{"cells":[{"metadata":{},"cell_type":"markdown","source":"![ll](https://preview.redd.it/12kjs65k2ah21.jpg?width=640&crop=smart&auto=webp&s=24b5dcf14075ff7c19e201514a6751770f71511a)\n\n# Mission\n\nAccording to ASHRAE,\n\n> Assessing the value of energy efficiency improvements can be challenging as there's no way to truly know how much energy a building would have used without the improvements. The best we can do is to build counterfactual models. Once a building is overhauled the new (lower) energy consumption is compared against modeled values for the original building to calculate the savings from the retrofit. More accurate models could support better market incentives and enable lower cost financing.\n\nOur task (yes, your and mine) is to embark on a quest to find such a suitable model. It will be used to predict the energy usages in buildings of various types all over the world for about 16 months, based on all their energy usage data from 2016. Besides various attributes of the buildings, this task obviously requires also weather data, as besides the time of day and week, weather is intuitively the most important determinant of energy use. Therefore the chosen buildings, of which there are over 9000, are clustered near 15 weather stations."},{"metadata":{},"cell_type":"markdown","source":"# Setting up"},{"metadata":{},"cell_type":"markdown","source":"The code in this section is from this excellent kernel: https://www.kaggle.com/caesarlupum/ashrae-ligthgbm-simple-fe/notebook#7.-Handling-missing-values"},{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n# Suppress warnings \nimport warnings\nwarnings.filterwarnings('ignore')\nimport gc\n\n# matplotlib and seaborn for plotting\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport seaborn as sns\nimport matplotlib.patches as patches\nfrom scipy import stats\nfrom scipy.stats import skew\n\nfrom plotly import tools, subplots\nimport plotly.offline as py\npy.init_notebook_mode(connected=True)\nimport plotly.graph_objs as go\nimport plotly.express as px\npd.set_option('max_columns', 100)\n\npy.init_notebook_mode(connected=True)\nfrom plotly.offline import init_notebook_mode, iplot\ninit_notebook_mode(connected=True)\nimport plotly.graph_objs as go\n\nimport os,random, math, psutil, pickle\nfrom sklearn.preprocessing import LabelEncoder\n\nfrom sklearn.metrics import mean_squared_error\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, StratifiedKFold, train_test_split\nfrom tqdm import tqdm\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Input, Dense, Activation\nfrom keras.optimizers import Adam, Adagrad\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import KFold\n\nimport warnings\nwarnings.filterwarnings('ignore')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Downloading and merging the training data"},{"metadata":{"trusted":true},"cell_type":"code","source":"def merge_meta(df, m_df):\n    temp_df = df[['building_id']]\n    temp_df = temp_df.merge(m_df, on=['building_id'], how='left')\n    del temp_df['building_id']\n    return pd.concat([df, temp_df], axis=1)\n\ndef merge_weather(df, w_df):\n    temp_df = df[['site_id','timestamp']]\n    temp_df = temp_df.merge(w_df, on=['site_id','timestamp'], how='left')\n    del temp_df['site_id'], temp_df['timestamp']\n    return pd.concat([df, temp_df], axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"building_meta_df = pd.read_csv(\"../input/ashrae-energy-prediction/building_metadata.csv\")\ntrain_df = pd.read_csv(\"../input/ashrae-energy-prediction/train.csv\")\nweather_train_df = pd.read_csv(\"../input/ashrae-energy-prediction/weather_train.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df = merge_meta(train_df, building_meta_df)\ntrain_df = merge_weather(train_df, weather_train_df)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Encoding"},{"metadata":{},"cell_type":"markdown","source":"### Converting timestamps to datetimes"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df['timestamp'] = pd.to_datetime(train_df['timestamp'])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Converting year built into age"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df['age'] = train_df['year_built'].max() - train_df['year_built'] + 1\ntrain_df = train_df.drop('year_built', axis=1)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Wind: En-compassing direction and Beau-tifying speed"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Convert wind direction into categorical feature. We can split 360 degrees into 16-wind compass rose. \n# See this: https://en.wikipedia.org/wiki/Points_of_the_compass#16-wind_compass_rose\ndef rose_petalize(x):\n    if np.isnan(x):\n        return x\n    else: \n        val=int((x/22.5))\n        arr=[i for i in range(0,16)]\n        return arr[(val % 16)]\n\n# Since this is a cyclical variable, convert it to x and y on a circle.\n# See this: http://blog.davidkaleko.com/feature-engineering-cyclical-features.html\ndef sinify_petals(X):\n    return np.sin(X*(2.*np.pi/16))\ndef cosify_petals(X):\n    return np.cos(X*(2.*np.pi/16))\n    \ntrain_df['wind_direction'] = train_df['wind_direction'].apply(rose_petalize)\ntrain_df['wind_sin'] = sinify_petals(train_df['wind_speed'])\ntrain_df['wind_cos'] = cosify_petals(train_df['wind_speed'])\n#train_df = train_df.drop('wind_direction')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"beaufort = [(0, 0, 0.3), (1, 0.3, 1.6), (2, 1.6, 3.4), (3, 3.4, 5.5), (4, 5.5, 8), (5, 8, 10.8), (6, 10.8, 13.9), \n          (7, 13.9, 17.2), (8, 17.2, 20.8), (9, 20.8, 24.5), (10, 24.5, 28.5), (11, 28.5, 33), (12, 33, 200)]\n\n# The original wind speed column is left in the dataframe, as it is \n# not certain that the new categorical alternative will be better. \nfor item in beaufort:\n    train_df.loc[(train_df['wind_speed']>=item[1]) & (train_df['wind_speed']<item[2]), 'beaufort_scale'] = item[0]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Reducing memory usage"},{"metadata":{"trusted":true},"cell_type":"code","source":"def reduce_mem_usage(df, verbose=True):\n    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n    start_mem = df.memory_usage().sum() / 1024**2    \n    for col in df.columns:\n        col_type = df[col].dtypes\n        if col_type in numerics:\n            c_min = df[col].min()\n            c_max = df[col].max()\n            if str(col_type)[:3] == 'int':\n                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n                    df[col] = df[col].astype(np.int8)\n                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n                    df[col] = df[col].astype(np.int16)\n                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n                    df[col] = df[col].astype(np.int32)\n                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n                    df[col] = df[col].astype(np.int64)  \n            else:\n                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n                    df[col] = df[col].astype(np.float16)\n                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n                    df[col] = df[col].astype(np.float32)\n                else:\n                    df[col] = df[col].astype(np.float64)    \n    end_mem = df.memory_usage().sum() / 1024**2\n    if verbose: print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))\n    return df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df = reduce_mem_usage(train_df)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Pickling"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df.to_pickle('train_df.pkl')\n#test_df.to_pickle('test_df.pkl')\n\ndel train_df#, test_df\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"![ll](https://vignette.wikia.nocookie.net/metroid/images/8/8a/Savestation.gif/revision/latest/scale-to-width-down/180?cb=20190728014411)"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df = pd.read_pickle('train_df.pkl')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Exploration"},{"metadata":{},"cell_type":"markdown","source":"### Energy usage distribution"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"fig = plt.figure(figsize=(20,13))\nfor i, (name, meter_group) in enumerate(train_df.groupby('meter')):  \n    ax = fig.add_subplot(2, 2, i+1)\n    meter_group = meter_group[meter_group['meter_reading'] < 5000]\n    sns.distplot(meter_group['meter_reading']).set_title('Distribution for meter {}'.format(name))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Meter reading autocorrelation"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"import statsmodels.api as sm\n\nbuilding_id = 500\ndata = train_df[train_df['building_id'] == building_id]\ndata = data[data['meter'] == 0]\nfig = plt.figure(figsize=(20,6))\n_ = sm.graphics.tsa.plot_pacf(data['meter_reading'], lags=300, ax=plt.gca())\nplt.xlabel('Lag (hour)');\nplt.ylabel('Autocorrelation');","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"There is very strong correlation with the previous hour's reading. There is also considerable negative correlation with the values at multiples of 24 h before."},{"metadata":{},"cell_type":"markdown","source":"### Building age"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"id_df = train_df.groupby(['building_id']).mean()\nsns.distplot(id_df['age'], bins = 15, kde = True, rug = False, norm_hist=False);","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The most common age is around 50 years. We can also see that there are three primary categories of age: about 0 to 30, 30 to 75, and above 75. We should try to encode the age with these.\n\nNext, lets look at the age distributions at each site."},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"fig = plt.figure(figsize=(20,35))\nfig.subplots_adjust(hspace=0.6, wspace=0.4)\nn = 1\nfor name, site_group in train_df.groupby(['site_id']):\n    ax = fig.add_subplot(8, 2, n)\n    id_df = site_group.groupby(['building_id']).mean()\n    nan_amount = id_df['age'].isnull().sum()\n    nan_ratio = nan_amount / len(id_df)\n    # Some out the sites that have no buildings with ages.\n    if nan_ratio < 1:\n        percent = np.around((1-nan_ratio)*100, 1)\n        text = 'Site {}, {} buildings, of which {} percent have age.'.format(name, len(id_df), percent)\n        sns.distplot(id_df['age'], bins = 15, kde = True, rug = False, norm_hist=False, ax=ax).set_title(text)\n        n = n+1","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Looking at the KDEs (the continuous lines), we see that the distribution of most sites is slightly skewed towards buildings less than 50 years old."},{"metadata":{},"cell_type":"markdown","source":"### Age vs. energy use\n\n**A mean of all buildings, for each meter type.**\n"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"fig = plt.figure(figsize=(20,13))\nn = 1\nfor name, site_group in train_df.groupby('meter'):  # use enumerate\n    ax = fig.add_subplot(2, 2, n)\n    id_df = site_group.groupby(['building_id']).mean()\n    id_df.plot(kind='scatter', x='age', y='meter_reading', color='red', alpha=0.4, title='Meter: {}'.format(name), ax=ax)\n    n = n+1","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The energy per area is quite independent of building age for all of the meters. However, for meters 0 and 1, there is a lot of noise for buildings newer than about 70 and 50 years respectively.\n\nNote the singular outliers of meters 1 and 3. "},{"metadata":{},"cell_type":"markdown","source":"**All sites for meter 0**\n\nOnly meter 0 has enough data to warrant a sitewise analysis of the age vs. the meter reading:"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"def plot_age_energy_sitewise(meter):\n    fig = plt.figure(figsize=(15,40))\n    fig.subplots_adjust(hspace=0.7)\n    n = 1\n    this_meter_df = train_df[train_df['meter'] == meter]\n    for name, site_group_df in this_meter_df.groupby('site_id'):  # use enumerate\n        buildings_df = site_group_df.groupby(['building_id']).mean()\n        ax = fig.add_subplot(8, 2, n)\n        buildings_df.plot(kind='scatter', x='age', y='meter_reading', color='red', alpha=0.4, title='Site: {}'.format(name), ax=ax)\n        n = n+1\n        \nplot_age_energy_sitewise(0)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"(The empty plots are sites that lack ages.) Again, there are no strong relationships; for most of them, newer buildings tend to have higher readings, but most buildings are at about the same level. The only interesting phenomenon is the distribution of site 5: the site could be some special region where many buildings have been intentionally opened on the same years. I fail to imagine what sort of district the latter could be. Most likely the stack are caused by slipshod data collection. "},{"metadata":{},"cell_type":"markdown","source":"### Primary use\n\n**vs. energy usage**"},{"metadata":{"scrolled":true,"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"def use_energy_plot(df, meter):\n    meter_df = df[df['meter'] == meter]\n    \n    temp_df = meter_df.copy()\n    le = LabelEncoder()\n    meter_df['primary_use'] = le.fit_transform(meter_df['primary_use']).astype(np.int8)\n    le_name_mapping = dict(zip(le.classes_, le.transform(le.classes_)))\n    \n    buildings_df = meter_df.groupby(['building_id']).mean()\n    del temp_df\n    \n    plt.subplots(figsize = (10,7))\n    sns.barplot(x = 'primary_use', \n                y = 'meter_reading', \n                data=buildings_df, \n                linewidth= 5,\n                capsize = .1\n               )\n    plt.title(\"Energy usage vs. primary use for meter {}\".format(meter), fontsize = 16, pad=20)\n    plt.xticks(le.transform(le.classes_), le.classes_, rotation=90)\n    plt.ylabel(\"Energy usage\", fontsize = 15);\n    plt.xlabel(None);\n\nuse_energy_plot(train_df, 0)\nuse_energy_plot(train_df, 1)\nuse_energy_plot(train_df, 2)\nuse_energy_plot(train_df, 3)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Meters 1, 2 and 3 have each one or two building uses with huge variances compared to the rest of the uses. Other than that, I am currently unable to extract any insights from these."},{"metadata":{},"cell_type":"markdown","source":"**vs. floor count**"},{"metadata":{"scrolled":false,"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"temp_df = train_df.copy()\nle = LabelEncoder()\ntemp_df['primary_use'] = le.fit_transform(temp_df['primary_use']).astype(np.int8)\nle_name_mapping = dict(zip(le.classes_, le.transform(le.classes_)))\n\nbuildings_df = temp_df.groupby(['building_id']).mean()\ndel temp_df\n\nplt.subplots(figsize = (15,10))\nsns.barplot(x = 'primary_use', \n            y = 'floor_count', \n            data=buildings_df, \n            linewidth=5,\n            capsize = .1\n           )\nplt.title(\"Floor count vs. primary use\", fontsize = 16, pad=20)\nplt.xticks(le.transform(le.classes_), le.classes_, rotation=90)\nplt.ylabel(\"Floor count\", fontsize = 15);\nplt.xlabel(None);","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"While somewhat interesting per se, this does not yield any insights for the feature engineering."},{"metadata":{},"cell_type":"markdown","source":"### Weather\n\nLets look at the relationships between weather features and the energy usage. Since many of the weather features are quite collinear, and there are so many that looking at all of their mutual interactions would take too much time and space, I will restrict this analysis to the relationship between the intuitively most important one, temperature and its relationship with intuitively second-most significant one, wind speed. (It is clear that across the four types of energy usage, the wind speed, precipitation etc. do not matter much if the air is at room temperature. Hence the temperature is the supreme weather variable.)"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"# Optimizing by numpy-vectorization.\n\ndef closest_index(a,x):\n    closest_index = np.abs(a-x).argmin()\n    if a[closest_index] - x >= 0:\n        return closest_index\n    else:\n        return min((closest_index + 1, len(a) - 1))\n\ndef weather_energy_matrix(df, feature):    \n    temperature_bin_edges = range(int(df['air_temperature'].min()), int(df['air_temperature'].max()))\n    feature_bin_edges = range(int(df[feature].min()), int(df[feature].max()))[1:]\n    \n    df = df[['meter_reading', 'air_temperature', feature]].dropna()\n    X = df['meter_reading'].to_numpy()\n    T = df['air_temperature'].to_numpy()\n    F = df[feature].to_numpy()\n\n    # Assigning each feature and temperature value into a bin (taking the upper bin edge).\n    F_ = [closest_index(feature_bin_edges,f) for f in F]\n    T_ = [closest_index(temperature_bin_edges,t) for t in T]\n    shape = (len(temperature_bin_edges), len(feature_bin_edges))\n    sums_matrix, counts_matrix = np.zeros(shape), np.zeros(shape)\n    for x,t,f in zip(X,T_,F_):\n        sums_matrix[t,f] += x \n        counts_matrix[t,f] += 1\n    M = pd.DataFrame(data=sums_matrix/counts_matrix, index=temperature_bin_edges, columns=feature_bin_edges)\n    return M\n    \ndef plot_weather_energy_heatmaps(df, feature, meter):\n    meter_df = df[df['meter'] == meter]\n    n = 1\n    fig = plt.figure(figsize=(40,60))\n    fig.subplots_adjust(hspace=1)\n    for site_id, site_group_df in meter_df.groupby('site_id'):\n        M = weather_energy_matrix(site_group_df, feature)\n        if ~M.isna().all().all():\n            ax = fig.add_subplot(8, 2, n)\n            sns.heatmap(M, cmap='Reds')\n            ax.set_title('Meter {}, site {}'.format(meter, site_id))\n            plt.xlabel(feature)\n            plt.ylabel('Air temperature')\n        label_freq = 3\n        for ind, label in enumerate(ax.get_yticklabels()):\n            label.set_visible((ind % label_freq) == 0)\n        for ind, label in enumerate(ax.get_xticklabels()):\n            label.set_visible((ind % label_freq) == 0)\n        n += 1","execution_count":null,"outputs":[]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"plot_weather_energy_heatmaps(train_df, 'beaufort_scale', 0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# This kernel takes too long to commit, so switched off.\n'''\nplot_weather_energy_heatmaps(train_df, 'wind_direction', 0)\n'''","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"These yield no insights. Probably because there is so much variation between the time series of the different buildings that their mean values simply tend towards a the same value across the heatmap."},{"metadata":{},"cell_type":"markdown","source":"### Wind direction per hour of day\n\nCopied from https://www.kaggle.com/chmaxx/ashrae-eda-and-visualization-wip#more-to-come..."},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"fig, axes = plt.subplots(nrows=4, ncols=4, sharex=True, sharey=True, figsize=(16, 12))\nfor sid in weather_train_df.site_id.unique():\n    row = int(sid / 4)\n    col = sid%4\n    tmp_df = weather_train_df[weather_train_df.site_id==sid]\n    tmp_df['timestamp'] = pd.to_datetime(tmp_df['timestamp'])\n    tmp_df = tmp_df.set_index('timestamp')\n    missing = 100 / len(tmp_df) * tmp_df['wind_direction'].isnull().sum()\n    tmp_df.groupby(tmp_df.index.hour)['wind_direction'].median().plot(ax=axes[row,col])\n    if missing !=0:\n        axes[row, col].set_title(f\"site {sid}, null:{missing :.2f}%\", fontsize=12, color=\"darkred\")\n    else:\n        axes[row, col].set_title(f\"site {sid}\", fontsize=12, color=\"darkgreen\")\n    axes[row, col].set_xlabel(\"\")\nfig.suptitle(f\"wind_direction per hour of day\", fontsize=18)\nfig.subplots_adjust(top=0.92)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Any insights that would prove useful in the modeling? Perhaps, but lets leave that for later. Unless you really insist."},{"metadata":{},"cell_type":"markdown","source":"# Missing data"},{"metadata":{},"cell_type":"markdown","source":"**Building metadata**"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"import missingno as msno\n\ndf = pd.read_csv(\"../input/ashrae-energy-prediction/building_metadata.csv\")\ndf['timestamp'] = pd.to_datetime(weather_train_df['timestamp'])\nmsno.matrix(df.set_index(['timestamp']))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Weather at each site**"},{"metadata":{"scrolled":true,"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"weather_train_df = pd.read_csv(\"../input/ashrae-energy-prediction/weather_train.csv\")\nweather_train_df['timestamp'] = pd.to_datetime(weather_train_df['timestamp'])\nweather_train_df = weather_train_df.set_index(['timestamp'])\nfor i in range(15):\n    site_df = weather_train_df[weather_train_df['site_id'] == i]\n    msno.matrix(site_df.iloc[:,1:], freq='M')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Feature engineering"},{"metadata":{},"cell_type":"markdown","source":"### Cyclical time features"},{"metadata":{"trusted":true},"cell_type":"code","source":"# I am not including the month in here, as the year should already be well covered by the week of year.\ndef cyc_time(df):\n    df['hour_of_day'] = df['timestamp'].dt.hour.astype(np.int8)\n    df['day_of_week'] = df['timestamp'].dt.dayofweek.astype(np.int8)\n    df['week_of_year'] = df['timestamp'].dt.weekofyear.astype(np.int8)\n    return df\n\ntrain_df = cyc_time(train_df)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Sinified cyclicals\n\nThis is needed for deep learning, but not for linear regression. [Here](http://blog.davidkaleko.com/feature-engineering-cyclical-features.html) is a great explanation of it."},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df['hour_sin'] = np.sin(train_df['hour_of_day']*(2.*np.pi/24))\ntrain_df['hour_cos'] = np.cos(train_df['hour_of_day']*(2.*np.pi/24))\n\ntrain_df['day_sin'] = np.sin(train_df['day_of_week']*(2.*np.pi/7))\ntrain_df['day_cos'] = np.cos(train_df['day_of_week']*(2.*np.pi/7))\n\ntrain_df['week_sin'] = np.sin(train_df['week_of_year']*(2.*np.pi/52))\ntrain_df['week_cos'] = np.cos(train_df['week_of_year']*(2.*np.pi/52))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Shifted series\n\nI expect the temps and the wind speed to have moderate shifted correlation with the meter reading. Others not much."},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"# Using numpy in addition to pandas due to the latter's slowness.\n\ndef shift_features(df, features, shift_n):\n    print('Shifting {}'.format(features))\n    total_values_array = np.empty((1,len(features)+1))\n    for x, g in df.groupby(('building_id', 'meter')):\n        \n        shifted_features = g[features].shift(shift_n).to_numpy()\n        shifted_features_with_index = np.column_stack((g.index, shifted_features))\n        # Concatenating this building's data (as extra rows) to all the data.\n        total_values_array = np.r_[total_values_array, shifted_features_with_index]\n    \n    # Exclude the first row, as it is the empty starting array.\n    V = total_values_array[1:,:]\n    \n    # Sort rows according to index to match with dataframe's indices.\n    V = V[V[:,0].argsort()]\n\n    # Adding the data to the dataframe.\n    for i, f in enumerate(features):\n        df.loc[V[:,0].astype(int), 'shifted {} {}'.format(shift_n,f)] = V[:,i+1]    \n    \n    return df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"features_to_shift = [\n                        'meter_reading',\n                        'air_temperature',\n                    ]\n\n\n#train_df = shift_features(train_df, features_to_shift, 1)\n#train_df = shift_features(train_df, features_to_shift, 2)\n#train_df = shift_features(train_df, features_to_shift, 3)\n#train_df = shift_features(train_df, features_to_shift, 4)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### STDs away from rolling mean"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"# Getting the weather one as a separate function from the energy,\n# as it is much faster to do it from the weather_df itself rather\n# than the merged train_df.\ndef roll_weather(df, features, long_window, short_window):\n    for x, g in df.groupby('site_id'):\n        rolling_now = g[features].rolling(window=short_window, center=False).mean()\n        rolling_mean = g[features].rolling(window=long_window, center=False).mean()\n        rolling_std = g[features].rolling(window=long_window, center=False).std()\n        target_values = ((rolling_now - rolling_mean) / rolling_std)\n        df.loc[target_values.index, target_values.columns] = target_values\n        return_df = df[features].add_prefix('rolling {}/{} '.format(short_window, long_window))\n        return_df[['site_id', 'timestamp']] = df[['site_id', 'timestamp']]\n        return_df['timestamp'] = pd.to_datetime(return_df['timestamp'])\n        \n        # Filling the first NaNs (left by the rolling) with the original data.\n        return_df.loc[:long_window, features] = df.loc[:long_window, features]\n    return return_df\n\n\n    \n# Using numpy in addition to pandas due to the latter's slowness.\ndef roll_energy(df, long_window, short_window):\n\n    all_target_values = []\n    all_index_values = []\n    for x, g in df.groupby(('building_id', 'meter')):\n        rolling_now = g['energy_per_area'].rolling(window=short_window, center=False).mean()\n        rolling_mean = g['energy_per_area'].rolling(window=long_window, center=False).mean()\n        rolling_std = g['energy_per_area'].rolling(window=long_window, center=False).std()\n        target_values = ((rolling_now - rolling_mean) / rolling_std).to_numpy()\n        \n        # Filling the first NaNs (left by the rolling) with the original data.\n        target_values[:long_window] = g.iloc[:long_window, df.columns.get_loc('energy_per_area')]\n        \n        all_target_values = np.append(all_target_values, target_values)\n        all_index_values = np.append(all_index_values, g.index)\n        \n    V = np.column_stack((all_index_values, all_target_values))\n\n    # Ordering the column of indices so that it would match with the dataframe.\n    V = V[V[:,0].argsort()]\n    \n    col_name = 'rolling {}/{} energy per area'.format(short_window, long_window)\n    df[col_name] = V[:,1]\n    \n    return df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"# Takes too long to commit this kernel, so switch off.\n'''\nlong_window = 20\nshort_windows = [2]\n\n# Using the original weather dataframe to do the rolling \n# due to the slowness of doing it for each building using the merged dataframe.\nweather_features_to_roll = ['air_temperature', 'wind_speed', 'cloud_coverage', 'precip_depth_1_hr', 'sea_level_pressure']\n    \nfor w in short_windows:\n    train_df = roll_energy(train_df, long_window, w)\n    rolled_weather = roll_weather(weather_train_df, weather_features_to_roll, long_window, w)\n    train_df = merge_weather(train_df, rolled_weather) #pd.concat([train_df, rolled_weather], axis=1)\n    \ndel weather_df\ngc.collect()\n'''","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Exploratory modeling\n\n![ll](https://media.giphy.com/media/jKhxkINMo429G/giphy.gif)\n\n### Trying a Keras Sequential model"},{"metadata":{"trusted":true},"cell_type":"code","source":"selected_features =  ['timestamp',\n                     'shifted 1 meter_reading',\n                     #'shifted 2 meter_reading',\n                      #'shifted 3 meter_reading',\n                      #'shifted 4 meter_reading',\n                      'shifted 1 air_temperature',\n                      #'shifted 2 air_temperature',\n                      #'shifted 3 air_temperature',\n                      #'shifted 4 air_temperature', \n                      'air_temperature',\n                      'hour_sin', 'hour_cos', \n                      'day_sin', 'day_cos',\n                      'week_sin', 'week_sin']","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Setting up the data:"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"def sequential_trial_sets():\n    kfold = KFold(n_splits=5)\n    cvscores = []\n    b_id = 589\n    n_hours = 600\n\n    building_n_df = train_df[train_df['building_id'] == b_id][:n_hours]\n    building_n_df = building_n_df[building_n_df['meter'] == 0]\n    building_n_df = building_n_df[~building_n_df['meter_reading'].isna()].reset_index(drop=True)\n    building_n_df = building_n_df[~building_n_df[selected_features].isna().any(axis=1)].reset_index(drop=True)\n\n    y = building_n_df['meter_reading']\n    X = building_n_df[selected_features]\n    \n    return X, y, train_test_split(X, y, test_size=0.3, random_state=3)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Making and trying the model:"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"def try_sequential():\n    X, y, (X_train, X_test, y_train, y_test) = sequential_trial_sets()\n    all_dates = X['timestamp']\n    test_dates = X_test['timestamp']\n    X_train = X_train.drop('timestamp', axis=1)\n    X_test = X_test.drop('timestamp', axis=1)\n\n    model = Sequential(\n            [\n                Dense(50, activation=\"relu\", input_dim=len(X_train.columns)),\n                Dense(1, activation=\"linear\")\n            ]\n        )\n    model.compile(optimizer=Adam(), loss=\"mean_squared_error\")\n    model.fit(X_train, y_train, epochs=10, verbose=1)\n    y_pred = model.predict(X_test)\n    plt.figure(figsize=(16,5))\n    plt.scatter(test_dates, y_pred, s=4, label='Predicted', alpha=1)\n    plt.scatter(all_dates, y, color='r', s=13, label='Actual', alpha=0.2)\n    plt.title('Predicted first {} hours for building {}'.format(n_hours, b_id))\n    plt.legend()\n    \n#try_sequential()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)\n\n**<center>Looking promising.</center>**\n![ll](https://media.giphy.com/media/YFis3URdQJ6qA/giphy.gif)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"This is not a clear sign of victory, though: I have yet to find out how biased the model is towards the training set."},{"metadata":{},"cell_type":"markdown","source":"### Trying linear regression in LightGBM\n\nInspired by https://www.kaggle.com/caesarlupum/ashrae-ligthgbm-simple-fe/notebook#10.-Baseline"},{"metadata":{"trusted":true},"cell_type":"code","source":"le = LabelEncoder()\ntrain_df['primary_use'] = le.fit_transform(train_df['primary_use']).astype(np.int8)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"categoricals = [\"building_id\",  \"meter\", 'day_of_week', 'week_of_year', 'hour_of_day']#, 'primary_use', 'hour_of_day']\n\nnumericals = [\"air_temperature\",\n              #\"dew_temperature\",\n              #'cloud_coverage',\n              #'square_feet',\n              #'floor_count'\n             ]\n\nshifted_numericals = ['shifted 1 meter_reading',\n                      #'shifted 2 meter_reading',\n                      #'shifted 3 meter_reading',\n                      #'shifted 4 meter_reading',\n                      'shifted 1 air_temperature',\n                      #'shifted 2 air_temperature',\n                      #'shifted 3 air_temperature',\n                      #'shifted 4 air_temperature',\n                     ] \n            \n\nselected_features = categoricals + numericals + shifted_numericals","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"params = {\n            'boosting_type': 'gbdt',\n            'objective': 'regression',\n            'metric': {'rmse'},\n            'subsample_freq': 1,\n            'learning_rate': 0.3,\n            'bagging_freq': 5,\n            'num_leaves': 330,\n            'feature_fraction': 0.9,\n            'lambda_l1': 1,  \n            'lambda_l2': 1\n            }","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Checking what the predictions look like:"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"def gbm_trial_sets():\n    building_n_df = train_df[train_df['building_id'] == b_id]\n    building_n_df = building_n_df[building_n_df['meter'] == 0]\n\n    y = building_n_df['meter_reading']\n    X = building_n_df[selected_features + ['timestamp']]\n\n    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.3) #split_data(X, y, 0.3)\n\n    all_dates, val_dates = X['timestamp'], X_val['timestamp']\n    X_train, X_val = X_train.drop('timestamp', axis=1), X_val.drop('timestamp', axis=1)\n    \n    return X_train, X_val, y_train, y_val","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"def try_gbm():\n    X_train, X_val, y_train, y_val = gbm_trial_sets()\n    \n    lgb_train = lgb.Dataset(X_train, y_train, categorical_feature=categoricals)\n    lgb_eval = lgb.Dataset(X_val, y_val, categorical_feature=categoricals)\n    gbm = lgb.train(params,\n                    lgb_train,\n                    num_boost_round=500,\n                    valid_sets=(lgb_train, lgb_eval),\n                    early_stopping_rounds=50,\n                    verbose_eval = 50)\n    y_pred = gbm.predict(X_val)\n\n    plt.figure(figsize=(16,5))\n    plt.scatter(val_dates, y_val, color='r', s=13, alpha=0.2)\n    plt.scatter(val_dates, y_pred, s=2, alpha=1)\n    x1,x2,y1,y2 = plt.axis()\n    plt.axis(('2016-01-01 00:00:00', '2016-01-25 00:00:00', y1, y2))\n    \n#try_gbm()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)\n\n![ll](https://media.giphy.com/media/JTxQbJ5RMgVHi/giphy.gif)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"# Feature selection\n\nIt is time to curate the optimal set of features from the ones given and the ones created above. I will use two approches: correlation, and backward elimination. \n\n### Correlation"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"from scipy.stats import pearsonr, spearmanr\n\n# Excluding the columns that are not weather or are cyclical.\nfeatures = train_df.columns.values[8:]\nfeatures = np.setdiff1d(features, ['energy_per_area', \n                                   'age', \n                                   'floor_count',\n                                   'square_feet',\n                                   'wind_direction', \n                                   'hour_of_day',\n                                   'day_of_week',\n                                   'week_of_year',\n                                   'beaufort_scale',\n                                   'wind_sin', 'wind_cos', \n                                   'hour_sin', 'hour_cos', \n                                   'day_sin', 'day_cos',\n                                   'week_sin', 'week_cos'])\n\ndef multicollinearity(df):\n    fig = plt.figure(figsize=(10,7))\n    corr = df[features].corr()\n    mask = np.zeros_like(corr, dtype=np.bool)\n    mask[np.triu_indices_from(mask)] = True\n    sns.heatmap(corr, mask=mask, annot=False, cmap='PiYG')\n    bottom, top = ax.get_ylim()\n    ax.set_ylim(bottom + 0.5, top - 0.5) \n\ndef meter_correlations(df, meter):\n    data = df[df['meter'] == meter]\n    all_corres = np.empty([1,len(features)])\n    all_corres[:] = np.nan\n    for i, g in data.groupby('building_id'):\n        building_corres = []\n        for f in features:\n            feature_data = g[['meter_reading', f]]\n            feature_data = feature_data[~feature_data[f].isna()]\n            # Proceed only if there is sufficient data of this feature.\n            sufficient_n = 200\n            is_suf = (~feature_data[f].isna()).sum() > sufficient_n\n            is_inf = any(np.isinf(feature_data[f]))\n            if is_suf & ~is_inf:\n                building_corres.append(pearsonr(feature_data['meter_reading'], feature_data[f])[0])\n            else:\n                building_corres.append(np.nan)\n        all_corres = np.vstack((all_corres, building_corres))\n    return np.nanmean(all_corres, axis=0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"# I will just paste an image that I got before, using this same code, instead of\n# lengthening the commit time by doing it again.\n'''\ncorres = [meter_correlations(train_df, i) for i in range(4)]\nfig, ax = plt.subplots(figsize=(20,5))\nax = sns.heatmap(corres, xticklabels=features, yticklabels=range(4), cmap='PiYG', annot=False, ax=ax, vmin=-1, vmax=1)\n# Removing for the plot's truncation caused by matplotlib 3.1.1 \nbottom, top = ax.get_ylim()\nax.set_ylim(bottom + 0.5, top - 0.5) \n'''","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{"trusted":true},"cell_type":"code","source":"multicollinearity(train_df)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Notes on individual features**\n\n*Dew temperature*: discard due to high collinearity with air temp.\n\n*Cloud coverage*: a non-trivial case. On one hand, it seems that it should have a significant impact when the air temperature is high; after all, people yearn for shadow in summer heat. However, there is  no definite threshold above which it would be valid to use the cloud coverage. Using the actual function that relates cloud coverage (and radiation angle) to temperature and projected energy could of course bring big value to the model, but the point of this project is rather to find a heuristic model. In other words, though an intuitive comprehension of the underlying physics is indispensable for this mission, I shall refrain from the actual equations, and focus on learning data science. As such, taking into account the awful sparsity of the cloud coverage feature at most of the sites, it is best to drop it from the upcoming modeling.\n\n*Cyclicals*: I did not include the cyclical features (wind direction, hour of day and period of year) here because correlation is not valid with them. However, the time-based ones must be significantly descriptive of the energy; this is apparent in the daily and yearly cycles of the series. The wind direction also could be important, as there are specific cold or hot winds in certain areas of the world (for example, in Europe when the wind is from the north, it is usually colder than otherwise, as it comes from Siberia). Although such heat or coldness is probably usually far better encapsulated in the air temperature, I suspect that the wind direction is worth trying.\n\n**Choose our team:**\n\n* n-1 energy\n\n* Air temp\n\n* Cyclicals: Hour of day, Day of week, and Week of year."},{"metadata":{},"cell_type":"markdown","source":"![ll](https://media.giphy.com/media/H5RSLry1qMrzq/giphy.gif)"},{"metadata":{},"cell_type":"markdown","source":"### Backwards elimination\n\nThis process is cumulative, i.e., when a feature is marked with REMOVE, the ones after that are done with it removed. Each new RMSE score is hence compared to the last one marked with REMOVE."},{"metadata":{"trusted":true,"_kg_hide-output":true,"_kg_hide-input":true},"cell_type":"code","source":"def backwards():\n    y = train_df['meter_reading']\n    X = train_df[selected_features]\n\n    lgb_train = lgb.Dataset(X, y, categorical_feature=categoricals)\n    del y, X\n    gc.collect()\n\n    history = lgb.cv(params,\n                    lgb_train,\n                    shuffle=True,\n                    stratified=False,\n                    nfold=5,\n                    num_boost_round=500,\n                    categorical_feature=categoricals,\n                    early_stopping_rounds=30,\n                    verbose_eval = 30)\n\n    'Last RMSE: {}'.format(history['rmse-mean'][-1])\n\n#backwards():","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**With all:** 29553\n\n**Without floor count:** 29716   REMOVE\n\n**Without square feet:** 30025   REMOVE\n\n**Without hour of day:**  27523  REMOVE\n\nThis is surprising, given the intuitively huge significance of the time of day for energy use.\n\n**Without day of week:**   34521  KEEP   \n\n**Without week of year:**  42546  KEEP \n\n**Withou cloud coverage:**   27273  REMOVE\n\n**Without primary use**:    26958    REMOVE\n\n**Without dew temperature:**   29371   KEEP\n\n**Without shifted 4 air temperature:**  27791   KEEP\n\n**Without shifted 4 meter reading:** 27323  KEEP\n\n\nHere I have the issue of not knowing what threshold I should use for removing or keeping a feature. For example, the first increase in the RMSE above, when the floor count is removed, is only 0.6%. There must be a robust technique for finding the threshold."},{"metadata":{},"cell_type":"markdown","source":"# Modeling and prediction"},{"metadata":{},"cell_type":"markdown","source":"### Preparing the testing set\n\nPutting the testing set through the same functions as the training set."},{"metadata":{"trusted":true},"cell_type":"code","source":"def prepare_test():\n    test_df = pd.read_csv(\"../input/ashrae-energy-prediction/test.csv\")\n    building_meta_df = pd.read_csv(\"../input/ashrae-energy-prediction/building_metadata.csv\")\n    weather_test_df = pd.read_csv(\"../input/ashrae-energy-prediction/weather_test.csv\")\n\n    test_df = merge_meta(test_df, building_meta_df)\n    test_df = merge_weather(test_df, weather_test_df)\n    test_df['timestamp'] = pd.to_datetime(test_df['timestamp'])\n    test_df = cyc_time(test_df)\n\n    features_to_shift = ['air_temperature']\n    test_df = shift_features(test_df, features_to_shift, 1)\n    #test_df = shift_features(test_df, features_to_shift, 2)\n    #test_df = shift_features(test_df, features_to_shift, 3)\n    #test_df = shift_features(test_df, features_to_shift, 4)\n    \n    test_df = reduce_mem_usage(test_df)\n    test_df.to_pickle('test_df.pkl')\n    gc.collect()\n    \n    return test_df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#test_df = prepare_test()\n#test_df = pd.read_pickle('test_df.pkl')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Defining the model\n\nSince LightGBM cannot be used as a base estimator in BaggingRegressor (as it does not belong to scikit), it has to be bagged manually.\n\nAs I want to maximize regularization, I will use only the validation set as the LightGBM's validation set, not the training set too."},{"metadata":{"trusted":true},"cell_type":"code","source":"def gbm_bag(df, features, cats):\n    folds = 5\n    seed = 387\n    kf = KFold(n_splits=folds, shuffle=True, random_state=seed)\n    models = []\n    i=0\n    for train_index, val_index in kf.split(df[features], df['building_id']):\n        i += 1\n        #print('Fold {}'.format(i))\n        train_X, val_X = df.loc[train_index, features], df.loc[val_index, features]\n        train_y, val_y = df.loc[train_index, 'meter_reading'], df.loc[val_index, 'meter_reading']\n        #train_y, val_y = np.log1p(df.loc[train_index, 'meter_reading']), np.log1p(df.loc[val_index, 'meter_reading'])\n        lgb_train = lgb.Dataset(train_X, train_y, categorical_feature=cats)\n        lgb_val = lgb.Dataset(val_X, val_y)\n        gbm = lgb.train(params,\n                        lgb_train,\n                        num_boost_round=500,\n                        valid_sets=lgb_val,\n                        early_stopping_rounds=30,\n                        verbose_eval = -1)\n        models.append(gbm)\n    return models","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def bagging_predict(bag, X):\n    return np.mean([model.predict(X) for model in bag]) ","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Predicting with a separate model for each building\n\nIn backwards elimination above, I did not use this method, but the in the next subsection; this method takes such a long time to finish that I have not enough time to conduct the backwards elimination process with this. As such, I shall instead use the set of 5 features chosen in the alternative feature selection approach, namely the correlation analysis."},{"metadata":{"trusted":true},"cell_type":"code","source":"categoricals = ['day_of_week', 'week_of_year', 'hour_of_day']\nnumericals = [\"air_temperature\"]\nshifted_numericals = ['shifted 1 meter_reading'] \n\ntrain_df_features = categoricals + numericals + shifted_numericals\ntest_df_features = categoricals + numericals","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"scores = []\n\ndef train_predict_separately():\n    for (_, train_g), (i, test_g) in zip(train_df.groupby(['building_id', 'meter']), test_df.groupby(['building_id', 'meter'])):\n\n        # This if is useful when having to stop and restart this script.\n        if (i[0] > -1):\n            print('Building {}'.format(i))\n\n            bag = gbm_bag(train_g.reset_index(), train_df_features, categoricals[2:])\n            scores.append([m.best_score for m in bag])\n\n            energy_shifts = 1\n            temperature_shifts = 1\n            preds = np.array([])\n\n            # Adding the last values from the previous year into the preds array,  \n            # as they are the shifted values of the first values of the new year.\n            start_energies = train_g.loc[train_g.index[-energy_shifts:], 'meter_reading'] #np.log1p(train_g.loc[train_g.index[-energy_shifts:], 'meter_reading']) \n            start_temperatures = train_g.loc[train_g.index[-temperature_shifts:], 'air_temperature']\n            preds = np.concatenate((preds, start_energies))\n            temperatures = np.append(start_temperatures.to_numpy(), test_g['air_temperature'].to_numpy())\n\n            # Iterating over the numpy array instead of using g.iterrows(),\n            # as the latter is too slow.\n            for j, row in enumerate(test_g[test_df_features].to_numpy()):\n\n                shifted_energies = preds[-energy_shifts:]\n                shifted_temperatures = temperatures[j:j+temperature_shifts]\n                row = np.concatenate((row, shifted_energies))\n                row = np.concatenate((row, shifted_temperatures))\n\n                # Reshaping due to LightGBM's 2D array requirement.\n                row = np.array(row).reshape((1,-1))\n                preds = np.append(preds, bagging_predict(bag, row))\n\n            test_df.loc[test_g.index, 'meter_reading'] = preds[energy_shifts:] #expm1(preds[energy_shifts:])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Takes too long to commit with this, so flipped off.\n'''\ntrain_predict_separately()\n\n# Mean score from validation sets.\nprint(np.mean([X[0]['valid_0'].popitem()[1] for X in scores]))\n\ntest_df = reduce_mem_usage(test_df)\ntest_df.to_pickle('test_df.pkl')\ngc.collect()\n'''","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Before submitting, lets check whether the predictions at least *could* be accurate by comparing the form of one building's prediction to that of its readings during the equivalent timeframe in the previous year. This will give an intuitive sense of its bias and variance. "},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"from dateutil.relativedelta import relativedelta\n\ndef test_for_building(b_id):\n    train_building_df = train_df[(train_df['building_id'] == b_id) & (train_df['meter'] == 0)]\n    test_building_df = test_df[(test_df['building_id'] == b_id) & (test_df['meter'] == 0)]\n\n    train_building_df = train_building_df.set_index('timestamp')['2016-04-01 00:00:00' : '2016-07-01 00:00:00']\n    test_building_df = test_building_df.set_index('timestamp')['2017-04-01 00:00:00' : '2017-07-01 00:00:00']\n\n    plt.figure(figsize=(16,4))\n    plt.scatter(train_building_df.index, train_building_df['meter_reading'], c='r', s=13, alpha=0.2,)\n    plt.scatter([ x - relativedelta(years=1) for x in test_building_df.index ], test_building_df['meter_reading'], s=2, alpha=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"'''\ntest_for_building(601)\n'''","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA54AAAD4CAYAAACEyjk9AAAgAElEQVR4Aey9eXAc2X3n+U0APACCIAEQJE7eN9lsHk2q79OSd8ceS3a3ZO/OemWPdntnbMkz9m6sPN2OXStmJsL+xx6P5Tm0Y2vlCa9HCqnllqUZW30f7ItXN5vNo3mCB4iDIEgAxEUAufF9r7KQVcgqZFUlgDq+GQFWVdbLd3xeVvJ93+/3fg/QIQIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAJhCThhE0aRrr6+3l27dm0UWSkPERABERABERABERABERABERCBPCNw5MiRGwAakqtVkXxiNj9TdB4+fHg2i1DeIiACIiACIiACIiACIiACIiAC80TAcZz2oKLLgk7qnAiIgAiIgAiIgAiIgAiIgAiIgAhERUDCMyqSykcEREAEREAEREAEREAEREAERCCQgIRnIBadFAEREAEREAEREAEREAEREAERiIqAhGdUJJWPCIiACIiACIiACIiACIiACIhAIAEJz0AsOikCIiACIiACIiACIiACIiACIhAVAQnPqEgqHxEQAREQAREQAREQAREQAREQgUACc7qdSmANdFIEREAEREAEREAEREAESp3A5CTc9nag9yZQXwdnzRqgTDaiUr8tiqn9upuLqTfVFhEQAREQAREQAREQgYIkQNHpnjwFDA+bVyNCC7IlqrQIBBOQ8AzmorMiIAIiIAIiIAIiIAIiMHcEem/CqakBqqrsKy2fOkSgiAhIeBZRZ6opIiACIiACIiACIiACBUqgvg7urVtwz52De/QoMDQETE4WaGNUbRGYTkDCczoTnREBERABERABERABERCBOSXANZ3OsmVAZyfQ2Aj39m275nNOa6HCRGD2CCi40OyxVc4iIAIiIAIiIALFREDBX4qpN/OvLQwkRDfbvXvNq7F40t123br8q6tqJAJZEJDFMwtoukQEREAEREAERKD0CCj4S+n1+Zy3mO62/f3Gzda81tfNeRVUoAjMFgFZPGeLrPIVAREQAREQAREoLgL+4C9smaxRxdW/edAas4VK7N5yWlvslip5UC9VQQSiICDhGQVF5SECIiACIiACIlD8BGiNOnkKDmCsUhQGOkQgUgJlZXDoWiv32kixKrP8ICDhmR/9oFqIgAiIgAiIgAjkOQFZo/K8g1Q9ERCBvCYg4ZnX3aPKiYAIiIAIiIAI5A2BQrNGKRhS3tw6qogIiACg4EK6C0RABERABERABESgCAkoGFIRdqqaJAIFTEDCs4A7T1UXAREQAREQAREQgZQE/MGQampsMKSUifWFCIiACMwugTDCcwuAD31//QD+OQDGd34JwNnYa+3sVlW5i4AIiIAIiIAIiIAIhCagrTlCo1JCERCB2ScQRnieAbA79rcPwBCAHwL4XQCvANgUe+VnHSIgAiIgAiIgAiIgAnlAgMGQnO3bgMpK8xoPjpQHdVMVREAESo9ApsGFngJwHkA7gM8DeDyG7DsAXgfw9dJDqBaLgAiIgAiIgAiIQB4SKLRgSHmIUFUSARGIjkCmwvNXAPx1rPhVAK7H3vN1ZYpqPQuAf+jp6UmRRKdFQAREQAREQAREQAREQAREQASKlUAmwnMhgF8A8C8yhPEtAPxDQ0ODm+G1Si4CIiACIiACIiACIpAtAW2pki05XScCIhAxgTBrPL0i/3sARwF0xU7wtSn2nq/dXkK9ioAIiIAIiIAIiIAIzD8Bbaky/32gGoiACFgCmQjP/8HnZsurfwTgyzGQfH1RUEVABERABERABERABPKIgLZUyaPOUFVEoLQJhBWeVQA+C+AFH64/iJ3jdir8jp91iIAIiIAIiIAIiIAI5AsBbamSLz2heohAyRMIu8aTW6jUJ9HqBcAotzpEQAREQAREQAREQATykEB8CxVaPltbEP+ch3VVlURABIqbQFjhWdwU1DoREAEREAEREAERKEYC2lKlGHtVbRKBgiQQ1tW2IBunSouACIiACIiACIiACIiACIiACMw/AQnP+e8D1UAEREAEREAEREAEREAEREAEipqAhGdRd68aJwIiIAIiIAIiIAIiIAIiIALzT0DCc/77QDUQAREQAREQAREQAREQAREQgaImoOBCRd29apwIiIAIiIAIiEBBEpichNveDvTeBOrrbDTaMtkLCrIvVWkREAFDQE8w3QgiIAIiIAIiIAIikGcEKDrdk6eA4WHzakRontVR1REBERCBTAhIeGZCS2lFQAREQAREQAREYC4IcN/Nmhqgqsq+0vKpQwREQAQKmICEZwF3nqouAiIgAiIgAiJQpATq6+D29wNDQ/a1vq5IG6pmiYAIlAoBrfEslZ5WO0VABERABERABAqGgLNmja0rLZ+tLXaNZ8HUXhUVAREQgekEJDynM9EZERABERABERABEZhfAmVlcNatA/inQwREQASKgIBcbYugE9UEERABERABERABERABERABEchnAhKe+dw7qpsIiIAIiIAIiIAIiIAIiIAIFAEBCc8i6EQ1QQREQAREQAREQAREQAREQATymYDWeOZz76huIiACIiACIiACpUFgchJmr05um1JfZ4MJlck+UNSdrz4v6u5V46YT0BNtOhOdEQEREAEREAEREIE5JUDR6Z48BQwPm1cjQue0Bipsrgmoz+eauMqbbwISnvPdAypfBERABERABERABLhtSnU13L4+4Pp1uMc/BiYnxaWYCbDPa2qAqir7Smu3DhEoYgJytS3izlXTREAEREAEREAECoRAfR3c48eBGzcAxwHKyozrrdlSpUCaoGpmSIB9fvIUnPFxuB98AFRUAENDcB58wL7PMDslF4F8JyCLZ773kOonAiIgAiIgAiJQvAS4zu/iRaDnBjAxASxfDmzZAmfDBkAWsOLtd8Cs43W2b4N77hzQcR1YuBDua6/Bfefdom63Gle6BCQ8S7fv1XIREAEREAEREIF5JhBf5zc6CpSXG0uXU1sLd3DQBBma5+qp+NkkUFYGY9FetAjYvAmorQVWrIB76dJslqq8RWDeCIQVnssBfB/AaQCnADwAoA7ASwDOxl5r560VKlgEREAEREAEREAECpGAf50frZz19UBlJWgJc9asKcQWqc4ZEnDWrgV6eoDz54HjJ4zlU+t7M4So5AVBIKzw/BMAfwdgK4B7Y+LzdwG8AmBT7JWfdYiACIiACIiACIiACIQlwHV+/f1mbR+tnM6ue+Dct89awrSdSliKBZ3OrOncuhXoHwDu2WEmHhTVuKC7VJVPQSBMcKEaAI8C+LVYHmMA+Pd5AI/Hzn0HwOsAvp6iHJ0WAREQAREQAREQgeImkMW+jHGrJi2frS2ychb3HRLcuooKOBs3Ai0tJsItAwyZ9b3r1gWn11kRKFACYYTnegA9AL4ds3YeAfDPAKwCcD3Wbr6uTMHgWQD8Qw/dCHSIgAiIgAiIgAiIQBES8NZrcosMsycnA8jMJB68dX7+dFkI2CLEWVpN8iLcAsYCzkkIHSJQbATCCE+m2QvgawDeB0C320zcar8FgH9oaGhwiw2g2iMCIiACIiACIiAChoB/vSZPMCqtX1CmwpQkNLm+zz19xuztGFrApspb5wuCgCzfBdFNqmSOBMIIz6sA+EfRyYNBhig8uwA0xayefO2Ofa8XERABERABERABESg9AllaraZZSsfH4dTVGbdLhxTDCtgg4kmi1ggcrR0NIjW/54Is3/NbI5UuApETCBNcqBPAFQBbYqU/BeAkgB8B+HLsHF9fjLx2ylAEREAEREAEREAECoQARR2j0WYcldZvKa1haA3rbsm1fibwUD03Esju8EQthoeN+6+C1mTHUVeJgAjkTiCMxZOl0M32rwAsBHABwK8DoGj9HoCvALgM4Iu5V0c5iIAIiIAIiIAIiECBEqDVas0amHVFvTfNaygLY7KldOcOgFbJKAIO+UUtseZiPS3Qbsnrassindfdo8pFSyCs8PwQwH0BRdP6qUMEREAEREAEREAERICWyvZ2Y1nMJMCQEaek5xeaFJ5h1ofORD1Z1CpozUzE5vT7bO6XOa2gChOBCAmEcbWNsDhlJQIiIAIiIAIiIAJFTMBvYaTbLC2MMx2x9X3O3j0mpXv0GNyLF8EgQ7keWbv/5lqwrg9HIJv7JVzOSiUCeUdAwjPvukQVEgEREAEREAERKFgCtDD29yOb9Zme9SvS9ZieqL1vn93aRYGF8uvWyuF+ya+GqDYiMDOBsK62M+ekFCIgAiIgAiIgAiJQ4gQC3WbDMvFbv3iN1mOGJVew6QLvF637LNj+VMXTE5DwTM9H34qACIiACIiACIhAeAK5bIuh9ZjhORdLyoD7hW7W3L81k3XCxYJD7ShuAhKexd2/ap0IiIAIiIAIiMBcE8jSYmWsX7z2xCe2xlzjyT+5x851D85vebJ8zy9/lT5rBLTGc9bQKmMREAEREAEREIFSJJD1Wk0KTP5VVMCpq4N7+oyJkluKDEu6zVr3WdLdX8yNl8WzmHtXbRMBERABERABEZh7ArlYrHK5du5bqhJngUDgus9ZKEdZisBcE5DFc66JqzwREAEREAEREIHiJpCLxSqXa4ubaum0TpGIS6evS6ylsniWWIeruSIgAiIgAiIgArNLIBeLVS7Xzm6rlLsIiIAI5EZAwjM3frpaBERABERABERABBIJBEQqTUyQ5lMu16bJVl+JgAiIwHwTkKvtfPeAyhcBERABERABERABERABERCBIicg4VnkHazmiYAIiIAIiIAIiIAIiIAIiMB8E5DwnO8eUPkiIAIiIAIiIAIiIAIiIAIiUOQEJDyLvIPVPBEQAREQAREQAREQAREQARGYbwISnvPdAypfBERABERABERABERABERABIqcgIRnkXewmicCIiACIiACIiACIiACIiAC801A26nMdw+ofBEQAREQAREQARFIJjA5Cbe9Hei9CdTXwezvWSZ7QTImfRYBESgcAnqCFU5fqaYiIAIiIAIiIAIlQoCi0z15ChgeNq9GhJZI29VMERCB4iQg4Vmc/apWiYAIiIAIiIAIREmAFsiLF+EePmJeMTkZZe7T8+q9CaemBqiqsq+0fOoQAREQgQImIOFZwJ2nqouACIiACIiACMwNgTm3QNbXwe3vB4aG7Gt93dw0VKWIgAiIwCwRCLvG8xKAAQATAMYB3AeAT8DvAlgLgN9/CUDfLNVT2YqACIiACIiACIjA/BHwWyBZC1og162btfqYNZ2xcpzWFrvGc9ZKU8YiIAIiMPsEMrF4PgFgd0x0sma/C+AVAJtir/ysQwREQAREQAREQASKj8BcWyDLyuCsWwfnvn3mFQosVHz3lFokAiVGIKzFMwjL5wE8HvviOwBeB/D1oIQ6JwIiIAIiIAIiIAKFTGDeLZCKclvIt4/qLgIiACCs8HQB/BQAX/8jgG8BWAXgeowiX1emIPosAP6hp6cnRRKdFgEREAEREAEREIE8JhCzQM6me+201vvFJtd63r4NZ/lyG+0WsJbQaRfphAiIgAjkJ4GwwvMhAB0xcfkSgNMZNIcilX9oaGigcNUhAiIgAiIgAiIgAiIwAwEvoBGj27rHjgGNjUBzMxxeN8trTGeomr7OlYB/UkH7tOZKU9cXCIGwazwpOnl0A/ghgAMAugA0xc7zld/pEAEREAEREAEREAERiIKAL6CREZ2dnYpyGwXXPMjDm1TQPq150BmqwpwRCCM8lwBYGqsR338OwAkAPwLw5dh5vr44Z7VWQSIgAiIgAiIgAiJQ7AR8AY3Mfp579gCVlXC2b1OU20Lve9+kgtmvVfu0FnqPqv4hCIRxteVaTlo5eTD9/wfg7wAcAvA9AF8BcBnAF2Np9CICIiACIiACIiACIhCGQBqXy8CARopuG4Zq/qepXQ73+HFgchKMWOw89mj+11k1FIEcCYQRnhcA3BtQTi+ApwLO65QIiIAIiIAIiIAIiEAIAp7LpVnHefKUuYLbqJhjPgIahaizkoiACIhANgTCCM9s8tU1IiACIiACIiACIlC6BNJYMhOg+F0u+YWCBiXgKdoPfbfgbNoEulBjaAjou1W0TVXDRMAjEGaNp5dWryIgAiIgAiIgAiIgAiEIeJbMGYPH+NZxuv39QH1diNyVpOAJqN8LvgvVgMwJyOKZOTNdIQIiIAIiIAIiIALpCYS0ZAau40yfs74tAgLq9yLoRDUhYwISnhkj0wUiIAIiIAIiIAIiMAMBWrROnjJ7btKS6bS2TL8grDuud2Wm6b3r9Jp/BLR+N//6RDWadQISnrOOWAWIgAiIgAiIgAiUGoEwFi3PHTcwsFAAsEzTB2ShUyIgAiIwbwQkPOcNvQoWAREQAREQAREoWgJhLFoh3XG55YYRnW+9DSxaBKxcaSypCkRUtHePGiYCRUlAwrMou1WNEgEREAEREAERyHsCYdxxASs6udUKRefp03DZsOrqYPfdoEbLRTeIis6JgAjMMQEJzzkGruJEQAREQAREQARKnIAnBHtuwFm61AhKZ/s2xN1zk/F4ltGVK63oHB2Fc2B/6vRJ18tFNwmIPoqACMwLAW2nMi/YVagIiIAIiIAIiECpEvCEIEZH4Q4MAA0r4KxbB5SlGJZ5W2+MjFhL5yMPp0+fDNYTrlVV4HpS46KbnEafRUAERGCWCcjiOcuAlb0IiIAIiIAIiEAJEvCsmr03zd6cxprpCUu/ECQapqHwTHHELaG8rrUltKUznl1Il954er0RAREQgVkgIOE5C1CVpQiIgAiIgAiIQGkT8KyagRFrMxWCYQIVpcGds3BNk7e+EgEREIGwBCQ8w5JSOhEQAREQAREQAREISyCNVTMjIZjOchq2LjkK17DFKJ0IiIAIpCOQYjFBukv0nQiIgAiIgAiIgAiIQFoC3rrMoSG4/f3G3dakz1BIepZTDA/DPXnKRLhNW66+FAEREIE8JSCLZ552jKolAiIgAiIgAiJQuARSWTU9IRnoghvU3DSW06DkOicCIiAC+UpAwjNfe0b1EgEREAEREAERKFwCqdxbMxWSma4HLVxiqrkIiECRE5DwLPIOVvNEQAREQAREQATyiIAnJOlye/48UF8/Peqtr7qpLKe+JHorAiIgAgVBQMKzILpJlRQBERABERABESg4AgHrOT0h6R7/2DTHWb7crN3kB7OXZ3IjU1lOk9PpswiIgAjkOQEJzzzvIFVPBERABERABESgMAmkWs9pBCb37qyrA6qq4HB/zxn28ixMAqq1CIiACEwRUFTbKRZ6JwIiIAIiIAIiIALTCdByefEi3MNHzCsmJ6enCTrjX89ZU2PFpZcuVdRb73u9ioAIiECREchEeJYDOAbgxzEG6wC8D+AsgO8CWFhkbNQcERABERABERABETBbmLgnPjFrMt0f/S3ctw8ilPhMIy7pcuts3wZUVppXzwU3Y9zZiuKMC9IFIiACIpAbgUyE5z8DcMpX3B8C+GMAmwD0AfiK7zu9FQEREAEREAEREIHiIEA32KEhoKsLqKiAe+xYqP0004rL2NpN5759dm0n3W2zODx3Xu3zmQU8XSICIjCnBMI+5VoB/ByA/xSrnQPgSQDfj33+DoAvzGnNVZgIiIAIiIAIiIAIzAWB+jqgsxMoLwcmJoDGxkS32VR1iEhc0rqa0tU3nTtvqnrpvAiIgAjMA4GwwvPfAPg/AXiLGuoB3AIwHqvzVQAtKer/LIDD/Ovp6UmRRKdFQAREQAREQAREID8JGMvlnj3A+DiwapUJCASK0Tk60lo107jzzlH1VIwIiIAIhCIQJqrtzwPoBnAEwOOxXGnxTD7c5BOxz98CwD80NDSkSpPiUp0WAREQAREQAREQgXkmQMvlww8Bba3W0llfh6zXZGbTFL9Vk9f7IuDG68E0rS1zW69s2qJrREAESpZAGOH5EIBfAPAPACwGUAOAFtDlAHg9rZ50xe0oWYpquAiIgAiIgAiIQHETmM/9NGnVPHkKnPV3+/uNwIzDns96xSuhNyIgAiIwM4Ewrrb/IiYs1wL4FQCvAvhHAF4D8EysiC8DeHHm4pRCBERABERABERABEQgEwIJQYq2bjERdTPe2iWTApVWBERABGaBQBjhmarYrwP4HQDnAHDN55+nSqjzIiACIiACIiACIiACWRLwBSlCWRnc02egKLZZstRlIiAC80YgjKutv3KvA+AfjwsADsTe60UEREAEREAEREAEiosAo8m2tyeu68xy25PIwKRZ7xlZGcpIBERABGaBQKbCcxaqoCxFQAREQAREQAREIP8IeNFknZoas8aSNXTWrcu+olEI2XTrPdPVLIqy0+Wv70RABERgBgISnjMA0tciIAIiIAIiIAIlSiBi62IUQjbbKLZRlF2id4GaLQIiEBEBCc+IQCobERABERABERCBIiOQrXXRj8FnaXSvXYOzfLnZB9TsS+fbFsV/Sdr32UaxjVhEp62jvhQBERCBAAK5BBcKyE6nREAEREAEREAERKA4CCREk92+Las9Mj1LI4MBobcX7vnzwNCQ2RYF9XVzB4oiur9/fsqeu1aqJBEQgTwmIItnHneOqiYCIiACIiACIjCPBLK1Lvqr7Lc0btgA99YtoLLS7MUZd5v1p5/pvc+CSuFq8ggR8CheFuvT2pKViJ6pavpeBERABNIRkPBMR0ffiYAIiIAIiIAIlDaBLIVeHJrfXXdwEM6ue3IKUORZUDMOeBSFiI43Sm9EQAREIHMCEp6ZM9MVIiACIiACIiACJUIga6EX4xO5pdFvQWUZ2awTLZG+y4tm5jpxkReNUCVEIBoCEp7RcFQuIiACIiACIiACxUggV6EXtaXRb0Ht7zdus8WIvVjalOvERbFwUDtEgAQkPHUfiIAIiECuBDSjnStBXS8Cc08gzO92ctIG4zl2DGhstNFoW1vmvq6+EiO3oPry1ttZIJDrxMUsVElZisB8EZDwnC/yKlcERKBoCGhGu2i6Ug0pIQJhfrcmze3bVnR2dsLZs2f+g/L4LahhxHMJ9em8NTVVP+ThxMW8MVLBIiCLp+4BERABEciRwPg43JdfAS5egru0GqithTs+HjrSZI6l63IREIFsCYSxRDEN991sbrZ/lZVAiAiy2VYp0+vCiOdM81T6zAmYfjjxibGO4+BBgBMUDz8Ec76vDygvBz44BOy/D05bW+YF6AoRKBICsngWSUeqGSIgAvNDwH3nXeCTk3Bv3AC6b8BZvxZYuNAMOJx16+anUipVBERgZgK1y+EePw7QKkUr4mOPJl4zW9aqVNaxxNLDfeq5AQwOwr150zx3wM967oRjF2UqBngaGgK6uoCKCrh0zW5rtYGfRkbsPbZ+nfnevXIlp6jGUVZbeYnAXBMom+sCVZ4IiIAIFBMB99IlYMN6OCtXAvW1cO+OA7SCHv/YDjaKqbFqiwiUEIFpbrbLlkXiZutZKTE8DPfkKTNJlTVWiprTp4H+fvvKzzrmnkB9HdDRAdC6efGStXByEoDnOzvt54kJ67JNkapDBEqUgIRniXa8mi0CIhABAVpEFi4EPv7Eisw7Q3AWLgC4Jqy3N7cBZQTVUxYiIAJpCPTdgrNpk123uWkT0HcrMXHMzdbZuBHO3r0msFAkbrZ+F9+aGmsVSyw5/KfFi4GtW4GlS+0rP+uYcwIm4FNTE3DtOsAlFxSdIyNmooLrgjkZiVWr7D1EMapDBEqUgFxtS7Tj1WwREIHcCdByAa752rUTuHARzpbNwMYNQFMTnGXLtL9e7oiVgwjMHoGZtiWZ6ftsaxZlvg0rgJ4eOM3NcGn15Gcdc0+Artrr18N1HGBszE5IchKA5x9+aMrttr4uEqv53DdQJYpANAQkPKPhqFxEQARKkQAtF3V1QGsrcO+9dp1VRQWcmhozCHRWK4hEKd4WanOeEqAL/Dvvwr14AVi0GM7q1XBoKVy0CM72bdMEwWxtWxJlvlHmlae9VjjV8iYBYs//+CRAchTiixfhMhARAGfnDrveM48CVhUOcNW0EAlIeBZir6nOIiAC+UEg2XKxc4eNeElB2toybSCbH5VWLURgjghEGUQngiob0fnqq8Ct23A/PQcc2Afs2wenrXV6sJeo6x6UXxRBgPyiJgJGyiJ7AmEmAcz63jfeBLq77X14+Ajw2Z+xVlGJz+zh68qCISDhWTBdpYqKgAjkG4HAgQYHD1EMKPOtsYVWn6CBvgZ2c9qLXhAd4wFw8pQpez4jPZtAYI5jPROql8A9fwHOtm2BLvFR1z3q/HLqSP42ZHXLCWHgxcmTADHOYDAhz8WW7xkbgMfwMOC68Qi48/nbCGyPTorALBCQ8JwFqMpSBESgRAgkDzRKpNmF0My8GugXArDZqKM/iA7z56B7HidlnLVr4R58F6hYAAzehNPcZCOOHtg/vfVR1z3q/KbXOPQZ89ug1Y1bQFGI9/batYjz2DehK19ACROeQXStvXIVLqPekve1DhtwiLEAGhvn/bdRQFhV1QInoKi2Bd6Bqr4IiIAIiEAAAf9AP9fIoQHZ61QIAnRFZ8CboSH7Os/RPJ0HHwCeeAzO8mVw6GZLF9s9e4Jd4qOue9T5hcCfMolndauvB7hGnRY4ntMRLQHfM8j8Bo4dg7N8OUDuTY3AihXA9m2KdBstdeWW5wTCWDwZm/tNAIsAMP33AfzfALgz+n8BwLjQRwH8KoCxPG+vqicCIiACiQSidMmMMq/EWupTpgSS19+2tmSag9LnSCDQFT3HPHO6vKICZV/6ItzPHIhvd4HFi822R6auPlfsqOsedX45ceAEANsas3gaITTPkwI5tSdfL/Y9g8xenrRsVlfD2bIF2LXLRiCm4PfccPO1HaqXCERIIIzwHAXwJLcoxfkAACAASURBVIBBAAsAvA3gvwH4HQB/HBOf/wHAVwD8+wjrpqxEQAREYNYJJLhD5bgOLcq8Zr3hRV5AXg30i5x1yuZl64o+mxM4sTq5ANyTp+DQ1TTod59t3VPBiDo/lpMNJ1o3+VdbC9y9a2vL9955n/hO1RSdD0cg4Rm0Zw9c7u88OAj3/Hlj9XQaVsDZu8dOAoTLUqlEoOAJhHG15fOZopMHhSf/eI5ilNZPHt8B8IXYe72IgAiIQOEQ8LlDMQhKTi5nUeZVOATzs6axgb5z3z5tV5CfPZSyVt4EDoOvUBTyc9YHxRkD6Rw+Yl6NwGJmRfBbzYaTueb0GTh081ywwPzxvXv6TG6cs+6gIr7Q/wx6+CGzdYp765ZpMF1uc763ixidmla8BMIIT7a+HMCHALoBvATgPAD+esZjaK4CSOXH9CyAw/zr6ekpXpJqmQiIQGESiHLtVZR5FSLNVIP8QmyL6hwdgUzviwhFYUpxVgy/1Ww4+a6JWzmrqszewzlNukV3txRnTp4IbWmBs2mTdbnNdaKzOEmpVUVOIIyrLRFMANgNYDmAHwLYFsCFVtCg41sA+IeGhoZUaYKu0zkREAERmHUCCe5QOe69GWVes97wWSjAG+Tny/YZs9BEZZkFgYzvC9/aOAYn4p64WR8+oeUwk1hk3Tn/rWbjFjtTo7Ph5LvGrPNkGbHgTzlxnqmu+t4S8PHP+d4WUxEoQAJhhafXNFo5Xwdwf0yE8npaPVsBdHiJ9CoCIiACBUMgyrVXUeZVMAB9FU0xyPel0NtSI0DBdfxjoKcH7sQEHG4fERN/qVAYUcjruAUFj2zXH/I6iqpjx+yWFbTseSLW+62uWWNcTOmKi5ERMNgQuPZuzZpI195lLL5TwfHOe0zGx82+pM7OHcHReb30sdcEwf3Yo/Zs3y3DJf5d0jX6mAGBdBMMWfZZBqUrqQjkPYEwwrMBAFegU3RWAvgZAH8I4DUAz8SCC30ZwIt531pVUAREQAREYPYIaDZ/9tgWaM4UXGbfwps3gb4+uPX1cJ54PH1rGOCGfxUVxgWU6w/52clwn0kj9hjQhdFEOzsDt04xaShwWc/jJ4BdOwGKTiDj8tI2KuJJGVNvrtWsi21Z4zFLWwlMccyQ5UzZ6ntLwPQLg1bV1EwLWpV1nwmuCBQRgTBrPJtiIvM4gEOxNZ4/BvD1WGTbcwDqAfx5EXFRU0RABEqJAGepgwKQlBKDCNpKi4nDfekqK82rLCgRQC30LCi4NmwAtm61kVQpPGPCLm3T/EIt27VwzGP5cjgbN8LZu9ful5gctZXW16Eh4FI7sKTKvvIzz0d5cFImyj1No+ATZfuUlyWQrl/SfSd+IlAiBMJYPCk49wTwuADgQMB5nRIBERCBgiJgROcbb1qXPg5MH3vUDpYLqhV5UFnPKiVrSh50Rp5UgYKrqwtObS3c8nI4W7fY6KkUdt7+hclikFWPwnoeJg/uX3nwILC0GujoBJqtdRQH9kcKMC62KT5yXEtuKhambZG2QJmFIpCuX9J9FypzJRKBwicQRngWfivVAhEQARFIQ8CsJeNm6vX1ZlN1fjZWmjTX6CsREIGZCSQLLq7XNPtnBrgi+nNLvi7+2Z9ohvfxa9KIPZOGeyweOWJ//3fvwrn3XjMJZdZ9phPHM5Sf8HXEkzJh2pZQvvch3RpEL41eMyPgZ1q73EyuIGDdbNZ9llltlFoE8pqAhGded48qJwIiMGcEHBPzEvBe56zgEizIP1CLamA/mxijqm9U+cxmW6POO0lwUcw51dVw+/qA7m644+PBgXySrsuqWmHyYJqHHwLaWq17LS2g4xNwZ8MDIsr+D9O2AGjp1iAGJNepEAQSmNK6v30bnN33wn3nXUy+9TactWvhPPiAXbNMbxB5hISgqiTFSkDCs1h7Vu0SAREITYARId3eXutqyzVoO3eEvlYJMyeQMFA7ecpkkGngmMxLzf6KqOobVT7Zt2QOr0wlsuhu+OGHwNlzwPAw0NIMlxFZuQ400yNVGZnmQxHH6La8rvemjYLLYEgrVkTqAZEX/e9fZxhrr4RQpjdMUvpkpj03MPn+B8CrrwMtTXDPc2Ua4Dz6SNKF+igCpUcgTHCh0qOiFouACJQUAYoeRtp0du+2r5qRnt3+9w/Usg0cM7s1TMw9qvpGlU9i7fLykyeyKC7pWsvPPIy7oeta0dnM2IWY2jbFfAr/T6oywucwldKfFy5fAV0lzRGlB0Q+9D+Ff5RBjqYQluY7b8ueo0fhnjsH99Ytuy3PocM2WNXgHaDMgXvpUmnyUatFIImAhGcSEH0UAREoQQIxtzXnvn12C4WgYCcliGXWmlxog9+g+tLalmkk5KB8Zg3yPGecSmTxt7VypXVtbWjIfq9M8uf+oNevG7dduu/mFInWq29sH0/3Rq9xBUZdXXQeEHnQ/xT+ijydw28j6XdvngH+LXu4Ty3voXVrgaFhYHwcuHbduNvmUKouFYGiISBX26LpSjVEBERABAqDwLQgG21tRsQZ4ZDNms9kl0vmd+UK0HPDWh9iYsKUm8WkwrT60i2zvT1UkBx/jwTl4/++qN5TZHE/Q1o0+/tNJFevfVG4thsLKt3jU+0PmnxPcAuXdH0fqy86Okyezu5dwMQEnE2bItvPMy/6P8u1oV7flfpr/HfPdcrHj5u9adHaCmf9eqC52WwlxYjMaGuzqC5eAp583K7xLHV4ar8IAJDw1G0gAiIgAiIwtwSSBr/GapBi0/UZK0aB8fZBG5W0vBzo7oFbVQmsWmX3bfz0U7uHZE+PySqrtaRJ9TUZ+S1kHR1w33rb5p8scIIEUCG6co+Pm2ApdBn0B0tJ1T/pRJbpA4pA/5YqqTJKdZ78N2yAy3WY3d0mIm28TIrdDCcGzLXsq795EVi0yAgH7gGKqqr0gjVV/YLOB91HQel0Ln8J8L6j6Dx7FqDFnfdK/4BdH1xdnbhVTksL8HP/IDh4Vv62UDUTgVklIOE5q3iVuQiIgAiUCIEggZXOwuTHQsvk4KAN8MSBHD+HFGdGYBw7BvT3wz19BhgeMX/O2jXAivqYq9s1OPv2WaETMl9/9QLf+y1kp08bcWu25Wm/DJeud1zPGAtSxXo5M2wfElhGHp1khE73tddMwB3zOlOwlHQiK913YdtM/v79QbdvSxSI3sRAVZWxuhqRm67vea/yr7bWBBTCp5/CZaCxJx4PWyOlKwUCvO9o6fzouH220LW2YQUwOgrnwP64yDSTK+nut1JgpTaKQAABCc8AKDolAiIgAjkT8Aux2uU2OwYsycaVNOfKzH4GmVqYEmo0MgJQvNF6dfEi0GSDziSkSfWBIrW8HC6jpHbfAO7eBZYvg/vRxwC5N66C4zhwz59PLyL8/eXvoxTnEyxkCxfadV0UzwwqUr0EcF24J08aawjXebkLFpjz7thYfHCaqkn5eN4ER2H/cKBNiyItn/4onSk4zdiWLK+LWzd7bsBZutRMVjAqrRNzs3avXQN6e61VdHAQTnPTzO7cM1hRZ2yLEhQ9Ad53nJAAA2Nxkouiky7ZjzwcmUt20UNUA0uagIRnSXe/Gi8CJU4gk0Fv2LSxdCbwCQe+69bBfeUVgNENaQG7ft1Az8rlMx+7i+1lkB26KI6Nwd2+HcZFkW6U6Wb8PZ49N+CeOgVQmI2OAlu2WBEXtq0UrTduwFmxAu7pc8DihUb0YfEiY8FyNm80bpig9YpusCkOI5zpOse1oRcuwl2zGly7ZdYQ0kK3caOxsPFyM/ikKyfTd3XbCK23+20b6OZLcXb0Q7PGkaLYiOGYOHWud1pLGveODGsRTlHnuTxN91rP0ml433NPQvHZTjyYe8fbM5P3BEXk3bszu/OyTygCrly1btax+8cl57Ex2xd0u6YoePIJU1ez5jSd5XkmK2pCi/WhJAnwvtt1D1z+doeGgM5OOHv2pH22lCQnNVoEUhCQ8EwBRqdFQASKn0Amg+WwaU26E58AtHZ1XId76rQZHNPV0hxVlXAnJ+1ApYCEx7S7wS+waa3k/oc3euHc7oe7eVN666JvDR5dbHHqtBWbFA8czNGKEPZg4KBt20wQIePO+tFxOI2NwKKFNtAHI6c2NdlInul4UyhTdH78CVxaTY+8CLS1WGvGsmVmLaFTV2fbyfpz/9HTp+HS4sb6st6Nq2zE1g8Owz1+wrZgdAyoXEy5ClSUm3a6dA1uay0oC4nz4ANmn1v36FEbRIVWaW4l4THN1LWVdHgPvfqaXStXWWn3O6To/OxTcZGbYFVNuifMby3mZo2OTsPZvXwFzuo2a41qbrT9wjqGqF98YoJpW1uKX0x4kz/+tbZefyax1scpAv77BD732qkUeicCIpCKgIRnKjI6LwIiUPwEQgxG4xDCpuUgjiJkeAQuI26evQBQbJp1jANAxQI4HdcxyZlyWvcYvKRhRWEIUQ5Uad2kpY9WyjtD1srZftlYOR0KrIEBs04uPjiLA0x6Q54M0nHmDEArFS2DtBTOYJlMysWK1J4eOM3NcLn1Bdf6dXVZAch+WLECztYtVuQcPpLa1ZmRKBmBkm1gIKKRUeDyVaC1Ge6pM3BcwN262a4V5cQBLWeLFsGhZZX153pSRkClNeTjE8C61cCka9eVDgxa99tlNVYMUxjPZBGe1tB5PlFRAdAKfOeObTuDNlWUT4nn2JrXoCi2gTXnvcSgUO8fsmsqJyaAji5rse7uAVY2THfnTc6IDMnyylXQwu2ePG2EsPvJKaClGTh3Hs5nDtg1ww0rUkbZjWcbxdrTeGb5/ybUZJrE6fSOLLH7ZDoAnRGB7AmUZ39p5lc2Nzf//rPPPpv5hbpCBERABGaDwOgo3KvX4JSV2S0f1qyGw+AiQUfYtHQXPXLUCo32K9btj3sM8uAaRB4T48CZs3CPfQj03QS4ufj4uFmfhig3rLelRfYv1/W5r79hAmu4H34EtF+2f/0DMBanJVVGTDKQD10z0x7Dw3B/+hLcEyetqyxFGVUL1xEuXGC3Kbhw0QbtoCBNwcXhd0x/dxwMKFT2wP1AUyOcyio4O7aj7MABG3jo1Gk4XAt69ZpJn9zPzIebv7vsO1o8e/tMnsaqR9HVdwvOygbresu9+WjtYyCkzi67lnTlSiNyGGnVTCZwCxGK0eEhODu2wdm+1bDBju3AkiWmrsl1SMsrH748f8EwZPv4mzHMuYUE7bn+fuDviG7NKfqM6c299MYbcGjl5n1E91hyp5DnJA0nE3Zsh7N9e+p8+FvjumkHcDmpwMkC3gt0e6b1nW7WE5PGAsoJgfh9EqJ++YB71uuQpj+9sk0/MeJ0mt+Ol3bGV4pYPuvOX5jxdz1jXvORILn+S5ea6MkF2575YKgyS4bAN77xDa4r+lZyg2XxTCaizyIgAiVDIG6VC+FaFzatSbdnD+hO6bS1wl282AgWl4MtrkdcusQKUL6vqIB76zac+/eb9HnvfkkLEwUXLXxLq4FrnUDtMmv5XNUAh26tB/aHX6M5eAfOqpWWCy1e/ExL4vd/EH5NbID1ISGiJAeLtND2xNb7USAFWRvLylD2zNOYpMWWls8N6+Ge/tQKodpaOEuqbJ3YxuvXreWTW7ZwywT/PqEUYXRLjYmrsieftJ85udHebsv2ghcV2i8tnVUzoB/izQuymnnWSteFs3Uz3I9O2N8BRSN/j9wHke637e1TVtV4hvZN/DfJfrjeadcIL15sOVP07tlt+4b9k65+SfmWzMd0/elBCOvp4aVP8xrKwprm+vn+alr9ub54YKDgI1bPN1eVX1oEZPEsrf5Wa0VABPwEHMdYOOmmaaxPaSw0tN4wzYxpmY6DZq73W7AATplj3P6chQvhrFsLjN011hgMjdggOBRxThmce3fBWVxp8vdXMa/eDw8DR47APXfBWjgZSIcusmxXTY2Ncrp0aThrHq2ZCyqA8jKAYq6vD7h3lxF17kuvms80gDJCrVOZPRdjYeF6zN5e+zcyYtxxA62NtOLV1RrB4zB4TnmZseQ5G9dby3hDvW1vfz9AQcSN4x98AA7FZ1+fdR2l6/CihbY/d++ecqEOe//kVYcnVibBqtnWau/fEFbpIKsZ3c+NtZKikJx3bAOW1cBhf69dDezbC7OmlpbsmFU1sTZU+LHfJPnTctrRAaxaCWd4BM76dXB27TJ1pCU8sL+nZVhaJxL6M5UVOKynRxh0noWVfU53eN47fG5wMohRoOfCGppstWTZ6Z77Xrt4HffqZb2Z/s4dgOudWf+GBusJkO5e9fLRqwiUCAFZPEuko9VMERCBPCAQs67QIhO3cm3dCrevD87QENzBAaD+Lpz6erhc60mxw7WeXGeY7wfXYG7eaDZMd7htCNdP0k2ypcVaprz1qjO1g21lhF9aqzo7gf33mcGce/Bda0HlgPfCBTiuC3CdXrYHLTYbNpjgQOjunnENadyKxuueedqWSuscXYBZ39u34X56Fs6WzfEAOLRUu6+9DnBLF7oQM9jSM0+bcrOtdl5e51kNeV9zfeaRI1aId3XDpUV42zazxtVYnP1BagKsZs7ePVNsGaDF2waF1mlGg6brM92VW1tCofBbmZ0vfN5aom/fLo0gQaEIBSTy+jNN9OmE30NywKUgS7a/35OLrF1u98CkeKOL+gP32yBdsXQzRh1Ozi+Tz7G6xqON85nAenD+Ik37vSLsc7zXutqfO28jWW/aaAOMMVF1deh71ctTryJQigTkaluKva42i4AITCeQ6SBqeg7Tz/gHdnRRjblaOv/bs2ZfSbP/Gy19mzfD2bkj/6Nocp0jAyJx+4CHB82aSGPt81xH0w06k+j4B7QmMmRbGyZ/8l9BMeuuboVDl8n+28Dqtty4ZLpFhr/PvDpv2ADnvn02sNKf/hmcpTUAt/1YsMAGwOGkgQlINGInEK51wD3xSbDwnI37zKvnXLyy/m++hcn//Fdm/SoYOIn7GXKN8uUrNqCWx9CrT5BLp5fGN+inAPBP1jA6bfw+8fJK9VpRYSzu6aLgprpU59MQ8PqJkw0XL2Lyb39sEvN5xcM9fSYzV1M+B69eg0u3ek4sMDqy58LPfXAZkI1eIFzn67s30tQw1Feem6z5ndI7gUHHuJ4/yO0+KEf/BNYHh+z+wPv3Wyvt6CgcL7pt0LU6JwIiECcg4RlHoTciIAKlTMAbmJg1hnTNDDkTHpqZN4DjYIqDL0YI5aAnC9EWusyoE/oFxODglHUrm3L8PGLXm4iw3JOTlsOhITi777V7MGYgaJOrEhcuHDgmW2ySE6f7zDrwr6kR7pmzto50n376l6ylemjYrjFkHhSiKY5Zv89SlBvVaVP/V14FRoaBa9eBgTs2KND6NXBpYaRVOWkwn1EfBNwXUdVd+WRJgJMNtHC//Ir1bqDlkq7r9H5Yvtx4ctCbwOXkA4NKpfq9MhAUJ2x4HZ+BJz6BywmDJx63kYnpKUDPghs3zBZIWdY2+LKY1Z37upq9eVnf8vLwVkr/BNb6dbYMenrQ0knRGaFIDm6AzopAcRCQ8CyOflQrREAEciUQ4A4Y5Yx7QvUKdHCdkYBIaHC4Dyb/xx+zbrEU/rQC5zqgi5I17xFGya2vBy61m8kD4+LJgfZTT8KlIOPaRUa4jVmEprV8Lu+zaYVHcIKisqrSuokzu49PAXSzpUs01/tSgCe7jEfZBxE0QVlkRsBsofTSy3AZfZh9yQjUFI60eJ4/b4Ui1z3GAmil/M3yvjh4cGqfXm5dFNs+yWw3tXWrnbypq7PClpGKa5fbylK05jJJ502a0YWb+/HyfvWiU7MtqcRyDFXCs++xR+N1ymkyK5a3XkSglAhIeJZSb6utIiACqQlwYHLiE+DqVYAWz0UL4R46NJWeAxMKil33WDE0w0Bl6sIiejfbAoL5062VW5Lk4+FZPbZsgdvUZPcM5VYrFMmPPjLdih3UBm8AzEF7BmsYg7Ka9XNBbsEUDwwGxWjMHMDfv98GiaKVlxGcn3oyvHtstg0Iqlcp/h6z5Zfhdea5yK1uyLjnBlxGIl61ykyumO8o4Pjd+LiJIJ3K6mnOxyJ+m22FuC0PJ2h4bcMKu4UO9+NlgC5u3UQLaMd1G5ztgQcyWpMZbyLvFe49zO2fzpyBSzdeBqNi2RS4p8+Y8lOKZS+j2X72eeXoVQSKnEAY4dkG4C8BNALgFBf3ZPkTAIyC8V0A3KztEoAvAegrcl5qngiIQJESMIMihsf/4JAJaoPO7tisvgOMjQLLlsFZ3Qr36DHgsz8D5+GH7IBpvnloED5nPZBg9Uh22w05ME2bx5y1JERBvK/oXnnsGNDYaAMrUWDTlfKJx+FSfPIz99lk5OZcLVIhquQlKXR3Za8dBfXKaMM1S63o5L3/2KP2XuB6Zu59S7dTWie5RdTFi/bZmLyUgL8RPjcZEdn/nXdf8Z7j5B+j3fJ7Tv5ReHKf4O3bE9dkUuS+865dY712rd2yKDYJ5Odq7pU33gTOfApaVc1evNyyh4KX4pOJWVaunhX+QvVeBEQgJYEwwnMcwP8O4CiApQCOAHgJwK8BeAXAHwD43djf11OWpC9EQAREYD4IhBVmnHWn1YauknS94sz4dRv1EEsqjYXHjW2vYQbjLc3Bg6s5bqMG4XMIPKS4TFujKPJIW0A0X5r7iqKTg3lG/2T04dgAfd6t0oXurhxNF0Wfi/9Z6XNxNRGGOdFAS2djoxWdjApLS+Lt2wBdZhkkjS64Y2Nwv/s9syURuA3OtWtA+2W7DpRrNxnUh14jjGrMZy6PWLlGdDJIF/OgJbK50brEMmgV9/Zds8ZY1GlZnfz+D+C+8prZX3fyrYNwOjpQ9qUvmux473rC1gQpij3Pjejktjt8xnd0mMjcjKjt7Nljn/lefWyt9K8IiMAsEAgjPK8D4B+PAQCMusH45p8H8Hjs/HcAvA5AwjMGRC8iIAL5QSAjYUY3Qq5R4wCErmWxdUxm37bRMWB8Ali0yFiAzCCJgTFqauJbAszorjUbSDQInw2qypMik5ZOik6u3eSWNwf25weXTN2V/YIql3WC+dH6WatFwrPy+HFTjrNpE9yBAfDVTMx5/Pgtnz0MLrRjh5mYM26xFJkXLln32MtXwMjdk9w6hUF9GMxn/TorQr0JGLqct7fbZyhFJ7cqGR211tOLl62opV2yo9OUb9aU8vl86LDJ071y1Xij8LMb23bJvy2Lw2BGTE8x290DZ9FCG8Coutru+0rXXopn7ssrq+es3VvKWAQ8AmGEp5eWr3Sr5eZb7wNY5ROkFKYr/Ql9758FwD/08KGiQwREQATmkkAGwsy4ET71pJ1lb2oyAxXANRuEuzd64SytBrZvM2vZzJ6QjgNGSTQboM+Xu1amg/C5ZK+yCpcAJ2G8fVY7OmyUUa7v89wi58s6xMkg/tHV8uZNG4CK7r9pDk/YzPskUZo6zutXnsXxzTfB7XFcijX2PZ9vtHhzso1rOu/bl1hN79nDgD20JI7dteswOVFx5izc5cuAS1eAmmobgIr5DA7a/vM/L2PPaBNx9qPjxlrq3LMTbk07MDIKh+s/lyyx9XBdu4XJurXApTfspAiF497d1srJ+7OmZsqNdtEiY6F1a2vhnDplJxbpZsv2NTfDWb8e7rlzcH/4N3C5vtwLaDZf93ciYX0SgaIjkInwrAbwAwD/HEB/BiS4JpR/aGho4P9ZOkRABERg7gh4g6MwwVw4C+8LEuP43c1877nVh0u3MW4pwD3hGJmRWwLMw1EwawbngU3oImURm0JFFnShPP4xzOQKv6EL+p07wPDw/Fr3PesY945kYJj+fmvNmkkkZDD5NAWidN4ZYc61lRcvwX3/MJwtm+Deum2Ep5lU45pN7reZdPifPVwzSddXMODUmU/N+kzj5lq3zHqKcLKgvx/uwCAc9hcnNrzDe0ZTwLa2AIN3zBpMZ+0aGzH3Zt/U+kw+o9euNVZT554dcI+fgLNrJ9DWZvPk/UtrLctjWq5FjQUsc5lmeNhMHJrnN9fz0yDC7ZsqK60Flc90Xhfbt9R4tqRby6xnh9eLehWBUATCCs8FMdH5VwBeiOXMxU98EtHaydfuUCUqkQiIgAjMIQH/4ChU6HsOOuhylcbtyj18BM7GjTbACvctjG0JMIfNmioqRH2nEutdEAFZxKaoGBYMxsKJlZt9dqDeuMoGjeE+nbW18bWeU1eFfJfrIN2Iio9N9NOMPA08YRNm8ilkU4ommcf05Em4V64Bq1bCvXjJCvrqJcDICLBli/XySG60/9lDocd75sJFuJs3AZcuAzu32/uHYo/LFFbUw3nwfisGfVbqhGf0M0/bUhisipN9ky7cEyfs/rD8hvcfI0pzjT1fn3zS1q1hRVwsJlcz/jl2H3B9p6nrmtVwj31kJg+dpka7RpURentvGsu+6/0OaPllhN2GBuNyTFdeHvx/Qs+OOF29EYFQBMIITwb9+vPY2s4/8uX6IwBfjgUX4uuLvu/0VgREQATmjoB/QOsbrMQtNt42KOk2N8+kttxA3T+r7m0JkEkeXlp/3b31UzNZcLxr9RoNAVnEpjjSBZIignuVDgxat3O6SHJA3t0Nl+v0aJXK4sh6kB77jRgr7MmTNoLu8Y/htjTD8YRKqvqwLfzLwDU3VVbFeJ59Yjw3GD12dMRGkOXzh5Fk6T7bc8OKPbq7pjv4PZ+vFGcUdnR9ZaRb5tHdY/e8ffKJ4K2o/AI2oAxn08YpK3x7OyZ/8t/grF8L54tfhLNhvRXJ3nV9t4yF06zb5P164pN4mZ7Add96G9i2zbjuOp+cgnu7Hy7X8HMN6H17reXU/ztg3hTj/E0kR8LVs8Mjr1cRCEUgjPB8CMCvcptoAB/Gcn0uJji/B+ArAC4DsOHEQhWrRCIgAiKQIwG/YBschHv2rJ3F5gCKblO0blxqN69BAS1yLD2yy7MejEdWA19GfqbZiuAo8vBVaU7eyiI2hZkuIJQRvAAAIABJREFUkBQetF4xwBYtXnSzpNWKlv3t2+w2GlNXhHvH+4Luuz094dZF++8jurZzHR/dImkJY534G6dL5gyH+X1l6po7Q55F9TWFEyPUTk7C+eg4XPb96BgcWi25RpPB1kJ4dHiizkSTPbAfTlsb3CtXrHU822eJHzSF4JUrcN97HxgesS6yXEv6pS9aYeml5W+Zk4K8f13XiGruA2gi6TIqbsyTxVgtuc5z4QI4FMm8pzhBsXmztZx6+X16Nu6ea4LM8V7077+rZ4dHSq8iEIpAGOH5Nn+zKXJ7KsV5nRYBERCBWSUQF2zV1XB/8ILd742RCru67Xo0Bp9YvMg+vrg+jS5g3gAqV4siZ9W9KI9DQ3YwnG1r82jGPM40h0i9UeSRLcpsr/MPmkO5Y2dbUAFcR8EABly5dQtYt9be5/xdxVwZjSjNoh28L4xl7ebNUOuiE+4jby/RlSsBBp/hmlO6WnKLFwrRdEce/b7SVXPevqNw6uqyLqS0PDL6LPuIB5+T6xuNaJux3wOsljMtWciozZwQodWRR+ViOEtrrFXWH6SIT3uuzaRlkiKSB9dscrLD5x7r/d5dWvE96z3byv8fdt9r2m3ScDlF+2UrwLkNzIYNNvCRbw9fLy8T4dd33hauf0VABJIJhBGeydfoswiIgAjMLwG/9cSs2xqw4fw5O8+oiYzEeO6CnTJbuAjuyAgcRkXs7TVrcrxZ76wbEeUsd5R5Zd2g2IVRDNLpmkcLNAev3AuVn9Osl821ypFcHzBojiTfAsyEVir3zh04+/fb4D1rVlsBElv3Zgba2UzceJa1FSts0CJvEigVI9+9aLZ14X6Lu3dPBZ+h6KQA9QepCcorn35fQfWb53MJwumJx62lktubMNgQraBcU5kH0YxZT3f/fcCLPzKRbN2xMTg1S6f3P3/Lu+6xQpMWTd6rXGrBSLeeSI393k2eDIrEtlK0MqKtt/aU11VXw/ncZ+19xgnGysrpkX317JjnO1jFFxoBCc9C6zHVVwREwIjHuPXk6jU4ixfbAenVawC3AljZAAwM2BD+d8ftmrQH7jd7zsUHHzlwjA9OODjOcZY7yrxyaJK1ENCNzLMucS2TZw3IJGO6rDEQBwUG3d0ComFmkp3SzjEBn+Cjq5MZlEexX61nWauttetEuS1ROgHrCUZaruhmOzAA9+9/CrS1GlFMURC3wqZBlDe/rzR1nNevAoRTPAosoxufPAWnvDzBYjgv9S0rQ9kzT8NtbIR79Kh5zjuPPz4lFH2V8vqc4tT8P9HZaaKQM8JtwsG2xyLeJpz3Pnj3oIJSeUT0KgI5E5DwzBmhMhABEZhzAhwcM6IgB6XcpJzrdB55CPjgkK0Ko3By/Q8DSHBtGq0lse0XnNVtuVc3YLCWdaZR5pV1JWKbuHOA39hoee3ZYwd1/rV2YdZqLV4MbN1qN4Gn+ORnHYVDIGmwzYon7IvoWY0ybJEnBsK6JHrpzbrQvj7jVmn20uX9yL0ZaZ0LY0nPk99XhrjyI3nSJISZtKPl0bhN3zTWRtNP6SYQ0rUk02cLJ0Aef8z8pcuWExqmXu2X4d4ZspNqdNMOe3j16rkBhxOZvN+yXdsctkylE4ESISDhWSIdrWaKQFER4ODYCyBBF1q6QQ0Pw/naV4Eyx1ppenvBPehcx4GzZ8+0tTlFxSOKxtAllhxpJaD4jAlG9+2DU1ZQuq5RiKQb8DO6ZU8PnOZm66o5UzTMXOruDRAphsKI4lzKKpFrPcHnCUS6W7oMzpOr1SdTARgTD0Z4Mrru0BCcFfU2SionnLIUwCXSjdE0M2kSgh4QCWtvfesmMy6Q91Wmz5YMCjH1/Ogjsz0MuG6VFvKZ1gPH8k9o48AAHFrZ0z3zMqiXkopAqROQ8Cz1O0DtLz0CRTBY5+DYBJDo6jKz0SZABEVTRbkZIBhraGxWnhbOnGblk++QIuCX3CTz2XOR5Qbw5y9Y0TgyAvfQIbtulpblpdVwb/aa5KmYmvNkFFs3ZYJ8UChkaxUJrKw9mTBAzGUQnKaMkvoq6N4mAPZdBG7lmbJk/xpXybt34XJLDk56rF9n6zPT2s5MC1P6aQTMb5lnfX3vHj0WiQXcpRvvSy/biS5OItD1OtfJBN/96167ZtZ2mkjI9H7p7AQO7J/WxsATQZZeCc9AVDopApkSkPDMlJjSi0CBE0gYrFMcXLlqgydw/0senBXOd+sRrSEMIHHpEsCgEZzRppXOG7hkal3JoE8T+BWT2PFcZDlg476NXV2YfP8QMHTHbOJuGHdch/PZJ+HW1RtigVYAihTvj2tGf/wTgG67Dz9kz/sGhznfZxogZnDnzpw01b1t+nk+Bt7sX271UVdnA8nQbXL/ffGtMWZukVLkRCDoOUorKP/fuHoVOHXarLlF7XJrEcxgcsnkQe+K4WG4zOtah/GSMBNVGeTjb5///jUTFpzwYhAqLrXwlg74L0j1PsDSmyqpzouACGRGQMIzM15KLQKFT8AbrFNoMHohQ9Tft8+8Bwd2O3cAYVwqk0lEKSiS8w74bGbj9+yZcgMNE+EyIJ+MT3n8kjcSzzijPLsg5iJrhDytS/wbGQaudlghyj0T21rsFga0LnsiP6gZ/I5paJGuqLB9FHNX8w8O/VscBGUz4zkNEGdElFGCfLu32b/c6qO+Hu7u3Xad3XwI4IwgFndi89y9chXuocPWWnnzJtw33jSTSoETUelw1NUCvS4wMAinpcXu1drenr1bq//+5YTFrVsmX1o6Tb1DCtogS2+6Zug7ERCB8AQkPMOzUkoRKA4CscE6OjqAjz8BmhvNzDX3K3MYlIeik2KBAT3o2sbw8qn+w/bEJsPtX7hgr21uzk64ZkqXs/G0orW1WhHkWWkzzSfT9EUqdrzBltnb7lI73OERYHwCaG0GenqBdauBJdXAspqZ3dboBnnwoL1vuK9q5WJwrZ4pwz84JPt0AnaGvvHqzDxyjS48Q1Gl8XWe3dvq3zy87fh/ASf5GKTNiW3xnsWaW25d4nKPTYpOutlGEXXcf/8ODlrLeDYTFUGW3jzsClVJBAqRgIRnIfaa6iwC2RLgAIF/XM/3xptwh+7A6e6xey5e7YDrTgKdXcDlq3AefQju6Chc7oG2655AAWqsV3S7ouX01ddhIsZyMMItNHIQFKGbN9cDBI/f+LhhlrDvW+hK52nCGEsO9icZCfj8ebNmFhUL7Lo6WkQ5MXH37oxua0Yw0Br98ivWKrKkKr6HKt1rzRYNuQarIca57v887bqoqpV3Qk/9G1XXRpcPn4H0ZuCab7rKcolGQ8P0/TRnKNFYR8vK7Fp9BoJbvtysK89qC6dYWXl3/87AQF+LQCkSkPAsxV5Xm0uWgBGKp88Yi6Tbcd3sc+mePA3c6gdWtxjBiZu3gC0bwSASOPEJnPv2wo1ZPKe5UnkulZfaAQ4caDUlXa4bpfjkICWVtXQue8GzzLK+nmU0i3p5/LytWeJrGeeyLbNdFgf769cDv/APreV6dBTYvBnO7nvDr/9lHg8/ZK3mPT12A/dly8xkhLN3j22BrJSz3ZOZ5y+hlzmzErvCPAO57dK2rXaNJ9ffPvaonZjMhEXsXqNYZJ7Ga8ELBJdJPv60un/9NPReBPKSgIRnXnaLKiUCs0Qg5ubo3rwJZ+0a4yrlUnQyOMTDD8J95XW7jQZFwo1eoGwYoMtlqjV9nkvl0mo4/QNwKT4vX4Hz5OO5r9eJEIEZLHEj9Jqa3DZCj9BNNMLmRZ+VtyVKa6u1QnAPu0xd1jgIZAAoj3t/v7WIa3AYfX8pRxGYKwJ8Bi5fDnBJRWur3aZqw4bsS8/1ecBJRUbIPf4xcOMGsGLFlIttJpOLEU1OZg9CV4pAaRCQ8CyNflYrRcASiLk5oq4O7qdn4WzaCKxssN8xkinfuy6c8nK4DEHf0gxQhKQIRR93qTxyBKivh3PhIrBvD5zPfMa6886Fu22Yvg0SjL6Z9tBW0CjdRMPU259mDgdGUbmsRZWPH4Pei4AIzCOB+XwGBjTbTCoyuNGZT+H23ICzssF6WniCNuCaoFORTU4GZa5zIiACcQISnnEUeiMCxU8gLgTq683MMEZHUPbII6bhDGlf9qv/yL7/8EM4a1cDDCzU05N6TR//c/cH+Nm1y1o6uf8jLVytLfkBNWCwlM1AI86PQra1JXP3shxoZFPfrIvzBm2ZWjmTC4wqn+R8ucb2nXfNdjrO2rVwHnzABMRKThb4eQ4FfGD5s3mymNs2m9yUd2gC8/kMDKwkJze5pGPhQiM6sWCB/ZzppGfQ5GSuz7/ACuukCJQ2AQnP0u5/tb7UCPiEgHP/ZxJaH4tPaM45jz8WX3djog2mi2zry9MMALz1OrMlzLIYXAcNlrLaCN3f1gR6c/CBA6PqahsJ8tQpuKdPA1/4fMb75yXU1GPZc8NaqLnFTsOKwEBSCdfNxQfW7fwFuK+/Dty6Zdz6TD9SdJ46BXAP1Y0b4L72mqmN86idQElZtVhbjUset3lhgC1G7123Fs7+/cCqlfnR7pQNSPoiQHy7V65MuTYX0x6zSU3Xx3kkMJ/PwKBmc7kHA9r19IBxC5zmJoDLSHg+k8ObnDTPnfPGgye0J0wm5SitCJQ4AQnPEr8B1HwRCCSQ7eAi2+sCKxF8MivLX1C9vIFGFNFVg6ua2VlPBKYKgMT6Hj+e6FKW7f55sZp5LDE4CFDIbt1qBnD8OuM1nalaO1O7Ulxn6vbCC3DPnAXGRoG33wH23GsHmdxjlFaO8XFjuXcvXcJMwjPeVgY7OnrMWOSxuBL4r39v84xZNyJrd4p2RXXaWHwpulesiItvbnPBdczmlQVlavWJqnLKRwTmiICZjOKenZfa4SxdamIUOBs2ZOyN4k1OmokpPv8Yr0CTN3PUiyqmlAhIeJZSb6utIjDbBLIUGRlVKyKXKG+gkS97QHrCKFUAJNbXpYv0ggVwNm+0M/IUX+nExUz94VlRz5wBhoaBa9fg7NuXPs+MOgvGcm4CDNFae/y4aUOq7XkSsma7uMcf12wx0NXEBNyjHwLLlwGDd4Cxu3AmXRxZuQ5/NrIWv9V+E3vXpLFyeG3lRMPFdmDRQtvmhQuACxfh3HNPpO1OaEsuH/yWTQZzYf2vXAbeP2T3U6TQ5Jptiu9HHobL7Y24R29np3GRj0eW9u4FWreHhqbWwe3cYQbptJaaeymHqM+5NFPXikBWBBhAqLoazuc+a38PDIRXWZl5NHVvcpJ7UnOpCD13GPeAvxe53GbVNbpIBIIISHgGUdE5ERCBrAjMJJ5Mpt4AOJVlb6aSo7JUegONfBlUzCSoWV9GiaXF7uw5gNvhMPgT99FLcczYH54VtavbBuZwHLjnz8N54vEUOWZxOtYut6/PRp1k/4exJNBVbmn1lMXz8lVg4SJggKJzFFhRD3d8HH9WuRHv3piA88oZfPsfP5C6gl5bTeTLOuD8JZM/g2m5XV048tK7+LPae/BbK25g79oVqfOZi298vxH33Dm7bcXKBkz+578ypXP9tXv2vF3XdueOfX36l6yV58pVuBcuAI2NCZGlTeRPWsi7ugxTCnpuB+QePgJ3x3ZgyRJZeeaib1VG9ASi+j+Bvzv+dl59HWjhdmCu3RYs+horRxEoWQISniXb9Wq4CMwCgZnEE6017e05rUPL2VLJwQXD79MyRJcqWnwoPjMJvT8L6LieyFgGg1x/PSHC2fclS4zV01j+VswgkPz9wTy45YB/PSfFXW0tsGG9jXBMCyqjE3NNb1RHrF3o7jZWS67FMmsrOzrg/PqvpQwKZOrwS78EvPqqTT8yavaKNRa8kWE4q1YBB/bjq/3DwLJGfO3JTWlrHLcYT07CefqXAAYnutkHhwyGhvBNtOK9GxPAT47j//3NJ9Pmle7Lo+038aevnMHXntqS3gKbJhP+Ro68cwLfbL+L3zz+KvY6g3DoVkwX48WLjGuws30r3P4Ba/les9oGWOI9THfbvXut9WdwEO6HH5k/vPwK3IEBW+q16zb4Fycuxu7CvfgTmDXfjY12H950VvQ09dZXIjAfBOLPKz7vcogtwN8drnNCr8l4W5i1olz3rkMERCAyAhKekaFURiJQZAQ8sZOJZTKdePLwUPhwQNzbawQUg/wYQcSB9diYDfpCFyf+VVRYYUg302RXwCwtlUb40vJDyxctfKyHZ/306jgPr9MGTy0tcN98y7hQkgNo2WIQjfbLwPZtxr2MLpXud78Ht6nJ7qe3dm1ikBz2BwX21avAu++ZVrncQoeuq8yDQXZGR4HhYWMhM6Jl545IRbhpF+8l1vX0p0bkUewduQP82Z/8FL/1zP3BAq2sDMcW1uHfjm/EV3e1YG9LM9z33ofDNY20eC6rAT79FHu3bsVfVI/AcSmq0gtxp7bW8uRasG1b4ZAn77sLF/G1pbfgTFTha+sX5dT7FJ3vnL1h8khrgU1XSu9NfLN9HO8OLIC7Yhf+4uBfwu3stnvq0kJLgUk35Pv3A/v2md+IuUeYp/83eP68XbN7s89ajulOXeZYS87AINyBQXtPLauBSysPjzVr8icata2R/hWB9AS853eW/yfEM+f/ddyflFuJcVKPzwZuJ6ZDBEQgMgLlIXL6CwD/D4AvA/h3sfRcSPMjAP8XgF8A8GMAIzPl1dzc/PvPPvvsTMn0vQiIwGwQ4OD/0iXg/AUjNpxly+ygM0VZTGsscNzT8+o1YOECcOCe7jB5cs3c3XE4tMLQcsbBvf9g+YcPW0vXRwyWcxa4fBnu0aPA0Q/hjI7CPfguHFrGaIGjlefOEFyuP8ygLv4iE96zfM5qcyabYq6r2wrczZum1zXhwln64PUL90CtqoRDQcQ1ewffgUtr361bcH/yd0B3FxxGa6Rwv3gR3ArHPXQY+PA40N8PnDxpB0wcLMX6yvQHLY3vvY8jHQN4vnI72i6cQjPGzHpOfHIS7tlzNhLk7dvG8kt33ml9FqLptPQ998KHWF23BE3LK6eucF2wHHjWhLEx0w7W5b2BCly/PYwv7GmLp/fnQxH37qXbuD5Rhi88sCkmmibhPPigFd5NTXDWrzf3hbnnOGhMcZj7mZMXnNDg+sdt24zrMvib6OhA06pafP5OO5o3r83J4sv2s020eCZwCKqX1/d0neZ9yXuRky+LF6Ht0+PoGBrHVycuo6l8HBgeBZobgaZG4HonnB3bzL67pv2+PvP/Bs3AeWLC3OcO201356oqYFUDUFdr76WVK6x1Z8UK89tz7r3Xckn+3QbVvwDO+e+nGfsjTXuiyidNEfGvoiorqnziFZvFN3lRV07C9d2yzwhuB7ZnT1H9Fmax+5S1CEwj8I1vfOM6gG8lfxFGePYBoPj8RZ/w/AaAkwB+GQA36vssgJeTM0/+LOGZTESfRSBCAikGsZ7AzFhInr9gB/R03aOFhWKSA3uvHA6UaSnjYJaiaXTUrBGjaGI6I1KDBq8xoWdcWy9cgsvALrRAMb/RmMWTbpX9A1b/UCQO3TFiwFgn3z5o3CQpHB0GW2HdMjk4uKAIOnsO7pWrcBiIoqrSuJwe63eDxVMm+WeYNlW/uLTKchsRWqW4nvNKB5zlNXZNp1NmxCUYJIfBZW7dNm6ozqKFcBiZ1usr8qdQvXIFz2Md3qtqwbW7Dj7/1t/avrtyFSgvs/zpbrtqFZyW7PZepeikpY+ii+LLE6GNt7rgvvGGDYpEoT80BKdmKdoqxnG9dhV+6+d3Jwg0fz4Ubx2dN/HVlXfRvGSBdSP97M+g7JGHgWXLcPTKbTx/YgjDd4bxR+2TWL1qWTyvaQNZ3s+0oLONtGZwTSOtuxRmS6tN/Y5WN+L5iw5WN9elzmeG/qW4oZBOFjnT6sPfES3aP3gBeOsg3IMHAXfSBgbqH0DTkgX4wo0zaC6fgMN1vJPjoHutacOWTcCOHXB27LBM/H1GoX/7tulfpjW/z+4e65a7pMqIWu6963D7mLYWgO63y5fDYdCm9evsfbFsGbznxgzNNV9Pa1uYiwLS+PPhfeTdQ8ksvUv96VOl8d9P/gkOL490r/78PUt28kRJuuu97/z5hGlXqjr780nVXq9Mvnr5vHyyE/vWZn9P+/NMfu+vU5i2JV/vffbqyjz8z48w7fTySH711y1MPuae5zN0cSWcPbut6Mz0/5ekSmRah0zTJxWnjyKQNwRyEZ7tAOh79D/6hOc3Afw2gEEA5wH8AQCeS3tIeKbFoy+LiYBfnFGQzWBdzLrp/nK45yEtOrQUehbF2/1x6xeFHS2Gxp3SLyRTFU7L49VrRnS6nP2lBTPmqnjkvZN4/sQdtF06g6Zrl8wWDmGtosaiw3qx7h9/bK1rFKP9gzZwDIUAZ50ZuZR/rgtn4UKg7yZw+Kh116QRlS6nS5dmbJ0yfcH+uHLFDrb37rGCenwCz7/XHRdPmQ5S/RgzGjyk6pfb/TjywUk8X74Jbbeuo2mRA9y9a6yTR+95EM9dXYi2JWW43jeE59c8hrbuK2jevsH2b6yvTJ0otA8dQdvgDXSMO/jap6/ZvHgvUMhWVpr9QanyzWBrBqt2qrYNj03i2OWb+J8eWI+/fv9SnOPna8ZMvY3Aqa2Fc/euCYrU8vABrPnMvfjT184mWEn9FkNGqf3CfWvQtHThNCv6sdsT+CevXMP5gUkcu1OG831jCdZT/0DW9GWK+9m7HylIn/90HO/2TqbPx9/RKd4HMUquj5lweOGHZr0xLl4yay1NhGVa+Rlxl5w4IcM1qJs2wtm40Z7j5ztD1jWavw1/X3N9sN9TgZNCFKKTE+Zep+Ase/QROGvX2jWyFJz8DXFyoqHe5kuLqv+5kaKN/tPJbfN/F/SefL76V4fwvcOXsWnl0rhI9+fzwcVecw8liyZ/fl765DR+/p9Zv2JGC7Q/vV+gePlTDHES5ExnP0Yn3IQ6++sTJh+vXcwz1TOGv6VDF+mu7WB789Rkir8+qa7114e/JbK5PXQ35T0dVuT99fvt+Mq330P14gW4p3UqoJm/TmHaloqR/3cfRuQzn6B7yJ9/xvmsqkHz2mbz/4GZhPzhR+bZFEZQ+8tNdQ+F6TOPZ/I97e9XvReBQiCQi/Bk+/iU8QvPfwXg92INp/j8lwD+MAUI+tb+RwDPLliwoPm3f5t6VYcIFBABCqQLF+C+9TZcbjuxoMJY9tK5IyYM/kK6qc5IxBOZfre8CxeN2KRlwz12zLoIUWjQ0hGz7sStX6kG3gD8/2nG/5Nd34SmhpppA34K2K8dG8TRvkmcGSnDl2pG4KxenWgVTdMYI/y8LSzo3sdgObS6VC7G0S278fz6p9BWU4GmJRXGEuXs3Q1wU3Bup9HZBYfBbxgUheKZLqX33pumtICvKLDq6qzLYlm5jeQ5MGAG8Ks3tAQOLv18/IOK5Ny9dC+f6sKRizfNoC+VpcHj3Fa3BI23uqcJfFoen//4Dt6bqMHLK7dgb+tSNK9eBWzbht/rXYZ3e8bRUVmDD+rW4L0FK9HRshq/+CgDJcVcRWPWZsO7rAwd13vxgbsUB5aVobmx1lg6MTKKo00b8fzibVi9cxNaDsT2yUxumO+zNzBKHjj/0U9P4VzXIMYmJswgnd8bd9PKsin3tcFBOE89ibJf/pIRP8/98HhcoHqDMvLl+zhn9hcFa5IV/bkXPsK57jtYVrUAv/Pf7YiX613nH8jyXPy+S3IDp4B97lAvVi9dgM9sbsL1yfIEN1l/Pl6f8ZxXjg9N/G0Qo4R8bt3Bc989jNYbV9HUddW6fXP95aJFcBg8qKLceAAcxRI8P9Zq++ZnHzf3rGGxug1OQ4NxWXba2uzWD8ZjYNRYuI2lky613d04evUWni/biLbVq9B8YJedXGhpAa+jS6/Jb+sWu+aZ6z/pvkwr+viEnZCJtyr1m4S2hbBUks+RS33o7h9JEET+fCgYg0STvxZMH5TGz/+fPrHZiAeKEKZP7jf+Zv/JX35g7t3ke9pfH06C/N2JDvO7Tk7HOvnzSRYN/nzYrlQC1nt+HLvSh2s3h9F3ZyzhGeLPh3UIEl5+Pmwrnz9M63f9Zj5eHd4625OyTf68KDopYDm59L8+tjH+lb9OM4l8tu9/+fZ7uNB9x5T/ywemgpixrsyL/fS5Hc3Tfs/xAmNvUt1DXt+zDzgJ5j2Pkvvdyy9MPh9dvTUjI3+5/me+nw/7gel4LlV9+F3QPe3VV68iUCgEohaevxuzcnrt5+dUwvNIzMf3Wxs3btQaT4+YXvOfQEzoUXDizbfMIA7nzgMfn7Bugxy4BbmSsmW0YjFwDV0lr10D3n8f7geH4H50HO6nn8I9cgTuqVNwPz0L9+zZ9GLWE74v/BB4ne6XfcCJT3C06w6e+7Afq0duoXldi123yEAy3KPv7HlrzaM76to1dnBJ18Sbd+0ge9NqdC6tAwfv3n/2npsk/9PjoPBM1wB++XO74wP+o5f7zH+aFEpvXehFz5iDlRPD+JLbBWflShMxM9n6Eu9kv2imW+7IiBGbR3uG8dzkWrTVVaJz/Vb8RuVunC+vwZn6VvzdsrVoq61E8+igtcRwU/DyMrvtB9fBuS6ObtuP59/pBC0Ev//i8WkWFA50vMEZDUkUR20j/WjqumIH3SaAxER8PWpTbVXg4NIbVHCw9r1D7dPK8drppVtZswij45NpLQ1x68BkOb5wYF2CwDes/+Y4fvbARhy90I3+yXJcX1qHz9/TBOeenVi9odUM3MYWLMI/3FyH3gkHYzW12Ni0HE2jA5ZvXx+OHjqN5356Dmvu24lvdlbgvTsL0bG8AV/Y1QRnYsKI1N9bvgfvjVahs7IGq+urAwdG3oCY98ryqkWB1hh+5w1wyYPt40C0eXWjseAFua95VtLHNq/Ev/rbE/ijvz+NmsqFOHHtNr78n97B3xy7ivKyMtNvzN8/WPPK+5e/eC9WVC+Kl+el4SvTeILjev8Inn+1Hat3bjRWDe+3y9/dwpEWAAAgAElEQVSAWUe6YAn+6S/sTRS9gCmT+fzrH5/AXx68iAvdgwliwOt7vnqcggbO/vq89P45HL0NvFzRgL23rqBp4CbQuBKYmARu3LSCcUkDfqNsO86PlKPj1h2zxpUi8dh4JZ4/0ofV92wy7TDBshit+MQJuN9/AWBAockJHL0+iOc+GsArY1U4OrwQL990sW/JBJo3xtbTeoKebrr8LV26gef6arG67zqa7t6Bs32beW4EtS+5L9jvXn+Tk3l+dPbDLyw8NrzWE18rl1Xi//jZbfF+9TNimp+/d2oiyPv98nqWx981RdNXHtkYFxfewJ78bwyOxK2TqSxfrBNFJwXVkkXlqFq0wCwr53OC9yYt98yLr169vXvcu8+8fvfyKS9zMDw2ERfULMMTU8yH7froSl+CkGEatse7v+6M3kVb/RKMjU9iYGQ8fr9R/LIezI+TWyeu3p4m3v39xfesZ8JETuycJ6L5rFpZszjOyt8uf160dFJ0/vbntiVYPP19z/TefeDPx+t71pmCmgf7/pf3TwlPnvOen55Y9H67/rzMxYDhwOdx8j1EPp5wiyqfVIy8dnn3hlcumfgn0vx9ljwh6fU9rf+8x3mPhBHMHge9ikC+EkglPJOifqSs/tpYAKGdsRRnAHCjNy4cbQLAcHhbUl4d+2Lfvn3uYboA6hCBAiAQ33KDm96/+4FdU8c1UgzeUl4O5+d/DmXPPG2ti0ntMde+9roZQLoUqtwzkANLrmEcHzeBQdxL7XDo1sZ1VbTiPfG43dbDnxcF29sH4XIrhEvtOFpWjW9Wb8VXxy7gm8t34r3yFbh/rAvfXjeKI93D+Ob4Kny17wT2raoya+AcRrx8+CFrHZycxNP/5hWc6BnBzobFxrrCgQsHXL9yYA3+ywftWLXMBoXh4Hpn23L84Dceidfm1//iXWOd2tm6zIjMocFhw6HKmcRzGyuw76FdcbdXE5beFw3X8IjtIYjOLmDjBqC3F7+OrUb0PFBfZlw+3716B1yxubAcGJkAapwJ/DvnNPY1VtugK9xCg+sEaTnbtw//+PwCvHOu11i9OHjkwfZ47ei6PYI7o+PmPAeEE5MuaiqAf7+/GnvLh+3get06Ixg4yKFVgAcHzxQmz+xbje8fbsfQ2IQ578+Plrb/8D8fSIjIykGE/9qfftJhBq5eHsN3J9A3OIba6oWoXGCX2FctqsDzP7fD5sP+bm/Hr7/4Kd7tGsODm1aYbUL+9QvHzJ6Vzz+yGk7TKvzpq2fRPzKOE1dumbZzkMr3D9QC336gFkeOX8Cf9lZiwFmAT4bKsWOZXcc5NDaOKncCT7cuxE97JvBbP7cLZ7ru4I9/esoMKFlP3hPs4x/85qOmzfzH3/dXbg6ZgTrPM505HMe0ge/J0asbv6+pXGAYkEUyX6b32uD1H7ny8H/me7LwR4kla6/P+MqJE177zL42fP/IlYT2rG9Ygt47YyZPr9+8ulJYsN3sb/bDmf+/vXOBrqus8vj/3Jt7k9y8mzRtkz5DW/qylIRHC1KhaOUhKBQHUMcB1KWwCsoI40iHtVwqOio6DlZQBwffqAMj4AN5qSMgBWxaoE1LoQ/6SPNo0rwf93Vm/b9z9u3J6U2atKTNTfdZKzn3nsc+3/fb3/nu3ufb3z4NnfjG45tRGAmjND/bbBP5PEfakZRHykE55Chl5XeRQ11Tz1xY3wji5sFUtx3E0mQLHgi+idqcUtzVWYoeBBHp7TLv5qzLLjPHVk3Mx5pVNaaNiC6cdp5j7sXc7k5cVf8KniyaidVNG1FTEsL1hWdgXdZEzIy1YU9WIRJWAMum5plXxXjLTL2srrTxnU1t5n2oS3P78MBp2Qhc9r7D5k+v+u5fU+3jqjNmpNoNZQh/MmP/UVWej8lFOan2v7O529yL0ia8bYH3l9wHvH+8bVDqK9x5vr8NShsj7+7+hOm76EyLnDXvW5RqK3TeZBHZbBNyD8k+uR73UafSbrznC0dp7zznunNn4cWdLYf1GV45dN6kT/C2C15bkg6be//CUwc4xrPKC0zx2Iakn+P57KfS3V+yXdq49GNyjrR7CqVMadP8zrqteXgjGjv6cc1Z07GloSOlS95L7McqSnKN3uQeFobSL4hee/rjqTYRCQcNG38Z5N6Tvsjbpry8DADAXFf6Z967Z8+ckLrveYxfTrq61bf1mTp89JyqQfn5GUk7YD/xpcdeM78p1AV1w/L85PntKWZsB/KAS3TG8Gn+Jomcu36/2bBnmSnHtN+pRWCbFf63X7wA15490EkXDrpWAmOVgGVZHHg8w1++o3U8vwGgxR315Ggne/J/8Qv3f1fH009Ev49lAubF6hw9YHbR1+qccEE3vBOS5OMjH4K1/JBzlqpPMonkb39nMo3aL77szFXkue6cRTCTJJ3QokJY715hfpysJUtgnVGTEsEPdNjWP/SEcSJW73kR3ymcjxeLZyIS7UEh4mjLykVxsh8TEn3oCeVgVyAfC9GJh8oajOOJiRNTBiRlrXpwEzZ3AgsLACsnxzihvI4YWfwsRoP8mNMhpRHOpaMnhklFNCy7URhImJE4bg/AxqSSXOSGsmBHo4gkorhjUT6qrW4T4mi//LKT5IbzCltbndDC2aegNqsE/9ZcgKZ+G9csqzKGA408I9N968PC3Dgeir+C9f0hrA1Mwy2XVaPmHOcZGI0jGqo0cOjUNbb1mnd+GwHuv5xQwIya8cc+CBsJWCi04rjvlBi25ZXj7q09ZqCpL5Y4ZAy4xq8YgRQlRgHXYjx4jRm55qp7nzWGBB13GlMMLaMxkW4RY5RyVs8vxF3PvGmSu6zKasVDPfnozclDXkmhGZ3dtKfdGKXhrCBYVm85+HlmxMIdpZ3mYceNByehI5hjnJZZoRjAxLLxXFM/8pV65WVnYVZ5vikvyyJOg/+hg3AW54HXo3Nff7DXlIV14/lieMp+cdSlfXmP4TnS1kR/YsxecGp56kHIYEYhzxdjmU6MjDgJZ15zRlmeMXilvrJmvScV5Zh9UlZ52LKnpTvl9FKWOBN/3tp4mEFJQ1OcHcqhnnn8nZe/I+WESnm4pgN8oL0HHVHb6GZSMIFe20KbFTZznvvsQ4myIskoZoUTYBKkze1Jo7NJbrbgdO28MNaDjlAEC2KtZgSzJ5wDKxRCYyyAHmQhL2CjsCgXHT3xAXVnmfNCFm6bATzZFsDK4iSe7IuYpE90ssS5Ekeax+eEgojGE+ZeI9OZE/MhuqZO6Qh6nTE+8OHCY6X98jivA8n9fl3w/uH9Le3Oe75clw4M24G0Mb8c6vr+688215e+wnzx/BMnSBwJ2SW6Zx/IerPMdAhEjrR/bx/De7mjN2ac3qHksH9lubmw3hzs5n3NNjK5ONc8pCF/zq30lovH+vsfcaC9cihXmEg/I+Xh2nsveu9DOUb0Kd/lHOmfZbtXj6I//7nUFZsA999//VLzEIAPKqR8lEVuXLidn9nepN5yvlxT1unqRZnzK4tSfYO0W/ZX0q8Ia78cL1vuY93Yllkmft7Z3GXagbf9iwxz/LRi+PsPbvc++PLL4X7qnW2IeuVn9r+FOVnmutzPer5050X8qIsSyBgCgzmew8lq+yAAzumcDuCTfBAN4H4AdDjvdF+c9mkATvzEEEg0udAQcHTX2CPAuZLra03oam1uGdaccqGT5KWswJljyGQcDFdLN8fQslB7MI47tkZNApgp9bvNiF0tIrhj/sWY3lqPKa1NTshuKGTeGZYutI0hu2veSGBdNA/1wVzcvOs5PD3hFHSFI+gKZiNuBdEVCKM5K4Jo0kYskIWJnS24umWrmTNJqLX92Vjz1Hb0HGhFXVsM+eEAYjYQS9iYUOyEczEslD+6dBxCnF/mGkUbdh80x3X1xcG/WCKJykgQVejGh/t2oTkGtARykLQCZj/nJLX1JdAcBTh37pmmKKbt32lC9+zde2D1R2HX78cvi6pwXV8VXuoNoSlqoTsBvLq3DZ+7ZKEJkWP4FA1GysuL9+Ln0RL8KDAVe6wItu5rxa9fazLhrpMKc/HEpv1oaOszRkxf7FA9GO4WT9qYV1mETyyfjdqdzbg22IxtsRC6rBAY5vu7Zgv9cdscR+OG5zPEmPXn/DOay9zONdlUluSiJD/byNvd0oXtdMB9oaFkxHIzPIshdXTSudCwmF6Wh5K8cOovEs4yn5sPduGnr7WgKWah2Q7juVg+ItFe7AoWoKknjvJsCy19jqHPOtG4os5OnVJg1gzHK88G/ri3Fz+KlqEvEEIgGUfUykJetBfhSA7K88P42IULTLgc68mFZWVYH50BymD9GHZHR/A3G/aa0C+GMJPx3tbe1JP6H1x3Nurq241DQDnCTkKMKStp20am17jsjyXR0RtFVtBh8YX3LzaMXt3ThsXTik1YID/nZgfx+K0r8JFls0wZGcLGMvzsb7tQ39ZrdMNyn1pRZEI1aaBzXhWP4zW4MKcO60NOrC+Nt4qSiNEN693eEzV65X1AJ4prlp+hm0zsUkYO/awHsOOAM4JH55RlY4IZOg1/fb0R0bjjVDEkODsUNNdjiB/D5UROPOG0scrSPHztvAo8tb0VXckgWNIWOwSKiMNCAEnYXNtJRANZmF1RjNuvrMEjG/aZcsh9yCvyfp0aCaAkBBTHe01WaY5qHgzmYH9WPtoQNn0CnU4+JJpZGsGOlj7Dk3VfOK0Y2VlBh0cS+HunBduy8FSLjR1tURPKzXDMxzbuA3UidaU+yc+ptXNvsL2zLbLt8N657LSpqKtvQ0ev02fQUCcbho6y/VIXRZEw9rT0mHqQHbdTB9IGKYehtBy1lHZOPUr7p17mVxQa/VPvvN/JhG2ZTqLonm2RIZm/WLfLXI9l9f6xnbIc1NkN5812EmQtnWl0TpnUN0Nw2R+wX5IyUQbLwoVrlnH2pALjMPIYHi/1Zhv0y2EDberoN/cO29C8ikJz/ucvXQTOS5XwUob97nY5Sd2ENdshwzOff6MJ7b0x0xalTOKskTVHDNlf9caSmFiUY+4N02e4YbZkzb/X93ek2FDH7LPIh7UUOQxhDgYDYJumLDrtvGcYhuu976XflHJInyWhyqIz7p9ckmseDrLdSIh03f528zCD57mYTXnYd7CO/H347EULzPHUN8sr9703NJZyZL6s/J7xfNZN+qFPnT83VX7KkbJT3ywT68Y6suzUGc8nE5bthndWYVtjx4D+49ltTYaxyOF9L4z8ciiLbDliygcZ/Pz1D55uQrI3vNWKaMI29fQmdDIV1X9KYIwTGCzUdjiO58MAvukmEPoWgA2uk/kTRlUB4PqITif5qOM5xluJFm8AASYjYfjqHfuz8VjuNGzKLkPtlDl4pmA6phWGUGFHYTEjKt9VmWahs8cEME+HynFw+gx8dsp5eGHqAmwurMSjkxehe2olfjjtTExDHyqqKmFFY847Kzn/kdko39wOe/t2TGtvxOuJMPoRwKSsJPYVlCEvEUVxrAfFiJkRTxqeeVYSlYF+3NH+CiomFqO2ajFu3l+An9a1YseBHjzX2I/GPtsYEo19QFvUNobbXVeeZn5Q77pyiXEmOAdFHAga8WIY0KgsyQshq7cbtxW2Y2VfA65ONmBCUQ7+nshHWW4A5SV56O2LGkOaRuSOvgCejuajpiSIKds2A60HYU2twMcLz0YXQmhLBDC1KIzOaNKELNH4+8Un32nm/jCbIw3GfZ0xNAdyjVFMw7wjYaG+vd8YdvyBp3EoRhHPZz1uv2gBzq4qNfXiHDLOm2EimpxwwISa0rnrCISQpJFvwYz6FeeFjdHFunNUQ+YP0Qik3M9fujA1N4vf32rpMYYWDfL/cx0QGqM0eGmA3mbK4CQSoQHzzaurTbnoTPHvmS0NxqCnYXegO24MGdbPspNIBoKIWkHMSXaiPDeANdWlWHSa4xDRAb7l3fNMmXgNzoOjQdTf2483+kOGU9BOYHrvQbSF8xC1LdQnQphdlos7rzjdOGisG+ds0RGg0SaGrNcApeH8l62NxtBivWiQkbOEF3Pekp+RtzwyT0mMebYlx2lxnDA6DZL4RYxRzn+Tz2J4c94X5wx6DX3qjMb67En5RgZvPx4viVQ+ttyZ9+ctD+eCXjh/sikz68y601Gh8/qJ5XNSPFcunIJPnj8H151bhdL8HGN00nC/ZHFFqmxsdzRAaXyKs8N26HU+vHK8bbFmwTTURBJm3uY/LpmM3b22cQiml0Zw86ws9Pf04Ia5+YgVFeGWixahemYpJuQ782rpDPM+rJwQwd3/UI0VZUHU7TsIOxzGzkQO4oEs8zBKDN6pucD8PBtfes9sLD9rDmh400GhIc92HgoGDrWDaNL0CfTbaZQ3tPcZh0ccdHE+vA+HbloxN/WgiPeMzF2koU5nLjsUMAY5naq7r65OtRca1svnlhuevF8/UO3MWeZ94pXDMsq9JPM9eYzckxzplocObDeUdfnpU41cr+7poPmdXNaRD6XoCPB+pizqjIlzzp1TnmpL3C5lZV8iuuf58iCJ+mC5xWFkpl6W5yurDpXHL0dk8v7g9VleOd/7c8L7LF3dvKypK2mL5gHZhEiKEds968X+hiPHfMAjfT6vKXLImm2LDwscxy6I/75hGVadMd1cX+Rw3i7vjRsvmGP6Pf5esPzcz3J673tul/rJ2qszOp98mMB+QZx/PqCQ3yDqnufRGZU+lG3F4eHUi+U5c5bT13vve3+9xDmV/v6eD5+BL16x2PTFdOrYdxzWn8lvh5s0jbyk76TOv31tDa4689D1uZ+sl812+jGpc+o3aBA53vuBHNmm2Z/xd4J9kTqd3jtCP2cKgcEcz+GG2r4t9dRQ27cFowo5jgQkhImhpOKk0GjjWMWasg586PJlsE6pMiUaMK9x2jQ8+MRGfOn5/UjQInAXyuFCWc5YgYVCxHBf2QFUd+wHFsx35jDyIGZ7rduC9XYeborOQocVRtCCkbc0uwcPHFxn5kXy0Ov7Z2JdzhQsDHYb+S3IRlMiy4yomA3uv8LsAG5dkI+H9zovqV9z6SLzIyfHSFgdjQMuMoeOP4Rcrvvun8B5mEuzOvFAbJPzzr9zlgH5+YfmS+46AM5J7O6LYn9nDH2cs5aM4gcHngWsAL5aeQ5aAtloiwEVpfnGCEp3LW4T/jlImLqXZyWxM+bMAaQBQV14nSFTyDT/WK97frcRq7HXvOPwxs6p6OBIUHYA379+mWHgrbvU1y/Ke4x3jg+PY3kYckijbrDzvfJEFkPBfvyXrWjqjOK2yX2w6zbj23kL8JnWDbh2ogXMnZN+/q9XGID1L2zGmsdfR2MUuG3fC5gb7MfaiUuwMi+Kp3Km4JZrzkX1TCecTU6VMlDfMo+RoWBc6va1G75SL45sMPRxOHUT+bKW67CuMmdquLJ4roQ2Up6ERcqctmMpjxjBUs7hrqVMdNRZDy7+e2W4so7lOLk/FpZkmfd79ljO6JzM7UtXP9GFhMIyrJHHCWPy3dnEkEJnbjTLR+eaIzJH0pnIpp6pH1mnK8dQ9RY5Iz1vMJleeTzmWHTllXU0bW+wMo50u5RjuPeUHO9n6t/u/z5UuUZybDo5cr63nfC4Y9GPXEdkS3393+W4I62P9jy/3LdLjl+uflcCY5XAYKG26niOVY1pucYEAf5YyNwxPpWVuUosXGEIeHF6kxPTxw2BAKyqKjMvs7bbwo0NxWYOJMMKvDP8ZhUEsLsraRxIOrAJBJwEI5HdQFWVCZkzlQ+HsX57I27qd51O91g6S987uxjVuXHYHBllOK2dh7XbetCRADZ3HZonRkeXb7PoTlrGyfreshJUz53ijNJyzulwFjfhDd8xuH7rXpMddfWUBKq7G014lgk1nlg2QKYYxHQ4ewLO/NAcm6GfHNNzriuJToYqgvxYc/5jdagPtbEcfKXWSdgwlHGdVibrsXMnbGYE7rSxtr3AJNfxO2Npzx1kI8tHg10ckKM2RIUx36fIrMQtLc5cWL5uZvE7nKRTR9KX1I8ZTrdsAfh6jkULzQMMaxFfszJrkFo4m4W1GGqcWyYJOjSxxZDoTthOv85GUpChzhXdM1ETk8pImxiJfD1WCSgBJaAETl4C6nievLrXmh8jATHQ5KnsPKsXv9rejQLEzIhfWyAHk+Nd+HLHBtQE+7G+N4CbJr8LHVm5Zm7VrfMieLLFxnuXzRuQOe+e37yMlaFOPNTgzLPj/JvO7DxcHW7FS4l8dNsBNMUD6Almm2Q4t1Ym8GSsALdcceago04sKx2hlrZudHRH8dlwPeY2v4W1mIrV/dtRs+Is81L64Tgigk2cNTpE2FwH5EVgLV0Km5ll+cqFNA4NR9/ueeRlrIw34yGr3CQ96gk6Diid4QUTc7HmytMPG4GTa+r6GAmII+vJLGzee3qMYvV0JaAElIASUAJKQAkciYA6nkcipPuVwDAJ0BG77mfrsa4ne8AZkVgvCqLdaM4tMXP0CuO9uDe2CTWLZ6UNlaSc9X/bhBs3J9BhH5pubdu2edWCCC9M9uO+U5OonhjBcB1Gk5F340aA7wmt3Qi0dQC52bBOPw049xxYfCemL4OuXM+/NrL4rtEtW53QWiZd4qtflpw2YJTTe545Z9064LXNJsnR+oNR3Dn9AjRZ2bj9koW4dunQo29eWfpZCSgBJaAElIASUAJKIHMIDOZ4Oi8Wy5x6aEmVwAknwGRCN896FR2vHERrMoi2gOOA9oRywT8uwWQC9+55BjWnue/oTJOAiHLW/mEnOuxu82qFwqCNpnjQZIgN2AkkOV8Lcdwb2oHq0nmO05lGTlogpRNM6C8zySKRAIoKzHs77bY2WA0NwFlnpj3tsI1MmcjQz2f+DPT2AFnO/ErrwIFBnU4jg9ef5r6ofucu1Fx8Hv541fvTvvP0sGvqBiWgBJSAElACSkAJKIFxR0Adz3GnUq3QqBMIBFBz7mI83PcnYFsd7O07UBsswldmnGcc0Y6sHNy293nUzKsAFi0yDmPaMMdAwLwrD27Sm+rdW7B+5wGsLV+Cla1v4snJC7E6WI+aJfNgVVamDWkdrK4m0+67lgMNDbBfeBEoyAdicfNOTev0wTPx+uUxYZLd3g6rpBj2rreA4iJYZaXA7j3gvnRhtpSRyvRbWQlcesnQTqr/ovpdCSgBJaAElIASUAJKYNwR0ORC406lWqHjQkASuWzabF78bpWUAOEw7N27nXDU3FxYM2cCk8qP7HSJrEceBRoazcgkOjth79kLa8X5wIwZww6xPazu8Tjs556HXVvrzO08/3wnC++REtW4ghgyi95ek+zG/s2jgJ106sW5nbNnDztc97By6QYloASUgBJQAkpACSiBcUlgsFBbdTzHpbq1UplIYEASn/p6YMoUkyUXvoyxx7Nupkx1W2Dl58PmnM2ubidTamT4802PZ3n1WkpACSgBJaAElIASUAInlsBgjqeG2p5YvejVlUCKQCo8lZlIzzrzyCOlqTNH74O3TNZVq5wLHWwDSiccCqcdvcurZCWgBJSAElACSkAJKIFxQkAdz3GiSK3GOCDA94Dy1SRpXk9ywmo3Fst0wmDohZWAElACSkAJKAEloASOlsAw3yB/tOL1PCWgBJSAElACSkAJKAEloASUgBI42Qmo43mytwCtvxJQAkpACSgBJaAElIASUAJKYJQJqOM5yoBVvBJQAkpACSgBJaAElIASUAJK4GQnoI7nyd4CtP5KQAkoASWgBJSAElACSkAJKIFRJqCO5ygDVvFKQAkoASWgBJSAElACSkAJKIGTnYA6nid7C9D6KwEloASUgBJQAkpACSgBJaAERpmANcry/eKbAbzl36jflcAYJ1AG4MAYL6MWb3QIqO5Hh2smSFXdZ4KWRqeMqvvR4apSR05A2+LImY2XMzJd9zMATBwvytB6KIHjSeDvx/Nieq0xRUB1P6bUcVwLo7o/rrjH1MVU92NKHSd1YbQtnrzqH5e611Dbk7dBa82VgBJQAkpACSgBJaAElIASUALHhYA6nscFs15ECSgBJaAElIASUAJKQAkoASVw8hIInrxV15orgRERWD+io/Xg8URAdT+etDmyuqjuR8ZrPB2tuh9P2szsumhbzGz9HUvpVffHQk/PVQJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSiB0SQwDcCfAWwBsBnAp92LTQDwFIA33HWJu30egBcA9AO4zVewYgAPAdjqylvm2y9fLwLwOoA3AfyrbPSsvwOgy/Pd/7EGwGvu+fcAkHfoftCtQxLAGf6T9PthBMaT7pcAWAdgIwCmTT/rsNrqBi+BTNT9XQD2pOkbrgPA91pT9/z7uLei+vkwApmm+wiA37u/K/yN+ndPjf4ZQB2AVwE8A4DvutMlcwiMpbb4IwA7Pf0If1PSLbMAvOjaRr8CEHYPWg6gFkAcwFXpTtRtAwiMJ91Pd+3oDW5fdMmAmuoXJaAEBhCYAqDa3VIAYBuABQC+7nEK6Rx+zT2mHMCZAGgE+h3PH3uMPnbGdET9C5NwbQdQ5XbYr7jXk+PoMP40jXEp+7l+CQCdWjqcjwO42N05H8CpAP6ijqcX16Cfx5Pun/S0A3b6bAO6DE4gE3W/FADL7X8oRcdz7eBV1T0+ApmmezqeF7h14O/Ks557ndu5n8uNAOgI6JI5BMZSW6TjORyH8dcArnERf89td/w6E8BiAD8ZppzM0dLolHQ86f4HnnZA+3nX6CBTqUpgfBJ4FMB73BFJdgxcuOYIpXf5gs/xLHSfFsroo/dY72c6jE94NnweAP+40Cnl6Cuv5zcu3UPMPo6oynItgO/LF3etjqcPyDC/ZrLu2aauduvJNvGLYdZZD3MIjHXde/Xk7xvU8fTSGfnnTNI9a/efAD6RppqnA3g+zXbdlDkETmRbHAW9HOQAAATZSURBVI7jSfvmAIAsF6nfnuHm4cjJHI0cv5Jmsu5pg37O0yb+dryw6Xs8jxdpvc5oEeATO/54M4xkEoD97oW45kjnUAtHMBnu9gAAhhvcDyAvzQmVbric7NoLgNu4rAbwmOe67uYBKx7Lc2Txni/bdD1yApmu+88A+Ibbtu72PMwYOYmT74xM0P2RtLLKDXFiqD9DuHQZHoFM0z2jaC5zw2r9NfyYGwHj367fM4PAiW6LpMRILoZt/weA7DTYSgG0ueG03K32RxpIR7Ep03XPgZiPuO3hDwBuPgoGR3WKOp5HhU1PGiME8gE8DIAGfMdRlIlPABmye5/rvHZ7QnW94tKNiNoAKgBwjibndw61DHb+UOfovqEJjAfdM8zuVtfp4PqHQ1dZ97oEMkX3Qynst54wt6cBMORflyMTyDTd8zfmQQCc17/DVz0afZymwYdPumQegRPdFkmMkVfMYcGpRMxxISNYXppqf3hpvD2fx4PuGWXFke6pADjVh9PFjotPeFwu8vboWaUogQEEQq7T+XMA/+vuaXRDXvmVoa9NA844/Auf/PGPo6VcOPJAR5SjD5L041PuMd4RCd6o9a6zOttNGMT4eM7bYfIhht/K+V90z+c5ssj58l3XIyMwXnT/T562+z+aXGhYjSCTdD9UhVrcZGc85r8AMPmYLkMTyETdcx4Vk91921e1dwNYA+ByTzvwHaJfxzCBsdAWiYeRXXwIzsSJjNySBHWcxkEbhFFcDLPlqLuE2qr9cWwNa7zontEWnPvLhck3cwCUud9HdSUNcVQvosKVwNtMgE/wODrErLbf8shmyCuNeWYQ5Jrx90MtDW6YI5P7cD7ohW62QWah9GaH430yBwAzw+1zJ+l/yM1GO9lzAc7joiPKxXs+v3cCYKIROrkfHcYoqSNF//sJjCfd8+HFu9ykQitcA9VfX/1+iEAm6v5Q6Qd+4oMxmRZA54N9mS6DE8hE3X8ZQJEneZ3UjlNDOL+KmdKP9HBUztH12CEwVtoiiUg/wjJ9AMAmF9N7fbiYh4JJiH45TNvId7p+dQmMJ93vdm1ejnoyySUdT04900UJKIE0BN7pPuXjvAYZWWSoAOcyMD09nzBzzdATLnQOObLJcFzOdeBnJhbiQgeRr7KgrEcAyCtY3N2pFeUzey6z2/JJdbrFn0DEewxDqvijwPOZzZIdGJcr3PLwiSVHbL1JjNxDdOUhMJ50z7qsB8AsyXwgoaNeHkWn+ZiJumembfY3fF0S15xXw+Wr7oMr6p5GIcPldBmcQKbpnqNKHIniAwX5jZJX5jC0mn29bOcDU10yh8BYaot/cl/TRtviZwAYAppuYT4LZtZnRBaja2QuKEN02S9xmhGjMPjqH10GJzCedM9Mtkxsxt8g9kUrB6+27lECSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKYKQE/h/r9UhbrihY6QAAAABJRU5ErkJggg=="}}},{"metadata":{},"cell_type":"markdown","source":"![ll](https://media.giphy.com/media/o14YPU6vooy0o/giphy.gif)\n\nAwful! It turns out that the scaling of the target by log1p before training the model causes this naughty behavior, as it makes the values too small. Why do small values prevent proper regression? It is a mystery, one of the great questions of the universe.  \n\nHere they are with the log1p removed (commented out and replaced with new lines above):\n![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"![ll](https://media.giphy.com/media/NJZMSqRY3rG9i/giphy.gif)"},{"metadata":{},"cell_type":"markdown","source":"Note that performing the same without bagging, i.e., with a standard model for each building, would yield excessive bias, that is, low reguralization; it would be odd if the very same patterns truly repeated from year to year, like here:\n![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"### Predicting with a single bag of models for the whole dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df_features = categoricals + numericals + shifted_numericals\ntest_df_features = categoricals + numericals","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def train_predict_holistically():\n    \n    bag = gbm_bag(train_df, train_df_features, categoricals)\n    \n    for (_, train_g), (i, test_g) in zip(train_df.groupby(['building_id', 'meter']), test_df.groupby(['building_id', 'meter'])):\n        if (i[0] > -1):\n            print(i)\n            energy_shifts = 1\n            temperature_shifts = 1\n            preds = np.array([])\n\n            # Adding the last values from the previous year into the preds array,  \n            # as they are the shifted values of the first values of the new year.\n            start_energies = train_g.loc[train_g.index[-energy_shifts:], 'meter_reading'] \n            start_temperatures = train_g.loc[train_g.index[-temperature_shifts:], 'air_temperature']\n            preds = np.concatenate((preds, start_energies))\n            temperatures = np.append(start_temperatures.to_numpy(), test_g['air_temperature'].to_numpy())\n\n            # Iterating over the numpy array instead of using g.iterrows(),\n            # as the latter is too slow.\n            for j, row in enumerate(test_g[test_df_features].to_numpy()):\n\n                shifted_energies = preds[-energy_shifts:]\n                shifted_temperatures = temperatures[j:j+temperature_shifts]\n                row = np.concatenate((row, shifted_energies))\n                row = np.concatenate((row, shifted_temperatures))\n\n                # Reshaping due to LightGBM's 2D array requirement.\n                row = np.array(row).reshape((1,-1))\n                preds = np.append(preds, bagging_predict(bag, row))\n\n            test_df.loc[test_g.index, 'meter_reading'] = preds[4:]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"'''\ntrain_predict_holistically()\ntest_for_building(601)\n'''","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)\n\n![ll](https://media.giphy.com/media/zXMRfbsHOAire/giphy.gif)\n\nThere are two possible reasons for this:\n1. Linear regression cannot use the categorical feature Building ID to discern which building it is predicting for.\n2. The various scales of the data for the buildings, i.e., some buildings have far smaller ranges of the target variable than other buildings. \n\nThe lesson here is obvious: **never use a single model for a dataset consisting of multiple time series.**","attachments":{"image.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA54AAAD4CAYAAACEyjk9AAAgAElEQVR4Aey9WZQcV3om9gVQ2LcqoAp7EYUdBAmgQbDRXAGS3RqNNCOpJXVL8qKR5PZpP9kzx8fH0vjFI5950DyN/eCjcY90xm2PZ7R0S2qpNaOjbpLgAi5NgGCDJBZiR2GrBagqVAGFpYDr8/03IysyKjIzIjMiMiLjj3OqIjMy4i7fvXHvv/+AHoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAopAWAScsDfGcd+KFStMX19fHEVpGYqAIqAIKAKKgCKgCCgCioAioAgoAhlD4OjRo8MAevzN6vBfSPI7mc4jR44kWYWWrQgoAoqAIqAIKAKKgCKgCCgCioAi0CIEHMe5FFT1rKCLek0RUAQUAUVAEVAEFAFFQBFQBBQBRUARiAsBZTzjQlLLUQQUAUVAEVAEFAFFQBFQBBQBRUARCERAGc9AWPSiIqAIKAKKgCKgCCgCioAioAgoAopAXAgo4xkXklqOIqAIKAKKgCKgCCgCioAioAgoAopAIALKeAbCohcVAUVAEVAEFAFFQBFQBBQBRUARUATiQkAZz7iQ1HIUAUVAEVAEFAFFQBFQBBQBRUARUAQCEUg1nUpgC/SiIqAIKAKKgCIQNwKPH8NcugTcvAWsWA5nwwZglspa44ZZy1MEFAFFQBFQBMIioIxnWKT0PkVAEVAEFIHcIECm05w4CWfpUjmz4c7GjblpvzZUEVAEFIEKBFSYVgGHfsknAsp45nPctNWKQG0EdIOqjU+7/arjPXNEb94SphMLF8Lhr0PDMDyrBnQmVnpFEVAEMo+AuXAB5q23gcePrfXGwQNwNm/OfLu1gYqAFwG1O/KioZ8VgTZBwNX2YHJStD1ictgmfdNuzERAx3smJjSvNbdvA3fv2vO9e1bzqe9EAFh6SRFQBLKOgPnsc+DmTcBx5Czfs95obZ8i4ENANZ4+QPSrItAWCPi1PdTyqJlhWwxtYCd0vGfAIj6dvEps1q8TjadDgs3VgBb9nVAt+Yw5oxdqIKDzpQY4Kf5kxG4DcM8pVq1VKQJxIKCMZxwoahmKQNYQ6OqEOX68bJLjHDyQtRZqe+JEgNo9+jMCot0TRivO8vNY1qxZ1qezJHAhuaYYTQ+kaMmpQbl7Fzh8GNi7F85LL2oApmmI9JMHAdeqIlGfaWVuPYjP/Og8/RQMNZ40te3uBr/roQjkDQFlPPM2Ytre1iOgm2Prx0BbUIGAX7sn33We1seo4o6CfaHGl0znwADQ0QFz7BjQu14DMBVsGoTu7tAwMDFhGZ9588SCIFYrGq5X7x6283D1auD6dWmaBgSbHiEXiwoTW9ffc/o2/aQIZBoBZTwzPTzauCwikIrkt9mOj4zC2bpVzAqFuBwZbbZEfT7LCPi0e2yqBKLQqK7ToxaA0fSPBfy0YrnVdHZ0AI8eAST2i25+XMBpELrL9+4Bp06Jpg0XLgBr1oR+NMyNsq9S+MH5SGHIqlU6H/3AMR0U/zo6bLTuU6flOwWNxC+RwGlTUzDvvQ9z8SKcvj44Lzxvx8jfNv2uCIREQIMLhQRKb1MEygh4/emWLrWLffnHjHzwB1YhkalHsRDIwzwt1ohkqrckVp29e4GpKUvkL1wo+U4z1UhtTHYQmD8f2LED4J7HM7/HeVDoQeEHhSCzZwM3buh8DMI3YF13heFJBBMUpvPNN4HxcZg33xQmNKhZek0RCIuAMp5hkdL7FAEXgRwwdUJU7nwSWLAAzs4nIaaXbvv1XAwEcjBPizEQGe7lurVAX58wEbpOZHicstC0nm5g8WLRevEMfo/zoHCUwg9qOqemRCii+5YPYJrVMkr3xx/DnD0LMzpqmfMAZtT3ZMNfqemkPymWLZOzfG+4NH1QEQDU1FZngSIQEYHyZsjFfv26bDJ1alYYcVTb7/ZczNP2gz03PRItyanTcJaX0s64Zny56YE2NE0Ekl5PvOVj/5ftvso5qUcZAXlnx8asZvjGjTJznmTgNJrXUtMpx/AwnF27yu3RD4pAIwgo49kIavpMsRGIytRpkJdiz5dW9T7qPG1VO7Xe1iDg1ZKwBerf2ZpxyEutSa8nSZefF5xrtbP0zhqmUlm0CGZkRCKZe5n2uIXh4tPJmAH08dy1y/p41mpjHn5Tmqylo6SMZ0vh18qLgIDrf5FoGPoiAKl9bAyBrG6yWW1XYyjn7ylNwZO/MdMWFxsBvrNMkzY8DDAn8axZElSI0W4l4m0SuboZyOjAy/KXefBD7ilKk7V2JJXxbC3+WnsREFDNQhFGObN9zOomm9V2ZXYgY25YklqSmJuqxRUJgZDMQ5EgcfvKd9asWGHzeK5cCYd+l2qp4MIjTLjkal66VHI28wc3BU35Jn5QmqwCjrS/hDGg3w7gE8/fbQD/DADDZP4IwJnSuSvtxmt9ikAuENAgL7kYprZtpHeTzVIU5rTbRYL23Dk8/sFfyx8/SyL2th34Oh0rmTY6z+6zxJn609UBTH9OAwFXIJVEhNY02p9oHXxnd++SVDZOVxfMxIRG/vUCHnZP6eqEOXNGcsbyjK5Obyn6OWEEwmg8TwP4UqkdswFcBfCXAH4PwOsA/qD0md9/N+H2avGKQO4QUM1C7oasvRqcVZPKJNsVoDURgvatt8tmaubmTZsDLwnztPaaQdobRSA9BLzMA2tVjV4F9k5vL9B/RfI0S15Nfk/yCFhLJZdoknWGLdvXNjKQZmBA/F7N7dsS/DFsUXpfegiEYTy9rfkqgHMALgH4JQCvlH78LoBDynh6odLPikAJAQ2aoFOhhQhkVfCRZLtcrYnXr1oIWKYjoKkaD35WoraFM1OrVgQCEEhSIBVQXd4umf5+mPFxsVIgc4X+/mBz0pg6FrSWBpqvxlRflGL8bXN2bJf0cWJKWyvjwMgonK1bbfqeu3eBkdEo1eq9TSIQlfH8DQD/sVTnKgDXS595XtlkW/RxRUARUAQUgbgRyKrgI8l2BWlNmCeQ5qRuYA4yoLzmk5oLQ6xmp3HPQi0vjwi04N1IUiCVxyGY0eagtS1Jq42065vR4RoX/G0jQ/nsPqAeHircqAFq8j9FYTznAvhFAP88YrO+DYB/GBoaivio3q4ItAECLdi82wA17YIi0DgCAYSFELQHD8B89rmU6zz9FGi2Zt49LL4+WL0auG5lqVmR6DcOgD6pCDSPgF+jxBITfzeSFEg1D0nrS6A5KSPb0mKDWB08kGybAtbSZCuMUHqDbVPhRgSME7g1CuP5cwA+BjBQagfPa0paT54Hq7TvOwD4h56eHua51UMRKBQCLdm8C4WwdlYRqEQgkLAgkbZ5s/y5d5sLFyzT2dEBDAwAq1ap+a0Ljp4VAb9GSU3TCzcnAtfSjKDQcNtUuNHSEYzCeP4XHjNbNvqvAfxWKbgQzz9oaU+0ckUgqwjo5p3VkdF2tSsCYQkLEtLUdJLpnD0buHED2P/ldkVF+6UIREOgQY1StEr07kgIpO2fGHYtjdSJmG6u1ja1MosJ4GSKCct4LgTwMwD+O08zGM32zwB8C8BlAN/0/KYfFQFFwEVAN28XCT0XDYGsEwD08aR5LTWdN27A2bsXZSl60cZK+1s8BOq8n+V3gcJTBmuhafqFC9YqYMVy+66oP3S688ZPT6xdU9wxqTJ/1cos3SkZtbawjOddAKVQgOUqbgJglFs9FAFFoAYCMzbvDRtq3K0/KQLtg0DWCQDvu0lNp3xXQrp9JqD2pCYCdd9Pn0ZJTNNPnIQ3WnTiPp81e1C8H71rFoUBEhytoGNSdf6qlVmmX4xZmW6dNk4RaAcESpu3Jmpvh8HUPkRCwEsALF1qNSWRCtCbFQFFIDEEoryf1C4d/1QsBMzICJzFi/V9TmxgahTspydoesu1deFCe6b7QFGOavOXWmGmmrl7155p2aJHZhBQxjMzQ6ENUQQUAUWgzRDIOAHgSswxOQlz4iT4XQ9FoDAIRHg/5d24eRO4dQs4fRrm3DmbjqgwYGW0oxHGMKM9aLxZVfpOrbCz80lgwQI5l7XEjdekT8aIQFhT2xir1KIUAUVAEVAEioBAecOnZLpWQu9WgeGVmLMNGrWzVSOh9bYAgUjvJ9+VzZthuruBwUFgxQr1h27BmPmrjDSG/odz/p0+x+i/Ij6uTl+f+CBLl3wm4oHdnJqCee99mIsXIc++8DzA6OZ6JI6Aopw4xFqBIqAIKAIFRSAMAdBKaCgxp38UICZZ4jPVyvZo3YpAmghEeT/5rgwMwOnqgpk922qU1B86zdEKrivKGAaXkNurpr8fZnxccsuKaW1/f+g8s8J0vvkm0N0NwzNz1B54ObdY5KnhynjmabS0rYqAIqAIKAKxIVBkbUFsIGpBhUBA35WMDnOVyK6hW9vs86ErSuDGRi1W2OePPgLuTgIPHgDLl1vN50svWncLWr5o1OYEBswWqYxnYtBqwYVEIM+LeCEHTDtdaAQKrC0o9Lhr56MjoO9KdMxSeML1U2800nCzz6fQxepVNGixIv7KNLUdGobDIERz58L55a/DvHsY5tgxm9uZabaoBd24sXr9+ktDCCjj2RBs+pAiEIxArhfx4C7pVUWgfREosqCoyH1v3xmtPSsSAnyHGWl4aAjm0SM4y5ZF91NvVGuYAZwb1sKzz/v3i58yLl4CNjwBrFsH88MfAmRE+68AfRtsfmdlPGMfaY1qGzukWmChEfAu4po+otBTQTuffQRcQVERo9oWue/Zn5naQkWgPgKiuWs20nCVyLD1a8/xHezz3btwtm8Hnn8Ozte+CoyNAXPmANduAI8eAcc/A+7dy3Ens9t01Xhmd2y0ZXlEoEHTjzx2VdusCDSFQBY0bl5BETtTpKi2Re57UxNXH1YEMoIA3+EmIw03rDXMAASu8CyqmXFQnw37c/8+sGA+zPAwnM2bxAQ3A91suyYo49l2Q6odaiUCQQtaK9ujdSsCWUWgUaIh1v4UWVBU5L7HOom0MEWgRQjwHaYv4uQkcOcOnG3bojckz767jQrPAvpM2s2sWwecPgNny2bgsbGBh6Ijqk/UQUAZzzoA6c+KQCQEAha0SM/rzYpAURBolGhoFJ8ADWuRBUVF7nujU0ifUwSyhIC8w8xjef68BMQxNBe9dKk4AXHIeH/2OXDtGnDjBpy9e4HHj4FG0vyQdtu0CcZxrOZz3jxg/vwsDXfbtEUZz7YZSu2IIqAIKAI5QiBljVs1DatELSxiAAkVkuXoZWlRUwOENQ0R9S1qfttXSwZr4UI4zzwjZ9y9Wyh3gdgZ755uCdTkrF0reZ3B73rEjoAynrFDqgUqAoqAIqAI1EMgdY1b2hrWegDo74pAxhEoC2sWL4Y5fhxmxQo4u3dB3t1GtEoZ728um5eyAC9TGMXMeKe+J2UKzPQao1Ft08Naa1IEFAFFQBFwEShp3Jxn91nTsKQJWRJoDJV/9649r1jutkTPioAiEIRASVgjJpzDwzZtx4mTIEOqRzYQcHp74SxZAnPhgpz5vVBHXOu6avdTmzaq8UwNaq2o7RHQhavth1g7mF8EVJqd37HTlqeAQND+VdKmYXAQoO/bypVgBNFCRX9OAfpmqjD9/TDj4yK8E8Faf384H8+g8U5a+NdMR6s8G9e6XtbuL10Kc+Kk1CZuGFXq1cuNI6CMZ+PY6ZOKQAUCmVm42mRDqQBXvygCURCo8g4U1p8zCnZ6byERCNy/NmwQLMzUlARscZYtE2sBZ/26QmKUyU436EIQON558nXnGn/hgg0uBMB5+qnmTMAbxDGTcyLjjVLGM+MDpM3LEQJDw8DEBAwTOjMiGr+3YCHP/YaSoyHXpmYTgUjvQBUmNZs901YpAgkhUIXwprCGWiW+U9R0Ok/0WgI/oWZosRERaNTHs8p4R6y9ZbcL0/m97wNXr0lgJebeZOCrhrWUjeLYMgTyW7EynvkdO2151hC4dw84dQro7gYuXADWrGlNC3O+obQGNK21rRAI+w6Q6Xz3MMyxY5KOAMyJR+l5CwRGbYW/diZ/CNQivF2CXt+LzI2r+HQypQp9PPv6ENrHs9Z4u710hXIUopO+YXqRnu7mNItu2U2eJY3KlavAokXity/m4EPDMCz35i1gxfJI7YzLZLfJbhXicWU8CzHM2slUEJg7F1ixAqDGk2d+b8VBc6jXXwfGJ4Ali+H8yq+0ohVaZ1YQcImHBjbjrHQhcjvCEFWAaHGE6ezoAAYGgFWr1H8tMtj6QDsgoIR3PkdRfDyZv3PePCtAo+DspRfr5rIMM96u5QgtuUSovmOHBJgiUpkQzpHpnJwEHj4E7k4Kc0z/TPohR/bTVOFKai+ARrVNDWqtqO0RePBgmukk88nvrTiuXgWuXbeLMc/8rkd7I0Dmkv4uR47KWZJol3pcJh4mJ2UzFpO59kZDJN3OzieBBQvAc5nI8vebzPjq1cCjR8Ds2ZKEnJJyPRSBwiGQdpTpMADXWNfCPF6Ie7iGMX8nBWcdHcJ8hlrj3fF+Zq/AZD4+NmPvENNqBpO6f99acj14MB1cqsXg0qcTWzYDq1dJ25yvviYaWQl+xdymbhCsFrdTq5+JQFiNZyeAPwLwNIXEAP4bAKcB/CmAPgAXAfwagJGZVegVRaAgCNAMhRJBd5Hm9xYc5tJFYHmXlXguXAB+d3CwBS3RKtNCwGUuAyW9Yc1O02psGvWElV6TyaR5LTWdN27A2bu3OpOaRru1DkVAESgjUHNdK99VoA9B1itcww4fFqZTBGgUpJEZDWkWXRPjkuWIxKyg+1B3d2aCS4nGlVF4PZY85b6QUbl9GxoEK5vvRljG8/8A8HcAvgGA9oMLAfwvAF4H8AcAfq/097vZ7Ka2ShFIAYGebjFDcdautXkC+b0Vx7z5MGfOwunpgRkagrN9eytaoXWmiUAt5jKk2Wmazc1KXWVNKImX/V+O5BOUlT5oOxSBtkWg1rrWtp2u3rEyY+U1JWXk4b17p/3UFy4U/8bqpfh+qYFxeX2kjydjVnh9PH3FpP41QLjobS9zmzLAIzVlcj1Mqpggxj7Mc6l3Pt8VhmE8lwI4AOC3S12l/SD/fgnAK6Vr3wVwCIAynvmeD9r6KAj4Fyk3cTMX8vXrWqY5YYABPPcV4NYtOJs3ScCBKN3Se3OIQA3msrwZt3heZhJVEi+M2MnG3bwVjUjJZIe0UYpAGyFQY11ro16G74rLJJIBvHYN5p135VnnheeB3vUV2r/QhdbCOIC5C11uC280tGK5eRPO5s3hfT1Jz2mguVRGLQzjuQnAEIB/B2APgKMA/imAVQBsCEB7Xlmlxd8GwD8MDbEYPRSB9kCgmvSx5UTsqpViZuPs2WM1r/yuR1sjUJO5zCnxkNaABb7HIc3U0mqj1qMIFBGBmutaEQEpMYm4cgX48CfAqpUwbx4CDh4QJiusea0Xutxi7Bf8U4DIOAdvvQ2cvwBMTcGsWAFn+fJg02Pf84yLoIHmvDMjuc9hGE/e8wyA/x7AhwBodkvT2rDHdwDwDz09PUKTh31Q71MEMo2AK32kIzsbSrMOhjVvcWqG3G4kmR7sjDdOmcvGB8j/Hkfwj2q8Un1SEVAE6iKg61oFRLK3k0H6438HMzIKh0LloSEwtQi1ew0dOcU4SGAoKVYY2HHxIpgvzsI5cQJm9+5AX88Zz09N2UBzDNLkBprb/+WGINWHaiMQJqrtFQD8I9PJ43slRnQAgJuokOfB0u96UgSKgQClj7dvS0Q5Od+7VykxY6Q5ErHcKKpEHE0EqNJG4jy7z4Y8Vx+FRGDWQtsEga5OmDNn5N3lGV2MpaeHIqAIKAIZQ4B7ufs39RDm+KfAhYtCY2Sspck3h36nExMwFy/KmYJ/OYyRdHYOY2zMnVs9qrlX4MgIuDzoH8tAc1NTGmjOIpLI/zAazxsA+gEwQgkj2X4VwInS32+Vggvx/INEWqiFKgIZRcCvWZSFjxHlfBKzGZK1rOTAyiiu2ixFQBFQBBSBgiLgM4GUfVaFp9OTgcLslT3A+fPChJqxMczq6pr+vSif7t2zuUW7u4FhG/yIKVYMNZ6PHwPbt8GhCXI1twkKHI8ft/dSWP/yy0DHbKss0EBzic6iMIwnG0Az2/+vFNH2PIDfAUBt6Z8B+BaAywC+mWhLtXBFIGsIcLHyBCYBF0I6/ftSMzA/Vjm3FPuQtCmfbtxZmynaniwjQJO1rVuttJtWCiOjWW6ttk0RaGsEVFBbZ3iZPuXRIzjbtwHjE0DfBrt21Xms7X4mreVLXxeUYqXt+t0GHQrLeH4C4NmA/lL7qYciUFgEKjbJsTE4lDzSXMMrMasVNS4B5CradOKk1FBV6pdA/VqkIpArBFJ+P3OFjTZWEUgbAa8JJOtOWlCbdv+arE80wEyf8vobgOMAA4Pi7iNavrg1w1kWYgelr4vir+oTOJoTJ6wZM4WPzIvKnM4vvWivNTlm+nglAmEZz8qn9JsioAhYBPyb5Lx5NoeWJzWD3yS3/D0pDP1t0o07KaS13DZAoPw+8r1pYRqklkCZZcKyJYBopS1HQAVBtYeAzCVTpyxeBIyNSSAcc+4csOGJ6maltUus+muWhdjldXtoGJFydnLNY8wNBoG83A88tRNYtMhiQKaTrlIdHfb33vWxY1oV7AL9ECa4UIHg0K4qAhERCAowRC3j5KTkj+LCzWAA1DimFuzH3yaa5uihCCgCwQik/X4Gt6IlV13CsmK9aklLtFJFwCJAhsLZ+SSwYEH1wDBFB4vCZPoxMihO5zIxvRXNcNy4eIXYrIv1ZuVwtZs93TDj48D9+9M0V402yprHlCv0BX34EPj8BJxly0D/UNy4YSPaPnpkI9xmqb81+pS3n1TjmbcR0/ZmCoGy1K2kLWGAIYfmL26KlRZoG6VNlOp99rnFihsU/+I2w8nUSGhjFAFFIDICXsKSD7dgvYrcZn2gtQgkrSV3GYpqQWFa2/ts1M54EjSxJbN17Tqc7VutpVWU1oUZxzxon6OuYVzjSA/19Ng/RsElvcb5RhNmNx0eXaZUaB9lRoW+VxnP0FDpjYpAAAK+TZKJas2Jk5LXkylWaLqX+kEGk38dHRLUyJw6Xda6pt4WrVARUASyi0AeCMvsolfIlrlacgbM417HI/EYAmGYpHYfDQ8GErl13zPAqdNwRkeBzk44vb2REAgzjn7Bevl7pJoSvjnqGkZmkvQRI+FSSbBihWUwScvRp5NmzGROVyyX4JEJt76QxSvjWchh107HhoBnM5CFat06oP+K+BA4fX2RN4PY2hVVChhbxVqQIqAI5AWBMiFZstgof89LB7Sd6SDg2efM1atwOjvTseop1Sv5Km/ehLN5Mwx98NJgdtNBNnQtXkYRIyPA0BCwrGRqO2cOTH9/NAFAGBrBJ1gP3dgUbyyvWSHXMLn/4IGyRRhNbMtl5KC/KUKbWFXKeCYGrRZcBAS8m4FIf8l0jo/LBkCNJ6JuBnGBFlUKGFe9Wo4ioAjkBwEltPIzVi1sqXefo28cNW5MQZS0VY9brzBZIyMw3d02cnwRTcK9jCIZcPooTk0BDGi4YAEkj3gU8+R2oRGirmG8f/Nm+WvhK1XoqpXxLPTwa+ebRsC7GdDM9sIFK3VsoY8n+1SW4IWUAjaNgxaQDQQ8mgl0ddo2MS+lazakfr7ZGCdthSKQJwS8+xyZHpp3MvhPnFGg3bVraBhuTmxz/bpoV83KlcCtW8DgIMzs2a1xYWn1eHV1whw/Ph2vYf164IsvbIAhnmltFeEoNI3gzjWvSa3ujRFmT3O3KuPZHH76dNER8EsN+/qsFJhMaKt8PDkmUaWARR/HNum/qyEQ/ysSKRRCUDORli9WHnFUIiSPo6ZtThMB7z43MQFn965oZp0h2uquXZiYAE6dAnbsAO7csdpVMrvd3eKPx4i3ZaYpRLltews1ncTowQOA2MyfH62rBaYRZK4x+KLm7Iw2Z2K6WxnPmIDUYoqJQHkDdDWLdPDv7xfn9FilwcWEV3sdFQGPZkIi9/H5Fmvfo3Yh7ftdgjfVYClpd1LrUwSaQGDGPrdhQxOlVXm0tHaJCSkZqQcPrE8ntauLFsF59RXLcBZVMzUyKkJErudkmAw1wHPmWDCZBmTNmmltaBWI9TIEI/EZPnECmLwHMB0LI9lqzs7UpocynqlBrRW1JQIBUkOJ8BfF16ItgdFOtQQBj2ainD6HREorte8uEFnVLHqYdYdtLaL/mDtGelYEghAI2OeCbmvqWmntEp/FCxdEi2cS0q421c5WPexZ22U9Z97Jq9dgzp+XnJNmbAy4dCl2TXSruttUvTX2GgoaJYfntevWR5YVPbVT1/2mAI/2sDKe0fDSuxUBRUARyCwCFZqJgwdsOykpj9MXq8HeZ1az6CfoWpECqUFM9TFFoF0QKK9d9PGk9o6moz3dalZbGuAyPq511YYNMFzbn3lGrFrEbLTIQjMvs0lh69iY9Q/2u5kQP5puM5fn8U+BBfMtfpqzM7WlRBnP1KDWihQBRUARSBiBNDQTjXYho5rFIIKu0S7qc4qAItAgAlleuxrsUqyPBeGjQrMyxBWCTZrOrl4NrF0rOdUrrFiI2cCAjX1As+0VK1CRUqVcon5ICgFlPJNCVstVBBQBRUARmEYgi0SSV0pepMi/Re339GzUT4pA7hFQoZlnCD2CTWE66fe6du0MN5MKzIruN+yBL82PynimibbWpQgoAopAQRGo2PDTMv2tw2BVSMn9JlltPE5F7XcbD6l2rYgIBGlBw+JQZ20MW0xm7vMINiWg3t698af8yUxn890QZTzzPX7aekVAEVAE8oFAM0RSgz2sy2B5pOSFCrha9IcAACAASURBVCxU1H43OI/0MUWg3RCouzbmrMOBgs2ACMiB/aa/rAQduqU5r1MYd2U8UwBZq1AEFAFFQBEIQCBpqXs9BssjJc9E5N8AiBK5VNR+JwKmFqoIpIBA3GtlvbWRXYq7ziRhCivYDOi3Yd71EyehKbWSHKDpspXxnMZCPykCzSOQp4W6+d5qCVlDIGfzL1D6HEcqohIO5upVCZ0vUQyZmsEXsTZQSp61MU2gPUXtdwJQapFxIZCztSuuboctJ/a1MoTwKfY6w3Y2yfuC+k1mdPFimJERYHAQZmqq2Dljk8QfgDKeCQOsxRcLgbZcqIs1hLnubeD8y7IZUYD0GTEwnmUcOjvBhPRmdBTO7l0zUzOElZLHOStIYF+4APPZ51KqRFRknwPMwuKstqKsVvS7ogH6RRGoRKD8zi5dKton/io5sStvK+63mNfKUMKnmOvMwuAF9Vs0nsePA8PDgOPIWsz5qPMvmRFTxjMZXLXUdkaglmS2DRfqdh7KtutbwPzLtBlRkPQ5jkHx4rB1qw0yEQNDG0fThMB+6+0ykUPGmEynEjlxoKtl5BYB7zvLThQ5J2XQIPrXyrVrRIAlODUSkdtdc2qti/46fRYjQc3M/LWAfpMZNStWiGkxVq6Es2yZzr8EB3JWyLIvAvgUwCcAjpSeWQ7gRwDOlM5dIcvS2xSBXCPgSmYxOSmSWX4vH1yob98GkznLWZMSl6HRDykgEDT/vATd0qV2Q02hKWGq4Ibv7HzSMoY7n5ypkQxTSNA9QTgE3deKaySombychM7y5fYzr+mhCGQNAVc7f+SoZXI4b5M6svzOJtXnCOX610o+Sr/EQDokQrm1bvXXye9teZAZ3b0LWLMGTlcXzMSEBBlqy75moFNRNJ6vAhj2tPn3ALwO4A8A8DP/ftfzu35UBNoTAS8hzx56JLPlhZn3pJUyoj1R1l41gEDQ/CtrPEmo3L49w8+xgWrieyRA+hxH4UE4lMudmoJ5732Yixfh9PXBeeH5smlVw9qDcuEhPlAYRbNa16yLDKgKqEIAp7ekjYAIWWkSfvcucPgwsHcvnJdetPM35sbUfGdjriuXxfnWSnPkaPJ+ib46M4lbLQu0CA3W+RcBrCZvjcJ4+qv6JQCvlC5+F8AhZTz9EOn3tkSAkllGQAsi5LlQ02yDHb95S86yoKXpv9WWoGunQiEQQCgUckMNwMGN0Gh+/DpAYnrTRpiPj8GcPg1n+3aYsTE49AlNOJ+n09sLbNokfqdYsgTOyy/Hp+kNNUl8N8VEuPlK1a/tgACFqmQ6BwaAjg6YY8eA3vXJmIUHvbPtgGFSfSAdkpZfYobXCNcCremItDr/kpqpM8oNa2pLOvrvARwF8O1SKasAXC995nnljNL1giLQhgiQkK9lHuguhEmawNSFNU0TqbqN0RtaikBpQ3We3WcJxoIKQcrv5aXLwIMHwNAwcP8+cPGSJahJYC9cKCH1RfOZ0KBJO86fB2j2PHs2MMsGs0iourrFlnEJch2o+7Te0NYIUBN/44adp48eAatXZ8pUv62xr9M5ESi65vrbt4ORu5NatzK9Rngt0DLmSlJnCAv7c1iN54sArpWYS/p1noqAGBlVYVaHhoYiPKa3KgIZRaCeZIwE7cQEzK1bwNy5lsCt5cAfZzdLkklz/NPpNBKUVmuEwDhR1rLyiECJQDF9G2CuXQcu94sWx9nYB2oehcBeuzZxc2SJZsuAQvTvZMTdzz63RGOrMPUSbmyDx3WgVU3SeluAQIBWS5ibvXutYIZM58KFrTELD2hbqlGgWzAcYapkEBya3GJoCIZBcV45GOax6Pk5s7xG1LJAC4eG3pUyAmEZTzKdPAYB/CWA/QBIza4paT155m9Bx3cA8A89PT1igRh0k15TBNoGgXv3gFOngO5u68e1hq9HOkdZMkkhz8gITHe3OMsXmphUoiWdyVevllaPg0ugiGbgJjB2G1i2VJg+BpNw9u61QY7S8M02pa3QPdfDLsnfXVyCXAeSrFfLzhQCsncE+HOKT2fveiuQcKOnptxyd19r2pwy5XYnWZ1gcvastd4YG4vkdxsZzwyvEWVXkqFhOBQgDg1Hd3Fq9d6U5ETJYNlhGM9FAGiSOw6An/8BgP8NwF8D+K1ScCGef5DB/mmTFIH0EZg/H9ixw5rxkfnk97QOV6tDsyhqXJkMefbsbAWUSQuLUj2hN1ndfBIdmdDjkFArygQK35Ff+yboa2n6+4Wgdvo2WD/LFMyQmbdTUqgwQigFQ08/lVCPwxVbgUsaTHe4ZuldaSNATTcjsn/4E5jRMeDsORvlc/OmtFsys74sa9xmtjadKxwvHtu22jOFWCOj9nO9/xHxzPQaUbJAoyhPYm84TmQ//VbvTfWGq91+D8N40peTWk4evP8/APg7AB8B+DMA3wJwGcA3S/foSREoJgIlxsVcvw7cuVPWpKCnOz08ujptwIGpKYAb0YoV4o9a3jjSa0l2agq5yZoLF2CYX5EMARmQgwdaawKZHQTjaUnIcYinsoBSAkzkJXdmWmbwpSa5+TrF5NZtpjvn3O9pngNwSbN6rSsjCNCf84MPYc5fBBYugKHlzqFDQMdsS9AvXRqZoG+oZ0ECwAxr3BrqYxwPNRMdOyqeeVgjmtlfmnk2jrEsWBlhGM/zAPYE4HITwFcDruslRaCQCJSlZoyMSd+t0VHJDdUSpo+MU0+PrT9lwjpzgx9ykxVGgCkuGLBheLj1vneZA7LJBoUchyZryf7jfDf519EhgYzMqdPy3WVIs98BbWE7IsB9yjA4y4L5cFavAh4+tBrQlIny8j7qZXTd/JFsi2rlZfoJXXHwgOxTvEDLibC0Rvm+dsIz7P6igo2WL19hGM+WN1IboAhkFgHPImauXpV0DFi8GM7WrdZfLG2mb2TU1s0gEIzSGdb0JrMAN98w2WQ5TvRf4kHtUpCGiddujQDjE5bo4ve8HUE5Kjuyscy3JbHT6PxImZif0UzPusUcojI2ZIb1KC4C1Gr9zNckMJ64h9y7B2ffPgkmJGvntWsSgMvZswfm3Dm7tyQxd6q8G62wTsj0ZOB4bd7cmFVOHjSYEcGv2F/WrrEBlBh4yTdHVbAREdgEbs8GRZJAx7RIRSANBLyLmESovHlTGD9z+3Zr/CrDSv3SACcrdbgENSOJkuCmOS0lxAwy4zmcri6YyUlrokyiq6vL82s+Ppr33od5803xHZQz+3ng5Ww0vg2JnYaBdU3iSwIQ5+CBhotq5EHvupV03tJG2qfPtAYBCSQ0axbMxYtw+vrgvPC81c73X4FhCqDVqy3Tef683eeSyHmre1hrBj/vtXr2F3GbYa51r9bcVQKoYKPlI62MZ8uHQBuQawS8i9jmzTYp/IIFLTMHqpD6qUlSeWqFMqOllpiEFnM7zptnUweUS8jHBxKMEk152TJpsBCQWWE8Vcs2PYkeG0mBINr1JYsBfk/z8K5brFdTqKSJfnbrovn3gZdnCquY3/aZZ2RNNMeOWeEcryUwd3QPy+70yE3Laq1vKtho+TAq49nyIdAG5BoB7yLGlAy7d6GlvloeqV+ucU2i8Y6QSYB79tfBYA2ffWbNcBmcid9zdlBL4Wo6mduN/qqS581nbtSKbqmWbRp1c+KEnYfMIUp/8BMn4GzdMn1D0p+861arrDOS7qOWHx2BasIhz3wp58+8ezeZnLe6h0UfN32iEgHPfPVbn1UINlauhGFArf/33wPbtmHWr/yy+N5zrxJhXAb2zcqOtcc3ZTzbYxy1Fy1CoLyI+XNIeVI1+H0MWtTU4lb7+DEk0fbtcYAMJRNttziFRVKDIaZxDCtPzSeDJC1YAExOphONsl6nakmh6z0b5fdqxLNbRr3f3fuSPrsCEPecdH2e8svrFsdELSM8yBT7Y7XI3hXzxTULZzwBnTvFnjAZ7X3FfPXPUY9g4/Gf/CnM3/4nOD09MD/8WzCqg/OV/elGcc4ohkk2SxnPJNHVstsfgdIiNiOHFH1ixseDfQzaH5VM9VA0bePjAJnNGzfENylQK01zw0WLbELuuXOtxNPnB5qpjgU1xmMqJ5pO+qwmZBIXVH3NazWk0DWfi/hjPc1qvd8jVtfQ7RW5PJnyKG1BiIf4aqgD+lBbIiAuCfSFX77cauI/+9z6wrdqvmRFSNSWo93GnQo7X7/4QphOUQ4Qji++ALZsEbotM/tmGw6TMp5tOKjapRYg4NXmcLP86CNg2TKYR4/gLFkCc/xTNd1owbBIlRybzk5g7Vr7Ry2gG3DI2ybmrTt1yvpIMq3KmjXeX/P3OSVGLywwNaXQYQsJc5/3XeT9fv/Fer+HqaPJe0TwwTnItrnmXE2WqY8rAk0hwEBXg4NA/xXg0aPqLglNVRLt4SwIiaK1WO/OFQLbtommk044ZmhItJ1cjxlwTa6pG0Iiw6mMZyKwaqGFQ8C7WDHUPE06b90CRkZguKH39IgUWSNItmBmeMem1kYyfz6wY4cNLtTdDUkp0ILmxlVlaoxe2AaHlUKHLa/affXGu97v1cqN83paWMTZZi2rrREgkyf+7xTMXbsOrFubvibej3AGhET+JhXqe5trnOnTKUnTqPn8yv6yj6eMMeee30y3UIOfXGeV8UwOWy25QAg4vb0iKaaPDKXFzrPPwty5YyXIY2PWXCkrJo8FGhd2NTQD1tMtkUadtWslaAb4Pc9HQZmbivEOyOdW/t3vl80k9UGa8DzPAW27IhAWARLaW7fCrFxp962entYGymO7syAkCotfK+5LmDFse43z3LmY9Ru/PmPkNGfsDEhivaCMZ6xwamFFRcD091ufzo0bYc6cARds2cRnz4ZDcw76ezI/Wi2NW1HBS7rfIRmwMkOiks6kRyTZ8j3jXS2fGwmLGX7ZFFK4ud6SbaGWrghkDwEyeQMDkr/YcN/a+WTLBTG6JteeJrEwhrWYV9U41x4A/bUhBJTxbAg2fUgRKCFQWrTNO+/a3I+MmOrP5yna0H7x51LTjQzPHA/DkuFWatOiIFCLcBoYBC5cgKFJPIOpMApwGoxnLUIvSt/0XkUgRgQyyeTpmlx7hGutb7WfLP9ak3nNs8a50XW20efKiOqHeggo41kPIf1dEaiBgLtoY948CUwjaeAXL56Rz1NNN2qAqD81j4BulsEY1iCcmHKGOdwklP4XZySolPPcV4LLifGqu2Y4S5dmI81NjH3TohQBRSBFBGqsb6FbUYN5zaQwImTHIq2z3v2T+WnpHtXZqetzSKyj3qaMZ1TE9H5FwIuAu2gzETGv378P59l9ABeyI0enI1aq75gXNf0cMwKRNtmY685ycTUJp/v34GzdIuaETucy4P69dLrirhnq850O3lpLKASqriFeotyNwKz7WShMk76p5voWtvJazGueNc4R1tmKuX/sGLB6tUTAZ2TbGVHRw+Kq91VFQBnPqtDoD4pACATcRZuRaxlMiOZ6V6+pxCwEdKndEoVwinJvah0IUVGETTZEae1zSw3Cydm4CebiJWtmOzwMfk/lcNcMhvBXn+9UINdKQiBQZQ2pIMpPnJSC1Bc6BJ5p3FJjfQtbfSzMa9jK0rwvyjo7NAxMTFi3i9mzgWvXhPHU9TmZAVPGMxlctdSCIOAu2pKnk8FJaJ6hErNMjX4UwinKvZnqZJRNNlMNT7gxNQQJzgvPS+U0uXV27YL7PeEWhY+ynHRDtHxFwItAtTWEDOnixTAjIxLt1kxN2TmsWk8vevn9HAPzmsXOu7QZNZZ1Y2v4c3gzrdqCBfWfy2LHc9AmZTxzMEjaxAwj4C7aTATPACULF1ozjRs3VGKWlWGrIskPbF6UewMLaM3FSJtsa5rYklprChI6OuAceFn+Um2cu2akEcgo1Y5pZXlGoOoaQob0+HFgeNjm+WR0dkZt1/mb5+GebnsN4dz0TW3+yZfDm3NbXKbavNut6p4ynq1CXuttLwS6Ou3mTJNbaj737FGJWVZG2Ds2JPoPHqjesmpS/+pPZOMXZWaCxyGngoTgzuhVRSBBBKqsIWRIDV1IuLcxavuyZer3luAwpF10TeFc2o2Jsb5I/Wq3HN4x4phEUcp4JoGqllkcBErSQjG1HRoCurqAjg6gd72kVSkOEDnoKQmnmzchY0Uia8OGGXnqqkr9c9A9bWIAAnkVJAR0RS8pAi1BgGvl7l0S4VMiMdMv+YneljRFK00AgXYQzgVpbSOky9J9P4F5VaNIZTxrgKM/KQL1EHClamRoQJ+X1aslATdGRus9qr+nhcDIKJytW62P0uioZT6rBcmoIvVPq6laT7wIOJJD9wrMhQtw+vog3+OtQktTBNoegVQJ8yAmQv1Jk5tjbSCcc+kwb4qqSOmydN9Pbn4FlKyMZwAoekkRqImAZ2M0V6/agEIrVwJMRD84CDN7tjil1yxDf0wPgdLGyrGB41hzsaVL1VwsvRFoWU2mvx9mfFz80RihEP396pvWstHQinOJgGe/QwrpVIKYCPUnrTNzmhijVIUKdbrR8M9BWttWpctquBPFeTAK4zkbwBEAVwH8YwAbAfwJgOUAPgbwmwAeFAc67WlREaD2xLz1tvV5oelmdzecLVvkzHQqzs4nrRlnE5tBUbFNot/uxspojNRK00epEGHS3fnHUPGM2scACj3dgSbGSeAeqky3jQzOlQRRG0SQaFCUUEOjNykCko/63cPTkdqvXxdQEmEES2uBeeddYN48KyBkbVwb9J2tORmbYtbbQdsXoLWtmi7Lv+f09oICSplnSexBNUeumD9GYTz/KQAmcVpagupfAfjXJebz3wD4FoA/LCaM2usiIWA++9xG+GPABWMsA7poEZxXX6kg6oVBPXESXvOPRDbsIoHfSF9LGysZUG7QEl79iV47Vo2Ul5NnXGIE1PR9+BNg1Upg1Srg4IHM+B+7bUzsHQkgSHIyfNpMRaDlCMj7yfRgjFswMGDXj4QYQXctEKbz1CkY+uRPTtrc2MoQ1J4LzQjY/IxYQOyD2pW3/ldXuFyROoVuFsyV7E2Xxb76BSn9V6xVzNKl4sfMZ5ROS3ZMZ4Usfj2AfwTgj0r3OwBeA/C90vfvAvh6yLL0NkUg/wjQZJMHkw0z0t+z++xiRV8ULm7UilJyOzEhmiYS1iJRy3/P89sDlwH1jlV+e1O/5S4xQi3F/fvAnbsiMBHBSf2n47/DfS+OHJX3Q6Jkum1cuFAENHG/IyRIaIEgOdlcSwT2LKgt8fdYS1QE8o0AmczVq4FHj+xexzRhK2jklsBRWgucTZsA5lF0tavMjX3ipBUaJlBtWxRJARsFjHfv2nOEMSoz/JOT+cU5aG8vpcua9U9+06bM6uiQOSR51l1BCvEiY0r6LKE9qC3mV8ydCKvx/N8B/M8AlpTqXwGA0VOmSt+vAFhXpW3fBsA/DDHqpx6KQM4RcJ5+CobBhCiRpWnt009V9Ki8kNNciJJb/rp4sfp9VqDUgi9kNkoaz0TMOlvQpZpVur6t9+/D3H8AZ/Ei6+Na86HkfnTfC0lGf/y4pGhwurpgxsZAMU4i5s9VzMjKbVEpd3IDriVnH4F6ayIZGDKAtJS4cQPO3r3JWYp4rBMkH/aSJVZAxPVBU7jUnEuBGr+aT3h+9Ar/eDkhjbanxtZ9dAUp1N5TaVCa07L3JLUHta63ma05DONJf85BAEcBvFLqSUndU9Evoa8rrtgv3wHAP/T09FS7J+AxvaQIZBMBMcOgZpOLmGsC5G2qu5CvXGmZzvv34ez/cnIbtrdu/VwVgQrfXI5fhkxOqza6iR9cYsQ8eACH/S2l+vELSpqoItqj9DWdmIA5fRoYHAI2TcGQMSRRuWCBCGbcNkcruIG73XeUUm4+3s7EVj146jEf9Z7X33OLQD0BTPl95Pvh7mFcSxI4vHU5xsAwGBwFvCMjVkj1qkt+JlB53ousImAL1S0Pw5+I8C9UI1K6KUiQ8sLzEnSOe4Czfp3SaSkMRRjG80UAvwjg5wHML/l4UgPaCYDPU+tJU9xrKbRXq1AEWo9AvUXes5CLppMbtgZHaPm4VfjmDg+D353Nm1versQaUJqnJOhmaHoTq7RGwQxwdOqUmPya4ZtwtmyWiNDCdD67r8aDEX8Kw0h53tG2J7bqwFeP+ajzuP6cZwSCBDBB60Ua+5dnXzVHjtqAfT09EileLIvoe6hH7AhUMPxexivMOhp7a5It0NtXEaRoYKFkAa9SehjG858D4B8Pipz+JwD/FYA/B/CNUnCh3wLwg9I9elIECo2ALG5ctBmEiAdNcvmXkKS40GBH7bzrm+ueoz6fx/s9BF1Lm8+ouvTdYgoivgt8R5iMfn01L43GWhuGkfISIIWXcgcxH2kwGo0Nrz4VJwIBAphMWIawXQMDkhNb0pPt2D5TeKb7aTwzocr+EGYdjacBKZbi66sGgEwRe09VYRhPz+0VH3+3xHT+SwDHAPxxxa/6RRFoRwTCSAG5IfKPzu30ITt12qbxUGKupTOinm9uSxuXdOVh5m3SbejppqM/nH37YM6ds/7R3oA/cdXvZaTY7+OfVprFl95PsULQd1LcBRi8JTE/27jGVcuJHYEgAczjv/mhNXFdvlzOrbAMmSG8dSOPlgINEQi1Iop9OlQW6F1H+Us7uiME9TFI469Cjsq50eS3qIznIQD843EewP7SZz0pAoVAILQUMGhBS5vIzQKzkZVZQY0zD6bAIdHy9FOFIlxCz1tBJ5l/FUSuL/VQrDV6tThkcPke0Mz38GGAwVFeelGtDzyAV4yL19TOc49+bFMEfBqgci+ZJoyHey7/kNIHv/D2yBFgzhyYkRGb45P+4mnvpyl1PTPVeNfRBCxTYu1no7ROQB+zsFfGik0GC4vKeGawC9okRSBFBIIYyiAJmX9BW7vGppDwBiRKWIqmC+j0vBAsTp2Gs7wUdt4lbKZvae9PQfM2bcKtGpEbM/JeRkoEDXfv2hyEDKfPnIS96+HQt+e9920o/b4+OAwwwRD7RTxSGpciQpvHPldYhnR3z4janlqfPGsWmU4c/wzYswu4cAFYsya1ZhS1Iu86mnV3hEZpnaA+mo+PTadX4eC3o6a3xZO6oDtti1HX6vOLgJ+hXL/O5ux86+1pP05GS3WJem6e9GGjRI7mbGmmb/Bs3IWP3Fl0LALmbX5fwiot5zvG/Lkl32rRand1wvzwby1TyVyEzEl48xYMTffefBPo7rZnasEPvFylYL2sCBQHAdm7KJjzCkmT6H49LZVnzcLDh8DupwHmW+zultzYSTRJy/QgkCeBVKP7e1AfPfMuiRgEHoQL+1EZz8IOvXa8EQSCJGTVfGK8PmQSpc9NUsyK05Ci6QI6PcRRsKhHEE2XmptPQfM2N40P2VCRelMANDws+UqZa9ehEGjvXqvpJNO5cGHJp/GEJWCZyoUWhUwiroxnSKT1trZBIGitS6lz9bRUFWsW/cKZz5M+nrdvA/QX1yNdBILmSsJWW6E7GGV/r1NoxbxT14M6aDX2szKejeGmTxUVgSAJGbFwfWHcs2+RBjUvjNKXYpJiXUCnJ2kULOoRRNOl5uhTtXmboy7UbSqFOfTlLfnxyueRUevT2bu+UoNDjecbb1gm9doN4NWD0xYLdSuK4Qbf+iDzMytEXAzd0yLygUDQWifWOX4LniTSTkXRUq1bC2fdWoDvszIDzU+uMOuP7x6ZF3RXSdNqK2RPo+zvdYsswl5ZF4Rkb1DGM1l8tfR2Q8C3GHPBC/KJ8W/ozo7tcHY+KcRvahunLqCNzb4oBFFjNRTjqYB3RaI9V7veLCpMDk7mraTxFAaU1wIO+nSaa9eAj44Au54Cc4nynS2byAc8E+cl//rAstOqO85+aFk5RoDvISM+Dw3BPHoEh9p/mqFfvSrXRJh67br9ja4jcQtG6mipKt4RCm0ZATvOfL85Hrpmmz4jZc7LLwMdsyuEcxX4nzgJTE1JjARajWTOdUdpnWanRKrPK+OZKtxaWd4RmLEYuwQjN2WPT8wMB3VKarlpur6feQciZ+2vOm5B/aB2+vjxsgaM5pp6REegGubVrkevofIJkXofPFDp41kK/BXkX+1s2QKsW2fNbxmAKA3zd7fJKtxwkdBzixDge4ibN4Fbt4CREZgVK+C8+ork2cWtEYCRoB88AC73JyKUKWuphobhLFkCDA2DsXTlemk/pXYtk4xOK8YsRoGd+MFTQEfrkOFhmEOHgLVrK7SZXA+9+Jtbt2ze5RSttloBs2h25d24JW4Z5fnYksa0Z6XKeLbnuGqvkkLAtxi7xKrXn1OqriPNTaR5MW5MibSvlYVy3BYvtuH4BwdhKL3dsGFaiu/FbmJi2nS6lW3Oe91B7woZwQAtSywCGVfq7QqBXA1NUDsoAGrFO+qOaSvrdtug52IjwPdi40YYmqdfvAR0dkq0Z0w9gvnx6zBjt+F0LgOe3JGMUKb0vpLZFMGQ48iZgyJr8927Fb7ZEqSvwCMWq8COY07hwvgEJHATx2LHjkom379GPf2U3S85b7Jm7uzdv1csr9zbI86ZWHGOWHdRblfGsygjrf2MBwH/YsyItQGHbJy8nuIirQtmwEC4lzhu1GK6ZpizZlVI8Suw++lPJfqpaMSoCRsZdUvRcxQEiDkjzNKk9cYNG+SHqRCCtCxRyq1xb8U40jyMR5V3thXvqNt0qZvEUikCr/ijkhh0mWX3Rj0rAkkh4K6J1HguWwrMng3T3w/McgAyFo5jrQEmJ+UdSqoZfs0avwszOjZmo1CX1o7y+5pYQzJecDUBWgPNdrq6YDiujElBzfa6dTO0mWW8vTQM16cMWm3NMB1mULlG/ZKZI3ZiAgxOh3nzRBOfxT43MOyZeUQZz8wMRcYbEqNEKeM9ndk8b9+XLYOzaJGkbXCY/6+3d+b9vOJqX9JYpNk+ppH4qx+IaZTZuVOi/7na2OAGFugqCfqpR8C588DoKLBnt/Wn85pWejZ1rFwJhfx4FwAAIABJREFUfPY5zPi4HUc1tW1osgjhwiA+588LAcmolMJ0Llhg05vcvw90dVnpdEM1+B7iexCgTXWe2WvTGQUweTMsFXxFJvaVBBz/Ojqsedup09NrRmKVegr2rmlNagg8perHHCHA95PmtSL0WLmy7OPJLjjPPSdRZDE4KNGfy0xIEv0LEFCxTYxgS/NP+eOaUXShTBUBWkNDMn8+sHWLNbNmXmNa/6xZLUxW2eyZ++ZjY31+6ffLMSE9k8FxEAEeGcXly2WP4ffQjKdvLQSFzadO2ajnmjO2oelV7yFlPOshpL8LArFKlHKGaYUWhVozbsxbt9qw7v391YOC+BY0WdwTWLSlfYxCODgEQ3+Zsdsw27Zaf52cYZ1EcwWfd96R4Ahm8h6c8xdgSPDTn8k9PJs67twBFi9yf9FzowhwrjMQxTPPWM0JTee4oVMDylx84+NwSPjG9E5wnAO1qSyff61i8qrh5xF2pB2so2JNK2mGhais1la93n4IUDi6e9d0funbt+E8YQWpEoGdWrHZs21QvJje0SAQZV/0Cagk+jvXh3b3JwwCpMq1MvPv1UBWubfuZfruklHjOkwrILqe0Ozaa/Z86C3g9BdWI7pwIQzvcwXqdStowQ1uRgH3HLIJM9ZCYkOzY541Z2xIFKPdpoxnNLwKe7f55KcSAdI8eAhn7hwYav4aNWXIG4oeAlGkw2x/iMhuMxY0MqxJaEBpGjIwYDeFOXNgBgbhbN5UXRubN/ybba+bZmPLZjhdncDEHQmqUN7IXZ8i1sN7e3pEsIDFiyHSTzW1bXwEPAy95N/jRs6AIUls6nxPN2+GYR3U1DBYCiX5pXH1BsrIhDWAD5tUfdg8a1raTG/jk0mfjBuBivfD77cXB4MTpsFkan0CKpo4Mn2KmqJ7AHSZvjhoCGo8g5grjrkbC+HjYzCX+uHs3AHQLJdrqtdKyNO0Vn8MyiwQqk0BVjKGVlGLF1tLFM0ZGwrGqDcp4xkVsaLef/o0zNnzwMIFMIPDYm6KX/56bNqKTMNKAtH1VWMQhocPbUAGalBcM8wg7abrK0Afmrlzk/MVoCbpi7PAjQFQW+cwGETJXycRRjfTgxXQOG+ajdmzgU0bRdJfoWnzbuouQ0A/UGoBqvjxBtSkl3wI+AlbCVxC7TPNuB4+jNd3jON2/bolkvgebNtmW8O6MhioxI9N+bsPw0S+unNcNUqJwJubQr3rnqfRsm9UY3CC9rpmNaL++cjUYzyyZqXgwSjXH3u6JWWO5OT0MlfLlsH8yZ/CHPspMHzL0hHsaOdSG3m4Snqq1LHgHKR7Ucl9wqF7EWkxCold14GgRvnmruxD/pgDLIfzOS3BS1A72/yaMp5tPsCxdY+M06KFwOQ9YAmjg45WBGeJrZ4MFiQEoWsKtLLH+gpysVq1qtzaIO2mOO27vgI0U1mzpnx/nB/MyAiclT0w9E3gwYWX2uiMSifj7HuYsmT8AtJsVHu2zADoxlMNovDXfYStOXcu/LMR76x4T1evtj5qNL8lc+UNVLJrF3DpMh6/8y7ET/uF563PacT6mr7dh03T5UUoQOd4BLD01goEgva6ZgWcQfNxRkoy3c8qxqGZL0F4S3lXr8Ic/QS4MQjcuw88egwsXggsWQQ8tXPagqSZymN4VuYg3YtKwQIZCEgYTzLGpeBU0kefQGTG3KWJsd9KxvVjrSZ4iaH9RS9CGc+iz4CQ/aeflnnrHYCR7ubNhbP76eIwNh5TIErZsG2rmAuSaC1HPCWT4s855pqzMIhKkr4CbF/3CjEjFc0nBQMTE2V/nZBD3L63kcDfvNky42F62UKGIEzzcn0PicdFiwC+E4wYyO9xmex73lOa7omZNMsvaTylTjKkZ85IlF2+k+bNNwVO58DL6cPqk74HEUqJNUrneGLQtkXBteZm0F6XBJGu+ZRl7SKzxBynYrUxMmJdap5+qrlAP/73f2oK5u138Pg7/xYYvmk1fmQ4794DOpfB+fovNVdf3C+Fu64zRgAPzldqP10NOf3Wue5zT+C9rhbUN3clNylppZT8meOGIa/lKeOZ15FLu929vWLCaS7329xeJOyyYnaRBhYlUyAhlsl8kmj1mmH6TYVc88yhIThr19pARDRvSeAo+zdQerd1s43sNjVlF1538U2gXi1SEYiMAEP3u1YASUQMDHoPL10G3v/AmrvTt5QRMvl+LlsmzTcXL6IVjOcM6XtSPuCRB0kfKDoCNedm0DvWJGBBwQubLLItHi/jMjAAc/oMnFUrRcgsqT5c5jGop9UEB1Wum/fet1Hx704C43cgUYbmzRFtp0NBO+MdJES/BDW/7jWv+wyVISUG1Cv8n8GIslD/3GVuUlrEBEQ8r9sGvaFhBJTxbBi6gj04NgbnH/08QJM1OpkzAIsbuKOdoeBCfe48zBtviHkeGHr85ZfLC7GLgXumdG1GcuWgazFiJmZOrmSP+adozjg0BENTFBKzcWmUYmyzFhUjAlWIiRhriK8o1wogieBCviBR7nv4mIHRqF29cwdmfAIO/XwHh2yfhofh0PS2FYdP+i6S+SQ0R63om9aZbwT8c3NoWHgRmaNdnXB2bBdrH/cda7azQvjTbJIMxPCwTYvEPY2CUzfVSwGDvEl6qNNfwFALyRgOdHciI0hcqMmrsl5UExxUvX7hvHWjYjqSjU8Ag8PAhvU2sNALz0tk4zKN0+xgx/C8tMXrPrNzJ0Az4Y8/trlfqRjhfuCzQpPUWvzBQ5MRk7KmNO20VjFgkccilPHM46i1os2UFA0MpGOSkBYhHaIeWai/9z2Yjz+xyZavXQc6O+2C5o5DjXLKQRpq3OMW0/CZkk/mZKPkjqaDzFfJDfzRIxjmSVTGs2Fo8/BgNWIik22n1JwRmHncuGH9nuPUynveBRIXfCfkoFScqSEWLgDI/DIA14MHwnQ69PFsxeGXvrtWEq1oi9apCHgR8M/NJUum066QDmDgLgo5334bZskSOK+8IpHUKwK2ecsL85nvKA+eKdxmdNGr1yTirdm6Bc5rr4Yppb3uIRPOFGmMVn99AObWCJxHj4B9z9S2OCNjxRQop08DFy/BXLsG53d+25rs3r5tr9+/D8M1kAqEefOFvhPsO2bDeek54Of/oQThk98pBMjSwXXe4z4jmmEqRShU/MlHwJefhbNvH8wXX1Sm5HG1xF6G3S9kqcHQZwmCPLdFGc88j16KbZfFh/V5JEVJVZ8WIR2qHi5CV67artLcZGAQ5sdvwFm/XjZiFwNz4qQNv10lJ17ZZMYlsg8eiJUhdPsiuTzPX4RjAPPwIRxKkfWIhkCSQoJoLQl3d442Tqe3F+bDn0hqJmzsAwNj4dKl+NIMcezePQxz7JiVfF+/boVlc+ZYwRHNa3c+CWfTJms6RuaUuXhJfKVMXKW5ps6YSHmb4zM6oBeSRMA/N+lj6JAhLKURowWQef9DsSKQdjDY3q//WsPvcdldhPsjhaaMeM18jGvXWObz8eNiWFj5B7W72wYOpMB77hyAfq+0PmO6j1oWZ7zvz/4c5uQpy4ydO4fHFBCsXg188CHEVPf2OBwGidy9S4Ks4bVXgPMXAK6Vz30Fs37hH6e+Jvq7H/o76TS6cXD+bNpYSi/n2By09WhWv5BFBYChYW/0RmU8G0WuaM8FSYqSwiAtQjpMPa4vAX0feJCZ4zVvHs8Akw6/CYyYEnFzpinLzZviUxCrJpJ9Yf4tbhpMpTI6Cmdjnw1qlNQ4tUO5AQS4y8RLqPkqgoRMdT1o4wzoV9qMVRBGZPLAlCdLl4gkXu5htGevBDrowXrXSv0V07SPjlhiq/8K0LcBePZZOD/ztWlmlGZY9+5Na3BaNcZprqk+/HI3x33t168JI+Cbm2JNQ+FqKf0OLveLxlN8/+7cAfiuNaEpqnAX4XpG83im+eB+SS3WypX5YYJiHBpn9y4rnKNrwPYtEFNRug2QMQ8SlHEdZJqRH78O89NPAcZ6oIbUGJi//zGwcYNNi3fvntVQT05aWmT3LoCMLJ//4Cdw6JZAaykGXevIEJtQbV8jTcY0XZwz4xN23SdNtP/L03sLnz1zFubQIbmPGlHnpRfhrFsnwlBz9CiwbZt8Fxxogss57QYmCsI7xrEuUlFhZtR8AHQWm8esSgC+B+B/BbARwJ8AWA7gYwC/CeBBkcArXF+rvfRxAxFESMddB8sLUY9IFZmv9C//ykrTyMxt3WIjzHmCC4nGs7QpV837SAkuD/dsv8Xzn305ftxuNEsWW3NC+uJwQ9GjKgJBBLho9X2+IU0zRlVb0PwPZck3N9pSEvigfjWb8qD5llqLCZGoX7shOYFx/DNg9+6mi3b7S99m8RHju8l0R6XySWDQP9slJPwanGaI5qYb34oCwgjdWtEurTOTCPjXGHP5MkD3k9Exy9gw6A2J/0YPH6Mr+X6/+AK4cFHStzFPYxGP8pp94wbMp59Zxsqgql+6rIOM7cBgOQvmA+cvSRYCSY0yOgbDQGsUTo9PgBFdnS2bBVaOr6Em9G/+EzA2DtNx2QYz+uADOK4mm4xXWjRglcF213m/UFjaTwHmydPAujXWpJgaUPeYmsJjaoD/w58CtLLZ0AsRnrjMJE2SmXmA+eo/+FD2inpWbG7Reo6OQBjG8z6A1wBMAJgD4F0A/xnA/wjgX5eYz38D4FsA/jB6E/SJTCIQsMBUe+njbr9/kyt/j7micrkegn1GFdwQS1I/iX75xBPAunUSZMkl8svP1CinwpSI5jOlaGrlZ5v8IAsvTZS2bIZEoaPJyapVDZs+Ndmc/DzuJ8AZtp7aMNdUk5rtrJve+Ik2ou/vVxPaiFgHk8QppfVrV1vJNNMy0eey2aPUX0PpPkPqL1lqNSVu+T6M/BqczI9xs/j4nw8hdPM/ot/bCIGA/T1Qg+Z22ff+YPt2mJ4PxfWEvtKy7/T2unc3f57lSABDMbslc8DvRTyk77OAp56ycSXIiNN/McgvnWN6/FPg7DngwUMbxZvM5527wBPrbQqrOXPgUOtJgUH/VTFLdXbssNrTa9cswszZOXhTgg0ZCu5onsvxJ3Pqc2PgA2XmOI3xcfc17hnXrsG8Q3YE0ja6Txiag7upujz7ikTt/cHfAKfO2vyk43dg9i8CLl60rSbT6Y1yzn0/R8LnNKCPs44wjCf3aDKdPMh48o/XyIz+l6Xr3wXwL5TxLKHRBqcgJjM1Yta/ySWFp78eLtxM8eAzr6B5oBkflwVWUqjQ+f7ZfRWtksXXay7o39i5KXNhZNS1JUuAx8ZqUF2JW0VpDXxhX55+yvpukOkk8U3mNq7yG2hSph8pjY+5elVMn2n2LLlPFy2COXvW5oD87HM4X32tti9NVjuZFcYi6D1g0Acy9iR4aPYaR5h+t7/LlsFQszlxB+D8r1J+KKFTVsc2hnbR15bmkVzvmI9YvsdQrhaRDwSC9veqDIT/HaZJJglz+miPjwPLO0XDxDJjcx8ZGYWzdat9f+/enc6XnQ94420lma3OTstUkZliOqiAfZ34MygTNXWi6Vy6WFKv0C8U3cstA0rinUGb+Hxfr2XS6PpAKy5qCEdv2z/SEJ1LbC5wfi4FahNzVJqzum4MtCrx0j3x9nxmaaV1HleuAIwVsGolzJuHgIMH7D5SJX0dlQaSn3TyvrU4GxwBTp6C80/+a6nDzedMaxmJcu7uJ/Ws2Ga2UK+EQCAM48liZgM4CmALgP8TwDkAowCmSnVcAbAuRH16S14QcCVLXl/GNn8ZAzdjSvkoRWR6kkeP4FAqFkJ7NKMsEnnnz1tT2NFRGPojkIFNc9HOy9xLoZ3l8eGGTp9b+sTSn4a+RbduWZ8iXmcgh4BNPoUmNlVFVhirMs5Ll5aDcfnNXsttbaLH5TK4bn3jV21JTL/g+uf4y/YLnfy/t/l3vzANDLCka1Gbj7qne0H7e5XxD3qHqeU0Z87YAhn/4NateOMWtDmt4RmJ+h+JBd1oGCiQGr1Zs8AxmfG+ki6ZnAQ4HmJOe8dqNOl6Qz9Zai7JSPFd378P2PslG5mYZtNkWnmQQaXG8N4DoHedBDMSJpWWKiyf5cbsJmErDvffXecNXZ/IhFNTyci/n31ugyGxGM7tksuJW6rzxBMSERjMTTr1CJjTAaxZXaE5Fou2XbvsNXfPDyjLLVPPjSMQlvF8BOBLADoB/CWAJwOqLDmwzfjl2wD4hyH63+iRDwQCFn73pQ96sUN3Kkh66r7koQuJ6UZfW8Tvy2deIZOaQYHIjIyMwKxYAefVVyob4CtHcPJt7KJJpeSQ5rA8SlLEWKWFKiWuHJda3zg+DMZEfw+m9+DZNX/m5s7DPdcqJ8xvQfMjyTmfdn21MPC9B67QRoimKoRureKq/haVkcwSRlU7leAPVcYlwRq16CwhwKinn3wiAlUyKrTskD0paF0Kmivz59ugLFeuwFm6zDI2UftX4x2sSmvUeCZq9Xm5n1iQ7pDxcXOaBgm/ZUyP20jDk/eAVd1WM/kLPy8MqfnkODB/no30yryg1FzSz5N5jFkec7O/esBai1D7uXoVsHWrWE5JGwhYEm4SUQaitM4bpoijWwXnq7tP19oD6B61YyvwyefAsnnAii44v/rL5cBJdKcSlypPW2LfozxlF/1jWMbTxYlazkMAnisxoXyeWs/1AEoG4u6t5fN3APAPPT091ZjT8s36IRsIBC78tV7skM0Okp7OkNyFLKvZ2yT6Gx3xyQSybzSFvXOnHLlP/L646dIMs7MTeP8DG9qdqRhoqlaK9hbUJwl7Timlt2xqPF2pJTeSZoIxeDtf2oxnmI1m3TfR24e0P7tSZCbnZqqAlT0wb71t54C7yVPI4DKjTbQvaH7ENuenpiD+KxcvWpPJF54XiXZmAiMECLCagDLco0HEKSX9niiFfC/NqdM1UyCFqyynd7ViXHIKVds2mwLV6wzytRDm3DlgwxMztWjsvH+uuClO5s4VhlOsRfhsxABAFesig+HQfJOm8SUrhSDCv+KZVkWjTntCkDahNY6bso2B054I9qd1OjpgbpJMBzB8C3hmj6RIMTcGrPnt7QmYS/1wdmwXzabz6qui4ZNo46RnyMgtW2o1pa++UjEfhCZMwk0iDJ7uml6KwSDReklbkfkMs0+PjWHWf/stmJ/8BObMOThbNmHWr/5KmJr1ngQQCMN49lDOUTKtXQDgawD+FYA3AXyjFFzotwD8IIH2aZGtQiAGJjOw6UHS0zg1H4GVBl+ckeJk2TI4PT3Tfk8Ms00TWUqGqe28ehXO9m0wb71lGVUGHeIR1Cf6VXiPdevg9K4XkxBeJkNTZu699zXwuWIzPnVKtHjOl7+sfls1sBQJLhlMBlvYtsVqormR0bScGm1KgKuZadYoN/CnoPkRx5x//BiPv/d94I1DEsnPnDtvq89QYITyHCcGPvOnQKxiuFjxPpSIUxZbJtx4bWoKDlM1uK4EQ8MSuKBi3IO0PzG0LwtFVIwLGQkSdkeOTs/5Nu57FvBveRvEDH3FdLotRloP0qKVArdIe0vvsMyVsTHxr8P58+J/CKb4oL/85k2WeQnTQc+6KIFiWNYzz0yb5AetkZ5nxC6lSpvDVJ+neyre12rrKLFYtND63NLctnMpnHVrrUbwzDnr48lxvjoAs2gRnJ/72bKWT8o/eKA2fUKa0BcdvNyuhMEsKwlonUQm+iv7y8GnyJTXbQeFJwMDcF56SaKoOzuftMGXEm63Fh+MQBjGcw0ABg+in+csAH8G4IcATpSYzn8J4BiAPw6uQq8qAh4E/NLTVmvl3NQmPNNPhSZEGzeCQYQk0TI32NWrYX7yETBvvl3EmRPrwvnyoj1DIsw+cYP0Bkegr+Cz++ILvuCBVOqiDx3NRWl2wgArDPpQJL8tVyLqZRhrEc+uFJlSf2qhGXCBjGhPt5XyBhE9XsyjfE5ozstm/KPXgXuTwJVrcNatgfipvPySZbJaFRjBOxZ+AQxx8/7uMve1xios1qVyJdIhCWGapfFZzgkS0B4zeqYSkEBhLkZLllQyprw/zjkQtg9p3OfDv/Da3zQwz1odtLbhOxfGAscnhKaAQoLdLFsqKYskLUfHbBswrJrWNKj/nnURN25YH0RXEFSNofQ8I+9vLfrBN8+FOYljnQnqS1LX3D64mj5PpNYZVTIaO3Fc2W3NZam53Nhn6ZkfvQGM3bFBDZcsAG6N2LF3C+EYb948TZ/QkoYRbD2WNGLh5ZsL7uOJnN2+M7ARg9HR3enOXRi67P39j+FQ486ovYy7QQu0GmNbZkxLwpPy90QaroXWQyAM43kcwN6Agihe3x9wXS+1AwIBJnyuaWkz3Su/8BlYACQKLBexwUHrkE+N5+LFZS0ICXshPteulahv5sMjQOcyWfic7dvLMAT1iTblomFxCdtaG2S5pAY/lDZj6QcZTxLcJLILpMUJ0nIFMg7uZsaNnNESuWnRXIcpbsJIThsYoqD50UAxMx6RoFdkOq9eF+kthQ2zXvNE4W3RO1YxFjQ3JxO3dWtZk8HvFdrHmJg8t17RvlDzz4oWLy6nw6l4H92IzyWMYs/rmdD6KWA2+c/FqZwLz6/9rUb0N1mvPp4BBDzrn7iWdHVZ650oFjj0JeR7ffIUzLGfilmmBJyh4C7C3KlYF/fulUBuXJNrMZQVz1TT/BFm9rPVqT9iGG73XcXEBHDqlI0EXoqVMmN/o9D8+efAFCiSw5hC1NdeBZi7cyGNFUsH97tVPdPabve65yzuG2++Kfe4EV+p7WR7ZIzjFBh66vV+lL7TBPv2bZi/+hvLLM+eBQzcBGbPgumYDWzusznWyRC7FmjeQtzPLsNMf9lLl2A+PqbWHS42LTiHYTxb0CytstUIBC48tV7ssA12F4AMaBNk4b56Tfw6meqEizt9XYRIph9FX19ZK8IUKM7+Z2URdJiomNHdSv6b4h/K/nj6NGOD7O0NTNUSFrZa95VTIzAJ8uPHcKi9YfuLpMUhA+HRaFUjgGaY7Dz/HLB2rQRcmLGR1wI9ym9JzXlqK+bMtTkxz120xATn5tSUTZVRklbXkwZH6Uqoez1jIe8IH/JqMrzaRxKIjBpNgrVZYsatd+VKy3Tevw9n/5crzbBKjKa8n5SQl97ZuAVFia2foQagzk0UunCtowaBWoO5c8vrXC2iv06p+nMOEHAZGRE6MEUYmUX6VdbQFlXtFvfA9Wvts8zLy8AzjcYtWLfWmoUyQF4thjLkWir9pJaMfos0z6QLTVzrTFVAEvihtKYx8rpEcH3wwO5zQQw+01Jt3GjH9IMPrXvQj1+3NMvePTB81y9fFSZUXH1276raYEk/4sttCboKuX6mzfjXutrUUmo555VX4ASZaA8MAkePwhz+ALg+ADAVCvOT8pg7x2o7qQGeP99qZkPQpxXzv5k+VEVOfwiDAM1nUzvWrl37L779bQlwm1qdWlFjCDDQiiza1AgxFPfkJJw9exorLKtPUTvIwDKM5rZqldV2cmNbvhwOTYaoFZk3F3g4BWflSuDhA2Dsts13NfUQWLvG+okF9c9x4HR1wSFT09VlpWxctGfPhrlyVRZOXo/jkMWU/qd9fTYU+uzZtu3cpLjxkugnYcF+UHvbjsfkpJViMifZyIgETxAfPl9fxQyzv9/6u9Cceu5cCSiUR2zM5ctwmAaGBCDHfNfTIhAxP/0pcOSIfX9PnJBzWRDiwyORr/fvyxyXOVcy/xZCl8KQDU8I4cN3gL9LzlSuLTQVb/a9cOvlemWMNW0nY8n33Pc+liMhlgCQNEkkZviO8N1nrkI+1+CR6fWTvsCcH8SJc2jHDjjM4xdT3xuETB9LA4Fz52UPEmaTDNnZszauQZR37/wFy/x0r4ADA4fvyb37wPbtmEVBXkgmlsyNMDLcE69ekz3WeWqn7JfNvHsUvJm/+Evg089gPjthLZrYZgq2mD4rSl/TGJNadZTWNBHgMe0J82ZybeMa5aMfZA0jvcKAeWc5zrNgLvfDGbsNCS706LFNQdKzHM7XvopZ9NestsaRzuHewTWCuS337rV+8fzeJD1h3n4Hj//v/wc4ew748CMJ+APSU/MXAAxyxfVpYNAGAnrzbUtzjd8BFi2waVDmz7O+rBR0MEovTb5feL5SwFgNU8/8b3uaqBoGKV7//d///etucFlvtarx9KKhn8sIiLaPphY83KS65V9j+OCa/ITVdATdT5O9Zk0/XLMhN/rswQPTfg6uCSCJV9ZPjSLzZDEYB+v+7HN7b1Db/Juvq43xan48GtKmEGXZjEzIvGoXL4npKLWgcWtxmmpjlh7mZrtoIczVh3C4sZMhCmMKHWacU+ynRDokY0eiatEi4KmnLAP30UeWoKBGi/PU64+cQvvKTC7nJRN78wjSZPDdJ/FE8zq2mWNBYQmZPv/7E6LdFfXW0poElRVSkxL0aNA1yRvn5pq7dw/OL3896LbWXKOWYMcOK6SiVoNrEteiuNaj1vRKaw2DgMdHMrRfpb9ctwzGEiAjwvlEIS33oChxBbg+MKUVtXknT8LQlPTrv2TnYgPvv9tMMbF95zBw5BPg1m1gxTKgrxe4clViOIiWn1r/HMz38prG9tKahVjThJZrpP8orWFm3jw41H4yJQqDDM2bC2fLZoBxH7je7nzSCltrYExGjof4eJZyW0ruXwrPeT3snulvI589elQsLuivieuDwPgdmH//H2F++Lc2x+bEHTgvfAXm3AWAOUjJBDMGx+g4MH8u0LXMWvmwv9u2wvnZf1CRjzOgyulL7txtsg/TBeqnRhBQxrMR1ArwTNDCE2e3o5o8BN3P9jRl+kFmkyHcz1+wRBgXdZrc0g+AWjEvU1yj80Ftm2G2mdSCxz6QcP/+X8BcuCQSQNB8mPm3XNMTbvB+QjxjTFQNeMP9FDKHKUP+C4FzexxO9wqbp2zJEith5Vzwbuy+jTnUOLutJb4XLlRGCSSh4yvTvb0ALqhXAAAgAElEQVSRs8wxag1JTHzxhZiUCRG3fLkEw5IIzUNDEH/ktMbbWw+tJRgVmlq1vj44X9pT7n+Z0ZmYwOO/+mtgcAhMSu+MjMLQ15Z52qIepffZDYiRuomxt73MG0cB1fgEsLwL4PdWH6WxMbQKYNoopomi7xiJVD0KgUCZYeGeENKv0g+MtwwKMKhFpC+1+MwHmYD6C3C/c0+kr+ip0zAUmC5YYN0EvvGrM99/77pSxyTf0ITz6jXLqHBNuDUG9LCuTyVtlgRUIhPHI0K5brNTPZO5Ij3CSr30SNA+4vaFAjzSNLdGQU2iWb3K0gJLl1rBJC19GnjnveM+g56IAgpdYth+mtI+nALoq3ntBnDhMrByhY26e/8+sHgRMDomEdsxdAtYush+Xtlt08Hs3GnjMkTYV2PrQ5T+6r0zEFDGcwYkekEQ6OiQxarMvMQNCze+ED555WqD7vf6ivHGKJsemVY6mdMscU6HmNxyoZPvJObp/0Lzv5IfgLSDiyX9Ya5dB+iT4uZ4DGqbT5qa1IInfRgbEwmkJIRe0Qdn3lyRKjqvHKyqyYjERJUHIcMfwjL2s5xyGHZufjTHFXNPN0w7TcX42ZdTjj0XH6GhIZhHjyBmTTXmm+BLc/VS1EhhCEsS6dhQdMsjwUHNJ4ksHkzd89wsmxx88ybrq8y5Hod/Tp3Gl+cVtR//9o9hLlyEs3MHzJmz8qR/PZF2k/mh2dS8edYkzLUkqFOX/+eafpUuUVaPePMX2uh3jgdN3x88ECLLvPEGzPvvWwac2oRSDuBGi2/kOabbMX/xF8DtccGbuYmdvV8K1p40UoE+k30E3DWD+xPXi5LFUCRGwluGu+5yz4yoBROGim4o167ZvZs5KIdvApyXPmaivK549uQZwl0XfREkDgFc63lw3758TSK8SmRU+rXS39GlAVJYF6WyBv+V4xJwvNgXn1WWW2wZoy1bYBa9a5k2RrVdtdK6lDCFCGkVvxDaLcBzrraWlgWGnnujfqRPJ/dFM3TTMp6bNgCfnrRCc/aR2k2uURRErFsDZ8lSmC/vsVraTRut6TStNLq7JRez7OM+eqtqm7xzt+pN+kPSCCjjmTTCWn4wAtywGLGMm86NG9aHoEQ4C4F06JB1it+3z+aOCrp/3Vqbm4kbSMRNTxpFInT16hKTscBqJ3bsENNE+hsYMg3j4zDDQ0BnlyXguShSOit+oCVzF3fzrdWOJBY8EtOf/NRGu6OPjRCzBubBQ8vUByNvr7oBRmjmxNQTOTE9qtal0Iy9TzMqJp4MxkOTJEpZOR+Zv9WXU87Ol5s2pDtzujJpNfN9Vjs4tzifqY0kxucv2GcaNCOtVo1c9/eJJrasp6dH3i1hfujLHEXQU7PCGj+WhDA0SzenTtvANf1XbA5bBjtytfBuESSkaCbMM9+taj5H7v01zhIQg7k5yewx7P6RI3btIFFM7TMFAXWItxrFR/uJZm40HSRxdORjedZ55kvie2ZOnoTz5JM1tevRKgt3tzl0COb0GTHDo4ZJfIP3fincw3pXeyEQkyAm9LobhB73xBUrYGhKOTJm39ulS4DPT4hQuIKxDCHcdasgY2O+/1fA7TPAgnkSARWre6y/ItcH7utcI4gBAw6FFCa65ad9ljZ+ccYGNHz4UCxcAi1CvBg9uUMYeOzZbfcsKhLInIWM6iprqS+40Iy1u0EgJJDQb/w6sG0bwDR1FDiwrMFhK4CkIqB3ndVOU0iwZjVw+D27j7L/7OfePbJ+ltNlhWU8G2lzTO9KI1W36zMaXKhdRzbj/RKNEdOYnD1rmT8ShHSMp/bue9+H+eQ4HJqE0G+REWUZgc17P1MA3H9gfS4ZnIR+C9Qw1CJcuYBcvGid18lk0EmdphxcvRhwYPs2S6w/NgD9OUdHS+Y5K60JEM0Zr9+w/glcHKn1JANKk8IYA5OEHTrpy9vvwHxxFnj8yIYbX94F58kdcH7u5+DQf9XTX2mni48bYITMKk0he3vzrfmoEzymjCnNkC73W60m5xZ9donJnDkwg8NwmID75CmAkvc5c2xYegZl4ByjORml6Zyr3d2YtX9/9fnG+UXsPzpqGY+JCTicIww2ReInroNtoSaLERxJTFEbywAdjP7nebcYZEq0+K5mIiA4RSxNIr58lyhUok8R28dcayOjmPUPf3bmHON7NDhg/XgY6OnJ7cLQN4QRCdj33pd1woxPyPwHpfvUqrzzruS1FYsFCgIY/XnHdEqkWPruLYRmZHy3GPRpcNCuEWSwuYbRrHio9DvbPHfOjEAh3qLi+kzG02FeYjL4A0NwuP7RTDKl+uPqh5bTPALcF8qBfZoJthN23a3WZL4ntDDhe0HTys5ldg3gOukNhFdaVyQgGYXMNdYv7nvOmtUwV/uBuR3Azm1w9uyGs20LQOHb6tUSUEmsLGjRxPWAf/TFJh1BYWGGDvPuuzCnv4CzYD7M4JANkrTvmZkt9GAk/p00YaXWcNEiG92b1j1hx53rki+4UFnIMLPmaFfcOcP1d+sW2RPopwlqZik07ZhtIxxTCFEyt3VImzHXtuxvT9r9metrlUBL0RpU++7QmNUuppC/anChQg57BjvtkR7RzM7ZvVsIYm4+hpqnBw9g/u5HwE1qlZYDmzcCbnAUmld86Us2GMHf/mc8JkH32itWu0ONCf9qHGKKQoKY+RsPH5YovdxoJFLc7t1lDYREVCMzcvq0jfZIxpepVcis8Vl/cKEktJk1+lH+iVo15h7tXiEmSmQ48MJzEtBFzJio5XnzkEh0RaP31desxoljcPKk1XLSx4IY0Lcx74dnbgWm5iATxD8m2SbzSbzIHHGzo3kXfT6vXbdYcpN+7wMhSJxf+6YgYwYGLBNDZmLH9po+nLJJb96Mxz963WqUybDSf7FBM9JqQyNzmswENfclywFKtikVlmtkgHkwcnMEU6tq9dW77hInNJ8TJoeMLiPYvnowMACESOG/8Q3Aa+EQFDijXsU0u3/heRsAjNo8mpjRT5Q4uNJwV+jinkOU2fAt9KGif+3atTBXr4qmUQjse/dtoA9qEyj0oLaFwTaYUiBhE1xn3z4RupiRUZvXj8Q4CfEC5ftteDzb7UGvdox9874nafaV78mO7XDGJ0DfY4d7KyO3+tKyuOtKKFNR7sevvYpZG/umfewZCInuM4xq6/o4EgP6OfNdJONLK5YG155EIWOO6ZU9VhDKM9sbcJTbzn69ctDewXfd9YnllZCWTknH+JDGuUJTKhpOfWEFYl2dwBO9MJN37d7L/nLvpIsThWSLFsLZv98K1IPSZQXg0vSlrLwrTXckOwWoqW12xiL7LSFh32TAlLIfAgmemzdtoBcSgvzjd5qOnrsM3HsIDI7YDXH30xIsheaPOP6p9bs8fU4ioJkTXwB7noLzz/6HaQKzGpLcXMk4UsJKgviNN4F9z0z7anKR5lEiGrnQmfc/gHPtBgxDfzMJM6VwXPgpIW31wc2ZGuLhm+LX6aYGcU2UxETnyFGbmJvRB5nTi+ksLl2Gef9DCTBCJtuhVJG+jTk/vHPL9c11sWDX5Heaf3KucRwpyFizxuYzpPkVTY6pmaRGkpEDqUGk1tNLjHATYgRcvgu1fDgpBFm8GM6eXcCNAcvYk9GI+2B7uCFTO8A/SrjZV74rbxyywRiowV+zpqq/b6xNKglhnN/5bYif0MWLmPWrv1KdqSJOlMxTw0EzdxKFUSJjehtPc7KvfXXal5WakT5rDk9/bPGzJbFDAtP1z/Y+H+Pn8pzh+HzlK3bOMdDS7A4r2ed8Y8qAnx6Hs3q1BAcTLaxLMMbYFrcoJoAX4RxTqTx8OJ2vuEj5fl0winouCecoDOEe5gaYkjWtBZjIe/LqK9YNge8EmSwGF/OuuWxXVOEu79+82Qqf+DyF2u+9L/SLBDpzo757hYkUzNURXrcAIjhPPw1DaysGKqM29+mng5sRBiPSP7QKucdo7mPAL/6CpJ2a0e+kY3xwj3rvfTz+w/8L+MknwMQ9ig6BeR3A1idsdNvJexJPgesk91BqsXkWKy7urczR7AoVgxGJ52oYV6p4aipMKcp4FmaoI3TUqzmiBIoHmTImHX/vPSu95zVqzb75jenF3d5Z/T/za5H5uXgJhhoJamYYhIOmqsyTSU0U7fwXzrfSL5o40mTw5CnJxyWR2mgmduU6MH7XMqdzZgPvHYVZ++dw6uUQI6N2+LA1geNiRsaCGgdqBcmMjo7B3BqB84pNAWEOv28XOppgslcbem3f2AZukAkTr9WBtL9wczbbtsEhMU2myQ237j5IB34GQiKTxU2Lpjj0aTn0luQkZdABQ9NhRnj1b/RuGXk615NMln5ncCDRcg4OwpAJpVaQ5rWc9xROMJw7w7iTWCFRfumSmCJXQMH7SkyMXOdnv9aA842a08+ZS27YauGYxiLOI2BTZHvByKXrSlFVyXylrdGOQrjUG7d6ePnWKzGh9aVvEQKFRCXHyKsBqFd2o797iEBz5CicF1+0Y0CBwL1JG1H0+39hTcnmdEgeQ/z9j6xFQlLErzsmL70oc1q0R5wb9P/le0BBDPvrn8eNYqDPZQ6Bsq8z1zb6qjPX9Jf2tG799zOICSEmQke6HnCt4JluMi7TwvUnRMCdhJoWXKx3TWPwNQq76e/ItcENmhT8ZOVVbzncKygwJx1AKxkK/IL8aStLSOybMJDXBywdx1pIV92fAs5ctn2kwJSxF9astAGSnvuKtRKJqun0YSC0ToQ1tkwbZXGeJDY6yRasjGey+OaydFmk3UhvDHdOWdTWrcI0mo+OWl/Ch1MwZN6YtiNk6gPJr0Vm7s4dgM7y1AaR6STRz2AcZJ46l1nGkv5Hsxl4ZKHN50SGdOKuZUS58N65bxfhqcfAkoXi5ygMgruZBCAvC8iePVbTyQWYJhwMwPL55zBvH7b+ntSEMngQiXb6OYyNW+f3ybtWE0bNEVMjNEK8NrkAzugSN+0v7YGhbxyj/bkmRO6NZI7XrrGbDBkpatzYb/pGMKLc4iUW/419yUl64+6z2zf/mYwf08rQ15FmpySi/bk5XSaNzCQ3OW6+9Nuk9J/tpK8sTXsoFSZWT/TCYYRA+gFRW+WJdOxw7nDzopSeRDuJggDzMEPNPv0JiTHnNBlC+qvEdARtiubjY1b7SS03CRYSmQ2Ez2+oid7x9gqt3PclaMN3x4W0RwNBwirWK2ow6Kf17L7K5nsYwcofUvjm6Z9owUuSegZgwo9eF78tTN6zgj0KOWqsYbG01oeF5vuNBdVcFCIB/bhmcb3iuuXOhTy03ru21FpPAvpS0W8KZOnykPR7FtAO2WdC5B4XAUHJTcacPS+WM87zz1s6ybXM8pfvx2fdOjymGesbh6y/+7atVlhKeoECJ+5xjGnQIkGTuC8xmjkZTP6RruMf92aKwHgenYBEHvrZr4mfvKyZpF8Ydb63tz7dQkzePTxNF3D/Zelhx57PnzkL8/3vW9/8r+y39F8pKrJ/CPR7OAQ0uFA4nIp117nzcEi00q6eLyoduJ94AmDqkY+OASO3gbv3bFL4xQvh/MzX7CZWCyW+wN/5IxvYZ+oRwMBAdybgkOCiScXZs3CeegqzDrwMc/mSaOSwZpVN/DwwZOucmAQmHwBkNhk0gJqrBXOtM/oL+4U5rghI4G8PN1o+Q4aDTC2d1cmkkLE4cdpqQNkvto3aUErbmLT49oR8djb1waGfJIO3MAABy4twJOGkXjOwEYPZMGACGY+HU3CeWC8+taKtvXXr/2fvPMDsOss7/z+jNuq9S5as3mUVS3LD3WAbg7EN2BCKgVA2kA01m5BCNnGSTSjJms1mE8ABQkxzr7jJVbZ677333kea+fb5ve89d869c+6dO02W5TnPI829557y9e8t//f/KgC3GznciYjylKY6VKvopU1R57QX2nvIvYrxglhH8kZCSJXoI2srDAvPPuvkVShBbLrt2zsDK95fPN5XXGZxR6ZYAnHCKELeQzzKKLTcV17u+TGZH0BpiQMeNFhR507uTSIemDEEOy5jjE0fSDP9MXFiWhXqdw6iBow3vIu68DcmzaItUOQmTarRFvV7We13AfENeNRpF2IXIcMgvqoIgUnRMVz7KyVStRCjhVEF8onWbVw4KOXe+BrQGAgowKcPH/H705Tk+Po6/LX6tWyhsHmLGyGItwUeTb8tX+5l7tdHmjFdEeMrSapSh/cUvJS1txDJGEIY5XgbyNEKlrf5hyZrARPciWlnbQANc/qUsYbaGEislU1WgAY8uCF7SbbehCKwnqN4l7cpTLBUy5ypbzVKrYORoYFOQkaBhwBOAmJfi5Dp5D+b1HDhiSc9NQnhOHhO2YuY64wB5DuMq7E8U1ulGrlNzIB/4pizfyOjINP17CJ17SQdP+kKJ2Vq01rRhDFmIA6LlyqqrFSYv8CRI2PG5Ozx+VWwNnnlFTe2045t2igqb1vyGmv3/79/V3j5VVc86ZPyNorGF4A75xfgXf69ELlQs+L5Lh8YqdVPsqMBhUW4xXODJ3DjJlfIiMHk37ZtUrfOilgAighqYc1ahfv/RVqz0T2JeKhsowsGbYRlkcUwIuYARRaFaPBFLgAuWSbtPegKZ2wZ69FZ6tzeFcmpExXddquiIRfXzki3YaPVJYLKG28Xgjpe1qW844Bb2YD4dmzvnlkgvSzUPbsrunSKyq67ruhCl9qe8clYoQf2CLSXJM8kdUbwC6GocBg/IucvGwExt5QdbxwCbUIhNuGWfFjLlytC0abtCKfFUtiurefwu+5at/41ldAR1zlW2FCAG1uwplF4D1DC3r0tv5cpiHimkwd1xLv99DNuXEBRsZjfvS6E4eFGaeM5eN+Ig8VIQQoKSC9gB0RxjWMHGe8IBIxlxtHevZ7GY/t2M9yYssVY2rLFxnZYuEjRyZMKePxhEkX5aIR2zxc4yG9m7cAGS55GFPAiczPZRA39nMMeC6oBjwpJ5nl/ob6PWQ6J80yM35LLEjM0YyyrJ0NzgB364UdsfQvA8TPswI3RP/YMxhFQLeLGSW5P/2N1J3dpRsCMMErMmOEkVyVXvvYLc8YH43XXLostNWG8VUtrcxibAykLZs60XKq2PtKezceF1QLGIg2r8l5Xati3Qcq8E5iNYwMTHltQJHUxMMX1xouWYXk1dukFCxR+9guPhye2/PLLVPXQw6r6/j+70nb8mM+XVasUfv0bR0uxXpXibauoUNUv/ktV//M+BWIZ33rL25o1H8WvSB1sboKIYN+EzA7eAWLTp05xWHTavpG/186fr+g0HsVKzxhw4qQiPHakAUP2GTjA4yRrywaQmQE5RkW8thAbNoShnb3h0ksVjRsjde2oaNJ4lX3p81KHdtKyle797NBW6tfb918URxwHjF28trThyBHFZb7Va6Rly9zDTcq5skjR5EnF70nOeBjjkRcwVhCyBLt95VlF73tv8qrmzwVaoJDi2Qy1LdBg7+bTOfC9qz3ekRhPI+5AiH75TVcCgUUcPaHwwM8ULrqoZo6+RCMaVGH/IReAsWYZoUhvafNW954eOmJXh1dfNUY6hGXzPrBR9Oohbdvl97DgsokM6Kto7GgJZfPSS42wI1vuxHtrfMyHvU2dojB7jitlKJgIyN2IOe3pucU6tTeCGeIMzFrYEAE+827LFUmOP8hU8LZmDqO3LyVBdnz95s21EtyEtWtNkLQYP/M+dZLwgF17jW9gDalPXPBifxPtXR8YZbFH5/yGV+3FF92626mjojvuyPk5/oIQbgmpj+102DbebOBGeACI4cW6CokUBhc8lK3b+JjIUO2b8pCJByJ2zzx7eEo5yDHWokzRkKEuMHCOPHUosbDbnjhp7H1YngObZkNhbhgeGAOkCqH8vXp5jB5zFahpqXAiL33j/R8LRaQPATUABLoeENqSC4Qhh9hZYnWBFtcjnjUggGY8pka88eJMBTyQjdWGBeJYYUo2z1OLFgoLF0vkHm1EKLa1YeLdrD1h7jwf21HkdcaYMnuOwpNPmfGKv1XwuZBrr/m4oFrAxjP9zZoxZIiiIUNcqH6b4JZ1atxSiXHyH4phkAMvJx7+cWNtXlfNnafwy4eyBuewc68qMcyQYm3zdpNLwo5d0sUXSZuQU06a8SwsWCT482HOLXZUPfyIwr/+SNq03WIXw5pN0sIlEimjLp0ibdzovAwpDzEytBUrnO2VdxGTesXlxdejvL3W8mTSr8fLjOMhgj0Y4qYk6VLKuwudMrgyayTKJkSQjcHQjpEX43eyLeGb2LpdAYcDhmAIES8a6Dk+N73i+Vgx5E2+pFaYsBndMFi0KfccxkOH1C2eGSQYXvLjJ7xZGEvIhs1Hg1qgWfFsUPNdoDfHAnG+0FVVpbKqKlXNXyKdqnBoBLk3gcqmJYdPNg/xoFyLIsnk7djeU1IsJoEz0J/WvqAgiO0/YCkIDDpHnswRwy3Hoi38PBPFlU1o6hSVffgus34lX1XssymnCOykVeEgVgBY4HDStmxx72evngaZJG2K5XykvG3bNZhMKFaMbdPHC5Tc9Nlc8Cpj4aRcaYJARtGw31gQUXggXkLR6NbVc5Tl38dGyqLJ4mlKwMma8W/eEk3yf1znpMLWJC8iTpP2IPaCv3wfPqzGq4DgVsH2Srww0CM2Ntpy6QofgxhEhg31cXD8hNHYkztNkHAwH5Jz4hQQ8Q0OweFNXD9kiCtZcbwi8YbEpOCZPnHclE2IHcjtmNrHNUpc+EQc2wjzrt58y5ls8fjGxqLCtzbZLyYwIZwwZ4YNtRhZxl6TknckU5fkxzmXWlNidkEHEAuNJ4L5lD+XSn1W3jw170ih+GOUQAwdzE8YG194Sbrnbjd2lPq+2q5LCKRG4EZe1YwQbv1EioW3Zlu9A22AV4HvpBFqasNUbWVv/r1xWyDe21mfli33NSOThsnGwnnc30ZECBoL+Clr7yOPqQoW8hi1Q53wxDGe8VCxF5CejfWI1G2kTmHNp4784xpiDC2ukJuDM6xigCasB4TQlp0m31i4DSFCXLNircJvfuspS4q1F2EaNp+qpBaRG+v3HXQPJHt9ESOZ7TXEdLJnsz7ARVHLUWOv7d9fAWjtggUeCkLKpuT+VcvzUn+m/hzx39SLGnYyGjpE+ubXc1JsibqsWKFo/FhTSKMJ4zzGs7YQIQzGtB1GyZHDndejWJ/lFZ02LfvEx1X181+4t3PwIEV33pl3VfPXurZAs+JZ1xZ7N1/PpnXN1YpuvkHh8WckYIRM4rblFlNXtGlYTF5705VLhNLhQwyuQO4u7drjwg6LLHEnLCYoDvHi1q2roptvtLg8rVrn8ZlnKxV+9RtVtWihsroKSBs3KTz8qC0kAbw+cERIZICWna10TyoxreTxRADF6kkewoYu2slNH08nwuj69Q6hwWO3dq3HqhEzCGMu1Om8kzZGmM0PkicZNNY8PBdbtjoz5U035nYDlkKuCVXWxmzMVf/4XWnAAH92715N6/mM69zQtsutVY1vAU88iiawZVLMALmscZXneoyA2wI/OnDIlU3GXNs27ikDdg3j8rXXSj/+iYxJmTECgzAkAyTVzljNPX3NYKd8h2kPwgvGTc+eCpm8lMC62DRN+SeeGWs6pDsYA2rbNFPKn3Mq48kiVte8fMeOS71zrjjnX7LjFaWtjgQg9S1sDYGrHgzN0TXX2DyxeQRtP2Opnv0TGwSM8It5DjEVHnTWlzlzpUunVrMkYz2fv1Bq6XHkKH3c3+C1JtGYpvhSBjwsxIkBWUMYxzONAhoL6Vu3+5oOLP+iixq9HIkiNX98O1uA/RclaskSF+IHDlAg3g+vWolEgQ0qfhxPnVSIkA9KUAgCMgLK3HZHrISlKxX+748UTZsskS7o6FHhkTTywr59jFQnrFxtKdpCu3JFN16TIdS72NdMjHbwRiBrMO7btHLGfHgkmB8onxi6gaxWnPVzrYLCqrWqeuJJ9yCy3qSVnXAe2hk5CX2tZWTvAG5vxDq1GMmMeRZvH0gW9rVCpEJxZ6TstSavNVKKpqxRkXhM2guZZOPGxpcfqMfwYZ5DPa4bY7YsUsAoyH6aySqQXfvj6/L/lpcrAM1tUy5t2iJTau1ZxXO+22NiA2KnTir77L0+XkizVo/9Jb9Y7/bv5zSIo1+/ft/5/Oc//25v83d2/VmMiQk4ftSVme5dFX3iYyq7+X3pi29cWxZfFvZWLcyDGb3/VpW99yZT7ixPE8I4MMUJ41T2/vdbzIlh+VngyNl0660SHiNLv9LRhTgC7c+eqR3nH5cBQ92mTar63z+UFi9zxlqUyqpKi+0gaN2o5W+9RWUzZkitWttGE73nKi9rI8U82SYC2QhQTQhHiGtDAUexyUB9LL0B1lk8bV27WrlDXpC8xbP16a0IoptMzIb1Qwx1pN6miB0S8WOm9PAbQueChWYwMMbTd0J8T6IPUz8Sn4lSSP3IuQocCIMF9U+2BwICbH5AdVDYIJVCQz1VYbEmWFTxalrOMLz4jDGexbjj+cQq028LF3n7LVvhsbrEyjFOgXwiULz6mseRLlvmpER4tIhFJE6kZQtFV12pMlJsJMuWWrEiJzOx2Easw3MmTvRNGeNJU8TRFilK9qeGxmtmH1SHD43wzoj1B+G3dy9TOi0WqsTYpxolzYu1Ys2xg/mMMoviZ0JxV6lXb4WlS515GPKvm25QRAxaI/afKcLEHeP9QHCDJIv1gpixaZfaPDDlFPg3BhTi6297v1/biOWo0U7NJ96WFjD44SuvKmDwYN1k/rAWQrwyamSTl8kMqI88akiRsGmLtHePRPgC+2CxY/duj0MGpXTgsHTkhHQcksMjHmu/aaO0fJU0b7G054CTvm3bIW1EST0rnTgtrdsodengcY4Y6jp1kPCSooz06yWNGSHtA7ERnLiwRxepZzckB0d54R3FSHnxQIO/utGmVWrZLU5694P6AhEAACAASURBVC7fH7ivS0fpyhmO0mKvGHSRKzEpe4CtGRit2Kv5lwn1qLWNirVfA38zTgL6CI4CxgxeyDhevba+a+C7bZ9kz4b8D8Xz7FlFKOS1xZjSxwcPGPt8NHCAI1rITFBCeW2ekN2hZUtntMfbiQE9pb8aWr0L9f7mGM8LtWebql6xtSfFcxEBRfxvX1SU8lvB4uBFmHapykjwTrwXyZqBII4Zo4gck1ge2fwyJEWpnpMpU5zwZf1GfE4W5wk0zqCrnClkeUwWCsgvaTNIk8Kmw4Zz6rQx4hqrZOIZKJz8a5IDDwTkNadOGjQ0wtMGfBjFCKU0jscENsRiR1sjOAJFRgEm5ylKDgQEeGf27VM0bVoN5d8sfB/5sLWReQBhcD1w0C2WkDnBsMqzm9gj2SRtmHgopBAcYd48F+rjNCixdTM5VokBxBqNJRwSKTaSrp0VKiqMmAjLrlmX8QYBh+KAVOvQIQXG6r79ztLKpgsDMuMYciys061bK7z2msO7ymFQPGnsrtHVV0sf+qCTEEGMQHunWcn9bSX9b+Odebpjh3sB8KAjEJBGpsj8Lenh76SLGqOuWNnrGftUo6kS0FYnohrsdP6ME2CszOPMnMOyX/a5z1bT/eN5qqentUY54hOJGE9bA9q2VVnCCxKI+WNdGT1K0Zmz9hcCLEsBFD+j+e+F0wKMPQwQMHBjkMS4y95zjg5TqtjvmQsgPzC2xvtcsTIQhgLUEo/nzv0OXaXsrKMolVt2uCJ6ptKfgvJ47JRDadkzGfz8i2PyMTKNHq3oG//doeUoGLTNpZdIa9Y7twQGoqEXe8whRkbkhs4dZTIQig+Q2cxcrlF04hfv/bTvCxiYUTSvu869brWt/cxZoMEoVuRLx/DJXhbvZzVedg5OsEaC/IEMjTWXkBEMu4Xq39Ai5a3rlm+4tnCk/HcShoEhtl17BfJzM+5KGWs8J3/dbKp65pf5XfC9GWr7Lujk+lSxBlyMNTtWTlIgHbW9IwtPYDInkjUbiVC8qPJcGDg50t7Rv7+iSRMNbmGWL3Ij4kmFGRLrYLKM9q3mf7bpsdmyKWEpJR3MJRPOadyjMdGSPB5WW8htenRTQLEB1ovyvWW71K2LQ0TYWBFE8cigMLFhcd+lU1V2+wcV5sz1+Nrx481rW6PGcTuilO3c6WQFtDeQz+HDLF6mRr7LGg95B5xgk8dIgOCORxMhBUGCWF5+S5A2WU7LZctcICF9TrcuJnxFw4dmSZdC2WZv6zhPJ7F/WFwxXGDxJSaQDQyDAZsZ+WZXrXHPMgy3kGbhVcLLNXqU7J1793o5aoFYldzajAX+ociwIWditUw4gHQozsVb4two+b3n2YW2VtHPCM8w0kKeBeyuNuEuvx5AAGe96fOJdDwYM1AW63jUWOtgwGT+JXLMZpVL5idlxRqPYBPDk+v4zqKX5yvCefltrbwYwlh7iJ0izRKMlc2QsqLN+o79kf2EuQEKIz6A2WJwq+uRpxzYmKll3lmc/cxXsrHxhLgYIqe2d6NEoKgyz9u1cWXT7gk+T4nLZL9EucyEIlq6Nbyd7PecbFcugWqJlThgoyCGPnS7GQujdet8bTeIa0835vbpIyM9hHyOMJg+vW3NZY5kSdNS2sHWpTVrjPE/ENpCvP/AgR4yA5qm2BrDnMUYRGwi+wncAatWW79l5bDa2qsJfqdO5oEFHXHwoAKoCVhy63qktBeP8Of7OmjK7arV2b2b3NmWS5vr6kJWR5wviDLQRqChSklnxvgoFJdf17o2X1+jBZqhtjWapPmEtQBxgaRKefoZhcefssUyYpEhZrCWjSW1BQvA4WL4hgk6LMzF4G3EE6B8stANGyKdPCFxD+x8WDQLpWtIFChAMY6icNJTWmhgP5V99Y9yUxiwKBbJe5d4XL0+4qENi5a4UgLFN5tpn16eJBp2UuDDKCwIvcQFKvgmhXeN1CmdOzlxAkIq8alALBOe2rRCmWUSBWWXkyVYHipSudB2efku0+5/x5xLpAJic2LzsjGGIsq4ZYyMHqWwdZsivDoo9MCeYEe+9RbPz8VYxZLbsZN7mPGETpliVu7wymtu9YYJt3dPac8+nw9AeiBtQTiiD7FwMybxbk6aqDLG7J49rtRAAISQV595lOwIxil5J4EAI4wBISKlzoAB1bkt65N6IPmOun5u4rlTozi8D4ZO8mESqwsTMUIGEPQS4FTJ5xkEcOZMn3e0KQaLuipflMeEs4QSiWCN8kk8ZwZtYP0Up9MpsDYmy9aQzzaWIUxh7KfB+/D4793n0DXWA8g4mC/56YgaUojme8+bFrDxwPrG2oRHj/QSsJxjWK4jjDALR4SVuUiu3mTlDeq9coUpLlG/Ps6pADIEY0d+aETiRn4LS5Y4GqVfb89BSnxmeStPwQEktn25e1INEtta0Rc+rWjcKGn3TkO1RJ/9hMq++AWFbdsUVq7yvQHYJjGFpGdhDSXUgv1g+HBFl83wdQSSw379FV1/rcpuucWNwXjSMvPJlEwMfYl2MI8Ze0Bi78FoHNLWmPx1k/UCwkWMzH361EnGSTRZ438kjACDKoZXlLMePVQG0qoRxo1xMyTbcNduVzohk0IJx7EwdowTLrE+Q6jHNaCXaOe0g/YjfICwG4u3rXREFMzhRcps45p1nLCI5SuMt6HsshkN37PTyngBn2sI1BZz7c8k9ZGMZf3fJP2zJIDvv5I0WBJBLB+RdPACbsN3V9VgjXv0cQkGW+AqLVuoau8+lf1llEt9HbdKmsBVimAde+Rib2r8vLS/Ccu9KQvXXGMWMOIfarWAUT7INbbvcBjr5EsEUQEbCZtO8shuIgkvWWNbGaPevRTMe3bChWUUTNhTRwyXYEo9edqttNTt2efc6rlvnxHcmPKPOloXOnPamXrecIM01BdjU5BiL2qyAd6pn7Feo/CtXq3QsaOMMAZCAqymSSspbUG6HmJnunR2UiZgtUCv4oOxi/DChobAgyEChRKrN0y4eIUQzPv1M5p2Nwa0UpgzT9GQwQYJM08oZYJsCGKoF160TdAIoVCI8dCWMkfiMiX/Mp5ff0Oa+bJvvnjLqXsck4dXn5Q9QIWL0PYnH9kYn23uNIb3scTCuJK33+c1AsqOnYZgKBlOlXhP2LjBv2HNZ7xs3FBnqL3Vf9FiieTt69Zb3ryyL3zeDUU2poAFnrDUJfX2zCbKXNLH5BpbaJ3u2sWF+gyU7+1kRi6pTs0X1b8FGA+NBSsH9UF6KNZS1scSYIwB4kBy9sI6iyEGtE/fy2pHLVFulBwUXAyMl7Z3CCrnJ05wYqHVaw1BBBtsdON1KvvG133u/e3f5LZXGowyIV9k5YkC88XkgaTMUuLzjJyONRkFmzUmkw3AiOv+/UcKK1aZQhTd8xFPZ3TVld4upcg4uTVsmm8ZTyxGPfNUEzJVnz0sbdzQjwkoLSnHAszACxY6cz9e6HHjjDiQ9E8YyGpN/YR3HwP+4SMKGZZ689TWRuAG+gRjLjIZ9y9foQAp3Lkg32qanjuvnloKjuispK9LWiCpo6T5kp6X9GlJL0r6e0n/I/Pvj8+r2jUXpu4tkFloTQjbudsZ3+KFZfN2E55zci5l3nBOlLXY+xDDdbEKbt3qWPwEfDet0lY+vENYzbp0drz/jder7K47ay6caZtIcpNJe0Gp5yy2prMCmzQesh5dLaG10b6frlCE1wZPHbEkQIc4Dh91eDHw2E4dFVav8YTKxMPW5aBvyev5q984PBTl7JO/V/2ERoIaVj/w3H4y5e7Rx6xuYely93becL0TZpByJjFGzJN19XuyaXXwQOZ7twymi8cQpRRv5Zp1ll4FIgyDQOEtuO026Wc/NwhuAOraoaPDsfr3c6WPzZRcni++5IYPPKPAdl98yWN+6jmubDwD3WQcAS9mcwQCFuew5C8xwMC0itD2N3oPxRs2FmqgzpSRROX1rGet5WOuEgvF+2B2bt/BhV8U77oe5Hpbu84FGmDRI+tBtEL9Fy5SePV1r/9Dj6oKrwApUhC21q9XeGuOQ+pBleBhyTN81bXYRa9nzpPKB2MAax9GlKNHzdNTanhC0ec3//jOaYECSlSDKlAfA1c8Z1mXYHkGnZBMLVZkrTD4O/vUiy8p6iZF93zUlV6ec/KUEWOFw0cU3XCdk/iwLqYdKUpmdv2P5QviGUsNWSjxecZwjceTA16G8R5aRJ7z8MrrhkoAmZDNjQ4MnyNRJj/x9vyf1kZ1KkksXy5eLLEmwReCoQ/CM9iV8XjGRuJxYxV+tVwBYyJwZ5RA2OJ37nRUBogvXo5yWuDI7vOk0yEsBk4RkCas00XGmYVCPLZSRnDJHkvmgLoY+guUp/m0t0CBWZnTPDsJx86cOSqJYLr+kj4oKQZ3/1TSy5KaFc+cpnvnfYkXWkGMQswPB3ET5GAmnpI4t4xVPKd2LIwJa1WtEzvn5hK/IMSzWCQWjPzvBZ/EQkO5SScA1BG20mHDXGDPvym5iRw6ZNcak2kcfxUr4vn3lfDdhEDiSMjNhcUXJQZmv+27pTUbFbp38RgVngVJAm1/8LC0ep3FEZpiAWX8yVMqi+EuJZbH3v3s76TN2xxmAqEOKWMyyZstvo1NsUcPhwMhqDYVuVIJbVXXSwL0/FiG2cgWLlVYsFjhpZcVfeD9KvvoR3L7mrFUm9Wf8cK4If4NIqFjxxVQRFEcW7U2dma8otH113meL6YKygTwaTa6Oz7klnieQX/zPO6FVIPvtW1+hRoAwevZ3ym8/KozMBpcuL2PbaC+HPxt7JjSQuVJnsfCTJwlYxKPfttyI6MwAaDQOG2IQBx76iAcoW1juDNzq46HwfxnTDdBCFIu+17HZyCwGPkG8xahlzojFKN4MjZfmOnznXRQMGq/8EKTKp4253/7kHmEMVAEwgzGjFagfBhTgBli0MMwM3y4GzGw9NeWuqGu7dJ8/dveAra3I+wTxvH4KgUMQnEOzEJzs7ZS18fAxRwBDk8sPnDHg4ey5Fu1etqZUxicgdRv36Gqf/+xI1BgJ9+2V+pQLk2f7IbVQkon+1q+EZvvSfkiVpCA8aN4QCREWxRYs2t9XqYdsyR4eDqTvAwoVcShQnZnaKcqhTlzPA0T5Y0No/Xtp9r6sdTfk21U2z0p67qtRzNfluYvUIAH4chRC0Vx2Hdf82AGSJzGjbF9NRB/26+vySQRey/Q1xEj7DpTUNnjpk8rXJK4vBBTYuxdsUKBvkyQq6XdTH+GAf09TR3jiL2bf81Ho7RAKYpn8kXAaidJmi3PGBcrpPxN4NSSt4j8KZZDZS/5dJqP87sFYgWyVy/ppusVHn5COkTqFDkdOMILsZb5kIOkslaXwO9z1RoIxBxr1vqGh0cKgT3/iBcYhHtYX/EQwN6GxauhJC0sxC+8qDDrLVcoUew3bZV27M14liPpxB6PWQHm2crzilousINHPScYHizyikFIhBI5cGDJgqt5Pbbt9M2exRTvG7ClzGFw0hQYUPz7ef8X8gGgW+s3SfsPGzwcdkI8vIF2qqMSDZyoKiYT4i8eYiyvpEWBmAOGYCy0o0ZZDAmKrimdNBSbKnlZJ13iSiCxl506m8BlGynzKx6TdWxYMxC8OFPasNlZdUktMGK4ENpiASj+i6CU9PTW8VV1vtzeO2mSwvMvuLUYRQfWVASn/DUj8/TY2JUkgCrZQ4qwtnevDL5HvC3GJGB/zJO6HkCtie+aONGg+znQ6xKfRf1hvAxvvOXEU+QdjI0BKMOgGY6QxqG9E4sRs92Eh8/57WZEJJ9dRNwySidEGwj99A2xTOf7+t2EbfSueTRKE0aFlat8bsLSTShBLJzXpyEY25n4O2Nax3PFHlpEQbI1AmPehg0+N1hPmbMYhUs4yJEcFi72Kzdskg4fkw4d93HN3zfmukEXNFP+ka8MTUaczSW1oXzxmmRK56pVzlUEF0AeMVf28XEbJozi2d+SHzIkeDl7Ee015GLplTekfQelVi2l0E6gdiJSu2Bco33idySfd75+pp3z847TzjDizpvv4Sm79zkEFifAhAmqevQxZSG0s+eoij1z8mRPMQfJEul+Jk820jdTASFumj5NZXd8qGgrWF9i7GdPwHFSZGxmH0RbDx9u6BTjijh+QmUZeHT2muYP9W6BuiieHSQ9JOmPJB2pwxuJCeWfevbsGXON1eH25kvPaQskBBDSnxgRy+NPmqUPkhSYV9MgB7aZUNDGFnTzN4qMZbKubUI8HhAzSy7dsYMiYInGdJf7JFukiAns1s2ET1M6oTSHICBTv6THNffu4t/M2ofSSRwHBwQ1EAshCKJkMjssrpBE1q2lljEbn6Rhg9xDunGLL5zduyq8/qYCpCDvvdG9M717ueJRbGGFwRWLN+/iHZBFZQ48PEZ8wPcEDCj+/Xz/azGdS5YqbNwstW4pde3kRT5+wtKs1JnplD4fPtTiBi1xOQICQkH7dp4LEQssZBIw5GK9px/xZiLUQyRDXMmSpU5YQXoXcrLijSX59YTxWSWxru1qMHjSuqA4YPkn6TlwtaRiFwsptQlCdX15bdfz3iuvUFi5stpavP+At0OyfMnnxMaueswxi5nCI4DRYe9+J4268fpqyHHyPbV8NgIgBGIMazBO4lmp60H9f/9zNkfDvv2KRnZXdOedlncuzJ0r7T3g+YwhusDrfS6EmYozAgbOEWDRPnnC0SkTJziChPbPCOCNvn7Xtf2ar2+6FojRCMBjMYKQ4ow1rYAXr5SC5CiRGOUQ7GuLn2N/Yq6jVGxY7wZgPH4ZxcSMTsX2MIw3rLMgfiDaI7UK+yY7NPexrxcwPNn+nu/1RcHIg5/bPADB1auXK50Q1CAPxZ7SUhqnxGsokyBNvHKGwtwFtn9EkCfGDO1ARBvYTyUWpdEus3YmzAIDN4YJjAqEGrAvkMbsJJkFMh7EENyziyIJsVkCQhtlQqEsFjbBAlx290dLLyvvxemFERRnA6RzJSA6TPbDGEkdcETggCj9rc1XFmmBUhXPVhml8xeSHs48D5qpvhkYLn/3FHlP80/vkBbILqwIIxnhuIoFnnyRxLpheUs7mkjQtQUMr1IDiX7CW7Mt9YgJ6mxaW7YKoRVInS38bL5szHi2IEtAQWBzJi8jHlwT2o4UtnimtUneObP2sTGTxoV8ed2rPBfpiY3SqTPVCihwW5j4iOncvsvY+CxtR6ykYqVettqf3oIcfHukaVOkKVPsXCFvkTGpTpnkGym5S7HeEqCPoI2nJs6FmQ8DyqtHyV8byWhQ6vssZ+mH77LLw7yF0v6DTg4EVOrMGevnbNuwkdSWOoPxcOiwp6CBJKiy0scCAhbC27wFlgvP0qXQnygSCPb004GDCrPnOiPeJRPdAg/Eh5jTehpPsu3QqrWz6eL5blEmdeh5bhSYbAFq+YDwF/+jnTHkIDx98AN+Pv/2hLErS+qRf02x77Q1bc47mdsYAWIvY7H78n4LW7e6AHrxxe7x3Lq1XrGpFrP5hd/3GCaEq507VYWX5o03pUEDpDV4zNtIMHreeENeKRr3K974KlI3MS45evWwsRO2bndiFhh1b7je2s7mxrk2VDRudZufVqQFLJVH794KS5YZI2zEmobxKg35U+Q5OT8x5zJKpClKeFRLUWQzc15HjznDOwY0YjMzHvjsOp3zMv8Cw7jF8JPWijW63zFpG+JoRi0ATQADadoRx1n/7gUJIy6Gw5FDFd1xuxMecU9GFojjDY3IEKWzWCqU5F6HMYnYUCCllApGc+6F7I3voD+YZ7QdB++jL2DWHTXSGclRxgjbADEBc+vkSxrWT/6mhv9PPYkZx3NJ/uj1GwxlZKnIMLBRbuJV58zzcYEnlzZ+fZana8LAgNEbDgsMBG3LjVnZ2iINQpvmIa5rLdirkZEgpCJedORwHzfFnsO6nYH2BtLP7Tsgmy+33uJyYbF7m3+rtQVKUTyZzT/OxHZ+P/HExyV9KkMuxN/HEr81f3yntUC8oMQkFCyOcdzDmDG+aMI+h7cQUpvkQtsIsY8Fm6sB3pDkM81iBtRRDrUIh9x6VUOxJYaADYINmRisq64wOvUGeUHigrCQoUziGWNhQ1iePFHq0V0iRuVUhTSgj6J7P6kItlTIkEaOUNizR2zQpjTjJWXR5hnkJyPFSt/2Bv+MRo8uuunbZkes49ixFt9lxWIxfvyJbO5DIEA5MKC47PX4W6Nt2XSbUrDNeNuMqOCFFyy+EwZaPHBGuZ4QiEqOZ0WQQpmhv6BkhzUWISFmVjxxwtLzBFKujBujcHK+b6jAqPfsl/BM/9dvpW6dFd36XutL4uosITjpDCZP9pglDBKlziMU6dHDHVKM52xgf2fwrUcfNckt1I8xi3GHF5B25sTJXMU/8eIcY1eCACpxSfrHDFFXFf2KNbtHd0XMj8rK+nkrS11ralv7YqXb4OzHFF7J5CzESIA3/OKBXsaP391ocy29gTAL75TKWngcHO205pC3FeXYtsM8Adn2L/iQ5h8uhBbAsEJ4QDRxvDDE8tmIxxpauboYjuK5A1IHlALwVci1MPISo403KrFOpxWN9RyVzWL6CX+YNk3h17/x+Ol2bW3/LLvzjrRbnVH6iaelZWudQ6FNS2nZKoX2z0mwYiPjkCN73AeNJdxQChjeUYqAjjKXkxwIoDvWrlOAkRYWa9h1W7VUeG2WtGuPGZhRuoyt/jTM9fsUOndSdPsHqsmPYk806wV7Dcob7Ly0B6E4Pbs3Tj+lt0idztqejlyyeo0rlzv3mME1LF+pMH+hrzOEmRCeBdKIekRl7oEmdhW5in/tMzlkMUjCLI+8OXCgUYlAFlQKhLbkghOHTBwopERAdkeMqNVzbc4IjCDs8xgKe3azfq56+BFnGy755c0XprVAKYontFqfkLRU0qLMQ/40o3D+WtJnJW2R9OG0FzSfewe0AJvBSzNV9cN/cSsgnpsrZkhf+LxvCsBYDALR3a10pKgohe0t3mTYSGKhmg0jP89dbPlLa6q6bGpp92fOGYx0/gLpeAsPaB810nMpYgGNPZws/JRtxy5ZPBT03StXyZQK4DhAfOrpBbFiED/JZj+gj8cCsuCOGKboQx8y7yvXxNbQqieedKIUFkqgowgMKDbrN0pdOnn84v5DHvfJJk7id+B00y4t3AooZhlCHciSYLi1eB+UT2JJSCJeCA5Z+KmFf0GQJyYYb/mmzQo7dii699O5JD+F7y79l+Q4w3oP4c+ll3qMBiQ8jz2pgGfnno8owvsI2yrpUWqLZwWOA9wJOBcWeWJiBw/yXKr0C0pSebkC9YREp+KMKxaMZyzVfN+C4B9JO/e6ZX/1GmnHbqnyrHTRAPM+RMSLInwASR02RNH06e59ZjymHViBEXDIvwoL8vhx5rlPu/TtOIdSbzT0CJPAuvHQkre0kEBZT7SEWd5hNESQIS8ubcg8QGCozzxNW2uSYyuzhtnalw/Xg6SF2LAMyzYxp4yNqscel2YvdKNSeWupc0fPJzhtqkH3DMbVhJ0UNm8yr7whOPB67j8idcmQMNFeGDGKrb9NWLbmR5/DFmAck+YHcpX1G519m9AMYqJLgB0WK2nWcME6WIvhKEduAN46ZIgztmOUZR6z5jEuix2sf9dcnUMQY+vhfcVuIhlgle9FrL8Vlb4uVwbfQ1evU1i60pFIL72iqjdmGQmXVq0xo2549AlF8EK0bqXw6htG+IfnOEJ2+McfSMvXWbhMQFHs3lXauNVJgijSgSMS8YzMs32HpdYtFFBAITnMrBukVjIGcHJtE/MKDBp5Y0A/CaZucmeyfjbm/lxLc6X+TBkqKhR27pJQOjF8Ui8M4MgpeJ3JGUxICv2556DEuteu3I3lsdG9ZUdFd9zmezDtkPEA1wlCm1rAlJMx0d6AAY5YKiUFDGOZ8Bhk4TjzACFXGa91yluaT9WhBc4pZHnKlClh3rx5dSjeu/vSBZsP6As/na0jJ8lo48eQnu21Ye/x+Kv9vXx4Dz3wmctyztXlS1i/XlXf/B/SWwt9IcEy37Oroj/4vMhBZ4yuwNcQwvEAIShz5J2LpjrUM363CYYxTBa4KhMexTPvXFEvWIrgV0xQuvcnb2rW2n1xEfL+BnMUsh5mYTl2hfll7Le8G+xr/vXjBnTWsm2H0y6t9ZyxSSaugrQo/fCypv/mZ1tUVqoykzi5ZRT0Z2Pb6fmK9pq1thoOfesl/fT9j+b2C3fT5+H7/6QAOUPLFu7Ju+kGj/VKGgoaIJRa/2OJXr1WEfGkePjuvKPRvTzZcdahg8KsWRKbYt8+XjcIf1AC8VgOHayyb33dBJbw6msez4rySTzrtdfWKJc998cPuJKKFw0adhTXZcv92WxKKLQDB7gX9NRphdnz3GAAs+22PVLLMt+Au3Vy+GdVpQtXWLaJH0HoO4wy0Nk97ONGGdwqrTzxWLC+w+pMnVDaIBVqTIEkDYYck9CUMDaqSC+DB3f3HofZEk/93puyRpW4HvHftDnLHNuy/3jO2hdfX/03fY7kz9fq6+v7KXd9SE5Z5CiOca0rtPxMK4VQPZ9vObhGr7Xtq6PlZCGTbl7xin5vx2J96drP62jLcluDPnvVEN0wtq++9sv52n34lO69coi+dctYJdf+zu1a6as3jdb3f7ci2x6cu3JETz21aIc9m/9alEV634S+embxDkOx8f3P+p7U5nnL9EDvS6TIk6y3rDyjgSf2a2PHPp7DNvMEhurZWogb2WveWrcvLTw+W47z6QM2n2QoP+02tn/nnD2CPth19FS2LVkz+3Qs149fy+R1rWOFWreIVIFCU/LBtZHG9e+oZdtJHOAH/ffnHxiv55bvyJY3Hi9JuYDrRvfrVMKeFFR2tnrP4C1pcyVfxkgbf7xzUPd2ObLILQPb2Dr/1LrqOjAbki0R73/JOUTdL25zVhtPMz6r50/cDo3xl/VkxbbDqpKvGS2rzuoTC57SxReA3AAAIABJREFUb8dcq6PtnAvglnWz1Pvwfv100i1W6Cr2vrigTPT4M3kFTxzW0Xaei7O+5Utre39WzXWt8LX1fXv974v7sNgT2lec0MkWbVSVkU+4NqqqNAOweUAzY48YvjON1OfMjZW7jmTnCuP2rikD9cDrG7JrQItIGt2fuVIKTU1uP8w4uFH/Ma6Fyr7y5WJVb/4t0QJRFJF+c2rilH1smlme/5bM92bFs0DDFDidJpAVuFSr/+62Qj/Ver7qkUcVvnOf05GzwLJbt2uj6ON3qezv7nNMf56yyEOTCuRnNrbRm9tP1Pqu5guargVKEXgQGBBOf7d4h85kJLJOZ07ozqPr9bPuY1Ql97Ld0oN8px2VFCLqV3JfvBN7dkHhIl9Iqfv7cjeKtPtR0P/89kt0z5T+tcd4YvRAQX1ppiuTMNGifBILmMnFGGGZJoYHeOmaNc58fPS4K4XrNnoKom6d3WKNoopRh99ROPv08gTqWPk5f+iINGSQoptuMOKMsk8CNEk5KFcyN2N+zFDKLSWfqqpS1a9+bfBrWHgNqvyh2y3naHK+WywSglmKIppV6rEQY70fO6ZobOvIP3mi5OKdrxcijOUbkUKoUgTMLHPwve+Jg9rVvnt8ypTFXp3LtfOg5+xl6V1xRz/d9eoBLdtbnYu0fZsWOn66MntfqR86tYp09Azidu5Wn1beUp95oV3HmliZ1E4zQnFsVDh39XUFNPm+8lZlOnWm2hpgCl+P9tqwJxOzm7y4CT+XNv5iFTN3rJVWrJp1L+2++l9lihAw9MwRK1T58zj+Pfm3ef4kW+P8+JxvZKJUaefqX9qgVbf1zHJh1P857547mxXPd1hf/8PTy+ttca17VdMF9vPJylb3OqXdEW+M8W/5G2TNdihlE4qf1vh/q5W2WAiqVuDyy96wt0ehSiEhJLudunHf0bASnk93+zjCe1DVMrbS+7kxZw9qeQss4a5woOh+4vhaPdR2sI6WtTav+mUVu9T18AE93XNMTqVan61QRcvWqV6InAub+EssgCVfUz3ukmf5XGyMNL0wmV7WYmXKL3/t3/PfEa8JyfNp56x1SMMUT97Mq+Jr7fc8jxDnPjOktWbuqtDGRrDjlbeQTpn3rbocyfdnilTnP3GdqveI3LUzHi/JqnMu/zvjJ36WtYd1XV36L17Ti91T6JqmH5+1NWxu3eM6VJc3v83w4pw8XakKcmrX+fD65r6Th8TvLfzAfAU490ovL31bPbaq68C1yXfaG/PmRfV9uU9u7G9p5YjLl1aG+Hp+iz+nlb++5Ux7pz+rej7Fc6mUfqpvOep6X25bpN3tYyr3usLn0p5wfpyLxzGlifSZid31x3dffn4U7R1QimbF8x3QSckijvn2k1krLFbO6UO7ZyEEXDduYBc99N+u0qg/fSJnM08+4/z4nDtxvUxp5xqvtGnQY/MQQaKDd8lgjp5+gjQC2ZhT4jWefMpj8SAr6d1LZZ/+ZDaWZNpfP6vDJ85kC/qd2yfonumDst9L/ZCFhsLUm4Egp8KNY8jjxg1OSgQjHLGGvXrpG1VD9NSGowLe9Lttp5VEduH5nDokd7zULFtyY4s8ziazw8WbocNgat5Z9zNN29/V5amuU/W53E9x3XLPnutvcXvULvCd65K9ne+LURtJD+itl/TV96bBpp2JE4cdMhNHafntFi8xin6DU0MsNHq0ygqx5yYqB5T1/hdX6yvXj9TkQYVjysKcuar6h+9Kr84xzObNH/qGNvcYZLCxP3v95/qnS+/Q0bYO1UPI+ouXf6x/mv7hHBheq7MVOtOydeLttX9sFUln4mGSuDzNgs869NePL83uF4nLEx+D/uL4Ev2s9WBtauUQwXidpC2+/J9ztf9YhYCZfm9aN/Pc//JQK/1g1Sl9bUZf/WDBvuzaRz3vXfS0vrXgOalda2nSOGnFGo8fpYDty6VLxvq5Q8fcmw+LLx7yE6ccBYCHsVtHv3f5aj+H5gLU/Nb3qexT8BVKD87erB88t9Kgxqy1SRTQp0+s0bfeesQgkAsGjtAPx9yorwxvq8mtTlu8+9dPDdDTh1rq5pNb9b1Xf67RN/1J1gPdMlSqtap0ImKVK3zEa/zN33/JYKUoYA989jIbMxiH/+ONjfr0FRcbPDq5PyRDHJJjjTFHKEj7VpEGHd2jP33r1/rSjE9m4di07V8eXaz7Oo5XZQYaXah0fbu21Z7Dp7L93rJFpJ///uXp4/nsWX317x7SU8fb6pYTm/S9ra8am+iCMZfqh9FA/eH7L7H5kAxToe7feXRJzut555ETFVnv+2W9WikcOqS3KtrbdZS/ezilPqePakXbXqasRSHo05te17eWvWixgAv6DNL9Q67SV5Y9p/unfkCzuw/NvCPoO7dPrNOemmzbtHmcHC+85NMHl+rnXcbktG3rlpEqzlZPNurQKqpUyyD1ObZfmzr2VovKs/r2lpd1t/bp3vaT9VbP4Zqxb63+o2ydpc4KrEPEno4YbggPC0mKc/Ref50g/gu//a3Cm3N8LTt2wmMhO7ZXdMcHVfblP3DZBCWdMJjfPuQEdpDUkc/yxhuMJK9YmFFOR9XxS/5Y5va0cw8+NU/ff3OH/kibdfeyl40DJPrsZ5xQadly3bukQm+daGNvt3Vi/2J9q3KLbul+jTa17OTr5qqn9VzvMZl+D+resVw//PjU9HFboB7Jfv/iz+Zk1yYuZ1et7k1/AB57kAMxsqHoXOF+EEWg/AjfWb9eY9d2V5Ui86Cu/Nv6owsLVOeCPd2seL7Dujbp8YzjOogD2nXolFj8v/fRyTZRx//ZU/W0fuY2CItE8nAdpG6CcSzIxM9BaGMCWywoZC4c5KuC0IAcWZl40WxsaB3jOeP3lPSXZycTGkMBH0MUMw+w8v70ZwpHj3maDNjQxo9TDHlECPrHZ5ard+e2uu+OiXVaKJNltJjZ48c95xnsnz17quy29zv5QUqKjxpKM0RPlL9/fyNi+MeVJ3K84wgMI/t01Kd+NMs21H5d2+qlDw/VlJ8s0/HqcGG1UFArVemUquFG5WdP62QLvG7x8h0pv1+Tdants5UdIhb6etcuRZMmpW6gyfGOoWXFfe+v7dE1f09CY0+etBynIqaSWMjytvrHwVfqgeNdat5X5zPxXKluo9IekbzP70jOu+o257dS5l78vFKvL7GUeWtBKXc1VKFPjrGv/Wq+xdvdOrGPvhvWK7w4U1G3rtLQTG672XPcYMMaQtqETp0s/raoEaeUSqRcY+P3n+9XeG6mMTUu6DdY949/r76y6GlNPn1ID/Yaqb+fepfOtmipT22epW/Nf0YP9h2t+6bfrcoWLdW5rEpfHdte9y0/pYRsK9b0ZAwhnqzjp87UiLHMVzS5LxmPGLdbcv5QDRSfZPxnt7YtNOvjI7Vg3W7dv+6U/vDWCZo8uEdKjZUa0//Lyh762yeW6szZSn1q+fP61uynszHGjmfDA5eZDyiY5NGFdZuD+QcygHHFkDVYayZmrmtH5xU4esJzGSOI//kfq+xj96SWLRY4/+DoSk36yf+VDhyWTp+V2reRBg/wWOmVa/1dPbsZYRqspVz3tcs+rGdGXKbuZ07of+94RWtOSfcNu8n6jvHbouqs2lVW6GgrOAwiYcBb+je+DsXvrWGoiI2Dmzbpl2V99E8bKvXVKT10NynLSePAHgJTK/D8BQu04GQL/bDVYH25d6Umz39F4ZkXbbz8/Yy7fQxtfEPfWvq8/mHwDD0w8ZasB7F7h9ZmGEg2CnGL0y/ubjFsfbq01Xev6qvJrU7ZnmCEP/RD5rBxPPNlTxGxb7/nfx4xzPIrxjHi1PHbDy3S7iOn9c2bx5gCGM/F+DkI8N+8eWzWGHB3ryrN+9Gv9KWKITrSolwzDm7Qf7TbqvlHKnV/m2H6yu4Fmrxjnae26t/HwwxQpgg7aNVSC9ReX5/yEe1t00mfHlquP/7998avapS/1CmOn/5UlxP65srn9Q/RAD3Qa3K2bYlpPXmmUjsPObT94i6t9ezXrzd5Yd4jL+iHVf305ZNrNWVYb2fQ3bDBc1NmuAQ0Zowz9L7+usJzL0nbdkndOyu64VrL6WvKaKzMzHxZ4ecPOlst84AJ3qu7yv7XfVkDtxH/LVpUHVMKjP+SS5SVkxqlZer5kFiOevoZhZWrnJkfNvEe3RVeeUML2nbTn13+ce1p20nf2PaG7t621IzmCwaN0g8HTNOXN83S5ON7tKC8m3446nr94Ycv15TLx9WzMH4bctnfPL4ku3b27VKuyYO75ax/yERb9x/LzpVYfi744jxZ9IoH12j/0Qp179has/60ccdowTJcAD80K54XQCemVSHHInzpwGrvXcwim9h8kvcjqMTW2m+226fwyKPGcBj27FV02XRFN92YTaeC4JD1Ctby3OQ7PD7uJQXYzuYssAXWUh6Q22nkiBrEKLY55sWSpnoCky8p9TN1qCUujvJWPfBTZ+Tkub16quzeT9UgnSn1lYWuyxECUPC6d/dcX5u3eD8grJw6pehDtzsRDgr8nDnGDGsU6+SMHHKxs7ZmPKZ3Pb3VyCUQRh76g/fYq2Nh6cujO2nSgc3Cg/FXy6rjxoCNDCw7o79aWB1oP/jwTo3eu0lPD51hG3N5y0gPfK6AFb1QBRPnbRNdv943aogGzp5V9IHbnAE0j934H55dmR2TEKzUOBDyiLl88SX7KcKS/J6rspZiOxmPVdiKT0Dnvtotx2PHyJmBMznUajy88ImccXnokKLOnfW1ufv19L4y3dK3pb43VPqVeuk7r+/MPuTBL16hzz0wW8cRijNHn3Jp/6kqncnAcJlX//nCP+v+ybdpdo9h5h24Zd9KfW/ub6X+fbXw9nv0f1oM0lduGJVj5MiO41qU+fi9dfpLjCepAf71xxKsyZBCgRCg7/BWnYEcqUxq00oi/92USf779GkqIxY0czTYSEM5/utBhe/+s5NDkf5j2MWejw02RcibyttIUyeq7HOf9RjaOqxNcTlr/Us5IMj6zwc9HpdYXJipjxyVdu31dkHfmjFVUc8envMVIqohg2XJz2HGPnhIC86U6ysvbct6FSH8mvydZ7LjA2PLn982Vt95bFlOkWKBKV6rU+dFos2T3sGJf/6kTmW03fYtpPmfHVdSOqOc8Z6HyKj6279T+PefSUc9JtUUSpTMkxVeCpRLlEwUSvLMnjhd7X7Aw3n4mAyeQTsyjrj29JlqBqBBfRX9yTdUMB1Gpq5V3/5zhV8/Kh3MENkgwMOeefx0dTkoV4e20rEMuRjvgm2Tg3JWVumum7+sFT2HqKyqUt+e+xvds2WxHhwxTf98yW362gcu0T0zLvbrC/wf73GGjyf91rgx0oAB0sJFlsc3ov87dXQjGOk6tmyzfcWYT0mRtXe/tGmH1BuG4dae4znTRhPv/a7OtMJ7FPTgF6/U13+1QDsyscAUhzXGvHysebUZVefNl44edW/arLdMUYDoDYZSyAKLKTWX/+3vTOjmnYzHHJQP8+PHP8kwfVcobNioaMjFxgRvbcz+9uZcnyexl49UGqSDYh9DqYJhPvbogUhqoqPq/h8qPPyY1fe6YXdqV/tuat2yTD/9nHuw4/0ya1xIkxlgrobR/7nnFZ5/0ZjLYbsNJ45Ly1Y6TwYGF+SuPl0VffubKvu9j3uNaKsnnlT4tx9LePndum8suNE9d6ns61+z6wrJB40mCzWwfW1P/7cfeS5V5jHM7eQhZ29gXlGvzm2lfr2dwR1jE3MPZtvBA3096Na1Gk1WQEatSzHv/JfXtGzrIbVv01I/une6zYsYpYBh4ZmvXVeXx9W4Nn5+jDSscUHzidQWKKR4Nt0sTy1G88nGbgE2gXgjyAoMwAOWLFFAoZkwvlqBTLwcASYWYoyFEohI586KYKNkI0rkW8yhQF+x0p6S/D3x2NyPKFAQjDz2tNNtk3/ydIXCqdOeUH7Xbk8qjH2Z90FhjRcDbx5PAl6XKEfuw+v2zepAIvtu3TwtCotd/oJXXq7ohusk6oiQQNtdXn+24EIltLQL3bs7IQ1lQKEiIfPq1QrQyrdtJ508Ic2d61ZQLOecJ68Xws2ggc5imminb79/XBY6GL8XoQS2Y/Owduqke/q00/9auV2nKqXyVi2ysQp/veiJDOtb0N+selr3j7w+aw2uqgo5Sk/87JL/kt4EjycCDqkbYDbevUdVs+dIc+dJFw/2PJZVVfrm6Hb6Zq++Uvd27iVB5Eoop9q8RVU/+09XwA8eVnj+JUW/d7fK/tuXqpXPvNQcKKbJZ2Sp/0uugI/DnHHZtq1+8NUr9P1E2e4ZNEj3L37eBDRgPBx4Df7nY0usbTu1KdP3Z3TVmrOt9Z3XYSKN1ObsaU3eu11fmfe4NOlWfWX1i5q8c7On3jh8VJOO/pt+/AdfVNmgvJgS5gkkR+QU5R8s0/ljuS71S15L+93g1n4tWeFCRGaMGoES11I9YsyAtmJEOnCgxvuT61Ly8aV+tvn6y9+4Rwth5vgpJ1/iAUcywY8IO0tWKMyb52zJmbzDpb6jpOvKylR2150KvXplDR4iyfuhQ0ZOxXyMrr5K0d0fNUZlLP/AfrNGkUy/wCs967JcYwrjw7yIVcHgmig5ScUTXSpe2+O1uliZ89u8b9sybTxaad31jdFYPUpbT7NzJCU9RnTnnZYCSK/PcQrcHp2dJAtirP0HfVxMGK3o6isVfvWwdOCQp5W4eICiu25XeOpZae0mqTMpIto73PDgYfeEkSZqyKDquVyssjBJo0TSvowPlE4UGRoNTxJKLYd9r/JzpH5AKAb2i+HCpqmjBkbt36x71s+3Z9yz4BXds3eVyu79WbES2G9h3TqFZSvcg8VzyRU4fpzCU7+zsRvWb3aDK3Nlzz5p3yFp/2GpaydpzAjPX8n87dbF22LBUhfSWwT1O7pXm7sN0GCdtDUYL83H/vUNcxoD+Y2hpTZX5s6Vtm6ztT4CGdC9u4VPBNZepmznzgp46g4cUNSrp6GQLKUUeTQH9C9az75d2tm6htAdj8fsDawX113rbKXbt1te2LB/v6X+Qp4gv6Ol/0I5gTL5GFBrT8ERweo9YbzKgGo2ocKZLSt1nTjejLrfbblBP2zXXv/9k9dl2zHeL7PXU7dM+rHsucwHSxm1ZKUpngEPO2mSyF3JWETJYg/AUIfCHyuePI8UWFfMUNi63a9HCSdPJ3JP5rD5d/V7lO07kFmsbefLQdopkFrMP+YUh6Eaynye0QYYoph7Hdv7/CfFCum1gA1fPsP/XnN1jX2j3lXknZIu7tk+25/33enwcQwJDT2+fevYGvJVQ5/5br6/WfG8kHofQYG8iW+8ofDmbBEDZYncr7nGF8M4TipPQLMclzNnekuQWmL8+NxWqa9CSP4k4hJh/MSqDZsnVnDYPMn7xPnlKxSIp0SASMujl1uS+n8rpQ5xvqf3vTcbe9kkGyIb0LixCuSE2r7D09TgUaKtsID3ivwvkCQOFPjRoyVyR44aZQt4OHZMESku8EgM6G+LbcGUOol2/eOLI/1ga6Sv3VwtBJPCwT0qQzT15q/rD2cv1+8dCMaEyd7R4KNdW4UtW+0xEZ8rKy2W1vocQW1qheUwC3Pmeywr+U3v/rAi8m4tXGiWevMwHT8hrVsvbdruQkx5K4Unn1EAhoTnM+2grTFeNMSAkWi/uL0pe/5zYwHtbGWwTYr+IPYOYeT4maAp771cKCBh42/0/Q1n9Udv/lo6clKTj27QA7v/1b0zzBM2UfMOVxpTbo1qpZWnxkV5J2JPcIE1IHk19SJJeyhroQhjx5mzLmisXi9Fp32uDupvMWJWVgxc5OlsyJFXPowTJCC3A0s6B4IcghqD0gSeTMzgnr3OJhwbsPzqxvmfchFTCpz3c59xT/2ChSa4R5dfng0XwLsejRwp8vHFIQQ2vouUIl9R5NIkRJZ52ZDjvmsH6f7XNujLozpocouTtedHjF+WMrbjn6Lhw1T213+l8MIL7uXCWAmrM2vRkiXGxhy95z0uiJJOZ916zzl7yURFGNvYezA6oWiwvh0+YnkSTQltW67o4sEljaVoxAiFi/p7rlzy5XZo5x7w3ft9ryHovbylJX7X2JGeBJ7rUBAmjJVYaw4e1Z/O+a3un3SbvrKK+MOM55XxdeKUwssewxbXPfXv5i3S/CV+L56uTu0UNm7x3I2M21MVLnh3au/e2RgxcPCIKaLR+LEKBw55SqYQFAb0dcW84qzum/sb3X/pB/SH0/vZq1GM/vL2CVmYa7Y8zOlDhz1+sOKMwqJlbgAg5/Du3dKWraqqOKOI/JjDhkojRnj4y+nTlku2NqWmNqHb1sHtOxRoC7y2G7corFrrTOAD+0s79lTDrtu28j7btcdTU7VqVb+cu9nKl/4BJdKMkFEXTdmxSw9Mq1TZwLqHXphCuH6Te9Lpc4wZrE149DB6xOs3CihKfuKwtv7wh93YTOqtbl0UjR6lCBktPph/BRTe+JK38y91iK69WuGRxyX6E6MP6/FJ5BX2rjLfy2gL/tE+KKktyszTrr59PbVexijXGHUpZnhvjOfXMEo0xkPfxc9oVjwvpM5HIH3xRYWXXnGh7cBBBYS3XbsklDugPUDW7rjDgsLjqsdePSygKJ3x9/j3+iqELFChbSZxMA9D2eQ7wixCyZHDigYOdAUUxXDyJH8ln2tJRJ0tW6kfShDWsxtwU7w/rZzHjru3Cksg8L1ePQ2iF86ctb+mZHJfrBD36+eKJh4XFu0Sy5ms1z3X9dfH8gwP5v1+32jflPee0JTpY/UXOyr1g1XH9bX35TKvplWj6DnShtDHq9ZIxBatWKWI3KE7d/sGjeDx4sueXoTYGLxYLbcr7PyhwrhRnuOShN94QrbtcKWTa+JYMeC0GzcUVjyLFq60H5PtV2xcIqDd9+Qy22hjKyvEIzFMMn7bPVMv0kef/Ttp41LflBF0+ccGjfBL3+LhI/8oQmLeUWp5kreZVySGsdeGWkDwueF6XzOwZOM4uugihd89596uwYMUTZ3sihaJzWOIKxfmKZBW1hIEjBrlO3rUvUAbtvg4Adrbu7srF1V7XJhHsCOur3cvR0qU6NFLtkttn2uUixsKrCVGRoHolTEG1fbstN+B4Kbl3U27trZzU6aP1gN92rmns/vgxvGaMDaGD8vZP+Jy2Jgxw0qGmIO4dWD2GCa6drWY9qhHDwXWL87hEVu9WhFQ1K3bFE0Yp+gjH8lB28TPrvEX79UnPuaGy23bFLF+jhmtAIpi1jwXhieN9z2FNeiSCZk8uWPccIfB78xZTY5O64EDb0p9Oki7Wvn869ROGjpIOlIdglDj/fEJlAyULaDHjHOgxaABYtZZzsXXYMRr3cKNwBhQMMCiuL/nCgklsU0bI6cJCxZJi5Zqcu8O+o+reqrsI9UQ9hxjRWauhe3bZfcAZbWcxUHhmecVXXWZexy3bDeFO/TsYcpnNGZM1rBqSmNclwJ/axW6qSM5vonbBH7OPzybHFt3uyKCIYA1DkUEBQ1j6siRNgZK9cQXKF7Jp5Frwo4djrQZP9b2XuZ3KW1Q4yWgdzhYs6kXyhfwYUI8du3zOl4xXdFdd+XeGs+fP/t2w5E4uU8+d99AgXzpi6rq3VvCeN6xo+3f4ZEnJVBtA/tazmzWCTOIrFknde2sqE8facZ0Uzqze1gjlbrWMdpI72l+TOO0QLPi2TjteF48BSUuHDgobd9VHfuy54ACAhkbAwoMcWFduuQKDrCxveeqgsK7KYfAeDZuFN5R+15KjdmQevSQevd0WBWb4ugRvrki/LZo4SQHWGL5x6LcUO9UgXJlF7piyloTvr9GsegToEabNvvGjOdv2jSFHj0UIexTlokT7LbUstO2pXrxCtUrqSSgwB0+7CQtR4/qnumj9bG7i8c31ahT2gn6FQ8HsU1s0IwjYHlHjrnlG0spCgVCGQolB8IJ8YV4EvAMoGSipENOgdDCPcC2eB4bXeuMEJD2/sY4V6j98p7N5hfH18Y/JSHt8TlTlCaOV9iwWYrjnvr0cGNRvz4O81akaNoUlX3gNo9rxSjE3JsxXQiaJqzFCh9jobaDcQ8E/+BBQ0EEYm3zDBD2iMSYMBIwBET6EBnrox9RBGMs5xBiU+5PU9RKEuwoXwJmD5QruuIyhV27fQwQTwqs9/RphVEV0qrVRhxjsMH+/Ruk7BVturxymbGnmIFs7z4jGkEAZdimtVGN98VtjtAaE9IUaN8a9xY7UeK4zT4iLgdrU6GxlX9NkmU4vifZZsOHu3HNZHTyQ7ez2HSDh3NuzBhp6lRFMdEcwmopR2yMu/l9piASJ2hr5vTp0ucypD60IWPjrdkyo+q11/r8wYNNPH2f3opGZQw7QNeHDlGYv1Dq10cRkMAp4BNqOQgdiB6V2rb2dQtGXyDDe/b7d26/qK+v9Zzfe8C9wQji48cpuuKKrBJoc/rkSUUzZmS95sViL3PmGuMmhnryzj37jezFlCDWyyPH3et0+Ij1RzEDWi01Tv+ZNQIj97790uFMTG2saHYol/p2k/YddsQCUGwMVxgjMNJcNDD9mY19FjkHYkNI+ZCHGHP1MFYZumPyRBmMGqMCccS9ugvvNaiIHIhsoT26rnOzsduioc9r3Vpld3809ynf+YuaubFBQiRCUkpaD3Of2vztAmyBZsXzAupUIGEGC4NwAYw9iyKWVxQZLKxAIBB0UXYKHfmCBV5Lnnv0qCmFbBQw9JUkTPKOXr0UDRtq3s2sYjlunG8AO3c6fHTECBfQCpWpMc6fbwt9ftwm1t8rr5Ago0gKftS9icqeI7gAacUi2a+fhz7VY0NO6yY2GqC1OlPhBDBbd7jnm3MQ77SCtOakx2fxAMYrMFMOBCks5ZwjLUPLvQ6VY1zjEUTYu/gijx3xO94R/9vme+MNCihPeH+JdaOP8XwgAAPPAvJ+7bUWsxWAweNRWvdAAAAgAElEQVQpmjnTLfYdO5qShoeNo6S5iJdu8WJp7ToXuCDEIHZoaJzKwJsuZ0ww54mlw4sVe0sz5wq+M6l0cGOp4yjPi2iKyZYtim68ztIbsYYIpYS6V5yWrr9W0UUXOVoiqQh7NRrv/7xyIbAXmo+0CcqmtRV5/0rsn7jNdeyYexCA0+/da3Uo2M6NV8Psk+JyYAAoVPYa12BIYkwk7qmLR9jaqh5eYptDlJzxFqNjChjj8o2qNuY/e6/0yqu+NzL3rn6PMZASu2dKKkaeEuL7y+74kKq455kXnXALhXLaVIMRh3kLpR7dXLHCaLJ+g8KOnbZeRePGKEJJTsTpF2q3bAflf8jMNYxJxHWGVRvcKAepEmgAjHKslRwY8/DEdexQlEgo/xWlfrd1HgK3x59O3JKJ0wBmOXKoNOi0oksmKHrvTa78YeiM+y5xV5N+TJvPdXyhzckvfsHRHbNmO5MyRgwIks5jiGwdq1n3yws4MKy9CingdX9L8x0XQAs0K54XQCdmq4CQ1wYoazsX1tl02rT0OBM2IiyflWdd0M3elPuhhmDBz/UVJhGKJ4yXkQ0gSGVINzRwgAndFsOXYU1EmHtXHQjLibhNW5xZuPM9vhUVqnr4EY/1GzFCCDrER9FPOQpqfdov0a+mdALJjuG8tRBOlNxXlAvPLczGxHRh/EAQYWySAxBmUkg5gIJ3OeueTmBL3btK+w76PcTRMHaJDx4z0iG3xGuNGqaob19p0yb3xsceuBTjyXk1vkzJvEplffsqPPSQx/bCCvvBD7iimYC8w+xqqIHOnntRa9aYQpojsJawqZtgiAEKK3+/vtZ9WObzFc/Uuc48Tngjbdxl2B1rjMF6Cnb5ioRB8YGxnTop4JkmMF88JOOVH2CeEvXtU5rSXfJgrXlhfrmy32te6mcSc8rE7lIU71iB2L/f+7qiwtu7lHt5a2ON91LKnneNoWAYfwkFqmjIBPfnKxtp5wq1b3y+gcY4W2dZm2jj2FObmZcF48Xjd8d/afft241YylL9bNjoSIDLLnMvL4rr6VO+JwPNJJVYJqWUMPxAdsdanoFmZ8dWqe2RmWvG43DxIGn4IGnrTid0YV2FU4G1lvhD1lviW4lDboqDtsOIdcM1Ck8/nzEUlvk7LxlrkGc8yGZcPRdEQgXqWOc2TnsOdQVu/o//q6aHL+365nPNLdDcAjkt0Kx45jTHO/wLGyiU/sARyYuGlZONh3xm5MHr11cR31F4Ch1sevmQPIhwdu82T1i8SebcXkTwSd3guTmz6dcQQnIefAF/iaFiGUXPYNAp1UXpDE8+ZdAv/lq2vOnTqj1QJXpVUoVTBBdYD4l74R9KHHFGY0Y3qgc6uu46Y7Yl0bh5M0lFAQMmMb+wS/IXVscRQx2qRQwwSgdeUSC3VRnSD1grFy+XRjhrI2PdCJcQ3h74qcLAAcawSDOaUJfwwtg4TGnft+0UwsvIEYr+9E9yilDDOzN4sLOprl3ncXDMRZgp6+olYr6hwMGgCSQOluFM/GZOAQoojvmeqTQDFW1cL8EuZf0wPw3smLEhBEgjaxPjIqHklAw3z6lkkS9xWeJ0PECT6Su8w8BK8w0+9EPiHIRuRdfKtFfHCgTKCXmP8W6XEieaKasxYtNWkKdAJlOqFzy/LAX6Puey/GsYn5Q1OR5pr3wDWlymPCNJ2nU572vsL3H/xgpnBjad7EMbw8yXWo7kHNCwYZYmiltsrhC3j3d++jRXbk+edHhnhoXa6p2/BxZot0LFiOcasHkbox+4VeGZ55zHgRjYDu1lhG3s+Z06GJtodMnEQo9r+HnWY/aOY8cVlq8wSD7plqLf/1xuaE/D31T/J9SxjYu+qICHr+g9xX7MH5uxIbXYPc2/NbfAO7AFqjPHn4PC9+vX7zuf//znz8Gb3p2vgKzBWAPPVkhHD3s83GWXKrpsmueVI3fmgAG2GZJWJPUghmrBQmnLFo83QmCATKRXL4NDkpfMNjwEwMwBNMk2W2I2t203WnjiN+yIIovlQIC0c9yXdo6LSStCPjIgUIePmDcrUI71G0zBsPol3pt5fc0/LOCbNtX9vppParIzVhcoxiESSmnT+MXh4UcUlbVwq3wEecVxY3uNgKMigCO88AwEmiJHah+xseHlJAaTdm3d2pXOIUP8e5Hn1eWnCHbl9u0VFi9x1lq+Q8gBMJHyY53nH/HJZg2PXAHlJSiksZLEtbBEnqlwun4gqihSkGbhLSK+COZfGH8xnqxd66lcUNTwunJ/Ux1NNOYsRm3FCms7i4XCM9ytq6LevYuOm9RqopQs8zyRlq8XYxTGKtYN+h9FFEWLuEpizRAigRwC/80fq+TrSxuDheZ2aoH8ZMGxmf/OigpbX7JszsybeJ0p8vy6/BSXxbxIL7zorKCkNoJx+ugxhfnzpaVLpTnz3IhCvyNkk15g/gJFbdsZ7BEPfbF5nSxTvBZErduYcmvxhay5zM8i6122rPQrKW1QNuirEtaD5PvjzxEkIXv2ONSU8QVTcd6cicuaXbe4hrlcyzoWv+Oc/k2Zk1llMbFX6fDhwvtXsQKv31BjDti8yZsXBqHdtr2ahZxx261bzX2x2LvSfovnGqnQuna1Z4r8scOGKAKuPWyoIjyhrVoqmj5V0Sc/oaiR1/ZksWxsMP46dTQeh+iaqxXdenOTvjP5/nf653g+s67WkKPOx8qlzK9i69X5WIWCZbqQ61aw0o3/w1/91V+R3Pzf8p/c7PHMb5F38neseXhRvvOX0l9UVVviEfQ5YPeLYUVxPZlgCYu9WfWhvUcIJT4TofTgIY8LybNWx4+w1AcbN5onxvJ2ZmjzLa9onEcMjwH35wky9gzgpCRp/8UvTfmAVCWsWaswd66zaCIEw/r32c9Y/bLvTftw9qyqfvtQbo7I2Nqedv3bda5Uy+vw4Qq//LULtseOK7r7I5Ymx1IX0Ec85+r3FK8FfUye0L17Ld7S+jQD4zOyGYwNpDt54y33fN59txDGsmMlrc+Kv9F/TY4tlEfesYUclpJILwDZBoI15w4c8XPAafkXpzbo38Njp4jxJCYUwgpiP/EgTLtU4YWXJOKmyIuGAAyc+6YbFJ551mMoEYJInD7rzYLkWV6gAv8n65CYOzlzBtgp43/myx6bR8zq9df5++rbdnFxUMSpA/nneBZ9fvpU/eK0gHcTC7ZwseehfOElhd88bGQqZZ/4PXt21S8eNIOTzTeQEdQ/GfcGdJ5YteeeU1i/0csFvT756ep74MnMh/LiPc3zmsUeHvN85sM16/vu/PsyZYHciHpa7sWuXRQNvkhh5SqFlastXQSQcQTF6JabFZ59Tlq4xI0nq9co+uof1a1/kmtBDK1/9lkFoPWQS82bXx13CLkUMfesqxhWGB+kk9m+Q9GZMwqwktcTJl9SLH+yrJm2y++n/CZ9u75nlcwE8sHGTv5YY3/IP1dor0tWJt/7m2n3fHRAk4/bRJ9APsZhuZtZIzEckeKH3NylEjcl61iXz5Tj3RzjWJe2Sru2wDqYdun5cC5tfjHW8/dGylrjXEP3xSZugNS6lbImNHG5LpTHNyueF0pP5tcjsRnl/5T8njrBEC5jMhFgVIVY5zJCeXjpJYXZcy0HGwqjQfp693LP5b59plwQ52lKEpM3T5g31sGf/qcE7fvZSoW5C6TRI6WXX3Xm0/btnN2S/I550MRkXfhsiZ1nvuLU7ks9ebax2L1DF41o8mRp4SKFffuNodG+51e6lu8mQL8xy5TLqEtnhSmTFac+MA/HXPJnHvF+evk193zOmGF5+UKco7EqKOAtoz9h3p0wvqYhgX5FAYuNDaRLgHikSxeZQkO6lCMnPF7z8HGpwxGPRT5+2msQpyCI8zZydvs+JyAiRgmlqwVJ4yMF8tIBTSTtBom8iQEFUvbWXIOUG5MzkN227VxRR0kolOuzSPulzQ8uz86PDNTZFYF1jhJAkX/xJSOKMqG8yPNr/Yk6HzmqQL369pFOnjDPhgmWsSJcyiYe983zLzrM2VIeZFJArN+sqs2eZ9Ug0B07KOzcZQpvAL75+BOKOnV2BWjRIsular+3bqXw5luGhsgK17VWKOWCAgJ8jStLXNNq3FeXE0BllyxReO4FaeFyZ0/euEOBVD5dO0ubd/g4hFV57iKFbTul1RuyDKbhxCyLaa6XkF9Vpap//X8KDz8mde4kzV+oqnh8QeKz5T/d0w/seMNGhcVLpd17pYH9DCKvnbsUXXllNdy5LvXm2qYQfPPWehsnpYzXupY97fq0+hQYa/nKYtrj8s9lxzzvyTeEJM811bgt1rYF6plfh+bvb2MLgPDCqLdxg8cBg2aBcIk1F9ROPQ1I56xGKfMrS66WNPak7JcN3hebupIpdWv0sI6mrsN5/PxmxfM87pxGLVqhTSplghUlh0gUKhbKLdUFQj4bLOyT27Yp4OGCtRYhAw8QcSjETcH8uHlzruAO1BN4EoQyxP5x/aYtUqvWTjoDpBI4JXC3Ygd1nDvXrgh4B9u1de8OuSJhbS2kMBV75tv9G4rbbe9XFNO/o2ihjJC3Dlgpyhfeyzx20mSxSYQeSPOAIrhjp6KL9rpwynMgfYL6H+Q03kf6CUUHyOXqNdb+gVxdKH4HD/lzevX033kJ/cv78aojHKNw8UzanN+vuNwIi8xDyTPwWHJP/A9lE5IhvvN+fg+nq9kY+d6lo8d7QpIB8RCJ42FXfmu2RD4+ngtrMwRajJVduxVNmihheCC33I5dsjjTZKOU+jllfnBrfhy09QnEPSRwJz8tHpTMeC/1VWnXMVeAGkcjhyug9EMehtIB9J35VaI3354DJJQ4WWJsMQDQ9hHKfJl7nfkOvPXkflPiLbXEzFctL1+gD0iZQJJ4lCLeC1yfcclcp//qeRQV4Ov5zAbfRt/FR0vyqmaUdNqPgc24hCEcxRODBx56GJo5t2Spe5zqYhjIeAXCSy/7eCbumfVs9lxv2zUbrG/Czj0OhcYwsGGrVFHp6+V7LpeAvdIf9e2LpLJy6JCiEKrrkZY2JUaXzJnrOYjvusvX/8T7ba0HbUEKk42bFC6dagRpNVICUdekZx1iHoPdx51Qj7/J+pRC4MNcz1cgi722gEJ5rjzANfbRxFpwXs6pYm35LvwtvPqaqn76c1vPYSCOmMMTJwguBDPsArU/n4+U+ZVqvGJc1gdR8HbWPa1ub2d5LrB3NyueF1iHFqpOdpMi9g2LPp4s4HFpZBgFNtQaz84I5QEY7Lr15pUzWO7I4aYQhXUbpOPHLWk18YOWo46HcF+CwMgEDJQocpwhuHUMii6f7l7ON+dKCFvEAPIelKWEYJMtE0LQL3+l8OSz0q49RqYQuA5vKSkkEOSGDnaP3Q0Z8ovszefxh7QFEOF/1pueT5G0LCRyLnYAmz1TIWuPTh09/Qj5tSAyQckHorp7vyt1LY+6t+u7P5C27JS6d3FFD+WOGMwTpxT69DJvY/g//9fhiO3auhKCoIoSuma91BZ25Q6+4cDmOHiQwvLVLsCj6LRvI3XIsC/DUEu/0sf9enus5s79XiOu5VmDB0prN9hfYucYM+Z1Q1lCEeAZsONykBx8YCY33MZN0nXXlJQawW/O+5/2R3AmFQnPunSq5fczQ8aChQrA0InZIj1K27YK9A1tzHimb0o50oxCsSLy2utuXICJlvjpxQsVYKckxhKikJ49S8vnSr+0b+f5BJkfHKSqaU2McGU12zDKPd48lC2MP8eAPVdI+w/7eJgx2b1s6zcrtFsnXTJeZeRS5EirR9pc9aur/2e9McVmm6V8Mdg3v44e5SzOrB3Jo77vST6j0Gf6k9yTo0d6nj7Kjzedg7GGkkm+WQw1RiDT3g03/E47ds4QY508acY1Tpds3UfZBQ69e58b4iDX6ttLWrvJ+4QUQuQMnLvI0wnRT5Rt/xHp9bfcMLFvn0FxTXDt319Vjz6Wy4id35ZWMf/P8jOT3/LNNw1SHFat8XExepQR04SXZkrrNpqBJ/rArZ4c/rGnfO6xnixeagaf8NIrZuzRgH6KZlzqUOBXZrnx6JU3VEXc/s5dCi+/7vl8p050ePzjT0qr1imcPC1dc5nKvvF1hddeszycIB2ia6+2JPTh5VecTRbegBuud14DDGXMtzi1DvOfNQWvEjHe5Atl3Myb79fRDhjKaPM4Z+rbhYqJQ01enKmobbnnyiXOb948h7Oz/k2fZpBZ2sOMP6yB5BolrCWNbKvUPTzR//X+WOp8LPW6ehfkHXQj8spPHpBen5PNXx2em+mcDT26W8yu7WGlrJ9vU7XNuEGfZhBONt8wbi9Z4nOPMZgJA0o79zYVu6TXNhtuSmqmel/UrHjWu+neYTfGSiIbNJ4TFgzgtKNGGomIKYOxtTeGgABPjPOZpVmfEcpZdPD02KbfRhHCLUQ3KDvHj3s6jKFDPG8XQgEH9y1YYFAy8w7Bptqls7R+i6eCAdKGwEZc1br17gUdNMA2WBToNEEO62H4l39zOnkEQDwUsPuSksNyQJZZXrPQvoOiiRNLE9atFG/vf2kLYNWixU6qAzSHfIHkcStWTBJmvz7LNwMUTb5zoIz06e3eQgRaWJAhh1iyXNq8079X7HNmWTw8tCuenV37FLZsc+8Z4wIvX7fOrnCt2yQBnW3dwpJqQ2IDU2d0043mFQkPP+7e6x7dFU2aYO8Pr7zuCvCAvir7+McU1q2TeX7wzF000MaBlflqjBIdjawiQPCCYIxCjBeUDbpLJ0V33q6y3/+cAt5A6nnrLe7drecGTvsHPPJLlkn9+0orV3m+U+Jl8R736SOr4/r10mUzZERKeKEZ0/F4L9Y3sYIZQ9uZTwjEzFNLQXRCAc8tUPX5S739gSmfOKlAMniUgkxcV9HXxHHezEvSLJhCn/Ey0/cD+vgc3LDZY2j79nSFkxQ4pLNBiD8jV0aPHHM2YpRUytm7t0OsGZdr1rjQxLggN2IRT3yyvEYq9uhjCqSkYAyNHSmtX28szvmJys2IFq87b7whTZrkaRrq2cfJctjaxLPJu8g6dCBD0oaRZOQwX+coH2tLn15uFOjVQ1q5TurdXdHUSRLeujRlIOdFKV9YQ1GI8NzDSs6cYizhha445R5VlDLyNDIfGfugBOif3QelAwsUZi+SuneWbrpOoV07hXkLHKK9anVqWyZLYe06e7Z7Wd9c4PO9vJW0c6/CrDk+R5nbQN3/5cc+51CGUci57s35CijFxzBeVEr7DivYenDS1xIMRBiuHviFj5+jJ738r8xWWLTM68waw2L27KuqQskFjYF3vlM7hSXLFUA22LnjCp07GBcAzKmkagpLlimaME6aONEg4DZXM8poYI9iPi5arPDWHCMtszQ9EPMwtuMxxL6Y73lFqSau+eWXDWlhaUEuv8yUX1tDi3m285WtDDNyLKyjEIennlL46S/MyBPo1gWLfWytWudt2L5cmrdQ4aIBEpD4g4d9bI4d7YgOIPjwMVxzdbI7z9nnrFE7Aa9M3aPzkU51Mcqcs9qcmxdZSBCGVOYT84d5fPi4wguv+L75q0ekSyepxb/c7yiGc1Osur2F9ZZ/sPvS96QHAonFfMqkzxNkVuyFEKCBTqKegwe7/NUY63XdSlz61efScFN6qS6YK5sVzwumK/MqkrfhxZ5NYzHdu88ZRjfgtWir6P03K5oxI/sA23gTSev5IS0+zpSijZtUxUaOQrN3vwKLDkIKXjQEI/4Bk+3QwSzL9iw2X8iDgHL27+teIrww40a6p6i8XGHBIo9nQgBE4C9v68I3FuoUy7RBPPcdcGGJMqAgIZwhBLGwWy7IIxJxUQgh75QjbQFkwTZG0u7eJrUs4CYE3HCdM1/Cphi3H9BJvCr9+7kSifKPdwvBBo8X7UZbwiTbppX3peWDrXIPWNyGKKzch9fxZIWfxStEKhRgQyhvxKx86Hb7lxW6IJzitw/f5UpwLMDBvgy5DJsZEDnYVeMyZ94Zli934wZji81sUH9FX/q8yj50u22Gdn3ePXFx6/SXtiUXH2PQ2hkyrk0mxEZ4IYFvY8jBs3/JRAWYgTPlTk2RE5PHoKDFeVkzRiGDSe7YYYo3Y9SgrrQr8OHFGZgsAj3C+d5D7gWGQKTUA+8obYXwjncZkiZInlDyYDSFpInnd2wvYXWHjIw+RNFhLAzu5/Oc/sbrytjZukPhgf+wEgQ8wpA7XTHDodZpeUILlNW8O7Tj9l3+3EXLbeyFFWtUBXz0hRcVXnrV4ZeTJigaM8qVqsNHFGa9pbKzZ13wrmUuFHh99rSxCKMAP/07qUc3ryeeO+IqaTvWM7xNbdqYd9/isEBi4MXjgE0UpmX6LwPvzD680AfWamKjZ8+WQpU0aKCtn1HfPvYMsa61O+XGNPoGBXjJCung0Yy3BIh8pf9jfu45qPDUcz5eWYcxUPTrIwGZL3LYvMSrzlocE3nR7/Hc5h2sqRwnXAG1cUGbsN6yRvDX2KpJh1TlHvP4ndwLVBmjIOsxZeXACHIEI2Wln+MVKKkbtmViZ4Mrn6wvGBJ5B32BQXHNejfAbNrmBjDivWNSqM1+Tv17u5eQMIW5i/2ZbdsoANkHlXPsmMJP/8uhjrBvA28GvdGihe+N732vwo9+XB22sXS5IvobSPWKVVaW6CN3KOrdx+KhLR5//FhFH/2oj+GfPOAx+hhcR41UePDX0pqNDmcfPdyJ0kAUxPUCZXDgkHQws7ah3O/Y7Z5w2oAxfmi7KeBhYF9TSi3UobzcjWxpRuI8ecD27gbOlbhb/397Zx5lR3me+acaLaBdQvu+ISQhQEIbIIGEFGycsVkE2HEyBjs4TsYnZ5ZzkrFn5o+ZyczkZDnJTE5mjiexQ2xnsGPFW7AxcQCxgxECbHZZSEhoXxBIAu3qmvN736rW7du3u2+rb3fXbb3fOd11l7pVXz311fe96/Oa0bo8lLISyUxZpBNlYGraj6YOFf8FxGSCcXg78x2KZxa+T3mxbRlR3uPPqPGP/1QN//U/F/OCGFMYG998U2lmbE0P7Pc0IIgE4TogSgL54v1DHsXBZ5DwTZvaYk0v5kVGr7oCgWoUz3slfVwS8Vnzsk5Qi+M7kqZiA5f0SThTu6KDccwOIFC6uCD4kKg+bFgzz2aKYIsHZeNmt7b1aVC695suxC9ebCezZHdeQdGPDPD2lpaKZ3au9KGH3QoLWQkWbIQLhAaECXKe8Mg98FM1XH+dLzK5hwcBh4bigDBCjih5e+R14pWZOUPpjh3SseMm3BqBxp5B7nHwX/r/3Dv7yqt+HIRiQgiRaVB+EG6Y2OkX7aIL2/cQ+p6F/U+ZAyNrQilE8SG3q602ZrQrcngCwJj3GBPIIVm40AS1lPuAMoKHC+EWzwr3ETzJwWQLlghH+T1GOKXxPjntXowhA7xg+Rk8MXjLTnoJCryPCB5LFrfwgpUridYvjsv+uRfez3T2P0KTCcXZPbZxlAllZ/eqzav+F1q4NmUu0v37lVx6qSB5spDQLM+W99X0u2Jd1sWLzoYnEao7YrgrnRtedgF75HBXeBDWEc7z+4IRBiWnmkYIKcXjX3vdwwu5v6P7uJcJoWDnblcqUR7wtvH8ThzvXuzde92QROkSFNQPyTs95WOFeeDZ51whQKE4ddoJpT72kWp6dXYf8kT3kcPaT9qS5e2i8H54XOnv/UfzBlg+I2Nt1zqlT/7M98XgdWF/NX79G2og5LlKD+vZEzd/ZURnkJNxXDDo09fCyJNly5Ti1QYDrPi7d1uIteXxYshCEULpxNMJEzgsoq2N3ean9Jx3hDGUoF173NiD4kOeOrm8Fw0w7zrXmUyb6t7YsWOU/uRhSXgTCSnNjESMDZ5TFDkiARBk8XBjTFpxXdmZK7xFyafOK9fDM8/xaGNHSrv2+xzLswc2hLabYZF70E9inEJSxrzLs8k+DRgpBvqcwPjlnuLJ3fOuj+Xs8BoxxA0hNmdnxyesmd+cyBRelF3WFPqEEsxrlO9DeOCzWr/MT+ue8XNjMKOvb+/wFI7dB/x4nJP+ofzRMoUv/YcfnY2eMIPPAKU/+onnpj74sCvMXDMs2bAb43kFo759lP7FV5SC+x4/R/rCy0rf3ubh+W9t89QPxvaDD7nRiOeYsbzhFdm10wfuIQ3c8meQ/bjOoyfdGJiHV4PBKe7xQc/L//CosZ+npDVU8HyaYYMyZYwVMOlANIJ3qo3/KNREbLC2Z8aWSl5QM34/9JAshJv1h3B2jI+dfGbb6FlhvyKSTHAl8Fzt3CcNyFJPGMtEUzD24agg+qKgjXtMhEu6abMSxjZr1zVXSxCi4fEkeoOxhhKa17HlWuBdINqqFobhgmIT3WobgWoUT8zZ/1vSN0sO9WVJj0j6I0m85u9LJd/Hy+5EIFcCyUXLC4nDPjnWQ3AIdaT+XLJypeW6pN/6jlus6SML+8HDSh9/WhZ6RM5IBUG7/HLyhcVJIt5zTxeLIRMNDUEA4cCEoCNmzU9Wr7KvmEyN6IbJh3BKLMsrlttkRDhVsugqt5JhRSPECa8sAhghhWXhi7l31nMIIcO5oCnM07wylOpgRWTBRinCU1jnzRQ1cAa/3EvYxjWZQsQYyRcxhI9MAEmWLzMlNsEbAt6QPFE2Aks+DJ4IWnNnyQikYB1GWOK+jhkpbd/r7wkFIxcUbBHC8ZJhMBg7ys+zd5+zzxJyU02r5OUt/x3jAY8tCx4Njw6eyS5oJiQw9sgVmzHdw88XLbSxbiU18nD0avoNzv36+0LMlvco/yzUKM8o6i++JOGtyRUKhHnCPlH2EEp5zoYPMq+k9a2aa86EQyzNevxp93oifHPP8aYgaNMQhCZPFN42I6tBYMC7RxgjNQIXLlTjX31V2rxVSi5yQrDtu9w7g5BMe3Or0gmvqOGOO/x9Nf9R5vOcu7e2u0Iz6EKfPwgtpq99KSeTRVHgFYIIi3BzPK87d9v47h+95dEAACAASURBVKwQa0Y3cOaYhLjSULYHDlRyw0ozLtj9wZBCZMaWLdLGjaZ0Wo1EQjtROhkf1TYMCG9uVLqd8PVT7u3iunhmMR7Ov0IWToqRafhw+4y5PUH5fPgxCcPAgUPSycxzwnkJnx59sXuI8ZiOH2v3rq0umUEL5uohg6QBh3zXfn2VfGy1RNmYv/6aGyzBBa8rXi685KRUjB6l5OO/agp5+vVvuaKJAeuqyy38Lt24ya/r8jmmaFgu9C9edYVw/Ggld/9Laes2z9GHTGvFMqUowOue8nvOvDN6uNf1hVSJZ4O5atJYn29OHZGQZng2aBgzWXuYqxrxjh52xa0xe37ycZSP2dxry5bvaGCK8M/zgneVc2JsI8QcxZxj09gyZtjaZ4kb7h4lheCE74tHk2vgdzT6ljc+GzbIjSvsM3yIz2vMo0Se0J8xI/za39jix+G3KPGEvefXuWuv0n/6qaUYpK+/bkfnnjL/m+cJ4yxRGhiDOxCNkHeztW0lgxvPSAtCGcL9GS+sEYRkQlZXw3601r8ifp4Qjs94wsB04IDLN1MmK334UemNTa6cnTnTvlG5Jy8O+YP6sbNmurEKOW3qVDM8EulkLOw2todZ5JOF2mLYytfunux7nLtHEahG8Xwi82yWdvQWSSuzD74h6bFQPEvhqfHrTLFspmjkCh7fkd8ImygeysGDjTjIlM49e8xDgxUfev+U8FmYKBEyEeRYdFlo2e7Zp5RSC5AOYYkqE7RbXFEeHsj5CO1hwaSZ2ZNtxl7K4mh5OWWMtFMmKRk6xEt8zJvrYUnkKt6eEUQcP+4CFoXMEc7xJpSE6+b9sXxALMvkD+CxYxGeNlkJ+S/IITBwvp2FwZGTNXRozYvO533ptm01Ck5pZxgrCC/geOQDGUvtmjVe141jlYaIMoYgrSD3acdOJyxBGD7wrrTkKg9jwzo/cZwvnCiYeDpy7wLnQhlE6EUoInQSWvgrr2hhNCjtYkdfGzEW4ZYv/twtq/0JMyMAowtaJY8xeS148TtaogXDzuNPKx11sYWma8GVXqaGsFaeI54bvG14UnnN84Sgi+JJKDo5mNQ/hbiJfLbMe93eVTcJh5z/3YNOmIRn7mcbXKg273XqAjPGHbzo1OdkH0L45s/3+3fxCDX87hc9XIpn9M2NrvwxvuyZT9zwg7KTzwntdY7pgjGIkgVJGc/r7my+4Novoj7ribMeMoxL5DEi7HMOnvuMabeKU7W9C6zBjPdDR9x7hVKEwlmqSJZ4eJiTMOhRNog899zj0/ZJyr41HH/p9xZPNAKpMeWeNC9gStjxkgVKRgxXIyQ9eDGHD1Vy9SIlsy9RevqUz+Pgwe/mXKLkumXuicabzVy7/BoXBMtOXfqWeR+2ZKsZO3eOs2tOmqhkzW3OXPyZ3/A1xqIsPPipmYKTeTDSa691FnEMn4wdvMTkAudG0c2bnZDrns85XqwhsHdD6gR5Vz7mDh9WI9f2zg7jIrB7jEJ+xTylG16Spky0ElMWwXN8ixs/8RwRBg4W3EMeIMYln4Eb5HWsd+w3c6qXwsmVw2wJs2cOtmDy1PEUo4ijGDIWWTdZYwjDxuOJgZXwaBrPgP25QmFrKxEEGHfz7zAY4Y3meaNfzJWL5vsxIF/js9EjlSxZaOdKf/Gy5V4bWzxKCmMDDy7KKcYRPL6E4mKQItf+qWc8ogGPLIaZ+VdIt92i9Cf/5Osg3qbL5iiZNav01nfuNWtITg6GwosBZdw4I7mClA2ehgRypEWLTPFNWCOYYzqSJtC5Hhbv16wfeKYJ2ydcFZxQ1jHAXzXfagZjNGj40u8Xr+95jzD8muzX4JExrC1jRisZdYOFqds8xti47jqbp3PDd24MyQ8T2/MPgWoUz0qoQKPpXP6+9di9SnvGZx1HAGWylXqIhLTQTDnM6OzTv1/rCxaWeSubMUSaN89Z8B57zBUMhAKEI0IFly5SCoMs4TwoESyOLKYsejt3utJJjl1ZaGazC8k9KHhGYBzF6g0bIwsbFnGEZ4Qojo1XZYmH8Vrf81BRhBzIjSqF/bDAEsqBNyAnasnZB/OOsA8L8xPPuuBFfuHEMUpmTleKJxVhBnpyFjnkDwQhPD5lXtP8cL15ayVVNm5SQngP3od8XDCWctp2jAl4VHJBAmUIkh6r+XhU2rTZF5hBBN4f8PqBhHoSNn3mtAmIGpcx7O5/14U0xvIbGz0skTCcGjUb/7CpoqAhgE2aeJY0qUbnyA/TDJ8qwyfz35ZvTfiCKGjPXmnubBPGqI8KQVEyaqQLjQjqKBAIk+aV7GtkI8mMaRLWZZ5TFnjKV+T3rvxE5e+5p8wBPNeLFjqByvr1Sn/5liue7J8bKHjOEBI5R5arZaF0eEogkOCZ/exdprwmhPTn4ZwQZVyQKYII5ljEqwyjs75hKNqzxwxHZqzavluaNlHJxz6i9OmfSa8iTJ+Rls43pRsvrCkGI4aZRxCBprPNPMgYNFA4UPARrsClpDVhnoeCw6BKuZD8fbX3JD8m8xF5vHjpiDDAu4ay8v6Hfr0Y7yDgQRnCpYcSc/iY0oMPK7n5Jg+J5l5BskNfZ81y5Q2G2oEDzPOULF3a/ljhtwiPpC+gtLAm4O3m2jBaoazgZWS/PhfY8YgAsJA7jBkN2/yz5cv8eSyJyEiYB3LSHkKV8RhjaGTNIi+abf/+tk3feMNZ0g++5wo/JZmIfNn2jhKec8YfnjuMqMxfl8+Tnv2Zz2sjhiu5Zqnnbf4zxtj3pfnz3DtMDjLGsNOnlKy5Rclll6nxz/6XRKkanjOMGTOnOys348zCgoe7YSufzzDUTpngERYoqhhIMKxOn+K5tETYoFRiLMHjS5gzXksU1IuHKFnzCVku9KvZOr5yuYRhg3kX4zGeoiuvMCXcjBgYPCDlefpZM4jg/U5HjXSFlWsbcFB6Z4/PCRhgYCff+aTfJ57XZ57z8zGeUcbpj95Q8hufzkdfTbZGDvaDH9ramr7wojPzUlbtuQ1m8KDerzW8XeQc4/WcML7YHr2aINP2QWx+/fEDZ3H6xctKbv6ELvjvf9D2Dwvwrc2DK65viqRqplAyR5Q8/8wZnY1GKcAlRxdqhMC5Kp4dOf0XJPGn/YSTRWsXAVM61/5DUy6ECSXTp5mQmD7/ouWSJHfeZtam9P4HfDEllAirLp4UQlezEBusTikLgimCx6wkieWesCiiuGH5ZdFFyOI9AgDbnIY+P05Zr3Phi3qdCVZorNRYtWFFxWuGtwzF5dgxJatvUMMdtzcdwYTM8omp6dvsRYmgXP5V/t5wggUTYSjPiSHULQsXxeKdbnzLLMimEFNjFAIT8snOt0buDQYBhDy25HrmrQRryg0kKDYIwwgHL2XhcNwvhLEJo13o2vKOCzKQY0ydLL3wst8D9kF4zuseIqjhGXj/UPuCb96farb0Z8gQJRATEb5DX8kN7opWgk+nDw+L8OLFZwmIeL9njxkE7LkdPco8HCn3ACMTXhuMQ1wbz9awoUpuvaVzxAyZt9Zw27NXKYYFhGOemxFDLcy2GVNuHt2QM7XyXKOQkcOzfJkSSj8QZv3iKz53TJ3oYfs8i9U27icKFaF4zD9Dh7iXi7D7cePUsHy5j8mMpZTzJ42pKnncqj1lxf3wIPOMrFjm1vxBAy3XPHeG2W8qjIdcqa94zPY+RJFgDI8f7cY6rv/gEX/eGAfggnGFZwkPL3+85jvmX8piUddzxAgnxrlvrdL977niOHOKdPN8jxYB4/Ya5B8cC0V46ztK9+6Xvv099zgPHiAtvELJyus9yiU7lhklyhhNy/Gw1AwMgXhVmYtQajG05GzOfDdpolBa0r//rudfnjqj9JEnpCvnejgx7LpPPW9hp8ldv26GVWNTx/hF5EF+LAjNWI8yQ6xgVSdM/rK5/ps8PaGxUQ2UZULhQ4ElFWTCBKV/c695nEzBPtOo5JIZSscf8Vy1qVPcI8z6iMFlyGAly65xhbFfP6Uz9nrdYEqKjRzpBHqUJWJfGI/79VPDF3/H73ee98bcRXj253/zLB75/Lx5s9K13/e0h5NnlF78ppJV13mOKURLlFrBuIuRaszFnmt9pGR9O0a5pNMuG8DMjEGL6ADGWw0bqTxmBAT3Q4fc4w3h2uxZPleghODxJmR75Eg7s8komZe8hl2pr0PxTGKAKcUJ4rl6aMyDM2ZUVCjLn/96uJzoY/chcK6K515JJGzh9WSbFYar2PG/lsSfRo0alWdgVNwxPnQEqBuYrt8gkVN37LhSBA3qWbLQZKUq0q/c61ZPlAiIGhBCEE6YyEoVKyzLWIfxAB4/ofSJp9xbxSJkIbcoChfa62TcOLe04tVAiGHx43iVBBYmHSz7KLbZ98ns2Uo3bTKLZgq7JZ6T29d4OGLpMSoIbudy7y10g5qJWOcR1hDGjp9U+sjjGUviaV+QyZVZfZ0LIix656HHk9y8ZmQ45BVWarknGxIqyALysDAEG7wtkGdgNUfRRxonPOyDD11QRkDOc6swZtAYl9yXfGxWOue5flYS8nhOIY7net5O/C432DTzjvEMYbRhDPNs4BFCaP3wA2ez5fMPjhqDYHLjarck10JgQ3kkpHraJAkCFOaEi4c3i06wS62EM8ootXhzciUU6Lvm+lyBIpBHKXQAq3TdOqVbtroAi9EIBZBcRhTuVvImSSGoZeP+pIsXSesecybjxtTylmp5jvJjcU7ua3osI/XB2EAOLWGUeDlR0ggPxTgAcRrjgeeRME1wh62Y+ZXSUzCK733fnzce0NfeUnryB+3mdzb16eQJJVfNt+OlEE79/M2zhDxElDz9vJc4uuwyH4c84uWMppXGZrnxAkWk0m8pXfTGZvcacp3MI8++5LmbzC/8vbNP6X1rfYxRXolQ/vLjM15uvUVpTqhD+kB5ZA3P2qob7K/p+jnnb31eWrvWveyUaXntDZvDzJOKkv/ceqV4Y6+83Oc+2MDnzLZ1xcJIS0OKYTxmfcJAilEpyw3GcGJKN7NqRshTPjdwnxu/eq/09k7Hg06+e0Tp/f/s3QWL0wdd8UShJOwR4zNzs5EtMQcTzdTHDVhEIzU2Kpk8qYlhvum6O/sCw8PJU0rIUWfLmECJ//EDtkwYKRvhtq0oKp09fV3/ftasFjjV9fVE5wOBdhA4V8Xzfkl3Z+RCbP+xnfPE1x1BAK8chB3vIoxmZBoIFuTRmQKQ0dnjXUIJOJZR2Of5lDDcZSG5Vv+PRH48G7CLQUSBhxJlc/wYF2KwuOf5IChlr7/ugjAhf4ReVRIkEJbXParGv7vPvZpTpyhdMN9JDLZudy/jwAGet1WqdHYEhyr2TebM9pwwwvIQnMktJOQXrxuLMYLEwIssnzS3tlcsc1HFuep5F0iEEE6bkeFUuKBc+EmffMpDP7Gio2wy7riPCLvmzcx8QCj1eOQYe4QHgjmeEfBHAMJogveEMVbjlve1mRJX43PU/HAVjC5mHQZbhHEIOHiuIaxBoZ8wVqI2LoYjBEjyD1EYa9AMP7w8eCspicSxr16qhtvXNDt6JZyxIFoJlB8/KFFvEaF21gwl9A1FAC9OR/uJocPyvtyTh2HMSMgywbxZp7rqTUODRWek48e3+6zUrAuc8847BCOpPZ+TJ5vHKP3eD11hILeQ/F5CS8nJ5fmbOkHJbTd7GSwUGu7Hfd9yrzXhzkbr7bYi+7JKY1sybbrSrdv8XORDovTaGpRdLV5m0h9K7km5ApXt2XxTQdFihxa/xWjKXEK4Kudm/kB1McNV5u3l+sgt37nT8k8ZnzYey5hVbdzmz1Xu5Wzeq5bveD4JHwZrPPusK6b8NyjFEAzL7e53rV/pyGFWisiYhom+oOxTFppuc9KK6+34ZiAlymDGDCsrxVxY6ZkyoxNrbb7esnaxTmO4y9d94MAIQZ4z2Ni8nEiLrrRwYjyqdv+IQMEIyHo4a7pHPEH+MmiwdOPqs+dvicA5fUKes0U6HflAyaWXOJHhlMlWOxbmU3I8G8gVjtYCAXAxM23g1AKb+KB3IlCN4vntjEgIyXGHJIoKwWa7VtI9kmBuubN3wtNDV4VXzsqLHHGPEcI++ZKQCJgVOCMdIcTGKNizsCss0nhFCf/B4kjezdatglzIfsfCSZ7HBRzrqCsRE8ZZ/p0JE4QdvfOO7W+lI6izCZHE1UtbAEHolCmdkBigYJKzBhPq9p0uOO97V+b1vDHzfLU4Quc/IFSHsiLJtUuV/vxlJYQjHzls4cR6+TUnkkARgoGRa8AK3skcvc73uoeOUC0ZTq4YIRSS30mYGAIohgmYbGGPxbCAV4bQzPSkl0vAe05JBdqlM1xJIgcJcpCxY5QsWVL7C8/7mgtqtT9D9xyx5DosfPzpJyxcjQgEE1rBmfy3cc5M2iS0drZ3JmRnoVJtMdCW9C8/pQnYeNgocs+cxPy0/iWll86w0FszUuQ7V7tFwUbBIAxw6GATYI1NdvKkmgvKbXap2melzYN08Muyc8J62YjXOK/3iveO57Esb6rZWSA5Wve4tPegewpRTPpeYCGm1Rp+jG2TZ3/rViXLlir90U9dCUT5weM6ZKCFnuZKVtP58Ti2Mbc2jdlK+5V8li5dIj35rHRsr68jRPRgyMLoSrQPjTkdYqCZM5tCzisev8K4bepvGy8sLBhDzC7YuLNw8TN4nY9I+wjnzzyvJw94Hy+fp+QjN/oYrXBOM3jCV8B15mO5wn4tusQ+t96s9BXKUxxyBZRrR9nkfvA6PSVNGa9k6WLz8Bqxz/d/IFJgzHgNCVy//sZ1kED6hMGAmrQ1NgajrOuTd7YYnw2/9qkWlxUflCFA+HXgVAZKvO3NCFSjeLaWhb66NwPTk9dmOTuLFig9eULmTSJvE+UTgf+9w2dJKOgkyiZEBliHsYZSAyoLkzUChh07lAwc5GQRkCCgIOA5YeGaNN7zqPr29RwzFN7Dh5yMKEmUEELUWokKBCC8EwiLuZLMZygmeFXpD3Xo8Jp2UWvyFM2dK0HCcOK4ElgpOTfU5NQXHDdGDXffZWFWdt1d1JfedlgT5Nas8ZIJlOmhQXoxc6bS733fc4xQOMZnJSfI96MG4eQJSj79Kcvxs0LR+CtWr5J5XHsbSF1xPTxD4EqkAR5I8uKz5zO57VbHscZC4zldBn3AGMGcgrKI4smWuQAGY+YXPEYdaXPmeEmVU6eVIFQvu7Y5m2xHjlXv+1YQRm2+a8PIgtLY8OXfV+P/+Yr05mYLwUxuvEHJb3+hesW9VAH+5J1qHD9O6be/6zwCk8YpueezLiTzvGetvX7Zbq0oWuW/hQugkfzJH97v8wmGUcq0EGHzzfvcsDpvtpJVK5sT07Ry/LyPHdqiIFJGBaUX5R13qm2zHFsPHnVPMIQ+kOS0cV9aeDI70Bk84TD7pt9a6yzYs2daTm/6yGPONI4nd/FCC6W2ORuiK+aP9eulffudIIy0Cj6HNboN40AHutVy11ri3/Lo8UkgEAj0IgSqUTx70eXWx6XYIvaF3/JC4Qj9CHHUjCOfcc8+z9FE6MtDHvke7yfsjuTnkBeFVZMF6MwZpadOKcGbCcEBOXejRngNQHJUyDlhPxYO8ld27vJQHQRf6ktNm14ZNAtdmuxEFIQBIhxDcw9LJsonllVYFmtMYtCsMyWLXZNXFgEF6/LUaUo+d3eTFbrZ7+JN+wiA7SUz3QhRujdjBcMFhByEV1p5nhNePPpf3GQCDuOIMZz8StimSqGr6jXhqSh1hNvv3edlba69xg045E4WQenMLwTv1PoXnFmWz/BSYmiCXIz5o4OhtsZADTFMPh+hwEarHgGUxps+qgtu+mj1v2lrzwsvVMOX/r3EX3c1FO67PiPxV94+99kmBl3GVpOXs3y/zr5n3JIHaay0RHuckfr3cbZ2DCyE3mLk5TPK2rS2Rna2H/wePO6+W+Ivb6xxhGZndZmbSHqYG5i3V67wUh35/rENBAKBQKBACITiWaCb0dSVTOjHypq+/77S519whRNvAuGLhN4h9KNEQiaA4kkuyJxLjfUSlkzhBaRsw8SJSg4fUfra674/NRfJj8GaO3eukqsWNA+PyUoBWKjV5ZcrD71q6lv2whb9z9/jOWlYVsnh+MTH1fi3X1e6/nmvQ7ZgQZfk9pX3pdn7EmW02efxpjYIgC8MktQfy8P+rIabl5PoUoGwNldQ6KPYc5VT1KPEgffQoU0EJEXqPLmgjRi2HnlUCZ4fyuWQT44C1Aobdlv9N4MbwnM+rjpalqStg8d39Y9AN83t9gwSeo7n9dks93n4MCU3fUTa9JbSBx9yb//kiUp+/VOtrpFdBjg4BElPl8EbBw4EAoGuRQC7Xbe1hQsXphs2bOi289X7iRoJafyrr0mvbfLw2kEXuaKJtxPCCfI1CX+6fpkVUbeaWQht1G67cZWTFJDfibeUsFNIhQiNPfKBMew1/OF/q0iF3Sncco9jqfBYJC9Npy4ufhwIdCMC8Sx1I9hxqkCgDIFKzx8Znlm+ZpORLda3MuDibSAQCAQC+LqSFyQtKsciPJ7liBTpPcyWm7dJR6nLRdHwo04aQf4T+VQw/1Gfc8hgZ7GF2Aev6IGDSl94ycJrrYQK7Jh4RykNQM7Ort1GBd9mXsq54tBNVulz7V78LhCoGwTiWaqbWxUd7YUItPL82brZVk5nL4QiLikQCAQCgVohEIpnrZDsiuNA4EBILLkk1DODzQ7inOFDvCB1zgJJqG2fvl7QnfwoGnmXe/cphXkSkiDo9NkfltppU5VAHBOW2q64a3HMQCAQCAQCgUAgEAgEAoFAIBAoQyAUzzJAivQ2WbRIxl53couH1VIbcfZMZ42loyil5HYePeYFyHdQLuWkeTeTq5c4yQc12N4/5OUYFi5Q8tGPGulHlxEzFAnA6EsgEAgEAoFAIBAIBAKBQCAQCBQCgVA8C3EbKneCEhQNEBxQl+vAu0ouv0zJ7bcr/erXlD7znBP74MWcPMkKkDdCOpTVfUOxTNet83qdeERpkAlRdiRaIBAIBAKBQCAQCAQCgUAgEAgEAt2IQCie3Qh2h08FO+SvrG5ZluKL/0oaO8ZJggYPUrJqldOulxQhphC9lVWBSOjoUa81RrmUaIFAIBAIBAKBQCAQCAQCgUAgEAh0MwKheHYz4LU4XTJjuvTJO9ssO2ChtCtXKB050k7ZVOurFh2IYwQCgUAgEAgEAoFAIBAIBAKBQCDQAQRC8ewAWIXZtRW2vWb9Y5+o9dUMkngTCAQCgUAgEAgEAoFAIBAIBAI9g0BDz5w2zhoIBAKBQCAQCAQCgUAgEAgEAoFAIHC+IBCK5/lyp+M6A4FAIBAIBAKBQCAQCAQCgUAgEOghBELx7CHg47SBQCAQCAQCgUAgEAgEAoFAIBAInC8IhOJ5vtzpuM5AIBAIBAKBQCAQCAQCgUAgEAgEegiBUDx7CPg4bSAQCAQCgUAgEAgEAoFAIBAIBALnCwKheJ4vdzquMxAIBAKBQCAQCAQCgUAgEAgEAoEeQiDp5vPul7Stm89Zz6ejCOeBer6A6HuPIxBjqMdvQa/sQIyrXnlbu/WiYgx1K9znzcliXJ03t7pbLzTGVcfhniJpVMd/Fr/oSQQ29OTJ49y9AoEYQ73iNhbuImJcFe6W1F2HYgzV3S2riw7HuKqL21R3nYxxVaNbFqG2NQIyDhMIBAKBQCAQCAQCgUAgEAgEAoFAIFAZgVA8K+MSnwYCgUAgEAgEAoFAIBAIBAKBQCAQCNQIgQtqdJw4TNch8ELXHTqOfJ4gEGPoPLnR3XyZMa66GfBeeLoYQ73wphbgkmJcFeAm9MIuxLjqhTc1LikQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEeiUCkyQ9KukNSa9J+jfZVY6Q9JCkTdl2ePb5bEnPSjoh6ffKEBkm6buS3syOd03Z9/nbmyRtlPSWpC/nH5Zs/1LSByXv42WxESjSGPq6pLcl/Tz7m19s6KJ3bSBQpHFF/ej/IemX2dz2r9vod3xVHASKNIaeLJmXdkn6YXFgip50EIEijavVkl7MxtZTkmZ28Fpi9+IgUKRxtSobV69K+oakPsWBKXpS7wiMk3RVdhGDM8FqrqQ/KVEKUQ7/ONtntKTFmRBWrngyOD+f7ddPEopoeYMcarOk6ZLY5xeSOF/eFkn6u1A8czjqYlukMYTieUddoBadbA+BIo2rz0n6pqScVZ15MFrxESjSGCpF63uS7ir9IF7XFQJFGlcYw+Zk6H1REmtgtPpEoCjjinVuu6RZGYx/IOme+oQ0el0PCPyjpBszjyQPAY0tHsrS9l/KPJ5DMk8TnoG2Gl7Qn5bs8B8k8UdDKcX7yvnC45mBUoebnhxDoXjW4YCpsss9Oa7WhyehyrtU7N16cgzlyGDgfU8Sa2a03oFAT44rZLOlGYzIUn/YOyCNq5DUU+NqVBaRmN+E6yT9JH9zPm5zi/P5eO1dfc1TJS2Q9JykMZJ2Zydk256FHw/mfkl/K+klSV+TNLBChydklpT8qx2S+Iz2u5LuLzlv9nFs6giBnh5DQEVI5MuS/qek/nWEXXS1dQR6elzNkPQpSRskPSjpkta7Gt8UFIGeHkM5LLdJekTS4fyD2NY1Aj09rogyQylAlvqMpD+qazSj8zkCPTmuDkjqK4kIRBpRZIQBn7ctFM+uufWDJBH+82/PcUEk/puQ3a9kyuuHJaG6pT2u5BFNJY2XdKck8juj1ScCPT2GQA2LL3nIhIOTp/yl+oQyel2CQBHGFQaM49lC/FVJ95b0L14WH4EijKEcpU9L+nb+JrZ1jUARxtW/k/SrkiZmhv8/r2tEo/Mg0NPjCpn81zLjPdE+RySdPp9vTSietb/7WDZQOu+T9P3s8HuzkFfeEvq6r53TYm3jD28piQaynQAAAo9JREFUDZIhFFGsJDnRy+9k+5RaTpgsIVrA00pSPIRDWyUNKHP1Z4eNTUERKMIYAhq880yakF/hfV9SULyiW9UhUJRxxdzGHEn7gaQrstexKT4CRRlDIHVxNic9UHzYooftIFCEcUVI5JUlctd3JF3bTr/j62IjUIRxBUKQiBJiiwz1REY0WmzkurB3oXjWFlw8kH+TMTWWWsoIeb07OxVbYs3banuyENpLs51gWns9+wxmUf7+r6TnszC1aRm5EFYVzsVCPFYS4QX8HY2cqrbgLtR3RRlDgJLnJdOnWyXByBatPhEo0riCgRSWP9qKjIQtexubAiNQpDEETET1/DjznhcYtuhaOwgUZVyRKzy0hAQGfg4qFESrTwSKMq5AL0+vI9qHyDHk92iBQE0QWJ55iMiJyz2ThG1gmSUPhXIqbAlbpKEcYv0nP+X97HVOkoBySQ4Ux0JQy0uwZD9t2nB8mNhgt/1PTZ82fxHkQs3xKPK7Io2hdZJeyRTO/5eFrBQZu+hb6wgUaVzB0I1xjLGFJRgvQ7TiI1CkMQRaj0minFi0+kagSOOKnGHmJSoEML7g24hWnwgUaVz9aWbEgLyKFLxogUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAINDLEfj/AO6QF0m97AkAAAAASUVORK5CYII="}}},{"metadata":{},"cell_type":"markdown","source":"### Creating the submission file\n\nI am here using the results from the approach of a separate model for each building. (Meaning that I did not run the whole kernel sequentially; I left out the section just above this one, as otherwise the test_df would contain the predictions from the wrong approach)."},{"metadata":{"trusted":true},"cell_type":"code","source":"#test_df[['row_id', 'meter_reading']].to_csv('submission.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# TODO \n\n* Outlier analysis. A fun little activity would be the finding of faulty meters: https://www.kaggle.com/juanmah/ashrae-outliers\n\n* Try the 24th hour meter reading, which has a big negative correlation with the current hour, as a feature.\n\n* Test how much imputation would improve performance.\n\n* When the time series contains huge constant regions, the models tend to unfortunately learn from it, even though there is no reason why the regions should repeat every year. Remove them and try again!"},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{"trusted":true},"cell_type":"markdown","source":"# Process insights\n\n* I had to comment out some code, as well as embed many of the plots manually instead of letting the code do it, as otherwise committing this kernel would have taken too long. In addition, I even had to comment some code that would not have taken long (the Sequential test and the LightGBM test) as the kernel ran out of memory during them, crashing the commit. A cleaner solution would have been to split the project into two or three kernels, as described [here](https://www.kaggle.com/c/ashrae-energy-prediction/discussion/114941)."},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.4"}},"nbformat":4,"nbformat_minor":1}