Cole Wins Above Replacement Portfolio.ipynb

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    "# Cole Wins Above Replacement Portfolio (CWARP™) Jupyter Notebook Tutorial\n",
    "\n",
    "__\"Your goal shouldn’t be to buy players. Your goal should be to buy wins.\"__\n",
    "__Peter Brand (aka. Paul DePodesta), Moneyball__\n",
    "\n",
    "What matters in sports is whether a player helps the team win. What matters in investing is whether an asset improves the\n",
    "risk-adjusted returns of your total portfolio. \n",
    "\n",
    "COLE WINS ABOVE REPLACEMENT PORTFOLIO (CWARP)™ is a new metric devised by Artemis Capital Management that measures whether any investment improves the Return to Risk of your Total Portfolio. The CWARP score evaluates the non-linearity and correlation benefits of alternative investments where Sharpe Ratios and other metrics fail. CWARP is quick to calculate while providing a practical assessment of how an investment contributes to portfolio success. Unlike Sharpe Ratios, high CWARP assets are additive, providing a convenient method for allocators to screen investments that improve the portfolio. CWARP™ eliminates investments that \"pad\" performance statistics with leveraged beta and concave returns. CWARP helps fiduciaries identify hidden gems, true diversifying investments and managers that help the return to risk of your Portfolio. The score offers similar insights derived from complete portfolio optimization, but it is much easier to implement and observe from a tear sheet.\n",
    "\n",
    "The logic behind CWARP is intuitive in that it answers a simple question: If you borrowed capital to allocate to an alternative investment, does it help or hurt your aggregate portfolio's risk adjusted returns? What allocators will find is that many managers with positive Sharpe Ratios may have negative CWARP scores and vice versa.\n",
    "\n",
    "__CWARP™ is easy to use and interpret:__\n",
    "\n",
    "_CWARP™ > 0 means the new asset is improving your portfolio by increasing:_ \n",
    "_1) Return to Downside Volatility;_\n",
    "_2) Return to Maximum Drawdown;_\n",
    "_3) or BOTH_\n",
    "\n",
    "_CWARP™ < 0 means the new asset is hurting your portfolio by replicating risk exposures you already own resulting in higher drawdowns and volatility_\n",
    "\n",
    "The following workbook provides step-by-step examples and code to replicate the CWARP score for and test various portfolios. Yahoo Finance API is used to provide stock data and create the Efficient Frontier. \n",
    "\n",
    "Please feel free to use and distribute this workbook and code freely as part of your investment process.  \n",
    "\n",
    "For full information on the theory and best implementation of CWARP please download the paper on our website.\n",
    "https://www.artemiscm.com/research-market-views\n",
    "\n",
    "www.artemiscm.com\n",
    "\n",
    "_Please see important disclaimers at the end of the notebook._"
   ]
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      "/usr/local/lib/python3.8/dist-packages/pandas_datareader/compat/__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  from pandas.util.testing import assert_frame_equal\n"
     ]
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   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from datetime import date\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
    "\n",
    "import yfinance as yf\n",
    "yf.pdr_override()\n",
    "import pandas as pd"
   ]
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    "__NOTES ON CALCULATION METHODOLOGY__ \n",
    "\n",
    "_In academic literature and across professional applications there is widespread disagreement over the proper calculation methodology for the Sharpe, Sortino, and Return to Max Drawdown Ratios. The most common methodology used by most professionals is the arithmetic mean and standard deviation of returns by period multiplied by the square root of the periodicity. Many others will point out that using logarithmic returns is more appropriate, or alternatively using geometric returns in the numerator(e.g. Geometric Sharpe Ratio). In some cases, the volatility calculation will take into account the subtraction of the risk-free rate, and in other cases, this is only applied to the numerator. For the benefit of the doubt, Artemis uses the most common methodology to calculate Sharpe and Sortino Ratios with arithmetic mean and standard deviations. In each respective case, we only subtract the risk-free rate from the numerator. Sortino Ratios assume the zeroing out of positive returns, as opposed to complete elimination, in the downside devation calculation. For the Return to Max Drawdown calculation, we assume the compound annualized growth rate of the asset minus the risk-free rate in the numerator. The maximum drawdown does not take into account the subtraction of the risk-free rate. In the original research paper, risk-free rate and the financing charges are applied by month to each respective return period. In the python code below, a consistent risk-free rate and financing charge is applied to the numerator, as opposed to a variable rate by period._ \n",
    "\n",
    "_While calculation methodology will numerically affect each metric slightly, and by proxy the CWARP score, they will not lead to radically different conclusions regarding the relative rankings of assets so long as there is consistency in the technique applied. If you prefer a different methodology, please feel free to edit the accompanying code to fit your preferences._ "
   ]
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   "source": [
    "# Traditional Risk to Return Portfolio Metrics\n",
    "To calculate the CWARP score first we must provide functions for classic risk-reward metrics like the Sharpe Ratio, Sortino Ratio, and Maximum Drawdown"
   ]
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  {
   "cell_type": "code",
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    "#Risk and Reward Functions##################################################################\n",
    "def sharpe_ratio(df,risk_free=0,periodicity=252):\n",
    "    \"\"\"df - asset return series, e.g. daily returns based on daily close prices of asset\n",
    "   risk_free - annualized risk free rate (default is assumed to be 0)\n",
    "   periodicity - number of periods at desired frequency in one year\n",
    "                e.g. 252 business days in 1 year (default),\n",
    "                12 months in 1 year,\n",
    "                52 weeks in 1 year etc.\"\"\"\n",
    "    # convert return series to numpy array (in case Pandas series is provided)\n",
    "    df = np.asarray(df)\n",
    "    # convert annualized risk free rate into appropriate value for provided frequency of asset return series (df)\n",
    "    risk_free=(1+risk_free)**(1/periodicity)-1\n",
    "    # calculate mean excess return based on return series provided\n",
    "    dfMean=np.nanmean(df)-risk_free\n",
    "    # calculate standard deviation of return series\n",
    "    dfSTD=np.nanstd(df)\n",
    "    # calculate Sharpe Ratio = Mean excess return / Std of returns * sqrt(periodicity)\n",
    "    dfSharpe=dfMean/dfSTD*np.sqrt(periodicity)\n",
    "    return dfSharpe\n",
    "\n",
    "def target_downside_deviation(df, MAR=0, periodicity=252):\n",
    "    \"\"\"df - asset return series, e.g. daily returns based on daily close prices of asset\n",
    "    minimum acceptable return (MAR) - value is subtracted from returns before root-mean-square calculation to obtain target downside deviation (TDD)\"\"\"\n",
    "    # convert return series to numpy array (in case Pandas series is provided)\n",
    "    df = np.asarray(df)\n",
    "    # TDD step 1: subtract mininum acceptable return (MAR) from period returns provided in df\n",
    "    df_ = df - MAR\n",
    "    # TDD step 2: zero out positive excess returns and calculate root-mean-square for resulting values\n",
    "    df2 = np.where(df_<0, df_, 0)\n",
    "    tdd = np.sqrt(np.nanmean(df2**2))\n",
    "    return tdd\n",
    "\n",
    "def sortino_ratio(df,risk_free=0, periodicity=252, include_risk_free_in_vol=False):\n",
    "    \"\"\"df - asset return series, e.g. daily returns based on daily close prices of asset\n",
    "   risk_free - annualized risk free rate (default is assumed to be 0). Note: risk free rate is assumed to be the target return/minimum acceptable return (MAR)\n",
    "               used in calculating both the mean excess return (numerator of Sortino ratio) and determining target downside deviation (TDD, the denominator of Sortino)\n",
    "   periodicity - number of periods at desired frequency in one year\n",
    "                e.g. 252 business days in 1 year (default),\n",
    "                12 months in 1 year,\n",
    "                52 weeks in 1 year etc.\"\"\"\n",
    "    # convert return series to numpy array (in case Pandas series is provided)\n",
    "    df = np.asarray(df)\n",
    "    # convert annualized risk free rate into appropriate value for provided frequency of asset return series (df)\n",
    "    risk_free=(1+risk_free)**(1/periodicity)-1\n",
    "    # calculate mean excess return based on return series provided\n",
    "    dfMean=np.nanmean(df)-risk_free\n",
    "    # calculate target downside deviation (TDD)\n",
    "    # assume risk free rate is MAR\n",
    "    if include_risk_free_in_vol==True: MAR=risk_free\n",
    "    else: MAR=0\n",
    "    tdd = target_downside_deviation(df, MAR=MAR)\n",
    "    # calculate Sortino Ratio = Mean excess return / TDD * sqrt(periodicity)\n",
    "    dfSortino=(dfMean/tdd)*np.sqrt(periodicity)\n",
    "    return dfSortino\n",
    "\n",
    "def annualized_return(df,periodicity=252):\n",
    "    \"\"\"df - asset return series, e.g. returns based on daily close prices of asset\n",
    "   periodicity - number of periods at desired frequency in one year\n",
    "                e.g. 252 business days in 1 year (default),\n",
    "                12 months in 1 year,\n",
    "                52 weeks in 1 year etc.\"\"\"\n",
    "    # convert return series to numpy array (in case Pandas series is provided)\n",
    "    df = np.asarray(df)\n",
    "    # how many years of returns data is provided in df\n",
    "    difference_in_years = len(df)/periodicity\n",
    "    # starting net asset value / NAV (assumed to be 1) and cumulative returns (r) over time period provided in returns data\n",
    "    start_NAV=1.0\n",
    "    r = np.nancumprod(df+start_NAV)\n",
    "    # end NAV based on final cumulative return\n",
    "    end_NAV=r[-1]\n",
    "    # determine annualized return\n",
    "    AnnualReturn = end_NAV**(1 / difference_in_years) - 1\n",
    "    return AnnualReturn\n",
    "\n",
    "def max_dd(df, return_data=False):\n",
    "    \"\"\"df - asset return series, e.g. returns based on daily close prices of asset\n",
    "    return_data - boolean value to determine if drawdown values over the return data time period should be return, instead of max DD\"\"\"\n",
    "    # convert return series to numpy array (in case Pandas series is provided)\n",
    "    df = np.asarray(df)\n",
    "    # calculate cumulative returns\n",
    "    start_NAV = 1\n",
    "    r = np.nancumprod(df+start_NAV)\n",
    "    # calculate cumulative max returns (i.e. keep track of peak cumulative return up to that point in time, despite actual cumulative return at that point in time)\n",
    "    peak_r = np.maximum.accumulate(r)\n",
    "    # determine drawdowns relative to peak cumulative return achieved up to each point in time\n",
    "    dd = (r - peak_r) / peak_r\n",
    "    # return drawdown values over time period if return_data is set to True, otherwise return max drawdown which will be a positive number\n",
    "    if return_data==True:\n",
    "        out = dd\n",
    "    else:\n",
    "        out = np.abs(np.nanmin(dd))\n",
    "    return out\n",
    "\n",
    "def return_maxdd_ratio(df,risk_free=0,periodicity=252):\n",
    "    \"\"\"df - asset return series, e.g. returns based on daily close prices of asset\n",
    "   risk_free - annualized risk free rate (default is assumed to be 0)\n",
    "   periodicity - number of periods at desired frequency in one year\n",
    "                e.g. 252 business days in 1 year (default),\n",
    "                12 months in 1 year,\n",
    "                52 weeks in 1 year etc.\"\"\"\n",
    "    # convert return series to numpy array (in case Pandas series is provided)\n",
    "    df = np.asarray(df)\n",
    "    # convert annualized risk free rate into appropriate value for provided frequency of asset return series (df)\n",
    "    risk_free=(1+risk_free)**(1/periodicity)-1\n",
    "    # determine annualized return to be used in numerator of return to max drawdown (RMDD) calculation\n",
    "    AnnualReturn = annualized_return(df, periodicity=periodicity)\n",
    "    # determine max drawdown to be used in the denominator of RMDD calculation\n",
    "    maxDD=max_dd(df,return_data=False)\n",
    "    return (AnnualReturn-risk_free)/abs(maxDD)"
   ]
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   "source": [
    "# Coles Win Above Replacement Portfolio (CWARP™) Code:\n",
    "\n",
    "CWARP begins with the assumption an allocator has a pre-existing or replacement portfolio (60/40 Equity-Bond or Passive Equity). The CWARP score first calculates the 1) Return to Downside Volatility (Sortino Ratio) and 2) Return to Maximum Drawdown of\n",
    "the ”replacement” portfolio. CWARP assumes the allocator borrows cash to layer an alternative investment on top of the replacement portfolio. CWARP recalculates the Return to Downside Volatility and Return to the Maximum Drawdown for the revised portfolio. CWARP is then calculated by taking the geometric average of the ratio between the Sortino Ratio and Return to Maximum Drawdown of the new and replacement portfolios.The CWARP shares inputs with the Sharpe Ratio, including manager returns, volatility, and the risk-free rate. Several new inputs include the returns of the replacement/base portfolio, financing rate, and the percentage allocation for the new investment. The standardized CWARP score uses a 60/40 portfolio or Equity Beta as the replacement strategy and a 25% weighting. If an asset can be portfolio margined without added cash (e.g., derivative-overlays), it may be appropriate to reduce financing charges to 0%."
   ]
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   "source": [
    "def cole_win_above_replace_port(new_asset,replace_port,risk_free_rate=0,financing_rate=0,weight_asset=0.25,weight_replace_port=1,periodicity=252):\n",
    "    \"\"\"Cole Win Above Replacement Portolio (CWARP): Total score to evaluate whether any new investment improves or hurts the return to risk of your total portfolio.\n",
    "    new_asset = returns of the asset you are thinking of adding to your portfolio\n",
    "    replace_port = returns of your pre-existing portfolio (e.g. S&P 500 Index, 60/40 Stock-Bond Portfolio)\n",
    "    risk_free_rate = Tbill rate (annualized)\n",
    "    financing_rate = portfolio margin/borrowing cost (annualized) to layer new asset on top of prevailing portfolio (e.g. LIBOR + 60bps). No financing rate is reasonable for derivate overlay products. \n",
    "    weight_asset = % weight you wish to overlay for the new asset on top of the previous portfolio, 25% overlay allocation is standard\n",
    "    weight_replace_port = % weight of the replacement portfolio, 100% pre-existing portfolio value is standard\n",
    "    periodicity = the frequency of the data you are sampling, typically 12 for monthly or 252 for trading day count\"\"\"\n",
    "    # convert annualized financing rate into appropriate value for provided periodicity\n",
    "    # risk_free_rate will be converted appropriately in respective Sortino and RMDD calcs\n",
    "    financing_rate=(1+financing_rate)**(1/periodicity)-1\n",
    "\n",
    "    #Calculate Replacement Portfolio Sortino Ratio\n",
    "    replace_port_sortino = sortino_ratio(replace_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "\n",
    "    #Calculate Replacement Portfolio Return to Max Drawdown\n",
    "    replace_port_return_maxdd = return_maxdd_ratio(replace_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "\n",
    "    #Calculate New Portfolio Sortino Ratio\n",
    "    new_port = (new_asset-financing_rate)*weight_asset+replace_port*weight_replace_port\n",
    "    new_port_sortino = sortino_ratio(new_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "\n",
    "    #Calculate New Portfolio Return to Max Drawdown\n",
    "    new_port_return_maxdd = return_maxdd_ratio(new_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "\n",
    "    #Final CWARP calculation\n",
    "    CWARP = ((new_port_return_maxdd/replace_port_return_maxdd*new_port_sortino/replace_port_sortino)**(1/2)-1)*100\n",
    "\n",
    "    return CWARP\n",
    "\n",
    "def cwarp_additive_sortino(new_asset,replace_port,risk_free_rate=0,financing_rate=0,weight_asset=0.25,weight_replace_port=1,periodicity=252):\n",
    "    \"\"\"Cole Win Above Replacement Portolio (CWARP) Sortino +: Isolates new investment effect on total portfolio Sortino Ratio, which is a portion of the holistic CWARP score.\n",
    "    new_asset = returns of the asset you are thinking of adding to your portfolio\n",
    "    replace_port = returns of your pre-existing portfolio (e.g. S&P 500 Index, 60/40 Stock-Bond Portfolio)\n",
    "    risk_free_rate = Tbill rate (annualized)\n",
    "    financing_rate = portfolio margin/borrowing cost (annualized) to layer new asset on top of prevailing portfolio (e.g. LIBOR + 60bps). No financing rate is reasonable for derivate overlay products. \n",
    "    weight_asset = % weight you wish to overlay for the new asset on top of the previous portfolio, 25% overlay allocation is standard\n",
    "    weight_replace_port = % weight of the replacement portfolio, 100% pre-existing portfolio value is standard\n",
    "    periodicity = the frequency of the data you are sampling, typically 12 for monthly or 252 for trading day count\"\"\"\n",
    "    # convert annualized financing rate into appropriate value for provided periodicity\n",
    "    # risk_free_rate will be converted appropriately in respective Sortino and RMDD calcs\n",
    "    financing_rate=(1+financing_rate)**(1/periodicity)-1\n",
    "\n",
    "    #Calculate Replacement Portfolio Sortino Ratio\n",
    "    replace_port_sortino = sortino_ratio(replace_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "\n",
    "    #Calculate New Portfolio Sortino Ratio\n",
    "    new_port = (new_asset-financing_rate)*weight_asset+replace_port*weight_replace_port\n",
    "    new_port_sortino = sortino_ratio(new_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "\n",
    "    #Final calculation\n",
    "    CWARP_add_sortino=((new_port_sortino/replace_port_sortino)-1)*100\n",
    "\n",
    "    return CWARP_add_sortino\n",
    "\n",
    "def cwarp_additive_ret_maxdd(new_asset,replace_port,risk_free_rate=0,financing_rate=0,weight_asset=0.25,weight_replace_port=1,periodicity=252):\n",
    "    \"\"\"Cole Win Above Replacement Portolio (CWARP) Ret to Max DD +: Isolates new investment effect on total portfolio Return to MAXDD, which is a portion of the holistic CWARP score.\n",
    "    new_asset = returns of the asset you are thinking of adding to your portfolio\n",
    "    replace_port = returns of your pre-existing portfolio (e.g. S&P 500 Index, 60/40 Stock-Bond Portfolio)\n",
    "    risk_free_rate = Tbill rate (annualized)\n",
    "    financing_rate = portfolio margin/borrowing cost (annualized) to layer new asset on top of prevailing portfolio (e.g. LIBOR + 60bps). No financing rate is reasonable for derivate overlay products. \n",
    "    weight_asset = % weight you wish to overlay for the new asset on top of the previous portfolio, 25% overlay allocation is standard\n",
    "    weight_replace_port = % weight of the replacement portfolio, 100% pre-existing portfolio value is standard\n",
    "    periodicity = the frequency of the data you are sampling, typically 12 for monthly or 252 for trading day count\"\"\"\n",
    "    # convert annualized financing rate into appropriate value for provided periodicity\n",
    "    # risk_free_rate will be converted appropriately in respective Sortino and RMDD calcs\n",
    "    financing_rate=(1+financing_rate)**(1/periodicity)-1\n",
    "\n",
    "    #Calculate Replacement Portfolio Return to Max Drawdown\n",
    "    replace_port_return_maxdd = return_maxdd_ratio(replace_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "\n",
    "    #Calculate New Portfolio Return to Max Drawdown\n",
    "    new_port = (new_asset-financing_rate)*weight_asset+replace_port*weight_replace_port\n",
    "    new_port_return_maxdd = return_maxdd_ratio(new_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "\n",
    "    #Final calculation\n",
    "    CWARP_add_ret_maxdd=((new_port_return_maxdd/replace_port_return_maxdd)-1)*100\n",
    "\n",
    "    return CWARP_add_ret_maxdd\n",
    "\n",
    "def cwarp_port_return(new_asset,replace_port,risk_free_rate=0,financing_rate=0,weight_asset=0.25,weight_replace_port=1,periodicity=252):\n",
    "    \"\"\"Cole Win Above Replacement Portolio (CWARP) Portfolio Return: Returns of the aggregate portfolio after a new asset is financed and layered on top of the replacement portfolio. \n",
    "    new_asset = returns of the asset you are thinking of adding to your portfolio\n",
    "    replace_port = returns of your pre-existing portfolio (e.g. S&P 500 Index, 60/40 Stock-Bond Portfolio)\n",
    "    risk_free_rate = Tbill rate (annualized)\n",
    "    financing_rate = portfolio margin/borrowing cost (annualized) to layer new asset on top of prevailing portfolio (e.g. LIBOR + 60bps). No financing rate is reasonable for derivate overlay products. \n",
    "    weight_asset = % weight you wish to overlay for the new asset on top of the previous portfolio, 25% overlay allocation is standard\n",
    "    weight_replace_port = % weight of the replacement portfolio, 100% pre-existing portfolio value is standard\n",
    "    periodicity = the frequency of the data you are sampling, typically 12 for monthly or 252 for trading day count\"\"\"\n",
    "    # convert annual financing based on periodicity\n",
    "    financing_rate=((financing_rate+1)**(1/periodicity)-1)\n",
    "\n",
    "    # compose new portfolio\n",
    "    new_port=(new_asset-financing_rate)*weight_asset+replace_port*weight_replace_port\n",
    "    \n",
    "    # calculate annualized return of new portfolio and subtract risk-free rate\n",
    "    out = annualized_return(new_port, periodicity=periodicity) - risk_free_rate\n",
    "    return out\n",
    "\n",
    "def cwarp_port_risk(new_asset,replace_port,risk_free_rate=0,financing_rate=0,weight_asset=0.25,weight_replace_port=1,periodicity=252):\n",
    "    \"\"\"Cole Win Above Replacement Portolio (CWARP) Portfolio Risk: Volatility of the aggregate portfolio after a new asset is financed and layered on top of the replacement portfolio. \n",
    "    new_asset = returns of the asset you are thinking of adding to your portfolio\n",
    "    replace_port = returns of your pre-existing portfolio (e.g. S&P 500 Index, 60/40 Stock-Bond Portfolio)\n",
    "    risk_free_rate = Tbill rate (annualized)\n",
    "    financing_rate = portfolio margin/borrowing cost (annualized) to layer new asset on top of prevailing portfolio (e.g. LIBOR + 60bps). No financing rate is reasonable for derivate overlay products. \n",
    "    weight_asset = % weight you wish to overlay for the new asset on top of the previous portfolio, 25% overlay allocation is standard\n",
    "    weight_replace_port = % weight of the replacement portfolio, 100% pre-existing portfolio value is standard\n",
    "    periodicity = the frequency of the data you are sampling, typically 12 for monthly or 252 for trading day count\"\"\"\n",
    "    # convert annual financing and risk free rates based on periodicity\n",
    "    financing_rate=((financing_rate+1)**(1/periodicity)-1)\n",
    "    risk_free_rate=((risk_free_rate+1)**(1/periodicity)-1)\n",
    "    # compose new portfolio\n",
    "    new_port=(new_asset-financing_rate)*weight_asset+replace_port*weight_replace_port\n",
    "    # calculated target downside deviation (TDD)\n",
    "    tdd = target_downside_deviation(new_port, MAR=0)*np.sqrt(periodicity)\n",
    "    return tdd\n",
    "\n",
    "def cwarp_new_port_data(new_asset,replace_port,risk_free_rate=0,financing_rate=0,weight_asset=0.25,weight_replace_port=1,periodicity=252):\n",
    "    \"\"\"Cole Win Above Replacement Portolio (CWARP) return stream: Return series after a new asset is financed and layered on top of the replacement portfolio. \n",
    "    new_asset = returns of the asset you are thinking of adding to your portfolio\n",
    "    replace_port = returns of your pre-existing portfolio (e.g. S&P 500 Index, 60/40 Stock-Bond Portfolio)\n",
    "    risk_free_rate = Tbill rate \n",
    "    financing_rate = portfolio margin/borrowing cost to layer new asset on top of prevailing portfolio (e.g. LIBOR + 60bps). No financing rate is reasonable for derivate overlay products. \n",
    "    weight_asset = % weight you wish to overlay for the new asset on top of the previous portfolio, 25% overlay allocation is standard\n",
    "    weight_replace_port = % weight of the replacement portfolio, 100% pre-existing portfolio value is standard\n",
    "    periodicity = the frequency of the data you are sampling, typically 12 for monthly or 252 for trading day count\"\"\"\n",
    "    # convert annual financing based on periodicity\n",
    "    financing_rate=((financing_rate+1)**(1/periodicity)-1)\n",
    "    new_port=(new_asset-financing_rate)*weight_asset+replace_port*weight_replace_port\n",
    "    return new_port"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data function to pull yahoo finance time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve_yhoo_data(ticker='spy', start_date='2007-07-01', end_date='2020-12-31'):\n",
    "    data_hold=yf.Ticker(ticker)\n",
    "    price_df=data_hold.history(start=start_date,  end=end_date).Close.pct_change()\n",
    "    price_df.name=ticker\n",
    "    return price_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple example of implementing CWARP Score for GLD (SPDR Gold Trust) as a diversifer on a 60/40 Stock-to-Bond portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.862679839852525"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cole_win_above_replace_port(new_asset=retrieve_yhoo_data('gld'),replace_port=retrieve_yhoo_data('spy')*.60+retrieve_yhoo_data('ief')*.40\n",
    "                            ,risk_free_rate=.005,financing_rate=0.01,weight_asset=0.25,weight_replace_port=1,periodicity=252)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discover Assets that Improve your Total Portfolio Risk-Adjusted Returns\n",
    "The code below references a 60% Stocks/40% Bonds as the replacement portfolio, and then iterates a wide range of portfolio diversifiers to calculate potential Efficient Frontiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up parameters for comparing various additions to a base replacement portfolio composed of 60/40 stock-bond portfolio\n",
    "# however, this may be modified to evaluate for any replacement portfolio of choice\n",
    "start_date='2007-07-01'\n",
    "end_date='2020-12-31'\n",
    "ticker_list=['qqq','lqd','hyg','tlt','ief','shy','gld','slv','efa','eem','iyr','xle','xlk','xlf']\n",
    "replacement_port_tik=['spy','ief']\n",
    "replacement_port_w=[0.6,0.4]\n",
    "replacement_port=sum([retrieve_yhoo_data(sym, start_date, end_date)*replacement_port_w[i] for i, sym in enumerate(replacement_port_tik)])\n",
    "replacement_port_name='Classic 60/40'\n",
    "replacement_port.name=replacement_port_name\n",
    "financing_rate=.01\n",
    "risk_free_rate=0.005\n",
    "weight_asset=0.25\n",
    "weight_replace_port=1\n",
    "periodicity=252"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Formatting to set up two tables below\n",
    "# Table 1 (risk_ret_df): Evaluate Sharpe, Sortino, Max DD etc for various indices/assets/tickers and CWARP/CWARP components when added at 25% weighting as a layer on top of replacement portfolio\n",
    "# Table 2 (new_risk_ret_df): Evaluate the new portfolio's (when each asset added as 25% weighting as a layer on top of replacement portfolio) Return, Downside Vol, Sharpe, Sortino, Max DD, RMDD, CWARP\n",
    "percentage_weight_asset = \"{:.0%}\".format(weight_asset)\n",
    "percentage_weight_replace_port = \"{:.0%}\".format(weight_replace_port)\n",
    "CWARP_W_String=str('CWARP_'+percentage_weight_asset+'_asset')\n",
    "\n",
    "risk_ret_df=pd.DataFrame(index=['Start_Date','End_Date','CWARP','+Sortino','+Ret_To_MaxDD','Sharpe','Sortino','Max_DD'],columns=ticker_list)\n",
    "new_risk_ret_df=pd.DataFrame(index=['Return','Vol','Sharpe','Sortino','Max_DD','Ret_To_MaxDD',CWARP_W_String],columns=ticker_list)\n",
    "new_risk_ret_df=new_risk_ret_df.add_suffix('@'+percentage_weight_asset+' | '+replacement_port.name+'@'+percentage_weight_replace_port)\n",
    "new_risk_ret_df[replacement_port.name]=np.nan\n",
    "\n",
    "prices_df=pd.DataFrame(index=retrieve_yhoo_data(ticker_list[0]).index)   \n",
    "\n",
    "# evaluate metrics for all tickers in list above\n",
    "for i in range(0,len(ticker_list)):\n",
    "    temp_data=retrieve_yhoo_data(ticker_list[i], start_date, end_date)\n",
    "    new_port = cwarp_new_port_data(new_asset=temp_data,replace_port=replacement_port,financing_rate=financing_rate,\n",
    "                                   risk_free_rate=risk_free_rate,weight_asset=weight_asset,weight_replace_port=weight_replace_port,periodicity=periodicity)\n",
    "    prices_df=pd.merge(prices_df,temp_data, left_index=True, right_index=True)\n",
    "    risk_ret_df.loc['Start_Date',ticker_list[i]]=min(temp_data.index).date()\n",
    "    risk_ret_df.loc['End_Date',ticker_list[i]]=max(temp_data.index).date()\n",
    "    \n",
    "    risk_ret_df.loc['CWARP',ticker_list[i]]=cole_win_above_replace_port(new_asset=temp_data,replace_port=replacement_port,financing_rate=financing_rate,\n",
    "                                                                        risk_free_rate=risk_free_rate,weight_asset=weight_asset,weight_replace_port=weight_replace_port,periodicity=periodicity)\n",
    "    \n",
    "    risk_ret_df.loc['+Sortino',ticker_list[i]]=cwarp_additive_sortino(new_asset=temp_data,replace_port=replacement_port,financing_rate=financing_rate,\n",
    "                                                                        risk_free_rate=risk_free_rate,weight_asset=weight_asset,weight_replace_port=weight_replace_port,periodicity=periodicity)\n",
    "    \n",
    "    risk_ret_df.loc['+Ret_To_MaxDD',ticker_list[i]]=cwarp_additive_ret_maxdd(new_asset=temp_data,replace_port=replacement_port,financing_rate=financing_rate,\n",
    "                                                                        risk_free_rate=risk_free_rate,weight_asset=weight_asset,weight_replace_port=weight_replace_port,periodicity=periodicity)\n",
    "    risk_ret_df.loc['Sharpe',ticker_list[i]]=sharpe_ratio(temp_data,risk_free=risk_free_rate, periodicity=periodicity)\n",
    "    risk_ret_df.loc['Sortino',ticker_list[i]]=sortino_ratio(temp_data,risk_free=risk_free_rate, periodicity=periodicity)\n",
    "    risk_ret_df.loc['Max_DD',ticker_list[i]]=max_dd(temp_data)\n",
    "    \n",
    "    new_risk_ret_df.loc['Return',new_risk_ret_df.columns[i]]=cwarp_port_return(new_asset=temp_data,replace_port=replacement_port,financing_rate=financing_rate,\n",
    "                                                                        risk_free_rate=risk_free_rate,weight_asset=weight_asset,weight_replace_port=weight_replace_port,periodicity=periodicity)\n",
    "    \n",
    "    new_risk_ret_df.loc['Vol',new_risk_ret_df.columns[i]]=cwarp_port_risk(new_asset=temp_data,replace_port=replacement_port,financing_rate=financing_rate,\n",
    "                                                                        risk_free_rate=risk_free_rate,weight_asset=weight_asset,weight_replace_port=weight_replace_port,periodicity=periodicity)\n",
    "    \n",
    "    new_risk_ret_df.loc['Sharpe',new_risk_ret_df.columns[i]]=sharpe_ratio(new_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "                                                                          \n",
    "    new_risk_ret_df.loc['Sortino',new_risk_ret_df.columns[i]]=sortino_ratio(new_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "    \n",
    "    new_risk_ret_df.loc['Max_DD',new_risk_ret_df.columns[i]]=max_dd(new_port)\n",
    "    \n",
    "    new_risk_ret_df.loc['Ret_To_MaxDD',new_risk_ret_df.columns[i]]=return_maxdd_ratio(new_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "    \n",
    "    new_risk_ret_df.loc[CWARP_W_String,new_risk_ret_df.columns[i]]=risk_ret_df.loc['CWARP',ticker_list[i]]\n",
    "    \n",
    "new_risk_ret_df.loc['Return',[replacement_port_name]]=annualized_return(replacement_port, periodicity=periodicity)\n",
    "new_risk_ret_df.loc['Vol',[replacement_port_name]]=target_downside_deviation(replacement_port, MAR=0)*np.sqrt(periodicity)\n",
    "new_risk_ret_df.loc['Sharpe',[replacement_port_name]]=sharpe_ratio(replacement_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "new_risk_ret_df.loc['Sortino',[replacement_port_name]]=sortino_ratio(replacement_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "new_risk_ret_df.loc['Max_DD',[replacement_port_name]]=max_dd(replacement_port)\n",
    "new_risk_ret_df.loc['Ret_To_MaxDD',[replacement_port_name]]=return_maxdd_ratio(replacement_port, risk_free=risk_free_rate, periodicity=periodicity)\n",
    "new_risk_ret_df.loc[CWARP_W_String, [replacement_port_name]]=cole_win_above_replace_port(new_asset=replacement_port, replace_port=replacement_port, risk_free_rate=risk_free_rate, financing_rate=financing_rate, \n",
    "                            weight_asset=weight_asset, weight_replace_port=weight_replace_port, periodicity=periodicity)\n",
    "first_col = new_risk_ret_df.pop(replacement_port_name)\n",
    "new_risk_ret_df.insert(0, replacement_port_name, first_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we evaluate each individual asset by CWARP score to see if it is helping the risk-adjusted returns of a 60/40 portfolio. We compare that data to outdated performance metrics like Sharpe and Sortino."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_c6604_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >tlt</th>        <th class=\"col_heading level0 col1\" >ief</th>        <th class=\"col_heading level0 col2\" >gld</th>        <th class=\"col_heading level0 col3\" >lqd</th>        <th class=\"col_heading level0 col4\" >shy</th>        <th class=\"col_heading level0 col5\" >qqq</th>        <th class=\"col_heading level0 col6\" >xlk</th>        <th class=\"col_heading level0 col7\" >slv</th>        <th class=\"col_heading level0 col8\" >hyg</th>        <th class=\"col_heading level0 col9\" >iyr</th>        <th class=\"col_heading level0 col10\" >eem</th>        <th class=\"col_heading level0 col11\" >efa</th>        <th class=\"col_heading level0 col12\" >xle</th>        <th class=\"col_heading level0 col13\" >xlf</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c6604_level0_row0\" class=\"row_heading level0 row0\" >Start_Date</th>\n",
       "                        <td id=\"T_c6604_row0_col0\" class=\"data row0 col0\" >2007-07-02</td>\n",
       "                        <td id=\"T_c6604_row0_col1\" class=\"data row0 col1\" >2007-07-02</td>\n",
       "                        <td id=\"T_c6604_row0_col2\" class=\"data row0 col2\" >2007-07-02</td>\n",
       "                        <td id=\"T_c6604_row0_col3\" class=\"data row0 col3\" >2007-07-02</td>\n",
       "                        <td id=\"T_c6604_row0_col4\" class=\"data row0 col4\" >2007-07-02</td>\n",
       "                        <td id=\"T_c6604_row0_col5\" class=\"data row0 col5\" >2007-07-02</td>\n",
       "                        <td id=\"T_c6604_row0_col6\" class=\"data row0 col6\" >2007-07-02</td>\n",
       "                        <td id=\"T_c6604_row0_col7\" class=\"data row0 col7\" >2007-07-02</td>\n",
       "                        <td id=\"T_c6604_row0_col8\" class=\"data row0 col8\" >2007-07-02</td>\n",
       "                        <td id=\"T_c6604_row0_col9\" class=\"data row0 col9\" >2007-07-02</td>\n",
       "                        <td id=\"T_c6604_row0_col10\" class=\"data row0 col10\" >2007-07-02</td>\n",
       "                        <td id=\"T_c6604_row0_col11\" class=\"data row0 col11\" >2007-07-02</td>\n",
       "                        <td id=\"T_c6604_row0_col12\" class=\"data row0 col12\" >2007-07-02</td>\n",
       "                        <td id=\"T_c6604_row0_col13\" class=\"data row0 col13\" >2007-07-02</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6604_level0_row1\" class=\"row_heading level0 row1\" >End_Date</th>\n",
       "                        <td id=\"T_c6604_row1_col0\" class=\"data row1 col0\" >2020-12-30</td>\n",
       "                        <td id=\"T_c6604_row1_col1\" class=\"data row1 col1\" >2020-12-30</td>\n",
       "                        <td id=\"T_c6604_row1_col2\" class=\"data row1 col2\" >2020-12-30</td>\n",
       "                        <td id=\"T_c6604_row1_col3\" class=\"data row1 col3\" >2020-12-30</td>\n",
       "                        <td id=\"T_c6604_row1_col4\" class=\"data row1 col4\" >2020-12-30</td>\n",
       "                        <td id=\"T_c6604_row1_col5\" class=\"data row1 col5\" >2020-12-30</td>\n",
       "                        <td id=\"T_c6604_row1_col6\" class=\"data row1 col6\" >2020-12-30</td>\n",
       "                        <td id=\"T_c6604_row1_col7\" class=\"data row1 col7\" >2020-12-30</td>\n",
       "                        <td id=\"T_c6604_row1_col8\" class=\"data row1 col8\" >2020-12-30</td>\n",
       "                        <td id=\"T_c6604_row1_col9\" class=\"data row1 col9\" >2020-12-30</td>\n",
       "                        <td id=\"T_c6604_row1_col10\" class=\"data row1 col10\" >2020-12-30</td>\n",
       "                        <td id=\"T_c6604_row1_col11\" class=\"data row1 col11\" >2020-12-30</td>\n",
       "                        <td id=\"T_c6604_row1_col12\" class=\"data row1 col12\" >2020-12-30</td>\n",
       "                        <td id=\"T_c6604_row1_col13\" class=\"data row1 col13\" >2020-12-30</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6604_level0_row2\" class=\"row_heading level0 row2\" >CWARP</th>\n",
       "                        <td id=\"T_c6604_row2_col0\" class=\"data row2 col0\" >35.075</td>\n",
       "                        <td id=\"T_c6604_row2_col1\" class=\"data row2 col1\" >21.355</td>\n",
       "                        <td id=\"T_c6604_row2_col2\" class=\"data row2 col2\" >19.863</td>\n",
       "                        <td id=\"T_c6604_row2_col3\" class=\"data row2 col3\" >10.370</td>\n",
       "                        <td id=\"T_c6604_row2_col4\" class=\"data row2 col4\" >4.964</td>\n",
       "                        <td id=\"T_c6604_row2_col5\" class=\"data row2 col5\" >3.055</td>\n",
       "                        <td id=\"T_c6604_row2_col6\" class=\"data row2 col6\" >0.176</td>\n",
       "                        <td id=\"T_c6604_row2_col7\" class=\"data row2 col7\" >-3.106</td>\n",
       "                        <td id=\"T_c6604_row2_col8\" class=\"data row2 col8\" >-5.413</td>\n",
       "                        <td id=\"T_c6604_row2_col9\" class=\"data row2 col9\" >-23.615</td>\n",
       "                        <td id=\"T_c6604_row2_col10\" class=\"data row2 col10\" >-24.563</td>\n",
       "                        <td id=\"T_c6604_row2_col11\" class=\"data row2 col11\" >-27.323</td>\n",
       "                        <td id=\"T_c6604_row2_col12\" class=\"data row2 col12\" >-33.079</td>\n",
       "                        <td id=\"T_c6604_row2_col13\" class=\"data row2 col13\" >-33.274</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6604_level0_row3\" class=\"row_heading level0 row3\" >+Sortino</th>\n",
       "                        <td id=\"T_c6604_row3_col0\" class=\"data row3 col0\" >29.875</td>\n",
       "                        <td id=\"T_c6604_row3_col1\" class=\"data row3 col1\" >18.216</td>\n",
       "                        <td id=\"T_c6604_row3_col2\" class=\"data row3 col2\" >13.874</td>\n",
       "                        <td id=\"T_c6604_row3_col3\" class=\"data row3 col3\" >9.440</td>\n",
       "                        <td id=\"T_c6604_row3_col4\" class=\"data row3 col4\" >3.735</td>\n",
       "                        <td id=\"T_c6604_row3_col5\" class=\"data row3 col5\" >2.768</td>\n",
       "                        <td id=\"T_c6604_row3_col6\" class=\"data row3 col6\" >-0.268</td>\n",
       "                        <td id=\"T_c6604_row3_col7\" class=\"data row3 col7\" >-6.900</td>\n",
       "                        <td id=\"T_c6604_row3_col8\" class=\"data row3 col8\" >-3.633</td>\n",
       "                        <td id=\"T_c6604_row3_col9\" class=\"data row3 col9\" >-21.711</td>\n",
       "                        <td id=\"T_c6604_row3_col10\" class=\"data row3 col10\" >-24.392</td>\n",
       "                        <td id=\"T_c6604_row3_col11\" class=\"data row3 col11\" >-24.580</td>\n",
       "                        <td id=\"T_c6604_row3_col12\" class=\"data row3 col12\" >-34.119</td>\n",
       "                        <td id=\"T_c6604_row3_col13\" class=\"data row3 col13\" >-27.888</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6604_level0_row4\" class=\"row_heading level0 row4\" >+Ret_To_MaxDD</th>\n",
       "                        <td id=\"T_c6604_row4_col0\" class=\"data row4 col0\" >40.483</td>\n",
       "                        <td id=\"T_c6604_row4_col1\" class=\"data row4 col1\" >24.577</td>\n",
       "                        <td id=\"T_c6604_row4_col2\" class=\"data row4 col2\" >26.166</td>\n",
       "                        <td id=\"T_c6604_row4_col3\" class=\"data row4 col3\" >11.308</td>\n",
       "                        <td id=\"T_c6604_row4_col4\" class=\"data row4 col4\" >6.208</td>\n",
       "                        <td id=\"T_c6604_row4_col5\" class=\"data row4 col5\" >3.343</td>\n",
       "                        <td id=\"T_c6604_row4_col6\" class=\"data row4 col6\" >0.623</td>\n",
       "                        <td id=\"T_c6604_row4_col7\" class=\"data row4 col7\" >0.843</td>\n",
       "                        <td id=\"T_c6604_row4_col8\" class=\"data row4 col8\" >-7.160</td>\n",
       "                        <td id=\"T_c6604_row4_col9\" class=\"data row4 col9\" >-25.473</td>\n",
       "                        <td id=\"T_c6604_row4_col10\" class=\"data row4 col10\" >-24.733</td>\n",
       "                        <td id=\"T_c6604_row4_col11\" class=\"data row4 col11\" >-29.966</td>\n",
       "                        <td id=\"T_c6604_row4_col12\" class=\"data row4 col12\" >-32.022</td>\n",
       "                        <td id=\"T_c6604_row4_col13\" class=\"data row4 col13\" >-38.257</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6604_level0_row5\" class=\"row_heading level0 row5\" >Sharpe</th>\n",
       "                        <td id=\"T_c6604_row5_col0\" class=\"data row5 col0\" >0.545</td>\n",
       "                        <td id=\"T_c6604_row5_col1\" class=\"data row5 col1\" >0.747</td>\n",
       "                        <td id=\"T_c6604_row5_col2\" class=\"data row5 col2\" >0.474</td>\n",
       "                        <td id=\"T_c6604_row5_col3\" class=\"data row5 col3\" >0.664</td>\n",
       "                        <td id=\"T_c6604_row5_col4\" class=\"data row5 col4\" >1.050</td>\n",
       "                        <td id=\"T_c6604_row5_col5\" class=\"data row5 col5\" >0.752</td>\n",
       "                        <td id=\"T_c6604_row5_col6\" class=\"data row5 col6\" >0.685</td>\n",
       "                        <td id=\"T_c6604_row5_col7\" class=\"data row5 col7\" >0.303</td>\n",
       "                        <td id=\"T_c6604_row5_col8\" class=\"data row5 col8\" >0.465</td>\n",
       "                        <td id=\"T_c6604_row5_col9\" class=\"data row5 col9\" >0.286</td>\n",
       "                        <td id=\"T_c6604_row5_col10\" class=\"data row5 col10\" >0.239</td>\n",
       "                        <td id=\"T_c6604_row5_col11\" class=\"data row5 col11\" >0.192</td>\n",
       "                        <td id=\"T_c6604_row5_col12\" class=\"data row5 col12\" >0.091</td>\n",
       "                        <td id=\"T_c6604_row5_col13\" class=\"data row5 col13\" >0.212</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6604_level0_row6\" class=\"row_heading level0 row6\" >Sortino</th>\n",
       "                        <td id=\"T_c6604_row6_col0\" class=\"data row6 col0\" >0.791</td>\n",
       "                        <td id=\"T_c6604_row6_col1\" class=\"data row6 col1\" >1.101</td>\n",
       "                        <td id=\"T_c6604_row6_col2\" class=\"data row6 col2\" >0.672</td>\n",
       "                        <td id=\"T_c6604_row6_col3\" class=\"data row6 col3\" >0.943</td>\n",
       "                        <td id=\"T_c6604_row6_col4\" class=\"data row6 col4\" >1.616</td>\n",
       "                        <td id=\"T_c6604_row6_col5\" class=\"data row6 col5\" >1.065</td>\n",
       "                        <td id=\"T_c6604_row6_col6\" class=\"data row6 col6\" >0.974</td>\n",
       "                        <td id=\"T_c6604_row6_col7\" class=\"data row6 col7\" >0.414</td>\n",
       "                        <td id=\"T_c6604_row6_col8\" class=\"data row6 col8\" >0.669</td>\n",
       "                        <td id=\"T_c6604_row6_col9\" class=\"data row6 col9\" >0.405</td>\n",
       "                        <td id=\"T_c6604_row6_col10\" class=\"data row6 col10\" >0.344</td>\n",
       "                        <td id=\"T_c6604_row6_col11\" class=\"data row6 col11\" >0.267</td>\n",
       "                        <td id=\"T_c6604_row6_col12\" class=\"data row6 col12\" >0.127</td>\n",
       "                        <td id=\"T_c6604_row6_col13\" class=\"data row6 col13\" >0.306</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c6604_level0_row7\" class=\"row_heading level0 row7\" >Max_DD</th>\n",
       "                        <td id=\"T_c6604_row7_col0\" class=\"data row7 col0\" >0.266</td>\n",
       "                        <td id=\"T_c6604_row7_col1\" class=\"data row7 col1\" >0.104</td>\n",
       "                        <td id=\"T_c6604_row7_col2\" class=\"data row7 col2\" >0.456</td>\n",
       "                        <td id=\"T_c6604_row7_col3\" class=\"data row7 col3\" >0.218</td>\n",
       "                        <td id=\"T_c6604_row7_col4\" class=\"data row7 col4\" >0.022</td>\n",
       "                        <td id=\"T_c6604_row7_col5\" class=\"data row7 col5\" >0.534</td>\n",
       "                        <td id=\"T_c6604_row7_col6\" class=\"data row7 col6\" >0.530</td>\n",
       "                        <td id=\"T_c6604_row7_col7\" class=\"data row7 col7\" >0.763</td>\n",
       "                        <td id=\"T_c6604_row7_col8\" class=\"data row7 col8\" >0.342</td>\n",
       "                        <td id=\"T_c6604_row7_col9\" class=\"data row7 col9\" >0.705</td>\n",
       "                        <td id=\"T_c6604_row7_col10\" class=\"data row7 col10\" >0.664</td>\n",
       "                        <td id=\"T_c6604_row7_col11\" class=\"data row7 col11\" >0.610</td>\n",
       "                        <td id=\"T_c6604_row7_col12\" class=\"data row7 col12\" >0.713</td>\n",
       "                        <td id=\"T_c6604_row7_col13\" class=\"data row7 col13\" >0.822</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f2788948eb0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "risk_ret_df.sort_values(by='CWARP', axis=1, ascending=False).style.set_precision(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we calculate the metrics of the brand new portfolios with the new asset layered on top at 25%. This is the raw data we can use to construct and Efficient Frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_ffc02_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >tlt@25% | Classic 60/40@100%</th>        <th class=\"col_heading level0 col1\" >ief@25% | Classic 60/40@100%</th>        <th class=\"col_heading level0 col2\" >gld@25% | Classic 60/40@100%</th>        <th class=\"col_heading level0 col3\" >lqd@25% | Classic 60/40@100%</th>        <th class=\"col_heading level0 col4\" >shy@25% | Classic 60/40@100%</th>        <th class=\"col_heading level0 col5\" >qqq@25% | Classic 60/40@100%</th>        <th class=\"col_heading level0 col6\" >Classic 60/40</th>        <th class=\"col_heading level0 col7\" >xlk@25% | Classic 60/40@100%</th>        <th class=\"col_heading level0 col8\" >hyg@25% | Classic 60/40@100%</th>        <th class=\"col_heading level0 col9\" >slv@25% | Classic 60/40@100%</th>        <th class=\"col_heading level0 col10\" >iyr@25% | Classic 60/40@100%</th>        <th class=\"col_heading level0 col11\" >efa@25% | Classic 60/40@100%</th>        <th class=\"col_heading level0 col12\" >eem@25% | Classic 60/40@100%</th>        <th class=\"col_heading level0 col13\" >xlf@25% | Classic 60/40@100%</th>        <th class=\"col_heading level0 col14\" >xle@25% | Classic 60/40@100%</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_ffc02_level0_row0\" class=\"row_heading level0 row0\" >Return</th>\n",
       "                        <td id=\"T_ffc02_row0_col0\" class=\"data row0 col0\" >0.101</td>\n",
       "                        <td id=\"T_ffc02_row0_col1\" class=\"data row0 col1\" >0.092</td>\n",
       "                        <td id=\"T_ffc02_row0_col2\" class=\"data row0 col2\" >0.100</td>\n",
       "                        <td id=\"T_ffc02_row0_col3\" class=\"data row0 col3\" >0.093</td>\n",
       "                        <td id=\"T_ffc02_row0_col4\" class=\"data row0 col4\" >0.082</td>\n",
       "                        <td id=\"T_ffc02_row0_col5\" class=\"data row0 col5\" >0.116</td>\n",
       "                        <td id=\"T_ffc02_row0_col6\" class=\"data row0 col6\" >0.084</td>\n",
       "                        <td id=\"T_ffc02_row0_col7\" class=\"data row0 col7\" >0.112</td>\n",
       "                        <td id=\"T_ffc02_row0_col8\" class=\"data row0 col8\" >0.090</td>\n",
       "                        <td id=\"T_ffc02_row0_col9\" class=\"data row0 col9\" >0.098</td>\n",
       "                        <td id=\"T_ffc02_row0_col10\" class=\"data row0 col10\" >0.091</td>\n",
       "                        <td id=\"T_ffc02_row0_col11\" class=\"data row0 col11\" >0.082</td>\n",
       "                        <td id=\"T_ffc02_row0_col12\" class=\"data row0 col12\" >0.086</td>\n",
       "                        <td id=\"T_ffc02_row0_col13\" class=\"data row0 col13\" >0.084</td>\n",
       "                        <td id=\"T_ffc02_row0_col14\" class=\"data row0 col14\" >0.074</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ffc02_level0_row1\" class=\"row_heading level0 row1\" >Vol</th>\n",
       "                        <td id=\"T_ffc02_row1_col0\" class=\"data row1 col0\" >0.077</td>\n",
       "                        <td id=\"T_ffc02_row1_col1\" class=\"data row1 col1\" >0.078</td>\n",
       "                        <td id=\"T_ffc02_row1_col2\" class=\"data row1 col2\" >0.089</td>\n",
       "                        <td id=\"T_ffc02_row1_col3\" class=\"data row1 col3\" >0.086</td>\n",
       "                        <td id=\"T_ffc02_row1_col4\" class=\"data row1 col4\" >0.080</td>\n",
       "                        <td id=\"T_ffc02_row1_col5\" class=\"data row1 col5\" >0.118</td>\n",
       "                        <td id=\"T_ffc02_row1_col6\" class=\"data row1 col6\" >0.081</td>\n",
       "                        <td id=\"T_ffc02_row1_col7\" class=\"data row1 col7\" >0.119</td>\n",
       "                        <td id=\"T_ffc02_row1_col8\" class=\"data row1 col8\" >0.097</td>\n",
       "                        <td id=\"T_ffc02_row1_col9\" class=\"data row1 col9\" >0.111</td>\n",
       "                        <td id=\"T_ffc02_row1_col10\" class=\"data row1 col10\" >0.131</td>\n",
       "                        <td id=\"T_ffc02_row1_col11\" class=\"data row1 col11\" >0.121</td>\n",
       "                        <td id=\"T_ffc02_row1_col12\" class=\"data row1 col12\" >0.130</td>\n",
       "                        <td id=\"T_ffc02_row1_col13\" class=\"data row1 col13\" >0.135</td>\n",
       "                        <td id=\"T_ffc02_row1_col14\" class=\"data row1 col14\" >0.132</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ffc02_level0_row2\" class=\"row_heading level0 row2\" >Sharpe</th>\n",
       "                        <td id=\"T_ffc02_row2_col0\" class=\"data row2 col0\" >0.910</td>\n",
       "                        <td id=\"T_ffc02_row2_col1\" class=\"data row2 col1\" >0.831</td>\n",
       "                        <td id=\"T_ffc02_row2_col2\" class=\"data row2 col2\" >0.806</td>\n",
       "                        <td id=\"T_ffc02_row2_col3\" class=\"data row2 col3\" >0.778</td>\n",
       "                        <td id=\"T_ffc02_row2_col4\" class=\"data row2 col4\" >0.737</td>\n",
       "                        <td id=\"T_ffc02_row2_col5\" class=\"data row2 col5\" >0.734</td>\n",
       "                        <td id=\"T_ffc02_row2_col6\" class=\"data row2 col6\" >0.712</td>\n",
       "                        <td id=\"T_ffc02_row2_col7\" class=\"data row2 col7\" >0.711</td>\n",
       "                        <td id=\"T_ffc02_row2_col8\" class=\"data row2 col8\" >0.690</td>\n",
       "                        <td id=\"T_ffc02_row2_col9\" class=\"data row2 col9\" >0.676</td>\n",
       "                        <td id=\"T_ffc02_row2_col10\" class=\"data row2 col10\" >0.562</td>\n",
       "                        <td id=\"T_ffc02_row2_col11\" class=\"data row2 col11\" >0.543</td>\n",
       "                        <td id=\"T_ffc02_row2_col12\" class=\"data row2 col12\" >0.537</td>\n",
       "                        <td id=\"T_ffc02_row2_col13\" class=\"data row2 col13\" >0.516</td>\n",
       "                        <td id=\"T_ffc02_row2_col14\" class=\"data row2 col14\" >0.478</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ffc02_level0_row3\" class=\"row_heading level0 row3\" >Sortino</th>\n",
       "                        <td id=\"T_ffc02_row3_col0\" class=\"data row3 col0\" >1.320</td>\n",
       "                        <td id=\"T_ffc02_row3_col1\" class=\"data row3 col1\" >1.202</td>\n",
       "                        <td id=\"T_ffc02_row3_col2\" class=\"data row3 col2\" >1.158</td>\n",
       "                        <td id=\"T_ffc02_row3_col3\" class=\"data row3 col3\" >1.113</td>\n",
       "                        <td id=\"T_ffc02_row3_col4\" class=\"data row3 col4\" >1.055</td>\n",
       "                        <td id=\"T_ffc02_row3_col5\" class=\"data row3 col5\" >1.045</td>\n",
       "                        <td id=\"T_ffc02_row3_col6\" class=\"data row3 col6\" >1.017</td>\n",
       "                        <td id=\"T_ffc02_row3_col7\" class=\"data row3 col7\" >1.014</td>\n",
       "                        <td id=\"T_ffc02_row3_col8\" class=\"data row3 col8\" >0.980</td>\n",
       "                        <td id=\"T_ffc02_row3_col9\" class=\"data row3 col9\" >0.947</td>\n",
       "                        <td id=\"T_ffc02_row3_col10\" class=\"data row3 col10\" >0.796</td>\n",
       "                        <td id=\"T_ffc02_row3_col11\" class=\"data row3 col11\" >0.767</td>\n",
       "                        <td id=\"T_ffc02_row3_col12\" class=\"data row3 col12\" >0.769</td>\n",
       "                        <td id=\"T_ffc02_row3_col13\" class=\"data row3 col13\" >0.733</td>\n",
       "                        <td id=\"T_ffc02_row3_col14\" class=\"data row3 col14\" >0.670</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ffc02_level0_row4\" class=\"row_heading level0 row4\" >Max_DD</th>\n",
       "                        <td id=\"T_ffc02_row4_col0\" class=\"data row4 col0\" >0.281</td>\n",
       "                        <td id=\"T_ffc02_row4_col1\" class=\"data row4 col1\" >0.290</td>\n",
       "                        <td id=\"T_ffc02_row4_col2\" class=\"data row4 col2\" >0.310</td>\n",
       "                        <td id=\"T_ffc02_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
       "                        <td id=\"T_ffc02_row4_col4\" class=\"data row4 col4\" >0.305</td>\n",
       "                        <td id=\"T_ffc02_row4_col5\" class=\"data row4 col5\" >0.437</td>\n",
       "                        <td id=\"T_ffc02_row4_col6\" class=\"data row4 col6\" >0.314</td>\n",
       "                        <td id=\"T_ffc02_row4_col7\" class=\"data row4 col7\" >0.436</td>\n",
       "                        <td id=\"T_ffc02_row4_col8\" class=\"data row4 col8\" >0.382</td>\n",
       "                        <td id=\"T_ffc02_row4_col9\" class=\"data row4 col9\" >0.382</td>\n",
       "                        <td id=\"T_ffc02_row4_col10\" class=\"data row4 col10\" >0.482</td>\n",
       "                        <td id=\"T_ffc02_row4_col11\" class=\"data row4 col11\" >0.462</td>\n",
       "                        <td id=\"T_ffc02_row4_col12\" class=\"data row4 col12\" >0.454</td>\n",
       "                        <td id=\"T_ffc02_row4_col13\" class=\"data row4 col13\" >0.541</td>\n",
       "                        <td id=\"T_ffc02_row4_col14\" class=\"data row4 col14\" >0.435</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ffc02_level0_row5\" class=\"row_heading level0 row5\" >Ret_To_MaxDD</th>\n",
       "                        <td id=\"T_ffc02_row5_col0\" class=\"data row5 col0\" >0.376</td>\n",
       "                        <td id=\"T_ffc02_row5_col1\" class=\"data row5 col1\" >0.333</td>\n",
       "                        <td id=\"T_ffc02_row5_col2\" class=\"data row5 col2\" >0.338</td>\n",
       "                        <td id=\"T_ffc02_row5_col3\" class=\"data row5 col3\" >0.298</td>\n",
       "                        <td id=\"T_ffc02_row5_col4\" class=\"data row5 col4\" >0.284</td>\n",
       "                        <td id=\"T_ffc02_row5_col5\" class=\"data row5 col5\" >0.277</td>\n",
       "                        <td id=\"T_ffc02_row5_col6\" class=\"data row5 col6\" >0.268</td>\n",
       "                        <td id=\"T_ffc02_row5_col7\" class=\"data row5 col7\" >0.269</td>\n",
       "                        <td id=\"T_ffc02_row5_col8\" class=\"data row5 col8\" >0.248</td>\n",
       "                        <td id=\"T_ffc02_row5_col9\" class=\"data row5 col9\" >0.270</td>\n",
       "                        <td id=\"T_ffc02_row5_col10\" class=\"data row5 col10\" >0.199</td>\n",
       "                        <td id=\"T_ffc02_row5_col11\" class=\"data row5 col11\" >0.187</td>\n",
       "                        <td id=\"T_ffc02_row5_col12\" class=\"data row5 col12\" >0.201</td>\n",
       "                        <td id=\"T_ffc02_row5_col13\" class=\"data row5 col13\" >0.165</td>\n",
       "                        <td id=\"T_ffc02_row5_col14\" class=\"data row5 col14\" >0.182</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ffc02_level0_row6\" class=\"row_heading level0 row6\" >CWARP_25%_asset</th>\n",
       "                        <td id=\"T_ffc02_row6_col0\" class=\"data row6 col0\" >35.075</td>\n",
       "                        <td id=\"T_ffc02_row6_col1\" class=\"data row6 col1\" >21.355</td>\n",
       "                        <td id=\"T_ffc02_row6_col2\" class=\"data row6 col2\" >19.863</td>\n",
       "                        <td id=\"T_ffc02_row6_col3\" class=\"data row6 col3\" >10.370</td>\n",
       "                        <td id=\"T_ffc02_row6_col4\" class=\"data row6 col4\" >4.964</td>\n",
       "                        <td id=\"T_ffc02_row6_col5\" class=\"data row6 col5\" >3.055</td>\n",
       "                        <td id=\"T_ffc02_row6_col6\" class=\"data row6 col6\" >-1.408</td>\n",
       "                        <td id=\"T_ffc02_row6_col7\" class=\"data row6 col7\" >0.176</td>\n",
       "                        <td id=\"T_ffc02_row6_col8\" class=\"data row6 col8\" >-5.413</td>\n",
       "                        <td id=\"T_ffc02_row6_col9\" class=\"data row6 col9\" >-3.106</td>\n",
       "                        <td id=\"T_ffc02_row6_col10\" class=\"data row6 col10\" >-23.615</td>\n",
       "                        <td id=\"T_ffc02_row6_col11\" class=\"data row6 col11\" >-27.323</td>\n",
       "                        <td id=\"T_ffc02_row6_col12\" class=\"data row6 col12\" >-24.563</td>\n",
       "                        <td id=\"T_ffc02_row6_col13\" class=\"data row6 col13\" >-33.274</td>\n",
       "                        <td id=\"T_ffc02_row6_col14\" class=\"data row6 col14\" >-33.079</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f26ba916d00>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_risk_ret_df.sort_values(by='Sharpe', axis=1, ascending=False).style.set_precision(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we plot the portfolios to visualize how high CWARP assets push your portfolio further out on the Efficient Frontier of Return and Risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kpKQ8N79NmzZoa2uzceNG8vPziYuL4+jRo/Ts2fOFfXndeoLwLhABpSAIVYKaKtR4eJ2ciOUgf/rCsgV3LpP3WzB6Iqj8z2vSpAlff/018+bNo0OHDkRFRbFu3To0NTWfW2fs2LGsXbsWGxsbNm3aVCpfU1OTdevWER0dTYcOHZg7dy5LliyhadOmL+zL69YThHeBikKhUFR2J/4tGRnZFBVVznCNjPRJT383//iJsVVPVW1sekWPeLpr7kuDyWdptulGgaU7+YXK6VVtbG9SdRybkZF+ZXdBEIS3TMxQCoJQ6VRVVVCkX3+lYBJAfikarfyHb6lXgiAIQnmJgFIQhEqnpXiK/OzeV69YkIci/Tqqqq920YUgCILwZomAUhCESqdWlEdRZupr1S1KvSwCSkEQhEomAkpBECqfQgG83v5mRX7uK98WRhAEQXizREApCELlU1EDNY3Xq6pjwH/o2kJBEIQqSQSUgiBUOrmaLhpNbV6rrvoHMgoKit5wjwRBEIRXIQJKQRAqnbxIFY1WLq9cT9WgHgU6Rm+hR4IgCMKrEAGlIAhVQoFOXdQbtHilOlodPyZPVect9UgQBEEoLxFQCoJQJTxVaKPhNAK1ug3LVV7b4RPktc0q7WEFwn/DypUrmTJlyltrv1evXsTFxb219gXh3yICSkEQqoxsFX00XX3RbOsKGtplllE1NKVGTz/yG9shV6j/yz0U3kV79+7F3d0dmUyGg4MDo0ePJiEh4V85d0REBLa2tq9cr7CwkOXLl+Pg4IBMJqNfv35kZWVJ+Vu3bsXe3h4rKysCAgKQy+VK9RMTExk8eLBSWnx8PObm5ixfvlwp/WVtCQKA+N9YEIQqJRs9NNr2o0ZLZxT3b1CUdgWFPBdVXQPU3rekQKcOOao6YmZSeCO2bNnChg0bmDt3Lg4ODmhoaHD8+HGOHDmCjc3rXSj2bwgODiYxMZHt27dTv359rly5gpaWFgDHjx9nw4YNfP/99xgbGzNhwgSCg4OVZlp///13OnXqJB3n5+ezYMEC2rZtq3Se8rQlCCBmKAVBqILyCyFbtRY5JpbkW31MoZ0X8tYf8VjnPZ5SQwST/0FHEzPxWnyJngF/4LX4EkcTMyvc5uPHjwkODiYwMJDu3bujo6ODhoYGLi4uTJ8+vcw6vr6+2NvbY21tzdChQ7ly5YqUd+zYMXr27IlMJsPR0ZFNmzYBkJmZybhx47CxsaF9+/YMGTKEoqLiOxO4uLgQExMDFM86rlu3jq5duyKTyXB3dyc1tfQN/x89ekRISAjz58+nQYMGqKio0Lx5cymgDAsLw8PDAzMzM2rVqsX48eMJDQ1VaiM6OhonJyfpeMuWLdjb29OkSROlcuVpSxBABJSCIFRhCoWCgoIi8vMLxa2B/sOOJmYSHHqLew/zUQD3HuYTHHqrwkFlYmIieXl5dOvWrdx1OnXqxMGDB4mNjaVly5ZKM3UzZ84kKCiIxMRE9u3bR4cOHYDiYM3ExITY2FhOnjzJ5MmTy7wZ/5YtW4iIiGDDhg2cPXuWhQsXoq1deuvH5cuXUVNT47fffsPe3h5XV1d++uknKf/KlStYWFhIx+bm5ty/f58HDx4AcO/ePe7fv0/Lli0BuH37Nrt27cLHx6fUuV7WliCUEAGlIAiCUKV9fyiNvHzlWem8fAXfH0qrULsPHz7E0NAQdfXy7/7y8PBAT08PTU1NJk6cSFJSEo8fPwZAXV2dq1evkp2dTa1atfjwww+l9PT0dO7cuYOGhgY2NjZlBpQ7duzAz8+PJk2aoKKigoWFBYaGhqXKpaWl8fjxY27cuMGRI0f49ttvWblyJSdPngQgJycHPT09qby+vj4AT548AYpnUh0dHaU+zJ8/Hz8/P3R1dUud62VtCUIJEVAKgiAIVVr6w/xXSi8vAwMDHjx4QEFBQbnKFxYW8s0339C1a1esrKxwcSm+d2rJbF1wcDDHjh3D2dmZTz/9lMTERABGjRrF+++/z8iRI+nSpQsbNmwos/20tDQaNWr00n6UzFr6+Pigra2NhYUFvXr14tixYwDo6OiQnZ0tlS/5vSRgfHa5++jRozx58oSePXuWea6XtSUIJURAKQiCIFRpRgZlP5bzeenlJZPJ0NTUJDIyslzl9+7dy5EjR9iyZQtnzpzh6NGjANKjP9u0acPatWuJiYmha9euTJo0CQA9PT1mzJjBkSNHWLt2LVu2bCE2NrZU+/Xq1ePmzZsv7Ye5uTmA0izns7+bmZnx119/ScdJSUnUrVsXQ0ND8vPziY+Px97eHoDY2FguXLiAvb099vb27N+/n5CQED777LOXtiUIzxIBpSAIglCleXWvh5aG8hKxloYKXt3rVahdfX19fH19CQoKIjIykqdPn5Kfn8+xY8dYsmRJqfJPnjxBU1MTQ0NDnj59yrJly6Q8uVxOeHg4jx8/RkNDA11dXVRVi//ERkVFkZycjEKhQF9fHzU1tTKXvAcOHMi3337LjRs3UCgUJCUllblXsVGjRtjY2LBu3TrkcjnXrl0jIiICZ2dnAPr27cvOnTu5evUqWVlZrF27lv79+wNw5swZzM3NpWVsPz8/Dh48SFhYGGFhYbi4uDBw4EAWLVr00rYE4VnitkGCIAhCleYiqw0U76VMf5iPkYEGXt3rSekVMXLkSOrWrcuaNWuYMmUKurq6fPjhh3h7e5cq269fP06cOIGjoyMGBgb4+fnxyy+/SPl79uxh3rx5FBYW8sEHH/D1118DkJyczLx588jMzKRmzZp88skn0gU7zxoxYgRyuZyRI0fy4MEDmjRpwurVq8vs97Jly/jiiy+wtbWldu3a+Pn5YWdnBxRfODR69GiGDRtGbm4urq6u+Pr6AsX7J5+9ultPT09pj6S2tjY1atTAwMDgpW0JwrNUFCVz9f8BGRnZlXa7ESMjfdLTH1fKud82MbbqSYyteqqOYzMy0q/sLgj/X8+ePQkODqZZs2aV3RXhHSOWvAVBEAThP0Aul9OvXz8RTApvhQgoBUEQBOE/QFNTk7Fjx1Z2N4R3lAgoBUEQBEEQhAoRAaUgCIIgCIJQISKgFARBEARBECpE3DZIEIRqQ0UF1NRUUVFRQaFQUFhYxH/nPhWCIAhVlwgoBUGo8tTUVNHIzabofjo5ly5R9OQxqrq66LRoiaqRCfnaehQWFlV2NwVBEP6zxJK3IAhVmqYiH/78g7trVpK2cQNZMSfI/uMPsmJiSNu0kburg1GcP4OWomLPdRaE/6q4uDg8PT1fu/7u3bv55JNP3mCPlI0ePZrQ0NC31r7wZoiAUhCEKktTUcDTk8dI376NwqysMssUPn7M/V07eRIViVaR/F/uoSC8mEKhYP/+/Xh5eWFnZ4e9vT0jR47k0KFDSuUyMjKYPHkyDg4OWFtbM3jwYP744w8pPy4uDgsLC2QymfTzbJC1YMEC2rVrx6BBg0hLS5PS9+7dy/z58ys8juPHjzN06FBkMhkdOnTg008/5ciRIxVutzw2btz42o97/P7773FxccHS0hI3NzeuX78u5e3duxdnZ2csLS0ZP348Dx8+VKp79+5dOnXqpJR248YNWrduzZQpU5TSX9YWwIkTJ/D29sbe3p4OHTrwySefsHPnToqKlFdXfvzxR9zd3WnVqhUzZswo1U5sbCw9evSgbdu2eHp6cvv2bSlPLpcTEBCAlZUV9vb2bNmyRcpLTU3l448/pn379nz11VdKbY4ePZrz588/72UsFxFQCoJQJamqqlD092UeRUWVq3zWyRMUXP4TNTXx35pQNRQWFuLv78+vv/6Kt7c3kZGR/P7770yYMIHt27cza9YsSh5Wl5OTQ+vWrdm9ezfx8fH079+fsWPH8uTJE6k9Y2NjEhMTpZ+SIOvcuXNcvHiRkydPYmVlxYYNGwB4/PgxmzZtYtKkSRUax2+//Yafnx/9+vUjOjqamJgYfH19iSrnv83KsmPHDnbu3MmGDRtITExk/fr1GBoaAnDlyhUCAwNZsmQJJ0+epEaNGsydO1epfnR0NI6OjkppQUFBtG7dWimtPG0tWbKEZcuW4eHhwYEDBzh58iSBgYGcOnWKcePGIZf/35dhY2Njxo8fz4ABA0qNKTMzkwkTJuDn50d8fDytWrXi888/l/JXrlxJcnIyUVFRhISEsHHjRqKjowFYv349/fr148iRI0RGRkoB5P79+3nvvfdKjetVif95BUGokjTlOWT+duCV6mQe/A2NvOy31CPhXXTp0iX69++PTCZj0qRJfP755yxfvlzK37hxIw4ODjg4OLBz507Mzc1JTk4G4MGDB3h7e2NlZYWHhwcrVqxQWvpdu3YtmpqabNmyBTs7O3R1ddHQ0MDKyorvvvuO7Oxs9uzZA0DDhg0ZMWIExsbGqKmpMWjQIPLz85Vm1J7n1q1bWFtbo6mpiZ2dHSkpKQAsX76cUaNGKT2r+1UpFAq++uorxo8fz8CBA9HX10dVVZX27ds/d+Zz/vz5ODk5YWVlhbu7OwkJCVLeuXPncHd3x8rKio4dO7Jo0SIA8vLymDJlCra2ttjY2DBgwADu378PgKenJzt27JDa+PXXX3Fzc0Mmk9GzZ08uXrxYqg9FRUWsWrWKL774gmbNmqGiokKjRo2kZ5Tv3bsXFxcX2rVrh66uLn5+fhw+fJjs7P/7/+Ofzz2PiIhAX19femZ6iZe1FRYWRlJSEtu2baNr167UrFkTNTU1WrRowTfffEOzZs2kLwEA3bt3p2vXrlJfn3X48GHMzMxwc3NDS0uLiRMnkpSUxLVr1wAIDQ1l/Pjx1KpVi6ZNmzJw4EBpJvvWrVt06NABfX19WrduTUpKCtnZ2WzYsIHJkyeX+V6+ChFQCoJQJSkeZlKQmflKdQqzsii6n/6WeiRUprwLUTxaNZwHC3vzaNVw8i5UfHZMLpfj4+ND3759iY+Pp0ePHkpL0dHR0WzevJnNmzdz6NAhYmNjleoHBQWhpaXFiRMnWLhwIbt27ZLycnJy2LlzJ4GBgeTn5xMQEICdnR2enp4EBARw5swZAgICCAkJKbNvf/75J/n5+bz//vtSWmZmJh07dsTFxYWFCxeSk5MDQLNmzUhISCA3N5fY2FiaNWvG+fPnuX79On369KnQa/T333+TmpqKq6trueu0bt2asLAw4uPj6d27N35+fuTl5QHFS/PDhg3j7NmzHD58GDc3N6A4EMrOzub3338nLi6OuXPnoq2tXartAwcOsHLlShYvXszZs2dZu3ZtmYFXWloaaWlpXL58GScnJ1xcXAgODpaWl69cuYK5ublUvlGjRmhoaHDjxg0A8vPzOX36NB07dgQgOzub4OBgAgICSp3rZW2tWbOGBQsWoKmpyddff429vT39+/dn4cKFhIWF4efnR1hYWLle23+eS0dHh0aNGnH16lUePXpEeno6FhYWUr6FhQVXr14FwMzMjJiYGLKysrh48SJmZmasWLECLy8vatasWa7zv4gIKAVBqHLU1FTJ/fvv16r79MrlN9wbobLlXYgiZ/9KirLSAQVFWenk7F9Z4aDyjz/+ID8/Hy8vLzQ0NOjRo4fSst+BAwdwd3enefPm6OjoMGHCBCmvsLCQQ4cO4evri46ODs2bN1fa5/e///0PW1tbdHR02LlzJ3fv3uXw4cMsXLiQ48ePU1RUhImJSZl77bKzs5k2bRoTJkxAX18fgCZNmhAWFsaJEyf4/vvvuXjxorQPrnnz5ri6uvLxxx+TmprKmDFjWLBgAbNmzSIkJIShQ4fi7+9P1nP2Ib9ISf+MjY3LXadv374YGhqirq7OyJEjkcvl0kyruro6N2/eJDMzE11dXSwtLaX0hw8fkpycjJqaGq1atSpzZnXnzp2MHj2aNm3aoKKiwvvvv0+DBg1KlSvZR3ry5En27t1LSEgIERER7Ny5EygO+Ete2xJ6enrSFoOEhAQsLCykPqxYsYIBAwZQr169Uud6UVvJyckYGxtjamrKsWPHiI6OJjw8nI0bNxIbG0tRURHa2trUqlWLzHJ8gX7RuUq+YDybr6+vL41p3LhxJCQk8OmnnzJkyBDy8/P566+/cHZ2xt/fn6FDh/Ljjz++tA/PIwJKQRCqHBUVKHqa+1p1i3KevuHeCI66sLcAACAASURBVJUt9/fvoSBPObEgrzi9Au7du4eJiQkqKipSWv369ZXyTU1NpeNnA5fMzEwKCgqU8p+tm5GRgYmJCQCXL1+mS5cu6Onp0bBhQ6ytrYHiwFFHR0epT7m5uXh7e9O2bVvGjRsnpRsZGdGsWTNUVVVp2LAhU6dO5eDBg1L+8OHDCQ8PZ8WKFRw4cAAbGxuKior49ddf2bp1K02bNlVaVi2vktm/e/fulbvOpk2bcHNzw9raGhsbGx4/fsyDBw+A4hnKGzdu4ObmxoABA6R9mH379sXBwUG6MGnJkiXk55e+c0NqaiqNGjV6aR9KZjdHjx5NzZo1ee+99xg0aBDHjh0Dimf2nl3ehuL3Q1dXF1Be7v7zzz+JjY1l+PDhZZ7rRW3983Pg6OhInTp1qFOnjrQ/s6ioiKysrDJnWstzridPnqCrqyt9lp7Nf3ZMBgYGrFixgvDwcIYNG8a8efOYPXs2GzZswMzMjC1btrBt2zZp+fxViYBSEIQqR6FQoKpT47XqqunpvuHeCJWtKOv+K6WXl5GREXfv3pUujAG4c+eO9LuxsTGpqall5tWuXRt1dXWl/Gd/NzQ0JD29ePtF8+bNOXLkCNnZ2aSkpHDmzBmysrKYO3eu0oUXJUvwJiYmBAUFvbDvJTf3/6f79++zfft2fHx8pOVRDQ0NWrduzV9//VWel0VJkyZNMDU1LXVV+vMkJCSwceNGVqxYwenTp0lISEBfX1/qa+PGjVm2bBmxsbGMGTMGX19fcnJy0NDQYMKECezfv59t27bx+++/l7kMbGpqys2bN1/ajw8++AANDQ2lLwvP/m5mZkZSUpJ0nJKSQn5+Po0bNwaUA8q4uDhu376Ns7Mz9vb20haIkhnpF7VlaGgoBePNmzfnxIkTZGRkkJGRwfHjx3n69ClLly7FyckJVdWXh2T/PFdOTg43b96kWbNm1KpVCyMjI6X8pKQkmjVrVqqd7du3Y2lpSfPmzbl8+TKtWrVCU1OT5s2bv9bnBERAKQhCFVRYqKBGGf8JlkcN8xZvuDdCZVOtWfeV0svL0tISdXV1QkJCyM/P59ChQ0q3TunRowehoaFcvXqVp0+fsmrVKilPTU2Nbt26sWrVKp4+fcrVq1eVbuNjaWlJXFwcubm5eHh4YGJiQvfu3Zk5cyYODg6sXr0aS0tLvLy8gOI9e76+vmhpabF48eJSwcWpU6e4ffs2CoWC1NRUvvnmG7p06VJqTIsWLWLixInUqFGD9957j/Pnz/PkyRPi4+Np2LDhK79GKioqzJgxgzVr1rBr1y6ys7MpKioiISGB2bNnlyr/5MkT1NTUqF27NgUFBaxatUppxmzPnj1kZmaiqqoq7dtTVVXl1KlT/PXXXxQWFqKnp4e6unqZAZaHhwebN2/mwoULKBQKkpOTlW6bU6JGjRr07NmTjRs3kp2dTVpaGtu3b6dz584A9OnTh6ioKBISEsjJyeHbb7+lW7du6OnpkZKSglwup2nTpgAMGjSIw4cPExYWRlhYGIMHD6Zz585s2rTppW198MEHpKWlce/ePZycnHB0dKRv37589tlntGvXjl9++QVdXV2mTZsm9b2goIC8vDyKioooLCwkLy+PgoICALp168aVK1c4ePAgeXl5rF69GnNzc6mv/fr1Y+3atTx69Ihr166xY8eOUrdcysjI4Oeff5a2cLz33nvExcXx5MkTLly48FqfExBPyhEEoYpS1DRE08QE+d275a6jUacOKga132KvhMqg3dmLnP0rlZe91bXQ7uxVoXY1NTVZuXIls2fPZsWKFTg5OdGtWzcp38nJCS8vL7y8vFBRUWHSpEns3btXyg8MDCQgIAB7e3uaNGmCu7s7cXFxQPG+tt69e7Nw4UKCgoKkq5lLFBQUoK7+f3+CExMTiYqKQltbm3bt2knp3333HTY2Nvz5559MnTpVWhrt1q1bqdsBxcbG8vjxY2kMbdq0wcnJic6dO/PBBx8QHBz8Wq9Tjx490NHRYd26dcyfPx8tLS3MzMwYNWpUqbIODg44Ojri6uqKjo4OXl5eStsCjh8/zldffUVubi7169dn+fLlaGtrc//+febMmcPdu3fR0dGhZ8+e9O3bt1T7bm5uPHz4EH9/f+7du0eDBg1YsmRJmfsoAwMDmT17No6OjtSsWZOBAwfi4eEBFM/0zZ07lylTpvDw4UPs7Oyk9+ifV3fXqFGDGjX+b8VER0cHTU1Nateu/dK2oHjZfdasWaxZs4apU6cydepUKe+fnwMovjvAs19ewsPDmTBhAhMnTqR27dqsXLmSoKAgpk6dStu2bVm2bJlU1tfXlzlz5uDs7Iy2tjZjxowpdS/NxYsXM378eGkpfNy4cfj6+rJt2zbc3d1f+/ZBKoqy5szfURkZ2RQVVc5wjYz0SU9/XCnnftvE2Kqnqj42VVUV1FP+Jm3zxnLXMRnmReEH5tSpo1elx1YRVf19K4uRkf7LC71E3oUocn//nqKs+6jWrIt2Zy+0Wjm/gd4pmzFjBiYmJkr39nuWubk5hw4dUrr6usTu3bvZsWMHv/zyC1AcLPj5+VFUVMRnn31GixYtyM3N5ciRI2zcuJHt27dLf9QrU1xcHKtWreKHH36o7K5UCWPGjOHTTz9VCiorKigoiKSkJHx9fZHJZBQVFXHy5ElWrFjB+vXrywyIqxsxQykIQpVUVKRAUb8Rdfr1JyPs5Y9dq92rN7zfpNK+NApvl1Yr57cSQL5N6urqrFy5kt27d7No0SL+/vtvNDQ0sLW1ZdmyZVUimBRKa9++Pba2tm+0zcDAQA4fPsy6deukPY4ymYygoKB3IpgEEVAKglCF5auoodFKRr26dcmMiECeeqdUGc169ajt1gtFg0bki//ShCpGVVUVDw8Paam1KmrQoMFrP9rwXTRmzJi30m63bt2UtlS8a8SS97+kOi5TlZcYW/VUncamoqKCZmEeKlkPyUu+QVHOU1RraKPVuDGKmgbkq2sr/duuTmN7VdVxbG9iyVsQhKpNfJ0XBKHKUygU5KlqgoExqrVNUP//t0zJLQkixTK3IAhCpRIBpSAI1UrxTKQIIAVBEKoScR9KQRAEQRAEoUJEQCkIgiAIgiBUiAgoBUEQhP8sFxcXYmJiXqvujBkzWL58+Rvu0du3cuVKVq5c+dr13/a4ZTIZKSkpb6194e0QAaUgCIIgvCFPnz7lu+++w93dnfbt2+Pk5IS/vz+XLl1SKve///2PESNG0L59ezp06ICvr6/0zGcoDvo+/PBDZDKZ9FMSZD1+/JhRo0ZhY2ODv78/hYWFUr3Zs2eX+7nbz6NQKAgJCaF3795YWlrSqVMnfH19X/sZz68qMTHxtR7/9/TpU7788ktsbW2xtrZm6NChUp5CoeDrr7/G1tYWW1tbvv7661LPQt+3bx/+/v5KaWFhYZibm7Njx45XaqugoIBt27YxePBgbG1tcXR0ZPz48Zw6dapUv2fPno2rqysWFhbs3r27VP7WrVuxt7fHysqKgIAA5HK5lHfr1i08PT1p27YtPXr0UPpyFBsbi4uLC/b29kREREjpWVlZ9O/fX+mRmG+CCCgFQRAE4Q1IT09n0KBBpKSksHDhQk6cOMH+/fvp3r07U6dOZdeuXVLZR48e8fHHH3P06FGioqLQ1dUlICBAqT03NzcSExOln5Iga9u2bbRo0YKYmBhu377N4cOHgeJA7N69e3Tv3r1C41iwYAEhISHMnDmT+Ph4Dh48SNeuXTl27FiF2n3bZs+ezaNHjzhw4ADx8fFKr+f27duJjIxkz549hIeHExUVxbZt25Tq//7770qPKXz06BHr1q3DzMxMqdzL2srJyWHEiBHExMQwY8YMjh07RmRkJEOGDGHp0qVKj1UEsLCw4Msvv6Rly5alxnT8+HE2bNjA1q1biYqK4tatW0qP0PT396dly5bExcXx+eef4+vrS2ZmJgALFy5k3bp1bNq0iblz50pfPJYuXcqYMWPQ09N71Zf4hURAKQiCIAhAbm4uM2bMoF27dvTs2ZONGzcqBRiXLl2if//+yGQyJk2aRF5enlJ9f39/hg0bRlBQEBYWFmhqaqKrq4urqys///wzW7Zs4ebNm0Dxc8Ld3NzQ09OjRo0afPrpp5w9e7Zc/bx16xa2trZoampiY2NDSkoKhYWFLFq0iFmzZlXoNbhx4wY//fQTy5Ytw87ODk1NTWrUqMFHH33E2LFjS5V/9OgR48aNo0OHDrRr145x48aRlpYm5e/evZsuXbogk8lwcXEhPDwcgOTkZD799FOsra2xtbVVei65ubk5ycnJQPF78tVXX+Hs7Iy1tTWffPIJubm5pfpx7do1jh49yrx586hduzZqamq0atVKyg8LC2PkyJHUq1cPExMTRowYQWjo/z2Bq6ioiJiYGBwdHaW0pUuX4unpiaGhodK5XtbWvHnzsLGxITg4GEtLS7S1tdHS0sLBwYGffvqJmJgYTp8+LZUfOnQodnZ2aGlplRpXWFgYHh4emJmZUatWLcaPHy+d6/r161y8eJGJEyeira2Nq6srzZs35+DBg0BxYNu8eXMsLCzQ0NDg4cOHnDt3jlu3btGzZ89S56ooEVAKgiAIVV58xilmnZ+Oz5kxzDo/nfiM0kuHFbVq1Spu3rzJ4cOH2bRpE2FhYVKeXC7Hx8eHvn37Eh8fT48ePZSWluPj49HQ0MDDw4PU1FSGDRuGra0tgYGBeHh4UKtWLcaOHSs95/ufTp8+XWomLCoqivbt29OrVy9+/vlnKb158+bExMSQm5tLQkICZmZm/PDDD3Tq1Om1loqfFRsbS7169WjTpk25yhcVFeHu7k5UVBRRUVFoaWkRFBQEFAc08+fP57vvviMxMVGaWQX49ttvsbe35/Tp00RHR/Ppp5+W2f7ixYu5ePEi27ZtIz4+nqlTp6KqWjp0OX/+PA0aNCA4OBhbW1v69OkjBVYAV65cwcLCQjq2sLDgypUr0vG5c+do2LAhtWvXlo4vXLjAJ598UupcL2orJSWF8+fPM3HiRLKysvDx8cHW1pYJEybg7e1Neno6/v7+5X5u+j/PZW5uzv3793nw4AFXr16lYcOGSjONFhYWXL16FYA6deqQlJREUlISKioq1KxZkwULFlT4S8fz/GsB5fXr1xk0aBCurq4MGjSIGzdulCpz4sQJ3N3dadWqFYsXLy53niAIgvDuis84xc/JP/BAXryU90Ceyc/JP7zxoPLAgQN4e3tjYGCAqakpnp6eUt4ff/xBfn4+Xl5eaGho0KNHD1q3bi3lnzx5Upr1Wbx4MTKZjJMnT9KlSxcuXLgAQIsWLfj7779LnTcpKYk1a9Ywbdo0Kc3NzY39+/cTGxvLvHnzWLNmDfv27QPAw8OD7OxsBg4ciI2NDRYWFuzZswcvLy8CAwMZOnToa1808/DhQ4yMjMpd3tDQEFdXV2rUqIGenh6fffaZ0uybqqoqV65cITc3F2NjYyloVldX586dO9y7dw8tLS1sbGxKtV1UVMSuXbuYOXMmJiYmqKmpYWVlhaamZqmyaWlpXL58GX19fY4fP87s2bOZMWMG165dA4qD22cDL319fXJycqS9j88udxcWFvLll18SGBhYZvD6orZOnTpF9+7dUVVVZf369ejp6XH8+HG8vb05deoUCoXiuZ+DspR1LoAnT57w5MkT6fjZ/CdPngAwd+5cFixYwOzZs/n666/55ZdfsLOzIy8vj1GjRuHp6Ul8fHy5+lEe/1pAOWfOHIYMGcLBgwcZMmQIgYGBpco0bNiQBQsWMGrUqFfKEwRBEN5d4XdCyVfIldLyFXLC74Q+p8bruXfvHqamptJx/fr1lfJMTExQUVEpMz8zMxMTExMALl++TJ8+fVBXV8fJyUlaMk1NTZXKlEhOTmbMmDF88cUXSkFVs2bNlIKoYcOGSTNuWlpazJs3j7179zJlyhQWLlzI559/Tnh4OAqFgh9//JFz584RHR39yq+BgYEB6enp5S7/9OlTAgMDcXZ2xsrKiqFDh5KVlUVhYSE6OjosX76cbdu24eDgwNixY6UAb+rUqSgUCjw8POjVqxc7d+4s1faDBw/Iy8sr16yrtrY2GhoafPbZZ2hqatK+fXtsbW05ceIEADo6OlKgBZCdnY2Ojo70fkZHR+Pk5ATAzz//jLm5OZaWlmWe60VtZWRkKH0OevbsiaamJq1ataJZs2ZA2Z+D59HR0VG6eKbkd11dXXR1dUtdWJOdnY2uri5Q/AXmhx9+YMeOHTRt2pRdu3bh7e3NrFmz8PHxYdGiRUybNq3UBUWv618JKDMyMrh06RK9e/cGoHfv3ly6dEnaOFri/fffp0WLFqirl36Az4vyBEEQhHdXycxkedNfl5GREampqdLxs78bGRlx9+5dpT++d+7ckX43NDSUArHmzZuzd+9eCgoKiI6O5sGDByQnJ7NixQo8PDykOrdv32bEiBGMHz+efv36vbR/Zf3hLwkaO3XqxOXLl2nVqhUqKiq0atXqta7KtrOzIy0tjfPnz5er/ObNm7l+/Tq//vorZ8+e5aefflLqq6OjI1u2bOHEiRM0adKE2bNnA8Wv5/z58zlx4gRz585l7ty50r7JEoaGhmhpaZXrFkLm5uYvzDczMyMpKUk6TkpKkmZL09PTuXfvHh9++CFQvOwfGRmJvb099vb2JCYm8tVXX0lL+S9q65+fgwMHDiCXy7l48SJXr14lPT2d+fPnM2jQoJeOqeRcz76PSUlJ1K1bF0NDQ5o1a0ZKSopSUJmUlCQFrs9atGgRkyZNQltbW/qcvPfeexQUFJSKxV7XvxKdlUTjampqAKipqWFsbExqaqq0X+HfUKfOm72i6VUZGem/vFA1JcZWPYmxVU/v8tjKYqhZu8zg0VDzzf79cHNzY8OGDbRt25anT58q7XOztLREXV2dkJAQhgwZQlRUFOfPn8fW1haADh06EBISQv/+/Zk+fTrTp0+nc+fOODo60q5dO2bNmsXUqVOlvYl3797Fy8uLoUOHlrlPLzIyknbt2lGzZk3Onz/PDz/8wOTJk5XK5OXlsXTpUtavXw/Ae++9R3x8PP379+fs2bNKS/bl1bhxY4YMGYK/vz/z5s1DJpOhUCiIjIzk9u3bpS7MefLkCVpaWtSsWZOHDx8qXcF8//59/ve//9GxY0e0tbXR0dGRlpAPHDiATCajXr161KpVCxUVlVLLy6qqqgwYMIBFixaxZMkS6taty7lz5/jwww9LLXvb2NhgamrK+vXrGTduHH/88QdxcXFMnToVgL59+7JlyxZpFnLLli3Svs3o6GgcHR2l2cqvvvpK6YKriRMn4urqKn0ZeFFbtra2+Pn54ePjw7hx4wgICKBz585YWlri4ODAwoULGTFihNKV+HK5HIVCgUKhoKCggLy8PDQ0NFBVVaVv374EBATQp08fjI2NWbt2Lf379wfggw8+oEWLFqxevZpJkyYRHR3NX3/9VeoeoydPniQvLw9nZ2eg+HNy6tQpTE1NkcvlGBgYlOOT8XL/qem+jIzs//8c4H+fkZE+6emPK+Xcb5sYW/UkxlY9VcexVTQA/qh+f35O/kFp2VtDRZOP6vevaNeUTJgwgTlz5tClSxeMjY1xd3cnJCQEAE1NTVauXMns2bNZsWIFTk5OdOvWTarbsWNHVq5cSXh4OB999JFUr0RBQYHSCtuOHTtISUlh1apVSkFYYmIiAPv372fmzJnI5XJMTEwYM2aMFEiUWLduHX369KFevXoADB48GD8/P+zs7OjcubNS/17FrFmzCAkJISgoiFu3blGzZk2sra3x8fEpVdbLy4spU6bQoUMHjI2NGTFiBJGRkUDxHsitW7cyffp0VFRUaNGiBV9++SVQfBHNwoULyc7Opk6dOsycObPMpe3p06ezdOlSPDw8yMnJwcLCgk2bNpUqp6GhwZo1a5g1axbfffcd9evXZ8mSJTRt2lR6bVJSUujTpw9QvA918ODBABw7dkzpqueaNWuWaltPT0/ar/iitho3bkyzZs1Yv3493t7erF69Wqmtf34OAEaNGiXtZUxMTGT27NmEhIRga2tLp06dGD16NMOGDSM3NxdXV1d8fX2lusuWLSMgIIB27dphampKcHCw0kSdXC5nyZIlrFmzRkqbPXu29NmaM2eONNlXUSqKN7V4/gIZGRm4uroSFxeHmpoahYWF2NracujQoTJnKFeuXElOTg7Tp09/pbyX90MElG+DGFv1JMZWPVXHsb2JGdX4jFOE3wnlgTwTQ83afFS/P+3rdHgDvXu+khmu8u5FvHv3LiNHjqRz5858/PHHNGzYkPT0dHbu3Mm5c+ekmcTKVjKDNXHixEruSeUrKCjA3t6eI0eOvLH7MmZnZzN69GiaNGnC8OHDadq0KY8ePSIiIoI9e/bw66+/lnmxT3X3r8xQ1qlThxYtWrBv3z769u3Lvn37aNGixb+63C0IgiBUX+3rdHjrAWRFmZiYsH37djZv3oyPjw9paWkYGhrStWtXFi1aVNndE8rw6NEj/Pz83uhNvvX09AgJCeGnn35ixowZpKSkoKurS6dOnfj222/fyWAS/qUZSii+6eiMGTPIysqiZs2aLF68mCZNmjBmzBh8fX1p3bo1CQkJTJ48mezsbBQKBfr6+ixYsABHR8cX5pWXmKF8O8TYqicxtuqpOo6tuu75fNUZyuoiLi4OQNr/KQhvwr8WUFYFIqB8O8TYqicxtuqpOo6tugaUgiCU37s57yoIgiAIgiD8a0RAKQiCIAiCIFSICCgFQRAEQRCEChEBpSAIgiAIglAhIqAUBEEQBEEQKkQElIIgCMJ/louLCzExMZXdjUrl6ekp3UrodbzN1zAhIQFXV9e30rbwZomAUhAEQRDekrt37zJ//nxcXV1p164drq6uLFq0iIyMDKVyoaGhuLu7Y2VlRadOnViyZAkFBQVSvqenJ61bt0YmkyGTyZSCrKSkJHr16oWtrS1btmyR0vPz8xk4cCCpqakVGkN2djYLFiygc+fOyGQyunbtyoIFC8jMLP189TfNxsaGgwcPvlbdlJQUxo0bh0wmw9bWliVLlkh5Dx8+xMfHB0tLS5ydndm7d2+p+oGBgWzfvl0pLSAgAHNzc5KTk1+prUePHrF8+XJ69+5N+/bt6dKlC7NnzyYlJUWp3L179/D29sbBwQFzc3Nu3bqllC+XywkICMDKygp7e3ul9xsgNjaWHj160LZtWzw9Pbl9+7aUt3HjRmxtbenVqxd//fWXlH7mzBnGjx//opeyXERAKQiCIAhvwZkzZ/jkk0+oW7cumzdvJi4ujh9//BFTU1MGDRrEn3/+KZV9+vQpX3zxBadOnWLHjh2cOnWKzZs3K7UXGBhIYmIiiYmJSkHW0qVLmTZtGuHh4axbt4709HQAtmzZQvfu3TE1NX3tMcjlcry8vLh69SobN27kzJkzbN++HQMDA86fP//a7b5tcrmcESNG0KFDB06ePEl0dDQfffSRlB8UFISGhgYnT57k66+/5ssvv+TKlStKbURHR+Pk5CQdJyQklAoAy9PWtWvXGDhwIIWFhQQHBxMbG8vu3buxtLRk5MiRnDhxQiqrqqqKo6Oj9HjMf1q5ciXJyclERUUREhLCxo0bpRvvZ2ZmMmHCBPz8/IiPj6dVq1Z8/vnnQHGgunPnTo4cOcInn3zCsmXLgOJHTy5evJgvvvjiVV/iUkRAKQiCIPyn/fnnn/Tp0wdra2smTZpEXl4eAL179+bo0aNSufz8fGxtbbl06RIAYWFhODs7Y2try+rVq5WWfh88eMDUqVNZs2YN3t7eNGjQAFVVVYyMjBg+fDjBwcFMmzZNmoUcMmQINjY2aGpqYmJiQp8+fTh79my5+n/r1i3s7OwwMTHh/fffJzU1ldu3b3Po0CGGDx9eoddmz549pKamsmrVKpo1a4aqqip16tTBx8dHKdgqce7cOQYNGoSNjQ0ODg4EBQUhl8sBUCgULFy4EDs7O6ysrOjTpw+XL18G4NixY/Ts2ROZTIajoyObNm0Cip/q06lTJ6n91NRUJkyYQIcOHbC1tSUoKKjMfoeGhmJsbMyIESPQ0dFBS0sLCwsLAHJycjh06BB+fn7o6upiY2ODi4sLe/bskeonJSWhr69PvXr1gOLAa/78+cyaNUvpPC9rSy6X4+vry5w5c5gyZQpNmjRBTU2NWrVqMWDAALZu3cq8efPIysoCoG7dugwdOpTWrVs/d1zjx4+nVq1aNG3alIEDBxIaGgrA4cOHMTMzw83NDS0tLSZOnEhSUhLXrl0jNTWVli1boqenh52dnRQYf//997i4uPDee+899zNQXiKgFARBEKq8RzExXPH358/hw7ni78+jN7hn78CBA2zcuJEjR47w119/sXv3bgD69u1LeHi4VO7YsWMYGxvTsmVLrl69yty5c/n66685fvw42dnZ3L17Vyr7008/MWjQICwsLIiLi6NXr144ODiwadMmRo4cScuWLWnbti3Hjx8vs0+nT5+mWbNmSmlLly7F1taWwYMHK+15NDMz48SJE6SlpXH79m0aNmzIggULmDZtGhoaGhV6bWJiYnB0dERXV7dc5VVVVQkICODUqVNs27aN2NhYfv75ZwBOnDhBQkICBw8e5MyZM6xYsQIDAwMAZs6cSVBQEImJiezbt48OHUo/t72wsJBx48ZRv359jh49SnR0ND179iyzH//73/9o0KABo0ePxtbWFk9PT2mZ98aNG6ipqfHBBx9I5S0sLLh69ap0HB0dTefOnaXjrVu3YmNjIwWlJV7WVkREBNbW1tjb2/PXX38xYMAAOnToQHBwMIMHD6ZBgwb069dP6XP2PI8ePSI9PV2pD8+e68qVK5ibm0t5Ojo6NGrUiKtXr9KoUSMuX75MVlYWsbGxNGvWjNTUVCIiIhg5cuRLz10eIqAUBEEQqrRHMTGkP8X0MgAAIABJREFUbt1Kwf/fd1iQkUHq1q1vLKj09PTExMQEAwMDnJ2dpaXojz76iGPHjpGdnQ1AeHi4tGz622+/4ezsLM0q+vr6oqKiIrV58uRJevbsiUKhwN/fn2nTpvH777+T9f/Yu/O4mvL/geOv23LThorSTPiiJBO6SUlDY022iMFYZxiDEIYsoywJk22MtSFj/1kmJDNM9j0RjW2kbG0iKlLRcru/P/p2vu6UKWKGmc/z8fB46JzPeZ/P59z7ePTus5xPZib5+fkA2NjYcPv27RL1CQkJ4erVq2q/6CdOnMihQ4c4efIkffr0YcSIESQkJAAwefJktm7dysiRI5k6dSoXL15EX18fCwsLRo4cyYABA9i/f/9rPZvHjx9TvXr1cpe3tbXFzs4OLS0tLCws6NOnD+fPnwdAS0uL7Oxsbt++jUqlol69epiamkrnbt68SVZWFlWqVOGjjz4qEfvy5cukpqYyadIkqdfRwcGh1Ho8ePCAffv2MXDgQE6ePImrqyteXl7k5eWRk5ODgYGBWnlDQ0Oys7Oln48dOyb1wKakpLB9+3bGjh1b4j5lxSr+HgD4+vrSu3dvTp06hZmZGampqcDLvwel3as4fmn3ysnJUTsHYGBgQHZ2NkZGRowYMYLBgwdz7NgxJk+ezJw5c/Dx8eHQoUMMGDCAkSNHcv/+/TLr8TIioRQEQRDeaak7d6L677BpMVVeHqk7d76R+C8mTLq6utIvbjMzM+zt7QkPDyczM1NtHl5qaqo0HFp8XXFvGxTNZzMzMyM9PR2lUomrqytaWlp07dpVKpOSkoKZmZlaXQ4dOsTixYtZs2YNxsbG0vEmTZpgYGCAXC6nR48e2Nvbc/z4cQA+/PBD1qxZw+7du2nbti3ff/89kydPJjAwkE6dOrFq1Sq+/fZbHj9+/MrPpmrVqtKczPK4c+cOw4cPx8XFBXt7e7777jsyMjIAcHZ2pn///vj7++Ps7Iyfn5+UrC9dupTjx4/TunVrBgwYQHR0dInYKSkpfPDBB2hpaZVZDx0dHezt7XF1dUUulzN06FAeP37M7du30dPTk+5bLCsrS+qFzczM5Pbt2ygUCgDmzp3LqFGjSiRrQJmxir8HALGxsXTr1g0tLS21+ZylfQ9Ko6enJ8Uv7V6l1SU7O1s636VLF3bv3k1wcDBxcXHI5XJsbGwIDAwkKCiIjh07EhgYWGY9XkYklIIgCMI7reAPK6LLOv4m9ejRg7CwMH799Vfs7OykX/ympqZqQ9zPnz9XS9iMjIx4+PAhxsbGaGpqcvz4cQoKCqQVwBERERw/flxtHuKJEyfw9fUlKChIbeiyNDKZDJVKVeL4ihUr6N27N9WqVSM2NhZbW1tpLmBxj+araNGiBadOnZKS7LLMnDmTunXrEh4ezsWLFxk/frxaPQcNGsSuXbvYt28fd+/eJTg4GIDGjRuzatUqzpw5Q7t27Rg3blyJ2Obm5qSkpKitfn8Za2trtR7jF/3nP/9BqVRy9+5d6VhMTIw0xeDUqVM0b94cTU1NoOizmj9/Pi4uLri4uADQp08f9u7dW2as4u8BQP369QkLC0OpVEpD3FevXmXz5s106dKlzDZVqVKF6tWrExMTU+q9rKys1M7l5OSQkJBQYurE8+fPWbx4MZMnTyY+Ph5zc3MMDAxo1KiR2urvVyUSSkEQBOGdpmVi8krH36R27drx+++/s3HjRrp37y4dd3Nz48iRI1y8eJG8vDyWLVumljg5OTkRHh6OTCZj4cKFBAYG0qFDB7S1tUlISGDr1q2sWLFC6vWKiIjAx8eHZcuW0bhxY7U6ZGZmcvLkSXJzcykoKCAsLIyoqChatmypVu7mzZucO3eOzz77DAALCwvOnj3Lo0ePuHv37mut9vbw8KBGjRqMGTOGW7duUVhYSEZGBkFBQVIP6YuKe8T09fW5desWW7dulc5dvnyZS5cukZ+fj66uLnK5HA0NDfLy8ggLC+Pp06doa2ujr6+PhkbJ9KRx48ZUr16dRYsWkZOTQ25uLhcuXCi13t26dePSpUucOXMGpVLJhg0bMDIyom7duujp6dG+fXuWLl1KTk4OFy5c4PDhw3h4eABFc2VfnD8ZHh7Onj17CA0NJTQ0FICgoCDat29fZqzmzZtLK/IDAgLYsWMHrVu3Jj4+Hl1dXb7//nvmz5/Phx9+KN0vNzdXWsiUl5cnLRID6N69O6tWreLJkyfcunWLn376iR49egDQvn174uLiCA8PJzc3lxUrVmBtbU29evXUns2qVavw9PTEzMwMc3Nz7ty5w6NHj4iMjKRmzZov+SaUrex+Y0EQBEH4G5n27EnK+vVqw94yuRzTnj3f+r0rVapEhw4d+OWXX2jfvr103MrKCj8/P77++muePXvGoEGDMDY2Ri6XA0XzMnv37o2rqyvNmzdn37590rWjR4+moKBAbeh25cqVPH36lK+++ko61rRpU4KDgykoKGDJkiXcvn0bTU1N6taty4oVK9QWggDMmjWLadOmST1rEyZM4Ouvv2bJkiWMGDHileZCFpPL5axfv56lS5cyZMgQMjMzMTExoW3btiUSXyiaz+nn58fatWuxsbGhU6dOnD17FihKNufOnUtSUhJyuZyPP/6YoUOHAkWryWfPno1SqaROnTosWLCgRGxNTU2CgoIICAigdevWANLq/D+qW7cuCxYsYMaMGaSlpfHRRx+xatUq6fOZMWMG33zzDS1atKBq1arMnDkTKysrVCoVp06dYtKkSVIsk1L+cDEyMqJSpUp/GguKEtu1a9cSGRmJk5MTO1+YplG8yv+PQ/gvPld3d3cAqeeweMV469atqVSpEsOGDZNWwRsbG7Ns2TL8/f3x8fGhSZMm0uuBit26dYtTp06xY8cOoKinfdiwYXTu3BkTExO+++67Em0tL5mqtD7zf6i0tCwKC/+e5lavbsjDh0//lnu/baJt7yfRtvfT+9i26tVLzj17VU/OnCF1504K0tLQMjHBtGdPqrRo8QZqV7bly5dz9+5dFi5c+NIy2dnZNGvWjPDwcKmX5+zZs3zzzTcMGzaMDh06YGRkxM2bN1m9ejX16tVj5MiRf0n9yzJw4EBGjx6Nk5PT312Vv93ly5fx9/cnJCTkjcW8ceMGXl5e9OnTh65du2JmZkZSUhKbNm0iNzf3pa8+et+IHkpBEAThnVelRYu/LIF80ePHj9m5c6faLivFjhw5grOzMyqVisDAQOrXr6/2Pr/mzZuzYcMGgoKCWLNmDU+ePKFWrVr06NGDfv36/ZXNEF7BmDFj3mg8a2trtm/fzurVq/n888959OgRNWrUwN3d/Y29suddIHoo/yLvY69CeYm2vZ9E295P72Pb3kQP5d9hx44dzJ07l27dupXaizRt2jTCw8NRqVTY2toyY8YM6tat+zfUtGJ27dqFo6PjG3m5tfDvJRLKv8j7+EugvETb3k+ibe+n97Ft72tCKQhC+YlV3oIgCIIgCEKFiIRSEARBEARBqBCRUAqCIAiCIAgVIhJKQRAEQRAEoUJEQikIgiAIgiBUiEgoBUEQBOG/kpKSsLa2Ltd+0e+6yMhIBg4c+NrX79q1S9rG8W348ssv2b1791uLL/y1REIpCIIg/Gu1adOGM2fOvPT8wIED+emnn0ocV6lU7Nu3j8GDB+Ps7IyLiwtDhgzhwIEDauXS0tL4+uuv+fjjj2natCl9+/bl0qVL0vnIyEgaNGiAQqGQ/r2YZM2ZM4dmzZrRp08f7t+/Lx3fu3cvAQEBFWk6ACdPnqR///4oFAqaN2/OgAEDOHz4cIXjlkdwcLC0D/Wr2rBhA23atMHOzg53d3fu3Lkjndu7dy+tW7fGzs4OLy8vHj9+rHbtgwcPpO0Ki929e5dGjRoxceJEteNlxQI4deoUI0aMwMXFhebNm/PZZ58REhJCYWGhWrnNmzfj6emJra0tU6ZMKREnIiKCjh070qRJEwYOHEhycrJ0Li8vj6lTp2Jvb4+Liwvr1q2TzqWkpNC7d28cHR359ttv1WJ++eWXXLly5WWP8Y0SCaUgCIIgvAKlUsmECRPYsWMHI0aM4NChQxw7dozRo0ezfft2fH19KX7Fc05ODo0aNWLXrl2cO3eOHj168NVXX5GdnS3FMzU1JTo6WvpXnGRdvnyZa9eucfr0aezt7Vm9ejUAT58+Ze3atYwbN65C7fj1118ZO3Ys3bt358SJE5w5cwZvb2+OHj1aobhv208//URISAirV68mOjqaH374ASMjIwDi4uKYPn068+fP5/Tp0+jq6jJr1iy160+cOEHLli3Vjvn7+9OoUSO1Y+WJNX/+fBYvXkyvXr3Yv38/p0+fZvr06Zw9e5bhw4eT98L+86ampnh5edGzlD3o09PTGT16NGPHjuXcuXPY2toyfvx46fyyZcuIj4/n6NGjbNy4keDgYE6cOAHADz/8QPfu3Tl8+DCHDh2SEsh9+/ZhYWFRol1vi0goBUEQhHde7PlkNk4/wsoxv7Bx+hFizyeXfVEZfHx8uHfvHiNGjEChULBmzRq189999x1RUVH4+/ujUCik3XJWrVqFXC5n3bp1ODs7o6+vj7a2Nvb29qxZs4asrCz27NkDQM2aNfniiy8wNTVFU1OTPn36kJ+fr9aj9jJJSUk0bdoUuVyOs7MziYmJUr2GDh2KgYHBa7ddpVLx7bff4uXlxaeffoqhoSEaGho4Ojq+tOczICAAV1dX7O3t8fT0JCoqSjp3+fJlPD09sbe3p0WLFsybNw+A3NxcJk6ciJOTEw4ODvTs2ZNHjx4BJXt/d+zYgbu7OwqFgk6dOnHt2rUSdSgsLGT58uV88803WFpaIpPJqFWrFlWrVgWKehTbtGlDs2bN0NfXZ+zYsRw8eJCsrCwpxvHjx3F1dZV+/uWXXzA0NMTZ2VntXmXFCg0NJSYmhm3bttGuXTsqV66MpqYmNjY2LFy4EEtLS+mPAIAOHTrQrl07qa4vOnjwIFZWVri7u6Ojo8OYMWOIiYnh1q1bAOzevRsvLy+qVKlCvXr1+PTTT6We7KSkJJo3b46hoSGNGjUiMTGRrKwsVq9ezddff13qZ/k2iL28BUEQhHda7Plkjm29QkG+EoCsjGcc21rUC1O/2YevHXfBggVcuHCBgIAAWvx3n/CkpCTp/Pjx47l48SLdunXj008/BYp6HENCQti3bx/5+fnMmDGDY8eOYWlpiYWFBZ6enkydOpWRI0fSvXv3Eve8fv06+fn51K5dWzqWnp5OixYtqFSpEu3atWPcuHHo6elhaWnJpk2beP78OREREVhaWnLlyhXu3LnD9OnTX7vdALdv3yYlJQU3N7dyX9OoUSNGjRqFoaEhGzduZOzYsRw5cgQdHR3mzJnDoEGD6N69O9nZ2cTFxQFFiVBWVhbHjh1DLpdz/fp1KlWqVCL2/v37WbZsGStWrKBRo0YkJCSgpVUyRbl//z73798nNjaWKVOmoKmpSffu3Rk9ejQaGhrExcWhUCik8rVq1UJbW5u7d+9ia2tLfn4+58+fl4aGs7KyWLp0KRs2bCgxtaGsWCtXrmTDhg3I5XIWLFhAaGgopqamNGvWjIYNGzJ27Fi6dOnC6NGjy3y2cXFxWFtbSz/r6elRq1Ytbt68SbVq1Xj48CENGjSQzjdo0ECammBlZcWZM2eoVq0a165dw8vLiyVLljB48GAqV65c5r3fFNFDKQiCILzTzu69ISWTxQrylZzde+Mvr8tvv/2Gk5MTenp6hISE8ODBAw4ePMjcuXM5efIkhYWFmJmZlTrXLisri0mTJjF69GgMDYu2o6xbty6hoaGcOnWKDRs2cO3aNSnZqV+/Pm5ubvTu3ZuUlBSGDRvGnDlz8PX1ZePGjfTv358JEyaQmZn5yu0orp+pqWm5r/Hw8MDIyAgtLS2GDBlCXl6e1NOqpaVFQkIC6enp6OvrY2dnJx1//Pgx8fHxaGpqYmtrW2rPakhICF9++SWNGzdGJpNRu3ZtPvyw5B8LxfNIT58+zd69e9m4cSO//PILISEhQFHCX/xsixkYGEhTDKKiomjQoIFUhyVLltCzZ09q1KhR4l5/Fis+Ph5TU1PMzc05fvw4J06cICwsjODgYCIiIigsLKRSpUpUqVKF9PT0Mp/tn90rJycHQO28oaGh1Kbhw4cTFRXFgAED6NevH/n5+dy4cYPWrVszYcIE+vfvz+bNm8usQ0WJhFIQBEF4p2VlPHul429TWloaZmZmAMTGxtK2bVsMDAyoWbMmTZs2LapXVhZ6enpq1z1//pwRI0bQpEkThg8fLh2vXr06lpaWaGhoULNmTXx8fAgPD5fOf/7554SFhbFkyRL279+Pg4MDhYWF7Nixg/Xr11OvXj21YdXyKh52TU1NLfc1a9euxd3dnaZNm+Lg4MDTp0/JyMgAihYP3b17F3d3d3r27CnNw/Tw8ODjjz+WFibNnz+f/Pz8ErFTUlKoVatWmXUo7t388ssvqVy5MhYWFvTp04fjx48DRT17Lw5vQ9Hnoa+vD6gPd1+/fp2IiAg+//zzUu/1Z7H++D1o2bIlJiYmmJiYSPMzCwsLyczMLHWIuzz3ys7ORl9fX/ouvXj+xTZVrVqVJUuWEBYWxqBBg5g9ezZ+fn6sXr0aKysr1q1bx7Zt26Th87dFJJSCIAjCO83ASPeVjr9NRkZGPHz4ECjqQTx8+DBZWVkkJiZy4cIFMjMzmTVrltrCi7y8PEaNGoWZmZk0D/NlZDKZtKDnRY8ePWL79u2MGjVKGh7V1tamUaNG3Ljx6j21devWxdzcvMSq9JeJiooiODiYJUuWcP78eaKiojA0NJTq+p///IfFixcTERHBsGHD8Pb2JicnB21tbUaPHs2+ffvYtm0bx44dIzQ0tER8c3NzEhISyqxHnTp10NbWRiaTScde/L+VlRUxMTHSz4mJieTn5/Of//wHUE8oIyMjSU5OpnXr1ri4uPDjjz9y4MABaVHUn8UyMjKSkvH69etz6tQp0tLSSEtL4+TJkzx79oxFixbh6uqKhkbZqdYf75WTk0NCQgKWlpZUqVKF6tWrq52PiYnB0tKyRJzt27djZ2dH/fr1iY2NxdbWFrlcTv369V/re/IqREIpCIIgvNOad7VGS1tT7ZiWtibNu1q/5Iryq1atmrTYpTzn7ezsiIyM5Pnz5/Tq1QszMzM6dOjAtGnT+Pjjj1mxYgV2dnYMHjwYgPz8fLy9vdHR0SEwMLBEcnH27FmSk5NRqVSkpKSwcOFC2rZtW6Ie8+bNY8yYMejq6mJhYcGVK1fIzs7m3Llz1KxZ85XbLZPJmDJlCitXrmTnzp1kZWVRWFhIVFQUfn5+JcpnZ2ejqamJsbExBQUFLF++XK3HbM+ePaSnp6OhoSHN29PQ0ODs2bPcuHEDpVKJgYEBWlpapSZYvXr14scff+Tq1auoVCri4+PVXptTTFdXl06dOhEcHExWVhb3799n+/btfPLJJwB07dqVo0ePEhUVRU5ODt9//z3t27fHwMCAxMRE8vLyqFevHgB9+vTh4MGDhIaGEhoaSt++ffnkk09Yu3ZtmbHq1KnD/fv3SU1NxdXVlZYtW+Lh4cHIkSNp1qwZW7duRV9fn0mTJkl1LygoIDc3l8LCQpRKJbm5udL7Ttu3b09cXBzh4eHk5uayYsUKrK2tpbp2796dVatW8eTJE27dusVPP/1U4pVLaWlp/N///Z80Z9PCwoLIyEiys7O5evXqa31PXoVYlCMIgiC804oX3pzde4OsjGcYGOnSvKt1hRbkFPvqq68ICAhgwYIFjBw5ssQilUGDBjFlyhS2bt2Kh4cHvr6+dOnShblz5+Lv7y+tZi5WUFCgtpgkOjqao0ePUqlSJZo1ayYdX7NmDQ4ODly/fh0fHx9paLR9+/YlXgcUERHB06dPad++PQCNGzfG1dWVTz75hDp16rB06dLXanvHjh3R09MjKCiIgIAAdHR0sLKyYujQoSXKfvzxx7Rs2RI3Nzf09PQYPHgw5ubm0vmTJ0/y7bff8vz5cz744AO+++47KlWqxKNHj5gxYwYPHjxAT0+PTp064eHhUSK+u7s7jx8/ZsKECaSmpvLhhx8yf/78UudRTp8+HT8/P1q2bEnlypX59NNP6dWrF1DU0zdr1iwmTpzI48ePcXZ2lj6jP67u1tXVRVf3f73cenp6yOVyjI2Ny4wFRcPuvr6+rFy5Eh8fH3x8fKRzf/weQNHbAZYvXy79HBYWxujRoxkzZgzGxsYsW7YMf39/fHx8aNKkCYsXL5bKent7M2PGDFq3bk2lSpUYNmxYiXdpBgYG4uXlJQ2FDx8+HG9vb7Zt24anp+dbf32QTFVa3/o/VFpaFoWFf09zq1c35OHDp3/Lvd820bb3k2jb++l9bFv16oZlF3qPFBQUMHbsWAoLCxk5ciQ2NjY8f/6cw4cPExwczPbt26Vf6n+nyMhIli9fzqZNm/7uqrwThg0bxoABA9SSyory9/cnJiYGb29vFAoFhYWFnD59miVLlvDDDz+UmhD/U4keSkEQBEF4BVpaWixbtoxdu3Yxb948bt++jba2Nk5OTixevPidSCaFkhwdHXFycnqjMadPn87BgwcJCgqS5jgWv7P035RMguih/Mu8j70K5SXa9n4SbXs/vY9t+6f1UL4vkpKSOHfuHJ6enn93VYR/AdFDKQiCIAj/QBYWFlhYWPzd1RD+JcQqb0EQBEEQBKFCREIpCIIgCIIgVIhIKAVBEARBEIQKEQmlIAiCIAiCUCEioRQEQRD+tTp37kxkZGS5yh48eBBXV1cUCgW///77W67Zm5OUlESbNm1e+/rIyMgSL9F+k6ZPn86KFSveWnzhryESSkEQBOFf65dffin3uwkDAwPx8/MjOjqahg0bSsdPnTrFiBEjcHFxoXnz5nz22WeEhIRQWFgolcnLy+Obb76hdevWKBQKPDw8OH78uHQ+KSkJa2trFAqF9O/FJCs4OBgnJyc6d+6stifzhQsX8PLyqsgjAODy5csMGzYMBwcHHB0d6dWrFzt37qxw3PLw9/dn1KhRr3XtL7/8gru7O3Z2drRr146oqCjpXEREBB07dqRJkyYMHDiwxFaOeXl5ODk5kZ2dLR17/Pix9Bm+qKxYUPQMx40bR6tWrXBycsLT05N169aRl5enVm7fvn307dtXivVH169fx9PTkyZNmuDp6cn169elcyqVigULFuDk5ISTkxMLFiyQ9lN/+vQpQ4cOxcHBgQkTJqBUKqXr/Pz8yr1v++sSCaUgCIIglMO9e/ewsrJSOzZ//nwWL15Mr1692L9/P6dPn2b69OmcPXuW4cOHS8lEQUEB5ubmbNq0iQsXLjBu3DjGjRtHUlKSWrzz588THR1NdHS0lGSlpqYSEhLC4cOH+eyzz6Qt+QoKCggMDOSbb76pULuio6MZPHgwzZo148CBA0RGRjJz5kxOnDhRobhv2+nTp1m4cCHz5s3j4sWLbNmyRdqvOj09ndGjRzN27FjOnTuHra0t48ePV7s+KioKGxsbtRfRL1y4UNo/u1h5Ym3atIlJkybRunVrdu/eTUREBAsXLiQ5OZl+/fqRmZkpla1atSqDBg1i2LBhJdqUl5eHl5cX3bp14/z583Tv3h0vLy/pe7R9+3YOHTrEnj17CAsL4+jRo2zbtg2Abdu2YWNjw5kzZ0hOTubgwYNA0eebmppKhw4dXvdRl4tIKAVBEIR/rTZt2nDmzBkACgsLWb16Ne3atcPJyYmxY8fy+PFj8vLyUCgUKJVKPDw8aNeuHQChoaHExMSwbds22rVrR+XKldHU1MTGxoaFCxdiaWnJ6tWrgaJ9oseMGYOFhQUaGhq0bt0aCwsLrl27VmYdU1JSaNiwIQYGBjg7O5OYmAjAhg0baNOmTYXfNTl//ny6d+/OV199hbGxMTKZDFtbW77//vtSyxc/I4VCQadOnaTEBSA+Pp4BAwbQtGlTnJycpH3JVSoVc+fOxdnZGXt7e7p27UpsbCwAU6ZM4bvvvpNiHDp0CA8PD+zt7WnXrt1LE9tly5bh5eWFnZ0dGhoamJmZYWZmBhRNT7CyssLd3R0dHR3GjBlDTEwMt27dkq7/497eFy9eJC4ursSL4MuKFRkZyc6dO9mxYwceHh6YmJigoaFB3bp18fX1pUePHmp7gLdo0YJOnTpJdX3RuXPnKCgoYPDgwcjlcgYNGoRKpeLs2bNA0XduyJAh1KhRAzMzM7744gt2794NFPVyOzk5IZfLcXBwIDExEaVSybx58/D19S31Gb5JIqEUBEEQ3nnXIw6w2qcXi4a0YrVPL65HvPnhu02bNnHo0CE2b97MyZMnqVKlCv7+/sjlcqKjowHYs2cPhw4dAmDlypXMmTMHuVzOggULcHFxoUePHsydO5fQ0FDGjh1LaGhoqfd69OgRd+/exdLSUu1469atadWqFVOnTiU9PR2AWrVqERsbS2ZmJhEREVhaWpKSksIvv/zCkCFDKtTmZ8+e8dtvv+Hm5lbua2rWrMmWLVu4cOECo0ePxsfHh9TUVAC+//57XFxcOH/+PCdOnGDAgAFA0bSAqKgowsPDuXDhAkuWLKFq1aolYl++fJnJkyczadIkoqKi2LJlS6lbGCqVSq5evUpGRgbt27enVatW+Pv78/z5cwDi4uKwtraWyuvp6VGrVi1u3rwpHXsxoVQqlcyePRs/Pz9kMpnavcqKtXz5cmbMmEHlypXZsGEDrVq1ws3NjSVLlrBy5Ur69evHpUuXePq07B2ubt68ibW1tVodrK2tpXvFxcXRoEED6VyDBg2Ii4sDoH79+pw5c4bnz58TFRWFlZUVmzZtolWrVlLP7dskEkpBEAThnXY94gAHNsznadoDQMXTtAcc2DD/jSeV27ZtY/z48dSoUQO5XM7o0aMJDw+noKCgRNn4+HhMTU0xNzfn+PHjnDhxgrBP1NvbAAAgAElEQVSwMIKDg4mIiKCwsJBKlSpRpUoVKTEslp+fz8SJE+nRo4c0vGpkZERISAhHjx5l165dZGdn4+PjI50bMWIEgwcP5tixY0yePJk5c+bg4+PDoUOHGDBgACNHjuT+/fuv3ObMzEwKCwupXr16ua9xd3fHzMwMDQ0NOnXqRO3atbl8+TJQtM/5vXv3SE1NRUdHBwcHB+l4dnY2t2/fRqVSUa9ePUxNTUvEDgkJoWfPnri4uEi9jn8cgoaihDw/P59ff/2VLVu2EBoayu+//86qVasAyMnJwdBQfctPAwMDab5kQkICSqWSunXrAkV/TDRu3BhbW9sS9/qzWLm5uaSkpKBQKLh58ybBwcFs2bKFnTt3cvnyZZRKJTKZDEtLS+7evVvms83Ozv7Teufk5GBgYCCdMzQ0JCcnB5VKRa9evcjKyuLTTz/FwcGBBg0asGfPHgYPHsz06dPp37+/Wk/wmyYSSkEQBOGddnLXagryctWOFeTlcnLX6jd6n3v37jFq1CgcHBxwcHCgU6dOaGhokJaWVqJsWlqaNGQZGxtLy5YtMTExwcTEhJYtWwJFQ+iZmZlqPXGFhYVMmjQJbW1t/Pz8pOP6+vo0atQILS0tqlWrhp+fH6dOnSIrKwuALl26sHv3boKDg4mLi0Mul2NjY0NgYCBBQUF07NiRwMDAV25z5cqV0dDQ4OHDh+W+JjQ0FA8PD+k5xcXFkZGRAYCPj4+U3HTu3JmQkBAAnJ2d6d+/P/7+/jg7O+Pn5ye17UUpKSnUqlWrzDpUqlQJgIEDB2JqaoqxsTFffPGFtNBJT0+vRPzs7GxpvuTx48ellesPHjxg48aNJeZFFvuzWI8fP6ZatWpA0ffA3t6emjVrYmBgQNu2baXy9+/fL3WI+4/09fX/tN56enpqi4iysrLQ09NDJpOho6PD7Nmz2bt3LxMnTmTu3LmMHz+esLAwVCoVmzdv5vLly29tbqxIKAVBEIR32tO01Fc6/rpq1KjBmjVriIqKkv5duXKl1ETAyMhIGuatX78+p06dIi0tjbS0NE6ePMmzZ89YtGgRrq6uaGgU/apVqVRMmzaNR48esWzZMrS1tV9al+Ihz+IVvMWeP3/O4sWLmTx5MvHx8Zibm2NgYECjRo3UVn+Xl66uLnZ2duVeAZycnIyvry9+fn5ERkZKQ6vFqlevTkBAAKdOnWLWrFnMmjWL+Ph4AAYNGsSuXbvYt28fd+/eJTg4uER8c3NzEhISyqxHlSpVqFGjhtrQ8Iv/t7KyIiYmRvo5JyeHhIQEaYrBiwnllStXePjwIZ07d8bFxYU5c+Zw5coVXFxcUCqVfxqratWq0h8c9evXJzo6msTERLKysjh8+DD5+fls3LgRY2PjUntk/8jS0pIbN26ofe43btyQ6v3HusTExJRYKAZISWOrVq2IjY3F1tZWmhv7Ot+T8hAJpSAIgvBOMzQp/Rfxy46/rs8++4wlS5ZIr4RJT0+X5kv+UZ06dbh//z6pqam4urrSsmVLPDw8GDlyJM2aNWPr1q3o6+szadIk6ZoZM2Zw69YtgoKCpB62YpcuXeL27dsUFhaSkZFBQEAAjo6OJYY/V61ahaenJ2ZmZpibm3Pnzh0ePXpEZGTka8+T8/HxkXo/i3saY2JiSu2xe/bsGTKZDGNjYwB27twpzeED2L9/vzT0XqVKFWQyGRoaGly+fJlLly6Rn5+Prq4ucrlcSrRf1KtXL3bt2iVNG3jw4IHaQpoXeXp6smnTJtLS0njy5Anr16/nk08+AaB9+/bExcURHh5Obm4uK1aswNramnr16vHs2TOuXLlC8+bNgaKk68iRI4SGhhIaGoq3tzc2NjaEhoaiqan5p7F0dHSoVq0aV69exdLSkqFDh9KvXz/69euHtbU14eHhJCcns3DhQqneSqWS3NxcCgoKKCwsJDc3l/z8fAAcHR3R1NRk48aN5OXlsXnzZgCprh4eHqxbt44HDx7w4MED1q1bR48ePdSeS25uLosWLZJW/1tYWHDu3Dny8vK4ePHiW5tPqfVWogqCIAjCG9LS8ysObJivNuytJdehpedXb/Q+xStqhwwZQmpqKiYmJnTq1Ela1f1HX375Jb6+vqxcuRIfHx9pziMUvdJHS+t/v2KTk5PZvn07crmcjz/+WDo+a9YsunXrRmJiIosXLyY9PR0DAwNatGghvR6o2K1btzh16hQ7duwAwNTUlGHDhtG5c2dMTExee36cvb09GzZsYOnSpaxatQpNTU1q165N//79S5S1tLRkyJAh9O3bF5lMRvfu3bG3t5fOX7lyhblz55KVlYWJiQnTpk2jZs2aJCUlMXfuXJKSkqRnMHTo0BLxGzduzLx586Sy1apVY/r06aXOo/Ty8iIjIwM3Nzd0dHRwd3dn5MiRABgbG7Ns2TL8/f3x8fGhSZMm0vM8e/YsdnZ26OjoACCXy9XmkBoaGqKlpSUd+7NYxfWYPn06mzZtYvDgwQwePFg6N2HCBLXvARQt7Jo6dapam3v06MG3336LXC5nxYoV+Pr6smjRIurVq8eKFSuQy+UA9O3bl8TERLp27QoUJeB9+/ZVix8UFETXrl2pUaOGdM3YsWNxdnbmk08+oX379iWe5ZsgU/2xP/0fLC0ti8LCv6e51asb8vBh2Su83keibe8n0bb30/vYturVDcsuVIbrEQc4uWs1T9NSMTQxpaXnV9g4v9336pWHv78/MTExeHt7o1AoKCws5PTp0yxZsoQffvih1BXKf7WkpCQGDRrEkSNH/u6qvBNmzpyJlZVVqQnz6woODmbPnj2MHz8eJycntLW1uXDhAosXL2bKlCk0bdr0jd3rXSV6KAVBEIR3no1zh3cigfyj6dOnc/DgQYKCgqS5bQqFAn9//3cimRRKsrGxoXXr1m805pdffknjxo358ccf8fPzIz8/n4YNGzJq1Kh/RTIJIqEUBEEQhApp3779WxtGfBMqV67MoEGD/u5qvDP69OnzVuI6Ojri6Oj4VmK/D8SiHEEQBEH4B6tcuTKff/75310N4R9OJJSCIAiCIAhChYiEUhAEQRAEQagQkVAKgiAIgiAIFSISSkEQBEEQBKFCREIpCIIgCH+wbNkyJk6c+HdX462ztrZ+7WuTkpKwtramoKDgDdbof4KCgpg2bdpbiS28eSKhFARBEIS34PLly4wbN45WrVrh5OSEp6cn69atIy8vT61cYGAgHTp0QKFQ0LFjR0JDQ9XOW1tbY2dnh0KhQKFQqCVZe/fu5eOPP6ZNmzacPXtWOp6QkEDfvn1RKpUVasOdO3fw9vbGycmJpk2b0rVrV9atW1fhuOUxYsQI5syZ81rXnjlzhh49emBnZ0erVq3Yt2+fdO769et4enrSpEkTPD09uX79eonr3dzcuHPnjvRzXl4e7u7u0v7frxLr9u3bfPPNN7Rp0wZHR0e6du3K0qVLycrKUit39uxZBg4cSNOmTWnTpk2JOElJSQwcOJAmTZrQsWNHzpw5o3Z+/fr1uLi4YG9vz9SpU6XvWUFBAePHj8fBwYGhQ4eq3TcoKIh169b92aMsN5FQCoIgCMIbtmnTJiZNmkTr1q3ZvXs3ERERLFy4kOTkZPr160dmZqZUVldXl1WrVnHhwgUCAwOZM2cOFy9eVIu3Z88eoqOjiY6OlpKsgoICFi1axK5du/Dz8yMgIEAqHxAQwNSpU9HU1HztNiQkJNC7d2/Mzc3Zu3cvFy5c4Pvvv+fq1atkZ2e/dty37ebNm0yYMIFx48YRFRXFnj17sLW1BYoSQy8vL7p168b58+fp3r07Xl5eakl+QkIChYWF1KlTRzq2du1aaf/yYuWJdeDAAYYNG8ZHH33E1q1biYyMJCgoCJlMRu/evbl3755UVk9Pj549e6rt//6iCRMm0LBhQyIjIxk/fjze3t6kp6cDcPLkSVavXs369es5evQoSUlJLF26VKqDTCbj7NmzGBoasn37dgASExM5cuQIAwcOrMjjloiE8iU0NGRoIIN8FcrsAgqyCyCvEA1V0TlBEATh/bd69WpatmyJQqHAzc2NiIgI6Vx+fj6TJk1CoVDQuXNnrly5AhRtszdmzBi1OAEBAVJCFxkZyc6dO9mxYwceHh6YmJigoaFB3bp18fX1pUePHsybN0+61tvbm3r16qGhoUGTJk1o2rQpv/32W5l1f/z4MWZmZpiamtKiRQsSExMB+PXXXzEzM6NJkyYVejZLly5FoVAwdepUTE1NAahbty6LFi2icuXKJcrv3LkTd3d3FAoFbdu2Zdu2bdK59PR0hg8fjoODA46OjvTr14/CwkLg5Z/BH6cdREVF0bdvXxwcHHB1dWXXrl2l1nvVqlX06dMHV1dXtLS0MDIyolatWgCcO3eOgoICBg8ejFwul/Zvf7F399ixY7i6uko/JyYmEhYWxldfqe8dX1as27dvs2DBArZs2UL//v0xMzNDJpPx4YcfMmbMGCZNmlRiT+/u3btTs2bNEm26c+cO165dY8yYMVSqVAk3Nzfq169PeHg4AKGhofTq1QsrKyuqVKmCl5cXu3fvBop6Nh0dHdHS0sLJyUn6nsyZM4fJkyeX2Gv8dYmdcv5AQ0NGYW4hqQmPuXEuicep2RQqi/b/lsnA0EQPS4U5H9Q3QVNX82/bG1wQBOHf5OmlB6QfvkPBk1y0quhg3LYOhk3MKhTz9u3bbNmyhZCQEMzMzEhKSpKSHIAjR46wfPly5s2bx5IlS5g9ezY7duygW7duLF++nMzMTCpXrkxBQQG//PILa9asAWD58uXMmDGDypUrs2HDBtauXYuuri7u7u7I5XJGjhxJ586defr0KYaG6vucP3/+nKtXr9KvXz+14/3790elUqFQKJgyZQoWFhYYGxvz+PFj7t+/z++//46lpSVZWVmsWrWK9evXV+jZAERERPD111+Xu7yJiQk//PADNWvW5Pz58wwbNoxGjRrx0UcfsW7dOszMzKRk8dKlS8hksjI/g2LJyckMGzaM2bNn4+bmRlZWFvfv3y+1Hr/99hs1a9aka9euZGRk0Lx5c3x9falatSo3b97E2toamex/HUPW1tbcvHlTGs4+fvy42ovgAwIC+Prrr6lUqZLafcqKtWbNGsaOHUuNGjXYt28fCxcuRKVS0bt3b5KTkwkICGDDhg3ExsZSv379P322N2/epGbNmhgYGEjHGjRowM2bNwGIi4ujbdu2avV49OgRGRkZ1K9fn9DQUDw9PYmMjKRZs2YcPHgQIyOjN7otpOihfIGGCh4nZRG+5gIRe2JIT8mSkkkAlQoyH+Vw8eAt9v8QRcr1NDREPikIgvBWPb30gId7Yyl4kgtAwZNcHu6N5emlBxWKq6mpSV5eHrdu3SI/Px8LCwupJwugadOmuLq6oqmpiYeHh7RXt6mpKQ4ODvz6669A0XCjkZERtra25ObmkpKSgkKh4ObNmwQHB7NlyxZ27tzJ5cuXUSqVyGQyLC0tuXv3bok6zZgxA2tra1q2bCkd27x5M0eOHGH//v2YmpoyYsQICgoK0NDQYObMmXh7e/Pjjz8SEBDAsmXLGDBgADdu3GDgwIEMHTqU2NjY13o+jx8/pnr16uUu/8knn1CrVi1kMhmOjo64uLgQFRUFgJaWFg8fPuTevXtoa2vj4OCATCYr8zMo9vPPP9OiRQu6dOmCtrY2RkZG2NjYlFqPBw8eEBYWxtKlSwkPDyc3N5fZs2cDkJ2dXSKJNzAwkIbwnz17xtWrV3FycgLg4MGDKJXKUrfWLCtWZGQkHTp04PHjx8yaNUuqz927d6WFTA0aNOD27dtlPtvS7mVoaCjdKycnRy3ZLC6bnZ2Nq6srFhYW9OzZE0NDQzp16sTy5cvx8fHhu+++o3///sycObPE3N5XJRLK/9JQwa2oexzfdoW852WvWFMWFHLul1gu7r+JZqEYAhcEQXhb0g/fQZWv3mulyi8k/fCdl1xRPrVr1+abb75h2bJltGjRgvHjx/Pgwf+S1GrVqkn/r1SpErm5uVIi0KNHD8LCwgAICwvDw8MDKErCiq+LjY3F3t5e6ll6sQfp/v37mJmp97AGBgYSFxfH999/r9br1axZM+RyOZUrV2batGkkJSVx69YtAJydndmxYwebN29GJpNx9epVPD09mTx5Mt9++y0jR47E19f3tZ5P1apVefjwYbnLHz9+nN69e+Po6IiDgwMnTpwgIyMDgKFDh1K7dm2GDBlC27ZtWb16NVD2Z1AsJSWl1ESzNDo6Onh6elKnTh309fUZPnw4J06cAEBfX7/EYpjs7Gz09fWBol5ZhUKBXC4nJyeHBQsWvPT5lRULQC6Xk5CQQM2aNbG1tUUul9OpUyfpfGnfg/LeKysrS7qXnp6e2vni/+vr6yOTyZg4cSJ79+5l9uzZrF69mr59+3LlyhWuXr3K5s2byc/PZ+fOnWXW48+IhBLQkMlIvv6IqycTXvnaxJhHXD52B01EUikIgvA2FPdMlvf4q+jatStbt27l6NGjyGQyFi5cWK7r2rVrx40bN4iNjeXYsWN07doVKErC0tLSAKhfvz7R0dEkJiaSlZXF4cOHyc/PZ+PGjRgbG0vzEqFovuLJkydZu3atWk9TaWQyGSqV+vCYSqXC398fX19fMjIyUCqVfPjhhzRu3JgbN268yiORODs7c+DAgXKVzcvLw9vbmyFDhnD69GmioqJo1aqVVE8DAwOmTJnC4cOHWbVqFevWrZOGv8vzGZibm5OQUL7f0X98FdKLybmlpSU3btxQe343btzA0tISKEqKi4e+4+PjSU5Opn///ri4uDBmzBgePnyIi4sLSUlJZcaSyWTk5eVRq1YtEhMTuXbtGnl5eezfvx+lUsm+fftITk6mUaNGZbbJ0tJS+h4Vi4mJke5lZWWl9jnHxMRQrVo1jIyM1OLcuHGD6Oho+vTpw40bN/joo4+QyWQ0atTotb8nxURCCeRn5XPhwK3Xvv72b/d5cj9bLNYRBEF4C7Sq6LzS8fK6ffs2ERER5OXlIZfL0dHRQUOjfL8WdXR0cHNzY8KECTRq1IgPPvhAOl6tWjWuXr2KpaUlQ4cOpV+/fvTr1w9ra2vCw8NJTk5WS5p++OEHfv75Z9atW1ciAYiLi+P69esolUqys7P59ttvMTU1pV69emrlfvrpJxo2bIiNjQ1Vq1YlNzeXmzdvcvbs2VIXeZSHt7c30dHRBAYGSj2V8fHxTJw4UW2VOhQllHl5eRgbG6OlpcXx48c5ffq0dP7o0aPEx8ejUqkwNDREU1NTmkNZns+ga9eunDlzhn379lFQUEBGRkapr+gB8PT0ZNeuXSQmJvLs2TNWr17NJ598AoCjoyOampps3LiRvLw8Nm/eDEDz5s2BoukLxWWtrKw4duwYoaGhhIaGEhAQgImJCaGhoZibm5cZS6FQcPToUapWrcqMGTMYM2YMXbt2xczMjIsXL3Lq1ClWrlwpLYopLCwkNzeX/Px8VCoVubm50jB0nTp1sLGxYcWKFeTm5nLw4EFu3LiBm5sbAB4eHoSEhHDz5k0yMzNZtWoVPXr0UHsuKpWK2bNn4+vri4aGBhYWFly4cIG8vDzOnz//2t+TYv/6RTmaMhkXj91BVcHFNVH742gzuIlI0QVBEN4w47Z1eLg3Vm3YW6atgXHbOn9yVdny8vJYtGgRt27dQltbG4VCgb+/f7mv7969Oz/99BNz585VO+7l5cX06dPZtGkTgwcPZvDgwdK5CRMmlFhVu3jxYrS1tenQoYN0bPjw4YwYMYJHjx4xc+ZMHjx4gK6uLgqFgh9++AFtbW2pbHp6Ohs3bpRWVWtpaeHn58fgwYPR0dEpUb/yqlWrFtu2bWPJkiV06dKFgoICPvzwQzw9PdHX11dLKg0MDPD19WXcuHHk5eXRunVrtXcpxsfHM3v2bNLT06lcuTKfffYZzZs3JyYmplyfwQcffMCaNWsIDAzE19cXQ0NDxo0bV+o8yl69enHv3j0+/fRTAFq2bCkNW8vlclasWIGvry+LFi2iXr16rFixArlcTmxsLHp6etIfB1paWmpzSKtUqYKGhoZ0TFNT86WxAL766ivGjBmDvb09nTp1UhvqHjduHCqVSi15Pn/+PIMGDZJ+bty4MY6OjmzatAko+p5MnTqVZs2aYW5uztKlS6VXGbVq1Yovv/ySQYMG8fz5c9zc3PD29lZ7Ljt37sTKykp6hVKHDh04ePAgzs7O2NnZ0adPn5d8E8pHpvpjv/k/WFpaVolV2YXPlexbdZ438RQ6DFGgU7X0v5irVzfk4cOnFb/JO0i07f0k2vZ+eh/bVr26YdmFyvA2VnlX1L1793B3d+f06dMlhqmDg4PZs2cP48ePx8nJCW1tbS5cuMDixYuZMmXKG11dWxHW1tYVHur8p1izZg0ZGRkvfQ/k69i7dy9Lly7F29ubVq1aoa+vz7Vr11i2bBndunWjW7dub+xef7d/fQ9lzpPcN5JMAjxMfEJtEzOUyn9Nji4IgvCXMGxi9rcnkC8qLCxk3bp1dOrUqdQ5j19++SWNGzfmxx9/xM/Pj/z8fBo2bMioUaPemWRSUPfhhx+WukNNRXTt2hVLS0vWrFnDggULePbsGfXq1aNv377/qGQS/uUJpaamBun33txf+ql3H/MfuxrAPyOh1NSUkZf3jIKCAlQqFZqaGmhr6yCTaZeYEC4IgvBvkZOTg4uLCx988AHBwcEvLefo6Iijo+NfWLNXN3r06L+7Cu+MF4ek3yQbGxsWL178VmK/S/7VCaVMBrnP8t9YvLxc5T8i0ZLJlDx7ls21a1e5fPky+fn/e0amptVxcnLG1NQUbW1dSnn/rCAIwj+anp4e0dHRf3c13og/7vgjCK/rX51QqlQgr/TmHoG2XPO/ryd4P5NKmQyUyuccOXKI27dLf79baupD9u4NQ0dHhw4dOvDBBxVbFSYIgiAIwvvvL1uTfOfOHfr06YObmxt9+vQpdYeAU6dO4enpia2tLYGBgWrnlEols2bNol27drRv356ffvqpwnVSKgsx+aDkfqSvy7RW1fc0lSxSUPCcnTtDXppMvig3N5e9e/dy61Zshd+uLwiCIAjC++0vSyhnzJhBv379CA8Pp1+/fkyfPr1EmZo1azJnzhyGDh1a4tzevXtJSEjgwIEDbN++nWXLlpGUlFTheulV0eFNvZPctHYVlMr3cwxYpcpj376fpV0NyuvQoUPEx8eLd3AKgiAIwr/YX5JQpqWl8fvvv9OlSxcAunTpwu+//056erpaudq1a2NjY1PiHV0A+/bt49NPP0VDQwNjY2PatWsn7aNaEdp6WnxQz7jCcfSrVkK3csVesvt3kckgIyOdlJSU17r+8OHDKJWil1IQBEEQ/q3+koQyJSUFMzMzNDU1gaKXgZqamr5SApOSkiK9bBSKtmG6f/9+heumVKlo3LoOsgr2sDV1swTt97WXrpBz5yJf++qMjAyysp68wfoIgiAIgvA++VctyjExKX1/1IKqShRt6/Lb4duvFbf2R9WxqG9CJT35n5Z7Ey/3fRvu379PUlJihYatL136jS5duqjt3vBP8a5+bm+CaNv76Z/ctndJZGQkPj4+nDhxotTzU6ZMwczMjPHjx//FNauYZcuWAa+/wvttt1uhUBAWFlbhrQCFv9ZfklCam5vz4MEDlEolmpqaKJVKUlNTMTc3f6UY9+7do3HjxkDJHsvyKG2nnGI1bU3JfvKcG+eSXymmeV0jGrety9PsXJ5m57603Lu8u0Vm5tOXPpfy0NCQce9eChkZT5HJ/lkJ5bv8uVWUaNv76X1s278pAX727BmbN29m//79JCUloauri4ODA0OHDqVhw4ZSud9++43vv/+ea9euoaGhgaOjI76+vpiamgJFSV9QUJC0jR8gJVlPnz5l3LhxXLp0CVdXV+bPny+NAPr5+dGyZUu1bRxflUqlYtOmTezYsYOkpCQqV66MnZ0do0aNwtra+rXjltfrvpLp2bNnBAYGsn//fgoKCmjQoAFbtmwBitq0cOFCQkJCgKLtGSdOnPjfN7MU+fnnnzl69CiLFi2SjoWGhjJ58mQCAgKkrRzLE6ugoICQkBBCQ0O5c+cOcrmcRo0aMWjQIGmv72J+fn6cO3eO+Ph45s6di6enp9r59evXs2bNGp49e4abmxuzZs2SvhdJSUlMnTqVy5cvY25uzvTp02nRogUAERERTJs2jdzcXL755hs6d+4MQGZmJoMHD2bTpk2lvpT/df0lQ94mJibY2Njw888/A0Ufmo2NjbQHZXl07NiRn376icLCQtLT0zl06JC0KfqbUChT0cClJi49bdCSa5ZZXqYhQ9GuLo7drCl8z/fvViqVFY5RtJn9+7kgSRAE4U14+PAhffr0ITExkblz53Lq1Cn27dtHhw4d8PHxYefOnVLZJ0+e0Lt3b44cOcLRo0fR19dn6tSpavHc3d2Jjo6W/hX32G3btg0bGxvOnDlDcnIyBw8eBIoSsdTU1AolkwBz5sxh48aNTJs2jXPnzhEeHk67du04fvx4heK+bX5+fjx58oT9+/dz7tw5tee5fft2Dh06xJ49ewgLC+Po0aPS3ufFjh07RqtWraSfnzx5QlBQEFZWVmrlyoqVk5PDF198wZkzZ5gyZQrHjx/n0KFD9OvXj0WLFrF8+XK1eA0aNGDmzJlqf3AUO3nyJKtXr2b9+vUcPXqUpKQkli5dKp2fMGECDRs2JDIykvHjx+Pt7S2tT5k7dy5BQUGsXbuWWbNmSb/rFy1axLBhw95oMgl/4SrvmTNnsnnzZtzc3Ni8eTOzZs0CYNiwYVy5cgWAqKgoWrVqxbp169i2bRutWrXi5MmTAHh4eGBhYUGHDh3o3bs3o0aNeuPd4YUyqFanKh2/aopDR0sqm+iVWAGuV1kH21a16TTCgdp2Zu99MgmUugjqVWlrayOT/QMehnWhjPMAACAASURBVCAI/yrXrl2je/fuKBQKvL29GTduHN99912pZX///Xd69OiBQqFg3Lhx5Oaqj0pNmDCBQYMG4e/vT4MGDZDL5ejr6+Pm5sb//d//sW7dOhISEgBwdXXF3d0dAwMDdHV1GTBgABcvXixXnZOSknByckIul+Pg4EBiYiJKpZJ58+bh6+tboedx9+5dtmzZwuLFi3F2dkYul6Orq0u3bt346quvSpR/8uQJw4cPp3nz5jRr1ozhw4errW/YtWsXbdu2RaFQ0KZNG8LCwgCIj49nwP+zd55RUV1Rw36GYQaliFhAA2oUEE0sIEURFcUo0dcWxF6IXRHRBHvAijUWAlYsRNTYUFGjxooNECUSW6RZsYAFQQFhmGG+H3zc1xEUbG+iuc9ady3vOefuU+bK7Nlnn73798fW1pamTZsybtw44RkrKytu374NQG5uLvPnz6dNmzbY2trSp08fcnNzi43j+vXrHD9+nNmzZ1OpUiWkUikNGjQQ6sPDwxk8eDDVqlXDxMSEQYMGsXv3bqG+oKCAqKgoWrZsKZQtXryYAQMGYGRkpNFXabJmz56NnZ0dgYGBWFtbU65cOXR0dGjRogWbN28mKiqK8+fPC+379euHo6MjOjrFD/aGh4fj7u6OpaUlhoaGeHp6Cn3dvHmTq1evMmbMGMqVK4erqyt169bl0KFDQKFiW7duXerVq4dMJiMjI4NLly5x9+7dj5IV6P/Mh9Lc3LzE2JFr1qwR/m1nZ/daXxWpVCoooR+TggI1ErkWZg2NMa1XhQJlAfl5hVq9tlyKlrYWWnItVKoCVO+xTfxvoly58u8tw9jYGKlUJmbOERER+SjExcVx5MgRMjIyqFixIu3atcPGxua9ZCoUCry8vPj+++/p27cvERER/PjjjyWGrlMoFIwePRoPDw/69evHsWPH8PHxYejQoQCcO3cOmUyGu7s7Dx48YNKkSSQkJODq6srff/9NWFgYw4cPZ8uWLUyaNKmY/PPnzxezhEVERODg4EDVqlXp168fffv2BaBu3bpERUVhb29PbGwsI0eOZOPGjbRq1eq9DS3R0dFUq1ZNcC8rjYKCAtzc3AgICEClUjF16lRmzZrFihUryMnJwd/fn7CwMOrUqcPDhw/JzCw8wPnLL7/g5OREaGgo+fn5gmHpVRYsWEBycjJbt26lSpUqXLx4ES2t4saLy5cvY2pqSmBgIHv27MHY2BgvLy9hJzMpKYl69eoJ7evVq0dSUpJwf+nSJWrUqCHsnF66dIkrV64wY8aMYhFl3iQrJSWFy5cvM2fOHJ49e8aUKVOIjY3F3t4epVKJn58fPj4+bNiwAXt7+1LXNykpibZt2wr3VlZWPH78mKdPn5KcnEyNGjU0LI316tUjOTkZKNwdjo+PB0AikVChQgU8PT2ZP39+qf2+C6JJ6TWoVAWF1ke5FjIDGTIDGRIdLdRSPtlYk6+jfHm99/4j1KSJrahMioiIfBTi4uIIDw8nIyMDgIyMDMLDw987/eHFixdRKpUMHDgQmUxG+/btadiw4Wvb5ufn4+HhgUwm49tvv9VoGxkZKVh9FixYgI2NDZGRkbRt25YrV64AhTmdb9wofvgzPj6eFStWMHHiRKGsQ4cOHDhwgOjoaGbPns2KFSsEtzF3d3eysrLo0aMHdnZ21KtXjz179uDh4cG0adPo16/fa62spZGRkUHVqlXL3N7IyAhXV1fKly+Pvr4+o0aN0rC+aWlpkZSURG5uLsbGxoLSrK2tzf3793n48CE6OjrY2dkVk11QUMDOnTv56aefhEgxTZo00fArLSI1NZXExEQMDAw4ffo0fn5+TJ48mevXrwOF1rqXFS8DAwNycnKEdMkvb3erVCpmzJjBtGnTSlRe3yTr7NmztG/fHi0tLVavXo2+vj6nT59m5MiRnD17FrVa/dr3oCRK6gsgOzub7Oxs4f7l+uzsbABmzpzJnDlz8PPz4+eff2bLli04OjqSl5fHkCFDGDBgAOfOnSvTOMqCqFCKAFIcHBze+emKFStiYPDhMg6JiIiIvMyRI0fIz8/XKMvPzxd8B9+Vhw8fYmJionGY4nWHRUtq+/LB0PT0dExMTABITEykc+fOaGtr4+zsLGyZFoXQe5nbt28zbNgwpk6dqqFUWVhYaChRAwcOFLYydXR0mD17Nvv27WP8+PHMnTuXH374gb1796JWq9m0aROXLl167Y7fm6hYsSKPHj0qc/sXL14wbdo02rRpQ5MmTejXrx/Pnj1DpVKhq6vL0qVL2bp1Ky1atGD48OGCgjdhwgTUajXu7u78z//8j3DA5WWePn1KXl5emQwe5cqVQyaTMWrUKORyOQ4ODjRt2pQzZ84AhfnXixQtgKysLHR1dYXP89SpUzg7OwPw22+/YWVlhbW1dYl9vUnWkydPNN6Djh07IpfLadCgARYWFkDJ78Hr0NXVJSsrS6MvAD09PfT09DTqiur19PSAwh8wGzduZMeOHZibm7Nz505GjhyJr68vo0ePZt68eUycOFFQqt8XUaEUQa2GSpWqCKcL3xYXFxek0k8zqLuIiMi/nyLLZFnLy0rVqlVJS0vT+EJ9XXzkktrev39f+LeRkZGgiNWtW5d9+/ahVCo5deoUT58+5fbt2wQEBODu7i48c+/ePQYNGoSnpyfdunUrdbwlffEXKY2tWrUiMTGRBg0aIJFIaNCgAQkJCaXKfBVHR0dSU1NfuwX9KuvXr+fmzZts376dCxcuaJyqBmjZsiUhISGcOXOGOnXq4OfnBxSup7+/P2fOnGHmzJnMnDlT8JsswsjICB0dHVJSUkodR2mnzy0tLYXtXyi0ChdZSx89esTDhw/5+uuvgcJt/6NHj+Lk5ISTkxNxcXHMnz+fWbNmlSrr1ffg4MGDKBQKrl69SnJyMo8ePcLf359evXqVOqeivl7+HOPj46lSpQpGRkZYWFiQkpKioVTGx8cLiuvLzJs3j3HjxlGuXDnhPTEzM0OpVBZLMvOuiAqlCAASiZzOnbtgaGj4Vs+1bt2aL7/88r3CDon8e5DJpMi1QSaTIJV+qoH6RT43Klas+FblZcXa2hqpVMqmTZtQKpUcPXr0tYqUtbU12trags/f4cOHNdo2a9ZMsCBOmjSJuLg4WrduzcGDB7G3t8fX15cJEyYIvolpaWmCP2afPn2K9Xf06FEyMzNRq9VcunSJjRs3avjSAeTl5bF48WKmTp0KgJmZGefOnUOhUHDhwoV3cmX68ssv6du3Lz4+PsTExKBQKMjLy2P//v0EBwcXa5+dnY2Ojg4VKlQgIyND4wTz48ePOXr0KDk5OcjlcnR1dYUt5IMHDwqHdwwNDZFIJMW2l7W0tOjevTvz5s0TQg/GxcWhUBTPzGZnZ0f16tVZvXo1SqWSP//8k5iYGFq0aAEUHuwNCQkhLS2NtLQ0QkJC+O6774BCpbxly5aCtXL+/PkcOHCA8PBwwsPDadCgAV5eXkLczTfJatq0KUeOHEGtVjNixAieP39O69atWb58OS1atGDu3Ln06NFD4yR+0Rqr1WqUSiV5eXkU/H8fsq5duxIWFkZycjLPnj1j5cqVQl+1a9emfv36LF++nLy8PI4cOSL47b5MZGQkeXl5tGnTBih8T86ePUtSUhIKheK9/x8V8Z8KbC7yZrS1y+Pu3pPDh/8o9RehTCajXbt21Kjx5f8/mSamXvxU0dKSIJe+oCD/Cc/vxaJSPEeipY2ukRXlK5ijVBuQrxSVS5F/jnbt2hEeHq6x7V30N+h9kMvlBAUF4evry5IlS2jZsiWtW7cu0UevqK2fnx8BAQE4Oztr9N+8eXOCgoLYu3cvXbp0ITQ0VON5pVKpEVFjx44dpKSksGzZMg0lrMgv9MCBA/z0008oFApMTEwYNmyYoEgUsWrVKjp37ky1atUA6N27N2PHjsXR0ZHWrVu/8/r4+voSGhrKrFmzhDiUtra2jB49ulhbDw8Pxo8fT7NmzTA2NmbQoEEcPXoUKPSB/PXXX5k0aRISiYT69eszY8YMoPAQzdy5c8nKyqJy5cr89NNPJSrAkyZNYvHixbi7u5OTk0O9evVYt25dsXYymYwVK1bg6+vLmjVr+OKLL1i4cCHm5ubC2qSkpNC5c2eg0A+1d+/eAJw8eVLj1HOFChWKydbX1xf8Fd8k68svv8TCwoLVq1czcuRIli9friHr1fcAYMiQIYIvY1xcHH5+foSGhtK0aVNatWrF0KFDGThwILm5ubi6uuLt7S08u2TJEqZMmYK9vT3Vq1cnMDBQIySjQqFg4cKFrFixQijz8/MT3q3p06cLMUzfF4n6Q22efwK8KbD5x+bTCkas5MWLLC5evMjff/+NUqkUaipXrkyzZs2oVq062trlUas/tbm9HZ/73J6mZ6KlvMej67tR5pWQPlMixbCaHQbVnHmh+HTcGj73z+1Tm9uHCGz+MU55l0SPHj3o3bs33bt3f+tn09LSGDx4MK1bt6Znz57UqFGDR48eERYWxqVLl1i9evUHH++78L6Zcj4nlEolTk5OHDt27IPFZczKymLo0KHUqVOH77//HnNzczIzM9m/fz979uxh+/btJR72+dQRLZQiJaBN+fIVad7cGTs7B5RKJWp1AVKpFJlMB6lUTkGBmv/OT5HPE3WBConiNg8SfoPXBaVXq8h8EIMi9wmVvnQj9xNSKkU+L2xsbD6KAnnu3Dlq166NkZER+/btIyEhQSMW4dtgYmLCtm3bWL9+PaNHjyY1NRUjIyO++eYb5s2b94FHLvIhyMzMZOzYsR80yLe+vj6hoaFs3ryZyZMnk5KSgp6eHq1ateKXX375LJVJEC2U/2d8ilaFsiLO7dOkgm4Wt2IDUavLlimpUq32aBs2Q6X69//J+Jw/t09xbv/m1Ivbtm3jl19+4cWLF5iZmeHj40Pr1q3/6WF9VGJiYoBCfz8RkQ+FaKEUEfkPIpVKyHpyrczKJEDm/TNUM2qISqX7EUcmIvJ/S69evcp84vZzQVQkRT4Gn6fdVURE5I3ItHLIuHfmrZ5R5edQoHj8kUYkIiIiIvIpIyqUIiL/RdQKVPnZpbd7BcWLR2hpiSe+RUREREQ0ERVKERGRslNQ9i1yEREREZH/DqJCKSLyX0QiQyItHmuvNLTLV/5gabpERERERD4fRIVSROQ/iFKti2G1t8vfLpHKkZUzEcNFiYiIiIgUQ1QoRUT+gyiVUMHYBii7P2QFE1vy1f/e8C8iIh+SoKAgxo8f/08P44MSExPDgAED3vn5Xbt2lZgm8kMxdOhQdu/e/dHki3xcRIVSROQ/iky3KlVqdyhb2/JVMDBpjlIpmidFREpCrVZz4MABPDw8cHR0xMnJicGDB3P48GGNdk+ePOHHH3+kRYsW2Nra0rt3by5evCjUx8TEUK9ePSGQu42NjYaSNWfOHOzt7enVq5eQCxtg3759+Pv7v/c8Tp8+Tb9+/bCxsaFZs2b079+fY8eOvbfcsrB27dpi6SXLyoYNG3BxccHa2poOHTpw8+ZNoW7fvn20adMGa2trPD09ycjI0Hg2LS2NVq1aaZTdunWLhg0bFvtRUZosgDNnzjBy5EicnJxo1qwZffr0ISwsTMjPXcSmTZtwc3OjQYMGTJ48uZic6Ohovv32Wxo3bsyAAQO4d++eUKdQKJgyZQpNmjTBycmJkJAQoe7Bgwf07NkTBwcH5s+fryFz6NChr81X/76ICqWIyH8UqVQHWYXGVDHvgkTy+lyu5SrUwqTeQHLzxfiTIiIloVKp8PHxYfv27YwcOZKjR49y4sQJvLy82LZtG76+voLvcU5ODg0bNmTXrl2cO3eO7777juHDh5Od/b9RF4yNjYmLixOuIiXr0qVLXL16lcjISJo0aUJwcDAAz58/Z926dYwbN+695vHHH38wduxYunXrxqlTp4iKisLb25uIiIj3kvux2bFjB2FhYQQHBxMXF8fq1asxMjICICkpiWnTprFw4UIiIyMpX748M2fO1Hj+1KlTxbIjzZo1i4YNG2qUlUXWwoULWbJkCe7u7hw8eJDIyEimTZvG2bNnGTFiBAqFQmhrbGyMp6dniWk+09PT8fLyYuzYsZw7d44GDRrwww8/CPVBQUHcvn2biIgIQkNDWbt2LadOnQJg9erVdOvWjWPHjnH06FFBgTxw4ABmZmbF5vWhEBVKEZH/MAqlNlK9Rpg2HkMV8y7o6FVDKjdAVs4IA5MmmDYcSaU6vXmh0BN9J0X+UTIexJB4Zip/Hx1J4pmpZDyI+SByg4ODadmyJTY2Nri6uhIdHV2szdChQ9m0aZNGWZcuXQTr48qVK5HL5YSEhODo6Iienh4ymYwmTZqwZs0asrKy2LNnDwA1atRg0KBBGBsbI5VK6dWrF/n5+RoWtddx9+5dbG1tkcvlODo6kpKSAsDSpUsZMmTIe6UPVKvVzJ8/H09PT3r06IGBgQFaWlo4ODi81vLp7++Ps7MzTZo0wc3NjdjYWKHu0qVLuLm50aRJE5o3by6knszLy2P8+PE0bdoUOzs7unfvzuPHhfFtBwwYwI4dOwQZ27dvp0OHDtjY2NCxY0euXr1abAwFBQUsW7aMqVOnYmFhgUQioWbNmlSsWBEotCi6uLhgb2+Pnp4eY8eO5ciRI2RlZQkyTp48ibOzs3C/f/9+DAwMcHR01OirNFnh4eHEx8ezdetWvvnmGypUqIBUKqV+/fosWrQICwsL4UcAQPv27fnmm2+Esb7MkSNHsLS0pEOHDujo6DBmzBji4+O5fv06ALt378bT0xNDQ0PMzc3p0aOHYMm+e/cuzZo1w8DAgIYNG5KSkkJWVhbBwcH8+OOPJX6WHwIxU46IyH8cpUqCUmWAlq41lSy/QgsVaiQUqHXIVQKvSfMtIvJ/RcaDGB5c24y6oNC6o8xN58G1zQBUrP7uWV9u3LjB5s2bCQsLw8TEhLt37xbblgTo1KkTW7dupX///gAkJydz//59WrduTU5ODmFhYRw4cID8/HymT5/OiRMnsLCwwMzMDDc3N6ZMmcKoUaPo1q1bMdnXrl0jPz+fWrVqCWXp6ek0b96ccuXK8c033zBu3Dh0dXWxsLBg48aN5ObmEh0djYWFBZcvX+bmzZtMmzbtndehaC0ePHiAq6trmZ9p2LAho0ePxsDAgNDQUMaOHcvx48fR0dFhzpw5DBw4kG7dupGdnU1SUhJQqAhlZWVx4sQJ5HI5165do1y5csVkHzx4kKCgIJYvX07Dhg25c+cO2trFVZbU1FRSU1NJTExk8uTJSKVSunXrhpeXF1paWiQlJWnkgK9ZsyYymYxbt27RoEED8vPzOX/+vLA1nJWVRWBgIBs2bNBQboFSZa1YsYINGzYgl8v5+eefCQ8Px9jYGHt7e7766ivGjh1Lp06d8PLyKnVtk5KSsLKyEu51dXWpWbMmycnJVKlShUePHlGvXj2hvl69eoJrgqWlJVFRUVSpUoWrV6/i6elJQEAAHh4eVKhQodS+3xXRQikiIgJAQYEaRb6M3Pxy5OXrkK/8p0ckIlLIw+t7BGWyCHWBgofX97yXXKlUikKh4Pr16+Tn52NmZkbNmjWLtfvmm2+Ij48XfNj27dtHu3btkMvl/PXXXzRt2hRdXV3CwsJIS0vjyJEjzJ07l9OnT1NQUICJiUmJvnZZWVlMnDgRLy8vDAwKD7zVqVOH8PBwzpw5w4YNG7h69aqg7NStWxdXV1d69uzJgwcPGDZsGHPmzMHX15fQ0FD69euHj48Pz549e+u1KBqfsbFxmZ/p2rUrRkZGaGtrM3jwYBQKhWBp1dbW5s6dO6Snp6Onp4e1tbVQnpGRwe3bt5FKpTRo0KBEy2pYWBhDhw6lUaNGSCQSatWqhampabF2RX6kkZGR7Nu3j9DQUPbv309YWBhQ6GJQtLZF6OvrCy4GsbGx1KtXTxhDQEAA3bt3p1q1asX6epOs27dvY2xsTPXq1Tl58iSnTp1i7969rF27lujoaAoKCihXrhyGhoakp6eXurZv6isnJwdAo97AwECY04gRI4iNjaV///707duX/Px8EhISaNOmDT4+PvTr16+Yxf1DICqUIiIiIiL/apS5JX8Bv668rNSqVYupU6cSFBRE8+bN+eGHH0hLSyvWTl9fH2dnZ/bv3w/A77//TpcuXYDCQzYmJiYAJCYm0rZtW/T19alRowa2trZAoeKoq6vpg5ybm8vIkSNp3LgxI0aMEMqrVq2KhYUFWlpa1KhRgwkTJnDo0CGh/vvvv2fv3r0EBARw8OBB7OzsKCgoYPv27fz666+Ym5trbKuWlaJt14cPH5b5mXXr1tGhQwdsbW2xs7Pj+fPnPH36FCg8PHTr1i06dOhA9+7dBT/Mrl270qJFC+Fg0sKFC8nPzy8m+8GDByUq969SZN0cOnQoFSpUwMzMjF69enHy5Emg0LL38vY2FH4eenp6gOZ297Vr14iOjub7778vsa83yXr1PWjZsiWVK1emcuXKgn9mQUEBz549K3GLuyx9ZWdno6enJ7xLL9e/PKeKFSsSEBDA3r17GThwILNnz8bPz4/g4GAsLS0JCQlh69atwvb5h6LMCmVKSgo+Pj507NiR1q1ba1wiIiIiIiIfC+1yld6q/G3o3LkzW7ZsISIiAolEwqJFi0ps16lTJ/bv309cXBx5eXk0bVq41W5kZMSjR4+AQgvisWPHyMrKIiUlhT///JNnz54xc+ZMjYMXCoWC0aNHY2JiwqxZs944PolEUmIygcePH7Nt2zZGjx4tbI/KZDIaNmxIQkLCW69DnTp1qF69erFT6a8jNjaWtWvXEhAQwPnz54mNjcXAwEAY65dffsmSJUuIjo5m2LBheHt7k5OTg0wmw8vLiwMHDrB161ZOnDhBeHh4MfnVq1fnzp07pY6jdu3ayGQyJJL/DYH28r8tLS2Jj48X7lNSUsjPz+fLL78ENBXKmJgY7t27R5s2bXBycmL9+vUcPnxYOBT1JllGRkaCMl63bl3OnDnDkydPePLkCadPn+bFixcsXrwYZ2dntLRKV71e7SsnJ4c7d+5gYWGBoaEhVatW1aiPj4/HwsKimJxt27ZhbW1N3bp1SUxMpEGDBsjlcurWrftO78mbKLNCOX78eCQSCZMmTWLhwoUal4iIiIiIyMfC2LwrEi3NzE4SLTnG5l3fS+6NGzeIjo5GoVAgl8vR0dF57Ze9s7Mz9+/fJzAwkI4dOwrtrK2tiYmJITc3F3d3d0xMTGjfvj0//fQTLVq0YPny5VhbW+Ph4QFAfn4+3t7e6OjosGDBgmL9nT17lnv37qFWq3nw4AGLFi2ibdu2xcYzb948xowZQ/ny5TEzM+Py5ctkZ2dz7tw5atSo8dZrIZFImDx5MitWrGDnzp1kZWVRUFBAbGwsfn5+xdpnZ2cjlUqpVKkSSqWSZcuWaVjM9uzZQ3p6OlpaWoLfnpaWFmfPniUhIQGVSoW+vj7a2tolrrm7uzvr16/nypUrqNVqbt++rRE2p4jy5cvTsWNH1q5dS1ZWFqmpqWzbtk0wdnXu3JmIiAhiY2PJycnhl19+oV27dujr65OSkoJCocDc3ByAXr16ceTIEcLDwwkPD6d37960bt2adevWlSqrdu3apKam8vDhQ5ydnWnZsiVdu3Zl1KhR2Nvbs2XLFvT09Jg4caIwdqVSSV5eHgUFBahUKvLy8lAqC32N2rVrR1JSEocOHSIvL4/ly5djZWUljLVbt26sXLmSzMxMrl+/zo4dO4qFXHry5Am//fab4LNpZmZGTEwM2dnZXLly5Z3ekzdR5kM5SUlJbNmypUyatYiIiIiIyIei6ODNw+t7UOamo12uEsbmXd/rQA4UWgoXL17M9evXkclk2NjYvNZiKJfLadeuHTt37tQI36Kvr0+nTp2YO3cus2bNEk4zF6FUKjUOk8TFxREREUG5cuWwt7cXytesWYOdnR3Xrl1jwoQJwtZou3btioUDio6O5vnz57Rr1w6ARo0a4ezsTOvWralduzaBgYHvtB7ffvsturq6rFq1Cn9/f3R0dLC0tGTIkCHF2rZo0YKWLVvi6uqKrq4uHh4eVK9eXag/ffo08+fPJzc3ly+++IKlS5dSrlw5Hj9+zPTp00lLS0NXV5eOHTvStWvxHwYdOnQgIyMDHx8fHj58iKmpKQsXLizRj3LatGn4+fnRsmVLKlSoQI8ePXB3dwcKLX0zZ85k/PjxZGRk4OjoKHxGr57uLl++POXLlxfudXV1kcvlVKpUqVRZULjt7uvry4oVK5gwYQITJkwQ6l59D6AwOsCyZcuE+7179+Ll5cWYMWOoVKkSQUFBzJo1iwkTJtC4cWOWLFkitPX29mb69Om0adOGcuXKMWzYsGKxNBcsWICnp6ewFT5ixAi8vb3ZunUrbm5uHzx8kERdxsS8I0aMYMyYMTRo0OCDDuD/kidPsigo+Gdin1StasCjR8//kb4/NuLcPk3EuX2afIpzq1r1886wpFQqGTt2LAUFBYwaNYr69euTm5vLsWPHWLt2Ldu2bRO+1P9JYmJiWLZsGRs3bvynh/KvYNiwYfTv319DqXxfZs2aRXx8PN7e3tjY2FBQUEBkZCQBAQGsXr26RIX4c6HMFkpTU1OGDh1Ku3btqFKlikbd2LFjP/jAREREREREPgW0tbUJCgpi165dzJs3jxs3biCTyWjatClLliz5VyiTIsVxcHAQfGE/FNOmTePIkSOsWrVK8HEssnx/zsokvIVC+eLFC9q0aYNSqdRI9yQiIiIiIvJfR0tLC3d3d2Gr9d+IqanpO6c2/BwZNmzYR5Hbrl07wR3hv0SZFEqVSkW1atUYNWoUcrm89AdERERERERE/lWYmZlhZmb2Tw9D5DOlTCdspFIpW7ZsKTFKvYiIiIiIiIiIyH+bMh/Z7tq1K1u2bPmYYxEREREREREREfkEKbPJ8dKlS2za3D4dJQAAIABJREFUtIl169ZRrVo1jcChmzdv/iiDExERERERERER+fdTZoWyZ8+e9OzZ82OORURERERERERE5BOkzAqleDJMREREROS/hFqtZurUqRw9epRatWoRFhb2Tw/pg2FlZfXOqffu3r1L27ZtuXr16kc5W7Fq1SpSUlKYM2fOB5ct8vEosw9lWFjYay8REREREZHPjT///JPIyEhOnjz5Vt91ly5dYty4cbRq1YqmTZvi5uZGSEgICoVCo92CBQto3749NjY2fPvtt8VyWltZWWFtbY2NjQ02Njb89NNPQt2+ffto0aIFLi4unD17Vii/c+cOvXv3RqVSveOsC7l58ybe3t40bdoUW1tbOnfuTEhIyHvLLQsjR458Z2UyKiqK7777Dmtra1q1asWBAweEumvXruHm5kbjxo1xc3Pj2rVrxZ53dXXl5s2bwr1CoaBDhw7FstCURdaNGzeYOnUqLi4uODg40LlzZwIDAzVSVEJhus0BAwZga2uLi4tLMTl3795lwIABNG7cmG+//ZaoqCiN+l9//RUnJyeaNGnClClThPdMqVTyww8/YGdnx5AhQzT6XbVqFSEhIW9ayremzArlnj17NK5169YxY8YM9uzZ80EHJCIiIiIi8m/g3r17mJqaoqurW+ZnNm7cyMSJE2nTpg27d+8mOjqaRYsWce/ePfr27cuzZ8+EtuXLl2flypX8+eefLFiwgDlz5nDhwgUNeXv27CEuLo64uDhByVIqlSxevJhdu3bh5+eHv7+/0N7f358pU6YglUrfed537tyhZ8+eVK9enX379vHnn3/yyy+/cOXKFbKzs99Z7scmOTkZHx8fxo0bR2xsLHv27BGy+ykUCjw9PenSpQvnz5+nW7dueHp6aij5d+7coaCggNq1awtl69atE1IvFlEWWYcPH2bYsGF8/fXXbNmyhZiYGFatWoVEIqFnz57cv39faKurq0v37t018ny/jI+PD1999RUxMTH88MMPeHt7k56eDhSmuAwODubXX38lIiKCu3fvCqk3Dx8+jEQi4ezZsxgYGLBt2zYAUlJSOH78OAMGDHif5S5GmRXKjRs3alwHDx5kxowZn3QqRhERERGR/zZpaWmMGTOGZs2a4eLiQmhoKAA7duzA19eXv/76CxsbGwIDA8nMzGTEiBE0a9YMe3t7RowYoZHoIyYmhp07d7J9+3a6du1K5cqV0dLSok6dOvj6+vLdd99p5H729vbG3NwcLS0tGjdujK2tLX/99VepY87IyMDExARjY2OaN29OSkoKAH/88QcmJiY0btz4vdYkMDAQGxsbpkyZgrGxMQB16tRh8eLFVKhQoVj7nTt30qFDB2xsbGjbti1bt24V6tLT0xkxYgR2dnY4ODjQt29fCgoKAAgODqZly5bY2Njg6upKdHQ0AEFBQYwfP16QERsbS+/evbGzs8PZ2Zldu3aVOO6VK1fSq1cvnJ2d0dbWxsjIiJo1awJw7tw5lEolHh4eyOVyBg4ciFqt1rDunjhxQiMNY0pKCnv37mX48OEa/ZQm68aNG/z8889s3ryZfv36YWJigkQiwdTUlDFjxjBx4kSmTJkiyGvUqBHdunWjRo0axeZ08+ZNrl69ypgxYyhXrhyurq7UrVuXQ4cOARAeHo67uzuWlpYYGhri6enJ7t27gULLpoODA9ra2jRt2lR4T+bMmcOkSZM+uLtCmRXKknBzc2Pnzp0faiwiIiIiIiIlcvlRNoF/3md2dAqBf97n8qP3t5QV5d62srLi1KlTbNiwgQ0bNnD69Gl69OjBzJkzsba2Ji4uDm9vbwoKCnBzcyMiIoKIiAh0dHSYNWuWIG/ZsmVMnz6dChUqsGHDBlq1aoWrqysBAQGsWLGCvn37cvHiRZ4/L56LPTc3lytXrmBhYaFR3q9fP5ycnPDy8uLu3bsAVKpUiYyMDFJTU4mMjMTCwoKsrCxWrlzJjz/++N7rEh0djaura5nbV65cmdWrV3PhwgXmzZvHvHnzuHr1KgAhISGYmJgQHR1NZGQkP/74IxKJhBs3brB582bCwsKIi4tj3bp1JaYmvHfvnpBzOzo6mvDwcOrXr1/iOIqU8c6dO9OiRQvGjx9PRkYGUGi9tLKy0ohQY2VlRXJysnB/8uRJDYXS39+fH3/8kXLlymn0U5qsNWvWMHbsWKpVq8aBAwdwcXGhTZs2rFy5El9fX1q3bo2WlhaJiYmlrm1ycjI1atRAX19fKKtXr57QV1JSEvXq1dMYx+PHj3n69Cl169bl7NmzKBQKYmJisLS05MiRIxgZGWFra1tq329LmRXKgoICjSs7O5tt27ZhYGDwwQclIiIiIiJSxOVH2ey/8ZRMRaH/XqZCxf4bT99bqbx8+TLp6el4eXkhl8upUaMGPXv21PC7exkjIyNcXV0pX748+vr6jBo1ivPnzwOQl5fHgwcPsLGxITk5mbVr17J582Z27tzJpUuXUKlUSCQSLCwsuHXrVjHZ06dPx8rKipYtWwplmzZt4vjx4xw8eBBjY2NGjhyJUqlES0uLGTNm4O3tzfr16/H39ycoKIj+/fuTkJDAgAEDGDJkSJkUlpLIyMigatWqZW7funVratasiUQiwcHBAScnJ2JjY4HCPOePHj3i/v37yGQy7OzskEgkSKVSFAoF169fJz8/HzMzM8Ga+DK///47zZs3p1OnTshkMoyMjF6rUKalpbF3714CAwM5dOgQeXl5zJ49G4Ds7Oxi+oq+vr6whf/ixQuuXLki5PY+cuQIKpWqxBSKpcmKiYmhffv2ZGRkMHPmTGE8t27dQqlUAoVK4Y0bN0pd25L6MjAwEPrKycnRUDaL2mZnZ+Ps7IyZmRndu3fHwMCAjh07smzZMiZMmMDSpUvp168fM2bMKObb+66U2d751VdfaWjjACYmJhq/zkRERERERD40EXcyyS9Qa5TlF6iJuJNJw6p67yz33r17PHz4EDs7O6FMpVJp3L/MixcvmDdvHqdPnyYzMxMo/OJWqVRkZGRQpUoVABITE2nSpImwhdm2bVvB5y01NRUTExMNuQsWLCApKYnQ0FCN71l7e3sA5HI5P/30E7a2tly/fh0rKyscHR1xdHQEID4+nitXrjBx4kRcXFz47bffePDgAb6+vmzfvv2t16VixYo8evSozO1PnjzJ8uXLuXXrFgUFBeTm5lK3bl0AhgwZwrJlyxg8eDAAvXr1Yvjw4dSqVYupU6cSFBREcnIyLVq0YPLkycXW5sGDByUqmiWho6ODm5ub4AM5YsQIBg0aBICenl6xwzDZ2dno6RW+P9HR0djY2CCXy8nJyeHnn38mODi4xH5KkwWFn1l8fDw1atQQXAM7duzIwYMHgcL3oH379qXOqaS+srKyhL50dXU16ov+raenh0QiYfz48YL7wIIFC+jduzeXL1/mypUrbNq0CV9fX3bu3EmfPn1KHUtplNlCeezYMY4ePSpckZGRnDhxotjJJxERERERkQ9JkWWyrOVlpXr16piZmREbGytccXFxrFmzpsT269ev5+bNm2zfvp0LFy4IST3UajUVK1bkyZMnANStW5e4uDhSUlLIysri2LFj5OfnExoaSqVKlQS/RCj0Vzx9+jTr1q3TsDSVhEQiQa3WVKzVajWzZs3C19eXp0+folKpMDU1pVGjRu8cFsjR0ZHDhw+Xqa1CocDb25vBgwcTGRlJbGwsrVq1Esapr6/P5MmTOXbsGCtXriQkJETwlezcuTNbtmwhIiICiUTCokWLismvXr06d+7cKdNYrKysNO5fVs4tLCxISEjQWL+EhATBxeDkyZOCPnP79m3u3bsnuBuMGTOGR48e4eTkxN27d0uVJZFIUCgU1KxZk5SUFK5evYpCoeDgwYOoVCoOHDjAvXv3aNiwYalzsrCwEN6jIuLj44W+LC0tNT7n+Ph4qlSpgpGRkYachIQE4uLi6NWrFwkJCXz99ddIJBIaNmz4zu/Jq5RZoQwJCcHU1FS4ik49iXGiREREREQ+Jobykk8sv668rDRq1Ag9PT2Cg4PJzc1FpVKRmJjIpUuXSmyfnZ2Njo4OFSpUICMjg2XLlgl1Ojo6VKlSRfCDHDJkCH379qVv375YWVlx6NAh7t27p6E0rV69mt9//52QkJBiCkBSUhLXrl1DpVKRnZ3N/PnzMTY2xtzcXKPdjh07+Oqrr6hfvz4VK1YkLy+P5ORkzp49W+Ihj7Lg7e1NXFwcCxYsECyVt2/fZvz48Rqn1KFQoVQoFFSqVAltbW1OnjxJZGSkUB8REcHt27dRq9UYGBgglUoFH8ro6GgUCgVyuRwdHR20tIqrJJ07dyYqKooDBw6gVCp5+vRpiSF6oPBcx65du0hJSeHFixcEBwfTunVrABwcHJBKpYSGhqJQKNi0aRMAzZo1AwpPSxe1tbS05MSJE4SHhxMeHo6/vz+VK1cmPDyc6tWrlyrLxsaGiIgIKlasyPTp0xkzZgydO3fGxMSECxcucObMGVasWCEciikoKCAvL4/8/HzUajV5eXnCNnTt2rWpX78+y5cvJy8vjyNHjpCQkCD4uHbt2pWwsDCSk5N59uwZK1euLBY3XK1WM3v2bHx9fdHS0sLMzIw///wThULB+fPn3/k9eZUyb3nv2rULX1/fYuV79+7ViI0lIiIiIiLyIWlT05D9N55qbHvLtCS0qWn4XnKlUimrVq1iwYIFtG3bFoVCQe3atRk3blyJ7T08PBg/fjzNmjXD2NiYQYMGcfToUaHe09OTadOmsXHjRjw8PPDw8BDqfHx8ip2qXbJkCTKZTGPrc8SIEYwcOZLHjx8zY8YM0tLSKF++PDY2NqxevRqZTCa0TU9PJzQ0VDhVra2tjZ+fHx4eHujo6DB37tx3WpeaNWuydetWAgIC6NSpE0qlElNTU9zc3NDT09NQKvX19fH19WXcuHEoFAratGmjEUvx9u3bzJ49m/T0dCpUqECfPn1o1qwZ8fHxLF68mOvXryOTybCxsSnRhe6LL75gzZo1LFiwAF9fXwwMDBg3blyJfpTu7u7cv3+fHj16ANCyZUtBb5HL5SxfvhxfX18WL16Mubk5y5cvRy6Xk5iYiK6uLl988YWwji/7kBoaGqKlpSWUSaXS18oCGD58OGPGjKFJkyZ07NiRjh07CrLGjRuHWq3WUJ7Pnz/PwIEDhftGjRrh4ODAxo0bgcL3ZMqUKdjb21O9enUCAwMFo16rVq0YOnQoAwcOJDc3F1dXV7y9vTXWZefOnVhaWgpb7+3bt+fIkSM4OjpibW1Nr169XvMmvB0S9av281coCuY6e/Zs/Pz8NOpSUlL4448/hOPr/3aePMmioOCN0/1oVK1qwKNHxU/2fQ6Ic/s0Eef2afIpzq1q1fc/vHn5UTYRdzLJVKgwlEtpU9PwvfwnPxZr165lz549/PDDDzRt2hSZTMaff/7JkiVLmDx58kc5XfsuvE+mnM+NNWvW8PTp09fGgXwX9u3bR2BgIN7e3rRq1Qo9PT2uXr1KUFAQXbp0oUuXLh+sr38LpVooiwKX5+fnawQxl0gkVKlShQULFny80YmIiIiIiAANq+r9KxXIVxk6dCiNGjVi/fr1+Pn5kZ+fz1dffcXo0aP/NcqkiCampqYlZqh5Hzp37oyFhQVr1qzh559/5sWLF5ibm9O7d+/PUpmEMlgoi1i6dCk//PDDxx7PR0W0UH4cxLl9mohz+zT5FOf2ISyUIh+WoKAgxowZ808PQ+Qzosw+lD/88ANPnz7l5MmTPH78mKFDh5KWloZaraZatWofc4wiHwgtbTUK6QvUqJFIQKtAikxVHpXqn1GyRURERET+GURlUuRDU2aF8ty5c4wZM4YGDRpw4cIFhg4dyu3bt1m/fj2rVq36mGMUeU/UOvlkqTM4/zSGK88uoyjIQ4KEivKKOFVuRW1dc8opDVArJaULExERERERERF5hTIrlHPnziUgIABHR0ch2Grjxo1fG15B5J9HIpGQp/OM8Ps7uPNCM46XGjVPFU/5/cEepBIpLlW/oZG+HZI82WukiYiIiIiIiIiUTJkVynv37glR+YuChcpkMlSq9wssK/LxyNN5xoY7a8nMz3xjO5VaxZGHh3iufIaToQsShahUioiIiIiIiJSdMgc2Nzc35/Tp0xplUVFRQnolkX8XBTp5bL/3W6nK5MucTY/mpiIRqVTc+hYREREREREpO2W2UE6ePJkRI0bQunVrcnNzmTZtGsePH2fFihUfc3wi70hGwRNScx+89XPHHh6hVg1zpKpyH2FUIiIiIiIiIp8jZbZQWltbs3fvXiwsLOjevTtmZmYsXbqUtWvXfszxibwDElkBUU/OvNOzmfkZPFU9/sAjEhERERH5VJg8eTK7du165+cHDBjAjh07PuCI/pf79+9jY2Mjutv9CylVoXzx4gUBAQGMHDmS9evX06dPHwYNGsTFixcZPnw4lStX/r8Yp8hbkKeVTeLz+Hd+Pi4jFpns/XLkioiIiIgUJzMzk6VLl9KpUyccHBxo27Ytfn5+pKSkaLQ7ceIEffr0wc7ODicnJ3766SeysrKE+smTJ9OgQQNsbGyEq0jJevDgAT179sTBwYH58+dryB06dCiXL19+rzkoFAqCgoJo37491tbWuLi4MGXKFO7evftecsvCF198QVxcHFLp239Hpaen4+Pjg62tLfb29vj4+Ah1CoWCKVOm0KRJE5ycnAgJCSn2/OrVq1myZIlG2bJly7CysiIqKuqtZL148YI1a9bg5uaGg4MDzs7O+Pj48Pfff2u0UygUeHt74+LigpWVFTExMRr1arWan3/+maZNm9K0aVN+/vlnXg4vfu3aNdzc3GjcuDFubm4aedD37dtHixYtcHFx4ezZs0L5nTt36N2791sr7aUqlLNmzSIiIgJzc3OioqIYM2YMAwYMwNLSkmPHjjF9+vS36lDk46NUKymg4J2ff6Z8hgrx15+IiIjIh+T69ev06NEDlUpFYGAg0dHR7Nq1C2trawYPHsyZM/+7s/T8+XNGjRrF6dOnOXDgAGlpaSxcuFBD3pAhQ4iLixOuIiVr9erVdOvWjWPHjnH06FFBgTxw4ABmZmY0bNjwvebh7e3N8ePHWbRoEbGxsezZs4cGDRoQHR39XnI/Nl5eXlSpUoUTJ04QFRXFkCFDhLqgoCBu375NREQEoaGhrF27llOnTmk8f+LECVq1aiXc37lzh0OHDmnk/S6LrEePHtGrVy9SUlKYO3cuZ86c4cCBA7Rv354JEyawc+dODXlNmjRh4cKFxfoB2LZtG0ePHmXPnj3s3buXiIgIIbe7QqHA09OTLl26cP78ebp164anpycKhQKlUsnixYvZtWsXfn5++Pv7CzL9/f2ZMmXKWyvtpSqUp0+fZv369UyYMIE1a9YQHR3NokWLGDdunJCc/L+GVAoSVQ4on4MyCy11Llpa/6aDLO83Fi2JFpL3lCEiIiLyITkel47Hgr/pOOUiHgv+5nhc+geRm5aWxpgxY2jWrBkuLi6EhoYKdQUFBQQHB/PNN9/QtGlTxo4dS0ZGBgB3797FysqKnTt34uzsjL29PVu2bOHSpUt07twZOzs7Zs2aJcgqsjRNnz6d8ePHU6dOHaRSKYaGhnTv3p1ff/2V2bNn8+zZM6AwdV+rVq0oX748hoaG9OzZk7i4uDLN6e7duzRr1gwDAwMaNmxISkoKWVlZBAcH8+OPP77XekVFRREVFcWKFSto1KgR2traGBgY0K9fP3r06FGs/Z07dxg4cKBgQfPx8RHmCBAcHEzLli2xsbHB1dVVUEovXbqEm5sbTZo0oXnz5sybN0+Ym5WVFUqlEoCMjAymTJlCixYtsLe3x9PTs8RxnzlzhtTUVCZOnIiBgQEymYyvvvpKqN+9ezeenp4YGhpibm5Ojx492L17t1CfmZnJrVu3sLGxEcpmzpzJ+PHjkcvlGn2VJsvHx4eBAwcya9Ys6tWrh1wuR09PD1dXV3777TdCQkK4c6cw1J9cLuf777/Hzs4OLa3iKlt4eDiDBw+mWrVqmJiYMGjQIKGvc+fOoVQq8fDwQC6XM3DgQNRqNWfPniUjIwMTExOMjY1p3ry5YCH/448/MDExoXHjxiWu45soVaHMyckRtrWrVauGrq6uEIfyv4YW+SizU7l2Kow/1kxjX9Akfl8+maMh/ty/dgZ1bjpSrX8+64xMIkMmeffQP5XlVZCoy+xeKyIiIvJROR6XTuDuuzzMyEcNPMzIJ3D33fdWKgsKChg1ahRWVlacOnWKDRs2sGHDBiGiycaNGzl69CibNm3i9OnTGBoaaiiJABcvXuTw4cMsXbqUuXPnsmrVKn799Vf279/PwYMHOXfuHAD79+/H1tYWJycnEhIS6N69O82aNSMwMJDevXtjampKt27d2Lt3b4ljPX/+PBYWFhplW7ZswcHBATc3Nw4dOiSUW1paEhUVxbNnz7h69SqWlpYEBATg4eFBhQoV3mvNoqKiaNSoEdWrVy9Te7VazYgRIzh9+jQHDx4kNTWVoKAgAG7cuMHmzZsJCwsjLi6OdevWYWpqCsCcOXMYOHAgFy5c4MiRI3To0KFE+RMnTuTFixfs37+fqKgovv/++xLb/fXXX9SuXZtJkybRtGlTunfvLnw2mZmZPHr0iHr16gnt69WrR3JysnB/5swZHB0dBavdwYMHkcvlODs7a/RTmqxz584hk8lwd3fnwYMHgrI9bdo03N3dMTQ0ZPjw4WzZsqVM65uUlFSsr6SkJACSk5OxsrISQj0CWFlZkZycTKVKlcjIyCA1NZXIyEgsLCzIyspi5cqV7/yjo9RT3iqVirNnz2rsyb96XxSf8nNFSwsUz1OJ2fcr969fhVfSn794nsHpHSuRyuTUc3Dh61ZdUUv/udy1Oio9rI2acD49pvTGJWBtaItSIW55i4iI/DvYcDiVvHzNv7t5+Wo2HE7Fxebdd8ouX75Meno6Xl5eANSoUYOePXty4MABWrZsydatW5k2bZqQXtjLy4s2bdoI1jGA0aNHo6OjQ4sWLdDV1aVTp06CEcbOzo6///4bBwcHIiMjcXd3B8DX15eePXvSvXt3du7cycOHDwGoX79+sW1WgMjISMLDw9m+fbtQNmDAACZNmoSBgQGRkZGMGzeOKlWqYGtry4gRI5gxYwbbt2+nb9++5Ofnk5CQgJeXFz4+PqSmptKhQwf69+//1muWkZFR4tbr66hVqxa1atUCoFKlSgwaNIhly5YBIJVKUSgUXL9+nUqVKmFmZiY8p62tzZ07d0hPT6dSpUpYW1sXk/3w4UNOnTpFTEwMhoaGADg4OJQ4jrS0NM6cOYO/vz/z5s3j8OHDeHp6cvjwYfLy8gAwMPjf720DAwOys7OF+5e3u7Oysli6dCnr168v1k9OTs4bZUVGRtKxY0cAFixYgI2NDevXrycyMlL4fOvXr8/+/ftfv6iv9Kevr6/RV05ODmq1muzsbI1xAOjr65OdnY2WlhYzZszA29sbuVyOv78/QUFB9O/fn4SEBJYvX45cLmfSpEllDg9ZqkJZuXJlpk6dKtxXrFhR414ikXDs2LEydfYpIpFAzpPb/LHWn/y8F29sq8pXcDXyD1JvxePSfwLIDP+PRqlJgRJsK9q/k0JZrVw1DPhnxi0iIiJSEo8y8t+qvKzcu3ePhw8fYmdnJ5SpVCrh/v79+4wePVpjq1FLS4snT54I9y8fTNXR0Sl2X6RgpKenY2JiAkBiYiJdunRBW1ubLl26sHr1aqDwME1RmyL++usvfHx8CAwMpHbt2kL5119/Lfzb2dmZzp07c+TIEWxtbalYsSIBAQFAoRW2X79+zJw5k+DgYCwtLZk3bx5ubm44Ojpibm7+VmtWsWJFbt26Veb2jx8/Zs6cOcTGxpKdnY1arRaspLVq1WLq1KkEBQWRnJxMixYtmDx5MiYmJsyZM4fAwEA6dOiAmZmZoMy/TGpqKoaGhoIy+SZ0dHQwNTUVtuX/53/+h1WrVnHhwgVh1zUrKwsdHR3h33p6ekDhGkZFRTFlyhSg8CBOly5dNBTgInR1dd8oKz09XegvMTERLy8vtLW1cXZ2xsjICCj5PXgdurq6GopvVlYWurq6SCQS9PT0NA5yAWRnZwtjcXR0FAyC8fHxXLlyhYkTJ+Li4sJvv/3GgwcP8PX11fgh8yZK3dc8fvz4G6/PWZkEyM9K49C6OaUqky/z5N4tTm4NQKLMKr3xR0JPbcjXFd7O8VqChG9NOqGVr/ORRiUiIiLy9lStWLILz+vKy0r16tUxMzMjNjZWuOLi4lizZg1Q6Oa1Zs0ajfrLly+X+cv+ZYyMjHj06BEAdevWZe/evahUKmGL+8qVK2zatIlOnToJz/z999+MGjWKuXPnlroTKJFINHYOi9i2bRvW1tbUrVuXxMREGjRogFwup27duiQkJLz1PJo3b86lS5dITU0tU/slS5YgkUjYt28fFy5cKHYKuXPnzmzZsoWIiAgkEgmLFi0C4Msvv2TJkiVER0czbNgwvL29BeW8iGrVqpGZmanhk/k6Xt36fRlDQ0OqVq1KfPz/RkeJj48XXAwuX77MF198IZwbiY6OZuPGjTg5OeHk5MSDBw8YN24cwcHBpcp69T3Yt28fSqWSU6dO8fTpU27fvk1AQIBgzS4NS0vLYn1ZWloCYGFhQUJCgsZ6JyQkFHOdUKvVzJo1C19fX54+fYpKpcLU1JRGjRq91TsiOsq9AS3yif1jE4rcnNIbv8LD20mk3br6jx3WkShktK/akVq6X5atPRK6fuFGFaqX+EdJRERE5J/Co301dGSaf0t1ZBI82ld7L7mNGjVCT0+P4OBgcnNzUalUJCYmcunSJQD69OlDQEAA9+7dAwqtS0ePHn2nvpo1ayb4Ofr7+7N9+3batGnD7du3KV++PL/88gsLFy4UfAgTExMZOnQofn5+uLgkG0KKAAAgAElEQVS4FJP3xx9/kJ2dTUFBAWfOnGHv3r3F2j158oTffvtN2NI3MzMjJiaG7Oxsrly5Qo0aNd56Hs2bN6d58+aMHj2aK1euoFQqycrKYsuWLYSFhRVrn52dja6uLgYGBqSlpWnErr5x4wbR0dEoFArkcjk6OjqCNXjPnj2kp6ejpaUlWDRfPZRibGxMq1atmDlzJpmZmeTn53P+/PkSx92uXTuePXvG7t27UalU/PHHH6SlpdGkSRMAunXrxsqVK8nMzOT69evs2LGD7777DoCTJ09q+Er++uuv/P7774SHhxMeHo6xsTEzZ86kX79+pcp6+T2YNGkScXFxtG7dmoMHD2Jvb4+vry8TJkygUaNGQn8KhULYls/PzycvL0/4nu7atSshISGkpaWRlpZGSEiI0JeDgwNSqZTQ0FAUCgWbNm0SxvAyO3bs4KuvvqJ+/fpUrFiRvLw8kpOTOXv27Fu9I2XOlPNfJD/nKXfjL77z8xeP78KkdkOQ6n7AUZUdaW55ulfvTXTGac6nx6BUK0tsZyQ3omO1LlSX1AKl+BtDRETk30WRn+SGw6n8P/buPS7n+3/8+OPqfJDEyCGZkdgcKunIkNNikRBDWJiJZRYqYqvRsDl85Nhs+WQbYSRzmozRQZj2dRjlMCoqJUVFV13X9fujX++PS1HkuL3ut1u3m97v1/v1fr3evdXzeh1z8ktpWE+bsX0b12r8JJSP4Vu7di2LFi2iV69eyOVyWrZsyaeffgogzYr19vbm5s2bNGjQgP79+9O7d+8nvtfAgQP57rvvSEpKwt7eXm1pmFmzZlFWVoaW1v/+JEdERJCXl8ecOXOYM2cOUL4GY8XYusjISObMmYNKpcLMzIz58+djb2+vds9Fixbh4+MjdXFOmjQJX19fNm/ejIeHx1MvH7RixQrWrl3L9OnTycnJwcTERAoyHzZ16lT8/f2xtbXF3NycQYMGsWHDBqA8UFqyZAmXL19GW1sba2tradLT0aNHWbhwIffv36dp06YsW7YMPb3KO7gtXryYr776CldXV0pLS7G3t69y4nC9evVYs2YNwcHBhISE0LJlS1avXi21OlbMwO/Zsyd6enpMnDhRGjN5+PBhgoODpbwquqYrVMzWr3jOj8vLycmJsLAwYmJiGDhwoNqqAkCl9wDgvffekz7UVCx1dPDgQczMzBgxYgTp6em4ubkBMHToUEaMGAGUzxBftWoVQUFBLFmyhFatWkljIyvk5eURGRkpLTWkpaXF3LlzGTt2LLq6uoSGhlZ6lo8iU/2LmqNu3SpEqaxZdTU1ZKQmRnPqQO1W+3fzmY9e/Tdp2NCInJy7tcrraWlow32Nu6Tdv8qp/BMUK+6hKdPkDZ03sK/vhLFGfbRL9Wv8bB72Muv2vIm6vZ5E3V4tDRu+vEmKr5KUlBR8fHwYPnw4bm5umJqakpGRwcaNGykpKak0g/xlCQgIkGaP/9vl5ubi7u7O0aNHH9ll/qSys7Px9vamR48eeHp60rx5c3Jycti2bRunT5+WxtS+bkQL5SMoy4r5+8yx6hNWI/PKOVo3bFl9wudIWQo6GNFGsyMWjd5GIVMgQ4amSgtlafl/ECX/ms8VgiAIL4WlpSVRUVGEh4czbtw4cnNzady4Ma6urnh7e7/s4glVuHv3LgEBAc8smAQwNTUlKiqK77//nilTppCVlYWJiQm9e/eW1tt8HYkWykcpK2TPmjkUFdyqPu1jvNO1Px17j8bExPC1a1WoqdexxaSmRN1eT/+WulWM0X7a3oUXRbRQvl5iY2Np1qwZ7dq1e9lFEV4jooXykWRoPMVeoQ/T1KrdLERBEISH6cvuoVWUiyL7MgCajVtTZvAG91SVx5gJwpN6mjGigiACykfQ0NbBoK4Jd/Nu1iofo/qmr3zrgSAIrweZDBR5N1DsX4U855raOU3Tlhj1msRdmVhHVhCEF09M6X0EFTq807V/rfKQaWhg+mZbEVAKgvBM6KuKKPplOYqHgkkARfbflOxfiQFPvsyZIAhCbYmA8hFUKhX1m7ZCW1f/qfMwb9cZHcPaLWshCIIA5a2TGgU3UOZnPjKN4lYGmndrtuC0IAjCsyQCysfQ0qtXq1bKDt0HolCJUQWCINSelpYmZRerX3mi7MofaGmJX+2CILxY4rfOYyiUYGnXl8ZvPflMty4DRmNgUnmfT0EQhKemkNcgTekzXeLkn27AgAEkJSW97GI8dwEBAWzfvv2pr/fy8mLr1tqty/woN27cwNraGoVC8VzyF14MEVBWQ6lpyLuen2Bm2anG19gNGE3LTj1QycQMb0EQng2FQommuVW16TTM3qGsTPkCSvTPsHv37ko7zDypgoICli1bxvvvv4+dnR29evVi7ty5pKenq6U7fPgwH3zwAba2tjg7OzNnzhwKCwul8wEBAbRv3x5ra2vpqyLIyszMxNPTEzs7OxYuXKiW74QJEzhz5kyt6iCXywkLC6Nv375YWVnh4uJCYGAgGRkZtcq3Jpo2bUpycjKaT7GySl5eHn5+fnTu3JkuXbrg5+cnnZPL5QQGBmJjY4OzszMRERGVrl+3bh1Lly5VO7Zy5UosLS1JSEh4orzu3bvHt99+i4eHB3Z2dnTv3h0/Pz/++usvtXRyuRxfX19cXFywtLSs9IFGpVLx9ddfY29vj729faX9z8+fP4+HhwedOnXCw8OD8+fPS+d27dpF165dcXFx4dix//VopKWlMWLEiOcatIuAsgZUWnVxGjyZXl4zaGD2VpVpNDQ0adnRAbcp83nTqhcqDbF8hyAIz45SqUJm2gp0Hv27RaZrgOyNN/kXLS/80pSVlW9le/nyZYYNG4ZCoWDFihUkJiayfft2rKys8Pb2Ji4uTrrm7t27TJ48maNHj7Jnzx6ys7NZvHixWr7jx48nOTlZ+qoIstatW4e7uzsHDx4kNjZWCiD37NmDmZnZU2+jWMHX15fffvuNb775hpMnT7Jz507at29PYmJirfJ93qZOncobb7zB4cOHSUhIkLYmBAgLC+PatWscOnSIyMhI1q9fz5EjR9SuP3z4sLQtIpQHXvv376dhw4Zq6arLKycnh+HDh5Oenk5oaChxcXHs2bOHvn37MnPmTLWtNgFsbGxYvHhxpfsAREVFERsby86dO4mJieHQoUPS1ohyuRwfHx8GDhzIiRMncHd3x8fHB7lcTllZGUuWLGH79u3MnTuX+fPnS3nOnz+fwMDApwraa0oElDWk0qpDgzet6T1mNoM+WYjjwA+xcvGgc19P3vWcgvv0b7AbNBk9kzdFy+QrQltDhY5Sjo6yFG3xpgv/APe0TKgzYDpoVxFU6uijP+BTirXqvfiCvcZcXFxISEggJyeHTp06cfv2bencuXPncHBwoLS0lO3btzNixAhCQ0Oxt7cnLCxMamn6/PPPmTFjBm+99Za0r/OQIUPYsGEDX375JXfu3AHAzc2Nd999F319fYyNjfH09CQ5OblG5czIyMDBwQEjIyM6dOhAeno6hYWFhIeH89lnn9XqGSQkJJCQkMDq1avp2LEjWlpaGBkZMWrUKIYNG1YpfVpaGmPGjJFa0Pz8/KQ6AoSHh9OtWzesra3p16+fFJSePn0aDw8PbGxscHJyknaFycjIwNLSUgrS8/PzCQwMpGvXrnTp0gUfH58qyx0XF0dWVhazZs3CyMgIbW1t3n77ben8jh078PHxwdjYmFatWjFs2DB27NghnS8oKODq1atYW1tLx4KDg5kxY4baftc1ycvPz48xY8YQEhJC27Zt0dHRwdDQkH79+vHTTz8RERFBWloaUL7H9rhx47C1tUVDo/Ifp+joaLy9vWncuDGmpqZ8+OGH0r2OHz9OWVkZY8eORUdHR9pv/tixY+Tn52NqakqjRo1wcnKSWsj37duHqakpnTrVvKf1aYgZI09ApVKh0tBDx9gMc6vm0jglpVKFSqVCrA708slkMnRLi1Hl3eJOfByluTkgk6FrZoaRvSMq43qUaOi+7GIKwlNRKFVomrXFYMhclOlnKL18AplMhmZrOzSatadI2+Qfu0xZydlD3D/8X5R3ctGo+wZ6Pcai277nM8u/YcOG2NnZsXfvXkaOHAnAzp07GTBgANra5Y0Ep0+fZsCAAcTHx1NWVsbu3bvp3Lkzzs7OpKSkMHv2bK5fv87IkSNJSEhg8+bNuLu7ExMTw+jRoyvd88SJE7Ru3Vrt2KZNm9i0aRNmZmZMmjSJfv36AWBhYUFCQgJvvPEG586dw8fHh+XLlzN27Fjq1q1bq7onJCTQsWNHmjRpUqP0KpWKSZMm0aVLFwoLC/nkk08ICwtjzpw5XLlyhR9//JFt27ZJe5UrleVDMBYsWMCYMWNwd3enqKiIixcvVpn/rFmzMDAwYPfu3RgYGDwy6P7zzz9p2bIl/v7+HD16FDMzM/z9/bGzs6OgoICcnBzatm0rpW/bti0HDx6Uvo+Li8PR0VFqtdu7dy86Ojp0795d7T7V5XX8+HG0tbUZOnQomZmZ+Pv7k5KSQr9+/fjrr7/Ytm0bH330EZs2bcLf37/a53vx4sVK96p4VpcuXcLS0lJtnLSlpSWXLl2ia9eu5Ofnk5WVxV9//UXr1q0pLCxkzZo1bNiwodr71pZot3lKSqUKhUKJQqEU3UuvCJkM9O7kkLNuNZnr1lB09gzyrCzkmZncPXGCGytXkBe5AYOSO4g5C8LrSibT4K5mfe636omsz6fQ+1NKWnanUNME5T906GTJ2UMU7wlDeScHUKG8k0PxnjBKzh56pvcZPHgwMTExACgUCnbv3s2gQYOk840aNcLLywstLS309PSIj4+nf//ylUCCgoLw9PQkLi4OU1NTbt4s3xSjXbt2XLlypdK94uPjiY6OxtfXVzrm5eXF/v37SUhIYNq0aQQEBPDHH38AMGnSJE6ePMno0aMZOXIkpaWlpKSk0LNnT/z8/Bg1ahQ//PDDU9U7Pz+/yq7XR2nRogXOzs7o6OhQv359PvzwQ06cOAGApqYmcrmcy5cvU1paipmZGebm5gBoaWmRlpZGXl4ehoaGWFlVHhN88+ZNjhw5QnBwMMbGxmhra2NnZ1dlObKzs4mLi8Pe3p64uDi8vb3x8fEhLy+P4uLy9ViNjP637aeRkRFFRUXS9w92dxcWFrJs2TLmzJlT6T7V5fXge7Bo0SKsra2Jj4+nV69enD17Fnj0e1CV4uJi6tSpo3av4uJiVCoVRUVFauUAqFOnDkVFRWhoaPDFF1/g6+vL999/z/z58wkLC2P06NGkpKTg5eXF+PHjSU1NrVE5npQIKIV/BJkM9O7eInPtGkrz8h6ZriQ9jaxvw9EvKXxkGkF4HSgUSkqUWpQoNVEo/qGR5P93//B/oaxE/WBZSfnxZ6hXr15cvnyZ9PR04uPjqVOnDh07dpTON27cWC19Xl4epqamAKSmpjJw4EC0tLQYOHCglCYzM1NKU+HPP//Ez8+PFStW0LJlS+n4O++8g4mJCVpaWnTv3h03NzcOHDgAQL169Vi+fDkxMTGMGTOGL7/8krlz5xIeHo6FhQURERFs3ryZy5cvP3G969WrR05OTo3T5+bmMn36dLp164aNjQ0zZ86Uhgq0aNGC2bNnExYWhpOTE9OnTyc7Oxsob6G8evUqrq6uDBkyhEOHKn8gyMrKwtjYGGPj6nd80tXVpVmzZgwbNgxtbW0GDBhAkyZNOHXqFAYGBgBqk54KCwsxNDQEQKlUkpCQIAWUK1euZODAgZiZVV6dpbq8Hn4P3NzcpJ+hiYkJUPV78CgGBgZqgW9hYSEGBgbIZDIMDQ3VygFQVFQklcXR0ZEtW7bwww8/IJPJOHv2LB4eHvj7+7Nw4UImT55MUFBQjcrxpF5YQPn3338zfPhw+vXrx/Dhw7l69WqlNAqFguDgYHr37k2fPn3UlijIyclh8uTJuLm54erqys6dO19U0YXXgI5CTu7WLSjv3682bdnt2xQcjEVbQ7QsC8LrQHkn94mOPy1dXV1cXV2JiYlh586daq2TQKXlmExMTKRArE2bNsTExKBQKKRWzrNnz/LDDz/w/vvvS9f89ddfTJ48mdDQUBwdHR9bHplMVmUPWFRUFFZWVrRp04bU1FTat2+Pjo4Obdq0ISUl5Ynr7eTkxOnTp8nKqtmi+EuXLkUmk7Fr1y5OnTpVaRaym5sbmzZt4tChQ8hkMr755hsA3nzzTZYuXUpiYiITJ07E19dXav2r0LhxYwoKCtTGZD7Kw12/DzI2NqZhw4ZcuHBBOnbhwgVpiMGZM2do2rQp9euXbz6SmJjIxo0bcXZ2xtnZmczMTD799FPCw8Orzevh92DXrl2UlZVx5MgRbt++zbVr11i+fDlDhw6ttk5QPrzh4XtZWFgA0Lp1a1JSUtSed0pKSqWhEyqVipCQEIKCgrh9+zYKhYJmzZrRsWPHp3pHauKFBZSff/45I0eOZP/+/YwcOZJ58+ZVSrNr1y7S0tL49ddfiYqKIiwsTFqyYOHChbRv355du3bx448/smzZMjIzH71jhPDvIrtbQMkTLG9R+GcyWkV3n2OJBEF4VjTqvvFEx2tj0KBB7Nixg99++61SQPkwBwcH9u/fD5TPot2yZQs9e/bk2rVr6Ovr85///IfFixfTrFkzoLz1asKECcydOxcXF5dK+e3bt4+ioiKUSiVxcXHExMRUSnfr1i1++uknpk6dCoCZmRlJSUkUFRVx9uxZmjdv/sR1dnJywsnJiSlTpnD27FnKysooLCxk06ZNbNu2rVL6oqIiDAwMMDIyIjs7m/Xr10vnrly5QmJiInK5HB0dHXR1daWJJzt37iQvLw8NDQ1p3OfDk1IaNWrEu+++S3BwMAUFBZSWlkrd6Q/r06cPd+7cYceOHSgUCvbt20d2djY2NjYAuLu7s2bNGgoKCrh8+TJbt25l8ODBAPz+++9qYyU3bNjAL7/8QnR0NNHR0TRq1Ijg4GBGjRpVbV4Pvgf+/v4kJyfTo0cP9u7dS5cuXQgKCmLmzJlqrd1yuZySkvJW99LSUkpKSqQgcdCgQURERJCdnU12djYRERHSvezs7NDU1CQyMhK5XC4Nc3BwcFB7Nlu3buXtt9+mXbt21KtXj5KSEi5dusSxY8ee6h2piRcyKefWrVv89ddf0rpN77//Pl9++SV5eXnSpwMoX/5g2LBhaGhoUL9+fXr37s2+ffuYMGECFy5cYOzYsQDUr1+ftm3bsnfvXry9vV9EFYRXmJaWBkX/939PdI2qrIzSGxnIWr8jxsAKwitOr8dYiveEqXd7a+mi12PsM79X586d0dDQ4J133pECwUcZOHAg3333HUlJSdjb26stDTNr1izKysrQ0vrfn9mIiAjy8vKYM2eONFavadOm7N69G4DIyEjmzJmDSqXCzMyM+fPnV1ojc9GiRfj4+EhdnJMmTcLX15fNmzfj4eHx1MsHrVixgrVr1zJ9+nRycnIwMTGRgsyHTZ06FX9/f2xtbTE3N2fQoEHSpA+5XM6SJUu4fPky2traWFtbExISAsDRo0dZuHAh9+/fp2nTpixbtgw9vcorFixevJivvvoKV1dXSktLsbe3p0uXLpXS1atXjzVr1hAcHExISAgtW7Zk9erVUlxRMQO/Z8+e6OnpMXHiRKmL+/DhwwQHB0t5VXRNV6iYrV/xnB+Xl5OTE2FhYcTExDBw4EAiIyPV8nr4PQB47733uH79OoC01NHBgwcxMzNjxIgRpKen4+bmBsDQoUMZMWIEUD5DfNWqVQQFBbFkyRJatWrFqlWr1Gal5+XlERkZKS01pKWlxdy5cxk7diy6urqEhoZWepbPgkz1Av6anj17Fn9/f+k/DUD//v35+uuveeedd6Rjbm5uLFiwQIriv/32W7KzswkKCmLWrFnUr18ff39/MjIyGDp0KG5ubs9tLIDwernx7bcUnTv3RNfU79+fBn37PqcSCYLwLD3vWd4PGjNmDG5ublUumfOwlJQUfHx8GD58OG5ubtLM5o0bN1JSUiIFUy9bQEAAdnZ2eHh4vOyivHS5ubm4u7tz9OjRZ7arVHZ2Nt7e3vTo0QNPT0+aN29OTk4O27Zt4/Tp06xbt+6Z3OdV9tosGxQQEEBoaCiDBg2iadOmalP9a+rWrcKXtqRGw4ZG5OT8M7tYX3bdtLU1UWlqSUtT1JRKplHtO/Gy6/Y8ibq9nl7HujVsaFR9omrotu/53ALIB50+fZq//vqL1atX1yi9paUlUVFRhIeHM27cOHJzc2ncuDGurq6iB+0VdffuXQICAp7pFqWmpqZERUXx/fffM2XKFLKysjAxMaF3797Sepv/dC8koGzSpAnZ2dkoFAo0NTVRKBTcvHmz0ppXTZo04caNG1ILZWZmJk2bNgXKu7krBvYCTJw4sdIgVOHfSaFQom9pSeGfNVsguIJOMzNK/qFr9gmC8OT8/f2JjY1lzpw5asu2VOeNN95g9uzZzJ49+zmWrnZ69+5dbRf+v0XLli3VZtc/K3Xq1MHX11dtKah/kxcyKadBgwa0a9eOX375BYBffvmFdu3aqY2fhPIxBVu3bkWpVJKXl0dsbKy0qOvt27elVfQTExNJTU1Vmzkn/HsplSp0WryJTLvmOxRp1a+PzKTBcyyVIAivm0WLFvHHH3/8I7uFe/fuTbt27V52MYR/sBfW5f3FF18QEBDA6tWrqVu3LosWLQKQlg7o0KEDgwYN4v/+7//o+//HtU2ZMkWajXT69GkWLFiAhoYGJiYmrF27Fn19/RdVfOEVV6pvhHH37uTHxtYofX3X/sh19BHbGwmCIAhC7b2QSTmvCjGG8vl4Veqmpyzhzp5fKEw+9dh0Jn37oWvnhFxW/eepV6Vuz4Oo2+vpdazbsxhDKQjCq+21mZQjCNW5r6FL3f5uGLRty+3YA5Q+tPODbnNzTPr0haZmNQomBUEQBEGoGfFXVfhHua+hg0ab9jRs2Rru5KMsLgYNGRqGdVAZGSPX0BHrTgqCIAjCMyYCSuEfR6lUcV9TF0xMweShkyKYFARBEIRn7oVtvSgIgiAIrxMvLy+2bt36sovxTCUlJeHl5fXU12/fvp0PPvjgGZZI3YQJE9ixY8dzy194fkRAKQiCIAjPgEqlYs+ePYwdOxZHR0ecnZ3x9vbm119/VUt369YtPvvsM7p27Urnzp0ZMWIE//fA9rFJSUm0bdsWa2tr6evBIGvBggV06dKF4cOHk5WVJR3ftWsX8+fPr3U9jh49yqhRo7C2tsbBwYHRo0dz8ODBWudbE+vXr5f2rX5S//3vf3FxccHKygpXV1f+/vtv6dyuXbvo2bMnVlZW+Pj4kJ+fr3Ztdna2tJVihatXr9KhQwdmzJihdry6vADi4uL4+OOPcXZ2xsHBgQ8++IBt27ZV2oDjhx9+wMPDg/bt2xMQEFApn8TERN577z06deqEl5eXtF0jlG9zGRgYiI2NDc7OztL21lC+jrenpyd2dnYsXLhQLc8JEyZw5syZRz3GpyYCSkEQBEGoJYVCgZ+fH1u2bOHjjz8mNjaWw4cPM3XqVKKioggKCpLGbxcXF9OhQwe2b9/O8ePHGTx4MB999BFFRUVSfo0aNSI5OVn6qgiyTp8+zblz54iPj8fGxobw8HCgfPeX7777jk8//bRW9di3bx/Tpk3D3d2dI0eOkJCQgK+vL4cOHapVvs/b1q1b2bZtG+Hh4SQnJ7Nu3Tppf+6LFy8yb948Fi9eTHx8PPr6+mr7eAMcOXKEbt26qR0LCQmptDd6TfJavHgxS5cuZejQoezdu5f4+HjmzZvHsWPHmDRpEnK5XErbqFEjfHx8GDJkSKU65eXlMXXqVKZNm8bx48dp374906dPl86HhYVx7do1Dh06RGRkJOvXr+fIkSMArFu3Dnd3dw4ePEhsbKwUQO7ZswczM7On3vP9cURAKQiCILzyjt86RtAZf6b8MZGgM/4cv3Ws1nmmpaVhZ2fHuXPngPJWKgcHB5KSkqpMv23bNlxdXenSpQvjx49Xay1as2YNOjo6RERE4OjoiKGhIdra2tjY2PDtt99SWFjIzp07AWjevDkffvghjRo1QlNTk+HDh1NaWqrWovYoGRkZdO7cGR0dHRwdHUlPTwdg2bJljB8//ol2+HmYSqVi4cKF+Pj4MGzYMIyMjNDQ0MDOzu6RLZ/z58+ne/fu2NjY4OHhwcmTJ6Vzp0+fxsPDAxsbG5ycnKQtCEtKSpgxYwb29vbY2toyZMgQcnNzgcrDDLZs2YKrqyvW1tb0799f+lk9SKlUsnLlSmbPnk3r1q2RyWSYm5tTr149oLxF0cXFhS5dumBoaMi0adM4cOAAhYWFUh6///473bt3l77fvXs3RkZGODo6qt2ruryio6O5cOECmzdvpnfv3tStWxdNTU3atWvHN998Q+vWraUPAQB9+/ald+/eUlkfdODAASwsLHB1dUVXV5dPPvmECxcucPnyZQB27NiBj48PxsbGtGrVimHDhkkt2RkZGTg4OGBkZESHDh1IT0+nsLCQ8PBwPvvssyp/lrUlAkpBEAThlXb81jF+uraR2/I8AG7L8/jp2sZaB5Xm5ubMmDGDmTNncu/ePWbPns3gwYOxt7evlDY2NpZ169axcuVKEhMT6dy5M35+fkB5i+O2bduYN28epaWlBAYG4ujoiJeXF4GBgfzxxx8EBgYSGRlZZTnOnz9PaWkpLVq0kI7l5eXh5OSEi4sLoaGhFBcXA9C6dWtOnjzJ/fv3SUxMpHXr1pw5c4a///4bNze3Wj2PK1eukJmZKe1QVxMdOnQgOjqa48eP8/777zNt2jRKSkqA8q75MWPGcOrUKQ4cOICrqytQHggVFhZy+PBhkpKSCA4ORk9Pr1Lee/fuJSwsjEWLFnHq1CnWrFlTZeCVlZVFVlYWqampdO/eHRcXF1asWCF1L1+8eBFLS0spvbm5Odra2ioWQOQAACAASURBVFy9ehWA0tJSTpw4gZOTEwCFhYWsWLGCwMDASveqLq/Vq1ezYMECdHR0+Prrr3F2dmbw4MGEhoYSHR3NtGnTiI6OrtGzffheBgYGmJubc+nSJQoKCsjJyaFt27bS+bZt23Lp0iUALCwsSEhI4M6dO5w7dw4LCwuWL1/O2LFjqVu3bo3u/6REQCkIgiC80mJu7KBUJVc7VqqSE3Oj9pM3PD09MTc3x9PTk5s3b6p1KT5o8+bNfPTRR7Rq1QotLS0+/vhjzp8/z/Xr1/nzzz+xt7fHwMCAbdu2kZ2dzYEDBwgNDeXo0aMolUpMTU2rHGtXWFjIrFmzmDp1KkZG5QvAv/XWW0RHRxMXF8d///tfzp07J42Da9OmDf369cPT05PMzEwmTpzIggULCAoKIjIyklGjRuHn58edO3ee+FlUlK9Ro0Y1vmbQoEGYmJigpaWFt7c3crlcamnV0tIiLS2NvLw8DA0NsbKyko7n5+dz7do1NDU1ad++fZUtq9u2bWPChAl07NgRmUxGixYtqtyPvGIcaXx8PLt27SIyMpLdu3ezbds2oDzgr3i2FerUqSMNMTh58iRt27aVyrB8+XKGDBlC48aNK93rcXldu3aNRo0a0aRJE37//XeOHDlCTEwM69evJzExEaVSiZ6eHsbGxuTl5VX7bB93r4oPGA+eNzIykuo0adIkTp48yejRoxk5ciSlpaWkpKTQs2dP/Pz8GDVqFD/88EO1ZXgSIqAUBEEQXmkVLZM1Pf6kPD09SU1NxcvLCx0dnSrT3Lhxg9DQUGxtbbG1tcXOzg6VSkV2dja3bt3C1NQUgNTUVHr16kWdOnVo3rw5nTt3BsoDRwMDA7U879+/z8cff0ynTp2YNGmSdLxhw4a0bt0aDQ0NmjdvzsyZM9m/f790fty4ccTExLB8+XL27t2Lra0tSqWSLVu2sGHDBlq1aqXWrVpTFa1/N2/erPE13333Ha6urnTu3BlbW1vu3r3L7du3gfIWyqtXr+Lq6sqQIUOkcZiDBg2ia9eu0sSkxYsXU1paWinvzMxMzM3Nqy1DRevmhAkTqFu3LmZmZgwfPpzff/8dKG/Ze7B7G8p/HoaGhoB6d/f58+dJTExk3LhxVd7rcXk9/B5069aNBg0a0KBBA2l8plKp5M6dO1W2tNbkXkVFRRgaGkrv0oPnH6xTvXr1WL58OTExMYwZM4Yvv/ySuXPnEh4ejoWFBREREWzevFnqPn8WREApCIIgvNJMdOo/0fEnUVRURGhoKEOHDiUsLKzKVkSAJk2aEBwczMmTJ6Wv06dPY2Njg4mJCTn/f2euNm3acPDgQQoLC0lPT+ePP/7gzp07BAcHq028kMvlTJkyBVNTU0JCQh5bRplMVuWGDLm5uURFRTFlyhSpe1RbW5sOHTqQkpLyxM/irbfeokmTJpVmpT/KyZMnWb9+PcuXL+fEiROcPHkSIyMjqaxvvvkmS5cuJTExkYkTJ+Lr60txcTHa2tpMnTqVPXv2sHnzZg4fPlxlN3CTJk1IS0urthwtW7ZEW1sbmUwmHXvw3xYWFly4cEH6Pj09ndLSUt58801APaBMSkri+vXr9OzZE2dnZ77//nt+/fVXaVLU4/IyMTGRgvE2bdoQFxfHrVu3uHXrFkePHuXevXssWbKE7t27o6FRffj18L2Ki4tJS0ujdevWGBsb07BhQ7XzFy5coHXr1pXyiYqKwsrKijZt2pCamkr79u3R0dGhTZs2T/WePIoIKAVBEIRX2sCmg9GWqbccast0GNj06ZaXedCCBQto3749CxYsoEePHnz++edVphsxYgTh4eFcvHgRKJ9VvXfvXgCsrKxISkri/v37DB06FFNTU/r27cucOXPo2rUrq1atwsrKirFjxwLlY/Z8fX3R1dVl0aJFlYKLY8eOcf36dVQqFZmZmXzzzTf06tWrUpm++uorPvnkE/T19TEzM+PMmTMUFRVx/Phxmjdv/sTPQiaTERAQwOrVq/n5558pLCxEqVRy8uRJ5s6dWyl9UVERmpqa1K9fn7KyMlauXKnWYrZz507y8vLQ0NCQxu1paGhw7NgxUlJSUCgU1KlTBy0trSoDrKFDh/L9999z9uxZVCoV165dU5sIVUFfX5/+/fuzfv16CgsLycrKIioqih49egDg5ubGoUOHOHnyJMXFxfznP/+hT58+1KlTh/T0dORyOa1atQJg+PDhHDhwgOjoaKKjoxkxYgQ9evTgu+++qzavli1bkpWVxc2bN+nevTvdunVj0KBBTJ48mS5durBp0yYMDQ2ZNWuWVPaysjJKSkpQKpUoFApKSkooKysDoE+fPly8eJH9+/dTUlLCqlWrsLS0lMrq7u7OmjVrKCgo4PLly2zdurXSkku3bt3ip59+YurUqQCYmZmRlJREUVERZ8+efar35FHETjmCIAjCK82ugQNQPpbytjwPE536DGw6WDr+tGJjYzl69Ci7du0CICAgAHd3d2JiYhg4cKBa2j59+lBUVMRnn33G9evXMTIywsnJCVdXV+rUqcP7779PaGgoISEh0mzmCmVlZWhp/e/PbXJyMocOHUJPT48uXbpIx7/99ltsbW05f/48M2fOlLpG+/TpU2k5oMTERO7evUufPn0A6NixI927d6dHjx60bNmSFStWPNUzee+99zAwMGDt2rXMnz8fXV1dLCwsGD9+fKW0Xbt2pVu3bvTr1w8DAwPGjh1LkyZNpPNHjx5l4cKF3L9/n6ZNm7Js2TL09PTIzc3l888/Jzs7GwMDA/r378+gQYMq5e/q6kp+fj5+fn7cvHmTZs2asXjx4irHUc6bN4+5c+fSrVs36taty7Bhwxg6dChQ3tIXHBzMjBkzyM/Px9HRUfoZPTy7W19fH319fel7AwMDdHR0qF+/frV5QXm3e1BQEKtXr2bmzJnMnDlTOvfwewDlqwOsXLlS+j4mJoapU6fyySefUL9+fcLCwggJCWHmzJl06tSJpUuXSml9fX35/PPP6dmzJ3p6ekycOLHSWpqLFi3Cx8dH6gqfNGkSvr6+bN68GQ8Pj2e6fJBM9S/a2PjWrUKUypdT3YYNjcjJuftS7v28ibq9nkTdXk+vY90aNjSqPtFrrqysjGnTpqFUKpk8eTLt2rXj/v37HDx4kPXr1xMVFSX9UX+ZkpKSWLlyJRs3bnzZRXklTJw4kdGjR6sFlbUVEhLChQsX8PX1xdraGqVSSXx8PMuXL2fdunVVBsT/BKKFUhAEQRBqSUtLi7CwMLZv385XX33FlStX0NbWxt7enqVLl74SwaRQmZ2dXZXLRNXGvHnzOHDgAGvXrpXGOFpbWxMSEvKPDSZBtFC+MK9jq0JNibq9nkTdXk+vY93+DS2Ur4uMjAyOHz+Oh4fHyy6K8A8jWigFQRAE4V/CzMwMMzOzl10M4R9IzPIWBEEQBEEQakUElIIgCIIgCEKtiIBSEAThFaahKUOBBko00NQUv7IFQXg1iTGUgiAIryCZhow792Ukpdzlz0t30ZDJcHynLp3baKGnIXtpEwwFQRCqIj7uCoIgvGJkGjLSbin5YuNVYhJySbtZwtXs+2z67SbBkX+TW6S+tZzwfHh5ebF161YAtm/fzgcffPCSS1R7AQEBbN++/amvf/CZPGs3btzA2toahULxXPIXni8RUAqCILxiikplrIzOoLSscitk0X0Fq2KuU6IQAeWrpqCggGXLlvH+++9jZ2dHr169mDt3Lunp6WrpDh8+zAcffICtrS3Ozs7MmTNHbcvCgIAA2rdvj7W1tfRVEWRlZmbi6emJnZ0dCxcuVMt3woQJnDlzplZ1kMvlhIWF0bdvX6ysrHBxcSEwMJCMjIxa5VsTTZs2JTk5GU1NzSe+Ni8vDz8/Pzp37kyXLl3w8/OTzsnlcgIDA7GxscHZ2ZmIiIhK169bt05tFxqAlStXYmlpSUJCwhPlde/ePb799ls8PDyws7Oje/fu+Pn58ddff6mlk8vl+Pr64uLigqWlJUlJSWrnVSoVX3/9Nfb29tjb2/P111+r7el+/vx5PDw86NSpEx4eHpw/f146t2vXLrp27YqLiwvHjh2TjqelpTFixIjnErSLgFIQBOEVIpPJuJpdwj258pFpcvJLuVUoWnFeJZcvX2bYsGEoFApWrFhBYmIi27dvx8rKCm9vb+Li4qS0d+/eZfLkyRw9epQ9e/aQnZ3N4sWL1fIbP348ycnJ0ldFkLVu3Trc3d05ePAgsbGxUgC5Z88ezMzMar2Vnq+vL7/99hvffPMNJ0+eZOfOnbRv357ExMRa5fu8TZ06lTfeeIPDhw+TkJCgtlVkWFgY165d49ChQ0RGRrJ+/XqOHDmidv3hw4fVti1MS0tj//79NGzYUC1ddXnl5OQwfPhw0tPTCQ0NJS4ujj179tC3b19mzpzJzz//rJafjY0NixcvrnQfgKioKGJjY9m5cycxMTEcOnSIzZs3A+XBqI+PDwMHDuTEiRO4u7vj4+ODXC6nrKyMJUuWsH37dubOncv8+fOlPOfPn09gYOBTBe3VEQGlIAjCK0RTU8aVG/erTZd9u1R0e9dSWloadnZ2nDt3DoDs7GwcHBwqtRRVZdGiRXzwwQfcvXtXamn6/PPPmTFjBm+99RaampoYGxszZMgQNmzYwJdffsmdO3cAcHNz491330VfXx9jY2M8PT1JTk6uUZkzMjJwcHDAyMiIDh06kJ6eTmFhIeHh4Xz22WdP/zCAhIQEEhISWL16NR07dkRLSwsjIyNGjRrFsGHDKqVPS0tjzJgxUguan5+fVEeA8PBwunXrhrW1Nf369ZOC0tOnT+Ph4YGNjQ1OTk7SXtgZGRlYWlpSVlYGQH5+PoGBgXTt2pUuXbrg4+NTZbnj4uLIyspi1qxZGBkZoa2tzdtvvy2d37FjBz4+PhgbG9OqVSuGDRvGjh07pPMFBQVcvXoVa2tr6VjFft06Ojpq96ouLz8/P8aMGUNISAht27ZFR0cHQ0ND+vXrx08//URERARpaWkA6OjoMG7cOGxtbdHQqByORUdH4+3tTePGjTE1NeXDDz+U7nX8+HHKysoYO3YsOjo6jBkzBpVKxbFjx8jPz8fU1JRGjRrh5OQktZDv27cPU1NTOnXqVOVzrC0RUAqCILxCVCqoo19964GBrgbw75mYU5CQwEU/P86PG8dFPz8KHuiGfFrm5ubMmDGDmTNncu/ePWbPns3gwYMfuxWfUqkkKCiI1NRUvv/+e4yMjNi9ezedO3fG2dmZlJQUhgwZgoODAytWrGDEiBE0a9YMd3d3YmJiqszzxIkTtG7dWu3Ypk2bsLOzw8PDg/3790vHLSwsSEhI4M6dO5w7dw4LCwuWL1/O2LFjqVu3bq2eR0JCAh07dqRJkyY1Sq9SqZg0aRJHjx5l7969ZGVlERYWBsCVK1f48ccf2bZtG8nJyXz33XfStoMLFixgzJgxnDp1igMHDuDq6lpl/rNmzeLevXvs3r2bhIQExo0bV2W6P//8k5YtW+Lv74+9vT1Dhgzh+PHjQHmwmJOTQ9u2baX0bdu25dKlS9L3cXFxODo6Sq12e/fuRUdHp9L+3tXldfz4cbS1tRk6dCiZmZlSsD1v3jyGDh2KsbExH330EZs2barR87148WKle128eBGAS5cuYWlpqfah0tLSkkuXLlG/fn3y8/PJysoiPj6e1q1bU1hYyJo1a2r9oeNxREApCILwClEolHRq9fh9n7U1ZTRtoMO/ZePcgoQEMjdsoOzWLQDKbt0ic8OGZxJUenp6Ym5ujqenJzdv3mT69OmPTFtWVsZnn31GQUEBa9asQV9fH4D4+Hj69+8PQFBQEJ6ensTFxWFqasrNmzcBaNeuHVeuXKmUZ3x8PNHR0fj6+krHvLy82L9/PwkJCUybNo2AgAD++OMPACZNmsTJkycZPXo0I0eOpLS0lJSUFHr27Imfnx+jRo3ihx9+eKpnkZ+fX2XX66O0aNECZ2dndHR0qF+/Ph9++CEnTpwAQFNTE7lczuXLlyktLcXMzAxzc3OgfN/ztLQ08vLyMDQ0xMrKqlLeN2/e5MiRIwQHB2NsbIy2tjZ2dnZVliM7O5u4uDjs7e2Ji4vD29sbHx8f8vLyKC4uBsDI6H/bfxoZGVFUVCR9/2B3d2FhIcuWLWPOnDmV7lNdXg++B4sWLcLa2pr4+Hh69erF2bNngUe/B1UpLi6mTp06avcqLi5GpVJRVFSkVg6AOnXqUFRUhIaGBl988QW+vr58//33zJ8/n7CwMEaPHk1KSgpeXl6MHz+e1NTUGpWjpkRAKQiC8Iox1pPRrYPxI8+7d30DI51/STQJ3Pz5Z1RyudoxlVzOzYfGoz0tT09PUlNT8fLyqtTF+aC0tDQOHjzIlClT1NLl5eVhamoKQGpqKgMHDkRLS4uBAwdKaTIzM6U0Ff7880/8/PxYsWIFLVu2lI6/8847mJiYoKWlRffu3XFzc+PAgQMA1KtXj+XLlxMTE8OYMWP48ssvmTt3LuHh4VhYWBAREcHmzZu5fPnyEz+HevXqkZOTU+P0ubm5TJ8+nW7dumFjY8PMmTO5ffs2UB5szp49m7CwMJycnJg+fTrZ2dlAeQvl1atXcXV1ZciQIRw6dKhS3llZWRgbG2Ns/Oj/BxV0dXVp1qwZw4YNQ1tbmwEDBtCkSRNOnTqFgYEBgNqkp8LCQgwNyz+0KZVKEhISpIBy5cqVDBw4sMrtKavL6+H3wM3NTfoZmpiYAFW/B49iYGCgFvgWFhZiYGCATCbD0NBQrRwARUVFUlkcHR3ZsmULP/zwAzKZjLNnz+Lh4YG/vz8LFy5k8uTJBAUF1agcNSUCSkEQhFeMBkrcHRvg0bUh+rr/+zVtbKjJh+81waltHbXZnv90FS2TNT3+JIqKiggNDWXo0KGEhYWRn5//yLRvvfUWX331FRMnTlRrZTIxMZECsTZt2hATE4NCoZC6uM+ePcsPP/zA+++/L13z119/MXnyZEJDQ3F0dHxsGWUyWZU/76ioKKysrGjTpg2pqam0b98eHR0d2rRpQ0pKyhM9BwAnJydOnz5NVlZWjdIvXboUmUzGrl27OHXqVKVZyG5ubmzatIlDhw4hk8n45ptvAHjzzTdZunQpiYmJTJw4EV9fX6n1r0Ljxo0pKChQG5P5KA93/T7I2NiYhg0bcuHCBenYhQsXpCEGZ86coWnTptSvXx+AxMRENm7ciLOzM87OzmRmZvLpp58SHh5ebV4Pvwe7du2irKyMI0eOcPv2ba5du8by5csZOnRotXWC8uEND9/LwsICgNatW5OSkqL2vFNSUioNnVCpVISEhBAUFMTt27dRKBQ0a9aMjh07PtU78jgioBQEQXgFacsU9OpYhy+8WhA0qgVzR7VgzsgW9LapjyaPngH+T6TVoMETHX8SCxYsoH379ixYsIAePXrw+eefPzb9+++/z2effcaHH34oTa5wcHCQxjnOnz+fLVu20LNnT65du4a+vj7/+c9/WLx4sTSGMDU1lQkTJjB37lxcXFwq3WPfvn0UFRWhVCqJi4sjJiamUrpbt27x008/MXXqVADMzMxISkqiqKiIs2fP0rx58yd+Fk5OTjg5OTFlyhTOnj1LWVkZhYWFbNq0iW3btlVKX1RUhIGBAUZGRmRnZ7N+/Xrp3JUrV0hMTEQul6Ojo4Ourq408WTnzp3k5eWhoaEhjft8eFJKo0aNePfddwkODqagoIDS0lKpO/1hffr04c6dO+zYsQOFQsG+ffvIzs7GxsYGAHd3d9asWUNBQQGXL19m69atDB48GIDff/9dbazkhg0b+OWXX4iOjiY6OppGjRoRHBzMqFGjqs3rwffA39+f5ORkevTowd69e+nSpQtBQUHMnDmTjh07SveTy+WUlJQAUFpaSklJiRQkDho0iIiICLKzs8nOziYiIkK6l52dHZqamkRGRiKXy6VhDg4ODmrPZuvWrbz99tu0a9eOevXqUVJSwqVLlzh27NhTvSOPI3bKEQRBeEUpFEr0NUFfGkb171wqqNGQIWRu2KDW7S3T0aHRkCG1yjc2NpajR4+ya9cuoHz9x4rJMw92Vz9s8ODBlJaWMnbsWDZu3MjAgQP57rvvSEpKwt7eXm1pmFmzZlFWVoaW1v/+3EZERJCXl8ecOXOksXpNmzZl9+7dAERGRjJnzhxUKhVmZmbMnz+/0kShRYsW4ePjI3VxTpo0CV9fXzZv3oyHh8dTLx+0YsUK1q5dy/Tp08nJycHExEQKMh82depU/P39sbW1xdzcnEGDBrFhwwagPFBasmQJly9fRltbG2tra0JCQgA4evQoCxcu5P79+zRt2pRly5ahp6dXKf/Fixfz1Vdf4erqSmlpKfb29nTp0qVSunr16rFmzRqCg4MJCQmhZcuWrF69Wmp1rJiB37NnT/T09Jg4caLUxX348GGCg4OlvCq6pitUzNaveM6Py8vJyYmwsDDp/YmMjFTL6+H3AOC9997j+vXrANJSRwcPHsTMzIwRI0aQnp6Om5sbAEOHDmXEiBFA+QzxVatWERQUxJIlS2jVqhWrVq2qNBQjMjJSWmpIS0uLuXPnMnbsWHR1dQkNDa30LGtDpvoX9ZvculX40rYra9jQiJycuy/l3s+bqNvrSdTt9fQ61q1hQ6PqE1WjICGBmz//TNmtW2g1aECjIUMwdnJ6BqV7NlJSUvDx8WH48OG4ublhampKRkYGGzdupKSkRAqmXraAgABp9vi/XW5uLu7u7hw9evSZLcGVnZ2Nt7c3PXr0wNPTk+bNm5OTk8O2bds4ffo069ateyb3eRWJFkpBEAThlWfs5PRKBZAPs7S0JCoqivDwcMaNG0dubi6NGzfG1dUVb2/vl108oQp3794lICDgma7nampqSlRUFN9//z1TpkwhKysLExMTevfuLa23+U8lWihfkNexVaGmRN1eT6Jur6fXsW7PooVSeDZiY2Np1qwZ7dq1e9lFEf5hRAulIAiCIPxL9O7d+2UXQfiHErO8BUEQBEEQhFoRAaUgCIIgCIJQK6LL+yXQ0NSgsAQK7ytRKFVoa8qoa6CJnqbypY3xFARBeJgmMuT3ylCUKtA10EamoyF+RwmCUCURUL5AGhoy8u/LiDtbwO//l889+f8WJ37DWJsB9g1429wAAy3Fv2aPXkEQXk0aCvhj30XSU3IB0Kujg7NHO+o0MhBBpSAIlYgu7xekTKHi71wFwRuvsu9EnlowCZBbUMp/f83i663p3JVr8AxXMRAEQXgimpoyUk9cl4JJgPuFcg7/dAbl/X/n4uqCIDyeCChfAJkMLt+4x/KfMygte/wn+9yCUpb+nEFxmeYLKp0gCII6ZYmSy6cyKx1XlCm5k1tcxRX/TF5eXmzdulX6ftmyZdjb2+Ps7PwSS/Xktm/fTkBAwFNfHxYWxowZM55hidQNGDCApKSk55a/8GKIgPIFUKg0+PFgFooadhPlFpSSfLkQTU3x4xEE4SV4XA/Jv7T75MaNG0RERLBnzx7i4+Ol42VlZWzevJkRI0Zgb29Pt27d8PHx4dixY2rX//3330yePBkHBwfs7OwYP348V65ckc5v376ddu3aYW1tLX1VBFllZWVMnz4dW1tbxo8fT2FhoXTd2rVriYiIqHX9du3ahYeHB9bW1nTt2pUJEyZw8uTJWudbE7t37660tWRNKBQKli1bRteuXbG2tsbd3Z07d+5I5zds2ICzszM2NjYEBgYif2DrToDk5GRpK8MKx48fx9LSkmXLlqkdry4vlUrFnj17GDt2LI6Ojjg7O+Pt7c2vv/5aqdzLly/Hzc2Nt99+m7CwsErnd+3aRc+ePbGyssLHx4f8/HzpXH5+PlOmTMHKyoqePXtK24YCXLhwgQEDBmBvb6/2TpSWljJs2DAyMyt/SHyWRMTyAhTcV3H5xr0numbfiTyKSv+dv7gFQXi5NLQ1sLBtWum4lrYGdRvov4QSvXw3btygXr16NGjQQDpWXFzMhx9+SEJCAgEBAfz+++/ExsYycuRIlixZwsqVK6W0d+/excXFhX379hEfH0+HDh3w8fFRu4eVlRXJycnSV0WQ9euvvyKTyTh27BhGRkZERUUBkJ6ezm+//YaXl1et6hYREUFoaCgff/wx8fHxHDp0iJEjR3Lw4MFa5fu8rVixguTkZKKiojh16hSLFy9GV1cXKN8vPDw8nA0bNnDo0CEyMjJYsWKF2vWHDx+W9uGG8sBrwYIFdOrUSS1ddXkpFAr8/PzYsmULH3/8MbGxsRw+fJipU6cSFRVFUFAQD+4h06JFC2bMmEH37t0r1enixYvMmzePxYsXEx8fj76+vtpe4yEhIWhraxMfH8/XX3/NF198wcWLFwFYsmQJs2bNIiYmhrVr15KTkwOU/3z79u1LkyZNnvZR14gIKJ8zTU0Z564+eRdRfmEZd4rFWCVBEF48hVJFa9umvNm+kdQgaVBXl56jOqKh+3KG46SeuE7kvN9Y/cluIuf9RuqJ67XOMy0tDTs7O86dOweU78Ps4OBQqfs1ISEBb29vbt68ibW1tdR9/OWXX2Jra8uKFSuwsrJCT08PXV1dunbtyo8//khCQgInTpwAoGPHjgwbNox69eqhra3NuHHj+Pvvv7l9+3a15czIyMDOzg4tLS3s7e1JT08HYMGCBfj7+6Ol9fTza+/evcuKFSuYN28effv2xcDAAG1tbVxcXPD396/yGl9fX5ydnencuTOjRo2SAhqA33//nf79+2NtbU23bt347rvvAMjLy2PSpEnY2tpiZ2fHyJEjUSrL5xK4uLiQkJAAlAdna9eupXfv3lhbW+Ph4VFly1pBQQGRkZHMnz+fZs2aIZPJaNOmjRRQRkdHM3ToUCwsLDA2NsbHx4cdO3ao5XHkyBG1oC4iIgJnZ2feeusttXTV5bVmzRp0dHSIiIjA0dERQ0NDtLW1sbGxXML1vgAAIABJREFU4dtvv6WwsJCdO3dK6QcPHkz37t0xNDSsVK9du3bh4uJCly5dMDQ0ZNq0aRw4cIDCwkKKi4v59ddfmTZtGoaGhtja2uLi4iLlnZGRgaOjI6amprRo0YLMzEyuX7/Or7/+yrhx46r8WT5LIqB8zmQyGXl3S5/qWnmpmEkpCMLLodQAq36t6D+5C+991Jk+H1ph8IY+ypewBEXqiesc3nSGwtvlPT2Ft+9xeNOZWgeV5ubmzJgxg5kzZ3Lv3j1mz57N4MGDK3W/Ojk58e2339KoUSOSk5NZuHAh6enpnDlzhk8++YQ7d+4wZcoU7O3tmTp1Kh9//DE5OTn4+fmxcePGKu998uRJGjZsiImJiXTs/Pnz2Nvb069fP1atWkVZWRkAbdq04dixY8jlcpKSkrCwsODAgQOYmJjQuXPnWj2D5ORkSkpK6NOnT42veffdd9m/fz+JiYm8/fbbauMr58yZQ0hICMnJyfzyyy84ODgA5cGaqakpiYmJxMfH89lnn1W5h3ZERAS7d+8mPDycU6dOERoaip6eXqV0qampaGpqsm/fPpydnenXrx8//vijdP7ixYu0bdtW+t7S0pLc3FwpgL958ya5ubm8/fbbAFy/fp2ff/6ZKVOmVLrX4/IqLi5m27ZtzJs3j9LSUgIDA3F0dMTLy4vAwED++OMPAgMDiYyMrNGzvXjxIpaWltL35ubmaGtrc/XqVa5evYqmpiYtW7aUzrdt25ZLly4BYGFhQVxcHFlZWVy/fp3m/6+9O4+rsloXOP7bA8iogFMaqKmEKJoYpqZYIIoDplbqzVLLIUsrreympokDWnbS1IPYcK4fDI+3YyWDEwmSolEKck+G4UAqDjgEmGymzR7uH1z3FUFk3ht8vp+Pn+R91/u+z4Mmz17rXWu5uRESEsJ//ud/YmVlVaXn14YsG1TPjEawsa5Z3a6Qcl8IYUZ6oxGljQolKgwAZlou6OeYU+hKyo7Y6Er0/Bxzikf7Plyre0+YMIEDBw4wYcIEoLS3qUox/fwzw4YNQ6lU8vnnn+Pg4EBiYiKnT5/mpZdewmg04unpWeY9yduuXr3KsmXLykyU6du3LzExMTz88MOcOXOGt99+G7VazaxZs3jqqadITk7mueeeo3fv3owcOZKXX36ZLVu2sG7dOpKTk3F3d2fRokVYW1tXK/+bN2/i7OxcrV7O559/3vT7N998k759+5KXl4ejoyNqtZqzZ8/SrVs3WrRoQYsWLQBQq9XcuHGDK1eu0LFjR3x8fCq8944dO3jvvfdMvYR3FnJ3unr1Knl5eZw/f574+HjOnz/Pyy+/TKdOnRg4cCAFBQU4ODiY2js6lu4nn5+fj7OzMwcPHsTX19dU1K5cudLU83e3yu51+0OAnZ0d//znP7l27Rr79+8nNzeXF154gbFjx9K2bdsy70FWpqCgwHT/2xwcHMjPz0elUpWJ43Ys+fn5ALz//vsEBwfz559/snDhQo4fP469vT2urq68/vrr5OXl8eKLLzJixIgqxVJdUrLUM4PBgPvD1X/nSKWE5rYy01sIIW73TFb1eHVNmDCB06dPM3ny5CoXZNnZ2bRt2xYo7S0bOXIk1tbWeHl50bVrVwCysrJMbW7Lyclh2rRpTJo0iaCgINNxNzc33NzcUCqVeHh4MGfOHGJjY4HSka758+cTExPDihUr+OKLL/iP//gPTpw4wW+//UZERAQlJSV899131c7dycmJ3NxcU2/o/ej1ev72t78REBBAnz598Pf3BzD1/G3YsIGDBw/i5+fHSy+9RGpqKgDTp0+nY8eOTJs2jSFDhvDFF19UeP+rV6/SoUOH+8Zxu9dyzpw52NjY0K1bN0aNGsXBgwcBsLOzKzN56fbvbxeMdw53HzhwgPz8fEaOHFnhsyq7191/D4YMGYKDgwNubm6m3mONRoOdnd19c6roWbevt7e3r/QcwMMPP8yXX37Jzp07GTJkCOvXr+f999/n448/ZuTIkYSFhfHRRx9VubitLiko65nRCO1crGhuV73i0MejOY7le/mFEOKB4+Bc8Yfyex2vjvz8fFatWsXzzz/Pxo0bq/zD1tnZ2TTp4dFHH2Xv3r1otVrS0tI4e/YsN27cYOXKlUycONF0zV9//cW0adPw9/fn9ddfr/T+CoWizESO206dOkVqaioTJ07k1KlT9OjRA4VCQc+ePTl16lQ1Mi/l7e2NtbU1cXFxVWofExNDfHw8W7ZsISUlhQMHDgCYYu3VqxdhYWH89NNPBAQEMG/ePKC0l23BggXEx8cTFhbGli1bSEpKKnf/hx56iMzMzPvGcXtY+M5h8zt/7+7uXub7kZ6eTqtWrXB2dqakpISjR4+aln9KSkrit99+Y+DAgQwcOJA9e/awdetW059RZfe6++9BfHw8Go2GixcvkpKSwq1bt1i2bBnPPfdcFb67pc9KT083fX3x4kVKSkro1KkTnTp1Qq/Xc/78+TKx3P4Ac6fQ0FAmTJhAq1atOH36NF5eXjg6Olb5+1sTUlA2AHsrI6MHtKpye4UCAn1cwGC4f2MhhGji+o/2QG1V9kO52kpF/9Ee97ii6kJCQvDy8iIkJISnn36apUuXVum6fv36sX//foxGI7NmzSIvL4+nn36a0NBQBg0axKpVqxg/fjzDhg0DSnuSpk+fTp8+fSpc0/HgwYP8+WfpQvIZGRls2rSJIUOGlGljNBpZsWIFixcvRqlU4urqSkpKClqtlmPHjuHm5lbt/B0dHXnrrbdYvnw5cXFxFBYWUlJSwsGDB1mzZk259vn5+VhbW+Ps7ExhYSFr1641ndNqtURHR5OXl4eVlRX29vYolaVlRkJCAhcuXMBoNOLo6IhKparwHcrx48ezfv16zp8/j9FoJD09vcKJSx06dMDHx4fNmzej1WrJyMhg9+7d+Pn5ATBmzBi+/fZbzp49y61btwgLC2PcuHEApKSk4OHhYRo+njt3LrGxsURGRhIZGYm/vz/jx49n9erV971X7969+eWXXygqKuL555+nbdu2DBs2jA8++IBBgwYRGhpK7969mTp1qin2kpISiouLMRqN6HQ6iouL0etLX+kYPXo0CQkJJCcnU1BQwPr16xk6dCgODg7Y2dkxdOhQNmzYQEFBASkpKcTHxzNmzJgy35uzZ89y9OhRXnjhBQBcXV35+eef+fPPPzl//ny9zfaWdygbgF5v5MkeLTh7uYCkk7cqbasAZoxoRyt7QObkCCGE6T3Jn2NOocktxMHZlv6jPWr9/mRcXByJiYmmtfwWLFjA2LFjiY6O5plnnqn02k6dOtG1a1c+//xzXnvtNUJDQ8uc1+l0Zd5L3L9/PydOnODs2bNlZgjv3r2b9u3b8/PPP7Nw4UIKCgpo2bIlzzzzDLNmzSpzz++++w53d3e8vLwAGDZsGPv372fAgAH07t27TG9odUybNo1WrVqxadMm5s+fj729PT169OC1114r13bs2LEcPnwYX19fnJycmDt3Ltu3bzedj4qKYsWKFej1eh555BE++eQTAC5cuMCKFSvIycmhefPmvPDCC6YJO3d65ZVX0Gq1TJs2jdzcXDp37lzue3vb2rVrWbRoEf369cPFxYW5c+cyYMAAoHTi0IwZM5gyZQpFRUUEBgby1ltvAaXF+52zux0cHMq8m2hjY4OtrS1OTk73vZeDgwNBQUGsWrWK5cuXm4rQ2+7+ewCwZMmSMn8HNm/ezOrVq3n22Wdxd3dn2bJlzJ8/n5s3bzJgwIAy91y6dCmLFi3iySefxMnJieDgYNzd3cvcf9myZXzwwQeoVKUfwt59913eeecdPvvsM1577TVat25d4fezthTGivrUm6jsbI3Z9qBt3dqRrBsFJP2ex65fsiksLt/72MbJiklD2vJIaysUxsbTO9m6tSM3buSZO4x6Ibk1TpKbZWnd2vH+jRohjUbDjBkz6Ny5My+//DJdunThr7/+Yvfu3URFRfGvf/3L1ENnTt9//z1Hjx7lo48+MncoFmHkyJFs2LChwqHimtDpdMydOxeDwcDrr7+Op6cnRUVFxMfH89VXX/HNN99UONmnqZEeygakRs/TXg74POpA5g0tJ87lU1Csx8leTV8PR1o6qrBRGU1rcwkhhLBcDg4ObN26lW3btrFgwQIuXryIvb09gwcPZv369RZRTIqytFotY8eOrbNiEkpnsG/cuJHvv/+e1atX88cff2BlZUW/fv1Yu3btA1FMgvRQNpi7exUUCgUqlRKFgv97j6LxFpGNscekqiS3xklysyxNtYeysfj999+5fPkyAQEB5g5FNGHSQ2kmpUWk7IQjhBCifnl6euLp6WnuMEQTJ/3xQgghhBCiVqSgFEIIIYQQtSIFpRBCCCGEqBV5h7KeKRRgjZLiaxps8krAaEShUoK9Gq2VAr3+gZkTJYQQQogmSgrKemSDAmN2EXnJWeRkaTDcUTwqbdQ4erfFzt2FImuF2WafCyGEEELUlhSU9cQWBZqfLlGQngOAUlV2iylDkY6/ki6Td/wqrcY8SnFzKykqhRBCCNEoyTuU9aAZCvKTrpiKycoYivXc2HkKm0I9FWxrKoQQ4g4xMTE8++yzeHt7M2jQIGbMmEFycjIeHh6mvbABwsLCKjw2ffp009cXL16kW7duFe7f7eHhQe/evfH29sbX15fVq1eb9lsGmDx5Mj179sTb25t+/frxxhtvcP369Upj/+qrrwgKCsLb2xt/f3+++uqrMuf9/f3p1asX3t7eeHt7M23aNNO5pKQk/P39GThwILt37zYdv3XrFuPGjUOj0VThu9ewNm7cWOG+5aJpkoKyHihvacn//c/7N/w/xhIDN3+8gDVSUQpRV5RqJXprKzQqFYVWarBWo1TK/2ON2ZYtW1i1ahWvvfYaR44cISEhgUmTJhEfH0/Hjh05duyYqW1ycjKdO3cud6xv376mr6OiomjRogV79+5Fq9WWe15UVBSpqalERESwZ88evvvuuzLnP/zwQ1JTU4mNjeXWrVvl9nG+m9Fo5OOPP+bYsWN89dVXbNu2rUxxCKX7OqemppKamsp//dd/mY6vWrWKzZs3849//INly5aZittPP/2UmTNnltmLWghzkIKyjlkrFeQdv1rt64ova1AVyULnQtQFjVbPv//S8t+n/+T7sznsOJ3NznM3yUWJUiX/7DVGeXl5bNiwgQ8//JBhw4ZhZ2eHlZUV/v7+vP/++/j4+JCcnAyAXq8nLS2NKVOmlDmWmpqKj48PUFrcRUZGMnfuXNRqNQcOHLjnszt27EifPn34/fffKzzv5OREYGAgZ86cqTSHmTNn0qNHD9RqNZ07d2bIkCEcP368SvkXFBTw6KOP0q1bN6ysrLh58ya//vorly5dYuTIkVW6B8Bbb73FwIEDefzxx3nxxRfLxHzw4EFGjhxp6pX9xz/+AUBOTg6zZs3Cx8eHJ554gkmTJpm2CL527Rpvvvkm/fv3x9/fn61btwJw6NAhPv/8c/bu3Yu3tzfPPPNMlWMUjZP8y1rH1MUGCs/drNG1BaeyUavlj0SI2lBaqfglK4/U6/ncuYhCnlZPzB85FEhB2SilpqZSXFzM0KFDKzzft29fU2/kyZMn6dKlCwMGDChzTKfT0atXLwBSUlK4evUqo0aNYsSIEURGRt7z2RkZGaSkpNCxY8cKz+fk5BAbG1ut3WiMRiPJycnl9pSeP38+/fv3Z9q0aaSnp5uOt2zZkvT0dNLT01EoFDRv3pyQkBAWL15c5WcCDB48mNjYWJKSkujevXuZIekPPviA5cuXk5qayq5du+jfvz9Q2jPctm1bkpKSOHLkCO+88w4KhQKDwcDrr7+Oh4cHhw4dIjw8nPDwcBITExk8eDCzZs1ixIgRpKamEh0dXa04ReMj/7LWMUOxHmo4t6bkeoEMegtRS1qFgt/+zK/wnMEIKdfyUVqpGjgqUVs3b97E2dkZtbriuaR9+/blzJkz3Lp1i5SUFHx8fOjUqRM5OTmmY4899hjW1tYA7Ny5k8GDB9OiRQuCgoJITEwkOzu7zD3HjRtH7969GTlypKln7k4rV67Ex8eHMWPG0Lp1axYuXFjlfDZu3IjBYOC5554zHfvkk084cOAACQkJ9OvXj+nTp3Pr1i0Ali1bRkhICEuWLOGTTz5h+/btDBgwgOLiYqZPn87kyZM5evTofZ/7/PPP4+DggLW1NW+++Sbp6enk5ZXuDa9Wqzl79iwajYYWLVrQo0cP0/EbN25w5coVrKys8PHxQaFQcOLECXJycnjjjTewtrbGzc2NCRMmsGfPnip/H0TTIbO8LYnBiEImegtRKwU6I5UtmJB5qxh9W3v58NbIODk5kZubi06nq7CodHV1pW3btiQnJ3Ps2DEmTpwIQJ8+fUzHbr8/WVRUxL59+1i5ciUA3t7etGvXjpiYGF5++WXTPXfu3EmHDh3Yu3cvn376KQUFBaaCFGDx4sWMHz++2rlEREQQGRnJP//5zzL3e/zxx02/nzVrFjt37iQ5ORl/f388PT35+uuvAbh+/TofffQR33zzDS+99BKLFi2iTZs2vPTSSyQkJKC4xwxPvV7PunXr2LdvHzk5OSiVpX1Kubm5ODo6smHDBsLCwvj000/x8PDg3Xffxdvbm+nTp/P3v//dNElo4sSJvPrqq1y+fJnr16+bXiO4/Yw7vxYPDumhrGOKWgxZKx2sMcqkASFq5X6rJajl/7FGydvbG2tra+Li4u7ZxsfHh2PHjvE///M/9OnTBygt0o4dO0ZKSoqpoNy/fz8ajYZly5YxcOBABg4cyLVr1yoc9lYoFIwcOZLevXsTGhpa6zy+/fZbvvjiC8LDw3nooYcqbatQKDAay386Wr16NfPmzcPGxobTp0/j5eWFq6srOp2OnJx7ry4SExNDfHw8W7ZsISUlxfTe6O1n9OrVi7CwMH766ScCAgKYN28eAA4ODixYsID4+HjCwsLYsmULSUlJtGvXDldXV5KTk02/UlNT+fLLL03xiweHFJR1zGCjwqqlbY2ute/RmpISmZgjRG3Yq5VYq+79g6ybiy2q/5tQIBoPR0dH3nrrLZYvX05cXByFhYWUlJRw8OBB1qxZA5QOe0dFRdGmTRvTrOfHH3+cqKgoNBoNvXv3BiAyMpLnnnuOmJgYIiMjiYyMZPv27aSnp3Pq1KkKn//qq6+yY8cObty4UeMcoqOjWbduHVu2bMHNza3MuStXrpCSkoJWq6W4uJivvvqK3NxcU2F825EjRyguLsbPzw8o7Zn9+eefOXPmDFqtFicnp3s+Pz8/H2tra5ydnSksLGTt2rWmc1qtlujoaPLy8rCyssLe3t7Ug5mQkMCFCxcwGo04OjqiUqlQKBT06tULe3t7vvjiC4qKitDr9Zw+fZpff/0VKH3v8/Lly6YJPKJpk4KyjpUoofkT7at9ncrRGkVz6/s3FEJUSq3T8ZRriwrP2amVdHexxaCTH3CN0bRp01iwYAGbNm1iwIABPP3002zbto2AgACgtKDMzs4uM3Ts6elJUVERPXr0wNbWlmvXrpGUlMTUqVNp3bq16ZeXlxe+vr73nJzj4eGBj4+PaeZzTXz22WfcvHmT559/3rTW5IcffgiUFnvBwcE88cQTDB48mMTERL788kucnZ1N12u1WtasWcMHH3xgOrZkyRKCg4N55ZVXWLp0KSrVvd8PHjt2LO3bt8fX15dRo0aZCuzboqKi8Pf3p0+fPvz3f/83n3zyCQAXLlzglVdewdvbm4kTJ/LCCy/Qv39/VCoVmzdvJj09nSFDhtC/f38WL15sWhNz+PDhAPTr149x48bV+PsmGgeFsaL+9CYqO1vTILvR2BoV5ESeQpdbbDqmVCnKbL14t5ajuqJvb9co9/Zu3dqRGzfyzB1GvZDcGqfmLvZcvFnEz1l5XCsoQa2Abi3teKyVHWqtrsJhxMaiMf65tW7taO4QhBD1TCbl1IMipZFWox/lxs5T6PPKL5Z7N6fBbhgfapzFpBCWqJlKiZNRz3DX5uiVChSAWq9HX1xS00UYhBBCVEIKynpgNEKBlYLWz3Uj79gV8tOzK2xn5WJLC183DC1tKGnEPSZCWCKjESjRmd7rkbeTRUMYNWoUV65cKXd82bJlDbK4d3R0dIVbSbZv377crjxC1CUZ8q5nViolVsUGDNcLKLx0C0OJAZWjFbZdXDDaq9EqafCY6lpjHIKrKsmtcZLcLIsMeQvR9EkPZT0r0RsoUUPrx9qi6+CIQlG6REOhzgAYQeYGCCGEEKKRk4KyAel0MugmhBBCiKanwQrKc+fOsWDBAm7evImTkxMff/wxnTp1KtNGr9ezcuVKEhMTUSgUvPrqq6ZdCLKzs1m4cCFZWVnodDr69evH4sWL77kNlxBCCCGEaBgNtg7l0qVLmTRpErGxsUyaNMm09tadYmJiyMzM5IcffuCbb75h48aNXLp0CYDNmzfTpUsXYmJiiI6OJi0tjR9++KGhwhdCCCGEEPfQIAVldnY2J0+eJCgoCICgoCBOnjxZbouoPXv2MH78eJRKJS4uLgQEBLBv3z6gdAun/Px8DAYDWq2WkpIS2rZt2xDhCyGEeMDNnz+fQYMG0adPHwIDA9mxY0eZ80lJSQwfPpzHHnuMyZMnc/nyZTNFKoR5NEhBmZWVRdu2bU0r+KtUKtq0aUNWVla5du3b//8uM+3atePq1asAzJ49m3PnzjFo0CDTrzt3QxBCCNG06XQ6bt68iU6na/Bnz5o1iwMHDnD8+HE2bdrEZ599xm+//QZATk4Ob7zxBnPnzuXo0aN4eXnx9ttvN3iMQphTo3kBcd++fXh4eBAeHk5+fj4zZ85k3759pq2dqqJlS4d6jPD+mvLSGZJb4yS5NU5NObeKZGdnExoaSmxsLEajEYVCQWBgIHPmzKFly5YNEoO7u7vp9wqFAoVCQWZmJl5eXuzfvx93d3dGjBgBwJtvvkn//v3JyMigS5cuDRKfEObWIAVlu3btuHbtGnq9HpVKhV6v5/r167Rr165cuytXrtCrVy+gbI9lREQEq1atQqlU4ujoiL+/P7/88ku1CkpzrEN5W2NcO66qJLfGSXJrnBpjbrUpgLOzs3nxxRfJzc1Fr///lTJ2797NTz/9xLZt2xqsqAwODmbnzp0UFRXRvXt3nnrqKQDOnDmDh4eHqZ2dnR0dOnTg7NmzUlCKB0aDDHm3bNkST09Pdu3aBcCuXbvw9PTExcWlTLvhw4ezY8cODAYDOTk5xMXFERgYCICrqyuHDh0CQKvVkpSUVOYToxBCiKYnNDS0XDEJpauC5ObmEhoa2mCxBAcHc/z4cbZt28bQoUOxtrYGoKCgAEfHskWzg4MD+fn5DRabEObWYLO8g4ODiYiIIDAwkIiICJYtWwbAzJkzOXHiBABjxozB1dWVYcOGMWHCBObMmYObmxsAixYtIiUlhdGjRzN27Fg6derEhAkTGip8IYQQDUyn0xEbG1uumLxNr9cTGxtb63cqJ0+ejIeHR4W/XnjhhTJtVSoVPj4+XL16le3btwOlPZIajaZMu/z8fOzt7WsVlxCNSYO9Q9mlS5dys+IAvvzyS9PvVSqVqdC8W4cOHdiyZUu9xSeEEMKyaDQa7rc7sMFgID8/nxYtWtT4OV9//XW1r9Hr9WRmZgKl71fu3LnTdK6goIDMzEy6du1a45iEaGwarIdSCCGEqA4HBwcUCkWlbZRKZb33BGZnZ7N7927y8/PR6/UkJiaye/duBgwYAMDQoUM5c+YMsbGxFBcXExoaioeHh7w/KR4oUlAKIYSwSGq1msDAQNOSc3dTqVQEBgbW+45pCoWC7du389RTT9G3b1/WrFnDokWLGDJkCAAuLi5s3LiRdevW0bdvX3799VfWrl1brzEJYWkUxvuNJzQhMsu7fkhujZPk1jg1xtzqY5a3SqXC2dm5QWd5CyHuTXoohRBCWKyWLVuybds2Ro0aRbNmzbCysqJZs2aMGjVKikkhLIj0UDaQxtirUFWSW+MkuTVOjTG3ulqIXafTmWZP1/cwtxCieuT/SCGEEI2CWq2u1WxuIUT9kSFvIYQQQghRK1JQCiGEEEKIWpGCUgghhBBC1IoUlEIIIYQQolakoBRCCCGEELUiBaUQQgghhKgVWTZICCGERTt58iQREREkJiZSXFxMs2bN8PX15aWXXqJ79+4NEkNERATff/89p0+fJigoiI8++sh07tKlSwwZMgQ7OzvTsRkzZjBnzpwGiU0ISyAFpRBCCIuk0+lYtmwZCQkJaLVaDAYDAIWFhcTFxXHo0CH8/PxYunRpvS903qZNG2bPnm0qaity7NgxWXBdPLDkb74QQgiLdLuYLCoqKnfOYDBQVFREQkICACtWrKjXWIYNGwbAiRMnuHbtWr0+S4jGSN6hFEIIYXHS0tLuWUze6XZRefLkyQaK7N78/PwYPHgwCxcuJCcnx9zhCNGgpKAUQghhcbZt24ZWq61SW61WS0RERD1HdG/Ozs58++23JCQk8P3335Ofn897771ntniEMAcZ8hZCCGFxEhMTTe9M3o/BYCAxMbHGz5o8eTJHjx6t8FyfPn3Yvn17pdfb29vTs2dPAFq1asWSJUsYNGgQGo0GBweHGsclRGMiBaUQQgiLc6+JL3XV/k5ff/11ja+tiEKhAMBoNNbpfYWwZDLkLYQQwuI0a9asXttXl06no7i4GIPBgF6vp7i4GJ1OB8C///1v/vjjDwwGA7m5uaxcuZInnngCR0fHeo1JCEsiBaUQQgiL4+vri1JZtR9RSqUSX1/feo0nLCyMXr168cUXXxAdHU2vXr0ICwsD4OLFi8yYMYM+ffowevRorK2tWbt2bb3GI4SlURgfoD757GwNBoN50m3d2pEbN/LM8uz6Jrk1TpJb49QYc2vduvo9dSdPnuTVV1+97yxvABsbG7788ks8PT1rEp4Qog4mBYuWAAASLUlEQVRID6UQQgiL0717d/z8/LCxsam0nY2NDX5+flJMCmFmMilHCCGERVq6dClAuZ1yoHSY29ra2rRTjhDCvKSgFEIIYZHUajUrVqywiL28hRCVk4JSCCGERevevTurVq0ydxhCiErIO5RCCCGEEKJWpKAUQgghhBC1IkPeQgghLN6FCxdIS0ujoKAAOzs7evToQceOHc0dlhDi/0hBKYQQwiIZjUYSEhIIDw/n7NmzqFQq9Hq96b9du3Zl6tSp+Pn5mbY7FEKYhwx5CyGEsDglJSUsWLCApUuXkpaWRnFxMQUFBWX+m5aWxtKlS1mwYAElJSU1ftbNmzeZM2cOvXv3xs/Pj5iYmDrMRIgHgxSUQgghLIrRaGTx4sUcPnyYwsLCStsWFhZy+PBhlixZQk03flu+fDlWVlYcOXKETz75hODgYM6cOVOjewnxoJKCUgghhEVJSEjgp59+ori4uErti4uLOXLkCD/++GO1n1VQUMAPP/zA3Llzsbe3x8fHB39/f6Kioqp9LyEeZFJQCiGEsCjh4eH37Zm8W2FhIeHh4dV+1vnz51GpVDzyyCOmY926dePs2bPVvpcQDzIpKIUQQliMCxcu1LiYO3PmDBcuXKjWNQUFBTg4OJQ55ujoSH5+fo1iEOJBJQWlEEIIi5GWloZKparRtSqVirS0tGpdY2dnh0ajKXNMo9Fgb29foxiEeFBJQSmEEMJiFBQUoNfra3StXq+noKCgWtd06tQJvV7P+fPnTcfS09Pp2rVrjWIQ4kElBaUQQgiLYWdnV6seSjs7u2o/b+jQoWzYsIGCggJSUlKIj49nzJgxNYpBiAeVFJRCCCEsRo8ePWrVQ9mjR49qX7d06VKKiop48skneffddwkODsbd3b1GMQjxoJKdcoQQoglSKkt3jjEYarY2o7l07NiRrl27VvtdSAB3d/cabcfo5OTEpk2bqn2dEOL/SQ+lEEI0JWo9Bc1yOalL4Yz+BMU2t1DUbATZbKZOnYqtrW21rrG1tWXq1Kn1FJEQ4n6kh1IIIZoIhdrImZKTRF34HiOlPZNqhZqXOrxMG6Vbo+mt9PPzIzY2lsOHD1dpcfNmzZoxcOBAnn766foPTghRIemhFEKIJqJIrSEmK9JUTALojDqisr6jxKp6C4Wbk0KhYOXKlfj6+t63p9LW1hZfX19WrFiBQqFooAiFEHeTHkohhGgi8nR/oTeWn9CSq82lyFiAPTZmiKpmrKysWL16NT/++CPh4eGcOXMGlUqFXq83/dfd3Z2pU6fy9NNPSzEphJlJQSmEEE2EtbJZhcdVChVqReP7516hUODn54efnx8XLlwgLS2NgoIC7Ozs6NGjR40m4Agh6kfj+xdGCCFEhRxogautG5cKL5Y53sepL7YGRwxmiqsudOzYUQpIISyYFJRCCNFEKLXWPN/+BX7KPci/b/4PVkor+rkM4DHHxzEUNd4hYY1Gw759+8jIyODWrVs0b96cLl26MHz48HL7cAshzEMKSiGEaELURXY87TiCJ52eQoECK50dhqLGMbv7bhkZGWzdupW4uDgUCgVFRUWmczY2Nqxbt46AgACmTJlCly5dzBipEEJmeQshRBNj0IG62A5VsS0GfeMsJvfu3cvUqVPZu3cvxcXFZYpJgKKiIoqLi8u0E0KYjxSUQgghLMqePXsICQmhqKgIg6HyNz8NBgNFRUWEhITUuKiMiIjg2WefxcvLiwULFpiOX7p0CQ8PD7y9vU2/QkNDa/QMIZo6GfIWQghhMTIyMli1alW5Hsn7uV1UPvroo9Ue/m7Tpg2zZ88mMTGxwoXUjx07hlotPy6FqIz0UAohhLAYW7duRavV1ujakpIStm7dWu3rhg0bRkBAAE5OTjV6rhBCCkohhBAWQqPREBcXd99h7nvR6/XExcWh0WjqNC4/Pz8GDx7MwoULycnJqdN7C9FUSEEphBDCIuzbt6/WO94oFAr27dtXJ/E4Ozvz7bffkpCQwPfff09+fj7vvfdendxbiKZGCkohhBAWISMjo9rvTt6tqKiIjIyMOonH3t6enj17olaradWqFUuWLOHw4cN13gMqRFMgBaUQQgiLcOvWrTq5T15eXp3c5263e0+Nxsa5FJMQ9UmmrQkhhLAIzZs3r5P7ODo6Vqu9TqdDr9djMBjQ6/UUFxejUqlIS0vD0dGRTp068ddff7Fy5UqeeOKJat9fiAeBFJRCCCEsQpcuXbCxsanVsLeNjU21lw0KCwvj73//u+nr6Oho3njjDR555BHWrl1LTk4ODg4OPPnkk6xdu7bGsQnRlCmMD1DffXa2BoPBPOm2bu3IjRv1MwxjbpJb4yS5NU6NMbfWravWo6fRaAgMDKxwLciqatasGbGxsbLHtxANTN6hFEIIYREcHBwICAhAqazZjyaVSkVAQIAUk0KYwQM15K1U1m45isb+/PokuTVOklvj1JRzmzJlCvHx8TUa9raysmLKlCn1EJUQ4n4eqCFvIYQQlm/v3r2mvbyrysbGhsWLFzN8+PB6jEwIcS8PVA+lEEIIyzdixAgAQkJCKCkpQa/X37OtSqXCyspKikkhzEx6KIUQQlikjIwMtm7dyv79+9Hr9WUKS5VKhUqlYujQoUyZMqXaM7uFEHVLJuUIIYSwOHq9nkuXLnHx4kWg/GLit7++dOkSly5dqrQXUwhR/6SHUgghhEXRaDTMmzePU6dOUVhYeN/2tra2eHh48Nlnn8kMbyHMRApKIYQQFkOj0TB16lSysrLQarVVvs7a2pp27doRHh4uRaUQZiBD3kIIISyCXq9n3rx51S4mAbRaLVlZWcybN6/aw9/z589n0KBB9OnTh8DAQHbs2GE6l5SUxPDhw3nssceYPHkyly9frta9hXhQSEEphBDCIhw+fJhTp05Vu5i8TavVcurUKY4cOVKt62bNmsWBAwc4fvw4mzZt4rPPPuO3334jJyeHN954g7lz53L06FG8vLx4++23axSbEE2dFJRCCCEsQnh4eJXemaxMYWEh4eHh1brG3d0da2trABQKBQqFgszMTPbv34+7uzsjRoygWbNmvPnmm6Snp5ORkVGrGIVoiqSgFEIIYXYXL17k1KlTdXKv33//nUuXLlXrmuDgYB577DFGjBhB69ateeqppzhz5gweHh6mNnZ2dnTo0IGzZ8/WSZxCNCVSUNbSuXPnmDhxIoGBgUycOJHz58+Xa6PX61m2bBkBAQEMHTq0zPs52dnZvPrqq4wePZoRI0YQHByMTqdrwAzurba53bhxg9dff92UW1RUVANGX7mq5Hb48GGeffZZvLy8+Pjjj8ucqyxvc6ptXpWdM7fa5hYaGsqoUaMYPXo0zz77LImJiQ0U+f3VNrfvvvuO0aNHM2bMGEaPHs3WrVsbKPK6s2vXrjpb+sdgMBATE1Ota4KDgzl+/Djbtm1j6NChWFtbU1BQgKOjY5l2Dg4O5Ofn10mcQjQlUlDW0tKlS5k0aRKxsbFMmjSJDz/8sFybmJgYMjMz+eGHH/jmm2/YuHGj6dPz5s2b6dKlCzExMURHR5OWlsYPP/zQ0GlUqLa5ffTRR3h5eRETE8O2bdtYt24dWVlZDZ1GhaqSm5ubGyEhIUyfPr3cucryNqfa5lXZOXOrbW69evXi22+/JSYmhlWrVvH222/XaL/o+lDb3AIDA4mOjiYqKort27ezZcsW0tPTGyL0OnPp0qU6+zCt0+lqNHlGpVLh4+PD1atX2b59O3Z2dmg0mjJt8vPzsbe3r5M4hWhKpKCshezsbE6ePElQUBAAQUFBnDx5kpycnDLt9uzZw/jx41Eqlbi4uBAQEMC+ffuA0vd18vPzMRgMaLVaSkpKaNu2bYPncre6yC09PR1fX18AXFxc6NatG3v37m3YRCpQ1dw6duyIp6cnanX5HUory9tc6iKvys6ZU13k5uvri62tLQAeHh4YjUZu3rxZ/8HfR13k5uDggEKhAKCoqIiSkhLT141FXl6exdxPr9eTmZmJu7t7mcK8oKCAzMxMunbtWhchCtGkSEFZC1lZWbRt2xaVSgWUfrpt06ZNuV64rKws2rdvb/q6Xbt2XL16FYDZs2dz7tw5Bg0aZPr1+OOPN1wS91AXufXo0YM9e/ZgNBq5ePEiqampXLlypeGSuIeq5na/e9wrb3Opi7wsVV3nFhkZSYcOHXjooYfqMswaqavc4uPjGTVqFH5+fsyYMaPMu3+Nwd1Dyw11v+zsbHbv3k1+fj56vZ7ExER2797NgAEDGDp0KGfOnCE2Npbi4mJCQ0Px8PCQbR6FqIAUlGa2b98+PDw8OHz4MIcOHSI5OdnsPV11ZcGCBfz555+MGTOGkJAQBgwYYPqhKYS5HD16lPXr1/Ppp5+aO5Q6NWTIEHbv3k1sbCxRUVH88ccf5g6pWlxdXeusZ1ytVvPwww9Xqa1CoWD79u089dRT9O3blzVr1rBo0SKGDBmCi4sLGzduZN26dfTt25dff/2VtWvX1kmMQjQ1ljWu1ci0a9eOa9euodfrUalU6PV6rl+/Trt27cq1u3LlCr169QLK9m5FRESwatUqlEoljo6O+Pv788svvzB8+PAGz+dOdZGbi4sLf/vb30xtZ86caRFDRVXN7X73uFfe5lIXeVmqusotNTWV9957j02bNtG5c+d6irZ66vrPrX379vTs2ZMff/zRYnKsiqCgICIiIurkPUqlUsno0aOr1NbFxYWIiIh7nn/yySebzId8IeqT9FDWQsuWLfH09GTXrl1A6SxFT09PXFxcyrQbPnw4O3bswGAwkJOTQ1xcHIGBgUDpp/JDhw4BpYvyJiUl4e7u3rCJVKAucsvNzTX9cEhKSuL06dOm98TMqaq5VaayvM2lLvKyVHWR26+//srbb7/Nhg0b6NGjR32FWm11kdud6yLm5OTwyy+/8Oijj9Z5rPXJzc2tzobpPT09cXV1rZN7CSGqRvbyrqWMjAwWLFjArVu3aN68OR9//DGdO3dm5syZvPXWW/Ts2RO9Xs/y5ctNuzfMnDmTiRMnApCZmcnSpUv5888/0ev19OvXjw8++MAiJkXUNreDBw8SEhKCUqnE2dmZDz/8EE9PT3OmZFKV3JKTk3nnnXfQaDQYjUYcHR0JCQnB19e30rzNqbZ5VXbO3Gqb23PPPcfly5fLTHpbs2aNRbxrWNvcVq1axZEjR1Cr1RiNRsaPH8/kyZPNnVa1HTx4kMWLF9dqcXNbW1tCQkIYPHhwHUYmhLgfKSiFEEJYBL1ez6xZszh58mSNtl+0trame/fufP755/K+thANTApKIYQQFkOj0TB16lSysrKqVVRaW1vTrl07tm7dKutECmEG8g6lEEIIi+Hg4EB4eDjdu3c3rRt6P7a2tnTv3l2KSSHMSHoohRBCWBy9Xs+RI0cIDw8nPT0dvV5fZga4Wq1GpVLRrVs3pk6dysCBA2WYWwgzkoJSCCGERbt48SK7du3i8uXL5OXl4ejoyMMPP0xQUBBubm7mDk8IgRSUQgghhBCiluQdSiEaqV9++UWWRhFCCGERpKAUwsymT5/O+vXryx2Pi4tj4MCBdbJziBBCCFGfpKAUwszGjRtHdHQ0d799Eh0dzejRoy1ikXshhBCiMlJQCmFmAQEB3Lx5k+TkZNOxv/76i4SEBMaOHUtISAiDBg1i0KBBhISE1GjBZyGEEKI+SUEphJnZ2NgwYsQIIiMjTcf27t1L586diY2N5d///jdRUVFER0dz4sQJNm3aZMZohRBCiPKkoBTCAowdO5bY2FiKi4sBiIyMZNy4ccTExDBnzhxatmyJi4sLc+bMITo62szRCiGEEGVJQSmEBfDx8cHZ2Zm4uDgyMzM5ceIEQUFBXL9+nfbt25vatW/fnuvXr5sxUiGEEKI8edtfCAsxZswYIiMjOXfuHIMGDaJVq1a0adOGK1eu4O7uDkBWVhZt2rQxc6RCCCFEWdJDKYSFGDt2LElJSfzrX/9i7NixAIwaNYqwsDBycnLIyckhNDSU0aNHmzlSIYQQoizpoRTCQri6uuLt7U16ejpDhgwBYPbs2eTn5/PMM88AMHz4cGbPnm3OMIUQQohyZOtFIYQQQghRKzLkLYQQQgghakUKSiGEEEIIUStSUAohhBBCiFqRglIIIYQQQtSKFJRCCCGEEKJWpKAUQgghhBC1IgWlEEIIIYSoFSkohRBCCCFErUhBKYQQQgghauV/Ae8tpK3W17P7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 674.225x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme(style=\"white\")\n",
    "sns.set(rc={'figure.figsize':(125,10)})\n",
    "\n",
    "#Load Data\n",
    "adjust_new_risk = new_risk_ret_df.transpose()\n",
    "adjust_new_risk['Portfolio']=adjust_new_risk.index\n",
    "\n",
    "#Plot Seaborn\n",
    "ax=sns.relplot(x=\"Vol\", y=\"Return\", hue=\"Portfolio\", size=CWARP_W_String,\n",
    "            sizes=(50, 400), alpha=.9, palette=\"muted\",\n",
    "            height=6, data=adjust_new_risk)\n",
    "plt.title('Efficient Frontier by CWARP');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__DISCLAIMER:__ \n",
    "\n",
    "_THIS RESEARCH IS BEING PROVIDED FOR INFORMATIONAL PURPOSES ONLY AND SHOULD NOT BE CONSTRUED IN ANY WAY AS A SOLICITATION FOR ANY ARTEMIS FUND, STRATEGY, OR INVESTMENT PRODUCT. NONE OF THE DATA PRESENTED HEREIN REPRESENTS REAL OR HYPOTHETICAL RETURNS ACHIEVED BY ANY STRATEGIES OR INVESTMENT VEHICLES OF ARTEMIS CAPITAL MANAGEMENT LP, ARTEMIS CAPITAL ADVISERS LP, OR ITS AFFILIATES. THIS IS NOT AN OFFERING OR THE SOLICITATION OF AN OFFER TO PURCHASE AN INTEREST IN ANY STRATEGIES OR INVESTMENT VEHICLES OF ARTEMIS CAPITAL MANAGEMENT LP OR ARTEMIS CAPITAL ADVISERS LP. ANY SUCH OFFER OR SOLICITATION WILL ONLY BE MADE TO QUALIFIED INVESTORS BY MEANS OF A CONFIDENTIAL PRIVATE PLACEMENT MEMORANDUM (THE \"MEMORANDUM\") AND ONLY IN THOSE JURISDICTIONS WHERE PERMITTED BY LAW.\n",
    "AN INVESTMENT SHOULD ONLY BE MADE AFTER CAREFUL REVIEW OF A FUND'S MEMORANDUM. AN INVESTMENT IN A FUND IS SPECULATIVE AND INVOLVES A HIGH DEGREE OF RISK. OPPORTUNITIES FOR WITHDRAWAL, REDEMPTION, AND TRANSFERABILITY OF INTERESTS ARE RESTRICTED, SO INVESTORS MAY NOT HAVE ACCESS TO CAPITAL WHEN IT IS NEEDED. THERE IS NO SECONDARY MARKET FOR THE INTERESTS, AND NONE IS EXPECTED TO DEVELOP. NO ASSURANCE CAN BE GIVEN THAT THE INVESTMENT OBJECTIVE WILL BE ACHIEVED OR THAT AN INVESTOR WILL RECEIVE A RETURN\n",
    "OF ALL OR ANY PORTION OF HIS OR HER INVESTMENT IN A FUND. INVESTMENT RESULTS MAY VARY SUBSTANTIALLY OVER ANY GIVEN TIME PERIOD. CERTAIN DATA CONTAINED HEREIN IS BASED ON INFORMATION OBTAINED FROM SOURCES BELIEVED TO BE ACCURATE, BUT WE CANNOT GUARANTEE THE ACCURACY OF SUCH INFORMATION. ANY AND ALL CONTENTS OF THIS RESEARCH  ARE FOR INFORMATIONAL PURPOSES ONLY. NEITHER THE INFORMATION PROVIDED HEREIN NOR THE PROGRAMMING AND QUANTITATIVE REFERENCE MATERIALS PROVIDED SHOULD BE CONSTRUED AS A GUARANTEE OF ANY PORTFOLIO PERFORMANCE USING CWARP OR ANY OTHER METRIC DEVELOPED OR DISCUSSED HEREIN. ANY INDIVIDUAL WHO USES, REFERENCES OR OTHERWISE ACCESSES THIS PAPER, THE GITHUB REPOSITORY, THE WEB-APP FOR CWARP EXPERIMENTATION OR ANY OTHER DATA, THEORY, FORMULA, OR ANY OTHER INFORMATION CREATED, USED, OR REFERENCED BY ARTEMIS DOES SO AT THEIR OWN RISK AND, BY ACCESSING ANY SUCH INFORMATION, INDEMNIFIES AND HOLDS HARMLESS ARTEMIS CAPITAL MANAGEMENT LP, ARTEMIS CAPITAL ADVISERS LP, AND ALL OF ITS AFFILIATES (TOGETHER, \"ARTEMIS\") AGAINST ANY LOSS OF CAPITAL THEY MAY OR MAY NOT INCUR BY UTILIZING SUCH DATA. ARTEMIS DOES NOT BEAR ANY RESPONSIBILITY FOR THEOUTCOME OF ANY PORTFOLIO NOT DIRECTLY OWNED AND/OR MANAGED BY ARTEMIS._"
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