cGAzsE7frGSMlai0zesa0bK06aBRCuN3M1gJ+CXQHJgJDQwjvxiQ9c+nfMbOhwOV458bTgJ+EEO5uxGxLE6q1zOCBokuA9YHP8eDRPiGEhxspy9L0tgVGZ5bPjdPNeNC5O7BRsjGE8ImZ7QlcDYzHR7O5FA9YSttQU5mJfgn0wpvQ/gc4OoSgPgDahuFx/kRu/bnAOfGznjOSVXOZifScabu64f3KdAM+AV4GvhVCGBm36xkjeTWVmUjPmBbKFAwWEREREREREZG8VtunkYiIiIiIiIiINJyCRiIiIiIiIiIiUkZBIxERERERERERKaOgkYiIiIiIiIiIlFHQSEREREREREREyihoJCIiIiIiIiIiZRQ0EhGRZsfM/mRmwcwub+q8rEhmNszMjl4Jxz3azN40s8VmNreOdCPidS2a7sukOye3bZGZvWpmp5lZ2d8OZtbTzK6KeVhoZvPN7AUzO8vM1sh890rnDmbWLabrbmYXmtl4M5trZjPN7Akz263gvN82s9vM7D9mtszMxtRwzfY3s1OqTV+wf3KNOjT0GI3BzAbHfO7R1HmpxMzGVPPbJde8EbK0UpjZFDO7tRHP9zUz+328vvNiORhcIW07Mzsz5nGhmf3bzA5qrLyKiEjz1az/0BERkbbHzFYFvhsXDzez00IIS5oyTyvQMPzf3j+vqAOa2XrA9cBfgR8AC+vZZSawX8H62QXrdgGWAl3wvF8MLAMuzZx/N+B+YAbwR2Ai0BHYETgR6Ar8DHgIGJjPPvAA8HYIYXpctw1wCHAT8CzQCRgOjDGz/UIID2b23x/YMqb7Sj3fO29/YA/gshr3E2kp1gKOBv4FPA4cWEfa84CfA2cBLwKHAnea2b4hhIdXdkZFRKT5UtBIRESam/2B1YGHgaHA3sCDde3Qxm0MtAduDiE8VUX6xSGEZ6s89nNJwM7MHgW2AI4lBo3MbE3gLuA1YI8QwoLMvo+Z2aXATgAhhJl4wOpLZrYr/mJ7dmb1U8Am2UChmY0EJgGnU1oWjg0hLItpqvnu0sqZWXvAWlGgeXm8G0LoAhBrmhUGjcxsHTxgdFEI4fdx9Wgz6wtchD+LRUSkjVLzNBERaW6OAubgNVs+j8slMk2D+pvZSDNbYGZTzewHcfv3zez12ExqtJltlNu/o5mdH5tiLI7z882sYyZN0qxncG7fpJlV78y6KWZ2q5kdamavxfyMN7NdMmnGAIOAnTNNssbUdSHMrJ+Z3RubaX1uZs+a2d6Z7SOA5BhPxGOOqOuYDRWDM/8GemZWHwOsDfw4FzBK9lkQQni8jsMeBSwG/pbZZ27+hT8uTwB6FOSpZvEaHQX0yPwWUzLb67zudRx371jmrrLYjM/MDoz7fxaPd6eZ9cztV2/5iem2M7PHzezjmK+3zeyaKr/2GubNE+fEpkp/NbO1Msd+xczuLfhOyX1Q8fub2dpmdp15M8HPzOw982aDPQrSHhrvzUVmNsnMDqhwzK3MbGxsKvWBmf0Kr5mWTxfM7LdmdoaZvYOXp83jtkHmTRs/jdd0pJltltt/LzN7xsw+ib/dG2b268z2TWJZmBHzMjX+hivlP17NbHszGxXzsiDmf/uCdCdb2pTseTPbKS6PSNKEEKptyrcXXqMv33TuVmBzM9uwod9HRERaPtU0EhGRZsO8qdUewJ9CCDPN+9k50MzWDCHMKdjlTuBPwO/xJkx/NrONgcHAGXgzqSuA24AdMvvdjDeBuwCv2bIT3iyjD3B4A7O/K9AP+BXeROw84EEz6x1CmBvzdyteK+hHcZ95lQ4Wr8VTwKfAScAneHOvh8ybjDwSz/Ei3izsRLwZysziI5Ycu+jf/6VVvGT2Bt7KLO8JfBhCGF/fOQvysCrwHeDBEEJR07hs2k5407aXaz1PBefhwa7tSJvqLYrnqua6F+XxSOAG4DchhPPjuuOB/8Ob2v0G+BpwDvCkmW0RQvg0c4g6y4+ZrQaMBJ7HA6qf4r/HTlV+5z8Ao4DD8NppFwDrAUPi9v8DrjCz9UII0zL7/Qh4J567ki4xz2fi5W894FTgaTPrH0JYGK/HHvi9+FDcvjZ+f3YE3kgOZmZdgX8C0/Hg3iLgNEoDllnDgLfx2jILgGlmtg/wj3iuI2K6XwBj47V/z8z64E0r78J/n8Xx2vTJHPshPIh9AjALD1wOZSX8x6uZbQE8Cbwav1PAn2NPmtmOIYR/x3THAJcDN+LPwI3w6/r1Bp56U/waT86tnxTnA/AyICIibVEIQZMmTZo0aWoWE978KAAD4/Jecfn4XLpz4vojM+vWBJYAHwOrZ9b/JKbtFZc3i8vn5I75y7h+i7g8OC4PzqUbFtf3zqybgr9YrplZt21Md3hm3RjgqSqvxe/j9+mbWdcef7n+V2bdHkX5rHDMETFt0fTzguu7Cv4fTGvjAYElwP6ZdK8B4xr4Wx8Wz7FfFWkvwPtS2rWONE8BY2o4/wjg/eW47sk16hDL7RfAMZntq+EBpz/njr8hHpw4uZbyk1neosbrnJTjR3PrvxfX7x6Xv4YHMX+VSbM2Hkw4o8Zztgc2iMc/ILP+aTwg0i6zbseYbkxm3W/jNdogs64zHrQJuXMFYBqwam79ZOCJ3LrV4zH+EJcPjvuvXuF7dK22jFZ5XaYAt9ax/S5gLvD1XJ5nA/fE5XbAe8DDuX0PjHkdUeHYFZ8TeJ9o0wvW9437fH9FfH9NmjRp0tQyJzVPExGR5uQo4M0Qwri4PAp/ISxrohZ9WesjeE2kGcCzIYRsDZ7X43yDOE9G4SpqigHehKwhxoXS2lCvxHml2hH12Q3/Ll/+738IYSnelGtLM1u9gcedgdewyU+3FKRdiAdDZuCBmzNDCPc18Lx5R8Xj1tlfipkdjte2OC+EMHYFnbsutV73y4FzgYNDCDdk1g/EX/j/amYdkgl/4X+dtBwm6is/b+IBhevM7Agz24Da3JFbvhMPxA2M3/FT/B44xtIR8obhTcLq7bjdzE4wH3FrPh50mxo39Yvb2+Pl7K6QaVYYvH+tKbnDDcR/g/cy6RbgnaYXeTSE8HkmLxvjtW/y1/4zYBzptZ+Al++/m9nB5n37ZH2M12C6yMyOjcddmXbDa97NTVbEZ9n9pM+l9eN0Z27ff+DXXUREZIVS0EhERJoFM9sWbwZxj5l93cy+jtd+uAfY0cw2Kdgt32RtcYV1kI6u1SXOP8ylm57bXquSJlYhhEW589aqC+V5BM+n4TWrGuKLEML4gumjgrQ7AtsDB+BN3y6y0j6e3gN61ZoBM+uO13y4LdTRYbGZfRuvEXRjCOHsSulWsFqv+2H4iHGjcuuTAMQoPDCRnTbHOwDPqrP8hBA+wZuSTQOuAaaa2USrflj0kt83hJDcK9l+h67Bg1RDzcyA44B7Qwgz6jqwmf047jsKr/GyPV52vsw/XmunYz4fRXkDuleZLpH/vZJrfyPl135f4rWPgcG98L+HbwGmm/c/NShuD3gTzPHAhcB/zPuROqFCPpZXXWUvKXfd47zkN4mBzVkNPO8c4OvxN8/nB4pHVhQRkTZCfRqJiEhzkdQm+kWc8o7Em5Atr+QFqBul/fN0y21Phq7vlNs//7K/sswmzVNWN7zJSFEfTyvaizGo84L56GSvA1ea2X/F2iKjgD3NbJsQwos1HPcI4ohvlRKY2e54bYp7SfuAagy1XvfdgceAR8xsaAhhflz/cZwPI+0bJuvTgnV1CiFMAA6KtWa2xZsM3hF/j4n17L5udiH2E7Um8EHm+BPNbCx+vRfizZOqufaH4k3BTs0cP9958iw8aLMu5dYF3s0sf1hHuiL5vriSa38m5cE8SAPJhBBG4yOFrQLsjPdt9FDsS2pWCOFt4MgYUPkvvJ+ra8xsSqjQv9VyqKvsJeUuCSqV1IqKNbm6NvC8k/CmqBtR2q/RgDh/tYHHFRGRVkA1jUREpMnFF9jDgOfw2hT5aQLw/YL/CW+I/xfnh+bWfy/Ox8R58hK7WS7dPstx7kXAqlWmfRKvYdU7WRFfDA8BXso1wVvpQgiz8BfqzYCkdssNeDDgKjPrnN/HzL4aOz/OOxJ4OQZBypjZQLy5zRPAEaGBo6TVo9JvUet1n4T3G7QxHjhaLa5/Bg8M9a1Qs+sNGiiEsCQ26/oV/rfcN6rY7bu55e/Efcfl1l8DfAvvs+k/IYR/VnHsr+IBoawf5PK8FHgBODjT/A0z2wHv0DtrHP4bbJBJ1xn4dhV5Ae9/agqwaYVrX9aheghhUfyuF+P9J22Y2x5ieT0lrso/F1aEJ/FaXl9LVsTP3yZ9Lr0fp+/k9t2fhv9n8KP47/e93PojgIkhBHWCLSLShqmmkYiINAf74DV4Tg0hjMlvNLPr8NGdBgOjl+dEsTbF34BzYo2NZ/A+VH4F/C2E8EpM96GZPQmcaWaz8OYgR1A6slKtXgWGm9kheC2nT+sIHlyO11J53MzOxjspHg5swvIFrjqZ2Y4F6z8repnOuQ4fxeqXZnZXCGF2bB51P/AvM7sSb6rVEW+idDzeue+XtT3MbGv8hfvU/MHj9v74iFWzgEuAbbKxwhgsSdL2wvvJAS8/y8zs4Lj8QgghW3sl71WgS2xqNB5YGH/7mq97COG12GxvNDDSzPYOIcwzs9OAq81sbbz/rU/w5mCD8I6fb6sjfyXMbF+8udh9+EhWnfFO3j+lPPBTZFMzuwn4e/wuv415eCKX7m58pLWdqfAbFXgU+IWZ/S8+uts38U6m887Ga2XdF+/ptfH+oKbn0l2OX/PHzOwc0tHTPqcKIYRgZicC/4gB6Tvw8rQuPtrc1BDCZXF0u93wfrXew2vqnIk3AZwYRzO7Argdr4HTHi8bS/DR3QAwsynAlBDC4Cqy1zNTRrPG4SPm7Qs8YWa/w2tQ/QIPyv0mfrdlZnYu8CczuwGvjdcH7/frE7yfqi+Z2bfwsrJ5XDUojk63IKkpFUKYYWaX4c+6T/GmqIfgv+N+iIhI27aye9rWpEmTJk2a6pvwF+F5wFcrbF8D78R2RFw+hzhyVS7dFHKjE5GOHrVHZl0n4Hy8NtEXcX4+0DG37/p457tz8RfbC4BjKB49rWxUJHKjtOHNTB7GX/RLRoyq8L37xWvzCd5c6Flg71yaFTV62sRMusLrG7cdR/moWL2Aq/BA2CJgPl6r5AxyI1PhL+FfAOtWyOOwOvIYakg7rJ5r0Rnv3HpOTD+lxutedo3w2kbv4wGA1eO6oXgwaR5eht/EO5YeUEv5iXm6HQ8YLcSHtn8Y2KGe7zk4HufA+PvPjeXvNqBrhX2uwwM0a1V5/66KB3VnxmM/iNfUKSn/Me1heE2gRXgtrQPwWjRjcum2BsbG7/oBHtQ9t6AMBOD8CvkaGPMyJx5nCh40G5jZ/g88YLQIb/p1J9Avbl8Hb0L5n/jbzcZrA+2VO89M4O9VXKcpBeU0mQ6OaXbAg6zzgQV4bbvtC451Mv7cWogHPXeN3/PyKs85JZeuPd789914LV5O8qRJkyZNmtr2ZCHkm4GLiIiISFsUa99NBsaGEL7f1Plp7mIH/W/gwbvnmzAf2+KB2iNDCEUjIYqIiDSImqeJiIiItHFmtjrebPBwYAPg0qbNUYsxCHi8MQNGsZPxE/GaWPPwPq3+F6+Fdndj5UNERNoG1TQSERERaeMyfTLNAM4LIVzVpBmSisysG97UcGt8BLw5eJO2M0IIU5swayIi0gopaCQiIiIiIiIiImXa1Z9ERERERERERETaGgWNRERERERERESkjIJGIiIiIiIiIiJSRkEjEREREREREREpo6CRiIiIiIiIiIiUUdBIRERERERERETK/H9UfpgCHyOW6QAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boundary_erc721_amount = \\\n",
    "    grade_boundaries_analysis(\n",
    "        distribution_df=address_agg_by_sum_amount_of_tokens_df,\n",
    "        value_column = 'sum_amount',\n",
    "        value_chart_label = 'Amount of ERC721 tokens by address, Log10',\n",
    "        value_name = 'Amount of ERC721 tokens',\n",
    "        chart_title = 'Distribution of Addresses by Amount of ERC721 Tokens',\n",
    "        max_show_value = 5000)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Analysis of Grade Boundaries. Fee spending to contracts\n",
    "Described in the [Extraordinary Hackers and Masters of the Great Web. Gas Analysis](gas__hackers_and_masters.ipynb)\n",
    "Jupyter notebook"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Distribution Rules. Masters of the Great Web\n",
    "\n",
    "<table style=\"text-align: left\">\n",
    "    <thead style=\"text-align: center\">\n",
    "        <tr>\n",
    "            <th rowspan=2></th>\n",
    "            <th colspan=3>Grade</th>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>1</th>\n",
    "            <th>2</th>\n",
    "            <th>3</th>\n",
    "        </tr>\n",
    "    </thead>\n",
    "    <tbody>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Amount of ERC721 tokens </td>\n",
    "            <td style=\"text-align: center\"> > 0 NFT </td>\n",
    "            <td style=\"text-align: center\"> > 12 NFT </td>\n",
    "            <td style=\"text-align: center\"> > 160 NFT </td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Owners of the Selected ERC721 tokens </td>\n",
    "            <td style=\"text-align: center\"> - </td>\n",
    "            <td style=\"text-align: center\"> ENS </td>\n",
    "            <td style=\"text-align: center\"> Gitcoin Kudos or LAND </td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Fee spending to contracts<sup>1</sup>, by contract creators, ETH </td>\n",
    "            <td style=\"text-align: center\"> > 0 ETH </td>\n",
    "            <td style=\"text-align: center\"> > 0.004 ETH </td>\n",
    "            <td style=\"text-align: center\"> > 0.477 ETH </td>\n",
    "        </tr>\n",
    "    </tbody>\n",
    "</table>\n",
    "\n",
    "<sup>1</sup> including contracts created by factories only\n",
    "\n",
    "### Distribution Rules. Cyberpunks\n",
    "\n",
    "<table style=\"text-align: left\">\n",
    "    <thead style=\"text-align: center\">\n",
    "        <tr>\n",
    "            <th rowspan=2></th>\n",
    "            <th colspan=3>Grade</th>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>1</th>\n",
    "            <th>2</th>\n",
    "            <th>3</th>\n",
    "        </tr>\n",
    "    </thead>\n",
    "    <tbody>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Owners of the Selected ERC721 tokens </td>\n",
    "            <td style=\"text-align: center\"> - </td>\n",
    "            <td style=\"text-align: center\"> - </td>\n",
    "            <td style=\"text-align: center\"> Unicorns, DRAGON or Cryptopunks </td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Urbit Owners </td>\n",
    "            <td style=\"text-align: center\"> - </td>\n",
    "            <td style=\"text-align: center\"> - </td>\n",
    "            <td style=\"text-align: center\"> here </td>\n",
    "        </tr>\n",
    "    </tbody>\n",
    "</table>"
   ],
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    "collapsed": false
   }
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  {
   "cell_type": "markdown",
   "source": [
    "### Create Distribution Table for ERC721 Tokens"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table erc721_analysis:erc721_analysis_distribution has been deleted.\n",
      "Table erc721_analysis:erc721_analysis_distribution has been created and filled 1,670,266 rows.\n"
     ]
    }
   ],
   "source": [
    "query_4 = f'''\n",
    "    WITH erc721_amount AS (\n",
    "        SELECT\n",
    "            address,\n",
    "            count(distinct token_address) as number_of_tokens,\n",
    "            ARRAY_AGG(DISTINCT token_address) as token_list,\n",
    "            sum(amount) as sum_amount\n",
    "        FROM `{PROJECT_ID}.{ERC721_ANALYSIS_DATASET_NAME}.{ERC721_AMOUNT_TABLE_NAME}`\n",
    "        WHERE amount > 0\n",
    "          AND address != '0x0000000000000000000000000000000000000000'\n",
    "          AND address != token_address\n",
    "        GROUP BY address),\n",
    "    urbit_owners AS (\n",
    "        SELECT\n",
    "            owner,\n",
    "            count(point) as sum_amount\n",
    "        FROM `cosmic-keep-223223.erc721_analysis.azimuth_points`\n",
    "        GROUP BY owner)\n",
    "    SELECT\n",
    "        '{MASTERS_AUDIENCE}' as audience,\n",
    "        CASE\n",
    "            WHEN (SELECT COUNT(1) FROM UNNEST(token_list) el WHERE el IN {erc721_tokens_manual_grade_3_tuple_str}) > 0 THEN 'Owners of the Selected ERC721 tokens'\n",
    "            WHEN (SELECT COUNT(1) FROM UNNEST(token_list) el WHERE el IN {erc721_tokens_manual_grade_2_tuple_str}) > 0 AND sum_amount <= {boundary_erc721_amount[2]} THEN 'Owners of the Selected ERC721 tokens'\n",
    "            ELSE 'Owners of ERC721 tokens'\n",
    "        END\n",
    "        AS segment,\n",
    "        address,\n",
    "        CASE\n",
    "            WHEN sum_amount > {boundary_erc721_amount[2]} OR (SELECT COUNT(1) FROM UNNEST(token_list) el WHERE el IN {erc721_tokens_manual_grade_3_tuple_str}) > 0 THEN 3\n",
    "            WHEN sum_amount > {boundary_erc721_amount[1]} OR (SELECT COUNT(1) FROM UNNEST(token_list) el WHERE el IN {erc721_tokens_manual_grade_2_tuple_str}) > 0 THEN 2\n",
    "            WHEN sum_amount > {boundary_erc721_amount[0]} THEN 1\n",
    "            ELSE null\n",
    "        END\n",
    "        AS grade,\n",
    "        sum_amount,\n",
    "        number_of_tokens\n",
    "    FROM erc721_amount\n",
    "    WHERE number_of_tokens > {min_number_of_tokens}\n",
    "\n",
    "    UNION ALL\n",
    "\n",
    "    SELECT\n",
    "        '{CYBERPUNKS_AUDIENCE}' as audience,\n",
    "        'Owners of the Selected ERC721 tokens' as segment,\n",
    "        address,\n",
    "        3 AS grade,\n",
    "        sum_amount,\n",
    "        number_of_tokens\n",
    "    FROM erc721_amount\n",
    "    WHERE (SELECT COUNT(1) FROM UNNEST(token_list) el WHERE el IN {erc721_tokens_manual_cyberpunks_tuple_str}) > 0\n",
    "\n",
    "    UNION ALL\n",
    "\n",
    "    SELECT\n",
    "        '{CYBERPUNKS_AUDIENCE}' as audience,\n",
    "        'Urbit Owners' as segment,\n",
    "        owner as address,\n",
    "        3 AS grade,\n",
    "        sum_amount,\n",
    "        1 as number_of_tokens\n",
    "    FROM urbit_owners\n",
    "'''\n",
    "\n",
    "if DROP_TABLES:\n",
    "    drop_table(table_name=ERC721_ANALYSIS_DISTRIBUTION_TABLE_NAME,\n",
    "               dataset_name=ERC721_ANALYSIS_DATASET_NAME)\n",
    "\n",
    "if CREATE_TABLES:\n",
    "    create_table(query=query_4,\n",
    "                 table_name=ERC721_ANALYSIS_DISTRIBUTION_TABLE_NAME,\n",
    "                 dataset_name=ERC721_ANALYSIS_DATASET_NAME)\n"
   ],
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Create Distribution Table for Spending Fee Analysis\n",
    "Distribution has been calculated in the [Extraordinary Hackers and Masters of the Great Web. Gas Analysis](gas__hackers_and_masters.ipynb) Jupyter notebook."
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
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