5.Scipy_optimization_Drag_min.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77bdf148",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.optimize import minimize\n",
    "import joblib\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%run 2.Hicks_Henne_NEW_Final.ipynb\n",
    "%run 3.Xfoil_runner_extract_value.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8bede254",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\backend.py:1398: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = joblib.load('model__24.joblib')\n",
    "scaler = joblib.load('scaler_file__24.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b55e805b-f7ff-4229-b774-5d8218ae06ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the current directory\n",
    "original_directory = os.getcwd()\n",
    "\n",
    "# Navigate to another folder\n",
    "os.chdir(original_directory + '\\\\Deformed_foils')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f4275fc9-4a66-486e-b783-e340c4355ce1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5572 0.00848\n"
     ]
    }
   ],
   "source": [
    "airfoil_name = '0rig_NACA0012'\n",
    "\n",
    "CL_org, CD_org = Xfoil_runner(alpha, Re, n_iter, airfoil_name)\n",
    "\n",
    "print(CL_org, CD_org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a95e83ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [\"x1\", \"x2\", \"x3\", \"x4\", \"x5\", \"x6\", \"x7\", \"x8\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1eaea160",
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective_function(design_parameters):\n",
    "    design_parameters = design_parameters.reshape(1,8)\n",
    "    design_parameters = pd.DataFrame(design_parameters, columns=feature_names)\n",
    "    design_parameters = scaler.transform(design_parameters)\n",
    "    \n",
    "    predictions = model.predict(design_parameters)\n",
    "    CL = predictions[0, 0]\n",
    "    CD = predictions[0, 1]\n",
    "    \n",
    "    return CD\n",
    "\n",
    "def CL_constraint(design_parameters):\n",
    "    design_parameters = design_parameters.reshape(1,8)\n",
    "    design_parameters = pd.DataFrame(design_parameters, columns=feature_names)\n",
    "    design_parameters = scaler.transform(design_parameters)\n",
    "    \n",
    "    predictions = model.predict(design_parameters)\n",
    "    CL = predictions[0, 0]\n",
    "    CD = predictions[0, 1]  \n",
    "    \n",
    "    return CL_org - CL\n",
    "    \n",
    "def area_constraint(design_parameters):\n",
    "    design_parameters = np.array(design_parameters).reshape((1, 8))\n",
    "    i = 0\n",
    "    y_mod = hicks_henne_overall(x, y , n, design_parameters, i, w, xM)\n",
    "    polygon1 = Polygon(zip(x, y))\n",
    "    area_orig = polygon1.area\n",
    "    polygon2 = Polygon(zip(x, y_mod))\n",
    "    area_mod = polygon2.area\n",
    "    return area_orig - area_mod\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "968bd949",
   "metadata": {},
   "outputs": [],
   "source": [
    "bounds = [(-limit, limit), (-limit, limit), (-limit, limit), (-limit, limit), (-limit, limit), (-limit, limit), (-limit, limit), (-limit, limit) ]\n",
    "#scale\n",
    "initial_guess = np.full(8,0.00)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "794f5a4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "from shapely.geometry import Polygon\n",
    "constraints = [{'type': 'eq', 'fun': CL_constraint},{'type': 'eq', 'fun': area_constraint}]\n",
    "# constraints = [{'type': 'ineq', 'fun': CD_constraint}]\n",
    "result = minimize(objective_function, initial_guess, bounds=bounds, constraints=constraints, method='SLSQP')\n",
    "\n",
    "optimal_design_parameters = result.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "067cecd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization successful!\n"
     ]
    }
   ],
   "source": [
    "if result.success:\n",
    "    print(\"Optimization successful!\")\n",
    "else:\n",
    "    print(\"Optimization failed. Message:\", result.message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7358f734",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 24ms/step\n",
      "Optimal Design Parameters: [[ 0.012       0.011978   -0.00708097 -0.01192201 -0.00586304  0.01175457\n",
      "   0.01198654 -0.012     ]]\n",
      "Optimal Lift Coefficient: 0.5572005\n",
      "Optimal Drag Coefficient: 0.0068624383\n",
      "Optimal Aerodynamic Efficiency: 81.1957\n"
     ]
    }
   ],
   "source": [
    "optimal_design_parameters = np.array(optimal_design_parameters).reshape((1, 8))\n",
    "optimal_design_parameters_df = pd.DataFrame(optimal_design_parameters, columns=feature_names)\n",
    "optimal_design_parameters_scaled = scaler.transform(optimal_design_parameters_df)\n",
    "Optimized = model.predict(optimal_design_parameters_scaled)\n",
    "CL_opt = Optimized[0, 0]\n",
    "CD_opt = Optimized[0, 1]\n",
    "optimal_efficiency = CL_opt/CD_opt\n",
    "print(\"Optimal Design Parameters:\", optimal_design_parameters)\n",
    "print(\"Optimal Lift Coefficient:\", CL_opt )\n",
    "print(\"Optimal Drag Coefficient:\", CD_opt)\n",
    "print(\"Optimal Aerodynamic Efficiency:\", optimal_efficiency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a1ca168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.012     ,  0.011978  , -0.00708097, -0.01192201, -0.00586304,\n",
       "         0.01175457,  0.01198654, -0.012     ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimal_design_parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "186b9979",
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "67552762",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_opt = hicks_henne_overall(x, y , n, optimal_design_parameters, i, w, xM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d088165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+/fZb/vnnn8DzK+vLXlxczPjx40lNTcVqtdK+fXueffbZCl2kanp/iBB1YevWrXTr1o1NmzYxd+5cNm/ezIwZM1i2bBk9e/YkNzf3qB8jLCyMZs2aHdU1jEYjiYmJddroUFslJSXMmzePSZMm8e6771Y4HhkZedgv9C6XC9AbA08//XTatm1LfHx8tR67LuqyMhLEixPKF198QXp6OmeddValx88++2zS09P56quvKhy78sorKSkp4YknnuDWW2+t8jFGjBjBK6+8wqBBg3jqqaew2+1ceOGF1S7j5s2bueKKK+jfvz/PPfcc0dHRjBgxIigw2rp1KwsWLOCiiy7i+eefZ+LEifz111/06dOHvXv3VvuxDjVnzhz69u1LYmIiV199NYWFhXzxxRfVPv+OO+5g7dq1PPTQQ9x7773cdttt3HfffQCMHTuW2bNnc//99wOwdOlSBgwYQHZ2Ng8//DDjxo3jxx9/pHfv3nVys+/QoUP5/vvvyczMDEr/4Ycf2Lt3L1dffTUAS5YsYdiwYURHR/PUU0/x5JNPcs4557BixYrDXr+sXm644YZKj5tMJq655hry8vIqXOv999/n5ZdfZtSoUUyePJm///6bc889l6ysLEBvLerfvz8As2fPDizVec4+n48nn3ySHj168Nhjj/Hiiy/Sv39/UlJSeOqpp2jTpg0TJkzg+++/r/I6HTp0CHrc2bNn88orr2A2m4P+aM2ePZsLL7yQsLAwnnrqKR588EHWrl3LmWeeGfQaLl68mMsvvxxN05g2bRpDhgzhxhtv5Lfffjvicyrz888/s3nzZoYNG4bFYuGyyy6r8peiymzYsIFhw4bRv39/XnrpJdq2bcvs2bM56aSTaN68eeB5dujQAaUUl1xyCS+88AIDBw7k+eefp3379kycOJFx48ZV+zGFqC+jRo3CYrGwePFi+vTpQ4sWLbjgggtYunQpe/bsCXzOgv5Fc+rUqQwbNozQ0FBSUlICjQRlxwEuvfTSoC/qDz/8MF27dg3kGzFiBEOGDOGJJ54gISGBqKgoHn30UTweDxMnTiQmJobmzZszc+bMwDllDQRlgxmMGDGi0saBsi/PTqeTCRMmkJKSQmhoKD169Khwk/isWbNo0aIFISEhXHrppdX+5eGjjz6iY8eO3HvvvXz//ffs2rUr6HjZ8ytzzjnnMHr0aO666y5iY2MZMGAA6enpfPLJJ7z//vtomhb4Ar9z504GDx4caFS76qqrAp/nldVlnalWZx4hjgP5+fkKOGKft0suuUQBqqCgQCml92UD1LBhwyrkLTtWZtWqVQpQd911V1C+ESNGKEBNmTIlkDZz5kwFqG3btgXS0tLSFKC+//77QFp2drayWq1q/PjxgbTS0lLl9XqDHmPbtm3KarWqRx99NCiNavaJz8rKUiaTKajveq9evSqtr7S0NDV8+PAKz+XMM8+s0K+6rI/wRx99FJTetWtXFR8fr/bv3x9I+/PPP5XBYFA33HBDhWuXr6fq9InfsGGDAtQrr7wSlH7HHXeosLAwVVJSopRS6s4771QRERE17g8+ZMgQBai8vLwq88yfP18B6uWXX1ZKHXw97Ha72r17dyDfzz//rAB19913B9IO1yf+0PdS2ftw5MiRgTSPx6OaN2+uNE1TTz75ZCA9Ly9P2e32oNfvSO8Tn8+nLrroIhUWFqb++ecfpZRShYWFKioqSt16661BeTMzM1VkZGRQeteuXVVSUpLKz88PpC1evFgB1e4TP3r0aJWamhro6152/qF9bQ/3/2rRokUVrtunTx/VqVOnoLQFCxYoQD322GNB6VdccYXSNE1t3rw56Nrl61L6xDctFfoe+3xKOYsaZ6nmfRz79+9XmqapJ554otLjt956q4qOjg78X0lLS1Ph4eFq2rRpasOGDerll19WRqNRLV68WCml/40p+/+fkZGhsrOzlVKV9+MODw9Xo0aNUuvXr1fvvPOOAtSAAQPU448/rjZu3KimTp2qzGaz2rVrl1KqYp/4/Px8lZGREVjuvPNOFR8frzIyMpRSSt1yyy2qV69e6vvvv1ebN29WzzzzjLJarWrjxo1KKaV++uknZTAY1FNPPaU2bNigXnrpJRUVFVWtvulnnXWWevXVV5VSSl1++eVBfyvLnl/5v3d9+vRRYWFhauLEiWr9+vVq/fr1Kjs7Ww0cOFBdddVVKiMjQ+Xn5yuv16u6du2qzjzzTPXbb7+pn376SZ1++ulBf6Pqq0984/++IUQDKSwsBCA8PPyw+cqOFxQUBOX997//fcTHWLRoEaC3SJc3ZsyYag/f17Fjx6BfCuLi4mjfvn1QP32r1RrY9nq95OfnExYWRvv27fn999+r9TiHmjdvHgaDgcsvvzyQNmzYMMaPH09eXh7R0dFHvMatt95arX7VZf0JJ02aRExMTCD95JNPpn///ixcuLBWz6G8du3a0bVrVz788ENGjx4N6HX18ccfc/HFF2O32wG9r35xcTFLlixh4MCB1b5+dd5P5d9L5Q0ZMoSUlJTAfvfu3enRowcLFy7k+eefr3YZDnXLLbcEto1GI926dWP37t3cfPPNgfSoqKgK76cjmTp1Kl9++SUff/wxHTt2BPRfMPLz8xk2bBg5OTlBj9ujRw++/fZb4OBrfe+99xIZGRnI179/fzp27Bi4d+JwPB4PH374IcOHDw90Kzr33HOJj49nzpw51WrhatmyJQMGDKjW8124cCFGo5GxY8cGpY8fP56PP/6Y//3vf4H3lDjOuEvgieTGeez79oIl9IjZNm3ahFKKDh06VHq8Q4cO5OXlsW/fvsAvZ7179+bee+8F9M/GFStW8MILL9C/f/9A19KoqCgSExMP+9gxMTG8/PLLGAwG2rdvz9NPP01JSUngF9fJkyfz5JNP8sMPPwR+7SwvMjIy8Dkwf/583njjDZYuXUpiYiI7d+5k5syZ7Ny5k+Rk/TWYMGECixYtYubMmTzxxBO89NJLDBw4kEmTJgWey48//hj423u4Ovvpp5+YP38+ANdddx3jxo3jgQceOOz9M23btg3qXgv631+73R6oqyVLlvDXX3+xbds2UlNTAf3X1k6dOvHrr7/yr3/967BlOxrSnUacMMoCqrLgqypVBWctW7Y84mPs2LEDg8FQIW+bNm2qXc4WLVpUSIuOjiYvLy+w7/P5eOGFF2jbti1Wq5XY2Fji4uJYs2YNBw4cqPZjlfef//yH7t27s3//fjZv3szmzZs59dRTcblcld4EVJnq1BHo9QTQvn37Csc6dOhATk5OtYK7Ixk6dCgrVqxgz549gN7XOzs7m6FDhwby3HHHHbRr144LLriA5s2bc9NNNx3xDwJU7/1U1Xupbdu2FfK2a9fuqLsRHfreiYyMxGazERsbWyG9/PvpcBYtWsQjjzzC5MmTg77gbdq0CdCD6bi4uKBl8eLFgRuay17ryp5zZa9/ZRYvXsy+ffvo3r174L25bds2+vbty9y5c6s1FGp135tlZU5OTq7wupUFTWXPSYjGpGowhG3Pnj0r7B96/1F1dOrUCYPhYOiYkJBAly5dAvtGo5FmzZoF/v9X5Y8//uD666/n1VdfpXfv3gD89ddfeL1e2rVrR1hYWGD57rvv2LJlCwDr1q2jR48eh31ulXn33XcZMGBA4LNw0KBBHDhwgG+++eaw551++ulHvPa6detITU0NBPCgN8ZFRUXVqo5rQlrixQkjMjKSpKQk1qxZc9h8a9asISUlhYiIiKD0spbb+lZVS3b5D+wnnniCBx98kJtuuompU6cSExODwWDgrrvuqtXY7ps2beLXX38FKg+2ykZeOZKGqqPqGjp0KJMnT+ajjz7irrvu4r///S+RkZFBLe7x8fGsXr2ar7/+mv/973/873//Y+bMmdxwww289957VV67Q4cOLFiwgDVr1nD22WdXmqfsvVbWel3fKnvvVOf9VJVt27Zx7bXX0r9/fx577LGgY2Xvs9mzZ1faeleXN7KV9X2/6qqrKj3+3XffVXoTc3nH2ntTHKPMIXqLeGM9djW0adMGTdNYt24dl156aYXj69atIzo6usLgDXXh0JGYNE2rNO1wf4cyMzO55JJLuOWWW4J+JSwqKsJoNLJq1aoKn1thYWG1LrPX6+W9994jMzMz6HPJ6/Xy7rvvct5551V5bmjokX8ZaUwSxIsTykUXXcRbb73FDz/8EBhhprz/+7//Y/v27dx22221un5aWho+n49t27YFBcObN2+udZkr8/HHH9O3b1/eeeedoPT8/PwKra7VMWfOHMxmM7Nnz67w4fnDDz/w8ssvs3Pnzkp/JaiNssmeNmzYUOHY+vXriY2NrZMPz5YtW9K9e/dAl5r58+czZMiQoO5IABaLhYsvvpiLL74Yn8/HHXfcwRtvvMGDDz5Y5a8oF110EdOmTeP999+vNIj3er188MEHREdHB1qaypS1Ype3cePGoJFfGntITYfDwWWXXUZUVBRz584Nan0DaN26NaB/CerXr1+V1yl7rSt7zpW9/ocqG/Z06NChQSMKlRk7dmzghuy6kpaWxtKlSyksLAxqjV+/fn3guDhOaVq1urQ0pmbNmtG/f39ee+017r777qAvqJmZmcyZM4cbbrgh6DPkp59+CrrGTz/9FNQdx2w24/V6673spaWlDB48mJNOOqlC18FTTz0Vr9dLdnZ2lYNPdOjQgZ9//jko7dDndqiFCxdSWFjIH3/8EfT37e+//+bGG28kPz//qIaZ7dChA7t27WLXrl2B1vi1a9eSn59f7w040p1GnFAmTpyI3W7ntttuq3BHe25uLv/+978JCQlh4sSJtbp+WZ/b1157LSj9lVdeqV2Bq2A0Giu0pH700UeBbiM1NWfOHM4666xAoFR+KauLuXPnHnW5yyQlJdG1a1fee+898vPzA+l///03ixcvZtCgQXX2WEOHDuWnn37i3XffJScnJ6grDVDhfWAwGDj55JMBfaSEqvTq1Yt+/foxc+ZMvvzyywrH77//fjZu3MikSZMqtAIvWLAg6LX65Zdf+Pnnn7ngggsCaWVfYsrXT0P697//zcaNG/n0008rvR9iwIABRERE8MQTT+B2uysc37dvHxD8Wpfv6rVkyRLWrl17xHJ8+umnFBcXM2rUqArvzbLhJj/55JPDvlY1NWjQILxeL6+++mpQ+gsvvICmaUGvkxCN4dVXX8XpdDJgwIDASCuLFi0KjET1+OOPB+VfsWIFTz/9NBs3bmT69Ol89NFH3HnnnYHj6enpLFu2jMzMzGp3tauN2267jV27dvHyyy+zb98+MjMzyczMxOVy0a5dO6699lpuuOEG5s+fz7Zt2/jll1+YNm1aYMS4sWPHsmjRIp599lk2bdrEq6++esTuj++88w4XXnghp5xyCp07dw4sV111FVFRUTUa5aoy/fr1o0uXLlx77bX8/vvv/PLLL9xwww306dOHbt26HdW1j0Ra4sUJpW3btrz33ntce+21dOnShZtvvpmWLVuyfft23nnnHXJycpg7d26glbGmTj/9dC6//HJefPFF9u/fzxlnnMF3330XmBCirlpXL7roIh599FFuvPFGevXqxV9//cWcOXNo1apVja9VNnRfVTfqpaSkcNpppzFnzhzuueeeoy16wDPPPMMFF1xAz549ufnmm3E4HLzyyitERkYGjYF+tK666iomTJjAhAkTiImJqdBqfMstt5Cbm8u5555L8+bN2bFjB6+88gpdu3at8saxMu+//z7nnXcegwcP5pprruGss87C6XQyf/58li9fztChQyv9QtimTRvOPPNMbr/9dpxOJy+++CLNmjUL3KwFB/tijh07lgEDBmA0Giu9Uaw+fPXVV7z//vtcfvnlrFmzJqgLWlhYGEOGDCEiIoLXX3+d66+/ntNOO42rr76auLg4du7cyVdffUXv3r0DQfC0adO48MILOfPMM7npppvIzc3llVdeoVOnThQVFR22LHPmzKFZs2b06tWr0uOXXHIJb731Fl999RWXXXZZnTz/iy++mL59+3L//fezfft2TjnlFBYvXsxnn33GXXfdVevPByHqStu2bfntt9+YMmUKV111Fbm5uSQmJjJkyBCmTJkSNGAA6Ddl//bbbzzyyCNERETw/PPPB93o/dxzzzFu3DjeeustUlJS6mSY38p89913ZGRkVGih/vbbbznnnHOYOXMmjz32GOPHj2fPnj3ExsZyxhlncNFFFwFwxhln8NZbbzFlyhQeeugh+vXrxwMPPMDUqVMrfbysrCy++uqrSueUMBgMXHrppbzzzjuMGjWq1s9J0zQ+++wzxowZw9lnn43BYGDgwIF13nhXqSOOySPEcWjNmjVq2LBhKikpSZnNZpWYmKiGDRum/vrrrwp5y4bv27dvX5XHyisuLlajRo1SMTExKiwsTA0ZMiQw3GH5of6qGgrvwgsvrPA4hw6pWFpaqsaPH6+SkpKU3W5XvXv3VitXrqyQrzpDTI4ZM0YBasuWLVXmefjhhxWg/vzzz0A5Kxti8tdff61wblVDTCql1NKlS1Xv3r2V3W5XERER6uKLL1Zr164NylPbISbL6927twLULbfcUuHYxx9/rM4//3wVHx+vLBaLatGihbrtttsCQ54dSWFhoXr44YdVp06dlN1uV+Hh4ap3795q1qxZgSHeypS9Hs8884x67rnnVGpqqrJareqss84K1G0Zj8ejxowZo+Li4pSmaUHvM6oYYvLQ9+jw4cNVaGhohTIfOqzioe+TsjqvbDl0SMhvv/1WDRgwQEVGRiqbzaZat26tRowYoX777begfJ988onq0KGDslqtqmPHjmr+/Plq+PDhhx1ismzY0+uvv77KPCUlJSokJERdeumlQWWvzv+ryuqiTGFhobr77rtVcnKyMpvNqm3btuqZZ56p8JrKEJNNW3WH8mvK0tLS1AsvvNDYxRA1UN33paZUDW5vFkLUyurVqzn11FP5z3/+w7XXXtvYxRGNZPv27bRs2ZJnnnmGCRMmNHZxhDjhlZaWsm3bNlq2bInNZmvs4tSL9PR07rrrLu66667GLoqopuq+L2vdJ97lcrFhwwY8Hk9tLyHEccnhcFRIe/HFFzEYDFWOYiKEEEIIURM17hNfUlLCmDFjAkOvbdy4kVatWjFmzBhSUlICkwkIcaJ6+umnWbVqFX379sVkMgWGLRw5cmTQOLJCCCFEfauv/u2i8dW4JX7y5Mn8+eefLF++PKiJv1+/fnz44Yd1WjghmqJevXqRm5vL1KlTGT9+PBs3buThhx9m+vTpjV00IYQQQhwnatwSv2DBAj788EPOOOOMoJE2OnXqFJhRS4gTWf/+/enfv39jF0Mcg9LT02s0y6IQQghRlRq3xO/bt4/4+PgK6cXFxY0+OYkQQgghRE3Jl2txLKnu+7HGQXy3bt0Cg+7DwXGv3377bXr27FnTywkhhBBCNIqyGTxdLlcjl0SIg0pKSgB9Jt3DqXF3mieeeIILLriAtWvX4vF4eOmll1i7di0//vgj3333Xe1K28T5fD727t1LeHi4/BohhBBCNBE+nw+lFPv27cNsNmMwyET2ovEopSgpKSE7O5uoqKjAl8yq1Gqc+C1btvDkk0/y559/UlRUxGmnncY999xDly5dal3wpmz37t0y6ogQQgjRBMXFxbFs2TJMJpnEXhwboqKiSExMPGLDsEz2VAcOHDhAVFQUu3btIiIios6u63a7Wbx4Meeff/4Rf1IRtVdn9awUeF364nEf3Pb5tz1O8Hr86U7wefxp/uM+L2gG0DT/2lDFfiXpaGAwgdkGZru+mOz6vskOJquevxHJ+7nhSF03DKnnhlGf9VxQUEBqaip5eXnH7WRPomkxm81HbIEvU+OvnUajkYyMjAo3t+7fv5/4+Hi8Xm9NL9nklX1TioiIqPMgPiQkhIiICPkDUY8C9RwejtlTBCX7obQASvPBWeDfPuDfPnD4fY7V78QamEP8AX5IuWC/XJrJBtZwsEeBLQpskQe3y6+tEWCo3gdMefJ+bjhS1w1D6rlhNEQ9GwwGCeJFk1PjIL6qhnun04nFYjnqAglR53w+PTAvyoRC/1Ju21iYSf992zGtuUVvEa8rBhMYLcGLyVJ1mmYEFChf8OLz6q38h6YHLUpv8Xc7yi0loMq+VCtwF+vLUdP0QN4eWS7AL7cd0gxC4yEsHkLjDq6FEEIIUWeqHcS//PLLgN7q/PbbbxMWFhY45vV6+f777znppJPqvoRCHInPpwfluVuDl/xdUJSlLz5PlacbgJDyCdYIf1AaWW474jD7Ufq+NUJv1S4Lzo+FG6S8bj2YDwruHQfTPOX2XSXgLPT/upAPjnx9XXrg4La7BFDgPKAv7Kx2UUz2GPoqO8bcNyE8wR/ox1Ue8Jus9VEbQgghxHGj2kH8Cy+8AOgt8TNmzAjqr2OxWEhPT2fGjBl1X0IhQG+NPrA7OEjP2+7f3qYHo0cSEgvhSXoAGZYI4friscfy419b6Xn+pZijUo6vANJoBqP/C0ld8LiqCPLzDqaV7IeibCjOhqJ9ULwPlBfNkUsEwI49R34cezREpEBEsn+dApFl+831tSXkyNcRQgghjlPVDuK3bdsGQN++fZk/fz7R0dH1VihxgivOgcy/9CXrb32ds0nvLlIVzQjRaRDdEmJa6UtUC3/Qnqi38Bor70up3G7yti6EyFQwSb/WwzJZ9NbzsBp0j/H5wJGH+8BefvnmS3p0bonJkesP8rMrBvw+t/6lwJGnv/5VCQT6/uA+MqXcfooE+kIIIY5rNe4T/+2339ZHOcSJyOeF/Vsgyx+wZ/6tB22FGZXnN1ogOv1gkB7TCmL8QXtkapVBumhkBgOENgNLBDnhW1GdBkFVN6cppQfvRVlQsAcO7IGCvVCwu9z2HnAVVS/QD4n1f7lLh6i04O3I5vKeEUII0WTValDU3bt38/nnn7Nz584Ks5w9//zzdVIwcRwq2As7foSdP8HePyDrnyq6wWh6YJ7YGRK7QOLJEN9Bb12txagoognRNAiJ0Zf4DpXnUUofCejAHj2gPzTYL9ir77uLoSRHX/asquSxjHrrfVBwn35wOzSu0YflFEIIIapS4yB+2bJlXHLJJbRq1Yr169fTuXNntm/fjlKK0047rT7KKJoipSBn48GgfeePkF/JTZDmEIjv6A/WO/sD9o5gDauYVwjQA+uyG48TOlaeRym9j37+Tsjbod8/kb+j3PZOfaz+/J36sv3/Kl7DHKJ3ySr79adZa4hpra8jmh8bNy4LIYQ4YdU4iJ88eTITJkzgkUceITw8nE8++YT4+HiuvfZaBg4cWB9lFE2B1w0Zf5YL2leCIzc4j2bQg/UWvSD1X3rAHtOqybauK6Vwenw4XF5K3F4cLi8ujw+3t2xRlW57vAqXt2I+r0/pI0mir33+bf8/lCo7TlC+srIAGAwaRk3DaNAwGDQMGhg1LZBuMOjHDqYRyGvUNExGAxaTAYtR86+NmP3bZqMBq39tMRkqpBkNx1Crtabpfebt0ZB0SsXjPp/eZadCcO/fLtijj8Szb72+HMpk0++/aNa6XHDfRt8OS5AWfCGEEPWuxkH8unXrmDt3rn6yyYTD4SAsLIxHH32UwYMHc/vtt9d5IcUxqmgfbF4CG7+GLd/4Jzsqx2SD5v+CFj2hxRmQ2l2fTKgR+XyKQqeHAoebAw43BQ43uUWlrMzS2PvDdopdPoqcHkpcHhxuHw6XhxKXlxKXHqQ73GXbHhxuL75jdW6nRmA0aJiNGjazEbvZiM2/2M0GbGYjVpNGfo6B5aV/E2o1HZLHv7YYAtuhVhOhFhOhVn07zGrCajIccRrqajEYICJJX9J6VjzuceqjIeVth7xt+ghI+zfr93DkbQNPKexbpy+HsoQdbLlv1uZg632zNno3ISGEEKIO1DiIDw0NDfSDT0pKYsuWLXTq1AmAnJycui1dJaZPn84zzzxDZmYmp5xyCq+88grdu3evMv9HH33Egw8+yPbt22nbti1PPfUUgwYNChwfMWIE7733XtA5AwYMYNGiRfX2HJospfTW9k2L9cB9zyqCZii1Rx8M2Fv00ltATfU7AVix00NOkZOcIif7Cp3sK3KRU+hkX5GTvGIXBaV6sK4H7B4KS91VBN5G2Lqx1uWwGA3YLUZ/67UBs1Fv1TYb9VZtkz/N7E87dNtkNGAyaBj8AaqmgYbmX/v3Nc2/fUi6Px+A16fwKoXPp/D6wKcUPqXw+g6uy9KD8+rH3V4V+DXB5fXh8hxcuwNrFUgvz+u/TqnbRz5VjSRk4I/9e2tdz0aDRojFGBTcB21bTYRaDqaH2UyE20yE28yE20xElNu2m41VfyEwWQ+2sh/K64EDO/WAfv8WyN1yMMDP36nfdJu5Rl8OFRILce0htp2+xLWD2Pb6TbbSei+EEKIGahzEn3HGGfzwww906NCBQYMGMX78eP766y/mz5/PGWecUR9lDPjwww8ZN24cM2bMoEePHrz44osMGDCADRs2EB8fXyH/jz/+yLBhw5g2bRoXXXQRH3zwAUOGDOH333+nc+fOgXwDBw5k5syZgX2r9TgaJ/xouUr0VvZNX8OmJRVHjkk8GdoNgHYDIfnUOusaU+r2siffwZ48B3vyHWQeKA0K1nOKXOQUOSlxeY98sUpYTQYi7WYi7GYibCZKC3Jp0yKF6FALYTYTIRYTNrOREIu+2M1GQiwm7IFtf7p/32Q88fpHq7Kg3+vDXS7Yd3q8OFw+Sj0Hf70odXspLnWxavVftGp3Ei6v/hqXuvXjDrfv4L7/l48Sl4dil5dipyfwOnt9isJSD4WlVU/eVV1Gg+YP8E2EW82BYD/ikMA/3GYm0n7IEpJKeOuWGNr2D76ox6m33u/3B/a5Ww4G+4V79Ztsd+TAjhXB55lDIbaNHtCXBfax7fQW/Xr+IiyEEKJpqnEQ//zzz1NUVATAI488QlFRER9++CFt27at95Fpnn/+eW699VZuvPFGAGbMmMFXX33Fu+++y7333lsh/0svvcTAgQOZOHEiAFOnTmXJkiW8+uqrQRNTWa1WEhMT67XsTYrPp9/ot+ZDWPs5uAoPHjOHQqtz9MC97fl6d4RaKCh16wG6P0gvC9h3+9c5Rc5qX8tuNhIbbiE2zEpcmJXYcCuxYVaahVoCQVeE3VQuaDdjMx/8suF2u1m4cCGDBnXBXNXQh6ICTdOwmPT+8lTje6/b7SY0aw2DzmpZ43r2+RQlbi8lTg9FTg/FTi/FLg/FzoOBfrE/vcRVlkdfF/iD/sJSd2DtU/oXgvwSN/klbqAak4UdwqARCPCjQsyB91ek3UyUvRWR9vZEJpiJTDcTGWIm2uQitnQHkcXbseRtgn0b9PkPcrfoI+lk/KkvQQ9i0vvex7WH2Lb+IN+/WEJrXGYhhBDHjxoH8a1atQpsh4aGNtgsrS6Xi1WrVjF58uRAmsFgoF+/fqxcubLSc1auXMm4ceOC0gYMGMCCBQuC0pYvX058fDzR0dGce+65PPbYYzRr1qzKsjidTpzOg0FmQYHeF9ztduN2H2ZCohoqu1ZdXvOwstdh+Pu/GP7+BK3wYJcHFZmKr+1AVNvzUS16Bc9oepiyebw+9uSXsiWnmK37itlabp1XcuTnFGoxkhxlIznKTmKEjbgwC7FherBetm4WZiHUcphuEZXy4XYf7ArS4PV8gjraerYawGo3Em0/ul97lFKUuLwUOvXgvqjUE9guLPVQ6HQH0su+AJR1yypweDhQ6qbU7cOnCHTV2pl75Mc9KAKb+V9E2XsRFWIhNkGjjWkfrbW9tPDtJsm9k9jS7UQUb8PkKYH9m/Sl/HNAg6g0VFx7VFwHfR17kh7om2zynm4gUs8Noz7rWV470ZTVapx40IPq7OxsfL7gfrEtWrQ46kJVJicnB6/XS0JCQlB6QkIC69dXMnoEkJmZWWn+zMzMwP7AgQO57LLLaNmyJVu2bOG+++7jggsuYOXKlRiNlQcL06ZN45FHHqmQvnjxYkJC6n6GyCVLltT5NctY3fk0z1tJ89wVRDkODgHpMoawN6oHu2J6kRvaDrwarHfA+mUVruH1QaYD9pZoZDs0shyQ5dDYVwpeVXVwHWpSRFshxnpwHVO2b4EQkwdNcwIH9BOc+uLdD1noS12qz3oWBx2r9awBEf4FAAMQ4l8O4fZBiQccHn1d4tX0tQccHu3gMS+UeA4eK/aAT2mUun1kup1kFuiNAT9gBVr6lzKKRHJpY9hLa20vbbU9tDPsoY1hDzEUQP52tPztelc3Px8aB8wJFNhSaGdP4Z///kShrTlF1kSUodYf9+IIjtX39PGmPuq5pKSkzq8pREOp8af6xo0bufnmm/nxxx+D0pVSaJqG11u7PsqN5eqrrw5sd+nShZNPPpnWrVuzfPlyzjvvvErPmTx5clALf0FBAampqZx//vlERERUek5tuN1ulixZQv/+/eu8m4e293cMP7+Otu5zNKW/ZspgRrXpj6/LlWht+pNispFyaJm8PjZlF/HP3gL+9i/rM4tweXwVHwS973nL2FBax4bSKi6EVrGhtIoLJS0mhFDrsRFU1Gc9i4OknvXPySKnl3yHi7xiN/kON3klbvJLXHrXHoebvGI3eQ7/fomd3x3x/ODqEnSdGApoZ9hNO20X7bTdtDXsob22iyitmGh3JtHuTCg8OMGVByPZ5hT22VtxIKw1pdHt8MWehDW+LbERdpqFWogOMZ+Q93YcDXlPN4z6rOeyX9KFaIpqHEXdeOONmEwmvvzyS5KSkupmuLdqiI2NxWg0kpUV3P6alZVVZX/2xMTEGuUHvbtQbGwsmzdvrjKIt1qtld78ajab6+WDvM6u6/PChv/Byun65EtlmneHU4aidboMLSSGsj/jSim27Cvit+15/LXnAH/vOcC6zMJKA/Zwq4kOSRG0jg+jdVworePDaBMXRnKU/dgaP/ww6uv1E8FO9HqOsUBMuL1G55S6veSXuMkrcZFX7GJ/sYtc/3p9sZMfi13sL3RCURYxJVtJdm2jDbv9gf5uwjUHye6dJLt3QsFy8PeWK1VmNqkUflQt2KBS2WNpSW5oG7SwRGIjbDQLtRAXrt9fUtZ9LTbMSly4Nei+khPdif6ebij1Uc/yuommrMZB/OrVq1m1ahUnnXRSfZSnShaLhdNPP51ly5YxZMgQAHw+H8uWLWP06NGVntOzZ0+WLVvGXXfdFUhbsmQJPXtWMi603+7du9m/fz9JSbW7YfOY5CqG1R/AT69B7lY9zWCGLlfAGXdA0smAHrRv3VfEyi37+Wnrfn7amlvpDabhNhOdkyPp0jySzimRdEmJJC0mBEMTCdaFaGpsZiOJkUYSI23Vyu90uvj4i/8R2qsP60q9lOTswJe9Dsv+DYQe2ERMyRYSndux4aSLtp0ubNdP9AGFkFsQxobdLVivUtmgUlnq09clHHz8UIuRuHDrwSXMesi+Tf8CEGbBLC38QghR52ocxHfs2LFBxoOvzLhx4xg+fDjdunWje/fuvPjiixQXFwdGq7nhhhtISUlh2rRpANx555306dOH5557jgsvvJB58+bx22+/8eabbwJQVFTEI488wuWXX05iYiJbtmxh0qRJtGnThgEDBjTKc6xTrhK91f2n6eDI09NskdDtJug+EiKS2Z5TzMpfdgYC9+zC4KDdajJwaosoTmkeFQjYW0jALsQxzWDQCDND67hQvaWxVTPgtOBMPq8+HGbWP/iy/sG992/IXoulYDsxFNHTuJaerA06ZY+WwDpvc9b5UtngSWV9biq/70/Cy+Fb5aNDzFUG+/HhNhIirMSF24iwmRrs110hhGjqahzEP/XUU0yaNIknnniCLl0qDslXl33CDzV06FD27dvHQw89RGZmJl27dmXRokWBm1d37tyJwXCwxadXr1588MEHPPDAA9x33320bduWBQsWBMaINxqNrFmzhvfee4/8/HySk5M5//zzmTp1atMeK97ng7/+C0sf0cemBohOhzNGoboOY+1+H1//lMn//v6OTdlFQadaTAZOaxHFGa2a0bNVM05JjZKfzYU4HhmMgQmtDB0vOThKqNsB+9ZD1lrI9i9Za6EokxSVRYohi36Gg/3tvQYz+aGtyLS2YrsxnfUqlTXu5mwoCiWn2IXHp8gr0fv+b8wqqrQoZawmA/ERVhLCbcRH6AF+XLiVhAgb8eHWQFp0iFmCfSHECa/GQXy/fv0AKvQXb6gbW0ePHl1l95nly5dXSLvyyiu58sorK81vt9v5+uuvKz3WZO34Eb6+D/b+oe9HtsB37oP8EXEuX6/bx6KXfmNn7sG78c1GjVNbRAeC9lNbSNAuxAnNbNcnbks+NTi9eH+5oP4f//Y6jK4imhVuoFnhBjoBF5blt0ej2nbE2awD+WFtybK3ZoepBZkOE/sKnWQXOgPr7IJSCko9OD0+duU62JV7+HH7LUaD3orvD/gTI21B2wkReuAfZpWWfSHE8avGQfy3335bH+UQRyt3Gyx5CNZ9ru9bwsk/fQxvugfwyVc5ZBX8HMhqMxvo0y6OgZ0TOfekBCLtcmOPEOIIQptBy7P0pYzPBwd26kF91lrI0rvksH8zOPLQdqzAtmMFiUAicArovwrGd4KEjtC5I8R3hGatKfUZyC5wkl1YGgjsswqdgbSygD+32IXL6wtMEnc4IRYjCREHg/rECBvx/nVChFUP/sNt+oRlQgjRxNQ4iO/Tp099lEPUllL6TasLJ4K7GKUZ2Nv6Kp4uvYzPl3tQag+gjx5zbod4BnZKpE/7OEIsx8bwjkKIJsxg0IPy6HQ46cKD6W6HPiNtWat9Wct9UZbeDz9vO2z4qtx1zNhi29EivgMt4jvogX2HDhDVTn+MclweH/uK/EG+P8DPPKBvZxWUklVQSmZBKYWlHkpcXrblFLMtp/iwTyM2zEpSpI2ECBtJkXprfmL57UibfGYKIY451fpUWrNmDZ07d8ZgMLBmzZrD5j355JPrpGCiGkoL4Ktx8NdHAOyNOp37nDew/O84wANAn3ZxXHdGGme3i8Vqkm4yQogGYLZDcld9Ka8452BAn/UPZK/TF3cxZP+jL0HXCYG4k/SgPr4DxHfAEt+RlMhEUqIOP0xnicsTFNhnFZSSecBJVmEpWQf0QD+7wInL6yOnyElOkZO/9hyo8noRNpM/oLeTFGEjKcpGfJiZXfkabbOLaBEbTtgxMveFEOLEUK1PnK5du5KZmUl8fDxdu3ZF0zSUUhXyNcXJnpqsPb/DxzdB3jZ8mpHp6kpeyLwIHwYibCau6pbKdWekkR4b2tglFUIIXWgstOqjL2V8Pjiwyx/Qrz0Y2OdsAHcJ7P1dX8qzReqBfVx7iG0Pce0gth1ENA+03IdYTLSMNdHyMJ+BPp8it8RF5gG9NT+zQF9nHNCD/owDDjIPlFLs8lJQ6qGgtKiSm3ONvL5On3cj3GYiKdJGUqSd5CgbiRF2kqJsQWnSoi+EqCvV+jTZtm0bcXFxgW3RyH6biVo4Ec3nJlOL447SUfyu2tE+IZwbe6czuGsKdou0ugshmgCDAaLT9KX9wIPpXo8+r0UgsPevc7dA6QHYuVJfyjOHQLM2B4P72Lb6dkwrMFUcccxg0IgNsxIbZqVzSmSVRSwsdQeC/IwDZYG+g715Djbu3keRMlNY6vEvlQX6B0WFmEmOtJMcpQf1+tpOSpQe6MeHW2XmXCFEtVQriE9LS6t0WzSCX9+Br8ahAQu93bnXfQuWsGY8NaAdV5ye2mRmRxVCiMMymvQW9rh20GnIwXR3KezfpN9Im7MBcjbCvo16cO8ugcw1+lKeZtT77ce2O9hqH9tOD+5DmsERRrAJt5kJt5lpmxAelO52u1m4cCGDBg3A6dPIPOBgb74e5O/1t+LvPVBKRr6DjAOlFDk95Je4yS9xszajoPKnbdBIjLCRHFXWem8nJdpO86iD29JtRwgB1QziP//882pf8JJLLql1YcQRrHpP7wMPvOG5kOe4jlv7tuL2c9rIh7oQ4sRgtkFiF30pz+vWb5jN2ajfVJuzyR/kbwJngR7k526Bjf8LPs8SDjHpEN0SYloGryOb6+PpV0OY1USb+HDaxIdXmaeg1E1Gfil7/SPrZPiD/j35Dvbm60G/x6fKjbyTV+l1ImwmUqJDSPG34KdE20mJCiHZvx0XZpWhNYU4AVQr8hsyZEjQ/qF94st/WEif+Pqhfp8NX9yJBrzjuYDlqWP45qpTaB4d0thFE0KIxmc0691nYtsGj5SjFBRm6sF90LIZCvaAqxAy/9KXQxnMENWiYnAf1QIiksEUVqMiRtjMRCSaaZ9YeaDv9Slyipx6gJ9fyp78kkCQvydPD+wPONx6//yMAtZV0ZpvNRn8gb2d5tEhNI+2B5aUqBDiw60y67YQx4FqBfE+ny+wvXTpUu655x6eeOIJevbsCcDKlSt54IEHeOKJJ+qnlCc4367fUJ+PxYBilud8tp1+H+9f0hmz9JsUQojD0zSISNKXVocMkewuhfydkLdNn2uj/DpvO3hdB1vwK2Ey2TnPGIEx90291T4i+ZAlBUJiKwyTWRWjQfOPa2+DFpXnKXJ69Jb8PAe7/S34ZQH+njwHWYWlOD0+tu4rZuu+yofWtBgNJEfZAgF+akxZoB9CarSd2DAJ8oVoCmrcB+Ouu+5ixowZnHnmmYG0AQMGEBISwsiRI1m3bl2dFvCEp3wcmH8X8fj4wtsT46CneaxXy8YulRBCNH1m28F+94fy+aBwb8XgPnebPppOyX40j4MwjwN2ZFX9GAYzhCfpQX14gt4HP2iJKbcdC5bD/7oaZjXRLiGcdgmVt+a7PD4yD5SyO6+E3fkOduc52J1Xogf9eQ4yC0pxeX1s31/C9v0llV6jrCW/LKhvHh1Ci5gQUmPspEaHEBVilu46QhwDahzEb9myhaioqArpkZGRbN++vQ6KJMqL2vsN8UXrKVAh+AY+yfUSwAshRP0zGPTW9cjmwbPUlnGX4s7bxc9L5nNGxxaYirOgYK/eRadgLxRm6N14fG59VtsDO6v3uCZ7cHBvjwJLGFjDD66tYXpffush6ZYwLJYQWkTZadGs8i8DHq+PzIJSf3CvB/i7cvX17jy9n/6RWvLDrSaax+gBfmqMHuDrQb7eom8zy+hoQjSEGgfx//rXvxg3bhyzZ88mISEBgKysLCZOnEj37t3rvIAnMmfBPk7N/hiAr+Ju4epepzRyiYQQQgB6K350OvvDTkJ1HgRmc8U8Xrc+S21ZcF+0D0r2V714XeBxQMFufTkaBhOYbAcXsw1MdkwmK83NdpqbrHq60QJmKyRZINWK12Ch2GMg32Ug36WxvxT2OSDLoZFRDNkODafbTGmmhYxMC9sx48BCibJRhB0nZhIibIGgvizAT2um78tNt0LUnRoH8e+88w6XXXYZLVq0IDU1FYBdu3bRtm1bFixYUNflq2D69Ok888wzZGZmcsopp/DKK68c9svDRx99xIMPPsj27dtp27YtTz31FIMGDQocV0oxZcoU3nrrLfLz8+nduzevv/46bdu2rffnciSbVn7OqZSwleYMGH6ffPAJIURTYjQfbM0/EqXAVaQH88VlgX2OPjO3qxCcheAs0vM4i6pIKwL8g074PPq+q+ox6ystMhDhXyrtlm85/PkeZaDEaaMow0bxXjvF2DigQtlNGH+pMIoMERhCorGEx2KPSiQiLoWYxBakJCWTEhMq93oJUQM1DuLbtm3LmjVrWLJkCevXrwegQ4cO9OvXr96DzA8//JBx48YxY8YMevTowYsvvsiAAQPYsGED8fHxFfL/+OOPDBs2jGnTpnHRRRfxwQcfMGTIEH7//Xc6d+4MwNNPP83LL7/Me++9R8uWLXnwwQcZMGAAa9euxWaz1evzORLXzt8A2NusJ63CDz/FuBBCiCZM0/xdZcL1Me1rw+cDT2nw4i7VW/c9TnD71x6Hnu51gsd1yNrp/0WgbF3qP6fsWodeu1S/rlvvemPSfERQQgQlUFVIUOpf9gGb9CS3MpJDJLnGWIqsibjDkjFGp2KLa0VEYms8blft6kSI41iNgni3243dbmf16tWcf/75nH/++fVVrko9//zz3Hrrrdx4440AzJgxg6+++op3332Xe++9t0L+l156iYEDBzJx4kQApk6dypIlS3j11VeZMWMGSilefPFFHnjgAQYPHgzA+++/T0JCAgsWLODqq69uuCdXiahcfcgzY+rpjVoOIYQQTYDBoN8Ye4SbY+uFz3ew5T/wS4F/35EPjjw8xfspyd9HaUEO3uL9GBz7CXHmEO47gFnzkkQuSb5ccGwEB3qQv1G/fGsg64xuJDSX+8KEKFOjIN5sNtOiRYtGGQve5XKxatUqJk+eHEgzGAz069ePlStXVnrOypUrGTduXFDagAEDAt1+tm3bRmZmJv369Qscj4yMpEePHqxcubLKIN7pdOJ0OgP7BQX6WL1utxu3212r51eZRM8uACKST6rT64pgZXUrdVy/pJ4bjtR1w5B6PoTRDnY72OOqzGL3L+W5vW5UUTZ7dm4m++/vCN3zPZ2dqyucGxoVV+d1La+daMpq3J3m/vvv57777mP27NnExMTUR5kqlZOTg9frDdxMWyYhISHQredQmZmZlebPzMwMHC9LqypPZaZNm8YjjzxSIX3x4sWEhNRdC0hbEolkC//8tJjNVUzqIerOkiVLGrsIJwSp54Yjdd0wpJ6rx6egwAX7HT5UcTahJbuJdO4h3rOHVN9u0sikpeap8vz/fTEfS0hknZappKTyYTaFaApqHMS/+uqrbN68meTkZNLS0ggNDQ06/vvvv9dZ4Y5VkydPDmrhLygoIDU1lfPPP5+IiIg6e5xVuz6DnC2kaFl0L3czrqhbbrebJUuW0L9/f8yVjTAh6oTUc8ORum4YUs8Vlbg87M5zsCvXwc48B/v3ZaJl/0NY/kYSSjfTlZ2013Zh0w5pAff3ny/Fwl5Tc/JD0nBFtMIY1wZbfBs27NjHhZdcWuf1XPZLuhBNUY2D+CFDhtRDMY4sNjYWo9FIVlbwpBpZWVkkJiZWek5iYuJh85ets7KySEpKCsrTtWvXKstitVqxWq0V0s1mc51+wJjTekDOJ6Tt/4ESRymREZVP7iHqRl2/fqJyUs8NR+q6YZxI9ez1KTIOONiZW8LuXAe78krYlVvCrv1FeHJ3kOTYREfDdjpqO7jIsINkLffgyeUGnnFqVvbZWlIc2RYVdxKhKZ1o1vIUQuLSaXXIDLdut5ut2QvrpZ5PlNdNHJ9qHMRPmTKlPspxRBaLhdNPP51ly5YFvkj4fD6WLVvG6NGjKz2nZ8+eLFu2jLvuuiuQtmTJEnr27AlAy5YtSUxMZNmyZYGgvaCggJ9//pnbb7+9Pp9OtbQ781JyVj1Bc7JY8t499B/zWmMXSQghxHFMKUVOkYtd/smfduWWBCaE2plbwt58B5rPTVttN538wfqZhh100HYQoTkqHYKy0N4cZ2xHzImdCUs7BWNSF6zR6TQ3yKRQQhyNGgfxZVatWsW6desA6NSpE6eeemqdFaoq48aNY/jw4XTr1o3u3bvz4osvUlxcHBit5oYbbiAlJYVp06YBcOedd9KnTx+ee+45LrzwQubNm8dvv/3Gm2++CYCmadx111089thjtG3bNjDEZHJycqP94lCeLSya5YnDuTjzJfrmzGX58ks555z+jV0sIYQQTZRSitxiF3vyHYGZWssH7HvyHZS6fYH8oTjooO2gk2EHl2jb6WTaTlttNxat4gAXPqMVX+xJmJJPhqRTILELJHQi3BqO/I4sRN2rcRCfnZ3N1VdfzfLly4mKigIgPz+fvn37Mm/ePOLiqr4r/WgNHTqUffv28dBDD5GZmUnXrl1ZtGhR4MbUnTt3Yij3M1yvXr344IMPeOCBB7jvvvsCE1KVjREPMGnSJIqLixk5ciT5+fmceeaZLFq0qNHHiC/jSzqdDe7zaL9/GenfjuYrw3tcePYZjV0sIYQQxyClFPuKnOzOc7Anz8HuPD1Q35PvCKQ53BUDcA0fqdo++mg76WDawamW3bTXdpHozaj8gWyRkHiyviTpa0NsWwxG6Z4iREOpcRA/ZswYCgsL+eeff+jQoQMAa9euZfjw4YwdO5a5c+fWeSHLGz16dJXdZ5YvX14h7corr+TKK6+s8nqapvHoo4/y6KOP1lUR61zqNS+z//W+pHsysS8byltZL3PT5ZdgNMgMrkIIcSJxe31kHijVA/J8B3vz9cC8bHt3vgOXx3fYa0RRSPewfZxuy6CTaRctPduId2zB7C03Ukv5S0SklAvWu+jbUS30CaqEEI2mxkH8okWLWLp0aSCAB+jYsSPTp09v8MmfThTmiARso78l581LSCjZwtC/b+PZnCxuvPYG4iOOjV8MhBBCHB2lFAWlHvbmO8g44GBPfil78vyBuj9YzyosRanDX0fTICncSpfIYrrasmlvyiDVu4v40h2EFW7B6MgBD1B0yIlGK8SfBAldILEzJHSChM4Q0nDDSQshqq/GQbzP56v0bm6z2YzPd/hv/6L2DFHNiR3zDfvfvpxm+3/j7sx7efPZnzGefTc39umAzSw3CAkhxLHM6fGSdcAZaDUvC9T3BvZLKXJWPU56GYvJQEqUnZRIG23DS2lvzSPduI8kXxbNSncSUrAFQ85G2FdY9UUiUyHuJH+w7l+atQFjrW+VE0I0sBr/bz333HO58847mTt3LsnJyQDs2bOHu+++m/POO6/OCyjKsUfR7N9fcWDuzURu/ZLR2sds/f4H7v35Ns696GouPjkJTX7eFEKIBuf2+sgqKCXjgH/xB+UZBxyBtH2FziNfCIgJtZAcZSMp0k7LcB/trbmkGXNI8mUR7dqLvWgXWv5OyN4Je4qrvpBmhJhWENceYtvpQXtcO2jWFqxhdfTMhRCNpVaTPV1yySWkp6eTmpoKwK5du+jcuTP/+c9/6ryA4hBmG5HX/wf1z6c4v5xEq9JMXnQ/whcff81Ny+9gSJ9uXNA5CYvJcORrCSGEOCKXRw/Qy4L0zAOl7D3gYG9eCet3Gnn87+/YV+Q8YjcXAKvJQItIMydFOGltLybNWkxzcwFxWgFRKo8wdy6mkn1QnA17s6D0wBGuqEFEMkSlQXSaHrSXBewxrcBUyZiPQojjQo2D+NTUVH7//XeWLl3K+vXrAejQoQP9+vWr88KJKmgaWufLsLXph2fZYxh+fYuLjT9xXu4ffPzx2Vz9xcX06n4G1/RoQXKUvbFLK4QQx6xip4fMAj0wzzxQSmaB3nqeecBJZoG+zikq34KusOEiHAdhmoMIHCRrDrpqJUQaS0m2e0i0uYkzO4kxuYgyOgjDQagqwerOx1iyD604Fw7TgF6BPUYP0MsC9cA6HaJSwVRx8kEhxPGvVp3fNE2jf//+9O8vY5Y3KlsEpgufhtOuw/353YRk/MYNpiVc51nKsh9OY9x3g4g8qQ/X9UynZ6tmmIzSOi9Ek6YUeF3gcYLXDV7nIdsu8DrRnA7iD/yJtl4BngrH8bpBecHnA+Xzb3vLrVUlaf708tt1IdAFUPNvV7KmbHXIMaUAVW5NYN+nfDjdXhwuN6VuD06Xl1K3h1K3F6fbi8vtxutxoXndmPFg1jy0xsNJeLDgCaRZ8GC26vsWzYuZI/RZd/uXIz5vI4TFQ2gchCXo22HxEBp/cDssAcKTwBZR42oVQhz/ahXEf/fddzz77LOByZ46duzIxIkTOeuss+q0cKKakk7GPHIpbP8/fD++imHT1/Q3rqK/cRV/bZ7NzPUDmWzrTe+O6Qzskkiv1s2wmuRGWCHqjVLgLoHSAnAWgrNAX1wlerqr2L/271dIK646L0cOnk1AT4Ct9fw8j2EGwO5fDqvWH4UayhqOw2fCHhWPZo0Aa3i55dD9cLBH+wP2BH3bIA0rQojaq3EQ/5///Icbb7yRyy67jLFjxwLwww8/cN555zFr1iyuueaaOi+kqAZNg5ZnY2h5NuRsgp9ew7f6A7p4tvO8ZQal3nf47s9T+PT3Htxr7k7PDmkM7JxIn3bx2C0S0AsRxOMER55/ydcD8NICcB7Qg/LSgnJphZUfVxUn1KlzBpM+LKDRrHepMFrBZEEZzBwochDRLB6DyQpGi/+4f20w6wGkZgTNAAajvm0ov28ol2b05zcE5zsaZa3n/m2f8lHs9FBc6qbY6aaw1K3vO90UlXoodnoocbopcbnx+RSa/2yF5l+ActsKDTQDoVYTYTYzYTYz4YG1hXC7hfCwUCJCQ7DZ7HodGi3+tbXctuXgdvk6tITh8XhYsnAhgwYNqnTUNiGEqE81DuIff/xxnn76ae6+++5A2tixY3n++eeZOnWqBPHHgti2cNELGPo+AKveRa2eiy13CwOMvzHA+BtO9Sbf/XMyX63pwf3GbnRumcoZrZrRs3UzOidHSLcbcXxQSg+uHfnlAvI8KD1k35EfnKc039/iXQc0g78VNlIfDcQSCuYQ/9pebjsELCFgDj1kXclxs80fVFqrbMn1uN185w8uDY0cXDpcXvYVOtlXpI/OEliKnGQXOMkudJJdWEpOkQuvr/pddGJCLcSHW0mIsJEYYSMhwkpCpI2EcBuJkTbiI6w0C7XKpHhCiONWjYP4rVu3cvHFF1dIv+SSS7jvvvvqpFCijoQ2g7Mnop01AbL+hn8WoNYuwLp/M+cbV3G+cRVOZea3be34eUsHpn3dgU2W9pySnhAI6jsmSVAvjgE+nx5cl+RCyX5w+NdB+7kV94+qNVwDexTYovQ+ydYIfap5a4R/P7zcdkTwdtlxS9hxOaul2+tjf5GryuA8p9DFviJ9vzrjnpfRNGgWaiUhwkp8uJX4cD0Yj4+w+ff17bgwq4zAJYQ44dVqdJply5bRpk2boPSlS5cGhpwUxxhN80+V3QXt3Acg6x9Y+5ke0OdspLfxH3ob/wHAqcys3taan7Z04MmvO7DJ3IHO6Ymc3DyKLimRdGkeSYLMEiuOhlJ6y3cgCM/xb5dfDgnSS/P1GzBrw2TT+x+XLbYo/3aUf6nsWLQejJ8gfZaVUhS7vOwv0kdiySlykVPkZH+59b4ip/+4iwOO6ty5eZDNbCAu3EpcmFVfh1uJDfMH6f7WdL3l3CKNBkIIUU01DuLHjx/P2LFjWb16Nb169QJgxYoVzJo1i5deeqnOC1gmNzeXMWPG8MUXX2AwGLj88st56aWXCAuresKK0tJSxo8fz7x583A6nQwYMIDXXnuNhISEQJ7KJkeaO3cuV199db08j0anafoMfYmd0frep/ef3/5/sGMFavsKrEWZ9NDW08OwHvgUlzLy97aW/LMlnW9VC17ztSA3tDWtmifROSVSD+xTIkmIsMpEUycipcBVVC7gzj3YSl6cExSUm0pyGJCXgenPYvBVv3U2iDVCD7BDmulTwYc004ffC2kGIdGH7Mfoec0n5jCrLi/szXdQ4Cxhf7GT3GIXucUu9he7yPUH5znFLnIKnewvdlLqrtmXJJNBo1mYpUJwrm/bygXrFsKsJvl8EEKIOlbjIP72228nMTGR5557jv/+97+APk78hx9+yODBg+u8gGWuvfZaMjIyWLJkCW63mxtvvJGRI0fywQcfVHnO3XffzVdffcVHH31EZGQko0eP5rLLLmPFihVB+WbOnMnAgQMD+1FRUfX1NI4tmqbP3hfXDv51M5pSsH8L7PgBtq9A7ViBpWAPp2mbOc2w+eB5Hti5NY4NW1qwTqXyqa8FWfY2hCW3o3V8JK3jQ2kdF0bruDBiwyzyx7upUEqfWKbSrinlu67kBe/7qtcqqwFBv+FYwg4G4hWWQwP0GH37BJ24xudTFJZ6yCtxkVuiB+GBgLzY6V/704r0NIfbBL/8X40ex242EhtuoVmo3lIeG2YhNsxKs3LruDArzcKsRNnNGKS/uRBCNJpaDTF56aWXcumll9Z1Waq0bt06Fi1axK+//kq3bt0AeOWVVxg0aBDPPvssycnJFc45cOAA77zzDh988AHnnnsuoAfrHTp04KeffuKMM84I5I2KiiIxMbFhnsyxTNMgto2+nD5CD+rztsPuX/UuOFn/4Mv6G0NhBi0M+2jBPvqzSj/XC66dRvbuiGW3imW7imOFiiXXnAhRLbDHtyQuOZ1W8ZG0jgslNSYEs/xsXj88rkNu3syv5IbO/Epu9MyvfR9yk80fcFcdlHus0fzfqrWcef5gzBHx+g2aJyC310d+iZv8Ej3ozvNv51Wa5tLzOtw1uumzjNmoERNqISZU76oSU24JBOnhVmJDrcSGWwix1OpPghBCiEZQ40/sX3/9FZ/PR48ePYLSf/75Z4xGYyDIrksrV64kKioq6Nr9+vXDYDDw888/V/qFYtWqVbjd7qCZZE866SRatGjBypUrg4L4UaNGccstt9CqVSv+/e9/c+ONNx629djpdOJ0HpzBr6CgAAC3243bXbO+oodTdq26vGaNhTeHDs2hw8E69jry0LLX+pd/UFn/QPZ6LF4H6VoW6WQFXyNfX9wbjOxVzdijYllNDKWWaJQ9BkNILNbIWEKjE4lslkBsXBIJ8QnYrA3T6npM1HMZT2m5ccUL0ZwF4CwCZwFaufSD+4Xl8ukjsWjumkwFWZEyh/q7rMSg/IG5svtbwu0xqJDgNSEx+ugpR+B2uylYV4TbHgsY4Vio71pSSlHk9HDA4eGAw01BqTuwfcDhpsDh4UCpmwKHHoAXODzkO9zkl7hrdKPnoUIsRqJDzHogHmIhJlTfji7bDtED9HCrxp8/r+Digf2wWKr7/0gdG/8HmpBj6rPjOFaf9SyvnWjKahzEjxo1ikmTJlUI4vfs2cNTTz3Fzz//XGeFK5OZmUl8fHxQmslkIiYmhszMzCrPsVgsFbrGJCQkBJ3z6KOPcu655xISEsLixYu54447KCoqCoyBX5lp06bxyCOPVEhfvHgxISFHDmZqasmSJXV+zbqRAloKJJ4PCT5s7jxCXDn+ZR+20hzMpfp+pHc/Zs1LmpZNGtn66V6gyL9kB1/ZpzQOEEqBFkGxIQyXMRSvwYIyWtGMVjSTBYPJitFkwWe04jVY8BqseAwHt30GEwoDoKE0DYUBpWngX+sjTWsozYAVje8WfoLSND1VeTEoD5ryYVBeNP9Stm3gkH3/2uhzHVyUE6PPhcnn9Kc5yx13YvQ5MR2yb1S1D/DKU2i4jSG4jKG4TaG4jaG4jGG4TSH+dVmaftxlDNPzm8LwGSoJ+sq/VgCU+JfdNS7bsfB+9iko9YLD4197odSj6etAuobDox9zeKDEo1ES2PaPQ15LGgq7CUL9S4hJEWou2y+/rR8LM0OICcwGD+AMvpiPwGvjBDL8i92kDzgg6t+x8J4+EdRHPZeU1NFwskI0ghoH8WvXruW0006rkH7qqaeydu3aGl3r3nvv5amnnjpsnrJZYevLgw8+GNg+9dRTKS4u5plnnjlsED958mTGjRsX2C8oKCA1NZXzzz+fiIi6mx7b7XazZMkS+vfv3+QnEvH5vPiKstAO7ETl7qAkby8l+dm4CvbhK87BWJqHxZVHqOcA4RRj0BTRFBFNkR6k1HJgkqZKWcICQxiqQ2Z+PLgfgTpkZkhljwZbNNgi0DQDVsDa2E/G72jfzx6vD4fbS5HTq0/84/JS7PJQ4vRS5PJS4vJQ7Dy4LnZ5KSr1UOh0U1jq0RenR584yFU3EzFZTAYibSYi7WYi7WYi7CYibWYiQ8xE2vz7/mORdjNRdjNRIfp2fY5ffjx9dhzLpJ4bRn3Wc9kv6UI0RTUO4q1WK1lZWbRq1SooPSMjA5OpZpcbP348I0aMOGyeVq1akZiYSHZ2cFOtx+MhNze3yr7siYmJuFwu8vPzg1rjs7KyDtv/vUePHkydOhWn04nVWnn4Y7VaKz1mNpvr5YO8vq7bsMxgTYNmadDqLCKByCpyKo+LwtxssrP3krsvg6LcTEoLc3E6inGXFuNxFqNcxWhuBzac2HFix4VdC9624kZvf/dh0NvhA/tG7WCawZ8WVAY0fJoJZdAXDCaUwYxmMIHRjGY0oxlNGPzb+NMx2cpN4uOfrMdsP5hmLp9WlqfcMVsEWMLQDAdn0T3Wbx30+hRurw+nx4fb66PU7fUvetDtcOn7RaUufsnS2L8qA5dPnwSo1OOl1OXFUS5/qf+corJA3emh2OWp8egp1WE1GQi3mYmwmQi3mQi3mf3r8tvmoEA8slwgbjMf27MdHx+fHcc+qeeGUR/1LK+baMpqHMSff/75TJ48mc8++4zISD0My8/P57777qN///41ulZcXBxxcXFHzNezZ0/y8/NZtWoVp59+OgDffPNNpX3zy5x++umYzWaWLVvG5ZdfDsCGDRvYuXMnPXv2rPKxVq9eTXR0dJUBvKh/mslCRHxzIuKbHzaf16fIK/EPlVfoYm9RKTmF+v6+Qie5JS4K/H2UDzg8FDjcuLyHCwT1gB7AR/VvurUYDdjMBkIsJiwmA2ajhtlo8C+H3zYZDViMGiajF5OhCE0rRtP0wF1fa4F9/F19DJoWnKfc/Rs+n8Kr1MG18qcdku71Vczr9Sk8XoXL6wsKyl0efQlsexUuj9efT9XwhksjbF1fg/wVmQwaoVYTYVYTIRYjoVYToVYjoRZ/mtWf5t8vC8QjKgnSZcIgIYQQTVWNg/hnn32Ws88+m7S0NE499VRAD3wTEhKYPXt2nRcQ9CEsBw4cyK233sqMGTNwu92MHj2aq6++OjAyzZ49ezjvvPN4//336d69O5GRkdx8882MGzeOmJgYIiIiGDNmDD179gzc1PrFF1+QlZXFGWecgc1mY8mSJTzxxBNMmDChXp6HqFtGg+YfYcMK1RxcqNTt9d946A7chJhXVMrKVX+S0qodRU6f3lXD3xrscOutwQ6XV1+7y7Y9lMWuLq8Pl9dHQWnd9Gdv6qwmA3aLEZvJqK/NRmxmAzaTgYK8HFokJxFiNWO3GA7JY8RuNgbS9eC8YoBuNR3brd9CCCFEQ6hxEJ+SksKaNWuYM2cOf/75J3a7nRtvvJFhw4bV689Sc+bMYfTo0Zx33nmByZ5efvnlwHG3282GDRuCblJ54YUXAnnLT/ZUxmw2M336dO6++26UUrRp04bnn3+eW2+9td6eh2hcZcFi+Vln3W435r2rGdS3dbXfw0opnB6960hJWYDv8gZasd1eX1Cr9qHbbn/g7/YoPD592+tVKPTh2hVKX6tK0vz7BPLox0D/YmPQtMBa3waDQcNYLt1oKL9NIM1kNGA1GjCbNCxGY+CXBYvJgMVo8O8bgvYtRgPmsrVRq3JkJ7fbzcKFCxk06BT5CVsIIYQ4SrUaFDg0NJSRI0fWdVkOKyYm5rATO6Wnp6NU8M/6NpuN6dOnM3369ErPGThwYNAkT0JUl6ZpgS8EUXU/IJEQQgghxGEd1cweERERrF69usJNrieasi8PdX2Xu9vtpqSkhIKCAmm5rEdSzw1D6rnhSF03DKnnhlGf9Vz2d/vQRkAhmoJqB/F79+6tMDOqvOl1hYWFAKSmpjZySYQQQghRU4WFhYHBOoRoKqodxHfq1Inp06dzzTXX1Gd5mqTk5GR27dpFeHj4YWd6ramy8ed37dpVp+PPi2BSzw1D6rnhSF03DKnnhlGf9ayUorCwsEIjpRBNQbWD+Mcff5zbbruNTz/9lDfeeIOYmBiuu+46+eACDAYDzZsffjjEoxERESH13ACknhuG1HPDkbpuGFLPDaO+6lla4EVTVe1Bku+44w7WrFnD/v376dixI1988QWvv/46sbGx9Vk+IYQQQgghxCFqdGNry5Yt+eabb3j11Ve57LLL6NChQ4VZWn///fc6LaAQQgghhBAiWI1Hp9mxYwfz588nOjqawYMHVwjiRd2xWq1MmTJFZo+tZ1LPDUPqueFIXTcMqeeGIfUsROU0VYMhZt566y3Gjx9Pv379eOONN4iLi6vPsgkhhBBCCCEqUe1m9IEDB/LLL7/w6quvcsMNN9RnmYQQQgghhBCHUe0g3uv1smbNmnodhUUIIYQQQghxZDXqTiOEEEIIIYRofNUeYlIIIYQQQghxbJAgvpFNnz6d9PR0bDYbPXr04Jdffjls/o8++oiTTjoJm81Gly5dWLhwYQOVtGmrST2/9dZbnHXWWURHRxMdHU2/fv2O+LoIXU3fz2XmzZuHpmkMGTKkfgt4nKhpPefn5zNq1CiSkpKwWq20a9dOPjuqqaZ1/eKLL9K+fXvsdjupqancfffdlJaWNlBpm6bvv/+eiy++mOTkZDRNY8GCBUc8Z/ny5Zx22mlYrVbatGnDrFmz6r2cQhxzlGg08+bNUxaLRb377rvqn3/+UbfeequKiopSWVlZleZfsWKFMhqN6umnn1Zr165VDzzwgDKbzeqvv/5q4JI3LTWt52uuuUZNnz5d/fHHH2rdunVqxIgRKjIyUu3evbuBS9601LSey2zbtk2lpKSos846Sw0ePLhhCtuE1bSenU6n6tatmxo0aJD64Ycf1LZt29Ty5cvV6tWrG7jkTU9N63rOnDnKarWqOXPmqG3btqmvv/5aJSUlqbvvvruBS960LFy4UN1///1q/vz5ClCffvrpYfNv3bpVhYSEqHHjxqm1a9eqV155RRmNRrVo0aKGKbAQxwgJ4htR9+7d1ahRowL7Xq9XJScnq2nTplWa/6qrrlIXXnhhUFqPHj3UbbfdVq/lbOpqWs+H8ng8Kjw8XL333nv1VcTjQm3q2ePxqF69eqm3335bDR8+XIL4aqhpPb/++uuqVatWyuVyNVQRjxs1retRo0apc889Nyht3Lhxqnfv3vVazuNJdYL4SZMmqU6dOgWlDR06VA0YMKAeSybEsUe60zQSl8vFqlWr6NevXyDNYDDQr18/Vq5cWek5K1euDMoPMGDAgCrzi9rV86FKSkpwu93ExMTUVzGbvNrW86OPPkp8fDw333xzQxSzyatNPX/++ef07NmTUaNGkZCQQOfOnXniiSfwer0NVewmqTZ13atXL1atWhXocrN161YWLlzIoEGDGqTMJwr5WyiETqZbbSQ5OTl4vV4SEhKC0hMSEli/fn2l52RmZlaaPzMzs97K2dTVpp4Pdc8995CcnFzhj4Y4qDb1/MMPP/DOO++wevXqBijh8aE29bx161a++eYbrr32WhYuXMjmzZu54447cLvdTJkypSGK3STVpq6vueYacnJyOPPMM1FK4fF4+Pe//819993XEEU+YVT1t7CgoACHw4Hdbm+kkgnRsKQlXojDePLJJ5k3bx6ffvopNputsYtz3CgsLOT666/nrbfeIjY2trGLc1zz+XzEx8fz5ptvcvrppzN06FDuv/9+ZsyY0dhFO+4sX76cJ554gtdee43ff/+d+fPn89VXXzF16tTGLpoQ4jgkLfGNJDY2FqPRSFZWVlB6VlYWiYmJlZ6TmJhYo/yidvVc5tlnn+XJJ59k6dKlnHzyyfVZzCavpvW8ZcsWtm/fzsUXXxxI8/l8AJhMJjZs2EDr1q3rt9BNUG3ez0lJSZjNZoxGYyCtQ4cOZGZm4nK5sFgs9Vrmpqo2df3ggw9y/fXXc8sttwDQpUsXiouLGTlyJPfffz8Gg7Sb1YWq/hZGRERIK7w4ocgnSiOxWCycfvrpLFu2LJDm8/lYtmwZPXv2rPScnj17BuUHWLJkSZX5Re3qGeDpp59m6tSpLFq0iG7dujVEUZu0mtbzSSedxF9//cXq1asDyyWXXELfvn1ZvXo1qampDVn8JqM27+fevXuzefPmwJckgI0bN5KUlCQB/GHUpq5LSkoqBOplX56UzKtYZ+RvoRB+jX1n7Yls3rx5ymq1qlmzZqm1a9eqkSNHqqioKJWZmamUUur6669X9957byD/ihUrlMlkUs8++6xat26dmjJligwxWQ01recnn3xSWSwW9fHHH6uMjIzAUlhY2FhPoUmoaT0fSkanqZ6a1vPOnTtVeHi4Gj16tNqwYYP68ssvVXx8vHrsscca6yk0GTWt6ylTpqjw8HA1d+5ctXXrVrV48WLVunVrddVVVzXWU2gSCgsL1R9//KH++OMPBajnn39e/fHHH2rHjh1KKaXuvfdedf311wfylw0xOXHiRLVu3To1ffp0GWJSnJAkiG9kr7zyimrRooWyWCyqe/fu6qeffgoc69Onjxo+fHhQ/v/+97+qXbt2ymKxqE6dOqmvvvqqgUvcNNWkntPS0hRQYZkyZUrDF7yJqen7uTwJ4quvpvX8448/qh49eiir1apatWqlHn/8ceXxeBq41E1TTera7Xarhx9+WLVu3VrZbDaVmpqq7rjjDpWXl9fwBW9Cvv3220o/c8vqdvjw4apPnz4VzunatauyWCyqVatWaubMmQ1ebiEam6aU/MYnhBBCCCFEUyJ94oUQQgghhGhiJIgXQgghhBCiiZEgXgghhBBCiCZGgnghhBBCCCGaGAnihRBCCCGEaGIkiBdCCCGEEKKJkSBeCCGEEEKIJkaCeCGEEEIIIZoYCeKFEKIWNE1jwYIFAGzfvh1N01i9enWjlkkIIcSJQ4J4IUSD83q99OrVi8suuywo/cCBA6SmpnL//fc3UslqJzU1lYyMDDp37lyn101PT+fFF1+s02sKIYQ4PkgQL4RocEajkVmzZrFo0SLmzJkTSB8zZgwxMTFMmTKlEUt3kMvlqlY+o9FIYmIiJpOpnkskhBBC6CSIF0I0inbt2vHkk08yZswYMjIy+Oyzz5g3bx7vv/8+Foul0nN2797NsGHDiImJITQ0lG7duvHzzz8Hjr/++uu0bt0ai8VC+/btmT17dtD5O3fuZPDgwYSFhREREcFVV11FVlZW4PjDDz9M165defvtt2nZsiU2mw2ATZs2cfbZZ2Oz2ejYsSNLliwJuu6h3WmWL1+OpmksW7aMbt26ERISQq9evdiwYUPgnC1btjB48GASEhIICwvjX//6F0uXLg0cP+ecc9ixYwd33303mqahaVrg2A8//MBZZ52F3W4nNTWVsWPHUlxcXMNXQAghRFMmQbwQotGMGTOGU045heuvv56RI0fy0EMPccopp1Sat6ioiD59+rBnzx4+//xz/vzzTyZNmoTP5wPg008/5c4772T8+PH8/fff3Hbbbdx44418++23APh8PgYPHkxubi7fffcdS5YsYevWrQwdOjTocTZv3swnn3zC/PnzWb16NT6fj8suuwyLxcLPP//MjBkzuOeee6r1/O6//36ee+45fvvtN0wmEzfddFPQ8xk0aBDLli3jjz/+YODAgVx88cXs3LkTgPnz59O8eXMeffRRMjIyyMjIAPTgf+DAgVx++eWsWbOGDz/8kB9++IHRo0fXrPKFEEI0bUoIIRrRunXrFKC6dOmi3G53lfneeOMNFR4ervbv31/p8V69eqlbb701KO3KK69UgwYNUkoptXjxYmU0GtXOnTsDx//55x8FqF9++UUppdSUKVOU2WxW2dnZgTxff/21MplMas+ePYG0//3vfwpQn376qVJKqW3btilA/fHHH0oppb799lsFqKVLlwbO+eqrrxSgHA5Hlc+xU6dO6pVXXgnsp6WlqRdeeCEoz80336xGjhwZlPZ///d/ymAwHPbaQgghji/SEi+EaFTvvvsuISEhbNu2jd27dwPw73//m7CwsMACsHr1ak499VRiYmIqvc66devo3bt3UFrv3r1Zt25d4HhqaiqpqamB4x07diQqKiqQByAtLY24uLig66amppKcnBxI69mzZ7We28knnxzYTkpKAiA7OxvQW+InTJhAhw4diIqKIiwsjHXr1gVa4qvy559/MmvWrKD6GTBgAD6fj23btlWrXEIIIZo+uQtLCNFofvzxR1544QUWL17MY489xs0338zSpUt59NFHmTBhQlBeu93eIGUKDQ2ts2uZzebAdlmf9rLuPxMmTGDJkiU8++yztGnTBrvdzhVXXHHEm2mLioq47bbbGDt2bIVjLVq0qLOyCyGEOLZJEC+EaBQlJSWMGDGC22+/nb59+9KyZUu6dOnCjBkzuP3224mPjw/Kf/LJJ/P222+Tm5tbaWt8hw4dWLFiBcOHDw+krVixgo4dOwaO79q1i127dgVa49euXUt+fn4gT2XKzsvIyAi0pv/0009H/fxXrFjBiBEjuPTSSwE9ON++fXtQHovFgtfrDUo77bTTWLt2LW3atDnqMgghhGi6pDuNEKJRTJ48GaUUTz75JKCPif7ss88yadKkCsEswLBhw0hMTGTIkCGsWLGCrVu38sknn7By5UoAJk6cyKxZs3j99dfZtGkTzz//PPPnzw+06Pfr148uXbpw7bXX8vvvv/PLL79www030KdPH7p161ZlOfv160e7du0YPnw4f/75J//3f/9XJ+PYt23bNnDz7J9//sk111wTaKUvk56ezvfff8+ePXvIyckB4J577uHHH39k9OjRrF69mk2bNvHZZ5/Jja1CCHGCkSBeCNHgvvvuO6ZPn87MmTMJCQkJpN9222306tWLm2++GaVU0DkWi4XFixcTHx/PoEGD6NKlC08++SRGoxGAIUOG8NJLL/Hss8/SqVMn3njjDWbOnMk555wD6N1ZPvvsM6Kjozn77LPp168frVq14sMPPzxsWQ0GA59++ikOh4Pu3btzyy238Pjjjx91HTz//PNER0fTq1cvLr74YgYMGMBpp50WlOfRRx9l+/bttG7dOtBP/+STT+a7775j48aNnHXWWZx66qk89NBDQX32hRBCHP80dehfSiGEEEIIIcQxTVrihRBCCCGEaGIkiBdCCCGEEKKJkSBeCCGEEEKIJkaCeCGEEEIIIZoYCeKFEEIIIYRoYiSIF0IIIYQQoomRIF4IIYQQQogmRoJ4IYQQQgghmhgJ4oUQQgghhGhiJIgXQgghhBCiiZEgXgghhBBCiCbm/wH71Php/NVzLQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, label = \"Original Airfoil\")\n",
    "plt.plot(x, y_opt, label = \"Optimized Airfoil\")\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "plt.title('Original Airfoil vs Optimized Airfoil')\n",
    "plt.xlabel('X-coordinate')\n",
    "plt.ylabel('Y-coordinate')\n",
    "plt.legend(loc=(1,1))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "201d2c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y-coordinate')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y_opt)\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "plt.title('Optimized Airfoil')\n",
    "plt.xlabel('X-coordinate')\n",
    "plt.ylabel('Y-coordinate')\n",
    "# plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0bbdf6bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_opt = pd.DataFrame({'x': x, 'y_modified': y_opt})\n",
    "path = \"Optimized_airfoil_1.dat\"\n",
    "df_opt.to_csv(path, index=False, sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a25d4e09-abdb-4db2-a80e-618d95f9204c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5361 0.00731\n"
     ]
    }
   ],
   "source": [
    "airfoil_name = 'Optimized_airfoil_1'\n",
    "\n",
    "CL, CD = Xfoil_runner(alpha, Re, n_iter, airfoil_name)\n",
    "\n",
    "print(CL, CD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "98daad60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Surrogate model optimization:\n",
      "Optimal Design Parameters: [[ 0.012       0.011978   -0.00708097 -0.01192201 -0.00586304  0.01175457\n",
      "   0.01198654 -0.012     ]]\n",
      "Optimal Lift Coefficient(Surrogate model prediction): 0.5572005\n",
      "Optimal Drag Coefficient(Surrogate model prediction): 0.0068624383\n",
      "Optimal Aerodynamic Efficiency: 81.1957\n",
      "Lift Coefficient for Optimal Design Parameter from XFOIL: 0.5361\n",
      "Drag Coefficient for Optimal Design Parameter from XFOIL: 0.00731\n",
      "Optimal Aerodynamic Efficiency from XFOIL: 73.33789329685364\n"
     ]
    }
   ],
   "source": [
    "print('Surrogate model optimization:')\n",
    "print(\"Optimal Design Parameters:\", optimal_design_parameters)\n",
    "print(\"Optimal Lift Coefficient(Surrogate model prediction):\", CL_opt )\n",
    "print(\"Optimal Drag Coefficient(Surrogate model prediction):\", CD_opt)\n",
    "print(\"Optimal Aerodynamic Efficiency:\", optimal_efficiency)\n",
    "print(\"Lift Coefficient for Optimal Design Parameter from XFOIL:\", CL )\n",
    "print(\"Drag Coefficient for Optimal Design Parameter from XFOIL:\", CD)\n",
    "Opt_eff = CL/CD\n",
    "print(\"Optimal Aerodynamic Efficiency from XFOIL:\", Opt_eff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8013af06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error%\n",
      "Lift Coefficient Error: 3.93592453429868\n",
      "Drag Coefficient Error: 6.122595296023478\n",
      "Optimal Aerodynamic Efficiency Error: 10.714526896022218\n"
     ]
    }
   ],
   "source": [
    "print(\"Error%\")\n",
    "print(\"Lift Coefficient Error:\", abs((CL_opt-CL)*100/CL))\n",
    "print(\"Drag Coefficient Error:\", abs((CD_opt-CD)*100/CD))\n",
    "print(\"Optimal Aerodynamic Efficiency Error:\", abs((Opt_eff-optimal_efficiency)*100/Opt_eff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9c9c9d4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Improvement in Efiiciency\n",
      "Lift and Drag Coefficient of Original Airfoil: 0.5572 0.00848\n",
      "Efiiciency of Original Airfoil: 65.70754716981132\n",
      "Lift and Drag Coefficient of Optimized Airfoil: 0.5361 0.00731\n",
      "Efiiciency of Optimized Airfoil: 73.33789329685364\n",
      "Improvement in Efiiciency: 11.612587070588448 %\n"
     ]
    }
   ],
   "source": [
    "Org_Eff = CL_org/CD_org\n",
    "print(\"Improvement in Efiiciency\")\n",
    "print(\"Lift and Drag Coefficient of Original Airfoil:\", CL_org , CD_org)\n",
    "print(\"Efiiciency of Original Airfoil:\", CL_org/CD_org)\n",
    "print(\"Lift and Drag Coefficient of Optimized Airfoil:\", CL ,CD)\n",
    "print(\"Efiiciency of Optimized Airfoil:\", Opt_eff)\n",
    "print(\"Improvement in Efiiciency:\", abs((Opt_eff-Org_Eff)*100/Org_Eff),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f1830e7-649d-44c7-8247-3e6d604b75f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac392efe-95d2-47e4-a919-4a9cf8c38702",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}