From 797da31a801db292020156bf6a8ade2bed9df028 Mon Sep 17 00:00:00 2001 From: MDKempe <58960264+MDKempe@users.noreply.github.com> Date: Thu, 10 Oct 2024 15:35:07 -0600 Subject: [PATCH 01/11] Update H2Opermeation.json I found an error in the AAA WVTR values. It's fixed now. --- pvdeg/data/H2Opermeation.json | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pvdeg/data/H2Opermeation.json b/pvdeg/data/H2Opermeation.json index 4f2502a..4e72e19 100644 --- a/pvdeg/data/H2Opermeation.json +++ b/pvdeg/data/H2Opermeation.json @@ -22,11 +22,11 @@ "source": "unpublished measurements", "Fickian": true, "Ead": 61.4781422330562, - "Do": 25790.6020262449, + "Do": 257.906020262449, "Eas": 5.88752263485353, - "So": 0.00982242435416737, - "Eap": 67.3656648679097, - "Po": 5559396276.60964 + "So": 0.0982242435416737, + "Eap": 66.9611315410624, + "Po": 189338932521.637 }, "W003": { "name": "Coveme", From e81014beba6179c013dccd4594bcf0238c549cf9 Mon Sep 17 00:00:00 2001 From: MDKempe <58960264+MDKempe@users.noreply.github.com> Date: Thu, 10 Oct 2024 17:31:12 -0600 Subject: [PATCH 02/11] Update Tools-Edge Seal Oxygen Ingress.ipynb --- .../Tools-Edge Seal Oxygen Ingress.ipynb | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/tutorials_and_tools/tutorials_and_tools/Tools-Edge Seal Oxygen Ingress.ipynb b/tutorials_and_tools/tutorials_and_tools/Tools-Edge Seal Oxygen Ingress.ipynb index 51f4ed2..db21127 100644 --- a/tutorials_and_tools/tutorials_and_tools/Tools-Edge Seal Oxygen Ingress.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/Tools-Edge Seal Oxygen Ingress.ipynb @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -192,6 +192,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "Oxygen ingress parameters loaded for the edge seal.\n", + "Oxygen ingress parameters loaded for the encapsulant.\n", "The edge seal is Helioseal_101_dry .\n", "The encapsulant is EVA .\n" ] @@ -220,7 +222,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[WinError 183] Cannot create a file when that file already exists: 'c:\\\\Users\\\\mkempe\\\\Documents\\\\GitHub\\\\PVDegradationTools\\\\TEMP\\\\results'\n" + "[WinError 183] Cannot create a file when that file already exists: 'c:\\\\Users\\\\mkempe\\\\Documents\\\\GitHub\\\\new\\\\PVDegradationTools\\\\TEMP\\\\results'\n" ] } ], @@ -240,7 +242,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAGyCAYAAABDdXhpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAADrY0lEQVR4nOzdd5xU1fn48c+dPtt7L+zSOwsoglIsgGgUyy9g8GtFvxKNiGiMaGIPRqNYvvaIEKNRYo0aG4qAlKhUgaWzsL2X2Z2ZnXp/f8zuwLoLzMBWeN6v130te+fce54Z0Xk859znKKqqqgghhBBCiE6j6eoAhBBCCCFON5KACSGEEEJ0MknAhBBCCCE6mSRgQgghhBCdTBIwIYQQQohOJgmYEEIIIUQnkwRMCCGEEKKTSQImhBBCCNHJJAETQgghhOhkuq4O4HTn9XopLi4mPDwcRVG6OhwhhBBCBEBVVerr60lJSUGjOYHxLLWLvfjii2qvXr1Uo9Gojhw5Ul29evUx269cuVIdOXKkajQa1aysLPXll19u1eb9999XBw4cqBoMBnXgwIHqhx9+GHS/Xq9XffDBB9Xk5GTVZDKpEydOVLdv396izcSJE1WgxTFz5syg3n9BQUGre8ghhxxyyCGHHD3jKCgoCOp7v1mXjoAtW7aMefPm8dJLL3H22Wfz6quvMm3aNHJzc8nIyGjVPi8vj4suuoibb76Zt956i7Vr13LrrbcSHx/PlVdeCcD69euZOXMmjz76KJdffjkfffQRM2bMYM2aNYwZMybgfp988kkWLVrE0qVL6devH4899hiTJ09m9+7dhIeH+2O6+eabeeSRR/y/m83moD6D5nsVFBQQERER3AcohBBCiC5hsVhIT09vkRMEQ1HVrtuMe8yYMYwcOZKXX37Zf27gwIFcdtllPP74463a/+EPf+CTTz5h586d/nNz5sxh69atrF+/HoCZM2disVj44osv/G0uvPBCoqOjeeeddwLqV1VVUlJSmDdvHn/4wx8AcDgcJCYm8sQTT3DLLbcAMGnSJEaMGMGzzz57wp+BxWIhMjKSuro6ScCEEEKIHuJkv7+7bBG+0+lk48aNTJkypcX5KVOmsG7dujavWb9+fav2U6dOZcOGDbhcrmO2ab5nIP3m5eVRWlraoo3RaGTixImtYnv77beJi4tj8ODB3H333dTX1x/zfTscDiwWS4tDCCGEEKeXLpuCrKysxOPxkJiY2OJ8YmIipaWlbV5TWlraZnu3201lZSXJyclHbdN8z0D6bf7ZVptDhw75f7/66qvJysoiKSmJ7du3s2DBArZu3cry5cuP+r4ff/xxHn744aO+LoQQQohTX5c/BfnLJ/9UVT3m04Bttf/l+UDu2R5tbr75Zv+fhwwZQt++fRk9ejSbNm1i5MiRbca/YMEC5s+f7/+9eQ5ZCCGEEKePLkvA4uLi0Gq1rUa7ysvLW408NUtKSmqzvU6nIzY29phtmu8ZSL9JSUmAbyQsOTk5oNgARo4ciV6vZ+/evUdNwIxGI0aj8aj3EEIIIQLh8Xj8y29E+9Pr9Wi12g67f5clYAaDgVGjRrF8+XIuv/xy//nly5czffr0Nq8ZO3Ysn376aYtzX3/9NaNHj0av1/vbLF++nDvvvLNFm3HjxgXcb/O04vLly8nJyQF8a8dWrVrFE088cdT3tGPHDlwuV4ukTQghhGhPqqpSWlpKbW1tV4dyyouKiiIpKalD6nR26RTk/Pnzueaaaxg9ejRjx47ltddeIz8/nzlz5gC+6bqioiLefPNNwPfE4wsvvMD8+fO5+eabWb9+PYsXL/Y/3Qhwxx13MGHCBJ544gmmT5/Ov//9b7755hvWrFkTcL+KojBv3jwWLlxI37596du3LwsXLiQkJIRZs2YBsH//ft5++20uuugi4uLiyM3N5a677iInJ4ezzz67sz5CIYQQp5nm5CshIYGQkBAp4t0BVFXFZrNRXl4O0DEDKydUPawdvfjii2pmZqZqMBjUkSNHqqtWrfK/dt1116kTJ05s0X7lypVqTk6OajAY1F69erVZiPW9995T+/fvr+r1enXAgAHqBx98EFS/qnq4EGtSUpJqNBrVCRMmqNu2bfO/np+fr06YMEGNiYlRDQaD2rt3b3Xu3LlqVVVVUO+/rq5OBdS6urqgrhNCCHH6cbvdam5urlpZWdnVoZwWKisr1dzcXNXtdrd67WS/v7u0DpiQOmBCCCEC19jYSF5eHr169Qq68LcInt1u5+DBg2RlZWEymVq81mPrgAkhhBDixMi0Y+foyM9ZEjAhhBBCiE4mCZgQQghxmnjooYcYMWJEp/e7cuVKFEVBURQuu+yyTu+/LQ899JA/ppPZUvBESQImhBBCnAKak4mjHddffz1333033377bZfFuHv3bpYuXdpl/R/p7rvvpqSkhLS0tC7pv8sr4YuOUW11UFLXSEyIgeQoWagphBCnupKSEv+fly1bxgMPPMDu3bv958xmM2FhYYSFhXVFeAAkJCQQFRXVZf2Dr4Ctoij+z6Iji60ei4yAnaJuWbGb83fn8fxP+V0dihBCiE6QlJTkPyIjI1EUpdW5X05BXn/99Vx22WUsXLiQxMREoqKiePjhh3G73fz+978nJiaGtLQ03njjjRZ9FRUVMXPmTKKjo4mNjWX69OkcPHgwqHjffPNNYmNjcTgcLc5feeWVXHvttf7fP/30U0aNGoXJZCI7O9sfX7NFixYxdOhQQkNDSU9P59Zbb6WhocH/+tKlS4mKiuKzzz5j0KBBGI3GFvs6dxVJwE5RERrfP9o6t6eLIxFCCNGdrVixguLiYlavXs2iRYt46KGH+NWvfkV0dDQ//PADc+bMYc6cORQUFABgs9k499xzCQsLY/Xq1axZs4awsDAuvPBCnE5nwP3++te/xuPx8Mknn/jPVVZW8tlnn3HDDTcA8NVXX/E///M/zJ07l9zcXF599VWWLl3Kn//8Z/81Go2G559/nu3bt/P3v/+dFStWcM8997Toy2az8fjjj/P666+zY8cOEhISTuYjaxeSgJ2iInS+IVWLRxIwIYQQRxcTE8Pzzz9P//79ufHGG+nfvz82m4377ruPvn37smDBAgwGA2vXrgXg3XffRaPR8PrrrzN06FAGDhzIkiVLyM/PZ+XKlQH3azabmTVrFkuWLPGfe/vtt0lLS2PSpEkA/PnPf+bee+/luuuuIzs7m8mTJ/Poo4/y6quv+q+ZN28e5557LllZWZx33nk8+uij/Otf/2rRl8vl4qWXXmLcuHH079+f0NDQE//A2omsATtFRTUnYKq3iyMRQgjRnQ0ePBiN5vB4TGJiIkOGDPH/rtVqiY2N9W/Ls3HjRvbt20d4eHiL+zQ2NrJ///6g+r755ps544wzKCoqIjU1lSVLlnD99df7629t3LiRn376qcWIl8fjobGxEZvNRkhICN999x0LFy4kNzcXi8WC2+2msbERq9XqT7QMBgPDhg0L7oPpYJKAnaKijDpwQYNsdCCEEOIY9Hp9i98VRWnznNfr+x96r9fLqFGjePvtt1vdKz4+Pqi+c3JyGD58OG+++SZTp05l27ZtfPrpp/7XvV4vDz/8MFdccUWra00mE4cOHeKiiy5izpw5PProo8TExLBmzRpmz56Ny+XytzWbzd2ueK0kYKeo6KYEzKpIAiaEEKL9jBw5kmXLlpGQkNAuW+jddNNNPPPMMxQVFXHBBReQnp7eoq/du3fTp0+fNq/dsGEDbrebp59+2j+K98vpx+5K1oCdomLNvv97sWokARNCCNF+rr76auLi4pg+fTrff/89eXl5rFq1ijvuuIPCwsITul9RURF/+9vfuPHGG1u89sADD/Dmm2/y0EMPsWPHDnbu3MmyZcv44x//CEDv3r1xu9383//9HwcOHOAf//gHr7zySru8z44mCdgpKtZsAMAmY5xCCCHaUUhICKtXryYjI4MrrriCgQMHcuONN2K3209oRCwiIoIrr7ySsLCwVlXyp06dymeffcby5cs544wzOOuss1i0aBGZmZkAjBgxgkWLFvHEE08wZMgQ3n77bR5//PH2eJsdTlFVWSTUlU52N/Wj2VtWz/jc/ShelaJzh7dYYCmEEKJnamxsJC8vj6ysLEwmU1eHE7CVK1dy7rnnUlNT02Yh1smTJzNw4ECef/75To+tV69ezJs3j3nz5rV67Vif98l+f8u38ikqIcwIgKpRqGt0H6e1EEII0fHS0tL4zW9+4/+9urqad999lxUrVnDbbbd1aiwLFy4kLCyM/PyuKVguE1SnqHCzDq1HxaNVqGxwEh1i6OqQhBBCnKbGjBnD3r17AVpshTRy5Ehqamp44okn6N+/f6fGNGfOHGbMmAEE//Rme5AE7BSl0Wgwu1QatAoVDU76dn3RXyGEEKcps9nc5pOMwW5f1J5iYmKIiYnpsv5lCvIUFtJUBL/K5jh2QyGEEEJ0KknATlE1u1cTqvo2I62WNWBCCCFEtyIJ2Clq/0+PEBG6D4Bqh+s4rYUQQgjRmSQBO0WVWVRC8Y2A1TplQ24hhBCiO5EE7BRVb1MIxQpArUumIIUQQojuRBKwU5TqDSOkKQGzuGUETAghhOhOJAE7VWkj/VOQlqYd7IUQQgjRPUgCdorSGuP8U5D1qiRgQggheo6XXnrJv/3PqFGj+P7774/a9sMPP2Ty5MnEx8cTERHB2LFj+eqrrzox2hMjCdgpSheW4h8Ba1Bku08hhBA9w7Jly5g3bx73338/mzdvZvz48UybNu2oWwatXr2ayZMn8/nnn7Nx40bOPfdcLrnkEjZv3tzJkQdHErBTVLwmnBBsAFglARNCCNFDLFq0iNmzZ3PTTTcxcOBAnn32WdLT03n55ZfbbP/ss89yzz33cMYZZ9C3b18WLlxI3759+fTTTzs58uDIVkSnqIwCL3vSfCNgNq0kYEIIcbpTVRW7q/MfyjLrtSiKElBbp9PJxo0buffee1ucnzJlCuvWrQvoHl6vl/r6+i7dZigQkoCdogyhRsLcLtCDXRfYX3whhBCnLrvLw6AHOn9tVO4jUwkxBJZuVFZW4vF4SExMbHE+MTGR0tLSgO7x9NNPY7Va/Rttd1eSgJ2idKE6IlyAHtxaDTaXhxC9tqvDEkIIIY7rlyNmqqoGNIr2zjvv8NBDD/Hvf/+bhISEjgqvXUgCdorShxsIdelRVA+qoqXS5iQj0tzVYQkhhOgiZr2W3Eemdkm/gYqLi0Or1bYa7SovL281KvZLy5YtY/bs2bz33ntccMEFJxRrZ5IE7BRliAhBbw0lFBsNhFPZ4JAETAghTmOKogQ8FdhVDAYDo0aNYvny5Vx++eX+88uXL2f69OlHve6dd97hxhtv5J133uHiiy/ujFBPWvf+JyFOWJHiwOUyEEqDLwGzObs6JCGEEOK45s+fzzXXXMPo0aMZO3Ysr732Gvn5+cyZMweABQsWUFRUxJtvvgn4kq9rr72W5557jrPOOss/emY2m4mMjOyy93E8koCdopbt2UJmrNVfjLXK5uriiIQQQojjmzlzJlVVVTzyyCOUlJQwZMgQPv/8czIzMwEoKSlpURPs1Vdfxe12c9ttt3Hbbbf5z1933XUsXbq0s8MPmCRgpyhdiA63y+jfD7K6UTbkFkII0TPceuut3HrrrW2+9sukauXKlR0fUAeQQqynKEOYGZfL6B8Bq3VKAiaEEEJ0F5KAnaJCIyJxuY3+7YgkARNCCCG6D0nATlERYZG4jxwBc3d+9WMhhBBCtE0SsFNUdFh0ixEwi0cSMCGEEKK7kATsFBUTEdNiEX69VxIwIYQQoruQBOwUFRcZ12IRfr0qZSiEEEKI7kISsFNUYmQiHo+eENWXgDUosghfCCGE6C4kATtFhRpDATC6HQDYtMffxFQIIYQQnUMSsFOZxovR7Rv5susC3wxVCCGEEB1LErBTmKrxoHf5EjCnTo/Lq3ZxREIIIYQAScBObToVg/Pw04+1DlmIL4QQovt76aWXyMrKwmQyMWrUKL7//vuArlu7di06nY4RI0Z0bIDtQBKwU5hiUPG4DZibFuJXWJ1dHJEQQghxbMuWLWPevHncf//9bN68mfHjxzNt2rQWG3C3pa6ujmuvvZbzzz+/kyI9OZKAncK0JqWpFpgNgIoGRxdHJIQQQhzbokWLmD17NjfddBMDBw7k2WefJT09nZdffvmY191yyy3MmjWLsWPHdlKkJ0fX1QGIjmMINfqr4VcRT7VdpiCFEOK0pargsnV+v/oQUAJ7Et/pdLJx40buvffeFuenTJnCunXrjnrdkiVL2L9/P2+99RaPPfbYSYXbWSQBO4WFhIfhLjf4i7FKAiaEEKcxlw0WpnR+v/cVgyE0oKaVlZV4PB4SExNbnE9MTKS0tLTNa/bu3cu9997L999/j07Xc9IamYI8hYVHxLTYD7LaIcVYhRBCdH/KL0bMVFVtdQ7A4/Ewa9YsHn74Yfr169dZ4bWLnpMqiqBFhcdQc8R2RFX2xi6OSAghRJfRh/hGo7qi3wDFxcWh1WpbjXaVl5e3GhUDqK+vZ8OGDWzevJnf/e53AHi9XlRVRafT8fXXX3PeeeedXPwdRBKwU1h0eDR7XEZiqQAgzykJmBBCnLYUJeCpwK5iMBgYNWoUy5cv5/LLL/efX758OdOnT2/VPiIigm3btrU499JLL7FixQref/99srKyOjzmEyUJ2CksPjIet9tIb/YBsAvPca4QQgghutb8+fO55pprGD16NGPHjuW1114jPz+fOXPmALBgwQKKiop488030Wg0DBkypMX1CQkJmEymVue7G0nATmEJkQm4XEYGNSVgZUYdtS43UXr5xy6EEKJ7mjlzJlVVVTzyyCOUlJQwZMgQPv/8czIzMwEoKSk5bk2wnkAW4Z/CokOiUVUNIR47iWoJAJvquuARZCGEECIIt956KwcPHsThcLBx40YmTJjgf23p0qWsXLnyqNc+9NBDbNmypeODPEmSgJ3CtBotquLF5TKS3TQK9t/Sui6OSgghhBCSgJ3iVI0Ht8tIH/YCsKG6oYsjEkIIIYQkYKc4r9aNy22gd1MCtsPlQFXVLo5KCCGEOL11eQIW7I7nq1atYtSoUZhMJrKzs3nllVdatfnggw8YNGgQRqORQYMG8dFHHwXdr6qqPPTQQ6SkpGA2m5k0aRI7duxoMyZVVZk2bRqKovDxxx8H/uY7g86L22Ukkzw0Xg91Gih0SEV8IYQQoit1aQIW7I7neXl5XHTRRYwfP57Nmzdz3333MXfuXD744AN/m/Xr1zNz5kyuueYatm7dyjXXXMOMGTP44Ycfgur3ySefZNGiRbzwwgv89NNPJCUlMXnyZOrr61vF9eyzz7ZZobdbMKg4XSYMuEhpLABgU521i4MSQgghTnNqFzrzzDPVOXPmtDg3YMAA9d57722z/T333KMOGDCgxblbbrlFPeuss/y/z5gxQ73wwgtbtJk6dap61VVXBdyv1+tVk5KS1L/85S/+1xsbG9XIyEj1lVdeaXHdli1b1LS0NLWkpEQF1I8++ug477qluro6FVDr6uqCui5QD7xyt/ryy7PUb77NVmd88lc1ccVmdcHWgx3SlxBCiI5lt9vV3Nxc1W63d3Uop4Vjfd4n+/3dZSNgzTueT5kypcX5Y+14vn79+lbtp06dyoYNG3C5XMds03zPQPrNy8ujtLS0RRuj0cjEiRNbxGaz2fjNb37DCy+8QFJSUkDv2+FwYLFYWhwdyRxqpr4+FoB+ht0AbKiVETAhhBCiK3VZAnYiO56Xlpa22d7tdlNZWXnMNs33DKTf5p/Hi+3OO+9k3LhxbW6PcDSPP/44kZGR/iM9PT3ga09ERHgUNlskqldDX70vAdvlduL2ykJ8IYQQoqt0+SL8QHc8P1b7X54P5J4n2+aTTz5hxYoVPPvss0eNtS0LFiygrq7OfxQUFAR1fbBio+NRVS32hliSKcbosuNUYJfV3qH9CiGEEOLouiwBC3bHc4CkpKQ22+t0OmJjY4/ZpvmegfTbPJ14rDYrVqxg//79REVFodPp0Ol82/tceeWVTJo06ajv22g0EhER0eLoSFGRCQBYG2LRoJLRmAfARotUxBdCCCG6SpclYEfueH6k5cuXM27cuDavGTt2bKv2X3/9NaNHj0av1x+zTfM9A+k3KyuLpKSkFm2cTierVq3yt7n33nv5+eef2bJli/8AeOaZZ1iyZEkwH0WHiouMA6C2PhqAPqqvHtj6io5deyaEEEKcqGBLVDkcDu6//34yMzMxGo307t2bN954o5OiPTFduitzMDueA8yZM4cXXniB+fPnc/PNN7N+/XoWL17MO++847/nHXfcwYQJE3jiiSeYPn06//73v/nmm29Ys2ZNwP0qisK8efNYuHAhffv2pW/fvixcuJCQkBBmzZoF+EbJ2lp4n5GRQVZWVod9ZsGKj4wHoL4hBoChpp/5gun8V0pRCCGE6IaaS0W99NJLnH322bz66qtMmzaN3NxcMjIy2rxmxowZlJWVsXjxYvr06UN5eTlut7uTIw/SSTyd2S5efPFFNTMzUzUYDOrIkSPVVatW+V+77rrr1IkTJ7Zov3LlSjUnJ0c1GAxqr1691JdffrnVPd977z21f//+ql6vVwcMGKB+8MEHQfWrqr5SFA8++KCalJSkGo1GdcKECeq2bduO+V7ohmUorE6r+sCDf1IfeuhP6jdf91U/+3agmvzNBjVxxWb1kK2xQ/oUQgjRMU6HMhTBlqj64osv1MjISLWqqqrdY+nIMhSKqsq+NF3JYrEQGRlJXV1dh6wHU1WV+x5bgNFjYtKw1XiiDnFv3VMURGXx3IAMZibHtHufQgghOkZjYyN5eXn+6blgqKqK3d35D2CZdeaAi5U7nU5CQkJ47733uPzyy/3n77jjDrZs2cKqVataXXPrrbeyZ88eRo8ezT/+8Q9CQ0O59NJLefTRRzGbzScV+7E+75P9/g5oCvKKK64I+savvPIKCQkJQV8n2peiKDg1DoweE0p9KkQdoq9jFwVksb62XhIwIYQ4Tdjddsb8c0yn9/vDrB8I0YcE1PZESlQdOHCANWvWYDKZ+Oijj6isrOTWW2+lurq6W68DC2gR/scff4zBYGhRv+pYx3/+8x8aGho6OnYRIK/WV6TWY0kBYJB2OwBrq1pvqySEEEJ0tWBKVHm9XhRF4e233+bMM8/koosuYtGiRSxduhS7vfuWXAp4Ef7zzz8f8IjW+++/f8IBifanan0/nQ2J6IGh4dtQvF4KXG6KG52kmAxdGp8QQoiOZ9aZ+WHWD8dv2AH9BupESlQlJyeTmppKZGSk/9zAgQNRVZXCwkL69u17YoF3sIBGwL777jtiYgKfqvriiy9ITU094aBE+1KMvgzMZotE8egJM1hJqikCYH2tjFQKIcTpQFEUQvQhnX4Euv4LTqxE1dlnn01xcXGLmbc9e/ag0WhIS0s7sQ+rEwSUgE2cONFfaDQQ55xzDkaj8YSDEu1LF+Wbe6+gHmO97xHe3g17AFgv+0IKIYToRubPn8/rr7/OG2+8wc6dO7nzzjtblai69tpr/e1nzZpFbGwsN9xwA7m5uaxevZrf//733HjjjSe9CL8jBVyIdc+ePbzxxhvk5+czf/58cnNzOzIu0Y5SUn37TVZo6jBZegHQ3+v757euRtaBCSGE6D5mzpzJs88+yyOPPMKIESNYvXo1n3/+OZmZmQCUlJSQn5/vbx8WFsby5cupra1l9OjRXH311VxyySU8//zzXfUWAhJwGYoLL7yQVatW8cQTT1BWVsZ3333HunXrOjq+U15Hl6EA+D5/LV8v+RKtquVXMQp1Q96kvDSWO5NeBUVh67jBJBr1HdK3EEKI9nMyZShE8DqyDEXAI2Aej4fBgwczd+5c/vznPwfdkeg6wxKHUGuoBaC+3rdnZlxsDQlVJYCsAxNCCCE6W8AJWHh4OPfdd5//9+a9F0X3F2mMpF7vm2qstBtR3EY0ei+9qvcBsE4SMCGEEKJTBZyA/elPf/IXZHU4HPzmN7/psKBE+3M3PQlZrqn1rwPr59gFyAiYEEII0dkCTsBycnL8fzYajf6nEUTPoInwFWGtUqwYLb7NwgfqdgCw1+agwunqstiEEEKI003gtSWO8OOPP7Jy5UrKy8vxer0tXlu0aFG7BCbaV3L8QFwHd6BHj8eSDEBiXA1xVaVUxibxQ62VXyVEdW2QQgghxGki6ARs4cKF/PGPf6R///4kJia2KLAWTLE10bkGxQ3iB+MaEhoTsNTHoACmaBvpu/OojE1ifW2DJGBCCCFEJwk6AXvuued44403uP766zsgHNFR+semsdxQS0JjAuUOLynOMDyGBnrX72EzY2UdmBBCCNGJAl4D5r9Ao+Hss8/uiFhEB0qJCsGGG4AybTWmpnVgA/AVZN1pbaTG5e6y+IQQQojTSdAJ2J133smLL77YEbGIDpQQYaTKGwpAneLBUNcLgKTYBmJqylGBH2RbIiGEEKJTBD0Feffdd3PxxRfTu3dvBg0a1Koe2IcffthuwYn2Y9JrqVd706hpwOQ14bQkARCW0Eh64UGqoxNYX9vAhfGRx7mTEEIIIU5W0CNgt99+O9999x39+vUjNjaWyMjIFofovmJN/agx1gBQa/Ftlq4PryWj/AAgBVmFEEJ0Dy+99JJ/+59Ro0bx/fffH7P922+/zfDhwwkJCSE5OZkbbriBqqqqTor2xAQ9Avbmm2/ywQcfcPHFF3dEPKIDpYdnUuupJtmeTLHXTu/GGNymavo69wCwo8GOxe0hQqft4kiFEEKcrpYtW8a8efN46aWXOPvss3n11VeZNm0aubm5ZGRktGq/Zs0arr32Wp555hkuueQSioqKmDNnDjfddBMfffRRF7yDwAQ9AhYTE0Pv3r07IhbRwZIjQ7BrfFsSFWsbMTatA0uJbiSqrhIv8IOMggkhhOhCixYtYvbs2dx0000MHDiQZ599lvT0dF5++eU22//3v/+lV69ezJ07l6ysLM455xxuueUWNmzY0MmRByfoBOyhhx7iwQcfxGazdUQ8ogMlRZio95hxapy40eCp860Di0mF9OKDAKyXhfhCCHFKUlUVr83W6YeqqgHH6HQ62bhxI1OmTGlxfsqUKaxbt67Na8aNG0dhYSGff/45qqpSVlbG+++/3+1n6oKegnz++efZv38/iYmJ9OrVq9Ui/E2bNrVbcKJ9JUWaqLYPosJUTqotlZoaA2bAFFNB+q6DbBs4mvW19V0dphBCiA6g2u3sHjmq0/vtv2kjSkhIQG0rKyvxeDwkJia2OJ+YmEhpaWmb14wbN463336bmTNn0tjYiNvt5tJLL+X//u//Tjr2jhR0AnbZZZd1QBiiMyRHmrHYBlGWuI1UWypFNjN9VAXVVE1mTQEAP9fbsbo9hMo6MCGEEF3klzvrqKp61N12cnNzmTt3Lg888ABTp06lpKSE3//+98yZM4fFixd3RrgnJOgE7MEHH+yIOEQnSIo0obqjcOgrASjVaBloTcQVVkpauIeI+los4VFstNiYEBPexdEKIYRoT4rZTP9NG7uk30DFxcWh1WpbjXaVl5e3GhVr9vjjj3P22Wfz+9//HoBhw4YRGhrK+PHjeeyxx0hOTj7x4DtQ0AnYTz/9hNfrZcyYMS3O//DDD2i1WkaPHt1uwYn2lRRpAsDkNNGgayDMHeZbBxZWSkKmnrSSg+SGj2B9bYMkYEIIcYpRFCXgqcCuYjAYGDVqFMuXL+fyyy/3n1++fDnTp09v8xqbzYZO1zKd0Wp9szjBrD/rbEEvwr/tttsoKChodb6oqIjbbrutXYISHSPMqCPcqMNly6bcXA5AXbXvX8aIhBrSSg4CsL7a0lUhCiGEOM3Nnz+f119/nTfeeIOdO3dy5513kp+fz5w5cwBYsGAB1157rb/9JZdcwocffsjLL7/MgQMHWLt2LXPnzuXMM88kJSWlq97GcQU9Apabm8vIkSNbnc/JySE3N7ddghIdJynSRFHNKDzJH5Ndn02pLYJwwBuWR3ppGgCb6m04vF6MmqDzcyGEEOKkzJw5k6qqKh555BFKSkoYMmQIn3/+OZmZmQCUlJSQn5/vb3/99ddTX1/PCy+8wF133UVUVBTnnXceTzzxRFe9hYAEnYAZjUbKysrIzs5ucb6kpKTVEKDofpIiTewtTyfRUI6KSpkzhr5eHV59A2kaLSG2emwh4Wyx2BgTFdbV4QohhDgN3Xrrrdx6661tvrZ06dJW526//XZuv/32Do6qfQU9xDF58mQWLFhAXV2d/1xtbS333XcfkydPbtfgRPtLjjQBCn0wUW2sRlW1qJYE32vpZtKbpyGlIKsQQgjRYYJOwJ5++mkKCgrIzMzk3HPP5dxzzyUrK4vS0lKefvrpjohRtKPkSN/TKPFqpn8dmLUmAoCYVAtpTQVZ11bWdkV4QgghxGkh6DnD1NRUfv75Z95++222bt2K2Wzmhhtu4De/+U2roqyi+0mN9iVgLtdQKkzfMpCBlFujCAM04Xmkl4cC8FO9HbdXRadpu+6KEEIIIU5cwAnYfffdx2WXXcaZZ55JaGgo//u//9uRcYkOkhblS8C2W4fijX4XDx6q7QlkA86wg6Q1jMXosNNoNLOtwU5ORPd+ZFkIIYToiQKegiwpKeFXv/oVycnJ/O///i//+c9/cDgcHRmb6ABp0b6EanednhEqVJmqsNsjUF0GVK2T1KTIw+UoamRbIiGEEKIjBJyALVmyhLKyMv71r38RFRXFXXfdRVxcHFdccQVLly6lsrKyI+MU7SQp0oSigMPtZbgphQpTBaDgqI0BIC7VQlqJ7/He78urujBSIYQQ4tQV1CJ8RVEYP348Tz75JLt27eLHH3/krLPO4m9/+xupqalMmDCBp556iqKioo6KV5wkg05DYrivIn5W+BAqzBUAVDf4EjBD9AEyy3xPQP5U34i3G1cRFkIIIXqqk6q0OXDgQO655x7Wrl1LQUEB1113Hd9//z3vvPNOe8UnOkDzQnx9yJnY9ZV4cFNj9ZWicEbsI6s+Cr3TQYOiYae1sStDFUIIIU5J7VY5NSEhgdmzZzN79uz2uqXoIGnRZjYeqmGvpg9DnA4qTVUYLfEAOMOKSYlIIrX0EAcz+rG+pp7BYYFvpCqEEEKI4ws6Abv88stRlNalCRRFwWQy0bdvX2bNmkW/fv3aJUDR/lKbnoQ8YDUyAhOrzBUk1iTiaghHH1ZPbFI16SVODmb0Y2VxOTelJ3RxxEIIIcSpJegpyMjISFasWMGmTZv8idjmzZtZsWIFbrebd999l2HDhrF27dp2D1a0j+YpyKIaOzkR2ZSbfAVZay1xAJjj9pNRbgPgpwZHt95NXgghxKll9erVXHLJJaSkpKAoCh9//PFxr1m1ahWjRo3CZDKRnZ3NK6+80vGBnqSgE7CkpCRmzZrFgQMH+OCDD/jwww/Zv38///M//0Pv3r3ZuXMn1113HX/4wx86Il7RDppHwIpq7QxPPYs6Qw1e1UVtQ9M6sMh99K+PRud2UafRstcm5UaEEEJ0DqvVyvDhw3nhhRcCap+Xl8dFF13E+PHj2bx5M/fddx9z587lgw8+6OBIT07QU5CLFy9m7dq1aDSHczeNRsPtt9/OuHHjWLhwIb/73e8YP358uwYq2k/aESNgYaln0mf3Umr0lYQ1rQOzR+aRYkojpayA/NRs1lVb6Bdq6sqQhRBCnCRVVXE7vZ3er86gaXPp0tFMmzaNadOmBdz+lVdeISMjg2effRbwPSC4YcMGnnrqKa688spgw+00QSdgbrebXbt2tVrjtWvXLjweDwAmkymoD1t0rtQoXzHWeoeb+ugR5DgcbAqvILZmCF6XHo2+kdjYetJKqslPzea7olKul3VgQgjRo7mdXl67Y1Wn9/u/z01Eb9R22P3Xr1/PlClTWpybOnUqixcvxuVyddttEoOegrzmmmuYPXs2zzzzDGvWrGHt2rU888wzzJ49m2uvvRbwzcUOHjy43YMV7cNs0BIbagCgwGFihCa8qR6YBoslFoCQuANklPtKUGywyjowIYQQ3VNpaSmJiYktziUmJuJ2u7t1kfigR8CeeeYZEhMTefLJJykrKwN8b/TOO+/0r/uaMmUKF154YftGKtpVarSZKqvTtxA/ZiB1jl3gdVHXkEBUbCnOqL0M2T4BjcdNlVbPoUYnvczGrg5bCCHECdIZNPzvcxO7pN+O9stZt+ZBg+48Gxd0AqbVarn//vu5//77sVgsAERERLRok5GR0T7RiQ6TGmXm58I6imrtTE49k/i927AplVia14FF7SNNP4vk8iKKkjNZU1lLr/TE49xVCCFEd6UoSodOBXaVpKQkSktLW5wrLy9Hp9MRGxvbRVEd30mlpREREa2SL9EzNC/EL6yxo6TkMKLRQWFYBfWWOFQVXCEVxIa5SS/1bSu1oqCkK8MVQggh2jR27FiWL1/e4tzXX3/N6NGju+36LwgwARs5ciQ1NTUB3/Scc86R/SC7OX8piho7pOSQ43BSGFmJx2PAbo0EfOvAMst868B+sjm7LFYhhBCnj4aGBrZs2cKWLVsAX5mJLVu2kJ+fD8CCBQv8a84B5syZw6FDh5g/fz47d+7kjTfeYPHixdx9991dEX7AApqC3LJlC1u3biUmJiagm27ZsgWHQ2pHdWep0b4nIYtq7RASwwhdNLWGWhSPi7r6BELC6nBE7WP4vlG85/FQoTWQb3eQIevAhBBCdKANGzZw7rnn+n+fP38+ANdddx1Lly6lpKTEn4wBZGVl8fnnn3PnnXfy4osvkpKSwvPPP9+tS1BAEGvAzj///ICfhOvOi96Ez5HFWAH6JwzDaN+Cx12JpS6e5OS92KP3kq6/hKSKQoqTMvm+vJqrM5O7MmwhhBCnuEmTJh0z31i6dGmrcxMnTmTTpk0dGFX7CygBy8vLC/rGaWlpQV8jOk/zdkTVVic2p5uQlBwG5/5IhakCsyULgMaIPOJNGnqVlVCclMm3BSWSgAkhhBDtIKAELDMzs6PjEJ0s0qwn3KSjvtFNUY2dvikjGLHZwWexFSRWDsXZaMZgsmOKyaNXmYN1wE92V1eHLYQQQpwSOr44h+i2mqchC2vtkJzDiEYn5WF1aN1u6iy+yveNMXsYXheDxuOhQmck3y5r+4QQQoiTJQnYaezIPSEJjWW4MR5VUdHaK6mr89X8skftIUOfSFJFIQCry6u7LF4hhBDiVCEJ2GksrelJyMIa30L86OTh9HK6sGgr/CNg9qj9xBl09Cr3Fbn7pqC4a4IVQgghTiGSgJ3GDhdjtflOpOQw3OEgL7oSmzUSt0uPqmvEFFVAn0rfRusb7G7ZF1IIIYQ4SSecgDmdTgoLC8nPz29xiJ6jeQSsoGkEjOQRjHA42J1Yg9btxdI8Cha9l+HVkWg8Hip1RvIbpSirEEIIcTKCTsD27t3L+PHjMZvNZGZmkpWVRVZWFr169SIrK6sjYhQdxD8CVn14BGxEoxOPFsz1lYenIaP3kK5P8K8DW1XafXeXF0IIIXqCoDfjvv7669HpdHz22WckJydL0dUeLD3GNwJW1VwLLCSG7NBkwj1e7JSjqUsCfAlYvN5AVuVBXz2wwhKuzUrtytCFEEKIHi3oEbAtW7bw6quvMm3aNEaMGMHw4cNbHMF66aWXyMrKwmQyMWrUKL7//vtjtl+1ahWjRo3CZDKRnZ3NK6+80qrNBx98wKBBgzAajQwaNIiPPvoo6H5VVeWhhx4iJSUFs9nMpEmT2LFjR4s2t9xyC71798ZsNhMfH8/06dPZtWtX0J9BV4k064kw+XLw5oX4muQRDHM4KAyvoL4+Fq9Hg8dQjzm8jAE1vrVfPzV6ZR2YEEKIDrF69WouueQSUlJSUBSFjz/++JjtP/zwQyZPnkx8fDwRERGMHTuWr776qnOCPQlBJ2CDBg2isrJ9pqCWLVvGvHnzuP/++9m8eTPjx49n2rRpR11LlpeXx0UXXcT48ePZvHkz9913H3PnzuWDDz7wt1m/fj0zZ87kmmuuYevWrVxzzTXMmDGDH374Iah+n3zySRYtWsQLL7zATz/9RFJSEpMnT6a+vt7fZtSoUSxZsoSdO3fy1VdfoaoqU6ZMwePxtMvn0xmaR8EK/NOQvnVguUm1aFwq9Q1xANii9zCsIgKtx021zsABqQcmhBCiA1itVoYPH84LL7wQUPvVq1czefJkPv/8czZu3Mi5557LJZdcwubNmzs40pOkBunbb79Vx44dq3733XdqZWWlWldX1+IIxplnnqnOmTOnxbkBAwao9957b5vt77nnHnXAgAEtzt1yyy3qWWed5f99xowZ6oUXXtiizdSpU9Wrrroq4H69Xq+alJSk/uUvf/G/3tjYqEZGRqqvvPLKUd/P1q1bVUDdt2/fUdv8Ul1dnQoE/dm1l/998yc18w+fqUvWHPCd2Petuv7xeHXI0iHqc7fNUd9Ycqn6zbfZ6o//+B/1+zu+UUf/4wM1ccVm9bV9BV0SrxBCnM7sdruam5ur2u32rg6lUwDqRx99FPR1gwYNUh9++OGT7v9Yn/fJfn8HPQJ2wQUX8N///pfzzz+fhIQEoqOjiY6OJioqiujo6IDv43Q62bhxI1OmTGlxfsqUKaxbt67Na9avX9+q/dSpU9mwYQMul+uYbZrvGUi/eXl5lJaWtmhjNBqZOHHiUWOzWq0sWbKErKws0tPTj/f2u430Np6EHOpwolFV3K5yf0FWW/Ru4vR6+lSXA/BtUWmXxCuEEOLEqKqKq7Gx0w+1k5eseL1e6uvriYmJ6dR+gxX0IvzvvvuuXTqurKzE4/GQmJjY4nxiYiKlpW1/uZeWlrbZ3u12U1lZSXJy8lHbNN8zkH6bf7bV5tChQy3OvfTSS9xzzz1YrVYGDBjA8uXLMRgMR33fDocDh+Pw9J3FYjlq287QPAXprwUWEkNoZDr9nE7KzRWE1A1A9Sq4zVUYQ6sYatHyHbDBqeJVVTTyEIYQQvQIboeD56/7f53e79y/v4/eZOq0/p5++mmsViszZszotD5PRNAJ2MSJE9s1gF8+Ramq6jGfrGyr/S/PB3LP9mpz9dVXM3nyZEpKSnjqqaeYMWMGa9euxXSUv2yPP/44Dz/88NHeXqdLj/GVoiioth8+mZLD8LLVbEyoYaRNob4hloiISmzRuxl8qDd6p4MGg5HcBjtDwkO6KHIhhBCipXfeeYeHHnqIf//73yQkJHR1OMcUdAIGUFtby+LFi9m5cyeKojBo0CBuvPFGIiMjA75HXFwcWq221WhXeXl5q5GnZklJSW221+l0xMbGHrNN8z0D6TcpyVd+obS0lOTk5GPGFhkZSWRkJH379uWss84iOjqajz76iN/85jdtvocFCxYwf/58/+8Wi6VLpywPT0HaDp9MyWHEoeW8nxTG5J98+0JGRFRij95F/MEzSC85yIHM/qworWRIeEYXRS6EECIYOqORuX9/v0v67QzLli1j9uzZvPfee1xwwQWd0ufJCHoN2IYNG+jduzfPPPMM1dXVVFZWsmjRInr37s2mTZsCvo/BYGDUqFEsX768xfnly5czbty4Nq8ZO3Zsq/Zff/01o0ePRq/XH7NN8z0D6TcrK4ukpKQWbZxOJ6tWrTpqbM1UVW0xxfhLRqORiIiIFkdXSm0qxlrf6KbO5ltHR0oOIxwOPFoFrb2CutqmdWAxvnVgfWt9T8GuKC7vkpiFEEIET1EU9CZTpx+dUS/0nXfe4frrr+ef//wnF198cYf31x6CHgG78847ufTSS/nb3/6GTue73O12c9NNNzFv3jxWr14d8L3mz5/PNddcw+jRoxk7diyvvfYa+fn5zJkzB/CNFhUVFfHmm28CMGfOHF544QXmz5/PzTffzPr161m8eDHvvPOO/5533HEHEyZM4IknnmD69On8+9//5ptvvmHNmjUB96soCvPmzWPhwoX07duXvn37snDhQkJCQpg1axYABw4cYNmyZUyZMoX4+HiKiop44oknMJvNXHTRRcF+rF0mxKAjLsxAZYOTghobkSGRkDycVLeHOLeHGn05Lkt/VFXBFVKO3lxLjs3IV8AWt4LLq6LXdO06sHq3h4N2B0PCzFIYWAgheriGhgb27dvn/z0vL48tW7YQExNDRkZGq9zgnXfe4dprr+W5557jrLPO8s9wmc3moGbmOl2wj02aTCZ1586drc7v2LFDNZvNQT+G+eKLL6qZmZmqwWBQR44cqa5atcr/2nXXXadOnDixRfuVK1eqOTk5qsFgUHv16qW+/PLLre753nvvqf3791f1er06YMAA9YMPPgiqX1X1laJ48MEH1aSkJNVoNKoTJkxQt23b5n+9qKhInTZtmpqQkKDq9Xo1LS1NnTVrlrpr166g3n9Xl6FQVVWd/sIaNfMPn6lfbCs+fPLZ4eq8F7PV2fcOVh+5/371o49Hq998m63ueOYJ9cs/fKj2+s8aNXHFZvXH2oYui7tZztrtauKKzeqKyq77DIUQojOcDmUovvvuOxVodVx33XWqqrbODSZOnHjM9iejI8tQKKoa3POhiYmJ/OMf/2hVxuGrr77i2muvpays7KSTwtOJxWIhMjKSurq6LpuOvP2dzXy6tZj7LxrIzROyfSffu4G/FyxnsSGK32w/h5BxhaSl7SSyYBLurVdzz6Bi9vQewp0pUfyhf68uibtZ0ndbALg2JZYn+/ecEiBCCBGsxsZG8vLy/Du5iI51rM/7ZL+/g14DNnPmTGbPns2yZcsoKCigsLCQd999l5tuuumoC89F99a8KXerhfgOBzXhCqGWw+vA7DG7iNPpGGCpBmBFaVWnx3s0Wpl+FEII0UMEvQbsqaeeQlEUrr32WtxuNwB6vZ7f/va3/OUvf2n3AEXH8z8JWd0yARvocGJQVSyaCiyWfqgqOENL0ZnqONNh5hNgu0ehwe0hTKftmuCPoJP8SwghRA8R9AiYwWDgueeeo6amhi1btrB582aqq6t55plnMHbSo6aifTXXAmvekBuA5OEYgMEOBweja1AbtVitvp0ObNF76GNLIKquCo+iYV1tQxdE3ZqMgAkhhOgpgk7AmoWEhDB06FCGDRtGSIgU4+zJmkfACmvsh7eMMEVAbF9GNDrZk6YSV1np35bIHr2L0MYIMgt9T6l8W9o+m7OfLJ0kYEIIIXqIgKYgr7jiCpYuXUpERARXXHHFMdt++OGH7RKY6DwpUWYUBewuD5UNTuLDm0YyU3IYvv9T/p4QTsyGCsprE0lN3YUtZhcpipahVgtbge8q67o0/maSgAkhhOgpAhoBi4yM9NdXioiI8Fd/b+sQPY9BpyEpwvd0xy8X4g9vdODVKNjVCurqEn3rwMKK0ZrqOcsbieL1kK9qKGx0dlH0h2kl/xJCCNFDBDQCtmTJEv+fly5d2lGxiC6UHh1CSV0jBdU2Rmb41nqRkkOc10uGR6Ugohqa1oGFhdVgi95Fcn1/ksuLKE7KYHV1PbNSYrv0PcgImBBCiJ4i6DVg5513HrW1ta3OWywWzjvvvPaISXSBtLYW4icNBUXDCJuVvalN68COKEcRYg+nV/M6sLKuL0chCZgQQoieIugEbOXKlTidraebGhsb+f7779slKNH52ixFYQyDuP4MdzjZk6oQX17hX4hvi95FpEdhuMP3BOT3tVY8wdX0bXeSgAkhhOgpAq4D9vPPP/v/nJub699rCcDj8fDll1+SmpravtGJTpMR40vA8o9MwABScsjJ3Yc1TkFxVFBX18+/DkxjquNMfRKLHY1YjCZ+rreTE9F1T8RKAiaEEKKnCHgEbMSIEeTk5KAoCueddx4jRozwH6NGjeKxxx7jgQce6MhYRQfKjD16Atbb5SIcDcXh1b+oB7abGG86GcUHAFhVbenUmH9JFuELIUTPt3r1ai655BJSUlJQFIWPP/444GvXrl2LTqdjxIgRHRZfewk4AcvLy2P//v2oqsqPP/5IXl6e/ygqKsJisXDjjTd2ZKyiAzWPgBXX2nF5vIdfSMlBAwxzuNib0nIdmC1mJ2ZrmH8d2DflNZ0ddgsyAiaEED2f1Wpl+PDhvPDCC0FdV1dXx7XXXsv555/fQZG1r4CnIDMzMwHwer3HaSl6ovhwI0adBofbS1GNnV5xob4XkoaCRscIWz0fpkUyYU0FxbVJpKbtwh6zixiXyiiNnW+AzdZGal1uovRB73B1wo7cS14SMCGE6PmmTZvGtGnTgr7ulltuYdasWWi12qBGzbrKCX9T5ubmkp+f32pB/qWXXnrSQYnOpygKGTEh7C1vIL/adjgB05sgYRAjanfzYhKEW1ruC6kx1jEsojex1eVUxSSwqqae6QnRnRa354h1/7IVkRBCHJ2qqqiuzh9EUfQafy3RjrJkyRL279/PW2+9xWOPPdahfbWXoBOwAwcOcPnll7Nt2zYURfGPQDR/uB6Pp30jFJ0mM/ZwAtZCSg7DyrahURRKQ3zrwBoaYggPr8YWs4sI1zCy8vdQFZPAt1WWTk3A3C1GwDqtWyGE6HFUl5fiB9Z1er8pj4xDMWg77P579+7l3nvv5fvvv0en67wZmJMVdBmKO+64g6ysLMrKyggJCWHHjh2sXr2a0aNHs3Llyg4IUXSW9Jg2SlEApI4kRFXpj4HdqS33hbTF7MTcEEp2wR4Avq2sw9uJ5SiOTMBkBEwIIU4vHo+HWbNm8fDDD9OvX7+uDicoQaeK69evZ8WKFcTHx6PRaNBoNJxzzjk8/vjjzJ07l82bN3dEnKITNC/EP1TVegQMYERDPRvTDIzc6FsHlpa2E1v0LmIavYzQa/jI6aAKI9sa7AwP75xyFG5ZAyaEEAFR9BpSHhnXJf12lPr6ejZs2MDmzZv53e9+B/jWqquqik6n4+uvv+62ReKDTsA8Hg9hYWEAxMXFUVxcTP/+/cnMzGT37t3tHqDoPEetBZYwCHQmRtjqeS8pltiqCnbW9UdVFVyhZSimarKihpJZtJ99WYP4tsrSiQnY4T9LAiaEEEenKEqHTgV2hYiICLZt29bi3EsvvcSKFSt4//33ycrK6qLIji/oBGzIkCH8/PPPZGdnM2bMGJ588kkMBgOvvfYa2dnZHRGj6CTNtcAKqm2oqnp40aRWD0lDGVG6CbdOodpQDQ4N9fUxRERUYYvZRaR7JNn56/wJ2PxeSZ0S85HV9yX/EkKInq+hoYF9+/b5f8/Ly2PLli3ExMSQkZHBggULKCoq4s0330Sj0TBkyJAW1yckJGAymVqd726CHhf84x//6C9F8dhjj3Ho0CHGjx/P559/zvPPP9/uAYrOk9a0HVG9w02tzdXyxZQckt0eEjQm9qZ6m+qB+ZIsW8xOjA2hZBfsBWCTxUaV090pMbu7ePsjIYQQ7WvDhg3k5OSQk+Nb/jJ//nxycnL8xd5LSkrIz8/vyhDbRdAjYFOnTvX/OTs7m9zcXKqrq4mOju7wx0xFxzLptSRGGCmzODhUbSM61HD4xZSRKMAIt8KuNIVf5VZQWJdEOjuwxewkdoeH3nGxxFWVUhmbxMpqC1cmxXR4zJKACSHEqWXSpEktajz+0tKlS495/UMPPcRDDz3UvkF1gKBGwNxuNzqdju3bt7c4HxMTI8nXKeJYe0IC5Fgq2JOqkFBejqUuHq9Xg9tcBeYKMlJzyM73PQ35TVXnbEvkkfxLCCFEDxRUAqbT6cjMzJRaX6ewo5aiiOsLhjBGWBuoD1FwUoviVKivjwN805CRajq9D/kexFhRXY/L2/HZkYyACSGE6IlOaA3YggULqK6u7oh4RBfLjPFVwM//ZSkKjRaSh9Pf6cSk6Nid4iGuspLaI9eB1YeQUl5AiL2BOreHH+oaOjxeScCEEEL0REGvAXv++efZt28fKSkpZGZmEhoa2uL1TZs2tVtwovNlxJoBOFRtbf1iSg76Q2sZog1nV7qTiXkVFNQmQqYvAYvb5iYpM4vsQ7vZPmAUX1bWcU50eIfGKwmYEEKInijoBGz69Omy3usUluGfgrS3fjF1JAA5jY18mqbw6x/K2WEZhNerBWMdakgJvWJz6HNwB9sHjOKrSguP9lE79O+LJGBCCCF6oqATsJ7wZIE4cc1rwIrr7DjdXgy6I2apU0cBMKK6iL/FR6N31qFxqVjq4omKLsUWu5MI73gyC/+NzuOmoBF2WhsZFGbusHhlEb4QQoieKOg1YNnZ2VRVVbU6X1tbK4VYTwHxYUbMei2qCoU1v1gHFpUJ5hiG222gKOxOdrdaB2awhGD0eMhsqgn2VWVdh8YrI2BCCCF6oqATsIMHD7b5FKTD4aCwsLBdghJdR1GUo5eiUBRIHUmk10u2IZpdaQrx5RX+BMwevQtjvZOEXtn0ObgLgC87OAHzSAImhBCiBwp4CvKTTz7x//mrr74iMjLS/7vH4+Hbb7/t1nsuicClx4Swu6y+dSkK8E1D7vuGHNXAljSFi7aWs6NhMB6PHgwNeMMLyIzJofcPn6OoKlvr7ZQ4nCQbDa3v1Q5kBEwIIURPFHACdtlllwG+EZLrrruuxWt6vZ5evXrx9NNPt2twoms0j4Ad+mUpCoAU30L84ZZqPk6ECEsNGpeX2toEYmOLsMXkEum9gFC7lfTqUvJjk/m60sJ1qXEdEqtb8i8hhBA9UMBTkF6vF6/XS0ZGBuXl5f7fvV4vDoeD3bt386tf/aojYxWdpFdcUwLW5ghY05OQlfl4tAr7ktVW+0LqLSEoioZee3071H9R0XHTkDIFKYQQp5bVq1dzySWXkJKSgqIofPzxx8e9xuFwcP/995OZmYnRaKR379688cYbHR/sSQh6DVheXh5xcR0zmiG6h8xYX223Q1Vt1AILS4DIdDLdLqL1YexMU0koLz+8ED96N4Z6O4lZvembtwOA72vrqXZ1zObcnVFtXwghROexWq0MHz6cF154IeBrZsyYwbfffsvixYvZvXs377zzDgMGDOjAKE9e0GUoAL799lu+/fZb/0jYkbp7ximOr1fs4SlIr1dFo/lFHa+UHJS6AobrY9iVZmHi7gq2WYficpnQ6xvxROSRGT2S0gPLSG9soMAUxpeVdcxKjm33WGUNmBBCnFqmTZvGtGnTAm7/5ZdfsmrVKg4cOEBMTAwAvXr16qDo2k/QI2APP/wwU6ZM4dtvv6WyspKampoWh+j5UqPM6DQKDreXsvrGNhr46oHlON3sTVWIrqlG6/ZQW5sI4FsHpqYD0G+/bxrys/LaDolVpiCFECIwqqridDo7/VA7+L/Tn3zyCaNHj+bJJ58kNTWVfv36cffdd2O3t1FQvBsJegTslVdeYenSpVxzzTUdEY/oBnRaDWnRZg5W2ThYaSM58heFVJvXgVUXYY9QKE5Q/PXA4uMPYYvJJWbf/0PRaMjc9gMMHsv3NQ3UutxE6U9o0PWoZBG+EEIExuVysXDhwk7v97777sNg6Jgn4QEOHDjAmjVrMJlMfPTRR1RWVnLrrbdSXV3drWflgh4BczqdjBs3riNiEd3IMdeBJY8AFAZVF6DX6NmR4vGtA6vxrQNrjNqPtr6BpKy+xNZWkoUbl6ryVaWl3eOUETAhhDi9eb1eFEXh7bff5swzz+Siiy5i0aJFLF26tFuPggU9HHHTTTfxz3/+kz/96U8dEY/oJnrFhrAKONhWKQpTBMT1w1i5m0EhKexKz2P0mgq2NQ7F6QjFYLTijdxLRvQISvbvZnhFAXnxWXxaUcvM5Jh2jVPWgAkhRGD0ej333Xdfl/TbkZKTk0lNTW1Rn3TgwIGoqkphYSF9+/bt0P5PVNAJWGNjI6+99hrffPMNw4YNa/XBLlq0qN2CE12neQQsv7qNETDwTUNW7iZHMfPvNIWYat86sJqaRBKTDmCLzSXCfQUA6ZvXwpQsVlXXY3F7iNBp2y1OScCEECIwiqJ06FRgVzn77LN57733aGhoICwsDIA9e/ag0WhIS0vr4uiOLugpyJ9//pkRI0ag0WjYvn07mzdv9h9btmzpgBBFV2iuBXawso0RMDi8MbfVQk24Qm2UtmkdWDLgqwemqzWj0WoxHdhFb6OuaRqyfWuCyWbcQghxamloaGDLli3+nCIvL48tW7aQn58PwIIFC7j22mv97WfNmkVsbCw33HADubm5rF69mt///vfceOONmM3mtrroFoIeAfvuu+86Ig7RzRy5BkxVVRTlF6UomhbiDy/dCwmhbEt1k1Bezu7aPgA0RhxE21BLYu++lOzZxTiHhf2E8El5Lb9Oar9pSBkBE0KIU8uGDRs499xz/b/Pnz8fgOuuu46lS5dSUlLiT8YAwsLCWL58ObfffjujR48mNjaWGTNm8Nhjj3V67ME44UfS9u3bx/79+5kwYQJms7ntL2nRY6VFm9EoYHV6qGxwEh9ubNkgcShoDcRZq0gP6c3O9GKG/FDONucw7PYozOZavNE7SY8YRsmeXQzYvw16j+G7agtVTjexhvZ5GlISMCGEOLVMmjTpmKUrli5d2urcgAEDWL58eQdG1f6CnoKsqqri/PPPp1+/flx00UWUlJQAvsX5d911V7sHKLqGUaclJco3dNvmk5A6AyQNAyDHlMiuNIXomhq0bjc11U31wGJzCff46oF5t/zAkDAzbhU+q6httzglARNCCNETBZ2A3Xnnnej1evLz8wkJCfGfnzlzJl9++WW7Bie6Vq+macg2n4SEw+vAXF5KYqAxxLcOrKZpHZg1Jte/DsxSUc5Fob5Rrw/L2q9gryRgQggheqKgE7Cvv/6aJ554otWTBX379uXQoUPtFpjoepn+LYmO9iRkU0X86mJQFHKb9oWsq01EVRVcoaVoGktJ6t0PgJFlB1GAH+qsFDQ62yVGWYQvhBCiJwo6AbNarS1GvppVVlZiNBrbuEL0VIGOgGWX5BJuCGdHqpeEsnI8HgNWazwAanQuKZlDALDnbmVclO8R4Y/aaRRMRsCEEEL0REEnYBMmTODNN9/0/64oCl6vl7/+9a8tnloQPd9xR8BissEUicbjYHh4FrvSm+uBuamuTgB805ARngwACnK3cUViFAAflNW0y/5gkoAJIYToiYJ+FO2vf/0rkyZNYsOGDTidTu655x527NhBdXU1a9eu7YgYRRfpFecbAcurPEopCo3GNwq2fwU5mjDWJYLboCG+ooLa2GQyMrZji91BdKEJjVZHfWUF0xQnBkVht7WRXGsjg8NOrkaLbEUkhBCiJwp6BGzQoEH8/PPPnHnmmUyePBmr1coVV1zB5s2b6d27d0fEKLpIRoxvBKy+0U2tzdV2o+Z1YLYGvBqF/Wk64ssrsFji8Xp1eIwWNO6DJPXxbQVRt2sHk+MiAPig9OSnIWUzbiGEED3RCRVjSkpK4uGHH27vWEQ3Y9JrSY40UVLXyMEqK9GhbWxh0ZSADSnbiy5cx88pTs7PLUdVh1Ffn0hkZBHE5JKoH0zx7p0U7PiZK2eewX8q6vigrJr7spPRaU68fpyMgAkhhOiJgh4BW7JkCe+9916r8++99x5///vf2yUo0X0cXgd2lIX4Kb6K+OaKPQyI7svOdIiprkbn9lBd5asHZo3dQbi7aR3Yzu2cHxNOjF5LmdPNypr6k4rPJQmYEEKIHijoBOwvf/kLcXFxrc4nJCSwcOHCdglKdB+9/FsSHSUBC0+EyHRAZYQ5ib0pCmgU4irKqalNAsAWvRttjR6NVkt9ZQWNVRVcmRgNwLslVScVnyzCF0II0RMFnYAdOnSIrKysVuczMzNb7M0kTg3Ne0LmVTYcvVHTvpAj3ODSKxSkGUkoL8faEIPHHYKqawRlN4nZvnVgBbnbuSo5FoCvKy1Uu9wnHJ/UARNCiFPL6tWrueSSS0hJSUFRFD7++OPjXvP2228zfPhwQkJCSE5O5oYbbqCq6uT+B7+jBZ2AJSQk8PPPP7c6v3XrVmJjY9slKNF9ZB3xJORRpY4GIKemFIAtKQ7iyysAhdqmqvhK3A7iUvoDUJi7ncFhZoaGmXGq6knVBJMRMCGEOLVYrVaGDx/OCy+8EFD7NWvWcO211zJ79mx27NjBe++9x08//cRNN93UwZGenKATsKuuuoq5c+fy3Xff4fF48Hg8rFixgjvuuIOrrrqqI2IUXSg73peAHWgqRdGmpoX4CcU/kxqWSm5a0zowj4eqKl89MFvsDkKPqAcGMDM5BoBlJdUnHJ8swhdCiFPLtGnTeOyxx7jiiisCav/f//6XXr16MXfuXLKysjjnnHO45ZZb2LBhQwdHenKCTsAee+wxxowZw/nnn4/ZbMZsNjNlyhTOO+88WQN2CsqMDUFRfKUoqqxH2T4oZQQoWqgvYURUP3anKSioxJWV+/eFbIw8gLYOFI0GS0UZlopyLk+IRq8o/NxgJ7fBfkLxyQiYEEIERlVVPB5bpx/tUXT7WMaNG0dhYSGff/45qqpSVlbG+++/z8UXX9yh/Z6soMtQGAwGli1bxqOPPsrWrVsxm80MHTqUzMzMjohPdDGjTktatJmCajt5lVbiwtrYbsoQComDoHQbOZow/mNSqEgJJaG8nNKUZFyOGPTGahRTLglZfSjbv4eC3G0Mnng+U+Ii+E9FHe+WVPNI39Sg43N72+FNCiHEacDrtbNy1dBO73fSxG1ota23MGwv48aN4+2332bmzJk0Njbidru59NJL+b//+78O67M9BD0C1qxfv378+te/5le/+pUkX6e4rDjf/o0HKo61EN+3DmyEzVdWYkuKk4TycgCqa3xPQ2ridhCd4FuIX7hzOwC/aVqM/35ZNQ5v8NnUkVOQXq8n6OuFEEL0bLm5ucydO5cHHniAjRs38uWXX5KXl8ecOXO6OrRjCnoEzOPxsHTpUr799lvKy8vx/uJLc8WKFe0WnOgesuNCWb2nggPHWoifNho2LqFP2V7CTGFsT7VwwYYadB4PlZUJJCblYo/NJaTycsC3EB/g3Jhwko16Shwuvqq0cGlCVFCxHTkFWVOzDhJ+FfT7E0KI04FGY2bSxG1d0m9Hevzxxzn77LP5/e9/D8CwYcMIDQ1l/PjxPPbYYyQnJ3do/ycq6BGwO+64gzvuuAOPx8OQIUMYPnx4iyNYL730EllZWZhMJkaNGsX3339/zParVq1i1KhRmEwmsrOzeeWVV1q1+eCDDxg0aBBGo5FBgwbx0UcfBd2vqqo89NBDpKSkYDabmTRpEjt27PC/Xl1dze23307//v0JCQkhIyODuXPnUldXF/Rn0N35n4SsOP6TkNqSrQyPG8bOdAWNqhJfVk5dXSKqqsEVWoa20YGiaKgtK6G+qhKtonBVkm8x/j+Lg39k+MgEzN5YEPT1QghxulAUBa02pNOPVvsItzObzYZG0zKd0Wq1AB2+/uxkBJ2Avfvuu/zrX/9i2bJlPPvsszzzzDMtjmAsW7aMefPmcf/997N582bGjx/PtGnTjlpPLC8vj4suuojx48ezefNm7rvvPubOncsHH3zgb7N+/XpmzpzJNddcw9atW7nmmmuYMWMGP/zwQ1D9PvnkkyxatIgXXniBn376iaSkJCZPnkx9vW+Krbi4mOLiYp566im2bdvG0qVL+fLLL5k9e3ZQn0FPEFApirh+YIwAl40RISnUhSnUJYSSUF6Gx2PAaUsBQBe2nbgMXx25wqanIa9qehpyVU09BY1HWeh/FEcmYIpyQjtrCSGE6EYaGhrYsmULW7ZsAXzf/Vu2bPF/Ry9YsIBrr73W3/6SSy7hww8/5OWXX+bAgQOsXbuWuXPncuaZZ5KSktIVbyEwapCSk5PV3bt3B3tZm84880x1zpw5Lc4NGDBAvffee9tsf88996gDBgxoce6WW25RzzrrLP/vM2bMUC+88MIWbaZOnapeddVVAffr9XrVpKQk9S9/+Yv/9cbGRjUyMlJ95ZVXjvp+/vWvf6kGg0F1uVxHbfNLdXV1KqDW1dUFfE1nK6i2qpl/+Ezte9/nqtvjPXrDpZeo6oMR6n+/e1AdsnSI+vqsker3Y8epDz74oLp06f9Tv/k2W13/9kz1g8efU5+acbH61SvP+S/9f5v3qokrNqtPHigOKrapP+1WE1dsVhNXbFaX/PzGib5FIYToEex2u5qbm6va7fauDqXDfPfddyrQ6rjuuutUVVXV6667Tp04cWKLa55//nl10KBBqtlsVpOTk9Wrr75aLSwsPOlYjvV5n+z3d9AjYHfddRfPPffcSQ/rOZ1ONm7cyJQpU1qcnzJlCuvWrWvzmvXr17dqP3XqVDZs2IDL5Tpmm+Z7BtJvXl4epaWlLdoYjUYmTpx41NgA6urqiIiIQKc7+kiMw+HAYrG0OLq7lEgzBp0Gp8dLUc0xykWk+aYhh9aUoFW0bE5qJLqmBn3TOjAAe2wuJrfvacfCXbn+S2c1LcZ/t6Q6qNpeHhkBE0KIU8qkSZNQVbXVsXTpUgCWLl3KypUrW1xz++23s2PHDmw2G8XFxbz11lukpgb/ZH1nCvoba82aNXz33Xd88cUXDB48GL1e3+L1Dz/8MKD7VFZW4vF4SExMbHE+MTGR0tLSNq8pLS1ts73b7aayspLk5OSjtmm+ZyD9Nv9sq82hQ4fajK2qqopHH32UW2655Vhvm8cff5yHH374mG26G41GISs2lN1l9RyobCAj9iiPEzetAwsp2syAXgPIzdiORvUSV15OiTYR1W3Cq7eioxoUhZriQqy1NYRGRTMtLpIonZYih4tV1fWcFxsRUGwyBSmEEKInCnoELCoqissvv5yJEycSFxdHZGRkiyNYv1ycp6rqMRfstdX+l+cDuWd7tQGwWCxcfPHFDBo0iAcffPCosYNv7rqurs5/FBT0jIXjAa0DaxoBo3IPOTGDqIgEW0wICaVlgAa7tRcA5tidRCf5quIX7fI92GDSavwbdP8ziA26WyRgGknAhBBC9AxBf2MtWbKkXTqOi4tDq9W2Gu0qLy9vNfLULCkpqc32Op3Ovw/l0do03zOQfpOSfHWrSktLWzy+2lZs9fX1XHjhhYSFhfHRRx+1GhH8JaPRiNHYRjHTbi6reUuiYz0JGZYAURlQm88IbRhvKQp7M/WkH/TVAyurjCMrEpSE7ZiLL6Cm5BCFO3fQ76xzAJiVEsviokr/Bt0x+uP/9TxyM24ZARNCCNFTnHAh1oqKCtasWcPatWupqKgI+nqDwcCoUaNYvnx5i/PLly9n3LhxbV4zduzYVu2//vprRo8e7U98jtam+Z6B9JuVlUVSUlKLNk6nk1WrVrWIzWKxMGXKFAwGA5988gkmkymYj6BHyQ5kBAwOb8xt9T0t+mNSA1G1tei9Xsqb1oE5og5gUH0Jc3NBVoDBYWaGNG3Q/XGAG3QfOQKmkQRMCCFEDxF0Ama1WrnxxhtJTk5mwoQJjB8/npSUFGbPno3NZgvqXvPnz+f111/njTfeYOfOndx5553k5+f7q9f+8lHTOXPmcOjQIebPn8/OnTt54403WLx4MXfffbe/zR133MHXX3/NE088wa5du3jiiSf45ptvmDdvXsD9KorCvHnzWLhwIR999BHbt2/n+uuvJyQkhFmzZgG+ka8pU6ZgtVpZvHgxFouF0tJSSktL8XhOvYrszZtyHzcBa5qGTCjNJTUsle3p+OuBOR2hqPYEUFTMYb5RsYr8gzQ2HK6wP7OpJtiy0sA26JY1YEIIIXqioL+x5s+fz6pVq/j00085++yzAd/C/Llz53LXXXfx8ssvB3yvmTNnUlVVxSOPPEJJSQlDhgzh888/929tVFJS0qI2V1ZWFp9//jl33nknL774IikpKTz//PNceeWV/jbjxo3j3Xff5Y9//CN/+tOf6N27N8uWLWPMmDEB9wtwzz33YLfbufXWW6mpqWHMmDF8/fXXhIeHA7Bx40Z/bbE+ffq0eF95eXn06tUr4M+hJ2jejqio1k6jy4NJr227YdoZvp+FGxhx1q/5T30hzkgzCaUlFCcn0dCQQbi5nJDEPYTFJNJQXUbR7lx6jzoTgMsTo3l4fxFb6+3sbLAzMOzYFZTd6uGdGCQBE0II0VMoapD1JOLi4nj//feZNGlSi/PfffcdM2bMOKHpyNOZxWIhMjLSX8Kiu1JVlRGPLKfO7uLLeeMZkHSUWN0OeDwNPE6WXbqQx7a9wqNfRBJ/yMvyqVOIjytjwKCv0dpjOPjdr6gq+InRl1zBxP+50X+LG7bl8UVlHb9Nj+fBPsd+jHjQ9z9T3bQj93PptczsM6m93rIQQnQ7jY2N5OXl+XdyER3rWJ/3yX5/Bz0FabPZ2lwkn5CQEPQUpOg5FEXxPwl5zIX4OiMkDQMgx+VLjJrXgRm8XqqqY8Gjw2OuRmfwPYxQtHNHi1s0T0N+UFaD23vs/z9wt/j/h47d7kIIIYRoL0EnYGPHjuXBBx+ksbHRf85ut/Pwww8zduzYdg1OdC+BrwPzTUP2qTxEuD6cralu/zowr1eHt6EXAFHxvnITZXn7cB3x9+n82Ahi9TrKnW5W1tQfs6sjn4KsLDh2WyGEEKK7CDoBe+6551i3bh1paWmcf/75XHDBBaSnp7Nu3Tqee+65johRdBPNT0Lur2g4dsOmhfiaog0MTxhOQTy4Q03El5QAUNeQDkBI8m6ModF4PR6K9+7yX67XKP6aYMtKjr0Y/8gRMFuDK7g3JIQQQnSRoBOwIUOGsHfvXh5//HFGjBjBsGHD+Mtf/sLevXsZPHhwR8Qouok+Cb6F+PvLj5OApfsW1FO6jZzYIaiKQlHvCBLKfU8+5pdHAaDG7sYQ6quzVviLacj/l+RLwL6pqqPBffSnSo98RXOMAr5CCCG6v8cff5wzzjiD8PBwEhISuOyyy9i9e/dxr1u1ahWjRo3CZDKRnZ3NK6+80gnRnpwTemzMbDZz8803t3csoptrTsD2lTcce8eCyHQIS4SGMnI0vms2Jjdy+c8WDF4vlvpQNPYYvOZqjNEu6suh6Ih6YABDw8xkm40csDv4usrCFU0jYkdSVRX3kYVYT7iqnRBCiO5g1apV3HbbbZxxxhm43W7uv/9+pkyZQm5uLqGhoW1ek5eXx0UXXcTNN9/MW2+9xdq1a7n11luJj49vUSWhuwn4K2vjxo2ce+65bW4eXVdXx7nnnsvWrVvbNTjRvWTGhqLTKFidHkrqGo/eUFH868CG1FejU3T8mGxFgaZRMAVXXV8AYtMrASjZtweP233ELRSmJ0QB8O/ytouyen/xu0YjI2BCCNGTffnll1x//fUMHjyY4cOHs2TJEvLz89m4ceNRr3nllVfIyMjg2WefZeDAgdx0003ceOONPPXUU50YefACTsCefvppzjvvvDYftYyMjGTy5Mn89a9/bdfgRPei12rIbNqIe9/xpiGb1oGZizczMHYgBxPBazYSX1wMQFW9r7yEOXkXWr0Zt9NB+cH9LW5xaVMCtqKqnjqXm19y/6KCiiIJmBBCHJWqqlg9nk4/gqx21UJdXR0AMTExR22zfv16pkyZ0uLc1KlT2bBhAy5X910bHPAU5A8//MC999571NcvueQSXn/99XYJSnRffRPC2V9hZV95AxP6xR+9YXNB1oKfyJlwI9sqt1HWN5aEQ751YAcrQ0nM1qOGVGOOG0pDiZ2iXbkk9+nvv8XAMDP9Q03stjbyRWUdVyXHtujilyUqJAETQoijs3m99F69rdP73T9hKKHaoxTvPgZVVZk/fz7nnHMOQ4YMOWq70tLSVuWxEhMTcbvdVFZWttjTuTsJeASsqKjIXwW+LWFhYZQ0PeUmTl3N68D2Hm8ELCUHFC3UF5MTlgHA1jQ3kXV1GL1eHC7Q1vh2EAhLtQNQvHtnq9scnoasbfXaL0fAZApSCCFOHb/73e/4+eefeeedd47b9pdrkptH3Y66VrkbCHgELD4+nt27d5OVldXm67t27SIuLq7dAhPdU8BPQhpCIXEwlP7MCIdvCHh1fBUXAvHl5RQmJWGvzUYft5OY9ApKN5go2p3banH/9IQonswrZXVNPVVON7GGw39l3b8Y1e7G/54JIUSXC9Fo2D9haJf0G6zbb7+dTz75hNWrV5OWlnbMtklJSZSWlrY4V15ejk6nIzY29ihXdb2AP5ULLriAP//5z22+pqoqCxcu5IILLmi3wET35H8S8ni1wMA/DRlXtpOM8AwOJDWtAyvyrQMrs/mGhfVxh9AawVZXS21pcYtb9A4xMTTMjEeFzytrW7zmaTUCJo9BCiHE0SiKQqhW2+lHMKNQqqryu9/9jg8//JAVK1YcddDnSGPHjmX58uUtzn399deMHj0avV4f9OfUWQL+xvrjH//Itm3bGDNmDP/617/YunUrP//8s3+j623btnH//fd3ZKyiG2iuhl9tdVLV4Dh2Y//G3D+Rk5CDV6NQ3S/BXw+swOJB35CMovESnuFbH1DUxjRk82L8j8tqW5xvtQg/+CUGQgghupHbbruNt956i3/+85+Eh4dTWlpKaWkpdrvd32bBggVce+21/t/nzJnDoUOHmD9/Pjt37uSNN95g8eLF3H333V3xFgIWcALWu3dvvvnmG6xWK1dddRUjR44kJyeH3/zmN9hsNpYvX06fPn06MlbRDYQYdKRFm4EAnoRsLshavIWcON+w9/Z0iKyrw+T14va6USoHABCZ4dtHtGhXbqvbNCdg62sbqHQefhqyVQImc5BCCNGjvfzyy9TV1TFp0iSSk5P9x7Jly/xtSkpKyM/P9/+elZXF559/zsqVKxkxYgSPPvoozz//fLeuAQZBFmIdPXo027dvZ8uWLezduxdVVenXrx8jRozooPBEd9QnIYzCGjv7KhoYk32M+fWYbAiJBVsVOfiStlVxlUwC4svKKUhOwlKbgQmISC8BJZOi3a0TsEyzkWFhZn5usPNlZR3/k+Lr0/PLJ5sl/xJCiB4tkJIVS5cubXVu4sSJbNq0qQMi6jgntGhmxIgR/PrXv2bGjBmSfJ2G+sQfroh/TIoCab5RsKzqfKKMUexKcKKajcQXFQFQ0hiN1hmG1mgnLNlGTXEhNktdq1v9qmkU7D8Vtf5zrUfATvANCSGEEJ1MVi2LoB25JdFxpfvWgSmFPzEiYQQerYKlX4p/HViJu5aQ8hEARGX7npZsqxzFxfGRAHxfU09NU1HWXyZgQgghRE8hCZgIWt/EYBKwMb6fhT8xMmEkALt66YiwWDB7vXi8HtwVTevAetUCapvTkL1DTAwMNeFW4atK3wiZJGBCCCF6KknARND6xPsK8pbUNdLgaL1FUAvNBVktReSE+LYfWhlfiQLEl5YBUFmXgOI2oQ+1ExLf2GYCBvCr+CgA/lPRnICd/HsRQgghuoIkYCJokSF64sKMQIAFWZN8W0gMaqjFoDGwJcYCIWb/OrBSg52wymG+e2fVU3ZgH+429u9qXge2qroei9vTqg6YEEII0VME9RRks9raWn788UfKy8vxer0tXjuyNoc4dfVJCKWywcG+8gaGp0cdu3HamVCyFUPxZobEDWFT+SYaBqaTsMe3dVWZpxpz+Qjqk34kMquBkh9dlOftI6XfwBa36R9qom+Ikb02B8sr60g1GTro3QkhRPd2Mhtci8B15OccdAL26aefcvXVV2O1WgkPD29Re0lRFEnAThN9E8L574Hq4+8JCb51YD/9DQp+IGfkpWwq38TebBMjNtYT4vFg04KtKgu8OkxRDoxRTor37GqVgIFvGvKZQ2UsOljGZYlR7f/GhBCiG2uu7G6z2TCbzV0czanPZvPVqOyIivpBJ2B33XUXN954IwsXLiQkJKTdAxI9Q/NC/L1l9cdv3PQkJCU/MzL2PhYDqxOryQHiS0s5lJpKqWonsWoQ1vificqqp3BXLqN/dXmrW/0mOYa/F1ey3+7g6YNl7feGhBCiB9BqtURFRVHe9CR5SEiIFKHuAKqqYrPZKC8vJyoqCq22/bdaCToBKyoqYu7cuZJ8neb6J/oW4u8qDSABi8qE0ASwljPc4/sPxbrQEuaFhxNfVMyh1FRKDHX0Lh/lS8B6Wzj0VeuNuQEyzEZWnzmQZw6W8vfiSlmIL4Q47SQlJQH4kzDRcaKiovyfd3sLOgGbOnUqGzZsIDs7uyPiET3EgKQIAIpq7dQ3ugg3HWN4VlF82xLt+ozI0u30ierDvtp92Ib0ImFbHgDl3lrMZaNh4FuYYx0oxlLqqyqIiEtodbs4g44/90tjdlo8f/7hLf7DqA55j0II0R0pikJycjIJCQm42nhgSbQPvV7fISNfzYJOwC6++GJ+//vfk5uby9ChQ1vNi1566aXtFpzoviJD9CRFmCi1NLKnrJ5RmTHHvqApAaPgR3Iyc9hXu48DvUMYtL6BULcHqw4qvS7C68ZQH72G2AG1FO3e2WYC1iw7xMj1fM1uNYx9Sv92fodCCNG9abXaDk0QRMcKOgG7+eabAXjkkUdavaYoCh6P5+SjEj1C/6RwSi2N7CwJJAFrKsha8CM5Z1zFe3veY21iHYOB+JJirOnpFGtqGHTobOqj1xDVx0Le1s0MPHviMW+rqs72eTNCCCFEJwq6DpjX6z3qIcnX6WVAkm8d2O5A1oEljwCtAazljDTGAbDKmIcmOor4Yl85ilJdLSHl/fHao9AZvdTWrTrubVVVht+FEEL0PCdViLWxsbG94hA90IDkIBIwvcmXhAEplQdJCEnApXpwDutLYrnvacZy6nDhJdx6PgAhqftxOY79d0xFEjAhhBA9T9AJmMfj4dFHHyU1NZWwsDAOHDgAwJ/+9CcWL17c7gGK7qt/om8h/q5SS2DF6tLPBEAp+MG/L2Re7zBCrTbCXC5UVMo0tSTbpgAQlmolb/v3x7ylTEEKIYToiYJOwP785z+zdOlSnnzySQyGw5XIhw4dyuuvv96uwYnurXdCKFqNgqXRTaklgNHQjLN8Pwt+JCchB4B1qb5Crs3bEhVratAXhmEtjUdR4OD+fx7zljICJoQQoicKOgF78803ee2117j66qtbPH0xbNgwdu3a1a7Bie7NqNOSHRcKBFgPrHkhfnkuIyP7ArCS3eiSk0koKQWgRFuLt8GFtm4cAGrYj3g8x0ruJAETQgjR8wSdgBUVFdGnT59W571er9QjOQ31D2YhflgCRGcBKn2tNYTqQ7G6bbhHDSKhqaBglWLBiZte5stwWPToTI0UFr591FvKCJgQQoieKOgEbPDgwXz/fet1Oe+99x45OTntEpToOYJ6EhL805Dagp8YET8CgLx+EYTY7YQ3OlCBEk0NCaZ4yjbH+17Pe+UYo2CSgAkhhOh5gq4D9uCDD3LNNddQVFSE1+vlww8/ZPfu3bz55pt89tlnHRGj6Mb6N1XE31liCeyC9DNh6zu+jblHXMza4rWsT2kgC4gvLKS+T29KNDX0LrJiKx6Is74CQ3g1RcXvkJF+Q+v7Ke72ezNCCCFEJwl6BOySSy5h2bJlfP755yiKwgMPPMDOnTv59NNPmTx5ckfEKLqx5hGw/RUNuDze41+Q3rQQv3ADI+OGA7DOtgPjwIH+chQlmhrcFXaS0gZRuikWgEOHXm01CqaqqkxBCiGE6JFOqA7Y1KlTWbVqFQ0NDdhsNtasWcOUKVPaOzbRA6RFmwkz6nB5VPIqrce/IH4AGCPBZWWIqkGn6Ci3l6OOHkp8eQUAVZoGGnHRr9cQavZE4aw34XRWUFTU8olIVXWjKLIbtxBCiJ7npAqxCqEoCv0Sw4AAn4TUaCD9DADMRVsYFDsIgLz+EZgbG4mw+pK4Uk0NCSGJqF6F0k3RAOQXLMbrPTzi5fW2rAEWSCkyIYQQojsIOgGLjo4mJiam1REbG0tqaioTJ05kyZIlHRGr6Kaa14HtLg10HVjzvpD/9dcD+298LYrBQEJxMeCrB0a5B50xjJq9EajucByOUioqvvLf5pfbEFmry07ynQghhBCdI+gE7IEHHkCj0XDxxRfz8MMP89BDD3HxxRej0Wi47bbb6NevH7/97W/529/+1hHxim5oYNOWRLnFQSZg+T+Qk+hLwDbWbidk9CgSynzlKEo0NbiKG0hI64/q0VCzbyAABQVL/bf55QiYva7qJN6FEEII0XmCfgpyzZo1PPbYY8yZM6fF+VdffZWvv/6aDz74gGHDhvH8889z8803t1ugovsanBIJwI5AE7C00aBowVJIjjERgH21+9CccQvxGzcBUKOxYnc66Nt3OMX7N1K6OYrYgTrqLJuxWH4mImJYqwTM7A5tvzclhBBCdKCgR8C++uorLrjgglbnzz//fL76yjc9dNFFF/n3iBSnvoHJ4WgUKK93UB7IlkSGUEgeBkBM+S6yIrMAODgoCpPDQWSdL5Er0dSQGJoGgLO+BpNyHnB4FOyXCVgdsjm8EEKIniHoBCwmJoZPP/201flPP/2UmJgYAKxWK+Hh4ScfnegRQgw6esf7FuJvL64L7KKMsb6f+ev9G3P/GFKGLimJhLKmbYk0NRgbTKAooDZQvedsAMrK/4PDUdZqDZhXDaAMhhBCCNENBJ2A/elPf+L3v/89l156KY899hh//vOfmT59Ovfccw8PPvggAMuXL2fixIntHqzovoam+qYhtxcFOA3ZvDF3/g+MTPQlYJsqNhM2aWLLdWCFVqKTMgAo2GwnMmIUquomP38xXq+jxS0VtxRlFUII0TMEnYDdfPPNrFq1itDQUD788EPef/99QkJCWLVqFbNnzwbgrrvuYtmyZe0erOi+BvsTsABHwJoLspZtJyfSt7fojqodGM8ZR3xFBagqtRob1kYrvfuMAMBhLSTC/BvAV5Ji776/tLilBuW43bpcddTWbqC6ei0uV4CxCiGEEO0s6EX4AGeffTZnn312e8cierAhKb5SFAEvxA9P9G3MXZNHWnURCeYEyu3l7O8TQriiEFVbS210NCWaGpKbEjSvu4S6Q9eTPfBODuQ9Q23tDy3vqRxOwFwuC1brHqzWvTRY92Kz7qPBuhens7zFJSEhWUREDPcf4WED0GiMJ/5BCCGEEAEIOgGbNGkSN954I7/+9a8xm80dEZPogQY1JWBFtXaqrU5iQg3HvyhjLNTkoRT6ylF8dfArNtXtYNpZY0goK/cnYP28vgRM9ZRRsKOCMy76HWFhA9iRexceT4P/dpqmBGz37ocoLPrHUbttbAxFVRXM5gZstjxstjxKSz8GQFEMhIcPJDx8CCEhWYSEZBEako3JlIqiaE/w0xFCCCFaCjoBGzVqFPfccw+33347M2bMYPbs2Zx11lkdEZvoQcJNerLiQsmrtLK9qI4J/eKPf1HGGNj6T8j/LyPHzOKrg1+xuXwzMyZNIuFvr7NnQH9KNLVQ7sYYEobD1kDx3n04G0cSH38Bo0e/z4qPb4dk3+0URaG09D/+5KuxMQSbLQqbNQqbLbLpiMLj0QOg0zkID68kPLySiIgqIiKr0WrtWCxbsVi2tghVUQyEhGT6kjJzFmZzOgZjAkZDPAZjAgZ9LBqNvj0/UiGEEKewoBOwp59+mieffJLPPvuMJUuWMGHCBPr06cONN97INddcQ2JiYkfEKXqAIamRvgSsONAErOlJyMINjLzYt55rS8UWzBP+SNzjvt/rNDYaqixk9B3K3q3r8TiLKd5TS69hcYSF9qX0+/+H50o96ECjc7F9x30oCuQfGkpp6RhiY2OJi4sjMzMWNSaWCnMYhRo9dS43+gYLzvISCg7sw5tfjoKKydRAeHglUVFW4uI9mM0W3O5iVNWJ1boXq3XvUd6Mgl4fjdGYgMEQj9GY2HQkHf6zIRGDIRZFkR3AhBDidHdCa8C0Wi3Tp09n+vTpVFRU8Oqrr/KnP/2J++67j4suuoi5c+dy3nnntXesopsbkhLBp1uL2RHok5Bx/cAcA/Zq+jbaCdOH0eBqIM9cT0SvXkTW1lIXFUWpppaMhMHsZT1edwkFO6vpNSwOr1dF4fCTj5qEXBSlgXprFGFD/0jm1F7stDbyZUMjuQ12aqvcQG3LGMwJMDgBo6KQiIdIuxV9dQVhDXVEVliJtFuJ03gZOCCBXr1MREY5aGw8hKOxGIezHKezEqezAlX14HJV43JVA7uO+pYVRYfBEI/JlIrZnI7ZlO77ac7wjaoZ4iVBE0KI08AJJWDNfvzxR5YsWcI777xDQkIC119/PSUlJVxyySX89re/5amnnmqvOEUPMKT5SchAa4Epim9boj1foC34geEJw1lbtJZN5ZuYPGkScRs3UhcVRZmmlkz9YAC8Hl8CBuB1e1E0Hv/ttPpGVFXhb+HPsLFeB7sLW3SnU6BPiInBYWYidVoO2h0ctDvJb3TgUFXy0YApHFJa1rDTedxE2K1E7LES47DTP/JsRqUlc3a/THpFhAMqLlc1DkcFTmc5DkcFDmcZDkc5DkcpDkcZDkcZTmclqurG4SjB4Sihrm5Dq49EozFgMqUTEtKLkJBsQkN6ExLq+6nXRwX4T0IIIUR3F3QCVl5ezj/+8Q+WLFnC3r17ueSSS3j33XeZOnUqStMi6BkzZnDZZZdJAnaaGdy0EP9QlY06u4tIcwBrojLOgj1fQP56Rg2axNqitWws28gV511L/Jdfsr9vH0o1tRgbDCiKBtVrobq4nPrqRgwmbYsEDGC3dxYbNRFoFRgbGcbgMDODwswMCjPRL9SEUdN6dMntVSl0ODlgc5BnbzpsTvLsDvIbHbi1OqrDIqkOi+QgsAl4p9YDmw6g93pI0UCf8BAGRkWQHZpAVoiR7FgjvQw6/78TAF6vG6ezAoejjMbGIuz2AuyN+b6f9gIcjmK8Xic2235stv3Aty3i1OtjmpKybEJD+xIa2pewsH4YDAkt+hFCCNH9BZ2ApaWl0bt3b2688Uauv/564uNbr/U588wzOeOMM9olQNFzRIUYSIs2U1hjZ0dxHeN6xx3/In9F/P+SM2keAJvLN2Mc/1eSm5KKKqUea7GFhPRelOUfODwNOTQOlMMJmNsRyrrka6DGyoykGJ4ZkBFQ3DqNQi+zkV7m1uUnjkzODtga2VZRzc6aOgpcXmoNRlwaLYeAQ/UOvq2vaHGtWaMhy2zw37uX2UCWOYxeIbGkRAxH+4ukyev1jY7Z7fnYbHlYbfuxWQ9gte3H4SjB5aqmrq661ciZThfhS8ZC+/kSs7B+hIX2w2CIDej9CyGE6HxBJ2Dffvst48ePP2abiIgIvvvuuxMOSvRcQ1MjfQlYkSWwBCwlB3QmsFUyFDN6jZ5KeyWF1iKSzj2X0MoKrGFhlKu19Eof0ZSAFZO/o4r0gTEoiorHowMdlNZO41uTFQ1we0b7PAxyZHJ2XmwEpCf4Xyuvrmb93v1sKCxhV1091QYzdeZQ6sxhNJhCsHu95FobybW23qNSp0Cq0UC6yUCG2fcz3WQgwxRNemgiKdHj/GU1ADwemy8ps+7HZjtAQ9MDAXb7QdxuC3V1G6mr29iiD4MhnrCwAYSF9vP9DOtPSEgftFqpcyaEEF0t6ASseb/Htnz55ZdceOGFJxWQ6NmGpEbyxfZSfg60Ir7OAKmj4dAajEUbGBw7mC0VW9hUvompF04l7plnsYaFUaqpZXC4b9Nur6eUvC2VDJmQiqJRcTjCwAhfxPpen54QRXZIxycZCTExTB8Tw/Qx4Ha7yc/PZ9++fezdu4XSykrqTSFYzKHUmcKwmENxRMVQHxpOpaLDpcKhRieHGp2tngsA0CsKyUY9KUY9aSYDqSYDKcZEUk3ppCZMJtOoJ0Kn9T2dacvD2rAHq3VPU2K2B7u9AKezgurqCqqrv/ffV1G0mM1ZhIcNaErKfIfRmCTTmEII0YmCTsBGjx7Nk08+ye233+4/53A4uOuuu1i8eDF2u71dAxQ9y/C0KAA259cEflHGWXBoDRxaz8jMkb4ErGwT08dNJ8nl5BBQqqlljGuor72nDI/HzU//OYiqHN6Au17v++s8N7PzS6HodDqys7PJzs5mypQpNDQ0cPDgQfLy8jh48CBVxQf8bb2A3WhGn5qONikFV3QsDaZQit1eChqdFDucuFSV/EYn+Y1OqLO22WeIVkOyQU+yUU+yaTjJhtEkxxpISdETp3UT4SnA2LgLu203DQ27aWjYhdtdh822D5ttH2Xlnx0RfxRhYf0JDxtIWNhAwsL6ExraT0bLhBCigwSdgL399tv87//+L59//jlLliyhtLSUWbNmAbB27dp2D1D0LMPTI1EUKKyxU17fSEK46fgXZY6F7/EtxD/zat7Y/gabyzejKApZQ4byg9NBhWLBXebAFBZOY0M9qqeC4r1azCEtbzUtLpKBYV2/Q0NYWBhDhgxhyJAhAFgslhYJWU1NDRzYAwf2oAVMwMD4eDIyMkjLyMCUkka93kiRw0Vho5Nih4uiI37WuD3YPF722x3stzuOGodWGUCiYShJRj1J0XoSdC6iqSLCU0SYax8hjdsxN24Fdy21tT+02N5JUbSEhGQ3jZINbBo1G9hUKkNGy4QQ4mQEnYBdccUVnHXWWVx33XUMGTIEq9XKDTfcwNNPPy1bEwnCTXr6JYSzu6yezfm1TB2cdPyL0s4ERQO1hxhuSkRB4aDlIJX2SjIumobx3XdxmEyU26rIzB7O7p/XEBZdg60+CfUXDzXe0QWjX4GIiIhg2LBhDBs2DPAlZIcOHfIfFRUV/mPjRt9arsjISDIyMhibmUlGRgZxcSlomp7itHm8lDpclDiclDhclDhcFDtclDpcFDmclDlclDvdeFQobnrtMDPQp+nwLRkI00Gc1kmsUkeUt4xw9yEivcXEWKuJtuYSU7aGSOrQ4EWvj2kaKRvQNFo2gNDQ3mg0AWw/JYQQAjjBOmAejwen04nH48Hj8ZCUlITRKFMVwmdkZhS7y+rZlF8TWAJmioCkoVCylciSbfSJ7sPemr1sLt/MBcMvIOH11ykwmSjV1JIeP5DdrCE0ohpbPaiKerjfmgZGRIQco6PuIyIigqFDhzJ0qG9a1Wq1kp+fT35+PgcPHqS0tJS6ujq2bdvGtm3bADCbzWRkZBw+kpPJDgk/ah9ur0q505eUlTh9SVrpkUfTaw0eLw0eaPAYOEg8EA8MgV8McmnwEKnWEuOqIqammuiaKmJYQwz/JkaxkGYOJSM8mdhw36L/8LABGAwBPIghhBCnoaATsHfffZff/va3jB8/nj179rBlyxZuuOEGvvrqK/7xj3+QnZ3dEXGKHiQnI5p3fixgc35t4BdljIOSrZC/npEJI9lbs5dNZZuYnDmZ9ORkClwuSjW1DDf2B6D8wHYS+11AbS3E1tdSExrJrIKKY/fRjYWGhjJw4EAGDhwI+NZVFhYWkp+fz6FDhygsLMRut7N79252794N+Nadpaamkp6eTkZGBunp6S1GoXUahRSTgRTTsUemGtweShwuyo5I0lr8dLood7rwqFpqlFhqiGV/Wzey+46wsnpiqCSGfxOntZJs0JFqDiMzLJ5eERn0jsomwiCj5UKI01vQCdjs2bN56qmn+O1vfwvA5MmT2bZtG7fccgsjRozAYglwG5omL730En/9618pKSlh8ODBPPvss8csc7Fq1Srmz5/Pjh07SElJ4Z577mHOnDkt2nzwwQf86U9/Yv/+/fTu3Zs///nPXH755UH1q6oqDz/8MK+99ho1NTWMGTOGF198kcGDB/vbvPbaa/zzn/9k06ZN1NfXU1NTQ1RUVFDv/1Q0MiMKgJ8La3F5vOi1AWytkzkWfnjZtxB/4B9ZtnsZm8o3AdB73DjWrVpFucaCzqIlPDae+qoKUnrXULMJxu/7mVv32Mkwtt2P1+nBa3PhtbrxWl14bS7QajBmR6IN7Z4baBuNRnr37k3v3r0B36hzSUmJPyErKCjAZrP5pzCbJSQktEjIoqOjj7teK0ynpa9OS9/Qo6/X86gqFU53U2J2eNqzOVErarRT6nBjVxUalHAaCCefXr4nDhqbjhoAF7CbEBpJ0NpJMiikms1khMaSERLtSxiNelJMekK12pP8FIUQovsKOgHbtGkT/fv3b3EuOjqaf/3rX/zjH/8I6l7Lli1j3rx5vPTSS5x99tm8+uqrTJs2jdzcXDIyWhfRzMvL46KLLuLmm2/mrbfeYu3atdx6663Ex8dz5ZVXArB+/XpmzpzJo48+yuWXX85HH33EjBkzWLNmDWPGjAm43yeffJJFixaxdOlS+vXrx2OPPcbkyZPZvXs34eG+aR+bzcaFF17IhRdeyIIFC4L9KE9Z2XFhRJh0WBrd7CqpZ2ha5PEvai7IWp7LyEhf0rGrehdWl5XMc85B8913ODQuqkoqGDz+PP778TJK9/0XrxKGAoR7DicZDf8txvpTGd4GX7KlurxtdAgooE8Nw9Q3GlPfKAwZESi67rkPo1arJS0tjbS0NMaNG4eqqlRVVfmnLfPz86murqa8vJzy8nL/OrLQ0FDS09P9R3JyMnp98EmnVlF8C/mNeqDtaV5VVbG4PYfXotmtHKwvo8BWS4nDQZlLQ4U3FBsh2DBx0GPiYNOoGdUNQEOL+0VqVVJNRlJNJn85Dl9yZiDVqCfZaECvkYcBhBA9k6Kqqnr8Zq1VVlaiKAqxsSdebXvMmDGMHDmSl19+2X9u4MCBXHbZZTz++OOt2v/hD3/gk08+YefOnf5zc+bMYevWraxfvx6AmTNnYrFY+OKLL/xtLrzwQqKjo3nnnXcC6ldVVVJSUpg3bx5/+MMfAN+UUGJiIk888QS33HJLi7hWrlzJueeee0IjYBaLhcjISOrq6oiIiAjq2u7s2jd+ZPWeCh6ZPphrx/YK7KLnR0L1fvjNMi7c/ixFDUW8OvlVxqWM4+WHHqIMmOgcxNAZo/n743NBUTD3Hk+53sZ410DCQ7wMHD+eus/zWt9bq6AJ1aMN0aMJ1eFpcOEus7Voohg0GLOjMPWNwtgvGn18z1hP1qyhoYGCggLy8/MpKCigpKQEj6flVk1arZbk5GTS09NJS0sjPT29U//eqapKpbWQA7X7OWgpIt9aQ5HDTplLQ5UaQxVxVBFLo3L8z14BEg2+0bJUo4HUI3+aDKQaDcTqtfLEphCiQ5zs93dQI2C1tbXcf//9LFu2zPcYPb7Rr6uuuorHHnssqOTD6XSyceNG7r333hbnp0yZwrp169q8Zv369UyZMqXFualTp7J48WJcLhd6vZ7169dz5513tmrz7LPPBtxvXl4epaWlLfoyGo1MnDiRdevWtUrAguFwOHA4DpcNCHbKtqcYmRHF6j0VbDpUE3gCljnWl4DlryMnIYeihiI2lW1iXMo40jIyKMvPp1xTh7HeQOqAQRTtyqV54EuvaolojPAnX+GT0jEPjkUT6ku4FEPrL2KPxUnj3hoce2to3FeLt8FF465qGnf5NvvWxZowDYjBNDAGY6/Ibjs61iwsLKzFOjKXy0VJSQkFBQX+w2q1UlhYSGHh4Y3KIyIi/AlZWloaycnJ6HQn9HzOcSmKQnxYOvFh6Yw54rzH48Bm20dDwy4aGlZS1nCQQw2VlLo0TUlZHNXEUkk81cRSpcThRu97kMDpYhO2NvszaRR/UpZyRHKWbmwubqvHFMgUuRBCtLOA/ytbXV3N2LFjKSoq4uqrr2bgwIGoqsrOnTtZunQp3377LevWrSM6Ojqg+1VWVuLxeEhMbFk2IDExkdLS0javKS0tbbO92+2msrKS5OTko7Zpvmcg/Tb/bKvNkettTsTjjz/Oww8/fFL36AlGZvj+HmwuqA38ooxxsPktOLSOkRNu4bMDn7G5fDMA2aNGsTE/n3KNBfu2fAZPvICiXbl4m56C1KElzBUGQPi56URO7XXc7rQRBkJHJRI6KhHVq+IqtfqSsT01OA5acFc10rC2mIa1xShGLaa+UZgGxGLqH402vPuXXNDr9f4nJsE3+lRTU+NPxgoLCykrK8NisbBjxw527NgBHB4lS01N9SdlUVFRHTqSpNUaCQ8fTHi4b41lX+AcwOmspsG6G2tDUzFZ6xqs1j24PHbqifAnZ5XNSZqSRK0mhUpiqfKaafSqx62VFqfXkWryTXGm/WIELdWkJ06vk1E0IUS7CzgBe+SRRzAYDOzfv79VYvLII48wZcoUHnnkEZ555pmgAvjlf9hUVT3mf+zaav/L84Hcs73aBGvBggXMnz/f/7vFYiE9Pf2k7tkdDU+PAuBQlY3KBgdxYQGUKckc5/tZvJmcaN8ozrbKbbi8LtJ69QKgWmnAWmih3/UTWLHkVTwcTsAAws5OIWJKZtDxKhoFQ0oYhpQwwiem43W4ceytxb6zmsbd1XgbXNi3V2HfXnV47VifKIx9ojBmRqLou/8oiqIoxMTEEBMTw/DhwwHfiGxxcbE/ISssLMRms/n//MMPvsKsISEhpKWl+ZOylJSUTqn7ZzDEEGMYS0z0WP85VfXS2FjclJjtocHq24bJav0aVXVB06yrCx01xFJJHDWadCyGftRo0qlS4ij3hFHi0mL3qlS63FS63Gytb3sXD5NGOTx6dkSClnbEyJpZRtGEEEEKOAH7+OOPefXVV1slXwBJSUk8+eSTzJkzJ+AELC4uDq1W22q0q7y8vM0+mvtpq71Op/OvRTtam+Z7BtJvUpKvdlVpaSnJyckBxRYoo9F4WtRMizTr6ZsQxt7yBjbn1zJ5UACfW3QviEgFSxHZlgrCDeHUO+vZU72HwXGDCdMbaHA5qVSdpHt09D1zLNsrnIBvCrLOWEPqr85pl9EKjVGHeUgc5iFxvtGxogbsTdOTrqIGXIW+o35lIeg0GHtFYOwThalPFPqUMJQesjjcaDSSlZVFVpZvH01VVamurqaoqMifhJWWlmKz2dizZw979uzxXxsbG0tqaqr/SExMPKEF/sFSFA1mc9r/b++94+yq6/z/52m33zu9ZpLMpAcCJCGAEIorUkRQcF1QV4Xlt66sZUFcxbL2pbr6dRVBcS2rrl1RUFGCFKXXQCCV9EzJ9NvLKZ/fH+fcO/dmSiYhmckkn+fj8Xm835/P+ZxzPvfMnTmv+ZT3h2CwjYb6c0vljmN6G5a7G5Wn0luoSm+mKbsR4bwCuT9VXEcAea2FdHA5Cd8ShrW5DNBEn4jSbWp05tzQHDlHsC2bZ9sEvWh1Xi/abG9Yc5Y3xDkrYNDm91Hv0ys2V5dIJJJJC7BiuIbxWLZs2bhDh2Ph8/k4+eSTWbNmTUWIiDVr1vDWt751zHNOP/107r333oqy+++/n1WrVpX+8J9++umsWbOmYh7Y/fffzxlnnDHp+3Z0dNDc3MyaNWtYsWIF4M4de+SRR7j11lsn/RmPdVbOqfEE2NDkBJiiuL1g636JuutJljcs52+df+OF3hc4vv54Zne0s2HzZnrVOJmnNnHSeRfx4k9/A8CW4Wdo6JjF8YfhJaeoCr7ZUXyzo1SdNxc7kSf36jD5V4fduWOJAnkvnwCUoE5gXhW+eVX451VjNIVmjCArLqypq6srRe03TZOenp4KUTY8PMzAwAADAwO89NJLAKiqSlNTE7NmzaK1tZVZs2aV/uGZClTVIBJZRCSyqKLccfIVG5an06+SzrxKJrOTgN1NINVNHfdVnKNpEcKheRjVC8n4j2NYb2dIaWGvE6Mrb9GZL7AnV6Azb5KxHQZMiwHT4qVxetF8ilJaLNDqibKRxQPuqs6ILsNuSCTHEpMWYPX19ezYsYO2trYxj2/fvv2AV0Ref/31vOc972HVqlWcfvrp3HXXXezatasU1+uTn/wknZ2d/PCHPwTcFY+33347119/Pe973/t44okn+O53v1ta3Qhw7bXXcvbZZ3Prrbfy1re+ld/97nc88MADPProo5O+r6IoXHfdddx0000sXLiQhQsXctNNNxEKhUr7XoLbQ9bT08Orr74KwLp164hGo8yZM4fa2toDehZHIyvnVvPzZ3fzzI7ByZ/kCTB2PsaKlW8tCbB3H/duZrePCLD005toPffvsXBDTPSkt9CgzDpMn6QSLeYnvLKJ8MomhBBYfdnSRP78tjgia5F9ZYDsKwMAqCEdX3sV/nluMprDM0aQgTuXrBjGokg6naarq4vOzs5SymQydHd3093dXaqn6zotLS20traWUl1dXWlLpalAVf1Evcj85dh2nky22GP2aqnnLJvdiW2nSCRfguRLwK/xA81Ai+LjjFA74dB8Qg3zCAUXYAY6GFJm0WNp7mbqOZOufIHOnLstVE/epCAEO7IFdmQL47azStfcMBuloU2jFBdtlt9Hi1wwIJEcVUxagF144YV8+tOfZs2aNfh8lROQ8/k8n/nMZ7jwwgsP6OZXXHEFAwMDfPGLX6S7u5tly5bxxz/+kblz3Tk8xcCTRTo6OvjjH//IRz7yEb75zW/S2trK17/+9VIMMIAzzjiDn/3sZ/zHf/wHn/nMZ5g/fz4///nPSzHAJnNfgI9//ONks1k+8IEPlAKx3n///aUYYADf+ta3KibUn3322QB8//vf56qrrjqgZ3E08rp5riBfu3uYbMEm6JvEf/hzV7t2zzMsP+9T7vm9axFClMR/r5qg0F2HlS1gK64As+3xX2yHE0VRMBpDGI0hIqtnIWxBYU+S/LY4+W3DFHYmcDIWufUD5Na7gkwJ6vg7qvB3xPC3V7lDltrMEWTgxhcr/nMC7tDl8PBwSZR1dXXR1dVFoVAoTfov4vP5aGlpKaXpEGXgTfwfQ5g5ToFMdieZ9NZSb1k6vZVMZqvbm+bNOWOfjRfC/hZODs3nrHAHoap5hFrmEQ7NQzWa2GvadOUKIxure5uqFzdYH7Zs4l7akM6N2+Y6Q2eWF6i2xe/FRSsTas1+A/8UP0eJRHJwTDoO2J49e1i1ahV+v58PfvCDLFni/tFav349d9xxB/l8nmefffaonFB+ODla44CB+1I+45YH6Y7n+PH/dxpnLpzEvoBCwJcXQKaf7JX3cMbfrsMSFn/6+z/R6G/k5ptvxnEcrsifQWyx4Ds73Phv0Y3Ps/LkS/i7j73vMH+qA0PYDoXOlCfI4hR2JBCFythcik/DNzfqibIqfG3RGTGpf384jsPg4GCFKOvu7sayrFF1DcOoEGUtLS1TOnw5GYSwyeU6SWe2ueIss5VMehvpzFZMc/xeXlUNEgq1Ewp1EAq5oiwU6iAYbMcw3N/5tGWXRFmXt6F6eU9aV75A1plcyMZ6Q6fVb9BSJtJavNTq99HsNwjJnjSJ5DUzZXHA2traeOKJJ/jABz7AJz/5yYrVh+eddx633367FF+SChRF4fR5dfzmhU6e2NY/OQFWnAe24R6Ce55lad1S1vWv44XeF7h43sW0tLTQ2dlJrxJHedkHEVCEuzLuSETRVPxzYvjnxOD1s0uCrLAjQX57nPz2BCLnrrjMbxl2T9IVfG1R/O1V+Drcc9Xg4YnLdThRVZX6+nrq6+tL88ls26a/v780VNnV1UVPTw+maZYi+hfRdZ3GxsYKUdbY2DglE/3HQlE0gsE5BINzoO71FcdMc6hMmG0jk9lOJrONbHYXjpMlldpAKrVh1DUNo84TZu2Egh0cH+rglFg7weAcNG1klakQgiHL7UXryptuKve97aHyZas6X0qNPR8N3OHOoihrLhNozT7XNvkN6gy5cEAiOZwc0F/1jo4O7rvvPoaGhtiyZQsACxYskPOdJOPyuvmuAHty24HMA1sNG+6BnY+zfPFprOtfx9retVw872La2tro7OxkLwPUG+7KPR2NmfKaKBdk0bPb3BWWezMUtsc9QRbHSZkUdiQo7EjAw7hhL5rC+Npj+Dti+Nqr0Ktm5kpaTdNoamqiqamJ5cuXA25PWVGUFXvJenp6KBQKpaHMIoqi0NDQQHNzc0UKhaZ31wLDqKG66mSqq06uKHcck1xujyfKtpFJbyOT3UEms51CoQ/THCAeHyAef3bUNf3+ZoLBuYSCcwmF2gmG2mkPtrO0Zg6aNnp7LyEEA6ZNT94VZsW9OrvK9u7s8hYNFIc7N04w3GkoCo0+vSTIWvwGTT5XsDX53LJmn05Ml7sNSCQHw0H9W11TU8Opp556qNsiOQo53ZsH9uLuYdJ5i7B/El+5YjywXU+yYvX7+BGUArLOnj2bp556ik1GD6rtzkXUROVwSmpokN3r1yEcB+G4PWNCCBACgQAB/nCYloWLidZOolfuMKKoCr6WML6WMJEzWt1J/f1Zt4dsR4LCjjjWQA6zJ43Zkyb9pDvBXav245sbwz83hm9uzJ3YP8PmkRVRVZXGxkYaGxtL8ckcx2FoaKgkxoo9ZplMprTfZXH1JbjR/MsFWVNTEzU1NVM+r2xfVNXwerg6gHMrjllW0p1rltlOJrODbGa762d3YFkJ8vke8vkehoefGnVdn6+RYHAOoeBct1cu5Aq1quAc6qPVLIuOOgVwfw+StuMJskJpM/XyjdV7Cib9BQtTCHdYNG9O+BkDqlISZE0+g0afTpPfsz6DRs+XPWoSSSUzb1xDMqOYXRtiVnWQzuEsz+0c4uxFDfs/qel4CFRBLs4K4YqsLUNbSBaSLF26lIULF7JlyxZe1t3hKp2Rl+zW557ivtu/Sj6TnlT7ovUNtC5ayqzFS2ldtJSGuR2o0zjvSFEUjIYQRkOI8CluPDo7WSC/I14SZWZXCns4T3a4j+yL7kxwxafhmxPFN8cbupwdnZHDlkVUVS2Fw1i2bBngbfadSJRWHxfT0NAQiUSCRCJREafMMIxSb1t5CgQC0/WxKtD1KLHoMmLRZRXlQggsa5hMZgeZ7E6yFdYVZ4VCL4VC75g9Z7oeIxicTTAwh2BwNoHgbFekBWYTCLQS0w1iusbi8PjPwXQEvQWTvXmT7oInzDxxtjdvsrdgsddbPJBzBDtzBXbmJl4IoytQb7hirMFn0OjXaTB0Gv0GDT6dRp9rGwzZqyY5Njjozbglh4ajeRJ+kY/+4kV+/fwe/vX187nhwiX7PwHgJ1fA5j/B+Tfypu4/sCe1h2+98VusnrUax3F49NFHeegvDyIUiFl+xJbHqK1rY3DA3eOwtrWNaL0r9hRFAUVxhykVBUVRSA4O0L9zx6i5Y7rfT8uCxbQuWkrbkuNoXbwUX/DI2pTbyVsUdicp7EyS35mgsDOByNuj6umNQXxzYq4omxNDb5w58cgOhFwux969e0uCbO/evfT29o452R+gurqaxsZGmpqaSrauru6ImvA/EaY5TDa7yxNlO8lmd5VSvrB3P2erBAItBAJtBANtrjgLtHlCrQ2/rxFFmXyvYdZ2KoRanyfM9hZMevOWe6zgxkg7EHyKQr1Pp96n02B4wszL1xs69T6j5NcaOsZR+L2WHPm81ve3FGDTzLEgwH713B7+/Zcvsnx2Nb/94OrJnfTYf8Oaz8Lii/hUWwf3bruX95/4fj604kOlKi/f/VvWPP4UZEyc/o2l8hUXXsI577kaTZ94snYhm6H71c10bd5A1+aNdG/eOKrnTFFUGjvmMWvJ8bQtOZ5ZS44jVFU96c8+FQhHYPVmSmIsvzOBPTB6bo/i19yAsnOipcCyWuTI39PyYHAch4GBgZIw27t3b2nfy7HQNI36+voKYdbQ0EBVVdW0D2MeCLadJZvbUybKdpPL7iab2+0tCBg/mj+AovgIBFpccRaYNZKCbQQDs/D7m1CUAxeqpiPoK5j0mRa9eVeo9RZMej3bX7DY6wm4lH3gC2qqdY16b5izzhNldT6dWkPbJ++WhVRV9rBJXjNSgM1wjgUBtmcow5m3PoSmKrz4ufOJTGYe2J5n4X/OhUA1v7j0Nr701H9yWvNp/M8F/1NRLfvKK3znP2/DdAbQNJ0LPvgRlq4+56DaKRyHgc7ddG3aQOfGV9izcT2JvtE9CjWtbcxafJw7bLn4OGpaWo+4P+Z2qkBhV9LrKUtQ2JNEFEa/2LQavyvG2lxhZrRGUCcTr22GUpxDVuwlK9pCYezhM5/PR0NDQ2mOWlGYRaPRI+5nvj+EcCgU+sjm9pDL7iGb3e35u8nmOsnnuxBidE9qOYqi4fc3E/C3euKsBX+glUCg1StrRdcjr6mdWduh37To84RZf8Gir2DRZ5oMFNwVnv2eHSgUQzEfGH5VodbQqdE113rCrNbQqTY0qnWdGkOjxnBtta5TrWvosqdNUoYUYDOcY0GAAZx124PsHszy/X86hb9b3Lj/E2wTbpkLZprN//gT/v7xTxDSQzz+zsfR1EqB8D9X3kzS7uGMM1Zz2gcuJ/W3v5H4058Qpgm2g7BtsC2EZSMcGywbNRwmeNJJBFeuIHD88ai+sXuCkgP97Nn4Cp0bXqFz4yv07945qk4wGqPVm0M2a/FxNM1bgD7O9aaL0mrLXQlPmCWw+rKw72+/6q24bItitEVc2xRC0WdOL9CB4jgO8Xi8JMaKqb+/H8cZ+/UeCARoaGgYlWKx2IwTZkUcxyKf7yGX6ySX6yTr2Vxuj2e73c3O94OuRz2R1oI/0OJaf4sr1vwt+P1N6Hr40LRZCIZMmwFPsA16/kDBYtDbHmrA9PyCzaBpUXgNr7yoplJlaNToOlW6RpWhUa1rVBuuQIvpmlvupZjhWV2TAXKPQqQAm+EcKwLs4796kV88u4f3nz2PT160dHIn/egy2Pog9gU3s3r7D0mbaX51ya9YXLu4otr//fNPGdab+LvV0GTvouvLX2OwZgm25sfRfNiqD1vz4ag+bM3AUX2ojkU400043U2k0Ed06QKCK5YTWrGC4IoV6ONsq5VNJenatJ7OTRvo2rSenq1bsM3Kl5Km6zTOW0DrwiWuMFu4hEjtgW3TNRU4OYvCniSF3Sm3p2x3Aic5xgtWVzBaIvhmuYLM1xZBbwjN2FWXk8W2bQYGBujt7aWvr68kzAYHBxnvz6bf7y/FPmtoaCjZ6urqGTPHbDyEsMkX+sjnusgVU76LXK67JNosa+wh3n0pijQ3NZVswLM+fxM+o/aA5qNN7jOI0t6dQ5bNYMGznmgbtmyGTIth02bQcu2QaZE8iGHRfQmoClFdI6ZpRHSVmCfMopprI7pKVNOI6hoRTSWia0Q9G9FUIppGWFPxq8qMFflHG1KAzXCOFQH22xc6ue7nazmuJcYfrz1rcif99b/gwS/B0kt4X02QJ7uf5DOv+wyXL768olpRgJ2c/BOhFx/g2ZNvIBucxGrLMvy5IcLpLiLpbsLpLqojFnUd9YSOW0Rg6VICS5eiNzWN+sNnmSa927eOiLLNG8jEh0ddP1rf4AqyRUvc1ZbtHfudozbVCCGwE+7QpdmZpLAnRWFPCpEbPYFaMVSMljDGrAi+1gjGrIjbU3YMRFi3LKtCmBXTwMDAuMJM0zTq6upK4qyY6urq8PtnZky3sbCstNuLlu8mn+uusLlcN/l8D7admtS1FEXH56t3BZqvEb+/2cs34vM1eLYRn6/2oOalHQimIxi2LDd+mmmXto4aMivLEl55uU1Y9qiO5teCrkBYc0VZSFMrfDdf9N25bqGyY0FVJaipBFWFQEVeJaApBFQVTYq7SSMF2AznWBFgA6k8q258ACHgyU+eS3PVJEIB7HoKvnc+hOr4xhs/wl3r7uIt89/CjWfeWFHth1f/mKSvlRNe/jZdLasZqFtGMGrQMDuK7tPQfWrJGj4N3adRyFkMdqUZ6EqRGhxnYrJwCGb7SqIsSpza1jB1i2YRXLQAX0c7/o4OtOrqkVOEYHhvN92bN9K1eSNdmzfQv2vnqNWWmmHQ2DGflvmLaF64mJYFi6lqHC3wphshBPZAjkJJkCUxO9OjtlMC3J6y5rAryFojGK1hjObwUT2nrJyiMOvv76evr4/+/v5SGm9FJkA0Gi2JsaKtq6ujurp6Ri0AmCyWlSKf31uKc5bL95Tl95LP91Io9MOkZ3ep+Hx1+H2N+Pz1+Ix6fL56fP4GfEada331+H316HrVIe9V2x+OECQ9QZa0HRKWTdJLCdtxrZfStkPKtklaDinLJmU7JG2blGVPeiuq14qhKARUBX+ZKAuobs+bT1XwKyp+TcGnuGVuuYpPVfApCkbJqhgKGKqKT1HQFQVdAV1VMEr5kaQpoCmKl1xfBe+YgqqAAqiKguZZFXfzFAV3lXvxz6fipZLvfSbfIf59mrKtiCSS10JdxM9JbdWs3T3Mw5t6ecepc/Z/UusK0IOQGeAkn7vbwkt9L42qlrfdFX8755xPItaBZqhc8uHlNMwZJxrlvudnXTE22JVioCvNwM5hBjpT5PMq2VAT2VATfQ3LS/WVbRahl/cSSd9PON1NVElQW++jam4dgXkd+Do6mN8xj6Wnn4ViGBSyGXq2bikJsu4tm8ilknR7Ky+5z71uMBqjecEiWhYspnn+QprmLZj2FZeKoqDXB9Hrg4ROcufuCUdgDWQxO1MUOlOu7UohcjbmnhTmnrIeDgX0hqA7hFkUZS3ho3L1pa7rpVhj5RTnmJWLsqJQS6fTJJNJkskk27dvrzhP0zRqampKgqyYamtrZ+QigCK6HkHXI4TD88et4zgWBbOfQr63JMry+R4KhX7yhV4K+T7XFgYAd3FBodAH++lcUxQdw6jF56tzxZmvDqPk13rHajGMGgyjDl1/7c9ZVRSqDJ0q47W9bi1HkHEc0rZNynJKYi1ju37adsiU5TO2Q8bxrO2QtR2yzojNOaLk58vEnSkEpu0G7GX/U/5mDF9e3MZ7Wqc38Pa+SAEmmTLesKSRtbuHeXDjJAWY7oPZp8L2Rzgp6W5ltCOxg+HcMNWB6lK1VFDBByRi7tZEr3/X4kmLLwB/UKdlfhUt80e2dxFCkE2aDHSlGOxMM7AnQf+2QYb6C1jopCOzSEdmVVxH7csT3tFNJP0k4fTdhHN7qa7RiM2pJzBvHkvmzeOkN12G8a/tJDMpul/dTM+rm+l+dRN9O7aRTSbY/sKzbH9hJLhmtK6BpnnzaepYQFNRlMVGb0MzlSjqSLDY0PIRUWYP5UYEWXcasyuFkzKxerNYvdlS0FgANepzhzC9XQCMljB6ffCoHMJUVZWamhpqampYtGhRxbFsNlsSY8U0ODjIwMBAad/M/v7+Udc0DKMkzmpraytSNBqd8T1nqqoT8DcT8DdPWE8Im0JhoCTKCoX+UsoXKvOWFUcIqxTEdjK4gq0Gn1EUZTUYvhoMvdrLV4+UG9UYRrUn2g59r6+uKsRUd74Yh3jU2haCnOOQswV5T5zlHKdUlnMcCo4gL1yxVnDceq7vWlMITEdQKFmnIm8KgS1cazlgCYFVzHvHbMGIpbLMwf277ACOAAeBI0avIxoP5QjcsE4OQU4zx8oQJMC6PXEuuf1Rwj6N5z97Hn59En+kHrkNHroRjr+MS/QBdiR28M1zv8nZbWcD4AiHz33s+zSnXPG17OxZnPOuxRNd8TUhHEFyMFcavhzYFWdgV5zhQRPHGfsXXLNyhDM9hNPupP9wpptY0KSqvZHAwoX4Fy5En9dBwtDo2b2Tnlc3sXfbqwx2d8IYv57RugYaO+bTMLeDxvYOGtvnEWs48oYvAXdOWVcKszuF2eWKMmuMGGUAaApGUwijOVyR1KhxRH62w4njOCQSCQYGBkal4eHhceeagdsTV11dTW1tLTU1NRW2uroaXT82/+92nAIFcxCzMEChmMyBkbw5gGkOUSgMYppDk56rNhoFXY9i6NXoRhWGXuVaowpdr8LQo+h6FboRQ9djGLpr3RRFVY+suaFHOkIIBCNCTJSJMm/zOWBkKPNQIocgJTOG41tjNET99CXzPLtjiNULJtEdPNcL3LrjMU48/XJ2JHbwYt+LJQE2nB8Gxf0VC7QKVv/DAh7rfIzne5/Hr/kJaAECeoCgHiSoBwnoAQJagMZQI7Ojsw/4xa6oCrH6ILH6IO0njrTfsR3ifVlXmHWmGOxOM7g7znB/AVsPkIi1k4i1V1xLtQuE1u0l9NSrhDOPEcruJRaF4+bUsGrJUtRzLyYZDjCQTtK7fSs9215lqLuT5EAfyYE+tj77ZOla/lCYhrkdbmrvoGF2O3Wz52D4p3fbHS3mIxirJbiktlTm5C3Mngxmd9oVZp4vCrYn0iqD4aohHb0pjNFcJs6aQqiBo/fPl6qqVFdXU11dzfz5lUN1lmURj8cZGBhgcHCwlIrizLKscXvOwN03s9gjV11dXeFHIpEZ33s2Hqrqm1SvWhHbzmOarhhzhdsgpjWMWRjCtIYwzWEvDZWsbacBgWUl3BWh4+91PkE7A+h6dCRprtW8oVtdi6DpYc9GymwYTQuhlWxwyue7TQeKsk/f1viZI46j9y+Y5IhDVRVev6iBXz63hwc39k5OgM06GTQ/pHs5KdjCPVTOA+vP9rOu+a9kjCRz/s7H//fAD3m+9/lJtWdWZBarW1ezetZqTms5jbBx8LGJVE2lpjlMTXOY+StH4pzZtkN8b9YVZN1phrrTDOxJEu/L4uAjFZ1NKjq78mKOQ+CFIYKP7SSU7SVsDVFfH2BReweRN59FujrMsLDp69xN347tDOzZST6TZs+Gl9mz4eWR6ygK1U3N1M9up35OO/Wz51I/Zy41za3Tut+l6tfxexuJFykOYZrd7qbj5t4MZk8aqz+Lk7EobI9T2B6vuI4W86E3hTCaXEGmN4UwGo9uYQZuD1dxPti+2LZNPB5ncHCQoaGhUdY0zdK+mTt3jo5pp+s6VVVVJfG3b4pEIsdMb6Sm+dE0N37ZZHEcE8uKY5oJLGsY04y74swaxjLjWFYS03Ktm09gemLNFW/gODkKhZw7p+01f4ZQZVKDaFoIVQuiacHKvBpA04KoagBV86OpQVQtgKYGULUAqupHVf1onh1JvsO+CvVoRQ5BTjPH0hAkwB/XdfOB/3ueeQ1hHvzo6yd30vffDDsfZdMbP83bt/6IsBHmsXc8hqZqPN71OO9f8/6K6n7NzwXtF2CoBhkrQ87Kucl2bdbK0pnqxHRGZpjqqs6KxhWsbl3NmbPOZFHNosP6onFsh0R/jqG9GYZ60gz3ZBjck2CoJ804QdkBUByLYLafUK6PsK9AdX2Aqjm16G1hzJjOYF8PfTu30bdrJ9lEfMxraIZBbcssatvmUNc2m7q2OdTNmkN1cwvaETY8JUwbszfribI0Zk8Ga28aOz7+Q9KqfOiNrhjTG925anpj8Kic+H8gCCFIp9MMDQ0xNDTE8PBwhR+Pxycc2gR3YUBVVVUpVVdXV+SrqqqO2SHO14rjWNh2yhVn+ybbtbaVxrLT2FYKy055Nu2dl8a2M6VeuKlEUXRPiBmoqg9VMVBUH6pqeOU+VEVHUXW3rmJU+oqGouj72JFE0UcBRUVB9Wx5fuTvtVK+DlJx/Zrq04hEFo3Z/oNFhqGY4RxrAiyRM1n5xTVYjuCRj72euXWT6HV66CZ45Fbs4/+eMwqvkLEy/Potv2ZRzSLu3Xovn3r0UwDois5lCy/j/Se+n6Zw04SXzJgZnt37LI92PsqjnY+yO7m74nhjqJHXt72e189+Pae2nIpfm5pYTcXJ/8O9GYb3ZhjuSTO0o5/hnjSJFDhi/CEFxbEIWXEi/gKxOj/h5gBavYZFitRgJwN7dtK/ZxdWfuywG6qmUdMyi9pZbdS2zqa2dRa1rW3UtLbhDx1hG5LnLMy9Gay9GVeY9WYw92ZwEuMLMzWsozd4wqwh6PoNQbSawFG5SfmBUuw9i8fjDA8Pj0qJRGK/Ag0gHA4Ti8WIxWJUVVWNspFIRIq0w4gQAsfJYduuILPsDLaVwraz2E4Gx855Qi2L7WRda2dw7CyOk8d2cjh2FtvJe9fJ4jg5HKeA4+RLaX/bVh1pLFn8n8ya9c5Dek0pwGY4x5oAA3jHXU/w5LZBPn/JcVy1umP/J2x7BH74Foi28M/LVvNUz9N87vTP8fZFb+cHL/+Arzz3FQD+cNkfmBObxOrKMdiV2MWjnY/yWNdjPNPzDFkrWzoW1IOsbl3N62e/nrPbzqYmUHNQ93itCEeQHMq5Q5rb9jKwpZt4d4pEwiEtQghlgpeacAj7LKLVOqF60MI5HHOAXGovyYFuhrp2Y+bHn7ASrqmltmUWNa2zqG2dTU1LK9XNrVQ1Nh1RvWZOxsTsy2J5gszqy2D2ZrCHJtiEWnNDbRieKNPrg27ojPogakhOiC5i2zaJRKJCpBX9Yn6ieGflFEVaNBotibWiH41GiUajBAKBY2a4cybiOBZCFDzRlkc4BRzHxHEKCGF6gq3MFxbCMRHCQgirlHeEiXAsBDZCjCREZV7ggHA8K0byxbJir58on3o/Im9mtb6D2trVh/QZSAE2wzkWBdhdf93KTX/cyJkL6vnxP5+2/xMKGbhlDjgmXz/vo3zn1V9y6YJL+dLqL/Ffz/wX/7v+f7nyuCv591P+/ZC0L2/neabnGR7a9RAP736Y3uzIknVVUVnesJyz285m9azVLKpZhHoETHR1HEFiVx99z7/K4OZOhjsTJOI2aSdINtiAo008/BYIqoTrbHy+OApDWIUBcqk+UoPdY0b2L6KoKlWNTdQ0t1LTMovqllZqml1xFqtvmNa5ZuU4BRurKMz6Mq7fl8Hsz4E1fsBPNaSX4qDpda4w0+uC6PUB1MlsKn8MIYQgm80Sj8dLQm1fm0wmse3J9Zzoul4SY8UUiURKtugHg0Ep1CTTghRgM5xjUYDtHEhzzpcfRlMVnv7UudRFJjG8990LYPeTPPp31/OvO35FbaCWNW9fw2cf/yx/2PYHPnryR7lq2VWHvK1CCNYPrufh3Q/z8O6H2Ti4seJ4baCWM1rP4IzWMzi99XTqg0dWoD8nnSa7cRPDL25iYMMehvcMkog75Py1ZIMNZIP1mEZkwmtohkUoksLwJVGUYaz8ALl0P+mhvViF8XuWVE0j1tBIdXMr1U0t1DS3UNXUQnVzC1UNTUfEhuXCEdjDeVeMeaLM6s9i9WcnnGcGoEYMV4zVBTxRNuKrQSnOxkIIQSaTKS0ESCaTJb+YTyaT5HKTXz6oqmpJkBVTOBwe08peNcmhRAqwGc6xKMAALvnGo6zrjHPjZcv4x9Pm7v+Ev3wR/vYVzBMu50J7G73ZXm456xbufvVunup+ipvOvIlL5l9y2Nvdnermod0P8XjX4zzd83TFUCXA4prFnDHrDF7X8jpWNK4gqAcPe5sOFCefJ795C7n168mtX09yw6sM7x4iq1eRCdaTDbjCLBtsIO+vhnF6+IQQKKQJhNMY/iSKEscxh8in+0jH+3GsCcJoKwqR2jqqG5upamymuqmZqqYRPxirmvYXpVOwsQZyWP2eKOtzhZk1kMNJTxwiXA3paLUB9NoAeq0rzLTagGtjfjnnbD+YplkSY+UplUqRSqVKfjab3f/FylBVlXA4TCgUIhwOl1IxHwqFKvxAIHDUhuWQvHakAJvhHKsC7FuPbOWW+zZyxvw6fvK+1+3/hFf/Aj9+G1TN5s43fJA71t7B8oblpMwUrw6/yrfP+zZntJ5x+BteRsEu8GLfizzW+RiPdz3OhsENFcd1VeeE+hM4pfkUTmk+heUNywno0xuXazyEaZLfto3cK64oy23YQG7DBuxsgWyglpwnyLKBerKhBnKxVjJGNQ5jDzEKIUCk0H0p/MEUmprEsYcoZAfIJvom7DkDMPwBqhqbiDU2UdXYRFVDUaC5eV9geoWtk7M8cZbFGnBFmTXgCjQntZ/9WzQFvdrvCrKaQMnqtQG0Gj9q+NgLPHuwWJZV2s6pKM7S6fSYNj/O4pOJUBSFYDBYEmZFPxgMjukHAgGCwSCGIX+GxwJSgM1wjlUBtnsww1m3PYSqwFOfeiMN0f0MQ+ZT7jwwYdP3/oc5f83VWMJCV3QsYZVWRU4ng7lBnux6kse6HuPpnqfpSfdUHDdUoyTIVjWv4sT6EwkZR9bqwnKE41DYsZPcBleU5TdsIPfKeuy4G95CoFDwxdzeskA9+eb5FOrmkgnUkbGD5Mbb41wIEFkUNUEglEHXU0AcqzBMPtVPNjU85g4A5QSiMaoamqhqaHRFWkMTscZG1zY0TmsAWqdgYw/mRoTZoJvsgSzWUN7dR2UCFENFqwmg1/j3sQG0aj9qRL7cDwbTNMlkMqTTadLpdIVfXlZMByPYimiaVhJjwWCw5AcCgYrk9/tH5f1+P4YhF3/MBKQAm+EcqwIM4K3ffIwXdw/zpbcez3tOb9//Cd85FzqfhUvv5GPxF/jTjj+VDj18+cPUBUcHppwuhBDsSe3h2Z5nebrnaZ7ueZreTOX+c7qic1zdcaxsWsnKxpWsbFpJlX9693ncH0IIrK4uchs3klu/odRTZnV3j6praQEKTfOwOpaRb5pPLtJERomQTCskB/OIcYSIu0oqSSCYxufPoGpJHDuOmRsim+ijkM3st53BWBWx+kZiDQ3EGpqI1TdS1djolTVNW1gN4QjsRN4VaEVhNpR3/aHchGE0SugKerUrxrQqP1q13xVpXl6v9qMYR8bih5mMZVlks9kKUZbJZMhms6Xyff1cLofjjL+oY7JomlYSY/smn883rh0rGYaBdoQshjnakAJshnMsC7Dv/HUbN/5xA6d21PKL95++/xPWfBYe+29Y/m6eO/1qrvrTVQBoisZz734OTT1y/8gIIdid3M0zPc/wdM/TPN/7/KgeMoAF1Qs4uelkVjSuYGXjSloik4/CPZ1YQ0NuD9mGjeQ2bCC/aSP5bdthjBVvis+HsWAR9sKTMGctJBebRdZXTTIpiPdlSfRlMfPjr5QTTg7dlyEQymL4UkASx4qTzwySifdj5vY/L8gfChOrbyDa4Imy+gZiRb+hkVCsCmUa5v4I08GK57GHPGE2lMMeymENe2WJwqRibKohvSTOtKoyoVblQ4v50ap8UqQdBoQQFAqFkhgrCrSin8vlyOfz5HK5MVNhoijMrwFN00pibN9ULNd1vWTL/fKyYtI0bcy8pmkV6WifPycF2AznWBZgncNZVt/yIIoCT37yXJpi+xk22nw//OQfoKYd8W9r+ft7/54tQ1toCDbw4OUPTk2jDyFdqS6e2/scz+19jud7n2d7fPuoOk2hJlY0rmB543JWNK5gUc0idHVmrLBzcjnyW14lt3ED+Q0byW3cSH7jRpzM2D1YenMzgcWL8S1aDB2LKNS3kzGqSQwWSPRlife74iw9wepEIQSKUiAYzREIZtCMDIgkdmGYfGaI9HA/+XRyv23XdJ1oXQPRunqi9Q2uWKtvIFbn2mhdPb7g1PeiCdvBjhewhz1R5iXXd0WbMCfXA6OG9JIYK7dqzIfmJTVkyAUDU4jjOBQKhZJIy+fzo1Lx+Fi2mEzTpFAoHJLeuNeCoiijBJmqquP6qqqiKMq4/kSpeL/ye5fbE088kblzJ7Hg6wCQAmyGcywLMIC33fEYz+8anlxQ1lwCbp0LwoGPrOcXPY/xpSe/xIkNJ/J/F/3f1DT4MDKQHeCF3hd4bu9zrO1dy4bBDdj7RJsO6SFOaDiBFY0rOKnhJE5sOJGYb+Z8b4TjYO7Z44mxTeQ2byK/cRPmnj1j1lcMA9+CBQQWLcS/aDH+xYvR2ueTUSMk+3Nuj1n/iDhL9OewJ4jrBeALOoRjBQx/Ft1IgUhiFeLk04OkhwdIDw0ixP5fXP5weESk1dWX+Q1E6+uJ1NZh+KZmB4UiQghE1sKKF7DjeTcNl9mEWz5ZkYaqoEU9MVYUZhEvH/WNHAtLoXYkYllWhSgrT2OVWZY1ypb7tm2XyvbN27Y97YJvIi6++GJWrVp1SK8pBdgM51gXYN97dDtf/P16Tppdze8+OIkoxd8+B7rXwtu+g7Xsbfxkw084uelkjq8//rC3darJmBle7n+ZF3pf4IW+F3ip9yWS5ujem/lV8zmp8SROajiJ5Q3Laa9qPyKCwx4IdjJJfvNmV5ht2kx+s5vG6y3TqqrwL1qEf+FC/IsWunbhQtRIlHS8QKJ/X2GWJd6fI7ufOVaKqhCuNgjFTPzBLJqWBpLYhQSFzBCp4X5SA/3kM+lJfa5gNEa0roFIXZ0rzGrriNbVE6mtJ1pX54q0KV4wUBRpdqJQEmR2fESc2Um33Embk99SUHHjomkRT5hFDLSoDzXiQ4saFVYN6lKsHaU4joPjONi2XZEsyyqVl9fZt8zdRskZMy+EmDAVGc9fuHAhra2th/TzSgE2wznWBVh/Ks/rbvoLliO479qzWNqyn2fw50/DE7fDyivhLV+fmkYeIdiOzdb4Vtb2rmVt71pe7HuRXcldo+rFfDFOqD+BExpO4IT6Ezix/kSqA9VT3+DXiHAczK4u8ps2ueJs02bymzZR2LkTxvlPW29udsXYggVuWrgA//z5qGF3z9FCziI5kPMEWs4VaEWRNpDD3k/PkO7XiNUFCFcp+EM5dD0NShrHTFDIDZMZHiA50E9yoH+/oTaKBMIRInX1RGtdQVaRalwbjMamfOWjsB3spImdyOMUxVrSTY4n0uxUwQ27cSBvERXUsCfWIgZa2BNmRT9sVPiKX5OrPiVHJFKAzXCOdQEG8K8/fo77Xu7hqjPa+fxb9tOTtelP8NMroGo2XLfO3ek+l4BEJzQunZoGH0EMZAd4qe8lXux7kbV9a3ml/xVy9ugo4nOicyoE2eLaxfj2sz3RkYqTz1PYupX8li3kNm8mv2UL+S2vjrkSs4jR2opvoSfKFizEv2A+/nnzSsIM3BWK6XiBxECWpNdjlux3hVmiP0tqOL9foeEP6cTqg0Rq/YRiDoYvh6qkEE4KMx8nEx8gNeim5ED/hPtvlqMZBpGaWiK1dYRr6ojW1hKuqXPLvPJITR1GYOrDbwhb4KRNV5ylPHGWMvexBeykichObq/ICjRlRJgVU0h3y0IGalj3rJcP6SiGKkWb5LAjBdgMRwoweGRzH1d+72liAZ2nP/1GAhOtzipk4MvzwczA+x6CWSvh6ytgcBv881+g7dCO8c80TMdk89Bm1vWt46W+l1jXv44diR2j6hmqwaKaRSyrX8bxdcezrH4Z86rmHdErSfeHnUiUxFj+1ZFk9/ePe47e2oJ/3nz88+fjWzAf//wF+OfPQ6saHQ7ENh2Sg17v2UCO5IDbi1bM5/YXgBUIRAxidQGitQEitX6CUdCNLAopHCtJNjnkCrShAZKeUMsm4pN+Br5giLAnysLVNSN+qcwt903T/onCclyxljJLoq2UT3l+2sTxjk96rtq+6Apq0BVjakgf7QeLfmVSAnJ4VDJ5pACb4UgB5m4kfdZtD9E5nOX/XXESl61om/iEX7wX1v8OzvwIvOGz8MUat/zv/gPO+djhb/AMI56P83L/y7zU9xIv9buiLJ4f/VIP6kGW1i7lhPoTOK7uOI6rO445sTkzbj7ZvlhDQxSKgmzLq+S3biW/bSt23/jCTKurwz9vHr558/DPn4evYx7+eR3oLS3jhqcoDW8WxdlArjTcmRzIkc/sv/fHH9KJ1LoCLeoJtVBMQzdyQAozFyc9NERqyF0skCqmwYFJhd8oovv9rkCrriVcXV0SZuGaGsJVNYSrawhVVxOKVaPp07fq1inYOGlzRJhlrFLeyYzk7bI89mt4pSmg+DXUQKUoU4M6akBz/UC579ZVPKsGNNBl79uxghRgMxwpwFy+/pctfHXNZk7rqOXn+4sJtu5X8Ov/D2rnw7t/5faAAVz+QzjurYe/sTOcYpDYV/pf4eX+l3l54GXWD6wfta8lQMSIsKR2SUmQHVd3HHNjc2e8KAOwh4fJb9tG/tVXKWzd5gqzrVsnHMpUgkF87e34O9rxtXfg63CTv6O9YjhzLPJZi+RAdkSkDboCrZhy+9lfEkAzVLf3rMbv9aIFiNb6idQG8AcFCilyqTipocGSQEsPFwXbEKmhwQMSauDuOhCuqiZcXU2oqoZQVTWhqmrCVdWEqqsJV9UQjFURqqpGn+YI7kIIRMHByZo4acu1GctLJk7W87PuMZG1SmUH3du2L6riCjSf5lq/J9j8Xpnf9Yu2vEzxaag+1yo+FdUvBd2RjBRgMxwpwFy6425MMEfAgx89h3kNkfEr55Nw23yw83DOJ+CRW9zyd/wUllw0NQ0+yrAdm+3x7bw88DIv97/MhoENbBraRN4ePZE8bIRZXLOYJbVLWFK7hKV1S5lfNR9DOzq2T3HSafLbd1DYtpX8tm2uONu+jcLOXWCOL5L0xkZPkLXjm9uOr30uvvZ2fG1tKJMQJoWcVRJlqcEygebZ9CSDsAajBpEaV6SVbO2I7wsIcsk4qeFBMsNDpIaGyMRdcZYZHiIdHy5ZcYBhBfzhMKFYNaGqKkKxaoKxmCvYPIEWjFYRisUIxqoIRmOoR1CEdmE5ODlXkImc7Yk0a6Ss6OdshGednFc3ZyEK9oEtRpgsCiVBpvg0VMO1iqGiGF55yXq+oaLo6sgxvXjMK9fL/FKZApoqh2APACnAZjhSgI1w9Q+e4cGNvfzL2fP41EX7mVD/k3fA5vvAH4N8wi2TAuyQYjom24a3sX5gvZsG17N5cPOYk/wN1WBB9QKW1C5hce1iFtcsZlHtohkVo2x/CNOksHsPhR3bKWzfTmHHDvLbt1PYvgN7YGD8EzUNo22WK8bmznXTnLn42uditLSgTHKIz7YcUkM5UoN5kkNFkZYfEWuDOazCJASTAqGoryTQwjV+ItV+L+8nXO0mTVPIppKjRFnGS27ZMJn4EJlEHGeMXQ8mbodCIBxxe89iMU+cVRGMxQhGR1IgGiUYdQXbdM1dmwzCEQjTdgVZ3hNleRuRt12xli/LFzzr5UXB8wvF5By6HrkDRVNQNE+Q6SPiTNHUkWOad0xTXL9Ypipl1qtfLPN8lH3qqa5FVVAUvONeAFVFAYXKYwpuwvWVirIyyr8nCu5eqtFDu/BICrAZjhRgI6xZv5f3/fBZYgGdxz95LhH/BC+mtT+B3/5rZZkUYIcdy7HYHt/OxsGNbBjcwMbBjWwc3EiyMHZ0+dZwK4tqF7G4ZnFJmLVF246KIcxy7Hh8RJDt2EFh504KO3ZS2LEDkZ1gyE/X8c2ahdHuibI5c/DNmY0xew5G2yxU3+RfGEII8hmrJNJSQzmSQ/mKfGoojzPJOVKBsFESY0VhFqn2E6pyxVu4yk/AC8AqhCCXTpGJD5ONx8kkhskUbSJB1hNs2UScTDJBLpXc74brY6FqOoFIZEScRaIEo1EC0RjBSJRANOqWhaMEIhECETevH8BzPFIQjiiJMacozEynTKB5x8yyctMpS17ectz6ljOS93w8/7D03B1hVF+2gMhph3ZrNynAZjhSgI1gO4LzvvoI2/rTfObi4/j/zpwgMn52CL68AJyyic1SgE0LQgi60l1sHHBF2abBTWwa2kR3euy5VEE9yILqBSysWcjC6oWurVlIbaB2ilt++BFCYPX2lsRYYccOCrt2Ye7aSWHnLsREe/8pCnpLM77ZI6LMN7sNo202vtltqFVVB9wbJBxBNmWWxFh6OE9qaEScpYbdsv3FQyuiagqhKh/hKq/nrMrv5X2EqvyujfkJRioj5TuOTS6VcgVZIu7ZBNlknGwyQTaRcG0xJRKTjqs2FrrP7wqycAR/OFLp71sWCuMPuykQiqD7/Udsr9uhQAgBthgRZrYA00HYDsIaKccWCHvECktU+o57rrAFOALhjBzHGSlHuN/D8jLhCFeQC9y8EOAAQrg63fFkinc+eOXFc/ZVMUKMFHlO1flzCS1vPKTPTgqwGY4UYJX85KldfOrudbRWBXjk43+HoU3QU/Kjy2Br2R6Q7/gJ1C2AXU9AoguizRCb5aVWCNZUdktLDivxfJzNQ5vZPLS5JMpeHXqVgjO26KgL1JXE2ILqBcyvns/8qvlEfBPMB5zBCMfB2ruXws5dbo/Zrp2Yu3ZR2LWbwu7diHF2ASiiRiIYs2fja5uF0TbbHeZsa8Noa8NobUUNBg+uXV5PWnrYE2RlwiwdL9oC2eTk5qQBqKpCMOYJs5iXqvwjfsxHqMpHMOrDFxi759vM58gmk+RSSbJeL1o2mSRXFGmpJPl0imwqSS6VIpdMkEulJrWt1IRt17QRURYK4w+F8AXLfM/6Q2F8wRD+YBBfKIQv6CZ/KIQRCKDO4BAvkrGRAmyGIwVYJTnT5sxbH6Q/VeBrVyzn0hWzxq/87Pfh99eN5DUf2BP0KOhBV4jFWqG2A9pOhdmnuaJtnNACkkOL5VjsSuxi8/BmtgxtKaU9qbH3ggRoDjczv3o+C6o8UVY9n3lV845aYQauCLIHBijs2o25uyjKdmHu2k2hc8+EITSKaHV1GLNmYcxqdYc5i6m1FaOlZb+rNveHbTtk4gXS8TyZ4QKp4TyZeJ50ouDauGuzBxgpX/epBKOuKCvaoh+MGoSiI74/bKBOMGlcOA6FXJZcUZSlUuTSKfJp1+bSKfKpMj+dJp8p2vSBz2ubAMMfwBcM4gsGMQJBfIEyP+jm3fLAiPUHMLy84fe7vr+Y/EfUIoZjESnAZjhSgI3m9ge38F/3b2ZJc5T7rj1r/O5/Kw9/+SI8fdeI8NKDbjDW2nmQ6nUj5Ce6IDPBCytY44kxT5DNWgm+1/ZykhwYGTPD1uGtbBl2BdnW4a1sHd5Kb7Z33HMaQ43Mq5o3kqpdWxuoPaqHjACcbBazs5PCnj2Yu/dg7tnj+nv2YHZ24qRS+72GVlWFPqsVo6XVFWWeMDNaW9Cbm9Hr68eNeXYg2LZDNmGSjufJJgpkEgUyiTyZeNEvlETbpBYRlKEobnDboiALRnwEI0apLBAxCJb5gbCBpk/uMwkhsPJ5cp4gy6VTFDIZ8pk0ec8Wyv1shkI2Sz6bcf1Mhnwmg2MfRPT/SaLpOoY/gB4IYPj86H4/us/n+j4fuj8w4vt86D4/umGUfM1njJQZPjTDh2YY6IbhWTevGQaa7llNOyTfi6MBKcBmOFKAjWY4U+CMWx4kU7D54dWncvaiholPGNoB2x6BpmXQciKMFQ7BzEGy2xVjiU7o3QC7n4bO52Df+FeKBs3LRkRZ2ylQ0y6HL6eBeD7O1uGtvDr8akmUbY1vpT87vqCO+WJ0VHXQHmunvaqdjqoOOmIdzI7OPmpCZUyEEAInkXAFWmcnZmcnZmeXa/fswezuxkmOvWiiAsPAaGrCaG5Gb2nxbDNGcwt6UyNGczNaTc0hfRkXchbZZIFMwnTFWtIVaEU/myyQTZpkk4VJBbYd82MFNFekhY2SKCv3/WG9VOYPGQTCOr7XECHfMs2SICvksq6fy1LIZjFzOQrZLIWcK97MfB4z55V71sy55QXPmvncQS1gOJSomoaq62i67goz3UDVNVRNR9M8q+ulMlXT0HQdRdVQNRVV1dxraFqpTFE1VNWNeaaoKoqqunnVFXxuuYKCUvrOFctRlJFFkN7fafefMKVUNHvZSTTMaT+kz0EKsBmOFGBj84V7X+H7j+3g1I5afv4vrzt8PRq2CT0vuWJs91Ow6ylIdo2uF24cEWOzT4WW5eALHZ42SfZLPB9ne3w72+Pb2Rbfxrb4NrYOb6Ur1YUYZ7xLUzTaom20x9qZE5tTYRtDjUfdysyJsJNJzK5uzK5OzK4urO5uzK4ut6ynB6u3d9wNz8tRDAO9qQm9uQmjqRm9qQmjqRG9sdEtb3T9A1nNOenPYDvkUq4YyyZMMsmCm08VrVk6nku7/sG+7RQFfCGdQMjAH9Lxhz1bzHvHfEEdf1DHF/Js0D022V63ySCEwDZNzHzOS3msfB4zl8Mq5DHNgpvP57EKBbcsn8c2C6W8ZZpYhTy2Z81CAds0S8kyC55184ezF2+qOO99H+LEN154SK8pBdgMRwqwsekazvL6/3qYguXwv1efyjn76wU7lAzvhj1Pw+5nXNv9Ejj7BOBUNGhYAq3LoXWFK8ial4FxcBOfJYeGnJVjZ2In2xPb2RHfwY7EDrbHXT9jjT+pPaAFmB2bzdzoXObE5jAnOoc5sTnMjs4+5sQZgLAsrN5ezJ4ezO5uV6B192Du7cHq2Yu5twe7f2DSPTFadfWIIGtoGJ0aXav6/YfvMzmCfNYilzJLgixb9L18Pm2Sy5jk0pbrp80DHhYdC91Q8XmCzBfQRvzyfMDzAzpGQMMX0DD8Or6gZwMa2jRtMi4cB9uyvGRiWyaOZWGZrrVNE9u2cWwLx3LtSN7CsW03OTaO7eDYNsKxR8ptG8dxEI6DEK4t5T3fXRHpuLsdOO7SR+GMlLkNFZ4Z+V4W/RPecD5zT1h+SJ+LFGAzHCnAxuc/f7+e/3l0O8e1xPj9h8+ccLLtYcXMQveLbi/Znqddm9o7up6iQeNSV4y1nOQKsqbjITB6Y2fJ1CKEoDfTy47EDnYmdlakPck9WGL8//B9qo/Z0dnMjs12bXQ2bZE22qJtzIrMwqfNvBhThwJRKGD19WHu3Yu1dy9mdw/W3r1Yfb2Ye3tdv7d34lAb+6DGYuj19aWk1deh1zd4+Tq0ujrXr6lBmaLYXpZpk8+4wi2fschninYMP2tRyHr5rIWZO3ST+MHtiTP8mpsCepmvYfg0dL9nfSqGX0P3aZ5VMXyugDN8Gtq+eUNF96loctujA0IKsBmOFGDjM5gucPZtD5HKW3z9nSt4y0mt090kFyHcuWTda6FrLXS94PrpvrHrV8+F5hPcOWrNJ7jCrHqunFN2hGA5Fl2prpIg25Xcxa7kLvYk99CZ7JxQnCkoNIWbSoKsLdLGrOgsZkXcVB+sP+Z6z8oRQmAPD2P19mH1uoLM6utz832V6UCEGnhira4Ora4Wva4eva4WraYWrbYGvdb19bpatNpatOpqlGlYMeg4gkKZKCvkXL+Qsz3r5bO2K9jyNmbOPW7mvTp5Gyt/aIXcRGiGim64YqwoyjQvX+krI2W6iqoraJprVc09Xm5VTUFVFddqaslXNK+sOMdLVVBUN3SJorrnuPO/RqLeK4obGV8pRsinGCl/5HOUC8micNV9h/Y7IAXYDEcKsIkpbtI9ty7EA9efM3FcsOmkKMqKYqxnHfS8DIlxwiv4otC4xO0xazxuJEWmcKhVsl8sx6I73c3uxO4KYbYntYc9yT1jbmBejk/10RppLaVZkVm0hl2/OdxMQ7ABTcaHchcOxONYAwNYff1Y/f3YA64t5t2yAazBQTiIbY+0qiq0mho3VVej1VSjV+Q9W1VVSpPZw3MqcByBlbcxCzamJ84qk1XyrYLj+gUbq2Bj5Z0Rv+C41iy3jjekd3Tz+n9czPFnTRDW6CCQAmyGIwXYxKTzFmff9hAD6QJfunQZ73nd3Olu0oGRGYS9L7tirGcd7F0HvRtHzykrEqp3RVnDEmhYDPWLXD/SKHvMjjCEEAzmBktibE9yD7uTu+lKd9GV6qIn3YMtJhYKuqLTFG6iJdxCa6SVlnALzeFmN4WaaYm0EDZkSJRyhONgx+PYg4NY/QPYgwNY/QNYA/3Yg0PYQ4NYg0PYg4NuiscP+l5qODwiyqqrUGOeOIvF0KpiqLEYWqyqzI+hRiJo0eik9/g8ErAtB9t0XEFm2timg2254qxYbltlyXSwLYFtOTi2l7cFjuXZku/gePWE45UXrS3c+WBe3p3P5YpN4aURH3dxjRdFXwj3969oKd9NaYwo+CAFmGQMpADbPz94bDufv3c9tWEfD370HKpDM3zOjW3CwFboXe+lDa4d3M64ESsDVVC/GBoWubZ+kRtAtmbu2GE3JNOO5VjszeylM9lJZ6qTrnQXnUnX9qR7JiXQAKJGlKZwU0mYNYWa3BRuKvlHc1Da14qwLHcYdHAQe2gYe3gYe2gIe3jIyw9hDXl+PI4dj+MkEq851IMaCqFGo2ixKGo0hhqNoEWinkCLoEYiqJEoaiSMFo2ihiOo4XApaZEwSigk52QdIoQQh/xZSgE2w5ECbP8ULIc3f/1vbOlN8a7T5nDTZSdMd5MOD4UM9G+Cvetd27fZtUM7YLztVFTdjVFWt6Ay1c6DaIuM8H8EYzs2fdk+ulJddKe76U53l3rOejKuQBtvk/N9CRthGkONNIWaaAw10hBsoCHUUPKL9liIg3YoELaNnUjgxOOuYCvZBHYigZ2I45T8BE4i7h5Lpfa7hdQBoSiukCsKs1BoJIVDKMEgasgrDwZRQ0G3LOD5gQBqMIQaCqIGAiiBAIrfjxoMohzle1xOBVKAzXCkAJscT24b4B13PYmiwN0fWM3y2dXT3aSpw8zBwKuVoqz/VbdsojlIesAVZzUdriCr7fD8DqiaDfoM70k8Bkib6VJvWVGY9WZ62Zvey96MmyYr0gBq/DXUBetKAq3kB12/PlhPfbCeiBGRL+eDRJgmdiqFk0hgJ1M4yQR2IunaVAonlcZJJrHTI76TSrnH0ulSmkwctteK4vePCLOAH9Xnd4WZ34/q96GU8j4Unw/V50MxXL+UDGPEGgaKoY/4uuuj6yi6gaJroGklX9E0KJarqpv3rKJ5dVUViuVH2HdSCrAZjhRgk+f6n6/lNy90smxWjN998Ey06QpLcaTgOG50/4Etrhgb2OoJtS0wvAsmGt5SVIi2QvUcdxizeq5n57h+tAW0mTOH5VgmY2ZcUZbZS2+ml95ML33ZPtdm+kq+Od68wzHwqT7qgnXUBepcW+bXBmqpDdRSE6ihNlBLtb8aXZXflUOJEAKRy+F4osz2RJnIZnEyGTelMyN+NouTSSOyOdfPZt262SxOLoeTzbjH8nkwJ/89OCJRVXc+bFGQFfP7RsMvijXPb/rEJ6h+22WHtClSgM1wpACbPH3JPOd+5WESOYsvvOV4rjyjfbqbdORimxDf7c4rG9zmDmOW+/tZvYeiuZuWV7Xtk2a7NtYKgWq5MGCGIIRgOD9Mb6aXgewAfdk++rP9pVSeT5vpA7q2gkK1v5qaQE2FKKsJ1FDjr6E6UE2tv5bqQDXVfjcF9eAR15txrCAsCyeXR+RzONnciC3kEfk8Tj6PyBcq87k8wiwgCqZnCzgF14qC6VrLQpgFsCy3zLIQZpm1LTAthG0jbNutZ3l5y3JXth7GXr/mL3yBmisuP6TXlAJshiMF2IHxoyd38pnfvkzQ0Lj3w2eyoFFOPj5ghHA3Kh/eBcM7XUFW9Id3uTsBTKa3xAi5QizaArFZrl9M0WaINLurN+W8oxlFzsoxkBtgIOulXKUdyg8xmB1kMDfIcH543K2fJsKn+qj2V1MVqCqJsiq/68d8Mar8VVT5qoj5Xb9YFtACUrgdxbgrGh2wbTcKvmWD4wk2cI85XtgMsY/vXmDE7hMVX6+rQzvE71gpwGY4UoAdGI4jeO/3nubRV/tZ2hLj7g+cQcCQcZQOKY7jRvqP73F70eJ7ytJuN2WHJnkxBcL1I4Is6qVwoyvOIo0jvj8qe9RmGJZjMZwfZig3xEBugOHccEmYDeWGSnYoP1TKH8hQ6L4YqkHMFyPqixLze9YXK6WoL0rEFyFqREd8X5So4fpSwEkOJVKAzXCkADtwehM5LvzvvzGYLvBPq9v53CXHT3eTjj3MrBt4Ntnt2kSnZz0/udcVcZMIs1BCD3iCrMFNoXoI15X5DW4+VA+hWrcHTr5MZxRCCLJWluH88EjKuTaej5MoJIjn48QLcdd6ZYl8YsIdCSaLruiEfWEiRoSw4dqIb8QPG2FCRoiwHiZsjE4hPUTQCLpWDqMe80gBNsORAuzgeGhjL//0g2cA+MgbF3H2onpWzKmZ5lZJKnAcyPRDssdNqZ4RP90LqT7P9kIhdeDX1wMQqoNgrSvIQrUj+WA1BGvcFCjzg9WgH74NnyWHByEEGStDIp9wBVkhQbKQLImzpJkkkU+QMlMkCglShRQpM0WykCRZSJIyUzjjhXI5SBQUgnqQkBEipIcIGa4omygF9IBrtUDJL5b7NT8BLYBf96zml7skHOFIATbDkQLs4Pnivev53mPbS/l7PrSaE9uqp69BkoOnkBkRY+k+SPe7NjNQlu93BV1mAOwD2zewAj3oCrFAVVkqz8fAHxuxJT/q+r6IjK82wygKuFQhRdpMkzSTpAtpUqaXLyRJm2k3WWnSBc+aaTJmpnQsY2X2u/3UoURX9ZIYC+gBfJoPv+avsAHNLfepPtd6vqEZFWWGapTsWL6u6uiqjqEaY/q64uY1VTum9zct57W+v+XaYcmM5RNvWkJjzM9df93GYLrA8zuHCBgaCqBrKmGfRtivEzQ01GM9ZMWRji8EvnY3btn+EMLtMcsMumIsOwiZIdfPDLjz03LDrq1Iw4BwV4Ams+7w6UG3N+IKMl8E/EU/6vq+CPjCI8eKvi/sJiPsfl4jOOLrQSnqDiOKopSGEV8rjnDIWTkyVoaMmSmJsrSZJmtl3WRmR3wrW6qTt/PkrBw5K0fWzrrWypbKcnauYo6c5VikHLc370hCVVQ0RSsJM13V0VQNTfHSGH7xHFVRXV8dXabihpYo5hVcX1EUFJSSLQrA8rLicLDC6L/1iqLw5o43s6p51ZQ+p/0x7QLsjjvu4Mtf/jLd3d0cf/zxfO1rX+Oss84at/4jjzzC9ddfzyuvvEJraysf//jHueaaayrq/PrXv+Yzn/kMW7duZf78+dx4441cdlll/I/93VcIwRe+8AXuuusuhoaGOO200/jmN7/J8cePzDfK5/P8+7//Oz/96U/JZrOce+653HHHHbS1tR2ipyOZCJ+ucs058/nzKz0Mpgt8/t7149YN+zRCfp2IXyfk04j4daIBNx8J6ET8BtHASFl4n7ohv07EpxPya0fuhuDHCori9UZF3dhlk8VxXGGWi4+TiscSkE+M2HLf8eYhFVIHN2w6EUaoLAW9tK8fcMXamNZLRhA0n5f372MDbgBeze+uTpVzmA4YVVHdYUcjBMFDf33bscnb+VLKWbmKfMEuVNiKMqeAaZulvOm4frHcdMySbznWSH2ngOVYmI5bXu6PtV2WIxwc4bymBRVTzXF1x0kBVs7Pf/5zrrvuOu644w5Wr17Nt7/9bd70pjexfv165syZM6r+9u3bueiii3jf+97Hj3/8Yx577DE+8IEP0NDQwN///d8D8MQTT3DFFVfwpS99icsuu4y7776byy+/nEcffZTTTjtt0ve97bbb+OpXv8oPfvADFi1axH/+539y3nnnsWnTJqLRKADXXXcd9957Lz/72c+oq6vjox/9KBdffDHPPfccmibH7qeKv1/ZRtdwFst2F8Q7QmDZgnTBKq1KThds0gWbvmT+Nd/Pp6uEfBohQyPg0wj5NIKGRtCnEzRUQj6dgKHi1zUChlbmqxV5n6biN1TP7pPXVQxNxdBVDE3Bpx15UaBnHKo6MlfsYBDCXXxQSEE+6aZCCvJevlAsS3spNdrPp8DMuKmQqYzHViyfMpQyoeaJsnJxpvvd45pRWaYaXpmv7LiXV/WRMlX3bLGu7vqq7vn75j1f1UHVRq5RXqbqboy6Yv4o/J3QVI2Q6gm8IwBHOCVRZgnX2o6NLWxMx8R27JJQs4VdOlbuW46FQGA7No5wsMVoK4RAIEriTgiBg1ORF4gRW+6XzaQqD4tSXn583ZG3WGta54CddtpprFy5kjvvvLNUtnTpUi699FJuvvnmUfVvuOEG7rnnHjZs2FAqu+aaa3jxxRd54oknALjiiitIJBLcd999pToXXnghNTU1/PSnP53UfYUQtLa2ct1113HDDTcAbm9XU1MTt956K+9///uJx+M0NDTwox/9iCuuuAKArq4uZs+ezR//+EcuuOCCST0DOQfs8CGEIGc6pPIWmYLlWZt03vVTOdcmc25K5c1SPpW3yORt0gWLdN4iXbApWId/a5CJMDTFFWWaK8p0VUX3ynRVQS+Vu76uKmiqe1xTlVJeVxXUsryquFZTFTTPVz1fVRjxS3VBVRRvqAA0tcxX3Doobh3VC0hdrK9Q9EHBHRooHWckgLVSypeVM3Ie++SVimsWn1hZ/X2u5R31ykeecfE+5fl9/XGPj1FWeWSM48JBNbOoVgbFzKBYGVQ7j2pmUSw3qV5SvDLVzqFYXir3S2UFFDvvJivnWS8/g3osJoNQVFA0hKqVhFmxrOR7x0p1FBWhaF4Edc31vXol30sjx1QExWupgIpQlLK62j754vkKUF6muNcpfdFV3C+z6rYVKspQFK+8GNl9xArvuPetr4z87uVF6VhZ3dIvUPnx4nXY5z6UysU++THPGXW8PF/2cxtVZ58KY+X3K7YnPh6atZRw/eiOndfCjJ0DVigUeO655/jEJz5RUX7++efz+OOPj3nOE088wfnnn19RdsEFF/Dd734X0zQxDIMnnniCj3zkI6PqfO1rX5v0fbdv305PT0/Fvfx+P+eccw6PP/4473//+3nuuecwTbOiTmtrK8uWLePxxx8fV4Dl83ny+ZFemEQiMWY9yWtHURSCPo2gTwNe+8o303bI5G1SBYtswSJbcMgULDKmTa5gkynYZE2brGfzlk3OdMiZnrVs8qZDvtxaDgXLIe+lQrHMdtj3XyPTFpi2DRxAaAfJDEYDIl46NCg4+LDwY+LHxIeJT7Fci1WWtzC8vIFVKvNhomOXykq+l3RsdMXCwPaO2ejetQzFRsPx8q5vYLllinsdFad0TMdGw8anjP99V4QDwjnqhKXk0PPU8Z/ltH/46HQ3o4JpE2D9/f3Ytk1TU1NFeVNTEz09PWOe09PTM2Z9y7Lo7++npaVl3DrFa07mvkU7Vp2dO3eW6vh8Pmpqasa9zljcfPPNfOELXxj3uOTIxdBUqkIqVaGpiexuOwLTdsWYaRWtoGA7WI6DZbvHLa+eaQsszzrCLbMdgeWIEevVtx2BLQS27VqneNzzbccdynWEW7doi+VCCBxR9CnVGSlzfQGeL3Acd3igaIV3vHgNdzihPD9yPhTzoqJcAOybpywgtle/vIzya45Tt3hspP7o0rHqVg6FjH/f8Rh1VOzn+L7V93t9DTCwCGIB+246dDjHQyZs237uq5QEmYOGjY6DWibS3HKndFwty6sVxxyUfcpVRCmvlJWpFbZ4rijVV/apUyxTECiK8MoFStk9in0/xfMo8928KN1DKUtqWVnxGso+ZWpZebFvq/x6lI6NlJfXd4+7eZTK/KjjZT+08rLyemPlK88b+aGP7vsS+8nvy/6/uJb/yBthmvZJ+PvOaxFCTDjXZaz6+5ZP5pqHqs6+7K/OJz/5Sa6//vpSPpFIMHv27AmvKTk2cYcFNRnpXyKRSF4ji6e7AWMwbcu56uvr0TRtVG9Rb2/vqJ6nIs3NzWPW13Wdurq6CesUrzmZ+zY3NwPst06hUGBoaGjcOmPh9/uJxWIVSSKRSCQSybHFtAkwn8/HySefzJo1ayrK16xZwxlnnDHmOaeffvqo+vfffz+rVq3CMIwJ6xSvOZn7dnR00NzcXFGnUCjwyCOPlOqcfPLJGIZRUae7u5uXX3553PZLJBKJRCKRACCmkZ/97GfCMAzx3e9+V6xfv15cd911IhwOix07dgghhPjEJz4h3vOe95Tqb9u2TYRCIfGRj3xErF+/Xnz3u98VhmGIX/3qV6U6jz32mNA0Tdxyyy1iw4YN4pZbbhG6rosnn3xy0vcVQohbbrlFVFVVid/85jdi3bp14p3vfKdoaWkRiUSiVOeaa64RbW1t4oEHHhDPP/+8eMMb3iBOOukkYVnWpJ9BPB4XgIjH4wf1DCUSiUQikUw9r/X9Pa0CTAghvvnNb4q5c+cKn88nVq5cKR555JHSsSuvvFKcc845FfUffvhhsWLFCuHz+UR7e7u48847R13zl7/8pVi8eLEwDEMsWbJE/PrXvz6g+wohhOM44nOf+5xobm4Wfr9fnH322WLdunUVdbLZrPjQhz4kamtrRTAYFBdffLHYtWvXAX1+KcAkEolEIpl5vNb3t9wLcpqRccAkEolEIpl5vNb3t9xTRSKRSCQSiWSKkQJMIpFIJBKJZIqRAkwikUgkEolkipECTCKRSCQSiWSKkQJMIpFIJBKJZIqRAkwikUgkEolkipECTCKRSCQSiWSKkQJMIpFIJBKJZIqRAkwikUgkEolkitGnuwHHOsWNCBKJxDS3RCKRSCQSyWQpvrcPdkMhKcCmmWQyCcDs2bOnuSUSiUQikUgOlGQySVVV1QGfJ/eCnGYcx6Grq4toNIqiKIfsuolEgtmzZ7N79265x+QBIp/dwSGf28Ejn93BIZ/bwSOf3cFR/tyi0SjJZJLW1lZU9cBndMkesGlGVVXa2toO2/VjsZj85TpI5LM7OORzO3jkszs45HM7eOSzOziKz+1ger6KyEn4EolEIpFIJFOMFGASiUQikUgkU4wUYEcpfr+fz33uc/j9/uluyoxDPruDQz63g0c+u4NDPreDRz67g+NQPjc5CV8ikUgkEolkipE9YBKJRCKRSCRTjBRgEolEIpFIJFOMFGASiUQikUgkU4wUYBKJRCKRSCRTjBRgRyl33HEHHR0dBAIBTj75ZP72t79Nd5OOaG6++WZOOeUUotEojY2NXHrppWzatGm6mzUjufnmm1EUheuuu266m3LE09nZybvf/W7q6uoIhUIsX76c5557brqbdcRjWRb/8R//QUdHB8FgkHnz5vHFL34Rx3Gmu2lHFH/961+55JJLaG1tRVEUfvvb31YcF0Lw+c9/ntbWVoLBIK9//et55ZVXpqexRxgTPTvTNLnhhhs44YQTCIfDtLa28t73vpeurq4DuocUYEchP//5z7nuuuv49Kc/zQsvvMBZZ53Fm970Jnbt2jXdTTtieeSRR/jgBz/Ik08+yZo1a7Asi/PPP590Oj3dTZtRPPPMM9x1112ceOKJ092UI56hoSFWr16NYRjcd999rF+/nq985StUV1dPd9OOeG699Va+9a1vcfvtt7NhwwZuu+02vvzlL/ONb3xjupt2RJFOpznppJO4/fbbxzx+22238dWvfpXbb7+dZ555hubmZs4777zSHsXHMhM9u0wmw/PPP89nPvMZnn/+eX7zm9+wefNm3vKWtxzYTYTkqOPUU08V11xzTUXZkiVLxCc+8YlpatHMo7e3VwDikUceme6mzBiSyaRYuHChWLNmjTjnnHPEtddeO91NOqK54YYbxJlnnjndzZiRvPnNbxZXX311Rdnb3vY28e53v3uaWnTkA4i77767lHccRzQ3N4tbbrmlVJbL5URVVZX41re+NQ0tPHLZ99mNxdNPPy0AsXPnzklfV/aAHWUUCgWee+45zj///Iry888/n8cff3yaWjXziMfjANTW1k5zS2YOH/zgB3nzm9/MG9/4xuluyozgnnvuYdWqVfzDP/wDjY2NrFixgu985zvT3awZwZlnnslf/vIXNm/eDMCLL77Io48+ykUXXTTNLZs5bN++nZ6enop3hd/v55xzzpHvioMgHo+jKMoB9WDLzbiPMvr7+7Ftm6amporypqYmenp6pqlVMwshBNdffz1nnnkmy5Ytm+7mzAh+9rOf8fzzz/PMM89Md1NmDNu2bePOO+/k+uuv51Of+hRPP/00//Zv/4bf7+e9733vdDfviOaGG24gHo+zZMkSNE3Dtm1uvPFG3vnOd05302YMxffBWO+KnTt3TkeTZiy5XI5PfOITvOtd7zqgjc2lADtKURSlIi+EGFUmGZsPfehDvPTSSzz66KPT3ZQZwe7du7n22mu5//77CQQC092cGYPjOKxatYqbbroJgBUrVvDKK69w5513SgG2H37+85/z4x//mJ/85Cccf/zxrF27luuuu47W1lauvPLK6W7ejEK+K14bpmnyjne8A8dxuOOOOw7oXCnAjjLq6+vRNG1Ub1dvb++o/3Qko/nwhz/MPffcw1//+lfa2tqmuzkzgueee47e3l5OPvnkUplt2/z1r3/l9ttvJ5/Po2naNLbwyKSlpYXjjjuuomzp0qX8+te/nqYWzRw+9rGP8YlPfIJ3vOMdAJxwwgns3LmTm2++WQqwSdLc3Ay4PWEtLS2lcvmumDymaXL55Zezfft2HnzwwQPq/QK5CvKow+fzcfLJJ7NmzZqK8jVr1nDGGWdMU6uOfIQQfOhDH+I3v/kNDz74IB0dHdPdpBnDueeey7p161i7dm0prVq1in/8x39k7dq1UnyNw+rVq0eFOtm8eTNz586dphbNHDKZDKpa+frSNE2GoTgAOjo6aG5urnhXFAoFHnnkEfmumARF8bVlyxYeeOAB6urqDvgasgfsKOT666/nPe95D6tWreL000/nrrvuYteuXVxzzTXT3bQjlg9+8IP85Cc/4Xe/+x3RaLTUg1hVVUUwGJzm1h3ZRKPRUXPlwuEwdXV1cg7dBHzkIx/hjDPO4KabbuLyyy/n6aef5q677uKuu+6a7qYd8VxyySXceOONzJkzh+OPP54XXniBr371q1x99dXT3bQjilQqxauvvlrKb9++nbVr11JbW8ucOXO47rrruOmmm1i4cCELFy7kpptuIhQK8a53vWsaW31kMNGza21t5e1vfzvPP/88v//977Ftu/TOqK2txefzTe4mr2FlpuQI5pvf/KaYO3eu8Pl8YuXKlTKcwn4Axkzf//73p7tpMxIZhmJy3HvvvWLZsmXC7/eLJUuWiLvuumu6mzQjSCQS4tprrxVz5swRgUBAzJs3T3z6058W+Xx+upt2RPHQQw+N+XftyiuvFEK4oSg+97nPiebmZuH3+8XZZ58t1q1bN72NPkKY6Nlt37593HfGQw89NOl7KEII8dp0okQikUgkEonkQJBzwCQSiUQikUimGCnAJBKJRCKRSKYYKcAkEolEIpFIphgpwCQSiUQikUimGCnAJBKJRCKRSKYYKcAkEolEIpFIphgpwCQSiUQikUimGCnAJBLJAaEoCr/97W+nuxmSSTAVP6sf/OAHKIqCoihcd911h/VeV111Vele8jsomelIASaRSCpebIZh0NTUxHnnncf3vve9UfvrdXd386Y3vWlS151JL8rPf/7zpWdQnh544IHpblrFz6c8XXjhhdPdNABisRjd3d186UtfOqz3+e///m+6u7sP6z0kkqlC7gUpkUgAuPDCC/n+97+Pbdvs3buXP/3pT1x77bX86le/4p577kHX3T8Xzc3N09zSw8fxxx8/SnDV1taOqlcoFCa/39shovjzKcfv909pG8ZDUZQp+V5UVVVRVVV12O8jkUwFsgdMIpEA7su8ubmZWbNmsXLlSj71qU/xu9/9jvvuu48f/OAHpXrlvVqFQoEPfehDtLS0EAgEaG9v5+abbwagvb0dgMsuuwxFUUr5rVu38ta3vpWmpiYikQinnHLKKNHT3t7OTTfdxNVXX000GmXOnDmjNqnes2cP73jHO6itrSUcDrNq1Sqeeuqp0vF7772Xk08+mUAgwLx58/jCF76AZVkTPgNd12lubq5IPp+Pq666iksvvZSbb76Z1tZWFi1aBMC6det4wxveQDAYpK6ujn/5l38hlUqVrlc876abbqKpqYnq6upSOz72sY9RW1tLW1sb3/ve9yb98ylPNTU1peNbtmzh7LPPJhAIcNxxx7FmzZpR13j88cdZvnw5gUCAVatW8dvf/hZFUVi7dm2pzvr167nooouIRCI0NTXxnve8h/7+/v22b1/y+Twf//jHmT17Nn6/n4ULF/Ld734XgIcffhhFUfjzn//MihUrCAaDvOENb6C3t5f77ruPpUuXEovFeOc730kmkznge0skMwEpwCQSybi84Q1v4KSTTuI3v/nNmMe//vWvc8899/CLX/yCTZs28eMf/7gktJ555hkAvv/979Pd3V3Kp1IpLrroIh544AFeeOEFLrjgAi655BJ27dpVce2vfOUrrFq1ihdeeIEPfOAD/Ou//isbN24sXeOcc86hq6uLe+65hxdffJGPf/zjpeHSP//5z7z73e/m3/7t31i/fj3f/va3+cEPfsCNN9540M/iL3/5Cxs2bGDNmjX8/ve/J5PJcOGFF1JTU8MzzzzDL3/5Sx544AE+9KEPVZz34IMP0tXVxV//+le++tWv8vnPf56LL76YmpoannrqKa655hquueYadu/efdBtcxyHt73tbWiaxpNPPsm3vvUtbrjhhoo6yWSSSy65hBNOOIHnn3+eL33pS6PqdHd3c84557B8+XKeffZZ/vSnP7F3714uv/zyA27Te9/7Xn72s5/x9a9/nQ0bNvCtb32LSCRSUefzn/88t99+O48//ji7d+/m8ssv52tf+xo/+clP+MMf/sCaNWv4xje+ceAPRCKZCRyuncQlEsnM4corrxRvfetbxzx2xRVXiKVLl5bygLj77ruFEEJ8+MMfFm94wxuE4zhjnltedyKOO+448Y1vfKOUnzt3rnj3u99dyjuOIxobG8Wdd94phBDi29/+tohGo2JgYGDM65111lnipptuqij70Y9+JFpaWsZtw+c+9zmhqqoIh8OldMoppwgh3OfT1NQk8vl8qf5dd90lampqRCqVKpX94Q9/EKqqip6entJ5c+fOFbZtl+osXrxYnHXWWaW8ZVkiHA6Ln/70p+O27corrxSaplW0LRwOiy9+8YtCCCH+/Oc/C03TxO7du0vn3HfffRXP/8477xR1dXUim82W6nznO98RgHjhhReEEEJ85jOfEeeff37FvXfv3i0AsWnTpjHb9v3vf19UVVVVlG3atEkAYs2aNWOe89BDDwlAPPDAA6Wym2++WQBi69atpbL3v//94oILLhh1/mS/VxLJkYycAyaRSCZECIGiKGMeu+qqqzjvvPNYvHgxF154IRdffDHnn3/+hNdLp9N84Qtf4Pe//z1dXV1YlkU2mx3VA3biiSeW/OIco97eXgDWrl3LihUrxpyfBfDcc8/xzDPPVPR42bZNLpcjk8kQCoXGPG/x4sXcc889pXz5HKsTTjihYt7Xhg0bOOmkkwiHw6Wy1atX4zgOmzZtoqmpCXDnlanqyGBDU1MTy5YtK+U1TaOurq702cbj7/7u77jzzjsryoqff8OGDcyZM4e2trbSsdNPP72i7qZNmzjxxBMJBAKlslNPPbWiznPPPcdDDz00qqcK3KHj4tDr/li7di2apnHOOedMWK/8Z9zU1EQoFGLevHkVZU8//fSk7imRzDSkAJNIJBOyYcMGOjo6xjy2cuVKtm/fzn333ccDDzzA5Zdfzhvf+EZ+9atfjXu9j33sY/z5z3/mv/7rv1iwYAHBYJC3v/3tFAqFinqGYVTkFUUpDTEGg8EJ2+w4Dl/4whd429veNupYuQDZF5/Px4IFC8Y8Vi60YGJhWl4+1ueY6LONRzgcHrdtQogJ2zBee/c9z3EcLrnkEm699dZR12tpaZmwfeXs7+dTpPw5HOxzkUhmKlKASSSScXnwwQdZt24dH/nIR8atE4vFuOKKK7jiiit4+9vfzoUXXsjg4CC1tbUYhoFt2xX1//a3v3HVVVdx2WWXAe58rh07dhxQu0488UT+53/+p3SffVm5ciWbNm0aV7AcCo477jj+93//l3Q6XRJnjz32GKqqTrqn6FC2ZdeuXXR1ddHa2grAE088UVFnyZIl/N///R/5fL7Us/fss89W1Fm5ciW//vWvaW9vL616PRhOOOEEHMfhkUce4Y1vfONBX0ciOZqRk/AlEgngrlrr6emhs7OT559/nptuuom3vvWtXHzxxbz3ve8d85z/9//+Hz/72c/YuHEjmzdv5pe//CXNzc1UV1cD7mrGv/zlL/T09DA0NATAggUL+M1vfsPatWt58cUXede73nXAvRzvfOc7aW5u5tJLL+Wxxx5j27Zt/PrXvy6Jjs9+9rP88Ic/5POf/zyvvPIKGzZs4Oc//zn/8R//cfAPaB/+8R//kUAgwJVXXsnLL7/MQw89xIc//GHe8573lIYfDyXFn095Kq5OfOMb38jixYt573vfy4svvsjf/vY3Pv3pT1ecX3zO//Iv/8KGDRtKvZAw0lv2wQ9+kMHBQd75znfy9NNPs23bNu6//36uvvrqUUJ6Itrb27nyyiu5+uqr+e1vf8v27dt5+OGH+cUvfnGInoZEMvORAkwikQDwpz/9iZaWFtrb27nwwgt56KGH+PrXv87vfvc7NE0b85xIJMKtt97KqlWrOOWUU9ixYwd//OMfS3OevvKVr7BmzRpmz57NihUrAFe01dTUcMYZZ3DJJZdwwQUXsHLlygNqq8/n4/7776exsZGLLrqIE044gVtuuaXUzgsuuIDf//73rFmzhlNOOYXXve51fPWrX2Xu3Lmv4QlVEgqF+POf/8zg4CCnnHIKb3/72zn33HO5/fbbD9k9yin+fMrTmWeeCYCqqtx9993k83lOPfVU/vmf/3nUis9YLMa9997L2rVrWb58OZ/+9Kf57Gc/C4wMy7a2tvLYY49h2zYXXHABy5Yt49prr6WqqqpiHttkuPPOO3n729/OBz7wAZYsWcL73vc+0un0IXgSEsnRgSLGmjwgkUgkkqOe//u//+Of/umfiMfjk563tS8/+MEPuO666xgeHj60jZsARVG4++67ufTSS6fsnhLJoUb2gEkkEskxwg9/+EMeffRRtm/fzm9/+1tuuOEGLr/88oMWX0Xi8TiRSGRUXLFDzTXXXDPmCk2JZCYie8AkEonkGOG2227jjjvuoKenh5aWFi699FJuvPHGccNyTIZkMsnevXsBqK6upr6+/lA1dxS9vb0kEgnAXZW578pUiWQmIQWYRCKRSCQSyRQjhyAlEolEIpFIphgpwCQSiUQikUimGCnAJBKJRCKRSKYYKcAkEolEIpFIphgpwCQSiUQikUimGCnAJBKJRCKRSKYYKcAkEolEIpFIphgpwCQSiUQikUimGCnAJBKJRCKRSKaY/x+tnmtP0a1dewAAAABJRU5ErkJggg==", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAGyCAYAAABDdXhpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAADluUlEQVR4nOzdd5xU1fn48c+dPtv7zPaCdKQtgoAUCyAaxPINJPgTCxKJRkQ0RjSxB4NfRePXHhFiYpQo9hBllV5Ueu8s29jed3Z3dsr9/THs4LoLzsA2luf9es1r2Tvn3vPckWQezjn3OYqqqipCCCGEEKLdaDo6ACGEEEKIC40kYEIIIYQQ7UwSMCGEEEKIdiYJmBBCCCFEO5METAghhBCinUkCJoQQQgjRziQBE0IIIYRoZ5KACSGEEEK0M0nAhBBCCCHama6jA7jQud1uTpw4QXBwMIqidHQ4QgghhPCBqqpUV1cTFxeHRnMW41lqB3v11VfVlJQU1Wg0qoMHD1bXrl17xvarV69WBw8erBqNRjU1NVV9/fXXm7X56KOP1N69e6sGg0Ht3bu3+vHHH/vdr9vtVh9//HE1NjZWNZlM6pgxY9Q9e/Y0aTNmzBgVaPKaOnWqX/efk5PT7Brykpe85CUvecnr/Hjl5OT49b3fqENHwJYuXcqcOXN47bXXGDlyJG+++SYTJ05k3759JCUlNWufmZnJNddcw8yZM/nnP//Jhg0buPvuu4mOjuamm24CYNOmTUydOpWnn36aG264gU8++YQpU6awfv16hg0b5nO/zz33HAsXLmTJkiX06NGDZ555hnHjxnHw4EGCg4O9Mc2cOZOnnnrK+7vZbPbrM2i8Vk5ODiEhIf59gEIIIYToEFVVVSQmJjbJCfyhqGrHbcY9bNgwBg8ezOuvv+491rt3b66//nqeffbZZu3/8Ic/8Pnnn7N//37vsVmzZrFz5042bdoEwNSpU6mqquK///2vt83VV19NeHg477//vk/9qqpKXFwcc+bM4Q9/+AMAdrsdi8XCggULuOuuuwAYO3YsAwcO5KWXXjrrz6CqqorQ0FAqKyslARNCCCHOE+f6/d1hi/AbGhrYunUr48ePb3J8/PjxbNy4scVzNm3a1Kz9hAkT2LJlCw6H44xtGq/pS7+ZmZkUFBQ0aWM0GhkzZkyz2N577z2ioqLo27cvDz74INXV1We8b7vdTlVVVZOXEEIIIS4sHTYFWVJSgsvlwmKxNDlusVgoKCho8ZyCgoIW2zudTkpKSoiNjT1tm8Zr+tJv48+W2mRlZXl/v/nmm0lNTcVqtbJnzx7mzZvHzp07ycjIOO19P/vsszz55JOnfV8IIYQQXV+HPwX50yf/VFU949OALbX/6XFfrtkabWbOnOn9c79+/ejevTtDhgxh27ZtDB48uMX4582bx9y5c72/N84hCyGEEOLC0WEJWFRUFFqtttloV1FRUbORp0ZWq7XF9jqdjsjIyDO2abymL/1arVbAMxIWGxvrU2wAgwcPRq/Xc/jw4dMmYEajEaPReNprCCGEEL5wuVze5Tei9en1erRabZtdv8MSMIPBQHp6OhkZGdxwww3e4xkZGUyePLnFc4YPH84XX3zR5NiKFSsYMmQIer3e2yYjI4P777+/SZsRI0b43G/jtGJGRgaDBg0CPGvH1qxZw4IFC057T3v37sXhcDRJ2oQQQojWpKoqBQUFVFRUdHQoXV5YWBhWq7VN6nR26BTk3LlzueWWWxgyZAjDhw/nrbfeIjs7m1mzZgGe6bq8vDzeffddwPPE4yuvvMLcuXOZOXMmmzZtYtGiRd6nGwHuu+8+Ro8ezYIFC5g8eTKfffYZ33zzDevXr/e5X0VRmDNnDvPnz6d79+50796d+fPnExAQwLRp0wA4evQo7733Htdccw1RUVHs27ePBx54gEGDBjFy5Mj2+giFEEJcYBqTr5iYGAICAqSIdxtQVZXa2lqKiooA2mZg5ayqh7WiV199VU1OTlYNBoM6ePBgdc2aNd73br31VnXMmDFN2q9evVodNGiQajAY1JSUlBYLsX744Ydqz549Vb1er/bq1UtdtmyZX/2q6qlCrFarVTUajero0aPV3bt3e9/Pzs5WR48erUZERKgGg0Ht1q2bOnv2bLW0tNSv+6+srFQBtbKy0q/zhBBCXHicTqe6b98+taSkpKNDuSCUlJSo+/btU51OZ7P3zvX7u0PrgAmpAyaEEMJ39fX1ZGZmkpKS4nfhb+G/uro6jh8/TmpqKiaTqcl7520dMCGEEEKcHZl2bB9t+TlLAiaEEEII0c4kARNCCCEuEE888QQDBw5s935Xr16NoigoisL111/f7v235IknnvDGdC5bCp4tScCEEEKILqAxmTjd67bbbuPBBx/k22+/7bAYDx48yJIlSzqs/x978MEHyc/PJyEhoUP67/BK+KJtlNvsnKisJyLAQGyYLNQUQoiuLj8/3/vnpUuX8thjj3Hw4EHvMbPZTFBQEEFBQR0RHgAxMTGEhYV1WP/gKWCrKIr3s2jLYqtnIiNgXdTMlQe58mAmL2/O7uhQhBBCtAOr1ep9hYaGoihKs2M/nYK87bbbuP7665k/fz4Wi4WwsDCefPJJnE4nv//974mIiCAhIYF33nmnSV95eXlMnTqV8PBwIiMjmTx5MsePH/cr3nfffZfIyEjsdnuT4zfddBPTp0/3/v7FF1+Qnp6OyWQiLS3NG1+jhQsXcvHFFxMYGEhiYiJ33303NTU13veXLFlCWFgYX375JX369MFoNDbZ17mjSALWRYVoPP9pK370l1QIIYT4qZUrV3LixAnWrl3LwoULeeKJJ/jFL35BeHg433//PbNmzWLWrFnk5OQAUFtby+WXX05QUBBr165l/fr1BAUFcfXVV9PQ0OBzv7/85S9xuVx8/vnn3mMlJSV8+eWX3H777QB8/fXX/L//9/+YPXs2+/bt480332TJkiX8+c9/9p6j0Wh4+eWX2bNnD3//+99ZuXIlDz30UJO+amtrefbZZ3n77bfZu3cvMTEx5/KRtQpJwLqosJNDqlUudwdHIoQQojOLiIjg5ZdfpmfPntxxxx307NmT2tpaHnnkEbp37868efMwGAxs2LABgA8++ACNRsPbb7/NxRdfTO/evVm8eDHZ2dmsXr3a537NZjPTpk1j8eLF3mPvvfceCQkJjB07FoA///nPPPzww9x6662kpaUxbtw4nn76ad58803vOXPmzOHyyy8nNTWVK664gqeffpp///vfTfpyOBy89tprjBgxgp49exIYGHj2H1grkTVgXVSY/mQC5pYETAghxOn17dsXjebUeIzFYqFfv37e37VaLZGRkd5tebZu3cqRI0cIDg5ucp36+nqOHj3qV98zZ87kkksuIS8vj/j4eBYvXsxtt93mrb+1detWNm/e3GTEy+VyUV9fT21tLQEBAaxatYr58+ezb98+qqqqcDqd1NfXY7PZvImWwWCgf//+/n0wbUwSsC4qzKgDB9QgGx0IIYQ4Pb1e3+R3RVFaPOY++Q96t9tNeno67733XrNrRUdH+9X3oEGDGDBgAO+++y4TJkxg9+7dfPHFF9733W43Tz75JDfeeGOzc00mE1lZWVxzzTXMmjWLp59+moiICNavX8+MGTNwOBzetmazudMVr5UErIuKMOo9CZgiI2BCCCFaz+DBg1m6dCkxMTGtsoXenXfeyYsvvkheXh5XXXUViYmJTfo6ePAgF110UYvnbtmyBafTyQsvvOAdxfvp9GNnJWvAuqhIs+dfL7Ud83StEEKILurmm28mKiqKyZMns27dOjIzM1mzZg333Xcfubm5Z3W9vLw8/va3v3HHHXc0ee+xxx7j3Xff5YknnmDv3r3s37+fpUuX8sc//hGAbt264XQ6+b//+z+OHTvGP/7xD954441Wuc+2JglYFxUZYACgVtu5hlyFEEKc3wICAli7di1JSUnceOON9O7dmzvuuIO6urqzGhELCQnhpptuIigoqFmV/AkTJvDll1+SkZHBJZdcwqWXXsrChQtJTk4GYODAgSxcuJAFCxbQr18/3nvvPZ599tnWuM02p6iqKouEOtC57qZ+OplFNQzfewRUlbyxA9BqJNcWQojzXX19PZmZmaSmpmIymTo6HJ+tXr2ayy+/nPLy8hYLsY4bN47evXvz8ssvt3tsKSkpzJkzhzlz5jR770yf97l+f8u3chcVFeQZAUNRKKt1nLmxEEII0Q4SEhL49a9/7f29rKyMDz74gJUrV3LPPfe0ayzz588nKCiI7OyOKVgui/C7qCCzHp1TxalTKK6xEx1k7OiQhBBCXKCGDRvG4cOHAZpshTR48GDKy8tZsGABPXv2bNeYZs2axZQpUwD/n95sDZKAdVGKomB2qlTrFEpsvlcmFkIIIVqb2Wxu8UlGf7cvak0RERFERER0WP8yBdmFBZzchahUpiCFEEKITkUSsC6q/OBagqgGoLReEjAhhBCiM5EErIs6+sNThAYeAqDcLgmYEEII0ZlIAtZFFVa7CaQGgHK7q4OjEUIIIcSPSQLWRVXbNN4ErNIpCZgQQgjRmUgC1kWpahCB2ACodEkCJoQQQnQmkoB1VZow7whYtVs25BZCCCE6E0nAuiiNMdo7AlatSgImhBDi/PHaa695t/9JT09n3bp1p2378ccfM27cOKKjowkJCWH48OF8/fXX7Rjt2ZEErIvSB8V7R8BqFNnuUwghxPlh6dKlzJkzh0cffZTt27czatQoJk6ceNotg9auXcu4ceNYvnw5W7du5fLLL2fSpEls3769nSP3jyRgXVS09tQaMJtGEjAhhBDnh4ULFzJjxgzuvPNOevfuzUsvvURiYiKvv/56i+1feuklHnroIS655BK6d+/O/Pnz6d69O1988UU7R+4f2Yqoi0rOVslM8IyA1cp/ZSGEuOCpqkqdo/0fyjLrtSiK4lPbhoYGtm7dysMPP9zk+Pjx49m4caNP13C73VRXV3foNkO+kK/mLsoQpCPY4QYD1OsU3KqKxsf/AQghhOh66hwu+jzW/muj9j01gQCDb+lGSUkJLpcLi8XS5LjFYqGgoMCna7zwwgvYbDbvRtudlUxBdlHaQB0hDZ7/vKqiUCHV8IUQQpwnfjpipqqqT6No77//Pk888QRLly4lJiamrcJrFTIC1kXpgw2YHEYMqp0GxUhxTQMRJkNHhyWEEKKDmPVa9j01oUP69VVUVBRarbbZaFdRUVGzUbGfWrp0KTNmzODDDz/kqquuOqtY25MkYF2UISQAbXUwgdTQgJFiWwM9ozo6KiGEEB1FURSfpwI7isFgID09nYyMDG644Qbv8YyMDCZPnnza895//33uuOMO3n//fa699tr2CPWcde7/EuKs5WnsNDgMBGKjnEhKaxs6OiQhhBDiZ82dO5dbbrmFIUOGMHz4cN566y2ys7OZNWsWAPPmzSMvL493330X8CRf06dP569//SuXXnqpd/TMbDYTGhraYffxcyQB66KWHtxBYniNtxZYWZ2zgyMSQgghft7UqVMpLS3lqaeeIj8/n379+rF8+XKSk5MByM/Pb1IT7M0338TpdHLPPfdwzz33eI/feuutLFmypL3D95kkYF2UNkCH02E8lYDVyyJ8IYQQ54e7776bu+++u8X3fppUrV69uu0DagPyFGQXZQwOwOEweouxljtkBEwIIYToLCQB66KCgkNxOE+NgFVKAiaEEEJ0GpKAdVEhwaE4HSbvCFilUzbkFkIIIToLScC6qPDgcBwOw6kRMHf7bz8hhBBCiJZJAtZFRYRE4PjRCFi1W6YghRBCiM5CErAuKiY0Bqfz1AhYNZKACSGEEJ2FJGBdlCXUgqpqMLnsANi0sgZMCCGE6CwkAeuiTHoTKCoGh6cCfp1W/lMLIYQQnYV8K3dhquLG6PRMPdbr9LhVtYMjEkIIIQRIAta1ad3oGzxPP6qKIrXAhBBCiE5CErCuTOdCcWgxqvUAlMp+kEIIIc4Dr732GqmpqZhMJtLT01m3bp1P523YsAGdTsfAgQPbNsBWIAlYF6YYwOEwEnCyFEVJjb2DIxJCCCHObOnSpcyZM4dHH32U7du3M2rUKCZOnNhkA+6WVFZWMn36dK688sp2ivTcSALWhekCNE22IyqxNXRwREIIIcSZLVy4kBkzZnDnnXfSu3dvXnrpJRITE3n99dfPeN5dd93FtGnTGD58eDtFem50HR2AaDvGIBPOmlMbcpfWOTo4IiGEEB1GVcFR2/796gNAUXxq2tDQwNatW3n44YebHB8/fjwbN2487XmLFy/m6NGj/POf/+SZZ545p3DbiyRgXVhQUAiO8lMjYGV2ScCEEOKC5aiF+XHt3+8jJ8AQ6FPTkpISXC4XFoulyXGLxUJBQUGL5xw+fJiHH36YdevWodOdP2mNTEF2YcGhkSenID0jYJKACSGEOB8oPxkxU1W12TEAl8vFtGnTePLJJ+nRo0d7hdcqzp9UUfgtPCSSfIeRCEoBOF5f38ERCSGE6DD6AM9oVEf066OoqCi0Wm2z0a6ioqJmo2IA1dXVbNmyhe3bt/O73/0OALfbjaqq6HQ6VqxYwRVXXHFu8bcRScC6sKiQKBwOExdxCIC9qoyACSHEBUtRfJ4K7CgGg4H09HQyMjK44YYbvMczMjKYPHlys/YhISHs3r27ybHXXnuNlStX8tFHH5GamtrmMZ8tScC6sKjQKBwOI31OJmAn9BrKHE4i9PKfXQghROc0d+5cbrnlFoYMGcLw4cN56623yM7OZtasWQDMmzePvLw83n33XTQaDf369WtyfkxMDCaTqdnxzka+ibuwmJAYVFVDgKuOWG0e+Uo8WyttjIsK7ejQhBBCiBZNnTqV0tJSnnrqKfLz8+nXrx/Lly8nOTkZgPz8/J+tCXY+kEX4XViQIQgUFYfDSA8OALCxqKqDoxJCCCHO7O677+b48ePY7Xa2bt3K6NGjve8tWbKE1atXn/bcJ554gh07drR9kOdIErAuTFEU3IoLh8NIdw4C8H1pTQdHJYQQQghJwLo4VePC6TyVgO112HG41Q6OSgghhLiwSQLWxbl1ThwOI3HkYXbWYldgn62uo8MSQgghLmgdnoD5u+P5mjVrSE9Px2QykZaWxhtvvNGszbJly+jTpw9Go5E+ffrwySef+N2vqqo88cQTxMXFYTabGTt2LHv37m0xJlVVmThxIoqi8Omnn/p+8+1A1blwNJjQoJJSexSAzRUyDSmEEEJ0pA5NwPzd8TwzM5NrrrmGUaNGsX37dh555BFmz57NsmXLvG02bdrE1KlTueWWW9i5cye33HILU6ZM4fvvv/er3+eee46FCxfyyiuvsHnzZqxWK+PGjaO6urpZXC+99FKLFXo7A8UINTWRAPRQPNOQm0qa34MQQggh2pHagYYOHarOmjWrybFevXqpDz/8cIvtH3roIbVXr15Njt11113qpZde6v19ypQp6tVXX92kzYQJE9Rf/epXPvfrdrtVq9Wq/uUvf/G+X19fr4aGhqpvvPFGk/N27NihJiQkqPn5+SqgfvLJJz9z101VVlaqgFpZWenXeb564u0H1WefvV/95ts09cUVk1XLyu1q/1W72qQvIYQQbauurk7dt2+fWldX19GhXBDO9Hmf6/d3h42ANe54Pn78+CbHz7Tj+aZNm5q1nzBhAlu2bMHhcJyxTeM1fek3MzOTgoKCJm2MRiNjxoxpElttbS2//vWveeWVV7BarT7dt91up6qqqsmrLQUGBlFfH4zLYaK79jCK6qZQdXGivqFN+xVCCCHE6XVYAnY2O54XFBS02N7pdFJSUnLGNo3X9KXfxp8/F9v999/PiBEjWtwe4XSeffZZQkNDva/ExESfzz0b4WERgEJtVQwm6om15QKwpaq2TfsVQgghxOl1+CJ8X3c8P1P7nx735Zrn2ubzzz9n5cqVvPTSS6eNtSXz5s2jsrLS+8rJyfHrfH9FRXiSyOrqKAC6OQ8D8L0sxBdCCCE6TIclYP7ueA5gtVpbbK/T6YiMjDxjm8Zr+tJv43TimdqsXLmSo0ePEhYWhk6nQ6fz7Op00003MXbs2NPet9FoJCQkpMmrLUWGxQJQXhUBQH/9DgDWlEhFfCGEEKKjdFgC9uMdz38sIyODESNGtHjO8OHDm7VfsWIFQ4YMQa/Xn7FN4zV96Tc1NRWr1dqkTUNDA2vWrPG2efjhh9m1axc7duzwvgBefPFFFi9e7M9H0aaiw2IAqKz2JKj9A3aBqnLE3kCB3dGRoQkhhBAt8rdEld1u59FHHyU5ORmj0Ui3bt1455132inas9Ohm3H7s+M5wKxZs3jllVeYO3cuM2fOZNOmTSxatIj333/fe8377ruP0aNHs2DBAiZPnsxnn33GN998w/r1633uV1EU5syZw/z58+nevTvdu3dn/vz5BAQEMG3aNMAzStbSwvukpCRSU1Pb7DPzV2RQJCoqLpcBTVUEQSFlxFfmkheWyLryan5pjejoEIUQQgivxlJRr732GiNHjuTNN99k4sSJ7Nu3j6SkpBbPmTJlCoWFhSxatIiLLrqIoqIinE5nO0fup3N4OrNVvPrqq2pycrJqMBjUwYMHq2vWrPG+d+utt6pjxoxp0n716tXqoEGDVIPBoKakpKivv/56s2t++OGHas+ePVW9Xq/26tVLXbZsmV/9qqqnFMXjjz+uWq1W1Wg0qqNHj1Z37959xnuhE5ahcLgc6rwnH1Yff/xxde27U9Rvvk1Tf/3efNWycrv6u33H26RPIYQQbeNCKEPhb4mq//73v2poaKhaWlra6rG0ZRkKRVVV2RiwA1VVVREaGkplZWWbrQeb++c5hDjCuDK6lobey/i+aCgvW/6AxaBjx4i+nbaIrBBCiKbq6+vJzMz0Ts/5Q1VV6pztvxWdWWf2+XumoaGBgIAAPvzwQ2644Qbv8fvuu48dO3awZs2aZufcfffdHDp0iCFDhvCPf/yDwMBArrvuOp5++mnMZvM5xX6mz/tcv799moK88cYb/b7wG2+8QUxMjN/nidbn0njWejkr4wHoH7oLrdNFIXCk1k73QP/+RyyEEOL8U+esY9i/hrV7v99P+54AfYBPbc+mRNWxY8dYv349JpOJTz75hJKSEu6++27Kyso69Townxbhf/rppxgMhib1q870+s9//kNNjZQ56CxUrWce3F4bgeLQYTbWk1SaBcDactmWSAghROfiT4kqt9uNoii89957DB06lGuuuYaFCxeyZMkS6uraf8TPVz4vwn/55Zd9HtH66KOPzjog0Qb0nr+0NhwEVyZRH3WMHjW7ybSksa68mhkJ0R0coBBCiLZm1pn5ftr3P9+wDfr11dmUqIqNjSU+Pp7Q0FDvsd69e6OqKrm5uXTv3v3sAm9jPo2ArVq1iogI35+W++9//0t8fPxZByValxrsybOLNJUEVPcCoC+7ANhQXoPTLcsAhRCiq1MUhQB9QLu//FlnfDYlqkaOHMmJEyeazLwdOnQIjUZDQkLC2X1Y7cCnBGzMmDHeQqO+uOyyyzAajWcdlGhdQRbPyGWxUoWhIg2AXqEHMNrtVLvc7KqWbYmEEEJ0DnPnzuXtt9/mnXfeYf/+/dx///3NSlRNnz7d237atGlERkZy++23s2/fPtauXcvvf/977rjjjnNehN+WfC7EeujQId555x2ys7OZO3cu+/bta8u4RCvql3oxdo0dt6JSX+WpXRYQUU9y/lEA1sg6MCGEEJ3E1KlTeemll3jqqacYOHAga9euZfny5SQnJwOQn59Pdna2t31QUBAZGRlUVFQwZMgQbr75ZiZNmsTLL7/cUbfgE5+HtWbPns2aNWtYsGABZrOZO++8k40bN7ZlbKKVXBo/gLXGr4iti6XYqWKuCcMZVEH3qr0cog/rymu4P6WjoxRCCCE87r77bu6+++4W31uyZEmzY7169Wo2bdnZ+TwC5nK56Nu3L7Nnz+bPf/5zW8YkWllqWAqVxjLAsw7MXNUTgH4n14FtqbRR63J3WHxCCCHEhcbnBCw4OJhHHnnE+3vj3oui89MoGhp0LgAKNeWYKz1PhCSHZhFcU0ODqrK50taRIQohhBAXFJ8TsD/96U/egqx2u51f//rXbRaUaH2KOQoVFZvSABWeefRASx3JuYcAqQcmhBBCtCefE7BBgwZ5/2w0Gr1PI4jzQ1jIACoNlQCU1wahOPXojG66lR8EYJ0kYEIIIUS78b22xI/88MMPrF69mqKiItzupmuHFi5c2CqBidbVLawvB4zfE9YQRrFSQ2xVGnURB+nj3sNnwO7qOsodTsL1Z/VXQgghhBB+8Pvbdv78+fzxj3+kZ8+eWCyWJgXWZFPnzqt3VBrfG8qANAo1paRVXERdxEFiw/OJLC+lNDySDeU1/CImrKNDFUIIIbo8vxOwv/71r7zzzjvcdtttbRCOaCtxoYE4lHoASjQ2jJXdAAg4uQ6sNHw468qrJQETQggh2oHPa8C8J2g0jBw5si1iEW0oNtSMwxHsKcgK1FZ69tQyRzSQWnAYgHXlsoG6EEII0R78TsDuv/9+Xn311baIRbShmBAjVfVplJ2sB1bsdKC3eZKwnu59KKqbY3V28uobOjJMIYQQ4oLg9xTkgw8+yLXXXku3bt3o06dPs3pgH3/8casFJ1qPSa+lgYspNn9ObF0sefoiBlR2wxFYSGRkObFFJzhhSWBdeTW/io3s6HCFEEKILs3vEbB7772XVatW0aNHDyIjIwkNDW3yEp2XNSCFElMBACeUau/G3IGWepJyjwAyDSmEEKLjvfbaa6SmpmIymUhPT2fdunVnbP/ee+8xYMAAAgICiI2N5fbbb6e0tLSdoj07fo+Avfvuuyxbtoxrr722LeIRbSguNJC8ejs1uhqCnEFUVsYAJxOwHUf5Ln0s68urUVVVnmgVQgjRIZYuXcqcOXN47bXXGDlyJG+++SYTJ05k3759JCUlNWu/fv16pk+fzosvvsikSZPIy8tj1qxZ3HnnnXzyyScdcAe+8XsELCIigm7durVFLKKNWUNNmOxR5AfkA5Bb40DjMKPRO7mo4TBal5PCBidHau0dHKkQQogL1cKFC5kxYwZ33nknvXv35qWXXiIxMZHXX3+9xfbfffcdKSkpzJ49m9TUVC677DLuuusutmzZ0s6R+8fvBOyJJ57g8ccfp7a2ti3iEW3IGmKi1tadggDPNGSOvgpTpWcaMiS6ivj8bADWV8g0pBBCdDWqquKurW33l6qqPsfY0NDA1q1bGT9+fJPj48ePZ+PGjS2eM2LECHJzc1m+fDmqqlJYWMhHH33U6Wfq/J6CfPnllzl69CgWi4WUlJRmi/C3bdvWasGJ1mUNNZFruwRd3FocigM0etTKJIjaS5DVRdKJo2QnpLG+vJrb46M6OlwhhBCtSK2r4+Dg9Hbvt+e2rSgBAT61LSkpweVyYbFYmhy3WCwUFBS0eM6IESN47733mDp1KvX19TidTq677jr+7//+75xjb0t+J2DXX399G4Qh2kNsqJlaVxR9HG4KzYUk1CZQVhmOGQiOs5O09xgAG8prcKsqGlkHJoQQogP8dB3ymdYm79u3j9mzZ/PYY48xYcIE8vPz+f3vf8+sWbNYtGhRe4R7VvxOwB5//PG2iEO0A2uoCYC4uhDyA/JJqE0gq1JDL1VBF1hNYs1xDA0NVGBgb00dFwf79i8WIYQQnZ9iNtNz29YO6ddXUVFRaLXaZqNdRUVFzUbFGj377LOMHDmS3//+9wD079+fwMBARo0axTPPPENsbOzZB9+G/E7ANm/ejNvtZtiwYU2Of//992i1WoYMGdJqwYnW1ZiAYUui0HIEFZVircLF1fE4QnIJjqklIf8Yx5J7sa68RhIwIYToQhRF8XkqsKMYDAbS09PJyMjghhtu8B7PyMhg8uTJLZ5TW1uLTtc0ndFqtQB+rT9rb34vwr/nnnvIyclpdjwvL4977rmnVYISbSPIqCPYqKPYNgC71u6tit9QFQ9AcKyGpDzPNOT68uoOi1MIIcSFa+7cubz99tu888477N+/n/vvv5/s7GxmzZoFwLx585g+fbq3/aRJk/j44495/fXXOXbsGBs2bGD27NkMHTqUuLi4jrqNn+X3CNi+ffsYPHhws+ODBg1i3759rRKUaDvWUBMHi3rSt6GB/IB8Iu2RlFaEEJUAYfEOkld6ErDvKm043Cp6jawDE0II0X6mTp1KaWkpTz31FPn5+fTr14/ly5eTnJwMQH5+PtnZ2d72t912G9XV1bzyyis88MADhIWFccUVV7BgwYKOugWf+J2AGY1GCgsLSUtLa3I8Pz+/2RCg6HysoSYOFxkZ4DKTYS6kX3k/cqoDiQJ0YUXEVIRirq+l1hTA9iobQ8OCOjpkIYQQF5i7776bu+++u8X3lixZ0uzYvffey7333tvGUbUuv6cgx40bx7x586isrPQeq6io4JFHHmHcuHGtGpxofbEn14ElaxKpMFTg1NipcQSh2INB4yIw2kHiyWlI2ZZICCGEaBt+J2AvvPACOTk5JCcnc/nll3P55ZeTmppKQUEBL7zwQlvEKFpRbKjnaRQjF6OgcsJcCCg0VHrWgYXFm73rwDZIQVYhhBCiTfg9ZxgfH8+uXbt477332LlzJ2azmdtvv51f//rXzYqyis4nPtyTgB1w9qAXDgrNhSTZkiitCMUaA+HxdpLXHAVgS6WNOpcbs9bvPF0IIYQQZ+BzAvbII49w/fXXM3ToUAIDA/nNb37TlnGJNpIQ5knAvrPFcpWxgQ+CCgHIr4nECuijcwmv0hJUU0VNUAibK22MjgjuwIiFEEKIrsfnoY38/Hx+8YtfEBsby29+8xv+85//YLfLps3nm4RwTw2YzEo3g00x2LV2HJoqamrCwWlE1dURGKUjKc8zCiblKIQQQojW53MCtnjxYgoLC/n3v/9NWFgYDzzwAFFRUdx4440sWbKEkpKStoxTtBJrqAlFAbvTTZ/w/gBkBp4ANNRXeOqlxCQFkdxYD0zWgQkhhBCtzq/FPYqiMGrUKJ577jkOHDjADz/8wKWXXsrf/vY34uPjGT16NM8//zx5eXltFa84RwadBkuw50lIR+gAujc0kB/omYYsrgwHICSu2rsQf0dVLVVOV8cEK4QQQnRR57S6unfv3jz00ENs2LCBnJwcbr31VtatW8f777/fWvGJNtC4ED/L0IP0ejulplI0bhelVTEAaKOOE2KrILyiBDfwnYyCCSGEEK2q1R5vi4mJYcaMGXz22Wc8+OCDrXVZ0QYSGp+EdCeQbnegKipQTk1NJKpLj2qwERQd+qNtiSQBE0IIIVqT32UobrjhBhSl+fY0iqJgMpno3r0706ZNo0ePHq0SoGh98SefhMyudnNdUApQxTFzDin2KOoqYgiIzMOaFEDSiWPs7DtUFuILIYQQrczvEbDQ0FBWrlzJtm3bvInY9u3bWblyJU6nkw8++ID+/fuzYcOGVg9WtI7GKci88jpi4gaT5HBwMLIAgOKqKACCY8u9I2D7bPWUNDg7JlghhBAXlLVr1zJp0iTi4uJQFIVPP/30Z89Zs2YN6enpmEwm0tLSeOONN9o+0HPkdwJmtVqZNm0ax44dY9myZXz88cccPXqU//f//h/dunVj//793HrrrfzhD39oi3hFK2gcAcurqIO4QaTX26kx1mJuqKOi0gKAEnWMgPp6okvyAdhQIaNgQggh2p7NZmPAgAG88sorPrXPzMzkmmuuYdSoUWzfvp1HHnmE2bNns2zZsjaO9Nz4PQW5aNEiNmzYgEZzKnfTaDTce++9jBgxgvnz5/O73/2OUaNGtWqgovUk/GgETI0dyOB6O58EB6G4y6mutqC6tLiNVUTExJJ04hjFUbGsL69hckx4B0cuhBDibKmqirPB3e796gyaFpcunc7EiROZOHGiz+3feOMNkpKSeOmllwDPA4Jbtmzh+eef56abbvI33HbjdwLmdDo5cOBAszVeBw4cwOXylCswmUx+fdiifcWHeYqxVtudVAX3IL3B89/tuCGbGDUOW1U0QeEFWBINJOceZWv/kayTdWBCCHFecza4eeu+Ne3e72/+Oga9Udtm19+0aRPjx49vcmzChAksWrQIh8PRabdJ9HsK8pZbbmHGjBm8+OKLrF+/ng0bNvDiiy8yY8YMpk+fDnjmYvv27dvqwYrWYTZoiQw0AJBb4yIhsgcxTid7LIUobre3HEWgtYjE/ONo3C6O1zWQXSc7HwghhOhcCgoKsFgsTY5ZLBacTmenLhLv9wjYiy++iMVi4bnnnqOw0FPA02KxcP/993vXfY0fP56rr766dSMVrSo+3EyprYG88jr6xg4iPedL/hukI+xIJZUVVkjehRp9CKMjjdjCXPJik1lfXsM0s7GjQxdCCHEWdAYNv/nrmA7pt639dNZNVdUWj3cmfidgWq2WRx99lEcffZSqqioAQkJCmrRJSkpqnehEm4kPM7Mrt/LUQvzDy/hvUCB6VwXFVUm4XTowVhEbl0xS3lHyYpNZV17NtLjIjg5dCCHEWVAUpU2nAjuK1WqloKCgybGioiJ0Oh2RkZ33O+uc0tKQkJBmyZc4PzQuxM8tr4O4gQyq90wv5hhyUFUt1ZXRAEQlukjJ9WzMvba8BvfJf1UIIYQQncHw4cPJyMhocmzFihUMGTKk067/Ah8TsMGDB1NeXu7zRS+77DLZD7KT85aiKK+DmD5c5IJgl5u9UcXoGxooq7QCYLLmEluUi97RQKnDyQFbfUeGLYQQoourqalhx44d7NixA/CUmdixYwfZ2dkAzJs3z7vmHGDWrFlkZWUxd+5c9u/fzzvvvMOiRYs6/a48Pk1B7tixg507dxIREeHTRXfs2IHdLgu2O7P4cM+TkHkVdaAzorH0ZZA9jw0xJn5xrISKCCukgjPyEGbNABLyM8lM6sm68mr6BJk7OHohhBBd1ZYtW7j88su9v8+dOxeAW2+9lSVLlpCfn+9NxgBSU1NZvnw5999/P6+++ipxcXG8/PLLnboEBfixBuzKK6/0Lmr7OZ150ZvwaFKMFTzTkEeOsTbATICjkhM1PXE5DaCvJT4hnuTcY2Qm9WRtWQ13JcZ0YORCCCG6srFjx54x31iyZEmzY2PGjGHbtm1tGFXr8ykBy8zM9PvCCQkJfp8j2k/jdkRltgZqG5wExA1i8J73ASgynAB6U1ERQ2RULhEJtSTvLwVgU2UNDreKXiNJthBCCHG2fErAkpOT2zoO0c5CzXqCTTqq653kldfRPW4Q/ex2DKrK7qgy+tTUUFFhJTIqF31MFtHrVcx1NmrNgWyrsjEsLKijb0EIIYQ4b7V9cQ7RaTVOQ+ZW1EF0bwxaI/3sdg4mKFgKCqmoiAXAHnGYUHM8yXmNT0NKVXwhhBDiXEgCdgH78Z6Q6Axg6cvgejvVAQqh9RXU1obiaDCjahuIT4gkubEcRVlNR4YthBBCnPckAbuAJZx8EjK3vHEh/iBvPbBScymKW6W8wrO9Q3B8hTcB21Zto8rpav+AhRBCiC5CErAL2KlirLWeA3GDGGi3o6iwI7qS8LJyKio89cA0MUcIr6kivKIElwobZBpSCCGEOGtnnYA1NDSQm5tLdnZ2k5c4fzSOgOX8aAQsxK3S3eniQIKCtaCAivI4AOpDjxETdhEpOYcBWF0mCZgQQghxtvxOwA4fPsyoUaMwm80kJyeTmppKamoqKSkppKamtkWMoo14R8DKTo6ARfcCnYlBdbXkR0BkVSl2eyD1tSGgcROTaCQl9wgAa2QETAghhDhrfm/Gfdttt6HT6fjyyy+JjY2VoqvnscQIzwhYaWMtMIMOrBczuHwvS0OCqQyuRedwUFZhJS6gioC4QpIOVKBxuzle10BWnZ1ks7GD70IIIYQ4//g9ArZjxw7efPNNJk6cyMCBAxkwYECTl79ee+01UlNTMZlMpKens27dujO2X7NmDenp6ZhMJtLS0njjjTeatVm2bBl9+vTBaDTSp08fPvnkE7/7VVWVJ554gri4OMxmM2PHjmXv3r1N2tx1111069YNs9lMdHQ0kydP5sCBA35/Bh0l1KwnxOTJwX+8EH/wyYX422JqiCksoqLcU47CFX2QIJeGuALPVPMamYYUQgjRytauXcukSZOIi4tDURQ+/fTTM7b/+OOPGTduHNHR0YSEhDB8+HC+/vrr9gn2HPidgPXp04eSkpJW6Xzp0qXMmTOHRx99lO3btzNq1CgmTpx42rVkmZmZXHPNNYwaNYrt27fzyCOPMHv2bJYtW+Zts2nTJqZOncott9zCzp07ueWWW5gyZQrff/+9X/0+99xzLFy4kFdeeYXNmzdjtVoZN24c1dWnko709HQWL17M/v37+frrr1FVlfHjx+NynT9PCDaOguWUnVqIb3W5iFO1HEjAsw6swoqqKjgCC4i3psk0pBBCiDZjs9kYMGAAr7zyik/t165dy7hx41i+fDlbt27l8ssvZ9KkSWzfvr2NIz1Hqp++/fZbdfjw4eqqVavUkpIStbKyssnLH0OHDlVnzZrV5FivXr3Uhx9+uMX2Dz30kNqrV68mx+666y710ksv9f4+ZcoU9eqrr27SZsKECeqvfvUrn/t1u92q1WpV//KXv3jfr6+vV0NDQ9U33njjtPezc+dOFVCPHDly2jY/VVlZqQJ+f3at5TfvblaT//Clunj9Mc+Bwn2q+niI+vBr3dWBb/dVN146TH388cfVjz8epn7zbZq66vkH1Lm/u0u1rNyu9li7S3W43B0StxBCXIjq6urUffv2qXV1dR0dSrsA1E8++cTv8/r06aM++eST59z/mT7vc/3+9nsE7KqrruK7777jyiuvJCYmhvDwcMLDwwkLCyM8PNzn6zQ0NLB161bGjx/f5Pj48ePZuHFji+ds2rSpWfsJEyawZcsWHA7HGds0XtOXfjMzMykoKGjSxmg0MmbMmNPGZrPZWLx4MampqSQmJv7c7XcaiT99EjKqB+gDGFxbg1OnUB3mJrCmhvKTVfENllysxXmYGuqpdLrYWV3bUaELIYTwg6qqOOrr2/2lnmFj7bbgdruprq4mIiKiXfv1l9+L8FetWtUqHZeUlOByubBYLE2OWywWCgoKWjynoKCgxfZOp5OSkhJiY2NP26bxmr702/izpTZZWVlNjr322ms89NBD2Gw2evXqRUZGBgaD4bT3bbfbsdvt3t+rqqpO27Y9NE5BemuBabRg7c/ggi0A7LTWYykopCQuluTkXTREHSBEdzFJOUc41K0fq8qqSQ8N7KjwhRBC+Mhpt/Pyrf/T7v3O/vtH6E2mduvvhRdewGazMWXKlHbr82z4nYCNGTOmVQP46VOUqqqe8cnKltr/9Lgv12ytNjfffDPjxo0jPz+f559/nilTprBhwwZMp/nL9uyzz/Lkk0+e7vbaXWKEpxRFTlndqYNxg0jL+Y4wRc/eeDsDvisgszoVl1MPhmoSk+NJyfUkYGvKqnkw1dpB0QshhBCnvP/++zzxxBN89tlnxMTEdHQ4Z+R3AgZQUVHBokWL2L9/P4qi0KdPH+644w5CQ0N9vkZUVBRarbbZaFdRUVGzkadGVqu1xfY6nY7IyMgztmm8pi/9Wq2ehKKgoIDY2NgzxhYaGkpoaCjdu3fn0ksvJTw8nE8++YRf//rXLd7DvHnzmDt3rvf3qqqqDp2yPDUF+aOpxLhBKMBAl4bNCQoxRYXggopKC5GRuYTE2kjdlgPA1iob5Q4n4fqz+qskhBCineiMRmb//aMO6bc9LF26lBkzZvDhhx9y1VVXtUuf58LvNWBbtmyhW7duvPjii5SVlVFSUsLChQvp1q0b27Zt8/k6BoOB9PR0MjIymhzPyMhgxIgRLZ4zfPjwZu1XrFjBkCFD0Ov1Z2zTeE1f+k1NTcVqtTZp09DQwJo1a04bWyNVVZtMMf6U0WgkJCSkyasjxZ8sxlpd76Sy1nHy4GAA0qtKsZkVaqKMRJSVectRKJZjhNqqiakswQ2slachhRCi01MUBb3J1O6v9qgX+v7773Pbbbfxr3/9i2uvvbbN+2sNfg9b3H///Vx33XX87W9/Q6fznO50OrnzzjuZM2cOa9eu9flac+fO5ZZbbmHIkCEMHz6ct956i+zsbGbNmgV4Rovy8vJ49913AZg1axavvPIKc+fOZebMmWzatIlFixbx/vvve6953333MXr0aBYsWMDkyZP57LPP+Oabb1i/fr3P/SqKwpw5c5g/fz7du3ene/fuzJ8/n4CAAKZNmwbAsWPHWLp0KePHjyc6Opq8vDwWLFiA2Wzmmmuu8fdj7TABBh1RQQZKahrIKa8lNCAUIrqBIZhBtTUQFsSueCfWggKOnRyps4cfISp4NEmZBygaeBmrSquZHOP7AxhCCCHE6dTU1HDkyBHv75mZmezYsYOIiAiSkpKa5Qbvv/8+06dP569//SuXXnqpd4bLbDb7NTPX7vx9bNJkMqn79+9vdnzv3r2q2Wz2+zHMV199VU1OTlYNBoM6ePBgdc2aNd73br31VnXMmDFN2q9evVodNGiQajAY1JSUFPX1119vds0PP/xQ7dmzp6rX69VevXqpy5Yt86tfVfWUonj88cdVq9WqGo1GdfTo0eru3bu97+fl5akTJ05UY2JiVL1eryYkJKjTpk1TDxw44Nf9d3QZClVV1cmvrFeT//Cl+t/dJ04dXHyt2vB4iDrk7wPV3zzUR10z8jL18ccfU//zn/7qN9+mqd8886D6u/tnq5aV29X+63erbreUoxBCiLZ2IZShWLVqlQo0e916662qqjbPDcaMGXPG9ueiLctQKKrq3/OhFouFf/zjH83KOHz99ddMnz6dwsLCc04KLyRVVVWEhoZSWVnZYdOR976/nS92nuDRa3ozc3Sa5+CKP8HGl7mj+wCOlJfx+qtuPr3hBpL7biU29jCmzNGs/6aMV+98jAaNlm8v6UnfIHOHxC+EEBeK+vp6MjMzvTu5iLZ1ps/7XL+//V4DNnXqVGbMmMHSpUvJyckhNzeXDz74gDvvvPO0C89F59a4KfdPF+IDDK6vozxYwRZpJqaokHLvtkSHMataUvKPA7CytGPLaQghhBDnE7/XgD3//PMoisL06dNxOp0A6PV6fvvb3/KXv/yl1QMUbc/7JGTZjxKwkwvxB5fmgSWS/QkK1oICdpT392xLFJSPNfpiEo/t41B8N1aWVXFvcstPrwohhBCiKb9HwAwGA3/9618pLy9nx44dbN++nbKyMl588UWM7fSoqWhdjbXAvBtyA4QlgzmCAXW1aFDYFluHtaAAl8tAdVUUAJY0HWnZhwDYXGmj2nn+7IEphBBCdCS/E7BGAQEBXHzxxfTv35+AgIDWjEm0s8YRsNzyulNbRigKxA0iUFXpZYpmf6JCUI3Nsy3RyWlIXUwBYdXlxNhtOFVYL+UohBBCCJ/4NAV54403smTJEkJCQrjxxhvP2Pbjjz9ulcBE+4kLM6MoUOdwUVLTQHTwyZHMuEFw9FsGq3r+GQH1ISas+QUUxcaRnOLZlsis60vK8QMU9Uzn29JqJkaHdei9CCGEEOcDn0bAQkNDvYXUQkJCvNXfW3qJ849Bp8Ea4nm6o6WF+OmVpaAoHEnSYS0ooLo6EqfDgFtfS0JSOElH9gDwbVlVu2+6KoQQQpyPfBoBW7x4sffPS5YsaatYRAdKDA8gv7KenLJaBiedLKp6ciH+oKJjkBTHZmstNx+0o6gK5RVWoqOzCU92kLg2H6PqJt/uYG9NHf2CZUpaCCGEOBO/14BdccUVVFRUNDteVVXFFVdc0RoxiQ6Q0NJC/OBYCLIQ6XKSEmBlf6KCweEgsryc8vI4AJTobHQuJ70qiwDIkHIUQgghxM/yOwFbvXo1DQ0NzY7X19ezbt26VglKtL8WS1EoCsSdLEehDycrBhwBBqx5ed4EzB52jIigMBIO7ADgG0nAhBBCiJ/lcx2wXbt2ef+8b98+715LAC6Xi6+++or4+PjWjU60m6QITwKW/eMEDDzrwA79l8H1DXysUTieYsZSUMge+8XU2sIICKwgNtVI4uGdcOnVbKuqpaTBSZTB7xJzQgghxAXD5xGwgQMHMmjQIBRF4YorrmDgwIHeV3p6Os888wyPPfZYW8Yq2lBy5GkSsMaCrCXZAGy22ogoK8PgdlN2shxFUFINwbZqurntqMDKMhkFE0IIcXbWrl3LpEmTiIuLQ1EUPv30U5/P3bBhAzqdjoEDB7ZZfK3F5wQsMzOTo0ePoqoqP/zwA5mZmd5XXl4eVVVV3HHHHW0Zq2hDjSNgJyrqcLjcp944OQWZUHKUGHMUexJUNKqKpbCI8jLPNKQz6ggaNPQ8uS1RRokkYEIIIc6OzWZjwIABvPLKK36dV1lZyfTp07nyyivbKLLW5fM8UXJyMgBut/tnWorzUXSwEaNOg93pJq+8jpSoQM8bgZEQloxSkcXgwCQyrMW4jHosOTnkxg3G7dKBqQKrtQ+FOzZBfE9Wl1XhcKvoNUrH3pQQQojzzsSJE5k4caLf5911111MmzYNrVbr16hZRznrhTr79u0jOzu72YL866677pyDEu1PURSSIgI4XFRDdlntqQQMPNOQFVkMVvV8pVXITQnEmlOAqmqpqLAQEZlHdJpK1MajhGug3OXm+8oaLgsP7rgbEkII0YSqqqiO9h9EUfQaby3RtrJ48WKOHj3KP//5T5555pk27au1+J2AHTt2jBtuuIHdu3ejKIq38Gbjh+tyyX6A56vkyFMJWBNxg2HvJwyuKgNgS2wdNx20E+pyUV4eS0RkHsa4EhR0DKqvYqUhhIzSKknAhBCiE1Edbk48trHd+417agSKQdtm1z98+DAPP/ww69atQ6c7fx4A87sMxX333UdqaiqFhYUEBASwd+9e1q5dy5AhQ1i9enUbhCjaS2JEC6UowLsQv3vBAYINwexMcAJgyTtBebnnydeG8GMYDTrSju8HYEVJpVTFF0II0aZcLhfTpk3jySefpEePHh0djl/8ThU3bdrEypUriY6ORqPRoNFouOyyy3j22WeZPXs227dvb4s4RTtoXIifVfqTBCx2ICgaNFV5DOo7gk213+HWa7Ecz+RQ0ijsdcEYzdXEJQdQ//1qjD0vJbOugUO1dnoGmtr/RoQQQjSj6DXEPTWiQ/ptK9XV1WzZsoXt27fzu9/9DvCsVVdVFZ1Ox4oVKzptkXi/EzCXy0VQUBAAUVFRnDhxgp49e5KcnMzBgwdbPUDRfk5bC8wYBFE9oXg/g/URrNUpFKQEE51ZjAaFsvJYYs3VhKfYUQ7bucSgsN6u8lVxpSRgQgjRSSiK0qZTgR0hJCSE3bt3Nzn22muvsXLlSj766CNSU1M7KLKf53cC1q9fP3bt2kVaWhrDhg3jueeew2Aw8NZbb5GWltYWMYp20lgLLKesFlVVmy6ajB8MxftJt3seutgWa+cXh51YXC7KyuOIjTuEYs0Fori47ATrA618VVLJfSmWDrgTIYQQ56uamhqOHDni/T0zM5MdO3YQERFBUlIS8+bNIy8vj3fffReNRkO/fv2anB8TE4PJZGp2vLPxe1zwj3/8o7cUxTPPPENWVhajRo1i+fLlvPzyy60eoGg/CSe3I6q2O6modTR9M24QAH2LszBqjWyLrwcgJiuLygorqluDK6CE0AgD8Xs2A7C9upYC+0+uI4QQQpzBli1bGDRoEIMGeb535s6dy6BBg7zF3vPz88nOzu7IEFuF3yNgEyZM8P45LS2Nffv2UVZWRnh4eJs/ZiralkmvxRJipLDKTlZZLeGBhlNvxqcDoM/fzsUDLmdX/WbcOi0xh4/gSkujqtJCaHg+1lQNR3dtZ/BVU9hWXceKkkqmx0d10B0JIYQ434wdO/aMD3EtWbLkjOc/8cQTPPHEE60bVBvwawTM6XSi0+nYs2dPk+MRERGSfHURp10HZukHWgPUlTM4JI0GvUJJcijh5eWYtFpKT25LFJhYhdPRwAjVDsBXJZXtGr8QQghxPvArAdPpdCQnJ0utry7stKUodAZPEgakq56RsR0JThQg3t5AeZmnHIUalYOiddMjx/NAxvryGmqc8vdFCCGE+LGzWgM2b948ysrK2iIe0cGSIzwV8LN/WooCvPXABlaVoVW0/GCtASDm6BFqa0Nx1Aehah1YknS4d20lzWykQVVZWVbdbvELIYQQ5wO/E7CXX36ZdevWERcXR8+ePRk8eHCTlzi/JUWaAcgqszV/8+Q6sID8XfSK6MXBBAVVoyHqwEFAoaTMCkBkspOCwwcYF+5J5mQaUgghhGjK70X4kydPlvVeXViSdwqyrvmbJxMw8ncwuO997C3dS1lKGJHHSokwmigvjyc27gjG+BJczhiG2Mp4E8goqcTudmPUtF0xPiGEEOJ84ncCdj48WSDOXuMasBOVdTQ43Rh0P0qaIruDIRgaqkk3WfgHsDtRZewxiK+1sc/pKUfhDirFGBJB6KFdWBPTKWhwsLasmnFRoR1zU0IIIUQn4/eQRFpaGqWlpc2OV1RUSCHWLiA6yIhZr0VVIbf8J+vANBqI99RlGVTvqQO20VLhOe/AQVwuAzWVnsKr1lQNuXt3cU20J+n6T7FMQwohhBCN/E7Ajh8/3uJTkHa7ndzc3FYJSnQcRVFOX4oCvNOQEYX7SQtNO7kOTCF83z60Gg0lJ8tRhCbZKDhyiAkhnq2Ivi6pxOGWzbmFEEII8GMK8vPPP/f++euvvyY09NR0ksvl4ttvv+3Uey4J3yVGBHCwsLp5KQo4tQ4sbyvpAyfwYeUxKpMjCcssIT4wkJKyOFLTtqGxFIASSPyJ40TqTZQ6nGysqGFMRHD73owQQgjRCfmcgF1//fWAZ4Tk1ltvbfKeXq8nJSWFF154oVWDEx2jcQQsq8VSFCcTsKJ9DI6cy4d8yL4khRGZEFtRQbY2DGd9MDpTNaHxdvL27eKaS8bxjxOl/Ke4QhIwIYQQAj+mIN1uN263m6SkJIqKiry/u91u7HY7Bw8e5Be/+EVbxiraSUrUyQSspRGwkDgIjgXVzRDVCMB6SzkAUXv24ClH4ZmGjE5Vydm7i2tPrgNbXlyJ6wzbSwghhBBr165l0qRJxMXFoSgKn3766c+eY7fbefTRR0lOTsZoNNKtWzfeeeedtg/2HPi9BiwzM5OoKNnbrytLjvTU78oqbaEWGHhHwaylmcQFxrE3XkXVKATuP0CA0UxpWRwAAQnlFGUeJV2nEKbTUuJw8kPlaa4phBBCADabjQEDBvDKK6/4fM6UKVP49ttvWbRoEQcPHuT999+nV69ebRjlufO7DAXAt99+y7fffusdCfuxzp5xip+XEnlqCtLtVtFoflL3LX4wHPjSsw7Mms4XthNUp0QTcqyI5EAzByusqG4tSlAVhpB6Cg/uZXxULP8uKOfLogqGhwV1wF0JIYQ4H0ycOJGJEyf63P6rr75izZo1HDt2jIiICABSUlLaKLrW4/cI2JNPPsn48eP59ttvKSkpoby8vMlLnP/iw8zoNAp2p5vC6voWGpxaiD/Y4tn94ECyFoDYkhLcbj22Cs80ZHiyney9O/lFdBgAXxZXyDSkEEJ0AFVVaWhoaPeX2sb/n//5558zZMgQnnvuOeLj4+nRowcPPvggdXUtFBTvRPweAXvjjTdYsmQJt9xyS1vEIzoBnVZDQriZ46W1HC+pJTbU3LRBnKcWGBXZDA7yPPm6NqaMoUDE1q1wySUUllsJisglIqWBnB27+fX0mYTqtBQ2OPmuooaR4bIYXwgh2pPD4WD+/Pnt3u8jjzyCwWBos+sfO3aM9evXYzKZ+OSTTygpKeHuu++mrKysU8/K+T0C1tDQwIgRI9oiFtGJnHEdmCkUonoAkFpdTIQpgt1xTlSNgvboMaLDIikviwfAYCml7MQxnDXV3qKsnxVVtMs9CCGE6PrcbjeKovDee+8xdOhQrrnmGhYuXMiSJUs69SiY3yNgd955J//617/405/+1BbxiE4iJTKANcDxlkpRgGcasuQQSt5WBscM5pv6b6hJjSH4aCHJRj1bKoJx1oahC6ggKN5Gzt7dXN9zAO/nl/FlcQXzuyeg++naMiGEEG1Gr9fzyCOPdEi/bSk2Npb4+Pgm9Ul79+6Nqqrk5ubSvXv3Nu3/bPmdgNXX1/PWW2/xzTff0L9//2Yf7MKFC1stONFxGkfAssvO8CTkzvc9C/HTb+Sb7G84lGIg/SjEnsgDrZbSsjgsARVEpNjJ2buLscNGEqnXUepwsr6imrERIe14R0IIcWFTFKVNpwI7ysiRI/nwww+pqakhKMjzkNehQ4fQaDQkJCR0cHSn5/cU5K5duxg4cCAajYY9e/awfft272vHjh1tEKLoCI21wI6XnGEEDDwJWIxnIf666DIAgjZvRqfVUVxuBSAkqYbsvTvRaRR+IdOQQgghzqCmpoYdO3Z4c4rMzEx27NhBdnY2APPmzWP69One9tOmTSMyMpLbb7+dffv2sXbtWn7/+99zxx13YDabW+qiU/B7BGzVqlVtEYfoZH68BkxVVRTlJ9OFln6gNUJ9BT1ULUH6ILbHVoNGg/t4Fgk3/pKsfDuqS48moI56+1FqykqZHBPO30+Usry4kgU93Bg0fv8bQAghRBe2ZcsWLr/8cu/vc+fOBeDWW29lyZIl5Ofne5MxgKCgIDIyMrj33nsZMmQIkZGRTJkyhWeeeabdY/fHWdUBAzhy5AhHjx5l9OjRmM3mlr+kxXkrIdyMRgFbg4uSmgaig41NG+gMEDsAcn9Ae2I7A2MGst6xHltaDIFH8knVwnFVS215AoFRmYQk1ZCzdxfDLhuL1aCnoMHB6rJqxkeFthyAEEKIC9LYsWPPWLpiyZIlzY716tWLjIyMNoyq9fk9/FBaWsqVV15Jjx49uOaaa8jPzwc8i/MfeOCBVg9QdAyjTktcmGfo9rQV8ROGeH7mbiHd4pmSPJJqAiA6MxOA/DILAOHJ9WTv3Y1WUZgUI9OQQgghLmx+J2D3338/er2e7OxsAgICvMenTp3KV1991arBiY6VcnIa8oxPQgLknUrA1sV41oHpv/+BQHOwd1siY0w1eYc3A3BDTDgA/y2pxOZytVX4QgghRKfldwK2YsUKFixY0OzJgu7du5OVldVqgYmOl+zdkuhnRsAK9tA3pBtGrZHNlhrQanDm5JAWH0dDQyDO6hgUBTAfp7KokEEhAaSYDdS63HxdUtU+NyOEEEJ0In4nYDabrcnIV6OSkhKMRmMLZ4jz1c+OgIUlQ0AUuB0Yig/QP7o/dUaF2m6eUa9kpx2A0pNFWRvXgSmK4h0FW1Yg21cJIYS48PidgI0ePZp3333X+7uiKLjdbv73f/+3yVML4vz3syNginJqFOxkQVaAY2metWORBw+hoHjXgYUm1pK9dzsAN1k9Cdjq8ipKGpxtdQtCCCFEp+R3Ava///u/vPnmm0ycOJGGhgYeeugh+vXrx9q1a1mwYEFbxCg6SEqUZwQss8R2+idS4psvxF9vqQDA9f33REdaqa6ORG0IQGN0Uly4EVVVuSjARP9gMy4VPi+SUTAhhBAXFr8TsD59+rBr1y6GDh3KuHHjsNls3HjjjWzfvp1u3bq1RYyigyRFeEbAquudVNQ6Wm6UcGoh/oDoAegUHRuiykCrxXHiBN0TrICG2rJkAAwReVQUnADgJotnFOzjQknAhBBCXFjOqg6Y1WrlySefbO1YRCdj0muJDTWRX1nP8VIb4YEtbGER55l2pPw4AQ219I7sze6S3dR1j8d8IIuEGs8i+xNlFrpb9xOS5NkXMjw2nutjwnnyyAm2VNWSVWcn2SxrCIUQQlwY/B4BW7x4MR9++GGz4x9++CF///vfWyUo0XmcWgd2moX45jCI6uH5c95W7zRkZjfPeUG7d6PXGigut4BbgyncTs7hdQBYjHouC/fs2yWjYEIIIS4kfidgf/nLX4iKimp2PCYmhvnz57dKUKLzOPUk5GkW4kOL68A2WD0jX/Xf/0BiQjIulwFnRRIAVbZN3jVlN56chlxWWH7GysdCCCFEV+J3ApaVlUVqamqz48nJyU32ZhJdQ+OekMdLzpCAeZ+E3MKgmEEoKKwJLwCdDmdhIT3jogEoK/PUjjNbiinLywXg2ugwTBqFI7V2dlTXtd2NCCGEOC+sXbuWSZMmERcXh6IofPrppz97znvvvceAAQMICAggNjaW22+/ndLS0rYP9hz4nYDFxMSwa9euZsd37txJZGRkqwQlOo/UHz0JeVoJl3h+5m4lVB9M9/DuNOgV6nt5Ft7HlhQDkF3qGTkNiq0la+93AATrtFx9cj/IZYVlbXELQgghziM2m40BAwbwyiuv+NR+/fr1TJ8+nRkzZrB3714+/PBDNm/ezJ133tnGkZ4bvxOwX/3qV8yePZtVq1bhcrlwuVysXLmS++67j1/96ldtEaPoQGnRngTs2JlKUcT0AX0A2Cuh5JB3GvJ4N8+52q3bCA4Io84eDDUxKBrIzzu1bVXj05CfFFbgcMs0pBBCXMgmTpzIM888w4033uhT+++++46UlBRmz55Namoql112GXfddRdbtmxp40jPjd8J2DPPPMOwYcO48sorMZvNmM1mxo8fzxVXXCFrwLqg5MgAFMVTiqLU1tByI63u1NOQuZubrQOz/fAD3S7ylCixlXqmr+3sRnW7ARgbEUKkXkepw8nqMtmaSAgh2oKqqrhcte3+auv1vSNGjCA3N5fly5ejqiqFhYV89NFHXHvttW3a77nyuwyFwWBg6dKlPP300+zcuROz2czFF19McnJyW8QnOphRpyUh3ExOWR3Him1EBZ2mVETCEMha70nAej8GwMqQXGYYjbhKSugeGcIOILcsmp7JEBhbTnH2UWJSuqPXKNxgCePt3BKWFZYz7uSUpBBCiNbjdtexes3F7d7v2DG70Wqbb2HYWkaMGMF7773H1KlTqa+vx+l0ct111/F///d/bdZna/B7BKxRjx49+OUvf8kvfvELSb66uNQoT6mIzJKa0zfyrgPbQpQ5ipSQFBw6aOjjGfGKys5Gg4ai6lCUhkB0RjeZBz73nv4/lggAviqppNrpapsbEUII0eXs27eP2bNn89hjj7F161a++uorMjMzmTVrVkeHdkZ+j4C5XC6WLFnCt99+S1FREe6T00iNVq5c2WrBic4hLSqQtYeKOebLQvyifWCvJt2SzvGq42ReFETP7eD4YTMxvfpTUJyLq7Q7mtgdlJWvAR4AYECwme4BRg7X2vmyuIJfx8oDHUII0Zo0GjNjx+zukH7b0rPPPsvIkSP5/e9/D0D//v0JDAxk1KhRPPPMM8TGxrZp/2fL7xGw++67j/vuuw+Xy0W/fv0YMGBAk5e/XnvtNVJTUzGZTKSnp7Nu3boztl+zZg3p6emYTCbS0tJ44403mrVZtmwZffr0wWg00qdPHz755BO/+1VVlSeeeIK4uDjMZjNjx45l79693vfLysq499576dmzJwEBASQlJTF79mwqKyv9/gw6O++TkMVnSMCCLRCWBKiQt827DmxjbDUAtT/8QO8+noKtxaXxnnMCjuB2eUa7FEXxLsZfViBFWYUQorUpioJWG9DuL0VR2vS+amtr0WiapjNarRagU9eX9DsB++CDD/j3v//N0qVLeemll3jxxRebvPyxdOlS5syZw6OPPsr27dsZNWoUEydOPG09sczMTK655hpGjRrF9u3beeSRR5g9ezbLli3zttm0aRNTp07llltuYefOndxyyy1MmTKF77//3q9+n3vuORYuXMgrr7zC5s2bsVqtjBs3jupqT0Jx4sQJTpw4wfPPP8/u3btZsmQJX331FTNmzPDrMzgfNCZgZxwBgx9NQ55aiL/SfBwlIABXZSWpJj0AmeWh4NJhCLaTe3S19/TGoqwbKmrIt59mwb8QQoguraamhh07drBjxw7A892/Y8cO73f0vHnzmD59urf9pEmT+Pjjj3n99dc5duwYGzZsYPbs2QwdOpS4uLiOuAXfqH6KjY1VDx486O9pLRo6dKg6a9asJsd69eqlPvzwwy22f+ihh9RevXo1OXbXXXepl156qff3KVOmqFdffXWTNhMmTFB/9atf+dyv2+1WrVar+pe//MX7fn19vRoaGqq+8cYbp72ff//736rBYFAdDsdp2/xUZWWlCqiVlZU+n9PecspsavIfvlQveuQ/qtPlPn3DTa+p6uMhqvreVFVVVXXch+PUfkv6qTunT1H39eylFi96R53/9F/Uxx9/XF31wS/Ub75NU9d/dU+TS0zaeki1rNyuvpZV2Ja3JIQQ56W6ujp13759al1dXUeH0mZWrVqlAs1et956q6qqqnrrrbeqY8aMaXLOyy+/rPbp00c1m81qbGysevPNN6u5ubnnHMuZPu9z/f72ewTsgQce4K9//es5D+s1NDSwdetWxo8f3+T4+PHj2bhxY4vnbNq0qVn7CRMmsGXLFhwOxxnbNF7Tl34zMzMpKCho0sZoNDJmzJjTxgZQWVlJSEgIOt3pl9bZ7XaqqqqavDq7uFAzBp0Gh0slr/wM1eq9I2A/gKqeqgd2kWcRf90PP5CS7FmUX13i+Wlr+KHJJRpHwWRvSCGEuDCNHTsWVVWbvZYsWQLAkiVLWL16dZNz7r33Xvbu3UttbS0nTpzgn//8J/Hx8e0fvB/8TsDWr1/Pe++9R7du3Zg0aRI33nhjk5evSkpKcLlcWCyWJsctFgsFBQUtnlNQUNBie6fTSUlJyRnbNF7Tl34bf/oTW2lpKU8//TR33XXXae8ZPIsFQ0NDva/ExMQztu8MNBqF1MjGacgzPAlpvRi0BqgthfLMZvXAajdvpk8/zzqwrLIoUEEbWEptba73EpOiw9ApsLumjkO2+ja6IyGEEKJj+Z2AhYWFccMNNzBmzBiioqKaJBOhof7Xb/rp4jxVVc+4YK+l9j897ss1W6sNQFVVFddeey19+vTh8ccfP23s4Jm7rqys9L5ycnLO2L6z8GlLIp0RYk8+iJG7hSEWzx6R3xqOogkJwW2zkejwrO0qcbnRlacAcPzwv72XiDTouDwiBJBRMCGEEF2X32UoFi9e3CodR0VFodVqm40oFRUVNRt5amS1Wltsr9PpvPtQnq5N4zV96ddqtQKekbAfP77aUmzV1dVcffXVBAUF8cknn6DX689430ajEaPxNMVMO7HUxi2JzvQkJHimIXM3Q84PJF/8SyJNkZTWl9IwoC+6dVtwb99BeHAU5dUlOEt7Q8RxiopW0Ie53kvcZAkno7SKjwvL+UOqtc2foBFCCCHa21kXYi0uLmb9+vVs2LCB4uJiv883GAykp6eTkZHR5HhGRgYjRoxo8Zzhw4c3a79ixQqGDBniTXxO16bxmr70m5qaitVqbdKmoaGBNWvWNImtqqqK8ePHYzAY+PzzzzGZTP58BOeVNF9GwKDJk5CKojRbB1b7/Xf07OWZhiwo8czPOzVHcDhOle8YHxVKgFZDdn0DW6pqW/M2hBBCiE7B7wTMZrNxxx13EBsby+jRoxk1ahRxcXHMmDGD2lr/viznzp3L22+/zTvvvMP+/fu5//77yc7O9lav/emjprNmzSIrK4u5c+eyf/9+3nnnHRYtWsSDDz7obXPfffexYsUKFixYwIEDB1iwYAHffPMNc+bM8blfRVGYM2cO8+fP55NPPmHPnj3cdtttBAQEMG3aNMAz8jV+/HhsNhuLFi2iqqqKgoICCgoKcLm6XiX3xk25fzYBSxzq+VmwGxpsDLF6piG968C2bqNXzzQAshvcKJVRKIpKcfE33ksEaDVcc3I7omUyDSmEEKIL8nsKcu7cuaxZs4YvvviCkSNHAp6F+bNnz+aBBx7g9ddf9/laU6dOpbS0lKeeeor8/Hz69evH8uXLvVsb5efnN6nNlZqayvLly7n//vt59dVXiYuL4+WXX+amm27ythkxYgQffPABf/zjH/nTn/5Et27dWLp0KcOGDfO5X4CHHnqIuro67r77bsrLyxk2bBgrVqwgODgYgK1bt3pri1100UVN7iszM5OUlBSfP4fzQeN2RHkVddQ7XJj02pYbhiZAcBxUn4AT20/VA9Me4o7oaFzFxUQUFaNTdNhxoC3ujzN0JXnZnxEXd+q/442WcD4qLOfLogqeuSgenUamIYUQQnQdiupnPYmoqCg++ugjxo4d2+T4qlWrmDJlyllNR17IqqqqCA0N9Zaw6KxUVWXgUxlU1jn4732j6B17hlj/fSvs+xSufBz3ZXMYvXQ0lfZKPthyKZqM9UT+dhbLzeEcP3GMIcYAzMPeBFXHmDFb0ek8iZ7DrTJg4x7KHC4+HNCNURHB7XOjQgjRidXX15OZmendyUW0rTN93uf6/e33FGRtbW2Li+RjYmL8noIU5w9FUXx7EhJOTUPm/IBG0TA4ZrDnvO6eJKp203f0GdDb06ROh7sqGBQnJSWn9hHVaxSuiQoD4LOiita7ESGEEKIT8DsBGz58OI8//jj19adqNNXV1fHkk08yfPjwVg1OdC4+rwNLOJmAnSzI2liOYr21AoC63bvpluDZHqJIqUZ7oi8AhUX/aXKZ62LCAFheUoHD3Xn38xJCCCH85fcasL/+9a9cffXVJCQkMGDAABRFYceOHZhMJr7++uu2iFF0Eo1PQh4tOkMxVoDY/qA1egqylh0j3epZB7bWeYA7ExNx5ORgOHSIYGMI1fYq1LJ04DtKS9fgdFaj03lGykaEBRGp11HqcLK+vJrLIzvvFK0QQgjhD79HwPr168fhw4d59tlnGThwIP379+cvf/kLhw8fpm/fvm0Ro+gkLorxrM86UvwzCZjOCHEDPX/O+YGe4T0J1AdS7ajGMbgXALXffUe37p6HF4rqA2moMKOqjibTkDqNwi+iPU9Dfl5c0ar3IoQQovN59tlnueSSSwgODiYmJobrr7+egwcP/ux5a9asIT09HZPJRFpaGm+88UY7RHtuzqoOmNlsZubMmbzwwgssXLiQO++8E7PZ3NqxiU6mMQE7WlTz83uB/mhfSJ1Gx6CYQQAcO1kPzLbpO/qeXAeWqy2DXE8yVli0vMllvNOQxZU0uN2tcRtCCCE6qTVr1nDPPffw3XffkZGRgdPp9JZ8Op3MzEyuueYaRo0axfbt23nkkUeYPXs2y5Yta8fI/edzArZ161Yuv/zyFjePrqys5PLLL2fnzp2tGpzoXJIjA9FpFGwNLvIrf2afRu9C/M0A3nIU6ywVANgPHSIhOBgNGmyKHX2x5/3GachGl4YFEWPQUel0sabs1HEhhBBdz1dffcVtt91G3759GTBgAIsXLyY7O5utW7ee9pw33niDpKQkXnrpJXr37s2dd97JHXfcwfPPP9+OkfvP5wTshRde4IorrmjxUcvQ0FDGjRvH//7v/7ZqcKJz0Ws1JEcGAHDk59aBNS7EL9oL9mrvQvwNtbsx9uoJgGPbNqzhnm2f6p2J1JcbUFVHk6KsWkVhUnQYIE9DCiHEuVBVFZvL1e4vP6tdNVFZ6dklJSIi4rRtNm3axPjx45scmzBhAlu2bMHhcJx1323N50X433//PQ8//PBp3580aRJvv/12qwQlOq/uMcEcLbZxuKiG0T2iT98wJBZCk6AyG/K20jdlJGadmQp7BQ2DRsKBg9g2baL3VRM4seoEBboqwo/HYgrPorDoS2Jjb/Be6rqYMBbllfB1SSX1Ljcm7VnvoCWEEBesWrebbmt3t3u/R0dfTKD2NMW7z0BVVebOnctll11Gv379TtuuoKCgWXksi8WC0+mkpKSkyZ7OnYnP32R5eXneKvAtCQoKIj8/v1WCEp2XdyH+z42AASSeXAeWsxm9Rs+A6AGec7t5RtFsm76jZ2/PvpD5mgoMeZ51YqWla7HbC72XuSQ0kFijnmqXmzXlMg0phBAXgt/97nfs2rWL999//2fbKkrT3VIaR91+erwz8XkELDo6moMHD5Kamtri+wcOHCAqKqrVAhOd048X4v+sxGGwZxnkeLZsGmIZwnf537EuqpRuej2O3FxC6+oI0JqpddWhUS6itiiIgJga8gs+JSX5LgA0J6ch38ot5rOiCiac3CdSCCGE7wI0Go6OvrhD+vXXvffey+eff87atWtJSEg4Y1ur1UpBQUGTY0VFReh0OiIjI/3uu734/KlcddVV/PnPf27xPVVVmT9/PldddVWrBSY6J59LUcCphfi5P4Db7V2I/13lDswDPKNhtZu+IyXRk9TXmLSU7vOsMczP/7DJuoHJJ5+G/LqkkjqXPA0phBD+UhSFQK223V/+jEKpqsrvfvc7Pv74Y1auXHnaQZ8fGz58OBkZGU2OrVixgiFDhqDX6/3+nNqLzwnYH//4R3bv3s2wYcP497//zc6dO9m1a5d3o+vdu3fz6KOPtmWsohPoFh2EokCZrYHSGvuZG1suBn0A1FdCyUEujr4Yg8ZAWX0ZjnRPCQrbxo30HeKpH3dCW4GS2x1UA7W1mVRWnnrqZXBIAPFGPTaXm5VlzZ/EFUIIcf675557+Oc//8m//vUvgoODKSgooKCggLq6Om+befPmMX36dO/vs2bNIisri7lz57J//37eeecdFi1axIMPPtgRt+AznxOwbt268c0332Cz2fjVr37F4MGDGTRoEL/+9a+pra0lIyODiy66qC1jFZ2A2aAlPsxT8+1n14FpdRDvGfUi+zuMWiP9o/sDcLCbZ1NT2/ffk5aagoJCpaaWEGMqzvIUAE7kf+S9lKIo3ppg8jSkEEJ0Ta+//jqVlZWMHTuW2NhY72vp0qXeNvn5+WRnZ3t/T01NZfny5axevZqBAwfy9NNP8/LLL3PTTTd1xC34zK+tiIYMGcKePXvYsWMHhw8fRlVVevTowcCBA9soPNEZXRQTRG55HUeKaxiW9jPz60mXwvF1nnVgQ25niHUIWwq3sD44n+7BwbgrK+HYMWICoiisLcYdFEH+dgOJV0JR0XJ6dP8TOp1nC6TJMeG8nlNMRkkVNpfrrJ6qEUII0Xn5UrJiyZIlzY6NGTOGbdu2tUFEbeesnucfOHAgv/zlL5kyZYokXxegi6L9eRLyUs/PHy3EB9hcso2AoZ41YrYNG+lx8mnISqOb8iMqRn08LpeNouL/ei81INhMsslAndvNN6UyDSmEEOL8JQWVhN+6W/xIwBKGAAqUHYOaIvpH90en0VFUW3RqHdimTfQa3AeAfG0FEaY4dA5PopaX94H3Uj+ehnzvROk5FfcTQgghOpIkYMJvftUCM4dBjCfRIud7zDozF0d5HoPel2YAoG7bNqzh4RgVAw7FhTkkkfJDEWg0BqqqtlPxo8X4U2Mj0CsKa8trWJRX0qr3JYQQQrQXScCE3y6K9hTkza+sp8bu/PkTvPtCNp2G3KTNRBcbi+pwULd1G8lRnlovjsBgcncfw2q5HoDs7FM7LFwUYOLxi+IAePLICbZX1bbGLQkhhBDtShIw4bfQAD1RQUbA14KsJ9eBZTdNwLYUbiVwxHDAU46i1wDPSFmpoQFbaRkRwZ7tiIqLM6itzfRebkZ8FNdGh+JQVX6z9ziVDh+SQCGEEKIT8espyEYVFRX88MMPFBUV4XY3LYr549ocouvqHhNESY2dw0U1DEgMO3PjpGGen/k7wFHPwJiB6BQdJ2wnaBjUC5aBbcMGetxzN3zzH8o0NaSYEyjLtBEVeQUlpSvJznmHXj2fBjxrwV7slcSe6oNk1Tdw/4EcFvVL6dRbTgghRGuSNbDtoy0/Z78TsC+++IKbb74Zm81GcHBwky89RVEkAbtAXBQTxKZjpb6tAwtPhcBosBVD/g4Cki6lT1QfdhXvYneqlosUBfuhQxhttUTqwih1VqALjSVn/x4u+eWdlJSuJD9/GWmpczAYPGUvQnRa3uqXwqSth1leUsmivBLuTDjD5uBCCNEFNFZ2r62txWw2d3A0XV9trWeZS1tU1Pc7AXvggQe44447mD9/PgEBAa0ekDg/ND4JebjQh82xFcWzL+SBLyH7O0i6lEssl7CreBc/1O+nX79+1O/ejW3DBrolpVJ6bDv1AWay9+xm3MzfERx8MdXVu8nN/Sdpafd5LzsgOIDHL4rj0cN5PHnkBOMiQ0g2G9vqloUQosNptVrCwsIoKioCICAgQEb/24CqqtTW1lJUVERYWBjaNqg76XcClpeXx+zZsyX5usD1tHgW4h8o8CEBg6YJGDDEOoRFexaxuWAz91420ZOArV9Hrzt+yw/HtlOir8NUVEJdVSXJSXeyZ+995J34Fykpv0WjMXgve0d8FP84UcoBWz0HbPWSgAkhujyr1QrgTcJE2wkLC/N+3q3N7wRswoQJbNmyhbS0tLaIR5wnelk9m2bnVdRRVe8gxPQzw7NJnsX25HwPbjeDYgahVbTk1eRhb6wHtmEjaX/5C3p01CsOYkKSyT2wl4sumYDBEENDQxHFxSuwWH7hvaxnc1l5lkQIceFQFIXY2FhiYmJwOBwdHU6Xpdfr22Tkq5HfCdi1117L73//e/bt28fFF1/cbF70uuuua7XgROcVGqDHGmKioKqeQwXVDEmJOPMJsQNAZ4a6Mig9TGB0T/pE9mF3yW52xNTSIzgYV2Uljv37iQ+0cNyWB8HRZO/ZTY9hI4mP+xWZx18mN/efTRIwIYS4UGm12jZNEETb8jsBmzlzJgBPPfVUs/cURcHlcp17VOK80NMaTEFVPQd8ScB0Bk9V/OPrIHsTRPdkiGUIu0t2s6VkO4MuvZTqjAxq1q+nR7c+HN+Vh82s4/jOnQDExU/leNarVFRupqbmIEFBPdvhDoUQQoi24ffcjdvtPu1Lkq8LS69Yzzqwg76uA0tqrAd2ah0YwOaCzQRedhkAtvUb6Dm0LwBl2jpqSkupt9VgMlqJjhoPQG7ee611C0IIIUSHOKfFM/X19a0VhzgP9bKebQK2CYDBMYPRKBpyqnOoTfdsxl23cydhwUbCCERVVALCkjlxcD8A8Qk3A1BQ8ClOp499CiGEEJ2Q3wmYy+Xi6aefJj4+nqCgII4dOwbAn/70JxYtWtTqAYrOq6fFsxD/QEGVb8XqEoaCooHy41CVT5AhiN4RngX42zW5GNLSwO3Gtuk7UiI92xK5g8I4um0HAOFhlxIQcBEul438gk/b4I6EEEKI9uF3AvbnP/+ZJUuW8Nxzz2EwnCoHcPHFF/P222+f4UzR1XSLCUSrUaiqd1JQ5cNoqCkELJ7pRXJOTkM2bktUsIXAy0YCULN+HRf17A5ApRGO79oNeNYYJpwcBcvKegOHo6o1b0cIIYRoN34nYO+++y5vvfUWN998c5OnL/r378+BAwdaNTjRuRl1WtKiAgE/6oE1lqM4uQ7sEuslAGwp3ELQqNEA2NasJW1ILzSqQp3GQW1lGQ67J8GLi/0lZnMydnsBhw4/2Yp3I4QQQrQfvxOwvLw8LrroombH3W631CO5APU8uQ7sQP7ZrQMbZBmERtGQVZVFTd8klIAAnMXFKCeysGjDATCGJJB3wLMOTKs107fP84CGgoJPKSr+ulXvRwghhGgPfidgffv2Zd26dc2Of/jhhwwaNKhVghLnj1ML8X2cDmwcASvYDfVVhBhC6BXRC4CtFbsJGjkCgJrVq0mxJAHgDAzmwMZt3kuEhg4mOfk3ABw48EdU1dkatyKEEEK0G78TsMcff5zf/e53LFiwALfbzccff8zMmTOZP38+jz32WFvEKDqxxor4Pk9BhsRBWDKobsjdDMAlFs805OaCzQSNHQt4ErCLejeuA3ORvW9Pk8ukpc4mKLAnDkcZdXU5rXAnQgghRPvxOwGbNGkSS5cuZfny5SiKwmOPPcb+/fv54osvGDduXFvEKDqxxinIo8U1OFxu30460zqwMWMAqN+zh7jEMAyqDofips5Wict5aopbozHSp8/zKIoeh6OidW5GCCGEaCdnVQdswoQJrFmzhpqaGmpra1m/fj3jx49v7djEeSAh3EyQUYfDpZJZYvPtpJ/WA7MM9q4DKzW7MPXvD0D9lo3E6aMA0IVYydrd9CGP4OA+pKXO9v7ucEhtMCGEEOcH2cVYnBNFUehhCQL8mIZM9qzzInczOBsINgR764FtKdxC8OVjAahZtZrUuGQAHAEB7F+/udmlkpJ+g0ZjBKC27tjZ34gQQgjRjvxOwMLDw4mIiGj2ioyMJD4+njFjxrB48eK2iFV0Uj1PrgPzeSF+VA8IiARnPZzYDpyahvzxOjDbpk1065EKQJXeRc6Bvc0updHoUJSTW5r6UAtWCCGE6Az8TsAee+wxNBoN1157LU8++SRPPPEE1157LRqNhnvuuYcePXrw29/+lr/97W9tEa/ohHqf3BNy3wkfEzBF+dE6sI1A03Vgxl690FmtqHV1BNcVEKiacCsqdQ3VuBzyxKMQQojzn87fE9avX88zzzzDrFmzmhx/8803WbFiBcuWLaN///68/PLLzJw5s9UCFZ1X37hQAPb4moABJI+EA19C1ka47H4GxZyqB1ZUW0TQ2DFUfLCU2g2rSTQmcaAhG31wNIe+30Pvywa2zY0IIYQQ7cTvEbCvv/6aq666qtnxK6+8kq+/9hTFvOaaa7x7RIqur09sCBoFiqvtFPmyJRFAcuMI2PfgdjVfB3bFFQDUfLuS1ARPPbB6s5kDm7a2evxCCCFEe/M7AYuIiOCLL75odvyLL74gIiICAJvNRnBw8LlHJ84LZoOWi2I8C/H3nKj07STLxWAIBnslFHrWdv14HVjApZeiCQrCWVxMQohnkX2NzknekebrwIQQQojzjd9TkH/605/47W9/y6pVqxg6dCiKovDDDz+wfPly3njjDQAyMjIYc7Kek7gw9IsL5VBhDXvyqriil+XnT9DqIHEoHP3WMw0Z259LrJewZO8SNhdsRmMwEHT55VR98QXa/d8T6g6gUlOLQ62lzmbHHGhs+5sSQggh2ojfI2AzZ85kzZo1BAYG8vHHH/PRRx8REBDAmjVrmDFjBgAPPPAAS5cubfVgRefVN96zDmx3no8jYHCqHMXJhfiN68Cyq7MpsBUQMsFTW64m47/Em6MB0AZFsn/trtYLXAghhOgAfo+AAYwcOZKRI0e2diziPNYvzlOKYu/ZJGBZG0FVCTYE0yeiD3tK97C5YDPXXjYOJSAAx4kTJIaEs684iwaziUObtzF44iVtcBdCCCFE+/B7BGzs2LG8++671NXVtUU84jzVOAJ2orKe0hq7byfFDQatEWzFUHoUaFqOQmMyETR6NACWimIAanQuCrMOoqpS9EsIIcT5y+8ELD09nYceegir1crMmTP57rvv2iIucZ4JMupIiwoEYK+v5Sj0JohP9/w5awNwKgH7If8HAELGe/YXdW/4kjB3ACjg0tooy69pxeiFEEKI9uV3AvbCCy+Ql5fHu+++S3FxMaNHj6ZPnz48//zzFBYWtkWM4jxxTuvAsk6tA9MqWnJrcsmvySdw9BgUoxHHsUPE6T1P2eqCItm/bnerxi6EEEK0p7PaC1Kr1TJ58mQ+/fRT8vLymDZtGn/6059ITEzk+uuvZ+XKla0dpzgPeNeB+VqKAn6UgHlGwIIMQfSJ7AN4piG1QYEEXnYZAHFuz19Xu9nE0W3bWylqIYQQov2d02bcP/zwA4899hjPP/88MTExzJs3j5iYGCZNmsSDDz7YWjGK80S/kyNge/L8qIifOAwULVTmQHkW8KNpyIKm05ARR/YBYNO5Kck/TFWp/+sQ3W4nDkclTqfN73OFEEKI1uL3U5BFRUX84x//YPHixRw+fJhJkybxwQcfMGHCBBRFAWDKlClcf/31PP/8860esOi8+p3ckii7rJbKWgehAfqfP8kYBHGDIG+LZxQsPJlLrJfwzp532FywGcCzObdWi2bnGsJSf0mFphbVWMPuVUcZ+T/9vJc6cWIpWypeQacNRNEYcDlrcLpqcDprcLlsOJ01uN2nkjazOYmgoD4EB/UmOLgvQcG9MRos3r/HQgghRFvxOwFLSEigW7du3HHHHdx2221ER0c3azN06FAuuUTKBFxoQgP0JEaYySmrY29+JSO6Rfl2YspITwJ2fAMMnMbgmMFoFS15NXmcqDlBXGgcAUMvoXbTd8QpoVRQiz44mt0rVzP8hj5otUZwQ4OjhMrK5lsVqUADBmoJoJYIaglAg0pUXREhdV9RXPyVt61eH0FwUB+CgnoSEJB68pWGwRAtiZkQQohW43cC9u233zJq1KgztgkJCWHVqlVnHZQ4f/WLCyWnrI49eX4kYMmXwYa/QtZ6AAL0AfSN6suu4l1sLtjM5IsmE3zlVdRu+g5rdR37QqAy2Iip8gBZe0pRFAvg5hUeIggHgYoDneKmVtVjU3XUosOttDzbbsSJVVNJDIVEuTOJchQQU15ITPnXxFCEgQYAtNogAgJSPAmZORWzOQmTKRajMRaTyYpGI5X5hRBC+M7vBKxxv8eWfPXVV1x99dXnFJA4v/WLD+W/ewrYlevHQvykS0HRQPlxqMyD0HiGWoeyq3gXPxT8cDIBu4LCZ57BsnMrCSOGkqstpTYujE1ffU9kTz2EWmhQtJShpQyTZ9gL4EeDVoqqYnA6MDgduDUabAYTdkVHljuSLCKBPk3aA0RSTox6Aosrn5jqAizVhVjYg4UCAjg1nanXR2Iyxf0oKYvzvIyenwZDFMppkkAhhBAXHr8TsCFDhvDcc89x7733eo/Z7XYeeOABFi1aJAVaL3ADE8MA2JFT4ftJphCIHQAntnvWgfWfwiWWS3h799tsLfRMKepjYzH17Uv9/p1cpd7OcvcOirRVHK7ewiWHg0i1byWpdx8ICKTC3oDd7SbMoCfcaCDSbCIywExkgJnAwCjMZjP19fUczcpmR14+B8oqKFG0VJkDqTYFUGUKpMocSINOTynhlCrh7Kdvs7BDqMHCCaxqHhZHgedVfRQr6wmktklbRdFjMsZiNMViNsVjMiVgNidhNidiNiefTNBkilMIIS4Ufidg7733Hr/5zW9Yvnw5ixcvpqCggGnTpgGwYcOGVg9QnF/6J4SiKJBbXkdRdT0xwSbfTkwe6UnAjq+H/lMYGDOw6TqwoDiCrryC+r170VbvZbx5IJ/rf6BKV4/NVoMZuLJvL3r16uVzrAkJCYwB3G43JSUlZGVlkZ2dTdaRg1RWVVGvM1BlDqTSHEhDWCTOqBiqA4IoREOJw0UVQVTRg8NKj2bXDlbqiVXKiOEE0a7jxKj5WOrzsdQfJJTvfzrQhlYbgNmUeDIpS8J8crozMCANgyFGkjMhhOhi/E7AbrzxRi699FJuvfVW+vXrh81m4/bbb+eFF17AbDa3RYziPBJs0tPTEsyBgmq2ZVVwdT+rbyemXAabXvHWAwvQB9A3si+7SnaxpXAL1wVdR/CVV1Hy8v9hW/sPgie/yLX16XyorsOpO7epPY1GQ0xMDDExMVxyySWoqkpJSQlHjhzhyJEjHD9+HFdRLhw61T4qMQlTShruaCvVAUFk2Z0cr7OTWWensMFJtWqiWo3jEHGgDGnSn1lxEautJoZiotRcIpzHiHYVEmMrJNq2BuPJdWeNtNrAk+vP0jwvcwqBgRcREJCGVutjgiuEEKJTOavNuF0uFw0NDbhcLlwuF1arFaNRFiELj0FJ4RwoqGZ7TrnvCVjScECB0iNQXQDBVtKt6Z4ErGAL13W7DmOP7ugTE3Hk5KC32gk8buKKup6sCD4MQEBAQKvErygK0dHRREdHM3z4cBoaGsjKyuLIkSMcPnyYsrIyirKOQ9ZxwFOYeEBCApNTUkhNTSXMauWEUyWzzs7xugaO19m9yVlevYM6VcsxZxjHCAO6g3J5k/4jNPXEKOVEqfmEu7KJchUSXV1EdPU2Ivn6RwmagsmUQGBgNwIDunmSssBuBAZchF4f2iqfhRBCiLbhdwL2wQcf8Nvf/pZRo0Zx6NAhduzYwe23387XX3/NP/7xD9LS0toiTnEeGZQUxvs/ZLM9q8L3k8xhYO0HBbs9o2D9buISyyUs3rOYLYVbAE9iFHzllZQtWULDsW9RDVeTRBKpudtReo0lMTGxTe7HYDDQvXt3unfvzsSJEykrKyMzM5Pjx4+TmZlJTU0NWVlZZGVlsWbNGnQ6HfHx8SQmJnJVYiKJiYne5NDudpNT30BWXQNZdXay6xvIrmsgu97ze7XLTZnbRBmxQCwog5vFE6bYiFQLiVALiaovIbK+mKjSPUSxikhKCKYKoyGGwMCLCAzs7v0ZFNgdvT6sTT4jIYQQ/vE7AZsxYwbPP/88v/3tbwEYN24cu3fv5q677mLgwIFUVflRBR147bXX+N///V/y8/Pp27cvL7300hnLXKxZs4a5c+eyd+9e4uLieOihh5g1a1aTNsuWLeNPf/oTR48epVu3bvz5z3/mhhtu8KtfVVV58skneeuttygvL2fYsGG8+uqr9O17ajH2W2+9xb/+9S+2bdtGdXU15eXlhIWF+XX/XdHgpHAAduVV4HC50Wt9nCJMGeVJwI57ErBBMYPQKBpyqnMosBVgDbQSPH4cZUuWUPPNV8Q8Np3a74oYEDSEdfvzqCm3ExzR9lNyERERREREkJ6ejqqqlJaWcvz4cW9CZrPZvAlZo6ioKBJPJmOJiYlcERWFooQ0ua6qqlQ4XWTVNZBT30BufdOfOfUN1LjcVKiBVJDGUaXlf+wYVDuRDSVENJQSVV5MJLuJZDWRFGPVu0gKjCAyKIXAwB6SmAkhRAfxOwHbtm0bPXv2bHIsPDycf//73/zjH//w61pLly5lzpw5vPbaa4wcOZI333yTiRMnsm/fPpKSkpq1z8zM5JprrmHmzJn885//ZMOGDdx9991ER0dz0003AbBp0yamTp3K008/zQ033MAnn3zClClTWL9+PcOGDfO53+eee46FCxeyZMkSevTowTPPPMO4ceM4ePAgwcHBANTW1nL11Vdz9dVXM2/ePH8/yi4rLSqQULOeyjoHB/KruTjBx+mw5JHw3Wuehfh49oXsHdGbvaV72VK4hV+k/QLzoEEYUlJoOH4c6vbiViKJMsUTXn2EktyadknAfkxRFKKiooiKimLIkCHe9WM5OTlkZ2eTk5NDaWkpJSUllJSUsH27Zw9Lk8lEQkICCQkJxMfHEx8fT0BAAOF6HeF6HQNDmk+nqqpKpdNFTn0DefUOcu0N5NU3kGd3kFvv+XNhg5MGxUg+8eQT3zxgJ1AJQRXVRFJMJDuI4BtitPXEmwwkBoSSGhxLSlgK4UE90OmC2/gTFEKIC5Oiqqr6882aKykpQVEUIiMjz7rzYcOGMXjwYF5//XXvsd69e3P99dfz7LPPNmv/hz/8gc8//5z9+/d7j82aNYudO3eyadMmAKZOnUpVVRX//e9/vW2uvvpqwsPDef/9933qV1VV4uLimDNnDn/4wx8AT6kNi8XCggULuOuuu5rEtXr1ai6//PKzGgGrqqoiNDSUyspKQkJCfv6E88St7/zAmkPFPHldX24dkeLbSbVl8FwaoMIDhyDYwvObn+fv+/7OTd1v4okRTwBQ8re/UfzCQsyDBmH8xe9x7a6hoC6LoF+NoNfwzjcFbrPZyM3NJScnh5ycHPLy8nA6nc3aRUZGNknKLBYLWq3Wr77sbjf5doc3Mcurb+CEN0Gzk1ffgM3989dRVDehVBCtqcSicxBn1JFgDiYlKJrU0ASSAkKJNujQyNOZQogL1Ll+f/s1AlZRUcGjjz7K0qVLKS8vBzyjX7/61a945pln/Eo+Ghoa2Lp1Kw8//HCT4+PHj2fjxo0tnrNp0ybGjx/f5NiECRNYtGgRDocDvV7Ppk2buP/++5u1eemll3zuNzMzk4KCgiZ9GY1GxowZw8aNG5slYP6w2+3Y7Xbv7/5O2Z4vBieFs+ZQMduyy31PwAIiTq0DO74OLv4fLrFewt/3/d27DgwgdPJkil/6K3XbtxPzcAAlu6qwmpPJP5IJnTABCwwMpGfPnt6RY6fTSWFhIXl5eeTm5pKbm0tZWRmlpaWUlpayc+dOAHQ6HbGxscTHx3uTsrCwsDOWpDBqNKSYjaSYW34oRlVVqpwuTtgd5NkdnKhvIKfORratnLy6WvIbVApdRpyKjgoiqFAjOOwAHEANUAyQC+Siw0W01k6sQSHebCY5MJwEcyBxRj0JJgNxRj2hOq2U0BBCiBb4nICVlZUxfPhw8vLyuPnmm+nduzeqqrJ//36WLFnCt99+y8aNGwkPD/fpeiUlJbhcLiwWS5PjFouFgoKCFs8pKChosb3T6aSkpITY2NjTtmm8pi/9Nv5sqc2P1/WcjWeffZYnn3zynK5xPhicHAbA9uwK/05MGd0kARtkGYSCQlZVFsW1xUQHRKOPiSFo9GhqVq2i+uvPqXBeRIQ+Cm39WQ3mtrvGRfrx8fEMHToU8IyS/Tghy8vLw263e0fNGgUGBnrPjY+PJy4uzq+nPxVFIVSvI1Svo3dQY9mYKCDZ28atqpQ6nGTVVJBZnU1WdQk5dTXk250UOHUUu8OoIAynoiXfFUB+HWyrA8rKgfIm/QVoIMFkIN5kJN5oIM6kJ95oIP7kz1ijHpOvawSFEKIL8TkBe+qppzAYDBw9erRZYvLUU08xfvx4nnrqKV588UW/Avjpv45VVT3jv5hbav/T475cs7Xa+GvevHnMnTvX+3tVVVWbPb3XkQYkhqEokF1WS0mNnaggH8uUpI6C716FzHUAhBhC6BXRi/1l+9lSuIWJqRMBCPufm6hZtYrKzz6DYZ7PU9eg4Ci0gaJ4thRSAVUF1fOj8c+oKhqTDm2kqdOMzgQGBtKjRw969PAUdXW73ZSVlXmTsby8PAoKCrDZbBw6dIhDhw55zw0PD2+SlFmtVgwGw1nHolEUog16oiOiGRIR3ex9h6OCipojZFUe4nh1Idm1lZyot1PkMlNCFGVEUUok1UootW44VNvAodqGFnryiNLriDPpSWiSoBlIMOqJNxlkqlMI0SX5nIB9+umnvPnmm82SLwCr1cpzzz3HrFmzfE7AoqKi0Gq1zUa7ioqKWuyjsZ+W2ut0Ou9atNO1abymL/1arZ7aVQUFBcTGxvoUm6+MRuMFUTMtxKSne0wQhwpr2JZVzvi+ftQDUzRQdhSqTkBIHOmWdE8CVnAqAQsaPRptVBSukhLvqRE5Bgpf3OZzjJogPcbkEAypoRhTQ9HHBqJoOscXvUaj8S7uHzhwIAAOh4OCggJvQpaXl0dZWRnl5eWUl5ezZ88ewPMPh5iYGOLi4rwvi8WCTndWZf+a0evDiA4fQnT4EH5cYtbhKKem5jA222FstasorTlOtq2cQoeGkpNJWSnRlBJFKVGUEYldMVHicFLicLKruuVtzPSKQqxR7x01a5zejDd5RtISjAaCdP6tlRNCiI7m8/8jN5ZrOJ1+/fqdduqwJQaDgfT0dDIyMpqUiMjIyGDy5MktnjN8+HC++OKLJsdWrFjBkCFD0Ov13jYZGRlN1oGtWLGCESNG+NxvamoqVquVjIwMBg0aBHjWjq1Zs4YFCxb4fI8XusFJ4Z4ELLvC9wTMHAbW/pC/w7st0RDrEP65/59N1oEpej2hk6+jbNE7ZNUcwhRiwmgKQm/w/D1Q3Z48DoVTI2KKgmcgRcFla8Bd46Bubyl1e0s91zRqMSSHYEwJwZgWiiEhGOUcq+y3Jr1e7y1j0aiuro4TJ040ScpqamooLCyksLDQ+9SlVqvFYrEQFxdHfHw8sbGxREdH+73I/8zxhRMePpTw8KHeYyPwjJjZbEc8iZntCDbbt9hqj1JvL6BGDfImZKde0ZQpFsqUGErVYByqxlMvrb4BsLXYd4hO4x05i29Mzk7+jDPqiTUa0HeS5FoIIcCPBCwqKorjx4+TkJDQ4vuZmZl+PxE5d+5cbrnlFoYMGcLw4cN56623yM7O9tb1mjdvHnl5ebz77ruA54nHV155hblz5zJz5kw2bdrEokWLvE83Atx3332MHj2aBQsWMHnyZD777DO++eYb1q9f73O/iqIwZ84c5s+f7y3AOX/+fAICArz7XoJnhKygoIAjR44AsHv3boKDg0lKSiIiIsKvz6IrGpwUzgebc9hyvMy/E1NHeRKwzLXQfwrpMekAHKs8Rll9GREmz2cbdtP/ULboHY5V7uVQ5Xou/Z/ZjPzlaJ+6UJ1uGnKrsWdW0XC8EvvxKlS7C/uhcuyHPOuYFL0GQ2oopm6hGLuFoY8L6jQjZI3MZjPdunWjW7duwMlF9lVV/P/2zjvOrqrc+7/dT29TzvRMJgnpQEIA6UoX4QpeLohKuXhVLqAErgrXCipNr7xeREAscK2gUgQFIUhRIBQTgoH0yaROnzn9nN3X+8faZ8850ydMZjLJ+ubzfJ61nr32Pmv2lP3LWms/q7293RVm7e3tUFXVjf3jH1TIiqLoirK6urr9IsoAOmIWiaxAJFK+JZNpZhxB1opcvijOXoKq7nGmigELPBKIDYgzrg5pqRn9XB36SBRdtg9pi0fatJE2VWzMqcP2gQNQo0ioVyTUDRJptC6jQmIvDDAYjKlj3ALs7LPPxle/+lWsWrVqyPoSTdPw9a9/HWefffaEPvziiy9GX18fvvWtb6GjowNLlizB008/jVmz6ILgjo4O7Nq1y20/e/ZsPP3007j++uvxox/9CHV1dbj77rvdHGAAcPzxx+Phhx/G1772NXz961/HnDlz8Mgjj7g5wMbzuQDw5S9/GYVCAVdffbWbiPW5555zc4ABwP3331+2oP7kk+nD/8EHH8QVV1wxoXtxMPKBFirI39mTRF434ZPH+ePWfDLw2g/pQnwAEU8EcyNzsS25DW93vY3TZp0GAFBaZsO7fDlgTbxvnMhDaQ5DaQ4DaASxCYzOHLS2FPQdaWjbk7BzZrkg84hQWgYEmVjtO+AEGcdxCIfDCIfDWLhwIQAqyhKJRJkg6+jogK7r7qhZkVJRVltb64qyyZq+LEUUgwiHlyEcXlYWt6wC8vntjjCjAi2fb0U+/wYIMTBoq0yo8KAPVcjIC5CWDkNSaEQfV4UeO4QuU0a7ZkEnBB2agQ7NANL5Yfvj4TnUKXTErHQtWl2JWGNTnQwGY7IYdx6wPXv2YMWKFVAUBddccw0WLFgAANiwYQPuvfdeaJqGf/zjHwflgvL9ycGaBwygD/4T73wRe5MF/PLTx+CkeUMXdA+LmgbubAaIBax8F4g04juvfwePbH4En1r4Kdx4zI1u0+Sjj+H/fvc4TCScEbAzR77uRPpuE5jdeajbktBak9C2p0C0cqXHeUQozSHIzSEos8OQ6wMH1JTlaBQX+Xd0dLgjY0VRNpjiZuVFQVZbW4t4PP6+FvrvW58NFAq7kc+3IpffjnzO8fltMM3MiOfxQgiGdwmyykIkxRYk+Dr0kRi6LC/aNQvtGk1gOx5CIu+KtKI4q3Pe6qxz3ur0src6GYxDginLA9bQ0IDVq1fj6quvxn//93+XvX14xhln4J577mHii1EGx3E4tiWGx9buxerWvvELME8IqDsS2LuGrgM78hIsr16ORzY/grXd5YvsQ2efBe73TwAEMLu6J6/vPAepxg+pxo/gifUgFoHRnoXaSgWZvjMNoppQN/VD3eRMsYo85MYAlOYwFWWNQfA+adL6NJmULvJfunQpgHJRVmqqqrrT7cU1ZcUkzDU1Na7V1tbC7/fvxz5L8Ptb4Pe3oPQniRAC3ehzBFkr8q44a4Oq7oFtpSFkX0M4+xrCKE24wcHjqYPP3wKxsgVZaR6Swiz0c9Xosf1o10zsVQ20azSZbcq03KnOTSNMdQJATBJckVZbtg6NlmtklnqDwWBMMBHr7Nmz8cwzzyCRSGDr1q0AgLlz57L1TowROa6lggqw7X0TO7H5JEeA/Z0KsDjdlHpT/yZk9SwCcgAAwPv9dLU9AezC8G/RTQacwEFuDEJuDAIfbKSCrCNbtobMzhnQ29LQ2waS64rVXshNIShNIcizghCrDrxpyyLDiTJCCJLJ5BBRlsvl3O2Vim9fAkAwGHQFWTweR01NDWKxGHh+/wkOjuOgyJVQ5EpEo8eWHbMsFYXCTnfELJ9vQ77Qhnx+O0wzA1XdC1XdC4BOd8sAagDUcjKO9TbB52uGLzoLPt9s2EozknwDekgIHZqJvZqOdpVOa7ZrdOeBvGWj37DQbxTwbnbkn8cKSXRFWa0zilbrkVArS673s+lOBuOgZp8WdkSjUTeBJIMxGsV1YP/ck0JOM+FXxvkjN/sk4NUf0IX4hKDGX4OGQAP2ZPdgXc86nFh/4v7r9DjgBA5yQxByQxA4qR6EEJi9Bbp+rC0FfVcGZm8BZje1/D+66HkeEXJTEEpTEHJTCHJD4IAdJQOouIlGo4hGo1i0aJEbz2Qy7qhY0fr6+pDJZJDJZNz/oAH07c3q6mpXkNXU1KC6uhoez/7ft1MQPAgE5iMQKN+/lhACw+hDPr+DirI8FWW5fBsKhV0gREc+vw35/LYh1+R5D+Z6Z2Gpbxa83lnwhZrh9c2C19MEVahCh053Gmh3toFqd4Rau2agQ9NRsGmi2z7DxPpRRFpI5FGryK4oq5GpWKtRBnyFxHKkMRgzlclfWctglNAY86Eh6sWeRAFv7ejHB+dXj+/EpuMAXgJSu4FEGxBrwfL4cuzJ7sHarrXTLsAGw3EcpCofpCof/EfTlBtWVoe+KwN9VwbazjSMPRkQtXxhPwCIVXSUTG6iI2xS3A9OOLAfqsFgEMFgEPPmzXNjmqahq6vLFWTFVBiGYQxZ7A8AkUgE8Xi8zPb3aFkRjuMgy5WQ5cohb2cSYkFVO5AvUHFWyO9wy6q6B7atIpvbjGxu85Dr8rwCr7cJVd4mNHlnwettgi/cBK+3CR7PLHCciKRpOaNmVJAVR9GKI2kdmoGsZbvTnZtHme6UOA5xRUStLLvCLK5IqJFFxItCjY2mMRgHJEyAMfY7x7VU4Pdr9mD19r7xCzDZDzQcDex6jY6CxVqwIr4CT7Y+iTVda/ZvhycJISDDu6gC3kV0FJBYNozOPPSdaei70tB2Z2D1qTB7CjB7CsivcUbJZB5SfZAKsgbqhZB8wKdIUBQFTU1NaGpqcmPFdWWloqyzsxOZTAbJZBLJZBKbNw8IGVEUUVVVhXg8jurqanfkLBAITNnXz3ECvN4GeL0NqIiVC33bNqCqe5Ev7EAhvxP5wk4UCjuQz+90xJnm5DvbOux1FaUOPm8TPN5GzPU2Yam3Ed7KRni9syBJA4t4M45I63REWacj2Dod69AN9OomDEKwRzWwRzVG/ZoCAo8aRUJcpgItLouD6jTGhBqDMXUwAcbY73zAEWCvb59oPrCTBwTYUVfgqDjNB7a+dz00S4MilO8osGf3ejz347uhqypsy4Ts8ULyeCB7vJC9PrcsebzwRyKomtUCZQL7KL5fOIGHXB+AXB8Ajq8D4IyS7c5Q20U90SzobSnobSn3XD4ou2vQ5MYA5IYgeM+B/+tbuq5syZIlbjyfz6O7u9sdJevq6kJ3dzcMw3DXmZXi9XrLBFlVVRWqq6vh9XoHf+R+/nokui7M1wwMSnto2yZUdS8KhV0oFHaiUNjlCLSdKBR2w7ZVqOpuqOruwVtmAgBEMQyvtxFeTyM83gb4PI1Y7G3AinATPJ468Hz5z7thE3TpjiDTBny3E+t0fNaykbVsbMtr2JbXRv36AgKPallCtTOCVi2LqHZEWrUsIi5LqJIlxCSBTX0yGO+TcaehYOwfDuY0FEXakwUcf8cLEHgO675xBoKeca552vEq8NA5gL8K+OJWEACn/v5U9BZ68eBZD2JFDZ06+uEnPg3d6tqnvkVr61A9ey7iLXMRnz0X1bNb4PEH9ulakwGxCcyePBVje6goM7pygD20rVjlhVwfgNTgTF3W+sHLM3cEw7ZtJBIJV4wVBVp/fz9G+jMVDAZdMVbqp2J92UQghEDXu52Rst3IF3ZBLeymYk3dDV3vHeMKHBQlDo+nAV5PAzzeeuo99fB4GuDx1ILnh08LkjUtdOlUnHVpBrp00/FUoHXrJjp1+gLBeBE4uodntSyhyhFp1bJI9xCVRVQ65UpJRJSJNcZByvt9fjMBNs0cCgIMAE753ovY2ZfHz69YgVMXjHM/TVMD7pgFmAXgP1cD8UX44stfxLM7nsW1R16Lzx3xOQDAQ/9+D5JWF2Y1iKhdsQCyxwNeEKGrBRhqAbqqQi8UaL2Qh66qSPd0I9PXM+zHRuK1qG6Zi9o581Az9zDEZ8+FNI0PdFu3YLRnB0bKdmdgJYYZyeABKe6HVE9HyOT6AN3fcobkJhsJwzDQ29vrCrKenh50d3cjlUqNeE4oFEJVVdUQm+oRs/Fimjmo6h5HkO1BobDbqVNvWcMnjx2gKNDqS6wO3pKyIIw+2ps1LXTqBro1E906HUnr0p2yZqLLifUbE8t8LHL0rc+iOKuQqECrLHpHqFU6x1geNcZMYcrygDEY74cPzK7Azr48Vrf2jV+AiQow6zig9QU6DRlfhKPiR+HZHc/ijc43XAEmCRWQlEWYvxBY9LFTx92nfDqF7u3b0NXWii7Hp3u6kOzqQLKrA1tW09QEHMejorEJNXPmoWbOYaiZMw+VTc0Q9kN2+OHgZaEkaz/FyurQ92Zh7M5A35OFvicDO2vA6MjB6Mi5b12C5yDV+CDXB6kwm4GiTJIkNwFsKaqqoqenxxVkRctms0in00in02htbS07JxAIoKqqyp0WLZaDweC0rrETRf+wb2sCxTc2+1FQ99BRM3UvVHUP1MIet2zbGjStE5rWiVRq+DWSkhSFx1MHj1IHxVMHj6cWHqUOHk8dFE8t/HIV5vo8mDvGrLzhvMVJRRr1PY5o6zFM9Djr03p0E0nTgklAR93GmezWJ/CokMQBk4WSMvUx1wSERDbCxpiZsBGwaeZQGQH747q9uO7hdVhQE8RfVo5vv0YAwCv/D3j+ZmD+R4BLfoPdmd0457FzwIHD0x97Gg3BBvz6P36LpBjHh04AFn7yg7Cyedi6DjHoB68oY35EKYVMmgqy1q3obN2CztatyPYPzWEmSBKqZ7UgPmcuauYchnjLXMTqG8Dz0zMFSAiBldYHBNneDIy9Wdj5YR56PAcp7oNUF4BcR0fMpFo/+PGmCDnAKRQK6O3tdcVZ0UYbMVMUxRVlpRaNRvfLNkyTSTERrVrYQ4WZuhcFtR2a2u6U98KysmNeh+NEKEocilILj1IDxeN4pRYeTy0UpQayXAmOG794120bfQYVYz26iV7dRK9holc3HG+iTzfR45SNfXgcCRwQFQcEWdSZ9oxKIqKigFhp3YmFJQHKFLxtyzi4YVOQM5xDRYAlcjqO+s4q2AR47aZTURcZ51TQ3jXAT04FlDBwYxvAC/jsc5/F6o7V+MzSz+ALy7/gCjDe0gCOh807a8yIDcHWIdgmeFgQOAsCZ0PkCWQZiNX4UDG/FvFlLYg1hCEpw4unbH8fOlu3OrYFna1boOVyQ9pJigfVs+egZs5cxFvmId4yD9GaWnDT9IeeEAIrqcHYm4W+l46SjSjKOECs8EKq8zujZFSUCcGp3W5of6JpGnp6elxxVvSJRGLENWYcxyEWi6GiosIVZRUVFaioqIDf7z/g30wtYhhpmnRWa4emdkBV26Fq7VDVDmhqOzS9C4SMPbXIcSJkucoRZjVUsHlqoMjxgboShyBMfMqeEIKMZaNPN908acVyr1PuN0wn0S0tZyewbm0wPoFHVBQQkQRERBERSUBYpBYRRYQlARGnHnbahEQBIZGHzMQbA0yAzXgOFQEGAP9632tYszOBWy9Ygk8eO2vsEwDAtoA7ZwNaCvjMC0D9UVi1cxVueOkGVHor8dyFz+EvKx/BLr3ufffPJ6iIxCRUtsRQcVgtYnV+xGr9kAe9bUgIQbKrwxkl20qnL7dvg6ENzdcke32ont3iCLK5qGmZi0j8ABBl7Vno7TkY7VkYe7Ow0kP3gAQAPihBqg1ArvVDqvNDqg1ArPQesNn89wXTNNHf3++KslIzjJHTO3g8HleMlVosFoMywZHX6ca2Teh6jyPQ6FSmqnVQgaZ1QlM7oOndAMb3uBDFEBVjcjVkpdotK0ocslwJRamGLFfvk1ArRbNtJBxB1qeb6DdNJAwLCcN04wnDQsI0kTBMJA0LSdMa51cxMl6ec8QYFWhBx4dEAQGBirSgU6cxHiGnnV/gERQEKDw3YwQ8Y3iYAJvhHEoC7J4XtuJ/ntuC0xfG8dPLV4x9QpHffgLY/Gfg9JuBE6+HYRs44/dnoE/tw10fvAun1n0IXW9uhuiRIIUDkMMB8LIII52DkcpBS+VgZPMwMgUYORVGTkW2K4VEew6pvICsUgVDHvne+zwWYnEvKloqUdEYQqw2gGitr0yY2baFRPted5Ssa/s29Oxog2kMFTay14f47Dmont2C6tlzUd3cglhdA3hh+t5gtLI6jPYcdEeQGR05mH2FYZ+3nMRDjPvoXpm1fnfPTMF/4Gb03xcIIchkMmWCrK+vD319fUgmk6OeGwgE3JGzUh+LxaZ8E/PJwrYN6HoPNK0LqrPeTNO6HBuo2/boqS5KEcUgZLkailxFhZpc5SbIleUqyIpTl6LguMn5/bAJQdq0kDItJAwLSZMKs37DRNqkAi1lWkgZxTI9njKt9zXiNhiRA4KCAL9IBVlAEBAQefgEHgGBCrWAwMPvtPEL9JiPp94vCLReEmeibmphAmyGcygJsPfaU/jI3a/AKwl4+xtnwCON8w/q6/cDf7kRaPkQcNkTAID/Xfu/+On6n+L4uuPx4zN+vM99IrYNfedOJNesR887O9C3ox/JFJDzViPnq4WuhEc81+8jdBqzpQIV9UHE6vyI1vjdqUzbstC3Z5ezwH8bulq3oWfn8KJMlBVUNTWjuijMZrWgomkWJHn6RlJs3YLRmYPRnoPRkXUX+BNj+IeQEJIhloqyON0ZgJMOvukawzDQ39/vCrJSy+dHf2MxGAy6YiwajbrlWCx2wKXPmCiEEJhmBpreBV3rdsRZNzSdCjVd64am90LXuyck1AAeshxzxFglZLkCklzh1Csgy9QkqQKyHIMg7J+3XS1CkHEEWnoYnzFtZEwLGaukblnIOMezlo3cJIq4wfAAvAIPL8/D6wgzWubg4WnZI/Dw8LTu4Xl4BI7GHQEnO8cUnoNS4mXnmMLxkHgOCs9B4jjIPA+Z4yAeRKPi44UJsBnOoSTACCH4wO1/RVdawy+uPAYnH1Y1vhO7NgD3HQeIXuCmnYCoYE9mDz782IcBAE9/7Gk0Bhsnr5+6Dq21Feqmzci8txW9rT1IdKnI8BHk/LXI+WuhjzhiRhDwEUTjXlTMrkBFYxjRWj+iNXTEzDJN9O/dja62VnTvaEV3Wyu6d7TBUIfuCchxPKJ19aiaNRtVs2aj2vH+aGza/pdLbAKzr0CFWUcORmceRmcOVv8I2+XwztqyoiCr8UOM+yBWHFzTmKUUCgVXnA32qjrytkIATThbFGZFK9aDweCUbNM0FRSFmq53Q9O66cia3g1d74Wu9VKv90DTe2AYCYx36rMIz3shyzFIUoyKMykGqViXopBci0GSIpCk8KSNsI2FTQhyFhVqWctG1rSQsWxkLQs5p55zhFrOGhBtWdNC3rKRt20ULBt5J563bOgHwGOcAyDzHESOCjPJEWjFsshREzi45dKYUOJ5lJQ5QAD1vOMBgOc48AB4jn42Bw5cSWe4kn5x4PCRqjCOCvsn9WtmAmyGcygJMAC46dF/4uG3duOK45tx878sHt9JhAD/cxiQ6wau+DPQTLeH+dyqz+G19tfw6SWfxsqjVu6/TsN5YHR1Qd20Cdqmzchs3Ym+PWkkkzaychVyPirMDDk44jX8HhvRKgUVs2OIzYoiGvchWuuH4hWQ6OxAd9s2dO/Yjq62VvTs2I5CJj3sdbyhMKqaZqGysRkVjbNQ1dSMisYmyJ7py3FlqyaMrrwjyoqWB1FHSD0gcpAqfXQq0zEx7ocY8xy0wgygOwD09/ejv78fiUTCLff39yM3zIsdpQiCgEgkgmg0Oqz3er0H5fSTbZswjH7oeg90vY+a0Qtd74Oh91GxZjhxvR+EDL+ecXQ4R4hFIIoRV5RJUhSS6HgpAlEMQ5LCJT44ZcJtNEybuMKsUPSOWMs7Ik21bag2gVpSLti2UyfQbRuaTaA6XnM9jRmExnSbOOWZJR2+N78Bl9ZVTuo1mQCb4RxqAuwv73biql+twawKH1764gfH/8D4w6eBd/8AnPxl4NSvAgCe3/k8rn/pesQ8MTx/4fOQhKlfg0RsG0Z7B/TtrdBatyOzbRf6dqWQ6DeR5SPI+WqQH3XEDFBkG5FKBRWzoojWhxCt9SNS7QXH5dG3ewe6d7ahx7FE+14QMvwURrg6jsqmZlQ2zkJFQxMqGpoQq2uAOE1rjgghsDO6O0pmdOVhdOVgduVHnMaESDc1F6t9kKq81Ff76ML/GZS7bF/QNA2JRMIVZsVyIpFAMpmEbY8+daUoCiKRiGvhcLisfrAKtFIIIbCsLHS9n4o2ox+GXvR9MIwEdCMBw0jAMPphGAmYZuZ9fCIHUQw4oi0EURwwSQxBFIMQpbATC0IUgtSLAYhiEIIQAM8f2GlORoIQApPQVCMaITAdYWYQAmOYskUITOcc0y6WadwgBDYBLBBYhE712o63QEcNieNtADYBbBAQAqdOZUxRzBDQ/7fTMi2cWxVhI2CMcg41AZbVTCz71nMwLIIX/usUtFSNc9uftb8Anvw80Hgs8OnnAACGbeDMP5yJ3kIvLlt0GVbEVyCpJZHUktAsDWEljKgSRVgJI6JEEPXQslfc/yNFhBBYfX3QWrdD396K9Oad6N/Vj0SviYztR94XR85XA80TG/EaPE8QrpARrQ8jUuNHNO5DICbCMnqQ6dmL3t070Lt7F3p37UAuOczmgqDTmJGaGsTqm1DR0DggzGrrpy27P7EJrIRKBVl3HmbRd48izHhAjHkhVnmpIKvy0XKVF7zv4Fr8Pxy2bSOdTqO/vx/JZNIVZUWfzY6d50uWZYTDYVeYFctFCwaDEKbxRZDpwrYNGGbKEWgpGGYCppFyRJrjzRQMIwnTTMEwUjDN1Dh2JxgfguCDKAQhiAGIYgCC4B/kAxCFAATRD1HwQRACEAQfBNFH2wh+Whd84HnPQS+yDySYAJvhHGoCDAA++dPX8eq2PnztIwvxHye1jO+kxE7gfw8HeBG4cQeg0Km+u9fejZ+s/8mEPl8RFNT6a3F0zdE4pvYYHFNzDGKjCKHJxkqnoW1rhbZ1K3KbW9G3vQfJrgKyJICcvwZ5Xxx5bxUIP7Kw8PhFROI+RKp9CMd98AYsEKsXaqYT/R270L93N/p274KaG/nBHKqqRqyugVp90TfCF45Myx/xUmFm9uRhdBdgdlNxRrSRc1TxfskRY1SUiVVeiJVeOp15iGxrYxiGK8hSqRSSyWSZjTW9CdCcZ8FgEOFwGKFQaFg/k3Kf7W9sW4dppmEYaZhm0hFmGZhmmsYdb5oZmIZTtjJOmyxse/T1gPsGB0Hwgue9jijzUnPqvOCBwHvAC95B3onzCnjB8bzixGS3Tsu0znEyeF6aUGLegw0mwGY4h6IA++nft+M7f96ID7TE8PBnjxv/if97BJDYAXzid8BhZwFA2WJ8ADix/kRElAgUQUFKSyGhJZDSUu7ImGkPvyZpXnQejq05FsfWHouj4kchOMparv2F2dcHbetWaFu2oLB5C5Jb25HoyiMvxZD3xpHzVSPvi0NXIqNexx9REKn2IlTlhTdogCcJGFoP8qlOJDr2oG/PrhHXlwGA4vMjWluHaG09tTrH19ZNyzozdyqzOw+zuwCjJw+zh4qzkfKXAQB4DmLMQ8VYZYkwq/RCCMoH9VqzwRiG4QqzVCo1rI01xQnQdWjBYBChUAihUKisXKwfqiNpE4UKuCwVZI4ws8wcLCsH08zCsrL0uJWDVfRWDpaVp2bmnFgetj30JZ6pguMkV5hxnAiek8DxklOXwHMiOF5yjongeBEcJ4HjhIEYJ9K1dBzvlHl6HAN1cDw4FD03qA7QlfcDS++50mX4HIdY9Phht/p6PzABNsM5FAXY7v48Tvrui+A44I2vnIbq4Dinwp78ArD2/4APXAOcfZsbvu6F6/Dynpfx1PlPoTE08tuQhBDkjBySWhLbktvwRscbeLPzTWxJbClrx3M8llQswfH1x+OEuhOwpHIJxGlap0EsC/rOXdC2bIa6eTO0zVuQ27wd6ZSJvLcaeV81Ct4q5L3VKPiqYUijT+kGogrC1V74wwSClAKsBPRCD3LJLiQ79yDV1TXiGjN6fgzR2npEausQiddSoVZTh3BN7bSkzLA1k4qx3gIVaE7Z7C2MPJ0JmstMKIqzCi/ESo/jDz1xBtApzmw2i1QqhXQ6PawfzzRnEb/f74qx4SwQCMDv9zOhNkkQYsGyCo7lYdkF2Fa+LGbbKiyr4HjVaaPCslXnuDbELEstqeuwbX0fX3KYfhbM/w7q6y+Z1GsyATbDORQFGACc/6NXsW53Erf8y2Jcfnzz+E5691HgD1cC8SXAf77qhk3bhGZp8Ev7tsCyX+3HW51vuYJsZ3pn2fGgHMQHaj+A4+uoIKsN1I5wpanDSqehbd4MddNmqJs2Qtu0GdrWrdBt0RFjjijzVlGR5o/D5EcXuv6IglCFBMWXgyCmYZv90Au9yCe7kexqRyE98l6KABCoqEQ0XuuKs0i8BpGaOkTiNZC9Y+zwPMkQm8DK6GWCzLV+la7iHQmRpyNnFR7HeyEUy1HPQf8ywEiYplm20XnRMplMWXk8I2lFikItEAi4vijOiuVAIABFUdjU5wECITZs2wAhepkws4kBYhsjekJM2MQEISaIbYIQC4QYIMSCTUyAWE7Mpm1gO3XL2SaLAISAwAaI7fjSeskSfPfXm7jxutp/Qyx2/KTeCybAZjiHqgArTkMe3RzF768a5y9Frhf43hxa/uI2IDDOPGITpDPXidXtq/Fq+6tY3b4aab18um52eDaOrzseR9ccjRXxFQiPkqx1KiGmCX3HDqgbN0HbvAnqxk1QN22C1dcHAsCQ/Ch4q5H3VqHgrYIaboAarkNOjMCwRx/h8wYlBKKg4oxPgVhJaIVeFNI9SHV3DLs3Ztn5oXCZIAtX1yAcr0GkumbK85oRy4aV0GD2OYKsT6XlPnVsccYBQliBGPPQETTHimXeLx3SQsG2bRQKBVeMlVo6nUY2m0Umk0Eulxtx/83hEEXRFWV+v7/MSmM+nw8+n4+NrDGmBCbAZjiHqgDrSBVw3O0vAABe/+/TUBMe5zTkfScAXe8CF/4cWPKv+7GHFMu28G7fu3ht72t4tf1VrO9dD7tkio4DhwWxBTi65mgcW3ssllcvR0Ae55udU4TZ0zMwUuaIMn3HDqBkpMIQfSh4q1AI1ECrnw8t2oi8UoGcqWCM3KGQPAKCMcDjy0EQ0iB2Eobaj0KmF5nezlHXmwGAKMkIx2sQro5TX+WUHZvK0TNiEVhJ1RFjRXFGBZrVr446rQkAnMxDiNKRMiGquKNmNKaA84qHtEArYts2crmcK8hKhVk2my0zXZ/4lJfH43EF2WDv9XpdoVY0NsLG2BeYAJvhHKoCDAAuvO81/GNnAl8/dxE+feLs8Z30l68Ar/8IWH458C93798ODkNKS+GNjjfc6cod6R1lxwVOwKKKRVgRX4Fl1ctwZPWRiHqiU97PsbALBWjbttHEshs30fVlmzbBHmYkyxQ80OsPg9G8BFrVbBR81cgRPzJZglxy9IejIPEIRDl4/XmIchYcScHUE1BzfcglupHp7R11zRkAeIMhhKvjCFU7wqwqjnBVNULVcYQqq6cszxl9GcCAmaAjZVYfnc40EyqsfpW+EDDGX1NOESBGFQgRR6A5XogoEKPOCNohtv5sLHRdd4VZLpcrs8GxsbaBGgme511h5vV6y6w05vF4yryiKAfN7gSMicME2AznUBZgD73ahpuf2oBlTRE8fvUJ4ztp6yrg1xcCwTrg+veAaf7j153vxludb+GtzrfwZueb2J3ZPaRNS7gFy6qXYVn1MiyvXo6GYMMB+b9tYtsw9u6lomzzFjqNuWkzjN1DvyYA4GQZwtz5sOYcDq1mHtRQLfJCGJmMjVRPAZl+DWSU6TyOA/xRCf6QDknJgefTsM0kDDWBQroX6d5uqNmxk2T6ozGEqqoRrooP+MoqhKrjCFZWTdnLAcS0qRhLaI53hJpTt7PG2BcROYhhBUKYirKiiREPLYcV8AqbXhuJ4hRoUYwN9qVWKBSQz+dhGOP4voyCx+MpE2ZFUxSlrF6MFa1YF8WZmYiVwQTYjOdQFmDdaRXH3v5XEAK8cuOH0BAdx1STqQHfnQPoGeDTzwONR+//jk6Azlwn3ux8E2u71uLt7rexPbV9SJtKbyWOrDoSR1QdgSOqj8CiikVQhOnbdHssrGwW2patriDTNm2CtnUr7BFGG4SKCiiHzYM8bz7MxvlQo40oKDGkUxbSPQWkegpI9xRgjjGd5wlICMY4KL4CJCkHkDRMIwEt149cshfp7i4Y2ti5lHzhCEJV1QhVVju+CqGqagQrqPf4p2bK2NYtWEmNCrOkRoVZkgo0Kzm+ETQA4DwihLAM0RFkQkgeEGxOnfewh/p4MQyjTJQNtsFxVVVRKBRgmiNsszVBBEEoE2aKokCW5TJfLI9mkiRBlmWIIpvmniqYAJvhHMoCDAAu/vFqvNHWj6+cswCfPXnO+E4qbkt03LXAWbfu3w6+T5JqEut61mFt91q83fU23u17d0guMpEXsSi2CIdXHY4jqo/AkVVHosZfM009Hh/F0TJt80B6DG3zZui7dg3sAVIKx0FqbIRy2Dx4DjsM8tx5sOpmQ/VUIp0wkCoKs17q1TFGiwSJRzCmIBAFZE8egpAFIWmYagJqrh/Zvh6kerqH3eR8MLLXVybKgpVVCFU6vqIK/mgMwhSMUhDLhpXSYSU1mClHlCU1Wk9qsFIaiDpyQtpSOFmAEJapOAs5wqykzodkCAHpkElUuz8wTbNMkKmq6pqmaWX10njR9mVt23gpijFJksrKoii6seHKoiiOaIIgjOiLdqgJPybAZjiHugD75es78fUn3sWCmiCeue6k8f0Cb3gS+N2lQKQJuO6fJcn3DnxUU8V7fe9hXfc6vNPzDt7peQf9av+QdtXeaiytWoqllUtxeNXhWFyxGD5palM57At2Pg+ttRXali3QtmyBumULtC1bYfX1DX+CJEFpboYybx6Uw+ZRP3cuSEUN0gmdjpj10hGzojgba2oTAHwhGcEKBf4wFWi8kAOxUjC0JNRsPzK9PUj3dI/5ggBAt3Pyx2IIVlQi5Ai0YEWlY7Q8VbsH2KoJK6VRoZaiosxMarDSTj2pjbprQBkc3UXAFWlBmQqzoETLQRmCY4dq6o39iW3bZYJssDgbzhuGAV3Xh7XJGpF7P/A8XybIilaM8zw/xIpxjuPK/ODYWFZkpPLChQvR0NAwqV8vE2AznENdgKXyBo657Xlopo3Hrz4ey5rGsWBdz9N0FEYe+OzLQN2R+72f+wtCCPZk91Ax1k0F2ZbEFlik/CHKczzmRua6gmxJ5RLMCc+BwM+M9UBmX9+AKNu6FfrWbaNOY3KKAnlOC5Q5c6HMnQtl3lwoc+ZAamgAAYdsQhskzFSke2lZy4/+IOIFDsGYB6EqLwJhHpKnAJ7PgtgZmFoShYwj0Pp6kO3rhTWOB5sgiggMEmXBWCWNxSoQrKyCNxiaGpGmWbDSjkhLl4iztA4rrcNO67Ay+ujpNgbB+0TwASrO+KAMoVgO0JG0Yoz3S+CEmfMfooMJy7JgmiZ0XYdhGK5YK5ZLzTTNIWXTNEc0wzDc65f6mSQfzj33XKxYsWJSr8kE2AznUBdgAHD9I+vw+Nt7cfGKRtx54eHjO+mRS4GNTwIn3gCc/s3928EpJm/ksaFvA9b3rsf63vX4Z88/0ZXvGtLOK3qxMLYQSyqXuNYQODAX+A8HIQRmezvUrVtpEtlt26Bu2Qp9+3YQTRv2HE5RILe0QJkzB8rcObQ8dy7kxkZwEt07U80ZyPSp7pRm0VK9KrJ9KuwxhIfkERCq8CBY4UUwJsPjN8ELOYBkYOkp5NP9yPT1INPXi0xfL90IfRx/RgVJQiBWQYVZrMIpVzhlJxaNgZ+CHFbEJrDzBhVlGUeUpR3BljFgZ2jcyuiANYFHBAfwXnFAmAUkOsoWkMEHJAj+kphfAucR2VufMxjLskY027aH1Afb4DghpMyXlgH6N2MkKx4vMljaLFq0CI2NI++Usi8wATbDYQIMeLOtHxf9eDV8soA3vnIagp6RN6F2Wf8H4NFPAxVzgWv/MaOmIfeF7nw31vesxz97/4l/9vwTG/o2IG8OHT2KKBEsrliMRRWLsLhyMRZXLEbcF58xogyg2y8Ze/ZA27YN2tZtdEpz27ZRhRkkCfKsJigtc+jIWcscyC2zoTQ3g/cP7JBgWzaySQ2ZXhXpvgLSvcWRM1rPp8Zel6P4RAQrPAhVeBGs8MAfESHJKjguC9tMo5BJINvf54i0PmT7HZE2HjgO/nDEFWiBaAyBaAWdAo3SmD9WAY8/MCXfU0IISMGkYiytw8o64sz1OuysQUVczhjXSwRl8HDFGF9qPifmE906X6zLM2PUl3HwwwTYDIcJMPpH/vS7XkZrTw63XrAEnzx21tgnaRn6NqSlAf+5Gogv2v8dPYCwbAs70jvwbu+7WN+7Hu/1vofNic0w7KGL12OeGBZVLKKizBFnM02UASXCrLUVWmsr9G2t0LZvh9baCjJK/iexthbK7NnOyFkL5NmzIc+eDbG6esg9MHULmX4V6V4Vmb4BYZbpozE1N3bKAsUnIhDz0FG0mMcVaYJQAJCFnk8h29+LbKLPEWh9yCaot63xrd8SJAn+SMwRaDH4YzG37o/GEIhE4Y/G4AkEp+z7XBxVs7MGFWhZR6jlHIFWLOeoH/datcGIPASfSEWZT3RMGqh7xRIvgfeKNAGuxM+4n3nGgQ0TYDMcJsAoP/nbdtz69EYsrQ/jqc+fOL6TfvNxYMszwAf/G/jgTfu3gzMA3dKxJbEF7/a+iw19G/Be33toTbYOWU8GUFG2MLYQC2ILsLBiIRbGFqIh2ACem3mLrQkhMDs7oW1rhda6Dfr2Nujbt0Nraxt58T8A3ueD3NzsCjJ5djMVas3N4H3Dv/CgqyYyfSoVZH1UpA2UxyfQRJmnwizmQcDx9I1OGYKkg9hZ5FOJEmHWj2yiD7n+PmQS/VDH8eJAEUEU4YtEEYjE4I9G4Y/E4I9E4Y9E4YtE4Y9EaDkchSiNY+R5EiGGDcsRbHauRJzlnXreHBQzJ7RubQgiR0WZxxFnjjArrfMeEZxXoDGPCM4jDMTZiwiMQTABNsNhAozSl9Xwgdv/CsMi+NPnT8SS+nHsr7jut8ATVwHVi4CrV+//Ts5AVFPF5sRmKsh638OG/g3Yntw+rCgLSAHMj813hdn82HzMCc+BJEztg3kyMRMJ6G07oLdth7Z9O/TW7dDb2qDv2QOMMtokVldTcdbcDHnWLMiznXJDA7hRMu/rqolMv+qKtEy/WlbPp8eReoAD/CEZgZgHgWhRnBUFmwJvQIBtZpFL9SOb6Ee2vx+5RB8tJ/qRS/Qjl0yMK4ltKR5/AL5wUZBF4ItE4A9HB3w44tpUizXAmQ7VLCrM8sbwvuCUC8Uy9e9LuBURefAeKs44RQDvEcApYlmMUwTwigDOQ6dKB9o5cdkxtu7toIAJsBkOE2ADXPubtfjTPzvwiWObcNsFS8c+oZAAvjcXsE3g2jVA5dz938mDgIJZwNbEVmzq34SN/RuxsW8jtia2QreHigORF9ESbsGC2AIcFj2MCrPofEQ8kanv+CRCdB36nj1UjLW1QWtrc4RaG6zEKOu1eB5SXR0VZbOaIM+aBampCfKsZsgN9aOKMwCwDBuZhIpsUZj1a8j0D9Sz/Rosc/QEtQDAixwCESrMAlHFMY/r/REFkgIU0kkqypL9yCUS1KeSyCUTyCcTyCVp2bYmlsJA8fnhC4fhDUXgD0fcsi8chi9EzRsKwxeOwBMIgJ/Gt3UJISC6NSDQCiZIUaCpAzG7YIKoFmzVBFFN2AWnvK9TpaPASTwVbLLgCjVO5gfqMg9OGiYmC/RcaaANL/ED7SUeEDg21TpFMAE2w2ECbIDVrX245CevwyPxePXGU1ERGEd2+F9+DGj9K3DaN4CT/mv/d/IgxbANbE9ux6b+TdjUvwmbE5uxqX8TMvrwoyjV3mrMi87DYdHDXD87PBuyMDX7Mu5PrGQS+s6d1HbsgL5jB7QdO2Ds2Dli2gwAA+KsqRFSY5PjG6lYa2goexlgJAghKGQMZBMDgqwo2LIJKtby48yYzwsc/BFHnEUU+KMe6l2T4Q8r4AUOai7rCLIEcqkk8skk8ikq0PIpJ+bYeNepuXAcvMGQI8pC8AWp9wZD8IbC5T4YgjcUmrLto8YDsZ2RN0eM2aoJW7WoSNOsAdGmWU7dhK1bJXXH6yYwtrZ+/3AAJ/JUqBW9xAOSAE7kaKzUnHYQOXCCUxaK7QbFhGI7Dhxfco7A0eN80RfbcwDvxIv+IIIJsBkOE2ADEELwL/e8ivV7U/jCafNwwxmHjX3SmoeAp64Dao8EPvfy/u7iIQUhBJ25TleQbe7fjM2JzcPudwkAIieiOdyMeZF5mBudi7mRuZgXmYf6YP2MXFs2GEIIzJ4eGDt3Qt+1C/rOXVSk7aJ+tBcBAECorITc2AipsQFygyPOGhsgNTZCrKqiD61xYJk2cikN2YSGXMIRaE45m1CRTWrjFmkA4A1KJaJMgT+swB+m4swfUeALy/AGZfA8B0IItFwOuVQChVQK+XTSEWcp5FMJFNJp5NMp5NMpFNKpCU+DFhFlBd5gCJ5gEN5A0CmH4HXqnkAQnmAQHn8Q3mAQnmAIis83rSNtY0EIAUziijOiF4VZiVgzLBDdpsccT3QLxCiJGU7diRX9hN9AnQ44ADwHcKWizInxHH2ZnSsp8xw9p5hslYNbh6PluJKyEyj/PIfgyQ3wLqqY1C+HCbAZDhNg5fz5nx245jdrEfFJeO2mU+GTx9gCJtcL/M88gNjAde8A0eYp6eehTM7IYWtiK7Ymt2JL/xbqE1tGHC3zCB60RFowNzLXtTmROajx1xwUwgygD1ert9cRZLuh794FY+cu6Lt3w9i1C1YqNer5nCxDqq93xFkDLdc7vqEeQmRimfYty0Y+pSPriLJcUnPKGvIpDdmkhlxKg22O788/x3PwOULNF1bgC8nwOSLNF3J8WIYvJEMoWaxumSbUbIaKslQShUyaWjqFQiaNfDoNNZNCPl2MZSY8HTrQSQ4enx+eQBCKPwBPIACPP0DFmlNWAgF4fAEo/gAUv5/G/H4oXt+4BfCBCCEEsAiIaQ+Is6JQK42Z1GASt+zGrUFx53ooLdtkoGwReo7jiQXApm1hkwNOEEYumIvAsbWTek0mwGY4TICVY9kEp37/Jezsy+Ob5y3Cv58we+yT7j8J6Pwn8PHfAo3HAskdgJYFIo1AqAEQZ/602IEOIQRd+S5sSWzBlsQWtCZbsS25DduT24ddWwbQRLIt4RbMicxx/ZzwHNQF6mZMhv/xYqXT0HfthrFrJ/Q9e2Hs3g19z24Ye/bCaG8f9YUAgL6xKRWFWV0dtfp6SPW0LMRiE173QwiBmjUGRtOSjqV15JMacikduZSGQlofT55ZF8UvwhdyRFqphWX4gjK8Ieo9QQnCoL0oCSHQCwWo2TQK6TQK2QwKmTTUonDLZKBmMyhkqS+aXhh7z8/R4Dgeis8H2eengszng+ILUO/3Q/FRk70+WvZ6aVufH7LPC8Xnh6R42NqrEohNhRixHXHo1N1YmQdNZuwIN1Is206ZOILO+Tkkbr08PtCgpB9OWa7zQ6zwTurXyATYDIcJsKH86vWd+NoT76I+4sVLX/ogpLE2DP7p6cCetwBeAgbnweJ4IFgHRGcBkVmOb6IJXGuWAtLk/kIyyrFsC7szu9GabMXW5FZsS25Da7IVO9I7hmxKXkTmZcwKz8Ls0GzMDs9GS7gFs8OzMSs0a0bshzlRiGnC6Oykomz3bhh722Hs2QNj717oe/fA6ukd8xqcxwOptnZAnNXVQqythVRLy1I8PuYLAiNhWzZdl5ako2f5tI5cSkc+pbk+n9aRT+lj7jIwGI9fcgQZ9d4gLXsCjlgLSvA6XvaKIwocyzSgZrOOZaDmqNdyWRSyWWjFej5H2+Sy0PI5aNksTGNyNsXmOB6y1wvJ64Xi9UH2eCH7HO/1QfZ66XHFQ72H1mXFA8nrg+zxQFI8kDyOycqMHpU7FGACbIbDBNhQVMPCiXe+gN6sjh9cfCTOX1Y/+gm//3fgvccG6sFaQPYDqT2AqY58Hi/SFBb1Rw1Y1XzgIBt9ORAxbAN7MnuwPbkdralWtCZbsT21HW2pNmjWCNnuAdT6a9Ecasas0Cw0h5vdcq2/9qAbNStiqyqM9nYYe/dSYdbeTkVaOzWzu3vsi3AcxMpKKspqaiDWxCHV1EKqiUMs+upqcOIYU/6jQGwCNW9QMeYIskKGehrTkE8byGd0qJmJjaoB9KUCT0CCNyBTH5Tg9UvwBGV4AxI8RfNLtO6XII4ja76p664w0/I56Pkc1HwOej4PNZeFXshDzdG4ls9BLxQcn4eWz0PL50Ds/bO6XlQUKsoUDyRFcYWZ5PFAlBVIigLROSbKCkRZdsoyPS4Xy7J7XJBkiLIEUSqWZfCCwEbv9gEmwGY4TIANz49e3IbvPbsZc6r8eHblyRBHGwXTskDHOiAQB8KNgOShcdsGct1AcheQ2EmnJhM7geROoHsjkOsZei3JD9QtA+qXAXXLaTnafNBvdXSgYNkWOnIdrhgr2o70DvSr/SOeJ/MymkJNmBWaRX2Q+qZgE6p9QzPeH0zYug6zo8MVZEZHJ4yOdifWAaOjY+QtnErheSrS4nFHkMVpOV4NMV4DMV4NKR4fMUntRCA2gZqjYq2Q0ZHP6CiUiLN8xoCadXxGh67uWyoIUeLLhJnHL0HxS/D4RVr2Ocd8IhS/BMUnwuOTIEjjH3kihMDUNWj5PPRCAYZaoGW1AL1AY0VvqAUaV1UYhTx0VYWuOvFCAYamwdDUce0tOplwHA9BkiBIIgRRgiBJECXJLQ94EbwoQhQl8KLT1okJogBeEMELgmO0LIhiWZ3jeVp3PCcI4Hla5zgOHO+04XmA52ic58Fxzk4GzoJ813ge9NXP4tdS/F3nBv5scxx8oTAU39hvIk8EJsBmOEyADU9aNXDKd19EIm/gjo8txcePaZrcDyCEjpDtXUOt/W1qenZoW2/MEWWOIKtbDoQmdzEnY2ySahJt6TbsSO3AjvQO7EzvxI7UDuzK7Bp2C6YiXtGLxmAjmoJNrihrDDaiMdiIal/1QTtyVoQQAiuRcMRYO8zOLphdnVSodXXC7OiE0d0NGGNn8QcAPhCAWF3tWBUkt+xYZSXEqirw3smb3jcNC4WMATVroJDVy8rUGyhkdKg5E2rOgJY1JjwdWooo8VBKRJnioyJN9olQvLQue0V6zDsQl70iZI8AfqxlE6NQFHSGpsFQCzBUlQo2TR2IaypMrVjWaFxVYeo6TF2DaehO3KlrGkzDgGXoNGbosMb5/T5YOOMz1+Lw08+e1GsyATbDYQJsZH72Shu+/acNiIcUvPTFD8G7vzfhtS2gd4sjytYC7WuBzneHrisD6GhbzdISOxyItbDpy2mgOGq2I73DFWS70ruwK7ML7dn2YbP+F5F4CfWBeleQNQYb0RBsQH2gHvWB+oNyzdlwENuG1dcHo7MLZncXjM5OmF3dMLu6YHR1uX6sVBul8IGAK8bEKuqFykqIlVUQKysgVlTQeiz2vqY+h/16CIGuWlCzBtScMeBzBrScMSDU8s6xvAktZ0ArmJPy9p6oCFA8AhVkrjATIXsFyIoIySvQuod6qegVAZJHgKTQuijvv/0riW3DMk2YjiizDAOWacAyDFegWYbpxizTgGWasEwTtuMt03DKBizLgm2asC0LtmXCMqm3i3Hbgm1ZILYN27addhZs2wKxLBBCYNs2iGO2bQPEHogR4izOt+m6eyeGYhxwRw4JSPmCfQAfuvwzWPKhMyb1HjIBNsNhAmxkNNPCqf/zMvYmC/jSWfNxzYemIdO9qQFd71Ex1v42sPdtoGcjTXsxGMkHxBdTQRZfDFQvBqoXAt7IlHebQTEsA+25duxM78Su9C7sTO/E7uxu7Mnswd7s3hFfBCgS88TQEGhAfbCe+kA96oP1qPfXo8ZfM6O3aZoohBDYuRzM7u4yM7q7YXb3wOzqgtnbC7OnB0QdZe3lYDgOQiQCsbISQmUFxFgFhIrYgK+ogBiLQaiogBCNgff79qMoIdAKJrS8CS1vOH5ArOkFC1rBhJ433HZ6sX3BhGVM8lowDpDkAVEmKQKtKwLEkrqoCJBkHmLxmFOmxkOSBQgSD1Fy4hLv1t/PaN2hDhNgMxwmwEbn8bf34PpH3kFQEfHylz+EmP8ASCmh54CuDTT1Red6al3vAeYIr8KH6qkQq144IMqq5rM3MKcZy7bQme/E7sxu1/Zk9lDL7hkxr1kRDhyqfFWoD9Sj1l+L+kA96gJ1qPXXojZQixpfzSEzglYKIQR2Nguzh4oxs7eH+p4eWL19VKT1UW/199O1mhOAk2UIsRiEWBRiNEbL0QgVadEYhEiE1qNRWo5EwE3R3pWWaUNXqSjTCxYVZwVzIKZaMFSL1lVzoFywYGgWDNWErtHyVOXR4nkOgsy7okwQnbI4INIEkRrvxASBG6iLTlngwAu0zgu0LggDZd7JlM8LHPiiF3gac7LkczztD8cNqhdj3EDi1fJyyRqw4hfG0d9Rt+x8zmTCBNgMhwmw0bFtgo/88BVs7Ejj309oxjfPWzzdXRoe2wL6Wh1R9k+6yL9rA5DeM8IJHE2JUTkfqDoMqFowUPaMYyNyxn4npaWwN7uXWmYv9mSpMOvIdqA92w7VGnuUJ6JEqCBzRFmtvxZxfxw1vhrU+GtQ6a2EyE/u9NtMglgWrESCCrKeXlj9fTD7+gd8Xx/Mfsf39Y3vZYJh4INBV4wJ4fCAL5YjtMyHQjQWCkEIhfY5dcf7ha4Ds2FoVKBRcWbB0C2YGvXFuqEVYzYs3Wmj2zB1GjN1yzEbpmHDMuxx7Td6sPHBT87H4pPGeKN+gjABNsNhAmxs/ralB5f9/E0IPIcnrz0Bi+tmkEBRU0D3JqD7PSrKujfS0bLCyG/0IVgLVM4DKubRfGWV84CKOTSPGVtjdkBACEG/2o+OXAf2ZveiPdtOLdeOjlwHOrIdyBrDvNAxCJ7jUemtRI2/BjW+GsT9ccR91Kp91a4dDHtsvl8IISD5PMxEAlYiAau/H2Y/9VbCKSeT9FjRp1Lv641CzuulYswRZXwoBCEYLPFBCMEQ+GCAHg8EIQT84INBatMk4MaC2ASmacPSbZgGFWeWSc0VaYZTNgdEm2XasC0yECuWnbht2bBNMlC2CCyreIwmXrVt4tZtJ7O+bdk0B6uTnJXYZEidAO9rVJAJMMYQmAAbH1f/eg2eXt+JIxojeOw/j4cwkzd1JYRuodS7GejZBPRsob53C5DpGPk8QQais6kgi7WUW6geYEkbDygyegYduQ505jrRnnWEWa4DXbkudOY60Z3vhknGt+1OVIki7qeirMpbhSpfFaq8VWX1mCd2SI+mDQexLFjptCPIkrBSSVjJlOOTsFIp2KkUrGQSZjIJO5WGlU7DzuzbHpaD4STJEWMBCP4AeL8ffKDo/dT7/RCKMZ9vwBfNqXNe7yGfmLWYAZ8QR5A5mfLJoAX3tFLmIDrTp5MJE2AzHCbAxkdXWsVp338ZWc3Etz66GJcd1zzdXdo/FJJA71agb6vjtznWCoySoJSKs+YBQRZtLsn8PwuQD721SAc6NrHRV+hDZ64TXfku13flu9CV60J3vhvd+e4Rt3IaDM/xiHliqPRWjmoxTwwBKXBQ50Z7vxDLgp3JUPGWSsNKp2Cn07AyGSeegZ1Jw0pnYGXSsIs+m4OdycDO5fZLvzivF3zRfF5wXl953eMF7/GA83rAe7zgvR5wisf1nEcBryg05lHAKdR4RQHn8YCTZTpqJ0ns52McMAE2w2ECbPz8YvUOfOOP7yGgiPjrf52CeMgz3V2aOmwbSO0eEGT92wcssXP4VBml+KuHbscUbgDCjmcC7YCEEIKUlkJXfkCQdRe60ZvvRXehGz35HvQUetBX6Bs13cZgFEFBzBNDhacCFV7HPBWIeWLUvDFElSgqvBWIKBE2sjZBiG3DzlExZmWzsLNZV5hZuRwVarkcjedysHNZWLkcSC4PO+9YLueWpzoxKwAqzmTZ8RJ4SaZ1SaI2pCwCoghOlMCJIo2L4qC4AAgCOEEcWuYFcAJPR/J5HpwglHsnDo53FtU7ZWeBvpt1tcwPLMKXGxshVlZO6j1iAmyGwwTY+LFsgo/d9xre2Z3EGYvieODSo9j/0gDAMuli//62ElG2g2b8T+wCtNTY1/BV0F0EIo3Uh+qBUB0VZ6E6IFADCOwhfKBi2RYSWgLd+W70FfrQW+gd0fLm+HN5FQkrYcQ8VJRFPVFElAhinhgiSsStF31EicAv+dnv5iRBCAFRVSrGCgXY+TxIoeCUC7ALTj1fgK2pIAUVtqqCqAXYBRW2WgDJF2BrGoim0TZqsazRa2vauBPxzlRqbrkF0YsvmtRrMgE2w2ECbGJsaE/joz96BYZF8O2PLsalB+tU5GRSSAxswZTYQcupPXRELbkbGCPdAgD6P81ADRVjoTr6okDQqQdrBupKiG3bdIBTMAvoK/ShT+0r94U+9Kv9SGgJ9BeoT6gJkH1Y+SxyIsJKGBElgrASdssRJYKQEqIxOUzLctht4xP3X44vxugQ2wbRdWqaBqLrsDUdxHDqhkFN14eWdR3EMEFMk9ZNA8Q0AdME0WmZWCZgWSCmRcsmTcoKy6Tn2hZg0eSrxLJpW7vEF5OwOh6E0LY2GUhlUpKQlQxaBFZ13RcQ/shHJvWeMQE2w2ECbOL89O/b8Z0/b4Qs8nji6hOwqI7dt32GEEBNUkGW3E1FWWo3kG6nltoLZNqBMRKWukg+uktAIA4EqsvLwRrq/dWAvxIQlf36pTHeP5ZtIaWnkFAT6Ff70a/2I6kmXXGW0BJuvV/tR0pLjbqZ+lgInICgHERIDpV7JeSWg1IQATlAy3IQAWmgzAQcYyphAmyGwwTYxCGE4D/+7x/466ZutFT58dS1J8KvsOmx/YZt043L03sdawcynY4Vyx005cZEUMJUiPmrSrxT9lUMNekQWvM3gymYBaS0FFJaCkktiaSWdMvFeEpPIa2lkdbTbmy8LxuMBs/x8It+BOQA/JIfQTkIv+RHQArQmOiHX/LDJ/ngl/xDzCf64JN88Ik+eEUvE3OMUWECbIbDBNi+0Z/T8eH//Ru60hr+dXkDvn/REdPdJYaeB7KOMMt2O9blWPeAz3WPf0StFMnviLEo3SDdGwV8ji/WXYsAnghNasuE2wEPIQSqpSKtpZHRM0jrA941LY2skUVGzyCrZ5HWy+vjTekxXjhwrhgrFWVeyTtQdqxY94ieci8M1D2iBx7BA0VU4BW8EHmRCbwZDhNgMxwmwPad17f34RM/eR02Af7fxUfggmUN090lxngoTnvmeh1B1uNY70C5kADyfQO2L4KtiOihYqxUlHlCdL2aJ0TriuM9YUAJlpscZC8gHOAQQlAwC8gZOWSNLHJGDhk9M6SeN/JuPW/kkTNzyOpZ5M28G9uXlxT2BZ7joQgKvKIXiqBAERR4RA9kQXbriqBAFmR4BBovHpP5krITL8YkXoIkSJB52fVu3Dkm8RJEXnQ9zx3a+cX2FSbAZjhMgL0/fvD8Fvzg+a3wyQJe+uIHUR3ywLBsmBaBR+LZ/zAPBggBtDQVYrk+uotAIeGItGK5f6CuJmk+NTWFSdtQT/I5YiwAKAEqypQAIPtpzI0HaEoPOUDPKSv7qUk+akzUHZDYxIZqqsibeVeQ5Q0q0ApmAQWzgLyZd8sFYyCmWRryZh6qqbpWMAtQLeo1S4NNDrxtgEROhMgPYyVxiZcgcAIEXnCPCbwwEOOokCvGeI53xV2xXvRFEzgBHMe5ngeNcxxH24CWOXAje6dcpFjmUBIDh2XxZWgJt0zqfZvxAuzee+/F9773PXR0dGDx4sX4wQ9+gJNOOmnE9i+//DJuuOEGvPfee6irq8OXv/xlXHXVVWVtHn30UXz9619Ha2sr5syZg1tvvRUXXHDBhD6XEIJbbrkFDzzwABKJBI499lj86Ec/wuLFA3sRapqGL37xi/jtb3+LQqGA0047Dffeey8aGsY/EsME2PvDsglOvPMFdKTovnyyyEN39jnjOcAvi/ArInyKgIAiuvWAIiDgoeWgUow55sT9sgifLNDzZQGKyATdjMK26RuehWSJKEsCapqKMy1Ny5pTL8a0zICZY+/3uM/wEhVoko9uzC75He+hMdHj1L2A6MRd7xk4Lio0LipOXCkxD03SW4yzraymFUIIDNuAaqnQTI2KNEuFZmkomAXolg7N0gbM1Mrquq1DtwaZTc8xLAO6rbtet3QYtjEQd8qGbezTm60znW8c9w3822H/NqnXfL/P72n9L9gjjzyClStX4t5778UJJ5yAH//4x/jwhz+MDRs2oKmpaUj7trY2nHPOOfjMZz6DX/3qV3j11Vdx9dVXo6qqCv/6r/8KAFi9ejUuvvhifPvb38YFF1yAxx9/HBdddBFeeeUVHHvsseP+3O9+97u466678NBDD+Gwww7Dd77zHZxxxhnYvHkzgsEgAGDlypV46qmn8PDDD6OiogL/9V//hXPPPRdr1qyBILA/dFOBwHP4QEsFHn97LwC44gsAbAJkNBMZbXLWhhQFnU8R4JNFeCQBXomHVxbglQSnLsAr07Ii8q5XBtdFHnLRCwJkpy6LPGRhwEsCB4HnmPDbF3h+YFoRs/btGqYO6NkSUZYG9Bwt6znnWJb6YtnIOcfy1JfWjRxQHAGxjQHhN1XwIhVkgkwFmaAAoky9IDkxmZaL7VyTSuISFZCCTEfyBNmpi46XSupi+TFedI4LA8d4saRtqQkAJwyqz9zfBY7j3ClDTOM2kZZtUUFWNMuASUyYdrkZtkHLxIRlW2Vli9B60dvEduvFsk1st71NbGqwYds2LGKBgLjHCEiZtwlNOWGDlkEAUvxHhvoipeKytFznr5vSezwepnUE7Nhjj8Xy5ctx3333ubGFCxfi/PPPx+233z6k/Y033ognn3wSGzdudGNXXXUV3nnnHaxevRoAcPHFFyOdTuOZZ55x25x99tmIRqP47W9/O67PJYSgrq4OK1euxI033giAjnbF43Hceeed+NznPodUKoWqqir88pe/xMUXXwwAaG9vR2NjI55++mmcddZZ47oHbATs/WNYNjZ1ZKBIPB3JkkUIAoe8ZiKrmcjrFrKaiZxTz2kWspqBrGbRmGoiq1NfbJPVTBR0CzndhGpM75QBxwGSwEPiOUgi75ZFgYcocBB5DiJPxZoo8LQucBB4WhZ4zvWucbQNz9E6zw204Z3jPAfwfHkbnoMrCHkOboxz28CZGnCO8XCmCGjd9c7XVdq2mMy62J4rbVeMATQLdkmd40rLTgMMPKeLbQbKxXh5u2K5bOpi0LHS84YcHyZWfgQAIeBtDZxZAGcWwBt58KYKzsiDNwvgTKduqeBM1YmpNOaUOUsb5GlbztTAWypg6eAsfaDNATjl9X4gHA9wAkiJOCvG3LJzjHA0izrhSsRbMc7RTOqEFwAUs6wLzrUGrKztoGM0Rq8JjgfAOW04t17WrqRe3pajfXDalF8DJedxIBjcHgNZ34vH3TZcyfUH2g7bBoOu5d7v0jYYod1wx53zy9qjvE3p78aQX5xB9TGF9+jHfXUL4K9sHOMaE2PGjoDpuo41a9bgpptuKoufeeaZeO2114Y9Z/Xq1TjzzDPLYmeddRZ+9rOfwTAMSJKE1atX4/rrrx/S5gc/+MG4P7etrQ2dnZ1ln6UoCk455RS89tpr+NznPoc1a9bAMIyyNnV1dViyZAlee+21EQWYpmnQtIE8Oel0eth2jPEjCTyWNoSHxAOKiOpJuL5lE+R1KuTyOhVtBcNCQbdQMCyoJeViXDNtaIYF1bChmeVeNS3opk3NsqEZ1JfGSiGEjuzpAKCPf7sZxkzE59jkIcCCAgMyDOo5AzJMx5yyGzMgwYIEExJH20iuWZA5elwstnHiImdCgQnBPWZBhAmJo/VirHhchAWBs92yG4MNARZkbuSfc47QZJ3cWNtvMRglvLH4Gzj23/5rurtRxrQJsN7eXliWhXg8XhaPx+Po7Owc9pzOzs5h25umid7eXtTW1o7YpnjN8Xxu0Q/XZufOnW4bWZYRjUbH3X8AuP3223HLLbeMeJxx4CHwHIIeCUGPNCWfRwiBYREYlg3DooLMLK2bBKZtw7AITMuGZRMYNi0bFj1m2QSWTWCWesuGWRKzbQKLELetRZyYDdiEwHaO2YTAtjFwnBCalLrEU6N9t2w68F+sl7YpPY84X2uxLdyyc7ykDb0vzhTEoHMxuD6obbGO0mthUBxkUL38OAYdL4+Wf+8GHys9Z/CEw5DphzHmI8aarhh+QkMAIMMAMFamrUmfDyn92gf3fozP4mBDdAQZFW42eFDRVu5pGx62I+Co8cOUOdcT9xjH0TJPaJwf1IYHKTuPd628Dc8NHEdJG3o+cc8HUBbnyq5Hb0wxVno9Z2zMjZfGOOeapecPHCPu5w6N0zK44nGUtBuIFcsl41lDrlkeK/8mD5yPIW0Hl8vPL7/OSAw+fzhM5cCbYZr213AGr20hhIy63mW49oPj47nmZLUZzFht/vu//xs33HCDW0+n02hsnNxhUcbMhuM4yCIHWWSvhjMYDMZkcNh0d2AYpu0vfGVlJQRBGDJa1N3dPWTkqUhNTc2w7UVRREVFxahtitccz+fW1NQAwJhtdF1HIpEYd/8BOpUZCoXKjMFgMBgMxqHFtAkwWZZx1FFHYdWqVWXxVatW4fjjjx/2nOOOO25I++eeew4rVqyAJEmjtileczyfO3v2bNTU1JS10XUdL7/8stvmqKOOgiRJZW06Ojrw7rvvjth/BoPBYDAYDAAAmUYefvhhIkkS+dnPfkY2bNhAVq5cSfx+P9mxYwchhJCbbrqJXHrppW777du3E5/PR66//nqyYcMG8rOf/YxIkkT+8Ic/uG1effVVIggCueOOO8jGjRvJHXfcQURRJK+//vq4P5cQQu644w4SDofJY489RtavX08uueQSUltbS9LptNvmqquuIg0NDeT5558na9euJaeeeio54ogjiGma474HqVSKACCpVGqf7iGDwWAwGIyp5/0+v6dVgBFCyI9+9CMya9YsIssyWb58OXn55ZfdY5dffjk55ZRTytq/9NJLZNmyZUSWZdLc3Ezuu+++Idf8/e9/T+bPn08kSSILFiwgjz766IQ+lxBCbNsm3/zmN0lNTQ1RFIWcfPLJZP369WVtCoUCufbaa0ksFiNer5ece+65ZNeuXRP6+pkAYzAYDAZj5vF+n9/Tngn/UIflAWMwGAwGY+bxfp/f7DUrBoPBYDAYjCmGCTAGg8FgMBiMKYYJMAaDwWAwGIwphgkwBoPBYDAYjCmGCTAGg8FgMBiMKYYJMAaDwWAwGIwphgkwBoPBYDAYjCmGCTAGg8FgMBiMKYYJMAaDwWAwGIwpRpzuDhzqFDciSKfT09wTBoPBYDAY46X43N7XDYWYAJtmMpkMAKCxsXGae8JgMBgMBmOiZDIZhMPhCZ/H9oKcZmzbRnt7O4LBIDiOm7TrptNpNDY2Yvfu3WyPyQnC7t2+we7bvsPu3b7B7tu+w+7dvlF634LBIDKZDOrq6sDzE1/RxUbAphme59HQ0LDfrh8Khdgv1z7C7t2+we7bvsPu3b7B7tu+w+7dvlG8b/sy8lWELcJnMBgMBoPBmGKYAGMwGAwGg8GYYpgAO0hRFAXf/OY3oSjKdHdlxsHu3b7B7tu+w+7dvsHu277D7t2+MZn3jS3CZzAYDAaDwZhi2AgYg8FgMBgMxhTDBBiDwWAwGAzGFMMEGIPBYDAYDMYUwwQYg8FgMBgMxhTDBNhByr333ovZs2fD4/HgqKOOwt///vfp7tIBze23346jjz4awWAQ1dXVOP/887F58+bp7taM5PbbbwfHcVi5cuV0d+WAZ+/evfjUpz6FiooK+Hw+HHnkkVizZs10d+uAxzRNfO1rX8Ps2bPh9XrR0tKCb33rW7Bte7q7dkDxt7/9Deeddx7q6urAcRyeeOKJsuOEENx8882oq6uD1+vFBz/4Qbz33nvT09kDjNHunWEYuPHGG7F06VL4/X7U1dXhsssuQ3t7+4Q+gwmwg5BHHnkEK1euxFe/+lW8/fbbOOmkk/DhD38Yu3btmu6uHbC8/PLLuOaaa/D6669j1apVME0TZ555JnK53HR3bUbx1ltv4YEHHsDhhx8+3V054EkkEjjhhBMgSRKeeeYZbNiwAd///vcRiUSmu2sHPHfeeSfuv/9+3HPPPdi4cSO++93v4nvf+x5++MMfTnfXDihyuRyOOOII3HPPPcMe/+53v4u77roL99xzD9566y3U1NTgjDPOcPcoPpQZ7d7l83msXbsWX//617F27Vo89thj2LJlC/7lX/5lYh9CGAcdxxxzDLnqqqvKYgsWLCA33XTTNPVo5tHd3U0AkJdffnm6uzJjyGQyZN68eWTVqlXklFNOIdddd910d+mA5sYbbyQnnnjidHdjRvKRj3yEXHnllWWxj33sY+RTn/rUNPXowAcAefzxx926bdukpqaG3HHHHW5MVVUSDofJ/fffPw09PHAZfO+G48033yQAyM6dO8d9XTYCdpCh6zrWrFmDM888syx+5pln4rXXXpumXs08UqkUACAWi01zT2YO11xzDT7ykY/g9NNPn+6uzAiefPJJrFixAv/2b/+G6upqLFu2DD/5yU+mu1szghNPPBF//etfsWXLFgDAO++8g1deeQXnnHPONPds5tDW1obOzs6yZ4WiKDjllFPYs2IfSKVS4DhuQiPYbDPug4ze3l5YloV4PF4Wj8fj6OzsnKZezSwIIbjhhhtw4oknYsmSJdPdnRnBww8/jLVr1+Ktt96a7q7MGLZv34777rsPN9xwA77yla/gzTffxBe+8AUoioLLLrtsurt3QHPjjTcilUphwYIFEAQBlmXh1ltvxSWXXDLdXZsxFJ8Hwz0rdu7cOR1dmrGoqoqbbroJn/jEJya0sTkTYAcpHMeV1QkhQ2KM4bn22mvxz3/+E6+88sp0d2VGsHv3blx33XV47rnn4PF4prs7MwbbtrFixQrcdtttAIBly5bhvffew3333ccE2Bg88sgj+NWvfoXf/OY3WLx4MdatW4eVK1eirq4Ol19++XR3b0bBnhXvD8Mw8PGPfxy2bePee++d0LlMgB1kVFZWQhCEIaNd3d3dQ/6nwxjK5z//eTz55JP429/+hoaGhunuzoxgzZo16O7uxlFHHeXGLMvC3/72N9xzzz3QNA2CIExjDw9MamtrsWjRorLYwoUL8eijj05Tj2YOX/rSl3DTTTfh4x//OABg6dKl2LlzJ26//XYmwMZJTU0NADoSVltb68bZs2L8GIaBiy66CG1tbXjhhRcmNPoFsLcgDzpkWcZRRx2FVatWlcVXrVqF448/fpp6deBDCMG1116Lxx57DC+88AJmz5493V2aMZx22mlYv3491q1b59qKFSvwyU9+EuvWrWPiawROOOGEIalOtmzZglmzZk1Tj2YO+XwePF/++BIEgaWhmACzZ89GTU1N2bNC13W8/PLL7FkxDoria+vWrXj++edRUVEx4WuwEbCDkBtuuAGXXnopVqxYgeOOOw4PPPAAdu3ahauuumq6u3bAcs011+A3v/kN/vjHPyIYDLojiOFwGF6vd5p7d2ATDAaHrJXz+/2oqKhga+hG4frrr8fxxx+P2267DRdddBHefPNNPPDAA3jggQemu2sHPOeddx5uvfVWNDU1YfHixXj77bdx11134corr5zurh1QZLNZbNu2za23tbVh3bp1iMViaGpqwsqVK3Hbbbdh3rx5mDdvHm677Tb4fD584hOfmMZeHxiMdu/q6upw4YUXYu3atfjTn/4Ey7LcZ0YsFoMsy+P7kPfxZibjAOZHP/oRmTVrFpFlmSxfvpylUxgDAMPagw8+ON1dm5GwNBTj46mnniJLliwhiqKQBQsWkAceeGC6uzQjSKfT5LrrriNNTU3E4/GQlpYW8tWvfpVomjbdXTugePHFF4f9u3b55ZcTQmgqim9+85ukpqaGKIpCTj75ZLJ+/frp7fQBwmj3rq2tbcRnxosvvjjuz+AIIeT96UQGg8FgMBgMxkRga8AYDAaDwWAwphgmwBgMBoPBYDCmGCbAGAwGg8FgMKYYJsAYDAaDwWAwphgmwBgMBoPBYDCmGCbAGAwGg8FgMKYYJsAYDAaDwWAwphgmwBgMxoTgOA5PPPHEdHeDMQ6m4nv10EMPgeM4cByHlStX7tfPuuKKK9zPYj+DjJkOE2AMBqPswSZJEuLxOM444wz8/Oc/H7K/XkdHBz784Q+P67oz6UF58803u/eg1J5//vnp7lrZ96fUzj777OnuGgAgFAqho6MD3/72t/fr5/zv//4vOjo69utnMBhTBdsLksFgAADOPvtsPPjgg7AsC11dXfjLX/6C6667Dn/4wx/w5JNPQhTpn4uamppp7un+Y/HixUMEVywWG9JO1/Xx7/c2SRS/P6UoijKlfRgJjuOm5OciHA4jHA7v989hMKYCNgLGYDAA0Id5TU0N6uvrsXz5cnzlK1/BH//4RzzzzDN46KGH3Halo1q6ruPaa69FbW0tPB4PmpubcfvttwMAmpubAQAXXHABOI5z662trfjoRz+KeDyOQCCAo48+eojoaW5uxm233YYrr7wSwWAQTU1NQzap3rNnDz7+8Y8jFovB7/djxYoVeOONN9zjTz31FI466ih4PB60tLTglltugWmao94DURRRU1NTZrIs44orrsD555+P22+/HXV1dTjssMMAAOvXr8epp54Kr9eLiooKfPazn0U2m3WvVzzvtttuQzweRyQScfvxpS99CbFYDA0NDfj5z38+7u9PqUWjUff41q1bcfLJJ8Pj8WDRokVYtWrVkGu89tprOPLII+HxeLBixQo88cQT4DgO69atc9ts2LAB55xzDgKBAOLxOC699FL09vaO2b/BaJqGL3/5y2hsbISiKJg3bx5+9rOfAQBeeuklcByHZ599FsuWLYPX68Wpp56K7u5uPPPMM1i4cCFCoRAuueQS5PP5CX82gzETYAKMwWCMyKmnnoojjjgCjz322LDH7777bjz55JP43e9+h82bN+NXv/qVK7TeeustAMCDDz6Ijo4Ot57NZnHOOefg+eefx9tvv42zzjoL5513Hnbt2lV27e9///tYsWIF3n77bVx99dX4z//8T2zatMm9ximnnIL29nY8+eSTeOedd/DlL3/ZnS599tln8alPfQpf+MIXsGHDBvz4xz/GQw89hFtvvXWf78Vf//pXbNy4EatWrcKf/vQn5PN5nH322YhGo3jrrbfw+9//Hs8//zyuvfbasvNeeOEFtLe3429/+xvuuusu3HzzzTj33HMRjUbxxhtv4KqrrsJVV12F3bt373PfbNvGxz72MQiCgNdffx33338/brzxxrI2mUwG5513HpYuXYq1a9fi29/+9pA2HR0dOOWUU3DkkUfiH//4B/7yl7+gq6sLF1100YT7dNlll+Hhhx/G3XffjY0bN+L+++9HIBAoa3PzzTfjnnvuwWuvvYbdu3fjoosuwg9+8AP85je/wZ///GesWrUKP/zhDyd+QxiMmcD+2kmcwWDMHC6//HLy0Y9+dNhjF198MVm4cKFbB0Aef/xxQgghn//858mpp55KbNse9tzStqOxaNEi8sMf/tCtz5o1i3zqU59y67Ztk+rqanLfffcRQgj58Y9/TILBIOnr6xv2eieddBK57bbbymK//OUvSW1t7Yh9+OY3v0l4nid+v9+1o48+mhBC7088HieaprntH3jgARKNRkk2m3Vjf/7znwnP86Szs9M9b9asWcSyLLfN/PnzyUknneTWTdMkfr+f/Pa3vx2xb5dffjkRBKGsb36/n3zrW98ihBDy7LPPEkEQyO7du91znnnmmbL7f99995GKigpSKBTcNj/5yU8IAPL2228TQgj5+te/Ts4888yyz969ezcBQDZv3jxs3x588EESDofLYps3byYAyKpVq4Y958UXXyQAyPPPP+/Gbr/9dgKAtLa2urHPfe5z5Kyzzhpy/nh/rhiMAxm2BozBYIwKIQQcxw177IorrsAZZ5yB+fPn4+yzz8a5556LM888c9Tr5XI53HLLLfjTn/6E9vZ2mKaJQqEwZATs8MMPd8vFNUbd3d0AgHXr1mHZsmXDrs8CgDVr1uCtt94qG/GyLAuqqiKfz8Pn8w173vz58/Hkk0+69dI1VkuXLi1b97Vx40YcccQR8Pv9buyEE06AbdvYvHkz4vE4ALqujOcHJhvi8TiWLFni1gVBQEVFhfu1jcSHPvQh3HfffWWx4te/ceNGNDU1oaGhwT123HHHlbXdvHkzDj/8cHg8Hjd2zDHHlLVZs2YNXnzxxSEjVQCdOi5OvY7FunXrIAgCTjnllFHblX6P4/E4fD4fWlpaymJvvvnmuD6TwZhpMAHGYDBGZePGjZg9e/awx5YvX462tjY888wzeP7553HRRRfh9NNPxx/+8IcRr/elL30Jzz77LP7nf/4Hc+fOhdfrxYUXXghd18vaSZJUVuc4zp1i9Hq9o/bZtm3ccsst+NjHPjbkWKkAGYwsy5g7d+6wx0qFFjC6MC2ND/d1jPa1jYTf7x+xb4SQUfswUn8Hn2fbNs477zzceeedQ65XW1s7av9KGev7U6T0PuzrfWEwZipMgDEYjBF54YUXsH79elx//fUjtgmFQrj44otx8cUX48ILL8TZZ5+N/v5+xGIxSJIEy7LK2v/973/HFVdcgQsuuAAAXc+1Y8eOCfXr8MMPx09/+lP3cwazfPlybN68eUTBMhksWrQI//d//4dcLueKs1dffRU8z497pGgy+7Jr1y60t7ejrq4OALB69eqyNgsWLMCvf/1raJrmjuz94x//KGuzfPlyPProo2hubnbfet0Xli5dCtu28fLLL+P000/f5+swGAczbBE+g8EAQN9a6+zsxN69e7F27Vrcdttt+OhHP4pzzz0Xl1122bDn/L//9//w8MMPY9OmTdiyZQt+//vfo6amBpFIBAB9m/Gvf/0rOjs7kUgkAABz587FY489hnXr1uGdd97BJz7xiQmPclxyySWoqanB+eefj1dffRXbt2/Ho48+6oqOb3zjG/jFL36Bm2++Ge+99x42btyIRx55BF/72tf2/QYN4pOf/CQ8Hg8uv/xyvPvuu3jxxRfx+c9/Hpdeeqk7/TiZFL8/pVZ8O/H000/H/Pnzcdlll+Gdd97B3//+d3z1q18tO794nz/72c9i48aN7igkMDBads0116C/vx+XXHIJ3nzzTWzfvh3PPfccrrzyyiFCejSam5tx+eWX48orr8QTTzyBtrY2vPTSS/jd7343SXeDwZj5MAHGYDAAAH/5y19QW1uL5uZmnH322XjxxRdx9913449//CMEQRj2nEAggDvvvBMrVqzA0UcfjR07duDpp5921zx9//vfx6pVq9DY2Ihly5YBoKItGo3i+OOPx3nnnYezzjoLy5cvn1BfZVnGc889h+rqapxzzjlYunQp7rjjDrefZ511Fv70pz9h1apVOProo/GBD3wAd911F2bNmvU+7lA5Pp8Pzz77LPr7+3H00UfjwgsvxGmnnYZ77rln0j6jlOL3p9ROPPFEAADP83j88cehaRqOOeYY/Md//MeQNz5DoRCeeuoprFu3DkceeSS++tWv4hvf+AaAgWnZuro6vPrqq7AsC2eddRaWLFmC6667DuFwuGwd23i47777cOGFF+Lqq6/GggUL8JnPfAa5XG4S7gSDcXDAkeEWDzAYDAbjoOfXv/41/v3f/x2pVGrc67YG89BDD2HlypVIJpOT27lR4DgOjz/+OM4///wp+0wGY7JhI2AMBoNxiPCLX/wCr7zyCtra2vDEE0/gxhtvxEUXXbTP4qtIKpVCIBAYkldssrnqqquGfUOTwZiJsBEwBoPBOET47ne/i3vvvRednZ2ora3F+eefj1tvvXXEtBzjIZPJoKurCwAQiURQWVk5Wd0dQnd3N9LpNAD6VubgN1MZjJkEE2AMBoPBYDAYUwybgmQwGAwGg8GYYpgAYzAYDAaDwZhimABjMBgMBoPBmGKYAGMwGAwGg8GYYpgAYzAYDAaDwZhimABjMBgMBoPBmGKYAGMwGAwGg8GYYpgAYzAYDAaDwZhimABjMBgMBoPBmGL+P1mlbqtKs/2QAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -321,7 +323,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Your results will be stored in c:\\Users\\mkempe\\Documents\\GitHub\\PVDegradationTools\\TEMP\\results\n", + "Your results will be stored in c:\\Users\\mkempe\\Documents\\GitHub\\new\\PVDegradationTools\\TEMP\\results\n", "The folder must already exist or the file will not be created\n" ] } @@ -343,7 +345,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, From a914310d605b9619ba18604f04ddf0810f29261e Mon Sep 17 00:00:00 2001 From: martin-springer Date: Mon, 18 Nov 2024 09:54:41 -0700 Subject: [PATCH 03/11] single_axis_initial --- pvdeg/geospatial.py | 53 ++++++++++++++++-------------- pvdeg/spectral.py | 80 +++++++++++++++++++++++++++++++++++++++++++++ pvdeg/standards.py | 37 ++++++++++++++------- 3 files changed, 135 insertions(+), 35 deletions(-) diff --git a/pvdeg/geospatial.py b/pvdeg/geospatial.py index c9cb0d2..85455f6 100644 --- a/pvdeg/geospatial.py +++ b/pvdeg/geospatial.py @@ -85,7 +85,7 @@ def start_dask(hpc=None): client = Client(cluster) print("Dashboard:", client.dashboard_link) - client.wait_for_workers(n_workers=1) + # client.wait_for_workers(n_workers=1) return client @@ -927,7 +927,10 @@ def elevation_stochastic_downselect( def interpolate_analysis( - result: xr.Dataset, data_var: str, method="nearest", resolution=100j, + result: xr.Dataset, + data_var: str, + method="nearest", + resolution=100j, ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ Interpolate sparse spatial result data against DataArray coordinates. @@ -965,12 +968,12 @@ def interpolate_analysis( # api could be updated to match that of plot_USA def plot_sparse_analysis( - result: xr.Dataset, - data_var: str, - method="nearest", - resolution:complex=100j, - figsize:tuple=(10,8), - show_plot:bool=False, + result: xr.Dataset, + data_var: str, + method="nearest", + resolution: complex = 100j, + figsize: tuple = (10, 8), + show_plot: bool = False, ) -> None: """ Plot the output of a sparse geospatial analysis using interpolation. @@ -982,7 +985,7 @@ def plot_sparse_analysis( data_var: str name of datavariable to plot from result method: str - interpolation method. + interpolation method. Options: `'nearest', 'linear', 'cubic'` See [`scipy.interpolate.griddata`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html) resolution: complex @@ -1000,7 +1003,9 @@ def plot_sparse_analysis( ) fig = plt.figure(figsize=figsize) - ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False) # these should be the same ccrs + ax = fig.add_axes( + [0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False + ) # these should be the same ccrs ax.patch.set_visible(False) extent = [lon.min(), lon.max(), lat.min(), lat.max()] @@ -1010,7 +1015,7 @@ def plot_sparse_analysis( extent=extent, origin="lower", cmap="viridis", - transform=ccrs.PlateCarree(), # why are ccrs different + transform=ccrs.PlateCarree(), # why are ccrs different ) shapename = "admin_1_states_provinces_lakes" @@ -1031,22 +1036,22 @@ def plot_sparse_analysis( plt.title(f"Interpolated Sparse Analysis, {data_var}") plt.xlabel("Longitude") plt.ylabel("Latitude") - + if show_plot: plt.show() return fig, ax + def plot_sparse_analysis_land( - result: xr.Dataset, - data_var: str, - method="nearest", - resolution:complex=100j, - figsize:tuple=(10,8), - show_plot:bool=False, + result: xr.Dataset, + data_var: str, + method="nearest", + resolution: complex = 100j, + figsize: tuple = (10, 8), + show_plot: bool = False, proj=ccrs.PlateCarree(), ): - import matplotlib.path as mpath from cartopy.mpl.patch import geos_to_path @@ -1061,7 +1066,7 @@ def plot_sparse_analysis_land( extent = [lon.min(), lon.max(), lat.min(), lat.max()] ax.set_extent(extent, crs=proj) - mesh = ax.pcolormesh(lon, lat, grid_values, transform=proj, cmap='viridis') + mesh = ax.pcolormesh(lon, lat, grid_values, transform=proj, cmap="viridis") land_path = geos_to_path(list(cfeature.LAND.geometries())) land_path = mpath.Path.make_compound_path(*land_path) @@ -1078,15 +1083,15 @@ def plot_sparse_analysis_land( proj, facecolor="none", edgecolor="black", - linestyle=':' + linestyle=":", ) cbar = plt.colorbar(mesh, ax=ax, orientation="vertical", fraction=0.02, pad=0.04) cbar.set_label("Value") utilities._add_cartopy_features( - ax=ax, - features = [ + ax=ax, + features=[ cfeature.BORDERS, cfeature.COASTLINE, cfeature.LAND, @@ -1097,4 +1102,4 @@ def plot_sparse_analysis_land( if show_plot: plt.show() - return fig, ax \ No newline at end of file + return fig, ax diff --git a/pvdeg/spectral.py b/pvdeg/spectral.py index b66cb63..4b5465e 100644 --- a/pvdeg/spectral.py +++ b/pvdeg/spectral.py @@ -135,3 +135,83 @@ def poa_irradiance( ) return poa + + +def poa_irradiance_tracker( + weather_df: pd.DataFrame, + meta: dict, + sol_position=None, + axis_tilt=0, + axis_azimuth=None, + max_angle=90, + backtrack=True, + gcr=0.2857142857142857, + cross_axis_tilt=0, + sky_model="isotropic", +) -> pd.DataFrame: + """ + Calculate plane-of-array (POA) irradiance using pvlib based on weather data from the + National Solar Radiation Database (NSRDB) for a given location (gid). + + Parameters + ---------- + weather_df : pd.DataFrame + The file path to the NSRDB file. + meta : dict + The geographical location ID in the NSRDB file. + sol_position : pd.DataFrame, optional + pvlib.solarposition.get_solarposition Dataframe. If none is given, it will be calculated. + tilt : float, optional + The tilt angle of the PV panels in degrees, if None, the latitude of the + location is used. + azimuth : float, optional + The azimuth angle of the PV panels in degrees. Equatorial facing by default. + sky_model : str, optional + The pvlib sky model to use, 'isotropic' by default. + Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'. + + Returns + ------- + tracker_poa : pandas.DataFrame + Contains keys/columns 'poa_global', 'poa_direct', 'poa_diffuse', + 'poa_sky_diffuse', 'poa_ground_diffuse'. [W/m2] + """ + + if axis_azimuth is None: # Sets the default orientation to equator facing. + try: + axis_azimuth = float(meta["azimuth"]) + except: + if float(meta["latitude"]) < 0: + axis_azimuth = 0 + else: + axis_azimuth = 180 + print( + f"The array azimuth was not provided, therefore an azimuth of {axis_azimuth:.1f} was used." + ) + + if sol_position is None: + sol_position = solar_position(weather_df, meta) + + tracker_data = pvlib.tracking.singleaxis( + sol_position["apparent_zenith"], + sol_position["azimuth"], + axis_tilt=axis_tilt, + axis_azimuth=axis_azimuth, + max_angle=max_angle, + backtrack=backtrack, + gcr=gcr, + cross_axis_tilt=cross_axis_tilt, + ) + + tracker_poa = pvlib.irradiance.get_total_irradiance( + surface_tilt=tracker_data["surface_tilt"], + surface_azimuth=tracker_data["surface_azimuth"], + dni=weather_df["dni"], + ghi=weather_df["ghi"], + dhi=weather_df["dhi"], + solar_zenith=sol_position["apparent_zenith"], + solar_azimuth=sol_position["azimuth"], + model=sky_model, + ) + + return tracker_poa diff --git a/pvdeg/standards.py b/pvdeg/standards.py index acdb176..40079c8 100644 --- a/pvdeg/standards.py +++ b/pvdeg/standards.py @@ -208,7 +208,7 @@ def standoff( weather_df: pd.DataFrame = None, meta: dict = None, weather_kwarg: dict = None, - tilt: Union[float, int] = None, + tilt: Union[float, int, str] = None, azimuth: Union[float, int] = None, sky_model: str = "isotropic", temp_model: str = "sapm", @@ -220,6 +220,7 @@ def standoff( x_0: float = 6.5, # [cm] wind_factor: float = 0.33, irradiance_kwarg={}, + tracker_irradiance_kwarg={}, model_kwarg={}, ) -> pd.DataFrame: """ @@ -239,7 +240,8 @@ def standoff( weather_kwarg : dict other variables needed to access a particular weather dataset. tilt : float, optional - Tilt angle of PV system relative to horizontal. [°] + Tilt angle of rack mounted PV system relative to horizontal. [°] + If tracker mounted, specify keyword '1_axis' azimuth : float, optional Azimuth angle of PV system relative to north. [°] sky_model : str, optional @@ -316,16 +318,29 @@ def standoff( solar_position = spectral.solar_position(weather_df, meta) - irradiance_dict = { - "sol_position": solar_position, - "tilt": tilt, - "azimuth": azimuth, - "sky_model": sky_model, - } + if tilt == "1_axis": + irradiance_dict = { + "sol_position": solar_position, + "axis_azimuth": azimuth, + "sky_model": sky_model, + } + poa = spectral.poa_irradiance_tracker( + weather_df=weather_df, + meta=meta, + **irradiance_dict | tracker_irradiance_kwarg, + ) - poa = spectral.poa_irradiance( - weather_df=weather_df, meta=meta, **irradiance_dict | irradiance_kwarg - ) + else: + irradiance_dict = { + "sol_position": solar_position, + "tilt": tilt, + "azimuth": azimuth, + "sky_model": sky_model, + } + + poa = spectral.poa_irradiance( + weather_df=weather_df, meta=meta, **irradiance_dict | irradiance_kwarg + ) T_0 = temperature.temperature( cell_or_mod="cell", From 4addfc93d2aa3305a18cde020f77bbc597ea8aae Mon Sep 17 00:00:00 2001 From: MDKempe <58960264+MDKempe@users.noreply.github.com> Date: Mon, 18 Nov 2024 09:56:59 -0700 Subject: [PATCH 04/11] Update standards.py I added code to calculate the temperature profile for an arbitrary effective gap. --- pvdeg/standards.py | 121 ++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 119 insertions(+), 2 deletions(-) diff --git a/pvdeg/standards.py b/pvdeg/standards.py index acdb176..fbddcab 100644 --- a/pvdeg/standards.py +++ b/pvdeg/standards.py @@ -484,8 +484,8 @@ def T98_estimate( ): """ Estimate the 98ᵗʰ percential temperature for the module at the given tilt, azimuth, and x_eff. - If any of these factors are supplied, it default to latitide tilt, equatorial facing, and - open rack mounted, respectively. + If any of these factors are not supplied, it default to latitide tilt, equatorial facing, and + open rack mounted as needed. Parameters ---------- @@ -633,3 +633,120 @@ def standoff_x( ).x[0] return temp_df + + +def x_eff_temperature_estimate( + weather_df=None, + meta=None, + weather_kwarg=None, + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + wind_factor=0.33, + tilt=None, + azimuth=None, + x_eff=None, + x_0=6.5, + model_kwarg={}, +): + """ + Estimate the temperature for the module at the given tilt, azimuth, and x_eff. + If any of these factors are not supplied, it default to latitide tilt, equatorial facing, and + open rack mounted, respectively. + + Parameters + ---------- + x_eff : float + This is the effective module standoff distance according to the model. [cm] + x_0 : float, optional + Thermal decay constant. [cm] + weather_df : pd.DataFrame + Weather data for a single location. + meta : pd.DataFrame + Meta data for a single location. + weather_kwarg : dict + other variables needed to access a particular weather dataset. + tilt : float, + Tilt angle of PV system relative to horizontal. [°] + azimuth : float, optional + Azimuth angle of PV system relative to north. [°] + sky_model : str, optional + Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'. + temp_model : str, optional + Options: 'sapm'. 'pvsyst' and 'faiman' will be added later. + Performs the calculations for the cell temperature. + conf_0 : str, optional + Model for the high temperature module on the exponential decay curve. + Default: 'insulated_back_glass_polymer' + conf_inf : str, optional + Model for the lowest temperature module on the exponential decay curve. + Default: 'open_rack_glass_polymer' + wind_factor : float, optional + Wind speed correction exponent to account for different wind speed measurement heights + between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM) + The NSRDB provides calculations at 2 m (i.e module height) but SAPM uses a 10 m height. + It is recommended that a power-law relationship between height and wind speed of 0.33 + be used*. This results in a wind speed that is 1.7 times higher. It is acknowledged that + this can vary significantly. + model_kwarg : dict, optional + keyword argument dictionary to provide other arguments to the temperature model. + See temperature.temperature for more information. + + R. Rabbani, M. Zeeshan, "Exploring the suitability of MERRA-2 reanalysis data for wind energy + estimation, analysis of wind characteristics and energy potential assessment for selected + sites in Pakistan", Renewable Energy 154 (2020) 1240-1251. + + Returns + ------- + T_x_eff: Pandas Dataframe + This is the estimate for the module temperature at the given tilt, azimuth, and x_eff. + + """ + + parameters = ["temp_air", "wind_speed", "dhi", "ghi", "dni"] + + if isinstance(weather_df, dd.DataFrame): + weather_df = weather_df[parameters].compute() + weather_df.set_index("time", inplace=True) + elif isinstance(weather_df, pd.DataFrame): + weather_df = weather_df[parameters] + elif weather_df is None: + weather_df, meta = weather.get(**weather_kwarg) + + solar_position = spectral.solar_position(weather_df, meta) + poa = spectral.poa_irradiance( + weather_df=weather_df, + meta=meta, + sol_position=solar_position, + tilt=tilt, + azimuth=azimuth, + sky_model=sky_model, + ) + T_inf = temperature.temperature( + cell_or_mod="cell", + weather_df=weather_df, + meta=meta, + poa=poa, + temp_model=temp_model, + conf=conf_inf, + wind_factor=wind_factor, + model_kwarg=model_kwarg, + ) + + if x_eff == None: + return T_inf + else: + T_0 = temperature.temperature( + cell_or_mod="cell", + weather_df=weather_df, + meta=meta, + poa=poa, + temp_model=temp_model, + conf=conf_0, + wind_factor=wind_factor, + model_kwarg=model_kwarg, + ) + T_x_eff = T_0 - (T_0 - T_inf) * (1 - np.exp(-x_eff / x_0)) + + return T_x_eff \ No newline at end of file From a636b6a9a624bf8cbda1a98f404a13c298fed219 Mon Sep 17 00:00:00 2001 From: MDKempe <58960264+MDKempe@users.noreply.github.com> Date: Mon, 18 Nov 2024 10:07:02 -0700 Subject: [PATCH 05/11] Update weather.py This adds in some functionality to provide a full set of meta data from another source to augment the PVGIS and NSRDB data. Doing this function call is not required and will default to not doing it for a geospatial analysis but default to do it for a single point look-up. --- pvdeg/weather.py | 58 +++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 55 insertions(+), 3 deletions(-) diff --git a/pvdeg/weather.py b/pvdeg/weather.py index dbe06f7..3af59fa 100644 --- a/pvdeg/weather.py +++ b/pvdeg/weather.py @@ -16,9 +16,10 @@ import h5py import dask.dataframe as dd import xarray as xr +from geopy.geocoders import Nominatim -def get(database, id=None, geospatial=False, **kwargs): +def get(database, id=None, geospatial=False, find_meta: bool=None , **kwargs): """ Load weather data directly from NSRDB or through any other PVLIB i/o tools function @@ -35,6 +36,9 @@ def get(database, id=None, geospatial=False, **kwargs): dask dataframe. This is useful for large scale geospatial analyses on distributed compute systems. Geospaital analyses are only supported for NSRDB data and locally stored h5 files that follow pvlib conventions. + find_meta : (bool) + if true, this instructs the code to look up additional meta data. + The default is True if geospatial is False. **kwargs : Additional keyword arguments to pass to the get_weather function (see pvlib.iotools.get_psm3 for PVGIS, and get_NSRDB for NSRDB) @@ -47,6 +51,8 @@ def get(database, id=None, geospatial=False, **kwargs): Dictionary of metadata for the weather data """ + if find_meta == None: + find_meta = not(geospatial) META_MAP = {"elevation": "altitude", "Local Time Zone": "tz"} if type(id) is tuple: @@ -97,6 +103,8 @@ def get(database, id=None, geospatial=False, **kwargs): # switch weather data headers and metadata to pvlib standard map_weather(weather_df) map_meta(meta) + if find_meta: + meta=find_metadata(meta) if "relative_humidity" not in weather_df.columns: print('\r','Column "relative_humidity" not found in DataFrame. Calculating...', end='') @@ -121,7 +129,7 @@ def get(database, id=None, geospatial=False, **kwargs): return weather_ds, meta_df -def read(file_in, file_type, map_variables=True, **kwargs): +def read(file_in, file_type, map_variables=True, find_meta=True, **kwargs): """ Read a locally stored weather file of any PVLIB compatible type @@ -163,6 +171,8 @@ def read(file_in, file_type, map_variables=True, **kwargs): if map_variables == True: map_weather(weather_df) map_meta(meta) + if find_meta: + meta=find_metadata(meta) if weather_df.index.tzinfo is None: tz = "Etc/GMT%+d" % -meta["tz"] @@ -265,12 +275,24 @@ def map_meta(meta): "Dew Point": "dew_point", "Longitude": "longitude", "Latitude": "latitude", + "state": "State", + "county": "County", + "country": "Country", + "Neighborhood": "neighbourhood" , + "country_code": "Country Code" , + "postcode": "Zipcode", + "road": "Street", + "village": "City", + "city": "City", + "town": "City", } # map meta-names as needed for key in [*meta.keys()]: if key in META_MAP.keys(): meta[META_MAP[key]] = meta.pop(key) + if "Country Code" in meta.keys(): + meta["Country Code"]=meta["Country Code"].upper() return meta @@ -930,4 +952,34 @@ def get_anywhere(database = "PSM3", id=None, **kwargs): meta = {'result': 'This location was not found in either the NSRDB or PVGIS'} weather_db = {'result': 'NA'} - return weather_db, meta \ No newline at end of file + return weather_db, meta + +def find_metadata(meta): + """ + Fills in missing meta data for a geographic location. + The meta dictionary must have longitude and latitude information. + Make sure meta_map has been run first to eliminate the creation of duplicate entries with different names. + It will only replace empty keys and those with one character of length. + + Parameters: + ----------- + meta : (dict) + Dictionary of metadata for the weather data + + Returns: + -------- + meta : (dict) + Dictionary of metadata for the weather data + """ + geolocator = Nominatim(user_agent="geoapiexercises") + location = geolocator.reverse(str(meta['latitude']) + ',' + str(meta['longitude'])).raw['address'] + map_meta(location) + + for key in [*location.keys()]: + if key in meta.keys(): + if len(meta[key])<2: + meta[key] = location[key] + else: + meta[key] = location[key] + + return meta \ No newline at end of file From 0e2aef7b18729fb641411ddbf764df74a3649468 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Mon, 18 Nov 2024 15:01:04 -0700 Subject: [PATCH 06/11] add top-level poa function --- pvdeg/spectral.py | 70 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 70 insertions(+) diff --git a/pvdeg/spectral.py b/pvdeg/spectral.py index 4b5465e..022df94 100644 --- a/pvdeg/spectral.py +++ b/pvdeg/spectral.py @@ -65,6 +65,66 @@ def solar_position(weather_df: pd.DataFrame, meta: dict) -> pd.DataFrame: ], ) def poa_irradiance( + weather_df: pd.DataFrame, + meta: dict, + module_mount="fixed", + sol_position=None, + **kwargs_irradiance, +) -> pd.DataFrame: + """ + Calculate plane-of-array (POA) irradiance using pvlib based on weather data from the + National Solar Radiation Database (NSRDB) for a given location (gid). + + Parameters + ---------- + weather_df : pd.DataFrame + The file path to the NSRDB file. + meta : dict + The geographical location ID in the NSRDB file. + module_mount: string + Module mounting configuration. Can either be `fixed` for fixed tilt systems or + `1_axis` for single-axis tracker systems. + sol_position : pd.DataFrame, optional + pvlib.solarposition.get_solarposition Dataframe. If none is given, it will be calculated. + kwargs_irradiance : dict + Contains kwarg arguments for the poa model based on mounting configuration. See + `poa_irradiance_fixed` or `poa_irradiance_tracker` for details. + + Returns + ------- + poa : pandas.DataFrame + Contains keys/columns 'poa_global', 'poa_direct', 'poa_diffuse', + 'poa_sky_diffuse', 'poa_ground_diffuse'. [W/m2] + """ + + if sol_position is None: + sol_position = solar_position(weather_df, meta) + + if module_mount == "fixed": + poa = poa_irradiance_fixed(weather_df, meta, sol_position, **kwargs_irradiance) + elif module_mount == "1_axis": + poa = poa_irradiance_tracker( + weather_df, meta, sol_position, **kwargs_irradiance + ) + else: + raise NotImplementedError( + f"The input module_mount '{module_mount}' is not implemented" + ) + + return poa + + +@geospatial_quick_shape( + 1, + [ + "poa_global", + "poa_direct", + "poa_diffuse", + "poa_sky_diffuse", + "poa_ground_diffuse", + ], +) +def poa_irradiance_fixed( weather_df: pd.DataFrame, meta: dict, sol_position=None, @@ -137,6 +197,16 @@ def poa_irradiance( return poa +@geospatial_quick_shape( + 1, + [ + "poa_global", + "poa_direct", + "poa_diffuse", + "poa_sky_diffuse", + "poa_ground_diffuse", + ], +) def poa_irradiance_tracker( weather_df: pd.DataFrame, meta: dict, From 4dcbdfbb2d5216df5bd8f0916602346cd39de82e Mon Sep 17 00:00:00 2001 From: MDKempe <58960264+MDKempe@users.noreply.github.com> Date: Mon, 18 Nov 2024 17:12:43 -0700 Subject: [PATCH 07/11] Merge branch 'single_axis_tracking' of https://github.com/NREL/PVDegradationTools into Kempe-Edge-Seals I found an error in the default tilt, it should be the absolute value for the southern hemisphere latitude. Fixed an error that occurs if kwargs can't be sent to a function. --- pvdeg/spectral.py | 12 +- pvdeg/standards.py | 2 + ...s - Module Standoff for IEC TS 63126.ipynb | 116 +++++++++++------- 3 files changed, 83 insertions(+), 47 deletions(-) diff --git a/pvdeg/spectral.py b/pvdeg/spectral.py index 022df94..e481b2c 100644 --- a/pvdeg/spectral.py +++ b/pvdeg/spectral.py @@ -131,6 +131,7 @@ def poa_irradiance_fixed( tilt=None, azimuth=None, sky_model="isotropic", + **kwargs_irradiance, ) -> pd.DataFrame: """ Calculate plane-of-array (POA) irradiance using pvlib based on weather data from the @@ -164,7 +165,7 @@ def poa_irradiance_fixed( try: tilt = float(meta["tilt"]) except: - tilt = float(meta["latitude"]) + tilt = float(abs(meta["latitude"])) print( f"The array tilt angle was not provided, therefore the latitude tilt of {tilt:.1f} was used." ) @@ -218,6 +219,7 @@ def poa_irradiance_tracker( gcr=0.2857142857142857, cross_axis_tilt=0, sky_model="isotropic", + **kwargs_irradiance, ) -> pd.DataFrame: """ Calculate plane-of-array (POA) irradiance using pvlib based on weather data from the @@ -247,17 +249,15 @@ def poa_irradiance_tracker( 'poa_sky_diffuse', 'poa_ground_diffuse'. [W/m2] """ - if axis_azimuth is None: # Sets the default orientation to equator facing. + if axis_azimuth is None: # Sets the default orientation to north-south. try: - axis_azimuth = float(meta["azimuth"]) + axis_azimuth = float(meta["axis_azimuth"]) except: if float(meta["latitude"]) < 0: axis_azimuth = 0 else: axis_azimuth = 180 - print( - f"The array azimuth was not provided, therefore an azimuth of {axis_azimuth:.1f} was used." - ) + print(f"The array axis_azimuth was not provided, therefore an azimuth of {axis_azimuth:.1f} was used.") if sol_position is None: sol_position = solar_position(weather_df, meta) diff --git a/pvdeg/standards.py b/pvdeg/standards.py index 555d2e0..4c38d75 100644 --- a/pvdeg/standards.py +++ b/pvdeg/standards.py @@ -659,6 +659,7 @@ def x_eff_temperature_estimate( conf_0="insulated_back_glass_polymer", conf_inf="open_rack_glass_polymer", wind_factor=0.33, + module_mount=None, tilt=None, azimuth=None, x_eff=None, @@ -734,6 +735,7 @@ def x_eff_temperature_estimate( weather_df=weather_df, meta=meta, sol_position=solar_position, + module_mount=module_mount, tilt=tilt, azimuth=azimuth, sky_model=sky_model, diff --git a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb b/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb index a88a81a..547fc0e 100644 --- a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -80,9 +80,9 @@ "Working on a Windows 10\n", "Python version 3.9.18 (main, Sep 11 2023, 14:09:26) [MSC v.1916 64 bit (AMD64)]\n", "Pandas version 2.1.4\n", - "pvdeg version 0.2.0+11.g7f12df3.dirty\n", + "pvdeg version 0.4.3.dev14+g08d739a\n", "dask version 2023.11.0\n", - "c:\\users\\mkempe\\documents\\github\\pvdegradationtools\\pvdeg\\data\n" + "C:\\Users\\mkempe\\Documents\\GitHub\\new\\PVDegradationTools\\pvdeg\\data\n" ] } ], @@ -112,14 +112,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'Source': 'NSRDB', 'Location ID': 145809.0, 'City': '-', 'State': '-', 'Country': '-', 'Clearsky DHI Units': 'w/m2', 'Clearsky DNI Units': 'w/m2', 'Clearsky GHI Units': 'w/m2', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Solar Zenith Angle Units': 'Degree', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Relative Humidity Units': '%', 'Precipitable Water Units': 'cm', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Cloud Type -15': 'N/A', 'Cloud Type 0': 'Clear', 'Cloud Type 1': 'Probably Clear', 'Cloud Type 2': 'Fog', 'Cloud Type 3': 'Water', 'Cloud Type 4': 'Super-Cooled Water', 'Cloud Type 5': 'Mixed', 'Cloud Type 6': 'Opaque Ice', 'Cloud Type 7': 'Cirrus', 'Cloud Type 8': 'Overlapping', 'Cloud Type 9': 'Overshooting', 'Cloud Type 10': 'Unknown', 'Cloud Type 11': 'Dust', 'Cloud Type 12': 'Smoke', 'Fill Flag 0': 'N/A', 'Fill Flag 1': 'Missing Image', 'Fill Flag 2': 'Low Irradiance', 'Fill Flag 3': 'Exceeds Clearsky', 'Fill Flag 4': 'Missing CLoud Properties', 'Fill Flag 5': 'Rayleigh Violation', 'Surface Albedo Units': 'N/A', 'Version': '3.0.6', 'latitude': 39.73, 'longitude': -105.18, 'tz': -7.0, 'altitude': 1820.0}\n" + "{'Source': 'NSRDB', 'Location ID': 145809.0, 'City': 'West Pleasant View', 'State': 'Colorado', 'Country': 'United States', 'Clearsky DHI Units': 'w/m2', 'Clearsky DNI Units': 'w/m2', 'Clearsky GHI Units': 'w/m2', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Solar Zenith Angle Units': 'Degree', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Relative Humidity Units': '%', 'Precipitable Water Units': 'cm', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Cloud Type -15': 'N/A', 'Cloud Type 0': 'Clear', 'Cloud Type 1': 'Probably Clear', 'Cloud Type 2': 'Fog', 'Cloud Type 3': 'Water', 'Cloud Type 4': 'Super-Cooled Water', 'Cloud Type 5': 'Mixed', 'Cloud Type 6': 'Opaque Ice', 'Cloud Type 7': 'Cirrus', 'Cloud Type 8': 'Overlapping', 'Cloud Type 9': 'Overshooting', 'Cloud Type 10': 'Unknown', 'Cloud Type 11': 'Dust', 'Cloud Type 12': 'Smoke', 'Fill Flag 0': 'N/A', 'Fill Flag 1': 'Missing Image', 'Fill Flag 2': 'Low Irradiance', 'Fill Flag 3': 'Exceeds Clearsky', 'Fill Flag 4': 'Missing CLoud Properties', 'Fill Flag 5': 'Rayleigh Violation', 'Surface Albedo Units': 'N/A', 'Version': '3.0.6', 'latitude': 39.73, 'longitude': -105.18, 'tz': -7.0, 'altitude': 1820.0, 'ISO3166-2-lvl4': 'US-CO', 'Street': 'West 8th Avenue', 'County': 'Jefferson County', 'Zipcode': '80419', 'Country Code': 'US'}\n" ] } ], @@ -132,14 +132,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'latitude': -43.52646, 'longitude': 172.62165, 'altitude': 4.0, 'wind_height': 10, 'Source': 'PVGIS'}\n" + "{'latitude': -43.52646, 'longitude': 172.62165, 'altitude': 4.0, 'wind_height': 10, 'Source': 'PVGIS', 'leisure': 'Hagley Golf Club', 'suburb': 'Central City', 'city_district': 'Linwood-Central-Heathcote Community', 'ISO3166-2-lvl4': 'NZ-CAN', 'Street': 'Uni-Cycle Cycleway', 'City': 'Christchurch', 'County': 'Christchurch City', 'State': 'Canterbury', 'Zipcode': '8440', 'Country': 'New Zealand / Aotearoa', 'Country Code': 'NZ'}\n" ] } ], @@ -189,20 +189,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 24.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The estimated T₉₈ of an insulated-back module is 89.6°C. \n", - "The estimated T₉₈ of an open-rack module is 63.8°C. \n", - "Level 0 certification is valid for a standoff greather than 9.3 cm. \n", - "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n", - "Level 2 certification is required for a standoff less than 3.0 cm.\n" + "The array tilt angle was not provided, therefore the latitude tilt of 43.5 was used.\n", + "The estimated T₉₈ of an insulated-back module is 69.4°C. \n", + "The estimated T₉₈ of an open-rack module is 44.0°C. \n", + "Level 0 certification is valid for a standoff greather than 0.0 cm. \n", + "\n" ] } ], @@ -220,22 +218,19 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "First calculation standoff = 9.3 cm.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "Second calculation standoff = 3.0 cm.\n", - "The estimated T₉₈ of an insulated-back module is 89.6°C. \n", - "The estimated T₉₈ of an open-rack module is 63.8°C. \n", - "Level 0 certification is valid for a standoff greather than 9.3 cm. \n", - "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n", - "Level 2 certification is required for a standoff less than 3.0 cm.\n" + "First calculation standoff = 0.0 cm.\n", + "Second calculation standoff = 0.0 cm.\n", + "The estimated T₉₈ of an insulated-back module is 45.2°C. \n", + "The estimated T₉₈ of an open-rack module is 32.7°C. \n", + "Level 0 certification is valid for a standoff greather than 0.0 cm. \n", + "\n" ] } ], @@ -273,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -317,15 +312,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The 98ᵗʰ percential temperature is estimated to be 89.6 °C.\n" + "The 98ᵗʰ percential temperature is estimated to be 49.5 °C.\n" ] } ], @@ -334,12 +328,40 @@ "T_98 = pvdeg.standards.T98_estimate(\n", " weather_df=WEATHER_df,\n", " meta=META,\n", - " tilt=META['latitude'],\n", + " tilt=-META['latitude'],\n", " azimuth=None,\n", - " x_eff=0,)\n", + " x_eff=10)\n", "print ('The 98ᵗʰ percential temperature is estimated to be' , '%.1f' % T_98 , '°C.')" ] }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 98ᵗʰ percential temperature is estimated to be 47.9 °C.\n" + ] + } + ], + "source": [ + "# This code will calculate the temperature for an arbitrary x_eff distance. This produces a slightly different value because it is set for a 1-axis tracker and x_eff=None indicating open rack.\n", + "irradiance_kwarg ={\n", + " \"tilt\": None,\n", + " \"azimuth\": None,\n", + " \"x_eff\": None,\n", + " \"module_mount\": '1_axis'}\n", + "\n", + "T_xeff = pvdeg.standards.x_eff_temperature_estimate(\n", + " weather_df=WEATHER_df,\n", + " meta=META,\n", + " **irradiance_kwarg)\n", + "print ('The 98ᵗʰ percential temperature is estimated to be' , '%.1f' % (T_xeff.quantile(q=0.98, interpolation=\"linear\")) , '°C.')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -353,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -399,19 +421,19 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[WinError 183] Cannot create a file when that file already exists: 'c:\\\\Users\\\\mkempe\\\\Documents\\\\GitHub\\\\PVDegradationTools\\\\TEMP\\\\results'\n" + "[WinError 183] Cannot create a file when that file already exists: 'c:\\\\Users\\\\mkempe\\\\Documents\\\\GitHub\\\\new\\\\PVDegradationTools\\\\TEMP\\\\results'\n" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABK4AAAG/CAYAAABxKJ/mAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB3m0lEQVR4nO3deVyU5f7/8fewDYuIO4gLoOGSe2kel9JKKXPNNrVcMstcOpmnXPKUZAZqHbOy7KunY5Zp1ilLK03TNM0WNbfUTBO3lEwzF8RB4Pr94Y85ToACM3Mz4Ovp43485L7vud/XdcMMMx+u+7ptxhgjAAAAAAAAwMf4FXcDAAAAAAAAgLxQuAIAAAAAAIBPonAFAAAAAAAAn0ThCgAAAAAAAD6JwhUAAAAAAAB8EoUrAAAAAAAA+CQKVwAAAAAAAPBJFK4AAAAAAADgkyhcAQAAAAAAwCdRuAKAYmSz2VwWPz8/lStXTtdff73+/e9/yxiTa//Y2NjiaaybXn75ZTVo0EB2u102m03t27e/7GPOnz+v6tWry8/PTzt27Mhznx9++EGhoaEKDAzUypUrPdzqoitKf31Ffj9nvvLz5yvtsEpp6q8vPC9iY2NzvfZeagkNDVVWVpbl7czLqlWrCtTmCRMm5HrsuXPnNH78eNWpU0fBwcGKjo7WwIEDdejQoXzz9u/fr2effVbDhg3T8uXLL9u+tLQ0vfjii7rxxhsVGRmpoKAglS9fXq1atdLTTz+tAwcOuNV/AMCVKaC4GwAAkPr37y9JysrK0i+//KKvv/5aa9eu1YoVKzR//nyP5+3bt09xcXFq166dVq1a5fHj/9WHH36oRx99VOXLl1e3bt0UFhamevXqXfZxgYGBeuSRRzRmzBi9+OKLmjVrlsv2o0ePqkePHkpPT9fLL7+sm266yVtdKJSi9he+zRvPG6ufi8XJF54XDodDN954Y64/CixZskRHjx7VzTffrOrVq7tsq1mzpvz9/a1sZr6ioqKcvy/+KisrS3PnzpUkXX/99S7bzp07p5tvvlnr1q1T1apV1b17d+3bt0+zZ8/WJ598om+++Ua1a9d2ecymTZt044036uTJk5Kk1157TRMmTNBTTz2VZ/63336rnj176siRIwoNDdXf/vY3RUZG6uTJk1q/fr2+/fZbTZkyRZ988ok6dOjg7qkAAFxJDACg2Egyeb0UL1u2zAQEBBhJZvHixS77x8TEuJ2bkpJiJJl27dq5fayC6Nu3r5FkVqxYUejH/vnnnyY8PNzY7Xbz22+/Odc7HA7Tpk0bI8kMGjTIk811mzv99QX5/Zx56ufPXTt37jR79uyxPNcbz5uCHLO4+utpvvy8iI6ONpLMjz/+WNxNKbLPPvvMSDI1atQwWVlZLtueeuopI8m0atXKnD592rn+X//6l5FkbrjhhlzHu/HGG821115rdu7caf78808zZswY4+/vb/bt25dr3y1btpiQkBAjyYwePdqcOXPGZXtWVpb54IMPTO3atc3s2bM902EAwBWDSwUBwAd17NhRffv2lSR99NFHxdsYD8i5FKVWrVqFfmxERIQeeOABORwOvfrqq871w4YN09dff622bdu6rPcF7vQXl1evXr1co0NKs9LSX199Xhw/flyHDx+W3W5X3bp1i7s5RZYz2uree++Vn9//3uKfP39er7zyiiTp1VdfVZkyZZzbRo4cqcaNG+urr77Sxo0bXY63YcMGPfXUU6pXr54iIiKUnJys8PBw/fDDDy77GWN03333KT09XYmJiZo0aZLCwsJc9vHz81PPnj21ceNGNW/e3KP9BgCUfhSuAMBHNWvWTJJ08ODBAu3/zTffqHv37qpcubLsdrtiY2M1dOhQHT582GW/xMRExcXFSZJWr17tMi/KgAEDPJ5ls9n05ZdfSpLi4uKcWYW5LGrEiBEKCAjQa6+9pvT0dE2fPl3//ve/VbNmTX3wwQcKCgoq8LG8qaD9Lej5ky5cSpYzF9CpU6f0j3/8Q3FxcQoMDNSIESMu26ZPP/1UAwcOVP369VW2bFmFhYWpSZMmSkpKksPh8FTX8+WNvuY359O+ffs0ePBgxcbGym63q3Llyrrzzju1devWS2alp6drzJgxiomJkd1u11VXXaXJkye7XE5WmOdNQc95QY95qTmuinp+C9LnHDt37lTfvn1Vu3ZtBQcHq3LlymratKlGjBihI0eO5Nmui/ni8+JiW7ZskSTVr19fAQElcxaNtLQ0ffzxx5Kk++67z2Xb2rVr9eeff6p27drO3ysXu/POOyVJixcvdllfpUoVrVmzxvn11q1bderUKUVGRrrs9/nnn2vbtm2qXr26xo0bd8l2RkREqGHDhgXvGAAAYo4rAPBZp0+fliTZ7fbL7jt37lwNGDBA2dnZat26tWrUqKEffvhBM2bM0IcffqhVq1Y555Jp2rSp7rjjDn3wwQeKjIzUrbfe6jxO27ZtPZ7Vv39/LV26VL/99pvuuOMO51/7o6KiCnwuYmJidOedd+rdd9/Vww8/rHnz5ik0NFQff/yxqlSpUuDjeFtB+luY83ex9PR0tWvXTvv371e7du10zTXXqHz58pdt0wMPPKC0tDQ1aNBAjRo10qlTp/T9999r3LhxWrFihZYtW+a1+Xus7OvatWvVuXNnnTp1Sg0aNFC3bt3066+/6sMPP9Rnn32mTz/9VDfeeGOux2VkZCghIUHbt2/Xddddp/r162v16tUaM2aMTp8+rYkTJ0oq3POmoOfcyudiUfosXbj5Qdu2bXXu3Dldd911uu6663T69Gnt3btXL730knr06KGqVatesp2++Ly4WE5hs0mTJoV6nC/58MMPlZaWpmbNmqlBgwYu23IKc9dcc02ej81Zn7NfjocfflhPPPGE1q1bp6ioKH3++edq0aKFWrZs6bLfp59+Kkm66667SmzhDwDg44r5UkUAuKIpnzmusrOzTatWrYwkM27cOJf9/zrH0IEDB0xISIgJCAhwmQ8rKyvLjBgxwkgyLVq0cHlMUefqKUqWMca0a9fOSDIpKSmFyrvYhg0bnOfLZrOZ999/v8jH+qszZ86Y8uXLe2weofz66873Sv9/fpoTJ04Uqi0LFy7MNd/MqVOnTJcuXYwkM2fOnFyPyevn7FLr8+LNvv61HSdPnjRRUVEmMDAw18/F8uXLTVBQkKlWrZpxOBx5Zl1//fXm999/d25bv369CQgIMKGhoS7zARX0eVOYc16QY3r6eV+YPvfv399IMh988EGudu3YscMcPnz4kufiYr70vLjYgAEDjCTzr3/9q8jHKIic/hdm+fLLLwt07ISEBCPJTJ06Nde2xx57zEgyjz32WJ6P3bx5s5FkrrnmGpf1WVlZJjk52cTExJjy5cube+65x2WewRw5cw2+/fbbBWorAACFReEKAIrRXwtXmZmZ5ueff3Z+kLLb7S7FlLw+wD799NNGkunbt2+u4587d8456fA333zjXF/UwlVRsozxTOHq1KlTpnz58kaSGTJkSJGPY4X8+uvO90qSWb9+vcfauHv3biPJ9OzZM9c2TxSuvNnXv7bjxRdfNJLM2LFj89w/p/hxcfElJ8vPz8/s2rUr12O6du2aq3Dg7uTseZ3zohau3Dm/helzp06djCS3CkM5fPV5cc011xhJZvny5fnuc/z4cfPAAw+YqlWrmjJlypibb77Z7Nixw7n9999/N3379jWRkZEmPDzcNGnSJFcRNTk52fTv379Qy86dOy/b/iNHjhh/f3/j7+9vjhw5kmv7gw8+mOuPIBfL+bmsU6fOZbPyUq9ePSPJLF26tEiPBwDgchjPCwA+wGaz5VoXHh6uOXPmXHZS5pw5SO69995c2+x2u+666y699NJLWrNmjf72t7+51U4rsy6WnZ2tPn366MSJE5Iu3Ka9JHLn/FWtWrXIkxrv3r1bn332mfbs2aO0tDRlZ2c75zLavXt3kY55OVb2dfny5ZKkHj165Lm9bdu2mjZtmtavX6+ePXu6bIuNjVWdOnVyPSZnXUHmcMqLt8+5O+e3MH2+9tprtWTJEvXr10///Oc/1bx5c5eJvz2huJ4XkpSVlaUdO3ZIkho3bpzvfv369dORI0f03XffqUqVKho3bpw6duyoXbt2KSwsTMOGDdOhQ4e0adMmRUZGav78+erdu7caNmzovMRxzJgxRW7npcybN09ZWVm69dZb87wEO+fnLq/fMxdvLyp3Hw8AwOVQuAIAH9C/f39JF+68VLZsWTVq1Eg9e/Ys0FwtORMX5zd5c876vCY4Liwrsy42duxYffLJJ2ratKnS09P17bff6uuvv1abNm1y7fvpp59q0KBBevrpp/XCCy/oyJEjuu+++zR58mQ99thjWrRokUJDQzVz5kzddtttkqTJkydrzZo1+uSTT5zHeOihh5zHSE1NVceOHTVv3jwFBwcXuR/unL+aNWsWOs8Yo8cff1wvvvhivh8uc+ZS8zQr+7pv3z5JyjX3zl8dO3Ys17rq1avnuW/OHEyFncDeqnPuzvktTJ+feOIJrV27VosXL9bixYsVERGhli1bqkuXLhowYIDCw8Pd6IVc2mjV8+Jiu3bt0rlz5xQZGZnvfHlpaWn67LPP9N5776lGjRqSpOTkZL388stauHCh7rvvPu3Zs0f333+/c76ve++9VyNHjtSPP/6Y59xcnpRzN8GcO9H+Vc73KC0tLc/tZ8+elSSXuw0WRqVKlbRr1y79/vvvRXo8AACXQ+EKAHzAm2++6fYx8vtrekG3+2rW3LlzNWXKFFWpUkUff/yxPv/8cz300EN64YUX8ixcbdu2TcePH1d2drZ27typ9evXq23bttq7d68mTZqkN954Q//4xz/07LPPOgtXW7duVaNGjVyO8fvvvys9PV3btm1TamqqGjVqpKVLl+Y7qqcwinL+ilIwW7BggaZOnarq1atr2rRpatWqlSpXrqzAwEBlZGTIbrd7fbSEFX3NysqSdGFy6NDQ0Hz3y6uw5cmfVcn6c16U81uYPpctW1YrV67U119/rcWLF2vVqlXOCeaTk5O1Zs2ay44KLSirnhcXy5mQvCATs1/8fTMXptvQpk2bdN9992nMmDF69dVX1aNHD0VFRemdd95Rdna2rr/+eudjJk2apJ9++qlQ7RszZswlC187d+7Upk2bVKZMmXxfm3KKe4cOHcpze876ohYBmzZtqq+//lo//PBDrjsaAgDgCRSuAKCEi46O1q5du5SSkpLn5T/79++XpMve+cvXsiTp+++/14MPPqigoCB9+OGHqlmzpvr166ennnpKixYt0p49e3TVVVe5PGbbtm3q2bOnhg0bJunCpU6SNGLECOclRc2aNdPatWudj9m6das6d+7scozbb79dI0aMkHRhxEeNGjV0/vx5t/pj9flbuHChJGnGjBnq0qWLy7a9e/d6JCM/Vva1evXq2rVrl/75z39e8nIvK1h1zq08vzabTW3btnXe6fD333/Xo48+qvnz5+vJJ5/UggUL3Dq+1c+Li+XcUfBSPzdhYWFKSEjQpEmT1LJlS1WuXFnjx49XZmamTp06JUlq0aKFQkNDVaNGDfn7+yskJETvvPOOIiMjncdZunSpVq9eXaj2DRgw4JKFq7fffluS1LNnz3yLtjlFuR9++CHP7Tnri/rc6dy5s1599VW9//77mjJlCncWBAB4nGcnKQAAWC7nL/rvvPNOrm0ZGRl6//33XfaTpKCgIElSZmam17OK6tdff1WPHj107tw5vf76687RVXa7XcOHD1d2dramTp2a63Hbtm1Tp06dnF/v2bNHAQEB6tixo3Pd7t27VatWLUnS+fPntWvXrlwjri4uZGVnZ2vfvn3OxxSVledPknNOsJzLmy723nvveSQjP1b2tUOHDpKkjz76yO1jXUpBnjeFPecl4bn4V5UrV1ZiYqKkC88VdxVnX3JGXF2uaPP222+rcePGat26tWrVqqWsrCzVr19flSpVkjFGHTt2lJ+fn44cOaJz585p7ty56tWrlz7//HPnMVatWuUcqVXQpX379vm2yRijefPmScr/MkFJatOmjSIiIvTLL7/kOT/gf//7X0nKVWgtqFtvvVUNGjTQoUOH9Nxzz11y31OnTmn79u1FygEAXLkoXAFACffAAw8oJCRE8+fP16effupcn52drSeffFK//vqrWrRo4TKpcaVKlRQYGKhffvnFeZmVt7KKIj09XT169NCRI0f02GOP6f7773fZPnToUIWFhenNN990mbfo/Pnz+umnn1w+hG7dulV16tSR3W53WZezz86dOyXJOaoh5xgXXzq0e/duZWRkqEGDBm71y6rzlyNn9MrMmTNdLnNas2aNnn/+eY9k5MfKvg4ePFiVK1dWUlKSZs+enetSvLS0NL311lv5XipVUAV53hT2nPv6c/H1119XSkpKrvVLliyR5P4cU5L1z4uLFWTElXShWDd79mwdOnRIR44c0dixY5WSkqL27dvrjz/+0J49ezR48GBFRUUpICBA3bt3V/369Z3z5nnDmjVrtH//fkVHR+umm27Kd7+goCANHz5ckjR8+HCXua6mTp2qrVu3qm3btmrRokWR2mGz2TR37lwFBwcrMTFRY8eOzTWfljFGixYtUvPmzbV+/Xrn+n79+qlevXrOkYoAAOTJ+zcuBADkR///du6F2T8mJibX+rffftv4+/sbm81m2rZta3r37m3q1q1rJJnIyMg8b6netWtXI8k0aNDA9O3b1zzwwAPmP//5z2XbUJSsdu3aGUkmJSWlQP3s1auXkWRuueUWk5mZmec+w4cPN5LMM88841y3bds24+/vb86dO+dcN3r0aNOrVy+Xx8bExJhFixY5+9OwYUOXYwQGBhqHw+Fct2DBAlO3bt0Ctd2YS/e3sOcvJSXFSDLt2rUrcH6OXbt2mbCwMCPJXH311aZXr17m+uuvNzabzTz++OP5/jwVdn1+vNXXvNqxdu1aU6FCBee2zp07m549e5rmzZs7z8GmTZsKnDV+/HgjycyePdtl/eWeN0U555c7pqee90Xpc5MmTZx9ueOOO8w999xjmjZtaiSZkJAQs27dujyPlRdfeV7kOH78uJGU6/mel927d5sjR44YY4zZu3ev6dSpk+nQoYPJzs42xhhz9dVXm+7du5tjx46ZrKws89lnnxm73W7mzJlT5PZdzoMPPmgkmSeeeOKy+6anp5uWLVsaSaZq1arm7rvvdn5dsWJFs3v3brfbs3btWhMZGWkkmdDQUHPzzTebPn36mM6dOzvXBwcHmy+++ML5mJyfib8+zwAAuBiFKwAoRp4qXBljzNdff226du1qKlasaAIDA03NmjXNkCFDzKFDh/Lc/7fffjN9+/Y1UVFRxt/f30gy/fv3L1A7CptVmMLVxIkTjSRTp04dc+LEiXz327t3r/H39zdVqlQx6enpxhhj5s2bZ+rXr++y32233Waee+4559cnT540ksy+ffuMMcaMGjXK9O7d27l93rx5LoUsY4x58sknzV133XXZtue4XH8Lc/7c/YC+Y8cO07VrV1OlShUTGhpqmjVrZmbOnGmMKXyBqrCFK2O809f82vHrr7+af/zjH6ZevXomJCTElClTxtSpU8fcc889ZsGCBS7FiaIWrgryvCnsOb/cMT31vC9KnxctWmQGDhxoGjRoYMqVK2dCQ0NNnTp1zEMPPVToYocvPS+MMWblypVGUq7ne17effddU716dRMSEmKio6PNyJEjTVpamnP77t27Tc+ePU2VKlVMeHi4qVu3rnnhhReK3LbLOXfunClfvryRZLZs2VKgx5w9e9Y89dRTpnbt2iYoKMhERkaa/v37mwMHDnisXadPnzYvvPCCadeunalcubIJCAgw5cqVMy1btjTjx483Bw8edNmfwhUAoCBsxnj5dkIAAAAAAABAETDHFQAAAAAAAHwShSsAAAAAAAD4JApXAAAAAAAA8EkUrgAAAAAAAOCTKFwBAAAAAADAJ1G4AgAAAAAAgE+icAUAAAAAAACfROEKAAAAAAAAPonCFQAAAAAAAHwShSsAAAAAAAD4JApXAAAAAAAA8EkUrgAAAAAAAOCTKFwBAAAAAADAJ1G4AgAAAAAAgE+icAUAAAAAAACfROEKAAAAAAAAPonCFQAAAAAAAHwShSsAAAAAAAD4JApXAAAAAAAA8EkUrgAAAAAAAOCTKFwBAAAAAADAJ1G4AgAAAAAAgE+icAUAAAAAAACfROEKAAAAAAAAPonCFQAAAAAAAHwShSsAAAAAAAD4JApXAAAAAAAA8EkUrgAAAAAAAOCTKFwBAAAAAADAJ1G4AgAAAAAAgE+icAUAAAAAAFDCffXVV+ratauio6Nls9n00UcfuWw3xigxMVHR0dEKCQlR+/bttX379uJpbCFQuAIAAAAAACjh0tLS1KRJE02fPj3P7VOmTNHUqVM1ffp0rV+/XlFRUerYsaNOnz5tcUsLx2aMMcXdiOLSv8fbxd0EAAAAwCOy/WyW5p23+1uaZ0/PtDQPQOk356O+Xs+Y7XdzkR97f/aKIj/WZrNp4cKF6tGjh6QLo62io6M1YsQIjR49WpLkcDgUGRmpyZMna/DgwUXO8jZGXAEAAAAAAHiBn5+tyIvD4dCpU6dcFofDUaR2pKSkKDU1VQkJCc51drtd7dq107p16zzVXa+gcAUAAAAAAOAFfv5FX5KTkxUREeGyJCcnF6kdqampkqTIyEiX9ZGRkc5tviqguBsAAAAAAABQGvm5cRn32LFjNXLkSJd1drvdrfbYbK7tMcbkWudrKFwBAAAAAAD4GLvd7nahKkdUVJSkCyOvqlat6lx/9OjRXKOwfA2XCgIAAAAAAHiBv3/RF0+Ki4tTVFSUli9f7lyXkZGh1atXq3Xr1p4N8zBGXAEAAAAAAHiBO5cKFtaZM2e0Z88e59cpKSnavHmzKlSooJo1a2rEiBFKSkpSfHy84uPjlZSUpNDQUPXp08eyNhYFhSsAAAAAAAAv8LPwOrcNGzboxhtvdH6dMz9W//799eabb2rUqFFKT0/X0KFDdeLECbVs2VLLli1TeHi4dY0sApsxxhR3I4pL/x5vF3cTAAAAAI/ItvCv+pJ03u7h61guw56eaWkegNJvzkd9vZ7xYcVbivzYnsc/92BLSi6fnOPq9OnTGjFihGJiYhQSEqLWrVtr/fr1zu3GGCUmJio6OlohISFq3769tm/fXowtBgAAAAAAcOXnV/QFF/jkqRg0aJCWL1+ut99+W9u2bVNCQoI6dOigX3/9VZI0ZcoUTZ06VdOnT9f69esVFRWljh076vTp08XccgAAAAAAAHiKzxWu0tPT9cEHH2jKlCm64YYbdNVVVykxMVFxcXGaMWOGjDGaNm2axo0bp549e6phw4aaM2eOzp49q3nz5hV38wEAAAAAACRJfv62Ii+4wOcKV5mZmcrKylJwcLDL+pCQEK1du1YpKSlKTU1VQkKCc5vdble7du20bt06q5sLAAAAAACQJ3+/oi+4wOdORXh4uFq1aqVnn31Whw8fVlZWlubOnavvvvtOR44cUWpqqiQpMjLS5XGRkZHObQAAAAAAAMXNz89W5AUX+FzhSpLefvttGWNUrVo12e12vfzyy+rTp4/8/f935xKbzfWbaIzJte5iDodDp06dclmyss57rQ8AAAAAAODK5udf9AUX+GThqnbt2lq9erXOnDmjgwcP6vvvv9f58+cVFxenqKgoSco1uuro0aO5RmFdLDk5WRERES7Ltt2LvdoPAAAAAABw5WLElft8snCVIywsTFWrVtWJEyf0+eefq3v37s7i1fLly537ZWRkaPXq1WrdunW+xxo7dqxOnjzpsjSK72pFNwAAAAAAAFAEAcXdgLx8/vnnMsaobt262rNnj5544gnVrVtX999/v2w2m0aMGKGkpCTFx8crPj5eSUlJCg0NVZ8+ffI9pt1ul91ud1nn7x/o7a4AAAAAAIArlJ9PDxcqGXyycHXy5EmNHTtWhw4dUoUKFXTHHXfoueeeU2DghULTqFGjlJ6erqFDh+rEiRNq2bKlli1bpvDw8GJuOQAAAAAAwAV+/lzy5y6bMcYUdyOKS/8ebxd3EwAAAACPyLZ4PpTzdmtnDranZ1qaB6D0m/NRX69nfFWvc5Efe8NPn3qwJSWXT464AgAAAAAAKOkYceU+ClcAAAAAAABewBxX7uMUAgAAAAAAwCcx4goAAAAAAMAL/Cyef7A0onAFAAAAAADgBX7W3seiVKJwBQAAAAAA4AWMuHIfhSsAAAAAAAAvYMSV+yhcAQAAAAAAeIE/I67cxl0FAQAAAAAA4JMYcQUAAAAAAOAFfgwXchuFKwAAAAAAAC/w8+dSQXdRuAKg41XLWJqXGlPWsqxylRyWZUlSkD3L0rzsbGt/EZ5NC7Q07/zv1vWvzElrf1bK/X7W0jz/zGxL8wId1j4XrHa2bJBlWacqhFiWJUlnIuyW5tnKWfs6Flk1zdK8MmXPW5YVFGTt8y7A4t9BZ06HWZr32xFr83TI2tfpmJ+OW5bll20sywJ8DSOu3EfhCgAAAAAAwAtsfhRu3UXhCgAAAAAAwAtsjLhyG6cQAAAAAAAAPokRVwAAAAAAAF5gs3GpoLsoXAEAAAAAAHgBlwq6j8IVAAAAAACAF/gxObvbKFwBAAAAAAB4ASOu3EfhCgAAAAAAwAtsjLhym0/W/jIzM/XPf/5TcXFxCgkJUa1atTRhwgRlZ2c79zHGKDExUdHR0QoJCVH79u21ffv2Ymw1AAAAAAAAPMknC1eTJ0/W66+/runTp2vnzp2aMmWKnn/+eb3yyivOfaZMmaKpU6dq+vTpWr9+vaKiotSxY0edPn26GFsOAAAAAABwgc2v6Asu8MlLBb/55ht1795dnTt3liTFxsZq/vz52rBhg6QLo62mTZumcePGqWfPnpKkOXPmKDIyUvPmzdPgwYOLre0AAAAAAACS5EcBym0+eQrbtm2rFStW6Oeff5YkbdmyRWvXrtVtt90mSUpJSVFqaqoSEhKcj7Hb7WrXrp3WrVtXLG0GAAAAAAC4mM1mirzgAp8ccTV69GidPHlS9erVk7+/v7KysvTcc8+pd+/ekqTU1FRJUmRkpMvjIiMjtX//fsvbCwAAAAAA8Fdc8uc+nyxcLViwQHPnztW8efPUoEEDbd68WSNGjFB0dLT69+/v3M9ms7k8zhiTa10Oh8Mhh8Phsi4r67z8/QM93wEAAAAAAHDF466C7vPJ2t8TTzyhMWPGqFevXmrUqJH69u2rxx57TMnJyZKkqKgoSf8beZXj6NGjuUZh5UhOTlZERITLsm33Yu92BAAAAAAAAEXmk4Wrs2fPyu8vM5j5+/srOztbkhQXF6eoqCgtX77cuT0jI0OrV69W69at8zzm2LFjdfLkSZelUXxX73UCAAAAAABc0biroPt88lLBrl276rnnnlPNmjXVoEEDbdq0SVOnTtXAgQMlXbhEcMSIEUpKSlJ8fLzi4+OVlJSk0NBQ9enTJ89j2u122e12l3VcJggAAAAAALzFj0sF3eaThatXXnlFTz31lIYOHaqjR48qOjpagwcP1tNPP+3cZ9SoUUpPT9fQoUN14sQJtWzZUsuWLVN4eHgxthwAAAAAAOACRk65zycLV+Hh4Zo2bZqmTZuW7z42m02JiYlKTEy0rF0AAAAAAAAFZbMx4spdPlm4AgAAAAAAKOkYceU+TiEAAAAAAAB8EiOuAAAAAAAAvMDG5Oxuo3AFAAAAAADgBX5c5+Y2ClcAAAAAAABewIgr91G4AgAAAAAA8AImZ3cfpxAAAAAAAAA+iRFXAAAAAAAAXmCzcamguxhxBQAAAAAA4AV+fkVfCiMzM1P//Oc/FRcXp5CQENWqVUsTJkxQdna2dzpmIUZcAQAAAAAAeIFVk7NPnjxZr7/+uubMmaMGDRpow4YNuv/++xUREaFHH33UkjZ4C4UroAAO1qlgaV5AfUvjFHfVSUvzbow4a1lWVIhlUZKk8EBrhwKfz7ZZnGdpnNIyrctKTbf2XB5NtzTO8u9dlrF2ULe/td8+RQda98NZNui0ZVmSFBVyytK8ckGWxqmc3cIXFkkVg7Msy7L6d4Ijy9q8tPMOS/OOnbP2hfqX09aez11Hgq3L2lnesixJitlyzNK80NMZluahZLFqcvZvvvlG3bt3V+fOnSVJsbGxmj9/vjZs2GBNA7yISwUBAAAAAAC8wd9W5MXhcOjUqVMui8ORdxG/bdu2WrFihX7++WdJ0pYtW7R27VrddtttVvbWKyhcAQAAAAAA+Jjk5GRFRES4LMnJyXnuO3r0aPXu3Vv16tVTYGCgmjVrphEjRqh3794Wt9rzuFQQAAAAAADAC2x+Rb8MeOzYsRo5cqTLOrvdnue+CxYs0Ny5czVv3jw1aNBAmzdv1ogRIxQdHa3+/fsXuQ2+gMIVAAAAAACAN/gX/UI3u92eb6Hqr5544gmNGTNGvXr1kiQ1atRI+/fvV3JyMoUrAAAAAAAA5MGNEVeFcfbsWfn5uRbJ/P39lZ1t8R16vIDCFQAAAAAAgBfYLLr1cdeuXfXcc8+pZs2aatCggTZt2qSpU6dq4MCBluR7E4UrAAAAAAAAb7BoxNUrr7yip556SkOHDtXRo0cVHR2twYMH6+mnn7Yk35soXAEAAAAAAJRg4eHhmjZtmqZNm1bcTfE4ClcAAAAAAADeYNGlgqVZ0ae396LY2FjZbLZcy7BhwyRJxhglJiYqOjpaISEhat++vbZv317MrQYAAAAAAPgfm5+tyAsu8MnC1fr163XkyBHnsnz5cknSXXfdJUmaMmWKpk6dqunTp2v9+vWKiopSx44ddfr06eJsNgAAAAAAwP/4+xV9gSQfLVxVrlxZUVFRzuWTTz5R7dq11a5dOxljNG3aNI0bN049e/ZUw4YNNWfOHJ09e1bz5s0r7qYDAAAAAABIYsSVJ/hk4epiGRkZmjt3rgYOHCibzaaUlBSlpqYqISHBuY/dble7du20bt26YmwpAAAAAADARfxtRV8gqQQUrj766CP9+eefGjBggCQpNTVVkhQZGemyX2RkpHMbAAAAAAAASj6fv6vgG2+8oU6dOik6Otplvc3mWn00xuRadzGHwyGHw+GyLivrvPz9Az3XWAAAAAAAgByMnHKbT4+42r9/v7744gsNGjTIuS4qKkqSco2uOnr0aK5RWBdLTk5WRESEy7Jt92LvNBwAAAAAAFzxmOPKfT5duJo9e7aqVKmizp07O9fFxcUpKirKeadB6cI8WKtXr1br1q3zPdbYsWN18uRJl6VRfFevth8AAAAAAFzBmOPKbT57qWB2drZmz56t/v37KyDgf8202WwaMWKEkpKSFB8fr/j4eCUlJSk0NFR9+vTJ93h2u112u91lHZcJAgAAAAAAr/Hz6fFCJYLPFq6++OILHThwQAMHDsy1bdSoUUpPT9fQoUN14sQJtWzZUsuWLVN4eHgxtBQAAAAAACA3GyOn3OazhauEhAQZY/LcZrPZlJiYqMTERGsbBQAAAAAAAMv4bOEKAAAAAACgRGOSdbdRuAIAAAAAAPAGLhV0G4UrAAAAAAAAL7Ax4sptFK4AAAAAAAC8wZ+7CrqLwhUAAAAAAIA3cKmg2yj9AQAAAAAAwCcx4goAAAAAAMALmOPKfRSuAAAAAAAAvIFLBd1G4QoAAAAAAMAbGHHlNgpXAAAAAAAAXmBjxJXbKFzBI1JjIizNO98g0NK8Rg1PWJrXvJKxNK9OuQxL86qHWZcX7B9iWZYkBfgFWZrnb7P2uWC1jOx0y7Iysx2WZUnSuaxMS/McWda+abL6PVqAn7Wvm3YLb20d5Gft65jdP8zSPKv752fztzYv29qfTUv5WftR4lz2GUvz0s5b+/7vD4e1v4cORJ63LGt7jLV929TU2p/NXdujLM2rtP20pXnlj6ZZmlfq+HFPPHdxBgEAAAAAAOCTGHEFAAAAAADgDcxx5TYKVwAAAAAAAN7ApYJuo3AFAAAAAADgDRSu3EbhCgAAAAAAwBu4VNBtFK4AAAAAAAC8gRFXbuMMAgAAAAAAwCcx4goAAAAAAMAbGHHlNgpXAAAAAAAA3sAcV27z2dLfr7/+qvvuu08VK1ZUaGiomjZtqo0bNzq3G2OUmJio6OhohYSEqH379tq+fXsxthgAAAAAAOAifn5FXyDJRwtXJ06cUJs2bRQYGKglS5Zox44d+te//qVy5co595kyZYqmTp2q6dOna/369YqKilLHjh11+vTp4ms4AAAAAABADgpXbvPJSwUnT56sGjVqaPbs2c51sbGxzv8bYzRt2jSNGzdOPXv2lCTNmTNHkZGRmjdvngYPHmx1kwEAAAAAAFxxqaDbfLKEt2jRIjVv3lx33XWXqlSpombNmmnWrFnO7SkpKUpNTVVCQoJznd1uV7t27bRu3briaDIAAAAAAAA8zCcLV3v37tWMGTMUHx+vzz//XA8//LD+/ve/66233pIkpaamSpIiIyNdHhcZGencBgAAAAAAUKy4VNBtPnmpYHZ2tpo3b66kpCRJUrNmzbR9+3bNmDFD/fr1c+5ns7kOuTPG5FqXw+FwyOFwuKzLyjovf/9AD7ceAAAAAABAFKA8wCcLV1WrVtXVV1/tsq5+/fr64IMPJElRUVGSLoy8qlq1qnOfo0eP5hqFlSM5OVnPPPOMy7rGdXuoab2enmw6AAAAAACApNwDbkqinKvfPOHiwUgF5ZOFqzZt2mjXrl0u637++WfFxMRIkuLi4hQVFaXly5erWbNmkqSMjAytXr1akydPzvOYY8eO1ciRI13WDb33v15oPQAAAAAAgErFiKsBAwZ4rABXagpXjz32mFq3bq2kpCTdfffd+v777zVz5kzNnDlT0oWK5YgRI5SUlKT4+HjFx8crKSlJoaGh6tOnT57HtNvtstvtLuu4TBAAAAAAAHhNKShcSVKTJk3UvXv3Ij/+o48+0tatW4v0WJ8sXLVo0UILFy7U2LFjNWHCBMXFxWnatGm69957nfuMGjVK6enpGjp0qE6cOKGWLVtq2bJlCg8PL8aWAwAAAAAAlC5NmzbV+PHji/z4ffv2la7ClSR16dJFXbp0yXe7zWZTYmKiEhMTrWsUAAAAAABAQfmV/DmuypYtq9DQULeOERISorJlyxbpsT5buAIAAAAAACjRSsGlgn/++afbx3jttdf02muvFemxFK4AAAAAAAC8oRSMuCpuFK4AAAAAAAC8oRSMuCpuFK4AAAAAAAC8oRQXrvbt26evvvpKR44ckcPhyHMfm82mp556yq0cClcAAAAAAAAokHPnzunBBx/UvHnzJEnGmHz3pXAFAAAAAADgq0rhHFejR4/WO++8oypVqujee+9V7dq1FRYW5rU8ClcAAAAAAADeUAovFVywYIEqVaqkzZs3Kyoqyut5pe8MAgAAAAAA+AI/v6IvhfTrr7/qvvvuU8WKFRUaGqqmTZtq48aNHu/SmTNndP3111tStJIYcQUAAAAAAOAdFl0qeOLECbVp00Y33nijlixZoipVquiXX35RuXLlPJ7VoEED/fbbbx4/bn4oXAEAAAAAAHiDRZcKTp48WTVq1NDs2bOd62JjY72S9fjjj+vee+/VunXr1Lp1a69kXOyKLlydu997k4flxc8v/5n2vaF8+bxvR+kNN4eftixLkuLLWnsu48pmWJpXI8zap2ZEULSleQEZ1p1Pc/aEZVmSJMcf1uZlWvuzafU1+mE2C/MCgqzLkiS/Uv4r2GRbHGjxxKdWPvfOWfy6cs7i15Wz5yyNM/ncrttbsrMtfC4EWPu6YgsLsTTPHmpxXkRlS/Mqhlv7fqxmGeuyGlWwbmSGJLWsYu3r2M81My3N29PW2t95h89Y99py6k+7ZVmlzaJFi3TLLbforrvu0urVq1WtWjUNHTpUDz74oMez7rrrLh06dEjdu3fXI488og4dOqhatWqy2fL+2axZs6ZbeQX6CRw4cKBbITlsNpveeOMNjxwLAAAAAADAp7nxh1iHwyHHX/7gYrfbZbfnLvDt3btXM2bM0MiRI/Xkk0/q+++/19///nfZ7Xb169evyG3IT+PGjVW+fHk988wzeuaZZ/Ldz2azKTPTveJugQpXb775pmw2m4xxb5QLhSsAAAAAAHDFcKNwlZycnKsoNH78eCUmJubaNzs7W82bN1dSUpIkqVmzZtq+fbtmzJjh8cLVJ598op49eyozM1OVK1dWTEyMwsK8d0Vbgcf83XLLLRo9enSRgyZNmqRly5YV+fEAAAAAAAAlihtTbYwdO1YjR450WZfXaCtJqlq1qq6++mqXdfXr19cHH3xQ5Pz8jB8/XsYYzZ49W/369cv3EkFPKXDhKioqSu3atSty0JtvvlnkxwIAAAAAAJQ4boy4yu+ywLy0adNGu3btcln3888/KyYmpsj5+dm5c6duuOEG9e/f3+PHzkuBClfdu3fXNddc41bQNddcoz///NOtYwAAAAAAAJQYFt1s6LHHHlPr1q2VlJSku+++W99//71mzpypmTNnejyrUqVKKlPGujs8FKhwtXDhQreDHnnkET3yyCNuHwcAAAAAAAD/06JFCy1cuFBjx47VhAkTFBcXp2nTpunee+/1eNadd96pd955R6dOnVLZsmU9fvy/KuX34gYAAAAAACgmFo24kqQuXbqoS5cuXs+ZOHGivvnmG/Xs2VOzZs1SXFycV/MoXAEAAAAAAHiDG5Oz+6ouXbrI399fX375perWravY2FhVq1Ytz0nabTabVqxY4VZeoQpXDodDP/30kyIjIxUVFeVWMAAAAAAAQKlm4Ygrq6xatcr5/8zMTO3Zs0d79uzJc19P3HGwwGfwrbfeUlRUlJo1a6Zq1aqpV69eOnv2rNsNyEtiYqJsNpvLcnGhzBijxMRERUdHKyQkRO3bt9f27du90hYAAAAAAIAisfkVffFRKSkpBV727t3rdl6BRlytX79egwYN0r333qtp06ZpzZo16tmzpypVqqTp06e73Yi8NGjQQF988YXza39/f+f/p0yZoqlTp+rNN99UnTp1NHHiRHXs2FG7du1SeHi4V9oDAAAAAABwpYuJibE0r0AlvHfeeUdZWVn617/+pYiICHXp0kW33HKL/vvf/3qtYQEBAYqKinIulStXlnRhtNW0adM0btw49ezZUw0bNtScOXN09uxZzZs3z2vtAQAAAAAAKJRSOOLKagU6EzmjmM6dO+dcd+7cOQUEeG9u9927dys6OlpxcXHq1auXc3hZSkqKUlNTlZCQ4NzXbrerXbt2WrdundfaAwAAAAAAUCilsHDlcDh04MABnT59Ot99Tp8+rQMHDigjI8PtvAKdiV69eikoKEgPPfSQtm3bptdee00rV65Unz593G5AXlq2bKm33npLn3/+uWbNmqXU1FS1bt1ax48fV2pqqiQpMjLS5TGRkZHObXlxOBw6deqUy5J1/rxX2g8AAAAAACA/v6IvPmrq1KmKi4vTli1b8t1ny5YtiouL00svveR2XoHORIMGDfThhx9q586datKkif7xj39o6NChmjhxotsNyEunTp10xx13qFGjRurQoYM+/fRTSdKcOXOc+/x1ZnpjzCVnq09OTlZERITLsvO/H3ql/QAAAAAAADabf5EXX/XRRx8pLi5Obdu2zXeftm3bKjY2VgsXLnQ7r8AlvE6dOumXX37R8ePHlZaWpldeeUVBQUFuN6AgwsLC1KhRI+3evdt5d8G/jq46evRorlFYFxs7dqxOnjzpstS/s6dX2w0AAAAAAK5gpfBSwV9++UVXX331Zfdr0KCBfvnlF7fzCn0mypcvLz+Lh6w5HA7t3LlTVatWVVxcnKKiorR8+XLn9oyMDK1evVqtW7fO9xh2u11ly5Z1WfwDA61oPgAAAAAAQKmQlpamsLCwy+4XGhqqU6dOuZ3nvdnV3fD444+ra9euqlmzpo4ePaqJEyfq1KlT6t+/v2w2m0aMGKGkpCTFx8crPj5eSUlJCg0N9dqcWwAAAAAAAIXmwyOniqpGjRrasGHDZffbuHGjqlat6naeT57BQ4cOqXfv3qpbt6569uypoKAgffvtt4qJiZEkjRo1SiNGjNDQoUPVvHlz/frrr1q2bJnz7ocAAAAAAADFrhReKpiQkKC9e/fqlVdeyXefV199Vb/88otuueUWt/NsxhhzuZ2uueYadevWTYmJiUUOGj9+vD755BNt3LixyMfwtHs+tnZydj+/y55qjypf3mFZVmy4tX2LL2ttXlxZ92/hWRg1wqwdDBkRlP/8cN4Q4IFbohaUOXvCsixJkuOMtXmZ1v5sWn53Eyt/YQdYM2+jk59PDnr2HJNd3C3wLiufe+csfl05Z/HrytlzlsYZh3XvjyRJ2RY+FwKsfV2xhYVYmqdQi/MiKlsaZwu39v2Yw8Ifl5MZv1kXJinldP437fKGn/+09j3EHov7d/iMdXmn/rRbliVJC7pbMO/1WTcmJw+93XPt8KCDBw+qUaNGOn36tLp27aqHHnpItWvXls1m0549ezRz5kwtXrxY4eHh2rx5s2JjY93KK9DL1ebNm9W0aVO3gg4cOKDNmze7dQwAAAAAAIASw4dHThVVjRo1tGjRIt15551atGiRFi9e7LLdGKNKlSrpvffec7toJRVijqs9e/borbfeKnLQnj17ivxYAAAAAACAEqcUFq4k6YYbbtDPP/+smTNnasWKFTp48KCkC0WtDh06aNCgQSpfvrxHsgpcuPr666/19ddfFznIGCObzdohjQAAAAAAAPC8cuXKadSoURo1apRXcwpUuBo/frxXGwEAAAAAAFDqlNIRV1aicAUAAAAAAOANVt/cyAtOnTqloKAgBQcHF/kY586dU0ZGhsqWLVvox5b8MwgAAAAAAOCLbH5FX3xE+fLlNWzYMLeOMWTIEFWoUKFIjy3l9+IGAAAAAAAoJj5UgCoqY4yMMR45TlFQuAIAAAAAAPCGUlC4kqS1a9dq4MCBbj2+qChcAQAAAAAAIF979uzRnj173DqGzWYr0uMoXAEAAAAAAHhDKRhx9eWXXxZrvs144kLFEmrniX8XdxO8qmxQlmVZwf4hlmVJUkhA4e9E4I5QW5ileebPXy3N05lj1uYd+9OyKHPipGVZkqRTZyyNM+ese55fyMu0NM8WbN3fV8z5bMuyikWWtf0z2da+vbD6uWAl47D2eWfSzlubZ/FzrzQ/1/3Cg6zNC7X2b+B+EXZr86qUsTRPlcpbGmeLrGhdWESUdVmSbBFVLc07a9IszUvLPGFpXnqmdb8X0s5bW+SpX36QBSnuFH1u9FgrSjJGXAEAAAAAAHiBO2OFinhlXalTpHLmwIED9Z///Oey+7355ptuTd4FAAAAAABQUhllF3nBBUUqXL355psFmhH+66+/1pw5c4oSAQAAAAAAUKIZN/7hAq9eQJqRkSF/f39vRgAAAAAAAKCU8tocV8YY/fDDD6pcubK3IgAAAAAAAHyWMW5c8sccV5IKUbi66aabXL5eunRprnU5MjMz9csvvyg1NVV9+/Z1r4UAAAAAAAAlEJf8ua/AhatVq1Y5/2+z2ZSamqrU1NR89w8MDFSXLl30wgsvuNVAAAAAAACAksitEVeQVIjCVUpKiqQLlwDWqlVLd955p55//vk89w0KClKlSpUUGBjomVYCAAAAAACUMFfKiKuMjAwtXbpU58+fV61atdSgQQMFBQV55NgFLlzFxMQ4/z9+/Hg1a9bMZZ23JCcn68knn9Sjjz6qadOmSbpQPHvmmWc0c+ZMnThxQi1bttSrr76qBg0aeL09AAAAAAAABWF0ZYy46t27t5YvX64aNWpo3759On/+vK666io1btxY7777rlvHLtLk7OPHj3crtKDWr1+vmTNnqnHjxi7rp0yZoqlTp+rNN99UnTp1NHHiRHXs2FG7du1SeHi4JW0DAAAAAACAtHz5cm3btk0xMTEyxmj37t3asmWLtmzZ4vax/TzQPq84c+aM7r33Xs2aNUvly5d3rjfGaNq0aRo3bpx69uyphg0bas6cOTp79qzmzZtXjC0GAAAAAAD4H2Oyi7yUJFFRUapataqkC/Oi16lTR3fddZcmTpzo9rELVLjy9/cv8hIQUKRBXRo2bJg6d+6sDh06uKxPSUlRamqqEhISnOvsdrvatWundevWFSkLAAAAAADA04wb/0qSxx9/XK+//rpXjl2gqlKNGjVks9m80oC8vPvuu/rhhx+0fv36XNty7mQYGRnpsj4yMlL79++3pH0AAAAAAACXU9JGThXVhAkTdOzYMX3zzTfq3LmzmjRpovr16xd5MNPFCnSEffv2uR1UUAcPHtSjjz6qZcuWKTg4ON/9/lpIM8ZcsrjmcDjkcDhc1mU4zivIzp0PAQAAAACA55W0kVNF9X//93/68ccftW3bNj3//PP66aefZLPZVLduXbfnuXK/9OVhGzdu1NGjR3Xttdc612VlZemrr77S9OnTtWvXLkkXRl7lXD8pSUePHs01CutiycnJeuaZZ1zWDR3VVcPHdPNwDwAAAAAAAEr3XQVTUlIUFxcnSercubM6d+7s3JaZmamffvpJP/74o9s5Ple4uvnmm7Vt2zaXdffff7/q1aun0aNHq1atWoqKitLy5cvVrFkzSVJGRoZWr16tyZMn53vcsWPHauTIkS7rUs6+4/kOAAAAAAAAlHLNmzfX8ePHJUndunVTo0aN1KhRIzVu3Fh169ZVw4YN1bBhQ7dzClS4OnDggCSpWrVq8vf3d35dUDVr1izwvuHh4bk6FhYWpooVKzrXjxgxQklJSYqPj1d8fLySkpIUGhqqPn365Htcu90uu93usi4oi8sEAQAAAACAdxhTei8VPHjwoPP/bdu21datW7V48WLt2rXLeZlgo0aNNHfuXLdyClS4io2NlZ+fn3bs2KE6deooNja2wJO122w2ZWZmutXIvxo1apTS09M1dOhQnThxQi1bttSyZcsUHh7u0RwAAAAAAICiKs2XCoaGhjr/P2rUKOf/MzMztWPHDm3bti3XFXVFUaDC1Q033CCbzeZsVM7XVlm1apXL1zabTYmJiUpMTLSsDQAAAAAAAIVxpUzOfrGAgAA1btxYjRs39szxCrLTXwtHf/0aAAAAAAAArowpvSOuLrZ06VI9++yzOn/+vGrVqqXGjRurSZMmaty4sWrUqOHWsf0KslOtWrU0evRot4IAAAAAAACuJMaNfyXJQw89pOuuu07Dhw9XfHy8vvvuOw0fPlyxsbFuH7tAI6727dun33//3e0wAAAAAAAAlC5paWmaOnVqrmmlTp8+7faxCzTiCgAAAAAAAIVjTHaRl5KkW7du2rRpU671nriJHoUrAAAAAAAAL7hSLhV88cUXNWbMGC1ZskTnz5/36LELdKkgAAAAAAAACseoZI2cKqoxY8boiy++0BdffKHAwEDVqVNHjRs3VqNGjTRmzBi3jl3gwtXmzZs1YcKEIoU8/fTTRXocAAAAAABASWVMyRo5VVRvv/22vv76a11zzTXas2ePtm7dqq1bt+rrr792+9gFLlxt2bJFW7ZsKdTBjTGy2WwUrgAAAAAAwBWnuEZcJScn68knn9Sjjz6qadOmeT2vatWqatGihQICAtSgQQM1aNBAvXv39sixC1y4ql27ttq0aeORUAAAAAAAAHje+vXrNXPmTDVu3NiyzMcff1yzZs3SkCFDPH7sAheu2rZtq//85z8ebwAAAAAAAEBpZPWlgmfOnNG9996rWbNmaeLEiZblTpgwQX/88YfWrFmjTp06qXHjxrr66qsVGBjo9rG5qyAAAAAAAIAXGGUXeSmKYcOGqXPnzurQoYOHe3Jp//73v/XMM8/Iz89PL7zwgq677jqFhYWpYcOGbh/7ir6rYL0si+t2Novz0iy8ljb9qHVZknT2gKVx5tQZa/OO/2lpXvYfaZbmZe4/ZVlW9h/plmVJ0vk0a/+iknXe2teVrEybpXnGwpex7Cxr+2b1ubT6Z8XP39rngtXfv8wM6/Ks/lnJtvr9kcV/R7XZrP3ZNMa6719QSJZlWZIUEp5haZ497LSleUFlfrc0z7/Sb5bmBVQPtyzLr+Iey7IkSZGVLI0LqRBhbV6Q3dI82UOty/IrfWNrst34teNwOORwOFzW2e122e15/wy8++67+uGHH7R+/fqihxbRbbfdpttuu835dWZmpn766Sf9+OOPbh+79P1UAAAAAAAA+IAsYyvykpycrIiICJclOTk5z5yDBw/q0Ucf1dy5cxUcHOzRPqxatarQjwkICFDDhg3Vq1cvt/MpXAEAAAAAAHhBtin6MnbsWJ08edJlGTt2bJ45Gzdu1NGjR3XttdcqICBAAQEBWr16tV5++WUFBAQoK6voo3JvueUWvfbaa0V+vLsKdKlgdnbx3L4RAAAAAADgSnSpywL/6uabb9a2bdtc1t1///2qV6+eRo8eLX9//yK3w9/fX4888oi2bt2q6dOnKyCg4LNOZWRkKCgoqMjZEiOuAAAAAAAAvCLb2Iq8FEZ4eLgaNmzosoSFhalixYpuT5C+du1aVatWTbNmzVKHDh107Nixyz7mjz/+0MSJExUbG+tWtkThCgAAAAAAwCuyTNEXX3HNNddo48aNatWqlb766itdd911uUZ35di7d6+GDx+umjVr6umnny5QketyKFwBAAAAAAB4gVUjrvKyatUqTZs2zf1OSKpcubK+/PJLPfDAA9q3b59at26thQsXOrd/8803uuOOO1S3bl299tprstlsGj58uH7++We3swt+YSIAAAAAAAAKLNuHRk65KzAwULNmzVKTJk00cuRI3XXXXXr44Ye1adMmffvttzLGKDIyUsOHD9fQoUNVvnx5j+RSuAIAAAAAAPCCLA+MnPI1Dz74oDZs2KC33npLM2bMkCTVrVtXI0eOVL9+/dyejP2vfPJSwRkzZqhx48YqW7asypYtq1atWmnJkiXO7cYYJSYmKjo6WiEhIWrfvr22b99ejC0GAAAAAAAovY4dO6ZnnnlGNWvW1Ntvvy1JKleunIwxCgkJ0a233urxopXko4Wr6tWra9KkSdqwYYM2bNigm266Sd27d3cWp6ZMmaKpU6dq+vTpWr9+vaKiotSxY0edPn26mFsOAAAAAABwQbYbi6/46aefNHjwYNWsWVMTJkzQiRMndM8992jDhg3av3+/OnfurE2bNum6667Tt99+6/F8nyxcde3aVbfddpvq1KmjOnXq6LnnnlOZMmWc10xOmzZN48aNU8+ePdWwYUPNmTNHZ8+e1bx584q76QAAAAAAAJKKd3J2T2nQoIFmzZqlwMBAPfroo9qzZ4/mzZuna665RmXKlNGiRYv0xBNPKDU1VTfeeKPeeustj+b7ZOHqYllZWXr33XeVlpamVq1aKSUlRampqUpISHDuY7fb1a5dO61bt64YWwoAAAAAAPA/2aboi6+oWrWqJk2apIMHD2rq1KmqWbOmy3abzabJkyfrrbfeks1m0/33368nnnhCxnimEz47Ofu2bdvUqlUrnTt3TmXKlNHChQt19dVXO4tTkZGRLvtHRkZq//79xdFUAAAAAACAXErD5Oz79u1TQMDly0f33Xef6tSpox49emjq1KnasWOH5s+fr7Jly7qV77OFq7p162rz5s36888/9cEHH6h///5avXq1c7vN5vrNN8bkWncxh8Mhh8Phsi7IcV52e6BnGw4AAAAAACDfGjlVVAUpWuW47rrrtH79evXo0UNLlizR3/72N+3YscOtfJ+9VDAoKEhXXXWVmjdvruTkZDVp0kQvvfSSoqKiJEmpqaku+x89ejTXKKyLJScnKyIiwmVJfukzr/YBAAAAAADgSlKtWjWtWbNG99xzj3bt2uX28Xx2xNVfGWPkcDgUFxenqKgoLV++XM2aNZMkZWRkaPXq1Zo8eXK+jx87dqxGjhzpsi7o9HyvthkAAAAAAFy5fGmSdSsFBwdr/vz5atSokdvH8snC1ZNPPqlOnTqpRo0aOn36tN59912tWrVKS5culc1m04gRI5SUlKT4+HjFx8crKSlJoaGh6tOnT77HtNvtstvtLutMBpcJAgAAAAAA78gqBZcKuuPJJ590+xg+Wbj67bff1LdvXx05ckQRERFq3Lixli5dqo4dO0qSRo0apfT0dA0dOlQnTpxQy5YttWzZMoWHhxdzywEAAAAAAC64UkdceZJPFq7eeOONS2632WxKTExUYmKiNQ0CAAAAAAAopNIwOXtx88nCFQAAAAAAQEl3pV8q6Ak+e1dBAAAAAAAAXNkYcQUAAAAAAOAFzHHlPgpXAAAAAAAAXsAcV+6jcAUAAAAAAOAFWYy4chuFKwAAAAAAAC9gxJX7mJwdAAAAAAAAPokRVwAAAAAAAF7A5Ozuo3AFAAAAAADgBVlcKug2ClcAAAAAAABewIgr913RhavMD5YVdxNKDXP2vLV557Iszcs+k2Ft3mlr886etPalICPduun1zjuCLMuSJEeav6V5mRnW/iJ0OLItzcu28KmebfHMmadPWvs6luGwNs9qVn///PxK75vQ0n4ug0OtneLV38I4P3+Lz2VIoKV5Zcpa+zs90G7t7zx7WKaleaF7f7csyx5m7e8gv/BDlubZQq19LviFW5snP+teyGyB1r5GBw4e6PUMJmd33xVduAIAAAAAAPAWLhV0H3cVBAAAAAAAgE9ixBUAAAAAAIAXMOLKfRSuAAAAAAAAvIA5rtxH4QoAAAAAAMALGHHlPgpXAAAAAAAAXkDhyn0UrgAAAAAAALyAwpX7uKsgAAAAAAAAfBIjrgAAAAAAALyAydnd55MjrpKTk9WiRQuFh4erSpUq6tGjh3bt2uWyjzFGiYmJio6OVkhIiNq3b6/t27cXU4sBAAAAAABcZZmiL7jAJwtXq1ev1rBhw/Ttt99q+fLlyszMVEJCgtLS0pz7TJkyRVOnTtX06dO1fv16RUVFqWPHjjp9+nQxthwAAAAAAOACClfu88lLBZcuXery9ezZs1WlShVt3LhRN9xwg4wxmjZtmsaNG6eePXtKkubMmaPIyEjNmzdPgwcPLo5mAwAAAAAAOHGpoPt8csTVX508eVKSVKFCBUlSSkqKUlNTlZCQ4NzHbrerXbt2WrduXbG0EQAAAAAA4GKMuHKfzxeujDEaOXKk2rZtq4YNG0qSUlNTJUmRkZEu+0ZGRjq3AQAAAAAAoGTzyUsFLzZ8+HBt3bpVa9euzbXNZrO5fG2MybUuh8PhkMPhcFnndz5L9kB/zzUWAAAAAADg/2PklPt8esTVI488okWLFunLL79U9erVneujoqIkKdfoqqNHj+YahZUjOTlZERERLsvkpTu913gAAAAAAHBFyzK2Ii+4wCcLV8YYDR8+XB9++KFWrlypuLg4l+1xcXGKiorS8uXLnesyMjK0evVqtW7dOs9jjh07VidPnnRZRt9a36v9AAAAAAAAV65sU/QFF/jkpYLDhg3TvHnz9PHHHys8PNw5sioiIkIhISGy2WwaMWKEkpKSFB8fr/j4eCUlJSk0NFR9+vTJ85h2u112u91l3XkuEwQAAAAAAF7CpYLu88nC1YwZMyRJ7du3d1k/e/ZsDRgwQJI0atQopaena+jQoTpx4oRatmypZcuWKTw83OLWAgAAAAAA5Ebhyn0+Wbgy5vLfWZvNpsTERCUmJnq/QQAAAAAAALCcTxauAAAAAAAASrrs7OJuQclH4QoAAAAAAMALuFTQfRSuAAAAAAAAvIDClfsoXAEAAAAAAHhBNoUrt1G4AgAAAAAA8AJGXLnPr7gbAAAAAAAAAOSFwhUAAAAAAIAXZJmiL4WRnJysFi1aKDw8XFWqVFGPHj20a9cu73TKYhSuAAAAAAAAvMCqwtXq1as1bNgwffvtt1q+fLkyMzOVkJCgtLQ073TMQsxxBQAAAAAA4AVWTc6+dOlSl69nz56tKlWqaOPGjbrhhhusaYSXULgCAAAAAADwguKanP3kyZOSpAoVKhRPAzzoii5cHfrkjKV5Ni7MLLGyM/0tzTvvCLM0LzPDZmmew5FtWVbmeWt/U2RnWdc3Scqw8FxKUnq6tXlWfv8cDmt/VtLPlt5zKUnZ3PvZY0JCrX0D4edn7e+EgEBr89LOZFma5+dvXf/sdmvPZfpZa382T5+ytn9Wn8/gUGvfbwYese6joNXPc5uftb+D/PytzbP5ZViaFxBk7XsWK8UN9n5GdnbRf/4dDoccDofLOrvdLrvdfsnHGWM0cuRItW3bVg0bNixyvq+glAIAAAAAAOBjkpOTFRER4bIkJydf9nHDhw/X1q1bNX/+fAta6X1X9IgrAAAAAAAAb8nOKvqIq7Fjx2rkyJEu6y432uqRRx7RokWL9NVXX6l69epFzvYlFK4AAAAAAAC8wJ1LBQtyWWAOY4weeeQRLVy4UKtWrVJcXFyRc30NhSsAAAAAAAAvcKdwVRjDhg3TvHnz9PHHHys8PFypqamSpIiICIWEhFjSBm+hcAUAAAAAAOAFVhWuZsyYIUlq3769y/rZs2drwIABlrTBWyhcAQAAAAAAeIE7c1wVhjGl947O3FUQAAAAAAAAPokRVwAAAAAAAF5g1aWCpRmFKwAAAAAAAC+gcOU+n7xU8KuvvlLXrl0VHR0tm82mjz76yGW7MUaJiYmKjo5WSEiI2rdvr+3btxdPYwEAAAAAAPKQnV30BRf4ZOEqLS1NTZo00fTp0/PcPmXKFE2dOlXTp0/X+vXrFRUVpY4dO+r06dMWtxQAAAAAACBv2Vm2Ii+4wCcvFezUqZM6deqU5zZjjKZNm6Zx48apZ8+ekqQ5c+YoMjJS8+bN0+DBg61sKgAAAAAAQJ64VNB9Pjni6lJSUlKUmpqqhIQE5zq73a527dpp3bp1xdgyAAAAAAAAeJJPjri6lNTUVElSZGSky/rIyEjt37+/OJoEAAAAAACQCyOu3FfiClc5bDbXb74xJte6izkcDjkcDtd1WVmy+/t7pX0AAAAAAODKlsVcVW4rcZcKRkVFSfrfyKscR48ezTUK62LJycmKiIhwWWbs+cWrbQUAAAAAAFeu7GxbkRdcUOIKV3FxcYqKitLy5cud6zIyMrR69Wq1bt0638eNHTtWJ0+edFmGXFXbiiYDAAAAAIArEIUr9/nkpYJnzpzRnj17nF+npKRo8+bNqlChgmrWrKkRI0YoKSlJ8fHxio+PV1JSkkJDQ9WnT598j2m322W3213WHecyQQAAAAAA4CUUoNznk4WrDRs26MYbb3R+PXLkSElS//799eabb2rUqFFKT0/X0KFDdeLECbVs2VLLli1TeHh4cTUZAAAAAAAAHuaThav27dvLGJPvdpvNpsTERCUmJlrXKAAAAAAAgELIZnJ2t/lk4QoAAAAAAKCk41JB91G4AgAAAAAA8AIKV+6jcAUAAAAAAOAFFK7cR+EKAAAAAADAC5jjyn1+xd0AAAAAAAAAIC+MuAIAAAAAAPACLhV0H4UrAAAAAAAAL6Bw5T4KVwAAAAAAAF7AHFfuo3AFAAAAAADgBYy4ch+FKwAAAAAAAG/INsXdghLvii5c7diYWdxNAAD4CLvd2r+G2e3+luYByMFfvj0lw5FtcZ6lcTp7xto8Hbc4D4DiirsBKJArunAFAAAAAADgLX6MuHIbhSsAAAAAAAAvsGVRuHIXhSsAAAAAAAAvYMSV+yhcAQAAAAAAeAGFK/dRuAIAAAAAAPACG4Urt/kVdwMAAAAAAACAvDDiCgAAAAAAwAv8mJzdbRSuAAAAAAAAvIA5rtxH4QoAAAAAAMALKFy5r0TPcfXaa68pLi5OwcHBuvbaa7VmzZribhIAAAAAAICkC5OzF3XBBSW2cLVgwQKNGDFC48aN06ZNm3T99derU6dOOnDgQHE3DQAAAAAAAB5QYgtXU6dO1QMPPKBBgwapfv36mjZtmmrUqKEZM2YUd9MAAAAAAADkl22KvOCCElm4ysjI0MaNG5WQkOCyPiEhQevWrSumVgEAAAAAAPyPX5Yp8oILSuTk7MeOHVNWVpYiIyNd1kdGRio1NTXPxzgcDjkcDpd15022Am0lsnYHAAAAAAB8HCOn3FeiqzY2m83la2NMrnU5kpOTFRER4bK8d2avFc0EAAAAAABXICZnd1+JLFxVqlRJ/v7+uUZXHT16NNcorBxjx47VyZMnXZa7y9SyorkAAAAAAOAKxBxX7iuRhaugoCBde+21Wr58ucv65cuXq3Xr1nk+xm63q2zZsi4LlwkCAAAAAAD4rhI5x5UkjRw5Un379lXz5s3VqlUrzZw5UwcOHNDDDz9c3E0DAAAAAABgknUPKLGFq3vuuUfHjx/XhAkTdOTIETVs2FCfffaZYmJiirtpAAAAAAAAzFXlASW2cCVJQ4cO1dChQ4u7GQAAAAAAALn4ZWcXdxNKvBJduAIAAAAAAPBVTLLuPgpXAAAAAAAAXsAcV+7jtnoAAAAAAADwSYy4AgAAAAAA8AImZ3cfhSsAAAAAAAAvYI4r93GpIAAAAAAAgBf4ZZsiL0Xx2muvKS4uTsHBwbr22mu1Zs0aD/fIehSuAAAAAAAAvMAvyxR5KawFCxZoxIgRGjdunDZt2qTrr79enTp10oEDB7zQM+tQuAIAAAAAAPACW7Yp8lJYU6dO1QMPPKBBgwapfv36mjZtmmrUqKEZM2Z4oWfWoXAFAAAAAABQgmVkZGjjxo1KSEhwWZ+QkKB169YVU6s8g8nZAQAAAAAAvMCdydkdDoccDofLOrvdLrvdnmvfY8eOKSsrS5GRkS7rIyMjlZqaWuQ2+ASDQjl37pwZP368OXfuHHklKIu8kp1XmvtGXsnOK819I6/kZpFHnq9mkVey80pz38gr2XlW981K48ePN5JclvHjx+e576+//mokmXXr1rmsnzhxoqlbt64FrfUemzGGezMWwqlTpxQREaGTJ0+qbNmy5JWQLPJKdl5p7ht5JTuvNPeNvJKbRR55vppFXsnOK819I69k51ndNysVZsRVRkaGQkND9f777+v22293rn/00Ue1efNmrV692uvt9RbmuAIAAAAAAPAxdrtdZcuWdVnyKlpJUlBQkK699lotX77cZf3y5cvVunVrK5rrNcxxBQAAAAAAUMKNHDlSffv2VfPmzdWqVSvNnDlTBw4c0MMPP1zcTXMLhSsAAAAAAIAS7p577tHx48c1YcIEHTlyRA0bNtRnn32mmJiY4m6aWyhcFZLdbtf48ePzHZ5Hnm9mkVey80pz38gr2XmluW/kldws8sjz1SzySnZeae4beSU7z+q++bqhQ4dq6NChxd0Mj2JydgAAAAAAAPgkJmcHAAAAAACAT6JwBQAAAAAAAJ9E4QoAAAAAAAA+icIVAAAAAAAAfBKFKwAAAAAAAPikgOJugK87dOiQZsyYoXXr1ik1NVU2m02RkZFq3bq1Hn74YdWoUaO4m+iWtLQ0zZs3L1f/2rRpo969eyssLKy4m1hkpblvUunun9V9K83nEsCVwRijL774Is/XsZtvvlk2m4088izPIq/k5wGAL7AZY0xxN8JXrV27Vp06dVKNGjWUkJCgyMhIGWN09OhRLV++XAcPHtSSJUvUpk0bj2Va+QF6x44d6tixo86ePat27dq59G/16tUKCwvTsmXLdPXVV3skrzT3TaJ/nuqf1X0rjnMp8SGFPPKszirNeb/++qu6dOmibdu2qWHDhi6vYz/++KOaNGmiRYsWqVq1auRd4XmluW/keT6vtL5mklfy8yjgXoEM8tW8eXMzYsSIfLePGDHCNG/e3GN527dvN9HR0aZcuXKme/fu5qGHHjIPPvig6d69uylXrpypVq2a2b59u8fy2rdvb3r16mUcDkeubQ6Hw/Tu3du0b9/eI1mluW/G0D9P9s/qvlmdZ4wxhw4dMk2bNjX+/v6mSZMmJiEhwXTs2NE0adLE+Pv7m2uuucYcOnSoxGWRR56vZpX2vG7dupmbbrrJHD58ONe2w4cPm5tuusl0797dI1nkley80tw38jybV5pfM8kr2XlW9w2+gcLVJQQHB5uffvop3+07d+40wcHBHsuz+gN0SEjIJYsN27ZtMyEhIR7JKs19M4b+ebJ/VvfN6jxj+JBCHnlWZ5X2vLCwMLN58+Z8t//www8mLCzMI1nkley80tw38jybV5pfM8kr2XlW9w2+gcLVJcTFxZn//Oc/+W7/z3/+Y+Li4jyWZ/UH6OjoaPPRRx/lu33hwoUmOjraI1mluW/G0D9P9s/qvlmdZwwfUsgjz+qs0p5XqVIls3Llyny3r1ixwlSqVMkjWeSV7LzS3DfyPJtXml8zySvZeVb3Db6BuwpewuOPP66HH35Yw4cP18cff6xvv/1W3333nT7++GMNHz5cQ4YM0ahRozyWV758ee3evTvf7Xv27FH58uU9lvfggw+qf//+euGFF7Rlyxalpqbqt99+05YtW/TCCy9o4MCBGjx4sEeySnPfJPrnyf5Z3Ter8yQpJCREf/zxR77bT5w4oZCQkBKXRR55vppV2vN69eql/v3767///a9OnjzpXH/y5En997//1f33368+ffp4JIu8kp1XmvtGnmfzSvNrJnklO8/qvsFHFHflzNe9++67pmXLliYgIMDYbDZjs9lMQECAadmypVmwYIFHs8aPH28iIiLM888/bzZv3myOHDliUlNTzebNm83zzz9vypcvb5555hmPZk6aNMlUrVrV2Gw24+fnZ/z8/IzNZjNVq1Y1kydP9lhOae6bMfTP0/2zsm/FkTd8+HBTo0YN8/7775s///zTuf7PP/8077//vqlZs6b5+9//XuKyyCPPV7NKe57D4TAPP/ywCQoKMn5+fiY4ONgEBwcbPz8/ExQUZIYMGZLnpd7kXXl5pblv5Hk2rzS/ZpJXsvOs7ht8A4WrAsrIyDCHDx82hw8fNhkZGV7LsfoDdI69e/eadevWmXXr1pm9e/d6JcMX+vbLL794LccX+uet750xxdM/q/pmdR4fUsgjr3T3rTjyjDHm5MmTZsWKFWbevHlm3rx5ZuXKlebkyZMezfhr3sqVK8krgXmluW9XSp63n+ul/TWTvJKbVxy/X1H8bMYYU9yjvpBbSkqKUlNTJUlRUVGKi4sr5hZ5TnH2LSgoSFu2bFH9+vW9llGav3dS6ezfkSNHNGPGDK1du1ZHjhyRv7+/4uLi1KNHDw0YMED+/v5eyT116pQ2bNig3377TdKF83nttdeqbNmyXsnauHGjy/fOW1nkkeerWcWVZ9XzHAA8zerXsCvhdwJ5JS8LxY/CVQly8OBBjR8/Xv/5z388dsz09HRt3LhRFSpU0NVXX+2y7dy5c3rvvffUr18/j2Tt3LlT3377rVq3bq26devqp59+0ksvvSSHw6H77rtPN910k0dyJGnkyJF5rn/ppZd03333qWLFipKkqVOneizzYidOnNCcOXO0e/duRUdHq1+/fqpRo4bHjr9p0yaVK1fOWTSaO3euZsyYoQMHDigmJkbDhw9Xr169PJb3yCOP6O6779b111/vsWNeyiuvvKINGzaoc+fOuvvuu/X2228rOTlZ2dnZ6tmzpyZMmKCAgACPZG3YsEEdOnRQXFycQkJC9N133+nee+9VRkaGPv/8c9WvX1+ff/65wsPDPZIHAN6QlpamefPmad26dUpNTZXNZlNkZKTatGmj3r17KywszLK2/Pbbb/q///s/Pf300x497qFDh1SuXDmVKVPGZf358+f1zTff6IYbbvBY1vHjx7V161Y1adJEFSpU0LFjx/TGG2/I4XDorrvu8uofwHLUqlVLn3/+ueLj472Wcf78eX366afavXu3qlatqttvv92jPyuHDh1ScHCwKlWqJElas2aNXn/9def7lWHDhqlVq1Yey/vXv/6lO++8UzExMR475uUsXrxYGzZs0K233qpWrVpp5cqVeuGFF5zvWR566CGP5qWnp2v+/Pl5/rHt5ptv9mgWAPiM4h3whcLYvHmz8fPz89jxdu3aZWJiYpyXfrVr187ltqKpqakey1uyZIkJCgoyFSpUMMHBwWbJkiWmcuXKpkOHDubmm282AQEBZsWKFR7JMsYYm81mmjZtatq3b++y2Gw206JFC9O+fXtz4403eiyvatWq5tixY8aYC5ebVa1a1URFRZmOHTua6tWrm4iICLNz506P5TVr1sx5V5lZs2aZkJAQ8/e//93MmDHDjBgxwpQpU8a88cYbHsvL+RmJj483kyZNMkeOHPHYsf9qwoQJJjw83Nxxxx0mKirKTJo0yVSsWNFMnDjRJCUlmcqVK5unn37aY3lt2rQxiYmJzq/ffvtt07JlS2OMMX/88Ydp2rSpV66TP3PmjJk5c6YZMGCAufXWW02nTp3MgAEDzKxZs8yZM2c8npef1NRUj8+/ZowxBw8eNKdPn861PiMjw6xevdqjWceOHTMrV640x48fN8YY8/vvv5tJkyaZZ555xuzYscOjWfmJi4szP//8s9dzMjIyzMKFC82UKVPM22+/7fGflYMHD5rff//d+fVXX31l+vTpY9q2bWvuvfdes27dOo9lvfDCC2bfvn0eO15BLFq0yDz99NPOfqxYscJ06tTJ3HLLLeb//u//PJ539uxZ88Ybb5j777/f3HrrraZz585m+PDh5osvvvBozvbt2010dLQpV66c6d69u3nooYfMgw8+aLp3727KlStnqlWrdsk7w3qap9+vHD582LRo0cL4+fkZf39/069fP5fXF0++XzHGmO+++85EREQYm81mypcvbzZs2GDi4uJMfHy8ueqqq0xISIjZuHGjx/JeeumlPBd/f38zduxY59ee0KpVK3PixAljjDFHjx41DRs2NEFBQSY+Pt4EBwebmjVrmkOHDnkkKyfvs88+M8YY89FHHxk/Pz/TrVs3M3r0aHP77bebwMBAs3jxYo/l2Ww24+/vbzp06GDeffddr18uNGPGDBMQEGCuvfZaU7ZsWTN37lwTHh5uBg0aZAYPHmxCQkLMtGnTPJa3e/duExMTYypWrOicvqFz586mZcuWxt/f39x1113m/PnzHsvzlfcqxvB+xVNKw/sVK9+rwHdQuPIhH3/88SWXF1980aNvzHr06GG6dOlifv/9d7N7927TtWtXExcXZ/bv32+M8ewbwVatWplx48YZY4yZP3++KV++vHnyySed25988knTsWNHj2QZY0xSUpKJi4vLVQwLCAjwypt3m81mfvvtN2OMMb169TLt27c3aWlpxhhjzp07Z7p06WLuvPNOj+WFhoY6v0/NmjXL9YHrnXfeMVdffbXH8mw2m/niiy/Mo48+aipVqmQCAwNNt27dzOLFi01WVpbHcowxplatWuaDDz4wxlz48OPv72/mzp3r3P7hhx+aq666ymN5ISEhLnOfZWVlmcDAQJOammqMMWbZsmUmOjraY3nG+NaHTD5gFo6VHzCNKd0fMvmA6bkPmO3btze9evXK8xw6HA7Tu3dv0759e49kGWPMli1bLrksWLDAo8/zfv36mb/97W9m/fr1Zvny5aZ58+bm2muvNX/88Ycx5sLris1m81hehw4dzKBBg8ypU6fM888/b6pXr24GDRrk3P7AAw+YHj16eCzPZrOZ6tWrm9jYWJfFZrOZatWqmdjYWBMXF+exrJz3Kw8++KBp2rSp849Rx44dM61btzYDBw70SJYxxoSHh5uUlBRjjDEtW7Y0kyZNctn+yiuvmGbNmnksz2azmdmzZ5vu3bubwMBAU7FiRfPoo4+abdu2eSzjYvXr1zczZ840xhizcuVKExwcbF599VXn9tmzZ5v69et7LK9Tp05m8ODBzvdeycnJplOnTsYYY37++WcTGxtrxo8f75EsX3qvYgzvVwqrNL9fsbogDt9A4cqH5Ixqybl7YV6LJ19Aq1SpYrZu3eqybujQoaZmzZrml19+8egLdtmyZc3u3buNMRcKAwEBAS4vztu2bTORkZEeycrx/fffmzp16ph//OMfzgn1rShc5VUw+/bbb0316tU9llexYkWzYcMGY8yF7+PmzZtdtu/Zs8eEhIR4LO/i/mVkZJgFCxaYW265xfj7+5vo6Gjz5JNPOr+/7goJCXEW5YwxJjAw0Pz444/Or/ft22dCQ0M9kmWMMTExMWbt2rXOrw8fPmxsNps5e/asMcaYlJQUExwc7LE8Y6z9kMkHzJL7ATMnr7R+yOQDpuc+YIaEhFzyd9u2bds8/jshv/crOes9+boSHR1tvvvuO+fX586dM927dzdNmzY1x48f9/gHzPLlyztHP2RkZBg/Pz+X/B9++MFUq1bNY3kPPfSQadq0aa4RF954z3Lxa0qdOnXMJ5984rL9yy+/NLGxsR7Li4iIMFu2bDHGXHi/kvP/HHv27PHo7/SL+/fbb7+ZyZMnm3r16hk/Pz/TokULM3PmTHPq1CmP5eX1nuXi17CUlBSP9i80NNRltIzD4TCBgYHOUf8fffSRx75/FMR5v1LYPKver1hdEIdvoHDlQ6Kjo83ChQvz3b5p0yaPvmCHh4fnOSx1+PDhpnr16uarr77ySuHKGGPKlCnjMspl3759Hi8OGGPM6dOnTb9+/Uzjxo3N1q1bTWBgoNcKV0ePHjXGXPg+XlxoMebCGxe73e6xvPvuu8888MADxhhj7rrrLvPPf/7TZXtSUpJp1KiRx/Iu/mV0sf3795vx48ebmJgYj/2sxMXFmSVLlhhjLny48/PzM++9955z+6effurRN9WPPvqoadiwoVmyZIlZuXKlufHGG13eiC1dutTUrl3bY3nGWPshkw+YJfcDpjGl+0MmHzA99wEzOjrafPTRR/luX7hwoUdHjlaqVMm88cYbZt++fXkun376qUef52FhYbkubTl//rzp0aOH8/e7p/NyPhQZk/s9y/79+z3+nmXhwoWmRo0a5pVXXnGu81bhKuf9SpUqVXIdf9++fR59v9KtWzczZswYY4wxt9xyS64RHrNmzTLx8fEey8vv/cpXX31l+vfvb8LCwkxYWJjH8nLeLxtjzK+//mpsNpv59NNPndtXrVrl0T9cRkdHu/zh98SJE8ZmszlfK/fu3eux7x8Fcd6vFIaV71esLojDN1C48iFdu3Y1Tz31VL7bN2/e7NHKf4sWLcxbb72V57Zhw4aZcuXKeewFu3Hjxs5ihDEXftldfInEmjVrPFr1/6v58+ebyMhI4+fn57UX60aNGplmzZqZMmXKmA8//NBl++rVqz36y+jXX381sbGx5oYbbjAjR440ISEhpm3btubBBx80N9xwgwkKCnJ54+Su/N4I5sjOzjbLli3zSNa4ceNM5cqVzaBBg0xcXJwZO3asqVmzppkxY4Z5/fXXTY0aNcxjjz3mkSxjLhQ37777bhMQEGBsNptp3bq12bt3r3P7559/7lI48wQrP2TyAbPkfsA0pnR/yOQDpuc+YI4fP95ERESY559/3mzevNkcOXLEpKamms2bN5vnn3/elC9f3qNzw9xyyy3m2WefzXe7p9+vNGrUyPz3v//NtT7ntaVmzZoefV2pV6+ey8jpTz75xDkK1xjPj6LOcejQIXPTTTeZW2+91Rw5csRrhavbbrvN3H777aZ8+fLOy21yfPPNNx4dAb9jxw5TsWJF069fP/Pss8+aMmXKmPvuu88899xzpl+/fsZut5vZs2d7LM/Pz++S71dOnjzpHHnpCcOGDTPx8fFm4sSJ5rrrrjP9+/c39erVM0uWLDFLly41jRo18uio2P79+5t27dqZnTt3mr1795p77rnHZWTJqlWrTI0aNTySRUGc9yuFYeX7FasL4vANFK58yFdffeVS3PmrM2fOmFWrVnksLykpyXnZQl6GDBnisTeeM2bMyFV5v9iTTz7pHEHkLQcPHjQfffSRVyaTTExMdFmWLl3qsv3xxx83vXr18mjmiRMnzOjRo83VV19tgoODTVBQkImJiTF9+vQx69ev92hWbGysc5SAt2VmZpqJEyeaLl26OIf+zp8/39SoUcNUrFjRDBgwwCvfw/T09Dwn5/QGKz9k8gGz5H7ANKZ0f8jkA6bnPmAaY8ykSZOcc2n5+fk5RyhUrVrVTJ482WM5xlyYa/Dtt9/Od/sff/xh3nzzTY/ljRo1yiQkJOS57fz586Zbt24efV1JTEw08+fPz3f7k08+aXr27OmxvItlZ2ebpKQkExUVZfz9/T3+ujJgwACX5a9/mHn88cfNLbfc4tHMPXv2mF69epnw8HDnCJrAwEDTunXrS15pUBSX+0Obp505c8YMGjTINGzY0Dz88MMmIyPDPP/88yYoKMjYbDbTvn17j7bnt99+M3/729+cz/PY2Fjzww8/OLe///775uWXX/ZIFgVx3q8UhpXvV6wuiMM3ULgCgGJg1YdMX/yA6ck3nqX5A6YxpftDJh8wPfcB82J79+4169atM+vWrXMZPVqSnT9/3pw8eTLf7ZmZmZbeoTItLc2cO3fOqxkbNmww06ZNc863Y5UzZ86Y9PR0rxw7OzvbpKammsOHDzvnHi2t0tPTPXqp81/9/PPPua5e8AYK4hTEC8rq9yt79uwx99xzjyUFcfgGmzHGCABQLFJSUpSamipJioqKUlxcXDG3yD2ZmZk6e/asypYtm+f2rKwsHTp0SDExMZa05+zZs/L395fdbvdaxsaNG7V27Vr169dP5cuX91pOXtLS0uTv76/g4GCPH9sYo6NHjyo7O1uVKlVSYGCgxzN8wblz53T+/HmFh4d75fi7d++Ww+FQvXr1FBAQ4JUMAPCm0vZeReL9Sml5v3KlvFeB5FfcDQCAK1lcXJxatWqlVq1aOd8IHjx4UAMHDrQk39NZAQEB+b4JlKTDhw/rmWee8Vje5Rw/flxDhgzxasa1116rRx99VOXLl7f0eydJf/zxh4YOHeqVY9tsNkVGRqpq1arON4Il+WczP8HBwQoPD/daXnx8vBo2bJiraOXpvPT0dK1du1Y7duzIte3cuXN66623PJZFXsnOK819I8+zeTt37tTs2bOVkZGhVq1aqXz58poyZYoGDhyolStXeiznr3k//fSTJOmnn37SkCFDvJIXEBCgX3/9Nd+81atXe7xodan+ffvttx4vWv01LywsTD/99JP+8Y9/ePX7t2vXLkmu/fvuu+88WrTKyfr5558VGRmpkydP6u9//7vXfjbhA4p3wBcA4K82b97s0eHpvpJFHnm+mlXS83bt2mViYmKcl/O0a9fOHD582Lnd03fHIq/k5pXmvpHn2bwlS5aYoKAgU6FCBRMcHGyWLFliKleubDp06GBuvvlmExAQ4DJnE3nkWZVndd/gGxizDgAWW7Ro0SW37927t0RmkUeer2aV9rzRo0erUaNG2rBhg/7880+NHDlSbdq00apVq1SzZk2P5ZBX8vNKc9/I82zehAkT9MQTT2jixIl699131adPHw0ZMkTPPfecJGncuHGaNGmSbrrpJvLIszTP6r7BRxR35QwArjQ5fynNmUwyr8VTfzG1Mos88nw1q7TnValSxWzdutVl3dChQ03NmjXNL7/84vFRH+SV3LzS3DfyPJtXtmxZs3v3bmOMMVlZWSYgIMBs3LjRuX3btm0evasteeT5YhZ8B3NcAYDFqlatqg8++EDZ2dl5Lj/88EOJzCKPPF/NKu156enpuebQevXVV9WtWze1a9dOP//8s8eyyCvZeaW5b+R5Pi+Hn5+fgoODVa5cOee68PBwnTx5kjzyijXP6r6h+FC4AgCLXXvttZf80Gqz2WQ8dMNXK7PII89Xs0p7Xr169bRhw4Zc61955RV1795d3bp180gOeSU/rzT3jTzP5sXGxmrPnj3Or7/55huXyxEPHjyoqlWrkkee5XlW9w2+gcIVAFjsiSeeUOvWrfPdftVVV+nLL78scVnkkeerWaU97/bbb9f8+fPz3DZ9+nT17t3bo0U58kpuXmnuG3mezRsyZIiysrKcX//17qhLlizx6BxC5JHni1nwHTbjyVdTAAAAAAAAwEMYcQUAAAAAAACfROEKAAAAAAAAPonCFQAAAAAAAHwShSsAAAAAAAD4JApXAAAAAAAA8EkUrgAAAAAAAOCTKFwBAAAAAADAJ1G4AgAAAAAAgE+icAUAAAAAAACfROEKAAAAAAAAPonCFQAAAAAAAHwShSsAAAAAAAD4JApXAADgkr777jvZbDbZbDYlJyd77LixsbGy2WweO56VBgwYIJvNplWrVnnkeDnnN2f56KOPcu2TmZmpN954Q8OGDdOLL76oc+fO5dpn8+bNuY61b98+j7QRAACgOAQUdwMAAIBve/vtt13+P3bs2GJsjTXat2+v1atXKyUlRbGxsZZkhoWF6c4775Qk1axZ02VbVlaWEhIS9OWXXzrXzZ49W998843CwsKc6ypUqKD+/ftLkpYuXarffvvNgpYDAAB4DyOuAABAvs6fP68FCxbIZrMpKipKO3fu1A8//OCRY69YsUI7d+70yLFKg0qVKunNN9/Um2++qWuuucZl2zvvvKOvv/5a7733ns6cOaMVK1bo0KFDmjZtmst+NWvWdB6jXr16FrYeAADAOyhcAQCAfC1ZskTHjh3TDTfcoIceekiS6wgsd9SuXZviSgFt2LBBt912m+666y6FhYXppptuUr9+/fT9998Xd9MAAAC8isIVAADIV06R6r777tN9990nSZo/f76ysrJy7Zsz79OllovnhMprjqt9+/bJZrOpffv2SktL08iRI1WjRg2FhITommuu0eLFi537vv/++7ruuusUFhamyMhI/f3vf1d6enqudtlstnwv93vzzTdls9mUmJjokr969WpJUlxcnEv78/LVV1/ppptuUnh4uMqWLavOnTtrx44deZ/QIqpSpYq2bNmi06dPS7owEu67775TZGSkR3MAAAB8DXNcAQCAPJ08eVKffPKJ7Ha77rzzTpUrV07XXXedvv/+ey1fvly33nqry/5t27bN8zhZWVnOYpe/v3+BsjMyMnTzzTfrl19+0d/+9jedOXNGX331lW6//XYtXbpU27Zt06hRo9SiRQslJCRozZo1euWVV3T8+HG98847Re5zmTJl1L9/f+f8UHfccYfKlCmT7/6LFy/WSy+9pIYNG+qWW27Rtm3b9Nlnn+m7777Tjz/+qKioqCK35WJ9+/bVpEmTVL9+fbVu3VqbNm3Svn37NGPGDI8cHwAAwFdRuAIAAHl67733dO7cOd1xxx0qV66cpAsjr77//nvNnTs3V+Fq0KBBGjRoUK7jPProo8rKylKXLl3Upk2bAmV/8803at++vX7++WeVL19e0oXRUffff7+GDBmiP/74QytXrtT1118vSTp8+LCaNWumefPm6dlnn1WtWrWK1Oeceabat2+v3377TS+88MIlJ2efNm2a5s6dq969e0u6UKS755579MEHH+i1117ThAkTitSOv4qJidHy5cs1cuRILVmyRPHx8VqyZImaNm3qkeMDAAD4Ki4VBAAAebr4MsEcvXr1UkBAgBYuXKgzZ85c9hhvvPGGXn75ZV199dWaN2+e/PwK9tbD399fs2bNchatJKlfv36qXLmy9uzZo+HDhzuLVpIUHR2te++9V9KFS/es0qdPH2fRKqfdTz75pFfa0apVK33zzTc6ffq0fvjhB3Xo0MGjxwcAAPBFFK4AAEAu+/bt09q1a1WhQgXddtttzvWVK1fWLbfcorNnz2rhwoWXPMaaNWs0dOhQVaxYUYsXL1Z4eHiB82NjY3XVVVe5rPPz81NMTIwkqWPHjrkeU7t2bUnSkSNHCpzjroSEhFzr6tSpY3k7AAAASisKVwAAIJe5c+fKGKO7775bQUFBLttyRmBd6u6C+/fv1x133KHs7Gy9//77hb50r1q1anmuDwsLy3d7zjaHw1GoLHdUr14917qcObGsbAcAAEBpxRxXAAAgl7lz50qSVqxYkWvS9ZyCzIoVK3TkyBFVrVrVZXtaWpq6deum33//Xa+99ppuvPHGQufndwe/gm4vqOzsbLce76l2AAAAIG8UrgAAgIvvv/9eu3btkiTt3r1bu3fvznO/7OxszZs3T//4xz+c64wx6tu3r7Zu3aohQ4ZoyJAhlrT5UgIDA/Odj+vgwYMWtwYAAACFwaWCAADARc4lgE888YSMMXkuy5Ytk/S/kVk5nn76aS1cuFA33nijXn75ZcvbnpeqVavq+PHj+uOPP3Jty+nHX+VcHpmZmenVtgEAAODSKFwBAACnzMxMLViwQJJc7pb3VzfddJOqVKmizZs368cff5Qkvffee5o4caJq1aql999/XwEBvjGwu127dpKkZ5991rnOGKPk5GStW7cuz8dER0dLknPkGQAAAIqHb7yjBAAAPmHJkiX6/fffVbduXTVr1izf/fz9/XXnnXfqtdde09y5czVp0iSNHTtW0oWiz8WXD15szJgxqlevnlfanp/Ro0frv//9r6ZNm6ZVq1apdu3a2rZtmw4ePKihQ4fqtddey/WYbt26ac6cOerTp48SEhIUEREhSfr3v/9tadsBAACudBSuAACAU85lgr169brsvr1799Zrr72md955R0lJScrKypIkrV27VmvXrs3zMQMGDLC8cNWgQQOtXLlSY8eO1ffff6+9e/eqTZs2eu+997Rp06Y8H9OzZ0+9+OKLmjVrlhYvXuyckJ7CFQAAgLVsxhhT3I0AAAC4ktlsNsXExGjfvn0eO2b79u21evVqpaSkKDY21mPHBQAAsBIjrgAAAHzAsWPHNGDAAEnS3//+d11zzTWFPsaBAwf09NNPS5J++uknTzYPAACgWDDiCgAAoJjZbDaXrxcuXKgePXoU+jibN2/ONTcZI64AAEBJRuEKAAAAAAAAPsmvuBsAAAAAAAAA5IXCFQAAAAAAAHwShSsAAAAAAAD4JApXAAAAAAAA8EkUrgAAAAAAAOCTKFwBAAAAAADAJ1G4AgAAAAAAgE+icAUAAAAAAACfROEKAAAAAAAAPonCFQAAAAAAAHzS/wPdVHVWKw0oEgAAAABJRU5ErkJggg==", + "image/png": "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", "text/plain": [ "
" ] @@ -469,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -514,7 +536,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -573,9 +595,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'pvdeg.scenario' has no attribute 'Geospatial_Scenario'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[15], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m geospatial_standoff_scenario \u001b[38;5;241m=\u001b[39m \u001b[43mpvdeg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscenario\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGeospatial_Scenario\u001b[49m(\n\u001b[0;32m 2\u001b[0m name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstandoff geospatial\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 3\u001b[0m geospatial \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 4\u001b[0m )\n", + "\u001b[1;31mAttributeError\u001b[0m: module 'pvdeg.scenario' has no attribute 'Geospatial_Scenario'" + ] + } + ], "source": [ "geospatial_standoff_scenario = pvdeg.scenario.Geospatial_Scenario(\n", " name='standoff geospatial',\n", @@ -677,7 +711,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "scrolled": true }, From a4623282f608c2634e0df832c9fe5dde112362d5 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Tue, 19 Nov 2024 09:40:33 -0700 Subject: [PATCH 08/11] allow single input dict for poa --- pvdeg/spectral.py | 238 +++++++++++++++++++++++++++++++++++++--------- 1 file changed, 191 insertions(+), 47 deletions(-) diff --git a/pvdeg/spectral.py b/pvdeg/spectral.py index e481b2c..1e87fbe 100644 --- a/pvdeg/spectral.py +++ b/pvdeg/spectral.py @@ -4,6 +4,8 @@ import pvlib import pandas as pd +import inspect +import warnings from pvdeg.decorators import geospatial_quick_shape @@ -69,11 +71,17 @@ def poa_irradiance( meta: dict, module_mount="fixed", sol_position=None, - **kwargs_irradiance, + dni_extra=None, + airmass=None, + albedo=0.25, + surface_type=None, + sky_model="isotropic", + model_perez="allsitescomposite1990", + **kwargs_mount, ) -> pd.DataFrame: """ - Calculate plane-of-array (POA) irradiance using pvlib based on weather data from the - National Solar Radiation Database (NSRDB) for a given location (gid). + Calculate plane-of-array (POA) irradiance using `pvlib.irradiance.get_total_irradiance` for different module mounts + as fixed tilt systems or tracked systems. Parameters ---------- @@ -83,28 +91,107 @@ def poa_irradiance( The geographical location ID in the NSRDB file. module_mount: string Module mounting configuration. Can either be `fixed` for fixed tilt systems or - `1_axis` for single-axis tracker systems. + `single_axis` for single-axis tracker systems. sol_position : pd.DataFrame, optional pvlib.solarposition.get_solarposition Dataframe. If none is given, it will be calculated. - kwargs_irradiance : dict - Contains kwarg arguments for the poa model based on mounting configuration. See + dni_extra : pd.Series, optional + Extra-terrestrial direct normal irradiance. If None, it will be calculated. + airmass : pd.Series, optional + Airmass values. If None, it will be calculated. + albedo : float, optional + Ground reflectance. Default is 0.25. + surface_type : str, optional + Type of surface for albedo calculation. If None, a default value will be used. + sky_model : str, optional + Sky diffuse model to use. Default is `isotropic`. + Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'. + model_perez : str, optional + Perez model to use for diffuse irradiance. Default is `allsitescomposite1990`. + Only used when sky_model is 'perez'. + **kwargs_mount : dict + Additional keyword arguments for the POA model based on mounting configuration. See `poa_irradiance_fixed` or `poa_irradiance_tracker` for details. + - For `module_mount='fixed'`: + - surface_tilt : float + Tilt angle of the modules (degrees). + - surface_azimuth : float + Azimuth angle of the modules (degrees). + + - For `module_mount='single_axis'`: + - axis_tilt : float + Tilt angle of the tracker axis (degrees). + - axis_azimuth : float + Azimuth angle of the tracker axis (degrees). + - max_angle : float + Maximum rotation angle of the tracker (degrees). + - backtrack : bool + Whether to enable backtracking for single-axis trackers. + - gcr : float + Ground coverage ratio of the tracker system. + + Returns ------- poa : pandas.DataFrame Contains keys/columns 'poa_global', 'poa_direct', 'poa_diffuse', 'poa_sky_diffuse', 'poa_ground_diffuse'. [W/m2] + + Notes + ----- + This function uses pvlib to calculate the plane-of-array irradiance based on the provided weather data and + mounting configuration. See the pvlib documentation for further information on the parameters. """ + # Allow legacy keys for tilt and azimuth + if "tilt" in kwargs_mount: + kwargs_mount["surface_tilt"] = kwargs_mount.pop("tilt") + if "azimuth" in kwargs_mount: + kwargs_mount["surface_azimuth"] = kwargs_mount.pop("azimuth") + + def check_keys_in_list(dictionary, key_list): + extra_keys = [key for key in dictionary.keys() if key not in key_list] + for key in extra_keys: + dictionary.pop(key) + warnings.warn( + f"Key '{extra_keys}' cannot be used for the selected `module_mount` and are ignored." + ) + if sol_position is None: sol_position = solar_position(weather_df, meta) if module_mount == "fixed": - poa = poa_irradiance_fixed(weather_df, meta, sol_position, **kwargs_irradiance) - elif module_mount == "1_axis": + arg_names = ["surface_tilt", "surface_azimuth"] + check_keys_in_list(kwargs_mount, arg_names) + + poa = poa_irradiance_fixed( + weather_df=weather_df, + meta=meta, + sol_position=sol_position, + dni_extra=dni_extra, + airmass=airmass, + albedo=albedo, + surface_type=surface_type, + model=sky_model, + model_perez=model_perez, + **kwargs_mount, + ) + elif module_mount == "single_axis": + args = inspect.signature(pvlib.tracking.singleaxis).parameters + arg_names = [param.name for param in args.values()] + check_keys_in_list(kwargs_mount, arg_names) + poa = poa_irradiance_tracker( - weather_df, meta, sol_position, **kwargs_irradiance + weather_df=weather_df, + meta=meta, + sol_position=sol_position, + dni_extra=dni_extra, + airmass=airmass, + albedo=albedo, + surface_type=surface_type, + model=sky_model, + model_perez=model_perez, + **kwargs_mount, ) else: raise NotImplementedError( @@ -128,14 +215,17 @@ def poa_irradiance_fixed( weather_df: pd.DataFrame, meta: dict, sol_position=None, - tilt=None, - azimuth=None, - sky_model="isotropic", - **kwargs_irradiance, + surface_tilt=None, + surface_azimuth=None, + dni_extra=None, + airmass=None, + albedo=0.25, + surface_type=None, + model="isotropic", + model_perez="allsitescomposite1990", ) -> pd.DataFrame: """ - Calculate plane-of-array (POA) irradiance using pvlib based on weather data from the - National Solar Radiation Database (NSRDB) for a given location (gid). + Calculate plane-of-array (POA) irradiance using `pvlib.irradiance.get_total_irradiance` for a fixed tilt system. Parameters ---------- @@ -145,54 +235,76 @@ def poa_irradiance_fixed( The geographical location ID in the NSRDB file. sol_position : pd.DataFrame, optional pvlib.solarposition.get_solarposition Dataframe. If none is given, it will be calculated. - tilt : float, optional - The tilt angle of the PV panels in degrees, if None, the latitude of the - location is used. - azimuth : float, optional - The azimuth angle of the PV panels in degrees. Equatorial facing by default. + dni_extra : pd.Series, optional + Extra-terrestrial direct normal irradiance. If None, it will be calculated. + airmass : pd.Series, optional + Airmass values. If None, it will be calculated. + albedo : float, optional + Ground reflectance. Default is 0.25. + surface_type : str, optional + Type of surface for albedo calculation. If None, a default value will be used. sky_model : str, optional - The pvlib sky model to use, 'isotropic' by default. + Sky diffuse model to use. Default is `isotropic`. + Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'. + model_perez : str, optional + Perez model to use for diffuse irradiance. Default is `allsitescomposite1990`. + Only used when sky_model is 'perez'. + surface_tilt : float, optional + Tilt angle of the modules (degrees). + surface_azimuth : float, optional + Azimuth angle of the modules (degrees). Returns ------- - poa : pandas.DataFrame - Contains keys/columns 'poa_global', 'poa_direct', 'poa_diffuse', - 'poa_sky_diffuse', 'poa_ground_diffuse'. [W/m2] + poa : pd.DataFrame + DataFrame containing the calculated plane-of-array irradiance components with columns: + 'poa_global', 'poa_direct', 'poa_diffuse', 'poa_sky_diffuse', 'poa_ground_diffuse'. [W/m2] + + + Notes + ----- + This function uses pvlib to calculate the plane-of-array irradiance based on the provided weather data and + mounting configuration for fixed tilt systems. See the pvlib documentation for further information on the parameters. """ - # TODO: change for handling HSAT tracking passed or requested - if tilt is None: + if surface_tilt is None: try: - tilt = float(meta["tilt"]) + surface_tilt = float(meta["tilt"]) except: - tilt = float(abs(meta["latitude"])) + surface_tilt = float(abs(meta["latitude"])) print( - f"The array tilt angle was not provided, therefore the latitude tilt of {tilt:.1f} was used." + f"The array surface_tilt angle was not provided, therefore the latitude tilt of {surface_tilt:.1f} was used." ) - if azimuth is None: # Sets the default orientation to equator facing. + + if surface_azimuth is None: # Sets the default orientation to equator facing. try: - azimuth = float(meta["azimuth"]) + surface_azimuth = float(meta["azimuth"]) except: if float(meta["latitude"]) < 0: - azimuth = 0 + surface_azimuth = 0 else: - azimuth = 180 + surface_azimuth = 180 print( - f"The array azimuth was not provided, therefore an azimuth of {azimuth:.1f} was used." + f"The array azimuth was not provided, therefore an azimuth of {surface_azimuth:.1f} was used." ) if sol_position is None: sol_position = solar_position(weather_df, meta) poa = pvlib.irradiance.get_total_irradiance( - surface_tilt=tilt, - surface_azimuth=azimuth, + surface_tilt=surface_tilt, + surface_azimuth=surface_azimuth, dni=weather_df["dni"], ghi=weather_df["ghi"], dhi=weather_df["dhi"], solar_zenith=sol_position["apparent_zenith"], solar_azimuth=sol_position["azimuth"], - model=sky_model, + dni_extra=dni_extra, + airmass=airmass, + albedo=albedo, + surface_type=surface_type, + model=model, + model_perez=model_perez, ) return poa @@ -218,8 +330,12 @@ def poa_irradiance_tracker( backtrack=True, gcr=0.2857142857142857, cross_axis_tilt=0, - sky_model="isotropic", - **kwargs_irradiance, + dni_extra=None, + airmass=None, + albedo=0.25, + surface_type=None, + model="isotropic", + model_perez="allsitescomposite1990", ) -> pd.DataFrame: """ Calculate plane-of-array (POA) irradiance using pvlib based on weather data from the @@ -233,20 +349,41 @@ def poa_irradiance_tracker( The geographical location ID in the NSRDB file. sol_position : pd.DataFrame, optional pvlib.solarposition.get_solarposition Dataframe. If none is given, it will be calculated. - tilt : float, optional - The tilt angle of the PV panels in degrees, if None, the latitude of the - location is used. - azimuth : float, optional - The azimuth angle of the PV panels in degrees. Equatorial facing by default. + dni_extra : pd.Series, optional + Extra-terrestrial direct normal irradiance. If None, it will be calculated. + airmass : pd.Series, optional + Airmass values. If None, it will be calculated. + albedo : float, optional + Ground reflectance. Default is 0.25. + surface_type : str, optional + Type of surface for albedo calculation. If None, a default value will be used. sky_model : str, optional - The pvlib sky model to use, 'isotropic' by default. + Sky diffuse model to use. Default is `isotropic`. Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'. + model_perez : str, optional + Perez model to use for diffuse irradiance. Default is `allsitescomposite1990`. + Only used when sky_model is 'perez'. + axis_tilt : float, optional + Tilt angle of the tracker axis (degrees). Default is 0.0. + axis_azimuth : float, optional + Azimuth angle of the tracker axis (degrees). Default is 0.0. + max_angle : float, optional + Maximum rotation angle of the tracker (degrees). Default is 45.0. + backtrack : bool, optional + Whether to enable backtracking for single-axis trackers. Default is True. + gcr : float, optional + Ground coverage ratio of the tracker system. Default is 0.3. Returns ------- tracker_poa : pandas.DataFrame Contains keys/columns 'poa_global', 'poa_direct', 'poa_diffuse', 'poa_sky_diffuse', 'poa_ground_diffuse'. [W/m2] + + Notes + ----- + This function uses pvlib to calculate the plane-of-array irradiance based on the provided weather data and + mounting configuration for single-axis tracker systems. See the pvlib documentation for further information on the parameters. """ if axis_azimuth is None: # Sets the default orientation to north-south. @@ -257,7 +394,9 @@ def poa_irradiance_tracker( axis_azimuth = 0 else: axis_azimuth = 180 - print(f"The array axis_azimuth was not provided, therefore an azimuth of {axis_azimuth:.1f} was used.") + print( + f"The array axis_azimuth was not provided, therefore an azimuth of {axis_azimuth:.1f} was used." + ) if sol_position is None: sol_position = solar_position(weather_df, meta) @@ -281,7 +420,12 @@ def poa_irradiance_tracker( dhi=weather_df["dhi"], solar_zenith=sol_position["apparent_zenith"], solar_azimuth=sol_position["azimuth"], - model=sky_model, + dni_extra=dni_extra, + airmass=airmass, + albedo=albedo, + surface_type=surface_type, + model=model, + model_perez=model_perez, ) return tracker_poa From 2731cd27eb400ee3160c7af6a9d7bfc6177b7cff Mon Sep 17 00:00:00 2001 From: martin-springer Date: Tue, 19 Nov 2024 09:48:18 -0700 Subject: [PATCH 09/11] update tracker keyword in standards --- pvdeg/standards.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pvdeg/standards.py b/pvdeg/standards.py index 4c38d75..e0d5181 100644 --- a/pvdeg/standards.py +++ b/pvdeg/standards.py @@ -241,7 +241,7 @@ def standoff( other variables needed to access a particular weather dataset. tilt : float, optional Tilt angle of rack mounted PV system relative to horizontal. [°] - If tracker mounted, specify keyword '1_axis' + If single-axis tracker mounted, specify keyword 'single_axis' azimuth : float, optional Azimuth angle of PV system relative to north. [°] sky_model : str, optional @@ -318,7 +318,7 @@ def standoff( solar_position = spectral.solar_position(weather_df, meta) - if tilt == "1_axis": + if tilt == "single_axis": irradiance_dict = { "sol_position": solar_position, "axis_azimuth": azimuth, @@ -766,4 +766,4 @@ def x_eff_temperature_estimate( ) T_x_eff = T_0 - (T_0 - T_inf) * (1 - np.exp(-x_eff / x_0)) - return T_x_eff \ No newline at end of file + return T_x_eff From db27db89006dce01de6a83e6e87607d50ec05ed9 Mon Sep 17 00:00:00 2001 From: martin-springer Date: Tue, 19 Nov 2024 11:49:00 -0700 Subject: [PATCH 10/11] update tests --- tests/test_spectral.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/tests/test_spectral.py b/tests/test_spectral.py index f1f8e50..f6497fc 100644 --- a/tests/test_spectral.py +++ b/tests/test_spectral.py @@ -58,7 +58,12 @@ def test_poa_irradiance(): weather dataframe, meta dictionary, and solar_position dataframe """ result = pvdeg.spectral.poa_irradiance( - WEATHER, META, solpos_expected, tilt=None, azimuth=180, sky_model="isotropic" + WEATHER, + META, + sol_position=solpos_expected, + tilt=None, + azimuth=180, + sky_model="isotropic", ) pd.testing.assert_frame_equal(result, poa_expected, check_dtype=False) From 1197068fec9e2f0f0abe3a0459ab8338d0d58a39 Mon Sep 17 00:00:00 2001 From: MDKempe <58960264+MDKempe@users.noreply.github.com> Date: Tue, 3 Dec 2024 20:35:30 -0700 Subject: [PATCH 11/11] Update Tools - Module Standoff for IEC TS 63126.ipynb I just fixed some minor descriptions. Nothing of any significance. --- ...s - Module Standoff for IEC TS 63126.ipynb | 64 +++++++++---------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb b/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb index 547fc0e..7112684 100644 --- a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -132,14 +132,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'latitude': -43.52646, 'longitude': 172.62165, 'altitude': 4.0, 'wind_height': 10, 'Source': 'PVGIS', 'leisure': 'Hagley Golf Club', 'suburb': 'Central City', 'city_district': 'Linwood-Central-Heathcote Community', 'ISO3166-2-lvl4': 'NZ-CAN', 'Street': 'Uni-Cycle Cycleway', 'City': 'Christchurch', 'County': 'Christchurch City', 'State': 'Canterbury', 'Zipcode': '8440', 'Country': 'New Zealand / Aotearoa', 'Country Code': 'NZ'}\n" + "{'Source': 'NSRDB', 'Location ID': '79584', 'City': 'Phoenix', 'State': 'Arizona', 'Country': 'United States', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 33.61, 'longitude': -112.14, 'altitude': 388, 'tz': -7, 'wind_height': 2, 'house_number': '3730', 'neighbourhood': 'Sunray Manor', 'ISO3166-2-lvl4': 'US-AZ', 'Street': 'West Rue De Lamour Avenue', 'County': 'Maricopa County', 'Zipcode': '85029', 'Country Code': 'US'}\n" ] } ], @@ -218,19 +218,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "First calculation standoff = 0.0 cm.\n", - "Second calculation standoff = 0.0 cm.\n", - "The estimated T₉₈ of an insulated-back module is 45.2°C. \n", - "The estimated T₉₈ of an open-rack module is 32.7°C. \n", - "Level 0 certification is valid for a standoff greather than 0.0 cm. \n", - "\n" + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "First calculation standoff = 7.0 cm.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "Second calculation standoff = 1.9 cm.\n", + "The estimated T₉₈ of an insulated-back module is 86.4°C. \n", + "The estimated T₉₈ of an open-rack module is 61.6°C. \n", + "Level 0 certification is valid for a standoff greather than 7.0 cm. \n", + "Level 1 certification is required for a standoff between than 7.0 cm, and 1.9 cm. \n", + "Level 2 certification is required for a standoff less than 1.9 cm.\n" ] } ], @@ -268,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -336,19 +339,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The 98ᵗʰ percential temperature is estimated to be 47.9 °C.\n" + "The array axis_azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The 98ᵗʰ percential temperature is estimated to be 64.4 °C.\n" ] } ], "source": [ - "# This code will calculate the temperature for an arbitrary x_eff distance. This produces a slightly different value because it is set for a 1-axis tracker and x_eff=None indicating open rack.\n", + "# This code will calculate the temperature for an arbitrary x_eff distance. \n", + "# This produces a slightly different value because it is set for a \"1_axis\" tracker (instead of a default of \"fixed\") \n", + "# and x_eff=None indicating open rack.\n", "irradiance_kwarg ={\n", " \"tilt\": None,\n", " \"azimuth\": None,\n", @@ -421,25 +427,19 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[WinError 183] Cannot create a file when that file already exists: 'c:\\\\Users\\\\mkempe\\\\Documents\\\\GitHub\\\\new\\\\PVDegradationTools\\\\TEMP\\\\results'\n" + "ename": "NameError", + "evalue": "name 'pd' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m standoff_series_df\u001b[38;5;241m=\u001b[39m\u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTilt\u001b[39m\u001b[38;5;124m'\u001b[39m: standoff_series[:, \u001b[38;5;241m0\u001b[39m],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAzimuth\u001b[39m\u001b[38;5;124m'\u001b[39m: standoff_series[:, \u001b[38;5;241m1\u001b[39m],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mXₘᵢₙ\u001b[39m\u001b[38;5;124m'\u001b[39m: standoff_series[:, \u001b[38;5;241m2\u001b[39m]})\n\u001b[0;32m 2\u001b[0m x_fig \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m16\u001b[39m,\u001b[38;5;241m4\u001b[39m))\n\u001b[0;32m 3\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPlot of $\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mit\u001b[39m\u001b[38;5;132;01m{Xₘᵢₙ}\u001b[39;00m\u001b[38;5;124m$ for all orientations for $\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mit\u001b[39m\u001b[38;5;132;01m{T₉₈}\u001b[39;00m\u001b[38;5;124m$=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%.0f\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m kwarg_x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mT98\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m°C.\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m15\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.08\u001b[39m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined" ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -739,7 +739,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "base", "language": "python", "name": "python3" },