{"metadata":{"celltoolbar":"Slideshow","kernelspec":{"display_name":"Python 3 (ipykernel)","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":"3.9.13"}},"nbformat_minor":5,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Crypto Forecasting ","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"### Problem setting ","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"This is a kaggle competition by G-Research.[Link](https://www.kaggle.com/competitions/g-research-crypto-forecasting/overview)\n\nOver $40 billion worth of cryptocurrencies are traded every day. They are among the most popular assets for speculation and investment, yet have proven wildly volatile. Fast-fluctuating prices have made millionaires of a lucky few, and delivered crushing losses to others.\n\nNonetheless being able to predict theses prices can help investors make money and maybe gain insight into the finicial crypto currency world.\n\nThis problem would be considered a time series regression problem where our target variable is coutinous representing 15 minute residualized returns from 14 popular cryptocurrencies","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"markdown","source":"### EDA","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"code","source":"import warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\nimport pandas as pd \nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import preprocessing\nfrom datetime import datetime,timedelta\nfrom pandas.plotting import lag_plot,autocorrelation_plot\nfrom sklearn.decomposition import PCA\nimport sys\nimport gc \nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.ensemble import RandomForestRegressor\nimport pytorch_lightning as pl\nfrom pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor\nfrom pytorch_lightning.loggers import TensorBoardLogger\nimport torch\nfrom pytorch_forecasting import Baseline, TemporalFusionTransformer, TimeSeriesDataSet\nfrom pytorch_forecasting.data import GroupNormalizer\nfrom pytorch_forecasting.metrics import SMAPE, PoissonLoss, QuantileLoss\nfrom pytorch_forecasting.metrics.point import RMSE\nfrom pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters\nimport torch.nn as nn\nfrom torchsummary import summary\nfrom ray import tune\nfrom ray.tune.search.bayesopt import BayesOptSearch\nimport random \nfrom ray.air import Checkpoint,session\nrandom.seed(42)\nnp.random.seed(42)","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"df=pd.read_csv('crypto.csv')","metadata":{"slideshow":{"slide_type":"fragment"}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/html":["
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timestampAsset_IDCountOpenHighLowCloseVolumeVWAPTarget
01514764860240.02376.58002399.50002357.14002374.590019.2330052373.116392-0.004218
1151476486005.08.53008.53008.53008.530078.3800008.530000-0.014399
215147648601229.013835.194014013.800013666.110013850.176031.55006213827.062093-0.014643
31514764860532.07.65967.65967.65677.65766626.7133707.657713-0.013922
4151476486075.025.920025.920025.874025.8770121.08731025.891363-0.008264
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"],"text/plain":[" timestamp Asset_ID Count Open High Low \\\n","0 1514764860 2 40.0 2376.5800 2399.5000 2357.1400 \n","1 1514764860 0 5.0 8.5300 8.5300 8.5300 \n","2 1514764860 1 229.0 13835.1940 14013.8000 13666.1100 \n","3 1514764860 5 32.0 7.6596 7.6596 7.6567 \n","4 1514764860 7 5.0 25.9200 25.9200 25.8740 \n","\n"," Close Volume VWAP Target \n","0 2374.5900 19.233005 2373.116392 -0.004218 \n","1 8.5300 78.380000 8.530000 -0.014399 \n","2 13850.1760 31.550062 13827.062093 -0.014643 \n","3 7.6576 6626.713370 7.657713 -0.013922 \n","4 25.8770 121.087310 25.891363 -0.008264 "]},"metadata":{}}]},{"cell_type":"code","source":"df_assest=pd.read_csv('asset_details.csv')\ndf_assest","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/html":["
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Asset_IDWeightAsset_Name
022.397895Bitcoin Cash
104.304065Binance Coin
216.779922Bitcoin
351.386294EOS.IO
472.079442Ethereum Classic
565.894403Ethereum
692.397895Litecoin
7111.609438Monero
8131.791759TRON
9122.079442Stellar
1034.406719Cardano
1181.098612IOTA
12101.098612Maker
1343.555348Dogecoin
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"],"text/plain":[" Asset_ID Weight Asset_Name\n","0 2 2.397895 Bitcoin Cash\n","1 0 4.304065 Binance Coin\n","2 1 6.779922 Bitcoin\n","3 5 1.386294 EOS.IO\n","4 7 2.079442 Ethereum Classic\n","5 6 5.894403 Ethereum\n","6 9 2.397895 Litecoin\n","7 11 1.609438 Monero\n","8 13 1.791759 TRON\n","9 12 2.079442 Stellar\n","10 3 4.406719 Cardano\n","11 8 1.098612 IOTA\n","12 10 1.098612 Maker\n","13 4 3.555348 Dogecoin"]},"metadata":{}}]},{"cell_type":"code","source":"#adding asset name to help with visualizations\ndf['Asset_Name'] = df['Asset_ID'].map(df_assest.set_index('Asset_ID')['Asset_Name'])","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"slideshow":{"slide_type":"fragment"}},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/html":["
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timestampAsset_IDCountOpenHighLowCloseVolumeVWAPTargetAsset_Name
01514764860240.02376.58002399.50002357.14002374.590019.2330052373.116392-0.004218Bitcoin Cash
1151476486005.08.53008.53008.53008.530078.3800008.530000-0.014399Binance Coin
215147648601229.013835.194014013.800013666.110013850.176031.55006213827.062093-0.014643Bitcoin
31514764860532.07.65967.65967.65677.65766626.7133707.657713-0.013922EOS.IO
4151476486075.025.920025.920025.874025.8770121.08731025.891363-0.008264Ethereum Classic
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"],"text/plain":[" timestamp Asset_ID Count Open High Low \\\n","0 1514764860 2 40.0 2376.5800 2399.5000 2357.1400 \n","1 1514764860 0 5.0 8.5300 8.5300 8.5300 \n","2 1514764860 1 229.0 13835.1940 14013.8000 13666.1100 \n","3 1514764860 5 32.0 7.6596 7.6596 7.6567 \n","4 1514764860 7 5.0 25.9200 25.9200 25.8740 \n","\n"," Close Volume VWAP Target Asset_Name \n","0 2374.5900 19.233005 2373.116392 -0.004218 Bitcoin Cash \n","1 8.5300 78.380000 8.530000 -0.014399 Binance Coin \n","2 13850.1760 31.550062 13827.062093 -0.014643 Bitcoin \n","3 7.6576 6626.713370 7.657713 -0.013922 EOS.IO \n","4 25.8770 121.087310 25.891363 -0.008264 Ethereum Classic "]},"metadata":{}}]},{"cell_type":"markdown","source":"### Meaning of variables ","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"markdown","source":"> timestamp - A timestamp for the minute covered by the row.\n\n>Asset_ID - An ID code for the cryptoasset. To decode which is which we also have another file assests.csv\n\n>Count - The number of trades that took place this minute.\n\n>Open - The USD price at the beginning of the minute.\n\n>High - The highest USD price during the minute.\n\n>Low - The lowest USD price during the minute.\n\n>Close - The USD price at the end of the minute.\n\n>Volume - The number of cryptoasset units traded during the minute.\n\n>VWAP - The volume weighted average price for the minute.\n\n>Target - 15 minute residualized returns","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"markdown","source":"Quoting from the kaggle competition \"\n`Target` is derived from log returns ($R^a$) over 15 minutes.\n\n$$R^a(t) = log (P^a(t+16)\\ /\\ P^a(t+1))$$\n\nCrypto asset returns are highly correlated, following to a large extend the overall crypto market. As we want to test your ability to predict returns for individual assets, we perform a linear residualization, removing the market signal from individual asset returns when creating the target. In more detail, if $M(t)$ is the weighted average market returns, the target is:\n\n$$M(t) = \\frac{\\sum_a w^a R^a(t)}{\\sum_a w^a} \\\\\n\\beta^a = \\frac{\\langle M \\cdot R^a \\rangle}{\\langle M^2 \\rangle} \\\\\n\\text{Target}^a(t) = R^a(t) - \\beta^a M(t)$$\n\nwhere the bracket $\\langle .\\rangle$ represent the rolling average over time (3750 minute windows), and same asset weights $w^a$ used for the evaluation metric.\"","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"code","source":"#set index to be timestamp \ndf.set_index(\"timestamp\",inplace=True)","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":9,"outputs":[{"name":"stdout","output_type":"stream","text":"\n\nInt64Index: 26755084 entries, 1514764860 to 1642982400\n\nData columns (total 10 columns):\n\n # Column Dtype \n\n--- ------ ----- \n\n 0 Asset_ID int64 \n\n 1 Count float64\n\n 2 Open float64\n\n 3 High float64\n\n 4 Low float64\n\n 5 Close float64\n\n 6 Volume float64\n\n 7 VWAP float64\n\n 8 Target float64\n\n 9 Asset_Name object \n\ndtypes: float64(8), int64(1), object(1)\n\nmemory usage: 2.2+ GB\n"}]},{"cell_type":"code","source":"#reduce dataframe instead of every minute to every 1 hour1\ndf_new = pd.DataFrame()\nfor asset in df.Asset_Name.unique():\n df_asset=df[df[\"Asset_Name\"]==asset]\n df_asset=df_asset.iloc[::60, :]#every 60 almost equivalent to 1 hours \n df_new=pd.concat([df_new,df_asset])#concat everything to recreate the dataframe\ndf=df_new\ndf.sort_index(inplace=True)#sort by timestamp again \n ","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":11,"outputs":[{"name":"stdout","output_type":"stream","text":"\n\nInt64Index: 445924 entries, 1514764860 to 1642981500\n\nData columns (total 10 columns):\n\n # Column Non-Null Count Dtype \n\n--- ------ -------------- ----- \n\n 0 Asset_ID 445924 non-null int64 \n\n 1 Count 445924 non-null float64\n\n 2 Open 445924 non-null float64\n\n 3 High 445924 non-null float64\n\n 4 Low 445924 non-null float64\n\n 5 Close 445924 non-null float64\n\n 6 Volume 445924 non-null float64\n\n 7 VWAP 445924 non-null float64\n\n 8 Target 433451 non-null float64\n\n 9 Asset_Name 445924 non-null object \n\ndtypes: float64(8), int64(1), object(1)\n\nmemory usage: 37.4+ MB\n"}]},{"cell_type":"code","source":"df.describe()","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/html":["
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Asset_IDCountOpenHighLowCloseVolumeVWAPTarget
count445924.000000445924.000000445924.000000445924.000000445924.000000445924.0000004.459240e+054.459240e+054.334510e+05
mean6.311914305.2462861703.1877931706.9447901699.9738731703.1900612.936162e+05inf-6.168263e-07
std4.086631895.6354127137.0943127146.9235987127.4124447137.1440152.443814e+06NaN5.513959e-03
min0.0000001.0000000.0012750.0013000.0012700.0012941.400000e-07-2.548139e+02-2.452924e-01
25%3.00000021.0000000.2828430.2837000.2819000.2828001.401295e+022.827911e-01-1.642355e-03
50%6.00000072.00000015.39076315.40860015.37235015.3890501.288659e+031.538982e+01-4.129166e-05
75%10.000000248.000000247.562342248.192500246.972500247.5808333.035195e+042.475605e+021.556067e-03
max13.000000165016.00000068610.32666768741.90000068580.93139168666.1457144.165365e+08inf2.727561e-01
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"],"text/plain":[" Asset_ID Count Open High \\\n","count 445924.000000 445924.000000 445924.000000 445924.000000 \n","mean 6.311914 305.246286 1703.187793 1706.944790 \n","std 4.086631 895.635412 7137.094312 7146.923598 \n","min 0.000000 1.000000 0.001275 0.001300 \n","25% 3.000000 21.000000 0.282843 0.283700 \n","50% 6.000000 72.000000 15.390763 15.408600 \n","75% 10.000000 248.000000 247.562342 248.192500 \n","max 13.000000 165016.000000 68610.326667 68741.900000 \n","\n"," Low Close Volume VWAP Target \n","count 445924.000000 445924.000000 4.459240e+05 4.459240e+05 4.334510e+05 \n","mean 1699.973873 1703.190061 2.936162e+05 inf -6.168263e-07 \n","std 7127.412444 7137.144015 2.443814e+06 NaN 5.513959e-03 \n","min 0.001270 0.001294 1.400000e-07 -2.548139e+02 -2.452924e-01 \n","25% 0.281900 0.282800 1.401295e+02 2.827911e-01 -1.642355e-03 \n","50% 15.372350 15.389050 1.288659e+03 1.538982e+01 -4.129166e-05 \n","75% 246.972500 247.580833 3.035195e+04 2.475605e+02 1.556067e-03 \n","max 68580.931391 68666.145714 4.165365e+08 inf 2.727561e-01 "]},"metadata":{}}]},{"cell_type":"code","source":"#check for missing values\ndf.isnull().sum()","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":["Asset_ID 0\n","Count 0\n","Open 0\n","High 0\n","Low 0\n","Close 0\n","Volume 0\n","VWAP 0\n","Target 12473\n","Asset_Name 0\n","dtype: int64"]},"metadata":{}}]},{"cell_type":"code","source":"np.isinf(df.VWAP).sum()","metadata":{},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":["1"]},"metadata":{}}]},{"cell_type":"code","source":"#add date \ndf[\"datetime\"] = pd.to_datetime(df.index,unit='s')","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"df.Asset_Name.unique()","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":["array(['Bitcoin Cash', 'Ethereum Classic', 'Ethereum', 'Bitcoin',\n"," 'Litecoin', 'Monero', 'Binance Coin', 'EOS.IO', 'TRON', 'Stellar',\n"," 'Cardano', 'IOTA', 'Maker', 'Dogecoin'], dtype=object)"]},"metadata":{}}]},{"cell_type":"code","source":"# check for time period for all of the data \nstart_date=(df.datetime.min())\nend_date=(df.datetime.max())\nprint('Data ranges is from {} till {}'.format(start_date,end_date))","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":17,"outputs":[{"name":"stdout","output_type":"stream","text":"Data ranges is from 2018-01-01 00:01:00 till 2022-01-23 23:45:00\n"}]},{"cell_type":"code","source":"#check time period for each asset\ndf.groupby('Asset_Name').datetime.min()","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":["Asset_Name\n","Binance Coin 2018-01-01 00:01:00\n","Bitcoin 2018-01-01 00:01:00\n","Bitcoin Cash 2018-01-01 00:01:00\n","Cardano 2018-04-17 09:11:00\n","Dogecoin 2019-04-12 14:34:00\n","EOS.IO 2018-01-01 00:01:00\n","Ethereum 2018-01-01 00:01:00\n","Ethereum Classic 2018-01-01 00:01:00\n","IOTA 2018-05-09 08:07:00\n","Litecoin 2018-01-01 00:01:00\n","Maker 2018-05-10 15:21:00\n","Monero 2018-01-01 00:01:00\n","Stellar 2018-02-16 23:53:00\n","TRON 2018-02-06 21:37:00\n","Name: datetime, dtype: datetime64[ns]"]},"metadata":{}}]},{"cell_type":"code","source":"df.groupby('Asset_Name').datetime.max()","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":["Asset_Name\n","Binance Coin 2022-01-23 23:22:00\n","Bitcoin 2022-01-23 23:23:00\n","Bitcoin Cash 2022-01-23 23:23:00\n","Cardano 2022-01-23 23:44:00\n","Dogecoin 2022-01-23 23:29:00\n","EOS.IO 2022-01-23 23:27:00\n","Ethereum 2022-01-23 23:45:00\n","Ethereum Classic 2022-01-23 23:23:00\n","IOTA 2022-01-23 23:01:00\n","Litecoin 2022-01-23 23:36:00\n","Maker 2022-01-23 23:42:00\n","Monero 2022-01-23 23:08:00\n","Stellar 2022-01-23 23:20:00\n","TRON 2022-01-23 23:27:00\n","Name: datetime, dtype: datetime64[ns]"]},"metadata":{}}]},{"cell_type":"markdown","source":"> Different time periods for different assets ","metadata":{"slideshow":{"slide_type":"notes"}}},{"cell_type":"code","source":"#check if it is sorted according to time \ndf.index.is_monotonic","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{}}]},{"cell_type":"code","source":"#check gaps between timestamp for each assest\nfor asset in df.Asset_Name.unique():\n df_asset=df[df[\"Asset_Name\"]==asset]\n print(asset,'gap counts are ')\n print((df_asset.index[1:]-df_asset.index[:-1]).value_counts().head())","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":21,"outputs":[{"name":"stdout","output_type":"stream","text":"Bitcoin Cash gap counts are \n\n3600 34438\n\n3660 596\n\n3720 214\n\n3780 121\n\n3840 75\n\nName: timestamp, dtype: int64\n\nEthereum Classic gap counts are \n\n3600 33056\n\n3660 1506\n\n3720 462\n\n3780 186\n\n3840 94\n\nName: timestamp, dtype: int64\n\nEthereum gap counts are \n\n3600 35535\n\n3660 52\n\n3780 2\n\n3720 2\n\n4140 2\n\nName: timestamp, dtype: int64\n\nBitcoin gap counts are \n\n3600 35541\n\n3660 49\n\n5580 2\n\n6420 1\n\n17280 1\n\nName: timestamp, dtype: int64\n\nLitecoin gap counts are \n\n3600 35491\n\n3660 91\n\n3780 3\n\n4500 1\n\n3900 1\n\nName: timestamp, dtype: int64\n\nMonero gap counts are \n\n3600 17064\n\n3660 2074\n\n3720 1292\n\n3780 950\n\n3840 877\n\nName: timestamp, dtype: int64\n\nBinance Coin gap counts are \n\n3600 32218\n\n3660 1641\n\n3720 690\n\n3780 289\n\n3840 191\n\nName: timestamp, dtype: int64\n\nEOS.IO gap counts are \n\n3600 35110\n\n3660 228\n\n3720 98\n\n3780 45\n\n3840 29\n\nName: timestamp, dtype: int64\n\nTRON gap counts are \n\n3600 33273\n\n3660 245\n\n3720 59\n\n3780 34\n\n3840 16\n\nName: timestamp, dtype: int64\n\nStellar gap counts are \n\n3600 28623\n\n3660 1205\n\n3720 505\n\n3780 318\n\n3840 200\n\nName: timestamp, dtype: int64\n\nCardano gap counts are \n\n3600 29739\n\n3660 1187\n\n3720 566\n\n3780 364\n\n3840 241\n\nName: timestamp, dtype: int64\n\nIOTA gap counts are \n\n3600 18870\n\n3660 1548\n\n3720 720\n\n3780 568\n\n3840 477\n\nName: timestamp, dtype: int64\n\nMaker gap counts are \n\n3600 11966\n\n3660 645\n\n3720 210\n\n3780 116\n\n3840 72\n\nName: timestamp, dtype: int64\n\nDogecoin gap counts are \n\n3600 12823\n\n3660 1182\n\n3720 856\n\n3780 651\n\n3840 574\n\nName: timestamp, dtype: int64\n"}]},{"cell_type":"markdown","source":"> almost all gaps are very close to the one hour interval","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"code","source":"#check for linear correlation between columns \nsns.heatmap(df.corr());\nplt.title('Heatmap of columns', fontsize = 10);","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"code","source":"#check for linear correlation between targets of different currencies\ndata = df[['datetime', 'Asset_Name', 'Target']]\n#Creating dataframe with a column for each different Assest ID\ndata_new = data.set_index(['datetime', 'Asset_Name']).unstack(['Asset_Name'])\n#remove vwap from column index\ndata_new.columns=data_new.columns.droplevel(0)","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"#check for linear correlation between targets of different currencies\nsns.heatmap(data_new.corr());\nplt.title('Heatmap of Asset correlation', fontsize = 10);","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAowAAAIuCAYAAAA151kzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC6NUlEQVR4nOzdd1gU1/s28HtoS69K0VBEpFlRYo1BoghqLIndqGBLLLFX9GsvRCNGjd2oqDHGJJYosWPvoqJGsKGIiRg7KioCe94/fNmfK7CC7g7F+5NrrrBnZs5zZlnh4Zw5ZyQhhAARERERUR70CrsBRERERFS0MWEkIiIiIo2YMBIRERGRRkwYiYiIiEgjJoxEREREpBETRiIiIiLSiAkjEREREWnEhJGIiIiINGLCSEREREQaMWEkIiqgZ8+eoXXr1rC0tIQkSXj06FFhN6nEaNCgAQYNGlRk6iGiV5gwEn3gwsLC0KpVqxzl+/bt03oypIs6C8PKlStx8OBBHDlyBCkpKbCyssrz2OfPn8PGxga2trZ4/vy5bG0sKe/12+R1nRs2bMDkyZMLp1FEJRATRiKiAkpMTISPjw8qVaoER0dHSJKU57Hr169HpUqV4Ovriw0bNsjYysIhhEBmZmaO8pcvX8raDltbW1hYWMgak6gkY8JIRPl25MgRfPrppzAxMYGzszMGDBiAtLQ01f6ff/4Z/v7+sLCwgKOjIzp16oQ7d+4AAJKSkhAYGAgAsLGxgSRJCAsLA/Bq+LB///4YNGgQbGxs4ODggCVLliAtLQ3dunWDhYUFypcvj23btqliZWVloUePHihXrhxMTEzg5eWFOXPmqLU3u/d04sSJsLe3h6WlJb755pu3Ji/r169HxYoVoVAo4ObmhsjISNW+Bg0aIDIyEgcOHIAkSWjQoIHGupYtW4bOnTujc+fOWLZsWY79EyZMgIuLCxQKBcqUKYMBAwao9i1YsAAVKlSAsbExHBwc0KZNG9U+IQRmzJgBd3d3mJiYoGrVqvjjjz/e+l7n5vDhwwgICICpqSlsbGwQHByMhw8fAgDS09MxYMAA2Nvbw9jYGJ988glOnjypOje7h2/Hjh3w9/eHQqHAwYMH0aBBA3z77bcYMmQISpUqhaCgIABAfHw8mjZtCnNzczg4OKBLly64d+9enm17n8/U60PSDx8+RNeuXWFjYwNTU1M0adIEV65cUe2PioqCtbU1duzYAR8fH5ibmyMkJAQpKSl5to3ogyKI6IMWGhoqWrZsmaN87969AoB4+PChEEKIc+fOCXNzc/HDDz+Iy5cvi8OHDws/Pz8RFhamOmfZsmVi69atIjExURw9elTUrl1bNGnSRAghRGZmpli/fr0AIC5duiRSUlLEo0ePhBBCBAQECAsLCzF58mRx+fJlMXnyZKGnpyeaNGkilixZIi5fviz69Okj7OzsRFpamhBCiJcvX4px48aJEydOiGvXromff/5ZmJqainXr1qldm7m5uWjfvr34+++/RXR0tChdurQYPXp0nu9HbGys0NPTE5MmTRKXLl0SK1asECYmJmLFihVCCCHu378vevXqJerUqSNSUlLE/fv386zr6tWrQqFQiAcPHoj79+8LhUIhEhMTVft///13YWlpKbZu3Spu3Lghjh8/LpYsWSKEEOLkyZNCX19f/PLLLyIpKUmcPn1azJkzR3Xu6NGjhbe3t9i+fbtITEwUK1asEAqFQuzbt0/je/2mM2fOCIVCIfr06SPi4uLE33//LX788Udx9+5dIYQQAwYMEGXKlBFbt24VFy5cEKGhocLGxkZ13dmfkypVqoidO3eKq1evinv37omAgABhbm4uhg8fLi5evCgSEhLErVu3RKlSpUR4eLhISEgQp0+fFkFBQSIwMFDVnoCAADFw4ECtfKZer6dFixbCx8dHHDhwQMTFxYng4GDh4eEhXr58KYQQYsWKFcLQ0FA0atRInDx5Upw6dUr4+PiITp065fn9JfqQMGEk+sCFhoYKfX19YWZmprYZGxurJYxdunQRX3/9tdq5Bw8eFHp6euL58+e51n3ixAkBQDx58kQIkTMJzRYQECA++eQT1evMzExhZmYmunTpoipLSUkRAMTRo0fzvJa+ffuK1q1bq12bra2tKskUQoiFCxcKc3NzkZWVlWsdnTp1EkFBQWplw4cPF76+vqrXAwcOFAEBAXm2I9vo0aNFq1atVK9btmwpxowZo3odGRkpPD09VUnL69avXy8sLS3F48ePc+x7+vSpMDY2FkeOHFEr79Gjh+jYsaMQIu/3+k0dO3YU9erVy3Xf06dPhaGhoVizZo2q7OXLl6JMmTJixowZanE2bdqkdm5AQICoVq2aWtnYsWNF48aN1cpu3rypSviyz3s90XtTQT5T2fVcvnxZABCHDx9W7b93754wMTERv/32mxDiVcIIQFy9elV1zPz584WDg0OebSH6kHBImogQGBiIuLg4te2nn35SO+bUqVOIioqCubm5agsODoZSqcT169cBAGfOnEHLli3h6uoKCwsL1XBtcnLyW9tQpUoV1df6+vqws7ND5cqVVWUODg4AoBqOBIBFixbB398fpUuXhrm5OZYuXZojVtWqVWFqaqp6XadOHTx9+hQ3b97MtR0JCQmoV6+eWlm9evVw5coVZGVlvfU6smVlZWHlypXo3Lmzqqxz585YuXKlqp62bdvi+fPncHd3R69evbBx40bV/X9BQUFwdXWFu7s7unTpgjVr1uDZs2cAXg3rvnjxAkFBQWrfj1WrViExMTHfbQSAuLg4NGzYMNd9iYmJyMjIUHs/DA0NUbNmTSQkJKgd6+/vn+P8N8tOnTqFvXv3qrXZ29tbFSs37/OZypaQkAADAwPUqlVLVWZnZwcvLy+16zA1NUX58uVVr52cnNQ+b0QfMoPCbgARFT4zMzN4eHiolf3zzz9qr5VKJb755hu1e+yyubi4IC0tDY0bN0bjxo3x888/o3Tp0khOTkZwcHC+JjwYGhqqvZYkSa0se2KJUqkEAPz2228YPHgwIiMjUadOHVhYWOD777/H8ePH83XNeU1UEULk2CeEyFedr9uxYwf+/fdftG/fXq08KysLO3fuRJMmTeDs7IxLly5h165d2L17N/r27Yvvv/8e+/fvh4WFBU6fPo19+/Zh586dGDduHCZMmICTJ0+q3oO//voLZcuWVatfoVAUqJ0mJiZ57su+7tzejzfLzMzMcpz/ZplSqUTz5s0xffr0HMc6OTnlKHvfz9Sb15Fb+evXkdtn8F2+90QlERNGIsqX6tWr48KFCzkSy2znz5/HvXv38N1338HZ2RkAEBsbq3aMkZERABSopy4vBw8eRN26ddG3b19VWW69VGfPnsXz589VidGxY8dgbm6Ojz76KNd6fX19cejQIbWyI0eOwNPTE/r6+vlu37Jly9ChQweMGTNGrfy7777DsmXL0KRJEwCvErYWLVqgRYsW6NevH7y9vXH+/HlUr14dBgYGaNSoERo1aoTx48fD2toae/bsQVBQEBQKBZKTkxEQEJBr/Py+11WqVEFMTAwmTpyYY5+HhweMjIxw6NAhdOrUCQCQkZGB2NjYd1rjsHr16li/fj3c3NxgYPD2Xz8XL17UymfK19cXmZmZOH78OOrWrQsAuH//Pi5fvgwfH58CXwfRh4hD0kSULyNHjsTRo0fRr18/xMXF4cqVK9i8eTP69+8P4FUvo5GREX788Udcu3YNmzdvzrEOnqurKyRJQnR0NO7evYunT5++c3s8PDwQGxuLHTt24PLlyxg7dqza7N1sL1++RI8ePRAfH49t27Zh/Pjx+Pbbb6Gnl/uPv6FDhyImJgaTJ0/G5cuXsXLlSsybNw/Dhg3Ld9vu3r2LLVu2IDQ0FJUqVVLbQkNDsXnzZty9exdRUVFYtmwZ/v77b1y7dg2rV6+GiYkJXF1dER0djblz5yIuLg43btzAqlWroFQq4eXlBQsLCwwbNgyDBw/GypUrkZiYiDNnzmD+/PlYuXIlgPy/1+Hh4Th58iT69u2Lc+fO4eLFi1i4cCHu3bsHMzMz9OnTB8OHD8f27dsRHx+PXr164dmzZ+jRo0e+349s/fr1w4MHD9CxY0ecOHEC165dw86dO9G9e/dcEz5tfaYqVKiAli1bolevXjh06BDOnj2Lzp07o2zZsmjZsmWBr4Pog1SI908SURGQ31nSQryacBAUFCTMzc2FmZmZqFKlipg6dapq/y+//CLc3NyEQqEQderUEZs3bxYAxJkzZ1THTJo0STg6OgpJkkRoaKgQIveJDq6uruKHH35QKwMgNm7cKIQQ4sWLFyIsLExYWVkJa2tr0adPHzFq1ChRtWrVHNc2btw4YWdnJ8zNzUXPnj3FixcvNL4nf/zxh/D19RWGhobCxcVFfP/992r73zbpZebMmcLa2jrXySwZGRnC1tZWREZGio0bN4patWoJS0tLYWZmJmrXri12794thHg1oSggIEDY2NgIExMTUaVKFbUZ4EqlUsyZM0d4eXkJQ0NDUbp0aREcHCz279+vOia39zo3+/btE3Xr1hUKhUJYW1uL4OBg1ff9+fPnon///qJUqVJCoVCIevXqiRMnTqjOzc+kk9ddvnxZfPHFF8La2lqYmJgIb29vMWjQIKFUKnM9T1ufqQcPHoguXboIKysrYWJiIoKDg8Xly5dV+1esWCGsrKzU2rpx40bBX5NEr0hC8AYNIiqZwsLC8OjRI2zatKmwm0JEVKxxSJqIiIiINGLCSEREREQacUiaiIiIiDRiDyMRERFRITpw4ACaN2+OMmXKQJKkfN13vX//ftSoUQPGxsZwd3fHokWLdNpGJoxEREREhSgtLQ1Vq1bFvHnz8nX89evX0bRpU9SvXx9nzpzB6NGjMWDAAKxfv15nbeSQNBEREVERIUkSNm7ciFatWuV5zMiRI7F582a1R1v27t0bZ8+exdGjR3XSLvYwEhEREWlReno6Hj9+rLalp6drrf6jR4+icePGamXBwcGIjY1FRkaG1uK8jo8GpEKTce+a7DG718j/0zq0oZyU93N6deERMmWNBwALbx16+0Fa1NTRT9Z4dnrGssYDgIpKeWPavv+TGgtkq8ETeQMCcJDkfU87pcv/b9HF5aGs8Z48kPc99bmyVecxtPV7KWLeqhyP2xw/fjwmTJiglfpv374NBwcHtTIHBwdkZmbi3r17uT6b/X0xYSQiIiICAKV2/noKDw/HkCFD1MoUCoVW6s4mSZLa6+w7DN8s1xYmjERERERapFAotJ4gvs7R0RG3b99WK7tz5w4MDAxgZ2enk5hMGImIiIgAQCgLuwX5UqdOHWzZskWtbOfOnfD394ehoaFOYnLSCxEREREAKJXa2Qro6dOniIuLQ1xcHIBXy+bExcUhOTkZwKsh7q5du6qO7927N27cuIEhQ4YgISEBy5cvx7JlyzBsmO7u02cPIxEREVEhio2NRWBgoOp19v2PoaGhiIqKQkpKiip5BIBy5cph69atGDx4MObPn48yZcpg7ty5aN26tc7ayISRiIiICIAopCHpBg0aQNOy2FFRUTnKAgICcPr0aR22Sh2HpHORlJQESZJUXcP06sNqbW1d2M0gIiLSnUIaki4OPriEMSwsDJIkqTY7OzuEhITg3LlzqmOcnZ2RkpKCSpUqFWJLtefx48cYM2YMvL29YWxsDEdHRzRq1AgbNmzQ+BfN69q3b4/Lly/ruKVERERUFH1wCSMAhISEICUlBSkpKYiJiYGBgQE+//xz1X59fX04OjrCwKD4j9g/evQIdevWxapVqxAeHo7Tp0/jwIEDaN++PUaMGIHU1NR81WNiYgJ7e3sdt5aIiKgQCaV2thLog0wYFQoFHB0d4ejoiGrVqmHkyJG4efMm7t69CyDnkPS+ffsgSRJiYmLg7+8PU1NT1K1bF5cuXVLVmZiYiJYtW8LBwQHm5ub4+OOPsXv3brW4bm5umDZtGrp37w4LCwu4uLhgyZIlasf8888/6NChA2xtbWFmZgZ/f38cP35ctX/Lli2oUaMGjI2N4e7ujokTJyIzM+8nCowePRpJSUk4fvw4QkND4evrC09PT/Tq1QtxcXEwNzcHADx8+BBdu3aFjY0NTE1N0aRJE1y5ckVVz5tD0hMmTEC1atWwevVquLm5wcrKCh06dMCTJ/I/wYGIiEgrlFna2UqgDzJhfN3Tp0+xZs0aeHh4vHWxyzFjxiAyMhKxsbEwMDBA9+7d1epp2rQpdu/ejTNnziA4OBjNmzdXm9UEAJGRkfD398eZM2fQt29f9OnTBxcvXlTVERAQgFu3bmHz5s04e/YsRowYAeX/vx9ix44d6Ny5MwYMGID4+HgsXrwYUVFRmDp1aq7tVSqV+PXXX/HVV1+hTJkyOfabm5urelHDwsIQGxuLzZs34+jRoxBCoGnTphqfSZmYmIhNmzYhOjoa0dHR2L9/P7777juN7yEREREVP8V/zPUdREdHq3rW0tLS4OTkhOjoaOjpac6fp06dioCAAADAqFGj0KxZM7x48QLGxsaoWrUqqlatqjp2ypQp2LhxIzZv3oxvv/1WVd60aVP07dsXADBy5Ej88MMP2LdvH7y9vfHLL7/g7t27OHnyJGxtbQEAHh4eavFHjRqF0NBQAIC7uzsmT56MESNGYPz48Tnae+/ePTx8+BDe3t4ar+vKlSvYvHkzDh8+jLp16wIA1qxZA2dnZ2zatAlt27bN9TylUomoqChYWFgAALp06YKYmJhcE9j09PQcD17XS0/X6Ur4REREBVJCh5O14YPsYQwMDFQtkHn8+HE0btwYTZo0wY0bNzSeV6VKFdXX2Q/2vnPnDoBXieeIESPg6+sLa2trmJub4+LFizl6GF+vQ5IkODo6quqIi4uDn5+fKll806lTpzBp0iSYm5urtl69eiElJQXPnj3LcXx+nyuZkJAAAwMD1KpVS1VmZ2cHLy8vJCQk5Hmem5ubKlnMfk+yr+VNERERsLKyUtumz1mksV1ERESy4izpPH2QPYxmZmZqPXc1atSAlZUVli5diilTpuR53uuP28lOwrKHi4cPH44dO3Zg5syZ8PDwgImJCdq0aYOXL1/mWUd2Pdl1mJiYaGy3UqnExIkT8eWXX+bYZ2xsnKOsdOnSsLGx0Zj0AchzprQQQmOyqela3pTbg9j1nvyrsV1ERERyKqx1GIuDD7KH8U2SJEFPTw/Pnz9/5zoOHjyIsLAwfPHFF6hcuTIcHR2RlJRUoDqqVKmCuLg4PHjwINf91atXx6VLl+Dh4ZFjy204XU9PD+3bt8eaNWtw69atHPvT0tKQmZkJX19fZGZmqk2uuX//Pi5fvgwfH58CXUNeFAoFLC0t1TYORxMRERUPH2TCmJ6ejtu3b+P27dtISEhA//798fTpUzRv3vyd6/Tw8MCGDRsQFxeHs2fPolOnTnn2tuWlY8eOcHR0RKtWrXD48GFcu3YN69evx9GjRwEA48aNw6pVqzBhwgRcuHABCQkJWLduHf73v//lWee0adPg7OyMWrVqYdWqVYiPj8eVK1ewfPlyVKtWDU+fPkWFChXQsmVL9OrVC4cOHcLZs2fRuXNnlC1bFi1btnzn94SIiKhY4ZB0nj7IhHH79u1wcnKCk5MTatWqhZMnT+L3339HgwYN3rnOH374ATY2Nqhbty6aN2+O4OBgVK9evUB1GBkZYefOnbC3t0fTpk1RuXJlfPfdd9DX1wcABAcHIzo6Grt27cLHH3+M2rVrY9asWXB1dc2zThsbGxw7dgydO3fGlClT4Ofnh/r162Pt2rX4/vvvYWVlBQBYsWIFatSogc8//xx16tSBEAJbt27NMexMRERUYnEdxjxJIr+P+iDSsox712SP2b3GMFnjlZM035eqbY+Q95qcurLw1iFZ4zV19JM1np1ezvuDda2iUt6YtjIvG7fVQP71Wh0ked/TTuny/1t0cXkoa7wnD+R9T32ubNV5jPTL2vl5pvD8RCv1FCUf5KQXIiIiohxK6KLb2sCEkYiIiAgoscPJ2vBB3sNIRERERPnHHkYiIiIioMTOcNYGJoxEREREAIekNeCQNBERERFpxB5GIiIiIoBD0howYaRCI/eaiACw/NRMWeNN9h8ra7zfHp2TNR4ABDlWlTWepWQka7z7yheyxgOAQ0iXNV6KkHddxDDlR7LGA4AT+vJ+H2caZcgaDwCc/7OXNV5C1iNZ4+2UIYYQXFYnL0wYiYiIiADew6gB72EkIiIiIo3Yw0hEREQE8B5GDZgwEhEREQEcktaAQ9JEREREpBF7GImIiIgAQMlZ0nlhD2MxlpSUBEmSEBcXp9M4YWFhaNWqlU5jEBERFTqh1M5WAjFhLMLCwsIgSZJqs7OzQ0hICM6de7XWnrOzM1JSUlCpUiUAwL59+yBJEh49eqTVdsyZMwdRUVFarZOIiIiKDyaMRVxISAhSUlKQkpKCmJgYGBgY4PPPPwcA6Ovrw9HREQYGur2zwMrKCtbW1jqNQUREVOiUSu1sJRATxiJOoVDA0dERjo6OqFatGkaOHImbN2/i7t27akPSSUlJCAwMBADY2NhAkiSEhYUBAJRKJaZPnw4PDw8oFAq4uLhg6tSpqhjnz5/HZ599BhMTE9jZ2eHrr7/G06dPVfvfHJJu0KABBgwYgBEjRsDW1haOjo6YMGGCHG8HERGR7nBIOk9MGIuRp0+fYs2aNfDw8ICdnZ3aPmdnZ6xfvx4AcOnSJaSkpGDOnDkAgPDwcEyfPh1jx45FfHw8fvnlFzg4OAAAnj17hpCQENjY2ODkyZP4/fffsXv3bnz77bca27Jy5UqYmZnh+PHjmDFjBiZNmoRdu3bp4KqJiIiosHGWdBEXHR0Nc3NzAEBaWhqcnJwQHR0NPT31XF9fXx+2trYAAHt7e9UQ8pMnTzBnzhzMmzcPoaGhAIDy5cvjk08+AQCsWbMGz58/x6pVq2BmZgYAmDdvHpo3b47p06erEss3ValSBePHjwcAVKhQAfPmzUNMTAyCgoK0+wYQERHJpYQOJ2sDE8YiLjAwEAsXLgQAPHjwAAsWLECTJk1w4sSJfJ2fkJCA9PR0NGzYMM/9VatWVSWLAFCvXj0olUpcunRJY8L4OicnJ9y5cyfPdqSnpyM9PV2tLEtkQV/Sz9d1EBER6RwTxjxxSLqIMzMzg4eHBzw8PFCzZk0sW7YMaWlpWLp0ab7ONzEx0bhfCAFJknLdl1c5ABgaGuY4VqnhH1pERASsrKzUtr9TL2tsGxERkZyEyNLKVhIxYSxmJEmCnp4enj9/nmOfkZERACAr6/8+rBUqVICJiQliYmJyrc/X1xdxcXFIS0tTlR0+fBh6enrw9PTUWrvDw8ORmpqqtlWy0l79REREpDtMGIu49PR03L59G7dv30ZCQgL69++Pp0+fonnz5jmOdXV1hSRJiI6Oxt27d/H06VMYGxtj5MiRGDFiBFatWoXExEQcO3YMy5YtAwB89dVXMDY2RmhoKP7++2/s3bsX/fv3R5cuXfIcjn4XCoUClpaWahuHo4mIqEjhsjp5YsJYxG3fvh1OTk5wcnJCrVq1VDOZGzRokOPYsmXLYuLEiRg1ahQcHBxUM53Hjh2LoUOHYty4cfDx8UH79u1V9xuamppix44dePDgAT7++GO0adMGDRs2xLx58+S8TCIiosLHZXXyJAkhRGE3gj5MXVy/lD3m8lMzZY032X+srPGWPDotazwAqG5ZTtZ4dpKxrPEei5eyxgMAPeR9/7AupGQ+kTVemP5HssYDgBP6L2SN96gQPjfOkqms8RKyHskab+fN7TqP8XzvT1qpxySwp1bqKUo4S5qIiIgIKLHDydrAhJGIiIgIKLHDydrAexiJiIiISCP2MBIREREBHJLWgAkjEREREcAhaQ04JE1EREREGjFhJCIiIgIKdeHuBQsWoFy5cjA2NkaNGjVw8OBBjcevWbMGVatWhampKZycnNCtWzfcv3//nWLnBxNGIiIiIqDQEsZ169Zh0KBBGDNmDM6cOYP69eujSZMmSE5OzvX4Q4cOoWvXrujRowcuXLiA33//HSdPnkTPnrpb/5ELd1OhGef2VWE3QefGxk6WNd6GyvIuFA4AozMSZI1X1UzeRZ/TlBmyxgOAsvpmssZ7IuS9xtKSQtZ4AHD65R1Z47U1kH9x8t1Cd71LuXHTt5A13sKk33Qe43n0LK3UY/L5kAIdX6tWLVSvXh0LFy5Ulfn4+KBVq1aIiIjIcfzMmTOxcOFCJCYmqsp+/PFHzJgxAzdv3nz3hmvAHkYiIiIiLUpPT8fjx4/VtvT09FyPffnyJU6dOoXGjRurlTdu3BhHjhzJ9Zy6devin3/+wdatWyGEwH///Yc//vgDzZo10/q1ZGPCSERERARobUg6IiICVlZWaltuPYUAcO/ePWRlZcHBwUGt3MHBAbdv3871nLp162LNmjVo3749jIyM4OjoCGtra/z4449af0uyMWEkIiIiAl4tq6OFLTw8HKmpqWpbeHi4xtCSpP4MeSFEjrJs8fHxGDBgAMaNG4dTp05h+/btuH79Onr37q21t+JNXIeRiIiISIsUCgUUivzdq1uqVCno6+vn6E28c+dOjl7HbBEREahXrx6GDx8OAKhSpQrMzMxQv359TJkyBU5OTu93AblgDyMRERERUCizpI2MjFCjRg3s2rVLrXzXrl2oW7duruc8e/YMenrqKZy+vj6AVz2TusCEMR+SkpIgSRLi4uJ0GicsLAytWrXSaQxtkOv9ICIikpWWhqQLasiQIfjpp5+wfPlyJCQkYPDgwUhOTlYNMYeHh6Nr166q45s3b44NGzZg4cKFuHbtGg4fPowBAwagZs2aKFOmjNbejtd98AljWFgYJElSbXZ2dggJCcG5c+dUxzg7OyMlJQWVKlUCAOzbtw+SJOHRo0dabcucOXMQFRX13vU8fvwYY8aMgbe3N4yNjeHo6IhGjRphw4YNOvvLg4iIiN5N+/btMXv2bEyaNAnVqlXDgQMHsHXrVri6ugIAUlJS1NZkDAsLw6xZszBv3jxUqlQJbdu2hZeXFzZs2KCzNvIeRgAhISFYsWIFAOD27dv43//+h88//1z1zdHX14ejo6PO22FlZfXedTx69AiffPIJUlNTMWXKFHz88ccwMDDA/v37MWLECHz22WewtrZ+/8YSERGVNO/4lBZt6Nu3L/r27Zvrvtw6k/r374/+/fvruFX/54PvYQRe3Zzq6OgIR0dHVKtWDSNHjsTNmzdx9+5dAOpDsElJSQgMDAQA2NjYQJIkhIWFAQCUSiWmT58ODw8PKBQKuLi4YOrUqao458+fx2effQYTExPY2dnh66+/xtOnT1X73xySbtCgAQYMGIARI0bA1tYWjo6OmDBhgsZrGT16NJKSknD8+HGEhobC19cXnp6e6NWrF+Li4mBubg4A+Pnnn+Hv7w8LCws4OjqiU6dOuHPn/xa2ffjwIb766iuULl0aJiYmqFChgiqpznbt2jUEBgbC1NQUVatWxdGjRwv83hMRERUZhfhowKKOCeMbnj59ijVr1sDDwwN2dnY59js7O2P9+vUAgEuXLiElJQVz5swB8Ooeg+nTp2Ps2LGIj4/HL7/8oprh9OzZM4SEhMDGxgYnT57E77//jt27d+Pbb7/V2J6VK1fCzMwMx48fx4wZMzBp0qQcN8ZmUyqV+PXXX/HVV1/leg+Dubk5DAxedSq/fPkSkydPxtmzZ7Fp0yZcv35dlfgCUF3Dtm3bkJCQgIULF6JUqVJq9Y0ZMwbDhg1DXFwcPD090bFjR2RmZmq8HiIiIip+OCQNIDo6WtXzlpaWBicnJ0RHR+eYgQS8Gp62tbUFANjb26uGd588eYI5c+Zg3rx5CA0NBQCUL18en3zyCYBXDwl//vw5Vq1aBTOzV4/9mjdvHpo3b47p06fnOXW+SpUqGD9+PACgQoUKmDdvHmJiYhAUFJTj2Hv37uHhw4fw9vZ+6zV3795d9bW7uzvmzp2LmjVr4unTpzA3N0dycjL8/Pzg7+8PAHBzc8tRx7Bhw1Sryk+cOBEVK1bE1atX8xWfiIioyOF9/nliDyOAwMBAxMXFIS4uDsePH0fjxo3RpEkT3LhxI991JCQkID09HQ0bNsxzf9WqVVXJIgDUq1cPSqUSly5dyrPeKlWqqL12cnJSGzp+XfaElrwW+nzdmTNn0LJlS7i6usLCwgINGjQAANV9m3369MGvv/6KatWqYcSIEbk+nuj1tmWv+ZRX23J7TFKmyHprO4mIiGTDIek8MWEEYGZmBg8PD3h4eKBmzZpYtmwZ0tLSsHTp0nzXYWJionG/phXbNSV4hoaGOY5V5vFhLF26NGxsbJCQkKCxLWlpaWjcuDHMzc3x888/4+TJk9i4cSOAV0PVAFQJ86BBg3Dr1i00bNgQw4YNy7Nt2deQV9tye0zS4dQLGttJREQkKyaMeWLCmAtJkqCnp4fnz5/nut/IyAgAkJX1fz1kFSpUgImJCWJiYnI9x9fXF3FxcUhLS1OVHT58GHp6evD09NRKu/X09NC+fXusWbMGt27dyrE/LS0NmZmZuHjxIu7du4fvvvsO9evXh7e3d649g6VLl0ZYWBh+/vlnzJ49G0uWLHnntuX2mKR6VhXfuT4iIiKSDxNGvBouvX37Nm7fvo2EhAT0798fT58+RfPmzXM93tXVFZIkITo6Gnfv3sXTp09hbGyMkSNHYsSIEVi1ahUSExNx7NgxLFu2DADw1VdfwdjYGKGhofj777+xd+9e9O/fH126dMnz/sV3MW3aNDg7O6NWrVpYtWoV4uPjceXKFSxfvhzVqlXD06dP4eLiAiMjI/z444+4du0aNm/ejMmTJ6vVM27cOPz555+4evUqLly4gOjoaPj4+LxzuxQKBSwtLdU2A0n/fS+XiIhIewpp4e7igJNeAGzfvl11D56FhQW8vb3x+++/q+7re1PZsmUxceJEjBo1Ct26dUPXrl0RFRWFsWPHwsDAAOPGjcOtW7fg5OSkWqXd1NQUO3bswMCBA/Hxxx/D1NQUrVu3xqxZs7R6LTY2Njh27Bi+++47TJkyBTdu3ICNjQ0qV66M77//HlZWVpAkCVFRURg9ejTmzp2L6tWrY+bMmWjRooWqHiMjI4SHhyMpKQkmJiaoX78+fv31V622lYiIqEgpocPJ2iAJPvqDCsk4t68Kuwk6NzZ28tsP0qINlcfKGg8ARmdovmdW26qafSRrvDRlhqzxAKCsvtnbD9KiJ0LeaywtKWSNBwCnX+Y+IU9X2hrI+zkFgN3ivqzx3PQtZI23MOk3ncd4vipcK/WYdI3QSj1FCXsYiYiIiAAuq6MBE0YiIiIigEPSGnDSCxERERFpxB5GIiIiIoA9jBowYSQiIiICSuySONrAIWkiIiIi0og9jEREREQAhJKzpPPChJGIiIgI4D2MGjBhpELzCJmyx/zt0TlZ41WUeSHtL8/Lu1A4ADT+prus8eocTJE1np9JWVnjAcBLme+jMpT57iRnYSRrPAA4IbJkjVcY/VTNYCdrvLZu/8gaTxa8hzFPvIeRiIiIiDRiDyMRERERAPAexjwxYSQiIiICeA+jBhySJiIiIiKN2MNIREREBLCHUQMmjEREREQAIHgPY144JP0BcHNzw+zZswu7GURERFRMMWEsJLdv30b//v3h7u4OhUIBZ2dnNG/eHDExMYXdNCIiog+TUqmdrQTikHQhSEpKQr169WBtbY0ZM2agSpUqyMjIwI4dO9CvXz9cvHixwHVmZWVBkiTo6fFvACIionfCZXXyxOyiEPTt2xeSJOHEiRNo06YNPD09UbFiRQwZMgTHjh0DAMyaNQuVK1eGmZkZnJ2d0bdvXzx9+lRVR1RUFKytrREdHQ1fX18oFArcuHEDd+7cQfPmzWFiYoJy5cphzZo1OeLnt+4dO3bAx8cH5ubmCAkJQUrK/z1hQ6lUYtKkSfjoo4+gUChQrVo1bN++XYfvGhERERUWJowye/DgAbZv345+/frBzMwsx35ra2sAgJ6eHubOnYu///4bK1euxJ49ezBixAi1Y589e4aIiAj89NNPuHDhAuzt7REWFoakpCTs2bMHf/zxBxYsWIA7d+6onZffumfOnInVq1fjwIEDSE5OxrBhw1T758yZg8jISMycORPnzp1DcHAwWrRogStXrmjpnSIiIpKZUGpnK4E4JC2zq1evQggBb29vjccNGjRI9XW5cuUwefJk9OnTBwsWLFCVZ2RkYMGCBahatSoA4PLly9i2bRuOHTuGWrVqAQCWLVsGHx+fd6p70aJFKF++PADg22+/xaRJk1T7Z86ciZEjR6JDhw4AgOnTp2Pv3r2YPXs25s+fn+N60tPTkZ6erlaWJbKgL+lrfB+IiIhkwyHpPLGHUWbi/0/ZlyRJ43F79+5FUFAQypYtCwsLC3Tt2hX3799HWlqa6hgjIyNUqVJF9TohIQEGBgbw9/dXlXl7e6t6LQtSt6mpqSpZBAAnJydVT+Xjx49x69Yt1KtXT63eevXqISEhIdfriYiIgJWVldoWm5r7sURERIVBKJVa2UoiJowyq1ChAiRJyjOxAoAbN26gadOmqFSpEtavX49Tp06peu0yMjJUx5mYmKglnvlJRvNbt6Ghodp5kiSp6n+97HVCiDxjh4eHIzU1VW3zt/LJ9VgiIiIqWpgwyszW1hbBwcGYP3++Wo9etkePHiE2NhaZmZmIjIxE7dq14enpiVu3br21bh8fH2RmZiI2NlZVdunSJTx69Ej1+l3rfp2lpSXKlCmDQ4cOqZUfOXIkx/B3NoVCAUtLS7WNw9FERFSkKIV2thKICWMhWLBgAbKyslCzZk2sX78eV65cQUJCAubOnYs6deqgfPnyyMzMxI8//ohr165h9erVWLRo0Vvr9fLyQkhICHr16oXjx4/j1KlT6NmzJ0xMTFTHvGvdbxo+fDimT5+OdevW4dKlSxg1ahTi4uIwcODAAtdFRERUJHDSS56YMBaCcuXK4fTp0wgMDMTQoUNRqVIlBAUFISYmBgsXLkS1atUwa9YsTJ8+HZUqVcKaNWsQERGRr7pXrFgBZ2dnBAQE4Msvv8TXX38Ne3t71f73qft1AwYMwNChQzF06FBUrlwZ27dvx+bNm1GhQoUC10VERERFmyTevDGNSCYD3NrLHvO3R+dkjTfHpLqs8b48P1nWeADw9JvussarczDnrRy65GdSVtZ4AKAv89/ySsj7a6Ayci4ppmvrX96QNV4HQ1dZ4wGAscy/zduW/0fWeA779uk8Rtqkr7RSj9m4nGsgF3dcVoeIiIgIKLGP9dMGDkkTERERkUbsYSQiIiICSuwMZ21gwkhEREQElNgZztrAIWkiIiIi0og9jEREREQAh6Q1YMJIREREBJTY50BrAxNGIiIiIoA9jBowYaRCs/DWobcfpGVBjlVljTf6WYKs8RrLvIg2AJgvXi5rvKtl6ssar6Wph6zxAOARMmWNd0v5XNZ4V/ReyBoPAEz0jGSNtzkrRdZ4AFDKQN4F0Q9et5Y13jpZo8lvwYIF+P7775GSkoKKFSti9uzZqF8/75936enpmDRpEn7++Wfcvn0bH330EcaMGYPu3XXze4AJIxERERFQaD2M69atw6BBg7BgwQLUq1cPixcvRpMmTRAfHw8XF5dcz2nXrh3+++8/LFu2DB4eHrhz5w4yM3X3xyYTRiIiIiKg0JbVmTVrFnr06IGePXsCAGbPno0dO3Zg4cKFiIiIyHH89u3bsX//fly7dg22trYAADc3N522kcvqEBEREWlReno6Hj9+rLalp6fneuzLly9x6tQpNG7cWK28cePGOHLkSK7nbN68Gf7+/pgxYwbKli0LT09PDBs2DM+f6+72EiaMRERERMCrIWktbBEREbCyslLbcuspBIB79+4hKysLDg4OauUODg64fft2rudcu3YNhw4dwt9//42NGzdi9uzZ+OOPP9CvXz+tvyXZOCRNREREBEBo6R7G8PBwDBkyRK1MoVBoPEeSJPW2CJGjLJtSqYQkSVizZg2srKwAvBrWbtOmDebPnw8TE5P3aH3u2MNI+SJJEjZt2lTYzSAiIiryFAoFLC0t1ba8EsZSpUpBX18/R2/inTt3cvQ6ZnNyckLZsmVVySIA+Pj4QAiBf/75R3sX8homjDoSFhYGSZIgSRIMDQ3h4OCAoKAgLF++HMpiuDBoSkoKmjRpUtjNICIi0h0tDUkXhJGREWrUqIFdu3aple/atQt169bN9Zx69erh1q1bePr0qars8uXL0NPTw0cffVTw684HJow6FBISgpSUFCQlJWHbtm0IDAzEwIED8fnnn+t06rsuODo6vrU7nYiIqFhTKrWzFdCQIUPw008/Yfny5UhISMDgwYORnJyM3r17A3g1xN21a1fV8Z06dYKdnR26deuG+Ph4HDhwAMOHD0f37t11MhwNMGHUKYVCAUdHR5QtWxbVq1fH6NGj8eeff2Lbtm2IiooCACQnJ6Nly5YwNzeHpaWlal2l102ZMgX29vawsLBAz549MWrUKFSrVk3tmBUrVsDHxwfGxsbw9vbGggUL1Pb/888/6NChA2xtbWFmZgZ/f38cP35ctX/hwoUoX748jIyM4OXlhdWrV6ud//qQdFJSEiRJwoYNGxAYGAhTU1NUrVoVR48e1c4bR0REVBgKoYcRANq3b4/Zs2dj0qRJqFatGg4cOICtW7fC1dUVwKtRvuTkZNXx5ubm2LVrFx49egR/f3989dVXaN68OebOnau1t+JNnPQis88++wxVq1bFhg0b0KNHD7Rq1QpmZmbYv38/MjMz0bdvX7Rv3x779u0DAKxZswZTp05VLeb566+/IjIyEuXKlVPVuXTpUowfPx7z5s2Dn58fzpw5g169esHMzAyhoaF4+vQpAgICULZsWWzevBmOjo44ffq0amh848aNGDhwIGbPno1GjRohOjoa3bp1w0cffYTAwMA8r2XMmDGYOXMmKlSogDFjxqBjx464evUqDAz4sSIiIiqIvn37om/fvrnuy+5kep23t3eOYWxd4m/2QuDt7Y1z585h9+7dOHfuHK5fvw5nZ2cAwOrVq1GxYkWcPHkSH3/8MX788Uf06NED3bp1AwCMGzcOO3fuVLtvYfLkyYiMjMSXX34JAChXrhzi4+OxePFihIaG4pdffsHdu3dx8uRJ1QKfHh7/97izmTNnIiwsTPVBHTJkCI4dO4aZM2dqTBiHDRuGZs2aAQAmTpyIihUr4urVq/D29tbiu0VERCQTPks6TxySLgTZU+UTEhLg7OysShYBwNfXF9bW1khIePUM4kuXLqFmzZpq57/++u7du7h58yZ69OgBc3Nz1TZlyhQkJiYCAOLi4uDn56dKFt+UkJCAevXqqZXVq1dP1Ya8VKlSRfW1k5MTgFezunKT2yKmQvAfJhERFR1CCK1sJRF7GAtBQkICypUrl+caS2+W57Y2U7bsYeWlS5eiVq1aasfp6+sDQL5ugC3I+k/ZDA0Nc5yf1wzwiIgITJw4UT2mnjn09S3f2jYiIiIqXOxhlNmePXtw/vx5tG7dGr6+vkhOTsbNmzdV++Pj45GamgofHx8AgJeXF06cOKFWR2xsrOprBwcHlC1bFteuXYOHh4faln2fY5UqVRAXF4cHDx7k2iYfHx8cOnRIrezIkSOqNmhDeHg4UlNT1TY9PQut1U9ERPTeCmnSS3HAHkYdSk9Px+3bt5GVlYX//vsP27dvR0REBD7//HN07doVenp6qFKlCr766ivMnj1bNeklICAA/v7+AID+/fujV69e8Pf3R926dbFu3TqcO3cO7u7uqjgTJkzAgAEDYGlpiSZNmiA9PR2xsbF4+PAhhgwZgo4dO2LatGlo1aoVIiIi4OTkhDNnzqBMmTKoU6cOhg8fjnbt2qF69epo2LAhtmzZgg0bNmD37t1aey8UCkWOZXne1oNJREQkqxKa7GkDexh1aPv27XBycoKbmxtCQkKwd+9ezJ07F3/++Sf09fVVS9XY2Njg008/RaNGjeDu7o5169ap6vjqq68QHh6OYcOGoXr16rh+/TrCwsJgbGysOqZnz5746aefEBUVhcqVKyMgIABRUVGqHkYjIyPs3LkT9vb2aNq0KSpXrozvvvtONWTdqlUrzJkzB99//z0qVqyIxYsXY8WKFWjQoIGs7xcREREVTZIoqXdnlmBBQUFwdHTMsVZicWNoVFb2mEGOVWWNd+lZ7g+O15XYAPnvCTVfvFzWeCZl6ssab2iZT2WNBwCPIO/C/reUz2WNV1rP+O0HadmVjIeyxiuM8ZNSBmayxjOUuc9p3Y1NOo+R2q2RVuqxWqG9EbqigkPSRdyzZ8+waNEiBAcHQ19fH2vXrsXu3btlXXuJiIjog8Ah6TwxYSziJEnC1q1bMWXKFKSnp8PLywvr169Ho0ba+SuIiIiI6G2YMBZxJiYmWp18QkRERHko+GOgPxhMGImIiIgACA5J54kJIxERERHAexg14LI6RERERKQRexiJiIiIAN7DqAETRiIiIiLwHkZNmDBSoWnq6Cd7TEvJSNZ4Vc0+kjVenYMpssYDgKsyL6T9/NZBWeO9+F9fWeMBwA/b7GSNZ60n76+Cm+KFrPEA4L+XqbLGq2fmKms8APg3K03WeLX15f2cUuFiwkhEREQEcEhaAyaMREREROCQtCacJU1EREREGrGHkYiIiAjgkLQGTBiJiIiIAAgmjHnikDQRERERacQeRiIiIiKAQ9IasIexCAsLC4MkSTm2kJAQ1TFHjhxB06ZNYWNjA2NjY1SuXBmRkZHIyspSq2vv3r0IDAyEra0tTE1NUaFCBYSGhiIzMzPP+G5ubpg9e7ZaWX7jERERFTdCqZ2tJGLCWMSFhIQgJSVFbVu7di0AYOPGjQgICMBHH32EvXv34uLFixg4cCCmTp2KDh06QIhXywNcuHABTZo0wccff4wDBw7g/Pnz+PHHH2FoaAilMv+f7PzGIyIiKpaUWtpKIA5JF3EKhQKOjo45ytPS0tCrVy+0aNECS5YsUZX37NkTDg4OaNGiBX777Te0b98eu3btgpOTE2bMmKE6rnz58mo9lW9TkHhERERUsrCHsZjauXMn7t+/j2HDhuXY17x5c3h6eqp6Ih0dHZGSkoIDBw7IEo+IiKg44pB03t6rh/HFixcwNjbWVlsoF9HR0TA3N1crGzlyJIyMXj0T2cfHJ9fzvL29cfnyZQBA27ZtsWPHDgQEBMDR0RG1a9dGw4YN0bVrV1haWuarHdl15SdebtLT05Genq5WliWyoC/p5ys+ERGRrpXUZE8bCtzDqFQqMXnyZJQtWxbm5ua4du0aAGDs2LFYtmyZ1hv4oQsMDERcXJza1q9fP9X+vO4bFEJAkiQAgL6+PlasWIF//vkHM2bMQJkyZTB16lRUrFgRKSkpBWpPfuLlJiIiAlZWVmrblceJBYpNREREhaPACeOUKVMQFRWFGTNmqHq5AKBy5cr46aeftNo4AszMzODh4aG22drawtPTEwCQkJCQ63kXL15EhQoV1MrKli2LLl26YP78+YiPj8eLFy+waNGifLXjXeK9Ljw8HKmpqWpbBcvy+YpNREQkBw5J563ACeOqVauwZMkSfPXVV9DX/7/hxCpVquDixYtabRzlrXHjxrC1tUVkZGSOfZs3b8aVK1fQsWPHPM+3sbGBk5MT0tLSZImnUChgaWmptnE4moiIihQhaWcrgQqcMP7777/w8PDIUa5UKpGRkaGVRtH/SU9Px+3bt9W2e/fuwczMDIsXL8aff/6Jr7/+GufOnUNSUhKWLVuGsLAwtGnTBu3atQMALF68GH369MHOnTuRmJiICxcuYOTIkbhw4QKaN28O4NX31dvbGydOnMi1HQWJR0RERCVLgSe9VKxYEQcPHoSrq6ta+e+//w4/Pz+tNYxe2b59O5ycnNTKvLy8cPHiRbRp0wZ79+7FtGnT8Omnn+L58+fw8PDAmDFjMGjQINU9hTVr1sShQ4fQu3dv3Lp1C+bm5qhYsSI2bdqEgIAAAEBGRgYuXbqEZ8+e5dmW/MYjIiIqjkrqcLI2FDhhHD9+PLp06YJ///0XSqUSGzZswKVLl7Bq1SpER0froo0frKioKERFRWk8pn79+ti2bZvGY/z8/LB69WqNx7i5ueWY0JKUlPRO8YiIiIojoWTHR14KPCTdvHlzrFu3Dlu3boUkSRg3bhwSEhKwZcsWBAUF6aKNRERERFSI3mkdxuDgYAQHB2u7LURERESFhkPSeXuvhbufPn2a41nE+V0ImoiIiKgoESV0hrM2FHhI+vr162jWrBnMzMxgZWUFGxsb2NjYwNraGjY2NrpoIxEREZHOcR3GvBW4h/Grr74CACxfvhwODg6cGUtERERUwhU4YTx37hxOnToFLy8vXbSHiIiIqFBwlnTeCjwk/fHHH+PmzZu6aAsRERFRoRFCO1tJVOAexp9++gm9e/fGv//+i0qVKsHQ0FBtf5UqVbTWOCrZ7PSMZY95X/lC1ngvRJas8fxMysoaDwBamuZ88pMuvfhfX1njGU9ZIGs8ANi9pZes8fRkvrWojoG9rPEAwN3MU9Z4x7LuyxoPAO5nPJU13gN9K1njUeEqcMJ49+5dJCYmolu3bqoySZIghIAkScjKkvcXJBEREZE2cEg6bwVOGLt37w4/Pz+sXbuWk16IiIioxGDCmLcC38N448YNTJ8+HbVq1YKbmxtcXV3VNiIiIiIqmAULFqBcuXIwNjZGjRo1cPDgwXydd/jwYRgYGKBatWo6bV+BE8bPPvsMZ8+e1UVbiIiIiApNYU16WbduHQYNGoQxY8bgzJkzqF+/Ppo0aYLk5GSN56WmpqJr165o2LDhO15x/hV4SLp58+YYPHgwzp8/j8qVK+eY9NKiRQutNY6IiIhILoU1JD1r1iz06NEDPXv2BADMnj0bO3bswMKFCxEREZHned988w06deoEfX19bNq0SadtLHDC2Lt3bwDApEmTcuzjpBciIiL60KWnpyM9PV2tTKFQQKFQ5Dj25cuXOHXqFEaNGqVW3rhxYxw5ciTPGCtWrEBiYiJ+/vlnTJkyRTsN16DAQ9JKpTLPjcliwU2YMEHn9x0QERHR2wkhaWWLiIiAlZWV2pZXT+G9e/eQlZUFBwcHtXIHBwfcvn0713OuXLmCUaNGYc2aNTAwKHDf3zspcMJIeQsLC4MkSTm2kJAQAK96YHXdZUxERETvRlvPkg4PD0dqaqraFh4erjH2m6vOZC9X+KasrCx06tQJEydOhKenfOuLvlNampaWhv379yM5ORkvX75U2zdgwACtNKy4CgkJwYoVK9TKcuuC1iUhBLKysmT7q4OIiKgkUArt3MOY1/BzbkqVKgV9ff0cvYl37tzJ0esIAE+ePEFsbCzOnDmDb7/9FsCr0V8hBAwMDLBz50589tln738RbyhwD+OZM2fg4eGBjh074ttvv8WUKVMwaNAgjB49GrNnz9Z6A4sbhUIBR0dHtc3GxgZubm4AgC+++AKSJKleZ1u9ejXc3NxgZWWFDh064MmTJ6p9QgjMmDED7u7uMDExQdWqVfHHH3+o9u/btw+SJGHHjh3w9/eHQqHAwYMH33peVFQUrK2t1dqxadMmtb9osofMly9fDhcXF5ibm6NPnz7IysrCjBkz4OjoCHt7e0ydOlV7byIREdEHwsjICDVq1MCuXbvUynft2oW6devmON7S0hLnz59HXFycauvduze8vLwQFxeHWrVq6aSdBe6CGjx4MJo3b46FCxfC2toax44dg6GhITp37oyBAwfqoo0lwsmTJ2Fvb48VK1YgJCQE+vr6qn2JiYnYtGkToqOj8fDhQ7Rr1w7fffedKgn73//+hw0bNmDhwoWoUKECDhw4gM6dO6N06dIICAhQ1TNixAjMnDkT7u7usLa2zvd5b5OYmIht27Zh+/btSExMRJs2bXD9+nV4enpi//79OHLkCLp3746GDRuidu3a2nvTiIiIZCS01MNYUEOGDEGXLl3g7++POnXqYMmSJUhOTlZNNA4PD8e///6LVatWQU9PD5UqVVI7397eHsbGxjnKtanACWNcXBwWL14MfX196OvrIz09He7u7pgxYwZCQ0Px5Zdf6qKdxUZ0dDTMzc3VykaOHImxY8cCAKytreHo6Ki2X6lUIioqChYWFgCALl26ICYmBlOnTkVaWhpmzZqFPXv2oE6dOgAAd3d3HDp0CIsXL1ZL/CZNmoSgoCAAKNB5b6NUKrF8+XJYWFjA19cXgYGBuHTpErZu3Qo9PT14eXlh+vTp2LdvHxNGIiIqtgprWZ327dvj/v37mDRpElJSUlCpUiVs3bpV9UCUlJSUt67JqGsFThgNDQ1VQ5YODg5ITk6Gj48PrKysCv1iioLAwEAsXLhQrczW1lbjOW5ubqpkEQCcnJxw584dAEB8fDxevHihSgSzvXz5En5+fmpl/v7+qq8Lct7bvNk+BwcH6OvrQ09PT60su825yW2JgSyRBX1JP48ziIiIPhx9+/ZF3759c90XFRWl8dwJEyZgwoQJ2m/UawqcMPr5+SE2Nhaenp4IDAzEuHHjcO/ePaxevRqVK1fWRRuLFTMzM3h4eBTonDcXP5ckCUqlEgBU///rr79QtmxZtePevKHWzMxM9XV+ztPT04N4Y0n6jIyMfLVPU5tzExERgYkTJ6qVVbPygZ+1b57nEBERyeldntLyoShwwjht2jTVhIzJkycjNDQUffr0gYeHR47ZwaTO0NCwwGtV+vr6QqFQIDk5uUDDyPk5r3Tp0njy5AnS0tJUyWZcXFyB2pdf4eHhGDJkiFpZ/8qhOolFRET0LgprSLo4KHDC+PqwZ+nSpbF161atNqi4S09PzzE13sDAAKVKlYKbmxtiYmJQr149KBQK2NjYvLU+CwsLDBs2DIMHD4ZSqcQnn3yCx48f48iRIzA3N0doaO5JV37Oq1WrFkxNTTF69Gj0798fJ06ceGu397vKbYkBDkcTEREVD1y4W8u2b98OJycnte2TTz4BAERGRmLXrl1wdnYu0H2EkydPxrhx4xAREQEfHx8EBwdjy5YtKFeu3HudZ2tri59//hlbt25F5cqVsXbtWp3fA0FERFRUKYWkla0kksSbN7HlITAwMNcVx9UqkyTExMRopWFU8nV3ayN7zPvKF7LGeyHkfVymrZ68i8QDgItkImu8/4U8lDWe8ZQFssYDgIZVe8kaT+8tP9u1rY6BvazxAEDu8YxjWfdljgjcz3gqa7x6xh/JGm9e0jqdxzhfrrlW6ql8fYtW6ilK8j0krel5x48fP8batWtzzIIlIiIiouIv3wnjDz/8kKMsMzMT8+fPx9SpU1G2bFlMnjxZq40jIiIikgtnSeftnR82vGbNGowbNw7Pnz/HhAkT8PXXX/PZxURERFRsldT7D7WhwBne9u3bMWrUKFy/fh3Dhg3DkCFD1Nb/IyIiIiqOCuvRgMVBvhPGEydOYOTIkTh27Bh69+6N3bt3o1SpUrpsGxEREREVAflOGGvXrg0TExP06dMHbm5u+OWXX3I9bsCAAVprHBEREZFceA9j3vKdMLq4uECSJGzcuDHPYyRJYsJIRERExRLvYcxbvhPGpKQkHTaDiIiIiIoqnU1rrly5MrZu3QpnZ2ddhaBirqLSWPaYhyDvWqFl9eWdEPZSKGWNBwCPkClrvB+22ckab/cWeRfRBoCYs0tljRfuP0bWeFmQf9zvh9uHZY3X2bGmrPEAwF7PVNZ4JfFRcZz0kjedJYxJSUnIyMjQVfVEREREWsUh6byVxD8QiIiIiEiLuNI2EREREVAIN0sUH0wYiYiIiMAhaU04JE1EREREGhXLhHHChAmoVq1aYTejyHBzc8Ps2bN1HicpKQmSJCEuLk7nsYiIiOQmhKSVrSQqcMK4atUqpKfnXJrk5cuXWLVqler14sWL4eDgoLGusLAwSJKUYwsJCVEdI0kSNm3aVNBmlhiPHz/GmDFj4O3tDWNjYzg6OqJRo0bYsGEDhMxL0js7OyMlJQWVKlWSNS4REZEclFraSqIC38PYrVs3hISEwN7eXq38yZMn6NatG7p27QoA6NSpU77qCwkJwYoVK9TKFApFQZv13oQQyMrKgoFB0bmt89GjR/jkk0+QmpqKKVOm4OOPP4aBgQH279+PESNG4LPPPoO1tbVs7dHX14ejo6Ns8YiIiOQkUDJ7B7WhwD2MQghIUs439J9//oGVlVWBG6BQKODo6Ki22djYAHg11AoAX3zxBSRJUr3Otnr1ari5ucHKygodOnTAkydP1No5Y8YMuLu7w8TEBFWrVsUff/yh2r9v3z5IkoQdO3bA398fCoUCBw8efOt5UVFROZK0TZs2qb0n2UPmy5cvh4uLC8zNzdGnTx9kZWVhxowZcHR0hL29PaZOnarxvRk9ejSSkpJw/PhxhIaGwtfXF56enujVqxfi4uJgbm6e63mzZs1C5cqVYWZmBmdnZ/Tt2xdPnz5V7b9x4waaN28OGxsbmJmZoWLFiti6dSsA4OHDh/jqq69QunRpmJiYoEKFCqqEPrch6QsXLqBZs2awtLSEhYUF6tevj8TERI3XRURERMVLvrvT/Pz8VEPGDRs2VOuJy8rKwvXr19WGkrXh5MmTsLe3x4oVKxASEgJ9fX3VvsTERGzatAnR0dF4+PAh2rVrh++++06VhP3vf//Dhg0bsHDhQlSoUAEHDhxA586dUbp0aQQEBKjqGTFiBGbOnAl3d3dYW1vn+7y3SUxMxLZt27B9+3YkJiaiTZs2uH79Ojw9PbF//34cOXIE3bt3R8OGDVG7du0c5yuVSvz666/46quvUKZMmRz780oWAUBPTw9z586Fm5sbrl+/jr59+2LEiBFYsGABAKBfv354+fIlDhw4ADMzM8THx6vqGzt2LOLj47Ft2zaUKlUKV69exfPnz3ON8++//+LTTz9FgwYNsGfPHlhaWuLw4cPIzJT3yR9ERETaoOS6OnnKd8LYqlUrAEBcXByCg4PVEhYjIyO4ubmhdevWBW5AdHR0juRn5MiRGDt2LEqXLg0AsLa2zjEUqlQqERUVBQsLCwBAly5dEBMTg6lTpyItLQ2zZs3Cnj17UKdOHQCAu7s7Dh06hMWLF6slfpMmTUJQUBAAFOi8t1EqlVi+fDksLCzg6+uLwMBAXLp0CVu3boWenh68vLwwffp07Nu3L9eE8d69e3j48CG8vb3zHTPboEGDVF+XK1cOkydPRp8+fVQJY3JyMlq3bo3KlSurrjFbcnIy/Pz84O/vDwA5enVfN3/+fFhZWeHXX3+FoaEhAMDT07PA7SUiIioKlBySzlO+E8bx48cDeJVAtG/fHsbG2nkOcGBgIBYuXKhWZmtr+9bz3NzcVMkiADg5OeHOnTsAgPj4eLx48UKVCGZ7+fIl/Pz81MqyE6OCnlfQ9jk4OEBfXx96enpqZdltflP2hJbchv/fZu/evZg2bRri4+Px+PFjZGZm4sWLF0hLS4OZmRkGDBiAPn36YOfOnWjUqBFat26NKlWqAAD69OmD1q1b4/Tp02jcuDFatWqFunXr5honLi4O9evXVyWLmqSnp+eYLJUpsmAg6edxBhERERUVBb6HMTQ0FC9evMBPP/2E8PBwPHjwAABw+vRp/PvvvwVugJmZGTw8PNS2/CSMbyYpkiRBqXw1Nyn7/3/99Rfi4uJUW3x8vNr9iNnxs+XnPD09vRyzk3N7ZnZu7dPU5jeVLl0aNjY2SEhIyP0NyMONGzfQtGlTVKpUCevXr8epU6cwf/58tXb27NkT165dQ5cuXXD+/Hn4+/vjxx9/BAA0adIEN27cwKBBg3Dr1i00bNgQw4YNyzWWiYlJvtsVEREBKysrtS3m8YUCXRsREZEuCUha2UqiAieM586dg6enJ6ZPn46ZM2fi0aNHAICNGzciPDxc2+2DoaEhsrKyCnSOr68vFAoFkpOTcySjzs7O73Ve6dKl8eTJE6SlpanO08W6hHp6emjfvj3WrFmDW7du5diflpaW672CsbGxyMzMRGRkJGrXrg1PT89cz3d2dkbv3r2xYcMGDB06FEuXLlXtK126NMLCwvDzzz9j9uzZWLJkSa5trFKlCg4ePJhrwvym8PBwpKamqm0NLSu+9TwiIiK5cFmdvBU4YRw8eDDCwsJw5coVtWHpJk2a4MCBAwVuQHp6Om7fvq223bt3T7Xfzc0NMTExuH37Nh4+fJivOi0sLDBs2DAMHjwYK1euRGJiIs6cOYP58+dj5cqV73VerVq1YGpqitGjR+Pq1av45ZdfEBUVVeDrzo9p06bB2dkZtWrVwqpVqxAfH48rV65g+fLlqFatmtrM52zly5dHZmYmfvzxR1y7dg2rV6/GokWL1I4ZNGgQduzYgevXr+P06dPYs2cPfHx8AADjxo3Dn3/+iatXr+LChQuIjo5W7XvTt99+i8ePH6NDhw6IjY3FlStXsHr1aly6dCnHsQqFApaWlmobh6OJiIiKhwInjLGxsfjmm29ylJctWxa3b98ucAO2b98OJycnte2TTz5R7Y+MjMSuXbvg7OxcoPsIJ0+ejHHjxiEiIgI+Pj4IDg7Gli1bUK5cufc6z9bWFj///DO2bt2KypUrY+3atZgwYUKBrzs/bGxscOzYMXTu3BlTpkyBn58f6tevj7Vr1+L777/PdRmjatWqYdasWZg+fToqVaqENWvWICIiQu2YrKws9OvXDz4+PggJCYGXl5dqQoyRkRHCw8NRpUoVfPrpp9DX18evv/6aa/vs7OywZ88ePH36FAEBAahRowaWLl2ar3saiYiIihoOSedNEgV8XIiDgwO2b98OPz8/WFhY4OzZs3B3d8fOnTvRo0cP3Lx5U1dtpRIm0qWz7DEPIVXWeDZ68i5C/1LIPxhiJnNPcVlhJGu83ZkF/0P4fcWcXfr2g7Qo3H+MrPEK45m0c28fljVeZ8eassYDgGeiYLdvva9Skrz/FucmrdN5jO0OHbRST8h/uXe0FGcF/nfbsmVLTJo0SXXfmiRJSE5OxqhRo95pWR0iIiIiKtoKnDDOnDkTd+/ehb29PZ4/f46AgAB4eHjAwsLirU8uISIiIiqqOOklbwV+cLKlpSUOHTqEPXv24PTp01AqlahevToaNWqki/YRERERyaKk3n+oDQVOGLN99tln+OyzzwBAtbQOERERUXGlZL6YpwIPSU+fPh3r1v3fjaft2rWDnZ0dypYti7Nnz2q1cURERERU+AqcMC5evFi1iPWuXbuwa9cubNu2DU2aNMHw4cO13kAiIiIiOSghaWUriQo8JJ2SkqJKGKOjo9GuXTs0btwYbm5uqFWrltYbSERERCSHAq0z+IEpcA+jjY2Naq3F7du3qya7CCEK/Ag/IiIiIir6CtzD+OWXX6JTp06oUKEC7t+/jyZNmgB49TxlDw8PrTeQSi7bQvj7IkU8kTWegaG8SxQbFsKSyLeUz2WNZ633znP13omeJP/wktwLaUfEyrskWp3KobLGA4CejnVkjTelzh1Z4wHAlKOOssar9rLkPd61pC6Jow0F/sn7ww8/wM3NDTdv3sSMGTNgbm4O4NVQdd++fbXeQCIiIiI5KAvhD8TiosAJo6GhIYYNG5ajfNCgQdpoDxEREREVMQUev1q5ciX++usv1esRI0bA2toadevWxY0bN7TaOCIiIiK5CC1tJVGBE8Zp06bBxMQEAHD06FHMmzcPM2bMQKlSpTB48GCtN5CIiIhIDnw0YN4KnDDevHlTNbll06ZNaNOmDb7++mtERETg4MGDWm8gERERUUm3YMEClCtXDsbGxqhRo4bGnGrDhg0ICgpC6dKlYWlpiTp16mDHjh06bV+BE0Zzc3Pcv38fALBz507VsjrGxsZ4/lze2ZJERERE2qKUtLMV1Lp16zBo0CCMGTMGZ86cQf369dGkSRMkJyfnevyBAwcQFBSErVu34tSpUwgMDETz5s1x5syZ93wH8lbgSS9BQUHo2bMn/Pz8cPnyZTRr1gwAcOHCBbi6umq9gURERERyKKyntMyaNQs9evRAz549AQCzZ8/Gjh07sHDhQkREROQ4fvbs2Wqvp02bhj///BNbtmyBn5+fTtpY4B7G+fPno06dOrh79y7Wr18POzs7AMCpU6fQsWNHrTeQ3l1YWBhatWqlen3z5k306NEDZcqUgZGREVxdXTFw4EBVj3FSUhIkSdK4TZgwQVWfl5cXjIyM8O+//8p8ZURERNqnrUkv6enpePz4sdqWnp6ea8yXL1/i1KlTaNy4sVp548aNceTIkXy1W6lU4smTJ7C1tS3gFedfgRNGa2trzJs3D3/++SdCQkKQmpqKBQsWYMuWLRg/frwu2khacO3aNfj7++Py5ctYu3Ytrl69ikWLFiEmJgZ16tTBgwcP4OzsjJSUFNU2dOhQVKxYUa0se0mlQ4cO4cWLF2jbti2ioqIK9+KIiIiKkIiICFhZWaltufUUAsC9e/eQlZUFBwcHtXIHBwfcvn07X/EiIyORlpaGdu3avXfb8/LOj0zYs2cPli9fjg0bNsDV1RWtW7fGsmXLtNk20qJ+/frByMgIO3fuVM1yd3FxgZ+fH8qXL48xY8Zg4cKFcHT8vycFmJubw8DAQK0s27Jly9CpUycEBASgX79+GD16NCQueEpERMXYu9x/mJvw8HAMGTJErUyhUGg8583foUKIfP1eXbt2LSZMmIA///wT9vb2BW9sPhUoYfznn38QFRWF5cuXqzLZjIwMrF+/Hr6+vrpqI72nBw8eYMeOHZg6daoqWczm6OiIr776CuvWrcOCBQvy9eF88uQJfv/9dxw/fhze3t5IS0vDvn37EBgYqKtLICIi0jltLYmjUCjemiBmK1WqFPT19XP0Jt65cydHr+Ob1q1bhx49euD3339XTULWlXwPSTdt2hS+vr6Ij4/Hjz/+iFu3buHHH3/UZdtIS65cuQIhBHx8fHLd7+Pjg4cPH+Lu3bv5qu/XX39FhQoVULFiRejr66NDhw5v7V3O7X6ODFEID5MmIiIqQoyMjFCjRg3s2rVLrXzXrl2oW7dunuetXbsWYWFh+OWXX1QTkHUp3wnjzp070bNnT0ycOBHNmjWDvn7Je+j4h0qIV+vS53dIedmyZejcubPqdefOnbFhwwY8evQoz3Nyu5/jrycX3qvdRERE2lRYT3oZMmQIfvrpJyxfvhwJCQkYPHgwkpOT0bt3bwCvhri7du2qOn7t2rXo2rUrIiMjUbt2bdy+fRu3b99Gamrqu114PuQ7YTx48CCePHkCf39/1KpVC/Pmzct3jxQVLg8PD0iShPj4+Fz3X7x4ETY2NihVqtRb64qPj8fx48cxYsQIGBgYwMDAALVr18bz58+xdu3aPM8LDw9Hamqq2tbMouI7XxMREZG2FdY6jO3bt8fs2bMxadIkVKtWDQcOHMDWrVtVyxWmpKSorcm4ePFiZGZmol+/fnByclJtAwcO1NZbkUO+E8Y6depg6dKlSElJwTfffINff/0VZcuWhVKpxK5du/DkyROdNZLej52dHYKCgrBgwYIci6vfvn0ba9asQfv27fPVw7hs2TJ8+umnOHv2LOLi4lTbiBEjNA5LKxQKWFpaqm2GEnupiYiIAKBv375ISkpCeno6Tp06hU8//VS1LyoqCvv27VO93rdvH4QQOTZdrlpS4GV1TE1N0b17dxw6dAjnz5/H0KFD8d1338He3h4tWrTQRRtJC+bNm4f09HQEBwfjwIEDuHnzJrZv346goCCULVsWU6dOfWsdGRkZWL16NTp27IhKlSqpbT179sSpU6dw9uxZGa6GiIhI+/gs6bwVOGF8nZeXF2bMmIF//vlH43AkFb4KFSogNjYW5cuXR/v27VG+fHl8/fXXCAwMxNGjR/O12OfmzZtx//59fPHFF7nWX7lyZS6tRERExRYTxry98zqMr9PX10erVq3UnipChe/NrmlXV1esWLEi3+dPmDBB7ckurVu3RlZW3jObz507V9AmEhERUTGglYSRiIiIqLgTfP5EnpgwEhEREaHkDidrAxNGIiIiIjBh1OS9Jr0QERERUcnHHkYiIiIivNtTWj4UTBiJiIiI8G5PaflQcEiaiIiIiDRiDyMREREROOlFEyaMVGi2Gsj//PEw5UeyxjsnvZQ1nrMwkjUeAFzReyFrvJtC3nh1DOxljQcAWTLfSVWncqis8Y6eXylrPACYWWOcrPFaHJB/AK+iYd4PVtCFy0Ylb5CSCWPeSt53m4iIiIi0ij2MREREROAsaU2YMBIRERGBs6Q14ZA0EREREWnEHkYiIiIicNKLJuxhLOIkScKmTZsKuxlFph1ERES6IrS0lUTsYSwCwsLC8OjRo1wTspSUFNjY2AAAkpKSUK5cOZw5cwbVqlWTtY2vt4OIiKgkUpbYdO/9MWEs4hwdHQu7CQCKTjuIiIhIfhySLuJeHwouV64cAMDPzw+SJKFBgwaq41asWAEfHx8YGxvD29sbCxYsUKvnn3/+QYcOHWBrawszMzP4+/vj+PHjqv0LFy5E+fLlYWRkBC8vL6xevTrPdiQlJUGSJGzYsAGBgYEwNTVF1apVcfToUe2/AURERDJRamkridjDWIycOHECNWvWxO7du1GxYkUYGb16qsfSpUsxfvx4zJs3D35+fjhz5gx69eoFMzMzhIaG4unTpwgICEDZsmWxefNmODo64vTp01AqX32sN27ciIEDB2L27Nlo1KgRoqOj0a1bN3z00UcIDAzMsz1jxozBzJkzUaFCBYwZMwYdO3bE1atXYWDAjxURERU/HJDOG3+zFyOlS5cGANjZ2akNEU+ePBmRkZH48ssvAbzqiYyPj8fixYsRGhqKX375BXfv3sXJkydha2sLAPDw8FCdP3PmTISFhaFv374AgCFDhuDYsWOYOXOmxoRx2LBhaNasGQBg4sSJqFixIq5evQpvb2/tXjgREREVKg5JF3N3797FzZs30aNHD5ibm6u2KVOmIDExEQAQFxcHPz8/VbL4poSEBNSrV0+trF69ekhISNAYu0qVKqqvnZycAAB37tzJ9dj09HQ8fvxYbcsS8j73lIiISBMOSeeNPYzFXPaw8tKlS1GrVi21ffr6+gAAExOTt9YjSerL2wshcpS9ydDQMMf52e15U0REBCZOnKhW5mPpiYrW7I0kIqKigU96yRt7GIuR7HsWs7L+r2fOwcEBZcuWxbVr1+Dh4aG2ZU+SqVKlCuLi4vDgwYNc6/Xx8cGhQ4fUyo4cOQIfHx+ttT08PBypqalqm7dVBa3VT0RERLrDHsYiIjU1FXFxcWplbw4h29vbw8TEBNu3b8dHH30EY2NjWFlZYcKECRgwYAAsLS3RpEkTpKenIzY2Fg8fPsSQIUPQsWNHTJs2Da1atUJERAScnJxw5swZlClTBnXq1MHw4cPRrl07VK9eHQ0bNsSWLVuwYcMG7N69W2vXp1AooFAo1Mr0JX2t1U9ERPS+uA5j3tjDWETs27cPfn5+atu4cePUjjEwMMDcuXOxePFilClTBi1btgQA9OzZEz/99BOioqJQuXJlBAQEICoqStXDaGRkhJ07d8Le3h5NmzZF5cqV8d1336mGrFu1aoU5c+bg+++/R8WKFbF48WKsWLFCbdkeIiKiko5PesmbJIQoqddGRVxb15ayx2yotJA13jn9l7LGcxZGssYDgCvSC1njvZB5spSr9PZ7gLUtS+ZfOXvS/5E13tHzK2WNBwAza4x7+0FatC0zRdZ4AFDR0E7WeKVh+PaDtGhS0hqdxxjj1kkr9UxN+kUr9RQlHJImIiIiQsmd4awNTBiJiIiIwHsYNWHCSERERISSe/+hNnDSCxERERFpxB5GIiIiIvAeRk2YMBIRERGB9zBqwiFpIiIiItKIPYxERERE4KQXTZgwUqFxkIxlj3lCX95FpuNf3pM13gmZF7UGABM9eRcL/+9lqqzx3M08ZY0HAD/cPixrvJ6OdWSNJ/ci2gAw7NQkWeOt9G0vazwAaGLgJGs84xKYXfEexrxxSJqIiIiINGIPIxEREREAwUHpPDFhJCIiIgKHpDXhkDQRERERacQeRiIiIiJwHUZNmDASERERgcvqaMIh6Q9cUlISJElCXFxcYTeFiIioUCkhtLK9iwULFqBcuXIwNjZGjRo1cPDgQY3H79+/HzVq1ICxsTHc3d2xaNGid4qbX0wYi6GwsDBIkoTevXvn2Ne3b19IkoSwsDD5G0ZEREQFtm7dOgwaNAhjxozBmTNnUL9+fTRp0gTJycm5Hn/9+nU0bdoU9evXx5kzZzB69GgMGDAA69ev11kbmTAWU87Ozvj111/x/PlzVdmLFy+wdu1auLi4FGLLgIyMjEKNT0RE9C6UWtoKatasWejRowd69uwJHx8fzJ49G87Ozli4cGGuxy9atAguLi6YPXs2fHx80LNnT3Tv3h0zZ858h+j5w4SxmKpevTpcXFywYcMGVdmGDRvg7OwMPz8/Vdn27dvxySefwNraGnZ2dvj888+RmJiYZ71KpRK9evWCp6cnbty4AQDYsmWLWrf3xIkTkZmZqTpHkiQsWrQILVu2hJmZGaZMmaKDKyYiItItoaX/0tPT8fjxY7UtPT0915gvX77EqVOn0LhxY7Xyxo0b48iRI7mec/To0RzHBwcHIzY2VmedNkwYi7Fu3bphxYoVqtfLly9H9+7d1Y5JS0vDkCFDcPLkScTExEBPTw9ffPEFlMqcfwO9fPkS7dq1Q2xsLA4dOgRXV1fs2LEDnTt3xoABAxAfH4/FixcjKioKU6dOVTt3/PjxaNmyJc6fP5+jDURERB+SiIgIWFlZqW0RERG5Hnvv3j1kZWXBwcFBrdzBwQG3b9/O9Zzbt2/nenxmZibu3dPNI2k5S7oY69KlC8LDw1UTVw4fPoxff/0V+/btUx3TunVrtXOWLVsGe3t7xMfHo1KlSqryp0+folmzZnj+/Dn27dsHKysrAMDUqVMxatQohIaGAgDc3d0xefJkjBgxAuPHj1ed36lTJ42JYnp6eo6/rrJEFvQl/Xe+fiIiIm3S1sLd4eHhGDJkiFqZQqHQeI4kSWqvhRA5yt52fG7l2sKEsRgrVaoUmjVrhpUrV0IIgWbNmqFUqVJqxyQmJmLs2LE4duwY7t27p+pZTE5OVksYO3bsiI8++ggxMTEwNTVVlZ86dQonT55U61HMysrCixcv8OzZM9Wx/v7+GtsaERGBiRMnqpV9bOWLmtaV8jiDiIhIXtp6NKBCoXhrgpitVKlS0NfXz9GbeOfOnRy9iNkcHR1zPd7AwAB2dnbv1ui34JB0Mde9e3dERUVh5cqVufbwNW/eHPfv38fSpUtx/PhxHD9+HMCr4efXNW3aFOfOncOxY8fUypVKJSZOnIi4uDjVdv78eVy5cgXGxsaq48zMzDS2Mzw8HKmpqWpbDSufd71sIiKiEsHIyAg1atTArl271Mp37dqFunXr5npOnTp1chy/c+dO+Pv7w9DQUCftZA9jMRcSEqJK/oKDg9X23b9/HwkJCVi8eDHq168PADh06FCu9fTp0weVKlVCixYt8NdffyEgIADAq8k1ly5dgoeHx3u1M7e/tjgcTURERUlhPUt6yJAh6NKlC/z9/VGnTh0sWbIEycnJquXzwsPD8e+//2LVqlUAgN69e2PevHkYMmQIevXqhaNHj2LZsmVYu3atztrIhLGY09fXR0JCgurr19nY2MDOzg5LliyBk5MTkpOTMWrUqDzr6t+/P7KysvD5559j27Zt+OSTTzBu3Dh8/vnncHZ2Rtu2baGnp4dz587h/PnznA1NREQlilIUzrNe2rdvj/v372PSpElISUlBpUqVsHXrVri6ugIAUlJS1NZkLFeuHLZu3YrBgwdj/vz5KFOmDObOnZtj3oI2MWEsASwtLXMt19PTw6+//ooBAwagUqVK8PLywty5c9GgQYM86xo0aBCUSiWaNm2K7du3Izg4GNHR0Zg0aRJmzJgBQ0NDeHt7o2fPnjq6GiIiog9P37590bdv31z3RUVF5SgLCAjA6dOnddyq/8OEsRjK7YPzuk2bNqm+btSoEeLj49X2i9f+gnJzc1N7DbzqGn99dldwcHCO4e686iMiIiqu+Nssb0wYiYiIiIB3fg70h4AJIxERERG0t6xOScRldYiIiIhII/YwEhEREaHwltUpDpgwEhEREYH3MGrCIWkiIiIi0og9jERERETgpBdNmDBSoemUnil7zJlGGbLGa2vwkazxCuNH3easFFnj1TNzlTXesaz7ssYDgM6ONWWNN6XOHVnjtTgg/+DWSt/2ssY7H79O1ngAsNhvnKzxzuunyxpPDryHMW8ckiYiIiIijdjDSERERAQ+uUwTJoxERERE4CxpTTgkTUREREQasYeRiIiICJz0ogkTRiIiIiJwWR1NmDASERERgfcwasJ7GIuQsLAwSJKE3r1759jXt29fSJKEsLAw+RtGREREHzQmjEWMs7Mzfv31Vzx//lxV9uLFC6xduxYuLi6F2LJXMjLkXfiaiIhILkIIrWwlERPGIqZ69epwcXHBhg0bVGUbNmyAs7Mz/Pz8VGXp6ekYMGAA7O3tYWxsjE8++QQnT55U7d+3bx8kSUJMTAz8/f1hamqKunXr4tKlS2rxtmzZgho1asDY2Bju7u6YOHEiMjP/7wkskiRh0aJFaNmyJczMzDBlyhQAwMKFC1G+fHkYGRnBy8sLq1ev1tVbQkREJAullraSiAljEdStWzesWLFC9Xr58uXo3r272jEjRozA+vXrsXLlSpw+fRoeHh4IDg7GgwcP1I4bM2YMIiMjERsbCwMDA7V6duzYgc6dO2PAgAGIj4/H4sWLERUVhalTp6rVMX78eLRs2RLnz59H9+7dsXHjRgwcOBBDhw7F33//jW+++QbdunXD3r17dfBuEBERUWFjwlgEdenSBYcOHUJSUhJu3LiBw4cPo3Pnzqr9aWlpWLhwIb7//ns0adIEvr6+WLp0KUxMTLBs2TK1uqZOnYqAgAD4+vpi1KhROHLkCF68eKHaN2rUKISGhsLd3R1BQUGYPHkyFi9erFZHp06d0L17d7i7u8PV1RUzZ85EWFgY+vbtC09PTwwZMgRffvklZs6cqfs3h4iISEeElv4riThLuggqVaoUmjVrhpUrV0IIgWbNmqFUqVKq/YmJicjIyEC9evVUZYaGhqhZsyYSEhLU6qpSpYrqaycnJwDAnTt34OLiglOnTuHkyZNqPYpZWVl48eIFnj17BlNTUwCAv7+/Wp0JCQn4+uuv1crq1auHOXPm5HlN6enpSE9Xf1D9S5EFI0lf43tBREQkF86SzhsTxiKqe/fu+PbbbwEA8+fPV9uXfUOtJEk5yt8sMzQ0VH2dvU+pVKr+P3HiRHz55Zc54hsbG6u+NjMzy7E/P7FfFxERgYkTJ6qVdTfzRg8L3zzPISIioqKBQ9JFVEhICF6+fImXL18iODhYbZ+HhweMjIxw6NAhVVlGRgZiY2Ph4+OT7xjVq1fHpUuX4OHhkWPT08v7o+Hj46MWGwCOHDmiMXZ4eDhSU1PVti7mXvluKxERka5xlnTe2MNYROnr66uGl/X11YdtzczM0KdPHwwfPhy2trZwcXHBjBkz8OzZM/To0SPfMcaNG4fPP/8czs7OaNu2LfT09HDu3DmcP39eNRs6N8OHD0e7du1QvXp1NGzYEFu2bMGGDRuwe/fuPM9RKBRQKBRqZRyOJiKiooRD0nljwliEWVpa5rnvu+++g1KpRJcuXfDkyRP4+/tjx44dsLGxyXf9wcHBiI6OxqRJkzBjxgwYGhrC29sbPXv21Hheq1atMGfOHHz//fcYMGAAypUrhxUrVqBBgwb5jk1ERETFhyRKat8pFXlHnFrLHnOmkbwLj9cTeSf9ulAY/5g3Z6XIGq+8obWs8W5mPZU1HgC46VvIGm9mnfuyxmtxQP67oe5kPJY13vn4dbLGA4DFfuNkjXdeP/3tB2nR4qTfdR6jwUeNtFLPvn/yHnErrtjDSERERARAyT60PDFhJCIiIkLhjNIUF5wlTUREREQasYeRiIiICJwlrQkTRiIiIiIwYdSEQ9JEREREpBF7GImIiIiAEvuUFm1gwkhEREQEDklrwoSRCo2Ly0PZYzr/Zy9rvN1KeRdEbgY7WeMBQCkDM1nj/ZuVJmu8+xnyL9xtr2cqa7wpRx1ljVfRMEvWeADQxMBJ1nhyL6INAN+cmSRrvKH+4bLGo8LFhJGIiIgIgGAPY56YMBIRERGB9zBqwlnSRERERKQRexiJiIiIwEkvmjBhJCIiIgKHpDXhkDRBkiRs2rQJAJCUlARJkhAXF1eobSIiIpKbEkIrW0nEhLEYuXPnDr755hu4uLhAoVDA0dERwcHBOHr0KAD1xI+IiIhIWzgkXYy0bt0aGRkZWLlyJdzd3fHff/8hJiYGDx48KOymqXn58iWMjIwKuxlEREQFwmV18sYexmLi0aNHOHToEKZPn47AwEC4urqiZs2aCA8PR7NmzeDm5gYA+OKLLyBJkuo1AGzZsgU1atSAsbEx3N3dMXHiRGRmZuYrblZWFnr06IFy5crBxMQEXl5emDNnjtoxYWFhaNWqFSIiIlCmTBl4enpq67KJiIhkoxRCK5suPXz4EF26dIGVlRWsrKzQpUsXPHr0KM/jMzIyMHLkSFSuXBlmZmYoU6YMunbtilu3bhUoLnsYiwlzc3OYm5tj06ZNqF27NhQKhdr+kydPwt7eHitWrEBISAj09fUBADt27EDnzp0xd+5c1K9fH4mJifj6668BAOPHj39rXKVSiY8++gi//fYbSpUqhSNHjuDrr7+Gk5MT2rVrpzouJiYGlpaW2LVrF28aJiIi0pFOnTrhn3/+wfbt2wEAX3/9Nbp06YItW7bkevyzZ89w+vRpjB07FlWrVsXDhw8xaNAgtGjRArGxsfmOy4SxmDAwMEBUVBR69eqFRYsWoXr16ggICECHDh1QpUoVlC5dGgBgbW0NR8f/e8zX1KlTMWrUKISGhgIA3N3dMXnyZIwYMSJfCaOhoSEmTpyoel2uXDkcOXIEv/32m1rCaGZmhp9++olD0UREVGwV9SHphIQEbN++HceOHUOtWrUAAEuXLkWdOnVw6dIleHl55TjHysoKu3btUiv78ccfUbNmTSQnJ8PFxSVfsTkkXYy0bt0at27dwubNmxEcHIx9+/ahevXqiIqKyvOcU6dOYdKkSaoeSnNzc/Tq1QspKSl49uxZvuIuWrQI/v7+KF26NMzNzbF06VIkJyerHVO5cmWNyWJ6ejoeP36stqUrlfmKT0REJAdtDUnn+jsvPf2923f06FFYWVmpkkUAqF27NqysrHDkyJF815OamgpJkmBtbZ3vc5gwFjPGxsYICgrCuHHjcOTIEYSFhWnsKVQqlZg4cSLi4uJU2/nz53HlyhUYGxu/Nd5vv/2GwYMHo3v37ti5cyfi4uLQrVs3vHz5Uu04MzMzjfVERESo7rfI3ubfupG/iyYiIipGcvudFxER8d713r59G/b29jnK7e3tcfv27XzV8eLFC4waNQqdOnWCpaVlvmNzSLqY8/X1VS2lY2hoiKysLLX91atXx6VLl+Dh4fFO9R88eBB169ZF3759VWWJiYkFric8PBxDhgxRK7vbsMU7tYmIiEgXtDUkndvvvDfnHrxuwoQJard/5ebkyZMAXi2h9yYhRK7lb8rIyECHDh2gVCqxYMGCtx7/OiaMxcT9+/fRtm1bdO/eHVWqVIGFhQViY2MxY8YMtGzZEgDg5uaGmJgY1KtXDwqFAjY2Nhg3bhw+//xzODs7o23bttDT08O5c+dw/vx5TJky5a1xPTw8sGrVKuzYsQPlypXD6tWrcfLkSZQrV65A7VcoFDn+sTzWYwc3EREVHdqa4Zzb7zxNvv32W3To0EHjMW5ubjh37hz++++/HPvu3r0LBwcHjednZGSgXbt2uH79Ovbs2VOg3kWACWOxYW5ujlq1auGHH35AYmIiMjIy4OzsjF69emH06NEAgMjISAwZMgRLly5F2bJlkZSUhODgYERHR2PSpEmYMWMGDA0N4e3tjZ49e+Yrbu/evREXF4f27dtDkiR07NgRffv2xbZt23R5uURERB+MUqVKoVSpUm89rk6dOkhNTcWJEydQs2ZNAMDx48eRmpqKunXr5nledrJ45coV7N27F3Z2dgVuoyS4BgoVkn9qfSZ7zO//y3nvhy5dzkqVNV4zFPyHwPvao/dY1nhPlS/ffpAW3c14Ims8APBRyPs5LSO9/X5mbUpD1tsP0jIXIe8KDhaFMKfvmzOTZI031D9c1nhzk9bpPEaF0jW0Us+Vu6e0Uk9umjRpglu3bmHx4sUAXi2r4+rqqrasjre3NyIiIvDFF18gMzMTrVu3xunTpxEdHa3WE2lra5vv1U3Yw0hEREQE7Q1J69KaNWswYMAANG7cGADQokULzJs3T+2YS5cuITX1VYfFP//8g82bNwMAqlWrpnbc3r170aBBg3zFZcJIREREhKK/DiPwqlfw559/1njM64PHbm5uWnmgBmcdEBEREZFG7GEkIiIiAiAEHyiRFyaMRERERACUxWBIurBwSJqIiIiINGIPIxERERGglckhJRUTRiIiIiJwSFoTJoxUaJ48kHexYABIyHoka7zy+gV79NL7auv2j6zxAODgdWtZ49XWl3dx8gf6VrLGA+S/V6jaS31Z4102kv9uKGOZ84Dz+unyBoT8C2lHxkbIGo8KFxNGIiIiInBIWhMmjEREREQoHk96KSxMGImIiIhQPJ70Uli4rA4RERERacQeRiIiIiLwHkZNmDASERERgcvqaMIhaSIiIiLSiAljMSVJksYtLCwsx3Hm5uaoWrUqoqKictSXlZWFH374AVWqVIGxsTGsra3RpEkTHD58WO24qKgoSJKEkJAQtfJHjx5BkiTs27dPR1dMRESkW0IIrWwlERPGYiolJUW1zZ49G5aWlmplc+bMUR27YsUKpKSk4OzZs2jfvj26deuGHTt2qPYLIdChQwdMmjQJAwYMQEJCAvbv3w9nZ2c0aNAAmzZtUottYGCAmJgY7N27V67LJSIi0jmlEFrZSiLew1hMOTo6qr62srKCJElqZa+ztrZW7Rs9ejQiIyOxc+dOBAcHAwB+++03/PHHH9i8eTOaN2+uOm/JkiW4f/8+evbsiaCgIJiZmQEAzMzM0K5dO4waNQrHjx/X1SUSERFREcEexg9IVlYWfvvtNzx48ACGhoaq8l9++QWenp5qyWK2oUOH4v79+9i1a5da+YQJE3D+/Hn88ccfOm83ERGRHDgknTf2MH4AOnbsCH19fbx48QJZWVmwtbVFz549VfsvX74MHx+fXM/NLr98+bJaeZkyZTBw4ECMGTMGrVq1emsb0tPTkZ6u/mzVlyILRpK8z7AlIiLKC2dJ5409jB+AH374AXFxcdi1axeqVauGH374AR4eHgWqQ5KkHGUjR47E3bt3sXz58reeHxERASsrK7VtyYNrBWoDERERFQ4mjB8AR0dHeHh4IDAwEL///jv69euH+Ph41X5PT0+1169LSEgAAFSoUCHHPmtra4SHh2PixIl49uyZxjaEh4cjNTVVbfva1v09roqIiEi7OCSdNyaMHxgPDw+0bt0a4eHhqrIOHTrgypUr2LJlS47jIyMjYWdnh6CgoFzr69+/P/T09NRmZedGoVDA0tJSbeNwNBERFSWcJZ03JowfoKFDh2LLli2IjY0F8Cph/OKLLxAaGoply5YhKSkJ586dwzfffIPNmzfjp59+Us2QfpOxsTEmTpyIuXPnynkJREREWie09F9JxITxA1S5cmU0atQI48aNA/Dq/sTffvsNY8aMwQ8//ABvb2/Ur18fN27cwN69e986qSU0NBTu7hxeJiIiKqkkUVIH26nIS6jQVPaYA18oZY1XXt9S1ngT3O7IGg8ABly3ljWel5R7b7euPECmrPEA+f+Sr/nS8O0HadFlI/l/7dgoc07c06WLeulvP0jLFDJ/ciJjI2SNZ1hK9x0TJiauWqnn+fMbWqmnKOGyOkRERERAiZ2wog0ckiYiIiIijdjDSERERASU2Akr2sCEkYiIiAgcktaEQ9JEREREpBF7GImIiIjAHkZNmDASERERAbyDUQMOSRMRERGRZoKomHnx4oUYP368ePHiBeMV05glPV5hxCzp8QojZkmPVxgxC+MaSTv4pBcqdh4/fgwrKyukpqbC0lL3T1Ip6fEKI2ZJj1cYMUt6vMKIWdLjFUbMwrhG0g4OSRMRERGRRkwYiYiIiEgjJoxEREREpBETRip2FAoFxo8fD4VCwXjFNGZJj1cYMUt6vMKIWdLjFUbMwrhG0g5OeiEiIiIijdjDSEREREQaMWEkIiIiIo2YMBIRERGRRkwYiYiIiEgjg8JuABEBSqUSV69exZ07d6BUKtX2ffrpp4XUKirKrl+/jszMTFSoUEGt/MqVKzA0NISbm1vhNIwIwIEDB/J1HH++FR9MGKnIS0tLw3fffYeYmJhcE6pr164VUsu049ixY+jUqRNu3LiBNxctkCQJWVlZOombmJiI2bNnIyEhAZIkwcfHBwMHDkT58uV1Eo+0KywsDN27d8+RMB4/fhw//fQT9u3bVzgNK8YyMzMxdepUdO/eHc7OzoXdHJ3IyMiAl5cXoqOj4evrq7M4DRo0yHOfJEmq/2dmZuqsDaRdTBipyOvZsyf279+PLl26wMnJSfXDRtdiYmLyTFKXL1+utTi9e/eGv78//vrrL9mub8eOHWjRogWqVauGevXqQQiBI0eOoGLFitiyZQuCgoJ0ElfuntTff/8da9euxeXLlyFJEipUqIBOnTqhTZs2Wo+V7cSJE9i3b1+u1zhr1iytxTlz5gzq1auXo7x27dr49ttvtRYnv86fP49ly5Zh9uzZWq9brs+NgYEBvv/+e4SGhmqtzvzav38/Zs6cqfYH3PDhw1G/fn2txjE0NER6errOf848fPgw1/Jnz55hzpw5mDt3Ltzd3XXaBtIyQVTEWVlZiUOHDskac8KECUJPT0/UrFlTtGzZUrRq1Upt0yZTU1Nx5coVrdb5NtWqVRMjR47MUT5y5Ejh5+enk5hHjx4V5cqVE3p6ekKSJLVNT09Pq7GysrJEu3bthCRJwsvLS7Rs2VK0aNFCeHp6Cj09PdG+fXuhVCq1GlMIIaZOnSokSRLe3t4iICBANGjQQLUFBgZqNZalpaU4ffp0jvLY2Fhhbm6u1Vh5SU1NFYsWLRIff/yxkCRJVK1aVesx5PzcCCFEy5YtxYoVK7ReryarV68WBgYGol27dmLOnDli9uzZol27dsLQ0FCsWbNG6/EiIiJEaGioyMjI0HrdecnKyhJLly4VH330kXBxcRHLly8XWVlZssWn98eEkYo8Nzc3ER8fL2tMR0dHsWrVKlliBQYGim3btskSK5tCoRCXL1/OUX7p0iWhUCh0ErNq1aqibdu2Ij4+Xjx8+FA8evRIbdOmyMhIYWtrK7Zs2ZJj359//ilsbW3FDz/8oNWYQghhb28vW7LRrFkz0bZtW5GZmakqy8zMFK1btxYhISE6jb1v3z7RpUsXYWpqKvT09MTIkSN19kePnJ8bIYRYtGiRcHR0FEOHDhW//PKL+PPPP9U2XfD29hazZs3KUR4ZGSm8vb21Hq9Vq1bCwsJCODk5icaNG4svvvhCbdO29evXCy8vL2Frayu+//578eLFC63HIN3jk16oyPv555/x559/YuXKlTA1NZUlpp2dHU6cOKGz+/nOnTun+joxMRH/+9//MHz4cFSuXBmGhoZqx1apUkXr8Z2dnTFr1iy0bdtWrfy3337DsGHDkJycrPWYZmZmOHv2LDw8PLRe95uqVKmCQYMGoXv37rnuzx46PX/+vFbjOjk54cCBAznuK9SF+Ph4fPrpp7C2tlYNWx48eBCPHz/Gnj17UKlSJa3GS0lJwYoVK7B8+XKkpaWhY8eO6NSpE+rUqYOzZ8/q7H44OT83AKCnl/fiIbq6p1ihUODChQs5rvHq1auoVKkSXrx4odV43bp107h/xYoVWomzf/9+jBw5EufPn8fAgQMxcuRIWFlZaaVukh/vYaQiLzIyEomJiXBwcICbm1uOhOr06dNaj9mzZ0/88ssvGDt2rNbrBoBq1apBkiS1SS6vJzfZ+3T1C6pXr174+uuvce3aNdStWxeSJOHQoUOYPn06hg4dqvV4AFCrVi1cvXpVll/8V65cQaNGjfLc36hRI53c5zd48GDMnz9fJ/fxvcnX1xfnzp3DvHnzcPbsWZiYmKBr16749ttvYWtrq/V45cqVQ9u2bTF//nwEBQVpTKy0Sc7PDYAc90jKwdnZGTExMTmuMSYmRieTb7SVEGrStGlTxMTEoFu3bti0aRMcHR11HpN0iwkjFXmtWrWSPeaLFy+wZMkS7N69G1WqVMmRpL7v5IXr16+/1/nva+zYsbCwsEBkZCTCw8MBAGXKlMGECRMwYMAAncTs378/hg4ditu3b+u8J9XExASPHj2Ci4tLrvsfP34MExMTrcXLNmzYMDRr1gzly5eHr69vjmvcsGGDVuOVKVMG06ZN02qdeXF1dcWhQ4fg4uICV1dXeHt7yxJXzs/Nm168eAFjY2Od1Z9t6NChGDBgAOLi4tT+gIuKisKcOXN0Hl8Xtm/fDgMDA6xbtw6//fZbnsc9ePBAxlbR++CQNFEuAgMD89wnSRL27NkjY2t068mTJwAACwsLncbJrUdKVz2pzZo1g4uLCxYuXJjr/t69e+PmzZv466+/tBYTAPr164dly5YhMDAQDg4OOWaivm/Pzrlz51CpUiXo6emp3daQG10kUocPH8ayZcvw+++/w9PTE507d8aIESNw7tw5+Pj4aD0eIO/nBgCysrIwbdo0LFq0CP/99x8uX74Md3d3jB07Fm5ubujRo4dW42XbuHEjIiMjkZCQAACqWdItW7bUSbw//vgDv/32G5KTk/Hy5Uu1fdoYtVm5cmW+jiuMGen0jgrv9kkiEkKIqKgoER0drXo9fPhwYWVlJerUqSOSkpIKsWXalZSUpHHTpsOHDwtDQ0PRtm1bcfz4cZGamioePXokjh49Ktq0aSMMDQ11MvPe3Nxc7XupbZIkif/++0/1dW4zh3U1e/h1T548EUuWLBG1a9cWkiSJBg0aiCVLlog7d+5oPZacnxshhJg4caJwd3cXP//8szAxMRGJiYlCCCHWrVsnateurfV4GRkZYsKECSI5OVnrdedlzpw5wtzcXPTr108YGRmJb775RjRq1EhYWVmJ0aNHy9YOKl7Yw0hFkq2tLS5fvoxSpUrBxsZG45phxX1Iw8vLCwsXLsRnn32Go0ePomHDhpg9ezaio6NhYGCg9WFMAPjvv/8wbNgw1TqTb/4Y0NVi4XLauHEjvv766xyfDxsbGyxevBitW7fWekxXV1fs2LFDZ8O1N27cgIuLCyRJwo0bN97aFm2aNGkShg0blmPiWUJCApYtW4bVq1fjwYMHyMjI0GpcuXl4eGDx4sVo2LAhLCwscPbsWbi7u+PixYuoU6dOnusLvg9zc3P8/fffsj2dx9vbG+PHj0fHjh3VrnHcuHF48OAB5s2bp7VYz58/x65du1RroXp6eqJRo0Y6uSWEdIsJIxVJK1euRIcOHaBQKN46tKGtIY0vv/wSUVFRsLS0xJdffqnxWG0mcaamprh48SJcXFwwcuRIpKSkYNWqVbhw4QIaNGiAu3fvai1WtiZNmiA5ORnffvttrouFa2sYbPPmzWjSpAkMDQ2xefNmjce2aNFCKzFf9+zZM+zYsQNXrlwBAHh6eqJx48Y6m22/YsUKbN++HStWrJBtRr9c9PX1kZKSAnt7+1z3Z2ZmYvPmzW/9t5Mfhfm5MTExwcWLF+Hq6qqWTMXHx6NmzZp4+vSpVuMBr+7TbtWqFcLCwrRed25MTU2RkJAAV1dX2NvbY9euXahatSquXLmC2rVr4/79+1qJs3nzZvTs2RP37t1TKy9VqhSWLVuG5s2bayUOyYOTXqhIej0JlOseFysrK1XiJOfSD+bm5rh//z5cXFywc+dODB48GABgbGyM58+f6yTmoUOHcPDgQVSrVk0n9Wdr1aoVbt++DXt7e42Tl3Q1G9zU1BRffPGF1uvNy9y5c2Wb0b9y5UqUKlUKzZo1AwCMGDECS5Ysga+vL9auXav1Hsa39S0YGBhoJVkECvdzU7FiRRw8eDDH+/f777/Dz89Pq7GyNWnSBOHh4fj7779Ro0YNmJmZqe3XdlLs6OiI+/fvw9XVFa6urjh27BiqVq2K69evv/X7nF9HjhxBmzZt0KJFCwwdOlR1j2t8fDwiIyPRpk0b7Nu3D3Xq1NFKPNI9JoxULGRlZWHTpk2qx2b5+vqiRYsW0NfX11qM1yckyLHsRLagoCD07NkTfn5+uHz5sioBuHDhgs6GqJydnbX2i0GT15cokXO5krlz5+brOG3PCJdzRv+0adNUk3qOHj2KefPmqW5lGDx4sE5uZZDrsZyF9bkBgPHjx6NLly74999/oVQqsWHDBly6dAmrVq1CdHS0TmL26dMHQO6rL+giKf7ss8+wZcsWVK9eHT169MDgwYPxxx9/IDY2VmtJ/5QpU9CtWzcsXrxYrbxu3bqoW7cuvvnmG0yePBlbt27VSjzSPQ5JU5F39epVNG3aFP/++y+8vLwghMDly5fh7OyMv/76S2eLawPA3bt3cenSJdW9N6VLl9Z6jEePHuF///sfbt68iT59+iAkJATAq19cRkZGGDNmjNZj7ty5E5GRkVi8eLFs903JqVy5cm89RpIkXLt2TYbW6IbctzLo6emhUqVKMDDQ3M+gi3VR5bZjxw5MmzYNp06dglKpRPXq1TFu3Dg0bty4sJumFUqlEkqlUvW9/O2333Do0CF4eHigd+/eMDIyeu8YNjY2OHDgACpXrpzr/nPnziEgIEAn94SSbjBhpCKvadOmEEJgzZo1qgWJ79+/j86dO0NPT0/rS6MAQFpaGvr3749Vq1apejj09fXRtWtX/Pjjj8X+/jQbGxs8e/YMmZmZMDU1zTF0qquJRPv378fMmTNVPcXZS4dkP6mE8s/e3h47duyAn58f/Pz8MHjwYHTt2hWJiYmoWrWq1u+109PTw9ChQ2Fubq7xuPHjx2s1LiDv5+bmzZt5LpZ97Ngx1K5dW+sxXyfX2o+69vq9oLm5ceMGfHx88OzZM5lbRu+KQ9JU5O3fvx/Hjh1Te3qFnZ0dvvvuO9SrV08nMYcMGYL9+/djy5YtqhiHDh3CgAEDMHTo0DzX93sfz549y3VNNF2spyfHk0je9PPPP6Nbt2748ssvMWDAAAghcOTIETRs2BBRUVHo1KmT7G3SNj09PY3DttocWiyMWxmGDx+e56QXXZH7cxMUFITDhw/Dzs5Orfzw4cNo1qwZHj16pNV4gDxrP75t3c7XaeNnjqenJ/bs2ZPnYwhze7INFXHyr+RDVDA2Njbi8OHDOcoPHTokbGxsdBLTzs5O7N27N0f5nj17RKlSpbQa686dO6Jp06ZCT08v162k8Pb2FrNmzcpRHhkZKby9vWVty8mTJ8X+/fu1Xu+mTZvUtt9//12MHj1alC1bVvz0009ajfXw4UPRr18/0aJFC7Ft2zZV+bhx48SUKVO0GksIIfT09FRrQMpJ7s9Nz549RfXq1cXjx49VZfv37xeWlpa5tkMb5Fj7UdO6nbpYw3PWrFnC1tZW/PXXXzn2RUdHCzs7O529n6QbTBipyOvSpYuoWLGiOHbsmFAqlUKpVIqjR4+KSpUqidDQUJ3ENDExEfHx8TnK//77b2FqaqrVWJ06dRJ169YVJ06cEGZmZmLnzp1i9erVwsvLS6eLQGd79uyZSE1NVdt0wcjISFy5ciVH+ZUrV4RCodBJzLx4e3vLmoyvWbNGtGjRQrZ4uvD6ouFykvtzo1QqRevWrUX9+vXF8+fPxZ49e4S5ubmYPXu21mNlK1++vNi9e7cQ4tXi79kJY0JCgrC2ttZKjLctgK7txdCzsrJEmzZthCRJwtvbW3zxxRfiiy++EF5eXkJPT098+eWXIisrSyuxSB5MGKnIe/jwoWjRooWQJEkYGRkJIyMjoaenJ1q1aiUePXqkk5ifffaZaNu2rXj+/Lmq7NmzZ6Jt27aiYcOGWo3l6Ogojh8/LoQQwsLCQly6dEkIIcSff/4p6tWrp9VY2Z4+fSr69esnSpcuLVuvZvny5cWiRYtylC9atEh4eHjoJGZe/v33X1mfonP16lWt/6Gxbds2cfDgQdXrefPmiapVq4qOHTuKBw8eaDWWEP+vvTuPqzH9/wf+uk/SaV8UIm0jkSnLWMYWGpK9MWbwsZVhxr4Mk49dljBfZJmxjiT5lC0TJkKW0RBZshVjiRgKURKhzvv3h1/3w3GqWbrPlvfz8ejx6NzX6X5fJ7fT+1z3db2vtwnHu3/gHz16RI8fP5Y8zvu0cd28fv2aOnbsSC1btiQzMzNauXKlWuIUk8vl4vX4bsJ45coVMjU1VWtsdYuOjqaePXtSvXr1qF69etSzZ0+KiorSdrfYv8BzGJnOs7KyQmxsLG7cuIG0tDQQETw8PNQ6/2X58uXw8/ODg4MDGjRoAEEQkJKSArlcjvj4eElj5efni/PCbGxs8OjRI9SpUweenp5qW3EaFBSEI0eOYNWqVRg0aBB++ukn/Pnnn1i7di0WLlyolpgTJ07E2LFjkZKSgpYtW0IQBCQmJiI8PBzLly9XS8zS1KhRQ2OxXr58iZUrV8LBwUHS837//fdYtGgRAODSpUuYOHEivvvuOxw+fBjfffed5KWhnJyckJOTg2nTpmHr1q3i6lZra2v07dsX8+bNg5WVlaQxAc1cNyXN7yveCWXAgAHw9vYWn6OOOcWaqP34VwXQ3yVl3cc+ffqgT58+kp2PaZG2M1bGypKbm1vibYuioiK13Tot9uLFC1q3bh199913NGHCBFq/fj29ePFC8jhNmjSh/fv3ExFRz549aeDAgXTv3j0KCgoiV1dXyeMREdWqVUuco2lubi7e8ouIiKDOnTurJSYRUUxMDLVq1YpsbGzIxsaGWrVqRb/88ovkcYqKiujNmzdKxzIzM2n27Nn0/fffK43MScnKyoqsra3FLysrKzIwMCBzc3OKjY2VNJapqSmlp6cTEdGsWbPoiy++ICKis2fPUrVq1SSNRUSUnZ1NderUIVNTU/rmm28oNDSUli5dSsOGDSNTU1OqW7euWkY2idR/3ZQ0v+/dx8Xfq2v0fffu3WRpaUkLFy4kExMT+r//+z8aOnQoVa5cmQ4cOCBJjL+au6ipfciLnT17lrp27aqRWEwaXFaH6axdu3Zh8uTJSElJUSlj8+LFCzRq1AiLFy/W++2ltmzZgjdv3iAgIADnz59Hp06dkJ2djcqVKyM8PFwtn87NzMxw5coVODk5wcHBATExMWjWrBnS09Ph6emplu3PNCkwMBCGhoZYt24dACAvLw/169dHQUEB7O3tkZqaitjYWHTp0kXSuO9vYymTyWBnZ4fmzZvD2tpa0lg2NjZITEyEh4cHWrdujUGDBuGbb77B7du34eHhIXm5kvHjxyMhIQGHDh1CtWrVlNoyMzPh6+uLzz77DKGhoZLG1YS/2pf7XVLvoFOsItZ+PHjwIA4cOABDQ0MMHTpU3JP7v//9L/bs2YOOHTti//792u4m+7u0nbEyVpqOHTvS+vXrS23fsGED+fr6qiV2SEgIbdiwocSYCxcuVEvMYvn5+XT27Fl69OiR2mJ4enrS0aNHiejt73nixIlERLR8+XKqWbOmWmKePn2akpKSVI4nJSVRcnKypLHc3NwoPj5efPzjjz+Svb29OOc1KCiI2rVrJ2lMTevevTt16tSJ5syZQ4aGhnTv3j0iIoqPjyc3NzfJ4zk5OYkj4SXZt28fOTk5SR5Xk9fNh+jdedpSCg8PJ0EQqEqVKiQIAtnZ2dHmzZvJ3NycAgIC6NKlS2qJy9SHE0ams+zt7UtcHVns+vXrZG9vr5bYTk5OJZbySUpKImdnZ8niaOuW+9KlS2n58uVE9LZUkLGxsbiYSF2rQZs2bUrbt29XOb5z505q1qyZpLFMTEzo1q1b4uPPP/+cRo8eLT6+cuUK2dnZSRqz2G+//Ub9+/enFi1aiElcRESE5LfB79y5Q127diUvLy+lkj3jx4+nMWPGSBqL6O1q5bt375bafvfuXbWsWtbkdfOuK1eu0L59+yg2NlbpS51evXpFd+/epTt37ih9Sa2wsJDmzJlDNWrUIAMDA3GRzfTp0yUr/9SgQQNasGABEb0tDyQIAjVu3Jhu3LghyfmZ5nHCyHSWXC6ntLS0UttTU1NJLperJbaRkZFSwlHs5s2bkv1RjImJITc3N8rPz1dpy8/Ppzp16tDu3bslifVX7ty5Qzt37qSUlBS1xTA1NRX/ML3r1q1bZGZmJmksGxsbunLlivjY3t6eIiMjxcc3b94kY2NjSWMSEe3YsYOMjY1p6NChZGRkJL7en376Sa1zQzWhRo0aZSa9v/32G9WoUUPyuJq8bojeXhteXl4lzmNU1/y+P/74g1q3bq1SrUBdcwo1UffRzMxMfA8tKiqiSpUqiXc1mH6SafuWOGOlcXZ2xpkzZ0ptP3PmjNrmE9WqVQu///67yvHff/9dshW2q1evRlBQUInbDJqYmGDy5Mn48ccfJYn1VxwdHdGrVy80aNBAbTGMjIyQlZWlcvzBgwd/uT/xP9WgQQNs3rwZAHD8+HFkZWXBx8dHbL9586ZaVkrPmzcPa9aswfr165W2W2zZsqVa91h++fIlnj17pvQlNT8/P0ybNk1lJyIAePXqFWbMmCHugy4lTV43ADBu3Di4uLggKysLJiYmuHLlCn777Tc0adIER48elTweAAQEBEAmk2Hv3r04e/Yszp07h3PnzuH8+fNquW4iIiKwbt069O/fHwYGBuJxLy8vXL16VZIY+fn5MDU1BfB2Lq9cLi91y0WmH7isDtNZvXr1wrRp09CxY8cSJ9lPnz4dAwYMUEvsoUOHYvz48Xjz5o2YaCQkJCAoKAgTJ06UJMbly5exatWqUtu9vb0xffp0SWIBwIoVK/72c8eOHStZ3GIdO3bElClTEBsbC0tLSwBATk4Opk6dio4dO0oaa8aMGejSpQu2bduGBw8eICAgAPb29mL7rl271LKt5LVr1+Dt7a1y3MLCQvIt5fLz8zF58mRs27YN2dnZKu1SbkMIAMHBwWjSpAnc3NwwatQo1K1bFwCQmpqKVatW4dWrV2KSLiVNXjcAcPLkSRw+fBh2dnaQyWSQyWRo3bo1FixYgLFjx+L8+fOSx0xJScHZs2fF36m6/fnnnyWWJVMoFHjz5o1kceLj48V/M4VCgYSEBFy+fFnpOVKW8GFqpu0hTsZK8+zZM6pfvz6Zm5vTiBEjaNmyZbR8+XIaPnw4mZubk4eHh9L2XVJSKBQUFBREcrlcvD1kYmJCwcHBpFAoJImh6Vvuzs7OSl+mpqYkCIJYAkYQBDI1NSUXFxfJYr7r3r175OrqSpaWltSuXTtq164dWVlZkbu7O2VkZEge78qVK7Rs2TKKjo5WmSe6du1aOn/+vOQxXV1d6eDBg0SkXIB506ZNVK9ePUljjRw5kurVq0fbt28nY2NjCgsLo7lz55KDg4PS7Xcp3bx5k/z8/FRu1Xbq1KnM+cbloenrxsrKSvx3c3V1pcOHDxPR2+Lr6pjGQPS2tJa6Sj2V5JNPPqHNmzcTkfJ1Onv2bGrdurUkMXSphA+TBieMTKfl5OTQiBEjyMbGRnyTsbGxoREjRtDTp0/VHj8vL49Onz5Nly5dooKCAknPXbduXfFNuyQRERHk7u4uacxiW7ZsoVatWtHVq1fFY1evXqU2bdqoLdkgervDzNq1a2nkyJE0ceJE2rRpE71+/Vpt8TRt0aJF5OHhQUlJSWRubk7Hjx+nyMhIsrOzk3y3EG3V0iQievLkCZ06dYpOnTpF2dnZao1FpNnrpnXr1rRr1y4iIurXrx/5+flRYmIiDRo0iOrXr6+WmAkJCdSiRQs6cuQIPX78WO1bdWqi7iOreDhhZHpBoVDQw4cPKSsrS7IRvrIEBgaWOHr5/PlzCgwMlCTG1KlTydHRkTIzM1XaHjx4QI6OjjR16lRJYr3P1dWVzp07p3L8zJkzkq4C17Zt27bR559/TvXr16ePP/6YPv/88xJX3Epp6tSpZGxsLH7AkcvlNH36dMnjmJqaitvJ1axZU9xe8tatW2rZTq54L+C/+tJ3+/fvp507dxLR2xHVevXqkSAIZGtrK+73LLX3F9aoe9EL0dvX6e3tTaampmRsbEytWrVSKkVVXqW9hzL9xYW7GSuBgYEBHjx4IG7ZV+zx48eoXr06CgsLyx0jLy8PLVq0QEZGBgYMGAB3d3cIgoC0tDRs2bIFtWrVQlJSEszNzcsd630mJiY4evQomjVrpnT89OnTaNeuneRFn4tt3rwZa9euxa1bt3Dy5Ek4OTkhNDQUrq6u6Nmzp2RxFAoF+vXrh+3bt6NOnTqoW7cuiAhXr17FjRs38OWXXyIqKgqCIEgWs6ioCImJifD09IRcLkdqaioUCgU8PDxgZmYmWZxiXl5eWLlyJdq2bQtfX194eXlh8eLFWLFiBX744Qfcu3dP0niBgYF/63lSb0kIaO66Kc2TJ09gbW0t6fXyrmPHjpXZ3rZtW7XEVafS3kOZHtNywsqYTsnNzaWcnBwSBIFu3LihdFvoyZMntGnTJklrP2rrlnu3bt3Iy8uLkpOTxRHb5ORkatiwIXXv3l0tMVetWkW2trY0b948ksvl4rypjRs3Sl5Ee8mSJWRjY0N79uxRaYuNjSUbGxsKDQ2VNCZR6eWY1EEbtTS1QVPXTWBg4N/6qghcXFzo8ePHKsefPn0q2RxmQRAoKytLknMx3cAjjIy9QyaTlTmKIAgCgoODMW3aNEnjEhEeP34MIoKdnZ3aRjKKPXr0CIMHD8b+/fvF8i+FhYXo1KkTwsPD1TIq4OHhgZCQEPj7+8Pc3BwXLlyAq6srLl++jHbt2uHx48eSxfLy8sL48eMxZMiQEts3bNiAZcuW4dKlS5LFBICmTZti4cKF+OyzzyQ979+RkZGBM2fO4KOPPlJreSRN09R1I5PJ4OTkhEaNGqGsP4u7du2SJN77cnJysGHDBqSlpUEQBHh4eGDIkCHiKmMpyWQyZGZmqvw/z8rKgqOjI169eiVJjKysLNjZ2ZX7XEw3cFkdxt5x5MgREBF8fHywc+dO2NjYiG2VK1eGk5OTWur3CYKgsTdWIsKLFy+wY8cO/Pnnn0hLSwMRoV69eqhTp47a4qanp6NRo0Yqx42MjJCfny9prOvXr6NDhw6ltnfo0AGjR4+WNCYAzJ8/H5MmTcLcuXPxySefiHXoillYWEges5ijoyMcHR3Vdn5t0dR1M3z4cERHR+PWrVsYMmQIBgwYoPT/X53OnDmDTp06wdjYGM2aNQMRYenSpZg/fz4OHDiAxo0bSxJn9+7d4vfvlrwB3k6pSEhIgLOzsySxAKBOnTp/+eH3yZMnksVj6sUJI9MrBQUFkMvlajt/8Vyh9PR0ODo6qn2kTxuICG5ubrhy5Qrc3Nzg5uamkbguLi5ISUlRKba+b98+eHh4SBrL2NgYOTk5pSZQz549g7GxsaQxAYiFq3v06KF07RARBEEod21EbdfS1AZNXTerVq1CaGgoYmJiEBYWhilTpqBr1674+uuv4evrq9b3ggkTJqBHjx5Yv369WIy8sLBQrAf722+/SRLH398fwNsPqIMHD1ZqMzQ0hLOzM5YsWSJJLOBt7U51jJAy7eCEkek8hUKB+fPnY82aNcjKysIff/wBV1dXzJgxA87Ozvj6668liXPx4kV8/PHHkMlkyM3NLfN2pZeXlyQxtUEmk8HNzQ3Z2dkaSxYB4Pvvv8eoUaNQUFAAIsLp06cRFRWFBQsW4Oeff5Y0VosWLbB69WqsXr26xPaffvoJLVq0kDQm8HaEWp1CQ0P/1vMEQagwCaMmrxsjIyP069cP/fr1w507dxAeHo6RI0fizZs3SE1NVcviJeDtCOO7ySIAVKpUCUFBQWjSpIlkcRQKBYC3SXhycjJsbW0lO3dJ+vbty4teKhKtzJxk7B/QxL6nRMqTtN/fR7aiFZvdu3cvtW7dmi5duqTRuOvWrSNHR0fxd+ng4EA///yz5HF+//13MjQ0pC+//JJOnTolLmY6efIk9e7dmwwNDSkxMVHyuEw9NHXdvOvOnTsUHBxMLi4uVLNmTcrLy1NbrKpVq5ZY0mb//v1UtWpVyeIkJSVRXFyc0rFNmzaRs7Mz2dnZ0bBhwySrNyuTyXjRSwXDCSPTeR999JFY/+zdXQnS0tLIyspKsji3b98WVwzfvn27zC99Z2VlJa6olcvl4m4vxV/q9ujRI7X/MYmJiSFbW1uV2nZVqlShHTt2qC3ub7/9Rv3796cWLVrQvXv3iOhtMW0pd/LIzc1V2b2GiKioqEgthZ51hbqvm4KCAvrf//5HHTp0ILlcTr1796Zff/21xN+1lMaMGUMODg4UHR1NGRkZdPfuXYqKiiIHBwcaN26cZHE6depECxcuFB9fvHiRKlWqREOHDqUlS5ZQ9erVadasWZLE4lXSFQ/fkmY6T1P7nr47R8rMzAxVqlQBANy9exfr16/Hy5cv0aNHD7Rp00aymMUSEhKQkJCAhw8fireNioWFhUkeb9myZZKf859Q960wAPj888/RqVMnxMfH4/r16wDeTsL39fWFiYmJWmLu3LkTAwcORP/+/XHu3DlxtWleXh5CQkIQFxdX7hi7du3C5MmTkZKSovI6CgoK0LRpUyxevBjdu3cvdyxd4OPjg5iYGFhZWSldN8+ePYO/vz8OHz4sSZyRI0ciOjoajo6OCAwMRHR0tPgeoG6LFy+GIAgYNGgQCgsLQUSoXLkyRowYgYULF0oW58KFC5g3b574ODo6Gs2bN8f69esBALVq1cKsWbMwe/bscsd6/32M6T8uq8N0XpMmTTB+/HgMGDBAqaxGcHAwDh06hOPHj0sW69KlS+jevTvu3r0LNzc3REdHw8/PD/n5+ZDJZMjPz8eOHTvEyeNSCA4Oxpw5c9CkSRPY29urTK5XVxkPTWvUqFGJCwcEQYBcLkft2rUREBCA9u3blztWly5dEBUVJU64nz9/PkaNGgUrKysAQHZ2Ntq0aYPU1NRyx3pXo0aNMGHCBAwaNEjpWk1JSYGfnx8yMzPLHcPX1xdfffUVhg4dWmJ7WFgYtm7divj4+HLH0gWllYB5+PAhatasKdmHRplMBkdHx1Kv02IxMTGSxCvJixcvcPPmTRARateuLfkHG7lcjuvXr6NWrVoAgNatW8PPzw/Tp08HANy+fRuenp7Iy8uTNC6rGHiEkem8WbNmYeDAgfjzzz+hUCgQExODa9euISIiAnv37pU0VlBQEDw9PREZGYnIyEh069YNXbp0ESfXjxkzBgsXLpQ0YVyzZg3Cw8MxcOBAyc75T7x8+VLlj646yr/4+flh9erV8PT0FEuHnDlzBhcvXkRAQABSU1PRoUMHxMTElHv3jvj4eKVacosWLUK/fv3EhLGwsBDXrl0rV4ySXLt2Dd7e3irHLSwskJOTI0mMy5cvY9WqVaW2e3t7iwmAPrt48aL4fWpqqlKyXVRUhP3796NmzZqSxRs0aJDGqyKUVif0fVLdZahWrRrS09NRq1YtvH79GufOnUNwcLDYnpeXJ9ZlZex9nDAynde9e3ds3boVISEhEAQBM2fOROPGjbFnzx507NhR0ljJyck4fPgwvLy80LBhQ6xbtw4jR46ETCYD8DZh/PTTTyWN+fr1a7Rs2VLSc/6V/Px8TJ48Gdu2bUN2drZKe3nLv5Tk8ePHmDhxImbMmKF0fN68ebhz5w4OHDiAWbNmYe7cueVOGN+/caKpGyn29va4ceOGSi27xMREuLq6ShLj6dOnZW5N+ebNGzx9+lSSWNrUsGFDCIIAQRDg4+Oj0m5sbIyVK1dKFi88PFyyc/2TmH+nWLhU/Pz88N///heLFi3CL7/8AhMTE6UpNhcvXsRHH32k9n4wPaW96ZOM6Z73J2q/u8iGiCgzM1PyVdJBQUE0Z84cSc/5V0aOHEn16tWj7du3k7GxMYWFhdHcuXPJwcGBIiMj1RLTwsKCrl+/rnL8+vXrZGFhQURvFzKZmZmVO5Y2/h2JiBYtWkQeHh6UlJRE5ubmdPz4cYqMjCQ7OztauXKlJDHq1q1LmzdvLrU9IiKC3N3dJYmlTbdv36b09HQSBIGSk5OVFp3dv3+fCgsLtd3FchsxYgRZW1tTgwYNaPny5ZSdna3WeA8fPqTWrVuTIAhkbm5OMTExSu0+Pj40depUtfaB6S8eYWQ6Lzk5GQqFAs2bN1c6furUKRgYGEhapwyAym0pdd+mKigowLp163Do0CF4eXmp3BJaunSp5DH37NmDiIgItGvXDkOGDEGbNm1Qu3ZtODk5YcuWLejfv7/kMeVyOU6cOKGygOnEiRNiMXaFQgEjI6NyxyoemXr/mLoFBQUhNzcX7du3R0FBAby9vWFkZIRJkyZJtrNMr169MG3aNHTs2BHVqlVTasvMzMT06dMxYMAASWJpU/EitIq8eELTxcLt7Oxw/Phx5ObmwszMDAYGBkrt27dvV1utSab/eNEL03nNmjVDUFAQevfurXQ8JiYGixYtwqlTpySLJZPJ0LlzZzFp2bNnD3x8fMQt3l69eoX9+/dLesu2rEUegiBItgr0XWZmZrhy5QqcnJzg4OCAmJgYNGvWDOnp6fD09MTz588ljzlv3jyEhIRg2LBhaNq0KQRBwOnTp/Hzzz9j6tSpmDZtGkJDQxEXF4eDBw+WK5Y2/h3f9eLFC6SmpkKhUMDDw0PSP8J5eXlo0aIFMjIyMGDAALi7u0MQBKSlpWHLli2oVasWkpKSYG5uLllMTdu9ezc6d+4MQ0NDpe3sStKjRw8N9Ur9iouFR0REqL1YOGP/FCeMTOeZmZnh4sWLKnPA0tPT4eXlJemKvsDAwL/1vI0bN0oWUxu8vLywcuVKtG3bFr6+vvDy8sLixYuxYsUK/PDDD7h3755a4m7ZsgU//vijuODE3d0dY8aMwX/+8x8AbxfgFK+aLo+K/u+Ym5uLKVOmYOvWreJ8RWtra/Tp0wchISHi4h599e7K6OL5wyWRYrtFXZKRkYHw8HCEh4fj9evXuHr1KieMTGdwwsh0XpUqVbB3716VrdxOnDiBrl27VogJ/poWGhoKAwMDjB07FkeOHEHXrl1RVFSEN2/eIDQ0FOPGjdN2F/VSfn4+Fi5cWGpNzVu3bkkaj4jw+PFjEBHs7Owq5N7npcnIyMCsWbP0Nukv9urVK/GWdGJiIrp164bAwED4+fmVmSwzpmmcMDKd17dvX2RmZiI2Nlasq5eTkwN/f39UrVoV27Zt03IP/7levXohPDwcFhYW6NWrV5nPVWfdt2IZGRk4c+YMateurfZ9ss+ePYu0tDQIggAPDw80atRIrfE0qV+/fjh27BgGDhxYYk1NTsSlc+HCBTRu3FivRxjfLxY+YMAAjRULZ+yf4kUvTOctWbIE3t7eYvkJAEhJSUG1atWwefNmLffu37G0tBSTieIkWBMOHz6M0aNHIykpSanWoqOjIywtLdGyZUusWbNGLbvZPHz4EH379sXRo0dhZWUFIhIXiERHR8POzk7ymJq2b98+/Prrr2jVqpW2u8L0wJo1a+Do6AgXFxccO3YMx44dK/F5mvjQyNhf4RFGphfy8/OxZcsWXLhwAcbGxvDy8kK/fv24yOw/1KNHD7Rv3x4TJkwosX3FihU4cuSIWnaX6dOnD27evInNmzejXr16AN4WZB48eDBq166NqKgoyWNqmouLC+Li4sTXx9SnIowwBgQE/K1pBPp+251VDJwwMqYjHj16hGvXrkEQBNSpU0ctI25OTk7Yv39/qQnN1atX4evri4yMDMljW1pa4tChQ2jatKnS8dOnT8PX11eynVC0KTIyErGxsdi0aZPa9qtmb1WEhJExfcK3pJle+OOPP3D06NESFxLMnDlTS72SRn5+PsaMGYOIiAjxtRkYGGDQoEFYuXKlpIlHVlZWmaOylSpVwqNHjySL9y6FQlFibENDQ72utff+3sM3btxAtWrV4OzsrPJ6z507p+nu6a2/mttbET5gMKZPOGFkOm/9+vUYMWIEbG1tUb16daU/zsVbBeqz7777DseOHcOePXvEuW+JiYkYO3YsJk6ciNWrV0sWq2bNmrh06ZJK8exiFy9ehL29vWTx3uXj44Nx48YhKioKNWrUAAD8+eefmDBhAj777DO1xNQEKfcV/6dOnz5d6gcpdRR816S/mttraWmJQYMGaag3jDG+Jc10npOTE0aOHInJkydruytqYWtrix07dqBdu3ZKx48cOYKvvvpK0hG/MWPG4OjRo0hOTlapdfjy5Us0a9YM7du3x4oVKySLWezu3bvo2bMnLl++jFq1akEQBGRkZMDT0xOxsbFwcHCQPGZFFhISgunTp8Pd3R3VqlVT+SCljoLvjLEPFyeMTOdZWFggJSVFpXB3RWFiYoKzZ8+qzCu8cuUKmjVrhvz8fMliZWVloXHjxjAwMMDo0aOVdgn56aefUFRUhHPnzqlsOSelQ4cOIS0tDUQEDw8PdOjQQW2xNM3V1RXJyckqpVFycnLQuHFjSeswVqtWDYsWLUJAQIBk52SMsdLwLWmm87788kscOHAAw4cP13ZX1KJFixaYNWsWIiIixFG/ly9fIjg4WKVYeXlVq1YNJ06cwIgRIzBlyhQUf14UBAGdOnXCqlWr1JIsKhQKhIeHIyYmBrdv34YgCHBxcRHL61SUgtO3b98ucRHGq1evJN89RyaTcfkexpjG8Agj03kLFizA0qVL0bVrV3h6eqosJBg7dqyWeiaNy5cvw8/PDwUFBWjQoAEEQUBKSgrkcjni4+NRv359tcR9+vQpbty4ASKCm5sbrK2t1RKHiNC9e3fExcWhQYMGqFu3LogIaWlpuHTpEnr06IFffvlFLbE1pXi/Y39/f2zatElp/l1RURESEhJw8OBBcUtEKfzwww+4f/8+li1bJtk5GWOsNJwwMp3n4uJSapsgCJJvt6YNL1++RGRkJK5evSrequ3fvz+MjY213bVy27hxI8aNG4fY2Fi0b99eqe3w4cPw9/fHjz/+qNcLGIq3cBMEAe+/pRoaGsLZ2RlLlixBt27dJIupUCjQtWtX/PHHH/Dw8FD5IMXFnhljUuKEkTGmVr6+vvDx8cF///vfEttDQkJw7NgxxMfHa7hn0nNxcUFycjJsbW3VHmvUqFHYsGED2rdvr7LoBeBiz4wxaXHCyJiWLViwANWqVcOQIUOUjoeFheHRo0d6vzq8evXq2L9/Pxo2bFhi+/nz59G5c2dkZmZqtmMS6tKlC6KiosRb0fPnz8eoUaNgZWUFAMjOzkabNm2QmpoqWUxzc3NER0eja9eukp2TMcZKwwkj0wv37t3D7t27kZGRgdevXyu16Xu9OWdnZ/zvf/9Dy5YtlY6fOnUKffv2RXp6upZ6Jo3KlSvjzp07pdZ3vH//PlxcXPDq1SsN90w6MpkMmZmZqFq1KgDVlf1ZWVmoUaOGpLuSODk5IT4+HnXr1pXsnIwxVhpeJc10XkJCAnr06AEXFxdcu3YNH3/8MW7fvg0iQuPGjbXdvXLLzMwsMZmys7PDgwcPtNAjaRUVFaFSpdLfagwMDFBYWKjBHqmfJj6Hz549G7NmzcLGjRt5G0LGmNpxwsh03pQpUzBx4kTMmTMH5ubm2LlzJ6pWrYr+/fvDz89P290rt1q1auH3339XWdzz+++/izui6DMiQkBAAIyMjEps1+eRRW1asWIFbt68ydsQMsY0ghNGpvPS0tIQFRUF4O1exy9fvoSZmRnmzJmDnj17YsSIEVruYfkMHToU48ePx5s3b+Dj4wPg7ahqUFAQJk6cqOXeld/gwYP/8jn6vEIaeLs6+v1FJ+quLanNLQkZYx8eThiZzjM1NRVHoWrUqIGbN2+KtQkfP36sza5JIigoCE+ePMHIkSPF+ZlyuRyTJ08udWWxPvkQVuu+P4paUFCA4cOHw9TUFIB6RlFnzZol+TkZY6w0vOiF6Tx/f3907doVw4YNQ1BQEHbt2oWAgADExMTA2toahw4d0nYXJfH8+XOkpaXB2NgYbm5upd7CZbonMDDwbz3vQ0ieGWMVEyeMTOfdunULz58/h5eXF168eIFJkyYhMTERtWvXRmhoKJycnLTdxXIZMmQIli9fDnNzc6Xj+fn5GDNmDMLCwrTUM6bLZDJZmbe9pVyRzRhjnDAypmUGBgZ48OCBWJKl2OPHj1G9evUKt4KYSSM2Nlbp8Zs3b3D+/Hls2rQJwcHB+Prrr7XUM8ZYRcRzGJneeP36NR4+fAiFQqF03NHRUUs9Kp9nz56BiEBEyMvLg1wuF9uKiooQFxenkkQyVqxnz54qx3r37o369etj69atnDAyxiTFCSPTeX/88Qe+/vprnDhxQuk4EUEQBL299WZlZSWurq1Tp45KuyAICA4O1kLPmD5r3rw5hg0bpu1uMMYqGE4Ymc4LDAxEpUqVsHfvXtjb26u9XImmHDlyBEQEHx8f7Ny5EzY2NmJb5cqV4eTkVCHqMDLNefnyJVauXAkHBwdtd4UxVsHwHEam80xNTXH27NkKuwXanTt34OjoWGESYaYZ1tbWStdM8dQGExMTREZGokePHlrsHWOsouERRqbzPDw8KkS9xXddvHgRH3/8MWQyGXJzc3Hp0qVSn+vl5aXBnjF9sWzZMqXHMpkMdnZ2aN68OaytrbXTKcZYhcUjjEznHT58GNOnT0dISAg8PT1VtkCzsLDQUs/+PZlMhszMTFStWlUsj1LSf0V9nqPJGGOs4uCEkek8mUwGQHWrNX1e9PLubeg7d+6U+Vx9rzPJ1Of48eNYu3Ytbt26he3bt6NmzZrYvHkzXFxc0Lp1a213jzFWgfAtaabzjhw5ou0uSO7dJJATQvZv7Ny5EwMHDkT//v1x7tw5cfvBvLw8hISEIC4uTss9ZIxVJDzCyJiWZWdno0qVKgCAu3fvYv369Xj58iV69OiBNm3aaLl3TFc1atQIEyZMwKBBg2Bubo4LFy7A1dUVKSkp8PPzQ2Zmpra7yBirQHiEkemkdxeFXLx4sczn6uuikEuXLqF79+64e/cu3NzcEB0dDT8/P+Tn50MmkyE0NBQ7duyAv7+/trvKdNC1a9fg7e2tctzCwgI5OTma7xBjrELjhJHppIYNG4qLQho2bFghF4UEBQXB09MTkZGRiIyMRLdu3dClSxf8/PPPAIAxY8Zg4cKFnDCyEtnb2+PGjRtwdnZWOp6YmAhXV1ftdIoxVmFxwsh0Unp6Ouzs7MTvK6Lk5GQcPnwYXl5eaNiwIdatW4eRI0eKi3zGjBmDTz/9VMu9ZLrq22+/xbhx4xAWFgZBEHD//n2cPHkSkyZNwsyZM7XdPcZYBcMJI9NJH8KikCdPnqB69eoAADMzM5iamirt9mJtbY28vDxtdY/puKCgIOTm5qJ9+/YoKCiAt7c3jIyMMGnSJIwePVrb3WOMVTCcMDKdV5EXhbxfKoh3e2F/R1FRERITEzFx4kRMmzYNqampUCgU8PDwgJmZmba7xxirgHiVNNNZf7UoJD8/X68XhchkMnTu3BlGRkYAgD179sDHxwempqYAgFevXmH//v16O0eTqZdcLkdaWhpcXFy03RXG2AdApu0OMFaa4kUhx44dQ7t27cRFIbm5uXj69Cm+/fZbLFy4UNvd/NcGDx6MqlWrwtLSEpaWlhgwYABq1KghPq5atSoGDRqk7W4yHeXp6Ylbt25puxuMsQ8EjzAynWVraysuCnn+/DksLCxw+vRpNGnSBABw9epVfPrpp1xChH2QDhw4gMmTJ2Pu3Ln45JNPxJHpYvq4ZSZjTHdxwsh01rv7LQNQKk4MAFlZWahRowbfsmUfpOLV9IDy3Fd93jKTMaa7eNEL02m8KISxklXELTMZY7qLE0am0wICAsRFIQUFBRg+fLjSohDGPlRt27bVdhcYYx8QviXNdFZgYODfet7GjRvV3BPGdNPx48exdu1a3Lp1C9u3b0fNmjWxefNmuLi4oHXr1truHmOsAuERRqazOBFkrHQ7d+7EwIED0b9/f5w7d04ccc/Ly0NISAji4uK03EPGWEXCZXUYY0wPzZs3D2vWrMH69ethaGgoHm/ZsiXOnTunxZ4xxioiThgZY0wPXbt2Dd7e3irHLSwsuNQUY0xynDAyxpgesre3x40bN1SOJyYmiqWnGGNMKpwwMsaYHvr2228xbtw4nDp1CoIg4P79+9iyZQsmTZqEkSNHart7jLEKhldJM8aYnpo2bRpCQ0NRUFAAADAyMsKkSZMwd+5cLfeMMVbRcMLIGGN67MWLF0hNTYVCoYCHhwfMzMy03SXGWAXECSNjjDHGGCsT12FkjDE9lJ+fj4ULFyIhIQEPHz6EQqFQar9165aWesYYq4g4YWSMMT00dOhQHDt2DAMHDoS9vT3vs84YUyu+Jc0YY3rIysoKv/76K1q1aqXtrjDGPgBcVocxxvSQtbU1bGxstN0NxtgHghNGxhjTQ3PnzsXMmTPx4sULbXeFMfYB4FvSjDGmJxo1aqQ0V/HGjRsgIjg7OyvtJw2A95NmjEmKF70wxpie8Pf313YXGGMfKB5hZIwxxhhjZeI5jIwxpodcXV2RnZ2tcjwnJweurq5a6BFjrCLjhJExxvTQ7du3UVRUpHL81atXuHfvnhZ6xBiryHgOI2OM6ZHdu3eL38fHx8PS0lJ8XFRUhISEBLi4uGija4yxCoznMDLGmB6Ryd7eGBIEAe+/fRsaGsLZ2RlLlixBt27dtNE9xlgFxQkjY4zpIRcXFyQnJ8PW1lbbXWGMfQA4YWSMMcYYY2XiRS+MMaZHunTpgtzcXPHx/PnzkZOTIz7Ozs6Gh4eHFnrGGKvIeISRMcb0iIGBAR48eICqVasCACwsLJCSkiKW0snKykKNGjVKXEHNGGP/Fo8wMsaYHnn/Mz5/5meMaQInjIwxxhhjrEycMDLGmB4RBAGCIKgcY4wxdeLC3YwxpkeICAEBATAyMgIAFBQUYPjw4TA1NQXwdqcXxhiTGi96YYwxPRIYGPi3nrdx40Y194Qx9iHhhJExxhhjjJWJ5zAyxhhjjLEyccLIGGOMMcbKxAkjY4wxxhgrEyeMjDHGGGOsTJwwMsYYY4yxMnHCyBj74Jw4cQIGBgbw8/PTWMzZs2ejYcOG/+hnnJ2dIQgCkpKSlI6PHz8e7dq1k65zjDH2FzhhZIx9cMLCwjBmzBgkJiYiIyND290pk1wux+TJk7XdDcbYB44TRsbYByU/Px/btm3DiBEj0K1bN4SHh4ttT58+Rf/+/WFnZwdjY2O4ubmJBbBfv36N0aNHw97eHnK5HM7OzliwYIH4s7m5ufjmm29QtWpVWFhYwMfHBxcuXAAAhIeHIzg4GBcuXBC39ns3blm+/fZbJCUlIS4urtTnJCcno2PHjrC1tYWlpSXatm2Lc+fOKT1HEASsXbsW3bp1g4mJCerVq4eTJ0/ixo0baNeuHUxNTdGiRQvcvHlT6ef27NmDTz75BHK5HK6urggODkZhYeHf6jtjrOLghJEx9kHZunUr3N3d4e7ujgEDBmDjxo0o3r9gxowZSE1Nxb59+5CWlobVq1fD1tYWALBixQrs3r0b27Ztw7Vr1xAZGQlnZ2cAb7fr69q1KzIzMxEXF4ezZ8+icePG+Oyzz/DkyRP06dMHEydORP369fHgwQM8ePAAffr0+Vv9dXZ2xvDhwzFlyhQoFIoSn5OXl4fBgwfj+PHjSEpKgpubG7p06YK8vDyl582dOxeDBg1CSkoK6tati//85z/49ttvMWXKFJw5cwYAMHr0aPH58fHxGDBgAMaOHYvU1FSsXbsW4eHhmD9//j/6nTPGKgBijLEPSMuWLWnZsmVERPTmzRuytbWlgwcPEhFR9+7dKTAwsMSfGzNmDPn4+JBCoVBpS0hIIAsLCyooKFA6/tFHH9HatWuJiGjWrFnUoEGDf9RXJycnCg0NpYcPH5K5uTlFREQQEdG4ceOobdu2pf5cYWEhmZub0549e8RjAGj69Oni45MnTxIA2rBhg3gsKiqK5HK5+LhNmzYUEhKidO7NmzeTvb39P3odjDH9xyOMjLEPxrVr13D69Gn07dsXAFCpUiX06dMHYWFhAIARI0YgOjoaDRs2RFBQEE6cOCH+bEBAAFJSUuDu7o6xY8fiwIEDYtvZs2fx/PlzVKlSBWZmZuJXenq6yi3ef8POzg6TJk3CzJkz8fr1a5X2hw8fYvjw4ahTpw4sLS1haWmJ58+fq8zP9PLyEr+vVq0aAMDT01PpWEFBAZ49eya+rjlz5ii9pmHDhuHBgwd48eJFuV8XY0x/VNJ2BxhjTFM2bNiAwsJC1KxZUzxGRDA0NMTTp0/RuXNn3LlzB7/++isOHTqEzz77DKNGjcLixYvRuHFjpKenY9++fTh06BC++uordOjQATt27IBCoYC9vT2OHj2qEtPKykqSvn/33XdYtWoVVq1apdIWEBCAR48eYdmyZXBycoKRkRFatGihklwaGhqK3wuCUOqx4lvfCoUCwcHB6NWrl0pMuVxe/hfFGNMbnDAyxj4IhYWFiIiIwJIlS+Dr66vU9sUXX2DLli0YPXo07OzsEBAQgICAALRp0wbff/89Fi9eDACwsLBAnz590KdPH/Tu3Rt+fn548uQJGjdujMzMTFSqVEmc1/i+ypUro6io6F/338zMDDNmzMDs2bPRvXt3pbbjx49j1apV6NKlCwDg7t27ePz48b+OVaxx48a4du0aateuXe5zMcb0GyeMjLEPwt69e/H06VN8/fXXsLS0VGrr3bs3NmzYgIcPH+KTTz5B/fr18erVK+zduxf16tUDAISGhsLe3h4NGzaETCbD9u3bUb16dVhZWaFDhw5o0aIF/P39sWjRIri7u+P+/fuIi4uDv78/mjRpAmdnZ6SnpyMlJQUODg4wNzeHkZHRP3oN33zzDUJDQxEVFYXmzZuLx2vXro3NmzejSZMmePbsGb7//nsYGxuX+3c2c+ZMdOvWDbVq1cKXX34JmUyGixcv4tKlS5g3b165z88Y0x88h5Ex9kHYsGEDOnTooJIsAm9HGFNSUlCpUiVMmTIFXl5e8Pb2hoGBAaKjowG8HeFbtGgRmjRpgqZNm+L27duIi4uDTCaDIAiIi4uDt7c3hgwZgjp16qBv3764ffu2OFfwiy++gJ+fH9q3bw87OztERUX949dgaGiIuXPnoqCgQOl4WFgYnj59ikaNGmHgwIEYO3Ysqlat+i9+S8o6deqEvXv34uDBg2jatCk+/fRTLF26FE5OTuU+N2NMvwhE/7+eBGOMMcYYYyXgEUbGGGOMMVYmThgZY0wLtmzZolSu5t2v+vXra7t7jDGmhG9JM8aYFuTl5SErK6vENkNDQ54nyBjTKZwwMsYYY4yxMvEtacYYY4wxViZOGBljjDHGWJk4YWSMMcYYY2XihJExxhhjjJWJE0bGGGOMMVYmThgZY4wxxliZOGFkjDHGGGNl4oSRMcYYY4yV6f8BUW6r/d/L2SQAAAAASUVORK5CYII=\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"code","source":"#check for linear correlation for close price\ndata = df[['datetime', 'Asset_Name', 'Close']]\n#Creating dataframe with column for each different Assest ID\ndata_new = data.set_index(['datetime', 'Asset_Name']).unstack(['Asset_Name'])\n#remove vwap from column index\ndata_new.columns=data_new.columns.droplevel(0)","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"#check for linear correlation for close price\nsns.heatmap(data_new.corr());\nplt.title('Heatmap of closing price correlation', fontsize = 10);","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":"> We can see a linear correlation between close prices however not between targets as target is a log transformed and normalized value I would be surprised to see the linear relationship still existing ","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"code","source":"#just to see the data\ndef plot_Target(df,assets):\n fig, ax = plt.subplots()\n for asset in assets:\n ax.plot('datetime', 'Target', data=df[df[\"Asset_Name\"]==asset],label=asset)\n\n ax.set_title('Time Plot of Target');\n ax.legend(loc='center left', bbox_to_anchor=(1, 0.5));\n plt.xticks(rotation=90);","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"plot_Target(df,df.Asset_Name.unique())","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":"> The target variable looks really messy and centered around 0 with not much variation ","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"code","source":"#just to see the data\ndef plot_close(df,assets):\n fig, ax = plt.subplots()\n for asset in assets:\n ax.plot('datetime', 'Close', data=df[df[\"Asset_Name\"]==asset],label=asset)\n\n ax.set_title('Time Plot of close price');\n ax.legend(loc='center left', bbox_to_anchor=(1, 0.5));\n plt.xticks(rotation=90);","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"plot_close(df,df.Asset_Name.unique())","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":"> The Bitcoin close is domintant as it has much higher values","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"code","source":"#let's plot again without bitcoin\nplot_close(df,['Bitcoin Cash', 'Binance Coin', 'EOS.IO','Ethereum Classic', 'Ethereum', 'Litecoin', 'Monero', 'TRON','Stellar', 'Cardano', 'IOTA', 'Maker', 'Dogecoin'])","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":31,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":"> We can see that they seem to be somehow following the same path","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"markdown","source":">As we already saw from the heatmap the prices at close time do look quite correlated so in our following analsyis we will focus on Bitcoin and Etheruim ","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"markdown","source":"> Stationary time series are easier to model and to predict so it is worth it to check for stationarity here.\nFor data to be stationary, the statistical properties of a system do not change over time.\n\n","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"code","source":"#To double check stationarty time series \ndef check_stationary(asset,df,window):\n \n min_periods=int(window/2)\n moving_average = df[df[\"Asset_Name\"]==asset]['Target'].rolling(\n window=window, # 1week window\n center=True, # puts the average at the center of the window\n min_periods=min_periods, # choose about half the window size\n ).mean()\n moving_std=df[df[\"Asset_Name\"]==asset]['Target'].rolling(\n window=window, # 1week window\n center=True, # puts the average at the center of the window\n min_periods=min_periods, # choose about half the window size\n ).std()\n fig, ax = plt.subplots()\n ax.plot('datetime', 'Target', data=df[df[\"Asset_Name\"]==asset], color='red',label='Target');\n ax.plot(df[df[\"Asset_Name\"]==asset]['datetime'], moving_average, color='black',label='Target mean');\n ax.plot(df[df[\"Asset_Name\"]==asset]['datetime'], moving_std, color='blue',label='Target std');\n ax.set_ylim([-1, 1]);\n ax.legend(loc='center left', bbox_to_anchor=(1, 0.5));\n plt.xticks(rotation=90);","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"#window of one week \n#We plot a moving average of one week against the target signal\ncheck_stationary('Bitcoin',df,168)","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":">The target looks very stationary due to the transformations done in's calcualtion which make sense however it could simply imply being white noise without any patterns.","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"markdown","source":"> We can check for autocorrelation with previous observations in the last week to check if we have white noise which is defined as mean=0 ,std=0 and 0 correlation between observations","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"code","source":"#Plot of target against itself lagged by 168 steps (one week)\nlag_plot(df[df[\"Asset_Name\"]=='Ethereum']['Target'],lag=168);","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":35,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"code","source":"lag_plot(df[df[\"Asset_Name\"]=='Bitcoin']['Target'],lag=168);","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":36,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":"> The lag plots shows a star shaped relation ship between the target variables and one week lag variables we will attempt to model this function ","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"code","source":"#Quick visualization of the variables per asset\ndef quick_visualize(df,asset):\n fig, axs = plt.subplots(3, 2)\n axs[0, 0].plot('datetime', 'Open', data=df[df[\"Asset_Name\"]==asset])\n axs[0, 0].set_title('Time Plot of open price for {}'.format(asset));\n axs[0, 0].legend(loc='center left', bbox_to_anchor=(1, 0.5));\n axs[0, 0].tick_params(axis='x', rotation=90)\n axs[0, 1].plot('datetime', 'High', data=df[df[\"Asset_Name\"]==asset])\n axs[0, 1].set_title('Time Plot of High price for {}'.format(asset));\n axs[0, 1].legend(loc='center left', bbox_to_anchor=(1, 0.5));\n axs[0, 1].tick_params(axis='x', rotation=90)\n axs[1, 0].plot('datetime', 'Low', data=df[df[\"Asset_Name\"]==asset])\n axs[1, 0].set_title('Time Plot of Low price for {}'.format(asset));\n axs[1, 0].legend(loc='center left', bbox_to_anchor=(1, 0.5));\n axs[1, 0].tick_params(axis='x', rotation=90)\n axs[1, 1].plot('datetime', 'VWAP', data=df[df[\"Asset_Name\"]==asset])\n axs[1, 1].set_title('Time Plot of VWAP price for {}'.format(asset));\n axs[1, 1].legend(loc='center left', bbox_to_anchor=(1, 0.5));\n axs[1, 1].tick_params(axis='x', rotation=90)\n axs[2, 1].plot('datetime', 'Close', data=df[df[\"Asset_Name\"]==asset])\n axs[2, 1].set_title('Time Plot of Close price for {}'.format(asset));\n axs[2, 1].legend(loc='center left', bbox_to_anchor=(1, 0.5));\n axs[2, 1].tick_params(axis='x', rotation=90)\n axs[2, 0].plot('datetime', 'Target', data=df[df[\"Asset_Name\"]==asset])\n axs[2, 0].set_title('Time Plot of Target {}'.format(asset));\n axs[2, 0].legend(loc='center left', bbox_to_anchor=(1, 0.5));\n axs[2, 0].tick_params(axis='x', rotation=90)\n fig.tight_layout()","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"quick_visualize(df,'Bitcoin')","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":38,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":"> We can see that the features of both currencies here are somehow are in aggreenment ","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"markdown","source":">I will also visualize a shorter version of the time series for only the last 3 months of data to also see recent trends and if they differ from the long term.","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"code","source":"df_reduced=df[df['datetime']>df.datetime.iloc[-1]-timedelta(days=90)]","metadata":{},"execution_count":40,"outputs":[]},{"cell_type":"code","source":"plot_Target(df_reduced,df_reduced.Asset_Name.unique())","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":41,"outputs":[{"output_type":"display_data","data":{"image/png":"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"]},"metadata":{}}]},{"cell_type":"markdown","source":"> As messy as the full dataset ","metadata":{"slideshow":{"slide_type":"skip"}}},{"cell_type":"code","source":"#just to see the data\nplot_close(df_reduced,df_reduced.Asset_Name.unique())","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":42,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAusAAAH0CAYAAACEkWPuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAADR+klEQVR4nOzdeXxM1//48ddk32QkkUhCEBKRNHZqCUXtFUtL7flIbS1KtZSqlqilrZYutHZiq3RDUY2taNVSVGwhiJ3ElkgkZL+/P/Kb+81kk4lseD8fj3l05t5z7z0z0uQ9577P+2gURVEQQgghhBBClDlGpd0BIYQQQgghRO4kWBdCCCGEEKKMkmBdCCGEEEKIMkqCdSGEEEIIIcooCdaFEEIIIYQooyRYF0IIIYQQooySYF0IIYQQQogySoJ1IYQQQgghyigJ1oUQQgghhCijJFgXophoNJoCPfbs2UNgYCDVqlUr7S6rLl++rNdHIyMjHBwceOWVVzhw4IBeW41GQ1BQkMHXuHnzJkFBQYSFhRVNp7M4duwYrVq1QqvVotFo+Prrr5/ofNWqVSMwMLBI+lYW7dmzR/1ZFEIIUbaYlHYHhHhWZQ9qp0+fzu7du/nzzz/1tvv4+ODm5sY777xTkt0rkNGjR9O/f3/S09M5ffo006ZNo02bNhw4cID69es/0blv3rzJtGnTqFatGvXq1SuaDv9/gwcPJjExkZCQEOzs7MrUF6GyqEGDBhw4cAAfH5/S7ooQQohsJFgXopg0bdpU77WjoyNGRkY5tgPY2tqWVLcMUqVKFbW/fn5+eHh40LZtW77//nuWLFlSyr3L26lTpxg2bBidO3cu7a6UaampqWg0GmxtbXP9uRRCCFH6JA1GiDIgtzQYjUbD22+/zYoVK/Dy8sLS0pJGjRpx8OBBFEXhiy++wN3dHRsbG15++WUuXLiQ47w7d+6kbdu22NraYmVlhZ+fH7t27Sp0P3UB3ZUrV/Jtd+rUKbp3746dnR0WFhbUq1ePlStXqvv37NlD48aNAXjjjTfUdJvHpdM87rzBwcFoNBrS0tJYsGCBet78JCcn88knn+Dt7Y2FhQUODg60adOG/fv353vc1atXGThwIE5OTpibm+Pt7c2cOXPIyMjQa7dgwQLq1q2LjY0N5cqVo1atWnz44Yd6baKjo3nzzTepXLkyZmZmuLu7M23aNNLS0vLtA2Sm6Pj7+7Nhwwbq1KmDhYUF1atX59tvv9Vrp0t1Wb16NePGjaNSpUqYm5tz4cKFPNNgDh06RNeuXXFwcMDCwoIaNWowduxYvTbnz5+nf//+ep/Dd99999h+CyGEKBgZWReiDNuyZQvHjh3js88+Q6PRMHHiRLp06cKgQYO4ePEi8+fPJy4ujvfee4+ePXsSFhamBqdr1qzhf//7H927d2flypWYmpqyaNEiOnbsyLZt22jbtq3B/dF9IXB0dMyzTUREBM2bN8fJyYlvv/0WBwcH1qxZQ2BgILdu3WLChAk0aNCAFStW8MYbb/DRRx/RpUsXACpXrvxE5+3SpQsHDhygWbNm9OrVi3HjxuX7ftLS0ujcuTN///03Y8eO5eWXXyYtLY2DBw9y9epVmjdvnutxd+7coXnz5qSkpDB9+nSqVavGli1bGD9+PJGRkXz//fcAhISEMHLkSEaPHs2XX36JkZERFy5cIDw8XD1XdHQ0L774IkZGRkyZMoUaNWpw4MABZsyYweXLl1mxYkW+7wEgLCyMsWPHEhQUhLOzM2vXruWdd94hJSWF8ePH67WdNGkSzZo1Y+HChRgZGeHk5ER0dHSOc27bto2uXbvi7e3N3LlzqVKlCpcvX2b79u1qm/DwcJo3b06VKlWYM2cOzs7ObNu2jTFjxnD37l2mTp362L4LIYR4DEUIUSIGDRqkWFtb57mvatWqetsAxdnZWUlISFC3bdy4UQGUevXqKRkZGer2r7/+WgGUEydOKIqiKImJiYq9vb3StWtXvXOmp6crdevWVV588cV8+3rp0iUFUD7//HMlNTVVSUpKUo4ePao0btxYAZTff/9dr59Tp05VX/ft21cxNzdXrl69qnfOzp07K1ZWVsr9+/cVRVGUw4cPK4CyYsWKfPti6Hl1fRo1atRjz7lq1SoFUJYsWZJvu6pVqyqDBg1SX3/wwQcKoBw6dEiv3YgRIxSNRqNEREQoiqIob7/9tlK+fPl8z/3mm28qNjY2ypUrV/S2f/nllwqgnD59+rF902g0SlhYmN729u3bK7a2tkpiYqKiKIqye/duBVBeeumlHOfQ7du9e7e6rUaNGkqNGjWUR48e5Xntjh07KpUrV1bi4uL0tr/99tuKhYWFEhMTk2/fhRBCPJ6kwQhRhrVp0wZra2v1tbe3NwCdO3fWS+/Qbdelp+zfv5+YmBgGDRpEWlqa+sjIyKBTp04cPnyYxMTEx15/4sSJmJqaYmFhQcOGDbl69SqLFi3ilVdeyfOYP//8k7Zt2+Lm5qa3PTAwkIcPH+aYeFtQxXHeP/74AwsLCwYPHmxwX3x8fHjxxRdz9EVRFHUS8Ysvvsj9+/fp168fv/32G3fv3s1xri1bttCmTRtcXV31/q10+fZ79+59bH9eeOEF6tatq7etf//+xMfH899//+lt79mz52PPd+7cOSIjIxkyZAgWFha5tklKSmLXrl28+uqrWFlZ6fX9lVdeISkpiYMHDz72WkIIIfInaTBClGH29vZ6r83MzPLdnpSUBMCtW7cA6NWrV57njomJ0fsikJt33nmHgQMHYmRkRPny5XF3d39sDvi9e/dwcXHJsd3V1VXdXxjFcd47d+7g6uqKkZFh4xb37t3LtcJM9r4EBASQlpbGkiVL6NmzJxkZGTRu3JgZM2bQvn17IPPfavPmzZiamuZ6rdwC/OycnZ3z3Jb9c8ntM8zuzp07QP5pSffu3SMtLY158+Yxb968XNsUpO9CCCHyJ8G6EM+gChUqADBv3rw8q3xUrFjxseepXLkyjRo1MujaDg4OREVF5dh+8+ZNvb4ZqjjO6+joyL59+8jIyDAoYDekL2+88QZvvPEGiYmJ/PXXX0ydOhV/f3/OnTtH1apVqVChAnXq1GHmzJm5Xkv3BSA/ueWc67Y5ODjobX/cly34vzkJ169fz7ONnZ0dxsbGBAQEMGrUqFzbuLu7P/ZaQggh8ifBuhDPID8/P8qXL094eDhvv/12iV67bdu2bNiwgZs3b+oFmqtWrcLKykr98mBubg7Ao0ePivS8hujcuTPr1q0jODjYoFSYtm3b8umnn/Lff//RoEEDvb5oNBratGmT4xhra2s6d+5MSkoKPXr04PTp01StWhV/f3+2bt1KjRo1sLOzM/g9AJw+fZrjx4/rpcL88MMPlCtXTq9/BVWzZk1q1KjB8uXLee+999R/q6ysrKxo06YNx44do06dOurdHSGEEEVLgnUhnkE2NjbMmzePQYMGERMTQ69evXBycuLOnTscP36cO3fusGDBgmK59tSpU9U87ClTpmBvb8/atWv5/fffmT17NlqtFoAaNWpgaWnJ2rVr8fb2xsbGBldX1zxHkgt6XkP069ePFStW8NZbbxEREUGbNm3IyMjg0KFDeHt707dv31yPe/fdd1m1ahVdunThk08+oWrVqvz+++98//33jBgxgpo1awIwbNgwLC0t8fPzw8XFhejoaD799FO0Wq1auvKTTz5hx44dNG/enDFjxuDl5UVSUhKXL19m69atLFy4MN90FMgcfe/WrRtBQUG4uLiwZs0aduzYweeff46VlZXBnwvAd999R9euXWnatCnvvvsuVapU4erVq2zbto21a9cC8M0339CiRQtatmzJiBEjqFatGg8ePODChQts3rw5xwJgQgghDCfBuhDPqIEDB1KlShVmz57Nm2++yYMHD3BycqJevXoEBgYW23W9vLzYv38/H374IaNGjeLRo0d4e3uzYsUKvetaWVmxfPlypk2bRocOHUhNTWXq1Kl51lov6HkNYWJiwtatW/n0009Zt24dX3/9NeXKlaNu3bp06tQpz+McHR3Zv38/kyZNYtKkScTHx1O9enVmz57Ne++9p7Zr2bIlwcHB/PTTT8TGxlKhQgVatGjBqlWr1FQTFxcXjhw5wvTp0/niiy+4fv065cqVw93dnU6dOhVotL1evXq88cYbTJ06lfPnz+Pq6srcuXN59913C/W5AHTs2JG//vqLTz75hDFjxpCUlETlypXp1q2b2sbHx4f//vuP6dOn89FHH3H79m3Kly+Pp6dnvpOQhRBCFJxGURSltDshhBCicKpVq4avry9btmwp7a4IIYQoBlK6UQghhBBCiDJKgnUhhBBCCCHKKEmDEUIIIYQQooySkXUhhBBCCCHKKAnWhRBCCCGEKKMkWBdCCCGEEKKMeq7rrGdkZHDz5k3KlStXoCW4hRBCCFH6FEXhwYMHuLq6YmQk447i2fZcB+s3b97Ezc2ttLshhBBCiEK4du3aY1f4FeJp91wH6+XKlQMy/2e3tbUt5d4IIYQQoiDi4+Nxc3NT/44L8Sx7roN1XeqLra2tBOtCCCHEU0ZSWMXzQBK9hBBCCCGEKKMkWBdCCCGEEKKMkmBdCCGEEEKIMsqgYL1atWpoNJocj1GjRgGZpZSCgoJwdXXF0tKS1q1bc/r0ab1zJCcnM3r0aCpUqIC1tTXdunXj+vXrem1iY2MJCAhAq9Wi1WoJCAjg/v37em2uXr1K165dsba2pkKFCowZM4aUlJRCfARCCCGEEEKUTQYF64cPHyYqKkp97NixA4DXX38dgNmzZzN37lzmz5/P4cOHcXZ2pn379jx48EA9x9ixY9mwYQMhISHs27ePhIQE/P39SU9PV9v079+fsLAwQkNDCQ0NJSwsjICAAHV/eno6Xbp0ITExkX379hESEsKvv/7KuHHjnujDEEIIIYQQokxRnsA777yj1KhRQ8nIyFAyMjIUZ2dn5bPPPlP3JyUlKVqtVlm4cKGiKIpy//59xdTUVAkJCVHb3LhxQzEyMlJCQ0MVRVGU8PBwBVAOHjyotjlw4IACKGfPnlUURVG2bt2qGBkZKTdu3FDbrFu3TjE3N1fi4uIK3P+4uDgFMOgYIYQQQpQu+fstnieFzllPSUlhzZo1DB48GI1Gw6VLl4iOjqZDhw5qG3Nzc1q1asX+/fsBOHr0KKmpqXptXF1d8fX1VdscOHAArVZLkyZN1DZNmzZFq9XqtfH19cXV1VVt07FjR5KTkzl69GiefU5OTiY+Pl7vIYQQQgghRFlV6GB948aN3L9/n8DAQACio6MBqFixol67ihUrqvuio6MxMzPDzs4u3zZOTk45rufk5KTXJvt17OzsMDMzU9vk5tNPP1Xz4LVaraxeKoQQQgghyrRCB+vLli2jc+fOeqPbkHOBAkVRHrtoQfY2ubUvTJvsJk2aRFxcnPq4du1avv0SQgghhBCiNBUqWL9y5Qo7d+5k6NCh6jZnZ2eAHCPbt2/fVkfBnZ2dSUlJITY2Nt82t27dynHNO3fu6LXJfp3Y2FhSU1NzjLhnZW5urq5WKquWCiGEEEKIsq5QwfqKFStwcnKiS5cu6jZ3d3ecnZ3VCjGQmde+d+9emjdvDkDDhg0xNTXVaxMVFcWpU6fUNs2aNSMuLo5///1XbXPo0CHi4uL02pw6dYqoqCi1zfbt2zE3N6dhw4aFeUtCCCGEEEKUOSaGHpCRkcGKFSsYNGgQJib/d7hGo2Hs2LHMmjULT09PPD09mTVrFlZWVvTv3x8ArVbLkCFDGDduHA4ODtjb2zN+/Hhq165Nu3btAPD29qZTp04MGzaMRYsWATB8+HD8/f3x8vICoEOHDvj4+BAQEMAXX3xBTEwM48ePZ9iwYTJaLoQQQgghnhkGB+s7d+7k6tWrDB48OMe+CRMm8OjRI0aOHElsbCxNmjRh+/btlCtXTm3z1VdfYWJiQu/evXn06BFt27YlODgYY2Njtc3atWsZM2aMWjWmW7duzJ8/X91vbGzM77//zsiRI/Hz88PS0pL+/fvz5ZdfGvp2RBlz69YtLCws0Gq1pd0VIYQQQohSp1EURSntTpSW+Ph4tFotcXFxMiJfBty7d4958+YB0K9fP/VOihBCCJGV/P0Wz5NCV4MRorASEhJIS0vLsT37HAQhhBBCiOedwWkwQjyJO3fu8N133wHQqFEj/P391X3W1tbq8+y1+IUQQgghnkcysi5KVEREhPr8yJEjPHz4UH2dkZGhPn9cbX4hhBBCiOeBBOuiRGUNyCFzca3c9j169KjE+iSEEEIIUVZJsC5KzJ07d/jzzz/1tt27d08N0tPT09XtWUfchRBCCCGeVxKsixKTdaGrrGbNmsWxY8f0RtYlWBdCCCGEkGBdlKCsk0azrn6blpbGb7/9RnJysrotKSlJb6RdCCGEEOJ5JMG6KDG6ha9q1apF48aNc+z/7bff9F4nJSWVSL+EEEIIIcoqCdZFidHVVjczMytQ+6wj7UIIIYQQzyMJ1kWJ0QXrJiYFK+8vaTBCCCGEeN5JsC6K3Z07d0hOTmb37t0A3L9/H4AmTZrke1z2Mo9CCCGEEM8bWcFUFKvr16+zdOlSvW0XL14EoHPnzlSrVg0TExN27txJrVq1cHZ25pdffiE9Pb3IgnVFUdiyZQsWFhZUqVIFOzs7nJyciuTcQgghhBDFSYJ1UayyB+oA7du3V597e3sD4OnpqW6zsbEhLi6uyNJg4uPjOXr0KAD//PMPAB999FGB03GEEEIIIUqLpMGIYqMoSq7b/fz88j1OVzWmKEfWs7tw4UKRnFsIIYQQojhJsC6KTW6lF999993HHmdklPljWVTBum5ia1anTp0qknMLIYQQQhQnCdZFsdm5c2eObeXKlXvscbpg/eHDh3z//ff89NNPKIrCjRs32Lp1q8H113NLp4mNjTXoHEIIIYQQpUGSdkWx0eWJA/Tu3RsHBwc1EM+PLg3mwIED3L59m9u3bzNt2jR1f0pKCj169Mjz+EuXLnHmzBlSUlLw9/fPdWQ9MTHRgHcihBBCCFE6JFgXxa5SpUr4+PgUuL0uoL927Vqu+48fP55rsH737l0iIiLYuXOnmqdesWJFXF1dAbC3t8ff359Vq1ZJsC6EEEKIp4IE66JYZM03f+211ww69nGj74qisHPnTtq1a6e3ffHixaSkpOht27ZtG23btgUyF2OqVKkSAKmpqaSkpBR4NVUhhBBPn/T0dFJTU0u7G0LoMTU1VbMICkKCdVEssv5yLEieelZ5jahntW/fPtq1a4eiKGg0GoAcgbrOrl27ADA3N8fMzAwTExPS0tJITEyUYF0IIZ5BiqIQHR2tLsInRFlTvnx5nJ2d1RgmPxKsi2KRNVg3tJ55vXr1CAsLU19369YNZ2dnFi9erNfuzz//5K+//sLW1paxY8c+9rzly5dHo9GoOexnzpyhefPmBvVNCCFE2acL1J2cnLCysipQQCRESVAUhYcPH3L79m0AXFxcHnuMBOuiWOgCYhMTkwJNKs3K39+fixcvUqlSJfr06aNuNzMz0xs9/+uvv4DMRY90o+dZaTQavRrrjo6Oevu3b99OkyZNDLoVJYQQomxLT09XA3UHB4fS7o4QOVhaWgJw+/ZtnJycHhuHSOnG58yNGzc4fvx4sV9HN7Juampq8LEmJia8++67eoE6ZNZof/vtt3M9RrcyqU7fvn0ZM2aM3rZmzZoBoNVq1W0zZ840uH9CCCHKLt3fHysrq1LuiRB50/18FmROhQTrz5FHjx6xZMkSNmzYwOnTp4v1WrofPkNTYHRyu2VpaWlJhQoVHnvsgAEDqFWrFnZ2dnq/rHVfHAIDA9VtRbXwkhBCiLJFUl9EWWbIz6cE68+JlJQU5s6dq76+ePEiAJcvXyY6OrrIr/ckI+uPY2trm+c+rVaLp6en+vr111+nfPnyDBo0SN1mZ2dX5H0SQgghhCgOEqwXk4SEBO7du1fa3VDdv39f71bL0aNH+eGHHwgODmbhwoV6ud1FoTiDdS8vL/V53bp1ee+993BzcwNg4MCBem3d3d0ZO3Ys7u7uettffvnlIu+XEEIIUVZcvnwZjUajV7BBPJ0kWC8Gx48f56uvviI0NJSrV6/y8OHD0u5Srrdbzp07pz7/5ptvCAoKKrJ8dt0E0+II1nV10wFOnDiBra0tQ4YMISgoKMck0rzUrl0bKHyajhBCCFHUAgMD0Wg0vPXWWzn2jRw5Eo1Go5fKKZ4PEqwXA1dXV9LT0zl//jzLly9n9uzZedYAf5yIiAj279//xCPf6enp+e7X1aLdsGHDE11HJyoqCiieYN3CwkJ9njVwN4SuvnpaWprkrQshhCgz3NzcCAkJ4dGjR+q2pKQk1q1bR5UqVUqxZwWbDCmKngTrxcDR0RFra2u9bXfu3CnUudatW8f27duJjIx8oj7pgnWtVkvLli3zbbtz584nuhbA+fPnAQo0IbQwRo0axcCBA/Hz8yvU8Vm/RMgvHyGEEGVFgwYNqFKlCuvXr1e3rV+/Hjc3N+rXr69uCw0NpUWLFpQvXx4HBwf8/f3zjRUyMjIYNmwYNWvW5MqVKwBs3ryZhg0bYmFhQfXq1Zk2bZp6Zxwy78ovXLiQ7t27Y21tzYwZM4rhHYvHkWC9mNSsWVPv9YYNG/S+JRtqzZo1OVZiu3fvHnfv3i3Q8bpg3djYmDZt2vD666/z/vvvq+UMs9q3b5/enYDIyEj++++/Ao/uZ2RkcPPmTSAzp7w4ODo64uHhUejZ/lnTXyRYF0KIZ5uiKDxMSSvxR2Hvir/xxhusWLFCfb18+XIGDx6s1yYxMZH33nuPw4cPs2vXLoyMjHj11VdzvVuckpJC7969OXLkCPv27aNq1aps27aNgQMHMmbMGMLDw1m0aBHBwcE5ShpPnTqV7t27c/LkyRx9ECVDEnaLSfPmzTl27Jj6+u7du3z33XeMHz++wOfI/j/c+vXr1f9REhISWLhwIRqNhnfffVctsA+ZqR1nz57F0dGRihUrAvrBupGRES+88AIANjY2uV57x44dNGvWjFu3bvHjjz+qbbN/CdH5999/2bp1K25ubnopN+XKlSvw+y1JWRdqSkxMzPNzEEII8fR7lJqOz5RtJX7d8E86YmVmeKgVEBDApEmT1Emi//zzDyEhIezZs0dt07NnT71jli1bhpOTE+Hh4fj6+qrbExIS6NKlC48ePWLPnj3qWiMzZ87kgw8+UKulVa9enenTpzNhwgSmTp2qHt+/f38J0kuZBOvFxN7eHmtraxITE9VtCQkJpKWlFXhSY9ZbUQBXr14lPT0dY2Njbt26pY4IX79+Xa9c4b59+9T/od955x3s7Oz0gvWsstYh7969O7/99hsAhw8f5siRI3plEi9evJhnsL5161YArl27prc96wJEZdWuXbvo379/aXdDCCGEADJTSLt06cLKlStRFIUuXbrkSCuNjIzk448/5uDBg9y9e1cd4Lt69apesN6vXz8qV67Mrl279P7mHz16lMOHD+uNpKenp5OUlMTDhw/Vto0aNSrOtyoKQIL1YmJsbMyoUaO4efMmISEhauAdExODk5NTnselpqZiZGSEsbFxrpNSf/zxR5o3b643ETQ+Pl59npKSwpEjR9TX8+fP5+OPP1Zz5rOOKANqX4yNjalTpw6JiYlqzrqiKNjb2xMXFwdkloHKTfYvFToBAQF5vs+yRJe7J4QQ4tlkaWpM+CcdS+W6hTV48GB11e7vvvsux/6uXbvi5ubGkiVLcHV1JSMjA19f3xyxwyuvvMKaNWs4ePCgXtnijIwMpk2bxmuvvZbj3FkLOWSfgydKngTrxcjKygoPDw8mT57M119/TVxcXL5VYdLS0pg7dy6PHj3Cw8ODxo0bA5kBtpWVFQkJCZw7d06v5CKgLmr08OFDFi5cSEJCgrovPT0dRVHYti3z9p8ul1ynUqVKDBo0CBsbGzVFJqus+dzR0dHs2bOH1q1b67VJSkrK9f3UqFEjz/daFpQrV44HDx7g7e1d2l0RQghRjDQaTaHSUUpTp06d1JihY0f9Lxr37t3jzJkzLFq0SC0asW/fvlzPM2LECHx9fenWrRu///47rVq1AjInskZERODh4VGM70IUBZlgWgI0Go2a+pLXKPSDBw+YN2+eOgn1woULbNmyBcj8Vptfmsbhw4fJyMjgn3/+0Rtl18l6i8vHxyfHfnd3d7U+edYFhyAzxSarPXv2EB8fz8WLF/nll19ITU0lPDw8xzmz5ruVVbpKMjLBVAghRFljbGzMmTNnOHPmTI4UVjs7OxwcHFi8eDEXLlzgzz//5L333svzXKNHj2bGjBn4+/urQf2UKVNYtWoVQUFBnD59mjNnzvDjjz/y0UcfFev7EoZ7ur5mPsV0wXpwcDAdOnSgefPmevsPHz6sppvoPHjwAABLS0tcXV1p2rQpBw8ezPX8x44d4/bt27nuy/oFoVevXvn208HBgeHDh7N48eI828ydO1d9fufOHXU0vkqVKjRu3BhXV9dCV2kpSbpa66dPn6ZXr15PRZ+FEEI8P7LOG8vKyMiIkJAQxowZg6+vL15eXnz77bc57nxnNXbsWDIyMnjllVcIDQ2lY8eObNmyhU8++YTZs2djampKrVq1GDp0aDG9G1FYEqyXkKyTSrdv354jWM8vb1o3aaRdu3Z6wfoHH3zAZ599BsCtW7f0JrPmJrc0l9zklp/2+uuv8/PPP+fYfuvWLfUbf+fOnXFxcXns+cuKrCW1bt68iYODg16enhBCCFGSgoOD892/ceNG9Xm7du1y3NnO+netWrVqOUpHvvfee3oj8B07dsyRYpPX+UTpkWC9hORXAebevXv5Buv37t1Tz/Hxxx+zf/9+atSogYWFBR06dGD79u38+++/j+2Dq6trgfqaPaAfMGAAbm5uebZPT0/HxMRELRP5tMh6x2HJkiVA5i+/Zs2a6d1y1N2xyG9isBBCCCFEcTA4Z/3GjRsMHDgQBwcHrKysqFevHkePHlX3K4pCUFAQrq6uWFpa0rp1a06fPq13juTkZEaPHk2FChWwtramW7duOXKjY2NjCQgIQKvVotVqCQgIyLEo0NWrV+natSvW1tZUqFCBMWPG5DuBszRlX8E060TP2NjYfI8dOHCg+tzY2JiWLVuqgXfW+uqP06NHjwK1s7a2plKlSlSpUoWpU6fi6emJhYWFWpvdwcGBt956S++YihUrFmjUvizJbf7Azp072bVrFzdu3CA0NJTw8HC+//57vv/+e8ltF0IIIUSJM2hkPTY2Fj8/P9q0acMff/yBk5MTkZGRlC9fXm0ze/Zs5s6dS3BwMDVr1mTGjBm0b9+eiIgIdYGcsWPHsnnzZkJCQnBwcGDcuHH4+/tz9OhRdUSzf//+XL9+ndDQUACGDx9OQEAAmzdvBjJHc7t06YKjoyP79u3j3r17DBo0CEVRmDdvXlF8NkWqXLlyPHz4UH2dvf56blxcXOjXr1+eOWuAXn317Fq2bMnff/8NwMcff5xjgkpejIyM1Jy1rHncr7/+Oq+//nqux+R3G62sqlmzJjt27Mixff/+/URERHDv3j29tKOzZ89Su3btkuyiEEIIIZ5zBgXrn3/+OW5ubnpL4FarVk19rigKX3/9NZMnT1brdq5cuZKKFSvyww8/8OabbxIXF8eyZctYvXo17dq1A2DNmjW4ubmxc+dOOnbsyJkzZwgNDeXgwYM0adIEyExTaNasGREREXh5ebF9+3bCw8O5du2aOso8Z84cAgMDmTlzZr4Bbmno06cPy5cvVwPzrKt8Jicn52jfuHFjunTp8tjz2tjY5FiWWMfPz4/GjRtTrlw5gydPGtq+SpUqBrUvCxwdHenfvz8//PBDjn261KOsfv31VwnWhRBCCFGiDMpb2LRpE40aNeL111/HycmJ+vXrq7m+AJcuXSI6OpoOHTqo28zNzWnVqhX79+8HMlfMSk1N1Wvj6uqKr6+v2ubAgQNotVo1UAdo2rQpWq1Wr42vr69eHnbHjh1JTk7WS8vJKjk5mfj4eL1HSbG3t2f8+PFqUJs1WM8tvaIggbpO1apV6du3L23atGHo0KEYGxvj5+eHhYUFtra2xVblJDAwkCpVqvDOO+8Uy/lLgq5kZUE9fPjwiSbcxMfHy4QdIYQQQhSYQcH6xYsXWbBgAZ6enmzbto233nqLMWPGsGrVKuD/FufJPtGwYsWK6r7o6GjMzMyws7PLt01uk/mcnJz02mS/jp2dHWZmZmqb7D799FM1B16r1eY7abK46FJRdBVegCLJs69VqxatWrWicuXKfPzxx7Rv3/6Jz/k41apVY/DgwTn+LZ8m+U38zc3s2bM5ceJEgdpmZGRw/fp19cvY6dOnmTt3Lrt27TK4n0IIIYR4PhkUrGdkZNCgQQNmzZpF/fr1efPNNxk2bBgLFizQa5d9JFdRlMeO7mZvk1v7wrTJatKkScTFxamPa9eu5dun4qCbhJnbyHrTpk3p0KEDb775Zon363llaLAO5JrnnptTp06xdOlSVq5cSUZGBn/88QeQucpcWZ0ILYQQQoiyxaBg3cXFJccKmN7e3ly9ehUAZ2dngBwj27dv31ZHwZ2dnUlJSclRASV7m1u3buW4/p07d/TaZL9ObGwsqampeZYQNDc3x9bWVu9R0nQj61mDdV3gZm5uTvPmzZ+qWuVPu6zBep8+ffQmS0PmxOCJEyfqLcdckKo3iqLwzz//AJmrwN66dQsrKyt1/59//vmEPYdr167x888/51hMSwghhBDPDoOCdT8/PyIiIvS2nTt3jqpVqwKZy9Y7OzvrjTympKSwd+9edRGghg0bYmpqqtcmKiqKU6dOqW2aNWtGXFycXu3wQ4cOERcXp9fm1KlTREVFqW22b9+Oubk5DRs2NORtlShdsL5582aCgoJISkri0aNHgGFlGEXRyFohx9bWNkf61f/+9z8sLS3p2rWrui0pKemx53348KHeF85FixbprTB77ty5J+k2AMuWLeP06dN89dVXOUqDCiGEEOLZYFCw/u6773Lw4EFmzZrFhQsX+OGHH1i8eDGjRo0CMtNSxo4dy6xZs9iwYQOnTp0iMDAQKysr+vfvD4BWq2XIkCGMGzeOXbt2cezYMQYOHEjt2rXV6jDe3t506tSJYcOGcfDgQQ4ePMiwYcPw9/fHy8sLgA4dOuDj40NAQADHjh1j165djB8/nmHDhpW5SjBZZZ9M+tlnn6krkGUdeRUlI+souYODg156iq2trTrSris7CnnPMbh48SLLli3jzp07uVb4yaqoa7Z/9913JTZxNSYmhm3btpXoBG0hhBBw+fJlNBoNYWFhpd2VMiM4ODjHXfFnjUHBeuPGjdmwYQPr1q3D19eX6dOn8/XXXzNgwAC1zYQJExg7diwjR46kUaNG3Lhxg+3bt+sFO1999RU9evSgd+/e+Pn5YWVlxebNm/VGOdeuXUvt2rXp0KEDHTp0oE6dOqxevVrdb2xszO+//46FhQV+fn707t2bHj168OWXXz7J51HsLly4kOe+Z/2HrayaOHEi48ePx8LCQp10bG5uzujRozE1NQUyg/rGjRurx+S2oNKqVau4du0a3333Hd9++22+1yzI6Lyhcis3aQhFUTh79qze3apDhw6xbNky9e4PwA8//MCBAwf45Zdfnuh6Qggh/k9gYCAajUZ9ODg40KlTJ72iBm5ubkRFReHr61uKPS068fHxTJ48mVq1amFhYYGzszPt2rVj/fr1BR6A6tOnT5HcrS7LDJ5d5+/vj7+/f577NRoNQUFBBAUF5dnGwsKCefPm5bt4kb29PWvWrMm3L1WqVGHLli2P7XNZYmpqmuuoao0aNUqlOo3QTz9q0aIFFhYW+Pj4qIG6TufOnTl8+DAAjx490vsCmhc7Ozu9+RnVqlXj8uXLpKamkp6ezrFjx7CwsDD4F69unkhW165do0KFCgadJ6tz584REhKCVqvl3XffBVAnxR49epQWLVoAcPfu3Tz7IIQQovA6deqkrpsSHR3NRx99hL+/v/r71tjYWJ0f+LS7f/8+LVq0IC4ujhkzZtC4cWNMTEzYu3cvEyZM4OWXXy7QIKalpeUzn0b8dK0P/wx44403qF27tl7Ki6+vLwEBAcVWD10UnLm5OX5+frmWozQyMlJ/IWRdjRb0S3FmP2bUqFEEBATwv//9j4CAAHVfTEwMW7Zs4ZdffjG4OszNmzf1+gw5J3YbSpdjHxcXx/Xr1/XeU0Em1QohhHgy5ubmODs74+zsTL169Zg4cSLXrl1T5yVlT4PZs2cPGo2GXbt20ahRI6ysrGjevLne/MLIyEi6d+9OxYoVsbGxoXHjxuzcuVPvutWqVWPWrFkMHjyYcuXKUaVKFRYvXqzX5vr16/Tt2xd7e3usra1p1KgRhw4dUvdv3ryZhg0bYmFhQfXq1Zk2bVqud6F1PvzwQy5fvsyhQ4cYNGgQPj4+1KxZk2HDhhEWFoaNjQ2QWTzkf//7H3Z2dlhZWdG5c2fOnz+vnid7GkxQUBD16tVj9erVVKtWDa1WS9++fXnw4IFh/xhliPwFLmGurq707NmTIUOGYGpqioeHB6+++mppd0sUkC5YX7x4sXqL7tKlS3zyySe5tk9LS8PR0ZEaNWpQvXp1jI2N1RF73Wq2QK7Vj/Jz5MgRABo1akSbNm1ynK8wzMzM1OdLly7l4sWL6mtra2sAvduS2e88CCFEmaUokJJY8o8nmEuUkJDA2rVr8fDwwMHBId+2kydPZs6cORw5cgQTExMGDx6sd55XXnmFnTt3cuzYMTp27EjXrl1z3B2dM2cOjRo14tixY4wcOZIRI0Zw9uxZ9RytWrXi5s2bbNq0iePHjzNhwgR1UGfbtm0MHDiQMWPGEB4ezqJFiwgODmbmzJm59jcjI4OQkBAGDBigt7iljo2NjVqtLTAwkCNHjrBp0yYOHDiAoii88sor+c79ioyMZOPGjWzZsoUtW7awd+9ePvvss3w/w7LM8CLTokg4ODgwceLEQtX5FqVHNwKenp7O2bNnqVWrFitXrtRrM2bMGDVnPWuJTh0TExNSU1P1vuXfunWrQGlQkZGRnDlzRk1FMTMzUydUFyZnPTU1lZiYGCpWrJhjBCRr7vrp06epW7euXhvdiL4QQpR5qQ9hVs6gsNh9eBPMrAvcfMuWLeqIcmJiIi4uLmzZsuWxdzdnzpxJq1atAPjggw/o0qULSUlJWFhYULduXerWrau2nTFjBhs2bGDTpk28/fbb6vZXXnmFkSNHAplzub766iv27NlDrVq1+OGHH7hz5w6HDx/G3t4eQK+k8cyZM/nggw8YNGgQANWrV2f69OlMmDCBqVOn5ujv3bt3iY2NpVatWvm+r/Pnz7Np0yb++ecftRrg2rVrcXNzY+PGjbz++uu5HpeRkUFwcLCarhoQEMCuXbvy/PJQ1kmkWIokUH/6ZP2FeePGjRw1/Y2MjNRfZJD7RFRdwL9+/Xp1W/bbcw8ePGDbtm00aNCA6tWrq9uzTrIG9FKqoqOjiY2NNWhF2fXr13PmzBn69u2bo69ZR/vPnTvHvXv39CaB5/ZFRAghROG1adNGXWgyJiaG77//ns6dO/Pvv/+qZbJzU6dOHfW5bq2W27dvU6VKFRITE5k2bRpbtmzh5s2bpKWl8ejRoxwj61nPodFocHZ2VksOh4WFUb9+fb2/b1kdPXqUw4cP6wXD6enpJCUl8fDhwxzV7nR3aR+X/nvmzBlMTExo0qSJus3BwQEvLy/OnDmT53HVqlXTm1fm4uKiVz75aSPRohCFpCgKMTExetv69eun9zq3wNnFxYXr16/rbcuawpKcnMycOXOAzFVQP/74Y70gOSt7e3u99JXDhw/ToUOHAr8H3S+7kJCQHBN0sv8iXLlypV65xuTk5AKtTiyEEKXO1CpzlLs0rmsAa2trvRHrhg0botVqWbJkCTNmzMj7MlnSEnW/k3UpKu+//z7btm3jyy+/xMPDA0tLS3r16pVjrlT21EaNRqOe43ETODMyMpg2bRqvvfZajn0WFhY5tjk6OmJnZ5dvwA3kWRHmcX978nsvTyMJ1oUwQNaR9bt376qrlNrb2/PWW2+pgXO/fv04ePAg3bt3z3GOTp06sXTpUr1tCQkJpKWlcf78eX788Ue9fTExMTg6Oub6S83MzAyNRkONGjWIjIx8bH33/GQtzwg5R86z11XPyMggNTVV78uCEEKUSRqNQekoZYVGo8HIyCjH72dD/P333wQGBqrz4xISErh8+bJB56hTpw5Lly4lJiYm19H1Bg0aEBERofdFIz9GRkb06dOH1atXM3Xq1Bx564mJiZibm+Pj40NaWhqHDh1S02Du3bvHuXPn8Pb2Nug9PM1kgqkQBshatjTrbPuYmBi9oNXLy4tBgwblWnYqt9H2Cxcu8Nlnn+UI1HXnBnLsa9CggTqyUK1aNcCw1JT79+8XuG1eFi9ezIMHD3L8IYmMjGTr1q1PPOlVCCGeJ8nJyURHRxMdHc2ZM2cYPXo0CQkJeqtoG8rDw4P169cTFhbG8ePH6d+/v8GjzP369cPZ2ZkePXrwzz//cPHiRX799VcOHDgAwJQpU1i1ahVBQUGcPn2aM2fO8OOPP/LRRx/lec5Zs2bh5uZGkyZNWLVqFeHh4Zw/f57ly5dTr149EhIS8PT0pHv37gwbNox9+/Zx/PhxBg4cSKVKlXIdDHtWSbAuhAE8PDye+BeElZVVjsmZ6enpeZa4Wr9+PT/99JPetvr169O5c2f1tW7+gyHBel4jNfXr1y/wOe7evcucOXOYN28eERERLF68mMjISFavXs2///7LqlWrCnwuIYR43oWGhuLi4oKLiwtNmjTh8OHD/Pzzz7Ru3brQ5/zqq6+ws7OjefPmdO3alY4dO9KgQQODzmFmZsb27dtxcnLilVdeoXbt2nz22WdqimbHjh3ZsmULO3bsoHHjxjRt2pS5c+fmm2dvZ2fHwYMHGThwIDNmzKB+/fq0bNmSdevW8cUXX6DVagFYsWIFDRs2xN/fn2bNmqEoClu3bn2uKpJplJJao7wMio+PR6vVEhcXp1bUEOJxjhw5kmMxrgYNGtCtW7cCnyM+Pp4///wz3yWjPT099WrJ6nTt2pWGDRvqbfv333/ZunUrPj4+9O7du0B9uHr1KsuXL8fe3p7OnTuzdu1aIHMV4mXLlqnVZWxtbdFqtVy7dq2A707fhx9+KKkyQogild/f76SkJC5duoS7u3uu+dJClAWG/JxKzroQBvL19c0RrDs6Ohp0DltbW/z9/dFoNBw7dizXNnnVkK1Xr16ObbrRjfwWoMjr/Kampnh6evL+++9jZWWFRqPRq04zaNAgbG1tiYiIoHr16iQnJ2NqasqXX35ZoOvExcUZ/PkIIYQQIpOkwQhhIAsLC1q0aKG3LftId0GYmJjkm1KT22j0yy+/nGtlGN02Q9JgsgbrkFmFQJcDn7VKgIODA6ampvj6+mJlZYWdnR02Nja0b9++QNd5kolRQgghxPNOgnUhCiH7qHdRp3lUrFhRLyddx8vLK9f2hclZzx6s5ya3leV0Hld5RpeX//DhwwL3SQghhBD6JFgXohCyBsW9evUq8vP37dsXOzs7Pv74YwIDA9XtWRd5yKowI+u60fPcgvWBAwdSo0aNfPPfsy5/bW5uTvv27Zk4cSKVKlWiffv26oqsMrIuhBBCFJ7krAtRCC1atCA2Npa6devi6+v7ROeqV68eYWFhlC9fXi2nqCvvaGxsTNWqVfHx8cHU1DTPhSl0AXf2RS7yk9/IuoeHx2Pr5dauXZv4+HiqVaumBuYAw4YNAzJXVAUZWRdCCCGehATrQhRC+fLlCQgIKJJzdenShfr161OpUiWOHDlC9erV9fZrNJrHVnjRLeVsSGCsC9YLm8JjZGREy5YtH9snGVkXQgghCk+CdSFKmampqVqLtmnTpoU6R9Zg/XHLMOsUJGf9SejuAsjIuhBCCFF4EqwL8QzQBevp6emkpKRgbm6OoiiEhoZy6NAhtd0HH3yg1nPNL2e9KOgmvf73338G1aAXQgghxP+RCaZCPAPMzMzU4Fg3kn39+nW9QB1g586d6nNdekpeefBPSvcFQgghhBCFJ8G6EM8IGxsbALZu3QpAREREjjZHjhxh9+7d/PTTT9y4cUPvuKLm7e2tPjdksSYhhBBC/B8J1oV4RuiW3D5//jx//PEH+/bty7Xd3r17CQ8P5+7du8D/VZ4paro66/D4muxCCCEgMDCQHj16qK+vXbvGkCFDcHV1xczMjKpVq/LOO+9w7949AC5fvoxGo8n3ERQUpJ7Py8sLMzMzdbBGPB0kWBfiGZE19zx7+kt+HB0di6M7GBkZqX2SYF0IIQxz8eJFGjVqxLlz51i3bh0XLlxg4cKF7Nq1i2bNmhETE4ObmxtRUVHqY9y4cbzwwgt628aPHw/Avn37SEpK4vXXXyc4OLh035wwiATrQjwjqlSpkuv2UaNG5Xtc1hHwoqY7twTrQghhmFGjRmFmZsb27dtp1aoVVapUoXPnzuzcuZMbN24wefJkjI2NcXZ2Vh82NjaYmJjk2AawbNky+vfvT0BAAMuXL0dRlFJ+h6KgJFgX4hnh5+eHq6ur3rbevXvj6OjIiy++qLddlzID/7f6aXFISEgA4MCBA8V2DSGEeBxFUXiY+rDEH4UNiGNiYti2bRsjR47MUQTA2dmZAQMG8OOPPxb4/A8ePODnn39m4MCBtG/fnsTERPbs2VOovomSJ6UbhXhGmJiYMHz4cACOHTvGw4cP8fHxAaBjx47Uq1cPZ2dnjIyMiI6OZuHChSXWtxMnTtCuXTu9LwlCCFFSHqU9oskPTUr8uof6H8LK1PDKWOfPn0dRFL2J+ll5e3sTGxvLnTt3cHJyeuz5QkJC8PT05IUXXgCgb9++LFu2jDZt2hjcN1HyJFgX4hlUv359vdfGxsZ6o+7Ozs4MGjSoRIPnmJgYCdaFEKII6EbUC7IAHmSmwAwcOFB9PXDgQF566SXu379P+fLli6OLoghJsC7Ec8rd3b3Yr1GjRg0iIyMBSEpKKvbrCSFEbixNLDnUv+AT74vyuoXh4eGBRqMhPDxcrzqMztmzZ7Gzs6NChQqPPVd4eDiHDh3i8OHDTJw4Ud2enp7OunXrGDFiRKH6KEqO5KwLIYpNv3791Oe6RZiEEKKkaTQarEytSvxR0JHv7BwcHGjfvj3ff/99jt+d0dHRrF27lj59+hTo/MuWLeOll17i+PHjhIWFqY8JEyawbNmyQvVPlCwJ1oUQxcbExISGDRsCSF1fIYQwwPz580lOTqZjx4789ddfXLt2jdDQUNq3b0+lSpWYOXPmY8+RmprK6tWr6devH76+vnqPoUOHcvToUY4fP14C70Y8CQnWhRDFqlKlSkDm6qlCCCEKxtPTkyNHjlCjRg369OlDjRo1GD58OG3atOHAgQPY29s/9hybNm3i3r17vPrqq7mev3bt2jK6/hTQKM9xoc34+Hi0Wi1xcXEy8U2IYhIREcG6desAmDx5st7iTUIIURj5/f1OSkri0qVLuLu7Y2FhUUo9FCJ/hvycysi6EKJYVa1aVX2ekZFRij0RQgghnj4SrAshilXWRZee4xt5QgghRKFIsC6EKFZZqxU8SbD+6NEjrl+/XhRdEkXk2rVrnDhxorS7IYQQzzSpsy6EKFZFFayHhoZy/Phx/P39adSoUVF0TZBZazkuLq5Ak9WySkhIUCemlS9fnipVqhRH94QQ4rknwboQolgVts4wZAaEkZGRJCUlqeXFdu3aJcF6EXjw4AG//fYbV65cITU1lYEDB+Lh4VHg4xctWqQ+v379ugTrQghRTCRYF0IUqycZWV+zZg3R0dF627IuEHLlyhWOHz9O+/btsbQs3EqBz5vk5GR++eUXzp8/r7d9z549VK1a9bHVetLS0sjIyODBgwfqtu3bt9O8efNi6a8QQjzvJFgXQhSrJwnWswfqOjExMdjb27NixQogs8pMbktyC32xsbFs2rSJS5cu5dh3/fp1dZGVQYMGcfDgQby9valXr57aJiEhgS+//DLXcyclJUmZPCGEKAYywVQIUex0AbshwXp+bb/99lsiIyPV12FhYTlGikVOGzduzDVQz27lypVERESwceNG9d8hPT09z0AdMr8ICCGEKHoGBetBQUFoNBq9h7Ozs7pfURSCgoJwdXXF0tKS1q1bc/r0ab1zJCcnM3r0aCpUqIC1tTXdunXLUeEhNjaWgIAAtFotWq2WgIAA7t+/r9fm6tWrdO3aFWtraypUqMCYMWNISUkx8O0LIUpCYYL1y5cv673u2bMnNjY26uvVq1fr7V+7di0xMTGF7+T/79+KFSuYO3fuM1kT/sqVK3qvra2tH3tMfHw8kDmqnpuKFSsCkJiY+IS9E0IIkRuDR9ZfeOEFoqKi1MfJkyfVfbNnz2bu3LnMnz+fw4cP4+zsTPv27fVyG8eOHcuGDRsICQlh3759JCQk4O/vT3p6utqmf//+hIWFERoaSmhoKGFhYQQEBKj709PT6dKlC4mJiezbt4+QkBB+/fVXxo0bV9jPQQhRjAoTrMfFxanPAwMDqV27Ns2aNcv3mOyDA4ZKSEjgypUrxMfHc/jw4Sc6V1lkZWUFgJOTE1OmTGH8+PGMGTOGN998k969ezNp0qQcVWH+/vtvAPbv369u031peu2119S5AlnnEgghhCg6BgfrJiYmODs7qw9HR0cg84/w119/zeTJk3nttdfw9fVl5cqVPHz4kB9++AHI/OO7bNky5syZQ7t27ahfvz5r1qzh5MmT7Ny5E4AzZ84QGhrK0qVLadasGc2aNWPJkiVs2bKFiIgIIHMyU3h4OGvWrKF+/fq0a9eOOXPmsGTJEnUUSAhRdhQmWDczM1Of61ZBTU1NzfeYtLS0QvTu/zx8+FB9/scffzxzd+t0n1+/fv0wMjJCo9Fgb2+Pi4sLPj4+mJubM3DgQL1jjhw5wsKFCzl06JC67b333iMoKIg6deqowXrWz04IUTiBgYFoNBreeuutHPtGjhyJRqMhMDCw5DsmSpXBwfr58+dxdXXF3d2dvn37cvHiRQAuXbpEdHQ0HTp0UNuam5vTqlUrdUTm6NGjpKam6rVxdXXF19dXbXPgwAG0Wi1NmjRR2zRt2hStVqvXxtfXF1dXV7VNx44dSU5O5ujRo3n2PTk5mfj4eL2HEKL4FSZY1wXe7u7u6vHVqlVT99va2qrP7ezsAJ74/+m7d+/qvc6eNvK0032mJiZ51xawt7enX79+9OzZU92WdaJvlSpVMDL6vz8dFSpUAODGjRtF3V0hnktubm6EhITo3a1KSkpi3bp1ZaJE6uMGTUTRMyhYb9KkCatWrWLbtm0sWbKE6Ohomjdvzr1799Rf5rr8RZ2KFSuq+6KjozEzM1P/sObVxsnJKce1nZyc9Npkv46dnR1mZmZ5Vo8A+PTTT9U8eK1Wi5ubmyFvXwhRSLkF64qikJqaypEjR3KdnKhLjTM2Nla3VatWjYEDBzJ27Fi9L+u60d1jx45x8+ZNg/qWnp6upur9/PPPevuSk5MNOldZlp6ern7++QXrAF5eXnh7e+fYXq5cOb2URPi/3/l37twpop4K8Xxr0KABVapUYf369eq29evX4+bmRv369dVtycnJjBkzBicnJywsLGjRooVe+t6ePXvQaDTq2hRWVlY0b95czVLQ2bx5Mw0bNsTCwoLq1aszbdo0vbuUGo2GhQsX0r17d6ytrZkxYwYACxYsoEaNGpiZmeHl5ZVjHpEoOgYF6507d6Znz57Url2bdu3a8fvvvwOZlQN0si+AoijKYxdFyd4mt/aFaZPdpEmTiIuLUx/Xrl3Lt19CiKKRNVhPTU1l1apVTJs2jZkzZ7JlyxaWLl2a45i8RoE9PDwoX768XppM9erV1eeLFy/OMYL/xx9/sGjRolxHhH766SfmzJnDhQsXcux7lkaQsv7xfVywrmvTrl07vbrrb7zxRo467LrBlejoaJKSkoqot0IULUVRyHj4sMQfhV21+Y033lBL0wIsX76cwYMH67WZMGECv/76KytXruS///7Dw8ODjh075phoP3nyZObMmcORI0cwMTHRO8+2bdsYOHAgY8aMITw8nEWLFhEcHKyWcdWZOnUq3bt35+TJkwwePJgNGzbwzjvvMG7cOE6dOsWbb77JG2+8we7duwv1fkX+nqjOurW1NbVr1+b8+fNqjePo6GhcXFzUNrdv31ZHXpydnUlJSSE2NlZvdP327dvqghrOzs7cunUrx7Xu3Lmjd56s+ZOQWUEmNTU1x4h7Vubm5pibmxfuzQohCi1rsP7LL7+o6XM6usniJiYm1K1bF0tLS7UUY9aR9dzOCaijRbrR3f/++4+GDRuq+3W/LyIiIvD19dU7j26Uac2aNeo2Y2Nj0tPTn6qcdUVROH78OGFhYTRo0IA6dero7c8arOf1mWbXokULWrRooZ4/t8EQJycn9fP67LPPGDlyZK53R4UoTcqjR0Q0aPj4hkXM67+jaP7/xG5DBAQEMGnSJC5fvoxGo+Gff/4hJCSEPXv2AJm/MxcsWEBwcDCdO3cGYMmSJezYsYNly5bx/vvvq+eaOXMmrVq1AuCDDz6gS5cu6roIM2fO5IMPPmDQoEFA5sDH9OnTmTBhAlOnTlXP0b9/f70gv3///gQGBjJy5Eggcx7LwYMH+fLLL2nTpo3B71fk74nqrCcnJ3PmzBlcXFxwd3fH2dmZHTt2qPtTUlLYu3evGog3bNgQU1NTvTZRUVGcOnVKbdOsWTPi4uL4999/1TaHDh0iLi5Or82pU6eIiopS22zfvh1zc3O9P9BCiLIha7Ce/Raszs6dOwkNDeXzzz8nMTGRc+fOAZlf5nOTNUXF0tKSUaNGqa83b96sll7MqwRjSkqK3u8ZHQ8PD2rXrg1kjshfv37doNGxiIgIjhw5QkREBEFBQeodyOKQkZFBaGgoJ0+eZM+ePWzcuJHLly+zfv16fv75Z73PSBesGxsb6+WcF1R+dy2zVvP6/vvv9V4LIQxXoUIFunTpwsqVK1mxYgVdunRR54cAREZGkpqaip+fn7rN1NSUF198kTNnzuidK+sXd91gqu736tGjR/nkk0+wsbFRH8OGDSMqKkpv0nijRo30znnmzBm9awP4+fnluLYoGgaNrI8fP56uXbtSpUoVbt++zYwZM4iPj2fQoEFoNBrGjh3LrFmz8PT0xNPTk1mzZmFlZUX//v0B0Gq1DBkyhHHjxuHg4IC9vT3jx49X02oAvL296dSpE8OGDWPRokUADB8+HH9/f7y8vADo0KEDPj4+BAQE8MUXXxATE8P48eMZNmyY3qQzIUTZYOgE019++UV9nteEqqxBuO78NWvWVIP8W7du4eLiojfpNOuI8qFDh9i1a1eO8z569EitcgWwdOlSGjVqhL+//2P7fezYMX777Te9bYcPH6ZLly6PPbYw/vnnHw4ePJjrvtOnT3P69Gnatm1Ly5Yt1RSV7GksRcHKykrvD/vff/9N69ati/w6QhSWxtISr//yLkBRnNctrMGDB/P2228D8N133+nt0/0uLUjqcdb/53X7sg5mTJs2jddeey3H9bOuSJzbmgyFSXsWhWPQ8Mr169fp168fXl5evPbaa5iZmXHw4EG1rNqECRMYO3YsI0eOpFGjRty4cYPt27dTrlw59RxfffUVPXr0oHfv3vj5+WFlZcXmzZv1/oiuXbuW2rVr06FDBzp06ECdOnX0Ji4YGxvz+++/Y2FhgZ+fH71796ZHjx75rq4nhCg9ul/gBR1xzbrKZqdOnXJt06BBAwAqV66sbuvVq5f6XLdIT9ZF13R/4PIb4a9WrVqOL/1HjhzJs69Zv4AcP3481zbFUYP80aNHuX7ZyG7Xrl2kpqayYMECAL3fx0UlICCAGjVqqK+zL2InRGnTaDQYWVmV+ONJgtdOnTqRkpJCSkoKHTt21Nvn4eGBmZkZ+/btU7fpJuznNjk8Lw0aNCAiIgIPD48cj/zuwHl7e+tdGzLXYjDk2qLgDBpZDwkJyXe/RqMhKCiIoKCgPNtYWFgwb9485s2bl2cbe3t7vfzR3FSpUoUtW7bk20YIUTbo/mBlTcuws7Pj1VdfZfny5XkeV7Vq1TxHgr28vBgyZIjeKLiZmRkajQZFUXL9HfLo0aM8fz+99dZbnD17lhYtWpCamsqZM2e4evVqvu9rz549HDlyhMqVK+Po6KhWpcnu2LFjahpfUVmyZEmu2zt27Mju3bv18u2zThbLayXSJ+Hi4kJAQABHjx5l8+bNskCSEEXA2NhYTSvJPs/E2tqaESNG8P7772Nvb0+VKlWYPXs2Dx8+ZMiQIQW+xpQpU/D398fNzY3XX38dIyMjTpw4wcmTJ9WqL7l5//336d27Nw0aNKBt27Zs3ryZ9evXq2vmiKL1RBNMhRCiIHTBetbR7OHDh+cZ3OroJk7ldc7cyq/ml2qzefPmXLfXqVNHXegNUCsmXL58meDg4Fy/MCQnJ6uTvc6ePcvZs2fVfZUqVcLMzEy9Q7Bnz54iD9azV3wAePXVV6lbty6VKlXi4cOHuQ6wvPHGG0Xaj6x0/566uxoFdfToUf7++2/8/f3x8PAojq4J8VTKL7X3s88+IyMjg4CAAB48eECjRo3Ytm1bjvLY+enYsSNbtmzhk08+Yfbs2ZiamlKrVi2GDh2a73E9evTgm2++4YsvvmDMmDG4u7uzYsUKSX8rJhKsCyGKnS5YP3DggLpNF9j169ePdevWodFoaNCggd7CZvlVd8pLy5Yt+fvvvwvcvk2bNjkmSunoRu1TU1NJS0vTK3mY30SqFi1a4O3tzcGDBwkNDSUlJYXw8HB8fHwK3K/HMTExIS0tjapVq+Ln54etra36ZSOvPH9zc/NirdTi4OAAZM4XuHz5MmlpaQUKvnft2sXDhw9Zs2YNI0aMKNS/uxDPguDg4Hz3b9y4UX1uYWHBt99+y7fffptr29atW+cYvKhXr16ObR07dsyRZpNVXgMgI0aMYMSIEfn2VxSNJ6oGI4QQBZFf3qaXlxdTp05l6tSpeiW/KleuXKh8T12JsoIIDAykVatWedYdt7KyUuu56xZuevjwIQsWLMj3dq/umKwrMf/000/88MMPj51ke+7cOX799dfHpqvo8km7d+9OzZo11UA9q+x/gMeOHZvvOZ+UrlpFamoqwcHBrFmzhqVLl3L69OkcqTEpKSlcuXIFRVH0JqeuXbu2WPsohBBPGxlZF0KUuBdffFHvtS4ot7GxUbcVdkGigiz4A5mTIqtVq5ZvG41Gg4ODA1FRUZw7dw5HR0cOHjyYYy0ICwsLvQWBdOs5ZP+yce7cOSIjI/McbVYUhR9++AHIzEnNa3It5L1oVFbNmjXD09MTOzu7AtdWfxK5XeP69evqyrBTp05Fo9GQlpbGL7/8wrlz5+jevTt2dnbql6Gs1XsATp06RWRkJF26dFHfq6IoKIpSqBKUQgjxtJHfdEKIYhcXF6f3umnTpo89pqgmKWatFqPTqVMnveol+dHlq+/YsYO0tDS9SbKQme8+evRovW1ZV1fNPrqd36TVrKPpBw8e5MSJEzx48CBHu4yMDLX02uO+nFSoUKFEAvWCuH79OqmpqXz66adqic09e/aogTpk3s3I6pdffuHYsWPMmDGDs2fP8uDBA5YuXcrSpUvVzyApKUkmtQohnlkSrAshSlzW+r3Z1atXD4CXXnqp0Odv3749xsbGBAYGMnToUHUVTh1DVibN+kXjwYMHOVZPtrGxyVGDOOtKyc2aNWPKlCnqez5x4kSegWX2LwLr169XR9p1FEXhypUr6uuC3kkoKbp6zblNqA0LCyM2NlavhGf2L3IpKSkoisLJkyfZv3+/3r6QkBDmzJnDjRs3uHnzJkuXLuXGjRvMnTuX7777Tu9zTUpKYu/evdy7d68o354QQpS4svVbXgjxXMgazGbXrVs3GjVqpK60Vxh+fn40adJEDWR1gaOuLrAuhaQgsi6+9M033+TYrxt5r1Klijpqnv3LiJGREe3bt2fz5s3cv3+fuXPn0rdvX6pXr05sbKw6QSy3EfCoqCjWrl1L3759MTY2JjIyUq8sZVkZNdepU6eOumJi5cqV+emnn9R9x44de2wd5rS0NA4dOkRoaOhjr3Xz5k21hKUuB75WrVpA5qrW//33H0eOHGHcuHGFfTtCCFHqZGRdCFHi8gswjYyMqFy58hMHoVlHnK2srGjXrh2NGjXC3Nychg0bFvg8+VVJgP8rrZZbznpWWctUpqamsnr1aiIjI9m+fbu6Pa9Fo86fP8/06dNJTk4mLCxMb19ZC9az0lWH0cnIyMhzDQ1ra2s1fagggXpuss4l0JXS1KURpaSkcPnyZb0vX0II8TSQYF0IUeyaNWtW2l0AwN/fn/fffx+tVlvgY3x9ffPdr6vColtRNa+ygzVq1KB69ep62yIiIvTqs2eVW63kY8eO5ZtCVNZknTCcXbNmzXj//fextLSkUqVKjBkzBnt7+1zbDho0iF69emFkZETnzp2pXbt2ru12795Neno6SUlJehVm4uLi2L59O8HBwXzyySe51qgXQoiySqM8ro7YMyw+Ph6tVktcXFy+Cw8IIZ5cRkYGMTExODg4PNES3KXhs88+0xs5z+rjjz/G2NiYtLQ0jh8/jpeXV75Ban4rPOvY2Ngwfvx4Nm3axH///ae3r0KFCty9exeA+vXr071794K/kVLw33//ERsbq1f73tHRkeHDh2NqakpqaioajQYTExP1boNO79698fLyUu8eZK91HxcXx549e7h9+zY3btwAMlOg/vnnH70+eHh4cOHCBfW1RqNh6tSpxfJ+RcnI7+93UlISly5dwt3d/an6ciueL4b8nMrIuhCiRBgZGVGhQoWnLlCHnJMlX331VV555RXeffddNZA0MTGhYcOG+Qbq+Wnbti2mpqbY2NjQs2dPIPNOQHa6QN3Pz49XXnmlUNcqSbrlyLMaOXKkmutvamqqBuBZ/2BVrFgRHx8fvTSf7JNptVot3bt31/v3yR6oA3qBOmRO0s0r5UgIIcoamWAqhBCP4efnR7ly5bC1taV69epF/oWjZcuW6iMrIyMjxo8fz4kTJ/Ry2wFq166tBrxPg/Lly3P//n1cXV0L9Pl17dq1wOf29PTMdburqys3b97Mdd/9+/dz5NQL8azSaDRs2LCBHj16cPnyZdzd3Tl27JhafUuUbTKyLoQQj2FsbEz9+vWpUaNGsdwZyG/VVRsbm1zLIOaVG19W/e9//6NVq1YMHDgwzzZZ69PnVh8/v+NefvllvW39+/enV69eeR6ju0MhRFly+/Zt3nzzTapUqYK5uTnOzs507NiRAwcOAJlB98aNG0u3k6LEyci6EEKUIm9v7wLVSg8ICGD16tVAZsrM05ZOZG9vT5s2bfJt06BBA44fP57nBNL81K5dmzNnzuDu7s7LL7+sfqYtWrTg6NGjDB06lLi4OH799VcSExOl/rook3r27ElqaiorV66kevXq3Lp1i127dpW5SdEpKSl6X65F8ZJgXQghSpCtrS3x8fFA5ujxq6++WqDjsq64+rSNqheUm5sbkydPxsjI8Ju+dnZ2vPnmmzm2t2vXjnbt2gGZpSRfeOEF/v33X1nxVJQ59+/fZ9++fezZs0e921a1alVefPFFAKpVqwag/s6oWrUqly9fBmDz5s0EBQVx+vRpXF1dGTRoEJMnTy7QQEB6ejrDhw/nzz//JDo6mipVqjBy5EjeeecdtU1gYCD379+nSZMmzJs3DzMzM/XaovhJsC6EECVo8ODBnDx5kkaNGunVXi+I0aNHc+PGjTxztJ8FxV03Xhe8GLIwlnj6KYpCWkrJ19g3MTMq8F0wGxsbbGxs2LhxI02bNs2xXsPhw4dxcnJixYoVdOrUSf1/Zdu2bQwcOJBvv/2Wli1bEhkZyfDhwwEKVPUoIyNDXcCsQoUK7N+/n+HDh+Pi4kLv3r3Vdrt27cLW1pYdO3bwHBcSLBUSrAshRAkqX758jomkBeXg4CCTIp+QLliXajDPl7SUDBa/s7fErzv8m1aYmhfsC6iJiQnBwcEMGzaMhQsX0qBBA1q1akXfvn2pU6cOjo6OQObvEN36DgAzZ87kgw8+YNCgQQBUr16d6dOnM2HChAIF66ampkybNk197e7uzv79+/npp5/0gnVra2uWLl0q6S+lQCaYCiGEeG5krdkuRFnTs2dPbt68yaZNm+jYsSN79uyhQYMGBAcH53nM0aNH+eSTT9SReRsbG4YNG0ZUVJTe4mD5WbhwIY0aNcLR0REbGxuWLFnC1atX9drUrl1bAvVSIiPrQgghnhuSBvN8MjEzYvg3eVddKs7rGsrCwoL27dvTvn17pkyZwtChQ5k6dSqBgYG5ts/IyGDatGm89tpruZ7rcX766Sfeffdd5syZQ7NmzShXrhxffPEFhw4d0mtnbW1t8HsRRUOCdSGEEM8NCdafTxqNpsDpKGWNj4+PWq7R1NQ0RwpXgwYNiIiIwMPDo1Dn//vvv2nevDkjR45Ut2VdSViUPgnWhRBCPDfKcrCelJTEuXPn8PLyyjG5UDz77t27x+uvv87gwYOpU6cO5cqV48iRI8yePZvu3bsDmRVhdu3ahZ+fH+bm5tjZ2TFlyhT8/f1xc3Pj9ddfx8jIiBMnTnDy5ElmzJjx2Ot6eHiwatUqtm3bhru7O6tXr+bw4cO4u7sX91sWBSQ560IIIZ4bugo8iYmJpdyTTGvWrCEoKIjExEQWLFjA+vXr+f3330u7W6IU2NjY0KRJE7766iteeuklfH19+fjjjxk2bBjz588HYM6cOezYsQM3Nzfq168PQMeOHdmyZQs7duygcePGNG3alLlz51K1atUCXfett97itddeo0+fPjRp0oR79+7pjbKL0qdRnuP6O/Hx8Wi1WuLi4rC1tS3t7gghhChmUVFRLFq0CICPP/642EtF5uXSpUusXLkyz/1BQUGPPceDBw+wsrIqtfdQmvL7+52UlMSlS5dwd3cvUM62EKXBkJ9TGVkXQgjx3LCyslKfr1+/vtT6sXv37jz3FST4vnPnDnPmzOGnn34qym4JIcogCdaFEEI8N7KOYJ0+fRqA1NRUIiMjycgouUVzbt26lee+KlWqPPZ4XSm/iIgIddupU6f0Xgshng0ywVQIIcRzI3ud6F27dqHRaPjrr7+oXbs2PXv2LJF+WFpakpycTIUKFfDy8uKff/6hR48ebNy4kbt376rt4uLiWLp0KQ8ePMDd3Z1evXpx6NAhvZz7+fPn6x1jb29PQEAAdnZ2T9THffv2ceXKFXr37o2pqekTnUsIUXgSrAshhHhuaDQaatWqxdmzZ4HMsnU6J0+epGbNmtSuXbtYrp2eno6RkRGKonD//n0ABgwYgJ2dHe3btychIQHIzEXPyMjgzp07/PPPPzx48ADIzHP/4osvcpw3a6AOEBMTw4YNGxg8eHCh+/rw4UN27twJwIoVK9Tl64UQJU+CdSGEEM+Vvn37snTpUq5fv55j36+//loswfqtW7dYsGABAFqtVt2edXKkrlINwCeffPJE17t69SpBQUF06NCBZs2aodFoDDo+Li5OfX7z5s0n6osQ4slIzroQQojnTq9evfLcl5qaWuDzKIrC+fPniY+Pz7fd3r171edZA+Gsk0kfN7HUx8dH7/X//vc/3nnnHQYOHMjLL7+Mg4MDbm5uem22b9/On3/+mWff85L1/WT9EiGEKHkysi6EEOK5U758eSZMmMDs2bOxtbVlzJgx6gIyM2fOpEOHDjRv3vyx54mMjGTt2rUAfPDBBzlKsN24cYPr169z5cqVHMcGBATk2GZmZkZKSkqO7fXq1aNHjx5kZGRgZGREamqqmkduZ2eHh4cHL730EpB5d+DkyZPqsX///Tc2NjZUrlyZSpUqAXDw4EFCQ0MB+Oijj4iMjMTR0RF7e3sANfUG4NGjR6SkpOTI9xdClAwJ1oUQQjyXrKys9OqZW1lZ8fDhQyBzRLpmzZrY29ujKArJyclYWVkRHh5OTEwMLVq0AODcuXPq8T/++CNdu3ZVA95z587xww8/6F1z7Nix7Nq1C3t7e6pXr56jT++//z6///47t2/fJj4+Xs1j161oamSUeUM8vwmfHTt2JCkpCRsbG44dOwbAH3/8AUDjxo3p0qWLGqgDfP/998TExAAwYcIErKysOH78uN45k5OTJVgXopRIsC6EEEKQGRDrgnWATZs2cfXqVfW1u7s7ly5dAmDnzp0MGzaMixcvqvsvXbrEt99+y6RJkzA2Ns4RqNvY2FC+fPl8K86YmprSo0cPIHPUfvXq1QAGVXaxsbFhwIABAGqwrnP48GFefPFFvW26QB0yV8hMT0/Pcc7cRvuFECVDctaFEEIIIDY2Vu911kAdUAN1nSVLluSoxAKwfPly1q1bl2N7WlqaQf3RjaZD5oh4YVSrVi3Htu+++y7P9rkF6iDBelkWFBREvXr1SrsbZUa1atX4+uuvi/06ly9fRqPREBYWVuzXkmBdCCGEKEK3bt0iMjIyx/akpCSDzlOpUiWaNm1Kt27dCrSqaW5ef/31Qh0H4Ofnh4ODAyDBekkIDAxEo9HkeHTq1Elto9Fo2LhxY+l1spTFx8czefJkatWqhYWFBc7OzrRr147169fnO2G6OLi5uREVFYWvr2+xX0vSYIQQQojH6Nu3LyEhIXnu9/DwoHz58hw5ckRve7du3Th48CC3b9+mTZs2Bl0ze6BWGNbW1gwYMIDLly9jZWXFjh078mxbu3ZtrK2tqV+/Pk5OTmg0GjXNR4L1ktGpUydWrFihty3rHZaSoigK6enpmJiUnTDx/v37tGjRgri4OGbMmEHjxo0xMTFh7969TJgwgZdffpny5cuXWH+MjY1xdnYukWvJyLoQQggBNG3aNNft/fr1w9PTU2/bq6++irW1tbpfVz4xuwYNGjB8+HCGDBmiTkotaZ6enrRv357mzZvz2muvAZm13idOnKiO2Ldu3ZqePXvSqVMnKlasqNZl100qfdqDdUVRSE1KKvGHoaO95ubmODs76z108xV0KU2vvvoqGo0mR4rT6tWrqVatGlqtlr59++pV9FEUhdmzZ1O9enUsLS2pW7cuv/zyi7p/z549aDQatm3bRqNGjTA3N+fvv/9+7HHBwcE5AuSNGzfq1fXXpeksX76cKlWqYGNjw4gRI0hPT2f27Nk4Ozvj5OTEzJkz8/1sPvzwQy5fvsyhQ4cYNGgQPj4+1KxZk2HDhhEWFoaNjU2ux82dO1f9Iurm5sbIkSPVidsAV65coWvXrtjZ2WFtbc0LL7zA1q1bgczUuAEDBuDo6IilpSWenp7ql6nc0mBOnz5Nly5dsLW1pVy5crRs2TLXu2yGKjtfmYQQQohS1LFjR1566SUePHigLmD05ptv4uzsnGNRobp161K3bl29bdnrkY8aNQoAExOTHPXPS4NGo6FOnTrUqVNH3TZkyBBu376tty2rZyVYT0tO5ttBedfWLy5jVv6CabZynoV1+PBhnJycWLFiBZ06ddJLjYqMjGTjxo1s2bKF2NhYevfuzWeffaYGwB999BHr169nwYIFeHp68tdffzFw4EAcHR1p1aqVep4JEybw5ZdfUr16dcqXL1/g4x4nMjKSP/74g9DQUCIjI+nVqxeXLl2iZs2a7N27l/379zN48GDatm2b65fmjIwMQkJCGDBgAK6urjn25xWoQ2YFpW+//ZZq1apx6dIlRo4cyYQJE/j++++BzP9PU1JS+Ouvv7C2tiY8PFw938cff0x4eDh//PEHFSpU4MKFCzx69CjX69y4cYOXXnqJ1q1b8+eff2Jra8s///xj8FyV3EiwLoQQQpAZzFpZWWFlZcXgwYOxtbU16LZ69oDe0dGxiHtY9FxdXXMNfrLbuXMn9evXL4EePd+2bNmSI/CcOHEiH3/8sfrzVL58+RzpFxkZGQQHB1OuXDkgs4b/rl27mDlzJomJicydO5c///yTZs2aAVC9enX27dvHokWL9ILuTz75hPbt2wMYdNzjZGRksHz5csqVK4ePjw9t2rQhIiKCrVu3YmRkhJeXF59//jl79uzJNVi/e/cusbGx1KpVq8DX1Bk7dqz63N3dnenTpzNixAg1WL969So9e/ZUVy7OWlL16tWr1K9fn0aNGgG5T9jW+e6779BqtYSEhKilVWvWrGlwf3PzRMH6p59+yocffsg777yjzrxVFIVp06axePFiYmNjadKkCd999x0vvPCCelxycjLjx49n3bp1PHr0iLZt2/L9999TuXJltU1sbCxjxoxh06ZNQGbe37x58/R+cV69epVRo0bx559/YmlpSf/+/fnyyy+lFqwQQognUqVKlRzbGjduzOHDh8vEKHlJ0Y0iJiYmlnJPnoyJuTljVv7y+IbFcF1DtGnTRr2ro6Or25+fatWqqYE6gIuLC7dv3wYgPDycpKQkNQjXSUlJyfEFTBeUGnqcof2rWLEixsbG6roBum26PmenSyfK/oW4IHbv3s2sWbMIDw8nPj6etLQ0kpKSSExMxNramjFjxjBixAi2b99Ou3bt6Nmzp3qnacSIEfTs2ZP//vuPDh060KNHjzwXSwsLC6Nly5b5roFQWIUO1g8fPszixYtz3DqbPXs2c+fOJTg4mJo1azJjxgzat29PRESE+g81duxYNm/eTEhICA4ODowbNw5/f3+OHj2q3tbp378/169fVxduGD58OAEBAWzevBnILC/VpUsXHB0d2bdvH/fu3WPQoEEoisK8efMK+7aEEEKIXLVu3Rp7e/vnaoTZz8+PH3/8sbS78cQ0Gk2RpaMUJ2trazw8PAw+LnuAqNFoyMjIAFD/+/vvv6sr2Opkn7yqm4dR0OOMjIxy5OWnpqYWqH/59Tk7R0dH7OzsOHPmTK7783LlyhVeeeUV3nrrLaZPn469vT379u1jyJAhaj+HDh1Kx44d+f3339m+fTuffvopc+bMYfTo0XTu3JkrV67w+++/s3PnTtq2bcuoUaP48ssvc1wrexpcUSrUBNOEhAQGDBjAkiVL9BZqUBSFr7/+msmTJ/Paa6/h6+vLypUrefjwobo4RFxcHMuWLWPOnDm0a9eO+vXrs2bNGk6ePMnOnTsBOHPmDKGhoSxdupRmzZrRrFkzlixZwpYtW4iIiAAyV5cLDw9nzZo11K9fn3bt2jFnzhyWLFlCfHz8k34uQgghhB5ra2uaNWuGxVMQ9BWVqlWrqs/zqsEuSo6pqanB/w4+Pj6Ym5tz9epVPDw89B753SUqyHGOjo48ePBA785LcdQdNzIyok+fPqxdu5abN2/m2J+YmJhrbviRI0dIS0tjzpw5NG3alJo1a+Z6vJubG2+99Rbr169n3LhxLFmyRN3n6OhIYGAga9as4euvv2bx4sW59rFOnTr8/fffuX5ZeVKFCtZHjRpFly5daNeund72S5cuER0dTYcOHdRt5ubmtGrViv379wNw9OhRUlNT9dq4urri6+urtjlw4ABarZYmTZqobZo2bYpWq9Vr4+vrq5dr17FjR5KTkzl69Giu/U5OTiY+Pl7vIYQQQhS1kiwhV5yyppUWRxAi9CUnJxMdHa33yLrwVrVq1di1axfR0dE5FvHKS7ly5Rg/fjzvvvsuK1euJDIykmPHjvHdd9+xcuXKJzquSZMmWFlZ8eGHH3LhwgV++OEHgoODn+gzyMusWbNwc3OjSZMmrFq1ivDwcM6fP8/y5cupV6+eXoUXnRo1apCWlsa8efO4ePEiq1evZuHChXptxo4dy7Zt27h06RL//fcff/75J97e3gBMmTKF3377jQsXLnD69Gm2bNmi7svu7bffJj4+nr59+3LkyBHOnz/P6tWr1UHmJ2FwsB4SEsJ///3Hp59+mmNfdHQ0kJl3lFXFihXVfdHR0ZiZmeVYOjl7Gycnpxznd3Jy0muT/Tp2dnaYmZmpbbL79NNP0Wq16uN5yjsUQghR/IYMGUL16tXp169faXelSGTNK05OTi7l3jz7QkNDcXFx0XtkLfk5Z84cduzYgZubm0HpWNOnT2fKlCl8+umneHt707FjRzZv3oy7u/sTHWdvb8+aNWvYunUrtWvXZt26dQQFBRXqvT+OnZ0dBw8eZODAgcyYMYP69evTsmVL1q1bxxdffIFWq81xTL169Zg7dy6ff/45vr6+rF27Nkf8mp6ezqhRo/D29qZTp054eXmpk0/NzMyYNGkSderU4aWXXsLY2DjP9RYcHBz4888/SUhIoFWrVjRs2JAlS5YUSQ67RjGgCOi1a9do1KgR27dvV0tWtW7dmnr16vH111+zf/9+/Pz8uHnzJi4uLupxw4YN49q1a4SGhvLDDz/wxhtv5Pifvn379tSoUYOFCxcya9YsVq5cmePbiKenJ0OGDOGDDz5g+PDhXLlyhW3btum1MTMzY9WqVfTt2zdH/5OTk/WuGx8fj5ubG3Fxcdja2hb0YxBCCCGeG3PnziU+Pp6hQ4fqFYIoTfHx8Wi12lz/ficlJXHp0iXc3d2fq5Ql8XQx5OfUoJH1o0ePcvv2bRo2bIiJiYm6ctS3336LiYmJOtKdfWT79u3b6j5nZ2dSUlJy3L7J3ubWrVs5rn/nzh29NtmvExsbS2pqao4Rdx1zc3NsbW31HkIIIYTIm+5vZdZFdoQQJcegYL1t27acPHmSsLAw9dGoUSMGDBhAWFgY1atXx9nZWW8545SUFPbu3auWumnYsCGmpqZ6baKiojh16pTaplmzZsTFxfHvv/+qbQ4dOkRcXJxem1OnThEVFaW22b59O+bm5jRs2LAQH4UQQgghstNVudiwYQM3b94kLi6ulHskxPPFoNKN5cqVw9fXV2+btbU1Dg4O6vaxY8cya9YsPD098fT0ZNasWVhZWdG/f38gc4njIUOGMG7cOBwcHLC3t2f8+PHUrl1bnbCqyxsaNmwYixYtAjJLN/r7++Pl5QVAhw4d8PHxISAggC+++IKYmBjGjx/PsGHDZMRcCCGEKCK6YD0lJUWthPHRRx9hYiLrKgpREor8/7QJEybw6NEjRo4cqS6KtH37dr1i+F999RUmJib07t1bXRQpODhYb+nctWvXMmbMGLVqTLdu3Zg/f76639jYmN9//52RI0fi5+entyiSEEIIIYpGbvWjExISnpmKN0KUdQZNMH3W5DdBRQghhBBw69YtVq9erVcar0+fPnmWsCsJMsFUPO2KbYKpEEIIIZ4vFStWZPz48XppL8/CqqZCPC0kWBdCCCHEY2UtySyEKDkSrAshhBDisfr06aP3+tGjR6XUEyGeLxKsCyGEEOKxbGxsGD58uPo6JSWlFHsjxPNDgnUhhBBCFIirq6v6PC0trRR78ny6fPkyGo2GsLCwYr1OYGAgPXr0KNZrFIWS+jxKmwTrQgghhCgwKysrANLT00u5J8+WwMBANBqN+nBwcKBTp06cOHFCbePm5kZUVJS6ts2ePXvQaDTcv3+/SPvyzTffEBwc/MTniY+PZ/LkydSqVQsLCwucnZ1p164d69ev5zkuRmgwCdaFEEIIUWC6qjAysl70OnXqRFRUFFFRUezatQsTExP8/f3V/cbGxjg7Oxf7glRarfaJ6+jfv3+f5s2bs2rVKiZNmsR///3HX3/9RZ8+fZgwYYKshGsACdaFEEIIUWAajQZ4unLWFUUhIyW9xB+Gjh6bm5vj7OyMs7Mz9erVY+LEiVy7do07d+4A+mkfly9fpk2bNgDY2dmh0WgIDAwEICMjg88//xwPDw/Mzc2pUqUKM2fOVK9z8uRJXn75ZSwtLXFwcGD48OF6dfSzp8G0bt2aMWPGMGHCBOzt7XF2diYoKCjf9/Lhhx9y+fJlDh06xKBBg/Dx8aFmzZoMGzaMsLAwbGxsAFizZg2NGjWiXLlyODs7079/f27fvq2eJzY2lgEDBuDo6IilpSWenp6sWLFC71oXL16kTZs2WFlZUbduXQ4cOGDQ517WyVrBQgghhCgw3YhocHAwH3/8sd7q42WVkprBzSn7S/y6rp80R2NWuM8nISGBtWvX4uHhgYODQ479bm5u/Prrr/Ts2ZOIiAhsbW3V1WYnTZrEkiVL+Oqrr2jRogVRUVGcPXsWgIcPH9KpUyeaNm3K4cOHuX37NkOHDuXtt9/ON/Vl5cqVvPfeexw6dIgDBw4QGBiIn58f7du3z9E2IyODkJAQBgwYoDfPQUcXqEPml77p06fj5eXF7du3effddwkMDGTr1q0AfPzxx4SHh/PHH39QoUIFLly4kKMS0eTJk/nyyy/x9PRk8uTJ9OvXjwsXLhT7HYiS8my8CyGEEEKUuH///ZdmzZrluu/48eNERkbi5eXFCy+8UMI9ezpt2bJFDWQTExNxcXFhy5YtGBnlTIQwNjbG3t4eACcnJzVt5cGDB3zzzTfMnz+fQYMGAVCjRg1atGgBwNq1a3n06BGrVq3C2toagPnz59O1a1c+//xzKlasmGvf6tSpw9SpUwHw9PRk/vz57Nq1K9dg/e7du8TGxlKrVq3HvufBgwerz6tXr863337Liy++SEJCAjY2Nly9epX69evTqFEjAKpVq5bjHOPHj6dLly4ATJs2jRdeeIELFy4U6PpPAwnWhRBCCFFgNWvW5Ny5cwCcO3cuz2D9xo0bnDhxgvLly5d6sK4xNcL1k+alcl1DtGnThgULFgAQExPD999/T+fOnfn333+pWrVqgc5x5swZkpOTadu2bZ7769atqwbqAH5+fmRkZBAREZFvsJ6Vi4uLXrpKVrr0H13KVH6OHTtGUFAQYWFhxMTEkJGRAcDVq1fx8fFhxIgR9OzZk//++48OHTrQo0cPmjfX/7fM2jfd4l23b99+ZoJ1yVkXQgghRIHpRjAhcxT3yJEjpKamqtt0wZZum6mpacl2MBcajQYjM+MSfxQkWM3K2toaDw8PPDw8ePHFF1m2bBmJiYksWbKkwOfQpcLkRVGUPPuVX3+z/ztqNBr13zo7R0dH7OzsOHPmTL59SUxMpEOHDtjY2LBmzRoOHz7Mhg0bgP+bE9G5c2euXLnC2LFjuXnzJm3btmX8+PF59k33HvLq29NIgnUhhBBCFJhWq2XIkCFAZrrDli1b+PvvvwG4cOECn3zyCUFBQURFRQFlI1h/Wmk0GoyMjPJcLdbMzAzQL6Pp6emJpaUlu3btyvUYHx8fwsLCSExMVLf9888/GBkZUbNmzSLpt5GREX369GHt2rXcvHkzx/7ExETS0tI4e/Ysd+/e5bPPPqNly5bUqlUr19F6R0dHAgMDWbNmDV9//TWLFy8ukn4+LSRYF0IIIYRBnJyc9F7r0mJ++ukndVt0dDQgwbohkpOTiY6OJjo6mjNnzjB69GgSEhLo2rVrru2rVq2KRqNhy5Yt3Llzh4SEBCwsLJg4cSITJkxg1apVREZGcvDgQZYtWwbAgAEDsLCwYNCgQZw6dYrdu3czevRoAgIC8kyBKYxZs2bh5uZGkyZNWLVqFeHh4Zw/f57ly5dTr149EhISqFKlCmZmZsybN4+LFy+yadMmpk+frneeKVOm8Ntvv3HhwgVOnz7Nli1b8Pb2LrJ+Pg0kWBdCCCGEQczNzfUC9oyMDH766adcyzlKsF5woaGhuLi44OLiQpMmTTh8+DA///wzrVu3zrV9pUqVmDZtGh988AEVK1bk7bffBjIrqIwbN44pU6bg7e1Nnz591BFrKysrtm3bRkxMDI0bN6ZXr160bduW+fPnF+l7sbOz4+DBgwwcOJAZM2ZQv359WrZsybp16/jiiy/QarU4OjoSHBzMzz//jI+PD5999hlffvml3nnMzMyYNGkSderU4aWXXsLY2JiQkJAi7WtZp1Ge4yWk4uPj0Wq1xMXFYWtrW9rdEUIIIZ4a27dvZ//+x5dD7N+/f5GlV+jk9/c7KSmJS5cu4e7ujoWFRZFeV4iiYsjPqYysCyGEEMJgBZ08mVupPSFEwUmwLoQQQgiDpaWl5blPl2Ndu3ZtdRKkEKJwpM66EEIIIQymq2ed3VtvvUXFihVxcHDA2dm5hHslxLNHgnUhhBBCGKxOnTokJyfzxx9/qNsmTZqEubk5IOkvQhQVSYMRQgghhMGMjIxo0qQJ3bt3BzJTXnSBuhCi6MjIuhBCCCEKrV69elSsWBFHR8fS7ooQzyQJ1oUQQghRaBqNBldX19LuhhDPLEmDEUIIIYQQooySYF0IIYQQQogySoJ1IYQQQoinwOXLl9FoNISFhRXrdQIDA+nRo0exXkMUnATrQgghhBBlQGBgIBqNRn04ODjQqVMnTpw4AYCbmxtRUVH4+voCsGfPHjQaDffv3y/SfnzzzTcEBwcX6TlF4UmwLoQQQghRRnTq1ImoqCiioqLYtWsXJiYm+Pv7A2BsbIyzszMmJsVbH0Sr1VK+fPlivYYoOAnWhRBCCPFMUxSFlJSUEn8oimJwX83NzXF2dsbZ2Zl69eoxceJErl27xp07d/TSYC5fvkybNm0AsLOzQ6PREBgYCEBGRgaff/45Hh4emJubU6VKFWbOnKle4+TJk7z88stYWlri4ODA8OHDSUhIUPdnT4Np3bo1Y8aMYcKECdjb2+Ps7ExQUFCh/i2E4aR0oxBCCCGeaampqcyaNavEr/vhhx9iZmZW6OMTEhJYu3YtHh4eODg4kJiYqO5zc3Pj119/pWfPnkRERGBra4ulpSWQuZLskiVL+Oqrr2jRogVRUVGcPXsWgIcPH9KpUyeaNm3K4cOHuX37NkOHDuXtt9/ON/Vl5cqVvPfeexw6dIgDBw4QGBiIn58f7du3L/T7EwUjwboQQgghRBmxZcsWbGxsAEhMTMTFxYUtW7ZgZKSfDGFsbIy9vT0ATk5OatrKgwcP+Oabb5g/fz6DBg0CoEaNGrRo0QKAtWvX8ujRI1atWoW1tTUA8+fPp2vXrnz++edUrFgx137VqVOHqVOnAuDp6cn8+fPZtWuXBOslQIJ1IYQQQjzTTE1N+fDDD0vluoZq06YNCxYsACAmJobvv/+ezp078++//xbo+DNnzpCcnEzbtm3z3F+3bl01UAfw8/MjIyODiIiIfIP1rFxcXLh9+3aB+iSejATrQgghhHimaTSaJ0pHKUnW1tZ4eHiorxs2bIhWq2XJkiUMHTr0scfrUmHyoigKGo0m1315bYecXzw0Gg0ZGRmP7Y94cjLBVAghhBCijNJoNBgZGfHo0aMc+3RfQNLT09Vtnp6eWFpasmvXrlzP5+PjQ1hYmF7++z///IORkRE1a9Ys4t6LoiDBuhBCCCFEGZGcnEx0dDTR0dGcOXOG0aNHk5CQQNeuXXO0rVq1KhqNhi1btnDnzh0SEhKwsLBg4sSJTJgwgVWrVhEZGcnBgwdZtmwZAAMGDMDCwoJBgwZx6tQpdu/ezejRowkICMgzBUaULgnWhRBCCCHKiNDQUFxcXHBxcaFJkyYcPnyYn3/+mdatW+doW6lSJaZNm8YHH3xAxYoVefvttwH4+OOPGTduHFOmTMHb25s+ffqo+eVWVlZs27aNmJgYGjduTK9evWjbti3z588vybcpDKBRClME9BkRHx+PVqslLi4OW1vb0u6OEEIIIQogv7/fSUlJXLp0CXd3dywsLEqph0Lkz5CfUxlZF0IIIYQQoowyKFhfsGABderUwdbWFltbW5o1a8Yff/yh7lcUhaCgIFxdXbG0tKR169acPn1a7xzJycmMHj2aChUqYG1tTbdu3bh+/bpem9jYWAICAtBqtWi1WgICArh//75em6tXr9K1a1esra2pUKECY8aMISUlxcC3L4QQQgghRNllULBeuXJlPvvsM44cOcKRI0d4+eWX6d69uxqQz549m7lz5zJ//nwOHz6Ms7Mz7du358GDB+o5xo4dy4YNGwgJCWHfvn0kJCTg7++vN5O5f//+hIWFERoaSmhoKGFhYQQEBKj709PT6dKlC4mJiezbt4+QkBB+/fVXxo0b96SfhxBCCCGEEGWH8oTs7OyUpUuXKhkZGYqzs7Py2WefqfuSkpIUrVarLFy4UFEURbl//75iamqqhISEqG1u3LihGBkZKaGhoYqiKEp4eLgCKAcPHlTbHDhwQAGUs2fPKoqiKFu3blWMjIyUGzduqG3WrVunmJubK3FxcQXue1xcnAIYdIwQQgghSld+f78fPXqkhIeHK48ePSqFnglRMIb8nBY6Zz09PZ2QkBASExNp1qwZly5dIjo6mg4dOqhtzM3NadWqFfv37wfg6NGjpKam6rVxdXXF19dXbXPgwAG0Wi1NmjRR2zRt2hStVqvXxtfXF1dXV7VNx44dSU5O5ujRo3n2OTk5mfj4eL2HEEIIIYQQZZXBwfrJkyexsbHB3Nyct956iw0bNuDj40N0dDRAjhqdFStWVPdFR0djZmaGnZ1dvm2cnJxyXNfJyUmvTfbr2NnZYWZmprbJzaeffqrmwWu1Wtzc3Ax890IIIYQQQpQcg4N1Ly8vwsLCOHjwICNGjGDQoEGEh4er+7MvVavks6xtXm1ya1+YNtlNmjSJuLg49XHt2rV8+yWEEEIIIURpMjhYNzMzw8PDg0aNGvHpp59St25dvvnmG5ydnQFyjGzfvn1bHQV3dnYmJSWF2NjYfNvcunUrx3Xv3Lmj1yb7dWJjY0lNTc139S1zc3O1ko3uIYQQQgghRFn1xHXWFUUhOTkZd3d3nJ2d2bFjh7ovJSWFvXv30rx5cwAaNmyIqampXpuoqChOnTqltmnWrBlxcXH8+++/aptDhw4RFxen1+bUqVNERUWpbbZv3465uTkNGzZ80rckhBBCCCFEmWBiSOMPP/yQzp074+bmxoMHDwgJCWHPnj2Ehoai0WgYO3Yss2bNwtPTE09PT2bNmoWVlRX9+/cHQKvVMmTIEMaNG4eDgwP29vaMHz+e2rVr065dOwC8vb3p1KkTw4YNY9GiRQAMHz4cf39/vLy8AOjQoQM+Pj4EBATwxRdfEBMTw/jx4xk2bJiMlgshhBBCiGeGQSPrt27dIiAgAC8vL9q2bcuhQ4cIDQ2lffv2AEyYMIGxY8cycuRIGjVqxI0bN9i+fTvlypVTz/HVV1/Ro0cPevfujZ+fH1ZWVmzevBljY2O1zdq1a6lduzYdOnSgQ4cO1KlTh9WrV6v7jY2N+f3337GwsMDPz4/evXvTo0cPvvzyyyf9PIQQQgghSpRGo8n3ERgYmKOdjY0NdevWJTg4OMf50tPT+eqrr6hTpw4WFhaUL1+ezp07888//+i1Cw4ORqPR0KlTJ73t9+/fR6PRsGfPnmJ6x8IQGkVRlNLuRGmJj49Hq9USFxcnI/JCCCHEUyK/v99JSUlcunQJd3d3LCwsSqmHhsk6D+/HH39kypQpREREqNssLS3RarVoNBpWrFhBp06dSExM5Mcff2Ty5MmEhobSsWNHIDM9uXfv3uzcuZMvvviCtm3bEh8fz3fffceKFSv4+eef6dGjB5AZrA8bNgzITCdu06YNkBms29nZsXv3blq3bl0yH8JzxpCfU4PSYIQQQgghnjaKopCR8ajEr2tkZPnYiniAWqQDUIPyrNuyKl++vLrvww8/ZM6cOWzfvl0N1n/66Sd++eUXNm3aRNeuXdXjFi9ezL179xg6dCjt27fH2toaAGtra3r37s0HH3zAoUOHCv1eRfGRYF0IIYQQz7SMjEfs2Vu7xK/butVJjI2tiuXc6enp/Prrr8TExGBqaqpu/+GHH6hZs6ZeoK4zbtw41q9fz44dO9TRdYCgoCA8PDz45Zdf6NWrV7H0VxSeBOtCCCGEEE+Jfv36YWxsTFJSEunp6djb2zN06FB1/7lz5/D29s71WN32c+fO6W13dXXlnXfeYfLkyXpBvCgbJFgXQgghxDPNyMiS1q1Olsp1i9pXX31Fu3btuHbtGu+99x7vvvsuHh4eBp0jt9SciRMnsmjRIpYvX07v3r2LqruiCEiwLoQQQohnmkajKbZ0lJLm7OyMh4cHHh4e/Pzzz9SvX59GjRrh4+MDQM2aNfVWls/qzJkzAHh6eubYV758eSZNmsS0adPw9/cvvjcgDPbEiyIJIYQQQoiS5+HhQc+ePZk0aZK6rW/fvpw/f57NmzfnaD9nzhwcHBzUktvZjR49GiMjI7755pti67MwnATrQgghhBBPqXHjxrF582aOHDkCZAbrr776KoMGDWLZsmVcvnyZEydO8Oabb7Jp0yaWLl2qVoLJzsLCgmnTpvHtt9+W5FsQjyHBuhBCCCHEU0q3CvyUKVOAzJSfn376icmTJ/PVV19Rq1YtWrZsyZUrV9i9e/djJ5AOGjSI6tWrl0DPRUHJokiyKJIQQgjxVHnWFkUSzx9Dfk5lZF0IIYQQQogySoJ1IYQQQgghyigJ1oUQQgghhCijJFgXQgghhBCijJJgXQghhBBCiDJKgnUhhBBCCCHKKAnWhRBCCCGEKKMkWBdCCCGEEKKMkmBdCCGEEEKIMkqCdSGEEEKIMiAwMBCNRpPj0alTJ7XN/v37eeWVV7Czs8PCwoLatWszZ84c0tPT9c61e/du2rRpg729PVZWVnh6ejJo0CDS0tLyvH61atX4+uuv9bYV9Hqi+EiwLoQQQghRRnTq1ImoqCi9x7p16wDYsGEDrVq1onLlyuzevZuzZ8/yzjvvMHPmTPr27YuiKACcPn2azp0707hxY/766y9OnjzJvHnzMDU1JSMjo8B9Kej1RPEyKe0OCCGEEEKITObm5jg7O+fYnpiYyLBhw+jWrRuLFy9Wtw8dOpSKFSvSrVs3fvrpJ/r06cOOHTtwcXFh9uzZarsaNWrojdA/jiHXE8VLRtaFEEII8UxTFIXE9PQSfxTlyPP27du5d+8e48ePz7Gva9eu1KxZUx2Bd3Z2Jioqir/++qtErieKl4ysCyGEEOKZ9jAjgxp/nSzx60a+VBtrY2ODjtmyZQs2NjZ62yZOnIiZmRkA3t7euR5Xq1Ytzp07B8Drr7/Otm3baNWqFc7OzjRt2pS2bdvyv//9D1tb2wL1Q3euglxPFC8ZWRdCCCGEKCPatGlDWFiY3mPUqFHq/rxG6xVFQaPRAGBsbMyKFSu4fv06s2fPxtXVlZkzZ/LCCy8QFRVlUH8Kcj1RvGRkXQghhBDPNCsjIyJfql0q1zWUtbU1Hh4eObbXrFkTgDNnztC8efMc+8+ePYuPj4/etkqVKhEQEEBAQAAzZsygZs2aLFy4kGnTpj22H4W5nigeMrIuhBBCiGeaRqPB2ti4xB9FOfLcoUMH7O3tmTNnTo59mzZt4vz58/Tr1y/P4+3s7HBxcSExMbFErieKjgTrQgghhBBlRHJyMtHR0XqPu3fvYm1tzaJFi/jtt98YPnw4J06c4PLlyyxbtozAwEB69epF7969AVi0aBEjRoxg+/btREZGcvr0aSZOnMjp06fp2rUrADdu3KBWrVr8+++/ufbDkOuJ4iVpMEIIIYQQZURoaCguLi5627y8vDh79iy9evVi9+7dzJo1i5deeolHjx7h4eHB5MmTGTt2rDqS/+KLL7Jv3z7eeustbt68iY2NDS+88AIbN26kVatWAKSmphIREcHDhw/z7EtBryeKl0Z5jivax8fHo9VqiYuLK/DsaCGEEEKUrvz+ficlJXHp0iXc3d2xsLAopR4KkT9Dfk4lDUYIIYQQQogySoJ1IYQQQgghyigJ1oUQQgghhCijJFgXQgghhBCijJJgXQghhBBCiDJKgnUhhBBCCCHKKAnWhRBCCCGEKKMkWBdCCCGEEKKMkmBdCCGEEEKIMsqgYP3TTz+lcePGlCtXDicnJ3r06EFERIReG0VRCAoKwtXVFUtLS1q3bs3p06f12iQnJzN69GgqVKiAtbU13bp14/r163ptYmNjCQgIQKvVotVqCQgI4P79+3ptrl69SteuXbG2tqZChQqMGTOGlJQUQ96SEEIIIYQoBhqNho0bN5Z2N556BgXre/fuZdSoURw8eJAdO3aQlpZGhw4dSExMVNvMnj2buXPnMn/+fA4fPoyzszPt27fnwYMHapuxY8eyYcMGQkJC2LdvHwkJCfj7+5Oenq626d+/P2FhYYSGhhIaGkpYWBgBAQHq/vT0dLp06UJiYiL79u0jJCSEX3/9lXHjxj3J5yGEEEIIUSoCAwPRaDRoNBpMTU2pWLEi7du3Z/ny5WRkZJR29wwWFRVF586dS7sbTz/lCdy+fVsBlL179yqKoigZGRmKs7Oz8tlnn6ltkpKSFK1WqyxcuFBRFEW5f/++YmpqqoSEhKhtbty4oRgZGSmhoaGKoihKeHi4AigHDx5U2xw4cEABlLNnzyqKoihbt25VjIyMlBs3bqht1q1bp5ibmytxcXEF6n9cXJwCFLi9EEIIIUpffn+/Hz16pISHhyuPHj0qhZ49mUGDBimdOnVSoqKilOvXrytHjx5VZs6cqdjY2CidO3dWUlNTS7uLoogY8nP6RDnrcXFxANjb2wNw6dIloqOj6dChg9rG3NycVq1asX//fgCOHj1KamqqXhtXV1d8fX3VNgcOHECr1dKkSRO1TdOmTdFqtXptfH19cXV1Vdt07NiR5ORkjh49mmt/k5OTiY+P13sIIYQQQpQV5ubmODs7U6lSJRo0aMCHH37Ib7/9xh9//EFwcDCQmQbcvXt3bGxssLW1pXfv3ty6dUvvPDNmzMDJyYly5coxdOhQPvjgA+rVq6fXZsWKFXh7e2NhYUGtWrX4/vvv9fZfv36dvn37Ym9vj7W1NY0aNeLQoUPq/gULFlCjRg3MzMzw8vJi9erVesdnTYO5fPkyGo2G9evX06ZNG6ysrKhbty4HDhwomg/uGVboYF1RFN577z1atGiBr68vANHR0QBUrFhRr23FihXVfdHR0ZiZmWFnZ5dvGycnpxzXdHJy0muT/Tp2dnaYmZmpbbL79NNP1Rx4rVaLm5uboW9bCCGEEE8ZRVF4mJJW4g9FUYqk/y+//DJ169Zl/fr1KIpCjx49iImJYe/evezYsYPIyEj69Omjtl+7di0zZ87k888/5+jRo1SpUoUFCxbonXPJkiVMnjyZmTNncubMGWbNmsXHH3/MypUrAUhISKBVq1bcvHmTTZs2cfz4cSZMmKCm42zYsIF33nmHcePGcerUKd58803eeOMNdu/ene97mTx5MuPHjycsLIyaNWvSr18/0tLSiuRzelaZFPbAt99+mxMnTrBv374c+zQajd5rRVFybMsue5vc2hemTVaTJk3ivffeU1/Hx8dLwC6EEEI84x6lpuMzZVuJXzf8k45YmRU61NJTq1YtTpw4wc6dOzlx4gSXLl1SY5jVq1fzwgsvcPjwYRo3bsy8efMYMmQIb7zxBgBTpkxh+/btJCQkqOebPn06c+bM4bXXXgPA3d2d8PBwFi1axKBBg/jhhx+4c+cOhw8fVjMoPDw81OO//PJLAgMDGTlyJADvvfceBw8e5Msvv6RNmzZ5vo/x48fTpUsXAKZNm8YLL7zAhQsXqFWrVpF8Ts+iQo2sjx49mk2bNrF7924qV66sbnd2dgbIMbJ9+/ZtdRTc2dmZlJQUYmNj822T/XYOwJ07d/TaZL9ObGwsqampOUbcdczNzbG1tdV7CCGEEEKUdbrByDNnzuDm5qY32Ojj40P58uU5c+YMABEREbz44ot6x2d9fefOHa5du8aQIUOwsbFRHzNmzCAyMhKAsLAw6tevrwbq2Z05cwY/Pz+9bX5+fmof8lKnTh31uYuLC5AZA4q8GfR1T1EURo8ezYYNG9izZw/u7u56+93d3XF2dmbHjh3Ur18fgJSUFPbu3cvnn38OQMOGDTE1NWXHjh307t0byJwtfOrUKWbPng1As2bNiIuL499//1V/uA4dOkRcXBzNmzdX28ycOZOoqCj1H3v79u2Ym5vTsGHDwn4eQgghhHjGWJoaE/5Jx1K5blE5c+YM7u7ueWYQPC77IGtKji6VZcmSJXrzAwGMjTP7bGlp+dg+FSaTwtTUNMfxT2Olm5JkULA+atQofvjhB3777TfKlSunjmxrtVosLS3RaDSMHTuWWbNm4enpiaenJ7NmzcLKyor+/furbYcMGcK4ceNwcHDA3t6e8ePHU7t2bdq1aweAt7c3nTp1YtiwYSxatAiA4cOH4+/vj5eXFwAdOnTAx8eHgIAAvvjiC2JiYhg/fjzDhg2TEXMhhBBCqDQaTZGlo5SGP//8k5MnT/Luu+9SuXJlrl69yrVr19TR9fDwcOLi4vD29gbAy8uLf//9V6/k9ZEjR9TnFStWpFKlSly8eJEBAwbkes06deqwdOlSYmJich1d9/b2Zt++ffzvf/9Tt+3fv1/tgyhChpSZAXJ9rFixQm2TkZGhTJ06VXF2dlbMzc2Vl156STl58mSOcjVvv/22Ym9vr1haWir+/v7K1atX9drcu3dPGTBggFKuXDmlXLlyyoABA5TY2Fi9NleuXFG6dOmiWFpaKvb29srbb7+tJCUlFfj9SOlGIYQQ4unzPJZu9Pf3V9LS0pSMjAylfv36SsuWLZWjR48qhw4dUho2bKi0atVKPc+aNWsUS0tLJTg4WDl37pwyffp0xdbWVqlXr57aZsmSJYqlpaXy9ddfKxEREcqJEyeU5cuXK3PmzFEURVGSk5OVmjVrKi1btlT27dunREZGKr/88ouyf/9+RVEUZcOGDYqpqamyYMEC5dy5c8qcOXMUY2NjZffu3eo1AGXDhg2KoijKpUuXFEA5duyYuj82NlYB9I55Xhjyc/pEddafdhKsCyGEEE+fZzlY1w2EmpiYKI6Ojkq7du2U5cuXK+np6Wq7K1euKN26dVOsra2VcuXKKa+//roSHR2td65PPvlEqVChgmJjY6MMHjxYGTNmjNK0aVO9NmvXrlXq1aunmJmZKXZ2dspLL72krF+/Xt1/+fJlpWfP/9fencdHVd/7H3+dM2v2DbISICD7Igiu2ItUQVurtZutXltte22t7bUu/PT22sXdqri0Wq2tFaqt2HtbtdYFQa1aLiqb4MIm+5YQyL7MPt/fH5MMCUwgIYFJyPv5eMwjyVk/85nvZD7nO99zzldMZmamSU1NNVOnTjXvv/9+fP6jjz5qhg0bZlwulxk5cqR56qmn2m1fxXrHutJOLWN66LpCfVB9fT1ZWVnU1dVp6IyIiEgfcajPb7/fz5YtWygrK8Pr9SYpwt5n5syZFBYWHnQtdEmOrrTTvjuAS0REREQO0tzczG9/+1vOPfdcHA4H8+fP5/XXX2fRokXJDk2OgIp1ERERkeOIZVm88sor3HHHHQQCAUaNGsXf/va3+IU8pG9RsS4iIiJyHElJSeH1119PdhjSQ47opkgiIiIiInL0qVgXEREREemlVKyLiIiIiPRSKtZFRERERHopFesiIiIiIr2UinURERERkV5KxbqIiIhIL2dZFi+88EKyw+g1cfQnus66iIiISC9wxRVXUFtbm7AYLi8vJycnB4CtW7dSVlbGBx98wKRJk45pjG3jkGNDxbqIiIhIL1dYWJjsEIDeE0d/omEwIiIiIr1c2+EnZWVlAEyePBnLsjjrrLPiy82dO5cxY8bg9XoZPXo0jz76aLvt7Ny5k2984xvk5uaSlpbG1KlTef/99+PzH3vsMYYPH47b7WbUqFE8/fTTHcaxdetWLMviueeeY8aMGaSmpnLiiSfy7rvv9nwC+jH1rIuIiMjxzRgINR/7/bpSwbJ6fLNLly7llFNO4fXXX2fcuHG43W4Afv/73/OLX/yCRx55hMmTJ/PBBx9w5ZVXkpaWxuWXX05jYyPTp0+npKSEF198kcLCQlauXEk0GgXg+eef58c//jEPPfQQ55xzDi+99BLf/va3GTRoEDNmzOgwnptvvpk5c+YwYsQIbr75Zi655BI2btyI06kysycoiyIiInJ8CzXDXcXHfr//vRvcaT2+2YEDBwKQl5fXbljK7bffzv3338+Xv/xlINYDv2bNGh5//HEuv/xynnnmGfbu3cuyZcvIzc0F4IQTToivP2fOHK644gquvvpqAK6//nree+895syZc8hiffbs2Zx//vkA3HrrrYwbN46NGzcyevTonn3i/ZSGwYiIiIj0cXv37mXHjh1897vfJT09Pf6444472LRpEwCrVq1i8uTJ8UL9QGvXrmXatGntpk2bNo21a9cect8TJ06M/15UVARAZWVld56OtKGedRERETm+uVJjvdzJ2O8x0jqU5fe//z2nnnpqu3kOhwOAlJSUw27HOmDYjjHmoGkHcrlcB63fGo90n4p1EREROb5Z1lEZjpIsrWPUI5FIfFpBQQElJSVs3ryZf//3f0+43sSJE3niiSeorq5O2Ls+ZswYFi9ezLe+9a34tCVLljBmzJgefgbSFSrWRURERHqJuro6Vq1a1W7agYV1fn4+KSkpLFiwgEGDBuH1esnKyuKWW27hmmuuITMzk8997nMEAgGWL19OTU0N119/PZdccgl33XUXF110EXfffTdFRUV88MEHFBcXc/rpp/P//t//4+KLL+akk07i7LPP5h//+AfPPfccr7/++jHMgBxIY9ZFREREeom33nqLyZMnt3v8/Oc/b7eM0+nk17/+NY8//jjFxcV88YtfBOA//uM/eOKJJ5g3bx4TJkxg+vTpzJs3L36pR7fbzcKFC8nPz+fzn/88EyZM4Je//GV8mMxFF13Er371K+677z7GjRvH448/zty5c9tdGlKOPcsYY5IdRLLU19eTlZVFXV0dmZmZyQ5HREREOuFQn99+v58tW7ZQVlaG1+tNUoQih9aVdqqedRERERGRXkrFuoiIiIhIL6ViXURERESkl1KxLiIiIiLSS6lYFxERERHppVSsi4iIiIj0UirWRURERER6KRXrIiIiIiK9lIp1EREREZFeSsW6iIiISD80dOhQHnrooWSHIYehYl1ERESkl6ioqOA///M/GTZsGB6Ph9LSUi644ALeeOONZIcmSeJMdgAiIiIiAlu3bmXatGlkZ2dz7733MnHiREKhEK+99ho//OEPWbduXZe3GYlEsCwL21b/bF+lV05ERESkF7j66quxLIulS5fy1a9+lZEjRzJu3Diuv/563nvvPQAeeOABJkyYQFpaGqWlpVx99dU0NjbGtzFv3jyys7N56aWXGDt2LB6Ph23btlFZWckFF1xASkoKZWVl/PnPfz5o/53d9muvvcaYMWNIT0/nvPPOo7y8PL5MNBrltttuY9CgQXg8HiZNmsSCBQuOYtaOf+pZFxERkeOaMQZf2HfM95viTMGyrE4tW11dzYIFC7jzzjtJS0s7aH52djYAtm3z61//mqFDh7JlyxauvvpqbrzxRh599NH4ss3Nzdx999088cQT5OXlkZ+fz9e+9jV27NjBm2++idvt5pprrqGysrLdPjq77Tlz5vD0009j2zaXXXYZs2fPjhf/v/rVr7j//vt5/PHHmTx5Mk8++SQXXnghn3zyCSNGjOhqCgUV6yIiInKc84V9nPrMqcd8v+9f+j6prtROLbtx40aMMYwePfqQy1177bXx38vKyrj99tv5wQ9+0K6gDoVCPProo5x44okAbNiwgVdffZX33nuPU0+N5eEPf/gDY8aMOaJt//a3v2X48OEA/OhHP+K2226Lz58zZw433XQT3/jGNwC45557+Oc//8lDDz3Eb37zm07lQtpTsS4iIiKSZMYYgMP2xP/zn//krrvuYs2aNdTX1xMOh/H7/TQ1NcV75N1uNxMnToyvs3btWpxOJ1OnTo1PGz16dLy3vivbTk1NjRfqAEVFRfEe+vr6enbv3s20adPabXfatGmsXr26ixmRVl0u1t955x3uu+8+VqxYQXl5Oc8//zwXXXRRfL4xhltvvZXf/e531NTUcOqpp/Kb3/yGcePGxZcJBALMnj2b+fPn4/P5OPvss3n00UcZNGhQfJmamhquueYaXnzxRQAuvPBCHn744XYNa/v27fzwhz/kzTffJCUlhUsvvZQ5c+bgdruPIBUiIiJyPEpxpvD+pe8nZb+dNWLECCzLYu3ate3qqra2bdvG5z//ea666ipuv/12cnNzWbx4Md/97ncJhUL795vSfvhNZw4EOrttl8vVbj3LsuLbbzutLWNMp4cDycG6fIJpU1MTJ554Io888kjC+ffeey8PPPAAjzzyCMuWLaOwsJCZM2fS0NAQX+baa6/l+eef59lnn2Xx4sU0NjbyhS98gUgkEl/m0ksvZdWqVSxYsIAFCxawatUqvvnNb8bnRyIRzj//fJqamli8eDHPPvssf/vb37jhhhu6+pRERETkOGZZFqmu1GP+6EqBmpuby7nnnstvfvMbmpqaDppfW1vL8uXLCYfD3H///Zx22mmMHDmS3bt3H3bbY8aMIRwOs3z58vi09evXU1tbG//7SLfdVmZmJsXFxSxevLjd9CVLlhw05Ea6wHQDYJ5//vn439Fo1BQWFppf/vKX8Wl+v99kZWWZ3/72t8YYY2pra43L5TLPPvtsfJldu3YZ27bNggULjDHGrFmzxgDmvffeiy/z7rvvGsCsW7fOGGPMK6+8YmzbNrt27YovM3/+fOPxeExdXV2n4q+rqzNAp5cXERGR5DvU57fP5zNr1qwxPp8vCZF1z+bNm01hYaEZO3as+etf/2o2bNhg1qxZY371q1+Z0aNHmw8++MAA5qGHHjKbNm0yTz31lCkpKTGAqampMcYYM3fuXJOVlXXQts877zwzceJE895775nly5ebM88806SkpJgHH3zQGGOOeNvPP/+8aVtOPvjggyYzM9M8++yzZt26deamm24yLpfLbNiw4ShkrO/qSjvt0Us3btmyhYqKCmbNmhWf5vF4mD59OkuWLAFgxYoVhEKhdssUFxczfvz4+DLvvvsuWVlZ8ZMgAE477TSysrLaLTN+/HiKi4vjy5x77rkEAgFWrFiRML5AIEB9fX27h4iIiEhvUFZWxsqVK5kxYwY33HAD48ePZ+bMmbzxxhs89thjTJo0iQceeIB77rmH8ePH8+c//5m77767U9ueO3cupaWlTJ8+nS9/+ct873vfIz8/Pz6/O9tu65prruGGG27ghhtuYMKECSxYsIAXX3xRV4LpBsuYAwYadWVly2o3Zn3JkiVMmzaNXbt2tSuiv/e977Ft2zZee+01nnnmGb797W8TCATabWvWrFmUlZXx+OOPc9dddzFv3jw2bNjQbpmRI0fy7W9/m5/85Cd873vfY+vWrSxcuLDdMh6Ph3nz5nHJJZccFO8tt9zCrbfeetD0uro6MjMzjzQNIiIicgzV19eTlZWV8PPb7/ezZcsWysrK8Hq9SYpQ5NC60k6Pyk2RjuTEggOXSbT8kSzT1k9+8hPq6urijx07dhwyJhERERGRZOrRYr2wsBCAioqKdtMrKyspKCiILxMMBqmpqTnkMnv27Dlo+3v37m23zIH7qampIRQKxZc5kMfjITMzs91DRERERKS36tFivaysjMLCQhYtWhSfFgwGefvttznjjDMAmDJlCi6Xq90y5eXlfPzxx/FlTj/9dOrq6li6dGl8mffff5+6urp2y3z88cftbnG7cOFCPB4PU6ZM6cmnJSIiIiKSFF2+znpjYyMbN26M/71lyxZWrVpFbm4ugwcP5tprr+Wuu+5ixIgRjBgxgrvuuovU1FQuvfRSALKysvjud7/LDTfcQF5eHrm5ucyePZsJEyZwzjnnALFLDJ133nlceeWVPP7440Bs3PsXvvAFRo0aBcTGuI8dO5ZvfvOb3HfffVRXVzN79myuvPJK9ZiLiIiIyHGhy8X68uXLmTFjRvzv66+/HoDLL7+cefPmceONN+Lz+bj66qvjN0VauHAhGRkZ8XUefPBBnE4nF198cfymSPPmzcPhcMSX+fOf/8w111wTv2rMhRde2O7a7g6Hg5dffpmrr76aadOmtbspkoiIiIjI8aBbV4Pp6w51NrmIiIj0TroajPR1Sb8ajIiIiIiIdJ+KdRERERGRXkrFuoiIiIhIL6ViXURERKSPuuWWW5g0aVKyw5CjSMW6iIiISC9wxRVXYFnWQY/zzjsPiN25/YUXXkhukHLMdfnSjSIiIiJydJx33nnMnTu33TSPx3NMYzDGEIlEcDpVJvYG6lkXERER6SU8Hg+FhYXtHjk5OQwdOhSAL33pS1iWFf+71dNPP83QoUPJysriG9/4Bg0NDfF5xhjuvfdehg0bRkpKCieeeCJ//etf4/PfeustLMvitddeY+rUqXg8Hv71r38ddr158+aRnZ3dLo4XXngBy7Lif7cO03nyyScZPHgw6enp/OAHPyASiXDvvfdSWFhIfn4+d955Z88l8TijQyYRERE5rhljMD7fMd+vlZLSrnDtjmXLlpGfn8/cuXM577zz2t1IctOmTbzwwgu89NJL1NTUcPHFF/PLX/4yXgD/9Kc/5bnnnuOxxx5jxIgRvPPOO1x22WUMHDiQ6dOnx7dz4403MmfOHIYNG0Z2dnan1zucTZs28eqrr7JgwQI2bdrEV7/6VbZs2cLIkSN5++23WbJkCd/5znc4++yzOe2003okX8cTFesiIiJyXDM+H+tPmnLM9ztq5Qqs1NQurfPSSy+Rnp7ebtpNN93Ez372MwCys7MpLCxsNz8ajTJv3rz43eK/+c1v8sYbb3DnnXfS1NTEAw88wJtvvsnpp58OwLBhw1i8eDGPP/54u6L7tttuY+bMmQBdWu9wotEoTz75JBkZGYwdO5YZM2awfv16XnnlFWzbZtSoUdxzzz289dZbKtYTULEuIiIi0kvMmDGDxx57rN203NzcQ64zdOjQeKEOUFRURGVlJQBr1qzB7/fHi/BWwWCQyZMnt5s2derU+O9dWe9wDoyvoKAAh8OBbdvtprXGLO2pWBcREZHjmpWSwqiVK5Ky365KS0vjhBNO6NI6Lper/X4ti2g0ChD/+fLLL1NSUtJuuQNPXE1LS4v/3pn1bNvGGNNuXigU6lR8h4pZ2lOxLiIiIsc1y7K6PBylN3K5XEQikS6tM3bsWDweD9u3b+/S0JXOrDdw4EAaGhpoamqKF/qrVq3qUnxyeCrWRURERHqJQCBARUVFu2lOp5MBAwYwdOhQ3njjDaZNm4bH4yEnJ+ew28vIyGD27Nlcd911RKNRzjzzTOrr61myZAnp6elcfvnlR7zeqaeeSmpqKv/93//Nf/7nf7J06VLmzZvXE2mQNnTpRhEREZFeYsGCBRQVFbV7nHnmmQDcf//9LFq0iNLS0i6NG7/99tv5+c9/zt13382YMWM499xz+cc//kFZWVm31svNzeVPf/oTr7zyChMmTGD+/PnccsstR/zcJTHLHDjYqB+pr68nKyuLuro6MjMzkx2OiIiIdMKhPr/9fj9btmyhrKwMr9ebpAhFDq0r7VQ96yIiIiIivZSKdRERERGRXkonmMpRE6qspP4fLxHetw/LYdO0dBn+Dz/EPXQoJhIhUluLq6QE99ChRJuaSJ0yhfqXXya0ezf5s28g+ytfoWb+fEK7y2letgzL68VyuXBkZZF/44048wcSWL8BR1Ym7sGDCe3eTXjvXhxZWVgeD86CAqyWa7hG6utpeu89og2NRAN+0k47Hc+wQ4/VE4g0NhLavRvL5aJp8f/h+/BDjN9HyqTJZH3pIpyHufaviIiIdI/GrPejMesmHMa/Zg3+NWup/tPTWJaFnZaOnZpCeF8VJhzGPawMV0Eh4co9eEaMxErxEqmpBWOI1NaSed65WB4PJhIBAymTJhGprqLupZcIl1cQqa2heekyInV1x/S5WS4X5oBru1ouF3ZmJnZqKuHKSkwg0H6+x0Px3XeR+fnPH8tQe0ykvp66F/5O1OfDM2ok6Z/5DFab208fiWgwCKEQwZ07sZxONl/0JUhwzdy2nAUFuEpKiDY34xk+HEwUEzUEN23EPWw4JffPwXIe3X4BE4lgAgHs4+DSbCJyeBqzLn1dV9qpivWjVKwHPv0U97Bh8eIp6vdjgkHC+/bhzC/AkZ6WcL1IbS1Vf3iS4LZtYFn4PvyQcHk5dloaKVOn4MzOwV1WhgkGqX3uOdyDBuEaNIj8G64HyyLw6acAhCr24P/4Y0K7d+MsLABjqH32Lz36HAGwLHA4IBxOPNvtxjN6NK7CQpwDBhCuribj7LNxDhxIcMd2IjW1+NeuIbRrN46cbNyDSvGvW4dvRZubV9g2zoIC3IMGYWdk0Pjmm0cUqp2eTspJkwmXlxP4dGN8unfCBFKnTiW0cyc5l3yD1JNPxnK5iPr9YNv4P/4Y3+oPSf+3z8ReU8s6aNvB7dsJbNpEqLwcR1YW4b17qZk/H+PzY7ndeMeOBWMI7dlDpKaGgT/6IVlf/CKBTZuw09Jwtbl1dGhPJRU//zkmFCTlpCm4CgtwDhxIyuTJBLdtp/n992hetpymd9/FBIPx9ZzFRQx6+GFSxo3rdE6aV66kZv6zODIyqF+0kMjefYdc3nK7cRYUENqxo9P7GHjtteRd+R8dHkiYSCThPBMOU/XEE4T3VdH8/ntE6urB6cBzwgl4hg0nUltLw8KFOHJyiNTVEW1uxjt+PBlnn03WhRfgKirqdIwi0reoWJe+TsV6Jx2tYj1UUcHGs2YAsQLK+PxEamri8535+ZS98Hx8CIEJhah+6mnqXnyRwPr1PRZHRzwjRuAsLsI9eAieYWU0LV2Ke/AQUk+ajO+TTwisXUvDotcBcA8ZgmvIYCzLpvmDD8AYLK8HR3oGwS1b4tv0TpxIysSJOHJzsFwuvKNHY6el4R01Cjst8YFJR4wx+FevJtLURNrJJ2O53e3nB4PUL1qEb9VqBlz9AyzLIlRejgkG8YwcGTt4iEbxr11LtKGBxrfewvKmkHflf+DMycFEIlT97nfs/dWvjyh/mV/4AhnnzsL2eqn+41OEyssJbtrU5e24SkoI7doFQNqZZ2J5PTS/v5RoQ0OXtuPIyyNSXQ3G4CwsJOcbXydcXQ1AaPsOHFmZOLKzCVVWMuCqH0AkTKi8nHBVFXtuv+OgbyTastxu8m+8Ec/IEaROmRIvqk0oRNUTT7Dv90+Qcc7ZhLZtx7d6Nc78fBzZ2aRMmkRo1y6a/u//4tsa/NQfST35ZPwff0LNM8/QvHQp0ebm+HvDVVICDgepkyeRMWsWVb/7Pb7Vq7uUiwN5x47FWVBApK4OR1YWxffdh52WivH7W75pWosjI52oP4AjJxtnXh71L7+Mb/WHeCeMJ+eSS+JDqTrDGENo504stwdHZgZN774HtkXq1JM7PEA/UsYYMAYsC8uyMMYQrauLPdfsbBxZWQetE9i8hboXXsA5cCB2RjpEolhuF46cXFwlxVhOJ6Fduwnt3o3/k08IbN5E0W23YTkcRBoaYu01Go19Y5WSEvsfYDtIm3YGzk5c71mkp3SmWB8yZAip+rZNeqnm5ma2bdumYv1wjlax3vj22+y87npMc/Mhl3Pk5LQr4tuxbdzDynAPHYq7dDApEycQ2l1OuLqK4LZtBNauI1RRgZ2amrC484w4Ac/oMQTWrQXLxjtuHO4hg8n64hd7rMcxXFVF05IleMeOjQ1/6GOC27ez8z+v6dQBkp2Rcegi2rbxDB+OMVEsy4r33KedcQYpkydjQqFYAbRuLcGNnSvs3WVlhKurMT7f/h50y8I5cCAmEiH9zGkU3n47tttN49tvs+P7V3VquwdxufCMiN3a2lVQSMF/3YSdmYnvg1WknjQZR3b2EW3WRKPs+I//oGnJu0cWV2t4paVknv95LNtB/WuvYblcOHNzsNPS8H34Ed4J4xn4ox9hORzUL3iNmv/5y2G/IegKZ0EBGeecQ8FP/gtsG6vN7bX9a9bE/g5HMKEQ/rVr2HP7HR0/l5ISci67DFdRIb4PVhFtbsZZVEhw02aiAT/GH6Bp8eLYuRxlZTjz83HmxQ7qU08+mbRp04jU11P+05/R+MYb7bbtyM4mUlt70D6tlBRSTzoJV3Ex9a++SrSxscdyE9+Hy0XGrFl4x47BkZdHxjnn4EhP7/H9tDLGQDiMMQbb7cYEg5hIBLvNrd2jPh+BTz/FVVyMIy8vdkDTwbc40vcc6vM7Go3y6aef4nA4GDhwIG63O+E3oiLJYIwhGAyyd+9eIpEII0aMwD5Mp5CK9aM0DMYEg7EhD9XVBD79FMvjIfDpRsJ79lD38ssHDxtxOMi9/HJMKMSAq3/QpV6qqM9HYPNm3KWlODIziTY1dbk3uz9rfQuE9+6l8e238X/4IQBpp5+OI28AqSdPxbJtwjU17P3Vrw4aTpRx3nnkz56Ne1BJu+lRvx87wdGyiUTwf/IJoT17cA4YgO+DVdS/8gruoUMJ7dxJpL6eQY88jGfYsPg64X37CFXswXPC8MTbNIbKOXOo/sOT8Wl2Vhau/IGxXtS0tPi3JZbXGztQrKrCPXQoQ+c/c9TaS6Sxkbq//73DArborrsIbNhA9dNP4y4bSrS+gXBlZXx+6eO/Jb0Lt8eGloOE719F07/+hbO4CNvtIbh1a3eexnEnffp0osEAlsNJtKmJSF0d4YoKjDGxA8MDb2fucmF7vbiKi7HcbiI1NUR9PlyDSghX7iVcXn7QPuyMDACizc04cnJw5mTjHlpG2r99huZ338NyOYkGgrFhcNu2A+AeOhTvuHG4BpcSravHkZdLeN8+IlXVRH0+LI8b0+wjsHFjvJ1YKSkYn29/qEMGkzJuPA1vvNHuPBWrpagHSJs2jeL77tUJ0n3Y4T6/g8Eg5eXlNB+m00wkWVJTUykqKsJ9wOiBRFSsJ+EE06jfT3DLFgIbNuD7+BNSTzmZtFNOSfi19bHwykflVNT5+c6ZujpKZ5hQCP+aNUSbm0mZMgW7E2+0oxKHMYSjBpdj/xG5CQbjw4aMMQf1JoWrq3FkZMROyA0GweE4Jj2Nm849L3YeBlDws5+S8/Wvd3jSaXDrVvzr1pFxzjk9dmKqMYbGt98msm8fJhzGVVKCZ9iw2LkTO3fiHjIEE4kQbWqKvw99q1YRrqykecVKap5++pDbt9PTY+c4hMPgdDL85Zew09JwDhgQby++Vauo+Z//TThkKvPzn8M7bjzhffsI792Ld/QowjU1+D9Zg3vwYEwoRMPrr7f7dsc7fjzZX/kyadOmEdi8GUd6Ot7x47G9Xnwffoh/zVosj4fg9m3xcyecAweSMWsmroKCTucu2tSElZraYc9ka24b33iDcFU1Tf/3fwedzN0bOYuLyLviCrK//nUsl4tIVRV2enq73nnpvTrz+W2MIRwOEznw4FMkyRwOB06ns9Pf+KhY70dXg0nkL8u2c9PfPgLgzBMG8NR3TsG2e/brwlAkyqI1e8hOdVFZH8DjtCnM8jK+JKtdoSmdU9ng55n3t/PQ67GTiQeke0hx23xtSinXnD2ix/cXjZput4nQnkoCG9aT/pnP9FBUx1bd3/9O3Usv4yzIJ7J3H94TJ+LIysKZk0PGrFlgWYSrqtj32GNkfPazh3yevo8+ovHtd2In3bpcAJ0aFx+uqSG4cSOekSOTdmDfGSYUip1DEgphAgH86zfEvj2ZOxeAlKlTsN0eHNlZWN4UAp9+ijMvL3aOy9gx1P7vX3EPHYqzsAATCmHZjtg3SmlpmHAEO8ULTifOgQPjJ2Y7srJofOcdap79C95RI3HmF+AZOZK0M04ntLscEw7hSEsj2txM09KlVN5zb7uTs9vKmDkzdn5LQSHO/IGx5+Tzxc4N8Hi6dA5DW1G/n/CePZhIFBMK4Vu1irp/vEi0vgE7JQVHbi7RxkaCu3biGVpGzmWXEamuIhoI4CoowL8uNlzPBPxEm5poXrYsdrGC3FwcOdkYn49IbS1Rnx/L4yFlwnjcQ4bgyM3DO2pk7ICriwfnkbo6oo2NsfNJehl9fkt/omK9n7/Zh/7XywdNO3t0PiMLM/jeZ4aRnerCH4qS4u74H/zGykb21PsZNjANCwu30yY3zc1HO+v42d8/ZtWO2oTrpbkdjCjIwLYgasC2YPO+JhyWxT1fmcg5YwuoagxQ1RQkzeMk1eUgJy1xL7YxhrXlDfz42Q/4tLKR6SMHcvrwPD4zYgBjizLZWeNj2dZqqpuCNAcjpHmczBpbQHF2Co5OFKLPf7CTBn+Yy04dctjC1RhDeZ2fhZ9UsKa8ngyvi5MG5zCiIJ10jxOvy0FuB88DoKoxwIc76whHDblpbp5cvIXKBj+/uGAcDyzawJvrKjtc95YLxtIYCDNn4YZ200cVZOB12Vw7cyQzRuXHp6/aUcv26ma8TpuNexup84Vw2TbZqS4mD87m3gXreX9LNTNGDWREQQYl2SlUNwXxhSL8+6mDGZKXlrCHXySR3jJm3L92LdXz/kjd3//epfWcAwfiKi3FPXQoRKNYKV5MKETzsmXY3pTYlatyskmbNi12/wi/n9COHYT3VVHzpz8dnSfTFbaNq6gIz8iRRGprCe7YgXvwYAIbNpA6dWpsaFNxcezKVTt30rx8ORAbNjXoN48c9UuwdoU+v6U/UbHeD9/sxhieencb/1i9m+XbOjjB9QBD8lLJ8DrJSnERChvq/SE+M2IAjYEw85cefBm/CSVZfLTr4Gutp3ucpLod1PtD+EPRQ+5zYIaHfY0B2rZQp20xMMNDQaaXb08bSjhiWLxxH89/sKtTzyOR7//bMIbnpzOhJIu8dDcD0jzxgjwUiVLdFOTUu/afzHfHReM5IT8dXzBCrS/Iym21vL+likZ/mIgx+IIR6v2JL2XZatiANIbnp/PJrjpOLM3m5KG57Khp5sOddazoxGuS4nIwbGAanx2dj2VZPPXuVmqbD3099FY5qS5SXA521/k7tXxnpXucLQcjNiU5KRRkeMnP9PLjs0cc8mBPJFmiwSANr72GiUTIPO88Kn5xS5cL+K6yMzPBGOzUVNLPOouMc87BBPyEq6qJ+pqp+9tzWKkpGJ8fEwrFrrjjcpF+5pk4BwzA8noJV1Ziud2kTjkpds5BTQ24XBCJYqemYoJBmpctI9LUSLSuPn7VqW5xOMi/4QZST56Ku6zsqJ5A3Bn99fNb+icV6/3szb6r1se0X7a/TnlOqot/3fRZXvmwnN//azOfVh7Z1SJcDotI1BA9oEWdP6GI75xZxoiCdDK9sa/9o1HD+j0NLN1STTAcpbzOT6rbQU1zkJc/Km9XeHqcNoHwoQv7tkqyUyjJSaGizs/26v0nF+Wkuhg+MB3btli6pbrD9YfkpbKtqmdOSsrwODmlLJd1FQ3sqvUdfoU2irK8lCcoqL8/fRg/+dyYdtMaA2FmPfA2u+v8FGd58YejfHXKIDbvbWLz3kYmDc7m1Y8q8IUOHrs5IN1DusfB1pbnfPqwPGqag6yraMDttJlQkkVFnZ+9DQGGDkhlQLqHTysb2dvQ+XHJM8cWcO05IxiSl0aqy0F5vZ+8NDdb9jVRUe9n6pAcMlraRluRqGFfY4CmQJgV22qwLYtUtwOvy8GyrdXsqQ/QGAjFv60YkZ9BfoaHSYOzGZDu6XR8/UUkauIHlpMGZ7OpsolPdtfxpcklZKe62VHdzI6aWDv4ZHc9G/Y0MHxgOtmpLnzBCHsbAjhsi/UVDViWxbaqJkpzUxlblMklpw6mJLvvj/f2ffQxltNBw+tvkH7WWTiys3BkZmKiUepffJHKBx7EVTqIrAsubLm+fxOe4SfEhvV4PNS/8ioNCxYAkHLSSbgHD45dUcm2yfrihXhHjepSPNFAAMvlOuLhNwDBnTsxgQDRZh/RhnoCm7cQ+PRTTCRMcOtWQrt34x4yhJRx47Azs8AYXIUFuMvKCG7fwe7Zs9tv0LYpuOlGcv793w/Z295aXhyNK7H0x89v6b9UrB+FN/uWfU28u6mKj3bVEghH8ThtIlHDxspGMlNcnD4sj+9P7/hSh5UNfj7eVUea24k/HMUidu+hFJeDYDiKLxRhUE4qBZmeeIHTOpSjrqXIXbWzljmvracg08NXp5Ty0a5aVu+oY/HG9pe0mzgoizsvmsCEQfvHwEajhhXba3hx1W7y0t2ckJ/Op3sa+euKnUwclMXEQdnU+oLsqvHx/pZqzh6dz43njY4P7fhkdx2rdtRSmpPKKWW5eF1d61Xdsq+JP723jXHFmQwdkMbk0mz8oSjBcJTKBj8/feFj/OEo+xoCFGZ5GZSTQk6qm3PHFXLSkGw8zv37q2sO8e7mfZxYmk1RVspB+7nvtXW88lFFp+IanJvKwAwPK7fXtOvtP39iEReeWMzADA+V9QEG56ZSkpPC3gY/J+RnxJczxtAUjPDX5TtYtq2GXTU+irK8VDUGaQ6FGZKXxmdOGECax8nMsQV4XY7Y1TkM2LZFZX2scM/PTHw91nAkyie76xlfktXh0J6V22sor/WTk+piXUUD44ozOXVY3kHLRVuK5I72ZYzh4131LNm0j6ED0hhblEl5nR/LgjfXVRIIRfGHIzzz/vZO5RZiw6KaghEGpHv47OiB7KzxdfmgoK2pQ3LITHHR4A9R2RDAtix+/oWxnDliAA3+MLtrffhDETxOB6FoFKdt4XLYVNT52VbVxJC8NE4szcYCslJcnR63X+8PkepyYIB6Xwi30054IAKx12zT3iY27Y0NJYtEDfX+MBYwqTSbwiwvpbmpeJ02zgTDjPwtB1/VTUFqmoNUNgTYuq+J2uYQDttiSF4qoYghN81Fgz/Mk4u3sHrn0bu7cIbHiQFGFWYwbEAaXpeDAekeCrM8DB+YzviSrPj/A38oQjhqSPc4CUeiVNT7SffEvr1rW9wFw1H2NgaIRg2luakYY3h/SzVvrN1DbpqHdK+TpVuq+XhXHV85qYRvnjYUr9vmmfe3s2xrNfkZXmqbg4woyKDeFzuwO2vUQCYPPrLrwic6cftAkYYGbK83fk5CX1e/YAF7H3kk4WVnnfn5ZF14AZlf+AImFCa4bRvNS5diQiHs1FTqFyxg4LU/JudrX+vZmFSsSz+iYv0ovNmf+Ndm7nh57SGXmTm2gPHFWTgdFo2BMMu3VrOrxtfloQm2BYZYMdHZYRBO2+KXX5nIV6cM6tK+jneBcIRPdtfzye56dtX4yE51UZydQorLwYSSLAZmeHDYFk2BMF6Xg0jU4LStHj8h93iytyHAb9/exB8Wbzn8wl0wID3Wix6JGiob/LidNkPy0qhsCLCrxse+xp69GsmwgWmcMTyPQCjKmSMGsL2qmT0NfiyseJsIthwsbUzwzVRpbqwdFWenMCDdQ1VjgFU7aqnp5HsWYu/b75xZRprbyYrtNXy4s7bT7/m2LAsGpnuoTHAQ5LQtMlNc5KS6GJSTSr0/RDhiiEQNxdkp5KW5iZrY3+NKsijK8rKtqplfv/Fpwm9tDpSV4mJUYQab9zayrzF2gmdOqqtdHtI9TvIzPUSjBo/Twe46Hw0tw8pKslPYU+8nfODXd0fge/82jB9MH06G18lHu+rYtLcJl8PitGF5NPhD5KZ5DnluydHQmZ7ocCRKUzBCUyBMUyBMKGLI8DpJcTsor/VT0xwkagxD89IYOqBnL8kaaWgg8OlGqn73OxrfeqvT62V/7WsU3X5bj8aiYl36ExXrR+HNvnxrNb9+cyNjCjPITXMTDEf5eHcdtc0h3j/E8IsDpbkd2JZFTpobj9OmKRAmaqCi/vAFvddlx8eEF2V5mVSazeC8VKaPiJ14qRtEyLE06baF8cIyN83NI5dOJifVzaiCDPY0+Jm/dAdZKS4a/WGWbNoXf5/c+5WJXDipGK/LEe+xTtTD3JYxhrc37GVHdTNOh82HO+uorPezakctVU37rwDiddm4bJtAJIrHYdMQCON12ZTmpOJy2KyrqD9oSNfRkOJykJ/pYUC6J96777At1pTXE45EO1XQO+zYELRhA9I4IT+dqqYgq3fUkpfuZk/Ltz2pbgfTThjApacOZvjAdBoDYVJdDiwr9j+lqjHIqMKMIzpR2B+K8Nb6SqIGlm6pxh+K4HLYBMIR6nwhlm+taZf7njB8YBrF2Sn4QxEG56ax8JMKGgL7zxVpHT43ODeVBn+I8SVZZKW42Fnj6/Ck9wM5bYvi7BR8oQi+YIRBOSmcPDSXVLeDvQ0BbNtixbYaUt2O+Gs3IN3DyWW5XDSpmKiB3bU+AuEom/Y2YgEN/jAel83Wfc2s2FZNTXOIQDiCw7KoqPfjdNhMHZKD22lT1Rhkb0OAWl+QVLeTpkC4S0MCB+WkUJjpZUC6h5w0N26HFRv25nXidto4bYvK+gClLe3D7bQZNiAdZ8uQxk17G7Eti5OH5rY778REIuxdsRrf/8yn+aWX2u3TzsrCO3YMgXXrwenAkZFJ6W8fwz14cKfj7gwV69KfqFg/xm/25mCYOa9t4NPKBnzBCJv3NTG2KJOiLC/nTywiJ9VNhtdJ2YC0Q17XOBQx7Kr1keJy4LAtapqDRKKGkpwUmgMRctJc7YaDiCTTnno/myobOeOEAUmNY/PeRtaWNzC+JJPi7JR2hWkkajDGxA8GguEoBkNVY5D7Xlt/0EnMpwzNJRyNMr4ki7w0D2vL6/lgRw3njivkhzNOwO2wSfc6qW4Kxg8+9jUE2LCngXUVDZw+PI+heal8fkJRh8NkIDaUq7zexzPvb6eqKUi620lxdgrjijMZXZRBmttJdqorPlzqQJ0ZtnEsBMNRXvukgn2NAU4anMOQvNhB0ZZ9TexrDDC2KPY/+OWPyslJdWNZkJniYmC6h5EFGVQ1BfhkVz2FWV6GD0zv8KTl3bU+IlHDoJyUhM87EjX89IWP+MuyHe0OxsYWZbKmvP6oPPejweWwSPM4cdp2/P+/07YoG5BGdVOwRw+ObAt+fPZICrM8NAUi2Bbcv2gDDtvi1gvH8ed3tzE1VMkFMyaw05HOx7vqcNgWZ44YwNIt1XxnWhluZ89eLUrFuvQnKtb1ZheRLmgMhEn39J5L2MmRMcaws8ZHRb2fsUWZpHmcBMIR/m/jPkYXZlJR76emKUhTMEJxlheP08H7W6qY+39bKclOYdoJA4hEozQGIowoSGdAuodAOMKq7bU80WbYV4bXiW3FhkuNKcrA44x9Y5ridvDZ0QMpyU7F67IJRQx5aW5W76xlT72f7BQ3uWluapqDhCKGyYOz41dcSvM42xW/oUgUYyBqTPxclw931tEcjLQU7gFqmkJUNvjxh6I0BcL4QhEiUcPuWh+haBSXw8aY2EUIjDHYloXX5cC26NJwrUQ+OzqfP1w+tUcPGvX5Lf2JinW92UVEpAc1+EOEIiZ+5aK+5MBx83XNIf68dBtLt1RTWR847LcPowoyGFmYwabKRtaU12Nb8NvLpjBrXGGPxqnPb+lPVKzrzS4iItJp9f4Qu2t9jMjP4J0NexlfkoVtQXaqu92VqPyhCE7bOux5JkcUgz6/pR/Rd7kiIiLSaZleF5mFsfMsZozO73C5vvatgkhvpfuDi4iIiIj0UupZPwoidQECm+ug9dtAy8Jy2rG/bQtHhhtnnheiBpw2lgUmFI09IgYTbLlesQUmSmy5litVALELqwO0/du0/mLFDsEsCyywbCv2eySKiYQhEsaEQmBscLiJBiItuzJgmdgObYNFNLa+FYWwD5rrMMGm2HRiz4NIABxuCAch0AjBJgg2xv62bLBdGFcmJmSIhi1M2MKEbaJhGxN2EPZ5iYbcmIiN7YyAFY0tYyxc2VEsrzuWi2iUlmDABsthYTktLNvCmAhETGwZE8VEYj9jl3kwWI5YXkzUwoQdsRy1vA6Ww8aZbePISsFKy4jfwMRK8WClZ4GJYJkwmDBYDnA4wekGbybYduzlNRHw10KoCUJ+iASJBWnH1otEgGhsXcsZy4nlxPa4YrcHtxyxdbwZsdiiEaxoCBMNQWj/62V8IaKhEMYfIeoLAxFMfQ1Ew4DBtnzY2Vm4hhSD7Y41DdPyekaCEGzAchistCzwpkPADy4vBBoxDdWYcCS2/0gUopFYe3OngbFi60cimGgkNi8SieXG7YHUTKxIAEwIbEfsLotOF9gOsFzgcGB53VgpmTBgVGyfwabYdkwUIqHY9t1p4E6P5Q1iba51fjQC0VDL7+HY360Nv3X9SDD2u+2MtUnbjuXWduz/6UrZPx/Ak9myvZaHicb2b9kt69kJHsm/qkq3GXN8PA8RkX6izxfrjz76KPfddx/l5eWMGzeOhx56iM985jNJjSm4eiXVr4QPv2Cfc+Dl5VpvGOIBMuhJoaN3k8UOBFoeAI1AVQ9vP9Jm+93lbHkkOmErAvgSTG99fQzQ0PJ76xUejuxOjp0XxMFmnPZbuJx7MBEwxkXsACwSO1Akyv6jzmibaTZRPBjjAVwYbCyCWIRiB5fxZfdvw2q3Ldpss7XItzAkLlbbb6t1XYNlGfZv04rt27IAKzbPAssKY1lhItEMiNr7t2O1j9GywmBFYgdCFmCcGJzEj+6NwRhHy7LRNutF28RmAc6W52G3/B37vd0007p8LAaDCxOOYnABzpbn0D5XVpvn2X777N++ifdEtDxC2DTtj7P1YCd+kG3FDuQsWqbHDo6s+P6jLc/ZBtvdEnsEE38edpvfowe8Hge8Tq3fF1tWbPt2y0GW1RKTbceOu7wuLKfBdoRbUtWSD6cXnC4sE2qT+9aDfBvL7cRyODEOTzweoqYl1yZ2YO7OjM2LhiEcwPhqYweTpqVjgZZOiGAThA78v2DAam1/rW1if9uI78fQ8lq0rGPbLU3SOuhhOVp/t9u8Lm0OQuM5slo6eGI5wtHSOeJyQXo2ONxYjpaDcW82pGTHfrrTwJsFYT+EfNBcBQ5X7AC5aR9kFEF2KSJyZPp0sf6Xv/yFa6+9lkcffZRp06bx+OOP87nPfY41a9YwuIdvwNAVoc1L8fi2Auz/sLOcLf8IPeDOif3dwpjWD7wIGINl/LEeW6vlg8C0+WAybT+g4MAPq/0fni2Plg9Dy4r1hsa2EYntC1p6ZqMHrNP6D721J7Hlg8KC9iOnrNi6B9Q9+z/sTazH1USAILbxYeHHouWn8WFbNS09mi7AYNt+LMsQJZVoNAWrtbhp+WCyrGjLh7YTg+OAD3ELyw62z5FltYQXbYmj9XlYGNwYKwtjpYDV0huNDXgwlju2/fgKsXyY+Fum/ZOO7wNaisi2r5ENprXwac2lm4OLodbfI/u/wWgtPE0w1lNPONY+Wr452R+XB0M6xvK22V6b3LQuZw6I24pi0eZ6zCbcUghHaV9Am3ava+trEHtuTozVdmxq2+fuwODFGC+h6GBCwROItb3Wwrl9rO3azgHFdmJt2/uB00iQ49btHXhevYkvffA8q+X5tubabpOP1vd4637BovU91cpuM79tO2h9HHiAkiiGQzEHxN36c//+Ds5r230eyGq3lbbTDv17Z5ZPvIzp0vJHvs/2r5XrENuSDpnYN3s2DdimHIsmYp8x4dj/R8tqaXEW4AAcpI5ZR8bl30pu3CJ9WJ++Gsypp57KSSedxGOPPRafNmbMGC666CLuvvvuw65/tM4mf/nhH+D2Pd9j2xMRkZ5n2nzLsv+DUAV8IlFsopbd8t2VTRQbY8UOZKPxA1XT7hCsVSQ8gov/6+UejUdXg5H+pM/2rAeDQVasWMF//dd/tZs+a9YslixZknCdQCBAILD/K8f6+qNzt7o3HbCwYOBR2baIiEhfMqWxlouTHYRIH9Zni/V9+/YRiUQoKChoN72goICKioqE69x9993ceuutRz22cN5IonU7j/p+RPoSi2j7oUDtmPiAkLZrHJkDt7J/iEtHY9X3R3Cw/esfvO3ObqfjgSWtY8MPHcOR7HP/fg4crtN7JR6W05n12jr8NmIDow4eWpWoV1hiWtvr/oFc+3/uH4KZOHN7nRqvLtIdfbZYb3Xg7YuNMR3e0vgnP/kJ119/ffzv+vp6Skt7/p/Ir75+HXBdj29XRERERPqXPlusDxgwAIfDcVAvemVl5UG97a08Hg8ej+dYhCciIiIi0m199qZIbrebKVOmsGjRonbTFy1axBlnnJGkqEREREREek6f7VkHuP766/nmN7/J1KlTOf300/nd737H9u3bueqqq5IdmoiIiIhIt/XpYv3rX/86VVVV3HbbbZSXlzN+/HheeeUVhgwZkuzQRERERES6rU9fZ727dJ1WERGRvkef39Kf9Nkx6yIiIiIixzsV6yIiIiIivZSKdRERERGRXkrFuoiIiIhIL6ViXURERESkl1KxLiIiIiLSS6lYFxERERHppVSsi4iIiIj0UirWRURERER6KWeyA0im1pu31tfXJzkSERER6azWz+1+fBN26Uf6dbHe0NAAQGlpaZIjERERka5qaGggKysr2WGIHFWW6ceHpdFolN27d5ORkYFlWckOp0fU19dTWlrKjh07yMzMTHY4vYby0j3KX2LKS89TTjum3OxnjKGhoYHi4mJsWyN65fjWr3vWbdtm0KBByQ7jqMjMzOz3/8wTUV66R/lLTHnpecppx5SbGPWoS3+hw1ERERERkV5KxbqIiIiISC+lYv044/F4+MUvfoHH40l2KL2K8tI9yl9iykvPU047ptyI9E/9+gRTEREREZHeTD3rIiIiIiK9lIp1EREREZFeSsW6iIiIiEgvpWJdRERERKSXUrEuIiIiItJLqVg/ju3Zs4fbbrst2WEkXSgU4oUXXuC+++7jT3/6E01NTckOqU9Tu4pRuzr61Nb2U3sT6b906cbj2OrVqznppJOIRCLJDuWYOuOMM3jllVfIzs5m7969nH322axfv54hQ4awY8cO8vPzWbJkCSUlJckOtU9Su1K7Olb6a1sDtTcR2c+Z7ADkyH344YeHnL9+/fpjFEnv8t577xEMBgG4+eabcTgcbNu2jcLCQqqqqrjwwgv5+c9/zh/+8IckR9o7qV0lpnbV89TWOqb2JiKt1LPeh9m2jWVZJHoJW6dbltXveqVs26aiooL8/HxGjRrFAw88wPnnnx+f/9Zbb/Htb3+bLVu2JDHK3kvtKjG1q56nttYxtTcRaaWe9T4sLy+Pe+65h7PPPjvh/E8++YQLLrjgGEfVO1iWBUBtbS1lZWXt5pWVlVFeXp6MsPoEtauOqV31LLW1Q1N7ExFQsd6nTZkyhd27dzNkyJCE82traxP2WPUHV1xxBR6Ph1AoxLZt2xg7dmx8Xnl5OdnZ2ckLrpdTu+qY2lXPUls7NLU3EQEV633a97///UNeEWDw4MHMnTv3GEbUO1x++eXx37/4xS/S2NjYbv7f/vY3Jk2adIyj6jvUrhJTu+p5amsdU3sTkVYasy79TlNTEw6HA6/Xm+xQ5DiidiXHktqbSP+hYl1EREREpJfSTZH6uJ07d3LzzTczY8YMxowZw9ixY5kxYwY333wzO3bsSHZ4vdKOHTv4zne+k+wwejW1q65TuzoyamtHRu1NpP9Qz3oftnjxYj73uc9RWlrKrFmzKCgowBhDZWUlixYtYseOHbz66qtMmzYt2aH2Kv35RiudoXZ1ZNSuuk5t7cipvYn0HyrW+7CTTz6ZM888kwcffDDh/Ouuu47FixezbNmyYxxZcr344ouHnL9582ZuuOEGfch1QO0qMbWrnqe21jG1NxFppWK9D0tJSWHVqlWMGjUq4fx169YxefJkfD7fMY4suQ51o5VW/fVGK52hdpWY2lXPU1vrmNqbiLTSmPU+rKioiCVLlnQ4/91336WoqOgYRtQ7FBUV8be//Y1oNJrwsXLlymSH2KupXSWmdtXz1NY6pvYmIq10nfU+bPbs2Vx11VWsWLGCmTNnUlBQgGVZVFRUsGjRIp544gkeeuihZId5zE2ZMoWVK1dy0UUXJZx/uN6q/k7tKjG1q56nttYxtTcRaaVhMH3cX/7yFx588EFWrFgR/zrU4XAwZcoUrr/+ei6++OIkR3js/etf/6KpqYnzzjsv4fympiaWL1/O9OnTj3FkfYfa1cHUro4OtbXE1N5EpJWK9eNEKBRi3759AAwYMACXy5XkiOR4oHYlx4ramohIYhqzfpxwuVwUFRXx1ltvEQwGkx1OrzN//vxD3tZcElO7OjS1q56jtnZ4am8i/ZN61o8zmZmZrFq1imHDhiU7lF5Feeke5S8x5aXnKacdU25E+if1rB9ndOyVmPLSPcpfYspLz1NOO6bciPRPKtZFRERERHopFevHmVdffZWSkpJkh9HrKC/do/wlprz0POW0Y8qNSP+kMesiIiIiIr2UetaPY2vXrtWJSAkoL4e3evVq7rjjDh599NH45fRa1dfX853vfCdJkSWX8nJs6b3aMeVGpP9Qz/pxbPXq1Zx00knxG41IjPJyaAsXLuSCCy5gxIgRNDQ00NzczP/8z/8wY8YMAPbs2UNxcXG/y5/ycuzpvdox5Uak/3AmOwA5ctdff/0h5+/du/cYRdK7KC/dc8sttzB79mzuvPNOjDHMmTOHCy+8kP/93//t8G6K/YHy0vP0Xu2YciMirdSz3oc5HA4mTZpEZmZmwvmNjY2sXLmy3/W8KC/dk5WVxcqVKxk+fHh82vz587nyyiuZP38+p5xySr/sQVZeep7eqx1TbkSklXrW+7ARI0Zw3XXXcdlllyWcv2rVKqZMmXKMo0o+5aV7PB4PtbW17aZdcskl2LbNN77xDe6///7kBJZkykvP03u1Y8qNiLTSCaZ92JQpU1ixYkWH8y3L6pc30VBeumfSpEn885//PGj617/+dZ544gmuueaaJESVfMpLz9N7tWPKjYi00jCYPqyiooJAIMCQIUOSHUqvorx0z/PPP88777zDgw8+mHD+/Pnz+d3vfpewcD2eKS89T+/Vjik3ItJKxbqIiIiISC+lMesiIpJ027Zto6KiAsuyKCgoUI9yG8qNSP+mMevHsdWrV+NwOJIdRq+jvHSP8peY8nJkHnzwQUpLSxk2bBinn346p512GsOGDaO0tJSHHnoo2eEllXIjIqCe9eOeRjklprx0j/KXmPLSNbfffjtz5szhv//7vzn33HMpKCjAGENlZSWvvfYat9xyC42Njfz0pz9NdqjHnHIjIq00Zr0P+/KXv3zI+XV1dbz11lv97jq8ykv3KH+JKS89r7S0lIcffpiLLroo4fznn3+eH/3oR+zatevYBtYLKDci0ko9633YP/7xD2bOnElBQUHC+f21aFBeukf5S0x56XlVVVWMGjWqw/kjR46kpqbmGEbUeyg3ItJKPet92MSJE/nxj3/Md7/73YTzW2+a0d+KCOWle5S/xJSXnnfWWWcxaNAg5s2bh9PZvu8oHA5z+eWXs2vXLt56663kBJhEyo2ItFLPeh82ZcoUVq5c2WHx4PF4GDx48DGOKvmUl+5R/hJTXnreww8/zKxZs8jPz2f69OkUFBRgWRYVFRW88847eDweFi1alOwwk0K5EZFW6lnvwwKBAJFIhNTU1GSH0qsoL92j/CWmvBwdDQ0N/OlPf+K9996joqICgMLCQk4//XQuvfRSMjMzkxxh8ig3IgIq1kVEREREei1dZ/04c/7551NeXp7sMHod5aV7lL/ElJeep5x2TLkR6Z9UrB9n3nnnHXw+X7LD6HWUl+5R/hJTXnqectox5Uakf1KxLiIiIiLSS6lYP84MGTIEl8uV7DB6HeWle5S/xJSXnqecdky5EemfdIKpiIiIiEgvpZ7141hTUxPvvPNOssPodZSX7lH+ElNeRETkaFCxfhzbuHEjM2bMSHYYvY7y0j3KX2LKS9eFQiFuvPFGTjjhBE455RTmzp3bbv6ePXtwOBxJii65lBsRaaViXUREkuLOO+/kqaee4qqrrmLWrFlcd911fP/732+3TH8dqanciEgrjVnvw3Jzcw85PxKJ0NjYSCQSOUYR9Q7KS/cof4kpLz1vxIgRPPjgg3zhC18AYNOmTXzuc59j2rRpPPnkk1RWVlJcXNwvc6rciEgrFet9WFpaGj/4wQ+YMGFCwvnbtm3j1ltv7Xf/zJWX7lH+ElNeel5qaipr1qxh6NCh8Wm7d+/ms5/9LFOnTuXee++ltLS0X+ZUuRGRVs5kByBHbtKkSZSWlnL55ZcnnL969WpuvfXWYxxV8ikv3aP8Jaa89LzCwkI2bdrUriAtLi7mzTffZMaMGR3muj9QbkSklcas92Hnn38+tbW1Hc7Pzc3lW9/61rELqJdQXrpH+UtMeel5n/3sZ3nmmWcOmt5alG7duvXYB9VLKDci0krDYEREJCm2bdvGunXrOPfccxPOLy8vZ+HChf2yF1m5EZFWKtZFRERERHopDYM5DkSj0Q6nb9++/RhH03soL92j/CWmvPQ85bRjyo2IqFjvw+rr67n44otJS0ujoKCAX/ziF+2uDLB3717KysqSGGFyKC/do/wlprz0POW0Y8qNiLTS1WD6sJ/97GesXr2ap59+mtraWu644w5WrFjBc889h9vtBvrnTTOUl+5R/hJTXnqectox5UZEWmnMeh82ZMgQ/vjHP3LWWWcBUFVVxfnnn09WVhYvvvgitbW1/fKmGcpL9yh/iSkvPU857ZhyIyKtNAymD9u3bx9DhgyJ/52Xl8eiRYtoaGjg85//PM3NzUmMLnmUl+5R/hJTXnqectox5UZEWqlY78NKS0tZu3Ztu2kZGRksXLgQn8/Hl770pSRFllzKS/cof4kpLz1POe2YciMirVSs92GzZs1i7ty5B01PT0/ntddew+v1JiGq5FNeukf5S0x56XnKaceUGxFppTHrfVhNTQ27d+9m3LhxCec3NjayYsUKpk+ffowjSy7lpXuUv8SUl56nnHZMuRGRVirWRURERER6KV26sY9ramrimWeeYcmSJVRUVGBZFgUFBUybNo1LLrmEtLS0ZIeYFMpL9yh/iSkvPU857ZhyIyKgnvU+bc2aNcycOZPm5mamT59OQUEBxhgqKyt5++23SUtLY+HChYwdOzbZoR5Tykv3KH+JKS89TzntmHIjIq1UrPdhM2bMoLCwkD/+8Y/xm2S0CgaDXHHFFZSXl/PPf/4zSREmh/LSPcpfYspLz1NOO6bciEgrFet9WGpqKsuXL++wZ+Xjjz/mlFNO6XfX41Veukf5S0x56XnKaceUGxFppUs39mE5OTl8+umnHc7fuHEjOTk5xzCi3kF56R7lLzHlpecppx1TbkSklU4w7cOuvPJKLr/8cn76058yc+ZMCgoKsCyLiooKFi1axF133cW1116b7DCPOeWle5S/xJSXnqecdky5EZE4I33aL3/5S1NUVGQsyzK2bRvbto1lWaaoqMjcc889yQ4vaZSX7lH+ElNeep5y2jHlRkSMMUZj1o8TW7ZsoaKiAoDCwkLKysqSHFHvoLx0j/KXmPLS85TTjik3Iv2binURERERkV5KJ5j2cT6fj8WLF7NmzZqD5vn9fp566qkkRJV8ykv3KH+JKS89TzntmHIjIoDGrPdl69evN0OGDImPZ5w+fbrZvXt3fH5FRYWxbTuJESaH8tI9yl9iykvPU047ptyISCv1rPdhN910ExMmTKCyspL169eTmZnJtGnT2L59e7JDSyrlpXuUv8SUl56nnHZMuRGRuGQfLciRy8/PNx9++GG7aVdffbUZPHiw2bRpU7/teVFeukf5S0x56XnKaceUGxFppeus92E+nw+ns/1L+Jvf/Abbtpk+fTrPPPNMkiJLLuWle5S/xJSXnqecdky5EZFWKtb7sNGjR7N8+XLGjBnTbvrDDz+MMYYLL7wwSZEll/LSPcpfYspLz1NOO6bciEgrjVnvw770pS8xf/78hPMeeeQRLrnkEkw/vDKn8tI9yl9iykvPU047ptyISCtdZ11EREREpJdSz7qIiIiISC+lYl1EREREpJdSsS4iIiIi0kupWBcRERER6aVUrIuIiIiI9FIq1kVEREREeikV6yIiIiIivZSKdRERERGRXur/A/HidbQsOqQeAAAAAElFTkSuQmCC\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":"> Difference to full dataset is that the currencies seem to be going down but bitcoin remains dominant ","metadata":{"slideshow":{"slide_type":"skip"}}},{"cell_type":"code","source":"#let's plot again without bitcoin\nplot_close(df_reduced,['Bitcoin Cash', 'Binance Coin', 'EOS.IO','Ethereum Classic', 'Ethereum', 'Litecoin', 'Monero', 'TRON','Stellar', 'Cardano', 'IOTA', 'Maker', 'Dogecoin'])","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":43,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":"> Difference to full dataset is that the currencies seem to be going down, however they still look quite correlated","metadata":{"slideshow":{"slide_type":"skip"}}},{"cell_type":"code","source":"check_stationary('Bitcoin',df_reduced,168)","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":44,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":"> Stationary as 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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":"> Same lag pattern","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"code","source":"quick_visualize(df_reduced,'Bitcoin')","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
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DAO8+5SY3cOBAwetWrVrBzc0NUVFRgumWlpZo167dO+MdPHgQhoaGpZ5q2bdvHziOw6BBgwTr6ODggEaNGim9s/Bd4uLikJiYiMGDB0Mme7ubmZqa4qOPPkJsbKzCKa+PPvpIrTYWLVqEs2fPKvwr/pm+S/fu3QWvS35e5dn3Sq6HGNtXrmvXroLX8toRHBysMP3FixeinQJXhuqd+HEBoG/fvkr3g0WLFr3zvfL61bdvX8H0Pn36QFdX+S0H79pn3qVkjRswYAAAKNS4du3awdLS8p3xDh48iICAAP57pMy+ffvQoEED+Pj4CL5TnTp1UrhDvqzk+ZY83dy8eXN4eXkp/L0pa80u7siRIwqf6549e9SKoezzys7ORkpKCgD165Oy9RBj+8opq3EcxwmuOdbV1UXNmjXL/B0UU5lv1KkMVlZWgtf6+vqlTs/OzgYA/rqQkp2l4l68eCEo4MpMmDABgwYNgkwmQ7Vq1eDh4fHOa3aeP3/O32lWnJOTEz+/PMSI+/z5czg4OCisk52dHXR1dRViOjg4KMSQT3v+/DmqV6+Op0+fYu/evdDT01PaZsnhGqytrQWv5XcJZmVllWkdVOVUMndl206Z1NRUODk5CTp2JT19+hSMMZWdMU9PzzK1VZw8X1WfcWFhIV6+fCm4wLus61Q8r6ZNm6qdW3Hv+rzKs++VXA8xtq9ceWqKqalpudsTOzeA6p26bG1tle4H8mFn3pUXAIXvpq6ursK+IVeRGqcsbvGaW5w6Ne5dNx89ffoUd+/eLXMdL4t31biSHRx16xsANGrUqMI36pSlxqlTn5SthxjbV05ZvTA2NoahoaHC9Ddv3pS7ncqi0U5lecm/VKtWrVJ551tZjs5Ur15d7T/C1tbWSEpKUpguvzC7vF94MeJaW1vj9OnTYIwJ/nikpKQgPz9fIWZycrJCDPk0+Y5oY2ODhg0bYt68eUrblP9RqCyqcqpZs6ZgWlkv4Le1tcXJkydRWFiosmNpY2MDjuPw999/Kx0qozzDZ8i3n6rPWCaTKRyFqOybEipDefa9kuuhzvY1NDRUuKkMKCrK2n4XaGWheqc58v306dOncHZ25qfn5+dXqKOrijxu8Y5OyZorp06NK3ljTEk2NjYwMjLCunXrVM5XV/EaV7JTm5iYqBBTG+sboH79V7YeZd2+8o5gyRonxnetqkiiU+nn54dq1arhxo0bKge6FUtgYCB2796NxMREQQdq48aNMDY25ou+ukfkyhpX3Vx37tyJPXv2oFevXoKY8vnFHT16FE+fPuX/QBUUFGDHjh2oUaMGXyS6du2KAwcOoEaNGmU6FVNRW7ZsEZw+PXXqFBISEvDpp5+WK15QUBC2bduGDRs2qDwF3rVrVyxcuBBPnjxROA1WXnXq1IGzszO2bt2KKVOm8IUoIyMDu3bt4u8IF5uBgUGZv5PKVMa+p872dXd3x5UrVwTTbt++jbi4uP9Mp5Lqnea0adMGQNHDCz744AN++u+//y4Y3aIybdmyBePHj+dfb926FUD5x1kMCgrCpk2bEBcXhzp16ihdpmvXrpg/fz6sra3h4eFRrnZKkp8C3rx5Mz8aAACcPXsWN2/eRGhoaKW0Uxp1v4fKVEb9L+v2tbe3h6GhoUKN+/PPP8vVrjaSRKfS1NQUq1atwpAhQ/DixQv06dMHdnZ2SE1NxeXLl5Gamoo1a9aI0vasWbP46wpnzpwJKysrbNmyBfv378fixYv5gVlr1KgBIyMjbNmyBV5eXjA1NYWTk5PKI3lljauOTz75BKtXr8aQIUMQHx8Pb29vnDx5EvPnz0eXLl3Qvn17wfI2NjZo164dvvnmG5iYmCA8PBy3bt0SDCv07bffIjIyEq1atcL48eNRp04dZGdnIz4+HgcOHMAPP/xQqeO+nTt3Dp9++in+97//4dGjRwgNDYWzszPGjBlTrnj9+/fH+vXrMXr0aMTFxSEgIACFhYU4ffo0vLy88PHHH8PPzw8jR47E0KFDce7cObRp0wYmJiZISkrCyZMn4e3tjc8++0ytdmUyGRYvXoyBAweia9euGDVqFHJycrBkyRK8evUKCxcuLNf6FHfnzh2lwzUVH4vP29sb27dvx44dO+Dp6QlDQ0N4e3uXuY3K2PfU2b6DBw/GoEGDMGbMGHz00UdISEjA4sWL+aFD/guo3mlO/fr10b9/fyxbtgw6Ojpo164drl+/jmXLlsHCwqLUy2bKQ19fH8uWLUN6ejqaNWuGU6dOYe7cuQgKCuKHJlPXt99+i4MHD6JNmzaYPn06vL298erVKxw6dAhffPEF6tati4kTJ2LXrl1o06YNJk2ahIYNG6KwsBAPHz5EREQEJk+ejBYtWqjVbp06dTBy5EisWrUKMpkMQUFBiI+PxzfffAMXFxdMmjSpXOtT3Pnz55V+N+rVqwdzc3OYmZnBzc0Nf/75JwIDA2FlZQUbGxu1BsyvjPpf1u0rv3Zz3bp1qFGjBho1aoQzZ87wPyzeB5LoVALAoEGD4OrqisWLF2PUqFFIS0uDnZ0dfHx8RH1aSJ06dXDq1ClMnz4dY8eORVZWFry8vLB+/XpBu8bGxli3bh1mz56Njh07Ii8vD7NmzVI5dltZ46rD0NAQUVFRCA0NxZIlS5CamgpnZ2dMmTIFs2bNUli+e/fuqF+/PmbMmIGHDx+iRo0a2LJlC/r168cv4+joiHPnzmHOnDlYsmQJHj9+DDMzM3h4eKBz586VfvRy7dq12LRpEz7++GPk5OQgICAA3333ncJ1JWWlq6uLAwcOYMGCBdi2bRtWrlwJMzMzNGrUSPBkjR9//BEtW7bEjz/+iPDwcBQWFsLJyQl+fn5o3rx5udoeMGAATExMsGDBAvTr1w86Ojpo2bIloqKi0KpVq3LFLG769OlKp4eGhmLu3LkAisZvS0pKwogRI5CWlgY3N7cyXWtWXGXse2XdvgMGDEBiYiJ++OEHrF+/Hg0aNMCaNWv4p2b8V1C905z169fD0dERa9euxYoVK+Dj44OdO3eic+fOlf44Qj09Pezbtw/jx4/H3LlzYWRkhBEjRmDJkiXljuns7IwzZ85g1qxZWLhwIZ4/fw5bW1u0bt2ar5smJib4+++/sXDhQvz000948OABjIyM4Orqivbt25f7qUVr1qxBjRo1sHbtWqxevRoWFhbo3LkzFixYoPKaVHWoevpRZGQkf5Bk7dq1+PLLL9G9e3fk5ORgyJAhSseBLE1F678623fZsmUAgMWLFyM9PR3t2rXDvn373psn5XGMMVbVSRDN4zgOY8eOxffff1/VqQAoGvh46NChOHv2bIVvPiGEkIo4deoU/Pz8sGXLFv7u7IoKCQnB77//rtERCAjRNMkcqSSEEEIqW2RkJGJiYtCkSRMYGRnh8uXLWLhwIWrVqoXevXtXdXqESAp1KgkhhPxnmZubIyIiAitXrkRaWhpsbGwQFBSEBQsWKAzbQggpHZ3+JoQQQgghFabRJ+oQQgghhJD3E3UqCSGEEEJIhVGnkhBCCCGEVBjdqCOSwsJCJCYmwszMTGsfT0WkjTGGtLS0dz7bnBCxUJ0jYqM6Jy3UqRRJYmIiXFxcqjoN8h/w6NGjSn2qESFlRXWOaArVOWmgTqVIzMzMABTtCObm5qK2lZNfgCZzjqicv2NkS9R3fvuoq8cvMuFgYQhdHXF+9R2/nYLvjtzBmoFNYG9BQ3KI5c2bN3BxceG/a6oUFBQgLy9PQ1m9//T19emIyb80Weey8wrQdK7qOrchpBmaerx98lZmbj6M9cX7E7fn4hMsi4jDn+Naw8pEX7R2/uuozolLT08POjo6lRaPhhQSyZs3b2BhYYHXr19XWrHNyMmHsb6Owmmm1ouO4fHLrDLFsDMzQEpaDuzMDHAmtP2736Cm8Oi7WHwojn8dvzAYeQWF0BOpA/tf9q7vGGMMycnJePXqleaTe4/JZDJ4eHhAX586EmLUudz8QujrKtYL92n7yxzD0lgPLzPz8IFrNfwxxq9S8ipu9t7rWP9PPP86fmEwGGN0CYAIqM6Jr1q1anBwcKiU7y8dqZSAmHvP0f/nWP71zK710LG+PZyrGaHuN4eQk19Y5lgpaTn8f++lpqOGrWml5lq8Qwm8/UPQv7kLFvRuWKltkdLJC62dnR2MjY3pD14lkF9DmJSUBFdXV9qmlejozacY/us5/vXPnzRFM3dLVDPWV6tDCQAvM4uOWF14+ArP03NgbWpQqbkW71ACb+vchMBamNShdqW2RUpHda78GGPIzMxESkoKAMDR0bHCMalTqeWy8woEHUoA+HbfDXy774bCsudntIepoS7m7ruJTbEJ74y99uQDzO/lrVY+UXEpGLr+LADg9twgwRGFMw9eqHzftjOPVHYq07Lz8PRNNmralX56g5RdQUEBX2itra2rOp33iq2tLRITE5Gfnw89Pb2qTue9kJVbIOhQAsCIjeeULnttdifkFxRi4cFb2H720Ttj/3jiPqZ38VIrn8gbT/n278/vApnsbUfl+O1Ule/77ugdlZ3KjJx8pOfkw96cLgmqLFTnKs7IyAgAkJKSAjs7uwqfCqdOpZYYvPY0/r7zDABQz9EcQQ0cMMTPne/Avcu+z1vzv8bn9GwAZ0sjeDtb4OqT11h48BYGtXSFUzUjwZHE609eK40Vuvsqtpx+CEtjPfwxxg8eNibYfuYhGjhbYOvph/xy5+JfoFVNGwDA/dR09P0xhp8n44DCEhdWFD89FJechk4rTwjm//xJU3SoZ1+m9SWlk19bZGxsXMWZvH/kp70LCgqoU6kGxhgGrz2Dk3eL6lzbOrYI9nZEcENHfPxT7DveXSRiUhuYGhT92Vr4UUN42Jigsasldp1/jB3nHmFUG08kvc7GX5cT+ffcTUlXGkte5wDgbGh72JoZ4PD1ZHg7W2DxoVv8cjeT36C+U9E16XeepmHIujNlXud7qekIXHZcMG1dSFO0q0t1rjJQnasc8u2Xl5dX4U6lpC90W7BgATiOw8SJE/lpjDGEhYXByckJRkZGaNu2La5fvy54X05ODj7//HPY2NjAxMQE3bt3x+PHjwXLvHz5EoMHD4aFhQUsLCwwePDgSrtmo7CQ4dGLTGw5nYD4Zxk4fjuV71ACwI2kN1gWeRsNwyJwPuElP33T8Obo11TxTsuISW3QoNiNOAAw2r8G/GraYLR/DVwJ64i5Pb0xpm1NxC8Mxozgol/tlx+/Rs/V/6DZvCOYs+8G4p9lwH3afr7QvszMQ8DSaPQK/wfT/riKrqtOIvLGU76N2H+PTN5PTUe7EoXz9twghHWrJ5iWnpMPAMgrKFToUALAsog4hWmkYuhUUOXT5DaVao3j28jIxfHbqUhNy8Gha8l8hxIAouNS8eXvV1Bv5mFcefz2B+62ES2hr+Qa7L+/CkBte+HZjFH+NdDcwwoLP/LGpZkd8HUXL/xf/8a4NaczQlq5AwCO3UrBuK0X0G5ZNH4+cR8PStQ5AGg27wg6rTiBUZvOo9XCY7hTrCMae7+ozl178hodVgjr1q05nTGopSv/WkfGofDfX9OZufkKHUoAGLZB+RFYUn5U5yqmMrefZG/UOXv2LPr27Qtzc3MEBARg5cqVAIBFixZh3rx52LBhA2rXro25c+fixIkTiIuL4+8e++yzz7B3715s2LAB1tbWmDx5Ml68eIHz58/zvfSgoCA8fvwYP/30EwBg5MiRcHd3x969e8uUn6qLix+9yMSHi6PUXt/fRvuimfvbOxsLCxlWHbsLK1N9DG7pplasG4lv0OX//lY7h5La1LbF+HY1cfnxa8wpdjo+YlIbQfGXX2/k5WiOm0lvSo3ZzN0SK/r5oLqlMQoKGfILC2GgW3l3pr1PSruAPTs7Gw8ePICHhwcMDel0W2XS1LbV9hoHqP4O3n6aho4rFH84vsuB8R+intPbOPkFhRj26zl4O5vjy0511Yr156UnmLD9kto5lNT7A2dMCKyFjTEJWHvyAT/96GR//pr0vIJC1Ao9CABo7m6FM/GqLwUCgG6NnPBNsBfs6FT4O1GdE19lbkdJnv5OT0/HwIED8fPPP2Pu3Ln8dMYYVq5cidDQUPTu3RsA8Ouvv8Le3h5bt27FqFGj8Pr1a6xduxabNm1C+/ZFdz9v3rwZLi4uOHLkCDp16oSbN2/i0KFDiI2NRYsWLQAAP//8M3x9fREXF4c6deqUO3dzo9JPl7XwsMLpEtcmftraQ9ChBACZjMOE9rXKlUPxoq2KroxDfsnz1yWcuJ2KEyWuL7o8qyMsVKzjuzqUAHA2/iUClx3HqDae+L9jdwEUHbnwrVF0vczrzDzcSn4DM0O9UtfjdVYeLIz0MGrTORy+/lQwr5adKe4/y0C3ho5Y0LshjPSp00q0i5RrHIByXTcY0spdYZ/W1ZFh47Dm5cqheyOnSulU/nHhCf648EQwrWSdKz66xbs6lACw93IiLj96hW6NHLE66h4AIGpKW3jYmAAAXmTk4vbTNNibG/LTlCksZJDJOHyy7gxfi92sjZHwPBMhrdzx4FkG6jqa4cuOdUQbQo6IZ8OGDZg4caJaZxBCQkLw6tUr7NmzR7S8SiPJb9nYsWMRHBzMF0y5Bw8eIDk5GR07duSnGRgYwN/fH6dOnQIAnD9/Hnl5eYJlnJyc0KBBA36ZmJgYWFhY8MUWAFq2bAkLCwt+mfKyMNLDX+OUD3FxbXYn7Bjli8uzOmJ6l7r4a5wfHizoghld6yldviJuzw1SOe/WnM64My8Io9p4Cqa397LHV53rIFTFRe8ffVBdaYeyUXULJUsXiV8YjLvzhLnk5BfyHUoA/I1KKWnZaPRtBPr9FIsu//c3Nscm4Hl6Dr7YcQk7zhadyur/Uyzcp+1Ho9kRcJ+2X6FDCQB3UtJRUMiw51Iitp99qDCfaIdHjx5h+PDhcHJygr6+Ptzc3DBhwgQ8f/68qlMTnZRrHFBU5+b0qK903r35XRC/MBgxX7fDZ21rYPPwFohfGIyw7sqXLy+O43BtdieV8y9+0wG35wbxp8nlatiaYFQbT3ziq/wM0NiAGkrrnEzFGcRGLtUQvzAY8QuDBdMfvsjkO5QAELA0GkDR5UQfzInExz/FImBpNK48foXsvAKsOnoHsfeLvvudVpyA+7T98Jx+AO7T9gt+3Cc8zwQAbDgVj+O3U/Hj8fs4cjNF5XYgVSMkJAQ9e/ZUmB4dHQ2O4/Dq1Sv069cPt2/f1nxyFSC5I5Xbt2/HhQsXcPas4g0sycnJAAB7e+FF0Pb29khISOCX0dfXh6WlpcIy8vcnJyfDzs5OIb6dnR2/TEk5OTnIycnhX795o/qoXMPq1XDz287wmnmIn9a/uQt/AbqFkR5Gtqmh8v2VQV9XhgcLuuDxyyxcePhS8IveUK/oyN3XXbxQzVgfiw7dwtdBdTHKvyinuOQ0zDtwUyGmmaHyr9OE9rWUXkdk8++NRbo6Mhz5wh/tlytefyR35sELvqDKzdhzDTP2XAMA/HHxCabuulrKGis3e+8N+Ne2hWcZhlai8TY15/79+/D19UXt2rWxbds2eHh44Pr16/jyyy9x8OBBxMbGwsrK6t2BJEhbaxygXp0b7OuO3h9UR/1Zh/lpw1t7QOff3pejhRGmdlbvlLa6TA10ETe3Mx48y8Cha8lYeeQOP8/y3wHLw7rXx/XE1zgb/xKT2tfmzwD9fScVG2MUR9Hwdq6mtK2FvRviq11XFKbbFBsYfc9YP/Rc/Y/KfO88TcP+q0mCad2/V718WY3efB7nZ7Sv9KGViLiMjIz4u7OlQlJ/IR89eoQJEyZg8+bNpZ73L3nRaVkGpS25jLLlS4uzYMEC/oJ3CwuLdz66zEhfByv6NeJfj/jQs5SlxcFxHFysjNG9kRMM9Yq+CgcnfChY5rO2NRC/MJjvUAJAHQczHJr4If4c64etn7aAh40J2taxxeftaiptp/jNRp/4uqHVv6eyd4xqyU+vaWeKK2EdFd4r1/fHGCyPLP8vtqthHbHv89bYOKw5fhzcBH9/FcDPa7fsODb/OwTT98fu4PtjRX94nr7Jhvu0/XCfth8BS6NRK/Qg3Kftx8w/r5U7D1I2Y8eOhb6+PiIiIuDv7w9XV1cEBQXhyJEjePLkCUJDQwEA7u7umDNnDgYMGABTU1M4OTlh1apVglivX7/GyJEjYWdnB3Nzc7Rr1w6XL1/m54eFhcHHxwebNm2Cu7s7LCws8PHHHyMtLU2j6wxod40D1K9zJga66P2BM/+6XzPNP9LRQFcHdR3MMb7d28uFjn/ZVrDMb6NbIX5hsOCSog9r2WLriBb4fbQvfh/ti/7NXbG8byN0qq/8zu0tp992QL/pWg/N3Is69Uv/97bO+7hUw6lp7VTm2mHFCUHHVx22Zga4GtYRWz9tgRnBXljSpyH+r39jfn6TuUdwPbHohqgjN57i7L+n6R8+z+TrXL8fY/j/33aGzuJUtQ0bNqBatWqCaXPnzoWdnR3MzMzw6aefYtq0afDx8VF479KlS+Ho6Ahra2uMHTtWY08bktSRyvPnzyMlJQVNmjThpxUUFODEiRP4/vvvERdXdPdwcnKyYBDPlJQU/pe9g4MDcnNz8fLlS8Ev+ZSUFLRq1Ypf5ulTxdOmqampCkcI5L7++mt88cUX/Gv5o6VK06txdbStbYeM3HxUt6y6IRE4jsOtOapPhytT1+HttU9RU9qWuqyR3ttrFvs2dUEdBzNk5OSjmrHwiSTmhnq4NLMDMnIL4Fyt6NdZnRkH1RrcHQAGtXTF3J7euJeajp3nHmGYnwfMDPUU7pAvPuxR8aOeALA0QtiBffAsg///jTEJ+LS1J1ytpTWMBWMMWXkFVdK2kZ7ik6BUefHiBQ4fPox58+Yp/Ep3cHDAwIEDsWPHDoSHhwMAlixZgunTpyMsLAyHDx/GpEmTULduXXTo0AGMMQQHB8PKygoHDhyAhYUFfvzxRwQGBuL27dv80c579+5hz5492LdvH16+fIm+ffti4cKFmDdvXuVuiHfQ5hoHlK/OLe/rg0ntayO3oLDSH7agDpmMUzgF/S6tatjw/9/UvfQj461q2uDyv3exd6xnj2F+7kq/807VjBA9pS1yCwr5GxqVDe7+ruva+zd3wfxe3vjrciLW/xOP7z72gZmhHlrVtOGHemOMYfy2i/x7gv/vZKnrUPx6/q//uIqgBg4KdVrbSaXOlceWLVswb948hIeHw8/PD9u3b8eyZcvg4eEhWC4qKgqOjo6IiorC3bt30a9fP/j4+GDEiBGi5SYnqU5lYGAgrl4VnuIcOnQo6tati6lTp8LT0xMODg6IjIxE48ZFv9Byc3Nx/PhxLFq0CADQpEkT6OnpITIyEn379gUAJCUl4dq1a1i8eDEAwNfXF69fv8aZM2fQvHnRReKnT5/G69ev+aJckoGBAQwM1D+1YGmiz5+GeV/1beaCpRG34VzNiO/YqSpU1Yz1Ua1YX83G1ABPXik+glL+GDag6BqtrLwCtJh3BBm5BZgRXHQNag1bU3wdpHrQ4xvfdkbdbw6pnF+aK09eSa5TmZVXgHozD797QRHc+LZTmZ/DfOfOHTDG4OWl/LPz8vLCy5cvkZpadB2Zn58fpk2bBgCoXbs2/vnnH6xYsQIdOnRAVFQUrl69ipSUFH7/XLp0Kfbs2YPff/8dI0eOBFD0pJwNGzbwd08PHjwYR48e1XinUptrHFD+OudiJa19pTzGBtREzL3naFXD+p3r617i5ht9HRlyC4Q/nvV0ZChgBZCPz3J7bhBeZOSi5YKjAIDZ3RuA4zj08HFGDx9nKMNxHG7PDULtGQfLtU5Xn7zGh7Vsy/XeqiKVOgcA+/btg6mp8IdWQYHqDvGqVaswfPhwDB06FAAwc+ZMREREID1dOBarpaUlvv/+e+jo6KBu3boIDg7G0aNHqVNZkpmZGRo0aCCYZmJiAmtra376xIkTMX/+fNSqVQu1atXC/PnzYWxsjAEDBgAALCwsMHz4cEyePBnW1tawsrLClClT4O3tzV8U7+Xlhc6dO2PEiBH48ccfARQNt9G1a9cK3xX5X2RnZoi4uZ2Vjj33LhuGNuPHhvu0tQei4lIwLcgL9Z3M8dOJ+xjSyh06Mg6mBrq4/m1ntWIb6ukgfmEwuq06iasqBoJf3Kchkl9nY3nkbawZ+AF+OfkA5xNe4vD1p+ja0Ent9SEVJx8FTX5EwNfXVzDf19eXH37n/PnzSE9PV3jaRlZWFu7de3uThLu7O9+hBIoeVyZ/dJkmUY2TLlMDXewZW77njB+a+CE/1m//5i6Ivf8Cc3o0gI2ZPlZH3cOk9rWgryuDg4Uh7s3vAhlX9rEF9XVliF8YXOqjLj9vVxNXn7xGdFwq5vRsgIjryfj7zjOcT3gpuU6llAQEBGDNmjWCaadPn8agQYOULh8XF4cxY8YIpjVv3hzHjh0TTKtfv75gEHNHR0eFH6tikVSnsiy++uorZGVlYcyYMXj58iVatGiBiIgIwR+MFStWQFdXF3379kVWVhYCAwOxYcMGwYewZcsWjB8/nr+Dsnv37vj+++81vj7vi/KONVnL3kxwyqr4nfCVdbfo75/5YtSm8+jh44RejavjyuNXqGVnJhhqaHxg0bVWh68n43zCS+y9nIjAunbo2Vj5EQJtZKSngxvfqr4bVuy2y6pmzZrgOA43btxQenfkrVu3YGlpCRsbG8U3/0v+B7ewsBCOjo6Ijo5WWKb4tUoln4zDcRwKC9W77EJTqMa9fzxtTVWeml9V7LpIAPyNTuq6+E0HfPxTLAb7uqFfMxccu5WCNrVslQ6pdjs5DX/feYaVR+6gjr0Zgrwr/kxoTZFKnQOKfjDWrCm8F6HkQwpKUnY9dUlVWc8k36ks+ceC4ziEhYUhLCxM5XsMDQ2xatUqhQv6i7OyssLmzZsrKUuizQx0dbBh6Nux8BpWr6Zy2b5NXbDnUtEj4CbuuAR3GxP4uCgun1dQiIk7LmH/laI7OXd95os9FxOx89wj/hrRPWP9lL5XLBzHqXVqpqpYW1ujQ4cOCA8Px6RJkwTXVSYnJ2PLli345JNP+OIaGyt8xF9sbCzq1i26q/iDDz5AcnIydHV14e7urrF1qExU40hlsDTRx+FJbfjXneo7qFy2uYcVNv178+JnWy7g9PRApWOP5hcUIvj/TiLuadFNbbFfB+LK41dY/088zsa/QH4hw5nQQNiZaW5gcqnUufKoU6cOzpw5g8GDB/PTzp3Tric0Seru7zVr1qBhw4YwNzeHubk5fH19cfDg22tFpPT4MiJN8kHY5Xqu/gebYuJRUOyC+ufpOagVepDvUALAR2tisCk2QXDTUc/V/wjuGL2bkob1/zxArpo3Jr2Pvv/+e+Tk5KBTp044ceIEHj16hEOHDqFDhw5wdnYWXOv4zz//YPHixbh9+zZWr16N3377DRMmTAAAtG/fHr6+vujZsycOHz6M+Ph4nDp1CjNmzNC6YixHdY5UtaAGwg7n139cRVyycDSE7LwC1Aw9yHcoAaDlgqMYuek8Yu4/528yaj7vqOBGx0cvMnHkxlOlR9hI6T7//HOsXbsWv/76K+7cuYO5c+fiypUrWvWYSkl156tXr46FCxfyh4t//fVX9OjRAxcvXkT9+vWxePFiLF++XPD4sg4dOggeXzZx4kTs3bsX27dv5x9f1rVrV8HjywYMGIDHjx/j0KGimzhGjhyJwYMHq/X4MvJ+4jgO+z5vja6r3t5F+c2f1/HNn2//qJsZlH23Ct19DaG7hUMU3UlJx/xe3hVPVsJq1aqFc+fOISwsDP369cPz58/h4OCAnj17YtasWYIxKidPnozz589j9uzZMDMzw7Jly9CpU9HpL47jcODAAYSGhmLYsGFITU2Fg4MD2rRpU+pdzlWJ6hyparo6Mmz9tAUG/HIaQNHz04/dSoG5oS7eZOfDSE8HwQ3LfkpcPrB714aO2Pfvj+0ZwV74tAqG0pOygQMH4v79+5gyZQqys7PRt29fhISE4MyZM1WdGk+yz/6Ws7KywpIlSzBs2DA4OTlh4sSJmDp1KoCiX+v29vZYtGgR//gyW1tbbNq0Cf369QMAJCYmwsXFBQcOHOAfX1avXj3B48tiY2Ph6+uLW7dulfki9tKeV0qkr7SL3osrOdjxkS/8UdPOFD+duIf5B24pfY+NqT7Ozejwztj0TNyiG2wmTpyIiRMnaqzNqti2VOdIVShrnftjTCv0Dn/7JKYtn7aAX00b/O+HUzgb/1LpexpVt8Cf41q/MzbVudJ16NABDg4O2LRpU7ljVOZ2lNTp7+IKCgqwfft2ZGRkwNfXt8ofX5aTk4M3b94I/pH3V9zcd99pvn1kS/j8+4i223ODEL8wGDXtioaPGNmmBtaHNFP6PnPD0p8PT/47qM6RqnTxm3f/uP1zrB8+cLVE/MJg/DOtHe7MC4Lfv+Nk/ja6Fb4OUv7UJHo6mfoyMzOxfPlyXL9+Hbdu3cKsWbNw5MgRDBkypKpT40nuU7169SpMTU1hYGCA0aNHY/fu3ahXr16pjy8r/mgysR5fpu6TJoi0Gejq8M8tr+tgBicLQ8GTM85MD0RLz7fXX+rrKu5qAXXtEL8wGN8PKLq7c+eooqFx7j/LEFyDRP57qM4RbWBpoo/4hcG4NrsT/xje3sVGvNg0vDkaFbvZ0LmakUJncZR/DdyZF4SmbkXfxxnBRePPnkt4iewqGqRcquSX83z44Ydo0qQJ9u7di127dvFDhWkDSV1TCRTd/XTp0iW8evUKu3btwpAhQ3D8+NtnRlfV48vK86QJIn26OjIcnPAh/92wMtGDq5UJ7JTcKalK14ZO6NrQCXnFBj8OWBqt9tM//ovi4+OrOgVRUJ0j2sTUQBdnQ9tDT0cGHRmHBs4WaOlpjXpOZbvkQU9Hht8/KxpU/2VGLubuvwkAqPvNIapzajAyMsKRI0eqOo1SSa5Tqa+vz1/A3rRpU5w9exbfffcdf31RVT2+rLxPmiDSV/yPcLu65b/5o+Qv/Pup6fCswsfakapDdY5oG8NiYzAOa+1RypKlK/kEudeZebAwpkt+3heSO/1dEmMMOTk58PDw4B9fJid/fJm8kBZ/fJmc/PFl8mWKP75MriyPLyOkMpyf8fY0RsQNxT/65L+J6hx5nxyc8CH//7EPnldhJqSySepI5fTp0xEUFAQXFxekpaVh+/btiI6OxqFDh8BxHD2+jEietenbo0ALD97CwoNFd4hPal8bE9rXUjuetj4VRsrEHjCD6hx533k5vj1tPmrTef7/5/fyxoAWrmrHozpXMZW5/STVqXz69CkGDx6MpKQkWFhYoGHDhvyAyAA9voy8Hwa1dMXm2IeCaTn56l3Qrq+vD5lMhsTERNja2kJfX1+rBsiVKsYYUlNTwXGcwqPQKgvVOfJf4FzNCE9eZQmmvcnOUysG1bmKYYwhNzcXqampkMlk0NfXf/eb3kHy41RqKxq/jZRXTn4B6sw4JJj2WdsamNpZODTHu75jubm5SEpKQmZmpqj5/tdwHIfq1avD1JSud6U6R8rrVWYufL6NFExTNiA61TnxGRsbw9HRsVI6lZI6UknIf4GBrg7a1LbFidupAIqewzulo/qnJPX19eHq6or8/HwUFNDQHZVFT09PcMSPEKK+asbCDoylsR6GtHJXOw7VuYrR0dGBrq5upR3hpU6lSOQHgGlwYFIeqz6qg0cvXVC9mjFkMg4Z6WkKy8i/W6WdbJCfphXrVC35b6M6Ryri8tetEf88A65WJtCRccjKSEdWiWWozkkLdSpFkpZW1AmgMdyI2NLS0mBhYVHVaZD/IKpzRFOozkkDXVMpksLCQiQmJsLMzKxch5Xlgwo/evSoUq9VEiuu2G1oIm+x26rsuIwxpKWlwcnJCTKZ5EcHIxJEdY7qnNgxqc5JCx2pFIlMJkP16tUrHMfc3FyU4iJWXLHb0ETeYrdVmXHplzupSlTnqM5pIibVOemgbj8hhBBCCKkw6lQSQgghhJAKo06lljIwMMCsWbMq/Tm7YsUVuw1N5C12W5pcB0KkQMr7GtU5zcQk0kI36hBCCCGEkAqjI5WEEEIIIaTCqFNJCCGEEEIqjDqVhBBCCCGkwqhTSQghhBBCKow6lYQQQgghpMKoUykBT58+xbffflvhOHl5edizZw+WLFmCzZs3IyMjoxKyK11l5F4VectJedsTIiVS3teozlGNI/9iROtdunSJyWQytd/n6+vLXr58yRhjLCUlhXl7ezN9fX1Wq1YtZmhoyFxdXdnjx48rOVuh8uSuDXnLSXnbEyIlUt7X/ot1TpvyJ9qDnv2tBa5cuVLq/Li4uHLFjY2NRW5uLgAgNDQUOjo6SEhIgIODA54/f47u3btj5syZWLt2bbniA+Lkrom85aS87QmREinva1TnFFGNI0pVda+WMMZxHJPJZIzjOIV/8unl+QXPcRx7+vQpY4yx2rVrs3379gnmR0VFMXd3d63LXRN5i5m/pteBECmQ8r5Gda5q8yfSQUcqtYC1tTUWLVqEwMBApfOvX7+Obt26lSs2x3EAgFevXsHDw0Mwz8PDA0lJSeWKKydW7mLnLSflbU+IlEh5X6M6pxzVOFISdSq1QJMmTZCYmAg3Nzel81+9egVWzqdphoSEwMDAAHl5eUhISEC9evX4eUlJSahWrVq54sqJlbvYectJedsTIiVS3teozilHNY6URJ1KLTBq1KhS75RzdXXF+vXr1Y47ZMgQ/v979OiB9PR0wfxdu3bBx8dH7bjFiZG7JvKWk/K2J0RKpLyvUZ1TRDWOKMOx8v40JJKXkZEBHR0dGBoaVnUqapFq3sW9D+tAiBRIdV+Tat5yUs+flA91KgkhhBBCSIXR4Oda4vHjxwgNDUVAQAC8vLxQr149BAQEIDQ0FI8ePRKlzUePHmHYsGEVjqPp3Csrbzkpb3tCpETK+xrVOfVQjftvoiOVWuDkyZMICgqCi4sLOnbsCHt7ezDGkJKSgsjISDx69AgHDx6En59fpbZ7+fJlfPDBBygoKCh3jKrIvTLylpPytidESqS8r1GdUx/VuP8m6lRqgWbNmqF169ZYsWKF0vmTJk3CyZMncfbsWbXi/vXXX6XOv3//PiZPnlyhnV6M3DWRt5yUtz0hUiLlfY3qnCKqcUQZ6lRqASMjI1y6dAl16tRROv/WrVto3LgxsrKy1Iork8nAcVypQ0VwHFehnV6M3DWRt5yUtz0hUiLlfY3qnCKqcUQZuqZSCzg6OuLUqVMq58fExMDR0bFccXft2oXCwkKl/y5cuFCRtPk2Kjt3TeRdvC2pbntCpETK+xrVOeUxqcaRkmicSi0wZcoUjB49GufPn0eHDh1gb28PjuOQnJyMyMhI/PLLL1i5cqXacZs0aYILFy6gZ8+eSue/61dmVeWuibzlpLztCZESKe9rVOcUUY0jSon9HEhSNtu3b2ctWrRgurq6/DNZdXV1WYsWLdiOHTvKFfPEiRPs4MGDKuenp6ez6Ojo8qbMq+zcNZW3nJS3PSFSIuV9jeqcENU4ogxdU6ll8vLy8OzZMwCAjY0N9PT0qjijspNy7oD08ydEKqS8r0k5d0D6+RPtRtdUahk9PT04OjoiOjoaubm5lR5/27ZtpT6uqyLEzF3MvOWkvO0JkRIp72tU51SjGkfoSKWWMjc3x6VLl+Dp6SmJuGK3oYm8xW5Lk+tAiBRIeV+jOqeZmERa6EillhKrr6+J3xBitKHJ3z5S3vaESImU9zWqc5qJSaSFOpWEEEIIIaTCqFOppQ4ePAhnZ2fJxBW7DU3kLXZbmlwHQqRAyvsa1TnNxCTSQtdUEkIIIYSQCqMjlRJw8+ZNUS58rqy4ly9fxty5cxEeHs4PVSH35s0bDBs2TCtiloe2b3tC3hfavq9Rnav6mEQCND0wJlHfpUuXmEwm08q4hw8fZvr6+qx+/frM1dWV2djYsGPHjvHzk5OT1W5DjJjlpc3bnpD3iTbva1TntCMm0X70mEYt8MUXX5Q6PzU1VaviFhcWFoYpU6Zg3rx5YIxh6dKl6N69O3777Td07txZa2KqIuVtT4iUSHlfozqnmZhE+uiaSi2go6MDHx8fmJubK52fnp6OCxcuoKCgQCviFmdhYYELFy6gRo0a/LRt27ZhxIgR2LZtG5o3bw4nJye12hAjpipS3vaESImU9zWqc5qJSd4DVXmYlBSpU6cO27Rpk8r5Fy9eLNdpBLHiFmdra8vOnTunMH379u3M2NiYrVmzRu02xIipipS3PSFSIuV9jeqcZmIS6aMbdbRAkyZNcP78eZXzOY4r16CyYsUtzsfHB1FRUQrT+/Xrh19++QXjx4/XipiqSHnbEyIlUt7XqM5pJiaRPjr9rQWSk5ORk5MDNzc3ScQtbvfu3Thx4gRWrFihdP62bdvw008/KS2emoypipS3PSFSIuV9jeqcZmIS6aNOJSGEEEIIqTC6+5uQfyUkJCA5ORkcx8He3r7SfoGLFZcQQtQlRj2iGkfk6JpKCbh8+TJ0dHQkE1fsNio75ooVK+Di4gJPT0/4+vqiZcuW8PT0hIuLC1auXKl1cQl5H1GdEzemGPWIahwpiY5USoRYVylo4uoHMdqorJhz5szB0qVLMX36dHTq1An29vZgjCElJQWHDx9GWFgY0tPTMWPGDK2IS8j7jOqcODHFqEdU44gydE2lFujdu3ep81+/fo3o6Gi1x/sSK67YbWgibzkXFxesWrUKPXv2VDp/9+7dGDduHJ48eaIVcQmRKqpz4sdURYx6RDWOKENHKrXA3r170aFDB9jb2yudX96iIlZcsdvQRN5yz58/R506dVTOr127Nl6+fKk1cQmRKqpz4sdURYx6RDWOKKWpATGJat7e3uyXX35ROb+8g8iKFVfsNjSRt5y/vz8bOHAgy8vLU5iXl5fHBgwYwPz9/bUmLiFSRXVO/JiqiFGPqMYRZehIpRZo0qQJLly4gOHDhyudb2BgAFdXV62JK3YbmshbbtWqVejYsSPs7Ozg7+8Pe3t7cByH5ORknDhxAgYGBoiMjNSauIRIFdU58WOqIkY9ohpHlKFrKrVATk4OCgoKYGxsLIm4YrehibyLS0tLw+bNmxEbG4vk5GQAgIODA3x9fTFgwACVz7atqriESBHVOfFjlkaMekQ1jpREnUpCCCGEEFJhNE6llgoODkZSUpJk4ordhibyFrstTa4DIVIg5X2N6pxmYhJpoU6lljpx4gSysrIkE1fsNjSRt9htaXIdCJECKe9rVOc0E5NIC3UqCSGEEEJIhVGnUku5ublBT09PMnHFbkMTeYvdlibXgRApkPK+RnVOMzGJtNCNOoQQQgghpMLoSKUEZGRk4MSJE5KJK3YbmsibEKJZVOfEj0mI2KhTKQF3795FQECAZOKK3UZlxszLy8NXX32FmjVronnz5li/fr1g/tOnT6Gjo6M1cQl5X1GdEy+mGPWIahxRhjqV5D9t3rx52LhxI0aPHo2OHTti0qRJGDVqlGCZ8lwhIlZcQghRlxj1iGocUYauqdQCVlZWpc4vKChAeno6CgoKtCKu2G1oIm+5WrVqYcWKFejatSsA4N69ewgKCoKfnx/WrVuHlJQUODk5qd2WWHEJkSqqc+LHVEWMekQ1jihDnUotYGJigs8++wze3t5K5yckJGD27Nlq75xixRW7DU3kLWdsbIwbN27A3d2dn5aYmIh27dqhadOmWLx4MVxcXNRuS6y4hEgV1TnxY6oiRj2iGkeUYqTKtWrViq1cuVLl/EuXLjGZTKY1ccVuQxN5y3l4eLAjR44oTH/y5AmrXbs2a9++fbnaEisuIVJFdU78mKqIUY+oxhFl6JpKLRAcHIxXr16pnG9lZYVPPvlEa+KK3YYm8pZr164dtm7dqjDdyckJx44dQ3x8vFbFJUSqqM6JH1MVMeoR1TiiDJ3+Jv9pCQkJuHXrFjp16qR0flJSEiIiIjBkyBCtiEsIIeoSox5RjSPKUKeSEEIIIYRUGJ3+1iKFhYUqpz98+FDr4ordhibyFrstTa4DIVIg5X2N6pxmYhLpok6lFnjz5g369u0LExMT2NvbY9asWYI75lJTU+Hh4aE1ccVuQxN5i92WJteBECmQ8r5Gda5q8ycSUrX3CRHGGBs/fjyrXbs2++2339jPP//M3NzcWHBwMMvJyWGMMZacnMw4jtOauGK3oYm8xW5Lk+tAiBRIeV+jOle1+RPpoE6lFnB1dWVRUVH862fPnrEWLVqwjh07suzsbJacnFyuoRnEiit2G5rIW+y2NLkOhEiBlPc1qnNVmz+RDjr9rQWePXsGNzc3/rW1tTUiIyORlpaGLl26IDMzU6viit2GJvIWuy1NrgMhUiDlfY3qnGZiEumjTqUWcHFxwc2bNwXTzMzMEBERgaysLPTq1Uur4ordhibyFrstTa4DIVIg5X2N6pxmYhLpo06lFujYsSPWr1+vMN3U1BSHDx+GoaGhVsUVuw1N5C12W5pcB0KkQMr7GtU5zcQk74GqPv9OGHvx4gW7du2ayvlpaWksOjpaa+KK3YYm8ha7LU2uAyFSIOV9jeqcZmIS6aPBzwkhhBBCSIXpVnUCpEhGRga2bt2KU6dOITk5GRzHwd7eHn5+fujfvz9MTEy0Kq7YbWgib7Hb0uQ6ECIFUt7XqM5Vbf5EGuhIpRa4ceMGOnTogMzMTPj7+8Pe3h6MMaSkpOD48eMwMTFBREQE6tWrpxVxxW5DE3mL3ZYm14EQKZDyvkZ1rmrzJ9JBnUotEBAQAAcHB/z666/Q19cXzMvNzUVISAiSkpIQFRWlFXHFbkMTeYvdlibXgRApkPK+RnWuavMnEqLpiziJIiMjI3b9+nWV869evcqMjIy0Jq7YbWgib7Hb0uQ6ECIFUt7XqM5pJiaRPhpSSAtYWlrizp07KuffvXsXlpaWWhNX7DY0kbfYbWlyHQiRAinva1TnNBOTvAequldLGJs1axazsLBgS5YsYZcuXWJJSUksOTmZXbp0iS1ZsoRZWlqy2bNna01csdvQRN5it6XJdSBECqS8r1Gdq9r8iXRQp1JLLFy4kDk6OjKO45hMJmMymYxxHMccHR3ZokWLtC6u2G1oIm+x29LkOhAiBVLe16jOVW3+RBroRh0t8+DBAyQnJwMAHBwc4OHhodVxxW5DE3mL3ZYm14EQKZDyvkZ1TjMxiTRRp5IQQgghhFQY3aijJbKysnDy5EncuHFDYV52djY2btyoVXHFbkMTeYvdlibXgRApkPK+RnVOMzGJxFXt2XfCGGNxcXHMzc2Nvy7F39+fJSYm8vOTk5OZTCbTmrhit6GJvMVuS5PrQIgUSHlfozpXtfkT6aAjlVpg6tSp8Pb2RkpKCuLi4mBubg4/Pz88fPhQK+OK3YYm8ha7LU2uAyFSIOV9jeqcZmKS90BV92oJY3Z2duzKlSuCaWPGjGGurq7s3r175f7FJ1ZcsdvQRN5it6XJdSBECqS8r1Gd00xMIn26Vd2pJUXXpejqCj+K1atXQyaTwd/fH1u3btWquGK3oYm8xW5Lk+tAiBRIeV+jOqeZmET6qFOpBerWrYtz587By8tLMH3VqlVgjKF79+5aFVfsNjSRt9htaXIdCJECKe9rVOc0E5NIH11TqQV69eqFbdu2KZ33/fffo3///mDlGPlJrLhit6GJvMVuS5PrQIgUSHlfozqnmZhE+micSkIIIYQQUmF0pJIQQgghhFQYdSoJIYQQQkiFUaeSEEIIIYRUGHUqCSGEEEJIhVGnkhBCCCGEVBh1KgkhhBBCSIVRp5IQQgghhFQYdSoJIYQQQkiFUaeSEEIIIYRUGHUqCSGEEEJIhVGnkhBCCCGEVBh1KgkhhBBCSIVRp5IQQgghhFRYuTuVHMeV6V90dDRCQkLg7u5eiWlXTHx8vCBHmUwGa2trdOnSBTExMYJlOY5DWFiY2m0kJiYiLCwMly5dqpyki7l48SL8/f1hYWEBjuOwcuVKlctyHIdx48ZVeg7aICwsDBzHabxddbZ/ZSht/woJCeGXO3DggMrv6vv8PagIqmOlq+o69ueff4LjOPzwww8q40RGRoLjOCxfvhzjxo0Dx3FITk4WLPPixQvIZDLo6ekhPT1dMO/x48fgOA5ffPGFYPqzZ89gYGAAjuNw7tw5pW2HhIQIPgMDAwPUqVMHs2bNQnZ2dhm3RMW0bdsWbdu21Uhbxe3YsQP169eHkZEROI4T5TsiFx0dXer+uWHDBn7Z8PBwweuSMX7//XfR8iSAbnnfWLJozZkzB1FRUTh27Jhger169eDi4oIJEyaUtynRfP755xgwYAAKCgpw/fp1zJ49GwEBAYiJiUHjxo0rFDsxMRGzZ8+Gu7s7fHx8Kifhfw0bNgwZGRnYvn07LC0tteoPnSZ9+umn6Ny5s8bbrYrt36dPH0yePFlhuq2tLf//Bw4cwOrVq8vVefivojpWuqquY8HBwXBwcMC6deswevRopXHWr18PPT09DB48GCdOnMDq1asRHR2Njz/+mF/m+PHj0NUt+nN38uRJQd2IiooCAAQEBAjibtq0Cbm5uQCAtWvXomnTpkrbNzIy4r8vL1++xLZt2/Dtt9/i1q1b2LFjRxm3RvmFh4eL3kZJqampGDx4MDp37ozw8HAYGBigdu3aorc7f/58hc8JAGrUqMH/f3h4OGxsbAQ/uInmlLtT2bJlS8FrW1tbyGQyhekAYG5uXt5mROXq6srn6+fnh5o1ayIwMBDh4eH4+eefqzg71a5du4YRI0YgKCioqlOpEpmZmTA2Nkb16tVRvXp1jbdf2ds/Ly8PHMfxf/SUsbe3V7pvaZOyrIe2oTpWdcqyH+nq6uKTTz7B4sWLce3aNTRo0EAw/9WrV9i9eze6d+8OW1tbtG3blj+yXLxTGR0djWbNmoExhqioKEGnMjo6GjKZDG3atBHEXrduHezs7ODm5oZt27Zh+fLlMDIyUsix5PclKCgI8fHx2LlzJ5YvXw5nZ2e1t01ZyOtgvXr1RIlfmtu3byMvLw+DBg2Cv79/pcSUr09patWqpfV1sCzr8T7TyDWVyk4byU/HrV+/HnXq1IGRkRGaNm2K2NhYMMawZMkSeHh4wNTUFO3atcPdu3cV4h45cgSBgYEwNzeHsbEx/Pz8cPTo0XLnKf+yJiQklLrctWvX0KNHD1haWsLQ0BA+Pj749ddf+fnyAgYAQ4cO5Q/Rv+sI0rvibtiwARzHIT8/H2vWrOHjVtSLFy8wZswYODs7Q19fH56enggNDUVOTg6/zP/+9z/Ur19f8L5u3bqB4zj89ttv/LQLFy6A4zjs3btXZXvy03aLFy/GvHnz4OrqCkNDQzRt2lTh85Of4r5w4QL69OkDS0tL/lepqtPfW7duha+vL0xNTWFqagofHx+sXbtWsEx5vjvv2v7v+vyAt6dgNm3ahMmTJ8PZ2RkGBgZKv9/qCAkJwerVqwEIT+nGx8cLltu0aRO8vLxgbGyMRo0aYd++fQqx7ty5gwEDBsDOzg4GBgbw8vLiY5d1PcqyfVWdTlb2uVZGvagoqmNVU8eGDx8OoOiIZEnbtm1DdnY2hg0bBgCwtraGt7c3oqOjBctFR0ejbdu28Pf3549MFp/3wQcfwMLCgp92+vRpXLt2DYMHD8aIESPw+vVr7Nq1q9T1Lq4sn4F8O0RGRmLo0KGwsrKCiYkJunXrhvv37wuWbdu2LRo0aIATJ06gVatWMDY25tdZ2envnJwcfPvtt/Dy8oKhoSGsra0REBCAU6dO8cswxhAeHg4fHx8YGRnB0tISffr0UWi7pJCQELRu3RoA0K9fP3AcJ2j/r7/+gq+vL4yNjWFmZoYOHToonBEora5XhLu7O65fv47jx4/z36uS+2xeXh5CQ0Ph5OQEc3NztG/fHnFxcQqxyrJflrYeZd2+7u7uSo+qlvxc5TV369atmDp1KhwdHWFqaopu3brh6dOnSEtLw8iRI2FjYwMbGxsMHTpU4VIPjWCVZMiQIczExETlPDc3N8E0AMzNzY21atWK/fHHH2z37t2sdu3azMrKik2aNIn16NGD7du3j23ZsoXZ29uzhg0bssLCQv79mzZtYhzHsZ49e7I//viD7d27l3Xt2pXp6OiwI0eOlJrrgwcPGAC2ZMkSwfTLly8zAGzAgAGCPGfNmsW/vnXrFjMzM2M1atRgGzduZPv372f9+/dnANiiRYsYY4y9fv2arV+/ngFgM2bMYDExMSwmJoY9evRIZU5liZuSksJiYmIYANanTx8+bmkAsLFjx6qcn5WVxRo2bMhMTEzY0qVLWUREBPvmm2+Yrq4u69KlC7/cDz/8wACwxMRExhhjeXl5zMzMjBkZGbERI0bwyy1atIjp6uqyN2/eqGxTvv1dXFxY69at2a5du9hvv/3GmjVrxvT09NipU6f4ZWfNmsV/V6ZOncoiIyPZnj17BPOK++abbxgA1rt3b/bbb7+xiIgItnz5cvbNN9/wy5T3u1Pa9i/L58cYY1FRUQwAc3Z2Zn369GF//fUX27dvH3v+/LnKdgGwMWPGsLy8PIV/8n3i7t27rE+fPgwAn1dMTAzLzs7mY7i7u7PmzZuznTt3sgMHDrC2bdsyXV1ddu/ePb6t69evMwsLC+bt7c02btzIIiIi2OTJk5lMJmNhYWFlWo+ybl9ldUHV51rRelFWVMe0s461bt2a2dnZsdzcXMH0Zs2aMWdnZ5afn89PmzBhgqBWPXv2jHEcxw4fPswOHjzIdHR02OvXrxljjD18+JABYF9++aUg7ogRIxgAdv36dfbmzRtmbGzM2rZtq5CXqu9Lr169GAB2+/Ztlesk37YuLi5s2LBh7ODBg+ynn35idnZ2zMXFhb18+ZJf1t/fn1lZWTEXFxe2atUqFhUVxY4fP87P8/f355fNy8tjAQEBTFdXl02ZMoUdOHCA/fXXX2z69Ols27ZtgnXU09NjkydPZocOHWJbt25ldevWZfb29iw5OVll3nfv3mWrV69mANj8+fNZTEwMu379OmOMsS1btjAArGPHjmzPnj1sx44drEmTJkxfX5/9/ffffIzS6roy8nqzY8cOpXVQ7sKFC8zT05M1btyY/15duHBBEMPd3Z0NHDiQ7d+/n23bto25urqyWrVqCb5DZd0vS1uPsm5fNzc3NmTIEIV1Lvm5yvN3c3NjISEh7NChQ+yHH35gpqamLCAggHXo0IFNmTKFRUREsEWLFjEdHR32+eefq9ymYqnSTqWDgwNLT0/np+3Zs4cBYD4+PoLCu3LlSgaAXblyhTHGWEZGBrOysmLdunUTxCwoKGCNGjVizZs3LzVXeTFetGgRy8vLY9nZ2ez8+fOsWbNmDADbv3+/IM/ixfjjjz9mBgYG7OHDh4KYQUFBzNjYmL169YoxxtjZs2cZALZ+/fpSc1E3rjyn0jqKxb1rWXlncefOnYLpixYtYgBYREQEY6yokABgGzduZIwxdvLkSQaAffXVV8zDw4N/X4cOHVirVq1KzUm+/Z2cnFhWVhY//c2bN8zKyoq1b9+enybfaWfOnKkQp2Tn4/79+0xHR4cNHDhQZdsV/e4wpnyblvXzkxeGNm3avLOd4u2p+rdp0yZ+ubFjxyp0xorHsLe3F3T2k5OTmUwmYwsWLOCnderUiVWvXp3/oys3btw4ZmhoyF68eFHqeqizfdXtVJa3XqiD6ph21jF5B+yPP/7gp127do0BYKGhoYJl5dt/69atjDHGdu3axXR1dVlaWhp78+YN09HRYfv27WOMMfbrr78yAOzAgQP8+zMyMpi5uTlr2bIlP23IkCGM4zh29+5dQVvy74u8c5Oamsq+++47xnEca9asWZnWqVevXoLp//zzDwPA5s6dy0/z9/dnANjRo0cV4pTsfGzcuJEBYD///LPKtuWd+mXLlgmmP3r0iBkZGbGvvvqq1Nzl+/9vv/3GTysoKGBOTk7M29ubFRQU8NPT0tKYnZ2d4O9CaXW9tPZU/Sv+Q6d+/fqC7VEyRvGDJYwxtnPnTv7HOGPq7Zeq1kOd7atup7JkXhMnTmQA2Pjx4wXTe/bsyaysrBTiiq1KhxQKCAiAiYkJ/9rLywtA0TUpxU+HyKfLTyWcOnUKL168wJAhQ5Cfn8//KywsROfOnXH27FlkZGS8s/2pU6dCT08PhoaGaNKkCR4+fIgff/wRXbp0UfmeY8eOITAwEC4uLoLpISEhyMzMVDjMX1ZixS1LuyYmJujTp49CuwD4w/01atSAu7s7jhw5AqDojktvb28MGjQIDx48wL1795CTk4OTJ0+iffv2ZWq7d+/eMDQ05F+bmZmhW7duOHHiBAoKCgTLfvTRR++MFxkZiYKCAowdO1blMpX13SlJ3c+vLOtTXN++fXH27FmFf6V9V0sKCAiAmZkZ/9re3h52dnb8fpWdnY2jR4+iV69eMDY2FmyfLl26IDs7G7GxsaWuh1jbV55/eeqF2KiOiR+3b9++MDMzw7p16/hp69atA8dxGDp0qGBZf39/yGQy/hR4dHQ0mjZtClNTU5iZmeGDDz7gT4FHR0dDV1eXP50LADt37sSbN2/408tA0U1FjDGlp+AzMjKgp6cHPT092NraYuLEiQgKCsLu3bvLtG4DBw4UvG7VqhXc3NwUTtNbWlqiXbt274x38OBBGBoaCvIvad++feA4DoMGDRJ89xwcHNCoUSOFywfKIi4uDomJiRg8eDBksrddC1NTU3z00UeIjY1FZmam4D3q1sFFixYprYP29vZljtG9e3fB64YNGwKo2H5Zcj3E2L5yXbt2FbyW15Xg4GCF6S9evND4KfAqvaLeyspK8FpfX7/U6fIhGp4+fQoACh2h4l68eCEo9MpMmDABgwYNgkwmQ7Vq1eDh4fHOaxSfP38OR0dHhelOTk78/PIQK25Z2nVwcFBYbzs7O+jq6graDQwMxKFDhwAUXW/SoUMHeHt7w97eHkeOHEGtWrWQlZVV5k6lg4OD0mm5ublIT08XXOOkbNuUlJqaCgCl3rxTWd+dktT9/MqyPsXZ2tqqvPu0rKytrRWmGRgYICsrC0BRjvn5+Vi1ahVWrVqlNMazZ88Er0uuh1jbFyh/vRAb1THx4xobG+Pjjz/G+vXrkZycDBsbG2zevBn+/v4K1+JVq1YNPj4+fKcsKipK8Ae3+HWVUVFRaNq0qeDH1tq1a2FoaIjOnTvj1atXAIo6Hu7u7tiwYQNmz54NHR0dfnkjIyOcOHECQNH+5ObmptZNXarqYHlrRmpqKpycnAQdu5KePn0KxpjKzpinp2eZ2ipOnq+qz7+wsBAvX74U3MSibh309PSs9DpoYGAAAHwdLM9+qawOVvb2lStPvTE1NS13e+qSzm2axdjY2AAAVq1apfJOsLL8cqlevbraX1Bra2skJSUpTE9MTBTkpi6x4pal3dOnT4MxJvhDlJKSgvz8fEG7gYGBWLt2Lc6cOYPTp09jxowZAIB27dohMjISCQkJMDU1LfPdeSXHkpNP09fXV9gJynJDknx4ncePHyscKZGrrO9OSep+flUxvua7WFpaQkdHB4MHD1Z5tNfDw0PwuuR6qLN9DQ0NBTeDyZXsuL6vqI6pZ/jw4fj555+xceNG1K5dGykpKVi2bJnSZQMCArBs2TJcuXIF169fx+LFi/l5/v7+WL58Oa5cuYL4+Hj079+fn3f79m2cPHkSQNFd9cocPnxYcBRYJpNVqKOjqg7WrFlTMK2sNcPW1hYnT55EYWGhyo6ljY0NOI7D33//zXeqilM27V3knTVVn79MJoOlpaVgujbWwfLsl8rqYFm3b2l1UKy/+2KSZKfSz88P1apVw40bNzQ+oHNgYCB2796NxMRE/tc3AGzcuBHGxsb8l7Dkr5/KilvZAgMDsXPnTuzZswe9evUStCufX3xZjuPwzTffCIbgaN++Pb788kskJCSgTZs20NPTK1Pbf/zxB5YsWcKfAk9LS8PevXvx4YcfCo4ElFXHjh2ho6ODNWvWwNfXV+kyYn13qurzK674d07Z0CfvYmxsjICAAFy8eBENGzbkf+mqQ53t6+7ujpSUFDx9+pQv0rm5uTh8+LDa7UoR1TH1tGjRAg0aNMD69etRu3ZtWFhYqDx9Ku9Uzp49GzKZTHB6W/7/s2fP5peVk48S8fPPPyt06rKystCjRw+sW7dOrctO3mXLli2C9Th16hQSEhLw6aefliteUFAQtm3bhg0bNqg8Bd61a1csXLgQT548Qd++fcvVTkl16tSBs7Mztm7diilTpvAdrYyMDOzatYu/I1xsxc++lEdl7JfqbF93d3dcuXJFMO327duIi4ujTqWmmJqaYtWqVRgyZAhevHiBPn36wM7ODqmpqbh8+TJSU1OxZs0aUdqeNWsW9u3bh4CAAMycORNWVlbYsmUL9u/fj8WLF/OnbGvUqAEjIyNs2bIFXl5eMDU1hZOTk6DQliduedy7d0/pUwTq1auHTz75BKtXr8aQIUMQHx8Pb29vnDx5EvPnz0eXLl0Ep7Lt7OzQoEEDREREICAggC8Q7du3x4sXL/DixQssX768zHnp6OigQ4cO+OKLL1BYWIhFixbhzZs3fLFXl7u7O6ZPn445c+YgKysL/fv3h4WFBW7cuIFnz55h9uzZon13xPz8gKLTKSWvZwSKxk6Uj1Pn7e0NoOi6o6CgIOjo6KjdOfzuu+/QunVrfPjhh/jss8/g7u6OtLQ03L17F3v37lUYFLwkdbZvv379MHPmTHz88cf48ssvkZ2djf/7v/9TuJ72fUV1TH3Dhg3DF198gbi4OIwaNUrlj6c2bdpAR0cHu3fvVji9Xa1aNTRq1Ai7d++Gnp4e/Pz8AAD5+fnYuHEjvLy8VHbounXrhr/++gupqamCBw9UxLlz5/Dpp5/if//7Hx49eoTQ0FA4OztjzJgx5YrXv39/rF+/HqNHj0ZcXBwCAgJQWFiI06dPw8vLCx9//DH8/PwwcuRIDB06FOfOnUObNm1gYmKCpKQknDx5Et7e3vjss8/Ualcmk2Hx4sUYOHAgunbtilGjRiEnJwdLlizBq1evsHDhwnKtT3F37txRWgeLj1ns7e2N7du3Y8eOHfD09IShoSFfG8uiMvZLdbbv4MGDMWjQIIwZMwYfffQREhISsHjx4kr7fmlcZd3xU567Jkve+adqiAxld5oxxtjx48dZcHAws7KyYnp6eszZ2ZkFBwcrLFeSqnaUQYm7Jhlj7OrVq6xbt27MwsKC6evrs0aNGim9O3Lbtm2sbt26TE9PT2mcksoaV9m2Ky1/Vf/k+Tx//pyNHj2aOTo6Ml1dXebm5sa+/vprfjia4iZNmsQAsHnz5gmm16pVq8x33Ba/a3X27NmsevXqTF9fnzVu3JgdPnxYsKz87rrU1FSFOMruEmas6O7HZs2aMUNDQ2ZqasoaN26ssB3L+91hTPX2L8vnp+q7/K72VP3z8/Pjl8vJyWGffvops7W1ZRzHMQDswYMHpeas7M7DBw8esGHDhjFnZ2emp6fHbG1tWatWrQR3o75rPcq6fQ8cOMB8fHyYkZER8/T0ZN9//73Ku78rWi/KgurYeoX3akMdk0tNTWX6+voMADtz5kypyzZv3pwBYFOmTFGYJ79jtvj+I79rfOXKlSpjHjp0SHBXb2nfl3eR3/0dERHBBg8ezKpVq8aMjIxYly5d2J07dwTL+vv7s/r16yuNU/IuYcaKhoqbOXMmq1WrFtPX12fW1tasXbt2guHaGGNs3bp1rEWLFszExIQZGRmxGjVqsE8++YSdO3eu1NxL28f27NnDWrRowQwNDZmJiQkLDAxk//zzj2CZ0up6ae2p+ld8BID4+HjWsWNHZmZmxvDvEDyl5Szfj8rzN+Jd61GW7VtYWMgWL17MPD09maGhIWvatCk7duyYyru/S+Yv/x6dPXtWMF3dbVxZOMYYq3jXlJCyi4+Ph4eHB5YsWYIpU6ZUdTqEEKJxGzZswNChQ3H27NkK33xCiLao0iGFCCGEEELI+4E6lYQQQgghpMLo9DchhBBCCKkwOlJJCCGEEEIqjDqVhBBCCCGkwqhTSQghhBBCKkySg59LQWFhIRITE2FmZqaVj6Ii0scYQ1pa2juf80uIWKjOEbFRnZMW6lSKJDExUeXzpwmpTI8ePeKfJkGIJlGdI5pCdU4aqFMpEvljwR49egRzc3NR28rJL0DTuUeg6j7+30f7oq7j2xwev8iEg4UhdHXE+dV38eFL/N/RO1j8UUPYmhuK0gYB3rx5AxcXF8Ej6JQpKChAXl6ehrKSLj09vXI9c/6/TJN1Liu3AM3mHVE5/6/P/eBpY8q/Tk3Lho2pgWhHUKNupeDH4/fww+AmqGZc9kehEvVQnat66tRGGlJIJG/evIGFhQVev34terHtvPIEbiWnlWlZMwNdpOXkw87MAGdC27/7DWraefYRvtp1hX8dvzC40tsgRd71HWOMITk5Ga9evdJ8chJVrVo1ODg40KncMhKjzjHGlG7/WqEHkFdQtj9Xpga6SM/JR3MPK+wc5VspeRW3LCIOq47d5V/HLwxWmTepGKpz2qGstZGOVErA+YSX+GjNKf719wMao5m7FezMDNBp5Qncfppe5lhpOfkAgJS0HMQlp6GOQ+m//tRVvEMJAO7T9gMARvl74usgr0pti5ROXmjt7OxgbGxMf/BKwRhDZmYmUlJSAACOjo5VnNF/T1RcCoauP8u//uWTpmjiZgkLIz14Tj+gVqz0f+vcmQcv8Cozt9KPJBbvUAJv69zkDrXxeWCtSm2LlI7qnLjUrY3UqdRy+QWFgg4lAIzbelHpsqenB8LUQBeLDt3CxpiEd8b+5e/7WPK/Rmrlc/r+c/T7KRYAcHdekOAU+uVHr1S+78fj91V2KrNyC/A8IwfVLY3VyoWoVlBQwBdaa2vrqk5HEoyMjAAAKSkpsLOzo1PhGpSdVyDoUALApxvPKV328qyOYIxh0aFb2Hbm0Ttjrz35AJM71lErn8gbTzHi3/bvz+8CmextR+XvO6kq37cs8rbKTmV6Tj7Ss/PhYEGXBFUWqnOaoU5tpE6llvhi5yX8ceEJAKCRSzV0qm+PYX4e+Gzz+TK9/7fRvrD/9/rFb3s0QLu6drA3N0TE9adYceQ2/tekOlytjLEs8jb/ntspyo9whv11HRtOxcPT1gS/Dm0OFytjHLyaBE9bU6w5fo9fbnNsAkL8PAAAya+z0WP1P/w8N2tjJDzPVJnvoxeZ+HBxlGDathEt4VuDCkNlkF9bZGxMHXV1yLdXXl4edSorGWMM/X6KxZkHLwAAfjWt0b2RE7o1ckL/n0+XKcahiR/CwkgPALCgd0PUsDVFI5dqOHA1Cev/iUcPHyfo68jw2/nH/HtuJr1RGuvrP67wndKzoe1ha2aAQ9eS0MDZAsuL1clbyWmo51R02vVuSjoGrz3DzzPUkyE7r1BlvvdT09Fu2XHBtL/G+aFh9WplWl9SOqpzmlPW2ijp+/MXLFgAjuMwceJEfhpjDGFhYXBycoKRkRHatm2L69evC96Xk5ODzz//HDY2NjAxMUH37t3x+PFjwTIvX77E4MGDYWFhAQsLCwwePLjSrtkoLGR49CITm2ITkPA8A9FxKXyHEig64rf4UBzqfnMIUXFvfxWvH9oMdmYGCvEOTfwQzdytBNPa1rGDl6M5xgfWxJnpgVjyv0b4PLAW4hcGI7SLF99O3x9j0G5pNBYduoVn6Tlwn7YfG07FAwDup2bgw8VRmLD9Ij7bcgGdVp5AdLF8riUWFevEV1loueCooP3ISf4Y1cZTMC3j31NS+QWFCh1KABip4sgEKT86FaQebdteUq1x8jxT03Jw6FoSUt5kY8+lJ3yHEgD+ufscU3ddRb2ZhwVnOdYPbQY9HcXP4e+vAlDXQXhN3acfeqKZuxW+Ca6HM9MD8d3HjbHkf41wd14Qhv37g/fIzRR8/ccVdFh+HD+fuI+E5xlwn7ZfcJSz2bwj6P9TLEZvvoDWi6IEHdHY+88BFHVO2y8XdhAvftMRfZu+vSNZV8ahsLDous/M3HyFDiUAhJQ4IksqTtv22/dRWbexZI9Unj17Fj/99BMaNmwomL548WIsX74cGzZsQO3atTF37lx06NABcXFx/N1jEydOxN69e7F9+3ZYW1tj8uTJ6Nq1K86fP8/3wAcMGIDHjx/j0KFDAICRI0di8ODB2Lt3b4XyVnaEriy2jmiBVjVs+JtrCgsZQvdchYGujkKhLY7jONiVuAO7drHrKOVFfk30PayJvgdl/ryUqHT6q8w83Eh8g5h/iy6//Fg/6OvK8HUXL3zdxYu/3mjijkuIvPFUZa5pOfkYsu4M5vf2hnM1IxQUMhQUMujrSvq3DyHlItUaBwB3U9LQfvkJtd93cMKH8HI0x515XQAAeQWFGL/tIpyrGcHFSvXRKJlMWOd0dWSoW6zOyTuQ8w7cxLwDN5XGKFnH5K4lvkbC8wzsuyKsg0cn+8NIXweL+zTC3J7eqD3jIPILGcZvv4h9V5JU5voiIxeTd17G113qwsZU8SABIVImybu/09PT8cEHHyA8PBxz586Fj48PVq5cCcYYnJycMHHiREydOhVA0S92e3t7LFq0CKNGjcLr169ha2uLTZs2oV+/fgDejrV24MABdOrUCTdv3kS9evUQGxuLFi1aAABiY2Ph6+uLW7duoU6dd1+fo+qOtdeZeWj0bYTK931YywZ/33kmmDbE1w2zezRQezupwhiDx9fqXfheVudntId1iUIp71SWlYm+DsYE1MSSw3EAgO0jW6KlZ9Fp8bTsPNxNSYeBrg5/SkqZjJx8mBjoInT3VWw5/VAwr4WHFa48fo1ujRwxq1t9mBhI87dVaXdFZmdn48GDB/Dw8IChIV3DVVbast2kUOMA1d/BZ+k5aDpX9fA/NqYGeJaeI5g2so0npnepvJv5xKxzl2d2hIWxnmCaunWuhq0J+jRxwaJDtwAAxyb7w9O2aEik9Jx83E9Nh4WRHtysTVTGyC8ohK6ODCHrz/BnkaxN9PE8Ixc9fJwQl5wGDxsT/F//xtATaQg5sVGd0w5l3daS/JaNHTsWwcHBaN9eOCTOgwcPkJycjI4dO/LTDAwM4O/vj1Onim52OX/+PPLy8gTLODk5oUGDBvwyMTExsLCw4IstALRs2RIWFhb8MuVlYayHXZ8pH+Li2GR/bBreAhe/6YDx7WpifUgzPFjQpVI7lEDR0cub33ZWOT/m63a4PrsTvuwk/MPiX9sWg1u6YUaw8sLf+wNnhQ7lu8QvDMb12Z0E0zJyC/gOJQB8/O+NQalpOfAOi0Cv8FPo8n9/4/fzj5GVW4Bv997AX5eLjiJ8su4M3KftR/1Zh+E+bb9ChxIATj94gay8Auw89xi/xsSrlS8RX0hICHr27FnVaVQpKdc4oKjTuEzFTYA3v+2MczPaI+brdhjl74lfhzXHgwVdKrVDCRTVucuzOqqcf2lmB9yZF4Qu3g6C6R+4VsOnrT0Q0spd6fvGBdRU6FCWplF1CzxY0AU3vhXWuXupGXyHEgB/qvzRi0w0mHUY3b//B/5LonE+4SWy8wqwPPI2Tt0tOuAQsDQa7tP2o2boQbhP2y+4LOl5Ri6AojNMt5LTcPBacplu3CSa061bN4V9Wy4mJgYcx8HIyAiLFi0SzJs6dSo4jsPRo8LLzQIDAzFgwADBtI4dO0JHRwexsbEKbYSEhIDjOHAcBz09PXh6emLKlCnIyMio4JpJ8PT39u3bceHCBZw9q3hdSnJyMgDA3t5eMN3e3h4JCQn8Mvr6+rC0tFRYRv7+5ORk2NnZKcS3s7PjlykpJycHOTlvf3m/eaP84nAAaOJmhathHeEd9vaIZZ8m1flfqZYm+vhCzbsV1WWkr4N787vgZtIbXE98jam7rvLzHC2K7vQaG1ATr7Py8NOJ+xjtXwPTguoCAK4nvlYaMzUtR+n0tUOaYvivitdLmhkWff1MDHRxaOKH6Lzyb5X5not/gXupwhuLpvx2GVN+u1z04h9g/Dbld8WXZvGhOHSu78Bv+9LQOHREE7S1xgHq1bmPmlRHkLcD6s08zE8b3toDRvpFp98dLYxEH2bMwkgPcXM742ZSGg5dS8YPxW40lA8zFD6wCUZuPIeIG0/xZac6GBtQEwBw4nYqf315cQzKT+4t79sIX+y8rDC9mrE+OI6Dsb4udn3mi4/WxKjM925KGvZfEW7/4qN//J/qVS3VnH030MPHiU63a4nhw4ejd+/eSEhIgJubm2DeunXr4OPjg7p16yIqKoo/IwEA0dHRcHFxQVRUFAIDAwEAubm5iImJwXfffccv9/DhQ8TExGDcuHFYu3YtWrZsqZBD586dsX79euTl5eHvv//Gp59+ioyMDKxZs6ZC6yapI5WPHj3ChAkTsHnz5lIPv5b8w1+WzkDJZZQtX1qcBQsW8Be8W1hYvPPRZWaGeoJfyKP9a5S6vBh0ZBwaOFugb9O3ue4f31qwzPQuXohfGMx3KAGgvpMF/hrnh83DW2BV/8b89C861FbazrUnb//w9G7sjEYu1QBAMChxXQdzXA1TfVShzw8xgo6vuuLmdsbeca0xIbAWZnevj+gpbfl57ZYdx9n4omtL1558gI3/Hr1Mfp0N92n74T5tP7p89zc8vj4A92n7MV/FNVlEfMePH0fz5s1hYGAAR0dHTJs2Dfn5RTeA7d27F9WqVUNhYdHduJcuXQLHcfjyyy/5948aNQr9+/evktzLQptrHKB+nTPW10WfJm9vZFF19E9MBro68HGphikd39an4vs/APz0SVPELwzmO5QA0Ka2LbZ+2gKbhjfHD4OaAADqOZpj6L83AJW09/Lbay6nBdVFo+oWAIClxY7YNnGzQszX7VTm2n75Caw4clvl/NLYmOrj0swO2DGyJeb0bIDvPvbBhqHN+PlN5x5B/LOiI1G7zj9GVFzRuINPXmXxde6jNaf4/99IZ3FE07VrV9jZ2WHDhg2C6ZmZmdixYweGDx+OgIAA/PPPP3x9S0tLw8WLFzFt2jRER0fz7zl9+jSysrIQEBDAT1u/fj26du2Kzz77DDt27FB6BNLAwAAODg5wcXHBgAEDMHDgQOzZs6fC6yapI5Xnz59HSkoKmjRpwk8rKCjAiRMn8P333yMuruiUaXJysmCAzpSUFP6XvYODA3Jzc/Hy5UvBL/mUlBS0atWKX+bpU8UbSlJTUxWOEMh9/fXX+OKLL/jX8kdLlSZ8YBM8T89BZm5BqRehi43jOLWffFN8SIxujZyQlVvAH4Eoqfgv+77NXODjUg1vsvNgZyb8o2lmqIcrYR3x6EUm6jsVFWTvsMNIy85XK7f+zV2xoLc37jxNw4ojtxEaXA8Gujrwrm4B738LPQDo6XD8Ezr+94Pw6MHMP4V3094odjfoTyfuY1ALN7haS2sYC8YYsvIKqqRtIz2dCh/lffLkCbp06YKQkBBs3LgRt27dwogRI2BoaIiwsDC0adOGL7xNmjTB8ePHYWNjg+PH396BGx0djUmTJlV0dUSjzTUOKF+dW/q/RpjYvhZy8wurtM7p6sjUrnOtatrw//+u98qHOgKA9l52GPmhJ7LzC2CsL/wz62hhhBNfBiA9J5+/Llzd6zGBog76rG71cPRmCradeYg5PRugmrE+Wnhao8W/16CXvGWi7dLoUmOeT3jJ///MP6+jW0MnWJpI6xGUUqhzurq6+OSTT7BhwwbMnDmTf89vv/2G3NxcDBw4EM+ePUN6ejrOnj0LX19f/P3336hduzb69OmDSZMmITMzE8bGxoiKikL16tVRs2bRDyLGGNavX4/Vq1ejbt26qF27Nnbu3ImhQ4eWnruRUaU85lJSncrAwEBcvSo8WjV06FDUrVsXU6dOhaenJxwcHBAZGYnGjYuOoOXm5uL48eP8tQlNmjSBnp4eIiMj0bdvXwBAUlISrl27hsWLFwMAfH198fr1a5w5cwbNmzcHUPRr4PXr13xRLsnAwAAGBuqfWrA2NcD7MDKjqg4lAMEF4vIbbgz1lC9vbqjHdygBoJqxntJOpYGuDDn5RUek7s3vgszcfP5ygpld6wEAatmbIXxgE4X3yl0N64S63xxSOb80V568klynMiuvQHAqUpNufNtJ4Y+rusLDw+Hi4oLvv/8eHMehbt26SExMxNSpUzFz5kxYWFjAx8cH0dHRaNKkCd+BnD17NtLS0pCRkYHbt2+jbdu2lbNSItDmGgeUv879Fx5sMKSVO/b8O1JGTbuiO89VfefLUjv0dWXIzX87Bub9+V2Qmp6DFvOLrqf7uktdcByH9vXs0b6e8h8CHMfh8syOpd4cWppria/xYS3bcr23qkilzg0bNgxLlixBdHQ0f5Rx3bp16N27NywtLWFpaQlnZ2dER0fD19cX0dHR8Pf3h52dHTw9PfHPP/+gQ4cOgvcDwJEjR5CZmYlOnYqu4R00aBDWrl1baqfyzJkz2Lp1K39KvSIkdfrbzMwMDRo0EPwzMTGBtbU1GjRowI/nNn/+fOzevRvXrl1DSEgIjI2N+YtYLSwsMHz4cEyePBlHjx7FxYsXMWjQIHh7e/MXznp5eaFz584YMWIEYmNjERsbixEjRqBr165lviuSvFVyDE11/PxJU/7/+zSpDndrY/w4uAkOTvgQXbwdsO/z1tCRcTAz1EP8wmDELwwutYNbnKGeDuIXBqOGreq7K2cEe2HCv0/I+GHQB2j+77pE3VL9VA0ijps3b8LX11dwJMDPzw/p6en8GIxt27ZFdHQ0GGP4+++/0aNHDzRo0AAnT55EVFQU7O3tUbduXVVNVDmqcdLV2NUSp6a1w+25QWq/969xfvz/9/Rxgru1MdYNaYb941sjqIEDjnzhD5mMg725IV/nDHTLVucsjPXeeZR1SsfaCGpQdDnWD4OaoJZd0TXmZ+NflvY2UgF169ZFq1atsG7dOgDAvXv38Pfff2PYsGH8MvJ6BhSdZZH/IPb390d0dDRycnIQGxuLdu3eXlKxdu1a9OvXD7q6RZ3b/v374/Tp0/xZDrl9+/bB1NQUhoaG8PX1RZs2bbBq1aqKrxiTOH9/fzZhwgT+dWFhIZs1axZzcHBgBgYGrE2bNuzq1auC92RlZbFx48YxKysrZmRkxLp27coePnwoWOb58+ds4MCBzMzMjJmZmbGBAweyly9fljmv169fMwDs9evXFVm998bJO6ns8cvMcr23sLCwkrMRyszJZyHrTrPtZxJYbn4Bi7r1lKVl5yldduTGs8xt6j7mNnUf23Pxsah5vUtp37GsrCx248YNlpWVxU8rLCxkGTl5VfJPnc9wyJAhrEePHgrTe/bsyYYOHSqYdvHiRQaA33//+usvZmFhwS5evMhsbW1ZYWEhmzRpEps6dSobOXIk69u3b6ltK9tuVU1baxxjVOek5GVGDusd/g/bcfYhy/u3zmXl5itdtnidi7n3TMOZCr2vdY4xxtauXcuMjIzY69evWWhoKHN3dxfE+OWXX5iJiQl79uwZ09XVZcnJyYwxxrZu3cp8fX1ZdHQ0A8Di4+MZY0X7tIGBAZPJZExHR4f/B4B99dVXfNwhQ4aw9u3bszt37rD4+HiWm5v7zlzLWhslOU6lFJQ2thaRrpN3nmHQ2rePlNs9phUau1oqLFdQyDB683l+sPfDE9tgU2w8Nse+HeLoz7F+/E1L5fG+jt8WEhKCV69eKVw0Hhoail27duHmzZv80crw8HBMmzYNr169gkwmw+vXr2FlZYXBgwcjIyMDv/32G/78808sWLAAL1++xIQJEzBmzBiVbUt5u1UFqnPvp02xCfhmzzX+9alp7eBUzUhhufyCQgT/30nEPU0DAJyZHogTd55hY0w8rjx+Xep7y+p9rXNA0Xi0jo6OWLJkCebNm4cRI0Zg5syZ/Pz79++jRo0amD59Onbv3o0bN24AKBp31t3dHRMnTsTvv/+O+/fvAwBWrVqF7777TqF2Hj16FAsWLMDjx4+hq6urssaW5r0cp3LNmjVo2LAhzM3NYW5uDl9fXxw8eJCfzyTy+DIiXX41hVfA9go/hZ1nH4Exxl8Un5mbjxrTDwieHtRp5QlBhxIAeqz+B78WG7Lkfmo6NsXEI7uKLjLXJq9fv8alS5cE/0aOHIlHjx7h888/x61bt/Dnn39i1qxZ+OKLLyCTFZUy+XWVmzdv5k8VtWnTBhcuXND66ynlqM6RqvZxM+HNVyM3nRPcxAMAWbkFqBl6kO9QAkDz+Ucx5bfLfIcSAFotPIZrT96+jn+Wgf1XkvjHWf6XmZqaol+/fpg+fToSExMREhIimO/p6QlXV1esWrUK/v7+/HQnJye4ubnhhx9+EFxPuXbtWvTp00fhEpphw4bh1atX2L9f/RvC1CWpTmX16tWxcOFCnDt3DufOnUO7du3Qo0cPvqDKH1/2/fff4+zZs3BwcECHDh2Qlvb2Sz9x4kTs3r0b27dvx8mTJ5Geno6uXbuioODtH/IBAwbg0qVLOHToEA4dOoRLly5h8ODBGl9fon04jsORL/wF077adQUeXx/ghxzq/v0/ZY4366/rcJ+2H73C/0G7ZcfxzZ/XMXf/jcpOW3Kio6PRuHFjwb9Zs2bhwIEDOHPmDBo1aoTRo0dj+PDhmDFjhuC9AQEBKCgo4DuQlpaWqFevHmxtbeHlJe64iJWB6hypano6Mvwx5u0NW9eevBEMN9Qw7DA+V2Nc4K6rTsJ92n4MWXcGbZdGY+zWC/jxxH0xUpec4cOH4+XLl2jfvj1cXV0V5gcEBCAtLU3hB7G/vz/S0tL4TuX58+dx+fJlfPTRRwoxzMzM0LFjR6xdu1aUdShO8qe/rayssGTJEgwbNkwSjy8j74eyDgFyYPyH+GTdaTxLL3rKxY6RLdHM3Qo//30fCw7eUvoeWzMDnA1V/rSF4t7n00JVRVu3G9U5UhXKWuf2fd4aXVed5F/LH607dP0ZRMUpv6mxkUs1/DnWT+m84qjOaYf38vR3cQUFBdi+fTsyMjLg6+tb5Y8vy8nJwZs3bwT/yPurtIHa5baPbIl6TuY4N6MDbs3pjAcLuqCFpzVkMg6j/Gtg47DmSt9XfLw78t9GdY5UpTOh7x5iZu+41mjgbIH4hcE4G9oe9+Z34YeOWz+0Oeb1Uv6YYX0dejrZ+0hyncqrV6/C1NQUBgYGGD16NHbv3o169eqV+viy4o8mE+vxZeo+aYJIm3wIozvzgtDIpRrcrI3xebu3T+M48WUAX1iBouGLSg6K26a2LR4s6IJve9QHAP7pF3dT0vHweaYG1oJoK6pzRBvYmRUNYXR9didYmehDR8ahvdfb782fY/0ED5SwNTOAjkxY5wa2cMO9+V349y3s7Q2gaLiijBz1HmxBtJ+kBj8HgDp16uDSpUt49eoVdu3ahSFDhgiemFFVjy8rz5MmiPTp6cj4UziMMRQUMrT0tC7zwOgcx+ETX3d84uuOvIK3Ax23WRKl9tM/yPuD6hzRJiYGurjwTYd/b0gEFh2+hY71HMo8eoWOjMMvQ4p+NL/MyMW0P4oG+K8/6zDVufeM5DqV+vr6/OOImjZtirNnz+K7777jry+qqseXlfdJE+T9wXEcvupc/oG1iz95CAAePMuAh43qgdnJ+4vqHNFGHMeB44Cvg8p/w1vJxz6+ysxFNWNpPQqSqCa5098lMcaQk5MDDw8P/vFlcvLHl8kLafHHl8nJH18mX6b448vkyvL4MkIqw5npb69hOnJD8Y8++W+iOkfeJxGT2vD/f+bBiyrMhFQ2SR2pnD59OoKCguDi4oK0tDRs374d0dHROHTokODxZbVq1UKtWrUwf/58lY8vs7a2hpWVFaZMmaLy8WU//vgjAGDkyJH0+DKiEXbmb++qm3fgJuYduAkA+KJDbYz/93GR6igsLHz3QoSnDduL6hx539W2N+P/f+Sm8/z/z+3ZAINauqkdTxv22/ddWbexpDqVT58+xeDBg5GUlAQLCws0bNgQhw4dQocOHQAAX331FbKysjBmzBi8fPkSLVq0QEREBMzM3n6BV6xYAV1dXfTt2xdZWVkIDAzEhg0boKPz9jmqW7Zswfjx4/m7J7t3747vv/9esytL/rN6f+CMPy48EUx7mZmrVgx9fX3IZDIkJibC1tYW+vr677zm7r+MMYbc3FykpqZCJpNBX7/qTsdRnSP/Bc7VjPDkVZZg2pvsPLViUJ0Tn7q1UfLjVGorGr+NlFdOfgHqzDgkmDa8tQe+6VpPMO1d37Hc3FwkJSUhM5PuJC8rY2NjODo6VmmnUkqozpHyepGRiw/mRAqmhXbxwog2noJpVOe0Q1lro6SOVBLyX2Cgq4N6jua4kVQ0BmDD6hb4Okj9G4D09fXh6uqK/Px8wZNUiHI6OjrQ1dWlIx2EaIBViRt2LI31MNTPXe04VOfEp05tpE6lSOQHgGlwYFIe20Ia4tHLTFSvZgyZjENmRrrCMvLvVmknGziOg56eHvT0aEB1UvmozpGKuPx1a8Q/z4CrlQl0qM69F6hTKRL5c3hpDDcitrS0NFhYWLx7QUIqGdU5oilU56SBrqkUSWFhIRITE2FmZlau02nyQYUfPXpUqdcqiRVX7DY0kbfYbVV2XMYY0tLS4OTkBJlM8qODEQmiOkd1TuyYVOekhY5UikQmk6F69eoVjmNubi5KcRErrthtaCJvsduqzLj0y51UJapzVOc0EZPqnHRQt58QQgghhFQYdSoJIYQQQkiFUadSSxkYGGDWrFmV/pxdseKK3YYm8ha7LU2uAyFSIOV9jeqcZmISaaEbdQghhBBCSIXRkUpCCCGEEFJh1KkkhBBCCCEVRp1KQgghhBBSYdSpJIQQQgghFUadSkIIIYQQUmHUqZSAp0+f4ttvv61wnLy8POzZswdLlizB5s2bkZGRUQnZla4ycq+KvOWkvO0JkRIp72tU56jGkX8xovUuXbrEZDKZ2u/z9fVlL1++ZIwxlpKSwry9vZm+vj6rVasWMzQ0ZK6uruzx48eVnK1QeXLXhrzlpLztCZESKe9r/8U6p035E+1Bz/7WAleuXCl1flxcXLnixsbGIjc3FwAQGhoKHR0dJCQkwMHBAc+fP0f37t0xc+ZMrF27tlzxAXFy10TeclLe9oRIiZT3NapziqjGEaWquldLGOM4jslkMsZxnMI/+fTy/ILnOI49ffqUMcZY7dq12b59+wTzo6KimLu7u9blrom8xcxf0+tAiBRIeV+jOle1+RPpoCOVWsDa2hqLFi1CYGCg0vnXr19Ht27dyhWb4zgAwKtXr+Dh4SGY5+HhgaSkpHLFlRMrd7HzlpPytidESqS8r1GdU45qHCmJOpVaoEmTJkhMTISbm5vS+a9evQIr59M0Q0JCYGBggLy8PCQkJKBevXr8vKSkJFSrVq1cceXEyl3svOWkvO0JkRIp72tU55SjGkdKok6lFhg1alSpd8q5urpi/fr1ascdMmQI//89evRAenq6YP6uXbvg4+OjdtzixMhdE3nLSXnbEyIlUt7XqM4pohpHlOFYeX8aEsnLyMiAjo4ODA0NqzoVtUg17+Leh3UgRAqkuq9JNW85qedPyoc6lYQQQgghpMJo8HMt8fjxY4SGhiIgIABeXl6oV68eAgICEBoaikePHonS5qNHjzBs2LAKx9F07pWVt5yUtz0hUiLlfY3qnHqoxv030ZFKLXDy5EkEBQXBxcUFHTt2hL29PRhjSElJQWRkJB49eoSDBw/Cz8+vUtu9fPkyPvjgAxQUFJQ7RlXkXhl5y0l52xMiJVLe16jOqY9q3H8TdSq1QLNmzdC6dWusWLFC6fxJkybh5MmTOHv2rFpx//rrr1Ln379/H5MnT67QTi9G7prIW07K254QKZHyvkZ1ThHVOKIMdSq1gJGRES5duoQ6deoonX/r1i00btwYWVlZasWVyWTgOK7UoSI4jqvQTi9G7prIW07K254QKZHyvkZ1ThHVOKIMXVOpBRwdHXHq1CmV82NiYuDo6FiuuLt27UJhYaHSfxcuXKhI2nwblZ27JvIu3pZUtz0hUiLlfY3qnPKYVONISTROpRaYMmUKRo8ejfPnz6NDhw6wt7cHx3FITk5GZGQkfvnlF6xcuVLtuE2aNMGFCxfQs2dPpfPf9SuzqnLXRN5yUt72hEiJlPc1qnOKqMYRpcR+DiQpm+3bt7MWLVowXV1d/pmsurq6rEWLFmzHjh3linnixAl28OBBlfPT09NZdHR0eVPmVXbumspbTsrbnhApkfK+RnVOiGocUYauqdQyeXl5ePbsGQDAxsYGenp6VZxR2Uk5d0D6+RMiFVLe16ScOyD9/Il2o2sqtYyenh4cHR0RHR2N3NzcSo+/bdu2Uh/XVRFi5i5m3nJS3vaESImU9zWqc6pRjSN0pFJLmZub49KlS/D09JREXLHb0ETeYrelyXUgRAqkvK9RndNMTCItdKRSS4nV19fEbwgx2tDkbx8pb3tCpETK+xrVOc3EJNJCnUpCCCGEEFJh1KnUUgcPHoSzs7Nk4ordhibyFrstTa4DIVIg5X2N6pxmYhJpoWsqCSGEEEJIhdGRSgm4efOmKBc+V1bcy5cvY+7cuQgPD+eHqpB78+YNhg0bphUxy0Pbtz0h7wtt39eozlV9TCIBmh4Yk6jv0qVLTCaTaWXcw4cPM319fVa/fn3m6urKbGxs2LFjx/j5ycnJarchRszy0uZtT8j7RJv3Napz2hGTaD96TKMW+OKLL0qdn5qaqlVxiwsLC8OUKVMwb948MMawdOlSdO/eHb/99hs6d+6sNTFVkfK2J0RKpLyvUZ3TTEwifXRNpRbQ0dGBj48PzM3Nlc5PT0/HhQsXUFBQoBVxi7OwsMCFCxdQo0YNftq2bdswYsQIbNu2Dc2bN4eTk5NabYgRUxUpb3tCpETK+xrVOc3EJO+BqjxMSorUqVOHbdq0SeX8ixcvlus0glhxi7O1tWXnzp1TmL59+3ZmbGzM1qxZo3YbYsRURcrbnhApkfK+RnVOMzGJ9NGNOlqgSZMmOH/+vMr5HMeVa1BZseIW5+Pjg6ioKIXp/fr1wy+//ILx48drRUxVpLztCZESKe9rVOc0E5NIH53+1gLJycnIycmBm5ubJOIWt3v3bpw4cQIrVqxQOn/btm346aeflBZPTcZURcrbnhApkfK+RnVOMzGJ9FGnkhBCCCGEVBjd/U3IvxISEpCcnAyO42Bvb19pv8DFiksIIeoSox5RjSNydE2lBFy+fBk6OjqSiSt2G5Udc8WKFXBxcYGnpyd8fX3RsmVLeHp6wsXFBStXrtS6uIS8j6jOiRtTjHpENY6UREcqJUKsqxQ0cfWDGG1UVsw5c+Zg6dKlmD59Ojp16gR7e3swxpCSkoLDhw8jLCwM6enpmDFjhlbEJeR9RnVOnJhi1COqcUQZuqZSC/Tu3bvU+a9fv0Z0dLTa432JFVfsNjSRt5yLiwtWrVqFnj17Kp2/e/dujBs3Dk+ePNGKuIRIFdU58WOqIkY9ohpHlKEjlVpg79696NChA+zt7ZXOL29RESuu2G1oIm+558+fo06dOirn165dGy9fvtSauIRIFdU58WOqIkY9ohpHlNLUgJhENW9vb/bLL7+onF/eQWTFiit2G5rIW87f358NHDiQ5eXlKczLy8tjAwYMYP7+/loTlxCpojonfkxVxKhHVOOIMnSkUgs0adIEFy5cwPDhw5XONzAwgKurq9bEFbsNTeQtt2rVKnTs2BF2dnbw9/eHvb09OI5DcnIyTpw4AQMDA0RGRmpNXEKkiuqc+DFVEaMeUY0jytA1lVogJycHBQUFMDY2lkRcsdvQRN7FpaWlYfPmzYiNjUVycjIAwMHBAb6+vhgwYIDKZ9tWVVxCpIjqnPgxSyNGPaIaR0qiTiUhhBBCCKkwGqdSSwUHByMpKUkyccVuQxN5i92WJteBECmQ8r5GdU4zMYm0UKdSS504cQJZWVmSiSt2G5rIW+y2NLkOhEiBlPc1qnOaiUmkhTqVhBBCCCGkwqhTqaXc3Nygp6cnmbhit6GJvMVuS5PrQIgUSHlfozqnmZhEWuhGHUIIIYQQUmF0pFICMjIycOLECcnEFbsNTeRNCNEsqnPixyREbNSplIC7d+8iICBAMnHFbqMyY+bl5eGrr75CzZo10bx5c6xfv14w/+nTp9DR0dGauIS8r6jOiRdTjHpENY4oQ51K8p82b948bNy4EaNHj0bHjh0xadIkjBo1SrBMea4QESsuIYSoS4x6RDWOKEPXVGoBKyurUucXFBQgPT0dBQUFWhFX7DY0kbdcrVq1sGLFCnTt2hUAcO/ePQQFBcHPzw/r1q1DSkoKnJyc1G5LrLiESBXVOfFjqiJGPaIaR5ShTqUWMDExwWeffQZvb2+l8xMSEjB79my1d06x4ordhibyljM2NsaNGzfg7u7OT0tMTES7du3QtGlTLF68GC4uLmq3JVZcQqSK6pz4MVURox5RjSNKMVLlWrVqxVauXKly/qVLl5hMJtOauGK3oYm85Tw8PNiRI0cUpj958oTVrl2btW/fvlxtiRWXEKmiOid+TFXEqEdU44gydE2lFggODsarV69UzreyssInn3yiNXHFbkMTecu1a9cOW7duVZju5OSEY8eOIT4+XqviEiJVVOfEj6mKGPWIahxRhk5/k/+0hIQE3Lp1C506dVI6PykpCRERERgyZIhWxCWEEHWJUY+oxhFlqFNJCCGEEEIqjE5/a5HCwkKV0x8+fKh1ccVuQxN5i92WJteBECmQ8r5GdU4zMYl0UadSC7x58wZ9+/aFiYkJ7O3tMWvWLMEdc6mpqfDw8NCauGK3oYm8xW5Lk+tAiBRIeV+jOle1+RMJqdr7hAhjjI0fP57Vrl2b/fbbb+znn39mbm5uLDg4mOXk5DDGGEtOTmYcx2lNXLHb0ETeYrelyXUgRAqkvK9Rnava/Il0UKdSC7i6urKoqCj+9bNnz1iLFi1Yx44dWXZ2NktOTi7X0AxixRW7DU3kLXZbmlwHQqRAyvsa1bmqzZ9IB53+1gLPnj2Dm5sb/9ra2hqRkZFIS0tDly5dkJmZqVVxxW5DE3mL3ZYm14EQKZDyvkZ1TjMxifRRp1ILuLi44ObNm4JpZmZmiIiIQFZWFnr16qVVccVuQxN5i92WJteBECmQ8r5GdU4zMYn0UadSC3Ts2BHr169XmG5qaorDhw/D0NBQq+KK3YYm8ha7LU2uAyFSIOV9jeqcZmKS90BVn38njL148YJdu3ZN5fy0tDQWHR2tNXHFbkMTeYvdlibXgRApkPK+RnVOMzGJ9NHg54QQQgghpMJ0qzoBUiQjIwNbt27FqVOnkJycDI7jYG9vDz8/P/Tv3x8mJiZaFVfsNjSRt9htaXIdCJECKe9rVOeqNn8iDXSkUgvcuHEDHTp0QGZmJvz9/WFvbw/GGFJSUnD8+HGYmJggIiIC9erV04q4YrehibzFbkuT60CIFEh5X6M6V7X5E+mgTqUWCAgIgIODA3799Vfo6+sL5uXm5iIkJARJSUmIiorSirhit6GJvMVuS5PrQIgUSHlfozpXtfkTCdH0RZxEkZGREbt+/brK+VevXmVGRkZaE1fsNjSRt9htaXIdCJECKe9rVOc0E5NIHw0ppAUsLS1x584dlfPv3r0LS0tLrYkrdhuayFvstjS5DoRIgZT3NapzmolJ3gNV3asljM2aNYtZWFiwJUuWsEuXLrGkpCSWnJzMLl26xJYsWcIsLS3Z7NmztSau2G1oIm+x29LkOhAiBVLe16jOVW3+RDqoU6klFi5cyBwdHRnHcUwmkzGZTMY4jmOOjo5s0aJFWhdX7DY0kbfYbWlyHQiRAinva1TnqjZ/Ig10o46WefDgAZKTkwEADg4O8PDw0Oq4YrehibzFbkuT60CIFEh5X6M6p5mYRJqoU0kIIYQQQiqMbtTREllZWTh58iRu3LihMC87OxsbN27Uqrhit6GJvMVuS5PrQIgUSHlfozqnmZhE4qr27DthjLG4uDjm5ubGX5fi7+/PEhMT+fnJyclMJpNpTVyx29BE3mK3pcl1IEQKpLyvUZ2r2vyJdNCRSi0wdepUeHt7IyUlBXFxcTA3N4efnx8ePnyolXHFbkMTeYvdlibXgRApkPK+RnVOMzHJe6Cqe7WEMTs7O3blyhXBtDFjxjBXV1d27969cv/iEyuu2G1oIm+x29LkOhAiBVLe16jOaSYmkT7dqu7UkqLrUnR1hR/F6tWrIZPJ4O/vj61bt2pVXLHb0ETeYrelyXUgRAqkvK9RndNMTCJ91KnUAnXr1sW5c+fg5eUlmL5q1SowxtC9e3etiit2G5rIW+y2NLkOhEiBlPc1qnOaiUmkj66p1AK9evXCtm3blM77/vvv0b9/f7ByjPwkVlyx29BE3mK3pcl1IEQKpLyvUZ3TTEwifTROJSGEEEIIqTA6UkkIIYQQQiqMOpWEEEIIIaTCqFNJCCGEEEIqjDqVhBBCCCGkwqhTSQghhBBCKow6lYQQQgghpMKoU0kIIYQQQiqMOpWEEEIIIaTCqFNJCCGEEEIqjDqVhBBCCCGkwqhTSQghhBBCKow6lYQQQgghpMKoU0kIIYQQQipMMp1KjuPK9C86OhohISFwd3ev6pR58fHxghxlMhmsra3RpUsXxMTECJblOA5hYWFqt5GYmIiwsDBcunSpcpIu5uLFi/D394eFhQU4jsPKlSsVlgkJCSnT5xMSElLp+VXU/PnzsWfPnjIvX9b1O3DggMrPkuM4jBs3rmKJk0pD9aV0VV1finvz5g3mzZuHpk2bwtzcHAYGBnB3d8ewYcNw4cIFfrkNGzaA4zjEx8dXes7aom3btmjbtq3G292xYwfq168PIyMjcBwnyvdCLjo6utR9csOGDfyy4eHhgtclY/z++++i5UmK6FZ1AmVVsjjOmTMHUVFROHbsmGB6vXr14OLiggkTJmgyvTL5/PPPMWDAABQUFOD69euYPXs2AgICEBMTg8aNG1codmJiImbPng13d3f4+PhUTsL/GjZsGDIyMrB9+3ZYWloq/YP6zTffYPTo0fzrCxcuYOzYsZg/fz4CAgL46ba2tpWaW2WYP38++vTpg549e5b5PX369MHkyZMVphdfvwMHDmD16tXl+iNONIvqS+mqur7I3bt3Dx07dkRKSgpGjx6N2bNnw9TUFPHx8di5cyeaNGmCV69ewcLColJz1Fbh4eEabzM1NRWDBw9G586dER4eDgMDA9SuXVv0dkv+LZGrUaMG///h4eGwsbHRyoMX/xWS6VS2bNlS8NrW1hYymUxhOgCYm5trKi21uLq68vn6+fmhZs2aCAwMRHh4OH7++ecqzk61a9euYcSIEQgKClK5TI0aNQQ7d3Z2NgCgVq1aSj8jdWVlZcHQ0BAcx1U4VmWwt7evlPUSU15eHjiOg66uZHbzKkP1peqUpb4AQEFBAXr16oVnz54hJiYGDRo04Of5+/tjyJAhOHjwIPT09MROucplZmbC2NgY9erV03jbt2/fRl5eHgYNGgR/f/9KiSlfn9JU1t8SMZVlPd53kjn9rQ5lp6fkpxvXr1+POnXqwMjICE2bNkVsbCwYY1iyZAk8PDxgamqKdu3a4e7duwpxjxw5gsDAQJibm8PY2Bh+fn44evRoufOU7yAJCQmlLnft2jX06NEDlpaWMDQ0hI+PD3799Vd+fnR0NJo1awYAGDp0KH9a4F1HyN4VV376KD8/H2vWrOHjlte5c+fw8ccfw93dHUZGRnB3d0f//v0V1l/ebkREBIYNGwZbW1sYGxsjJycHjDHMnz8fbm5uMDQ0RNOmTREZGan0NNCbN28wZcoUeHh4QF9fH87Ozpg4cSIyMjL4ZTiOQ0ZGBn799Vd+/SrjdFJISAhWr17NtyH/V/JU3KZNm+Dl5QVjY2M0atQI+/btU4h1584dDBgwAHZ2djAwMICXlxcfW05+emfTpk2YPHkynJ2dYWBgwH+Py/LdVXVaNywsTOFzr4z9SaqovlRNfdmzZw+uXr2Kr7/+WtChLC4oKOidf9TXrVuHRo0awdDQEFZWVujVqxdu3rwpWOb+/fv4+OOP4eTkBAMDA9jb2yMwMFDhNO+OHTvg6+sLExMTmJqaolOnTrh48WKp7Rdf98jISAwdOhRWVlYwMTFBt27dcP/+fcGybdu2RYMGDXDixAm0atUKxsbGGDZsGD+vZL3KycnBt99+C6//b+++o6K42j+Af3fpvUtRqooVLKiIRBEL2Mur0ViwJZbYYnuDLfZu3mjUaKIxxopGjf7sgg0bxILYxQaIShVFelnu7w+yI8vuIm3WHXw+53COzMze++y49+HuzNx7GzSArq4uLCws4Ovri6tXr3LHMMawceNGNG3aFHp6ejAzM0P//v3l6i5pxIgR+OKLLwAAAwcOlMuXR44cgZeXF/T19WFkZITOnTvL3QWQ5pOIiAj0798fZmZmMhckKsrJyQn3799HaGgo91kq2U7z8/MxZ84c2NnZwdjYGJ06dUJUVJRcWWVpi6W9j7KeXycnJ4VXVUv+v0rz+549exAYGAhbW1sYGhqiZ8+eSExMRHp6OsaMGQNLS0tYWlpi5MiRyMjIqNiJrCwmUMOHD2cGBgZK9zk6OspsA8AcHR1ZmzZt2N9//80OHTrEXF1dmbm5OZs6dSrr3bs3O3bsGNu9ezeztrZm7u7urLCwkHv9zp07mUgkYn369GF///03O3r0KOvRowfT0NBgZ86cKTXW6OhoBoCtXr1aZvvt27cZADZ48GCZOOfPn8/9/ujRI2ZkZMRq167NduzYwY4fP84GDRrEALCVK1cyxhhLS0tj27ZtYwDY3LlzWVhYGAsLC2NxcXFKYypLuUlJSSwsLIwBYP379+fKLYvz588zAGz//v3ctv3797N58+axQ4cOsdDQULZ3717m4+PDrKysWHJyMnec9L3UrFmTjRkzhp08eZIdOHCAFRQUsFmzZjEAbMyYMezUqVNsy5YtzMHBgdna2jIfHx+ujMzMTNa0aVNmaWnJfvrpJ3bmzBn2888/MxMTE9ahQwfu/zYsLIzp6emxbt26ce/v/v37pb43AGz8+PEsPz9f7kda7tOnT1n//v0ZAK7csLAwlpOTw5Xh5OTEWrVqxf766y924sQJ1r59e6apqcmePXvG1XX//n1mYmLC3Nzc2I4dO1hwcDCbPn06E4vFbMGCBXLnu2bNmqx///7syJEj7NixY+zNmzdl/uwqajeMMTZ//nxWMlVUtj2pO8ov6pdfxowZwwCwhw8flno+pKQxR0dHc9uWLVvGALBBgwax48ePsx07djAXFxdmYmLCHj9+zB1Xr149VqdOHbZz504WGhrKDh48yKZPn87Onz/PHbN06VImEonYqFGj2LFjx9jff//NvLy8mIGBwUdziDQ2e3t7NmrUKHby5Em2efNmVqNGDWZvb8/evn3LHevj48PMzc2Zvb09W79+PTt//jwLDQ3l9hXPe/n5+czX15dpamqyGTNmsBMnTrAjR46w2bNns6CgIO640aNHMy0tLTZ9+nR26tQptmfPHla/fn1mbW3NEhISlMb99OlT9ssvvzAAbNmyZTL5cvfu3QwA8/PzY4cPH2b79u1jHh4eTFtbm126dIkrQ5pPHB0dWWBgIAsJCWGHDx9WWqc0t+3bt09hzpWKiIhgLi4urFmzZtxnKSIiQqYMJycnNmTIEHb8+HEWFBTEHBwcWN26dVlBQQFXTlnbYmnvo6zn19HRkQ0fPlzuPZf8f5XG7+joyEaMGMFOnTrFfv31V2ZoaMh8fX1Z586d2YwZM1hwcDBbuXIl09DQYJMmTVJ6Tvn0WXUqbWxsWEZGBrft8OHDDABr2rSpTIJfu3YtA8Du3LnDGCvqoJibm7OePXvKlCmRSFiTJk1Yq1atSo1VmvRXrlzJ8vPzWU5ODrt58yZr2bIlA8COHz8uE2fxpP/VV18xHR0d9uLFC5kyu3btyvT19dm7d+8YY4xdv36dAWDbtm0rNZbyliuNacKECWUqV0pRp7KkgoIClpGRwQwMDNjPP//MbZcm3GHDhskcn5qaynR0dNjAgQNltkv/MBVvhMuXL2disZhdv35d5tgDBw4wAOzEiRPcNgMDA4UNWxkASn927tzJHTdhwgS5zljxMqytrdn79++5bQkJCUwsFrPly5dz2/z9/VmtWrVYWlqazOsnTpzIdHV1WWpqKmPsw/lu166dzHHl+eyWt1NZ0fYkBJRf1C+/dOnShQHgvph9TMlO5du3b7kvkMW9ePGC6ejocJ3vlJQUBoCtXbtWadkvXrxgmpqacn+409PTmY2NDRswYECZYuvbt6/M9itXrjAAbMmSJdw2Hx8fBoCdPXtWrpySnY8dO3YwAGzLli1K65bmy//9738y2+Pi4pienh77/vvvS41dUW6XSCTMzs6Oubm5MYlEwm1PT09nNWrUYG3atOG2SfPJvHnzSq2nZH3Kfop/uWnUqJHM+ShZRsn/+7/++ov74s9Y+dqisvdRnvNb3k5lybimTJnCALDJkyfLbO/Tpw8zNzeXK1cVquXtb2V8fX1hYGDA/d6gQQMARbdMit92kW6X3ja6evUqUlNTMXz4cBQUFHA/hYWF6NKlC65fvy5zS1WZwMBAaGlpQVdXFx4eHnjx4gV+++03dOvWTelrzp07h44dO8Le3l5m+4gRI5CVlSV3a6Gs+Cq3NBkZGQgMDESdOnWgqakJTU1NGBoaIjMzU+72EwD069dP5vfw8HDk5uZiwIABMttbt24td5vj2LFjaNy4MZo2bSrzf+bv78+N4q2MAQMG4Pr163I/pf1fluTr6wsjIyPud2tra9SoUYP73OXk5ODs2bPo27cv9PX1Zd5Ht27dkJOTg/DwcJkyS56zqvrsKou/Iu2puqL8wn+5lREWFobs7Gy524329vbo0KEDd3vT3NwctWvXxurVq/HTTz/h1q1bKCwslHnN6dOnUVBQgGHDhsn8n+nq6sLHx6fM+WXIkCEyv7dp0waOjo44f/68zHYzMzN06NDho+WdPHkSurq63O1xRY4dOwaRSIShQ4fKxG5jY4MmTZpUKDdGRUXh9evXCAgIgFj8oVthaGiIfv36ITw8HFlZWTKvKZmrPmblypUKc661tXWZy+jVq5fM7+7u7gAq1xZLvg8+zq9Ujx49ZH6X5pLu3bvLbU9NTf0kt8A/qyf4zc3NZX7X1tYudbt0sEliYiKAohG/yqSmpsr8QVHku+++w9ChQyEWi2FqagpnZ+ePPqP45s0b2Nraym23s7Pj9lcEX+WWZvDgwTh79ix++OEHtGzZEsbGxhCJROjWrRuys7Plji8ZnzQmRUmk5LbExEQ8ffpU6UP7KSkpFX0bAIoGcrRo0aJSZVhYWMht09HR4c7FmzdvUFBQgPXr12P9+vUKyyj5Pkqes6r67CpS0fZUXVF+4bdcBwcHAEB0dDTq169foZgA+TYijSskJARA0fOxZ8+exaJFi7Bq1SpMnz4d5ubmGDJkCJYuXQojIyPu/0z6rGlJxTtWpbGxsVG4reT5URSzIsnJybCzsyu1/sTERDDGlHbGXFxcylRXcR87t4WFhXj79q3M865lfU/F46rqnKujowMAXM6tSFtUlHOr+vxKVSTHGBoaVri+ivisOpUVZWlpCQBYv3690tFnZfm2VKtWrXI3CgsLC8THx8ttf/36tUxs5cVXucqkpaXh2LFjmD9/PmbOnMltz83NRWpqqsLXlPyDKE0I0oZfXEJCgszVSktLS+jp6eGPP/5QWHZVvz8+mJmZQUNDAwEBAZgwYYLCY5ydnWV+L3nOyvPZ1dXVRW5urtz+ynbASekov5SNv78/Nm/ejMOHD8vkkPLEBEBpXMVjcnR0xNatWwEUjXb+66+/sGDBAuTl5eHXX3/ljj1w4AAcHR3LHYtUQkKCwm116tSR2VbWAZJWVla4fPkyCgsLlXYsLS0tIRKJcOnSJa5TVZyibR/zsXMrFothZmYms11dZvIoriJtUVHOLev5LS3nCuFvlCLUqSwDb29vmJqa4sGDByqfsLpjx444dOgQXr9+zX3LB4AdO3ZAX1+f++CX/MZVVeVWFZFIBMaYXCP7/fffIZFIylSGp6cndHR0sG/fPvznP//htoeHhyM2NlamU9mjRw8sW7YMFhYWch2vkopfHaxKxf9P9PT0yv16fX19+Pr64tatW3B3d+e+fZZHeT67Tk5OSEpKQmJiIpc48/LycPr06XLXS8qO8kvZ9O7dG25ubli+fDl69OihcAT46dOn0bZtW4UjwL28vKCnp4ddu3bhyy+/5La/fPkS586dU3p1ytXVFXPnzsXBgwe5ydX9/f2hqamJZ8+elfs2bnG7d++Wef3Vq1cRGxuLb775pkLlde3aFUFBQfjzzz+V3gLv0aMHVqxYgVevXsk9SlRR9erVQ82aNbFnzx7MmDGD62hlZmbi4MGD3IhwvlU2l1dFWyzP+XVycsKdO3dktj1+/BhRUVHUqazODA0NsX79egwfPhypqano378/atSogeTkZNy+fRvJycnYtGkTL3XPnz8fx44dg6+vL+bNmwdzc3Ps3r0bx48fx6pVq7hJfmvXrg09PT3s3r0bDRo0gKGhIezs7GQSekXKrSrGxsZo164dVq9eDUtLSzg5OSE0NBRbt26FqalpmcowNzfHtGnTsHz5cpiZmaFv3754+fIlFi5cCFtbW5lv5lOmTMHBgwfRrl07TJ06Fe7u7igsLMSLFy8QHByM6dOnw9PTEwDg5uaGCxcu4OjRo7C1tYWRkRHq1atXaiyJiYlyzzNK36d07jg3NzcARc8Cde3aFRoaGuXuHP7888/44osv0LZtW3z77bdwcnJCeno6nj59iqNHj8pNzl1SeT67AwcOxLx58/DVV1/hv//9L3JycrBu3boyd/pJxVB+KRsNDQ0cOnQIfn5+8PLywrfffss9xxobG4sDBw7g6NGjePv2rcLXm5qa4ocffsDs2bMxbNgwDBo0CG/evMHChQuhq6uL+fPnAwDu3LmDiRMn4ssvv0TdunWhra2Nc+fO4c6dO9wVUicnJyxatAhz5szB8+fP0aVLF5iZmSExMRHXrl2DgYEBFi5c+NH3dOPGDXzzzTf48ssvERcXhzlz5qBmzZoYP358uc8PAAwaNAjbtm3DuHHjEBUVBV9fXxQWFuKff/5BgwYN8NVXX8Hb2xtjxozByJEjcePGDbRr1w4GBgaIj4/H5cuX4ebmhm+//bZc9YrFYqxatQpDhgxBjx49MHbsWOTm5mL16tV49+4dVqxYUaH3U9yTJ08U5txatWqhVq1aAIpy7t69e7Fv3z64uLhAV1eXy8NlURVtsTznNyAgAEOHDsX48ePRr18/xMbGYtWqVWq5SEiZfZLhQVWgIqMzS44wVDYVh7KRy6Ghoax79+7M3NycaWlpsZo1a7Lu3buXOsK5tHoUQYnRmYwxdvfuXdazZ09mYmLCtLW1WZMmTRSOwgwKCmL169dnWlpaCsspqazlKjp3H6PoHL58+ZL169ePmZmZMSMjI9alSxd27949uRFw0pGRJUduM8ZYYWEhW7JkCatVqxbT1tZm7u7u7NixY6xJkyZyIykzMjLY3LlzWb169Zi2tjY3Nc/UqVNlpnWIjIxk3t7eTF9fX24UuSIoZSSit7c3d1xubi775ptvmJWVFROJRDIjUZWdU0WjAaOjo9moUaNYzZo1mZaWFrOysmJt2rSRGSH6sdH2Zf3snjhxgjVt2pTp6ekxFxcXtmHDBqWjvyvbntQZ5Zdtcq9Vl/zy7t07tnjxYta8eXNmaGjItLS0mIODAxs6dCi7cuUKd5yiKYUYY+z3339n7u7uXE7o3bu3zBRAiYmJbMSIEax+/frMwMCAGRoaMnd3d7ZmzRqZqWcYKxrh7+vry4yNjZmOjg5zdHRk/fv3/+g0UNLYgoODWUBAADM1NeVGpj958kTmWB8fH9aoUSOF5ZQcJcwYY9nZ2WzevHmsbt26TFtbm1lYWLAOHTqwq1evyhz3xx9/ME9PT2ZgYMD09PRY7dq12bBhw9iNGzdKjb209nz48GHm6enJdHV1mYGBAevYsaPM/wljH0ZNF59Griz1KfuZM2cOd2xMTAzz8/NjRkZG3BQ8pcUsbTslP5dlaYsfex9lOb+FhYVs1apVzMXFhenq6rIWLVqwc+fOKR39XTJ+ZX8ry3uOq5KIMcaqvKdKiApJH9yfP38+Zs+e/anDIYSQUv35558YOXIkrl+/XunBJ4SoE7r9TQTl9u3bCAoKQps2bWBsbIyoqCisWrUKxsbG+Prrrz91eIQQQshnizqVRFAMDAxw48YNbN26Fe/evYOJiQnat2+PpUuXlmu+MkIIIYRULbr9TQghhBBCKk3tV9TZuHEjnJ2duVUiLl26VOrxoaGh8PDwgK6uLlxcXPDrr7/K7N+yZQvatm0LMzMzmJmZoVOnTrh27ZrMMdKF4ov/KJqklhBCCCGEFFHrTuW+ffswZcoUzJkzB7du3ULbtm3RtWtXvHjxQuHx0dHR6NatG9q2bYtbt25h9uzZmDx5Mg4ePMgdc+HCBQwaNAjnz59HWFgYHBwc4Ofnh1evXsmU1ahRI8THx3M/d+/e5fW9EkIIIYQImVrf/vb09ETz5s1l5oVq0KAB+vTpg+XLl8sdHxgYiCNHjsisIz1u3Djcvn1b6VqzEokEZmZm2LBhA4YNGwag6Erl4cOHERkZWbVviBBCCCGkmlLbgTp5eXm4efOm3HJcfn5+uHr1qsLXhIWFwc/PT2abv78/tm7divz8fIXrQGdlZSE/P19u7cwnT57Azs4OOjo68PT0xLJly8q1ZmdhYSFev34NIyMjtVyOiggfYwzp6ekfXeuXEL5QniN8ozwnLGrbqUxJSYFEIpEb0Wttba1wvVSgaM1URccXFBQgJSVF4QL2M2fORM2aNdGpUydum6enJ3bs2AFXV1ckJiZiyZIlaNOmDe7fvy+3IL1Ubm6uzBqer1694lZWIYRPcXFx3IoShKjS69evYW9v/6nDIJ8BynPCoLadSqmS334ZY6V+I1Z0vKLtALBq1SoEBQXhwoUL0NXV5bZ37dqV+7ebmxu8vLxQu3ZtbN++HdOmTVNY7/LlyxUuyxUXFwdjY2Ol8X4OLj9NwbidNyEWAXcW+H/qcKqN9+/fw97eHkZGRqUeJ5FIkJ+fr6KoPl9aWlrQ0ND41GGolPSzp4o8l50nQculZ5Tu/7+J3qhtZcj9nvw+B5ZGOrxdQb0enYqlJx5g6/CWsDDU4aUOQnnuU6loPlPbTqWlpSU0NDTkrkomJSUpnY/QxsZG4fGamppyVxh//PFHLFu2DGfOnIG7u3upsRgYGMDNzQ1PnjxResysWbNkOpzShmBsbPzZdypvvn4JsY4+AHz254IPyv5oMsaQkJCAd+/eqTagz5ipqSlsbGw+m1vB0vdZlXlO2YUD95nHuTyiSN8tt7h/a2uIkScpRAtHMxz4tk2VxFXchnNP8GPwYwCA77priFnRvcrrILIoz6leRfKZ2nYqtbW14eHhgZCQEPTt25fbHhISgt69eyt8jZeXF44ePSqzLTg4GC1atJB5nnL16tVYsmQJTp8+XaYlsnJzc/Hw4UO0bdtW6TE6OjrQ0aFvq4qo71Cw6k2aaGvUqAF9ff3PpqPzKTDGkJWVhaSkJABQ+KgNKd2ZB4n4ZscN7vctw1qghaMZzAy04TTzeLnKypMUAgBuxL5FUnoOahjpfuQV5SPtUEpJ4wvsUh/ftq9dpXWR0lGeq3qVyWdq26kEgGnTpiEgIAAtWrSAl5cXNm/ejBcvXmDcuHEAiq4Ovnr1Cjt27ABQNNJ7w4YNmDZtGkaPHo2wsDBs3boVQUFBXJmrVq3CDz/8gD179sDJyYm7smloaAhDw6JbJzNmzEDPnj3h4OCApKQkLFmyBO/fv8fw4cNVfAaqBwbqVaqaRCLhEq2y54BJ1dLT0wNQdHekRo0an92t8MrIyZfIdCgBYHSJ36XuL/RHQSHD4mMPcODmy4+WvTn0Oeb2KN/z7afvJ2DszpsAgOfLukEs/tBRufQkWenrVp56pLRTmZ6Tj/ScAtiZ6pUrFqIc5Tn+VDSfqXWncuDAgXjz5g0WLVqE+Ph4NG7cGCdOnICjoyMAID4+XmbOSmdnZ5w4cQJTp07FL7/8Ajs7O6xbtw79+vXjjtm4cSPy8vLQv39/mbrmz5+PBQsWAABevnyJQYMGISUlBVZWVmjdujXCw8O5eglRd9Jni/T1ld8uJFVPer7z8/OpU1kCYwxjd95E8INEAEDH+jXQzc0W3d1t8eWviqd8K+nQ+DYw0Cn6s/Xjl03Qr3kt1LYywKrTUThw8yUmd6iD5IxcBF2L414TnZKpsKyZB+9g7/Wi467P6QQrIx0cvxMP91omWH06ijsu+EECujQuulLzNCkdAVs/LJZhYaCNN5l5SuN9mpSBTj+Fymz7fVgLdGpIS8pWBcpz/KpIPlPreSqF7P379zAxMUFaWtpn/xzhgiP38efVGACgZ4+qUGmfsZycHERHR3OrURHVqOrzvnz5csyePRvfffcd1q5dC6Coc7Zw4UJs3rwZb9++haenJ3755Rc0atSIe11ubi5mzJiBoKAgZGdno2PHjti4caPM6Nm3b99i8uTJOHLkCACgV69eWL9+PUxNTcscX2mfQcYYktJzcT0mFS2dzHH1WQqm7rv90TL3jPbE4C3/yG2/HOiLWmaKOw+MMbzPLoCJftFjToWFDEuOP8QfV6IBAB3q10DMm0wM9XRENzdbtF5+Vq6M1i7mCH+eKrd9gm9t/Ne/Ph4lvEeXtbIruj1c1AU//N897oqppliEx0u6QiwWISuvAA3nnVYYL+XBsqM89+lU5Pyq9ZVKQgj5XF2/fh2bN2+WG0i4atUq/PTTT/jzzz/h6uqKJUuWoHPnzoiKiuJGyE6ZMgVHjx7F3r17YWFhgenTp6NHjx64efMmd8Vh8ODBePnyJU6dOgUAGDNmDAICAuSeS6+IqIR0+K+9WO7XnfyuLRrYGnOdrtwCCQb8Fg4Hc32lHUqgaBCHtEMJAGKxCM5WBtzv5x4VPRu26NgDLDr2QGEZijqUABCfloPYN5k4cSdeZvuZae2gp62BH79sgmV93eA69yQKChnG7fpwNVaZsTtvYHHvxqhhXPSH+mOzmhAiFNSpJIQQNZORkYEhQ4Zgy5YtWLJkCbedMYa1a9dizpw5+M9//gMA2L59O6ytrbFnzx6MHTsWaWlp2Lp1K3bu3MnNv7tr1y7Y29vjzJkz8Pf3x8OHD3Hq1CmEh4fD09MTALBlyxZ4eXkhKioK9erVq1T8tqalX9WwNNRBSkauzLaxPi5oYCt7JUpHUwP/N8G7QjEMaeWAHw7fq9Bri/s74hX+jpBdxvfuAj8Y6X7oxGprfpiU+2MdSgA4fT8RD+Lfo39ze6w5UzToJ3hqO7haF30pyM6T4OyjRNQ01UMzBzOl5eRLCqGlIcagzeEIe/4GAOBiZYDnyZkY5uWIf56non09K/zXvx40NWjicCESiUQ4dOgQ+vTp86lDKRP6lBHe0RMWpKxEIlGpPyNGjPhksTk5OXG3oPk2YcIEdO/eXWZRBgCIjo5GQkKCzMphOjo68PHx4VYau3nzJvLz82WOsbOzQ+PGjbljwsLCYGJiwnUoAaB169YwMTFRumJZeRjramH9oGYK912b0xE35nbCP7M7YoJvbWwb2RLRy7thVtcGla63OLFYhLsL/JTuj5zXGY+XdMVE3zoy25s5mGJ6Z1eM8nZW+Lrx7WvLdCg/ppWTOWJWdJe75R2Xms11KAHAb03Rld2Xb7PQYN4pTNxzC303XkXEi7eQFDKsCXmMK09TAABtlp+F08zjqDvnJJxmHuc6lADwPLnoGdIdYbGISkzHbxef48zDpDLHS1QrISEBkyZNgouLC3R0dGBvb4+ePXvi7Fn5RzSEgK5UEt5Rl5KUVXz8h1uM+/btw7x58xAV9WHQhHREYlnl5eVBW1u7yuJThb179yIiIgLXr1+X2yedrULRymGxsbHcMdra2jAzM5M7Rvr6hIQE1KhRQ678GjVqKF2xDJBfOez9+/dKj+3ZxA4dG9SQea7w6y+cuel9rI118V//+kpfXxWMdLXwaHEXPE3KwN8Rr7hnLAHAVL/oczHDvx7uv07D+ahk/NCjIb7+oqgzeelJsszxUk3tTRXW9eOXTTBjv/wzo0a6H/7MHvzWC/02KR+U9DgxHafuyZ7//2ysfCd/3K6buDG3EyzLMEk73YpXnZiYGHh7e8PU1BSrVq2Cu7s78vPzcfr0aUyYMAGPHj361CGWG12pJLyjC5WkrGxsbLgfExMTiEQi7nctLS2MGzcOtWrVgr6+Ptzc3GSmCwOA9u3bY+LEiZg2bRosLS3RuXNnAMCRI0dQt25d6OnpwdfXF9u3b4dIJJKZMPnq1ato164d9PT0YG9vj8mTJyMzM5MrNzY2FlOnTuWumvIhLi4O3333HXbt2lXqg/HlXWlM0TGKjv9YOcuXL4eJiQn387ElGvW1NdHd7cMcd1+2UP0ye7paGmhc0wSzun3owF763lfmmG0jWyFmRXeuQwkAbetaYc9oT+wY1Qr7xrTGgBa1sG5QM3RWMnL774gP0xsFdqmP1i7mAIAV/T48E+vhaI7wWR2Vxuq35iJ+CnmsdH9paprq4fY8P+wd0xoLezXC+kHNsGNUK25/iyVn8DQpHQDwf5GvcPVZ0VXPl2+z4DTzOJxmHkefX67AedYJOM08XuE4SNmNHz8eIpEI165dQ//+/eHq6opGjRph2rRpCA8PV/iau3fvokOHDtDT04OFhQXGjBmDjIwMbv+FCxfQqlUrGBgYwNTUFN7e3twXTgA4evQoPDw8oKurCxcXFyxcuBAFBQVV9p7oSiUhnwnGGLLzJZ+kbj0tjUp3xHJycuDh4YHAwEAYGxvj+PHjCAgIgIuLi8xt3O3bt+Pbb7/FlStXwBhDTEwM+vfvj++++w7ffPMNbt26hRkzZsiUfffuXfj7+2Px4sXYunUrkpOTMXHiREycOBHbtm3D33//jSZNmmDMmDEYPXp0pd5HaW7evImkpCR4eHhw2yQSCS5evIgNGzZwV20TEhJkJiQuvtKYjY0N8vLy8PbtW5mrlUlJSWjTpg13TGKi/LN/ycnJSlcsA5SvHFaaX4Y0x8zULORLCuFSbBlFVdPSEJd71HWb2pbcvz1dSp8H0cb4w5eA7m62GOfjgtyCQuhqyU7FYmOii/BZHZFXUAgHi6LBR+Wd3B0ARno7YX7PRrj0JBnbrsRgad/GMNHXQmsXC7T+N9aSjx51+qn0wVORce+4f687+wQj2zjBzEBYV/qFkudSU1Nx6tQpLF26FAYGBnL7Fc3CkJWVhS5duqB169a4fv06kpKS8M0332DixIn4888/UVBQgD59+mD06NEICgpCXl4erl27xsV0+vRpDB06FOvWrUPbtm3x7NkzjBkzBkDRtIpVgTqVhHc0+bl6yM6XKJ3ihG8PFvlDX7ty6aZmzZoyncFJkybh1KlT2L9/v0ynsk6dOli1ahX3+8yZM1GvXj2sXr0aAFCvXj3cu3cPS5cu5Y5ZvXo1Bg8ejClTpgAA6tati3Xr1sHHxwebNm2Cubk5NDQ0YGRkBBsbm0q9j9J07NgRd+/eldk2cuRI1K9fH4GBgXBxcYGNjQ1CQkLQrFnRM4t5eXkIDQ3FypUrAQAeHh7Q0tJCSEgIBgwYAKDosYJ79+5x58XLywtpaWm4du0aWrUqupr1zz//IC0tjet4KlLRlcPszav/PII9m9rh71tFA3qkncWSHUopGxPZq9CaYhEKCmXzpK6WGDqaGkjLLpqL8fmybkjOyIXnsqJn7aTPoLata4W2da0U1iMSifBkaVfUnXOyQu/p3us0pWWrK6HkuadPn4Ixhvr1y/4IyO7du5GdnY0dO3ZwHdENGzagZ8+eWLlyJbS0tJCWloYePXqgdu2iSfgbNPjwrPLSpUsxc+ZMbiEXFxcXLF68GN9//z11KquT5PRc/PfAbQxu5QC/Rvz9waqox4npOH4nHqPbucBQp/wfGbr9TaqCRCLBihUrsG/fPrx69Yp7vq/kt/ySS69GRUWhZcuWMtukHSmpmzdv4unTp9i9eze3jTGGwsJCREdHyyRmPhkZGaFx48Yy2wwMDGBhYcFtnzJlCpYtW4a6deuibt26WLZsGfT19TF48GAAgImJCb7++mtMnz4dFhYWMDc3x4wZM+Dm5sYN/GnQoAG6dOmC0aNH47fffgNQNKVQjx49Kj3y+3PV3tUKB79tg9pW8ledPubY5C+4OTD7e9TCzdi3WNKnMcz0tbHh/BNM96sHsVgEa2Pdcl9tlV6hLe1q6Aw/V8SlZmPfjThsHd4CK04+wpOkDFyPeSu4TqVQSK8il+cOzsOHD9GkSROZnOft7Y3CwkJERUWhXbt2GDFiBPz9/dG5c2d06tQJAwYM4O5q3Lx5E9evX5f5Qi2RSJCTk4OsrKwqmUSeOpVqYOnxB7gQlYwLUckKE8aTxHTs/ucFxrevzc1rpkrSUYlvs/KwqHfjjxxdfTDG8PJtNmqZ6VWLB9f1tDTwYJH/J6u7sv73v/9hzZo1WLt2Ldzc3GBgYIApU6YgL092RZOSnUxFzwmWvC1YWFiIsWPHYvLkyXL1Ojg4VDr2qvT9998jOzsb48eP5yY/Dw4O5uaoBIA1a9ZAU1MTAwYM4CY///PPP2VWxdi9ezcmT57MjRLv1asXNmzYoPL3U12IRCJ4OCqf/qc09W2MlXYWNw7xULi9vG7P88NXW8Ix0bcO2tezQujjZLRztZK5ULCyf9Hzn/tvvMSTpAysO/sEDW2N0aWx+l3sUEYoea5u3boQiUR4+PBhmacLKu2ZZ+n2bdu2YfLkyTh16hT27duHuXPnIiQkBK1bt0ZhYSEWLlzITUdWXFVNHk+dSjWQXGy+tuiUTDhbyv5R7LXhCrLzJYhKSEfQmNYqielxYjoO3XqFce0+rGN7+2XaR1934OZLFEgK8VWrD3+IY958WCYtLjUL9ub6ZR5hyBjD/dfv4WJlUOnbp+W18cIzrD4dxa2oUVWuRaciJ1+Cdq7yVwCeJWfAzkQPetpVv8SfSCRS+TmsSpcuXULv3r0xdOhQAEUdwSdPnnz0KmL9+vVx4sQJmW03bsiuK928eXPcv38fderITi9TnLa2NiQS1T+rdeHCBZnfRSIRFixYwC0rq4iuri7Wr1+P9evXKz3G3Nwcu3btqqIoiboz0dfCye/acr93KzaAqiTvupY4db9oFPq4XTdxZWYH1FSwZnm+pBBey89xc47emNsJ5x4m4c+rMXgQXzQzQGkrIfFBKHnO3Nwc/v7++OWXXzB58mS5L8Pv3r2Te66yYcOG2L59OzIzM7njr1y5ArFYDFdXV+64Zs2aoVmzZpg1axa8vLywZ88etG7dGs2bN0dUVFSpea6yaPT3JxL27A3G7LiB+LRsmdvDvj9ekDtW+tBx8YeopRhjuPXiLffcjdTz5AxsuvAMWXkVG9Xlt+YiNl14hgVH73/0WMYYnidnIDtPghn7b2Pm33eRnP6ho3zl6Yc51NquOo/w52/QculZ/F/kK0XFyTh5LwE91l9Gw3mn8TgxHUnpOcgrKFR6fOL7nCqbF1O6/u8v558hLTsf91/Ldqozcgtw6l48svPK3tEoLGQY8FsYhv1xDakl1gy+EZOKjv8LRfd1l5S8+vNWp04dhISE4OrVq3j48CHGjh1b6vQ3UmPHjsWjR48QGBiIx48f46+//sKff/4J4MO3+8DAQISFhWHChAmIjIzEkydPcOTIEUyaNIkrx8nJCRcvXsSrV6+QkpLCy3sEgE2bNsHd3R3GxsYwNjaGl5cXTp788EwcYwwLFiyAnZ0d9PT00L59e9y/L9tOc3NzMWnSJFhaWsLAwAC9evXCy5cvZY55+/YtAgICuJHcAQEBMqPhyefrq5ayg6++2hyGGzGyKw5l5hag7pyTMpPYt1hyBt8fvMN1KAHgi5Xnce/Vh9wZ+yYTx+68hqSQnovauHEjJBIJWrVqhYMHD+LJkyd4+PAh1q1bBy8vL7njhwwZAl1dXQwfPhz37t3D+fPnMWnSJAQEBMDa2hrR0dGYNWsWwsLCEBsbi+DgYDx+/Jj74j1v3jzs2LEDCxYswP379/Hw4UPuamZVUftO5caNG7l1Jz08PHDpUul/cENDQ2WGy//6669yxxw8eBANGzaEjo4OGjZsiEOHDlW63vIatCUcwQ8S4bX8HK4+eyO3f/XpR/gt9BkGbf4wrUBBoXxnKuRBIvpuvIpuP3+IjzGGDv8LxcpTj/DjaflpIZ4lZ+B9Tj6S0nMw+9BdPEsumo5AUSO/9eKtzO8v32bhm+03cO5RIl68yUJaVj7+uBKDDv8LRcDWD+v1tlx6BrkFEmTkyndqv9ocjpSMXHy3N5Lb9ijhPfpvuooN555g2l+RSE7PxfA/rmH87gjuGL81F9Fq6Vm4zj2JuNQsfPnrVZz+99v0q3fZmLgnAp7LzsJ5VtFVqew8CRhjiEvNwtbL0UhKz0FUQrpcPAWSQmQqiLM4j8Uh6L7uMi4+TsY/z9/g9b/1jdsVgYX/drwLJIVYfOwBgu8n4PW7bJnXv8nIxdVnKcgt1iFOzczD8+QMnLoXD5/V5xF48A4A4HlKJvIKCnHpSXK5OqzV3Q8//IDmzZvD398f7du3h42NTZluGzk7O+PAgQP4+++/4e7ujk2bNmHOnDkAwA06cXd3R2hoKJ48eYK2bduiWbNm+OGHH2RGWC9atAgxMTGoXbs2rKz4e86sVq1aWLFiBW7cuIEbN26gQ4cO6N27N9dxlC7TuGHDBly/fh02Njbo3Lkz0tM/fLanTJmCQ4cOYe/evbh8+TIyMjLQo0cPmSutgwcPRmRkJE6dOoVTp04hMjISAQEBvL0vIhxaGmIcm/QF93tcajb6/xrGTT3kNPM4xu26Webyeqy/DKeZx9Frw2X4rL6AiXtuYcul53yELijOzs6IiIiAr68vpk+fjsaNG6Nz5844e/YsNm3aJHe8vr4+Tp8+jdTUVLRs2RL9+/dHx44ducdW9PX18ejRI/Tr1w+urq4YM2YMJk6ciLFjxwIA/P39cezYMYSEhKBly5Zo3bo1fvrpJzg6OlbZexIxNV7uZN++fQgICMDGjRvh7e2N3377Db///jsePHig8Dmn6OhoNG7cGKNHj8bYsWNx5coVjB8/HkFBQejXrx+AopUk2rZti8WLF6Nv3744dOgQ5s2bh8uXL3MjSMtbryLv37+HiYkJ0tLSYGwsu/TYu6w8NF0UUqFzYm2sgzUDm8LV2gjLjj/kRhsCwB8jWiD4fiL2Xo+TeY2ThT5+H94CdWoYYdzOm9xtDWX2jWmNgZvl58hyMNfHi9SsCsWtjIejGVo4mmHPtRdIzyn7VdUv6lji8r+rS/w61EMuwe3+xhNDfv9H0Uuxqp87/BpZw1RfG4wxrhO6b0xrtHAyh6SQ4dfQZ5Wepy2wS32MbeeC9eeecitnWBpqIyUj79+4m2PcrojSigAANLQ1xvHJX8g9LlDaZywnJwfR0dHcFyMia+nSpfj1118RFxf38YPLga/zbm5ujtWrV2PUqFGws7PDlClTEBgYCKDoqqS1tTVWrlzJLdNoZWWFnTt3YuDAgQCA169fw97eHidOnOCWaWzYsKHMMo3h4eHw8vLCo0ePyjxYp7TPIBG+sk51dGzSF+ix/jL3+/5xXmjpZI6v/7yOs48Ur+bTxN60TEtwUp77dCpyftW6U+np6YnmzZvL9NgbNGiAPn36YPny5XLHBwYG4siRI3j48CG3bdy4cbh9+zbCwopWMRg4cCDev38vczupS5cuMDMz4yZSLm+9ipTWECoyJxn5vP3Xvx4mlFhOjpJt2W3cuBEtW7aEhYUFrly5gkmTJmHixIky62pXhao+7xKJBPv378fw4cNx69Yt6Orqonbt2oiIiOCmFAKA3r17w9TUFNu3b8e5c+fQsWNHpKamysxT2aRJE/Tp0wcLFy7EH3/8gWnTpsnd7jY1NcWaNWswcuRIhfEoWlHH3t6eOpXVVFkugByf/AUa2ZkAAFIycmGurw2x+MMX4F3hsZirYA32Fo5mOPCt8umrpCjPfToVOb9qe/s7Ly8PN2/elFm/FgD8/PyUrk0bFhYmd7y/vz9u3LiB/Pz8Uo+RllmReoGiZPv+/XuZH0KqivT5TlIxT548Qe/evdGwYUMsXrwY06dPL3Wgy6d29+5dGBoaQkdHB+PGjcOhQ4fQsGHDUpdpLL4EI1/LNJZ3RR0ibKb62ohZ0R33FvrDRK9ovfOBLT78n5/8ri3XoQQAS0MdmQ4lAAxt7Yjny7qhVxM7AMCagU0AADdi3yp8PIoIm9oOkUpJSYFEIik1eZaUkJCg8PiCggKkpKTA1tZW6THSMitSL1CUbBcuXPjR96XGF4YJqbbWrFmDNWvWfOowyqxevXqIjIzEu3fvcPDgQQwfPhyhoaHc/k+1TGNFVtQhwmeoo4nIeZ3BGCAWi9DQzhhetS3gam308Rej6DXrBjXDukHNkJMvwdR9RWukN55/utzzbhL1prZXKqXKmzyVzUdXWjJVVGZ56501axbS0tK4H2XPauWWMnKZEEKAoumL6tSpgxYtWmD58uVo0qQJfv75Z241n5JfcJUt01jaMRVZplFHR4cblS79IZ8HkUjEXYUc3sapzB3KkkquMlRyFgwibGrbqbS0tISGhkapybMkGxsbhcdramrCwsKi1GOkZVakXqDsyVZLQ21PuVKK5ierjpo5mGKsj8snqVtXq/TPRUNb+uP9OWOMITc3F87OztwyjVLSZRqlyysWX6ZRSrpMo/SY4ss0SpVlmUZCqsKZaT7cv8Ofy89+QoRLbXs42tra8PDwkEmMABASEqI06Xl5eckdHxwcjBYtWkBLS6vUY6RlVqTe8tAQixCzojsCWssP4d82siViVnRHxA+dsXFIc4WvH9POBSO9ndDdzRbbRrbE6Snt0LdZzXLHcX1OJ5nfnS0N0LWxDS597yt37MkpbXFjbie4WBrIPE8DAL2b2uHp0q4I7PLxycGNdTXhVvPD8zcNeOooNbE3ldv2z+yOiFnRHXcW+OHR4i44NukLjPOpjeCp7RCzojvOTvfB3jGtMatrAzxe0pV7XZ+mRc8BTevsipgV3RXeqlk7sCm+qGMpM5Ixcl5nzO/ZEKb6WjLHnpvuI7fUZSsnczxa3BX7x8nPSyZV0c5uoYJpqAh/quJ8z549G5cuXUJMTAzu3r2LOXPm4MKFCxgyZAhEIhG3TOOhQ4dw7949jBgxQukyjWfPnsWtW7cwdOhQpcs0hoeHIzw8HKNHj6ZlGolK1KlhyP17/O4IbpqiXeGxFSqP8hw/KnJe1faZSgCYNm0aAgIC0KJFC3h5eWHz5s148eIFxo0bB6DolvOrV6+wY8cOAEUjvTds2IBp06Zh9OjRCAsLw9atW7lR3QDw3XffoV27dli5ciV69+6N//u//8OZM2dw+fLlMtdbFRStmNLcvujBenMDbXRzs4W2plhuou/ALvWhUeJB6DUDm+J/XzbBq3fZuPA4GZceJ+NdVj6+busM91om0BSLYWmojdTMPFgY6sjVO7tbfYwptnLOjlGt8DgxHUuOF42iN9Yt6hidm9EeAPBP9BvEvCmaWujnr4pGoI5p54KWTmZo/G+n8czDREzcc4srs7WLOfaO8UJyei5aLj0DANjzjSeiEtOx6tQj3H2VhnxJ2Z833fW1J4b+Oy/mL4Obo5ubDQ7cfImw52+wrK8bLj5OxpzD95Ccnos/RrSA9b/LW0rfS+OaJlysAFDb6kOS09YUy3Qe1371YZQtUDRdxvcH7iA6pWiloD7NaqLPvx37k9+1hYWhNkz1tTHS2xkjvZ1x6l4Cvj9wGz9/1QwuVoa4Na8zJIVF82c+TsxA+3pFcx42LvbAe0lNFXSUS6OtrQ2xWIzXr1/DysoK2tra1WKpSXXFGENeXh6Sk5MhFouhra1d4bISExMREBCA+Ph4mJiYwN3dHadOnULnzp0B0DKNpHqwNdFFfFqOzLZ3WeW7FU55jh+VyWdqPaUQUDQVyKpVqxAfH4/GjRtjzZo1aNeuHQBgxIgRiImJkVnGLDQ0FFOnTsX9+/dhZ2eHwMBAuc7ggQMHMHfuXDx//hy1a9fG0qVL5dbCLK3esvjY/G0xKZlo/+MF+NazwpROrnCyNOBG10klvc9Bq2Vn4elsjn+iU/GfZjXx08CmZY7hY4LvJ+Dy0xTM69EQmgpuy4c/f4OapnqwN5ddYisy7h0Ctv6D77vUV3jFVUo6ddL5Ge25pSff5+TDfUEwAODeQn+Zq3bpOfnwXHYWWSUm/PZvZI0pnVzRtdgE7zEruqNAUqgw7uJy8iVyz/BUhfc5+WiyMBiN7UxwtNgkwcqUdVnKafsiZeYelbr4X184WMj+P3zsM5aXl4f4+HhkZVXt3KJEOX19fdja2laqUykkNE8lqajifwuk5nRrgNHtZO/KUJ77dCqSz9S+UylUZUm2mbkF0NfWKLWzIe2MpGXlw1hPU22+hRUWMrmpI0qKfZOJlIw8eDjKTm2yMywGEIkUdkhj32Rixv7bMNDRRL/mtWBlpAMPRzNoaYix6cIzrDz1CF9/4YwfejSsyrdTITn5EmhpiOWuHFcF6Tf2nhsuIzuvEOGzOsh1oMvyGWOMoaCg4JOsWf250dDQgKam+rRRVaBOJamM4nM2Wxvr4Eog5Tl1UdF8Rp1KnqSlpcHU1BRxcXGUbKsIYwwvUrPgYK7/2fzhLpAUopAV3ZIvSTqdy7t372BiovzWOSF8oTxHKoMxhpg3mXAwN1D65ZzynLCo9TOVQiZdh5fmcCN8S09Pp2RLPgnKc0RVKM8JA12p5ElhYSFev34NIyOjCl1Vk347q+orAHyVq4o6VBE7n/VUdbmMMaSnp8POzg5isdpO5ECqMcpzVVuHqnIcX3XxUSblOWGhK5U8EYvFqFWrVqXL4WuCYVVMXCzk2PmspyrLpW/u5FOiPMdPHaqcWF4I8VOeEw7q9hNCCCGEkEqjTiUhhBBCCKk06lSqKR0dHcyfPx86OvKTlatjuaqoQxWx81mPquInRCiE3Nb4qEOVOULo8RP1RAN1CCGEEEJIpdGVSkIIIYQQUmnUqSSEEEIIIZVGnUpCCCGEEFJp1KkkhBBCCCGVRp1KQgghhBBSadSpFIDExEQsWrSo0uXk5+fj8OHDWL16NXbt2oXMzMwqiK50Qo4dEH78hAiFkNtaVcT+KXOE0OMnaoQRtRcZGcnEYnG5X+fl5cXevn3LGGMsKSmJubm5MW1tbVa3bl2mq6vLHBwc2MuXL6s4WllCjp0x4cdPiFAIua1VJHZ1iFtK6PET9UFrf6uBO3fulLo/KiqqQuWGh4cjLy8PADBnzhxoaGggNjYWNjY2ePPmDXr16oV58+Zh69atFSofEHbsgPDjJ0QohNzW+IhdlTlC6PETAfnUvVrCmEgkYmKxmIlEIrkf6faKfIMXiUQsMTGRMcaYq6srO3bsmMz+8+fPMycnp8829uoQPyFCIeS2xkfsqswRQo+fCAddqVQDFhYWWLlyJTp27Khw//3799GzZ88KlS0SiQAA7969g7Ozs8w+Z2dnxMfHV6hcKSHHDgg/fkKEQshtja/YVZUjhB4/EQ7qVKoBDw8PvH79Go6Ojgr3v3v3DqyCq2mOGDECOjo6yM/PR2xsLBo2bMjti4+Ph6mpaYXKlRJy7IDw4ydEKITc1viKXVU5QujxE+GgTqUaGDt2bKkj5RwcHLBt27Zylzt8+HDu371790ZGRobM/oMHD6Jp06blLrc4IccOCD9+QoRCyG2Nj9hVmSOEHj8RDhGr6FdDIniZmZnQ0NCArq7upw6l3IQcOyD8+AkRCqG2NaHGLSX0+EnFUKeSEEIIIYRUGk1+riZevnyJOXPmwNfXFw0aNEDDhg3h6+uLOXPmIC4ujpc64+LiMGrUqEqXI+TYAeHHT4hQCLmtqTr2qs4RQo+fCANdqVQDly9fRteuXWFvbw8/Pz9YW1uDMYakpCSEhIQgLi4OJ0+ehLe3d5XWe/v2bTRv3hwSiaTCZQg5dkD48RMiFEJua58i9qrMEUKPnwgHdSrVQMuWLfHFF19gzZo1CvdPnToVly9fxvXr18tV7pEjR0rd//z5c0yfPr1SjV7IsQPCj58QoRByW+MjdlXmCKHHT4SDOpVqQE9PD5GRkahXr57C/Y8ePUKzZs2QnZ1drnLFYjFEIlGpU0WIRKJKNXohxw4IP35ChELIbY2P2FWZI4QePxEOeqZSDdja2uLq1atK94eFhcHW1rZC5R48eBCFhYUKfyIiIioTNleHUGOX1iPk+AkRCiG3NT5iV2WOEHr8RDhonko1MGPGDIwbNw43b95E586dYW1tDZFIhISEBISEhOD333/H2rVry12uh4cHIiIi0KdPH4X7P/Yts7rHDgg/fkKEQshtjY/YVZkjhB4/ERC+14EkZbN3717m6enJNDU1uTVZNTU1maenJ9u3b1+Fyrx48SI7efKk0v0ZGRnswoULFQ2ZI+TYGRN+/IQIhZDbWlXHruocIfT4iTDQM5VqJj8/HykpKQAAS0tLaGlpfeKIyk7IsQPCj58QoRByWxNy7IDw4yfqjZ6pVDNaWlqwtbXFhQsXkJeXV+XlBwUFlbpcV2UIOXZA+PETIhRCbmt8xq6KHCH0+Il6oyuVasrY2BiRkZFwcXERRLmqqEMVsfNZj6riJ0QohNzW+KhDlTlC6PET9URXKtUUX319VXyHEHLsfNZD398IkSXktsZHHarMEUKPn6gn6lQSQgghhJBKo06lmjp58iRq1qwpmHJVUYcqYuezHlXFT4hQCLmt8VGHKnOE0OMn6omeqSSEEEIIIZVGVyoF4OHDh7w8+FxV5d6+fRtLlizBxo0buakqpN6/f49Ro0apVbnloe7nnpDqQt3bGh/5SB1yHMDPuacc95lS9cSYpPwiIyOZWCxWy3JPnz7NtLW1WaNGjZiDgwOztLRk586d4/YnJCRUqA6+yi0vdT73hFQn6tzW+MhH6pLjGOPn3FOO+zzRMo1qYNq0aaXuT05OVqtyi1uwYAFmzJiBpUuXgjGGH3/8Eb169cL+/fvRpUsXtSu3JCGfe0KERMhtjY98pKocB/BzjijHEUXomUo1oKGhgaZNm8LY2Fjh/oyMDEREREAikahFucWZmJggIiICtWvX5rYFBQVh9OjRCAoKQqtWrWBnZ1fuOvgqtyQhn3tChETIbY2PfKSqHAfwc44oxxGFPuVlUlKkXr16bOfOnUr337p1q0K3EfgqtzgrKyt248YNue179+5l+vr6bNOmTRWqg69ySxLyuSdESITc1vjIR6rKcYzxc44oxxFFaKCOGvDw8MDNmzeV7heJRBWaVJavcotr2rQpzp8/L7d94MCB+P333zF58mS1KrckIZ97QoREyG2Nj3ykqhwH8HOOKMcRRej2txpISEhAbm4uHB0dBVFucYcOHcLFixexZs0ahfuDgoKwefNmhcnzU5RbkpDPPSFCIuS2xkc+UlWOA/g5R5TjiCLUqSSEEEIIIZVGo78J+VdsbCwSEhIgEolgbW1dZd/A+SqXEELKi498RDmOSNEzlQJw+/ZtaGhoCKZcVdRRleWuWbMG9vb2cHFxgZeXF1q3bg0XFxfY29tj7dq1alcuIdWREHKFKuuo6jL5yEeU40hJdKVSIPh6SkEVTz+oc+yLFy/Gjz/+iNmzZ8Pf3x/W1tZgjCEpKQmnT5/GggULkJGRgblz56pFuYRUZ+qcKz5FHVVVJh/5iHIcUYSeqVQD//nPf0rdn5aWhgsXLpR7vi++ylVFHaqIHQDs7e2xfv169OnTR+H+Q4cOYeLEiXj16pValEuIUAk5V/BRh6pyHMBPPqIcRxShK5Vq4OjRo+jcuTOsra0V7q9oUuGrXFXUoYrYAeDNmzeoV6+e0v2urq54+/at2pRLiFAJOVfwUYeqchzATz6iHEcUUtWEmEQ5Nzc39vvvvyvdX9FJZPkqVxV1qCJ2xhjz8fFhQ4YMYfn5+XL78vPz2eDBg5mPj4/alEuIUAk5V/BRh6pyHGP85CPKcUQRulKpBjw8PBAREYGvv/5a4X4dHR04ODioTbmqqEMVsQPA+vXr4efnhxo1asDHxwfW1tYQiURISEjAxYsXoaOjg5CQELUplxChEnKu4KMOVeU4gJ98RDmOKELPVKqB3NxcSCQS6OvrC6JcVdShitil0tPTsWvXLoSHhyMhIQEAYGNjAy8vLwwePFjp2rafqlxChEjIuYKPOlSZ4wB+8hHlOFISdSoJIYQQQkil0TyVaqp79+6Ij48XTLmqqEMVsfNZj6riJ0QohNzW+KhDlTlC6PET9USdSjV18eJFZGdnC6ZcVdShitj5rEdV8RMiFEJua3zUococIfT4iXqiTiUhhBBCCKk06lSqKUdHR2hpaQmmXFXUoYrY+axHVfETIhRCbmt81KHKHCH0+Il6ooE6hBBCCCGk0uhKpQBkZmbi4sWLgilXFXWoInZCiOoIOVfwUQflOCJE1KkUgKdPn8LX11cw5aqijqoqNz8/H99//z3q1KmDVq1aYdu2bTL7ExMToaGhoTblElJdqXuuUHUdVVkmH/mIchxRhDqV5LO2dOlS7NixA+PGjYOfnx+mTp2KsWPHyhxTkSdE+CqXEELKi498RDmOKELPVKoBc3PzUvdLJBJkZGRAIpGoRbmqqEMVsQNA3bp1sWbNGvTo0QMA8OzZM3Tt2hXe3t74448/kJSUBDs7u3LXw1e5hAiVkHMFH3WoKscB/OQjynFEEepUqgEDAwN8++23cHNzU7g/NjYWCxcuLHfj5KtcVdShitgBQF9fHw8ePICTkxO37fXr1+jQoQNatGiBVatWwd7evtz18FUuIUIl5FzBRx2qynEAP/mIchxRiJFPrk2bNmzt2rVK90dGRjKxWKw25aqiDlXEzhhjzs7O7MyZM3LbX716xVxdXVmnTp0qVA9f5RIiVELOFXzUoaocxxg/+YhyHFGEnqlUA927d8e7d++U7jc3N8ewYcPUplxV1KGK2AGgQ4cO2LNnj9x2Ozs7nDt3DjExMWpVLiFCJeRcwUcdqspxAD/5iHIcUYRuf5PPWmxsLB49egR/f3+F++Pj4xEcHIzhw4erRbmEEFJefOQjynFEEepUEkIIIYSQSqPb32qksLBQ6fYXL16oXbmqqEMVsfNZj6riJ0QohNzW+KhDlTlC6PET9UedSjXw/v17DBgwAAYGBrC2tsb8+fNlRswlJyfD2dlZbcqtLrFXh/gJEQohtzU+6lBljhB6/ERAPu04IcIYY5MnT2aurq5s//79bMuWLczR0ZF1796d5ebmMsYYS0hIYCKRSG3KrS6xV4f4CREKIbc1PupQZY4QevxEOKhTqQYcHBzY+fPnud9TUlKYp6cn8/PzYzk5OSwhIaFCUzPwVW51ib06xE+IUAi5rfFRhypzhNDjJ8JBt7/VQEpKChwdHbnfLSwsEBISgvT0dHTr1g1ZWVlqVW51iZ3PelQVPyFCIeS2xkcdqswRQo+fCAd1KtWAvb09Hj58KLPNyMgIwcHByM7ORt++fdWqXFXUoYrY+axHVfETIhRCbmt81KHKHCH0+IlwUKdSDfj5+WHbtm1y2w0NDXH69Gno6uqqVbmqqEMVsfNZj6riJ0QohNzW+KhDlTlC6PETAfnU998JY6mpqezevXtK96enp7MLFy6oTbmqqEMVsfNZj6riJ0QohNzW+KhDlTlC6PET4aDJzwkhhBBCSKVpfuoASJHMzEzs2bMHV69eRUJCAkQiEaytreHt7Y1BgwbBwMBArcqtLrFXh/gJEQohtzU+6lBljhB6/EQY6EqlGnjw4AE6d+6MrKws+Pj4wNraGowxJCUlITQ0FAYGBggODkbDhg3VotzqEnt1iJ8QoRByW+OjDlXmCKHHT4SDOpVqwNfXFzY2Nti+fTu0tbVl9uXl5WHEiBGIj4/H+fPn1aLc6hJ7dYifEKEQclvjow5V5gihx08ERNUPcRJ5enp67P79+0r33717l+np6alNuaqoQxWx81mPquInRCiE3Nb4qEOVOULo8RPhoCmF1ICZmRmePHmidP/Tp09hZmamNuWqog5VxM5nPaqKnxChEHJb46MOVeYIocdPBORT92oJY/Pnz2cmJiZs9erVLDIyksXHx7OEhAQWGRnJVq9ezczMzNjChQvVptzqEnt1iJ8QoRByW+OjDlXmCKHHT4SDOpVqYsWKFczW1paJRCImFouZWCxmIpGI2draspUrV6pdudUl9uoQPyFCIeS2xkcdqswRQo+fCAMN1FEz0dHRSEhIAADY2NjA2dlZrctVRR2qiJ3PelQVPyFCIeS2xkcdqswRQo+fqDfqVBJCCCGEkEqjgTpqIjs7G5cvX8aDBw/k9uXk5GDHjh1qVa4q6lBF7HzWo6r4CREKIbc1PupQZY4QevxEID7t3XfCGGNRUVHM0dGRey7Fx8eHvX79mtufkJDAxGKx2pRbXWKvDvETIhRCbmt81KHKHCH0+Ilw0JVKNRAYGAg3NzckJSUhKioKxsbG8Pb2xosXL9SyXFXUoYrY+axHVfETIhRCbmt81KHKHCH0+ImAfOpeLWGsRo0a7M6dOzLbxo8fzxwcHNizZ88q/I2Pr3KrS+x81qOq+AkRCiG3NT7qUGWOEHr8RDg0P3WnlhQ9l6KpKftf8csvv0AsFsPHxwd79uxRq3JVUYcqYuezHlXFT4hQCLmt8VGHKnOE0OMnwkGdSjVQv3593LhxAw0aNJDZvn79ejDG0KtXL7UqVxV1qCJ2PutRVfyECIWQ2xofdagyRwg9fiIc9EylGujbty+CgoIU7tuwYQMGDRoEVoGZn/gqVxV1qCJ2PutRVfyECIWQ2xofdagyRwg9fiIcNE8lIYQQQgipNLpSSQghhBBCKo06lYQQQgghpNKoU0kIIYQQQiqNOpWEEEIIIaTSqFNJCCGEEEIqjTqVhBBCCCGk0qhTSQghhBBCKo06lYQQQgghpNL+H12ui+DFGy87AAAAAElFTkSuQmCC\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":"> Same as full dataset except that prices are decreasing ","metadata":{"slideshow":{"slide_type":"skip"}}},{"cell_type":"markdown","source":"### Feature Engineering ","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"> I will construct some features which could help our predication of the target variable.\n\n > Firstly I will need to split the dataframe into dataframes of the same asset to ensure that the features are valid and then do the same transformation on each indivdual dataframe and in the end concat the results again.\n \n > There are 7 infinty values are replace with nans that are then filled later \n \n > I will construct log returns of the Close, open , high , low , and vwap columns The justification for this since the target variable is sort of a log return plus log reutrns and since logarithmic returns are additive over time unlike simple returns. \n \n > I will construct an expotentially weigthed mean series varaiable for the Close, open , high , low , and vwap columns to give higher weights to closer observations. \n \n > Next I will constrcut a lag variable for the Close, open , high , low ,target and vwap columns as feeding in lag variables can sometimes help with time series prediction. \n \n\n ","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"markdown","source":" > Since the time gaps are not consistent even though most of them are 1 hour apart I will use the last observation to fill in for the gaps.\n \n > Next I will fill the na values with the last valid observation since na values are a small percentage of the total and then in case the first value is nan I will use back filling afterwards to ensure no nans anywhere.\n \n > Finally I will scale all input variables to be in the range of 0 and 1 using max - min normalization which is basically X_scaled = (X-X_min)/(X_max-X)","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"code","source":"# define function to compute log returns https://lucaslouca.com/Why-Use-Logarithmic-Returns-In-Time-Series-Modelling/\ndef log_return(series, periods=15):\n return np.log(series).diff(periods=periods)","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":48,"outputs":[]},{"cell_type":"code","source":"df_new = pd.DataFrame()\nscaler = preprocessing.MinMaxScaler()\ncols=['Count', 'Open', 'High', 'Low', 'Close', 'Volume', 'VWAP',\n 'Close_log', 'Open_log', 'High_log','Low_log', 'VWAP_log',\n 'Close_ewp', 'Open_ewp', 'High_ewp', 'Low_ewp',\n 'VWAP_ewp', 'Close_lag', 'Open_lag', 'High_lag', \n 'Low_lag', 'VWAP_lag','Target_lag']\ndf.replace([np.inf, -np.inf], np.nan, inplace=True)\n#we have to do the transformation per asset \nfor asset in df.Asset_Name.unique():\n df_asset=df[df[\"Asset_Name\"]==asset]\n #calculate log return\n df_asset[['Close_log','Open_log',\n 'High_log','Low_log','VWAP_log']] = (df_asset[['Close','Open','High','Low','VWAP']].apply(log_return))\n #calculate expontential weighted series\n df_asset[['Close_ewp','Open_ewp',\n 'High_ewp','Low_ewp','VWAP_ewp']] = (df_asset[['Close','Open','High','Low','VWAP']].ewm(alpha=1/15).mean())\n #introduce lag variables\n df_asset[['Close_lag','Open_lag',\n 'High_lag','Low_lag','VWAP_lag','Target_lag']] = df_asset[['Close','Open','High','Low','VWAP','Target']].shift(15)\n #forward padding for each missing X interval\n df_asset = df_asset.reindex(range(df_asset.index[0],df_asset.index[-1]+3600,3600),method='pad')\n \n df_asset.fillna(method=\"ffill\",inplace=True)#filling values that are nans \n df_asset.fillna(method='bfill',inplace=True)#if first value is missing we drop it\n \n df_asset[cols] = scaler.fit_transform(df_asset[cols])\n df_new=pd.concat([df_new,df_asset])#concat everything to recreate the dataframe\ndf=df_new\ndf.sort_index(inplace=True)#sort by timestamp again \ndf[\"datetime\"] = pd.to_datetime(df.index,unit='s')#create matching datetime again ","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":49,"outputs":[{"name":"stderr","output_type":"stream","text":"/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/Users/youssefmecky/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arraylike.py:397: RuntimeWarning: invalid value encountered in log\n\n result = getattr(ufunc, method)(*inputs, **kwargs)\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:13: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_log','Open_log',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:16: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_ewp','Open_ewp',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3488916155.py:19: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_asset[['Close_lag','Open_lag',\n"}]},{"cell_type":"code","source":"#confirm no nulls\ndf.isnull().sum().any()","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":100,"outputs":[{"execution_count":100,"output_type":"execute_result","data":{"text/plain":["False"]},"metadata":{}}]},{"cell_type":"code","source":"#check that gaps are all apart x hours now \nfor asset in df.Asset_Name.unique():\n df_asset=df[df[\"Asset_Name\"]==asset]\n print(asset,'Gaps')\n print((df_asset.index[1:]-df_asset.index[:-1]).value_counts().head())","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":51,"outputs":[{"name":"stdout","output_type":"stream","text":"Bitcoin Cash Gaps\n\n3600 35616\n\nName: timestamp, dtype: int64\n\nBitcoin Gaps\n\n3600 35616\n\nName: timestamp, dtype: int64\n\nEthereum Classic Gaps\n\n3600 35616\n\nName: timestamp, dtype: int64\n\nLitecoin Gaps\n\n3600 35616\n\nName: timestamp, dtype: int64\n\nMonero Gaps\n\n3600 35616\n\nName: timestamp, dtype: int64\n\nBinance Coin Gaps\n\n3600 35616\n\nName: timestamp, dtype: int64\n\nEOS.IO Gaps\n\n3600 35616\n\nName: timestamp, dtype: int64\n\nEthereum Gaps\n\n3600 35616\n\nName: timestamp, dtype: int64\n\nTRON Gaps\n\n3600 34730\n\nName: timestamp, dtype: int64\n\nStellar Gaps\n\n3600 34488\n\nName: timestamp, dtype: int64\n\nCardano Gaps\n\n3600 33063\n\nName: timestamp, dtype: int64\n\nIOTA Gaps\n\n3600 32535\n\nName: timestamp, dtype: int64\n\nMaker Gaps\n\n3600 32505\n\nName: timestamp, dtype: int64\n\nDogecoin Gaps\n\n3600 24417\n\nName: timestamp, dtype: int64\n"}]},{"cell_type":"code","source":"df.head()","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/html":["
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Asset_IDCountOpenHighLowCloseVolumeVWAPTargetAsset_Name...Open_ewpHigh_ewpLow_ewpVWAP_ewpClose_lagOpen_lagHigh_lagLow_lagVWAP_lagTarget_lag
timestamp
151476486020.0034960.8266910.8266390.8248200.8259390.0012450.825023-0.004218Bitcoin Cash...0.8914140.8959260.8879120.8899930.8259390.8266910.8266390.8248200.8250230.530628
151476486010.0040360.1630300.1647430.1609350.1631950.0041120.162937-0.014643Bitcoin...0.1657040.1675020.1635850.1655790.1631950.1630300.1647430.1609350.1629370.151985
151476486070.0001300.1445910.1432500.1477050.1456890.0004290.145325-0.008264Ethereum Classic...0.1773780.1698620.1802990.1773410.1456890.1445910.1432500.1477050.1453250.435197
151476486090.0076400.5292820.5326540.5241050.5263500.0033690.527684-0.009791Litecoin...0.5623590.5671070.5576850.5620490.5263500.5292820.5326540.5241050.5276840.326989
1514764860110.0008910.6232330.6210850.6239270.6217210.0009640.621932-0.024506Monero...0.6693600.6697600.6703630.6700260.6217210.6232330.6210850.6239270.6219320.350696
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5 rows × 27 columns

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"],"text/plain":[" Asset_ID Count Open High Low Close \\\n","timestamp \n","1514764860 2 0.003496 0.826691 0.826639 0.824820 0.825939 \n","1514764860 1 0.004036 0.163030 0.164743 0.160935 0.163195 \n","1514764860 7 0.000130 0.144591 0.143250 0.147705 0.145689 \n","1514764860 9 0.007640 0.529282 0.532654 0.524105 0.526350 \n","1514764860 11 0.000891 0.623233 0.621085 0.623927 0.621721 \n","\n"," Volume VWAP Target Asset_Name ... Open_ewp \\\n","timestamp ... \n","1514764860 0.001245 0.825023 -0.004218 Bitcoin Cash ... 0.891414 \n","1514764860 0.004112 0.162937 -0.014643 Bitcoin ... 0.165704 \n","1514764860 0.000429 0.145325 -0.008264 Ethereum Classic ... 0.177378 \n","1514764860 0.003369 0.527684 -0.009791 Litecoin ... 0.562359 \n","1514764860 0.000964 0.621932 -0.024506 Monero ... 0.669360 \n","\n"," High_ewp Low_ewp VWAP_ewp Close_lag Open_lag High_lag \\\n","timestamp \n","1514764860 0.895926 0.887912 0.889993 0.825939 0.826691 0.826639 \n","1514764860 0.167502 0.163585 0.165579 0.163195 0.163030 0.164743 \n","1514764860 0.169862 0.180299 0.177341 0.145689 0.144591 0.143250 \n","1514764860 0.567107 0.557685 0.562049 0.526350 0.529282 0.532654 \n","1514764860 0.669760 0.670363 0.670026 0.621721 0.623233 0.621085 \n","\n"," Low_lag VWAP_lag Target_lag \n","timestamp \n","1514764860 0.824820 0.825023 0.530628 \n","1514764860 0.160935 0.162937 0.151985 \n","1514764860 0.147705 0.145325 0.435197 \n","1514764860 0.524105 0.527684 0.326989 \n","1514764860 0.623927 0.621932 0.350696 \n","\n","[5 rows x 27 columns]"]},"metadata":{}}]},{"cell_type":"code","source":"df.describe()","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/html":["
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Asset_IDCountOpenHighLowCloseVolumeVWAPTargetClose_log...Open_ewpHigh_ewpLow_ewpVWAP_ewpClose_lagOpen_lagHigh_lagLow_lagVWAP_lagTarget_lag
count476680.000000476680.000000476680.000000476680.000000476680.000000476680.000000476680.000000476680.000000476680.000000476680.000000...476680.000000476680.000000476680.000000476680.000000476680.000000476680.000000476680.000000476680.000000476680.000000476680.000000
mean6.5198460.0108850.1778960.1763070.1804560.1778040.0115550.181674-0.0001350.565887...0.1858150.1848760.1865000.1858160.1786480.1787550.1771540.1813070.1824720.474797
std4.0670800.0264200.2031710.2022780.2032060.2030520.0266850.2025950.0076190.101877...0.2148140.2142750.2152530.2148120.2028770.2030100.2021170.2030240.2026580.118476
min0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000-0.2452920.000000...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%3.0000000.0009260.0381560.0374110.0392160.0381930.0013640.039518-0.0017250.524633...0.0352350.0351350.0353450.0352350.0384980.0384670.0377190.0397150.0395230.367529
50%7.0000000.0034230.0856670.0845200.0893290.0856380.0043750.097341-0.0000120.582889...0.0873840.0866140.0879310.0873950.0878520.0878590.0864630.0910980.0977040.490050
75%10.0000000.0103610.2691610.2669770.2724870.2691120.0114830.2700070.0017260.627857...0.2881150.2864860.2892860.2881120.2692130.2692410.2670900.2726530.2701020.575063
max13.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000000.2727561.000000...1.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n","

8 rows × 25 columns

\n","
"],"text/plain":[" Asset_ID Count Open High \\\n","count 476680.000000 476680.000000 476680.000000 476680.000000 \n","mean 6.519846 0.010885 0.177896 0.176307 \n","std 4.067080 0.026420 0.203171 0.202278 \n","min 0.000000 0.000000 0.000000 0.000000 \n","25% 3.000000 0.000926 0.038156 0.037411 \n","50% 7.000000 0.003423 0.085667 0.084520 \n","75% 10.000000 0.010361 0.269161 0.266977 \n","max 13.000000 1.000000 1.000000 1.000000 \n","\n"," Low Close Volume VWAP \\\n","count 476680.000000 476680.000000 476680.000000 476680.000000 \n","mean 0.180456 0.177804 0.011555 0.181674 \n","std 0.203206 0.203052 0.026685 0.202595 \n","min 0.000000 0.000000 0.000000 0.000000 \n","25% 0.039216 0.038193 0.001364 0.039518 \n","50% 0.089329 0.085638 0.004375 0.097341 \n","75% 0.272487 0.269112 0.011483 0.270007 \n","max 1.000000 1.000000 1.000000 1.000000 \n","\n"," Target Close_log ... Open_ewp High_ewp \\\n","count 476680.000000 476680.000000 ... 476680.000000 476680.000000 \n","mean -0.000135 0.565887 ... 0.185815 0.184876 \n","std 0.007619 0.101877 ... 0.214814 0.214275 \n","min -0.245292 0.000000 ... 0.000000 0.000000 \n","25% -0.001725 0.524633 ... 0.035235 0.035135 \n","50% -0.000012 0.582889 ... 0.087384 0.086614 \n","75% 0.001726 0.627857 ... 0.288115 0.286486 \n","max 0.272756 1.000000 ... 1.000000 1.000000 \n","\n"," Low_ewp VWAP_ewp Close_lag Open_lag \\\n","count 476680.000000 476680.000000 476680.000000 476680.000000 \n","mean 0.186500 0.185816 0.178648 0.178755 \n","std 0.215253 0.214812 0.202877 0.203010 \n","min 0.000000 0.000000 0.000000 0.000000 \n","25% 0.035345 0.035235 0.038498 0.038467 \n","50% 0.087931 0.087395 0.087852 0.087859 \n","75% 0.289286 0.288112 0.269213 0.269241 \n","max 1.000000 1.000000 1.000000 1.000000 \n","\n"," High_lag Low_lag VWAP_lag Target_lag \n","count 476680.000000 476680.000000 476680.000000 476680.000000 \n","mean 0.177154 0.181307 0.182472 0.474797 \n","std 0.202117 0.203024 0.202658 0.118476 \n","min 0.000000 0.000000 0.000000 0.000000 \n","25% 0.037719 0.039715 0.039523 0.367529 \n","50% 0.086463 0.091098 0.097704 0.490050 \n","75% 0.267090 0.272653 0.270102 0.575063 \n","max 1.000000 1.000000 1.000000 1.000000 \n","\n","[8 rows x 25 columns]"]},"metadata":{}}]},{"cell_type":"markdown","source":"### Feature selection","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"> I will split the data to test , train and validation where I will use the validation set for PCA and hyper parameter tuning \n\n> I will use Principal component analysis to select featrues as it is an orthogonal linear transformation to project it to a lower dimensional space while keeping information loss to a minimum it is based on SVD\n\n","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"code","source":"#test data are the last 3 month validation the 3 months before that and training all the data before that\ndf_test=df[df['datetime']>df.datetime.iloc[-1]-timedelta(days=90)]\ndf_valid=df[(df['datetime']<=df.datetime.iloc[-1]-timedelta(days=90)) &(df['datetime']>df.datetime.iloc[-1]-timedelta(days=180)) ]\ndf_train=df[df['datetime']<=df.datetime.iloc[-1]-timedelta(days=180)]","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"X=df.drop(['Target','Asset_Name','datetime'], axis=1)\nY=df['Target']","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"\nY_test=df_test['Target']\nY_valid=df_valid['Target']\nY_train=df_train['Target']\n\nX_test=df_test.drop(['Target','Asset_Name','datetime'], axis=1)\nX_valid=df_valid.drop(['Target','Asset_Name','datetime'], axis=1)\nX_train=df_train.drop(['Target','Asset_Name','datetime'], axis=1)","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":56,"outputs":[]},{"cell_type":"markdown","source":"## PCA","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"markdown","source":"PCA is the most common featrue selection technique in stock market prediction according to Htun, H.H., Biehl, M. & Petkov, N. Survey of feature selection and extraction techniques for stock market prediction. Financ Innov 9, 26 (2023). https://doi.org/10.1186/s40854-022-00441-7 ","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"code","source":"pca = PCA(svd_solver='randomized',n_components=3)#randomized is an approximate solution of the svd but which is quite faster\npca.fit(X_valid)","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/html":["
PCA(n_components=3, svd_solver='randomized')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"],"text/plain":["PCA(n_components=3, svd_solver='randomized')"]},"metadata":{}}]},{"cell_type":"code","source":"print(pca.explained_variance_ratio_)","metadata":{"slideshow":{"slide_type":"fragment"}},"execution_count":58,"outputs":[{"name":"stdout","output_type":"stream","text":"[0.96686758 0.02952356 0.00258641]\n"}]},{"cell_type":"markdown","source":"> Looks like the first Principal compnonet(PC) accounts for most of the variance in the dataset \n\n> For the purpose of this project I will use the first 3 PC this was after a few try and errors ","metadata":{"slideshow":{"slide_type":"fragment"}}},{"cell_type":"code","source":"X_test=pca.transform(X_test)\nX_valid=pca.transform(X_valid)\nX_train=pca.transform(X_train)","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":59,"outputs":[]},{"cell_type":"markdown","source":"### Modelling\n> I will start by a very simple linear regression model to check how it preforms, providing to the model the reduced dataset.\n\n> Next I will try a stacked LSTM RNN that is also fed the reduced dataset.\n\n>Finally I will use a state of the art model namely temporal fusion transformer which takes care of featrue selection so it will be fed all of the dataframe without the PCA.","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"#### A.Base model: Linear regression","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"code","source":"from sklearn.metrics import mean_absolute_percentage_error\nreg = LinearRegression().fit(X_train, Y_train)\nY_pred=reg.predict(X_test)\nmse=mean_squared_error(Y_test,Y_pred)\nprint('MSE on test set:{}'.format(mse))","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":60,"outputs":[{"name":"stdout","output_type":"stream","text":"MSE on test set:2.4179288756775408e-05\n"}]},{"cell_type":"code","source":"#Prediction plot\nplt.plot(np.arange(len(X_test)), Y_test, color=\"black\",label='True signal')\nplt.plot(np.arange(len(X_test)), Y_pred, color=\"blue\",label='predicted signal');\nplt.legend();","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":61,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":"#### B. Time series based models\n##### LSTM","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"markdown","source":"> RNN 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7V2BI/3Fev5GMYyfxJwYAvIfHEasbr2RvZn4tIVife6XEKNjhxKuu9JPyA4qL8jlJltW7wISAh+X2TDQr8MXLAzWOxcW/6xm/1h2V6GxGGR9hAEwNwe6Z7uq0ztiSE3OjBlOf8dVYPe+PIh4SdYeeFyGXyZAvk5+sSLO9Z5h+wqHesPyxHdf6W01QNYhWybcEFLmfB3EYGasD6fjWstjjSc6At7uTXRd9Xg5OHT968siUib36sTIfFHo9PPL5GbLui/AEyXM9XiEtH16cZ8wGZGnQO5h6Jn2QEmrw4Od9tsEPwa7qVRuCoRB58CqBbtzRlcuUACrpOdtWlTrQcCVRGROrrojYADPjpcbNN9H3NOzAlxKJ0pYTqaupQpIqI6GrVZJPuN79SClvQA01qVzgbQNLk6d9u7/Hj74TciTaLThKT3J0MP2vtz1orrfbQ1MQDwIc5gcqh9OYaElCN1EQzcQwNvxWSDcwrHbvyVIRvwQu10Y40Y9jr/OxKjTA7Pf2I3OJh05SB/R3WpH8EvOk2bJrkkq7c53Zv7qYxePCYB+P7qRWU4u53JC+8PfU/SeUdKqP6h8ITwHNB9BqgIRWCP44Nf9u5IIeMDnO3VCLCowClGgKPpSgtff8mg9LxSe6gTVbc+n6dcKj+zpM0t5Ksks8UDV6RZSXHXZxK4bC3qyVNRc9BOwyGsinx4Na0NDyU5I+GWJdND/nfdY/2y/GIoipsqOPY9IUrkCbLJqCS2uxYcgNhlXaNk47xYc+fEATqj2gL+UqyN5MyWA8dLyw8I+lynldbN64/O8DGj1PlT/21vW2a2Yq6g/p4Gvu92INcDmmC60L8DeX/wiT2FrFVZekhi2amzw6Vgx7BJ+5oaXKSuWF8w5BPLCg4IFBHdNI7q7ldVo0jQY0zcwlEaXnEO3kQ9+RjLRcZ+CZHMSktloEhKnPW8I+SYE91QndGK3yIPB/ulc8okmV/4cR1i61u0tJSx5LckpZiJGjvoErErlBwlya3Jr/scwi/Wc0kgFqrjwJKLvFialTTJDtQHStkJc9I3jc/fGb3tG89NTu9gW1E6aqPd2itaiTadRTPcMPlRrnMI98/fstCfEHot6MdQa/86CJ5iC8a/lI+EAeZKXJoiNeLlRTgTp18qG8+Vm2/rrEoNszL5Y9apcxjFq+KvTjCJffwn9FKJC2eZSdDf6XVtzt65BGi8bjLyHykRiF0ra/v1XG2/+MrdYMYtIvXK3c5Tr4c1t1DCtPqeQBV78s/k47c38SIqLwb6RQdpp1QNElOcIJQaaGm/9X14FcZDJB0JjofPlI0YbaMOdFAEsswrEX83xm932UjmXfuzMSTg5AUB06OBuM0dnRiZgDWEcFQ9jQcZpyfTpe506Cm1aTNmqAkjaNqsuS1y1MOVg7svLC/+ho6n+Z5wzv6XmyNWu4mA3mZ8SqrgMtPA2HuchAb5T2zuImgofo5TJV4i5kkF3V/ck2mQj31xiN02Rfa1HXFH04F/PWG2q5KiOzRAc9AF/9Q37Xpf8kDw/qWK+QlkiO36d+MqTEQX37PAlj3VizfH0p73pPoHwzvbzwzLYqPR0RKQSVVIKjAqWsiLC4aE8gEN0MSD4s9+6BUkGzgdpb2txIqZi33xPEM31IoHNojFYPzLpfEcqPfVsqHlA+quKkY/rCJYQBf8ZOsfBgRi/+9FRhTMaZqTSeJEKCDXlNy+49/A3M5X3Xx0qRXNb3r2FkU8JIzWUUb4TyDWjzqCqNTnAi7a9YCl7DGzlpo98W877pVwnLEHAqfZxpcLG3GlHYzQwSs8ha9m+2vIkKb9myzjKQCKhTXpVDabU3aPcl/2Yjv7pEPPx7x5iQj9EJcSkO2zFp23oxCJm1hfwoodrZHXt3xO42BViSTb9kr2dhrA+VL2uJpMlFrUpy3AdLB0A+6CLHO/ilU2K8ZM00hP4/ihEh0GrGLWXmPQcNzjdbmWWke5UxtsDGn6sZ6Fo+9n/pXykjeSFDclw83+2vvPlGtkIximJpIk7jgJ1adZDnSbh9JbWGfw8t78hnJ6rF1RVtYCJphvHgecnGxiXFbSwq1OoeEinkPadHrnzM7cbPSUx8y/jX1OeYCrSMU8eiYmVeibyaGT7nCoZslaV5c8VPpO+y6bXRD/EHNwzGkCkB71OxuwM3wQaQQhZj5CJaKX29nWucproSbkqKdrJv1vxWO6P4cK7CzP26XdqIsJKUlysAT6TCSI30sEO60QOyq5eZ2E7FhTP9yWwrLVclsdPY1pSnuiKSDlSvbkyYofPiCMbznlEbxsf/2RDPJzo6UHQyfJJuy/ktUC+zV4UsxUKEw3T5rxY1/OTobzefCzGClhWyvLE6/UGO8xdDfMJsVo6rOj0Czn9EyOv+VrKvrrLTsKA2yEmqwyqyk1oE/PISoq00D+zd5SFzjRCm7I4+gOTsbehtYiwRwFV5sYLruG5aVr82PoDWXUjwy6/NBos/vB2xM8IB//E8KR2nM/GVDGRApRtWMszvbY5+tkLjpObpM9gX24mWoQ9EKOMjAacMM8sGY61yVZ/aCsjR7ZO2CKWjQCGpygWq11+ZiT92lG4icn27x0aZjPMW3NtYcCah3BTehhPjC+hKfAY2EMESdQzgzuCZSnflWUD1Bzbr43JPdiVFEilfD/x3AU5FbWRcttseizG8/gRSdbxHnz4zwTFWEF9kQIwNnNvEjGq2cBE+hwKlbXARUHG6NhK2f+vkQpdH4O5JIIXF51lEQldJ58H1y6VfKd8MHVIHJw3DB2w96QNs1yzZUmeb+SgIkYPEj+4nVRU0qnNUGdTgwSEI6+KAr5CXhjTedCtoYm63BUwvAEaE0IZVNk+H1JZNfTZ4O6u3pW7d/Y36IYzaSPaI+0oIgXciR0oVf5MzIRY0IxU9wSXLIeoRwSSl+17c4YwmNWtJArkrsWLDAfCZwBjR1e9cvEcpN0iYR+oDm09SLIrbxXJ2OEy7Bt8kuanlU9l+gCceH2lwRc+k9bmMtAo8WKT/XLuDGqWNTvCDG+tQj0uKo8XzvuMrVheqRNU6d2g1mbaPZD1nbSiPdigTeaATryk5Vi3+GMJQAFMfnTaO1mWt0Bqi/ttTcarlcBjh3bVqnXogbbs+iZjI4mXlq2lIWKwI+K2ISyaG9wNFWgsp3vv6O/WZ9t3dR7bSG5cjjKR1MiSiUKC/jXsXb6qh7Z/opzxceOfjApbPkzxgVi9+65nWC1oPh/4j/hsAPnKqjqAHZ/kSPOVFBfIjCU3M7CTdL5D70T+R6tLM+MLp3zisUv22XWDun/CB0Ak3E5imENugEdecYU+pmi8/RcDoKVDVMOUSokhoHgcG3++3fhPbKOk1CnmEqGo9scYCiN1Zt+UTgWTkfMyPAdf1RBVpz9Ao+lVysooCJT7MT0dCemktTe1xr3xaVYaRAVxejBmJQf+jVyQ9tgs5RDLfFv5O6Me95hsmfY3hNmMb1Y7t644EaIUI6Xw8odK+X8Y5UHdxdp36uAnBdnnaxe87FnIaCJdBiiEijlhOlORxyX/+IE0uma2/4aJJEmCc6PlJ0MZL6TUYYYWzXvi87Zg6+T0bRd6C8v8mRfnaNUMqCnpPqYlOWoVl9raZe8YCRJ8X1YvhoXWha7GzDirJj9pQv6AlVHiluz+c6kz2vwrugTNt+K/K11SpcnoLGKasDyKojN/QZBJpVYgTqTBm2gt7W7d9u48OIly5LSqgTM6GmaZtPIoZhJFPdaxflTNqQt+4J2cEWVnuYnE+4ZuoJMIitJqLgxQAi3FUBzGPgdm78Myz6CiX5UUU7LiQQiOx5TBD2EGjViSD8nsE6TzSNL3drjKd4KOKODVXpmBiLfHtvs3rQ4u3ZOZBZIpowPdSMYoexvnNXHDeJpNmgouSuP13PUACqBkdv1v+kppBNvGZDMLAJgeglGG4EnE+P2WVTdCgKklWGd3MCdt1COQTeERjXoo5w9N0mH3P/7VxCwbcSLxQImKe4+i1mECDb3pM3ow6NG9OUuzdLAh3UgCYfoDj/FE7wBZ2tKne83vFf5GPJYaYThhMT1iH7lY3f4VWfnW4CDJsOv2Jd6fYmr7bE1kHKJTsc0UzBTcNaXJ0iISALMID9nEgRB/BtpJ+B2gjmQdgdn69sle3nPwckCKo91MNgvUTDi+Jn6E5YSGCYQmunt3UCwVTfC7ROxHYVpgpOqJwnNtJJXMY+NEN3H3OEx8RLX8JTHopuSVgHMM8Zo299T+7qCPei1GF0WQTtpwF7jfI8yvLUB+cV8z4ClRYTOJexIw2DFjjGyTZQEKmkJbRsWXdaZBXmsEHyfYAjaSqJ4Gcg8yFInrgTUCOZ3E26GiVuJqzyDCsPJBLgKzVxc+sVcaqFYQB40FqMhAplaHdXrqpxjuSeh+M/Q4b0euTyWiSLPePu+8iAGRET+22H5uNbfmVL6uaBf5XMSqmJ5lwoXg7/rxqPYYbVqLvlEoIDpMRFrTABdN+ifqLz5TX/oWDYcdoLyAeCj2IiqPm2OUaZD+ZeW1KHyPXjcTLYH8weOXT4Kn0Ej/S61CoWxRSQWOJN13VsNYoSa1+TT1mArBdWntiziItbrYJ9zzTdtuSXASdcrh42/KTRiR4l2eNig5W9GJP6fzSZEfy+YECGL6961TcwHP97XR0zFRC6+Ef6lSutn9ALHipuX7vXB5ReO1nKFij865f6QTAvBJQ/OROQe0J6gT5Qp+SvDjIoj6mzIJAEkE9SdeZM2w1hrzlptSSFI8Mp/toi+bLdVx6FFt+2sDkxC5PrKiPqrNd9eHu8mxae8SCRwjIttAxQHBl2sw1gjpq9uV/THsdooY1pPd7aeTPtGTFG02uo+BgUhmnv6M3TMzmZ3jbz+xQjohvBVXUNgD4Q5zyTZw2y1jTBPsGer471tKxlzsUkRdCNAVzW1wYIL6QOG9r8NyIaVzqjSulreXowW0/q8yVtjURHHrd55Z5CZ9kXrdLeEezF8A7Ja2PVBUo7bBCM7pwO3weygNoxWw/2Zf5V2SuArJc7Tf6hTENPUkQLePtVfXxqfPUsSkeRZvnOgIVAN3SLm/2ctXtIHER3Z3GRkr/PIkzb2eAnT/eDQLi3G9tU5sjYFAkja6MYKkh/5tZtMhIcaq5kVOd3ne8nk8LSKBNELFhxn5kW/QULMOlz0aFaV7ZG5PC/FtNIFJnaz1FJs7QAOx+ZkNuEp+PVk6XJqNIDBHBOehoOyVUo8gOkkpehlvZB33ggGNrnvczYdcqLkixso7vrV7xZDIrFd7eZaydq69ZJV02rvOmaoz9cTbiotXt5Hgw3a7NubjGAocSOOjHTH/XXWJ7I5b9zp/84ecSG0pyjQtlJ+DiBnQs/zaBP1aCqgxx8LFLklhk+BojV71BamsQUOEA0ZrGuwUjKoOGud7Ub/rwbYcDpgxtQNWgZylpAhmPhcxylHg/9NUAZQSRFneAc0ZiAUzpyYgITkDEpM4BdOe0G428cfwqovgdmMRTM6OPv2J8CpwRO9Nw/mhu+Egnh8ppFHoFlNaM/EsMOwKEV6rDYWwfIGWgpM09RMSqC22IkYHlxjb/C6dGDql7r/r4romyTSthDaZ3xBhneyImDL0l19qltqqPABk38Vd+crGA3nctmQiJ8f5YQ8FNGs97oZmO8WHe5EPgCvJbsvezmYv666ubJEpth8VGPPX5pQoXXdv6kQxuPUxnXueG6yXZo7U1XvjhIvSUo94aOHVDXSTbNmal8FsC8VtnS2QNNf+Xx8Zc6eLO1Sn989dr75nuUzs+USaej2REcIhyyi09i0b3xsnMWIFvBTjDQpd5qokfHACjoC8y/eq+7ehQcKXJYaNi1AtN9a3bpjUIHVQ/w8Ac2b+uU6T5uB1luqgh5T6O1Ku7CajWN9prJC9XvIMPPA7CWlhN4jHvFbMEoRhFPE7iDnsBQC/z38IWxRtsvZXaq4t8OYR4w70xLYNfCTCSgy2YF/Kwtkk1V2sg+/aiGx+1GZI8zQivIlifoYE3s+8lX8J+gPpgOPL+4vvXsPbkY9yFltrQegOUzshTOa4h/55SM8LkmW0h2p/FtNvK/JptPQk27hW2dpQDPcuOmrj1hH6CRID5+44HgtrvJzkEx/UZ3XSKWNY/+n2p7B2snhrrrv6Mdn9DHcCuVCGlQNSm0jnQ/hVUGtoH5sgqp2uGJwb5NTMdKiHEcEKFLk/I865weo+qKEp0nhHk01VEw06gkZYCgmEjLMFdjutNn5jVxvfOjOvL4j11m8nGO42IV8hSXLa/T/wHP8dYIQSobV+2pyvz67QdB/yVaiHLWsqS6rTuKDgUeYsVU5G0BdppyV7enlbRvdkAxEI3E6F2w6cEyn2jZUuFT6+DWWyt5LtafpgNq5F4CYIU/85VuSvfTy+UtCz2AMcG/rV81AkhV81mQHBIzhrsoMVcLigfw3YrzuVpA2FzkMb5guUVY6K/AFCRf3UMgRccae+g4PrVBcThrQiZU0sQ9+gSSBNTMQGxpI1t+eiYDHns3rG/pBFvpfzyoI+21dvzrheDGI9RasrjKZKUXN0UGJqlBd+3d0ZxWgpAd4BYJzI4f3kex0x36XiCYq0lQERE63ufIsrVBnmriouV0AzXM2Z5iKv8nNotqnveQBW2oTCLnckQF7ey6fRN7hUZvCRkPC1vqROy/SoqCsrZ3yBotW4t1IIguTcrEenppMLRV9fFRsCJeRCboui06tLffm1O06gc/yWSQ0r11GxddBZz6HRbDa5q6VK/xgBuy2wwOgm3ClRQ7WTgyuD0bExeevE1RPP8x6GXnkdn6t7JdO97oZHsrcElwsHfm5ayNya7RgXV1FHBPGW+wIAND7iOBeRoZfNX0fH86xLj9DIgwaRKK0RjqA2Ojsbp7ycmfEOVOxtgVMxRUvI5hcddw06DpmiZBCyZy0mhK6RZr9wOveb3W2hWQ1XeIpa8vCqaMCfgnwBY54WmJeK9lMZ7ITF5BQ5o6k3sAXiB5m47M8GRfGM0l6qByICqLinCksGHQe6rYtt1YWIrCMpePOaUYkzGF/IZ0BGTImk9Z/Mlv7EQA649w8B+sBNH3SvKsiMTYDycp5s/sYI9/zVl2mESBB2Rg5azD+ApUkoIM+Nu49VXy5uGz66r+Qz2k+OtoMZcjtb+aYADYVM8Q8fKdLQJkgCaOIH4gz3uG1qfFsFJQ0aV8daM6766bXeX3np3dzSilHOC2is25GM1MnobJgh6zq94w92BLyyLsm3j3gN1eWhfHleyOo65tgKq9aTrzMLD7wcO5yZS4Mqwyjeu0eGFcrBVP3PTrm7kewFH9cvw6vDzQzJtkO740bHdYzW+3FCl/5+QkLe+w/IRuLNQ2G9rVKdBSH7VfXl/64AY28Bf+g27UqeUoQq8OjyCb6d2CVZdRtUW0HrB/wxtkNA0o7IjngDR/+21NxqwxhUGcGCKoREiPf1ld2B+pOv/jqlurV5xrQT45ifUTPIySWDuGNPfUIILF898qWnGB40yZhYgKMMC5bqrr7dMWBUz6P040dq/MedElAVKwRSUb9jU5B/JEdnhAgCPtlHum80pLnHQZkqGfJZD/kZPPlRZeYX5UfwdTXLqz9FFmRWFJqB3kknw4pOfDF1Eh6wLLSZc/CVOJcPLTkc3+0WiIIWY0vdqmY2gVUlkNNn2othykHa5bnnLVwrEAXP9DJ6VjPkP1ET8w5i0G/6bs8fT21R6MTEy5ZxstftbbKY9SeqIKyU33L73fEs7I/lRpXnnsnGO8TJnPNPPCG39JdE+hjMoAnyA25Cwtg9b84U6g3rJN4cREJW+5LsBgqAdsLdvWcen5MNo+G+k3HuxAIz4+aZyyEtaDrX455XDxAA12tbEabC/QM82A2cZOtyBL0+sSfKemeIFrxc0oWxZzHCmou5KeWqFI6PuUCj1R3AfEaEYblvuqoAtU9du4XLZYtP3SPMmpw/J47WP5EzDLg08fowssMp7Or2uf/AjITkilgsNZuXmgUWg9S7aaJMy/IyPlGTjdXucQ3N+zIj4AyB7idX/gqAntAWNPwvrozS1tmiCW/NVISZlAyLw+U834iA+nBabz+qjlmzAenZOdm63lv1yIG1tyFK+6sGbwZ5B3oyl7pzSoueGFf9Vog1HDgl7qvBnEU5TmU5WcyieA3UtiXz+x+IX7o0caT8ABUHf4CVMBgLaazRPo8U8uV6ZTPwKURgMP2BADAOYzLbegC2Uy07fmwD36mfyJ0lS2uUijyFfL+Li/557O8E7TQ/sg+BGmwkjOxxflOfyQdchmwZkpnxdib2jAbURO8K2GRkAD+AAAk1Zj6g8JQIgguL3pvPHOQqPRJFpDhEpZRmMgK1Fz63VJbZOsi7ThlaXO1oPW7mr4OvHqnw3SwM9AM+ONugufvMh46wA2FUYOc5H6dPSK4e1ld0tS7SPVZLpYwX6o+XTEFprsATR6xtDNfWK0/rnUGQlzRCUevd5uTdkh3qiAAL0J0j9p2/kSjgHUuFF+hRH+l0KbST4AAAABFWElGugAAAEV4aWYAAElJKgAIAAAABgASAQMAAQAAAAEAAAAaAQUAAQAAAFYAAAAbAQUAAQAAAF4AAAAoAQMAAQAAAAIAAAATAgMAAQAAAAEAAABphwQAAQAAAGYAAAAAAAAASAAAAAEAAABIAAAAAQAAAAYAAJAHAAQAAAAwMjEwAZEHAAQAAAABAgMAAKAHAAQAAAAwMTAwAaADAAEAAAD//wAAAqAEAAEAAADQAgAAA6AEAAEAAAAIAQAAAAAAAA=="}}},{"cell_type":"markdown","source":">LSTM\n\n![1*Zrht4QBK5_hAxif17ED4ew.webp](attachment:1*Zrht4QBK5_hAxif17ED4ew.webp)\n\nrefernece 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"}}},{"cell_type":"code","source":"from torch.utils.data import Dataset,DataLoader\n#Create dataset and dataloader with sequence to input for LSTM\nclass timeseries(Dataset):\n '''\n Time series dataset class which takes in our X and Y and produces a sequence of 15 previous datapoints (last 15 hour)\n if the there is not enough datapoints it pads the X and Y with the last valid observation\n '''\n def __init__(self,x,y,sequence_length=15):\n self.x = torch.tensor(x,dtype=torch.float32)\n self.y = torch.tensor(y.to_numpy(),dtype=torch.float32)\n \n self.len = x.shape[0]\n self.sequence_length=sequence_length\n\n def __getitem__(self, i): \n #To create batch sequence input to lstm for x and y\n if i >= self.sequence_length - 1:\n i_start = i - self.sequence_length + 1\n x = self.x[i_start:(i + 1), :]\n y=self.y[i_start:(i + 1)]\n else:\n padding_x = self.x[0].repeat(self.sequence_length - i - 1, 1)\n padding_y=self.y[0].repeat(self.sequence_length - i - 1)\n x = self.x[0:(i + 1), :]\n y=self.y[0:(i + 1)]\n x = torch.cat((padding_x, x), 0)\n y=torch.cat((padding_y, y), 0)\n\n return x, y\n \n def __len__(self):\n return self.len\n\ndataset_train = timeseries(X_train,Y_train,sequence_length=15)\ndataset_val = timeseries(X_valid,Y_valid,sequence_length=15)\ndataset_test = timeseries(X_test,Y_test,sequence_length=15)\n#dataloader\n \ntrain_loader = DataLoader(dataset_train,shuffle=False,batch_size=32)\nval_loader = DataLoader(dataset_val,shuffle=False,batch_size=32)\ntest_loader=DataLoader(dataset_test,shuffle=False,batch_size=1)","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":62,"outputs":[]},{"cell_type":"code","source":"#Simple LSTM model\nclass LSTMmodel(torch.nn.Module):\n '''\n Simple lstm followed by a fully connected layer\n All the weights and biases are initialized from U(-sqrt(k),sqrt(k)) where k=1/hidden_size\n \n '''\n def __init__(self,input_features,hidden_features,num_layers=1,drop_out=0):\n super(LSTMmodel, self).__init__()\n self.input_features=input_features#number of input features\n self.hidden_features=hidden_features# The number of features in the hidden state \n self.num_layers=num_layers #number of lstm layers\n self.drop_out=drop_out#specify dropout parameter\n self.lstm = nn.LSTM(input_size =self.input_features, hidden_size =self.hidden_features,num_layers =self.num_layers ,batch_first=True,dropout =self.drop_out) \n self.linear = nn.Linear(self.hidden_features, 1)#fully connected layer to produce output \n \n \n\n def forward(self, x):\n lstm_out, _ = self.lstm(x)\n pred = self.linear(lstm_out)\n \n return pred","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":63,"outputs":[]},{"cell_type":"code","source":"class EarlyStopper:\n '''\n Early stopping mechanism to stop learning if validation doesn't imporve by min_delta for a number of epochs\n called patience\n '''\n def __init__(self, patience=1, min_delta=0):\n self.patience = patience\n self.min_delta = min_delta\n self.counter = 0\n self.min_validation_loss = np.inf\n\n def early_stop(self, validation_loss):\n if validation_loss < self.min_validation_loss:\n self.min_validation_loss = validation_loss\n self.counter = 0\n elif validation_loss > (self.min_validation_loss + self.min_delta):\n self.counter += 1\n if self.counter >= self.patience:\n return True\n return False","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":64,"outputs":[]},{"cell_type":"code","source":"def train_lstm(config):\n '''\n Function to train_lstm and validate as well takes as input the config for hyperparameter optimization\n '''\n epochs=10\n model=LSTMmodel(input_features=3,hidden_features=2,num_layers=config['NL'],drop_out=config['DO'])\n criterion = nn.MSELoss()#I use MSE loss \n optimizer = torch.optim.Adam(model.parameters(), lr=config['LR'])#use adam optimizer\n train_loss=[]#list of train losses\n val_loss=[]#list of validation losses\n early_stopper = EarlyStopper(patience=3, min_delta=0.0001)#early stopping \n #check length of loader \n for i in range(epochs):\n running_loss=0\n for step, (x, y) in enumerate(train_loader):\n\n y_pred = torch.squeeze(model.forward(x))\n loss = criterion(y_pred,y)\n loss.backward()\n optimizer.step()\n running_loss+=loss.item()\n train_loss_epoch=running_loss/len(train_loader)\n train_loss.append(train_loss_epoch)\n #print('epoch',i,'training_loss',train_loss_epoch)\n \n running_val_loss=0\n with torch.no_grad(): \n for step, (x, y) in enumerate(val_loader):\n y_pred = torch.squeeze(model.forward(x))\n loss = criterion(y_pred,y)\n running_val_loss+=loss.item()\n val_loss_epoch=running_val_loss/len(val_loader)\n val_loss.append(val_loss_epoch)\n if(not(config['Out'])):\n tune.report(mse=val_loss_epoch)\n if(val_loss_epoch<=min(val_loss)):\n torch.save(model.state_dict(), 'best-model-parameters.pt')\n if early_stopper.early_stop(val_loss_epoch):\n #print('early stop')\n break\n \n\n #print('epoch',i,'val_loss',val_loss_epoch)\n if(config['Out']):\n return model","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":80,"outputs":[]},{"cell_type":"code","source":"analysis = tune.run(\n train_lstm, config={\"LR\": tune.grid_search([0.000001, 0.001]),\n \"DO\":tune.grid_search([0.1, 0.3]),\n \"NL\":tune.grid_search([2, 4]),\n \"Out\":False},metric=\"mse\",mode='min',\n )","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":81,"outputs":[{"name":"stderr","output_type":"stream","text":"2023-03-06 14:16:36,162\tWARNING callback.py:108 -- The TensorboardX logger cannot be instantiated because either TensorboardX or one of it's dependencies is not installed. Please make sure you have the latest version of TensorboardX installed: `pip install -U tensorboardx`\n"},{"output_type":"display_data","data":{"text/html":["
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Tune Status

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Current time:2023-03-06 14:37:44
Running for: 00:21:08.34
Memory: 6.0/8.0 GiB
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System Info

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Resources requested: 0/8 CPUs, 0/0 GPUs, 0.0/2.88 GiB heap, 0.0/1.44 GiB objects\n","
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Trial Status

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Trial name status loc DO LR NL iter total time (s) mse
train_lstm_1f308_00000TERMINATED127.0.0.1:99046 0.11e-06 2 10 811.3250.0114635
train_lstm_1f308_00001TERMINATED127.0.0.1:99054 0.31e-06 2 7 626.2720.00801787
train_lstm_1f308_00002TERMINATED127.0.0.1:99055 0.10.001 2 5 456.0590.146285
train_lstm_1f308_00003TERMINATED127.0.0.1:99056 0.30.001 2 7 621.9560.00985838
train_lstm_1f308_00004TERMINATED127.0.0.1:99057 0.11e-06 4 10 1256.73 0.0025027
train_lstm_1f308_00005TERMINATED127.0.0.1:99058 0.31e-06 4 10 1257.14 0.0206702
train_lstm_1f308_00006TERMINATED127.0.0.1:99059 0.10.001 4 7 986.5290.011962
train_lstm_1f308_00007TERMINATED127.0.0.1:99060 0.30.001 4 4 659.0670.0129949
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Trial Progress

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Trial name date done episodes_total experiment_id experiment_tag hostname iterations_since_restore msenode_ip pid time_since_restore time_this_iter_s time_total_s timestamp timesteps_since_restoretimesteps_total training_iterationtrial_id warmup_time
train_lstm_1f308_000002023-03-06_14-30-12True a7387372bf35478287204ee8309c70420_DO=0.1000,LR=0.0000,NL=2youssefs-MacBook-Air.local 100.0114635 127.0.0.199046 811.325 60.4239 811.325 1678109412 0 101f308_00000 0.00288677
train_lstm_1f308_000012023-03-06_14-27-13True d1af454e221f419c929e030bf3fa52101_DO=0.3000,LR=0.0000,NL=2youssefs-MacBook-Air.local 70.00801787127.0.0.199054 626.272 84.9365 626.272 1678109233 0 71f308_00001 0.015239
train_lstm_1f308_000022023-03-06_14-24-23True d895ebc0c7194b9a9c9a01b22de5ee362_DO=0.1000,LR=0.0010,NL=2youssefs-MacBook-Air.local 50.146285 127.0.0.199055 456.059 90.0795 456.059 1678109063 0 51f308_00002 0.00904799
train_lstm_1f308_000032023-03-06_14-27-09True 56cede66f8b04ccdbb802ac40c4dca5c3_DO=0.3000,LR=0.0010,NL=2youssefs-MacBook-Air.local 70.00985838127.0.0.199056 621.956 85.6434 621.956 1678109229 0 71f308_00003 0.0058639
train_lstm_1f308_000042023-03-06_14-37-44True e173a83a007a4fda8b492d37c011c7244_DO=0.1000,LR=0.0000,NL=4youssefs-MacBook-Air.local 100.0025027 127.0.0.199057 1256.73 91.9128 1256.73 1678109864 0 101f308_00004 0.00781417
train_lstm_1f308_000052023-03-06_14-37-44True f517a30a822944dfb3d0f52b4c0be98f5_DO=0.3000,LR=0.0000,NL=4youssefs-MacBook-Air.local 100.0206702 127.0.0.199058 1257.14 91.8864 1257.14 1678109864 0 101f308_00005 0.00423503
train_lstm_1f308_000062023-03-06_14-33-14True 3994669dfc78499fb8ce2b746271e6776_DO=0.1000,LR=0.0010,NL=4youssefs-MacBook-Air.local 70.011962 127.0.0.199059 986.529 102.694 986.529 1678109594 0 71f308_00006 0.0362999
train_lstm_1f308_000072023-03-06_14-27-46True 0459f9c467ed44f69be42b0ca15279aa7_DO=0.3000,LR=0.0010,NL=4youssefs-MacBook-Air.local 40.0129949 127.0.0.199060 659.067 149.78 659.067 1678109266 0 41f308_00007 0.00515413
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error+=torch.sum(y_pred-y)**2\n y_out.append(y.tolist()[0][-1])\n y_pred_out.append(y_pred.tolist()[-1])\n mse=error/len(test_loader)\n","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":84,"outputs":[]},{"cell_type":"code","source":"print('MSE on test set:{}'.format(mse.item()))","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":85,"outputs":[{"name":"stdout","output_type":"stream","text":"MSE on test set:0.3142533600330353\n"}]},{"cell_type":"code","source":"np.shape(y_pred_out)","metadata":{},"execution_count":118,"outputs":[{"execution_count":118,"output_type":"execute_result","data":{"text/plain":["(30239,)"]},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(np.arange(len(y_out)), y_out, color=\"black\",label='True signal')\nplt.plot(np.arange(len(y_out)), y_pred_out, color=\"blue\",label='Predicted signal');\nplt.legend();","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":86,"outputs":[{"output_type":"display_data","data":{"image/png":"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"]},"metadata":{}}]},{"cell_type":"markdown","source":"### State of the art model\n#### Temporal Fuison transformer :Temporal Fusion Transformer (TFT) is a Transformer-based model that leverages self-attention to capture the complex temporal dynamics of multiple time sequences.","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"markdown","source":"\n![tft1.jpeg](attachment:tft1.jpeg)\n> Inputs model can work 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"}}},{"cell_type":"markdown","source":"![tft2.jpeg](attachment:tft2.jpeg)\n> Model architecture \n\nReference :Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021). Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4), 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Main features \n- gating mechanisms: skip over unused components of the model architecture\n- variable selection networks: select relevant input variables at each time step.\n- temporal processing of past and future input with LSTMs (long short-term memory)\n- multi-head attention: captures long-term temporal dependencies\n- prediction intervals: per default, produces quantile forecasts instead of deterministic values","metadata":{"slideshow":{"slide_type":"subslide"}}},{"cell_type":"code","source":"#We need a timestep unit of an increase one in each index basically we calculate hours and use that \ndf_train.reset_index(inplace=True)\ndf_train[\"timestamp\"] = df_train[\"timestamp\"]\ndf_train[\"time_idx\"]=((df_train.timestamp/(60*60))).astype('int')","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":87,"outputs":[{"name":"stderr","output_type":"stream","text":"/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/1888610158.py:3: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_train[\"timestamp\"] = df_train[\"timestamp\"]\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/1888610158.py:4: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_train[\"time_idx\"]=((df_train.timestamp/(60*60))).astype('int')\n"}]},{"cell_type":"code","source":"\ndf_valid.reset_index(inplace=True)\ndf_valid[\"timestamp\"] = df_valid[\"timestamp\"]\ndf_valid[\"time_idx\"]=((df_valid.timestamp/(60*60))).astype('int')","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":88,"outputs":[{"name":"stderr","output_type":"stream","text":"/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/4244872979.py:2: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_valid[\"timestamp\"] = df_valid[\"timestamp\"]\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/4244872979.py:3: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_valid[\"time_idx\"]=((df_valid.timestamp/(60*60))).astype('int')\n"}]},{"cell_type":"code","source":"df_test.reset_index(inplace=True)\ndf_test[\"timestamp\"] = df_test[\"timestamp\"]\ndf_test[\"time_idx\"]=((df_test.timestamp/(60*60))).astype('int')","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":89,"outputs":[{"name":"stderr","output_type":"stream","text":"/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/1152990966.py:2: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_test[\"timestamp\"] = df_test[\"timestamp\"]\n\n/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/1152990966.py:3: SettingWithCopyWarning: \n\nA value is trying to be set on a copy of a slice from a DataFrame.\n\nTry using .loc[row_indexer,col_indexer] = value instead\n\n\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n df_test[\"time_idx\"]=((df_test.timestamp/(60*60))).astype('int')\n"}]},{"cell_type":"code","source":"max_prediction_length = 15#max prediction sequence\nmax_encoder_length = 15 #This is the maximum history length used by the time series dataset.\ntraining = TimeSeriesDataSet(\n df_train,#data frame\n time_idx=\"time_idx\",#incremental time index \n target=\"Target\",\n group_ids=[\"Asset_ID\"],#every asset_ID is a different time series\n max_encoder_length=max_encoder_length,\n max_prediction_length=max_prediction_length,\n static_reals =[\"Asset_ID\"],#variables that don't change in time\n time_varying_known_reals=[\"timestamp\"],#variables that change in time but are known in advance\n time_varying_unknown_reals=['Count', 'Open', 'High', 'Low', 'Close', 'Volume', 'VWAP',\n 'Close_log', 'Open_log', 'High_log','Low_log', 'VWAP_log',\n 'Close_ewp', 'Open_ewp', 'High_ewp', 'Low_ewp',\n 'VWAP_ewp', 'Close_lag', 'Open_lag', 'High_lag', \n 'Low_lag', 'VWAP_lag','Target_lag'],#unkown variables in time \n add_relative_time_idx=True,# variable to encode relative time index\n add_encoder_length=True,# variable to indicate where in the sequence are we \n)","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":138,"outputs":[]},{"cell_type":"code","source":"validation =TimeSeriesDataSet(\n df_valid,\n time_idx=\"time_idx\",\n target=\"Target\",\n group_ids=[\"Asset_ID\"],\n \n max_encoder_length=max_encoder_length,\n max_prediction_length=max_prediction_length,\n static_reals =[\"Asset_ID\"],\n time_varying_known_reals=[\"timestamp\"],\n time_varying_unknown_reals=['Count', 'Open', 'High', 'Low', 'Close', 'Volume', 'VWAP',\n 'Close_log', 'Open_log', 'High_log','Low_log', 'VWAP_log',\n 'Close_ewp', 'Open_ewp', 'High_ewp', 'Low_ewp',\n 'VWAP_ewp', 'Close_lag', 'Open_lag', 'High_lag', \n 'Low_lag', 'VWAP_lag','Target_lag'],\n add_relative_time_idx=True,\n add_encoder_length=True,\n)","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":139,"outputs":[]},{"cell_type":"code","source":"test=TimeSeriesDataSet(\n df_test,\n time_idx=\"time_idx\",\n target=\"Target\",\n group_ids=[\"Asset_ID\"],\n max_encoder_length=max_encoder_length,\n max_prediction_length=max_prediction_length,\n static_reals =[\"Asset_ID\"],\n time_varying_known_reals=[\"timestamp\"],\n time_varying_unknown_reals=['Count', 'Open', 'High', 'Low', 'Close', 'Volume', 'VWAP',\n 'Close_log', 'Open_log', 'High_log','Low_log', 'VWAP_log',\n 'Close_ewp', 'Open_ewp', 'High_ewp', 'Low_ewp',\n 'VWAP_ewp', 'Close_lag', 'Open_lag', 'High_lag', \n 'Low_lag', 'VWAP_lag','Target_lag'],\n add_relative_time_idx=True,\n add_encoder_length=True,\n)\n# create dataloaders for model\nbatch_size = 32 \ntrain_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=8)\nval_dataloader = validation.to_dataloader(train=False, batch_size=batch_size,num_workers=8)\ntest_dataloader = test.to_dataloader(train=False, batch_size=batch_size,num_workers=8)\n","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":140,"outputs":[]},{"cell_type":"code","source":"pl.seed_everything(42)#random seed\ntrainer = pl.Trainer(gpus=0)\n#early stopping mechanism \nearly_stop_callback = EarlyStopping(monitor=\"val_loss\", min_delta=1e-4, patience=2, verbose=False, mode=\"min\")\n\nlr_logger = LearningRateMonitor() # log the learning rate\ntrainer = pl.Trainer(\n max_epochs=10,\n gpus=0,\n enable_model_summary=True,\n callbacks=[lr_logger, early_stop_callback],\n )\n\n\ntft = TemporalFusionTransformer.from_dataset(\n training,\n learning_rate=0.0001,\n hidden_size=4,#hidden size of network \n attention_head_size=1,#number of attention heads\n dropout=0.1,#drop out factor \n hidden_continuous_size=4,# hidden size for processing continous variables\n loss=QuantileLoss(),#default loss for tft\n reduce_on_plateau_patience=4,# patience after which learning rate is reduced by a factor of 10\n optimizer='adam'#usign adam optimizer \n)","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":141,"outputs":[{"name":"stderr","output_type":"stream","text":"Global seed set to 42\n\n/Users/youssefmecky/opt/anaconda3/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:467: LightningDeprecationWarning: Setting `Trainer(gpus=0)` is deprecated in v1.7 and will be removed in v2.0. Please use `Trainer(accelerator='gpu', devices=0)` instead.\n\n rank_zero_deprecation(\n\nGPU available: True (mps), used: False\n\nTPU available: False, using: 0 TPU cores\n\nIPU available: False, using: 0 IPUs\n\nHPU available: False, using: 0 HPUs\n\n/Users/youssefmecky/opt/anaconda3/lib/python3.9/site-packages/pytorch_lightning/trainer/setup.py:200: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n\n rank_zero_warn(\n\nGPU available: True (mps), used: False\n\nTPU available: False, using: 0 TPU cores\n\nIPU available: False, using: 0 IPUs\n\nHPU available: False, using: 0 HPUs\n\n/Users/youssefmecky/opt/anaconda3/lib/python3.9/site-packages/pytorch_lightning/utilities/parsing.py:262: UserWarning: Attribute 'loss' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['loss'])`.\n\n rank_zero_warn(\n\n/Users/youssefmecky/opt/anaconda3/lib/python3.9/site-packages/pytorch_lightning/utilities/parsing.py:262: UserWarning: Attribute 'logging_metrics' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['logging_metrics'])`.\n\n rank_zero_warn(\n"}]},{"cell_type":"code","source":"#Use validation and training set to find optimal LR tests by default 100 lRs and it has an early stop thershold \nres = trainer.tuner.lr_find(\n tft,\n train_dataloaders=train_dataloader,\n val_dataloaders=val_dataloader,\n max_lr=10.0,\n min_lr=1e-6,\n)\n\nprint(f\"suggested learning rate: {res.suggestion()}\")\nfig = res.plot(show=True, suggest=True)\nfig.show()\n\n","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":142,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0696f6ed0f7146978bc1301627d0bc7a","version_major":2,"version_minor":0},"text/plain":["Finding best initial lr: 0%| | 0/100 [00:00"]},"metadata":{}},{"name":"stderr","output_type":"stream","text":"/var/folders/w4/_b3n8mvx2nx_86hmhz6jwnbm0000gn/T/ipykernel_92480/3782390496.py:12: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n\n fig.show()\n"}]},{"cell_type":"code","source":"new_lr = res.suggestion()\n\n# update hparams of the model\ntft.hparams.lr = new_lr","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":143,"outputs":[]},{"cell_type":"code","source":"trainer.fit(\n tft,\n train_dataloaders=train_dataloader,\n val_dataloaders=val_dataloader\n)","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":144,"outputs":[{"name":"stderr","output_type":"stream","text":"\n\n | Name | Type | Params\n\n----------------------------------------------------------------------------------------\n\n0 | loss | QuantileLoss | 0 \n\n1 | logging_metrics | ModuleList | 0 \n\n2 | input_embeddings | MultiEmbedding | 0 \n\n3 | prescalers | ModuleDict | 216 \n\n4 | static_variable_selection | VariableSelectionNetwork | 238 \n\n5 | encoder_variable_selection | VariableSelectionNetwork | 3.2 K \n\n6 | decoder_variable_selection | VariableSelectionNetwork | 246 \n\n7 | static_context_variable_selection | GatedResidualNetwork | 88 \n\n8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 88 \n\n9 | static_context_initial_cell_lstm | GatedResidualNetwork | 88 \n\n10 | static_context_enrichment | GatedResidualNetwork | 88 \n\n11 | lstm_encoder | LSTM | 160 \n\n12 | lstm_decoder | LSTM | 160 \n\n13 | post_lstm_gate_encoder | GatedLinearUnit | 40 \n\n14 | post_lstm_add_norm_encoder | AddNorm | 8 \n\n15 | static_enrichment | GatedResidualNetwork | 104 \n\n16 | multihead_attn | InterpretableMultiHeadAttention | 76 \n\n17 | post_attn_gate_norm | GateAddNorm | 48 \n\n18 | pos_wise_ff | GatedResidualNetwork | 88 \n\n19 | pre_output_gate_norm | GateAddNorm | 48 \n\n20 | output_layer | Linear | 35 \n\n----------------------------------------------------------------------------------------\n\n4.8 K Trainable params\n\n0 Non-trainable params\n\n4.8 K Total params\n\n0.019 Total estimated model params size (MB)\n"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"","version_major":2,"version_minor":0},"text/plain":["Sanity Checking: 0it [00:00, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7aee0ca762eb435ca6d9d3b85376d2fc","version_major":2,"version_minor":0},"text/plain":["Training: 0it [00:00, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{}}]},{"cell_type":"code","source":"#predictions\ntft_pred=tft.predict(test_dataloader )\nactuals = torch.cat([y for x, (y, weight) in iter(test_dataloader)])","metadata":{"slideshow":{"slide_type":"skip"}},"execution_count":145,"outputs":[]},{"cell_type":"code","source":"#Mean squared error calculation \nmse=((actuals - tft_pred)**2).nanmean().item()\nprint('MSE on test set:{}'.format(mse))","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":146,"outputs":[{"name":"stdout","output_type":"stream","text":"MSE on test set:2.3956537916092202e-05\n"}]},{"cell_type":"code","source":"#Plot of prediction vs the true values\nplt.plot(np.arange(len(actuals[:,-1])), actuals[:,-1], color=\"black\",label='True signal')\nplt.plot(np.arange(len(actuals[:,-1])), tft_pred[:,-1], color=\"blue\",label='Predicted signal');\nplt.legend();","metadata":{"slideshow":{"slide_type":"subslide"}},"execution_count":147,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"markdown","source":"### Conclusions \n> Linear regression model is actually not that bad in comparison to the other models given the complexity of the other models as it has the lowest MSE\n\n> However from the visualiztions it looks like the tft is more able to capture the variations. \n\n>Maybe it is worth exploring simpler models for this problem something to captrue the star shape function we saw in the lag plot would be intreseting ","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"### Limitations and possible improvements \n> Use bayesian search instead of grid search\n\n> Use full data set\n\n> Optimitze hyper parameters in a wider scale \n\n> Use auto encoder as feature extracter \n\n> Look into the attention maps per variable for the tft to better understand what has been learned\n","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"### References\nhttps://lucaslouca.com/Why-Use-Logarithmic-Returns-In-Time-Series-Modelling/ \nhttps://medium.com/open-machine-learning-course/open-machine-learning-course-topic-9-time-series-analysis-in-python-a270cb05e0b3 \nHtun, H.H., Biehl, M. & Petkov, N. Survey of feature selection and extraction techniques for stock market prediction. Financ Innov 9, 26 (2023). https://doi.org/10.1186/s40854-022-00441-7 \nhttps://www.relataly.com/feature-engineering-for-multivariate-time-series-models-with-python/1813/ \nhttps://www.kaggle.com/code/kanncaa1/time-series-prediction-tutorial-with-eda/notebook \nhttps://neptune.ai/blog/select-model-for-time-series-prediction-task \nhttps://builtin.com/data-science/time-series-model \nhttps://towardsdatascience.com/the-best-deep-learning-models-for-time-series-forecasting-690767bc63f0 \nhttps://pytorch-forecasting.readthedocs.io/en/stable/tutorials/stallion.html \nhttps://medium.com/analytics-vidhya/pytorch-for-deep-learning-lstm-for-sequence-data-d0708fdf5717 \nhttps://stackoverflow.com/questions/71998978/early-stopping-in-pytorch \nhttps://pytorch.org/tutorials/beginner/hyperparameter_tuning_tutorial.html \nhttps://pytorch-forecasting.readthedocs.io/en/stable/tutorials/stallion.html \nLim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021). Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4), 1748-1764.\n","metadata":{"slideshow":{"slide_type":"slide"}}},{"cell_type":"markdown","source":"### Thank you","metadata":{"slideshow":{"slide_type":"subslide"}}}]}