{"cells":[{"cell_type":"markdown","metadata":{"_uuid":"d6996868a117c237300d06626ac7cff0507e3819"},"source":["# Bitcoin LSTM Model with Tweet Volume and Sentiment"]},{"cell_type":"markdown","metadata":{},"source":["## Load libraries"]},{"cell_type":"code","execution_count":137,"metadata":{"_uuid":"e777e8fcb088070e8264d936b82cfbf74d91e6a8","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["['$RECYCLE.BIN', '03_IntelliJ IDEA', '2019版安装教程.docx', '46-clion2020破解-无限重置插件', 'Adobe_Premiere Pro 2023', 'anaconda3', 'BaiduNetdisk', 'BaiduNetdiskDownload', 'cent browser', 'CLion 2020.1', 'CLion 2020.1pjb', 'CLionProjects', 'CLIPART', 'CloudMusic', 'CN', 'Config.Msi', 'DAUM', 'Document Themes 16', 'DouyuPCClient', 'DTL8Folder', 'DumpStack.log.tmp', 'Epic Games', 'FFOutput', 'FIRECOLUMN.jpeg', 'FIRECOLUMN1.jpeg', 'FIRECOLUMN2.jpeg', 'firegod.jpg', 'FormatFactory', 'GameDownload', 'Games', 'Git', 'Github', 'GOG Games', 'HuyaClient', 'iGame', 'InfoClient', 'JetBrains', 'kaggle', 'KDR', 'MATLAB_2016', 'Microsoft VS Code', 'MSOCache', 'Netease', 'network', 'Office16', 'Office2016简体中文64位(专业版)', 'opencv', 'pagefile.sys', 'PhotoshopCS6', 'Program Files', 'Program Files (x86)', 'Programs', 'PyCharm 2021.3', 'python', 'qqpcmgr_docpro', 'Recovery', 'Riot Games', 'Stationery', 'steam', 'System Volume Information', 'TECENT_FILES', 'Temp', 'Templates', 'Tencent', 'thunder', 'Thunder.exe', 'Thunder9', 'Tongchuan', 'V2RAYN', 'VSCODEcode', 'WeChat', 'WeChat_Files', 'WECHAT_FILES_COMPUTER', 'yk_temp', 'zd_zts', 'zlad', '斗鱼视频', '新建文件夹', '群星 豪华中文 v3.8.1+银河典范DLC+全DLC+赠品+3.8.2升级补丁 安装即玩', '迅雷云盘', '驱动人生C盘搬家目录']\n"]}],"source":["import pandas as pd\n","import re\n","from matplotlib import pyplot\n","import seaborn as sns\n","import numpy as np\n","import os  # accessing directory structure\n","\n","\n","# Input data files are available in the \"../input/\" directory.\n","# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n","\n","import os\n","\n","print(os.listdir(\"/\"))\n","\n","# set seed\n","np.random.seed(12345)"]},{"cell_type":"markdown","metadata":{"_uuid":"07abc867e57ccf5d705cafa32e4a8b45b6072351"},"source":["## Data Pre-processing"]},{"cell_type":"code","execution_count":138,"metadata":{"_kg_hide-input":false,"_uuid":"bd4ae8de3e746a9f1114ba28b8089ba269d26e02","trusted":true},"outputs":[],"source":["notclean = pd.read_csv(\n","    \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/LSTM/cleanprep.csv\", delimiter=\",\", on_bad_lines=\"skip\", engine=\"python\", header=None\n",")"]},{"cell_type":"code","execution_count":139,"metadata":{"_uuid":"669580e3ae15f1b020a110cc646fdb3039b5796b","trusted":true},"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>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2018-07-11 19:35:15.363270</td>\n","      <td>b'tj'</td>\n","      <td>b\"Next two weeks prob v boring (climb up to 9k...</td>\n","      <td>0.007273</td>\n","      <td>0.590909</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2018-07-11 19:35:15.736769</td>\n","      <td>b'Kool_Kheart'</td>\n","      <td>b'@Miss_rinola But you\\xe2\\x80\\x99ve heard abo...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2018-07-11 19:35:15.744769</td>\n","      <td>b'Gary Lang'</td>\n","      <td>b'Duplicate skilled traders automatically with...</td>\n","      <td>0.625000</td>\n","      <td>0.500000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2018-07-11 19:35:15.867339</td>\n","      <td>b'Jobs in Fintech'</td>\n","      <td>b'Project Manager - Technical - FinTech - Cent...</td>\n","      <td>0.000000</td>\n","      <td>0.175000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2018-07-11 19:35:16.021448</td>\n","      <td>b'ERC20'</td>\n","      <td>b'Coinbase App Downloads Drop, Crypto Hype Fad...</td>\n","      <td>0.333333</td>\n","      <td>0.500000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                            0                   1   \n","0  2018-07-11 19:35:15.363270               b'tj'  \\\n","1  2018-07-11 19:35:15.736769      b'Kool_Kheart'   \n","2  2018-07-11 19:35:15.744769        b'Gary Lang'   \n","3  2018-07-11 19:35:15.867339  b'Jobs in Fintech'   \n","4  2018-07-11 19:35:16.021448            b'ERC20'   \n","\n","                                                   2         3         4  \n","0  b\"Next two weeks prob v boring (climb up to 9k...  0.007273  0.590909  \n","1  b'@Miss_rinola But you\\xe2\\x80\\x99ve heard abo...  0.000000  0.000000  \n","2  b'Duplicate skilled traders automatically with...  0.625000  0.500000  \n","3  b'Project Manager - Technical - FinTech - Cent...  0.000000  0.175000  \n","4  b'Coinbase App Downloads Drop, Crypto Hype Fad...  0.333333  0.500000  "]},"execution_count":139,"metadata":{},"output_type":"execute_result"}],"source":["notclean.head()"]},{"cell_type":"code","execution_count":140,"metadata":{"_uuid":"11d661717e2fa606dbfaa7898d54083f2a15fdf8","trusted":true},"outputs":[],"source":["# -----------------Pre-processing -------------------#\n","\n","notclean.columns = [\"dt\", \"name\", \"text\", \"polarity\", \"sensitivity\"]"]},{"cell_type":"code","execution_count":141,"metadata":{"_uuid":"bcecb8820242c5f313e7e6edb48cec3a7e241ca8","trusted":true},"outputs":[],"source":["notclean = notclean.drop([\"name\", \"text\"], axis=1)"]},{"cell_type":"code","execution_count":142,"metadata":{"_uuid":"e856a75e156884c699d9e1d9a8aa932f3c285375","trusted":true},"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>dt</th>\n","      <th>polarity</th>\n","      <th>sensitivity</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2018-07-11 19:35:15.363270</td>\n","      <td>0.007273</td>\n","      <td>0.590909</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2018-07-11 19:35:15.736769</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2018-07-11 19:35:15.744769</td>\n","      <td>0.625000</td>\n","      <td>0.500000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2018-07-11 19:35:15.867339</td>\n","      <td>0.000000</td>\n","      <td>0.175000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2018-07-11 19:35:16.021448</td>\n","      <td>0.333333</td>\n","      <td>0.500000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                           dt  polarity  sensitivity\n","0  2018-07-11 19:35:15.363270  0.007273     0.590909\n","1  2018-07-11 19:35:15.736769  0.000000     0.000000\n","2  2018-07-11 19:35:15.744769  0.625000     0.500000\n","3  2018-07-11 19:35:15.867339  0.000000     0.175000\n","4  2018-07-11 19:35:16.021448  0.333333     0.500000"]},"execution_count":142,"metadata":{},"output_type":"execute_result"}],"source":["notclean.head()"]},{"cell_type":"code","execution_count":143,"metadata":{"_uuid":"b5e5d6910c1ea456703c74e53b54eefa96f98b12","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1413001 entries, 0 to 1413000\n","Data columns (total 3 columns):\n"," #   Column       Non-Null Count    Dtype  \n","---  ------       --------------    -----  \n"," 0   dt           1413001 non-null  object \n"," 1   polarity     1413001 non-null  float64\n"," 2   sensitivity  1413001 non-null  float64\n","dtypes: float64(2), object(1)\n","memory usage: 32.3+ MB\n"]}],"source":["notclean.info()"]},{"cell_type":"code","execution_count":144,"metadata":{"_uuid":"a1ea711e8022f1620861a616c650d5e462f24d37","trusted":true},"outputs":[],"source":["notclean[\"dt\"] = pd.to_datetime(notclean[\"dt\"])"]},{"cell_type":"code","execution_count":145,"metadata":{"_uuid":"fe559ef0103b5fcd5fdcb650e161f9160f730905","trusted":true},"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>dt</th>\n","      <th>polarity</th>\n","      <th>sensitivity</th>\n","      <th>DateTime</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2018-07-11 19:35:15.363270</td>\n","      <td>0.007273</td>\n","      <td>0.590909</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2018-07-11 19:35:15.736769</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2018-07-11 19:35:15.744769</td>\n","      <td>0.625000</td>\n","      <td>0.500000</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2018-07-11 19:35:15.867339</td>\n","      <td>0.000000</td>\n","      <td>0.175000</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2018-07-11 19:35:16.021448</td>\n","      <td>0.333333</td>\n","      <td>0.500000</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                          dt  polarity  sensitivity            DateTime\n","0 2018-07-11 19:35:15.363270  0.007273     0.590909 2018-07-11 19:00:00\n","1 2018-07-11 19:35:15.736769  0.000000     0.000000 2018-07-11 19:00:00\n","2 2018-07-11 19:35:15.744769  0.625000     0.500000 2018-07-11 19:00:00\n","3 2018-07-11 19:35:15.867339  0.000000     0.175000 2018-07-11 19:00:00\n","4 2018-07-11 19:35:16.021448  0.333333     0.500000 2018-07-11 19:00:00"]},"execution_count":145,"metadata":{},"output_type":"execute_result"}],"source":["notclean[\"DateTime\"] = notclean[\"dt\"].dt.floor(\"h\")\n","notclean.head()"]},{"cell_type":"code","execution_count":146,"metadata":{"_uuid":"b4697f4adece1e8a274fe6606f9a674bfbbda29b","trusted":true},"outputs":[],"source":["vdf = (\n","    notclean.groupby(pd.Grouper(key=\"dt\", freq=\"H\"))\n","    .size()\n","    .reset_index(name=\"tweet_vol\")\n",")"]},{"cell_type":"code","execution_count":147,"metadata":{"_uuid":"7c2bab6be8891336327fb384ca0d13e823f1a89f","trusted":true},"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>dt</th>\n","      <th>tweet_vol</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2018-07-11 19:00:00</td>\n","      <td>1747</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2018-07-11 20:00:00</td>\n","      <td>4354</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2018-07-11 21:00:00</td>\n","      <td>4432</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2018-07-11 22:00:00</td>\n","      <td>3980</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2018-07-11 23:00:00</td>\n","      <td>3830</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                   dt  tweet_vol\n","0 2018-07-11 19:00:00       1747\n","1 2018-07-11 20:00:00       4354\n","2 2018-07-11 21:00:00       4432\n","3 2018-07-11 22:00:00       3980\n","4 2018-07-11 23:00:00       3830"]},"execution_count":147,"metadata":{},"output_type":"execute_result"}],"source":["vdf.head()"]},{"cell_type":"code","execution_count":148,"metadata":{"_uuid":"6c9b6512f070658ee9be513d9a3d08f038b4ba5f","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 302 entries, 0 to 301\n","Data columns (total 2 columns):\n"," #   Column     Non-Null Count  Dtype         \n","---  ------     --------------  -----         \n"," 0   dt         302 non-null    datetime64[ns]\n"," 1   tweet_vol  302 non-null    int64         \n","dtypes: datetime64[ns](1), int64(1)\n","memory usage: 4.8 KB\n"]}],"source":["vdf.info()"]},{"cell_type":"code","execution_count":149,"metadata":{"_uuid":"3113badcf8541c9456793fb593d88b4d9a930831","trusted":true},"outputs":[],"source":["vdf.index = pd.to_datetime(vdf.index)\n","vdf = vdf.set_index(\"dt\")"]},{"cell_type":"code","execution_count":150,"metadata":{"_uuid":"3db4137a0ed499cf52ab62fd096f9c58526397ce","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","DatetimeIndex: 302 entries, 2018-07-11 19:00:00 to 2018-07-24 08:00:00\n","Data columns (total 1 columns):\n"," #   Column     Non-Null Count  Dtype\n","---  ------     --------------  -----\n"," 0   tweet_vol  302 non-null    int64\n","dtypes: int64(1)\n","memory usage: 4.7 KB\n"]}],"source":["vdf.info()"]},{"cell_type":"code","execution_count":151,"metadata":{"_uuid":"587438c72be5b4c592f54f3008494cc7b0366769","trusted":true},"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>tweet_vol</th>\n","    </tr>\n","    <tr>\n","      <th>dt</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-07-11 19:00:00</th>\n","      <td>1747</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 20:00:00</th>\n","      <td>4354</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 21:00:00</th>\n","      <td>4432</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 22:00:00</th>\n","      <td>3980</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 23:00:00</th>\n","      <td>3830</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                     tweet_vol\n","dt                            \n","2018-07-11 19:00:00       1747\n","2018-07-11 20:00:00       4354\n","2018-07-11 21:00:00       4432\n","2018-07-11 22:00:00       3980\n","2018-07-11 23:00:00       3830"]},"execution_count":151,"metadata":{},"output_type":"execute_result"}],"source":["vdf.head()"]},{"cell_type":"code","execution_count":152,"metadata":{"_uuid":"9e63070b3cc0b2ac014d25f3c168475ac88b1f54","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1413001 entries, 0 to 1413000\n","Data columns (total 4 columns):\n"," #   Column       Non-Null Count    Dtype         \n","---  ------       --------------    -----         \n"," 0   dt           1413001 non-null  datetime64[ns]\n"," 1   polarity     1413001 non-null  float64       \n"," 2   sensitivity  1413001 non-null  float64       \n"," 3   DateTime     1413001 non-null  datetime64[ns]\n","dtypes: datetime64[ns](2), float64(2)\n","memory usage: 43.1 MB\n"]}],"source":["notclean.info()"]},{"cell_type":"code","execution_count":153,"metadata":{"_uuid":"5dbb645ae2ed48a41817c25a838a696d8f093e1d","trusted":true},"outputs":[],"source":["notclean.index = pd.to_datetime(notclean.index)"]},{"cell_type":"code","execution_count":154,"metadata":{"_uuid":"9cb9150c0f449d84d0d8bb32831c8fba910d6e3c","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","DatetimeIndex: 1413001 entries, 1970-01-01 00:00:00 to 1970-01-01 00:00:00.001413\n","Data columns (total 4 columns):\n"," #   Column       Non-Null Count    Dtype         \n","---  ------       --------------    -----         \n"," 0   dt           1413001 non-null  datetime64[ns]\n"," 1   polarity     1413001 non-null  float64       \n"," 2   sensitivity  1413001 non-null  float64       \n"," 3   DateTime     1413001 non-null  datetime64[ns]\n","dtypes: datetime64[ns](2), float64(2)\n","memory usage: 53.9 MB\n"]}],"source":["notclean.info()"]},{"cell_type":"code","execution_count":155,"metadata":{"_uuid":"a1b401b8cd71c7f3ee69ce42582b824f9f9e3ffb","trusted":true},"outputs":[],"source":["vdf[\"tweet_vol\"] = vdf[\"tweet_vol\"].astype(float)"]},{"cell_type":"code","execution_count":156,"metadata":{"_uuid":"880f9d8202a051412f66ded691159757d4ca4292","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","DatetimeIndex: 302 entries, 2018-07-11 19:00:00 to 2018-07-24 08:00:00\n","Data columns (total 1 columns):\n"," #   Column     Non-Null Count  Dtype  \n","---  ------     --------------  -----  \n"," 0   tweet_vol  302 non-null    float64\n","dtypes: float64(1)\n","memory usage: 4.7 KB\n"]}],"source":["vdf.info()"]},{"cell_type":"code","execution_count":157,"metadata":{"_uuid":"1a7774f6ab6de7c40f559ead0afc141d4cbe234a","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","DatetimeIndex: 1413001 entries, 1970-01-01 00:00:00 to 1970-01-01 00:00:00.001413\n","Data columns (total 4 columns):\n"," #   Column       Non-Null Count    Dtype         \n","---  ------       --------------    -----         \n"," 0   dt           1413001 non-null  datetime64[ns]\n"," 1   polarity     1413001 non-null  float64       \n"," 2   sensitivity  1413001 non-null  float64       \n"," 3   DateTime     1413001 non-null  datetime64[ns]\n","dtypes: datetime64[ns](2), float64(2)\n","memory usage: 53.9 MB\n"]}],"source":["notclean.info()"]},{"cell_type":"code","execution_count":158,"metadata":{"_uuid":"59d942ba991e435fa9c86420000b4d019eb40fa2","trusted":true},"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>dt</th>\n","      <th>polarity</th>\n","      <th>sensitivity</th>\n","      <th>DateTime</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1970-01-01 00:00:00.000000000</th>\n","      <td>2018-07-11 19:35:15.363270</td>\n","      <td>0.007273</td>\n","      <td>0.590909</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>1970-01-01 00:00:00.000000001</th>\n","      <td>2018-07-11 19:35:15.736769</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>1970-01-01 00:00:00.000000002</th>\n","      <td>2018-07-11 19:35:15.744769</td>\n","      <td>0.625000</td>\n","      <td>0.500000</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>1970-01-01 00:00:00.000000003</th>\n","      <td>2018-07-11 19:35:15.867339</td>\n","      <td>0.000000</td>\n","      <td>0.175000</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>1970-01-01 00:00:00.000000004</th>\n","      <td>2018-07-11 19:35:16.021448</td>\n","      <td>0.333333</td>\n","      <td>0.500000</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                      dt  polarity   \n","1970-01-01 00:00:00.000000000 2018-07-11 19:35:15.363270  0.007273  \\\n","1970-01-01 00:00:00.000000001 2018-07-11 19:35:15.736769  0.000000   \n","1970-01-01 00:00:00.000000002 2018-07-11 19:35:15.744769  0.625000   \n","1970-01-01 00:00:00.000000003 2018-07-11 19:35:15.867339  0.000000   \n","1970-01-01 00:00:00.000000004 2018-07-11 19:35:16.021448  0.333333   \n","\n","                               sensitivity            DateTime  \n","1970-01-01 00:00:00.000000000     0.590909 2018-07-11 19:00:00  \n","1970-01-01 00:00:00.000000001     0.000000 2018-07-11 19:00:00  \n","1970-01-01 00:00:00.000000002     0.500000 2018-07-11 19:00:00  \n","1970-01-01 00:00:00.000000003     0.175000 2018-07-11 19:00:00  \n","1970-01-01 00:00:00.000000004     0.500000 2018-07-11 19:00:00  "]},"execution_count":158,"metadata":{},"output_type":"execute_result"}],"source":["notclean.head()"]},{"cell_type":"code","execution_count":159,"metadata":{"_uuid":"80c4f3eb29f41c661c5a39cc3c9222baff3c8476","trusted":true},"outputs":[],"source":["# ndf = pd.merge(notclean,vdf, how='inner',left_index=True, right_index=True)"]},{"cell_type":"code","execution_count":160,"metadata":{"_uuid":"76da5613eb8a3d1c7a79e00cefa5ea48ce8a98b0","trusted":true},"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>dt</th>\n","      <th>polarity</th>\n","      <th>sensitivity</th>\n","      <th>DateTime</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1970-01-01 00:00:00.000000000</th>\n","      <td>2018-07-11 19:35:15.363270</td>\n","      <td>0.007273</td>\n","      <td>0.590909</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>1970-01-01 00:00:00.000000001</th>\n","      <td>2018-07-11 19:35:15.736769</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>1970-01-01 00:00:00.000000002</th>\n","      <td>2018-07-11 19:35:15.744769</td>\n","      <td>0.625000</td>\n","      <td>0.500000</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>1970-01-01 00:00:00.000000003</th>\n","      <td>2018-07-11 19:35:15.867339</td>\n","      <td>0.000000</td>\n","      <td>0.175000</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>1970-01-01 00:00:00.000000004</th>\n","      <td>2018-07-11 19:35:16.021448</td>\n","      <td>0.333333</td>\n","      <td>0.500000</td>\n","      <td>2018-07-11 19:00:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                      dt  polarity   \n","1970-01-01 00:00:00.000000000 2018-07-11 19:35:15.363270  0.007273  \\\n","1970-01-01 00:00:00.000000001 2018-07-11 19:35:15.736769  0.000000   \n","1970-01-01 00:00:00.000000002 2018-07-11 19:35:15.744769  0.625000   \n","1970-01-01 00:00:00.000000003 2018-07-11 19:35:15.867339  0.000000   \n","1970-01-01 00:00:00.000000004 2018-07-11 19:35:16.021448  0.333333   \n","\n","                               sensitivity            DateTime  \n","1970-01-01 00:00:00.000000000     0.590909 2018-07-11 19:00:00  \n","1970-01-01 00:00:00.000000001     0.000000 2018-07-11 19:00:00  \n","1970-01-01 00:00:00.000000002     0.500000 2018-07-11 19:00:00  \n","1970-01-01 00:00:00.000000003     0.175000 2018-07-11 19:00:00  \n","1970-01-01 00:00:00.000000004     0.500000 2018-07-11 19:00:00  "]},"execution_count":160,"metadata":{},"output_type":"execute_result"}],"source":["notclean.head()"]},{"cell_type":"code","execution_count":161,"metadata":{"_uuid":"cbbc14d816ca6e85b9368345e25a0554575d5708","trusted":true},"outputs":[],"source":["df = notclean.groupby(\"DateTime\").agg(lambda x: x.mean())"]},{"cell_type":"code","execution_count":162,"metadata":{"_uuid":"203b32ff58cd7779ec2e15c2219fc9bc164c997b","trusted":true},"outputs":[],"source":["df[\"Tweet_vol\"] = vdf[\"tweet_vol\"]"]},{"cell_type":"code","execution_count":163,"metadata":{"_uuid":"1fa92fbc902d03143e639647bfb4f05fe2206d02","trusted":true},"outputs":[],"source":["df = df.drop(df.index[0])"]},{"cell_type":"code","execution_count":164,"metadata":{"_uuid":"e23b1147cd72317238160a7924c57f0dbe1ec1cc","trusted":true},"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>dt</th>\n","      <th>polarity</th>\n","      <th>sensitivity</th>\n","      <th>Tweet_vol</th>\n","    </tr>\n","    <tr>\n","      <th>DateTime</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-07-11 20:00:00</th>\n","      <td>2018-07-11 20:27:49.510636288</td>\n","      <td>0.102657</td>\n","      <td>0.216148</td>\n","      <td>4354.0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 21:00:00</th>\n","      <td>2018-07-11 21:28:35.636368640</td>\n","      <td>0.098004</td>\n","      <td>0.218612</td>\n","      <td>4432.0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 22:00:00</th>\n","      <td>2018-07-11 22:27:44.646705152</td>\n","      <td>0.096688</td>\n","      <td>0.231342</td>\n","      <td>3980.0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 23:00:00</th>\n","      <td>2018-07-11 23:28:06.455850496</td>\n","      <td>0.103997</td>\n","      <td>0.217739</td>\n","      <td>3830.0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-12 00:00:00</th>\n","      <td>2018-07-12 00:28:47.975385344</td>\n","      <td>0.094383</td>\n","      <td>0.195256</td>\n","      <td>3998.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                               dt  polarity  sensitivity   \n","DateTime                                                                   \n","2018-07-11 20:00:00 2018-07-11 20:27:49.510636288  0.102657     0.216148  \\\n","2018-07-11 21:00:00 2018-07-11 21:28:35.636368640  0.098004     0.218612   \n","2018-07-11 22:00:00 2018-07-11 22:27:44.646705152  0.096688     0.231342   \n","2018-07-11 23:00:00 2018-07-11 23:28:06.455850496  0.103997     0.217739   \n","2018-07-12 00:00:00 2018-07-12 00:28:47.975385344  0.094383     0.195256   \n","\n","                     Tweet_vol  \n","DateTime                        \n","2018-07-11 20:00:00     4354.0  \n","2018-07-11 21:00:00     4432.0  \n","2018-07-11 22:00:00     3980.0  \n","2018-07-11 23:00:00     3830.0  \n","2018-07-12 00:00:00     3998.0  "]},"execution_count":164,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":165,"metadata":{"_uuid":"35828731c32ee7de7ccbfcdcfb941f96e1f3c683","trusted":true},"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>dt</th>\n","      <th>polarity</th>\n","      <th>sensitivity</th>\n","      <th>Tweet_vol</th>\n","    </tr>\n","    <tr>\n","      <th>DateTime</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-07-24 04:00:00</th>\n","      <td>2018-07-24 04:27:40.946246656</td>\n","      <td>0.121358</td>\n","      <td>0.236000</td>\n","      <td>4475.0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-24 05:00:00</th>\n","      <td>2018-07-24 05:28:40.424965632</td>\n","      <td>0.095163</td>\n","      <td>0.216924</td>\n","      <td>4808.0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-24 06:00:00</th>\n","      <td>2018-07-24 06:30:52.606722816</td>\n","      <td>0.088992</td>\n","      <td>0.220173</td>\n","      <td>6036.0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-24 07:00:00</th>\n","      <td>2018-07-24 07:27:29.229673984</td>\n","      <td>0.091439</td>\n","      <td>0.198279</td>\n","      <td>6047.0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-24 08:00:00</th>\n","      <td>2018-07-24 08:07:02.674452224</td>\n","      <td>0.071268</td>\n","      <td>0.218217</td>\n","      <td>2444.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                               dt  polarity  sensitivity   \n","DateTime                                                                   \n","2018-07-24 04:00:00 2018-07-24 04:27:40.946246656  0.121358     0.236000  \\\n","2018-07-24 05:00:00 2018-07-24 05:28:40.424965632  0.095163     0.216924   \n","2018-07-24 06:00:00 2018-07-24 06:30:52.606722816  0.088992     0.220173   \n","2018-07-24 07:00:00 2018-07-24 07:27:29.229673984  0.091439     0.198279   \n","2018-07-24 08:00:00 2018-07-24 08:07:02.674452224  0.071268     0.218217   \n","\n","                     Tweet_vol  \n","DateTime                        \n","2018-07-24 04:00:00     4475.0  \n","2018-07-24 05:00:00     4808.0  \n","2018-07-24 06:00:00     6036.0  \n","2018-07-24 07:00:00     6047.0  \n","2018-07-24 08:00:00     2444.0  "]},"execution_count":165,"metadata":{},"output_type":"execute_result"}],"source":["df.tail()"]},{"cell_type":"code","execution_count":166,"metadata":{"_uuid":"ddf02f8ddd2fd8bebf6b9fa14b5ff2ec59791bbf","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","DatetimeIndex: 301 entries, 2018-07-11 20:00:00 to 2018-07-24 08:00:00\n","Data columns (total 4 columns):\n"," #   Column       Non-Null Count  Dtype         \n","---  ------       --------------  -----         \n"," 0   dt           301 non-null    datetime64[ns]\n"," 1   polarity     301 non-null    float64       \n"," 2   sensitivity  301 non-null    float64       \n"," 3   Tweet_vol    301 non-null    float64       \n","dtypes: datetime64[ns](1), float64(3)\n","memory usage: 11.8 KB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":167,"metadata":{"_uuid":"3e6947d5d388fa9236f4e2114af6aef320be1445","trusted":true},"outputs":[],"source":["btcDF = pd.read_csv(\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/LSTM/btcSave2.csv\", on_bad_lines=\"skip\", engine=\"python\")"]},{"cell_type":"code","execution_count":168,"metadata":{"_uuid":"5a5abaedcfeda71909e4dca98bc13ac6bd82c043","trusted":true},"outputs":[],"source":["btcDF[\"Timestamp\"] = pd.to_datetime(btcDF[\"Timestamp\"])\n","btcDF = btcDF.set_index(pd.DatetimeIndex(btcDF[\"Timestamp\"]))"]},{"cell_type":"code","execution_count":169,"metadata":{"_uuid":"9625fcc29b9aef64c1b0700ea5fa083f8fcd5650","trusted":true},"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>Timestamp</th>\n","      <th>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Close</th>\n","      <th>Volume (BTC)</th>\n","      <th>Volume (Currency)</th>\n","      <th>Weighted Price</th>\n","    </tr>\n","    <tr>\n","      <th>Timestamp</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-07-10 01:00:00</th>\n","      <td>2018-07-10 01:00:00</td>\n","      <td>6666.75</td>\n","      <td>6683.90</td>\n","      <td>6635.59</td>\n","      <td>6669.73</td>\n","      <td>281.73</td>\n","      <td>1875693.72</td>\n","      <td>6657.70</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-10 02:00:00</th>\n","      <td>2018-07-10 02:00:00</td>\n","      <td>6662.44</td>\n","      <td>6674.60</td>\n","      <td>6647.00</td>\n","      <td>6647.00</td>\n","      <td>174.10</td>\n","      <td>1160103.29</td>\n","      <td>6663.38</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-10 03:00:00</th>\n","      <td>2018-07-10 03:00:00</td>\n","      <td>6652.52</td>\n","      <td>6662.82</td>\n","      <td>6621.99</td>\n","      <td>6632.53</td>\n","      <td>231.41</td>\n","      <td>1536936.22</td>\n","      <td>6641.70</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-10 04:00:00</th>\n","      <td>2018-07-10 04:00:00</td>\n","      <td>6631.17</td>\n","      <td>6655.48</td>\n","      <td>6625.54</td>\n","      <td>6635.92</td>\n","      <td>120.38</td>\n","      <td>799154.77</td>\n","      <td>6638.52</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-10 05:00:00</th>\n","      <td>2018-07-10 05:00:00</td>\n","      <td>6632.81</td>\n","      <td>6651.06</td>\n","      <td>6627.64</td>\n","      <td>6640.57</td>\n","      <td>94.00</td>\n","      <td>624289.31</td>\n","      <td>6641.32</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                              Timestamp     Open     High      Low    Close   \n","Timestamp                                                                     \n","2018-07-10 01:00:00 2018-07-10 01:00:00  6666.75  6683.90  6635.59  6669.73  \\\n","2018-07-10 02:00:00 2018-07-10 02:00:00  6662.44  6674.60  6647.00  6647.00   \n","2018-07-10 03:00:00 2018-07-10 03:00:00  6652.52  6662.82  6621.99  6632.53   \n","2018-07-10 04:00:00 2018-07-10 04:00:00  6631.17  6655.48  6625.54  6635.92   \n","2018-07-10 05:00:00 2018-07-10 05:00:00  6632.81  6651.06  6627.64  6640.57   \n","\n","                     Volume (BTC)  Volume (Currency)  Weighted Price  \n","Timestamp                                                             \n","2018-07-10 01:00:00        281.73         1875693.72         6657.70  \n","2018-07-10 02:00:00        174.10         1160103.29         6663.38  \n","2018-07-10 03:00:00        231.41         1536936.22         6641.70  \n","2018-07-10 04:00:00        120.38          799154.77         6638.52  \n","2018-07-10 05:00:00         94.00          624289.31         6641.32  "]},"execution_count":169,"metadata":{},"output_type":"execute_result"}],"source":["btcDF.head()"]},{"cell_type":"code","execution_count":170,"metadata":{"_uuid":"1c5a996b39b6e9cd662a0629f6cc3d9548dc6a6a","trusted":true},"outputs":[],"source":["btcDF = btcDF.drop([\"Timestamp\"], axis=1)"]},{"cell_type":"code","execution_count":171,"metadata":{"_uuid":"ab08a7a1c73e134c48277a952355a5306ad5c779","trusted":true},"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>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Close</th>\n","      <th>Volume (BTC)</th>\n","      <th>Volume (Currency)</th>\n","      <th>Weighted Price</th>\n","    </tr>\n","    <tr>\n","      <th>Timestamp</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-07-10 01:00:00</th>\n","      <td>6666.75</td>\n","      <td>6683.90</td>\n","      <td>6635.59</td>\n","      <td>6669.73</td>\n","      <td>281.73</td>\n","      <td>1875693.72</td>\n","      <td>6657.70</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-10 02:00:00</th>\n","      <td>6662.44</td>\n","      <td>6674.60</td>\n","      <td>6647.00</td>\n","      <td>6647.00</td>\n","      <td>174.10</td>\n","      <td>1160103.29</td>\n","      <td>6663.38</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-10 03:00:00</th>\n","      <td>6652.52</td>\n","      <td>6662.82</td>\n","      <td>6621.99</td>\n","      <td>6632.53</td>\n","      <td>231.41</td>\n","      <td>1536936.22</td>\n","      <td>6641.70</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-10 04:00:00</th>\n","      <td>6631.17</td>\n","      <td>6655.48</td>\n","      <td>6625.54</td>\n","      <td>6635.92</td>\n","      <td>120.38</td>\n","      <td>799154.77</td>\n","      <td>6638.52</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-10 05:00:00</th>\n","      <td>6632.81</td>\n","      <td>6651.06</td>\n","      <td>6627.64</td>\n","      <td>6640.57</td>\n","      <td>94.00</td>\n","      <td>624289.31</td>\n","      <td>6641.32</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                        Open     High      Low    Close  Volume (BTC)   \n","Timestamp                                                               \n","2018-07-10 01:00:00  6666.75  6683.90  6635.59  6669.73        281.73  \\\n","2018-07-10 02:00:00  6662.44  6674.60  6647.00  6647.00        174.10   \n","2018-07-10 03:00:00  6652.52  6662.82  6621.99  6632.53        231.41   \n","2018-07-10 04:00:00  6631.17  6655.48  6625.54  6635.92        120.38   \n","2018-07-10 05:00:00  6632.81  6651.06  6627.64  6640.57         94.00   \n","\n","                     Volume (Currency)  Weighted Price  \n","Timestamp                                               \n","2018-07-10 01:00:00         1875693.72         6657.70  \n","2018-07-10 02:00:00         1160103.29         6663.38  \n","2018-07-10 03:00:00         1536936.22         6641.70  \n","2018-07-10 04:00:00          799154.77         6638.52  \n","2018-07-10 05:00:00          624289.31         6641.32  "]},"execution_count":171,"metadata":{},"output_type":"execute_result"}],"source":["btcDF.head()"]},{"cell_type":"code","execution_count":172,"metadata":{"_uuid":"7000094373f2dae8315bbff318a20335ee987063","trusted":true},"outputs":[],"source":["Final_df = pd.merge(df, btcDF, how=\"inner\", left_index=True, right_index=True)"]},{"cell_type":"code","execution_count":173,"metadata":{"_uuid":"22368613df3390e7e29c51f4930414e2a616ad41","trusted":true},"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>dt</th>\n","      <th>polarity</th>\n","      <th>sensitivity</th>\n","      <th>Tweet_vol</th>\n","      <th>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Close</th>\n","      <th>Volume (BTC)</th>\n","      <th>Volume (Currency)</th>\n","      <th>Weighted Price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-07-11 20:00:00</th>\n","      <td>2018-07-11 20:27:49.510636288</td>\n","      <td>0.102657</td>\n","      <td>0.216148</td>\n","      <td>4354.0</td>\n","      <td>6342.97</td>\n","      <td>6354.19</td>\n","      <td>6291.00</td>\n","      <td>6350.00</td>\n","      <td>986.73</td>\n","      <td>6231532.37</td>\n","      <td>6315.33</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 21:00:00</th>\n","      <td>2018-07-11 21:28:35.636368640</td>\n","      <td>0.098004</td>\n","      <td>0.218612</td>\n","      <td>4432.0</td>\n","      <td>6352.99</td>\n","      <td>6370.00</td>\n","      <td>6345.76</td>\n","      <td>6356.48</td>\n","      <td>126.46</td>\n","      <td>804221.55</td>\n","      <td>6359.53</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 22:00:00</th>\n","      <td>2018-07-11 22:27:44.646705152</td>\n","      <td>0.096688</td>\n","      <td>0.231342</td>\n","      <td>3980.0</td>\n","      <td>6350.85</td>\n","      <td>6378.47</td>\n","      <td>6345.00</td>\n","      <td>6361.93</td>\n","      <td>259.10</td>\n","      <td>1646353.87</td>\n","      <td>6354.12</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 23:00:00</th>\n","      <td>2018-07-11 23:28:06.455850496</td>\n","      <td>0.103997</td>\n","      <td>0.217739</td>\n","      <td>3830.0</td>\n","      <td>6362.36</td>\n","      <td>6381.25</td>\n","      <td>6356.74</td>\n","      <td>6368.78</td>\n","      <td>81.54</td>\n","      <td>519278.69</td>\n","      <td>6368.23</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-12 00:00:00</th>\n","      <td>2018-07-12 00:28:47.975385344</td>\n","      <td>0.094383</td>\n","      <td>0.195256</td>\n","      <td>3998.0</td>\n","      <td>6369.49</td>\n","      <td>6381.25</td>\n","      <td>6361.83</td>\n","      <td>6380.00</td>\n","      <td>124.55</td>\n","      <td>793560.22</td>\n","      <td>6371.51</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                               dt  polarity  sensitivity   \n","2018-07-11 20:00:00 2018-07-11 20:27:49.510636288  0.102657     0.216148  \\\n","2018-07-11 21:00:00 2018-07-11 21:28:35.636368640  0.098004     0.218612   \n","2018-07-11 22:00:00 2018-07-11 22:27:44.646705152  0.096688     0.231342   \n","2018-07-11 23:00:00 2018-07-11 23:28:06.455850496  0.103997     0.217739   \n","2018-07-12 00:00:00 2018-07-12 00:28:47.975385344  0.094383     0.195256   \n","\n","                     Tweet_vol     Open     High      Low    Close   \n","2018-07-11 20:00:00     4354.0  6342.97  6354.19  6291.00  6350.00  \\\n","2018-07-11 21:00:00     4432.0  6352.99  6370.00  6345.76  6356.48   \n","2018-07-11 22:00:00     3980.0  6350.85  6378.47  6345.00  6361.93   \n","2018-07-11 23:00:00     3830.0  6362.36  6381.25  6356.74  6368.78   \n","2018-07-12 00:00:00     3998.0  6369.49  6381.25  6361.83  6380.00   \n","\n","                     Volume (BTC)  Volume (Currency)  Weighted Price  \n","2018-07-11 20:00:00        986.73         6231532.37         6315.33  \n","2018-07-11 21:00:00        126.46          804221.55         6359.53  \n","2018-07-11 22:00:00        259.10         1646353.87         6354.12  \n","2018-07-11 23:00:00         81.54          519278.69         6368.23  \n","2018-07-12 00:00:00        124.55          793560.22         6371.51  "]},"execution_count":173,"metadata":{},"output_type":"execute_result"}],"source":["Final_df.head()"]},{"cell_type":"code","execution_count":174,"metadata":{"_uuid":"d67a1db2a748641cee09b861bca24255edeb0d56","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","DatetimeIndex: 294 entries, 2018-07-11 20:00:00 to 2018-07-24 01:00:00\n","Data columns (total 11 columns):\n"," #   Column             Non-Null Count  Dtype         \n","---  ------             --------------  -----         \n"," 0   dt                 294 non-null    datetime64[ns]\n"," 1   polarity           294 non-null    float64       \n"," 2   sensitivity        294 non-null    float64       \n"," 3   Tweet_vol          294 non-null    float64       \n"," 4   Open               294 non-null    float64       \n"," 5   High               294 non-null    float64       \n"," 6   Low                294 non-null    float64       \n"," 7   Close              294 non-null    float64       \n"," 8   Volume (BTC)       294 non-null    float64       \n"," 9   Volume (Currency)  294 non-null    float64       \n"," 10  Weighted Price     294 non-null    float64       \n","dtypes: datetime64[ns](1), float64(10)\n","memory usage: 27.6 KB\n"]}],"source":["Final_df.info()"]},{"cell_type":"code","execution_count":175,"metadata":{"_uuid":"9c86baae1119fb26becd9e8478b655061dd9e5f1","trusted":true},"outputs":[],"source":["Final_df = Final_df.drop([\"Weighted Price\"], axis=1)"]},{"cell_type":"code","execution_count":176,"metadata":{"_uuid":"29ebcdfbc4a47788ae7ad96a427012c2b8e217c3","trusted":true},"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>dt</th>\n","      <th>polarity</th>\n","      <th>sensitivity</th>\n","      <th>Tweet_vol</th>\n","      <th>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Close</th>\n","      <th>Volume (BTC)</th>\n","      <th>Volume (Currency)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-07-11 20:00:00</th>\n","      <td>2018-07-11 20:27:49.510636288</td>\n","      <td>0.102657</td>\n","      <td>0.216148</td>\n","      <td>4354.0</td>\n","      <td>6342.97</td>\n","      <td>6354.19</td>\n","      <td>6291.00</td>\n","      <td>6350.00</td>\n","      <td>986.73</td>\n","      <td>6231532.37</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 21:00:00</th>\n","      <td>2018-07-11 21:28:35.636368640</td>\n","      <td>0.098004</td>\n","      <td>0.218612</td>\n","      <td>4432.0</td>\n","      <td>6352.99</td>\n","      <td>6370.00</td>\n","      <td>6345.76</td>\n","      <td>6356.48</td>\n","      <td>126.46</td>\n","      <td>804221.55</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 22:00:00</th>\n","      <td>2018-07-11 22:27:44.646705152</td>\n","      <td>0.096688</td>\n","      <td>0.231342</td>\n","      <td>3980.0</td>\n","      <td>6350.85</td>\n","      <td>6378.47</td>\n","      <td>6345.00</td>\n","      <td>6361.93</td>\n","      <td>259.10</td>\n","      <td>1646353.87</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 23:00:00</th>\n","      <td>2018-07-11 23:28:06.455850496</td>\n","      <td>0.103997</td>\n","      <td>0.217739</td>\n","      <td>3830.0</td>\n","      <td>6362.36</td>\n","      <td>6381.25</td>\n","      <td>6356.74</td>\n","      <td>6368.78</td>\n","      <td>81.54</td>\n","      <td>519278.69</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-12 00:00:00</th>\n","      <td>2018-07-12 00:28:47.975385344</td>\n","      <td>0.094383</td>\n","      <td>0.195256</td>\n","      <td>3998.0</td>\n","      <td>6369.49</td>\n","      <td>6381.25</td>\n","      <td>6361.83</td>\n","      <td>6380.00</td>\n","      <td>124.55</td>\n","      <td>793560.22</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                               dt  polarity  sensitivity   \n","2018-07-11 20:00:00 2018-07-11 20:27:49.510636288  0.102657     0.216148  \\\n","2018-07-11 21:00:00 2018-07-11 21:28:35.636368640  0.098004     0.218612   \n","2018-07-11 22:00:00 2018-07-11 22:27:44.646705152  0.096688     0.231342   \n","2018-07-11 23:00:00 2018-07-11 23:28:06.455850496  0.103997     0.217739   \n","2018-07-12 00:00:00 2018-07-12 00:28:47.975385344  0.094383     0.195256   \n","\n","                     Tweet_vol     Open     High      Low    Close   \n","2018-07-11 20:00:00     4354.0  6342.97  6354.19  6291.00  6350.00  \\\n","2018-07-11 21:00:00     4432.0  6352.99  6370.00  6345.76  6356.48   \n","2018-07-11 22:00:00     3980.0  6350.85  6378.47  6345.00  6361.93   \n","2018-07-11 23:00:00     3830.0  6362.36  6381.25  6356.74  6368.78   \n","2018-07-12 00:00:00     3998.0  6369.49  6381.25  6361.83  6380.00   \n","\n","                     Volume (BTC)  Volume (Currency)  \n","2018-07-11 20:00:00        986.73         6231532.37  \n","2018-07-11 21:00:00        126.46          804221.55  \n","2018-07-11 22:00:00        259.10         1646353.87  \n","2018-07-11 23:00:00         81.54          519278.69  \n","2018-07-12 00:00:00        124.55          793560.22  "]},"execution_count":176,"metadata":{},"output_type":"execute_result"}],"source":["Final_df.head()"]},{"cell_type":"code","execution_count":177,"metadata":{"_uuid":"d3a7cffb45d346d2aad9045e0bc7e0ac28cda13f","trusted":true},"outputs":[],"source":["Final_df.columns = [\n","    \"dt\",\n","    \"Polarity\",\n","    \"Sensitivity\",\n","    \"Tweet_vol\",\n","    \"Open\",\n","    \"High\",\n","    \"Low\",\n","    \"Close_Price\",\n","    \"Volume_BTC\",\n","    \"Volume_Dollar\",\n","]"]},{"cell_type":"code","execution_count":178,"metadata":{"_uuid":"64ab938cd9c18c54653364110fc6617abf4ed6a6","trusted":true},"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>dt</th>\n","      <th>Polarity</th>\n","      <th>Sensitivity</th>\n","      <th>Tweet_vol</th>\n","      <th>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Close_Price</th>\n","      <th>Volume_BTC</th>\n","      <th>Volume_Dollar</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-07-11 20:00:00</th>\n","      <td>2018-07-11 20:27:49.510636288</td>\n","      <td>0.102657</td>\n","      <td>0.216148</td>\n","      <td>4354.0</td>\n","      <td>6342.97</td>\n","      <td>6354.19</td>\n","      <td>6291.00</td>\n","      <td>6350.00</td>\n","      <td>986.73</td>\n","      <td>6231532.37</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 21:00:00</th>\n","      <td>2018-07-11 21:28:35.636368640</td>\n","      <td>0.098004</td>\n","      <td>0.218612</td>\n","      <td>4432.0</td>\n","      <td>6352.99</td>\n","      <td>6370.00</td>\n","      <td>6345.76</td>\n","      <td>6356.48</td>\n","      <td>126.46</td>\n","      <td>804221.55</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 22:00:00</th>\n","      <td>2018-07-11 22:27:44.646705152</td>\n","      <td>0.096688</td>\n","      <td>0.231342</td>\n","      <td>3980.0</td>\n","      <td>6350.85</td>\n","      <td>6378.47</td>\n","      <td>6345.00</td>\n","      <td>6361.93</td>\n","      <td>259.10</td>\n","      <td>1646353.87</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 23:00:00</th>\n","      <td>2018-07-11 23:28:06.455850496</td>\n","      <td>0.103997</td>\n","      <td>0.217739</td>\n","      <td>3830.0</td>\n","      <td>6362.36</td>\n","      <td>6381.25</td>\n","      <td>6356.74</td>\n","      <td>6368.78</td>\n","      <td>81.54</td>\n","      <td>519278.69</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-12 00:00:00</th>\n","      <td>2018-07-12 00:28:47.975385344</td>\n","      <td>0.094383</td>\n","      <td>0.195256</td>\n","      <td>3998.0</td>\n","      <td>6369.49</td>\n","      <td>6381.25</td>\n","      <td>6361.83</td>\n","      <td>6380.00</td>\n","      <td>124.55</td>\n","      <td>793560.22</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                               dt  Polarity  Sensitivity   \n","2018-07-11 20:00:00 2018-07-11 20:27:49.510636288  0.102657     0.216148  \\\n","2018-07-11 21:00:00 2018-07-11 21:28:35.636368640  0.098004     0.218612   \n","2018-07-11 22:00:00 2018-07-11 22:27:44.646705152  0.096688     0.231342   \n","2018-07-11 23:00:00 2018-07-11 23:28:06.455850496  0.103997     0.217739   \n","2018-07-12 00:00:00 2018-07-12 00:28:47.975385344  0.094383     0.195256   \n","\n","                     Tweet_vol     Open     High      Low  Close_Price   \n","2018-07-11 20:00:00     4354.0  6342.97  6354.19  6291.00      6350.00  \\\n","2018-07-11 21:00:00     4432.0  6352.99  6370.00  6345.76      6356.48   \n","2018-07-11 22:00:00     3980.0  6350.85  6378.47  6345.00      6361.93   \n","2018-07-11 23:00:00     3830.0  6362.36  6381.25  6356.74      6368.78   \n","2018-07-12 00:00:00     3998.0  6369.49  6381.25  6361.83      6380.00   \n","\n","                     Volume_BTC  Volume_Dollar  \n","2018-07-11 20:00:00      986.73     6231532.37  \n","2018-07-11 21:00:00      126.46      804221.55  \n","2018-07-11 22:00:00      259.10     1646353.87  \n","2018-07-11 23:00:00       81.54      519278.69  \n","2018-07-12 00:00:00      124.55      793560.22  "]},"execution_count":178,"metadata":{},"output_type":"execute_result"}],"source":["Final_df.head()"]},{"cell_type":"code","execution_count":179,"metadata":{"_uuid":"d256322016d09fba98d1685d09a164dd6a9ef842","trusted":true},"outputs":[],"source":["Final_df = Final_df[\n","    [\n","        \"Polarity\",\n","        \"Sensitivity\",\n","        \"Tweet_vol\",\n","        \"Open\",\n","        \"High\",\n","        \"Low\",\n","        \"Volume_BTC\",\n","        \"Volume_Dollar\",\n","        \"Close_Price\",\n","    ]\n","]"]},{"cell_type":"code","execution_count":180,"metadata":{"_uuid":"4ec1b8f62ba36cdbab04f3c5bd985222692c7bcf","trusted":true},"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>Polarity</th>\n","      <th>Sensitivity</th>\n","      <th>Tweet_vol</th>\n","      <th>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Volume_BTC</th>\n","      <th>Volume_Dollar</th>\n","      <th>Close_Price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-07-11 20:00:00</th>\n","      <td>0.102657</td>\n","      <td>0.216148</td>\n","      <td>4354.0</td>\n","      <td>6342.97</td>\n","      <td>6354.19</td>\n","      <td>6291.00</td>\n","      <td>986.73</td>\n","      <td>6231532.37</td>\n","      <td>6350.00</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 21:00:00</th>\n","      <td>0.098004</td>\n","      <td>0.218612</td>\n","      <td>4432.0</td>\n","      <td>6352.99</td>\n","      <td>6370.00</td>\n","      <td>6345.76</td>\n","      <td>126.46</td>\n","      <td>804221.55</td>\n","      <td>6356.48</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 22:00:00</th>\n","      <td>0.096688</td>\n","      <td>0.231342</td>\n","      <td>3980.0</td>\n","      <td>6350.85</td>\n","      <td>6378.47</td>\n","      <td>6345.00</td>\n","      <td>259.10</td>\n","      <td>1646353.87</td>\n","      <td>6361.93</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 23:00:00</th>\n","      <td>0.103997</td>\n","      <td>0.217739</td>\n","      <td>3830.0</td>\n","      <td>6362.36</td>\n","      <td>6381.25</td>\n","      <td>6356.74</td>\n","      <td>81.54</td>\n","      <td>519278.69</td>\n","      <td>6368.78</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-12 00:00:00</th>\n","      <td>0.094383</td>\n","      <td>0.195256</td>\n","      <td>3998.0</td>\n","      <td>6369.49</td>\n","      <td>6381.25</td>\n","      <td>6361.83</td>\n","      <td>124.55</td>\n","      <td>793560.22</td>\n","      <td>6380.00</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-23 21:00:00</th>\n","      <td>0.107282</td>\n","      <td>0.235636</td>\n","      <td>5164.0</td>\n","      <td>7746.99</td>\n","      <td>7763.59</td>\n","      <td>7690.16</td>\n","      <td>237.63</td>\n","      <td>1836633.86</td>\n","      <td>7706.00</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-23 22:00:00</th>\n","      <td>0.094493</td>\n","      <td>0.271796</td>\n","      <td>4646.0</td>\n","      <td>7699.13</td>\n","      <td>7759.99</td>\n","      <td>7690.50</td>\n","      <td>63.31</td>\n","      <td>489000.25</td>\n","      <td>7750.09</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-23 23:00:00</th>\n","      <td>0.074246</td>\n","      <td>0.231640</td>\n","      <td>4455.0</td>\n","      <td>7754.57</td>\n","      <td>7777.00</td>\n","      <td>7715.45</td>\n","      <td>280.46</td>\n","      <td>2173424.81</td>\n","      <td>7722.32</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-24 00:00:00</th>\n","      <td>0.080870</td>\n","      <td>0.219367</td>\n","      <td>3862.0</td>\n","      <td>7722.95</td>\n","      <td>7730.61</td>\n","      <td>7690.17</td>\n","      <td>496.48</td>\n","      <td>3830571.66</td>\n","      <td>7719.62</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-24 01:00:00</th>\n","      <td>0.090717</td>\n","      <td>0.212626</td>\n","      <td>4620.0</td>\n","      <td>7712.46</td>\n","      <td>7727.70</td>\n","      <td>7691.14</td>\n","      <td>163.99</td>\n","      <td>1264085.79</td>\n","      <td>7723.22</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>294 rows × 9 columns</p>\n","</div>"],"text/plain":["                     Polarity  Sensitivity  Tweet_vol     Open     High   \n","2018-07-11 20:00:00  0.102657     0.216148     4354.0  6342.97  6354.19  \\\n","2018-07-11 21:00:00  0.098004     0.218612     4432.0  6352.99  6370.00   \n","2018-07-11 22:00:00  0.096688     0.231342     3980.0  6350.85  6378.47   \n","2018-07-11 23:00:00  0.103997     0.217739     3830.0  6362.36  6381.25   \n","2018-07-12 00:00:00  0.094383     0.195256     3998.0  6369.49  6381.25   \n","...                       ...          ...        ...      ...      ...   \n","2018-07-23 21:00:00  0.107282     0.235636     5164.0  7746.99  7763.59   \n","2018-07-23 22:00:00  0.094493     0.271796     4646.0  7699.13  7759.99   \n","2018-07-23 23:00:00  0.074246     0.231640     4455.0  7754.57  7777.00   \n","2018-07-24 00:00:00  0.080870     0.219367     3862.0  7722.95  7730.61   \n","2018-07-24 01:00:00  0.090717     0.212626     4620.0  7712.46  7727.70   \n","\n","                         Low  Volume_BTC  Volume_Dollar  Close_Price  \n","2018-07-11 20:00:00  6291.00      986.73     6231532.37      6350.00  \n","2018-07-11 21:00:00  6345.76      126.46      804221.55      6356.48  \n","2018-07-11 22:00:00  6345.00      259.10     1646353.87      6361.93  \n","2018-07-11 23:00:00  6356.74       81.54      519278.69      6368.78  \n","2018-07-12 00:00:00  6361.83      124.55      793560.22      6380.00  \n","...                      ...         ...            ...          ...  \n","2018-07-23 21:00:00  7690.16      237.63     1836633.86      7706.00  \n","2018-07-23 22:00:00  7690.50       63.31      489000.25      7750.09  \n","2018-07-23 23:00:00  7715.45      280.46     2173424.81      7722.32  \n","2018-07-24 00:00:00  7690.17      496.48     3830571.66      7719.62  \n","2018-07-24 01:00:00  7691.14      163.99     1264085.79      7723.22  \n","\n","[294 rows x 9 columns]"]},"execution_count":180,"metadata":{},"output_type":"execute_result"}],"source":["Final_df"]},{"cell_type":"markdown","metadata":{"_uuid":"3ef466549870c90a637c88c45e259816a34c07d2"},"source":["## Exploratory Analysis"]},{"cell_type":"code","execution_count":182,"metadata":{"_uuid":"dcf4e169e5eb2c6b476f442314b66717bd524c5b","scrolled":true,"trusted":true},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjwAAAGhCAYAAABlH26aAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd3gU1f6H3y3Z9N57QiAJCR0SekdEBEHA3hW514Y/O3i9WLEB14ZYECwoInalSe+QQof0hPTe+262/P6Y7GQ3CZBAAojzPg8P2dnZmTO7M+d8zrcdmcFgMCAhISEhISEhcQ0jv9INkJCQkJCQkJDobiTBIyEhISEhIXHNIwkeCQkJCQkJiWseSfBISEhISEhIXPNIgkdCQkJCQkLimkcSPBISEhISEhLXPJLgkZCQkJCQkLjmkQSPhISEhISExDWP8ko34GpgyJAhaDQa3N3dr3RTJCQkJCQk/tEYDAZkMlmH9i0pKUGlUhEfH3/BfSXBA6jVanQ63ZVuhoSEhISERLdQXFyMRqNps12hUGBlZYWjoyNyececPuXl5ajVary9vS+5Xbm5udjb2+Po6IjBYKCqqgqVSoWNjU2HPq/VaunoghGS4AE8PDwA2LFjxxVuiYSEhITE352d8dl8+stJFtwXzaAwjyvdHADuueceamtrefnll8VtTU1NnDlzhv/97394enry/fffd8iysmDBAmJjY7tkzDx+/DheXl54eXmRm5vLxIkTeeutt5g1a1aHPj9x4sQOn0sSPBIS1yBNWj1yuQyFvGNmYQkJia5jy6EsGtQ61m1NvmoED4CdnR0DBgww2xYVFUVdXR0ffvghJ06caPN+d3M5zycFLUtIXGMUlNZx+0ub+OTnE1e6KRIS/zgaNVpScyoASMwsJ7Og+gq36ML06dMHgPz8fAA2bdrErFmzGDhwICNHjmTRokVUVVWd8/ONjY0sW7aMyZMn06dPHwYNGsQDDzxAYmKiuM+CBQu47777ePnllxk0aBBTp05Fp9MRFhbGRx99JFp3ABYuXMiECRPYvXs3YWFh7N+/3+x88fHxhIWFceTIkU5dpyR4JCQuE6WVDbz5VSxJWeXdep7jKcVomnTsPZaHTt8x37bEP4viinoWfLyfvw5nXummXHMkZ1Wg1bU8d5sPnr2CrekYZ88KbfT392fFihU8/fTTDBgwgA8//JDHHnuMv/76i3vuuYfGxsZ2P//888/z888/M2/ePFavXs3ChQtJTU3lmWeeMYuviY+Pp6CggI8//phnnnkGhUIhvufh4cHy5csBeOSRR1i+fDmjR4/Gw8OD33//3ex8v/32G0FBQQwePLhT1ym5tCQkupjy6kYOncxn/BB/bKwsxO0b9mdw6FQB1XUa3n5sVLedP6e4FoAGtZasgmqatDpcHKxxd7butnNK/L34ekMCZzLKyCuuZVJ0oOT67EJOp5cB4OVqQ2FZPbuO5PLQTX1QWSgu8Mnux2AwoNVqxddVVVXExsbyySefMHDgQAICAvjkk0+49dZbWbRokbhfaGgod911Fz///DN33XWX2TE1Gg11dXW89NJLTJ06FYDo6Ghqa2t5++23KS0tFTOgtVotr732Gl5eXm3aplKp6N27NwABAQFEREQAcPPNN7NmzRrq6uqwtbWlsbGRzZs3M2/evE5fv2ThkZDoYtZtS+bTX0+x5VCW2faU7EoAEs6WUVmjRq83sPtIDj/tTEWr03fZ+XMKa8S/f92TxnMf7ePNr2K67PgSf2/ScirZezwPgMpaNclZ5ei68P673Oh0ek6ll14113A6oxSAWeN74epoRYNaK4qggtI6Xlt1mAMn88X980tqOXQqv91jdTVxcXFERkaK/0aMGMHTTz9Nnz59WLZsGcePH0ej0TBt2jSzzw0ZMgRfX19iY2PbHFOlUrFq1SqmTp1KUVERhw8fZt26dezatQvALDPMycmpXbFzPmbPnk19fT3btm0DYNu2bdTX1zNz5sxOXr1k4ZE4Dz/tTMXexoLrhwVd6ab8rcgpEgRHdlGL716nN4h+fYMBNh44S3xiIWm5gl9cLhM6yC45f3GL4Nl9JBeAjLwqmrR69hzNwdnBisHhnl1yLolzcza/Ci9XW6wtO97NVlQ38t1fSdw8ric+bracTi+jV4ATVqqu66q/+ysJEO45vQHWb08hNaeS4X29efyWAV12nsvFb3vS+WpjAvdO7c0tE0PbvK/V6WnS6s1+h7KqBqwtlWYW2EulrqGJ7XHZJGcJz3nfEFfScz3563AWcYmFDAr3YO1fScQlFBGXUMTdU8K57bow3v4mjrP51fzv/8bQy9+5y9rTHpGRkbz66qsAyGQyLC0t8fb2xs7ODkCMiXFzc2vzWTc3N2pqatpsB9i3bx9vvvkmGRkZ2NraEh4eLqaVm7q0bG1tO93mwMBAoqOj+e2335g5cya//fYbI0aMwNOz832YZOGRaJecohq+3pjAip9Pom6SahR1hqLyekCYzRnJLqymUdPyPa7blkxabhUWSrn4uqyq4ZLPXdfQRFlVWz+73gBHkor44IfjvPV1HE3aq2M2fK1yPKWY+ct2s+y7zgVV/rQrlb8OZ7FmUyJ7juXx4icHeH1VDHq9gQZ1x+uNmFJbrxE/V9vQxNHkYgDumiK4D44kFVNdp+Gvw1nnPMal0qDWciKl5KLafyGOpQjXE3OmsN333/oqjvte3SI+l4Vldcx7cztvfx3XZW1oUGt5ccUBvvj9NE1aPZ4uNvi62xHVWxiU4xKKqKxRs/9EiyXn2y1JnMko42y+MDEyTpS6E1tbW/r27Uvfvn3p06cPvXr1EsUOgKOjIwClpaVtPltSUoKzc1tBlp2dzWOPPUbv3r3Ztm0bR44cYe3atYwfP77L2j179mxiY2NJT0/n0KFDzJ49+6KOIwkeiXYxzlL0egOFZXUX2FvCSJNWT1mlIFxMv7eUbOH7NI2jcba35JMXJhIW6EyDWsfcxdv411vbyS+tvejzG6079jYq5K3iMvYeE9wYao2O9LzKiz6HxIXZd1wY2GITCsWBtiMYXR9nMso4fLoAgJNppby+OobbX9rEp7+c7FQ7tsZkccd/N/PHvgwATqSUoNcb8POwY/roHqLgNmIUJBU1jfx1OLPLhPHav5J46bODbD6U2SXHM2IwGEjLqQQgNaeSBrXW7P2swmpiEwppUOtEt9Hp9DI0Wj0n0kq7ZDKn1xtY8m08GflVONqpeOimPrzz+ChkMhn9e7ljoZRTVF7Pqj9Oo9Xp6eXvJKaq/7gjRTxOUfmlT3gulf79+6NSqdiwYYPZ9vj4ePLz8xk0aFCbz5w+fRq1Ws28efMICAgQ6/js27cPoFMi1zSI2ZTrr78ea2trXnnlFWxtbZk0aVKHj2mKJHiuMAaDgfrGpivdjDYYB2iA/JLLK3hqG5p46+tYMz+3wWBAr2+Zpe4+mtulcS9dRUllPcbEqPJqNY3NHbBRQI4d6Ed4oDPWlkpeenAoni42PDKrH1YqBVqdgfzSOlb+dvqiz5/bPEvs4etATz9htmYc1OISWmbASZndmyn2T8ZgMBCfWNT8N+yIy+7Q5+oamjibL7g4K2vVxJxu+b3iE4vQ6w3sO54nPgcXorpOw5d/ngEQj3UkSWjX4HBPrC2VTBjibxawbBQMa/9KZvmPJ/hld2qHznUhjPf/oVMFF32MnKIaftiWTJNWR0VNI5sPniW7sIa6RqHNer2BxLPm9/W2mJbv/lhyCYCYJq7XG8jqQMq4wSBM+kor2xckx1NKiEsoQqWU898HhzJzbAiujsLExspSSd+egnto91HBvXzjyGD69xK2HUkqFo9T3Alh3F04OTkxb9481q9fz90PP82+fftYt24dTzzxBD179uTmm29u85nIyEiUSiVLlizhwIED7Nq1iyeeeILdu3cDUF/f8euyt7cH4NChQ5w40VJWw9ramhtvvJHY2FhuvPFGVCrVRV2fJHiuMFsOZXLbfzZ1+cznYjCd7SSbCZ6LtzhcDCt/O8XBkwVmJud121KYvWADKdkVrPr9NMu+O8KmA1dfumdRmfnDXdBs5TEKyNAAZ956bBRfLZpMaIBgHg7xc+K7125g2ZNjUCpkxCcWiQNmQWkdVbXqDp8/u0j4rfw97XlszgDuvzGCmWNDAMxcaglnJcHTXZzNr6a8usWtuD0uu0PlARLOlmE6Gdbq9FipFAwK88DWSolKKaemvomswo7Vdfn+ryRqG4TJVFpuJTq9QRxgB4cLFobHbxnAj29Nw0olzKyraoUAU6MQ2H+8a4Jpc5szB89klNGo0V5g7/b54o/TfLslia2Hs/hmYyIrfj7J4q/Mg2hPpbe4Ypq0OnbG54ivT6eXomnSkVnQUk8mPbfyvOfMKqjmX2/v4OE3t/Pvd3a063Y+2Gw5mhgVQFigS5v3bxgeJE46evo5MmqAL31C2sbIFFd0XvBkFlTzn08O8MO2ZLH//mZTAvPe2s6HPxwzqwFU2yDcO5U15+9PbrnzQTz73syJo/H8+99CeviUKVNYu3Ztu8s9BAYGsmzZMoqKinjkkUfE7K41a9Ygk8k6tMaVETs7Ox544AG2b9/Oww8/TFNTizFg3LhxAB2uwNwe3R60rNfrWb58OT/++CM1NTVERUWxaNEi/P39L/i5efPm0b9/f5544gmz9zZv3iwWKurRowcvvPACw4cP787L6DY2HcwE4Ms/zxAe6Myp9FIig10J8XO6rO34ZlMCv+5O57E5/Rk1wMfsQckvvbwWnv3NGSRG9HoDmw+eRavTs/HAWdFScTKtlJvGhFzWtl2I1u6LgtI63J1tyG62vIQFOqNUyFEqzOcaKgsFoQHOTB8dwq+70/jwh2NMHhbIj9tT8Haz5ZMXJl6w5HujWitabgI87enh60gPX0dxZmlKUmZ5pxbo+6eQV1LL2fwqRvbzuejvxihWB4S6k5pTSUlFA++tPcqjc/qdN0j2TIbgzlLIZaJAiuzhyqKHhmEwGHhtVQxHk4s5dKqA77Yk0a+XGzeNbv/+/21POhuaJwRymWC5OXyqgPLqRixVCiJ7uIr7WijlONpZ0lheT1WtGm83W/E+ziyoJre4Bj8P+4v6LkCwNNXUC0KqSavnTEbZeYPm121L5tDJAgK87Jk+uoc4MchoDvBPzKwgOVu4z41xck52llTWqs0Ez56jedTUa3B1tMJgEMpFmMbMAGLSgClJWeWs/O0UD8/sy9GkYvEcao2OU2mljBvcMnbp9AbR9TiiX/vrSg3r482Pb01DBqKbOcTXEWtLpZkLrrOCp0mrY8m38WQX1nAyrZTtcdnMm9mXH3ekit9NRn4V7z81Tjjn6EfRZlfwV0wmt00KO+dx121LxiFgGA4Bw3hgWkS7yRRvv/222espU6YwZcoUdHoDBoNB7N+SkpLO+RkjycnJZq8XLFjAggUL2uy3Z88eevfuTb9+/c7Z9gvR7RaeFStWsHbtWl5//XXWrVuHXq9n7ty57S5iZkSj0fDiiy+KPkBTDh8+zHPPPcftt9/Or7/+yvDhw5k3bx7p6endeRndQn5prSgsGtRa5i/bzcrfTvN/7+3h1S8O06S9fMHCe47lodXp+XD9MT75+aSZ2by9mJKqWjVfb0wQTfBdRaNGi6ZV3EBGfhUVzbOS3UdzqakXVH9SVnm3BEFeCu0JnoQMYebu42aLi4PVeT9/+3WhBHk7UFGj5odtKegNkFdSJ17/ucgqqObhN7eT2Cx4gn0dxfd83c0zI5QKGRU16k7Flhi52r7vrubtr+N455t4cRC7GIyCfEQ/Hx6cHolcLmPPsVxeWxVz3u/PGL8zYUjLgNq/l7uwRIhCLrpGftiWTMyZQlb9cYbsVtYeg8HA2r+SWPWH4BadM6GXmPljzM7q19OtTU0YJztLQHClaZp0ZhYqU9dyVa2aHXHZ1Nafu/9uTV6xef+x91geh08XtBsfdDa/irV/JZGRX8Xuo7l8tP44IGSvVTZbOo+nFlPYypI6bVQwIMTx7DuWR6Nay7dbEpvf6yHGzOw8kkN1XUvb24tle+7DfaRkV/LpLyfbuLESM8s5m1/FzvjsZhdaGVW1GuysLdq12hhRyGVmMXUKhdxMdIJQmNTUEng0qZhnP9zL40t2svavJFqzblsK2YU1ONiqcHO0orCsntdXC+Un+oQIx87Iq6KuoYnaeo2YJZrejsgzkldSyy4Tq1hSVsU5922NVqfniaU7+b//7e7ScINvvvmGV155hfXr1zN37txLOla3Ch6NRsPq1auZP38+48aNIzw8nPfee4/CwkK2bt3a7meOHj3KrFmziI+Px8HBoc37K1euZNKkSdx7772EhITwwgsvEBkZyddff92dl3JRtI7P0er0rP7zjOjTP3RS6FS9XVsGJC9XG9GtYRrR352UVjaI/mODAdEM7OooDM75JXUknC3j0Kl8dDo9dQ1NLPr8ED/tTGX1H2cuePxGtVaMQbgQxk4fwLLZzH6kecYMmB2jqlYjDto743O6rWpsbnEN8YlFHXqIjYHKttbCTL6grE6cdRoHrPNhY2XBW4+Nol+rfc8nLHV6Ax+uP0ZlrRpPFxuevG0AYQEt2RS+7i1ZGF6uNqL1MLGTcTzfbErg1hc3dtil0tWUVTXw4Q/HeH/dUTYdPNvhWBYQ7ptXVh7i9v9s5NF3d3IitaTNPvklLROQHXE5aJp0pOVWUlWr7rDQyyqoJimrArlcRnSEJ5OHBvLWoyOxUik4k1HGnnasbSBMeNKa3SuzJ/QSBcmAUHdxn77Ng5jxsvV6A6uaY3Q27M9gzsINPLF0F99vFWbM99zQm3un9iakOZbLmAU0ZqBfm/M72AkxEVW1mjaWhgPN/dAfe9N5+M1tvL/uGK+tiulwFe+8EuG8SoUw4O+Mz2Hxl7Gs/qNtrNo3mxIxGARRJpMJFqby6kYzq4zR7WbKiH4+DO/rjV5v4N1v43ls6S7KqhrxdLHhptEtgscYuG9rJTg3hMKcLc+1aaKBpkkvZjwOac62OpNRxmurYnjv+2N8+stJ0UIfHenVxmp7Ifo2C6TQACcUchlanYEKE6G5fkcKyVkVZBUKsUumrkB1k45fd6cB8Oic/rz12CjsrC0wGARx9cStA/B2tcVgECaGJ9NKxfvG1I13Nr/K7HneEZeN3gBuTkIMUnInJpU5RTXkFNWSVVgjxmx1BfHx8fz+++/cd999beoDdZZuFTxJSUnU1dWZuZscHByIiIggLq79lMA9e/YwevRofvvtNzGAyYher+fo0aNt3FdDhw495/GuJB+tP849r/zF8ea0yR1x2fy6O43lP56gtl4jBvDdPC6E/z44lKfvHMSnL0zk9smCuXHj/q6JUTmbX8XJtLYdvBFjoF8PX0funhIubjfONMurG/nPJwd486s45r65nX+/vYOMPGEATsgsP28mh8Fg4K1v4nj1i8NmGQntUV2nEevGgGBC1ur0xDULHmMnBYKZHgTXTHJWOe99f5TlP54Q29VVNGn1vLjiAK9+cZh/vbWd5AssC2EUYEbBUlBax+lmwXO+GaApdtYWvPHvEXzzyvWM6u8DYNbhG9HrDZxILWH1n6dJya7ExkrJO4+PYlJ0oJk7xsbKAmd7YQYf7ONI7yAhzqB1gOeFOHAin0aNjl3xghhIOFt22Yq9NWq0vL46hm2x2eyIy+GTn0/y864LB9RmF1ZTVF5PfmktR5KKqWvUklNUwzebEgCob2yirqEJvd5AbIJ5kPCLnxzgqff2cPfLW1j02SHqGi6cXPDnfiEbalgfLzFwNSLYVawP8+kvJ7ll4QZe/eKw2UCSlFmOTm/Aw9kaX3c7Ft4XxZO3DSDYp8VS19PPCWtLQQj19HdCqZBxNKmYbTFZrNmciFqjI6u56OS8mX25dVIoMpmMnibucWtLJcP6tC38ZrTwVNW2WP6M1siz+dVU1apZ9cdpGtSC1Tkxs1wccFtT19DE8x/t48s/z2AwGMT4nZH9fLGzbnHpbTmcScLZMtZtS6awrI5TaaXEJxahkMt4bE5/QpqtlMeSi83ibowM7+uNq6MV/p52+Lrb8cI9Q5gzoRcyWUsA8APTI1FZKBjaxwt3Z2tRJA8I9cDO2gKtzsA738SJkyXTmB+A0uaYHeNzmFVYI1p9Nh/KZF+z+33coLYi8kJMGR7I9cMCeXB6H1FgGMWmTqcXBTAIIte0b0vJqqBJq8fFwYoRfb3xcrXlhXuHYGttwZwJvfBxs6N3sPCcJ5wt53hKS/9fXNFAdZ2GxLPlzF+2mxeW70fTHP9jjPG6bVIocrmM8mo1pZXtLyfRGtNJmTE4viv48MMPOXbsWLturs7SrTE8hYVCB+Ltbe7b9PDwEN9rzVNPPXXO41VXV1NfX9+mUuP5jnclsbNRoWnSseTbIyx7cgzrm32rWp2eNZsTSc6uQCYTfLzOJq6O64cGsW5rCsnZFaRkV4g+7IuhrKqB5z/ah6ZJx2cLJ+Hl2rbwU8JZwarSp4crt10XxvC+3hxNLmby0EC2HMqipl4jrg1jfNjtbVTo9HrqG7XEJxay+WAmowf4ct3QQLNj7z6ay9Hmh+jX3WncOKqHWadn5ExGGa+sPGQWWAvCjMsY8Hv3Db357NdTyGUwdpAfu47kkphZLs6yALbFZPGvWRfv423NsZRi0Z1UXNHAqj/O8O4To8X3dTo9P+5MJcTXkagIL3GwGBjqzqFTBWTkVYlWPuMMvSPIZDKc7a0I9nFk/4n8di08O+Ky+bDZ5A/CjN44yLbGx92Oiho1Qd4OBHk78NuedBKb43hyi2vx87A7b8xKk1Ynzn6PJZdQVtXI7qO59A1x47m7B+PsYIWmSYdMJmuT6nwpNKi1/HU4kwMn8knPrcLBVsWo/j5sOpjJt1uSyCupxUql5L4bI9oU+CurauCp9/dib2MhCnl/TzsKSutIya7km00J/LwzFb0BPJytRYuiTCZYzZKbLTUGg4HjqSW89NlB3vjXCNF615ry6kYxXmraqB5m780cG8LWmCzx/ohPLOLQqQJG9BMG0tPN8TtGN4fRomCKQiFnUnQgu4/k8uRtA9l7LJcfd6SK90Cglz03juqBh7O1WYxMT38n8e9R/X3aLWLo2I7g6eXvxMm0UhrUWhLOlqE3CILp4Rl9+HD9cb7bksjEKH+c7a3Q6w3sOZZLeKALiZllJGaWk5hZjo21UhQ8YYHO3HdjBFqdnuU/HudkWikvLBcWhdwZlyO6fK4fFoiPux0DwzxIy63iWHIJxlvTWCwRhEyz/7t9IAqFvPmzMu67MYIbRwYTl1CIhVLOiL7C2KOyUHD3lHDe+/4YAME+DtQ1NnE8pYSYM4XEnCnEzlrFdpOMutLKevGZCA1wxsPFRhRSQd4OZBZU42RvyUPTIxl4ESui21hZiIUePV1sKCqvp7i8nohgV7KLalBrdNhYKYns4UpcQhGpOZVEBAv3x5mzLfeLsY0DQj34/vUbxNcRwS7sjM8h4WwZZa1Ey/GUYr7cIIj+uoYmcotrcba3FEXVsD7ebDmcSXpuFcnZ5bg7+4qfra7TsPrP04QGODN1RLC4PSOvZVJ2NLmYe6dGdPo76W66VfA0NAiDY+sUMktLy/OuvHoujAuXtXc8tbrjmSyXi7umhHMipYSM/Coee3enWWyKcZAe0c/HTOwAONlbMmqAD7uP5LI1JksUPN9uTuTAyXz+++BQfEzcFCBYUlb/eYbM/GrcnKy5Y3IYHi42fLUhQRQRp9JKzQTPjztS2HssT8zkMD5MAV4OBHgJ7kQfN1uSswUT8gv3DsFCIcfB1pIgHwfe+/4oh04V8NH649TUN3HmbDmDe3vi4mDFqfRS1m1NFn3AFko5dY1a3vwyFpWFnAemRRLo3eKy/HZLIo0aHR4uNgyN9GLzwUy0OiHA0Rj/cv2wQBLOluPvYYefpz27juSyvdn1IJMJ7rhdR3O5f3okll20bo3RBREV4dnc6VSgbtKJx98Rn8N3W5JQWSh4/6mxYnzAoHBP5HKZ+N16u9meU4ycjx7Ns1xTC0+jWouVpZLDzanGPXwdGTvQlxtMOp/WTBziT0llAyP6+eBoKzw/WYXVrNuWwtq/krh+WCD33NCbXUdyGdHXGw8X82yM/JI6caDJyK8is9kMfipdGLRe+9dw/vPJAZQKOR89O77L1g36cUeKGISpVMhYcF8UfXq4UtvQxN5jeeyIE2bkDWotT91hXiPkWLKwiGpZlY6Nzc/bwDAPgrzV7DueJx4XBDFrZMaYEH7bI8QE/uf+aNycrPnvZwdJy6lkw4EMs4DPrMJqPvvlFHWNTWQX1qDV6QnydqBPq/gMlYWCRQ8N5fBpoS7P1pgsvtmUILpCjAHLF7ICzpvZl7k39UEul+HrHs6RpGJxkLrz+nBRQJni72mPlUpBo0bHxKiAdo/raOLSUiqEQd3TxQYPZ2uyCmtEQebpYsOk6AD+2JdBZkE1CWfLGdnPRxTfPf2dCPBsscx/uzlJzADz9bAT61DdPaU3zy9vidE0ZjM621tyT/NAOTDMgx93pHIspRinZgvl4N7CcwgQHujcbhC4m5N1u8/C2EH+/L4ng4z8KiKCXRnS25O/XAURejS5mLe/EbwExmBiozULBPd+RJCLKHieuWswVioFTnaWWHWikva5MH4vxvvQ6BLq5e9EaIAzcQlFYr0haAlwjww2zwoznbQY+/PEs4L1UCGXMTDMg/jEIj5af9xscplVWM3ZfOEB7+nniJO9JWEBzoLgyaogPNCF55fvw83RGnWTjoy8KnbG5xAa4CxaEE0nZem5VVTUNOJsf/6YxctNt7q0rKyEi20doKxWq7G27nznb2lp2aXH624sLRQsvD8KbzdbUezcPK6nOJNRKmTcdw4VPHqAoKiN1pfKGjU/70olt7iWj3483savmlNUw2970jmeWsL2uGy+2ZRIUma5WYaOsdMCwUWwfnsKmQXVotWmd3DblErv5oDXAC97RvbzYWgfb3oHu2BtqRQ7dWMQsaZJxw/bkjEYDHzy80lOpglpoJE9XHnytoGAMEAeSSo2G2xSsis4nV6GQi7j7UdHMW9mXxxshY7MGJzo7GCFhVLB8/cM4Y7rw+kd5IJMhmiKveeG3rg7W4vm9I/WH7/klcIb1Fqxeuvt14XhbG+JVmcgtdnipNMb+Hlnqnjtxtmqu7M1ni42zJvZV/Trt47J6SjBPoIozCupRdOkY2d8Nre8uJGN+zM4lS6YqZ+4dQCzxvc67wKQ1w0NZNV/riPI2wFnByu8XG0wGGD9diHe46/DWTz67k5W/XGaFz85IGbVGDFdrgIEd1qQtwPuztYUlNXx5P92U1zRQH5pnRgncakYDAbxWNNGBfPhM+PpG+KGTCbj8VsGcOukUGaODUEuE1wRu4+YuyOMdVcAcbAI9XdmyvAWK2SfEFe+eeV6sWZRkLcDd14fzrhBfjxx6wCiI73o4esozlZNa+MA/Lg9lVPppWTkVaHV6Qn2ceDJ2wa2ay0L8HLg1kmhPHRTJI52KvJK6th/Ih91k04c4FoLpfYw9h8WSjnP3jUYexsVkT1cGdan/SwhpULOc/cM4dE5/Ylo5xmH9i08ni42uDsLwtc4wHo42yCTyUS3aHJWBQaDQcwIS8upFAO+jdYq48Dq59EySesd7MK8mX25Y3IYbz4yUozveXhmX9ECHB7ogrWlguo6DdnNrjqjRcHeRoWfZ+cyxxRyGa//ewSLHxlB355uhPg58eic/iy8LwofN6Gfc3Gw4p3HR4kCEARXuo2VhXg9vfydCPJ2wMvVtkvEDoBn8/dsdGmZlrHo1WyhMwYd63R60bUecZ77xc/DrtkSL/SDYwf5iVbmRo0OhVxGWKAwmc4qqBYt8YOarYPGFPuEs2UcPJlPSUUDiZnlosA2GOCL309jMAiZWcbtxtAD0+fvaqFbLTxGV1ZxcTEBAS0zi+LiYsLCzp0Wdy6cnJywsbGhuLjYbHtxcfFFratxOfByFVKKT6eVUlxRz4SoAPJLaok5U8i0UT3wdmt/bRHjTZ5bXEt9o7BGi9GtdDq9jG2x2Uw2cR8Zgy0dbFVU12k4cDKfvFJjTRY7copqxU4LIO5MkZnCP1cG0fhB/iRklPHQTX3adOKms1F7GxU19UJ5+n493ckpqsFCKeftx0YR4uuITCYjPa+K7MJqjiQVcyylGL3egEwmrNkFMGagrzjTsbGyoLxaLbpRHGzNrXpuTtY8f88Qisrq6RPiSligC3q9gW+3JJGRV0VGXhUThvi3yYQAodS+laXygkGGMWcKUWt0eLvZ0svfiYgerhw4kc+Zs2X0CXHj0Kl88kvrxNlzTb0GuQwend0fEAqMRQS7sOtILtNGntv6cj5cHKzE7zazQLDIAKz68wxNWj0Otip6mMR5dJTwIBcKy+rFewoQrVNF5fUs/fYIL88dJg6uua0ybUAQ725OVvznk4PUN7YEVP65P4OJUf6XnPKelltJUXk9lioF902NMBtcrC2V3HNDb/Hv77cms+Lnk4QFuuDtZoteL7ihWtMrwAlvV1v69XSjpKKBZ+8ajLO9Fa/OG8FPO1MZ3scba0slz9w12Oxz0ZGeyH4SsoDKqhpwdbSmUa3l8BlhcH9sTn96B7mYWS3PhY2VBeMG+fP73nRSsytwdbRCq9Pj4mB5zv7gXPh72vPVoskmbp32iY44/4KNjiZZWrUNwn1gtPAAnG0ezIyvwwKd2Xwok+SsclKyK8ziS+obtSgVcl6eO4xl3x0h5kwhlioFbq0snNNHt7j9Xpk7nLLqRjFWBgRBN2FIABubxZSVSsHgcA8W3BeFq4PVRa3w7mCrol9Pd7NtVpZKXnl4OLuO5HBddCDuzta4O1mLwdGuzfE1k6IDqG/UthsDdakYhaWxyKup4DFaUPJK6qhtaKKgtJYGtQ5bawsCvc59v8lkMob18WJ7XDa3TgzljuvDOZ3WkrZ/y8RQnB0sSc6q4Gx+tSiojAHexkyvtNwqbJstaYFe9liqFNx2XRjvfBPPmYwyHnx9KwPDPKhtaEKpkHHd0EB+25PO6fRSs4zDq4FuFTzh4eHY2dkRExMjCp7q6moSEhK4++67O308mUzGoEGDiI2N5ZZbbhG3x8TEMGTIkC5rd1ejkMvob5Jt8dgt/RnWx4uxg859MzjbW+HmZE1pZQOpOZVsaS5M2DvIhcTMcr7dnMj4wX5YKAVzsVHwjOjnQ1puJWk5wj8LpZyF90Xz+JKdFJXXU1rZgJuTNXuOCZYf46AV3k7BLIBB4R6semlyu+8Fejtga21BXUMT90ztzeHTBRxNKmbpd0KhqegIL7P4owenR6LV6bnzv5uprtOQnldJfKJQV0QmE9pixPiAFTbPNu1t2lbWHNXf1+z1rPE9cXe2ZtPBTJKzKkjOKhcFj1anJy6hiC2HMzmWXExPPyfe+PeI89ZFMaYXj+ov1GSJCHbhwIl8EjKE2dXmZjfJjLEhlFQ0sDM+h4dm9DGLvwj2cTQLPO0sMpmMYB8HTqaV8v3WZLEmiDFQfEBz2nJniQhyEQPEJwzxx95GRWWNmuuiA3httVDv5fDplhgTY4aP0bVnb2PByP4+WFoouHlcT37dncYtE3vx+94MMvKqSDhb3q7YbI/Es+U42KnMssmgpehdVG/P886kb5sUysm0Us5klLHk23ievXsw9Y1aqus0KBUyUdTZWVvg7WqLTCZj8SMjzeoQOdiqeHB65DnP4WxvRWiAM8lZFcQmFHHD8KAWQewquFs7I/D8m60TOUU12DeL+cgebhclErvCfWh0c1bXqdE0CfeWh4sNHs0DsdFYanR1Gi0DaTmV/LFXCNR2tFOJIiE8SKgm/uxdg/nij9P08HU8731q2j+a8vDMvoQGOPHzrjSGRnohk8kY2Y7b7lLxdrPlzutbEjbcnW3EGj1GoaZUyJk1vme7n79UegU4AYIF/ERKiVi3KzTAGUc7SzF+KCmzXMyaiwh2ueCz//gtA7jvxghR0IYFOuPjZouzgxW3TgoVhdXxlGL0BqGfDW/+bT2cbfBytaGwrJ5jzUHPj8zuLz7XD8/oI6TuVzWyLVaIffL3tG+xGplkfy377ggp2RW899TYLl2wtbN0q+BRqVTcfffdLF26FBcXF3x9fVmyZAleXl5MnjwZnU5HeXk59vb2ovvrQjzwwAPMmzePiIgIxowZw88//0xiYiKLFy/uzkvpUpztrZgUHXjB/Xr5O1Fa2cD67SkUlddja23By3OH8ei7OymvbmTf8TwmDBGEpDHGI8jbgV7+TnyUcxwQTMD+zUXo0nKrmot+eYjR+BOH+HdoVtoeCrmMx2b3Jym7nElR/vTv6cbjS3eJg/H4wW0zF5QKOQOaA3pNs6oeuqmPmTCwaTaLFjVbeOxtLvyQWCgVTBgSQEW1muSsCpKaze37T+Sz+o/TlJosqpmaU8m7a+J5/p4hZg+gwWCguEIQhUaTrDEANNLoE2/OTDNWox4zwBdfdzvumhIuDhBdycAwD042Z7BAS5E1ME9b7gy9g1vEyHXRAWbWuhljevDjjlR+3pXK8L7eyGQycpsrOF8/NJBR/X3wdbcT45gemBbBTaN74OZkLS5CueqP07z7xGjRilZS0cCSb+OZPDSQSdEt1t5T6aW8uOIASoWMe6dGMGNMiBgovP+E4M4aNcBc2LZGoZDz9J2DeHLZblJzKvnXWzvE+2dQmCdZzZlavfydzARFZ8XF0EgvkrMqOHyqgBuGB4mThjEDfTt9rAATwaNo/o6MbqIrgTFGpry6JRbS00TwGDG+9nGzw87aQoilas5UeuqOQbzenK5utKJYWSovaQV2hVzGhCEBYj93uTBd885YnqM7CfRyYMIQf3bG57Bo5SEMBqGEhNHq3svfieLyel794rD4mY5YT+RymSh2QPg9Pl0wEYNBeC/QS7gPjYJ2WB8v8X4EIXW+sEwQM0qFXPQ8AEwZHsS4QX5sPHCWrzYKAdDebrai1SmrsEZci3H30Vwh4PwSwwwulW4vPDh//nzmzJnDSy+9xB133IFCoWDVqlVYWFhQUFDAqFGj2LRpU4ePN2rUKN58802+//57br75Zg4fPsynn35KSMjVVXG3KzDeXCebzZCTogKwtbYQi2z9tiddjOUxWniCvB0YM8AXFwcrHGxVzJkgVMmM7CEMaCdSS9h7XCgyGOhlf9Fix8jogb48PKMvFkoFPu524vnsbSxEX3BrjBkNRrFz15RwZrSqmGwUIcb4IAdbSzpKuBhfUM5nv57i3TXxlFY14minYvb4niy4LwqVhYIjScXc/9pffPH7aTG4+OddacxdvI13vomjpl6DjZVSnLEE+bRUR91zNBe1Roe1pQJfD3sUCnm3iB0QLF/XDxMEskwG/3kwGkuVQgxCvBgCPO0Z1d+HMQN8xeBGI9NH90CllJOSXclLnx7kuQ/3ktEckOjnac+EIeYl9GUymZhWe/t1YdhaW5CaU8mP21vKEGzYn0FiZjmf/3bKrGjd+uZ9tDoh6P7VLw5TUdNIak4lxRUNohvjQng42/Dy3GH07+WGQi4TXWzRkZ7iitWRnciSaw9jjMzR5GKefn+PGDw7ZuD5BVl7+HsK1qzSqkYSm+P0Qptn+VeC1s+Xk50lNlYWuLuYu6E8ml/L5TJCA1ust6MH+DI43JNxg/1QKeWMPEfV4b8L7k4tz7Lx3u5u7p3aGyuVAr3egK21BU/cOkB8b9a4ngQ199WWKiGWsbWFu6PIZC1FEO1sVGaCrnXQu2ntsNAApzbWRCtLJbMn9BL77zED/PBxs0WllKPW6Cgsr2NXsyV5QJgHdu1Y6i8n3b60hEKh4LnnnuO5555r856fn1+bstKm7Ny5s93tM2fOZObMmV3VxKsWUzUtl8u4qdnnPWV4ED9sT+FsfjXHU0pESxAIgsfKUslHz47HYDCI6j4qwpPf96az51ieWNxv8rALW5k6yy0Te2EwCCbtc6UnDzIZpCcPDeS2SaFt9rGxMr81jUHMHSHEzxFFcw2JjQfOIpMJA/Eck4Ju1pZKPv/1FHkltcL3cjSXh2f24Ydtwv1orJHUv5e7aKVQyGX06+lGzJlC1jfXFOrp53xRsQSdQaxL4ueEjaWS8EAX3n5sFGqN7qI7Y7lcxgv3RrX7nrO9FROjA9h8MFMU20a8XM4v6tycrPn3rH4s++4I67anMHqgLz5uduxttoY0qLVsOHCW268LIy2nkuMpJcjlMu6cHMb67SkcTS5mwfL94j0SHeHVbhp1e4QHufDGv0dS39hEel4VtfUaoiO9UWu09PR3EhMBLhZ/T3vuuzGCNZsSSM2pRCaDWyeGihmNncHORoWzvSUVNWrqGrUo5LJLcn1eKhZKObZWSnEhTmMsjXur+8tU1IcHunA0qRgrlYKHbhLcgU/cMoB/3dyvTYmAvxvmFp7LI3hcHa159q7B7D+Rzx2Tw8wycUMDnPno2fHkl9SislB0qQgL9HKgrKoRGyuluKipEdNki/O5qOfO6MMdk8PEkg3+Xvak51aRmV/NruZkgvGDr3w8z9/7rrzG6enfMoMa2c9H9J/b26iYPDSQP/dlsGZzIg80xx54OFuLN1zrIN9+Pd3o5e9Eak4lBWVCoO3EbjATWygV3GVSvLA9PF1suGNyGDX1mnaDoYE2tU7ai+E5F1YqJcE+DqIPfmQ/HzP/PAii65MXJnAkqZgvfj9NXkktS7490uZYg1pZUKIiPIk5UyjG0lyuWblMJuOG4UHi657dvNbaHZPDqK7V4OFiw56juZRXNxLk7WBm7j4XYwf6sudoLvGJRaz9K5mpI4LM3Il/7E3H39Oedc0VgccM9OW268IY1tebRZ8dJL+0joIyIVB11IDOx2vYWFmIVWyNr8+Vjt1Z5kzoRViAM9vjhKSBjsYptYe/p71Y4ynIx6HLUvkvFtPFg6c2B9k721uhVMjR6vRYqhRm/crEIf4cSy5m+qgeoihQKORYd7Li8NWIqdBzc7p8qdVD+3gz9BzZdkCbciRdQbCPA0eTi4mO8BJjQo24Olrj72lPTlHNBTNNTfvsIG8H0nOr2Hwwk6LyeqwtFd0S7N1ZJMFzFWNnbUFYgDNpuZXMGmceLHfLxF5si8kiNadSHDiCvM89Q5TJZNx+XZi41sqEIf7nLKB2OWgtQFrTOrDN3rZzptDwQBdR8Nx2XfsZgTKZjCG9PenX041318SLKeiPzu7Hp7+eAoOBQa3cKa0LwvW6hKKQVzPO9lYsuE+wAN02KZQfd6S0+S7OhUwm496pvYlPLGLf8TxKmlNtJwzxJyW7gtziWt7+Wqh5Ym9jwR3Nv0+gl5ASvvzHExgMYG2pOKdb9ErSt6dbh5YJuRB+HnaiBa2X/5W/j0wz9oxB1XK5DHcnofSAh7O12eTEw8XGrAjntYSphad1dtm1hrFUSutimUZeuHcIZ/Or6d+r4/GCRvebMVNyVH/fDltqu5Mr3wKJ8/Lfh4ZSXacROyAjzvZWzBgbwg/bUsROM8jn/Kb1qAhPege5kJFfZZYSejVi28al1TnBExXhxYYDZxk3yE98+M6FykLBgvui+GVXGrbWFtwwIhgfdzuatPo2cTmujtaE+DmKC/CZuh2vVWytLbh/2rkzmNoj2MeRMQN92XssTyw+OX6wH3dP6c0P25PZfTSX8EBn/u/2QWbm+UlRAfyyK4380jqiI7y7rIDk1Yhpgb6r4T6aPb4nv+1J55WHh5ltN9Za6q4YtasRJztL3J2tqW/U4ul6bV+3o53leasiB3o5nDf9vT1M+1ylQsat7YQtXAkkwXOV42hnaRZlb8qscT1JyCinsLwOexvVBddzkcmEwltqja7TAuJy08bC08lgt0HhgsuqvaU02kOpkJs9lOebzUT19iI9twone8s2MQ4SLTx0Ux+UCjkGg4Egb0f693IXiwY+Ort/uym1CoWc/7t9EN9vTeLWSb2uQKsvH6aF8y5l+Ziu4r4bI5gzMbTN0i9GofNPEjwymYz3nxqHVqe/KiwTfzdMk2FuGBHc4X64u5F+yb8xNlYWvPnoyE59xtJC8beYNdtat9yaMhkXFd3v59G5SqwdZWKUP9tis5gw5NKL613LuDhYtVnuwcj56of0DnbhtX+N6K5mXTUE+zhiqVJgY6nE36PrYzM6i0wma3edu9EDfDmdUdot9W+uZq72SeHVjLO9Fb2DXCiramg3KeVKIQkeiasSUwuPrZVFt2dCdQYvV1u+WnT9lW6GxN8cB1sVS54YjaWFokPB4FeKQeEerHzxuivdDIm/Ge88PgqtztCliwlfKpLgkbgqMU1L72zAsoTE34UrmYouIdGdyGQyLJRXz0QVLkPhQQmJi8HWxMIjmZYlJCQkJC4VSfBIXJWYurQ6G7AsISEhISHRGknwSFyVmAYtSxYeCQkJCYlLpdsFj16v58MPP2T06NEMGDCAhx9+mJycnHPuX1FRwTPPPENUVBTR0dG8+uqrNDQ0mO0zefJkwsLCzP4tWLCguy9F4jJioVSIwW6ShUdCQkJC4lLp9qDlFStWsHbtWt5++228vLxYsmQJc+fO5c8//0SlajuQzZ8/n4aGBr766iuqq6v5z3/+Q319Pe+88w4A9fX15OTk8NlnnxEZ2VIMraOrrUv8fbC1sqCyVi1ZeCQkJCQkLplutfBoNBpWr17N/PnzGTduHOHh4bz33nsUFhaydevWNvsfO3aM2NhY3nnnHSIjIxk+fDivvfYav//+O0VFwsrEaWlp6PV6Bg4ciLu7u/jP3r57aq5IXDmMmVpSlpaEhISExKXSrYInKSmJuro6hg8fLm5zcHAgIiKCuLi4NvvHx8fj7u5OSEiIuC06OhqZTMaRI8LCjsnJybi5ueHoKKVzXuvY2bS/EKqEhISEhERn6VbBU1goLMbo7W2++quHh4f4nilFRUVt9lWpVDg5OVFQUAAIgsfGxob58+czatQopk+fzldffYVer++mq5C4Uswa34tR/X0YGNrxReskJCQkJCTao1tjeIzBxq1jdSwtLamqqmp3//bieiwtLVGr1QCkpqZSXV3N9ddfz2OPPcaRI0dYsmQJVVVVPPnkkxfVzuLiYnQ6HRMnTryoz0t0L1u/vtItkJCQkJC4GikoKECh6NhySd0qeIyBxBqNxiyoWK1WY23ddtFFKysrNBpNm+1qtRobG2HhupUrV6JWq8WYnbCwMGpra/nkk0944oknkMs7b7SytLRs97wSEhISEhISVy9KpbJdQ0m7+3ZnQ4zuqeLiYgICAsTtxcXFhIWFtdnfy8uL7du3m23TaDRUVlbi4eEBCNai1hcXGhpKfX09VVVVODt3ftXh+Pj4Tn9GQkJCQkJC4u9Dt8bwhIeHY2dnR0xMjLiturqahIQEoqKi2uwfFRVFYWEhWVlZ4rbY2FgABg8ejMFgYNKkSSxfvtzsc6dOncLd3f2ixI6EhISEhITEtU+3WnhUKhV33303S5cuxcXFBV9fX5YsWYKXlxeTJ09Gp9NRXl6Ovb09VlZW9O/fn0GDBvHUU0/xyiuvUF9fz6JFi5g5cyaenp4AXHfddaxatYoePXrQp08fDh06xBdffMF//vOf7rwUCQkJCQkJib8xMoPBYOjOE+h0Ov73v//xyy+/0NjYSFRUFIsWLcLPz4/c3FwmTpzIW2+9xaxZswAoKyvj1VdfZd++fVhaWjJlyhQWLlyIpaUlAFqtls8++4xff/2VwsJC/Pz8ePDBB7n11lu78zIkJCQkJCQk/sZ0u+CRkJCQkJCQkLjSdPvSEn8HhgwZgkajwd1dqvciISEhISHRGQwGAzKZ7Iocr6SkBJVK1aHkI0nwIKS963S6K90MCQkJCQkJM5qamqiurkatVqPX65HL5VhaWmJvb9/hdOyuJDc3F3t7exwdHTEYDFRVVaFSqcTSMeXl5ajV6jZFhM9FVVUVNTU1+Pn5AcJ4XFNTg5ubW4c+r9Vq6aijShI8IKa879ix4wq3REJCQkJCQiA1NZVbb72VgQMHcuutt+Lq6kphYSHffvstSUlJfPPNNwwYMOCytun48eN4eXnh5eXVbhzuvpjTrNlwnCcfuIHIHq4XPF5hYSGFhYXidSxYsIDY2NgOj8edKRgsCR4JCQkJCYmrkC+//BJnZ2dWrlyJUtkyXE+aNIkpU6awYsUKPv/888vapgsJrKRCAwX19uyIy+6Q4DGKp8tBt9bhkZCQkJCQkLg4SktLMRgMbdaKtLGx4cUXX+SGG24Qt23fvp1Zs2bRt29fRo4cyRtvvEF9fb34/kcffcR1113H7t27mT59On369OH666/nt99+Mzv2119/zZQpU+jbty+jR4/mlVdeoba2Vnw/LCyMjz76SLTuACxcuJAJEyYA8Oua98nY8RZl1Y3897//ZeTIkW1CRhYvXszQoUNpamrio48+EgsRL1iwgF9//ZW8vDzCwsL45ZdfmD17Nrfffnub7+b+++/ngQce6NT3KQkeCQkJiX8gOUU1NKi1V7oZEudh3Lhx5Ofnc/vtt/Pdd9+Rnp4uxqtMmTKFm2++GYA///yTxx57jB49evDxxx/z+OOP88cff/Doo4+axbeUlJTw2muvce+99/L555/j5+fHCy+8QHp6OgAbNmxgyZIl3HXXXaxatYrHHnuM33//nddff71N2zw8PMQiwI888oj4t1ojiJvyqkZmzJhBaWmpWfFhvV7P5s2bufHGG7GwsDA75qOPPsrYsWNxd3fnhx9+YNy4ccyZM4djx46ZFSQuKCggJiZGdKN1lMvi0tLr9Sxfvpwff/yRmpoasRaPv79/u/unpqayZMkSTpw4gVwuJyoqigULFuDj4wMItX0GDhwoLihq5PHHH+eJJ57o9uuRkJCQ+DtzJqOMBR/vZ8wAX567Z8iVbo7EObjzzjspKSlh1apVvPbaawA4OzszatQo7r33Xvr164fBYGDp0qWMHj2apUuXip8NCgri/vvvZ8+ePYwbNw4QFuhevHgxw4cPF/cZP348e/bsISQkhNjYWPz8/LjrrruQy+VER0djY2PT7mLfKpWK3r17AxAQEEBERAQAjc2Cp6yqgcGDx+Hr68uGDRsYMWIEADExMZSUlDBjxow2xwwICMDFxQWVSiW6zqZNm8bbb7/N77//zvz58wH4/fffsbW15brrruP999/v8Pd5WSw8K1asYO3atbz++uusW7cOvV7P3Llz212ws6KiggceeAArKyvWrFnDypUrKS8vZ+7cuaLAyczMRK1W8/vvv7N//37x34MPPng5LkdCQkLib03smUIA4hIL0en0F9j7n0l6biUJZ8uudDN48skn2bdvH8uWLWPOnDnY2dnx559/cuutt/LNN9+QkZFBYWEhEyZMQKvViv+ioqKws7PjwIEDZsczjcExxs4YXV/Dhg3j7NmzzJo1i+XLl3Pq1CmmT5/OPffc06G2Nml1aJoEwVNT34RGq+emm25i+/bt4ni/ceNGgoKC6N+/f4eOaW9vz+TJk/njjz/Ebb/++itTp041W5S8I3S74NFoNKxevZr58+czbtw4wsPDee+99ygsLGTr1q1t9t++fTv19fW8++67hIaG0qdPH5YsWUJ6ejpHjx4FIDk5GTs7O8LDw3F3dxf/2dradvflSEhISPztOZMhDOQNah1nC6qvcGuuPnQ6Pf/59CD/+eQgtfVtJ+aXG0dHR6ZNm8bixYvZvn07v/76KyEhISxZsoTKykoAXn31VSIjI83+1dbWUlxcbHYsa2tr8W+5XJAARrfX1KlTWbZsGTY2NqxYsYI5c+YwceJENm3a1KF2llY2mr2uqBbcWlVVVezbtw+NRsPWrVu56aabOnX9c+bMIScnh/j4eI4dO0ZmZman3VlwGVxaSUlJ1NXViSY0AAcHByIiIoiLi2PatGlm+w8fPpwVK1aYKTfjj1JdLTyYycnJhISEdHfTJSQkJK45GtVa0nIrxdcJGWX09HO6Yu25Giksr6euoQmA3JJawgNdLnsbioqKmD17Nk8++SS33HKL2XsRERE89dRTPPbYY2JA8PPPP090dHSb4zg6OnbqvNOmTWPatGnU1NSwf/9+Vq5cyXPPPcfgwYPFNS3PRUllvdnrsqpGInsE069fPzZv3oxcLqe6urrTgic6OpqAgAC2bNmCXC6nR48eF5WO3+0WnsJCwXTaugiRh4eH+J4pfn5+DBs2zGzb559/jpWVlbjCekpKClqtloceeoiRI0cya9Ysfv/99266AgkJCYlrh+SsCnT6lkDWhLPlV7A1Vyd5xS1ZSQWldVekDW5ubiiVStauXdsmXhUgIyMDS0tLevXqhaurK7m5ufTt21f85+npybJly0hISOjwOf/v//6Pxx57DBBcSTfccAOPPvooWq22jaUIQKFQmL0urWwwe11WJbyeMWMG+/btY+PGjQwaNOic8bvQYuAwRSaTMWvWLLZv387OnTvFYO3O0u2Cp6FBuODWFSEtLS3b/RFbs2bNGr799lueffZZXFwElZ2amkplZSX33HMPq1at4vrrr2fhwoX89NNPXX8BEhISEtcQp5vdWZ4uQmXchLNlHa5Ue7kwGAzEJxaJA+blJtdE8BReguDJK6mlsOziPq9QKHjllVdISUlh9uzZfP/998TGxrJnzx7efPNNPvjgAx5//HGcnZ156qmnWLduHW+88QYHDhxg8+bNPPTQQyQkJBAZGdnhcw4bNozt27fzzjvvcOjQIf766y8++OADgoKCCA8Pb7O/vb09AIcOHeLEiROUVLQWPIKLa+rUqdTV1bFp06Z2g5VNcXBwoLS0lD179piJrFmzZlFcXEx+fv4Fj3Euut2lZXRNaTQaMzeVWq028yW2xmAw8MEHH/DJJ5/wyCOPmAVNbdiwAZ1OJ8bshIeHk5+fz6pVq5gzZ043XYmEhIRE59HpDWyLyaKqVo2jnSWTogNQKq5cRRBjIO5NY3rw5Z8JVNSoKSirw8fN7oq1qTUHTxbw9jdx9PR34n9PjunSdZo6Ql5Ji+DJv0jBklVQzVPv78FKpeTLRZOxtFBc+EOtGDduHOvXr2fVqlV8+umnlJeXo1KpiIiI4L333mPy5MkA3HLLLdja2vLFF1/www8/YGNjw6BBg1i6dOl5rSmtuf3222lqamLdunWsXbsWKysrhg8fznPPPdcmhRzAzs6OBx54gB9++IE9e/Zw19MrzN4vrxYEj4uLC6NGjeLAgQNMmTLlvG2YNWsWe/bs4bHHHmP+/PnMmzcPAE9PT8LDw3Fzc7uga+1cdLvgMbqyiouLCQgIELcXFxeLxYZa09TUxMKFC9mwYQMLFy7k/vvvN3u/vcjs0NBQsyhuCQkJiStFo1rLum3JRPZwJSOvim+3JInvFZXXc9+NEVekXfWNTaILa3C4J4dPFXIqvZTf96TzyOyOZc1cDrYczgQgLaeS1JxKQgOcL+v5TQXPxVh4dDo97/9wjCatniathtPppQwOv7hBOjIykv/9738X3G/q1KlMnTr1nO8/8cQT7ZZtSU5ONnt9zz33nDcrq/X+CxYsYMGCBQC8vPIQXgNuI8jbgcyCatHCA/Dpp592qF2hoaFs3ry5zX5FRUUkJSXx4YcfnrNtF6Lbpxnh4eHY2dmZFR6qrq4mISFBjMlpzfPPP8+WLVtYtmxZG7FTXV1NdHQ0v/zyi9n2U6dO0atXry5vv4REd5KUVc7G/RlXnUvhasRgMJBwtkxMe21NeXUjsWcKr4rv8qddqfy8K43XVsWw9i9B7AzpLQx4v+xKJfEKxc2cSC1Bq9Pj7WaLj5std0wWJp1bDmVyNr9trZXLiU5vYGd8NkeTizmRWiJu33ww87K3xSyG5yIsPJsOZpKWUym+PprUNv7lWsTo0goLFARqV7gkExMTWb58OXPnziUoKEis6HwxdLvgUalU3H333SxdupQdO3aQlJTEU089hZeXF5MnT0an01FSUkJjo6AEf/nlFzZt2sRTTz1FdHQ0JSUl4r/GxkYcHBwYNmwY7733Hnv27CEzM5PPP/+cP/744x9ddNBgMPDhD8dYsznxSjdFohV6/bkH4Pe/P8qnv54iLrGoQ8dKy63kuQ/3kpT5zws03Xc8jxeW7+fjn060+/5H64/z+uoYdsRlX+aWmdOg1rJx/1nxtd4A4wb7seihoYwf7IfeAB//dPyyCLOyqgYSzpbRpBVEYlyCcJ9FRXgik8no29ONkf180Bvgk59Por2CNXk27s/gve+P8fLnhzAYwM1RsOTvPZ53WVPDaxuaqKxtiS+tqtVQVasWv8OOEHOmAIA+IcJaUke6SfAYDAYSz5bTqLnyFbMNBgOlzVla4aLgaTzfRzqEWq3myy+/RKfT8b///a/doOaOclkcyfPnz2fOnDm89NJL3HHHHSgUClatWoWFhQUFBQWMGjVKzPPfsGEDAO+++y6jRo0y+2fc580332Tq1Km8/PLLTJ8+nU2bNvHhhx8yevToy3E5VyW5xbVsi81m/fYUqVz8RWAwGDiRUkJGXtfOcmNOFzBn4Qb2HM1t8159YxN5JcLs0VgIzhR1k46XVx5i+Y8tg+PWmCySsir4fmtym/2vdU6klgKw91guFdXmHammSSdaBX7fe2UtZttis6htaMLbzZbn7x7C7PE9eXR2f2QyGfNm9kVloSCrsIZUEwtAV6PXG3jp0wPc/9pWXli+n/XbU9HrDaKwju7dsljjg9MjsbFSkphZzpcbznRbmy7EziM5Zq/vmRqBv6c9miYdx00sPt1NXnENAC4OVjjaCck2D7y+lfte3UpNB4SXXm8Qf9u7rg9HLpddUvDy+dh1JIfnl+9j7V9Xvj8oq2qkQa1DLoOw5jT+8urGS34WBwwYwJEjR9i0aVO7gdOd4bIsLaFQKHjuued47rnn2rzn5+dn5hNcvXr1BY9nZ2fHwoULWbhwYZe2szv5cUcKf+zLYPG/RxDg5dDlxzfNKsgtrqGX/+X1eXcVv+5O42RaKU/eNhAne8vLcs6yqgaWfneE0+ll2Fgp+XrR9VhZds2j8e2WJJq0epZ+d4QxA33Ngi/P5rcUfItLKMJgMJi9f+BEvmgKH9Lbk2F9vMUU2ROpJdTWa7CzMc9+NNKk1aNUyC5rsOcfe9NRKOTcODK4W46fnlcJgFZnYGtMFrdd1xIDmJRVTpNWsE5kFlRzJqOMPiFuZBdWY6VS4tGckdTdGAwG/tyXAcDNY0MYPdCX0QN9xfftbFQM6+PF3mN57D6a222xKWcyykSBCHAqvZSoXE8qa9RYWyqJMFnF2sPFhqfuGMTiL2P5Y28GUb09GRDq0S3tak1+aS1f/H6aAb3cSc+tQiGX8cD0SGrrmxg70JdTaaXkFNWQW1xLbUMTGXmV9A1x69b72tiX+nnYoWnSUVWrEWNxkrMqRNfkuT9fQ32jFkuVgt5BLvQOcuFMRhlHkoq7/Nk4fFqYKCVkXL6K0Dq9gW82JtDTz4nRA33ZHpuFg50l1c1WsZ7+Tni5Cs9bk1ZPZa0aZ/vOVUTuLqTFQy8DWp2eX3enUVmj5q+YrAt/4CLIbZ6VAOQU1Z5nz6sXTZOOb7ckEZ9YxLtr4i9byfs1mxM5nS50GPWNWhI66S4SzN3tt9XBtkWQnEwrNXsv06TCbXl1Ixl5VTSqtSxZE88vu1LZFttyr6z+4wxNWp0oeHR6AwdOFhBzuoDYhEKzdNCC0jruWrSJx5fuYt+xvMti7cgqqGbl76f59JeTbawvl0JqTgXzl+3iSFIRWQUt9/iWQ5lm98fJ5sHdOA7+sS+DrMJqnvzfHl74eP953YpdSXJWBYVl9VipFIwf3H52jHH73mO5nXIhdeZ33BkvWEt6+TsBwr1mtIANCHXHQmne9Q/r480NI4IA2HigxR23/0Qex1Na3DF6vRBHdTGur/LqRtJyK6mua7GS/LE3g7iEIlb+fhqAgWEezBgTwl1TwlEo5Ph6CJljuUW1rPztFP/55GC71tCuxCh4fN3tcHYwH6g7EueUnFUBQE8/JxQKOdERgkDaejirS59Fg8EgVszOKa7p0mMfOJnP2r+S2j3midQSftmdxoqfT5BbXMMHPxznra9i2XVEsGIPDPXAQqkg0EtIWT+SePXEL0mC5zJwKq2UmnqhamfM6e4JqjTNKjAVP38nTme0BKSeSi89ZzxSeXUjv+5OY1tMVpeYiRMyBIHj5iSUSTjVSpicj5KKBu5/bSuvrzrc7vv1Ju7FLYcyzd5r3XnGJxax6WAme4/n8eWGBE6nlyGXgaOdioKyOrbGZFNS0VLJdMXPJ3jjy1heXxXDv9/eTnHze4mZ5TSodWQX1vDut/G8tiqmS0RIcUU922KySMutJD6xiO2xWaLo2Hs8T9wvObviks9lZMP+s5zNr+b9dcfQ6vTYWilxsFVRWtVIbEJL3JNRTN40OgSZDA6dKmDJmni0Oj2llQ0UltfxzjdxvPlVrFnRva7G6Loc1tf7nFbCgaHuONqpqKrVEJfQscH7bH4Vt/1nE191wOXUqNZy4KTwe9w3NQKFXEZdQxP7j+cDEBHcftXgac3Wh9iEIiqqG/nrcBbvfCPcP7XNVYd3xGXzwvL9fPrLyQ6128j3fyVx36t/8dR7e5i/bBeNzc9F62DecYP8zF77GQVPSY0o2JKzKzh0qoB5b20nPbeStJxKnv9oH6k5wn2n0+l57/ujvLB8H1W1F6711hrjRCTQ2wEPZ3PLYGb+hZfhMN7/xjiWSdGBWKoUZORXtZn0XAo5RTWieKxv1Iop4F3Bip9O8P3W5Hbdrsb4wdqGJvY131NanUG8tgGh7gCMHiBYNveZ9A1XGknwXAYOnMwX/y4qryersIYmrZ6n3t/Dm1/Fdsk5TLMKsgv/PoLHVPwdSRIGMH9PYWbw6+40zuZXoW7S0aDWivt+uzmR1X+e4cP1x3nmg72XFGhZVasWszBuHissV9Ja8JzNrzIrxW9KWm4lWp2eU+ll4uDfpNWTlluJXm+g3CRo7+DJfL7ZlCAGPxo7z/693ABBEP2+N83s+IPCPblxZA9AGGz0hhYrhl5vwNZKiZOdJRqtnr3HhI7FtLy7hVIuWMy+jadJq+flzw+x4uf2g37Ph05v4PVVMXy4/jhPvbeHV784zAc/HGd7XDYGg4F9x1o6tZQuFDzGGWxljTBwhfg5MXloIACbmi0R9Y1N4jmnj+7B1BHCwJ1l8hzsP57P/hP5HDpVwLHklkG2KycfWp2efSeE72HsQL9z7qdQyJkUJZTo+OzXU2YWD1P0egMxpwuoa2jiz30ZNKi17IzPabfN1XUa9p/Io0mr5+CpAhrUOrxcbejXy00UDRnNAvtcyyQEeDnQO8gFvd7Ah+uP8+kvwn3SpNWLwuxMcw2f7bHZosC+EAWldazfkQqAUiGjrKqRbbHZFJTWUVBWh0IuY9wgPwaEujOsr3lFfmPbs0xSnPNL69gak0VBaR3bY7P5fV86iZnl/L5HcCV+tTGBnfE5JJwt54MfjnX6NzZORIJ9HLh5XAjjBvlx5/VC7EhH1h0z3otGd6WDrYrrmn/vX3ennfNzneV0KzdWbivLfoNae1GWzdp6jXhPtjeWmCZMmFqhAawtFWL8jlHwHE8tuSjh2R1Igqeb0en0HDolROw7N8ekxJwuILuwmrScSg6dKhDXbLlYDAZDmxieriLxbDkLV+w/54DfWQwGAzlFNeh0erbHZnPXoi3c+d9NvLLyEDHN/ui7rg9nRD9v9AZ4fXUMd7y0iVtf3Mg9r2whq6CapKyWB666TkN6O23TNOn4bksSWRfooIyzMX9PO7GzTc2tpL6xiapaNS+uOMD8Zbt55oO97ZqzjeJCq9OLwmndtmSeem8P2+OyxWyPoZFe6A3w445Uvt2chE5vILNQaNu9UyPwcbOltKqR8mo1ro5WjBvkh0wGM8b0EFM8jbOtQC8Hhvf1pqefI0ufHMPdNwidsVF0GBfwu/26MN5+bBQgLB+QmFnG0eRiNh/MFAVER9l3PI/MgmpUSjm2VkosVUIRtTMZZaTlVpql7hpN+pdKSUUDReXmg2oPX0emDA9CJhM60o37M3j6/b3o9Aa8XG3wdLHh3qm9RWudURz+1VzXBYTAb6Gd5cxesKHLBqH4xCKqajU42qnEWe65uP26MHzdbSmrajQToAdO5PPouzuIPVPIriM5vPFlLIs+P8j+ZiFVUaOmsKyt0PhmUwLvfBPPhz8c47vmNPhJUQHIZDKCvFvWUlIqZPTwPffaSpOHBojXotUZsLUWis0Z+7CcIqFv0ekN/LYnXfxccUU9j767k9/2tP0uV/95Gq1Oz8BQdx6e2ReAX/ekiSKqd7ALz9w1mNf/NaJNcT4vV1sUchlaXcvAXVBSR3bzs5OaU0lqdiUgxHEdSSoS26VUyIhLKOKtr+M4fLrgnNdsSlWtWhRWQd4OuDpa88xdg8XvJa+k1qwsgsFgEN3ZBoOB2DOFotA2PrcAM8YKlscjScVdVj3a6IY3kmPS76fmVHDPK1v44IdjnT6u6bNsHEsqahpZ8PF+ft6ZajahaV1ZuU+Im+gu9XG3I8TPEb3ewEGTSf+VRBI83UhOUQ0LPt5PdZ0GexuVWPMi5kyheSXPUnNl/tWGMyxpnpGfi73Hclnw8X7KqxuprtOIJmcQZlRNWh25xTW8vPIQp9Ivzoyq1el5f91RTqeXsc4kK8hgMFBS0cDp9FLRNN1RVv95hkff3cnDb23ngx+OUVOvoaa+iSNJxRSV1yOXy+gf6s5DN/XBUqWgpKJB/B6qajVsjckSxV1ogBMAZzLaxtxsOZzJum3JrP7z/C4A4+AcFuCCh7MNXq426PWCb/zX3Wnid6dv1cEbMX3gjbMho4UoLqEQvd6AXAYL74vikdn9ANhzLJeC0lrUGh0qpZwQX0f++9BQbK0EF8jMsT15+s5BfP/6VAaEeohxGEa83Wx58f5o3ntqHH4e9ozo54NCLiMjv4qcohrR7eXmZE0vfydsrS3Q6w2i+Vn4zjoe5KjV6cVaMrddF8a6xTfywj1DAGHAMbpKgrwdmrdVdInbyGhNMCXE1xFPFxsxcPTTX0+RV1KLk50lj80RCufZWFnwnweiuXFkMLdODAWg2OR3ij1TSHl1IwdOFtCk1fPjjtROpRy3Rt2ko76xiZW/nQKEGJ0LVVK2slTy7F1DkMkEkVNUXs/22GzeXRNHTlEtG/ZncDxFcOGkZFfSoG5pX0I734tx2+6juRSX1+PmZM1NYwSLZbBPS5JEiK8TqvNU/B01wJeoCE/69XRj7ow+vPKwsK7hkaRiGtVas/jAvw5lsvuIYHHaGZ9DTlEN22PNSwIkZZZz+HQhcrmMuTP6MDEqACc7S0oqGsR7alDYuQOklQo5Xq62Zttyi2vE3zM9r0rsS4vK6/lpp2BJumF4EHNv6gMIYm3xl7HsNskC2xmf0+4kzmh19Xa1xcaqpbKwi4MV9jbCc5TdLPoaNVreWB3L7S9tIj23kjWbE3l9dQx6vYHQACdcHVtWEvBytRWfj+SsCgpK60jO6lysoCk6vYHTzX2T0XVmFKMAX29MQK3RcfBkfqct4KZrhxl/7193p3Mmo4yvNiZQ19i2zzdaXScOCTDbPmaAYOn8ZXca6nPUz7qcSIKnG/lywxmSsiqwtlTwyKx+DG7upDPyqswCVk3dUak5Ffy8K429x/LO69//YXsKZzLK2H0kVxQA7s7W2Fop0Rsgv6SO3/akczSpmHe+iWP/iTye/2jfBf2pabmVoqn6r0OZ5Dff/EeSiqit16DTG1j8ZSwPvrGVhSsOdCqN9URqiSgajEJh2qhglswfLUb19w5ywc7aAg9nG565cxDjBvux+JERPH6LMJgJLhRwcbBkZD/BZNreAGCc/aTnVZ7XpG3sdIyzsX49hZn5sZQSMRXWGNuw91huGz+5qeDJKqxBrzeIliCjqHC0sxTdGJYqBWVVjWIxtQBvBxQKOX4e9rz56CgenB7JtFHByGQycXZtb6PCx62l0zf92/j+wOZBY9/xPHEBP3dn6+YZvtDR7jf57TsqghvUWt5YHUNBaR1OdpZMHy2414xZgHkltaL14ZaJvbC2VNCg1pl1vp3FmLljHKDGDfJD3mypCWle1Xv2+F4o5DKc7C25ZWIvPlkw0SyzqKefE/+e1Y/+rSwtKgtFc4G7HPF3qqnXEHORgbA74rK5ZeEG7n/tL4orGvB0sREnNheip78TfUMEd+ZPO1NZ/uNxjDoxIbNcHNCMGO+HxFZB9Q1qrZmFF+BfN/fFujmGKMhE8JhaHdrDSqVk0UPDWPzISGaMCSEswBlPFxs0TTq2xmbRoNaikMsYFOaBRqtn2dqjrN+eIsbilLRaPNIoQCYM9ifAywFLCwW3XyeIUOPAOfA8ggda3FpGNCYTwdaDufG5nxjlz42jevDu46MZN1gYdD//7TRVtWoSz5bz3vdHWfxlrNg36PQGCkrrOFtQ1eY7A8wsZZn51WiadLz6xWFiEwrRNOlY9ccZfm/u22aMCeHlucPbXIfR1ZOYWc5/Pj3A88v3X7Q1/lhyMRU1auysLZjY7C4zipOTaSVihl6jRtduqQ293tBmoU8jphaenOIa6hubzCykAPY2LWJQZaHg0dn9+GHxVEb29zHbb8rwQNwcrSgsq+eHbVc+df6ypKX/U5k1ric9fB25YXgQro7WGAwG7G0sqKlvMjOx5pXU0ajWYkBQ0ka2xWYzop9Pm+PWNjSJ1oSMvCqxI/Rzt6NerSU5q4LsohpxhlhVq+Gdb+IByCyoIiLYhayCGgK97c1mIceSi3l55SHcnW346JlxfN98gyoVgkn54ClhRmw6OCRlXth9kXi2nG82J4iVR6+LDiDYxxGlQtbsnpDxzuOj+X1POmNMUniH9/VheF/h+t2dBEFU39xJhvg5EdlD6EDOZJQJlpTmUdFYjMt47eXVjWbXaUSnN5DSbA43DgTREZ5sjcli77Fc0Y99y6RQ0vOqSMwsZ9PBs9w9pbd4DNN4mezCagrL6mjUCDMZY6C6S3MBNZWFgkFhHhw6VcAfzanLQ0zKzffwdTynuyE0wFkUn96tBA/AyH4+xCcWcSy5WBx03JvdOoFe9pzJKDOzArYeTM/Fhz8c40hSMSoLBf93x0BxEHWyt8Td2ZqSigaKKxpQyGUMDvekl78zJ9NKSc4qF4VWZ/n+r2R2m9QtGtHPh0HhHlTXacT4rsgerqx5dQrWlsrzWlOCfcy/z1njerJuWzLHkovN4hO2Hs5iVH/f1h8/L8UV9Xz26ykMBmhQ65DJ4MnbB5pZBi7EmIG+nEwrFQPa+/dy42x+NdV1GtQaHXK5jAGh7mTmV3HH5HA+/ukEp9JK+XlnKhHBrvQOduFsfhUGg+AyH9HPBwdbFcP6tMTCmP4O54rfORcymYzhfb35bU86v+wS3FU+7rYsemgo329N5oftKfy8Kw11c+G7+kYtFdWNfPLLSZQKOTFnCpHJYNb4nuIxbxzVg0BvBzYfzMTR3pKQ87jYQBA8MZ0oD+RkbykK8t7BLvT0dyIzv5rMgmq+2ZSIv6cgoEorG8gsqCbYx5FvNyeK4gza3jfCNgdOpZeSWVCNWqPldHqZKPCNE4jQACceuimy3bT5sAAnthwSRLKxb4hPLMLPw77jF9eMMZNuUnSA2GcYXVo/bEsBBHeuwSCIwNblD37bk86XG84wb2ZfcRJjpLC0pU8rKqtj44Gz1DdqcbBViX3ihCEB/Lk/A73eQA8fYdJm085zaGNlwbyb+/HmV7H8siuNWeN7YWfd8eejq7ksFh69Xi8WBhwwYAAPP/wwOTk559w/NTWVefPmMXToUIYPH878+fPJzzf3AX733XdMnDiRfv36ceedd5KQkNDdl9Fp+oS4cfeU3uJgK5PJxAfJ1DQspM/u5s7/bubAiZZZ+NGk9lcLNg0ay8ivFE26fp72BDQPCHuP5VJUXo9CLkPRLARUFsLD+fiSXby88hD/fnsH731/lMeX7OTdNfF89ONxDAYoLq/no/XHqarV4OFiw62ThBnrn/syWLNJ+J6njRKsHnmltee1oOj1Bpb/dJzT6WU0anT08HXk4eaH7IYRwWLH4OJgxQPTI8UZfGu8XG1wdWxJEQ3xdSLEzwlLlYLahiYzi0JBaZ1ZpdSz58isyC2qoUGtxUqlEGsjDQzzwMZKSVWtBoNBCKB2cbAS62ccPGkeC2DqKskuqhEDQ00xrUExNLKl4JuttQUzmgOlL0SvZvcdtC94jIItLbdKFIVGwdOe8MgqrLlgIKFOL6xYDfDfB6PbrAVk6mqL7OGKrbUFvYOEAXVHXPvBtRfCYDBwMq2lyJxcLiMi2IXxg/2ZMcb8u7K3UV3QdSRYC4XvwcvVhtEDBAF9JqOMylq1GONzPLWE4vJ6yqoaONE8Udh1JIcXVxw4Z3Duip9O0KDW0jvIhTf+NYKl88eIFpuOMryvj/h8AtwxOdzsGD18HXll7jC+WnQ9w5tjzPJL6/hqYwIf/HAUQHTN9PQXrFrGAFsjLg5W+LrbYaVSEBniSmcZ0TzpMMa2+Hvao1DIufP6cHzd7YTgWJOfemtMFodOFYjW5KGRXqJQNdInxI3n7hnCvJl9L1hTx2jhkckQ7y/jayMBXi3Hj47wEic/IATuG91bh08XkJ7b8ozGJxZR39hklooP0MOn7TMT4if03QdO5vNncyXte6dGmD3Ts8b3Ouf1GC08RrEDcCy5YwUVDQaD6BIqLKsTEzxuGB4kfj+VNWoKy+rEYOYbm4P3T2e0ndwYn+s1mxPajDGmFh69AdZvFwTUPTf0Fq91ZD8fcazpeY4+28jwvt7cNLoHkT1cUSou7yKwrbksgmfFihWsXbuW119/nXXr1qHX65k7dy4aTdvshIqKCh544AGsrKxYs2YNK1eupLy8nLlz56JWCx30r7/+yrvvvsuTTz7JL7/8gp+fHw888ADl5Vd/uf32ZvBxCUXkl9ah1enRG4S0vt5BLugNiLUNTDE1aecW14p1MgK9HIiKEG5IY0Gq8CAXXn14OP93+0AWPzICECxEMplg7twZn0NWYQ37jueZuWf2nxAE5g3DgxjfbBLOLKimrlFLTz9HHpgWiUIuQ63RtVs+vFGt5XR6KYdPF5BdWIO1pZL3/m8s/3tyjGgl6AwymYxIk2JpIX6OKBVywppnLqbxHq1dXOeqnZGU1ZJNYSoKTTswYwbVoHAPZDLBT27sIDRNOrPg37ziWtFiZIqpUBvS21N0z8wa17PDsx3TGZq3a9tVrX3c7bBUKUQTv72NhZgWHWgieCyUcnzdhc+3F8dTWFYnZnbkFtfQqNFhbamgb8+2QbimxS2N990NI4KwVClIzCy/qHTU3OJayqvVWCjlPHHrAJ67ezCOdpdWgNI4yQgPdMHf0x4HW5UYY+Trbke/nm4YDLA1Nov/fnaIlz47SFxCIV9tOMOp9FLWb09Bp9OTWVAtZuJlFVRzJKkYpULG/NsG0D/U/aKKCDrYtrgj+4S4EtnDlb4moiQi2AWZTCgg6WhnaSYc8krqqKpViwP4uQYemUzG4kdG8MHT43BpVVemI4QFOuPi0PIbGNsgl8u4aUyPNvsfS2kZxP087Lj7ht5t9ukMoQHOyGXC/6b9Z/9eLffk9FEt7Rjax4vWRPRwRaWUU11nXgrgSFIxO+Jy2lSnb8/CM6KfD25O1pRWNpBXUou1pYIJQ/y5ZWIv5DJBdJla1lrj624nxukZOZ1eysm0Ev7cl9EmZrO+UcjOe+nTA9z+0iZuWbiBw6cL2BYruPUHhrrj426HjZWFGBKwZlMier0BPw87JkQJ9Z4SMsrMYur0eoNYxLNBLbjjTLO5jKU+jLFejRodjnYqxg/x54V7h/D5wkn0DnYRv+f2vu/WPDyzL4sfGYmV6so6lbr97BqNhtWrV/Pss88ybtw4AN577z1Gjx7N1q1bmTZtmtn+27dvp76+nnfffVdcFX3JkiWMGzeOo0ePMnz4cD799FPuvvtubrrpJkBYamLSpEn8+OOP/Otf/+ruS7okgtuZORgHqX493QgNcOaGEUEcSy4mMbOcw6cKmDNBWBRV3aSjorrRzMJjMLRYMIb09sDZ3ooAL3vRXD8g1N0sjuGhmyJJOFvOPTf0Ji1XWIm4l78Te47mkpZbya2TQln5m1AETKmQMSkqACd7Sx6Z3Y+EjHIc7FRMH9UDlYUCL1cb8krqyCupFbNihDYZePOrWI6llIiD+40jg+nZKvi2s/QJcRNTr42de7+ebpxMK+VoUjGTogLYdSSHA81izWhuPlftjNbxO0ZGDfAVhaaxU7W3UdHL34mU7EqOJZcwKTqA0mbho7JQoFTIqG/UmlnojJgOMo52lsye0IvMguo2puTzEeLriK+7LRZKhZmAMqKQywj2dhBFnOnvEWhS2TvQy55eAc7kldRy5myZmct037E83v02nkFhHiy8P0rMfunh62RmhTBiauExFldzdbTmlgm9+HZLEl9uSGB4X582Re7ao7SygbjEIhoahdlv7yAXMRDyUpkwxJ8zGWVMig4QhbMx6yjYx5HoSC9OppXy88408Vn85JeTlFcLYnZXfA7F5fUcSynB3kbFzeNCxAFycLjnRbkkTLl3am8slHLuniJYZvr2bLHwRASbW2Qem9OfYynF/HUoi8paNak5lWKMxvlm2u25dDuKXC5jWB9vNhnjzkxE14TB/nyzKZG6hibsbVTU1GvE/unO68M7HM90PgK8HFj65BhcHKzEZxuELLQTqSUo5HLGDPRla0wW9Y1aMyFkxEIpJzTQmdPpZWZBt4mZ5WIsy11Twtl3PA97GxXuzm2/LyuVkgemRbDk2yMAjBvsj42VBWGBLnz47Hic7CzbfU6MyOUyegU4czylBKVChp21ispaNf/55CAAx1KKWXBvlCg0Vv52mu2t1oX7c1+G2F5j7A4IKeA/7kgV62ENCvOgh48j1pYK6hq1ZBVUi2KxsLyO+kYhFktvMLDveB51DU08d88QLJRycQI7oJc7sc3icOqIYDGDzmhhvuO6MG4cEdymOOPVTLcLnqSkJOrq6hg+vCWIy8HBgYiICOLi4toInuHDh7NixQpR7ADiYmHV1dWUlZWRmZlpdjylUsmQIUOIi4v7GwielpmDm5M15dWNorqePDSQsc2Ft4zug9ScCnEJgXe/iSc2oVAUER4uNhQ3p+2GBTiLndotE3qxbK1g7m6dHjtzbE9mjhX+9ve0F6u+Gv83GAxsPZxFVmENw/p4i8s7TB0RLNY3MeLjbkdeSR35JbVmnczR5GJxlqc3gEopb3cm2FkG9HJHIZfh7mwtDvrRkV58uyWJY8nFfL0xQYyNAcHPvPHAWTEQ0UhhWR0OtioxJT2s1cx8YKg7Hs7WqJt0Zu6FAaEepGRXcjxFEDxGi5iHszW21hYkZ1WILi4vVxsxfbh1h3Dv1IhOX7uFUsFHz45HJpOZmetN6eHrKAoeY8wTCK4zY7xNkLcj4YEubD6YSUpWBWfzq/ji99Pcc0NvtjQHJh5NLub1VTFix9Y6S8xIeJALPXwdcXeyxse9xeo0c1xP/twvdMxnMkrPuUyBTqdHo9Wj1xtYuGI/hWX14r1tOuhfKiP6+ZgJuz4hpoLHgRF9vfnM2sIsxsnU2qnR6sX7uaZewzebEkUr5fnq7XSUYB9HXrw/Wnzt72lPgJc95VWN9OlhLngiewhWoJKKBnbG53AqrVTMGjK6XLqDEX19RMFjamWyslTy3weHkllQTW5xDRv2nxWtCa2DjS8FozXR1J07MMyD5+8ZgqWFAhsrC5Y9OQaDgXM+H5HBrmJQs5OdJTZWSvJL6ygqr8fW2oKbRvfgtkmh53WxjR7gy/bYbJKyyrnJZMIS2MHlgsIDXTieUkKfEDdcHa3YEdcS2hGXUMRnv57iiVsHAC0W2FnjejIg1J1Fnx8Si/tZKOVERbS4mCcM8efHHS0xSIPCPVAo5ET2cCM+sYiDJ/MJ9nGgrqGJ9ByhPwzxc+TGkcF8/NNJjiYX88O2ZCZFCyLKxkpJRLALsQmFWCjlbfp+EOpJ/Z3EDlwGwVNYKChEb29zU5+Hh4f4nil+fn74+Zl3Ip9//jlWVlZERUVRUFBwzuMlJSV1ZdO7BT8Pe5QKOVqdnkAveyyUcgpK65DJzMWJm5M1/p725BTVcCKtlKjenhxrdl3pDcIgNmaArxhoZ2pWHN1sodAbDPS6gH+1NTKZsJbNjztS28QCtMbX3Y44isg1SbHX6Q18tUGI85nS7F8O8LTvkrVUvN1seefxUdjbqsROKcjbQRR+RrHjYKvCxcGKm0b3YOOBs+QV16Ju0mFpoSC/tJZH39mJn4edGPcT2srCY6FU8N5T44TCfiYupwGh7qzfnsKJ1BL0eoOY/u3uZM3I/j5m9WeG9/UR67u4dlGnYKE8dzoxCJYYI25O5ucM8XWkpKKBnn6OYhprel4V329N5mRaKUu+OyJej7WlgpNppWKHey7BY2mh4IOnx7W7Paq3F9vjsolLLGojeKpq1Sz+MpbUnEp0ej1uTtaiwDBa1jsbC9MZ+vRoOXawjyMqCwXjBvmx4cBZLFUKfNxsRavpddEBbGtOtX76zkGk5lSKRQCtVAqiIs+/rtLFIJPJeOexUTRp9ed054UFOrMzPocthzPR6w24OFhdlLuqo0SGuBLoZY9Gq29j0TKKsNb1jLpS8BgJ9nFELpfh6WyDg63KLNBccP2d+7Om1rIevo6MHuDD+u2phAc5M3Nszw4Fm8tkMhbNHUaTVn9RrvmbxvSguk7NjSODKSyrZ0dcDj39nZg5JoSl3x3h4Ml8HpvTn3q1VoylmT2hFw62KnoHuYjhDIPDPcza6+dhT3igM0lZFVgo5aL7f2KUP/GJRWyLFSyC22KyCG629IT4OjFhSABymYxla49yIrVEFNjebrZER3rx444UbhoTctnWNexuul3wNDQ0m/1V5oscWlpaUlV14XVJ1qxZw7fffstLL72Ei4sLGRkZ5zyeMcbnasZCKSfAy56MvCp83e2QyWQUlNbRy9+pTec2MNSdnKIajiUX42irokmrx655tj68r49ZoN5wkwqlCoWcV+e1TYvsKIPDPdsEqLaHMRYkv6QlyG399hQyC6qxtVJyzw29zdaS6grCWmWZyGQyhkV6iWLH08WGzxZOQiGXYTAYxBL+azYl8sC0CE6nC/5sY3EwTxebdsVYe+0OD3TBSqWgslZNVmG1OEi7O9tw/bAg3J1t+OL3U4T4OZnFGnTnQGSKabaLe6uS+A9MjyQ0wJnJwwJRKuRtsgWNlsKIYBeG9/Vm1R9nxJn6xbgih0R4sj0um/iEIh6e0ZfKGjUf/HCMiVH+ZOZXm8WhlVQ0oFTImDEmhJ93pWFrpey2RTWhRSTX1KlFMTdzXE8SMsuZFBWAjZWS99cdw9XRikdm98PW2gI/DzvGD/ZneF9v4hIKKSyrZ2ikd7fFJJxrUVgjxu/HGKBuzHbsLpQKOe89NRa5TIbiHIHipsswyGSYWf26Cjcna5Y8Mfqi+pXwICEWSG8QBM+k6EAmRXfebapUyC8YLH8u7G1UPDJbKLER4OXA//5vDIFeDshkMlQ/HKO2oYn80lrRreThYiNe69iBvuJzM7KdjMLrhwWSlFXBwFAP8b4cGumNo52K8mo1fx0WCm4aY76MCSJG63xmQbUY8Oztaou/pz3fvzH1si5A3N10u+AxuqY0Go2Zm0qtVmNtfW6/ssFg4IMPPuCTTz7hkUce4Z577mlzPFMudLyrib4hbmTkVREe6IKDrYr4xCIxE8KUgWEe/LEvg2PJxWJMhtGMC0JUvq21BUHeDpccR3AxGAVPXnEtp9IFi8D3WwUr29wZfbtc7JyLYX28RcEzbVQP0Y8uk8mYM6GXUCNjbzpanb7NDPBCdUlMsVDKCQ0Q0q7TcirF9G9jFtCgMA9WPD8RMF9ewdnh8syOAr3tUchl6PQGsxgeAB83O25pLsIHgnCMTyyidSLV2EF+TBjiz88706isFe4vb9e2WWEXYmCoO0qFjPxSweW593ge8YlFnEovRdUc0/N/tw+kd5AL+0/k09PfiUFhHvQJccPBVtWhuJ+LRS6X8e7jo1BrdOIkw9PFRrRW6fQGqmo19A5ywUKp4KHmDB8Q4jgW3BvFTztTufP6S49PuViCvB1QKeVomi0N00d1zwr1plzIwmga9+LhbNOmanJXcbFi2MbKgl7+ziRnV3SroO4MpoH/Pf2dSDhbTlJmBdV1wuTd1EI/sr8vX21MQCGXiTFzpkyMCsDeRmVmsbZQCvW/fm4uKWAUfNDiAnV2sMLHzZb80jo27Bf60cHhglX2WhI7cBkEj9H1VFxcTEBAS5BVcXExYWHtdxhNTU0sXLiQDRs2sHDhQu6///52jxcS0pKmWlxcjKdn15uXu4P7buzNuMF+9PBxRKvTEx7kQp92TPh9mtP4iisaRLO6aaaSk70lq1+67pwzru7Gx10YCAvK6nhxxQFx+5ThQaIv+HIQEeyCn4cdjWptm/POHCtkQn3ww3H2HM0VrWLGGhWmaa4doYevIyfTSsnIqxKXPWgtLkAw51uqFKiUcpwuMcuoo1goFYQHuXAmo4zgC9TACQt0FlNTR/bzITWngga1lpH9fLBSKbl5XE++3HCG3kEuF9Xp2VhZENnDlROppcQlFom1m9QaHWqNDndna8YN8kOhkHPrpBYhZqyg3N2cL4hXIZeZ1Y1pTYifEy/cG9UdzeowSoWcXgHOnMkoY+qIoAtahC4H7ibPQXe4s7qCJ28fyJmMMoZ1ILPochMW6CIInqxycbkh07gsJ3tLlj05Brlc1q77TSaTMbSdLLEpw4PYdDCTAC97rosOYPmPJ1Aq5OJq5iCMK0KmsAGlQs6wdibg1wLdLnjCw8Oxs7MjJiZGFDzV1dUkJCRw9913t/uZ559/nm3btrFs2TJuvPFGs/dcXV0JDg4mJiZGDFzWarXEx8dz5513du/FdBEWSoWYUaGSK9rNKgAhIHBkP1/2HMsVXQ6tgxg7U+Ssq2ntqhnWx4veQS5MH92x2jJdhUIh5/2nx2HQG9pdoXr8YH8+/+0UtQ1Nokn46TsHk1tUw3WdzAQyuo1ScyrJLhLiPNpLYbWxsuDdx0ejUJzbBdAdLLwviuKKerNU9PYwDdQeM9CXx2/pj05vEC0eM8aG4OxgaSawO0t0hBcnUkv5Y286xRUNyGSI6yJNG9njign1a4W5M/pw8GS+meXuSuJoZ4mFUk5TO3E+Vwv+nvZtagJdLRhj65KzKlA3Fy9tnXkX0MHgaFO8XG35atFkLJQKFHIZFTVqPF1szCx2fUJcxUn1kN4eV7Q4YHfS7YJHpVJx9913s3TpUlxcXPD19WXJkiV4eXkxefJkdDod5eXl2NvbY2VlxS+//MKmTZt4/vnniY6OpqSkpaaDcZ8HH3yQxYsXExgYSN++ffn8889pbGxkzpw53X05l52HZ/bhRFoJlTVq7G1UV9XDKpPJmDBECIp76o5Bl2123h7nM58rFHIigl05klSMwSCk24/s17F06dYYA/6MGV72NhbnrCh8vkUauwtHO8sO1a0JDXDGxkqJTCZU8m0tnBVymZi5d7FMig5g3bYUMXMtNMCZG0cGcyqtlKkjgi7p2BLCYHihom+XE7lchpuTNQWldVethedqJrzZ2my67NC5CrF2FtPn+/br2npWTAO6x3RB5uHVymWpAjR//ny0Wi0vvfQSjY2NREVFsWrVKiwsLMjNzWXixIm89dZbzJo1iw0bNgDw7rvv8u6775odx7jPrbfeSk1NDe+//z6VlZX06dOHL7/8EheXzrkn/g442lny5G0DWfxlLKP6+5wz5fJK8dQdg9DpDeetP3E10DfEjSPN6/34edhfdIyIn7udGDsBQm2gq+036Qi21hYsnT8Gmaz7rIQ2VhbcMrGXuIDr0Egvxg/2v2QhJXH1MqS3J9tisujXq/uy7K5VXBys8HC2FicIvu62ly0O0tPFhqgITypq1Gbp7tcaMsPF1H+/xpg4UQg03bFjxxVuybmpa2jC2lL5txxcrwZSsit45oO9gFCz4qk7Bl30sZ75YI9YUbm9tWgkWlA36Xj03Z2UVTaw/LnxV62rQ6LraNLquzXo/Frmj33pbNh/lj49XJk+uke77nIJczozfkuLh/5NsL1GfaqXixBfR7HycnvVrjtDsI+jKHj6dWGBvGsRSwsFS58YTXW9RhI7/xAksXPx3DQ6hJsucwzkPwnpzpT4R6BQyBkz0A+lQs6gsPYr/3YUY+Cyo53KrBaSRPs4O1h1uBKthISERHchWXgk/jE8MqsfD06PvOSYlWF9vdlyKIuxg/yuuToVEhISEtcqUgwP0LdvX3Q6XZvlKiQkJCQkJCSuXgoKClAoFJw6deqC+0ouLYRlKZRKydglISEhISHxd0KpVGJp2bHirpKFR0JCQkJCQuKaR7LwSEhISEhISFzzSIJHQkJCQkJC4ppHEjwSEhISEhIS1zyS4JGQkJCQkJC45pEEj4SEhISEhMQ1zyXlYn/22Wfs37+fNWvWiNsSExNZvHgxp0+fxsXFhfvvv597771XfF+v17N8+XJ+/PFHampqiIqKYtGiRfj7+3fpMTrDkCFD0Gg0uLu7X9TnJSQkJCQk/mkYDIYrXny1pKQElUpFfHz8Bfe9aAvPd999x/vvv2+2raKiggceeICAgAB+/vlnHnvsMZYuXcrPP/8s7rNixQrWrl3L66+/zrp169Dr9cydOxeNRtNlx+gsarUarVZ7UZ+VkJC4OmjS6qmu0yAV2pC4FigvLyc3N/e8/4qLi69Y+6qrq6mtrb0i5y4uLhavXavVolarO/S5Tlt4ioqKePnll4mJiSEoKMjsvfXr12NhYcFrr72GUqkkJCSErKwsPv/8c2bPno1Go2H16tU8++yzjBs3DoD33nuP0aNHs3XrVqZNm9Ylx+gsHh7C2kpX82rpEhIS52fBx/s5k1HGM3cOYtzgi7P2SkhcLWRnZ1NeXi6+XrFiBQkJCSxfvlzcZmdnR8+ePa9E8wgLC+Pxxx/niSeeuOznvueeewBYs2aNuFp6R+i0hefMmTNYWFjwxx9/0L9/f7P34uPjiY6ONqtaPGzYMDIzMyktLSUpKYm6ujqGDx8uvu/g4EBERARxcXFddgwJCYl/HgWldQCUVDZc4ZZISFw6AQEBDBgwQPzn4uKCSqUy23alxM7flU4LngkTJvDRRx+1Gy9TWFiIl5eX2Taj9aSgoIDCwkKANmtWeXh4iO91xTEkJCT+WaibdJRXNwJQXtV4hVsjIdH9vPXWW0RHR6PX68VtL774ImFhYWRnZ4vbvvrqKwYNGiSGfMTHx3P33XfTv39/oqOjeeGFF8wsSQD5+fk8/fTTREdH079/f+677z4SEhLE98PCwgBYvny5+PeFUKvVDB48mHfeecdsu1arZdiwYbzxxhsA6HQ6vvvuO6ZPn06/fv0YN24cS5cu7bDb6nx0aZZWY2MjKpXKbJtxjQu1Wk1DgzDzam8f48V0xTEkJCT+WRSX14t/l1VLgkfi2mfcuHFUVVVx+vRpcdvhw4cBzLwd+/btY+TIkahUKuLi4rj//vuxsrLi/fff58UXXyQ2NpZ7772XxsbmCUN5Obfffjtnzpzhv//9L8uWLUOv13PXXXeRnp4OwA8//ADAnDlzxL8vhKWlJddffz2bN2/GdEWrAwcOUFFRwYwZMwBYtGgRb731FpMmTeKTTz7hrrvu4ttvv+XRRx/lUlfC6lLBY2Vl1SZw2ChCbGxssLKyAmh3H2tr6y47hoSExD+LwrI68W/JwiPxT2Dw4MHY2tpy6NAhQIj5ycvLIzIyUhQ8jY2NxMXFMX78eACWLVtGcHAwn332GePHj2fmzJmsXr2ajIwMMTHo66+/prKyktWrVzN9+nQmTZrEqlWrcHV15YMPPgBgwIABAHh5eYl/d4QZM2ZQUFDAkSNHxG0bN26kR48e9O3bl7S0NH766Sfmz5/Pk08+yciRI3n44Yd59dVX2b9/P3v37r2k76xLBY+Xl1ebqHHja09PT9EN1d4+np6eXXYMCQmJfxaFZSYWniophkfi2kelUjFy5EgOHjwIwKFDhwgODmby5MnExsYCEBMTQ1NTE2PHjqWhoYETJ04wduxYDAYDWq0WrVaLv78/ISEhHDhwQDxO79698fT0FPeRy+WMGTNGPNfFEh0djY+PDxs3bgQEQ8X27dtF646x3TfeeKPZ52688UYUCgUxMTGXdP4uFTxRUVEcOXIEnU4nbjt8+DDBwcG4uroSHh6OnZ2dWaOrq6tJSEggKiqqy44hISHxz6Kw3MTCU6NGr5dy0yWufcaOHcuxY8dQq9UcOnSI6OhooqOjycvLIz8/n3379tGvXz9cXV2prq5Gr9ezcuVKIiMjzf6lpKSIRoTKykqOHz/eZp/vvvuOmpoaMazkYpDJZEyfPp0tW7ag0+nYtWsX9fX1TJ8+HYCqqiqANjXxlEolzs7O1NTUXPS54RILD7Zm9uzZfPHFF/znP/9h7ty5nDx5kq+++opXX30VEBTp3XffzdKlS3FxccHX15clS5bg5eXF5MmTu+wYEhIS/yyKTCw8er2Bqlo1zg5WV7BFEhLdz9ixY9FoNMTHxxMTE8NLL71E3759sbGxITY2lr1793LzzTcDYGtri0wm4/77729jQQHEkBB7e3uio6N5/vnn2z1n6/jZzjJjxgw+++wzYmJi2LRpE1FRUfj6+gLg6OgICMUEjdsAmpqaqKiowNnZ+ZLO3aWCx9XVlS+++ILFixdz88034+7uzvPPPy9+4QDz589Hq9Xy0ksv0djYSFRUFKtWrcLCwqLLjiEhIfHPwjSGB4TAZUnwSFzruLu7ExERwdq1aykvLyc6OhoLCwsGDx7M+vXrycrKEuN37OzsiIiIICMjg759+4rHaGxsZP78+YwdO5aePXsSHR3Nn3/+SXBwMHZ2duJ+b7zxBk1NTaLxQS6/OAdRSEgIkZGRbNy4kT179vDSSy+J70VHRwNCXM+8efPE7Rs3bkSn0zF48OCLOqeRSxI8b7/9dptt/fr1O2/UtkKh4LnnnuO555475z5dcQwJCYl/BgaDgcLmLC07awtqG5qEwGW/K9wwCYnLwLhx4/j4448JDg4WXUFDhw5l6dKl+Pj4EB4eLu779NNPM2/ePJ555hluuukmdDodq1ev5sSJEzz66KMA3H///fz+++/cf//9PPjggzg7O7Np0ybWr1/PwoULxWM5ODhw9OhR4uLiGDJkSKeWmJgxYwbvvPMOSqWSKVOmiNt79uzJzTffzIcffkhDQwNRUVEkJiayfPlyhg4dyujRoy/pu5IWD5WQkPhbU1mrRq3RIZdBWKBg8pZS0yX+KRhXHDBaR0AQPCC4vEwZNWoUq1atorCwkPnz5/P888+jUCj48ssvxWwrT09P1q1bh6+vL6+88gr//ve/OXnyJIsXL+b+++8Xj/Xvf/+b06dP8/DDD1NQUNCpNk+bNg2ZTMb48eOxt7c3e2/x4sU89thj/Pnnn8ybN4/vvvuOe++9l5UrV160VcmIzHCpie3XAMbS1NLSEhISfz+SMst57qN9eDhbM7i3J5sPZnLbdaHcPaX3lW6ahIREN9OZ8btLY3gkJCQkLjfG+B0vV1tcm+N2pFo8EhKXD51Od8GigDKZDIVCcZla1D6S4JGQkPhbY4zf8XSxwdVREDySS0tC4vJx3XXXkZeXd959oqOjWbNmzWVqUftIgkdCQqINVbVqYs8UMjDMAzenq7uCuamFx8VBaOvlsvA0afWUVzfSqNbiZG+Jo53lZTmvhMTVxCeffNJm9YPW2NraXqbWnBtJ8EhISJjxx950vt6UiKZJR5C3A+8/PQ6FvOMZGJcbY5VlL1cbPF1tAMgvqaVJq8dC2T15GTX1Gt75Jo6TaaUYLfnWlkref2osPu525/+whMQ1RkcXEL3SSFlaEhISImVVDaz68wyaJh0yGWQWVLP7SM6VbtZ5KTKx8Pi42WJvo0Kj1ZOeW9kt56ttaGLRZwc5kSqIHQulHEuVgga1lg/XH79qqjyrm3TEnC7g282JFFfUX/gDEhLXOJLgkZCQENl6OAu93kBEsAv3TY0A4NstSTRqtFe4Ze2jadKJ8TqeLjbIZDIigl0AOJNR1i3n/HF7Cmm5VTjYqnj/qbH8/PY0lj87HiuVgjMZZaz9K+mKih6DwcC+Y3k8vHgbb3wZyw/bU/jwh2NXrD0SElcLkuCRkJAAQKvTs+VwJgA3jgxm+ugeuDlZU1rZwMrfTl/Zxp2D4op6DAbBneRgK5S8j+zhCkDC2fJuOeexFGHNoYdn9iXEzwmZTIaXqy33T4sE4IftKbz5VSxanb5bzn8h1v6VzLvfxlNRo8bV0Qq5XMaJ1FJScyquSHuMlFU1kJJdQXpuJWVVDWQWVFNRIwWXS1w+pBgeCQkJAPafyKe8Wo2TnSXD+/pgoZTz5G0DWPT5IbbGZNE7yJlJ0YFXupmAIHRW/3EGOxthORkvVxux0muL4ClDrzcgv8j4oy2HMtl8KJNn7xqMv6dQHK22XkNmQTUA/Xu6me0/dUQQcrmMlb+dIuZMIXuO5jKktycFpXWEB7lcVBs6SpNWR3JWBRXVan7YngzAbZNCue26UD5af5xdR3L5aWcqC++LvsCRuocfd6TwzabENtuVChk3jAjmnht6Y20pDUdXIwaDAY1Wj6XFlU0p7wokC881SlWtWpo9SXSY+sYmvvzzDAA3jgoWg30HhHpw5/VCafqPfzrBqbTSK9ZGI41qLW+sjuHAyXz+OpwFCPE7Rnr4OmKpUlDb0ERO0cWvrvzzrlQy8qr4/NdTYo2RhLPlGAzg627bZq0umUzGDcODuGOyEMD56+40nv9oH899tI+Y052rRHsu/jqcycsrD1FZoxa3NWq0LPr8EAtXHODdb+MxGOD6YYHcfUNvLJQKZk/oBcDBkwX8sS+9S9rRGTRNOn7bI5zXxcEKJ3tLZDJhGRCtzsCf+zJY+dupy94uiQuj0+l59YvD3L1oM4e76B6+kkiC5xriVFopx1OKOZVWysNvbmPuG9uuiZtUonsxGAx8vTGB8upGvF1tuXlcT7P3b50Yysh+Pmh1BhZ/GUNWYfVla1tyVjkvLN/H5oNn0en0xCcWsejzQ5zNN2+Dp4uN+LdSIScsQFhi4tstiTRpdZ0+b1F5vZj9dTy1hJgzhQCcShcEX58Qt3N+dsrwICxVCrIKa8gvFQKqv9mciO4S43oMBgPfbkniaFIxP+5MAQQ30eLVsZxOL8NCKUellBMe6MzDM1sWhwz0cmDm2BAAVv52mh+2JV9SO1qj0+mpb2w65/v7T+RTXafBzcma1S9dx5pXpvDbuzfx/RtTWXBvFAC7j+ZSXXf+tGaJ7kGvN5BfWttu4cD121M4klRMo0bH21/H8dmvJ9mwP4Pa+r/nb9XlNsSioiLGjBnTZvtbb73FrFmzSExMZPHixZw+fRoXFxfuv/9+7r33XnE/vV7P8uXL+fHHH6mpqSEqKopFixbh7+8v7nOhY1xr6PUGtsZk0a+n2zlTXnfGZ/Pe920DE9/6Kpan7xzM2EFXx0qKBoOBlOwKMguqsbNRMTTSC6VC0t1XiiatjhU/nWR7XDYA/57dr43pWi6X8dSdgyivbiQxs5xXvzjMkidG4+rYvfV5mrR63vv+KHkldSScLefLDWdoUAviRaWUM3aQH9tihXa3rhV0y8ReJJwt5/DpQl77IoYXH4julMvkVFqJ2evFX8bi7WqLukk4f59mt1l72NuomDDEn80HM5HLwFKlILuwhj1Hc5kwxP+cn7sQeSW1omVny6Es7G1U/LQzFbVGh6VKwWvzhtM7yKXdRRwfnB6Jg62KbzYl8u2WJIK8HRjax/ui22LKW1/HcSq9lKXzx4iuPyNanZ6NBzIAuGF4EIrmZ93oZhzRz5sePo5k5FexIy67jdiW6F6Kyut57/ujnMko49ZJodxzg7Acy4nUEn7bk87RpCIAevo7kZZTyYb9ZwH4ZlMit00K5eZxPYlPLMLW2kJ0JV/NdLngSUpKwtLSku3bt5s9ePb29lRUVPDAAw8wYcIEXn31VY4fP86rr76Kra0ts2fPBmDFihWsXbuWt99+Gy8vL5YsWcLcuXP5888/UalUHTrGtcbuo7l8/NMJvFxteOnBobz9dRz+nvZMHhrI+u0p1DZoyCsRZpIWSjlNWj1DenvibG/JtthsPlx/nCAfBwK9HK7odeh0et77/hh7juWK29ydrXnqjkH0Pc+MWaL7+G1POtvjspHL4IHpfRgU5tHufpYWCl56cCjPf7SXvJI63v46jnceH33R8TEd4c99GeSV1GFrpUTdpKNBrcPexoLxg/2ZProHLg5WouAJaDXQDgj14JW5w3jjyxiOp5bw308P8vLDw7C3UXXo3CdSBUvOtFHB5BXXcjy1hILm9HeAyB7nv19vmRBKZn41Ywf6Uq/W8s2mRD5af5zKmkZuHtezUytLGzmV3pJ1pmnS8d2WJAB6B7kw7+a+9PRzOudnZTIZt0wMpby6kQ37z7Js7RFuvy6caaOCUV1CbEajWktcYhF6vYF125J57u4hgPCsbzmcxU87UiitakSpkHHd0IB223XDiCA+/ukEmw9lMmNMSLfeUxItNGl1LPh4P6WVDYDggr1+WCA1dRpeWXlYDLq/YUQQ/5rZl11HcsgqrOF4SgmZBdV8tTGBjQfPUlIhfH5opBePzO7X7ROhS6HLBU9KSgpBQUF4eLTtOL/++mssLCx47bXXUCqVhISEkJWVxeeff87s2bPRaDSsXr2aZ599VlwB9r333mP06NFs3bqVadOmsX79+vMe41rE6JYqLKvn+Y/2Ud+oJbe4lkOnzN1V4wb78ejs/mTkVREe6AwyGSWVDRxPKeHtr+N494nRHe7wL4UDJ/PZfPAsWYU1hAc6MzjcE18PO37bnU5sQiEKuYx+Pd04m19NSUUDb30Vx4fPjLvqK/pei8QlCDO4h27qw01jQs67r4OtilceHs78ZbtJyqpg7/E8xnWT5bC0soF124QBfe6MvoQFOlNZq6Z3kIuZRfD9p8aSnF3BgFD3NsfoH+rO4kdG8srKQyRnV7BkTTwvPzz8gkUUDQYDJ5stPMP6eNO/lzv1jU3sO57Hn/syCPZxxN35/Pequ7M17z4xGhDq4SRmlhOXUMSXGxJwdrBi/ODOW3pON7vTevk7kZpTiYVSztwZfbhheFCHBdRDN/Uhu7CGk2mlfLnhDCfTSnh57rCLEmAAqbmVYgr+vuN5DArzIKuwhtgzBeIkzMnOkgemR+Jsb9XuMcYO8uPLDWcoKK0j5kwhw/t2jeXp78KOuGxOppXy71n9zKyQBoOBqloNTvbdU737bH41pZUN2Flb4OtuR3J2BR//dIKC0jq0Oj2Dwjx4YHokgV72yGQyMWFBrzfwV0wWn/96kpKKBlRKOTq9gZgzhSRnV7Dg3qir1trT5YInOTmZkJD2O874+Hiio6NRKltOO2zYMD777DNKS0vJz8+nrq6O4cOHi+87ODgQERFBXFwc06ZNu+Ax3NyuLUuBpknHseRi8XV9oxZbKyUujtbkFNUwbrAfEwb7o2nSMaS3JwqF3Oxme+bOwTz5v93kFtfy0qcHWXBvlFlGy8VSWFbHkm/juWl0iJm7bM/RXJZ+d0R8ffh0IYdPF4qvlQoZL9wbxbA+3jRqtCz4eD/puVW8uyaeF+4dclXPDq416hubSM4WUpWHddC94eVqy5wJvVizOZGvNyZgb2NBaIBzlwhpg8GATCbDYDCw4ucTNKh1hAc6M2GIP3K5rI27BCDEz4mQ81g2QgOcWfzISJ79cB/HUkpYtzWZu6aEi+83qLW89/1R0nIrGdnPhxljQqiqVVNercZCKRezq2ysLLh+WBDXDwvq9HVZWij474ND+WpDAr/sTmPj/rOdFjwGg4HTzRae+26MAIMgqjpb1Vmp+H/27js8qip94Ph3eskkk94bIQRCEnqVoiDYCy6IvaKLDQsWVnF/6rrgig27siKubV3dFWwUFaSHEumkEdJ7L5PpM/f3x5CBmAABE0A4n+fxkdx75sydIcx955z3nFfO3/48mjUZJbz39R5+za7ml19LmDis4+hLV2QXHl76L0mw8IvD0+q+ehU3XdyPi0bFoVIefRRJp1Fy+ZhefLX6AF/8lMOo1PDf/fl0IlwuN8hkp2U38VaLg3e/3oPN7iI61MC1FyZ5zy38Yie//FrCnVemMOX87p/qyz30b79ffCDXTU7i8Tc2sCPbc68JCdDx2M1DO/13LZd7kvN7RfixaU85l54Xj9Pp5qVPf6Wwopln/pnO249PbJdXd6bokRGegIAAbrrpJgoKCoiLi+Pee+9l/PjxVFZWkpSU1K5920hQRUUFlZWeG2NERESHNm3njtfH2Rbw7MmrxWp3EWTUYjRoyC9rYuafBjBuUBS1jZZ2q1M64++r4W8zRzP33U3klzXx5xd+JjHayLN3j/5ddX9WpheSW9zI2//dTUKUkZXphYQF6fnq5wMATB4Ry4ShMewvqGNHdjWl1SaG9A3lqvEJJB1KKNWqlTxxyzAefnUdWYX1/PmF1dw/bcBJf/gez0ff7+enbcWMSo3gopGxJMUGnNIP1jPNvoOeZdsRwT6EnsCH09Xn92ZFeiG1jRae/ecWDDoVj9w4hBH9w0/6Wr5df5DPV2Vz19WpWO0utmdWoVTImTV90O+e4ugVaeSBawfy6uc7+OKnHAL9NNQ1WymqaKaitpWiSs9KrmXrDrJ+Z5l3P59hyWHdthRXJpNxzQWJfLvhIDmH9qI5VqD2WxV1rdQ3Wz0J2XEBaNUn/9GtUMiZPDKORpONj5dn8cE3+0iKDSA6tGNAeTw5RZ6b5qThsaTvq8BHq2RgnxAGJAYzrH84Bp2qS/1cPb43323IJ7+siW37K7stv+h4SqtbeOqdTVhsTpJiA7hgSDTjBkf9rvf3RKzJKMFm9+SFfbshH7lMxq7cGpLiAliT4dnhfPG3+wnx1zNmYGS3Pnfbl52kGH/6xQVyxxUpZBbUEeCnZcr5vY/7JaZffGC77RZemjWOZ/6ZTmZBPe99vYf/mzHyjPt87da/VafTSX5+PomJifzlL3/BYDDwww8/8Oc//5klS5ZgtVpRq9u/iRqN56Zrs9mwWDxzgZ21aWpqAjhuH2ebbYdWh4zoH87NlyZTXW8mMcYf4LjBTpu4cD/m3TuGd/+3h+zCevJKm/j3jznc86cBJ31duw54hvwtNicPvrK23SZrMWG+3Dt1ICqlnLTEYK6ffPQ6K5HBBubfN4ZFS/eSVVjPG//ZRVigT7cPiZZUtbB0bR5uCX7cWsSPW4voFenHfVMH9ugeKdUNZr5afYCpExI7/H1JksTCL3ay+4BnWqFXpLHdOafLfcxvxierqt7Mqi2F3iXbg/p0nA46Fo1KwaM3DuG/aw5QUm2iut7M84u38sC1g7h41Mnt07N8cyGtViev/2eX99j1k5OI7aa8swlDY8gva2LZuoO887897c756tXcclky323Ip6SqhfpmK34+amZek3aU3k6Ov6+G89IiWb+rjO83FvDQ9YMBKK818dPWYi4aGUdEsA9ut8TXa/OorjczeWQsCVH+LFrqWbbdL/73BTtHuuaCRDbuKie/vInZC9cx+8ahXR7pA8/vaHaRZ4Tn4lFxPHjdIICTuskZDRouH9OL//2Sx3tf7yEpNqDDsv/uUFDexK/Z1YwfFIVMJuO5D7bQcCgRfE9eLXvyavl6bR6vPDQevbZrwdrJkiSJFemFAMhk0Nhi46MfMoHDn6+RwT6U17by2hc7iA337XSU82QdOBTw9Dn0BfRPExL504STH0nSapQ8cO0gHnzlFzKyqvhydS5TJ/RBqZCzJqOE4spmbrk02Zu4fjp0a8CjVCrZunUrCoUCrdbzy5qamsqBAwdYvHgxWq22Q0XVtiBFr9d7H2O3271/bmuj03mmOo7Xx5mkut7M8s0F5BY3Mio1/Lg5Er9lsTnZvLccgBEp4fj5qL3fPk9UXLgf/7h/LHvyapj77mZWphcyblAUFpuTLfsq0KgVJMcHMjotst3QbmZBHRt3l3PhsBjvN9Imk438siZvG6fLjb+vBo1KQaPJxqxrB51Q0cbEaH9efGAsCz7JYOPucv7x8Xb+b8ZIzFYnFbWtTBoR26WVXMWVzbz11W5uuKgvgw8l3+7MqSanuIGsgnrcEgxIDCbQqGXz7nIKypv5y9sbuXp8byYOiyEuovuTuj/6PpMNu8qobbTwzF2j2p3buLvc+y3u+Q+3cunoeFotDq6f3Jev1hzg618OMPeOkQxLDuu265EkiVc//7XdLsQDO8l/OZ7U3sGk9g7G4XTzwTd7Wb65kPe+3k1smC/JvU4sgKyuN1NWY2p37KZL+rUb3u8Od1yRQpPJxi+/lhIR5MNlY+Jxu2HMwEjCAvWMTo3gr+9vprTaxCM3DOmR6dXLxvRi/a4yft5ejE7rmcr56/ubqWmwsCajhFnTB/Hj1iJvft6K9EJ0GiUWmxO1SsGMK1O77VqUCjnP3j2KBZ9msO9gHQs+yWDBA+O8X6iOp7LOTJPJjlIhp3e08Xd/m7/2wiS27KukrMbEvI+28cJ9Y7ut+KvnC4TE8x9upabBwifLM2nbJSAsUM9jNw1lf34dy9YdpLTaxMfLs475hdDpcrMmo4Tk+MCTCkLcbomla/MoqWpBo1Zw7cQ+fLoyG7kMhvcPZ3tmJQlRRl64fyzzPtzGrgM1LPgkg5ceHOcNeOuaLDic7i598XU43axMLySnqIE7ruyPRqXw5lj16eLfd1fEhPky/cIkPv8xh09XZLNlXyVP3jqcN7/chdPl5urxvXskkO2qbh+366wEfJ8+fdi4cSPh4eFUV1e3O9f2c1hYGE6n03ssNja2XZu2aqzH6+NM8u7Xe8jI8iSF7j1YS1y43wndXL5df5Amk52IIJ9OkzJPxoDEEIYlh5GRVcVf3t74m+fLJzk+kFsvS8bllliZXsjG3Z6Aa/mmAu9NqK1CdFy4L8P7h7P3YC0PXTeYqBADdqfrpL6BymQyHrpuMKXVJgormnn09fXeKtTVDWZuPVTX6Vi+WnOArMJ6Fn+7jzcfm8DyzYW8v3SPtx+ZDP48JY24CD/+PCWNd/+3hw27yvh6bR5fr83jnj8N4PIxvU742o/GZHF4E84zsqooP3RTzyyoo7y2lZ+2elYYqZVyahos3p1oW8wO1v5agtMl8fZ/d/POExO7bRfafQfr2gU7bQnkJ0ullHPPnwbQZLKzaU858z/axjN3j6Kq3kxJVQtatZLeUUb6xQccdbRqx6EcteT4QKZN7INBr6J/r+5PepTLZTx8/RCuGteb2HDfDquT/H01LJx9AS2tPZcompIQxC2XJvPJiiy+25DPdxvyvefqm60898EWwBOMDO0Xyq/Z1Vhsns/Fh68b3OVgpKsC/LT8feZ5zPtoG9szq3jhX9uYc+twgoxaahosJMUGtJtSrG+2siq9kPOHRvNrlufvrXe0sVtGIn10Kv5vxkhmv76enKIG1v5awuSRHUcMK2pbMehVR51ykSSJ+R9to6TKxN/vOY9PVmSRWVDHsH5h1DRYUCrk3hHpvrEBPHzDYKJDfekXH0hitD9Pv7+ZHzYV4O+rYfKI2A6Br8Pp4sWPM9i63xOUvD77ghN6nS6Xm/kfbWdbpmf0/ooxvZg2sQ86jZLe0f6kJATRZLKh0yhRqxTMvnEID76ylsKKZh58eS0z/+RZkTfr5bW0WuzMmj7omDug55c18eLH2717QsnleHPIwoP0vyu1oTPXTe5LoFHLR99nklfSyF/f34zT5aZvD43anYhuDXgOHDjAddddx7vvvsvIkSO9x/ft20diYiLJycl88cUXuFwuFArPP5AtW7bQq1cvgoKC8PX1xWAwsHXrVm/A09zcTGZmJjfffDMAw4cPP2YfZ5I/TUgkMtiHqnozW/dX8toXO/jLbcMpLG9mb14t0ycnHXWpeHOrna/X5gGeb7vduVfNjKtSKK5qoaXVhl6rYnRqBMhg9fYSsgrrefKdTd62Mpln59qDpU18vDwLf4OG7EPz9oOSQj0JlEf4PcPtWo2SF+4fyxv/2Un63gqUChlOl8T/1hxgeHL4MUcO7A4XWw8lRxdVtvDu13tYsbkQgMRoI6XVJi4aGecdxfHVq3n85qGMGxTJj1uLyciq4oNv9hIX7ouPTsWajBL2HKilvNbEHVemcNl57QOhuiYLvnr1MZf0btxVhsN5eKrvr4vSqa5vX7U6OtTAX24bzpv/2YVcLiOrsJ4ftxZ5z9c2WvjixxzuuDKla2/iMTSZbHy60hNUXXZePCkJQWg1yt+dcCyTyXjo+sFU1LWSX9bEI6+t69AmJEDHi/ePIyRAh8stsWFXGeFBevrFBXoDniH9QhmRcvJ5QF0hl8uOGTQo5LIeC3baTJ+URGy4Lx8vz6SkyoSvXs1f7xzJB9/upbreQmKMP9dNSqJffCA2h4uiimYUctkJ5fycCIVCzuwbhzJ74Toqalt59PX13nMTh8WQFOPPyi1FTBgazeqMEoorW/hpezHWQ4HY+YO7b6VeZIiB6Rf2Ycn3mSxbf5BJI2K9I0cHShr457J9ZBXWEx6k583HJnT6ebN5b4V3ocTsheu8U1bfb/LsIXP7Ff0ZlRqBWiXvsHJsYFIIF4+KY9WWIj5bmc1Xqw8wa/og72rEospm3v5qN1mHkrXzy5qoabAcd9UeQIvZTm2jhR+3FLEtsxK1SsFdV6dyyag4ZDJZuxmAI4OQAD8tf7ltOAs+2U5FXSt/W7yV5PhAWg5t/vf6f3ZRUNHMDZP7YvjNv+Vt+yt58ZMM7A4XRoOaJpOddTvLsB/6XEqKCTjudZ8ouVzGxaPiUSnlvPbvnd5A6/Kx3fdl8mTJpM62VzxJbreb6dOnY7FYeO655wgICODLL7/k888/53//+x9BQUFceumlTJw4kbvuuos9e/bw7LPP8txzz3HNNdcAnmXoX3zxBfPnzycqKoqXXnqJ0tJSvv/+e1QqFXV1dcft40RdeOGFAKxevbq73op2rDYnD7+2rsOwvY9WSUpCMLtyq5HJZYQG6BicFMrV5/fm81XZrN5eQq9IPxY+csEp2Zuisq6VD7/bz8GyJux2FyNSwrnsvHgSoox88VMun6/KRqmQI5eB3enmmbtGdet0SxtJktiXX0dksA8f/ZDJ2l9LiQnz5a3HJhz1fUjfW878j7Z3OD51QiK3Xd7/mMPtkiTxwr+2d1jm30anUfLPpyZ5P4R25lTz7AdbGNE/jLl3jOz0MQCPv7Ge7KIGhvQN9d7UZTLo3yuI+Ag/jAYNFwyJJiLYx3sdc97a6P0wvWhkHD9uLUIhl/H67AuOO+WWXVhPRrZnT5TrJ/f1BmOFFc38b80BNu4uw+mSUCpkLHpycpc+pE+EyeLg7x9uZX9+HT46FaNSw7HaXOzJq6XFbGd0WgRP3jacd/63h5WHchdSEoLIL2vEYnPxykPjvQnt5wJJkiivbcVHq+rxIKsrqhvMfLYym3U7Sr277h5vc+j4CD8WPnJ+t+ZlmCwO7nx+FRabi3uuSaNffCD+vhoeenUtTabD6Qw3XtzPW8ajjdPl5v4Fa7w32TbBRi21TVb8fTX886lJx/xi5nK5WZ1Rwo9biryJvVeNSyAi2IcPvtmHyy2hVSvwM2iorjdz37SBXDo6/pivqcVs54GXfqG++XC5n6duH87otK4nIltsTt76ahfrd5Z5j41Oi/B+bvn7anjxgbFEBntW7jWZbNzzj9WYLA6G9Avl8ZuG8srnO7yzDgCP3DDkd22GeSwut8TDr3pGpvwNGj786+QeyUk8kft3twY8ALW1tbzyyits2LCB5uZm+vfvz2OPPcawYZ4Nqfbs2cO8efPIzMwkJCSEO++80zt6A+ByuXj11Vf5+uuvsVqt3p2Wo6MPf4s4Xh8nqqcDHjj8YbJ2Ryl+ejWBRm27PJgj+WiVtFqdyGTwwn1jz4g9Ddxuz/x32z+WhCgjC2aN6/GCciaznbvm/USr1cmcW4dR22ghIsinwyqOBZ9ksGFXGSkJQezP9yzfTUkIYt69Y7q03NRktvPkO5sorGhGqZAxLDmM84dE8981BzhY2sTFo+K49sIkAnw1PPjKL97573eemNjpHH7b8ny5XMaHT0/m/aV7qWm08Oer0445UpVVUM9f3tlIbJgvC2dfwD/+tY0t+yq90z378usoqmjmuslJ7aZ9Nu0p5x//Ohzw/emCRO64MoXc4gbmvLUBp8vzzzwhysgNF/U9oeTUE+Fwuth9oJZ+8YHeFTqFFc08/OpaXG6JvnEB5BQ1IJOBXCbzllvw81Hz8bOXnJalwUJ7ZqsDmUzGr9lVvPTpr8hlMGlEHOt2lCCXy7l/6kDe/GoXVruzxz6f/vnNXr5df3i6ry2PKT7Cj0tGxfHe0r1o1Ar+esdI8kobWb+rDKOPmuoGM2U1rRgNam66uB/vfr2HcQOjuG/aQL5ancuQfqEMSOxaeoDLLfH5qmy+/Dm33fGRKeH8eUoaa3eU8smKLEb0D+evM47+xQfgjf/s5KdtxZ4vjHIZN13clz9N6HPC74vD6eKv76ezP7+O0WkRPHX7CHZkV7No2V7KakzEhBm45vxETBbPlhObdpfTK9KP1x72BKXZRfU8/sYGZDK47bL+/GnCyW2C2VX78+t48ePt3HBxv+MGhSfrtAY8f0SnIuBpY3O4UMplON0Sn67IQpJgwtBo9FoVBeVNfLk6l4OlnkDomgsSubMbpjK6i9nq4JeMEnrH+NP3FC7p/nh5Jl+tPuCd4pLJPN9MftpajFaj4OpxvXlu8RYcTjevPDSez1ZlU1zZwosPjCU0oOuJ7G3/FI58Xbtza3j6/c3en/VaJWar0/vzZefFc8+fBvD9xgJWbSnELUkEGXXkFNVjsbm4blISNx/arr2rSqtb8PPR4HfoA/y+BWu8S1fb+OpVvPrw+YQH+SBJkneOv29sADnFnoDimbtG8dH3mRRWNJPWO5g7r0zp9hyQrlry3X7vFC3A/dMGMrhvKBmZldQ0Wk7oRiScOqXVLaiUCsIC9ZitDtxuCYNeTWl1CyaLg35xPbPCsb7Zyj/+tZ0Ws53qBgt2hwutWsFrj5xPVIiBv7y9sV0u2pHa8l7GDIikocWK0Ufzu0bI0/dW8Nq/d2C1O7n1sv5MPRQkFJQ38eAra9GoFdw3dQD+Bi1xEZ5N+ow+ahQKOZIk8cuvpbz27x0AvPjA2N+dn9ZqcbBxdxljB0bhc+hLRX2zldkL11HX1LFg9G+fc/eBGnQa5VkzmioCnhN0KgOe47Hanfzrh0xMFgezrh30u7Z9P1s0ttiYMe8n7I5jF4Eclhzm3fvB7Za6bRrw3f/tZtOeclotTm+y48RhMazJKEGrVjAoKaTd5optBiQG87eZ5/3uUYtVWwpZ/O0+wgJ96BPjT355EwdLm9BrlQQZdfSJ8fdey5K/XsTib/d7a2OBJ1/p3TkTuz058UQ4nC6+31iAWqWgf6/AdkvwBeFYmkw21u0sJSkmwLuFREOzlU9WZLFpTzkGnYppE/sgl8vQqpUM7x/W7UvKG1qsmMyOdqO5kiRx5/M/UttJkOHvq2H84Cjyy5q8G0Zeel48900d2K3XdaS8kkZe+fxX/HzU+OrVHChpZMLQaG6/4sz50twTRMBzgs6kgEfo3H9+ymHZuoPM/NMA/rs6l6LKFqJCDLRaHTS22IgJM/DSrPHebzw9oW3Xa5vDxbhBUcx6+RfvpnUyGdxyaTJJsQFU1LZS12TlynEJJ72NwLHUNVl44s0NVB+qYdPm6vG9uevqVMxWB2995QnS3G6JR24Y3GObOQrC6dS2O/fpsiajhO835qPXKqlusFDxm9wh8OwuP21iEtMnJXXbMnvhMBHwnCAR8PwxtH24NTRb2bKvgnGDojBZHKzbWcqk4R2Xj/a0shoTG3aVYbU5GdovjLTfsbz7RNkdLooqmymqaOaTFdm43G5ee/iCdonI9c1W6pos9OmBlRiCIHQkSRIut8TGXWXsyaslLsKPEf3DvQsThO4nAp4TlJaWhsvl6lDSQhD+KCTJM8okCIJwLqmoqEChULB3797jthXja3hKUxxZjFQQ/mhEsCMIwrlIqVR6y0sdjxjhEQRBEAThrCdGeARBEARBOOuJgEcQBEEQhLOeCHgEQRAEQTjriYBHEARBEISzngh4BEEQBEE464m12MCwYcOw2+2EhIhaPoIgCILwR1FTU4NarSYjI+O4bUXAA9hsNlyuY9dpEgRBEISzjQTY7S7UKnmXynTYbDZMJhM2mw23241CoUCr1aLT+yCXK1DI5SgUMuwOFy63hFajpCe3CXM6nXR1dx0R8AChoaGAKC0hCIIgnDusNifPLd7CvoN1GHQqzhsQicPpol98IKNSIwjw1VBR10pmfj0HShrI2racH7/5F5G90vCJHILZpUFmb6AmbxN1dVWEDZyOMXoQg/qGsiO7GgA/HzUpCUEM7BPCZefFd3vts7bSEl3RrQHP1q1bufXWWzs9Fx0dzerVq3n33XdZuHBhh/M5OTneP3/22Wd8+OGH1NTUkJqaytNPP03//v2950tLS3n++efZvn07er2eadOmMWvWLBQKUVlcEARBEDrTZLLxyYos5DIZ/eID+X5jPgdKGgEwWRz8uLUIgF9+LeXd/+1BqZDjdLk956syKd/+EUFJk/FJmgyA/lC/MWGDqdr1OVW7v0TjF86ObM/xAF8NDS020vdWkL63gvMGRBDgqz2VL7mdbg14Bg8ezMaNG9sd27VrF7NmzeK+++4DPIHN1VdfzeOPP95pH0uXLmXBggU8//zz9O/fn0WLFnHHHXewYsUKAgMDcTgczJgxg/j4eL744guKi4uZO3cucrmcBx98sDtfjiAIgiD8obWY7bzw0XYsNge1TVYaW2wArEgvBECnUfLc3aOpbjBTUtWCQi5ja2YlB0ubcLrcKBVy+sT4s3XXBvwCIxgwZgojUiIY0T+MqBADJosDjVqBUprAxIkTiJT2Et0nla9eu5O5c59mQ3oGWzatRaPR8s4b+3jsscfalYL4+eefeeeddzhw4AB+fn5ceumlzJ49G73eE069+eabfPvtt8ydO5dXXnmFgoICoqKiuPfee5kyZcoJvRfdGvCo1ep2ib9ms5kXXniBa665hqlTpwKQm5vL9OnTj5og/N5773HzzTdz1VVXATB//nwmTZrEV199xcyZM1m1ahXl5eV8+eWXGI1GkpKSqKurY8GCBdxzzz2o1erufEmCIAiC8If16Yos9h6s9f4cE2YgMdqfvNImhvYL5cpxCYQG6EnuFehtc8PF/bA7XNQ1WQkyajG1NDH6rYPMmDGDJ56Y1K7/AL+2ERs95513Hlu3pvPRP9/iq9fgzTffYODAgbz15hscPHiQhQsXUlNT453l+e6773jssce48sorefjhhykrK+O1114jLy+PJUuWeKe/ampq+Nvf/sa9995LVFQUixcvZs6cOaSlpZ3Qe9GjOTzvvfceFouFOXPmAGC32yksLCQhIaHT9nV1dRQWFjJ69OjDF6hUMmzYMLZv387MmTPJyMggJSUFo9HobTNq1ChMJhNZWVkMHDiwJ1+SIAiCIJwSkiSxM7eGZpONyBADvaP9UchlWGxODpQ00NhiQ61SMKhPCFpNx9t5QXkTKw+N5My4KhV/Xw2jUsPRqo9/61erFEQE+wBQVlYGQFRU1DEfExcXx+rVq2lqagIgMDCQ9957D6VSyfnnn49cLueFF15g1qxZJCQk8PLLLzNu3Dhefvllbx/x8fHcfvvtrFu3jgsuuAAAi8XCvHnzvLFBfHw8EyZMYN26dcd9HUfqsYCnvr6ejz76iEcffRR/f38A8vLycLlcrFq1innz5mGz2Rg+fDiPP/44oaGhVFZWAhAREdGur9DQULKzPZOClZWVhIeHdzgPnjLxIuARBKE7SJKEWwKF/OSSLCVJ4ruN+WQW1HP31akEGXXdfIXHtiOnmlazg1Fp4aiUXctvdLklNuwqo9Vsp1eUkeT4wA5Jpm3THMcjSRKZBfVs2FWGQadi4rAYfHQqmkw26pqsqFUKAv20hAboUHShvzNdc6udkqoWkuMDkZ/k70wbSZI4UNLIZyuz2ZFT7T0e6KfBz0dDcWUz7iMWJl0wNJpHbxzaro+C8iZe+Gg7bgnGDIhkyvm9f9f1AKhUqmO2a8ujbWt/5ZVXolQeDjMuvvhiXnjhBbZv3w547uczZ87E6XR62wwfPhyDwcCmTZu8AQ/AoEGDvH9uiwHMZvMJvY4eC3g+//xzfH19ue6667zHcnNzAdDpdLz++uvU1dXx6quvcuutt7Js2TIsFgtAh2kpjUaDzeaZd7Rarfj5+XU4D3jbCIIgnIg9eTW889/dRAQbiA3zJX1vBdUNZuRyGaPTIpg2sQ+9Io0dHldVb2Z/fi3FlS2YLA50GiWpCUHERxr5Zv1BvtuQD0BZtYkX7h+LQXfsG8bvJUkSFpuTb9bn8/kqz5dEf4OGe/40gDEDI71t6putNLfa2ZZZSV2TlVGpEciAf/+YQ1Zhvbe/icNi6BcfyMrNhdidLlrMdppMdqJDDYzoH86UC3oT4KvF5XKzMr2Qn7cXU1VvYUCfYIoqmimtNnn7+s/PuZ1es0at4KHpgxk3+NijB6eD2epg38E6gv11xIX7egMzt9vzHhoNGmx2Jx+vyGL19hLsDhdD+4Vy39SBhATojroiaVduNQdKGtGoFew5UEt5rQlfvZq+cYEE+mn4fmMBVfWem7lKKScpNoDCimbqm23UN3vucyEBOsIC9ew7WMf6HaXceFE/74jMzpxq/v7hVuxON6EBOmZclfq73oe2kZ22kZ6jKSkpwcfHxzvIERYW1u58UFAQAE1NTTQ2NgLw3HPP8dxzz3Xoq7q6ut3POt3hLwxyuefvoavL0dv0WMCzbNkypkyZglZ7OCN7ypQpjB8/nsDAw3OFffr0Yfz48axZs4bY2FjAM/V1JJvN5n2xWq220/OAN8lJEAShqzbsKuPVz3fgdLkpq2klI6vKe87llli/s4xt+yt59u7RNLbYKKluobrezN6DtVTWdfyGuWzdwXY/+2iVFFY0M+vlX7jsvHiuHt8bterwiIvF5qSq3kxMqOGERzosNifvfb0HvUZJ3/hA/r0qm/LaVu95Px81jSYbL36yndvrUzhvQAQvfZpBbnFju35WbC70/lmnUZKSEMSOnGrWZJSwJqOkw/OWVpsorc5j+eYCrr0widLqFn75tdR7ftPucgC0agVjB0ZR22Rh94EaJAn0WiXB/jocDjd1TRZsdhfvfr2bIf1C8enhgPB49ufXkVvcQG2ThZoGC7tyq7HYDu/RplbK0aiVOJwurHYXvno1KqXMG4TIZPBrdjUz5v2ETqNgdFok8RF+1Ddb0WmUqJRyDpQ0kr63otPnzyw4HGxq1QqG9w/nxov7Eh3qi8PpZk9eDXaHm6RYf++I4TP/TGdHdjWLlu0lyKhFIZfx8/YS7E43Q/qG8uhNQ/Hz+X25rUFBQQwaNIhVq1bx0EMPeQOOI5lMJjZt2sTEiRO9xxoaGtq1qa315BIFBgZ6By6eeOIJRowY0aG/I9NWukuPBDzZ2dmUlJRw5ZVXdjh3ZLADnukof39/KisrGTlyJOCJ7Hr3Pjz8Vl1d7Y0Uw8PDvSNFR56HjtGkIAjnnt25NfgZ1N4RmfS9FWzYVcbFo+IY2MezWKK8xkRFXSsF5c18vDwTSYLRaRFEBvtQ02hhVGoEyfGB1DdbWfL9fvYdrOMvb2/s8FxyuYykGH96R/tjNGhobLGyZV8FjS024iONTJvQh+gwA/+3KJ3aRgsfL89iy74KHr5+CJIk8cVPuWzdV4Hd6SY+wo97/jSAlISgTl+X0+XGanehlMvQapS43RKv/XuH9+b5/aYCb1uDTsWtl/dn8ohYPvhmHz9sKmDJ9/tZ8v1+z3XLQKtRkhQbQFigni37KtCoPaNTN17cz3tswScZyICbLulHn9gA9Bol/r4asgrr+fqXPA6UNPLJiizAM/V32+X96RPjz+4DtQT7axk3KAq91hPEuFxukMnaTRG6XG5mvfILJVUm3vt6DwF+WvRaJb46FRa7Z7Qk2F/HJyuyCDBouGhU3HGnBq12JwdLm3C7JZJ7BR51+q3V4mBXbg0l1S3Y7C7yy5u8e8ccKTRAR4vZgcXmxO50Y3ce/sLdYvb8OSLYh/unDcRo0PDGf3ZysLQRi83VabAInt+bUanhuFwSCYemDhtNNrbt94y4XTA0monDYtrl2qiUcob263iPu3ZiH3ZkV7cL1AGGJYfx1O0jUCm7Z7rwgQce4K677uLVV1/lsccea3fO5XLxzDPPYLVaueuuu7zH16xZw5///Gfvz6tWrUImkzFq1CgiIyMJCgqitLSUGTNmeNtUV1fzxBNPcP3113sHQbpLjwQ8GRkZBAUF0a9fv3bHX3vtNVauXMnKlSu9Q32lpaU0NDSQmJhIUFAQvXr1YuvWrd7kJKfTSUZGBjfeeCPgmd9btmwZJpMJg8EAwJYtW/Dx8enwfIIgnFv2Hazl6fc3I5fLuGpcAkUVzezMrQE8IzmD+oQgIbH7QG27x10xphd3TUnrkK8T7K/jmRmj+L9F6WQV1hPop2FovzD8fTUkxweSkhDkvaG3mXnNAFxuqd2N5oO5k9mws4zF3+4jt7iR+xasafcYuVxGYUUzf3l7I2MGRmKxOqlpNKOQy/E3aLDanRwoacTllpDLoF98IHaHi7zSJpQKOf17BZJdWM/lYxOYfmEfDHr1EdeTRlSIgf+uOUB9s5WYMF/+b8ZIwoN8vG0euHZQh/dyVGoEi56chEIh67B3ytiBUYwZEMnaHaV88M0+TBYHj9441Dstldo7uEN/nY1eKRRybr2sP/OWbGPtjtIO5z9dkUWAn5baRk+6w5erDzD3jhEMS25/43e63PyaVcXqjBK2Z1bidHmmOnx0KuIj/NCqFTS32ukbF8DotAh25dawfFMBrVZnu34UchkjU8MJD/QhyF9Lr0gjqQlBuCUwme1Y7S6sdicKuYywQD27cmuoqG3lopFx3qThVx8+H4fTTV5JI7/8WkKL2U6wvw6b3YXT5UanVTJhaAyJ0f4dXu+EoTEdjh1PSkIQ4wdFsS+/lvPSIlGrFPj6qLlyXEK3BTsA48aN4y9/+QsLFiwgKyuLqVOnEhoaSmlpKf/+97/Jyspi3rx57e7Du3bt4rHHHuPqq68mOzubN998k+nTpxMT43mdjzzyCP/3f/+HQqFgwoQJNDc3884771BVVUVKSkq3XXsbmXSik2Bd8NRTT1FRUcGSJUvaHd+3bx/XX389U6dO5fbbb6e2tpb58+ej1Wr5/PPPkclkfPnll8ybN49nn32WtLQ0Fi1axPr161m+fDmBgYHYbDauuOIK4uLieOyxxygtLWXu3LnccsstPPDAAyd1vW07NYqdlgXhj23+R9s6TBco5DIGJYXw6xHf3mUyiA3zxelyc8lozzTTsXaAtTtcHChpJCk24HfdRCrrWnnzy11kFtTjdLkZlRrOdZP6EhLgGcVYtaXohPqTy2DW9MFMGhGL2y0dM1nW6XKTU9RA7yhjpyt6TpbV5qTV6jjppGxJknjp01/JLqpncFIobrdEq9VBq8XBnjxPYBrsryPIqCWnqAFfvZpZ0weyP7+erMI6mkx2TBZP+zaBfhrcbmg0HTuvMyrEh37xgfjoVKgUciaPjCMqxHBSr+NcsWvXLv71r3+xY8cO6uvrCQkJYcyYMdx2220kJiZ62/Xt25fbb7+dqqoqfvnlFwICApg+fTozZ85st0nw8uXL+eCDDzhw4AB6vZ4hQ4bw8MMP07dvX8CzD89bb73VbnPitv4feOABli1bBnTt/t0jAc/dd9+NwWDgtdde63AuPT2d119/nZycHNRqNRdeeCFz5sxpN1+3ePFiPv74YxobG707LScnJ3vPFxUV8dxzz5GRkYHRaPTutNzZvGJXiIBHEP74quvN3D3/J9wSXDcpiX35daT2DuKCIdFEh/qSX9bEgZIGbHYXw/qHERl8+m5sTpcbq83ZbiQGIKeonl9+LSU8SE+vSCMut+TdKK5/r0CC/XXUNVnZlVuDVq2gb1xAu5Gas4kkSfzyaynZhfXccFFfDHoVj7+5gYOlTZ229/fVcMGQaC4cHktcuC9uCfLLGqmsM2OxOdGoFPy0rYjCimb69wri/CHRjE6N+N0rqoTOtQUks2bN6tHnOZH7d4+Wlli+fLn3z22lJWJiYjAajchkMlQqFaGhod6pqTZarRalUokkSd7/jqRQKFCpVMdsIwjCuWX55gLcEgzqE8LNlyZ3OJ8QZSQhqvsTIU+GUiHvEOwA9I0LpG9cYCePOCwsUM/Fo+J66tLOGDKZjInDYpg47PA0zxO3DGPOmxtxud2MSo1gQJ8QwgP1KBVyekX6tZs2U8igT0wAfWICvMfOHxJ9Sl+DcGY5paUlulIWQpSWEAThZOw64MnVmTyyexMdhTNHZLCBD/96EXK57KT3RxLOXae0tMT3339/3LIQorSEIAgnyu2WvHu+nCmjOELP6M5EXKHn/Dbn5kzQo785vy0tcbyyEMcrLdGVPgRBOPfUHtrPRSGXnbU5LYIg/D49FvC0lZa45557vLsuHq8sxLFKS7SdO14fgiCce9pGdyJDfLpU9kAQhHNPj30ydFZawmq1dlo2Ajy7JXe1tMSx+hAE4dxTWt0CQHSo72m+EkEQzlQ9FvB0VlrieGUh2tqK0hKCIJyIthGe6FCxh4ogCJ3rkYDnaKUlwsPDOxQEO7IsRNtUVmdtjiwtcaw+BEE495RWtQU8YoRHEITO9UjAc7TSEsOHDyczMxOT6XAF3SPLQhxZWqJNW2mJ4cOHd6kPQRDOPYentMQIjyAIneuRgCczM9O7LfSRJk2aREhICA8//DDZ2dn8/PPPvPrqq9x5553evJw777yTJUuWsHTpUvLy8njqqaewWq1Mmzaty30IgnD2kySJ7zbk8/R7m2g4tBuxCHgEQTiaHikeWlNT412ZdSSNRsMHH3zAc889x/Tp0zEajdx4443cd9993jbTp0+npaWFhQsXektLLFmyxFtlvSt9CIJw+pksDr5bf5BtmZW0WpykJQbTanVQUdOK2eagb2wg11zQm4Qoo7eOlcstkVNUT1FFM/3iA5HJZOSXNTIgMYRgfx2SJCGTyahrsvD5qhx+3Hq49lSgn6ZDIU9BEIQ2PVJL649G1NIShBNjd7jIL2tCpZRT12ylut5MYrQ/EcE+WGxOiqta+OeyvVTWmY/bV5BRS1piMP4GDWt3lHprRx1JrZQTH+nHwdImZDIZTpcb8BQBHT8omtziBiYOj+H6yR1HlgVBOHudtlpabZYtW8aiRYsoKSkhNjaWBx54gEsvvRSAd999l4ULF3Z4zJG7Mn722Wd8+OGH1NTUeIuH9u/f33u+tLSU559/nu3bt6PX673FQ4+swCoIwskpqWqhvMbEkH6hqJQKXG6JrfsqaGi2olDI0agV/PvHHCpqW4/bV2ignhsm98VoULPvYB1Gg5rYcD8Uchk/bSsmfW85dU1W1v5a6n2MQaciPtKPnKIGAMKD9JRUmcgtbjzUwvMdrV9cANdN7suwZLFYQRCE4+v2gOebb75h7ty5PPXUU4wbN44ffviB2bNnEx4ezuDBg8nJyeHqq6/m8ccf7/TxopaWIJw+xZXNPPbGBiw2J/4GDSNTwymsaPYGH0fy0SpRqRT46tWEBujIKWrAZHGgUsoJDdCTkhDEHVf09xbJHN6//Yahg/uGYrU7ySlsYO/BWqoazIxKiWBkajhKhRy7wwV4SgnsyaulrslKv/gAVAoFapUco0HT82+IIAhnjW4NeCRJ4vXXX+fWW2/lpptuAuDee+8lIyODbdu2MXjwYHJzc5k+fXq7mltHErW0BOH0KKxoZv5H27DYnMjlMhpNNlZt8eTI6LVKBiWF4HC6aWi20jcukJsvTcagO5wzI0kSbokTKuqoVSsZmBTCwKSOnwdq1eER24F9Ov+8EARB6KpuDXgKCgooKyvrsP/O4sWLAc+GgoWFhSQkJHT6+OPV0po5c+Zxa2kNHDiwO1+SIJz1JEni5c9+Zf3OMgBCAnS8NGsc+WVN7Mmrxe2WmHJ+IiEBumP2I5PJUIgC1oIgnKG6PeABT5X0GTNmkJmZSXR0NPfeey8TJ04kLy8Pl8vFqlWrmDdvHjabjeHDh/P444+3q5fVWS2t7Oxs4Pi1tETAIwgnZlduDet3liGXyxiZEs5tl/cnyKgjyKjrMA0lCILwR9Wt+/C0bQY4Z84crrjiCj788EPGjBnDfffdR3p6Orm5uQDodDpef/115s2bR35+PrfeeitWq1XU0hKE0+C/aw4AcPmYXjx1+wiiQsReNoIgnH26dYRHpfLM58+YMYNrrrkGgOTkZDIzM1myZAmLFi1i/Pjx3j11APr06cP48eNZs2YNsbGxwNlTS0uSJGoaLFTUtuJyS7gliaq6VirqzFTWtVJZ14pGraBPTACXjo4nLsIPAPehtqLq89mvoraVFrOd3tH+VNSaKChrxs+gZveBGvYcqMVksdNqceJyuxmZEkFitJGaRgs1DRaqG8w0mmzEhvnRLz6AAF8t/r4a/HzU6LVKXC4Js9WJxeZEr1OiUsjZl1+HyexAqZShUshpaLGxJ68WuVzGlPG9T/fbIQiC0GO6NeBpq2WVlJTU7nhiYiJr164FaBfsgGc6yt/fn8rKSkaOHAl4amP17n34w/e3tbTaRoqOPH/k858pPvxuP8vWHTxuu9ziRpZvLiA53vPeFJQ3Y7U7CTLqMOhURIcaeOi6wWg1PbKLgHCKuNwS+WWNuFwSWYX1/Li1yFv00kenotXiOObjf95ezM/bOx6vrDOzLbPyd13b+YOjCA08s74wCIIgdKduvYOmpKTg4+PD7t27GTZsmPd4bm4usbGxvPbaa6xcuZKVK1d6d1YtLS2loaGBxMTEdrW02hKX22pp3XjjjYCnltayZcswmUwYDJ6h9zO1lpZapUCrVhBk1KFWeUZrQgP0RAT7EB7kQ3iQHrPFycY9ZWzeU0FmQX27x9c2WqhttFBY0UxYoJ7br0g5HS/jpFlsTrbsq8DPR82QvqHIZDLMVgflNZ6RLZvDRUOzlaTYgNO+xFiSJAormmlssWHQq4iP8MNkdrBlfyVWmxOFQoafj4aYUANxEX5HHX2TJImqejPZRQ0cKG7A4XIT6KelpdXO1v2VVNW334hPIZehUStotTiQy6B3tD8mi4PIYB/OHxJNaIAevVaJyeLgl4wSmlvthAToCA3QExqgx89HTU5xA8WVzTSZ7DSabDS32rFYHSiVcvQaFVqNgpZWO61WJ8m9AgkP1ONySzicbjRqBRFBPkweEXsq3mZBEITTplsDHq1Wy1133cXbb79NWFgYAwYM4IcffmDTpk189NFH+Pj4sHjxYp599lluv/12amtrmT9/PkOGDGHcuHGAp5bWvHnziIuLIy0tjUWLFnWopbVw4UIefvhhHnvsMUpLS8/YWlq3XJrMLZcmH7fduMFRFFY0U1zZjAwZMeG++PmoqW4wk1fSyPtL97Js3UEmDIshLtyvx6631eJgwacZ1DdZ8fNR4+ejxuF0U9Nooa7Jgt3hQqNSolbJ8TNoiAo2UFTZTIvZTkKUEZ1aic3hwuZwYXe4KK5swXRo1CIqxAenS6K6wcxv9/ZWKuRcMCSau6ekeksD1DdbKasxYbE5CfHXkZFVRWZBPXqtEqNBQ4CvhmHJYfjq1RRWeEbEVAo5Rl8NTS02rHYXRoOayGADKpWcnKIGiitbqG4wYzI7UMhlqFRyrDYXFpuTitpWKuoOb6SnVStwutw4XZ1vRK7TKNBrVfjoVOg1SupbbNQ2enLQ3O6jb16u1yrx1avxN2iYPDKOsQMjUasUHChpICxQT5Dx6Cuh0noHd348sfPjgiAIwmE9UlpiyZIlfPrpp1RVVdG7d29mzZrFpEmTAEhPT+f1118nJycHtVrNhRdeyJw5c9otM1+8eDEff/yxt5bW008/TXLy4cChqKiI5557joyMDIxGo3enZbn85HJezvTSEvOWbGXLvkp89SpGpISTV9KIVq2kV5SRhEg/IoMNaDUKiitbaDTZcDjd6DRKAnw1BBl1FFZ4gpIAXw3+vloUChl1TVZ89SoCfLW43G6iQ33557K9bNxd3q3XHh6kp/FQANLG36DB4XR5RiC0Ku+OvQlRRkalRrD7QA378+u69Tq6Qq1SEBGkp6HFM0oC0Dc2gIgQH5xONw0tNgrLm2i1Oo/Zj1IhIyHKSN+4QE8w1GzFV68mLsKP8wZEoFWLqUlBEITucCL3b1FLizM/4KlrsvDMonSKKlt6/LkUchkPXDsIpVJOs8mGUikn2F9HsFGHVqPA7nBjszupbbJSVm0iMsSHQD8tBWVNuNwSapUCjVqBWqXAz0dN/15BmMx29ufXYTRoiA41dJi+2nuwlhc/3k6T6XAyukwGEUE+aNQKKuvMRIcauGBING5JornVTnFlCztyqnG53MSE+WLQq7E5XDS22PA3qNFqlDS22LwJ43HhviREGYkI8sGgV+OWJOwOF3qNEp1WhZ+PmpSEIHQaJW63RFFlM3K5rMOImtst0WK202p1YLY4abU4aLU6MBo0hAXqkcnAV69ut2meIAiC0DNEwHOCzvSABzwJr+t3llJQ3ky/uABcLon88iYKypuobjDTanESE2YgNECPUiHHYnN6p6KiQ30JMmppMtloaLbhcLkJMmoxmR00mmzIgPJDoyy3Xd6faRP7nPLXV1nXyn/XHECSICbMwNiBUQT7H3ujO5vDhSRJxxwxcTjdOJwuUUVbEAThLCQCnhOUlpaGy+XqsOHhucTllnC7JVRKsRReEARB+GOoqKhAoVCwd+/e47YVyQR4Ni787d4+5xqFXHZCNZAEQRAE4XRTKpVdXrAkRngEQRAEQTjrifkLQRAEQRDOeiLgEQRBEAThrCcCHkEQBEEQznoi4BEEQRAE4awnAh5BEARBEM56Ylk6MGzYMOx2OyEhIaf7UgRBEARB6KKamhrUajUZGRnHbStGeACbzYbTeez6SIIgCIIgtFdbV0d5eQU2h6vT8xUVFdTX13f4c1cd7zFOpxObzdalvsQIDxAaGgqc2aUlBEEQBKG7NJlsPP3eZppb7YwbFMVV4xIIDdR36bGSJNHYYuOb9Qd5+7V5OOvyCRj+CMH+Oi4cFoNCIedASQM+OhXfvv8oo0eP4h//+AeZmZkYDAZiY2O7fJ0TJ05kxIgR/OMf/+j0fFtpia4QAY8gCIIgnOWsdieF5c30ifHH5ZaYt2QbhRXNAHyz/iArNhdw/pBogow6fPUqFHIZzWYHpdUtNLfa8fNRU1jRTHEnRaxVSjkBvhpqGy385+fcdufqmq38ml3F+0v3cPfVachP447+3RrwbN26lVtvvbXTc9HR0axevZp3332XhQsXdjifk5Pj/fNnn33Ghx9+SE1NDampqTz99NP079/fe760tJTnn3+e7du3o9frmTZtGrNmzUKhEBWqBUEQznQut8RT72zEanfx0qxxqFXis7snlFS18Gt2Nb0i/Fj0zV6KK1sI9NPicrtpMtnx0Sq56+pUVmeUsO9gHT9tK+5y33IZRAT7EJIYTOGBSv45dzI/byumsKIZu8NFfIQfW/dXkr8aGltsLN9UwL9ff4hRo0Z6R2uqq6t54YUX2LRpE5IkcemllxIQEMB3333HmjVrvM/lcDhYsGAB33zzDa2trQwZMoRnnnmGuLi4E3o/ujXgGTx4MBs3bmx3bNeuXcyaNYv77rsP8AQ2V199NY8//ninfSxdupQFCxbw/PPP079/fxYtWsQdd9zBihUrCAwMxOFwMGPGDOLj4/niiy8oLi5m7ty5yOVyHnzwwe58OYIgCEIP2JlTTWaBJy9jR041o1LP3cLNPcV9aBSnrMbU7nh9sxUAf18Nj900lIF9QrhweCzbs6rILqzHZHZgsjhwutwYdCoign0I8NXS3GojLNCHpNgAVEo5PjoVKqWcv/zlZwoBhUzi4pEx7Z7r8vPi2PiFhqTewTz50Hhm3va695zdbue2227DbDbz1FNPYTAYWLRoEVlZWR0WEC1fvpyxY8fyj3/8g9raWl544QUeeeQRvv766xN6T7o14FGr1e0u1Gw288ILL3DNNdcwdepUAHJzc5k+ffpRV0S999573HzzzVx11VUAzJ8/n0mTJvHVV18xc+ZMVq1aRXl5OV9++SVGo5GkpCTq6upYsGAB99xzT5eLiAmCIPS0j5dnsiOnmr/eOZIgo+50X84xOV1uymtMxIT5IpP17LTDj1uLvH/esLNMBDwnqabBwrbMSsYMiMTfV9Pu3LbMSspqTKiUciQJ4iP9mHPLMIorW1Aq5QxIDEap8KxbkslkjOgfzoj+4Sd1HWVlZaSkpBz1/Ngxo+kTE9Du2Lfffkt+fj7/+9//SE1NBWDUqFFMmjSpw+PDwsJ45513UKlUABQVFfHuu+9iMpk6tD2WHs3hee+997BYLMyZMwfwRHSFhYUkJCR02r6uro7CwkJGjx59+AKVSoYNG8b27duZOXMmGRkZpKSkYDQavW1GjRqFyWQiKyuLgQMH9uRLEgThLPPj1iIOljZy2+X90WtVJ9VHc6sdm91FSMDhoGbF5gK+Wn0AgGXrDjLjqtRuud6eIEkSzy/eyo6caiaPiOW+aQO9N8Pfw+WWUPwmZ6Ohxcq2/ZXen7dmVmK1OdFqREppmyaTjf+uOcCOnGoMOhW9Io2kJQbT3GrHZLZjsTkprmzh1+wqnC6Jbfsree7Po9v18fUveQBcNS6Bmy7ph0IuRy6XER7k0+3XGxISwrvvvtvpuXvvvbfT41u2bCEmJsYb7AAYDAYmTJjA1q1b27UdMGCAN9gBT4oMQHNz8wldZ4/9htXX1/PRRx/x6KOP4u/vD0BeXh4ul4tVq1Yxb948bDYbw4cP5/HHHyc0NJTKSs8/goiI9tF+aGgo2dnZAFRWVhIeHt7hPHiWr4mARxDOXZIkndDoREZWFW9+uQuAkioTt16WTIvZjlatJDRQT4i/7qhJlpIkIUmQWVDH3z/cis3h5m9/Hk1aYjCb95Tz/tK93rY/bi3ihov6nnRA1VWSJPHFT7ksXZvHoKQQhiWH4aNTMbRvaLuAwmpzsnF3GS43hAfpqahtZUdONQA/bSumqLKZmy5OZmBSCHIZtFocaNRKVMrOg6CiymY+/HY/IQE6RqVG4Oej5t8/5vBrdhUGnYq+cYGMTAknr7SRX7OqcLkl+sYG0GiyUVVvZntWFeMGRfXoe9NTJEmiuKqF0ioTyGBE/zBUyq7nJEmShFsChVxGflkTyzcXsG5HKVb74WXemQX1/LCp4Kh97MippqiymbhwP6x2J5+tzCarsB6lQs6V4xJO6HpOhlqtJi0t7ajnOtPQ0EBQUFCH450d0+vbrx6Tyz2/h263+4Sus8cCns8//xxfX1+uu+4677HcXE/2tk6n4/XXX6euro5XX32VW2+9lWXLlmGxWICOb5BGo/Gus7darfj5+XU4D3R5Lb4gCGeff/+Yww+b8nnythGkJHT80PythhYrr3+x0/vz3oO1PP7mhnZtfLRKxg+J5sqxCcSE+eJ0uamobWXPgRqWrjtIbaMFCU++BMD8j7YxMjWcNRklSBKMHxxFflkTpdUmftxaxJTzE7v1NYPnhllWYyK7sIEt+yrYemj0JH1vBel7KwDoFenHX24bTrPJzq4DNfywqYDGlo6fl+MGRbE9s5Lc4kae+Wc6vno1SoWMhkNt9Volfj5q/HzURAYbGJUWQU2Dmc9XZWOxeW7Qq7YUteuzxewgI6uKjKwq7zGtWsEtlyWzK7eG/645wJc/5zIqNeKoAdWpUt9spbnVjtPp5mBZExW1JuqardQ3WdGoFUwYEkOfWH90GiV2h5vNe8u9ibpt+sUFMPvGoYQH6TsE3+l7y/l8VQ5ymQyVSk5Di43GZisut+RZ5dRk9bZNiDIydUIikgR78mrJK2kk0KjF36BBrZITGWIgOT6Q/645QPreCr5dn891k5N4fvFW7/Vce2GfM3YqNSwsjMLCwg7H6+rqeuw5eyzgWbZsGVOmTEGr1XqPTZkyhfHjxxMYGOg91qdPH8aPH8+aNWu8a/Ptdnu7vmw2Gzqd5y9Nq9V2eh46RoGCIJwbHE4X36zLo9XqZMEn23l99gT8Dy2TbWixEhFswKA7PLridkss/PdOGk024iP8uOOKFF77YgdKuQyjrwarzUlVvYVWq5MVmwv5cUsRI1LC2ZNXS6vF0eH5R6aE02iykVPUwOrtJQBcel48M6ek8dO2Yt7+724++j4Ti9XJ1Il9cLrcZBbU02SyoVEriAoxEB/hd0KjU1/8lMPXv+Rhc7i8AReAXC7jlkuTaWi2UlpjIq+kkYLyZma+0H6fsfAgPdGhvuSXNVHfbCUx2sjsG4fQZLLx9S95rN5eTIu5/Wet2erEbHVSWWcmt7iRtTtKvefSegcTGeLD/vw66pos9IsL5LbL+yPhydPJLqonMdqfwX1DSe0dhFatJC7cjx+3FlFY0cy/fsjk4lFxGA0aDDrVKVu+3GSysXxzIWsyiqmsMx+z7fbMqk6Pq5RyekcZKalqIbuogT+/8DMGnYq4CD8C/bTIZTLsTpc3AO1MbZMVpULG6LRILj0vntSEIO/vw/lDoo/6uCnn9yZ9bwU/bSti7Y5S7A4X/r4aHpw+iOEnmZNzKowYMYL//e9/ZGVlkZycDHgGNDZs2NBjubg9EvBkZ2dTUlLClVde2eHckcEOeKaj/P39qaysZOTIkYBnqVrv3r29baqrqwkLCwMgPDzcO1J05HnA20YQhHPLjuxqWq2e3dLrm20890E6I1Ii+OKnHNxuCZkMJgyNITxQz7asKjQqBfvz61Ar5Tx281Diwv345NlL2vXpcrnZd7COZesPkpFV5b1Z6TQKokJ9mTQ8luH9w3C63EQE+dDcaufLn3PRapT0jQ1geP8wZDIZk0bEsjevlvW7yvj8xxx+3FqExe7qEDil9g7iTxckIgEFZU1YbE7Cg3yIDPGhpdXB9qxK/A0akmIDKK028dnKbO9jVUo5fWL86RcXyJiBkSTFHk4Qraxr5fkPt1Jc2YK/r4bk+ECGJ4dxwdCYQwmtnhGiIKMOpUJOkFHH3VPSuOPKFA4UNwKeESKHy01zq51mk52mVht7D9bya1YVIf56hiaHccXYXkfN+0mM9u/0uL+vhnunDuDFjzP4Zv1Bvll/EPAEbX56NTIZKJVyYsN8GdI3lInDYjDoO78ZOpwucooayCttRKdRERGsJyk2ALPVScuhfWSMBg3NrXZ+2FRAbnEDxZXN7UZV5DLw9VEjQ0ZsuC9xEX4EG7UE+mkprTaxYVcZdc1WbIemmxJj/Jk0PJbzB0dh0KsprzXx+hc7PaudLA7253ccrbh8TC+GJYfhcLoI8NUS4KdFJoOqejPRoQYCfLUdHnMsyfGBDEsOIyOrCrvDRVy4L/931yhCA87sAYArrriCRYsWcf/99/PQQw/h5+fHkiVLqKurIzIyskees0cCnoyMDIKCgujXr1+746+99horV65k5cqV3si1tLSUhoYGEhMTCQoKolevXmzdutWbuOx0OsnIyODGG28EYPjw4SxbtgyTyYTBYAA8yU8+Pj4dnk8QhHPD+l1lAIzoH86+/FrySpvIK20CwFevosXsYE1GSYfHzbg6lbhwvw7HARQKOQOTQhiYFEJGVhV78moZ2CeYwUmhnY4+GA0a7p7SMY9BqfAEVSNSwlny/X7vDTY0UE9UsA9Wu4uDpY3sO1jHvoMnNpx/48X9uGhkLH4+mqNOB4UH+fDWYxOw2Jyd5hDJZDKiQ307ve7kXoe/oGoBX72aqEMLbEelRnD31Z3nbZyIsQOjKLvUxOrtJTSbbLRanbjdEo2mw1NuNQ0Wfs2u5uMVWdw3dSCDk0LILKzH36DB7ZbILKjju435NJnsx3gm0GmUuNwS9t+UQegdbeSa8xO9OU9Hc/OlnpGIthG13/4eRAYbePGBcdgdLspqTBRWNNPSascteR4TF+HL0H6dfzE/2QBFJpPxfzNGUt9spbHFRlyEX7cknPc0pVLJ4sWLmTdvHs8++yxKpZKrrroKf39/CgqOnq/0e8gkSZKO3+zEPPXUU1RUVLBkyZJ2x/ft28f111/P1KlTuf3226mtrWX+/PlotVo+//xzZDIZX375pfcNSEtLY9GiRaxfv57ly5cTGBiIzWbjiiuuIC4ujscee4zS0lLmzp3LLbfcwgMPPHBS19u2NbUoLSEIfzxWm5Obn12Jze7ilYfGE+Cr5Z/f7GVHTjU3XdyPKef35kBJI//6IROH082Fw2NpaLHio1VxxdhePb4E+0g2h4steyvw0akY0vdw4FRdb+aTFVkUVjQjk0FsmB++Pioqalspr20FPNNmrRYHhRXNNJlsjE6LZMZVKaf0+k8Fh9NNc6uN5lZP8GK2OjlQ0sjq7YdzZRRyGS53x1uXv0FDcq9AHE43BeVN1DVZkcvAR6fCZHHQdrfrE+PP5JFxxIX7Ehvme9RRI6HnHDhwgPz8fC666KJ2v8PTpk0jPDyct956q0v9nMj9u0dGeGpqarwrs46UmprKP//5T15//XX+9Kc/oVarufDCC5kzZ473BU+fPp2WlhYWLlxIY2MjqampLFmyxDsVptFo+OCDD3juueeYPn06RqORG2+80buxoSAI55b9BXXY7C5CA/X0ifFHJpPx1O0jcLslb0CRFBvAvHvHnOYrBY1K0Wk+RmignkdvGnoarujMo1J6ptWOTLZNSQjiqnEJfLYqmy9/zsXllogL98XucCOTQXiwDxOGRDNuUBSKQ6MbbfWefHQq1CoFTpebshoTdoeLxGj/sy5Q/KMxm8089NBD3HjjjUyePBmXy8Xy5cvZt28fjz32WI88Z4+Wlli+fLn3z22lJWJiYjAajchkMlQqFaGhod6pqTZarRalUnlo2afnvyMpFApUKtUx2wiCcG5o20m2d5Sx3U3sdNbsEbpfWzL2yJRw1CoF8RGdT0W2kclkBPgdzodRKuRHnb4UTr2BAweycOFCFi9ezLJly5Akif79+/PBBx8watSoHnnOU1paoitlIURpCUEQTkRFjWfKJzK4+zdUE848RyZkC39sl1xyCZdccsnxG3aTU1pa4vvvvz9uWQhRWkIQhBPRluMSGWI4TktBEM5lPZrK/dvSEscrC3G80hJd6UMQhHNLea1nSkuM8AiCcCw9FvC0lZa45557vAnMxysLcazSEm3njteHIAjnDofTTXW9Z7O4CBHwCIJwDD0W8HRWWsJqtXZaNgI8uyV3tbTEsfoQBOHcUVXfilvylCoI9DuxDdsEQTi39FjA01lpieOVhWhrK0pLCILQFW35OxHBPmKZsSAIx9QjAc/RSkuEh4d7y0C0ObIsRNtUVmdtjiwtcaw+BEE4d5R7V2iJhGVBEI6tRwKeo5WWGD58OJmZmZhMJu+xI8tCHFlaok1baYnhw4d3qQ9BEM4NFpuT0uoWACJDRP6OIAjH1iMBT2ZmJn379u1wfNKkSYSEhPDwww+TnZ3Nzz//zKuvvsqdd97pzcu58847WbJkCUuXLiUvL4+nnnoKq9XKtGnTutyHIAinV3W9mR051VQ3mCmpaqG4shmXW6KuycLu3Bo27S6nuuHYlamPZuu+Cp56ZxPXzf2BVVuKAIgIEgGPIAjHdkpLS3SlLIQoLSEIZy6r3clnK7PRqBSMHRRFTJgvit/saLx6ezFvfbUbp8vd7rhSIcPpktr9PHlkHBOHxhDgpyWzoI7lmwqwO930ivRDLpNhd7hxS56ii5HBBvbn1/HDpvaFBeVyWbsil4IgCJ3pkYDn8ssvZ9GiRaSlpREbG8sDDzzApZdeCnjKTWzatAnw5N688847vPPOO+Tk5HgfL0pLCMKp53K52bq/ksRof0IDOy4AsDlczFuyjV25NQD85+dclAo5EcF6okIMRAYbyCttZE9eLQCBfloaW6zeCtVWuwu5XEZksA9KhZzCimZWbC5kxebCDs+VX9bU7ucNu9qfv2p8AleN640MUChk7eouCYIgdKbbA55vvvmGuXPn8tRTTzFu3Dh++OEHZs+eTXh4OIMHDyYnJ4err76axx9/vNPHi9ISgnB6/Gt5FkvX5qGQyzhvQCSJ0f6YLHZKq02UVpuoqG3F6XKjVStI7R3MngM12J1uSqpMlFQdzqmTy+DaSUnceJEnp05+qLJ1TYMZf4MGrcbzsbMnr4ZVW4r4NasKu9NNsFHHhSNiiA71pbSqBblchlqlwO2WOFDSSH2zJ3i6ZFQcI1MjOn0NgiAIR9OtAY8kSbz++uvceuut3HTTTQDce++9ZGRksG3bNgYPHkxubi7Tp09vV4LiSKK0hCCcegXlTXyz/iAALrfEhl1lbNhV1qGdv0HDE7cMIy0xGLdborbRQmmNifIaE2U1Jvx8NEwaHktIQPsRF4VcRvhv8mwGJIYwIDHEOzorlpULgtCTujXgKSgooKysrMNy9MWLFwOe/XUKCwtJSEjo9PHHKy0xc+bM45aWGDhwYHe+JEE4qzmcbn7YlM/yTYW43RLnDYhg6oQ+/JpVRXFVC74+aqJDDESH+hIVaiDEX+etQi6XywgN1BMaqGdI39CTvgYR6AiCcCp0e8ADnqKhM2bMIDMzk+joaO69914mTpxIXl4eLpeLVatWMW/ePGw2G8OHD+fxxx9vVz6is9IS2dnZwPFLS4iARxC67sNv9/H9oSRgPx81d1+dRrC/TlSkFgThrNOty9Lb9saZM2cOV1xxBR9++CFjxozhvvvuIz09ndzcXAB0Oh2vv/468+bNIz8/n1tvvRWr1SpKSwjCKVRZ18qK9EIAbru8P+88MZFgf5H8KwjC2albR3hUKhUAM2bM4JprrgEgOTmZzMxMlixZwqJFixg/frx3iTlAnz59GD9+PGvWrCE2NhY4O0tLuNwSJrMdp8uNyyXhliTkMhkGvQq9VnW6L084hcxWB60WZ4c8lzY2h4t9B2vJL2tCc6hGVFSIgYggH7QaJWarg+2ZVTS02AgN0JEQZSQsUI/N4UKjUhxzishsdaBUyJHJZPzrh0xcbonBSSFMm9inp16uIAjCGaFbA5620g5JSUntjicmJrJ27VqAdsEOeKaj/P39qaysZOTIkYBnuXrv3r29bX5bWqJtpOjI80c+/5mioLyJZesOklNUT1W9ud0eJEfy1asY0jeMtMRg/HxUSBKoVQpCA3Ro1Er8fNToND2yg8BpZzLbMejP/kTzrIJ6ahrNaNVK3vpqFw0tNhJj/Anx13kDYJfLTbPZTlFF81F/VzRqBU6nG5e7/Xm5DNwSRIX40C8+kCaTnbomCza7i5gwX2x2F8VVLdQ3W1HIZeg0SkwWBwC3XJbc469fEAThdOvWu2hKSgo+Pj7s3r2bYcOGeY/n5uYSGxvLa6+9xsqVK1m5cqX3W2hpaSkNDQ0kJia2Ky3RlrjcVlrixhtvBDylJZYtW4bJZMJg8NTPOVNLS3y8PIuMrKp2x+RyGUq5zLtU1+F002J2sG5nKet2lnbaj06j5JWHxhMT5nsqLrvbuNxSh03p2kiSxL9+yOR/v+RxxdhezLxmwCm+us5ZbE4KypvoFWkkv8yzcik1IYhJI2JPeiRuw84yXv78V9y/CVLyShrJK2ns9DHBRi39E4JwHVoJVV7TSovZjs3uAiAy2IdeUUaq6s0Uljd5A6SymlbKDtWXatNWYLONyy1hsjgI8NVww8X96BMj8nUEQTj7dWvAo9Vqueuuu3j77bcJCwtjwIAB/PDDD2zatImPPvoIHx8fFi9ezLPPPsvtt99ObW0t8+fPZ8iQIYwbNw7wlJaYN28ecXFxpKWlsWjRog6lJRYuXMjDDz/MY489Rmlp6RlbWuLWy5JJSQgiPsKPuHA/Av00KBTt06bMVgdFFS1s3ltOabWJVosDmQzMVic1jRasNicWm5O3/7ub+feO8a6Q6W65xQ38+8cccooaCAvSc+XYBDSH9kBxSRJl1SZcbjch/jpCAvRoVApazHbkchkalQKNWoFGpUChkFPfZOV/vxxg78Fagow6okMNRAb74OejweZw0dBspdFk825g9/3GAvrE+DNxWGy7azKZ7WzaU0H63nLqmqwoFTIuH9OLCcNijxpIHY0nuHThdEk4nW6cLs8Ovmqlgi37KsjIqsLhdJNVWIfF5kKnUWK1O5EkSN9bwQff7iPQT0vvKH+SYv0JMmrx99Vi0KmwO13YHW7Ka0xs2lNOdYPn781qdyGXgcPlRpIgJEBHk8nO+YOjuG5yX/YdrMXucCGXy1EcCoJ1GiUJUUZCA3QdpqZazHZaLQ7kchkh/ofP2xwuTGY7SoWc3QdqKKtpJdBPS7C/FqVCTnFlCzqNgugwX2JCfWm1OqhttNAnJgCVskeqywiCIJxxZFIPbFG8ZMkSPv30U6qqqujduzezZs1i0qRJAKSnp/P666+Tk5ODWq3mwgsvZM6cOe2WmS9evJiPP/7YW1ri6aefJjn58LB7UVERzz33HBkZGRiNRqZNm8asWbOQy0/uw/vCCy8EYPXq1b/jVfeMqnoz97+0BpvdxdXjezNuUCR2p5uwAH2nu+G63BKNLVYCfLVdDo6qG8w89Mpa7xTHqTSwTzC7D3h25o0ONeDvq0Eh95QgyCmq73RqR69VEhPqi49OhdnqoL7FhlopR6dRtvtPo1Zgd7iorDOTV9qIw+nu0FdndBoFFptnJGV0WgTFlc0dRk1O1OQRsTxw7SBkMrEMWxAEobucyP27RwKeP5ozOeABWLo2jw+/29/heICvhkCjllaLg8YWGwG+WhpNNiw2J756FQa9mpoGC2qVHL1WhUGnQq9VIpPJaGi2YjRoCAvUk1/eRHFlC4nRRv48ZQC/ZleRkV2FRqVAqZDjliQignzQqBTUNFqobjBjd7jx81HjdkvYHC5sDhd2hwuXS0KrUZDWO5gp5/em1eKpaF1R14rJ7EClkhPkp0OpkNE72p9+8YG8/sUO1u4opbPfxPgIP8YPjqJ3tD+F5c38d00uLebfH5gpFTJAhtPlJixQz8Wj4gjw1RARbCA5PpD9+XUgg7TewUiSRJPJTmVdK1mF9RRWNNNostHYbMNsc6BSKlCr5Pjq1AzvH0a/+EC0agVatdKbnN5ZcCoIgiD8PiLgOUFnesAjSRKrt5ewbmcpJVUtqFUKqurNHXJCfg8frZKFsy/osBvuqdJitpNT1IDF5sTllnC7JXpHG4kL92vXzuF0U3ZoZ1+r3YlGpSTIqMXhcmOxOb1TgJZDU0pqpQJ/Xw194wII9PNM8SgVMu8oi8Pp8q5aEgRBEP5YRMBzgtLS0nC5XB02PDyTSRLePBSZTIZCJjv0Z1Ao5DidbiRJ8uQMSeA+VGC1LUaSH2rvcnvySzQqhcjnEARBEP5QKioqUCgU7N2797htz861zidIo9F02NvnTCeT0SFAUXB4lOJY5/jNGUEQBEH4I1IqlV1esCRGeARBEARBOOuJOQxBEARBEM56IuARBEEQBOGsJwIeQRAEQRDOeiLgEQRBEAThrCcCHkEQBEEQznpiWTowbNgw7HY7ISEhp/tSBEEQBEHoopqaGtRqNRkZGcdtKwIewGaz4XK5TvdlCIIgCMIfkiRBTU01AKGhoafseZ1OJ13dXUcEPBz+yzlTS0sIgiAIQk+QJInGFhu+PmqUCnmHc7sP1FBY0UxcuB9ymYzyulYOljZS12RFrZJz08X9WL29hK/X5mHbuRijQc3tD85ny74KHE43I1PDCfLTEhliYGCf7p9FaSst0RUi4BEEQRCEc4TV7qSoopnCimaaW+1s3lNOXmkTSoUMg06Nw+Wmf69A4iP82JVbw4GSxmP2tzOnGovNM0PSanHQanHwyYos7/nCimbvnz959hL8fTU98rq6QgQ8giAIgnCWcrklvlmXR/reCvLLm7E7Ok/fcLokGk02ALZnVrE9swoAtVLOgD4hlNeYUCjkBBu19I72JzzIh5XpBeSVNgEw5fze/DtTh93hYszASAYmBqNRK/k1qwqHy01smC9Gg5pNmzbx9ttvk5OTg1KpZOzYsTz22GNERESwevVq7rvvPpYuXUr//v0BWLZsGXPmzOHvf/871157LQBZWVlMmTKFL7744oTeCxHwCIIgCMJZ6j8/5fDvH3PaHfPzUZMY7Y/RoCYu3I9JI2Kx2V20Wh24XBLp+yqob7KSGOPPeWkRBPhpO+17zMBIFi3dQ7C/jpsvSWbtVz4A/OXW4d42E4fFeP/cFrxcccUVzJw5k4aGBt544w2uu+46li5dyujRo1Gr1WzevNkb8GzZsgWAjIwMb8Czfv16AgMDGThw4Am9FyLgEQRBEIQ/AJPZTkmViZLqFkqqWggP8uHyMb2O2n5HTjVf/OQJdm6+tB9jB0YR4KtBp1Eikx2toDQkxvh36XoMOhWzbxzapbZut5uXX36ZsWPH8sorr3iPDxkyhMsuu4zFixfzxBNPMGLECNLT07nrrrsASE9PJyUlhe3bt3sfs2HDBs4//3zk8hPbWUcEPIIgCL9R12TBz0eNSqk43ZfyhyRJEoUVzVTWmekXH0CA7+ERArdboqS6hZyiBkL8dQzue+pW9JyJJElCkkAuPxyAFFc28+2GfHbl1hxKJJZotTi9U05H6hPjT1JsQIfj6XsrePnTDCQJLh4Vx3WT+vbkyziugoICampqePTRR9sdj42NZfDgwWzbtg2ACy64gFdeeQW73U5ZWRmVlZXMnTuXWbNmUVZWhtFoZOfOndx8880nfA0i4BEE4ZzmdktUN5hxuyXyy5tYk1HC9swqQgP13D91IEP6dX5DLq814XJJBPvr+H5jPq0WB9demERDi5X0vRUUljczuG8oFw6POea36e5UWdfKwdImKutaySqsx99Xw91T0tCoDgduJrOd/fl11DZZcbrcOJ1umlrtFFc2U91gRqVUMKJ/ONdf1BdJkvh8VTYr04swGtT46tVY7U5G9A/nqvG98fNRA1BVb+a1f+9AkiTUKgWF5c3tbs46jQKXSyLIX0dzq51WiwMAuQzenXMhkSGGU/L+nIgmk43mVjshATqKK1uw2Jz0ijSiUsqx2V04XW6CjFqcLon/rs6ludWOv6+G1N7B9IsLQKHofPSh1eJg6do8cosbACioaMZkttMr0ojRoKG6wUxxZctRryvYqCUmzJcmk5388iZWphd2CHg27S5nwSfbcUswLDmMu6ekdd8bc5IaGxsBCA4O7nAuODiYzMxMwBPw/P3vf2fHjh3k5+fTq1cvJkyYgF6vZ/v27ej1emQyGWPHjj3haxABjyAI56wmk43/W5ROfllTh3PV9Wae+Wc6D04fxLhBUWQW1uNwuCisaGbr/krv6hWlQo7T5QZg1ZYiTIdu5gDrd5WRkVXFXVenEuyv6/brd7kl0veWYzI7qKxrZem6g7jd7fckKasxcf+0gRgNGjKyqvjnsr20mB1H6dEjv6yJzII6GlpslFR5br4tZrv3fEF5Mz9sKuAvtw0nOT6Qf/xrmzd5tY1apSAsUEdJlcm7iqeithUAjVqBVq2gyWRnRXohM65K/d3vxe/VZLLxn59zqW20oFLI2by3HKfr2Pu7pCQEER6kZ/X2knbHQwP13DA5CZ1Whd3hwmp3cbC0kdJqE4UVzd6A70hHroZSyGUMSw7jktHxaNQK5DIZWrWCiGAf9FoVAJkFdcx5ayPrd5Ux46pUGk02ftpahMPlZvmmQtwSXDg8hlnXDjpq8HUq+fv7A1BbW9vhXE1NDQEBnqAtJiaGhIQE0tPTKSgoYMSIEahUKoYMGcLWrVtRKBQMHz4cg+HEg2QR8AiCcFaSJInKOjO+PmoMOs9NwuF0UV7TSrPZTlWdmW/WH6Swohm5XIZSISc6xEBq7yAuHB7L8s0FrNpSxDv/283nq7KpbbK2618hl6GQy7A73YQF6pHJoLLOjEwGQ/qGEhliYPmmAjbtKWdbZiW3X96fq8b39j6+yWTj3a/3oJDJmHZhH3pFGr3nKutaqao3ExlsIMBPQ1W9mV+zq4gL92NAoucbcm5xAx9+t5/Mgvp215UYbSQsyIfYMF+WrTvIvoN13PvimnZtwoP0xEf4oVYpUMhl+PqoiQw2EB1ioLTGxAff7GNPnufGZNCpmPmnAfgdGt2xOVz8b80BiipbeGZROiEBOs/7rFdx19WpOF0SseG+JEQaUasUNJlsmK1OZDKoabSg0yjpFeHHrznVPL94K6u3F3PzpcneUagDJQ00mewMSgrx7gvjdkvIZHRppMxsdVDXZEUhlxEe5IPT5cbtltBqDt/uXC4336w/yJqMEppa7WhUClotjnbBKniCNrvDhUGnwqBXUVln9p6TyWB/fh378+uQyeDyMb1oMtnZmVNNdb2Z1/+z66jXGB1q4KpxCaiUciJDDPgbNBwsbcLmcKLTqBjYJxiDXn3M15kcH0hsuC/FlS08/NpaquvNHBnrjk6LYNb0wSjkp2Z08Xh69epFSEgI33//Pddcc433eElJCbt27eLWW2/1HrvgggvYunUrJSUlPPXUUwCMHDmSL774ApfLxYwZM07qGkTAIwjCWcNsdfDiJxlU1bXSYnbQ3GpHq1Zwyeh4DpQ0klNU3+Fbe4Cvhvn3jSE61Lfd8fumDsRkcbBpdzm1TVYCfDWEBOgI8dczsE8wo9Mi0WoUlNe0EhNmwOWS2LSnnKTYAGLCPH1NGBrN4m/3sz+/jg++3YdSKWfF5kLkchlmq8N7A12/q4yUhCD69wqkoraVzXvKcR9lcCHYX4fN7vKOuOg0CpLjg7A5XFw1LoHzBkR6247oH87i7/aRW9SA3emZgrl4VDzXXtinwyZzbQYmhRAf4cePW4tISQhiVGqEd+qqzZgBkbzxn12s21lKZZ0ZhVzG7BuHMiw5rEN/RoMGo8Gz90p4kI/3+NB+YYQG6KhusLDku/3ERfixZnsx2UWeqZ6IYB9Gp0bQaLKxcVcZA/qE8PQdI9qNVrQFQs2tdr7bkM+WfRUUHTEdpFbKsTvdyOUyhvQNJTrUQKvFQXZRPSVVpg7XGhfuy4XDY2kx2xmREk7f2ACaTHb8fNTI5TKsNifIQK1UUFLVwv8tSqe+2cotlyZz7YVJAFhtTpauO8jmPeVo1Qq0GiUKuYz4CD/iI42EB+pJjPHv8P6f6LSeTCbjyrEJvP3f3d7fo+H9w4gI9sHfoOHq8b1PS7BTWVnJRx991OF4UlISs2fP5sknn+TRRx/lqquuoqGhgbfeeguj0cgdd9zhbXv++efz4YcfAjBixAgARo0a5U12njBhwkldm0zq6p7MZ7G2nRrFTsuC8Mf20ff7+d8ved6f5TI6BA4+WiX+vlqCjFoSooxcPqZXuxvxkax2Jx9+t58Ag4ZrJiSiVZ/4d0RJknjrq938uLWow7nQAB19YgPYvKec334ShwXqqWm04HZLyOUykuMDOVDS6N1HRatWMDIlgpsv7XfU62/jdLlxutwndf3Hel0HShpptTiICjUQGqA/4T6Wrs3jw+/2tzumVMjRaRSdTrtdc0Eiw5JDWb65kK37KnG63N6buuuIv2i9VonT6cbudB/1uX10Km67vD99YwOw2DwjV2m9g1Epuz7902K2U1Zjom9swCnL0zqSJElkFtRjc7gI8dd5A+3T5ZZbbvEmH//WtGnTmDdvHqtWreL9998nNzcXg8HAuHHjmD17NhEREd62DoeD0aNHExwczMqVKwFwuVyMGDGCsLAwli9f7m17IvdvEfAgAh5BOBtU1LZy34I1OF1u7vnTAPrE+BMf4cf6nWVsy6wkKTaAMQMiCQ/Sn/Kbk8Xm5OFX11Je20pa72AmDouhvNbElWMTCPDTUttoYf3OUqobPFM+YwZGkhjtj8stYTLbUSrk+OhUtJjtFFY046NVERVqaJeM/EckSRJrMkpYu6MUi83J6NQIJg6LQatRsvbXEkqrTbgliUA/LR8vzzpmX4nRRq4+P5FBfULw99Xgcrmpqjfjo1PR3GonfW8FZqsDtUpBVIiBQUkh3pEn4Y/rtAU8W7dubTcPd6To6GhWr15NaWkpzz//vDfbetq0acyaNQuF4vA/3M8++4wPP/yQmpoaUlNTefrpp72bEAFd6uNEiIBHEP74Xvv3DtZklDA4KYTn/jz6tHzjPpaGFiv78+sYmRJxQqMIgseH3+3nm3V5BBp1DEgM5spxCYT463C63Di8eVRn1t+50PNO5P7drTk8gwcPZuPGje2O7dq1i1mzZnHffffhcDiYMWMG8fHxfPHFFxQXFzN37lzkcjkPPvggAEuXLmXBggU8//zz9O/fn0WLFnHHHXewYsUKAgMDu9SHIAjnnqxCT/LulAsSz8gbX4CvlrEDo073Zfxh3XllCrdf3r/dfjWCcCK6NeBRq9WEhByuhmo2m3nhhRe45pprmDp1Kt9//z3l5eV8+eWXGI1GkpKSqKurY8GCBdxzzz2o1Wree+89br75Zq666ioA5s+fz6RJk/jqq6+YOXMmq1atOm4fgiCcW2wOF5V1niXPvSL8TvPVCD1FBDvC79Gj46rvvfceFouFOXPmAJ5aGCkpKRiNh5dfjho1CpPJRFZWFnV1dRQWFjJ69GjveaVSybBhw7zbSh+vD0EQzj2lVS1IEvjqVae1GrMgCGeuHgt46uvr+eijj7jnnnu8Gw5VVlYSHh7erl1oqGcX04qKCiorKwHaZWu3tWk7d7w+BEE49xQf2hwvNtzvjJzOEgTh9OuxgOfzzz/H19eX6667znvMarV2mHLSaDzfxmw2GxaLBaDTNjabrUt9CIJw7mnbDTj2NC/LFQThzNVjAc+yZcuYMmUKWu3honFarRa73d6uXVuQotfrvW07a6PT6brUhyAI55622kOx4SLgEQShcz0S8GRnZ1NSUsKVV17Z7nh4eDjV1dXtjrX9HBYW5p3K6qxNWFhYl/oQBOHc0xbwnO6N1wRBOHP1SMCTkZFBUFAQ/fr1a3d8+PDhZGZmYjId3tJ7y5Yt+Pj40K9fP4KCgujVqxdbt271nnc6nWRkZDB8+PAu9SEIwrnFandSWe9ZoSVGeARBOJoeCXgyMzPp27dvh+OTJk0iJCSEhx9+mOzsbH7++WdeffVV7rzzTm9ezp133smSJUtYunQpeXl5PPXUU1itVqZNm9blPgRBOLM4XW4Kypv4aWsR364/6M25OVkut0R2YT3/+iGTue9uOrRCS42/2DlXEISj6JHioTU1Nd6VWUfSaDR88MEHPPfcc0yfPh2j0ciNN97Ifffd520zffp0WlpaWLhwIY2NjaSmprJkyRICAwO73IcgCD3P5ZY6LU4oSRKNLTa+3ZDPqi2FuCWwO1w4flPXKCrEwKjUcEalRqBRK8gqrGfjrnJqGy24JAm3WyLIT8vApBD8fDyVuusardQ2WThQ3Eijqf0ihWHJoWKFliAIRyVqaSFKSwhCVzhdbmx2F7VNFj74Zh97DtTQNy4QjVpBabWJID8tLkmiosZEq9XZ4fF6rZLeUf4oFDL2HaztULX8ROm1Sob1C2NIv1ASoozEhfuJjekE4Rxz2kpLCILwx7f3YC1ZBfWEBuiQyWRkF9Wzbkdpp9Wr28o5ANQ2WtqdS4gycsNFfYkKMaBUyAkL1HsDErPVwa9Z1WzZX8GvWVUgkxEb5svotAiSYgNQKmTIZDKKKprJLKjH7nShVioI8tcSbNQRFWKgX3ygqEklCEKXiYBHEAQA3G6J/645wCcrjr1juVwGg5JCueHivuSXNSED4iL8aGyxIZNBZLCB8GCfY1by1mtVjBscxbjBUbQNMnc2HZUUG8DkkXG/63UJgiCACHgEQQA27S7n4+WZlNd6VjsN6ReKw+FGJoNAo5YJQ2JIivVHo1agVMi9wUm/uMDf/dwi70YQhFNBBDyCcI4rqzGx4NMM3G4JH62S265I4dLR8af7sgRBELqVCHgE4Rz3+cps3G6JQUkhPHX7CHQa8bEgCMLZR3yynQIHShqoa7Jis7sorTbR0GLF4XQTG+ZL/15B9I0LEKtLBMCzfDunuIF9ebWUVpvw9VET4KvBz6BBLgOLzUV9s5WCsiYkJPrFBZLcK5DIYANWuxOlQo5WrUStklPdYKah2Ya/rwaZDJpMdoorW5AkiSCjliCjjtLqFtbvKgPgjitSRLAjCMJZS3y69SC7w8X7S/fy49aiY7YL8NUwKi0Cp9PNzpxqbA43/r5qRqZE8KcJifjqxYaKZwOXWyKnqJ7qBgs5hfXsOlCD0+VGpZSjUiiw2J3UNlo67FdzLLsP1HbLtY0bFEVClLFb+hIEQTgTiYCnB7339R5+2laMTAaJ0f6oVQoig30IDdQjk0F+WRO7cmtoaLGxYnNhu8e2mO2UVB0gq7CeF+4bc9YkdrrcEq0WByazHR+dCuMZsjOu1e6kpsGCXqskyKhrd66sxkRZjYm+sQHHvF63W6Km0UJJVQul1Sbqmiwo5DICjVpqGixs2lNOTYPlqI9vE+CrIa13ML2jjbRanTQ0W2lu9RTM1agUGH01xIX7IUkSWYX1ZBbUUd9sQ69V4nK5sdicOF0Sfj5qQgJ0ntVTgEGvJjrUs0S8rslKXZMFH52Kof3C+NOExN/1/gmCIJzpRMDTgyJDDMSEGbj76jQG9w3ttI3D6WZPXg1b91WCDM5LiyDQT0tBeTNv/Gcn+/Pr2LKvgtFpkafkmtP3lrNuZxnFlS2olHJsdicNLTYkCXQaBYF+WnpH+6NVKzlQ0oDd6UYGyGSe1TZtYVl9s5VGkx2jQU2grxatRkFJlYn6Zmu75+sdbeSWS5MZ2q9j4Vez1UFVvZnKulYaW2y0mB20mO1EBPswon84FpsnSGk02fA3aDDoVbjdEi63hNGgJirEAEBlnZkDJQ3kFjdSVd+KQiHH6XTjliSC/XUUljeTXVRP2xacMWG+TBgazcA+IezMrebfq3JwuduWToNapSAiyAe1So7d4cbhdGGzu2ixOLDZXcd8fw06FQlRRiKCfRjaLwx/gwaHy4XTKaHVKAjw1RIepO9ygHvJUZKLHU63dy8bQRAEoYd2Wl62bBmLFi2ipKSE2NhYHnjgAS699FIASktLef7559m+fTt6vZ5p06Yxa9YsFIrDe3Z89tlnfPjhh9TU1JCamsrTTz9N//79vee70seJOFN3Wv5kRRZf/pxLRLAPLz4wlgBfLQ6ni69WH0CllJOSEESvSCMut0RRRTM5RfXUN9twSxKxYb4Y9Coamm1ISMhlMhQKOdEhBnRaJQdLG/HzUZMUG+Ad0diVW81f308/Ja9Np1FgsXmCA7kM7rwqlSvHJvDDpgLW7Silsr6VJpP9dz1HRJAPJou90w3zOr8mJTa7E3cn/yJCAnRdGp1RKmREhRiIDvMlxF+H0+mmrtlKsL+OpBh/Rg+IPOb+NIIgCELXndadlr/55hvmzp3LU089xbhx4/jhhx+YPXs24eHhpKamMmPGDOLj4/niiy8oLi5m7ty5yOVyHnzwQQCWLl3KggULeP755+nfvz+LFi3ijjvuYMWKFQQGBuJwOI7bx9li6oREftxaREVtK/cvWMNdV6ey72AdP20r7rbnkMtg8sg4br4kmc9X5QAwKjWcS0f3QkJCrVQQ4KdBLpNhtjmprjeTVViP3eGiX3wgBp0KCUDy1FCS8Pzf36DF6Kum2WSnvtmK2eokOtRAWJAeX70apUJOQ4uVT5Zn8dO2Yj74Zh/frj9I9W+CCj8fNWGBegL9tPj5qNFplezPr+NgaRM6jZLQAB3+vhoaW2yYbU4UchlymYzqBgsVdZ49ZZQKOQlRfiTFBBAd5ovbLaFSypGA6nozQUYto1IjCPbXYTLb2bKvklVbCqlptODvq+Hy83oxaUQsZqsTu9OF2eqkvMaEJOHJv1HK0agV+GhVhAXqUSjE7r+CIAhnmm4d4ZEkiQsvvJCLL76YOXPmeI/PmDGDESNGEBUVxZNPPsnGjRsxGj0Jkv/5z39YsGAB6enpqNVqLr74YiZNmsTjjz8OgNPpZNKkSdxwww3MnDmT77///rh9nKgzdYQHoKC8idf+vYOC8mbvMbkMhvQL42BpIw0tngKKQUYtSbEBRAb74JY8j7PZXQT6aVHIZbgkCbvDRWFFMxark97RRlpaHeSXNwGgUSuw2V2olHL++dSkDnksPUWSJL7bkM+/fsjE7nQjl8u49dJkBvcNJSxQj49O1enj7A4X6mOMlLRaHOzPryPAT0N8hFGUIBAEQTgLnbYRnoKCAsrKyrjyyivbHV+8eDEAzz77LCkpKd5ABWDUqFGYTCaysrKIjo6msLCQ0aNHH75ApZJhw4axfft2Zs6cSUZGxjH7GDhwYHe+pNOuV6SRVx8+n6Vr8/j3jzk4nG5uuaw/0yb2AaC51Y5SIUOv7TwwOJ79+XV88M1e8ko9gc8lo+NPWbADnryfq8b3Znj/cJZvLmBUagQpCUHHfdyxgh0AH52KESnh3XWZgiAIwh9ctwc8AGazmRkzZpCZmUl0dDT33nsvEydOpLKykvDw9jeh0FBPMm9FRQVKpedyIiIiOrTJzs4GOG4fJxPwVFdX43K5vJHimcrllnC53Ly7S8G787q3b8/KHjdf7FTznze7t+8T8fnpe2pBEAThD6aioqLL+bvdOs5vMpkAmDNnDldccQUffvghY8aM4b777iM9PR2r1dphykmj8SzztdlsWCye/I3O2thsnqmb4/VxMjQajTfYOpMp5LLjjmycLJ1Gia9ejVjUIwiCIPxRKJVKbwxw3Lbd+cQqlWdaZcaMGVxzzTUAJCcnk5mZyZIlS9Bqtdjt7VfetAUper0erVYL0Gkbnc4zzXK8Pk5GRkbGST1OEARBEIQ/hm4d4QkL8+ylkpSU1O54YmIipaWlhIeHU11d3e5c289hYWHeqazO2rT1fbw+BEEQBEEQfqtbA56UlBR8fHzYvXt3u+O5ubnExsYyfPhwMjMzvVNfAFu2bMHHx4d+/foRFBREr1692Lp1q/e80+kkIyOD4cOHAxy3D0EQBEEQhN/q1oBHq9Vy11138fbbb/P9999TXFzMu+++y6ZNm7jjjjuYNGkSISEhPPzww2RnZ/Pzzz/z6quvcuedd3rzcu68806WLFnC0qVLycvL46mnnsJqtTJt2jSALvUhCIIgCIJwpB7ZaXnJkiV8+umnVFVV0bt3b2bNmsWkSZMAKCoq4rnnniMjIwOj0ejdJVkuPxx7LV68mI8//pjGxkbvTsvJycne813pQxAEQRAEoU2PBDyCIAiCIAhnkm5fi93Y2Mirr77K2rVrMZlM9O3bl0cffZRhw4YBcMcdd7B58+Z2jxkxYgSffPIJ4Flx9Y9//IOVK1ditVqZOHEic+fOJTAw0Ns+PT2dl156iYMHDxIREcGskLo5dwAAaJRJREFUWbO4/PLLT/qahw0bht1uJyQk5KT7EARBEATh1KqpqUGtVndptXW3BzyzZ8+mpqaGV199laCgID755BNmzJjB0qVLSUhIICcnh2effdY7xQWHl7ODZzfmjIwM3nzzTdRqNc888wwPPvggn376KQAHDx5k5syZ3HHHHbz00kusXbuWJ554gsDAwHY7NJ8Im82Gy3XsKteCIAiCcKSamhrsdjuRkZHIjrKJWWVlJXK53LtB7tE0NTXR0tJCdHR0T1xqj6moqOhw/5TL5ajVavz8/Ly5tfX19ZjN5mP2pVar271PDocDk8mE1WrF5XKhUChQq9X4+vp6+3U6nXR5okrqRoWFhVJSUpKUkZHhPeZ2u6VJkyZJCxculGpra6WkpCRp//79nT6+srJS6tevn7R27Vrvsfz8fCkpKUnasWOHJEmS9Ne//lWaNm1au8fNnj1buvPOO0/6uidOnChNnDjxpB8vCMLpYzLbpdkL10pz3tog/bS1UHK53Kf7koRzxHfffSclJSVJP/30U6fn9+3bJyUlJUlLly49bl9vvPGGlJSU1M1X2PMmTJgg3X333dLOnTulnTt3Stu3b5d++OEHaerUqdKIESOk2tpaSZIkqaioyNtm586d0t133y2NGTOm3bEDBw54+121apU0cOBAadq0adJ///tfacuWLdKyZcuka665RkpLS5M2btwoSdKJ3b+7dYQnICCARYsWkZaW5j0mk8mQyWQ0NzeTk5ODTCajV69enT7+119/BTy1sdr06tWLsLAwtm/fzuDBg8nIyGg3OtTWft68eUiSdNQoWxCEs1NmQR25xY2Apzacn0HDiP6ijprQ8yZPnozRaOTbb7/tcF8CWLp0KQaDgYsvvvg0XN2pExgYyKBBg9odS0tLY9KkSaxcuZKbbrqJ2NhYYmNj2z1GrVZ3eBxAcXExc+bMYdy4cSxcuLBd6YiLLrqIG264gTlz5rBmzZoTus5uXdbk5+fH+eef3255+KpVqygqKmLcuHHk5ubi6+vL3/72N8aPH88ll1zCwoULvTsnV1VVERAQ0GGb6NDQUCorK4Gj19KyWCw0NDR058sRBOEPwGJztvu5rtFymq5EONdoNBquuOIKb87qkRwOBz/88AOXX345Op2OTZs2ceONNzJ06FBGjhzJo48+SkVFxVH7njhxIn/5y1/aHfv666/p27cvpaWlALz55ptccskl/PTTT1xxxRWkpaVx9dVXs3PnTnbt2sW1117LgAEDuOKKK0hPT2/XV25uLjNnzmTIkCEMGTKE+++/n5KSkm56Z2hX4PtEffLJJ9jtdp5++ukOdbJ0Oh1z5sxh6tSpNDU1nVC/PbqOe8eOHTz55JNcdNFFXHDBBeTm5mKz2RgwYAAffPAB9957L1999RVPP/00ABaLpdO9dI5XS6vt59+WnBAE4exnsbXPHzBZHKfpSoRz0dSpU7HZbKxatard8fXr11NfX8+1117LsmXLuPPOO4mIiODVV1/lySefZOfOnVx33XXU1dX9ruevrKzkH//4B/fccw+vv/46zc3NPPjgg8yePZtrr72Wt99+G0mSeOSRR7BarYCn0Pf1119PXV0dL774IvPmzaOkpIQbbrjhpK5HkiScTidOpxO73U55eTnz5s0jODiYSy+99IT727BhA/379z9q9YTRo0fzyCOPnPBCox6rmPnzzz/z2GOPMWTIEF5++WUA/va3vzFnzhxv5JeUlIRKpeKRRx7hiSee6LROFrSvpaXRaDq0afu5rY0gCOcOq739CI/JLAIe4dRJSUkhOTmZ7777jqlTp3qPL1u2jL59+5KSksK9997L2LFjeeWVV7znhwwZwmWXXcbixYt54oknTvr5LRYLzzzzDOPHjwcgLy+PV155hXnz5nk37DWbzTz44IMUFBSQnJzMW2+9hU6n46OPPsJgMACeIGLSpEl88MEHzJkz54SuYdmyZSxbtqzdMZlMxksvvdRuhXVXVVZWttt7r7v0SMDz6aefMm/ePC655BJefPFF7wiMUqnsMMzVp08f4PBUVWNjI3a7vd0ozpG1tCIiIjqtpaXX6/H19e2JlyMIwhnst1NaYoRHONWmTp3K/PnzqaqqIiwsjMbGRn755ReeeOIJCgoKqKmp4dFHH233mNjYWAYPHsy2bdt+9/MPGTLE++fg4GAABg4c6D3m7+8PQHNzM+ApxzRixAi0Wi1Op+ffj8FgYNiwYR22jemKCRMmcP/99wOe0Z76+npWrFjBY489hsViYfr06SfUn0Kh6JGV090+pfX555/z/PPPc9NNN/Hqq6+2C1xuueUWnnzyyXbt9+7di0qlIj4+nqFDh+J2u73Jy+AZequqqvLW0ho2bFiHX5AtW7YwZMgQsdOyIJyDrIcCHpXS8++/VQQ8wil25ZVXolQqWb58OQA//PADMpmMq666isbGRuBwIHKk4OBgWlpafvfzt43SHOlYMx6NjY0sX76clJSUdv/98ssvHQYUusLf35+0tDTS0tIYMGAAF1xwAS+++CLnnXceL7300gkHL5GRkZSXlx/1vMPhoLa29oSvs1tHeAoKCpg/fz6TJ09m5syZ7S5Iq9Vy8cUXM3/+fAYMGMDYsWPZu3cvCxYsYMaMGRgMBgwGA5dffjlPP/008+fPR6fT8cwzzzBixAhvJvctt9zCNddcw8svv8w111zDunXrWLlyJR988EF3vhRBEP4g2kZ4gv11VNS2YrKIXD7h1PL392fSpEl899133HHHHXzzzTdMnjwZf39/7+hKZzfompoaAgICjtrvbwOF4+1j01W+vr6cd9553HHHHR3OKZXdFxakpqayefNmGhoaOg34jmbs2LH861//oqamptM8nXXr1nH//ffz1ltvndD1dOuQyKpVq3A4HPz000+MHTu23X/z5s3j5ptvZu7cuXzyySdcdtllvPzyy9x+++089NBD3j6ef/55Ro8ezQMPPMCMGTNISEjgjTfe8J7v06cP77zzDuvWrWPKlCl89dVXvPTSSye96aAgCH9sVrvnphBs9HyjFVNawukwdepU9u/fz7Zt29i9e7c3f6ZXr16EhITw/ffft2tfUlLCrl272k1HHclgMHhXJ7c5cvbj9xgxYgR5eXkkJyd7R2ZSU1P56KOP+Omnn7rlOcAzg2M0Go8Z1HXmpptuQqVSMW/evE6DvjfeeIOAgABv3lJXdesIzz333MM999xzzDY33XQTN91001HP6/V6/v73v/P3v//9qG3Gjx9/wi9UEISz0+ERHi0gprSE0+O8884jMjKSv/71r0RHR3u/hMvlcmbPns2TTz7Jo48+ylVXXUVDQwNvvfUWRqOx01EW8OTFvP/++7z//vsMHDiQNWvWsGXLlm651vvuu4/rr7+emTNncsMNN6DRaPjPf/7Dzz//3G6Aoavq6+vZtWuX92eLxcKyZctIT09n9uzZHZaW/3975x0eVZX+8c+U9N4TEgihhNAJvQgKAuKKFRFFV1dX1wquqCj2svjDVRGlCCguNiyoKAhK74SElt57r5Oe6XN/f0zmJpMEEiABAvfzPDxk7tx758yZc8/5nvd9z3vaIygoiLfeeotXX32V+++/n3vvvZeAgAByc3P53//+R15eHhs2bGiVwqY9umyVloSEhMSlwCJ4fDwcAWmVlsTlQS6Xc+edd7J69WoWLlxolQT3rrvuwsnJiXXr1vH000/j7OzM5MmTWbRo0VmXVj/++OOoVCo2bNiAXq/nhhtuYOnSpTz55JMXXdawsDC+++47Pv74YxYvXowgCISGhrJ69WpuvPHG877fwYMHOXjwoPja0dGRkJAQ3nzzTebPn39BZbzzzjsJDg7mq6++YsWKFVRUVODj48PIkSNZuXIlffv2Pe97Srulg/gD79279zKXREJC4nx58dNDJOdU8tTdw1nzcwwyGfz239uQy6Ws6xISVzvnM35LFh4JCYlujejScjO7tATBfMzJweZcl0lISJwFk8mEyWRq97zODHC+FHSv0kpISEi0QN0YtOzqZIutUo7OYKJOrZcEj8Q1T1x6OUdjC/nHLYOwt+v4cP/KK6+wZcuWds9LSUm5mOJdciTBIyEh0a2x5OFxsFPi7GiDqkZLXYMOP0/HDl1fUFZHqaqB8AG+XVlMCYlLznc7k0nIrGBwHy8mjwjs8HXPPPPMORcXdVckwSMhIdGtsbi07O2UODnYmgXPeazUem9jFLnFtax7+UZ6+LRO4HYxaHQG6hr0eLtL29605HRyKXqDkXFDAi53Ua5aqmq1Vv93lKCgIIKCgrqiSJcVKTWxhIREt8VgNKE3mGMNHOyUODe6sTq6NN1kEigoNe9yXVhe3+nlW77pNI8u3U1eycVn072aMBhNLN0YxXsbo1DVaC53ca5aaup1Vv9f60iCR0JCottiSToIYG+rFON2Omrhqa7TYjSZF6qe7yy4PeoadETGF2E0CSRlqzr13t2d6jotOr0RkwDxGee/RYBE+xhNgph1vKa+c9u2BYPRxOKVh/n4+9Ndcv/ORhI8EhIS3RZL/I5SIcdGKcfZsVHwdDAXT0V1k3WhsrZzLQ2nU0pp1FIUV3S+9ag7U1nTNADHZ1ZcxpJcvdQ16LAknantotxUeSW1JGWr2H8qD72h8zf77GwkwXOFU1hWR0xa2eUuhoTEFYlaDFg2Z3IVXVqajgoetfh3VV3nzoJPJJWIfxdXdM4eSFcLzes6QRI8XUJzN1ZXWXgsEwZBgNJKdTtnX34kwXOF8/7XJ3lt7TEy8qsud1EkrkLq1Xp+3pfWbX386mYrtIAml1ZDx75PRbP4kaqazhsUjCaBU0lNu05LFh5rKpvVe25xLdWdLDYloLahueBp+3nILqohp7jmgj+juYW0RHXli3pJ8FzBCIJAbmOw45nUMg6fKeCjTafQ6q9806FE92DLwXS+2p7Ipp3Jl7soF4RG17RCC8DZwRboeAyPtUur8wbdtLxKaht0WHYXkASPNS3rOjFLinHqbKwtPK0Fj1pr4KVVh3l51ZELHlNUzSykkuC5xqmq1RJ3EQF5NfU6DEbzCpTo1FI++zWGA6fyOZFY3M6V589Pe1KZu+QPsosuXO1LdD9yGn/v7hpUq9Y0WnhsLYLn/IKWrV1anRfDk1VortdBIV6AOYZC2sW9iZbuQ8mtdeFYrJwtaS5yaut1tNxFKre4hgaNgTq1/oJXETa3kJZ0A1EvCZ4u5JMfz/DKmqOk5lZe0PXNl2vGpJWLgWdFXbB89khMARqdkYjYwk6/t8SVi2Updk5RDVq9kbiMcjEQuC2MRhM/7UnlZLP4lMuJJcuyvSWGx/H8lqVbWXg60aVl6fxDerji5my2Ov28N5VXPzva6avBuiMWl1bfIDfAbBGTOH9OJ5cy79Xt/LIvrdV7zQWPzmBCq7O24jQXOTkXONFt/vxIMTwSAGQWVF/Qdc0bU3M6W/CYTAIFZeZ7pudfWFkluh8mk0BxY1symgRWbY7mlTVH+Wp74lmv2X8qn2/+TGL5ptOtZoyXA81ZY3jOX/DUqfUXvdLE1Lgsy2Le9/N0wt/LCYBf9qcTm17OkZiCi/qMqwGLS2v0QD/A3Eda0gOcjejUUpZ9dcIqNuVa50xqKYJgHSBvoaUbq+XrnOImwdNRy77eYLSyilZYubQkC881jX9javsL9d83b0zN6ewEaeVVanSNPtyMgqpOvbfElUt5tRqdoWmDwAOn8gE4mdy29cZoEvh5XypgDogsKKvr+kK2Q/MsywC+HuZnrqiivkMJ7VQtnrGqWutBQRAEIuKKyCpsfyJwJqWUe1/bwdZDGc0EjwP+nk5W53VFgsPOICGzgpU/RdPQwRVuF0NVYwqAoX28sbNVoNEZKWynPW3cnsjR2EL2nczr8vJ1F/Ibk2bmFte0moC0XJnVUvDkXoCFZ/XPMTzy7i7OpJgD8qWgZQkRv8aZ3YU2BFVjY1IqzJGPcrn5/8628OQ362gqqjWdno9E4srkbANMcUVDm2I7Iq5QtAQCJJ8j7icjv+qSJJQTLTyNMTx+no6EBXtgMgntDoxqrYH6xhggJ3vz9c3jeEwmgfW/xfHexije+eL4OS1aGp2BVT/HoNYaOHSmwNrC4229p1d7A/vFUtegY/uRTBo0ehIyK/jn0t38FZHd7nXf/ZXMrsgcdkXmdGn5oCnJo6ebPX16mN1a6edYiVrboBMt5ZZB/mpHpze2u4LKMumobdC3cpW2FDillQ0k56jEdmzl0urASi2d3sjh6EJMgnmPLr3BaPUZ1XW6c7rDrwQkwdOFWDYvLL5AwWMJCLt5YgjjBvvz+J1DAXNsT2c2rPxS64C1jMvg1hIEgZyiGtHSJNH1WCwNDm3sotzWqpk/jmQBTYHBSdltx13oDSZeW3uMV9ceo7C8awcnSwxP8+8wY1wwAHuics4pUiyizsFOQYC3eXLSfPXQ97tSxO9cXq2xmhG35Oe9aZQ2PueZhdXiQODr6djawlPW+RaeugYdR2MLMZoEvv4zibVb4vjs11i+2p5IqaqBNb/EcDTm3PF5ln4g+Sy/a0s0OgPf70zmxU8P8dyKgx0Wclq9URSaHq72YhzPuQRPfEa5mESvZX91tbJhazzPfLCfqIS2F6noDSarybRFtGh0BiprNK0Ez4ofzvDip4c5ElNIg0ZPWbOYG1WNtt3UAHEZ5WL/nJJTycHTZouwjVIuupJLKq9sK48keLoQfy+z4Cm5wKRjFnNhsL8rrz0yjr9NDBEHmwsVUW3RcsZ0qdxalbUaVv4UzV8R2Sz//jTPfLif9b/FXZLPlmgaeCcMbdq8saefCwCJLVbN1NTrSMoyH7t/Vhhw9pVd2UXV1Kn1mEwCh850bbyKpoVLC2DyiEAc7BQUlNWfM4uv5fnydHXA3cUeaApcNpoE0SpieeZi09q2WFXXadlyIB0AmQxxby8nBxucHWwI7eWOXAZBvuaNSUsqG8TVlxeDySSI1tgNWxNY9tUJft2fxvG4IsDsorT8RoIAH206ZTXINadBoxfFXnJOx1bs7Tyew6ZdKSTnVJKeV8WSNUc6JEYslggbpRwneyX9gtyBc0+0YprV/bVg4TGaBA5HmwVq1FlW5RZX1IsxY9AUk/P+1yf559LdpOSYhaunq7ltW9y/JxKLxTr0cLETJ+btWXlOJppd3RaPw9otceL9Lfe40t1akuDpQiyNoLZB1+FVI82xzEC93OzFY5aZaFEnzpwtmyf2DnAFLp2FZ8uBDHZF5rD65xgxfuTUFbL6pyv5fmcy722MuiSxEufCYn0ZEOzBvBmh3DQ+mHtnhAKQkGUtFE4ll2ASzG1k8ohAwGwSbyvBn6WjBTh0Jr/Tg5sLy+s4nVyKySS0yrRs/lvJlHDzTs+bdibToNFzOqW0VVCsRfB4udnj4WIHNLm0UnJUVNVpcXKw4fbr+wIQm26d8bymXkd1nZY/jmShM5jo39OdAb08xPctz38vf1dWvjCVDxZMxt5WgckkXPTAIAgCH313iofe3klMWpk4KG7em9oqx83UUUEM7O2J3mDijyOZbd6voIVb+2zCqDkWi9HNE3sT7O+CqkbLf785aTUIt4UlfsfdxQ6ZTCYKnsyCqrNe27zuq2q1HU4s2V3JyK8Sg7ObP0/NaSn8copqqK7Tciq5RBTd0CS0LSRkqchtFDe9/F3Eft+SSqEtBEEQY/v+MXswchniqi8vtybB0zJetapWy2e/xFwxiXMlwdOFONrb4OpkXpJ6IR2cJeiyLcHTmWZxy4Nz/UjzIJGaW3nRg1RSlooVP5xm2+HMs8YEJWSaZ20OdgrcXeyQy2WUV2sovULMoqoaDZ/+eKZDAasdpaJazfe7U4iIK2L15pjLutLJ0oYCvZ15YNZAnpk7giF9vQHzqo3Ne1PJaQyGtJjVxw72x83Zjh6N7TAuo7UFpblLJK+krt0VIHVqfYddmXqDkSWrj/Lm5xG8vPqI6AZp6Za7Z3ooNko58RkVPPn+Xt5cH8GXW+OtzrEshe7h44y7RfA0WniOx5u/75hBfowc4Ct+V6NJwGQS2H4kk0f+s4tH/rObrYczALhraj/6BLqJ97cMAmAWPc6OtuLze7EB38diizgUXYAgwNpfY0X3hVprrse+QW5i0sO7pvZnztR+AOw8nt1m3paCFoNnfGb5Odu9qkYjWoLuuTGUd5+YiKO9kqzCmnZdZxZBZhGZQb7O2NsqUGuNRKe23kanolpNXkkdMlmTtS3/CgiY70qaT/xyimvanBxZ2pC9rVns55bUciq5hJZdSqCPteApVTUQ2fg89/RzoX9PdwB+2JXSpjD5YXcKD729k+KKBpQKOTPHBTN2sL/4vpebAyGNcVgt3W8btsaz41g2n/9u/exdLiTB08WIbq3zXLKnNxiprjN3YhaTJEAPb3PjLerAyq/qOi2bdiaLsQVt0aDRi8Jq2uie2NkqqKjWkHGBS+ktrPstlr0n8lj/WxzPfnSA8irrGaNGaxAtSStfmMbG12eKg0XSFZJ19ds/k9gdlcuX2xI67Z4HTuWLHdKh6AJ2R+V22r3PB73BKLbJAJ+mGBNPV3v6BbkhCPD1jiSe+WA//3hnl7jsdcwg8zJiiwhYvyW2laBNyTX/fpYBbdtha6uCySQQk1pGdGopP+1J5YE3/mTJmiMYjCZ2Hs9pM2bhZFIJ+07mcTS2SGyvSdkqUazb21oLHl8PR26b3AcwxycAbD+aJcaZCIIg5hIaOcBXFDyF5WY3gcUtNH5IAH0D3XCyV1Kv1pOSo+KTH8+wdkscWp0Rnd5Ig8aAv5cjE4b2oE+gu1iG5oLHQo/GwSezoJqoxGIyC6qt3FuWQONzubxq6nWs/TVWfN2Wi+f2KX157eFxLHloDL0DXBkzyJ8AbyfqNQb+PJaN3mDkz4hsvtqeyC/70lrFJ33ywxkWfnSAX/ent1mG4/FFCAIM6OWBt7sDHi723HG9WVR9tzMJo9HEtsOZPLfiIInNrIXlVWoxyaBHoxtRoZAzc7w57urz3+OsrBMAWw+Z209oLw8x3ie/5PwET12DjmVfneDAqe6xwutUStO2JIJAm7ncLO7DMYPM4iO3uIaoxNYW8h4+Tq2OWQT9iP4+3Dq5DwOCPahT63lt7THi0sv5z5eRPPPBPk6nlPL9zmRRpE4cFoCDnZLZk/qI93K0V3Lj6J7IZGbXo0WI5RTVcPCM2XKfmFVx1lXHl5LW0YrdBJPJxKpVq9i8eTO1tbWMGTOGN954g549e3bK/detW8eRI0f45ptvOnR+ZGQkDz74YJvv2Th6Ej9lPVq9ickjAlE0rrY6F5ZO2kYpF61E0GThScmppLJWw5mUMnR6I27OdlRUqylRNaA3mJh9XQgbtiZwMqmEyPhiPvr3FJSK1vrWMvt2d7HD09WeUWG+HIstIiKuSDQ1W9DoDJRVqunh7YRCIadOrcfJXolMZv19SisbyMivRi4zr1Ipqqhn6f8iWfrkJBztbcTyG00C3m72+Ho4IJPJGNTbk/S8KhKzKkRr06UkJq0MO1sFYcGe1Kn1HGyMP4nPKKdBoxfLfr6YTAJvfR6BqkaDTm/uzEN7uZOaW8X3O5O5cXRPFM1+m9ziGjzdHJDLYOn/ovD3cuKZucNb1fPF8GdENgajuf693Rys3vvPE5M4ElPA0ZhCErJUosBwc7YltKfZZfPAzQM5k1pGQVkdr6w5yr0zBjB5RCA19TqKKxqQyeDxu4ax7KsT7I7KJTjAldun9KWiWs2nP0ZzulmHDpCaW8Wb6yOITS9HLoMPFk4htNE9FJVYzH++jEQQmlZT/W1ib1JzK8W8UW0FXs+9MZSYtDIc7My/W1xGOas2xzBnWj983B3EGeuIUB/R2nM6pZR/vLOTylotNko5Iwf4olDIGdbfh4i4Il5ZcxSjSUAul/HobUMwCQK7InP4xy2DUMhl9Al0FT+/TcHT+Px+91ey1bEXHxjN74czOBJdiMFoYsbYXiycFy6eo9EZ2H8yj75B7vy0J5WqOi09/ZwxmZpm+jPG9mJ3VC5yuYxRYX5W/YZcLuOO6/vy2S+xfLU9gV2ROVZWJotLsE+gm1VOnB92pzBtdE9REFqwWHGax3/dPqUP2w5nUlBWz4srD5OWVwXAW58f593HJ+DmbMfCj/aLVqjm97xvZhiHTheQX1rHbwfTmXuj2bVaUa0W3XD3zhjAyaQSYtLKyS+txWg0WT0352L70SyOxhZyOqWEUQP9cHG0pUGjJ6OgmkEhXh3qk9siI7+K2PRybpkUgq1Nk1tVbzCRVVhN/57u5/3cVtZoSGsUOINCPEnMUpGcU8mIUF/xnIpqNbnFFsHjR0RcIWqtkcj41pMFS3wamMcPyyrfXv4ujBnkj1wu4+3HJvDm5xGk5FTyymdHxfPf/jwCk2DOl/TYHUPEAPxh/b3Fc1wcbfH1dGRUmB8nk0r4KyKbf942hG//ShInd4IAR2IKuX1K3/Oqi86m2wqeNWvWsGnTJpYtW4a/vz8ffPABjz76KNu2bcPW1rb9G5yD7777jhUrVjB69OgOXxMeHs6RI0esjkVHR/PMggV49r9RnKUUV9Rz74wBra5Xaw3Y2SjEpecWNezpam/1wAzs7YlSISO7qIYH39p51vLsOZEr+lgzC6vZcqCpE2nO1sbZ97BGV8aEoT04FlvEsdhC+gW5o5DL6Onnwme/xBCTVoZJMHfQfp6OnEktY/wQf158YLTVw2556AaGePHve8NZtOIQ6fnV/Ov/9jAoxAuD0YS88TsN6uMlfr+BIZ5sPZxpFQybnKNi3ZY4vFztufW6PgwP9Tnrd74Y9p/KY/mm0ygVcla9OJUzKaWim8VgFDidUsp1wwPPer3eYCSnqLbRlWDdwcWll3OmmaneRinntUfGseDD/ZRXazieUMykYT0A+O1gOhu2JuDt7kB4qA+x6eXEppdzw8gghvZr6mSMJoHCsjoqqtUMCPa0GvBTcyv58NtT2NkqGNbfm/tmhuHsYIMgCMhkMtRaA5v3mDOzzpsxQGxzFpwcbLhpfG9uGt8bvcHIsdgiDkcXcN2IQPFcJwcbXv/nOBavPEx+aR0ffneKw9EFhDdafnr6uTBpWA8e/NtAvt6RxBe/x7PvZB75JbXoDCZslXK83BzQG4wM6uPFoTMFxKabXZwmwbyi5I1/jiOzoJpPfjwjdpz1GgMKuYx5MwZgq5TzxvoIsgprCA5wpSVODjZ8/NwNgHm2ufCj/cRllBOXUY67s3mwHdLXCwc7JcP6+fDY7UP45s8kcTY7c1ywWK+P3DqY8io1aXlVyGXw/PyRYpxQ80482N8VuVyGySScRfA0uRdslXIUCjmF5fU8t+Kg1Xm7o3IZ2NuTlNxKbG0UnEoqscrfo1TIeX7+KE4ll/LNn0mA2Y03sLcnTg42VmLHwk3je5NZUM3O42ax4+JoQ59AN2LSykURMmtCb774LQ4nBxtcnGzJLa5lzS8x3HpdHwrL63F2tEGtMRCbXo5MBhMb2y2YXfgL543gv9+cFMWOu4sdVbVa3lwfQZCvi/g5AGHBnuLfzg42/GP2IFb8cIZv/0omLNiTsN4erNsSh85gYlCIJ6PCfMUYkV/2p7PtSBZP3jWU6WPN1iFBEDiVXMrh6AIMRhO9A1y584Z+yGUydjVaUtVaI1sPZTIlPJB3N0RSVFFP3yA3xg0OoEGjJzzUlyBfZ9Q6Az19XVo9G80xGk2899UJSlUNlKgauGd6KOn5VfTyc+Gj706RnFPJjLG9WHDPCLFPMJoE0vMq6RPoho1S0fqeJoHlm05jEqBfT3cmDe9BYpbKKo7n1/1p/O+PpuSgwf6uTAkPYt/JPAxGE65OtvTwdiK58ZrmX2HO1H6s2hwDwNxp/a2e5+aix93FDn3jajqZDB66ZZBV25XJZHywcDLbj2ZxR2OM280Te3MyqYQ9UbmEh/pyPL4YuQxumtCbP49lczi6QBI8F4JOp+PLL7/khRde4IYbbgDg448/ZvLkyezatYvZs2df0H1LSkp48803iYyMpHfv3ud1ra2tLT4+TYNxQ0MD//d//8e4SdOpdB8jHv9xdyouDjak5FZSUFaHWmtAqzNSWqnG292B1x4eS59AN7HDaO7OArNCf/tfE/j4+zOUV6kJ8HYi0MeZ6jot3u4O+Ho4Ep9ZLrqLLDOE7/5KpkTVQGFZPWVVDQzo5UloL3eONW4lMXe6WQyNGeiHUiEjv7SO9zZGtfqeSoWMwvJ6sfM9Hl/M48v2Uq/WM6CXB/fPCuN4vMUd4I+/lxNvPDqOjzedprC8nohGV4GFwX28xL8H9jZ3gNlFNXz64xmq6rScagxOTQciE4qZNyOUe2cMoKxSja+Hg9UMT6M1kF9Wh1ZnJLekluRsFdcN7yGafM9GRFwhn/xwBgCD0cTqzTGiC87T1R5VjYbIhOKzCh6d3sirnx0lOaeS68ODePbeEdgoFaLA+LNxtY9SIcdgNDFhSAAeLvbcNL43P+1JZcv+dKpqNMSkl4v1U16ltnJ3/bA7haH9vBEEgfiMClZujhZnav5ejsy/KYyySjVGo4nfDmXQ0LjsN7uohpjUMtyc7UjIrGBQiBe1DTqq6rQEeDsxfWyvc9aNjVLB9SOD2rS4Bfo489lLN7L9aBab96YSmVAsxgZYBrO7p/VHqzfyy750MY9KWLAHC+4ZQS9/s0gxmgTyS+rILKxm9EA/0vOryCup5bH39oifNSjEk2B/V/6MyGZyeKD4XHy4cApqrUFcFns2ggNcefeJiew7mcehMwXiXk5jGjP9Atw2pS+TwwPJLa6ll58LHs2ePX8vJz5YOIVjsYW4O9tZic/m2NooGNHfh6TsCvo1xkY0p3kA6b/vG8ngPl68vu4YucW1+Hs58vz8Uew8nsOeE7l8+lO01bUujrbUq3WYBPMA1DfIHVcnO349kE4vPxf8vZqyOreFQi7j6buH4+/lRFZBNQ/NNlulHnl3F5ZY4aF9vVj78nScHJRkFFTzypqjRMQVtXpuAeZNHyBanC2MHxLA0icm8cmPZxjcx4t/3jaYtz4/TlK2ipTcSpQKOR8/dz1O9jZ4u1v3bdNG9yQ6rYwDp/J598vjuDnbUVzRgFwG/7hlMDKZzKr+dHojKzfH4O5iT6CPMx9+d5LU3Crx/UNnCkjJqWT62F6Uqsz3MQnwy/40ftmfJrrOMvKrxf7yt4MZ4vXD+nmz4J4R+Ho4til8DscUiuEC249msfdELpoW2zfsjsrFz9ORe6aHUlWn5aPvThGTVk6/nu68eP8oyqvVBPm64OlqT3mVmi+3JRDdaGl+dl64mPk7Nq2M+Ixy7G2VfL0jSbx/SA9Xevq5sHBeOH6ejvy4J5Vpo3tyw8ggXvj0EFPCg0RLqauTLZOGB/LtX8l4utqLiw8sODnY8M6/JogTl4z8av77zQmmjw0WA5ubExbsaSVaR4X50dPPhbySWt798jgAN4zqybzpofwVkU1KTiXVdVrcnO1a3etSIROuhPzw50lsbCxz587lr7/+IiQkRDx+3333ERoayttvv31e97vxxhsBePXVV9myZQuLFy9m9erVFBQUWLm09u/fz8qVK0lPT8fPz49bbrmFp556qk2L0vLly9m8eTOfrt/EOxvN/vaBvT3b3aTRwU4hPugAN47pyb/vHdnqPLXWQF5JLf2C3Fs9jPVqPas2RyMAz88fxarN0edMwjZhaACv/GOs+PrtL45zMqkEW6UcezslNfU6woI9ePbecDxd7dkVmUNtg56efi6s3hzd6iG3sH7JdLFDNBhNHI0pNIuYpBLR4rHqhalWs/Mnlu1tFdB53fAeODvaisuEHe2VNGgMONorcXawoapOh0IOGp2xVcAewC2TQnBysCEyvojyag1TRgTi6+mIqkZDTZ1O9DOPCvMlJq0Mg9F8EzdnWxbeE867X0ZiZ6ugb6Ab9nZKbJVmd56vhyN9A92IyygXfeJgFqW9A1yJTi3FycGWyhoNRpPABwsnU1hWz6gwX9yc7SivUvPPpbtbrUz528Te7DuZh0ZnZHAfL1JyVBiMAp6u9tQ26MSO2s5Wga1SLu6x1pzBfby4ZWIIX2yNE92jzVEq5Lz68Fgxtf/FkpKj4r2NUdRrDIwO8+MfswdZDb4V1WqOxxfTy8+FIX29WlnBSlQNRMYXMXNcMBkF1azbEkteSS1ODjZMH9OLu28MxdFOSUJWBf2C3Nt0YXWUqMRi3vtfFIIgsPbl6a0G7YvFaBLQ6gxtukAtyQwDfZy5tTHGqF5tXkU2coAvTg42VNZqePL9fdSr9YwbbI69sbNVcMeUvlTVaSlRNTBygK9Yh7UNOmyU8lZxTB3lrc8jOJVcilwu45dls61c34ejC9h3Mo+c4hoCvJzILa6lqk7LiFAf3npsQodcQQ0aPW+siyAlt5L5Mwdw301hZz1XozPw6mdHReHi7GDDi38fLcaM1TXoeOy9PTjYKwn2dxXjsCxWNQc7BdPHBuPqZMtPe1Kt4oH+NrE3cRkVYsK9oX29eeKuoew9kde4s72MyISixhW1MjGOSi4DFydbeng7c8OoIAIa2/WX2xLILqrB18NB3EfK1cmWmnodrk62TBvdUxRQQ/t6k1lY3eZqXbkMvN0dKKtSi/3Xiw+MYkp4EIIg8PYXxzmVXIqNUo5SIUOtNTJxWACL/z4GuQyrZ0mjNWBnq0Amk1FTr8PRXolSISenuAZ3ZzvcnO3Q6o3IwMoqf67fzt5WeU5LV3OSs1UsXnUYQTAL7LUv34i/lxMbtsaTmlvJ2/+acMHt9GxYxu+9e/e2e263FDy7du1iwYIFxMTEYG/fNEt49tln0Wg0rFu37rzu11aFvfzyy1aC59ChQyxYsIAlS5YwceJEcnNzeffddwkLC+OTTz6xup9KpeKGG27g+eef56GHHuJYbCEhPdyQy2UsXnkIuVzO1FFB9Atyx8XJFoVchqerPSt+OCMG9CkVcmaND+a+m8LaNE+fL6dTStl7IpcgH2cGBHsSn1nO4egCaut1vL9gMsH+TaKjVNXA7qhcpo4KwtvdgeyiGvoGurXpL88rqSWjoJoAL0f+jMjm8JkCdAYTA3p58OGzU9osi05vZO2vschkslaxKbnFNZxILMFgMuHqaEsvf1cGhXgik8n4dX86//vDHEBsma21xN3ZDicHJe4u9rg523IstvXMtC1um9KHh2cP5ofdKfy4O5WBvT1ZNH8kPu4OPPzurlZLfVsil8uYP3MAWw6ki0nVmjOwtyf/XTC51fFv/0zi4Jl8Aht/l1FhvoT28iAmtYw9J3J5+NbB/LQnle1Hs5o+SwYzx/fm4dmDMJkEvtgaT0Z+Nb17uGKjkOPpZs/dU/tjb6ektLKBNT/H4OXmwMxxvUjJqcTOVsn4If6dPtOyzEbbMtVfCEaT0KpD7yxSclQ0aAyiC+5Ko6i8npp6LQOazaC7iqMxhSz7+gTB/i6senHaOc/V6AwkZqkY0serQwOmBb3BSHZRDf2C2o9pMRhNpOZWUlGlYVAfT7xaxJg1aPTY2pgtqJ/+GM2RGHPc08Deniz++2i83c3nx6SW8fEPp6mo1qBUyFmx6HrsbZXEpZcT2sudnn4urcoiCAKCYF4U8skPZ9qdoNrbKli3ZDp/HMnEx8ORmeOCKa6ox9nBBjdnO7YcSOer7YliTFRID1ceuHkg67fEUaJqEC3IFob09eKhvw0irHfT767VG1n6ZaQ4SQzwduLDhVM6ZVzoCr74PZ7fD2Uwe1IIj981rMs/76oXPL///juLFy8mKSkJubxpEF68eDGlpaVs3LjxvO7XEcEzf/58Bg8ezKuvviqec/z4cR566CH27t1LUFCTyX/VqlV8//337N2710qQAaKboy30BiMxaeXY2SgIDnDt8gZt+ek7c0DR6o2k51UR5OvcJabLmNQyZHIY2NuLvJJadHoj7i52CII5cLVlcOWRmAJOJpWgVMjp39MDHw8H9p/KQzCBj4cDMhkM6estziAFQaCovB4/Lydx9ppTXENytgonBxs0WgM6gwlHexvySmopKKtDIZdxw8ggxgzyp06tJya1jLzSWob08SIlp5KI+CL+eesQBoZc2OClN5gHADsbBa5Otrg623b6LEni2kUQBHZF5tI30K1NN9yVjlproKyygUBfl1YWJ0EQmqX3cGjr8nNiMJrEfEsxaeUciy1EozMgCOYkk7dM6sNNjSvMzkZqbiV/RWQzZpAf4wYHIJfL0OgMqDUGPFztKa6op6xSTU8/l1b9lwWTSSA9vwqlQk5PP+dOm1R0BSaTQFK2igHBHm0ulOlsrnrBs3PnThYuXNimhUen0/HZZ5+d1/2GDh2K0WgkIKBpxYFKpcJgMODrax4ICwoK2hQrgiDg5eWFg0PTw1RUVISDgwPu7u4X8O0kJCQkJCQkOkJRUREKhYK4uPaz9HfLaaJFmJSWltKrV1PQZWlpKQMGtF4B1R52dnbodOfO3CkIAi4uLjg5tfb3N7cy6XQ6jEYjjo6tV2hISEhISEhIdB5KpbLDK7O7peAJCwvD2dmZyMhIUfDU1NSQmJjIAw88cN73O3nyZKtjLV1ac+bMISgoyCpeJzIykq+//pq33npLXKH17bffsmbNGg4fPnwhX01CQkJCQkKiC+iWmZZtbW154IEH+PDDD9m7dy/Jyck899xz+Pv7M3PmzC75zMcee4ydO3eyatUqsrKyiIiIYMmSJdTW1lotR09MTLwgK5OEhISEhIRE19EtLTwADz74IAcOHODpp59GEARcXV155513sLG5sGy47TFr1iw+/vhj1q1bx9q1a3F3d2fatGm88MILVueVlZVJsTsSEhISEhJXGN0yaBngkUceoaysjDfffBMvLy+++eYbfvnlF7Zs2UKfPn3av4GEhISEhITENUO3FDw5OTnMnDmTTZs2MWrUKMAcVDxz5kxmz57Ns88+e5lLKCEhISEhIXEl0S1dWh4eHqxfv56hQ4eKx2QymTm7ZE3Ned9v9OjR6HQ6q1gcCQkJCQkJiSubsrIybG1t21x81JJuGbTs6urK9ddfb7UUbefOneTk5DB5cutstu2h1WoxGFpnx5WQkJCQkJC4cjEYDGi1586Eb6FbWnhacvr0aZYsWcLMmTPFzUTPB0tywY5kapSQkLjyMBhN/HEki5QcFU/OGX7Fpt2X6B48/PDDJCQkcOTIkbPmeLn11ltxdXXlu+++O+e9Vq5cyapVq0hJSemKonYZ06ZNo6CgQHwtl8txcnKif//+zJs3jzvuuOOC7jl27FiWLVtGZGQkDz74IF9//TXjxo274HJaMi13hG4vePbs2cMLL7zAyJEj+fDDDy93cSQkJC4xdWo9r645SmahecfrsYP9mTqq52UulUR3Zs6cORw7doxDhw4xffr0Vu8nJCSQmprK+++/fxlKd+m4/vrreeqppwCzJaWyspI///yTl156iaSkJJYsWXKZS3h+dGvB8+2337J06VJmzZrF+++/3+FsixISElcPydkqUeyAefdwCYmLYcaMGbi5ubF169Y2Bc+WLVtwdnbmpptuugylu3R4enoyYsQIq2MzZszAx8eHjRs3MnPmTHHhUHegW8bwAGzatIl3332X+++/n+XLl0tiR0LiGkWttY6/q1dL8XgSF4ednR2zZ8/mwIED1NXVWb2n1+vZvn07t9xyCw4ODhw9epT58+czatQoxo0bx/PPP09RUdFZ7z1t2jRefvllq2O//vorAwYMID8/HzC7wWbNmsXu3buZPXs2Q4cO5fbbb+fMmTNER0czd+5chg0bxuzZs4mIiLC6V2pqKo8//jgjR45k5MiRPP300+Tl5XVSzZh55plnsLOz44cffhCPabVaVq9ezaxZsxg6dCgzZ85k/fr1mEymDt93z549zJ8/n/DwcIYMGcKsWbOsXIaRkZEMGDCAH374galTpzJy5Eg0Gs057mhNtxQ8WVlZvPfee8yYMYPHH3+c8vJyysrKKCsro7a29nIXT0JC4hLSUvDUqSULj8TFM2fOHLRaLTt37rQ6fujQIVQqFXPnzuW3337jkUceISAggOXLl7NkyRLOnDnDvHnzqKiouKjPLy4uZtmyZTzxxBN88skn1NTUsHDhQhYtWsTcuXNZvXo1giDw3HPPiYN+VlYW9957LxUVFbz//vssXbqUvLw87rvvvosuT3NcXFwYNmwYp06dAsxpYZ544gm++OIL5s6dy9q1a5k1axYrVqzgzTff7NA9LYmEBw8ezJo1a1i5ciU9e/bknXfeISYmxurcVatW8dJLL/HGG2+cl7GjW7q0du7ciV6vZ/fu3ezevdvqvTvvvJNly5ZdppJJSEhcalpbePSXqSQSVxODBw9m4MCBbNu2jTlz5ojHf/vtNwYMGMDgwYN58sknue666/joo4/E90eOHMnf/vY3NmzYwOLFiy/489VqNW+++SZTpkwBID09nY8++oilS5dy9913A9DQ0MDChQvJyspi4MCBrFq1CgcHBzZu3IizszMAEyZMYPr06XzxxRe89NJLF1yelnh7exMbGwuYReCxY8dYvnw5t9xyCwCTJk3C3t6eTz75hAcffJD+/fuf837p6enceeedvPrqq+Kx8PBwxo0bR2RkJMOHDxePz58/n1mzZgFma1hH6ZaC54knnuCJJ5643MWQkJC4AtC0tPA0SIJHonOYM2cO7733HiUlJfj5+VFVVcX+/ftZvHgxWVlZlJWV8fzzz1td06tXL8LDw4mKirrozx85cqT4t7e3N4DVwG/ZxsiSf+748eOMHTsWe3t7MdWKs7Mzo0eP5tixYxddnuYIgoBMJgMgKioKpVIpihALt912G5988glRUVHtCp5HH30UgPr6erKyssjNzSUuLg4Anc7aajtw4MALKnO3dGlJSEhIWLBYeLzd7AGo10iCR6JzuPXWW1EqlezYsQOA7du3I5PJuO2226iqqgKahEhzvL29OyW8wmKlaY6Dg8NZz6+qqmLHjh0MHjzY6t/+/fspLS296PI0p6SkBH9/fwCqq6vx8PBAoVBYnWNJ5tuRulCpVCxYsIDRo0dzzz33sHLlSjF+quWGEI6OjhdU5m5p4ZGQkJCwIAoedwfKqzWShUei03B3d2f69Ols27aNhx9+mN9//50ZM2bg7u4uWlfKy8tbXVdWVoaHh8dZ72s0Gq1eNzQ0dEp5XVxcmDhxIg8//HCr95TKzhvuq6urSUhI4PbbbwfAzc2NyspKjEajleixiKxz1YWFF154gczMTDZu3Eh4eDi2trao1Wp++umnTiu3ZOGRkJDo1jQXPCBZeCQ6lzlz5pCQkEBUVBQxMTFi/ExISAg+Pj788ccfVufn5eURHR1t5Y5qjrOzM8XFxVbHLMG/F8vYsWNJT09n4MCBDB06lKFDhzJkyBA2btzYKt71Yli7di16vZ558+aJn2swGPjrr7+sztu6dStAh5aunzp1ipkzZzJu3DgxEPnQoUMA57XS61xIFh4JCYluTUvBI1l4JDqTiRMn0qNHD15//XWCgoKYMGECYM48vGjRIpYsWcLzzz/PbbfdRmVlJatWrcLNza1NKwvA1KlTWbduHevWrWP48OHs27eP48ePd0pZn3rqKe69914ef/xx7rvvPuzs7Pjxxx/Zs2cPn3766XnfT6VSER0dDZitUhUVFezcuZM//viDJ554QtzPcsqUKYwbN47XXnuNkpISwsLCiIqK4vPPP+fOO++kX79+7X7WsGHD2LZtG4MHD8bf35/Tp0+zfv16ZDIZarX6vMveFpLgkZCQ6NZotGb3gE+j4FFrDRiNJhQKyYAtcfHI5XLuvPNOVq9ezcKFC8VAXYC77roLJycn1q1bx9NPP42zszOTJ09m0aJFZ92M+vHHH0elUrFhwwb0ej033HADS5cu5cknn7zosoaFhfHdd9/x8ccfs3jxYgRBIDQ0lNWrV5/XFgwWDh48yMGDBwHzBt2urq4MGjSITz/91CrpokwmY926dXz66ads3LgRlUpFUFAQixYtOqvwa8myZct49913effddwHo3bs3b7/9Nlu3bu3QxqAdQSa0jAa6BrE0BGkvLQmJ7scLnxwiJbeSJQ+N4f++OgHApndvxsVRSkYqcW2TkFnBsbhC/n7zQOxtr077xvmM31dnDUhISFwzqHWNy28dbbC3VaDRGalr0EuCR+Ka57u/konLKGdgb0+uGx6IyWTqUDxMZwY4X0lcnd9KQkLimsESw2Nvq8TZwQaNznheyQfX/xZHXHo5/10wGQc7qUu8VJRXqTEYTfh7OV3uoly1VNaaMzBX1WoBeOWVV9iyZUu713W3nd07ivR0S0hIdGssiQcd7JQ4OdiYl6afx/YS+07mUa/Wk5FfxZC+rXOqXAyHzxQQlVTMgrkjsLVRtH/BNYLJJPDcioPo9Eb+9/pMHO1tLneRrkosG+nW1pv/f+aZZ7j//vs79TMMRhNymQy5XNb+yZcZSfBISEh0a9TNBI9zoxuroxuIanQG0RpUVaft1HKZTALrf4ujqk7L+CEBTBrWo1Pv352pbdCJVofU3EpGhPpe5hJdfQiCQG3jisXaxjYeFBREUFBQp32GVm/kyff34u/pxHtPTeq0+3YV0jIGCQmJboveYMRgNK+7cLBT4tRoKeiohUdV07TTcnVt5wqezMJqUUSVVHROYrmrhapmdZ2SU3kZS3L1Uq8xYDKZnw2LhaezKSito6xSTVxGOVq9sf0LLjOS4JGQkOi2qLVNnay9rQJnR7Pg6WgMj6q6SfBUdrKF50xKUyr/ElV9p967u9PcmpYsCZ4uoa6hSeTUNHSN4Gk+YSiv6pxcOV2JJHgkJCS6LRZ3lq2NAoVCjpODxcLTQcHTrMOu6mQLz6nk5oJHsvA0x9rCo2q1V5LExVPTzKpzNgtPaWXDRQmV5s9PaTdo45LgkZCQ6LZYApYdG1dXOV8hgqdBoyc5WyW+lgSPNdXNLDy1DXqKyiULWGdT28yqU9uGhUerN/LsRwf498cH0BsubOuGyuaCp1Ky8EhISEh0GeKSdDvzCiiLhaejLq2KZi6t6k50aSVkVmA0CeIy91JVg2TFaEbLAPHkHNVZzpS4UGrbsfAUlNZRp9ZTXaejqLzugj6j+YShrPLKF/WS4LnC+fbPJF5efYQGaUNEiS6gtLKBFT+cpvACO7zLTUOzFVpwkRaeThQ8hY0WixGhPshloDOYOt1l1p2x1IVSYV7KLMXxXBgmk0B6XhW6NgKGm8ft1GvM2600p6C06ZnPK+kEwSPF8EhcLH8cySQhs4Lj8UVUVKuJTi1t/yIJiQ7y+6EM9p7I4/td3TPRmKZZ0kE4fwtPV7m0LPEMPbyd8Grc42v70Sz+76uoDouxqxmLuBwU4gVAZn715SxOt+VQdAHPrTjIN38mtXqvtt66nbVsd/mlteLfucU1F/T5lTVNz0ypZOG5tskvrWXfybwLNmXXq/XUa8wd+smkUt75IpLX10WQmFXRmcUEICqhmHc2HJdmodcYucXmTi8x09ymjKZzt1VBEDgaW0hW4ZUxQIk5eOytBU9Hd0xvvkpLozOKAupCqVPrEQRBjNnx9XTE18MRgB/3pHIstohDZ/Iv6jOuBiz9zKgwc/6drMLqVhaIlqTnV/H573FodBf3G11NpDS6ApOyW7sEW8bt1LRwa+U3s/DkltTSEY7GFvLexigxg7OqVorhkWhk/ZY4Pv7+NHEZ5Rd0fXPFfCy2kMzGQSY1t/PNvz/uSeFEYgl7TuR2+r0lrlzyGzu60ko1uyNzmPPSNn47mHHW84/HF7PsqxP858vIKyImRcyy3GjhseyYXlRRL3bKZ0MQBCsLD7R2awmCwJGYAjIL2hd4h6MLuP/1HXy/K0V8dn09HPHzdLQ6r7Ds0gbodvR32hOVwzMf7KO4ouvLZ4mXGtjbCwc7BTqDyWoAbov1W+LYeiiTfSfzurx83QWLWyqvpLbV79wybqelAMova+7S6pjg+eqPRCLiivh5bxqCIFhZeCqq1O1OmC43kuDpQiyzzqzCCzMXljVTzM0bkmVW3lkYTQI5lpl+F1iPJK5MGjR6yptZONb+GovRJLAnKqfN800mge/+MpvOSyvVV8TKmpYxPP5eTgwI9sBkEjhw6tyWFLXWgEZnjn1wczZnaG4ueIxGEyt/iub9r0/yzobj5xQOdWo967fEYRLgRGKxONv19XDAv6Xg6eJ4qdLKBjZsjaeiWs2JxGLufvkPftqT2u51f0Zkk1Ncy4HTXWuBEgRBtPB4uNoR0sMNgIxziMoGjZ6UxoleXif3f1cqFdVqdkXmnNPyVdD4DDZoDJRXWYv3lrl3TiaVsOKH09Q26DCZBAqaCZ6Csrp2LWxF5fUUNYrh3VE5FFc0YGi8Ri6XYTQJVqu2rkQkwdOFBPo4A1g1rPPhbD7RjpofO0pJRT3axo4/KUslZueUuLppOaPWNS5NzSmubdM6cjSmUBTGAHEZZxfHx+OL2B3ZtnDqTCyCxTK5AJg+phcAu6NyzylSLCu0nOyVohWmuUt34/ZEdkfliuee67n79s8kUSxlFdaIMUS+Ho74ebW08HS+4Kmu03LgVB5Go4lv/0zit4MZrNocw7d/JaMzmPjmzyT2nzq3ZcRieepo5mON1sC3fybx3IqDLPhwf4fjQNRag9jW3J3t6BvkDkBGQdVZr0nIrBD7pfYsQVcLn/0Sy8qfos9qddfpjVYroyxWmnq1eZm/xaJj2eNq89409p7IY8fRLMqr1Wh1RhRyGXa2CgxGQRQzZ6N5/Khaa+SH3ea4PxdHW7wbLatXehyPJHi6EFHwXOADapklernZA+DqZJ6FtmW+vBiaW6Dq1HrySq+NGdS1jsVSqFS07gbi0lu7YX8/ZHZ1uTvbARCf2barVqM18P7XJ/n0p+gucb82p2mn9KaNOSePCMTWRkFeSe05P9/izvJ0s8fd2fyMWQSP0WhiT6PYsTx3sWltf9/yKjV/RWQDoGic6YLZamRvp6R/Tw/kMgjyNfcHxRUN7c6mO4LJJIiD2oat8Xy06TSb96VxMqkEMM/om7viPv0x+qwDUk29TgxqTc2t7FD/8tfxHH7ck0p6XhXZRTUsWXO0Q64/izC0t1Vgb6ekb2Cjheccgcuxzdpj/jXQP+kNRqLTygCIz2x7YlFUXk/znym3xNyP/99XUTz5/l6xPv08rAV3ck6lOCYFeDvR088FaN+tdSbVXJ5e/ubzLa5FT1c7fD0sgufKjuORBE8XEtjYwV3ojM7SOd1xfT8W3jOCZU9fh1wuo0FjsMofcrFkFVl3NElZlyYnhlZv5I8jmaTmVnI0tpDnVhzkYBeb0yWasAwc44f4i8eCGzuzmBaDe2llAym5lchk8MhtgwGIz6hoc2BMyakUTd17uzgmTK2xdmmBOXD5uuHmjTq/35WC3mAiI7+qVVlFweNqj7uLWcRZYksSs1XUqfW4ONpy6+Q+AK1i8SprNVTWaPjjSCZGk8DQvt6E9vIQ3/dpHGh6+rmw5qUb+WDhFGyVcowmoVMGhk9+PMMDb/xJXEa5KHI2700TN4y0cNP4YAb38cJgNLHjaFab92reR9XU6zqUKPFYbCEAs68LoV9Pd2rqdfz3m5PtxnFYRKWlzi0WnsyC6rNal2MaB3+A8mrNVZ+mIzm7UrS6p51FtLf0HOQW11JZoyEmrdzqN+jh49Ti3ipxUhvk60yvRsGTlN325xhNAoXldcQ2/gZP3jUMF8em3e09Xe3FwPyWk/vU3Er+uXR3l/cDHUUSPF2IxcJTXq25oNUflqWtfp4OzBgXTE8/F3p4mxtvZ8bxZDdaeCwz90sVx7PlQDrrtsTx/CeHWPbVCdLzqvh5X9ol+ezLyfc7k3lv4+Vfnmxx0Qzp6838mQO4eUJvHvzbIMB6gAGIiCsCzMuIJwwJQCGXUV6lbnNgTGjWfg6dKWgzR8jF8PuhDJZ9dYLU3ErUutaCB2De9FAUchmnkktZ8OE+/v3xwVZL79PzqgBz3I9l8LUMxpHxxQCMGeTHiFAfwGz1MpkEBEFg+9EsHl26h0eX7mbHMbOIuG1KH/o0WivAemYd6OOMs4MN/o3P78XG8ZxILGbfyTxMgjn2yiJyLHU9INgDuVyGTAZ3XN+Xu27oB5itMm31RS3Lk5hVcc4Zf2WtRkwWOGdqf959fCIujjYUlNVxuJ1VaBZR6dbY3/T0dcZWKUetNVi1nebnW6zQDo0JJi914Pel5kwz91FBWb3VvlhNx82/maVOcktqOZ3SOm2JZRyyUKfWi/FtQb4uDO/vDcBvB9PFFYQmkyBaT9/7XxSP/99e6jUGnB1sGBjixdTRPcX7ebjaM7SvOb3A4eh8cWIhCAJf/B5PqarhiunXJcHThbg42orm8MILCPC0BC37NOs4LebEzozjySoydyYzxpljH+IyKi46jud0cinvbojk6x2JZ+04TyaWiH83upnJLqpptXLmclFcUc/S/0V2qsWrqLye73enEBFXxPtfnRAtIZeD/MZkY738XLjvpjCeuns4Q/p6oZDLKFE1sODD/XzxezzH44s4El0AwMRhAY1uGncA0ZXTnOaCuU6tJzKh+KxlqGvQ8f2uFKISzecYGwVFSwRBwGgSKK6o58ut8RyNLeT5Tw5xNMZsZWgpeHr4OHPzxN6AecAA+GVfmtW+QaeSze1v5ABfMWi5uDEjclRjmccO9qd/kDsOdkrq1HoyCqpY/XMMa3+NRac3ojOYUGuNBHg5MWaQPyE9XMX7+7YIVoamwSevpJa0vEoqqtVW39fU+B3P5VJSaw2s/TVWfN3W5Gf2pBDefmw8bz46niBfF0YP9CPAy4l6tZ5dUTnoDSZ2Hs/hmz+T2HIgvVVczMffn+Gp/+5j6+G2V+xFxhcjCNC/pzve7g44O9hwZ6Oo+mF3Ckajia2HMnj2owMkNHPJqGo0JDdaEiwTLIVCLg6gX/we38pCtONYNgB9erjRJ9AdOH+3llZv5PPf46w2dL2SsbiPLKTmVrU6xyL6RoX5AeY2ZbH0Ncffy6nVsbRGsT9uiD9TR/Xk5om9EQRYvuk0O4/n8MyH+3jk3V3sOJZFVGIxMpnZIjf3RvNEYua4YPFeMhlMHNYDO1sFBWX14u8bl1EuLpfPL63r8EqwrkTZ/ilXJiaTiVWrVrF582Zqa2sZM2YMb7zxBj179mz/4ktIoI8zNfUqCkrrrGZ/7aHVG0Vfd/NlrWZ/a1GHAgTzSmr5eV8at0wKsTK1N6derRctSbdMCuGPI1mUV6lJzKpgSF/vDpe3OYIgsHZLLEXl9UQlFrPtcCbvPj6RsN6e4jm1DTrS8swPxornrsfZ0ZZlX0WRnl9NdGoZ00Zf/t/xy20JHI8vprpOx38XTL6oe5VXqdEbTPwZkS363aPTyvh6RxKP3Dr4rNcVlNXhYKfE09X+oj6/JaoaDcWNO3gH+TXNAB3tbbh1ch+2Hs4ku6iG7KIaMXYHYOJQs6volkkhJOdU8sv+dNxd7Jh9XR+UCjkGo0nMmjt+iD/H44v54vd4+vd0FzveE4nFfPtXMjKZOQ9OZa0WpULOK/8Yw/rf4vBwseedxyeIyQTrGnS89flxquu19Al0wySYB8vmK6paCh6Ae2cM4HRyKbY2CpQKGen51azbEsv9swZiZ2PunBVyGSNCfUhrHFBOJpXw6Ht7KFU1oFTICQ/1QaGQM6SvFycSS1iy5ihanRG5DB65bQhgFn0P3zoYhVwmrjgCxLiG5lgstBu2JojH+vd05+WHxvBXRDZ7onKprNUyZ2o//jG7qV0YjSZOJJXQO8CVn/akUlqpxtfDAblcRnGF+fmdOiqI/afykctljAzzEydbYA5cvXVyH9b/FseXWxPYHZlLdlFTH2Kpv75BblaxNJt2pjB1VE9cHJvuBU0WvwlDA8Rjt0wKYcuBDArK6lmy5qg42L2z4ThLn5yEq6MtCz7aT0OjG9JiVQP4+80DORJtXv6/63g2N08MAczutd8OpgNw97T+xKSXkZBZYbWkuiP8eSybrYcy2X8yny9fm4G9nRKD0URxRT2BPs7IZLLzup+FwrI6ErNUTB0VhKJZLJwgmN2WLVMSdITKWg0Z+VUADO3rTVxGOal5lYxszFkEZquXJWZnzCB/IuKKaNAY2pxcWMQ8mEWjJb3JwN6ehAWb++Qn7hxGg9rAwTP5rNocLZ7/2S9mYT1zXDDPzB0hHg/2bxL2Qb4uONrbMGlYD/adzGPPiVwGhnjy427zykCZDATB3GYs8UKXi24reNasWcOmTZtYtmwZ/v7+fPDBBzz66KNs27YNW1vb9m9wiQj0cSYpW8X63+PYFZnDk3OG0aOFibEtLNH3DnYKMV0+QLCfuaGdSCzh1/3pJGRWoNUbcHawpapOS3mVGoPRxG2T+7DzeA6F5fWcTCrh0+dvwMutdQdsCYzzdnfAy82B64b3YHdULvtO5lkJnsyCamLSyiivUjM81Ac/D0eiEosZO9jfqvGD2W9bVF6Pna2CPj3cSMpW8dbnEfzztiEM6uOF3mAiv7QWk2AWcBYffvgA30bBUyoKHkEQyCioxt5WQZBv1z0sDRo9G7Ym4Giv5OHZgykoqxM79aRsFcUV9W3OlDpCvVrPvz8+QE29Dhul2fx80/hgdh7P4Y8jmdxxfV9R0FTVavl6RyL9e7rTJ9CNl1Ydwc3Zjs9emoajvc25PkbEaBI4nVyC0SQQ2sujTbH0v20JCILZ9WGZaVv4521DuGd6KKeSS0nMqiAyvhhVjYZh/bzF1Rg3jOpJbkktm/emsWFrAlsOpPPEXcPwcnNAqzPi7GDDgnvCKSg7Ql5JLUtWH+G2KX1JyKxo1Skr5DIMRhPvbIgEzEG9X25N4Km7h6PRGnhv4wlxSbJlcH9u/kiqarV8/P1pAKvB3YKbsx1rXroRhVxGco6KFz89zPH4Yo7HFxPYGNcwMMQTR3sbhvX35qFbBvH9zmRxAnDjmJ5inT88ezBF5fWiJeSZuSOY0TjLvX1KX/EzgwNckcvAJLRt4Qnwbnr25TJAJiMtr4p/vbfHyrKx5WAGw/v7kJJbiZ2Ngoi4IpKyVWJQtEwG/75vJLFp5eJqmXkzBjAg2BMXR5s26+NvE3uTklPJwTP5ZBfV4GSvJCTQjfiMCtF9cdO4YD4vjsfRXomzgw0FZfVs2BrPndf3o7C8HmcHG+o1etF10lzwONrb8PTc4Xz47UlR7Lg521Jdp+ONdREEB7iIYgewmoS5Odsxf1YYn/8WzxdbEwjr7Umwvytf70ikQWMgpIcrk4b3oKLGbKH7cXcqR6ILeOyOoaKFAyCnuIZDZwowGk30DnDl+pFBAOxqXDFY26Djr+M5TB7Rg/98GUl6fjUjw3yZMCSABo2eEaG+BPk6o9UbW4m8lphMAv/5XyR5JXUUltdx19T+pOVW0r+XBx99d4qTSSWthKslKaWvh6O4eqrlPVf+FI0gmMXn+KH+xGWUW62c23oog89/jxdf9w5wZdwQf47FFqE3mHBysCHQx0m0CjU3Ft5+fV/xmbnj+qZ2K5fL+Pd94ai1BqISiwnwckKtM1BVq0Uul3H3tP6tyvr5K9PZfyqf2deZxen0sb3YdzKPw9H5jB7oS2x6OUqFjLunhfLD7hQi4gq5Z3roOeu0q+mWgken0/Hll1/ywgsvcMMNNwDw8ccfM3nyZHbt2sXs2bMvbwGbYQlcrqrVEl1bZs7p8fgE0vOrKFE1oNEa0BtMFJbX08Pbibum9sNGqRCDGn08HK1mH+Fhvvh5OlKiauB/fyS0+ZkA//sjUfy7pl7HexujeGrOcEpUDZRXq+kb6E5ID1e+ajxv6ihzx3DjmF7sjsrlSEwBWUU1GI0meng7c7QxQBFg6+FM8e8f96Qyb3ooaq2BfkHujBsSwP5G//CEIQE8ffdw3vw8gsQsFZ/+FC1eZ1lVM3JA06wlPNSXzXvTOJNaRnK2ipp6HTuOZXEq2dy5Dgj2YME9I1oJrLZo0OgpKKujp5+LaCk4G3VqPW9/HiFaJvw9HcUB1sLh6ALm3nj2h3XfSbNInDd9AEP7WVvGdhzLorrO7IPX6Y34ejjw5Jzh5BbXkpStYsuBdG69rg/p+VV8uS2BElUDu6NycXG0xWgyJ8f7eV+aGF+j0RnYeiiT6NQyKms1XDc8kDnT+qGq0SAIsPEPs2UKzIPq/bMG4u3uQHKOigG9PMxLmE/nI5PB43cObXN26+Joyw0jg7hhZBCP3zGU9PyqVrEAf795II72Nmw9lIGqRst7G0/g6WoWT4NCvHB1suU/T0xkyeojFJbX8+U2c3uVycwiYWBvT3R6I/17efDcxwdQa82DTJ1ax58R2SRlqyhR1aPWGnGwU+LqZEuJqoHeAa6Eh/ogk8nw83QkNbdS3KKgJYrGQSUs2JMX7h/FnqhcYtPLRDfX6MbBUiYzd+rXhweRVVhNcICrlYWmp58Lnz4/lT0ncvFwsWP8kIDWHwbY2SgYGOJFSk6luPqoOc0DSP915zDCB/jw2tpjlFWqcXG05ck5wzh4Op/IhGLeWB9hda3FggYw98ZQhvb1xsvNnl/3pxHo60wPb6dWv5FVXSjkPDd/JL6eDmQV1vDY7UNQKOQ8unS3eM6Qvt6sWTwNZwcbUnIreevz4+w9kcfeE01L2i3N5fYpfVtNQiYN64HzYxP4+PvTDA7x4sm7h/PGumOk5VURn1GBXC7jgwWTcXWyFQNdLdwyqQ+nkks5nVzKm+sj8PdyEoXTg38bhFwus/q8grJ63v/6BO8/MxlvdwfW/BzDkZhCq3sm51QyJTzQyp3yw+4Uft6XKj6Tpxs/00xTvzllRCBPzx1+1onGicRicQ+qn/elsTsyl6o6s0CwhAT8sj+dHj7OzBwXjFprYNVP0RyKLmBEfx9efmgM1XVavNwdsLNR0KDR8/WOJE4klmCjlLNg7gjx947PKCcjvwobpZyN25vK2NPPhZ5+zrxw/yi+dE3gjyNZXDe8B9PH9OLFlYe5PjyIAcFmYenmbMv4If54utrj7W7PuBZtWKmQ8/JDYziVXMLgPl6k5VWx9MtIbp4Y0uZkz9/LiftmDhBfD+njJVoIl319EjCPJ7dMCuGnPSmk51dTVau1suxdamTClZAu9TyJjY1l7ty5/PXXX4SEhIjH77vvPkJDQ3n77bfP63433ngjAHv37u3UcgJExBXy3sYTANgq5egMJtHE1xYDennQu4crUQnFVNZqGT3QjzcfHW91ToNGz8/70kjKVjGsnw9+ng7UqfV4uNjj5WZPcraKr7YnIpPJWDgvnHVbYq1mVhYss0VPVzs+e+lGHO1tEASBf/3fHnEm3ZzRA/3wcrPnSHQBap0Rf0/HVrFJvh4O1DboUWsNvPXYeEaF+aHRGdhxNJvtRzOprtdhNJowGM0VYDkHzEsx57/+p5hbxYJSIcMkmGc/TvZKRob5kVdSS5CvM86OttTUa5HLZNQ16CmsqEenN1JTpzXPsj0ceOTWIdjZKohNL0dVrWHcYH883eyprNXQoDHw/c5kyqs1KBUysVwWLJYYfy9Hpo7qiZ2NAhulnHqNAR93e4L8XEjLrWL9b3GAWWDMHN+bYH8X4jMrcLK3ISqxmKpaLVPCA8kqrOGBWWFMHNaDU8klvPX58Vb17ORgI+ZxsbdVoNEZsVXKuW5EIHUN5jiSlqv0WrYppUJOoI+TVd6clsya0Jun7x5+1vc7it5g4usdiWKGZlulnBf/PloUBQ0aPXuicjnWaNK+9boQerUQrQdP57P1cAZP3DWMk4klbGoWYOzv5cjCe8Lx9XTkpz2pzBzXiwHBnlwoO4/niGb7VS9MJTigfQF9Pmj1RurV+jYta3qDiWVfnSDI15l/zB6ETCajolrN4egCrhseiLe7A8UV9Tz1333oDSb69XTHz9MROxsF998URomqgcLyOqaP6SW6UIor6nG0b9uq0xFeWXOUuIxy5DL4edls0QopCAJ/RmSz90QuOcW1BHg5UdSYs2tALw/+7+nrsFG2HQYqCIIopGvqdSxZc4Tc4lpun9KXR28fctay1DboWLTioNj/KBVynr57ONPHmuMLK2s0PPKf3SgUMoJ8ncnIr0apkONor6SmXodMBuMG++PmbMeuyBwEAWxtFOj0RqaEB5KYpRLjuIJ8nfnXHUPZdzKPmgYdCrmMMyllVnF1Nko57i52uDnZEujjwtTRQaKoXL7pNEnZKlwcbcX0ABZRam+rYNzgAA42BgFPG92TxKwKq37V0v862isJ6eFGVmG12E8/M3c4N43vjdEk8OpnR0nIrMDZwQZbGzmqGvO48Mo/xqCQy60sRRXVatxd7MVFBS5Ottg1pmhwc7bD1ckWg9GEIHDW3645BqMJhVzWYZefxZIKZqvRupdvxN/LiQ1b40nNreTtf01odwJ6vpzP+N0tBc+uXbtYsGABMTEx2Ns3dSrPPvssGo2GdevWndf9ulLwaHQGlm86zeA+XvTwdhLN9v5ejgT7u+LkYINcJsPD1Y4dx7KtNj30dLXn3/eGE97MCtJRsotqEASBkB5u5BTX8N1fyUTEFeHr4UBwgCvpeVVUNq5IWTR/JFNHNcXMHDydz9c7ErlhVE/8PB3JKqhm0vAeootLbzBiMArYKuVs3pdGQkYFnm72RCUUiyuPPFzs+N/rM6382mDuCFNyK1n6ZZT5gVhyo9UD8Ov+dHYez8ZgEnBxtCHQx5l7ZwzAycGGZV+daHPPmLNh6eg6gr+XI0seGsuGrfHEppdjq5TzwM0DmTEumAff+gu9of3g4t4BrlZxEc3xdnfg81emW+W8EQSBl1cfITFLhVIho5efKwN6e3D/TWH8sj+d/afyeH7+SH7ck0p8iyR/Ph4OzJ3WH6VCzlc7Eqmu02Fro0Ahl+HuYse/7w1nUIgXO4/nsG5LLE72NkwYFkBGfhW2NgrGDfbnlkl9OtTpdZT4jHLq1XqG9/fBvo2YmvOhuKKe3JJaXB1tGRDsccExFmfjWGwhdWq9VfDllURytoqyKjUTh/UQrVRdxZ6oXD758QwB3k6sXzL9nOdW12k5lVzC2EH+OLfj8mlOvVpPUraK8AG+7X6fBo2ek0kllFepCR/gaxUXBea4GUd7G5QKGW99cVx09/h7OfLS38fQrzGgft/JXFZvjhGTHH6wYDIKhYyohBLCenswrJ+3KO4sqLUGdHojReX1fPDdKdG9eTaUChkrX5jKL/vS8fVw4M6p/UjLq8LL1R5/Lye+2BrPtmYWcU9XO+6dGcY3OxKpbdBbWYMAAn2ceHj2YCvrS4NGz6trj4mrCt2d7Vix6Po2QxSuBFZtjmbn8Rymj+nFs/eGd/nnXfWC5/fff2fx4sUkJSUhlzd12IsXL6a0tJSNGzee1/26UvC0JD2/CoVcRu8A11adeFF5PX9GZGNrI6dPDzfGDvZvMynchaLTG7FRypHJZGJsjGWA6gw0WgOx6eWk5VURPsDnrG4GMAtBBM5rYNTpm7J79u/pQX5pLTq9CTdnWwTBbA0J9HUW3R8Odkq+3pFEdGoZSoWMkEA3vFztORZXBII54RyYg/fmTQ/F3k5JXYOOA6fzGT3QTzTjHjidz8nEEhztlWj1RnR6s4ulqKKeElUDcpmMKeGB/P3mgZxKLuVEYjFF5fUMCPYkp7iGk0klLJwXzg2N8QQt66ysSo2/l1Mr8WGZJZdXmfe5srVR4Oxog5uzHSNCfUShqNUbqazRnDUuoE6tFy1TEhItMRhNfL8rhUEhnlbxMN0BQRDIL60jI7+KMYP8xc1jLTRo9GQV1mCjlJ914cbZMBpNlFWpqa7TUl2nIzqtjKMxBWK8E8i44/q+zL8p7Jz3iYgr5I8jWYwZ5Mes8b2xt1NSWaOhrEpNSA83UnMrKVHV08vPlZBAtzYFoUZn4ExKGTZKOWHBHuclNi81eoOJk0nFhA/w7XRrTltc9YJn586dLFy4sE0Lj06n47PPPjuv+w0dOhSj0UhAQNt+eQkJCQkJCYkrj6KiIhQKBXFxce2e2y2nfBZhUlpqnVOhtLQUP7/zn6HY2dmhVHbL+G0JCQkJCYlrFqVSiZ1dxwKhu+UoHxYWhrOzM5GRkfTqZQ5mq6mpITExkQceeOC873fy5MnOLqKEhISEhITEFUS3FDy2trY88MADfPjhh3h6ehIYGMgHH3yAv78/M2fOvNzFk5CQkJCQkLjC6JaCB2DhwoUYDAZee+01NBoNY8aMYcOGDdjYdCw5m4SEhISEhMS1Q7cMWpaQkJCQkJCQOB+6ZdCyhISEhISEhMT5IAkeCQkJCQkJiaseSfBISEhISEhIXPVIgkdCQkJCQkLiqkcSPBISEhISEhJXPZLgkZCQkJCQkLjqkQSPhISEhISExFWPJHgkJCQkJCQkrnokwSMhISEhISFx1SMJni7CZDLx6aefMnnyZEaMGMFjjz1GXl7e5S7WFUNJSQkDBgxo9e/XX38FICkpiQceeIARI0Ywbdo0vv7668tc4svLunXr+Pvf/251rL06utbbYFt19tprr7Vqc9OmTRPfvxbrrKqqijfeeIMpU6YwcuRI7rvvPqsNlSMiIrjrrrsYPnw4s2bNYvv27VbXa7Va3n77bSZMmEB4eDjPP/88KpXqUn+NS0p7dfbwww+3amfN2+K1WGcVFRW8+OKLjB8/nvDwcP71r3+RkZEhvn9J+jNBoktYuXKlMG7cOGH//v1CUlKS8MgjjwgzZ84UtFrt5S7aFcGBAweEoUOHCiUlJUJpaan4T61WCyqVShg3bpywZMkSIT09Xfj555+FoUOHCj///PPlLvZl4dtvvxXCwsKEBx54QDzWkTq6lttgW3UmCIJw9913C8uXL7dqcxUVFeL712KdPfzww8Ls2bOFEydOCJmZmcLbb78tDBs2TMjIyBDS09OFoUOHCsuXLxfS09OFL774Qhg0aJBw7Ngx8fqXX35ZmD59unDixAkhJiZGuOOOO4T777//Mn6jrudcdSYIgjBhwgRh06ZNVu2ssrJSvP5arLN58+YJc+fOFWJiYoT09HRhwYIFwnXXXSc0NDRcsv5MEjxdgFarFcLDw4XvvvtOPFZdXS0MGzZM2LZt22Us2ZXD+vXrhVtvvbXN99auXStcd911gl6vF4999NFHwsyZMy9V8a4IiouLhccff1wYMWKEMGvWLKvBu706ulbb4LnqzGQyCSNGjBB27drV5rXXYp1lZ2cLoaGhwsmTJ8VjJpNJmD59urBixQrh9ddfF+6++26raxYtWiQ88sgjgiCY6zssLEw4cOCA+H5mZqYQGhoqnD59+tJ8iUtMe3VWXl4uhIaGCgkJCW1efy3WWVVVlbBo0SIhJSVFPJaUlCSEhoYKMTExl6w/k1xaXUBycjL19fVMmDBBPObq6sqgQYM4ceLEZSzZlUNKSgp9+/Zt872TJ08yduxYlEqleGz8+PFkZ2dTXl5+qYp42UlISMDGxoatW7cyfPhwq/faq6NrtQ2eq85yc3NpaGigT58+bV57LdaZh4cH69evZ+jQoeIxmUyGTCajpqaGkydPWtUHmNvZqVOnEASBU6dOiccshISE4Ofnd83WWUpKCjKZjJCQkDavvxbrzM3NjY8++ojQ0FAAVCoVGzduxN/fn379+l2y/kwSPF1AcXExAAEBAVbHfX19xfeudVJTU1GpVNx///1MnDiR++67j0OHDgHm+vP397c639fXF4CioqJLXtbLxbRp01i5ciU9e/Zs9V57dXSttsFz1VlqaioA33zzDdOmTWP69Om888471NbWAtfmc+vq6sr111+Pra2teGznzp3k5OQwefLks7YztVpNZWUlJSUleHh4YGdn1+qca7XOUlNTcXFx4Z133mHKlCnMmjWLFStWoNPpAK7JOmvO66+/zoQJE9i+fTtLly7F0dHxkvVnkuDpAtRqNYDVAwFgZ2eHVqu9HEW6ojAYDGRmZlJdXc2CBQtYv349I0aM4F//+hcRERFoNJo26w6Q6q+R9upIaoOtSU1NRS6X4+vry9q1a3n55Zc5cuQITz31FCaTSaoz4PTp0yxZsoSZM2dyww03tNnOLK91Oh1qtbrV+3Bt11lqaiparZZhw4bxxRdf8OSTT7J582Zee+01gGu+zh566CF++eUXZs+ezdNPP01CQsIl68+U7Z8icb7Y29sD5g7B8jeYfzgHB4fLVawrBqVSSWRkJAqFQqyfIUOGkJaWxoYNG7C3txdnQxYsjdrR0fGSl/dKpL06ktpga5588knmz5+Ph4cHAKGhofj4+HDPPfcQFxd3zdfZnj17eOGFFxg5ciQffvghYB5QWrYzy2sHB4c22yFc23X2zjvv8NJLL+Hm5gaY25mNjQ3PPfccixcvvubrrF+/fgAsXbqUmJgYvv3220vWn0kWni7AYnYrLS21Ol5aWoqfn9/lKNIVh5OTk1XDBejfvz8lJSX4+/u3WXeAVH+NtFdHUhtsjVwuF8WOhf79+wNmd9a1XGfffvstCxYsYOrUqaxdu1acXQcEBLRZH46Ojri4uODv709VVVWrweparjOlUimKHQvN29m1WGcqlYrt27djMBjEY3K5nH79+lFaWnrJ+jNJ8HQBYWFhODs7ExkZKR6rqakhMTGRMWPGXMaSXRmkpaUxcuRIq/oBiI+Pp1+/fowZM4ZTp05hNBrF944fP05ISAheXl6XurhXJO3VkdQGW7N48WL+8Y9/WB2Li4sDzLPOa7XONm3axLvvvsv999/P8uXLrdwGo0ePJioqyur848ePM3LkSORyOaNGjcJkMomBuABZWVmUlJRcs3X297//nSVLllidHxcXh42NDb17974m66y8vJxFixYREREhHtPr9SQmJtK3b99L159d/IIzibZYvny5MHbsWGHPnj1WOQN0Ot3lLtplx2g0CnPmzBH+9re/CSdOnBDS09OF9957TxgyZIiQkpIilJeXC2PGjBFeeuklIS0tTfjll1+EoUOHCr/++uvlLvpl46WXXrJaYt2ROrrW22DLOtuzZ48QGhoqrFy5UsjJyREOHDggTJs2TVi0aJF4zrVWZ5mZmcLgwYOFp59+2ipnTGlpqVBTUyOkpqYKgwcPFj744AMhPT1d2LBhQ6s8PIsWLRKmTZsmHD9+XMwp0zL/0dVEe3X2zTffCAMHDhQ2bdok5ObmCtu3bxfGjRsnLF++XLzHtVZngiAIjz76qDBz5kwhKipKSElJERYtWiSMGTNGKCgouGT9mSR4ugiDwSD897//FcaPHy+MGDFCeOyxx4S8vLzLXawrhrKyMuHll18WJk2aJAwdOlSYN2+ecOLECfH9mJgY4Z577hGGDBkiTJ06Vfjmm28uY2kvPy0Hb0Fov46u9TbYVp3t2LFDuOOOO4Rhw4YJkyZNEpYtWyZoNBrx/Wutzj777DMhNDS0zX8vvfSSIAiCcPDgQWH27NnCkCFDhFmzZgnbt2+3ukd9fb3w6quvCqNHjxZGjx4tLFq0SFCpVJfj61wSOlJn3377rXDzzTeLz+Znn30mGI1G8R7XWp0JgiDU1NQIb775pjBp0iRh2LBhwiOPPCKkpqaK71+K/kwmCILQKTYrCQkJCQkJCYkrFCmGR0JCQkJCQuKqRxI8EhISEhISElc9kuCRkJCQkJCQuOqRBI+EhISEhITEVY8keCQkJCQkJCSueiTBIyEhISEhIXHVIwkeCQkJCQkJiaseSfBISEhISEhIXPVIgkdCQkJCQkLiqkcSPBISEhISEhJXPZLgkZCQkJCQkLjq+X97KvGZNHu7ggAAAABJRU5ErkJggg==","text/plain":["<Figure size 640x480 with 8 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# --------------Analysis----------------------------#\n","\n","values = Final_df.values\n","groups = [0, 1, 2, 3, 4, 5, 6, 7]\n","i = 1\n","pyplot.figure()\n","for group in groups:\n","    pyplot.subplot(len(groups), 1, i)\n","    pyplot.plot(values[:, group])\n","    pyplot.title(Final_df.columns[group], y=0.5, loc=\"right\")\n","    i += 1\n","pyplot.show()"]},{"cell_type":"code","execution_count":183,"metadata":{"_uuid":"e86896cdb80dd037dfb7b7a9c88b493c2b31a6e6","trusted":true},"outputs":[{"data":{"text/plain":["2640.49"]},"execution_count":183,"metadata":{},"output_type":"execute_result"}],"source":["Final_df[\"Volume_BTC\"].max()"]},{"cell_type":"code","execution_count":184,"metadata":{"_uuid":"55588535d921e707c05bfa25eb404a679cadd0c2","trusted":true},"outputs":[{"data":{"text/plain":["19126407.89"]},"execution_count":184,"metadata":{},"output_type":"execute_result"}],"source":["Final_df[\"Volume_Dollar\"].max()"]},{"cell_type":"code","execution_count":185,"metadata":{"_uuid":"1cd2d1d24fa3ff7eaa5ef3b97d102b2b8987ff74","trusted":true},"outputs":[{"data":{"text/plain":["96945.04000000001"]},"execution_count":185,"metadata":{},"output_type":"execute_result"}],"source":["Final_df[\"Volume_BTC\"].sum()"]},{"cell_type":"code","execution_count":186,"metadata":{"_uuid":"ab617ef71a24fc5e6d93f6559b276807e0749fe2","trusted":true},"outputs":[{"data":{"text/plain":["684457140.05"]},"execution_count":186,"metadata":{},"output_type":"execute_result"}],"source":["Final_df[\"Volume_Dollar\"].sum()"]},{"cell_type":"code","execution_count":187,"metadata":{"_uuid":"f15e357b4aaace7439c520730dd1f431889aac6d","trusted":true},"outputs":[{"data":{"text/plain":["10452.0"]},"execution_count":187,"metadata":{},"output_type":"execute_result"}],"source":["Final_df[\"Tweet_vol\"].max()"]},{"cell_type":"code","execution_count":188,"metadata":{"_uuid":"568881a0cdc80c611bacfbdc3a8f52ad283f024e","trusted":true},"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>Polarity</th>\n","      <th>Sensitivity</th>\n","      <th>Tweet_vol</th>\n","      <th>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Volume_BTC</th>\n","      <th>Volume_Dollar</th>\n","      <th>Close_Price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>294.000000</td>\n","      <td>294.000000</td>\n","      <td>294.000000</td>\n","      <td>294.000000</td>\n","      <td>294.000000</td>\n","      <td>294.000000</td>\n","      <td>294.000000</td>\n","      <td>2.940000e+02</td>\n","      <td>294.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.099534</td>\n","      <td>0.214141</td>\n","      <td>4691.119048</td>\n","      <td>6915.349388</td>\n","      <td>6946.782925</td>\n","      <td>6889.661054</td>\n","      <td>329.745034</td>\n","      <td>2.328086e+06</td>\n","      <td>6920.150000</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.012114</td>\n","      <td>0.014940</td>\n","      <td>1048.922706</td>\n","      <td>564.467674</td>\n","      <td>573.078843</td>\n","      <td>559.037540</td>\n","      <td>344.527625</td>\n","      <td>2.508128e+06</td>\n","      <td>565.424866</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.051695</td>\n","      <td>0.174330</td>\n","      <td>2998.000000</td>\n","      <td>6149.110000</td>\n","      <td>6173.610000</td>\n","      <td>6072.000000</td>\n","      <td>22.000000</td>\n","      <td>1.379601e+05</td>\n","      <td>6149.110000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.091489</td>\n","      <td>0.203450</td>\n","      <td>3878.750000</td>\n","      <td>6285.077500</td>\n","      <td>6334.942500</td>\n","      <td>6266.522500</td>\n","      <td>129.230000</td>\n","      <td>8.412214e+05</td>\n","      <td>6283.497500</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.099198</td>\n","      <td>0.214756</td>\n","      <td>4452.000000</td>\n","      <td>7276.845000</td>\n","      <td>7311.380000</td>\n","      <td>7245.580000</td>\n","      <td>223.870000</td>\n","      <td>1.607008e+06</td>\n","      <td>7281.975000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.106649</td>\n","      <td>0.223910</td>\n","      <td>5429.750000</td>\n","      <td>7422.957500</td>\n","      <td>7457.202500</td>\n","      <td>7396.427500</td>\n","      <td>385.135000</td>\n","      <td>2.662185e+06</td>\n","      <td>7424.560000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>0.135088</td>\n","      <td>0.271796</td>\n","      <td>10452.000000</td>\n","      <td>7754.570000</td>\n","      <td>7800.000000</td>\n","      <td>7724.500000</td>\n","      <td>2640.490000</td>\n","      <td>1.912641e+07</td>\n","      <td>7750.090000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         Polarity  Sensitivity     Tweet_vol         Open         High   \n","count  294.000000   294.000000    294.000000   294.000000   294.000000  \\\n","mean     0.099534     0.214141   4691.119048  6915.349388  6946.782925   \n","std      0.012114     0.014940   1048.922706   564.467674   573.078843   \n","min      0.051695     0.174330   2998.000000  6149.110000  6173.610000   \n","25%      0.091489     0.203450   3878.750000  6285.077500  6334.942500   \n","50%      0.099198     0.214756   4452.000000  7276.845000  7311.380000   \n","75%      0.106649     0.223910   5429.750000  7422.957500  7457.202500   \n","max      0.135088     0.271796  10452.000000  7754.570000  7800.000000   \n","\n","               Low   Volume_BTC  Volume_Dollar  Close_Price  \n","count   294.000000   294.000000   2.940000e+02   294.000000  \n","mean   6889.661054   329.745034   2.328086e+06  6920.150000  \n","std     559.037540   344.527625   2.508128e+06   565.424866  \n","min    6072.000000    22.000000   1.379601e+05  6149.110000  \n","25%    6266.522500   129.230000   8.412214e+05  6283.497500  \n","50%    7245.580000   223.870000   1.607008e+06  7281.975000  \n","75%    7396.427500   385.135000   2.662185e+06  7424.560000  \n","max    7724.500000  2640.490000   1.912641e+07  7750.090000  "]},"execution_count":188,"metadata":{},"output_type":"execute_result"}],"source":["Final_df.describe()"]},{"cell_type":"code","execution_count":189,"metadata":{"_uuid":"2de9b3ea2a880f80a4929a1a704fc24a5b6631de","trusted":true},"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>Polarity</th>\n","      <th>Sensitivity</th>\n","      <th>Tweet_vol</th>\n","      <th>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Volume_BTC</th>\n","      <th>Volume_Dollar</th>\n","      <th>Close_Price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Polarity</th>\n","      <td>1.000000</td>\n","      <td>0.380350</td>\n","      <td>-0.167573</td>\n","      <td>0.179056</td>\n","      <td>0.176277</td>\n","      <td>0.180088</td>\n","      <td>-0.062868</td>\n","      <td>-0.052646</td>\n","      <td>0.178456</td>\n","    </tr>\n","    <tr>\n","      <th>Sensitivity</th>\n","      <td>0.380350</td>\n","      <td>1.000000</td>\n","      <td>0.053903</td>\n","      <td>0.194763</td>\n","      <td>0.200611</td>\n","      <td>0.190222</td>\n","      <td>0.097124</td>\n","      <td>0.112425</td>\n","      <td>0.193203</td>\n","    </tr>\n","    <tr>\n","      <th>Tweet_vol</th>\n","      <td>-0.167573</td>\n","      <td>0.053903</td>\n","      <td>1.000000</td>\n","      <td>0.237185</td>\n","      <td>0.262207</td>\n","      <td>0.234330</td>\n","      <td>0.541112</td>\n","      <td>0.545850</td>\n","      <td>0.250448</td>\n","    </tr>\n","    <tr>\n","      <th>Open</th>\n","      <td>0.179056</td>\n","      <td>0.194763</td>\n","      <td>0.237185</td>\n","      <td>1.000000</td>\n","      <td>0.997128</td>\n","      <td>0.998799</td>\n","      <td>0.217478</td>\n","      <td>0.277600</td>\n","      <td>0.997217</td>\n","    </tr>\n","    <tr>\n","      <th>High</th>\n","      <td>0.176277</td>\n","      <td>0.200611</td>\n","      <td>0.262207</td>\n","      <td>0.997128</td>\n","      <td>1.000000</td>\n","      <td>0.996650</td>\n","      <td>0.270551</td>\n","      <td>0.329816</td>\n","      <td>0.998816</td>\n","    </tr>\n","    <tr>\n","      <th>Low</th>\n","      <td>0.180088</td>\n","      <td>0.190222</td>\n","      <td>0.234330</td>\n","      <td>0.998799</td>\n","      <td>0.996650</td>\n","      <td>1.000000</td>\n","      <td>0.202895</td>\n","      <td>0.263863</td>\n","      <td>0.998058</td>\n","    </tr>\n","    <tr>\n","      <th>Volume_BTC</th>\n","      <td>-0.062868</td>\n","      <td>0.097124</td>\n","      <td>0.541112</td>\n","      <td>0.217478</td>\n","      <td>0.270551</td>\n","      <td>0.202895</td>\n","      <td>1.000000</td>\n","      <td>0.995873</td>\n","      <td>0.243875</td>\n","    </tr>\n","    <tr>\n","      <th>Volume_Dollar</th>\n","      <td>-0.052646</td>\n","      <td>0.112425</td>\n","      <td>0.545850</td>\n","      <td>0.277600</td>\n","      <td>0.329816</td>\n","      <td>0.263863</td>\n","      <td>0.995873</td>\n","      <td>1.000000</td>\n","      <td>0.303347</td>\n","    </tr>\n","    <tr>\n","      <th>Close_Price</th>\n","      <td>0.178456</td>\n","      <td>0.193203</td>\n","      <td>0.250448</td>\n","      <td>0.997217</td>\n","      <td>0.998816</td>\n","      <td>0.998058</td>\n","      <td>0.243875</td>\n","      <td>0.303347</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               Polarity  Sensitivity  Tweet_vol      Open      High       Low   \n","Polarity       1.000000     0.380350  -0.167573  0.179056  0.176277  0.180088  \\\n","Sensitivity    0.380350     1.000000   0.053903  0.194763  0.200611  0.190222   \n","Tweet_vol     -0.167573     0.053903   1.000000  0.237185  0.262207  0.234330   \n","Open           0.179056     0.194763   0.237185  1.000000  0.997128  0.998799   \n","High           0.176277     0.200611   0.262207  0.997128  1.000000  0.996650   \n","Low            0.180088     0.190222   0.234330  0.998799  0.996650  1.000000   \n","Volume_BTC    -0.062868     0.097124   0.541112  0.217478  0.270551  0.202895   \n","Volume_Dollar -0.052646     0.112425   0.545850  0.277600  0.329816  0.263863   \n","Close_Price    0.178456     0.193203   0.250448  0.997217  0.998816  0.998058   \n","\n","               Volume_BTC  Volume_Dollar  Close_Price  \n","Polarity        -0.062868      -0.052646     0.178456  \n","Sensitivity      0.097124       0.112425     0.193203  \n","Tweet_vol        0.541112       0.545850     0.250448  \n","Open             0.217478       0.277600     0.997217  \n","High             0.270551       0.329816     0.998816  \n","Low              0.202895       0.263863     0.998058  \n","Volume_BTC       1.000000       0.995873     0.243875  \n","Volume_Dollar    0.995873       1.000000     0.303347  \n","Close_Price      0.243875       0.303347     1.000000  "]},"execution_count":189,"metadata":{},"output_type":"execute_result"}],"source":["cor = Final_df.corr()\n","cor"]},{"cell_type":"code","execution_count":190,"metadata":{"_uuid":"768ef0d890519b8ca9686dd5e1053773d11f9780","trusted":true},"outputs":[{"data":{"text/plain":["2018-07-17 18:00:00    2640.49\n","2018-07-17 19:00:00    2600.32\n","2018-07-23 03:00:00    1669.28\n","2018-07-18 04:00:00    1576.15\n","2018-07-20 17:00:00    1510.00\n","2018-07-18 19:00:00    1490.02\n","2018-07-23 19:00:00    1396.32\n","2018-07-12 07:00:00    1211.64\n","2018-07-16 10:00:00    1147.69\n","2018-07-23 08:00:00    1135.38\n","Name: Volume_BTC, dtype: float64"]},"execution_count":190,"metadata":{},"output_type":"execute_result"}],"source":["Top_Vol = Final_df[\"Volume_BTC\"].nlargest(10)\n","Top_Vol"]},{"cell_type":"code","execution_count":191,"metadata":{"_uuid":"42e5a8f8c638bba891be985cda197f344a79428f","trusted":true},"outputs":[{"data":{"text/plain":["2018-07-23 22:00:00    0.271796\n","2018-07-19 20:00:00    0.262048\n","2018-07-21 19:00:00    0.256952\n","2018-07-20 22:00:00    0.246046\n","2018-07-22 06:00:00    0.245820\n","2018-07-19 19:00:00    0.244655\n","2018-07-19 21:00:00    0.244215\n","2018-07-18 20:00:00    0.243534\n","2018-07-18 21:00:00    0.243422\n","2018-07-18 18:00:00    0.241287\n","Name: Sensitivity, dtype: float64"]},"execution_count":191,"metadata":{},"output_type":"execute_result"}],"source":["Top_Sen = Final_df[\"Sensitivity\"].nlargest(10)\n","Top_Sen"]},{"cell_type":"code","execution_count":192,"metadata":{"_uuid":"e79dcf077343fc5adf4de7f7b211f68a4d91f86a","trusted":true},"outputs":[{"data":{"text/plain":["2018-07-22 05:00:00    0.135088\n","2018-07-16 03:00:00    0.130634\n","2018-07-19 20:00:00    0.127696\n","2018-07-15 10:00:00    0.127469\n","2018-07-22 06:00:00    0.126299\n","2018-07-15 06:00:00    0.124505\n","2018-07-16 05:00:00    0.124210\n","2018-07-22 09:00:00    0.122784\n","2018-07-15 13:00:00    0.122411\n","2018-07-22 12:00:00    0.122021\n","Name: Polarity, dtype: float64"]},"execution_count":192,"metadata":{},"output_type":"execute_result"}],"source":["Top_Pol = Final_df[\"Polarity\"].nlargest(10)\n","Top_Pol"]},{"cell_type":"code","execution_count":193,"metadata":{"_uuid":"dfd4bd69741ceffa845c6d78bd31855868f96f1c","trusted":true},"outputs":[{"data":{"text/plain":["2018-07-17 19:00:00    10452.0\n","2018-07-17 18:00:00     7995.0\n","2018-07-17 20:00:00     7354.0\n","2018-07-16 14:00:00     7280.0\n","2018-07-18 15:00:00     7222.0\n","2018-07-18 14:00:00     7209.0\n","2018-07-18 13:00:00     7171.0\n","2018-07-16 13:00:00     7133.0\n","2018-07-19 16:00:00     6886.0\n","2018-07-18 12:00:00     6844.0\n","Name: Tweet_vol, dtype: float64"]},"execution_count":193,"metadata":{},"output_type":"execute_result"}],"source":["Top_Tweet = Final_df[\"Tweet_vol\"].nlargest(10)\n","Top_Tweet"]},{"cell_type":"code","execution_count":194,"metadata":{"_uuid":"6990a33baca941477f5636922b89a5a58df11345","trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1100x900 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","\n","sns.set(style=\"white\")\n","f, ax = plt.subplots(figsize=(11, 9))\n","\n","cmap = sns.diverging_palette(220, 10, as_cmap=True)\n","ax = sns.heatmap(\n","    cor,\n","    cmap=cmap,\n","    vmax=1,\n","    center=0,\n","    square=True,\n","    linewidths=0.5,\n","    cbar_kws={\"shrink\": 0.7},\n",")\n","plt.show()"]},{"cell_type":"code","execution_count":195,"metadata":{"_uuid":"8f93944d12561a30247c6584b52e891e22c26ad6","trusted":true},"outputs":[],"source":["# sns Heatmap for Hour x volume\n","# Final_df['time']=Final_df.index.time()\n","Final_df[\"time\"] = Final_df.index.to_series().apply(lambda x: x.strftime(\"%X\"))"]},{"cell_type":"code","execution_count":196,"metadata":{"_uuid":"a9ad630b18148762dcfc1fff2fb0976fe7a2284e","trusted":true},"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>Polarity</th>\n","      <th>Sensitivity</th>\n","      <th>Tweet_vol</th>\n","      <th>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Volume_BTC</th>\n","      <th>Volume_Dollar</th>\n","      <th>Close_Price</th>\n","      <th>time</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-07-11 20:00:00</th>\n","      <td>0.102657</td>\n","      <td>0.216148</td>\n","      <td>4354.0</td>\n","      <td>6342.97</td>\n","      <td>6354.19</td>\n","      <td>6291.00</td>\n","      <td>986.73</td>\n","      <td>6231532.37</td>\n","      <td>6350.00</td>\n","      <td>20:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 21:00:00</th>\n","      <td>0.098004</td>\n","      <td>0.218612</td>\n","      <td>4432.0</td>\n","      <td>6352.99</td>\n","      <td>6370.00</td>\n","      <td>6345.76</td>\n","      <td>126.46</td>\n","      <td>804221.55</td>\n","      <td>6356.48</td>\n","      <td>21:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 22:00:00</th>\n","      <td>0.096688</td>\n","      <td>0.231342</td>\n","      <td>3980.0</td>\n","      <td>6350.85</td>\n","      <td>6378.47</td>\n","      <td>6345.00</td>\n","      <td>259.10</td>\n","      <td>1646353.87</td>\n","      <td>6361.93</td>\n","      <td>22:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 23:00:00</th>\n","      <td>0.103997</td>\n","      <td>0.217739</td>\n","      <td>3830.0</td>\n","      <td>6362.36</td>\n","      <td>6381.25</td>\n","      <td>6356.74</td>\n","      <td>81.54</td>\n","      <td>519278.69</td>\n","      <td>6368.78</td>\n","      <td>23:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-12 00:00:00</th>\n","      <td>0.094383</td>\n","      <td>0.195256</td>\n","      <td>3998.0</td>\n","      <td>6369.49</td>\n","      <td>6381.25</td>\n","      <td>6361.83</td>\n","      <td>124.55</td>\n","      <td>793560.22</td>\n","      <td>6380.00</td>\n","      <td>00:00:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                     Polarity  Sensitivity  Tweet_vol     Open     High   \n","2018-07-11 20:00:00  0.102657     0.216148     4354.0  6342.97  6354.19  \\\n","2018-07-11 21:00:00  0.098004     0.218612     4432.0  6352.99  6370.00   \n","2018-07-11 22:00:00  0.096688     0.231342     3980.0  6350.85  6378.47   \n","2018-07-11 23:00:00  0.103997     0.217739     3830.0  6362.36  6381.25   \n","2018-07-12 00:00:00  0.094383     0.195256     3998.0  6369.49  6381.25   \n","\n","                         Low  Volume_BTC  Volume_Dollar  Close_Price      time  \n","2018-07-11 20:00:00  6291.00      986.73     6231532.37      6350.00  20:00:00  \n","2018-07-11 21:00:00  6345.76      126.46      804221.55      6356.48  21:00:00  \n","2018-07-11 22:00:00  6345.00      259.10     1646353.87      6361.93  22:00:00  \n","2018-07-11 23:00:00  6356.74       81.54      519278.69      6368.78  23:00:00  \n","2018-07-12 00:00:00  6361.83      124.55      793560.22      6380.00  00:00:00  "]},"execution_count":196,"metadata":{},"output_type":"execute_result"}],"source":["Final_df.head()"]},{"cell_type":"code","execution_count":197,"metadata":{"_uuid":"15e69117506d3e0b9e269af2ed60f433963232ed","trusted":true},"outputs":[],"source":["hour_df = Final_df"]},{"cell_type":"code","execution_count":198,"metadata":{"_uuid":"d0d23c4a61c4b08c50a1d2c5252c6b89883f235f","trusted":true},"outputs":[],"source":["hour_df = hour_df.groupby(\"time\").agg(lambda x: x.mean())"]},{"cell_type":"code","execution_count":199,"metadata":{"_uuid":"b775bacdd82ac2d5e32d2da61caa710d684959e9","trusted":true},"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>Polarity</th>\n","      <th>Sensitivity</th>\n","      <th>Tweet_vol</th>\n","      <th>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Volume_BTC</th>\n","      <th>Volume_Dollar</th>\n","      <th>Close_Price</th>\n","    </tr>\n","    <tr>\n","      <th>time</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>00:00:00</th>\n","      <td>0.090298</td>\n","      <td>0.211771</td>\n","      <td>3976.384615</td>\n","      <td>6930.237692</td>\n","      <td>6958.360769</td>\n","      <td>6900.588462</td>\n","      <td>322.836154</td>\n","      <td>2.228120e+06</td>\n","      <td>6935.983077</td>\n","    </tr>\n","    <tr>\n","      <th>01:00:00</th>\n","      <td>0.099596</td>\n","      <td>0.211714</td>\n","      <td>4016.615385</td>\n","      <td>6935.140769</td>\n","      <td>6963.533846</td>\n","      <td>6894.772308</td>\n","      <td>318.415385</td>\n","      <td>2.243338e+06</td>\n","      <td>6933.794615</td>\n","    </tr>\n","    <tr>\n","      <th>02:00:00</th>\n","      <td>0.102724</td>\n","      <td>0.204445</td>\n","      <td>3824.083333</td>\n","      <td>6868.211667</td>\n","      <td>6889.440000</td>\n","      <td>6842.588333</td>\n","      <td>158.836667</td>\n","      <td>1.105651e+06</td>\n","      <td>6870.695833</td>\n","    </tr>\n","    <tr>\n","      <th>03:00:00</th>\n","      <td>0.105586</td>\n","      <td>0.214824</td>\n","      <td>3791.666667</td>\n","      <td>6870.573333</td>\n","      <td>6909.675833</td>\n","      <td>6855.316667</td>\n","      <td>328.811667</td>\n","      <td>2.385733e+06</td>\n","      <td>6888.139167</td>\n","    </tr>\n","    <tr>\n","      <th>04:00:00</th>\n","      <td>0.103095</td>\n","      <td>0.208516</td>\n","      <td>3822.916667</td>\n","      <td>6887.420000</td>\n","      <td>6911.649167</td>\n","      <td>6872.603333</td>\n","      <td>271.692500</td>\n","      <td>1.949230e+06</td>\n","      <td>6890.985000</td>\n","    </tr>\n","    <tr>\n","      <th>05:00:00</th>\n","      <td>0.108032</td>\n","      <td>0.215058</td>\n","      <td>3904.166667</td>\n","      <td>6891.468333</td>\n","      <td>6911.175833</td>\n","      <td>6869.017500</td>\n","      <td>213.315000</td>\n","      <td>1.524601e+06</td>\n","      <td>6890.451667</td>\n","    </tr>\n","    <tr>\n","      <th>06:00:00</th>\n","      <td>0.104412</td>\n","      <td>0.210424</td>\n","      <td>3760.250000</td>\n","      <td>6889.327500</td>\n","      <td>6907.070833</td>\n","      <td>6868.484167</td>\n","      <td>183.329167</td>\n","      <td>1.281427e+06</td>\n","      <td>6891.371667</td>\n","    </tr>\n","    <tr>\n","      <th>07:00:00</th>\n","      <td>0.100942</td>\n","      <td>0.209435</td>\n","      <td>4056.000000</td>\n","      <td>6891.645833</td>\n","      <td>6908.654167</td>\n","      <td>6858.290833</td>\n","      <td>329.882500</td>\n","      <td>2.263694e+06</td>\n","      <td>6878.757500</td>\n","    </tr>\n","    <tr>\n","      <th>08:00:00</th>\n","      <td>0.099380</td>\n","      <td>0.210113</td>\n","      <td>5095.583333</td>\n","      <td>6878.635833</td>\n","      <td>6903.660833</td>\n","      <td>6851.435833</td>\n","      <td>368.109167</td>\n","      <td>2.616314e+06</td>\n","      <td>6885.867500</td>\n","    </tr>\n","    <tr>\n","      <th>09:00:00</th>\n","      <td>0.099369</td>\n","      <td>0.204565</td>\n","      <td>4650.083333</td>\n","      <td>6886.526667</td>\n","      <td>6915.735000</td>\n","      <td>6852.922500</td>\n","      <td>397.702500</td>\n","      <td>2.740251e+06</td>\n","      <td>6888.328333</td>\n","    </tr>\n","    <tr>\n","      <th>10:00:00</th>\n","      <td>0.099587</td>\n","      <td>0.203848</td>\n","      <td>4932.833333</td>\n","      <td>6888.095000</td>\n","      <td>6921.905000</td>\n","      <td>6870.502500</td>\n","      <td>318.982500</td>\n","      <td>2.194532e+06</td>\n","      <td>6905.600833</td>\n","    </tr>\n","    <tr>\n","      <th>11:00:00</th>\n","      <td>0.097061</td>\n","      <td>0.203488</td>\n","      <td>4996.333333</td>\n","      <td>6904.373333</td>\n","      <td>6928.490000</td>\n","      <td>6885.326667</td>\n","      <td>256.131667</td>\n","      <td>1.766995e+06</td>\n","      <td>6907.590000</td>\n","    </tr>\n","    <tr>\n","      <th>12:00:00</th>\n","      <td>0.099139</td>\n","      <td>0.207495</td>\n","      <td>5446.583333</td>\n","      <td>6908.016667</td>\n","      <td>6942.194167</td>\n","      <td>6890.370000</td>\n","      <td>314.837500</td>\n","      <td>2.200126e+06</td>\n","      <td>6922.389167</td>\n","    </tr>\n","    <tr>\n","      <th>13:00:00</th>\n","      <td>0.097565</td>\n","      <td>0.208969</td>\n","      <td>5565.083333</td>\n","      <td>6922.530000</td>\n","      <td>6948.463333</td>\n","      <td>6902.968333</td>\n","      <td>260.825000</td>\n","      <td>1.808326e+06</td>\n","      <td>6925.079167</td>\n","    </tr>\n","    <tr>\n","      <th>14:00:00</th>\n","      <td>0.098485</td>\n","      <td>0.212563</td>\n","      <td>5736.583333</td>\n","      <td>6925.589167</td>\n","      <td>6952.400833</td>\n","      <td>6908.944167</td>\n","      <td>288.322500</td>\n","      <td>2.033892e+06</td>\n","      <td>6936.545833</td>\n","    </tr>\n","    <tr>\n","      <th>15:00:00</th>\n","      <td>0.099428</td>\n","      <td>0.212782</td>\n","      <td>5686.500000</td>\n","      <td>6934.180833</td>\n","      <td>6965.962500</td>\n","      <td>6915.395833</td>\n","      <td>326.230000</td>\n","      <td>2.313765e+06</td>\n","      <td>6937.513333</td>\n","    </tr>\n","    <tr>\n","      <th>16:00:00</th>\n","      <td>0.098327</td>\n","      <td>0.215643</td>\n","      <td>5647.250000</td>\n","      <td>6937.987500</td>\n","      <td>6970.406667</td>\n","      <td>6914.132500</td>\n","      <td>411.930833</td>\n","      <td>2.977255e+06</td>\n","      <td>6947.045000</td>\n","    </tr>\n","    <tr>\n","      <th>17:00:00</th>\n","      <td>0.099674</td>\n","      <td>0.219544</td>\n","      <td>5482.333333</td>\n","      <td>6947.205000</td>\n","      <td>6986.905000</td>\n","      <td>6928.375833</td>\n","      <td>421.290833</td>\n","      <td>3.020724e+06</td>\n","      <td>6946.573333</td>\n","    </tr>\n","    <tr>\n","      <th>18:00:00</th>\n","      <td>0.098512</td>\n","      <td>0.223854</td>\n","      <td>5340.666667</td>\n","      <td>6946.270833</td>\n","      <td>7020.676667</td>\n","      <td>6917.940000</td>\n","      <td>621.599167</td>\n","      <td>4.405309e+06</td>\n","      <td>6985.155833</td>\n","    </tr>\n","    <tr>\n","      <th>19:00:00</th>\n","      <td>0.094025</td>\n","      <td>0.227546</td>\n","      <td>5345.583333</td>\n","      <td>6984.812500</td>\n","      <td>7048.856667</td>\n","      <td>6948.802500</td>\n","      <td>635.320833</td>\n","      <td>4.639426e+06</td>\n","      <td>6990.852500</td>\n","    </tr>\n","    <tr>\n","      <th>20:00:00</th>\n","      <td>0.096545</td>\n","      <td>0.221846</td>\n","      <td>4829.692308</td>\n","      <td>6941.196923</td>\n","      <td>6968.019231</td>\n","      <td>6903.869231</td>\n","      <td>396.382308</td>\n","      <td>2.764029e+06</td>\n","      <td>6937.162308</td>\n","    </tr>\n","    <tr>\n","      <th>21:00:00</th>\n","      <td>0.100486</td>\n","      <td>0.227031</td>\n","      <td>4586.076923</td>\n","      <td>6937.536923</td>\n","      <td>6961.624615</td>\n","      <td>6902.450000</td>\n","      <td>320.918462</td>\n","      <td>2.235449e+06</td>\n","      <td>6929.238462</td>\n","    </tr>\n","    <tr>\n","      <th>22:00:00</th>\n","      <td>0.101451</td>\n","      <td>0.231768</td>\n","      <td>4215.769231</td>\n","      <td>6928.340769</td>\n","      <td>6960.293846</td>\n","      <td>6899.097692</td>\n","      <td>201.525385</td>\n","      <td>1.425684e+06</td>\n","      <td>6924.276923</td>\n","    </tr>\n","    <tr>\n","      <th>23:00:00</th>\n","      <td>0.096164</td>\n","      <td>0.218966</td>\n","      <td>4081.230769</td>\n","      <td>6924.337692</td>\n","      <td>6960.037692</td>\n","      <td>6893.017692</td>\n","      <td>259.863846</td>\n","      <td>1.851852e+06</td>\n","      <td>6928.529231</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          Polarity  Sensitivity    Tweet_vol         Open         High   \n","time                                                                     \n","00:00:00  0.090298     0.211771  3976.384615  6930.237692  6958.360769  \\\n","01:00:00  0.099596     0.211714  4016.615385  6935.140769  6963.533846   \n","02:00:00  0.102724     0.204445  3824.083333  6868.211667  6889.440000   \n","03:00:00  0.105586     0.214824  3791.666667  6870.573333  6909.675833   \n","04:00:00  0.103095     0.208516  3822.916667  6887.420000  6911.649167   \n","05:00:00  0.108032     0.215058  3904.166667  6891.468333  6911.175833   \n","06:00:00  0.104412     0.210424  3760.250000  6889.327500  6907.070833   \n","07:00:00  0.100942     0.209435  4056.000000  6891.645833  6908.654167   \n","08:00:00  0.099380     0.210113  5095.583333  6878.635833  6903.660833   \n","09:00:00  0.099369     0.204565  4650.083333  6886.526667  6915.735000   \n","10:00:00  0.099587     0.203848  4932.833333  6888.095000  6921.905000   \n","11:00:00  0.097061     0.203488  4996.333333  6904.373333  6928.490000   \n","12:00:00  0.099139     0.207495  5446.583333  6908.016667  6942.194167   \n","13:00:00  0.097565     0.208969  5565.083333  6922.530000  6948.463333   \n","14:00:00  0.098485     0.212563  5736.583333  6925.589167  6952.400833   \n","15:00:00  0.099428     0.212782  5686.500000  6934.180833  6965.962500   \n","16:00:00  0.098327     0.215643  5647.250000  6937.987500  6970.406667   \n","17:00:00  0.099674     0.219544  5482.333333  6947.205000  6986.905000   \n","18:00:00  0.098512     0.223854  5340.666667  6946.270833  7020.676667   \n","19:00:00  0.094025     0.227546  5345.583333  6984.812500  7048.856667   \n","20:00:00  0.096545     0.221846  4829.692308  6941.196923  6968.019231   \n","21:00:00  0.100486     0.227031  4586.076923  6937.536923  6961.624615   \n","22:00:00  0.101451     0.231768  4215.769231  6928.340769  6960.293846   \n","23:00:00  0.096164     0.218966  4081.230769  6924.337692  6960.037692   \n","\n","                  Low  Volume_BTC  Volume_Dollar  Close_Price  \n","time                                                           \n","00:00:00  6900.588462  322.836154   2.228120e+06  6935.983077  \n","01:00:00  6894.772308  318.415385   2.243338e+06  6933.794615  \n","02:00:00  6842.588333  158.836667   1.105651e+06  6870.695833  \n","03:00:00  6855.316667  328.811667   2.385733e+06  6888.139167  \n","04:00:00  6872.603333  271.692500   1.949230e+06  6890.985000  \n","05:00:00  6869.017500  213.315000   1.524601e+06  6890.451667  \n","06:00:00  6868.484167  183.329167   1.281427e+06  6891.371667  \n","07:00:00  6858.290833  329.882500   2.263694e+06  6878.757500  \n","08:00:00  6851.435833  368.109167   2.616314e+06  6885.867500  \n","09:00:00  6852.922500  397.702500   2.740251e+06  6888.328333  \n","10:00:00  6870.502500  318.982500   2.194532e+06  6905.600833  \n","11:00:00  6885.326667  256.131667   1.766995e+06  6907.590000  \n","12:00:00  6890.370000  314.837500   2.200126e+06  6922.389167  \n","13:00:00  6902.968333  260.825000   1.808326e+06  6925.079167  \n","14:00:00  6908.944167  288.322500   2.033892e+06  6936.545833  \n","15:00:00  6915.395833  326.230000   2.313765e+06  6937.513333  \n","16:00:00  6914.132500  411.930833   2.977255e+06  6947.045000  \n","17:00:00  6928.375833  421.290833   3.020724e+06  6946.573333  \n","18:00:00  6917.940000  621.599167   4.405309e+06  6985.155833  \n","19:00:00  6948.802500  635.320833   4.639426e+06  6990.852500  \n","20:00:00  6903.869231  396.382308   2.764029e+06  6937.162308  \n","21:00:00  6902.450000  320.918462   2.235449e+06  6929.238462  \n","22:00:00  6899.097692  201.525385   1.425684e+06  6924.276923  \n","23:00:00  6893.017692  259.863846   1.851852e+06  6928.529231  "]},"execution_count":199,"metadata":{},"output_type":"execute_result"}],"source":["hour_df"]},{"cell_type":"code","execution_count":200,"metadata":{"_uuid":"b91b3d741a17c83cf34f54a8832828027819f42f","trusted":true},"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>Polarity</th>\n","      <th>Sensitivity</th>\n","      <th>Tweet_vol</th>\n","      <th>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Volume_BTC</th>\n","      <th>Volume_Dollar</th>\n","      <th>Close_Price</th>\n","    </tr>\n","    <tr>\n","      <th>time</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>00:00:00</th>\n","      <td>0.090298</td>\n","      <td>0.211771</td>\n","      <td>3976.384615</td>\n","      <td>6930.237692</td>\n","      <td>6958.360769</td>\n","      <td>6900.588462</td>\n","      <td>322.836154</td>\n","      <td>2.228120e+06</td>\n","      <td>6935.983077</td>\n","    </tr>\n","    <tr>\n","      <th>01:00:00</th>\n","      <td>0.099596</td>\n","      <td>0.211714</td>\n","      <td>4016.615385</td>\n","      <td>6935.140769</td>\n","      <td>6963.533846</td>\n","      <td>6894.772308</td>\n","      <td>318.415385</td>\n","      <td>2.243338e+06</td>\n","      <td>6933.794615</td>\n","    </tr>\n","    <tr>\n","      <th>02:00:00</th>\n","      <td>0.102724</td>\n","      <td>0.204445</td>\n","      <td>3824.083333</td>\n","      <td>6868.211667</td>\n","      <td>6889.440000</td>\n","      <td>6842.588333</td>\n","      <td>158.836667</td>\n","      <td>1.105651e+06</td>\n","      <td>6870.695833</td>\n","    </tr>\n","    <tr>\n","      <th>03:00:00</th>\n","      <td>0.105586</td>\n","      <td>0.214824</td>\n","      <td>3791.666667</td>\n","      <td>6870.573333</td>\n","      <td>6909.675833</td>\n","      <td>6855.316667</td>\n","      <td>328.811667</td>\n","      <td>2.385733e+06</td>\n","      <td>6888.139167</td>\n","    </tr>\n","    <tr>\n","      <th>04:00:00</th>\n","      <td>0.103095</td>\n","      <td>0.208516</td>\n","      <td>3822.916667</td>\n","      <td>6887.420000</td>\n","      <td>6911.649167</td>\n","      <td>6872.603333</td>\n","      <td>271.692500</td>\n","      <td>1.949230e+06</td>\n","      <td>6890.985000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          Polarity  Sensitivity    Tweet_vol         Open         High   \n","time                                                                     \n","00:00:00  0.090298     0.211771  3976.384615  6930.237692  6958.360769  \\\n","01:00:00  0.099596     0.211714  4016.615385  6935.140769  6963.533846   \n","02:00:00  0.102724     0.204445  3824.083333  6868.211667  6889.440000   \n","03:00:00  0.105586     0.214824  3791.666667  6870.573333  6909.675833   \n","04:00:00  0.103095     0.208516  3822.916667  6887.420000  6911.649167   \n","\n","                  Low  Volume_BTC  Volume_Dollar  Close_Price  \n","time                                                           \n","00:00:00  6900.588462  322.836154   2.228120e+06  6935.983077  \n","01:00:00  6894.772308  318.415385   2.243338e+06  6933.794615  \n","02:00:00  6842.588333  158.836667   1.105651e+06  6870.695833  \n","03:00:00  6855.316667  328.811667   2.385733e+06  6888.139167  \n","04:00:00  6872.603333  271.692500   1.949230e+06  6890.985000  "]},"execution_count":200,"metadata":{},"output_type":"execute_result"}],"source":["hour_df.head()"]},{"cell_type":"code","execution_count":201,"metadata":{"_uuid":"ebc381771aa7b44f8556e818a2a95dbdb82f58dc","trusted":true},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAlAAAAJZCAYAAABm7QuNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1xX1f/A8RcbAUFBBHOLCrkYAkqJKZmZmgs1B25zZOFeiQPNjXuk5i7KkYiVlqaWaV8BwVUC7q2AA1QQ+LB+f/Dj6ie2Ih/E9/P7+Dy+fs4599z3vVm8Oefcc7UyMjIyEEIIIYQQBaat6QCEEEIIIV43kkAJIYQQQhSSJFBCCCGEEIUkCZQQQgghRCFJAiWEEEIIUUiSQAkhhBBCFJIkUEIIIYQQhSQJlBBCCCFEIUkCJUQJJ3vdCiFEySMJlHjj9OnTB1tbW7VPgwYNaNGiBb6+vjx69KhQ/U2aNAkPD48iic3W1pYVK1YAoFKpmDNnDj///HOR9F0YK1asyHaPHBwc6NixI9u2bVPaBQcHZ2uX0+fWrVvKMcnJyWzevBlPT08aN26Mq6srPXr0IDAwMM9k0cfHh3r16nHv3r1c2wwbNgwPDw/S09PzvcaAgIBssQk4e/YsH374ISqVCgAPDw8mTZqUY9tbt25ha2tLQEBAnn3++OOPDBkypMhjFUKTdDUdgBCaUK9ePaZPn658T0lJ4dy5cyxevJiIiAh++OEHtLS0ij2u7du3Y21tDUBMTAxbtmxh7ty5xR7H8/EApKenEx8fz19//cX06dPR0dGhW7du1K9fX2kDcO7cOWbOnMm0adOoX7++Ul6xYkUA7t+/z+DBg7l79y59+vShUaNGpKen88cffzBp0iRCQ0OZNWtWjvfe09OTnTt3snfvXvr375+t/sGDBxw9epThw4ejrS2/G76I5ORkJk6cyPjx49HX1y+yfj09PfH39+fHH3+ka9euRdavEJokCZR4I5mYmODg4KBW5uLiQkJCAsuXL+fMmTPZ6ouDJs6Zl//G07x5cyIjI9m2bRvdunXLdh+Tk5MBqF27do7XMnHiRKKioti+fTs1atRQylu0aMFbb73F4sWLadmyJe+//362Yx0dHbGxseHnn3/OMYH6+eefSU9Pp0uXLi9yqQL4/vvv0dXVpVWrVkXar5aWFkOHDmXmzJm0b98eQ0PDIu1fCE2QX9OEeE6DBg0AuHPnjlK2b98+unTpgqOjI++++y7Tpk3Lc5ovKSmJRYsW0bp1axo0aICTkxMDBgwgIiJCaTNp0iT69evH9OnTcXJyom3btqSlpSlTeLdu3VKSiMmTJ+Ph4cGff/6Jra0tx44dUztfaGgotra2hIWFZYvl7t27NG7cmD59+ihlycnJtG3blnbt2ikJT2GYmpq+0OhcREQEx44dY9CgQWrJU5b+/fvTu3dvjIyMcu3D09OTf//9l6tXr2ar2717N++88w5vvfUWAP/88w+DBg2iSZMmODk5MWzYMC5evJhr33369FG7T/BsijI4OBjInPZr2LAhoaGheHp60rBhQz788EMOHz7MlStX6NevH/b29nzwwQfs3btXra87d+4wZswYXF1dsbe3p1+/foSHh+caT5aDBw/Sq1cvHB0dadCgAW3atMHf3x/I/GfZuHFj5s+fr3ZMamoqTZs25auvvgIyR1j9/Pxo3rw5jRo1YtCgQQQGBqpNYapUKjZt2kT79u3zjSk3WVN6mzZtok2bNtjb27Nr1y4AWrZsSXJysvJdiNedJFBCPCfrB3PVqlUBWL16NWPGjMHBwYHly5czYsQI9u/fT58+fUhKSsqxjwkTJrBr1y6GDBnCxo0bmTx5MhcvXmTs2LFqa3xCQ0O5e/cuq1atYuzYsejo6Ch1FStWZOXKlQAMHz6clStX4u7uTsWKFdmzZ4/a+QIDA6lRowaNGzfOFkulSpWYNGkSISEhyg+uRYsWcePGDRYtWoSBgUGe9yM1NVX5PH78mF9++YW//voLLy+v/G5lNkePHgXIdb2YgYEB06ZNw83NLdc+OnbsiK6ubrZ1YZGRkURGRtKtWzcAgoKC6NmzJwBz5szhq6++4u7du/To0YPLly8XOvbnpaamMnbsWHr06MHXX39NmTJlGDduHMOGDaNFixasWbOGihUrKqNtAA8fPqRHjx6cO3eOqVOnsmjRItLT0+ndu3ee8fz555+MGDGC+vXrs3r1alasWEHVqlWZOXMmZ86cwcDAgA8//JBff/1V7e/W33//TWxsLB07dgRg2rRpbNmyBS8vL1atWkWFChWYOnWq2rmCg4OJjo6mdevW2eLIyMhQ+7uQ9cltrdmKFSv49NNPWbBgAe+++y6Q+c+3ZcuWGlnTJ8SrIFN44o2U9QMhy6NHjwgJCeHrr79WftN/9OgRX3/9Nd27d2fatGlK27p169K7d2927dpF79691fpVqVQkJCTg4+ND27ZtAXB1dSU+Pp558+Zx//59LC0tgcwfxDNnzlTWPD1PX1+ft99+G4Bq1apRr149ADp37sy3335LQkICxsbGJCUl8euvv+a5QLdbt24cOHCABQsWUK5cObZu3cr48eOxs7PL9z49v44pi4eHh3JthXH37l0AqlSpUuhjs1SoUIEWLVrwyy+/4O3trZQHBgZSvnx5JTlbtGgR1atXZ926dUpi2qxZMz744AOWL1/OsmXLXjiG9PR0hg0bpiRrjx8/ZvTo0fTr148BAwYAULZsWWW0zNrami1bthAXF8cPP/xA5cqVgczp0LZt27Js2TKWL1+e47kuXbpE586dmTJlilLm6OhIkyZNCA4Oxt7eno4dO7Jr1y7CwsJwdnYGYO/evdSqVYuGDRty48YNdu/ezcSJE5X43N3duX//vtpoZlBQEKamptSsWTNbHIGBgQQGBhb4Hn300Ud4enpmK2/YsCH79u0jPj4eExOTAvcnREkkCZR4I504cSJbcqCtrc0777zDzJkz0dLS4vTp06hUqmxTGs7OzlSuXJmQkJBsCZS+vj4bNmwAIDo6mqtXr3Lt2jX++OMPAOXJJoBy5crlmDzlxdPTk7Vr1/L777/TqVMnfv/9d54+fUqnTp3yPO6rr76iffv2fP7557i6ujJw4MACne/HH39U/pyYmMg///zDmjVrGDRoEJs3b1YbNctPVtu0tLQCH5MTT09Phg8fzpkzZ7C3tyctLY2ff/6Zjh07oq+vz9OnT/nnn3/4/PPP1eIzNTWlZcuWHDly5KXOD5lJTBYLCwsA7O3tlbJy5coBmckVwPHjx3n77bexsrJSEndtbW2aN2/OTz/9lOt5Bg8eDEBCQgJXr17lxo0b/PPPP8Czv0uurq689dZb7N27F2dnZ5KTkzl48KCSVAcHB5ORkUGbNm3U+m7fvr1aAnXz5k0lufuvli1bMmLEiGzl9+7dY/jw4dnKs5L//6pcuTJpaWlERUVRu3btXK9biNeBJFDijVS/fn18fX2BzAWuBgYGVKpUSe234qx1ThUqVMh2fIUKFXjy5EmOfR89epQ5c+Zw5coVjI2NsbOzU9b1PD/NYmxsXOi4q1evjqurK4GBgXTq1InAwEDeeecdrKys8jzOysoKNzc39u/fT4sWLQq8hqlhw4Zq311dXbG0tGT8+PEcOnQox+me3GT9cL5z506uPzyjo6OpWLFinvE1b94cS0tLfv75Z+zt7Tl27Bj3799XRoSePHlCRkZGof+5FUZOoydlypTJtX1cXBzXr1/PcUQPMpPTnI5/+PAh06dP5+DBg2hpaVG9enVllCnr75KWlhYff/wxO3fuxMfHhz/++IOnT5/y8ccfK33As0Qvy3+/x8fH53oN5cqVy/Z3Ach1C4jc1rFllRfFPwMhNE0SKPFGMjY2zvEHwvPMzMyAzEfva9WqpVZ37949ZZ3U827cuMGIESNo1aoVa9eupWrVqmhpaeHv76+sAXpZnp6efPnll1y+fJnjx4/j5+eX7zHHjh1j//79vP3226xYsYIPPvggx/gLImuh/bVr1wp1XLNmzQA4cuRIjglUamoqHTt2xMnJidWrV+faj66uLp06dSIgIIDJkycTGBiIg4OD0mfZsmXR0tLi/v372Y69d++eMjqUk/+Ojj19+rQgl5avsmXL4urqyoQJE3Ksz23LgHHjxnHlyhU2b96Mo6Mj+vr6JCYmsmPHDrV2HTt2ZO3atQQHB7Nv3z5cXFyUhDUrub5//76ywB6eJVZZypcvT0xMzAtfY0Fk/VJSvnz5V3oeIYqDLCIXIhf29vbo6+vzyy+/qJWHhoZy584dnJycsh3z77//kpyczJAhQ6hWrZoykpKVPBVmV/Hcpsc+/PBDypQpw4wZMzA2Ns73kfMnT57g4+PDO++8w3fffYepqSlffvnlC+9wfvbsWYAcn6TLS506dWjevDnffPMNN2/ezFa/du1aYmNj6dChQ759eXp68uDBA44dO8aff/6ptreQkZERDRo04Ndff1VLiJ48ecKff/6Z42J7yBxVylr0nSWnJxtfhKurK1evXqVmzZo0bNhQ+ezZs4cff/wx13/WYWFhtG7dmiZNmihJ1l9//QWgtoDbxsaG+vXrs3fvXo4cOaJ2Dxs3boyOjg6///67Wt8HDhxQ+/7WW28RFRX1Sne+j46ORkdHJ98RUyFeBzICJUQuypUrx5AhQ1i1ahV6enq0bNmSW7dusWzZMmrXrk3nzp2zHVO/fn10dXVZuHAhAwcORKVSERAQwJ9//gkUbkSjbNmyQOb6GRsbG2WNTZkyZWjXrh3bt2+nZ8+e+W54OGfOHGJjY9m6dSsmJiZMnTqVESNG8N1332V7bP+/Tp8+rfw5LS2Nc+fOsXz5curWrUuLFi0KfC1ZfH196devH927d6dv377Y29uTkJDAb7/9xt69e+nRo0e2tTo5qVmzJk5OTsyZMwcg26L2sWPHMmjQIIYMGUKvXr1ISUlh3bp1qFSqHNfyQOY6n8OHDzN37lw8PDwIDQ0t1MLpvPTv3589e/bQv39/Bg4cSPny5dm3bx87duxg8uTJuR7XqFEjfv75Z+rXr4+1tTUnT55k3bp1aGlpkZiYqNa2Y8eOzJ8/H11dXbV7WLVqVTw9PVm8eDEpKSnY2dnx+++/K+vysjYdfffdd1m3bh0XLlzA1ta2SK77v7IWuuc13SnE60ISKCHy8MUXX1ChQgW+++47tm/fTrly5WjTpg2jRo3KcZ1H9erVWbRoEStXrmT48OGYmZnh4ODAt99+S58+fZQ9mwrCxMSEAQMGsH37do4cOcLff/+Nnp4ekLnx5Pbt2/PdNPLIkSMEBAQwfvx4qlWrBkCrVq1o3bo1ixYtonnz5lSvXj3X4z/55BPlz3p6elSsWJG2bdsycuTIF9qp+q233mL79u1s2bKFX375hXXr1qGvr0+tWrVYtGhRoZ7u69q1K19++SWenp7Z1pO5ubmxadMmli9fzpgxY9DX18fZ2Zn58+dTp06dHPvz9PRUnljbtm0bLi4uLF++XNkO4WVYWVmxbds2Fi1axIwZM0hOTqZGjRrMnj07z525582bx6xZs5g1axaQOern6+vLTz/9RGhoqFrb9u3bs2DBAlq2bKkk31mmTp2KkZERGzduJD4+Hjc3N4YPH86qVauUv8fOzs5YWFhw5MiRV5JAJScnExwczKhRo4q8byE0QStD3lQqxGtn+vTpnDlzpshGSETpFRcXx19//YW7u7va2qP58+cTEBCgbBIKsHHjRn744QcOHDhQ5K8yCgwMxM/Pj4MHD8pO5KJUkBEoIV4jW7du5cqVK+zYsYOFCxdqOhzxGihTpgyzZ8/m7bffpl+/fhgZGXH69Gm+++47hg4dqta2V69e+Pv789tvv/HRRx8VWQzp6els3LiRzz//XJInUWrICJQQrxFvb2+OHj3KJ598wqRJkzQdjnhNREREsHTpUk6fPk1iYiLVqlWjR48e9O7dO9tI08mTJ5k0aRK//PJLkb1QeOfOnfz222/KHmlClAaSQAkhhBBCFJJsYyCEEEIIUUiSQAkhhBBCFJIkUEIIIYQQhSQJlBBCCCFEIck2BiWYw+dLXrqP0ytHF0EkQgghBKRH1S2SfrStLxRJP5qk8QQqPT2dlStXsnPnTp48eYKLiwvTpk1TXnQaERHB7Nmz+ffffzE3N6d///707ds3zz5//fVXVqxYwa1bt6hVqxYTJ07Ezc1NqY+NjeWrr77ir7/+QktLi3bt2jFhwoQ8Xy+QXxxZ16Gjo8PHH3+MtbU1kPtLQgvi8NyhaGtrEXEjhmV7jhF5K+8XfTraVOaLj9+lbuUKPElM5o8zl1/43EIIIcR/pZOef6MCKA3TXxq/htWrV/P9998za9Ystm3bRnp6OoMHD0alUhEbG8uAAQOoVq0au3btYsSIEfj5+bFr165c+wsKCmL8+PH06NGD3bt34+bmxpAhQ7h8+Vky4e3tzfXr19m8eTPLli3jyJEjzJgxI9c+CxLH6tWrKVu2LMOHD8fQ0JDly5fz888/v9S98f3+d6Z9ewA9XR02jelO9Yq5v8HcxtqCNZ93QZWaxoSNe1n7axBtXe1e6vxCCCGEyJlG94FSqVQ0bdqUcePG0atXLwAeP36Mu7s7s2fP5vbt23z33Xf88ccf6OpmDpYtXryY/fv3s3///hz7HDRoEGXLlmXp0qVKWY8ePahbty4zZ87k1KlT9OjRg3379mFjYwPAsWPHGDx4MEeOHMnxLeFr167NMw6VSkWLFi04cuSI8q6yrOs4fPgwFhYWL3R/sqbwDPV12ec7iAMnLzBv5x85tv3i43fp3dKJlpPWkKhKAcDz3YZM7dnqhc4thBBC/Ffy3VpF0o9BpStF0o8maXQEKjIykoSEBLXpNVNTU+rVq8eJEycIDQ3F1dVVSVoAmjZtyrVr17h//z4AkyZNwsPDA8icRjt58qRafwBNmjThxIkTAISGhmJpaakkTwCurq5oaWkRFhYGQEBAALa2tty6dUs5Jq84IiMjiY2NJTo6Ott1xMbGvvR9SlKlEhMXT5UKZrm20dfTITUtjaSUFKXsUULSS59bCCGEyJJORpF8SgONJlBRUVEAVKpUSa28YsWKREVFERUVpawler4O4O7duwBMmTKFH3/8Ecgc9Xn69GmOx2SdKzo6Otv59PX1KVeunNJn27ZtOXbsmNIuvziioqJIT0+nQoUKam169epFrVovn61XrWCGzVsWXI56kGubwOPnABjX5T3MjA2xsbZgaNumL31uIYQQIkt6Ef2vNNDoIvLExEQg+0JrAwMDHj16RFJSUo51AMnJyQCULVtWqUtKSsq1v6z2iYmJOS7sfr6NoaGh2gsv84sjt+v4+OOPOX36NA4ODjlef0EY6Okwq08bVClpbPvzdK7tLt99wNI9x5jcvSW9WzoBcOfBoxc+rxBCCCFyp9EEKitJUalUaglLcnIyZcqUwdDQEJVKpXZMVpJjZGSUrb+spCanY7KesMupz6w2OfWZ2zHPx5Hbdfz444+0bds2xz4LwshAjyVDOlC/hhXj1//C3dgnubYd8IELIzs2Y9uR0xw6c4nyxmX49KMmL3xuIYQQ4r/S5PW5Co1O4WVNkcXEqD+eHxMTg5WVFdbW1jnWATku9i5XrhxGRka59gfk2KdKpSIuLk6Zlvuv/OLI7Tp27dqV62L3gtg0+hMcar3FxI37+POf3Bfc6WhrMaRNE/aGRDBv5x+cuHCTA6cuMGT5jy98biGEEOK/ZA3UMxpNoOzs7DAxMSE4OFgpe/z4MeHh4bi4uODi4kJYWBhpaWlKfVBQEDVr1szxyTYtLS2cnJwICQlRKw8ODsbZ2RkAFxcXoqKiuH79ulKf1b5x48Y5xplfHHZ2dlSpUkVZZ/X8dZibmxfmlqipZF6W4SsDOHzmUp7typsYUcZAj9NX7qiVx8YnvvC5hRBCCJE7jSZQ+vr6eHl54efnx6FDh4iMjGT06NFYW1vTunVrPD09iY+PZ8qUKVy6dImAgAA2b97M0KFDlT6ePHnCw4cPle8DBgxg7969bNq0icuXL7NgwQIiIiLo168fAPb29jg5OTF69GjOnj1LUFAQ06ZNo1OnTsooVVJSEvfu3VMSpvzi0NfXp2fPnri6unLp0iW162jWrNkL35/+i7dz8vLtfNs9fPKUuIREnGpXVisvZ2yYyxFCCCFE4aWRUSSf0kCj+0ABpKWlsXjxYgICAkhKSlJ2Iq9SpQoAZ8+eZfbs2YSHh2NpacnAgQPx8vJSjp80aRIhISEcPnxYKQsMDGT16tVERUVRu3Ztxo8fr7a1wYMHD/D19eXo0aMYGBjQpk0bJk+erKyhCggIYPLkyRw6dKjAcaSlpXHu3DlsbW1ZtWoVAIMHD8bU1PSF700fvx/UvickqbgSlZks2laxJCU1Tfn+SXN7Jnf3YOexs/x+8gLlTMowqLULtlVynpYUQgghCuvencr5NyoAy7fyHxwo6TSeQImCC714k8HLMtc17fMdyJ2Hj5XvAG1d7Ojr0Zha1ubEJSRx8vJt2jS21VS4QgghShlJoJ6RBKoEk5cJCyGEKEmibr9VJP1YV76Tf6MSTuMvExZCCCHE66F0bIFZNGQESgghXoDT8JcbIT75tYwOi9fPnSIagXpLRqCEEOLN9IffcFSpaQRFXGdZwFEePnla4GN7tnR8hZEJ8eqUlifoioJGtzEQQojX1fSt+1m0808a16nMis87oatdsP+cVqtYjs87vfuKoxPi1UjLKJpPaSAjUEII8QL+Opv5doAnT5NZ5d2FRjaVOHkx7yeLtLW08O37IY/ikyhjrlccYQpRpGQN1DMyAiWEEC8hLiHzJebljMvk27bPB40xNzVi0/6QfNsKIUo2GYESQogXUMHUGH09HUZ1cedeXDzBkTfybF+rkgVD27nx+coAKluYFVOUQhStNLQ0HUKJIQmUEEK8gAPzhwDwKCGJIUt2kpCkyrWtjrYWs/p/SODf/3Dy4m1JoMRrK72UrF8qCjKFJ4QQL+Cz5bsY9fUeLt+5z2rvLtSwKp9r20EfNcGkjAHLA48VY4RClF6BgYG0bduWhg0b0q5dO3799Vel7tatWwwdOhQnJyeaNWvG0qVLlXfbZvH39+f999+nUaNG9OrVi/Dw8ELHIAmUEEK8gKCIG/x19gojVgSQnJLKZx1yfrLOtoolAz90Ybb/QVJS09DR1kJbS6ZBxOspDa0i+byMPXv2MGXKFHr37s3evXtp3749Y8aM4dSpU6SkpDBo0CAAtm3bxowZM/jhhx+Ud9QC7N69mwULFjBy5EgCAgKoUqUKAwYM4OHDh4WKQ6bwhBDiJSSnpBF+PQabSuY51rewt0FfT5c1o7oWc2RCFD1Nr4HKyMhg2bJl9O3bl969ewMwfPhwQkNDCQkJ4fbt29y5c4cdO3ZgZmZG3bp1efDgAQsWLGDYsGHo6+uzZs0avLy86NChAwBz5syhVatW7Ny5k6FDhxY4FkmghBDiJejr6mBXrSJX7z7IsX7XsX/465+ramXNG9ZkaHu34ghPiFLl6tWr3L59m48//litfMOGDQDMmDGD+vXrY2b2bJ1h06ZNiY+PJyIigipVqnDt2jXc3J79+6erq4uzszMnTpyQBEoIIV61d+pVR09Xh97vO1HRzJgpG/cpdbZVLFGlpnE16iH3HyVw/1GC2rG137Io7nCFKBLpGUUzAvX+++/nWX/o0KEcy69ezfxl5OnTpwwaNIjw8HCqVKnC8OHD8fDwICoqCmtra7VjKlasCMDdu3fR1c1MeypVqpStTWRkZKGuQdZACSHEC/Ab2oGpXh+QmJxCn/k/8M/VKKVu0bCPmdzTQ4PRCfFqaHoNVHx8PAATJ06kffv2bNy4kXfffZfPPvuM48ePk5SUhL6+vtoxBgYGACQnJ5OYmAiQY5vk5ORCxSIjUEII8QLeGbki17r2PhvzPPbnoHB8+31Y1CEJ8drIbYQpP3p6mTv4Dxo0iM6dOwPw9ttvEx4ezqZNmzA0NESlUt9SJCsxMjIywtDQECDHNmXK5L8Z7vNkBEoIIYQQBZKGdpF8XpSVlRUAdevWVSuvXbs2t27dwtrampiYGLW6rO9WVlbK1F1ObbL6LigZgRJCiBdw8uvRmg6h1PhAu9tLHf97+s4iikTkp6jWQL2o+vXrY2xszJkzZ3B2dlbKL1y4QLVq1XBxcSEwMJD4+HhMTEwACAoKwtjYGDs7O/T19alZsybBwcHKQvLU1FRCQ0Pp1atXoWKRBEoIIYRG7bq/iZTkFMIOnOGbid8RF/Moz/YNmtkxcHYvatlXJyHuaTFFKUDz2xgYGhoyePBgVq1ahZWVFY0aNWLv3r38/fffbN68GQcHB5YuXcqoUaMYN24ct27dYvHixQwcOFBZ9zRw4EBmz55N9erVadiwIevWrSMpKYmuXQu31YjGp/DS09NZvnw57u7uODg48Omnn3Lz5k2l/vDhw3h6euLo6IiHhwfz588nKSkpzz7z22G0ILuU/tfx48fp0qUL9vb2tGnThr1796rVJycn4+vri5ubG46OjowdO7bQm3IJIcSbyG/AKr4evZlG79Vjzr4v0dHVybVt9XpVmH9gKinJKXz1yRK+nSmjT2+azz77jC+++IIlS5bQtm1bfvvtN1asWEGTJk0wMDBg/fr1pKen0717d3x9fenVqxefffaZcnz37t3x9vZm6dKleHp6cvv2bTZt2oS5ec57ueVGKyMjQ6Nvtlm5ciXfffcd8+bNw9ramoULF3Lr1i1+/vlnzp49S9++ffH29qZNmzZcv36dadOm8c477zB37twc+9u9ezczZsxg1qxZ1KtXj3Xr1nHkyBF+/fVXzM3NSUlJoX379tSoUYOxY8dy48YNZUdTb2/vHPu8fPkynTt3ZsCAAXTo0IE///yTxYsXs379emUIcPLkyYSGhjJ37lz09fWZPn06xsbGfPfdd6/s3gkhRGmQNYXn3Nqeub/5MLbFdM7+lfOrNQbO7kmXUe3pWnEQSQmZv0zLFF7xOXqtdpH0417jUpH0o0kaHYFSqVRs3LgRb29vWrRogZ2dHUuWLCEqKooDBw6wbds2mjRpwrBhw6hRowbvvfceo0eP5ueff862gj7L8zuM1q5dmzlz5lCmTBl27sz8F2z//v3cuXOHBQsWULduXVq1asWYMWPYsmVLrn1u2bIFW1tbRo8ejY2NDYMGDaJNmzasX78egOjoaAIDA/Hx8cHZ2ZlGjRqxePFiTpw4walTp17NzRNCiFLm8YMnAJhamOTaRs9Qn9SUVJKfFu6Rc1E00tEukk9poNGriIyMJCEhQW1HUFNTU+rVq8eJEycYOHAgEydOVDtGW1ublJQUZS+IPn360KdPHwAePHiQ5w6jAKGhoXnuUgqwYsUKbG1tlfrQ0FC1PrOOCQsLIyMjg7CwMKUsS82aNbGyslLOK4QQImfm1uWwrlGRTxf04cGdh5w8+E+ubfdvPAzAsMX9KGtuQvV6VYorTCHUaHQReVRU5sZzOe0IGhUVRb169dTKU1JS2Lx5Mw0aNFDmKleseLYXS179Ze0wmt8upfb29gwcOJAePXqo9ZvTMYmJicTGxhIdHU358uWVzbr+ex1CCCFyt/3ONwA8fhjPeI8ZPH2SmGvba+dusn7id3y+cjBdRrYrpghFFk0vIi9JNJpA5bUj6KNH6k9hpKamMmHCBC5evIi/v79SXq5cuQL1l7WRVlJSEqamptnq4dlmW8bGxhgbGyv1Oe1smvVdpVKRmJiYrf6/5xVCCJGzSR/OQt9Qn65jP2be/sw1UDfP38mx7ScTOzF4bm/2rPqNYwHBmFYoy9TtY4o54jdXWkbpmH4rChpNoJ7fETTrz5B9R9D4+HhGjRpFSEgIK1eupFGjRvn297zn+8tvl9KcGBgYZDsm63uZMmVy7DOn6xBCCJFd2O9nlf/fEL6E/rN6Mqv7omzttHW06e3jycHv/mLlFxueVWwvrkiFeEajqWRBdgSNiYmhd+/enD59mg0bNvDee++9VH/57VKaW785HWNkZETZsmWxtrYmLi4uWxL1IjubCiHEm0qVpOJC6BVq1M95XVM5S1PKGBty7n/nizkykSUdrSL5lAYaTaDs7OwwMTEhODhYKXv8+DHh4eG4uLjw6NEj+vXrx8OHD/H398fFxSXP/iwsLJQdRrNk7TCadayLiwvh4eHKInRQ36U0J87OzoSEhKiVBQUF4eTkhLa2No0bNyY9PV1ZTA6Zb4yOjo7ON2YhhBCZ9Az0qONUk7tXYnKsj4t5zOMHT2jY7O1ijkxk0fSrXEoSjU7h6evr4+XlhZ+fH+bm5lSuXJmFCxdibW1N69atmTp1Kjdv3mT9+vWYm5tz79495Vhzc3N0dHSIi4sDnq2Fym+H0VatWuW7S2lCQgJPnz7F0tISyHzSr3Pnzvj5+dG5c2eOHDnCb7/9pmxjYGVlRbt27fDx8VG2TZg+fTqurq44ODgUz80UQojXlPOHDugZ6OI5qj0Wlc2Z23uZUmfjUIOU5FRuRNwiPT2drTN28PmKQTx98pS/dh7HtIJpHj0L8epofCPNtLQ0Fi9eTEBAAElJSbi4uDBt2jQqVaqEo6NjrouwDx06RJUqVZQtDL799lulbsOGDWzdupW4uDgaNGiAj48Pb7/97DeW69ev4+vrS2hoKGZmZnTt2pUvvvgCbe3MrHjFihWsXLmS8+efDRP/9ddfLFy4kGvXrlGlShW++OIL2rZtq9Q/ffqUOXPmsH//fgCaN2+Oj48P5cuXL7qbJYQQpVByooqnTxI5H3KJjVO+5+o/N5S6b6+sIvraPcZ5zFDK3u/tTtcxH1OtXhUe339ChcqF20FavLifr+S8BrmwPq51tkj60SSNJ1BCCCHebPIy4dfHnisORdJPx1qni6QfTZKXCQshhBCiQNIySscC8KJQOlZyCSGEEEIUIxmBEkIIoVGlYQrOfuSSl+7jzLLRRRDJq1VanqArCpJACSGEEC/prznDUaWmcfz8dZb8dJSHT57m2d6xVmW8279L3coVeJKYzOGzl4sp0peTLjuRK+ROCCGEEC9p6vf7Wbj7TxrbVGb10E7oauf+49XG2oK1n3VBlZrG+E17WftbEO2cc96HUJRcMgIlhBBCvKQj/14B4EliMl8P74J9zUqEXb6dY9u2znZkZMCo9T+RqEoBQEdbm6mftCq2eF+UTOE9IwmUEEIIUUTiEpIAKGec+3tQDXR1SE1PIyklJdtxJZ08hfeMpJJCCCHES6pgakxlc1PGdHQn5lE8Qedv5No2MPgcAOM6vYeZkSE21hYMa9O0uEIVRUQ20hRCCCGKyKOEJAav3MmFO/fzbNft3UZM7toSnf9fK3X7wSMqW5gVR4gvZcvFd4qkn351/lck/WiSTOEJIYQQL2nY6l3o6+nSr2Vj1nzWhYHLd3ItJjbHtgNbuTDy42Zs++s0h85eopxxGYZ82KSYI34xafIUnkLuhBBCCPGSjp+/wZF/rzB8TQBJqlQ+b/duju10tLUY0roJe0MjmLvrD0Iu3uTA6QsMWfVjMUcsXpaMQAkhhBBFJDkljfCbMdhY5/yC4/ImRpQx0OPUlTtq5Q/jE4sjvJeWjiwizyIjUEIIIUQR0dfV4e0qFbn14FGO9Q+fPCUuIREnm8pq5eWMDYsjvJeWlqFdJJ/SQEaghBBCiJf0jl119HV18GrhRMVyxkzauk+ps6tsiSo1jSvRD0nPyODrX48zuasHCUkqDpy+QHnjMgxs5aLB6AtO9oF6RhIoIYQQ4iUtGdSBp8kq/r0RRa9FP3DxuafwFg/6mDsPHzN4ZeY6p21Hz/AkMZm+LRvTsUk94uKTOHnlNnZVKmoqfPECZBsDIYQQ4iW9KS8TXhnpUST9fG53uEj60SQZgRJCCCFEgcgU3jOSQAkhhHitNe+48KX7+GvP+Jc6/nUYPRJFSxIoIYQQr70RA1pQ18aKkT7b821rYmzAp17uNHerSxlDPa5cz3vXcPFMeil5gq4oaPxOpKens3z5ctzd3XFwcODTTz/l5s2bObb18fHBwyP/+ddff/2Vtm3b0qhRIzp16sTx48fV6mNjYxk7diwuLi64urri6+tLYmLee3BERETg5eWFg4MDHh4ebN269YWvQwghRNH5pKMzn3Qq2FNs2tpaLJjWlWZNarNmyxGmzt9DfPzr8SLfkiANrSL5lAYaT6BWr17N999/z6xZs9i2bRvp6ekMHjwYlUql1u7gwYPs3Lkz3/6CgoIYP348PXr0YPfu3bi5uTFkyBAuX76stPH29ub69ets3ryZZcuWceTIEWbMmJFrn7GxsQwYMIBq1aqxa9cuRowYgZ+fH7t27Sr0dQghhChaQ/u9x5MCJkGtmr+NXW0rxvv+yP4/znHi1DV85gW+2gBFqaTRBEqlUrFx40a8vb1p0aIFdnZ2LFmyhKioKA4cOKC0i4mJYerUqbi6uubb5zfffEOrVq3o27cvNjY2TJw4kfr167NlyxYATp06RUhICPPnz6d+/fq4ubkxc+ZM9uzZQ3R0dI597tixAz09PWbOnImNjQ2enp7079+fdevWFeo6hBBCFL3RPtu5dDWmQG1bvFOX0+duqU3bqVLSXlVopU56hnaRfEoDjV5FZGQkCQkJuLm5KWWmpqbUq1ePEydOAJCRkcGkSZPo2LFjjgnUpEmTlGm99PR0Tp48qdYfQJMmTZT+QkNDsbS0xMbGRql3dXVFS0uLsLAwAAICArC1teXWrVvKMa6urujqPlsy1rRpU65du8b9+/cLdB1CCCFejTPhtwrctnbNily7cZ9uHzdm+7ohHN41hnWL+rzC6EoXmcJ7RqOLyKOiogCoVKmSWnnFihWVus2bN3Pv3j3WrFnD2rVrs/UxZcoUUlJSAHj8+DFPnz7F2to61/6io6OznU9fX59y5cpx9+5dANq2bYu7uzvm5uZKnHXr1s3WJ8Ddu3cLdB1CCCE0r5yZES3eteVJfBKrN/9JcnIKvT2baDos8RrSaAKVtXBbX19frdzAwIBHjx4RGRnJypUr8ff3z9YmS9myZZU/JyUl5dpfcnKycs6c+nq+jaGhIYaGz95LlJSUlGOfAMnJyflehxBCiJJBV1cHE2MDho77lnsP4oHMEaz920ZpNrDXRGmZfisKGk2gspIUlUqllrAkJyejo6PDuHHjGD58OHZ2dgXqLyup+e/C7eTkZMqUKaOcM6eF3cnJyRgZGeUaZ059AhgZGeV5HVnnFUIIoXmJiSqu33qgJE+ZZSkajOj1UlpeBFwUNHonsqa8YmLUF//FxMSgUqm4ePEiK1euxNHREUdHR9auXcudO3dwdHQkNDQ0W3/lypXDyMgox/6srKwAsLa2zlavUqmIi4tTpuX+K6djsr5bWVnleR1Z5xVCCKF5t+7Eoqero+kwXlvpaBXJpzTQaAJlZ2eHiYkJwcHBStnjx48JDw/nk08+4cCBA+zZs4fAwEACAwPp0aMHFStWJDAwkAYNGmTrT0tLCycnJ0JCQtTKg4ODcXZ2BsDFxYWoqCiuX7+u1Ge1b9y4cY5xuri4EBYWRlrasyc1goKCqFmzJhYWFnleh4vL6/GGbSGEeBMEhV2hds2KVK9irpSZljXM4wghcqbRKTx9fX28vLzw8/PD3NycypUrs3DhQqytrfnwww/R09NTa29mZoauri7Vq1dXyp48eUJKSoqy4HvAgAEMGTKEevXq0bx5c3bt2kVERASzZ88GwN7eHicnJ0aPHs2MGTN4+vQp06ZNo1OnTspoUVJSEk+ePMHc3BwdHR08PT1Zv349U6ZMYfDgwZw9e5bNmzfj6+ub73W0bt26OG6lEEKIHFSvaoG+rg4X/3+bg50/h/HR+w2YP9WTb747SlJyCn27ueXTi8giU3jPaPxVLt7e3qSmpuLj40NSUhIuLi5s2LAhW/KUm9mzZxMSEsLhw5lvdm7WrBlz5sxh9erVLFmyhNq1a7NmzRpl2wItLS1WrlyJr68v/fr1w8DAgDZt2jB58mSlz3379jF58mQOHTpElSpVsLCwYP369cyePZvOnTtjaWnJhAkT6Ny5c5FdhxBCiKI3ZmgrrCua8cmQzH374hOSGTHpe4b1e4/RQ1uhq6vDPxG3NRzl6yM9o3RMvxUFrYyMjAxNByGEEEK8qJLwMuE3xZSzXYqkn9mNAoqkH03S+AiUEEIIIV4PaZp/A1yJIQmUEEIIIQpEpvCekQRKCCHEa620TL99VO/Llzr+1/A5RRSJKAhJoIQQQogSYMfxqWRkZBDyZyQbF/9G7P34PNu7NLfFa8T7VLOpyKO4hGKJMV2m8BRyJ4QQQogSYOHEHazwDeRtx2rM2TAIHd3cf0Q7vVOb6av6cO1iFL5ffMuujUeLJca0DK0i+ZQGkkAJIYQQJcCJv85zbP+/LJu2mxp1rLBvYpNr2+6ftuDSudss8Qng9PHL/Px9UDFGKkCm8IQQQogSJTUl860X+ga5/4heOnUXOhp4JY0sIn9GEighhBCiBNDT18XGrhIjpnbgzo0HnPz7Yq5to27FKn82MjbAwa12cYRIuuxErpAESgghhCgBVu/+gio1LUlKVDHL2x9Vcmq+x5SvUJbv/5qcb7uiklZKXgRcFCSVFEIIIUqAVV/9xJTBGzkTdBnf1X1xerdOvseoklOYNGA9s0d/XwwRiufJCJQQQghRApw+fjnz/4OvsPankXQb1DzPaTyAhCdJnAm+UhzhAbIG6nkyAiWEEEKUIOlp6Vy7EI1FRdMc67W1tXBv0xCbtysVc2SZa6CK4lMalI6rEEIIIUqJMkb62DlU5eqFqBzr09MzGDD6QwaM/rCYIxPPkyk8IYQQogRwaW6Lnr4u3QY3x8jYAP9Vh5Q6m7crkaJK48blGAD8Vx1i3LxufD6tI8cO/It1lfLFEmO6LCJXSAIlhBBClADj53dDR0ebf05cZYxPgJIsAUxd7kX07Vgm9l8PwKGfTpGclEK3wc15v4MjiU9VxRJjadlFvChoZWRkZGg6CCGEEOJN9zq8THjAiQFF0s8ml01F0o8myQiUEEIIIQqktCwALwqSQAkhhHjjfWQ57KWO//XempeOoThGkF6WbGPwjCRQQggh3ng7Li4iIz2DkN//YePM3cTGPM6zvcsHDfAa355qdSvx6GF8MUUpShKNj8Wlp6ezfPly3N3dcXBw4NNPP+XmzZtKfUxMDGPGjMHZ2ZkmTZowduxYHj58mGefx48fp0uXLtjb29OmTRv27t2rVp+cnIyvry9ubm44OjoWqM9bt24xdOhQnJycaNasGUuXLiUtLU2tjb+/P++//z6NGjWiV69ehIeHF/JuCCGE0ISFn21ixfjvedvFhjk/jkRHN/cfj04t3mb6t59xLeI2vn2/Zteq34sxUs1KR6tIPqWBxhOo1atX8/333zNr1iy2bdtGeno6gwcPRqVSoVKpGDhwIHfu3GHr1q2sW7eOyMhIJk6cmGt/ly9fZujQobi7uxMQEEC3bt2YMGECx48fV9rMmDGDY8eOsWLFCrZs2cKVK1fw9vbOtc+UlBQGDRoEwLZt25gxYwY//PADq1atUtrs3r2bBQsWMHLkSAICAqhSpQoDBgzINzETQgiheSd+/5djP51k2ejvqPF2Zezd7XJt231kGy6ducGSkd9y+q9Ift7wZ/EFqmHpGVpF8ikNNDqFp1Kp2LhxI+PGjaNFixYALFmyBHd3dw4cOIBKpeL27dv8/vvvVKhQAYBJkybh6+tLfHw8JiYm2frcsmULtra2jB49GgAbGxvCw8NZv349bm5uREdHExgYyJo1a3B2dgZg8eLFtGnThlOnTuHo6Jitz/3793Pnzh127NiBmZkZdevW5cGDByxYsIBhw4ahr6/PmjVr8PLyokOHDgDMmTOHVq1asXPnToYOHfoqbp8QQogilpqS+QJffQO9XNssHfUtOro6xRVSiSKLyJ/R6J2IjIwkISEBNzc3pczU1JR69epx4sQJjh07RtOmTZXkCcDd3Z2DBw8qydOKFSuwtbVV6kNDQ9X6A2jatClhYWFkZGQQFhamlGWpWbMmVlZWnDhxAoDg4GBsbW0JDg5W+qxfvz5mZmZqfcbHxxMREcGDBw+4du2a2nl1dXVxdnZW+hRCCFFy6RnoYte4JiPm9+DO1RhO/pn7Eoyo6/e5fTkaACMTQ95p51BMUYqSRKMjUFFRmdvUV6qk/j6fihUrEhUVRUxMDM7OzqxatYrAwEBSU1Np1qwZ48ePx9Q08x1BAwcOpEePHmp9WltbZ+svMTGR2NhYoqOjKV++PAYGBjmeE8DR0ZFjx44pCVNufQLcvXsXXV3dXK8jMjKy8DdGCCFEsVr9pw9ValuT9FTFrP5rUCWl5HtMeStTvv93QTFEV3KUlum3oqDREajExEQA9PX11coNDAxITk4mPj6ewMBAzp8/z6JFi5g5cyZhYWF89tlnZO3/aWxsjKWlpXJsUlJStv6yvqtUKhITE7PVP3/OrPaWlpZKu5z6zErAkpOT870OIYQQJduqiduY0m0ZZ45G4us/AqeW9fI9RpWYwqTOS5g9aF0xRFgyyCLyZzQ6AmVoaAhkJjZZf4bMpKRMmTLo6upiZGTEokWL0NPLnI82MzOjW7du/PPPPzRq1ChbnwYGBqhU6lvaZ30vU6YMhoaG2eqfP2ducf73mKzEyMjISO06CtqnEEKIkuP0X5mzBaePnmftsWl0+6I1J//I+0nqhMeJnDl2vjjCE8+Jjo6mefPm2crnzp1Lly5diIiIYPbs2fz777+Ym5vTv39/+vbtq7RLT09n5cqV7Ny5kydPnuDi4sK0adOoWrVqoeLQ6AhU1pRXTEyMWnlMTAxWVlZYW1tTs2ZNJXkCqFOnDpC5rUBufebUn5GREWXLlsXa2pq4uLhsyU7WOXNibW2dY58AVlZW+V6HEEKI10N6WjrXIu5gYV0ux3ptbS3cOzbGpmHhftiWFiXhKbzIyEgMDAw4evQox44dUz5t27YlNjaWAQMGUK1aNXbt2sWIESPw8/Nj165dyvF5Pf1fGBpNoOzs7DAxMVEWawM8fvyY8PBwXFxccHFxITIykqSkJKX+woULAFSvXj3HPp2dnQkJCVErCwoKwsnJCW1tbRo3bkx6erqymBzg6tWrREdH4+LikmOfLi4uhIeHEx//bLO0oKAgjI2NsbOzw8LCgpo1a6pdR2pqKqGhobn2KYQQouQpY2yAnXMtrobfzrE+PT2DAT6dGDC1U/EGVkKUhATqwoUL1KhRg4oVK2Jpaal8DA0N2bFjB3p6esycORMbGxs8PT3p378/69ZlTrNmPf3v7e1NixYtsLOzY8mSJURFRXHgwIFCxaHRBEpfXx8vLy/8/Pw4dOgQkZGRjB49Gmtra1q3bk2PHj3Q0dFh7NixXLx4kbCwMHx8fGjSpAn169cHICEhgXv37il99unTh7Nnz+Ln58fly5fZuHEjv/32G4MHDwYyR4zatWuHj48PwcHBnD17ljFjxuDq6oqDgwOQeYPv3bunZKOtWrXC0tKSUaNGERkZycGDB1m8eDEDBw5U1j0NHDiQTZs2sXv3bi5dusSXX35JUlISXbt2LcY7KoQQ4kW4fNCAd9o5MGfXKIxMDPBf8ItSZ9OwKtXqPntIyH/hLzRuWZ/PF/TEwd2WNl7NNBHyG+v8+fPY2NjkWBcaGoqrq6vycBdkPjV/7do17t+/n+/T/4Wh8Ve5eHt7k5qaio+PD0lJSbi4uLBhwwb09PQwNzfH39+fuXPn0q1bN/T19WnVqhWTJk1Sjt+4cSMrV67k/PnMeeg6deqwevVqFi5cyJYtW6hSpQoLFy5Uu1mzZs1izpw5fP755wA0b94cHx8fpf7UqVP07duXrVu30qRJEwwMDFi/fj2+vr50794dMzMzevXqxWeffaYc0717d548ecLSpUuJi4ujQYMGbNq0CXNz81d9C4UQQryk8asGoKOrzT//u8iYUd9y48JdpW7q5mFE33zAxE6LATi0I5jkxBS6eX/I+92bkpjw5jwsVFRP4b3//vt51h86dCjXugsXLlC+fHl69+7N1atXqV69OsOHD6d58+ZERUVRt25dtfbPPzWf39P/haGVkfU4mxBCCPGGKgkvE34dtPsr97d2FEaS77k863NLoFJTU3FwcKB27dpMmjQJExMT9u7dy6ZNm9i0aRPTpk2jffv2jBw5Ujnm5s2btGrVCn9/f27fvs2ECROIiIhAW/vZJNyECROIiYlh8+bNBb4GjY9ACSGEEOLNktcIU150dXUJDg5GR0dHeQK+QYMGXLx4kQ0bNhTqqfmcnv4vVCwvdAVCCCFee/Yjl7x0H2eWjS6CSDTvTRlBelklYQ8nY2PjbGV16tTh2LFj+T41n5qaqpRVq1ZNrc3zbzUpCEmghBDiDRW08HMSk1P469wVlv3yNw+fPM2zfe1KFozu4E7D6pVQpaZx/Pz1YopUlBSa3on84sWLfPLJJ3z99dc0adJEKf/333+pXbs2b7/9Ntu2bSMtLQ0dncz3FQYFBVGzZk0sLCwoW7as8vR/VgKV9fS/l5dXoWKRtwIKIcQbaqr/fqb/cIDKFmZ8O6oHJobZ39KQxbysEd983hWLskZM/X4/C3f/SWObysUYrSgJNL2NgY2NDbVq1WLmzJmEhoZy+fJl5s6dy+nTpxk+fDienp7Ex8czZcoULl26REBAAJs3b2bo0KFA/k//F4aMQAkhxBvq99MXAQi5cJPAKf3o/74zK/f+L8e2LRvYYG5iRJ/F27j14BEATxKT+Xp4l2KLVwhtbW3WrFnDokWLGDVqFI8fP6ZevXps2rRJefpu/fr1zJ49m86dO2NpacmECRPo3Lmz0kdeT/8XhiRQQgjxhktKSeWfa1G416uZawJloJc5HZKQ9GyBblxCUo5tReml6Sk8gAoVKjB37txc6xs1asT27dtzrdfR0WH8+PGMHz/+peKQKTwhhBBUrWBGlQpmudbvP3WBmEfxTO7akgqmxlQ2N2VMR/dijFCUBJqewitJZARKCCHeUJamxqRnZNCnhRO1rC3Q1cn9d+oHT57y1Y5DzO/Xlg+dMp9WeiQjUOINJgmUEEK8oQ7OGkJ6ega/n7nIrv/9Q8cm9XNt+1FjW2Z7teH30xcJDPoXfT1d+rVsTOPaVYoxYqFpGaVk9KgoSAIlhBBvqI6zN/MoIYnYhES+6v0hj57mPqI0vI0bZ67eZeKWfUpZ0PnrhPgVzc7U4vVQEvaBKilkDZQQQryhrsXEEpuQCMDbVSsSeSsm17aVzMty5uodtbLklLRXGp8QJZmMQAkhxBuuqW01aleqwOZDYbm2uRodi0Ott9TK9HV1XnVoooQpLQvAi4IkUEII8YZqaluNSuXLMrbTe5y6cpu9oRFKXS0rc/R1dYi8fQ+AVfv+x9JBHVjYvx27g/5FX1cHrxZOmgpdaIisgXpGKyMjI0PTQQghhCh+CUkqniQmc/DMRVbt+x9Pk1OUuvWfd+Utc1PaztyolL1jV50hHzbl7SoVeZqs4t8bUTSvX0sToQsNeff3iUXSz98fzC+SfjRJEighhHhDycuERWG5HZhUJP0cbz2vSPrRJJnCE0IIIUSByBTeM5JACSGEEKJAZBH5M5JACSGKle2sl582Oj9Vpo2Kgky/CfHiNL4PVHp6OsuXL8fd3R0HBwc+/fRTbt68qdSfO3eOPn364OjoSIsWLfDz80OlUuXRI/z666+0bduWRo0a0alTJ44fP65WHxsby9ixY3FxccHV1RVfX18SExPz7DMiIgIvLy8cHBzw8PBg69athboOIUSm4HHDOTpqCPM6fIiFsVGBj9PR0mL7gB583rzpK4xOCJGXjIyi+ZQGGk+gVq9ezffff8+sWbPYtm0b6enpDB48GJVKRWxsLAMHDqRWrVoEBgYya9YsAgICWLp0aa79BQUFMX78eHr06MHu3btxc3NjyJAhXL58WWnj7e3N9evX2bx5M8uWLePIkSPMmDEj1z5jY2MZMGAA1apVY9euXYwYMQI/Pz927dpVoOsQQjwz+af9zDnwJy7VK/NNz07oauf/nyF9HR0WdWmLQ5VKxRChECI36WgVyac00GgCpVKp2LhxI97e3rRo0QI7OzuWLFlCVFQUBw4cICwsjLi4OMaPH0/16tVxd3fn448/5ujRo7n2+c0339CqVSv69u2LjY0NEydOpH79+mzZsgWAU6dOERISwvz586lfvz5ubm7MnDmTPXv2EB0dnWOfO3bsQE9Pj5kzZ2JjY4Onpyf9+/dn3bp1BboOIcQzhy9c4dfwC0zfe4j6laxwzCcpaly1MjsH9aRpjarFFKEQQuRPowlUZGQkCQkJuLm5KWWmpqbUq1ePEydOYG5uDsAPP/xAWloat27d4siRI9jb2yvtJ02ahIeHB5A5jXby5Em1/gCaNGnCiRMnAAgNDcXS0hIbGxul3tXVFS0tLcLCMnfhDQgIwNbWllu3binHuLq6oqv7bMlY06ZNuXbtGvfv38/3OoQQ2cUlZr53rZxRmTzbff1JB+48ekKX9f7FEZYQIg8ZGVpF8ikNNLqIPCoqCoBKldR/A61YsSJRUVE4OTkxfPhwli1bxpIlS0hLS6Np06ZMmzZNaTtlyhRSUjI3f3v8+DFPnz7F2to6x/4AoqOjs51PX1+fcuXKcffuXQDatm2Lu7u7ksBFRUVRt27dbH0C3L17N9/rEEI8Y2lijIGuDhNauRPzJJ7/XbmRZ3uvrTu4EPOgmKITQuRFnsJ7RqMJVNbCbX19fbVyAwMDHj16RHx8PFeuXKF379506NCBmzdvMnfuXKZOncr8+Zm7mJYtW1Y5LikpKdf+kpOTlXP+t/6/bQwNDTE0NFTrN6c+AZKTk/O9DiHEM8dGDwEyR6D6fruThHzWCUryJIQoiTSaQGUlKSqVSi1hSU5OpkyZMixcuJBHjx6xfPlyAOrXr4+ZmRn9+/enf//+vP3222r9ZSU1/124ndVf1jlzWtidnJyMkVHOTwTldExWsmVkZJTvdQghnhnovwsDXV0GNm3Mxl5d6LN1J1cexGo6LCFEAZSWJ+iKgkbXQGVNecXExKiVx8TEYGVlRVhYGA0bNlSry1r/dO3atWz9lStXDiMjo1z7A7C2ts5Wr1KpiIuLU6bl/iunY7K+W1lZ5XsdQohn/r5yg8MXrjDo+wCSUlMZ2fJdTYckhCggWQP1jEYTKDs7O0xMTAgODlbKHj9+THh4OC4uLlhZWXH+/Hm1Y7K+16xZM1t/WlpaODk5ERISolYeHByMs7MzAC4uLkRFRXH9+nWlPqt948aNc4zTxcWFsLAw0tLSlLKgoCBq1qyJhYVFvtchhMguOTWNf+/GUMfSXNOhCCFEoWk0gdLX18fLyws/Pz8OHTpEZGQko0ePxtramtatW9O/f3+OHj3K0qVLuXHjBsePH2fy5MnKVgEAT5484eHDh0qfAwYMYO/evWzatInLly+zYMECIiIi6NevH5A5guXk5MTo0aM5e/YsQUFBTJs2jU6dOimjRUlJSdy7d09JmDw9PYmPj2fKlClcunSJgIAANm/ezNChQwt0HUKI7PR1dKhvXZGbsbJOUIjXhYxAPaOVkaHZGc20tDQWL15MQEAASUlJuLi4MG3aNKpUqQLAkSNHWLVqFRcvXqR8+fJ88MEHjBw5UlmvNGnSJEJCQjh8+LDSZ2BgIKtXryYqKoratWszfvx4tS0GHjx4gK+vL0ePHsXAwIA2bdowefJkZQ1VQEAAkydP5tChQ0ocZ8+eZfbs2YSHh2NpacnAgQPx8vIq8HUIITIN/j4AfR0d+jVxwrFKJby27uTM7cynVd+2tkSVmsbl+w9zPPb81NGsOHKcL95zy7FeCPFq1d8zo0j6OdexaPrRJI0nUEKIN0tSSioJKhVnb0ex5I+/OR9zX6k79MVAbsc9pu+3P+Z4rCRQQmhWvcAZRdJPeKei6UeT5GXCQohiZT9vRa5176/YmOexWS8ilgRKCKFpkkAJIYQQokBKy/qloiAJlBCiUGotX/RSx1+ZOraIIhFCFDdJoJ6RBEoIUShXvLMnQLklVX/1H0wVU7NXHZIQQhQ7SaCEEIXSZcf3AFQ3M8Pvg4/Y9u/ZXNsO2/sT+jo6amUB3Xu90viEEK+OPHX2jCRQQohCOR11F20tLaY3b0nE/XvM/OuPXNuG31Pfnd8kh/dQCiFeHzKF94xGN9IUQryeejVoRIOKVvj8cZCU9PQCHzfCpekrjEoIIYqPjEAJIQrFSE+PUU3fZXdkOGejowp83FsmZenv4PgKIxNCvHIyh6eQBEoIUSjd6jXAzMCAr0ND8m/8nAGOTqjS0jDQkf/sCPG6kim8Z2QKTwhRKH0aOXDw6mWuxsUW+Bh9HR2612vIznP/vsLIhBCi+EgCJYQolFrlzfnpfEShjnGvVoOyBgbsKeRxQoiSJSOjaD6lgYylCyEK5WlKCoevXi3UMR41a3HjURz/xES/oqiEEMVBpvCekQRKCFEo52KiSU5LLdQxjtaVCLt75xVFJIQoNpJAKWQKTwhRKBcfPsi1rra5OfUsK6qVaWtpYVPenEt5HCeEEK8bSaCEEIXyODk517qZLVqxpl0HtbLyhobo6ejwKCnpVYcmhHjFZA3UMzKFJ4QolAX/O5prXa+AHdnKHiQmvvQLiIUQJUQpSX6KgoxACSGEEEIUkoxACSGEEKJA5Cm8ZySBEkIUyhXvsZoO4aU5frbkpfs4tXp0EUQixGtGpvAUJWoKb+3atfTp00etLCIiAi8vLxwcHPDw8GDr1q359vPrr7/Stm1bGjVqRKdOnTh+/LhafWxsLGPHjsXFxQVXV1d8fX1JTEzMs8/84khPT2f58uW4u7vj4ODAp59+ys2bNwt45UKI4vTnwuEcmDuEmX0/xLysUb7t3RvU5NsJPQla9gW/zR7MWM/3iiFKIURJVmISKH9/f5YuXapWFhsby4ABA6hWrRq7du1ixIgR+Pn5sWvXrlz7CQoKYvz48fTo0YPdu3fj5ubGkCFDuHz5stLG29ub69evs3nzZpYtW8aRI0eYMWNGrn0WJI7Vq1fz/fffM2vWLLZt20Z6ejqDBw9GpVK98D0RQrwa07fux+/HP2lcpzIrR3RCVzv3/xS2tLdh6bCOPE1WMXH9Xvx+PIKrbdVijFaIkiMjQ6tIPqWBxqfwoqOjmT59OsHBwdSoUUOtbseOHejp6TFz5kx0dXWxsbHh+vXrrFu3Dk9Pzxz7++abb2jVqhV9+/YFYOLEiZw6dYotW7Ywc+ZMTp06RUhICPv27cPGxgaAmTNnMnjwYMaMGYOVlVW2PvOLQ6VSsXHjRsaNG0eLFi0AWLJkCe7u7hw4cID27dsX3Q0TQry0I/9cAeDJ02RWf9EFe5tKhF28nWPbYe3cuBr1kBErd5Oalg7AyUu3OTR/aLHFK0SJIVN4Co2PQJ07dw49PT1++ukn7O3t1epCQ0NxdXVFV/dZnte0aVOuXbvG/fv3AZg0aRIeHh5A5jTayZMncXNzU+unSZMmnDhxQunT0tJSSZ4AXF1d0dLSIiwsDICAgABsbW25detWgeKIjIwkISFB7bympqbUq1dPOa8QouR5lJC5N5WZcZlc29S0Nud4xDUleQJ4+OTpK49NCFGyaXwEysPDQ0mA/isqKoq6deuqlVWsmLnL8d27d6lQoQJTpkwhJSUFgMePH/P06VOsra2zHRMVFQVkjnhVqlRJrV5fX59y5cpx9+5dANq2bYu7uzvm5uYFiiOr7//2+/x5hRAlRwVTY/T1dBjVxZ17cfEER97ItW1cfCKVzE3VyvKa8hOidCsd029FQeMJVF6SkpLQ19dXKzMwMAAg+f93Qy5btqxaeyDHY7LaJyYmZqv/bxtDQ0MMDQ0LHEfWAvSc2jx69KgglyqEKEa/zxsCZI5ADVm6k4Sk3NcqBh4/x6cfNaH/B87sOX4OAz1dRnR4p7hCFaJkkSk8RYlOoAwNDbMtws5KcoyMsj85k5XU5HRMmTJlcu0zq01OfRYkjqxkS6VSqSVez59XCFFyDF+xCwNdXfq2aszqL7oweMlOrkXH5th27d7j6Gpr89nH7zCyszspqWkE/P1PMUcsRAkhCZSiRCdQ1tbWxMTEqJVlfc9psXe5cuUwMjLK8Zis9tbW1hw8eFCtXqVSERcXp0zLFTaO1NRUpaxatWpqbWxtbfO9TiFE8QqKyJyyC4q8TsC0foz4+F3Gr/8lx7Zp6Rks33OMNXuPU6WCGTGPEohPTOaT9xyKMWIhRElToifyXVxcCAsLIy0tTSkLCgqiZs2aWFhYZGuvpaWFk5MTISEhauXBwcE4OzsrfUZFRXH9+nWlPqt948aNXygOOzs7TExMCA4OVuofP35MeHg4Li4uL3DlQojikJySRvj1GGq9ZZ5rm8Z1quD2dnVUqWlciXpIfGIyOtqyDkS8oTK0iuZTCpToBMrT05P4+HimTJnCpUuXCAgIYPPmzQwd+uzx4SdPnvDw4UPl+4ABA9i7dy+bNm3i8uXLLFiwgIiICPr16weAvb09Tk5OjB49mrNnzxIUFMS0adPo1KmTMkqVlJTEvXv3lIQpvzj09fXx8vLCz8+PQ4cOERkZyejRo7G2tqZ169bFdbuEEIWkr6uDXbWK3L6f+1rFVo51mNq7ldrC8Y5uDYojPCFKnIyMovmUBiU6gbKwsGD9+vVcvXqVzp07s3LlSiZMmEDnzp2VNrNnz6Zr167K92bNmjFnzhx++OEHOnfuTFBQEGvWrFG2LdDS0mLlypVUqVKFfv36MWrUKJo3b662kea+ffto1qyZ8lReQeLw9vama9eu+Pj40LNnT3R0dNiwYQN6enqv+C4JIQrrnXrVadGoFqs+70xFM2O+2fds9Ni2iiW1rJ+NSP149CwWZY3w7dsaV9uqeHk4MemTlpoIWwhRgmhlZJSWXFAIIQomSZXK02QV/16LYuVPf3Px9n2lbu+sgdx58JhPl/6olDWxq4Z3x2bUrGTOg8dP+TkonOHt3XLqWohSrfqGBUXSz/VBE4qkH02SBEoI8caRlwkL8WKqr19YJP1cHzy+SPrRpBI9hSeEEEIIURKV6G0MhMjSsvX8lzr+jwMTiygSURrI6JEQL0ZL5qwUkkCJ18K+PaO5cvUeGzb/xanTub9243na2lqsWOJFSOiVVxydEEK8ISSBUrzQFN7u3buJjo4u6liEyNX0mYE8iU9i3lfdqFXTMt/2eno6TJ3cgXpvv1UM0QkhhHjTvFACNXPmTM6ePVvUsQiRqxNhV5nmu5vomMc4N66ZZ9uGDarw9fK+ODpWL6bohBDiDSEbaSpeKIGytrYmPj6+qGMRIk8pKWn0HfgNO34MybPdbF9Pou89Zuhnm4snMCGEeFNkFNGnFHihNVCffPIJs2fP5tSpU9ja2mJsbJytTadOnV42NiEUv+8bx+UrMaxac5h//r2VZ9uRY/25eu1+nm2EEEK8gFKS/BSFF0qg5s2bB8COHTtyrNfS0pIEShQpn+kB9OzRlIVzu/OZ97dcuXov17aSPAkhhHjVXiiBOnToUFHHIUSegk9c4ey/t/DfMoRenzTlq3k/azokIYR488gIlOKF1kBVrlw5348QRS0xUcW58NvUrm2l6VCEEOLNVMIWkV+9ehVHR0cCAgKUsoiICLy8vHBwcMDDw4OtW7eqHZOens7y5ctxd3fHwcGBTz/9lJs3bxb63C80ArVy5cp823z++ecv0rUQedLV0SE5OUXTYQghhNCwlJQUxo0bx9OnT5Wy2NhYBgwYgIeHB76+vpw+fRpfX1+MjY3x9PQEYPXq1Xz//ffMmzcPa2trFi5cyODBg/n555/R19cv8PmLPIEyMTGhYsWKkkCJImda1pAG9Suz//d/NR2KEEK8kUrSTuQrVqzAxMRErWzHjh3o6ekxc+ZMdHV1sbGx4fr166xbtw5PT09UKhUbN25k3LhxtGjRAoAlS5bg7u7OgQMHaN++fYHP/0IJVGRkZLayp0+fEhoayowZM5g6deqLdCtErt5pWhuvXm5kZMD257YxqG1TkZSUNK7feKDB6IQQ4g1RQhKoEydOsH37dgIDA5VECCA0NBRXV1d0dZ+lN02bNmXt2rXcv3+fO3fukJCQgJubm1JvampKvXr1OHHixKtPoHJiZGRE8+bNGTFiBAsWLGD37t1F1bUQ+Ez+mH/+vYX3GH/u3XuilM+a3oWo6EeMHv+DBqMTQghRGO+//36e9Xk9rPb48WMmTJiAj48PlSpVUquLioqibt26amUVK1YE4O7du0RFRQFkO65ixYpKXUEV+bvw3nrrLS5fvlzU3Yo3XNuOS3Is79l3TZ7HZb2EuH+fZkUekxBCiOI3Y8YMHB0d+fjjj7PVJSUlZVvHZGBgAEBycjKJiYkAObZ59OhRoeIosgQqIyODqKgo1q9fL0/hCSGEEKVQUa2BetHtkAIDAwkNDeXnn3PeysbQ0BCVSqVWlpycDGTOlBkaGgKgUqmUP2e1KVOmTKFieaEEys7ODi2tnB9DzMjIYMGCBS/SrRC5+uPARE2HUCo4Dc15JK8wTq4dXQSRCCFeSxp+j92uXbt48OCB2rongOnTp7Nv3z6sra2JiYlRq8v6bmVlRWpqqlJWrVo1tTa2traFiuWFEqgRI0bkmECZmJjQokULatSo8SLdCiGKwXv2tVjyWUc+XbSTsAt5vxbHsXZlPu/0LnWqVOBJYjJ/nJLpeSGE5vj5+ZGUlKRW1rp1a7y9venQoQN79uxh27ZtpKWloaOjA0BQUBA1a9bEwsKCsmXLYmJiQnBwsJJAPX78mPDwcLy8vAoVywslUF988cWLHJavtWvXcuzYMb799lul7PDhw6xatYorV65Qvnx5PvzwQ0aOHKk29PZf/v7+bNy4kXv37tGgQQN8fHyoV6+eUn/r1i1mzZrFiRMnMDIyomvXrnzxxRfKzc7J8ePHWbhwIZcvX6ZSpUp88cUXtGvXTqlPTk5m3rx5/PbbbyQlJeHh4cGUKVMwNzd/ybsiRNExMzZkSu9WBWpbq5IFq0d14fSlO0z6Zi+W5UwY2cX9FUcohCjRNPwUnpVVzhspW1hYYGVlhaenJ+vXr2fKlCkMHjyYs2fPsnnzZnx9fYHMtU9eXl74+flhbm5O5cqVWbhwIdbW1rRu3bpQsbzwGqiHDx+yceNGQkJCePz4MeXLl8fZ2Zn+/ftjYWFR6P78/f1ZunQpzs7OSlloaCiff/453t7etGnThuvXrzNt2jTi4uKYO3dujv3s3r2bBQsWMGvWLOrVq8e6desYMGAAv/76K+bm5qSkpDBo0CBq1KjBtm3buHHjBlOmTEFbWxtvb+8c+7x8+TJDhw5lwIABLFy4kD///JMJEyZgbm6uPAo5Y8YMQkNDWbFiBfr6+kyfPh1vb2++++67Qt8LIV6Vyb08SE1LL1Dbtk3syMiAMV//ROL/b16qq63NFK+CJWBCiFKohGxjkBsLCwvWr1/P7Nmz6dy5M5aWlkyYMIHOnTsrbby9vUlNTcXHx4ekpCRcXFzYsGEDenp6hTqXVkZGRqFvR1RUFJ988gkPHz7EwcEBS0tL7t27x6lTpyhfvjw//vhjrlnif0VHRzN9+nSCg4OxtramQoUKygjUuHHjePDgAZs2bVLaBwYG4uPjw8mTJ3PcMfTDDz+kVatWjB8/HoDU1FRatWpFz549GTp0KL/88guTJ0/m2LFjmJmZAbB9+3YWLFjA8ePHc+xz2rRpREREsHPnTqVs7NixxMXFsWHDBqKjo2nRogVr1qzhvffeAzK3l2/Tpg3btm3D0dGxgHdWiFfrUUIS8344zNzBbfOdwhvTrTmd3m3Ae6NXk/Vfifed6rBwaMH3SRFClC61liwukn6ujB5TJP1o0gu9C2/hwoXo6uqyb98+vv32WxYvXsy3337Lr7/+iqGhIUuWFHyh6rlz59DT0+Onn37C3t5erW7gwIFMnKi+eFhbW5uUlBTi4+MB6NOnD3369AHgwYMHXLt2TW2DLF1dXZydnTlx4gSQOapVv359JXmCzE224uPjiYiIADJ3N31+MVloaKhan1nHhIWFkZGRQVhYmFKWpWbNmlhZWSnnFaIk8Nv+J/cfJRSo7Z6/zwEwttt7mBkbUquSBUPaN83nKCFEaaaVUTSf0uCFpvCOHTvGl19+SdWqVdXKq1atqmykWVAeHh54eHjkWPf8uiXIfO/N5s2badCggbK2aMWKFUp9XhtkZe2eHhUVhbW1dbZ6yNxky97enoEDB9KjRw+1fnM6JjExkdjYWKKjoylfvryy18TzbQq7MZcQr9Le4Aga161SoLaX7zxgWcAxJvVsSa/3nQC4c79w+6QIIUqZUpL8FIUXSqDS0tIoX758jnXm5ubK6FBRSk1NZcKECVy8eBF/f3+lvFy5csqf89ogK2sfiKSkJExNTbPVw7O9IoyNjTE2Nlbqc9qYK+u7SqUiMTExx6m/588rxOum/4cueHdpxvY/TnP41CXKmZRhcLsmmg5LCCFKhBdKoGxtbfn5559p3rx5tro9e/Zk20b9ZcXHxzNq1ChCQkJYuXIljRo1yrHd8xtkPe/5DbLy22QrJwYGBtmOyfpepkyZHPv873mFKAl0tLXQ0dZS/qytpUV6DssgdbS1+LRdE/YFRzB/2x9KeeiFmxzyG1Zs8QohShgZgVK8UAL12WefMWjQIB49ekTbtm2VReR79+7l2LFjLF++vMgCjImJ4dNPP+X27dts2LABFxeXXNtmTd3FxMRgY2Oj1kfWonZra2suXLiQ7RyQ++ORlSpVynFjLiMjI8qWLYu1tTVxcXGoVCq1kajnzytESXDi61HKn9eM7sqd+49oP2VjtnblTYwoY6DH6Ut31MpjnyS+6hCFECVYaVm/VBReKIF69913mTdvHn5+fvz1119KeYUKFZgzZw4ffPBBkQT36NEj+vXrR3x8PP7+/vnuEmphYUHNmjUJDg5WFn2npqYSGhpKr169AHBxcSEwMJD4+HhMTEyAzE22jI2NsbOzy7FfZ2dnQkJC1MqCgoJwcnJCW1ubxo0bk56eTlhYmHLeq1evEh0dnWfCJ0Rx6z3ne+pVq8gUr1bM/u4gZy7fybHdwydPiYtPxLFOZX7866xSXs449/3XhBDiTfLC+0B16tSJVq1aERERga6uLmZmZujr66Otrc2dO3d46623Xjq4uXPncvPmTdavX4+5uTn37t1T6szNzdHR0SEuLg54thZq4MCBzJ49m+rVq9OwYUPWrVtHUlISXbt2BaBVq1YsXbqUUaNGMW7cOG7dusXixYsZOHCgMnqUkJDA06dPsbS0BDKf9OvcuTN+fn507tyZI0eO8Ntvv7F+/Xogc+SqXbt2+Pj4MGfOHMqUKcP06dNxdXXFwcHhpe+DEEUl4no0RgaZe51ci47l0p0HSp1tVUtUqWlcvfuQ9IwM1v58nIk9PUhIVHHw5AXKmZRhQBv5hUCIN5qGX+VSkrxQAnXjxg3GjBnDuXPncm2TtSXAi0pLS2Pfvn2kpKTQr1+/bPWHDh2iSpUqyq7oWXtHde/enSdPnrB06VLi4uJo0KABmzZtUp7aMzAwYP369fj6+tK9e3fMzMzo1asXn332mdL3xo0bWblyJefPnwegTp06rF69moULF7JlyxaqVKnCwoUL1bY2mDVrFnPmzOHzzz8HoHnz5vj4+LzUPRCiOC0a9jF3HjxmyOIfAdj+5xmeJCbj1aoxHd6pR1x8Eqcu3ca2akUNRyqE0BiZwlO80EaagwYN4t9//6VXr15UqVIFbe3s20k9v+unEKJkkJcJCyFeRp15L//fEICLk17//4680AjUyZMnmT59Op06dSricIQQQgghSr4XSqCMjY2V9UFCCCGEeEPIFJ7ihRKojh07snXrVpo2bYqOjk5RxyREkWvRZv5L9/HnbxPzb1TCyfSbEOJlyDYGzxQ4gZo8ebLy59TUVI4ePcoHH3xAo0aNsm0WqaWlxZw5c4ouSiFeUsD3IyhTRp8z/9xk1brD3Lz1MM/2NWtUYNiglrxtV4mUlDROhF0tpkiFEEK8DgqcQAUHB6t9z3o33NmzZ7O11dKSxxxFybJu0xESnqro1/tdlszvQf+hG4iPz/k1O+XLGbFkXk9i7j9m3qJ9GOjrMnRQi+INWAghSiIZgVIUOIE6fPjwq4xDiFfqt9//BeDsvzfZtnkYndo78d224zm2fdetDuXKGfHZ6G+5czcOgPiEZBbO7l5c4QohRMkkCZQi+/4DQpRijx4lcvqfmzRxqZVrG339zN8rEp4+G6F6/FheYSKEEOIZSaDEG+f2nViqVTHPtf7PvyK5/+AJIz/7AHNzY6ytzBg2uEXxBSiEECWUVkbRfEqDF36VixCvq8REFUZGBrnWP4xNYPGKA0yb1AGP994G4LG8RFcIIcRzJIESb6S8NuB/v8XbfDm+PUeOnmffgbPo6+vyiacr9g2rFmOEQgghSjJJoMQbx8hIn/iEnJ/AA+jv1YxzEbeZOe8npSz05DUO/DS2OMITQoiSq5RMvxUFWQMl3jiVK5Xnxs0HudZbVTTl3/DbamUqVeqrDksIIUo8WQP1jCRQ4o1iZlYG+4ZV89wY88athzSsX0WtTF9PdtwXQggyiuhTCsgUnngjtGvTiEePE+nv1Yz4+GT27D2l1FWvZoGeng6XLscAsHHrUb6a1oUZUzqy77ez6Onp0K2Li6ZCF0IIUQJpZeS1mlaIUuLJkyS0tLX499ytbK9yWbqgJ9ZWZvTot0Ypc21ckz693qFubSueJqqIPH8Xtya1NRG6EEKUGHbTlxRJP5G+r/97OSWBEm8EeZmwEEK8vLenFU0CFTHz9U+gZA2UEEIIIUQhyRoo8UYoCaNHbj0XvXQfx3+QrRSEEBokc1YKSaCEKCZ71wxHW1uLM5G3WfHdn9yOeZRn+wZ1KjG8hzu2Na1ITFJxOPhCMUUqhBA5Ky1bEBSFEjWFt3btWvr06ZNrvY+PDx4eHvn28+uvv9K2bVsaNWpEp06dOH78uFp9bGwsY8eOxcXFBVdXV3x9fUlMzPtVHREREXh5eeHg4ICHhwdbt25Vq09PT2f58uW4u7vj4ODAp59+ys2bN/ONVbw5Fm8+zOy1+6lkacqqaZ9gksfrZGyqVWDFlG48TVTx5ZKf+HrbMd5valuM0QohhMhLiUmg/P39Wbp0aa71Bw8eZOfOnfn2ExQUxPjx4+nRowe7d+/Gzc2NIUOGcPnyZaWNt7c3169fZ/PmzSxbtowjR44wY8aMXPuMjY1lwIABVKtWjV27djFixAj8/PzYtWuX0mb16tV8//33zJo1i23btpGens7gwYNRqVQFun5R+h0KOs+xsMtMXf4LVhZlae5sk2vbHh815nF8EpOX/ETIP9fZ99c5Vv9wtBijFUKIHMg+UAqNJ1DR0dEMGzYMPz8/atSokWObmJgYpk6diqura779ffPNN7Rq1Yq+fftiY2PDxIkTqV+/Plu2bAHg1KlThISEMH/+fOrXr4+bmxszZ85kz549REdH59jnjh070NPTY+bMmdjY2ODp6Un//v1Zt24dACqVio0bN+Lt7U2LFi2ws7NjyZIlREVFceDAgRe7MaLUehSfBICpSZlc26zb8TdjFgSQmpaulKWmpr3y2IQQIk+SQCk0nkCdO3cOPT09fvrpJ+zt7bPVZ2RkMGnSJDp27JhjAjVp0iRlWi89PZ2TJ0/i5uam1qZJkyacOHECgNDQUCwtLbGxefbbv6urK1paWoSFhQEQEBCAra0tt27dUo5xdXVFV/fZkrGmTZty7do17t+/T2RkJAkJCWrnNTU1pV69esp5hXirohnmZkaM7e9BQqKKv0Iv5dr2Xmw8l2/cB8DQQBeXBtUY1qNZcYUqhBAiHxpfRO7h4ZHnuqbNmzdz79491qxZw9q1a7PVT5kyhZSUFAAeP37M06dPsba2VmtTsWJFoqKigMwRr0qVKqnV6+vrU65cOe7evQtA27ZtcXd3x9zcHICoqCjq1q2brU+Au3fvKn3/t9/nzyvErmWDAUhWpTLeL5A7+Swiz/Lbus8w0Ncj7kne6/SEEOJVk0Xkz2g8gcpLZGQkK1euxN/fH319/RzblC1bVvlzUlLm1Mh/2xoYGJCcnAxAYmJijn0938bQ0BBDQ0O1fnPqEyA5OVlZgJ5Tm0ePCvZDUpR+YxcEkJ6eQaf3GzFvTAfGzAvgzPnbeR6jo6PNeL89GOjp0LdjE8qVzX3aTwghXjlJoBQan8LLTXJyMuPGjWP48OHY2dkV6JispOa/C7eTk5MpUybzB4+hoWGOC7uTk5MxMjLKsd+cjslKtoyMjJRkK6/zCvG/U1cJOnONyUt+4srN+4zs2yLfY9LS0jnxz3WOnbzC6Hm78m0vhBCvlKyBUpTYBOrMmTNcvHiRlStX4ujoiKOjI2vXruXOnTs4OjoSGhqa7Zhy5cphZGRETEyMWnlMTAxWVlYAWFtbZ6tXqVTExcUp03L/ldMxWd+trKyUqbu8zitElowMOHv+DrWqWOTapplTLRzsKquVJSTKE51CCFFSlNgEqlGjRhw4cIA9e/YQGBhIYGAgPXr0oGLFigQGBtKgQYNsx2hpaeHk5ERISIhaeXBwMM7OzgC4uLgQFRXF9evXlfqs9o0bN84xFhcXF8LCwkhLe/YUVFBQEDVr1sTCwgI7OztMTEwIDg5W6h8/fkx4eDguLi4vfhNEqdWgTqU8N9L8pG1jxg9qhbaWllJmaW5SHKEJIUSutDKK5lMalNg1UIaGhlSvXl2tzMzMDF1dXbXyJ0+ekJKSoiz4HjBgAEOGDKFevXo0b96cXbt2ERERwezZswGwt7fHycmJ0aNHM2PGDJ4+fcq0adPo1KmTMlqUlJTEkydPMDc3R0dHB09PT9avX8+UKVMYPHgwZ8+eZfPmzfj6+gKZa5+8vLzw8/PD3NycypUrs3DhQqytrWndunVx3C7xGmjW2Ia0tHTat2hAgzpv8eXSn5S6GpXN0dfT5cK1zFHMTQFBLPuyK7NGtmfPobOUMy3DwM5uuXUthBDFo5QkP0WhxI5AFdTs2bPp2rWr8r1Zs2bMmTOHH374gc6dOxMUFMSaNWuUbQu0tLRYuXIlVapUoV+/fowaNYrmzZurbaS5b98+mjVrpjyVZ2Fhwfr167l69SqdO3dm5cqVTJgwgc6dOyvHeHt707VrV3x8fOjZsyc6Ojps2LABPT294rkRosTz/bwtM0Z8hJmJIZ9/tYMjJ55tYzB+YCvmjemgfD8ZfpORc37E3MyIOaM+Zmx/D85fy3mfMiGEEMVPKyMjQ/JJIYqBvExYCPG6azhuSZH084/f6CLpR5NK7BSeEEIIIUoYGXJRvPZTeEIIIYQQxU1GoIQoJjL9VnQcvni5aYTTK17/6QMhNEJGoBSSQAkhXltlyxjwxcfv4mFfGyMDPS7euc/KX/7HiQs3NR2aEKWSVv5N3hgyhSeEeC1pa2mxcngnWjS0Yemeo4xd/wtPniazclgn6rxVIdfjHGq+VYxRCiFKKxmBEkK8lj5ytqNeNSt6zvfn0t0HAIRdusXOyX1ws6vOxTv3czyu/wfOxRmmEKWLTOEpJIESQryWWjnUIezSbSV5AlClptFx1uY8j/v3WhQtGtq84uiEKJ1Kyy7iRUGm8IQQryXbKpZcufuA3i0c2TdjICeWeuM/vheONpXzPG79gZA864UQeZCXCSskgRJCvJbKm5ShlWMdurzTkMWBRxm97ieSVCl8/VmXPNdACSFEUZAESgjxWtLT0aFsGQOGrw7g4OmLHAu/xhdrAnmarGLgB/ISbyFeCRmBUsgaKCHEa+lpsoqrUQ+JiYt/riyFM1fvYFvFUoORCVF6yRqoZ2QESgjxWrpxLw49XZ1s5bo62iSnpGogIiHEm0QSKCHEa+nYuavYVrGkppW5UmZmZIhDzbc4dflOrsfJ6JQQL0Gm8BQyhSeEeC35/3mKDk3rs2JYJ1b+8jeJySl82qYJGcCWQ6FKO9sqlqSkpnEl6iEAM3p9oKGIhXj9yRTeM5JACSFeS08Sk+m/ZDujOjRjcjcP9HS1OXX5DgO+20H0c+uilgz+mDsPHzN4+Y9UtjDj7apWGoxaCFFaaGVkZEg+KYR4rcjLhIXQDMfPXu7fvSynVr/+/w7KCJQQQgghCkSm8J6RBEqIAqqxxu+ljr82bFwRRSJkBEkIoWmSQAlRQLs79+LIzWssDf1fvm272tbnU3sXapiWI/ppPD+eP1cMEQohxCsmI1CKErWNwdq1a+nTp49aWUxMDGPGjMHZ2ZkmTZowduxYHj58mGc/x48fp0uXLtjb29OmTRv27t2rVp+cnIyvry9ubm44OjoWqM9bt24xdOhQnJycaNasGUuXLiUtLU2tjb+/P++//z6NGjWiV69ehIeHF+LqRUnnaPVWgdoNaOiEX8uPuBT7gKH797A09H90t2vwiqMTQohiINsYKEpMAuXv78/SpUvVylQqFQMHDuTOnTts3bqVdevWERkZycSJE3Pt5/LlywwdOhR3d3cCAgLo1q0bEyZM4Pjx40qbGTNmcOzYMVasWMGWLVu4cuUK3t7eufaZkpLCoEGDANi2bRszZszghx9+YNWqVUqb3bt3s2DBAkaOHElAQABVqlRhwIAB+SZmonTR1tLCu7Ebf928xojff+bPm1cJuBDOwH27NR2aEEK8NK2MovmUBhqfwouOjmb69OkEBwdTo0YNtbpffvmF27dv8/vvv1OhQubLQSdNmoSvry/x8fGYmJhk62/Lli3Y2toyenTmGgkbGxvCw8NZv349bm5uREdHExgYyJo1a3B2dgZg8eLFtGnThlOnTuHo6Jitz/3793Pnzh127NiBmZkZdevW5cGDByxYsIBhw4ahr6/PmjVr8PLyokOHDgDMmTOHVq1asXPnToYOHVqUt0yUYBXKGFHesAyHr19WK78Qe19DEQkhhHgVND4Cde7cOfT09Pjpp5+wt7dXqzt27BhNmzZVkicAd3d3Dh48qCRPK1aswNbWVqkPDQ3Fzc1NrZ+mTZsSFhZGRkYGYWFhSlmWmjVrYmVlxYkTJwAIDg7G1taW4OBgpc/69etjZmam1md8fDwRERE8ePCAa9euqZ1XV1cXZ2dnpU/xZnisSiYlLY3KZc3Uyk31DTQUkRBCFCGZwlNofATKw8MDDw+PHOuuXr2Ks7Mzq1atIjAwkNTUVJo1a8b48eMxNTUFYODAgfTo0UM5JioqCmtra7V+KlasSGJiIrGxsURHR1O+fHkMDAyytYmKigLA0dGRY8eOKQlTbn0C3L17F13dzNtYqVKlbG0iIyMLdT/E6y0pNZVfLp+nbwMHLjy8z/6rF6lQxojp7+b8d1wIIV4nWrJ1pELjI1B5iY+PJzAwkPPnz7No0SJmzpxJWFgYn332GVn7fxobG2Np+ezdVklJSejr66v1k/VdpVKRmJiYrR7AwMCA5ORkpb2lpaXSLqc+sxKw5ORkEhMT1c6TU5/izTHlr98JvBDB/BYfcnbgF+zt2pew6NzfzSaEEOL1o/ERqLzo6upiZGTEokWL0NPTA8DMzIxu3brxzz//0KhRo2zHGBgYoFKp1MqyvpcpUwZDQ8Ns9ZCZCJUpUybHOHI6JisxMjIywtDQUO08BelTlF5PU1OYeGQ/vn8fpnJZU24/eczT1BTGuLyr6dCEEOLlyACUokSPQFlbW1OzZk0leQKoU6cOkLmtQE4qVapETEyMWllMTAxGRkaULVsWa2tr4uLisiU7MTExWFnl/I4sa2vrHPsEsLKyUqbucmqTW5+i9PKoVovG1m/xNDWFi7EPeJqagoWhkabDEkKIl1YSnsJ78OAB48ePp2nTpjg6OjJkyBAuX3724E5ERAReXl44ODjg4eHB1q1b1Y5PT09n+fLluLu74+DgwKeffsrNmzcLHUeJTqBcXFyIjIwkKSlJKbtw4QIA1atXz/EYZ2dnQkJC1MqCgoJwcnJCW1ubxo0bk56eriwmh8y1VtHR0bi4uOQaR3h4OPHxz15QGhQUhLGxMXZ2dlhYWFCzZk1l0TlAamoqoaGhufYpSq/e9e2Z0rSFWtnARk6aCUYIIUqZESNGcP36ddatW8ePP/6IoaEh/fv3V9Y6DxgwgGrVqrFr1y5GjBiBn58fu3btUo5fvXo133//PbNmzWLbtm2kp6czePDgHGen8lKiE6gePXqgo6PD2LFjuXjxImFhYfj4+NCkSRPq168PQEJCAvfu3VOO6dOnD2fPnsXPz4/Lly+zceNGfvvtNwYPHgxkjhi1a9cOHx8fgoODOXv2LGPGjMHV1RUHBwcgcyru3r17ys1s1aoVlpaWjBo1isjISA4ePMjixYsZOHCgsu5p4MCBbNq0id27d3Pp0iW+/PJLkpKS6Nq1azHeMaEJjhUrUc302VN3m/85iZP1W0x9pwVub1VlrMu7jHBqmkcPQgjxmtDwU3iPHj2icuXKfPXVVzRq1AgbGxs+++wzYmJiuHjxIjt27EBPT4+ZM2diY2ODp6cn/fv3Z926dUDmz/eNGzfi7e1NixYtsLOzY8mSJURFRXHgwIFCxVKiEyhzc3P8/f1JTU2lW7duDB8+nIYNG7Jy5UqlzcaNG2nWrJnyvU6dOqxevZojR47QqVMndu7cycKFC9W2GJg1axZubm58/vnnDBo0iFq1arF8+XKl/tSpUzRr1oxTp04Bmeuq1q9fT3p6Ot27d8fX15devXrx2WefKcd0794db29vli5diqenJ7dv32bTpk2Ym5u/ylskSoDdXXrj3fjZ36+jt67jffAX3KvUYONHXfioVl2mHzukwQiFEKJoaHoKz8zMjEWLFlG3bl0AHj58yObNm7G2tqZ27dqEhobi6uqqPB0PmdsOXbt2jfv37xMZGUlCQoJaTmBqakq9evUKve2QVkaGPJMoREHIy4SFEG861/6Li6Sfsjf35ll/6FD+v3ROnTqVHTt2oK+vz9dff02zZs34+OOPad68OePHj1faXbp0iXbt2vHjjz9y9+5dvvjiC86cOaM8AAYwcuRIkpKSWLt2bYGvoUSPQAkhhBCiBClBG2n269ePXbt20b59e0aMGMG5c+eKdduhEr2NgRAliYwgiaJUfeOCl+7j+sAJRRCJEAVXVO+xK8gIU35q164NwOzZszlz5gzfffddobYden4E6kW2HZIESgghNMTHtSUNLKzo8eu2fNs2sLBinFMzGlWohLaWFv88iCqGCIX4Dw0v+nn48CHHjx/nww8/VNY5aWtrU7t2bWJiYvLddig1NVUpq1atmlqb518LVxAyhSeEEBrwaQMXPm1QsG1Oqpctx/a2PTHU1WPCsd8Ye3Qf+try+69489y/f58xY8Zw/PhxpSwlJYXw8HBsbGxwcXEhLCyMtLQ0pT4oKIiaNWtiYWGBnZ0dJiYmatsOPX78mPDw8EJvOyT/BgohhAZMcn6PR8lJ+TcEBtRrTGJqCgN+30ViagoA/7t7g8i+o19liEJkU1RTeC+qbt26NG/enK+++oqvvvoKMzMz1q5dy+PHj+nfv7/y1PyUKVMYPHgwZ8+eZfPmzfj6+gKZa5+8vLzw8/PD3NycypUrs3DhQqytrWndunWhYpEESgghNKDnr9sY49Qs/4bApUcP+OafE0ryBKj9WYhiUwIe3F+8eDGLFi1i9OjRPHnyBGdnZ/z9/XnrrbcAWL9+PbNnz6Zz585YWloyYcIEOnfurBzv7e1NamoqPj4+JCUl4eLiwoYNG9TeelIQso2BEEJoQPWNC9j2UQ+AAq2BynZ82XL81W1IUYclRJ6a9l5UJP0E+Y8tkn40SUaghBDiNWOgo8vi5m01HYZ4A2l6Cq8kkQRKCCFeI8a6+nzTqjP2lpU0HYp4E0kCpZCn8IQQ4jVRybgsP7bvReOKlfn8j580HY4QbzQZgRJCiNeAbfkKfPthdwx1dOmzfwch0bc0HZJ4A2mlazqCkkMSKCGEKOEqGZfFv80npKWn47nXn4txDzQdknhTyRSeQhIoIYQoYeqUs0BfW4dzDzN3UJ7R9H0syxgz+e/9mOjp4yjrn4TQOEmghBCihJnl9gFVTMxotnMtetravF/VBoC5736o4cjEm06ewntGEighhNCQ3PZ/er48JT2d2ptz3ntHXiYsip1sHamQBEoIIYQQBSIjUM/INgZCCCGEEIUkI1BCvEYe3KnyUsdbvCWPvpcUMv0mXksyAqUoUSNQa9eupU+fPmpl586do0+fPjg6OtKiRQv8/PxQqVR59vPrr7/Stm1bGjVqRKdOnTh+/LhafWxsLGPHjsXFxQVXV1d8fX1JTEzMs8+IiAi8vLxwcHDAw8ODrVu3qtWnp6ezfPly3N3dcXBw4NNPP+XmzZuFuHpRGp0+fZo+ffrg4ODAO++8w8SJE3nw4Nkj6MePH8fLywsXFxfeffddvvjiC27cuJFrf8tX6zNilGGBzj1jtgHvtDRW+9ja2vLbb78pbS5fvsywYcNwdHTE1dUVb29vrl27ptaPSqVi0aJFvPfeezRq1IjOnTuzd+/ewt0IIUSpoJVRNJ/SoMQkUP7+/ixdulStLDY2loEDB1KrVi0CAwOZNWsWAQEB2do9LygoiPHjx9OjRw92796Nm5sbQ4YM4fLly0obb29vrl+/zubNm1m2bBlHjhxhxowZufYZGxvLgAEDqFatGrt27WLEiBH4+fmxa9cupc3q1av5/vvvmTVrFtu2bSM9PZ3Bgwfnm+yJ0uvff/+lb9++GBsbs3LlSsaNG8fff//NiBEjAAgLC2PQoEGUL18ePz8/fHx8uHbtGj179uThw4fZ+tu4cSPbdhb8beEXL2nzgUcq61YlKp/t27fTtGlTAG7evEnPnj25ePEi06ZNw8/PD4BPPvmEW7eejVSNHj2ajRs30qFDB9asWcNHH33ElClT+Pbbb1/m9gghxGtN41N40dHRTJ8+neDgYGrUqKFWFxYWRlxcHOPHj8fExITq1avz8ccfc/ToUSZMyHn4+5tvvqFVq1b07dsXgIkTJ3Lq1Cm2bNnCzJkzOXXqFCEhIezbtw8bm8xHg2fOnMngwYMZM2YMVlZW2frcsWMHenp6zJw5E11dXWxsbLh+/Trr1q3D09MTlUrFxo0bGTduHC1atABgyZIluLu7c+DAAdq3b190N0y8NhYuXEi9evVYvXo12tqZv6uYmJgwe/Zsbt68yTfffIONjQ3Lli1T6p2cnGjRogW7d+9m0KBBQGaiM3/+fA4fPoyJccF+dUtWwY2bWvTomkaDes+2DrZ4y0H585YtW0hMTGTXrl1UrVoVgGbNmtG9e3eWLFnCokWLCA8P5+DBg4waNYrhw4cD8M4772BkZMSiRYvo2LEjpqamL32vhBCvCXkKT6HxEahz586hp6fHTz/9hL29vVqdubk5AD/88ANpaWncunWLI0eOqLWbNGkSHh4eQOY02smTJ3Fzc1Prp0mTJpw4cQKA0NBQLC0tleQJwNXVFS0tLcLCwgAICAjA1tZW+S08NDQUV1dXdHWf5ZtNmzbl2rVr3L9/n8jISBISEtTOa2pqSr169ZTzijdLbGwsISEh9OzZU0mOAFq3bs2RI0eoWrUq9vb29OvXT63eysqKsmXLqk3jzZ07l+vXr7Nlyxbq1C7YexSuXNUmLU0rz/ZXrlyhdu3aSvIEoK2tjYuLC0eOHAFQRm5btmypdmyTJk14+vQpISEhBYpHCFE6yBTeMxofgfLw8FASoP9ycnJi+PDhLFu2jCVLlpCWlkbTpk2ZNm2a0mbKlCmkpKQA8PjxY54+fYq1tbVaPxUrViQqKgrIHPGqVEl9F199fX3KlSvH3bt3AWjbti3u7u5KAhcVFUXdunWz9Qlw9+5dpe//9vv8ecWb5fz586Snp2Nubs7YsWM5fPgwAB988AE+Pj6YmpoqIzrPCwkJ4dGjR9SpU0cpGzVqFHXq1EFLS6vA5794KTMp+3mfLuO/1OHRYy3qv53OlKlnlF9Aypcvz4ULF0hJSUFP79nU4M2bN3ny5AlxcXGUL18egDt37mBnZ6e0yUrwZJ2fEOJNpfERqLzEx8dz5coVevfuzc6dO1m2bBnXrl1j6tSpSpuyZcsqiU5SUhKQmRA9z8DAgOTkZAASExOz1f+3jaGhIZaWlujo6Cj95tQnQHJysrIAPa/zijdL1hqmL7/8EkNDQ1avXs2ECRP4448/GDp0KBk5DIM/fPiQqVOnUrFiRTp16qSU161bt1DJEzxLoBITwXdqMr4+ySSroG/fvkRGRgLQpUsX7t27x8SJE7l58yaxsbFs3ryZo0eP/v+xibi6ulK1alW++uorjh8/Tnx8PKGhofj5+aGlpcXTp09f5PYIIV5XGUX0KQU0PgKVl4ULF/Lo0SOWL18OQP369TEzM6N///7079+ft99+W619VlLz34XbycnJlClTBshMjnJa2J2cnIyRkVGOceR0TFZiZGRkhKGhoXLerD//97zizZI1Klq/fn1mz54NgJubG6ampowZM4a///6bZs2aKe1jYmIYNGgQMTExbN68GRMTk5c6f9fOKbzrlkZT1zSlzLlxGj36VmDNmjUsXbqUd999l4ULFzJnzhzlqbp33nmHTz/9lBUrVmBoaIi+vj4bNmzgyy+/pH///gBYWlri4+PDqFGj5O+3EG+Y0jL9VhRKdAIVFhamLMrOkjX9cO3atWwJVLly5TAyMiImJkatPCYmRlkcbm1tzcGDB9XqVSoVcXFxyrTcf1lbW+fYJ2SuWUlNTVXKqlWrptbG1ta2IJcqShljY2Mg+9ohd3d3AMLDw5UE6vz58wwbNoyEhATWr1+fbS3gi6heLYPq1dLUysqaZE6LZ41AAXTo0IF27dpx8+ZNypQpg5WVlbKoPWtxePXq1fH39+fBgwfExcVRvXp17t69S0ZGBmZmZi8dqxBCvI5K9BSelZUV58+fVyvL+l6zZs1s7bW0tHBycsq2sDU4OBhnZ2cAXFxciIqK4vr160p9VvvGjRvnGIeLiwthYWGkpT37gRQUFETNmjWxsLDAzs4OExMTgoODlfrHjx8THh6Oi4tLYS5ZlBJZT5T+d+QyK9nOGqkMCgqiV69eZGRk4O/vn+vfwcI6eFiH4BM62cqTk5OVKe/Lly8TGBiIjo4ONWrUUH7JCA8Px9bWFh0dHZKSktizZw83b97EwsICGxsbdHV1OXfuHJA5wiaEeIOkZxTNpxQo0QlU//79OXr0KEuXLuXGjRscP36cyZMn06JFC2VB65MnT9T2zBkwYAB79+5l06ZNXL58mQULFhAREUG/fv2AzBEsJycnRo8ezdmzZwkKCmLatGl06tRJ+QGSlJTEvXv3lITJ09OT+Ph4pkyZwqVLlwgICGDz5s0MHToUyFz75OXlhZ+fH4cOHSIyMpLRo0djbW1N69ati/OWiRLCxsaGypUrs3fvXrX1TocOHQLA2dmZ8PBwhg0bRqVKldi+fbvawvGXFfiLHguX6PP/M4kA3LunxcmTJ2nSpAkAFy9eZOLEiVy5ckVpc+nSJY4dO0arVq0A0NPTY9asWezYsUNpk5qaynfffUe1atWyPVwhhCjlZA2UQisjp9WsGjJp0iRu376ttkHfkSNHWLVqFRcvXqR8+fJ88MEHjBw5UlmvNGnSJEJCQpSnnAACAwNZvXo1UVFR1K5dm/Hjx6ttMfDgwQN8fX05evQoBgYGtGnThsmTJytrqAICApg8eTKHDh2iSpXMV2ecPXuW2bNnEx4ejqWlJQMHDsTLy0vpMy0tjcWLFxMQEEBSUhIuLi5MmzZNOV68eX777TdGjRpFmzZt6N69O5cuXVL2B1u+fDmdO3fm4sWLLFq0KNv+Y+bm5mrTwVl6dK8NwKqlSWrlV69poUrRwrZO5rYFYae0GTnOEFfnNLp7pvL4MWzcqk9qWhV++uknypYtS2JiIh06dKBs2bKMHDmS+Ph4FixYgKGhIbt27VLWYS1evJgtW7YwYcIEatasib+/P3/88QerVq3KNkUphCjdWny0oEj6+fPX1/9VRiUqgRKitMlKNM6fP4+ZmRkff/wxo0ePJjo6WhnlyUnnzp2ZN29etvLcEqgRowy5G6VFwLZnryQKDdNmwxZ9Ll3RRlsLmrimMWXqUd566y2lzfXr15kzZw5hYWEYGBjQvHlzRo8erbYeMCUlhZUrV7Jnzx4ePXqEnZ0dI0aMUFsEL4R4M0gC9YwkUEK8RuRlwkIITWrRZn6R9PPnbxOLpB9NKtFP4QkhhBCi5JBtDJ6RBKqUS496uUW+2tYXiigSURRkBEkIIUqGEv0U3pvq9OnT9OnTBwcHB9555x0mTpzIgwcPlPo///wTT09PHBwcaNmyJcuXL8/2uPy4ceOwtbXl7fe01D77/8z73Lt/hY/7g8MH8JEXbN26Nduu2cePH8fLywsXFxfeffddvvjiC7V3t/3XvHnz6NOnT2FvgxBCiJJGnsJTSAJVwvz777/07dsXY2NjVq5cybhx4/j7778ZMWIEAMeOHWP48OHUrVuX1atXM2jQIDZt2sSsWbPU+omMjKR9+/b8sDpD7dPEKfdz7/wFvpynxXtN4et50OnDzORn7dq1SpuwsDAGDRpE+fLl8fPzw8fHh2vXrtGzZ0+17SSybNy4kU2bNhXNzRFCCKFRWhkZRfIpDWQKr4RZuHAh9erVY/Xq1WhrZ+a3JiYmzJ49m5s3b7J27Vrq16/P3LlzgcxXb8TGxvL1118zefJkjIyMSE5O5urVq/Tr1w+H+j8X+NzrvoMP38tg3LDM726N4fqDDnz33XcMG5ZZ+M0332BjY6PsVg2Zu1u3aNGC3bt3M2jQICDzJbPz58/n8OHDlC1btqhujxBCCFEiSAJVgsTGxhISEsK8efOU5ASgdevWyoacc+bMUd6zlkVPT4/09HRll+sLFy6Qmpqa7VU3+VkzDwwN1Mv09PTUXohsb29Pq1at1OKzsrKibNmyatN4c+fO5ebNm2zZskV5l6EQQojXXLqmAyg5JIEqQc6fP096ejrm5uaMHTtW2Rz0gw8+wMfHB1NTU6pWraq0j4+P53//+x8bN26kXbt2yrvLst51tnPnTg4egEePodHbMP4zsK+X+/ltamT+f0YGPHoCv/+VuSnpwIEDlTbDhw/PdlxISAiPHj1S20l71KhR1KlTBy0trRe9HUIIIUqY0jL9VhQkgSpBstYQffnllzRv3pzVq1dz7do1Fi9ezM2bN/n++++VhCQmJkZ5MW3VqlUZPXq00k9ERAQAiYmJLJoGcY/hG3/oPwq2fQ22NnnHcfoc9BqReZ4GDeoyYMCAPGOeOnUqFStWpFOnTkq5vOJDCCFEaSaLyEuQrKm5+vXrM3v2bNzc3OjZsyczZszg5MmT/P3330pbQ0NDNm/ezNKlS9HX1+eTTz4hOjoaAC8vL9avX8+CBQtwdYTW78GGRVDGENZ8m+Op1VS2hi3LMpgzKYN79+7Ro0cPEhMTs7WLiYmhX79+xMTEsHLlSuXVH0IIIUopeQpPIQlUCWJsbAyQ7f1iWSNN4eHhSpmpqSlubm589NFHrFu3jgcPHrBz504AatWqpRyjtC8LTg3h/OX846hYAVwdoPNHsGjRIq5evcr+/fvV2pw/f15J2tavX4+9vX1hL1cIIcTrJiOjaD6lgCRQJUiNGjUAsu3plLU43MDAgH379qklUgBVqlTBzMyMmJgYAP6PvTOPq2n7//8rU8Z7TZdcrtnt1FGaNVOIm7EMmaKEFIUQQi4ZQuZEmee4IkPGa7jGqLgyVJSK0qRBheb3749+Z387nal0TPezno/HfrD3Xmu11t5r7/0+a73fr3X+/Hncvn1bpPyCQqD5z+L/9oePwLkrQGIlnUZV1XKnKUHZABAaGoqxY8eCiHD48GFoa2tXuY0MBoPB+HFRIPls/wWYAfUd0aVLF7Rt2xYhISFC4pVXr14FAOjq6mL9+vVYv369UL5nz54hJycHysrKAIDAwEAsXbpUyBBLywAePQX0NMX/7Tq1gSXrgD2BwscF04aCsp8/f45p06ahTZs2OHbsmJDjOIPBYDAY/yswJ/LvCAUFBbi7u2PWrFmYPXs2Ro0ahdjYWGzcuBH9+/eHqqoqXFxcMH/+fCxduhQDBgzAmzdvsGXLFvz+++8YPnw4AMDZ2Rn29vZwdnaG7eDyKLxt+4GffwLsbf7v7/37DGjeFGjfFlBUBKaMA3z3As2bAT01gehYwO+ABwwNDWFqagoAWLRoEUpKSuDi4oKUlBSkpKRw5TVv3hzt27f/mpeMwWAwGF+T/8j0mzxQoMrrdDC+OdevX8e2bdsQExODn3/+GYMHD8bs2bNRr149AMDFixcREBCAV69eoWHDhujbty/mzJmDn3/+v/m5e/fuYevWrYiJDkctBcBYD5gzDfi19f/9HZVeChg2gLB6Yfk+EXDsDHD4FPAmudy4GjR0KlxcXKCoqIg3b96gb9++EuttZWUFb29vkeOCZVwOHqyCBzuDwWAwvlv6Ga+USzlXbi+SSznfEmZA/cdhiwkzGAwGQ14wA+r/YFN4DAaDwWAwqgYbc+FgBhSDwWAwGIyqwewnDmZA/cf5HqbgOm/eUOMyXs10k0NNGAwGg8GQD99cxiAnJweenp4wNTWFlpYWxowZg/DwcO78vXv3YG1tjR49emDAgAEICQmRWebhw4fRp08fqKurY+zYsSK6SUlJSXB0dISWlhaMjY2xadMmlJaWSi1TVj0KCwuxbNkyGBgYQFNTE3PmzOGWZvlf5/akyXg+3RUhY21h2U22T5amUhscGT4SkU4zEDp5Kjx7mcnMw2AwGIwvjwKRXLb/At/cgHJzc8OjR4+wYcMGBAUFQUVFBQ4ODnj16hXi4uLg6OgIExMTnDx5EiNHjoS7uzvu3bsnsbxTp05h7dq1mDlzJk6ePIl27drB3t6eM2aKi4vh4OAAoFwv6c8//8TRo0exbds2iWVWpR5//vknbt++ja1bt2L//v149eoVXF1d5XSVfmy2hz3AlDPBeJ6Rji1/DIRph44S0/JatsQh6xH4UFQE55CzWHfnNgZWwehiMBgMxleAKZFzfNMpvMTERNy5cwdHjhzh1KyXLFmCW7du4ezZs8jMzISysjK3UG6XLl3w/Plz7Nq1CwYGBmLL3LFjB8aPH48hQ4YAAFatWoW+ffvir7/+gqOjIy5duoS3b9/i+PHj+Pnnn/H7778jMzMTa9euxbRp0zipgIrs379faj3S0tIQHByMHTt2QEdHBwCwYcMGDBgwAI8ePYKmpgT1yv8RDj+JBADcefMaHZo2Ra8OHXEzMUFs2kmaWsgpKIBzyFkUl5Vxx9dZDPgaVWUwGAwGo0p80xGoZs2aISAgAGpqatwxBQUFKCgoIDc3F+Hh4SKGkr6+PiIiIjilbltbW05nKDMzEwkJCUJ56tSpAx0dHYSFhQEAwsPDwefzhTST9PX1kZ+fj6ioKADA1q1bOeVtQR5p9YiIiOCOCejUqRNat27N/V1GOaP+Ogavmzcknl9/9y4czpwSMp4q/p/BYDAY35AyOW3/Ab6pAfXTTz+hV69eQqM+ly5dQmJiIkxMTJCamgolJSWhPK1atcKnT5+QnZ0NoNzY2bp1KwAgNTUVANCmTRuRPIJzksoEwKlqT5o0SWgtOVn1SEtLQ7NmzaCoqCjx7/4vc9l2Il64zMJl24no17mL1LRpH/IR/e4dAKBBnTow+q095hoafY1qMhgMBkMGzAfq//iuovAePnyIhQsXwsLCAr1790ZBQYHIlJpgX7DOW9OmTblznz59EkojQFFREYWFhQCAgoIC/PTTTyLnAXBpGjVqhEaNGnHnZdXj06dPYqf+Kv7d/2W2hz1AxscPGKemge2DhmDS6VMSp/AqEuHojPp16iDr/99XBoPBYHxj/iPGjzz45k7kAv7++29MmjQJGhoa8PHxAVBugFRcEBf4P8OpQYMGImXUr19fKI2AwsJCLn39+vXFngeAhg0biq2brHqIK7Py3/1f5lR0FG6/fo3p58/iZWYmXPT0ZeapU6sWpp4NxuQzwYj//6ONDAaDwWB8L3wXBtShQ4fg4uICMzMz7NixgxsRatOmDdLT04XSpqeno2HDhmjSpIlIOYKpO3F5WrcuXwROSUlJ7HkAXBpx5Uqrh5KSEnJyckSMqIp/lwGUEeHum9fg//8pU2mUlJXh9uvXuBb/CpNOn/wKtWMwGAyGTFgUHsc3N6COHDkCLy8vjBs3Dhs2bBCaCtPR0cGDBw+E0oeGhkJLSwu1aolWvUWLFujUqRPu37/PHSspKUF4eDh0dXUBALq6unj+/Dny8/OFymzUqBF4PJ7YOsqqh7a2NsrKyjhncgCIj49HWloa93cZ5dSpVQsFJSUSz5t36gzdX9sKHcsTM7rHYDAYjG8AcyLn+KYGVHx8PFatWoV+/frB0dER7969Q0ZGBjIyMpCXlwdbW1tERkbCx8cHcXFx2LNnDy5evIjJkydzZeTk5CAnJ4fbnzRpEvbu3YtTp04hNjYWHh4eKCgowIgRIwAAffv2xS+//IJZs2YhOjoaf//9NzZs2IBJkyZxxtuHDx+QkZHBlSmrHq1bt8bAgQOxePFi3L9/H5GRkXBzc4Oenh40NDS+/IX8QahbqxZMO3REWHKSxDSTNLXgZd4HtRQUuGNKjRt/jeoxGAwGg1FlFIi+3Vjajh07sHHjRrHnrKys4O3tjZs3b2LdunVISEhAu3bt4OLiAktLSy6dQMLg4MGD3LHdu3fjwIEDyMnJQffu3bF48WKoqKhw5xMTE7Fs2TKEh4fj559/xogRI+Di4sKNam3duhW+vr6IiYnh8siqx8ePH7Fq1SpcunQJAGBqaorFixejWbNmcrhSPzYL/r6M1Px82GlowqDdbxhxPBDPMsqnRLs2b456tWvj+f83WPXb/YYDVsNxJS4WR58+QYsGDTBDTx9dmjf/lk1gMBgMBoABGp5yKefiv8vlUs635JsaUIz/Dd59/IBGdevhWUY61t25jbC3ydy5I8NHot1PP8F0727umH673zBb3wC8lr+glAg3ExMwRFn89CqDwWAwvh4DeiyRSzkXH3vJpZxvyXclY8D4b6K301/iubFBf4kcC016A5sTb4SOMQOKwWAwGN8TzIBiMBgMBoNRNdikFQczoBhfnFcz3b51FRiM/ySa08X7kFaVR9tmy6kmjP8ZmAHFwQwoBoPB+EG5vmYaateuhTvP4rE+6Cbe5X6Qmt6keydM/UMf3dq2RE4+U/hnMGrCN9eBYjAYDMbnsTLwKlYFXkWPzr/Cb4Y16ojRxxNg1qMLNjkOxcfCIszfHQKfoH++Yk0Z/xmYDhQHG4FiMBiMH5S/H70EALxIysBfiyagn/bvuBAWLTbtNEsDxKdlYfq2Uygp/Y98wRhfnf/KQsDygI1AMRgMxg/Oq9QsxKdlwZjfUWKaTkrNce95AjOeGDWDLeXCwQwoBoPB+A/wJiMHHVtJFpzNyf+ENs1/+oo1YjD+2zADisFgMP4DfCgoQqP69SSeD773DH00u8Gunw6aNW4ApWaiC7IzGDIpI/ls/wGYDxSDwWD8RyiTMjXif/4e6tSuBedBhpg5zATFJaVfsWaM/wz/kek3ecAMKAaDwfgP0Lh+PeQXFEo8X1pG2HL6NnaE3EO7lj8j/f0H3PJx/oo1ZDD+WzADisFgMP4D/PZLUzxNTJV4XrtbO9SrUxv3ohLxKjXrK9aM8Z+CjUBxMB8oBoPB+MHprNQcndu0wL2oRIlp+mp2w5KxfaVqRTEYMvkOovBycnLg6ekJU1NTaGlpYcyYMQgPD+fO37t3D9bW1ujRowcGDBiAkJAQofyFhYVYtmwZDAwMoKmpiTlz5iArq/o/KtiTxGAwGD8oA7SVMUBbGb7TrfAiOQNXHr7gzim3+wWdlf4vKu/ErUi0aNIQyyZYQE/5N4w31/oWVWYwaoybmxsePXqEDRs2ICgoCCoqKnBwcMCrV68QFxcHR0dHmJiY4OTJkxg5ciTc3d1x7949Lv+ff/6J27dvY+vWrdi/fz9evXoFV1fXatdDgYiNxzEYDMaPyIeCIpSUliE0KhE+Qf8ILeUSsnwS3mbmYsrmE9yxnrz2cB1ijE5tmiMz9yPatfz5W1Sb8QPzR+e5cinnwiufz8qXmJgICwsLHDlyBNra2gAAIoKFhQUGDRqEzMxMREVF4a+//uLyzJkzBzk5Odi9ezfS0tLQu3dv7NixA7169QIAxMfHY8CAAQgMDISmpmaV68J8oBgMBuMHxXjONonnBnruETl2P/o1xkUf4fbZYsKMakPyEWLt06eP1PNXr14Ve7xZs2YICAiAmpoad0xBQQEKCgrIzc1FeHg4+vbtK5RHX18fK1euBBEhIiKCOyagU6dOaN26NcLCwqplQLEpPAaDwWAwGD8EP/30E3r16oV69f5P8+zSpUtITEyEiYkJUlNToaSkJJSnVatW+PTpE7Kzs5GWloZmzZpBUVFRJE1qquQgDHGwESgGg8H4QfkeRpD+6ORW4zIuxG+QQ00YXwU5ef1IGmGqLg8fPsTChQthYWGB3r17o6CgQMi4AsDtFxUV4dOnTyLnAUBRURGFhZJlQMTBDCgGg8Fg1IjJi4agW/d2mD/GT2baeRvHwXyY9leoFeOL8B2piP/999+YO3cutLS04ONT7lOlqKiIoqIioXSC/QYNGqB+/foi54HyyLwGDRpU6+9/8yk8WeGIQUFBGDx4MDQ0NGBhYYGAgACUlkpW0C0rK8OWLVtgYmICDQ0NTJkyBW/evBFKExUVhfHjx0NDQwPm5uY4cOCAzHpeuHABlpaWUFdXx7Bhw4Q8+gEgOzsbc+bMga6uLvT09LBs2TJ8+vSpmleDwWAwfiysJ/fC8Mm9q5y+s8qvuH76IWZbbeY2xg/EdyBjAACHDh2Ci4sLzMzMsGPHDm5Krk2bNkhPTxdKm56ejoYNG6JJkyZQUlJCTk6OiBGVnp6O1q1bV6sO39yAkhaOeObMGSxduhTjx4/HmTNnMGvWLPj7+2P79u0Sy/Pz88ORI0fg5eWFwMBAlJWVYfLkydzFys7Ohr29Pdq3b4+goCBMnz4dPj4+CAoKklhmaGgo5s2bh9GjR+PUqVMwMDDA1KlTERcXx6VxdXVFYmIi9u3bh82bN+Off/7Bn3/+KbfrxGAwGN8jk+YPQn5u1X4s1q1XB+06t8K/d18i+t9EbmMwqoPgGz9u3Dhs2LBBaEpOR0cHDx48EEofGhoKLS0t1KpVC9ra2igrK+OcyYHyKLy0tDTo6upWqx7f1IBKTEzEnTt38Oeff0JHRwedOnXCkiVL0KpVK5w9exZHjx7FsGHDYGNjg/bt28PS0hKTJk3CiRMnxJZXVFSEPXv2wNXVFb179waPx8PGjRuRmpqKy5cvAwCOHz+OunXrYvny5ejSpQuGDx8OOzs7BAQESKznzp070bdvX0yYMAFdunTB/PnzwefzsX//fgDAo0eP8ODBA6xZswZ8Ph8GBgZYvnw5Tp8+jbS0NPlfOAaDwfhOWDBuO149T65S2o7KSqhTt3aV0zO+Q77xCFR8fDxWrVqFfv36wdHREe/evUNGRgYyMjKQl5cHW1tbREZGwsfHB3FxcdizZw8uXryIyZMnAwBat26NgQMHYvHixbh//z4iIyPh5uYGPT09aGhoVKsu39SAkhWOOHfuXDg4OAjlqVWrFt6/f8/tm5ubY8GCBQCA6OhofPjwAQYGBtz5n376CaqqqggLCwMAhIeHQ09PD3Xq/J/7l76+PhISEvDu3TsAwIIFC2Bubg6gfErw4cOHQmUCQM+ePYXK/OWXX9ClSxfuvJ6eHhQUFISsXAaDwfiv8fTBqyqn7azaFgAwwKYnDt//E2di1mLd8RlfqmqML8E3NqAuXbqE4uJiXLlyBcbGxkLbypUr0a1bN/j5+eGff/7BsGHD8Ndff2HdunVC33AvLy8YGBhgxowZcHBwQOfOnbFly5Zq1+WbOpELwhErIghH9PDw4ESyBOTl5eHo0aMwMTHhjp04cQJ169YFAC4EsU2bNkL5KoYnpqam4vfffxc5DwApKSlo2bIlFi1ahOLiYgBAbm4uPn78KDYsUlBmWlqayN+sV68emjZtipSUlCpeDQaDwfhvIzCgFBvWg7frQfzUrBFGTTP/xrVi/EhMmzYN06ZNk5rG1NQUpqamEs83bNgQK1aswIoVK2pUl+8qCq9yOGJFPnz4AGdnZxQWFsLd3Z073rz5/y1VIHDarhyiqKioyI1aiQtxFDifCUIYmzRpwp0rKCiQWKYgvTzDIhkMBuO/ypn9t3D/6jM8vBnDHfv3zguciFz1DWvFqBZl8hHS/C/w3RhQ4sIRBWRkZMDR0RFJSUnYvXs32rVrJ7aM+vXrAyj3hRL8HxAOTxQXwigwcho2bChSpsC4EpdHWpmCNOLKZDAYjP9Fkl9lIPlVhtCxD3kF36g2jM+Crf7G8c2j8ADJ4YgAEBcXh1GjRiEzMxOHDx8W8peqjGAaTVwIoyA8UUlJSex5AGJDGJs2bYqGDRtWu8yioiLk5ORw04MMBoPxv47pQA1omfwuOyGD8QPwzQ0oaeGIb968wcSJE9GgQQMEBgaiW7duUsvi8Xho3Lgx7t+/zx3Lzc3F8+fPufBEXV1dRERECGlJhYaGolOnTmjRooVImQoKCtDS0hIJi7x//z50dHS4MlNTU5GY+H/huIL0lf24GAwG43+VP8YaYMaKkahTtzZ3rEVrtqDxD8V3ogP1PfBNDShZ4YgeHh4oKirChg0bUKdOHe5cRsb/DQFnZWUhLy8PQLmf0vjx4+Hj44OrV68iOjoas2fPhpKSEiwsLAAAw4cPR35+PhYtWoTY2FicPHkS+/btg6OjI1dmXl4esrKyuH17e3uEhIRg7969iIuLw9q1axEVFYWJEycCAHr06AEtLS3Mnj0bkZGRCA0NhaenJ4YNG1ZtYS4Gg8H4r9C+a2t0+f+O4wBwdOsVtGrbDJ4Bk6BtykPvIVrwPuL0DWvIqDZlJJ/tP4AC0bczBXfs2IGNGzeKPWdkZIQ7d+5IzBsTU+6EaG5uDj09PXh7ewMASktLsWHDBpw8eRIFBQXQ1dWFp6enkN9UZGQkVq5ciefPn+OXX37BpEmTMH78eO78ggUL8ODBA1y7do07FhwcDD8/P6SmpqJr166YN2+eUFhkZmYmli1bhlu3bkFRUREDBgzAwoULRRYsZDAYjP8Sf3Ryw5qjzgAgspTLmqPOaN2uOexM/i/aScOwG8bN6o9Oym1QRoSIf6LRe4jWV60z4/P54xfpEXBV5ULGDrmU8y35pgYUg8FgMH5s2GLC/1sMaDlVLuVcfCdZvPpH4buJwmMwGAwGg/Gd8x+ZfpMHzIBiMBgMBoNRNdikFQczoBhS6T1gTY3LuHFxvhxqwmAwvkfY9BvjfxVmQDGkcjHYDZ8KinDvfhx27v0H2Tkfpabv1LElpjmYQYXXBsXFpQiLiP9KNWUwGAzGF4cpkXN8cx0oxvfN6vUhWLvhAn5t0xTbNtqiUUPRJWsENGvaEBu9x6BZs4bwXn8evjuuQkO9/VesLYPBYDC+KEwHioONQDGk8s+tcrmIh48TcWDnFIwe2RO7998Sm9bIoBuaNm0I59kH8TYlBwCQ/6EQ61aO+lrVZTAYDAbjq8BGoBhVorCwBFExb2Gg10Vimnr1yu3xDx//bwHl3NxPX7xuDAaDwfg6UFmZXLb/AsyAYlSZX9s0RRulphLP37gZjXeZeZjp3A/NmzeCUuufMW1y769WPwaDwWB8YdgUHgebwmNIpUXzxiAijLDSQcf2LVG7tmSbOyv7AzZsvQzPBUNg3ksFAJCbx0agGAwGg/HfgxlQDKkEHZmOsjLCP7djcPbCv/ijn5rEtH16q8Bj3iD8cysG5y9Hol69OrAZrocear99xRozGAwG44vBhDQ5mAHFkMqEyTvxPu8T3r//hIVzByI3r0BiWrvxxngWlYzl3me4Y+EPE3D5zJyvUVUGg8FgfGnov+G/JA+YDxRDKq+TsvD+ffk03O9dW+NlbJrEtK1b/YSnz5OFjhUVlXzR+jEYDAaD8S1gBhSjSmhrdkSnjr/g9r2XEtO8TsqCGr+d0LF6dWt/6aoxGAwG4ytBZSSX7b8Am8JjSEVbsyNat/oJzlPN8eRZEq5ce8ad69C+BerWrY3YuHQAwJ4Dt7DC0xp/LhqK8xcjUbdubYy01v1WVWcwGAyGvGFTeBwKRP+ReELGF+Hjx0LkfyjEzdsvsPvALXz6VMSd27R2DJRa/4zRE3dwx/S0O8F2rCF+79oaHz8VITomBQY9u36LqjMYDAZDzvSrbSOXcq6UHpNLOd8SZkAxpMIWE2YwGAyGAGZA/R9sCo/BYDAYDEbVYFN4HGwEisFgMBgMBqOasCg8BoPBYDAYjGrCDCgGg8FgMBiMasIMKAaDwWAwGIxqwgwoBoPBYDAYjGrCDCgGg8FgMBiMasIMKAaDwWAwGIxqwgwoBoPBYDAYjGrCDCgGg8FgMBiMasIMKAaDwWAwGIxqwgwoBoPBYDAYjGrCDCgGg8FgMBiMasIMKAaDwWAwGIxqwgwoBoPBYDAYjGrCDCgGg8FgMBiMasIMKAaDwWAwGIxqUudbV4BRNXJzc/HTTz8BAD58+ICgoCAkJiaiU6dOGDZsGBo3blztMrOzs5GcnIwOHTqgSZMmn1WvrKwsNGvWDAoKCp+V/3+Vjx8/omHDhtz+06dPkZSUhA4dOkBFRaXK5aSmpiIzMxPFxcVo0qQJfv31VzRo0OBLVLlKREZGIi0tDV27dkWnTp2++t//9OkTLl++jLS0NHTr1g29e/dmfZOB1NRUtGrVCrVqVX/MICUlBenp6ejYsSN+/vnnaucnIly7do17X5uamqJ27drVLgcAAgICMHr0aO5bwPjGEOO7Ji0tjUaOHEk8Ho8sLS3pzZs3NGjQINLS0qLhw4eTnp4e9erVi5KTk6WWc+TIEXJyciJ/f38iItqyZQvx+XxSVlYmFRUV8vLyopKSEon5d+3aRc7OzkREVFpaSps3byZNTU3i8Xikp6dHfn5+n93GpUuXUmZm5mfnrypbt26ljx8/Ch0LCgoiS0tL0tLSIhsbG7pz547UMoKDg2n8+PE0ePBgWrduHeXl5Qmdz8zMJHNzc4n5s7KyaMqUKdSvXz8iIsrNzaXx48eTsrIyKSsrE4/HIzs7O5FyK3P69Gnq06cP8Xg8oY3P59P48eMpLCxMan5JDBo0iN6+fSsznbm5OWVlZXH7eXl5ZGtrK9SOefPmUWFhodj88rgXeXl5NGPGDNLQ0CAXFxdKSUmhvn37Eo/HI319fVJWViYbGxuJ17Jv37506tQpmW2VRU37hDzIyMigK1eu0OvXr4mIKCoqiqZPn06DBg2iGTNmUGRkpMS8paWltG3bNjIzMyMtLS2aNWuWyPskIyODeDye1Dr8+++/3PuFiOjevXvk6OhIAwcOJCcnJ6l90sPDg16+fFmVpn4Wmpqa3LWRxoEDB8jR0ZEOHjxIxcXFNGvWLOLxeKSsrEx8Pp/Wrl0rNX9eXh7NmjWLtLW1adKkSZSTk0MTJ04kZWVl0tfXJx6PR1ZWVkLPTmWSk5MlbhoaGhQWFsbtM74tCkRE39qIY0jGzc0Nb9++xdSpUxEcHIwnT57g119/xfbt2/HTTz/h48ePcHNzg6KiIjZv3iy2jD179mDLli0wMTFBREQEjI2NcePGDbi7u0NDQwPR0dHw9vaGra0tHB0dRfJv3boV+/btg729PWbMmIGdO3fC398fTk5O6Nq1K2JiYrBz5044Ojpi8uTJYusQFhYmsY1TpkzBypUr0apVKwCArq5utfJXRlx+AFBRUcHt27fRokULAMCZM2ewcOFCjBw5Et26dcOTJ09w7tw5bNmyBebm5iL5//rrLyxbtgxDhw5FrVq1cO7cObRq1Qp79+7Fr7/+CgB49+4dTExMEBUVJbYOc+fORXR0NBYsWABjY2N4enoiIiIC3t7e6NatG2JiYrB48WJoaGjAy8tLbBkhISHw9PTElClToKKigqSkJOzduxdTpkyBkpISLl26hLNnz8Lf3x+GhoYi+X19fSVeux07dmDMmDHcL+0ZM2aITcfj8XDnzh3uWnp5eeH27dtYt24dunXrhsjISCxatAj9+/fHvHnzRPLX9F4AwNKlSxEREYFRo0YhODgY79+/R4sWLeDr64tWrVrh9evXmDlzJrp37y72WvJ4PNSqVQtWVlaYP3/+Z/2ql0efqCmPHz/G5MmTkZeXB0VFRWzZsgVz5syBsrIyevTogZiYGNy/fx/79u2Djo6OSP6AgAAEBARg0qRJUFBQQGBgIAoLC7Fnzx6oqqpybTA2NkZ0dLTYOly8eBFubm4wNDTErl27cP36dTg7O8PU1BRdu3bFixcvcPfuXfj6+sLMzEwkP4/HQ6NGjeDp6YmhQ4d+1nWYMGGCxHPh4eFQU1ODoqIiAODAgQMiaXbt2gU/Pz8YGhoiLCwM6urqiIqKwsKFC6GsrIx///0XPj4+cHBwwJQpU8T+HU9PTzx48ADjxo3DxYsXkZ+fj7KyMmzbtg3t27dHWloaZs6ciS5dumDlypViy+DxeBJHTYkICgoK3L9fqk8xqsi3td8YsujZsydFRUURUfmvGxUVFZFfclFRUWRoaCixjH79+tH58+eJiCg6OpqUlZUpKChIKM2VK1eof//+YvP36tWLLl++zO1bWlqK/HIPCQkhMzMziXVQUVHhRkkEoxTiNkm/co2NjWuUn4hIWVmZ3r17x+1bW1sL/WImIvL39ycrKyux+QcNGkTHjh3j9pOTk8nS0pLMzc0pPT2diGT/UtfT06PHjx9z+2ZmZnT79m2hNOHh4aSnpyexjMGDB1NISIjQsefPn1OfPn2E2jFixAix+QXX0tjYmMzMzIQ2Ho9HpqamZGZmJnXUpPK17Nu3L129elUozY0bN8jExKRK+at7L4iIDAwM6NGjR0REFBsbS8rKyiLPRlhYGPXs2VNiHa5cuUJmZmZkaGhIBw8elDhiJoma9onx48eTra1tlTZJjBs3jhYvXkx5eXm0a9cuUlNTI09PT6E0GzdupDFjxojNb2FhQRcuXOD2BaOienp6FBsbK7MNREQDBw6k7du3c/sjR46kTZs2CaXx8/OjYcOGic2vrKxMa9euJT6fT5MmTaLo6GiJf0sSkyZN4kYdFyxYILTx+XxycXHh9sXRr18/unLlChER3blzh3g8Hl28eFEozfnz56W+54yMjCgiIoKIykcelZWV6ebNm0JpHj16RKamphLLOH36NOno6JCdnR3dvXuX7t+/T/fv36fQ0FBSV1enM2fOcMcY3xbmRP6do6CgIPRrhIhQp04dkTRlZWUSy8jIyICamhoAQFlZGbVr1+Z+WQrg8XhIS0sTmz87OxvKysrc/vv378Hj8YTSqKmp4d27dxLrcODAAbRp0wb6+vo4e/Ysrl69iqtXr+Lvv/9GgwYNcPDgQW5fHGfOnIGKigr4fD7+/vtvLn/lTVJ+caSlpaFXr15Cx/r374+4uDix6ZOSkmBgYMDt//rrr9i/fz8UFBQwefJk5Ofny/ybZWVlqFevHrdfp04dkZGPn3/+GUVFRRLLePPmjYif1O+//463b98iIyMDADBw4EDExsaKzX/69GmYmZnh559/xrZt23Dt2jVuq1+/Pg4dOoRr167h6tWrEutQ+RdyWVkZ2rdvL3Ssc+fOyMvLk1hGRap7LwCgoKAAzZo1AwB06dIFHTp0EPH/atq0qdRnQ1NTEyEhIRg+fDjWrVsHc3NzbNiwAS9evKhSvWvaJ4yNjREeHo7MzEy0bdtW6iaJ58+fY+rUqWjcuDHs7e1RWlqKUaNGCaWxsrKS2Kb09HTw+Xxuv0mTJvD390fbtm0xZcoUie+Firx+/RoDBw7k9pOSktC/f3+hNIMGDZJ6PydNmoTjx4/jw4cPGDZsGJydnXH79m2p968iu3fvxoIFC/Dq1St0794dq1ev5ra6deti3rx53L440tLSuOfKwMAAtWvXRocOHYTS8Pl8ZGdnS6xDYWEh1ycbN26MWrVqcfsCfvrpJ3z69EliGUOGDEFwcDCKioqwadMmtGnTBnp6eujZsydq1aoFDQ0N6OnpQU9Pr0rXhfHlYAbUd46hoSGWL1+OGzduYNGiRWjatCl27tzJPYBFRUXYsmULNDU1JZbRpUsXnD17FkD59A8R4ebNm0Jprl+/jo4dO4rNr6amhj179nD7vXr1Evm4nj59Gt26dZNYBx0dHZw5cwbNmzeHk5MT0tLS0LZtW7Rr1w4AoKSkJPVD0axZM2zfvh1JSUm4d+/eZ31ogHKndwE8Hg+pqalC5xMTE7lppcq0atUKz549EzrWsmVLBAQEIDU1FU5OTigoKJD6901MTLB8+XKuHsOGDcOuXbtQWloKoPx+bt68GT179pRYRpcuXXDkyBGhY+fPn4eioiJatmwJoHzKUzAlWpnmzZvDz88Ptra2sLOzQ0BAgNQ6i4OIcOjQIfz999+Ij4+HqampSJ84c+aMVEfymtwLAFBXV8euXbu4D+ylS5eEDIH8/Hz4+PhAS0tLalsaNGgANzc3XL9+HaNHj8aFCxcwdOhQGBsbw8HBAXPmzJGYt6Z9wtHREYsWLUJKSgqmT58u9NGvvEmiadOmSEpKAlDu8FxaWor09HShNKmpqRKnKNu1a4fQ0FChYw0bNoS/vz+ICJMnT0ZmZqbEvw8Av/32G+7cucPtq6ioiEz3RUZGonXr1lLLUVVVRWBgIPz8/PDhwwdMnjwZJiYmmD17Nvz9/XH8+HGp+e3s7HDw4EEEBgZiypQpUn/UVaZDhw64ceMGgPIfCFeuXOHeTwJOnTqFrl27SixDW1sbGzduxMuXL7F27VrUr18fhw8fBlXwlNm3b5/ID9jKtG3bFocOHUKvXr0wfPhw/PXXX1VuB+Mr8k3HvxgyyczM5JyMtbS06PLly7R582bS09MjGxsb6tmzJ+nr61NcXJzEMu7cuUNqamrUs2dP4vF4tHjxYrK3t6dFixZRYGAgLVu2jPh8PgUHB4vN//jxY9LQ0KCRI0fS/v37KSgoiPT09MjNzY22b99Ojo6OxOfzRaaiJBEcHEx6enq0YcMGKi4uJg0NjSo5eBIR/fXXX+To6FiltJVRU1MjHo9HOjo6ZGNjQ9bW1mRubs45M586dYpMTU3J29tbbP6dO3eSnp4e+fv7U2pqqtC58PBw0tTUpH79+kmd6khPT6eBAweSpqYmOTs706pVq0hXV5d69+5N9vb2ZGBgQEZGRpSYmCixjJs3bxKPxyMbGxtau3YtzZ49m/h8PufI7+XlRXw+X2SaVhyvXr0ia2trGjduHL1586bK92LRokU0cuRI0tDQ4KZVVVVVKScnh4iI7OzsxE6BCKjpvSAievr0KdcPK3Pjxg1SV1enXr16UUJCgtj8PB5PaBqxIlFRUXTo0CHy8PCgKVOmSKyDPPoEEZGjoyO5uLhITSOJTZs2kbGxMXl7e9OAAQNo0KBBNHbsWIqIiKDCwkKKjIwkS0tLWr58udj8J06cID6fT0uWLBG5VrGxsWRkZER6enpS23Dq1Cni8/m0bt06ioqKovDwcOrbty8FBgZSREQE7dmzh7S1tWnv3r1i80u6F2/fvqX9+/eTi4sL9e3blzQ0NKp0TQoLC8nLy4v09fXp4sWLVXIiDwkJIVVVVdq8ebPIucePH5ONjY3M99zr16/JwsKCex4OHz5Mnp6e1L9/f3Jzc6NBgwaRurq60DS+LB4/fkwWFhbk6OhI6urqVX5XMr48zID6QcjNzRXyzwgJCaHly5eTv78/52shjVevXtG+ffvo7NmzVFJSQqmpqeTg4EAaGho0YMAAIT8OSfnnz59PRkZGQj5H6urqZGdnV+35+Ddv3tCYMWPIysqK1NTUvspLobS0lGJjY+n8+fO0ceNGcnJyor59+3LXVU9Pj1xdXenDhw9i85eVlZGfnx+ZmprSvXv3RM4/fvy4Sh/LwsJCCgoKIjc3N7KysqJ+/frRH3/8Qfb29uTn5yc1QkdAeHg4OTk50cCBA8ne3l7IJ+3QoUN09+5dmWUIKC4uJh8fH9LR0SE+n1/te5GYmEhXrlyhbdu2ccfc3d1FfKIqUtN7IeDdu3diP0avXr2iQ4cOcQadOCr7YX0O8uoTaWlpdO3atc+qQ0lJCW3ZsoWGDh1KkyZNotjYWLp48SJ1796d8xscO3Ys5ebmSizj9OnTNGbMGHr48KHIudevX9P48eNltiE4OJjMzc05X8SK7wktLS2pkbryuBfi+Oeff8jIyIh4PF6V+vXNmzfp5MmTIsfDw8PJxcVF7PWpTHFxMUVFRXEGdVFREfn7+5OjoyN5enpSTExMtdvx8eNHWrhwISkrKzMD6juCReExqk1+fj4+fPiAOnXqoFmzZp+lrQKU+81s374dJ0+exKFDh9CmTRs517R65OXlVVkPi/5/FExlSktLERkZKXVK9XvlwYMHOHXqFObNm4fmzZt/07pU5158Lg8ePICWlpaIT+Hn8r31idTUVDx+/BhKSkpQV1evsR5Wenq6xGnhisTHxyM+Ph75+fmoU6cOlJSUwOfzuQg4cfj6+sLBweGLaJhlZWXh5s2bsLCwENJe+xEoKytDbm4up/NWv379b10lRgWYAfUDo6WlhdOnT+O3336TmTY/Px/Pnj3jfGuysrIQHBzMiTcOHToUTZs2rXYd5CHsVlWRu+TkZERERGDIkCEAgFevXuHw4cNISkpC+/btMW7cOIl+XBWRJGLZsWNHEed4cdRU1PTdu3f4999/wePx0K5dO0RHR8PX1xeJiYno2LEjpk6dyjn9S6KkpASXL19GWFgYUlJSUFRUhAYNGqB169bQ1dWFhYWFTLG+t2/fIiwsDFlZWSguLkbjxo3Rvn17aGpqolGjRlLzBgcHw9LSUsghPjQ0FHv27EFqaiq6desGZ2dndOnSRWIZRUVFePToEXJzc6GjoyPibFtYWIgLFy5g2LBhUusijuo8G5XJyspCcnIy2rVrJ1KnL0VRUREiIyNF7kVNxEirKwCZnZ3NtTctLQ1BQUGcIKm1tXWVjI8v0Q558Oeff8LV1VXmD4OwsDAEBQXh/fv3MDU1xahRo4Seo/fv38PFxUWsDIKA06dP48SJE1wZ06ZNE3onZGVlYeTIkVKDNMLCwuDr64tHjx6huLiYO96iRQuYmJjA0dGxSu86xpeFGVDfOQsXLpR47uzZszA3N+c+dpIcTaOjozF58mTUr18ff//9N+Li4jBu3DgA5c6fr1+/Rt26dbF//36xH7y3b99KrMPAgQOxc+dOTvNG8G91qMrH7v79+5g2bRo6dOiA4OBgPHr0CBMnTkSHDh3QpUsXxMbGIjk5GXv37oWGhobYMrKzszF//nwkJCTg8uXLyMvLg7OzM6cxpaCgAH19fWzdulWsEZSeno4ZM2bgyZMn6Ny5M6eF9fbtW3Tq1Alv3rxBgwYNcOTIEYnXoaaaPUB5hJODgwPS0tKgqqqKVq1aQVFREYWFhUhPT8fz58/x66+/YteuXWLrUVpaCk9PTwQFBXHHBKOJ7969Q4MGDTBt2jRMnTpV4v2orON069YtTJ06FcbGxpyOU2RkJPbu3SvWiTslJQWTJ0/morIaNGiAuXPncv0SkK2fVNNno7CwEGvWrMHjx48RFBSEoqIiLFmyBGfOnOGiXYcNG4alS5eibt26Ev9WTdm+fTsCAgK4wJBatWpxTsdt2rTBnDlzhCLcxHHw4EHcuXMHxsbGGD16NObNm4eLFy8CAGrXro2JEyeK1eMCyvv1tGnTEBUVBT6fjxUrVsDOzg5EhN9++w3x8fFo0aIFDh48KNUJfPv27fD390dBQQEXPVzddoijqsZwTbXmrl27hhkzZkBPTw+1atVCaGgoevTogR07dnAGqKw+KQ9dsLt378LJyQlDhgzhdN5OnjyJqVOnon79+rh8+TKePHmCAwcOCAVNML4+zID6zhk8eDBiY2PRtWtXkRGiiIgIdO/enRsaP3jwoNgybG1t0axZM6xcuRJNmjTB1KlTUadOHWzcuBGKioooKCiAu7s7Pnz4gN27d4vkl4ewW01F7oYPHw4NDQ0sWrQItWrVgq2tLTp16oTly5dzaZYvX46oqCgcPXpU7N+pqYhlVUVN69Wrhy1btoitw/jx49GpUyfMnz8fx44dw+bNm2FlZYVly5ZxaTZt2oQHDx6IRNoJmDp1KkpLS7Fp0yax01y5ubmYPXs26tatix07doic37p1Ky5cuIAVK1ZAVVUVSUlJWLlyJczMzDBy5EhcunQJK1euhKurK2xtbcXWobKQ5tixY9GjRw/Mnz+fS7N69Wo8efJEbDtmzpyJrKwsrFu3DgoKCggICMCRI0fg7OwMFxcXALI/NDV9Nry8vHDlyhW4urpixIgRWLduHc6ePQsPDw+uP6xduxaDBg3C3LlzxdZBmihpZcSJkh48eBABAQFwd3fn7sXmzZsxYcIEqKmp4eLFi/D398fq1aslGh81FYCcM2cO0tPTYWdnh6NHj+LJkyfQ1NTExo0b0aBBA+Tm5sLV1RXNmjXDxo0bxdahpu2Qxw9FVVVVzmCT9lmT9J6ytrZGv3794OTkBKA8anDGjBlo2bIlDhw4gMaNG1epT9ra2nIyEm/fvsWUKVNQUFCAwMBA/PLLLzLLsLGxwdChQzF27FjuWGhoKFavXo3Tp08DAFauXImYmBipI2GMr8DXdrpiVA9BNImRkRFdv35d6FxVI6Y0NDTo1atX3L6RkZHI0g4vX76UGOEiD2G3morcqaurC0WmGRgY0LNnz4TSxMfHU48ePSTWoaYilvIQNa0YDVRaWkqqqqr09OlToTQJCQmkra0tsQwNDQ2ZQoNRUVGkpaUl9lyvXr04sT8BGRkZpKOjQ8XFxURULqwqWG5GHJWdfg0NDUXuR1xcnMT7Ie7+HThwgJSVlenAgQNcnaQ5Ltf02TAyMhJytq8opCjg5s2bZGRkJLEMBwcHbjmjyqKkFTdJoqQWFhYiQouvX78mIyMjKi0tJaLyKLlBgwZJrENNBSB1dHS4fp2cnEzKysoijvlPnz6V2idr2o5BgwYRj8ejQYMG0fjx44U2FRUVGjlyJLcvibCwMDIzMyM7Ozt68eIFJSUlUVJSEhdd+uDBA+6YOMT1mbi4ONLX16eJEydSUVGRzD4proyMjAzq06cPDRkyhPLy8qpURuVoSMG7IiMjg4jKr62mpqbEMhhfB6YD9Z1Tr149LF68GKtXr8aSJUuwdOlSFBYWVquMJk2aICUlhdtv1aqViMBfTk6ORAdOeQi71VTk7pdffsHz58+5/Y4dO4pMLSYmJkr19aipiKU8RE1rqtkDlN9PWeKGb9++lehw+uHDBxF/lkaNGuHDhw/IyckBUD7CJBDlFEflEclOnTqJ9KmsrCyJjuClpaUiTsW2traYNm0aVq9ejQsXLkj82wJq+mx8+PBBaEqquLhYZMrzt99+kyqGuXPnTgwdOhQ///wzzp07JyRKWnGT5O+SkZEh8jdbt26NrKwsTntJX1+f6zPikIcApGCK8tdff4WRkZHQcyJA3DF5tSMoKAjjxo1DdnY2HBwccPDgQW5TVFTE+vXruX1J1FRrrnnz5khMTBQ61rlzZ2zbtg2PHj2Cu7s7p9cmCXloxbVr1w6XLl0SOnb37l3UqlWLG2mNi4v7rIWNGfKFGVA/CCYmJjh9+jTS09MxZMgQREZGVjmqZujQoXB3d+fEMx0dHbFmzRrEx8cDAJ49e4Y///wTAwYMkFiGPITdaiJyN2bMGHh4eODo0aPIzc3FrFmz4OPjg9u3byMtLQ2XL1/GkiVLYGVlJbGMmopYykPUVHAv1qxZg8mTJ6Nbt27YtWsXHj58iKKiIjx58gTLly8Xu16YgBEjRmDBggU4duwYEhMTOYOvqKgIb968QVBQEBYtWgRra2ux+TU1NbF69Wq8f/8eQLkhuGHDBrRs2RItW7ZEWVkZDh48KKQ+XxkiQp8+fTBs2DDMmTMHderUgbe3N1eXsLAwLFu2DKampmLza2howNfXV8RYnTVrFiwtLeHu7s6Jv8ric58NXV1dbNy4kXPS7d+/P06cOCGUZt++fVId+hUUFLB8+XIoKirCz8+vSvWtCJ/Px7Zt24QchQ8ePIgmTZrgl19+AVAuECrN/6emApA6OjpYt24dZ2Dt3r1bKJgiISEBy5cvF7uuorzaIY8fikC5+vf69evh4uICJycnbNy4ESUlJVXKa2lpCU9PT4SEhCA3N5c7rqWlhXXr1uHKlSuYOXOm1DJGjhyJpUuXIiAgQOhHTufOneHn54dnz55h0qRJUsuYPHkyNm7ciDlz5uDIkSNYv349Zs6cCRsbG9SpUwc7duzAnDlzMGbMmCq1i/EF+cYjYIzP4OjRo6StrU0qKipVmsIrLi4mDw8PbqrB2tqatLW1icfjEZ/PJx6PR/b29jI1dwTUVNjtc0TuiIi2bdtGGhoapKKiQj179qQePXpwOjc8Ho8WLVpEJSUlEvPXVMRSHqKm8tDsKSsro61bt5KWlpZQ+wWbtrY2bdiwgZs6qUxCQgIZGhqSpqYmjRgxgkxNTUldXZ2bBrO1tSU9PT1unTlxpKam0j///EM7d+6kuXPn0pAhQ6h79+6cEKaWlhaNHDmSMjMzxeZ/+fIlGRkZkba2tsjUb3FxMc2aNUvm2obiqM6zER8fT0ZGRtS7d29auXIlJ4ppY2NDixcvpiFDhpCGhgY9efJE5t99+PAhbdy4sVp1JSp/lnr06EFmZmY0c+ZMGjVqFPF4PPrrr7+IiMjV1ZXU1NREpigrUlMByNevX5O5uTnNmjVL5NzFixdJWVmZhg0bxk0ffal2CMjMzKRp06aRhYUFPX78uMrvh8pUV2uuoKCAFi1aRN27d6c7d+6InL969Srp6OhI7ZPy0gU7e/YsDR06lDQ0NMjCwoJ8fX2pqKiIiMrXNTxx4oTU/IyvA3Mi/0F59eoVzp8/jwkTJlRZQiApKQl37twR0WjR1dWFtrZ2tf7+p0+f4OXlhZMnT+LKlSufFS5+8+ZNeHh4IDMzE5cvX65SGZ8+fUJ4eDgSEhKE2qCjo1MlHamioiKcO3dO4nUYPXq0zNB1QQSdYErj/PnziIiIQOvWrWFlZcX94q4On6PZU1xcjKioKKSlpeHTp0+oX78+lJSUwOPxpE63AOWO5idPnsTr16/RqlUr/PHHH9y0T0REBDp37lztEP7S0lIu5Ds2NhZdunSR2o6cnBxcuXIFRkZGYqMFT548iZCQELGBDdKIj49HSEhIlZ6NnJwcLoJNXH9wcHD44iH4b968wYEDB7h7MXToUC4C8/Lly+jSpYtUOQigPAry3bt3IiOwERER2L9/P+zt7aWOjBYVFSE1NVVkPcPU1FRER0fDyMhIZiSiPNpRkcDAQPj4+ODjx48yR+Ek8Tlac4IoQnG6Vbm5ubh58yYGDRpU7boI+JG14hjCMAPqB0YeGkzv37//pnPpNRW5y87ORnJyMjp06PDFhRflSVJSEnJzc7nFnStSXFyMf//9V2yotSwiIiKgpqYm04CSJ1lZWUhJSUFhYSEaNmyIVq1a1UiIs7p9UpIuV8eOHWFlZSVTl4shmapqtH0pqmMMCyguLsaHDx/E6tqVlZUhNTVVqtyK4IdJamoqCgsLOX01FRWVKouuhoeH48GDB8jKykJRURGaNGmC9u3bw9DQsEqGYFFREeLi4vDbb7+hcePGSEtLw549ezi9uQkTJnxz4WEG2BTe905ycrLETUNDg8LCwrh9SSxYsIDy8vKEjm3ZsoU0NTWJx+ORsbExN9QuDhUVFdq6davc2lSZpUuXSpzqqciRI0fIycmJ/P39iYho69at3BSkiooKeXl5SZ3Cmzhxothh9erw7Nkz2r17NxdBdvToUerbty+pqamRlZWVzCmKnJwcbp04Ho9HJiYmIkt4yIrSkUZVpzvi4uLI09OTLC0tSVNTk/h8Puno6NDAgQNp6dKlQlGbkjh58iQNGDBAZNkOHo9Hf/zxh8S1FYlE+2RZWVm1+iRR+fInI0eOJB6PR5aWlvTmzRsaNGgQaWlp0fDhw0lPT4969eol8dnw8PCgly9fymynLGrSJ7Zu3cpNewoICgoiS0tL0tLSIhsbG7HTSRWRR7+WRHWmz7Kzs8nf35+mTJlCgwYNIgsLCxo6dChNnTqVdu7cSdnZ2RLzJicnU1lZmdCxzMxM2rlzJ3l5edGxY8eElrISR0FBAXl4eHDT4SNHjhSJcJX2bAmmxnV0dIT6s2DT1tYWWq5IHB8/fuSebwMDA1JVVSU+n0+jRo3i9j09PSVOrxOVP5tmZmakrKxM+vr69OTJEzI1NaW+ffuSq6sr98y+ePFCal0YXx42AvWdIw8Npsqih/v378fmzZvh5OSE33//HZGRkdizZw8WLVqEESNGiK2DoqIitLW1sWzZss8aSq+pyN2ePXuwZcsWmJiYICIiAsbGxrhx4wbc3d2hoaGB6OhoeHt7w9bWFo6OjmL/Do/HQ506deDs7MxpYVWHf/75B87OzmjUqBE+ffqEadOmYfv27bC2toaysjIeP36M8+fPY+vWrRKdwBcvXozHjx9j6dKlUFBQwM6dO3Hz5k14eXlh+PDhAMr1j4yNjUVWsxdgbm4usU+8ffsWrVu35ka1xEV/3bt3D46OjlBVVYW+vr6IEGdoaChiYmLg7+8vMbLy4MGD8PHxgZ2dHVdGvXr1UFRUhPT0dNy7dw8HDhyAu7u7kJ6NgJr2SaDqulyKiorYvHmzSH4ej4dGjRrB09MTQ4cOFfs3ZFHTPlH5Opw5cwYLFy7EyJEjOUHSc+fOYcuWLTA3Nxdbh5r265pqtAFAVFQU7O3t0aBBA+jo6Ij0qfDwcBQWFmLv3r1igxMqX4eEhASMHTsWZWVlaNeuHRISEtCiRQscOHBAopjn2rVrcf78ecyePRsKCgrYt28f4uLi4OvrCxMTEwDSn63NmzfjyJEjmDNnjtjn4t69e9iwYQNsbW3FanoBwIoVK/Ds2TNs2rQJrVu3Rn5+PpYuXYq2bdvCzc0N4eHhmDt3LqytreHq6iq2jKlTp6J27dqYNWsW/vrrL5w5cwY9e/bExo0bUadOHRARFi9ejNTU1GpPbzPkzDc13xgykYcGU2XNnsGDB9PRo0eF0gQGBtIff/whNj+Px6OIiAgaPnw49ejRg7y9vaU6lIpDRUWFG3UR9+uu4uiFOPr160fnz58nIqLo6GhSVlamoKAgoTRXrlyh/v37S6yDsrIyHTx4kBtpqe7irdbW1rR9+3YiKtcs4vF4tGfPHqE0/v7+NGzYMIllmJiYUHh4uNAxb29vUlVVpUuXLhGR7BGoRYsWkbKyMk2YMIG2bt3KbVu2bCE+n09eXl7cMXFYWVnRmjVrpLZ1zZo1ZG1tLfG8ubm52EVXKxIUFER9+vQRe66mfZKo5rpcysrKtHbtWuLz+TRp0iSZ2lriqGmfqHwdrK2tuRHWivmtrKwk1qGm/bqmGm1ERGPHjiV3d3eJI8AlJSU0b948iTpOla+Ds7MzOTg40KdPn4io/P7a2dmJdXQXYGZmJqRFJQhG6NGjB/fMSXu2jIyM6O+//5ZYPlH5O8bU1FTieSMjI3r+/LnQsby8PNLQ0OBG0EJDQ6l3794Sy9DR0eECUQoLC0lVVVVElys2NlaiXh3j68EMqB+ApKQkGjt2LI0aNUpoOL2qQpqVX05GRkYiw7+vX78mNTU1qfnLysooMDCQjI2NSV1dnWbPnk3Xr18XmYIQhzxE7t68ecPtq6qqch9PAYKyZF2HlJQUcnNzI2VlZRo6dCgFBgZSTk6OzDaoq6tzdSgoKCAejyfy0U1ISCB1dXWJZWhra4udHluwYAGpqalRWFhYlabwrly5QgYGBuTt7c1F5xBVrU+oq6vLnKKTJoIp+DvSog2Jyl/ykq5FTfskEZG+vj53/fPy8ojH44lEDkZHR5O+vr7UOjx79oxsbGyIx+ORk5MT3bp1S+oUS0Vq2ifEXYfq9qma9msior1795Kuri4dOnRI6HhV3zHq6uoUGxsrNU1sbKzE57PydTA2Nhb5oREZGUk6OjoSy9fQ0BCJoC0pKSEHBwfS1dWlly9fSn22tLS0ZLYhOjpa6nOhr69PDx8+FDqWk5NDPB6Pc1NISEiQKoJpbGxM//77LxGVT2MqKyuLTNE+fvyYjI2NpdaV8eVhOlA/APLQYHr27Bny8vIAlOuaxMbGCp1/+vQplJSUpJahoKAAGxsbXLt2DcuWLUNWVhamT58OHR0d9O/fH6NHj5aYt6Yid126dOF0gUJCQkBEnK6VgOvXr1dpgU0lJSWsX78ep0+fhoqKCry9vWFsbIxRo0bB09MTGzZsEJuvRYsWePXqFQBAUVERa9euFXFUffLkidTrqKKiInapGS8vL2hpaWHatGl48OCBzDb07dsXwcHBePHiBYYPH46XL1/KzCOgXbt2uHv3rtQ0N2/elLruWffu3bF3716JoqFEhF27dnECj+KoaZ+Uhy4XUL4ESGBgIPz8/PDhwwdMnjwZJiYmmD17Nvz9/XH8+HGJeeXRJwS6ZED5dFxqaqrQ+cTERG5qSxqf26+Bmmm0AeVCt5KmnAU8efJE4oLllUVqmzZtKiLs26BBA6lRnV26dOHW/xNQu3ZtbN68GW3atMHkyZO5eyUOHR0d+Pj4SBRO/fDhA9avXy81YtnAwABLly7lNPY+fvyIpUuXokOHDmjevDmys7OxZcsWiet1AuXP9qJFi3D48GG4uLjg119/hb+/P6cr9e7dO3h7e0NfX19iGYyvA/OB+sGIjIzEvHnz0KlTJ9y7dw/nzp2T6ZNkYWGBpKQkEBFatGiBhg0bIjc3F3///TcaN24MX19f7N27F5MnT+bWgapIZf+Eirx//x4PHz5EVFQUMjIysHTpUpltOH36NFatWoXRo0fDxcUFurq6OHPmjNR23L17F9OmTUPDhg3x/v17jBgxAsnJyfj111+hpqaGmJgYHD9+HCtXrpTozyKpHYWFhbh9+zbCw8MRFRWFd+/e4dy5cyL5t2zZgmPHjmHRokWwtLQUOpeSkoLAwEAcOHAAs2bNwsSJE8XW4eHDh3BwcICSkhLWrFkDdXV17tyHDx8wdepUPHz4EAAk+rRVZu/evfDz84OTkxO2bt0q81peuHAB8+bNg4WFBQwMDKCkpCTkv/TgwQOEhIRg1apVGDJkiNgyoqKiMGnSJNSrVw+6urpo06aNUBnh4eHIz8/H7t270b17d5H8Ne2TQLnhMXPmTISFhaFRo0bw9vZGVFQUDh8+jE6dOiEhIQEKCgo4fPgwOnfuLJJfUn9ISUnBlStXhPrDo0ePxNahpn1CXV0dxcXFaNy4Mbp06YLi4mLk5OTg3LlzaNCgAYKDg7Fx40ZYWloKrTNYlXZUtV9XpKioCGvXrkVISAj+/PNPLFy4sEoL+R4+fBjr1q3DhAkToK+vL7ZP7d27F66urrC3txfJz+Px0LNnTygrK6NLly6cz6SPjw8AyFyvEyhfDNjFxQX6+vpwd3cX8rXKyMjAhAkTkJyczEXZVSY5ORmTJk1CamoqVFVVRfr0s2fP0KpVK+zatUvi9Xj37h3s7e0RGxuLZs2aITc3F02aNMGOHTvQo0cPjBkzBhkZGQgICBDbJ4Fyo2v58uW4evUqmjdvjlWrViE6OhorVqxAixYtkJOTg3bt2uHAgQOc3yjj28AMqB+Qz9Fg+vTpE16+fImYmBi8ePECL1++xK5du1CnTh3069cPffv2xbx588SGK1deOFYeJCUlwd3dHQUFBYiNjUVISIjMdsTHx+PmzZto0aIF/vjjD7x79w6LFi1CREQElJSUYG9vzy3iKY6atqOsrAybNm1CaWmpyMr2V69exZIlSzBp0iQ4ODhI/aUcHx+P06dPY+jQoSIaQ8XFxdixYwfOnTsnspyDNKKjozF37lzExcVVSVMrNDQUO3fuxMOHD7kRG6D8V762tjbs7e1hZGQktYycnBwcO3YMERERSE1NRUFBARQVFdGmTRvo6OhgxIgRUuUMatInK/K5ulzy6NdV7ROTJ0+WmD8+Ph4vXrwQug4hISGoV68eevbsCX19faxevVqizMeXeD4/R6Pt1KlT8Pf35wxXAUSETp06YdKkSRg5cqTYvGfPnuXaHxMTg7S0NCgoKODevXto2rQptzrAvn37pI5qhoeH49ixY3BwcBBSUwfK5S5WrVqFCxcu4PHjx2Lzl5SU4OLFi2L7tK6uLvr27StTIqSkpAQ3btxAYmIiWrVqBVNTU06W4/Xr12jbtq2IdElV+Pfff/Hw4UMoKSnBzMxM4tJbjK8HM6AYMhGM9FR16Ziq8jkidzXh1KlTGDhw4BfRSKooIvmtKCoqwuPHj9GjR48qt5GIkJuby30oJE2x/Bfx9fWFg4PDF/sQlZWV1Vg/KS8vT6a+2Zfq15+r0ZaVlYXU1FRO3LVNmzbV1gXLzc3FixcvOCHO48ePw9TUVKabQVWQx32pLj+qXh1DOsyA+oHR0tKq0vC6NOQhxlkdaiJy96XE5QQv/A4dOqBRo0ZS02ZnZ+P69evIzc2FkZERunXrJnT+48eP2LNnj8QwZwCIiYnBvXv3oK6uDi0tLdy5cwc+Pj5ISEhAp06d4OTkhH79+kmtR2FhIV6+fImuXbuifv36iIqKwqFDh5CWloZu3bph4sSJ1frYfPr0CZcvX+by9+7dW6bBXJM6vH37Fm3atBH6G1lZWTh58iRSU1Px+++/Y9iwYVKNAlVVVTg7O0u91t+S6jxbb9++RVhYGLKysrgpvfbt20NTU1Nmn5RGdYQwHz9+jLCwMBEBSV1dXaGpZml8/PhRyNh6+vQpkpKS0LFjR5ERoaqSnZ1dZVX8nJwcHD58GGFhYcjMzERxcTEnYmlsbIzBgwdXW+YBAAYPHoyAgIAqvV+OHj2KW7duQUNDA1OnTsXWrVvh7++PkpIS1KpVC2PHjsXChQu/+Q8uRs1hBtR3zsKFCyWeO3v2LMzNzbkX7OrVq8Wme/v2rcQyBg4ciJ07d3JGizSF3ppQWFiI5cuX48yZMygpKYGamhqWLl0KPp/PpXn37h1MTEzE+ie8evUKkydPxtu3b9G8eXMEBARg+vTpqFevHlRVVREbG4uUlBQcO3ZMxKgRMGHCBPj6+nIftKKiInh6euL06dMgItSrVw8ODg4SFwyNjY3FxIkT8fHjR65NdnZ2cHd3r1IbgHLdoOnTp6NRo0acRsyKFSswYMAA9OjRAzExMTh58iQ2bdqEvn37ii3j1atXsLOzQ3p6On799VesWLECzs7OaNu2Lbp27Yrnz58jNzcXR44cEbt0Rn5+PhYuXIjbt2/DxMQEHh4esLW1RVJSEpo2bYrs7GxoaGhg165dElW8a1oHeej+1FSfjIiwfft2nDhxAu/fv4epqSnmzZsn9AzIup81fbZKS0vh6emJoKAg7lidOnXQrFkzvHv3Dg0aNMC0adMwderUarVNQFV+ZGVnZ8PV1RVhYWFo3bq1iP5RWloa9PX1sXnzZokK8dnZ2Zg/fz4SEhJw+fJl5OXlwdnZmfNlUlBQgL6+PrZu3SqxT928eZMzPAYOHIiTJ09i7dq1eP/+PX766Sc4OTnBzs5OYjtev36NsWPHonHjxuDxeEhOTkZUVBRGjBiBnJwc3L17F0pKSti3b5/YETFfX1+JZe/YsQNjxozh2i/JaJeHXp20PlWZL/W+ZlQNZkB95wwePBixsbHo2rWryKhNREQEunfvzoncHTx4UGwZNRXjDA4OrnJ9hw0bJvZ4TUXu5CEuV9lXZPPmzfjrr7/g4eHBiTeuW7cO9vb2Yj9YkydPRsOGDeHj44NatWrhwIED2LBhA4YMGYJVq1ZxbZD2wbW2tkbv3r3h6uqK8+fPY+7cuZg2bZqQqN6BAwdw+vRpoY9qRRwdHaGoqAhnZ2fs27cPFy5cwMCBA7Fy5UooKCigpKQE8+fPx/v377Fr1y6R/EuXLkVERARGjRqF4OBgvH//Hi1atICvry9atWqF169fY+bMmejevTu8vLy+SB0q34vp06ejsLAQvr6+qF+/PvLz8+Hi4oKmTZti48aNYuugoqKCw4cPY9WqVYiNjcWYMWPg4OCAli1bik1fmYCAAAQEBGDSpElQUFBAYGAgCgsLsWfPHqiqqgKQLWpa02dr69atuHDhAlasWAFVVVUkJSVh5cqVMDMzw8iRI3Hp0iWsXLkSrq6usLW1Fft3aiqEOWfOHMTHx2PDhg1io1jj4+MxZ84cdOnSBevWrRP7d+bOnYvo6GgsWLAAxsbG8PT0REREBLy9vdGtWzfExMRg8eLF0NDQENungoOD4eHhAR6Pxxnne/fuxaRJkzjDIyAgAPPnz5fo5+js7IxWrVpxIrWC9t6/fx/btm1Dfn4+nJ2d0bp1a7HtMDExwbt379CyZUuRdf9SUlLQqlUr1K5dGwoKCmIFaoHy4IjZs2fjjz/+QExMDIYOHYpVq1bB2tqaS/P333/Dx8dHJGJQgKamJgoKCsSeEyCtTzG+Il9LL4HxeRQWFpKXlxcZGRmJLAlRVY2WmopxDhkypEYimEQ1F7mTh7hcZa2ZAQMG0JkzZ4TShISESBR/1NXVFdGJuXz5MqmqqnLClLI0nKqiZ/X69WupOjEVNZgEOjGCZUQExMTESNTcMTAw4PSSYmNjSVlZWUSAMiwsjHr27PnF6iAP3Z+a6pNZWFjQhQsXuP3c3FwaP3486enpcfdZ1v2s6bPVq1cvioiIEDqWkZFBOjo6VFxcTETlml/9+vWTWIeaCmFqampSZGSkxPKJynWHpD1benp6Qs+jmZkZ3b59WyhNeHi4xDIGDhxIhw8fJqJyoUkej0d79+4VSnPq1CkaNGiQxDpoaWmJ6JsVFxcTn8+n3NxcIirvk5LqkJmZSU5OTjRw4EARMcyqvmvloVeXkJBA5ubmNHz4cK7vSNoY35bqTwYzvir16tXD4sWL0atXL3h4eMDc3BweHh5iVwqXxJAhQ6CtrQ13d3ds2rQJPj4+3JB+rVq1oKGhIXWI//jx43B2dsb79+8RGBj4WT4E2dnZ6NChA7dfp04d+Pj4wNHREU5OTjhy5IhUB+b69etzmkH5+fkoLS3lptIEfPjwoVqOtB8/fhSJ6FFTU0NGRobY9PXq1UNhYaHQsX79+mHx4sVYtmwZWrVqJXOV9l9++QVRUVFo164dYmJiUFpaitjYWCH/kJcvX0qMHAPKr4Ugcq558+YYNWqUSH8QhE+Lo6CggPMp6dKlCzp06CDiSN20aVOJGk/yqIM8dH8qlmVjYwNra2uEhIQgODgY06dPB1CuedWsWTMEBgaK5EtPTxeaQm7SpAn8/f0xfvx4TJkyBUePHpXpp1LTZ+vDhw8iDtqNGjXChw8fkJOTg5YtW4LH40nskwCwe/du7Nu3D35+fhg8eDDGjRvHnbt48SLmzZsntQ7169dHcXGx1HbKGhEpKysTevbq1Kkj4vv1888/o6ioSGz+pKQkmJqaAgB69uyJWrVqiegc6ejoYNmyZRLr8PPPPyMmJkYosvXNmzcoLS3lfMCKi4sl+oM1b94cfn5+OHbsGOzs7ODg4FDtqVOBXp2Tk5OQXl3F51uWXl2HDh0QEBCAESNG4P379zL9IRnfDiak+YNgYmKC06dPIz09HUOGDEFkZGS1ouJqIsapqKiITZs2ISsrC3v37v2c6tdY5E5e4nJXr15FdHQ0CgsLYWhoyGkuCbhz547Ej42enh68vb1FPmaCqaM1a9YgICBA6t8fP3485s+fz+nhaGlp4dChQzh79izi4+Nx8eJF/PnnnyKaQhUxNjaGl5cXJzy5fPlyzs+orKwMd+7cweLFiyX6UKmrq2PXrl2cgXTp0iUhQyI/Px8+Pj7Q0tL6YnUgIsyePRurVq3CsWPHoKysjD179nDnCwoKsGXLFqipqUmsQ+X+X7duXQwbNgz79u3D3bt34evri6FDh0oMe2/Xrh1CQ0OFjjVs2BD+/v4gIkyePBmZmZkS/76AmjxbmpqaWL16Nd6/fw+g/Lps2LABLVu2RMuWLVFWVoaDBw+KXT+uIjURwuzfvz/nE1fZwCkpKUFoaCgWLVqE/v37SyzDxMQEy5cv50RBhw0bhl27dqG0tBRAub/h5s2bOTmCyvz222+4d+8eAOD+/fsoLS3Fv//+K5QmIiJCqs/PoEGDsHTpUhw/fhxxcXG4desWXF1dYWhoiEaNGuHmzZtYuHChxHUqBdjY2CAwMBCXLl3C+PHjkZSUJDV9Rdzc3LB9+3bo6+tj7ty5GD58OEJDQ7F48WIcO3YMy5cvx5o1a6T6cgHl78tp06aJnf5mfEd82wEwxudw9OhR0tbWJhUVlSqvlF6Rx48fk4WFBTk6OpK6unqVy7h69Sp5eHhU++8J8qqqqopdcyw9PZ0GDBhAampqEqdLPnz4QPPnzycdHR2ysLCg8PBwOnToEPF4PDIyMiI+n0/9+/entLQ0iXWws7MjQ0NDUlZWJhUVFdLV1aUePXpww/sLFy4kPp9PR44cEZs/JSWFBg0aRDweT2g6UsDatWtlTmUSla8R5+zsTEuWLKH09HRuukwwRerm5iZ15fnMzEyysbGhOXPmiJwLCQkhZWVlmjp1KuXl5YnN//TpU9LT0yM3NzeRczdu3CB1dXXq1asXJSQkfLE6nDlzhtatW0dTpkwhU1NT7rplZ2cTUfmUkJ6enshUSkUqTwNWlxMnThCfz6clS5aItDU2NpaMjIxIT09P5v2sSHWfrYSEBDI0NCRNTU0aMWIEmZqakrq6Ojddb2trS3p6eiJL1EhCMOWvr69PFy9eJE1NTZl1KCwspAULFpCKigqpqqqSkZERmZmZcc+ViooKzZkzh1uXThzp6ek0cOBA0tTUJGdnZ1q1ahXp6upS7969yd7engwMDMjIyEhkqRUB586dIz6fT9bW1qSurk7Ozs40ZswY8vPzo1u3btGuXbtIW1tbZFqvIkVFRbRkyRIhd4OJEydSeno6ERFNnTqV3N3dJfbJyhQXF5OPjw/p6OgQn8+v8nvy1atXtG/fPjp79iyVlJRQamoqTZ48mTQ0NGjAgAF07NixKpXD+P5hTuQ/KPHx8QgJCcGECRM+S4Lgc8Q4a0pNRe7E8TnicllZWYiJiUFMTAxevnwJLy8v1KpVC7a2thgwYIDQFEhlioqKEBERgW7duol1Vr5//z5CQkKwfPnyKrcBKL8fcXFxUFJSqrITdG5ursi9z87Oxrt37yRGIgrIzMxEcnKySHh6fHw87t69i0GDBkmMuJJXHSqXU13dH3nok505cwaBgYGYN2+eyJIvb968gYeHB6fkXVWq+2zl5ubi5MmTeP36NVq1aoU//viDm+6OiIhA586dqxzGL+BzhDDT0tLw8OFDpKWlcRpOSkpK0NbWrpLidVFREc6dO4c7d+4gPj4e+fn5qFOnDpSUlKCrq4vRo0dLbUdoaCiuXbuGFi1awNbWFm/fvoWrqytevXqFBg0aYMKECZg1a5bM+52bm8uJWEpbjqiqPHjwAKdOncK8efOqrWfF+G/DDKgfhPDwcDx48ABZWVkoKiritE0MDQ1rZPy8f/++Sh/Kr8W3ELmrLjXRsgLKPzSPHj1Cbm4udHR0RD4qhYWFuHDhgsSIRgFJSUnIzc2FsrKyiK9OcXEx/v33X+jq6la5XZ+DPOpQVlaG3NxcTrOnfv36n12foqIiXL16ldOikqWmXhXS09N/yCUzPlcI83sjOzsbP//8c5XfC19CU+vcuXMwNzev0nVMSEjA2bNnOWkMgW+XgPz8fKxcuVKi7ExYWBg0NTWFfE3j4uJw6NAhTiNt4sSJzJj7Hvi2A2AMWXz8+JHs7OyIx+ORgYEBqaqqEp/Pp1GjRnH7np6eUlePX7BggdCwdVlZGW3ZsoU0NTWJx+ORsbEx/fXXXxLz9+3bl06dOlWjdkycOFFkRfFvQV5eHoWGhnL7mZmZtHv3blq2bBnt27ePm0ISR0FBAXl4eFD37t2Jx+PRyJEj6enTp0JpZEVtvX37liwtLbnIRQ0NDTp06FC1ysjJyeH6BI/HIxMTE7p27VqVyygtLaVt27aRmZkZaWlp0axZsyg5Ofmr1oGI6MGDBzRhwgRu6lawGRkZ0YIFCyg+Pl5iXiIiHo8nNIUnmArm8Xikr69PysrKNG7cOG6KVhyFhYX0/Plz7vlITU2lVatWkaOjI61evZrevn0rtQ41RUVFhbZu3frFyl+6dCllZmZ+sfIr8uDBA5o/fz5NmzaNjhw5QiUlJULnc3JyyNbWVmoZaWlpdP78edqzZw9t376d9u3bRxcvXuSm4aRRUlJCHh4eQpHBfD6fjI2NicfjkaamJvn7+39W26oyFUpUHmnYo0cP6tevH1lYWBCPxyNXV1ehKXlZz0Xlfv348WNSV1engQMHkqurK1lYWJCenh69fPnys9rCkB8sCu87Z/369SgoKMCNGzfQunVrTnyxbdu2OHbsGMLDwzF37lz4+voKaQlVJDg4GHPnzuUE7A4cOIC9e/fCycmJ0z9auXIlAGDEiBEi+QVTGWFhYZg/f/5nTRmGhoYiPDwczs7OmDp1arUj+eQhLhcdHY3Jkyejfv36+PvvvxEXF8dN1/32228ICQnBzp07sX//frHij5s3b8adO3ewYsUKTstq7NixQlpWQLkjsCS8vb3RvHlz7N69GwoKCggICMCKFSuQlZUFFxeXKrVv3bp1ePfuHQ4ePAgFBQXs3LkT06dPh5eXF4YPHy6zHrt27cKePXuE9I+sra2F9I9ktaOmdbh79y6cnJwwZMgQ9O/fH0lJSTh58iSmTp2K+vXr4/Llyxg+fDgOHDgg5OBekcplr1u3jnMW/uWXX5CUlARXV1esXbtWrPaQLHHWW7du4fjx41LFWWtKWVkZtybh54iBAuDEKsURHBwMXV1dbgRN3GigPJ6ta9euYcaMGdDT00OtWrXg5eWFM2fOYMeOHdwId3FxscS6FhQUYNmyZQgODoaCggKaNm3KiXnm5ORAQUEBw4YNw9KlSyVG2vr5+eHRo0c4cuSIVE2tBg0aiNXUkqXpZWFhwe1LmtJdv349hg8fjiVLlgAoD9Dw8PCAs7MzduzYUaX3XuV+vWnTJvTv3x9r1qyBgoICSktL4e7uDm9vb+Zk/q35hsYbowoYGRmJONLm5eWRhoYG96smNDSUevfuLbGMys62gwcPpqNHjwqlCQwMpD/++ENi/itXrpCZmRkZGhrSwYMHpTo5Syrj4MGDpKOjQwMHDhQZrZCFhoaG0CiFuE2WA/f48ePJxcWFG5GYMmUKOTk5UUFBARERffr0iVxcXGjSpEli89dUy4qoXIOpsl7SgQMHSFlZmQ4cOFClMkxMTEQ0k7y9vUlVVZUuXbokswx56B/VtA6jRo3idH8E3Lt3j4YMGcLtr1ixQuqIReV+3bt3b7p7965QmgcPHpC+vr7Y/FOmTKFp06ZRdHQ0eXl5ka6uLs2YMYPTXyorKyMPDw+J/YGofARJVr8UbOLg8XgUERFBw4cPpx49epC3tzdlZGRI/Huy6vA5Om3yeLasrKzIz8+P23/8+DGZmJiQlZUVN7onrT8sW7aM044qKioSOldUVES3bt0ic3NzWr58ucQ61FRTy8/Pj/h8PtnZ2dHJkye5LSgoiNTU1Gjnzp3cMUloaWmJBCRERESQhoYGF7Qh69mq3K+NjIxENO+io6Olakkxvg7MgPrO0dfXp4cPHwody8nJIR6Pxw3NJyQkSBVeFPdAvnjxQijN69evSU1NTWr+jx8/0vr160ldXZ2MjIxo/fr1FBMTU6V2CMpISUkhNzc3UlZWpqFDh1JgYCDl5OTIzC8PcTkNDQ0hoT0jIyMRAcGXL19KfDFpaGiIRBGVlJSQg4MD6erq0suXL2W+HCsaKRXZuHEjqaio0Pnz52WWoa2tLSIYSFQ+VaumpkZhYWFSyxAnCvjhwweysrIiMzMzSk1N/Sp1qPyhKS0tJVVVVc6AkCUoWvEZICo3DCtHeMbHx0ssQx7irAJxSGtra6GPrrhNHDUVAyUqFz01MzMjOzs7evHiBSUlJVFSUhIn2PjgwQPumDjk9WxV7lNxcXGkr69PEydOpKKiIqn9QU9PT2hqXRz37t0jAwMDied1dHRERCs/fvxIKioqXJ+SJWIpiKJ0cXERei9VVUjT1NRUxIgjKjfcVFRUaNWqVVWawqvYr62trUWiMJ8+fSrxhwHj6/F9e+syYGBggKVLlyI+Ph5Aufjj0qVL0aFDBzRv3hzZ2dnYsmULNDQ0pJbz7NkzTohSS0uL0+8R8PTpU5mLzzZo0ABubm64fv06Ro8ejQsXLmDo0KEwNjaGg4MD5syZI7M9SkpKWL9+PU6fPg0VFRV4e3vD2NgYo0aNgqenJzZs2CA2n0BcLi4uDu/fv4eenp7ETRJNmjRBSkoKt9+qVSvk5+cLpcnJyZEYyVdTLSsA0NDQgK+vr4jezqxZs2BpaQl3d3ecPXtWahkqKio4evSoyHEvLy9oaWlh2rRpePDggcT88tA/kkcdLl26JHTs7t27qFWrFuecHxcXJzXAgYiwdu1a7N+/H7dv34auri5OnjwplGb//v0SF7GVhzirtrY2/Pz8EBMTg3bt2sHKykriJg2BGOi1a9ewbNkyZGVlYfr06dDR0UH//v0xevRoiXl1dHRw5swZNG/eHE5OTkhLS0Pbtm3Rrl07AOXPXNu2bdG2bVux+eXxbDVv3hyJiYlCxzp37oxt27bh0aNHcHd35zShxEFEMoNZGjVqJFXQUx6aWurq6ggODkaTJk0wePBg3LlzR2qdKtOrVy8sW7YM//77r5A4ad++feHh4YH9+/fLjNAlIowZMwbTp0/Hhg0boKSkhK1bt3LXLzk5GatXr5Z6PxhfiW9qvjFkkpGRwWkPGRgYEJ/PJ319ffr333+JiGj06NHUp08f7pe0OPr168cN8xsZGVG/fv2oZ8+e3ND61q1bSUtLS2gIviKVnRorEhUVRYcOHSIPDw+aMmWKxDpIKqOgoID+/vtv8vb2pokTJ9LAgQMllkFEtGPHDho1apTUNJLw8fEhIyMj+ueff4iI6OLFizR06FBuJOXp06c0cOBAWrZsmdj8NdWyIiof4TIyMiJtbW2RX/SCKUFZ0yWCKYEBAwaIjJjk5+fT2LFjpU4byUP/qKZ1CA4OJh6PR25ubnT48GHy8fEhLS0t8vLyIiKi7du3k5aWllSnXz8/P5o1axZZWlqSqqqqiJZU//79SU1NTWLwwp9//kkDBw6kQ4cO0dixY7lRnNTUVCIqf/bGjBlDc+fOlVgHAatWraLhw4fLTFcZac9WTk4OXbt2jbZt20Z//vlnlcoLDg4mPT092rBhAxUXF1d55ISo5s+WmZkZnTt3jt6/fy907tKlS8Tn88nGxkZif3Bzc6ORI0eKHdUkKh+NHD58OM2cOVNiHeStqXXp0iXS19en5cuXV/k65uTkkL29PfF4PO49U5HDhw8Tn8+X+WwdOXKE/vzzTxozZgzp6OgQj8fj3tcaGhrUp0+fz9IAZMgXZkD9ABQXF9OVK1do165ddObMGaGh5cTERJFoF3F8/PiRHj9+TMePH6cVK1bQxIkTOb+Avn37kre3t8RIvpoKFsqrjJpSXFxMHh4exOPxuGkXbW1t4vF43EvN3t6ePnz4ILGMsLAwmjt3rshUARHR+/fvaf78+aSuri61HtnZ2XT8+HGJUypBQUFS/W6IysX6Nm7cKPaDU1RURFu2bCELCwuJ+U+fPk1jxowRmR4mKp/mGD9+PCkrK3/ROpw9e5aGDh1KGhoaZGFhQb6+vpz/y8aNG+nEiRNS/35FBNF0wcHB3LGNGzeKRElWRB7irAJKSkqkRvtJ4ks8F2/evKExY8aQlZUVqampfZUPbUFBAS1atIi6d+9Od+7cETl/9epVzhAQR3Z2No0fP56L6Bw1ahSNHz+eRo0aRb179yYej0djxoyRGVH4/v172rt3Ly1btoy2b98u9AMhPDycsrKyqtWu1NRUsrOzI2Vl5Wpdx8TERIn94dWrV7Rjx45q1SMlJYX7/82bNyk/P79a+RlfBqYD9YMgTXuIiJCSkiJVe6gmPHjwAFpaWp+1Bp6AU6dOYeDAgdVaq+5L8ebNG9y5cwcJCQkiYn/a2to1Lv9H0LKqCj+q/lFN+Rxx1s8lOTkZbdq0kXt/KSsrw/bt23Hy5EkcOnQIbdq0kWv5kigoKICCgoLYtTpzc3Nx8+ZNqetFPnr0CBEREUhNTUVBQQEn5qmrq4sePXp8yapL5e3bt2jdurXMtRElER0djcTERHTs2FHmsjySyM7ORnJyMjp06CBxjUnG14UZUN85hYWFWL58Oc6cOYOSkhKoqalh6dKlQqHd7969g4mJiUy15Iqq0R8+fEBQUBASExPRqVMnDBs2jJM5qEx4eDi0tLTk+pLPyclBYWEhGjZsWKWXwfciLldYWIiXL1+ia9euqF+/PqKionDo0CFOuNHOzk6m+vGrV6+wf/9+hIeHIyUlBUVFRWjQoAFat24NHR0d2NnZSV1sFABiYmJw7949qKurQ0tLC3fu3IGPjw8SEhLQqVMnODk5SV2EtKZif9IYPHgwAgICpH605XE/fX194eDgIGTgnDx5Ert370Zqaiq6devGrYUmjtTUVJl+f1UhOzsb169fR25uLoyMjEQkDz5+/Ig9e/ZgxowZInlPnz4NCwuLL2KklZSUcAsSy6KoqAhxcXH47bff0LhxY6SlpWHPnj3cR3/ChAlf7AeaPHn+/DlCQ0Ohr68PVVVVBAYGYvfu3UhLS0PXrl3h6uqK3r17S8x/+vRpnDhxgnsupk2bJvRezMrKwsiRI3H16lWx+YuLi7F582bcunULPXr0wJIlS7BkyRIEBwcDKPd16927NzZu3ChVMPbo0aO4desWNDQ0MHXqVGzduhX+/v4oKSlBrVq1MHbsWCxcuPCzDTqGfGAG1HfO2rVrcf78ecyePZvTHoqLixPSHnr37h2MjY0RHR0ttoz09HTMmDEDT548QefOneHv7w8nJye8ffsWnTp1wps3b9CgQQMcOXJE7EuSx+PB0NAQGzdurJFqeWhoKHbt2oWIiAghZ9CGDRtCS0sLU6ZMkegYqaKigtu3b6NFixYAgMjISNja2uK3335Dly5dEB0djZycHBw+fBhdu3YVW0ZZWRl27Ngh9IJ0d3cX+tBLM0ZfvXoFOzs7pKen49dff8WKFSvg7OyMtm3bomvXrnj+/Dlyc3Nx5MgRsTpSAHDv3j04OjpCVVUV+vr6aNWqFad3k56ejtDQUMTExMDf31/itfjnn38wffp0NGrUiNMFW7FiBQYMGIAePXogJiYGJ0+exKZNm8Qu5hsREQEHBwe0atUKCgoKeP36NSwsLLBu3TpuhFCWUe7r6yv2OADs2LEDY8aM4fqKOMNBHvezchlnzpzBwoULMXLkSHTr1g1PnjzBuXPnsGXLFpibm4vNP2zYMCxfvhx169aV2B5pxMbGYuLEiZzzeWFhIezs7ODu7s6lkXYteTwefv/9d2zdupVbvuVzuHbtGu7cuQMjIyOYm5tj/fr1OHDgAIqKitC0aVO4uLhg7NixYvPK0sOKjY1FSkrKF9XDkoaWlhZOnz4tUyPrn3/+gbOzMxo1aoRPnz5h2rRp2L59O6ytraGsrIzHjx/j/Pnz2Lp1q9gFhf/66y8sW7YMQ4cORa1atXDu3Dm0atUKe/fu5d6Lsp6L9evX4+TJkxg8eDD++ecfKCkpIT4+HqtXr4aGhgaio6Ph4eGB3r17Y/78+WLL2LNnD7Zs2QITExNERETA2NgYN27cgLu7O1eGt7c3bG1t4ejoWM2ryZAr33D6kFEF5KE9NHv2bLKxsaGrV6+Si4sL9e7dm8aOHcs5e3748IEcHR3J1dVVbH5lZWWysLAgQ0NDOnv27Ge1IyQkhPh8Prm5udHx48fpxo0bdO/ePbpx4wYdP36c3NzciM/n08WLFyXWoaKviL29Pc2bN4/KysqIqNwHxc3NjRwcHCTWwd/fn7S1tWnbtm3k5+dHpqam1LNnTyFdpoyMDIm+P1OnTiUXFxeKiorifJ0WLlzI1aG4uFhmHaysrGjNmjWSLxQRrVmzhqytraWWsXnzZiIqv64qKircvoD9+/dLLGPMmDFCejoXL14kLS0tcnBw4PziZPUpgbqzsbExmZmZCW08Ho9MTU3JzMyMzM3NxeaXx/2sXIa1tbWI07m/vz9ZWVlJzK+vr0+WlpZiQ8+rgoODA7m4uFBhYSEVFxfT7t27ic/n08KFC7k00q6lQC1dXV2dduzYUW19NaJynzlVVVUaNmwYqaur06JFizjV7Rs3btCmTZuoR48eEn3K5KGHderUqSpv4liwYIHEjc/nk4uLC7cvCWtra9q+fTsRlWur8Xg82rNnj1Aaf39/GjZsmNj8gwYNElroNzk5mSwtLcnc3JxTQpf1XAi0rIjKfdGUlZU5XTQBd+/elfhcEJUH/Zw/f56IyvWelJWVKSgoSCjNlStXqH///hLLYHwdmAH1nSMP7aGePXtyTs95eXmkoqJCYWFhQmmioqLI0NBQbH4ej0fJycnk7e1NfD6frKys6MqVK9Vqxx9//EG7d++Wmmb37t1kaWkp9pw8xOVqKiCpoaHBRTtmZmaSsrKyiChmTEyM1Dqoq6tLjDQSEBcXRz169JB4XkNDg968ecPtq6qqiji1S9NQkofYX2ZmJjk5OdHAgQNFhF6rErEkj/sprozK0ZEJCQkSnfp5PB7FxsaSi4sLqaiocMZxddDV1RXR9bp8+TKpqqpyhrK0a8nj8SgjI4MTmTUxMaF9+/YJLb0ki0GDBtHx48eJqNyg5vF4IsbSsWPHaMCAAWLzy0MPa8iQITUS8xREGg8aNIjGjx8vtKmoqNDIkSO5fUmoq6tzz0VBQQHxeLxq9Qdx/TYjI4P69OlDQ4YMoby8PJnPRcUlXwS6ZpWfj9evX5OWlpbEMqryfMvSs2J8HX58T9f/OPLQHlJQUBBaooCIRBzCFRQUUFZWJjY/EUFRURHz589HcHAw2rVrB1dXV5iammLNmjW4efMm0tPTRbSNKvL27Vuxw+YV6d27N5KSkqrUhtatW4vUt6SkRKpfQXp6upDvWJMmTeDv74+2bdtiypQpSEtLk1q/+vXr49OnTwDKdW9GjRol4iybm5sr1aerXbt2uHv3rtS/c/PmTal+VL/88gs3hRATE4PS0lIRXa+XL1/il19+EZu/cePGIjpPWlpaWLduHS5cuFAlv6fmzZvDz88Ptra2sLOzQ0BAgMw8FZHH/QTKfVIE8Hg8pKamCp1PTEzkpvgqQ0Ro2rQptmzZgoCAACQnJ8PKygo2NjY4fPgw4uLiZLajXr16KCwsFDrWr18/LF68GHv27MG+ffuk5iciKCgoYPz48bh06RIGDhyIrVu3wsjICK6urjh69CgePXrE6cCJIzExkfPzsrCwgIKCArp37y6URl9fX+KSLfLQwzp+/DgMDQ3B5/Px9OlTREdHi90kTX0FBQVh3LhxyM7OhoODAw4ePMhtioqKWL9+PbcviRYtWnDvQkVFRaxdu1Yk6ObJkycS/d5atWqFZ8+eCR1r2bIlAgICkJqaCicnJ6k6VADA5/Oxd+9efPz4EXv37kWtWrVw5swZoTSnTp2SOhXapUsXTgsuJCQERISbN28Kpbl+/bpMP0nGV+Cbmm8MmchDe8jNzY3Gjh1L169fJ1dXV9LX1ydnZ2dO5biwsJCcnZ3JyclJbH5xodZv3rwhX19fsrKyqtJSD9bW1iLTTJXx8fERWsqjch0sLCzI2dmZ1q9fT87OzjRp0iROwiEpKYnGjRsncRqSSPiXekXS09Opd+/eNGjQIIqOjpbYjrlz55KNjY3YRTxLS0vp9u3b1L9/f4k6UkRE58+fJz6fT7Nnz6bjx4/TzZs3KTQ0lG7evEknTpwgd3d34vP5dPr0aYll7N+/nzQ1NcnFxYUMDAxozJgxZGNjQ2fOnKFXr17RhQsXyMTEhDZt2iQ2/5IlS2jIkCH06NEjkWUzDh48SMrKyuTi4iL1flbk1atXZG1tTePGjeN+GVdlBKqm91PQ73V0dMjGxoasra3J3Nyc69enTp0iU1NT8vb2lliHyv36wYMH5OHhQfr6+sTj8UhNTY2MjIwk1mH27Nlka2srdrHbtWvXEo/Ho5UrV0qdwqtch/z8fDp16hTNmDGDWxRZ2r2wsLCgkJAQbj8sLEwk3P/QoUM0aNAgsfnlpYeVm5tLZmZmFBAQIDWdNG7evEnGxsbk6enJLbFUVQ2mzZs3k6GhodC1EPD27VvasGEDaWho0L59+8Tm37lzJ+np6ZG/vz/XdgHh4eGkqalJ/fr1k3ovnj59Sj179uRG4zZt2kQuLi5kZ2dH69evp6lTp5KKiopYjSgBd+7cITU1Na6cxYsXk729PS1atIgCAwNp2bJlxOfzhSQ7GN8GZkD9ANRUeygzM5PT9dHS0qLLly/T5s2bSU9Pj2xsbKhnz56kr68vUYxTllZNXl4ehYWFcfP24rh79y6pq6vTqFGjaMOGDXT06FEKCgqio0eP0ubNm2ncuHHUvXt3unXrltj88hCXq6mAZGZmJtnY2NCcOXNEzoWEhJCysjJNnTpV5vTLvXv3aNKkSaShoSE0vaGhoUEODg6cD4U0goKCyNnZmZYsWULp6ekUFhbGvXCVlZXJzc1Noj+NPMT+KlNcXEw+Pj6ko6NDfD5f5gdPHveztLSUYmNj6fz587Rx40ZycnKivn37cu3W09MjV1dXibpe0kQsy8rK6MWLF3Tq1CnauXOnxDqkpKRw008VfRUFrF27VqoBVBUdqJSUFJEptYocOHCANDQ06NChQyLnYmNjyc3NjVRUVOjMmTNi88tTD+vq1avk4eEhM500MjMzadq0aWRhYUGPHz8WmhaTRmlpKa1fv57Wrl0rcu7vv/8mAwMDqfeyrKyM840UJ776+PFjmQYUUbmm1ZUrVziNtby8PPL09KRBgwaRg4ODxHdcRV69ekX79u2js2fPUklJCaWmptLkyZM58dqKvlqMbwczoP4jSBLBrEhubq7QRzUkJISWL19O/v7+Yn9BCzAzM6u2AJ04YmNjacmSJWRpaUlaWlqkqqpKmpqaZGlpSZ6enmJHdqTxOeJy0gQkX79+zYn5SaOy0jIRUVZWlsj6grIoKyujnJwcSk1N5dSza8LHjx/pyZMnVV6MVt5if0RE9+/fpwULFsgUPBSHvMUCZQlbykvEsrCwkO7evSvxuoeGhtKSJUvEnhs/frzY/lRdAgMDadeuXSLHb968SdbW1mJHZWTx6NEj2r17N4WEhFRpTT55c/ToUdLW1iYVFZUai4FW5f1YEUEwQ2VKSkrEvjsY/5swGQMGoxKyBCSLiooQGRmJrKwsFBcXo3Hjxmjfvj06depU5b/xuZpcFfn48SMaNmzI7T99+hRJSUno2LGjxPXfpJGSkoL09HR07NhRplyFvPWLqqsLJgkiwrVr17hraWpq+k20cv4rooepqalo1arVNxOGjY+PR0hICCZMmMA9L9Xh06dPuHz5MqfT1rt3byHfO3HU9NmUpRU3ceJEqfpjixYtgr29vUT5Dsb3AzOg/keQJt6oq6uLCRMmVMsAEPDnn3/C1dVVpoDl9yKEKUswcOLEiVIFILdv346AgADOmbxWrVoQPEJt2rTBnDlzMHDgQIn5a6rJBZR/nOfPn4+EhARcvnwZeXl5cHZ2RlhYGIByB219fX1s3bpV4sv+4MGDuHPnDoyNjTF69GjMmzcPFy9e5AIMJk6ciHnz5klshzz0i2qiCwaUOzwvWbKEEy3csGEDZs6cidDQUDRr1gw5OTlQUVHB7t270axZM5H8fn5+GDlypERn+6pSU9HDmn5wv+SzVVUNJqDmIpSAZOOlY8eOsLKykmq85OfnY+HChbh9+zZMTEzg4eEBW1tbJCUloWnTpsjOzoaGhgZ27dolthx5PJvy0Irj8Xho1KgRPD09MXToUIntZXx7mAH1nWNrayvzF5OAAwcOiD1eU/FGwYdZHFOmTMHKlSu5ERtdXV2x6WoqnCgpgkgc0l5uNREMPHjwIAICAuDu7g5VVVUkJSVh8+bNmDBhAtTU1HDx4kX4+/tj9erVEo0oNzc3vH37FlOnTkVwcDCePHmCX3/9Fdu3b8dPP/2Ejx8/ws3NDYqKiti8ebPYMubOnYvo6GgsWLAAxsbG8PT0REREBLy9vdGtWzfExMRg8eLF0NDQgJeXl0j+Xbt2wc/PD4aGhggLC4O6ujqioqKwcOFCKCsr499//4WPjw8cHBwwZcoUsXXg8XjQ0dHBkydP4OzsDHt7+2ot03P+/Hm4u7ujf//+EvvkpUuXsH79evTv319sGZ6ennjw4AHGjRuHixcvIj8/H2VlZdi2bRvat2+PtLQ0zJw5E126dMHKlSvFtqFVq1bYsGEDdHR0qlz3itRU9FAeH9yaPlsTJkyQ2L7w8HCoqalx0aaS3jE1FaGUh/GydOlSREREYNSoUQgODsb79+/RokUL+Pr6olWrVnj9+jVmzpyJ7t27i30uqvps1qtXD1u2bBFbB0dHRygqKsLZ2Rn79u3DhQsXMHDgQKxcuRIKCgooKSnB/Pnz8f79e+zatUtsGTweDw4ODti/fz969uwJd3f3z17+hfGF+WaTh4wqsWPHDlJRUSFLS0upYnPSBOZqKt6ooqJSI40XopoLJ2poaHB1kLTJqkNNBQMtLCxEHIVfv35NRkZGnI/FiRMnJEY7EdVck4uo3Dm6olNxRfE+AeHh4RJ1e/r168fpeN25c4d4PJ6IgOn58+fJzMxMYh1qql9UU10wonLdJ4EApkCXq/L9efToEZmamorNL3C25/F4tGTJks/yh6qp6KE8xFlr+mxNmjSJlJWVycbG5rNFLGsqQllTsV8iIgMDA3r06BERlftbKisrizxbgmALccjj2ZSHVpzgfj579oxsbGyIx+ORk5MT3bp1q9q+XIwvCzOgfgAOHTpEmpqaQuJq1aGm4o1hYWFcaPOLFy8oKSmJkpKSuJD1Bw8ecMckUVPhxISEBDI3N6fhw4fT/fv3pW6SqKlgoKampohoYmFhIamoqHAfiaSkJKkvR319fU6OIi8vj3g8HvfSr3gd9PX1pbajYkRmv379KDIyUijNy5cvJdZDXV2du1dlZWXE5/NFIjwTExOrLGKZmZlJ3t7epK2tTerq6uTi4kJHjhyhhw8fSux3PXr0qFKflBZdqqOjw5UhuA9PnjwRKUNXV1dsfkEU3tWrV6lPnz6koaFBK1eulFmvitRU9FCeH1wBnyNKunfvXtLV1RWJ5KuqhEBNRSjlYbxoamoKRddaWFjQ06dPhdK8fPlSYn+Qx7Opr68v9DeXLFki8s4ICwsj3Lp4FwAAiOJJREFUExMTiWVUvp/Xrl2jCRMmkLKyMhkaGtKsWbNox44dLBLvO4AJaf4AjBs3Dnp6eli7du1n5a+peKOOjg7OnDmD5s2bw8nJCWlpaWjbti3atWsHAFBSUkLbtm3Rtm1bieXXVDixQ4cOCAgIQFxcHN6/fw89PT2JmyRqKhjI5/Oxbds2FBcXc8cOHjyIJk2acH40ly5dkuovYmhoiOXLl+PGjRtYtGgRmjZtip07d3I+VUVFRdiyZQs0NTUllmFiYoLly5dzIpLDhg3Drl27UFpaypWxefNm9OzZU2z+Dh064MaNGwDK78uVK1e4eyng1KlTVXZibd68OebPn49//vkHy5YtAxFhy5YtGDNmDCwtLcXmqSgWKIlTp05JFQvU1tbGxo0b8fLlS6xduxb169fH4cOHOZ80ANi3bx9UVVXF5hekMzc3x/nz5zFnzhzcuHEDlpaWsLa2ho+PD86dO4fbt29LrENNRQ/lIc4qD1FSOzs7HDx4EIGBgZgyZQrevXsnMa04aipCWVOxXwBQV1fHrl27uDSXLl0SEs7Nz8+Hj48PtLS0xOaXx7NpbGwMLy8vTth2+fLl3NRrWVkZ7ty5g8WLF4tdo7JiOytiZmaG/fv34/r163B0dERpaSlOnDjxWQt9M+QL84H6QUhPT8ezZ89kqnmL48KFC5g3bx4sLCxgYGAAJSUl1KtXD0VFRUhPT8eDBw8QEhKCVatWYciQIVLLOn36NFatWoXRo0fDxcUFurq6OHPmjEwnUx6Phw4dOqBr167o0qUL4uLiUFBQgICAANSuXRvJycmYP38+WrRoIdH3BwD8/f1x7do1HDt2rNrXYdmyZQgLC8OYMWNw/vx5pKSkoEOHDvD29kbr1q3x7t07uLq6om3btli3bp1I/sjISEyYMAHNmzeHuro6UlJSEBkZCS8vL4wYMQIzZ87E9evXsWXLFokrvmdlZWHmzJkICwtDo0aN4O3tjaioKBw+fBidOnVCQkICFBQUcPjwYXTu3FlsGRkZGbC3t8fbt29hYGCAdu3a4dSpU2jUqBE6deqE6Oho1KpVC0eOHEH79u1F8p8/fx7z5s2Do6MjXF1dRdq4atUqPH36FP7+/jAyMhJbBx6Phzt37khU+QbKI7jS09Ohrq4ucu7evXuYNm0aeDwe9PX10aZNG5E++fjxY2zfvh3GxsZiy3/z5g0mT56MxMRE1K5dG4sWLUJMTAzu378PPp+PFy9e4PXr1zh48KDYOlT2HQLKP9wRERH4+++/ERYWhhcvXqCkpESigvbdu3cxbdo0NGzYEO/fv8eIESOQnJyMX3/9FWpqaoiJicHx48excuVKsQ7B8+bNw5s3b7BixQoRg7WsrAz37t2Dl5cXDA0N4enpKbYO8nq2gHIjYe3atQgJCcGff/6JhQsXVsmJfNeuXdi5cyccHBwwdOhQoR9jERERmDJlClq2bIk3b96IvZZz5sxBamoqpkyZglOnTuHBgwfQ0tKCj48PGjRogKKiIsyePRtEBD8/P7F1ePbsGSZNmgRjY2OsX79e6Nw///wDV1dXNGvWDPv37xcb+CCPZzMrKwvOzs5o164dfHx8hM6dP38ebm5u6NWrF9avXy/RIb4qzxbj+4AZUP8jhIaGYufOnXj48CH3iwoAGjRoAG1tbdjb20v8WFYmKSkJ7u7uKCgoQGxsLEJCQmS+YB8+fIiYmBi8ePECMTExePnyJfLz8xEWFobGjRtDU1MTLVq0wN69e6sU8fM5fPz4EcuXL8fVq1fRvHlzrFq1CtHR0VixYgVatGiBnJwctGvXDgcOHJAoYyD4IL9+/RqtWrXC0KFDOQfky5cvo0uXLhKdfSuSl5cHRUVFbrTr/PnziIiIQOvWrWFlZSUzMqyoqAjnzp3DnTt3EB8fj/z8fNSpUwdKSkrQ1dXF6NGjxUaeCbh16xbevXsHKysroeMRERHYv38/7O3tpf7StrW1xbZt2z4rtFxAXFwc9u/fj4iICKSmpqKgoACKiopo06YNdHR0YGtrK3MUrKSkBLGxsWjWrBlat26N4uJi7N27Fw8fPkTr1q0xbtw4/P7772LzVuVDVVZWhpycHKkRbPHx8bh58yZatGiBP/74A+/evcOiRYsQEREBJSUl2NvbY9SoUWLzyuOD+yWerZs3b8LDwwOZmZm4fPmyzHxEhB07diAwMBBr1qyBvr6+0PnIyEjMnTtXogElD+MFADIzM5GcnCxiMMfHx+Pu3bsYNGiQTImOmj6bgHA0oYDs7Gy8e/dO6jIuAODr6wsHBwe5SYQwvhzMgPofg4iQm5vLfawqrxVVVcrKyrB9+3acPHkShw4dkhr6L4nU1FQuPPvWrVvQ0tJCo0aNPqs+NeHff//Fw4cPoaSkBDMzM/bi+h9h4cKFWLRoUZU0t740NfngikMez1ZWVhZu3rwJCwsLIb0xWdD/X9+vMqWlpYiMjJRqmMvDeAHK30+5ubkoLi5GkyZNZK6pKC/Cw8OhpaUlN92sb9UORtVgBtR3TnBwcJXTDhs2TOK57OxsXL9+Hbm5uTA2Nhb5Zf/x40fs2bMHM2bMqPLfy8rKQmpqKjp06CDz5Vzxhf65yNJwmjBhgsQQZ3F8jnjj48ePcf/+fUydOhVA+cjevn37kJSUhPbt22PSpEmfHRIvD6ZOnYoVK1ZIFQIFyqUpgoKCOM2eUaNGCekUvX//Hi4uLhLD1oODg2FpaSnkLxYaGoo9e/YgNTUV3bp1g7Ozc5VG4ypTVW0xaQwePBg7d+6scZ+rKo8fP0ZYWBhSU1NRWFiIBg0aQElJCTo6OmKnD2URGRmJtLQ0dO3a9bP02YCqi3nKU7hRHgKxNSEsLAy+vr549OiRkK9iixYtYGJiAkdHR4n+aKqqqnB2dq7WO7AyPB4PhoaG2Lhxo8yRLmlIa4exsTGmTZvGFhP+DmAG1HfO0KFD8eLFCwCAtFuloKAg0U8jNjYWEydO5BymCwsLYWdnB3d3dy6NNI2WCRMmwNfXl3sxFhUVwdPTE6dPnwYA1K1bFw4ODpg5c6bE+qmoqGDYsGFYvnw56tatK6PVotRUw0lATcQbL168CDc3NxgaGmLXrl24fv06nJ2dYWpqiq5du+LFixe4e/cufH19P8tXrapIM6o9PT0xa9YszvgQZ1Rfu3YNM2bMgJ6eHmrVqoXQ0FD06NEDO3bs4F760voDIOo/dOvWLUydOhXGxsbo1q0bnjx5gsjISOzdu1es0648tMV8fX0llrFjxw6MGTOGa4+4j6K4qZKTJ09i9+7dnBHo6uoKQ0NDiX8nOzsbrq6uCAsLQ+vWrUX0rNLS0qCvr4/NmzeL/aD26dMHJ06c4KZb8/Pz4ezsjAcPHgAof64HDx6MFStWSNXZqomYpzyEG2uq4yQP4+Xu3btwcnLCkCFDoKKigqSkJJw8eRJTp05F/fr1cfnyZTx58gQHDhwQci4XwOPxoKioCG1tbSxbtuyzXAkE/mgCUc9BgwZ99XYwviJfNeaPUW0KCgpo0qRJNHz4cE6vqLo4ODiQi4sLFRYWUnFxMe3evZv4fD4tXLiQSyMtxLhyWO2mTZvIyMiIQkJC6OXLlxQUFET6+vrk7+8vsQ7Kysqkr69PlpaWnHZPdaiphhNR+dp/fD6f3Nzc6Pjx43Tjxg26d+8e3bhxg44fP05ubm7E5/NFNJEEDBw4kLZv387tjxw5kjZt2iSUxs/Pj4YNGyaxDhU1tWRtkqioifU5ulxWVlbk5+fH7T9+/JhMTEzIysqK03GS1h+IRPvEmDFjyNvbWyjNqlWraMyYMTKvw+dqixkbGxOPxyNjY2MyMzMT2ng8HpmampKZmRmZm5uLzV95MeHTp0+TqqoqLV26lA4dOkTz588nPp9PV69elVgHNzc3srKyovj4eLHnX716RVZWVjR37lyx5ytfx+XLl3OL6H78+JFCQ0OpT58+YhfIFbB7927q0aMHzZgxgwwMDGjevHmkq6tLf/31F718+ZLOnj1LRkZGEtc2VFZWprVr1xKfz6dJkyZxofzVoao6Ti4uLhLroK6uTvb29p+97t2oUaPo8OHDQsfu3btHQ4YM4fZXrFhBtra2YvPzeDyKiIig4cOHU48ePcjb27vK60pWLCM5OZm8vb2Jz+eTlZUVp7n2tdrB+HowA+oHIDc3l8zMzCggIOCz8uvq6opokVy+fJlUVVU5gc3qGFADBgwQWdk9JCSE+vTpI7EOPB6PYmNjycXFhVRUVDjxwKpSUw0nopqLN6qpqQm93A0MDETa8Pr1a1JTU5NYvkDg0tramk6ePCl1k8SrV6/I2tqa7OzsKDU1VehcVXR7xKWJi4sjfX19mjhxIhUVFVXbgDI0NBTRL/rS2mKZmZnk5OREAwcOpOfPn8tso6w2WFtbi/wI8Pf3JysrK4llaGpqimhwVebx48cS+2XlOvTt21fEYLtx44ZU3aCainnKQ7ixpjpO8jBeNDQ0hHSgiMoXEVZVVeXKev36NWlqaorNL7gOZWVlFBgYSMbGxqSurk6zZ8+m69evV2lB5Yr38+XLl9z7zsTEhLy9vemff/6htLQ0oUXd5d0OxteD6UD9ADRp0gSLFy9GQkLCZ+WvV68eCgsLhY7169cPixcvxp49e7Bv375qlffx40eoqKgIHVNTU0NGRobEPESEpk2bYsuWLQgICEBycjKsrKxgY2ODw4cPIy4uTurfrKmGE1C+HIysqbXevXsjKSlJ7LnffvsNd+7c4fZVVFQQHR0tlCYyMlKinhZQrl3k5+eHmJgYtGvXDlZWVhI3SXTq1AnHjh2Dmpoahg4divPnz0ttU2WaN2+OxMREoWOdO3fGtm3b8OjRI7i7u3OaUpKo7CTcqVMn5OfnCx3LysqS6HsjD22x5s2bw8/PD7a2trCzs0NAQIDUOssiLS0NvXr1EjrWv39/qX2zfv36Qj4q4pClf1SRsrIyEemJzp07c31fHBkZGVBTUwMAKCsro3bt2iLaVzweD2lpaVLrqaqqisDAQPj5+eHDhw+YPHkyTExMMHv2bPj7++P48eNS21ETHSciQocOHfDXX39h4cKFOHfuHPr06QM3NzfcuHFDKHJYEu3atcOlS5eEjt29exe1atXigmXi4uJk+iYpKCjAxsYG165dw7Jly5CVlYXp06dDR0cH/fv3x+jRo2XWBQC6du2KLVu24PLly7CxscH9+/fh6OgIU1NT9OjR44u3g/HlqSM7CeN7wNzcHObm5p+VV09PD97e3li/fr1QFMuYMWOQlJSENWvWyFxr7urVq1BXV0enTp1gaGiIhw8fCjmd3rlzp8o+A8bGxjA2NkZYWBiCg4Ph6+uLFStWoG7duvjpp5/EChf27dsXixYt4jScfv31V/j7+6NTp06chpO3t7dI+HRFBKKHlbWPKiJNvHHKlClYvHgxkpKSMGjQIDg7O2PBggUoLCxEt27d8PjxY2zbtk2mH4e2tjbGjRuHNWvW4MSJE1LTSqJOnTpwc3ODiYkJ5s+fj2vXrmHp0qVVymtpaQlPT0/MmTMHJiYmnG+blpYW1q1bBzc3N6SkpEgtg4jQp08fdOzYEV26dEGdOnXg7e2NwMBA1KtXD2FhYVi+fDlMTU0lltG4cWOsX78ep0+fhpOTE6ctVl1sbGygp6eHuXPn4ubNm/D29q5y3qysLM6Pi8fjITU1VWjdscTERKkyB/3798fChQuxZMkS6OnpCRnwJSUlCA8Px5IlSySu50dEOHToEPh8Prp06QJTU1NcvXpV6Nk6c+aMVEdyQb92cnISEvPk8XhcGmlinuKEG83MzJCSkoIrV64gPDwcJ06cwLt37yTKMQhEKAU6TgIRyoo6TrJEKAV1sbGxgbW1NUJCQhAcHIzp06cDKDcsmjVrhsDAQLF5J0+ejAULFiAmJgba2tpISUnBkSNHYGNjgzp16mDHjh3YuXOn2DUJxV2HunXrYtiwYRg2bBjev3+Phw8fIioqSuoPRXG0a9cO06dPx/Tp05Gfn4/o6GipZdS0HYyvB3Mi/x9AIFAXGxuLgIAAmJiYCJ1ft24ddu/eLdER3d7eHi9evEBmZiZq1aqFxo0bo6ioCLdu3UKTJk3g4eGBM2fOcAaOOMSJFgogIsTGxuLZs2d49+4dJk+eLJJGHhpO8hBvPH36NLZs2YLk5GQoKCgIOfY3atQIkydPhpOTk9i8FRGMoFU1+k8aubm5WLZsGcLDw5GZmYkLFy5INWYLCwvh5eWF06dPw9/fX8RJ+tq1a5g/fz7y8/MlOpGnpaUJaQ+9ePECr169woMHDzhtsS5dumDHjh1Viqb7HG2xypSUlGDz5s0IDAzEp0+fZF4HdXV1FBcXo3HjxujSpQuKi4uRk5ODc+fOoUGDBggODsbGjRthaWmJ+fPniy2jqKgIS5cuxenTp6GgoIBmzZpxfSonJwdlZWWwtLTEihUrxIagL168GC9evMDLly9RUFAAIkLt2rVx9+5d/Pzzz7C3t0doaCg2bdok0QirqZinPIQba6rjJO39UNl4kfZD4dy5c9i1axcSExPRqlUrDBkyBI6OjqhTpw42bdqE3377DcOHDxebVx7XwdzcHEFBQVI12KqCuHZMnToVdevWldkOxteDGVA/AAkJCQgICEBYWBgyMzM5TZD27dvD2NgYtra2Modzi4qKEBERgW7duqFly5Yi5+/fv4+QkBAsX75cYhlZWVmIiYnhxPq8vLxQq1Yt2NraYsCAARg3bpzEvF9KXbe6Gk7yEG8EyqMCExIShAQs+Xy+yDIcX5Pg4GCcPHkSPj4+MmUMgPKpJQUFBbF1zs3Nxc2bN6sVRVRaWspFecXGxqJLly5i9YAkIQ9tMQB48OABTp06hXnz5kk13srKyhAfHy9kBL58+RIhISGoV68eevbsCX19faxevVqmDlJaWhoiIiKQnp6OT58+oX79+lBSUoK2tnaV7gVQPtolqIOzszMAYP78+ejfv7/M0eeaiHnKU7jxc3Wcvgf17eTkZLRp00ZuGk6M/z7MgPrOefbsGcaPHw9NTU0upPXGjRuwt7dHcXHx/2vvvMOiuL7//0YTNR81H42JJR8TY9Tswsqy9CoKiWgEC6CigIqgokRUsPeANYoFxIKgCFaMIBpF0VhRBCkKmogRO0pRkSIg9fz+4Md8WXZnWdxFN8m8nmcfHmbmnj139s7MmXvveV/8/vvvKC0txf79+6Uu2yGLqqoqFBQUSA2o/k7k5+ejY8eOTXpYK5OMjAxG64ZN9VoWZWVlOHPmDHJzc9GnTx8MGDDgnepS58c333wjNgwlL8nJycjLy0OvXr2aVD4/Px/Z2dmMplbnzp0b7XVShi6YNCoqKnDu3DlGQ4mtJ1EeiouL5e4hrKysRElJiVRhWiJCdnZ2kzTK/m3U9Zg11zXcFG2x58+fIykpCfn5+UwP5ddffw1tbe1G9e6UqaklTVusS5cu0NfXfydtMQ7lwwVQKo6Liwv09PTE5tWcPn0a+/btw759+1BdXY25c+eirKwM27dvZ7Vz/vx5XL16FaamprC0tMSGDRsQHh6OiooKdOjQAZ6ennB0dJTpy+vXr5mu6dzcXERGRjIPfTs7O7nUivPy8pjen/o3BR0dnUbf1Hft2oXU1FRs3boVNTU1CAwMxJ49e1BWVoZPP/0ULi4ujQ6fyRLj7NmzJ8aNG8f6oKusrIS/vz/i4uKgpaWFpUuXYunSpYwuk5qaGgYMGIBNmzaxKgbX6cNcuXIF/fr1w6JFizBu3DhkZWWhQ4cOeP36NUQiEUJCQlhFBxX1o7KyEhs2bMDVq1dhZmaGn376CZMmTcLNmzeZ8oMGDcL69etlanYdPXoUO3fuxKNHj8SGMtXU1NCzZ0+4u7uz6gopqgtWZ6P+sM+LFy8wfvx4PHr0iDmXenp62L59e5OGSusCyd69ezcaEJeXl8PX1xfHjx9HVVUVNDU1sXz5cjF9nsY0tRQVNa3jXa9PIsL27dtx5MgRxoe5c+eKXQeN1QGo7ZUNCwtDcnIysrOzUVFRIfbQHz9+/DuLgsqDotpi1dXVWLZsGSIjI5ltH330ETp27IiXL1/ik08+wdSpUxkRXWkoQ1NLUW0xjvfI+03642gqslJa8/Pziag2rV1PT4/VRmRkJGloaNCIESNIKBTS4sWLSVtbm4KCgujixYu0efNm0tLSoiNHjkgtn5ubS7a2tsTn88ne3p7u3LlDhoaGZGBgQPb29qSjo0MDBw6USKmvT1lZGS1YsID4fD6pq6uTsbExDRgwgIyNjUldXZ00NDRo0aJFrOm9AQEBpKOjQ1u2bCEiop07d5Kuri6FhITQxYsXKSgoiPT09Cg4OJjVh/v375OFhQXxeDwyNjamW7dukbm5Of3www80Y8YMGjJkCGlra9Nff/0ltbyfnx+ZmJjQmjVraPDgweTi4kL9+/en+Ph4Ki0tpdTUVBo8eLCEHlJ9li1bRtbW1hQWFka2trZkaWlJo0aNotzcXCIievz4MY0YMYKWLFnCakNRP/z8/MjMzIxWr15NFhYWZGtrS1ZWVnTjxg0qLS2l+Ph4srS0lKk9FB4eTkKhkDZu3Ejx8fGUmZlJT548oczMTIqPj6cNGzaQlpaWhJ5NHYrqgtXZqC8BMHfuXLK3t6e8vDwiInr69CnZ2tqynsuKigpas2YN2djY0Nq1a6m4uJgcHBzENKhmzpxJFRUVrD788ssv1L9/f4qOjqZjx46Rra0tCYVCunz5MnPMixcviMfjSS1/7tw5UldXpwkTJtDEiRNJXV2dxowZQwUFBWLlZUlKKHp9BgUFka6uLm3dupW2bdtG5ubmZGhoKCZLIasORETx8fGkqalJDg4OtGnTJtq/fz8dOXKE9u/fT5s2bSIHBwcSiUSUmJjIakNRFNUWCwgIoB9//JFSUlKorKyM7t27Ry4uLhQWFkalpaV09OhR0tPTo/DwcFYflKGppai2GMf7gwugVBwrKyuKiIgQ25aWlkYaGhr09u1bIqrV1DE1NWW1YWNjQ4cPHyaiWr0mPp8vESxFRETQ4MGDpZb39vYmZ2dn+v3338nNzY0MDAzI3d2d0UUpLCykCRMm0KxZs1h98PHxIQsLC7py5YrEA6miooLi4uLI0tKSfH19pZbv378/nTlzhvl/yJAhdPToUbFjTp48SRYWFqw+KCrGWec/Ue3DmcfjUWxsrNgxdcEHG8bGxnTjxg0iqtWt4vF4Eno5SUlJZGhoyGpDUT/qgi2i2rbE5/PFHvhERJcvXyYzMzNWHywtLWVqVRHVBu5s2mCK6oIRSQZQAwYMYOpVx/Xr18nIyEhqeWUEkhYWFmLnrrKykmbNmkVaWlqUnJxMRLIDIGWImip6fVpZWdGpU6eY/4uKisjZ2ZkMDAwY/bjGfLC1tWU05dj45ZdfyM7OTuq+o0ePyv1hQ1Ftsf79+0sE8y9evCA9PT3mHnH27FkaOHAgqw/K0NRSVFuM4/3BBVAqzu7du5k3/YsXL9LBgwfJ3NycvL29iYjoyJEj1L9/f5m9HpqamsxNo7KyktTV1SXejB4/fkxCoVBqeT09Pebh9uzZM+LxeBIilrdv3yZdXV1WHwwMDCghIUFmXa9du0bGxsZS9wmFQnr8+DHzv6mpaZNFLBUV49TW1mbEGet6ARsKOD558oR0dHRYfdDW1hbrUbSysqLbt2+LHXPv3j3S19eXaUMRP7S0tMREJrW0tCR8ePjwoUyhPpFIxJxLNjIzM1nbVP3gJy4ujuzs7IjP59Po0aNp3759EsKv0uDz+fTq1SvmfysrK4l2LaseyggkRSKRWLskIqqqqiI3NzfS19ene/fuyQw+lCFqquj1Kc2HkpISsrW1JQsLC8rJyWnUB6FQSA8ePGDdX1cvNmHVYcOGKaxMT1Qr4unt7U3ff/+9WDAkj7Bq/fNYR2lpKamrqzMClnXBGBsNg/rz58/T+PHjicfjkYmJCc2aNYt27Ngh8VJcH2Nj40Z7ZRMTE7kASgXg0g1UHBcXF3h4eODgwYNwd3fH6tWrYWpqCh8fHwBAamoq7OzsMGfOHFYb3bp1Q1paGoDaMf3w8HCJbJi4uDiZk9Dr5ql8+eWXMDU1lSpYKUvEkogaHa9v27Ytq+igpqYmdu/ezfzfv39/nDt3TuyYY8eOyVwHT1ExToFAgNDQUJSWliI0NBQtWrTA8ePHxY45evSoTB+EQiFCQkIYQcHY2Fix+TJv3ryBn5+f1PXjlOXHd999h6ioKOb/1NRUsUnjNTU1CAkJQd++fVl96Nu3L0JDQ2UKI4aEhEgIrkrDzMwMkZGRCA8PR+/evREYGAgbGxsIhUKZk8CJCOvWrUNYWBiuXLkCfX19sXoBQFhYmJgeUn3qpC+A2t+ldevWEpOMv/rqK5SUlLD60KtXL5w+fVpsW8uWLeHv749u3bph0qRJePDgAWt5ZYiaAopdn927d0dCQoLYtv/85z8ICgoCEWHSpEl49eqVzO/v3r074uPjZR5z+fJlVpHZw4cPw8TEBAKBALdv30ZGRobUj6w5WMD/aYt5enpi2rRp2LRpE6qqqmSWqUNbWxtr1qxBYWEhgNr2tXHjRnz++ef4/PPPUVNTg71798pMsJCmqRUWFoYLFy7A3d0d1dXVOHLkCNasWcNqo05b7MqVK6ioqBDbV1VVhYSEBCxevJhV1oLjPfJBwzeOJvHq1Su5u4HrEx4eTiKRiPbt2yexLzMzk7y9vUldXV1ieZY6pk6dSu7u7sycq4Y8fPiQRo8eTbNnz2b1wdvbm0aNGsX6lvrkyROyt7enmTNnSt2flpZGIpGIRo0aRWFhYRQZGUkGBgbk7e1N27dvJ3d3dxIIBMzQljR+/vlnsra2pn379pGjoyPT3V83N+TFixc0duxY1rkFt2/fJkNDQ+ZNefPmzeTp6UkuLi60YcMGmjJlCqmrq9OlS5dYfbh9+zbjd0MuXrxIQqGQ+vfvLzHvTZl+XL16lTQ1NWn58uUS+xISEmjAgAGko6NDt27dYvXhzz//JCMjIzI3N6fZs2eTn58fBQQEkJ+fH82bN48sLS3JwMCA1UbDdejqU1NTQ3/99RcdPXpU5py2bdu20axZs2jIkCGkoaHB9FC8fv2aiIgGDRpEmpqadO3aNanlG65lWF1dLbbeZHV1NS1evFjmmmPnzp0jDQ0NqfNd8vLyaPDgwaSpqcnac+Ln50cWFhZ04sQJZt24OmJjY0kgEDBDQWwoen0eOXKEBAIBLV26VKLdZWZmkqmpKRkYGMj0ISYmhgQCAXl5edHhw4fp8uXLlJCQQJcvX6YjR47QvHnzSCAQ0LFjx1htKLpkVUOePn1KY8eOJVtbW4llmKTx6NEjMjExIW1tbRo5ciSZm5uTUCikCxcuEBHRuHHjyMDAgBmCl0bDHqh3oby8nBYsWMDMDTU1NSULCwsyNTUlgUBA6urqNHv2bCorK1PoezgUh8vC+5uQnJyM69evIz8/HxUVFYwOlImJiVyCgxEREXjz5g3c3NzEtsfFxWHz5s1wc3PDkCFDpJZ9+vQpXFxcIBQKsWnTJrF9sbGxmDlzJtTV1REcHMwqiVBQUABPT08kJyfjiy++kBCxzMnJgba2NgIDA1lTjR8+fIigoCBcuXIFL1++ZLa3bt0aOjo6mDZtGgwMDFjPgTLEOAsKCpCcnIxOnTpBW1sbb968wfr165GamoouXbrAxcWl0dT5V69e4dmzZxKpyA8fPkR8fDxsbGwa7a1T1I+//voL2dnZEkuX3LlzB8ePH4ejo2Oj7aqgoAARERGsmlojR45k/S2VrftTl135119/MdlPmzdvxsCBA1lXrK8ToLSzs8PPP/8sti8xMRELFixAUVERwsLCZPbGJScnIyIiAm5ubhK9XUVFRVi9ejVOnTrF9ALXRxmipsq4Po8fP45Dhw5h7ty5EmrhT58+xaJFi5CcnCyzByghIQHBwcFITU0VW3qlTlh14sSJMDU1ZS1fV99z585h1apVMo+Tl6ZqixUVFSEqKgpPnjxB586d8eOPP6JHjx4AgJSUFHz77bcyRTKVqamVm5uL1NRU5ObmvrO2GEfzwgVQKk5ZWRk8PDyQkJCAjh07orCwEGpqahAIBHj69CmjPLx8+fJmFYCrqKhATk6OxDBfTk4OMjIyYGpqKlc6+o0bN8QeuHU3BX19fZnrQzXkzZs3KCkpYdKMFal7U8U4m4v6aegc7wdlBJLyUFNTI7ONKipqqqzrUxZ5eXlyPbiJCEVFRUxALU0b633zIbTiampqUFRUxAgfs0mbcPx94QIoFWflypX4448/sHnzZnTp0gVv3rzB8uXL8b///Q/e3t5ITk7GnDlzYGdnJ3ONt8rKSpw8eZJVzVxWzw0b/xQhzqbUo6qqCmfOnEFSUpJUrRsrKysxDR9pXL58GXFxcRCJRLC2tkZUVBTWrVuHwsJCfPrpp5g2bRpcXFxk2nj27BlSUlIwbNgwALUaPPv370dWVhZ69OgBR0dH1rXPgFqRvsTEREbTJiEhAXv27EFWVha+/vpruLq6Qk9PT6YPL1++xM2bN8Hn89G9e3dkZGQgMDCQEfOcMmUKs8itNF6/fo0LFy6gqKgIpqamEnO2SktLsXv3bplrC+bl5eHGjRsQiUTo0qULLl26hNDQUEb/aNq0aXLNw1IEZbQJRcQb2cjPz0dOTg569Oghlw1l6LwVFRUxayuWlJQgMjKS0VgbMWIEq7aZslBEK05DQwMeHh6NrmXZGElJSQgMDMSNGzfEFpru1KkTzMzMMHXqVJnXJsffBy6AUnHMzMwQHBws9hB48+YN+vXrh8TERLRq1YoZbrhw4YJUG/n5+Rg/fjzy8vLQo0cP5OTk4PXr17CwsEBOTg7+/PNPGBsbY8uWLay9L8oQ4lQFFKlHVlYW3NzckJubCw0NDQmBuz///BNffvklQkJCWMU4o6OjsWjRIvD5fDx48AAuLi4IDQ2Fq6srRCIRMjIysHPnTsyfP5916Y3ExERMnToVPXr0QHR0NG7cuIEJEyagR48e6NWrFzIzM/Hs2TOEhoZCJBJJlD99+jS8vb1hYmKCkJAQXLhwAR4eHjA3N0fv3r3x119/IT4+HoGBgbCwsJDqQ1paGiZNmsQs3REQEIDZs2eDx+NBS0sLd+/eRWJiIvbs2SM1EMvMzMSECROYSfzl5eVwcXHBvHnzmGMaE29MS0uDi4sLysrK0K5dOyxevBiLFy+GsbExeDwe0tPTkZ6ejrCwMNZFbBVdJknRNqEM8cbx48cjMDCQCVwqKiqwbNkyHDt2DEDtBHM3NzfMnDlTavm8vDxMnToVd+7cgUAgwMqVK+Hi4gIiwldffYWHDx+iU6dO2Lt3L+sk8Ly8PEyfPh23bt3Ct99+i6CgIEybNg3Pnz9Hz5498fTpU3zyySc4cOAA67UhKxju3bs3PDw8ZAbDW7ZswZ49ezBx4kRMnz4dwcHBjB+9e/fG3bt3mUV4pa23yefz0bp1a+jq6sLHx+edeh7j4+Mxbdo0DBs2jFk5IioqClOmTEGbNm1w5swZ3Lp1C+Hh4axDy40t7F4fTt3+A/OhJl9xyIeRkRGlpqaKbSsoKBBL4X706JHMlPM5c+bQTz/9RCUlJURUOzn2l19+oUWLFhFR7WTLoUOHsmowKSrEqQy2bNki94cNResxefJkcnV1paKiIqn7CwsLydXVldzd3Vl9sLa2ZsQlExISiM/nU2hoqNgxR48eJRsbG1YbdnZ25OvryyQUODs709KlS8WO8fHxoTFjxrD6sH37dub/hpOpiWonaI8YMYLVBycnJ1qyZAkVFxdTSEgIaWpq0rJly8SO2bRpE40dO1ZqeTc3N/L09KTy8nKqrKykXbt2kUAgoIULFzLHNJY67+TkRD4+PlRcXEx+fn6krq5OGzZsEDtm1apVrD7cvn2bRCIRTZw4kdatW0czZswgoVBImzZtonXr1pGVlRWZmZlJyBTUR9E2oSzxxvoTlzdv3kympqZ08uRJunfvHkVGRpKRkREFBQVJLa8MnTcvLy9ycHCgc+fOkaenJw0YMIAcHR2ZifElJSXk7u5OM2bMkFr+5s2bJBKJiMfjka6uLkVFRZG6ujq5urrSL7/8Qk5OTqSpqSlxL6yPolpxfD6fUlJSyN7enrS0tGjt2rWMfIG8jB49WkI89tq1azRs2DDm/5UrV8pMTBCJREyCCNtHHkkHjuaHC6BUHC8vLxo6dCiTvVZSUkIzZ86kQYMGERFRfn4+eXt708SJE1lt1BfEq6OsrIw0NTWZoCo9PZ1VjFNRIU4icZXgxj7ScHNzIz6fTwYGBmRhYcH6kSViqWg9RCJRo8rCd+7ckakDpaWlRU+fPmX+19DQkNCeaUxrpqEmlrGxsZhqNFFt5hWb5k7DjCRjY+Mma2pJ06JqqCX16NEjVu0hfX19iTZ55swZ0tDQYAQZGwug6utZFRcXE4/Hk6jHgwcPWLWoJkyYIBFwnzp1ipycnIioVs/Jy8uLpk6dyuqDom1CmeKNdQwePFgio/bkyZOsoqbK0HkzNDRkbBQXF5O6urqEQOydO3fIxMREanlFg2EixbXi6s5jTU0NHTp0iMzMzEgoFJKXlxdduHCBCShlIWvliLpg7MmTJzJfeB89ekSWlpZkb29PiYmJMj8cH5aPPnQPGIdsFi1ahIkTJ2LIkCHo2LEjioqK0L59e+zYsQMA4OHhgRcvXmDnzp2sNlq1aoXs7Gz06tWL2VZYWIiKigpGI+WTTz6R0Byp4/Hjx0x2kJWVFdTU1CSykoyMjGRmzuzduxceHh7o3r07nJ2d5at8PYKDg7Fw4UKkpqYiOjparvkYDVG0Hu3bt0dubq5MHZjnz5/LnCz61Vdf4dq1axg1ahQSExNRXV3NzCOqIyUlRWbX/BdffIE///yTmTD8zTff4Pnz59DQ0BCrK9vQ01dffYWrV69izJgxAGrXlMvIyBDzIT09nXW4BgA6dOiArKwsfPXVV8jOzkZ1dTXy8vLEhiVycnKYYaWGtGrVCuXl5WLbBg4ciCVLlsDHxwedO3eWOWkaALOWYd26hl5eXhJzfZ48ecKa6ZeWlsboqdVhZWWF2bNnM/OBPD09WYdSAcXbRElJiURbbtu2LUpKSph5eXw+Hy9evGC135DS0lKJoS5NTU2ZNhTVeVNTUxOboE1E+OijjySOYdMNu337NtasWYN27drB3d0dwcHBElnBY8eOxYgRI1h9qNOKq8uorNOKq9+uG9OKq/PTwcEBdnZ2OHnyJKKjo/HTTz8BqNW76tixIw4dOiS1bPfu3REbGys25BofH48WLVowk+nv378vc1i4R48e2LlzJ0aOHInCwkIMHDhQpr8cH5APHcFxNE5lZSWdPXuWQkJC6Pjx42LrZD1+/Jiqqqpkll+2bBmz/EdZWRndv3+fnJ2daeTIkURU+2Y4fvx48vT0lFreysqKTp48yfyflJQkpgBNRLRv3z6Zw05ERMnJySQQCOj69esyj2OjvLycbGxsaP369e9UXtF6+Pv7k7GxMR06dIgePXrErNtXXl5OT548oSNHjpCRkRH5+fmx+nDixAkSCARkZ2dHQqGQPDw8aOzYsbRt2zaKi4ujkJAQ0tXVlRjWq09ISAhpa2vTgQMHqLCwkBITE2nQoEEUFxdHOTk5FBsbS/369aNNmzZJLX/06FESCAS0fv16unPnDiUnJ9MPP/xAhw4dopSUFNq9e3ejPmzevJnMzMxo7dq1NHjwYLKxsSFHR0dKSUmh8vJySk9PpyFDhrAOC3t5edG4ceOYdevqs27dOuLz+bRq1SqZPVC+vr40aNAgqQr3hYWFFBERQSYmJhLDk3UoY5kkRdvE5MmTafz48cw1XVNTQytXrqR+/foRUW3vxerVq8nBwYHVBx6PRxEREXTnzh16+/YtLViwQKJeBw8eJGtra6nllaXz5ujoSBcuXKAZM2aQkZEReXh4ML025eXl5OHhQdOmTZNa3tTUVKzHaseOHRK6TRcvXpS5VJOiWnGytMkKCgro/PnztHXrVvr5559ZfYiOjiY+n0/e3t60f/9+8vPzIx0dHVqxYgUREW3fvp10dHRYh1Prs2PHDho9enSjx3F8OLgA6m/G69evKScnh3XOhTSKi4vJ1dWVGTfn8/n0448/MsOCzs7O5OTkxCxo2xBFhTjrs3r1arK3t5fb94akpqayBgaNoWg9ampqaMuWLaSjoyN16FFXV5c2btzYqNjptWvXaNWqVbRjxw4qKSmhe/fu0Y8//kg8Ho9EIhFt3LiRampqZNrYunUriUQiUldXJ0NDQ9LS0hLzZfHixTID6+joaLK0tGTaRP3lMnR0dMTWZ5NGVVUVBQQE0PDhw8nV1ZUyMzPp9OnT1LdvX8YHR0dH1naanZ1NNjY2UpdPIaoNohqb51FWVkbe3t7MXL76nD59mjQ0NMjHx4d1MWBlLJOkaJtQhniji4sLmZiYEI/HI3V1ddLX1yctLS3m3C9cuJAEAgEdOHBAavknT56QpaWl1DlOp0+fJh6PRyNGjJA5H+jVq1fk7OzMtJ8zZ86Qv78/GRgYkIODAxkaGpKRkRHr8j+KBsN1PHjwgObPn0+mpqZibVooFJKLi4vMYS9liGASEf322280fPhwEolEZGVlRYGBgUwb3LRpU7POFeV4v3BZeH8DEhISEBISgpSUFLGlTv7zn/9AR0cHkydPlkuG4M6dO3j8+DE6d+4MTU1Nptu+pKSk0TRnRYQ461O3fEr79u0bPbY5UEY9KisrcefOHQmBOz6fL3OYozFev36N//73v3JrWpWVlSE5ORmPHj3Cmzdv8NFHH6Fr167Q09NrVDCwjgcPHkiUFwgEUvWI5CEnJwdpaWno2rUrhEKhTN2diooKpKSkoE+fPlIlJBITE3Hy5En4+vrK/E5pGkulpaWoqamRmTZPRNi5cyd27dqFoqIitGrVCjY2Nli0aBGT1delSxf89NNPjcoQKNImFBVvrCM/Px93797F3bt3ce/ePaxYsQItWrTAuHHjMHjwYDg5ObGWVZaOVF1WZl2dY2JikJKSgi5dusDW1lZiCak63r59i8WLF6NNmzYSQ+ixsbHw9vaGg4MDFi5cKLeeVVO14p49e4Yvv/zyvWpFcfy94QIoFScmJgbz5s3DoEGDYGRkJJEmnZCQgNjYWGzYsKHJayNlZ2cjLy8P33zzTaPK1xz/R1ZWFoqKisDj8SQerJWVlbh58yb09fWllt22bRtGjRrF+iBpCpWVlSgpKZEqVEhEyM7OblKac0ZGBqPZ8913372zXykpKdDU1JQ7mMzJyRGTEPjyyy+bJGZaWloqNo/o9u3byMrKwjfffMO6Dl5D8vPz0aFDB6WJ0f4dry1lt6d34V2DYUC511ZDTpw4AUtLS7nmXr558wZ//PEHDA0NAdS2rejoaEajbfjw4TLFRRcvXoyJEyeid+/eynKfo5ngAigVZ8iQIRg5ciRcXV1Zj9m9ezciIyNx8uRJ1mP27t2Lq1evwszMDGPGjMHcuXNx+vRpZrLnhAkTMHfuXKllk5KSoK2tLTYp9P79+9i3bx9ycnLw3XffYcKECazLdtQnLy+PUSIvLy9nBAd1dHRkqhwrSxulbsmPuonHubm52L17NxM4jBs3jrV8YWEhZs2axSy8+sUXX8DHx0dMK6kx7SI+n4/OnTtj48aNjQpVslFeXg5fX18cP34cVVVV0NTUxPLly8UmcMvyo7KyEv7+/oiLi4OWlhaWLl2KpUuXIjo6GkDtJNoBAwZg06ZN76SerKOjg2PHjjWqo3P8+HEEBATg2bNnYttbtmwJbW1tzJw5U+Y5ev36NebPn49Hjx7hzJkzKC4uhoeHB5KSkph6GBkZYcuWLVIfvtu2bcPo0aMVFoJteG3NmzcPp06dkuvaAoA///wTCQkJMDIygoaGBg4dOoRdu3Yx+kczZszAgAEDFPJRFoq2pzoePHiAsLAwJCcnSxUUHT9+PHr27Nlk/+QNXpRxbbEhb5vOyMjApEmT0KZNG/z++++4f/8+0/P31Vdf4cmTJ/j4448RFhYmltTTsB5t27bFsmXLmGWJOFQTLoBScUQiEY4ePSrzxvPgwQPY2tpKXWsLAEJCQrBt2zaYmJggKSkJQqEQd+7cwcKFC8Hj8XDz5k34+fnBzc0NkydPliivrq6OK1euMNlM6enpGDduHL766iv06tULGRkZKCgowP79+1nfmt6+fQsfHx9ER0dDTU0NHTp0YHrSCgoKoKamhhEjRmD58uVSey60tbXFhi+lQURQU1Njvck/ePAAkyZNwvPnz/HZZ59h586d+Omnn9CqVStoaGggMzMT2dnZiIiIkJqps2TJEqSlpWH58uVQU1NDcHAwLl++jBUrVsDe3h5A7YPGzMwMGRkZUn3g8/mwtrZGTEwMRo0ahZkzZzZ5Pbh169YhJiYGXl5eUFNTw549e3D//n0EBgaiX79+jfqxYcMGREVFYejQobh06RK6du2Khw8fYs2aNYyY56JFizBgwADMnz9fqg+WlpasQx3Pnz9Hly5dmN65c+fOSRxz8uRJLFu2DJMnT2YEB0NDQzF58mR07doVsbGx+O2336SuD1fHnDlzkJGRgQULFsDMzAzLli1DSkoK1q5diz59+uDu3btYsmQJRCIRVqxYIVFeGQ9cRa+tS5cuwcPDA23btkVZWRmmTp2K7du3w87ODjweD2lpaYiJicGWLVtYRU0VRdH2BADXrl2Du7s7NDQ0WHvK7969i6CgoCaveiBv8KLotcXn81nbdN29pQ62e8y4cePQsWNHrFq1Cu3bt8eUKVPw0UcfYdOmTWjdujXevn2LefPmoaSkBLt27WL1w83NDWFhYTA0NMS8efNkZnlyfEA+wLwrjiZgZ2dH/v7+Mo/x8/MTE2pryMCBA+ns2bNERHT16lXi8/l0+vRpsWNiYmJYM1waTq6cOHEizZ07l5noXFVVRd7e3uTm5sbqg4+PD5MJ2HBSb0VFBcXFxZGlpSVr1pYytFEmT55MU6dOpYyMDFqxYgXp6+vT9OnTGb2dmpoaWrRoEbm6ukot369fP0pOThbbtnbtWtLQ0KDY2Fgialy7qC7T59y5c/T999+TSCSiVatWMRP65cHCwkJs4nVlZSXNmjWLtLS0GP9k+VH3OxDVak7xeDzG/zri4+NlamotXryYeDwejR8/XkzENCAggAQCAa1YsUKmsOnQoUPFMiKJiP78808xraKgoCAmU1QaBgYGYnpF9etVR3JyMhkYGEgtz+PxyNvbm/h8Pi1duvSdJhArem3Z2dkxoqbh4eHE5/Np9+7dYscEBQXJFDVdsGCB3B9pKNqeiIhsbW0Z/S42fvnlF7Kzs5O6r36CC5topCydOCLFr61t27aRQCAgFxcXioqKYj6RkZGkqalJwcHBzDY2RCKR2PeZmppSenq62DH37t2TqfNWd7/9448/yMHBgfh8Pk2bNo3i4uIaTVDheL9wAZSKEx8fT0KhkEaPHk0bN26kgwcPUmRkJB08eJD8/f3JycmJ+vbtS3Fxcaw2hEIhZWVlEVFtkCAQCCQE5h4/fsx6UTcMoExNTSWE9jIyMmTeFAwMDKRm2NTn2rVrZGxszLo/MzOTRCKRmNpwU9DT02OygMrLy0lDQ0OiHpmZmawPXF1dXak34wULFpCmpiYlJSU1+qCpfy7Ly8tp7969NHDgQOLz+WRra0vr16+n3377TebvKRKJJNSxq6qqyM3NjfT19enevXsy/ZAmgvnnn3+KHfPkyROZgqBEtQKPxsbGtHbtWrGgWCQSSaSgS6tDw3NZVVVF6urqjLRBVlaWzDZVXwCSqDaYacrDShnBrKLXllAoZIRV3759S3w+X0KY89GjR6xioERES5cuJR6PRxYWFuTs7CzzIw1F21NdPRo7b/fv32cVd1VG8KKMaystLY2srKzI09NTTC5GnjZNVPuSdfXqVeZ/W1tbio+PFzsmKSlJ5n2u4f32/PnzNH78eOLxeGRiYkKzZs2iHTt2SEhVcLx/lDNjkqPZMDY2RlRUFHg8Hn7//XesX78eS5cuxbp16xAbG4tevXrh6NGjMDMzY7XRo0cPXLx4EUDtvJCzZ8+ie/fuYsccPXqUdfitoUhely5dJATxqqqqZM6XIaJGJ9O2bdtW5jBdr169MHXqVISEhMi0w0abNm1QXFwMoHaiZ11GYH1KSkpYJz+rq6vj4MGDEttXrFgBHR0dTJ06FdevX5fpQ/3z2KpVKzg7OyM2NhZ79+6FgYEBrl27hoULF0od7qmjV69eOH36tNi2li1bwt/fH926dcOkSZPw4MED1vICgQChoaEoLS1FaGgoWrRogePHj4sdc/To0UYFB3/44QdER0fjr7/+gr29Pe7duyfz+IZ1OHDggNi2mJgYtG7dmpmTlJSUJHNeXL9+/eDr64v8/HwAwIgRIxASEoLq6moAtfPd/P39mcm8DaH/P3vB0tISMTExmD17Ni5evIghQ4bAzs4Ofn5+OHHiBK5cucLqg6LXVqdOnZjfqnXr1li3bp3EBONbt26ha9eurD74+vpiypQpKC8vh7+/P/bu3cv6kYai7QmoFZCMj4+Xeczly5dZxVmnTZuGAwcO4Pnz57hw4QIsLS1ha2sLOzs7tGzZEoMGDYKtrS1sbW1Z7Svj2hIKhYiOjkb79u0xdOhQXL16VWadGjJ8+HDMmzcPly9fBgC4u7vjl19+wcOHDwEAf/zxB37++WcMHjxYrnoAgIWFBcLCwnDhwgW4u7ujuroaR44cwZo1a5rkG0cz8KEjOI7m5+TJk6ShoSF1KDAtLY0cHBxkCszxeDyysrIiDw8P2rBhA3l4eJCrqyujM5SVlUVOTk6s61wR1QrtjRo1ivUt9cmTJ2Rvb08zZ85segXl5OeffyZra2vat28fOTo6koWFBbm4uFBOTg4R1Q5TjB07lubMmSO1fEpKColEIho8eLBEz9WbN2/I0dGx0WEGebRmqqurJQQ+63Pu3DnS0NAgV1dXid6KvLw8Gjx4MGlqarL6cfv2bTI0NGR83bx5M3l6epKLiwtt2LCBpkyZQurq6nTp0iWZftZn9+7dpKenR7t27ZLrbf3y5cvE5/PJwcGB1q1bR15eXiQQCBj9qRUrVpBAIKDIyEhWG3l5eWRtbU3a2trk4eFBq1evJn19fRowYABNnDiRjI2NydTUlHUtO2nCiTU1NZSUlERr1qwhOzs7RteKDUWvLX9/fzIxMZEYziQiev78OW3cuJFEIhHt2bOH1Yc6v8eMGUNLliyReZw0FG1PRLXDlAKBgLy8vOjw4cN0+fJlSkhIoMuXL9ORI0do3rx5JBAI6NixYzJ9KS0tpUWLFlG/fv2YcyZv748yrq36xMbGkpGREfn6+srtQ2VlJS1atIhZdsrOzo50dXWJz+eTQCAgPp9PEydOZJbQetd6cKgG3CTyvwGKZI7VERcXh5cvX0q8waWkpCAsLAwTJ05kXbE+NTUVd+/exV9//cVozLx58wZJSUlo164dtLW10alTJ4SGhrJO9CwoKICnpyeSk5PxxRdfoFu3bmjVqhUqKiqQl5eHnJwcaGtrIzAwUK5svnehtLQUvr6+OHfuHD777DOsXr0aGRkZWLlyJTp16oSCggJ0794d4eHhrD0fDx8+xLFjxzB8+HCJif2VlZXYsWMHTpw4gdjYWKnlFy5ciMWLFzeakt0YycnJiIiIgJubm0SqflFREVavXo1Tp06xJhYUFBQgOTkZnTp1gra2Nt68eYP169cjNTUVXbp0gYuLi8xeTWlkZGRgzpw5uH//Ps6cOdPopN+UlBTs2rWL0T8aNmwYs1TH/v378e2338LY2FimjYqKCpw4cQJXr17Fw4cPxfSs9PX1MWbMGFYNJT6fj6tXr8qcaFxTU4OCggKZbVKRa6umpgabN29GdXW1RKbeuXPnsHTpUri6umLSpEms31/H/fv3kZiYCEdHx0aPbYii7Qmo1asLDg5GamoqysrKmO2ffPIJdHV1MXHiRJiamsrlz5kzZ7B8+XIMGTIEUVFROH78eKPtSVnXVn1yc3OxYMECXLt2DWfPnm3UhzqysrJY26Surq7MsoGBgXBzc2uSlAfHh4ELoFQcRTPHmoucnBxmWCEuLg46OjqNinECwI0bNxgZg7dv3zKCg/r6+tDS0pJZVlF9FTZu3ryJ1NRUdO3aFRYWFv+YG5c0TZ3mpqKiAmlpadDS0lJIVPR90BwPXGVSXV3dqIDn+0Te9kREKCoqwtu3b9G6det3uiaBdw9emoOGmaXvQmFh4d9GE4xDPrgASsWZMmUKWrZsiVmzZuHXX3/F8ePHYWhoiE2bNuGjjz4CEWHJkiXIyclhTYuNjo7GkCFDxB5oCQkJ2L17N3JyctCnTx94eHiw6pKwUVVVxSx4qgjy3FiUoa8ii5ycHHTu3FnmA2LgwIH46aefZC5oqghDhw7Fzp075VIRT0pKQmRkJAoLC2Fubo7Ro0eL3dwLCwvh6emJ8PBwqeVfvnyJmzdvgsfj4auvvkJGRgYCAwOZXs3JkydDU1NTpg910hVJSUliQphff/01zMzMMHToUIkFZZVZ3sXFBVOnToWRkVEjZ6t5efbsGVJSUjBs2DAAtS89+/fvZwJ7R0dHfPPNN1LLKkuLio38/Hx07NixUXVtRdvT8+fP0a1bN7Hvyc/PR1RUFKMVN2LEiHcKqp8/f46uXbsq9DIg77V17NgxHDlyhDkPU6dOFQuw8/PzMWrUKKnSHID0oHzLli0IDQ1FWVkZPv/8c8ycORMjR45857pwqA5cAKXi6OvrIyIiAt9++y0qKiqgra2NgwcPQigUMsfcv38fjo6OSExMlGqjoY5TXFwcpkyZAjMzM/Tp0we3bt1Ceno6QkNDoaOjI9XG+fPncfXqVZiamsLS0hIbNmxAeHg4Kioq0KFDB3h6esocOlD0xqIMfRVZyKM1w+fz0aJFC9ja2mL+/Pn49NNPm/w9gYGBrPt27NiBsWPHMsHk9OnTpR53/vx5TJ8+HQYGBmjRogUSEhKgpaWFHTt2MGVlCR+mpaVh0qRJzLIbAQEBmD17Nng8HrS0tHD37l0kJiZiz549rPpIT548gaOjI9q1awc+n49nz57hzp07GDlyJAoKChAfH4+uXbtiz549Uoe/FC0P1P4eH330ETw8PJj20FTS0tKQmJiIKVOmAKh9sdizZw+ysrLw9ddfw9XVVaZGVGJiIqZOnYoePXogOjoaN27cwIQJE9CjRw/06tULmZmZePbsGUJDQyESiaTWQRnij7t27UJqaiq2bt2KmpoaBAYGYs+ePSgrK8Onn34KFxcXTJs2TWpZRdsTIHmPefToERwdHVFTU4Pu3bvj0aNH6NSpE8LDw1knkrMhb/Cj6LX166+/wsfHB8OHD0eLFi1w4sQJdO7cGaGhocwUiaaeh7CwMPj7+2PatGn47rvvkJ6ejt27d2Px4sVcEPVP4ENNvuKQDzMzM7p58yYR1S7YyePx6Nq1a2LHpKWlkZmZGauNhpMSx44dK7FA6urVq2ns2LFSy0dGRpKGhgaNGDGChEIhLV68mLS1tSkoKIguXrxImzdvJi0tLZmLZDacsLtnzx7S1tamnTt30sWLFykgIIBEIhH9+uuvUssrQ19l3LhxrB91dXUaPXo08780eDwenT17liwsLMjExIT27t1L5eXlrN8nDTMzM+Lz+WRmZkYWFhZiHz6fT+bm5mRhYSFTg8nW1lZssd+0tDTq168f2draUnFxMRHJ1u1xcnKiJUuWUHFxMYWEhJCmpiYtW7ZM7JhNmzaxtgciomnTptHy5cvFFj0OCwsjDw8PIqpdwHrcuHGsE/IVLU9U+3vs3buX9PT0yNrams6fP896rDROnTpF6urqjH7Z+fPnic/n05QpU2jdunU0adIk0tDQkGnXzs6OfH19GX0eZ2dnWrp0qdgxPj4+NGbMGNY6KKpFFRAQQDo6Oozm1s6dO0lXV5dCQkLo4sWLFBQURHp6ehQcHCy1vKLtqa4e9X338PAgNzc3KisrI6La39PFxUXqgsVEJKYl1vAjEAho5cqVMnXFiBS/tmxsbMSkAZ49e0ZDhgwhS0tLRlqjqedh6NChdPDgQbFjDh06RD/++COrDXV1dVZNrIYfjg8LF0CpOIpmjhFJXtQmJib0xx9/iB0jS6PFxsaGDh8+TES1WUd8Pl8iWIqIiKDBgwfL7UNTbyzK0FdxdXUlHo9HDg4OEgKDAoGAPD09ZQoO1tWhtLSUNmzYQEKhkExNTWnDhg109+5d1u+tz6tXr2jatGlkbW0tob0kb6aPtOPu379PRkZGNGHCBKqoqGiyDtTt27fFjnn06BHp6uqy+qCjoyORUVlZWUkCgYCKioqIiOju3busmlqKlif6v98jOzubvL29icfj0fDhw+nQoUNiGj5sWFtbMyKWRESjRo2izZs3ix2zbds2mSKWQqFQLMvP2NhY4tp6+PAh67WlDC2q/v37i2mjDRkyhI4ePSp2zMmTJ1nFPBVtT0SS17eZmZmE6Gx6ejrp6elJLa+MFwtFry1px7x48YK+//57GjZsGBUXFzf5PJiamtJff/0ldsyTJ09IU1OT1Uad+KudnZ2YJpa0D8eHhQugVJySkhKaP38+6enpkZWVFSUnJ9O+ffuIz+eTqakpCQQCGjRoEOXm5rLaaNj74+TkJKHYnZSUxNqLpampyYgFVlZWkrq6ukS68+PHj2WK/Sl6Y/Hz8yNTU1Mmtf706dM0fPhw5kFz+/Ztsra2Jh8fH1YfiIhCQ0NJX1+f9u3bJ7Zdnhtswzq8evWKtmzZQj/88APze7i6upK3t7dMO0S1waKBgQEFBQU1yQciIktLS6ligCkpKSQUCmnWrFmUk5MjU4m8LvjMysoiHo8n0cuSkJDA+sCts3Hq1CmxbQ8ePCA+n09v3rwhotrfxMjIqFnKE0n+HhkZGbRgwQISiUTUt29fGjVqFC1dupQ2bNggtbympqbY+TY2NpYQwWzsYff999+L1WPs2LGMMnkdFy9eJHNz80br8K7ijw2DOFNT0ybVQ9H2RFR7j6kvD2BjYyMRSN67d4/09fWlllfGi0Ud73ptWVlZSbRJotpg0sDAgJydnenp06eNBlCXLl1iXgI8PT0pJiZG7JiYmBgaOHCgTF+Sk5NJIBDQ9evXZR7H8WHhAqi/KTdu3KBdu3bRyZMnqbS0VOaxPB6PtLS0aPjw4eTt7U0TJkwgW1tbZvjp+vXrZGNjQ4sWLZJa3srKSkynJikpSUJLZd++fWRjYyPTB0VuLMrQV6kjIyODbGxsaNKkSfTixQsiku8GK003qI47d+7Qvn37aNGiRTR58uRGfSCqDRjs7OzIycmJnj59KveDws/PjywsLOjEiRNUWFgoti82NpYEAgGzBIQ0Nm/eTGZmZrR27VoaPHgw2djYkKOjI6WkpFB5eTmlp6fTkCFDWJfVISLasGEDGRgYUEREBGVmZtLly5fJxsaGWQbn0qVLNHToUFq4cGGzlCdi/z3evn1Lv//+O61du5YmTJhA1tbWUssPGTJErBfU1dVVoufmxIkT9MMPP7D6EBISQtra2nTgwAEqLCykxMREGjRoEMXFxVFOTg7FxsZSv379aNOmTXLXoalaVE5OTrR8+XLm/0WLFlFgYKDYMVu2bGFdRkXR9kREzLI+q1atokOHDtHs2bNp9uzZzP6ysjLy9PRkXSapDkVeLOrzLtdWcHAw8911Pfx1JCcnk7a2NhPcsjFw4EBmCM7U1JQGDhxIhoaGzFDoli1bSEdHR2zIlI3Vq1eTvb29HLXl+FBwAdTfiOzsbLp9+zbduHGDMjMzGw2c6sjJyaFLly5RcHAwzZkzh4YNG0Z9+/Zlyuvo6NCoUaNYBebCw8NJJBJJ9NoQ1S594u3tTerq6nT8+HFWH5R1Y3ny5AkdPHiQ1qxZQ4sXL6bly5fT9u3bJYYLGqO8vJxWrFhBRkZGdPr0abFhLTaaQ+CusrKS/Pz8SE9PjwQCgVwPirdv39LixYupb9++YsOadZw7d4709PRYb/RVVVUUEBBAw4cPJ1dXV8rMzKTTp08zD2o+n0+Ojo5MsCuNiooKWrp0qdh6ZRMmTGDmikyZMoXmzZvH/L7KLk+k+O9x9OhREggEtH79erpz5w4lJyfTDz/8QIcOHaKUlBTavXs36erqUmhoqEw7W7duJZFIROrq6mRoaEhaWlpi81QWL17MiM6+Sx0aE39MS0sjkUhEo0aNorCwMIqMjCQDAwPy9vam7du3k7u7u0wxT0XbExHR8ePHaf369TR58mQyNzdn1q97/fo1EdUu5WRgYCDRuySNd32xaEhTr62amhratm0bmZubS8wzJao9z40FUES1YqBpaWl0+PBhWrlyJU2YMIFZb/OHH36gtWvXyrWmXVVVlcxrkOPDw2Xh/Q04fvw4AgIC8OzZM7HtLVu2hLa2NmbOnNnkDJ76GjOZmZno1auXzFTniIgIvHnzBm5ubmLb4+LisHnzZri5uWHIkCEyv7OsrAz37t1jRDnv3buHkJAQfPTRRxg4cCB++OEHzJ07971qF12+fBmLFi3Cq1evGhV/vH79OnR0dN4p26sxrl+/jqNHj2Lu3LlyC4m+ffsWampqaN26tcS+oqIiXL58GTY2NnL7kJOTg7S0NHTt2hVCobDR1Pe673n8+DE6d+7c5OwqRcsfPXoU1tbWCulNHTt2jLm21NTUUP922LZtW0yaNIk1e60+ZWVlSE5OxqNHj8SEE/X09GRmjylLi+rhw4cICgrClStX8PLlS2Z769atoaOjg2nTpsHAwECmDWW2p6KiIvz111/Mfenw4cMwNzeXuSRNfaqqquDv749Dhw6hrKwMp06demcdqHe5tohIavuvrq5Geno6qzAqx78LLoBScU6ePIlly5Zh8uTJUFdXR1ZWFkJDQzF58mR07doVsbGx+O233xAUFAQTExOZtrKyslBUVAQejychCFdZWYmbN29CX1+/OaujEA8ePEBYWBiSk5ORnZ2NiooKfPLJJ+jSpQv09PQwYcIECXVwecjPz8fly5dhZWWF//znP40er+zzSEQ4f/48Hj9+jG+++Qb9+/f/IAKK2dnZyMvLwzfffCO34F9paanYObt9+zaysrLwzTffSCha1+fYsWOwsrJSimhpZWUlSkpKpAo21tTUICcnp1Gl/gcPHkgEPwKBQGowwUZOTo6YntWXX375QURZ37x5g5KSEnz00Ufo2LGjQi8kJ06cgKWlpVzXRXPwLsHP8+fPkZSUhPz8fFRWVqJdu3b4+uuvoa2tLZfYrzIoKipiZE5KSkoQGRnJaKyNGDGiyQFzWVkZzpw5g9zcXPTp0wcDBgyQ6wWHo5n5gL1fHHIwdOhQiXWy/vzzT/r++++Z/4OCgmjkyJGsNgoKCsjFxYUZUujXr5/EpOHGskuKi4spISGB+f/Vq1e0a9cu8vHxoT179jBd9e9CUFCQxNyLhsTHx5OmpiY5ODjQpk2baP/+/XTkyBHav38/bdq0iRwcHEgkEklMjm9KPUJDQ2XWQ1nncdasWaSrq0uurq5UUFBAEyZMIB6PR0ZGRszk4fz8fJn1UJTw8HByd3envXv3UmVlJc2aNYupl0AgoHXr1sksn5+fT5MnT2bmrBUVFZGzszPxeDxm+MbFxYV1CI7H49HQoUPp0aNH71yH8vJyWrRoETP0OGrUKIlswsZ+D2ncuXOHTp8+LXdm5bFjx+j777+XSDEXCATk7OxMSUlJMsvfvHlTbM7PtWvXyN3dnaytrWnatGmNlq+j/jX05s0bCgsLI19fX9q7d6/MoVBZyDO0rSwb5eXllJCQQGfOnJHa/t++fSsxR60+VVVVtGjRIqYN8ng8EggETIZfnfRKc5Kbm0ujRo0iPp9PQ4YMoadPn5KNjQ3p6OiQvb09GRgYUP/+/enZs2esNoqLi2n69OkkEonI09OTsrOzmUQVIyMjJpP4XX9TDuXB9UCpONra2oiKihLrWamuroampiYuXbqEL774As+ePYONjQ1u3Lgh1caSJUuQlpaG5cuXQ01NDcHBwbh8+TJWrFgBe3t7ALUCcWZmZsjIyJAorwwV8OfPn7PW0draGsHBwUwvgbTeAjs7OxgZGWHevHmsdtatW4fExERERkZK3a9oPRQ9jwCwbNkyXL9+HU5OTjh9+jTevHmDmpoabN26FV9//TVyc3Mxc+ZM9OrVC6tWrZJqIzo6mvUcNESaanpISAi2bdsGExMTJCUlQSgU4s6dO1i4cCF4PB5u3rwJPz8/uLm5sa5cP2fOHGRkZGDBggUwMzPDsmXLkJKSgrVr16JPnz64e/culixZApFIhBUrVkiU5/P50NPTw61bt+Dh4YGJEyc2eShu3bp1iImJgZeXF9TU1LBnzx7cv38fgYGB6NevHwDZv0dlZSX8/f0RFxcHLS0tLF26FEuXLmXOr5qaGgYMGIBNmzahTZs2Un1QtIf49OnT8Pb2homJCUJCQnDhwgV4eHjA3NwcvXv3xl9//YX4+HgEBgbCwsJCqg95eXmYPn06bt26hW+//RZBQUGYNm0anj9/jp49e+Lp06f45JNPcODAAanXFp/PZ+3NoAZDWWwCkgsXLpS6HQB+++03WFpaMr0/a9askTgmOzsbkydPRmZmJoDa9fPmzJnDXJ9A4yKWW7ZswalTp7By5UpoaGggKysLq1atgoWFBUaNGoXY2FisWrUKM2bMwLhx4yTKyxLibAibyK23tzeeP3+OKVOmIDo6Grdu3cKXX36J7du349NPP0VpaSm8vb3RunVr+Pv7S7WxfPlypKSkYPTo0YiOjkZhYSE6deqEwMBAdO7cGU+ePMHMmTPRt29fqdcWx3vkAwdwHI1gb29PK1euFNt2/PhxEolEjAjh0aNHycrKitVGv379JCZZr127ljQ0NCg2NpaIZL+pOzs7k6enJzOhcfLkyTRt2jR6+/YtEcmXYVPXKyHtU7ev7q80hEJho9o4srSslFEPRc8jUW2KeUpKChH9nzDq5cuXxY65ceMGa9o7EdGwYcPEzh3bh82PgQMHMqn2V69eJT6fT6dPnxY7JiYmRqaMgYGBAaWlpTH/W1hYSExSrtOzkQafz6cXL14wQpj9+vWjPXv2NOmt2sLCQuzc1fWkaWlpMb+TrN/Dz8+PTExMaM2aNTR48GBycXGh/v37U3x8PJWWllJqaioNHjxYQnS2Por2ECtDi8rLy4scHBzo3Llz5OnpSQMGDCBHR0emR6qkpITc3d1pxowZUstv27aNBAIBubi4iGkMRUZGkqamJgUHBzeqO2RjY0N8Pp9sbGzI2dlZ7KOurk6jRo1i/pfGjBkzyNnZmbKzsyknJ4d8fX2Jz+dTQEAAc0xj11b//v2Za6t+GT09PWYS99mzZ1kzfd3c3Jgs34ZaVPU/srSoDA0NGQmJ4uJiUldXl+hBvHPnDpmYmLDaMDY2phs3bhBRbZIOj8eTsJGUlESGhoasNjjeD1wApeJcvnyZ+Hw+OTg40Lp168jLy4sEAgGTrbZixQoSCAQUGRnJakNXV1dq8LFgwQLS1NSkpKQkmTcnZaiAHzt2jPT09MjFxYXi4+MpMTGREhMTKSEhgYRCIR0/fpzZJo0hQ4ZIzQKsT2hoqMxAUtF6KHoeiYj09PQYG+Xl5aSurk63bt0SO+b+/fusejlEtUMZrq6uZG9vzzwYmoJQKGR0vWpqakggEEjoBj1+/Fjm76mnpydWZuDAgU06l/Wzz169ekVr164lXV1dEgqF5OnpSQcOHKDU1FSZQbNIJBLTPyKqHcZxc3MjfX19unfvnszfo37Q9/TpU+LxeEwgXEd8fLzMB2bDNlXng7q6OpNRmJWVxXoelKFFpYyHdlpaGllZWZGnp6eYCKm8GXB1Wa2mpqZ04cIFsX3y2JAmQBoeHk48Ho/Cw8OJqPEAqmGbJKrNiFNXV2fkSuqy+qRRU1ND8+fPp4EDB8olhyINIyMjRiOvuLiY+Hw+EwzVkZGRIVPfTFtbW2xo28rKSmJoWpamFsf74/0u1c7RZPr164d9+/bhs88+w6VLl1BQUICVK1cymUE9e/ZEcHAw7OzsWG2oq6vj4MGDEttXrFgBHR0dTJ06FdevX2ct3759e2RnZzP/d+7cGW/evBE7pqCgQOaE2WHDhiE6OhoVFRXYvHkzunXrBgMDAxgaGqJFixYQiUQwMDBgzRSaPn061qxZA29vb/z666+Ii4tDYmIi4uLiEBkZifnz58PPzw8//fRTs9VD0fMIALq6uti0aRPu3buHdevWoU2bNti/f79Y9teePXugoaHBaqN169bYvHkz8vPzERoaKvP7pNGjRw9cvHgRQO0w1dmzZ9G9e3exY44ePYrevXuz2ujXrx98fX2Rn58PoHaoMCQkBNXV1QCAiooK+Pv7w9DQsFF/PvvsM8yfPx+XLl2Cj48PiAgBAQEYO3aszMzOXr164fTp02LbWrZsCX9/f3Tr1g2TJk3CgwcPWMsXFBTg66+/BlA7bNyyZUuJTK/u3bujoKBApg8HDhwQ2xYTE4PWrVszCwQnJSWhc+fOUst/9dVXuHr1KvO/urq6xHBjenq6zAxFNTU1sWE2IpLIFFVTU0NNTQ2rDaFQiOjoaLRv3x5Dhw4V80keWrVqhSVLlmDNmjVYunQpli9fjvLycrnLV1dXS0zYHzduHKZOnYo1a9bg1KlTjdrQ1tbGmjVrUFhYCKD2PGzcuBGff/45Pv/8c9TU1GDv3r3g8XhSy6upqcHX1xetW7fGtm3b5Pa9PiYmJvD19cXFixexePFidOjQAcHBwSgrKwNQe10EBATIzOITCoUICQlhfq/Y2FgIBAJm/5s3b+Dn58e6binHe+TDxm8c74OUlBQSiUQ0ePBgsWEXotrJpo6OjjLXVlKWCjhR7Vve1q1bSV9fn1keRt633GvXrpGrqyuJRCKxoSqRSERubm6sOjfKqoei55GotjfBysqKeDweaWho0P79+2nZsmU0aNAg8vb2JhsbGxIKhRL2pXHu3DlW8VNZnDx5kjQ0NMjf319iX1paGjk4OMjUDSIiysvLI2tra9LW1iYPDw9avXo16evr04ABA2jixIlkbGxMpqamEj1Edcijf5SdnS3zPJw7d440NDTI1dVVQhk/Ly+PBg8eTJqamjKHpn18fKikpIRCQkKob9++EsN1/v7+5ODgwOqDoj3EytCi8vb2JkdHR7pw4QLNmDGDjIyMyMPDg9F5Ky8vJw8PD5o2bRqrjfrExsaSkZER+fr6vpMG06tXr2jq1KlkZWVFaWlpck0inzJlCs2aNUvq2pKzZ8+mvn370u7du2VeW48ePSITExPS1tamkSNHkrm5OQmFQqZHbNy4cWRgYCDRI9SQ1NRUVuHTxnj16hWTTKGjo0Nnzpwhf39/MjAwIAcHBzI0NCQjIyO6f/8+q43bt28zOl4NuXjxIgmFQurfv79CCRgcyoGbRP43ICkpCZGRkSgsLIS5uTlGjx4tluZeWFgIT09PhIeHs9p4+PAhjh07huHDh0uk+ldWVmLHjh04ceIEYmNjJcpWVVVh+fLliIqKwqefforu3bvj8ePHKCkpQcuWLVFdXQ1jY2MEBgbKne6cnp6OuXPnomfPnrh27RpOnDght84LEaGoqAhv375F69atpaavS0MZ9VDkPNb3IzMzEx07dkSXLl1QWVmJ0NBQpKamokuXLnBycsJ3330nV53elbi4OLx8+RK2trZi21NSUhAWFoaJEyc2qnVTUVGBEydO4OrVq3j48KGYBIC+vj7GjBmDjh07Si07btw4bN26lUn1fleSk5MREREBNzc3CdmEoqIirF69GqdOnUJaWppE2T/++ANubm5Mj8XUqVNx//59FBcXQ1NTE3fv3kVcXBx27NgBc3NzVh9SUlKwe/duRs9q2LBhzOT9/fv349tvv4WxsTFreUW1qPLz8zFz5kwkJSWhbdu2WLt2Le7cuYP9+/ejZ8+eePToEdTU1Bhf5CE3NxcLFizAtWvXcPbs2XfSYDp06BD8/PxQWlqK2NhYmTYyMzPh4uKCt2/fYtu2bWI90VVVVZg7dy5OnToFNTU11knkQO1vHhUVhSdPnqBz58748ccf0aNHDwC1v9O3337L2iaVSXFxMVq3bs0kRsTExCAlJQVdunSBra0tvvjiC5nlX716hWfPnkEoFIptf/jwIeLj42FjYyO31AhH88EFUCrO+fPnMX36dBgYGKBFixZISEiAlpYWduzYwVxAjWWnKIusrCzWh6Wurm6T7ZWVlWHFihWIiopq9Ca9bds2jBo1qtEbjzwoux4cqk1NTQ2rFlJBQQGSk5PRqVMnaGtr482bN1i/fj0TzLq4uMDMzOy9+Pnw4UOJNtkULSpFH9rSeP78Obp27frOWlIPHjxATEwMxo8f32iwXFBQgLNnz8LU1FRqtmBUVBROnjyJXbt2vZMvH5rCwkIu6PmHwQVQKo6dnR0GDhzIvIGmp6dj+vTp+PzzzxEeHo527drJFUAVFBTg8OHDUkUo9fX1MXLkSLl7cpSNPDcWPp+Pzp07Y+PGjU1WXVeWD0BtyviNGzcgEonQpUsXXLp0CaGhocjNzUXv3r3h4eEBdXX1d/Jh6NChCA4OblStOTAwEG5ubmJztaKiorBr1y7k5OSgT58+mDFjRqPCqopSXl6Oe/fuoXfv3mjTpg3u3LmDffv2MWJ/EyZMkFkXRcvLYsqUKVi5ciXr3CNlER0djSFDhohJMCQkJGD37t3Mb+Hh4cEq71FHRUUF0tPTJcQf30UY9l149OgRdu7ciaSkJDEx0K+//hpmZmYYN27c3+Lh31xiuzo6Ojh27FijPXENleWJCIGBgQgNDUVZWRk+//xzzJw5EyNHjnzneujr62P8+PHvrW1wsMMFUCqOtrY2jh8/LnbhPnjwAE5OTuDxeAgODkZhYaHMAOrOnTuYOHEiPvnkE+jp6aFz585o3bo1ysvLkZeXh+TkZJSXl2P37t0y1aMVQdEbC5/Ph7W1NWJiYjBq1CjMnDkTnTp1UsgHoFY7Rl4f0tLS4OLigrKyMrRr1w6LFy/G4sWLYWxsDB6Ph/T0dKSnpyMsLIx1+EuW1syOHTswduxY5kHFpjWjrq6OK1euMPU/fvw4Fi5ciFGjRqFPnz64desWTpw4gYCAAFhaWsp1bprKgwcP4OLigry8PHz55ZdYuXIlPDw88L///Q+9e/fGn3/+iaKiIhw4cEBq8KBoeUC2Htby5csxc+ZMRr1amh6WMoKfhr9FXFwcpkyZAjMzM+a3SE9PR2hoKOuk3+3btyMoKIhZSqX+MF63bt0we/ZsWFtbs/ogC3ke/H/88QecnZ2hra3NaFldvHgREydORGVlJX7//XeUlpZi//79zKR7ZaOhoQEPDw/WNi8P165dg7u7OzQ0NGBkZCRxn0tISMDdu3cRFBQkNVlFUS0rQLI9hIWFwd/fH9OmTcN3332H9PR07N69G4sXL2a9zyhaD473BxdAqTjff/89fHx8JIYRUlNTMXHiRFhaWmLBggUYMGAAawDl5OSE7t27Y/Xq1VKXCKmursbChQuRnZ2NvXv3Nks9FL2x1JVPS0vD6tWr8erVK4waNQpjx46V+01MUR+cnZ3x3XffwdvbG0FBQdi1axcmTZoEb29v5pjVq1fj9u3bEplZdfTr1w8vX77E559/jo8//lhsX3Z2Njp37oyWLVtCTU0N586dk2qDz+fj6tWrTD3s7e0xaNAgTJkyhTlm586dOH36NKKioiTKjxs3Tu5lINjm1bm7u6N169bw8PDAnj17cOrUKVhbW2PVqlVQU1NDVVUV5s+fj8LCQoSEhCi9PFD7cvH27VsAgKzbGNu8GWUEPw1/C0dHR2hpaWH+/PnMMWvWrMGtW7ektom9e/di586dmDdvHiP+6O/vj/Hjx0NTUxOnT59GUFAQ1qxZwxpEKfrgd3FxgZ6enljwcvr0aezbtw/79u1DdXU15s6di7KyMmzfvl3q9ygqQsnn89G6dWvo6urCx8fnneZcKSq2O3ToUGRmZqJ3794SvfEpKSno27cvM5zKdp9s2B6GDRsGR0dHjBkzhjkmIiICYWFhiImJaZZ6cLxH3v+8dY6m4OfnRxYWFnTixAmJ5U5iY2NJIBCQg4ODzOwUoVBImZmZMr8nMzOTVR9FXV2dVQSz4YeNhllXQ4cOpYMHD4odc+jQIfrxxx8bLV9eXk579+5lVka3tbWl9evX02+//UZxcXHN5oOWlhaTTVRcXEw8Hk9Cd+bBgwckFApZfXj16hVNmzaNrK2tJVamlzfjqWE9TE1NJbLQHj16xOrHjh07SF1dnYYMGUILFiyQ+WFDJBIxmUR1gqANdXzu3r3L2qYULU9Ue67t7OzIxcWFcnJyJOw3di4bnsexY8dKZOGtXr2axo4dK7cNExMTiXrIEni1srKSEFJ98uQJmZqaUnV1NRERHTlyhGxsbFh9UFTEUiQSSWR0VVdXk4aGBrOkyoMHD0hPT4/VB0VFKPl8PqWkpJC9vT1paWnR2rVrGe0meVFUbFdRLSsi6dfmX3/9JXZMY7peyhAN5ng/KH9ZeQ6lMn36dLx+/RoLFiyQWA7CysoKAQEBYm+70vjiiy+QkZEhcyji1q1brHOg9u7dCw8PD3Tv3h3Ozs7vVI+G5OfnS0zYNjExYV2+pH6PSatWreDs7AwnJyekpKTg999/x7Vr1xAWFoaqqiq5J9M31Yd27dohNzcXX331Fdq1awcvLy+JxUmfPHkic2jxs88+w7Zt2xAREQEXFxe4ubmJ9RzJS35+PvM9fD4fOTk5Yvo2jx8/ZvXD3d0d7dq1w4YNGxAUFCShASUPbdq0YbRtPvvsM4wePVpisnNRURHat2/fLOWBWg20iIgIBAQEYPjw4Vi2bJlM3ajGePz4MZYsWSK2zcHBAREREaxlGvbk9ezZU0JbLD8/n7UeL168kJgw3aVLF+Tn5+PVq1f44osvYGRkhJUrV7L6EBkZiXXr1uH06dOYPXs2BgwYwOzT1tbGhg0bZPbodO7cGYmJiUy2GlC7KDQAJhv11atXMiezBwcHY+HChUhNTUV0dHSTFx8mIvTo0QO//vorDh8+jMDAQBw4cADff/89hg0bBkNDw0YXZu7evTvi4+Nl9khfvnyZVVOrTsuqf//+WLRoESwtLbFo0aImLSgN1A6Jamtro3379tDR0UFmZib69OnD7L99+7bMuX2K1oPjPfKhIzgO+SgrK2OWHGlIYWEh/fbbb6xl9+3bR1paWrRhwwa6evUq3b9/n54+fUr379+na9eukb+/P4lEItq9ezerjeTkZBIIBHT9+vV38p/H49GlS5eYZVQ8PT0pJiZG7JiYmBjWZRbk0Q2qrq6mV69eNZsPvr6+NGjQILHFiOsoLCykiIgIMjExkViKg426HhQnJydGIVmet9w6bSM9PT1ycHAgOzs7srS0ZHR/jh49Subm5jKXICEicnd3J09PT7l8bcicOXPIwcGB7t27J7Gvurqarly5QoMGDWLV1FK0fEOuX79OFhYWNHv2bCoqKpLrXPL5fLE25eTkJKGEn5SURGZmZqw2eDweaWlp0fDhw8nb25smTJhAtra2jJ7R9evXycbGhlWvy9nZmby8vKiiooLZFhISIrYEzq5du2jo0KEy60JUq0llZmZGy5YtY+4V8pyH3bt3k1AopI0bN9LFixfp4MGDZG5uzugQHTlyhPr3799oeyovLycbGxtav359o742pOH1XVFRQUePHqUJEyaQhoYGaWhokJWVlUxNrpiYGBIIBOTl5UWHDx+my5cvU0JCAl2+fJmOHDlC8+bNI4FAQMeOHWvUn3fRsiKqVeSv67E3NTWlgQMHkqGhIbNE0ZYtW0hHR4fRCWvuenA0L1wA9TehoqKC0tLSKDY2lo4fP05nz56l9PR0uZfyiIqKokGDBkmsScfj8Wjw4MGMqKUsVq9eTfb29u/kv6I3lgULFii8+riiPpSVlZG3t7fUh+Hp06dJQ0ODfHx8xB6GjVFZWUl+fn6kp6dHAoFArpt0dXU1ZWZmUkxMDG3atImmTZtGP/zwA/PQNjAwoBkzZjS6HEVubi6dP39ebl/r8+rVK3JwcKDZs2dL7Dt58iTxeDyaMmUK62+maHlpFBYWkpeXF5mbm8t1LhUNfoiIcnJy6NKlSxQcHExz5syhYcOGUd++fZlgVkdHh0aNGsUa2KelpZGWlhZZWFjQzJkzafTo0cTn8+nXX38loto14jQ1NSWGlNh4lwd/TU0N7dixg/T19YnH45GmpiYtXLiQOfeLFi0if39/qqqqavT731WEsmEwW5+CggI6f/48bd26lX7++WeZdhQV223IwYMHSVdXl9TV1eUWFC0tLaW0tDQ6fPgwrVy5kiZMmMDcp3/44Qdau3YtMzz7vurB0Txwk8hVHCLC1q1bERYWhuLiYon97dq1g6urKzw8POSyl5+fj5ycHJSVlaFNmzbo1q0bk6nUGNXV1SgtLZU5rCKLsrIy3Lt3D3fv3sVff/2Fe/fuISQkBB999BEGDhyIH374AXPnzn1nzZn35YM0XaHS0lLU1NSIZfg1hevXr+Po0aOYO3eu3L8HG8XFxe/8GzWVoqIiCX2f169f4+XLl2LDFs1VXhrR0dGIioqCn5+fTBmD3Nxcph3U/X3w4AGuX7+OTz75BLq6uujVqxd27NjRpN+kurqaSdbIzMxEr169ZE7af/r0KcLDwxnxx+HDhzNSHWfOnEGvXr0alUFoSFNELOuTn5+PDh06NOs1KI2Gk68Vhd5RbFcaTdGyUjbKrAeH8uECKBXH398fBw4cwOzZs6WmtF67dg0bN27EuHHj3ikF+MSJE7C0tGzynIUPgSpq1RARzp8/j8ePH6Nnz54wNzeXmukor41vvvkG/fv3V8iGvH4oQ3tIURuq4EN9mhr8NIcPyuLhw4c4efKk3A/+vLw8pKSkICcnB+Xl5YzukK6urlIEbGXx7NkzdOvWTemBW1lZGc6cOcNoiw0YMEDuDNT67Ny5E2PGjGn0PD5//hzdunUT+478/HxERUUhJycH3333HUaMGCEmncFG/ZeLkpISREZGMtf3iBEj3vlljUN5cAGUimNmZgYfHx98//33rMf8/vvvWLFiBS5dutRk+/IKxAG1Acw333wDoPaBceHCBWRlZaFHjx7o37+/RFq+Mm0oQ6tGUTXzN2/eYOnSpYiLi4OWlhY2btyImTNnIiEhAR07dkRBQQHU1dWxa9cu1uUiVMXG9u3bsXPnTmYid4sWLZqsPaSoDVXwAVA8+Kmv4/QuPijrxYAtANLR0ZHZE/f27Vv4+PggOjoaampq6NChA/OSVlBQADU1NYwYMQLLly+X+eBXVMSyoqICN27cQFFREfT09CTabnl5OU6dOiVV0wuovS4WLlyIK1euoF+/fli0aBHGjRuHrKwsdOjQAa9fv4ZIJEJISIjU4OP58+esvllbWyM4OJiZ8C9NKR2QlMZ49OgRHB0dUVNTg+7du+PRo0fo1KkTwsPDWSeB5+XlYfr06bh16xa+/fZbBAUFYdq0aXj+/Dl69uyJp0+f4pNPPsGBAwdY/eB4T7z3QUOOJqGjo9OoBEFGRobMlNaG854azoFqTIbg6dOnZGNjQxYWFkRUO+/DysqK1NXVydjYmPh8PllbW1Nubi6rD4ramDBhAm3ZskVs26lTp8jJyYmIiKqqqsjLy4umTp0q8zz069ePkpKSWI+RxdKlS2nQoEEUHh5Ojo6ONGzYMLKxsWEWzM3JySEHBweZc2ZUwUZ4eDiZmZnR8ePHKTMzky5evEi2trZ09OhRyszMpMDAQNLU1KQTJ06w+qCoDVXwgYho27ZtpKWlxcwxqS/ZUScfIgtFfbh9+zaJRCKaOHEirVu3jmbMmEFCoZA2bdpE69atIysrKzIzM2NdlJmodm7eggULiM/nM9fTgAEDyNjYmNTV1UlDQ4MWLVokdaFeIiIfHx+ysLCgK1euSMzfq6iooLi4OLK0tCRfX19WH+Lj40lTU5McHBxo06ZNtH//fjpy5Ajt37+fNm3aRA4ODiQSiSQm6dfx/Plzsra2Fpvrs2/fPrFjXrx4IVMqZdmyZWRtbU1hYWFka2tLlpaWNGrUKOae8vjxYxoxYgQtWbJEanl57pN1f9loOBnew8OD3NzcqKysjIhq5U9cXFxo1qxZrDa8vLzIwcGBzp07R56enjRgwABydHRkZGxKSkrI3d2dZsyYwWqD4/3ABVAqzpQpU2jq1Kmsk2nfvHlDkydPJldXV1Yb27ZtI4FAQC4uLhQVFcV8IiMjSVNTk4KDg5lt0nB3dycnJycmkJs1axaNGTOGuVHk5uaSs7OzzAtaURvK0Krh8Xjk7e1NfD6fli5d2mhWX0NMTU0pJSWFiP5Pu6ihhs+NGzfI3NxcpW0oQ3tIURuq4IMyAjBFfVDGi4GiAZCBgYHUzNL6XLt2jYyNjVn329ra0i+//CLTxi+//EJ2dnZS982YMYOcnZ0pOzubcnJyyNfXl/h8PgUEBDDHNBZAGRsb040bN4ioVteOx+NJvCwlJSWRoaGh1PLHjh0jPT09cnFxofj4eEpMTKTExERKSEggoVBIx48fZ7ax0TCAMjMzo+TkZLFj0tPTZd6nDA0NGX254uJiUldXl6jHnTt3yMTEhNUGx/uBC6BUnKysLLKysiKhUEhjxowhLy8vmj9/Pnl5eZGTkxOJRCKysrJqNEMkLS2NrKysyNPTkwoKCpjt8qQ56+rq0t27d5n/+/XrJ3FT+OOPP0hXV7fZbFhZWVFERIREnTQ0NJiU7aSkJDI1NWX1oS7T59y5c/T999+TSCSiVatWNSpaV4eenh5zbHl5Oamrq9OtW7fEjrl//z7p6+urtA1tbW2JXs06O3l5eURU2+5kiVgqakMVfFBGEKeoD8p4MVA0ANLX15cQhG1Ieno6aWtrs+5XVPzR2NhYQoA0PDyceDwehYeHE1HjAZS2trbYubSysqLbt2+LHXPv3j2Z11ZWVhY5OjrS6NGjxe6L8kqM8Pl8sYxLGxsbiXo15oORkREjjFtcXEx8Pp8JDOvIyMggIyOjRv3haF7eb6oFR5P53//+h5MnT2LVqlXg8/koKytDbm4uSktL0adPH6xatQq//fZbo3OYhEIhoqOj0b59ewwdOhRXr16V24ePP/4YpaWlzP/SJlISkczJmYraGDNmDFatWoVNmzbh0qVLOHToEDw9PTF48GC0bt0akZGRmDNnDoYOHcrqA/3/eSmWlpaIiYnB7NmzcfHiRQwZMgR2dnbw8/PDiRMncOXKFanldXV1sWnTJty7dw/r1q1DmzZtsH//frFlRPbs2QMNDQ1WH1TBhkAgwNatW1FZWcls27t3L9q3b8/MD2ssc0tRG6rgQ2MilgBgZGSErKysZvOhTsSyPk0VsSSiRudItW3blpmj1ZB+/fph2bJlePjwodT9T58+hY+PD8zNzVnt14k/ykKW+GN1dbVEHceNG4epU6dizZo1OHXqlEzbQO09LiQkBDU1NQBqz7tAIGD2v3nzBn5+fqzL8gC199t9+/ahf//+sLe3x6+//tro99aHiODl5YXVq1cjIiICPB4Pu3fvZva/ffsWAQEB0NTUZLVhYmICX19fXLx4EYsXL0aHDh0QHBzMzPOrqKhAQEAA63qbHO+RDxe7cXwoYmNjycjIiHx9feV6s1q2bBn9+OOPjOjhgQMHyNnZmRlWfPHiBTk6OkrV9FGWDWVo1UjTmqmpqaGkpCRas2YN2dnZUd++fVnfcp88eUJWVlbE4/FIQ0OD9u/fT8uWLaNBgwaRt7c32djYkFAopLS0NFYfVMGGMrSHFLWhCj4oQ8RSUR+UIWLp7e1No0aNYu0BevLkCdnb29PMmTOl7n/9+jU5OzsTn8+nfv360ejRo8nZ2ZlGjx5NAwYMID6fT2PHjpUpUquo+OOUKVNo1qxZUudpzZ49m/r27Uu7d++W2QN1+/ZtMjAwYM5dfS5evEhCoZD69+8v0ePHRl2vvbu7OwmFQrl6oI4fP07r16+nyZMnk7m5OTNn6vXr10RU21toYGAgsYxTfV69ekXOzs7E4/FIR0eHzpw5Q/7+/mRgYEAODg5kaGhIRkZGzFJIHB8OLgvvb0BzpO/n5uZiwYIFuHbtGs6ePSvzTb+kpATTp09HYmIivvvuO3z99ddISEhARUUFunTpgmfPnqFnz57Ys2cPq46LMmzU8a5aNfJozdTU1KCgoIBV96eqqgqZmZno2LEjunTpgsrKSoSGhiI1NRVdunSBk5MTvvvuO5l+qIINZWgPKWrjQ/uQnp6O8ePH47PPPoNQKER2djbS09OxYsUKjBw5EjNnzsSFCxcQEBAgtjyKMn0gIuzcuRO7du1CUVERWrVqBRsbGyxatAjt2rXD4sWL0aVLF/z000+sshQFBQXw9PREcnIyvvjiC3Tr1g2tWrVCRUUF8vLykJOTA21tbQQGBsrUs7px4waSk5ORm5uLt2/fok2bNujatSv09fWhpaXFWq6OhIQEBAcHIzU1lektAcBoak2cOBGmpqZSy2ZmZsLFxQVv377Ftm3bYGBgwOyrqqrC3LlzcerUKdaFoet49eoVnj17BqFQKLb94cOHiI+Ph42NTZPulaWlpVi5ciWioqIavU9Ko7CwEPfu3WPaw+HDh2Fubi5zKZc6iouL0bp1aybzMSYmBikpKejSpQtsbW2bXVqCo3G4AErFUUb6viyeP3+Orl27yhWMJCYm4urVq3j48CHevHmDjz76iLnBDh48WC5tE0VtKKJVs3DhQixevJjTT+FgaA4Ry3dFURFLRQMgZUHvKP5YUFCAs2fPwsTEBP/73/8k9kdFReHkyZPYtWuXkj3m4Hg3uABKxXFxcYGenp6YSObp06exb98+7Nu3D9XV1Zg7dy7Kysqwfft2VjuqKELZFJSlVaMoyjiPqmCD80F5KMOHDyliCSiu4aQqyKqHvr4+xo8fL7MeyjgPivqgLBsczQ8XQKk42traiI6OFlspvaamBpqamrhy5Qo6duyIhw8fYvTo0UhKSpJqQxm9WB/6YVc3qXLFihUwMDAQE9ysrKxEYmIili9fjgEDBmDp0qXN4oMyzqMq2OB8+D8UbdeK+qAKIpbXrl2Du7s7NDQ0pK52kJCQgLt37yIoKEhsaE3ZNFYHFxcXRoS3OeqhjPOgKjY43g9cAKXiDBo0CG5ubhg9ejSzLT09HWPHjkVqaipat26N5ORkzJo1izV7TNFeLFV42BkaGiIgIACGhoas5yohIQHe3t6s2UCK+qCM3kBVsMH5UIsy2rWiPijjxUDRB66dnR2MjIwwb948qfYBYN26dUhMTERkZKTU/YGBgaxlGyJtySllBA2K1kMZ50FVbHC8Jz7M3HUOeVFGlo6iWjPKEPtT1IYytGoU9UEZmj2qYIPzoRZltGtFfVAFEUtFNZyIiNzc3IjP55OBgQFZWFiwfiwtLZulDsqohzLOg6rY4Hg/cDpQKo6Liws8PDxw8OBBuLu7Y/Xq1TA1NYWPjw8AIDU1FXZ2dpgzZw6rDUW1ZtLS0iT0laysrHDjxg28fv0aLVu2ZLKA2FDUhjK0ahT1QRmaPapgg/OhFmW0a0V9IAU1nADg/v37GDVqlEwbI0eOxP3796XuU1TDCQCCg4MxfPhw/Pe//8WJEydw/vx5qZ9z5841Sx2UUQ9lnAdVscHxfvjoQzvAIRs1NTW4u7vD3d1dapbOqlWrGrVRJ0L57Nkz6OjoIDs7G9u3bxcTodyyZQurCGXdQ6L+PKx3fdi9q42lS5fC09MTQ4YMkZmqvWzZsmbzQdHzqCo2OB9qUUa7VtSHuheDX375ReocpaaIWMqaVCzrgTt9+nTMnTsXKSkpMDY2RteuXcWurevXr+PkyZNYvXo1q301NTX4+vrC3t4e27Ztk/lC1xx1UEY9lHEeVMUGx/uBmwP1N0GRLB1SUGsmNDQUmzdvhouLi9hDQk9PDxs2bGAeEj/++CPmz58v1Qdl2ABqU7XrzkNTU7UV9UHR86gqNjgfalFGm1TUB2VoOJ06dQpz586FlZVVow/cYcOGSbWhiIZTfW7cuIFLly5h1qxZjR6r7Dooox7KOA+qYoOj+eECKBVH2en776I1owoPO2WgTB8U1exRFRv/Zh+U3SYVqYciLwaA8h649I4aTspAmUGDovVQxnlQFRsczQcXQKk4ykrfB5SjNaMKD1w2ysvLcerUKYwYMaJZfVDGeVQFG5wP/4eibfJD6zjV8U944P4T6sDx74ALoFQcZaTvK6sXS1Uedmy8fPkSZmZmyMjIaBYflHEeVcEG54M4irTJ9yHw2pQXA1Xmn1IPDo46uEnkKo4ysnTqNENCQkJk9mL98ssvUnuxVOlhJ4vPPvuMNctHGT4oeh5VxQbnQy3KaJPKqEdjFBcXY8GCBc0WeCiq4SQvql4PZZwHVbHB8X7geqBUnNmzZ+Pp06cys3S8vLzQvXt3bN68WaoNRXuxlDGMqAwbVVVVOHPmDJKSkqQub2BlZSVzroqiPiijN1AVbHA+1KKMNqmMejRGTU0NsrOzpa4PByj+wJ00aRKuXr2KTz/9FG3btmUtq6amJvMFpTFk1UMZQYOi9VDGeVAVGxzvB64HSsVRRvq+or1YJ0+eZH1IfPzxxzAzM8OqVavg7e3N+qBR1EZWVhbc3NyQm5sLDQ0NdO7cGf/9739RXl6OjIwMREVFYcuWLQgJCcGXX37ZLD4oozdQFWxwPtSijHatjHrI82LAFjwBwM2bN+V+4EoLPoKDg7Fw4UKkpqYiOjqakXBoKorUQ9E6KKMeyjgPqmKD4/3ACWmqOB06dMDevXtx4MABjB8/HpqamujRowc0NTXh5OSEQ4cO4cCBA6wpzoDiIpSq8LDz9fVF9+7dERcXhwMHDmDz5s345ZdfsHnzZhw4cACXL19Gt27d4Ovr22w+KEPMUxVscD7Uoox2ragPWVlZsLa2xqJFi3D37l20adMGX3zxBT7++GNkZGRg4cKFGDp0KJ4/f87qg6IilnUaTq1bt8a2bdtknA12FK2HonVQRj2UcR5UxQbH+4EbwvsXoKjWjDKGERW1oa2tjUOHDoHH47HWMyMjA05OTkhJSWkWH5Sh2aMKNjgfalFGu1bUhylTpqC6uhqbN29G+/btJfYXFRXBy8sLH3/8MXbs2CHVBwCoqKiAvb09+vfv32QRyzreVcMJUE49lFEHQLF6KKO8KtngaF64AOofgLzZLe+qNaMKDztzc3OsXLlSZo/E+fPnsXTpUly9erXZ6qHIeVQ1G/92H5TVHhTxQRkvBvV9+FAPXGXVgwsaOP5OcAHUPwB50/cV5UM+7AICAnDo0CHMnDkTRkZGYg+73NxcXL9+HX5+fhg5ciRmz57d7PXg+OfwIduDMl4MVIF/Sj04OJqEkhcn5vgAVFdXU1ZWlkI23r59S0ePHlWOQ81ATU0NbdmyhXR0dIjP50t8dHV1aePGjVRdXf1B/VTGeVQFG5wPykOWD/7+/mRsbEyHDh2iR48eUXl5ORERlZeX05MnT+jIkSNkZGREfn5+79FjSRo7j/+UejR3eVWywaE4XA/U3wBF0/flQdFeLGWI5Mljo7KyEnfu3EFubi7KysqY3gI+n//O+lFN9UEWyugNVAUbnA+1KKNdy/KBiLB161aEhoaitLRUYn/btm3h5OSEmTNnKqTc39zt+n3Uo7l/i/dRXpVscCgOF0CpONLS9+vE/vLy8vDnn3/iyy+/lJm+Lw+Nac00xj/lpqCoD4qeR1WxwflQizLapDw+NPeLwftq181Zj/f1WzRneVWywaE4XACl4igrS6e5e7H+KTeFxnxQxnlUBRucD/IhT5t8Hz3EivI+2nVz8z5+C1Vpk3+H34ODC6BUHmVktyijF+ufclNQxAdlnEdVsMH58H8o2ibfVw9xY3zodv2h66CMeqhKm1SV34OjcbgASsVRRnaLor1Y/5SbgqI+KKM3UBVscD7Uoow2qaweYkVQhXb9oeugjHqoQptUlg2O98SHmbvOIS/KyG4RiUSUkZEh83vu3LlDOjo6UvdNnjyZXF1dqaioSOr+wsJCcnV1JXd3d1b7yrChKIr6oOh5VBUbnA+1KKNNKqMeiqIK7VpRVOG3UIU2qSwbHO8HbikXFcfT0xOOjo5Yt24dBg8eDC0tLairq0NLSwtWVlZYs2YNRo8eDS8vL1Yb7du3R25urszvef78Odq0aSN1X1JSEubNmyf1bQgAPv30U8ydOxdJSUms9pVhQ1EU9UHR86gqNjgfalFGm1RGPRRFFdq1oqjCb6EKbVJZNjjeD1wApeLULZ6ZkJCAiIgIBAQE4JdffoG/vz8iIiIQHx8PLy8vmanBI0eOxIIFCxAREYHHjx+joqICQO3SCU+fPkVkZCQWL14MOzs7qeX/KTcFRX1Q9Dyqig3Oh1qU0SaVUQ9FUYV2rSiq8FuoQptUlg2O98NHH9oBDvn4+OOPIRQK36msp6cnWrRogXXr1jWq0SKNugtaHhVwNpRhQ1EU9UHR86gqNjgfalFGm1RGPRRFFdr1h66DMuqhCm1SWTY43g/cJPJ/Ee+q0UJKEMlThg1FUZYPytC6UQUb/3YflNkmm1vHSRaq1K7fFVX6LVThulCWDY7mhQugOOTmn3JTUAUfOFSHf0p7+CfU459QB45/D1wAxcHBwcHBwcHRRLhJ5BwcHBwcHBwcTYQLoDg4ODg4ODg4mggXQHFwcHBwcHBwNBEugOLg4PjXwU395ODgUBQugOLg4FAK48aNA4/HE/v07dsXAwYMgI+PDwoLC+W2tWDBAlhaWirFLx6Phy1btgCoFSNcvXo1fvvtN6XY5uDg+PfCCWlycHAoDQ0NDSxfvpz5v7KyEn/88Qc2btyIO3fu4ODBg1BTU3uvPkVERKBr164AgLy8PISFhWHNmjXv1QcODo5/HlwAxcHBoTTatWsHkUgktk1fXx8lJSUICAhAWlqaxP7m5n1/HwcHx78DbgiPg4Oj2enbty+A2vXMACAmJgZ2dnbQ1taGqakpli1bJnOI7+3bt9iwYQOsrKzQt29f6OjoYOLEibhz5w5zzIIFCzBhwgQsX74cOjo6GDJkCKqrq5khvKysLHz//fcAgIULF8LS0hIXL14Ej8fDlStXxL4vOTkZPB4PKSkpyj4VHBwc/xC4AIqDg6PZefjwIQDgq6++wrZt2+Dt7Q2RSISAgAD89NNPiI2Nxbhx4/D27Vup5efNm4fIyEhMmTIFu3fvxsKFC3Hv3j3Mnj1bbEJ4cnIysrOzsXXrVsyePRstW7Zk9nXu3BmBgYEAgGnTpiEwMBD9+vVD586dcezYMbHvi46OxjfffANdXV1lnwoODo5/CNwQHgcHh9IgIlRVVTH/FxYW4vr169i+fTu0tbXx9ddfY/v27Rg9ejSWLVvGHPfdd9/ByckJkZGRcHJyErNZUVGBkpISLFmyBEOGDAEAGBgY4M2bN1i7di1evnyJL774AgBQVVUFX19fZs5TfVq1agV1dXUAwNdffw0NDQ0AgK2tLfbu3YuSkhK0bdsWb9++xalTpzBlyhTlnhwODo5/FFwAxcHBoTSSkpIgEAjEtrVo0QImJibw9fXFzZs3UVFRARsbG7Fj9PT08L///Q/Xr1+XCKBatWqFXbt2AQByc3Px8OFDPHr0CBcuXABQG2DV0aFDB6nBkyzs7e0RFBSEs2fPYsSIETh79ixKS0sxYsSIJtnh4OD4d8EFUBwcHEpDIBDAx8cHAKCmpobWrVujW7duaNeuHQAwc4o+//xzibKff/45iouLpdqNi4vD6tWr8eDBA7Rt2xZ8Ph//+c9/AIhrOrVt27bJPvfo0QMGBgaIjo7GiBEjEB0dDRMTE3Tp0qXJtjg4OP49cHOgODg4lEbbtm2hqakJTU1N9O3bF3369GGCJwD473//CwB4+fKlRNkXL16gY8eOEtufPHmCn376Cerq6jh79ixSUlJw4MABWFhYKM1ve3t7XL9+Hffv38e1a9dgb2+vNNscHBz/TLgAioOD472hpaWFVq1a4cSJE2Lbk5OT8fz5c+jo6EiUuX37NsrLyzFlyhR8/fXXjI5UXFwcgKapitefVF6fQYMG4ZNPPsHPP/+Mtm3b4ocffpDbJgcHx78TbgiPg4PjvdGhQwdMmTIFW7duxccffwwLCwtkZWXB398fvXv3hq2trUQZgUCAjz76COvXr4erqysqKioQFRWFixcvAgBKS0vl/v727dsDAK5du4ZevXpBS0sLAPDJJ5/A2toaERERGDt2LFq1aqV4ZTk4OP7RcD1QHBwc7xVPT08sX74cCQkJmDp1KgIDAzF48GAcOHCAmddUnx49emDDhg3Izc3FtGnTmOy9vXv3Qk1NDcnJyXJ/d7t27TBx4kT8/vvvmDx5MiorK5l9AwYMAADY2dkpVkEODo5/BWrErarJwcHBgeXLlyMtLQ3R0dEf2hUODo6/AdwQHgcHx7+a8PBwPHjwAIcPH8b69es/tDscHBx/E7gAioOD419NcnIy4uLiMGHCBAl9Kg4ODg42uCE8Dg4ODg4ODo4mwk0i5+Dg4ODg4OBoIlwAxcHBwcHBwcHRRLgAioODg4ODg4OjiXABFAcHBwcHBwdHE+ECKA4ODg4ODg6OJsIFUBwcHBwcHBwcTYQLoDg4ODg4ODg4mggXQHFwcHBwcHBwNJH/B1nU8igzs+hTAAAAAElFTkSuQmCC","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# sns Hourly Heatmap\n","hour_df[\"hour\"] = hour_df.index\n","result = hour_df.pivot(index=\"hour\", columns=\"Polarity\", values=\"Volume_BTC\")\n","sns.heatmap(result, annot=True, fmt=\"g\", cmap=\"viridis\")\n","plt.title(\"Polarity x BTC Volume avg(Hr)\")\n","plt.show()\n","\n","# sns daily heatmap?"]},{"cell_type":"code","execution_count":202,"metadata":{"_uuid":"33b6d6339bf05215e76ea3455f451695101e7304","trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["hour_df[\"hour\"] = hour_df.index\n","result = hour_df.pivot(index=\"Volume_BTC\", columns=\"hour\", values=\"Tweet_vol\")\n","sns.heatmap(result, annot=True, fmt=\"g\", cmap=\"viridis\")\n","plt.title(\"BTC Vol x Tweet Vol avg(Hr)\")\n","plt.show()"]},{"cell_type":"code","execution_count":203,"metadata":{"_uuid":"21a2d551ead3bba9e8fe836ae149dbd75698c058","trusted":true},"outputs":[],"source":["# ----------------End Analysis------------------------#"]},{"cell_type":"code","execution_count":204,"metadata":{"_uuid":"7f17e26175a1a9f0d16bad12c115c15baff5599f","trusted":true},"outputs":[],"source":["# ---------------- LSTM Prep ------------------------#"]},{"cell_type":"code","execution_count":205,"metadata":{"_uuid":"12815ec88c823657a84575e0a664327909f8c9cc","trusted":true},"outputs":[],"source":["df = Final_df"]},{"cell_type":"code","execution_count":206,"metadata":{"_uuid":"2b5734e6d6ab2eb2e33e8c5531726306d31d1a6a","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","DatetimeIndex: 294 entries, 2018-07-11 20:00:00 to 2018-07-24 01:00:00\n","Data columns (total 10 columns):\n"," #   Column         Non-Null Count  Dtype  \n","---  ------         --------------  -----  \n"," 0   Polarity       294 non-null    float64\n"," 1   Sensitivity    294 non-null    float64\n"," 2   Tweet_vol      294 non-null    float64\n"," 3   Open           294 non-null    float64\n"," 4   High           294 non-null    float64\n"," 5   Low            294 non-null    float64\n"," 6   Volume_BTC     294 non-null    float64\n"," 7   Volume_Dollar  294 non-null    float64\n"," 8   Close_Price    294 non-null    float64\n"," 9   time           294 non-null    object \n","dtypes: float64(9), object(1)\n","memory usage: 33.4+ KB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":207,"metadata":{"_uuid":"76108a35d9f6d5396d7a34077f68105c7f7e2ae9","trusted":true},"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>Polarity</th>\n","      <th>Sensitivity</th>\n","      <th>Tweet_vol</th>\n","      <th>Volume_BTC</th>\n","      <th>Close_Price</th>\n","      <th>time</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-07-11 20:00:00</th>\n","      <td>0.102657</td>\n","      <td>0.216148</td>\n","      <td>4354.0</td>\n","      <td>986.73</td>\n","      <td>6350.00</td>\n","      <td>20:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 21:00:00</th>\n","      <td>0.098004</td>\n","      <td>0.218612</td>\n","      <td>4432.0</td>\n","      <td>126.46</td>\n","      <td>6356.48</td>\n","      <td>21:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 22:00:00</th>\n","      <td>0.096688</td>\n","      <td>0.231342</td>\n","      <td>3980.0</td>\n","      <td>259.10</td>\n","      <td>6361.93</td>\n","      <td>22:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 23:00:00</th>\n","      <td>0.103997</td>\n","      <td>0.217739</td>\n","      <td>3830.0</td>\n","      <td>81.54</td>\n","      <td>6368.78</td>\n","      <td>23:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-12 00:00:00</th>\n","      <td>0.094383</td>\n","      <td>0.195256</td>\n","      <td>3998.0</td>\n","      <td>124.55</td>\n","      <td>6380.00</td>\n","      <td>00:00:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                     Polarity  Sensitivity  Tweet_vol  Volume_BTC   \n","2018-07-11 20:00:00  0.102657     0.216148     4354.0      986.73  \\\n","2018-07-11 21:00:00  0.098004     0.218612     4432.0      126.46   \n","2018-07-11 22:00:00  0.096688     0.231342     3980.0      259.10   \n","2018-07-11 23:00:00  0.103997     0.217739     3830.0       81.54   \n","2018-07-12 00:00:00  0.094383     0.195256     3998.0      124.55   \n","\n","                     Close_Price      time  \n","2018-07-11 20:00:00      6350.00  20:00:00  \n","2018-07-11 21:00:00      6356.48  21:00:00  \n","2018-07-11 22:00:00      6361.93  22:00:00  \n","2018-07-11 23:00:00      6368.78  23:00:00  \n","2018-07-12 00:00:00      6380.00  00:00:00  "]},"execution_count":207,"metadata":{},"output_type":"execute_result"}],"source":["df = df.drop([\"Open\", \"High\", \"Low\", \"Volume_Dollar\"], axis=1)\n","df.head()"]},{"cell_type":"code","execution_count":208,"metadata":{"_uuid":"afdac4c3b1b4b8b8fc989aff7589d4e4c4bdb19f","trusted":true},"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>Close_Price</th>\n","      <th>Polarity</th>\n","      <th>Sensitivity</th>\n","      <th>Tweet_vol</th>\n","      <th>Volume_BTC</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-07-11 20:00:00</th>\n","      <td>6350.00</td>\n","      <td>0.102657</td>\n","      <td>0.216148</td>\n","      <td>4354.0</td>\n","      <td>986.73</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 21:00:00</th>\n","      <td>6356.48</td>\n","      <td>0.098004</td>\n","      <td>0.218612</td>\n","      <td>4432.0</td>\n","      <td>126.46</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 22:00:00</th>\n","      <td>6361.93</td>\n","      <td>0.096688</td>\n","      <td>0.231342</td>\n","      <td>3980.0</td>\n","      <td>259.10</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 23:00:00</th>\n","      <td>6368.78</td>\n","      <td>0.103997</td>\n","      <td>0.217739</td>\n","      <td>3830.0</td>\n","      <td>81.54</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-12 00:00:00</th>\n","      <td>6380.00</td>\n","      <td>0.094383</td>\n","      <td>0.195256</td>\n","      <td>3998.0</td>\n","      <td>124.55</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                     Close_Price  Polarity  Sensitivity  Tweet_vol  Volume_BTC\n","2018-07-11 20:00:00      6350.00  0.102657     0.216148     4354.0      986.73\n","2018-07-11 21:00:00      6356.48  0.098004     0.218612     4432.0      126.46\n","2018-07-11 22:00:00      6361.93  0.096688     0.231342     3980.0      259.10\n","2018-07-11 23:00:00      6368.78  0.103997     0.217739     3830.0       81.54\n","2018-07-12 00:00:00      6380.00  0.094383     0.195256     3998.0      124.55"]},"execution_count":208,"metadata":{},"output_type":"execute_result"}],"source":["df = df[[\"Close_Price\", \"Polarity\", \"Sensitivity\", \"Tweet_vol\", \"Volume_BTC\"]]\n","df.head()"]},{"cell_type":"code","execution_count":209,"metadata":{"_uuid":"74a7fd9d0a2a4936aae7db927fe185fdc17952f0","trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1100x900 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["cor = df.corr()\n","import matplotlib.pyplot as plt\n","\n","sns.set(style=\"white\")\n","f, ax = plt.subplots(figsize=(11, 9))\n","\n","cmap = sns.diverging_palette(220, 10, as_cmap=True)\n","ax = sns.heatmap(\n","    cor,\n","    cmap=cmap,\n","    vmax=1,\n","    center=0,\n","    square=True,\n","    linewidths=0.5,\n","    cbar_kws={\"shrink\": 0.7},\n",")\n","plt.show()"]},{"cell_type":"markdown","metadata":{"_uuid":"49b989963d666f6a13789dbcea23264469691fdb"},"source":["## LSTM Model"]},{"cell_type":"code","execution_count":210,"metadata":{"_uuid":"e31a80ceef1f69fbc2eeb31f1fdb5094c9e82e28","trusted":true},"outputs":[],"source":["from math import sqrt\n","from numpy import concatenate\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.preprocessing import LabelEncoder\n","from sklearn.metrics import mean_squared_error\n","from matplotlib import pyplot\n","\n","from pandas import read_csv\n","from pandas import DataFrame\n","from pandas import concat\n","from keras.models import Sequential\n","from keras.layers import Dense\n","from keras.layers import LSTM\n","\n","\n","# convert series to supervised learning\n","def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n","    n_vars = 1 if type(data) is list else data.shape[1]\n","    df = DataFrame(data)\n","    cols, names = list(), list()\n","    # input sequence (t-n, ... t-1)\n","    for i in range(n_in, 0, -1):\n","        cols.append(df.shift(i))\n","        names += [(\"var%d(t-%d)\" % (j + 1, i)) for j in range(n_vars)]\n","    # forecast sequence (t, t+1, ... t+n)\n","    for i in range(0, n_out):\n","        cols.append(df.shift(-i))\n","        if i == 0:\n","            names += [(\"var%d(t)\" % (j + 1)) for j in range(n_vars)]\n","        else:\n","            names += [(\"var%d(t+%d)\" % (j + 1, i)) for j in range(n_vars)]\n","    # put it all together\n","    agg = concat(cols, axis=1)\n","    agg.columns = names\n","    # drop rows with NaN values\n","    if dropnan:\n","        agg.dropna(inplace=True)\n","    return agg"]},{"cell_type":"code","execution_count":211,"metadata":{"_uuid":"f418eb58dc832a1516e76147c6fcca0217990ffc","trusted":true},"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>Close_Price</th>\n","      <th>Polarity</th>\n","      <th>Sensitivity</th>\n","      <th>Tweet_vol</th>\n","      <th>Volume_BTC</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-07-11 20:00:00</th>\n","      <td>6350.00</td>\n","      <td>0.102657</td>\n","      <td>0.216148</td>\n","      <td>4354.0</td>\n","      <td>986.73</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 21:00:00</th>\n","      <td>6356.48</td>\n","      <td>0.098004</td>\n","      <td>0.218612</td>\n","      <td>4432.0</td>\n","      <td>126.46</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 22:00:00</th>\n","      <td>6361.93</td>\n","      <td>0.096688</td>\n","      <td>0.231342</td>\n","      <td>3980.0</td>\n","      <td>259.10</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-11 23:00:00</th>\n","      <td>6368.78</td>\n","      <td>0.103997</td>\n","      <td>0.217739</td>\n","      <td>3830.0</td>\n","      <td>81.54</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07-12 00:00:00</th>\n","      <td>6380.00</td>\n","      <td>0.094383</td>\n","      <td>0.195256</td>\n","      <td>3998.0</td>\n","      <td>124.55</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                     Close_Price  Polarity  Sensitivity  Tweet_vol  Volume_BTC\n","2018-07-11 20:00:00      6350.00  0.102657     0.216148     4354.0      986.73\n","2018-07-11 21:00:00      6356.48  0.098004     0.218612     4432.0      126.46\n","2018-07-11 22:00:00      6361.93  0.096688     0.231342     3980.0      259.10\n","2018-07-11 23:00:00      6368.78  0.103997     0.217739     3830.0       81.54\n","2018-07-12 00:00:00      6380.00  0.094383     0.195256     3998.0      124.55"]},"execution_count":211,"metadata":{},"output_type":"execute_result"}],"source":["values = df.values\n","cols = df.columns.tolist()\n","cols = cols[-1:] + cols[:-1]\n","df = df[cols]\n","df = df[[\"Close_Price\", \"Polarity\", \"Sensitivity\", \"Tweet_vol\", \"Volume_BTC\"]]\n","df.head()"]},{"cell_type":"code","execution_count":212,"metadata":{"_uuid":"bdbdccbe6c18cc63036eb434066d64a5bcb1cd7f","trusted":true},"outputs":[],"source":["scaler = MinMaxScaler(feature_range=(0, 1))\n","scaled = scaler.fit_transform(df.values)"]},{"cell_type":"code","execution_count":213,"metadata":{"_uuid":"f415bc8c3a803e9d1b30355d0751a1510c9379c5","trusted":true},"outputs":[],"source":["n_hours = 3  # adding 3 hours lags creating number of observations\n","n_features = 5  # Features in the dataset.\n","n_obs = n_hours * n_features"]},{"cell_type":"code","execution_count":214,"metadata":{"_uuid":"800c5b4a11d9e3e61b15fc10a3d1ee3bad101074","trusted":true},"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>var1(t-3)</th>\n","      <th>var2(t-3)</th>\n","      <th>var3(t-3)</th>\n","      <th>var4(t-3)</th>\n","      <th>var5(t-3)</th>\n","      <th>var1(t-2)</th>\n","      <th>var2(t-2)</th>\n","      <th>var3(t-2)</th>\n","      <th>var4(t-2)</th>\n","      <th>var5(t-2)</th>\n","      <th>var1(t-1)</th>\n","      <th>var2(t-1)</th>\n","      <th>var3(t-1)</th>\n","      <th>var4(t-1)</th>\n","      <th>var5(t-1)</th>\n","      <th>var1(t)</th>\n","      <th>var2(t)</th>\n","      <th>var3(t)</th>\n","      <th>var4(t)</th>\n","      <th>var5(t)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3</th>\n","      <td>0.125479</td>\n","      <td>0.611105</td>\n","      <td>0.429055</td>\n","      <td>0.181916</td>\n","      <td>0.368430</td>\n","      <td>0.129527</td>\n","      <td>0.555312</td>\n","      <td>0.454335</td>\n","      <td>0.192380</td>\n","      <td>0.039893</td>\n","      <td>0.132931</td>\n","      <td>0.539534</td>\n","      <td>0.584943</td>\n","      <td>0.131741</td>\n","      <td>0.090548</td>\n","      <td>0.137210</td>\n","      <td>0.627175</td>\n","      <td>0.445375</td>\n","      <td>0.111618</td>\n","      <td>0.022738</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.129527</td>\n","      <td>0.555312</td>\n","      <td>0.454335</td>\n","      <td>0.192380</td>\n","      <td>0.039893</td>\n","      <td>0.132931</td>\n","      <td>0.539534</td>\n","      <td>0.584943</td>\n","      <td>0.131741</td>\n","      <td>0.090548</td>\n","      <td>0.137210</td>\n","      <td>0.627175</td>\n","      <td>0.445375</td>\n","      <td>0.111618</td>\n","      <td>0.022738</td>\n","      <td>0.144218</td>\n","      <td>0.511893</td>\n","      <td>0.214693</td>\n","      <td>0.134156</td>\n","      <td>0.039164</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.132931</td>\n","      <td>0.539534</td>\n","      <td>0.584943</td>\n","      <td>0.131741</td>\n","      <td>0.090548</td>\n","      <td>0.137210</td>\n","      <td>0.627175</td>\n","      <td>0.445375</td>\n","      <td>0.111618</td>\n","      <td>0.022738</td>\n","      <td>0.144218</td>\n","      <td>0.511893</td>\n","      <td>0.214693</td>\n","      <td>0.134156</td>\n","      <td>0.039164</td>\n","      <td>0.135117</td>\n","      <td>0.589271</td>\n","      <td>0.500135</td>\n","      <td>0.095922</td>\n","      <td>0.045637</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.137210</td>\n","      <td>0.627175</td>\n","      <td>0.445375</td>\n","      <td>0.111618</td>\n","      <td>0.022738</td>\n","      <td>0.144218</td>\n","      <td>0.511893</td>\n","      <td>0.214693</td>\n","      <td>0.134156</td>\n","      <td>0.039164</td>\n","      <td>0.135117</td>\n","      <td>0.589271</td>\n","      <td>0.500135</td>\n","      <td>0.095922</td>\n","      <td>0.045637</td>\n","      <td>0.111700</td>\n","      <td>0.722717</td>\n","      <td>0.212514</td>\n","      <td>0.113362</td>\n","      <td>0.045561</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.144218</td>\n","      <td>0.511893</td>\n","      <td>0.214693</td>\n","      <td>0.134156</td>\n","      <td>0.039164</td>\n","      <td>0.135117</td>\n","      <td>0.589271</td>\n","      <td>0.500135</td>\n","      <td>0.095922</td>\n","      <td>0.045637</td>\n","      <td>0.111700</td>\n","      <td>0.722717</td>\n","      <td>0.212514</td>\n","      <td>0.113362</td>\n","      <td>0.045561</td>\n","      <td>0.111101</td>\n","      <td>0.649855</td>\n","      <td>0.365349</td>\n","      <td>0.111752</td>\n","      <td>0.053607</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   var1(t-3)  var2(t-3)  var3(t-3)  var4(t-3)  var5(t-3)  var1(t-2)   \n","3   0.125479   0.611105   0.429055   0.181916   0.368430   0.129527  \\\n","4   0.129527   0.555312   0.454335   0.192380   0.039893   0.132931   \n","5   0.132931   0.539534   0.584943   0.131741   0.090548   0.137210   \n","6   0.137210   0.627175   0.445375   0.111618   0.022738   0.144218   \n","7   0.144218   0.511893   0.214693   0.134156   0.039164   0.135117   \n","\n","   var2(t-2)  var3(t-2)  var4(t-2)  var5(t-2)  var1(t-1)  var2(t-1)   \n","3   0.555312   0.454335   0.192380   0.039893   0.132931   0.539534  \\\n","4   0.539534   0.584943   0.131741   0.090548   0.137210   0.627175   \n","5   0.627175   0.445375   0.111618   0.022738   0.144218   0.511893   \n","6   0.511893   0.214693   0.134156   0.039164   0.135117   0.589271   \n","7   0.589271   0.500135   0.095922   0.045637   0.111700   0.722717   \n","\n","   var3(t-1)  var4(t-1)  var5(t-1)   var1(t)   var2(t)   var3(t)   var4(t)   \n","3   0.584943   0.131741   0.090548  0.137210  0.627175  0.445375  0.111618  \\\n","4   0.445375   0.111618   0.022738  0.144218  0.511893  0.214693  0.134156   \n","5   0.214693   0.134156   0.039164  0.135117  0.589271  0.500135  0.095922   \n","6   0.500135   0.095922   0.045637  0.111700  0.722717  0.212514  0.113362   \n","7   0.212514   0.113362   0.045561  0.111101  0.649855  0.365349  0.111752   \n","\n","    var5(t)  \n","3  0.022738  \n","4  0.039164  \n","5  0.045637  \n","6  0.045561  \n","7  0.053607  "]},"execution_count":214,"metadata":{},"output_type":"execute_result"}],"source":["reframed = series_to_supervised(scaled, n_hours, 1)\n","reframed.head()"]},{"cell_type":"code","execution_count":215,"metadata":{"_uuid":"a3192a321f850ad620ea55e29e24aeae5d31c6f1","trusted":true},"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>var1(t-3)</th>\n","      <th>var2(t-3)</th>\n","      <th>var3(t-3)</th>\n","      <th>var4(t-3)</th>\n","      <th>var5(t-3)</th>\n","      <th>var1(t-2)</th>\n","      <th>var2(t-2)</th>\n","      <th>var3(t-2)</th>\n","      <th>var4(t-2)</th>\n","      <th>var5(t-2)</th>\n","      <th>var1(t-1)</th>\n","      <th>var2(t-1)</th>\n","      <th>var3(t-1)</th>\n","      <th>var4(t-1)</th>\n","      <th>var5(t-1)</th>\n","      <th>var1(t)</th>\n","      <th>var2(t)</th>\n","      <th>var3(t)</th>\n","      <th>var4(t)</th>\n","      <th>var5(t)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3</th>\n","      <td>0.125479</td>\n","      <td>0.611105</td>\n","      <td>0.429055</td>\n","      <td>0.181916</td>\n","      <td>0.368430</td>\n","      <td>0.129527</td>\n","      <td>0.555312</td>\n","      <td>0.454335</td>\n","      <td>0.192380</td>\n","      <td>0.039893</td>\n","      <td>0.132931</td>\n","      <td>0.539534</td>\n","      <td>0.584943</td>\n","      <td>0.131741</td>\n","      <td>0.090548</td>\n","      <td>0.137210</td>\n","      <td>0.627175</td>\n","      <td>0.445375</td>\n","      <td>0.111618</td>\n","      <td>0.022738</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.129527</td>\n","      <td>0.555312</td>\n","      <td>0.454335</td>\n","      <td>0.192380</td>\n","      <td>0.039893</td>\n","      <td>0.132931</td>\n","      <td>0.539534</td>\n","      <td>0.584943</td>\n","      <td>0.131741</td>\n","      <td>0.090548</td>\n","      <td>0.137210</td>\n","      <td>0.627175</td>\n","      <td>0.445375</td>\n","      <td>0.111618</td>\n","      <td>0.022738</td>\n","      <td>0.144218</td>\n","      <td>0.511893</td>\n","      <td>0.214693</td>\n","      <td>0.134156</td>\n","      <td>0.039164</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.132931</td>\n","      <td>0.539534</td>\n","      <td>0.584943</td>\n","      <td>0.131741</td>\n","      <td>0.090548</td>\n","      <td>0.137210</td>\n","      <td>0.627175</td>\n","      <td>0.445375</td>\n","      <td>0.111618</td>\n","      <td>0.022738</td>\n","      <td>0.144218</td>\n","      <td>0.511893</td>\n","      <td>0.214693</td>\n","      <td>0.134156</td>\n","      <td>0.039164</td>\n","      <td>0.135117</td>\n","      <td>0.589271</td>\n","      <td>0.500135</td>\n","      <td>0.095922</td>\n","      <td>0.045637</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.137210</td>\n","      <td>0.627175</td>\n","      <td>0.445375</td>\n","      <td>0.111618</td>\n","      <td>0.022738</td>\n","      <td>0.144218</td>\n","      <td>0.511893</td>\n","      <td>0.214693</td>\n","      <td>0.134156</td>\n","      <td>0.039164</td>\n","      <td>0.135117</td>\n","      <td>0.589271</td>\n","      <td>0.500135</td>\n","      <td>0.095922</td>\n","      <td>0.045637</td>\n","      <td>0.111700</td>\n","      <td>0.722717</td>\n","      <td>0.212514</td>\n","      <td>0.113362</td>\n","      <td>0.045561</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.144218</td>\n","      <td>0.511893</td>\n","      <td>0.214693</td>\n","      <td>0.134156</td>\n","      <td>0.039164</td>\n","      <td>0.135117</td>\n","      <td>0.589271</td>\n","      <td>0.500135</td>\n","      <td>0.095922</td>\n","      <td>0.045637</td>\n","      <td>0.111700</td>\n","      <td>0.722717</td>\n","      <td>0.212514</td>\n","      <td>0.113362</td>\n","      <td>0.045561</td>\n","      <td>0.111101</td>\n","      <td>0.649855</td>\n","      <td>0.365349</td>\n","      <td>0.111752</td>\n","      <td>0.053607</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   var1(t-3)  var2(t-3)  var3(t-3)  var4(t-3)  var5(t-3)  var1(t-2)   \n","3   0.125479   0.611105   0.429055   0.181916   0.368430   0.129527  \\\n","4   0.129527   0.555312   0.454335   0.192380   0.039893   0.132931   \n","5   0.132931   0.539534   0.584943   0.131741   0.090548   0.137210   \n","6   0.137210   0.627175   0.445375   0.111618   0.022738   0.144218   \n","7   0.144218   0.511893   0.214693   0.134156   0.039164   0.135117   \n","\n","   var2(t-2)  var3(t-2)  var4(t-2)  var5(t-2)  var1(t-1)  var2(t-1)   \n","3   0.555312   0.454335   0.192380   0.039893   0.132931   0.539534  \\\n","4   0.539534   0.584943   0.131741   0.090548   0.137210   0.627175   \n","5   0.627175   0.445375   0.111618   0.022738   0.144218   0.511893   \n","6   0.511893   0.214693   0.134156   0.039164   0.135117   0.589271   \n","7   0.589271   0.500135   0.095922   0.045637   0.111700   0.722717   \n","\n","   var3(t-1)  var4(t-1)  var5(t-1)   var1(t)   var2(t)   var3(t)   var4(t)   \n","3   0.584943   0.131741   0.090548  0.137210  0.627175  0.445375  0.111618  \\\n","4   0.445375   0.111618   0.022738  0.144218  0.511893  0.214693  0.134156   \n","5   0.214693   0.134156   0.039164  0.135117  0.589271  0.500135  0.095922   \n","6   0.500135   0.095922   0.045637  0.111700  0.722717  0.212514  0.113362   \n","7   0.212514   0.113362   0.045561  0.111101  0.649855  0.365349  0.111752   \n","\n","    var5(t)  \n","3  0.022738  \n","4  0.039164  \n","5  0.045637  \n","6  0.045561  \n","7  0.053607  "]},"execution_count":215,"metadata":{},"output_type":"execute_result"}],"source":["reframed.drop(reframed.columns[-4], axis=1)\n","reframed.head()"]},{"cell_type":"code","execution_count":216,"metadata":{"_uuid":"6d524d80ae37802727a0b61d2199d94a0fc4e2dc","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["   var1(t-3)  var2(t-3)  var3(t-3)  var4(t-3)  var5(t-3)  var1(t-2)   \n","3   0.125479   0.611105   0.429055   0.181916   0.368430   0.129527  \\\n","4   0.129527   0.555312   0.454335   0.192380   0.039893   0.132931   \n","5   0.132931   0.539534   0.584943   0.131741   0.090548   0.137210   \n","6   0.137210   0.627175   0.445375   0.111618   0.022738   0.144218   \n","7   0.144218   0.511893   0.214693   0.134156   0.039164   0.135117   \n","\n","   var2(t-2)  var3(t-2)  var4(t-2)  var5(t-2)  var1(t-1)  var2(t-1)   \n","3   0.555312   0.454335   0.192380   0.039893   0.132931   0.539534  \\\n","4   0.539534   0.584943   0.131741   0.090548   0.137210   0.627175   \n","5   0.627175   0.445375   0.111618   0.022738   0.144218   0.511893   \n","6   0.511893   0.214693   0.134156   0.039164   0.135117   0.589271   \n","7   0.589271   0.500135   0.095922   0.045637   0.111700   0.722717   \n","\n","   var3(t-1)  var4(t-1)  var5(t-1)   var1(t)   var2(t)   var3(t)   var4(t)   \n","3   0.584943   0.131741   0.090548  0.137210  0.627175  0.445375  0.111618  \\\n","4   0.445375   0.111618   0.022738  0.144218  0.511893  0.214693  0.134156   \n","5   0.214693   0.134156   0.039164  0.135117  0.589271  0.500135  0.095922   \n","6   0.500135   0.095922   0.045637  0.111700  0.722717  0.212514  0.113362   \n","7   0.212514   0.113362   0.045561  0.111101  0.649855  0.365349  0.111752   \n","\n","    var5(t)  \n","3  0.022738  \n","4  0.039164  \n","5  0.045637  \n","6  0.045561  \n","7  0.053607  \n"]}],"source":["print(reframed.head())"]},{"cell_type":"code","execution_count":217,"metadata":{"_uuid":"fc43ba4acac4903b8591ecb475fb497a060a67e7","trusted":true},"outputs":[{"data":{"text/plain":["(200, 20)"]},"execution_count":217,"metadata":{},"output_type":"execute_result"}],"source":["values = reframed.values\n","n_train_hours = 200\n","train = values[:n_train_hours, :]\n","test = values[n_train_hours:, :]\n","train.shape"]},{"cell_type":"code","execution_count":218,"metadata":{"_uuid":"80174c6721da8a6dad4adcfa13d98ea20f9c1070","trusted":true},"outputs":[],"source":["# split into input and outputs\n","train_X, train_y = train[:, :n_obs], train[:, -n_features]\n","test_X, test_y = test[:, :n_obs], test[:, -n_features]"]},{"cell_type":"code","execution_count":219,"metadata":{"_uuid":"ffcdaebb530fc706edbd5c56b6b9550d7dac65fe","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(200, 3, 5) (200,) (91, 3, 5) (91,)\n"]}],"source":["# reshape input to be 3D [samples, timesteps, features]\n","train_X = train_X.reshape((train_X.shape[0], n_hours, n_features))\n","test_X = test_X.reshape((test_X.shape[0], n_hours, n_features))\n","print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"]},{"cell_type":"code","execution_count":220,"metadata":{"_uuid":"6ca3338a33277cd4984e64115440e5b6adcf919c","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/50\n","34/34 - 3s - loss: 0.2547 - val_loss: 0.6327 - 3s/epoch - 79ms/step\n","Epoch 2/50\n","34/34 - 0s - loss: 0.2190 - val_loss: 0.5658 - 194ms/epoch - 6ms/step\n","Epoch 3/50\n","34/34 - 0s - loss: 0.2099 - val_loss: 0.5187 - 212ms/epoch - 6ms/step\n","Epoch 4/50\n","34/34 - 0s - loss: 0.1882 - val_loss: 0.4559 - 207ms/epoch - 6ms/step\n","Epoch 5/50\n","34/34 - 0s - loss: 0.1617 - val_loss: 0.3725 - 192ms/epoch - 6ms/step\n","Epoch 6/50\n","34/34 - 0s - loss: 0.1316 - val_loss: 0.2751 - 201ms/epoch - 6ms/step\n","Epoch 7/50\n","34/34 - 0s - loss: 0.0985 - val_loss: 0.1715 - 230ms/epoch - 7ms/step\n","Epoch 8/50\n","34/34 - 0s - loss: 0.0694 - val_loss: 0.1069 - 199ms/epoch - 6ms/step\n","Epoch 9/50\n","34/34 - 0s - loss: 0.0595 - val_loss: 0.1018 - 182ms/epoch - 5ms/step\n","Epoch 10/50\n","34/34 - 0s - loss: 0.0517 - val_loss: 0.0893 - 191ms/epoch - 6ms/step\n","Epoch 11/50\n","34/34 - 0s - loss: 0.0496 - val_loss: 0.0819 - 193ms/epoch - 6ms/step\n","Epoch 12/50\n","34/34 - 0s - loss: 0.0463 - val_loss: 0.0754 - 197ms/epoch - 6ms/step\n","Epoch 13/50\n","34/34 - 0s - loss: 0.0435 - val_loss: 0.0713 - 195ms/epoch - 6ms/step\n","Epoch 14/50\n","34/34 - 0s - loss: 0.0407 - val_loss: 0.0668 - 196ms/epoch - 6ms/step\n","Epoch 15/50\n","34/34 - 0s - loss: 0.0390 - val_loss: 0.0589 - 194ms/epoch - 6ms/step\n","Epoch 16/50\n","34/34 - 0s - loss: 0.0377 - val_loss: 0.0595 - 207ms/epoch - 6ms/step\n","Epoch 17/50\n","34/34 - 0s - loss: 0.0352 - val_loss: 0.0541 - 209ms/epoch - 6ms/step\n","Epoch 18/50\n","34/34 - 0s - loss: 0.0342 - val_loss: 0.0528 - 189ms/epoch - 6ms/step\n","Epoch 19/50\n","34/34 - 0s - loss: 0.0328 - val_loss: 0.0506 - 182ms/epoch - 5ms/step\n","Epoch 20/50\n","34/34 - 0s - loss: 0.0311 - val_loss: 0.0471 - 230ms/epoch - 7ms/step\n","Epoch 21/50\n","34/34 - 0s - loss: 0.0302 - val_loss: 0.0467 - 187ms/epoch - 5ms/step\n","Epoch 22/50\n","34/34 - 0s - loss: 0.0296 - val_loss: 0.0436 - 185ms/epoch - 5ms/step\n","Epoch 23/50\n","34/34 - 0s - loss: 0.0292 - val_loss: 0.0430 - 190ms/epoch - 6ms/step\n","Epoch 24/50\n","34/34 - 0s - loss: 0.0276 - val_loss: 0.0440 - 175ms/epoch - 5ms/step\n","Epoch 25/50\n","34/34 - 0s - loss: 0.0268 - val_loss: 0.0402 - 183ms/epoch - 5ms/step\n","Epoch 26/50\n","34/34 - 0s - loss: 0.0260 - val_loss: 0.0407 - 176ms/epoch - 5ms/step\n","Epoch 27/50\n","34/34 - 0s - loss: 0.0246 - val_loss: 0.0383 - 181ms/epoch - 5ms/step\n","Epoch 28/50\n","34/34 - 0s - loss: 0.0255 - val_loss: 0.0393 - 177ms/epoch - 5ms/step\n","Epoch 29/50\n","34/34 - 0s - loss: 0.0238 - val_loss: 0.0447 - 188ms/epoch - 6ms/step\n","Epoch 30/50\n","34/34 - 0s - loss: 0.0220 - val_loss: 0.0386 - 179ms/epoch - 5ms/step\n","Epoch 31/50\n","34/34 - 0s - loss: 0.0234 - val_loss: 0.0458 - 184ms/epoch - 5ms/step\n","Epoch 32/50\n","34/34 - 0s - loss: 0.0215 - val_loss: 0.0416 - 190ms/epoch - 6ms/step\n","Epoch 33/50\n","34/34 - 0s - loss: 0.0233 - val_loss: 0.0372 - 182ms/epoch - 5ms/step\n","Epoch 34/50\n","34/34 - 0s - loss: 0.0231 - val_loss: 0.0423 - 187ms/epoch - 6ms/step\n","Epoch 35/50\n","34/34 - 0s - loss: 0.0213 - val_loss: 0.0400 - 178ms/epoch - 5ms/step\n","Epoch 36/50\n","34/34 - 0s - loss: 0.0220 - val_loss: 0.0399 - 185ms/epoch - 5ms/step\n","Epoch 37/50\n","34/34 - 0s - loss: 0.0212 - val_loss: 0.0404 - 189ms/epoch - 6ms/step\n","Epoch 38/50\n","34/34 - 0s - loss: 0.0218 - val_loss: 0.0375 - 184ms/epoch - 5ms/step\n","Epoch 39/50\n","34/34 - 0s - loss: 0.0210 - val_loss: 0.0358 - 181ms/epoch - 5ms/step\n","Epoch 40/50\n","34/34 - 0s - loss: 0.0229 - val_loss: 0.0353 - 190ms/epoch - 6ms/step\n","Epoch 41/50\n","34/34 - 0s - loss: 0.0204 - val_loss: 0.0376 - 190ms/epoch - 6ms/step\n","Epoch 42/50\n","34/34 - 0s - loss: 0.0198 - val_loss: 0.0392 - 190ms/epoch - 6ms/step\n","Epoch 43/50\n","34/34 - 0s - loss: 0.0195 - val_loss: 0.0400 - 189ms/epoch - 6ms/step\n","Epoch 44/50\n","34/34 - 0s - loss: 0.0189 - val_loss: 0.0339 - 187ms/epoch - 5ms/step\n","Epoch 45/50\n","34/34 - 0s - loss: 0.0205 - val_loss: 0.0423 - 184ms/epoch - 5ms/step\n","Epoch 46/50\n","34/34 - 0s - loss: 0.0196 - val_loss: 0.0327 - 182ms/epoch - 5ms/step\n","Epoch 47/50\n","34/34 - 0s - loss: 0.0207 - val_loss: 0.0448 - 191ms/epoch - 6ms/step\n","Epoch 48/50\n","34/34 - 0s - loss: 0.0230 - val_loss: 0.0394 - 186ms/epoch - 5ms/step\n","Epoch 49/50\n","34/34 - 0s - loss: 0.0218 - val_loss: 0.0418 - 191ms/epoch - 6ms/step\n","Epoch 50/50\n","34/34 - 0s - loss: 0.0226 - val_loss: 0.0420 - 190ms/epoch - 6ms/step\n"]}],"source":["# design network\n","model = Sequential()\n","model.add(LSTM(5, input_shape=(train_X.shape[1], train_X.shape[2])))\n","model.add(Dense(1))\n","model.compile(loss=\"mae\", optimizer=\"adam\")\n","# fit network\n","history = model.fit(\n","    train_X,\n","    train_y,\n","    epochs=50,\n","    batch_size=6,\n","    validation_data=(test_X, test_y),\n","    verbose=2,\n","    shuffle=False,\n","    validation_split=0.2,\n",")"]},{"cell_type":"code","execution_count":221,"metadata":{"_uuid":"c29c3ceacdfbee6afb4e8ba921426cb495e82f15","trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot history\n","plt.plot(history.history[\"loss\"], label=\"train\")\n","plt.plot(history.history[\"val_loss\"], label=\"test\")\n","plt.legend()\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def predict(model, date_train, X_train, future_steps, ds):\n","    # Extracting dates\n","    dates = pd.date_range(list(date_train)[-1], periods=future, freq=\"1d\").tolist()\n","    # use the last future steps from X_train\n","    predicted = model.predict(X_train[-future_steps:])\n","    predicted = np.repeat(predicted, ds.shape[1], axis=-1)\n","    nsamples, nx, ny = predicted.shape\n","    predicted = predicted.reshape((nsamples, nx * ny))\n","\n","    return predicted, dates\n","\n","\n","def output_preparation(\n","    forecasting_dates, predictions, date_column=\"date\", predicted_column=\"Volume USDT\"\n","):\n","    dates = []\n","    for date in forecasting_dates:\n","        dates.append(date.date())\n","    predicted_df = pd.DataFrame(columns=[date_column, predicted_column])\n","    predicted_df[date_column] = pd.to_datetime(dates)\n","    predicted_df[predicted_column] = predictions\n","    return predicted_df\n","\n","\n","def results(\n","    df, lookback, future, scaler, col, X_train, y_train, df_train, date_train, model\n","):\n","    predictions, forecasting_dates = predict(model, date_train, X_train, future, df)\n","    results = output_preparation(forecasting_dates, predictions)\n","    print(results.head())"]},{"cell_type":"code","execution_count":226,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading data from https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/LSTM/LSTM_model.h5\n","32208/32208 [==============================] - 0s 1us/step\n"]}],"source":["# if you need a model trained, you can use this cell\n","import tensorflow as tf\n","from tensorflow.keras.models import load_model\n","from tensorflow.keras.utils import get_file\n","\n","model_url = \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/LSTM/LSTM_model.h5\"\n","model_path = get_file(\"LSTM_model.h5\", model_url)\n","LSTM_model = load_model(model_path)"]},{"cell_type":"code","execution_count":null,"metadata":{"_uuid":"3907ffedaa9463fd7ba9d9e46b19b3ab791bb158","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["3/3 [==============================] - 0s 2ms/step\n","Test MSE: 12919.827\n","Test RMSE: 113.665\n"]}],"source":["# make a prediction\n","yhat = model.predict(test_X)\n","test_X = test_X.reshape(\n","    (\n","        test_X.shape[0],\n","        n_hours * n_features,\n","    )\n",")\n","# invert scaling for forecast\n","inv_yhat = concatenate((yhat, test_X[:, -4:]), axis=1)\n","inv_yhat = scaler.inverse_transform(inv_yhat)\n","inv_yhat = inv_yhat[:, 0]\n","# invert scaling for actual\n","test_y = test_y.reshape((len(test_y), 1))\n","inv_y = concatenate((test_y, test_X[:, -4:]), axis=1)\n","inv_y = scaler.inverse_transform(inv_y)\n","inv_y = inv_y[:, 0]\n","# calculate RMSE\n","mse = mean_squared_error(inv_y, inv_yhat)\n","print(\"Test MSE: %.3f\" % mse)\n","rmse = sqrt(mean_squared_error(inv_y, inv_yhat))\n","print(\"Test RMSE: %.3f\" % rmse)"]},{"cell_type":"code","execution_count":null,"metadata":{"_uuid":"3316296dd7c8cdeaf06dac98b9555ef40aa0c5b3","trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.title(\"Real v Predicted Close_Price\")\n","plt.ylabel(\"Price ($)\")\n","plt.xlabel(\"epochs (Hr)\")\n","\n","actual_values = inv_y\n","predicted_values = inv_yhat\n","\n","# plot\n","plt.plot(actual_values, label=\"Actual\", color=\"blue\")\n","plt.plot(predicted_values, label=\"Predicted\", color=\"red\")\n","\n","# set title and label\n","plt.title(\"Real v Predicted Close_Price\")\n","plt.ylabel(\"Price ($)\")\n","plt.xlabel(\"epochs (Hr)\")\n","\n","# show\n","plt.legend()\n","plt.show()\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["# Acknowledgements\n","\n","Thanks to [Paul Simpson](https://www.kaggle.com/paul92s) for creating [Bitcoin Lstm Model with Tweet Volume and Sentiment](https://www.kaggle.com/code/paul92s/bitcoin-lstm-model-with-tweet-volume-and-sentiment). It inspires the majority of the content in this chapter."]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.16"}},"nbformat":4,"nbformat_minor":4}