s-p-data-anomaly-detection.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"\"\"\"\n    Title: Anomoly Detction in Time Series Data  \n    Author: Sahil Sangani  \n    Dataset: S&P 500 Stock Data  \n    Dataset URL: https://www.kaggle.com/camnugent/sandp500?select=all_stocks_5yr.csv  \n    Anomoly Type: Sudden Price Change  \n    Data Range(Years): 1986 - 2020  \n   \n    \n    =============================================\n    Dataset Information\n    =============================================\n    File type: CSV (Comma Separated File)\n    Number of Columns: 7 \n        1. Date\n        2. Open Price\n        3. High Price \n        4. Low Price\n        5. Close Price\n        6. Volume \n        7. Company Name\n\"\"\""},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        pass\n#         print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":88,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\nfrom matplotlib.pylab import rcParams\nimport plotly.express as px\nimport plotly.graph_objects as go\n%matplotlib inline\nimport tensorflow as tf\n\nsns.set(style='whitegrid', palette='muted')\nrcParams['figure.figsize'] = 14,8\nnp.random.seed(1)\ntf.random.set_seed(1)\n\nprint('tensorflow version:',tf.__version__)\n","execution_count":89,"outputs":[{"output_type":"stream","text":"tensorflow version: 2.3.1\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df1 = pd.read_csv(\"../input/sandp500/all_stocks_5yr.csv\")\ndf1.head(2)\n","execution_count":90,"outputs":[{"output_type":"execute_result","execution_count":90,"data":{"text/plain":"         date   open   high    low  close   volume Name\n0  2013-02-08  15.07  15.12  14.63  14.75  8407500  AAL\n1  2013-02-11  14.89  15.01  14.26  14.46  8882000  AAL","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>open</th>\n      <th>high</th>\n      <th>low</th>\n      <th>close</th>\n      <th>volume</th>\n      <th>Name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2013-02-08</td>\n      <td>15.07</td>\n      <td>15.12</td>\n      <td>14.63</td>\n      <td>14.75</td>\n      <td>8407500</td>\n      <td>AAL</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2013-02-11</td>\n      <td>14.89</td>\n      <td>15.01</td>\n      <td>14.26</td>\n      <td>14.46</td>\n      <td>8882000</td>\n      <td>AAL</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df1.drop(['open','high','low', 'volume', 'Name'], axis = 1)","execution_count":91,"outputs":[{"output_type":"execute_result","execution_count":91,"data":{"text/plain":"              date  close\n0       2013-02-08  14.75\n1       2013-02-11  14.46\n2       2013-02-12  14.27\n3       2013-02-13  14.66\n4       2013-02-14  13.99\n...            ...    ...\n619035  2018-02-01  77.82\n619036  2018-02-02  76.78\n619037  2018-02-05  73.83\n619038  2018-02-06  73.27\n619039  2018-02-07  73.86\n\n[619040 rows x 2 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>close</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2013-02-08</td>\n      <td>14.75</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2013-02-11</td>\n      <td>14.46</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013-02-12</td>\n      <td>14.27</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013-02-13</td>\n      <td>14.66</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2013-02-14</td>\n      <td>13.99</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>619035</th>\n      <td>2018-02-01</td>\n      <td>77.82</td>\n    </tr>\n    <tr>\n      <th>619036</th>\n      <td>2018-02-02</td>\n      <td>76.78</td>\n    </tr>\n    <tr>\n      <th>619037</th>\n      <td>2018-02-05</td>\n      <td>73.83</td>\n    </tr>\n    <tr>\n      <th>619038</th>\n      <td>2018-02-06</td>\n      <td>73.27</td>\n    </tr>\n    <tr>\n      <th>619039</th>\n      <td>2018-02-07</td>\n      <td>73.86</td>\n    </tr>\n  </tbody>\n</table>\n<p>619040 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"'''\n    The S&P 500 Index or the Standard & Poor's 500 Index is a market-capitalization-weighted index of the 500 largest U.S. publicly traded companies. The S&P is a float-weighted index, meaning company market capitalizations are adjusted by the number of shares available for public trading.\n    Source - www.investopedia.com\n'''\n# df = pd.read_csv(\"../input/mydata/myData.csv\")\ndf = df1","execution_count":92,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df","execution_count":93,"outputs":[{"output_type":"execute_result","execution_count":93,"data":{"text/plain":"              date   open   high    low  close    volume Name\n0       2013-02-08  15.07  15.12  14.63  14.75   8407500  AAL\n1       2013-02-11  14.89  15.01  14.26  14.46   8882000  AAL\n2       2013-02-12  14.45  14.51  14.10  14.27   8126000  AAL\n3       2013-02-13  14.30  14.94  14.25  14.66  10259500  AAL\n4       2013-02-14  14.94  14.96  13.16  13.99  31879900  AAL\n...            ...    ...    ...    ...    ...       ...  ...\n619035  2018-02-01  76.84  78.27  76.69  77.82   2982259  ZTS\n619036  2018-02-02  77.53  78.12  76.73  76.78   2595187  ZTS\n619037  2018-02-05  76.64  76.92  73.18  73.83   2962031  ZTS\n619038  2018-02-06  72.74  74.56  72.13  73.27   4924323  ZTS\n619039  2018-02-07  72.70  75.00  72.69  73.86   4534912  ZTS\n\n[619040 rows x 7 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>open</th>\n      <th>high</th>\n      <th>low</th>\n      <th>close</th>\n      <th>volume</th>\n      <th>Name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2013-02-08</td>\n      <td>15.07</td>\n      <td>15.12</td>\n      <td>14.63</td>\n      <td>14.75</td>\n      <td>8407500</td>\n      <td>AAL</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2013-02-11</td>\n      <td>14.89</td>\n      <td>15.01</td>\n      <td>14.26</td>\n      <td>14.46</td>\n      <td>8882000</td>\n      <td>AAL</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013-02-12</td>\n      <td>14.45</td>\n      <td>14.51</td>\n      <td>14.10</td>\n      <td>14.27</td>\n      <td>8126000</td>\n      <td>AAL</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013-02-13</td>\n      <td>14.30</td>\n      <td>14.94</td>\n      <td>14.25</td>\n      <td>14.66</td>\n      <td>10259500</td>\n      <td>AAL</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2013-02-14</td>\n      <td>14.94</td>\n      <td>14.96</td>\n      <td>13.16</td>\n      <td>13.99</td>\n      <td>31879900</td>\n      <td>AAL</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>619035</th>\n      <td>2018-02-01</td>\n      <td>76.84</td>\n      <td>78.27</td>\n      <td>76.69</td>\n      <td>77.82</td>\n      <td>2982259</td>\n      <td>ZTS</td>\n    </tr>\n    <tr>\n      <th>619036</th>\n      <td>2018-02-02</td>\n      <td>77.53</td>\n      <td>78.12</td>\n      <td>76.73</td>\n      <td>76.78</td>\n      <td>2595187</td>\n      <td>ZTS</td>\n    </tr>\n    <tr>\n      <th>619037</th>\n      <td>2018-02-05</td>\n      <td>76.64</td>\n      <td>76.92</td>\n      <td>73.18</td>\n      <td>73.83</td>\n      <td>2962031</td>\n      <td>ZTS</td>\n    </tr>\n    <tr>\n      <th>619038</th>\n      <td>2018-02-06</td>\n      <td>72.74</td>\n      <td>74.56</td>\n      <td>72.13</td>\n      <td>73.27</td>\n      <td>4924323</td>\n      <td>ZTS</td>\n    </tr>\n    <tr>\n      <th>619039</th>\n      <td>2018-02-07</td>\n      <td>72.70</td>\n      <td>75.00</td>\n      <td>72.69</td>\n      <td>73.86</td>\n      <td>4534912</td>\n      <td>ZTS</td>\n    </tr>\n  </tbody>\n</table>\n<p>619040 rows × 7 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"================================\")\nprint(\"Datatypes of columns:\\n\", df.dtypes)\nprint(\"================================\")\nprint(\"Shape of dataframe: \",df.shape)","execution_count":94,"outputs":[{"output_type":"stream","text":"================================\nDatatypes of columns:\n date       object\nopen      float64\nhigh      float64\nlow       float64\nclose     float64\nvolume      int64\nName       object\ndtype: object\n================================\nShape of dataframe:  (619040, 7)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv('../input/mydata/myData.csv', parse_dates=['date'])\nprint(\"Datatypes of columns:\\n\", df.dtypes)","execution_count":95,"outputs":[{"output_type":"stream","text":"Datatypes of columns:\n date     datetime64[ns]\nclose           float64\ndtype: object\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"Let's visuliaze all closing price between year of 1986 to 2018."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Used ploty.graph method as go; created an empty figure first\nfig = go.Figure()\n\n# Now populating figure by adding a trace. It helps to add various types of plots in the same figure.\n# For line plot, passing mode = 'line' to the go.Scatter function\n# For scatter plot, passing mode = 'marker'\n# Legend value is closing price here.\n\nfig.add_trace(go.Scatter(x=df.date, y=df.close, mode='lines', name='close'))\n\n\n# fig.add_trace(go.Scatter(x=df.date, y=df.close, mode='marker'))\n\n# Next we update our figure layout to show the legend\nfig.update_layout(showlegend=True)\nfig.show()","execution_count":96,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>                            <div id=\"316f7ce7-c753-4797-98ca-1f06338ddd30\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"316f7ce7-c753-4797-98ca-1f06338ddd30\")) {                    Plotly.newPlot(                        \"316f7ce7-c753-4797-98ca-1f06338ddd30\",                        [{\"mode\": \"lines\", \"name\": \"close\", \"type\": \"scatter\", \"x\": [\"1986-01-02T00:00:00\", \"1986-01-03T00:00:00\", \"1986-01-06T00:00:00\", \"1986-01-07T00:00:00\", \"1986-01-08T00:00:00\", \"1986-01-09T00:00:00\", \"1986-01-10T00:00:00\", \"1986-01-13T00:00:00\", \"1986-01-14T00:00:00\", \"1986-01-15T00:00:00\", \"1986-01-16T00:00:00\", \"1986-01-17T00:00:00\", \"1986-01-20T00:00:00\", \"1986-01-21T00:00:00\", \"1986-01-22T00:00:00\", \"1986-01-23T00:00:00\", \"1986-01-24T00:00:00\", \"1986-01-27T00:00:00\", \"1986-01-28T00:00:00\", \"1986-01-29T00:00:00\", \"1986-01-30T00:00:00\", \"1986-01-31T00:00:00\", \"1986-02-03T00:00:00\", \"1986-02-04T00:00:00\", \"1986-02-05T00:00:00\", \"1986-02-06T00:00:00\", \"1986-02-07T00:00:00\", \"1986-02-10T00:00:00\", \"1986-02-11T00:00:00\", \"1986-02-12T00:00:00\", \"1986-02-13T00:00:00\", \"1986-02-14T00:00:00\", \"1986-02-18T00:00:00\", \"1986-02-19T00:00:00\", \"1986-02-20T00:00:00\", \"1986-02-21T00:00:00\", \"1986-02-24T00:00:00\", \"1986-02-25T00:00:00\", \"1986-02-26T00:00:00\", \"1986-02-27T00:00:00\", \"1986-02-28T00:00:00\", \"1986-03-03T00:00:00\", \"1986-03-04T00:00:00\", \"1986-03-05T00:00:00\", \"1986-03-06T00:00:00\", \"1986-03-07T00:00:00\", \"1986-03-10T00:00:00\", \"1986-03-11T00:00:00\", \"1986-03-12T00:00:00\", \"1986-03-13T00:00:00\", \"1986-03-14T00:00:00\", \"1986-03-17T00:00:00\", \"1986-03-18T00:00:00\", \"1986-03-19T00:00:00\", \"1986-03-20T00:00:00\", \"1986-03-21T00:00:00\", \"1986-03-24T00:00:00\", \"1986-03-25T00:00:00\", \"1986-03-26T00:00:00\", \"1986-03-27T00:00:00\", \"1986-03-31T00:00:00\", \"1986-04-01T00:00:00\", \"1986-04-02T00:00:00\", \"1986-04-03T00:00:00\", \"1986-04-04T00:00:00\", \"1986-04-07T00:00:00\", \"1986-04-08T00:00:00\", \"1986-04-09T00:00:00\", \"1986-04-10T00:00:00\", \"1986-04-11T00:00:00\", \"1986-04-14T00:00:00\", \"1986-04-15T00:00:00\", \"1986-04-16T00:00:00\", \"1986-04-17T00:00:00\", \"1986-04-18T00:00:00\", \"1986-04-21T00:00:00\", \"1986-04-22T00:00:00\", \"1986-04-23T00:00:00\", \"1986-04-24T00:00:00\", \"1986-04-25T00:00:00\", \"1986-04-28T00:00:00\", \"1986-04-29T00:00:00\", \"1986-04-30T00:00:00\", \"1986-05-01T00:00:00\", \"1986-05-02T00:00:00\", \"1986-05-05T00:00:00\", \"1986-05-06T00:00:00\", \"1986-05-07T00:00:00\", \"1986-05-08T00:00:00\", \"1986-05-09T00:00:00\", \"1986-05-12T00:00:00\", \"1986-05-13T00:00:00\", \"1986-05-14T00:00:00\", \"1986-05-15T00:00:00\", \"1986-05-16T00:00:00\", \"1986-05-19T00:00:00\", \"1986-05-20T00:00:00\", \"1986-05-21T00:00:00\", \"1986-05-22T00:00:00\", \"1986-05-23T00:00:00\", \"1986-05-27T00:00:00\", \"1986-05-28T00:00:00\", \"1986-05-29T00:00:00\", \"1986-05-30T00:00:00\", \"1986-06-02T00:00:00\", \"1986-06-03T00:00:00\", \"1986-06-04T00:00:00\", \"1986-06-05T00:00:00\", \"1986-06-06T00:00:00\", \"1986-06-09T00:00:00\", \"1986-06-10T00:00:00\", \"1986-06-11T00:00:00\", \"1986-06-12T00:00:00\", \"1986-06-13T00:00:00\", \"1986-06-16T00:00:00\", \"1986-06-17T00:00:00\", \"1986-06-18T00:00:00\", \"1986-06-19T00:00:00\", \"1986-06-20T00:00:00\", \"1986-06-23T00:00:00\", \"1986-06-24T00:00:00\", \"1986-06-25T00:00:00\", \"1986-06-26T00:00:00\", \"1986-06-27T00:00:00\", \"1986-06-30T00:00:00\", \"1986-07-01T00:00:00\", \"1986-07-02T00:00:00\", \"1986-07-03T00:00:00\", \"1986-07-07T00:00:00\", \"1986-07-08T00:00:00\", \"1986-07-09T00:00:00\", \"1986-07-10T00:00:00\", \"1986-07-11T00:00:00\", \"1986-07-14T00:00:00\", \"1986-07-15T00:00:00\", \"1986-07-16T00:00:00\", \"1986-07-17T00:00:00\", \"1986-07-18T00:00:00\", \"1986-07-21T00:00:00\", \"1986-07-22T00:00:00\", \"1986-07-23T00:00:00\", \"1986-07-24T00:00:00\", \"1986-07-25T00:00:00\", \"1986-07-28T00:00:00\", \"1986-07-29T00:00:00\", \"1986-07-30T00:00:00\", \"1986-07-31T00:00:00\", \"1986-08-01T00:00:00\", \"1986-08-04T00:00:00\", \"1986-08-05T00:00:00\", \"1986-08-06T00:00:00\", \"1986-08-07T00:00:00\", 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</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"# Data Preprocessing\n    1.Standardizing the target-vector by removing the mean and scaling it to the unit variance, using the standard_scaler function from sklearn.preprocessing\n    2.Split Data into train and test set\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_size = int(len(df) * 0.8)\ntest_size = len(df) - train_size\n\ntrain_set = df.iloc[0:train_size]\ntest_set = df.iloc[train_size: len(df)]\n\nprint(f\"The shape of train_set is: {train_set.shape}, and test_set is: {test_set.shape}\")\n\n'''\n    Why not used sklearn.model_selection.train_test_split(*arrays, **options)?\n    --> In time series data shuffling should not perfomed. Order should be maintained\n'''","execution_count":97,"outputs":[{"output_type":"stream","text":"The shape of train_set is: (6553, 2), and test_set is: (1639, 2)\n","name":"stdout"},{"output_type":"execute_result","execution_count":97,"data":{"text/plain":"'\\n    Why not used sklearn.model_selection.train_test_split(*arrays, **options)?\\n    --> In time series data shuffling should not perfomed. Order should be maintained\\n'"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Now let's standardize the data set.\n\nfirst we import and instantiate standard_scaler,  \nThen fit the train set close feature to standard_scaler  \nThen transform the train set close feature by standard scaler  \nThen we transform test set close feature too.  "},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\n\nscaler = StandardScaler()  # Instantiate SC\n\n# Next we fit scaler on training data to learn its statistical properties, \n# Then we transform both train and test data with these statistical properties.\nscaler.fit(train_set[['close']]) # Use double square brackets for 2D array.\n\n'''\n    scaler.fit() function will learn statistical propries of target values (Only pass training data) \n'''\n\ntrain_set['close'] = scaler.transform(train_set[['close']])\ntest_set['close'] = scaler.transform(test_set[['close']])\n","execution_count":98,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:13: SettingWithCopyWarning:\n\n\nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\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/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:14: SettingWithCopyWarning:\n\n\nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\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","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Let's see the first few standardized data.\ntrain_set.head()","execution_count":99,"outputs":[{"output_type":"execute_result","execution_count":99,"data":{"text/plain":"        date     close\n0 1986-01-02 -1.516118\n1 1986-01-03 -1.513027\n2 1986-01-06 -1.513578\n3 1986-01-07 -1.506029\n4 1986-01-08 -1.520001","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>close</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1986-01-02</td>\n      <td>-1.516118</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1986-01-03</td>\n      <td>-1.513027</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1986-01-06</td>\n      <td>-1.513578</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1986-01-07</td>\n      <td>-1.506029</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1986-01-08</td>\n      <td>-1.520001</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(train_set[['close']])\nprint(train_set['close'])","execution_count":100,"outputs":[{"output_type":"stream","text":"         close\n0    -1.516118\n1    -1.513027\n2    -1.513578\n3    -1.506029\n4    -1.520001\n...        ...\n6548  0.904583\n6549  0.870288\n6550  0.956445\n6551  0.962244\n6552  0.986881\n\n[6553 rows x 1 columns]\n0      -1.516118\n1      -1.513027\n2      -1.513578\n3      -1.506029\n4      -1.520001\n          ...   \n6548    0.904583\n6549    0.870288\n6550    0.956445\n6551    0.962244\n6552    0.986881\nName: close, Length: 6553, dtype: float64\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"In times-series data modelling project, we need to split dataset into sub0sequence before training the model. Here, we are using LSTM AutoEncoder which is type of RNN; which requires input sequences. So, we are diving data into specific time-steps (Here, 30 days).   \nIn LSTM netorks, reshape of data is needed.   \nShape: n_samples by n(time_steps) by n(features)  \nHere,  \nwe have only one feature so --> n(features) = 1  \ntime-sequence of 30 days -->  n(time_steps) = 30 \n\n\n**Why LSTM?**  \nThe LSTM will leverage autocorrelation to generate sequence predictions.  \n\n**What is Autocorrealtion?**  \nAutocorrelation Function (ACF), which is the correlation between the time series of interest in lagged versions of itself.  \n\n**Backtesting: Time Series Cross Validation** (For Information Only)  \nCross validation is the process of developing models on sub-sampled data against a validation set with the goal of determining an expected accuracy level and error range. Time series is a bit different than non-sequential data when it comes to cross validation. Specifically, the time dependency on previous time samples must be preserved when developing a sampling plan. We can create a cross validation sampling plan using by offsetting the window used to select sequential sub-samples. In finance, this type of analysis is often called “Backtesting”, which takes a time series and splits it into multiple uninterupted sequences offset at various windows that can be tested for strategies on both current and past observations.\n\nReference: https://www.business-science.io/timeseries-analysis/2018/04/18/keras-lstm-sunspots-time-series-prediction.html"},{"metadata":{"trusted":true},"cell_type":"code","source":"def create_sequence(X, y, time_steps=1):\n    \"\"\" Reshape data for LSTM network.\n\n    @Param:\n    X is the feature of the training set\n    y is the target of the training,\n    Note that X and y are identical in this\n    Case given only `close` feature.\n\n    @Return:\n    Return X and y which are both arrays, \n    reshaped by:\n    n_samples by n_timesteps by n_features\n\n    \"\"\"\n\n    Xs, ys = [], []\n\n    for i in range(len(X)-time_steps):\n        Xs.append(X.iloc[i: i + time_steps].values)\n        ys.append(y.iloc[i + time_steps])\n    \n    return np.array(Xs), np.array(ys)","execution_count":101,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Next let's create our X_train and x_test, y_train and y_test\n\ntime_steps = 30\n\nX_train, y_train = create_sequence(train_set[['close']], train_set['close'], time_steps)\nX_test, y_test = create_sequence(test_set[['close']], test_set['close'], time_steps)\n\n# Let's see the first index of X_train and y_train\nprint('X_train last index is:\\n', X_train[-1],'\\ny_train last index is:\\n', y_train[-1])","execution_count":102,"outputs":[{"output_type":"stream","text":"X_train last index is:\n [[0.92720661]\n [0.95258626]\n [1.01048734]\n [0.98156076]\n [0.99601207]\n [0.9459238 ]\n [0.89612312]\n [0.89497277]\n [0.84064258]\n [0.82880353]\n [0.76589363]\n [0.75841634]\n [0.83961205]\n [0.84593899]\n [0.97000931]\n [0.96430548]\n [0.96358651]\n [0.99426257]\n [0.9975938 ]\n [1.00368109]\n [0.93978859]\n [0.98973306]\n [0.94486931]\n [0.91913017]\n [0.88579392]\n [0.89521243]\n [0.90458301]\n [0.87028812]\n [0.95644473]\n [0.96224443]] \ny_train last index is:\n 0.9868811449578108\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(f'X_train shape is: {X_train.shape}, y_train shape is: {y_train.shape}')\nprint(f'X_test shape is: {X_test.shape}, y_test shape is: {y_test.shape}')","execution_count":103,"outputs":[{"output_type":"stream","text":"X_train shape is: (6523, 30, 1), y_train shape is: (6523,)\nX_test shape is: (1609, 30, 1), y_test shape is: (1609,)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Build an LSTM Auto-Encoder in Keras\nHere we'd build an LSTM auto-encoder network and visualize its architecture and data flow. Here are the steps:\n\n    1.We train an auto-encoder on data with no anomalies\n    2.Then take a new data point and try to reconstruct it using the auto-encoder\n    3.If the reconstruction error for the new data point is above some set threshold, we'd label that data point as an anomaly. LSTMs work fantastically with Data that has temporal properties to them."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Let's define variables we'd need for our LSTM as a function of Training data.\n\ntimesteps = X_train.shape[1]\nnum_features = X_train.shape[2]\nprint(timesteps)\nprint(num_features)","execution_count":104,"outputs":[{"output_type":"stream","text":"30\n1\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Let's import the required building libraries\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, LSTM, RepeatVector, TimeDistributed","execution_count":105,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Next we use the Sequential layer and basically stack all needed layers in it.\n# tf.keras.Sequential(layers=None, name=None)\n\n\nmodel = Sequential([\n    # We pass the input shape to the model\n    # Here, Units = 128\n    LSTM(128, input_shape=(timesteps, num_features)),\n    Dropout(0.2),\n\n    # If subsequent layer is an LSTM, we need to duplicate \n    # This using the repeatVector layer. This replicates the \n    # Feature vector from the LSTM layer output, timesteps times.\n    RepeatVector(timesteps),\n\n    # The encoder has been done, now we mirror the encoder \n    # in reverse fashion.These are the steps for creating \n    # The decoder. Note the RepeatVector layer acts as a bridge\n    # Between the encoding and the decoding modules. It prepares \n    # The 2D array input for first LSTM layer in decoder. \n    LSTM(128, return_sequences=True),\n\n    # The return_sequence=True makes each timestep return a \n    # signal. Let's add another dropout layer\n    Dropout(0.2),\n\n    # Next we add the timedistributed layer (TDL) at the end,\n    # So we get the output with the right shape\n    TimeDistributed(Dense(num_features))\n        \n    # TDL creates a vector of length=num_features outputted from\n    # The previous layer. Since prev layer outputs 128 features,\n    # TDL creates a 128 long vector and duplicates it the\n    # num_feature times.\n])","execution_count":106,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(loss = 'mae', optimizer = 'adam')\nmodel.summary()","execution_count":107,"outputs":[{"output_type":"stream","text":"Model: \"sequential_2\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nlstm_4 (LSTM)                (None, 128)               66560     \n_________________________________________________________________\ndropout_4 (Dropout)          (None, 128)               0         \n_________________________________________________________________\nrepeat_vector_2 (RepeatVecto (None, 30, 128)           0         \n_________________________________________________________________\nlstm_5 (LSTM)                (None, 30, 128)           131584    \n_________________________________________________________________\ndropout_5 (Dropout)          (None, 30, 128)           0         \n_________________________________________________________________\ntime_distributed_2 (TimeDist (None, 30, 1)             129       \n=================================================================\nTotal params: 198,273\nTrainable params: 198,273\nNon-trainable params: 0\n_________________________________________________________________\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# Train Model\nTime to train the model, after compiling it. Let's apply the early stopper regularization technique called call_backs. This way, we don't need to hard-code the number of epochs the model will train on. Rather by specifying a metric to watch, we can stop training the moment the model stops improving in learning and starts over-fitting."},{"metadata":{"trusted":true},"cell_type":"code","source":"# This is one way to implement the callback function based on achieving,\n# A certain level of performance. But we'd use the one below.\n\nclass myCallback(tf.keras.callbacks.Callback):\n    def on_epoch_end(self, epoch, logs={}):\n        if(logs.get('acc')>0.9):\n            print(\"\\nReached 90% accuracy so cancelling training!\")\n            self.model.stop_training = True","execution_count":108,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The code below means, we monitor the validation loss metric and if it's not reducing for 3 consecutive epochs, then we stop the training and take the minimum validation loss parameters."},{"metadata":{"trusted":true},"cell_type":"code","source":"callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3, mode='min')","execution_count":109,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Next, let's train the model. Batch size is the number of features the model\n# Should train before updating its weights. It's a hyper-par we can sure tweak.\n# We set epochs real high to give leeway for callbacks to work.\n# validation_split:- keep specified percent for validating, use rest to train\n# Make sure value of callback is in a list.\n# Finally, dont shuffle time-series data. order matters. So, shuffle=False\n\nmodel.fit(\n    X_train,\n    y_train,\n    epochs = 1000,\n    batch_size = 32,\n    callbacks = [callback],\n    validation_split = 0.1,\n    shuffle = False\n)","execution_count":110,"outputs":[{"output_type":"stream","text":"Epoch 1/1000\n184/184 [==============================] - 2s 10ms/step - loss: 0.1650 - val_loss: 0.1764\nEpoch 2/1000\n184/184 [==============================] - 1s 7ms/step - loss: 0.1136 - val_loss: 0.0569\nEpoch 3/1000\n184/184 [==============================] - 1s 7ms/step - loss: 0.0902 - val_loss: 0.0510\nEpoch 4/1000\n184/184 [==============================] - 1s 6ms/step - loss: 0.0806 - val_loss: 0.0539\nEpoch 5/1000\n184/184 [==============================] - 1s 8ms/step - loss: 0.0763 - val_loss: 0.0447\nEpoch 6/1000\n184/184 [==============================] - 1s 7ms/step - loss: 0.0778 - val_loss: 0.0468\nEpoch 7/1000\n184/184 [==============================] - 1s 6ms/step - loss: 0.0753 - val_loss: 0.0773\nEpoch 8/1000\n184/184 [==============================] - 2s 8ms/step - loss: 0.0766 - val_loss: 0.0391\nEpoch 9/1000\n184/184 [==============================] - 1s 8ms/step - loss: 0.0717 - val_loss: 0.0284\nEpoch 10/1000\n184/184 [==============================] - 1s 7ms/step - loss: 0.0728 - val_loss: 0.0347\nEpoch 11/1000\n184/184 [==============================] - 1s 6ms/step - loss: 0.0694 - val_loss: 0.0554\nEpoch 12/1000\n184/184 [==============================] - 1s 8ms/step - loss: 0.0770 - val_loss: 0.0574\n","name":"stdout"},{"output_type":"execute_result","execution_count":110,"data":{"text/plain":"<tensorflow.python.keras.callbacks.History at 0x7f46576df4d0>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"# Evaluate Model\nLet's now plot our metrics, which is the training loss and validation loss. We use matplotlib this time, since we have no need for interactive plots. Loss values are stored in the history.history object. So we can use this to plot the losses. The history object is a dictionary so we choose the keys we want to plot."},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10,8))\nplt.plot(model.history.history['loss'], label='Train_Loss')\nplt.plot(model.history.history['val_loss'], label='Val_Loss')\nplt.legend()  # Show the legend\nplt.show()","execution_count":111,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x576 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Here we can see that our validation loss was consistently lower than training loss. This means we under-fit on the training data. We can adjust this by tweaking on some of the hyper-parameters like model architecture and batch size and others"},{"metadata":{},"cell_type":"markdown","source":"Well, we still need to detect anomalies in our test data. So let's make some predictions and calculate the MAE on the training data."},{"metadata":{"trusted":true},"cell_type":"code","source":"# First let's get some predictions on the training data\nX_train_pred = model.predict(X_train)\n\n# Next let's compute the mean absolute error of X_train_pred and X_train.\n# MAE is so easy to compute:- find mean of abs(X_train_pred - X_train)\n# Let's save it in a Data Frame\n\ntrain_mae_loss = pd.DataFrame(np.mean(np.abs(X_train_pred - X_train), axis=1), columns=['Errors'])\n\ntrain_mae_loss.head()","execution_count":112,"outputs":[{"output_type":"execute_result","execution_count":112,"data":{"text/plain":"     Errors\n0  0.517725\n1  0.518227\n2  0.519823\n3  0.521949\n4  0.520541","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Errors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.517725</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.518227</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.519823</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.521949</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.520541</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Next, let's evaluate the model on the test data"},{"metadata":{"trusted":true},"cell_type":"code","source":"model.evaluate(X_test, y_test)\n\n# Model.evaluate shows us the total loss for the predictions. While the MAE we computed above was for each prediction.\n# This loss is 0.37 for the test data ","execution_count":113,"outputs":[{"output_type":"stream","text":"51/51 [==============================] - 0s 4ms/step - loss: 0.3680\n","name":"stdout"},{"output_type":"execute_result","execution_count":113,"data":{"text/plain":"0.36795541644096375"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Let's see the distribution of the MAE loss for the train data"},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10,8))\nsns.distplot(train_mae_loss, bins=50, kde=True) # kde is kernel density estimate tht's over laid on the distplot.\nplt.show()","execution_count":114,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x576 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAkwAAAHUCAYAAADIuXdwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeXxcZ333/e+ZVbNp363F+57EiUz2hBAgYU/YCgVuduhKe79ankLvUugDBZ60dIc2LUtyc/cuewmEAGFJgJA9ih3ieN8k21pGy0ia0YxmPc8fY9mOY3lkSzPnzMzn/XrplUhnNOfn45Hm6+u6zu8yTNM0BQAAgAU5rC4AAADA7ghMAAAABRCYAAAACiAwAQAAFOAq5pPncjnNzs7K7XbLMIxingoAAGBJTNNUOp1WIBCQw/H8MaWiBqbZ2Vnt37+/mKcAAABYVuvXr1coFHre14oamNxu96kTezyeYp7K9nbt2qWtW7daXYbluA55XIc8rkMe1yGP65DHdTit1NcilUpp//79p/LLmYoamOan4Twej7xebzFPVRa4BnlchzyuQx7XIY/rkMd1yOM6nGbFtTjXMiIWfQMAABRAYAIAACiAwAQAAFAAgQkAAKAAAhMAAEABBCYAAIACCEwAAAAFEJgAAAAKIDABAAAUQGACAAAogMAEAABQAIEJAACgAAITAABAAQQmAACAAghMAAAABRCYAAAACiAwAQAAFEBgAgAAKMBldQGwn2g8o3gyu+Bxv9epkJ+XDgCgevCuhxeIJ7PqPxBb8HjfuiCBCQBQVZiSAwAAKIDABAAAUACBCQAAoAACEwAAQAEEJgAAgAIITAAAAAUQmAAAAAogMAEAABRAYAIAACiAwAQAAFAAgQkAAKAAAhMAAEABBCYAAIACCEwAAAAFEJgAAAAKIDABAAAUQGACAAAogMAEAABQAIEJAACgAAITAABAAQQmAACAAghMAAAABRCYAAAACiAwAQAAFOCyugCUVjSeUTyZPe9jUmmzRNUAAFAeCExVJp7Mqv9A7LyP2djtK1E1AACUB6bkAAAACiAwAQAAFEBgAgAAKIDABAAAUACBCQAAoAACEwAAQAEEJgAAgAIITAAAAAUsqnHlzTffLI/HI6/XK0n68Ic/rBtuuKGohQEAANjFojt9//M//7PWr19fzFoAAABsiSk5AACAAhY9wvThD39Ypmmqr69Pf/Inf6La2tpi1gUAAGAbhmmaBbemHx4eVkdHh1KplD796U9rdnZWn/vc5wo+eTKZ1K5du5alUCwPl79Zj+6aOO9jrt3WpUd2Hl/w+DVbm5SJjy93aQAA2MLWrVtPrduet6gRpo6ODkmSx+PR2972Nv3e7/3ekk9cbfr7+9XX11f080TjGcWT2QWPp9KmenqD532OYNCnnt7eBY+3twfV1rDw8fMp1XWwO65DHtchj+uQx3XI4zqcVuprcb6BnoKBKR6PK5vNKhQKyTRN/fCHP9SmTZuWvUgsj3gyq/4DsQWPb+z2lbAaAAAqQ8HANDExoQ996EPKZrPK5XJas2aNPvGJT5SiNgAAAFsoGJi6u7t1zz33lKIWAAAAW6KtAAAAQAEEJgAAgAIITAAAAAUQmAAAAAogMAEAABRAYAIAACiAwAQAAFAAgQkAAKAAAhMAAEABBCYAAIACCEwAAAAFEJgAAAAKIDABAAAUQGACAAAogMAEAABQAIEJAACgAAITAABAAQQmAACAAghMAAAABRCYAAAACiAwAQAAFEBgAgAAKIDABAAAUACBCQAAoAACEwAAQAEEJgAAgAIITAAAAAW4rC4AFyYazyiezC54PJU2i15DNmtqNJJc8Ljf61TIz0sLAFA5eFcrM/FkVv0HYgse39jtW7ZzZbKmIrG06gIueVynByPn0jntPZxY8Pv61gUJTACAisK7Gp7nyHBCv352SkdH5jQVy8iUVBdw6s0vbpXP67S6PAAALMEaJpwyFcvoh09M6PBwQg0hl7ZvCOnFl9UrlsjqvscmlMkWf7oPAAA7YoQJpzz0myk5HYY+8tYeDU+mT33d53Hox09O6udPT+qW7Y0WVggAgDUYYYKk/FTc0dE5XbmxVnWB5+fodV1+XbO5VvuPJ/T4nhmLKgQAwDqMMEGZrKmHnp1SQ9Cly9YEz/mYvvUhTc9m9OS+qDb3+lUbcJe4SgAArMMIE7TjYFTTs1ndeFm9nA7jnI8xDEM3bWtQe4NH33t4Qtkc65kAANWDwFTlovGMntoX1ZpOn3paa877WKfD0PaNIU3NZnRoaOG2AgAAVBoCU5V7dPeMTNPU9ZfULerxK9tq1Frv1s6DUZkmo0wAgOpAYKpi6UxOB0/EtWVlQLWLbDRpGIZuuqxeo5G0RiZTRa4QAAB7IDBVsWPhpLI5aXXHhXUHv2pTrbxuQzsPLdxxHACASkJgqmJHRhLyuA11Nnsv6PtqPA5tWRnQoaGEovFMkaoDAMA+CExVyjRNHRmZU29bzYJ3xp3Ppavz7Qd+c5hRJgBA5SMwVanRSEqJZE6r2y9us96Q36U1nT49d3RWqUxumasDAMBeCExV6vDwnAxD6mk7fyuB89m2Jqhk2tTewfgyVgYAgP0QmKrUkeGEVjR5VeO5+JdAe6NHLfVu7RmYXcbKAACwHwJTFZqezWgymtGqjosfXZLyLQbWdvoUnkqz+BsAUNEITFXoyHC+S/eqi1y/dKY1nfnnODw8t+TnAgDArghMVejIyJwaQy7VBZe+93JDyK3GkIutUgAAFY3AVGXiyayGxpNadYHNKs9ndadPQ+NJJZLZZXtOAADshMBUZXYPxJUzpVXtS1u/dKY1HT6Zyo9cAQBQiQhMVebZwzH5PA61NXqW7Tlb6t0K+ZxMywEAKhaBqcocOJFQd6tXDuPCu3svxDAMre706Vh4Tqk0TSwBAJWHwFRFYomspmezamtYvtGleWs6fcrmpIEw03IAgMpDYKoi4amUJKm1CIGpo8kjn8ehw0zLAQAq0NLvK0fZCEdSMgyppc697M/tMAyt6qjRgRMJzSWzGo0kz/k4l79Z0XhGIT8vPQBA+eBdq4qMTqXU0eiR21WcgcXVnT7tHojrN0dm5XSe+xyDAxNqb28nMAEAygpTclXCNE2FI2n1LmGz3UK6W2rkdhl65lCsaOcAAMAKBKYqEY1nNZfKqafVW7RzuJyGulu82jMYl2maRTsPAAClRmCqEvMLvos5wiRJ3a01moxmNDNL128AQOUgMFWJ0UhKDkPqbFr+O+TO1N2SH8E6NkZ7AQBA5SAwVYnwVFrNde6iLfieVx90qSHo0rHwue+SAwCgHBGYqoBpmgpPpYrSf+lshmFoQ49fx8eSyrGOCQBQIQhMVWAqllEqbaq1vviBSZI2dvs0l85pfCpdkvMBAFBsBKYqED4ZXNoalr9h5bms7/JLYh0TAKByEJiqQDiSktMhNYZKE5jqAi411bKOCQBQOQhMVWB0KqWWeo8cDqNk5+xuqdHQRFKZLOuYAADlj8BU4XKmqbGptNpKsOD7TF2tXmVz0vAEo0wAgPJ3QYHp85//vDZs2KD9+/cXqx4ss0g0o0zWVGt9aabj5q1o8sphSMfGCEwAgPK36MD03HPPaefOners7CxmPVhm4Ui+w3cpWgqcyeN2qK3Rw8JvAEBFWFRgSqVS+uQnP6lPfOITMozSrYPB0o1GUnK7DDUEXSU/d3dLjcKRtOZSuZKfGwCA5bSowPRP//RPet3rXqfu7u5i14NlFp5Kq6XObUnQ7T650e9xpuUAAGWu4LDDjh079Oyzz+rDH/7wRZ9k165dF/29laS/v3/Jz+HyN2twYGLB410NXRocOC5JMk1pYtqh7iZTgwMDLzi+mOdYyvGcKTkdDu05PCZP5vTdciMjIzp+ePy8NVSD5Xg9VAKuQx7XIY/rkMd1OM0u16JgYHryySd1+PBhvfSlL5WUf7N73/vep89+9rO6/vrrF3WSrVu3yuv1Lq3SMtff36++vr4lP89oJKmeaHDB48GgTz29vZKkWCKrTG5Y3R0N6ukNvuD4Yp5jqce7hsY1E8+op7ddkjQ4MKD29na1NZy/hkq3XK+Hcsd1yOM65HEd8rgOp5X6WiSTyQUHeQoGpg9+8IP64Ac/eOrzm2++WXfeeafWr1+/fBWiKCKxfIfvxlDp1y/N62zy6NHROSWSWfm8TsvqAABgKejDVMEi0YwkqaFEHb7PpbM5P7I4NJGyrAYAAJbqgoceHnjggWLUgSKIRNNyuwwFaqzLxW31Hjkd0tB4Ums6fZbVAQDAUjDCVMEi0YwaQi5LW0E4nYbaG70aouM3AKCMEZgq2GQ0o4agddNx8zqbPBqbSiuVph8TAKA8EZgqVCqd0+xc1tIF3/M6m70yJQ1Pso4JAFCeCEwVKhKzfsH3vPZGjwwjv44JAIByRGCqUJFovqVAgw1GmDwuh1rr3axjAgCULQJThYpEM3IYUl3A+sAkSZ1NXo1EUsqyjAkAUIYITBVqMppWXcAlp8MemyV3NnuVy0mRWasrAQDgwhGYKlQklrHFdNy8zqZ8A8uJmD0CHAAAF4LAVIGyOVPTsYwtFnzPq/E41FTrJjABAMoSgakCzcxmlDPtseD7TJ1NHk3O5gMdAADlhMBUgSbn95CzQdPKM3U2e5XNGRocnbO6FAAALgiBqQLNtxSwQ9PKM82vY9p3PGFxJQAAXBgCUwWKxDIK1DjkcdvrrzfocyrgNbXvWNzqUgAAuCD2ekfFsohE07Za8H2mxoCpg0MJmSbrmAAA5YPAVGFM01Qkaq+WAmdqDErReFYnxtlXDgBQPghMFWZ6NqtUxlSjTUeYmgL5kaXnBmIWVwIAwOIRmCrMaCQ/ctMQtOcIU7BGCtQ4tHuAdUwAgPJBYKowI5MnA5NNR5gMQ1q3wq/njrJHCgCgfBCYKsxoJCW3y1Cgxr5/teu6fDoxntRULGN1KQAALIp931VxUUYjKTWEXDIM+25Bsn6FT5K0e4BRJgBAeSAwVZix6bTqA/ZcvzSvt71GLqdBYAIAlA0CUwXJ5kxNRjOqs3lg8rgcWt/lYx0TAKBsEJgqSCyelWnK9oFJkjb3BnRwKKFkOmd1KQAAFERgqiDT8fwi6toyCExbVgaUyZraf5z2AgAA+yMwVZDp2Xxgqgs4La6ksM09AUks/AYAlAcCUwWZmc3I5TAUqLF/YKoNuNTd6mUdEwCgLBCYKsj0bFZNdfZuKXCmLb0B7RmIK5djI14AgL0RmCrIzGxGTbX27PB9Lpt7A4rNZTUYnrO6FAAAzovAVCFM09T0bEbNdeUTmLb0zq9jYuE3AMDeCEwVIpk2lcqYaimjwNTR5FFD0MXCbwCA7RGYKsT8HXLlNCVnGIY29wZY+A0AsD0CU4WYORmYymlKTsr3YxqJpDQxk7a6FAAAFkRgqhDlOMIk5Rd+S/RjAgDYG4GpQszEs/J5HarxlNdf6ZpOn7xug2k5AICtlde7KxY0PZtRnd/+W6KczeU0tKHbzwgTAMDWCEwVYmY2o9oy2BLlXLb0BnRoOKFEMmt1KQAAnBOBqQJkc6aiiazqymDT3XPZ3BtQLiftPUY/JgCAPRGYKkAsnpVp5vdnK0ebegMyDBZ+AwDsi8BUAabj+TvkynWEKVDj1Mq2GhZ+AwBsi8BUAeZ7MNX6y3MNk5Tvx7R3MK5slo14AQD2Q2CqANOzGTkcUsBXvoFpc29AiVROR0YTVpcCAMALEJgqwMxsVrV+lxyGYXUpF23LypMNLI+y8BsAYD8EpgowHS/flgLzWus9aq5z6zkWfgMAbIjAVAFmZjNlu+D7TFtObsRrmqxjAgDYC4GpzM2lckqmzbLs8n22zb0BTcykFZ5iI14AgL0QmMrcqTvkKmCEafNKvyT6MQEA7IfAVOZO92Aq7zVMkrSqzSef10E/JgCA7ZT/sESVO92DqXz+KrNZU6OR5DmPre6o0a4jsRJXBADA+ZXPuyzOaXo2K5/HIY+7fAYL59I57T187n5LQZ9Lu4/GFUtkFSzjvlIAgMpSPu+yOKeZ2UxFrF+a19HokSlp7yDTcgAA+yAwlbmZeFahMt4S5WxtjR452IgXAGAzBKYyZpqmYolMWe8hdzaPy6Ge1hoaWAIAbIXAVMYSyZyyOSlURgu+F2Ndl0/7jsWVYSNeAIBNEJjK2Ew8K0kKVdji6HUrfEqmTR0aYiNeAIA9EJjKWCyRbykQrKApOSkfmCTWMQEA7IPAVMaip0aYKmtKriHkVnuDhwaWAADbIDCVsWgiK7fLkNdtWF3KstuyMqDdA2zECwCwBwJTGYvGMwr5nDKMygtMm3sDisQyGp5IWV0KAAAEpnIWTWQr7g65eVtWBiSJ9gIAAFsgMJWxaDxbcXfIzetu8SpY42ThNwDAFghMZSqdyWkulau4O+TmORyGNvf6GWECANgCgalMxRKV2YPpTJtXBnQsnNTMbMbqUgAAVY7AVKZOtRSo0DVMkrSlN7+OaTcb8QIALEZgKlPR+RGmCp2Sk6R1XX65nAb9mAAAliMwlaloPCNDUqCmcgOT1+3Q+i6fdh0hMAEArEVgKlPRRFYBn1NOR+X1YDrTJauCOnAirkQya3UpAIAqtqjA9Pu///t63etep9tvv11ve9vbtGfPnmLXhQJiFdxS4EyXrAoqm5N2D8StLgUAUMUWtWL4jjvuUCgUkiT97Gc/0//6X/9L3/3ud4taGM5vJpFRe4PH6jKKbnOvXw6H9OyRmPrWh6wuBwBQpRY1wjQfliQpFotV5FYc5cQ0TcXiWQUrbNPdedmsqdFIUqORpGbiGa1qr1H/geipr0XjtBkAAJTWot9x/+Iv/kIPP/ywTNPUl770pQs6ya5duy64sErU39+/5Odw+Zt14NCEcqZTmblpDQ5MPe94V0OXBgeOL/j9hY4vx3MUOh6LxQp+/yM7Tx83soYOhQ196/7n5HJK12xtUiY+ft4/QzlYjtdDJeA65HEd8rgOeVyH0+xyLRYdmD796U9Lku655x79zd/8jb74xS8u+iRbt26V1+u98OoqSH9/v/r6+pb8PKORpGob3ZLG1LOiRT0dvucdDwZ96untXfD7Cx1fjuc43/HBgQEFg8EL+n6zZk4HR8flCrapp7VG7e1BtTWc/89gd8v1eih3XIc8rkMe1yGP63Baqa9FMplccJDngu+Su/322/X4448rEoksuTBcnNM9mCpzSu5sHY0eGYY0NJ60uhQAQJUqGJhmZ2c1PDx86vMHHnhAdXV1qq+vL2phWNjpLt+Vf5ecJHncDrXWu3WcwAQAsEjBIYpEIqE//uM/ViKRkMPhUF1dne68804WflsoGs/K4zLkdVdPG60VzV7tPBRTOpOzuhQAQBUqGJiam5v1zW9+sxS1YJGiiUzVjC7NW9Hs1dMHYhqJpKwuBQBQhapniKKCVHJLgYV0NnllSDrBtBwAwAIEpjIUTWRVW2UjTB63Qy31bp0YIzABAEqPwFRmkqmc5lI5BatgW5SzrWj2ajSSUop1TACAEiMwlZmJaFpS9bQUONOKZq+yOenQUMLqUgAAVYbAVGYmZk4GpiocYepsyjc/3TvIRrwAgNIiMJWZiZn8PmrVdpecJHk9+XVMBCYAQKkRmMrMxExahiEFaqovMElSd4tXB4cSmktlrS4FAFBFCExlZmImrWCNUw5HdTYO7W6pUTYn7Toya3UpAIAqQmAqMxMzaQWrcDpuXkeTRy6noR2HYlaXAgCoIgSmMjM5k6nKBd/z3C6H1nb6tPNg1OpSAABVpPruTS9j2ZypyWhaPW01Vpdiqc0r/frvh8Y1FcuoPshLuBii8YziyYXXifm9zqpsbQGgevEbr4xEohllc9V5h9yZtvQG9N8PjeuZw1G9+NIGq8upSPFkVv0HFp727FsXJDABqCpMyZWRsan8xrPVPCUnSSvba+T3OrTzIOuYAAClQWAqI+Hpk4Gpyv9l73QYumxNkMAEACgZAlMZGZuq3i7fZ7tsTVAjkZSGJ9mMFwBQfASmMhKeSsnvdcjj5q/t8rUhSWKUCQBQErzzlpHwVFpNtW6ry7CF7havmmpdtBcAAJQEgamMjE2l1Fhb3euX5hmGoW1rQtp5OKZczrS6HABAhSMwlRFGmJ5v29qgZmazOjIyZ3UpAIAKR2AqE/FkVrFElsB0hm1r8uuYdjAtBwAoMgJTmZi/Q47AdFpznVvdrV49fYDABAAoLgJTmQifbFrZxBqm59m+LqRdR2c1l8pZXQoAoIIRmMoEI0zn1re+VumMqd8cpr0AAKB4CExlIjyVktMh1QcYYTrTJasC8rodemr/jNWlAAAqGIGpTIxNpdVc55HDYVhdiq143A5dtjqgp/axjgkAUDwEpjIRnkqppZ7puHPpW1+r4cmUhsbZJgUAUBwEpjIRnkqptc5jdRm2tH1Dvr3Ak0zLAQCKhMBUBrI5U+MzaUaYFtDZ5NWKJg/TcgCAoiEwlYHJaFq5nNRazwjTQvrW1+rZIzEl07QXAAAsPwJTGZhvKdDKCNOCtm8IKZk2tesI7QUAAMuPwFQGwpF808oWRpgWdOnqoDwuQ0/tZ1oOALD8CExlIDydH2FiDdPCvG6HLl0dJDABAIqCwFQGxqZSCvqc8nudVpdia33rQzo+ltTIJO0FAADLi8BUBsJTadYvLcL29bWSxCgTAGDZEZjKwNhUijvkFmFFs0ftjR49sZd+TACA5UVgKgN0+V4cwzB09aZa7TwU01wqa3U5AIAKQmCyudm5rGbncowwLdLVm+qUzpjqP0B7AQDA8iEw2dzY1HxLAUaYFmPLyoCCNU49vmfa6lIAABWEwGRz4VNNKxlhWgyX09CLNob0+N4ZZXOm1eUAACoEgcnmxqbzI0wEpsW7elOdZmaz2jM4a3UpAIAKQWCyuXAkLZfTUEPQZXUpZaNvfUgup6HH93C3HABgeRCYbC48lVJzrVsOh2F1KWUjUOPUpasDenQ3gQkAsDwITDY3Np1mwfdFuHpTnU6MJ3V8bM7qUgAAFYDAZHM0rXyhbNbUaCS54Ec0ntFVm/Jdvx9jWg4AsAxYGGNj2ayp8Rm2RTnbXDqnvYcTCx7vWxdUW4NXazp9enT3tN50Y2sJqwMAVCJGmGxsIppWLie1MMJ0Ua7eVKs9g3FNxTJWlwIAKHMEJhsLT823FGCE6WJcvblWpin2lgMALBlTcjY2drJpJSNMF2Z+jVOwxqHGkEsP7ozosjWBU8f9XqdCfl76AIDF413DxsYYYbooZ65x6mmt0bNHYnp094w87vyAat+6IIEJAHBBeNewsdGptGr9TtV4nFaXUrbWrPBp56GYjozMaUO33+pybCMazyiezC54PJVmWxkAOBOBycbGplJMxy1RR6NHgRqnDpyIE5jOEE9m1X8gtuDxjd2+ElYDAPbHom8bG5uipcBSGYahtSt8GhydUyqds7ocAECZIjDZWHgqpZY6RpiWak2nT9mcdHSErt8AgItDYLKp2bms4smcWhsYYVqqziaPAjUOHTgRt7oUAECZIjDZVDgyf4ccI0xLZRiG1nT6NDA6p1SGaTkAwIUjMNlUeL4HUx0jTMth7Qo/03IAgItGYLKp0z2YGGFaDh1NHvm9Dh08sfAedAAALITAZFPh6ZRcTkP1QTo/LAfHGdNyyRTTcgCAC0NgsqmxqbRa6txyOAyrS6kYa1f4lMmaeubwwv2HAAA4FwKTTYWnUkzHLbPOZq98XoceZzNeAMAFIjDZVHgqrRaaVi4rh2FofZdfzxyaVSyx8LYgAACcjcBkQ5msqcmZNNuiFMH6Lr8yWVMP75qyuhQAQBkhMNnQxExaOVNqY4Rp2bU1uNXW4NYDOyNWlwIAKCMEJhuabynACNPyMwxD12yu07NHZk9dZwAACiEw2dB800o23i2OazbXyjSlXzzDtBwAYHEITDYUPjny0czGu0XR1uDRhm6/HmRaDgCwSAUDUyQS0Qc+8AHdeuuteu1rX6s//MM/1OTkZClqq1pjU2nVBpyq8ZBni+XmbQ06MjKnIyN0/gYAFFbwHdkwDL3//e/X/fffr3vvvVfd3d363Oc+V4raqlZ4KqU21i8V1Y2X1snhEKNMAIBFKRiY6uvrddVVV536fNu2bRoaGipqUdUuPJViwXeR1Qfd6lsX0i92TimXM60uBwBgcxe0UVkul9PXvvY13XzzzRd0kl27dl3Q4ytVf39/wceYpjQy6dSK2oT6+ydecNzlb9bgwAu/Pq+roUuDA8cv+vhyPEeh47FYrKjPX+j4ilCTjh8e18o6Q0/uc+q/f/K0VrUs+PCiWczroViW+jqav4bLwcrrYCdchzyuQx7X4TS7XIsLCkyf+tSn5Pf79Y53vOOCTrJ161Z5vd4L+p5K09/fr76+voKPiyYySn3nOW1dv0J9fS98Fx+NJNUTDS74/cGgTz29vRd9fDme43zHBwcGFAwGi/b8izne3h5UW0OvtlyS1b07d+v4bKve9IruBR9fDIt9PRTLUl9H89dwqay+DnbBdcjjOuRxHU4r9bVIJpMLDvIselXxHXfcoYGBAf3jP/6jHA4WIxfL2HxLgQZaChRbjcepGy6p069+M6VEkq1SAAALW1Ty+Yd/+Aft2rVLX/jCF+TxsLammOZbCrTQUqAkbt3epEQqp4eenba6FACAjRUMTAcOHNCdd96pcDist771rbrtttv0B3/wB6WorSqFIzStLKXNvX51tXh1/1MLr+cBAKDgGqZ169Zp3759pagFksamU3K7DNUFLmh5GS6SYRi6pa9RX/nxsI6PzamrpcbqkgAANsRiJJsJT6XVUueWw2FYXUrVeOkVDXI4pPufoiErAODcCEw2E47Qg6nUGkNuXbWxVj9/OqJMlp5MAIAXIjDZzOhUSu0NBKZSu6WvUZFYRk/um7G6FFszTQIlgOrEQhkbSaZzikQzaiMwldyLNtSqIeTS/U9N6prNdVaXYwuDo3N6ZPe0ovGssjlT2ZypXE7qafOqIehUW0N191YDUF0ITDYSjuRbChCYSs/pNPSyKxr0nYfGNDmTVmNt9d6lGI1n9MX7hrTz0KzqAi6tW+GT02HI6TSUzZnaOxjXJ//PgK7cMKl3vLxd63/fGK8AACAASURBVFb4rS4ZAIqOwGQjowQmS93S16Rv/XJMP3t6Ur91U5vV5ZScaZracTCmx/fMyDCkqzfX6oq1ITmdz78B4aqNtZqYSevHT07qjz5/QG+6sUXvfUWHDIMbFQBULgKTjYycDEztjQSmYspmTY1Gki/4utslbej26b7HJ/TGG1vlrLI7Ffv3R/Xo7hmt6qjRe25t19h05pyP87gdes3VTXrrS9r05R8N6du/GtPsXFZ/cFtX1V0zANWDwGQjo5GUXE5DDUH+WoppLp3T3sOJcx5b1e7TvmOTempfVFdtqi1xZdbZdyyuR3fPaF2XT7dub1RTrXvBwDQvUOPUh27vUq3fpW/8IqxEMqc/fXOPXE5CE4DKw11yNjIaSamtnh5MVlrd6VN9wKUfPDZudSklc3xsTj/rn9SKZo9efkXjBU2tGYahd9/aoXff2q5fPDOlz/zXUaUyuSJWCwDWYCjDRkYjKbUxHWcpp8PQTdvqdc/D4xoaT6qzubLvBJuYSeu+xydUH3Tp1Vc1v2C90mK95aY2+b1O/ev3T+jvv3VMH3lrD2uabCAazyh+no2l/V6nQn7eBoDF4CfFRkYiKV3XyS3tVrvpsnrd++i47nt8Qh94dafV5RRNKp3TvY+My+009Lprm+X1LG3A+bXXNCuezOru+0e0vsuvN9zQskyV4mLFk1n1H4gteLxvXZDABCwSU3I2kUhmNTObpWmlDdQHXbpuS51+8tSk5lKVO730yHPTiiayetVVTcv2pvlbL27VdVvr9OUfD2nnoeiyPCcA2AGBySbmWwq0Ephs4TXXNCs2l9Uvn4lYXUpRHDge17NHZnXZmqDaG5dv2tEwDP3Jm7rV1ezVZ782oPBUatmeGwCsRGCyidFIWpIYYbKJrSsDWtlWo3sfm6i47UBSmZzuun9EIZ9TV29e/jsB/V6n/vIdq5TJmPrr/zyqZLpyR+kAVA8Ck02MnOwLRNNKezAMQ6+9plmHhhLaOxi3upxl9a1fhDU0kdJN2+rlcRXnV0BXi1d/9pZeHTiR0BfvGyrKOQCglAhMNhGOpOV1G6qnB5NtvGRbvfxeh773SOW0GBgMz+nrvwjr6k21WtnuK+q5rtpUqzfe0KL7Hp/QE3vZ1BhAeSMw2cRIJKXWBg+3YtuIz+vUK69s0kO7pk6tMSsX0XhGo5Hk8z5GJuf0d98alNdt6E03Lu0Otvlu6Qt9ROP5ppfvvKVdK9tr9I/fOabp2fM3wgQAO2M4wybyTSuZjrOb265r1j0Pj+m7vx7T7752hdXlLNq5bic/cCKu/ccTuvnyBvm8S/u30vm6pUunb1f3uBz6s9/q0R994YD++bvH9bG39/KPAgBliREmmxiNpNhDzoZa6jy66bIG/fjJyVOjJuUomzP16HMzaqp1aVOvv6TnXtXh07tuadcjz03rZ09X5l2HACofgckGZueyiiWyLPi2qTfe2KJkOqcfPD5hdSkXbdeRWU3PZnTtljo5LBjhef31LbpkVUD/du8JjUy+cONjALA7ApMNzK+PITDZ06p2n7avD+neR8aVKsNb5FPpnJ7YO6OuZq9622osqcHpMPSnb+6RIelz3zqmbK6yWjUAqHwEJhsYmSQw2d0bb2xRJJbRz3eU35TS0weimkvldO3WupKtHzrXonDJ1Ntf1qbnjs7qv34+UpI6AGC5sOjbBuZHmGhaaV+XrQ5q3QqfvvPQmG7d3iiHozwWLscSWe04GNO6Ll9JA/lCi8K9bofWrvDpG78I65ot7JsIoHwwwmQDo5GUfB6HQn6n1aVgAYZh6I03tujEeFKP7SmfnkJP7J1RLmfqms32CCeGYeimy+oV8rn0t98YVDprdUUAsDgEJhsYjaTUdrIH07n655z5kUqz9sMq12+pV3uDR19/cLQstkuJRNPafXRWl6wOqi5gn8Fkn9ep972qXYPhpH76LL+CAJQH+/wWrWIjkZTaTrYUOFf/nDNt7C5ud2YszOk09Ns3t+kfvnNMj+2Zsc2ozUIe2zMjl8vQizaErC7lBS5ZFdRrr2nSvY9OaMfBqC5fa78aAeBM/PPOYqZp0rSyjLz08gZ1Nnn0nz8bUc7Gd3odHZnTwRMJbVsTlM9rz6ne976iUy0hU3/3rWOKJsq3x1U1KzQiXs69y4CzMcJksWg8q0QyR9PKMuF0GnrD9S36/PdO6IdPjOtFG2rP+Ti/16mQ37ofr+88NKYat0OXr7PvyE2Nx6E3XZnVfzyY1r9+74Q+8tZeq0vCBSo0Ij7f8R2oBLySLTY6lb9DrpU75MrGFeuCagi69LUHwpJhnLMRpJVvFM8eienZI7O6bmudvG57DyKvaJDe/rJ2ffUnI7pqY61u2tZgdUkAcE72/m1aBWgpUH4cDkNXbqrVZDSjg8cX3k/NCqZp6u77h1UfdOnS1UGry1mU37qxVZt6/PrC905obKq8NjkGUD0ITBajaWV5WrfCp6Zalx4/edu+XTy5L6rdA3Hddm2TXM7y6BXldBr68G/1KJMz9XffPmar6wkA8whMFgtHUgrUOBT02XNhLs7NMAxdubFOU7GM9h2LW12OpPwGu//7J8PqaPTohkvqrS7ngnQ2efU7r+nUM4di+s5DY1aXAwAvQGCy2MjJHkwoP2s6a9Ra79aju2eUyli/x9zPn47o8PCc3nlLe9mMLp3p1u2NuuGSOt19/7B2HopaXQ4APA+ByWLDkyl1NHqtLgMXwTAM3XhpvWbnsnpqn7Vv8IlkVnf/ZFgbu/168aXlNbo0zzAM/c83dqurxavPfm1AYdYzAbARApOFsllTI5MpdTYzwmQn59o4dqFu6x1NXm3s9mvHwaimYtb1nPnWL8OKRDP64Gs6S7bBbjH4vU795TtWKp0x9en/e1SptPUjdwAg0VbAUuHplDJZUyuaGGGyk4U2jp13drf1a7fW6dBwQg89O6XXXtNc7PJeIDyV0nceGtNNl9VrU0+g5Oe/GPOh1OVv1mgk+bxjbpeh97+qQ//y3RP6t3tP6I/f0G1RlQBwGoHJQkPj+SmHDgJTWQvUOHXlxlo9vGtaR0cSWtle2u1r7vrxsCTpPa/oKOl5l2I+lA4OTKgneq72B4Zec3WTfvDYhFa1+/S6a0sfRAuJxjOKJxfePdjq5qUAlhc/zRYamsj/y3pFM4Gp3F22Jqjnjs7qod9Mq7u1pmTn3Ts4q188M6W3vqRVrRW2vc4brm/W+HRad/7ghGoDTt10mb2aWha7y3WhQCYRyoBS4ifNQifGk/K6HWoM8ddQ7pyO/ALw7z8yrqcPRHVlCTa8zeVM/cd9Q2oIufTmF7cW/Xyl5nAY+uhv9+pjdx3W5745qJDPqb71596KphIVCmQSW48ApcSibwsNTyTV2eQp60W6OK23rUZrV/j0xJ4ZHR4ufgfw7z0yrj2Dcb33FR3y23SD3aXyuh36q3euUm9bjT71nwPaMzhrdUkAqhSByUInJlLqZDquorxkW4P8NU7dee9QwemUpRgYndNd9w/rms21eunl9pqqWm6BGqc+9Z7Vagy59PG7j+hICcIoAJyNwGSRfEuBJHfIVZgaj0O3bG/U2HRa//q9E0U5RzqT0+e+OSi/16k/en1XVYxQNobc+sz7VsvrdujD/35Qzxw6/1QVACw3ApNFRqdSyuZED6YKtKLZq9dd06Sf74jowZ2RZX/+rz0Y1sGhhD70+i7VB93L/vx21d7o1d//3lo117n1sbsO6xdFuLYAsBACk0WGT94h18kIU0V63bXN2tzr1+fvOa7hyWThb1ikvYOz+sYvRvXSyxt03Za6ZXvectFa79HnfnetNvb4dcc3BvXtX4VlmmzWez6maSqdyWl2LqvpWEZpG2zjA5Qjbq+wyImTPZiYkqtMToehP3tLr/7gn/fpL+86or/5wBo11i5tNCgSTetvvzmoppBbv/vaFctUafkJ+Vz69HtW6+++Nagv/2hYh4YS+v3bVijkq+5fZ6Zpangipf0n4jo0lDj1EUtklTsrUwZqnKoPOvWbQ1G97IpGXb4uJKej8qd2gaWo7t8wFhqaSKrG41ADLQUqVluDR3/1rlX6y7uO6CNfOqQ73n/xoWkqltZHv3RIEzMZffq9qxX0VeZdcYvlcTv0+7etUGOtW99/ZFzPHIrpva9s1yWrTjfBXGqPokJ9kM7cIscqc6mcHts9rSf3R9W/P6rRSP4fYi6noVXtNdq2Jqh4MieP25DH7ZDLYSh2cqRpajajJ/dF9cvfTKu13q1XvKhJL+9rVHNd9UzzAheCd2uLnBinpUA12LoyqE+9Jx+aPvqlQ/r/PrBGjaELe0OaimX05186rNFISp9892ptWVke258U21wqp+7WGr35xa36af+k/u5bx7V1VUDXbqmT1+1Yco+iQn2Qzt4ipxRM09RkNKOB0TkNjM7p376f317J53Fo29qg3vziFm3qCaintUYup6HRSPK8f4bLVgd0eHhOP3piQl/96Yj+8+cjevONrXrHy9rlcvK7CTgTgckiwxNJreoo/S9clN7WlUF96t2r9Jd3H9FHv3hIn33/mkV/7/RsRn/+5UMamkjq/333Kl26+lzbiFS31gaP3vKSNj22e1o7DsZ04Hhcl60JWRJoiiGVzunYWFIDo3MaHJ1TNJEf9WoMufSyKxr04svqtaU3ILfrwpekGpLWd/m0vqtL4UhK3390Qt/4RVhP7J3RB1/doTWdfhpjAifxk2CBbNbUSCSl6y+pt7oUlMjWVUF98t2r9PG7j+gDf79X168ztPXSnLzuc7/J5XKmHt09rbt/MqJwJKW/etcqbVtT/O7h5crlNHT9JfXa0O3XE3tn9MTeGT17OKbbrmvWK69sKqttYxLJrPYei+vxPdN6an9MwxNJ5cz8psTdLV5t3xBSb1uNQn6X+tYF1dZw8esgz95o+vJ1IYX8Lj24I6K/vPuo3v7SVr3lpjZGwgERmCwxGjnZUoAF31XlklVB/csfrtNXfjysn+6a0Y5je/WuW9p1zeY61bgdcjoNZXOmHt41ra89MKqjo3Na0eTRJ9+9WpetYWRpMVrqPXr11c0am07p4ImEvv5gWF9/MKz1XT5dv7Ve122ts83PnWmamolnNTA6p0cPGvr1wLH8Qu3hhHI5yTCk5lq3tq0NqretRh1N3pIszF67wqf2Ro9+9vSk/vdPRhVN5PT+V3YQmlD1CEwWOHGqpUD5/KsXy6OrpUYf/x+r9O0f9+tXB336u28dk3RMUn4EweUwlEjl1N3q1Z+9pUc3XlrP3UsXoaXOo5df3qDxG9N6cl9+QfRXfjysr/x4WPUBl3ravFrT4dPGnoBa6t1qCLpUH3SpxnNhi+lN01Q2J2WypjLZnNIZ8+T/m3rObejw8Jxiiaxm4hlF41lNz2Y0Pp1WeCqlsam0Eqn5W/ydqvVPa1W7T2++sVVbVwbUVOfSnkFrupoHfU7ddm2zdh+d1X8/NKZcztTt1zW/4HF2WPgOlAqByQJD84GJbVGq1qoW6Q23rNMTe2c0NJFSIpXVXCqnZCqnrauCum5rHUFpiebSOR0fT6mjyavXXOPVTDyjI8NzCkdSOjGe0nNHZ/Xdh8ef9z1et0NejyG305BhGMpkTOVkKpfLT5PmzNP/nQ9Li+V0SCG/S821bnW11OjytSG1NXjU0+pVNHxAL77u0ueN4oxGlq9/18UwDEOvv6FZw5Mp3fPwuMam07pi3fOnhStlnRiwGAQmCwyNp+TzONQQ5PJXM4fD0NWbq6/5pFVq/a7nTW1essqvVFqamElr6uRt9tOxjJLpnLI5U9F4RuHptByGIYeR//s6878tdW5FYhm5nEZ+dNDpkMtp5D93Gtqy0q/2Rq+CPqdq/S75vY4Fp7X6o7LllJfDMHTzFQ3KZPNTxS6nwY0HqFq8Y1tgaCKpzmavLX9BAtXC43Kou8WrNZ3nHiUpdEv+xm6f9h5beMpsfZd/SQuy7cJhGHr59kZlshP65TNTCtY4tXqBawZUMgKTBYYmkgv+kkZlyGbN806p1AQbSlgNsDROh6FXXNmkb/8qrJ/viKi1wVP1zVNRfQhMJZY52VLgBloKVLSzb9c+24oQbzYoLy6noVu2N+obD4b1s/5J3XaOReBAJWPz3RIbjaSUy+V3tAeActIYcuuGS+p0bCypHQcXnq4EKhEjTCV2Yjw/TdNBSwHgvApNa3JLuzW2rAzke0c9N60bttZaXQ5QMgSmEhs+2VJghU2a5wF2VWhak1varWEYhm6+vEFfe2BUd90/otdf33JR27IA5YZXeYmdmEjK53WonpYCAMqUz+vUy/saNRpJ66n9UavLAUqCwFRiQ+MpdTbRUgBAeeturdGLNoS040BU07MZq8sBio7AVGKD4Tn1tDIdB1S6+TVYC31E4+UfMm67tkmGYejhXdNWlwIUHfNCJTQ7l9XYdFor22qsLgVAkRVag9W3LqiQf2m/gq1eGN8Qcmv7+pAe2zOj42Nz6mrhdxsqF4GphAZG5yRJPQQmAMvADgvjL18X0nMDs/rVb6b11pu9crDcABWKKbkSmg9MjDABqBQup6HrttZpYiat3UdnrS4HKBoCUwkNjM6pxuNQaz09mABUjrWdPnU2efTo7hklUzmrywGKomBguuOOO3TzzTdrw4YN2r9/fylqqlhHR+fU21ojh4MhayxNNJ4574JimjqilAzD0I2X1msuldOOg7QZQGUquIbppS99qd75znfq7W9/eynqqWgDo3O6ciOdcbF08WRW/QcW3pqCpo4otZZ6j9Z2+vTMoZi2rQ2pxsMEBipLwVf09u3b1dHRUYpaKtpsUpqKZVi/BKBivWhjrVIZUzsPMcqEylOSu+R27dpVitPY2uh0fhouOX1M/f2DCz7O5W/W4MDEgse7Gro0OHC8aMdLcY5YLFbU57f6+GIes2Jrk/r7+8/7HOdj9etkOY8PDgxYUsOKUJOOHx5f8Hixr/Hq5i6NjCROneuZPc+/Di53jQYHTiz4/ctRQzGOd9Q7tGP/jJo9U8/7M56Ly5HVXCzyvK8t5eeiknAdTrPLtShJYNq6dau83upu1vjYN56WJL38hkvUVOte8HGjkaR6osEFjweDPvX09hbteLHPMTgwoGAwWNQ/g9XHF/eYmPr6+s77HOdj9etkuY4PDgws+Lhi19DeHlRbw8LHi32NPT6fDo87Jemc12Fjt7U/qxd73F+f0tceCGsiVS+PL3jqz3gufeuCatuw+tTn/f39S/q5qBRch9NKfS2SyeSCgzz0YSqR0RlDQZ9TjSEuOWAHVjd9rFTNdR6t7qjRzkMxvTmZtbocYNnw7l0io9OGVrbVsIccYBN2aPpYqa7cWKvDD4b14M4pren0W10OsCwKLvr+67/+a914440aGRnRe97zHr361a8uRV0VxTRNhWekXhZ8A6gCLfX5UaYHd0zRlwkVo+AI08c+9jF97GMfK0UtFWtiJqO5tMEdcgCqxpUba/X1B8N69khM2zfQTgXlj0YZJTAwmh/2Zw85ANWipd6jjd1+PXMopmyW9WAofwSmEjh6cg85puQAVJOX9dUrnsxp37G41aUAS0ZgKoGB0TkFa0zVBVhjD6B6bOz2q7nOracPRmWajDKhvBGYSuDoyJzaavllAaC6GIahy9cGFYlmTo20A+WKwFRkuZypwXBSbax5BFCF1nX5FfQ5teM8ex8C5YA5oiIbnUopmc6ptY4RJpzm9frO2zTR73Uq5OfHE+XP6TB02ZqgHt41rdFISm0NHqtLAi4Kv5GLbGAkPwzNlBzOlMpK/ef5F3ffuiCBCRVj68qAntw7ox0HonrFlU1WlwNcFKbkimx+3r6FKTkAVcrjdmjrqoAOnkhoejZjdTnAReGfsEU2MDqn1nq3atz8ksDisc8ZKs1la0LaeTCm3xyK6YZL660uB7hgBKYiOzSU0KoOn6SF96wCzsY+Z6g0QZ9T67r8em5gVldtqpXHzQQHyguv2CKKJjI6NpbUhm42nwSAbWuCSmdMPTcwa3UpwAUjMBXR/mP5EYKNBCYAUGuDR51NHj1zKKZcjmlllBcCUxHtOxaXYUjruwhMACBJ29aGFI1ndXiYZQooLwSmItp3bFZdLV4FapxWlwIAtrCqo0a1Aad2HqSRJcoLgalITNPU3uNxpuMA4AwOw9C2NUENT6Z0aIhRJpQPAlORjERSmpnNsuAbAM6yqScgj8vQ/U9NWl0KsGgEpiLZNxiXJG3sDlhcCQDYi8ft0JaVAT21L6rwVMrqcoBFITAVyd5jcXndDq1sq7G6FACwncvWBCVJ9z46bnElwOIQmIpk37G41q3wyek0rC4FAGwn5Hdp+/qQfvTEhBLJrNXlAAURmIogncnp0HCC9UsAcB63vKhRs3M5/aSftUywPwJTERwenlM6YxKYAOA81nb6tKnHr+89PK4sjSxhcwSmIth3bH7BN4EJAM7n9de3aHgypSf2zlhdCnBeBKYi2HcsrsaQS811bqtLAQBbu3ZznVrr3frur8esLgU4LwJTEew7NqsN3X4ZBgu+AeB8nE5Dr7u2Wc8emdVQxOpqgIURmJZZNJ7RiYkU03EAsEiveFGTfB6HHj7AWxLsi1fnMptfv7SBhpUAsCiBGqdu2d6oZ48ZGp9OW10OcE4EpmW271hchiGt6/JZXQoAlI3br2uWaUr3PMxaJtgTgWmZ7RmMq7e1Rn6v0+pSAKBstDd6dUm3qfsen1A0nrG6HOAFCEzLKJnOadfR2KmW/wCAxbtxY05zqZy+/wjbpcB+CEzLaNeRmJJpU9vXh6wuBQDKTnuddPWmWn3vkXG2S4HtEJiW0ZP7ovK4DF2ymhEmALgYv3VTq6KJrH70BNulwF4ITMvoqf1RXbo6KK+bywoAF2NTT0CXrQ7qv38dViqTs7oc4BTe2ZfJ8GRSJ8aTTMcBwBK95SWtmpjJ6OdP08kS9kFgWiZP7YtKkrZvqLW4EgAob9vWBLVuhU/f+mVY2Syb8sIeCEzL5Kn9UXU0erSi2Wt1KQBQ1gzD0Fte0qbhyZQefIZRJtgDgWkZpNI5PXMopu0bmI4DgOVwzaZare306T9/Nqo0a5lgAwSmZbDr6KyS6Zy2r2c6DgCWg8Nh6N23tms0kuKOOdgCgWkZPLVvRm6XoUtpJwAAy+aKdSFdujqgrz04Sl8mWI7AtAye2h/VJasCqvFwOQFguRiGoXff2qGpWEb3PEz3b1iLd/glGo2kdGwsyXQcABTBpp6Artlcq2//KqyZWfaYg3UITEv01P4ZSdKLWPANAEXxrls6lEjl9M1fhq0uBVWMwLRET+yZUXsD7QQAoFh622p087YGff/RcY1NpawuB1WKwLQEEzNpPXUgqhsurZNhGFaXAwAV63+8vF2GpH+/b8jqUlClCExL8JP+SeVy0ite1GR1KQBQ0doaPPrtm9v08K5pPbF3xupyUIUITBcplzN1/5OT2rYmqM4mpuMAoNjeeEOLulu9+tfvn9BcimaWKC0C00XacTCm0UhKr7iy0epSAKAquF0O/eFtXRqNpPT1B0etLgdVhsB0kX705IRq/U5ds7nO6lIAoGpcujqol13RoG//KqyB0Tmry0EVITBdhEg0rcd2T+tlfY3yuLiEAFBK73tlp3xepz5/z3GZpml1OagSvNtfhJ8+HVE2J71iO9NxAFBq9UGX3vfKDu06OqvvP0oHcJQGgekC5Rd7T2jrqoC6W2usLgcAqtItfY26ckNIX/rhsA6ciFtdDqoAgekC/eZITEMTKb2SVgIAYBmHw9CfvrlH9UGXPvNfA5qdY3NeFBeB6QL94NEJBWucum4ri70BwEq1AZc++tZehadS+qf/PsZ6JhQVgekC7DoS08PPTet11zbL6+bSAYDVtqwM6F0vb9dDz07rvscnrC4HFYx3/UXK5kzd+YMhNde59eYXt1pdDgDgpDfd2Krt60P6j/uGtGdg1upyUKEITIv0k6cmdWgoofe9skM1Hi4bABRbNJ7RaCS54Ec0npF0ej1TS51bH7/7iI4MJyyuHJWId/5FiCWyuvsnw9q6MqAXX1pvdTkAUBXiyaz6D8QW/IgnTy/0rg+69Jn3rZHX49BffOWwhsaTFlaOSkRgWoT/+/MRReNZ/c5rO2UYhtXlAADOoa3Bo8+8b7WyOVN//uVDGptOWV0SKgiBqYDB8JzufXRct25v1NpOv9XlAADOo6e1Rn/93tWKJrL6iy8f1sRM2uqSUCEITOeRTOf0j985phqPQ++6pcPqcgAAi7BuhV9/9c5VGptO64+/sF/7jtHYEktHYFpANmfqjq8PaO+xuP74Dd2qD7qsLgkAsEiXrg7q739vrVxOh/6f/zioB3dGrC4JZY4UcA6maepfv39Cj+6e0e++plM3XMJCbwCwm2zW1Ghk4cXdzbVu/ePvr9On/+uo/uYbgzo6Mqd3vrxdTidrUXHhCEzn8PUHw/rh4xN6040tuu26FqvLAQCcw1w6p72HF24h0LcuqLYGrz7z3tX6t3tP6Ju/DOup/TP60O1d2tgTKGGlqAQEpjOYpqnvPzKur/50RDdva9B7bmXdEgAUy9kjRC5/8/M+T6WXZ6sTt8uhD93epcvXhvTvPzihP7nzoF55ZZPefWu7Qj7eBrE4vFJOikTT+qfvHtfje2Z05YaQ/ucbu+RwMGwLAMVy9gjR4MCEeqLBU59v7PYt27kMw9ANl9Srb31I/+enI/r+I+N6eNe0Xn99s151ZZNCft4OcX68QiT9eteU/uWe40okc/rgqzt127XNhCUAqEB+r1O/85oVeukVDfrKj4Z19/0j+toDYd3S16Dbr2tRZ7PX6hJhU1UbmLI5U08fiOoHj03oib0zWrfCpz99c49622qsLg0AUGRrO/36zPvW6MhIQt/99Zh+9OSk7n1sQhu6/bp2c62u2Vyn7lbeD3DaogLTkSNH9NGPflRTU1Oqr6/XRhSYlgAACkJJREFUHXfcoZUrVxa5tOVnmqaGJlL61W+m9OMnJxSeSqs+6NK7bmnXm25slYs7JwCgqqxq9+lP3tSjd9/SoZ/0T+rR3dO66/4R3XX/iFY0ebR5ZUAbuwPa0O3XyrYa7rCrYosKTJ/4xCf0tre9Tbfddpu+973v6eMf/7i++tWvFru2JZmdyyocSSk8ldaRkYT2DMa199isZmbzew9dvjao97+qU1dvqpXbRTsqAKhmjbVuvfUlbXrrS9o0NpXSY3tm9OS+GT2+Z0Y/7c/3cPK4DHU0edXR6FFHo0ftjV41hFyqD7pUF3Cp1u9Sjcchj8tgWUcFKhiYJiYmtHv3bt11112SpNe85jX61Kc+pcnJSTU2Np73e00zf4dDKlW8/XyeG5jVAzsiiiezSiRzSiSzmpnNKpHKPe9xHY0e3bg1oDWdPm3pDai13iNJymXTOmP/xqJKJgtvBplJp+TQwq38sxlnUY8X+xwel6lsxto/ox2uoZnLyaHcgset/jOU6rjHZS74OLvUWIrj57oOVv+sWnH87Ouw1OfPpFNaxK/dF6j1SbdcEdItV4RkmqbGptM6PJTQ0dE5hadSCkfm9PCJGaUyC9/F53YZ8rgc8rjyd+l5XA65XIYcZ+xHakjKyZSZO/nJqbo9+s6OvZIkp2E8b1Rr/tvnv5LL5ZeYnP31+f9xOgw5HZJx8guGcfI5DEMOY/7z/NFTz3HG5w7DOON7zjim09/rOPV9Z3yc9TzGGc9z6tj888o49XXTlEyZMk0pZ0qjo4b2TByTaZpqrvPo5ssbCv8FLsF8XpnPL2cyzHN99Qy7du3SRz7yEd13332nvvaqV71Kf/u3f6stW7ac98TRaFT79++/mJoBAAAssX79eoVCoed9raiLvgOBgNavXy+32y3jjFQNAABgN6ZpKp1OKxB4YWPTgoGpo6NDo6OjymazcjqdymazCof///buNqSpPY4D+PfqzW5RQUkPO0FZL9KlPSEpFSeM+bB0x+WyFEoKkYgisCCJoEAiyzdRSr7oRUUvojBRaFmQmphFGiEsckYtJXE+lBJmadrxd19E495r3P317pzTXb8PCBv7w777co77sZ2d0weTyf9JHUNCQiZMaIwxxhhjP6s//vjxryP9Hu0cHh4Os9kMp9MJAHA6nTCbzX6PX2KMMcYYCxZ+j2ECAI/Hg2PHjmFwcBBz5sxBcXExli9frkc+xhhjjDHDCQ1MjDHGGGO/Mj4BEWOMMcaYHzwwMcYYY4z5wQMTY4wxxpgfPDAxxhhjjPnBAxNjjDHGmB8BH5iGh4eRn5+PpKQkWK1WPHjw4Ifrent7kZOTg9jYWDgcjkDHMER7ezuysrKQkpKCrKwsdHR0TFijqioKCwuRmJiIpKQklJeX6x9UYyI9NDY2wuFwICYmBsXFxfqH1IFIDxcvXkRaWhrS09PhcDjw8OFD/YNqTKSHiooKKIoCu90ORVF++ot7T4VID9+9efMGa9asCcp9Q6SH0tJSbNiwAXa7HXa7HYWFhfoH1YHoNlFdXQ1FUWCz2aAoCt6/f69vUI2J9FBQUODbHux2O6KiolBbW6tvUAqw0tJSOn78OBERtbe308aNG2loaGjCusHBQWpubqa6ujrKyMgIdAxD5OTkUFVVFRERVVVVUU5OzoQ1lZWVlJubS6qqUn9/P8myTJ2dnXpH1ZRIDx0dHfTixQs6d+4cnT17Vu+IuhDpoaGhgT5//kxERG63m2JjY2l4eFjXnFoT6eHjx480Pj7uu52QkEBut1vXnFoT6YGI6OvXr7R79246cuRIUO4bIj2UlJQE5Wv/J5EuXC4Xbd26lfr6+ojo23vnyMiIrjm1JrpvfOd2uykuLo6+fPmiRzyfgH/CdPfuXWRnZwMAIiIiEBMTg4aGhgnrZs+ejfXr12PmzJmBjmCI/v5+tLa2wmazAQBsNhtaW1sxMDDwt3XV1dXYsWMHQkJCMG/ePCQmJuLevXtGRNaEaA9Lly7FypUr8fvvml7O0DCiPciyjBkzZgAAIiMjQUT48OGD7nm1ItrDrFmzfNebHBkZwdjYWFBdf1K0BwC4dOkSEhISEBERoXNK7U2mh2An2sXVq1eRm5uL+fPnA/j23jl9+nTd82plKtvErVu3oCgKwsLC9IoJQIOv5LxeLxYvXuy7bzKZ0NPTE+in+el0d3dj4cKFCA0NBQCEhoZiwYIF6O7unrBOkiTf/WDrR7SHYDeVHqqqqrBkyRIsWrRIr5iam0wPtbW1SEtLw5YtW5CXl4fIyEi942pGtIe2tjY0NjZi7969BqTU3mS2hzt37kBRFOTm5qKlpUXvqJoT7cLj8aCzsxO7du1CRkYGysrKQEF0vunJ/q8cHR3F7du3sX37dj1jAhC4+O4/ZWRkwOv1/vCxx48f/+dAjP2KmpubceHCBVy+fNnoKIaxWCywWCzwer04ePAgNm/e/EtdgmlsbAwnTpzAmTNnfG8ev6rs7Gzs378f06ZNw6NHj3DgwAFUV1dj7ty5RkfTnaqqePnyJa5cuYLR0VHk5eVBkiRs27bN6GiGqKmpgSRJMJvNuj/3pAemysrKf31ckiR0dXX5Ls7b3d2N+Pj4qaX7HzGZTOjt7YWqqggNDYWqqujr64PJZJqwzuv1YvXq1QAmfuL0fyfaQ7CbTA8tLS04evQoysrKgm5AmMr2IEkSVq1ahfr6+qDpQ6SHd+/e4e3bt9i3bx8AYHBwEESEoaEhnDp1yqjoASW6PXz/+gkANm3aBJPJhFevXiEuLk7vyJoR7UKSJFitVoSFhSEsLAwWiwUulytoBqbJ/o+oqKgw5NMlQIOv5KxWK27evAkA6OjowPPnzyHLcqCf5qcTHh4Os9kMp9MJAHA6nTCbzb7B8Tur1Yry8nKMj49jYGAANTU1SElJMSKyJkR7CHaiPbhcLhw+fBglJSWIjo42IqqmRHvweDy+2wMDA2hqasKKFSt0zaolkR4kSUJTUxPq6upQV1eHPXv2YOfOnUEzLAHi20Nvb6/vttvtRldXF5YtW6ZrVq2JdmGz2dDY2AgiwtjYGJ48eYKoqCgjImtiMu8ZPT09ePbsme94J90F+ijyT58+0aFDhygxMZGSk5Pp/v37vsfOnz9P169fJ6JvvwSRZZni4+MpOjqaZFmmkpKSQMfR1evXrykzM5OSk5MpMzOTPB4PERHl5eWRy+Uiom+v++TJk2SxWMhisdCNGzeMjKwJkR6ePn1KsizTunXraO3atSTLMjU0NBgZO+BEenA4HBQfH0/p6em+v7a2NiNjB5xID6dPn6bU1FRKT08nRVHo2rVrRkbWhEgPfxWsvxQT6aGgoIDS0tJIURRyOBxUX19vZGTNiHShqioVFRWR1Wql1NRUKioqIlVVjYwdcKL7RllZGeXn5xsVk34jCqKjxxhjjDHGNMBn+maMMcYY84MHJsYYY4wxP3hgYowxxhjzgwcmxhhjjDE/eGBijDHGGPODBybGGGOMMT94YGKMMcYY8+NPjV4zNHaRoIYAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Okay, so let's set a threshold of say 0.65 on the x_axis. This means any data above 0.6 is considered and labeled an anomaly"},{"metadata":{"trusted":true},"cell_type":"code","source":"threshold = 0.65","execution_count":115,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# First let's get some predictions on the testing data\nX_test_pred = model.predict(X_test)\n\n# Next let's compute the mean absolute error of X_test_pred and X_test.\n# MAE is so easy to compute:- find mean of abs(X_test_pred - X_test)\n# Let's save it in a Data Frame\n\ntest_mae_loss = np.mean(np.abs(X_test_pred - X_test), axis=1)","execution_count":116,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#  Detect Anomalies \nFirst we build a data frame with the loss and anomaly values(those exceeding the treshold)  \nThe data will begin after the time steps in the original test data set"},{"metadata":{"trusted":true},"cell_type":"code","source":"test_score_df = pd.DataFrame(test_set[time_steps:])\n\n# First lets add the loss column containing the test_mae_loss\ntest_score_df['loss'] = test_mae_loss\n\n# Next, we set the threhold column\ntest_score_df['threshold'] = threshold\n\n# next let's create an is_anomaly column that tells if a loss is above or equal \n# to the threshols set\ntest_score_df['is_anomaly'] = test_score_df.loss >= test_score_df.threshold\n\n# lastly, let's track the closing prices\ntest_score_df['close'] = test_set[time_steps:].close","execution_count":117,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_score_df.head()","execution_count":118,"outputs":[{"output_type":"execute_result","execution_count":118,"data":{"text/plain":"           date     close      loss  threshold  is_anomaly\n6583 2012-02-08  1.216856  0.056160       0.65       False\n6584 2012-02-09  1.221625  0.056245       0.65       False\n6585 2012-02-10  1.199313  0.056122       0.65       False\n6586 2012-02-13  1.221194  0.056263       0.65       False\n6587 2012-02-14  1.218150  0.054837       0.65       False","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>close</th>\n      <th>loss</th>\n      <th>threshold</th>\n      <th>is_anomaly</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>6583</th>\n      <td>2012-02-08</td>\n      <td>1.216856</td>\n      <td>0.056160</td>\n      <td>0.65</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>6584</th>\n      <td>2012-02-09</td>\n      <td>1.221625</td>\n      <td>0.056245</td>\n      <td>0.65</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>6585</th>\n      <td>2012-02-10</td>\n      <td>1.199313</td>\n      <td>0.056122</td>\n      <td>0.65</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>6586</th>\n      <td>2012-02-13</td>\n      <td>1.221194</td>\n      <td>0.056263</td>\n      <td>0.65</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>6587</th>\n      <td>2012-02-14</td>\n      <td>1.218150</td>\n      <td>0.054837</td>\n      <td>0.65</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Let's see the last 10 rows\ntest_score_df.tail()","execution_count":119,"outputs":[{"output_type":"execute_result","execution_count":119,"data":{"text/plain":"           date     close      loss  threshold  is_anomaly\n8187 2018-06-25  4.493228  0.735808       0.65        True\n8188 2018-06-26  4.507583  0.781334       0.65        True\n8189 2018-06-27  4.451431  0.791482       0.65        True\n8190 2018-06-28  4.491406  0.821036       0.65        True\n8191 2018-06-29  4.496343  0.810404       0.65        True","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>close</th>\n      <th>loss</th>\n      <th>threshold</th>\n      <th>is_anomaly</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8187</th>\n      <td>2018-06-25</td>\n      <td>4.493228</td>\n      <td>0.735808</td>\n      <td>0.65</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8188</th>\n      <td>2018-06-26</td>\n      <td>4.507583</td>\n      <td>0.781334</td>\n      <td>0.65</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8189</th>\n      <td>2018-06-27</td>\n      <td>4.451431</td>\n      <td>0.791482</td>\n      <td>0.65</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8190</th>\n      <td>2018-06-28</td>\n      <td>4.491406</td>\n      <td>0.821036</td>\n      <td>0.65</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8191</th>\n      <td>2018-06-29</td>\n      <td>4.496343</td>\n      <td>0.810404</td>\n      <td>0.65</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10,8))\nsns.distplot(test_mae_loss, bins=50, kde=True) # kde is kernel density estimate tht's over laid on the distplot.\nplt.show()","execution_count":120,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x576 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAlcAAAHUCAYAAAAX9w1vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdd3Rc933n/c+dihnMoBeCaGwgQbGKYLFENVO0KNuyKMdRsWI7sR/L68ixz5NnE9vPWa8d2dpkqWTlbBTn0UbrZCP3KIkl06K6ZBWqQxRJsFf03oHBDAYz9/kDIiyZBAECF7hT3q9zeA6IezH48mIw8+GvfK9hmqYpAAAAWMJhdwEAAACphHAFAABgIcIVAACAhQhXAAAAFnLZXYAkxeNxDQ8Py+12yzAMu8sBAACYlGmaikajyszMlMNx/jhVQoSr4eFhHT9+3O4yAAAApm358uUKBoPnfT4hwpXb7ZY0XqTH47G5GvvV1dVp9erVdpeRlLh2M8e1mzmu3cxx7WaOazc7s7l+o6OjOn78+ER++V0JEa7OTQV6PB55vV6bq0kMXIeZ49rNHNdu5rh2M8e1mzmu3ezM9vpNtpSJBe0AAAAWIlwBAABYiHAFAABgIcIVAACAhQhXAAAAFiJcAQAAWIhwBQAAYCHCFQAAgIUIVwAAABYiXAEAAFiIcAUAAGAhwhUAAICFCFcAAAAWIlwBAABYiHAFAABgIcIVAACAhQhXAAAAFiJcAQAAWMhldwEAEttgaEyhSGzS436vU0E/LyUAcA6viAAuKhSJqfbE0KTHa6oChCsAeB+mBQEAACxEuAIAALAQ4QoAAMBChCsAAAALEa4AAAAsRLgCAACwEOEKAADAQoQrAAAACxGuAAAALES4AgAAsBDhCgAAwEKEKwAAAAsRrgAAACxEuAIAALAQ4QoAAMBChCsAAAALEa4AAAAsRLgCAACwEOEKAADAQoQrAAAAC7mmOqG3t1df//rX1dDQII/Ho8rKSn33u99VXl7eB8574IEH9NOf/lRFRUWSpA0bNug73/nO3FQNAACQoKYMV4Zh6Itf/KK2bNkiSdq1a5f+5m/+Rn/5l3953rm33HKLvvGNb1hfJQAAQJKYclowJydnIlhJ0vr169XS0jKnRQEAACQrwzRNc7onx+NxfeELX9C2bdv0uc997gPHHnjgAT3yyCPKzs5WYWGhvvrVr+ryyy+f1uNGIhHV1dVdWuUA5oXLX6DX6ronPX7F6nyNhbrmsSIASAyrV6+W1+s97/NTTgu+3/e+9z35/X595jOfOe/YHXfcoS9/+ctyu93au3ev7r77bu3Zs0e5ubmzLjLd1NbWqqamxu4ykhLXbuYmu3btvRFVDAYm/boFCwIqzq2cy9ISHs+7mePazRzXbnZmc/2mGhSa9m7BXbt2qb6+Xn/7t38rh+P8LyssLJTb7ZYkbd26VSUlJTpx4sQMSgYAAEhe0wpX3//+91VXV6cf/OAH8ng8Fzynvb194uMjR46oublZixcvtqZKAACAJDHltOCJEyf04IMPatGiRbrjjjskSWVlZfrBD36gu+66S1/72te0Zs0a3X///Tp06JAcDofcbrfuu+8+FRYWzvk/AAAAIJFMGa6qqqp07NixCx576KGHJj7etWuXdVUBAAAkKTq0AwAAWIhwBQAAYCHCFQAAgIUIVwAAABYiXAEAAFiIcAUAAGAhwhUAAICFCFcAAAAWIlwBAABYiHAFAABgIcIVAACAhQhXAAAAFiJcAQAAWIhwBQAAYCHCFQAAgIUIVwAAABYiXAEAAFiIcAUAAGAhwhUAAICFCFcAAAAWIlwBAABYiHAFAABgIcIVAACAhQhXAAAAFiJcAQAAWIhwBQAAYCHCFQAAgIUIVwAAABYiXAEAAFiIcAUAAGAhwhUAAICFCFcAAAAWIlwBAABYiHAFAABgIcIVAACAhQhXAAAAFiJcAQAAWIhwBQAAYCHCFQAAgIUIVwAAABYiXAEAAFiIcAUAAGAhwhUAAICFCFcAAAAWIlwBAABYiHAFAABgIcIVAACAhQhXAAAAFiJcAQAAWIhwBQAAYCHCFQAAgIVcdheAxDAYGlMoEpv0uN/rVNDP0yUZ8bMFgPnFKyokSaFITLUnhiY9XlMV4A04SfGzBYD5xbQgAACAhQhXAAAAFiJcAQAAWIiFFsA0sCgcADBdvBsA08CicADAdDEtCAAAYCHCFQAAgIUIVwAAABYiXAEAAFiIcAUAAGAhwhUAAICFCFcAAAAWojEPkOSmanA6GjXnsRoAAOEKSHJTNTitLvfNYzUAAKYFAQAALDTlyFVvb6++/vWvq6GhQR6PR5WVlfrud7+rvLy8D5wXi8V077336uWXX5ZhGPrSl76kW2+9dc4KBwAASERTjlwZhqEvfvGLeuqpp7R7926Vl5frb/7mb847b/fu3WpoaNDTTz+tX/ziF3rggQfU1NQ0J0UDAAAkqinDVU5OjrZs2TLx9/Xr16ulpeW88/bs2aNbb71VDodDeXl52r59u5588klrqwUAAEhwl7TmKh6P62c/+5m2bdt23rHW1lYtXLhw4u8lJSVqa2ubfYUAAABJ5JJ2C37ve9+T3+/XZz7zmTkppq6ubk4eNxnV1tbO6/dz+QvUUN896fHSYL6aTnfNY0UzNxfXLpGvz1S1leWWqaF+8in699d+oWuXyP/2RDLfv7OphGs3c1y72Zmr6zftcLVr1y7V19frwQcflMNx/oBXSUmJWlpatHbtWknnj2RNx+rVq+X1ei/pa1JRbW2tampq5vV7tvdGVDEYmPT4ggUBFedWzmNFMzNX1y6Rr89UtQUCPlVUTl7budonu3aJ/G9PFHb8zqYKrt3Mce1mZzbXLxKJXHRAaFrTgt///vdVV1enH/zgB/J4PBc858Ybb9QjjzyieDyunp4ePfvss9qxY8eMigYAAEhWU4arEydO6MEHH1RHR4fuuOMO7dy5U1/5ylckSXfddZcOHjwoSdq5c6fKysp0ww036LbbbtNXvvIVlZeXz231AAAACWbKacGqqiodO3bsgsceeuihiY+dTqfuuece6yoDAABIQnRoBwAAsBDhCgAAwEKEKwAAAAtdUp8rJK/B0JhCkdikx0ej5jxWk3i4PgAAqxCu0kQoElPtiaFJj1eX++axmsTD9QEAWIVpQQAAAAsRrgAAACxEuAIAALAQa66QFKZacO73OhX083QGANiPdyMkhakWnNdUBQhXAICEwLQgAACAhQhXAAAAFiJcAQAAWIhFKkgLdGAHAMwXwhXSAh3YAQDzhWlBAAAACzFyBaSZsZip9t5RjY7FVVbgtbscAEg5hCsgDfQORnW0IaTm7ojae0cVj49/3umQ9tb16+o1OQpMviQNAHAJCFdACoubpp6p7dXu17pkmlJhjkfrlgRUWuCVy2nobFtYrd0R/cOvmuV1OZWzYFCXLwvaXTYAJDXCFZCiegejevadXrX1jGpJSYauW5+rzAznB84pL8pQTVVA4VFT3/mno/qv/3xa/8/vV2jb5bk2VQ0AyY9wBaSgw/XD+s27vXI5Df3RjmIFfC4ZhjHp+ZXFGfridTHtrsvWX/9rg7oHovr9awov+jUAgAtjtyCQYvadHNRz7/SqJN+rO69foE0rsqYVknwe6XufX6Jr1ubon55s1Q+faJ2HagEg9TByBaQI0zT11rFBvXFkQEsX+rRjY56czksbefK4HPrG7RUK+pz695c7dVllppYuzJijigEgNTFyBaQA0zS1t65fbxwZUHWFXzduuvRgdY7DYeg/3bRQSxf69MCjTRoIjVlcLQCkNsIVkORM09RLB/q17+SQ1izJ1PYNuXI4ZrdWyu1y6D/fWq6hkZj+5ek2mSa3BwKA6SJcAe8Zi5kKRWLqGxpTR9+ouvqjCR8qTNPUIy916sDpIa1fFtC1a3MsW4S+eIFPn/3IAtUeH9LxphFLHhMA0gFrrpDWRqNxHW8KaferXTrbHj7veIbbodJCr9p6AsrwOJUTSKxfmZ+/0KE9b/Ro9eJMXbU62/LdfZ+6ulAvH+jTi/t7VVrgVcDnnPqLACDNJdY7BTBPegejqj0+qBPNIxqLmSrJ82hTdVB+r1MelyGPy6HRsbiauyJq7IzokRc7JUnLSn3aXJ2l/Cy3zf8C6ZevdOrhZ9p05aosbagKzjhYxWKm2nsjcvkL1N4bOe/4H96wQPf86Kxe2NerT1xZMNuyASDlEa6QVkzTVN2ZYb1S1y/DkJaX+bVqkV/Xrs3WsabzR66qKzIlSQVZLu1+vVv7Tw3pZPOIqt4LWXk2hazH3+jSPz7eoqtWZ+vzNy7Qu6eGZ/xY4WhcR0+PqKG+WxWDgfOOV5f7tKU6S3sP9au5K6JS7kcIABdFuELaGA7H9Pw7vTrbHlZFkVfXb8ibmOaaatSnINutKy7L1vqlAb17ckj7Tw/pZMuINlQFtbk6az7Kn/DYq516cHeLNldn6eu3V6hnMDrn33PNkkztOzne5uH3ri6c8+8HAMmMcIW0cLh+WD99rl3RsbiuWZujtUsyZzSN5vM6dcWqbK1fFtCrh/pVe3xQp1pGlJPpVHHu3I/o/NtLHfrhE626clW2vnlHhdyu+dmT4nY5tHFFUC8d6FdTZ1hlhfS+AoDJsFsQKe/1I/26/9+a5Pc6dMeHi7VuaWDWC799Xqeu35CnnVsLFI+b+qufNeiBR5s0HI5ZVPX5fvZCu374RKuuWZuj//fTlfMWrM5ZtSigzAyHXj8ykPC7KAHAToQrpLQXD/Tq3h+fVXmhV5+6psjyNVIVRRm68/pi7diYqyff7NZ/+v4xvX6k39LvER2L63/9ulkPP92m6y/P1ddvq5Brhg1CZ8PlNLRxRZZau0fV2Hn+wncAwDjCFVLWM7U9uu/nDaquyNTXby9Xhmdunu5ul0Of3las+/+4SkGfU/c8fFZ/9bN69Q3Nfi1Ue++o/vwfT+nRvV3aeWWB/vT3y2fced0KqyozFfA59cZhRq8AYDKEK6Skp97u1v3/1qh1SwO69/OL5fPOfX+mFeV+/d2fVOmz2xfo1UP9+uL/OKqfvdCuUGRmU4WvH+7Xn/zdcTV2hPVf/qBSX/5EqZyz7Lw+W06noU0rgmrrHVX9BfqCAQBY0I4U9ObRAf3dL5tUUxXUtz+7SB63Q/3D83N/PLfLoTuvL9ZVa7L1T0+26uGn2/To3k7dfm2xPv6hfHndF///jGmaOtIQ0mOvdumlA32qKvXpm5+u1ML8xGl/sLIyU7XHB/Xm0QFVFrOwHQB+F+EKKaWpR/rnx+q1tMSn//IHlfJMEWbmSkVRhv7ic4t1pGFYDz/dpof2tOjnL7Tr8mUBrV8W1IaqoIpzPYrHTQ1HYhoaianuzLB+9WqXTraMKDPDoduvK9Kd1xfLM88L16fidBi6vCqoF/f3qa1n1O5yACDhEK6QMlq6IvrRXqdyAy7d84fzMxU4lZUVmfqrLy7V/lNDeqa2R/tODuqlg+ML3n1eh8Kjcb1/6VJFkVd/ckuptq3PTYj6J1Nd4ddrh/p14PSQbvpQvt3lAEBCIVwhJQwMj+mvftYg05S+9/klyg3af3ua91u3NKB1SwMyTVMNHRHtOzmolu6IAj6Xgj6nAj6nSvI9WlU5s/5b883jcuiyykwdOD2k3sHovPT4AoBkQbhC0huLmfq7XzareyCqP7o6prLCxH2jNwxDlcUZKbFWac2SgN49NaTf7O9TdcX5t80BgHSVWIs5gEtkmqae39erky0j+rPbKlTBDNW8yQm4VFmcoRf29yk6Fre7HABIGIQrJLW3jw/qWGNIv3d1ga5ek2NbHbGYqfbeyKR/BkPzs1txvq1bGtDAcEwvH7S2cSoAJDOmBZG0TjaH9PrhAa0o9+sTNi+qDkfjOnp6ZNLjNVUBBf2p9+tWUeRVca5bv3qtS9suz7W7HABICIxcISm1947qmdpeleR5tO3y3KRYBJ6KDMPQ9g25OtYY0rHGkN3lAEBCIFwh6fQPj2n3a13yex362IfybbnPHn7rqtXZ8nkc2v1al92lAEBCIFwhqYRH4/rVq12Kx0194ooC+RO4F1S68Hmd2r4hVy8e6NPAPHXCB4BElnqLQDAnzi3Ynozf65zzNUVjMVO/fr1LA6Ex3bK1UHlZv+1lda4+l7/ggnWORrnJ8Fy6cXO+dr/erRfe7dXOrYV2lwMAtiJcYVrsXrAdN009U9uj1u5R7diUp9KCD/ayOldfQ323KgbP77lUXe6bs9ogLSnxaXmZT0++1aObryxgDRyAtMa0IBKeaZr68bPtOtk8oq2rsrW8zG93SbiAHZvydbY9zMJ2AGmPcIWEFo+b+vvHmvX8vj5dviygy6voBJ6orl2bI6/boafe7rG7FACwFeEKCWs8WDVpzxvd+tiWPG1dnc10UwLLzHDqmrXZ+s3+PoUiMbvLAQDbEK6QkOJxUw882qQn3uzR7dcV6dZrCglWSeDGTfkKj8b18oE+u0sBANsQrpBwBkfG9L0fn9WTb/Xo09uK9Yc3LCBYJYmVFX6VF3n15FtMDQJIX4QrJJTjTSF99YETevv4oL5800J9dnsxwSqJGIahj27K19HGkM62Tb67FABSGeEKCcE0Te1+rUv/+cGTMk1Tf/2lpdq5lanAZLTt8ly5nAajVwDSFn2uYCvTNLXv5JB+8lybDteHtHlFUH92W0VK3uQ4XWRnunTlZVl6fl+vvnBjiTxu/g8HIL3wDgZbmKap2uOD+unz7TrSEFJBtltfvaVMN27Kk8PBaFWy27EpXy8d7Nerh/t13bpcu8sBgHlFuMK86R6Iat/JQe07MaR9pwbVOzimwmy3/uSWUn2kJk8eFyMcqWL90oCKcz166q0ewhWAtEO4guUGQ2M63jSi060jau6KTPzpHRq/qW92pkvrlwa0aUVQ16zNkZtQlXIcDkM7Nubp4Wfa1NoTUUmed+ovAoAUQbjCrJmmqbqzw/o/T7XpeFNILd2jE8dyAy6VFni1uTpLFUUZWrc0oMULMpj6SwPba3L142fb9NRbPfqjHSV2lwMA84ZwhRkzTVONnRG9eWRArT2jygm4dFllpnZszFNVmV/LSn0K+niKpavCbI82Lg/qmdoefXb7AjmdBGoA6YF3PsxIz2BUz7/Tq9aeUQV8Tn3uI8X61DVFrJvCB+zYlK83f3xWbx0f0IdWZttdDgDMC8IVLlnf0JgefaVT8bh03bocXVaZqc3VQYIVzrO5Oku5AZeeequHcAUgbfBuiEsyGBoPVrG49HtXF2rNkgDTPZiUy2loe02u3jw2oO6BqN3lAMC8IFxh2obDMf3ylS5FxuK6ZWuB8rLcdpeEJLBjY77icenZd+jYDiA9EK4wLUMjMT36SqdC4ZhuvqJAhTkeu0tCkigt8Grtkkw9+VaP4nHT7nIAYM6x5grT8tjeLvUNjenmrQUqyadn0XwaDI0pFIlNenw0mviBZcfGfP31vzbowJkhrV8atLscAJhThCtMqXsgqteODGjd0oDKCzPsLifthCIx1Z4YmvR4dblvHquZma2rs/X//cqpp97qIVwBSHlMC2JKe+v6leFxaNOKLLtLQZLyuh368OU5eqWuXwPDY3aXAwBzaspwtWvXLm3btk0rVqzQ8ePHL3jOAw88oCuuuEI7d+7Uzp07dc8991heKOzR2BlWfXtYN27KU4aHLI6Z++imfI3FTL3wbq/dpQDAnJpyWvD666/X5z73Of3BH/zBRc+75ZZb9I1vfMOywmA/0zT1al2/gj6nrl2brVOtEbtLQhJbXOLT8jKfnnyrRzdfWSDDoIUHgNQ05VDExo0bVVLCfcHS0fGmEXX0RfWhy7K4uTIssWNTvs62h3WsMWR3KQAwZyx7x3z88cf1iU98Ql/4whe0b98+qx4WNonFTL12uF8F2W6tKPfbXQ5SxLVrc+R1O/TU2/S8ApC6LNkteMcdd+jLX/6y3G639u7dq7vvvlt79uxRbm7uJT1OXV2dFeWkhNraWksfz+UvUEN996THy3LL1FDfNPH3M52GBkMOrV42qsaGBpXnffD47yoN5qvpdJelNb/fpdTfUF9/0eNTff1cHJ/N9bnUn91sjs/k2l3qv21VqUPPv9OtTQs75E2hPrRW/86mE67dzHHtZmeurp8l4aqwsHDi461bt6qkpEQnTpzQ5s2bL+lxVq9eLa+XHkq1tbWqqamx9DHbeyOqGAxMejwQ8KmisnLi72+c6VB+lqmNa8ouePx3LVgQUHHu5Mdna7r1N9TXX7DOqeqf6+OzuT6X+rOb6fGZXrtL/bf5Cob1nx88qUHnIl1Zkz/tr0tkc/E7my64djPHtZud2Vy/SCRy0QEhS8JVe3u7iouLJUlHjhxRc3OzFi9ebMVDwwZ9Q1G19Y5q6yputDtfLtYoNBmahF6KlRV+VRR59cSbPdqxKTXCFQC835Th6t5779XTTz+trq4uff7zn1dOTo4ef/xx3XXXXfra176mNWvW6P7779ehQ4fkcDjkdrt13333fWA0C8nl3GLj5ay1mjcXaxSaDE1CL4VhGPrYlnw9uLtFRxqGtbIi0+6SAMBSU4arb33rW/rWt7513ucfeuihiY937dplbVWwjWmaOtYYUlmhVwGf0+5ykKJuqMnTj55p06N7uwhXAFIO++vxAW09o+ofjqmaUSvMIZ/XqRs35euVuj519I3aXQ4AWIpwhQ841hiSy2lo6cLUmopC4rn5ygJJ0u7X5m6XKQDYgRs3Y0IsbupE04iWlGTI4yZ3wxoXW6xfUxXUnje6dee2Yvm89kxDX6w+SfJ7nQr6eakEMH28YmBCfVtY4WicpqGw1MUW61cWZ+itY4N6prZ3YiRrvl2sPkmqqQoQrgBcEoYnMOFoY0g+r0MVRRl2l4I0UZLv1ZKSDD32aqfi8dRqOQEgfRGuIEkKhWM60zai5WV+ORzcUBfzZ8fGPLV0j+rNYwN2lwIAliBcQZL07qkhxeNiShDzbuOKoAqz3fqPlzvtLgUALMFCghQx1aLcqbp8H20IKTPDoaKcFLrZWwKJxUy190YmPZ5qXdgvhdNh6JNXFeofH2/RwTNDWrN48lv9AEAyIFyliKkW5V6sy/d449ARlRdmyDCYEpwL4WhcR0+PTHo81bqwX6qPbcnXIy916MfPtmnXXcvsLgcAZoVpQairP6qhcEzlRdw0G/bwuh26/doiHTg9rP2nJv9PAgAkA8IV1Ng5Pl1VVsguQdjno5vzlZ/l0o+fbZNppu80KYDkR7iCGjvCWpDn4V6CsJXH7dBt1xar7uyw3mX0CkASY81VmovFTLV0j+qq1dm21jHbBflIDTduyptYe7V+acCSNYA8twDMN8JVmmvtiWgsZtq+oHo2C/KROjxuh26/rkg/eKxZ75wYUs3y4Kwfk+cWgPnGtGCaa+yMyDCkZaW8wSAx3LAxT4XZbj38TCtd2wEkJcJVmmvsiGhBrse2m+YCv8vjcuhzNyzQ8aYRPfNOj93lAMAlY1owjUWicXX0jmrjitlPvUzVJNPvdXLzW0zbtvW5euLNbv3Tk626clW2gj6eOwCSB69Yaay5MyJTUrkFN2qeqklmTVWAcIVpczgM3X1zmb7298f1o6fbdPfOMrtLAoBpY1owjTV2huVyGlqQ57G7FOA8Sxf69PEt+Xr8jW6dapk8uANAoiFcpbHGjohKC7xyOrjlDRLTZ29YoIDfqX/4VRONRQEkDcJVmhoaGVPv0JjKC7nlDRJX0OfSF3aU6HB9SM/t67W7HACYFhbBpKmJW97M0/0Ep1rwTiNHTOYjNXl64q0ePbSnRRuqgsoLuu0uCQAuinCVptp6RuVxGyrImp83qqkWvNPIEZNxOAz96afK9bW/P66//fdG3fOHiy3p3A4Ac4VpwTTV3juq4hwPb1JICpXFGfrCR0v01rFBPf5Gt93lAMBFEa7S0FjMVHd/VMW57BJE8vjEhwpUUxXU/97ToqbOsN3lAMCkCFdpqLNvVHFTKqYFA5KIw2HoT3+/XF63Q/f9okFjMdbpAUhMhKs01NY7KkmMXCHp5Ge59bVPlulE84h+/Gyb3eUAwAURrtJQe++ogj6nMjO4nyCSz9bVOdqxMU//+mKH3jw6YHc5AHAewlUaau8ZZdQKSe2Pby7VkhKf7vtFvVq6Jm/xAQB2IFylmZFITAOhGOEKSc3rdui/fmaRHIah7/34rMKjMbtLAoAJhKs0M7HeisXsSHLFuR5989OVaugI6/v/zu1xACQOmoimmfbeURmSCnPoco3kt6EqqDs+XKSfPt+hhfke3bgp77xz6P4PYL4RrtJMe8+o8rLc8rgYtERq+EhNrt44Oqhf/KZDkWhcZYUZHzhO938A84132DRimuZ4Z3bWWyGFGIah7RtylRNw6Yk3ezQYGrO7JABpjnCVRvqHxxSJmlrAeiukGI/boY9vyVcsbmrPG900GAVgK8JVGmnvjUqSinNZb4XUkxt064aaPHX0RfXi/l4WuAOwDWuu0khbz6jcTkN5WYQrWCcWM9XeO3mvqflcUL5koU+bVgT11rFBFed6tHpxYN6+NwCcQ7hKI+29oyrMccthGHaXghQSjsZ19PTIpMfne0H55pVZau8b1Yv7+1SQ7WZBO4B5x7RgmoiOxdXZP8p6K6Q8h2Fox8Z8BfxO7XmjWwMscAcwzwhXaaKle1TxODdrRnrI8Dj0sS35ikRN/e89rYrFWX8FYP4QrtJEfXtYEuEK6aMw26Ntl+fqVEtYe+v67S4HQBphzVWaaOqMyOs2FPA57S4FmDcryv2KjsX1wrt9Kspxq7oi0+6SAKQBRq7SRFNXRAXZHhksZkea+eTWAi3M9+g3+/s0MMz6KwBzj3CVBuKmqZbuURVk04IB6cfpNPSRjeP3HHxuH/2vAMw9wlUa6B8aU3TMJFwhbWX5XbpqdbaaOiM6dHbY7nIApDjWXKWBrv7xzuyEKySiqZqQ+r1OBf2zf6latShTJ5pH9EpdvyqKM5RlwWMCwIXw6pIGuvqjchhSXpBwhcQzVRPSmqqAJeHKMAxdf3mufvp8u55/p1c7txawBhHAnGBaMA109UdVnOuRy8kbCdJbVqZLW1dnq51DjbIAACAASURBVJHpQQBziHCVBroGoiot9NpdBpAQVi/KVFmBV3sP9WskErO7HAApiHCV4sKjcQ2NxFRWQPNQQBqfHrxmXY6iUVNvHh20uxwAKYhwleK6+kclSaUFjFwB5+RnubVqcaYOnhlSW8+o3eUASDGEqxR3bqcg4Qr4oC0rs+RyGnp0b5fdpQBIMYSrFNfVH5XP61B2JhtDgffze53atCKog2eG1dgRtrscACmEcJXiugai9LcCJrFuaVD5WS69UtevOJ3bAViEcJXC4nFTPQNRFWQRroALcTkN7byyQF39UR2tD9ldDoAUQbhKYb1DY4rF6cwOXMyGqoAW5Hn0+pEBxWKMXgGYPcJVCvvtbW9owwBMxjAMbanO0nA4piMNNBYFMHuEqxTW1T8qh0PKDbKYHbiY8iKvinLceufEoOJxRq8AzA7hKoV19UeVF3TL6eC2N8DFGIahjSuy1D8c04nmye9zCADTQbhKYV397BQEpmtJSYbygi7VHh+Qyc5BALPAfFGKCkViCkXihCskvVjMVHtvZNLjo1FrgtC50aun3+7Rmdawliz0WfK4ANIP4SpF/XYxO+EKyS0cjevo6cmn6qrLrQtBVaU+vX7EqbeOD2hxSYYMgyl1AJeOacEUNRGu6HEFTJvDYaimKqiO3qgaOycfLQOAiyFcpajewaj8Xod8XqfdpQBJZWVFpjIznKo9Pmh3KQCSFOEqRXUPjCkvyKgVcKmcTkPrlmaqqTOi7oGo3eUASEKEqxRkmqZ6BqPKy2JJHTATl1VmyumQDp4ZsrsUAEmIcJWChkZiio6ZymO9FTAjPq9TVaV+HW0IaSQSs7scAEmGcJWCegbHJIlpQWAW1iwJKDpm6tXDA3aXAiDJEK5S0Ll1Innc9gaYsQV5HhXluPXcO700FQVwSaYMV7t27dK2bdu0YsUKHT9+/ILnxGIx3XPPPdq+fbs+8pGP6JFHHrG8UExfzwA7BQErrFkSUEv3qA6e4YbOAKZvynB1/fXX6yc/+YlKS0snPWf37t1qaGjQ008/rV/84hd64IEH1NTUZGmhmL7xxexMCQKztbzMr8wMh3a/1mV3KQCSyJThauPGjSopKbnoOXv27NGtt94qh8OhvLw8bd++XU8++aRlRWL6xncK0oYBsILLaejqNTl69XD/RGNeAJiKJWuuWltbtXDhwom/l5SUqK2tzYqHxiX67U5B1lsBVth2eY5MU3rizW67SwGQJBLqHbiurs7uEhJGbW3tJZ3v8heoob5b7f2S5NRYqFsN9b99MyjLLVND/eRTtal0vKG+PqHru9TjXDt7j5cG81VVHNfuV9tUndssxyS3G7zU31n8Ftdu5rh2szNX18+ScFVSUqKWlhatXbtW0vkjWdO1evVqeb1eK0pKarW1taqpqbmkr2nvjahiMKCuE4OS+rWyqlwZnt8OTAYCPlVUVk769alyvKG+/oLnJUp9MznOtbP3+IIFAd16fbb+20/q5ciqUs3yrPPOmcnvLMZx7WaOazc7s7l+kUjkogNClkwL3njjjXrkkUcUj8fV09OjZ599Vjt27LDioXGJzu0UfH+wAjA7m6uzFPQ59XRtr92lAEgCU74D33vvvbrmmmvU1tamz3/+8/r4xz8uSbrrrrt08OBBSdLOnTtVVlamG264Qbfddpu+8pWvqLy8fG4rxwWxUxCwnsfl0IfX5+q1w/0aHBmzuxwACW7KacFvfetb+ta3vnXe5x966KGJj51Op+655x5rK8MlO7dTcGVFpt2lACnnIzW5+tVrXXpxf59u+lCB3eUASGDMHaWQwfd2CuazUxCw3NKFPi1akKFnmRoEMAXCVQrpee+2N7n0uAIsZxiGbqjJ07GmkOrbw3aXAyCBEa5SyLkbNuez5gqYEx9enyOnQ3r2nR67SwGQwAhXKYSdgsDcygm4tbk6S8/t61Usxs2cAVwY78IppJudgsCc274hT72DY6o9MWh3KQASFOEqRcRNU70D3FMQmGubVgSVlenUM7VMDQK4MMJViugZGFM0xk5BYK65XQ59eF2u3jg6oOFwzO5yACQgwlWKaO6KSBIjV8A8uG5djqJjpl491G93KQASEOEqRUyEK9ZcAXNuRblfC/I8euFdel4BOB/hKkU0d0fYKQjME8Mw9OF1Odp/akg9g1G7ywGQYHgnThEtXaOMWgHz6Np1uYqb0ssH+uwuBUCCIVylgHjcVEt3hOahwDyqLM7QkpIM/WY/4QrABxGuUkBnf1SRqKm8IDsFgfl03bpcHW0MqWfI7koAJBLCVQo4d58zdgoC8+vadTmSpAONhs2VAEgkhKsU0NDxXrhiWhCYV0U5Hq1elKn9DQ6ZJrfDATCOcJUC6tvDysl0sVMQsMF163PUOWjoTFvY7lIAJAjejVNAfXtYpQUeu8sA0tJVq3PkMEx6XgGYQLhKcvG4qYaOiBYWeO0uBUhL2ZkuLSs29eL+PsXjTA0CIFwlvfGdgnGV5hOuALusKTfV2R/V0caQ3aUASACEqyR3bqcg04KAfVYuNOV2GXqJhqIARLhKeufCFdOCgD0GQ2MKZBdo7eJM/WZ/r1q7w2rvjUz8GQyN2V0igHlG18kkV98eVl7QpcwMp92lAGkpFInptbpuFeYUqvbEkHa/3q2ywoyJ4zVVAQX9vNQC6YSRqyTX0BFWZXHG1CcCmFOLFmTI7TR0omnE7lIA2IxwlcTO7RSsKCJcAXZzuxxaVJKhky0jirFrEEhrhKsk1tE3qkg0zsgVkCCWl/oVHo2rqTNidykAbMRCgCRW3z7+As7IFTB3YjFT7b2Th6XR6G9HqSqLM+RxGTrRFOI/PUAaI1wlsfr37ilYWZyh4TA7koC5EI7GdfT05Ouoqst9Ex87nYaWLPTpVOuIPhwz5XRyQ2cgHTEtmMQa2sPKz3Ip4GOnIJAolpf5NRo1J/7zAyD9EK6SWENHmClBIMGUFXqV4XHoRBPd2oF0RbhKUuwUBBKT02Fo6UKfzrSGFR2L210OABsQrpJUOzsFgYRVVeZTNGbqbDtTg0A6IlwlqYb3dgoSroDEU1rgld/roKEokKYIV0nq3GJZpgWBxOMwDC0r9ets24hGIjG7ywEwzwhXSaq+LayCbDc7BYEEVVXmUywu7Ts5ZHcpAOYZ4SpJnW0f0SKmBIGEVZLnUcDn1BtHB+wuBcA8I1wloVjcVGNnRBWEKyBhGYahqlKf6s4MazBEk18gnRCuklBr96iiYyYjV0CCqyrzKxaXXj3Ub3cpAOYR4SoJ1beP70BipyCQ2Ipy3CrKcevFA312lwJgHhGuktDZ9rAMQ6oo8tpdCoCLMAxDm6uztP/UkPqGonaXA2CeEK6SUEN7WMW5HmV42CkIJLotK4OKm9IrB5kaBNIF4SoJnW0Ps94KSBJlBV5VFHn14kGmBoF0QbhKMqNjcTV3RVhvBSQJwzB0zdocHTo7rK5+pgaBdEC4SjLNXRHF4mLkCkgi167NkWlKLzN6BaQFwlWSqX/vRrCMXAHJo6wwQ0sX+tg1CKQJwlWSqW8Py+GQSgvZKQgkg1jMVHtvRBuqAjrWGFLdmUG190Ym/tBgFEg9LrsLwKWpbw+rtMArj4tcDCSDcDSuo6dH5PeO7+795d4u1SzPmjheUxVQ0M9LMZBKeIdOMvXsFASSUlamS8W5Hh1vGrG7FABzjHCVRMKjcbX2jKqyiHAFJKPlZT519UfVO8iuQSCVEa6SSGNnWKYpVS4gXAHJaFmpX5J0opnRKyCVEa6SyNm28Z2CTAsCySngc2phvkfHm0IyTdPucgDMEcJVEmnoCMvtMlSSx05BIFktL/Ord3BM3QPsEgRSFeEqiZxtC6u80Cun07C7FAAztLTUJ0PSiaaQ3aUAmCOEqyRS3x6meSiQ5Pxep8qKvEwNAimMcJUkhsMxdfZHWW8FpIDqcr8GQjG1dI/aXQqAOUC4ShINE7e98dlcCYDZWrrQJ7fL0NGGYbtLATAHCFdJ4sxEuGIxO5Ds3C6Hli306UTziCLRuN3lALAY4SpJnGkdkd/rUHGux+5SAFigusKv6Jip2uODdpcCwGKEqyRxunVES0p8Mgx2CgKpoLTAq6DfqVcPDdhdCgCLEa6SQDxu6nRrWEtKWG8FpArDMFRd7teh+mF19XM7HCCVEK6SQFvPqMKjcS1ZyE5BIJVUV2TKNKXn3+21uxQAFiJcJYFTreP3IWPkCkgtOQGXqkp9evadHnpeASmEcJUETreOyOGQKosYuQJSzdbV2WrsiOh4EzdzBlIF4SoJnG4ZUUVhhjxuflxAqtm0Iii3y9Bz7/TYXQoAi/BunQROt7GYHUhVmRlObV2VrRfe7aPnFZAiCFcJrn94TF39US0uYUoQSFUf3ZyvoXBMLx3os7sUABYgXCW40+8tZl+6kJErIFWtWZypskKvnniz2+5SAFiAcJXgzoWrxQsIV0CqMgxDH9ucryMNIZ1pZWE7kOwIVwnuTGtY+Vlu5QRcdpcCYA5t35Art8vQHkavgKRHuEpwp1pHtIT1VkDKC/pdumZNjp7b16uRSMzucgDMwrTC1ZkzZ3T77bdrx44duv3223X27NnzznnggQd0xRVXaOfOndq5c6fuueceq2tNO6NjcTV2sFMQSBcf3ZyvkUhcL7KwHUhq05pr+s53vqM777xTO3fu1GOPPaZvf/vbevjhh88775ZbbtE3vvENy4tMVw0dYcXiLGYH0sVllX4tKs7Qnje6deOmfLvLATBDU45cdXd36/Dhw7rpppskSTfddJMOHz6snh4a3s210y1hSdz2BkgXhmHoo5vzdaJ5RCeaQ3aXA2CGphy5am1tVXFxsZxOpyTJ6XSqqKhIra2tysvL+8C5jz/+uF555RUVFhbqq1/9qi6//PJLKqauru6Szk9ltbW1en2/Qx6noZazdWqrv/j5Ln+BGuonXwhbllumhvqmtDjeUH/+xUqk+i71ONcu8Y9LF7520/n60mC+mk53Tfw93yG5nU79y+PH9Mma9GgqWltba3cJSYtrNztzdf0s24J2xx136Mtf/rLcbrf27t2ru+++W3v27FFubu60H2P16tXyer1WlZS0amtrVVNTo5/XntTSUlObNlZN+TXtvRFVDAYmPR4I+FRRWZnyxxvq6y94XqLUN5PjXLvEPy5pxj+/BQsCKs794PHrWxr1/L5e/dmda1J+p/C51ztcOq7d7Mzm+kUikYsOCE05LVhSUqL29nbFYuO7V2KxmDo6OlRSUvKB8woLC+V2uyVJW7duVUlJiU6cODGjoiGZpqkzrSNMCQJp6JNXFWp0zNTjr3dNfTKAhDNluMrPz9fKlSv161//WpL061//WitXrjxvSrC9vX3i4yNHjqi5uVmLFy+2uNz00dEX1XA4riUsZgfSTkVRhjZXZ2n3693cbxBIQtMab/6Lv/gLffOb39Q//MM/KCsrS7t27ZIk3XXXXfra176mNWvW6P7779ehQ4fkcDjkdrt13333qbCwcE6LT2WnWsa7NC9ZQI8rIB196upCfeOhU3puX68+tpmdg0AymVa4Wrp0qR555JHzPv/QQw9NfHwucMEax5tCcjqkxUwLAmlpzeJMVZX69B8vd+rGjXlyOAy7SwIwTXRoT1DHGkNavMAnr5sfEZCODMPQ711dqOauiN48OmB3OQAuAe/cCShujo9cLS/3210KABtdvTpHRTlu/fvLnXaXAuASEK4SUNegFIrEVU24AtKa02nolq2Fqjs7rGONNBUFkgXhKgE19YyvrVhBuALS3o5NecrMcOiRlzrsLgXANBGuElBTjyG/16GyAhqqAunO73XqE1cUaG9dv063jthdDoBpIFwloMYeQ8vL/OwOAiBJ+r2rC5WZ4dDDz7TZXQqAaSBcJZhINK72fqYEAfxW0OfSp64p0htHBnSkYdjucgBMgXCVYE42jyhuGixmB/ABt1xZoOxMl37E6BWQ8AhXCebcjiDaMAB4P5/XqduvK9K+k0M6cHrI7nIAXAThKsEcaxxWtt9UXtBtdykAEszHtuQrP8ulf3m6VaZp2l0OgElM6/Y3mD9HG0Mqz+NFE0gXsZip9t7IpMf9XqeC/vGXaq/boU9vK9bfP9qst44NanN11nyVCeASEK4SSO9gVB19UW2oIFwB6SIcjevo6clbLNRUBSbClSTdUJOnf3uxU//8VKtqqoJyOtlVDCQapgUTyLGm8fVWZYxcAZiE2+XQFz++UGfbwvrVa112lwPgAghXCeRYY0gOh7Qwx+5KACSyKy/L0sblQf3o2TZ1D0TtLgfA7yBcJZBjjSEtKs6Qh8laABdhGIb++OZSjcVMPfR4i93lAPgdhKsEEY+bOt4UUnV5pt2lAEgCC/O9uv3aIr14oE/7Tg7aXQ6A9yFcJYjmroiGw3E6swOYtluvLVJJnkf/8KtmjY7F7S4HwHsIVwniSMP4YnbCFYDp8rgd+uObS9XUGdF/vNxpdzkA3kO4ShAHTg8pO9Ol8kKv3aUASCKbVmTp6jXZ+slz7TrZHLK7HAAiXCUE0zT17qkhrVuSKYeDnjUALs2f7CxTdqZL//3nDRqJxOwuB0h7hKsE0Nw1qu6BqNYtC9pdCoAklJXp0p/fVqGW7oj+16/ZPQjYjXCVAN49Nb7TZ/2SgM2VAEhW65YGdNu1RXrq7R69fLDP7nKAtEa4SgD7Tw2pMNutknyP3aUASGKf2b5AK8r9+p//0aj23lG7ywHSFuHKZvG4qQOnh7RuaUCGwXorADPnchr6xh0VipvSX/20XuFR2jMAdiBc2exse1gDoZjWL2VKEMDsleR59We3Vuh4c0h//a8NisW5Vykw3whXNtt/akiStJZwBcAiV67K1l0fW6hXD/Xrn55otbscIO1wFzubvXtyUKUFXhVms94KgHVu2Vqgtp5R/ccrnVqQ59EnriiwuyQgbTByZaOxmKmDZ4a1jlErABYzDENfummhtqzM0oO7m/X6kX67SwLSBuHKRieaQxoZjbPeCsCccDoMffOOCi1d6NN/+0m9XjtMwALmA9OCNppYb0V/KwCTiMVMtfdGJj3u9zoV9E/+Uh4dM/V/f6pM/+ORRt3747P60k0L9aGVWRPHHQ5D8Yssep/q8QGcj98YG+0/NaQlJRnKzuTHAODCwtG4jp4emfR4TVXgouEnFInpaOOIPlKTp92vdenB3S060TyiyyozJUnV5T4dbZz54wM4H9OCNhmNxnW4fljrGLUCMA88boduvrJAFUVePfdOr949OSjTpE0DMBcIVzY50hDS6Jip9dxPEMA8cbscuulDBVpSkqGXD/bruXd6FR2j0ShgNcKVTV4/0i+3y9DqxZl2lwIgjTidhj62JV+bqoM60hDS/f/WpMHQmN1lASmFcGWDeNzUK3X92rg8KL/XaXc5ANKMYRj60MpsfXxLvjp6o/rFCx1q6gzbXRaQMghXNjjeFFJXf1RXrc6xuxQAaWzJQp/+/PZyeT0O/fKVLr10oI9pQsAChCsbvFLXL5fT0Jb3bYcGADssyPPojg8Xae2STO0/NaSfP9+h1u7JWz8AmBrhap6Z5viU4IZlAWVmMCUIwH5ul0PXrsvVJ68qUNw09W8vdeqlA30KjzKKBcwE4WqenWwZUXvvqK5aw5QggMRSVpihT28r1prF46NYP3qmTc++06tYjJYNwKWgM9w8e+Vgv5wOfaBDMgAkCo/boevW52rV4ky9fKBfP362Xc/v69Vt1xZp3dJMGYbxgfOn6uA+GBpTKBKb9HhGIHfGtU712HSXh1141s2j8SnBPq1fGuQXHkBCK8z26JNXFWg0GtcvftOhv/2PJhXluLVxRZaWlGRMhKzpdIivPTE06fHS4MyXR0z12HSXh1141s2jM21htXSP6tZri+wuBQCmZBiG1i0NyOVy6FhDSG8fH9CeN7qVn+VWzfKglpX67C4RSEiEq3n0ysE+OQzpQyuz7S4FAKbN6TB02aJMVVf4dbwppLePDerpt3v0ykGHmjvDuvXaYuVnue0uE0gYhKt5YpqmXq7r15olAeUEuOwArBGLmWrvnbx1wmjUusXoDoeh6opMrSj3q6EjogOnh/SrV7v169e7tWVltrZvyNXG5UG5XeyVQnrjXX6e1LeH1dQZ0c4rC+wuBUAKCUfjOnp6ZNLj1eXWT90ZhqHK4gxVFmeovNCjN44M6vl9vXr1UL+y/E5duzZH163PVXW53/LvDSQDwtU8eertHrmchrauYkoQQOooyvHoix9bqM/vKNE7Jwf13Du9eurtHu1+vVt5QZfWLwsoK9Ol0nyvHA5j6gcEUgDhah4Mh2N66u0eXbMmR7lB1iUASD1Op6FNK7K0aUWWhsMxvXl0QHvr+vXKwX6NjpnyuA2VF2aoosiryuIMdvEhpfHsngfP1PZoJBLXLVcxJQgg9WVmOPXh9bn68PpcNXSM6NG93apvH1FDR0SnWsanMHODLlUWZWjDIlPVy+LyulmnhdRBuJpjsbipx17t0mWVflWVsv4AQHrxuh1aVurTslKfTNNU7+CY6tvDaugI6+CZIb17SvrRi3VaXupTdYVf1RV+LV7gk8s5PoVII1AkI56xc+zNowNq6xnVF24ssbsUALCVYRjKy3IrL8uty6uCio7FNRoZ0TunomrsjOhQfZckye00VJLvUWmBV9s35GpLdbacTtZrIXkQrubYo3s7VZTj1pWXsZAdAN7P7XJocWGGMjMzJUkjkZiauyJq6oqouTOi1w4P6LXDA/J5HFq1KFPrlga0dklASxf65GRxPBIY4WoOnWoZ0YHTw/q/PlrC/7oAYAo+r1PLSv1a9t4SilA4Jq/b0Nn2iA6eHtIPn2iVJPm9Dq1eHNCSkgyZkgqy3XIYvMYicRCu5tBjr3Yqw+PQjk15dpcCAEnHn+FUTVVAn7jCK0nqGYjqwOkh7T89pIOnh/Tm0QFJktdtaGGBV2UFXpUWelWQ5T7vBtPAfCJczZHewaheeLdPH92Up6CPywwAs5WX5dZ163N13fpcSdKRhiE98WbPxFTimdawJCnD7dDCAo96Bka1dVWOKoszLthjazA0plAkNun3m2ox/Wy/HqmLn/oc+clz7YrHTd18ZaHdpQBASsoLulVdkanqivE1W4OhMTV1jgetlq6IfvJch37yXIeCPqdWL87UmsXja7YWLxgPW6FITLUnhiZ9/JqqwEXD0Wy/HqmLn/ocONYY0p43u3XzFQUqK/TaXQ4ApIWg36WVlS6trBwPW5VFXrX2jOrA6SEdPDOs1w6PTyMGMsbD1uIFGYqLNVuwHuHKYrG4qb9/tEm5AZc++5EFdpcDAGmrINutVYsC2r5hfN1rR9+oDr4XtA6cHtLrR8bDlsdtaGG+V6XvrdsqyCFsYXYIVxZ7/PVunWwZ0Tc/XaHMDKfd5QAA3lOU49H1G/J0/Xth60jDkJ58q1fN77V+ONs2vmbL4xoPW+09EV25KkfLFvrY8Y1LQriyUM9gVP/ydKsuXxbQNWty7C4HAJJeLGaqvTdywWOjUXNWj50XdGtFuV8rysdbPwyNxNRyrs9WV0T/+mKn/vXF8V3fy8t8qi4f7yBfWZwhp8OY9fdH6iJcWeihx1s0Ombq7pvL2AYMABYIR+M6enrkgseqy32Wfq+Az6nl5X4tfy9sleS59eKBfjV3RdTYEdGB08OSJLfLUGmBVx9aGVSGhx2BOB/PCIu8frhfv9nfpzu3FbOIHQBSQHamS8vL/FpeNh62hsPjI1vNXRHVt4f18xc6JUn5WW5VFmdo0YIMleR5Ltj2AemFcGWBIw3D+u8/r1dVqU+3XVdkdzkAgDmQmeFUVZlfVWV+maap3IBLz7/bp7NtYb17clDvnBiU122oomg8aK0o86k41+6qYQfC1Sw1dYb1F/9yRvlZbn33jxbL63bYXRIAYI4ZhqEFeR5tqApqQ1VQkWhcjR1hnW0L62x7WCeaR/TsO71aVZmpLSuztKU6S2WFXpaMpAnC1Sx0D0T1X/7ptBwOQ9/7/BLlBNx2lwQAsIHX7Zi4L6JpmmrvHVV4NK5DZ4f1wyda9cMnWrUw36Mt1VnasjJbqxZlyjXHOxDpIG8fruoMDYbG9O3/c1qDoZh2fWmpFuazzgoAcG5Uy6uaqoDuvrlMHX2jevPIgN44OqDdr3frl3u75PM6tH5pQBuXZ6lmeVDFuR7L66CDvH24qjNw8MyQ7vtFg/qGxvQXn1usqvfu4A4AwO8qyvHopisKdNMVBRqJxPTOySHVHh9Q7fHBia7xJXkerVkS0NrFmVq7JKDCHOvDFuYP4eoSxGKmfvp8u37+QrsW5Hn0P768bGIXCQAAU/F5ndq6KltbV2XLNE01dUZUe2JQB04P6dVD/Xr67R5JUmG2W8vL/aouH9+tuHShj8bUSYRwNU3Hm0L6X79u1uH6kLZvyNUf31wqv5cnOoD0drEmn9LsG30ms6mujcNhyOM2dMVlWbrisizF3wtbRxtCOtUyopPNIe2t6584vyhnvOVDZXGGygq9GuySKvujygu6aP+QYAhXFxGPm3rz6ID+/ZVO1Z0ZVmaGQ39+W4W2Xc7eWgCQLt7kU7K+0Wcymc61Odp4/vH8bI/ysz3645tL5XU7dbwppLNtIzrTFlZ9e1j7Tg5pLGZKcumHLx6Wx2WoINutvCy38oJu5QVdygu65XJKHX1RZWY45fM65HU7CGHzZFrh6syZM/rmN7+pvr4+5eTkaNeuXVq0aNEHzonFYrr33nv18ssvyzAMfelLX9Ktt946FzXPqUg0rsP1w3r35JD2HhrvzFuU49ZdH1uoHZvyGJYFAMybnIBLm6uztLk6a+JzYzFTHX2jeun1QwrkV6i1Z1Td/VF1D0Z1qjmktwbHNDIav+DjedyGMtwOZXgcemFfr/Kz3Ar6x3cNBn1OZb3v46DfpaDfqUCGk3srXqJphavvfOc7uvPOO7VzatpZ8AAACJ9JREFU50499thj+va3v62HH374A+fs3r1bDQ0Nevrpp9XX16dbbrlFV1xxhcrKyuak8NmKjsXVNzQ2cQ+p5q6IzrSFdbh+WNExU06HdFllpj6zvVhXr87hiQUASAgu5/iNpasWmKqpKbjgOaFITCeaQnrz2KCGwzGNROKKROMKj/72TygSU29zVIMjMQ2NxBS/yAxuIMOpgN/5XgBzKTPDIX+GU5nv/fFnOBSY+Pjc5x3jf/emXzibMlx1d3fr8OHD+ud//mfp/2/v/kKa+vs4gL93Nq0wrWZlk349IeTaI9aFQfCgUGa4ahP6Y7soKaR50YVQEAol5Z+gFV1UGDx1UVFXeZHBFAnxYillBYGEZWCGkkvTPf2s1Lad8/1d+Evq53x2nn5z5/j0foEXnu9Xee/j2Ofj8bgDwOFwoLa2FoFAAGazeXpfc3MziouLIUkSzGYzCgoK0NLSgiNHjkQNIcTUTzQYDP7s44jq4+cw/t30DiO/h/B5XJ4x1SeaDLCkLsCefy3BP/+xGNbfFmFh4tRZqnA4iHB4zqJF9PXr7H+njyQcCkJCaNZ1OWz8JdYTTSLiPr3k+5l11k7/67PVTi/59LwuFAUSIp9lifb10b53OBTEf3sp1fvrZrT8wOy9wghgeTLw23IJQOQ3t85em4TlS6b+K1FRBCaCCj5PyPgyKePLhIzPk1ND1/ikjE8/HA9h+D8yJr7KGP+qIBSOfl2dyWTAAqOEBBOQmCAh0SQhIcGARJOERKMBRpMBRskAyQBIBgMkCVOf/3nMKBlgMABGaWpdYGp2UASgCEAogBCAIgQkyYBdm1OxfEn09578X3vtN9/mlW/zy18ZxGwrf3rx4gUqKirQ1NQ0fWznzp24cOECsrKypo85nU6cPXsWGzZsAABcv34dQ0NDOHXqVNSQnz59wuvXr6M/GiIiIiKdyMzMRHJy8ozjurigPSkpCZmZmUhISOCtAYiIiEjXhBAIhUJISkqKuB51uLJYLBgaGoIsyzAajZBlGcPDw7BYLDP2DQ4OTp+58vv9SE9PVxVSkqSIkx8RERGRHi1cuHDWtah3GU5NTYXNZoPX6wUAeL1e2Gy2H663AgC73Y6GhgYoioJAIIDW1lYUFhb+zehERERE80vUa64AoLe3F5WVlRgbG0NKSgo8Hg8yMjLgdrtRXl6O7OxsyLKMmpoadHR0AADcbjdcLtecPwAiIiIiPVE1XBERERGROlH/LEhERERE6nG4IiIiIoohDldEREREMcThioiIiCiGOFwRERERxRCHK4309fXB5XKhsLAQLpcLb9++nbFHlmVUV1ejoKAA27dvR0NDQ/yD6pCa2tXX12PXrl0oKirCnj178PDhw/gH1SE1tfvmzZs32LhxIzweT/wC6pja2jU3N8PpdMLhcMDpdGJkZCS+QXVITe1GR0dRVlYGp9MJu92OM2fOIBzvm7rqkMfjQX5+PqxW66y3iWOviExN7easVwjSRElJiWhsbBRCCNHY2ChKSkpm7Ll3754oLS0VsiyL0dFRkZeXJwYGBuIdVXfU1M7n84nx8XEhhBAvX74UOTk5YmJiIq459UhN7YQQIhwOi4MHD4rjx4+Lc+fOxTOibqmpXVdXl9ixY4cYHh4WQggxNjYmJicn45pTj9TUrq6ubvq5FgwGxb59+0RTU1Ncc+rR06dPxeDgoNi6davo6emJuIe9IjI1tZurXsEzVxoYHR1Fd3c3HA4HAMDhcKC7uxuBQOCHfc3NzSguLoYkSTCbzSgoKEBLS4sWkXVDbe3y8vKwaNEiAIDVaoUQAh8/fox7Xj1RWzsAuHbtGrZs2YK1a9fGOaU+qa3dzZs3UVpaihUrVgAAkpOTsWDBgrjn1RO1tTMYDPjy5QsURUEwGEQoFEJaWpoWkXVl06ZNM24391fsFZGpqd1c9QoOVxrw+/1IS0uD0WgEABiNRqxcuRJ+v3/Gvu/vz2ixWPD+/fu4ZtUbtbX7XmNjI9asWYNVq1bFK6Yuqa3dq1ev0N7ejsOHD2uQUp/U1q63txcDAwM4cOAAdu/ejatXr0L84u/TrLZ2R48eRV9fH3Jzc6c/cnJytIg877BXxEYsewWHK/q/9uTJE1y6dAkXL17UOsq8EAqFUFVVherq6ulmSOrJsoyenh7cuHEDt2/fhs/nw/3797WONS+0tLTAarWivb0dPp8Pz54949kXiptY9woOVxqwWCwYGhqCLMsApl6Qh4eHZ5y+tFgsGBwcnP7c7/f/8mdf1NYOAJ4/f44TJ06gvr4eGRkZ8Y6qO2pq9+HDB/T396OsrAz5+fm4desW7t69i6qqKq1i64La5116ejrsdjsSExOxePFibNu2DV1dXVpE1g21tbtz5w6KioogSRKSk5ORn5+Pzs5OLSLPO+wVf89c9AoOVxpITU2FzWaD1+sFAHi9XthsNpjN5h/22e12NDQ0QFEUBAIBtLa2orCwUIvIuqG2dl1dXTh27BguX76MrKwsLaLqjprapaeno7OzE21tbWhra8OhQ4ewf/9+1NbWahVbF9Q+7xwOB9rb2yGEQCgUwuPHj7F+/XotIuuG2tqtXr0aPp8PABAMBvHo0SOsW7cu7nnnI/aKnzdXvYI3btZIb28vKisrMTY2hpSUFHg8HmRkZMDtdqO8vBzZ2dmQZRk1NTXo6OgAALjdbrhcLo2Ta09N7fbu3Yt37979cEHs+fPnYbVaNUyuPTW1+96VK1cwPj6OiooKjRLrh5raKYoCj8cDn88HSZKQm5uLiooKSNKv/Xusmtr19/fj9OnTGBkZgSzL2Lx5M06ePAmTyaR1fE3V1dXhwYMHGBkZwbJly7B06VI0NTWxV6igpnZz1Ss4XBERERHF0K/96xQRERFRjHG4IiIiIoohDldEREREMcThioiIiCiGOFwRERERxRCHKyIiIqIY4nBFREREFEN/ABzCoK4vJ/uOAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = go.Figure()\nfig.add_trace(go.Scatter(x = test_score_df.date, \n                         y = test_score_df.loss, \n                         mode='lines', \n                         name='Test_Loss'))\n\n# Let's create another line chart to plot the threshold at 0.65\nfig.add_trace(go.Scatter(x = test_score_df.date, \n                         y = test_score_df.threshold, \n                         mode='lines', \n                         name='Threshold'))\n\n# Let's update the figure with the legend\nfig.update_layout(showlegend=True)\nfig.show()","execution_count":121,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>                            <div id=\"9a40bb36-bae0-4980-9ae8-2bec3eec920c\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if 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And seems like we are thresholding extreme values really well. All the values above the threshold are considered anomalies because their reconstruction error esceeds the threshold of 0.65\n\nSo let's create a Dataframe with only the extreme values and plot them and explore them with more granularity"},{"metadata":{"trusted":true},"cell_type":"code","source":"anomalies_df = test_score_df[test_score_df.is_anomaly == True]\nprint(anomalies_df.shape)","execution_count":122,"outputs":[{"output_type":"stream","text":"(84, 5)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"anomalies_df.head()","execution_count":123,"outputs":[{"output_type":"execute_result","execution_count":123,"data":{"text/plain":"           date     close      loss  threshold  is_anomaly\n7474 2015-08-25  2.457439  0.725614       0.65        True\n7475 2015-08-26  2.632149  0.792964       0.65        True\n7476 2015-08-27  2.745147  0.701318       0.65        True\n7976 2017-08-22  3.859192  0.651471       0.65        True\n8089 2018-02-02  4.601217  0.655149       0.65        True","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>close</th>\n      <th>loss</th>\n      <th>threshold</th>\n      <th>is_anomaly</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7474</th>\n      <td>2015-08-25</td>\n      <td>2.457439</td>\n      <td>0.725614</td>\n      <td>0.65</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>7475</th>\n      <td>2015-08-26</td>\n      <td>2.632149</td>\n      <td>0.792964</td>\n      <td>0.65</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>7476</th>\n      <td>2015-08-27</td>\n      <td>2.745147</td>\n      <td>0.701318</td>\n      <td>0.65</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>7976</th>\n      <td>2017-08-22</td>\n      <td>3.859192</td>\n      <td>0.651471</td>\n      <td>0.65</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8089</th>\n      <td>2018-02-02</td>\n      <td>4.601217</td>\n      <td>0.655149</td>\n      <td>0.65</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"So while plotting the anomalies plot, let's inverse_transform the data back to its original state to see the real value of the plot"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = go.Figure()\nfig.add_trace(go.Scatter(x = test_score_df.date, \n                         y = scaler.inverse_transform(test_score_df.close), \n                         mode='lines', \n                         name='Close_Price'))\n\n# Let's create another line chart to plot the threshold at 0.65\nfig.add_trace(go.Scatter(x = anomalies_df.date, \n                         y = scaler.inverse_transform(anomalies_df.close), \n                         mode='markers', \n                         name='Anomalies'))\n\n# Let's update the figure with the legend\nfig.update_layout(showlegend=True)\nfig.show()","execution_count":124,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>                            <div id=\"90733468-1d42-48c1-96f4-48595ed5116f\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"90733468-1d42-48c1-96f4-48595ed5116f\")) {                    Plotly.newPlot(                        \"90733468-1d42-48c1-96f4-48595ed5116f\",                        [{\"mode\": \"lines\", \"name\": \"Close_Price\", \"type\": \"scatter\", \"x\": 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Note that we can still adjust the thresholds and other parameters to get better results."},{"metadata":{},"cell_type":"markdown","source":"<b><h3>Summary:</b></h3>\n\nWe have combined two very powerful concepts in deep learning, \n1. LSTMs\n2. Auto-Encoders\nAnd we've combined these two to create an anomaly detection module to predict anomalies in certain price changes in the S&P 500 Index Data."},{"metadata":{},"cell_type":"markdown","source":"**Key points:**\n\n1. The three essential components of an Auto-encoder are: 1. The Encoder, 2. The Decoder and 3. the Loss function."},{"metadata":{},"cell_type":"markdown","source":"# References:  \n    1.https://github.com/Lawrence-Krukrubo/Anomaly_Detection_in_Time_Series_Data_with_Keras/blob/master/anomaly_detection_in_time_series_data_with_keras_and_tensorflow.ipynb"},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}