Portfolio_Optimization.ipynb

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    "## An Introduction to Portfolio Optimization in Python\n",
    "\n",
    "In investing, portfolio optimization is the task of selecting assets such that the return on investment is maximized while the risk is minimized.\n",
    "\n",
    "In each of these cases, the task of optimizing assets involves balancing the trade-offs between risk and return, where return on a stock is the profits realized after a period of time and risk is the standard deviation in an asset's value. Many of the available methods of portfolio optimization are essentially extensions of diversification methods for assets in investing. The idea here is that having a portfolio of different types of assets is less risky than having ones that are similar.\n",
    "\n",
    "Finding the right methods for portfolio optimization is an important part of the work done by investment banks and asset management firms. One of the early methods is called mean variance optimization, which was developed by Harry Markowitz and, consequently, is also called the Markowitz Method or the HM method. \n",
    "\n",
    "The method works by assuming investors are risk-averse. Specifically, it selects a set of assets that are least correlated (i.e., different from each other) and that generate the highest returns. This approach means that, given a set of portfolios with the same returns, you will select the portfolio with assets that have the least statistical relationship to one another.\n",
    "\n",
    "For example, instead of selecting a portfolio of tech company stocks, you should pick a portfolio with stocks across disparate industries. In practice, the mean variance optimization algorithm may select a portfolio containing assets in tech, retail, healthcare and real estate instead of a single industry like tech. Although this is a fundamental approach in modern portfolio theory, it has many limitations such as assuming that historical returns completely reflect future returns.\n",
    "\n",
    "Additional methods like hierarchical risk parity (HRP) and mean conditional value at risk (mCVAR) address some of the limitations of the mean variance optimization method. Specifically, HRP does not require inverting of a covariance matrix, which is a measure of how stock returns move in the same direction. The mean variance optimization method requires finding the inverse of the covariance matrix, however, which is not always computationally feasible.\n",
    "\n",
    "Further, the mCVAR method does not make the assumption that mean variance optimization makes, which happens when returns are normally distributed. Since mCVAR doesn’t assume normally distributed returns, it is not as sensitive to extreme values like mean variance optimization. This means that if a stock has an anomalous increase in price, mCVAR will be more robust than mean variance optimization and will be better suited for asset allocation. Conversely, mean variance optimization may naively suggest we disproportionately invest most of our resources in an asset that has an anomalous increase in price.\n",
    "\n",
    "The Python package PyPortfolioOpt provides a wide variety of features that make implementing all these methods straightforward.\n",
    "- Mean Variance Optimization\n",
    "- Hierarchical Risk Parity (HRP)\n",
    "- Mean Conditional Value at Risk (mCVAR)"
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    "There are several many python libraries that suppors portfolio optimization. However, we use PyPortfolioOpt herein.\n",
    "    \n",
    "PyPortfolioOpt can be installed using command:\n",
    "- pip install PyPortfolioOpt"
   ]
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  {
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   "source": [
    "import pandas as pd\n",
    "from pypfopt import EfficientFrontier\n",
    "from pypfopt import risk_models\n",
    "from pypfopt import expected_returns"
   ]
  },
  {
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   "id": "bafb0a6d-3840-4458-a7d8-d7be956f790d",
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   "source": [
    "import yfinance as yf"
   ]
  },
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   "id": "5acac37a-e532-4cbf-8ef3-4681c406571b",
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   "source": [
    "companyList_1 = [ \"PFE\", \"JNJ\", \"GOOGL\", \"FB\", \"AAPL\", \"COST\", \"WMT\", \"KR\", \"JPM\", \"BAC\"]\n",
    "companyList_2 = ['GOOG', 'AAPL', 'BABA', 'AMZN', 'GE', 'AMD', 'WMT', 'BAC', 'GM', 'T', 'UAA', 'XOM', 'RRC', 'BBY', 'MA', 'PFE', 'JPM', 'SBUX']"
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   "source": [
    "Construction of the data table\n",
    "- Here we use the close prices of all the stock prices\n",
    "- Duration 1 year\n"
   ]
  },
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   "id": "deef4905-7681-4aaf-82ea-80566d9b92d3",
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   "source": [
    "historical_prices_df = pd.DataFrame()"
   ]
  },
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   "id": "17bd9d65-a457-4be9-8c80-5b1fa75f362f",
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   "source": [
    "for ticker_id in companyList_2:\n",
    "    ticker = yf.Ticker(ticker_id)\n",
    "    historical_prices_df[ticker_id] = ticker.history('6mo')['Close']"
   ]
  },
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       "                                 GOOG        AAPL       BABA        AMZN  \\\n",
       "Date                                                                       \n",
       "2023-12-28 00:00:00-05:00  141.119415  193.071426  75.597015  153.380005   \n",
       "2023-12-29 00:00:00-05:00  140.769806  192.024185  75.861275  151.940002   \n",
       "2024-01-02 00:00:00-05:00  139.401367  185.152283  73.169769  149.929993   \n",
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       "2024-01-04 00:00:00-05:00  137.883087  181.432098  73.071899  144.570007   \n",
       "\n",
       "                                   GE         AMD        WMT        BAC  \\\n",
       "Date                                                                      \n",
       "2023-12-28 00:00:00-05:00  102.228363  148.759995  52.164497  33.439625   \n",
       "2023-12-29 00:00:00-05:00  101.678658  147.410004  52.190979  33.232353   \n",
       "2024-01-02 00:00:00-05:00  100.340256  138.580002  52.733913  33.459366   \n",
       "2024-01-03 00:00:00-05:00   98.834557  135.320007  52.737221  33.094173   \n",
       "2024-01-04 00:00:00-05:00   99.312561  136.009995  52.227398  33.360664   \n",
       "\n",
       "                                  GM          T   UAA         XOM        RRC  \\\n",
       "Date                                                                           \n",
       "2023-12-28 00:00:00-05:00  35.998123  16.205858  8.95   98.467644  30.686291   \n",
       "2023-12-29 00:00:00-05:00  35.719685  16.244583  8.79   98.261261  30.298109   \n",
       "2024-01-02 00:00:00-05:00  35.848957  16.699585  8.68  100.600334  30.656431   \n",
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       "2024-01-04 00:00:00-05:00  35.292084  16.602776  7.94  100.561028  30.148809   \n",
       "\n",
       "                                 BBY          MA        PFE         JPM  \\\n",
       "Date                                                                      \n",
       "2023-12-28 00:00:00-05:00  76.738998  425.060364  27.941795  168.274734   \n",
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       "\n",
       "                                SBUX  \n",
       "Date                                  \n",
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     "execution_count": 50,
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    "historical_prices_df.head()"
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5ZDQ6qcMDsKZ6F+/+aTVet0xiZhjFeXWERpn49ZMXHjf4URSFz/6+mZJ9dcR1szLx7tb71giCcPraM8xYBCjCaTuXrrnX7ePzlzdjq3Ux7rZsEjPDT2k/az47wMZvDwNgtOhI6R2JxWrAEmLAEmrAHHr0Z1OIvs1BhCwrbP2+kLVfHMTnlTFadIy8oQc9h8b5b+aKovDjh/vYtrSI2PRQrvpdDgbzyStDj3SGPbCpEkmCwZenMejytHYFOOeC1Z8dYFPzvw2oNSs9hsSe8DU1JU0seH4DXpcPc6iBcbf2Ijk7QgyvF4QOJgKUc+Rm+UtxLl3zVZ/s9/evkDQSw67pTs6lye26ESmywpxHf8Le4GbQZakMnJCKwdSxraU1JU0smbuLisONAOoIlUnpRCYFs+rj/WxdUujfNq5bKJfe3pv9Gys4sLECr0dG0khIzbVEkqSeq9Pmob7SgUYnMX56b7oP/GXOl+Ns8vDun1bjsnuJSQ3hukcGtymXSk1pE9/9dwc1JWqTm9GiIyYtlN4XJfxir5UgnGkiQDlHbpa/FOfKNS/Pb+DjFzagKJDYM4zivXUAZI9MYMyUzDYnBDuSLdVg1jH9xYtOKf9GW8g+mU2LCli/MB/Zp75NdQYNXrcMwMCJqexcUYzL7m3zPvVGLZfP6NumTq7nsj1rStn4zWHG/Sab2LQTfwgey+v28dPH+9m9qhSfR/av7zUinpE39ERvPP3UAYJwPhOZZAXhZ3wemSXv7EZRoMeQWC6dns2O5cWsnL+XXStL0EgSo6b0bFNNyoEtlQCk9YvstOAE1FEwgy9LI61vFGs/P0Dxvjo8TnXKhtG/zqTPqES6D4jmi39uwWX3EpkUTN/RiYRGm0FWm4IUpflRVn+OTQvtErlXOlvWhfFkXdgyQeHJ6AxaRk/J5KIbelBdZGP/hgo2f1/A7p9KKd1fz5BJaWQMikHzC2sWE4SuSAQowi+eoiis/HAvtaVNmEP0jLpRDUT6jknCYNLy/dzd7FhRjKSVGHlDjxMGKYqicHCzGqB0zzkz1f5RScFMmtkf2SdTVWRDkiT/HDcxqaHc9KcLcDR6iEoOFn0mOohWqyEmNbR5hFEEi9/eRV25ncWzd7Hms4PkXJpMr+EJnVKjIssKFYca0Bk0mIIMmEP0AYHwiebFEoRfEhGgCF1eY40TR6ObmNS2V9Ufa+uSQnXUigQX39IrIOV65oXxyLLCD+/sYfvSIjSSxIjrM457A6gqtNFY7URn0JDc+8w2k2iab5o/FxxuIji86zatneuSsiKY8ueh7FhexLalRTTWOFk5fx/rFx6i75hE+o5NwhzccSOefpy/l+3LiwPWmYL0mIL1uOwenE1ewmLM9B2TRNaweNHsJPxiiQBFOCPKDtaz9YdC9EYt5hAD0ckhpPSOOG7nUkVW2L26lD2r1ap1gIETUrjwmu5t/vaoKAr7NpTz08f7ATXfRVrfqBbb9RqegOxTWPZeHlt/KESjlRh2bevHObC5AoDU3pHtngRPOHeZgvQMvjydnHEp7FldyubFBTRUOVn/1SE2Lyqg1/B4IhKDQVHwuGWcNjcuh48+oxKISmr7jM5Fe2r8wYk5RI+zyYsiKzibPDibPP7tasvsrJi3l7VfHKT3yAT6jkk654JURVYo2V/HgY0V2Bvd6E06DEYtepMWg0mH3qjFYNJisOgJjTJhjTKjE++584oIUIQz4scF+yjPbwhYp9VpSOoVTrecaNL7Rfnzbvh8Mj/M3c3edeXqhhKgwKbvCrA3uBkzLStgaKyiKLidPlxNHlx2L067h/oKBztXFlNVaAPU5Gj9L0k+bvl6j0xEUdSU55sXFyBpJS68uluLIOVI8063AdGne0mEc5DOoKXP6CSyRyZyYFMFmxcVUFnQ2KLG44jD26u48Q8XtGmiRLfTyw//2wNAn1GJjP51phqc2D3Y6904mzwYLXoMJi2Htlex9YciGiodbPqugC2LC+k+KIb+lyS3q1NwZ1AUBa9bxmX3YA42BEwfUF/p4PCOKsrzGyjOq6Wp3t2ufSdlhTN6SiZhsZaOLrbQBYlRPMJpO9k1b6hy8L8/rkaSYNDlaTgbPRTuqaG+wuHfRpIgPiOMbjnRFO2p4dD2ajQaiSFXpJM1LI6CXTX+WW4ljYQ5RI/eoMXl8OKye/2z3/6cTq8he2QCwydntCnfx7alRaycr87wPHhSGkOv7OZ/rmhPDZ+/vAWNTmL6iyMxtiH3iPDLpigKRXm17FlVisflQ9JIaHUaLCEG8rdX0VDpIK1fFJfP6Nsi2PV5ZGx1Lmy1Tmy1Lg5uruTglkpCIkzc9OcLTjp0XZYVDm2rYuuSwoDJFBN6hHHp9N6tJvjrKD6PzKHtVeRvq8Le4MZl9+Kye3A7vLiavMjN70dziJ7L7u5LfEYYh7ZX8e2/d+DzHh0dZTDr6D4gmsikYDwuHx6nD4/Ti7v5Z7fTi7PJQ0OVE7dDHa2m1WkYckUaA8anBsxqLZycx+WjpqSJ6hKbOlGmBGn9okjIsJ6xjt9iFI/QpezboNaEJGaG+2/4iqJQU9pE/pZKDm6porKgMWDWWq1ew8S7+vibZLJHJGAJMfD93F24mrzYW/nmpdVpMAbp1Pb6ID2pfSLJviihXSna+41NQvbJ/PTRfjZ8dUgNkialo8iKv6mo90WJIjgRADXPTHJWBMmtDNvOHBbHR89v4NC2Kn78cB96k5baUjuNNU5stU7/5Ig/N/bmrDbl1dFoJLrlRNMtJ5rKgka2Lilk34ZySvbVsWTuLq76XU6bh86fjM8nU5xXS2VBI9XFTRTsqsbVdJLh7RI4Gj18/vIW+o5JZNsPRciyQkxaKGl9I4lNDyWxR3ibJmhUFIX6Sgcr5u2lcFcNaz47iNvpY9g13Tvk/H6JjgSw6r+ZjeqSJhqqHPCz73JblxRiDtEz9Kpu9B6ZeHYKexyiBqULWr16NRdddBETJ07kq6++anWbDz74gGnTpvHb3/6Wf/3rXwHPLVu2zD9LMUBMTAwXXXQRL774on925LS0NO6//37uv//+0y7vya75vP+3juoiG2OnZZF9UUIre4CGagf5W6o4uKWSpjoXF9+SRUKPllleZZ+Mo9GDvcGNx+XDaFEDEqNF16Ht05sWHWb1JwcAGHp1N0IiTHz/9i4MJi3TnhnWqWnghV+OY2vkWqPVawgONzYvJrr1jz6t5sOakiYWPLcer0dm1E09/RMvniqnzcOOFcXsWFFMU50r4Lkgq4GeQ+OISAjCaNFjNOswWo4sepBg0Vs7ObStyv+aHkNiueS2XqecvVhRFHYsL2bFPPWaXnFvf1L7RJ76Cf5CleyrZcX8fVQX2Vo8Zw7RE5kYTGRCMC6nl/ytlf5gs/fIBEbe2LNT0yeIGpRzXG5uLrNmzSI3N5eSkhISElre1HNzc3n44Yf597//zUsvvdRqYJCXl0dISAj79u3jrrvu4sorr2Tbtm0t5urpTDWlTVQX2dRveyf44A2NNNP/kuQT9hMBdSRLUJix03N5DByfiiIrrPnsIGs/P+gfKTFwYqoIToQ26zsmkYYqBxWHGwiPCyIiPojQKHXUVXCEEVOQvkOHDEckBDHs2u6snL+PVZ/sJ7lXxCn316gpaeLLV7dgq1UDE3OInqSsCCITg4hNCyWhZ/hJm1guu7sPKz/cx44VxfQZmcjIm3qeVrPMkfQAtaVNbF9ezOK3d/KrBwdiCtY3d6o9f29pdeV2Dm2v4tC2Kn8SSqNFR7cB0UQmBBOZGEREQjCW0MDPL58vky2LC1jz+UF2riyhrsLBVb/r3yVy/Zy//5pdlM1mY/78+WzYsIGysjLmzJnD448/HrBNfn4+q1at4uOPP2bp0qV88skn/PrXv26xr5iYGMLCwoiPj+fPf/4zU6dOZf/+/WRmZp6p02F/c/NOcu+IU54N92wZNDEN2aew7st8PC4fweFG+l984gBKEI4lSRIXXd/jjB6z7+gkDm6ppDivjmXv7+GaBwa2ex9lB+tZ+K+tuJq8WGPMDLk8jYxBsW1qjjmWRqth9JRMhv2qe4cGDyOu60FZfgOVBY3Me2YdoE7nkNI7gl7D4knrF9WptQAn0lDlwGHzoDdo0Rk1GIw6dEYNWp2mw/PXVBXZ2LOmlMPbqwMmLZUkteP/BVeln3QIvFarYdDENCITg1n01k6K82rJ31ZF9wFnf3qH8yNAURTwnHzG2Q6nt6h/Ke3w4YcfkpWVRWZmJtOmTeP+++/nscceC/jDfvvtt5k0aRJWq5Vp06aRm5vbaoByLLNZnULe7W5fr/nToQ7zVYfl9hh84snauqohk9KRNBLblhYxekqmGOYodHmSRuLim3vx3hNrKM6ro/xQQ5tH9jhsbrYuKWTrkkK8bpnY9FCumNm/TaOQTqSjazaO9FH7+o3t1JQ2+bMlH95ezeHt1ZiC9PS8IJas4fFEJ7d9mPepqi62sWNFMQW7amiodLS6jaSR0Bs06I1adUi1SUvPC+JOWmv8c4qiUFnQyMZvDnOwOas1qH2SEnqGkdY3ivT+UYRGmdu137S+UfQbm8TGbw+zfVmxCFDOGI8dnm2970OnerwEDEHteklubi7Tpk0DYOLEidTX17N8+XLGjBkDgCzLzJkzh1dffRWAm266iYceesjfB6Q1paWl/O1vfyMxMfGM1Z4oisKazw5QV25Hp9eQ3r9l/pFzxeDL0hg0MVVk7xTOGaFRZjIGx7B3bTlblxQy/vbeJ9zeVutiy/cF7FxZ7J/rKaV3BBPu7NNlm01Co8zc9KcL/L/XljWxZ3UZeWtKaap3s22pmlgvKjmYXsPj6Tkk7oSBltvpZcfyYkKjzCfM0XSs6mIbG74+xP5NFf7OpxqNhMVqwOP24XXJ/lFLiqymQ3A7fdDcyb/icCOhUSbS+5+435HH7aM4r5bDO6o5vKOaxmqn+oQE3QdEkzEoluTsiNPuvN97VCKbvjtMcV4tNaVNRMS37/7V0brmX955Ki8vj3Xr1vHpp58CoNPpuPHGG8nNzfUHKIsXL6apqYnLL78cgKioKC699FJmz57NM888E7C/pKQkFEXBbrfTv39/Pv74YwyGzu8/cSQ42fSdOmvwiOt7dNkPubYSwYlwrsm5JIW9a8s5sLEC27XdW03kVl/pYNOiw+xZXYrsVe+w0SkhDL4sjfT+UR02CuhMCI8LYtivujP0qnQKd9eye1Up+dsqqSq0sXL+Pn76eD/p/aLIviiB5F4RAe9pRVZYnLuTQ9urAbWGJiU7gu4DokntG9WiebrsYD0bvz0c0AG4+8BoMofGkZgZHvB5J/tkPG4Zr8unDqV2qcOn964rZ9ePJSx7L4/4jDBMQXpqSppwO72YQwzIPpmCXTUU7KimeG9dwPBsjU6i+4AYBl+WRkRCxwURIREm0vpFkb+1ih3Lixl1U88O2/epOLfvGm2lt6i1GWfjuO2Qm5uL1+sN6BSrKApGo5HXXnsNq9VKbm4uNTU1/iYbUGtVtm3bxlNPPYVGc7TddeXKlYSGhhITE0NISOdXcx6xfmG+PzgZdVNP+ozqWkPXBOF8EJ0SQkKPMEr21bF9WTHDfnV0SG5duZ31X+ezb32FP4dQfIaVwZelkZwdcU4H5BqthtQ+kaT2icRp87B3fRm7V5VSVWjjwKZKDmyqJL1/FKOnZPo726/98iCHtlej1aud8BsqHeRvrSJ/axUajURiVjjdB0SjKLDrxxIqCxrVg0nQPSeaIVekE5kYfNzyGM2aFrUbsemhlO6vo7bMzvdzdiF7ZQp31x73vIIjjKT1iSKlTyRJmeGdNsVB39FJ5G+tYs+aUi68pttZ/XJ5fgQoktTuppYzzev18s477/DSSy8xfvz4gOeuueYaPvjgA66//no+//xz5s2bR+/eR6tsfT4fF110EYsWLWLixIn+9enp6YSFhZ2pUwBg108lrP/qEAAjb+xx2sMcBUE4df0vSaZkXx07VxaTnhNFRHwQmxcXsOm7w/4ak5TeEQyamEZCj7CzW9hOYArW029sMv3GJlNV1MiuH0vZubKY/K3qSJfUPpHo9Bp2ryoFYOy0LHpeEEt1cRMHNldwcHMlNSVNFO6qoXBXjX+/Gp1E5gVxDBifQnjcqd1bdHotF9/Si09e3Mjh5pobjUYiKMyIvdGN4lOI72EltXcUqX0iCY+3nJHAMSkrnLBYC3XldvauLaPP6LP3GX5+BCjngIULF1JbW8vtt9+O1WoNeG7y5Mnk5ubidDqJjIzkhhtuaPGHevnll5ObmxsQoJxMcXExW7ZsCViXmppKeHjL/CNt2t/eWpa/lwfAoMtS6TdWjHgRhLMprV8UoVEmGqqcfPz8Rv+0EQAp2REMvbrbKU/Cea6JSgph1E0h9B6ZwA/v7KbicCP71pf7n88Zl0zm0LjmbYOJSgpm6JXdqCu3c2BzBflbq5B9Cj0viCXzwrgOmSAyrpuVC67qxsZvDtFzSCyDLksjNMqsdvpVOCuZciWNRJ9Rify4YB/526rPaoAiErV1EVdeeSWyLLeamG3dunUMHToUSZKYMWNGi8RsoI7+ufnmmykuLmbHjh2MHTuW2tra49agpKWlcfjw4Rbr3549h1tuvQVQK55+HggpioLsU/B5ZLweGa/Hh73JQUHhYbZ/UY+zXqbnBbGM+032OV1NLAi/FNXFNtZ8fpDS/XW47F6CrAYuuqEn3QdGn7fv0SNZVhuqHNgb3BgtOgZcmnLWcn8oitKl/i1cdg8l++tJ7RPZ4UFSexK1iQDlPHJkEi+Py4vbqXbWOt4cNkcYzTosViOSRk1b7WzytHiNx+umuLSQnV81kNA9knG3Zrc7X4IgCJ1LkRUaqp0EhRnQ6cVweeHsEJlkhRaa6l3Y6938PB6VJAm9UU0o5PPIeJw+/0RfgDoZn6PlnBtanQadQYNWr8UnS1gcBm74wxCCgsUso4LQFUkaCWt0+3JjCMLZJAKU84DL7vHPoyFJEnqT1p8WWmcIzG6oKAqo/yF7ZewN6jTvoM48agkxoDdqA4YfOp0KWq3mrGVuFARBEH55RIDyCyf7ZBpr1KQ+5hADweHGE7Z1SpIE6n9oDFpCo8wEhxtR4JQn+BIEQRCE9hIByi+crdaF7FPQ6tTx/afSEasrTBolCIIgnF9EgHIOkGUFjvTybmVkTWsURcHReLR5JiTSdFaGrAmCIAjCqRABShdyJKgACVOQDiQJe4Mbe4NbnfCwmSRJSJojj5J/OLCkkdDp1cmonE0ef3BiCTWc86nmBUEQhPOLuGv9jL+TqKKAJJ3RWgd7g9vfmdVWpybpkX0thwErioLiA3/GpWO4fvZ7cLgJc8jpzUQqCIIgCGeaCFCO4Wh0+zuUHqE3ajGYdRjMOnR6Tacl0/G4fP7gRKvT4PPKyD4FjVZDcLgRo0WHoqi5DNSpxfFnGzy6Ts1z4nb5kFAnfjKc5uyWgiAIgnA2iLvXsVqJPY7MPtlU50Kj1WAwNwcsJl2H1a7IskJDlQMAo0VHaJQZr1vG55MDjiNJgOhHIgiCIJwHRIByDKNFj8Gk8/fr8HkV3E4vbodXTWDmk3HaZJw2tW/HkdoVo0XXrsyMPo+s7tfpw+vx4fOo02hrtBIhESZ/8jQ9ItujIAiCcH4S40ePodFIaHUaNBoJSVI7nFpCDITFWIhKCsYaY8EcYvAnJDtSs1JT2oSjsWWW1iNkn4yzyUNjtYPqYhvVJTYaa5y47J6jwYlG4sHHZqLVadUOr5JEZGQkEydOZNu2bS32effdd6PValmwYEGL55588kn/PiRJwmq1MnLkSJYvX95q+Z577jm0Wi0vvvjiqV46QRAEQehQIkBpI0kjYTTrCIkwEZkYTERCEMHhJvRGLSjQWOOkocpJQ5UahFQWNlJdYqOmxEZVkY2GKgcOmwefVw1I9EYtQVYj1hgLkYnBRCYFo9FqmDhxIqWlpZSWlrJkyRJ0Oh1XXHFFQFnsdjvz5s3j4YcfZvbs2a2Wt3fv3v79rF69mh49enDFFVdQX1/fYtvZs2efcF+CIAiCcKaJAOUU6fRaLKEGwmItBIUZATWlvLNJDUIU+eiMvwBavQZziAFrtJmo5BDC44IICjNiNOvQ6o52vjUajcTFxREXF0dOTg6PPvoohYWFVFZW+o+9YMECsrOzefTRR1mxYgWFhYUty6fT+feTnZ3N008/jc1mY+/evQHbLV++HIfDwdNPP01DQwOrVq3qrEsmCIIgCG12XvRBURQFh9fRafuXLGDUSjhtHjRaCb1Jh1YrYUANXHQG7SnNU2Oz2Xj33XfJyMggMjLSvz43N5dp06ZhtVq57LLLmDNnDn/605+Oux+Xy8Xbb79NWFgYmZmZAc/l5uYyZcoU9Ho9U6ZMITc3l+HDh7e7rIIgCILQkc6LAMXhdTD0/aFn/Lhrf70Wi759s/suXLiQ4OBgAJqamoiPj2fhwoVoNGqAs2/fPtasWcMnn3wCwLRp03jwwQf54x//GDAEevv27f792O12QkJCmD9/fsD01g0NDXz00UesXr3av6+RI0fyz3/+0/9aQRAEQTgbRBNPFzN27Fi2bNnCli1bWLduHRMmTOCyyy7j8OHDgNpfZMKECURFRQFw+eWXU19fzw8//BCwn8zMTP9+Nm7cyIwZM7j++uvZsGGDf5sPPviA7t27079/fwBycnJITU1l/vz5Z+hsBUEQBKF150UNillnZu2v156V47ZXUFAQGRkZ/t/feustrFYr//3vf3nqqaeYO3cuZWVl6HRH/+l8Ph+zZ8/mkksu8a8zGAwB+xkwYACfffYZL7/8Mu+++y6gNu/s3LkzYF+yLDN79mxuv/32dpddEARBEDrKeRGgSJLU7qaWrkKSJDQaDQ6Hg6+//prGxkY2b96MVns0R8qOHTv4zW9+Q11dHWFhYcfdl1arxeFQ++Js376dDRs2sGzZMiIiIvzb1NTUMGbMGPbs2UNWVlannZcgCIIgnMh5EaCcS1wuF2VlZQDU1tby2muvYbPZuPLKK3n55ZeZNGmSv0nmiOzsbB544AHee+89Zs6cCYDX6/Xvp7Gxkfnz57Nr1y4eeeQRQK09ueCCCxg1alSLMgwZMoTc3FyRF0UQBEE4a0QflC7m22+/JT4+nvj4eIYOHcr69etZsGABvXr14quvvmLy5MktXqPRaPjVr35Fbm6uf93OnTv9+8nJyeHDDz/kjTfe4JZbbsHtdvPuu++2ui+AyZMn88477+DxeDrtPAVBEAThRCTleOlP2+Cvf/0rjz32GPfddx8vv/wyAE6nk4ceeoh58+bhcrmYMGECr7/+OrGxsf7XFRQUMGPGDJYuXUpwcDC33norzz33XEBfiBNpaGjAarVSX18fMCrlyPHz8/NJT0/HZDKd6qkJ7SCuuSAIgtAWJ7p//9wp16CsX7+ef//73/Tr1y9g/QMPPMCXX37JggULWL58OSUlJVx77bX+530+H5MmTcLtdrNq1Srmzp3LnDlz+POf/3yqRREEQRAE4RfmlAIUm83G1KlT+e9//0t4eLh/fX19Pbm5ufz973/n4osvZtCgQbz99tusWrWKNWvWALBo0SJ27drFu+++S05ODpdddhnPPPMM//rXv3C73R1zVoIgCIIgnNNOKUCZOXMmkyZNYty4cQHrN27ciMfjCViflZVFSkqKPxnY6tWr6du3b0CTz4QJE2hoaGDnzp2tHs/lctHQ0BCwCIIgCILwy9XuUTzz5s1j06ZNrF+/vsVzZWVlGAyGFkNdY2Nj/SNKysrKAoKTI88fea41zz33HE899VR7iyoIgiAIwjmqXTUohYWF3Hfffbz33ntntDPkY489Rn19vX9pbXI8QRAEQRB+OdoVoGzcuJGKigoGDhyITqdDp9OxfPlyXnnlFXQ6HbGxsbjdburq6gJeV15eTlxcHABxcXGUl5e3eP7Ic60xGo2EhoYGLIIgCIIg/HK1K0C55JJL2L59u3+Oly1btjB48GCmTp3q/1mv17NkyRL/a/Ly8igoKGDYsGEADBs2jO3bt1NRUeHfZvHixYSGhpKdnd1BpyUIgiAIwrmsXX1QQkJC6NOnT8C6oKAgIiMj/etvv/12HnzwQSIiIggNDWXWrFkMGzaMCy+8EIDx48eTnZ3NzTffzAsvvEBZWRl//OMfmTlzJkajsYNOSxAEQRCEc1mHp7r/xz/+gUajYfLkyQGJ2o7QarUsXLiQGTNmMGzYMIKCgrj11lt5+umnO7oogiAIgiCco04rk+zZIjLJdi3imguCIAhtcUYyyQqdZ/Xq1Wi1WiZNmhSw/tChQ0iShFarpbi4OOC50tJSdDodkiRx6NAhAMaMGYMkScddli9fDsBtt92GJEn89a9/DdjnZ599hiRJnXeigiAIgnAcIkDpgnJzc5k1axYrVqygpKSkxfOJiYm88847Aevmzp1LYmJiwLpPPvmE0tLSgOXw4cP06dOHwYMHM3ToUP+2JpOJ559/ntra2s45KUEQBEFoBxGgdDE2m4358+czY8YMJk2axJw5c1psc+utt/L2228HrHv77be59dZbA9ZFREQQFxcXsDzzzDNUVVXx6aefBjTHjBs3jri4OJ577rlOOS9BEARBaI/zIkBRFAXZbj/jy6l07/nwww/JysoiMzOTadOmMXv27Bb7ueqqq6itreXHH38E4Mcff6S2tpYrr7zyhPt+/fXXeeedd/j4449JSkoKeE6r1fLss8/y6quvUlRU1O5yC4IgCEJH6vBRPF2R4nCQN3DQGT9u5qaNSBZLu16Tm5vLtGnTAJg4cSL19fUsX76cMWPG+LfR6/X+4OWiiy5i9uzZTJs2Db1ef9z9rlixgvvvv5/XX3+d4cOHt7rNr371K3JycnjiiSfIzc1tV7kFQRAEoSOdFzUo54q8vDzWrVvHlClTANDpdNx4442tBgvTp09nwYIFlJWVsWDBAqZPn37c/RYUFHDddddx1113cccdd5ywDM8//zxz585l9+7dp3cygiAIgnAazosaFMlsJnPTxrNy3PbIzc3F6/WSkJDgX6coCkajkddeey1g2759+5KVlcWUKVPo1asXffr0YcuWLS326XA4+NWvfkXv3r15+eWXT1qGUaNGMWHCBB577DFuu+22dpVfEARBEDrK+RGgSFK7m1rONK/XyzvvvMNLL73E+PHjA5675ppr+OCDD5g4cWLA+unTp3PPPffwxhtvHHe/d9xxBzU1NXz33XfodG375/7rX/9KTk4OmZmZ7T8RQRAEQegA50WAci5YuHAhtbW13H777Vit1oDnJk+eTG5ubosA5c477+T6668nLCys1X2++OKLLFiwgC+//BKv10tZWVnA81arFXMrtTx9+/Zl6tSpvPLKK6d3UoIgCIJwikQflC4iNzeXcePGtQhOQA1QNmzYQENDQ8B6nU5HVFTUcWtGXn/9dTweDxMnTiQ+Pr7FMn/+/OOW5+mnn0aW5dM7KUEQBEE4RSLVvXDaxDUXBEEQ2kKkuhcEQRAE4ZwmAhRBEARBELocEaAIgiAIgtDliABFEARBEIQuRwQogiAIgiB0OSJAEQRBEAShyxEBiiAIgiAIXY4IUARBEARB6HJEgCIIgiAIQpcjAhRBEARBELocEaB0MWVlZdx3331kZGRgMpmIjY1lxIgRvPHGG9jtdgDS0tLUGZp/tvz1r389y6UXBEEQhI4hZjPuQg4ePMiIESMICwvj2WefpW/fvhiNRrZv385//vMfEhMTueqqqwB1Mr8777wz4PUhISFno9iCIAiC0OFEgNKF3HPPPeh0OjZs2EBQUJB/fbdu3bj66qs5dl7HkJAQ4uLizkYxBUEQBKHTnRcBiqIoeN3yGT+uzqBBkqQ2bVtdXc2iRYt49tlnA4KTY7V1X4IgCIJwrjsvAhSvW+Y/9y0/48e965+j0Ru1bdp2//79KIpCZmZmwPqoqCicTicAM2fO5PnnnwfgkUce4Y9//GPAtt988w0jR47sgJILgiAIwtl1XgQo57J169YhyzJTp07F5XL51//+97/ntttuC9g2MTHxDJdOEARBEDrHeRGg6Awa7vrn6LNy3LbKyMhAkiTy8vIC1nfr1g0As9kcsD4qKoqMjIzTL6QgCIIgdEHnRYAiSVKbm1rOlsjISC699FJee+01Zs2addx+KIIgCIJwPhB5ULqQ119/Ha/Xy+DBg5k/fz67d+8mLy+Pd999lz179qDVHg2yGhsbKSsrC1gaGhrOYukFQRAEoeNIyrFjV88RDQ0NWK1W6uvrCQ0NDXjO6XSSn59Peno6JpPpLJXw1JWWlvLss8/y1VdfUVRUhNFoJDs7m+uvv5577rkHi8VCWloahw8fbvHau+++mzfffPOMl/lcv+aCIAjCmXGi+/fPnRdNPOeS+Ph4Xn31VV599dXjbnPo0KEzVyBBEARBOAtEE48gCIIgCF2OCFAEQRAEQehyRIAiCIIgCEKXIwIUQRAEQRC6HBGgCIIgCILQ5YgARRAEQRCELkcEKIIgCIIgdDkiQBEEQRAEocsRAYogCIIgCF2OCFAEQRAEQehyRIDSBa1evRqtVsukSZMC1h86dAhJktBqtRQXFwc8V1paik6nQ5Ikfyr8I9sfWUJCQujduzczZ85k3759Z+p0BEEQBKHdRIDSBeXm5jJr1ixWrFhBSUlJi+cTExN55513AtbNnTuXxMTEVvf3/fffU1paytatW3n22WfZvXs3/fv3Z8mSJZ1SfkEQBEE4XSJA6WJsNhvz589nxowZTJo0iTlz5rTY5tZbb+Xtt98OWPf2229z6623trrPyMhI4uLi6NatG1dffTXff/89Q4cO5fbbb8fn83XGaQiCIAjCaTkvAhRFUfA4nWd8URSl3WX98MMPycrKIjMzk2nTpjF79uwW+7nqqquora3lxx9/BODHH3+ktraWK6+8sk3H0Gg03HfffRw+fJiNGze2u4yCIAiC0Nl0Z7sAZ4LX5eKVW68748f93dyP0JtM7XpNbm4u06ZNA2DixInU19ezfPlyxowZ499Gr9f7g5eLLrqI2bNnM23aNPR6fZuPk5WVBaj9VC644IJ2lVEQBEEQOtt5UYNyrsjLy2PdunVMmTIFAJ1Ox4033khubm6LbadPn86CBQsoKytjwYIFTJ8+vV3HOlIrI0nS6RdcEARBEDrYeVGDojMa+d3cj87KcdsjNzcXr9dLQkKCf52iKBiNRl577bWAbfv27UtWVhZTpkyhV69e9OnThy1btrT5WLt37wYgPT29XWUUBEEQhDPhvAhQJElqd1PLmeb1ennnnXd46aWXGD9+fMBz11xzDR988AETJ04MWD99+nTuuece3njjjXYdS5ZlXnnlFdLT0xkwYMBpl10QBEEQOlq7mnjeeOMN+vXrR2hoKKGhoQwbNoxvvvnG/7zT6WTmzJlERkYSHBzM5MmTKS8vD9hHQUEBkyZNwmKxEBMTw+9//3u8Xm/HnM05bOHChdTW1nL77bfTp0+fgGXy5MmtNvPceeedVFZWcscdd5xw39XV1ZSVlXHw4EG++OILxo0bx7p168jNzUWr1XbWKQmCIAjCKWtXgJKUlMRf//pXNm7cyIYNG7j44ou5+uqr2blzJwAPPPAAX375JQsWLGD58uWUlJRw7bXX+l/v8/mYNGkSbrebVatWMXfuXObMmcOf//znjj2rc1Bubi7jxo3DarW2eG7y5Mls2LCBhoaGgPU6nY6oqCh0uhNXhI0bN474+Hj69u3Lo48+Sq9evdi2bRtjx47t0HMQBEEQhI4iKacyFvYYERERvPjii1x33XVER0fz/vvvc9116oiZPXv20KtXL1avXs2FF17IN998wxVXXEFJSQmxsbEAvPnmmzzyyCNUVlZiMBjadMyGhgasViv19fWEhoYGPOd0OsnPzyc9PR1TF2/W+aUQ11wQBEFoixPdv3/ulEfx+Hw+5s2bR1NTE8OGDWPjxo14PB7GjRvn3yYrK4uUlBRWr14NqCnc+/bt6w9OACZMmEBDQ4O/FqY1LpeLhoaGgEUQBEEQhF+udgco27dvJzg4GKPRyG9/+1s+/fRTsrOzKSsrw2AwEBYWFrB9bGwsZWVlAJSVlQUEJ0eeP/Lc8Tz33HNYrVb/kpyc3N5iC4IgCIJwDml3gJKZmcmWLVtYu3YtM2bM4NZbb2XXrl2dUTa/xx57jPr6ev9SWFjYqccTBEEQBOHsavcwY4PBQEZGBgCDBg1i/fr1/POf/+TGG2/E7XZTV1cXUItSXl5OXFwcAHFxcaxbty5gf0dG+RzZpjVGoxFjO3OKCIIgCIJw7jrtTLKyLONyuRg0aBB6vT5ghty8vDwKCgoYNmwYAMOGDWP79u1UVFT4t1m8eDGhoaFkZ2efblEEQRAEQfiFaFcNymOPPcZll11GSkoKjY2NvP/++yxbtozvvvsOq9XK7bffzoMPPkhERAShoaHMmjWLYcOGceGFFwIwfvx4srOzufnmm3nhhRcoKyvjj3/8IzNnzhQ1JIIgCIIg+LUrQKmoqOCWW26htLQUq9VKv379+O6777j00ksB+Mc//oFGo2Hy5Mm4XC4mTJjA66+/7n+9Vqtl4cKFzJgxg2HDhhEUFMStt97K008/3bFnJQiCIAjCOe2086CcDSIPStcirrkgCILQFmckD4ogCIIgCEJnEQGKIAiCIAhdjghQuog333yTkJCQgIkTbTYber2eMWPGBGy7bNkyJEniwIEDpKWlIUkS8+bNa7HP3r17I0kSc+bM8b/mRMuyZcs6+SwFQRAEoW1EgNJFjB07FpvNxoYNG/zrVq5cSVxcHGvXrsXpdPrXL126lJSUFLp37w5AcnIyb7/9dsD+1qxZQ1lZGUFBQQAMHz6c0tJS/3LDDTcwceLEgHXDhw8/A2cqCIIgCCcnApQuIjMzk/j4+IBajGXLlnH11VeTnp7OmjVrAtYfOxPx1KlTWb58eUCG3dmzZzN16lT/TMcGg4G4uDj/YjabMRqNAevaOlmjIAiCIHS28yJAURQF2e0740t7B0iNHTuWpUuX+n9funQpY8aMYfTo0f71DoeDtWvXBgQosbGxTJgwgblz5wJgt9uZP38+06dP74CrJwiCIAhnXrtT3Z+LFI9MyZ9XnfHjJjw9HMmgbfP2Y8eO5f7778fr9eJwONi8eTOjR4/G4/Hw5ptvAuqM0C6XKyBAAZg+fToPPfQQf/jDH/joo4/o3r07OTk5HXk6giAIgnDGnBc1KOeKMWPG0NTUxPr161m5ciU9e/YkOjqa0aNH+/uhLFu2jG7dupGSkhLw2kmTJmGz2VixYgWzZ88WtSeCIAjCOe28qEGR9BoSnj7zHUAlffviv4yMDJKSkli6dCm1tbWMHj0agISEBJKTk1m1ahVLly7l4osvbvFanU7HzTffzBNPPMHatWv59NNPO+QcBEEQBOFsOC9qUCRJQmPQnvFFkqR2l3Xs2LEsW7aMZcuWBQwvHjVqFN988w3r1q1r0bxzxPTp01m+fDlXX3014eHhp3q5BEEQBOGsOy9qUM4lY8eOZebMmXg8Hn8NCsDo0aO59957cbvdxw1QevXqRVVVFRaL5UwVVxAEQRA6hQhQupixY8ficDjIysoiNjbWv3706NE0Njb6hyMfT2Rk5JkopiAIgiB0KhGgdDFpaWmtDk9OTU1tdf2hQ4dOuL+6urpW18+ZM+cUSicIgiAIZ8Z50QdFEARBEIRziwhQBEEQBEHockSAIgiCIAhClyMCFEEQBEEQuhwRoAiCIAiC0OWIAEUQBEEQhC5HBCiCIAiCIHQ5IkARBEEQBKHLEQGKIAiCIAhdjghQBEEQBEHockSA0oXcdtttSJLkXyIjI5k4cSLbtm1rse3dd9+NVqtlwYIFre5r//79/OY3vyEpKQmj0Uh6ejpTpkxhw4YNnX0agiAIgnDaRIDSxUycOJHS0lJKS0tZsmQJOp2OK664ImAbu93OvHnzePjhh5k9e3aLfWzYsIFBgwaxd+9e/v3vf7Nr1y4+/fRTsrKyeOihh87UqQiCIAjCKROTBXYxRqORuLg4AOLi4nj00UcZOXIklZWVREdHA7BgwQKys7N59NFHSUhIoLCwkOTkZAAUReG2226jR48erFy5Eo3maAyak5PDfffdd+ZPShAEQRDa6bwIUBRFwePxnPHj6vV6JEk65dfbbDbeffddMjIyiIyM9K/Pzc1l2rRpWK1WLrvsMubMmcOf/vQnALZs2cLOnTt5//33A4KTI8LCwk65PIIgCIJwppwXAYrH4+HZZ58948d9/PHHMRgM7XrNwoULCQ4OBqCpqYn4+HgWLlzoDzb27dvHmjVr+OSTTwCYNm0aDz74IH/84x+RJIl9+/YBkJWV1YFnIgiCIAhnluiD0sWMHTuWLVu2sGXLFtatW8eECRO47LLLOHz4MACzZ89mwoQJREVFAXD55ZdTX1/PDz/8AKi1RYIgCIJwrjsvalD0ej2PP/74WTluewUFBZGRkeH//a233sJqtfLf//6Xp556irlz51JWVoZOd/SfzufzMXv2bC655BJ69uwJwJ49exgwYMDpn4QgCIIgnAXnRYAiSVK7m1q6CkmS0Gg0OBwOvv76axobG9m8eTNarda/zY4dO/jNb35DXV0dOTk5ZGdn89JLL3HjjTe26IdSV1cn+qEIgiAIXZ5o4uliXC4XZWVllJWVsXv3bmbNmoXNZuPKK68kNzeXSZMm0b9/f/r06eNfbrjhBsLCwnjvvfeQJIm3336bvXv3MnLkSL7++msOHjzItm3b+Mtf/sLVV199tk9REARBEE5KBChdzLfffkt8fDzx8fEMHTqU9evXs2DBAnr16sVXX33F5MmTW7xGo9Hwq1/9itzcXAAuuOACNmzYQEZGBnfeeSe9evXiqquuYufOnbz88stn+IwEQRAEof0k5RzsVdnQ0IDVaqW+vp7Q0NCA55xOJ/n5+aSnp2Mymc5SCc8v4poLgiAIbXGi+/fPiRoUQRAEQRC6HBGgCIIgCILQ5YgARRAEQRCELkcEKIIgCIIgdDkiQBEEQRAEocsRAYogCIIgCF2OCFAEQRAEQehyRIAiCIIgCEKXIwIUQRAEQRC6HBGgCIIgCMK5omIPrH4dDi4/2yXpdCJA6WLKysq47777yMjIwGQyERsby4gRI3jjjTew2+0ApKWlIUkS8+bNa/H63r17I0kSc+bMOcMlFwRBEDpF7SFY+RK8PhxeHwrfPQbvXAUf/Bpq8jvnmD4vuJs6Z99tpDurRxcCHDx4kBEjRhAWFsazzz5L3759MRqNbN++nf/85z8kJiZy1VVXAZCcnMzbb7/NTTfd5H/9mjVrKCsrIygo6GydgiAIgtAeHgds/QD2LYb8FRASBxfcBRnj1HU7PoKi9Ue31+gh+QIoWAN5X8H+72H4vXDRA2AMOb2yFG+EHZ9A8SYo3QIj7ocxj5zePk+DCFC6kHvuuQedTseGDRsCgoxu3bpx9dVXc+y8jlOnTuUf//gHhYWFJCcnAzB79mymTp3KO++8c8bLLpwdPq+H6qJCdAYjEQmJZ7s4giC0h6LAvF/DgR+OrqveD988/LMNJUgfCX2ug15XgiVCber55mHIX67Wrmx+Fy55AvpPAc0pNI7s/Q7mTQXZc3Rd+Y5TOq2Ocl4EKIqiIMuOM35cjcaMJElt2ra6uppFixbx7LPPHrcG5Nh9xcbGMmHCBObOncsf//hH7HY78+fPZ/ny5SJAOQ9UFxey6M1XKDuwD9nnRdJouHzW/5E1fNTZLpogCG21IVcNTnRmGPUQdL9Yrb1Y+2+o3gdJQ9SgpPc1as3KsWKy4JbPYc9XsOgPajPQ5/fA+v/CxL9CyoVtL8eBpTD/ZjU46X4J9L0OEgdDZEZHnm27nRcBiiw7WLa87xk/7pjR29FqLW3adv/+/SiKQmZmZsD6qKgonE4nADNnzuT555/3Pzd9+nQeeugh/vCHP/DRRx/RvXt3cnJyOqz8QtfkcTn58u/PUV1UAIDOYMTrdvH1K39DURR6jRh9lksoCMJJ1RyERX9Sfx73JFz4W/XnxEEw5A616cdwkvuHJEGvK6DHpbDmDVjxNyjZDLMnqDUtlzwJURlqTY3HAa5GdXE3P1bvV5uKdn0BPhdkXQHXzwGtvhNPvO3OiwDlXLZu3TpkWWbq1Km4XK6A5yZNmsTdd9/NihUrmD17NtOnTz9LpRTOpGVz36K6qICgsHBueOI5wuLiWfyf19ixdDHfvPoSbrudfuMmtrn2ThCEM0yW4bN7wGOHtJFqn5NjSdLJg5Nj6Yxw0f2Q82v44Rm1uWf3l7DnazAGg8sGiu/E+8i4FK6b3WWCEzhPAhSNxsyY0dvPynHbKiMjA0mSyMvLC1jfrVs3AMzmlvvS6XTcfPPNPPHEE6xdu5ZPP/309AosdDmN1VWU7N1Def5+JMDr8bBtybcgSVx270NEJCQBMP6uWUiSxPYfFvH9W/+iOG8X4+64B4Op7X+DgnA2+BrdSFoJjaXr3Bg7XfFGKFgNegtc/a9T6zPSmuAYuOpVGDoDvn8S9n0HzvpjNpDUjrRHluBYSB6qNgd1GwMabceUo4O0K0B57rnn+OSTT9izZw9ms5nhw4fz/PPPBzRLOJ1OHnroIebNm4fL5WLChAm8/vrrxMbG+rcpKChgxowZLF26lODgYG699Vaee+45dLrOiZckSWpzU8vZEhkZyaWXXsprr73GrFmz2jwSZ/r06fztb3/jxhtvJDw8vJNLKXQmn9dL5aGDlOzbQ0nebkr27qGxurLVbS+4+jpS++b4f5c0Gi69817C4hL4cd477F65lPKD+7nygUeJSk49Q2cgCG0nO700LCnAtqoESa8h4vpMzL0jz3axzox9i9THHuMhvBPen7HZMPVDdQiyz3M0INFbOi4YOgPaFREsX76cmTNnMmTIELxeL48//jjjx49n165d/hvqAw88wFdffcWCBQuwWq3ce++9XHvttfz0008A+Hw+Jk2aRFxcHKtWraK0tJRbbrkFvV7Ps88+2/FneA55/fXXGTFiBIMHD+bJJ5+kX79+aDQa1q9fz549exg0aFCL1/Tq1Yuqqioslq4dgAkntvbTD1nzyXy87sBmPEmjITo1nfiMTLQ6HS6HnaCwcIZfP7XFPiSNhguuvo6EzF589fLz1BQX8t4fHmTc7ffQe/Ql/u3Wf/Exm779kivvf4SEnr06/dyE85uiKHhKmvBW2PFUOfDVuZBtbtxFjchNXnUbn4/q/+0ieFQi1glpSNozfxN17q2l/tt8FK+MxqxHY9b5F6n50ZAQhLFb2OkfbP9i9bHH+NPf14lEpHfu/juZpBw7drWdKisriYmJYfny5YwaNYr6+nqio6N5//33ue666wDYs2cPvXr1YvXq1Vx44YV88803XHHFFZSUlPhrVd58800eeeQRKisrMRgMJz1uQ0MDVquV+vp6QkNDA55zOp3k5+eTnp6OyWQ61VM7a0pLS3n22Wf56quvKCoqwmg0kp2dzfXXX88999yDxWIhLS2N+++/n/vvv7/VfYSFhfHyyy9z2223nZEyn+vX/Gxz2mz8+7e34PW4MQWHkNAzi4SevYjvkUVcRo9Taqax19fx9WsvcXjbZgD6jL2Ui39zNzuXLWHJ7DcAiO+RyZRn/ib6qgidxlfvouajvbj21bX6vC7ajPXydFwH6rH9WAyAIS2UyClZaK3GNh/Hsasa++YKJIMWTZAOjUWP1qJHY1F/PrJOY9G1CH4URcH2Uwn1Xx2ENtwNzf2iCLuqO9rg1u9VPpsbb7UTSadB0knq8fQaJK2krnNXI/29p7rxQ3shJLbV/ZxtiqKAApKmYz8fTnT//rnTalOpr1fbtiIiIgDYuHEjHo+HcePG+bfJysoiJSXFH6CsXr2avn37BjT5TJgwgRkzZrBz504GDBjQ4jgulyugg2hDQ8PpFLtLi4+P59VXX+XVV1897jaHDh064T7q6uo6tlBCp9r941K8HjdRKWnc8vwrSB1QBWuxhnHtY0+y9tMPWbXgfXYsXUzRrh3UVZSpG0gSpfvyOLR1E+k5LWvmzjRFUXDn1+MutOEusaG4fGiC9GiC9WiD9IE/Nz9K+lNrL1d8CrLdg8/mQba5QVFvihpD12p/78oUWcFd1IjrQB2KR1Y7dTY78qPiU7CtKUVxeEEnYUgORR9tRhtuQhusR2s1YuxuRdJqMPeKxJgWSs2CvbgPNVD+ymYipmRiylCbrT2Vdqr/txskMPeKwNQrEkNyCJJGwramlLrP97cpuADQhBow947E3DsKb7UDx/YqXPvrALAMisUyIAbZ4UVxeJEdXmSHB9nhxdfowbmnGse2KlwH6gkdl0LQ4NiAv8OmDeXUfb5fvSbHIellIhiGOcneocGJoij46l24CxsBMGdHtloTJTu9uItsuIsaUdw+DAnB6BOD0YYZkSQJ2e3DvqWCxmWHsY7vhiUnpsPK2F6nHKDIssz999/PiBEj6NOnD6CmaTcYDISFhQVsGxsbS1lZmX+bY4OTI88fea41zz33HE899dSpFlUQuixFUdi25DsA+l0yoUOCkyM0Gi3DJk8hMTObr155kbryUgByJkxCq9Oz8avPWLXgPdL6DzztWhRFUbBvKKdh8WEMaaFYL++GLqxt34AVn0ztgr3Yt7Te3+Z4JIMWyaRF0khIeg3acBO6CBOG1FDMvSPRGLS4S2w0rSnFU2lHtnmQmzzIdm/Lfek1mLIisAyIwZQV0eHfGn8pvPUubD+VYN9SgdzgbtNr9InBRNyYiT7mxM3Q5j5RxMYFUf3ebjylTVTl7iB0XCrm3pFUvrUd2aYmEGsst9O4rAit1YghJQTH9ioALANj0EVbkO3qv7H6b938s10NMlBAbnDTtLqUptWlRw8ugXVSN4JHJJzwveAuaqRmwV685XbqPj9Aw5ICLP2j0YYa8ZQ1Yd9cAYDGApLOgOKTUTwKik8GnxpBKR4NNTxITNx2TrdbsKesCceOKn/AceQaAehiLIRd2Q1NkB53YSPugkbchY14K+2tBnOSUYsu2oyvxul/j9R9sQVLTic3Q53AKQcoM2fOZMeOHfz4448dWZ5WPfbYYzz44IP+3xsaGvzZUwXhXFa2fy9VBYfQ6Q30Gjm2U46R0qc/Nz//CivenU1wRCQjp9yKvaGerYu/oWz/XvK3bKDbgCGnvH9fvYvaT/bhzKsFwLGtCueeGoIvSsSQHIIuyowuwtTqtznFI1P9/m6cu2tAI2HuHYk+IRhtkB5fU3NAYXOrPzcHGL4mD/gUFLcPxX106KS30oELaFpTSp1Biy7Wgqf522QLEmqtTJAexenDV+/Csb0Kx/YqtOFGLDkxSEYtklbCnB2JLvL8Hg0l2z3UfZWv3oBl9e4mGbWYeoajCdYfveEd6THQ/KCLtRB8YXyb+5TooszE3NOf2s8P+APehiWHQQZ9fBDBFyXi3FuLc09N87+ZWrMeMjaZ0PGpJwwuFFlBdnhxFzXi2FKJc18tunATpuwIzH2i0EefvB+fISmE2FkDaFpXSuPSfHyNHmw/lRx7pQjVvUeI70MkUwT0vkzNLdJtDIrWhOJyU/2Xt3F5e1O95wJiLvO1q+ZOURQUlw9vpYPGFUX+4MxPI6GPs+Crd+GtsFOV23omWG2YUa2BMmrxFNvwlNtRXD48RTb1LJqqcB/8AcuAKBTl0rPWDHxKAcq9997LwoULWbFiBUlJSf71cXFxuN1u6urqAmpRysvLiYuL82+zbt26gP2Vl5f7n2uN0WjEaGx7e+Qpk2VQZNCeF6Ovj1JkNYmPsw4c9ep4eZ0RdCZ1TLxGp2Y6NAQFVOUKp2/bkm8B6DnsIkxBwZ12nODwCC6f9X/+34PCwsmZMIkNX37Cqg/fJz1ncLs/hBRZoWltKfXfHkJx+UAnETI6GdeBOtyHGmj8ofDoxhrQRZjVYCXKjNZqxFthx3mgDl+NE3QaIqf1wpwVcfLjKooaVDR51OPKCrLLh6/WiafSgWNnFb5qpxqcaMDcNxpzdgSaYAPa4ObmIoveX0uiKAqeYhv2LZXYN5Xjq3XRuPRo2e3bqoidmdOua9NRFEVBccv+GgDFK4O3uflA09y/QSMhaSXQqIvGpD1u/4hT4bO5qXprB54ydeI4YzcrwSMSMWWGI+k6vjOrpNcScV1PjOlW6j5Tm0v0icFE394HjUVP0KBYFI8PZ14tjl3VGFJCCb4w/uT71Uhog/SYMyMwZ5787wwAe42azKz6gPrYUIxkrya4+gBB7kM49CNwyz2QFSsKeoK0X2PS7gBTKNir1Xwkm98FvQWp+8VIMb2I0P6Xct+reGvDqZmfR8QNmWiMWhRFwXWwHl+NU615ccn4Gt34Glz4Gt3IDW58jW4Ud2DzkalXBMaMMAzJIRjig5D0WmSHl4bFh7GtKUXSa9Tnjlm0IYF/H4pXxlvtwPbjNqr+9TLeku2EXn4Z8U/98az2UWvXnVhRFGbNmsWnn37KsmXLSE8P7CE8aNAg9Ho9S5YsYfLkyQDk5eVRUFDAsGHDABg2bBh/+ctfqKioICZGbdtavHgxoaGhZGdnd8Q5nTp3o5rdT2dWh2SZQsEQ/Mu6Kcs+NRjxOtQkQW4HeJ20qPNztzKTpc4MQVFq4KLRgqRVh6ydej/r85rL3sSeVSsA6HfJxDN+/CFXXsvWRV9TfnAf+9evpscFw0/6GkVR1MBibx32LRV4itVvXPrkECKu64E+NghlXAqOLZU4dlXjrXLgrXKgeGT/zz8nGbVE3Zrd5tERkiT5R1W0xnpZGu7DDXjKmjBlRqALP3HHbUmSMCSFYEgKwTohFfvWKtyHG1BkBfumcjyFjXhrnSfdT0fxNXmwbyzHlV+P+3BDq01SJ2NICyVocBz6+CA1qJEVtQOpUYs23Nh6bZaiINu9+Gqd+OpdoNUg6TXUfX4Ab4UdTYieyGnZGFNP3LGxowQNisWQFIxzbx1BqfVoNr+hzkGTdQWSOQxznyjMfaI65mAeBxRtgKJ1ULWvOSjZD47a475E0puxZMdjMQC75qoBScIAmLQE4vpDwSo1Df2er6C+EPYshD0L0UoQ2WMVlfsn4dxZTcUrmwgZm0zTujLcBcep8fv5sZtrr0IvSUEf1zIlhcasI+yq7lgvT1cD2JM0WUo6DRqjh8oXHsBXX0/w2LEkPP9XJO3Z7ZfVrlE899xzD++//z6ff/55QO4Tq9XqTyQ2Y8YMvv76a+bMmUNoaCizZs0CYNWqVYA6zDgnJ4eEhAReeOEFysrKuPnmm7njjjvaPMy400bxNJZBY2ngOq0RgqLVN0YXS2LTZooMtnL1zeZ1tb6NpFWDMnOYes5ep7rIXnUcvdum7qcVTq9Cfkk16Zv/iik+Cy5/sX1ZEM9Te35azlevvEhEQhK3/f2NM/5NRVEU1s2Zz/pFHxOcEM0tL76K2+5gxXuzqa8oR6c3EKSxEoSVIF8IZk8QJpcZre/o+0AyarFOSCPowvjjfggqsoKv0e0PULyVDnx1TnSRZgwpoRjTQ7tskq6Kf2/DnV+P9YpuhFzU+ZMxOnZVU/vJvoC+BABoJXW4q16j1lpIqM1cPgVkBUVufmxu+jpRh1HJqMWUEYY+MVj9hl7rwlvrxFfrbPHt3H/4UANRd/ZtUzNImx2psVZkNZmYrUz9nGosV39uLFd/r9gFVXuPKYwRsiap880c28m0qVr9vArQyoXwusBRA7YKKFwLh35SE6fJnpbbAoQmQkQ3dV6asBT1S1pQNKSOUL/EAvi80FAM1uSWeUYUBcq2qYHK7oVq4DN1AU6lP7Uf5uGrP9qXR9JrMKRb0eg1akAZYkAbakAb0ryEGtCEGjqlQ3f5X5+nZs4cjD16kPbRAjSd1GrRaaN43nhDHZ44ZsyYgPVvv/22f0jrP/7xDzQaDZMnTw5I1HaEVqtl4cKFzJgxg2HDhhEUFMStt97K008/3Z6idI6QOLBEHp2rwFGvzk/QUASNJWqQYokG/Tk0lNbrUieR8tiPrtPoQW9uXizqo9YQWFP08wBD9qrfEBx16s+KrNbGHPkAUGRoKoctG9Q34K/ngfk0Esd5XeoEVk0V6s96s/qBEJGuvuHrCtQPmfC00zvOWVS4S81unD7gFJpXmr/tasy6dnfo9Nnc2DdXYt9YRmJZItHJd/Fj+SdsXfQ1ectWEFEXRYapN2GGGPSalh9SPtmLHC0RdWGGv4PgiUgaCZ3ViM5qhO5h7Srr2SQ7nSiuQ0A4jp1VnRagyE6v2lyxrRLHzmoAdDFmggbHYUgLRR8bhGTQtPlvxFvvwr6pHPuWShSnF3Qa/+gMxeFFcflw7Kz2H+vnNEE6JL23uYMnaCwSYVcnoIs00bR6NfWff4HidhE+bRqWgQPbfqKHfoIlT0H5TvU9fbyAoNVC6aHbaKgvgso9sPMTdabdWxeqn1uf3AkHl7V9f60JiYeUYRDbWw1GIjPUzxtDG5JmanXHT7gmSRDfX13GPq5+fkkSJiD2voHUfXEAx85qLINiCb04BW1oxzTP+RobaVi4EK3VStCIEWit1uNu6y4spOa99wCIefjhTgtO2uu08qCcLWcsD4rsU2+CTZWBNQ/GEDWCNoZ27eYft10NFhSfWkNiTVTL3JFzLcgyTkeTes01pZi+uFP9NhSTDdM+htCENu+HxhK1evXAD7DlPTUg+jlrCrga1P4yR5jD1W84AUt3tbq1C/cnmn3/3dSWFnPNw3+i+6ChJ91e8Sk4dlbh2FmNO78eX4MbbaSJ4AviMfYMR3F48DV51U6MzW9rRWn+nwL4FJx5NTh21/g7Oh7hU3wcbNxKalA2Bu3R942iUfBYvLjNLhxGBxX1h9i1bTkRKcnc8sKrv9gcKu7CQop+dx/uQ+UET/groBD32BB01o7pLOutcuDYXYNzTzWu/Iaj/x4SBI9MwnppKpK+4/t3KLLa38aZV6Pm6jBLKPYqPOX5ePJ349y+Hk9xQauvlYxGlJ/NBxY0fBjmwYPRhoWhCw9HGxaG9shjWBiKw4Znw9f41n+MsXEFOuPxh99iiVK/JAbHqktILATHqZ9b6aPAZFX/lks2wfxb1C+OkRnqPDO2I8Pn23DNNHr1y6Y5AuL7qV980kZAeHqnf557yspw5eUhNzUhOxxIBiPaiHD0iYkY09I67DiO7TsofvBBPIXNfam0Wsw5OQSPHk3w6FEYe/YMeO8WP/ggDV9/Q9CIEaTkvoWiKGwqqCPeaiIhrGM7iLenBkUEKG2hKGqtSlOlenM8QmtUq/ssEWpH0lPZr5oJR/3Z51abUqC5n4dODS5kn7qNztj248gyVOWpzTR6i1rToOucqDjgmtfth3cnqx8Y1mSY9gmEJcOP/4B9i9XAJe0itbmsap86pXjVfqg5EFjLA+o3mvj+au1OUyUUrT9ahavRq4FJU8XxCxbRDcb+AXpf2+XSO9tqqvn3jFuRJA335L7fagdZxePDW+fCV+fCU9aEbXWp2qG0A+iTggkaHIs5O5Kaz/bh2nW0rV2K1BN2cTqGpGB0URa1A2Yzp83Gv++5Fa/LxfV/epaUPv06pDydSfF6kW02FFkGn6+5OcQHsowiy8h2B57SErylpXhra/HV1VH/6WfIjY1IJhPmoQ+iDU/DW/kDwUPiMPfri6lPH7Qn+XD9OdnhpXFZodo3pzKwL44u2oypVySW/tEYEjuvszSAc/du6j/7DNuKlbjz81vdxpCWhjY8HEmrxVdfh+tgPvh8aEJCCJ10Ofh81H36GXjb1z/GmBhK6KSriJg2BU1QqPq5JklqX7/2fHGqOQhzrlSDFICoTLjxfxCdeeLXdSJFUQJu+orXizMvD8fmLTg2bcK+eTPe0tLjvj7s+uuI/cMf0JzGfctTXk7tvHlUv5ULHg+6+Hi0wUG49u0P2E4XF0fwqFHoExJw7N6N7bvvUCSJ6pdz2aqP4pNNxRysauKeMd15eGLWKZenNSJA6cyspl4XNFWp3+79s0NKalukOVyN8k8UxR8Jdhy1ai2AIjcHHVLbqjw1OvWGrTOBzqAGSTpjy+CloVhtY9XoILpXp9YktLjmtYfgf9eqQYc5Qq1xqjvctnMLT4eYXtD/JugxIbDcrka1I5slEqKz1PN3N6nzTdQcDFzKth2dJCs8Xf2GlDgYrElqmSLS1cDyVFXtg8J16gRb1vZX/ed9s4ySL7aQZu2DKTREHVobZsRX7/IvR9KAH0sTpCNoSDzGHmHo44Jw7qzGtq4Ub7XTn9BM0kpqHwXpmMfmB120haDBsQEd6xRZ4dC7q/Hsqcc6KoWY8dknbDb6/q3X2br4a7oPHso1v/9Tu8/9THIfOkTB7XfgKS5u92vNOTkkvvwPaj/eirvQgrdsO441RxMoGlJTMfXti7lvH0x9+6KPj0cbFoamlYk9FY+Pyv9uP9oJUiNh7GbFlBWBOSsCXVTnD2P2VlZSeO+9OLduC1ivT0jA1Lcvpj69Mffti6l3b7QhIQHbyE4nnqIi9LERaPI+g8Zy3MH9qF99EG/xIXwlB/FVluCtq8fnlPG5NKCof0Nak4ImyIKn+mhQpkuIJ2rGDLyVldjXrEW229FFR6OLilIfY475OToabVQUmp9nGa85CJ/OgOieMOE5ddbes6Q6dzZV//kP2pAQjD17ItvtOLZtQ7H/7EuXRoMxIwOt1YpkVocd+2qqce0/AIqCsVcvon83C21oKJLBgOJyIbtcKM2L7Gz+2e3GmNEdy5AhoNXStHo1dfPm0fjDUvCp96WQSy8l/v89g9ZqxV1UTNPKFdiWr6BpzRoUZ8svOl+mD+f1/tf6fzfrtdwyLJXHLu/Y6TBEgHIm0q7LPjXIaKpsHgXTTGdSO1WZfnbhPQ51yJqj9gSByJEptqXmNlqvWtOg0ak/t+gA9vOXa4/2KTlSsxDeDczHb3vsCK1e86YqeO96tToW1Gsy8kGoK1Rn8dTo1OrZqB7N7b091Dbcjmp+ctlg7Rvw0yuBtV5HSFroOUGdnjyiu9rObAhWH3XG1qt6ZZ/aYW/VayjbPsLp64GXbNzBlyAHJaKxmJGCgtFYTEhGLRqTTs2lodOA24lStgdPgwVXtRlvRcvRLK2RDFq0YQa0YSbMvSKwDIo96xlPq4sLmfPgDJAkbn/5P4TFnXyI5+ly2e1UFR6mquAQlQWHqCo4RHVRAZFJyfzqkScxWiwoHg+2FSvQRkRgGTAAuamJ/BtvxL3/wNEdSRJoNGpCPI0GtFo0BgO6+Hj08fHooqLQWq3oU5IJu/pqJIMBT4Wd8r9vRM3yVYKvvhHZ6URCav47kQAFb9l2PId/RBcfQ+TttxN2/fVoDAYUWaH6vd04d1YjmXSEX9MdU1YEGtOZa35UFIXCu++macVKJL2e4Jw0QmNKsPRMQpfcUw3YI7qpwXx4qvoe8HnUJuLaQ+r7uSoPNv0vsHnVGNry/aUzo6QMQ44fjtR9DJr0wSBJeCsrsa1YQeW//oW35Pg1CcejDQtDspiRkNCEhhJ+002ETb4WSX/yzwzZ6cRbVY2vphrZ7kAyGtCYzRgzMpBOY5JaRVGo+NvfqMmd3erzmuBgzDk5mAcOwDJgAOZ+/dC0MhGs7aefKPn9w/hqatp1fE1oKNrQUDxFRf515sGDiJg2jZAJE1ptgpWdTuzr12NbvoLdeYV83RTEgYhkdIMuoMnjIyLIwFX9E7isbzzBxo7/GxUBypmcF0ZRmnOI1Kq9yI/UquiD1GBDo1U723qPuSFJWnW0jDkCdEYk3Yk7RT3xxBM8+eST6g3S61I77vof3cfvcGaO6JCZMmVZbTfWHKeZ5LjX3GVTO8WZwmDEfWfnG46zHg6vUms7SreoAaW9Rq1hOh5JezRYObIAVO1FcdtxyoMpd92BTpN0/H2chKLIFNv3EztET8LgUXjqDPga3WitRrRhRrRWI7owo5optQv28/jkuSfI37KRtJxBjJ42vcNmTJZ9Pqp2bKM8bzdVJUVUl5VSXVmOzXb86S0So+MYm9GX+k8+oaKpAaPXR8IllyK7Xdi+X4IuOpq0jxagi4k55WtZ9o+NeMvtJ91ObqrCvX8xvtqDaEK0hF13J5IpA+fuWtBKRN/eF2O3jvvCoHi9eCsr8ZSV4S0rw1NWDrKP8KlTA5oKat5/n/Knn0EyGEh7fy6mLyYdbU5uQVL7gjRVtf65Ep6u9t3Y/4M6mAAJEnKg21i1RjF56AkHEsgOB9X/fYvGxYswdOtO0LBh6KKj8FZW4a2qwltZecxjJd7KKvC0/qVOn5xMyKWXqjVXJiPemhp81TXNj9V4q6vxVVcj/7wmo5mxRwbxz/0Vc5/exy3v8biLiqn8+99p+PprAKIfehBzv/649u1D0ukwDxiAMaN7m4fqesrLqXj+BVwHDyI77ChuDxqDAclkQjIa0RiNSEYjksmIJGmwb9qEr1rtp6cJDsZ69dWE3XgDpp49/ftsdHqotrmxu304PF6aXD7/zwXVDv7xvTo66pmre3PzsLR2X4NTIQKUszVxnexVhyo3tZay+0gzUIT6eEwz0LEp/ufPn8+f//xn8vLy/OuCg4MJDj7Jzf1I8OJpAlcToKh9P06lb4x/lz7s9XU01dchSRLW6FiMrUT/58pkgS67HYPZjOLw4ivMQ5f/AdLeb9Qgxt0UGEQCiqJBwYiCEZ8SiVMeiEMegUfOAMAtu6h0FZKaXEOIfAjF4UD2gKJYkDEffcSIhBf0BnSUIHu28X6+BbdsZ2bP1Ri1PojpDRmXQL8bIa7PGb82is+Hr74eXUTbmr0Kd27jw2f+4O+Mm5iVTXrOYBJ79aahsoLC9WtprCjDqNVhlLQYZQWD14fB7UbvcGJwugjPGUDYVVeh6HTsyP0PeTu3Uo0Pj671D3ST20uI002I00WIw41WltmaEotPqyG23obdoKfRbMTg8XLxrsNoAPR6Ut+Zi6WVOb7aw1vtwLm3uZ9Oc9OZdKQJTSMh29w0/liM3Hj8ZtqIX2dh6Rd90mM1rVlD46JFhN1wA6aswPZ/xeej8rXXaFq1Cm9ZOd7KSrW/2c+E3XQj8U8+CYDrwAHyr52M4nIR+/jjRPSV4POZar+0ix442kRam6/+fGzgYgiByO4QHKMODOg5Qc2MqtGqnzflO9SA5XSaS0/C65PZtaeQHdsPEKFXuDAtEnnndqr+8x98VVUn30EzSa9Xm4qCLChuD96qKrUJRqsl6u67iJox46S1Mb76euwbN9L43SLqFy5Um1M0GuKfeYawydee8LWtKW9w8tW2UvZVNHL7Rd3IiGn5Oe+TFZweH06PD5dXxunx4fEppIYbkXfuwFtVRfCIEQE1M3a3l5e/30fuj/n45BPf4m8dlspTV5+5zxwRoJztm6XXpb7JPU6146sxRK1FaEM/kDlz5nD//fef9Qn/PE4ntWXFyL7AD7+gsHD0JjOKonYwVGQZp8tJQUERtXu2E5+WfsKU7YqisHfNj8heL6n9B2IJ7dzmpyP2rFrBV6+8SHpKDkNM45HcIJm06OODAAnF40Nx+VDcXhS3D9kjw/Fa1HQSe2rWsqt2FR7ZRURCElOf+4c667DLBg0laue9+mL1Z6+TiuCBbNtZiMliQWuvZNWi5cSG65nat5ymbfupP2TGVadHo1PQBAWhiUlFE98TTVgkmuAgtCGhhE4Yjz6xY4e6KoqC7YcfqHjp77jz89Ub2M3T2vTaoj072bjgfxzYsR2F9tdM6HwyIQ4XDoMOp+HojUEjK1i9MqGKhFXSYjUYCTeYMVosaIwmJJMRjcmMZDJSWFPBisL9LfY9BjOW7buIe+pJwq+/vt1lOxWy20fTmlKce2vxlNqQm7zIzlq8JVvxFq0j+r6bCb/xhlZfq8gyrn37qHz1VWzfLwFAMhiIefhhwqf+2l/zU/3WW1T87aXAF+t06GNi0MXFoYuMpHHxYgCSc9/C2L07h2+9Fc/hAoJGjCD5v/9Benuimv/jkifUZteAgihqzUl9AQTFqH22OrkGr8HpIb+yiYpGF5WNLioanVQ0uqhocFHZ6ORQtZ16x9HAL8So46qcBO6/KBndkm9xHzqMr64O2elEFxGONjISXWQkUlg4jZZQqvVBlGvMlHq0lDW68fhk0qKC6GHwkPjOv7B9q2ZzNvXrR+KLL2BITcVns+HOz8d14ADug/m48w/iOnBQ7VR8zC3TMmwYdTfexjdKDHllDVzWN57rBiahOUEfLqfHxzc7SvlwfRFr8qv9uzPpNfzh8l6kRgbx3trDrDpQ7Q9GWhNvNfHoZVlc1T8BRYHSBicHKmzsq7Dx9k/5FNWqX7gsBi0Wg675URvwe99EKzPGdEfXxqkIOoIIUH4WoCiKgr2VbxmdzaJpe+6CI7pKgFJbWozLbkdnMBAUHoHH6cRe33qZPD4fJWXlbPrgbew1VVz3x/9Hat+cVrfdt24VX7zUnJBPkojrlkFazmDScwYSl9ETTSckw3M7Hcy+/25MdhOjYq8PGEbbJpKa3MqYbsWUGUGdvpL5LzyOOdSKVqvFVltDeHwiIVHR6I0m9EYjepMJvUF9rCo8zIENa1vsNufCkaR88d0Je/YfS2u1kvSv17AMHuxf562qouThh3EfOkzIZROxXnU1psyeJ9iLyrl3L7bly2lctBjn9u0BzyU8/1esV199wtf7amuoe+Vxqj9eik3RUx4aRHWwmbogEyaPlwiHB2tICB6zGY9Bj0unxQ04FR9Orweny8mxHz0GJHr1yaHnxeNJGDQYnantnUa3LfmOtZ9+SNbwkVQczufQlo0Mu+7XDJ14ZYvOnqekoQS2zVdzcHS/WF30Jy+f7PSiyG6q/vkKNXPnglZL8uv/Inj0aLy1tTi3bcOxdSuOrdtwbN+OfGSWdq0WY2ZPXLt2AxB8ySXE/79n8FVVqTUhHg9Rs+4leNQodLGx6s34mGaEsmf+H7XvvYcuLg7JaMBzuAB9YiKpH7yPXqqFf12gNmM+uEttyjlLDlc38dbKfD7cUIjLe+LP5xCjjgvSI9hb0UhhjXrjjQ4x8vKNOfRJtLJ0TwWbCmoprXdS3uCkrN5Jlc3189H0LfRPDuPNhGoanvsLckMDksWCNjgYb8XxRwca0tMxDR7CxuwR/L1Iz6HqwOajASlhTBmSgs3lpc7uptbuodbupt6hPh6ustPoOvrtZ2BKGDqthnX5J+9/YtBqMOo0yIpCU/M8VIlhZmqa3Dg8voBtE8PMPHNNby7O6rgZkzuCCFB+FqA0+Xx0X7G9tV11qgOj+hLUzlTBnRWg+DwefF4PepP5pEGT1+OhquAQAFEpaeiaqz2dtkaamsslaSQkjQZJ0uD1+SgsLqZw1XL2rVrO4ITLyAjNwdw3mpDRSf7skz6vhzkP3UNDWQUh4VHY6qqRFRkF9cPJFBxCar8B9Bw6nPTMQfiqnfga1UnikGV0kWZ00WbQSDi37ca+Yw/6qHi0kbFI5lAUp695enS1FsSQGkrQwBjWf/UJtcsO0itsGDpJT5W7hJWlCwg2hnPB6GtJyxmIxqhTZ8c1aNAYtUh69WfJoFWzdx5zzdZ+toAfP5hLckg43X0aljdVHxmwcEI9Bl6AT4JDWzYi+3wMK6gkvLYBbVgYoZdfTvDoUSi2WuS8Zfj2/YhcXYrslZA9EvbqIFw1EpJeT+wfHifoopH46usoundWiwBHn5JC8MiRBI8aieWCCwJGlchOJ6WP/8Hfbg4gmUxE3HILss1G7fvvg1ZLxC23oDGb1KG5/lwqMigKrp2bsK3dTPM/G8ZILZE9qtAaZLjsefT9R2NISTlh50Of10ttSRFlu3ei0WjoOfoSdD8fpXEKtn3/LYv/+xoJmdlMefqF09uZsx4+uwfyvg7Moqy3QI9LoddV6qPpxLWAiqJQ+vgfqP/0UySzGV10NJ6ClrlGJKORoOHDiXnwAQwZGdT+710qXnwRxeNBFxuLNjQE1779BI0eRfKbbx73fSzb7Rz81a/wHFaPoU9KInXuHLX27bs/wOrXIPNymPLBqV+bU+T2yvywp4IPNxSyLK/CH0BEhxiJt5qIDjYSE2okOsRETIiRmBAj8VYzveJD0Gk1yLLCmoPVPPHFTvZV2JAk0EoS3uNEIjqNREyIkViribhQE7GhJnQaifyqJtbl19Do8pIVF8LcK1JwPPknHBs2+F+rREThS0rBnZiMMz4ZW2wSBdYEtjt0rMuvoaxBHRwRZNAyJiuG9Mgg3v4p3x84nEhimJkbBidz7cBEkiMsyLLC3NWHeO6bPRh1GiYPTOLagYlEhxgx6bSY9FoMOg3a5poZp8fHWysP8q+lB/yBiV4rkRYZRPfoYPomWblteBpBndDJ9XSJAEUEKAEURaGq8DA+jwedwUBweATGoODjfsA1VlfRVFeL0WIhPP7kTQpHrnliTBx7/7qQOG3a0SclMKSEoo+xUFGaj31/NZHGeDRS4HWRFR+yIiMjo0WL9jT6zgSQ1MynWkndn7FHGJark1j89mv+Wo2Y9O4Mv34q3QYOaVON16fPPcnBLRvoVVxFelU9dr2OBrMRn0ZqXjT4NBJejQZZIyEpkFzTQLDLgy46Gm9UJLZD+QQ7XARddBFJ/3y51Z79lG2Hze/B9g+RG2ooWRtBY2HLXDaG1FQi774b29IfaFy2PKBDoWQ0YhkyhOAL+mFOtFD+xns49peCRiJoSA7BE64gZNw49N4SlIMrKZ27kvqVbXuvGMNlwq8ZT9gDLyEtnAXb5kHqRXDbQnUDRW779BBV+2H1q+rre19zyqO56ivKeGvWHUgaDTNz52G0nEZq9m8eVUeCAaQMh9hs2PudOq/KERq92jG015XQZ/JxO4IrHg+Fd/+WpuYpP0D9Jm7u1w9T/36Y+/XHlNmzRR8I565dFD/4EO5Dh9TDhYbyr5ufocZs5Y6R6YzuGa3+zSoKFG+CHR9B1T7sMddT8Pvn0MXEqMFJQoLaof7vvcBeBVPmQeZlJ70EGw/Xsq+8kUn94gkxnfoIO7vby/trC/jPioNUNB5N9ja6ZzR3j+7GsG6R7aptdrh9PL1wFx+sU4OwHjHBjM2KISXC4g9EYq1GooKMx21uyStrZOpba6myuTDrtfi8XjIrD+LW6CgMicF+klqymBAjd43qxpQLUvyBQHmDk1eW7ONwtZ0wi55wi4Ewi54wi4Fwi54wi57oYBO9E0JbLVe9w4NRp8Gkb9v7pqLRye7SRpLDzSRHWNCfwaaaUyUClLPdxKMoKF4Fk1eBY9sPm/vU+dt0JXWSJsl4dKRGZwQoboeDmpKigHXHC1QUWaay4BCyz0dYXDwGnQlfvRvFJ6tlPTJ7aXN2UkUBp9vJ4aICQlc70ZS68Sk+dtSuoFfPkRiqTi3QkBUfukgz+ggLss6H09aISQnCW2nHa7fjUtx4FDdu2YlbduHxOY/+LDuRFIWU4CwiTOpIm0ZqielnJWhIIsaM7gBsXfQ1K96fg8epVhnHZfRkxPVTSe0/8Lgfloqi8K+pv8Ll8zKyqome99yrjg4xGsHrRfF4UI48ejwoHi+e0hJsK1bg3LY9oP3aes01xD/z9MmHSXrd8MkdKDs/p3p/HPU16XiKSlA8HoJGjiTxby/601j7KgqxL/4U28qV2Lbsx1vXMt+BxiCTNKKGoFg3ZF+t1hQ0pwlXZKjdH4S7UYd/GgP/323z6/UKIePHY5r6gpqoENTh468OUkeWpY2Eit1qrqCQeLUfQ1iy+mhNVpewZDW5llanjrB6/4ajE7NZk6HvdRCSAMHRal+II500TdaT9onI/d2d1JWXcvXv/0TG4KHqjMCKfMLmQ6/HQ3XhYcLi4jFagtTZa/91AbLPi/Tr+UiZzZM5Koo6Gmz3l+q8KlVHO7MT1VO98Ud2b/UYst1O/cKF6BMSMfftc8LU4wGva2qi7Lnn1I6Z9z7CDbuPNlFOiq3jwfhtdCv/Dqn2mKRrWiPei55BO+I3SAaD2h/u49vVCeuC4+CBnSfsE6coCm8sP8DfvstDVtQmlhuHJBMRbKC83olRr+WSrBgGp0X4v9UrioLLK2NzeWlyebG5vOSVNbLqQDU/7KmgpkmdcyY6xMjkgUlcPziJ7tGnN7JvW1EdwUYd3U6yH9nno3Dndvat+wmP08nF02dgtFjIr2pi2ltrKa472kHerNdiNmgx6TSYDFr1d71aixERZCAzLoRe8SEM7x7V5kBCOEoEKJ3QSVZR1GBD8crqhFw+GbzNj6hThEs6CcXlQ26eAr7NmqdIVxR45713eOhPj1BVUI6k16i5NNo518rPNVRW4Gy0YTaHoNXpcNmb/EOHtTodppAQDGYzkl6L026jvrwMvc6INSS6TTOpOr1uDpcUELK0Cb1HS3lqKUu/nY0kabjqrkeJNMWT9+1ynFUNOLUNpP60SB2WLWlBo0VqfkTSoGi0bEmPp1RrJzk+iXRDEKvyd+PRSBh8MlEuLyVmPUgSweGRGMxm3E4HHrsd98/6NQAE6cLQa/T0LS8kpEAdWqxPTsYycADasHA8QRZ211Wwa+9OvM01Dwk9ezH8hqmk9OnfIlApXvQd83JfRSPLTJ/xf1gvvrjN/w7e2lo8JSX4qqqQzGYsQ9pWYwOonW9zx0PFTojpjTLwN/gi+qNL7qEm6ytYBWv/DfsW+V+iKOBu0GErNWIrNWGvNGIIN5J05zCMFjvs+uzo/iUtZIxTh8nbytV9BscGBgjBMerP4WkQ2kruk8VPwE8vt/l6YLKqwcz+JeoIqujmb/etjoJrdmTyziPlislSE/olDVHzdZRu5fvv1rN1wy4GTLiCnAv789lrr1FbXY9Wp23uH2RGbzSr/YSMJrxuF5WH8/F5vYSGhzH5z88TsfJRdq5eww+VmfQaexnj7pjZenkq89RgZX2uOlWDyQqTc9Vr2RGdS2WfOvdM0XqU6gN8X2Lgvb0ahgeVMNq9gkzpaFORV2tGk3UZGlcj7Fc7ytL9YrUse7+F/BXq9bvxfyg9xtPo8lJv91Dv8FBn91DncFPX/Pu6/BqW760EBeJCjNQ0upAUcB1J+9IszKLHrNdic3mxu30nHDGSEmHhnjHduXZgEgZd53/T93k9FGzfyt61P7F/w1qcjUeHqudMmMQl02cAanPJ/gobkcEGooKN50QtxLlMBCinGKDITi++RrcaEDRXm/qDkeP0pD4uSVL7MOg1HEnkRHNme0Ddt4I68+gxb+p3PnyP/3vqUSp2Hq1Glo70idAAGuno9NmSBBq1PwgaqdWbnSLLNBSVY9IEqYmlTkCdrsUDCug0x4yqsOjRBOlQmgMyfy1Qc5ZSp9vJ4cLDJBqjCUmOQDLrWPTvV9ixdDFanQ6DJQhHQz1anY5RwdGYl/9I6FVXYhk4EE9xCZ7iYjzFxbhLivFVVtFoMvBjjySUYwIzjSwjH5OHpffwUYybcX9AvwVFUfB5PLidDhp37ODQH/6Ao6kRo8dHmMOFNjxcTXneSk4Fl07LwegwDkdbkZuvY2xEFBeMGkfaiFFozGbqv/qKLR/8jy1hZqJNQdwyd/4Jr2eHqz0E/xlzwinggeZsvNnqzTu6l5qZN6pH84wJmqM1NmU7YN1/1FFmF9x1+jlz3HZ1SgNTqJoLw5qs3rTrCtUOpvVFavNIfaE6nPXYBF89xsP1c9RAacdHanNFUwXYKo8+uk80FX3zewzY1xDJF8XZhOqdSCjUe9rW4VZCQUHCrFfICCpje93RIOzGp54nKesEuTIay2H+NChap/4e2QP6XKsGB/E5atbj9vB5YPP/YPmL6jU83maSjhVKfz51D2OxPJCoiHB+OyqdKY75aJarcwgdIRuCeTP+//FFfXeKah3YXK18+VAgxieR4dWS6NOQrjWgOI/2qXBbNJBsoSxcy7eHqwNG1xwryKDFYtSREGZmWLdIhndXl84aLaLICvWVDhprmyjYvpmCHWupOLgNr/tozYhGZ8FgTsPZuAskiV//v78Rn9GxqfFtNdUU7tqOJTSMlL4tv+AIIkA55QDFZ3Pjq3MdfwNJUtOIayW1uUMrQfMbTvH4ULxKc62HVu1c2YY/TkVRmoe3+kAjMfe9//HgIw9RlV+mrj9J7/YW5dNr1MBIpwFZwevwIHnUf2JJr1EDnGOCJNnnQ/b5kNCg+VmKfsmkQxtiQGM8cTVma9dc9vn48h/PsX/9GkDtbDvxznupvWEKitNJ+icfY8rObrEv2eXCuWsXP777NtuLDgLQp+9Axtx1LwdWLGXf+tV0u2g0fa/81Ukvh7uggII778RbVk7kHXcQecftIMs0rVmD6+BBfHV1+KprcBcU4M7Px1dbi1On5WBMGAWRVuTmACmy0U6Psloi7E62JUVTFBnK4MuuYvRtd520DB2u9hBsnQ+HVqpp/4/kbjGGwoBpMOSO4zYxdCmyTw1C9n+v9jcZcd/J+514HOr0DU2V6qOtDArWqHM8OWpAZ4a4vjgdTl7/MdQ/9Nlqkpk82IvWXoWnsQaPx4NH1uKRNXgU9W871mTDEJPBJ5v1lDuPNhdEJCRRU1JEXEZPfv3M39QMtMfjdcF3j6vZVn3HfI7ozOpkd6P+D5IvaP21iqLm4indqja3bV+g5iUBNWlgwgDcET1ZtmErqZSRmtoNU8510OtKGjQh/G/1YWb/mE91czPK6J7RvDbOQkjhD3BoJfVVxfy29mZWO1MCDmvSawgz6UlHT5oTYhpkDK6T3xI0Golxd/SmKdqARoIgo45go44gow6LXnvCYbYnoygKjsYGaooLqSkpxtVkI2PIha32h1Nkhf2bKlj18TpqCpchew4CxwRNUhBaQwYafQ80uiQkSYO76Wtk9x6iU9MZf/fv2LF0MeX5+/G6XPi8HqJS0kjrP4i0/gMJjTpx3prKw/nsW7eKmpJif3bjI9IHDOaS6TOwxnStUTRnmwhQTrUGxeNDcctHZ3+FwGDkOLUUnUnxyOpwRZ/in6VWnexMUQcWKMpJm5MUFLw6L5bY8NZTH/t8uJqaQAaNLKHR6tAFG5Ha+G3neNfc63az8v05mEJCGHLVddiXLaN41u/QJybS/fvFJ7yWXo+HTV9/TnRKGukDBh93u5NR3G4Uj6f1Tqg/46urw33oEK78Q9Tu2c3WvO0ctDf4R+iEKhJukxGny9nmGYg7laKoyQG9LjU1eUfOUn0ukX1qZuCQBH+/ivcff4DSA/swBQcz5ZmXiEhovrkpipqjyFahNmXZysHnhfSREBKHu2wvX/3tCYpKGxn3m7tIGTyS3PvuwuN0cPnvfk+vEaNPXh5nA+R9A7u/ULMYO44ZPtptjFpOR+3RxVmnPvrcgfsJioZRv4dBt4HOyNI9FfxmznpSIiyseLhlrqHaWicLlhzk43WF7JO8pERZuDQ7lvWHatlSWAdA30Qr913Sg5QwM1Kli6Id1eRvqcRxTII5nUFDau9IEjPDiU4NITzWglanweeVObS9ml0/llCyrw6NVuLyGf1I7RPZ9n+rn1EUhaLdOyjZu4fakmJqSgqpLSnG2dQyy21qvwGYQ0Ip3Z+Hs7GR8b/9PVuWKFQcqsTVMBeUJgC0+lBCovsQlZxDRGJ3zCFGTMF6TEF6dHoNi2dvoKHsv6Cc4Mtos4jEZNL6DySt/0CSsvugNxztpF5dVMj/Hv0dvmNrZCWJmNRuVBcdxuf1ojMYGX79rxl4+dVoTyOlfmtcdg+7V5VSdqAeufkecORurjdqiUwMIio5hPhuVgzmrjOaRwQoZztR2xmmKEcCFgXFI6u1MT4FJLDb6nF5HVjj4zC0MolZR2jrNS9++GEavviSiNtuI/bRRzqlLB2tobKC1R9/wK4VPyD7jlZ1z/jve2csyZzQfvvWr2b9Zx8x5tY7SOjZ/snOZJ8PTfMIvDUfz+OnD98lJDKakVNuISm7LyGRUa2+ZsPCT3E0NjByyq3q6xVF7TC89g11RJZykiGoQdEoaaOpj7oE67ArkUxH87j85atd/HdlPjcOTOL3w7tRXdxEdZGN6hIb1UU2muqPBjg1BvjC6KRS21x7KsHdo7pzS1Y8+9eWs29DOU7b0Rur0aIjrV8U3XKiScmOQHeC+Z5kn8yi3J0c2FSJVqdhyBVpdB8YQ1jMiUdMeT0+1nx2ENmnMGBCEkW71rP+i4+pPHSw5caSRGhUDBEJiSiKwuHtWwI6mANotGb0QVORPavwOHYRFpfA5bMeIq57zxN++SnOq+Xj5/+Hp2kRoEGj74HW0AOk5kEV3mJ8nkMovjKObSLT6vV0H3whl94xE4PFzLwnHqF07x5i0jPIGj6S8IQkEjN7YQ4Jpbq4kO/f+hdFu3YAEJGQypCrf0NwRBqKopCcHYHuFDvYOhrdrP/qELtXl+J1nXxIs0YnkdQznPT+UaT1iyI4/OzeF0WAcp4FKK3xejw0VlXgstvR6nREpaR1Wu2Pw+Egf/9+YioqCI6Pb7XpRnG72TviIuTGRlLffw/LwIGdUpbO4rTZ2LduFfvXryYmPYMRN0w920USzhCPy8nsB36LrfpoWnVrbBxJvfqQnN2XpF590BuNLPznCxTuVGcKnvS735P189qWmnzY8bE6/YQ5TJ39/Mhiav7dEMTG7w6z5rODdBsQzcDruxMUZEDjknn4pVWYqj10Q+0P1pqQSBNuhxeX3YsiQWOamYxRCVyQEUnh0lK2/nC0b5s5RE+3ATF0z4kmITMMbTv6h/h8Mt/9Zwf5W49ek8jEYLoNiKb7gGgiEoICPm8cjW6+fmM7ZQfrUeRGvI5v8LnVkYU6o5HuAy8gMjmFiIQkIhKSCItPCKitqK8oY/O3i/F6fMSk92DFu3NxNZUgaUNRfA1Ikoabnn6BhJ6BUwMcz/ZlRSx7dyWSJgStLoiwOAtx3a1EJ4dQcbjh/7N33nFyVvX+fz91+szuzvbdlE2vkEBoEZQqYKWoFyuoV38WUK/XawOv1967glcU7OXaUEBBBKT3kIQS0pNNsr1Mn2eedn5/nJnZ3WzKptLms6/zOmeeeerZZ875nG9l29ohCuksvtuN72zDd7cjfGkDFa5ro75tMbvW/RMwCTZcyqJT57P0ZR0M7cixZfUgqf4ChaxNYXQNTuEuENKbTgscix58CXUt9bzsjfPpXFBPdsSiVHBpnLb30A8g1VlP39fDA3/eTKnsvNDQHmHByW0YQa1qk93zzIP0bV6L70coFcJYBRNFDYMSQlFCNM2QRLTr2EaSHZOvKYQ4opqCGkF5ERMUp1TCymUppFPVFy3e1ELocETURCYn8y0LYVnlukSxWKB7YADt819A7e0ldu65NH/kPzGnTasel7vnXna8611oTY3Mveuufevya6jhOYB/PNXHf//lKTrrQ7xuXojGHY/St/4pBrZuQYiJtmGaruO5Y0an7fMW8sbPfe2ArymE4Of//QC5QTmh9Wo+AyFYZmkTSEkgrNPQHqGxI0pDR5RQ1MbKbqd/y3oyQ8OUirMY3NWIoqgEwjqRugAjPVIFMuf4ZhasbGPagnrUQzBa9TyfZ+7vZdNjA+zakJKq5zISzSFmL2+iriVCdsRi/UN9ZAaLqOp2rNTf8L0iYGCETqBz8cton9uKEdQwgzpGQJPtgI4R1MinSjxx1052rU+N9ZOXwc7+CiGkDdaJr30dp73psgO6/+FdOTRDJZ4MTuoH3xcMbMuwdc0QW9cOMdKTQ3i92Lkbq6okAD18Fnrg2H1eR/hFPPtu3OJTAChqBNVcgmbOQ9N97Px6hDdAsrOB9rnthGIxApEooVicSF090YYkVt7kgRt2MrBNkqRkZ5SXXDyHzgVjanvh+9z1y+t47OYb9vPkapmsBNGMIEZAxwiaRJPLKORnYeUcNE3gu88w/5QVnHXp4VVl1wjKi5CgOJZFZmgApzSmVzVDIeKNzYcUoVMIgbBt/GwWL53GLxYn7VPyfbqHhgj98le4Dz4Ivo9iGLR+9rPUXXgBwrbZ8Z73kL//AereeAltn/70Qd9PDTUcCXjlCKWO59NZH+JPq3Zx9b82T9gnbGocN72eFe0h5opBQsPb6dvwFP1bNuF7Hg3tnZz1zvfxxy/+N77n8pYvfZuWWXOmdH3X89nQn+PXt22i7YEULgJbgfC4EMW9ms9gvcYX/t/x+P4wvRvWseuZp9m1fh2Zwf5J54zUNxOInUMhK400gxGDsy5dyMxjJqunDhVW3mHrmiG2rB5kx9MjeHsw7g/HMozuvB7he8SbZxCIvZLsyNTHaEWBcCKAa3sEwjoLV3rc88tv0DhtBm/8/NerEa+PBFIDBbatHWJwew8bHrgWK9tPfftc3vCpL5IZLvHY37fR/fQIyY4Is5Y10T63jlDMlLYvYQPNUOl+ci3//PEPGO3dRyb1fUJD0aIkmhppntnGktPPrtrnuY7DLVd/i/X33w3AsnNfhaqqpAb6KKbTFLJpipk09h7G7/FQjVmo+nTc0mPgZ2nuWslbv/zJg7zfPaNGUF5EBEUIQTGTJjs8VJWYBMIRgrEYgXDkgEV1wvPwC0WZ7rtYxC8UEN5EPadimqjBIEogiBoMUFIUtu/cSdesWbC9m4GvfJn8/Q8A0Pzxj1F45FFyt9+OEggw83e/nZShtYYaDhSW4/F4dwrH85ndHKU1HmQ4V2Jnqsi0+jBNsTH1gJV3yKdK+J4oF1/WvsB2PFb1pPnRUzvZOChXxjFfYYGtMcNVmaHoqM1B/qwW2ZSdOLjrqsKSjgQndIRZHHOYPXcW9naLx2/4Eemdq3BmHsdT817J9uECYVPjjSdO5+LjO/F8wbrezLiSZX1/Ftv1OcXSOdUyGImrzDl3Gt79QwxkLW72CmzWfd62KMCMx35Hqn9iegNFUWma0UXHgkVohsGTd/wDK58jFE9w5tu/yuCOPMtfPoNo/eRIxIcbtuWyvWJ8m3OIJ4PEm4Ksv/dqejeuo2v5Cl77kSvRdINUf4FtTwyRGbZwLBfH8nBKHrbl4ZRcbMtDUWDO8S0seVkHsYaJY3pudIRAOIwROHpjfamQZ9MjDzJ7xUkEI2MeX77n71ci5ToOGx+6j/UP3MPW1Y+haQZdx62gkG2mb/MoRqDE7OVxHCvPSM8wIz0DeG62qiKaAEXhnHe9n3knn8pfv/4Fdjz9BKqmce57P8SivSRsdW2bYi5DMZMhOzzK0M4RMoMWg91b6dt4B8IfkwKGYgmWn38Rp1x88cF11F5QIyjPc4IiXBcvk8EvFlEMA8U0Za3rMr9JOQmh73lkhgawctLiPRiJEm9qrhr3VeA7Dt7wML5lSdVKuSjjawFePoefz08yRkNRUEMhtEQCLR6fEPnU9wW5QpGtW7eS0uroaIwzuynKwJe/zMjPfj52CtOk85qrib7kJUeu42p4wcLzBU/uSnPvpiHu3zzEI9tGscet0ivR3gEaIia/f88pzG6Kkh2x+M1nH8Kx9m1MOKD6rI35zFINZoz6aLvFDApEdLrO7GCH77B2MMcjPWl2ZK1q0DJDwMsLBoscHd/txc7+BtDYUv9q+ukjrwVZF12AqWvY3p5DB0RMjctyQcIFnzPftoCFK9sBuQj53SM7uPWOB1j2zB9xiwWMYIi2ufPpmL+QjvmLaZs7DzM0ZqRqFwv873svwy4WeNMXvjHleB9DO7bz+C03suGh+0k0NTPv5FOZc8Ip1Le1H5JdwhN3/oN//PC7GIEgl33zmv26774Y4No2iqqi6Tq25fL7Lz1Kqr8wab+6ljAved1M6lsgNzJCbnSELY89xNP33AlANNlIbngIMxTi1R/+JDOPWX5Q9zPUvY1/XPt9iuk0x73ytSw545wJdkCHCwdCUJ47vkfPAfi2jdvfj1ZXhxqJHHU7Cb9Uwu0fwMtmJpOE8VAUfEOnoKlUht1IIEjIMMG2oeyt4xeLuMPDeOn0vs+3++kNEzUcQg2FUMJhMr5G3vHwBfhpG9cv4fk+rifwhEC4NgPZEv9z5xPsynqcOqeRd130ThZFYwz94AcohkHnD75fIyc1TBlCCDYP5rl/8xD3bhziwS3DZKyJgcVa4gGiAZ3twwVcX6AqMkz5SN7mbT95mD+9byX33bBRrsoVgQX4CHxF5jn0AU+BBl+h2Vc5O60ivTYU2mYnmH1cM/WtYR64YTNDO3I8c+N2AGYDs1FQtDBKQMVSwbM8Qq48/+ZwM9OKLeD2M2v0BmaV7zeabOIRW6pXOupkArxFbXEWlkvChV//z0MoqkLXMWMTuOc4zBteTf+Tv8N1bNrnLeSCj/03oeje7crMUJgZS5ex8eH72fr4Y/skKL7vsWXVozz+97/S/eSa6nYrm6F/yyYZKiASpXnWHKYvOZY5K06moaNzyoSlkE5x9y+vB2DlG95cIydljFe9m0Gd8969hD9+7bEqmTYCGsedO4Pl50xHM+RclGiW2afnnfwSIg1JHvnLH8gNDxGpq+eiT3yG5pmzJl9oimicPpM3fe7rh/BEhx81Cco4uIODOP1Sl6toOmosKqUXFQmGaUopxmG2cBaehzs4iDs8XCUSajCIGo0iPA9RKiFcV+Z68X1sTcMydQSgCkHYdtDGGaipgQDoupSGVLaFI2h1ZbdYz5dZan1fGvuV22oohBqPy+OR0pGdqSKpwm7xGXaH5zDUu5Pr1uS4f1umGpblO5cs42xrB1p9A8H58w5bf9Xw3MeG/iwPbR0hbGjjgnhp1WBeQUOjN12ke7jAQLZE1nLIWC6jeZuRvM1TPZlqttgKYkGdU2YlecmcRl4yJ8nsJumB4Hg+I3mbhohJpujwuh8+wNahPE0Bgzf1a5go/D5SYrvpM6MhXCUEC1pjLGyL02jqrLl9B0/cuZO61ggnv2YWnQvHjA8912fVrdvZsnoQK+dg5R1ce7IUJFIX4Ox3LqJ1VoKtqx7ir1//AigmihJG+ClaZi3mpA9dRVMsQF14sl3Yqlu388CfN1PX1IPCasxgiFiyiW2rHyU3KmOpzDruBF71oY9NSaWx9vZbue1H36Ntznze9IVvTPreyud48s7bWH3rTaQHyuOeojLnhJM55uzzyAwOsP6Bu9n1zNMTDIABIvUNJJpaiDU2EUs2jivyczieQFFVtqx6hNt+/ANyw0M0zejiLV/69iQJbw1jsC0Xp+RJQ2FT22+ak9X/+Bs7nlrLS9/89udNQLiaiuegQ91beKOjeOm0JAR7giJT3o9XvVTIxIFKXIQQeKOjuAMD1eup0ShGSwvqHmKW+L5PdnCAYk5acptmgGgojOL74Lr4joOfy42TlihoiTh6Mol6AFldhRAUHY+eVJGC7aGgkIyaGJqKqkjdu66p6KqCpik4pRLbtm2jq6uLwYLPV29dz41rejh+Rj1/fO/KA+qTqeLJXWke2TZCSzxIZ32IjroQDRGzFlr6WYDj+eQsl/qIiRCC6+7bxpf/vg7nQNND7AZTV1kxo75MSBpZ0h6fUqj0HSMFLr7mfmYMepxuGdgRjdOuWMLCtsQ+088LX0w575Vje5KslAmLU/Jon1tHMDKm/ixk0kCAW3/0IFse+Sbgc8FHv8js44+p7uP7gsxgkeGeHA/9ZQuD2+7FLd7F+PgbIMX4J1/4Bpaeee6UJ/js8BA/et9loCi890e/nBC357Gbb+De3/0Ct2xUH4zGWHrWuSw75xXEm5onnMdzHYZ2dNOzYR1bVj3CjifXTCIsu0PTdcJ19WSHZF6lupY2XvuRK2mcPnNK917DCxc1gnKQBMWxS2QGBghGIxioKLZdjUQqa5fdB44KFE1DSyTQm5r2mZ1WhrYv4aUzkgjZJYqGjqNraLqOEQqhGSaaYaAbBroZQFEUXNsm1d+La9ugQKwhSTgxOTKs8Dx5Xs9DSyRQy2JEIUQ1kZeiKONS6YxlIrUcj7ztMZq3KTpSzKirCtMbwkT3kWp99z7vz1ic8qXb8QXc/V9nMD15CCnvd4Pvyyyr37xtw6TEZCFDo6M+RGd9iGn1YS5Y3sHxM+oP27VrgC2DOVbvSNGWCNEUC3Dz2l5+8eB2hnIlOupCNMYCrClHLT1+Rj2RgE6+nN02a7nkbdl2PEF92GB6MkJbPEgiZBAL6tRHTBoiJtMbwhw/o/6gs8Vu7c/yty+vgqLHmW9byMKVe0hueJTglDx+/IH/ppBaQzA+n1Mv+RBDu3KM7MoxvHMUpzSI7w3juzvx7acBOObs82ibM5/0YD/xxmYWnnbGQXmp/Oy/Lmeoe9uEKLhrbvsb//zx1YAU6y8/79UsPPVlUzY0ta0iwzu6yQ4PkhkaJDs8RHa4Ug+RT41WF0mKonL8qy5g5evfdFQNWWt47qJmg3KQKOVyOCULpyRFy2YwRDSZJFCWZgghJFmpEhZZ+7kcwnVxR0bwcjnMmTOrxKB6XLGIl8ngZTIIW6pMfEWhEDTxyiThNW+4hMWLFvK5q66sHqsZBn/4y1/5+JVXsf7xx1B1nbrmVgaGh6lvbmXevHk8+eST1f0VTUNvaKh+fuCBBzj11FM57Yyz+e5PJye3U1CqAX78cVxVURTqQgYt8QCmfmCTREs8yMrZjdy7aYi/rN7FFWfNPaDjd8fO0QKrulN0D+e5Z+MQD22V4u4TuxpwPZ9dqSL9mRLFclbSTQPSaPgXD27nwuUdXHLCNLYM5elNW7xyaRvzWw9PTJgXE3aMFPjO7Rv506qde82ssCtVZFeqiKmpXPnKhbztlBl7lWg5nn/Essa6jsfgw4NQ9AgnTOad8OyKvo2Axis/8HZ+/9kPYWXWc+fPfojw0vjeMIjJCRBPveRtnHjB6w+LNLBr+QqGurex7fFHWfiSl7Hxofu5/Sc/BODkiy9h5evffMDXMcsGum1z92zX4rkOuZERssODRBsaqWtpPeTnqOHFiRpBGYdQPIGq6Vj5LHaxiG0VGenZSSASIdbQiF62R8E0YVxuFyEEfi6H09ODsG3srVsxOjokgSkTkwkqI0XBjYQp+B5CCFRNI97YhG6amIEgoVgcz3VwSiWZnbfsu26GwiSaW9B0nZ/+9Ke84Q1v4O677+ahhx7ipJMmB9MRQnD1/17Lm97+bv70218y0NdLc+vElaRAVDVCqqJU7QXqw8YhZR69YHkH924a4obVu7j8zDlTGgQtx6MvbdFTnui2Dxf414YBntyVmbBf0FD57GuW8PoVY4Z6JdejN2Wxc7TIztECD28d4c+rd/Hnx2Wp4Ed3b+bLFx3DBcsnJx6bKoQQPNWT4cEtw/hCoCoKx3TWcWJXw/4Pfp5hIGPx/Ts38ZuHu6sqm2On1ZEu2OxKFVncnuAdp3bxsrlNPNmTZn1flpVzkixo3ffK6EiQEyEEa+/YyWO3bKvmljn2zGlVA8NnE9MXz2H6khPpfvJhvNKaCd+F6+pp7JxOsnM6XctX0LXs+MN23a5lx/PIX/7A1jWruO3a7/Pknf9ECJ9jzjrvoMjJVKDpBonmlueNTUQNz13UCMo4aLpOOJEgnEjguQ750VEK2TSlfJ5SIU84liBS3zAp6ZOiKGixGEpXF/a2bZKkbNs2cR9VRY3FEOEwOauAY0kpjREMUtfcilp2H3ZVg2IggRoEIgKvWMDTDISiMKLHGRmxQAh+9OPr+OxXvkm0oZlv/eCHfGn2EhTk5B0LGtILoneYP/3h9/zm5jsYHR7kgVv+xFVXfrKcC1FUcyIKIRCAqamHbcA6d3ELV/5ZZfNgnid3ZVjamUAIQW/a4oldabqHC+xKFelNF+lJWfSmiwzl9myMqypyUpzdFGV6Q5hXHtPG7KbohH0CusbMxggzGyVxvOTE6Vy6ciZf+vs6Ng3kWdAao+R6PLJtlA/9bjWPd4/yn+fOJ74P1dXuGM6V+OOqnfzhsZ1s6J+czOyl85r4+HkLWNS+78n5uQYhBNfft42/runhTSdO56LjOsiVXH541xZ+ev9WLEcahJ46p5GPnDufZdPqqseNf18qtiIHA8/x2bp2iM759QSjBxdwq2djint/vxGAaEOAY8+cxjFnTtvPUUcP577nPdz72zChWJxkmZA0dE7bpzfOoaJ93kLMUJhiJs3af94CwPxTTuOsf39vzV6rhuc8XhQEpWL0OZX9oGKXoaAnGgiHYuRGhyjlCxSHRxkZTROOJwjX1aFOMopV8Tum4ezYiXAcwpEgWjCIGouhhMMU0inyqVE5sKsq0YYkwVickbzDSD5H0fbI2y4D2fHeCxr5chKrgi2f4aH77qZYLHDsyacRSzZz6YXn8aGrPk84HCFjwUBWGr7d8Mc/0DVnLicftxTlHZfyH//xH1x15SfLqdCP7OAUCxqcs6iFm9b28oW/PU0saPB4d4qh3L4ziAYNlfY6afTaURdi+fQ6zl7YQjJ64P74x06r47fvPqX62fMF37ptA9+/cxM/e2A7f13Tw3tPn82sxii252O7spTGtW3Xx/Y8tg8X+MdT/dUYFqau8tK5TcRDOoWSx+3P9HP3hkHu2TjIBcs6+PA585jWcPhsb44UPF/wuZue5qf3bwNg9Y4UP7xrM4PZEtmSlPodN72Oj5w7n5WzJ5KPwzXBCSG47bqn2Pz4IPHGIK/+wLL9Jp7bE9bcLvPMzD+5lTPfuuCQwrgfCcSbmnnFFR85qtfUdJ1FLz2D1bfeTNfyFZz42tfRsWBxjZzU8LzAi4KgFB2PRf9961G/7tOfPZegqVMqFsju2oFbTssdiEQwE0nyrmB7Xw7XH3NZNDSVZDRQzWth6CrJiImqKMxMRhDA5//0G17/+jfQ1RSjq2kFXV1dPHrn33njm98qjRFLLr6Am37/K95x6dtojgU5//zzecc73sFdd93F6aefflSe/8LlHdy0tpcHt4ylm9dUhXktMeY0R2mvC9KeCNFeF6ItEaSjLkRd2Dhig6emKnzk3PmsmFnP5256ms2Deb74t2cO6BzHdia45MTpvGJpG4nQ2Ep/+3Cer926npvW9vLnx3dx89pe3nbKDN5/xpyqd8u24QIt8QBh89n/2eVKLvdtGuI3D3fzr/XS0+Li4zq5/Zl+tgxJ9/QFrTH+69z5nLmgeUr/k95NKZ66t4dwzGTmMUlaZyWmRBLW3rGTzY/Le8gMWfzpa49x9tsXEUlIA3FFLZMhZZyBt6pUQ5+rqkJqoMDWtTJx3fHnzXjOkZNnE2dc+m5OveRtBMKR/e9cQw3PITz7I+ULGL7nkR4YppiVNhSKpmEHE4z4Ou7wWNhsU1NpjgcIB3QSIYOOuokuxuGya2Q8ZJBKpbjxrzdw7733VmMpXPq2t/K7X/6My//fv0tyIwTPrF/P6lWPcvONfwFA13X+7d/+jZ/85CdHjaC8bF4TbzppOiM5m+Nm1HHc9HoWtycI7SOV+9HA6fObOXVOI39ctZPfPbIDzxeYuiqLVq51rdoO6CrRgM65i1tZ2pnY4zlnJCN8/03H8e6Xpvjy35/h/s3D/Pjerfzu0R2cv6SV+zcPs3O0SEPE5HOvXcJZC5v58T1buP6+bTRGA7zllBlctLxjn26wB4uC7bJ2Z5pV3aOs682ysT/LpoEcbpkEm5rKN95wLK8+tp100eG3D3czrSHMeYtby9K2PSOfKpEaKFDI2Gx4uJ9ta8cy2z5+WzearhKpDxCtCxCtlyVSF5R1IoCiSkJy/x83AXDCK2eyde0QQzty3PjdNXu77ATEm0K84r1LeeqeHhAwfXGS+tbaRDweqqbVyEkNz0u8KNyMp6ri8cuuuK7n4/oCzwPXF7i+j+cJ2RYCz/PRnTwht4hazmpqGWFKZrQafjskSiRFAbXsllzUQuT0CKKsWqkYpCZCOnVhKSF5zWteQzKZ5Prrr59wX9/+9rf51re+xfbt27n66qt5//vfjzYuFoIQAt/3Wb9+PfPmyYBoH/3oR/na1742ab9AIEBvby+JxJ4n2oPBCyG9wOGEEIK7Ngzy5b8/wzN9k700ABIhg3TRmbAtYmqcsaCZ85e0cfr8poMiK0IIdowUWdU9Wi3rerOTXLIBZibDnD6/mdcd38mSjqm/D57r8/CNW1n1j+0TvO4VVWH+ya0IT7D9yWGsvLP3k+yGuSuaOeedi7Etj3/98hl6Nsps3EKUVa9CupgLAZRr3/MRAsyghi/ALXm85gPLmLbohWesXEMNLxTU3Ix3g6IoR0CsXo/ruuRGRrCyaUJYmJqKq6vYVgldyMHZVTSyegxXMwmbMpJmNKATMjXU3cTm8+fP5x//+MekK61atapKPH7yk5/wn//5n1x22WUT9nnf+97Hddddx5e//GVc1+XnP/853/jGN3j5y18+Yb8LLriA3/zmN7znPe85jH1Rw3goisLp85t56dwm/rqmh8e7RzmxK8mpcxr5yX1bufrOTaSLDq3xIB89bz7posPPH9jO1qE8N63t5aa1vVUbl/OXtHL2whYS4b0bjg5kLW5fN8B9m4Z4cMvIHu18WuNBjptRx5KOBPNbYsxvjdEcMll3fy/hQRvRPtHgVQiBa/uUCg6lgouVH6uf+NdOhnZII+FEc4hIIkBdc4hl50yvSi98X5AbscilSuRHS+RGS+RSFvnREtnREoV0+R4VaJkZ5/S3LJCJLkM6575ryZT62co5/O2Ha+ndlAagoT1C58Ja3Jsaanih4EUhQTnSKGYzZAYHmNCVioIeTeAHY4RMjYip71NcDrBlyxYWL17Mu971Lv793/+dQCDAzTffzMc+9jFuvPFGWltbWb58OevWrWPBbhmBr7nmGj772c+yY8cObrrpJv7t3/6NgYGBSZKSj33sY9xxxx088sgjh+35axKUA8MzfRke707x2mXtVeLs+4I1O1Pc8lQftzzZx/bhsaRhuqpwyuwki9ritMSDdDVGOHlWkqCh8puHd/DFv60jZ7m0eAodnoqvKjQ2hujqTLB4dh0r5jXS2Tgm4i9mbTY+OsCjf9tadcedfVwTJ792NlvWDLLuvl4yQ0X8fUSCDUYMznjLAmYtf3bzqriOx52/eIYND/fz8n9fzNwVNdfWGmp4LqMWSfZZmCydkkUxk0FRVRnPJBhCO4jIj4888ghXXnklq1evxrZtFixYwMc//nEuuOACrrjiCu644w6eeuqpScf19fXR0dHBn//8Z6699lp83+fmm2+etN/DDz/MSSedxJo1azjmmGMmfX8wqBGUwwshBM/0ZbnlyT5ufapvj2qioKEyrT7MxoEcJ1g6JzsGwX1oMYMRg3DCxMo5FDJj7tzRhgCFlI2/l+hrqqoQiOgEwgaBsKwTjUGOP38mkbrDn+n0YGFbLmbwRSEQrqGG5zVqBKU2WR5V1Pr8yGLrUJ5/rR9gx0iR/ozF6h0pdqWkkfViT+cVWUmEjYBG+7w6AAppm0LGppjZA/lQoK45zLFnTWPhS9oY2ZXnnz99mpGePMnOKMec0cm0hQ0EwjpGQKu5pNZQQw2HDTUblBpqeAGhqzHCjIaZVRVhRcKy+pkhsn/ZgYPHsrOncfIFs9H0ie61whdYBadKWIygRrI9ihEYM55umh7jDVeeQHbIItEcqhGSGmqo4TmBGkGpoYbnCIQQ5FM2o/15Un0FRvsKjPblSfUXyKVKTF/YwEvfOI9EU5g5DRHWPbaREcujpSvOyRfORttD7A9FVQhFTUJRk+Q+ovtrmkpdy3M/sFwNNdTw4kGNoNRQw7MI23LZumaIjY/207MhhVPauyFJ99Mj/OazD9Mxr46eDSlcx8cIapzzjsV7JCc11FBDDc9n1AhKDTUcRQghGNieZfuTw+xaP0r/1gyeOxZJWFEVEk0h6lvD1LeGqWsJU98aQTdV7vvDJnY+M0r3UzIyb7wxyEsvmU+iKbS3y9VQQw01PG9RIyg11HAIEEIw2J2VEVXTdtXWI58uUcjIz47tkWgKkWgKMdidJTc6MU5JoinE3BNamH1cE/WtkUl2JBW85oPL2Lp6iNRAgWkLG2icFq3Zi9RQQw0vWNQISg01HARS/QWevHsXmx8fIDey7wSIACM9eUZ6ZI4bPaAxY1EDnQsb6JhXR11LeEpEQ1GUZz3uyPMSmR4oZSFYB6E60PfiHl0che6HYNs9kNoOkWaItcH0k2DGS0B9FlI0CAF2Xt6bqkG8/ejfQw01PEuoEZQaXpQQQlDMOmSGiqCApqu7FaXaVnVJHkoFl+yIxRN37uSZB/uqCR31gEbz9BiRhEk4ESAcNwknTCLxAOGEiaarpAeLpAYKxJNBpi1sQH+W8xG9oOG5kB+AnY/Ao9fDljsnfq+HIFQvyUqwDhQVhjdBrm/v54y1wdyXQ6QRAjEIxCGYGNeOQ3LO3skPgF2AHQ9CYUQSplKmXJeLlYb8IKR3QrYPxB7skY6/DM7/GujmXq6RlyXavJ9Oepbg2oAY66eex+GRn0B+CMJJiCQh3Fhul+toMySmQU1a+KJDjaDU8KKB63hsXTPE+gf76NuSplRwp3ysosjF7HjMWJpk0Uvamb5o/4SjriXMDJIHc9s17AvFFNz9NXji9+CWACEne+GP20mRZMJKy+/dImSLkO2ZfL6G2dB1GjQthMIwjG6DjbdCthdW/Wzf96IFoPMEaF0CmgGqDpopS/9TsOEWcAr7PsfeoBrgu/DYT2FoE5x5FWR2wchWGNkCo+U61y/3b1kKSy6EYy6BxD7ctw4GvWth4GkpzYl3lMmGUiYQu9W5fuh/GvrWwo6HoXc1+J4kc2ZYEpSpIJiA9uWw8NVw3GWgHYGpSwhJVM0oxNsO//lrOGDUArXVcMh4PvT5ltWD3PHzdRNJiQLR+gCKouC5frkIfMffa2TVQFinpSvOCa/sonXW4Uu4WMM4+J4kBxVpgJ0HOydrpzDWLgzDql9AcWTyORQNEp2w5GI4/lKonwm+L6UWxVGwUpLcWCnwHElMGufIiXB3uCXYdDvsemw3yUcGrHK7MFQmQPtBYpq8l4rUJRAbk8IEYlJikJgGsVZJclDkRG6EYeNt8Id3gL3nBJR7hB6Ek98Lp/4HmDFw8pJIVSQwvi/PZ6Xls1hpWUpZKcFIzoZYu5QyZXvgn5+BJ/8w9evvD6oOiy+CGadIyVJhRPZlfkj+fwvDUprkj0s82bwIzv+qJJKHC04R/nqFJLoAdTNg+ikw/WRZN84DteYpdzhQiyT7HJ8sX2h4rvf5Mw/2csfPn0H4gmhDgPkntTJ7eTP1reG9Sj6ELyaQFuELghEDzagNUocM34dSuqxeKYvtS1nYfCc8c7OUNFipqZ+vaQGc9Wm5KlcUOdlHGo+uzUhl9b3tXil1EZ4kWp4tCU44CYteA+3HHZqqYmAd3PA+aVfTMAsauqC+S9aVNsC6G2H1r2DHQ/Kzok1UGelBSVRKGSakpJ4SFJh2oiQTmR5JHoSQ59m9DiageTG0LIKOFfI4PSglMNlemHP2/u1qXBsG18HWu+Geb0iCCTD7THjpRyW5qcBzpJosNzCuHoDcYLkekO/FnLNh3nnyXjI98LePSOmOUv59T5DAId/V6SfDrDNg2RsnE1krLaVZTfPBeB541QkBg+thaH2ZCI7IvhO+/O2luiG1A5ZeDKf952G9dI2gPIcny73B8zxOO+00Wltb+dOf/lTdnk6nWbJkCW9729v4whe+wE033cTXvvY1Vq1ahed5LF68mPe///0Tshtv27aNrq4uVFWlu7ubjo4xEW9vby/Tpk3D8zy2bt3KzJkzD/nen6t9PtqX55kH+1h1y3YAFpzSyhlvWYBaixly6BBCiu89W6owPFsOaJldsg1yVZrrlyvg3IC08ciVJwnhSTuQjuOl6mLbfRNXyQBGREoPzIgUu5uRcSUqpQrty+HYNx4Zkf/zHUJIsvePT8Hwxn3vqwXkpBtMSMmOGYFsv1QdeWO5m5h5Grz889C+7Ije+l5RGIE7Pi9VXRXCZcaktKkyuR4sQg3whp9D27HSfqn7QWkztPPRiao5MwrH/FuZ3OyUpHFog/zOCEvi1LwIvJIkVxPqkiRBrUth2kny/TePUoDE/LC0x9p8J2y+Y88qzt1xzCVw0f8e1tuohbp/HkLTNH7605+ybNkyfvWrX/HmN78ZgCuuuIKGhgY+/elP873vfY8PfehDfOxjH+Oaa67BNE3+8pe/8J73vIcnn3ySr3/96xPO2dHRwc9//nM+8YlPVLf97Gc/o6Ojg+7u7qP6fIcKp+SRT5XIpUrYRRen5OGUPOyiS6noYhfKteViF13yaZvMYLF6/DFndnLq6+ai7Cej9IsWvi8HWy0gJyenIMXs+cExkXt+UNap7dIOoTB0aNcsjsKmf459rp8J818JC14pB+8a6Tg0KArMP18a96Z3ypW9GZGEw8rIOpiQEidjLwsL35NSFiHkxBqqO6qPMAnhBnjVN+ElH4B7vwWP/2qyykvRpAQt0gzRpt3qZvnerf+bJMWKApEmaFkMr/yGfAcB5pwlC0jJQt9a2P4APP5LKc159CeT7y0Ql331zE2y7AtP3yBrPQSzz5D/p+krpUrtYCVsqW7YcCts/IdcGHSeIAnQ0AZJSnrXMEFapgeh9RjZJ6E6+dtXVAhEpZqxbjo0zj24ezlMeHFIUIQ4eOO0Q4ERPuCX7bvf/S7/8z//w1NPPcXDDz/M61//eh555BEaGhqYPXs2V1xxBd/4xjcmHPO9732PD3zgAzz44IOcdNJJVQnKVVddxe9+9zs2bNhQ3Xf+/Pn827/9G5/73OeeNQmK7/kUcw5WpeSdCZ+dkouqqSiaQmaoyPDO3KTYIVOBqil0zq9n3kmtzDuxpRYzZE/wHFj7f3Kw398qe3coqpSeuCVpS1A3Tdp96GURt25CtAWirXIQjJXraKucGAefkXYdQshV56EMzjW8OGGlJWn2ytK3SJOUzE3FXsRzJJk5ENsSIaT0Yd2NciKPd0pVW8dxUo3X94QkCbm+MVufCXUAXEu+990PSjXXeIQaJLGYfrI8Z6RpTLJlRif+PpyiNDzefDts+IckTvtDyxJJiGafKW1rngV1VE3Fs/tkaefhi89C/IBP9sgVywFACMGZZ56Jpmk88cQTXHHFFVx11VV861vf4sMf/jA9PT20tU20MLdtm2QyyTvf+U6+/e1vVwnKww8/zCte8Qr+/Oc/c+qpp3Lvvfdy0UUXcfPNN3PiiSceVYIy2pdn3X299G1JM7A9OyF66lShBzSidQGCER3d1DCDOmZIwwzpmCGdQLk2gzqBsE7zjBiBsHGoj/f8gZ2Xq6Ti6ET3VTs31g7WQfNCuWLafAes//uY54eqyxUzAlDkajXcKAfJSGO5NEmi0bpU2hYY4xYBNXJRQw1ThxCS0Kz/m/wt9qyWaqC9QVHHyIoRkTZP4/dXVCl5nPtyKQna8TD0rJLt2WfCrNPlb/dZRk3F8zyGoihcc801LFy4kKVLl/Lxj38cgA0bNpBIJCaREwDTNJk1a9YESQmAYRi85S1v4brrruPUU0/luuuu4y1veQuGceQmbd/zWXd/L91Pj9A0Lcq0RUk2PTbA2tt3TPSMUSAYMQhFDYJRg2BE1qGogRHQ8T0fzxNE6wIkO6M0tEUIhPWaFKQCpyj15IPrYXizHIh2PSbtOQ4UkWZYeTmseIcc+NyiXO0diIql9n+poYYDg6JA2zGynP5xaafS94S0e+l+UBoSW2npbeaXDViLo2NGwiDj88w8DeadK0lIuGHsuyUXHfVHOtw4YIJy991387WvfY3HHnuM3t5e/vznP3PBBRdUvxdC8OlPf5prr72WVCrFS17yEq655hrmzh3TZY2MjHDFFVdw4403oqoqF198Md/5zneIRqOH5aEmwQhLacbRhnFwxk/XXXcd4XCYrVu3snPnzkOScrzjHe9g5cqVfPGLX+T3v/89DzzwAI4jxaGu42FbLr4nvVQ0Q8UwtT3aaQghcG0f154cPKpk2zgljw2P9PPUnX3ViKlbHh/kob9ure43Y0mSOcc30zorQbwphPp8tAdxS3LQ0Aypytib7n6qEEJKPdb9Va6IYu1S91s3bUwPHKqXg1lqh/Ry2XgrbL9fiop3R6xdxr0wo+PcV8ttMyLF4QNPS0PV6adIT4au0yYGGDtAqV8NNdRwGKCb0Hm8LKe8f2y7EPK3XnEBr7iE1898watFD5ig5PN5jj32WN7xjndw0UWTGdpXv/pVvvvd7/Kzn/2Mrq4uPvWpT3Huuefy9NNPV8X/b37zm+nt7eW2227DcRze/va38+53v5tf//rXh/5Ee4KiTGnQFb5AwFGdOH1fSGmBK/Bsj/vuu59vfetb/Om3f+Xr3/4Kl771Mm74/c1Ma59JOp1m07qtdHR2oGqKLKqK4zls3ryZM844Y9L5Fy1czLx583nD6/6NeXPm01Y/kzVr1wAyXHvcnGiboygygiq7dYHn+tXIqbvDcW2snMMTN23FSvt0RrewdMEQW4vHs2mjQawhyMqL5zBzaePh6bRDRc/j0jIfyu9GTHouVAwGq54MsTH9dCkLD14D939fusjKg6W6ZNqJ0tbCKUoC45ZrpygHFt+T718gJj8XR+WqyM7LGB75wX3frxmV+u3U9onb4x3S4yA5WwYWm/kSGb/hBTxg1VDDiw6KIm1FjNBzQkVzNHHABOX888/n/PPP3+N3Qgi+/e1vc9VVV/Ha174WgJ///Oe0tLRwww03cMkll7Bu3TpuueUWHnnkEVasWAFII89XvOIVfP3rX6e9/dnLNVEqumSGiqjaWJhzRa0QAaVKCuQ2FUUpz+OeLY1w3ZJciRoRKR73PYTv4QkNz5PBwHynHFvDE3iOz3gToEKxwHsufxeXvuWdnLTiJXzjS9/j9PNWcu2P/5fzznoV//O5T/GNb36Tz1z1hQn3fe31PySfz/OKs14rk9ZlpF4yPVhkeFeOf7vwTXzsU//JVz//TWn7Ub5mhYxUnsktefi+wHX2EGIbgaHaBAwXpWoJLuuS6hDUi5zSeRfTG24hWnwadsIs1eDMc96CsuxNELekUZp2kOql0W3w8LXSOr55sYyk2bJo7HvHki6u6Z2yzg3IFUbnCWORNF0b7vw83PddmFLsB2WMsJTSewjEJaQ0YuDpg3umCvQQzD1H6o9z/ZDeIaUlqW4Zu8HOyYIipR4Vz4ym+TUyUkMNNbxgcVhtULZu3UpfXx9nn312dVsikeCkk07igQce4JJLLuGBBx6grq6uSk4Azj77bFRV5aGHHuLCCy+cdN5SqUSpNGYMlMlkDudtV1Ex3PQ9ge9JN9YKFHw0xUbHQVPGFRxUZe8GnwqykxWhowgdDwNEuWDgYqIoMt/Ll7/wORQFvvKVLxMJh1jSuIAvf/ErfOLKj/Hq176Kz33mC3zyqo8TjoR4/UWXoKk6f7/1Zr741c/w3nddzrJjjscuuhQyMm6BVyYal136Tt5wyRtobEpiBgziA1L1lOyIkuwYU6sJIUmT54nKBhTfRiv2ojo5SUz28KiKEBgiS1fp9wSLO6RnR9N86HsC5bHr4bHrx3oj0ihXAcE66eZmBGWtB+UKQQ9KwleJLeAU5SRcTFElFaPbpGFZ12mSNKR37dvlNZyUkgU7NxavYNYZUkoifCjlylFBx0XT9Cph09NjEpPkHDj9E7D4QnlcYVhKYnY+LKUh1ecIStKhB+QzKdqYoaoelCqbSh4XMyL7am8SPscq52bplQHJorVkgTXUUMOLA4eVoPT1yWRbLS0tE7a3tLRUv+vr66O5eWIiK13XaWhoqO6zO770pS/xmc985nDe6h4RCZQIRwcRQsVHBd9D8W0U30ZlT1IFCSEUXEw8YaApDjqlau4WgYKqCDTFRVNcYKLdgEBFCcS465En+PF1/8u/7ryTZHNd5cRc/t63c+Nffs/lV7yTf/7tryycP5Ovf/u7/O+Pr64GavvB1Vdz6dsuxfcEru1hDkkpRSgRINkRlSobxiIf6nuJhqooCrqpyZfC96S9QraPKjFQNDnhViN0lnNuOB6YBVjwaqhvhWVvlkRk+/1w33dkHpJKuOr84P5VGnvD7DPludf9FZ7+i4wsOR5GWKo9Ep3y+oPr5bUrIbNBuvG95nuw8FX7vpZjjYUyt9IyKFT7ceMMRzVJtBa+av/nOhQYQRmCvXHOkbtGDTXUUMNzEM8LL55PfOITfPjDH65+zmQyTJs27fBfyCuhlDIowB6ncNWQq2I9gNACCNXEU0xsW8XKe3ieFC8oisAwFcxwgEBIR6gCpRJFsBJVsNxWfBdKaV52zHTc7kfkNQbXA4qc0D2bW3/xTXn91DZec/IcXvPb75YTkZX96nUTnDToAcxogGOOX8C+vMeXLVu25++FkBKLYjknxvhIjYl2KRXYk0rBsmDEhrM/DePdjGeslAVkILDCsIwPkO2Tk79jSXuNSl2x21BUmfuieaFUsSBkHW/Dsiz64ycw0HAhxcHtNDQ20dg2nYZp8zDjzZPvzy5I49P0Dmn7MecciE0k0HuEUZaEPFezwtZQQw01vMBxWAlKa6s04Onv75/gDtvf38+yZcuq+wwMDEw4znVdRkZGqsfvjkAgQCCwjzTmhwmuFsYJdyCEtNNQAM0w0c0gqhGckNujLDtARS7cwwmB7wqUsr3KJGj6ZDG+KGdWtcordacgScmEkN+KVAUo2lioZOFJd1Lflcm/dkeFSClaObeEKOeWKMe3qJxXYeyzEPJc491UNVO6sVW8SA4FqirVE9EmGUNjCigUCjiOQyQSYXh4mPtv/zNPPPEEvj9Oz7Q+BWwEbicej9PY2EgymaSxsZG5c+fS0NAw5sp3GFAqlejt7aWvr4+RkRF0XccwDEKhEIlEgng8TjweJxKJoNaSi9VQQw01HDQOK0Hp6uqitbWV22+/vUpIMpkMDz30EO9973sBOOWUU0ilUjz22GMcf/zxANxxxx34vs9JJ510OG/ngFHyIF3YLY6EVQJKqGoWXdfRdR1N0ya0VVWVBqfG1CZx3/fxfR/P89A0Ey3aghJrlVIGpzBGEhRVkprdk575blkCU5bEjG9XCI49Ma+JTN2loCB2d9CZCEUtZ1VtLBOjqRMT3/cpFAoUi0U8zyOZTKJpB56wTQjBfffdx+23375HSU88HqelpYVQKMTIyAjDw8MUi0UymQyZTIYtW7YA8Pe//52uri7mzJmD67q4rksymaS9vZ2GhgYURUEIgRCi+j8ZX8ZvT6fTrF69mnXr1uG6+481oqoqsViMeDxOLBbDMAwMw6CpqYk5c+agaRoPPPAAa9asIZFIcOKJJ7J06VJM0zzg/qqhhhpenBC+jzs4iGKaqMEgSjD4gooVdcAEJZfLsWnTpurnrVu3snr1ahoaGpg+fTof+tCH+PznP8/cuXOrbsbt7e3VWCkLFy7kvPPO413vehc//OEPcRyHyy+/nEsuueRZ9eABmQ+nMkEoilIlEZVJyrZtbNuedJyqqlWiUtlXUZQqgamcp3Ku3SddTdMwDEMaqXpe9VhdVzBwME3pTlyZTIVQEGoAoZgILYJv+NXvdFXBUDyEa2PZDkXHx/UE3rhLqorkHaqiyLq8zQgECIRj6Lq0YRFCGgu7rjtpwh5fSqUS6XSaq6++mlwuV72OaZrMmDGDSCRCPp/Htm0ikQiRSIRgMFidtE3TrLZ1XefRRx9l/fr11f+DEAJFUVi4cCErV66ks7Nz0v8gn88zPDxcLT09PWzZsoWtW7eydevWSfsfCuLxOG1tbTQ2NiKEwHEc8vl8lSBls9kqqUmnd/f8mQzLsrjxxhu5+eabCQaDBIPBqtSw0g4Gg3R0dDB37lxCIRme2nEcdH3fwesq91YphUKhKplqamqira2NcDiMEKL6Hk4VjuOwa9cutm3bRj6fp6Ojg2nTplXJXw011HDkkLv3Pvq/9CXszZsnbFdCIYzWVurf+hbqLr4Y9ShoH44UDjjU/b/+9a89xtu49NJL+elPf1oN1PajH/2IVCrFqaeeytVXX828efOq+46MjHD55ZdPCNT23e9+d8qB2o52NmPf96srcK88YVfKoWQK0DQNz9u78e14VCbqqeJA99/9WGDKx7uuy65du7jvvvvI5XJVkrcnMjdVaJrG+eefz3HHHYdlScPicPjAAt+lUilWr17N0NAQpmmiaRr9/f309vZWg9XtDaqqTiiGYTB//nyWL19Oe3v7Pidgz/PI5XJVwpLL5XAch1KpxI4dO+ju7sb3fbq6ujjllFMYGhri4YcfJpVK7feZVFWlrq6OXC6HbduEQiGam5uJxWLVd7NCQiqkcKpQFIU5c+awfPlykslkVRpWqSvtXC7H8PAwqVRqj+9IJBJh+vTptLS0VNVc4/tLURQURSEYDBIKhchkMmzevJnu7m5Jssuka4yQ7700Nzdz1llnMWfOnBopquEFD9+yyN97L6nf/4HcXXfJjRWPjD1Ab24mfMIJ6I1JtIakrJNJ9EppakI5gpHF94RaLp7DTFD2hfGSEc/zqhOaEKIqeahsq0hZNE2rDtIVyYzrupOOdV0X27b3SGIqx+9eHMepThqaphEOh6uTc+Xce1JhVCQhe5q4NU2bcM/jJ25N03Ach+7ubtra2kgkEui6ju/79Pf3s23btqodiWma1Qmucq09lWAwyDnnnENHR8cR+5+Nn7jHP0/l+Y4kSqUSlmWRSIx5Vvm+TyaTqX5Xca2vtHO5HJs3b2Zw8MA9oFRVrUquwuFw1T5mYGCA/v7+iTY9B4hoNMrMmTOJxWLs3LmTnp6eKZPuw4mZM2cya9YskskkbW1t0vboeQ7Xdenv70fTNJqamvaqLt2dtPm+j+M4uK5LLBar2UIdIQghsLduQ5QszNmzUY+Qeta3LHL33EP2llvJ3XknfqEcXFPXaXjzm2h8//tRIxGEZeEXi/jFIrm772b42h/j7sUztgItmaT10/9N/OUvPyL3vifUCMpRJChHA57nVdUc48ueUCE3QggMwzjgVWWFaFWOqxCTfeGF2OfPVYyMjJBOp4nFYoTDYdLpNAMDA+Tz+apdVCgUqhKSSCRCIBDY6/+wQrAVRSGVSrFmzRqeeOIJHMchHA4TCoUIhULVdjgcJhwO09DQQDKZJBaLTTi34zj09PTQ3d3N6KjMGTJ+iKm0fd/HsiwKhQKmaTJ79mxmzZqFaZpVwl8hjHsrvu/z8MMP8/DDD08iRXV1dXR1ddHR0UFrayuhUGiCBHR8XVHJqqpKc3Mz9fX1h+3/5Xke/f39DA4OViVqpVKpqi7evaiqSjgcRtM0BgYGqs9lGAbNzc1YlkU2m60uJPY3fM+bN483velNh+15Djd84fNAzwP05HvQFR1DM9BVHUM1MFQDXdGJB+IsTi5+1iVkwvexN2+msHo1xVWPk3/ggTECYBgEurrQkg1osThqPIYWi6PFY6iVOh5Hi8dRI1EUXTow6E2NaLEYAPaOHeTuvBPhuBidnSB8sv+4jdy//jVGSgC9vY34y8+l7g2vJzBr1l7v17dtcv/6F86uHrzhIdyhYdyRYbyhYdzhYdyRESi/R4kLL6Tlk5+o3suRRI2g1CbLo4pan9fwbGJ0dJQnnniC4eFhhoaG6O3tPSSpUGNjIzNmzMAwDFRVJZlMMnPmzAm2NbvblXmeV5V4jY6O0tfXR09PDzt37tyvOnFfCIVCVenmweIjH/nIkctzdpBwfIc/bfgTv1j3C7Zntu93/0XJRXxw+Qc5pf2UI05UhBB4Q0OUNm7E2rCB0saNlDZspLRpE6JYnLCvYpoooRD+FGzN9gajvR01GqW0W7LX8dDb24ifex7x884leMwxh6UPhG0z+P0fMHzttSAEWkMDTR+4grrXvQ5FP3IRSGoEpTZZHlXU+ryG5xJKpRLbt29n+/bt9PX10dfXVzUoHu+FV6krqk/Hcejv79+rVELX9aoK5UCGzUAgUFV/xuNxgsEgpmnusVQ84WzbpqWlhfr6eoQQDA4OMjQ0RDgcJhqNVqVieyuGYfCjH/2Ivr4+LrzwQo499tjD1b2HBZ+855PcuOVGAGJmjOOaj8MTHq7v4vgOru9W2zuyOyi6khi8tPOlfPm0LxMzD/9K31q/gYGvfx3rySfxRkf3uI8SChFasoTgsccSOeVkwscfjxII4Pb0UNq0CS+dxstm8bM5vGwGP5PFy2Twsxm8TFZuy+XBdRG+j5/Njp1cVQmfdCJ6QxJn5078QoHIqacSP/88gkuXHjFiVnj0UXqv+hT2tm3VZ0RRwHWpe/3raf3UVYf1egdCUJ4XgdpqqKGGFweEEAjHAc9DMYyDWskFAgHmzZs3wTB/v9f1fQoPPojf1sb2fJ6BgYGqcXxPTw+7du3ar3t5xdsqGo3S1tZGW1sbnZ2dNDU1HZIdiKIotLS0TIrQvT/MnTuXvr4+Nm7ceFQJyubUZv66+a/cvOVmokaUn5z7E5KhZPX7GzffyI1bbkRVVD6y4iNcPPdiwvvI/D5ijfCTJ37Cb5/5LXfvvJu3/O0tfP/M7zMtfviCdWZuuZWeT3xiTEKiKJjTp6PMm0uxaxa55ibSoRAjts3A4CCpVIquLVtY0dDAzJkzKYZCWHPnlg/de/gChMDQNKZNm4au63jpNKUNG3CHhwmfeCL6FGynhBAUCgWCweBBhXHYHeEVK5h1418Z/e3vGPr+9/HGSYPEIUj/DgdqEpQaDhm1Pq+hAnd0lNxdd5H71104O3ag1dWh1dfLUpdAq69HL382p09Hb2vD6e5m5Oe/IHPzzXiZjIwHVIGqSqKyl2J2dRF/1SuJnn76QRspFp96iv7PfZ7i6tUowSBNH/wgDW97K8q4wd9xHLLZ7ARD90pdaT/X0N3dzXXXXUcwGOSjH/3ohHsUQpBzcji+g+dLyYUrpNTC8z0EgpnxmRhTTO6ZslL8bevf+Ovmv/LU8FMTvju+5Xiuffm1GKrBjuwOXn/j68k7ed637H2899j3Tvl5nh5+mituv4LgQJDppeksW7GMN5/yZqJmtPpMByNlGP7JdQx87WsUQiGGTjsVa8UKRj2P4ZERsuMlHIcR0WiU448/nkWLFlFfX1+1Oert7a2qFSs2ZiMjIxPK6OgojuMQCATo6uqqhnIYH5ZA13Vs266qF3f3SqzYXJmmSTQarb4bvmXh9Pai6DqKpqFGImjjjPkPB2oqnufpZHnZZZfxs5/9DJDi5M7OTl7/+tfz2c9+tvos43+AsViM+fPnT8geXcEf//hHvve97/H444/jeR6zZs3ida97HZdffvlh93B4Pvf5swW7u5vcv+6iuPpxQsuPo/7Nb0J5Dk5yU4X1zDMMX3cdmb/9HaYQyK4CNRzGLxb36iY55fPEYoSWLyO09BjMri60+jpU08Tu3oG9fTteKoVfKOAXi4hiAb9QrHo8ODt2yOurapUcmTNnoibi4HqEV6yg4Z3vwGh+fqU98DyPr33ta1iWxTve8Q46p3Vy85abuWvnXTzW/xhDxX0k2AQ6oh28f9n7eUXXK9B2Cxbp+i69+V6eGXmmek63HGBSV3RO7TyVl3a+lG88+g3yTp7Xz3s9SxuXcv1T17M1vZXjmo/jJ+f+BF2duoSsVCrxx7/8kQ1Pj9lq7IrvwqvzcPIO2ODpHr7pgwmKqaAFNIygQSAYIGSECOpBmsJNnDvzXBY1LMIZGuJfb30bm2fOoH8vkcwjkQiNjY3V0tLSQjgc5sknn2TVqlUUCgU0XcMIGJiaWVX/7Y0UZLNZ8vmJEcArMbSeDaiqWlUbVlz8K7ZVixcv5vTTTz+s16sRlIOcLH3bRliWXLWpqhywNE2upPbhOXO4cNlll9Hf38/111+P4zg89thjXHrppbznPe/hK1/5CiAJyvXXX895551HJpPh6quv5gc/+AGrVq1i6VIZQv7KK6/kK1/5Cv/xH//BhRdeSHt7Oxs3buSHP/whL33pS/ngBz94WO/7SBGUlJUiZsYmDY7PR7iDgwz96FqsJ57A3rYNb7eYJ+GTT6b9K1/BaJn6JFjaspXsbbeRvf12vHSK0NJjCB23nNCiRQTmzkWN7CVD8n4gfH9K77vd3U32tn+Sve02iqtXV7cHFiwgesbphJYswctm8VIpvNEU3uhotbjDw9g7dlTJTORlL6XhrW8jMG8uaigkIxq7DsIpF9sea5eLb5UoPPgA6Rtvwu3vP6hnrSD+ylfS/F8fIXf33Qx89Wv44wIOgjSGjL/m1QQXLMScMR1z+nSM9vajHkPiQPGHP/yBJ598ktNOO42n6p/i2ieunbSPqqjoio6malUPGsu1KLjSc2RabBontp7IgoYFbMts47H+x9g0uglXTCSiCxsW8prZr+H8rvOrKp07u+/kA3d+YMJ+iUCC/3vV/9EebWfr1q3ccsstDA8PV6VRFfug8dIpx3HI5XJYloWiKERaI+R6J/6P9gUfn5JWoqSVsDSLnJEjHA7TuasOXxtTL0WborRPa6euoY7GxkaaG5uJR+MEtSABLUDRLbIzt5P1I+v55/Z/ct+u++RkrvrVZ3v1rFdzbNOxpEtp0naaVCkl2+XiuA7HcRzR/iiZ4Uw1zlMgEKgGKx0eHiafz1NXV0dDQ8OkEo/HGRgYYNOmTfT19VXDEViWhWVZeJ5XtWsCJqmaKnZUpVJpn/ZUxx9/PK9+9aun3M9TQY2g7DZZCiGqRlb7QnGoH2VgeC/fKijaOMKiaZLEaBqqaaLU1eErAk0Zc8sN6aEDIjWXXXYZqVSKG264obrt4osvZuvWraxatUrehaLw5z//uRqZN5vNEo/H+c53vsMHPvABHn74YU466SS+/e1v75GIpFIp6urqpnxPU8GRICi/XvdrvvrIV1mUXMS1L7+WiHFwk+3RhBACPA/heTgli4Kdo1jKMXDLjag/+AVGYVzQNF0nfPzxBBcuZPR3v5O6b11HjURQTAPVDKAEZFHLngLhFSuou/ACfNtm8FvfJvuPf+zzfozp0wnOn0dg3nwC8+YRmDcXP5ulsGoVpfUb8PN5nHwOTVFBU8FxsHfuwunpAUBLJGSpq5tQ+4UC+QcekJKHClSV+Hnn0vD2dxBaumRq/eU42Dt2oAYCGIcQ80Z4HtbTT1NcuxZr7RM4vb1SYmJZmJ0dmDNnojU2oobCqKEQajiEEgrJz+EwenMT5rjoxO7QEIXHVqEYOsKyGPnlryiWf38ToGkYHR2Y0yVhCS1fRmTlStRolOLjj1NcsxZFU1GjMRRdw7dKCNtGS8TR6hvQkw0yaFZ9PUo4fFgWQJZr4Qu/atOxevVqbrjhBkINIX6Z+CUAly2+jJd1voxFyUUE9SCqMllyV3SL/OaZ3/DjJ35M1p6o5gi5IRqsBmJ+jCatiY5IB12JLurMuupkV7HBCAQCPF14mpt6b6I10spJrSexomkFYTVcjUJ+IIjFYrzuda9jxowZdO/o5sbbbsRzPZINSepideQLeXL5nAwuWChSKpZwS/uW6JmlEqPKZu6Z1U3e2ENus/2gLlBHxIhQdIuMWCMHfLzhGRi+QTQWZU7DHE6fdjqvm/c6DPXIkt+CUwABTtGpuq5XYndVyGE8HieZTO7/ZAeAGkHZbbIsOAVO+vXRz/Nz7xl/ISSk9GX3olTaqioNAVWVd7znPaRGR/njz36GGo3y9JYtvPzcc5kxYwYPPvggMJGguK7L97//ff7jP/6Da665hve85z188IMf5LrrrmNkZOSAwpbvDsdzsDyLoBZEczyE48j71HWpnxw3mE6FoHipFLm77sLp6cHp70cNBDE62jHa29Hb2jDa29Hq6hAIvvnoN/nZUz8lXoD2EXipO4s3JM9GRQUhEK4rV9S2jXDs6urat8faAHUXXED8Na85bJIvy7X47TO/Ze3QWnLbt9D8VB8LtznM2VoimvNQ9/NL2tICN56kos+ayX9e+A26WhYAUhLS85GPYD399P5vovzO4HmgqkRWriT28nMw2jsorl5NcfVqSuvX4x5EQLcDhq4TXrGC2DlnEzv7bIwDNOJ8LsP1XXJ2jqJbpCXcQvHhh8n96y7sHTtwurdjd+9A7MX1VzGMAzYuVIJBtIZ6tEgUDF3+Pjo7MWfMIJ8ZovDkk3jdO3ENBdtUsVUfT/h4wsUVXrX4+BRNhfTyWcy+8C0Y9S3c9QsZcbQn3MM0cxrHzjqW41pbCa/fMKbaUpALLkWmQVV0jeDSpTgz23mk/xHWDq5l/cB6WvtaoXvqUaanghUrVnDKKacAVGPTeJ6H6zg4hQK+5xFMJKqxYA50XKtEV66ke0in0/QO9tL95ON03PUIs0aH4bff5Y6Be7m/534ypQwlr4TlWViuheNP/F82BBvojHWysn0l5844lzn1cwDwfI/7e+7nr5v/ymBxkLpAHYlAQhZT1nWBOizP4p/b/8k9O+/B9vcc6XlWYhbvXfZeVFQGi4MMFgYn1MPFYTzhyVgxqo6maGiqJmtFI2JE6Ep0MatuFo7nMFQcYtgaZrg4zFBxiKHiEAW3gKZoLE4uZlnzMjzhMVQcoi5Qxxvmv4F59VM3Mj8Q1AjKc4Sg3LXsekLa1CUK777ySn5z880ETRPX8yiVAzf9+ppruPiii1AMA72urmq9XSwW8X2fmTNn8thjj9HQ0MArXvEKdu3axZo1a6Z8Xd9xEKUSiqqS8QuMlFI4bgnTFSTyENrDb8jXVXxdQ+gaJQV29A0wuOsRGuqbWZBciBEME5g5E8U0yf7zn/T+z2fwhvat83YCGsMJlazm0DYKUWvKj7BXxF/1Kurf9CZG//xH0rfcAoUi+D5OJMDorEZGuhoQ8ShaMIQRCKGHwpiBCGYwQiAcJRCMEgzH6El3c9ftPyW5bZRF3YLW1NTvoRBUeOrVixCvfyXXrfspI9YIuqozPTadjmgHWTtLd2Y74eEC8yNdLIjOYl5kFrNCnSS1ONg27sgImZv/RqFMVI2XnMz6S06gccnxHNd8HLqqsyu3i7WDa+nJ9zDaux1/81bMbb3Ed4zS0mMxbUhgGbChQ2Fzm0IuBCUDfAUC6Jw18xySsxbitTYiVCCdhUwOJZ1DyeZRMrJGgHvMPNxj57PdG2DD6AaGi8PUBetIBpOc1HYSL2l/yWFXzVUkoVk7S9bOknNyGJpBW6SN+kD9IRPRglPg1m238qeNf2L14Orq9mOajuE7Z3yHxlDj2L2Uk7TZ27fjdHdT2rSZ/IMPUnrmGQC0pkYiJ5yAYph4+Rw4LkoohGIYeJk03sgoXjlYlrAOw4u+B/jAUzMUVp90FqY6eRXcODiIadsovkAV/sTa9wnYJaLhCEyfRi/Qqyi4ZTupupFRYtksoWIRzfdAgIKYVJfMAPlohEI4DKIclVrX0Rwbw7JYMDREmyqlzn6hULUT8guFCf0SXLSI6JlnknjVKzFnzjywfrBt/ExGuvtmMlKy09XFzg9+iMJDD5F897tp/vB/7PV4z/coeSVKXglTMw+bNNdyLYpukYAWwBMem1ObWTO4hp888RNGS3t2cz6aWNm+kksXX8rK9pWH9bw1gnKQKp79wfM9Sn6JkluqvrAlr4SmaCT9CCHLw3FL2G4JH4FmBvA0OWgqgCIqtVL9rAsFQ6hoQuU9n/gkvQMDfPtznyU/PMIPfvozNF3nms98pnoP4aVL+c6nPsWZp5zCtr4+PvqlL/HNz32Os848E8UweNUb3kBvf/9eCYrU3VtlY0FpJCimYNRo66D5suyOku/TPTiI9vkvoPb2Vrf7mkqhOUa0V7qtpZrDZBZ0EGhpxSpksHbtRB8YJZn2qduTZFVR8FoaeCI8wmACGsJNnNx2EuFgnD5nmCE3RU4pkfUtshRJiwJpP09GFOkcElzwoNjj/R4OCE1FWTwP//glWMvmQWcboUCEYCBC2IzKOhjF0ANSHViePAcKA3z8no/zSN8jU7pOQ7CBJY1LWJJcQtgIow2MsKH3Sf7qPoYnZJTRsB4mZsboL+zbDkNXNObWzeW0aS9lZftK2iJtqIrKZx/4LPfsuufQOmQ3dEQ7OL/rfBqCDUSNKBEjImtT1mF9TKVRiXdRcApsTG1k/ch6+vJ9kog42SohydrZ6jPvjqAWpDncTEukhdZwK60RWeqD9STMBIZmjKkfKIeFR37uzfdyZ/ed3Ndz36RxQub/FnREO7j6rKuZVbf3yJ0gbY38fB5jxowpESYhBKJQwB0dxRsaophN8eCO+7hr460YvcO0jwgcTWFweozSjFYazXqaiBJXw0T0CGEjREQLE9bDhPUQYS1MducWBm7+C3UbZJTTdDxO9/TpBC2LQKlE9/Tp7OrsOKBM5RXUjYxy7FNPMc0wyrZ5oAZDqKEQSjg0pkYrq9KE4+KOjuDs6sF64gnEIeTnAkDTqLvoIhovf39VYicch8zf/07+vvvwUjIOiZdJV2OQ7JMAqipz/nkbxrOcqHY8MnaG/13zv9yz6x4SZoKmcBNNoaYJdWOoEV3Vq78d3/fxhCeL75Eqpdic2szWzFZCeohkMEkylKQx1DihpEtpHu1/lCeHniSsh0mGkqweWM0/u/+JL3zOmXEO3zz9m4f3+WoE5dn1KPGFT9bOMmqNknemrtO88vIryWayfPfn3wVAdXwuPOMi3vX2t/K2iy9C96BhwVJ+++1v85qzzgLgkSee4KL3vY/HbriB5mSSj3z5y/z8z3+m56GHMANBKb4FoKwa2Zvo2TRxPRvNkyQKVUXRNJRYlELMxFZ9VEWV37meXBG6Horr4loOO3f1Yn3/64jt2/B9n5ANkbIE3FfgLycr/OFUFUefPChGjSjLE4s5UZnFIq+ZmVoz8dnzMWfMQA0GuXXbrXz6/k+Td/LSrgelasC3L8zbKfjgXzzqc/DQfIX7T4yRmDWfZKSR5rxO4+YR4t3DKMVSVTWklBwUx0VxXVTbQ3V9dEeyHH9mO10nv5z48ScQXnECWvQgjVCFYGduJzuyO+jJ9RA1o8yIzSCgBXhq+CnWDq7lyaEneWb0mapnxJ6wOLmY3nxvVe+tKzqLGhcxMz6TlnCLnLDDLbREZLsh2LBHewNf+Pzmmd9w4+Yb8YSHgoKqqPL/rSioqNXPqqIiEDieg+M7tEXamN8wn5ZwC+lSmh3ZHfx9298n2S0cTuiKTsyMETWjWK7FYPHwqbOmx6Zz0dyLOL/rfJrCTfTkenjfP99Hd7YbVVHHiNZuhCtqRJlXP48VrSuYUzdnj/28O3J2jp25nezK7mJnbifdmW7+sf0fpEopAJLBJO865l1cPPdigvqBj2XOrl2k/vgnUn/8Y9WIWKurI3LqqWhveTN9jEXEHW88WVGx5NNpUtu24RcKtIfDTEsk6Fy6lNCSJagHMbb6pRLW2rV42SxaXR1qJIKfz0uDcSFQw9ImSAmFUMMR1Ij87Ody5P51lyQh994rT2YYBBcsILhgPrl778MdtzDaIxQFNRZDi8UQnlcNUR9/9avp+NpXD/hZnm8QnsAvOPh5By8vaz/voAQ0QouSqEG9vJ+PcAU9di+/Wvcrzu86n2Oajjms91IjKM8hl1df+BNWa6Is/qy0bc+m6BaxPIsP/r8Pkk6lufqXVxMxIgS0AL//3e/5n0/8D/9c9U80U2NJ0xK++9Pv8PLzzsLwBLoHb3vz/2PurC6++slPsGbVWs5445v42sc+xvvf8pZJ95PKZIg21OEFNLyAgQiYEDRJORlKbomwHmZGfDrqAYjnx/e5YRqsHVrL6v7HEb39mJt7KE1rxOjqQlf16kCcCCRY1ryMY5uOZXZi9n7VATuzO7ny3itZNSCNFZtCTZzUdhIt4RaSoSTJoFwdVNqmZnJ79+387Zm/YJcKvHLp63hF1ysOaqB/tlDySqwfWc8TQ0+wbngdrnDRFI3mcDOvnvVqZtXNwhc+z4w8Q97Jszi5eJ8Br44Wim6RW7bewhNDT5BzcuSdPDlb1pWSc3IIBApK1XMkoAWYmZjJ/Pr5zIjPIGbGiJkx4mZcEhIjSsyMTTI+tz2b/nw//QVZevO99OX76M/3k7al54TruzLKKrtlVUYhYkRY2b6SM6efycKGhZMkH6PWKB/+14d5tP/RKT1/zIyxoGEB8+vnE9AC+MLH9m0KToGck6Mn18PO3E7SpT2HR++IdvDmhW/ebwCzqUJ4Hu7QEFp9/RFLaHe0UFi1ioFvfJPiY49N2K41NlL3uosxOztl7ptEOe9NJf9NNDrBjd/P53F6e2UwtoPoE7/k4aUs3NESwnIRngBVITAjjt4weYwRni/JQdbBt1xUU0MJamgRAyVUzt7tC/yii5+z8fMuftFFuB7C8RGuj3DExM+2j7A9RMnDr9SWrIXnl506FIQlz8XeZnpdJTivHj9nY/fkwBUoAQ0tbhI6ponEOTMOuH/2hRpBeQ4RlAPBnrx4XNdl5syZfOhDH+LD//lhNFXj1//3a8551TnYno3t2dx2621c/rbL+fsjf6elrYVvfeab/PSan/GOd7+N8847k+aWZrZt6+ZXv/g9y05Zzlv+31v3eH1N1ZidmD3l4EwVHK0+93yPR/ofIWbEWJhcOKVVag01HA4IIRgqDo0RLidH3pZ1zskxao2ydnAtqwdXH5A6uT5QT0e0g85YJx3RDpY2LeVlnS87oPggLxZ4OVuu/B0ft3cX9rZnsLdtwuiYTviUsxC2gqIqKJoCmirr8ufKZF39TldR9PL3ioKXtbF3ZHF6cpIAgDQgFpSLnCa9jJzEveG9q42M9gh6Uxg/a+PlHEk4CvtQo+sqakiT+3hHcDpWQA3pqBEDNWKgRQycwSLuwN6l0dGV7dS9ZvZhvY1aqPsXEHRd5/LLL+erX/0q732vjLoYMkITDPb+/fX/zjc//U1+94Pf8fXvfp2vfPWrrDjhBK773+v49S/+DyEEM7tmctHFF/Hud72bWDRW1Vf6wsfzpfV/Mpg8YHJyNKGpGie3nfxs30YNL0IoiiJtAGja536O77A5tZl1w+vYnNqMJzw0RcPQDCJGhJAeojXSSme0k85Y5/PCff5IwC845B/tx+nL4w4WEY6HYmgohopilutyWzg+9vYM7tDuxC8JShJnGAprp+ABd5ihBHX0+gBqxABVQZQ87O4MTk8ep2cPqn0FSQ5COsLxpYSk5IHr42fHjOWUkI5W3q/aD7oK5bryWTE1KYkJqCgBvdzWUAMaaAr4AuEJ1ICGGjVQQ4YkaOMghMDZlcPaMIpWF8CcFkOLmXhZGy9jo0Wf3fmgJkF5geNgwz8fCGp9XkMNNUwVfsFh8EdP4PQdYMwRBdSwjqLJ5I5+zqmqLdSogRYzQUgPK+EJ8ATC88u1LPi+dG/aw7n15jBmZww1pJe9GeQXyri2GtIxOiIYbVG0yOTJ28vZWOtH8QsuWsyQ9xU1JUEIGyjqbgTB8fEyJfyiW91X0V/YkuGaBKWGKo40Oanh2YNfcqVeXd931Ffh+WU9tY87XMTpzeMOF1E0uRpDLYvGdQU1LMW/iq4ifDnII4Rsl1dk40XffrGsV887+CUP4XhoURM9GUSrC8jBtixiH2uPE7mrUvcuSlK3rsVNtIYgqvnsRQ/2MjZe1karC8gJ8UX6GxJC4I2W8AuOfC/COr7lSZVFyRt7D6A8oZc9FittBdSgPuH/6Vsug9c9idOXR40aRE9pR28OoQalVEGWsp2FXW4LMKfFCMyIS/JQuT9fkpSq1GCqz+ULhOuD60vNjRAoxoGdY2/QoiaR46ceD0gxVPRk6JCv+0JFjaDUUMNhghByoq3qnXOO1JsXx+mfyxO7YqqYM+KYHVE5WVe+dqXhm+9IUiFsWXzLwxkoSHIxWMAdkcZ5AKgKalBDCeqoAW3sHGUjuiOq1z5CUEwNVGRQw3I9oa3Kla0aMzE7YxitYYTr4xclScIXUoxe9loQrj9GNCqr2HGTKgC+wB0o4GXGXGGVgCaJlRAIgVwBV+wcxteqMkbEyqoJNaBJXX/MRNEVaQBZdCWRK3nVWthjbTViEJidwOyI4Y4UcXryeClLvlNFd6wPQEoLKvYRorIB0FTMjijm9BhGSwStXhJFZ1cOe1cOvyD7o/JeiPHHVpq+wB0sIKw9u3UfKKqk1/HwCy5qWKfpXUsxWg5exaWoClr8wA1cFVWR79ezSIJrmBpqBKWGGqisxmzcVAkvXcJL2bJOl/BSUgSrJ4PorREUBdyhIl7arkoZfMvFyzngHljgFcVUUQJadfV4UGTCF9LAruCyz+lEVdASJkZ7FKMpJCcixx+Tjjg+fkFO6PhibOIdV1eIQWViV0M6asyU3ggBaTvgZW3coSJ+1paidbcsci/XwiuvXn3K1wE1oKPoCm7aRhRdSazK2GePDFvY2zIH3mf7QtlWwM85kkCUpngvhwF+3sEdKJBnb26zYv/34EqbDXv7YegXTUGLGHgFV77bqiLVFUGtrK6Q70HVUmA3w1Kv4CKKLn5+LLyBEtJpfOehkZMaXhyoEZQajjiEELhDRZydOdx0CT8jJ3ZFL1vaa0q5LdUVCPBzNl5WDmqKUd5v/GpXRU6w5ZWgcPyqD79wffCFnDyj0lq9YngnHB/fcvEL0p3Pyzl4qZJcNfv7HvrdoSKs33+ER8XUUGNl3XNEisar35VF317Owd6Wxi+4CHsPpEZTymLnstGgqaEngxhtUYyWcFWFgpAuj8Jyqy6Giq5Uj1ECGmrF8PB5otv2C1JaIHxRneiEL2vK20S57Q5b2DuzuINF2e8hXZI+Vb5TFY8FxSg/+7gJVMDE/7kCen0Qoz0qJVGOh5sqyX0q756o2DLsqS6/i65flYh4OVu+7558H5WQlHJV1BKy1iW5MzW8kSLWphROXx69IYTZEUVvDE18jyr3rCjydzDeTkKRkhp7RxZ7RxZ3uCjVNCUPsz2C0RlDS5hS0qMpVduKPdlcaA0hjKZQ9b0Rjr9fdeLe/p/VfhSgN4WqcTdqqGFfqL0lLxCMt3VWFKWcuK48WcPYKnh/5xDI8UpXx87jivLk70+YNORB4JZK+HmH0Rs3Y+SRagnXr4q8nf4CfvYQI0geDShIG4hEAK0uIOtEAL0ugBLUJcnql4Z9ejKEXh+Acj8pQW3MGG6KomPhS+ImXL9sra+hmgdOJtSgDonAQT3ycxHS3mFq3gOBmYkD0vkfCBRDw2g6ynFl2iKEFjfuf799QIuZGM3hw94vVZJ3gFDDBuYU/5811DAeNYIyDqKyGhovRB2v4y3roSdtK++qwJj/fcUQUJl4nupKsHz8eOPDSgAeeZFxB45f2E9Y5O/2XeUmKiuc/UgE9glFGk0KV4yRkb3Ad11826O0IYOX3cu+uoLZEUNvCKLGTdlHFev6ihSkTIJADrJq1ASlvHLz/ImPXO4jRS9LXyrSmHHtamCkglO166gESFJDepVQVIiIFjMnueFNwJy6A+3FfUJRFYzmZz+wWg011FDDcxE1gjIOfkGK+58bOEhyMV66AWPiWX8PZGZ3VMS8ZRIlHFHdLj0wxqlaKnaGCmiOFF/HzphG0AhIEbuuVl37tLhJYHr8oFdgNdRQQw01vPhQIyjjUfYO2H3beP1upR7vSjce440B9yh5qOiNK+cY7wFQXv1PugcYdx1lYo6v3XetSGUo224chIukEBU3PCF1zvq+z6NZAjWoE1nYUouDUkMNNdRwABDCw3XzGMa+Y4K8GFEjKOOglcP/Hi5IqUUlklAlRsCRj6lwqFdQFGmgSU1tXEMNNdRwWOB5RQqFreQLmynkt5AvbKKQ30y+sBUhbKKR+TQ2nkl9w0uIx45B18e8nIQQ+H4R183jeTlcL4/wHYTwEMLF84p4fhHfK+J5Fr6wUVBRFA1fOPheCd8fK55v4fslTDNJe9sbiEQObzj7w4UaQTmCGO+GV0MNNdRwKPC8Aq6bxXXz+H4JISoTlIcvxiYrTQ2SSByHqj43EwP6voPtDOPYoyiKhqoG0LQQuh5FVUNHZRF3oPB9B9fNoigqup6o3qMQHr5fcQAQVQJhWT3kC1soFDZTyG+lUNiCVerZ5zVy+fXk8uvZtv0aQCUU6sT3bTwvj+vm2XMI3ENHd/ePqa9fSUP9SiKR2QSDHWhaBE2PomtRNO3Zk4rXCMpzCJdddhk/+9nPqp8bGho44YQT+OpXv8oxx8iU1+N/vJqm0d7ezute9zq+9KUv8eCDD3L22Wdz5513cuqpp1b3y+fzLF26lIsuuoivf/3rR++BaqhhPxDCp1TqK68st5DLrSOXXYftDJcN0Hc3Wp+4TddjBIMdBMxmPK+A46YJBTtpbb2AuroTEMLFsnrwvDxC+Ah8KNdCyLbvW5TsQWx7GNNMEostIRKejaoefRGi79v099/IwOCt+L6NgoLtjFIs7sB1U1M+j2E00NryGmLxpSiKJmOSeAU52ZVr37MQwq0Sm92Jjufmcb0cQnhyotLDuG4ex0nh+yU0LYiqBuT5qwuxcr7osl5cUXQ0NYCi6JTsASyrF8cZ3sedq+h6BE2LolcmSD2KpoXRtDDh0Ezq6lYQjx+Lpo1FYJXkLY+ux8r3dGAkRwiPkZF76R/4O7Y9iOtmJBl0MjhuBt8fywOkaWFMsxnPy2HbIxwIcdD1OiKRWYTDs4lEZhMJzyESmYOmhRkeuYfhoTtJpR+jVOqlWOzewxkUNC2CrkdRFENKuxUdTQuhqWFULYimhVEVo/qOq6qBqgaqRau2TVLpVQwN3cHo6P2Mjt4/6Wptba9n0cIvH1BfHk7UCMpzDOeddx7XX389AH19fVx11VW86lWvort77GW9/vrrOe+883AchzVr1vD2t7+dSCTC5z73Oa644gouu+wy1qxZQyQiRYQf/ehHCYVCfP7zn39WnqmGFzby+S2k049SKG6nWNyBpgYxzUY0LYzvW+PEz5acJH0Lz8tTKvVTKvUjxD4yve4Htj1IobBlwrZRoKf3/9D1OK6b42BXnopioKomoWAn0dhCwuFZcvCfMMgHUNUgqmrKiVpRUBWjvAKNoCC3KahlwqPgulkcN43rpHHclKydFLYzTH//zZRKewvSBpUJXEoaNBRFR1V1FEWvfi6V+rDtIXbs/OlBPffRgKJo6HodIMpSggLy/+SXpURZSvvxV1DVILoeqxKvsXOb6HoMw4ij65USQ9fjGHoCw6gjEGjFDDRhFXeSzT3F0ODt+5VwVOB5BYrFbXt7MjQtQiDQTDg8i0h4FuHwLMLhLsLhWZhmw17P29Z6AW2tFwBglfooFrZL4lEma5oWQdNCKIcxi/sMoFjcSX//TeTzG8gXNlEqDcjfrJdH055dL8MXBUERQiCKU0+BfrighA5cXBkIBGhtbQWgtbWVj3/845x22mkMDg7S1CQzqdbV1VX3mTZtGq997WtZtWoVAF/84he55ZZb+NjHPsb3v/997rzzTn784x9z//331wxYn2Nw3RylUl9ZRDwhqcm4QajSlhOTrtehaUc25okQokwa5ApMrqj98mcP182Qy28gm32KwcHbyOc3HNL1FEUnFJpOODSTaHQ+0dgigoH2CX1QzfNSdTWTnx03hVXciW0PVkXSqdQj9A/8DdeVkVRVNYRhJKj0ZZU0lPtVVU1MsxHTSGKVeslmn8Lzcgjh4HlOVfR+tGCaTXR2vIVgsA2BwNDjBEPTCQU70LTofscU33cZGbmH/v6bsJ1h+b8UAk2PyNW3FpFESwtKEjaO3IyvNS2MrsdQFBXXy+O5eTQ9gmHUo6kBaevgl8pSqWr8BSnhqgaWdcp2Dw6m2Ugw2E4g0IxhNEyYaMdsLHJllUa23M6V6yKumyWXW0cq/Wj5d2Nh29ak5xfCxnGG9yOpmQxdT9Da+lpi0UVjpMaIY5RJjqZFEcKlVOqlVBpA12NVIl55Hw8XgQgGWgkGWg/5PFNBKNTJzJnvmbR9bBx49vDiICjFIuuPO/6oX3f+qsdQwgfPQHO5HL/85S+ZM2cOyWRyj/ts2LCBO+64g8suuwyAYDDIz3/+c1auXMk555zDhz70IT75yU9y/PFH//mfLQjh4XlFNC1yWPXZnlfEsnrwhYOmBlEUHdfNVEXeFXsAgVdtIzw8v4RdGqRkD1Aq9VGyerFKvdUJ9EChaRGCwXaCwU6i0fnEYksIBTvwygZw5WA7Y0HRx3mTqVoQ02hAIEilHiaVegRVNUnEl2OYDQwN3c7Q0B0HdG+KolOXWEEkMpdgqBPh25TsIXyvWJ4IQ2hqEE0LldshNC2EGWgiGGjDNJtR1UMYiuonfmxru4h58z5NPr+RQKAF02w6oPdACB/HGcX3bXzfkqqn7NMUrZ0TDQ09q7xPCd+3qiTO9x08L4/nFeQ7sJt/v6Lo6OWVvGEkMPS68kRYRzS6gJaW1xwSCVVVncbGM2hsPOOgz3E04Hkenufh+z6qqqLrQQKBPY+Xtm3T19eHrp9OXcJFiByxmE40qiGEQaFgUCj4KEoJRSmCUgQKIAoICgiRR/h5fD+HL9I4ziC2PUDAbCEWW0w8sRzhL2Hz5m76et2y6sRDUVIoShpVlZ6MmqZhmiaGEaNUKmFZmxFCEAwGMU0Ty7IoFAq4buUcslSOVxQFXddpbm6mra0NVVUZHR0lm82i6zqGYaCqYwTH9/1qP+1ecrkcqVSKQqEwqb90XSeRSJBIJABwHAfbtqt1pa1pGo2NjTQ2NhKPx4lGo+i6jmVZWJZFMBikvr5+0vmPFl4UBOX5hJtuuoloNApI25G2tjZuuummCS/tG9/4RjRNw3VdSqUSr3rVq/jEJz5R/X7FihV84hOf4KKLLmL58uVceeWVR/UZHCfFwMDfyeU3UihswXUzCN+Veu7yqlzXIpiBJkwjKWuzEdOUta5Fy6uoDIXCVnL5DZRK/WMibcVAUXUQAtfLVkXClVIR9wbMFurqTiAcmVO2NShRLHZTKGzB84roRgJDj8upXLgI38EX5fsULr7vVNueZx2QDcBUoesxNHVsUB6zrxDlCa+8VXhVdYXn5cnnN5LPb2R4+M7Dch99fTdMeV9VDUr9eWQu9fWn0NR4TllCcfDwPI9UKoVt23iehxACXdfRNG1CGb+tMujvCZoWJB5fOqVrCyGqA3coFCpPQmMLgnC4i6bGsw7p+aQkyi3bBEy2kfA8D8uySKVSPPnkM6RSKYQQjHqjWJpFXbSOumgdyVCSmBnbY99MpV/2B9/3cV0Xx3FwHAfXdfE8rzpxZjIZRkZGyOVy2LZdnYhVVa1ee3zteR7FYpFCoUA2myWbzZLP5ykUCjiOg0CQM3Lk9TwlrYSt29iGja3blLQSlmbhCY9kPkl7vp2GUgO6OJRpSwXqiUan0dDQQDAYRFVVhoe3MDj40CGcd+rwFA9btXFUB8M3CHrBiuXOcw4nnngir3jFK561678oCIoSCjF/1WP73c8vT6KKopYNv8oJX6rYLftneZucOPcg1gsGx1bVVVG5R0VULttURc2+b3P66S/lBz/4Loqik0pluOaaazj//PN5+OGHmTFjBgDf+ta3OPvss/E8j02bNvHhD3+Yt771rfz2t7+tXvpTn/oUn/3sZ/n4xz+Orh+9f/PQ0J2se+YT2Pbg/nfOHtl7Kdn99A/ctPcdJkuH9wutbNXueRZCOFKvbdShqsHye1AuqGVRuYqimgTMJsxAM4FAC8FAK4FAG8FgG7oem/K1hfDLthuDWNYuisVusrmnyWafxLaHpIRCDUgVBozlWRk3+HleAccZwfdLRKPHEIudgKJ45PNPUCr101C/kqbm84hG5lZ/A/KZFEDD98F13bJI3mdkZIQ1azbS399PX18fAwMDmKZJIpEgEomgquoei6IoOI6DZVmk02kGBwfxvAPPnGsYBoFAAMMw8DwP13UxTZP6+npisRiu61ZXi67rTiqO61DwChSVIiW1hBJQSLYkaWxsJBlJ0hRtoi5WJ58nGKEuUEdIn6i6laJwMWERsTvy+QIbN25k27ZtFAqF6grVsizyVp68n8dWbSzNoqAXyJpZ+kJ9ZM3JPxJFKGhC/o8rUrJqrQh0X2emN5PlgeXMC85D1/QJ/W5ZFvl8nnwxT9pNk/JTZMiQVbLk1BxFvUhBL2CrNrZqgwJBN0jIC6H7OgoKQS9IR76D5mIzKnt/bh+fkcAII4ERbE1Oyq7h4tf5lLQSKTOFozl7Pb6CVDzF5vhmAHShEyVKa7GV5nQzCTtBfayeWDSGJSwKfoGCX6DoFylSxBIWFhYlpURJKYGAuB0nPhjHVVwKeoGiXsRtcjFjJpquScIgpEqxQh4UoRAmzDRvGi1uCwWjwKAxSJEiqquiuipBI0goEMLTPLJeloyfqdY5kSPn5ygx0bjG8A2SIkm9V0/CSZB0kjQ4DaioeySflXYkEiGRSBCNTlb72bZNKpUinZbSH8MwME2zLP0Za9u2zeDgIMPDw1XyCFICEwwGj+rcsScoQuwnjvlzEJlMhkQiQTqdJh6fGNzGsiy2bt1KV1fXAdtc2PYIlrXroO5JVQOoWrDMW7wy2bHHrYL3j/e+9yrS6Sy//vV35AZFRQiNjvbjef/738n//M9HCYen8dvfXstrXnMulWHp9//3Vy677ArWrv0Xc+bMrhrOBQJN/OEPv+HVrz4Hzyvg+045L9hEPb404htnha9oZaM/o7zqcyas5qGS+0eWUsll+/YePO//GE1JQhAIzKCp6Sxi0bmYZmNV+oGioqDgujlsexDbHqJkD2HbQ9XPrpstG7XFCAWnlVUHHeUkcWUphy8HtYrxm65Hq8dULPkz2SdJpR6lVOotkwWdULCDcHgWuh6T6hk3I8mEqpd18eOKalS3qWqAQKD1sAZT8jyPbDaLoigYhlGdPCzLolgs7rFtWZZceZYnxYq4drxIeW8i4UrZE0zTJBqNEgqFCIVCCCEolUrV81faB0MipgrHdNCCGoqmIFSB4zs4voPru9W2UAS+4leLp3i4iouruniKh6d4CGUsT5SKiia06qrVVm0czamuYCsT8IFAExqmZxLwAph+ufZMEuEEzY3N1MfqUWwFr+iRz+fJ5rOMFEaqE6Gt2tiaXb0HT917n6qoRIniChcHB0fZ/2Q+HkFXEokmqwlHdST5MbKkzTQ5IzfWVweJkBIiqkbHNoiJdUqkcNj3PQe1IDNiM6gP1JMwEiSMBHE9TkyLEVNjuMLlicIT3Nd/HyPWyCHd73MFmqIRMSLknTyemPz/D+thljYtpTnUTF2wjvpAPfXBeuoD9fJzsJ6AFkBTNHRVx1CNCbW6hwWzEIKsk6U/309fvo/+Qj8lr0QymCQZSjK/YT4RLYLv+xjGkfNg29f8vTteFBKUqcJ1PUCjHOv9gI6t6KX3BiFUQEEIhXIoWYSo6CVBUUT52mq59qQ7pO+iqgqFQhrHkZl0PU+6+o1BXjeXG8JxJlqJl0r9WNbUrNMPFq4rDdzSmVUIodDfv4TNm5agqoKWljSNjUaZ6YfQdb3M6GMYxnQMwyAaMTDqDAxDFk3Tqiv0ykq3oqcer8+tiOZLJZtstqJfzWLbw7iuSzLZSEfHOwmFQti2TTqdZufOnXR3d5PN7pyUYHFfRVVVgsENqKaKaqhEDTkoV3TOvu9PErtXPruuy9DQEENDQ9i2Xb3vXC53QP0sEDiqg6M6KEKSSl/xEQhc1ZWrU8VFQa6wNV9DFzqqkJO0JjQ0NFTUSSJl27YZGZn64C8QlNQSRswgkUwQa4gRqYsQjofJ23lS+RQ5K4cnPFzfHStC1p7wiBkx6gP1ZMiwKruK7tye3CqPDiJGhIZgAwEC+LaP7dhyFS6KuMJFICaQoqJepKjvwfA+XS7jESqXfUBFJRFI0BBsoC3aRnuknRPaTmBl+0ri5tggXvJKjFqj2J50Qa4sOIQvfy/CF2xPbefmzTdz98Dd5MmzObGZzYnNe7yupmg0BZtoDbXSGmmlLdJGR7yD9lg7jaFG4gF57YHCAP2FfhxPEsZ1I+u4ddutjFgjFL19OyDUB+o5vuV4WiItRI0oYSNMQAsQMSLMr5/PnPo5GPtx6X4jb0QIQdEtMmwNs3F0I3d038HdO+9mtDQxw3jMiBEPxImb8bHajJMIJIibcRzfYcPoBrakthAxI3REO2gNt1b3VRUVX/hyDMKvtj3hsTW9lXt23cNAYUCSiMaltEZaKbpFCm5BEmnPIagHaQw10hhqpCnURGOokWQoSVOoiWQoSdSQUg/bs9mW2cbG0Y1sTm1mw+gGHh94nIyd4aHeg1c5hfQQ02PTmRabRt7J01+QpKTgTrZXGY/Zidl0JbpQFRVVUTm57WQunnfxQd/HoaJGUMZBiDC5XF3lExNJyt6XWYoiUFUXtbwSEkKSD9/XqsRkKnAcnULBZ8sW6dWRyYxy/fXXk8sVOPvsc3Ec6TY8MuKwa5eFELB161a+8pVrmTNnFvPmrwBFQ8EHxS3fm4qihBDCxBd61dq+MjmPl4TItjT0UxQPRfURvooQaplYTewLeQoFx/FwHJPt249lcHAZ+ZzU3/u+y65du9i16+CkUocLuq7juodmje4pHusT69mQ2ICneDSUGmi2pHg56kQxfAMff2yFP64tECgoqEKV9i6KHPjcsIurSRFzTstR0kqSn6pUpQiU+ayv+GT8DCVx6LmiFBRMzcRQDUzNJKgFCapBgkoQFRVVjBn0oZQDDo4TuhXcAjtzO8k7ZddOAQyXyyFAVVRCeghd1dEVvXqPhmpgaMZYWzXQNblaDGkhwkaYkB4ioAXkqlKV6g9f+NUJI6AHJk1U1XYgTmAfRqlCCIrFIrlcjpIokXEyZJwMaSdNxpb1qDXKwPAAg6ODFO0iwhT4ho9pmASMAHWROqbXTact0kZ9sJ6EmSARSFTvJWJE9rjq3R0BLUBrZN/eHTOTM3nZ7JfheA4P9D7ArdtuZcPoBpKhJC3hFmbGZzKvfh6z62bTFGqq9te+0BHtmPD5Qi7koyd8lPUj6ym6xTEVUzlOTeVzMphkdt3sKT3b/qAoCmEjTNgIMy02jTOnnwmA67tYrrRViRrRKT3PoUAIwWBxkGQwecjXMjWTefXzmFc/r7rNFz4bRzfy9PDTpEopRkujjFqjpKyx9mhpVJJF4eL53phBfBlFt8j60fWsH53sfZYIJGgNt9ISaSGgBRguDtNf6GdXbheb05vZnB4js3EzzsXUCMpzAhYW6UBaioXRqqvMSXrecXX1r/zD1BUdQzFQFIEnSri4UxPGCHA0hzvvvJPly5cDEI1G6ZrTxbd+/C3mrVzOYDli4RVX/Bcgf7BNzU2cePKJ/Ncn/ot8SUEpCRAKCjKKZKEYJp0NIhSBoExaxulXK1rWibcyNomOl1jIw0RVL6uosvYUB6EGIL6ChiaP1iYTLaIx3ZxOrBAjnUqTyWTI5XJVq/Txhni7l/GSDV3Xq5II3/flKrEsXamoRir61PE6VlVVGRgYYHh4uEpOTNOkoa2BTDLDgDZAb6mXwbKtjIKCpkjpgqqoVP/K7T67j1F3bKU2EhxhJHiUxM27aQlDegjP9/CEVxXrmppZneg84WG5FpZnUXJLsvZK+GU1nUBQ8kqUvBL7kb7vEwoKESNCSA9VS1APVuuAFqiSDV3VJ4igFUUhU8owYo0QNsK8rPNlnNpxKjFz6jY5RwuKohAOhwkfgkfeswFDM3hp50t5aedLj8j5dVVncePiI3LufcHK5cgMDdDQ3olumuiqTtSM7v/AQ4AQAs9xsK0ijmWhliyGve0I3ycQjpBobkHZhw3SgUBVVOY3zGd+w/wpH+P53gR1aKqUojvTzc7cTiJGhNZIK63hVprDzYSNPb/Hw8Vh1gyuYaAwICWGwmdO3ZzD8kwHi5oNyjgM5QcZzk007lSEgipAEbKtVNpMbEsIPBU8VZIVVSio4yUPYs+yFKFQXm2Drwh8VeCpAl8dd13KxEgZp+Z9jhh++47PwM4BvrLpK/TaE4NMxYwYHbEOVEVFV3RCRoiIHiFiRAgbYaJGdEI7rIcJakECegBTMwloAUJ6CF/47MjuYFduFxEjQlukjZgZI+fkyNrZseLIOu/kCWpBwmoYx3XI+Tl25nfyWP9juP7BSVNaI63854r/ZFnTMh7oeYBVA6vYmt7K9sx2LNeaMAHvrg+uqDoUFFRFxdAMokaUqBGlOdxMR7SDpnDT2PGKjqZqaIqGpmoYqkEylKQt0kZI34++YPf/j+9RSKdJjwzi4qJGQ4iAhlXMUyxksVyLol/CwsbTBHZZ+lYR8yqKIsmaKslaUA/SGeukM9qJqT03w6nXcOAQvo/nuniug+e6BMJhNF2qXlzbJjs8SD6dophJ4zoOhhlADwTwXRfXLuHaNk5J1q5dwnVshO+jlKUM+dQIueEhSoUCnuvg+z6BcJhgJIYRkJ5NiqrKYHeqWjYwV0BRpRee5zGwbQv9WzeDEKiaTkvXbALRKI5l4bkOumFK0mIGdqtNjEAA3QwQCIeJJZuIJRtxShb51Ci5kWGyw0Nkh4co5XPYVhG7UKBULGIXC9jFAv4+7K+MYIjGadMJxRMEQmGMYBAjEByrA4HqZ90MVBd9ZjhMOFFPKBaXRtYKqJqOqlUM0w/g/ycEvufiWCU8d2zl4VhW9Xlsq/w8VhG7HBssUldPpK6eRHMLsWQTqqaV+yWFbhhEG/Yc4uJgUbNBOUiEXINE7lCMg5Rnr0MrxEWpSEDkZkFZMl8mOZOlORPVWFWbufL5hFouFeNaZeyQiiTJdT2ytsbKdU1kLA09EUWJBtiV3YXtltC9HgxPRfWhYPgMBzwcXZIwTxX42ljbUwWGq9KcCtCYCmA6kuTty5ZPFUqV/I0njMMhl9GoQzHooXkKuqewzI7R5MdpUusJESCAIYOAiYnysYlSMlBRiOlRhlfdyh3KbWimwTGqxkKrBduKS2lOMIgRDGEGg5jBEEYwhBEMYoZCBKMxglEpHXBLJYQQxJKNxBub0E0T4QsUVcEMhQmEI6ja3kXHnuvSu/EZejY8g24GiNbXo2ga+ZERcqPD5EZHKKRGyadS5FMjFNLpAzLWVjUdrWwLpJTtaWQtB05V19le/iwQ+K70UpMTikogHCIYjWOWjW2lcbMM5lUx7hW+j+s4uHYJx7JwrCJOOXRoNYdVNThb5Z2cnHBT0w30QAAzFCIUjRGKxYkmG6lrbSNeHmzHDi9PgGXpX6UWvpDXtyzMcJhYYxPByP4Doj0fIHyf7U+uofvJNaQH+skM9mMXi5KEOOXiOri2g+9NJu7BaAxV0yikU0f/5veBQDhCqZCnd9PRC6BXgV4mZpquo6gqxUwaxyrSu/Hw3YuiqGimgREIYpbHlQrZMcskp5TPkRsZppBJ45RKOCUL4R9avh5V09FNo0pejnvFaznj0ncdjkc6KNQIyjiomoY2znpZqjHkoKuW6z21KwMnQuCWf/RCiOrAXh3oFGXSYCsHbR/fkysEz3PxHbmKqQi3FFUtz6GVwX4Ps3VFOjNBojMV7GXfyoTvAd64jXva1QPNU2gZNIiNaEARKNJMBIjs8ZijgaZUgK69fjtKCTgQi478/nc5bNADAQKhMGY4QiAcxgyG8FwXx7JI9fdUB5ApQ1EIxxMI36eYy1aDt6mahqKqeK5b3eZ7rlyJHe6Heh6hKq4XEIrHiTc2EU78//bOO06q6vz/n9tmZne2spVd2EIRg4IRCSrxK0UUFGxRgz/5CqgxERWNmmjEKJCoRP2KilGRCGJHQQGDiAYCBGKhL0162aVsZcvs1Fue3x+37MwWmIXdmS3n/XrNzuyt537Oufc+5znPOSdJX0QUYmyZSHY7bDGxsMXEwhEXZxiaIjieg7+2FqdOHkd1STGUQACaqkBVdJ1VVYWmKNCMMUfIGLyM4znwvID4lFR0+9mF6Nq7DziehxII6B4IY3s9v1T9eIqiv6Q4Dqqi4NCWDXCVh9Hlvwl8tXXdnCW7A87kZMQkJEKUbFD8fsgBPwRRDPJW1HksTE+BpunGqTMpGXEpKXA44yBIEniOh9/rgdflgmp4W8wmXBhGrPm/GROVlJGJ7hdeBGdSMqpLS3By/x6oigKbwwFBkqAEZMubE/xd59kJwOd2oaa8DLUV5bDFxCA2KRlxSV0Qn5KKuJRUxMTFwxar56PNEaPff0a+Sg47+HpxJ5qq4tSJYzh1vAg+txsBjxsBnw+y36ef1/jW//dZFRRN0+B3u+GpqoQiB0KOSaRB8fuh+P3w1tSPug4T4x0j2ez69ThiYIuJ0StPxjcRwVNdhdrKU6gpLYaqKAh4dUNVlGyn9RpFAtbE00bRC7BquTrrr4M5aRo1fGASaZYxo4/TYgzrbcSRAOZXPePEDIzUT2K5DFVFAWlBxaTebv5AAIWFRdBOlSHgqoarohye6iqrxmrWcHlBhM9VA091FQI+H1Q5AFWWdaNOka0HLy+IyOzZG1nn/QxxyV3q3J31a7RElqFoXSOvf5Omoqr4JCqOFcLrqrFcv7GGO9MRF697CUSxQayR+aKuf2fwAg9eEI326AA0VTU8JjH66KOGJyDg8+oPKOM74HHD566Fr7ZWH0nSZtO7/FWUw1VRBk1RAY4DqWqDB1VTOOITkNNXH4istqoSmqogLrkL4rqkwJnUBc7kZMN12wXOpGTEJiRa3gRNVeH3eiDZHRCDDHJVUSD7fQh4vcbLTwEZXZNDvoNerBzHWfmjlxcVAY8b3loXZJ/PKlN1QbfGcPNcXU1UstkhxcRAstl1HUgDtNB4r+DMqPtJIWn2uWrgcdWgpqwUVcUnUXuq3Hq5WUHhwS9A42N5v+x2+Gpr4XWd3Qi/bRW704neg36J1O45SEhLhz02DqJNgiBK+j0gSRAlm3E/SBBtEnhegM/jhrvyFFRFQUJaeofxKrU1iAhKQB+fxfRIqopcZ9T49E/A77V+yz4v7M44xHVJQWxiku5VMZuR7A4IZzF+iaapqK2ogCIH4EzqAttZTNUSDqyJpwOgD6vcePZYYViIeQAANHZJREFUD/qIpOTMMQa8zwdbTAzyrxjSro3CtoBeg/HA7/EY3269zdjjAS9KsDkccCZ3QVpO3lkH5fGCgJi4hsGogihCEOPgcLZuwGFbRwkE4K2t0bvvguCproarvEw3XKymzjpj3vw2DSW/xw1fbS38HrfuPdA0SI4YJGdlIymjq2GsCxBEUW9OE0XwotF8ZjSdmc2Omqqi/Fghju3egbKjh8GLIkTDkDD3EQQRvChYsQvmgHEEIC03H71+cZlu/DWTmLj4RssJo2XhOA6SPfrPTZ4XkJCWHu1khMAMFAajDSGIImLiExAT33IDwjGah2izIb5LqvV/fJdUZOT3jFp6kjK7otfAS6N2fgYjWjADhcFgMBidnipZwVFfACoRRI6DxHEQrG9A4jn4NUJFQMEpWQEBEIx1HDjwAGIFHrECDwfPQ+T1ffVjwfpfCm5KPweICLWqhipFhWYE+WsEaCD4NUKlrKBaUeHXCAFNg0IwvgkyAbKxTANBI0AhgkJU14TKAQMTnLg+Pemc03q2MAOlCYiCpm0zMtH8TUaG1g1xpsMFfRP0DFfNQFfjrzk9Sr3B5sEH9ViQeA62FirE5rWoxjUAVmhKyKjUBIJCgGK0/dt4/cbSC3NdwW0sYEkOyHApKj46UYFKTkCVot8YXlWDR9VQragoDyhwqSpieB5xogC7MYIuDw580DcH/aa38RwcPI94kUeSKCJFEtHNIaGbwwY7zxt5oadLBaAavxUCNEN3kdOPIRg6aiA9TZoGjfRrtHEceC5o8H+ubmQY838ThQiyRogReGTaJXSRRHhVDbWqBs04n8hxEHkOIgeIxgOOCKhVVbgUFT6N4NM0yBpZ169Bzx8CrP141B2D53RtqmQVZQEZLrUuUt/GcXAIukvfp2rwahpU0sunijotVAJU1Gli4+uaCO28+VDlQIC+LRECGoXkv/m/RgQ7z8POm7rq8NDT6xB4xBrn0IzzqkH5YuaX+VCsVTWUBWRUyApkzdSCrHzhgbo84syx63RNgu9PhQBZI2tfkeNg5+uuVzA0FYx8Cf5tvoTMtDsFHimSiGRJRKzAW2UFRp5xgFFe68otH1xYgu49n0Zwqxo8qgqvRuCh3+NmGkQrPfr95lY11CoqKmXVehEmiAJijBekR9Pg1wiypiEQlDeypueVGpQ/PMdBI/Oe5o2Xo54XwXljlRkjf7Sg46pE0Iw8U408M/PcfNbVvSD1deaLUqO6ZarVU07X0Lyn/JpWlwaEbm+GviWIAuJFHhoBNYoKj6ZZ5SL4+Swa+ajnNWcZEGJQnos8hxijTJQFFBT5Ajji9eOUHPmAUDvPIUEU4BR4S3+g7jkEBPUoDHr4EoBKWYX3HHvtnAlvlsYMlLZCRUDGMb8c3sBqrQzHARJX94APtmrR8GfovvX+11r5eiigoEpR8VZRKY5pZzKqVMDfmfuHMDoqEschRuAgcfqIwSoBblWF0gaeJ4zwSLeJsPEcVMPYVanO6FNIN3RSbSKSJQEcYBkVpmHv0zS4VQ1+y1Ohl4Om8GuEsoCCs+9npRs5AscZBptu/Ek8hyRRQJIowiEYnpsgD45oGNymgW5WLkSeC5kid0BCdAcnZAZKfZoqTJw5gw6MgatCa9z6rmR5JzijdiZa68xD19Ui6jwyxr7Gb9nwVgSa6E58pqQ2eT/UdeIJ8d4Aeq3KrM0BsGrLolGwxaBrrn8uRRPgFniMSo1DQLQhSRT0WoEowGHcKKk2CfEiD69KqFVUyEG1Jf3mNmpbxm+/UUusUVRUKypKAzKO+WQc9wUgE1nam14Lq0aMulqTHFTrN4kR9NoxDw4BjeAnrUEeBOeXmUcAIPH6+TyqhmK/7smwcRycgu6lqfPiGLX54PMa3qBYgYfdqMmaLzHz4QAOllfBPJZZs1SIkCiKSLOJSBQFy7MT0HStCIDDqDVLQV4XXQ+9pslD19dnaAvj2gKke7p8KlneADPPgx9sZlngjPOatTfB8G6YNWyfph/Pr5FemzXObeaLYHiIzJptjMAjzSYiVRJhN7rUmzVzILh2bsySFfS/uW1wmnkjL1Qi+FQ9naYXSDPyxlyvBuktG94Oj+HBOKUoOCXrnsBwbAyZCLJCaDD0r1UG9GvV6p2zvgHj4DnECjy6SCKSRRE8Z3gNVM3y8Nh53csocbzl7bRZeR+aP7wR7GveD+YynkOD/OCD8so8rhi03Lzfgr1XwR7QYE8oZx2rbn/TK2zmoS2onJnb8EY5MdOhEeBS9eeAyHGIN5pRAN0oMM8L6JU5hUKbLOr/DhDBp2rwaRpSJBE5MTbkOGzIj7HDKbb8MPmmN0omgqLpzSuA/rzxGc84t6Lq+WDcS5rxLDI97vpX0JSuHIznqgjnacZMau8wAyWIJElEvCiENMdwpmHSQs0t4UBEuutWI+tmDT776R6W1MhGvPkQaqVr8HEEvyTiz/nZnaoXj6IRRP70mpovVyGC5YfRspBhRARIs5pGTUPJnFbUbBLzGcaQGZPgFHjEiwJiBb7JMmAeRyGyar+MjgPP6d4KG6DPA8sIm5aZPKCDoNcaeEg8r9fGDNdZpIyTiRMnguM4TJo0CXaeh1MUECPwsPE8fj95MmyCgHvvvhs2nrc+m3/8ETGShJuvvx42njfannnYhbqPxPOtZpx0Zs5knABmbZBp357hDM+RU9C9gnGGhzDRiFHpIolIs0nIdtjQM9aB850x6ON0oLfTgSyHDfGicNoywBllxG48dxgMhg7zoAThCSio8cq6m81wY5pwwb421LnezOA4m8hDEnjdvXmGF1LdTMJGDdsI8lI1Qrfu3bFgwQK89H8vw+mMBc9x8Pl8+Pjjj5GTk9PgWHPnzsXkyZMxd+5cnDhxAllZWeesQ3tCM4IFZZWgqBoCqgZF1bVMiJGQ4NAnpvPJKio9+iBoAs/BLgiItQuQhKZtdAoKDNaIEFA0+BUNPlmFX9EQUDQj2JCsvLSCArW6335FQ0WtHxW1AbgDCgKKBlXTBwgTeKMpUOAh8JzVZKiXI8Ota7jI7RKPGEmAXRT0/XgeosBBMr8FHpLAQeCND8eBN74FXv/tEHk47SLsIs8G3WIwGG2aTmGg6CP1nTna2VUbwMnqZg4hXg8O+kuH4zgIkv4SsGJNgiLdG6PGK6P3z/qh6Ohh/P3djzD65l+D5zgsX7IIGVnd0C0nFzU+GQdLa8FxgNftxoIFn2LZqnU4VHgcs976Bx567HH9mlH3gg3RAqEGkrWdsZJg9KQxXnLBxwnZN+iAiuxHqcuPlz7cjGK3Br+iQtEICQ4JSbH6SKVuvwKfrEHgOYhCUM8Zw6ALjufRiOAJqHAHFEg8j1i7AIcoWL2nqj0yymr9qPbKUM8QAWwT9Je3J9B4hL5oGAX1tWgiizoMwYZ0ZoIDP89JwgVZCYiRBIiC3qJvldcgYwtAUJyO0fNDIygqQVY1KBrBIfGId0hwSLzVam7Gipi/AXPAM/34XlmFV1bhl3UjUDPijBBkpAWXEbPc2EUeCQ7RMLoESIahFmywibxu/NUn2D4zDUyR5+GQeNhEHiLPg+dhGYCiER/TGOb+qnGviMb9o2p6U62iaiFGbV0ZC763dC31feqeV5LAwyEJsIs8ZMMIV7XQe1s/Blm6SqJ+/TbBqDjxgKLq+aSn0diW46xKlnltPG9OTqrnDQCIhuEbMAx0vXlT1xfQ44HI6nVjGuyGJhoZ+WnEupl5FKSPGctiVgzNb7OXjk9RrYqBrOi98Jx2AfEOCXaRt54p1vk0I87IOLepq2bGgSh6pcbsNRNQNLh8Mmr9iuUxE4PKj55mPQpEMY5lEzk4RMHKZ9Vs2gtOPx/8f901hd6LHPR5Auv2sQk8eJ6DomqQVT2twd+KSkHliEKf0/XiFM0KU6geet7yPAenTYDDJuihBYo+S7zTJiLWLiAjwYHspOZNTtqSdAoDRQlomPPw2oifd/DjF0GwnbnRse6Bq39uvv1/sfSzjzH65l9DI8IXCz7ADbfdgU3fr4eqEtwBfa6ExYsWIq9nb3TJzsPVN9yCl6ZNwf/77UMRrxmToiKgaNhb4sJxV5TnbuD0FwrPAz5Zf5ibtolodLtUjG7GQF236uZgE3jYRR6SaHg9oBt05kM++EHEG02FqXF2pMTZ4LSLxkuDs17u5kNTUbWQl1Xwg0clwB/0EleNfVRNf1iZniPZeHmFPpRhPUADiv7i04KssONVXhyv8uKr7SebumQGg9EJGTuwO164tX/Uzh9VA+WNN97ASy+9hOLiYlx00UV4/fXXMWjQoGgmqUXpkRYH0SZYFnRDjwEXUpNNirUBAQl/fOBezPrbX+CUK6FphIJNP+LTBQvwwObvEecQkdslFhqA5Z9/hP83bhy6JjrwqxvGYNofJuNgwQYM/p8hAIJqrVaNVf8bXBsN+d9Ikzm+iFmJq9u+blvreBwQ8AtQa2x49qYLIUh22I0Xd41PRqVbBscBTrsIhyToTTKq1qCmRahrJuE5DnF2EbE2AYpGuvdF0azzJsfakBZvR1KsZHlIJKOmGFxT9skqymv9UFRCSpwNcXbR0jqgaPAGVHhkRY+W5+oZiiE66flkFwXYxMZr4+0FVdMNXK9htREBh8prsbWwCgdLay1DB4BlbDX4DjqeaYCZtT6B15vTav36OUzzz/QWmL+Da34CxyHGJsAhCbr3QtCbsEKMteAeVkGeSL+sweVT4A4olodC0bQQj45Z26xLR3BfOn2ZaWjKGsEnq5anQz5dH9FmYhN0z0xj95PlyeD12jvP13k0ZEX3MPkVFZJhHJtl0Cyn+m/9OGa6A4qqN31qug6S4Ukyr9V0kwR7dKzehFSX/wSyPB02w5sj8pxlDDd4tgWVE44zygdnBBMbxrPZJCsYngrLY2PU9oObVgHoZUPkYZcEy5NR61fg8smWR0nVyKoohDRxWstgLbOJunfJLNeiYHjibPorUdFCvRWyUaYIuueI5wBZ1cuKqpHlwYFxjWba65p+9eep+dvMNyJYHi3LC6eR5d0Jeb7xHCTRGABOqIsrDL4/OWNByD3KIVSXoN+qRvAa3mrR0EXV6jzY6QnNnyKhJYmagfLpp5/i0UcfxezZs3HppZfi1VdfxciRI7F3716kp7fsfACijcdvXxvSoscM97xn481IS0vD6NGj8eH774OIMHr0aORmZxouRx6JsTbs3bsXWzZtwj+XLkVavN5z5v/dPhaLP/0QvxozsqUv5bT4OA0xkoC++altqhePQxLQLbnxfvw2UX9ZJEJqdH1HReA5JDgkJDjqrjsz0YHBPVNPs1fnRtXqPFOnw3TVA7CaGMz4H9OIYzAY4RM1A2XmzJm49957cddddwEAZs+eja+++grz5s3Dn/70pxY9lz4ZU/vq33X33XfjwQcfBKB7muozd+5cKIoSEhRLRLDb7fj73/+OxMTEiKWVwejI6LXO9vX8YDA6AlHpZhwIBLB582aMGDGiLiE8jxEjRuD7779vsL3f70dNTU3Ip6MzatQoBAIByLKMkSNDPSKKouD999/Hyy+/jG3btlmfgoICZGVl4ZNPPolSqhkMBoPBaBmi4kEpLy+HqqrIyMgIWZ6RkYE9e/Y02H7GjBmYPn16pJLXJhAEAT/99JP1O5hly5ahsrIS99xzTwNPyS233IK5c+fivvvui1haGQwGg8FoadrFQG1PPvkkqqurrU9RUVG0kxQREhISkJCQ0GD53LlzMWLEiEabcW655RZs2rQJ27dvj0QSGQwGg8FoFaLiQUlNTYUgCCgpKQlZXlJSgszMzAbb2+122O3RjSaOBPPnzz/t+iVLlpzxGIMGDbLGOGAwGAwGo70SFQ+KzWbDJZdcglWrVlnLNE3DqlWrcPnll0cjSQwGg8FgMNoQUevF8+ijj2LChAkYOHAgBg0ahFdffRVut9vq1cNgMBgMBqPzEjUDZezYsSgrK8MzzzyD4uJi/PznP8eKFSsaBM4yGAwGg8HofER1JNkHH3zQGuuDwWAwGAwGw6Rd9OJhMBgMBoPRuWAGCoPBYDAYjDYHM1AYDAaDwWC0OaIag3K2mON8NDbkfSAQgKZpUFUVqqpGOmmdElVVoWkaamtrEQgEop0cBoPBYLRRzPd2OON1tUsDxeVyAQC6d+/eYF1ubi5mz54Nr9cb6WR1asrLyzF69GgcPXo02klhMBgMRhvH5XKdcVJbjtrhsKOapuHEiROIj48Hx4VOYR4IBFBSUoK8vDw4HI5WS4Oqqti+fTv69+/fYK6czobP58ORI0eQkZEBm83W6DY1NTXo3r07ioqKGh2+v7PD9Gk+TLPwYDo1H6ZZeJyNTkQEl8uFrKws8Pzpo0zapQeF53l069at0XU+nw9lZWUQBCEihkNLnmfixIl47733AACSJCEnJwfjx4/HlClTsH79egwbNqzBPk899RSeffZZrFmzptH1AHDy5MlGpxBoKQRBAM/ziIuLO6NR2NT8Qgwdpk/zYZqFB9Op+TDNwqO5Op3Jc2LSLg2UjsyoUaPw7rvvwu/3Y/ny5XjggQcgSZI1BcDevXtDCkJcXFzI/vXXA0B6enrrJ5zBYDAYjBakUxgoRATF72/RY6qqCjUQgOzzQWvCgyLa7Q2aoM6E3W63vB2TJk3C4sWL8eWXX1oGSnp6OpKSkprc/0zrGQwGg8FoD3QKA0Xx+zFrwq2tcuz1p1n30HuLIJ1jHExMTAwqKirO6RhtAbvdjqlTp3aKWanPBqZP82GahQfTqfkwzcKjtXVql0Gyp8Pn8+Hw4cPIz8+34iFkn6/VDJTT0VwDZeLEiaiqqsKSJUtARFi1ahXGjBmDyZMnY/To0Rg2bBicTmfIPkePHkVKSooVg1J/fW5uLnbt2tUi19MUjWnOYDAYDMa50Ck8KKLdjofeWxSV8zaXZcuWIS4uDrIsQ9M03HHHHZg2bRo2btwIAFi3bh3i4+Ot7ZOTk0P2r79ekqSzTD2DwWAwGNGjUxgoHMedc1NLpBg2bBjeeust2Gw2ZGVlQRRDsyg/P/+0MSZnWs9gMBgMRnugUxgo7Qmn04levXpFOxkMBoPBYEQVZqB0MEpLS+Hz+UKWpaSksKYeBoPBYLQrmIHSwejTp0+DZd9//z0uu+yyKKSGwWAwGO2Z2traBuNtRQo2m3E9zE5N0ejcNH/+fCxZsqTRdUOHDgURNRlfYq5v7NPaxomqqiF6dbCOYefMqVOnUFJSYk2kqGlalFPU9ikqKsKKFSuinYw2z8GDBzFt2jQcOHAg2klpNxw5cgSTJk3CN998E+2ktGmOHj2KkSNH4oknngAQnecWM1CCKCoqwuHDhwGg2QOsdUaICEVFRThy5AhKS0sxa9YseL1ecBzHjBTo+jz00EO4/PLLccMNN+Daa69FVVUVeJ5n+pyG/fv3Izc3F7/61a+wf//+aCenTUJEmDRpEnr37o2TJ082OfUHI5QpU6bgZz/7GcrLy+HxeNh92AhEhN/97nfo1asXfvjhB6xduxaapp1x3pzWgBkoADweD/bt24eKigqcOnUK1dXVAJgn4HS4XC7s2rULtbW1SE1NhSiK+Ne//oUnn3wy2klrE3z11Vfo27cvNm3ahL///e/47W9/i+LiYkyePBkAM4BPhyzLGDlyJFJSUvDss89GOzltjk8++QSpqanYsGEDNmzYgLffftsaf4g9s5rm3//+N9auXYslS5Zg4cKFuPnmm9l9WI+ZM2ciKSkJ27Ztw5YtW/D8889DkiSUlJREJT0sBgWA2+2GJEnIz89HRUUFjh07hsTERMsTwApxKKqqorKyEnFxccjJyUEgEECXLl1w44034vPPP0d1dXXYk0F1VNasWYMxY8bgueees2Z43rp1K2RZjnLK2j4FBQWw2WxYuHAhfvnLX+Kuu+7C0KFDo52sNsN7772HhIQELFu2DF27dsXOnTtx4sQJ9OrVC5mZmYiNjWXPrUaYP38+evbsiZEjR+KHH37AsmXL0LNnT1xxxRXo3bt3tJMXdfbv34+lS5fitddew8SJEwEAlZWVKCgogKqqABDxcsU8KACSkpKQkZGBxMREpKWlQVGUqFmM7QEigtPpRFpamtVcwXEcFEWBy+VCQkJCp6/JPf7443jggQcs46SkpAQbNmxATk4Ovv/++yinru0R3L4tSRJyc3Nx2WWX4aqrrsLUqVMB6BUJBvDiiy+C53m8+eabuPXWW3H99dfjsccewxVXXIF7770XAPPQBaNpGjweD06cOIFrrrkGr7zyCm688Ubs3LkTzz77LIYPH47PP/882smMOrm5uVizZo1lnJgxjz169MDq1asBRL5cdToD5eTJkygqKkJZWZn1UJQkCbGxsQD0uW9SUlJQXFwMVVVZPAUaaiaKIlJSUhoMq+9yuZCfnw+O4zrVA3LGjBl45JFH8Pbbb1uBsGlpacjLywMAzJ07F926dYMgCFi5ciWuv/56PP744/B6vVFMdXSpr1lw+/b27dtRU1MDAPjoo4/w/fff49prr8WQIUOwbdu2KKU4OjRWtvr374/rrrsOL774ouVp+vDDD/HKK69gyZIlVrNYZ31uNVa2zOf7vHnzUFBQgE8++QSLFi3CwYMHMWDAAGt5Z6K+TjabDRzHWe9FjuOQlpYGv98PvzHZbsTLFHUwvF4v7d69m7xeb4PlO3bsoJ07d9KBAwdoy5YttGfPHnK5XEREpGmata3b7aadO3fSkSNHGqzrTISrmcfjod27d9Ntt91GM2fODFnXkdmzZw/17duX+vXrR2PHjqXk5GQaOnQo/fDDDyHbffDBB7Rq1SpLky+//JJEUaTdu3dHI9lRJRzN7rrrLvriiy+IiOijjz6iuLg4EgSB3n///WglO+I0pdP69euJiKi6upqmTJlChw4dCtnvpZdeoqSkJJJlORrJjipNafbdd98REdEnn3xCkiRR9+7d6dixY9Z+mzdvpq5du9LKlSujlfSIEu5zS1VVIiK64ooraMKECUQU+ed6p/GgVFVVQRAE9O3bFz179sSFF15oNeX4fL4QT4nD4UBaWhoqKiqsXikulwuKokT5KiJLuJpxHAdVVXHgwAEMGTIEgG59Hzp0CEDH7Vb71VdfITExEVu2bMGCBQuwe/duVFZWYubMmTh48KC13bhx4zB8+HDLq9S/f3/wPI89e/ZEK+lR43Sa7d27FwAgiiIWLVqEK6+8Eg8++CD+8Ic/ICUlxSpPnYGmdJo1axb27duHhIQEPPHEE8jPzw/ZLzs7GzabDT/99FOUUh49mtLslVdeQWFhIYYPH46hQ4dCFMWQmIqLL74Yfr8fhYWFUb6CyBDOc8vstRMIBHDeeeehrKwMtbW1rImnNSAi+Hy+kNFUJUlC165dEQgEUF5eDgDWC5fneSQmJiI+Ph6HDx/Gnj17sH///k5loDRHM0Cf0Tg5ORkDBgzA7t27MXToUPTr1w9erzcq3dNaG0VRsGvXLqSnp0MQBABAZmYmnnrqKRQWFmLu3LnWtvVv6iVLluDyyy/H8OHDI5rmaHMmzT744AMAeq+6r776Cn369MHWrVsxdepUTJ06FdOnT+8URt2ZdJo/fz4AICEhocG+5qCM/fr1i2SSo86ZNPvHP/6B9PR0PPbYYygpKcHrr7+OoqIicByH5cuXo1evXhgxYkSUr6L1Cfe5xfM8NE2DzWZDamoqTp48ibi4uIg38XS8N0cjmO1qZk3eFLlLly5wOp1wu93weDwh+xARFEWBx+OBw+HARRddZHXl6ww0VzNZlpGbm4snn3wS/fv3R7du3XDy5EnExMREJf2tjSiK8Pv98Hq90DTNqpHddtttuOSSS/Djjz9i69at1vbmGDuTJ0/G3/72N9x+++1ITEzsVHECZ9Js3bp1OHLkCJ555hmsXbsWc+bMQW5uLgDgvvvuwwsvvIAePXpE8xIiQnPLVmFhIY4cOYIHH3wQS5Yswfjx4wF0rhiU02k2cOBArF+/Htu3b8fIkSMxa9YsfPzxxxg+fDhuvfVW3H777RgxYgSys7OjfBWtT3PKlvnsv+qqq1BQUICDBw9GPrYwog1KEaB+DIrZZlZTU0MbN24kt9vdYPn27dupoqLCOkZtbS3t2LGDdu3aRR6PJ8JXEH2aq5nX66WVK1dSXl4eDR48mDZv3hydhEcIRVGIiGj16tXE8zxt3bqViMhq91+zZg316tWLPvvsMyIi2r9/Pz355JOUk5NDgwcPpoKCgqikO5qEo1mPHj1o4cKF0Upim6C5ZWvfvn302GOPUWZmJl1++eW0ffv2qKQ7moSjWc+ePenTTz+19tm4cSO9/fbb9MQTT3Sa+7G5Zctk0aJFdM8991B5eXnEY1A6hIFy5MgRKioqIiLduGjMQFFVlfbs2UN79+4NWU5EtH37djp+/Lj1vyzLViBoJJkwYQLdeOON1m8ABIAkSaKePXvS9OnTrcK0evVqAkBJSUkNAoI3bNhg7dsUPp+P/H4/ETUMfGquZrW1tfTDDz/Q8uXLz+Hq2xbBZcq8sU3MPPB6vTRkyBAaMWIEEYXqY+aXud1///tfWrt2bSSSHjVaQrO//OUvDZZ3NFqybHk8Hlq9ejWtWrUqEkmPGi1ZtjoyLamTuX8078V238SzdOlS5OfnWyN0mu1qZLg3zbgSVVWRlZUFl8uF0tJSa39FUcDzPERRtPYTRTFqkyMFM2rUKJw8eRL79+/HY489hmnTpuGll14K2SY+Ph6LFy8OWTZ37lzk5OQ0edyqqirs2LHDCgoz3XZnq5kgCEhISMCwYcPO/aLbAE2VKdMdagbZVVdXY/r06Vi7di1mz55t6VdZWQmn04mUlBQAetD14MGDceWVV0bhaiJDS2nWpUsXAB13HI+WLlsxMTEYOnRoh45naumy1VFpaZ3M/aN5L7Z7A2XDhg249NJLUVhYaA22Q0Gj3ZWUlGDL5s2oKquE0x6LrmmZOFF4HEcPHEZNRRVOFh6HFlARZ4+FFlBBsgYtoLbIh86xDdhutyMzMxO5ubmYNGkSRowYgS+//DJkmwkTJmDevHnW/16vFwsWLMCECROaPK7b7YbT6UQgEEBlZWXjmm3ZgurqasTHxyMrKwsnTpzA0aNH4XK5cPLkSWiahvj4+HO6vrZKY2VKVVXrhp01axZiY2OxYsUKDBkyxAri/N3vfod169bhr3/9K1wuF6666qpoXkZEYZqFB9Op+TDNwqMj6tRuh7o3u0FVV1fjF7/4BTweD1577TVcc8011qimhw4dgsvlQk5WDvyz9uKEsW8qAKAWNTgODkAygFPY2tSpzpqsvwwGZxNa7HgxMTGoqKgIWXbnnXfipZdeQmFhIXJycvD5558jLy8PAwYMaLA/BXUJdjqd0DQNJSUlSExMBM/zUBQFhYWFcLlcyM3NtWppXbt2Bc/zqKystLwuPXr06HABsE2VqRtuuAGSJKG6uhoPPPAAVq9ejX/84x+48847AQBPPfUUEhISsHDhQjzwwAPgeR4LFizA+eefH+Uran2YZuHBdGo+TLPw6Mg6tVsPijnE+oEDBzBu3DjcfPPNqKiowIIFC6xtMjIycOGFFyIlNSWKKT13iAgrV67EN99808CVm56ejmuvvdbqejhv3jzcfffdjR7HbLrx+Xzo0qULkpKSoCgKysrKrG1MzVJTU0PGhsnIyECfPn3Qq1cvXHDBBQ1Gke0INFWm3nrrLWub3//+99izZw/Gjx8fMuri5MmTsWbNGixduhTbtm3DoEGDonUZEYVpFh5Mp+bDNAuPjqxTu/CgLFq0CElJSbjgggvQtWtXAHWuK0EQEAgEcNlll+FXv/oVFi1ahPPPPx9OpxPdunXTM48nZP1lcMTTzUnnZv8tW7YMcXFxkGUZmqbhjjvuwLRp0xpsd/fdd+Phhx/G//7v/+L777/HwoULsXz5cgCwhjAG6jwopuERFxeH5ORklJeXw+12IyYmBhkZGSHjlgS3P3IcB7vdfk7X1FZoTpmaO3cufvzxR/Tr1w+PPvqopSeAEK14nm8wcFZHgmkWHkyn5sM0C49Op1PEwnHPgvfff5/S09Np0KBBlJaWRr/85S9p8eLF1vpTp05RZmam1RvlkUceofPOO49WrFgR0m24vVC/F8+IESNo//79dPTo0QZDV5u9eCorK0mWZcrMzKShQ4fSjTfeSFu3bqVZs2YRAPrpp5/o1KlT1n6yLNO2bdusYYwLCwtp06ZNtHnzZqqtrT2rdDc1vUBb5GzKlMPhoJiYGNq0aVOUUh1dmGbhwXRqPkyz8OisOrXJJh5FUfDaa69hxowZeP7557Fu3TosWbIEPXv2xJw5c6yJi7xeL4YMGYIvvvgC/fv3xwcffIDBgwdbvUuA9j1YkdPpRK9evZCTkxNyTfURRRF33nkn1qxZg1GjRiE7O9sadMhut6O8vNxy6Wmahri4OFRVVWHXrl2oqKhAQkJCiHXdnjVrinMpUyNGjEBubq6lixkV39FhmoUH06n5MM3Co7Pr1CYNFLfbjbKyMkyYMAF33XUXbDYbBg8ejL59+6KmpgayLAPQBf/ss88wfvx4XHnllVZ3XFEUUVxcDKDjdlesz7Rp07B9+3bccMMNSE1NtVx4DocDqhrao6iyshKHDx9GfHw8LrzwQnTr1g02mw1FRUUAOqZm51KmXnjhBeTl5eGRRx4BUNf9rqPDNAsPplPzYZqFR2fXqc3EoOzfvx+9evUCx3FITEzErbfein79+llzAvA8j+7du8Ptdlu1/e7du+OTTz5Bfn6+FdzjcDjg8XgQHx8fMq5He0DTtNN6ShrDvMbY2Fj07NkTMTExIcGtNpsNmqZZGthsNvTo0QN2u90KdBVFEcnJySGGTHvR7HS0VJlKSkrCTTfdBJfL1aH0aQymWXgwnZoP0yw8mE5BRLxRqR6ffvop5eXlUZ8+fWjQoEH0zjvvhKw3YyWIiO644w6aOHEiEREFAoEGx9I0jbxeL+3atatdxEPUZ+TIkfTAAw+ccbuKigoqKCigHTt20O7du6m0tDRkffDIfwcPHrSmZA/Wsv625zJaYFuLQWnpMkXUcFTGjgbTLDyYTs2HaRYeTKeGRNVA+fbbbykvL4/eeOMNWrFiBT366KMkSRLNmTMnZKh60/Do378/ffDBBw2OE5wJbe1lGQ6nTp2if/7zn2S320MCnxqjurqaCgoKqKSkhKqqqqwg19LSUqsAm5qpqko7d+6k8vLyBsdpyeGL25LmrVGmOjpMs/BgOjUfpll4MJ0aJyoGivlynD59Ol1yySUhFuD9999PAwcOpC+++CJkn+PHj1NeXh7t27ePiPRJsh555JEGx25LL8twuemmmyg7O5umTJnSpOFgLj9+/Djt2rUrxJo+cuQI7dq1K6S3DhGR3++ngoICSwuv10uFhYUtnv62oHlrlqmOCtMsPJhOzYdpFh5Mp9MTlSBZsx1s9+7d6NmzJyRJsoJ9nn32WTgcDixdutQKdAWAlStXonv37ujatSsefvhh9O3bF0ePHoUsy+2+18nixYtx7NgxPPfcc022EZrLvV4v7Ha71R4JANnZ2eB5HlVVVZaOAFBTUwObzQZJklBYWIhdu3bB7/dD07R2r1l9WJlqPkyz8GA6NR+mWXgwnc5AJKygb7/9liZPnkyvvPIK/fjjj9byOXPmUHx8vOWWMq3HOXPm0HnnnUerV68mIt3KvO222yg5OZlSUlLoggsuoI0bNzZ6rrZQm28Jqqur6ejRo1RcXBwyPklpaSlt3rw5ZMZhc/mOHTuopqaGiHTNDhw4QFu2bKGtW7fSzp07z3qckzMRDc0jWaY6Ckyz8GA6NR+mWXgwnZpHqxooJ06coDFjxlB6ejqNGzeO+vXrR4mJiVbG7N27l7Kzs+npp58mIrIGmSEiyszMpFdeeYWIiNxuN40ZM4a6detGCxYsOO0527uB4vf7ad++fbR161Y6ePAg7dy5k7Zs2WIZF16vl7Zt20bHjh0jotDAqW3btlFxcTER6W2R+/bto4KCglYftC6SmkejTLV3mGbhwXRqPkyz8GA6nR2tZqC43W6aMGECjR071upFQkQ0aNAgK/q4pqaGnn32WYqJibFiI0zPwJAhQ+g3v/mNtV+4o+G1ZwNFURQ6dOgQHThwgHw+n7V89+7dloaKotCJEydo8+bNViE2NduzZw8dPnzY2q+1PCb1iZTm0SpT7RmmWXgwnZoP0yw8mE5nT6vFoMTGxsJut2PixInIz8+HoigAgOuuuw4//fQTiAjx8fG44447MGDAAPz617/G0aNHwXEcCgsLUVpaiptuusk63iWXXNJaSW0zCIIAnueRmpoKu91utScmJibC5/OBiCAIArp06YLY2FgcPHgQfr8fHMfB7/dDlmUkJSVZx+toE/qxMtV8mGbhwXRqPkyz8GA6nT0cUetF1ciyDEmSANRNCT1u3Dg4nU7MmTPH2u748eMYOnQoFEXBwIED8d133+H888/Hxx9/jIyMjGad0+fz4fDhw8jPz4fD4WjR64kEpk5A3eR+hw4dAs/zyMvLs7YLBALYu3cviAhOpxO1tbVwOBzo0aOHpXmkiKTm0ShT7R2mWXgwnZoP0yw8mE5nR6uOJBv8ojRfukePHsW9994LACG9UL7++mts3rwZP/74I55//nlMmDChNZPWZmlsJuFAIIDU1FQAdSPH2mw29O7dGx6PB263G9nZ2dY2HRlWppoP0yw8mE7Nh2kWHkynsySS7UkHDx6kjIyMkDa04GCglqA9x6A0hs/no23btoXEkzQ2Imw0iabmkShTHQ2mWXgwnZoP0yw8mE7hEZFxUMio9a9fvx5xcXFWG9r06dPx8MMPo7S0NBLJaPOUlZVh0qRJyMnJgd1uR2ZmJkaNGoWCggI4nU7k5eWB4zgIggBBEJCVlYV77rkHlZWV1jHmz58fEocSDMdxWLJkCQBg+fLlsNls2LJlS8g2L7/8MlJTU0P63bdFWJlqPkyz8GA6NR+mWXgwnZpHRAwUs6liw4YNuOWWW/Cvf/0L+fn5ePPNN3HzzTcjPT09Eslo89xyyy3YunUr3nvvPezbtw9ffvklBg0aBEVRrJkrJ02ahL1796KwsBAfffQR/vOf/+Chhx5q9rmuu+46jB8/HuPHj7em7N69ezf+/Oc/44033kBmZmZLX16LwspU82GahQfTqfkwzcKD6dRMIuWq8Xq91KtXL+I4jux2O/3tb39rtfPUb27QNI38fn/EP82Z76ayspIA0Jo1a6xlqqrS9u3baePGjbRp0ybq1q2b1R/e5K9//Sv17dvX+v/dd9+lxMTERs8BIGSun5qaGsrNzaUnnniCZFmmgQMH0m233RZ2mk2i1cQTqTLVkWCahQfTqfkwzcKD6RQ+rRokG4zD4UBeXh6uvvpqzJw5M6I9bGRZxvPPPx+x85lMmTLFmg77TMTFxSEuLg5LlizBZZddZg1nb7fbkZCQgO7du0MQhJB9jh8/jn/+85+49NJLzyp98fHxmDdvHkaOHInDhw+jqKgIK1asOKtjRYNolqn2CtMsPJhOzYdpFh5Mp/CJmIECACtWrGjwkmXoiKKI+fPn495778Xs2bMxYMAADBkyBGPHjsV5551nbffEE0/gz3/+M1RVhc/nw6WXXoqZM2ee9XmHDx+OW2+9FQsWLMCnn36KlJSUlriciMHKVPNhmoUH06n5MM3Cg+kUHhE1UKKVIZIkYcqUKVE5b3O45ZZbMHr0aKxbtw4//PADvv76a7z44ot45513MHHiRADAH//4R0ycOBFEhKKiIkyZMgWjR4/Gf/7zn7PS9/jx41ixYgViY2Oxbt06/PrXv272MaIJu8mbD9MsPJhOzYdpFh5Mp/CIymzGkYbjONhstoh/mpqZ+HQ4HA5cffXVePrpp/Hdd99h4sSJmDp1qrU+NTUVvXr1Qu/evTF8+HC8+uqr+O6777B69WoAQEJCAtxut9Wv3qSqqgqAPiptMPfeey8uueQSLFu2DG+99RbWrl3b7DQzGAwGg9HSdAoDpT3Tt29fuN3uJteblrjX6wUA9OnTB4qiYNu2bSHbmd2Jg5uL3nnnHaxfvx5z587FsGHDMGnSJNx9992nPR+DwWAwGJGAGShthIqKCgwfPhwffvghtm/fjsOHD2PhwoV48cUXceONN1rbuVwuFBcX4+TJk9iwYQP++Mc/Ii0tDYMHDwYAXHDBBbjmmmtw9913Y9WqVTh8+DBWrFiB+++/H2PHjkV2djYAfRTDRx99FP/3f/+H3NxcAMALL7wAjuPwpz/9KfICMBgMBoMRRKvOxRMN2utcPH6/H9OmTcO3336LgwcPQpZldO/eHbfddhumTJmCmJgY5OXl4ejRo9Y+aWlp+MUvfoHnnnsOP//5z63lVVVVmDp1Kr766iucOHEC3bp1w80334ynn34acXFxICJcffXVEAQB33zzTUg61q9fj6FDh2LVqlUYMmRIWGlvr5ozGAwGo+3CDBTGOcM0ZzAYDEZLw5p4GAwGg8FgtDmYgcJgMBgMBqPNwQwUBoPBYDAYbQ5moDAYjFZh4sSJ4DgOHMdBkiRkZGTg6quvxrx58xqM03M6TjdDN4PB6Lh0WAOlg8X+tmmY1oymGDVqFE6ePIkjR47g66+/xrBhw/Dwww9jzJgxUBQl2sljMBhtmA5noJjDy3s8niinpPMQCAQAsOGbGQ2x2+3IzMxEdnY2BgwYgClTpmDp0qX4+uuvMX/+fADAzJkz0a9fPzidTnTv3h33338/amtrAQBr1qzBXXfdherqassbM23aNAB61/w//OEPyM7OhtPpxKWXXoo1a9ZE50IZDEaLE9G5eCKBIAhISkpCaWkpACA2NvashpxnhIemaSgrK0NsbCxEscMVJ0YrMHz4cFx00UX44osv8Jvf/AY8z2PWrFnIz8/HoUOHcP/99+Pxxx/Hm2++icGDB+PVV1/FM888g7179wLQZ/4GgAcffBC7d+/GggULkJWVhcWLF2PUqFHYsWMHevfuHc1LZDAYLUCHGwcF0JsciouLrflnGK0Lz/PIz8+HzWaLdlIYbYiJEyeiqqoKS5YsabDu9ttvx/bt27F79+4G6xYtWoT77rsP5eXlAPQYlN///vch93NhYSF69OiBwsJCZGVlWctHjBiBQYMG4fnnn2/x62EwGJGlQ1Z5OY5D165dkZ6eDlmWo52cDo/NZgPPd7jWQkYrQkSWZ3PlypWYMWMG9uzZg5qaGiiKAp/PB4/Hg9jY2Eb337FjB1RVDZlbCtCbfVJSUlo9/QwGo/XpkAaKiSAILC6CwWiD/PTTT8jPz8eRI0cwZswYTJo0Cc899xy6dOmC9evX45577kEgEGjSQKmtrYUgCNi8eXODe9xsAmIwGO2bDm2gMBiMtse///1v7NixA4888gg2b94MTdPw8ssvW164zz77LGR7m80GVVVDll188cVQVRWlpaX4n//5n4ilncFgRA5moDAYjFbD7/ejuLgYqqqipKQEK1aswIwZMzBmzBiMHz8eO3fuhCzLeP3113H99dfjv//9L2bPnh1yjLy8PNTW1mLVqlW46KKLEBsbi/POOw/jxo3D+PHj8fLLL+Piiy9GWVkZVq1ahf79+2P06NFRumIGg9FSsMABBoPRaqxYsQJdu3ZFXl4eRo0ahdWrV2PWrFlYunQpBEHARRddhJkzZ+KFF17AhRdeiI8++ggzZswIOcbgwYNx3333YezYsUhLS8OLL74IAHj33Xcxfvx4PPbYY+jTpw9uuukmbNy4ETk5OdG4VAaD0cJ0yF48DAaDwWAw2jfMg8JgMBgMBqPNwQwUBoPBYDAYbQ5moDAYDAaDwWhzMAOFwWAwGAxGm4MZKAwGg8FgMNoczEBhMBgMBoPR5mAGCoPBYDAYjDYHM1AYDAaDwWC0OZiBwmAwGAwGo83BDBQGg8FgMBhtDmagMBgMBoPBaHP8f9hKc8jPtpA/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "historical_prices_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c1fbc219-240c-4528-9654-2f223c5b3bbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate expected returns and sample covariance\n",
    "mu = expected_returns.mean_historical_return(historical_prices_df)\n",
    "S = risk_models.sample_cov(historical_prices_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a62c6f0e-3a12-425c-9bcd-bf4ea1efbbfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GOOG    0.769263\n",
       "AAPL    0.233803\n",
       "BABA   -0.084841\n",
       "AMZN    0.677650\n",
       "GE      1.501075\n",
       "AMD     0.151754\n",
       "WMT     0.707744\n",
       "BAC     0.384844\n",
       "GM      0.615456\n",
       "T       0.344935\n",
       "UAA    -0.419250\n",
       "XOM     0.368406\n",
       "RRC     0.196621\n",
       "BBY     0.211013\n",
       "MA      0.086393\n",
       "PFE    -0.010286\n",
       "JPM     0.408547\n",
       "SBUX   -0.303034\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7e6e9996-31b8-4ade-b191-f0d1bc26e6cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GOOG</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>BABA</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GE</th>\n",
       "      <th>AMD</th>\n",
       "      <th>WMT</th>\n",
       "      <th>BAC</th>\n",
       "      <th>GM</th>\n",
       "      <th>T</th>\n",
       "      <th>UAA</th>\n",
       "      <th>XOM</th>\n",
       "      <th>RRC</th>\n",
       "      <th>BBY</th>\n",
       "      <th>MA</th>\n",
       "      <th>PFE</th>\n",
       "      <th>JPM</th>\n",
       "      <th>SBUX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>0.076775</td>\n",
       "      <td>0.019083</td>\n",
       "      <td>0.014640</td>\n",
       "      <td>0.033006</td>\n",
       "      <td>0.005018</td>\n",
       "      <td>0.033061</td>\n",
       "      <td>-0.000115</td>\n",
       "      <td>0.005291</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>-0.001730</td>\n",
       "      <td>0.012427</td>\n",
       "      <td>-0.001429</td>\n",
       "      <td>0.007774</td>\n",
       "      <td>-0.001024</td>\n",
       "      <td>0.003723</td>\n",
       "      <td>0.004342</td>\n",
       "      <td>0.000747</td>\n",
       "      <td>0.010597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>0.019083</td>\n",
       "      <td>0.060428</td>\n",
       "      <td>0.018454</td>\n",
       "      <td>0.014891</td>\n",
       "      <td>0.006659</td>\n",
       "      <td>0.021777</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>-0.001520</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>-0.002442</td>\n",
       "      <td>0.014637</td>\n",
       "      <td>-0.003406</td>\n",
       "      <td>0.005491</td>\n",
       "      <td>0.017632</td>\n",
       "      <td>0.005413</td>\n",
       "      <td>0.003382</td>\n",
       "      <td>-0.006608</td>\n",
       "      <td>0.008574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BABA</th>\n",
       "      <td>0.014640</td>\n",
       "      <td>0.018454</td>\n",
       "      <td>0.129157</td>\n",
       "      <td>0.009515</td>\n",
       "      <td>0.002553</td>\n",
       "      <td>0.015597</td>\n",
       "      <td>0.016178</td>\n",
       "      <td>0.005017</td>\n",
       "      <td>0.011296</td>\n",
       "      <td>0.006514</td>\n",
       "      <td>0.029068</td>\n",
       "      <td>0.010209</td>\n",
       "      <td>0.020166</td>\n",
       "      <td>0.014699</td>\n",
       "      <td>0.010041</td>\n",
       "      <td>0.019189</td>\n",
       "      <td>0.009411</td>\n",
       "      <td>0.006991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>0.033006</td>\n",
       "      <td>0.014891</td>\n",
       "      <td>0.009515</td>\n",
       "      <td>0.061596</td>\n",
       "      <td>0.022915</td>\n",
       "      <td>0.045434</td>\n",
       "      <td>0.001360</td>\n",
       "      <td>-0.002418</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>-0.009951</td>\n",
       "      <td>0.014971</td>\n",
       "      <td>-0.003060</td>\n",
       "      <td>0.001491</td>\n",
       "      <td>0.005557</td>\n",
       "      <td>0.006514</td>\n",
       "      <td>-0.001559</td>\n",
       "      <td>0.001337</td>\n",
       "      <td>-0.001709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>0.005018</td>\n",
       "      <td>0.006659</td>\n",
       "      <td>0.002553</td>\n",
       "      <td>0.022915</td>\n",
       "      <td>0.082118</td>\n",
       "      <td>0.037542</td>\n",
       "      <td>-0.001363</td>\n",
       "      <td>0.009998</td>\n",
       "      <td>0.021595</td>\n",
       "      <td>-0.002401</td>\n",
       "      <td>0.019628</td>\n",
       "      <td>0.001945</td>\n",
       "      <td>0.010398</td>\n",
       "      <td>0.016234</td>\n",
       "      <td>0.010660</td>\n",
       "      <td>-0.010862</td>\n",
       "      <td>0.010732</td>\n",
       "      <td>0.001443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMD</th>\n",
       "      <td>0.033061</td>\n",
       "      <td>0.021777</td>\n",
       "      <td>0.015597</td>\n",
       "      <td>0.045434</td>\n",
       "      <td>0.037542</td>\n",
       "      <td>0.237053</td>\n",
       "      <td>-0.003718</td>\n",
       "      <td>0.004119</td>\n",
       "      <td>0.013155</td>\n",
       "      <td>-0.012247</td>\n",
       "      <td>0.014757</td>\n",
       "      <td>-0.003282</td>\n",
       "      <td>0.013943</td>\n",
       "      <td>0.022993</td>\n",
       "      <td>0.019143</td>\n",
       "      <td>-0.016155</td>\n",
       "      <td>0.014358</td>\n",
       "      <td>0.037658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMT</th>\n",
       "      <td>-0.000115</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.016178</td>\n",
       "      <td>0.001360</td>\n",
       "      <td>-0.001363</td>\n",
       "      <td>-0.003718</td>\n",
       "      <td>0.028050</td>\n",
       "      <td>0.004059</td>\n",
       "      <td>0.005123</td>\n",
       "      <td>0.003019</td>\n",
       "      <td>0.010399</td>\n",
       "      <td>0.001318</td>\n",
       "      <td>0.001814</td>\n",
       "      <td>0.003542</td>\n",
       "      <td>0.002374</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>0.003313</td>\n",
       "      <td>0.007280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAC</th>\n",
       "      <td>0.005291</td>\n",
       "      <td>-0.001520</td>\n",
       "      <td>0.005017</td>\n",
       "      <td>-0.002418</td>\n",
       "      <td>0.009998</td>\n",
       "      <td>0.004119</td>\n",
       "      <td>0.004059</td>\n",
       "      <td>0.041732</td>\n",
       "      <td>0.026031</td>\n",
       "      <td>0.012689</td>\n",
       "      <td>0.025780</td>\n",
       "      <td>0.012620</td>\n",
       "      <td>0.017218</td>\n",
       "      <td>0.020063</td>\n",
       "      <td>0.005218</td>\n",
       "      <td>0.013375</td>\n",
       "      <td>0.024152</td>\n",
       "      <td>0.009478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GM</th>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>0.011296</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>0.021595</td>\n",
       "      <td>0.013155</td>\n",
       "      <td>0.005123</td>\n",
       "      <td>0.026031</td>\n",
       "      <td>0.065175</td>\n",
       "      <td>0.012217</td>\n",
       "      <td>0.026466</td>\n",
       "      <td>0.008517</td>\n",
       "      <td>0.021823</td>\n",
       "      <td>0.025208</td>\n",
       "      <td>0.010801</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.019190</td>\n",
       "      <td>0.007191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>-0.001730</td>\n",
       "      <td>-0.002442</td>\n",
       "      <td>0.006514</td>\n",
       "      <td>-0.009951</td>\n",
       "      <td>-0.002401</td>\n",
       "      <td>-0.012247</td>\n",
       "      <td>0.003019</td>\n",
       "      <td>0.012689</td>\n",
       "      <td>0.012217</td>\n",
       "      <td>0.037284</td>\n",
       "      <td>0.018313</td>\n",
       "      <td>0.008793</td>\n",
       "      <td>0.011248</td>\n",
       "      <td>0.013848</td>\n",
       "      <td>0.004524</td>\n",
       "      <td>0.016846</td>\n",
       "      <td>0.007260</td>\n",
       "      <td>0.002391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAA</th>\n",
       "      <td>0.012427</td>\n",
       "      <td>0.014637</td>\n",
       "      <td>0.029068</td>\n",
       "      <td>0.014971</td>\n",
       "      <td>0.019628</td>\n",
       "      <td>0.014757</td>\n",
       "      <td>0.010399</td>\n",
       "      <td>0.025780</td>\n",
       "      <td>0.026466</td>\n",
       "      <td>0.018313</td>\n",
       "      <td>0.120960</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>0.014982</td>\n",
       "      <td>0.034265</td>\n",
       "      <td>0.010125</td>\n",
       "      <td>0.010624</td>\n",
       "      <td>0.018056</td>\n",
       "      <td>0.022062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XOM</th>\n",
       "      <td>-0.001429</td>\n",
       "      <td>-0.003406</td>\n",
       "      <td>0.010209</td>\n",
       "      <td>-0.003060</td>\n",
       "      <td>0.001945</td>\n",
       "      <td>-0.003282</td>\n",
       "      <td>0.001318</td>\n",
       "      <td>0.012620</td>\n",
       "      <td>0.008517</td>\n",
       "      <td>0.008793</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>0.033388</td>\n",
       "      <td>0.031787</td>\n",
       "      <td>0.006427</td>\n",
       "      <td>0.004896</td>\n",
       "      <td>0.007084</td>\n",
       "      <td>0.011617</td>\n",
       "      <td>0.004702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RRC</th>\n",
       "      <td>0.007774</td>\n",
       "      <td>0.005491</td>\n",
       "      <td>0.020166</td>\n",
       "      <td>0.001491</td>\n",
       "      <td>0.010398</td>\n",
       "      <td>0.013943</td>\n",
       "      <td>0.001814</td>\n",
       "      <td>0.017218</td>\n",
       "      <td>0.021823</td>\n",
       "      <td>0.011248</td>\n",
       "      <td>0.014982</td>\n",
       "      <td>0.031787</td>\n",
       "      <td>0.092595</td>\n",
       "      <td>0.016166</td>\n",
       "      <td>0.006833</td>\n",
       "      <td>0.008464</td>\n",
       "      <td>0.008080</td>\n",
       "      <td>0.010758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBY</th>\n",
       "      <td>-0.001024</td>\n",
       "      <td>0.017632</td>\n",
       "      <td>0.014699</td>\n",
       "      <td>0.005557</td>\n",
       "      <td>0.016234</td>\n",
       "      <td>0.022993</td>\n",
       "      <td>0.003542</td>\n",
       "      <td>0.020063</td>\n",
       "      <td>0.025208</td>\n",
       "      <td>0.013848</td>\n",
       "      <td>0.034265</td>\n",
       "      <td>0.006427</td>\n",
       "      <td>0.016166</td>\n",
       "      <td>0.107209</td>\n",
       "      <td>0.010377</td>\n",
       "      <td>0.007868</td>\n",
       "      <td>0.014033</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>0.003723</td>\n",
       "      <td>0.005413</td>\n",
       "      <td>0.010041</td>\n",
       "      <td>0.006514</td>\n",
       "      <td>0.010660</td>\n",
       "      <td>0.019143</td>\n",
       "      <td>0.002374</td>\n",
       "      <td>0.005218</td>\n",
       "      <td>0.010801</td>\n",
       "      <td>0.004524</td>\n",
       "      <td>0.010125</td>\n",
       "      <td>0.004896</td>\n",
       "      <td>0.006833</td>\n",
       "      <td>0.010377</td>\n",
       "      <td>0.020460</td>\n",
       "      <td>0.003248</td>\n",
       "      <td>0.006730</td>\n",
       "      <td>0.010876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFE</th>\n",
       "      <td>0.004342</td>\n",
       "      <td>0.003382</td>\n",
       "      <td>0.019189</td>\n",
       "      <td>-0.001559</td>\n",
       "      <td>-0.010862</td>\n",
       "      <td>-0.016155</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>0.013375</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.016846</td>\n",
       "      <td>0.010624</td>\n",
       "      <td>0.007084</td>\n",
       "      <td>0.008464</td>\n",
       "      <td>0.007868</td>\n",
       "      <td>0.003248</td>\n",
       "      <td>0.059075</td>\n",
       "      <td>0.010655</td>\n",
       "      <td>-0.006583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPM</th>\n",
       "      <td>0.000747</td>\n",
       "      <td>-0.006608</td>\n",
       "      <td>0.009411</td>\n",
       "      <td>0.001337</td>\n",
       "      <td>0.010732</td>\n",
       "      <td>0.014358</td>\n",
       "      <td>0.003313</td>\n",
       "      <td>0.024152</td>\n",
       "      <td>0.019190</td>\n",
       "      <td>0.007260</td>\n",
       "      <td>0.018056</td>\n",
       "      <td>0.011617</td>\n",
       "      <td>0.008080</td>\n",
       "      <td>0.014033</td>\n",
       "      <td>0.006730</td>\n",
       "      <td>0.010655</td>\n",
       "      <td>0.034938</td>\n",
       "      <td>0.009904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SBUX</th>\n",
       "      <td>0.010597</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.006991</td>\n",
       "      <td>-0.001709</td>\n",
       "      <td>0.001443</td>\n",
       "      <td>0.037658</td>\n",
       "      <td>0.007280</td>\n",
       "      <td>0.009478</td>\n",
       "      <td>0.007191</td>\n",
       "      <td>0.002391</td>\n",
       "      <td>0.022062</td>\n",
       "      <td>0.004702</td>\n",
       "      <td>0.010758</td>\n",
       "      <td>0.023387</td>\n",
       "      <td>0.010876</td>\n",
       "      <td>-0.006583</td>\n",
       "      <td>0.009904</td>\n",
       "      <td>0.088745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GOOG      AAPL      BABA      AMZN        GE       AMD       WMT  \\\n",
       "GOOG  0.076775  0.019083  0.014640  0.033006  0.005018  0.033061 -0.000115   \n",
       "AAPL  0.019083  0.060428  0.018454  0.014891  0.006659  0.021777  0.000187   \n",
       "BABA  0.014640  0.018454  0.129157  0.009515  0.002553  0.015597  0.016178   \n",
       "AMZN  0.033006  0.014891  0.009515  0.061596  0.022915  0.045434  0.001360   \n",
       "GE    0.005018  0.006659  0.002553  0.022915  0.082118  0.037542 -0.001363   \n",
       "AMD   0.033061  0.021777  0.015597  0.045434  0.037542  0.237053 -0.003718   \n",
       "WMT  -0.000115  0.000187  0.016178  0.001360 -0.001363 -0.003718  0.028050   \n",
       "BAC   0.005291 -0.001520  0.005017 -0.002418  0.009998  0.004119  0.004059   \n",
       "GM    0.000144  0.002435  0.011296  0.003451  0.021595  0.013155  0.005123   \n",
       "T    -0.001730 -0.002442  0.006514 -0.009951 -0.002401 -0.012247  0.003019   \n",
       "UAA   0.012427  0.014637  0.029068  0.014971  0.019628  0.014757  0.010399   \n",
       "XOM  -0.001429 -0.003406  0.010209 -0.003060  0.001945 -0.003282  0.001318   \n",
       "RRC   0.007774  0.005491  0.020166  0.001491  0.010398  0.013943  0.001814   \n",
       "BBY  -0.001024  0.017632  0.014699  0.005557  0.016234  0.022993  0.003542   \n",
       "MA    0.003723  0.005413  0.010041  0.006514  0.010660  0.019143  0.002374   \n",
       "PFE   0.004342  0.003382  0.019189 -0.001559 -0.010862 -0.016155  0.002892   \n",
       "JPM   0.000747 -0.006608  0.009411  0.001337  0.010732  0.014358  0.003313   \n",
       "SBUX  0.010597  0.008574  0.006991 -0.001709  0.001443  0.037658  0.007280   \n",
       "\n",
       "           BAC        GM         T       UAA       XOM       RRC       BBY  \\\n",
       "GOOG  0.005291  0.000144 -0.001730  0.012427 -0.001429  0.007774 -0.001024   \n",
       "AAPL -0.001520  0.002435 -0.002442  0.014637 -0.003406  0.005491  0.017632   \n",
       "BABA  0.005017  0.011296  0.006514  0.029068  0.010209  0.020166  0.014699   \n",
       "AMZN -0.002418  0.003451 -0.009951  0.014971 -0.003060  0.001491  0.005557   \n",
       "GE    0.009998  0.021595 -0.002401  0.019628  0.001945  0.010398  0.016234   \n",
       "AMD   0.004119  0.013155 -0.012247  0.014757 -0.003282  0.013943  0.022993   \n",
       "WMT   0.004059  0.005123  0.003019  0.010399  0.001318  0.001814  0.003542   \n",
       "BAC   0.041732  0.026031  0.012689  0.025780  0.012620  0.017218  0.020063   \n",
       "GM    0.026031  0.065175  0.012217  0.026466  0.008517  0.021823  0.025208   \n",
       "T     0.012689  0.012217  0.037284  0.018313  0.008793  0.011248  0.013848   \n",
       "UAA   0.025780  0.026466  0.018313  0.120960  0.002732  0.014982  0.034265   \n",
       "XOM   0.012620  0.008517  0.008793  0.002732  0.033388  0.031787  0.006427   \n",
       "RRC   0.017218  0.021823  0.011248  0.014982  0.031787  0.092595  0.016166   \n",
       "BBY   0.020063  0.025208  0.013848  0.034265  0.006427  0.016166  0.107209   \n",
       "MA    0.005218  0.010801  0.004524  0.010125  0.004896  0.006833  0.010377   \n",
       "PFE   0.013375  0.007000  0.016846  0.010624  0.007084  0.008464  0.007868   \n",
       "JPM   0.024152  0.019190  0.007260  0.018056  0.011617  0.008080  0.014033   \n",
       "SBUX  0.009478  0.007191  0.002391  0.022062  0.004702  0.010758  0.023387   \n",
       "\n",
       "            MA       PFE       JPM      SBUX  \n",
       "GOOG  0.003723  0.004342  0.000747  0.010597  \n",
       "AAPL  0.005413  0.003382 -0.006608  0.008574  \n",
       "BABA  0.010041  0.019189  0.009411  0.006991  \n",
       "AMZN  0.006514 -0.001559  0.001337 -0.001709  \n",
       "GE    0.010660 -0.010862  0.010732  0.001443  \n",
       "AMD   0.019143 -0.016155  0.014358  0.037658  \n",
       "WMT   0.002374  0.002892  0.003313  0.007280  \n",
       "BAC   0.005218  0.013375  0.024152  0.009478  \n",
       "GM    0.010801  0.007000  0.019190  0.007191  \n",
       "T     0.004524  0.016846  0.007260  0.002391  \n",
       "UAA   0.010125  0.010624  0.018056  0.022062  \n",
       "XOM   0.004896  0.007084  0.011617  0.004702  \n",
       "RRC   0.006833  0.008464  0.008080  0.010758  \n",
       "BBY   0.010377  0.007868  0.014033  0.023387  \n",
       "MA    0.020460  0.003248  0.006730  0.010876  \n",
       "PFE   0.003248  0.059075  0.010655 -0.006583  \n",
       "JPM   0.006730  0.010655  0.034938  0.009904  \n",
       "SBUX  0.010876 -0.006583  0.009904  0.088745  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "db17b038-1322-4d24-b8ef-1dcfcdcd6ac5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict({'GOOG': 0.1358, 'AAPL': 0.0, 'BABA': 0.0, 'AMZN': 0.00291, 'GE': 0.27498, 'AMD': 0.0, 'WMT': 0.37475, 'BAC': 0.0, 'GM': 0.0, 'T': 0.10287, 'UAA': 0.0, 'XOM': 0.10869, 'RRC': 0.0, 'BBY': 0.0, 'MA': 0.0, 'PFE': 0.0, 'JPM': 0.0, 'SBUX': 0.0})\n",
      "Expected annual return: 86.0%\n",
      "Annual volatility: 11.4%\n",
      "Sharpe Ratio: 7.39\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.8599543143786224, 0.11366833285896805, 7.3895190793444705)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optimize for maximal Sharpe ratio\n",
    "ef = EfficientFrontier(mu, S)\n",
    "raw_weights = ef.max_sharpe()\n",
    "cleaned_weights = ef.clean_weights()\n",
    "ef.save_weights_to_file(\"weights.csv\")  # saves to file\n",
    "print(cleaned_weights)\n",
    "ef.portfolio_performance(verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2acb6fc6-1111-4d40-82b5-a20edc94f34e",
   "metadata": {},
   "source": [
    "This is interesting but not useful in itself. However, PyPortfolioOpt provides a method which allows you to convert the above continuous weights to an actual allocation that you could buy. Just enter the most recent prices, and the desired portfolio size ($10,000 in this example):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a2e4ff4d-5005-4607-af7d-1a33083cc4bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6bd376e0-2f95-440b-8c51-b88bf4749c42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete allocation: {'WMT': 55, 'GE': 17, 'GOOG': 7, 'XOM': 10, 'T': 55}\n",
      "Funds remaining: $49.83\n"
     ]
    }
   ],
   "source": [
    "\n",
    "latest_prices = get_latest_prices(historical_prices_df)\n",
    "\n",
    "da = DiscreteAllocation(cleaned_weights, latest_prices, total_portfolio_value=10000)\n",
    "allocation, leftover = da.greedy_portfolio()\n",
    "print(\"Discrete allocation:\", allocation)\n",
    "print(\"Funds remaining: ${:.2f}\".format(leftover))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d4702ad5-800d-4e63-a588-0a2bf53daead",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pypfopt import plotting\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d02f0e8c-4e1e-4a7c-abcc-dd7729fa0d7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program Files\\Python312\\Lib\\site-packages\\cvxpy\\problems\\problem.py:1407: UserWarning: Solution may be inaccurate. Try another solver, adjusting the solver settings, or solve with verbose=True for more information.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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Gfn5+kMvl8PHxwbRp0ypfV6PRYObMmWjSpAmcnJzQqVMn7N+/HwCwf/9+jB8/HgUFBZBIJJBIJIiJiQEAfPzxx2jRogXs7e3h6emJp556yqjfDyIiS6Yp12JfYg7mbD2DiCV78djKP7B01yUkpFeUujZNFHglsgV+mtoNR2Y/giVPhuGxNt4sdVQjjtjdh1qtxvLlyxEbG4vMzEz4+PggKioK06dPh7Ozs0Hes6ioCDNmzEBYWBjUajXmzZuHYcOGISEhAStXrsT27dvx7bffws/PD+np6UhPTwcAbNmyBcuXL8fmzZsRGhqK7OxsnDp1qvJ1p06divPnz2Pz5s3w8fHB1q1b0b9/f5w5cwYRERFYsWIF5s2bh8TERACAs7Mzjh8/jmnTpuHLL79EREQEbty4gT/++MMgXzcRkbUo0pQj7lIudp7Nxt6LOVBr/llPzt5Wim7N3fFISw/0CfGAp6u9iEnJ3LDY3YNarUbPnj2RkJAAnU4HAMjIyEBMTAy2bduGuLg4g5S7J598ssrnX3zxBdzd3XH+/HlcvXoVLVq0QLdu3SCRSODv71+539WrV+Hl5YXIyEjY2trCz88PHTt2rHxs7dq1uHr1Knx8fAAAM2fOxM6dO7F27VosWrQICoUCEokEXl5eVV7TyckJgwYNgouLC/z9/dGuXTu9f81ERJauoLgMey5ex86z2Yi7lAtNua7yMQ8XOfq28kRkS090CWwEe1tOeqD6YbG7h+XLl1cpdXfodDokJCRg+fLlmDt3rt7f9/Lly5g3bx7++usv5OXlVb7/1atXMW7cOPTt2xfBwcHo378/Bg0ahEcffRQAMHz4cKxYsQLNmjVD//798dhjj2Hw4MGwsbHBmTNnoNVqERQUVOW9NBoNGjVqdNcsffv2hb+/f+Vr9u/fH8OGDYOjo6Pev24iIkuTW6jB7+ezsfNsNg4n56Nc988Fc34NHdG/tRf6hXqhndINUikXBaYHx2J3D7GxsdVK3R06nQ6xsbEGKXaDBw+Gv78/1qxZAx8fH+h0OrRu3RqlpaV46KGHkJqail9//RW7d+/GiBEjEBkZie+//x5KpRKJiYnYvXs3du3ahcmTJ+O9995DXFwc1Go1ZDIZ4uPjIZNV/ZfgvUYdXVxccOLECezfvx+///475s2bh5iYGBw7dgxubm56/9qJiMzdzaJS7DyXjZ9OZeJISj7+1eUQ7OmCfq290D/UCy29XXiHB9I7iSAI1e/2ayZUKhUUCgUKCgrg6upa5bGSkhKkpqaiadOmsLev3/UJMpnsrsUOAKRSKbRa7V0fr4/8/Hw0btwYBw4cQPfu3QEABw8eRPfu3bF161YMHTq0yv6//fYb+vfvj/z8fDRs2LDKY4mJiQgJCUF8fDycnZ0RHBxc5XX/a9OmTXjhhRdQWFhY4+NAxfV/bm5u+Oabb/DEE0882BdrJfTxs0hEpq2wpAy7L1zHT6eycOBSbpWRuXBfBfq39ka/UE80czfMtdlk2e7Vd/6LI3b34OPjg4yMjHs+rm8NGjRAo0aNEBsbC29vb1y9ehWzZs2qfHzZsmXw9vZGu3btIJVK8d1338HLywtubm5Yt24dtFotOnXqBEdHR3z11VdwcHCAv78/GjVqhFGjRmHMmDFYunQp2rVrh9zcXOzZswdhYWEYOHAgAgICoFarsWfPHoSHh8PR0RF79+5FSkoKevTogQYNGmDHjh3Q6XQIDg7W+9dORGROSsq02HsxBz+dysTeizlVrplr5e2KweE+GBTmDWVDXrpCxsNidw9RUVGIiYmpcdROKpUiKipK7+8plUqxefNmTJs2Da1bt0ZwcDBWrlyJXr16Aag4Nfruu+/i8uXLkMlkePjhh7Fjxw5IpVK4ublhyZIlmDFjBrRaLdq0aYOffvqp8hq6tWvX4q233sKrr76Ka9euoXHjxujcuTMGDRoEAIiIiMCLL76IkSNHIj8/H9HR0YiMjMQPP/yAmJgYlJSUoEWLFvj6668RGhqq96+diMjU6XQCjqbdwJb4DOw4k1VlweBm7k4YHOaDweE+aO7BkTkSB0/F3kNNs2KBivLVtm1bg82KJcvCU7FE5u9qfjG2nMjAlhMZyLh5u3J7EzcHDA73weBwb7TyduU1c2QQPBWrJ87OzoiLizP6OnZERCQ+taYcO85k4fv4DBxNvVG53Vlug0Fh3niyvS86+DdgmSOTwmJ3H87Ozpg7d65BZr8SEZFpEQQBx6/cxNdHr+LXM9m4XVZxqlUiAbo1b4yn2vvi0VZecLDjOnNkmljsiIjI6t0sKsWWExnYfCwdSTnqyu3NGjvhyfa+GNauCXzcHERMSFQ7LHZERGSVBEHAkZQb2HysYnSuVFtxLbWDrQyDw70x8mE/POTnxlOtZFZY7IiIyKrcKCrF9/Hp2Hw0HSl5RZXbQ31c8UxHPwxp6wNXe1sRExLVn8UXOzOe9EsWgj+DROITBAEnrt7EV0eu4pczWSj9e805JzsZhrRtgmc7+qGNr0LklEQPzmKLna1txb+2iouL4eDA6yJIPMXFxQD++ZkkIuNRa8qx9eQ1bDxyBRez/7mrTusmrhjVyR9Dwn3gJLfYX4VkhSz2p1kmk8HNzQ05OTkAAEdHR14nQUYlCAKKi4uRk5MDNze3avfoJSLDuZitwpeHr2DbyWuViwjLbaQYEu6D5zr7I1zpJm5AIgOx2GIHAF5eXgBQWe6IxODm5lb5s0hEhlOm1WHX+etYdyityrpzzdyd8Fwnfzz5kC8Ujhw5J8tm0cVOIpHA29sbHh4eKCsrEzsOWSFbW1uO1BEZWJ5ag6//uoqNf11FtqoEACCTStAv1BPPdfZHl2aNeMaGrIZFF7s7ZDIZf7kSEVmYk1dvYsPhK/jldFblUiWNne3wTEc/jOrkDy8Fb+FH1scqih0REVmGcq0Ov57NxucHU5GQfqtye1ulG8ZG+OOxNt6Q2/Af8mS9WOyIiMjkFdwuw+ajV7H+UBoyCypOt9rJpBgU7o2xXQI4GYLobyx2RERkslLzirD2z1R8H5+B4r9ntzZyssOozv54rrMfPFx4upXo31jsiIjIpAiCgONXbiL2QAp2X7iOO2t8h3i54PmuTTGkrQ/sbXm6lagmLHZERGQStDoBu85n49MDKTh59Vbl9j4hHpjQrSkiAjm7leh+WOyIiEhUJWVafB+fgc/+SEFafsWdWuxkUjzxUBNM7N4MzT2cRU5IZD5Y7IiISBQFxWVYfzgN6w+lIb+oFACgcLDF6M7+GBPhz+vniOqBxY6IiIwqu6AEnx9Mwaa/rlbe7su3gQMmdGuKER2UvHcr0QPgnx4iIjKKlFw1Yg+k4IcT1yoXFG7p7YoXezbDwDbesJFJRU5IZP5Y7IiIyKDOXivAx/uT8OvZ7MoZrh0DGuKl3oHoFeTOCRFEesRiR0REBnHy6k18tDcJey7mVG57JMQDL/UKRIeAhiImI7JcLHZERKRXf6Xk48O9STiYlAcAkEqAQWE+mNw7ECFeriKnI7JsLHZERPTABEHAwaQ8fLgnCUfTbgAAbKQSDGvXBJN7N0fTxk4iJySyDqIWu5iYGMyfP7/KtuDgYFy8eFGkREREVBd3Ct2K3ZcRf+UmgIo16EY87IsXegRC2dBR5IRE1kX0EbvQ0FDs3r278nMbG9EjERHRfQiCgEPJ+Vi+6xKO/13o5DZSPNvJDy/0CISXgmvQEYlB9BZlY2MDLy8vsWMQEVEtHUrOw4pdlytPudrZSDGqkx9e6hkID1cWOiIxiV7sLl++DB8fH9jb26NLly5YvHgx/Pz8xI5FRET/EX/lJt7/LRGHU/IBVBS6Zzv64aVegfBkoSMyCaIWu06dOmHdunUIDg5GVlYW5s+fj+7du+Ps2bNwcXGptr9Go4FGo6n8XKVSGTMuEZFVupClwtLfE7H7QsWyJXYyKZ7uqMTkXs15ypXIxEgE4c5ykeK7desW/P39sWzZMkyYMKHa4zVNtgCAgoICuLpyCj0RkT6l5KqxfPdl/HQqE0DFsiXD2ysxLbIFmrg5iJyOyHqoVCooFIpa9R3RT8X+m5ubG4KCgpCUlFTj47Nnz8aMGTMqP1epVFAqlcaKR0RkFbILSvDBnkv49ngGtLqKf/sPCvPG9L5BCHR3FjkdEd2LSRU7tVqN5ORkjB49usbH5XI55HK5kVMREVmHguIyrI5Lxto/U6Epr7iX6yMhHpjxaBBCfRQipyOi2hC12M2cORODBw+Gv78/MjMzER0dDZlMhmeeeUbMWEREVqWkTIv1h9Lw8f5kFNwuAwB08G+AWQNCeOsvIjMjarHLyMjAM888g/z8fLi7u6Nbt244cuQI3N3dxYxFRGQVtDoBW05kYPmuS8gqKAEABHk64/V+IXikpQckEonICYmorkQtdps3bxbz7YmIrJIgCIi7lIslv17ExexCAICPwh4zHg3GsHZNIJOy0BGZK5O6xo6IiAzr7LUCLP71Av5MqliLztXeBlP7NMeYLgGwt5WJnI6IHhSLHRGRFci4WYz3f0vEtoSKpUvsZFKM6xqAyb0C4eZoJ3I6ItIXFjsiIgumKinDx/uS8cWfqSj9e6br0LY+ePXRYCgbOoqcjoj0jcWOiMgClWt1+ProVazYfRn5RaUAgM7NGmLOY63QxpdLlxBZKhY7IiILIggC9l7MwaIdF5CcWwQAaObuhDcHtORMVyIrwGJHRGQhLmar8NbPF3AwKQ8A0NDJDq9EtsAzHf1gK5OKnI6IjIHFjojIzOWpNVj6+yV8c+wqdELFxIjx3QIwpXdzuNrbih2PiIyIxY6IyEyVlGmx9s80rNqXBLWmHADwWBsvzOrfEn6NODGCyBqx2BERmRlBEPDbuWy8veMC0m/cBgC0aaLA3EGt0LEpbwFGZM1Y7IiIzMj5TBUW/HwOR1JuAAA8XeV4vV8IhrVrAinvGEFk9VjsiIjMQL5ag6W7LmHz0Yrr6OQ2UkT1aIYXewbCSc6/yomoAv82ICIyYWVaHTYcvoIVuy+hsKTiOrqBYd6YPSAEvg14HR0RVcViR0Rkog5ezkPMT+eQlKMGAIT6uCJ6cCivoyOiu2KxIyIyMek3ivHWL+fx27nrACrWo3u9XzCGd1BCxuvoiOgeWOyIiEzE7VItVu9PwicHUlBaroNMKsGYLv545ZEgKBy5Hh0R3R+LHRGRyO4sX7Lw5wu4dqti+ZKIwEaIGRKKIE8XkdMRkTlhsSMiElFyrhox28/hj8sVtwHzUdjj/wa1woDWXryvKxHVGYsdEZEIijTl+HBvEj4/mIIyrQA7WcXyJZN7B8LRjn81E1H98G8PIiIjEgQBv57NxsKfzyOroAQA0DvYHdGDQxHQ2EnkdERk7ljsiIiMJCVXjeh/nXb1beCA6MGhiGzpwdOuRKQXLHZERAZ2u1SLj/ZdRuyBv0+72kjxYs9ATO4VCHtbmdjxiMiCsNgRERnQrvPXEbP9XOVs155B7pg/hKddicgwWOyIiAwg/UYx5v90Drsv5AComO06b3Ao+oV68rQrERkMix0RkR5pyrVYcyAFH+5NgqZcBxupBJN6NMPLfZpztisRGRz/liEi0pM/k/Iwd9tZpOQVAQA6N2uIhY+3RgsuMkxERsJiR0T0gHJUJXjrlwvYfioTANDYWY65g1piSLgPT7sSkVGx2BER1ZNWJ+DLw2lY+vslFGrKIZUAozv749V+wXC1571dicj4WOyIiOrhVPotzNl2BmevqQAA4b4KvDW0Ddr4KkRORkTWjMWOiKgOCm6X4b3fLmLjX1chCICLvQ1e7x+CZzv6QSblaVciEheLHRFRLQiCgO2nMrHw5wvIU2sAAMPaNcGbj7WEu4tc5HRERBVY7IiI7iMlV425P57Fn0n5AIBm7k54a2hrRAQ2FjkZEVFVLHZERHdRUqbF6v3JWL0/GaVaHeQ2Urzcpzkm9WgGuQ1vBUZEpofFjoioBgcv52Huj2eR+veadD2D3LHg8VD4N+KtwIjIdLHYERH9S26hBm//ch7bEirWpPNwkSN6cCgea+PFNemIyOSx2BERAdDpBGw+lo4lv16AqqQcEgkwtksAZjwaxDXpiMhssNgRkdVLzC7Em1vPIP7KTQBA6yauWDSsDcJ83cQNRkRURyx2RGS1bpdqsXLvZaw5kIJynQAnOxlefTQYY7r4w0YmFTseEVGdsdgRkVWKu5SL/9t2Buk3bgMAHm3liZghofBxcxA5GRFR/bHYEZFVyS3UYOHP57H9VMXkCG+FPeYPCcWjoV4iJyMienAsdkRkFXQ6Ad8cT8fiHRWTI6QSYFxEU8x4NAjOcv5VSESWgX+bEZHFS8opxJs/nMXRtBsAKiZHLB4Whja+CpGTERHpF4sdEVksTbkWH+/7584RjnYyzOgbhHERAZwcQUQWicWOiCzSXyn5eHPrGSTnVtw5ok+IBxYObY0mnBxBRBaMxY6ILErB7TIs+fUCvj6aDgBo7CzH/CG8cwQRWQcWOyKyCIIg4Nez2Yjefg65hRoAwDMd/TCrfwgUjrxzBBFZBxY7IjJ7WQW3MXfbOey+cB0A0MzdCYuHtUGnZo1ETkZEZFwsdkRktnQ6ARv/uoJ3diZCrSmHrUyCl3oGYnLv5rC3lYkdj4jI6ExmWtiSJUsgkUjwyiuviB2FiMxAUk4hRnx6GHN/PAe1phwP+bnhl2ndMePRYJY6IrJaJjFid+zYMXz66acICwsTOwoRmbjSch0+iUvGR3uTUKrVwclOhtf7h2B0Z39IpZwcQUTWTfQRO7VajVGjRmHNmjVo0KCB2HGIyISdvHoTgz88iGW7LqFUq0PvYHf8PqMnxkYEsNQREcEEit2UKVMwcOBAREZGih2FiExUcWk5Fv58Hk+sPoTE64Vo6GSHD55uiy/GPcx16YiI/kXUU7GbN2/GiRMncOzYsVrtr9FooNFoKj9XqVSGikZEJuLPpDzM+uE00m/cBgAMa9cEcwe1QkMnO5GTERGZHtGKXXp6Ov73v/9h165dsLe3r9VzFi9ejPnz5xs4GRGZgoLbZVj0ywV8c7xioWEfhT3efqINegd7iJyMiMh0SQRBEMR4423btmHYsGGQyf6ZvabVaiGRSCCVSqHRaKo8BtQ8YqdUKlFQUABXV1ejZSciw9p1/jrmbD2DnL8XGh7TxR+v9w+Bs9wk5nsRERmVSqWCQqGoVd8R7W/JRx55BGfOnKmybfz48QgJCcEbb7xRrdQBgFwuh1wuN1ZEIjKyfLUG8386j+2nMgEAzRo7YcmTYejYtKHIyYiIzINoxc7FxQWtW7euss3JyQmNGjWqtp2ILJsgCPjpdBZitp/DjaJSSCXApB7NMD0yiGvSERHVAc9rEJGocgpL8H9bz+L38xW3Awv2dMG7T4UhXOkmbjAiIjNkUsVu//79YkcgIiMRBAFbT17D/J/Oo+B2GWykEkzp3RxTejeHnY3oKzEREZklkyp2RGQdsgtK8ObWM9h7MQcA0LqJK957KhwtvTkJiojoQbDYEZHRCIKA7+IzsPDn8ygsKYedTIr/RbZAVI9msJVxlI6I6EGx2BGRUWQXlGD2D6exLzEXABCudMN7T4UhyNNF5GRERJaDxY6IDEoQBHwfn4EFd0bpbKSY0TcIE7s1hQ1H6YiI9IrFjogM5rqqBLN/+OdaunClG95/KgwtOEpHRGQQLHZEpHeCIODHhExEbz+HgttlsJNJ8UrfFojq3oyjdEREBsRiR0R6lafWYM7WM/jtXMW6dG2aKLB0RDivpSMiMgIWOyLSm51ns/Dm1rO4UVQKG6kE0x5pgZd6BXLGKxGRkbDYEdEDKyguQ/T2s9iWUHGP1xAvFywdEY5QH4XIyYiIrAuLHRE9kLhLuXjj+9PIVpVAKgFe6hWIaY+0gNyG93glIjI2FjsiqpciTTkW7biAjX9dBQA0beyEpSPC8ZBfA5GTERFZLxY7Iqqz+Cs3MePbBFzJLwYAjIsIwBv9Q+Bgx1E6IiIxsdgRUa2Vluuwcs9lfLw/CToB8FbY4/3h4ejavLHY0YiICCx2RFRLl68XYvq3CTh7TQUAGNauCWKGhELhYCtyMiIiuoPFjojuSacTsOFwGhb/ehGach3cHG3x9tA2GBjmLXY0IiL6DxY7IrqrHFUJZn5/Ggcu5QIAegS5472nwuDpai9yMiIiqgmLHRHVaOfZbMz+4TRuFpdBbiPFnIEtMbqzPyQSidjRiIjoLljsiKiKIk05Fvx0Ht8cTwcAhPq44oOn26K5B28JRkRk6ljsiKjS6Yxb+N/mBKTmFUEiAV7oEYgZfYNgZ8NbghERmQMWOyKCVifg0wPJWPb7JZTrBHgr7LFsRFt0CWwkdjQiIqoDFjsiK5ddUILp3yTgcEo+AOCxNl5YNKwN3BztRE5GRER1xWJHZMV2nb+O174/hVvFZXC0kyFmSCiGt/flBAkiIjPFYkdkhUrKtFi04wI2HL4CAGjdxBUrn26HZu7OIicjIqIHwWJHZGUuXy/Ey1+fxMXsQgDApO5N8Vq/EE6QICKyACx2RFZCEAR8dzwD87afRUmZDo2d7fD+8HD0CvYQOxoREekJix2RFVBryjFn6xn8mJAJAOjWvDGWjQyHhwvvIEFEZElY7Igs3NlrBZi66QTS8oshk0owo28QXuoZCKmUEySIiCwNix2RhRIEAV8duYKFP19AqVYHH4U9Vj7TDh0CGoodjYiIDITFjsgCFZaUYfYPZ/Dz6SwAQGRLD7w/PJxr0xERWTgWOyILcy6zAFM3nURqXhFspBK80T8EE7s35dp0RERWgMWOyIJ8c+wq5v54DqXlFadeP3z2IbT3byB2LCIiMhIWOyILcLtUi3k/nsV38RkAgN7B7lg2oi0aOPHUKxGRNWGxIzJzaXlFeGnjCVzIUkEqAV59NJizXomIrBSLHZEZ+/1cNl797hQKS8rRyMkOK59ph67NG4sdi4iIRMJiR2SGtDoBy3YlYtW+ZABAe/8GWPXsQ/BScMFhIiJrxmJHZGZuFJVi2tcncTApDwAwLiIAcwa2hK2M93olIrJ2LHZEZuR0xi28+GU8MgtK4GArw5In2+Dxtk3EjkVERCaCxY7ITHxz7CrmbjuHUq0OTRs74ZPn2iPYy0XsWEREZEJY7IhMnKZci5jt5/D10XQAQN9Wnlg6Ihyu9rYiJyMiIlPDYkdkwjJv3cZLG0/gVPotSCTATC5lQkRE98BiR2SiDifnY+qmE8gvKoWboy0+eLodega5ix2LiIhMGIsdkYkRBAGfH0zF4l8vQqsTEOrjik+eaw9lQ0exoxERkYljsSMyIbdLtZj1w2n8mJAJAHiiXRMseqIN7G1lIicjIiJzwGJHZCLSbxQj6st4XMhSwUYqwf8NbImxEQGQSHg9HRER1Q6LHZEJOHg5D1O/PoFbxWVo7GyHVc8+hE7NGokdi4iIzAyLHZGIBEHAmj9SsOTXi9AJQLivAqufaw8fNwexoxERkRmqV7ErKirCkiVLsGfPHuTk5ECn01V5PCUlRS/hiCzZ7VIt3thyGttPVVxP91R7X7w1tDWvpyMionqrV7GbOHEi4uLiMHr0aHh7e9f7GqDVq1dj9erVSEtLAwCEhoZi3rx5GDBgQL1ej8hcpN8oxgtfxuP839fTzRvcCqM7+/N6OiIieiD1Kna//vorfvnlF3Tt2vWB3tzX1xdLlixBixYtIAgC1q9fj8cffxwnT55EaGjoA702kak6lJSHKZtO4GZxGRo52eHjUbyejoiI9KNexa5BgwZo2LDhA7/54MGDq3z+9ttvY/Xq1Thy5AiLHVkcQRCw9s80vL3jArQ6AW2aKPDpaF5PR0RE+iOtz5MWLlyIefPmobi4WG9BtFotNm/ejKKiInTp0kVvr0tkCkrKtJj53Wks+Pk8tDoBw9o1wXcvdmGpIyIivarXiN3SpUuRnJwMT09PBAQEwNa26s3IT5w4UevXOnPmDLp06YKSkhI4Oztj69ataNWqVY37ajQaaDSays9VKlV94hMZVXZBCV74Kh6n0m9BKgHmDGyF57tyfToiItK/ehW7oUOH6i1AcHAwEhISUFBQgO+//x5jx45FXFxcjeVu8eLFmD9/vt7em8jQ4q/cxItfxSO3UAM3R1t89MxD6NaisdixiIjIQkkEQRDq8oTy8nIsWrQIzz//PHx9ffUeKDIyEoGBgfj000+rPVbTiJ1SqURBQQFcXV31noXoQXx7LB3/t+0sSrU6BHu6YM2YDvBrxPu9EhFR3ahUKigUilr1nTpfY2djY4P33nsP5eXl9Q54Lzqdrkp5+ze5XA5XV9cqH0SmpkyrQ8z2c3h9y2mUanXoH+qFHyZHsNQREZHB1etUbJ8+fRAXF4eAgIAHevPZs2djwIAB8PPzQ2FhITZt2oT9+/fjt99+e6DXJRLLjaJSTNl4AodT8gEAM/oGYWrv5pBKeT0dEREZXr2K3YABAzBr1iycOXMG7du3h5OTU5XHhwwZUqvXycnJwZgxY5CVlQWFQoGwsDD89ttv6Nu3b31iEYnqYrYKE9cfR8bN23Cyk2HZyLboF+oldiwiIrIidb7GDgCk0rufwZVIJNBqtQ8Uqrbqcs6ZyJB2ns3CjG9PobhUC7+GjlgzpgOCvVzEjkVERBagLn2nXiN2/703LJG10ukEfLDnMj7YcxkA0LV5I3z0zENo4GQncjIiIrJG9Sp2RAQUacox49sE/HbuOgBgfNcAzHmsJWxk9Vr3m4iI6IHVq9gtWLDgno/PmzevXmGIzEX6jWJM2nAcF7MLYSeT4q2hrTHiYaXYsYiIyMrVq9ht3bq1yudlZWVITU2FjY0NAgMDWezIoh1KzsOUjSdws7gMjZ3l+HR0e7T3byB2LCIiovoVu5MnT1bbplKpMG7cOAwbNuyBQxGZIkEQ8OWRK5j/U8X9XsN8Ffh0dHt4K3i/VyIiMg31mhV7N2fOnMHgwYORlpamr5e8J86KJWMpLdchevtZfH00HQAwtK0PljwZBntbmcjJiIjI0hl8VuzdFBQUoKCgQJ8vSSS6PLUGL30Vj2NpNyGRALP6hyCqRzNIJFx0mIiITEu9it3KlSurfC4IArKysvDll19iwIABeglGZArOZRYgakM8rt26DRe5DVY+0w69QzzEjkVERFSjehW75cuXV/lcKpXC3d0dY8eOxezZs/USjEhsv5zOwqvfJaCkTIemjZ2wZkwHNPdwFjsWERHRXdWr2KWmpuo7B5HJ0OkErNh9CSv3JgEAegS548On20HhaCtyMiIionur10qqzz//PAoLC6ttLyoqwvPPP//AoYjEUqQpx4tfxVeWukndm+KLsR1Y6oiIyCzUa1asTCZDVlYWPDyqXmuUl5cHLy8vlJeX6y3gvXBWLOnTfxcdXvxEGzzZ3lfsWEREZOUMNitWpVJBEAQIgoDCwkLY29tXPqbVarFjx45qZY/IHBxOzsfkjfG4WVwGd5eKRYcf8uOiw0REZF7qVOzc3NwgkUggkUgQFBRU7XGJRIL58+frLRyRMXx15Apitp9D+d+LDseO7gAvhf39n0hERGRi6lTs9u3bB0EQ0KdPH2zZsgUNGzasfMzOzg7+/v7w8fHRe0giQyjT6jD/p3P46shVAMDjbX3wDhcdJiIiM1anYtezZ08AFbNi/fz8uEArma0bRaWYvDEeR1JuQCIBXusXjJd6BvJnmoiIzFq9ZsX6+/vj4MGDeO655xAREYFr164BAL788kscPHhQrwGJ9C0xuxCPrzqIIyk34GQnw5rRHTC5V3OWOiIiMnv1KnZbtmxBv3794ODggBMnTkCj0QCouKXYokWL9BqQSJ9+P5eNJz7+E+k3bsOvoSO2TumKyFaeYsciIiLSi3oVu7feeguffPIJ1qxZA1vbf9b36tq1K06cOKG3cET6IggCVu1LwgtfxaOoVIsuzRrhxyldEeTpInY0IiIivanXnScSExPRo0ePatsVCgVu3br1oJmI9KqkTIvXvz+N7acyAQCjO/tj3uBWsJXV6981REREJqtexc7LywtJSUkICAiosv3gwYNo1qyZPnIR6UV2QQmivjyO0xkFsJFKEDMkFM919hc7FhERkUHUq9hNmjQJ//vf//DFF19AIpEgMzMThw8fxquvvop58+bpOyNRvSSk30LUhuPIKdSggaMtPh7VHl0CG4kdi4iIyGDqVexmzZoFnU6HRx55BMXFxejRowfkcjlee+01TJw4Ud8Zieps28lreH3LaZSW6xDk6YzPxjwMv0aOYsciIiIyqHpdZCSRSDBnzhzcuHEDZ8+exZEjR5CbmwuFQoGmTZvqOyNRrel0At7ZeRGvfJOA0nIdIlt6YMtLESx1RERkFepU7DQaDWbPno0OHTqga9eu2LFjB1q1aoVz584hODgYH3zwAaZPn26orET3pNaUI+rL41i9PxkA8FKvQHw6ugNc7G3v80wiIiLLUKdTsfPmzcOnn36KyMhIHDp0CMOHD8f48eNx5MgRLF26FMOHD4dMxtsxkfGl3yjGxPXHkXi9EHY2UrzzZBsMa+crdiwiIiKjqlOx++6777BhwwYMGTIEZ8+eRVhYGMrLy3Hq1Cmu2k+iOZp6Ay9+FY8bRaVwd5EjdnR7tPNrIHYsIiIio6tTscvIyED79u0BAK1bt4ZcLsf06dNZ6kg03xy7iv/bdhZlWgGtm7hizZgO8FY4iB2LiIhIFHUqdlqtFnZ2dv882cYGzs7Oeg9FdD/lWh0W7biIL/5MBQAMbOON94eHw8GOlwIQEZH1qlOxEwQB48aNg1wuBwCUlJTgxRdfhJOTU5X9fvjhB/0lJPoPVUkZXt50EnGXcgEA0yODMO2R5hw5JiIiq1enYjd27Ngqnz/33HN6DUN0P2l5RZiw/hiSc4tgbyvFshFt8Vgbb7FjERERmYQ6Fbu1a9caKgfRfR1KzsPkjSdwq7gMXq72+GxsB7RuohA7FhERkcmo150niIxt419XEP3jOZTrBLRVuiF2dHt4uNqLHYuIiMiksNiRSSvX6vDWLxew7lAaAGBoWx8seTIM9racJEFERPRfLHZksgpul2HqphP443IeAOC1fsGY3CuQkySIiIjugsWOTFLq35MkUnKL4GArw/KRbdG/tZfYsYiIiEwaix2ZnEPJeXjpqxMouF0Gb4U91ozhJAkiIqLaYLEjk7Lpr6uY9+NZlOsEhCvdsIaTJIiIiGqNxY5MglYn4O1fLlTeSWJIuA/efYqTJIiIiOqCxY5EV1hShmlfn8S+xIo7SbzaNwhT+/BOEkRERHXFYkeiSr9RjAnrj+HSdTXsbaVYOrwtBobxThJERET1wWJHojmedgNRX8bjRlEpPF3lWDOmA8J83cSORUREZLZY7EgUP5zIwKwtZ1Cq1aF1E1d8NuZheCk4SYKIiOhBsNiRUel0ApbuSsSqfckAgP6hXlg2MhyOdvxRJCIielD8bUpGc7tUixnfJuDXs9kAgMm9AjHz0WBIpZwkQUREpA8sdmQU11UlmLThOE5nFMBWJsGSJ8LwZHtfsWMRERFZFBY7Mriz1wowcf1xZKtK0NDJDp+Obo+HAxqKHYuIiMjiSMV888WLF+Phhx+Gi4sLPDw8MHToUCQmJooZifRs1/nrGP7JYWSrShDo7oRtk7uy1BERERmIqMUuLi4OU6ZMwZEjR7Br1y6UlZXh0UcfRVFRkZixSA8EQUDsgWREfXkct8u06N6iMX6Y3BV+jRzFjkZERGSxJIIgCGKHuCM3NxceHh6Ii4tDjx497ru/SqWCQqFAQUEBXF1djZCQaqNMq8PcbWex+Vg6AGBUJz/EDAmFrUzUf0cQERGZpbr0HZO6xq6goAAA0LAhT9WZq4LiMkzeFI8/k/IhkQD/N7AVnu8awNuDERERGYHJFDudTodXXnkFXbt2RevWrWvcR6PRQKPRVH6uUqmMFY9q4Wp+McavO4rk3CI42smw8ul2iGzlKXYsIiIiq2EyxW7KlCk4e/YsDh48eNd9Fi9ejPnz5xsxFdVW/JUbmLSh4vZg3gp7fDa2A0J9FGLHIiIisiomcY3d1KlT8eOPP+LAgQNo2rTpXferacROqVTyGjuR/ZhwDa99fxql5RW3B/t87MPwdOXtwYiIiPTBbK6xEwQBL7/8MrZu3Yr9+/ffs9QBgFwuh1wuN1I6uh9BEPDR3iQs3XUJANC3lSc+eLotbw9GREQkElF/A0+ZMgWbNm3Cjz/+CBcXF2RnV9xqSqFQwMHBQcxodB+l5TrM+uE0fjhxDQAwqXtTzBrQEjLeHoyIiEg0op6KvdtMybVr12LcuHH3fT6XOxHHreJSvPhVPI6k3IBMKsH8IaF4rrO/2LGIiIgsklmdiiXzciW/COPXHUNKbhGc5TZYNeoh9AxyFzsWERERwYRmxZLpi79yE5M2HMeNolL4KOzxxfiHEeLFkVIiIiJTwWJHtfLL6SxM/zYBpeU6tGmiwOdjO8CDM1+JiIhMCosd3ZMgCPj0QAqW/HoRABDZ0gMrn2nHma9EREQmiL+d6a7KtTrM234Om/66CgAYFxGAuYNaceYrERGRiWKxoxqpNeWYsvEE4i7lQiIB5g5shee73XudQSIiIhIXix1Vc11VgvFrj+F8lgr2tlJ88HQ79Av1EjsWERER3QeLHVWRmF2I8WuPIrOgBI2c7PD5uIfRVukmdiwiIiKqBRY7qnQoKQ8vfBmPQk05mrk7Yd24jvBr5Ch2LCIiIqolFjsCAGw9mYHXvz+NMq2AhwMaIHZ0BzRwshM7FhEREdUBi52VEwQBH+9Pxnu/JQIABoZ5Y+nwcNjbykRORkRERHXFYmfFyrU6RG8/h41/L2cyqXtTzB7QElIuZ0JERGSWWOysVHFpOaZ9fRK7L+RAIgHmDWqF8V25nAkREZE5Y7GzQvlqDZ5ffxyn0m9BbiPFB0+3Rf/W3mLHIiIiogfEYmdlruQXYewXR5GWXww3R1t8NqYDOgQ0FDsWERER6QGLnRU5k1GA8euOIk9dCt8GDlg3viOaeziLHYuIiIj0hMXOSsRdysVLX8WjuFSLVt6uWDf+YXi42osdi4iIiPSIxc4KbD2Zgde+O41ynYCuzRvhk+faw8XeVuxYREREpGcsdhYu9kAyFu24CAAYEu6D94eHw85GKnIqIiIiMgQWOwul0wlY/OsFrPkjFQAwoVtTzHmMa9QRERFZMg7dWKAyrQ4zvztVWepmDwjB/w20/FKnVquxcOFCKJVKyGQyKJVKLFy4EGq1WuxoRERERiERBEEQO0R9qVQqKBQKFBQUwNXVVew4JuF2qRaTN8ZjX2IuZFIJ3n0yDE+29xU7lsGp1Wr07NkTCQkJ0Ol0ldulUinatm2LuLg4ODtzBjAREZmfuvQdjthZkFvFpXju87+wLzEX9rZSrBnT3ipKHQAsX768WqkDAJ1Oh4SEBCxfvlykZERERMbDYmchrqtKMPLTI4i/chOu9jb4akIn9AnxFDuW0cTGxlYrdXfodDrExsYaOREREZHxcfKEBbiSX4TnPv8L6Tduw8NFjg0TOiLEy7pOTWdmZj7Q40RERJaAI3Zm7kKWCk99chjpN24joJEjtrwUYXWlDgB8fHwe6HEiIiJLwGJnxuKv3MTITw8jt1CDEC8XfPtiFygbOoodSxRRUVGQSmv+cZZKpYiKijJyIiIiIuNjsTNTf1zOxXOf/QVVSTna+zfANy90gYeL9d4ibPr06Wjbtm21cndnVuz06dNFSkZERGQ8LHZm6Pdz2Ziw7jhul2nRI8gdX07oCIWDdd8izNnZGXFxcYiJiYGvry+kUil8fX0RExPDpU6IiMhqcB07M/NjwjXM+PYUtDoBA1p7YcXTbSG3kYkdi4iIiAykLn2Hs2LNyNdHr+LNrWcgCMATDzXBu0+GwUbGQVciIiKqwGJnJtb9mYqYn84DAEZ39sf8IaEWf4swIiIiqhsWOzPwSVwylvx6EQDwQo9mmDUgBBIJSx0RERFVxWJn4j7YfRnLd18CAEx7pAWmR7ZgqSMiIqIasdiZKEEQsHz3ZazccxkA8Fq/YEzp3VzkVERERGTKWOxMkCAIWPr7JXy0LwkA8OZjIYjqEShyKiIiIjJ1LHYmRhAEvPtbIlbvTwYA/N/AlpjYvZnIqYiIiMgcsNiZEEEQ8N6/Sl304FYY37WpyKmIiIjIXLDYmZDluy/j479LXczgVhjHUkdERER1wNVtTcQH/5ooMXcQSx0RERHVHYudCfgkLrlySZM5j7XEhG4sdURERFR3LHYi23A4rXLx4df7B2NSD06UICIiovphsRPRd8fTMe/HcwCAl/s0x+ReXKeOiIiI6o/FTiS/nsnCG1tOAwCe79oUM/oGiZyIiIiIzB2LnQgOJeXhf5sToBOApx9WYu6glrxNGBERET0wFjsjO51xC5M2HEepVocBrb3w9rA2LHVERESkFyx2RpSaV4Rxa4+hqFSLrs0bYcXTbSGTstQRERGRfrDYGUmeWoOxXxzFjaJStGmiwKejO0BuIxM7FhEREVkQUYvdgQMHMHjwYPj4+EAikWDbtm1ixjGY26VaTFh/HFdvFEPZ0AFfjHsYznLe9IOIiIj0S9RiV1RUhPDwcKxatUrMGAal1QmYtvkkTqXfgpujLdaN7wh3F7nYsYiIiMgCiTpsNGDAAAwYMEDMCAb37s6L2HX+OuxspPhsTAcEujuLHYmIiIgsFK+xM6Dvjqfj0wMpAID3h4ejQ0BDkRMRERGRJTOrC700Gg00Gk3l5yqVSsQ093Y87QbmbD0LAJjWpzmGhPuInIiIiIgsnVmN2C1evBgKhaLyQ6lUih2pRjmqEry08UTlWnWvRPKuEkRERGR4ZlXsZs+ejYKCgsqP9PR0sSNVU6bVYfLGE8gt1CDEywVLR4RDyrXqiIiIyAjM6lSsXC6HXG7aM0oX77iI41duwkVug9XPtYejnVl9i4mIiMiMido61Go1kpKSKj9PTU1FQkICGjZsCD8/PxGT1c/Os9n44s9UAMDSEeFo2thJ5ERERERkTUQtdsePH0fv3r0rP58xYwYAYOzYsVi3bp1Iqeonq+A2Zv1wGgDwQo9meDTUS+REREREZG1ELXa9evWCIAhiRtALrU7AjG9O4VZxGcJ8FXj10WCxIxEREZEVMqvJE6bqsz9ScDglH452MnzwdDvY2fDbSkRERMbHBvKAUvOKsGzXJQBA9OBWvK6OiIiIRMNi9wB0OgGztpyGplyH7i0aY0QH01xXj4iIiKwDi90D2HwsHX+l3oCjnQyLhrWBRML16oxFrVZj4cKFUCqVkMlkUCqVWLhwIdRqtdjRiIiIRCMRzHj2gkqlgkKhQEFBAVxdXY363gXFZej1/j7cLC7D3EGtMKFbU6O+vzVTq9Xo2bMnEhISoNPpKrdLpVK0bdsWcXFxcHZ2FjEhERGR/tSl73DErp5W7r2Mm8VlaOHhjLFd/MWOY1WWL19erdQBgE6nQ0JCApYvXy5SMiIiInGx2NVDSq4a6w+lAQDmDmoFGxm/jcYUGxtbrdTdodPpEBsba+REREREpoGNpB5W7rmMcp2A3sHu6BHkLnacKqzh2rPMzMwHepyIiMhSsdjVUVpeEbafqigOprYQ8Z1rz2JiYpCRkQGdToeMjAzExMSgZ8+eFlPufHx8HuhxIiIiS8ViV0er9ydDJwC9g93RuolC7DhVmNO1Zw8yshgVFQWptOYfXalUiqioKH3HJSIiMgucFVsHeWoNuizegzKtgC0vdUF7/4YGf8+6UCqVyMjIuOvjvr6+SE9PN2Kimj3orFbOiiUiImvCWbEGsvXENZRpBYT7Kkyu1AHmc+3Zg44sOjs7Iy4uDjExMfD19YVUKoWvry9iYmJY6oiIyKpxxK6WBEFA3+UHkJSjxtvDWmNUJ9Nb4sRcRuzulxOoyBoVFYXp06ezqBERkVXjiJ0BnM4oQFKOGva2UgwON82L883l2rPajBxa4qQPIiIiQ2Oxq6W9F3MAAH1CPOBqbytymppNnz4dbdu2rVbu7lx7Nn36dJGSVVXbWaumOOmDiIjIlLHY1VLcpVwAQK8gD5GT3J25XHt2r5HF/+KCw0RERLXHa+xq4VZxKdot3AVBAI7MfgReCnuDvZc1uNus1ruRSqXQarVGSEZERGR6eI2dnp29poIgAAGNHFnq9OC/I4v3wwWHiYiIaofFrhYSrxcCAIK9XEROYjmcnZ0xd+5cpKenY8GCBWYx6YOIiMjUsdjVwqXsO8XO8IsgWyNzmfRBRERk6ljsauHardsAKk7Fkv6Zy6QPIiIiU2cjdgBzUFhSBgBQOJjmMieW4M6p2blz54odhYiIyGxxxK4WCkvKAQAuJrp+HRERERHAYlcrqr+LnbOcA5xERERkuljsauGN/sF487EQ+LhZzlInarUaCxcuhFKphEwmg1KpxMKFC3n7LiIiIjPGBYqt0N0WCL4zC5UTFoiIiEwHFyime1q+fHmNd334771ZOapHRERkXjhiZ2XUajWaNGkClUp11318fX1x4cIFjuoRERGZAI7YUY3unIK9V6kDgIyMDDz++OM4efLkfUf1iIiIyHSw2FmRO6dga2Pv3r2422CuTqdDbGysHpMRERGRPrDYWZHY2NhqI3D1lZmZqZfXISIiIv1hsbMi+ixjPj4+enstIiIi0g8WOyuirzImlUoRFRWll9ciIiIi/WGxsyJRUVGQSh/skN+ZFTt9+nQ9pSIiIiJ9YbGzItOnT0fbtm1rXe6kUin69OkDX19fSKVS+Pr6IiYm5oGXOuH6eERERIbBdeysjFqtxvLlyxEbG4vMzEx4eXlBEARkZ2dXmQVrqPXqeNcLIiKiuuE6dnRXzs7OmDt3LtLT06HVanHt2jVcunQJ8+fP1/vIXE1qe9cLQ6nraCFHF4mIyJxwxI6MSqlUIiMj466P+/r6Ij093SDvXdfRQo4uEhGRKeCIHZms+y25Ysj18eo6Wij26CIREVFdsdiRUd1vyRVDro93rwWaa7qbRl33p/vjqW0iIsNisSOjuteSK4ZeH6+uo4Viji5aojuntmNiYpCRkQGdToeMjAzExMSgZ8+eLHdERHrAYkdGdbclV4yxPl5dRwvFHF20RDy1TURkeCx2ZFTOzs6Ii4tDTEyMUWbh/ltdRwvFHF20RDy1TURkeJwVa8L+u+acj48PoqKiMH36dM7GrAfOihWXTCa7a7EDKr6vWq3WiImIiMwDZ8VaAF6PpH91HS0Uc3TREvHUNhGR4XHEzkQtXLgQMTExNY5wSKVSxMTEYO7cuSIkI6of/kwTEdVPXfoOi52JEnMhXyJD4KltIqL6MbtTsatWrUJAQADs7e3RqVMnHD16VOxIouNSG2RpeGqbiMjwRC9233zzDWbMmIHo6GicOHEC4eHh6NevH3JycsSOJipLvR6JC9Rat//eqzg9PR1z585lqSMi0hPRi92yZcswadIkjB8/Hq1atcInn3wCR0dHfPHFF2JHE5UlLrXBCSFERESGJWqxKy0tRXx8PCIjIyu3SaVSREZG4vDhwyImE5+YC/kaCheoJSIiMixRi11eXh60Wi08PT2rbPf09ER2dna1/TUaDVQqVZUPS2WJ1yNxgVoiIiLDshE7QF0sXrwY8+fPFzuG0dy5HslSloDghBAiIiLDEnXErnHjxpDJZLh+/XqV7devX4eXl1e1/WfPno2CgoLKDy73YV4sdULI/XDCCBERGYuoxc7Ozg7t27fHnj17KrfpdDrs2bMHXbp0qba/XC6Hq6trlQ8yH5Y4IeR+OGGEiIiMSfRZsTNmzMCaNWuwfv16XLhwAS+99BKKioowfvx4saORnlnihJD74YQRIiIyJtGL3ciRI/H+++9j3rx5aNu2LRISErBz585qEyrI/FnihJD74YQRIiIyJt5SjMiAZDLZXYsdUDFaqdVqjZiIiIjMjdndUozIUlnrhBEiIhIHix2RAVnjhBEiIhIPix2RAVnjhBEiIhIPix2RAVnjhBEiIhIPJ08QERERmTBOniAiIiKyQix2RERERBaCxY4sHu/VSkRE1oLX2JFFu3Ov1v/e1uvOrFROYCAiIlPHa+yI/sZ7tRIRkTVhsSOLxnu1EhGRNWGxI4uWmZn5QI8TERGZExY7smi8VysREVkTFjuyaLxXKxERWRMWO7JoxrxXK5dVISIisXG5E7J4arUay5cvR2xsLDIzM+Hj44OoqChMnz5db0udcFkVIiIylLr0HRY7Ij1YuHAhYmJiapyBK5VKERMTg7lz54qQjIiIzB3XsSMyMi6rQkREpoDFjkgPuKwKERGZAhY7Ij3gsipERGQKWOyI9IDLqhARkSlgsSPSA2Muq0JERHQ3LHZEeuDs7Iy4uDjExMTA19cXUqkUvr6+iImJ4VInRERkNFzuhIiIiMiEcbkTIiIiIivEYkdERERkIVjsiIiIiCwEix0RERGRhWCxIyIiIrIQLHZEREREFoLFjoiIiMhCsNgRERERWQgWOyIiIiILwWJHREREZCFY7IiIiIgsBIsdERERkYWwETvAgxAEAUDFzXGJiIiILNGdnnOn99yLWRe7wsJCAIBSqRQ5CREREZFhFRYWQqFQ3HMfiVCb+meidDodMjMz4eLiAolEInYci6NSqaBUKpGeng5XV1ex49Bd8DiZPh4j88DjZB6s8TgJgoDCwkL4+PhAKr33VXRmPWInlUrh6+srdgyL5+rqajV/eMwZj5Pp4zEyDzxO5sHajtP9Ruru4OQJIiIiIgvBYkdERERkIVjs6K7kcjmio6Mhl8vFjkL3wONk+niMzAOPk3ngcbo3s548QURERET/4IgdERERkYVgsSMiIiKyECx2RERERBaCxc7KrVq1CgEBAbC3t0enTp1w9OjRu+67Zs0adO/eHQ0aNECDBg0QGRl5z/1JP+pyjP5t8+bNkEgkGDp0qGEDEoC6H6dbt25hypQp8Pb2hlwuR1BQEHbs2GGktNarrsdpxYoVCA4OhoODA5RKJaZPn46SkhIjpbU+Bw4cwODBg+Hj4wOJRIJt27bd9zn79+/HQw89BLlcjubNm2PdunUGz2nKWOys2DfffIMZM2YgOjoaJ06cQHh4OPr164ecnJwa99+/fz+eeeYZ7Nu3D4cPH4ZSqcSjjz6Ka9euGTm59ajrMbojLS0NM2fORPfu3Y2U1LrV9TiVlpaib9++SEtLw/fff4/ExESsWbMGTZo0MXJy61LX47Rp0ybMmjUL0dHRuHDhAj7//HN88803ePPNN42c3HoUFRUhPDwcq1atqtX+qampGDhwIHr37o2EhAS88sormDhxIn777TcDJzVhAlmtjh07ClOmTKn8XKvVCj4+PsLixYtr9fzy8nLBxcVFWL9+vaEiWr36HKPy8nIhIiJC+Oyzz4SxY8cKjz/+uBGSWre6HqfVq1cLzZo1E0pLS40VkYS6H6cpU6YIffr0qbJtxowZQteuXQ2akyoAELZu3XrPfV5//XUhNDS0yraRI0cK/fr1M2Ay08YROytVWlqK+Ph4REZGVm6TSqWIjIzE4cOHa/UaxcXFKCsrQ8OGDQ0V06rV9xgtWLAAHh4emDBhgjFiWr36HKft27ejS5cumDJlCjw9PdG6dWssWrQIWq3WWLGtTn2OU0REBOLj4ytP16akpGDHjh147LHHjJKZ7u/w4cNVjikA9OvXr9a/xyyRWd8rluovLy8PWq0Wnp6eVbZ7enri4sWLtXqNN954Az4+PtX+UJF+1OcYHTx4EJ9//jkSEhKMkJCA+h2nlJQU7N27F6NGjcKOHTuQlJSEyZMno6ysDNHR0caIbXXqc5yeffZZ5OXloVu3bhAEAeXl5XjxxRd5KtaEZGdn13hMVSoVbt++DQcHB5GSiYcjdlQvS5YswebNm7F161bY29uLHYcAFBYWYvTo0VizZg0aN24sdhy6B51OBw8PD8TGxqJ9+/YYOXIk5syZg08++UTsaPQv+/fvx6JFi/Dxxx/jxIkT+OGHH/DLL79g4cKFYkcjuiuO2Fmpxo0bQyaT4fr161W2X79+HV5eXvd87vvvv48lS5Zg9+7dCAsLM2RMq1bXY5ScnIy0tDQMHjy4cptOpwMA2NjYIDExEYGBgYYNbYXq82fJ29sbtra2kMlkldtatmyJ7OxslJaWws7OzqCZrVF9jtPcuXMxevRoTJw4EQDQpk0bFBUVISoqCnPmzIFUyrERsXl5edV4TF1dXa1ytA7giJ3VsrOzQ/v27bFnz57KbTqdDnv27EGXLl3u+rx3330XCxcuxM6dO9GhQwdjRLVadT1GISEhOHPmDBISEio/hgwZUjlbTKlUGjO+1ajPn6WuXbsiKSmpsngDwKVLl+Dt7c1SZyD1OU7FxcXVytudMi7wbpwmoUuXLlWOKQDs2rXrnr/HLJ7YszdIPJs3bxbkcrmwbt064fz580JUVJTg5uYmZGdnC4IgCKNHjxZmzZpVuf+SJUsEOzs74fvvvxeysrIqPwoLC8X6EixeXY/Rf3FWrHHU9ThdvXpVcHFxEaZOnSokJiYKP//8s+Dh4SG89dZbYn0JVqGuxyk6OlpwcXERvv76ayElJUX4/fffhcDAQGHEiBFifQkWr7CwUDh58qRw8uRJAYCwbNky4eTJk8KVK1cEQRCEWbNmCaNHj67cPyUlRXB0dBRee+014cKFC8KqVasEmUwm7Ny5U6wvQXQsdlbuww8/FPz8/AQ7OzuhY8eOwpEjRyof69mzpzB27NjKz/39/QUA1T6io6ONH9yK1OUY/ReLnfHU9TgdOnRI6NSpkyCXy4VmzZoJb7/9tlBeXm7k1NanLseprKxMiImJEQIDAwV7e3tBqVQKkydPFm7evGn84FZi3759Nf6euXNcxo4dK/Ts2bPac9q2bSvY2dkJzZo1E9auXWv03KZEIggcTyYiIiKyBLzGjoiIiMhCsNgRERERWQgWOyIiIiILwWJHREREZCFY7IiIiIgsBIsdERERkYVgsSMiIiKyECx2RERERBaCxY6IrFpAQABWrFih99eRSCTYtm0bACAtLQ0SiQQJCQkP/D5ERPfCYkdEZmvw4MHo379/jY/98ccfkEgkOH36tF7fc926dXBzc6u2/dixY4iKiqrxOUqlEllZWWjdujUAYP/+/ZBIJLh165ZesxERsdgRkdmaMGECdu3ahYyMjGqPrV27Fh06dEBYWJhRsri7u8PR0bHGx2QyGby8vGBjY2OULERkvVjsiMhsDRo0CO7u7li3bl2V7Wq1Gt999x0mTJiALVu2IDQ0FHK5HAEBAVi6dOk9X3PZsmVo06YNnJycoFQqMXnyZKjVagAVI23jx49HQUEBJBIJJBIJYmJiANz7lO6/T8WmpaWhd+/eAIAGDRpAIpFg3Lhx2LBhAxo1agSNRlPluUOHDsXo0aPr/s0hIqvEYkdEZsvGxgZjxozBunXrIAhC5fbvvvsOWq0WLVu2xIgRI/D000/jzJkziImJwdy5c6sVwX+TSqVYuXIlzp07h/Xr12Pv3r14/fXXAQARERFYsWIFXF1dkZWVhaysLMycObNOmZVKJbZs2QIASExMRFZWFj744AMMHz4cWq0W27dvr9w3JycHv/zyC55//vk6vQcRWS8WOyIya88//zySk5MRFxdXuW3t2rV48sknERsbi0ceeQRz585FUFAQxo0bh6lTp+K999676+u98sor6N27NwICAtCnTx+89dZb+PbbbwEAdnZ2UCgUkEgk8PLygpeXF5ydneuUVyaToWHDhgAADw8PeHl5QaFQwMHBAc8++yzWrl1bue9XX30FPz8/9OrVq07vQUTWi8WOiMxaSEgIIiIi8MUXXwAAkpKS8Mcff2DChAm4cOECunbtWmX/rl274vLly9BqtTW+3u7du/HII4+gSZMmcHFxwejRo5Gfn4/i4mKDfy2TJk3C77//jmvXrgGomKgxbtw4SCQSg783EVkGFjsiMnt3rqUrLCzE2rVrERgYiJ49e9b5ddLS0jBo0CCEhYVhy5YtiI+Px6pVqwAApaWl+o5dTbt27RAeHo4NGzYgPj4e586dw7hx4wz+vkRkOVjsiMjsjRgxAlKpFJs2bcKGDRvw/PPPQyKRoGXLlvjzzz+r7Pvnn38iKCgIMpms2uvEx8dDp9Nh6dKl6Ny5M4KCgpCZmVllHzs7u7uO9tWWnZ0dANT4OhMnTsS6deuwdu1aREZGQqlUPtB7EZF1YbEjIrPn7OyMkSNHYvbs2cjKyqoc5Xr11VexZ88eLFy4EJcuXcL69evx0Ucf3XXCQ/PmzVFWVoYPP/wQKSkp+PLLL/HJJ59U2ScgIABqtRp79uxBXl5evU7R+vv7QyKR4Oeff0Zubm7lrFsAePbZZ5GRkYE1a9Zw0gQR1RmLHRFZhAkTJuDmzZvo168ffHx8AAAPPfQQvv32W2zevBmtW7fGvHnzsGDBgrue3gwPD8eyZcvwzjvvoHXr1ti4cSMWL15cZZ+IiAi8+OKLGDlyJNzd3fHuu+/WOWuTJk0wf/58zJo1C56enpg6dWrlYwqFAk8++SScnZ0xdOjQOr82EVk3ifDvNQKIiEh0jzzyCEJDQ7Fy5UqxoxCRmWGxIyIyETdv3sT+/fvx1FNP4fz58wgODhY7EhGZGd7fhojIRLRr1w43b97EO++8w1JHRPXCETsiIiIiC8HJE0REREQWgsWOiIiIyEKw2BERERFZCBY7IiIiIgvBYkdERERkIVjsiIiIiCwEix0RERGRhWCxIyIiIrIQLHZEREREFuL/AWgSHcCoTwUAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ef = EfficientFrontier(mu, S, weight_bounds=(None, None))\n",
    "ef.add_constraint(lambda w: w[0] >= 0)\n",
    "#ef.add_constraint(lambda w: w[2] == 0.15)\n",
    "#ef.add_constraint(lambda w: w[3] + w[4] <= 0.10)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plotting.plot_efficient_frontier(ef, ax=ax, show_assets=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c083c5d-5a5c-430d-8643-3579a09692d6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}