Critical Transitions Auto.ipynb

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    "# Critical Transitions"
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   "source": [
    "Implementation of \"Topology Data Analysis Of Critical Transitions In Financial Networks\" by Marian Gidea, 2017. arXiv:1701.06081v1 [q-fin.MF]"
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   "source": [
    "**To Investigate**\n",
    "\n",
    "Rolling Baseline\n",
    "Make normalization independent of the time-frame\n",
    "Daily updating\n",
    "Local renormalization\n",
    "Put/Call Ratio or other signals\n",
    "Outside correlations; for instance, junk bonds with S&P500\n",
    "Different short and long points\n",
    "Superlevel sets. The problem is that a node should be distance 0 from itself.\n",
    "Effects at higher topological dimensions\n",
    "Kalman filter\n",
    "Sampling and majority vote\n",
    "Upper-quantile holdings or weighted correlations.\n",
    "Periodicity to detect cointegration"
   ]
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    "import quandl\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import dionysus as d\n",
    "from scipy.spatial.distance import squareform\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import os\n",
    "import subprocess\n",
    "import re\n",
    "\n",
    "quandl.ApiConfig.api_key = # Your key here.\n",
    "\n",
    "data_directory = # Your data directory here.\n",
    "\n",
    "def quandl_return_symbol(s):\n",
    "    \"\"\"Rewrite a ticker symbol for the Quandl EOD Database.\"\"\"\n",
    "    # Quandl symbols have '.' and '-' replaced with '_'.\n",
    "    s = re.sub('[.-]','_',s)\n",
    "    # EOD is the dataset and .4 is the column containing closing prices.\n",
    "    s = 'EOD/' + s + '.4'\n",
    "    return s\n",
    "\n",
    "def get_returns(symbols, start_date='2016-12-30', end_date=None, log_returns=False):\n",
    "    \"\"\"Get percent closing returns from Quandl for a list of symbols.\"\"\"\n",
    "    returns = quandl.get([quandl_return_symbol(s) for s in symbols],\n",
    "                         start_date=start_date, end_date=end_date, transform='rdiff')\n",
    "    \n",
    "    # Convert to log-returns if necessary.\n",
    "    # Log return is log of 1 + percent return. Compare the definitions.\n",
    "    if log_returns:\n",
    "        returns = np.log(returns + 1)\n",
    "    \n",
    "    # Rename the columns\n",
    "    returns.columns = symbols\n",
    "    # Check and drop symbols with missing data.\n",
    "    dropped_returns = returns.dropna(axis=1)\n",
    "    print('Missing data from symbols:' + str(list(set(returns.columns) - set(dropped_returns.columns))))\n",
    "    return dropped_returns\n",
    "\n",
    "def normalize_returns(returns, window=13):\n",
    "    # Local renormalization. This is just computing a rolling z-score.\n",
    "    x = returns\n",
    "    r = x.rolling(window=window)\n",
    "    m = r.mean()\n",
    "    s = r.std(ddof=0)\n",
    "    z = (x-m)/s\n",
    "    z = z.dropna()\n",
    "    return z\n",
    "\n",
    "def quandl_short_symbol(s):\n",
    "    \"\"\"Rewrite a ticker symbol for the FINRA NYSE Database.\"\"\"\n",
    "    # Quandl symbols have '.' and '-' replaced with '_'.\n",
    "    s = re.sub('[.-]','_',s)\n",
    "    # FINRA/FNYX_ is the dataset and .1 is the column containing short volume.\n",
    "    s = 'FINRA/FNYX_' + s + '.1'\n",
    "    return s\n",
    "\n",
    "def quandl_volume_symbol(s):\n",
    "    \"\"\"Rewrite a ticker symbol for the FINRA NYSE Database.\"\"\"\n",
    "    # Quandl symbols have '.' and '-' replaced with '_'.\n",
    "    s = re.sub('[.-]','_',s)\n",
    "    # FINRA/FNYX_ is the dataset and .3 is the column containing total volume.\n",
    "    s = 'FINRA/FNYX_' + s + '.3'\n",
    "    return s\n",
    "    \n",
    "    \n",
    "def get_short_ratios(symbols, start_date='2016-12-30', end_date=None):\n",
    "    short_vol = quandl.get([quandl_short_symbol(s) for s in symbols], start_date=start_date, end_date=end_date)\n",
    "    short_vol.columns = symbols\n",
    "    total_vol = quandl.get([quandl_volume_symbol(s) for s in symbols], start_date=start_date, end_date=end_date)\n",
    "    total_vol.columns = symbols\n",
    "    # Pandas division requires the column names to match.\n",
    "    short_ratios = short_vol.div(total_vol)\n",
    "    \n",
    "    # Rename the columns\n",
    "    short_ratios.columns = symbols\n",
    "    # Check and drop symbols with missing data.\n",
    "    dropped_ratios = short_ratios.dropna(axis=1)\n",
    "    print('Missing data from symbols:' + str(list(set(short_ratios.columns) - set(dropped_ratios.columns))))\n",
    "    return dropped_ratios\n",
    "\n",
    "\n",
    "def compute_correlations(returns, window=15, method='pearson', direction='both'):\n",
    "    \"\"\"Compute rolling correlations for a dataframe containing returns.\"\"\"\n",
    "    dates = []\n",
    "    cors = []\n",
    "    \n",
    "    # Whether to use ordinary, downside, or upside returns.\n",
    "    if direction == 'both': pass\n",
    "    elif direction == 'down': returns[returns > 0] = 0\n",
    "    elif direction == 'up': returns[returns < 0 ] = -0\n",
    "    \n",
    "    for k in range(len(returns)-window+1):\n",
    "        window_returns = returns[k:k+window]\n",
    "        date = returns.index[k+window-1].strftime('%Y-%m-%d') # On the first day with window 4, it would be index 3, which is the fourth day.\n",
    "        # print(date)\n",
    "        dates.append(date)\n",
    "        c = window_returns.corr(method=method)\n",
    "        cors.append(c)\n",
    "    correlations = pd.concat(cors,keys=dates)\n",
    "    return correlations\n",
    "\n",
    "def distance(c, sets = 'sublevel'):\n",
    "    \"\"\"Compute distances from correlations. Choose sublevel sets or superlevel sets.\"\"\"\n",
    "    d = np.sqrt(2*(1-np.round(c,2)))\n",
    "    \n",
    "    if sets == 'sublevel':\n",
    "        return d\n",
    "    elif sets == 'superlevel': # Broken.\n",
    "        return 2 - d\n",
    "    \n",
    "def compute_distances(correlations, sets = 'sublevel'):\n",
    "    \"\"\"Apply the distance function to each correlation in a dataframe.\"\"\"\n",
    "    distances = distance(correlations, sets)\n",
    "    return distances\n",
    "\n",
    "def compute_ripser_diagrams(distance_matrix, maxdimension, maxdistance):\n",
    "    \"\"\"Computes the peristence diagrams using Ripser.\n",
    "    Takes a distance matrix and returns a list of persistence points ordered by dimension.\"\"\"\n",
    "\n",
    "    # Save the distances into a temporary file for Ripser to read.\n",
    "    np.savetxt('temp.distance', distance_matrix, delimiter=',')\n",
    "    \n",
    "    # Loading persistence files from Ripser into Dionysus\n",
    "    temp_distance = 'PATH/temp.distance'\n",
    "    subprocess.call('/bin/ripser/ripser PATH/temp.distance --format distance --dim ' + str(maxdimension) + ' --threshold ' + str(maxdistance) + ' > PATH/temp.persistence', shell=True)\n",
    "    \n",
    "    diagrams = []\n",
    "    current = []\n",
    "\n",
    "    for line in open('temp.persistence'):\n",
    "        point1 = re.match(' \\[(.*),([^ ]*)\\)', line)\n",
    "        point2 = re.match(' \\[(.*), \\)', line)\n",
    "        if line.startswith('persistence'):\n",
    "            diagrams.append(current)\n",
    "            current = []\n",
    "        elif point1:\n",
    "            gps = point1.groups()\n",
    "            point = tuple([float(x) for x in gps])\n",
    "            #print(point)\n",
    "            current.append(point)\n",
    "        elif point2:\n",
    "            gps = point2.groups()\n",
    "            point = tuple([float(gps[0]), float('inf')])\n",
    "            #print(point)\n",
    "            current.append(point)\n",
    "    del diagrams[0] # The first diagram is empty because of awkward parsing. Improve.\n",
    "    os.remove('temp.distance') # Cleanup\n",
    "    os.remove('temp.persistence')\n",
    "    return diagrams\n",
    "\n",
    "\n",
    "def convert_to_dionysus(diagrams):\n",
    "    \"\"\"Converts a diagram in list form to a Dionysus object.\"\"\"\n",
    "    return [d.Diagram(diag) for diag in diagrams]\n",
    "\n",
    "def compute_dionysus_diagram(distance_matrix, maxdimension, maxdistance):\n",
    "    \"\"\"Compute the persistence diagrams as a Dionysus object.\"\"\"\n",
    "    diagrams = compute_ripser_diagrams(distance_matrix, maxdimension, maxdistance)\n",
    "    diagrams = convert_to_dionysus(diagrams)\n",
    "    return diagrams\n",
    "\n",
    "def compute_persistence_diagrams(distances, maxdimension=2, maxdistance=2.5):\n",
    "    \"\"\"Compute persistence diagrams from a dataframe of distance matricies.\"\"\"\n",
    "    diagrams = []\n",
    "    index = distances.index.levels[0] # This is the dates.\n",
    "    for date in index:\n",
    "        #print(date)\n",
    "        diag = compute_ripser_diagrams(distances.loc[date], maxdimension, maxdistance)\n",
    "        diagrams.append(diag)\n",
    "    diagrams = pd.DataFrame(diagrams, index)\n",
    "    dionysus_diagrams = diagrams.apply(convert_to_dionysus)\n",
    "    return dionysus_diagrams\n",
    "\n",
    "def compute_base_diagram(fill, dim, maxdimension=2, maxdistance=2.5):\n",
    "    \"\"\"Compute baseline diagrams.\n",
    "    fill is the correlation to fill the array with, n is the square dimension,\n",
    "    pdim is the max persistence dimension, and pdist is the max persistence distance.\"\"\"\n",
    "    e = np.empty((dim,dim))\n",
    "    e.fill(fill)\n",
    "    np.fill_diagonal(e,1.0)\n",
    "    dists = distance(e)\n",
    "    diagrams = compute_dionysus_diagram(dists, maxdimension, maxdistance)\n",
    "    return diagrams\n",
    "\n",
    "def compute_wasserstein_distances(base, diagrams, q=10):\n",
    "    \"\"\"Compute the series of Wasserstein distances from some baseline for a given dimension.\n",
    "    Pass in list of baseline diagrams and a series of Dionysus diagrams, with q as the Wasserstein paramater.\"\"\"\n",
    "    w_distances = []\n",
    "    for k in range(len(diagrams.columns)): # Iterate over the dimensions of diagrams. First column is dim 0, etc.\n",
    "        # Need better checking on this. There could be empty dimensions?\n",
    "        wdists = diagrams[k].apply(lambda x: d.wasserstein_distance(base[k], x, q=q))\n",
    "        w_distances.append(wdists)\n",
    "    w_distances = pd.concat(w_distances, axis=1)\n",
    "    return w_distances\n",
    "\n",
    "# def compute_wasserstein_distances(base, diagrams, q=10):\n",
    "#     \"\"\"Compute the series of Wasserstein distances from some baseline for a given dimension.\n",
    "#     Pass in list of baseline diagrams and a series of Dionysus diagrams, with q as the Wasserstein paramater.\"\"\"\n",
    "#     w_distances = []\n",
    "#     for k in range(len(diagrams.columns)): # Iterate over the dimensions of diagrams. First column is dim 0, etc.\n",
    "#         wdists = []\n",
    "#         dates = []\n",
    "#         window = 30\n",
    "#         for l in range(len(diagrams[k])-window+1):\n",
    "#             base = diagrams[k][l]\n",
    "#             diag = diagrams[k][l+window-1]\n",
    "#             date = diagrams.index[l+window-1]\n",
    "#             dates.append(date)\n",
    "#             dist = d.wasserstein_distance(base,diag,q=q)\n",
    "#             wdists.append(dist)\n",
    "#         w_distances.append(pd.DataFrame(wdists,index=dates))\n",
    "#     w_distances = pd.concat(w_distances, axis=1)\n",
    "#     return w_distances\n",
    "\n",
    "def normalize_distances(distances, decimals=3, inverse=False):\n",
    "    \"\"\"Normalize a dataframe of distance series to (0,1). If inverse is true, multiply the series by -1 before transforming.\"\"\"\n",
    "    i = -1 if inverse else 1\n",
    "    normalized_distances = []\n",
    "    for k in range(len(distances.columns)):\n",
    "        scaler = MinMaxScaler(feature_range=(0,1))\n",
    "        scaler = scaler.fit(i*distances[k].reshape(-1,1))\n",
    "        normalized = scaler.transform(i*distances[k].reshape(-1,1))\n",
    "        #normalized = normalized.flatten() # Why this?\n",
    "        normalized = np.round(normalized, decimals=decimals)\n",
    "        index = distances.index\n",
    "        normalized = pd.DataFrame(normalized, index=index)\n",
    "        normalized_distances.append(normalized)\n",
    "    normalized_distances = pd.concat(normalized_distances, axis=1)\n",
    "    return normalized_distances\n",
    "\n",
    "\n",
    "\n",
    "# The main function of the library. Compute a normalized series of distances given a list of asset symbols.\n",
    "\n",
    "def compute_distance_series(symbols, start_date='2016-12-30', end_date=None, log_returns=False, normalize=False,\n",
    "                            window=15, method='pearson', direction='both', \n",
    "                            sets='sublevel', persistence_dim=1, \n",
    "                            wasserstein=10, base='anti'):\n",
    "    \"\"\"Base should either be 'anti' or a date.\"\"\"\n",
    "    print('Getting returns.')\n",
    "    returns = get_returns(symbols, start_date=start_date, end_date=end_date, log_returns=log_returns)\n",
    "    if normalize:\n",
    "        returns = normalize_returns(returns, window=13)\n",
    "    \n",
    "    print('Computing correlations.')\n",
    "    correlations = compute_correlations(returns, window=window, method=method, direction = direction)\n",
    "    print('Computing distances.')\n",
    "    distances = compute_distances(correlations, sets=sets)\n",
    "    print('Computing diagrams')\n",
    "    diagrams = compute_persistence_diagrams(distances, maxdimension = persistence_dim)\n",
    "    \n",
    "    dim = len(returns.columns)\n",
    "    if base == 'anti':\n",
    "        base_diagram = compute_base_diagram(-1, dim=dim, maxdimension = persistence_dim)\n",
    "        inverse = True\n",
    "    else:\n",
    "        base_diagram = diagrams.loc[base]\n",
    "        inverse = False\n",
    "    print('Computing wasserstein distances.')\n",
    "    wasserstein_distances = compute_wasserstein_distances(base_diagram, diagrams, q=wasserstein)\n",
    "    \n",
    "    print('Normalizing series.')\n",
    "    distance_series = normalize_distances(wasserstein_distances, inverse=inverse)\n",
    "    return distance_series\n",
    "\n",
    "def compute_short_series(symbols, start_date='2016-12-30', end_date=None, \n",
    "                         window=15, method='pearson', \n",
    "                         sets='sublevel', persistence_dim=1, wasserstein=10, base='anti'):\n",
    "    print('Getting data.')\n",
    "    short_ratios = get_short_ratios(symbols, start_date=start_date, end_date=end_date)\n",
    "    print('Computing correlations.')\n",
    "    correlations = compute_correlations(short_ratios, window=window, method=method)\n",
    "    print('Computing distances.')\n",
    "    distances = compute_distances(correlations, sets=sets)\n",
    "    print('Computing diagrams')\n",
    "    diagrams = compute_persistence_diagrams(distances, maxdimension = persistence_dim)\n",
    "    \n",
    "    dim = len(short_ratios.columns)\n",
    "    if base == 'anti':\n",
    "        base_diagram = compute_base_diagram(-1, dim=dim, maxdimension = persistence_dim)\n",
    "    else: # In this case it is a date.\n",
    "        base_diagram = diagrams.loc[base]\n",
    "    \n",
    "    print('Computing wasserstein distances.')\n",
    "    wasserstein_distances = compute_wasserstein_distances(base_diagram, diagrams, q=wasserstein)\n",
    "    print('Normalizing series.')\n",
    "    distance_series = normalize_distances(wasserstein_distances, inverse=True)\n",
    "    return distance_series"
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    "import matplotlib.pyplot as plt\n",
    "# Set the default figure size.\n",
    "plt.rcParams[\"figure.figsize\"] = [20,12]"
   ]
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    "# S&P 500\n",
    "# ETF: SPY\n",
    "SP500_symbols = ['ABT', 'ABBV', 'ACN', 'ACE', 'ADBE', 'ADT', 'AAP', 'AES', 'AET', 'AFL', 'AMG', 'A', 'GAS', 'APD', 'ARG', 'AKAM', 'AA', 'AGN', 'ALXN', 'ALLE', 'ADS', 'ALL', 'ALTR', 'MO', 'AMZN', 'AEE', 'AAL', 'AEP', 'AXP', 'AIG', 'AMT', 'AMP', 'ABC', 'AME', 'AMGN', 'APH', 'APC', 'ADI', 'AON', 'APA', 'AIV', 'AMAT', 'ADM', 'AIZ', 'T', 'ADSK', 'ADP', 'AN', 'AZO', 'AVGO', 'AVB', 'AVY', 'BHI', 'BLL', 'BAC', 'BK', 'BCR', 'BXLT', 'BAX', 'BBT', 'BDX', 'BBBY', 'BRK-B', 'BBY', 'BLX', 'HRB', 'BA', 'BWA', 'BXP', 'BSK', 'BMY', 'BRCM', 'BF-B', 'CHRW', 'CA', 'CVC', 'COG', 'CAM', 'CPB', 'COF', 'CAH', 'HSIC', 'KMX', 'CCL', 'CAT', 'CBG', 'CBS', 'CELG', 'CNP', 'CTL', 'CERN', 'CF', 'SCHW', 'CHK', 'CVX', 'CMG', 'CB', 'CI', 'XEC', 'CINF', 'CTAS', 'CSCO', 'C', 'CTXS', 'CLX', 'CME', 'CMS', 'COH', 'KO', 'CCE', 'CTSH', 'CL', 'CMCSA', 'CMA', 'CSC', 'CAG', 'COP', 'CNX', 'ED', 'STZ', 'GLW', 'COST', 'CCI', 'CSX', 'CMI', 'CVS', 'DHI', 'DHR', 'DRI', 'DVA', 'DE', 'DLPH', 'DAL', 'XRAY', 'DVN', 'DO', 'DTV', 'DFS', 'DISCA', 'DISCK', 'DG', 'DLTR', 'D', 'DOV', 'DOW', 'DPS', 'DTE', 'DD', 'DUK', 'DNB', 'ETFC', 'EMN', 'ETN', 'EBAY', 'ECL', 'EIX', 'EW', 'EA', 'EMC', 'EMR', 'ENDP', 'ESV', 'ETR', 'EOG', 'EQT', 'EFX', 'EQIX', 'EQR', 'ESS', 'EL', 'ES', 'EXC', 'EXPE', 'EXPD', 'ESRX', 'XOM', 'FFIV', 'FB', 'FAST', 'FDX', 'FIS', 'FITB', 'FSLR', 'FE', 'FSIV', 'FLIR', 'FLS', 'FLR', 'FMC', 'FTI', 'F', 'FOSL', 'BEN', 'FCX', 'FTR', 'GME', 'GPS', 'GRMN', 'GD', 'GE', 'GGP', 'GIS', 'GM', 'GPC', 'GNW', 'GILD', 'GS', 'GT', 'GOOGL', 'GOOG', 'GWW', 'HAL', 'HBI', 'HOG', 'HAR', 'HRS', 'HIG', 'HAS', 'HCA', 'HCP', 'HCN', 'HP', 'HES', 'HPQ', 'HD', 'HON', 'HRL', 'HSP', 'HST', 'HCBK', 'HUM', 'HBAN', 'ITW', 'IR', 'INTC', 'ICE', 'IBM', 'IP', 'IPG', 'IFF', 'INTU', 'ISRG', 'IVZ', 'IRM', 'JEC', 'JBHT', 'JNJ', 'JCI', 'JOY', 'JPM', 'JNPR', 'KSU', 'K', 'KEY', 'GMCR', 'KMB', 'KIM', 'KMI', 'KLAC', 'KSS', 'KRFT', 'KR', 'LB', 'LLL', 'LH', 'LRCX', 'LM', 'LEG', 'LEN', 'LVLT', 'LUK', 'LLY', 'LNC', 'LLTC', 'LMT', 'L', 'LOW', 'LYB', 'MTB', 'MAC', 'M', 'MNK', 'MRO', 'MPC', 'MAR', 'MMC', 'MLM', 'MAS', 'MA', 'MAT', 'MKC', 'MCD', 'MHFI', 'MCK', 'MJN', 'MMV', 'MDT', 'MRK', 'MET', 'KORS', 'MCHP', 'MU', 'MSFT', 'MHK', 'TAP', 'MDLZ', 'MON', 'MNST', 'MCO', 'MS', 'MOS', 'MSI', 'MUR', 'MYL', 'NDAQ', 'NOV', 'NAVI', 'NTAP', 'NFLX', 'NWL', 'NFX', 'NEM', 'NWSA', 'NEE', 'NLSN', 'NKE', 'NI', 'NE', 'NBL', 'JWN', 'NSC', 'NTRS', 'NOC', 'NRG', 'NUE', 'NVDA', 'ORLY', 'OXY', 'OMC', 'OKE', 'ORCL', 'OI', 'PCAR', 'PLL', 'PH', 'PDCO', 'PAYX', 'PNR', 'PBCT', 'POM', 'PEP', 'PKI', 'PRGO', 'PFE', 'PCG', 'PM', 'PSX', 'PNW', 'PXD', 'PBI', 'PCL', 'PNC', 'RL', 'PPG', 'PPL', 'PX', 'PCP', 'PCLN', 'PFG', 'PG', 'PGR', 'PLD', 'PRU', 'PEG', 'PSA', 'PHM', 'PVH', 'QRVO', 'PWR', 'QCOM', 'DGX', 'RRC', 'RTN', 'O', 'RHT', 'REGN', 'RF', 'RSG', 'RAI', 'RHI', 'ROK', 'COL', 'ROP', 'ROST', 'RLC', 'R', 'CRM', 'SNDK', 'SCG', 'SLB', 'SNI', 'STX', 'SEE', 'SRE', 'SHW', 'SIAL', 'SPG', 'SWKS', 'SLG', 'SJM', 'SNA', 'SO', 'LUV', 'SWN', 'SE', 'STJ', 'SWK', 'SPLS', 'SBUX', 'HOT', 'STT', 'SRCL', 'SYK', 'STI', 'SYMC', 'SYY', 'TROW', 'TGT', 'TEL', 'TE', 'TGNA', 'THC', 'TDC', 'TSO', 'TXN', 'TXT', 'HSY', 'TRV', 'TMO', 'TIF', 'TWX', 'TWC', 'TJK', 'TMK', 'TSS', 'TSCO', 'RIG', 'TRIP', 'FOXA', 'TSN', 'TYC', 'UA', 'UNP', 'UNH', 'UPS', 'URI', 'UTX', 'UHS', 'UNM', 'URBN', 'VFC', 'VLO', 'VAR', 'VTR', 'VRSN', 'VZ', 'VRTX', 'VIAB', 'V', 'VNO', 'VMC', 'WMT', 'WBA', 'DIS', 'WM', 'WAT', 'ANTM', 'WFC', 'WDC', 'WU', 'WY', 'WHR', 'WFM', 'WMB', 'WEC', 'WYN', 'WYNN', 'XEL', 'XRX', 'XLNX', 'XL', 'XYL', 'YHOO', 'YUM', 'ZBH', 'ZION', 'ZTS']\n",
    "\n",
    "# Dow-Jones\n",
    "# ETF: DIA\n",
    "DJIA_symbols = ['BA','GS','MMM','UNH','HD','MCD','AAPL','IBM','JNJ','CAT','TRV','UTX','CVX','V','JPM','DIS','AXP','WMT','PG','XOM','MSFT','DWDP','MRK','NKE','VZ','KO','INTC','PFE','CSCO','GE']\n",
    "\n",
    "# NASDAQ\n",
    "# ETF: QQQ\n",
    "NASDAQ_symbols = ['AMZN', 'CMCSA', 'PCLN', 'NFLX', 'CHTR', 'SBUX', 'TSLA', 'MAR', 'JD', 'FOXA', 'SIRI', 'ROST', 'DLTR', 'CTRP', 'FOX', 'EXPE', 'ORLY', 'LBTYK', 'WYNN', 'NCLH', 'ULTA', 'HAS', 'DISH', 'QVCA', 'VIAB', 'TSCO', 'LBTYA', 'LVNTA', 'DISCK', 'DISCA', 'LILAK', 'LILA', 'KHC', 'WBA', 'COST', 'MDLZ', 'MNST', 'AMGN', 'GILD', 'CELG', 'BIIB', 'REGN', 'ISRG', 'VRTX', 'ESRX', 'ILMN', 'ALXN', 'INCY', 'CERN', 'MYL', 'ALGN', 'BMRN', 'IDXX', 'XRAY', 'HSIC', 'HOLX', 'SHPG', 'CSX', 'PCAR', 'AAL', 'CTAS', 'VRSK', 'FAST', 'JBHT', 'AAPL', 'MSFT', 'FB', 'GOOG', 'GOOGL', 'INTC', 'CSCO', 'NVDA', 'AVGO', 'TXN', 'PYPL', 'ADBE', 'QCOM', 'BIDU', 'AMAT', 'ADP', 'ATVI', 'MU', 'CTSH', 'EBAY', 'INTU', 'EA', 'LRCX', 'ADI', 'FISV', 'ADSK', 'WDC', 'PAYX', 'MCHP', 'NTES', 'SYMC', 'SWKS', 'CHKP', 'XLNX', 'KLAC', 'MXIM', 'CA', 'CTXS', 'STX', 'MELI', 'AKAM', 'TMUS', 'VOD']\n",
    "\n",
    "\n",
    "# Real Estate\n",
    "# ETF: IYR\n",
    "IYR_symbols = ['AMT', 'SPG', 'CCI', 'EQIX', 'PLD', 'PSA', 'WY', 'AVB', 'EQR', 'HCN', 'DLR', 'VTR', 'BXP', 'SBAC', 'ESS', 'O', 'HST', 'NLY', 'GGP', 'VNO', 'CBG', 'HCP', 'ARE', 'MAA', 'IRM', 'UDR', 'DRE', 'EXR', 'SLG', 'REG', 'FRT', 'CPT', 'AGNC', 'KIM', 'ELS', 'VER', 'WPC', 'GLPI', 'SUI', 'KRC', 'CLNS', 'AIV', 'FCEA', 'LPT', 'OHI', 'DEI', 'NNN', 'HTA', 'JLL', 'LAMR', 'ACC', 'STWD', 'BRX', 'DCT', 'CONE', 'NRZ', 'HIW', 'HPP', 'HHC', 'AMH', 'MPW', 'GPT', 'SFR', 'CUBE', 'HPT', 'RLGY', 'EPR', 'SNH', 'PK', 'STOR', 'APLE', 'HR', 'RLJ', 'RYN', 'CUZ', 'WRI', 'COR', 'LSI', 'SHO', 'SRC', 'EQC', 'FR', 'SBRA', 'TWO', 'CIM', 'RHP', 'MFA', 'OUT', 'PGRE', 'LHO', 'GEO', 'OFC', 'EGP', 'DOC', 'NHI', 'BDN', 'TCO', 'CXW', 'BXMT', 'JBGS', 'UNIT', 'RPAI', 'PDM', 'EDR', 'WRE', 'UE', 'CXP', 'PEB', 'DDR', 'SKT', 'AKR', 'XHR', 'LXP', 'DRH', 'PCH', 'ALEX', 'CLI', 'IVR', 'QCP', 'KRG', 'WPG', 'FCPT', 'CBL', 'MAC']\n",
    "\n",
    "# Finance\n",
    "# ETF: XLF\n",
    "XLF_symbols = ['BRK.B', 'JPM', 'BAC', 'WFC', 'C', 'GS', 'USB', 'CB', 'MS', 'AXP', 'PNC', 'BLK', 'AIG', 'MET', 'SCHW', 'BK', 'PRU', 'CME', 'COF', 'SPGI', 'MMC', 'BBT', 'ICE', 'AON', 'TRV', 'STT', 'ALL', 'AFL', 'STI', 'PGR', 'MTB', 'DFS', 'SYF', 'MCO', 'AMP', 'TROW', 'WLTW', 'FITB', 'NTRS', 'HIG', 'KEY', 'CFG', 'RF', 'PFG', 'LNC', 'HBAN', 'IVZ', 'BEN', 'CMA', 'L', 'CBOE', 'ETFC', 'UNM', 'CINF', 'AJG', 'RJF', 'AMG', 'XL', 'RE', 'ZION', 'TMK', 'NDAQ', 'LUK', 'PBCT', 'BHF', 'AIZ', 'NAVI']\n",
    "\n",
    "# Consumer Discretionary\n",
    "# ETF: XLY\n",
    "XLY_symbols = ['AMZN', 'HD', 'CMCSA', 'DIS', 'MCD', 'PCLN', 'NFLX', 'SBUX', 'TWX', 'NKE', 'LOW', 'CHTR', 'GM', 'F', 'TJX', 'MAR', 'TGT', 'FOXA', 'CCL.U', 'DLPH', 'YUM', 'ROST', 'VFC', 'DLTR', 'DG', 'RCL', 'CBS', 'NWL', 'ORLY', 'EXPE', 'AZO', 'MHK', 'OMC', 'MGM', 'DHI', 'BBY', 'HLT', 'KMX', 'GPC', 'LEN', 'ULTA', 'WHR', 'WYNN', 'LKQ', 'COH', 'FOX', 'LB', 'WYN', 'DISH', 'HAS', 'BWA', 'DRI', 'PVH', 'NCLH', 'TIF', 'VIAB', 'GT', 'HOG', 'HBI', 'PHM', 'IPG', 'SNI', 'KSS', 'KORS', 'TSCO', 'CMG', 'M', 'LEG', 'AAP', 'GRMN', 'GPS', 'HRB', 'MAT', 'NWSA', 'RL', 'JWN', 'TRIP', 'SIG', 'FL', 'DISCK', 'UAA', 'DISCA', 'UA']\n",
    "\n",
    "# Gold\n",
    "# ETF: GDX\n",
    "GDX_symbols = ['NEM', 'ABX', 'FNV', 'GG', 'AEM', 'GOLD', 'RGLD', 'KGC', 'BVN', 'AU', 'GFI', 'IAG', 'SBGL', 'BTG', 'PAAS', 'AUY', 'PVG', 'NGD', 'HL', 'AGI', 'EGO', 'TAHO', 'CDE', 'SSRM', 'AG', 'SAND', 'HMY', 'FSM', 'MUX', 'WPM', 'KL', 'OR', 'KLDX']\n",
    "# Unavailable: NCM, EVN, NST, CEY, CG, 2899, IGO, DGC, EDV, OGC, RRL, TXG\n",
    "\n",
    "# Biotech\n",
    "XBI_symbols = ['ALNY', 'JUNO', 'EXAS', 'NBIX', 'PBYI', 'BLUE', 'SRPT', 'SGEN', 'AVXS', 'CLVS', 'ABBV', 'UTHR', 'EXEL', 'ALKS', 'AMGN', 'BIIB', 'FOLD', 'BIVV', 'ACAD', 'IONS', 'TSRO', 'BMRN', 'VRTX', 'GILD', 'PTLA', 'REGN', 'INCY', 'ALXN', 'OPK', 'RDUS', 'CELG', 'ONCE', 'GBT', 'LGND', 'BPMC', 'LOXO', 'AGIO', 'ARRY', 'ESPR', 'DVAX', 'EGRX', 'ICPT', 'MYGN', 'CARA', 'RARE', 'FGEN', 'SAGE', 'IMMU', 'ACOR', 'IRWD', 'MDXG', 'EDIT', 'ARNA', 'XON', 'PRTA', 'ALDR', 'SGYP', 'HALO', 'IMGN', 'RXDX', 'RGEN', 'AMAG', 'PTCT', 'TGTX', 'EBS', 'SPPI', 'XLRN', 'AKAO', 'FLXN', 'FPRX', 'CALA', 'HRTX', 'FMI', 'MNTA', 'SGMO', 'ENTA', 'LJPC', 'INO', 'CHRS', 'LXRX', 'VNDA', 'INSM', 'KERX', 'EPZM', 'AKBA', 'CYTK', 'RTRX', 'AIMT', 'PGNX', 'ZIOP', 'RGNX', 'ACHN', 'XNCR', 'CTMX', 'MGNX', 'PDLI', 'BCRX', 'GHDX', 'ADRO', 'GERN']\n",
    "\n",
    "# Alerian MLP\n",
    "AMLP_symbols = ['MMP', 'EPD', 'ETP', 'MPLX', 'WPZ', 'PAA', 'BPL', 'WES', 'EQM', 'SEP', 'ANDX', 'EEP', 'DCP', 'TCP', 'NS', 'GEL', 'ENLK', 'SHLX', 'PSXP', 'AM', 'TEP', 'BWP', 'DM', 'RMP', 'HEP']\n",
    "\n",
    "# Oil and Gas\n",
    "XOP_symbols = ['CRZO', 'SM', 'WLL', 'OAS', 'HFC', 'CLR', 'MRO', 'PBF', 'WPX', 'SWN', 'CXO', 'RSPP', 'DVN', 'NFX', 'QEP', 'FANG', 'MUR', 'VLO', 'PXD', 'APC', 'XEC', 'EOG', 'MPC', 'OXY', 'COG', 'COP', 'HES', 'CHK', 'PDCE', 'EGN', 'PSX', 'GPOR', 'RRC', 'MTDR', 'PE', 'CPE', 'NBL', 'APA', 'ANDV', 'CVX', 'AR', 'XOM', 'EQT', 'LPI', 'DK', 'CRC', 'REN', 'CDEV', 'SRCI', 'XOG', 'SN', 'KOS', 'DNR', 'GPRE', 'INT', 'JAG', 'SGY', 'REI', 'TELL', 'REX', 'REGI', 'PARR', 'SD']\n",
    "\n",
    "# Semi-Conductors\n",
    "SOXL_symbols = ['ADI', 'AMAT', 'AMD', 'ASML', 'AVGO', 'CAVM', 'CRUS', 'CY', 'ENTG', 'IDTI', 'INTC', 'KLAC', 'LRCX', 'MCHP', 'MKSI', 'MPWR', 'MRVL', 'MSCC', 'MU', 'MXIM', 'NVDA', 'ON', 'QCOM', 'QRVO', 'SLAB', 'SWKS', 'TER', 'TSM', 'TXN', 'XLNX']\n",
    "\n",
    "\n",
    "# Large-Cap\n",
    "# ETF: IVV\n",
    "IVV_symbols = ['AAPL', 'MSFT', 'FB', 'AMZN', 'JNJ', 'BRKB', 'JPM', 'XOM', 'GOOGL', 'GOOG', 'BAC', 'WFC', 'CVX', 'PG', 'PFE', 'T', 'UNH', 'C', 'V', 'VZ', 'HD', 'INTC', 'GE', 'KO', 'CSCO', 'CMCSA', 'DWDP', 'MRK', 'PM', 'PEP', 'DIS', 'ORCL', 'BA', 'ABBV', 'MMM', 'MA', 'IBM', 'MCD', 'WMT', 'AMGN', 'MO', 'NVDA', 'HON', 'MDT', 'GILD', 'BMY', 'AVGO', 'ABT', 'TXN', 'PCLN', 'UNP', 'UTX', 'SLB', 'ACN', 'GS', 'USB', 'ADBE', 'NFLX', 'UPS', 'LLY', 'CAT', 'PYPL', 'TMO', 'QCOM', 'SBUX', 'CELG', 'LMT', 'TWX', 'NKE', 'CVS', 'CB', 'NEE', 'MS', 'COST', 'AXP', 'LOW', 'CRM', 'PNC', 'BIIB', 'CHTR', 'CL', 'COP', 'WBA', 'MDLZ', 'DUK', 'AMAT', 'AMT', 'AET', 'GM', 'AGN', 'BLK', 'AIG', 'MET', 'GD', 'FDX', 'EOG', 'DHR', 'ANTM', 'SCHW', 'BK', 'MON', 'RTN', 'ADP', 'SO', 'NOC', 'D', 'SPG', 'CI', 'OXY', 'ITW', 'F', 'SYK', 'CSX', 'PRU', 'BDX', 'ATVI', 'CME', 'KHC', 'TJX', 'MU', 'COF', 'CTSH', 'PX', 'EMR', 'SPGI', 'DE', 'CCI', 'MMC', 'ISRG', 'KMB', 'BSX', 'BBT', 'JCI', 'PSX', 'ICE', 'AON', 'NSC', 'EXC', 'MAR', 'INTU', 'EBAY', 'AEP', 'STZ', 'HUM', 'TRV', 'VRTX', 'HPQ', 'HAL', 'EA', 'ETN', 'EQIX', 'STT', 'APD', 'ECL', 'KMI', 'PLD', 'ESRX', 'ALL', 'DAL', 'TGT', 'VLO', 'LRCX', 'ADI', 'AFL', 'LYB', 'WM', 'SHW', 'BAX', 'FIS', 'REGN', 'TEL', 'ZTS', 'PSA', 'LUV', 'PPG', 'ILMN', 'MCK', 'GIS', 'MPC', 'STI', 'PCG', 'SRE', 'GLW', 'PGR', 'CMI', 'ALXN', 'FOXA', 'CCL', 'FISV', 'APC', 'SYY', 'ADSK', 'ED', 'DLPH', 'WDC', 'ROP', 'YUM', 'WY', 'APH', 'DXC', 'PPL', 'EIX', 'MTB', 'PCAR', 'SWK', 'ROST', 'EL', 'PEG', 'DFS', 'AVB', 'XEL', 'SYF', 'EQR', 'PH', 'AMP', 'MCO', 'ZBH', 'HCN', 'ADM', 'PXD', 'ROK', 'IP', 'BCR', 'DLR', 'MNST', 'WMB', 'HPE', 'VFC', 'IR', 'TROW', 'CERN', 'FTV', 'HCA', 'DG', 'DLTR', 'WLTW', 'EW', 'VTR', 'COL', 'MCHP', 'A', 'FITB', 'RHT', 'WEC', 'RCL', 'AAL', 'CBS', 'MYL', 'TSN', 'PAYX', 'NTRS', 'CAH', 'OKE', 'HIG', 'KEY', 'NWL', 'FCX', 'DTE', 'ES', 'INCY', 'SYMC', 'SWKS', 'CXO', 'CFG', 'NEM', 'NUE', 'ORLY', 'RF', 'KR', 'BXP', 'DVN', 'PFG', 'EXPE', 'SBAC', 'XLNX', 'MTD', 'ESS', 'KLAC', 'LNC', 'CLX', 'AZO', 'MHK', 'OMC', 'HRS', 'VMC', 'Q', 'MGM', 'AME', 'DPS', 'CNC', 'INFO', 'LH', 'UAL', 'WAT', 'AWK', 'ALB', 'ETR', 'WRK', 'GPN', 'LVLT', 'ANDV', 'HBAN', 'K', 'IVZ', 'APA', 'BLL', 'RSG', 'DOV', 'TAP', 'DHI', 'BBY', 'AEE', 'MSI', 'O', 'ALGN', 'HLT', 'HSY', 'BEN', 'LLL', 'HST', 'IDXX', 'TXT', 'FE', 'XRAY', 'CMA', 'CAG', 'KMX', 'MLM', 'FAST', 'CMS', 'L', 'VRSK', 'GPC', 'TDG', 'NBL', 'EMN', 'EFX', 'SNPS', 'NLSN', 'HSIC', 'BHGE', 'CTAS', 'DGX', 'NOV', 'GGP', 'ABC', 'MAS', 'CBOE', 'CNP', 'VNO', 'FMC', 'CDNS', 'HCP', 'SJM', 'UNM', 'URI', 'LEN', 'ETFC', 'TSS', 'MKC', 'IFF', 'NTAP', 'PNR', 'CBG', 'CTXS', 'WHR', 'FTI', 'ULTA', 'COO', 'XYL', 'MRO', 'COH', 'WYNN', 'ARE', 'CHD', 'IT', 'LKQ', 'FOX', 'CINF', 'COG', 'ADS', 'ANSS', 'MAA', 'AJG', 'PKG', 'RMD', 'PRGO', 'HES', 'LB', 'BFB', 'CHRW', 'STX', 'KSU', 'HAS', 'WYN', 'RJF', 'DISH', 'XEC', 'EXPD', 'AMG', 'IRM', 'XL', 'GWW', 'BWA', 'FBHS', 'HOLX', 'ARNC', 'CA', 'DRI', 'EQT', 'DRE', 'UDR', 'CTL', 'LNT', 'PVH', 'EXR', 'NCLH', 'PNW', 'AMD', 'TIF', 'RE', 'SLG', 'VAR', 'JNPR', 'DVA', 'ZION', 'VRSN', 'WU', 'JBHT', 'REG', 'AVY', 'SNA', 'TMK', 'CPB', 'UHS', 'QRVO', 'AKAM', 'VIAB', 'AOS', 'FRT', 'CF', 'NI', 'SEE', 'NDAQ', 'GT', 'HOG', 'HBI', 'PHM', 'HRL', 'PKI', 'ALK', 'LUK', 'SNI', 'IPG', 'FFIV', 'KIM', 'ALLE', 'KSS', 'KORS', 'NRG', 'MOS', 'AES', 'TSCO', 'COTY', 'JEC', 'CMG', 'SCG', 'AIV', 'AYI', 'RHI', 'FLIR', 'PBCT', 'XRX', 'M', 'LEG', 'GRMN', 'MAC', 'AAP', 'SRCL', 'BHF', 'GPS', 'FLR', 'FLS', 'HP', 'NFX', 'PWR', 'HRB', 'AIZ', 'EVHC', 'CSRA', 'NWSA', 'MAT', 'JWN', 'RL', 'FL', 'TRIP', 'RRC', 'SIG', 'DISCK', 'NAVI', 'CHK', 'UAA', 'PDCO', 'UA', 'DISCA', 'NWS']\n",
    "\n",
    "# Mid-Cap\n",
    "IJH_symbols = ['TTWO', 'NVR', 'TFX', 'MSCI', 'CGNX', 'HII', 'CC', 'TRMB', 'SIVB', 'BR', 'IEX', 'RGA', 'STLD', 'ATO', 'INGR', 'LDOS', 'EWBC', 'CDK', 'SEIC', 'JKHY', 'CPT', 'UGI', 'MAN', 'TER', 'DPZ', 'WCG', 'ODFL', 'KEYS', 'Y', 'IPGP', 'STE', 'OA', 'ABMD', 'WR', 'PTC', 'GGG', 'WST', 'FDS', 'LII', 'ARW', 'LW', 'SBNY', 'OGE', 'AFG', 'CPRT', 'PII', 'RPM', 'CSL', 'GXP', 'KRC', 'HUBB', 'TOL', 'TTC', 'OSK', 'WRB', 'MKTX', 'SCI', 'THO', 'WAB', 'COHR', 'NDSN', 'JBLU', 'LOGM', 'HFC', 'PACW', 'JHG', 'DCI', 'TYL', 'WTR', 'LPT', 'NYCB', 'BIVV', 'OHI', 'LECO', 'FTNT', 'MSCC', 'DEI', 'NNN', 'ZBRA', 'TDY', 'EV', 'OLN', 'CFR', 'JLL', 'FAF', 'LAMR', 'BRO', 'OZRK', 'SNV', 'RS', 'ACC', 'STL', 'LYV', 'VVC', 'CRL', 'CTLT', 'ATR', 'ULTI', 'RNR', 'ACM', 'RGLD', 'GNTX', 'POST', 'CONE', 'DCT', 'HIW', 'KNX', 'HRC', 'CBSH', 'MDU', 'CY', 'SON', 'UTHR', 'EXP', 'AVT', 'WBS', 'CW', 'WSO', 'DNKN', 'JBL', 'POOL', 'SIX', 'ARRS', 'PNFP', 'WEX', 'NFG', 'VVV', 'ORI', 'BRCD', 'X', 'EGN', 'MPW', 'TRN', 'NATI', 'LFUS', 'AGCO', 'PB', 'CRI', 'TECH', 'BLKB', 'SLM', 'IDA', 'UMPQ', 'EME', 'FICO', 'GWR', 'FHN', 'WTFC', 'FNB', 'VSM', 'BIO', 'EPR', 'MDSO', 'R', 'WGL', 'SKX', 'DNB', 'CR', 'MPWR', 'HLS', 'TCBI', 'CASY', 'WWD', 'BC', 'MMS', 'DAN', 'ASH', 'HBHC', 'HPT', 'ROL', 'THG', 'SNH', 'BMS', 'WSM', 'LSTR', 'ITT', 'MASI', 'WPX', 'CLB', 'TDC', 'MUR', 'PTEN', 'MD', 'CLGX', 'SMG', 'LPX', 'RIG', 'TEX', 'SLAB', 'OGS', 'BCO', 'HR', 'NCR', 'CNK', 'PRI', 'SNX', 'FSLR', 'ASB', 'UBSI', 'MBFI', 'HE', 'CHFC', 'OI', 'SWX', 'HAIN', 'CUZ', 'THS', 'RYN', 'NEU', 'SABR', 'COR', 'IDTI', 'NJR', 'CBRL', 'CBT', 'EPC', 'LSI', 'POL', 'WRI', 'FR', 'KEX', 'HOMB', 'RBC', 'VMI', 'CNO', 'SBRA', 'SF', 'BOH', 'MSM', 'JCOM', 'DLX', 'DST', 'BKH', 'FLO', 'CRUS', 'VSAT', 'KMT', 'BDC', 'SXT', 'LM', 'PNM', 'TKR', 'TXRH', 'CABO', 'FULT', 'CNX', 'UMBF', 'LNCE', 'INCR', 'CREE', 'SAIC', 'LIVN', 'TECD', 'LHO', 'CATY', 'GEO', 'ILG', 'WAFD', 'MANH', 'NUS', 'AN', 'TUP', 'OFC', 'AKRX', 'VLY', 'ENS', 'CAA', 'PBF', 'TPX', 'BBBY', 'CAR', 'CIEN', 'MNK', 'JACK', 'TCF', 'NUVA', 'KMPR', 'TCO', 'SAFM', 'CXW', 'VSH', 'JBGS', 'MOH', 'ACIW', 'UNIT', 'CLH', 'KLXI', 'NWE', 'MSA', 'DY', 'ENR', 'UFS', 'FII', 'BXS', 'WEN', 'TGNA', 'ATI', 'AMCX', 'NTCT', 'CHDN', 'ACHC', 'AAN', 'IDCC', 'SWN', 'GHC', 'TDS', 'PBI', 'HELE', 'KBR', 'TPH', 'EDR', 'MTX', 'MUSA', 'UE', 'ESL', 'AHL', 'LANC', 'INT', 'PBH', 'MDRX', 'NYT', 'JWA', 'CVG', 'MTDR', 'AEO', 'CRS', 'MIK', 'GATX', 'BIG', 'GVA', 'SBH', 'SKT', 'SFM', 'DKS', 'TRMK', 'DECK', 'CMP', 'CMC', 'IBOC', 'ATGE', 'KBH', 'LPNT', 'GMED', 'GPOR', 'CVLT', 'PAY', 'SLGN', 'ESV', 'PCH', 'MLHR', 'MDP', 'ALEX', 'WOR', 'CPE', 'MCY', 'HYH', 'URBN', 'CLI', 'ACXM', 'GME', 'UNFI', 'QEP', 'ODP', 'CAKE', 'BID', 'CTB', 'OII', 'BWLD', 'SM', 'PZZA', 'CARS', 'NBR', 'GNW', 'OMI', 'WERN', 'SAM', 'HNI', 'PLT', 'DBD', 'EAT', 'QCP', 'RDC', 'WPG', 'DRQ', 'GEF', 'KN', 'ENDP', 'DDD', 'DNOW', 'SYNA', 'HSNI', 'SPN', 'DDS', 'THC', 'DO', 'AVP', 'ISCA', 'DF', 'FTR', 'TR']\n",
    "\n",
    "# Small-Cap\n",
    "# ETF: IJR\n",
    "IJR_symbols = ['MKSI', 'ALE', 'HCSG', 'LITE', 'SR', 'IBKR', 'NKTR', 'AEIS', 'B', 'CACI', 'JBT', 'SIGI', 'CMD', 'CHE', 'STMP', 'AVA', 'VAC', 'LCII', 'FFIN', 'FIVE', 'NEOG', 'EGP', 'EVR', 'PDCE', 'PRA', 'FCFS', 'IART', 'LGND', 'ASGN', 'NGVT', 'SANM', 'DAR', 'GBCI', 'FUL', 'MOGA', 'TTEK', 'WWW', 'ICUI', 'CBU', 'BCPC', 'ITRI', 'LAD', 'PSB', 'SJI', 'TREX', 'SMTC', 'ROG', 'WAGE', 'GDOT', 'AIT', 'HI', 'ONB', 'COLB', 'CVBF', 'HQY', 'UNF', 'HAE', 'GWB', 'IIVI', 'AJRD', 'HOPE', 'SKYW', 'KFY', 'MRCY', 'FMBI', 'EE', 'ABM', 'FNGN', 'SHOO', 'AKR', 'SFNC', 'MYGN', 'SGMS', 'CCMP', 'TIVO', 'SLCA', 'BLD', 'BRKS', 'UFPI', 'RLI', 'BYD', 'OLLI', 'POWI', 'PRLB', 'BGS', 'LXP', 'DRH', 'DORM', 'CCOI', 'VIAV', 'EXLS', 'PLXS', 'SSD', 'PRGS', 'UCBI', 'PLAY', 'MATW', 'CWT', 'SEM', 'AMN', 'SUPN', 'MGLN', 'ROIC', 'AWR', 'OMCL', 'JJSF', 'KWR', 'MLI', 'AXE', 'INDB', 'EFII', 'TREE', 'PLCE', 'BANR', 'KS', 'MMSI', 'EXPO', 'AKS', 'NWN', 'WTS', 'MDCO', 'MTH', 'SFBS', 'CPS', 'BRC', 'ABCB', 'LTC', 'SRCI', 'HA', 'NSP', 'HMN', 'AEL', 'MDR', 'FLOW', 'NWBI', 'FELE', 'IRBT', 'ALGT', 'ARI', 'FWRD', 'NPO', 'FFBC', 'HSC', 'MEI', 'NBTB', 'AIN', 'CHSP', 'PFS', 'VG', 'PENN', 'LTXB', 'KALU', 'FOXF', 'INN', 'PPBI', 'RH', 'QLYS', 'SPPI', 'AAT', 'FCN', 'MNRO', 'OSIS', 'HMSY', 'CBM', 'NSIT', 'AAWW', 'INGN', 'HF', 'SCL', 'KLIC', 'SAIA', 'CORE', 'ATU', 'KAMN', 'WDFC', 'WABC', 'NTRI', 'DSW', 'TGI', 'AXL', 'BOBE', 'GPI', 'WD', 'ESE', 'EIG', 'BHE', 'KRG', 'KRA', 'MINI', 'MDC', 'NTGR', 'FCPT', 'RMBS', 'IOSP', 'EBIX', 'PATK', 'STBA', 'BOFI', 'UVV', 'NP', 'CTRE', 'AMWD', 'TTMI', 'GBX', 'FCF', 'ITGR', 'SFLY', 'TILE', 'EBS', 'CSGS', 'MXL', 'AAON', 'IBP', 'FIX', 'APOG', 'ELY', 'WGO', 'RGEN', 'GTLS', 'SCSS', 'AMED', 'CUB', 'FN', 'BPFH', 'OCLR', 'IPXL', 'ASIX', 'BCC', 'WNC', 'GIMO', 'ADC', 'SXI', 'LZB', 'CBL', 'CALM', 'CVCO', 'TVTY', 'DIOD', 'BABY', 'CNMD', 'NAVG', 'PLUS', 'HUBG', 'CAL', 'MSTR', 'SWM', 'VREX', 'BMI', 'FSS', 'CENTA', 'SPXC', 'MDXG', 'THRM', 'VVI', 'TNC', 'CNSL', 'WETF', 'AIR', 'VSTO', 'AZZ', 'PRAA', 'OSUR', 'TIME', 'RAVN', 'BRKL', 'ACOR', 'AMSF', 'RMAX', 'USCR', 'MANT', 'MATX', 'EPAY', 'HTLD', 'AAXN', 'CRZO', 'KEM', 'ECOL', 'SYKE', 'LABL', 'ABG', 'EGHT', 'CHCO', 'GOV', 'SCSC', 'CVGW', 'ENSG', 'EGOV', 'ABAX', 'JCP', 'BGG', 'AVAV', 'OXM', 'BCOR', 'OIS', 'LHCG', 'XPER', 'BEL', 'CCC', 'GIII', 'MNTA', 'SEDG', 'WDR', 'CHS', 'SONC', 'TMP', 'SHLM', 'FSP', 'BGC', 'SCHL', 'NSA', 'TPRE', 'CATM', 'ARR', 'VRTU', 'COKE', 'GNCMA', 'ROCK', 'IPCC', 'TBI', 'GES', 'ALG', 'HAFC', 'KOP', 'LDL', 'GCI', 'DEL', 'ADTN', 'RPT', 'LL', 'SAFT', 'IRDM', 'LGIH', 'ALOG', 'ANF', 'SBSI', 'LNN', 'DPLO', 'ACLS', 'ANDE', 'CEVA', 'STRA', 'ASTE', 'SSP', 'EXTN', 'IPHS', 'HLX', 'WING', 'NE', 'WWE', 'SPTN', 'FBP', 'WIRE', 'MTRN', 'CUDA', 'PJC', 'SPSC', 'UHT', 'INVA', 'UNT', 'UEIC', 'TYPE', 'PMC', 'GTY', 'RGR', 'BANC', 'STC', 'SMP', 'CIR', 'TRST', 'NBHC', 'BEAT', 'OFIX', 'MTSC', 'VRTS', 'CMO', 'DIN', 'RRGB', 'CTS', 'RTEC', 'IRT', 'VECO', 'CLDT', 'ARCB', 'CPF', 'AROC', 'UFCS', 'CBB', 'GLT', 'CUBI', 'ECPG', 'NEWM', 'USPH', 'LMNX', 'NFBK', 'SMCI', 'TLRD', 'CAMP', 'ANIK', 'UVE', 'KELYA', 'ETH', 'MHO', 'NR', 'HMST', 'CPLA', 'NX', 'IPAR', 'ITG', 'MRTN', 'AEGN', 'NCI', 'CKH', 'NMIH', 'CLW', 'CROX', 'LPSN', 'AAOI', 'SXC', 'CRAY', 'DFIN', 'CENX', 'BFS', 'PETS', 'PAHC', 'CASH', 'GPRE', 'NANO', 'CECO', 'PEI', 'PGTI', 'RRD', 'GFF', 'HT', 'ENTA', 'PRFT', 'TMST', 'COHU', 'MED', 'DCOM', 'CYH', 'LCI', 'CRY', 'QSII', 'ORIT', 'UBA', 'SHAK', 'AMPH', 'CYTK', 'FARO', 'PRSC', 'UFI', 'INTL', 'HSKA', 'ANGO', 'SSTK', 'AHH', 'PLAB', 'VIVO', 'SVU', 'RUTH', 'RYAM', 'JBSS', 'LKSD', 'ECHO', 'AVD', 'BJRI', 'TTEC', 'ATNI', 'EGL', 'HSTM', 'NPK', 'CRVL', 'AFAM', 'WLH', 'EXPR', 'LNTH', 'ESIO', 'LMAT', 'MPAA', 'AMAG', 'RGS', 'RCII', 'SAH', 'MYRG', 'EZPW', 'MHLD', 'GCO', 'KND', 'REX', 'CTRL', 'PARR', 'GHL', 'ANIP', 'ENVA', 'CMTL', 'DXPE', 'MCS', 'NLS', 'WSR', 'CCRN', 'BKE', 'TWI', 'IIIN', 'LION', 'MCRI', 'XOXO', 'TG', 'MYE', 'HVT', 'IVC', 'WRLD', 'SNCR', 'OPB', 'BKMU', 'HAYN', 'EGRX', 'FRGI', 'CDR', 'FORR', 'RECN', 'LMOS', 'MOV', 'BKS', 'DNR', 'OFG', 'FINL', 'HSII', 'DAKT', 'HZO', 'FOSL', 'PGNX', 'CENT', 'SRDX', 'FF', 'SUP', 'MTRX', 'ESND', 'HWKN', 'BH', 'VDSI', 'CHUY', 'EHTH', 'TISI', 'PKE', 'VRTV', 'VICR', 'SPOK', 'ASNA', 'CPSI', 'CLD', 'BBG', 'APEI', 'UIHC', 'ACET', 'CATO', 'HCI', 'DEPO', 'BELFB', 'TTS', 'ZUMZ', 'TTI', 'DSPG', 'ICON', 'RRTS', 'PERY', 'DGII', 'BRS', 'HIBB', 'FTK', 'LOCO', 'QNST', 'POWL', 'BNED', 'SENEA', 'SCMP', 'SCVL', 'FRAN', 'HLIT', 'GEOS', 'ORN', 'KOPN', 'KIRK', 'FTD', 'ERA', 'AGYS', 'TESO', 'ZEUS', 'GIFI', 'QHC', 'FRED', 'LQDT', 'LXU', 'RT', 'BGFV', 'PES', 'VRA', 'CRR', 'DHX', 'VSI', 'MCF', 'BBOX', 'GRB']\n",
    "\n",
    "# Small-Cap Value\n",
    "RZV_symbols = ['KELYA', 'MHO', 'RH', 'CMTL', 'EXTN', 'ESIO', 'QNST', 'ASIX', 'BCC', 'MTH', 'DPLO', 'GBX', 'ECPG', 'IVC', 'SEM', 'SENEA', 'SCSC', 'GCI', 'MDC', 'PMC', 'NSIT', 'WNC', 'SKYW', 'RRTS', 'AEL', 'NE', 'DAR', 'RGS', 'ARCB', 'UIHC', 'NEWM', 'UVV', 'ANF', 'BHE', 'SANM', 'AAWW', 'SAH', 'ITGR', 'GES', 'TBI', 'INTL', 'GPI', 'GIFI', 'AXL', 'LAD', 'CENX', 'AFAM', 'TPRE', 'HZO', 'ZEUS', 'PRSC', 'BANC', 'RRGB', 'ENVA', 'WRLD', 'CPS', 'HVT', 'PENN', 'PERY', 'BGG', 'TIME', 'ANDE', 'HUBG', 'GIII', 'IPCC', 'VRTU', 'LKSD', 'MHLD', 'GLT', 'VRTV', 'KIRK', 'AXE', 'BNED', 'FCN', 'GPRE', 'HSII', 'ECHO', 'ERA', 'MTRX', 'MYGN', 'ABG', 'SCVL', 'ESND', 'SIGI', 'KFY', 'AIR', 'UFCS', 'IRDM', 'MTRN', 'EZPW', 'CORE', 'STC', 'PLXS', 'SEDG', 'LHCG', 'COHU', 'HMN', 'EXPR', 'EIG', 'MANT', 'SCHL', 'BKS', 'OPB', 'AEGN', 'HCI', 'BGC', 'HMST', 'UFI', 'CAL', 'RRD', 'BRS', 'SYKE', 'LXU', 'CMO', 'ITG', 'KS', 'RYAM', 'PARR', 'FRED', 'CHSP', 'WIRE', 'FOSL', 'CROX', 'MOV', 'NAVG', 'KND', 'SPOK', 'GCO', 'ASNA', 'TMST', 'DXPE', 'OFG', 'CYH', 'CACI', 'WDR', 'CLW', 'KAMN', 'APEI', 'SAFT', 'POWL', 'RT', 'QHC', 'SXC', 'BGFV', 'DGII', 'RECN', 'FINL', 'SUP', 'ARR', 'SVU', 'CLD', 'FTD', 'JCP', 'VSI', 'CATO', 'BBOX']\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The short way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "ivv = compute_distance_series(IVV_symbols, start_date='2016-12-01', normalize=False,\n",
    "                              window=15, method='pearson', direction='both',\n",
    "                              base='2017-10-10',wasserstein=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The long way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing data from symbols:['DWDP']\n"
     ]
    }
   ],
   "source": [
    "returns = get_returns(DJIA_symbols, start_date='2017-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "norm_returns = normalize_returns(returns, window=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "returns15 = returns.rolling(15).mean().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "cors = compute_correlations(returns, window=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "dists = compute_distances(cors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "diags = compute_persistence_diagrams(dists, maxdimension=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "base = compute_base_diagram(-1, len(diags))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "wdists = compute_wasserstein_distances(base, diags, q=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "dia = normalize_distances(wdists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "series = pd.concat([dia1, dia2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IzijKMo37tTsamIjE58ymeeSZi5S4nXzlfbeQME0+/pOzS1rrSjpyeZjNdaXU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4tHU9t5cMvP4R3/IETT726D9p3ZHzDQrVEoUj6HTUeRiYluTKHIpGJR+4m2XED9RCrbljfjdlh5\n6cAJcQKrs8gaxNSzqo6eKT1A0bj+qV2aUaNnSQOOomQcfnwHjPXBhx4pqUgE0FIrhJoBrcWOudj6\nCYgHYe/9mm4uyzL+UAzfheYoigihKGHzAtBbxOjfDkUoWlCu6Jl3DljsWSefVfLUs0f3duLzOLh6\nZctMv5QJeBy28se4RhVHUY4+jVAsgdUipbvZCuF12hiL6ojlzga2/pFwvSy8Aj7yBHziRVj/fm19\ndBkEY4k8jqIFIt4my/mnng0ocd7mVbqeOycODyx7m+gp0jrps0iiyRS1qlBU01DeqWemo6gkVIqj\nSI2e9QW0bbL98KXTfO/F0/zuFYv5k7dnF1e9ThsOm4W+iAW2fxY+s1ds5h14MPuDBrpEVLQQqqMo\nViVrEROTCsIUikwqlt1n/DhsFi5dcOEVILrsVt62qoUTRw+KKy5oR1EZT4YsFnHCrctRpJRZD7eL\nqTc3/CMs2lryl1YSRxHAvA2w6ErY9X3Rw1OAYCxJLJEq2lFU67LT5HHQ7g8W9TjThnKSFLeLPrTu\nkTCywRO29kHVUVSm6JnFAg0LpziKGtyOip16FojEefZIL7evn4e9HJPgisDrsuWebFUqRvNHz8Kx\nFDV2q+bhAx6ndXomtVUSq2+GL/bAPT+BJdvFaHkDhHJ1FIEQipJRCA5QY7ditUiMRbP8nAdPistS\nRc8AVt0MQ2eg/1jpHjMHsiwTS6TwJobA2yocRZHh8ohU4SHTUVQiLppby0e2LeHqFdqK28tFo9uB\n1SJpip49ureTLz9xhFsvmcM/3LEu5zFOkiSaPQ4Gx5TPME+zcNode3rq+zISgNioTkeRGT0zMdFL\nZa3WTEwy2H3Wz4YFDUWN6K5mblzXhiesFHc26nMUhWNJ0T9Q1Y4ig9EzvdicmjuKUimZWFJxFIWU\nkb/1C8vysupcdmwWqbiOIpWtnxCiwr8she9cCfe/Bx77DLzwLzB4asJN/coirVihCET8rGocRUr0\nLG4X0bNIPEUgbEw86BgK47Jb0ruuZaFh8RRHUaPbnhb6Ko3T/UFiiRTbZ/gEJxu1zmlyFLkaxne3\nJxGOJzRNPFPxOm0ELzRHERQ9YSyRTBFLpHJ3QdUpkZjAeSRJwuvMISIOnABnnRBZSsWqm8Xl8fJP\nP4slxTHCE/cr0bN64awtx8m06SgqGU6blX+4Yx2NJfh8LgaLRaLZ6ygYPXvxeD9//rP9XLGsia+9\nd0PBvtEmr4PBzDXP6lthtAu690+8YaBLXJrRMxOTsmIKRSYVSTCa4K2uAJcvnTqW80Lh7avbWGxR\nRnTrjJ6pPSWN1ewoshsoszaCzanZUaQurp12C4QGxJWe7FNBisVikfB5HaURitbcCbd/Ay59r5hu\nExwQEYcXviIKI1PjP+PBoHg+n7f4984SXxUJRUr0LKZEzwC6A8ZOmjr8IRY0ujW7QwzRsGhq9Ew5\neahEV1Fc+dtJ93tVCpEAc20j0yAUdefsJwLhctEy8UzFOx3i1iwkpHRB5S2zhgmF1ln7qwZPgG+F\nYVdTzueec6lwUJSZWCKFRIqaeEb0DEofP0tERfTZfeGu5WYrLbXOvELR+aEQn7z/DVa21fL9D1+m\n6djv8zjxZ8btV94ISFO7u5QesbSwm490mXWVrEVMTCoIUygyqUj2tg+TTMkXZJG1Sr3bzmX1I4xI\nteCq03Xf4ZCwyjfMcI69GNIdReUWiqxOETXQgNqX5LRZhdgC4C6fQ6LZ62RgrAQn/RYLbP49uO3f\nRGzjE7+FvzgBd31fnPCc/k36puoirclTvBtmkc9D10i4vPHBUhGdKhQZnXzWMRROl3mXjYZFEOyf\n0LugdpINV+Dks7jyd2y3VtAES1mGn76fz3R8fhrKrHugNns/EagFy9qFIo8pFBkirEyXyx09Uxyi\nI+JEtNZlYzTbz3ngROn6iTJZfQuc3wXBwdI/dgbRRIoGxrDIyfHoGZR+8pnqvDUdRbOOFq8zb/Rs\n52k/oViSf3/feuo0TrkUm2MZax5vCyy4XGxsZaLHUeTwiEtTKDIx0Y0pFJlUJLvO+rFIsHnxhb0L\ntcbl50yyhVP9Y7rupzoKzI4iDdicYtdTA+prEdEzZSHvLo+jCFShKPdre/30IB3FTBVb9y7wtMLO\n76WvGlSEomLLrAEWN7mRZTg/VAXdAMoJUrFCkSzLnPeHWNhYpn4iFdVlONKRvkp1EA4VW4BeBlQ3\nnq2S+okO/xLOvUJr9CxStExFvipjvXkdReF4nt6cLIjoWeUKRZ/4nz387aMHZ/plTEH9mamlwFNw\n+0SBbkA4impdNkYnd0HFgsLRUKqJZ5msuhnkFJzYUfrHziCWSOGThDiejp5B6SefhRWhyOwomnUU\nchSd6BvDbpVY0eLNeZvJ+DyOtKs5zeqboXvfuDgE4//Oc0xNY7WDxWZ2FJmYGKCCVmwmJuPsPuNn\nzdw6ajXuQsxWWhM9dMitPHu4V9f9hhRHUaOnen9+DlUoKufUM9ApFKmOIiV6ZnON71aVgWavc7zY\ncRLJlMwf/HgP33nhpPEnsDnh8o+JkxKlq2hcZCz+vaNOPmuvhvhZJAA2F3HGv+9uA0LRcCjOaDTB\nwulwFAEMjRdaq78z9e+/kkgkRRmp1qleZScRhWf/Duzi73dF6kz5Yq6plOIoyl5kDbMverb77BCv\nnCyvK8YIoUKOIkkSLoURVSiyT/05l6PIWmXuBnHyW+aeolgiRYukiKPljJ6ZjqJZS2uti4GxGKlU\n9gL0k31jLPF5dG0O+LxOIvEUoVjG39yqW8RlZvws0Ck2ubROPLS7TaHIxMQAFbJiMzEZJ5ZIsbdj\n6IKOnQGQSmIbPU/Es5Adh3p03XU4rJzs11Szo2iaomc2pyjx1EBE6bdw2a0iGuBuLm1HxSSavQ76\nx6JZp2+dGRhjLJpIxwwNs/mjYvKb4ioaDsWxWUSJa7EsUoSic4NVMPksGgBnHYmMRW+vxtG/mbQr\nDq9piZ7BhMlnqoOwkjuK7LYKiZ7tvFf87G7/OgDrpDPlc+iE/ZCK53cUGYieRROp9M+1khiNxPEH\nY5wdDKaPmZWCKhTldBSBmHymRM+ydhQNnBCXzWUQiiwWWHUTnHxO8waGEaKJFM0oolA5o2emo2jW\n0lLrJJmS052YkznVP8bKNu1uIhh3Mk/YIGtdIxy0md1dgS5tsTMVu1s4AU1MTHRhCkUmFcdbXSNE\n4im2LL3AFxaBTkgl8C1Yxd6OYfp0nLSmO4pmwdSzWLmjZ1btZdYTHUWDZV/8NnudxBKprB0ZB86L\nRX7RY71r2+Did8O+ByASYDgcp8FtL0kRc4vXidth5Vwx8bjpIhIAV13a+QLGHEVpochXZqHI2ybe\nuxmF1o0V7ChKR88sFbDsGOuHF/8NVt4El/4OIVcb6yznyufQGe0Wl948HUXxJDX5xItJqEJuJcbP\nOvxi516W4USvvth0uVGdCnljfnUL0mW5XleWqWeDJwEJmpaX50WuugViY3D25fI8PpOjZxlCUamj\nZ6ajaNbSUit6DPuyxM8i8STnBoO6YmcwPkRjQuRekkR315nfjnfyBbq0FVmr2GtMR5GJiQEqYMVm\nYjKR3WfEwuKCdxQpkZKVF12MLMPd977Gh/9zF3/5vwf492eP8+Cu9pzdRUPBGDV2a+VNGNKBOvVs\nWhxFCW0OjHRHkd0qomee8o76bq5VFk1ZFmIHO1WhqASiwNZPiBOTfT9hJBSnvkQl6JIksajJXSXR\nsxFw1ZNQJsA1e51FOYoWNpZZKLJYoGHhBKGoxm7FYbOkHYWVREVFz174ithdvvHLAIw1rmGddLZ4\n0TUXo0p0uKCjSPvPRhWKKjF+1p4hDB/rHZ3BVzKVtKMo19QzENPHRrshmcheZj1wXDj67K7yvMhl\nbwNbzdRJTyUkmkjSLI0gS1aoaRzvKCp19Mx0FM1aVKEoW0/R2cEgKRlWtNXqekyfMkTDP7lnb9XN\nYkPv9Avi/0cNOIrMMmsTE91UwIrNxGQiu88OsbTZk/4QumAZOgvAgmVr+IubVrNmbi0joRjPH+vj\nG785wV89cpDfv2931ruqrpBqRj2hnB6hSKOjKJ7hKAoOlHXiGQixAsg6+eyg6igqxYni/M2wYAvs\n+h4joUhJS9AXNbmrw1GkRs8UQWNBY40hR1GHP0Sz14GnBNG9gjQsmiAUSZJEo9vOcLDyHEXxdJn1\nDEfP+o7AG/eJbq4WMbUq4ruYFVInoWCZRA3VUZS3oyiBW4ejyJN2FFVWtAtIF+zbLBLH/n/23jTO\nkas+93+qVKoq7ep1prune1bPjO2xPXgbG4xtsIGwhcQJ1w5mDQQTsq83F8glJP9PINtNyEogCYQl\nQAh72CHeMB7vNraH2Zdepnepta9V9X9x6mjrklQlVamr1ef7Rrakrq7pltR1nvM8z28hucFnUw8V\nivzeNtEzTQVS8whJAopltX6owsopZ2JnFK8P2HMridoYxI7toKhHz0ryIBGdPQIghpyZeib4qiPK\nGX3DSLC5UHR6iWxiduooWtfNuPNFgBQGTnyTuIpycYtCkY8JRQxGBzChiOEqVFXD4xdiuG7X1p52\nBoD0Z3A8uMgO/MpL9uGf33QtvvqrN+HR996Ok//fK/G7L9+PC6tZw8lMa9nipp54BpDpSB6e68HU\nM9l0R1F99CzmvKNIvxBbbZh8pqganr9IFmC2uSCO3APEzmJf4qhtjiKAFFpPx7JNCy9dA42e6Y6i\nHQM+JHKlyjhts0zHss4XWVOiU3UdRQCZfNasM2IjKem/f+9GO4q+815ACgG3/p/KXcq2K+DhNGDp\neWe+Z0rvmGsbPbPSUUSemy64TxScjmURlgUcHAvhhEujZ/5WjqLwDnKbnKsM1Kj0FGkaKf53osi6\nlgOvBBLTwMKPHTl8QVExzCVQ9tX8DZMjDkw9izM3UZ9ScRQZTGY9tZgGxwF7RqwN+6COopXGyWeC\nCOx9KXDyO5VYqCWhSGRl1gxGJzChiOEqTi+nsZYtsdgZQBxFkR1ktGcDXg+Pmy4ZAQA8fiG2/kuz\npUpfyWZGEnjnJhFRPKKFjiK9zJorA8VUTzqKgIa8PkhJZK6kYDgo2hM9A4DLXgeExvDK7FcRtVEo\nmhoKoFhWsZiy7s7pKQ1l1jv06NiCxfjZdCzrfJE1JTpFurIK1cV41O/tvuDcAUr6+3hDo2envgec\n+QFwy/+ue+9y44cBAN4lh8a5p+ZJvKdJVKmkqCgpmqUy65BMo2fucxRNx7KYGvJj/7aQex1FrUS5\niN59kphdH/FLXgRKGWcdRQBw6WvJ36anP+vI4QslFcNcEqp/pHqnL+rM1DPWT9SXBCQBftFj7Cha\nTmNq0G+5/sAneuAXPYgZTXs98CogswQc/2/y/yx6xmA4DhOKGK7iUb2faMsXWQOko2hgV9OHLx8P\nQ/byePx8fN1jxFHUH0KR89Ez2UJHETkXX1n/mTscPRvwe8FxwHLDRRONnd24dxj5kk2Tjzxe4Nq3\n44j6NPZyc90fT2fnIJ185vKLNNpRVBM9A4D5hPldyJKi4uJarodC0U5ym5ip3uVzqaNoo6NnqgJ8\n932kgPi6X6p7yDe8E2taAP7YMWe+d3qxdT9RyYR40UDA1WXWRCw9uD2ExWTBVVP4soUyOA6QhVaO\nohqhSBfkKs7NlZPk1mmhyD9IFsbP/qfpv0/ryK2/NqAUdUeR6m9wFDkx9czPHOL9ykhIMhaKFtOW\nY2eUwYCI1caOIgC45GUAxwOPf5z8v9Uy66LLr0EYDBfChCKGq3jsfAwjIal3Cy03Ez9fXQga4PXw\nODwZxRMXjISi0qaPngGAJHgqvUCOIVhwFOnn4ivqP3OHo2eCh8egX1znKHp2LgG/6MFVO0gB6brx\nzR1SesFbUNC8uCn+ZVuOBwC7hoj13NWF1kqJ7DbKkYqgQeNjVgqtL67loGroYfRM/3yonXwW8BpP\nPUstAj/6eyKYbADljY6enfousHwceMl7yHu+hqDsxfPqLkTWHBKKUvMt+4lovM/wtT0AACAASURB\nVNFS9EzvM7LrvW8XiqphNp7D5CBxFAHAiQX3FFpniwp8Xg94voVgKYcBKaJHzxqEotXT5Nbp6BkA\nHL6bOAZPfcf61158GvjzPcCMcY9hsaRgGAky8YwiM0cRwxojwfVCUVlRcW4lg32jnQlFQ0Fp3TUP\nACKeTt5QjVu3EN/X4Q2w6BmD0QFMKGK4ip/MJ3HVjqgto7k3NcUMsdi2cBQBwLU7B3FsPlm3q6xp\nGtZyfRI98/LOdxR5JECpXpT87heewYe/f8rwqXkaPSvpu67+IWfPDSR+1jj17Nm5BC4fD1e6hOzq\nKUryEXxNuRGXLX69MnWvW8ajMgSew4VYxpbjOUJBX8hKYSi6oDERpY4i80IRnfbUM6F7SB/PvVjt\n1on6RSRyRWiNJbhPfAL47nuBM//Tm3NrgEZIvRvlKHrkI2QH+rLXrXvIL3rwvLYLg+nTRDS0m9QC\nEDQhFHUUPXOXULSYzKOoqLqjKAzAXZPPMkXFXGl4ZAJIzCIk6R1F9Oe8cgoQgy2FP9vY+1Lyunnq\nM9a/9tR3SSH33OOGDyuFFGSuBC5YIxT5og50FMVYR1EfMxqWsNQQK5+J51BU1I6FouGAuH7qGeXA\nT5Fb3wDpHTILK7NmMDqCCUUM16BpGmZiOewcYm6iikOgnVC0awCKquHpmerFXapQhqJqiPo2v6NI\n9PQieiYB5aoQc9+JZTx8dsXwqdRRJBboyF9nHUUAMByqdxSVFRXHLiZxaCJSKVpN2VRou5Yr4W+V\nnwV4Afivt3UeeahB8PCYGPC5O3pGd9HlcKV0OewTEJYFLLpZKPIPEmfDzCOVuwb8XpQUDZnGEu6Z\no+T2iU/05twaKKsqBJ7bmE2ApeNkrPJ1bzfsfOM4Dmc8eyFoxWq0yC5UVY+etZp41j/Rs9r3wLaw\nhIjP6ypHUa5YNvdzjuwgQlHFUaR/xq7qE8968Tr2CMBVdxLRJ71k7WvPPUBul48bHzq7DADggzUd\nRXZHz1SVxN+Yo6hvMXIUndKF4U6FosGAuH7qGWX/K8mtldgZoAtFzFHEYFiFCUUM17CaKSJXUjA5\nwMaoIn6e3LYRiq7eOQCOI5E9Ch2N3RcdRd4elFlToUjTUFJUrGYKTXezqLvJW+hN9AwgjqLavP6Z\n5QxyJQVX7ogg3BiL6JJEroQZbRtO3PBBYO4J4Pvvt+W4U4P+zSEUSWEoevTMy/PYHpEtO4pED49t\nYePSYkeYOgJMHyWLMqASOY3XvoZVhURQPCJw8tskhtZjSoq2cbGzRz5CusiufmvTp1wQ95H/mH/G\n3u+dXQXUsqmOIivFr14PD1HgXecoqhWKOI7DgW0hVwlF2aJiTigKTwCxcxg691Xs4JaQpkLRyqne\nxM4oh+8GNAX48efNf00pVxWPl4yFIiFLNkM84ZpJfHIUKKYBxabXVCFBXE3MUdS3jIQkJPNl5EvV\njYnTy2S4QjfRs9VMYb0rFiAi7fABYHC3tYN6/UA5V/k7yWAwzMGEIoZrmI0TtZ9OG9rS0NhPi44i\nAAjLXhzYFqrrKaJFtgP90lHUC6EIGqCUsJQqkOnHTXazCmUVHAd4cqukVFGOOntuIONia6Nnz84R\nUeOKicj6otUuSejdNqX9rwWuvwc4+o/AT77e9XF3DvlxYdXN0TN9MpMcqXTpeDwctkd8lqaezcSy\n2DHgg6dV/4ndTN1IXAC6E4a+7+smny0+T6b03fTbRLR45j96d346xbK6MUXWuTjwzOeAK14PBJpH\nRePyJAqcDMzbPI48NU9uTXQUmYpE1RCSBNcJRTOxLDw8h3E9unlgewgnFlPGi74NwLRQtPclgKYi\n9I1fxg+l38TP33sb8Nk3kOJ4p4usaxk5AExcCzz9H4DZn+HMI4BSJFMRl48bfp03T4QiIVQjFPn0\nv2d29RRl9Q0s5ijqW0ZC6yeznl5KY3tYrjierTIcFFFSNKSMPts4DnjzV4DX/I21g9KYWpm5ihgM\nKzChiOEaZvSdyJ4VwbqZ+HlSvmfCsXLtrgE8eSGOsu6EWMuRBeJAoA8cRUKPOooAQClgQZ9wFc8W\noarrL64LZRWSwIPLrpKLX975j9DhkIhMUaksJp+dXUNA9GD3cLByIZa2LXpGBLKoXwRe/ifA+NXA\nV34FiJ3r6rg7BwNI5suumn5UR54KRWGU9KlnXp7HWFjGgkVHUc8/vyZvILd6tIw6Cesmn03rsbMX\n3A3sfBHw5CfNLzptoqyqEDfCUfTkp8ji4Mg9LZ/m90mY9u4BFuwWihbIbcvoGVkQWYmeASR+5sbo\n2XhUrrjH9m8PIZUvW3LmOUm2WDYnyF32OuAPpoF7HsT7lV/EmcgRPcbFAZPXO36edbzgbmDpGHDx\nKXPPP3s/iQ9f/RYiIhvE1sT8KgBAqHMUkeEItsXP6NQ15ijqW6hQVBs/O72U7thNBJDoGdB8ww7h\ncetubq/+d5nFzxgMSzChiOEaZuJEKNrBomdkqsPALlM9CNfuHESmqOC4bu+ni/FIH3QUEaHIaUeR\nHhMqF7CQIBc7qlYV3GoplBRIggfIrvQkdgaQ6BlQ3bEjRdYReHhu/USeLqEulKjPS5xWr/84wAH4\nwlvrepysMqX3jrk2fkYdRVIYim5N9/ActkVkLKcLlUlo7ZhezfZ+YuPQXtKVpYtBA0ZC0cxREqWJ\nTAJXvxmInQXO/7Cnp1kqb0D0TCkDj34M2HkTsP2Klk8NSgJO8buJo8jOeEK6vVDUSfQMIEJRurAx\nU+yaMR2rfw8c3K5PPnNJobVpRxFAOoLGrsTXxVfh8zveA/z6k8AfLgN7bnXyFNdz+R3k79TTJp2A\n5x4AJq4BdlxL/t+gp0gqEqGo7u8YdcjaJRQxR1HfMxIk109UKNI0DWe6FIqG9GueVaPJZ53i1dcV\nRRc7mxkMF8KEIoZrmI3nMBgQKyWdW5r4eWCgdeyMcu2uAQCoxM9oN0k/TD0TBb5SIO0YdFR2uVAX\nMzK6SKGOImRjPZl4BpCySABYThdIkfV8ElfsIDu/QckZoSisT1PDwC7gZ/4JmH8a+M57Oz4uLai/\nEHOpUJSvRs8qjiIPh7GIDE3DurJOIxLZEpL5cu/L+DkOmLqhIhTRjqJErdA5fRSYPEKee9nriHPg\nyX/v6WmW1A2Inp38FpCYBm54V9unhmQBx7GbRPTi3Tno6qCOouC2pk/JdVBmDdDomQNT2rqgUSzd\nP6oLRS7pKbIkFOkEJaH6GWtQhu44vihw8DXAs18ASm2cWfkEcPFJYPfNwMhBct/yiXVPkwurWEOw\n/t9Do2etJp99+V3A137d3Hnn6NAHJhT1KxVHkX69NJ/II1NUuhOKqKOo2eSzTmCOIgajI5hQxHAN\nM7EsK7IGSCQkfqFtkTVlIurD9rCMx3WhiDph6Oj0zYwkeFA06eboGOooUgpYrBWKDC5SCmUVkpcH\nMis9E4qGK7trRZxeTiNfUnHFBBGKZK8Hooe3tcw6JAv1HTsHXw3c+KvAYx8DTv+go+PSheO0W3uK\nasusVQ0efTrXdr2U2kxsZnojo7OTR4i4kVokbjAAcb3UHmszQHKOdBkBZGf1yjuBY1+r7vj3gJKi\n9T569sg/A5Gp6qScFgQlAc8oujhvZ/wsNU8cFYLU9Cl06pnPsqPIg4yLHEXpQhmrmWLdeyDi92Is\nIuOka4SiMvwWN6NCsoC0TZ+xHXP4DcTpc/JbrZ934WFSIL37ZiJOyhFDR5GvFEMMDR17ZqJn0w+T\nQQdmqDiKBsw9n7HpGAoSUWcpSYSiU0vdFVnXHrNp9KwTKkKRSzerGAyXwoQihmuYjedYkTVARIhS\npm2RNYXjOFy7awCP65PP1rJksS9s1IQhG5EEHoWS0x1FVUfRfCJfSfsZXaTkSwrkXkfPQuT8VtIF\n/HiWCBqHdKEIIIuYyujmLknkSsbT8m57P+lyOvM/HR3XLwrwi576gmU3UUiSTjCPgJKqVoSy7REi\nFC2aKLSunfbUc6gINHMUgodHSBaq0TM6/WjqSPX5V78ZUArWJil1SanXZdYLzwHnHwSufweJELUh\nIAl4tjhOul3sLLROLbSceAZUo2e+Td5RNNPkPbB/W6gSjd5oskUFfouCXFASjIt1e8meW0l89KnP\ntH7euQfI5seO64mDcOSgoaMoUIwhwUfq75TblFmrKpC8CKRNTk3MxXo29IGxMXg9PAYDYsVRdFoX\nii6xpaPIgegZcxQxGJbY/CtJRl+gqhrm4jnsGGSOIqzpE89MOooA4NqdA5hP5DG3lkM8W+yLiWcA\nIHl721G0mMhj91AAABDLGEfPfIJGSjp75CiiF00rqQKem0sgIHqwZzhQeTwoCzZGz4qIGnVbCSIw\nvN9wwWEWn9dTWRC7jnwCkMMAgLKiwUuFos3iKBq7iryOp4koNOAXq8Xh0w8DYhAYvbz6/O1XkKLy\nHpZal1W1tx1Fj3yE7CJf/WZTTw9JAuJFDtrIQWD+GfvOI7XQsp8IINEzniPCuBVCsgsEjBqaiaUH\nt4dwejldGbiwUaiqRoQiy44ir22fsR3De4Cr7gLO/ABIzjd/3rn7icPQq/9dGzlg6CgKlONY4xsE\nnHbRs+wKmaaWWSH9X+3IxohI1IOhD4yNYzQkVeLZp5dSGPB7Kz1DnSAJHoRkwaHoGXMUMRhW6PtP\n70JZwRs+dhRPTcfbP5mxYSylCigqKnMUAaSfCDDdUQQA1+4iHQCPn48hni31RT8RQC4YnBeK6juK\nLh0jgsGKgaOoUFYw6MkRa7+/N44iSfAgLAsVR9HlExHwNdGwkGzfiOy1Zo4iABg9aLjgMIvsZqGo\nkAQk8nun0TOATBCTBL4yDa8V07EshgJipTeqpwgiKa+dfhgA6SejO7yYfgTYcd16V801byGTlGYf\n78kpFpUelllnVkmfy5V3mo69BGUBmgaUR68g0TO7BDSTjiKf1wPOxPCCWgLi5nEUFcsqzm9wmX2+\n3GEXlOySLqjDd5O/PU992vjxzAqw+ByJnVFGDhKBJ7NS99RQOY4k3/DeEGTisG0WPUvO6f+hkWO2\nIxdj/URbgJE6oSiNS/Resm4YCoj2CkUiE4oYjE7oe6FoIZHHj86s4qlpm6Y4MBxhVp94xjqKUC1S\nNRk9A8iObUD04PHzcSSyRUT6xFEkCjwKZYfFBd1RpJXzWEjmsWPAh4jPi5hRR1FJxTCnRyh6FD0D\ngOGQhIVkHj+ZT+LKifq4QEjy2hc9y5aad1uNHAQSM0ChswiJT/Qg71ahKJ+o9HOUlKrzheNIofVC\nsr0FfiaW3Rg3EWXyCBE4illct2sQj5yNYXFpiSwcp25Y//xDP0fidk9+oienV1ZUeHsVPXvob4By\nHjhyj+kvCUrkdZ8bOgRklqsl1N2gKiSmE2peZA2QOJTPzMj2BgKSgGxRgaL2xhXWjulYFiFZWPcZ\ncmC7OwqtaZ9ToJsy641kaC/p2/rR3wLp5fWPn3+Q3O6+pXpfpdC6RuQv5eHXMkgJDSIOxxEHULPo\nWWKu+t9m4mfZGJt4tgUYCRKhSNM0nFpKY28XsTPKUFBi0TMGwwX0vVBEJ7/knV5sMrpiJr6BsQ23\nEb9ASihF8z8LwcPjBVMDePxCvM8cRTxKigbVyYWQh1ik05kMimUV28IyhoIiVptEzwapUNTDndLh\noIRHzsVQKKuViWcUW6NnuRZC0eil5LbD+JnP66lMd3Id+WRd9Ky2S2dbWDbtKNqQfiLK1A2AWgbm\nnsCbb9wFRdPw4L3fBKAZC0VSCDh0B/Dcl6pT3xykVoBzlKWfAEf/EXjBG6uvWRMEZSLUJAf0r2mM\nn6kq8MzngeWT5s8luwpoSntHUbFs2eUCVKceZoouEDFQfQ80OqP2jQbBc8CJxY0Viujnj1VRjpZZ\naz2Kabbk5X9CXBH3/en6x849AIghYPwF1fuMhKIMEZkygoHbzhdtHj1L1gpFS+3PlTmKtgQjIQnL\n6QJWM0WsZUtdFVlThgKi4WZdx9DoWdGlAzUYDJeydYQip0dsM7piJkYWYhPRLeQoSsySiUSNxM9b\nchNRrt01gOMLSSwm8/3TUSSQxZOjk8/0aURrKVLCuD0iE9tzs+gZ9EV1j6JnANmxo0XQVzQ6imwS\nijRNa15mDVQXHEs/6ej4ru4oqomelVUNQk2nBnEUte4oKisq5tZyGysUTV5PbqePYmrIj9sv3YbY\n8QehcR5g4lrjr7nmrWTR+U8vBD7xGuBL9wDf/wDw6Mc6/j03o6ho3RXszzwG/OONwHNfbP4cTQO+\n+Xukk+n2D1g6fEgXXVaD+wFw9ZPPcmvA5+8GvvxO4Ju/Y/6gKb1Lpl1HkR49swoVt9wSP2smlspe\nD3YNB3BiwXlBshVUULPsKJIFlFXN+Ri0GYYvAa59O/DEJ4DFY/WPnXsA2PnC+phpeJyIR7UCf4aI\nPBnRQMSRI82jZ4nZ6n+bchTFmaNoCzASklAsq3hSn7zbTZE1ZSgoGsb/O6bSUcQcRQyGFbaMUOR4\nfIXRFbPxLEZDEuQOLpY3LV9+F/D31wJP/0f9/WsXLBVZU67dOQhNI66Xpq6QTQYtdy04KfTqQlFS\nF4q2hWUMBSTj6FlZxQAVinoZPdPHxQYlAbuGAnWPhWV7omfpQhmKqhmXWQPkNSnIHfcUyaIHObcK\n9rWOIrV+Ote2iIzFRKGlm+DiWh6Kqm2sUOQbAEYuBWaOAgDe9sJdOFQ+hrXwAUBqcuE+cQ3wsj8m\nHUZKCbjwEIm1fPN3gS+81dbTKysqxE6jZ8e/Afz7a8hr78u/DMw2Gc/93BdJ/Oa2/2v5/UlFl5Tq\nIxEf6iia/zHw0VuAU98Fpl5IFuPxC+YOSuNrbRxFJHpm/W9fQHKPUKSqGmZjzcXSg9tDOLmY7vFZ\n1bOgl9Jb/VmHZPL3NGlTxLdrbv0DImx/933V+xJzwOppYM8t9c/luPWF1npsLes1EopaRM+Sc9XX\nsploJnMUbQlGQuQa6uGzqwBgk6NIQjxbtM9NzqJnDEZH9L1QlMyRCyhHF5qMrpmJ5bBjq/UTxS+Q\nqMhXfhn4+m8ApTxZrCVmLRVZUw5PRUE7jvsmeubVhSInhV4PEUZSGWJJHovIGAwaFynmSwoimn4R\n3aOpZwAqE0QOTYTriqwBIh6lC93HIqhjKdLstcN7yG52x44iHnnXRs+qHUXlmjJrABgLyygqaksb\n/IZOPKtl6gZg5lFAVXDjrjCu9pzBA/m9zV8bHAe86DeA138cePt3gN96DnjfEnDkXUDsHIlb2UTH\n0bNHPwZ8/o3A6GXAu48Sd87n3kDGdNeSTwLfeS+J3VzzVsvfJqDHkdKFErD9SuIoeuozwL++DCgX\ngbd+E7jjowA44JnPmTsodRQFW3cU5YodOook8jVu6M9ZTOVRVNSm74H920I4v5rZsPjpj06v4Nc/\n+xS2hSVcucPauHbqNku74OcMgIgvt/xvMgHt1PfIfeceILe1RdaUkYMNjiIiFOWNHEWtomeJOWBw\nLyBF2kfPSjniVjRZJs/YvIzo1ycPn1lFQPRgLCJ3fcyhoAhF1Sqb/V3j8QK8l5VZMxgW6XuhqBo9\nc+kChQGAdBRt+CKrl2gakF4gC7KbfovYyP/tFcCFH5GpJh04ioKSgMvGiStiINAf0TPRQ4UiJx1F\n5KImlcmQzdeQhOGAiHi2uK4ktlBWEdGSxMovdD7+1SrD+oVYY+wMINEzVSOuhG6gn5XRVm60kUu7\n6ihyZVdcuQAohWr0TFHhrYmebdcveucTzeNnlbHgQy4QigpJYOkn4BafhYwCvpvajcfOW5j6yXuA\noX3kZ5IxKMztkJLV6JmqAt97P3E3XfJy4K3/TZwRv/A5oJgGPnd3/e7wfR8icZhX/xX5N1gkRB1F\n+TIwdiWwNg189d0k0nfPA8DUESA6SRbiT3/GnIiW0uM57YSiktJhRxF5r9KS5o1kWp9otrPJe+Dg\n9hA0jUxF6jVffXoOb/n4oxiLyvjyu1+EQYt/H2kXlBsEuQrXvYOINt95L9lgOvcAiXmNXr7+uSMH\nyHsjGyP/r0fPCpLBZker6FlyDohMAMHR9tEzKuSGx03+gxibFeooOr6Qwr7RoOXpjUbQ96hRV2TH\niH4mFDEYFmFCEWPDKSsq5hN5TA5sIaEoFweUIhCeAG7/I+Cu/yA7+J++gzzeQUcRQOJnAPoneqbv\nsjsrFJGLnGw2i+GgBK+Hx2BAhKYB8Wy9i6RQUhFSEkCgd24ioBo9u8JgJzwo27OIqTiKWr12Rg8C\nydmOyo9lt5ZZ038LdRQ1lFlvjxCn40Ibocjr4bA93P1OaldMHiG3M0eB6UcAACfFy/CJH52zdpzo\nFLldm7bt1EpWpp6Vi8CX7yHTy655G3DnZwBRj1xuu4w4ey4+BXzt14jovvg88MhHiJNo4pqOzo+K\nAelCmYhBvADc9NvAm74CBEeqT3zBG0k8ePpH7Q+amiddZkJrYSLXcfTMUz3nDaYilrZwFAHA8R72\nFGmaho/cfwa/8bmncfXUAL7wrhdivIMeRCoiuuHnXEEQSbH1ygmy0XTuAWD3iwHe4LKe9sut6EXs\n6WVkIIMzGphBo2eNLkRVIeJPeIIIn+0cRRWhaMLSP4ux+aBCEQBbJp4B1c0x23uKmFDEYFhiCwlF\nLHrmVuYTpN9jS0XPKt0V+k7zwVcD77yXXNBx+o5+B9ywhwgYnVwMu5FKR5GTThRdKMrnMpWFPo16\n1caNNE1DoawgqCR6WmQNAEf2DOEXrp/ErQdG1j1G+zPShe4s2ms58m+NtipCH+l88pns1jLrgr5w\nrSuzrome6Y6iVoXWM7Esdgz46yJrG8LALiC4HZg+Ckw/DESn8NIjh/Gd5xcxt2ahmyEySW4TNgtF\nRotYI47+A/DsfwIvfR/wmr+uL+cFyOflS98HPPsF4Id/DXzjd4nQd9v/7fj8ArXxoolrgPfMA7e/\nf7076eBryGvlqc+0P2hqoW2RNaB3FHUUPXNPR9FMLAuea/63Z+dQAJLA48RCbyafKaqG93/teXzo\nW8fx2qvG8cm3X9/xBopdYrztHHgVsOvFwPf/iAj4RrEzgDiKgGpPUWYZMUQhCgbvR1+UuJoLDb+n\n9CKZ4BeZINctbR1F+oQ0JhT1PRGft+L+vmQ0ZMsxh/TNMXsnn/lYRxGDYZG+F4qSVChyY+SBAYDE\nzgAX9Hv0krQuFAVrFhFDe4F3fB9498NAuHX5aTNecfk2/Pev3VTZvd3sVIUiB4VeDxGFCvlcJWY0\npNueV9JV23NZ1aBqgL+81tN+IoBciH3wjisRltcvdOhud7LLRUwletaq32qUjlq23lPkEz3udHbS\nmEVdmXX1T+NwUIKH59o6ilzx+cVxJCI1fRSYeQSYvAFvvnEXNE3Dpx42WcAMkIgVYDyVsUPKigav\nYFJIO/U90hN08++Rf5MRL/4d4NDPAT/4AHH3vOwDXRXnigIPSeCrrpFmLiDRD1z+s8CxrwKFNjGq\n1Lwpoajz6Jl7nC7TsSzGo76mPVQensMl24I4sdgboegf7z2NTz58Ae+8eQ8+fOfhygTNTqCfu3YM\nDbAVjgNe8afVkd+7bzF+XmSSuCmowJ9ZwqoWNv6Z6M7KdfGzBBV+dph0FNHns+hZv8NxXMVVZEeR\nNVATPUvbGD3z+oEicxQxGFboe6GoMvWMOYpcy2ycKPxbKnpGuysaFxFeX3X3rwM4jsMhgx6bzQq9\nkHX0/esRAM6DQj5bcRQNGuxmUbHKX17r6cSzdoRs6s8wFT2L7gIEH7BkffKZz+tBSdFQUlz2WWwU\nPatxBnl4DqMhqaWjiIwFd4mLb+pGIDFDdvynbsBE1IdXXL4dn3ts2nz0TwqRCErCPqGoaLbMupAm\nhdx7X9L6eRwH/PTfk7jdrhcDh9/Y9TmGZAEpM6LL4buBUgY49pXWz0svmhOKigp8otD2eY0EXCYU\ntZv6d2BbGMcXUlhM5tf1v9nNsfkk9owE8J5XXbpuAIBV3CTIrWPsSuC6twPD+5s7kXmeXFfQQQTp\nZSxpYWNHkazHmxsnnyVnyS3tKCqmqgKVEYk5UmRtFG9j9B3DdgtFftpRxKJnDMZGYv3KZJORYI4i\n1zOrW9bHohvc79FLqKPIxCJiKyP2InoGQBMkaLlijaNoffSsUFIAaPCV4j13FLWiEj2zwVEke3nI\nrSIwPA+M7O/MUaQfN19SOpt+5RQG0TPZW39+28JyU0dRIltCIldqu0juGbSnCCDl1gDe+sJd+NZz\nC/jq03O46/opc8eJTjnQUWTi9z79MKCWgD23tn+u6Afe9m0AmnE3i0WCkmDufTR5PVmUP/0fpLPI\nCFXRhaLW7tCyoqKoqB1FzySBh8BzroieTcdyuP3S0ZbPOTQRxhefnMWRP/0BPDyHbSEJY1Efdgz4\n8LsvP2CrKy+eLWI4YM/AARo9c83Us0Ze+RckLtaqRHjkIHD2fgCAllnCsnq4Eheqw6cLRY2TzxI1\nUTJazp5eBAb3GH+/5BxxHzG2BCNBCaLAY9KmCgnBw2PA78WqrR1FLHrGYFhl6whFzFHkWmbjOYxF\nmlvW+5LUIpmcRQtaGYbQ6FnRyegZAJWXIKKEQd1RNKDHr2qLFAtlFX4U4FGLLhOKqKOoy46ibNFc\nh8fIQeDcg5aPvyfxMAbBI1dSKuKWK6g4iqpTzwSp/k/jWETGqSbTmmh01jVC0fYryM4p7610Sl2/\nexCXjYXx8YfO487rJs1NpYlOAatnbDutsqKZK7M+ex+Jg07daO7ANghElKAsmBNdOA44/AbgB38M\nxM4aL5Yzy2TxbmLiGYCOomccxyEoCxvudMkUylhJF9oKPW84MoVdQwHMruWwkMhhPpHH/Foe33l+\nAYWSio+8qbMiciPimVLTCWxW8Xp4yF7enNtsI+B5tA0IjBwAnvksmXyWjWEFYUheI0dRk+hZco58\nrvgGiKMIIPGzVkJRhPUTbRVecfk2TERla5Mt2zAYEO2deub1A7mYfcdjaM4mYgAAIABJREFUMLYA\nfb8yr0bPmKPIrczEs5jYSkXWgN5d0XoBwUDF2eFoRxEAhfdCQqlSXEx3s2I1Fyn5koJBThcVXBQ9\ns3PqWdRnYmz0yEEgdXH9jnMr7vsz3PbEu/F/vZ9Evugy0Z5GLJqUWQOtHUV02pMrOooAwOMFDrwS\nOPiqiojCcRz+17U7cGIxhYstupbqiEyS6Jk+/UjTNJxbaRE1aYGmaSirmrnNgLP3kZ4lb+//JgRE\nk9EzALjqFwCOB57+rPHjlYEFrR1FNA4odyAUAeScN1ooMiuWSoIHLzk4ijfdsBO/94qD+H//6zA+\n+84b8PabduM7xxZwvsPXlxGxbLHSc2IHQcnrvjJrK9DJZxd+BA4aVrSIsaOoWfQsMUvcRBxX7yhq\nRmKO9RNtIV5/7SQ+8LpDth5zKCjZ6ygS/cxRxGBYpK+FIlXVkMxTRxETitzKTCy3tfqJAHKBFWSx\ns3ZUOoocFopKnAiRK2FbzXjzwYBYd5FSKKsYgi4U9XjqWSuCogCOQ9e73YlcCZFWRdaUUX3yGR21\n3I77PgTc96coihG8gn8c+Uyi/df0kkISAFcVihQNQoPzZSwiI10oG7q2XCcUAcDP/xvwsx+pu2tC\n/4yNmb3wjk4BxTSQiwMAHj6zipf85X14/qL1319JIWJTW6EovQQsPmcuduYAIdlk9Awgi+C9LyUu\nDdXg88msUEQdRR1EzwASl3MieqaqGrTGEelNmF7tzlX3lht3wcvz+LeHznX09Y1omoa1bBEDNgpF\nYVlwX5m1FWj34XniBl3RIhXHbh3Nome1DqGKUNSk0LqUI84NNvGM0QVDAdHmjiIWPWMwrNLXQlGq\nUKabocg7vNBkdEahrGAxlcekW4pge0VqgTmKTCD1qKOoCAESSpWOIkDfzWoosx7g9Ik9Loqe8TyH\noNj9IiaRKyFqNnoGVItRW3HvB4H7PggcvhvP3vSP8HFFSKe/2dV52k4+ScqbdfdN49QzAJXXxaJB\nofV0LIsBv9dwIp2bGAyQ84tlzQpFdPIZ6Sk6r4sBT16IW/7etMC8bfTs3APkds+tlr+HHQQli+6c\nw28grqvzD6x/LDVPbtv00GWLnUfPACAgeRxxFL3uHx7CH//3MVPPpWJpp0LRaFjG6w6P4z8fn0Hc\nhoVhulBGSdEqEWI7cEPEryuiOwFBBs7/EAAVigxec2IIAGc89Yx2DvmHiJuumaMoeZHcMqGI0QVD\nQdH+qWeszJrBsERfC0XJXHWKD3MUuZOLa3loGrBjKzmKNM1UySmjpsza4Y6xvOaFn1cq020AfTer\n5iKlUFIwBF0oCrhHKALIIsaW6JmZhVV0pz5qucXkM00D7v1T4P4PkWlUP/13KIwfwQV1FJGTX+zq\nPGvJFssdx6EqFJIVNxFgHD2j0/DmDWJbMyamPbmBAX2KzJpZoSiiC0X65LMV/b3w3FzS8vemQpHQ\nrk/o7L0k+jJ22PL3sAPLYsCBV5NOl6c+s/6x1AIArtrn0oRsl9GzoOxFumDv9U2mUMazcwl86uEL\nmIm1X1jNxLIISYK5z48m/NLNe5Avqfj00QsdH4NCJzjS17wdmC46dyu8Bxi+hDj2AKyiydQzniev\n6droWblIrlmoo4j3AIHR5kJRomZCGoPRIUMBCWu5Esp2TUr1+oAiE4oYDCv0tVBE+4m2hSXHoyuM\nzqAXoXZNStgUFFJkV6NNySmjGj0rOjxSPa8KCAv1i62hoFg/9aysVjuKXBQ9AyxGZpqwlisiamZh\nxfNkFHMzR1FFJPozMhHqp/8O4D3wiQK+ot6EyMKPgOR8V+dK+dcHz+EVf/NARcToiHyiUmQN6NGz\nBkFjLEI+n4x6iqZjWXfFzppA+1piZh0bUX06mu4ooj/jZ+e6iJ4ZLUwpmgacuQ/YfTNZiG4AQclr\n7X3klYEr7wSe/xJw+gf1j6UXSJeZp7V4ku86euaxPXp2dpmIr2VVw9//z+m2z6fvAVMl6U3Yvy2E\nWw+M4N8fPt/1xh59jdvZURSyQYzfcKgbFMRRZCgUASR+Vhs9S80D0OodQsHR5tEz5ihi2MBQUISm\nAfGsTZFPbwBQCmQiJYPBMEVfC0XJilAkQ1G1yq4mwz3MxkleeMcmWGjZRqW7gnUUtaNXjqKMKiDg\nqb94GGzYzSJCUQoq7yVRJRcRkr1IFTq/mMqXFORLqrmpZwDpKWrmKHr+S8ADfw684E3Aa/+uEuny\niR58WXkROGjAs1/o+FxrmY5lUSyr+OITs50fJJ+oTvqBHj1rcBSNhsmY7UahqKyomIvnbJuu5CRh\n2Queg/loj28AEIPAGnEU0b6uk4spy1FQ+rdXbBU9i50FkrMbFjsDiBhQVFRr/76Xvo9Ml/v8m4C5\nJ6r3pxZMfcZXo2edDaENiPY7Xc4skwl/N+0bxn89OVvpIGrGtE2uune+eA9W0kV85am5ro5D45Wm\nhG+TBCXv5o6eARWhSOVFJOE37igCdEdRjVBEhZ9ah1BwW4vomf55zMqsGV0wFCB/d22bfEYHJLCe\nIgbDNH0tFFFH0WiIxAZY/Mx9zMSz8Hq4SrRjS5DWhSLmKGqLh+fg9XCOdxSlyx74PfWLgKFA/W5W\noaxgECmovkEy+cVFBKXudrtrY7qmGDlIdpkbC09VFXjgL8nC+bV/Wze63Of14Lw2htXolcCPP9/x\nudaylCIXkJ9/bMZ08e46GqNnBmXWsteDwYCIhYaOovlEHmVV2xTRM57nMOAXzXcUcVx18hmAZd1R\nVFY1nFxIW/reZd1R1DJ6duZ/yO2eWy0d205o9NSS8CJHgDf+F3EPfeb1wIruwEnNmxpYQMusfWJn\nl2NB2f4y69NLaXh4Dh+84woIPIe/v/dU0+eqqoaZeA5TNoilN+4dwmVjYXzswbNQ1Q7fz6jGK+13\nFG3iMmugIhSV5SEAXHNHkRytj54ldeGOdhQB5Pol1aKjyD+0IZMLGf3DUFB3wdo1+awiFLH4GYNh\nli0hFG3Td4PzDrsSGNaZiWUxHvXBw7tr4e0o9OKKOYpMIQkeR6OjiqohrfCQuQahKFgf1SmUSPRM\n9bmrnwjoPnq2pn9Wmu4YoRGGRlfRyW8DS8eAF/92nUgEEKEIAM6MvYb0ZCw81/H5UpZSBYgCj7Mr\nGTx6LtbZQfLJuuhZSVENp3NtD8s4uZjCd59fwL//6Dw+9K3j+MOvkn/DZoieAcBAQEQ8Y2GxG50E\n1khnzEq6gEMT5Of0nMXJZzQ62jJ6dvY+IDIFDO6xdGw7CehCUcZq509oO/CmLwPggE//LHETmXQU\n5Yrkfevr0FEUlARkiuXOhVIDTi+lsXPQj8lBP95wZApffHIOF1aNu8DOrmRQLKu2iKUcx+GdN+/B\nmeUM7j3RJNZkgliGdhTZV2Yd0vur7Pw59xz9c7sok79hhmXWwPromVHnUHAUyCwZT/xLzDE3EaNr\nhoNk7bZsV6G1GCC3TChiMEyzJYSi0RAVipijyG3MxnPYsZX6iQDmKLKIKPCOOopW0wXkNS9krn4B\nTXejaaF1vqxgkEtBc6VQ5EWyG6FId01FfSZ34EcNhCJNAx78S1J2ffkd676ElvWeGL4d4AVbXEXL\nqQJedWg7QpKAzz0209lB8ok6R5GiaobC9eSgD4+dj+Odn3oC7//a8/jXH57FmeU0bt4/gismIuue\n70YG/aL5jiKA9BTp0bOVVAFXTw0gJAuWe4oqU8+abQioCnDuQWDPLRvq1qOOoo5inEN7gbu/AGRW\ngU//HJBZNjWwgEbPfB12FAUkAapWdSbZwZnlNPaOBgEAv3zLXgg8h78z6Cq6uJbDL37iMYQkATft\ns6e37dVXjmEsIuNjD57t+BjxTBE8B1snEQb1nzP9fW1KBnYBHhF5ifwNa+4oaoyezQFSpD5yHdwG\nqGUgZzAFMTlX7z5iMDpgTJ82enFtfTdgR7DoGYNhmb4XigSew4C+4HM6vsKwzmw8i8mtNPEMIDvN\ngq+uF4XRHEngUXTQUTSfyKMIL7yoXxzS3azVWkcRkuAC7iqyBrqPRaxVOj1MLqwiU2Ty2VKNUHTu\nAdLRctNvAp717gi6EF5DGNj3MtJT1EWpZFlRsZopYGoogNe9YBzffHYeCaull5pGomc178WSuj56\nBgDvf+3l+OibrsHXf/UmPPbe23HiT16JB3//pfjkL16PkI0LUieJ+r2Im42eASR6ll9DIbOGZL6M\nkaCEQ+MRPG9RKKLRMyOnFgDg4tNAIbGhsTOAvI8Ai9GzWiauBu78FBFQNdWUo+jCahayl698b6t0\nFJdrQVlRcX41g70jRCgaDcu4+8hOfPmpOZyvmTA4t5bDXR89inimiE++/XrsGg7Y8v29Hh6/+KLd\nOHo2hh/PrrX/AgPi2SIG/CJ4G53K9D2+qQutPQJw+R1YHrkRACA2ez82Rs8Sc+snmNFpfkY9RUmD\n5zMYFgnokxQvrtkk7Hj1tQZzFDEYpul7oSjs80LWFygseuYussUyVtLFTRPbsI30IhDa5rqeG7ci\nCbyj0bOFZB4FzQuvVr+AbnQUFcoqhrgU+OCIY+fSKSFJQKGsdiyorVntKOJ5YOQAsFwz+ezBvyKd\nLFe9wfBLvB4eAs8hX1aAq+4kHS7nH+zofAEi4GkacYzedd0UCmUVX3naYgluKUd2xeV6R5HXoEtn\nPOrDyy/fjit2RDASkmxdhPaKwYBVR9EkACAxT9wdwyEJhybC+MlCytJwiLbRs7P3ktvdt5g/Nweo\niC7ddP7suw34mY8AnP4eacN9J5Zw456h5iJaG6jAlLSpP+dCLIuSomGf7igCgHfdugdeT9VVRESi\nhxHPFvGpdxzBC6YGbPnelLuun0RIEvCvPzzX0dfHs0XzordJglRE7GJogCu4459xcs+bAQCSt8XU\ns3IeKOlOjuTs+glm1BHdKBQVs8RlxKJnDBsYj/hsFIp0R1GRCUUMhln6XiiK1AhFzFHkLuboxLOt\nFj1LLZgqOWUQJMHj6NSzxWQeRQjwqPULgAG/CI6rdhSVinmEuSz4oDsdRUDnC1zqxIlYWVyNXFp1\nFM0+Dpy7H3jhr5KR4U3weT3IFVVg/ytJ3OuZzuNny3qR9UhIwqGJCA5NhPHZR6etdYgUkuRWj55p\nmtY0etYPDARExLNF8z+j6E4AQGaJLNiHg+RnXSyrOLVovtC6bfTs7H3A9iuADRZhg12+jypc+Xrg\nD2aAXTe1fNr5lQzOr2Zxy/7O/93U+bhiU+HrmSXye907UnUIjYZkvPHITnz5qVk8dHoFd330Yaxl\nS/j024/g8GTUlu9bS0j24uYDI3h6pkNHUaZka5E1QMR4YJM7inToxktzR5HusKSuIkNHERWKGrqk\n6IQ0Fj1j2MB41Ic524Qi2lHEomcMhln6XigK+7yQ9V1M5ihyFzNxourv2IrRsxDrJzKL5HW2o2gh\nkUeJE8Ep9YWJHn1KFI2e8XnSxcAH3NdRFKzEIjrb7U7kSvDwXGUxZIrRg6RvKxcHHvx/JK5wzdta\nfoksekiXilcGLnsd8JOvdby7t5Qiu920g+6u66ZwfCGFH89aiEXRhZC+MCpVIlL9KRQN+kWUFM28\nEBIhjqLi6nkAwHBQxCG9j8lKoXUlembkKCpmgZlHNjx2BtgsBkjBtk954NQyAODWA6Mdf5uqUGRP\n4evpZV0oGq0//3tu2QtR4HH3vzyCRLaEz7zjCK5yQCSiTER9mE/kOyqPptEzO6FifD8IRdR5KrWa\negaQnqJSHsiurBd+Qk0cRUm9+Jo5ihg2MBGVbRSK2NQzBsMqfS0UJfPlOkcRK7N2F7O6o2hyqzmK\n0ovMUWQB0eNw9CyRh1eUwZXXL7SGAiJW9Z16IadP1fK711HU6SJmLVdExOcFZyUOOXIpuX3uS8CJ\nbwA3/HLbxbHs5aufw1fdBRTTwIlvdnTOS8mqowgAXnd4HD6vB597bNr8QfK6o0gXihR9JLen1Rj3\nTQzt6zM9+SwwAngkaHFSaD0clLB7KICA6LHUU0QdRYKRo2j6YUApukIoss1RZJL7Tixj55C/q36f\nYX0640rKJqFoKY1tYWldEfRISMI7b96LwYCIT7/jCK7c4ZxIBJAi22JZrQj1Vohl7BeKev3acJKq\nUNRi6hlAJp8l9Thvo6NIDJLOl0ahKNHk+QxGB4xHfUjly/ZEa5lQxGBYpj+vhnWSDdEz5ihyFzOx\nLCSBryz0tgTFLIm7MEeRaSSvs2XWC8k8RNkHaAqg1C8CajtdvIVVcqdLy6yBLoSibAlRs/1EFDr5\n7Pt/RCzd17+z7ZeQ6JkuFE29kDhWnvmcte+rUxs9A0hc5TVXjuFrT19ExuxirqCLHXr0rKSPeu5b\nR1GA/I5jZguteR6ITkJIEaGIdjNdPh6xNPmsEj0zirqcvQ/wiMDUjaaP5xQ+rwc8Z18xdCvyJQUP\nn1ntKnYGkIish+fsi54tV4usG/mt2y/BI++5zXGRCADGImRRN29x4pGmaVjLliqiqF3QMutevDac\nphI9a+soSlSFosaOIo4jhdbrHEV69CzEHEWM7hmPdvY5YIhIo2dMKGIwzNLXQhHpKBIge2n0jDmK\n3MRMLIcdAz5rLobNTnqB3JoYm8wgSILH8TJrWdbjjw3xs6GgiJUMuU8s6mOA/e6LnoWk7qNnYatC\nUXgHEYgKSeC6XwT8g22/xOf1VMd48zxw+c8SoaCQsnzOS6kCon5v3a74XddPIlNU8N8/vmjuIBVH\nERGKFD0iZeh86QOoyyJuxaURmYScuYigJFQ2XS6fCOPYfLLiwGoHjfStW5iWi8DxbwCTR6oX8RsI\nx3EISEJPXCOPn48jV1Jw64HuhCKe5zAYECvCaTdomoYzS+m6IutaOI7ruHTbKuNRfTR2wlrsJFNU\nUFRUDNhdZi3ZWxq+kRRNC0VrVYdQo1AEkJ4io+iZf7hlVx2DYRYqFNlSaF1xFLGOIgbDLH0rFGma\nVimzpgsJJxebDOvMrmW3YD+RflEVZI4is5CpZ86JvIuJPHx+fZHaED8bCkgVR5FUoEKRex1FnS5w\n17Il61OC6OQzjwjc8CumvkSuFYoA4JKXA2oJOPeAte8N0lE0Eqx3I149NYBLRoP47KMz5g6S18ty\nGxxFnh4thnsNLfi1OvksXJivRJwA4NB4BPmSirPL5gqtm0bPHvowEDsD3Gju9dMLQj0Siu47sQTR\nw+OGPd0LzyNByZaOoqVUAelCualQ1EuqjiJrizoqgtrtKLJlIp5LKCoKBJ5rXtpPy6xza607h4Kj\nxmXWLHbGsIkJXSiypafIq6832NQzBsM0/Xk1DLKrpKiaHj1jjiI3MhPLYXJwi/UTpebJbYh1FJmF\nCEXOiLypfAmZooKAX38dNghFgwERa9kSSooKXykOFRzgs3cUtB10Gz1L5DqIngHALb8PvPbDQNic\nQ84neuo/hyePkK6L09+3/K2XUwWMhuuFIo7jcNf1U3h6Zg0nFky4lKYfIb9PXbitlC73q6OIdhSZ\njZ4BQGQKYSWO8RrDzxU7yELSbPzMMHq2fAJ44M+By+8ADrzS/Pk4TFAWehIvuv/kMo7sGYRftFAg\n34ThkD1C0enKxLONF4qGAiJED4/5hLXICX1tD9rcUeThOQRET1+UWRdKanM3EVDtKMoniKPINwiI\nBpt6Ro6ixJyx+4jB6ICRkASB5+xxFPEewCOx6BmDYYG+FYoSOX3cc21HkYOuBIY1kvkSErnS1nMU\n0YsqVmZtGknwoOBQv9iCvggJBPSFUUP0jLoo4tkifOU1pLkg4Ol+YWc3wYpQ1FksYi1bRLSThdWB\nVwKH32D66XUdRQAgiKTE+PT3AYvTjZZSBYyG1scbXqJHeZ5rJ2KoCnDqu8TVpP9Oq2XW/SkUhSQB\nAs9ZdBRNAQD2SdVR5XuGA5C9PJ6bS5o6RHWanH7JoSrAV3+VxM1e+efmz6UHBHvgKJpby+HUUrrr\nfiLKcFC0paOICkVucBTxPIftERkXLQpFsYqjyN7oGdA7EdFpikobocjjJbHivF5m3cwhFNxGpl7W\nbrAkZ5lQxLAND/0csHPyGYueMRim6V+hKFsViugIUFZm7R4W9Yu/scgWy7GnFgDea6rPhUEQBR5F\nxSGhKEleh6GAcfRsMEAcK7FMEYHyGlJ8xJHz6BZJ8EAUeKQ6WOAqqlaZEOk0vsboGQDsuw1YmwZW\nTpk+jqZpWE4VDIvwfSLZGGj7mpl5FMjFgP0/VbmrZelyH8BxHAYCojVHUXQSALDbG6vcJXh4XDoW\nxnMXzTmKyo0l4Y/9CzD7KPBTHwKC9ogldhGUvR29j6xw/4llALBNKBoJSlhOFzoaJV/LmeU0QpKA\nUZcMmBiLyJajZ2v6tZ/dU88AUmjdF9Gzslq5Lm6KHCHRs8Qc6aMzIjhKbjPk9YxCmriQWPSMYSPj\nUR8u2lFmDZD4GXMUMRim6c+rYVQdRWF95LMk8Ciw6JlroIWQHbkYNjPpRbILt5UKvLvEyfcudRRF\nQyFyR2NHke4oWk0XEVQSSHucn/bTKWFZ6CgWkaxxXzqN3Bg9A4B9t5NbC/GzZL6MQlk1XNCKusjT\ndlLeyW8R0XbfbZW7yrqjSOjTqWcAieRYcRSVQ2TRN8kt191/xUQExy4moZootKa/C6/AE1Hw+x8A\n9t4GXHmnhTPvDSFJQNrhwuL7Ty5hIuqzzbkzHJRQLKtdC1ynl9LYMxp0zYCJ8ajPcvSMvrYHbe4o\nAojbrB/KrAvlNo4igMTP8npHUStHEVDtXqQTz5ijiGEjO6I+ezqKAN1RxIQiBsMs/S8U6SNNZa/B\nAoWxYSRz5II2LLsvxuMoqQXWT2QRyetcR9Gi7iiKhJqVWetCUYYIRRnBnY4igCxiOhGK1nJUtO2R\no6jY8DkcnQKGDwCnv2f6OMsp8nszchR59QVQqZ2j6MS3gV0vqha3otpR1K9TzwASyYlnzC92Y/wQ\nSpoH27R6oejQeATpQhnnVzNtj1GJnnEc8PXfJHe+9m9cKZg7HT0rKSoeOr2Km/eP2CbIDIfI51S3\nk89OL6WxzwX9RJSxiIyFZN70dD2AxIR5rnrtZychuTdF505DHEWe1k+SoyR2lk80F36oUEQj9ckW\nE9IYjA4Zj/osfw40RfSz6BmDYYG+FYoad8klgWfRMxdBd+VCDlzMuZr0IhOKLCIJHpRVDWUH4mfz\niTwG/F6Ist6VpTQ6iogQsZouIKIlkPW611EUkr0dOSESvRaKSsr6iMy+24HzD5meRrKkL4iNhCLq\nKGopLq6eAVZOAPvrS5RpRErg+/ZPIwYDImIWomfLWQUL2iCGyvWltZdPkElxz11s31NE37visf8E\nzvwAuP39le4jtxGQnO2heeJCHOlCGbcesC9yN6x/Tq10IRQl8yUspQrYOxpo/+QeMRb1QVE1SwJY\nXO9b4x0Qe0MdujbdRqGsVD4nmyJHgKXj5L8jzaJnzYQigwlpDEaHjOufA3RjrytY9IzBsETfXg1X\nyqz9VUeRkyO2GdZIVqKBPXIUFdvveveE1Hz14ophCtql4ERP0WIyj21hmUzCAIBy/YVI1OcFzwGx\ndB5hLYmc130TzyidLmLWdNEg4nM+BuoTPVA1g9/lvtuISHfhIVPHoQtHozJrU9Gzk98mtwd+qu5u\nGj3z9HH0bMAvVkaIm2ElXcQchhHKz9fdv39bCKKHb18aDuKiiXBp8N99D5l0d907LJ93rwjKAjJF\nxVSkrhPuP7kMgefwwr1Dth2TCqbdFFqfoUXWLnIUjesdhhcT5h0A8UwJAw6J3kGHRcReYTp6VtZ/\n7s0cQgFd7EwvkdtK9IwJRQz7GI/qnwN2xM+8PtMbUgwGo4+FomS+BJ4DgvroWdnLHEVuIpmn0bMe\nOIqOfwP40BQQO+f892pFuUAmhDBHkSXoBW3bzpkOWEjmSaG6oIsk5fqFFs9zGAyISCdiEKCiKLpX\nKOo0etZLR1FlAmWx4Xe580WA4ANOmYufLSWbO4p4noPXw7UWFk98Cxi5FBjYVXc3jZ55+9xRFM8W\nTQshK6kCZrUR+LJzdfd7PTwOjoXMCUWqhiOeU+ByceClf0jGFLuUkESuGTJFZwSB+08s45qdA7a6\naSuOonTnjiI3TTyjjEV8AIB5C0W2sUzRkSJrYKuVWde4Z5t1FAki4BusOooSs0BgFBDcUYbO6A8m\nouRzwJaeIm+ARc8YDAv07dVwIldC2Oet2I9lrwd55ihyDcl8CaLAVxaOjvLQhwG1DMw94fz3agW9\nmGKOIkvQLgUneooWEgVsj8iAoDtTyusXJIMBEeUU2TEtuFgo6nQRQ6cERXs09QzA+slnXhnY/WLT\nhdbL6QIkgW/acSZ6+ObCYm4NmH54nZsIqEak+rnMesAvQtVgupR3JV3AnDYMT2YRUOq/5tBEBM/N\nJdpO2yqVVRzgdaFp+6GOzrtXBPXXlBOCwFIyj2PzSdx6YNTW4w74RfBce6FoKZXHqcWU4WNnljPw\nejhMDfptPbduoE6CeSuOomwRAw4UWQPV/ipbulI2EFOOokp3GweEWjiEgtvqo2fMTcSwmTFdKLJl\n8hkrs2YwLNHXQlHtFB9ZYGXWbiKZK/fGTTT3JDDzCPnv5ePOf79W0MkgzFFkCbrzWbDZEVgsq1hJ\nF/Tomb6wUNZHNwYDIrTMCgCgJNkXF7GbkNzZRB4qFIV7IRSJ5He5TigCSE9R7AwQO9v2OEvJPEbD\nUtMyYFFoIRSd/j4Rjg+8at1DlalnfVxmTadBmZ18tpIuYJEfAaep1Q4SnUPjESTzZczEWi/kS4qK\nffwcENwO+NwrtgJEDADgSMTo/pOkEPyW/fb1EwGAh+cwGJDaCkV/9q0T+Jl/eAirBs87vZTGrqEA\nhHbdNT0k4vPC5/VYWiDGs0XHomch2Vm3Wa8w5Sjy6Y6i4GjVcWtEcLQ+etasz4jB6JCgJCDi89oX\nPWOOIgbDNO65IrCZRqFIYtEzV5HMl3rTT/ToRwExCEQmgaWfOP+9+JpjAAAgAElEQVT9WpFeILdM\nKLKE5KXlxPYKvUv65Kzt4daOoqGgBOhCUVl27yKXTuSx2q2ylisiKAnw9mCBWHEUNU4+A4hQBACn\nf9D2OEupAkaCzeMNLYWiE98C/MPAxDXrHqqUWbtosWw31G0RN1lovZIuIiPrLoG16brHDlUKrVvH\nz0qqhn2YA0YOWDzb3kMdRd2Omjfi/pPLGA1JuHQsZPuxh4Ni29LncytpZIoK/uHeM+seO7OcdlXs\nDAA4jsNYVDbtKNI0DfFsyTFHERWKNntPUVExOfUMaD/BrNZRlGCOIoYzjEd9NglFfqDkks5SBmMT\n0LdXw+uEoh47igplBdOrWRw9u4qvPXPRnrb+PiKZKznvKEovAc99ETj8BmDiamDpmL3HXzxWLW80\nQ0oXioJMKLKCU9EzOiJ8KChVOxXK6xfPQwERWjYGAFB8g7aeg52EZAGaBmQtfs41flY6SaWjyEj0\nG9oLDOw21VO0nCoYFllTRIE37ihSSsDp7wH7X2HYk0M7ivraUeSnjiLz0bNCUHcJrM3UPXZgewgC\nz+H5dkJRScFuzAIjB62fcI8JOegoOr6QwtVTA02dcN0wEpKw3KbMem4tB44DPn30Ambj1fhFoaxg\nOpbFXhcVWVPGIz5cTJi7fsoWFRTLauU1bjdBiXxObvbJZ4WyYj561qyfiBIcJUJRIQUUEu2FJQaj\nAyaisj0dRaKfOYoYDAv0tVBUG6WQvS12mG1iMZnHz/3Tj3DNn3wPB973bdz8F/firo8exa9/9il8\n+AenHP3em41kvlzZnXOMxz9OokTX30OKa2Pn7P0D8fk3At/+P+afn1oAOB4IDNt3DlsAekFrt1BE\nxQrZy1eFImX9jvxQQEJYJSPAVZ+bo2d0EWMtfpbIlnpSZA1UHUV5I0cRQFxF5x8ESq0XhkupgmGR\nNcXraSIUTR8F8glg//p+IqAmetbPHUUB8rs2O/lsOVWAFp4AwAGJeqFIEjwI+7yVQvRmBIuL8CO/\nqRxFTnQUpfNlx5y0I0EJKy0cRYWygsVkAXdeOwlwwN98v3pNcmE1C0XVXOcoAoCxiIwFk44iGqd0\nrsyavjasR3zdRLGsVqZDNoVGz8JtomTBbcSJu6RH+1n0jOEAtjqKlCKgbG6xl8HoFX0rFCUbO4q8\nzjuKnppewxMX4rhx7xB+52X78Rc/fyU+/fYj2DXkR6yLsbX9SKpByLOdchF4/F+BfS8DhvcBo5cC\n0IDlE/YcX9PIhI+FZ81/TXqBTARx8cQfN1LpKLI5ekY/D2SvB/BQR5FBmXVQRIRLI695IUgBW8/B\nTmi3itXd7rVcD4UisUmZNeWSl5GiyemHmx4jX1KQyJUw2kIoalpmffLbpI9q70sNv65Ey6z7fOoZ\nAMQsRM8GwwESmW2IngHk/dmuP2w4p0+cHL3U2sluAAHROUdRulCuuFLsZjhEOoqaFYvTnp/rdg3i\nLTfuxJeenMVJvdjajRPPKGNRH5ZShcp7sxW0b82xMmtdKEpuckdRsaxWIt1NodGzdo4iGqW/+CS5\nZdEzhgOMR31I5suWN8LW4SXF2KzQmsEwR19eDWuatr7M2ssj77CjiBZJ/uFrLsOv3XYJXn/tJG66\nZBgjIamjktl+Jpl3OHp27CvEDn3kXeT/Ry8jt3b1FOUTxH0SOwsUTf7BSS0CITbxzCqSQ44iuriV\nBQ/g8QLgDKNnwwERUaSxhmD7AtANhO52WxaKssWeR8+aCkW7biJCTovpZ/RzdjTcXCiSjDqKNA04\n8U1g982AZLwgVrZAmbXP64Ek8KYcRYqqIZYpYCggAdEpQ6FIFPi2780RKhRthuiZQ44iVdWQKZYR\nlJzZKBgOiiiU1abnPRcnu/E7Bnx49637EBAF/MV3yMbJGV0o2jPiPiF8PCJD02Aqvk/Fz8GAQ2XW\nHYrxbqNgxlE0sAvYeROw59bWzwvqE/wuPkVuWfSM4QDj+uSzeZMx1KZ49amOLH7GYJjCvaueLsiV\nFJQUrU6I6MXUM7qAGWzYzQrL7a35W42kgxZ8AMAjHwGGLqk6Bwb3kAWoXT1FdMoHNGDFpEspvcD6\niTqg0lFkcxk9jZ5JXh7gOBI/M3IUBUREuQzWtGBF6HAjVaHIYvQsV0LE58wOfCMty6wBQAwAO1/Y\nUiha0uM1raJnhmXWK6eIsNskdgbUdBT1cfSM4zgMBkRTU8/i2SJUjYgQiEyui54BTUS5BrYXLyDO\nRQG/ezu+KAHJGaEoW1KgaVVXit0M6+XuK03cy7STaGLAh4GAiHtu2YPvHVvEExfiOL2cxkTUB7/Y\ngwETFhmzsECk4mfUsegZuabc9GXWZbV9R5HoB972DWD7Fa2fF9Q3v+aeBMABoTFbzpHBqGUiSjoJ\nu+4pqghFzFHEYJihL4UiKsoYRc+a2bLtYDlVwIDfu256UNjnZY6iGvIlUjjpmKNo9nFg7gngyD0A\njZB4BGB4P7B83J7vQad8AKTU2gypRTbxrAOcmnqWr3UUAUQoUgzKrIMSolwaCQRc7iiyXrRK3Ze9\nip5Vyqxbifb7XkbepwbuFQBYSuqOIqtl1ie/RW5bCUW6o6gXE+A2kgG/aGrqGd38GA5JQHSSxG3V\n+t+dJHjavjfHihcwK0x1fsI9xOvhIXt524WijH48KkTZDRWKmk0+m43n4OE5MuURwNtetBvDQQl/\n9u3jOL2UdqWbCCCOIgCm+knoa9qpMutOxXg3oagayqrWfuqZWahQtHKSuIuE3mw6MLYWE1Ei8HTd\nU8SiZwyGJfryathYKOKhakBJcU4oWkkbF6xGfF4kc5t7B8pOqGjmWEfR0X8CpDBw1V31949eal/0\nrFYoMuNSUspAZpkJRR1ALfJ2l9FXO4qomGjsKBoKiIggjTUtCGkTOIqsLHCzReK+jPYoeta2owio\nCjnHvmr48DKNnlnpKFo6Djz2L8C2K4jg0YSySr7G08fRMwCmHUUrKfKc4aAePVPL1emNOm2jZ5qG\nidJ5XPRuDqEIINOt7I4X0fdl0GGhiIp7jczGsxiLyBD0z9OAJODXb9uHR8/FcGw+6cp+IsC6o4jn\nnLu28IseeHhuU0fP6OdiW0eRWeQowHsBaCx2xnCMkZAEgecqEdqOEXVBnEXPGAxT9KVQREWZWqGo\nOmLbufjZSrpYuVirJSwLSOZLUFXnRKrNBP39hJ2w4CfnST/RC94ISKH6x0YvJdGJfLL770OjZ5Ep\nc0JRZgmAVt19Y5im6iiyuaNIP15F/BFkw46iiM9biZ652VFULbM2v9u9povqPXMU6T+/XLHF73J4\nHzB+NfDjzxs+vJzMg+PWR3xrqYuePftfwMdeSi4MX/1XLc+PbiR4+7jMGiBlv/Fs+9fJakZ3FAUl\nYFifWNZQNN42epaah1/LYl7c1enp9pyQLNjuKKJxJaeEIrpJ1UwomlvLYUIXXSh3XTeFqUE/NM2d\nRdYA+XmFZAHzJpwEMb1vzSmhl+M4BCVhUzvE6XvVtr9lPF/tKWJF1gyH8PActkdk5ihiMHpMX14N\nN3MUAdW4iROspAvGQpHPC00D0sXNuwtlJ446ip74OIlGXP9L6x8b0Sfu2DH5LL1IdtF2vtCcS4nu\nwjNHkWWqIq8zjqLKBbMgGjqKeJ7DAJfGmsujZwFRAMdZi56t6VGNXpVZCx4eoodv7SgCiBtw4VnD\nWOdSipQrCy3iYV4PD7VcAL75+8AX3w5sPwTc8wAwdaTlt1Woo6iPO4oAYNDvNeUoojGmkaAETN1I\nhPGnPlX3HKmdo0iP+y5IOzs/4R4TlASkbRYDMg47igYDIngOWGkRPdsx4K+7TxR4/N4riAB4aDzi\nyHnZwXjEh4tmHEXZkmMTzyhhn7CpHUUFhXz22uYoAqpCUWSHfcdkMBoYj/oq0xs7piIUMUcRg2EG\n9656usBIKJLMdGN0yUqqiVCkd4ckWaE1gOrPwRFH0bkHgB3XkfLqRuhoZjsKrTPL5OJo22VAah7I\nxlo/n0bVWJm1ZapTz+x97xZKCjiuViiSDTuKUMpBRhEJLWhfr4MD8DzZ7bayiElk6Wdl73olZC/f\n/nP48jsAzgP8+HPrHlpOGUd8axnRVvDX2fcAj/4zcMO7gbd+w9RuN3UU9fPUM4A4ihK5EsptRo6v\npIsQPTwZPMDzwNVvAs7eB8TOVZ5jWBxeiy7ML0m7bDjz3hCU7HcUpRzuKPLwpKR82aDMulhWsZDM\nY8eAb91jr71qHA/+/ktw1WTUkfOyg7GojPmEiY6iTBEDDvUTUUKSd1N3FNGhEPYKRbpTmjmKGA4y\nEfXZV2ZdzHR/QgzGFmDLCEW0RNWp6Fm2WEamqGA4tP4ihTpn2OQzQjJPo2cOuBhWTwMjB4wfi+4k\nfyTs6ClKLxKhaPRy8v/txKeKo4hFz6xSEYpsn3qmQhJ4cJwuCniMHUXIrQEAKbP2uvsjM2RRKOp1\n9AwgPUVNp55RgiPAvtuBH38BUOt/70upQst+Ihz7Kn7n7DuwW5sGfv7jwE99EPCY+/fRqWdbocwa\naP83aSVdwFBQrL5HDt8NcHydq6htmfXycSS5EAriUNfn3SsCFt9HZqCOopBDU88AEhE0ip7NJ3LQ\nNBgKRQAwOeg3vN8tjEVkzJtwEsSzJeeFIlmoXMNsRmjJv63u2Er0jHUUMZxjPCpjIZmH0k2NR2Xq\nGXMUMRhm6Mur4USuBI6rvyCj3RhORc/qSj8boGPgWaE1IeVU9Cy3Rpw+w5cYP87zRERatkso2lbj\nUmpzzIqjiAlFVuE4jpQTt3E/WKVQUurH3QsSUDaIbeTiAOD6jiKATD6zstud2AihyOtpHz0DgKvu\nBFIXgfMP1t293EwoyqwAX3gr8J9vRlLchrvxQeDQHZbOjUbP+txQVInntJt8ti5OHZkgU+me+gwp\n6IeJMuvlEzjHTUJw+XunlpAsIGNzVDztsKMIINcfRlPPaAHsRBOhyO2MRXxYzRTbOhHjmSIGA85+\nlpHP2M17LUc3XOwVinSnNIue/f/svXmcJNdd5Xtij1yrsrbe1d3VUqvV2m3Jsi3JeBG2bLBZLPAi\nAzbYYpiHwSwewMOYGQa/wWae35sBf/gAMgyLV7wD3kFeZCxbu9Rae++u3qqy1twztvfHjRu5VERk\nRGZkVkbm/f6TUlVWZVZ1Zca9555zfow+snMyAcO0sFjoIX7mCEWso4jBCEJ8Vm4h2KhoyCgi+KbV\nfqCxzD1AJ/G4RSKc6FmM7cpR0iizjnhBt3yc3E57CEUAMHc4IkfRIjlFy+4E1Ang0lP+9y9cAJIz\ngZ0NjFYUkY/eUaSZrYvlTkIRUq3C0hAStoR3zY6eTQ40ehZQKLrydYCcAZ74tPMh07Tcp0s+9Xng\nIy8Cnvln4JW/j789/FE8Z4SPQWimBUngGg6aEYWOD18pdXYUzaTb/jZe+AtA8SJw9OsAOpRZWxaw\n+AxOcrtj5dIiHUXxmnoGADNp2dVRtGALRXtyw+0c8mLHhAoAuOjTU2RZFlbK/Y+eZVUx1tEzeuAS\nafSMCkST8ZlsyIgfO+0y/p4KrVmZNYMRivis3EKwXtEw0XZCrvTbUVRsKv1sY4JFz1rYqGqQBK4x\nljwqlo+S2+nLve8zdxVx95SWu38c07A7irYBHEfiZ53Ep8IlVmTdA4rERx4breptjiJBAQwXoahK\nomdxcBSl1bDRM9JBE/lr0YeELAQT7KUEcPgngKe/CNTJom6lXIduWg1HUWkZ+PTPEyfRxB5SWP2y\n90KU5a4caIZp9W1i0jCRs10XnQqt8wWXSZ5XvJq89z3ytwBo9Mzjd11aAqprOGbthhSjgvC0Lbha\nVnSTSotVHSLP9fU9ZDZDomftz3thtQyeA7bbgkvccDaIPj1FFc1AXTf7XmadCfkeO2w0pp5FeOhx\n3ZuAd3yVdRQx+gqd2niul0JrFj1jMEIx3LueLlmvaJum+PS7o4gKRV5TzwBWZk3ZqGjIqlL0p/b5\no6QAN7fP+z40KtZL/Ky8DFhmI0Y2dxURivw2FcWLLHbWA76b0S6pagZUMXj07FfuvAmZfvRqRUjo\n6FmZiOqDdNAkpAAdRZTr3wTUC8BzXwbQmMI1l1UBrQp8/GeA574CvOoPgHf+KymXByALAgzTCt1l\noBkmJH4kL4stTAWInlmWheVSDTPt7i1BIl1FR78ObJz3L7O2J54ds3bFzlGkGVak7zmlmo60Kvb1\ntTaTVlDVTJTaXl8LaxVsz6qx+jdohjqK/HqKqOg51feOIvIeG6WIOEjoGjhSR5GkAntfEt33YzBc\noO8DPTmKeJ4MLmGOIgYjEPFcNXRg3RYimmlEz/rbUTTdbtMHKZjlOMS6ADFKNqp69P1EACmyzu0l\nY869mA3YKeSH0zdkFzhuOwzU1oGNc95fwxxFPdFxBHcX1HSz1UnTQSj68VsOR/r4/aCb6NlkP16L\nPgTuKAKAvbeRglQ7frbojGuXgX/6NeDcw8Bdfw3c/puA0Ij00E2Q7zQuF3TDghgj50u35JzombdQ\ntF7RoBmW6+EHXvBzRCx/9GMkemaYMN1EOXvi2XNmvIQi2m8Y5eSzQk1HSu5f7AxoHFTl23qKFlYr\n2B3T2BlAOooA+E4+Wy0Npm8to4owLWwS4+ICfU+UY/R6ZDAAItJmVbE3oQggbmXmKGIwAjGSVwp3\nRxGNnvXPUTSZlFwXwzzPIaOIzFFks1HR+jP5ZfmYfz8RQKzRyoS3UFRcBM4/5v892oup52wB4ZLH\n5DPTbJRfM7qCuBYijp5pRqv9XlQAw2XjXFklTjUlE+nj94OMEm4iz3pFG2iRNQCocgihiOeBa38G\nOPZNoLiExQ3iKLj82F8DT3wKeMV/Bq56/aYvozGn0EKRaUEYA0eRKglIygJWfYSihkvWRXifmgf2\nvwx49O9AK3dco35LzwLKBM4ZE7ES4GiPUJQ9RaWa3teJZwAc91d7T9G51YrnxLM4kJAF5JISzvt0\nFFF33FSfo2f0kCuuPUVO9GzIJ3gyGG7snExEIBQlnTg7g8Hwp6crBcdxd3Ic9xzHccc4jvtdj/v8\nLMdxT3Mc9xTHcR/v5fGC4hc9q/YxeuZ68mqTTUhMKLLZqG52fPWMaZIya6+JZxSOa0TF3PjMLwL/\n0GFSUnGJ3FJHkTP5zEMoKi0BlgFkdvh/X4Yn/XAUVTWzdbEsKIDushGprAKJHPnbGXIyqoi6bgaK\n2C5uVHHk/LrT/zEoEpIQrpj8+jeT189Tn8NSsYZX8I9i8nsfAA7/JPCy97p+Ce2BCdtTpBtmrLp0\neiGXlLHiEz1b8pnkCQB4wS8Aa2ewf/0hAHB/fS4+C8xeCd2wYuVgoJPJonQUFWt6XyeeAQ1Rr3ny\nmWaYuLAeb6EIIK6iCz4bRCoUDaKjCEBse4pqzFHEiDG7JhO9dRQBRChi0TMGIxBdXyk4jhMAfATA\nawEcBvAWjuMOt93nCgC/B+BWy7KuBvCeHp5rYDbchCKxv9GzpYLLdJgmsqrEpp7ZFKo6somIF8wb\n5wC9Akwf6HzfuUOko6i9Y+Dkd8ko7vIyUFnz/nrqKErZQlEiB2R2egtF9nQg7Lyx83NjuKKIIcWF\nAJDoWbOjSAV0D0dRIhfpY/cL2qEUxAnx/i8+hZpu4tdf1UFcjZhQ0TOACLHbrwUe/yTMS8/hT6U/\nA7f9WuAn/9xTvJO7FIoMczyiZwBxXgRzFHkIRVe9HkjkcNXFzwPw6P9behbm7JUwLcQretYXocjo\nu1BEh2k0O4ourldhWsCumAtFOydVXPBzFNl/y/2eekbfY+PuKIq0o4jBGBC7cgmcW+1R5GHRMwYj\nML1cKV4E4JhlWScsy6oD+CSAn2i7z7sAfMSyrFUAsCxrsYfHC0RVM1DTzU0dOMoAomezGe+JIhMJ\nyRkLP+5suHRI+XLsm8D/vhGoFbzv40w8C7DpnTtMNv9U8KF8648b/7122vvri4uAlAKUdONj2w57\nC0WPfZw8r903dX5uDFf6MfWsprVNPRNlf0dRDKCRmU6n3V958gK++tRF/MYdBzE/m/a9b9QkZP8y\na9O08I2nL7UWUV/3ZuD8I7j76K+hzsvAWz4ByN6dK912FGmmBXEMomcAcV6slL03u77RM4BENa9/\nC/Zc+jdMYWOzkFvKA+U8jOmD5O4xEuBSfYieFauaI0D1i6mUDI4DlooNAXBhlWyI4txRBNiOIh+h\naKWsgeOw6ZAwaqijKK6dkzWjD1PPGIwBsXMygY2q3ptQKyUBrRTdk2IwRpheVsS7AJxt+v8F+2PN\nHARwkOO473Ec9wDHcXe6fSOO4+7hOO4hjuMeWlpa6uEpNSaLtS8WaBQh6vgKJV+s+zuKEiLWWfQM\ngB09C7OYO/kdYOUEKa71Yvk4uZ2+vPP3c4uKnfwOcPp+Ms0HAFZPeX998VIjdtb8PZeeB4y2xePy\nceDMvwM3vDUW0aVhpT16VtdNnF0p49iij3jYAdJR1FxmrQKmRmKMzcRIKAoSi1gva3j/l57C1Tuz\neNft+wf11BxU21HkNTXowVMreNffPYRP/PBM44PX3gVwPNL6Oj489QfAxG7fx5AFsgkKX2ZtQuTH\n43U6lZQ6OooEnvN3aLzgFyBYOt4i/Ntm95ZdZK1NXQkgXlEXKhSV6lF2FBlIKf3dnIsCj6mk3OIo\nWrBP32MfPZtUsV7RUPb4N1kt1TGRkCD0+fWbpUJRTNdzNa0PU88YjAFBo/J+onFHZNZRxGAEpZcr\nhdvVuH3lLwK4AsDLAbwFwL0cx01u+iLL+kvLsm6yLOum2dnZHp4SHDGmXSjiOI5sNvvgKKpqBoo1\n3b+jiEXPAJB4QlUzncVWIKgItPCg933yRwE5HWyyGC2fXiSjm2FZxE2U2QG86v3kY6t+jiKXYuq5\nqwGjBqwcb/34458AOJ70rDC6RhZ5nMyX8Po/vR83/dE3cfD3v4LbP3Qf7vjwd/DsxY2uvme1feqZ\nYG+IjbbJZ7ESiuxYRM37veYDX34aK6U6PvjG6yBuweY9Ybu4vET7vO2G+Oj9JxuTtDLbgdd+CP9F\n+R2sTXeOcHY99cy0+r7RHBZyHaJny8U6plIyeL/fx9whXNrxSrxb/Dws+n5KWSL/X8/ZjqIY/V7T\nfeooSiv9L46fSSstU88WVivguMbksLiy037+5z36SVbLdUz1OXYGNEfP4ukoqjuOIiYUMeLHrkmS\n3DjXS6F1ag4oXAh23x/8JXDpqe4fi8GIOb1cKRYA7Gn6/90Azrvc54uWZWmWZZ0E8ByIcNQ3vIQi\ngJxk9yN6tuSMbPYWiiZYmTWAxuIqlKNo+Ri5XfBzFB0jbqIgrp3UDJCcaTiKTn4HOP094LbfJBtS\nZaJz9MzNUQS0upRMA3jsE8CBV5Jpa4yuuf2KWVw2lcRUSsYdV83hPXdc4XTrnMp3dzJU0wynuwwA\ncRQBgN4uFK3FSCjydxR971gen35oAfe8bB7X7JoY5FNzoOKcV/xsrULEi5P5Er75TFM89EXvwpcq\n12LOJ+JLaXQUhXu/J2XW47GBmkrKKNR0TzGt04AGytEX/XeUoGLnv/16a8fX0nOAnEY9ScR7KUYb\nU+r8KUUkFJmmhVJdR7rPjiIAmMm0OorOrVWwLaPG3kGyY4K87i+su28QV8v1vhdZA/Evs66zMmtG\njKGOop4mn01fTnpNO7mKakXgK+8FfviX3T8WgxFzerlSPAjgCo7j9nMcJwN4M4Avtd3nCwBeAQAc\nx82ARNFO9PCYHfEXivi+lFk7XQ4Zv+iZhFLdgBayXHXUoGJZ4DHBpkFiZwBw7qHNBdSU5aPBYmcU\nOvnMcRPtBF7w8+RzucvCO4pmryTOoUttcbaNhUacjdE1b3nRZfjqe16Gv/3FF+GP33gd3nPHQdx9\ny2UAgKW2UdBBqW4qs7Zfv81CkaEBtY3YCUVu3Srluo7f/dwT2D+TGniBdTPUUeRVaL1m9+bsmFDx\nV99tXC5KNR3luoHZTGfxgm6CwkaN9TEqs6ab6jWPyWdLHeLUFC6zHe/T3onk8hHg2x9s+gZk4lnd\ndoXFSYBLydRRFM3BUlkzYFlAOoyTtktm0krLe+LCajn2sTOgKXLi4ShaKWnIJfvv2EpIAkSei22Z\ndU0nkx19nYIMxpAyl1Eh8FyPQtE8uV096X8/ekidP9b9YzEYMafrlZtlWTqAXwXwNQDPAPi0ZVlP\ncRz3hxzHvcG+29cALHMc9zSA+wC817Ks5V6ftB8dHUURF+ICDUeRf/Qs3qdQUUELIAOXWa+fBYw6\nsOMGMmZ+7czm+2gVYO0sMBNi8zt3mGxkTnyLdAjd/puAZDsVJvd6O4r0GlBd2ywUSQlg6kCro+ix\njwHqBHDl64I/L0ZgptMKeA5Y2gifVdcME4Zpbe4oAlqjZ9V1chsboch7Is+Hv/48zq5U8Mc/fW2r\nQDZgEnInoaiOhCTgnbfP48FTq3j0zCoAYNF+n50LIhSJZBOkGR7Csge6YcUqItULU7ZQtOIhFOUL\nNV+XLEUWeXzNvBkX5+8C7v8wcOYH5BNLzwGzh6AbVCiKz++V5zmkZCGQo+jJhXX8zff8Nxz0+/R7\n6hlAo2etZdZxn3gGANuyKjgOOO/hKFor1/s+8QwgNQYZVYztWq6um6zImhFbBJ7D9qzqGUENxJQ9\nHXm5gwDkCEXPd/9YDEbM6emIz7KsL1uWddCyrAOWZX3A/tj7Lcv6kv3flmVZv2lZ1mHLsq61LOuT\nUTxpP3yFIrE/0TPaqeF30k2jVuMeP6Mb2MDRM/pGTTt+zj20+T4rJwFY4R1F9SLwL78FZHc13EQA\nkNtHBCk391LJLltvj57R70mFouo68Mw/Adfc1RCgGJEi8Bym04ojIISBvg+0CCaC/fptdhRViEgR\nF6HIa+rZY2fX8NffO4m7b7kMt8xPb8VTc6C/c8/oWVnDZFLCm27eg4wq4t7vkk34oi0IBnMUdVlm\nbZrjM/XM3lSvuPQUWZZFomcBftdUbH3m+veRkvHP3wOsL6b5XdQAACAASURBVADFi8DslY6LNk6O\nIoCIOkGmnv3jw2fxgX95xrOcHWh0HaUHIBTNZhRUNAOlmg7dMHFxvToSjiJZ5DGTVnwcRXVH/Ow3\nGVWKraOorpuxjyEyxpu5rOIc0HfFNBWKjvvfj+4/SouNtSCDMWaM3NVi3SfaREZs9y96Np3y7yhq\nfn7jykYlpKOIvpFf9Qbi+HDrKVo+Sm7DCkUAKZ++/TfJqGfK5F4yJr14afPX0Y+5CUXbriaiVb0M\nHPkc+R43sthZP5lNd7dgoO8DLWXWbtGzmAlFsshDEfmWEt66buJ3PvMEtmVV/O5rD23hsyPQ6JmX\naL9a1jCRkJBWRNx9y1585cgFnFkuO3GauWwwlwvQXZn1uETP6KZ6tbT5mlSs6ajpZqDoGXUnlLkk\n8FN/QWK7n3ob+eTsIac8N24CXFoRUQww9axY1aGbFkoewie9D/2e/YY6m/PFGi4VatBNC7tzyb4/\n7iDYOaG6OooqdQM13cTkABxFAGLtKKrpBusnYsSanoVaJUNSAe3DZ9ppdhyx+BljTBm5q8V6RUNa\nEV2n+fTPUVTDRELyPaVxHEUxPYWKig3HURRwwbx8DFCypAx6543uk8/yXQhFs/aGObsbuPHnWj+X\n20du3XqKiovk1stRBItE2h77OHmMnS8I/pwYoZnL9uYoUjqVWcdMKALIImqjaRPz5986jucuFfBH\nP3mNE03bSjpFz9YrjQjJ21+6DwLP4a+/dxKLGzR61s8y6/GJnuVS5G/BLXpGXbJByqxbftd7Xwrc\n9h7g/KPkk7NXOtEzGgeMCylFDBQ9K9j38ep6AhrRs8EIReS1ky/WsLBCylpHwVEEkMltbmOx6d/w\nVGow728ZVYztWq6um1CkkVv6M8aISITaqQOdHUX5o+TgGGDxM8bYMnJXi/WK5ho7A4ijqF9l1p1O\nXqmDhjpqxhUavQvuKDpGbKIcB+x6IXDh8dbJOgB5s8/sAJR08CeSmARu+iXgdX/S6iYCgJx9YXDr\nKXIcRds2f27uanL7zJeAhR+SEusgU9gYXTOX6c5RRN8HWhbMgv0aNtyEoslun+LAIYso8jo7eqmA\nP7vvKN5w/U686iqXv9ktINEherZqR88AYPuEijdcvwuffugsji4WIfIcJgPEVrt1FGmG6XrIMIpQ\nMW7VJXrmDGgIIBTR6FmNXltf/j5g27WAnAEmLotx9CxYRxF1C/m5hQsD7igCgKVCHQurxH2za3JE\nhKJJFRfWKptifvRveBAdRQB1NMRzLVfTTeYoYsSabNthWFdMdxCKLIvsPy6/A+AlJhQxxpaRu1ps\nVHTP/htV6pOjqFDvuKBm0TPCRlWDwHNIygHLFOnYewDYfRPZxF860nafkBPPKD/+YeCQS9H0JJmm\n5esoSs1u/tzUfuJKeeDPAU4ArntT+OfECMVsRkG+WINphistdu0ochxFTSfWsXQUiSjWdBimhf/0\n2SeQVkT8wesPb/XTclADTD2bbJpe9K6X7Ue5buCzDy9gNqMEmtZDN0JhhSLDHB9HkSTwyKiia0dR\n3hZfpwNFz9omzIky8LbPAj//BYDn4x09CzD1rFAj1/T1sve1nQpOgad99gDt8Foq1nDOngy0c0SE\nop0TCZTqxqZN4qrtKMoNrKMovtEz1lHEiDvZKBx90wdI91B1w/3zxUukx3T2ELkvTS4wGGPGyF0t\nNioaJjxiTaok9KWjaClA6SeNWsXVrhwVGxUdWVUEF8Rpo1XJNDMqAu26idyea+spahaTokBKEMfQ\n2qnNnyteIqJBuwsJAHgBmL2SCA1X/CiQGQ4Hxygzl1Ghm5bn5CYvGh1FzUIR7Shq+l5UKFInenma\nA4VuYv7u+6fw6Jk1/MHrr8Z0AGfIoKDRMzfR3rIsrFfqLV0jh7Zn8bKDs6gbZqCJZ0DDURT2/Z50\nFI3cZdGTqZTsbLKboY6ioFPPgDZRLrONCPtAbKNn6YDRM+ooWvM5BCoO0FFEu6fyhRoWVsuYyyhb\nOuUwSvbPpAAA//pMa3/gyoAdRcTREM+1XN0wW6d9MhgxI6OKqOsmar1MsaaTz7x6iqgwNHM5majM\nHEWMMWXkrhZ+0TNV5PvkKOo8RjghCRB5buynnm1UteA9Katt08wmdhMBZ6Fp8llpmWzmZ66I9olO\n7vVwFF1yj51R5mznxg1vjfb5MFxxTs9Dxs9qTkdRc5m17Shqj56pE0QEjAlpRcTp5TL+5GvP4eVX\nzuInbti51U+pBb/oWaluQDOsTfGye26fBxBs4hnQ5Cgywk89k8bEUQSQjbWbo2ipWAfHIdAUKdrz\n5bVo12LqKAraUVR0Ooo6C0WD6CiSBB5TKZl0FK1WsGtE+okA4BWH5nDDnkn8939+GsvFxvt0I3o2\nmI6irO3aDOtkHQZqGnMUMeINTY305Oqj+wqv+JkzJOcKYOYg2Y8Y471/Y4wnI3e16NxRFK1QVNUM\nFGp6xw0Mx3GYSEgselbRwhVZA41RlhxHXEXnmoSi5jfzKMl5CUWL7kXWlCtfC+y+GTh4Z7TPh+EK\ndZiELbSu6i7RM8F+DbeXWccodgaQ/ox8sQYOwAd+6tpg7r0B4ghFLn1xtBC43Rlw6+XTuOOqOdx6\n+Uygx6AbIU0Pt5HTDQvCGAlFfo6iXFIO5K6S7ClxXu4tzXYUxa2jKK2ITreQHwXHUeTtaixWdYg8\nNzAnx0yaCEXn1iojM/EMAASew4fuug7Fmo7/9k9POx9fLWvgOHiu/aImo0qwLKAUYCresFEzTMhi\nfA4+GIx2aIS3p4P3qf3k1lMoOg6ICSC7iwhFpk6mGjMYIXn49Gpv7rctJl4rtwD4O4qij541Sj87\nn7xmExEUsMWcQlUPV2QNNCyiALD7heTj5ZXW+0wfQKRM7gU2FjafIBQX/R1Fh38CeOc33aNpjMih\nE7AWNzZPwvGDllmrzWXW4qgIRWQR9TuvPTSUJbZ0s+zWUURdGRNtzgCO43DvL9yMd9y6P9BjCDwH\ngedCTz3TjPGKnuWSMlZLre9xxZqO7x5dwmVTwQQGjiMCiFcfFHUUxS16llJIvEHzcaWR+AP5vN8h\nUKmmIx00ch0BM2kyDfL8WmVkJp5RDm7L4FdfcQW+9Ph5J4K2Wq5jIiEN7LVL32Pj2FNU11n0jBFv\nMkoEjiIpAUzs8Y+eTR8AeL6RWGDxM0ZIFlbLeOOf/zu+9Nj5vj/WkXPr+I1PPeY6oKQXRupqUddN\nVDTDWyiyy6zbJ2b0QpgxwllVZNGzqhZOKEpvA9Rs42O7bya35x4ht/mjZCIBHWEZFbm9gGUC6wut\nHy8uAikfRxFjoDQXt4aBqvtq88mqIxS1lVnHTCj6sWt34J237cfbbon4NRERPM9BlXgn/tcMFYqC\nTDbrhCx4ixdeGKY5NmXWABkn3h49+6N/fhrnViv4/R+7KvD3UUTex1EU3+gZAN/4WfPn/MqsCzUd\nKbn/sTPKTFrBsxcK0Axr5IQiAPiVlx/Aldsy+P0vHEGhqmGlVB9YPxEAJz4fx56imm6w6Bkj1kQS\nPQOAqXkfR9GxxgH0NBOKGN1xbLEIALgU8jA7LEfOrePue3+Azz96Dl987Fyk33ukrhb0RM9bKOJh\nWg0rfBTQ6TCBhCIWPbOn0gWNnh3fXFK980YAXCN+tnyMvNkLES/CqfC01hQ/qxUBreQfPWMMlIQs\nIKOIWNwIGT2zHUWKm6PIaC6zXoudUHTTvin8/o8fDjQdbKtISIK7o6gS3fQi2cfl4oVuWBCF4f29\nRU0uJaOiGU5f1DeevoRPPngW/+FHDuCmfVOBv4/s49alZdZSzDanaYWIyEUfoaj5c34dRaWaPpB+\nIspMWnFeX8PoKuwVWeTxwbuuw6WNKj741WexVtYG1k8ExNdRpBsmLq1XMT2g6XAMRj9womdRTD6j\nqYRm9DqweqohEKlZIL2dTT5jhOZkvgQAWCn1b+//5MI63vpXDyCtiLhsKomvHLkY6feP18qtA1SE\nyfo4ioBGP0kUONGzACWrJHo25kJRWEfR1Hzrx5QMMHdVo9A66olnlJwtFDX3FBXtSSt+0TPGwJnN\nKqHLrGlXWYujSBgNR1EcSEiCa5n1apSOIpHvoszail2XTi9M2S6M1XIdS4UafvezT+Dwjizec8fB\nUN+HOIrcr6v03yBuJeFpO95QqnmvF5qFAt+OIjt6NihmMg0hYJQ6ipq5Yc8k3nHrfvzDA2fw+MJa\noOL1qGgIRfFazz17sYBS3cAL97JrGiO+RPb6m74cqK41qiwoq6cAy2gdkjNzRaMTlcEICBWK3Log\no+CJhTXcfe8DyKgSPnnPi/GTN+zEg6dWHG0iCkZqRdxJKFIk77HM3UI3qEFOaLKqhI1KvE6gokQz\nTJTrhue/TwuVNaC05C4C7XohcO5hwNCBlRNkfGXUZHcDnNDqKCouklvmKBoqZtPdCEW0o8gtema/\noZsmWUQwoShyVNndUbRuX0zbO4q6QRa841Be6KY5VmXW1Lm1Uqrj9z73BAo1Hf/fm28IHU1RJO/f\ntU6FopgJcKkAjiK6UUnKQoepZ4YTZRsEzVNYRzF6RvmtVx/EnqkEClUdk1sQPYubo+jh06sAEMot\nyGAMG9FFz+xoWXv8zOk+bdpbzBwk0bMIq0sYow8VipYj7g0CgMfPruHue3+AbELCp375xdgzlcSd\n1+yAaRF3eFTEa+XWgY0O0TNa4FdzmbbTLfliDVlVbN1wejCRkLBR0SLtSIoT9E09E+RklRbMuQlF\nu28CKivAyW+TmFA/HEWCCEzsYo6iGDCXVbFYCJf/pe6HllJPjgMEueEoqm2QniomFEVOwu6La2et\nrCEpC87I9V7wK1h2w7IsaIYVO+dLL1AXxp9/+zi++cwifufOQzi4LRP6+/j1QWmxjZ6R61SQ6Nnu\nXMI3Vl6sasgMMnpmO5xn0nKgtUlcScoi/sdPXQcAmA4wUCQqaHw+bsNJHjy1gh0T6kjGERnjQ1oW\nwXE9Tj0DGh1E7fEzZ5py05CcmYNAdZ0cYDMYATmxZDuKIhaKjpxbx9s++gNMJomTiDqHr9qRwd7p\naONn8Vq5daBzRxFZMEU5pi5frAeKnQFkcVE3zMgnr8UF+qYeKHq27CMU7bqJ3D7+Sfs+V2y+TxTk\n9nk4iphQNEzMZciEnzBUNROywG/u8RHVRkdRhZy+MqEoerw6ilbLWiSxM4A4WMIIRaat3wsxK13u\nBdrr8i9PXMBLD0zjHS/d19X3USTvjqK6U2YdLwEuSJl1QyhKdph6ZjgOpUFAHUW7RjR21sxtV8zg\n3p+/CW/v8m+3G7KOoyhe0bOHT68yNxEj9vA8h7Qs9i7UTu4lyYH2yWfLx4DkTOvab4smn62V62Pf\nbRtXqpqB8+sVANg0NKRXPnr/SfAch0/e85KWeDnHcbjz6u3492N53wEbYRipFXFHocg+0axG6Cha\nKtYCFVkDjcXFuE4+o46iQNGz5WMAOGDKZRz23FWAlAKe+Sfy/zN9Eoom9252FHECkGQLrWFiNqOg\nXDd8N3TtVDWjtcia0uwoYkJR30jI7h1F65V6ZBESWeR9R5u340znGqcya/t3nVFF/M+fub7rAnRF\n4FH3OIBxyqxjFj0L4iii17RdkwmU64bnIVSxpjudR4OArklGOXbWzB2Ht2HHxOB+VkXkIQlcrKJn\n59YquLBexU2sn4gxAmQTUu+vP1EGJi/bHD3LH9u8r5ixe/sGLBS9+xOP4n2fe3Kgj8mIhtPLZVgW\nOcyOuqOoXNc93aF3XrMdumnhX5+NJn4Wr5WbD5Zl4YuPncP2rOo5JlXtQ0dRvlhr6QPwgwok41po\nTX/ubJDo2fIx8gYuuvxueQHY9QJArwDqBJCcjviZ2uT2AqVFoF4m/19aBFKz5PEZQ8Oc7egL4yqq\n6aZ7JENUGx1FTCjqG4oooOIi2K+VNUxGNL0obJm1blJBY7yEolcf3ob/52eux84e4ih+HUWaQXqf\n4tb9FM5RRH53bie/pmmhVNedKWqDYDotQ+Q57J0afUfRVsBxHDKqFKtDv4dOkcJeVmTNGAUyqhjN\nXspt8tny0c1phuwuQEoOfPLZwmoF59YqA31MRjSczBcBADfty6FcNyLVHuq66dklef3uSeyYUCOL\nn42MUPTNZxbxyJk1/PodV3guSBtCUYQdRYUaZgJm46nTaVxthBsdysZb6DTNbNcLye30FaRbph9M\n7iO3a2fIbXGRFVkPIbNUKNoI3lNU0wyobo4ikTmKBkFCdu8oWi3XoxOKQkbPDNv5Mk7RM57n8Jc/\nfxNeffX2nr6PIvKe3X+aacYudgY0yqx9haKqDp4Dtk+oAOBq9S5rBiwLA516Jgk8/v6XbsEv3ebi\nyGVEQkYVY+UoeujUKtKKiEPbw3eQMRjDBnn9RSEUXU6G4tDuWK9BOjxPPjZgR9F6RRtbc0HcOWEX\nWb/gMrKHiNJVVDdIfYYbPM/hNVdvx3eeXwqVtPBiJFbEhmnhT772LOZnUviZF+72vB/dGEal6lU1\nAxtVPUT0zC5AHNPJZ46jqJNQZFnECuonFO22e4r6UWRNye0lt7SnqHiJCUVDyFyGbNKWQoyDrOqG\ne2GyoACG/X2YUNQ3EhLvET3TIo2ehRGKNJNO54qfqLHV+Lm3NN3yXNAMM4ooQBZ4FGve6wUSKRMd\nF/OayyEQXagNcuoZALzkwDSmA65NGOGJbKM6IB48tYIbL5uEGMPXIoPRTlaNIHoGkMln9WKjg5TG\n0NwqLejkswFhWRYRisZ0zxh3Ti6VMJdRnA6hKHuKNN3yjfPfec121HQT33qu9/L1kbhifOHRc3j+\nUhG/9eorfS+CjqMoojJrOu5uNnCZ9ZhHz+w3u47Rs+Il8sbtKxTdDIADZq+M7gm2M2kLRbSnqLjI\niqyHECd6thFCKNJMD0eRAuhUKFojt+pkr0+R0YZbmbVlWSR6FlGZtSx6x6HcMEzqKGJCUVgUUfDs\n59EMM7a9TylF8D2RK1R1ZFTJccG5OYpoPC09YKGI0V8ySkQb1QGwUdXw3KUCi50xRoboomfz5JbG\nz5yJZy77j5mDwNrZRh1FnynVDRimNbZ7xrhzMl/C/pmUM102SqGoZnhHzwDg5n1TmE7J+MqRCz0/\nVuyFoppu4MPfeB7X7prA6671t8/TUdheFvmw5O1OlKCOorGPnlU18ByQkjssmOkbdvNoynYy24G3\n/wtw8zuje4LtpOcAMQGsngJMk0XPhpTJpARJ4EI5imq6AdXNUdQiFK0CcprE0RiRosqbhaJiTYdu\nWp4dc2EJ21FEy6ylMYqeRYUseEfPdNOMXZE1JaWIHcqsNWRUEZMJb0dRscqEolEkTtGzR8+swbLI\n5oHBGAUyUTmKqCBEJ58tHyNDa3Iusd2ZKwBYm6ekAcDS88C9dwBnftD7c7JZs6NKdd2MtN+GMRhO\n5kuYn01hKkX2/lEKRX4dRQA58Hz11dtw37OLPf/txHP11sTHHjiDc2sV/M6dh8B16KqJ2lGUtzem\nMwEdRRknejamQlFFQ1oRO0/WcYSiDrGyfbcCajaaJ+cGx5FC7bXTQHUNMDXmKBpCOI7DbFoJ7Shy\nnXrWLhSx2FlfSEgC6rrpuHgAUmQNABMRdRQpITuK6HSuuLpfthJF8hbl6h0s0sNMuoNQRKNn9BBo\nzaWDoMQcRSMJmboUj7XcQ6dWIPAcbtjD3LGM0SCbIEKtZVmd7+zHxB4y7ZZGzvJHSe2E2wGh3+Sz\n+/4IWHgQ+NTdxHUUAc2mAuYqihfrZQ3LpTr2z6Scw8/VSIUiw1coAoA7r9mBUt3A/UfzPT1WPFdv\nNsWajj+77xhuvXwat10x0/H+1EEQVZm1IxQFLLNWRAGqxGMjJqdQUVOo6sGLrAUFmPDumxoYub0k\nela0xwwyR9FQMptVsVgIXmZd1TwcRe0dRQm2sO4HCZcJlFQoiip6JoUVilj0rGv8yqyJoyiev9OU\nInacepZWRWRUERzn7hYubFFHEaO/kOhLPNZyD51axeEdWfY3yBgZMqoEw7RQduk6DAUvALl9TdEz\nn37U6QMAuM2Tzy4eAZ7+InDtz5KDxk++FaiXenteaBOKWE9RrDi5TP7998+kMZmUwXHAiks0vVv8\nyqwpL5mfRlYVe55+Fmuh6N7vnsBKqY7/9JpDge6vRFxmnS8SdTBo9Awg8TO3HoNxYKOqIasGEYqO\nA1PzwzGGfnIvcRQ5QhFzFA0js2kFS4UwjiLDcRi2ICqAbqv+zFHUNxIy+d03x8/WKuT3nktFFz3T\nQkTPdKfMOtaXxS3Bt8zaiG/0LN1JKKrqjkt2IiE5Ymcz9OszA5x6xug/GVVCsaa3uCKHEc0w8ejZ\nVdZPxBgp6PtpZPGzlROkYmL5GJmm7IaUICmDdkfRt/8YULLA6z4EvPGjwMUngS/8x8YktS7ZYI6i\n2HIyXwQA7J9JQeA5TCakSB1FQYaEyCKPO67ahm8+cynUWrideK7eACwXa/ir75zAa6/ZjusD2mkV\nkQfHkdHYUbBUqCGjiO4bTg+yqjS2L/iNio5sIsBiefmYfz/RIMntBWobJH8MMKFoSJnLhhOKarpf\n9Mx2JlVWWZF1n6Dvmc2Tz6J2FIWdeuZEz5ijKDSKKMAwLegui5G6bsV20lKn6FmhpjsblsmE5Ooo\nKjJH0UhCh3L4/X0MA0+f30BVM3HTPiYUMUYHeugcSfxzap4IRetnAb3iv/+YOdjqKLr4JPDMPwEv\n/hVysHjw1cCP/iHw9BeAb3+op6fV6igaz31jXDm5VALPAZdNkYlnuZSMFZdoerfUO5RZU+68ZjvW\nKxoeOLHc9WPFc/UG4CP3HUdVN/Hbrwk+9YrjOCgij2qIzYMfS8Va4H4iSjYxxkJREEeRoQMrJ/s7\n9j4MdPLZwg/JLYueDSWzaQXLpXpg1byqmVBcy6xVwGCOon7jHj0jv/eoOorCllnT6BnrKAqPMyjC\n5dqqmybkmP5OydQz74Ml6igCgImk7F5mzTqKRpKGo2G413MPnV4FANy0lxVZM0YHp/M1kslnB8gB\n4cnvkP+f8XAUAUQoWj5G3EcA8K0/BpQJIhRRXvpu4Lo3A9/6v4Gnv9T102rtKBpuQZrRyol8CXum\nko6YM5WUI+4oCiYUvezgLFSJx33PLnX9WLEViu57bhGvPDSHA7PpUF+niEJ00bNCDbMhYmeAHT0b\nU2V4o6J17ihaP0NKo4dFKMrtI7dnf0j6a5Q+lmczumYuS16Hy8Vgb8Q1zYDq5igSZLJgsCwmFPWR\nhlDUEBYajqKIomcCD82wYAaMhlA3jMimnoWGLljcHFxxjp75TT3TDRMVzUBaIdc0Eivf/P5TrOoQ\nec4R0xijQcZxNAz3Bu6hUyvYnUtg+4S61U+FwYgM+vqLRECh+43nv2r/v59QdAWglYGNc8CFJ4Bn\n/7nhJqJwHPD6/wXsugn4/C8T11EXNEeZmaMoXpzMl7B/JuX8fy4lD3TqGUWVBOzOJXFhvdL1Y8V2\n5bJUqGF3LhH661TJu3QzLPliDTOZcJuarCqObSnZRlXv7CiikweGRiiyHUVrp0nsrMNkPcbWMJch\ni+CghdZV3aujSCUdRfUSESyZUNQX3DuKNKRkIdDFLwiOeBHQVeQ4ilj0LDTUnefmKNJ0K7YurbQi\nolR3n6xDnUbN0TM3R1GppiOliB2nsjLiRaQdKX3Csiw8dHoVN7F+IsaIMZGI8PU3ZUfNjt8HyGkg\ns937vs2Tz779wc1uIoqkAm/+GCCngG/8QVdPa91eEwGsoyhOWJa1SSiaSkYnFFmWhXqIA7iwHa7t\nxFIoqtQNFGt6qBJpiioJqOrRlVmHfQ7jGj0zTAvFpj4HT+jkgWERitSJRk8Ni50NLbN2BHRxo/Ob\noWFa0AzLfeqZaDuKKsSuz4Si/uB0FDUJRavlOiaT0biJgEYcKrBQRDuKYup+2Up8HUVmvB1FlgXX\nyTqFGrmOp6lQlHQvsy7UdBY7G0Ei7UjpE2dXKlgq1HDTPhY7Y4wWjqMoCqdNZgcgJgCtRGJofqI+\njaUd+SxxE73kP3pPx81sB264GzjxLaAUfkT5ekXDtqwKWeDH1mAQRxYLNZTrBubbHEWr5brroVNY\nNHutGtSlPJtRsFQcM6GIjqWfDdkPBABqRNGzum5ivaKFFoomEhI2KlrgOMSoULRV/47Rs+VjRKFP\nzQzgWQWEuopYkfXQMme/FwR5M6zZQrF7mbUKGDWgskL+nwlFfSHhUma9XtYwGVE/EdCYXha00JpO\nPYur+2UraXQUbb62xjl6RgUet8ln9CQ7ozQcRRvVzdf2EhOKRpJIO1L6xIOnyHWMFVkzRo1IHX08\n3yiw7nRInZolB8iPfYzcurmJmrn2LsAygKc+H/pprdt1HdmEONTvM4xWTiyVAAD7moSi6ZQMzbAi\nGX5ADz87TT2jzIyjo2jR/oHD9gMBJHpWjSB6tlwizyG0o0iVYFpAqT5e6jB9k8sGcRRNzw9XxIsW\nWjNH0dBCX4dBHEX09a+6qfGC7WgpLpJbJhT1BRo9q25yFEUnFPm5XNxgU8+6x7fM2rAgxVR8owKP\n2+LOKalWG2XWlrV541Ks6c59GKNDp44i3TCxsFoe5FPaxEOnV5FRRRycy2zp82AwoiYhCRB5LjpH\n39Q8ufXrJwLI3oTGz17yq0Qs8mPbNcDsIeJACslGRcPd9c/g/bgXWnEl9NcztoaTeSIUtXcUAcBq\nqfe/V7qmDVrTMJtRUK4brgdeQYilUNSLo0iRonEUUXVuJh2yoyhBT6HGSyiiBd4dHUXlZSDtkw/e\nCpijaOiRRR65pBSoo4i+/j07igCgcJHcMqGoLyRcomdrFS3S6Bk9bQk6Cc9xFLEy69DIPkJRmCz9\nsJFyHEWb1wzUJZtuchQBwFqltYegWDOc78MYHTo5Gj77yAJu/9B9+P7x7scS98pDp1bwwr058Ez8\nZowYHMcho0botKGOIr+JZ5Tt15G14S2/3Pm+HAdcPIeWKwAAIABJREFUcxdw5vvA2tlQT2m9XMdr\ni5/FG/Sv4n2n3g48++VQX8/YGk7mi5BFHjsnGj3KUymyPlhxGXgRlm6EIgBdu4piuXqjQlE3HUWK\nyKMa8IQ5yHMIK1bRXPu6S5fBKNNwFHUQiuplQApfUt5XmKMoFsxl1EBvhHQz6y4U2a/nIhOK+oln\n9KyTkByC0I4iWmYdU/fLVtIosx6t6FlKIT+Xm6OoYH+MCgYTVChqu7YXq5oTT2OMDqokkO4Qj43q\nsxcLsCzgt//x8S2JjayV6zi6WGRF1oyRJaNK0ZXJU5dQEKHoR/8b8Cv/3tlNRLnmp8ntU58L9ZSS\nlfNIG+v4RvoNWMMk8Mm3AJ99F1Bm7qJh5mS+hP3TqRaBPpekjqLohKLAZda2TpHvsqcolqs3uhmc\nDunmAcjFvRaBoyhfIP/Y3XQUAcOda+8HtIiNOqo80SqAnBzAMwpBbh+5ZULRUDObUZxYqh/UUeRa\nBEejZ4VL5JYJRX1BlcnvnjqKLMuyHUXRC0VuLhc3WPSse2jf11hFzxxHEfmbpX+77ZPPSjXDEZwY\no0VGFT03qqeXy5hKybi4UcV//dJTA35mwNPnNwAAN+xh1zDGaJJNeL/+QnPNG4Gf/XviFuqEkgGy\nO4N/7+kDwM4XAE9+JvCXmKaF/fXnAACP5F6HX058CHj57xGx6SO3AE9/CYigGJkRPSfaJp4BwJQd\nPYti8pnTURTUUZQeU0dRLil1dUqpSkLgjYMfS906ihIRNvXHiMCOIq0ESEMmFO27Hbj9t4H5V2z1\nM2H4MJcJVtgWLHp2ARCU4XO3jQiywIPnGv8WhZoOw7ScU5dIHiPk1DMaUYur+2UrkX2KwzXDjO0k\nuZRPmXXRZeoZQJwcrffTHTGJMVr4CUWn8iW8eH4K/9crLsfnHjmHLz95YaDP7cwK6UfaOz1k6ykG\nIyIyihTdXkpUgMNv6F8/6rV3ARefAPJHA929WNdxLXcSBiehMHEFVqoc8PLfBd51H5DZBnz654C/\newNw4fH+PF9GV+iGiTPLZeyfbRWKclEKRXq4MuvZEMN+3Ijl6m2pUOsqdgaQAtuoOorSiui+2fTB\niZ6Nm1AUtKNIqwyfUCSpwKv+C6Ckt/qZMHyYzRKhqNP4SVpm7T71jJZZXyJuomEqVR8hOI6DKglO\n9IxGcScijJ4pIaeeGXb0TGCOotCoPo4izbACL2iGjUwHRxHHASm7mH0iQd47mjculmWhVNeRZo6i\nkYREXzav5XTDxNnVMvZOp/DuV16O63dP4H2ffxKXNjp36EXF2dUyRJ7Djgl1YI/JYAwSP6F26Lj6\npwFwgV1F62UN13HHsZ49iFQy1Uih7LiOiEWv/RBw8QjwFz8CfO6XQ/cfMfrDwmoFumltchRlFBEi\nz0XTUWQfarqmIlyYSsngubFzFNW7KrIGiIugV6HohydX8OmHzuLwjmzor21Ez2Ly5hYRG23Fn64Y\nOmDUh08oYsSC2bSCumF2FGFpj0rHMmsWO+srCUlwomer9sWzL46igEKRxjqKukYWyGvJy1EU1+iZ\nn6OoYI+952wx2a2jqFw3YFlgU89GlGxCdHU0XFivQjMs7J9OQRJ4fPhNN6CqGXjvZ57oeJARFWdW\nKtg5mYitm4/B6EQ24S7UDiXZHcC+24AjnwkUGVsv13ANfxLFmeuQTUio62Zj7ypIpEj71x4Fbv11\n4KnPA3/6QuCb/xXQBidGMzZDJ57NtwlFHMchl5Ij7SgKGj0TeA7T6WCJCzdieQXJF3twFEm84yjo\nhh+cWMbb/+aH2DGh4s/eemPor6cLxnGLnhXsQk/f03rNHiU7bB1FjFgwlyUiT6c3Q8dR5NdRRB1F\njL6hNglFdHMdZUeRFNZRRKNnbOpZaBodRe5l1nHdrCZlARznIRRV9ZaSalnkkZKFlo4i6kRiU89G\nk4ziXqZ7aplsFmjs68BsGv/5dVfhO88v4R8eOD2Q53Z2pYzLpthaijG6kKlnMTp0v/YuYPlYoLhY\n/dJRZLkKtLnrkaX7xnZRLDFJirXf/RBw9U8C9/+/wMN/049nzgjICVsoancUAcB0So40ehamJmF2\n3ISinqJnkoCqbnR1qvPAiWW84/88iB0TKj5xz4udjWkYBJ5DRhHHMHqmB4id2UIR64VhdMGc7TLs\nVGgdyFFk1JlQ1GcScsPdSTfX/Siz1gJ2FNGpZ0JM3S9bCRVda22HMJZlQTOs2PY+cRyHlCyiWNss\ngBWr+ian0ERCanEUUaHI10nLiC1e0ZdTy2Qts69ps/C2F+/FjxycxQe+/Ixz6txPzq6UsYcJRYwR\nJqNKKNr9hrHgqjcAvERcRR3gLz4KAOB2vaCp29ZDFJu8DPjpvwRSc8ClwRfnMxqczBeRVUWnvLqZ\nXFJ23PO9oIUsswaAmYwyPh1FpZqOct3oOnqmiDwsi/QmhOH7x5fxjr95EDsnE/jkPS/BXKb73Hc2\nIY3f1LOq5owR9sQRijYrsQxGJ2Ydocjfeks3s+5CUdP7ChOK+kqiqaOIFgBPbmmZNbkmMEdReLx+\n11R8k2MsvqUUwSmubqZoR8+amUjKWK80FoLFIJFrRmzx6ig6nS9BlXjn8AIgouMH33gdqpqJrxzp\nb7F1qaZjuVTHnil26MYYXajTxq1DbihJTgGXvwo48jnA9F+XJJYeR9lSkNh12Om27bhvnJoHVk5G\n9WwZXXAyX8L+2bQTSW9mKiJHUS1kmTUwYo6iTrGwvK2IzaS721DQzWHVxSLvxfePL+MX/8+D2J1L\n4BPvenHXIhUlm5C8leERZaOidXYU1ZmjiNE9dFHeMXpGHUVuanyLUDQZ2XNjbCbhEj2LssyaXkSD\nTrk07IUbK7MOj/O7brt+6wbtfRq6pUZgUoqIkoujqFDTkW6b4jmZkFrcwiXmKBppMqqIUt3Y5Gg4\ntVzCvunUps3C9gkVCUmIpKfCj7OrZC3FomeMUcYRUOKU0LjmLmDjHHDm+753m1g5gqesvZhIJZBN\nBKwsmT4ArByP6pkyuuDkUmlTPxEll5KwWu79b7XehaNoNqMgX+w87MeNoVu9HV8q+v4g+S7H0lMU\nKhQFLLSuagbu+fuHsGcqgU/c07tIBBAVfPwcRbrzpu6JViG3MnMUMcKTVkQkJAGLG506ishrX3Fz\nFAnMUTQoVFlAxRYWVst1pBUx0oiSErbMmjqKYux+2SpEgYfAc6gbrddVuqCJa/QMINNK3KeeaS0d\nRQCJTjZHzwqso2ikoS7pYlv87NRyGfumPTYLSQkrpf6u/86ukLXUnhwTihijC339xWbyGQBc+VpA\nTPjHzwwd08VnccQ6gIQkNDmKOvycU/NA4QJQ73+0lbGZSt3A+fWqaz8RAEwlZayV6z1HJemaNujU\nM4BoJpphdVV7M3SrN9OyfN02SwVyEtN1R5FHl4IXD59eRaGq4/dee1XXj9nOREKKlwIeAcRR1Cl6\nZr+5salnjC7gOA6zGaVjRxF1LXZ2FDGhqJ8kJB5VO3q2XtYi7ScCwk89M0wLAs+5WoYZnVFEftN1\nVXOEovj+TomjyEUocomeTSalljJr+nUdY9eMWOIWCTFMC2eWy9g7476OmbQ3C/3kzApzFDFGH5pS\niM3kMwBQ0kQseuoLgO7xPrD0LCSzhmPSQXAc19RRFMBRBAArJyJ8woyg0CEGXkJRLiXDtNBzR3FX\nZdYBExduDJ1QBDRss24s9egoUkM6iu4/lofIc3jR/qmuHs+N7DgKRVUtuKOIRc8YXTKX6ZzDrekG\nRJ5zj8MwoWhgtETPKn0UioJ2FJkmi531gCLym2J+uuPSGsqlRiBSno6izWXW2YSE9bLmuKLZ1LPR\nJuMyjejiRhV1w/R2FKWkSApN/Ti7UkZaESN/T2UwhonG6y9GjiIAuP7NQGUFOPp198+ffwQAcDZx\nJQB3QdqVqXlyu8ziZ1vBSZ+JZwCcgutee4q6KbOeTY+YUHRureL5uXyhBo6Da6N4EBpCUbDNw/1H\n83jBZblIF3pZVYrfG1sPmKaFYk13iuc8oXZJFj1jdAlxFPmXWVc109uyyTqKBkbz1LPVch25CIus\ngUZvTlBHkW5YkJhQ1DWyyG/6XWsjED1LKyJK9dbrtWFaKNWNTU6hyYSMumE6AiibejbaNBwNjb+P\n0/ZmYe+0n6Oo39EzMvGMuSMZo0xGjaGjCAAOvApIbwMe+7j75889ghKXQjF5GQBAlXhIAte525YK\nRcxRtCVQoWhfB6Go14MCus4K21EEoKvJZ0O5eltY9RGKijXkknLXC0+6QQxSZr1aquPI+XXcevlM\nV4/lxUSCjHTUA550x51iXYdloXOZtcbKrBm9MRcoema4TzwDWEfRAFGbHEXrZS3SImsAdowsfPSM\n0R2KKKCme3UUxff3mlKETWXWXgIQdXBQa3mxqkPkuVBdAoz44NaRctKOH/h1FPXbUXRmpYw9ObaO\nYow22Th2FAGAIALXvQk4+jWguLT58+cfxTHhcmSTZD3KcRyyHhMWW1AyRIBihdZbwvGlIrZnVc+D\nIXoY2qujyCmzHtfoGc9xWPCLnhVqjoWqG+gGMUhH0fdPLMOygNuumO768dygXT2xe3PrEhqzCx49\nY44iRnfMZVUUqrpvtLSqmd5CEYueDYyE1HAU9SN6xnEcZIF3XC2d0Awz1s6XrWako2dt1+qiR/fQ\npC12UsdIqaYjpYjM2TGiuDkaTi+XoYg8tmdV16/JJWWsVzSYPRaaemFZFs6ullk/EWPkycRx6hnl\nhrsBUwee/HTrx/UacOkpHMEB53oC2JUlQfaMUweAZeYo2gqOL5UwP+u9f3UcRT0KRTU9/AFcVhUh\nC/xoCEWSwHV0FM1kuo8oqJLtKArQUXT/sTzSiojrdkcbQQmcNx0RqF2yY5k1jZ4xRxGjS4LkcGu6\nAUXyeOvjBYC3/06ZUNRXEpIAzbBQ102s9SF6BhBrbrt44YVuWBBj7HzZavyiZ2KMnVppWUTdMFt+\nNiocpZVWcXMi2SoUFVwKrxmjg5uj6FS+hL3TSfAef/O5JCk07df6b6lYQ1UzcZlH9I3BGBVkkYci\n8s50yVgxdwjY9ULg0Y8BzZO+Lx4BTA0P6/tbXNZZVQwmiE3NM0fRFmBZFk4sFX2FIsdRFEH0TBb4\nUAdQdNjPSETPZJHHOR+haKlY62n6mNNRFCB69r1jebx4firy01Aaweq1+Twu0AURK7Nm9JvZLHlv\n8IufkY4iD0cRAIgqwAmAko366TGaSMjk32CpWINpIfLoGUBcLkHLrHXTgsgP3SUxNrg5ipyOohhH\nr2hhdfPks2JNa/kcZTJBFoLrlbrzNUwoGl0aQlGro2ivR+wMIGXWQO/xAy/OrpB11J4cE4oYo0+s\nhwPd8FZg8SngwuONj9lF1j+sXdYqFCWkYOLy9DxQvATUilE/W4YP+WIdhaqO+Zm0530SsoCEJPTs\nKNIMM1Q/EWUmwLAfN4Zu9SYLvG/0LF+o9xY9E4OVWZ9dKeP0chm3RdxPBDQ2RB2LyUYEJ3oWpKNI\nSgLMps/okjknh+tdaF3TDcdZ6IogkyJr9nfYV6hof8EeXjDZD0eRsNnl4oVumsxR1APujiJyUhom\nSz9s0EEWzZPPClX3jqJ2R1GxtnkyGmN0UEQBssg7fw+maeH0Sgn7fNw89H1utU+F1mdXyPp5D4ue\nMcaAjCrGt8bjmjeSXszHPtb42PlHYSZnsGDNtOyZsmpAQWzqALllhdYD5cQSEeYOzHkLRQCJn62U\nenvvr+vdCUWz6RERiiSRx0ZVd3XblGo6KpqBmUwvjqJg0bPvHcsDAG67InqhiEawxiZ6Zr+Jd3YU\n2UIRg9EltLDN31FkOIKxK6LCYmcDIEGFonUi6uX6MMrZTbzwQmdl1j3hVmY9EtEzWwxqnnzWsaOI\nllnXjEgnpjKGj6wqOmu5S4Uqqprp7yiyhaK1PhVan7GFot2szJoxBpAp0jHdSyVywKEfA578R9JN\nBADnHkFt7noAXJujSAzWUTRNhSIWPxskx5dIdcq8x8QzSi4lYaUUXqxphkbPwjKbUZAfieiZ/cO7\nxc+oEtaLo0hxHEX+QtH9x/LYllVwYNZfHewGKpiMS/SM2rLbF9WbqDOhiNEb0ykFPNepo8j0dxQx\noWgg0OjZRVsoirrMGggpFBkmJBY965pRjZ5RoacleubhKErKAiSBa5p6piHDhKKRhmxUyd/DqTwR\nabwmngENQbyfjqJtWcV7YAODMUJk1IACyrBy491AZRV47iskLpZ/Dhu5awCgraMoqKNontwuM6Fo\nkJxYKkIReeya9Bfoc0kZKz2+99cNE5IY/vBtNqNguVQPPXF96FZvjlC0tlkookpYL44iWmLrV3Bq\nmhb+/fgybr18pi/TShrRs/EQimjErqNQpJUBmQlFjO4ReA5TKX/VvKoZnTuKmFDUd6ij6Px6/6Jn\nkhCio4iVWfeEX/QszgJcWiF/p83xBi9HEcdxmEjITVPPDKQUtmEfZZqjL6eXyanyvpnO0bN+OopY\nPxFjXAg0Nn6YmX8FkNkJPPZx4OITgGUinyVCUfOaKJuQUNPNzoOY5BSQ3s6iZwPmRL6E/TMpzyEG\nlOmU3HNHUS+OIssK3483dKs3evLo1lNEXQIz6e43FIrIg+OAms+L7ekLG1gp1fvSTwSQU0eB5+Jr\nlwxJVTcgCRzETn/YWpkVWTN6ZiIh+rr1qloHR9Gr3g/c9ht9eGaMZqho7ziK+lBmHTZ6FueI1FZD\nometv2vdcRTF9/facBQ11gz0BDslbz78mExKTpl1saZvmozGGC0yTRvVU8tlyAKPHRPe65isKkLg\nOaz2SShaWK3gMtZPxBgTsokYdxQBZNLu9W8Gjn0DeO7LAIALqUMA2h1FmycsejJ9gAlFA+bEUjFQ\nAikXhVBkmJD9Drs9oGksv2oON4ZOKBJ5DglJwIJL9Iy6BGZ7cBRxHAdF5FH12TzQfqJb+yQUcRyH\nrOq/mR0ljKDThLQKIPnnOxmMTkwkpA5CkeFvyz/0Y8Del/bhmTGaae8o6sfUMzmMo8g0O4vZDE9k\nl+gZ/d1HPTl0kKQ9omdpRXQ9PZxISFgra7AsC6W67jiSGKNJu6Noz1TCt+uM4zhMJqS+RM/quonz\n6xVWZM0YGzJBI1nDzA13A5YJ/OAvgOwuLFqTALBp6hkQsNt2ap5FzwZITTdwZqWM+dnO+9eppIxC\nTQ98gOlG12XWdNhPyJ6ioVy97col3B1FxTo4jvyie0GVBF/73v3H8rhiLo1tWbWnx/FjIiGNzdQz\nzTCDndTXS8xRxOiZyaTsKxSRjiK2edtqaEfRhfUKMqrYF5EmXEcRcxT1Aukoai+zHoXo2eapZ8Wa\n5jn2ftIWisp1A5YFNvVsxCFCUcNR5NdPRMml5L5Ez86vVWBZbOIZY3zIKCJqutnTxnvLmbkc2HML\nYNSBnTc669f2jiIgYGXJ9AGgtAhUN/rydBmtnFkuw7QQSCjKpXqPHtd1E0o30TPbUZSPu6MIINMa\n3DqKlgo1TKfknjcUqugtFFU1Aw+eWumbm4iSTcS4qT8kgbs/tArrKGL0DD3R94J0FA3lW99YQR1F\nS4VaX4qsASJehIqesY6irlGkzY6i0YqetXYUeQlAE0niaKTCEpt6NtoQR4MOy7JwernkO/GMkktK\noXsigkAnnrHoGWNcoE6bWPcUAcANbyW3tlAkC3xLRUJjWnYAgwEttGbxs4HQmHjWOXo2ZQtFK70I\nRV2WWc9kyGOPhKNody7hGT2b6WHiGUWVeFQ1983DI2dWUdXMvvUTUbKqfzxmlCBjp4NEz8osesbo\nGb/omWVZqOkmFOYo2nKoUGRajZHRUSOLYcqszWARWYYrikBEOcuynI/RqWdx/r1KAg9Z5FGsNxbo\nBTt65sZkQm4RirzuxxgNMqqIimbg/HoV5bqB/T5F1pTJpOx7mNEtVCjaM8Wc2YzxgA4UiPXkMwC4\n5i7g2p8FrvlprFfqyCaklmFKoRxFUwfILROKBsKJfBFAQEeRvdZdKfbmKOqmzDopi0grou9UaDeG\ncvW2azKJtbLWYvUGyMlzFEIRKd10dxR971geAs/hlvmpnh/HDxI9GxOhyDAhBXIUsTJrRu9kExIK\nVR2GaW36HHU8+JZZMwaCKjfEun70EwF2RxErsx4IVHxtFuZo9KybRc0wkVbETY4irymek0kJxZru\nWMuZUDTaZOwN3JFz6wAQ2FHUjzLrs6ukTHtbpn+1CQzGMEFff7F3FClp4I1/BUzNY72iYSLRet0I\n3VEEACusp2gQHF8sYS6jOH+LfkThKNKM7jqKANJTNBJC0e4cEQvOtbmK8sVaT0XWFD9H0f3HlnHj\nnslA/+C9kE2I8VfAA2IEjXTUyyx6xuiZCR8rcs1+3atdTAxgREuiydU12U9HUZiOIhY96xoa52yO\nn2kjED0DgJQioFjdXGbtBo1RnlsjJe1MKBpt6DQiKhQF6ihKyli1C8+j5OxKGbtziY4jmhmMUSHU\nNLCYsF7RNq2JGo6iAD+nnAQyO4Fl5igaBCfyxUBuIgDIpci/Yy+Tz0iZdXd7mNn0iAlFzYXWlmXZ\n0bPeNxSKR5n1elnDkwtrfe8nAog6vN6HhcIwogWZemaagF4BJCYUMXqDCkVu8bOq7SRUmKNoy5EE\n3nHw5PrUUSSxqWcDg55w1V2EojhHzwAgrUgo1hprBj9HEX3/oQddrKNotKGHik+eW4fIc9g52dnN\nM5mUUddNVHyGqnTD2RU28YwxXmTCRLJiAnEUta6JVImHJHDBu22n5pmjaABYloUTSyXMz3buJwKa\nomel7v9ea11GzwDbUTQaHUXkQtfcU1Ss6ahqZkSOIgFVl1PmB04uw7QwEKFoNq2gbphjMflMDzL1\nTLf/rZlQxOiRST+hyF6YM0fRcEBdRZP9ip6FLbNmJ/Fd4+4osqeexdyplVaElugZ6Shy/5t1hKI1\nctDlJSgxRoNmR9GeqWQgsZkK46td9BQ9fHoFyx4L/TMrZdZPxBgrMiPqKGoXijiOQ1YNUVkyPd+x\no2itXMd9zy3iw19/Dm+79we48Q+/jk89eKbbpz2WrJTqWK9oOBBQKJIEHllV7Cl6XDdMyF26tLuJ\nng3lCmYmLUMR+RZHUd4ufoqkzFrksehykvPkwjoEnsN1uyd6foxOzGXJqdOlQhUTfTpNHxZImXWH\nP2qNCUWMaKCvJ7ey0EZHEROKhgFVFlCo6ZgYijLrAM5HhidejiJJ4FpKOeNIShGdKVWmaflOPaOR\ngQXmKBoLqKMhX6zj2l3B1o50RPJqqY5dk8GFHcuy8HMf/SFeemAG9/7CTS2fW69oWK9obOIZY6wI\n1d0TE9bLm4UigE7LDiiITR0ASktAdQNQsy2fOpUv4Z6/fwjPXyIlzDwHHNqeRalm4Mi5Dbzp5p5/\nhLHBmXgWMHoGkJ6iXqZeakZvjqJCVfec/O7GUK5gOI7DrlwC59YajiKqgEUz9cw9evb0hQ1cPpse\nyCZym+2MurRRxcFtmb4/3lZCyqw7/FHXyYuNdRQxesU3ema/7pUui+AY0UIdRf2KnjVP4uokVuhm\nAOcjwxPFduk1D4rQg8SOY0BKEZ2JUiV7+lnGc+pZa/SMdRSNNs2OsSBF1kAjfhD2VLlQ01GuG/jX\nZy/h+FKx5RT7rP33yYQixjhB319HpfPVMC1sVHVHAGsmq4ohHEV08tlxYOeNLZ96fGENz18q4j/8\nyAH8yMFZXLd7AilFxEv/x7+iXI82DjvqnFgiYtuBmWCOIoAcFPTkKNJ7KLO2NZR8iPjZ0K7gdueS\nLdEz+kNFET1TRPcy66fPb+DwzqzLV0TPNttRtLgRzgIWR/QgZdaOo4jZphm94S8UMUfRMOFEz/ok\nFNGLKY1A+cHKrHvDiZ41XVvresCJl0NOWm5MPXPG3vtMPQOIo0jkOSZKjzjNQtG+6WAiTbfRMzpS\n2bKAj95/suVzVCii1Q0Mxjgg8BzSihj/qWc29OfwdhSF6CgCgOXNPUU0pveLt+7DSw5MO67XhCyg\noo2G4DYoTuRLkEUeu3LB965Tyd4cRb0IRTMZckgRJn42tCuYXZMJV6EoKkdR86knACwXa7i4UcXh\nHYMRiuaytqOoUB3I420lJNLRSSiyHUVScPseg+FGEEeRysqshwJVpkJR/6JnAALFz7QgzkeGJ26/\n61H5naYU0Zl6Rm+9nEI0ilTRDKQUMfaxO4Y/zRNy984EW7/Q97u1kKfKy/bmYs9UAp99eKGlq+is\nXdVwWUCxisEYFbKqODIdRXTd6tbbGKqjKLef3Lr0FJU8DjtSisgcRSE5sVTE/ulU53qVJnI9RM9M\n04JuWl2vq2bTxKQyEkLR7lwCK6U6yrbNe6lQA8+RbF+vqNJmR9HTFzYAYGCOoqQsIqOIY+IoMjvH\nD+p2HxVzFDF6RJUEyCLvekFlHUXDRcIW7PpWZi1s7s3xwgjSpcbwxImeNV1bdaP7Bc0wkVZFlOoG\nTNNCoYOjSOA5p+CYxc5GH1nknYOHfQGjZ9R1thpy8g3dXPzGHQdR0038/QOnnc+dWSljIiE5Y7QZ\njHEhE0ZAGXKoUOTuKBKDR+zkJJDd5SoUFWs6eK7h6KYkJIEJRSE5vlQK1U8ENDqKupl6Tg/iuo6e\n2amsMJPPhnYFt9u2cdGcf75Yw1RKiWQhT6aeGS3/SE+ft4WiATmKAOIqWhwHR1GY6JnMHEWM3plM\nSKyjKAY0omf9chSR7x9EKNKCvE8xPGlMPWssNDXDhNTldI5hIq2Qv6OyZjiOIq+OIqDx98yEovEg\no0oQeM5Zt3ZCEnhklPCTb6iD6EX7p/CqQ3P4u++fdq5pZ1cqrJ+IMZZkRtBR5DbkKJSjCCDxM4/o\nWdrF7ZqUBcecwehMXTdxZqXclVBU001UQhRKO49JhaIuD+Cm0yMUPaM5axo/WyrUMJOOZjOhSgIs\nq9Ui//SFDeycUJ1pFINgW1bFpXFwFIWKnjG4x5jyAAAgAElEQVRHEaN3JhKS69SzRvSMOYqGgYQd\nPXM7PYsC2o8T1FHEyqy7x23qWd0wIY1ImTVALPu0oyjj49ygjhEv1xFjtMioInbnEqHcc5Mpqevo\n2XRKwbteNo+VUh2ffWQBAOko2jPF1k+M8SObkFCojYOjSEJNN4NPrJo+QMqs2yjWdNfrV1Jm0bMw\nnFkpwzCtlqECQZiyD5K6iZ/R9VW3h92SwGMqJY+KUEQueAt27nqpWI+kyBpo/IKb42eDLLKmEKFo\n9B1FmmFC7LSAcsqs2YkYo3cmvBxF9E2WdRQNBaokIKuKfYt8NXpz/Bc/lmXZQhH7u+iWhqNoBKNn\ntlBUrOmNjiIfEYgu8lPMUTQW7M4lcXXI9WMuKYcvsy7VkZAEJGQBt+yfwrW7JvDR756EbphYWK1g\nD3MUMcaQjCpiozIaThh6wOkqFNnXnMDuqal5oLwMVNZaPlys6kgpmw9LE7KAChOKAkMnns2HFIqo\nISVs9BhoCEXdRs8AMvlsJISi2bQCWeAdR1G+UHPGuvWKItEuBfKCqNQNHF8qDjR2BgBzGQWLG7Wu\ncopxItBJfZ06ithCh9E7XkJRjTmKhorXXL0dP/eSvX37/orjcvF/j6VT0UZhQtdW4eYoIocE8f+d\npuSGo4hOnfGLldHomV88jTE6fOStN+JDd10f6mtySTm0o2ilVHeiAxzH4V0vm8eJfAmf+OEZ1A0T\ne9jEM8YYQqJn4+EoAhBi8tkBctvWU1Sq667Xr5TMOorCcHyJ7FvDR8/Iv+NKyPd/oLG+6uUAbjaj\njEZHEc9z2JVLYGGtAsuysFSsYSYiR5HadvL53KUCTGtwRdaUuayKumG6bmhHCdJRFNBRJLOFDqN3\nPIWiHm2bjGh5zdXb8d7XHOrb9w869cwwiVAkMEdR1zhl1s0dRT1M5xgmUs2Oopr/1DMAmEiI9tcx\nQXocyKhS6D6qXFIKvVHIF2uYbqpHeN0127FrMoH/+fXnAYB1FDHGkqwqoVDVR+LQfaOi2QX5m68d\ntKg+cE/RtLtQVKjqSLtEzxKyyBxFITixVMRMWgk9QCCXpI6iLoSiHsusASIU5UdBKAJI/GxhtYJC\nTUddNyNzFNEXIM150iLrq3dORPL9g7ItS36eUe8p0k0TUseOIjr1jC10GL2TTbiX/lU1AxzXfREc\nI17IQrAya82kpzTxd79sFTTO2Rw903RzJF5rGdvyX6yS6FlSFnzjkpMJWmbNJlAx3JlMyljrYupZ\n8+RfUeDxi7ftdw5FmFDEGEcyqgTdtLoqBx421iua5xTYrH0AEXjyWW4/AG5ToXWxpru6XZOygLph\nQutwsMYgnMiXcCCkmwhoTG/vpaOol3XVrJ1mCspQr+B2TSZwbrWMvJ2lm8lEV2YNNDqKnr6wjowi\nBp5YERXbsioAjHxPkW4EGDutlQFBAXh2AsvonYmEhEJNd5wilKpmQBWFTdMeGKOJWxzKDcOOnrEy\n6+6hC5faKEbPaJl1nTiKOrlHnDJr5ihieJBLyijU9FCbMiIUtR6YvunmPcioIjgO2DnJyqwZ40cm\nbHfPELNe0TyHe4R2FEkqkN21qdC6WHW/hiXt4SIsfhaME0vF0P1EAPl35LkuhaIIHEUzabllndaJ\noRaKducSyBfrOGv3FM2m1Ui+r2qffFZti/xT5zdw1c7swDePcxnqKBptoUgzAkTP6mUWO2NEBt2o\ntV9Qa7rpvP4Zo0/QMmvqKBJGwP2yVbiVWY9O9IwsoIs1A4Wa3nGaGV3os6lnDC9ydk+F23RONyzL\nwnKpvmn6b1oR8e5XXo5XXDnX0+aBwYgrtLtnFHqKfIWisB1FADA9vyl6VqzproMW6BRaFj/rzEqp\njtWy1pWjiOc55JJyTx1FvUbPwjDUV5XddjHf42dJY3v0jiIDhmnh2QuFgRdZA8BchghfiyHax+OI\nYZqdT+q1CoudMSKDXmjX2oSiqmY4XSqM0YdGyTo5inRaZs0cRV3DcRxkgW8ts9bNkYjz0dPXkj31\nrFNJNS2zZlPPGF7Qv5GghdZFu4KhOXpGuedlB/DXb7850ufHYMQF6ihaH4HJZ8EcRSF+zvQ2oJR3\n/vf/Z+/NgyTJ7yrP52dcGZF3Zd3V3XV0dbX6FJeQEGIACZBG3MOO7cwYMAsGKxDsLjvGrtmMrbEH\nc2sExoxgmDVYsDF2ZlmBBrFIQoDQARJSt7pbakl1dVdlXVl5xx3h1/7h/vOMzIzwI8Lj8Iz3+Seq\n4vSKivDw3/P33te2HbfMustJDDG0od5O//s4bPYmnsUXigA3ftZXR1ES0bOYppuJFopOeVGwF29v\nAwCWkuooUveiZ69v1tAwrNijTZMgp7ujoR8ecUeRaTnh8QOjRqGIJIb4oT1YaN006CiaJrq5XLqx\nV2adflFjnGRUeV+ZtWnbR8JRlNMUyJInFLVMFEPKK31HEYUi0oN5z/W6HdFRJGIK3YQiQqaZvbHx\n6XcU7dR7C0VZTYYqS/EcRdlZoFX2/1o3LDhO94mcOUbPInPTm3h2vo/oGeBGjwfqKBqho2iij2JE\nZ9BLd3aheFatJNgr3bT8IutRTzwTrJSyyZVZ17eAf3mx+21Pfj/wg7+ZzOvExIwSPzAagMZ8PUmG\n3kKR1XWaBDmaiDJrwwqehiJ6Qo6CqDFOMpp8oKPoaETPJElCQVfdqWdNM3SwxqWVGTx1ahZPnRrt\ngAySHvzJNxEdRZveomJxhkIRIZ0Ip81R6CgqNww/YnYQSZJ6DmrpSaYENHcBxwEkCVXvPermKBId\nRUehFHzY3NioQldkP/kUl9m8htWteuzHGQlNPYvDRAtFx4pZaIqErVoby8VMYmd7Ox1F1x9WoSkS\nLh4rJvLccTlWyuBhJSFHkZoF3vxzh69/7ZPA1/4/wLaBMYx/Nm07/P+uXQP0/ix8hBykl1DUMm1k\nKBRNDVHLrE3PUXQUipfHycHoWds8GmXWgHtgXW2aqDSN0O6hubyO//KzbxnRlpE0Ml+IFz3bqnpC\nUSEZZz0hRwXh8IzltJlALNtBpWX6HZvdKGXV6FPPANdRZJvuwCC9gGrLfY+CyqxrrfQLbsPmxsMa\nzi3m+9Yl5nIavhRH8PPwy6wHOAE3l9NiDW6ZaKFIkSWcnMvh1mY99AxeHPwya8PCq/fLuHCsOLYS\nwJViFp99bSuZJ9PzwLf/k8PXv/i7wB++xy00W7qQzGtFxHEc96xylI6i7HhcXeToEeQoyrDwc2rY\nE4qCz5CZnHqWCBlN2ecoMm17oAOaSaKQUVFrm26ZNSNlZEBE9GyrxugZIYNwVKaeCadQr+gZgPiO\noqznam2WPaHIPRbq9huW09zrWGYdzs2NKi4e6y92Brj/x1EHGXTSSiB6JssSlmYyuBF+V/f+fb/S\niDjljftcimmVCiLTUWb96r3yWPqJBMdKWTysNOE4wdGIgTjxjHt5/4vDe40eiOnkSpiTyaizo4gk\nhj8d4qBQZNqMnk0Re1PPwhxF7u3qGByXR4mMKqPVYVs3ovTTpYRCRkWlKTqKKBSRwchpCnRVjuwo\n2qi5FQWMnhGyn7yuQJGl1HcU7UYRirJazI4ib33b3AWAwOiZmO7JjqJgDMvG7c163/1EgDuZuWFY\noW73gyRRZg3Ei59N/FGx6Ck6OBJ0EISj6M52AxvV1lgmngmOFTMwLCdyoWFfLF8GlMxYhCKRpwwv\ns6ZQRJIjqynIaocPwluGhSwdRVOD+DGNGj1TjoioMS50Vd4nyhnW0SizBoCZjIL1SguOw5JqMjiS\nJGE+r0XuKNqqtpHVZOR1fvYI6USSJBSzarxpYBNIJKEop/bpKPKEooDomV9mzY6iQG5v1WHaDh4b\nQCjqlXoII4kya+DICUWueBC3fCkIXZEhScAL3jS1cRVZA26ZNYDkeoq6oWjAypPA/ZeG9xo9ENOE\nQkckt+tudI6QhJjNaV07iugomh7EfidUKPKiZxodRQPhOor2C0VHJnqmq1jzJpSGdRQREoX5vB5r\n6hn7iQjpTjGrpt5RtBPZURSno2jOvfQmn4l4XveOIhE9S7fgNmzExLPHlvvv1S35QlG8yWdJlFkD\n8cw3E38EJxxFSXYUSZKEjCr7E8+eGKOjaKXk/rsSm3zWixPPuELRMCNuXRALsPDoWYOOIpIo3YQi\ndhRNF5IkQVdktMKiZ1GdjySQw46ioxM9m8mo/qKejiKSBHN5LXL0bLPWZuyMkB6UslrqO4qiOYr6\nmHoGdDiKegtFOU2UWdNRFMTN9SoA4PzSINEzd1/et6NowBNw5xaji1wTv2LyO4oSFIoAN5pi2g7O\nLOQCv5TDRjiKxJnKoXHiGXdHsf36cF/nAIYtxk4HLBYcBzBqFIpIovQSiugomi50VY4+9Yxl1gOR\nURW0vOJw23Zg2c7RiZ51uIjEOGZCBiGOo2iz1mKRNSE9KGbV1E89i9ZRpKJl2v7vbCh+9GwHwN5E\ns0IXoUiRXRNFg9GzQG6u17A0o2M2YDpdGOL/OG6hdduyIUkYeAr8e74t+mCriT+Ce/7cPN77ty7g\n2y4fS/R5s6q7WBxnPxGwF6lbrwzZUXTyWfdyxPEzET0L/FBbbcCxAS03oq0i04ArFO0/w+RGzyZ+\nt0cSRFdl367bC7/M+oiIGuOiM3q2d5LgaLynnQfWjJ6RJJgv6JEdRVvVNoUiQnpQPAKOIuEUKoU4\nioAYE946p54BqLRMZFS5Z3SpkFFRZ/QskBvrVTw2gJsIcEfUA/05itz6nNGd1Jz4IzhNkfHfv/3x\nxF0/YrF45cRsos8bfzsUzOa04TuKjl0BZG3khdZigRbY/dF2857Q+897EnKQ2Zy+z6LrOI4XPaOj\naJrQlQiOIouOoiTojJ75vU9HKHrW7c+E9ItbZm2ETr11HAebtXbiznpCjgpHJXqW1eRA17tws0aO\nn2lZQNH3TT0LmtqZ0xROPQvh5kZtoH4ioH9HUcu0B+4nisvEC0XDQnwRx1lkLVgpZYYvFKkZ4NgT\nY3MUBfZUGHX3ktEzkiCzuf39D4blwHZAR9GUESt6dkREjXGxz1FkHTFHkb538E6hiCTBfF6HZTuh\n5bT1toWWadNRREgP3KlnKY+e1Y1QU0Qp5/72xCu0nt3XURT0+5XXFTQoFPVkp97GVq09sFBU6tNR\nZFj2yHtWj8YRXB9kPKHoyYkQirLDL7MG3J6ie18caaG1YUWInhkN95JCEUmQ2ZyGWtvyF6xNL9PN\njqLp4mDBcjfEZ0Tl1LOByKiK/163raMV5+uMngWdkSUkKqLQNCx+tll1b6dQREh3SlkV1bYJ2x7t\nwJ4k2Wm0w4WiuI4iwC209qaeVZtm134iQV5XUKNQ1JMb3sSz88uDRc8UWUIxq/YdPRslR+MIrg8y\nqoy5vIYTs9lxbwqWi5nhdxQBrlDU2AJ27wz/tTzMKD0VfvSMQhFJjllx5sXbEQunQ4ZC0VQRJXpm\nscw6EXRVRssrwhTRM/2IuLQ6z8IGHWgTEpV5rww1rNB6s+YeHy5SKCKkK8WsBscBqinu19ltRHEU\neUJRnOLuGI6inK6gkeL3cNjc8CaePTagUAR0H7gTRtuyodFRNBq+/pF5vOvpEyMthOrFSimLh5Xm\n8JXwk8+5lyOMn5mxHEUssybJISYSiB1x01vAjtq2ScaLrspoRe0oOiKixrjIdLzXRy16Jgqss5p8\nZP5NZLwIR9F2iKNoq+bevsiOIkK6IioFxAnBNLLbMGM4iuJGzzxHUSu4oyivq+woCuDmeg2aIuHM\n/ODr1bl8H0LRGBxFU3ta7H98x+Vxb4LPSjEDw3KwXW8P90Bg5UlAUtxC6yfeNbzX6UB0fwQWmhqe\no0hjmTVJjtkDGeAWo2dTSaQya38/RQFgEHRVhmk7sGxnL853RN5T4SKaySQ7WINML8JRFBo9E0IR\nHUWEdEUU/IbFzCeZcsMIncS911EUx1FUAsr3ALCjaFBurldxdiGfyHHNwR7VKLRZZj2drJTc+NvD\nYcfPtByw/PhIHUWWHaH7g44iMgRmc+5B9Z6jyP0sZukomiqidBSJiGyg85GEIiYKtk3b76c7atGz\nEvuJSEKIzqHtWkj0jB1FhAQiFs8i+pxGokTPcpoCVZbidRR1Rs+apu+O7UZe59SzIG5u1AbuJxLM\n5fS+omcUiqaQYyXXRTT0yWcAcOLZkQpFRpSx021v6plORxFJDjqKCOAeQBqhZdaeo4hl1gMhYp0t\n0zpyBeG+o4hCEUmIUlaDLIU7irZqLWRUGXmdv12EdENXvJMUKXUUGZaNais8eiZJEko5LX5HUWsv\nehZcZq2izo6irpiWjVubtUT6iQC3b2o3ToQQLLOeWo4VPUfRqCafVdeA8v3hvxY6uz+CHEWeUERH\nEUmQg0KRcBSxo2i6iFZm7TmKjoj7ZVz49v8OR9GoixeHxYwuomcUikgyyLKE2ZyGrQjRs6WZzER0\nahIyiXT+9qQR4RASQ1iCKGXVeB1FmVnAqKPdaqJl2iiGlFnTUdSd1e0GDMvBY8vJmBrcMus2nBiT\nyOkomlJG6yh6xr0ckasoUqTDF4o49Ywkhy8U1feXWdNRNF3oarhQFMn5SELZcxTZHWXWR+M9LWTc\n/QaFIpIk83k9dOrZVq3N2BkhAWRSLhSJHrL5CN/zvhxFAOrlLQDBv2F5TYFpO6l9H4fJTW/i2fmE\nhKK5vAbDctCIEZeko2hKyagK5vPa8DuKAOD4UwCk0QlFVpQyawpFJHl0VUZOUw53FFEomiqiCEV7\n+yn+JA6C3lUoOhrvqarIyGoyo2ckUeby4YWmm1UKRYQEkXZH0UurOwAQWmYNuJHV2B1FABqVbQDA\nTLZ3vC3viUgstD7MzXV38NJjS8lEz8TJ7J2QEwWdsMx6ijlWzI7GUZSZAZYuupPPRoCYJhTYU9Gu\nA7IKqDwQIsniWjsPdhRxtzdNRCmzFtEzGooGQ5RZt0zrSIpvl1aKuHisOO7NIEeI+bweWma9VWtz\n4hkhAfgnKVLaUfTi6g6KWTVSUXIpp6LcjBE9y7riU6MSwVHk9aDVjenrKWoaFtYDDBs31qtYKOiR\nXF9RmDtQjxEFg9Gz6eVYKYO1Hh/QD710D5+8tp7ci514ZuTRMzXQUdSgm4gMhbm81qWjiI6iaUJX\nZLTCome2A02R2AEyIBltz1HU9susj857+qGfeQt++m3nx70Z5Agxl9fDHUW1FhZnKBQR0gsRx0mr\no+iFW9t49swc5Ai/l/06ioya5yiKIhRNoaPoX330a/iuf/OX/knlg9xcr+GxpeSGLvXtKGL0bDpZ\nKWXxsIujqN428T/9/sv49598LbkXO/EsUL4LVBMUn3pgRun+MGoUishQKOU6hSI6iqaRKNEzy3aC\ne9RIJDIdB+ti3z/qs1+EpIn5vBbYUVRvm2gaNhYKmRFuFSHpIs0dRdWWiatrFTx/dj7S/fvtKDJq\nbrwtKD6d86oZ6q3pE4o+9/o2NmttfOraRtfbb25UEyuyBtz/RyCeo6ht2SMfEMIjuAnhWDGD9UoL\ntr2//fxjr66h1rbQSHJc4QgLrUX0LDB+YDQAnUIRSZ7O6FnTZJn1NKIrbvQsaLKEYdnQjsgY93HS\n6Sg6ah1FhAyD+YKOhmH5JzIOsll13UaMnhHSmzR3FL28ugPbAZ47Oxfp/qWsiqZh93S+HCLjRs/M\nuicUBTqK3NvqSa45U4Bh2fjK/TIA4MMvH54Kvls3sFFtR4oGRmUu7wpFcdxhLTqKppeVUham7Rwa\nk/r/vnAXAGK1oody4mn3cgQ9RaYVYepZu05HERkK+zqK/OgZd3vThK7KcBzXNdQL03KC47EkEiLW\n2T6i0TNCkmY+7wpAveIHYhoSy6wJ6U3nIIW08aJXZP3cmWiOollvn7FVC46s+niOIruxCwAoBjiK\n8hnRUTRdjqKraxW0TRuLBR0fe3XtkAh3Y8OdePZYgkKRHz1rRPx/hHtsNeo1DFdME8JKybUVdxZa\nr1dafjdRonnR7Cyw8NhohCJRZh029YxCERkCBx1Fuiqzh2bK8M80BpRcmrYDhY6igdk7WLcYPSMk\nAvPeWeVei76tmttdyY4iQnqz11GUPoHjhVvbOL9cwGy+9zSyTkRPjpjCFYo+A0gynKYrFBUidBRN\n29SzL91135v3fvtFVFomPnl1f/zMn3iWYPRsJqNCkSWWWZNoHCtlAQAPOwqtP/TSPd+OmPiXdkSF\n1qZ/VjkoelYHtNzQt4VMH7M5DfW2BcOy0TJsZLlonTqilFyalg2NjqKBEWe6WsZe9IyOIkJ6M+c7\niroLRXvRM3YUEdKLKCeEJhHHcfDi6k7kfiIAuHjMdbVcW6tEe4AsA5ki5FYZkgTkA+oX8pqInk2X\nUPTK3V0UMyr+7jecxVxew4df2R8/u7lehSpLOLuQnKlBkiTM5rTIZdamZcN2Rh/n56ppQjhWdA8C\nOgutP/jiHTx1ahZPn5pN/kt74llg5zawcT3Z5z1AZEeRnpxKS4hAZIB3GwZapsV+oikkSneBZTN6\nlgSdB+t+RxHFWUJ6Ml9wf6N6FVr70TM6igjpSVo7im5t1rFVa+O5GELRcjGDUlbFtYfV6C+UnYXc\nKmNGVwMnq+X8qWfT1VH0yp1dvOHULHRVxjuuHMfHXl3b1xt3c72Gs4v5xEWauY7UQxhCBKWjaEpZ\nLoromesourZWwZfulvF9z51CTleTdxRdfqcbQfvNvwVc/Uiyz92BX2Yd5Chq01FEhsNsx1SBpmH7\nZbtkeojSXWDYTrDrkURCdBS1DAuGiJ6xzJqQnoiOou0ejqKtWhu6KqOg8yQHIb2I4hyeRF5cdUfW\nP38uWpE14DpRLq4UYwtFqlEOnHgG7EXPpslR1DZtfOVBBU+ddruc3vn0CVRbJv7y6t5k8BvrVTy2\nlFw/kaAURyjyPtsss55SMqqChYLudxR98MW7UGQJ737mJPK6grZl+zGuRFi6CPzkJ4C5s8B//DvA\nn/1vgJ38jiFSmbXRYEcRGQpi/ORO3UDTsJBVebA9bfgHkEEdRZbNiFQCZDpEOUbPCAlHuF6DomdL\nBZ3deoQEIEkSdFVGK2XRsxdu7WAmo+LisWKsx108NoPrcYSizCx0sxI48QwActr0CUWiyPoNp1yh\n6E3nFzGf1/DHXvzMsh3c2qzjfIL9RIJZOopIHI4VM3hYacG2HfzhF+/hWy4uYbmY2VN4k26hX3gU\n+IcfA577e8Bf/gvgd38AqHkFXrYFrH0Z+MJvAX/wHuAv/llfL+FHzwKFohqFIjIUhKOo3PCEIkbP\npo4olnS3zJoLsUHpfK9Ny4YkhZwkIGTKyagKCrrSM3q2VWsxdkZIBDKKnEpH0TNnZmP/Tl5cKWKr\n1sZmtRV+ZwDIzkI3q4FF1gAgyxJymoLGFEXPRJH1055QpCky3vHkXvzsznYdbcvG+QQnngnm8n04\nikYsFAV/YshIOVbK4mG5ic+9voW7Ow38o+96HMBeZrTRtlDKRmvFj4yWA77314Az3wh8+BeAX38r\nsHgBuPsC0PaK0iQFkFXgrb8AyPEW2qblQJYQmIlFuw7oFIpI8nRGz1qmjSyjZ1NH9DJrfjYGRZUl\nyJLrKDJtB5rCKYOEhDGX13tGzzZrbSywyJqQUHQ1XUJRvW3iK/cr+OlvPR/7sX6h9cMqFmci7B+y\nJeSsKooh0TPAjZ9Nk6Po5bu7KGZVnFvcW4e+8+kT+L2/WcUnrq77x5BJTjwTxCmzZvSMYKWYwVq5\nhQ++cBcFXcHbrxwHMKLM6PP/APiHHwXyi0BjG3jmR4Dv/3XgZ18A3vU+wGq55dcxMWwbatCH2jIA\n26CjiAyF/R1Flt+hQqYHcfbFCIqescw6ESRJQkZV/DJrjW4iQkKZL2jYrvWOni0W6CgiJIy0CUWv\n3NmFZTux+okEF1f2hKJIZGeRt2uh0TPANSdMk1D0pbu7eOrU7L6TWm96zI2fffjl+7ix7r7Hjw3D\nUZTTUG4asL30TRDjip4N5CiSJOm7ALwfgALgNx3H+ac97vdDAP4zgK93HOfzg7zmUWallMV6tYU/\nfuU+vusNJ3wnUc4fVzhkK+DJZ4Gf+uTh62teodfGNTeuFgPLcoIXC0bdvaRQRIbAwTLrhQK18Wkj\nUvTMctilkxC6KqPlxaQ58YyQcObzOrZ6Rs8oFBESBV2VA7sIJ40Xbu8AAJ49E33imeB4KYuZjIrr\na5VoD8jOIo86inr4b7LrKJqO6FnbtPHV+xX82Jsf2Xe9qsj4rjecwB9+8S5UWcJcXsPCEPbDpZwG\nxwEqTROz+eDEUOocRZIkKQB+DcB3A7gC4O9KknSly/2KAN4L4LP9vta0sFLKwLIdVFomvv+5U/71\n+Y7o2VhYuuReblyN/dDQ7g+j4V5y6hkZApriTovZbRhomhYy7CiaOvypZ4GOIptTzxIio8pomTba\nFifJERKFy8eL+Mq9MsrN/WJRvW2iYVjsKCIkAroio2WkRyh68fY2Hl0q9CVASJKEC8dmcHUtoqMo\nU4IMBwt6d+diJ3ldnRpH0dW1CtqW7U886+RdT59AvW3hj165P5R+IsCNHQOI1FNkpLDM+hsAXHcc\n56bjOG0Avwfge7vc738F8M8BNAd4ralguZgF4ApGbzq/6F8/9nGF+QU3krZ5LfZDTTuk+6Ndcy/1\n5LOfhAB7GeCWYXPq2RQSqaOI0bPEEPZ/07Kh8z0lJJTveeoE2paNP311bd/1m1V3UUdHESHhpMlR\n5DgOXlzdwXNn4sfOBBePzUSOnjnZEgBgQW6E3jevK+MzJoyYV7wi66dOHRaKvvHRBSwUdLRNG48t\nDWeN2pl6CKM1pjLrQV7tFIDVjr/f8a7zkSTpOQBnHMf5owFeZ2pYKbmFZN/37Kl9LpzcCIWizWqr\n+4Jq8aIbPYuJadFRRMZLyRs/2TItZFhmPXVkGD0bKcJRZFg2o2eERODZM3M4NZfDh1++v+/6La+3\niGXWhISTpo6iO9sNrFdaeO5c/NiZ4OQxiaYAACAASURBVOLKDDaqrZ79Zp201SIAYF6JJhRNi6Po\n5Tu7KGVVnF04XH/ixs/cruBh9BMBe0LRTiPC/6H32R714JVBXq3bUbXfxiRJkgzgfQD+h9AnkqSf\nlCTp85IkfX59fX2ATUo3T56cxY9+8yP4sTfv7wHK625HUcMYbma0bdr4jn/9CfyHT712+Mali31F\nzwzLCf5Q+x1FdBSR4TCb01D2OoroKJo+tAiOIsMKKd0nkcmoiicUUXwjJAqSJOF7njqOv7y2vu/M\n8p5QREcRIWHoSnqEohdX3X6iwRxFrvhzfT3cVVSX3TXWnFwPvW9OV9EwegtFLdPC+z529Uj0GH3p\n7i6eOj3bczrru585CQB44kRxKK8/l4/uKBKf7UyKHEV3AJzp+PtpAPc6/l4E8AYAfyFJ0usAvgnA\nhyRJ+rqDT+Q4zm84jvN1juN83fLy8gCblG50Vcb/8u4ncXw2u+/6UUXPXr1fxnbdwN2dLjuSpUtu\nqXV9K9ZzWrYdHOkQQpHOMmsyHGY9R1HTsJClo2jq8MusAyzplk1RIyl0VUbLtFxHEcU3QiLxzqdP\nwrAcfKwjfrbpCUVL7CgiJJSMpgR2EU4SL9zaRk5TcPl4/wLEhWPe5LMIPUVVyb1vCeFCUV5TUGv1\nFoG+8Po23v/xa/jM9c2IWzqZtEwLX31QxlOneot13/TYIv7k578F33ppONqE7yjqMcygk3FNPRvk\n1f4GwEVJkh6VJEkH8F8B+JC40XGcXcdxlhzHecRxnEcA/DWAd3PqWXxyIyqz/sKtbQBAudFlByEK\nrTevx3pOI6zMui0cRYyekeEwl9ewVW/DtB1kWWY9dYgfVSOwzNqhoygh9kXP+J4SEolnTs/i1FwO\nf/zKXvxsq9YCQEcRIVFIm6Po6dOzAx13nJrLIa8ruPYwfPJZFa6jqBhFKMoEdxRt1V0BO4oLZpK5\n+qAKw3K69hN1cvl4qafjaFDidBT5ZdZpiZ45jmMC+BkAHwHwFQD/yXGcL0uS9EuSJL07qQ0krroL\nDN9R9IIQippdPrBLF93LmPEz07KhBU2+YfSMDJnZnIbNqnvAPWrLJhk/epSOItuGRkdRIvhl1rYD\njWXWhERCkiS88+kT+OS1dex6Z5c3q23oioyZjDrmrSNk8smoMtrm5HfrNA0Lr97bxXNn++8nAgBZ\ndiefXY9QaF1x3JPxebsWet+8rqBuWHAcp+vtohOp61oxRbx8143/Pd1l4tmoyGoKMqocK3qWJkcR\nHMf5Y8dxLjmOc95xnP/du+6fOI7zoS73fRvdRP2hKjJ0RR6qUOQ4Dj5/y42VVZpdHEVz5wBFjy0U\nWWGOIoOOIjJcZnMabO/3jo6i6UOcfWmFlFkH7qdIZERHUduko4iQOLzzqRMwLAcfffUBADd6tlDQ\nh3Y2m5CjRFqmnn353i4My8HzZ/vvJxJcODYTKXq2I4QiJ/y+eV2FZTs938ttT8jumj5JEV+6u4vZ\nnIbT8+Ndf87mNP/kQBBpLLMmIySnK2gMsTjs3m4Ta2XXdVHupmwqKrBwPvbkM7fMOsLUM52OIjIc\nhLUTADuKphA9Upk1o2dJkdHYUURIPzx9ehan53P4sBc/26q1sch+IkIikZbo2Yu3XSfLswkIRReP\nFfGg3Ax191TaEmpOBlkr3FGU04LrTkTJftqjZy/f2cXTAUXWo2Iur0V6L1tpdBSR0THscYWin+jK\niVJ3RxHQ1+QzK6z7o+3ttOgoIkOitE8ooqNo2pBlCZoihZRZ2yyzToiMwugZIf0g4mefuraBnXrb\ndxQRQsIRsedJ5/XNGubyGo4Vs+F3DuGiV2gdFj+rtkxUkIduhvcZiQFKtR5rzu16+qNnLdPC1bUK\n3hDSTzQKZnMadhrt0PuJY9g0TT0jIyTnZUaHhWjg/4ZHF3p/+ZcuAVuvAWb4B1pgWHZI9KwBQALU\nwXeYhHSj01HEjqLpRAs502haTvB0RhIZ11HkRs/o0iIkHu986gRM28FHX13DZrWFRQpFhERC9wYp\nTDoPdls4XkpmzXNxRUw+CxaAqi0TZScPLYJQtDdAqbtpQDiKuqZPUsLXHlRgWA6engihSMduhBif\nYbodGoyeka7k9eAW+kH5wq1tPHtmDvN5HfW2BbPb2felS4BjAduvRX7e0LPKRh3Q8gAz+GRIzOX3\nDrQzdBRNJWFnGt39FH8OkyCjKr6jaNTTOQhJO0+dmsWZhRw+/PJ9bNXaWChkxr1JhKSCtDiK1spN\nrCQkFJ2ezyOjyqE9Ra6jqAClVQ59zoLuluf3SrFsH4GpZy/f2QWAiXEU7dajOIosKLI08j5NHsWl\nhLymoj6kjqJ628Sr98t447l5FLPuDqJr/KyPyWem7UAJm3qm5+NsLiGx2NdRpFIomkbCugtMO8T5\nSCLjntUVHUV8TwmJgyRJeOdTJ/Gp6xuoty12FBESEV2RYdoObLv7tK5J4UG5mZijSJElnF+ewbWw\n6FnTREMuAM3d0OcU0bOeQlHNK7PuVVOSAr50dxfz+fEXWQPRO4rapj2Wk28UilJCboiOopfv7MKy\nHbzx3Lzf55KYUGSFjJ1u19lPRIYKy6yJrsowenQUOY7jlu5TKEqEjGf/Nxg9I6Qv3vX0CVjeYpfR\nM0KikfGO7yZ58plh2diotrBSSs4peHFlJlJHUUOZAZrhjqK96FlIR1HKHUVvODX+ImvAXaPU2lbP\nY1RB27RHXmQNUChKDcMssxZF1s+dnfMdRV17ijJFoHgy1uQzt8w6LHrGiWdkeJS8zzTgxmLI9KGr\nMlo9foTFycdA5yOJjK7IcBygYViM8xHSB0+eLOHsguu0Zpk1IdEQbotJ7ilar7TgOMDKbHK9rJdW\niri700C11dvhU2maaKnFiI4i95i51iXF0jQsfy2aVqHIcRzcWK/i8ZXiuDcFwN7J7DBXUduiUEQC\nyA0gFLVMKzC29sKtbZxfLmAur6OUdT+wvQut400+Mywbalj0jI4iMkRURcZMxv3ho6NoOgmKnomz\nOCyzTgZxVrfWshg9I6QPxPQzAIyeERIRMaxkknuKHpSbAJBY9AwALniTz24EuIpqLROGOuMKRU5w\nNC8oeibcRMvFDCot03c+pola20LLtHEsQVfXIMzlIwpF5nh6H7lqSgl5XUGjz6lnv/j7r+AH/91f\ndf1CO46DL9zexhvPzQPAnqOoVwP70iXXURSyoxGYoY6iBqDTUUSGi1DssyyznkoyASWXpi0mSVDU\nSALh2mtbNh1FhPTJ3/+mc/ihN57GlRPjL1slJA0It8UkR8/Wdl2hKKkyawC46AlFQT1F1ZYJUy8B\ntgGYzcDnywdEz0Q/0SOLruOxmsKeos1qCwCwOCGDAkTly06djiIyAHm9/zLr1a06vnK/jA+/cv/Q\nbTc3atipG75QNOt3FAU4ilploLoW6bVNywkuiW3X6CgiQ0d8rjNj2MmS8RM0DcWyXKGI0bNk6DyQ\noVBESH+cnMvhX/7wM35fCCEkGD1NjqIEo2dnF/LQFRnXHlZ63qfaMmHrXtQqJH6WD5h6JhxFjyy6\nJ/h7pk8mmA0hFE2IW3POW5+ERfnapkVHEelNTlPQNOy+2vzFF/n9f3r1kKtI9BMJoWgvetbLUSQK\nraP1FJm2DS00esapZ2S40FE03WiK3PMso2Hb3n3oKEqCzD6hiO8pIYSQ4aMrnpt1goWitXILmiJh\nIZ+cSKEqMh5bLuD6Wm9HUaVpwsl47sQQoSiryZAkoNHFnLBV84SiJVcoijKta9LYqLr/hqWZyXAU\nifXJTqMdeD+WWZNAfCtgH/GzcsPEsWIGN9Zr+KOX7+277YVb25jNaXhsybUuzvjRs16OokvuZcSe\novAya0bPyPChUDTdBDqKbOEooqiRBJ2F8XQUEUIIGQViEd0yhzP4JwnWyk0cK2YhJ3y8ceHYTEj0\nzICUFUJR8OQzSZKQ07r34gpH0TkvepbGQutNTyiaGEeRJxruMnpGBiGoXCyMctPAu54+icvHi3j/\nx6/tcxV94dY2nj875++0FFnCTEZFpZejqHjSnVIW0VFkWA5URs/ImJnNadAUiWLAlBKlzDrQ+Ugi\nw+gZIYSQUZOK6NluM9HYmeDisSJWt+tde4VMy0bTsCHlojmKAHfNWevyXMJRdG4hvdEz0VE0KRMl\nxWTmnRDRzWCZNQki52VGu+0EgjAsG/W2hbm8hp/79ou4uV7Df3nJdRXt1g1ce1j1Y2eCYlbt/eWX\nZWDpQmRHkWnZUIM+2EaD0TMydJ44UcT55ZlxbwYZE7oq+4LQQUyvo4hTz5KB0TNCCCGjRiyiJ1ko\nWis3E514Jri4MgPHAW6sH3YV1VruulHJz7lXNHdCny+vq12jZzt1A6WsivlCtEldk8hmrY1iVt3n\nfh4nYjJz2HvZsmxodBSRXuS8yEzdiFdoLZxBpayKdzx5HJePF/ErH78G07LxwqrbT/T8AaGolNV6\nl1kDe5PPIhA49cy2AZNCERk+P/rmR/EnP//WcW8GGRO6KqMVMvWMbrNkoKOIEELIqPGjZxM69cxx\nHDwoNxOdeCa4tOKeCL26drjQutJy13NawVvrtYKjZ4DrKOqWYNmqtbFQ0P1JXT0nZE8wG9XWxPQT\nCWZzWqhQ1DZtOopIb/qNnokPXimnQZYl/Px3XMTNjRo+9NI9vHBrG7IEPHN6bt9jilk1+Mu/dAnY\nvQ2066Gvb9oB0TOz4V7qFIoIIcMjo/Yuszb9Mmv+HCZBp6OILi1CCCGjIDPh0bNKy0S9beH4bPIi\nxbnFAnRFxte6CEXVlrueyxSEoyg8epbTla6duNv1NuYLOmZ0FZKU1uhZG4sTEjsTzOa08I4i0xrL\n5GYeGacEMSI1bvRMFI2JMt+3XzmOJ06U8Kt/dh2ffW0LT5wooZBR9z2mlNN8BborYvLZ5vXA13Yc\nxy2z7tX9IYQmOooIIUMkqKPIj57RUZQILLMmhBAyaiZdKFrbbQLAUBxFmiLj4soMXr132C1U9ZIl\n+cIMIGuhZdZAiKMor0OWJZSy4S6YSWSz1pqYImvBXD6Co4hl1iSIfh1FQu0VNkHhKnpto4bPvbZ1\nqJ8IiOgoAkJ7ikSko+cCzKBQRAgZPkFTz/z9FN0vidB5IDMOmzQhhJDpY9LLrB+UXaFoGB1FAPDk\nyRJevVeG4zj7rheOokJWA7Kz0RxFmopa6/A6cLvW9qd0lXJqaqeeLU5g9Ixl1mQg9oSieHlQIfiU\nspp/3duvrODKiRIAdBWKQjuKFs4DkEJ7ivZKYnt8zHyhiFPPCCHDQ1MComfe9T2djyQWjJ4RQggZ\nNb5QNKEdRQ88R9Ewpp4BwJUTJWzW2nhYae27XghFxYwKZEuRhKJCplf0zMCCV2Rdymoo95qQPaFY\ntoOtehtLkxg9i+Ao0tTRH1PxyDgl9Dv1bM9RtBcvkyQJv/jdl7FSyuBNjy0eeow79cw8pEr7aFlg\n/lwER5Ho/ghxFOmFkH8FIYT0j67KsGw3CnuQUOcjiUWGZdaEEEJGzKRPPVsrDy96BgBXTs4CwKH4\nmYiezWRV11HUZ5l1o22hYViY90SW2ZyWOkfRdr0Nx8HkOYq86FnPdTdEmfXoJ7XxKC4l5L2pZ90U\n3iDEl7jTUQQAb720jM/+z9+BY112WKWcBst2gl8rwuQz4SjqOU2oTUcRIWT4BFnSQ52PJBb7O4oo\nvhFCCBk+aYiezeU1ZLXhLPafOFEEAHz53n7HkHAUzWTUWNGzg8aE7XobALAgomcp7CjarLr/hknr\nKJrNaWibNppG789u22RHEQkgN0BHkSJLfnQtCkJUCu0p2rzmjrjvwV73R6/omTf1TKOjiBAyPPwz\njV0s6cL5yJhUMmQ0OooIIYSMFrGIbpnx1kmj4sFua2j9RABQzGo4t5jHq/cPOIpER5GuAplo0TPX\nUbQ/WbJVc0WWfR1FKZt6tll1Y3mLhclyFM3l3Pe0l/DmOA7LrEkwGVWGLPUz9cxEKatCkqIvgopZ\nN6YW2FO0dBEwm8Du7Z538RdgPcusa+4lHUWEkCESNA2FU8+SpbNskUIRIYSQUZCG6NmwYmeCKydK\nXaNnBV2BLEueoyg8epbTFdgO0Op4L3e88e0L+6Jn6eoo2vDErqUJdBQBwE6j3fV2wztO1cdwQpNH\ncSlBkiTkdbUvR5GYeBYVcf9Apfjkc+7lB38aqDzoepfQBZhwFOmcekYIGR5BJZd7gjZ/DpNAliU/\ncsboGSGEkFEgSRJ0RUZrUsusy02slIbrZLlyooTXN+u+iwhwHUUzngEgavSs0CXFsiWiZx1l1g3D\nmlhhrhvCUbQ0YR1Fc3n3Pd2td193i2NXOopIIDldQcOIO/XMONRPFIZwFAUqxSeeAX7gN4H7XwQ+\n8C3A658+dBcRPet5VrktHEWMnhFChkdgR5EfkaWokRSip4iOIkIIIaNCV+WJFC4My8ZGdbjRMwB4\n8pQ70forHfGzSst0+4kAVygyaoAVHBnLewOUOidtb3tunHk/ehbBVDBhbFbbUGTJd/BMCnuOoh5C\nkfeZ1sdwTMWjuBTRrYU+jHLT3DfxLAp+R1HYl//pHwZ+4s/ccYu//beBT/8K0JFnFWOne5ZZ+x1F\njJ4RQoaHmBTB6NloEMIcXVqEEEJGRWZChaL1SguOA6zMDjt6dnjyWbVpYkYYBrLu7WhVAp9H9OJ2\n1p1s1dqQpD1RQ1ymafLZZq2FhYLuxvAmCPFe9uoo8oUilVPPSAA5rQ+hqA9HUUk4ipoR3EvHngB+\n4s+BJ94FfOwfA//p7/u2xj1HUS+hSEw9Y/SMEDI8xD6o2wGk4QnadL8kh+iE0tXJOhgjhBBydJlU\nR9GDchMAhu4oWillsFDQ9wlFtZaJmYwnMAihqLkT+Dz5LtGz7XobpazmDygSJoQ0TT7bqLaxWJis\nfiIAmM0Hi257QhEdRSSAvK7ELrPe7Uco8pTNwDLrTrIl4Id/G3jH/wF89Y+Bj/8SgL0z9Uqvs8pG\nHVCzAM86E0KGyF5H0eH9p2WL/RRFjaQQ7zfFN0IIIaNCV+WuXYTj5qEnFA27zFqSJLfQuiN6Vu2M\nnmXcaFpYT5FwFNU6o2d1wy+yBjrTJ+kptN6otiaunwgAZnQVsrRXGH6Qtn9Ck2XWJAC3zDpmR1HT\n8JXKqGRUGZoixWuzlyTgTe8BHnkzcO9FAIARNna6XaebiBAydPbG5nZxFLGjKHGEo0ilUEQIIWRE\n6MqEOop2PUfRkKNnAPDkyRK+9qDiu6UrTRMzmQPRs5DJZ6KjqNOcsF1rY75jPZnK6Fm1jcUJm3gG\nuENAZnNaaPQsQ0cRCSIXs6OoZVpoGrYfJYuKJEkoZbXojqJOli8D61cBx/HP1Gs9HUUNCkWEkKEj\nflzFiNFOLHGmhs7GxNgrs6b4RgghZDRMbvSsBU2RsJAfvkhx5WQJbcvGjfUqANdRVPSnnkVzFHWd\nelZr73cUhfTqTCKb1RYWC5PnKAJc4a1nmTWnnpEo5HUFDSO6UFTx7IClPtrdSzmtPzvh0iWgXQEq\n9301u3eZdQ3QKRQRQoZLYJm1iJ5R1EgMP3pG8Y0QQsiImNTo2Vq5iWPF7EhKlK+ccMWgV++V4TjO\n/uiZ7yiKFj1rHOgoms93i56lQyhqtC3U2tZEOooARHIUiWPZUcKjuBQRd+qZsAPG7SgCgGJW7dNR\n9Lh7uf7VPUdRYPSME88IIcPF7yjqWmYd4nwksREOLm0MZ78IIYRMJ7oio2WMXii6tVnDs7/0UXz1\nQfdI14Pd5khiZwDw6FIBGVXGl++V0TJtWLaDwkGhqBUtelbf11HUxnyHoyirydAVOV5NyRjZrLUA\nAEuTKhTldezW211vM+goIlHIaWqsMuuy7yiKFz0DXHGpr9zp8mX3cv1qR5l1wNQzrRD/NQghJAbB\nZdYhzkcSm70ya76nhBBCRoOuymiNwVF0/WEVO3UDf/KlB11vXys3hz7xTKAqMi6fKOHVe2U/WTIj\nomd6EYAU6ijyp555KZZG260y6XQUSZKEUk5NjaNos+qKMJMcPQtzFLHMmgTiOopMOM7hno1uDO4o\n6kMlLiwD2Tlg/avhY6cNOooIIcNH/LgGOoooaiRGhtEzQgghIyYzpo4isV769PWNQ7c5joMH5ebQ\nJ551IiafiWRIUTiKZNmdfBZSZp1RZUgSUG+5QtGW53RZKOxfT5ayvcWNSUM4iiY1ejYXIBSJQSx0\nFJFAcroC2+k+uacbQuXtq6Moq/WnEkuSGz/buOpHz3pOEzIa7CgihAydoOiZZTtQZAmSRKEoKTKq\nAkWWRtLHQAghhACizDp68iIphCDz4u0dVFv7T7JXWibqbQvHZ0fnZLlysoTdhoGra26htd9RBLjx\nsxBHkSRJKOiqX3eyXXOFovkDZdylXJ/pkzGw4TmKlmYm21Fk24fNIKJ3i1PPSCD5LuViQYjc6Egd\nRYArFK1/bW/sdK/FQrvGqWeEkKGT8QoAu4nshm0zdpYwuirToUUIIWSkZFRlLGXWourDtB187rXN\nfbet7TYBYOSOIgD43GtbADqiZ4A7+SxEKAJcc0LDcP9dW0IoKnQRivpdK44YP3o2qY6ivAbbAart\nw+8ny6xJJA5mRsPYcxT10VGU01BvW358LBZLjwP1DSgNd2ep9oofGA0KRYSQoSMcRSJm1olpOdAo\nFCVKRpUZOyOEEDJSdGU80bNy04CmSMioMj51bb9Q9KDsCkWj6igCgCdOFCFJwOded7flkKMopMwa\n2D9Aabvew1GUVVPjKNqstpDTFL+oe9KY9dI/O7XD7+c4y6wn890iXcl5H+5GF7WxG+WGAVWWkNPi\nK5BFT32uNE0sFGKqr16hdW73BgAtpMyaQhEhZLhEiZ6R5PjOKyso9uFkJYQQQvpFH2NH0WxOw+Xj\npUM9RQ88R9Gopp4B7tSyR5cKePWeKwgdEop2V0OfI6cph6JnB9eDsymKnm3W2hPrJgL2nE4btRbO\nLu5fG7PMmkQi7wk+9ajRs6aBUk7rq3tDxNUq/fQULV8CABTKNwD0KLN2HFcoYkcRIWTIKLIERZa6\nTj0zLLt34T7pi7c9fgy/+N2Xx70ZhBBCpohxCUXlhoFiVsObLyzha2sVPKw0/dvWyqOPngFu/EzU\n3eyLnmWiRc/EACUA2KobkKQ914vAjZ4ZkYcsjZONaguLE9pPBOx1J4mIXCdtllmTKPjRs4hC0W7D\nPPSljopwFImeo1iUTgNaHqWKKxR1LbM2W4Bj01FECBkJvSzppuX0LtwnhBBCSCrQVXksHUWVpolS\nVsVbLiwBAD5zfS9+9qDcxFxeQ7aPdMcgXDlZ8v98uMw6PHpWyOwvs57LHU6IlLIaDMtBI2IlyjjZ\nrLaxPNGOIlco2qi2Dt3WHmP0jEJRisjFLrM2UMr2ly4Uk9L6chTJMrB0CcXqTQA9yqyNuntJoYgQ\nMgJ6nWk0bad3jxohhBBCUoGuyDAsp+vkqGFSabqOoisnS5jLa/hUR/zswW5rpP1EgidPzgKA353k\nIzqK7GBBLacp/npzu94+1E8E7HXg9mUqGDGbtRYWC5PrKFr0Yn2bXYSill9mTaGIBCAKuOJGz/pB\nRM/K/QhFALD8OOZqrwEA1G4fbCEUMXpGCBkBmtL9TKNp23QUEUIIISnH7yMcsauo3DRRzKpQZAnf\nfH4Rn76+4cex1spNHBuDUCQmnxUy6v4KkmzJTXS0q4GPP1hmfXDiGbAXRet7rTgiHMfBZnWyO4qy\nmoJiRsVGl+iZYdnQFbmvKplBoVCUIvaiZ9HLrEt9For60bN+xx4uXcJM6wEKaHR3FJXvuZfZuf6e\nnxBCYpBRZf+sTCcmy6wJIYSQ1COcM91+64dJpbm33nrLhWXc323i5kYNgBs9O14avZNluZjBcjGz\nP3YGuI4iIHTyWU7fi55t1YzujiJhKpjwQutyw4RpOxPdUQQAS8VM9+iZaY+lyBqgUJQq/OhZxCxo\nuWn6tsC4CCdS319+b/LZeeled6Ho+p8Ckgw88pb+np8QQmLQM3pm2RzlTgghhKScoAmnw6TiOYoA\n+D1Fn76+AdOysVEdT/QMAJ49M4fl4gFxRJygr28efkAHnWXW27U2FgqHjQdirbg74ULRRs0VX5Ym\n2FEEuPGzXmXW4+gnAoD+VAQyFuKWWQ/iKBIKdKVfR9Hy4wCAi9Ld7mfrr34EOP0NQH6hv+cnhJAY\nuN0FLLMmhBBCjiKiw2WU0TPDslFvWyh6662zi3mcWcjhU9c28J1XVuA4wMrseISiX/6Bpw67qxYe\ncy83rwMnnun52IKuoGFYcBwHWymPnm1UXKFokjuKAHfy2Y31w5HAcQpFPI2aIrJqdKGoaVhomXbf\nHUWKLKGYUfv/8s8/CktScUm5dzhTWXkA3P8icPE7+3tuQgiJiR4QPevqeiSEEEJIashoo3cUVb0T\n6p0JjrdcWMJf3dzEvZ0GAIzNUbQ0k8GpudyBKy+6iY71rwU+NqercBxgq9ZG27R7RM/il1k3DQs/\n9TtfwLW1SuTHDMpmzXXpTHJHEeBun9jWTtoWhSISAVmWvBb68C+kcAL1O/UMcHuK+nYUKSq2smdx\nQb57+LZrH3MvL72j720jhJA4LM7oWK8czn67Zdb8KSSEEELSjK64J9RHKRSJdVKxI8Hx5gtLqDRN\nfPTVNQDAypiEoq6oGddV9PArgXcTKZa7nti10HXqWfzo2VcfVPAnX36AD710L/JjBkVMEpt0oWhp\nJoPtehvmAUdc27Khjek4lUfHKaOzhT4I4QTq11EkHjtIQdl65hwuoJtQ9FGgdApYeUPfz00IIXE4\nt5DH7c26P4lEYFp0FBFCCCFpZxwdRWK9Vew4Mf/N55cgScAfvOiugY6PKXrWk+XLERxFnlC07QpF\n3aJnmiIjryux1oqrW+7U6xdub0d+zKCISWLdxK5JYmlGd11c9f2uorZp+7HKUUOhKGXkdAWNKEKR\n96Xtt6MIGNBRBGAt+whOS2uAij6RUAAAIABJREFU0dy70mwDN/7cjZ2NYcwfIWQ6ObdYQKVlYuuA\nrde02VFECCGEpB1fKLKidbkmgX9ivmO9tVDQ8eTJEtbKLWiKNHkCxfLjwNYNd03WA+EouuMJRd3K\nrAH33x2npuS2JxS9tLoLy3ZC7p0Mm7UW5vPaxLvHl7ypbBuVw0JRhtEzEoXojqLDmdm4xP3yH2RN\nPwcFjrszEtz+K6BdAS6+ve/nJYSQuJxbzAMAXt+s77vetGyonHpGCCGEpBrhuujWRzgs9qJn+9db\nb/amnx0rZiFPmmt5+TJgm8DWzZ53Kejuv0dEz+Z6iF2lnBorenZn2z0Gq7ZMXHs4mp6izWobizOT\nXWQNwN/Gzdr+mgSWWZPI5HQVdWN0jqJBhKL72ln3D+tf3bvy2kcBRQce/da+n5cQQuJybrEAALi9\nVdt3PcusCSGEkPQjFtOjFIp6rbfe4glFExc7A/zJ1PvWZwfIRegoAtzJZ3HKrG9v1f0x9S/e3on8\nuEHYrLax2CU6N2mI92WjekAoYpk1iUo+Ypm1UHdnB+woGiR69kA7DQsysH5178prHwUeeQuQmen7\neQkhJC5nFnKQJOD1jYOOIkbPCCGEkLSTGUNHUaVHguPrH1mArspjm3gWyOJFAFJgT1G+o6NIlnp3\n3vYTPXvT+SUsFHS8cGs0PUUbtZYf65pkfEdRdX/0zGCZNYnKSMuss65QdLD8NSoNR8cD6Riw4e2I\ntl4DNq4CFzntjBAyWjKqgpOzOdzaPOgo4tQzQgghJO2Mo8xaCEUzmf1CUVZT8Mvf/xR+/C2Pjmxb\nIqPngflzgY6izqlnc3kdSg/ndSmnRY6emZaNeztNnF3I4bkzc3hxdYSOogmfeAa4k8p1Rcb6QUfR\nGMus+y+wIWMhepm1CV2RByq/KmZVWLaDettCIRP/o2LaNm4pZ3FKKNbXPupeXvzOvreJEEL65dxi\nHre2DjiKGD0jhBBCUo9YTI966lleV7qecPrBN54e2XbEZvmJQEdRzuso2m0YOL9c6Hm/2RgTsu/v\nNmHZDs4u5JHTFHz8qw+xWzcwm+/f1BBG27Sx2zCwWJh8R5EkSVic0Q85ithRRCITx1FUyqmQBpgs\nJtxI/fYUmZaDO8oZYPM6YJnA1Y8AixeAxfN9bxMhhPTLucUCbh0qs3ZYZk0IIYSknIwmpp6N0lFk\nDNQHOzaWHwc2r7nrsy7kNcX/83zA1LZSVkWlZcKOMMFs1TtRd2Y+j+fPzgMAvnhnuK6ibW/UfBoc\nRYA7+exgR1GLQhGJSl5XUY/QUVRuDL7jEg3+/fYUmbaDu+oZwGoDD18FXv8UY2eEkLFxbjGPrVp7\nn/ht2jYdRYQQQkjKGYejqNI0D008SwXLl9312fZrXW/OZzqEooAi6FJOg+MA1Qhr09tCKFrI4+kz\nc5AlDL2nSIguSykRiro6iix7oITQIFAoShk5XUEjytSzponiAP1EwF6Df1RL4UEMy8Y97Zz7l7/5\n94DVAi69faBtIoSQfnlkMQ8AuN3hKmKZNSGEEJJ+xtFRVG4aKRWKgief6Yrs9xL1mngG7KVPduvh\na8XV7ToUWcKJ2SxmMiourRTxwu3hCkVCdElDmTXQ3VHEMmsSmbymwLAcGCG2StdRNNiOa1BHkWU7\nWNPPuH956fcAfQY4+80DbRMhhPTL2QU3Z/96R6H1OH+ACSGEEJIMvlA00uiZOdDgoLGxdMm97CEU\nSZLkx88CHUXZ6DUlt7caODWX8/ucnjs7jy+u7kSKrfXLZs0VXRZTIhQJR1HnIKlxllnz6Dhl5LwW\n+jBXkdtRNKCjKIGOorY6AxRPuvbGx94GqOmw/hFCjh7nPEdRZ0+RZTs9p3kQQgghJB2IxXRr5NGz\nFApFmRlg9mxIobW75lwo9P73lXKuqaDciBY9O7uQ9//+/Nk5VJombqxXo251bISjKC0dRcszGbQt\nG+UOkwbLrElk8l4Lfdjks3LDTKyjqNx3R5F3pl7YGy+xn4gQMj4KGRXLxQxudTqKbEbPCCGEkLQj\nSRJ0RR5t9KyR0ugZ4K7PejiKAHeAEgDMBUTPZkX0LEJNyZ2tOs4s5Py/P+cVWr94e3iF1hvVNnRF\nRrGP6d3jQAham178zLYdmLZDoYhEI6e7/2Vhk8/E1LNBGLSjyBRn6pcvu1dc+M6BtocQQgbl3EIe\nrx9wFLHMmhBCCEk/uiqjZYZ3uSZFpTn4ifmxsfw4sHENsLu/X8KcENhRFDF6VmuZ2Ky1cabDUfTY\nUgGzOW2oPUWb1RYWZ/SBpoCPEtGltOE5oUSMkkIRiUROc7+0QZPPmoaFtmkPvOPKagp0Re67o8gQ\nY6ff9B7gh38bKJ0YaHsIIWRQzi0W/DJrx3E8oYg/hYQQQkja0dXROYqahoW2ZafYUXQZMJvAzq2u\nNwtHUdjUMyDcVLC67U08m98TimRZwrNn5obqKNqstVMTOwOAxYIrFAlHkS8UsaOIREF8aYOiZ0LV\nTaJcrZRT++4osmwbmiIBc2eAJ79v4G0hhJBBObeYx4NyE422BcNyywI1Rs8IIYSQ1DPK6Jm/3kqr\nUHTsCfeyR0/RXkdRb6GlmFEhSeFCkThB19lRBADPn53H1YeVvteaYWxUW774kgaWiu57LSafic8y\nHUUkEkIoCoqeiUKx2SSEoqzWf/TMYkksIWSyEIXWt7fqsLxJGwodRYQQQkjq0VV5ZFPPROIilVPP\ngNDJZ76jKN/73yfLEooZNbTPdnW7AQD7omcA8NzZOTgO8PLqbtStjsVmNV2OooW8DknqiJ6ZdBSR\nGOQiCEWiUCwJhbuYVfuOnpm2w7HThJCJ4pHFAgDg1mYNhu3+ANNRRAghhKSfzAijZ2J9lNroWbYE\nlE4BD3sJRSpkCaFVJqVcuKlgdauOmYx6SHR69uwcJAlD6SlyHAcb1Zbf+5MGVEXGfF6no4j0hz/1\nzOgt3iQbPdP6tgOalk1HESFkohCOolubdVhe9Ixl1oQQQkj6GWVHkRBHimktswYCJ5+tlLI4PZ+H\nHHKMVMqGrxVvb9VxZiF/qFS6lNVwYXkGLw5BKKq1LbRMG4sB0blJZLGgY/NAmfW4jBcUilJGtOiZ\ncBQNvuMaxFFk2A7P1BNCJoq5vI7ZnIZbW3uOIoXOR0IIIST1jCV6lmqh6DKwcRWwD79n7/32C/h/\nfupNoU8xm9P8NEsvVrfqODOf63rb82fn8eLqDhzHibbNERGF0IspchQB7uQzOopIX+QilVmLzOzg\nVshBOoo4TYgQMok8spjHrc06TFFmTUcRIYQQknp0RUZrZNEz4ShKafQMcB1FRh3YXT10U15XcayU\nDX2KUk71+3G74TgOVrfrh4qsBc+dncNO3cBrG7Xo2x0B0fOTpo4iwN3ezdp+RxGFIhKJvJYiRxGj\nZ4SQCeTsYgGvb9Y6yqy5nyKEEELSzkijZ0dCKLrsXvaYfBaFsOjZerWFpmEfKrIWPH9uHgDwwu2d\nvreh6+tWXFfOchodRZX9jqIMo2ckCqoiQ1fkYKGoaUBXZWQ9UWkQSlkNDcOC0YeN02L0jBAygTyy\nmMfd7QYahrsfZek+IYQQkn5GXWYtS0BBT7FQFDL5LAph0bPVrToA9HQUXVieQTGjJt5TdGfbfd1T\nc90jb5PK0oyOSstE07AYPSPxyekKGu2AMuuGmVheVqjk/biKTMvh2GlCyMRxdiEP24Fvc1YpaBNC\nCCGpR1dltMzeJ9OTpNI0MZNRQ8ueJ5r8AjCzMpijKKeh3u5tKljdagBAT0eRLEt4/HgRN9arfW9D\n99d1J63N5dPVISWmtG3V2r5QxDJrEpm8roQ6ipLoJwL2Jqf101Nk2DYdRYSQieORpQIA+AclnHpG\nCCGEpB9dGV2ZdblhpHvimWD58kCOolKIqeC25yg63aPMGgDyGRUNI9n/t9XtBk7P5w5NWpt0RPn2\nRrXli290FJHI5HQFdSO4oyg5R5H7PHEdRbbtwHHAMmtCyMRxbtE9q3Xjoeco4n6KEEIIST2j7Sgy\n/RPqqWb5suso6nPq2Kzn2OkVP7u9VcdKKRNYiZJVZbQC1rb9sLrVu0B7klnyyrc3q22WWZP45HUl\ndOpZUjsuoRIHlZR1Q4ydZqSDEDJpLM9kkNcV31GkcD9FCCGEpJ5RCkWVppHuImvB8uNAuwKU7/b1\ncGFO6JU+Wd2q48x8sGCT1RQ0ExSKHMfBne1Gz7jbJCOiZ+vVlj/BT2f0jEQlr6moB3QUVRqGL/AM\nihCcKjGFIjFNiJEOQsikIUkSzi7kfaFIo6OIEEIIST0ZVRmto+hICEVi8ll/8TO/pqTHWjGKsyer\nyWgmGD3bqLbRMCycCYi7TSqLnY4iMfWMjiISlVyoo8jAbEKOIqGUlxvxomeG5QlFnCZECJlAHlks\n+JFaOh8JIYSQ9KOro+soqjSTq/oYK75Q1F+htVhzdouetU0b98vNUGdPVlPQTLCEfNWbeJZGR1Fe\nV5HXlX0dRSyzJpEJKrN2HAe7DSO56FmIStwL0/tg01FECJlERE8RwP0UIYQQchTQFRmG5cC2u/ft\n/OuPXcU//oMvJfJalaZ5NKJnhUUgUwJ2bvf18L3o2WFTwd2dBhwnXLBJOnq2upVeoQhw42eb1Zbv\nKBpXR9ER+HRPH7kAoahp2DAsJzGFe0ZXIUmuvTIOfvSMZ+oJIRPIucWC/2c6HwkhhJD0IxbUbctG\nVj5cnvzXNzexXWsP/DqO43gdRUfAUQQA2TmgudvXQ8Wk7W6mAiHYhEbPVDd65jhOIlPK7mw3AARP\nWpsIHAe49wLw5T8AdleB7/91QM1gcUbHRkf0jEIRiUxeV9DoobqKL6n40g6KLEuYyag9C8p6YbCj\niBAywdBRRAghhBwtMp1CUZcpW5WmiVor3snvbtTaFmwnufXW2MnN9i0U5TQFmiJ1XSve9p09wYJN\nxvu/apnd/9/isrpVx9KMjrw+gf8/jgPc/QLw5Q8Cr34I2O1wcr31HwErV7A0k8HqVh1ty4Ykje84\ndQLfPRJGXu9dZi2+pElmZktZze/yiIolOopYEksImUD2CUV0PhJCCCGpRzgvWoYNZA/fXm4YqAX0\nvEZFDPmho8gdEFLKal07ila36tAVGSvFLv8ZnS/viUNNw0pGKNqu43TIpLWR064BX/yPwGc/AGxe\nB2QNOP9twNt+EciWgP/77wG1dQDA0oyOF2/voG3a0BU5EZdVP1AoSiE5TUHTsGHbDuQDCuOeoyi5\nHVcxq8buKDJsr6OICzBCyARyYjYHXXFLLyloE0IIIelHjBHvVWhdbhpotK2BI06ij+dIdBQBQHYW\n2H6974efWcjjY6+u4b3ffhErpT1RyBVscofWqwfJ+UJRMkXkq1sNPHNmLpHnGpjdu8DnfgP4wm8B\nzR3g5PPA9/5b4PL3ALl59z4PvYlzvlCUwVathZYnFI0LHh2nkLzufpm6xc/EjivJcY2uoyhumTUd\nRYSQyUWRJZz2rNCMnhFCCCHpx+8oMg8LDrbtoNoyYdrOwJPRxLroSEw9A1yhqLHT98P/6Q8+hVrL\nxE/+X5/fV0p9e6seqVA6q7n/b0kUWpuWjbs7DZwZdz+RbQN/9N8B738a+MyvAI++FfjxjwA/8WfA\nc//1nkgEAIVl97K2AQBYLOiwHeBhpTm2fiKAQlEqEUJRt0LrYTiKSjm1a5N9ECYdRYSQCecRr9Ca\n+ylCCCEk/QQJRZWWCccbhlZrDSZIiEqOI+Uo6jN6BgCXj5fwvh95Fi/d2cUv/v7LcLw3enWrEVpk\nDXREz8zBhaL7u01YtjP+iWeb14HP/5/Ale8F3vsi8CO/A5z9JqCbky03D0jynqOomAEA3N2hUERi\nkvOKuRrdhKIhdBQVs1rs6Nmeo4gLMELIZCIOXjROPSOEEEJSjx896yYUdaxlBi20Lh/FjqJ2BbD6\nf1/e/uRx/MLbL+EPvngPH/jETew2DOw2jNAia6DTUTR49Gx1O9qktaFTuedevvFHgflHgu8ry0B+\nyReKFguuUHR/pzFWoeiIyKDThe8oMg5/mctDULiLWRXVmDtUU0w94wKMEDKhPHmyBF2V/X0qIYQQ\nQtKLmJ7VtnrXcwBArcdQoKiI9daRmXqWnXUvW2Ugv9D307zn2y7gqw8q+Ocf+aov1kVyFKl7ZdaD\ncmerAQA4M+4y68oD97J4Mtr9C8t+9Gy5qAMA1qstnF+eGcbWReKIfLqni1xQ9KxhIKPKiTTGC4pZ\nFdWmGav4zfSyvxodRYSQCeUHnj+NN19YOjpnBAkhhJApRjiKWl0cReV9jqJBo2dHsKMIcONnAwhF\nkiThX/zQM7i1Wcf7/vQqAESaPpbRkhOKVrfrkCXgxFzwpLWhU/YcRcXj0e5fOOwochywzJrEI+99\nmbpGz5pGov1EADCT0WDaTiw7oHAUKRSKCCETiiJLODk35rJDQgghhCRCUEdRuZFg9KxhQlMkZMYY\nC0oUXyjqv9BakNMV/MY/eCOWvZ6deGXWCUTPtuo4MZsbf61A5T6QKQGZiI6gwrIvFM3mNL++JfHo\n2frVyHeloyiF5L2Oou6OIhOzSQtFXoyt0jJ8N1MYjJ4RQgghhBBCRkUmqMy6uScO1QeMnlWaBkpZ\nLXLSYuLpdBQlwInZHH77x74Bn7mxEWldKpIwrQTKrFe3G5F6kYZO5T5QPBH9/jPH/OiZLEtYKOh4\nWGklLxR94p9FvitX8SlkL3p2eCe32zBQSriBXzxftRl9pyqiZyyzJoQQQgghhAwb31FkBUfPqglM\nPTsyE88AIDfnXiYkFAHAlZMl/Dff8li0lw9Iy8Rldas+/n4iACjfB0oxhKLCklsobrgdS0szriMr\nUdfazirw5Q9GvjuFohQiildHFz3zhKIYNs09RxGFIkIIIYQQQshw8TuKukSYOsusB3UUlZvG0eo3\nTNhRFPvlE+ooahoWHlZakeJuQ6fyIJ6jqLDsXnquosUZt9A60QjdZz8Q6+4UilJIPqTMOuliNSEU\nVWI5ilyhaOz5UEIIIYQQQsiRJ8xRJJIOcac5H6TSNI/OxDNgAoQir6OoS2QwDne2vYln446e2TZQ\n7VcocnuKlj1HUWJl1o0d4Au/BbzhByI/hKv4FCKiZ40uqmt5CDsuv6MojlBku190llkTQgghhBBC\nhk1QmXWlaWBxRocsDV5mXWkaKGaOkKNInwEk2RUTxkBWTcZRtLpdB4DxR89q64BtDiQUCUdRYh1F\nL/w20K4Cb/qZyA+hUJRCdEWGIkuHbJOO4wzFUSSeL1b0TDiKZH7ECCGEEEIIIcMleOqZO/CnkFFR\nG7CjqNw4Yh1FkuS6isbkKJJlCboiDzz17M6WJxTFjZ799b8Dfie60yaUyn33Mm5HEeALRaKjKBGh\nyGwDf/0B4NG3AiefjfywI/QJnx4kSUJeUw5Fz6otE6btDK2jqNJRAheG7yhiRxEhhBBCCCFkyGRC\nomelrIZyw0zEUZT0emvsjFEoAoCMJg/sKLq9VYeuyn5sKxLNMvDnv+y6bWwbSMLkIISi4snojznk\nKEpQKPryB4HKPeDdvxLrYbR7pJScrhwqs/7QS/cAAE+fnk30tWb6mXpmC0cRhSJCCCGEEELIcPHL\nrLs5ipoGilkVhczhk+1xMC0btbZ1tBxFAJCdG6tQlNUUtMwBo2dbDZyez0GOs/78/H8AWruAYwHN\nhKJ3ZXdNjuLx6I/RC4CW98usl0T0bNCOIscBPvOrwPJl4MJ3xHoohaKUktf37+RMy8YHPnEDz5yZ\nw5seW0z0tTRFRlaT+4qeqSyzJoQQQgghhAwZSXIjTN07ikyUvOjZIGXW4rFHauoZMHZHUVYbPHq2\nul3H2TixM6MB/NWvAWrW/Xt9a6DX96k8cDufZlbiPa6wlHz07OZfAGuvAN/8s27EMAZcxaeUnK7u\nK7P+o5fvY3Wrgfe87TykmB+CKMxkNFRi7FQNi2XWhBBCCCGEkNGhq92FItHjWtDVQz2vcRDDfUpH\nzlE0m5yjpg9y2uG0TFxWt+rxiqxf/F1XmPmm/9b9e31zoNf3qdwDCscAJeZnpLB8WCga1HTxmV91\nBaunfjj2QykUpZR8R/TMth3827+4jksrM/iOJ2IqlxEpZtVYU88sET1jRxEhhBBCCCFkBOiqjLa1\nX3BwHMefDF3IKKgOUGZd9jpb6ShK+OU1Bc0Bome7DQPlpokzC7loD7AM4NPvB858I/DE33avq2/0\n/fr7qDyIFzsTdAhFCwUdeV3BQkHvfzvWvgzc+DjwDT8JqDF6mzyOmBQ6PeR1xS9i+9OvrOHqWhX/\n5keejZfJjEExq6Iaq8zaFYroKCKEEEIIIYSMgm7Rs3rbgmU7KGbd6NkgjqJy4yg7isYoFKnKQGXW\nq2LiWVRH0Sv/GdhdBd75r/YmjiXlKCrfB+bPxX9cYQm4/xIAV/D8k597K46V4gs8Pn/1a27v0df9\neF8Pp6MopeS8qWeO4+DX/uIGzizk8K6nY4zgi8lMzDyv6CjSkmiOJ4QQQgghhJAQdFU+VGa9FxfT\nkNfVgaaeiSnQR27qWW4OMOruKPUxkBmwo+jOticURekosm3gU+8DVt4AXHw7kE9YKKrcG8xR5Ljr\n6LOLeWQ1pb9tsEx32tnTfwfIL/T1FFzFp5S8rqBhWPjMjU28tLqDn/rW80Mtjp7JxIuembYNWcLQ\nHE6E/P/t3XuUY2d55/vfK21pq6qkqu6u6pvddrfvF4gxpjHGQIy5xDa5EAJMIJAQDhzWMGEmmZzk\nDDOslTmLM5mVhMzkrEyYNYcTCAkwBAaYCQMGMwE7GBwutjE2xrf2pd337uruKtVFUunynj/evVWq\nqi2VbiWpVN/PWrW2Stra+23V2yrtp57neQEAAIBaUT2KstXgjqe0H9dCB6Vn4fXQUK56JvUtqyiV\n6DSjKCepyYyix78iTT8pvep3XYPn5KjkjVRXHOtIMS/lzkuZC1p/7thOqVLqTq+oM4+5wN+BV7V9\nCAJFm9RI0tPiUlkfvfuQdmV8vfmGfRt6vnSLPYqKZSuPbCIAAAAAPRJVepbNBYGioPQsVyxX+6m2\naqh7FEl9DRStzgRrxZHzi8qkPE2MrvNzsVa69z9IOy6Vrv3l5ftHJ7uz6tncCbcdb6PSZ2yn23Yj\nYHXsAbe98Ia2D8GV/CY1moxrer6g+54+q//9VZe2n5bWpPFUoqXSs3KlIo9G1gAAAAB6xDWzjs4o\nyqQ8jSVdJlC7fYqGN6Ooz4EiL9Zxj6Kmsome/pZ04iHplf9SitVcP49Ndqf0bO6k27ZVehaUwAUN\nrTty7EFpZLu0/ZK2D0GgaJMaTcZlrTQxktCvveziDT9f2KPI2uai7y6jiEARAAAAgN7wG/UoGnEZ\nRZLaLj+byxc1kogrsYEtP/qiGijqQtlTO6fvtPTsfK65Fc/u/Y/S+IXSdW9bef9otwJFx922rdKz\nXW7brUDRBTe40ro2DdkM3zpGki4C+ps3H6i+4W2kdMpTuWKVa/I/cLliN7RnEgAAAADUiuxRtKL0\nzF1DLbSZUZTNlYYvm0gagB5FsaavM1ez1jaXUXT6Menwd6SX/VPJW7Xs/OiktNiFkq9sN0rPOgwU\nLS1Ip38qXfiSjg7Dlfwmdc3ecV26c0y/efOBnpwvfEOcb7JPUalSIaMIAAAAQM/4kc2sl8vFwtKz\ndlc+mysUh2/FM6nvGUUjibjyxUrT1Su1zswVVChV1l/x7MG/kWIJ6fpfW/vY6FT3ehR5qeXAWytG\nJ9220x5FJx6WbLnjQNEQhkO3hluv2qVbr9rVs/Olg6yluUJJzZyV0jMAAAAAvRTZoyhXVNKLKZWI\nazTMKGq79GxYM4r626PID/rtFkqVlnvvHjm/KEm6uFGgqJiXfvxZ6ZpfWO4FVGt0UipkpdLS2myj\nVsydkDJ72yv5invSyI7OM4q60MhaIqMITQrfEJtd+YzSMwAAAAC9FLnqWb6k8WCVsrTfWUZRNlc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3c0c05748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "series.plot()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.4"
  },
  "name": "Critical Transitions Auto.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 2
}