Cubic_spline_interpolation_datapoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4a871fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = np.array( [1, 3, 5])\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "#find t from step size\n",
    "t0=1 #<<t0 is 1\n",
    "h=2  #<<h is step size \n",
    "t_values = np.arange(t0, 5+h, h) #<<t2 is 5\n",
    "t = [round(i,5) for i in t_values.tolist()]\n",
    "print(\"t = np.array(\",t,end='')\n",
    "print(')')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ecd165d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S0(1< t<=3) = 2.78000 + 19.31250(t-1) + -0.00000(t-1)^2  + -0.63812(t-1)^3\n",
      "S1(3< t<=5) = 36.30000 + 11.65500(t-3) + -3.82875(t-3)^2  + 0.63812(t-3)^3\n",
      "A(2) Exact=12.64353\n",
      "A(2) Euler =  2.31628\n",
      "Error Euler =  10.32725\n",
      "A(2) Cubic Spline = 21.45438\n",
      "Error Cubic Spline=8.81084\n"
     ]
    },
    {
     "data": {
      "image/png": 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MEAB58jsjI0NpXVVcFwcPHiwAECQSiXDz5k2V9U+ePFF8djJhR0S6wjHsiIj0SMnxWGxsbHQXiBodOnTApEmTVJYbGhpi3bp1AACpVIrt27drvc0TJ04oxoNasWIFfHx8NJY1NCz/PErz58/HN998A1tbW8WyiIgI7NmzB++//z46deoEd3d3rcaTkkgk+Pzzz9Wu+/jjj9GwYUMAwKZNm8odpzbeffdd+Pr6qiy3tLTEqFGjAAD//POPyvq1a9cCAAYOHIjRo0er3baVlRUWLFgAANi9e3e1zur79ddfw8jISGX58OHDFW1e3XFoa926dRAEAX5+fvjggw/UljEyMsKyZcsAAEeOHEFqaqracra2tliyZIlW+61o+/j5558VY1N9/fXXKrNBA/JZjotngT5w4IBW4z6VR/H7OigoqNQx5Cpj48aNAAADAwN88803EItV/0QdPnw4BgwYAADYvHkzZDKZ2m2Zmpriiy++ULtuypQpiueVaUelmThxIgDgr7/+KrXcqFGj0Lt3b5XlBgYGim1cu3YNUqlUaf3mzZsByGcCX7Rokdptf/nllzA2Ni537JpYWlqW+Th37lyV7U8bjx49wh9//AFAPrarg4OD2nIDBgxQjGNZ2niqH3zwAVxcXCoUS/Fr5OzsXKH62jAzM8Nnn32mdt2YMWMAAEVFRZg5cyY8PT1VyvTp0wf169cHAJWx7Kr6XGrL2dkZCxcuVLuu+L1aUFCAGzduKK2r7HUxISEBhw8fBgC8+eabaNmypUp9R0dHjbEREdUUJuyIiKhKDB8+XOO6Fi1aKL5AnD9/Xuttnjp1CoD8C+z48eMrF6AGb7/9NqKiorB161aMHTsWbm5uSutjYmLw3nvvoW/fvqVODBAQEIB69eqpXWdsbIzBgwcDkA+QrSnRUBkvvviixnXFic64uDil5Tk5Obhw4QIAoG/fvsjKytL48PPzAyBPGkdERFR5/IB8oHJvb2+16wwMDBRt6NnjKI/iJErfvn2RnZ2t8XiLz5kgCLh27ZrabfXu3bvM2YWLVbR9FCdBXFxc0KlTJ43bL/mF/eLFi1rFVNKVK1cwbdo0tGzZEtbW1jAwMFAMFl/82hcVFeHhw4fl3rY2io/T398fTk5OGssVH2dqairu3Lmjtkznzp2VkvAl2dnZKSbfqUw7On36NCZNmgRvb29YWlpCLBYrztdLL70EAIiNjUVmZqbGbWjzni0oKFBKGAuCgKCgIADyJLtEIlFbv379+ujZs2e5j0ufnDx5EoIgQCQSoUePHqVev1q1agVA3s41KX7dKqJ169YAgC1btmDv3r1l/oOnIjp37gxra2u165o0aaJ43r9/f7VlRCIRGjduDEDeNkuq6nOprb59+2r8Z1vJf9A9+16t7HXxwoULiutsaX+7jBgxoowjICKqXuXvjkBERDpT8gt/Wlqa7gJRo7TebwDg6+uL8PDwcs0CWJwcaNKkSaVmci2LpaUlJk2apOhJlJycjHPnzmH37t3Yt28fZDIZzpw5gwULFuCrr75Suw1tjh8AsrKykJSUBHt7+yo9BkdHR43rzMzMAECld1RERITii+WcOXMwZ84crfaVmJio9AWxqpR2DIDm49BWVlYWnj59CkDes7C4d2FZNPVYK/7yq42Kto/iGViLk2aalFxf3pk2P/roI6xYsUKrnpPp6enl2ra2KnqcmnrGlKYy7aioqAivv/46tm7dqlX59PR0jdcubd6zgHKc6enpimu/Nm2qrF5+2qrOXrUVVTwzuCAIih6qZSmt92l53s/PWrx4MQ4cOICMjAyMHTsW1tbW6NGjB7p06YLevXujY8eOWs/orklp7aVkDzNtyj37z6eqPpfaqsh7AKj8dbHk89LeR46OjrC2tq626x4RUVnYw46ISI94eHgonhffKlpbWFhYaLW+tB4nz8rIyACAak3WqWNnZ4ehQ4di7969OHjwoOKL1o8//oiCggK1dbQ9fqB850BbFbkluKJfQvLy8ipUryzaHkNFkwdVfbwlv1CWpaLto/h5WfVLvkfK07727t2LTz/9FIIgoHv37ti5cydCQkKQmJiIjIwMZGZm4vbt24ryz96eWVWq8jirsx19/vnnimTd4MGDsX//foSGhiIpKQmZmZnIzMxU3GoHlH6+KhJnVlaW4nl52lRdVJH3c2nXrvK8n5/l4eGBa9euYcKECTAzM0N6ejoOHTqEBQsWoHPnzmjcuDF27NhR4e0D8l7GVVXu2bZf1edSWxV9r1b2esH3ERHpC/awIyLSI507d4ahoSGkUinOnj1bqW1p+99+bb+gZ2dnl7q++A/k8iTfrKysAFRPgktbgwYNwsCBA3H48GHk5OTg3r17iluCStL2+IGaT0BqUvKLyO+//46hQ4fqLpgaUPJ4v/rqK8yePbvG9l3R9lH8vOT68tQvy7fffgtAfitqYGCg2rHjquP2vmdZWloiLS2t2o6zqhSfrzFjxmDPnj1qyxSPrVUdSrbh8rQpXajqz5hnFZ8La2vrWtHjvEmTJti+fTs2btyIy5cv4+LFizh16hROnjyJyMhITJgwAUlJSTV63dFWbTuXZansdfHZ95G5ublW2yAiqmnsYUdEpEcsLCzQr18/AEBgYCDCw8MrvK2SY2+VNjbbkydPtNpeWT3+ite7u7trtT0AaNq0KQD5rbG6/KO55G01mm6j0/b4LSwsFAN/65q7u7siQaNpnLa6xNraGnZ2dgBq/ngr2j6K3y8hISGl1i85nlt53mPFg7mPHj1abbIOgFIPu+pS3cdZFVJSUhATEwMAeOWVVzSWq87zZW1trZh8Rds2pStV/RnzrOLb8tPT06ttXM2KMDY2RteuXfH+++/j6NGjePjwoeKzbNmyZSgqKtJxhKpq67nUpLLXi5LPS3ufxMbG8nZYItIpJuyIiPTMvHnzAMhvEZkxY4bWt3UlJiYqjTljZ2enmEUwNDRUY72jR49qtf3ff/9d47qQkBCEhYUBALp166bV9gD5gNSAfNyoyt5OVBnFX9IBzWPuBAYGapxNtLCwEIcOHQIAdOnSRWNipKZZW1srBuzesWNHjfSkqi7FM8uW9WW4OOF98OBBpVmXq1tF20f37t0ByNtgabOa7t27F4D8djh/f3+t4yruDVbaefv5559L3Ya25740xcd54cIFxTiD6hQfp62tLZo3b17h/VVEyZ5zmo5VJpNh165d1RaDSCRC165dAchnMNZ0i35ycjLOnDlTbXFoo6o/Y55V/F4G/ps5tzZydXXFm2++CUCe9E1ISNBxRKqq4lyWnN27upOSlb0u+vv7K66zpf3t8ttvv1VFuEREFVY7vjEQEZHWevbsqfjj/+TJkxg/fnypvReKy7Vr106pJ4OhoSHatWsHANi+fbvamUtPnz6Nffv2aRVXcHAwdu7cqbJcKpUqbgEyNDTExIkTtdoeAPTp00fxpXzhwoWKgbHVKe9tVUFBQVi9enWZPfeuXbum+KO9WbNmKrPIFsvLy1MkU5+1YsUKxax8U6dOLVec1e29994DADx69AgzZ84scwbb0l4DXSrulVZasgcA5s6dC0A+PuLkyZPLvH2xqo63ou1j/PjxiplAZ8+erTbe4OBgbNq0CQAwbNiwcvXgLB5o/48//lCb/N+2bRtOnjxZ6ja0PfelKT7uoqIizJw5U20sv/32G44dO6YoX9OJb3t7e8WtdAcPHlRbZunSpdX+HpkyZQoAICEhAcuWLVNb5v3336/WW3O1UdWfMc9q1qwZBg0aBEA+tmBgYGCp5TMyMlRmR60qpSUkgf8mUDIwMNA406suVcW5LHndqcy1QBuVvS7a29srZgX+4Ycf1PbUi4uLw/Lly6sjfCIirTFhR0Skh9atW4eBAwcCAHbt2oVmzZph5cqVuHz5MuLi4pCUlITbt2/j+++/R0BAAPr27avUS6zY66+/DkB+W9zIkSNx48YNpKWl4f79+1i+fDkGDRqk9W1n7u7ueO2117Bo0SKEh4cjJSUF586dw4ABAxRf+OfNmwdnZ2etj1MkEmHr1q2QSCRISUlB586dsXLlSoSEhCA1NRVPnjzBiRMnMHPmTIwYMULr7QJAamoq5s+fj0aNGuG1117D7t27ERoaipSUFCQmJuKff/7BRx99hJ49eyq+DKxcubLU49+4cSNeeeUVXLlyBSkpKbh9+zbeeecdLF68GIC8d+Ho0aPLFWd1GzFiBMaNGwcA+P7779GjRw/8+uuviIqKQlpaGh4/foy///4bS5YsgY+PjyLBV9u0b98egHzm2x9//BGpqamQSqWQSqVKvT3at2+PBQsWAAAOHTqE9u3bY+vWrXj48CHS0tIQGxuLoKAgrFmzBh06dCh3u9Kkou3D3t4eS5YsAQBcvHgRAQEBOHHiBJKSkhAVFYWvv/4a/fv3R2FhIaysrLBmzZpyxTVmzBgAwJkzZ/Dqq6/i6tWrSE5Oxq1btzB37lxMnTpVMYOtJsXn/sCBAwgMDER2drbi3JeVAC7WqlUrTJs2DQCwf/9+DBo0CEFBQUhOTsaDBw+wdOlSvPrqqwAAFxcXLFy4sFzHWRUMDAwU7WH79u2YNWsW7ty5g+TkZFy+fBmTJ0/GkiVLyjxflTVs2DAEBAQAAJYvX44ZM2bg9u3bSElJwZUrVzB27Fhs3bpVaZKiysrKyirzoW64gKr8jFFnw4YNcHBwQH5+Pvr164d3330XQUFBSEhIQEpKCsLCwrBv3z5MmTIFzs7OCAoKqvC+SuPr64sXXngB3333Ha5evYqEhAQkJibi2rVrmDt3Ln788UcAwMsvv1ypyS2qU2XPZdOmTRW3a69ZswYREREoKChQXAuqUlVcF1evXg2JRIK8vDz06tULmzZtwpMnTxAfH499+/ahW7duyMvLUxwTEZFOCEREpJekUqnw8ccfC2ZmZgKAUh8GBgbCm2++KWRkZChtQyaTCYMGDdJYr0ePHsKff/6p+P3Ro0dK9R89eqRY99tvvwnNmzfXuK0xY8YIUqlU5ThKbuPvv/9We6ynT58W7OzsSj3Gnj17luv8nT59WjAwMCjz3AEQzMzMhB9++EHtdtzc3AQAwqJFi4Tx48dr3Iafn58QFxdX5jaetWjRIgGA4ObmprLu77//1vjalLRlyxZFOXUKCgqEd955RxCJRGWei+HDh2vcjyYl49yyZYvK+kmTJmn1Gvbs2VMAIEyaNEllXV5enuDl5aVV25DJZMLy5cu1ev3btm2rsq/SXq/Syla0fchkMuH9998vNU47OzshKChIbf3S2lB2drbQoUOHUuMKDg4u9T169+5dwcTERG39kueorHaYn58vvPrqq6Uep7u7u3Dv3j219UtrHyWV5/V7VlxcnODh4VHqNfPIkSNaXTM1Xe8Eoez3dkJCQqnX2/Hjx5f6umuj+H2p7UPdfir7GSMIQqnXDkEQhLCwMKFFixZaxfjHH38o1S2rTWpLm323bt1a43u8NNq0a20/C8raVmXOpSD8d61R9ygZl7bvwdJe+8peFwVBEH7//XdBIpGorWtiYiIcOnSoUtcLIqLKYg87IiI9ZWBggKVLlyIiIgJr1qxB37594eLiAlNTU0gkEjg6OqJfv35YuXIlIiMj8cMPP6jMqigSifDbb7/hq6++Qps2bWBmZgZLS0u0a9cO69atw+nTp0udPa0kW1tbBAcHY/HixfDz84O5uTmsrKzQrVs3/Pzzz9izZw8MDAwqdKy9evXCw4cP8emnn6Jz586wtbWFsbExnJ2d4e/vj2XLlmHjxo3l3mZcXBy2bduG119/He3bt4ednR0MDQ1hYmKCRo0aoXfv3vj0008RFhamuA25ND///DO2bNmCrl27wtbWFqampvDz88OyZctw+fJlODg4VOj4q5uRkRHWr1+PGzdu4O2330bz5s1hZWUFAwMD2NjYoE2bNnjnnXdw6tQpxZhAtY1EIsGZM2fw7rvvwsvLS2nA+2eJRCIsXLgQ4eHh+OCDD9C2bVvY2NjAwMAAVlZWaN68OaZOnYpDhw7hwoULVRZjRduHSCTCmjVrcOHCBYwbNw6urq6QSCSwsrJCmzZt8PHHHyMsLKxcY9cVMzMzQ2BgIBYtWgRvb29IJBJYW1ujTZs2+PTTT/HPP//A3t6+1G34+PggKCgIo0ePhrOzs9JYVuVhbGyMnTt34siRIxg+fDgcHR1hZGQEGxsbdOnSBatXr0ZISAi8vb0rtP2q4ODggMuXL2POnDnw8PCAkZER6tWrhy5dumD9+vU4ffo0TE1Nqz2OBg0a4PLly/j000/RvHlzmJqawtbWFl27dsWWLVvKHHewplTlZ4wmnp6euH79Onbu3Ilhw4bB2dkZEokExsbGcHR0RJ8+fbB69Wo8ePAAgwcPrqIjU3b16lWsXr0aL774Ipo1awYrKysYGRnBwcEB/fr1w08//YR//vmn1n4GFKvsuVy0aBF++OEHdO3aFTY2NtV623pVXBeHDh2KGzduYMKECWjUqBGMjY3h5OSEcePGITg4WHGbMBGRrogEQcvRyomIiEiFu7s7oqKisGjRIsWtjUTF2D6IiIiIqCLYw46IiIiIiIiIiKgWYcKOiIiIiIiIiIioFmHCjoiIiIiIiIiIqBZhwo6IiIiIiIiIiKgWYcKOiIiIiIiIiIioFuEssURERERERERERLWIoa4DqMtkMhmePn0KS0tLiEQiXYdDREREREREREQ6JAgCMjMz4ejoCLFY842vTNhVo6dPn8LFxUXXYRARERERERERUS0SExMDZ2dnjeuZsKtGlpaWAOQvgpWVlY6jqbzc3FycPn0aANC7d2+YmprqOCLSJbYHKontgUpie6CS2B7oWWwTVBLbA5XE9kAl1dX2kJGRARcXF0XOSBMm7KpR8W2wVlZWdSJhZ2RkBDMzMwDyY6orbxaqGLYHKontgUpie6CS2B7oWWwTVBLbA5XE9kAl1fX2UNbQaZwlloiIiIiIiIiIqBZhwo6IiIiIiIiIiKgWYcKOiIiIiIiIiIioFmHCjoiIiIiIiIiIqBZhwo6IiIiIiIiIiKgWYcKOiIiIiIiIiIioFmHCjoiIiIiIiIiIqBZhwo6IiIiIiIiIiKgWMdR1AKQqPz8fGRkZyM/P13UoSmQyGVxcXAAAiYmJEIuZ732esT3IWVlZwdLSEiKRSNehEBERERERUR3BhF0tk5GRgczMTNSrVw/169evVUkAmUwGKysrAPIkxfOaoCE5tgf5OUhPT0d8fDwaNmyo63CIiIiIiIiojnj+vmHXYvn5+cjMzISjoyNMTU1rVbKOiFSJxWLY2trCwMAA2dnZug6HiIiIiIiI6ggm7GqRjIwM1KtXj4k6Ij1jbW2NrKwsXYdBREREREREdQQTdrVIfn4+TExMdB0GEZWTsbExCgsLdR0GERERERER1RFM2NUy7F1HRERERERERPR8Y8KOiIiIiIiIiIioFmHCjoiIiIiIiIiIqBbRm4Td5MmTIRKJSn3k5eWprXvx4kW8/PLLaNCgAUxNTeHr64tly5ZpLE9ERERERERERKQrhroOoLw8PT1hb2+vdp1YrJp/3LlzJyZNmoSioiI4OTnBxcUFd+7cwSeffIJDhw4hMDAQZmZm1R026Sl3d3dERUXh0aNHcHd313U4RERERERERPQc0JsedsUWLFiA8+fPq30YGxsrlY2MjMTUqVNRVFSE1atXIyYmBteuXUN4eDi8vLxw+fJlzJs3T0dHQhXl7u5eZm9LkUiErVu36jpUIiIiIiIiIqJy07seduWxZs0a5Ofno1+/fvjggw8Uy93c3LB582Z07doVP/74Iz7++GM4ODjoMFLdyc4GQkKAzEzA0hLw8wPMzXUdlXZK620J4Ll9TYmIiIiIiIhIv9XZhJ0gCPj9998BAFOnTlVZ7+/vD29vb4SGhuLgwYN48803azpEnQoNBb77Dti+HUhP/2+5tTUwcSIwYwbg7a27+LSxYMECTJ48WddhEBERERERERFVKb1L2O3btw8HDhxARkYG7O3t0bVrV0ycOBHW1tZK5aKjoxEbGwsA6Nq1q9ptde3aFaGhoQgODq6ShN3jx4+Vfs/MzAQA5ObmwsjIqMz6MpkMMpms0nGUZd064P33RZDJRCrr0tOB9euBb78V8PnnAmbNUo5P3XNdqanzVdP70he1rT3omkwmQ25urq7D0JmSk/hwQh9ie6CS2B7oWWwTVBLbA5XE9kAl1dX2oO33Rr1L2B0+fFjp971792LRokXYtWsXBgwYoFgeHh4OAJBIJHB0dFS7rcaNGyuVrSwXFxe1y0+fPq3VxBYuLi6wsrKqklg02bBBggULTMssJ5OJMHeuCHl5uZg+PV9lfVZWVnWEp5Xi5FBeXh4yMjLKLB8dHY1WrVrBxcUFt27dUlvG1tYWAJCamqp2X1lZWWr3derUKWzcuBFXr15Feno66tevj4CAALz//vvw8PAoNY5t27Zh27ZtCA8PR1ZWlsq+9Yku20NtkZSUhOvXr+s6jFrh7Nmzug6BahG2ByqJ7YGexTZBJbE9UElsD1RSXWoPOTk5WpXTm0knmjRpghUrVuDmzZvIyMhAZmYmTpw4gU6dOiE1NRVDhw7FlStXFOWLkx82NjYQiVR7kgGakzR1VViYGB99ZFKuOh99ZIKwML1pJjXqww8/xMiRI3Hs2DEAgLe3NzIzM7Fr1y707NkTwcHBGuvOnTsXs2fPRkJCAjw9PVV6iBIRERERERHR80tveth9/PHHKsv69u2Lnj17onv37vjnn38wf/58nDp1CsB/3SWfnTm2JIlEAkD77ohliYmJUfo9MzMTvr6+6N27t1Y95xITE6u1h93PP6u/DbY0MpkIO3ZYYu1aATKZTNGTysLCAmKxbhJ5xfs1MTHR6nxZWFgo6pVV/tn1xfuysLBQWvfDDz/g+++/h4eHBzZu3IiAgAAAQFFREVatWoWPP/4Yr7/+Ou7fvw8TExOlOJ4+fYpffvkFv//+O4YMGQJA3gZNTcvu+Vib1Jb2UFvUr18fPj4+ug5DZ/Ly8hT/9erRo4ei3dPzie2BSmJ7oGexTVBJbA9UEtsDlZSXnIxbu3fDMDcXLfz9YdS6tf7MklkKbe4UBPQoYaeJsbExli1bhv79+yMwMBCpqamwtbVVvLELCgo01s3Pl9/qWVWJEmdnZ6Xfi18EU1NTrfYhFovLTHpUNJ/3+efAzz9XrO769SJs2SL6t6fifz3BMjLUJ//69AEuX0aJchXbb1mmTp2qdkKRYqmpqbCxsVE6p2WdX03rS742BQUFWLp0KQwMDLB//360adNGqdxHH32EGzduYP/+/di/fz8mTJigtO2ioiIsXboUQ4cOVdQz1/OLjjZtt64Ti8V6l3StLiYmJjwXpMD2QCWxPdCz2CaoJLYHKont4Tn27yyZJtu3o6e+zpJZisLCQq3K6X3CDgC6dOkCQN7jJyIiAu3atVPc7pqWlgZBENTeFlt8K2xxWX3w7zwW5RYVpTwbbHnJO1Jp1zsvJ6ficZaHp6cn7O3tNa43NKye5n3x4kXExcWhQ4cOSsm6koYMGYL9+/fjzJkzioRdSRMnTqyW2IiIiIiIiIj01tq1wHvvATKZagbiv1kygS++AGbPrvn4alCdSNiVnIFVKpUCkCdzAHkvuqdPn8LJyUmlXkREhFLZuqwOTaiisGDBAkyePLnG93v79m0AQGRkJLp166a2TFpaGgDgyZMnKuvq16+P+vXrV1t8RERERERERHpn7Vpgzpyyy8lk/5Wrw0m7OpGwCwkJUTwvvi3V1dUVDRs2RFxcHIKCgjB69GiVekFBQQCATp061UygVcDSsmL1/h0+rcLMzAADA+GZpep73JmZVTxOfZD+b1fFxMREJCYmllpW3fiI+n77KxEREREREVGVCg2V96wrj/feAwYM0OvbY0tTJwad+uKLLwDIZ+ks7kknEokwbNgwAMCmTZtU6ly4cAGhoaEwMjJSDPyvDzIyKvaYN09+u3dFWFsDiYlAWpqA6Oh0REenIy3t2eTdf06dUt63rhXfDi0I6mPOzs4u1/aKJ48YN24cBEEo9REYGFip2ImIiIiIiIjqvO++k/ecKw+ZDNiwoXriqQX0ImH3119/4cMPP8SjR4+Ulqenp2PmzJnYvXs3AOCTTz5RWv/BBx/A2NgYJ06cwJo1axQJm6ioKEyZMgUA8Prrr6Nhw4Y1cBS6ZW4uH5uxIiZNkvea01fFPdo09YZ78OBBubbn6+sLALhz507lAiMiIiIiIiJ63mVnA9u3V6zutm3y+nWQXiTssrOz8dlnn6Fx48ZwdnZGx44d0aZNG9jb22P9+vUQiURYtGgRXnnlFaV6Hh4e+OmnnyAWizFv3jy4uLigbdu28PT0xP3799GuXTusWbNGR0dV82bMAMo7kadYDEyfXj3x1BQ7OztYW1sjNzdX6fbpYhs3bizX9rp374769evj5s2b7EFHREREREREVAGCIOBBygMcO/BFxWfJTE8H7t6t2sBqCb1I2LVr1w4LFy5E7969YWBggDt37iA0NBROTk6YOHEiLl68iMWLF6utO3HiRJw7dw6DBg1Cbm4u7t69i8aNG2Px4sU4f/78czWemLe3fCKV8vjiC/2/HVwkEqF///4AgLlz5yJLPuUtAGDbtm3YvHlzubZnYmKCpUuXAgBGjRqF33//XeV22zt37mD+/PmKcRKJiIiIiIiInmfpeek4GXESy88ux6Bdg9BgTQN4rvfEmhOLKrfhzMyqCbCW0YtJJ1xcXLB8+fIK1/f398ehQ4eqMCL9VTyByr+zJGskFgNffgnMmlUjYVXIihUrSu0dN3r0aMycORMAsGTJEhw5cgQnTpxAw4YN4eXlhdjYWMTGxmLDhg2YXs5uhNOnT0d0dDQ+++wzDB8+HPXq1UOTJk1QVFSEyMhIpKSkAAB69epV8QMkIiIiIiIi0kNFsiLcTbyLS48vyR9PLuFe4j0IUB1bPtO4kjuro7Ne6kXCjqrW7NnyiVQ2bJDf7l2y56m1tXzMuunTa3/PuvDwcISHh2tc3759e8Vzb29vnD17FgsWLEBQUBDu37+PVq1a4ccff8SgQYPKnbADgJUrV2Lw4MH49ttvce7cOdy8eRMWFhZwdnbG0KFDMWLECPTp06dCx0ZERERERESkL+Kz4hH8JFiRoLv89DKyCrLKrgggxB5IkwA2+RXYsbU14OdXgYq1HxN2zylvb2DdOmDFCvnt3pmZ8qS0r698goraLDIyskL12rRpg6NHj6pdp2kG2bL25e/vD39/f6327+7urnE/RERERERERPqgoKgAN+Ju/Nd77vElPEp7VHZFNTzNPdG+YXtE9IhB27/Ol38D+j5LZimYsHvOmZsDHTroOgoiIiIiIiIiqgkyqQyyXBlkOTIU5RRBlvvfT1mODEW5RZDlyMtIs6V4nPMYV/Ov4pr0Gm4INxBiGIICUUG592uZawmfxz7wfewLnyc+8HnsA8s8+e2sUkRDwAWIUMrYXc+qC7NkloIJOyIiIiIiIiKiWkIQBAgFAoqyiiDNlEKWLUNRVhGKsv99/PtcZXnxshx5wq0ou0jpeXGCTijQfOdXnlEewhqF4a7zXdx1uYu7zneRbJksX1mODJJYJkaTuCbweSJP0Pk+9oVzsjNEEKktnwNXPMR0NMW32u+kLsySWQom7IiIiIiIiIiIKkEmlaEoowjSDCmKMovkzzOfeZ5RhKLMEsuziv77maX8uyCt/uGUBAh4Wu+pPDnnfBchziF42PAhZOJy9HL7V/2M+orec76PfdEsthlMCk3KtY3HGAkAaIINpfe004dZMqsAE3ZERERERERE9NySSWUoSi+CNE0KabpU/rPkI12ebJOmS+UJufRnnmdIIcspf5KrpuUY5yDUKVSRoLvrfBfp5ullV3yGkdQIzZ42g+9jX/g99oPvY180yGigUk5sKobY5N+HpMRzEzFEEpHqOokYgsksxGQNgfWd3bC4tR8G+Zn/bVCfZsmsAkzYEREREREREZFekxXKIE2RojC1ENJUqfLzfx+FKSV+L5GQK8oq0nX4VU4mkuGx3WNFz7m7zncRaR9Zod5zrmJXtDNuh3bm7dDeuj1aWLeAqbmpPCFX/DARw8DUQPFcZCSCSKT+9teyeQLoh9ykNfhn61YY5uaiXUAATNq2rf2zZFYhJuyIiIiIiIiIqHYQAOQB+VH5KMwsRGFSIQqTCyFNlqIwuVCedFPzvCizbiTdxCZiGFgYwMDSQP7T/N/n5gYQm4v/W2b+33qxuRjZptm4KdzE1fyruJJ9BVfSryC1ILXc+zc3MkdHp47o7NwZXZy7oJNzJ9ib21fDkWoTjDnSPD0BAEL79oCpqW7i0BEm7IiIiIiIiIioWgiCAGm6FIUJhShIKEBh/L8/E/9NxhX/TCpEQWIBrBKsICoU4QZu6Dp07YgAAysDGFoZKv+0NJQn2ko+t/x3/b/PFUk5C3kZsbkYYkNxmbuUCTLcT7qPi48v4mLMRVx8fBF3o+5CQPnHvfOy80IXly7o7NQZnZ07w8/eD4ZipopqA74KRERERERERKQ1QRAgTZGiIK5A9ZFQIE/Oxf/3XCjUPpGkaRbR6mJgaQBDG8P/HtaGSr8bWP+73kq+zsDKQPmnuQFE4uqNOSM/A/88+QcXYi7g4uOLCH4cjNS88vees5JYoZNTJ3Rx7oLOzp3RybkT6pnWq4aIqSowYUdEREREREREkEllKEwoRP6TfBQ8LUD+U/lPlaRcfEG5knDVTWwmhpGdEQzrGcKonhEMbQ1haPvMc1v5esXzf5Nx2vRoq0mCIOBBygNciLmgSNDdSbhTod5zvg18Fcm5Ls5d4NPAB2JR7Tpe0owJOyIiIiIiIqI6TpohRf7jfOTH5Mt/PslXJOQUibn4AkDHk50K5gJk1jJYu1hDYi+BUX0jGNX/NxlnZ/RfYq7EcwMTA90GXQk5hTm4/OSyIjl38fFFJOUklXs7NiY2it5zXVy6oKNTR9iY2FR9wFRjmLAjIiIiIiIi0mNF2UXIi8pDXnSePBlXMjH373NdTcpgWM8QxvbGMLI3Uv7ZwOi/x79JOamZFCf/PgkA8O/nD9M6NsmAIAiIyYhR9J67EHMBN+NvQiqTlntbxb3nujh3gb+LP7zqe7H3XB3DhB0RERERERFRLSUIAgqTC5EflS9Pyv37yI/+73dpcvkTPpVhaGcI44bGqg8H+aNkUk5spH0SSZar4+59VaywqBA34m4gKCZIkaB7kvmk3NuxklgpbmstnrmVvefqPibsiIiIiIiIiHRImiVF3qM85D3KQ25ErvLzyDzIsmsgkSUCjOyNIHGUwNjRGBJHCSROEhg3MoaxY4mknL0xxMbsyaVOck4yLj6+iKDoIFx4fAGXn1xGrjS33NvxsvOCv4u/ovccx557PjFhR0RERERERFSNBEFAQXwBch/kIjc8F7kPcpEX8V9yrjCxsFr3L5KIIHGWQOIsgYmLCSTOEhg7GUPiJJEn6JzkPePK0xvueScIAsKSwxAUE4Sg6CAExQThfvL9cm/H3MgcHZ06KhJ0nZ07w87MrhoiJn3DhB0RERERERFRJQmCgIK4AkVCrmRyLvdBLoqyqmkMOQPIE3GuJjBxM4HERZ6YU/x0lk/cIBKJqmf/z4k8aR6uPL2iSM5diLmA5Nzkcm/H3cYd/i7+8Hf2h7+LP1o4tIChmKkZUsVWQVQHBAYGYsmSJbh69SoyMzMBAI8ePYK7u7tuA6sCkZGR8PDwgJubGyIjI3UdDhERERE954pyi5Ablouc+znyR6j8Z+796knKiU3EMHE3gcRNAhO3f5Nyrv89N3Y0htiQPeOqWmJ2IoJignA++jyCYoJw9elVFMrK1xPSSGyEto3aoqtLV3kPOpcucLR0rKaIqa5hwo70iru7O6Kiosost2XLFkyePLn6A6oCa9euRVpaGmbPng0bG5ty1w8JCUH//v1RUFAADw8PtGzZEgBgYmJSxZFWj8oePxERERFRdShILEDO3Rxk382WJ+X+TczlR+cDQhXuSARIXCQw8TCBaWNTpZ8mjU1g7GDM3nHVTBAEPEh5gPPR5xUJuorc3lrfrL4iOefv4o92jdrB1KhuzXRLNYcJO9JLnp6esLe317jewcGhBqOpnLVr1yIqKgqTJ0+uUMJq06ZNKCgowLvvvouvv/666gOsZmUdv5GREby8vODk5FTzwRERERFRnadIzIVkIzskW/G8KseVExmLYNrEFKZN/314msK0iSlMGpvAxNWEkzjUsMKiQlyPu66UoEvITij3drzre6OrS1f5w7UrPOt5MrlKVYYJO9JLCxYs0JsedNUtNDQUAPDiiy/qOJLq4eTkpDhGIiIiIqKKKsouQvadbGTdykL2rWxk38mu0sScyFgE06amMGtm9l9i7t/knMRJApEBEzm6kpmfiUuPL+Fc9Dmcjz6PS48vlXv2VhNDE3Rw7KBIznVx7sLJIahaMWFHpOdyc+UfNKam7GpNRERERCTIBORF5SHrpjwxV5ygy32QWyW3sho5GMHMy0z+8P7vp4m7CZNytUR8VjzOR59XJOhuxN1AkVC+8QUbmDVAV9eu6ObSDV1du6Jto7YwNjCupoiJVLHfLdV5r7/+OkQiEfr27QtBUP2E/uSTTyASidCiRQvk5+crlkdERGDVqlUICAiAi4sLJBIJGjRogAEDBuDw4cOl7jMlJQWLFi1CmzZtYGVlBQsLC/j4+GDatGm4fv06AGDr1q0QiUSKMfk8PDwgEokUj8DAwFL3MXnyZKVyvXr1UtQt7n1YvA9NvREDAwMhEokQEBCgcblMJsO6devQvHlzmJiYwMHBAVOnTkViYmKNHH9kZCREIpHGCTSSk5Mxb948eHl5wdTUFLa2tggICMDOnTvVvt4lz0l+fj4WL16Mpk2bwsTEBC4uLpg7dy6ys7M1HhsRERER1R6yAhkyb2QidnMswt4Jw7Wu13De5jyCGwcjZFgIIhdFIml/EnLDy5msEwEmjU1gN8gOLvNd4L3VG20vtUXX1K7oGtcVbc60gdePXnCZ6wK7l+xg2sSUyTodEQQBD1MeYsv1LZhycAqarW+Ghl80xMhfR2Jd8Dpcjb2qVbKumV0zvNb6NWwasgn337mP+Pfj8fuY3/Ge/3vo7NyZyTqqcexhR3Xe2rVr8ffff+PkyZNYt24dZs+erVgXHByMFStWwNjYGDt27IBEIlGsW7FiBTZt2gQLCws4OjqiZcuWePLkCY4fP47jx4/js88+w/z581X2d/PmTQwcOBBPnz6FWCyGt7c3jI2NERERgR9++AF5eXnYunUrHBwc0LVrV1y5cgX5+flo37690v6tra1LPa5mzZqha9euuH37NjIyMtC8eXNFnWbNmlXyrP1nwoQJ2LVrFzw9PdG0aVPcv38fmzdvRnBwME6dOqUUc00ePwA8ePAAvXv3RkxMDIyNjdG8eXOkpaXhzJkzOHPmDE6cOKFI0D2rsLAQ/fr1w7lz5+Dr6wt3d3eEh4fjq6++wp07d3DixInKnzwiIiIiqjJFeUXIvpWNzGuZyLqWhcxrmci+nQ2hoHLd5kw8TGDuZw4zPzOY+5nD3NccZt5mMDA3qKLIqSrJBBlCEkJwNuoszkWfw9mos4jNii3XNgzFhmjbqC26uXRDN1d5Dzp7c81jpBPpAhN2VOdZWFjg559/Ro8ePfDhhx+ib9++8PPzQ05ODiZMmICioiKsWLECrVq1Uqo3YsQIvPHGG+jYsaNSwufcuXMYM2YMFi5ciJEjR6JJkyaKdRkZGRgyZAiePn2KAQMG4KeffoKzs7NS3ejoaADyMedefPFFxcy3v/76q8ZeZOosWLAACxYsQEBAAM6cOYP169er9JSrrAsXLqBBgwYIDg5Gx44dAQBhYWHo06cPQkJCsHPnTkyZMkVRviaPXxAEvPLKK4iJiUHPnj2xd+9exWQjx44dw8iRI7F9+3Z07twZ06dPV6n/66+/wtPTE6GhoYoE56VLl9C/f3/89ddfOHbsGAYMGFDuc0ZERERElScrkCH7djYygjOQeTlTnpwLyQbKd1ejEkM7Q1i0soBFSwuYtzSHeQtzmPuYMzFXyxUWFeJa7DVFgu589Hmk5qWWaxvmRubwd/FHN9du6O7aHR2dOsLc2LyaIiaqGkzY6YkLThdQlFmJT6cqIvzbj1yE8nX3NrA0gP8T/yqL47XXXsNrr72mcX1qaqrSjKP+/v6YN28eVq5cifHjxyM4OBhz585FeHg4evTogffff19lG5omcejevTuWLVuG119/HXv37sWCBQsU63744QdER0fDx8cHBw4cUOl91r1793IeqW4VFhZi/fr1imQdIO+9N2/ePMycORMnT55UStjV5PGfOnUKV65cgUQiwZ49e5RmBh4wYAAWLVqEefPmYdWqVZg2bZpKLzupVIpt27Yp9Ubs3LkzXn/9dXz55Zc4evQoE3ZERERENUEAxHFiJO1NQt71PGT+k4nM65kQ8ivWc05kKIKZjxnMW5orknMWrSxg3NCYM3jqgQJZAcJzwnH1wlVcfHoRF2IuIKcwp1zbsDe3VyTnurl2Q+uGrWEoZvqD9AtbrJ4oyiyqFQm72sLT0xP29pq7LBsaqjbtJUuW4NixY7h+/ToGDRqEv/76C1ZWVti+fTvEYvXDOSYmJmLXrl0IDg5GQkIC8vLyAADp6ekA5Ld/lnTw4EEAwKxZs1SSVfrI1tYWw4cPV1neoUMHAPLx5UqqyeMvvmV11KhRaNiwocr6adOm4eOPP0ZUVBTu378Pb29vpfWtW7dG+/btVeoVH1tEREQ1RE1EREREhSmFyLiUgYzgDKRdTIPlJUuIM8V4iIfl3pbYVAyL1hawaGsBy7aWsGhjAXNfc4glHK5dX2QXZOPi44s4E3kGgY8CEfwkGIVCIfBA+2142High1sPdHftju5u3eFZz5PJWdJ7TNiRXlqwYIHGiRQ0MTIywo4dO9CuXTv89ddfAICvv/4abm5uasufOHECo0ePViTn1ElJSVH6/d69ewDkPbXqgpK3+5ZUnCx9dnKGmjz+sLAwAICvr6/a9ZaWlnBxccGDBw8QFhamkrAr69iysrKqMFoiIiKi55MgCMh9kIv0oHRkXMhAelA6cu4q95YSazkXooGlASza/JuYayf/aeZlxske9ExGfgbOR5/HmcgzOBt9FleeXoFUJi3XNvwa+Ckl6JytnMuuRKRnmLDTEwaWtWNchcrcElsbNG3aFK6urggLC4O1tTVGjBihtlxaWhrGjh2L9PR0TJw4ETNmzICXlxesrKwgFotx8uRJ9O3bF4WFhUr1MjIyAEDpdlx9Zm6uflyH4h6Jz87CWpPHX5xQK62npYODAx48eIDMzEyVdeU9NiIiIiIqmyxfhsyrmf8l6C6kozChsOyKzxBJRLBsawnLjpaw6mgFy/aWMG1qCpGYyTl9k56XjvPR5xEYGYjAqEBci70GmSDTur6ByABtG7VVJOi6uXaDnZldNUZMVDswYacnqnL8t4qSyWSKhExx4krfLFy4EGFhYRCLxUhPT8ecOXPw008/qZQ7evQoUlNT0aVLF7WzjMbExKjdvqWlJVJTU5GWlqax515NKo5bU/Lp2R5ylVWTx29hYQEASEhI0FgmPj5eERcRERERVb2inCJkXMxAWmAa0s6kIeOfjHKPPSeIBMicZHAIcICtvy2sOlrBvIU5xMb6932DgLS8NJyLOoczUWcQGBmI63HXy5WgMxQZwtPME4NaDMILTV9AF+cusJTw73l6/jBhR8+Ns2fP4ssvv4SZmRkOHTqEESNGYOPGjRgyZAgGDx6sVLZ4bLYuXbqoHfvg2bHrivn5+eH8+fO4dOmSyqyzmlTn2ArFvcgSExPVrn/woBwDQ2ihJo+/eLKIu3fvql2fmZmpSKyWnFiCiIiIiCquKLsI6RfSFQm6zH8yIRSWL0FnWM8Q1v7WsOpiBUkbCYJTgwFzwL+fP0xNTaspcqou6XnpOBd9Dn8/+huBUYG4HntdcWeWNkwMTdDFuQt6uvVEp4adkHEvAxKxBP169GN7oOcaE3b0XMjIyMCkSZMgk8mwZs0a9O7dG99++y3GjRuH119/HXfu3EGDBg0U5Ys/GIp7aJWUnJyMTZs2qd3P0KFDcf78eaxfvx6vvfYajI2Ny4yteF+5ubkVObRSNW7cGABw48YNSKVSpck4ZDIZtmzZUqX7q8nj79+/P9asWYNff/0Va9asUZl44ocffkB+fj7c3Nzg5eVVrm0TERERkVxRdhHSg/5N0AWmIfNyJgRp+RJ0ps1MYd3VGtZdrWHlbyUfd+7fW1tzc3OBE9UROVWXzPxMxS2uf0f+jauxV8vVg87MyAzdXLuhp1tP9HTriQ5OHWBsIP/ekJubixP32SCIAGg5uieRnps5cyYiIyPRr18/zJgxAwDw6quvYsyYMUhISMCbb76pVL579+4AgF9++QUnT55ULI+NjcWIESMglaofFPXNN9+Em5sbQkJCMHz4cDx58kRp/fnz57Fz506lZcVJtTNnzlTuINVo1aoVHB0dERsbi0WLFilujc3Ly8Ps2bM19k6rqJo8/t69e6NDhw7Iz8/HK6+8onRr7IkTJ7BkyRIAwP/+9z/OEEVERESkJaFIQMY/GYhaEYUbvW7gfL3zuNX/FqJXRiPjYkaZyTqRsQhWXa3gMs8FzQ82h3+CPzrd7wTvzd5oNLURzH3MOQ6dnskpzMFfD//CglML0GVTF9iussXAXQOx+sJqXH56ucxknbmROfo36Y+VfVbi4tSLSJufhuPjj2NB9wXo6tpVkawjImXsYUd6acWKFdi4caPG9aNHj8bMmTMBAL///ju2bdsGW1tblR5lGzZswLlz53DgwAFs2bIFr732GgCgXbt2GDlyJPbt24e+ffuiadOmsLCwwJ07d2BqaorPPvsMs2fPVtmvpaUlDh48iAEDBuDw4cNwdXWFj48PjIyM8OjRI6Snp2PSpEkYN26cos6YMWNw+PBhTJ8+Hd9++y3s7OQDqK5duxatW7eu1HkyMDDAqlWrMGHCBKxYsQI//fQT3NzcEBYWBplMhpUrV+L999+v1D5KqsnjF4lE2LVrF3r16oXAwEC4urrCz88PGRkZilt9J0yYgLfeeqvKjo+IiIioLsp9mIuUv1KQejIVaafTIE3VfsZOkUQE6y7WsAmwgU2ADSw7WcLApHZMOEcVU1BUgODHwTj96DROR57GxZiLKJRpP3GIhbEFurt2R4B7AHq69UTbRm1hZGBUjRET1U1M2JFeCg8PR3h4uMb17du3ByC/pbW499x3330HR0dHpXLFSbwBAwZg1qxZ6NWrF9zd3QEAO3fuhI+PD37++WdERUXBzs4OI0eOxOLFixEbG6tx361atcKdO3fwxRdf4I8//sCjR49gYGAAZ2dnvPrqqyoJpAkTJiA1NRWbNm1CeHg47ty5A0A+U21VGD9+PCQSCVatWoWQkBBERESgT58+WL58eakTNlRUTR5/06ZNcf36daxatQoHDx5ESEgIJBIJevTogTfeeAPjxo1j7zoiIiKiZ0jTpfIE3V+pSP0rFXmP8rSuKzYRw8rfSp6g62kDy45M0Om7IlkRrsddx6mIUzgdeRrno88jpzBH6/rmRubo5toNvdx7oZdHL7Rt1BaGYqYaiCpLJGiaPpIqLSMjA9bW1khPT4eVlVWZ5R8/fgxnZ+caiKxi6sIssVR12B6U1fb3b3XLzc3FiRPy8Ub69eMAwc87tgcqie2BnsU2UfMEQUB2SDZSjqQg+Ugy0s+nA0Xa1RUZiWDd1Ro2vW1g08sGVh2sIJZU3d99bA81TxAEhCSGyHvQPTqNwMhApOena13f1NAUXV27opd7LwS4B6CDY4cq60HH9kAl1dX2oG2uiGlvIiIiIiKiOqYouwipp1MVSbr86Hyt65q3NIftC7aw7WsLm+42MDBnDzp9F50ejVMRp3Dy0UmcfnQacVlxWteVGEjQxaWLvAedey90dOoIiaGkGqMlIoAJOyIiIiIiojoh92Eukv9MRvKRZKQFpkEo0O5mKmMnY9TrWw+2L9jCpo8NJA2ZjNF3Kbkp+PvR3zj16BRORpxEeIrm4YSeZSAyQAenDujt3hu9PXrD38UfpkZ1o2cTkT5hwo6IiIiIiEgPCYKArGtZSDqQhKQDSci+k61VPZFEBNtetqg3oB5s+9nCzNuM4/7quTxpHs5Hn8fJiJM4GXES12KvQYD2o1+1cmiF3h690cejD7q7dYeVpOwhnYioejFhR0REREREpCdkhTKkn02XJ+kOJiE/RrtbXSWuEti9ZId6A+vBtpctb3PVczJBhlvxt/DXw7/wV8RfOBd9DnlS7ScP8azniT4efdCncR8EuAegvln9aoyWiCqCCTsiIiIiIqJarCi7CCnHU5B0IAnJfyZDmiots47IUATrbtaoN7Ae7AbawcyXvej03eOMx4oE3cmIk0jMSdS6rr25PV5o/AJe8HgBfRr3gau1azVGSkRVgQk7IiIiIiKiWkaaJUXyoWQk/pKIlGMpkOXJyqxjWM8QdoPsYDfYDvX61oOhNb/u6bPsgmwERgbixMMTOBFxAqFJoVrXtTC2QIB7APp49MELjV+AXwM/JmyJ9Ayv4ERERERERLVAUXYRkg8nI+GXBKQc1i5JJ3GVoP6w+qg/tD6su1lDbCiugUipOsgEGW7G3cSJhydw/OFxnI8+j0JZoVZ1DUQG6OzcGX0b98ULjV9AR6eOMDIwquaIiag6MWFHRERERESkI0W5RUg5koKEXxKQ/GcyZDllJ+nMW5qj/lB5ks6itQV7Tumx+Kx4RQ+6Ew9PICE7Qeu6zeyaoW/jvujbuC8C3ANgbWJdjZESUU1jwo6IiIiIiKgGyfJlSDmWgoS9CUj6Iwmy7DKSdCLAupu1vCfdy/Vh2ti0ZgKlKldQVIALMRdw7MExHH94HDfibmhd187UDi80fkGepGvSl+PQEdVxTNgRERERERFVM0EQkHExA/E/xyNhb4JWE0dYd7NGgzEN0GBEA0gaSWogSqoOUWlROPbgGI49PIZTEaeQWZCpVT0jsRG6unZF/yb90bdxX7Rp1AZiEW95JnpeMGFHRERERERUTXIf5iLu5zjE74hH3sO8MstbdbGC/Rh7NBjZABInJun0UZ40D2ejzsqTdA+O4V7SPa3rNrNrhn6N+6F/0/4IcA+AhbFFNUZKRLUZE3ZERERERERVqDClEAm/JCD+53hkXMgos7xlR0tFks7E1aQGIqSqFpEagSPhR3D0wVH8/ehv5EpztapnLbFGn8Z90K9xP/Rr0g8eth7VHCkR6Qsm7IiIiIiIiCpJVihD8uFkxG+PR/LhZAgFQqnlzZubw2G8AxqMaQBTd45Jp2/ypfk4F30OR8KP4Ej4EdxPvq913faO7TGgyQAMaDoAnZw7wVDMr+VEpIpXBiIiIiIiogrKCc9B7KZYxG2NQ2F8YalljRyM4DDOAQ4THGDRirO76puY9BgcfXAUR8KP4GTESWQXZmtVr75ZffRv0h8Dmg5Avyb9YG9uX82RElFdwIQdERERERFRORTlFiHptyTEboxFWmBaqWXFpmLUH1YfDhMcYPuCLcSGnDRAX0hlUlyMuYjD4YdxJPwIbifc1qqeWCRGZ+fOil507RzbcbIIIio3JuyIiIiIiIi0kHUzC7EbYxG/Ix7StFJmeRUBNr1t0HBCQ9QfXh+GlvzapS9Sc1Nx7MEx/Bn+J46GH0VqXqpW9ezN7fFi0xfxYtMX0bdJX9QzrVfNkRJRXcdPDqIyLF68GEuWLMGiRYuwePFiresFBgaiV69e6NmzJwIDA6stvpri7u6OqKgoPHr0CO7u7orlM2bMwO7du7FlyxZMnjxZZ/ERERERVQdpphQJuxMQ+1MsMq9kllrWpLEJGk1tBIeJDjBx5uQR+kAQBIQmheLPsD/xZ/ifCIoOQpFQVGY9EUTo4NQBA5sOxEDPgexFR0RVjgk70ltBQUHYtm0bzp49i9jYWOTl5aF+/fpo06YNhg4dildeeQXm5ua6DpOIiIiI9FD2vWw8+fYJ4rfHoyhTcwJHZCxCgxEN0Oj1RrAJsIFIzHHpart8aT7ORJ2RJ+nC/sSjtEda1bM1sUX/pv0xsOlA9G/an2PREVWz7GwgLMwGubmGsLUVoV074Hn6is+EHemdnJwcvPbaa/jll18AACYmJmjSpAlMTU3x5MkTHD58GIcPH8Ynn3yC48ePo0WLFjqJ08zMDF5eXnB1ddXJ/muKg4MDvLy8YG1tretQiIiIiCpFJpUh+VAynnz7BGmn0kota97cHI3eaASHcQ4wsjOqmQCpwpJzknH0wVH8cf8PHHtwDJkFpfeWLNbcvjkGeQ7CS81eQmfnzpzRlagGhIYC330HbN9ugvT0norl1tbAxInAjBmAt7cOA6whvNqQXiksLES/fv0QFBSEhg0bYtWqVRg1ahRMTU0VZe7evYuvv/4amzZtwsOHD3WWsOvYsSNCQ0N1su+atGjRInzxxRcQi3kLABEREemngsQCxG6MxdMNT5Efk6+xnNhcDIdXHNDo9Uaw7GjJWV5rufDkcPxx/w/8EfYHzkefh0yQlVlHYiBBb4/eGNRsEF7yfAluNm41ECkRFVu7FnjvPUAmAwDla2x6OrB+PfDtt8AXXwCzZ+sgwBrEhB3plSVLliAoKAgODg64ePGi0lhqxXx9ffH9999j/PjxTCIRERERkUYZ/2TgybdPkLAnAUKBoLGcRWsLOM5whP1Ye04gUYsVyYpw6fElRZIuNEm7f547WjriJc+XMKjZIPTx6ANz4+fonjuiWmTtWmDOnLLLyWT/lavLSTtmM0hvpKen4+uvvwYArF27Vm2yrqRu3brB399f8fvkyZMhEomwdetWteUXL14MkUhU6sQScXFxmDp1KhwdHWFiYgIfHx98/vnnkEpVZwkLDAyESCRCQECA2m3l5OTg888/R+fOnWFjYwMzMzN4enpiwoQJOHPmTKnHVlJ2djaWLl2Kli1bwtzcHCYmJnBxcUFAQAA+++wzFBYWKspGRkZCJBLB3d0dgiBg/fr1aNGiBczMzGBvb48JEyYgOjpa630D8kknDAwMVM5ryfOZnp6O2bNnw9XVFRKJBE2bNsWyZcvUnrdioaGhmDJlCtzd3SGRSGBnZ4eXXnoJp0+fLld8RERERCXJCmWI3x2Pqx2v4lqna4jfHq82WScyEsH+VXu0CWqDdtfawfENRybraqHcwlz8cf8PTD04FY2+aIRuW7ph9YXVZSbr2ju2x5KAJbj25jU8nvMYPw7+EUO8hjBZR6QjoaHynnXl8d578np1FT9xSG8cPnwYmZmZaNCgAUaOHFnj+09OTkbHjh3x9OlTtGjRApaWlggNDcUHH3yAoKAg7N+/X+sefdHR0RgwYADu3bsHAPD09ISlpSUiIyOxY8cOxMTEaDWzrFQqxQsvvIBLly5BLBYrtvP06VOcO3cOZ86cwbRp02BjY6NS9+2338aGDRvg6uoKX19fhISEYMeOHTh+/DjOnTsHLy+v8pwejdLT09GlSxeEh4ejefPmMDAwwMOHD/HJJ58gOjoaP/30k0qdX375BRMmTEBBQQEsLS3h6+uLuLg4HDlyBEePHsW6devw7rvvVkl8RERE9HyQZkoRuzEWj9c+Rn605ttejR2N4TjNEY3eaARJQ0kNRkjaSs5JxuHwwzgQegDHHx5HTmFOmXUkBhL0adwHQ5oNwaBmg+Bk5VQDkRKRtr77rvg2WO3JZMCGDcC6ddUTk64xYacnnL50Qma+dgOj1kaWEks8mfukUtu4cOECAKBr164wNKz5pvv999/Dx8cHZ8+eVfTuO3v2LIYMGYIDBw5gw4YNePvtt8vcTlFREYYPH4579+6hffv22L59O3x8fBTrb9y4gYsXL2oV08GDB3Hp0iW0atUKf/75J5ydnRXrEhMTsWvXLhgbG6vUe/LkCTZu3Ijdu3dj7NixAOQJybFjx+LkyZOYOHEiLl26VCXjsnz77bfo0qULTp48CUdHRwDAoUOHMGzYMGzcuBHvvfcevEuMGHrr1i1MnDgRYrEYP/74I6ZOnapIhB46dAjjx4/HnDlz0KNHD7Rq1arS8REREVHdlvc4D0++foKnPz5FUbrm2V6te1rD6R0n1H+5PsRGvBGptolMi8TB0IM4cP8AzkWdQ5Gg+bUs1sCsAQY1G4QhXkPQt3Ff9p4jqqWys4Ht2ytWd9s2YMWKujl7LBN2eiIzP1PrmYzqqidP5Ak/Dw8PnexfKpVi69atSrfi9ujRA8uWLcPMmTPx+eefY8aMGWUmuX777TdcvXoV9vb2OHbsGOzs7JTWt27dGq1bt9YqpvDwcADAlClTlJJ1ANCgQQPMmjVL47HMnDlTkawDADs7O+zcuRNubm74559/EBgYiF69emkVR2kMDQ2xc+dORbIOAAYPHoyXX34Zv/32G44ePaqUsFuyZAny8/Oxbt06vPHGG0rbGjx4MD799FO8++67iolFiIiIiNTJupmFmC9ikLA7AYJU/fh0YjMxHCY4wOltJ1i0sKjhCKk0giDgTsId/HbvN/we+jtuxt/Uqp53fW8M9RqKIV5D0NGpIwzEBtUcKRFVVkiIfEKJikhPB+7eBTp0qNqYagP+64j0RmamPGFprqPUeZcuXdC2bVuV5VOmTIGJiQkiIyNx//79Mrdz8OBBRb1nk3Xl5eLiAkB+u3BOTtm3ApSkrjegvb294nbj48ePVyq2YgMGDFBJJgJAh3+vqBEREYplBQUFOHLkCAwMDDB58mS12xsyZAgAlGucPyIiIno+CIKAlBMpuNnvJq60voL4n+PVJuuMGxnDY6UHujzuAq/vvZisqyVkggzBj4Mx/6/5aPZNM7T8viUWn1lcarJOBBH8Xfyx+oXVuP/Ofdx7+x5WvrASXVy6MFlHpCcyK9k3qbL1ayv2sNMTlhJLXYdQKVURv6WlfBvZ2dmV3lZFlLxttSRzc3O4uLggPDwcYWFhSr3F1Cket65z586Vjmno0KFwd3fHiRMn4OjoiAEDBqB79+4ICAiAn5+fxnpGRkZo2rSp2nXFxxkWFlbp+ACgSZMmapfb29sDALKyshTLwsLCkJeXB2NjYwwcOFBtPUGQ/9Fd3OOSiIiISCgSkLgvEVEropB9S/PfimZ+ZnB53wUOrzhALGHfhdpAKpPiduZtXEy/iBkbZuBp1tMy6xgbGOOFxi9gqNdQDPYajIYWDWsgUiKqLpaVTBdUtn5txYSdnqjs+G9VQSaTISMjAwBgZWWl9QQLVcXJST4w7KNHj2p0v8WKE0zqODg4IDw8XNELsDTF51DdRBDlZW5ujnPnzuGTTz7Bvn37sHfvXuzduxcA4Ovri1WrVmHQoEEq9ezs7DS+fg4ODgCg1bFoG6M6xfsvTsAB8gkqAHlPu6CgoFK3m5eXVyXxERERkf6SFcqQsCsBUSuikBuWq7Gc7Qu2cH7PGfX616uSMXqpcvKl+TgZcRK/3fsNB+8fRHJucpl1rCXWGNRsEIZ6D0X/Jv31vkMDEf3Hzw+wtq7YbbHW1vL6dRETdqQ3/P398e233+LChQuQSqXlnnii+I+zkgmiksrquZeYmKhxXUJCAoD/egGWprhMWlpamWW14ezsjM2bN+PHH3/E1atXERgYiH379uHKlSsYOnQogoKC0KlTJ6U6ycnJkMlkapN25TmWqmZhIb8dxcnJCY8fP67x/RMREZF+kOXLELctDtGfRSPvkfp/4okMRbAfaw/n95xh2ZrJHV3LLczFiYcnsO/ePvxx/w9k5GeUWcfB3AHDvIdhmM8wBLgHwNhAdTI1ItJ/5ubAxInA+vXlrztpEmBmVvUx1QZM2JHeGDhwICwsLJCQkIB9+/YpTZigjeKeXpoSbw8ePCi1fvGtrM/KyclBdHQ0AKBZs2ZlxuHn54fr16/j0qVLePnll8ssry1DQ0N06tQJnTp1wvz58/HKK69gz5492Lx5s0rCrrCwEA8fPoSnp6fKdoqPU5tjqWqenp4wMjJCbGwsUlJSUK9evRqPgYiIiGqvopwixG6MRfTqaBQ8KVBbxsDSAI5vOcJplhNMnE1qOEIqKacwB0fDj2LfvX34M+xPZBVklVnH3cYdw72HY7jPcHR27sxx6IieE2+9Vf6EnVgMTJ9ePfHUBhy4gfSGjY0N3n33XQDA7NmzERkZWWr5oKAgXLhwQfF748aNAQCXL19WKfv48eMyJ1m4cOECbty4obJ88+bNyMvLg5ubG7y8vMo4Cvm4c8X1UlJSyixfUcVj5D19qn4ckO+++05lWWJiIn799VcAQL9+/aotNk3MzMzQv39/yGQyfP311zW+fyIiIqqdpJlSRK+OxiWPS3gw64HaZJ2hjSHcF7ujc2RnNFnThMk6HckqyMLeO3sx6tdRaLCmAUb+OhJ77uwpNVnnYuKCUQ6jcGHSBUTMjMAX/b9AV9euTNYRPUf++KP8db74AihjCHm9xoQd6ZXFixejS5cuiI+PR5cuXfDzzz+rjGUWFhaGt99+GwEBAYrbOwHgxRdfBAAcOHAAR44cUSyPjY3FuHHjIJVKS923oaEhJk+ejKioKMWy8+fP45NPPgEAvP/++1qNiTJ06FC0b98eCQkJGDhwoMrMsjdv3sSGDRvK3A4AfPXVV1i7di3i4+OVlkdHR2Pjxo0AoHZmW0NDQ3z33XeK5BwApKSkYPz48cjLy0P79u3Rq1cvrWKoasuWLYNEIsHy5cvx2WefITdXeTya2NhYrFu3Dt9//71O4iMiIqKaI82UIurTKFxyv4SI+REoTChUKWPUwAiNP2uMzlGd4b7IHUb1jHQQ6fMtqyALu2/vxrC9w9BgTQOM3T8W++7uQ05hjsY6bRu1xae9P8X1qdex3ns9xjUah9YOrTnGINFz6NIl4OOPtS8vFgNr1wKzZ1dXRLUDb4klvWJsbIwTJ05g8uTJ2L9/PyZOnIi33noLTZo0gampKZ4+faqYPdTZ2VlpJlQfHx9MnToVmzZtwksvvQQPDw9YW1vjzp07aNq0KWbMmIF169Zp3Pdbb72FP/74A02bNkXz5s2Rm5urSLYNHjwYM2bM0OoYDAwMsH//fvTr1w/BwcHw9vZGs2bNYGlpicjISCQnJ6Nnz56YrkXf3qioKKxbtw5z5syBu7s77O3tkZGRgfDwcBQVFaF58+aYO3euSj0nJycMHDgQo0ePhpubGxo0aICQkBDk5ubCzs4O27dv19kfS61bt8bu3bsxfvx4fPjhh1iyZAm8vb1hbGyM2NhYxMTEAADmz5+vk/iIiIio+hXlFuHphqeIXhmNwiTVJB0AGDcyhss8Fzi+4QgDc/bEqmnZBdk4HH4Yv4T8gsPhh5EnLXtCsI5OHTHSZyRG+I5AY1v53S+5ubmIQlQZNYmoLhOJACcn4N+RpuDuDrz4IrBrl4D09P++l1pby8esmz69bvesK8aEHekdCwsL7Nu3D+fOncO2bdtw7tw5REZGoqCgAPXr18dLL72E4cOH45VXXoGpqalS3e+//x5ubm7Ytm0bYmJiUFBQgLfeegvLly/H2rVrS91v/fr18c8//2DhwoU4evQokpOT4eXlhSlTpmDu3LnlmjXX1dUVV69exfr16/Hrr78iLCwMMpkMTk5OGDRoEKZOnarVdqZNmwZbW1ucPn0aDx8+xI0bN2Bra4sOHTpg3LhxmDp1qso5KPbtt9/C29sbP/30E+7cuQMLCwsMHz4cn376Kdzc3LQ+luowbNgw3L17F1999RWOHz+O+/fvw8DAAE5OThg2bBiGDh2KIUOG6DRGIiIiqnqyQhniNschclmkxjHqJG4SuP7PFQ0nN4SBCRN1NSm3MBdHwo/gl7u/4M+wP0vtQVfM38UfI31GYrjPcLjZ6PZvTCKqnTp1Am7cAN54Azh0CPj9d6B1a2Dp0jxs3foPcnMNERDQDm3bmuDfoemfCyJB05SZVGkZGRmwtrZGeno6rKysyiz/+PFjODs710BkFSOTyZCRIZ/NycrKqlwJKqodIiMj4eHhATc3tzLHACwL24Oy2v7+rW65ubk4ceIEAPn4h5oSxfR8YHugktge6Fnq2oRQJCB+VzwiF0ciL0J9Ty3TpqZwXeAKh/EOEBs933931KQ8aR6OPTiGX0J+wR/3/0B2YXap5UUQobtbd0WSzsnKqdTyvEZQSWwPzzdBAO7eBfz85L/X1fagba6IPeyIiIiIiEgnBEFA4v5EPPrkEXLuqu+tJXGVwH2ROxwmOkBsyERdTZDKpDgVcQp7Qvbgt3u/ISM/o8w63Vy7YYzfGIzwGYFGlo1qIEoiqmtEov+SdcSEHRERERER1TQBMLxuiJAlIci+rr7HlpGDEdwWusHxTUeIJUzUVTeZIENQdBD23NmDX+/+isScxDLr+Lv4Y7TvaIz0HVlmTzoiIiofJuyIiIiIiKjGZF/Phvkn5jC8bYhsqCbrDG0N4TrfFU7vOHEyiWomCAKuxV7Dnjt7sDdkL2IyYsqs08mpE0b7yZN0rtauNRAlEdU1ixYBnTvLJ5YgzZiwIyIiIiKiapcXk4dHCx8h/ud4GKr5GmJgYQDnuc5wmesCQ2t+TalO95PuY9ftXdh9ZzfCU8LLLN+2UVuM8RuD0X6j4W7jXv0BElGd9ccfwNKl8udz5wIrVwLGxrqNqbbiJyHRc8Td3R2cZ4aIiIhqkjRTiujPovH4y8eQ5clU1otNxHB6xwku811gXJ/f2qpLXFYc9tzZgx23duBq7NUyy3vZeeGV5q9gbPOx8KrvVQMRElFd9+QJ8Npr//3+5ZdAVhbwww+6i6k2Y8KOiIiIiIiqnEwqQ+zGWEQuikRhQqHKekEswH6SPZoubwqJo0QHEdZ9GfkZ+P3e79h5eydOPToFmaCaMC3J1doVY/3G4pUWr6CVQyuIRKIaipSI6rqiImDcOCAl5b9l1tbAhx/qLqbajgk7IiIiIiKqMoIgIOVICh5+8BA599TP/FrYrhB5k/LQ+fXOkJgyWVeVCooKcOzBMey8vRN/3P8DedK8Usvbm9tjlO8ovNL8FXRx6QKxiBN8EFHVW7kSOHNGedlPPwHu7joJRy8wYUdERERERFUi80YmHr73EGmn09SuN29pDpcVLgiWBtdsYHWcIAi49PgStt/cjl/u/oKU3JRSy1sYW2C4z3CMazEOvT16w1DMr4VEVH2CgoDFi5WXvfEGMGqUTsLRG7wyExERERFRpRQmFyJiQQRif4oF1AyXa+xoDI/lHmg4sSHyCvKAEzUfY10UkRqBHbd24OdbP+NByoNSyxqKDfFi0xcxrsU4DPYaDDMjsxqKkoieZ6mpwKuvym+JLebjA6xdq7OQ9AYTdkREREREVCFCkYCnPz3Fo4WPIE2RqqwXm4vhOt8VLnNdYGBuoIMI6560vDT8GvIrfr71M85FnyuzfDfXbhjXYhxG+Y6CnZldDURIRCQnCPKedNHR/y2TSIC9ewEz/s+gTEzYERERERFRuaVfTEf4O+HIupalulIMNJraCO5L3SFpyDHqKquwqBAnHp7A9lvbcTD0IPKL8kst79vAF+NbjMcrLV6Bu417zQRJRPSMn34C9u9XXvbll0CLFrqJR98wYUdERERERForiC9AxP8iELc1Tu16m142aLquKSxaWNRwZHXPrfhb2HJ9C3be3onEnMRSy9qb22Nci3GY0HICWjdszRleiUinQkKAWbOUlw0dCkyfrpNw9BITdkREREREVCaZVIan3z7Fo08eoSijSGW9xFmCJl80QYNRDZgsqoSknCTsur0LW29sxfW466WWNTE0wVDvoZjYciL6NunLySOIqFbIzQXGjgXySkxS7ewMbNoE8ONBe7yiExERERFRqdLOpCH8nXBk38lWWScyEsHlfRe4LnCFoQW/XlREYVEhjj04hi03tuDPsD9RKCsstXxPt56Y2GoiRviMgLWJdQ1FSUSknaVLgTt3/vtdLAZ27gTq1dNdTPqIn6hERERERKRWQUIBHsx9gISdCWrX2/a3hefXnjBrxtHDK+J2/G1svbEVO27vQEK2+nNcrJldM0xsORHjWo7juHREVKu99548Yffnn/LfP/4Y6NFDtzHpIybsiEjB3d0dUVFRePToEdzd3XUdDhEREemIIAiI2xqHh+89hDRVdfZXE3cTNF3bFHZD7Hj7azml5aVh1+1d2Hx9M67GXi21rJXECmP9xuK1Nq+hk1Mnnmsi0gv16wN//AGsXy9P2n30ka4j0k9M2JFeKU4oFROJRLCwsIC1tTW8vb3RqVMnvPrqq/D19a3S/aalpWHt2rWwsbHB7Nmzq3TbddmBAwdw48YNDB06FK1bt9Z1OERERKSFnAc5CHsrDGmn01TWiSQiuP7PFa7zXWFgalDzwekpmSBDYGQgNl3fhN/u/YY8aZ7GsiKI0LdJX0xuNRlDvYfC1Mi0BiMlIqoaIhEwcybw7rsct66imLB73mVny6dvycwELC0BPz/A3FzXUZXJ09MT9vb2AIC8vDwkJSXh5MmTOHnyJD799FOMGDECP/zwA+zs7Kpkf2lpaViyZAnc3NzqdMKuSZMmMDExgZGRUZVs78CBA9i2bRvc3d2ZsCMiIqrlZIUyxHweg6ilUZDlyVTW2w2yQ9N1TWHamAkkbT3OeIytN7Ziy40tiEiNKLWsZz1PTG49GRNaToCLtUsNRUhEVL2YrKs4JuyeV6GhwHffAdu3A+np/y23tgYmTgRmzAC8vXUXXxkWLFiAyZMnKy1LSkrCzp07sXz5cuzfvx8hISG4dOkSrK05EK+2Tp06pesQiIiISAcygjNw/437yL6tOqmEcUNjeH7jifrD6/OWTC0UFBXg0P1D2HR9E44/PA6ZoJr8LGZpbIkxfmMwufVk+Lv48/wSkV7KygIsLHQdRd0j1nUApANr18p70q1fr5ysA+S/r18vX792rS6iq7D69etj1qxZuHLlCho1aoTQ0NA63RuOiIiIqLKkmVKEzwzHtS7X1CbrGr3VCB3udUCDEQ2YTCrD/aT7eP/E+3D+0hkjfx2Jow+OakzWdXftjq0vb0Xse7H4achP6OraleeXiPRScrI8fbBwIVBY+gTXVE5M2D1v1q4F5swBZJr/0wdAvn7OHL1L2gGAm5sbvvvuOwDAjh07EBMTo7Q+IiICq1atQkBAAFxcXCCRSNCgQQMMGDAAhw8fVtne5MmT4eHhAQCIioqCSCRSehTLzc3F7t27MXbsWHh5ecHCwgIWFhZo3bo1li9fjuxs1T+CyxIQEACRSITAwED8888/eOmll1CvXj2Ym5vD398fBw4c0FhXEATs2LEDPXv2hI2NDUxNTeHt7Y358+cjJSVFbR13d3eIRCJERkZqjCM0NBSjRo2Cvb09GjVqhICAAPzyyy9K5SMjIyESibBt2zYAwGuvvaZ0zhYvXlzuc0FERERVK+nPJFz2u4wn658AgvI6M28ztD7bGl7fe8HIpmqGyqiL8qR52HlrJ3pu7Qnvb73xxcUvkJiTqLZsQ4uGmN91Pu6/cx9nXzuLSa0nwdy49g9FQ0SkiSAAr78OREcDK1YAPXsCz3yVpErgLbHPk9BQ+fzK5fHee8CAAbX69lh1hgwZAkdHRzx9+hQnTpzA1KlTFetWrFiBTZs2wcLCAo6OjmjZsiWePHmC48eP4/jx4/jss88wf/58RflmzZqhffv2uHLlCiQSCdq3b692n1evXsWrr74KQ0NDNGzYED4+PkhPT0dISAhu3ryJ33//HefPn4epafnHfTl37hyWL18OY2NjeHt748mTJ7h48SKGDRuGL774AnPnzlUqLwgCxo8fj127dgEAGjduDBsbG9y5cwerV6/G3r17cfr0aTRu3LhccVy9ehWLFy+GSCRCs2bNEBUVhZs3b+KVV16BVCrF+PHjAQAmJibo2rUrwsPDkZCQoDTmIAC4urqW+xwQERFR1ShMLkT4u+FI2J2gsk5kJILbQje4/s8VYgn/t6/J3cS7+OnqT9h+aztSctX/IxQADEQGeKnZS5jaZioGeg6EoZhfv4io7tiwASjZh+TiReDkSXkSjyqPn8LPk+++K7tn3bNkMvm7UM+IxWJ06dIFAHD58mWldSNGjMClS5eQkZGB+/fv4/Lly3j69CnOnj2LRo0aYeHChXj48KGi/IIFC/Drr78CABo2bIjz588rPYq5uLjgl19+QWpqKmJiYnD58mWEhYUhJiYGI0eOxLVr17B69eoKHc/SpUsxfPhwxMXF4fLly3jy5Am+/vprAMD8+fNx8+ZNpfLffvstdu3aBUtLS5w4cQIPHz7E1atXERUVha5duyIqKgqvvvpqueP48MMPMXnyZCQkJOCff/5BWFgYZs2apYijqKhI6Ty9+OKLAOTnsOQ5mzJlSoXOAxEREVVO0qEkXG5+WW2yzrqbNdrfbA/3Re5M1qmRW5iL7Te3o9vmbvD7zg9rg9dqTNY1s2uGz/p8hpg5MTg49iCGeA1hso6I6pTbt4Fn+o1g1CigRF8ZqiR+Ej8vsrPlE0xUxLZt8vp6xsVFPrtWQoLyH6QvvvgiOnXqpDJOSPfu3bFs2TIUFRVh79695d6fm5sbRo0aBYtnRtts2LAhtm/fDmNjY+zcubPc2wWAevXqYcuWLTD/dwZfkUiEd999F8OHD4dUKsWXX36pKCsIgiIxuHTpUvTt21cplr1798LY2BjBwcE4ffp0ueLw9fXFunXrYGJioohj4cKFaNiwIZ4+fYpbt25V6PiIiIioehWmFeLe5Hu4M+QOCuIKlNYZWBmg2ffN0PpMa5j78BbNZ91JuIOZR2fC8UtHTDowCUExQWrLSQwkGN9yPM5MPoPQt0Mxv9t8NLJsVMPREhFVv5wcYMwYID//v2VubsCPP3JW2KrEf/M8L0JCVCeY0FZ6OnD3LtCuXdXGVM2Kk1uZmZkq6xITE7Fr1y4EBwcjISEBeXl5AID0f8/Rsz3WtCWTyXDo0CGcOHECERERyMrKgiDIB4URiUQIDw9HTk4OzMzMyrXdqVOnKpJkJc2YMQO//fYbjh8/rlh27949xMTEwMTEBG+88YZKHScnJ4wYMQK7d+/GiRMn0Lt3b63jmDJlCsRi5Ty/kZERWrZsibi4OERERKBNmzblODIiIiKqbinHU3D/9fvIf5yvss5uiB2abWgGiaNEB5HVXvnSfOy7uw8brmzQmKAr5tvAF2+2fRMTWk1APdN6NRQhEZHuzJkD3Lv33+8GBsCuXYCNjc5CqpOYsHteqEla1Wh9HcjKygIAWFlZKS0/ceIERo8erUjOqaNpUobSpKWlYeDAgbh48WKp5VJTU8udsPPx8Sl1eXx8PDIyMmBlZYWwsDAA8nHiipOWz/Lz8wMARVltNWnSRO3y4vHpis85ERER6Z40U4qH7z9E7I+xKusMrA3gud4TDuMdODtpCQ9THuLHqz9i843NSMpJ0ljOxNAEY/zG4M12b6KLcxeeQyJ6bvz6q7wnXUlLlgD+/rqJpy7jLbHPC0tL3dbXgejoaABQmuwgLS0NY8eORXp6OiZOnIhLly4hNTUVRUVFEAQBf/31FwCgsALzUc+dOxcXL16El5cX9u/fjydPniA/Px+CIEAQBDg5OVV42yWPQdPy4p6ExUkzTXUAwMHBQamOtjQlAIt73RX3JiQiIiLdSv07FVdaXlGbrLPtb4sOdzqg4YSGTDQBkMqkOBB6AAN2DEDT9U2x+sJqjcm6FvYt8M2L3yD2vVhsHboV/i7+PIdE9NyIjASevYkrIAD43/90EU3dxx52zws/P8DaumK3xVpby+vrEZlMpujp1rFjR8Xyo0ePIjU1FV26dMHWrVtV/sCKiYmp0P6kUil++eUXAMDBgwfh5eWlsj4uLq5C2wbkt/CWtdzy36Rq8Rh6z47dV1J8fLxSHSIiIqobinKKEPG/CDxZ/0RlnYGFAZp82QSNXm/EJBOAp5lPsfHaRvx07Sc8znissZyJoQnGNh+Lt9q9hU5OquMgExE9D6RS4NVXlVMKdnbAjh3yW2Kp6jFh97wwNwcmTgTWry9/3UmTADOz8s8wq0MHDhxAXFwcjIyM0K9fP8XyyMhIAECXLupvXdA0dl1Zf5glJiYiOzsb9erVU0nWAcCdO3cUM6hWxL2SAwSoWe7g4KC49bdZs2YA5D0Ms7KyVCbBAICQkBClstWFf9ASERHVnIx/MnBv/D3khueqrLPpZQOvzV4wdTfVQWS1hyAIOBt1Ft9c/ga/3/sdRYLmv8+87Lwwrf00TGw1kWPTEdFzb/Fi4NnRn7ZsAf69kYyqAW+JfZ7MmAGIy/mSi8XA9OnVE081iYqKwjvvvAMAmDhxouJWVAAwNZX/kVrcw6yk5ORkbNq0Se02i+vl5qr+AVxyfUZGhtoyxbO2VtSmTZuQn686UPR3330HAEpJSR8fH7i6uiIvLw8bN25UqfP06VPs378fANC/f/9KxVWWss4bERERVZ5QJCBqZRSud72ukqwTm4nRdH1TtDrZ6rlO1mUVZOH7K9+jxYYWCNgWgH1396lN1hmKDTHKdxROTTyFe2/fw+zOs5msI6Ln3t9/AytWKC+bORMYPFg38TwvmLB7nnh7A198Ub46X3whr6cHkpKS8PXXX6N9+/aIjY2Fr68vvvzyS6Uy3bt3BwD88ssvOHnypGJ5bGwsRowYAalUqnbbDRo0gKWlJRISEtT2drOxsYGfnx+kUinmzJmDgoICAEBRURFWrVqFvXv3wtjYuMLHlpycjKlTpyI7OxuA/L/D3333HX777TcYGBhg7ty5irIikQgffPABAGDRokU4deqUYl18fDzGjh2LgoICdO7cGb169apwTNpo3LgxAODs2bMc346IiKga5D3Ow80XbuLRgkcQpMqftVZdrdD+Zns4v+MMkfj57PV+P+k+Zh2dBacvnTD98HSEJIaoLedq7YrlvZYjenY0fhn1C3p79OadAkRE/9q2DSj5da5VK2DVKt3F87xgwu55M3s28NVXZfe0E4uBtWvl5WuhFStWoFu3bujWrRs6dOgADw8PNGjQALNmzUJSUhJGjRqFc+fOqcwQ265dO4wcORKFhYXo27cvPD090aZNG7i6uuLatWv47LPP1O5PJBJh1KhRAIC2bduiQ4cOCAgIQEBAgKLMypUrIRKJ8MMPP6BRo0bo0KEDGjZsiP/9739YuHAhGjVqVOHj/eSTT/Drr78qtuvs7Iy3334bgiBg5cqVaN26tVL5t99+G6+++ioyMjLwwgsvwNPTE+3atYOrqyvOnTsHV1dX7Ny5s8LxaGvYsGEwNjbGnj174OHhgR49eiAgIABbt26t9n0TERHVdYm/JeJKyytIC0xTWi4yEqHx6sZoc6YNzJqWb2b6uqBIVoQ/7v+Bfj/3g/e33vj6n6+RkZ+htuyApgPwx9g/EDEzAgt7LEQjy4r/vUZEVFdt3gysXg0YGspHy9qzBzAx0XVUdR/HsHsezZ4NDBgAbNggT5WXHDXS2lo+Zt306bW6Z114eDjCw8MByCdZsLGxwQsvvIBOnTph3Lhx8PHx0Vh3586d8PHxwc8//4yoqCjY2dlh5MiRWLx4MWJjVWdSK7Zu3TpYWlri4MGDuHnzpspsr4MHD8bRo0exdOlSXL9+Hffv34efnx/Wrl2LcePGYfv27RU+3u7du+PcuXNYvHgxLl68iPz8fHTu3Bnz5s3DsGHDVMqLRCLs2LEDAwYMwE8//YSbN28iJiYGbm5uGDp0KObPnw87O7sKx6OtJk2a4NChQ1ixYgWuX7+O6OhoCIKglOgkIiKi8inKLsKDuQ/UzgBr6mUK392+sGzz/E0slZyTjI3XNmLDlQ2ISo/SWM7GxAZTWk/B9A7T0bRe0xqMkIhIP4nFwAcfAD17AlFRtTpVUKeIBN6nVm0yMjJgbW2N9PR0lZ5e6jx+/BjOzs41EFkJ2dnA3btAZiZgaQn4+sonqFBDJpMhI0P+30krKyuIyzseHpVbQEAAzpw5g7///rvWJbnYHpTp5P1bi+Tm5uLEiRMA5GMqFo9fSM8ntgcqie2hamVez8TdV+4i977q+LCN3myEpl82hYF57Z6ur6rbxJ2EO/g6+GvsuLUDuVLN4+a2cmiFdzq+g1dbvAozo+ev52FtxWsElcT2QCXV1fagba6IPeyed+bmQIcOuo6CiIiIiEohyAQ8XvsYEf+LgFCo/P92Q1tDeP3khQYjGugouppXJCvC4fDDWBe8DqcfndZYzlBsiJG+I/FOh3fg7+LPcemIiEhvMGFHRERERFSL5cflI3RSKFJPpKqsswmwgfd2b5i4PB+DCaXnpWPz9c345vI3iEiN0FiukUUjTGs/DW+0fYPj0hERldOhQ8DAgYBB7e6wXecxYUdEREREVEulnkrF3VfvojBBeexcGAAeyzzgOs8VIoO632vsftJ9rP9nPbbe2IrswmyN5bo4d8GsTrMw3Gc4jAyMajBCIqK6Yc8e4JVXgIAAYMcOwMlJ1xE9v5iwIyIiIiKqZQSZgKgVUYj8JBJ4ZsRpk8Ym8N3lC6tOZY+RrM8EQcDpR6fx5aUvcST8iMZyRmIjjGk+BjM7zkQHJw71QkRUUY8eAW+9JX8eGAi0bAkcPAh066bTsJ5bTNgR1WKBgYG6DoGIiIhqWEFSAUInhCLlWIrKOoeJDvBc7wlDq7r7Z3y+NB+77+zGV5e+wq34WxrL2ZvbY3r76Xir3Vu87ZWIqJIKC+U96/6dVxCAfHbYJk10F9Pzru5+0hMRERER6Zn0S+m4O+ou8h/nKy0Xm4nR7PtmaDihoY4iq35JOUn4/sr3+OafbxCfHa+xXNtGbTGr0yyM8RsDiaGkBiMkIqq7PvkECA5WXrZtG9CI/w/RGSbsiIiIiIh0TBAEPPn6CR6+/xCCVPkeWDNvM/jt84O5n7mOoqte9xLvYe2ltdh+azvypHlqy4hFYozwGYFZnWZxtlcioip28iSwapXysjlz5BNPkO4wYUdEREREpEPSDCnuT72PxH2JKuvsX7FHsx+bwdCibv3ZLggCbmXdwoZ9G3A84rjGclYSK7zR9g282/FduNm41WCERETPh4QEYMIEQCjxv6I2bYCVK3UXE8nVrU9+IiIiIiI9knUrCyEjQ5Abnqu0XGQsQtO1TeE4zbFO9SYrLCrEnpA9WBa2DI9yH2ks527jjlmdZmFKmymwktTtyTWIiHRFJgMmTwbi4v5bZm4unylWwhEHdI4Ju1pGJpNBLBbrOgwiKgdBEMouRERE9IzYLbEInxEOWZ5MabmJuwl8f/WFVfu6k6jKzM/ExmsbsTZ4LaLTozWW83fxx9zOc/Gy98swFPOrChFRdVq3Djh6VHnZt98CzZrpJh5Sxk/BWsTKygrp6emwtbXVdShEVA7Z2dkwMzPTdRhERKQnZPkyhL8TjtiNsSrr7AbbwXubN4xsjXQQWdWLzYzF18Ff4/ur3yMtL01tGQORAUb4jsCcznPQ2blzzQZIRPScunYNmD9fedm4ccDEibqJh1QxYVeLWFpaIi4uDomJibC2toaxsbGuQyKiUgiCgOzsbKSmpsLR0VHX4RARkR7Ij81HyIgQZFzMUF5hADRe0Rgu77tAJNb/W2DvJd7D5xc+x47bO1BQVKC2jInYBG+0fQPvdX2P49MREdWgzExg7FigsPC/ZU2aAN99B9ShURj0HhN2tYhIJEKjRo0UCYDCku+eWkAmkyEpKQkAUL9+fd66+5xje5AzMzODo6MjDAwMdB0KERHVchnBGbgz7A4KYpUTWMaNjOG7xxc2PWx0E1gVEQQB56PPY/WF1fgz7E+N5RqaN0Q/q37oZ9cPw3sPh6mpaQ1GSURE774LhIf/97uhIbB7N2BVd0ZiqBOYsKuFzM3NYW5uruswVOTm5uL69esAAB8fH/5x9ZxjeyAiItJe7JZYhE0Lg1CgPO6pdXdr+P3qB2MH/b2zQibIcDjsMD4L+gwXYi5oLOfbwBfvd3kfw5oOw5nTZ2owQiIiKiYI8t50YrF80glAPiNshw66jYtUMWFHRERERFRNZIUyPHzvIZ6sf6KyznGGI5qubQqxkX72Ui8sKsTekL347PxnCEkM0Viup1tPfOD/AV70fBFikRi5ubkayxIRUfUSiYCPPwZ69wZefRXw8QHmztV1VKQOE3ZERERERNWgIKkAd0fdRVpgmtJykZEInt95wvF1/Rz/NKcwB5uvb8bnFz5HVHqU2jJikRgjfEbgA/8P0MGJ3TaIiGqbrl2BGzcAqVTe245qHybsiIiIiIiqWOaNTNwZegf5UflKy40bGsNvvx+s/a11FFnFpeam4rvL32Fd8Dok5iSqLSMxkGBKmyl4r8t7aFKvSQ1HSERE5WFrq+sIqDRM2BERERERVaGEvQkIfS0UslyZ0nLLjpZo/ltzSJwkOoqsYmIzY/HVpa/w/ZXvkVmQqbaMlcQKb3d4G7M6zYKDhUMNR0hERFT3MGFHRERERFQFhCIBEQsjELMqRmWdwyQHNPu+GQxM9GdW8ai0KKwKWoXN1zcjvyhfbRkHcwfM6TwH09pPg7WJ/vUaJCKq6+Li5GPUffkl0LChrqOh8mDCjoiIiIiokqRZUtwbdw/JfyQrrzAAmn7RFE4znSASiXQTXDmFJYfhs/Of4edbP0Mqk6ot09i2Meb5z8Ok1pNgYmhSwxESEZE2ZDJg0iTgxAng1Clg+3agf39dR0XaYsKOiIiIiKgS8p/k4/bg28i6nqW03NDOEH6/+MG2t34MEnQ7/jZWnF+BX0J+gUyQqS3TyqEV/tftfxjpOxKGYn6VICKqzb74Qp6sA4CEBGDAAODaNaBNG93GRdrR67lAPvroI4hEIohEIixfvlxjuYsXL+Lll19GgwYNYGpqCl9fXyxbtgx5eXk1GC0RERER1TWZNzJxtdNVlWSdeUtztLvcTi+SdZefXMbQPUPR8vuW2HNnj9pknb+LPw6/ehjX37qOsc3HMllHRFTLXb4MLFigvGzSJCbr9IneftLeu3cPa9asKbPczp07MWnSJBQVFcHJyQkuLi64c+cOPvnkExw6dAiBgYEwMzOrgYiJiIiIqC5JPpyMkDEhkGUrJ7jsBtvBZ5cPDC1q95/a56PPY/nZ5Tj+8LjGMn08+uCjHh+hp1tPvbmll4joeZeRAYwdC0hLjGrg6Ql8843uYqLy08sedoIg4K233oKRkRF69+6tsVxkZCSmTp2KoqIirF69GjExMbh27RrCw8Ph5eWFy5cvY968eTUYORERERHVBY+/fozbQ26rJOucZjmh+e/Na3WyLjAyEL229UL3Ld01JusGNRuEi1Mv4uTEkwhwD2CyjohITwgCMH06EBHx3zIjI2DPHsDCQndxUfnpZcJu06ZNOHfuHD755BO4uLhoLLdmzRrk5+ejX79++OCDDxR/aLi5uWHz5s0AgB9//BHx8fE1EjcRERER6TeZVIbwd8PxYNYDoGSuTgx4fuMJz7WeEBnUvuSWIAj4+9Hf6Lm1J3pt64XAyECVMiKIMNJ3JK69eQ2HXjmEzs6daz5QIiKqlO3bgV27lJetWgW0baubeKji9C5hl5iYiPnz58PX1xdz5szRWE4QBPz+++8AgKlTp6qs9/f3h7e3NwoLC3Hw4MFqi5eIiIiI6gZpphR3Xr6DJ988UVpuYGGAFn+2gNPbTjqKTDNBEHAq4hR6bu2J3tt742zUWZUyBiIDTGg5ASEzQvDrqF/RphEHOCIi0kdhYcDbbysvGzgQmD1bJ+FQJdXevvoazJkzBykpKfjtt99gZGSksVx0dDRiY2MBAF27dlVbpmvXrggNDUVwcDDefPPNSsf2+PFjpd8zMzMBALm5uaXGqi9KTtLBCTuI7YFKYnugktgeqKS60h7yH+cjbEQYcm7nKC03djaG134vmLUwQ25uro6iUyUIAv6O+hufBn2Ki08uqi1jKDbEOL9x+KDzB2hs2xgAauQY6kqboKrB9kAlsT1UXH4+MHq0BNnZ//XLcnAQ8N13edDXU1lX24O2n7V6lbA7deoUdu7cifHjx6Nnz56llg0PDwcASCQSODo6qi3TuHFjpbKVpen23NOnT9e5iS3OnlX97yw9v9geqCS2ByqJ7YFK0tf2IH4ghvmn5hCnKt+cIm0iRcbCDCTGJgKxOgruGYIg4EbmDeyN34vQ7FC1ZQxggD52fTDCfgQcDBzw4PIDPMCDGo5UTl/bBFUPtgcqie2hfDZtao6bN5sofheJBMyYcQE3biTpMKqqU5faQ05OTtmFoEcJu7y8PEybNg3W1tb4/PPPyyyfmpoKALCxsdE4SK6tra1SWSIiIiKikgyvGsJstRlE+cp/TxZ2LkTO7BzAREeBPUMQBNzMuondsbtxP+e+2jKGIkP0qdcHIxxGwN7YvoYjJCKi6nLligMOHWqitGz48HC0alU3knXPK71J2C1fvhwPHjzAN998AwcHhzLLF3eXNDY21lhGIpEAqLqu/zExMUq/Z2ZmwtfXF71794aVlVWV7EOX8vLyFFntHj16wMSklvyFSjrB9kAlsT1QSWwPVJI+t4fEHYmIWBEBFCkvbzS7EVyWu0Akrh2TS5yLPoel55ci6HGQ2vXGBsaY1GIS3u/8PlysNE/YVlP0uU1Q1WN7oJLYHsovJwd4803l89ShgwybN7vAyEj31/zKqKvtISMjQ6tyepGwu3fvHtasWYO2bdti+vTpWtUpfiELCgo0lsnPzwcAmJqaVj5IAM7Ozkq/F78IpqamVbaP2sLExKTOHRNVHNsDlcT2QCWxPVBJ+tIeBEFAzOoYRPwvQnmFAdDsu2ZwfFP9cCs17ULMBXz898c4/ei02vXGBsZ4vc3r+F+3/8HFunZ+adOXNkE1g+2BSmJ70I6pKbB7NzB+PBAbC1hZAXv3imFlVbfOXV1qD4WFhVqV04uE3YwZMyCVSrFhwwaIxdpNbFt8u2taWhoEQVB7W2zxrbDFZYmIiIjo+SbIBDyY+wBP1inPBCs2E8Nvnx/sXrTTUWT/ufzkMj4J/ATHHhxTu97YwBhvtn0T87vNh7OVs9oyRERUd/TuDdy8Cbz2mjxx5+Gh64ioKuhFwu769esQiUQYMmSIyrr09HQAwKpVq/DNN9/AxcUFly9fhqenJwB5L7qnT5/CyclJpW5EhPy/psVliYiIiOj5JcuX4d7Ee0j8JVFpuaGdIVoebgmrTrod4uRG3A188vcnOBR2SO16I7ERpraZioU9FjJRR0T0nGnQADh0CNAwhD/pIb1I2AFAUVER4uPjNa7PyspCVlaW4lZYV1dXNGzYEHFxcQgKCsLo0aNV6gQFycf56NSpU/UETURERER6QZohxZ1hd5B2Ok1puYm7CVoebwmzZma6CQxASEIIPgn8BL/d+03tegORASa3noyPenwEdxv3mg2OiIhqDSbr6hbt7i/VseLbWtU9Jk2aBABYtmwZBEFAZGQkAEAkEmHYsGEAgE2bNqls88KFCwgNDYWRkZHanntERERE9HzIj8vHjZ43VJJ15q3M0eZCG50l6yJSIzDh9wlosaGF2mSdWCTGhJYTEPpOKDYO2chkHRHRc0AQAC3nLCA9pxcJu4r64IMPYGxsjBMnTmDNmjUQBAEAEBUVhSlTpgD/Z+++o6Mq9y6O75n0hAChhN5rQhVFFBQUVAQUQUB6E6zYEMF2sSE2RLGBivQOIlWqgIqCihVIgdB7TyCkZ+a8f+Q14RhUhMycmcn3s9Zd19lnyuY6l0x+85znSBo8eLDKli1rZU0AAABYJDUhVb81/03nfz9vyovfVFxXfXOVgsoFub3T0eSjevjLh1XnwzqauXWmDBmm4zbZ1KN+D8U8HKPpnaerZomabu8IALDGlClSVJS0YYPVTeBqPj2wq1atmiZOnCi73a4RI0aoUqVKatKkiWrVqqUdO3bo6quv1pgxY6yuCQAAAAuc23JOvzX/Tel700156W6l1XBVQ/kXc+/uMWfSzujptU+rxvs1NOHnCcp2Zue7z91Rd2vrQ1s1p8sc1S1V1639AADWiouTHn1UOnJEatNGGjlSys7/owI+wqcHdpLUr18/bdy4UXfccYfS0tIUGxur6tWr66WXXtJ3332nsLAwqysCAADAzc6sOaPfb/5dWaeyTHmFRyooek607EHu+5h8PvO8Xv32VVV7r5re2vSW0rLT8t2nXc12+uX+X7TwnoWqH1nfbd0AAJ4hPV3q0UNKTc25bRjS5MnS/1+HEz7Iay468XemTp2qqVOn/uN9mjdvrmXLLn41LQAAABQuJxeeVGzPWBlZ5lNNq42upsrPVpbNTbt2p2en65OfP9HojaN1MvXkRe9zQ+Ub9Frr13RjlRvd0gkA4JlGjJC2bs27bbNJM2dKJUta1wmu5fUDOwAAAOBSHZt+TPED4yXnBaGfVGdiHZUbWM4tHRxOh2ZsnaEXNrygg+cOXvQ+jcs21mutX9PtNW932wARAOCZli6VPvjAnD3/vHTzzdb0gXswsAMAAEChcHj8YSUMSTBl9mC7ohdEq9QdpVz++oZh6MuEL/XMV88o5mTMRe9Tu2Rtjbp5lLpGd5Xd5vO71wAA/sXhw9LAgeaseXPpxRet6QP3YWAHAAAAn3fgrQPa8/QeU+ZXxE/1l9VXxE0RLn/9zQc36+mvntbGAxsverxS0Up6sdWL6t+4v/ztfEQHAEgOh9S7t3TmTF5WrJg0e7bkz48Kn8e/YgAAAPgswzC0d+ReHRh9wJT7R/ir4cqGKtqsqEtff8epHXpu/XP6Iu6Lix4vFVpKz9/4vB685kEF+we7tAsAwLu89pr0zTfm7LPPpCpVrOkD92JgBwAAAJ9kOA3tGrpLh98/bMoDIgPUaG0jFWlYxGWvfST5iF7++mVN+m2SHIYj3/GwgDANu36YhjUfpqJBrh0aAgC8z/ffSy+9ZM7uv1/q2tWSOrAAAzsAAAD4HMNhaMf9O3Rs8jFTHlQpSI2+aqTQ2qEued2z6Wf11vdv6d0f3lVadlq+4/52f93f5H6NbDVSZYuUdUkHAIB3S0yUevWSnBdcICk6Wnr3Xes6wf0Y2AEAAMCnODOdiusbp5PzT5ry4BrBaryusYKrFPypp5mOTH3y8yd6+ZuXdTrt9EXv0y26m0a3Hq1aJWsV+OsDAHyDYUj33ScduGAnh+Bgae5cKdQ13zXBQzGwAwAAgM9wpDkU0y1GZ748Y8pD64Wq0dpGCioXVKCvZxiGFscv1tNfPa2EMwkXvc9NVW/Sm7e8qWsrXFugrw0A8D2HDknffmvO3nlHatDAmj6wDgM7AAAA+ITs89na3nG7kjYkmfLwa8LVcFVDBZQMKNDX+/HQjxq2Zpi+P/j9RY83LNNQb97yptrWaCubzVagrw0A8E2VKklbt0p9+0pffSV17iw9+KDVrWAFBnYAAADwetnJ2drabqvOfX/OlBe7sZgaLG8g/6IF97F3b+JePbvuWc2LmXfR45WLVdarN7+q3g17y26zF9jrAgAKh7JlpdWrpY8+knr3lvjOp3BiYAcAAACv9nfDuojbIlR/UX35hfoVyOskpiVq9MbR+uCnD5TpyMx3vGhQUT13w3N6rNljCgkIKZDXBAAUTna79OijVreAlRjYAQAAwGtln/v/Yd0m87CuVKdSip4bLXvQla9wy3RkavyW8Xrlm1eUmJ6Y77i/3V8PXv2gXmj1gkqHlb7i1wMAAGBgBwAAAK+UfS5bW2/fqnOb/zKs61JK0XOiZQ+4smGdYRhasmOJnlrzlHYn7r7ofTrV7aQ32ryhOqXqXNFrAQAKpwMHpIMHpRYtrG4CT8OmGgAAAPA6fzesK921dIEM6/449ofaTG+jzvM6X3RY17R8U30z4Bst6r6IYR0A4LJkZ+fsUdeqlfTqq5LDYXUjeBJW2AEAAMCrZJ/9/2HdD38Z1nUrrahZUVc0rDt+/rhGbhipz379TIaMfMerFKui19u8ru71u3NBCQDAFRk1Svruu5x/HjlSWrdOWrVKCgqythc8AwM7AAAAeI3ss9n6o+0fSv4x2ZSXvuf/h3X+lzdEy8jO0Hs/vqdXv31VyZnJ+Y6HB4brfy3/p8eaPaZg/+DLeg0AAP70zTc5q+ouFBXFsA55GNgBAADAK2QlZWlr261K/ukvw7rupRU18/KGdYZhaFH8Ig1fO1x7EvfkO2632XVfk/v0ys2vKDIs8rK7AwDwp9Onc06FdTrzsvr1pbFjresEz8PADgAAAB4vKylLW2/bquQt5mFdZI9I1Z1R97KGdb8d/U1DVw/VN/u/uejx1tVa692276phmYaX1RkAgL8yDGnQIOnw4bwsJESaOzfnv4E/MbADAACAR/vbYV3PSNWd/t+HdSdTTur59c//7T51NUvU1NjbxurO2nfKZrNdUXcAAC40fry0ZIk5GzdOqlfPkjrwYAzsAAAA4LGykrK09datSv75L8O6XpGqO+2/DeuyHFkav2W8Xvz6RZ3NOJvveLGgYnqh1Qt65NpHFOgXeMXdAQC40Nat0rBh5qxLF+m++6zpA8/GwA4AAAAeKft8tra135Z/WNc7UlHTomTzu/TVb+v2rNNjqx5T7MnYfMfsNrvub3K/Xrn5FZUOK33FvQEA+KvUVKlHDykjIy+rXFmaOFFiMTcuhoEdAAAAPI4jzaHtHbfr3OZzprxMnzKqO7XuJQ/r9iXt07A1w/RF3BcXPd66WmuNaztODco0uOLOAAD8nSeekOLi8m77+UmzZ0sREZZVgodjYAcAAACP4sxwKubuGCVtSDLlkb0iL3lYl5qVqje/e1NvbXpL6dnp+Y5XLV5VY28bq851O7NPHQDApRYsyFlJd6GXXpJatLCkDrwEAzsAAAB4DGeWU7E9Y3Vm1RlTXqpzKdWd9u/DOsMw9Hns5xq2ZpgOnjuY73iIf4ieveFZPdX8KYUEcDk+AIBr7duXf4+6m26Snn3WijbwJgzsAAAA4BEMh6H4/vE6teiUKS/RroSi50T/6wUmYk7E6NGVj2rDvg0XPX5PvXs05tYxqlyscoF1BgDgn/j7S40aSd9+m3O7RAlp5sycU2KBf3Lpl9UCAAAAXMRwGtrxwA6dmHPClBe/ubjqLawne9Dff2xNzkjWU2ueUuNPGl90WNcgsoE29N+geV3nMawDALhVxYrS+vXSyy/nDOmmTJEqVLC6FbwBK+wAAABgKcMwtOvxXTo26ZgpL9q8qOovrS+/kIsvQzAMQ/Ni5mnYmmE6knwk3/GI4AiNunmUHrjmAfnb+dgLALCGn5/0wgtSnz5S9epWt4G34JMLAAAALGMYhvY8s0eHPzxsyotcXUQNVzSUf5GLf1yNPRmrR1Y8ctEVdTbZdP/V9+vV1q+qVGgpl/QGAOC/YliH/4KBHQAAACyzf9R+HXzLfHGIsPpharS6kfyL5f+ompyRrFHfjtK7P7yrbGd2vuPNKjTTR+0/0tXlr3ZZZwAAAFdjYAcAAABLHBhzQPte3GfKQmqHqOHahgooGWDKDcPQgtgFenL1kzqcbF6NJ0klQ0rqjVve0L1X3Su7jW2aAQDWGD9eSk2VnnxSsvPjCFeAgR0AAADc7vD4w9ozYo8pC64arEbrGimobJApjz8Vr0dWPKJ1e9fle54/T38d3Xq0SoaWdGlnAAD+yW+/SUOHSpmZ0ldfSdOmSWXKWN0K3oqBHQAAANzq+OzjShiSYMoCKwSq0bpGCq4YnJulZaVp9MbReuv7t5TlzMr3PNeUv0bj249X0wpNXd4ZAIB/cv681KNHzrBOklavljp1kjZtkmw2S6vBSzGwAwAAgNucXnla8f3jTVlAZIAar2uskOohudmqXas0ZMUQ7Unc89enUImQEnq9zesadNUg+dkvfgVZAADc6bHHpJ078277+UnvvsuwDpePgR0AAADcIvnHZMV3iZeRbeRm/sX91WhtI4XWCZUkHUk+oqGrh2p+zPx8j7fJpsFNBuu1Nq9x9VcAgMeYM0eaMsWcvfqqdN111vSBb2BgBwAAAJezH7Brx4s75Exz5mUhdjVY3kBFGhaRw+nQ+C3j9fz655WcmZzv8Y3LNtbHHT5Ws4rN3FkbAIB/tGeP9MAD5qxNG2nECGv6wHcwsAMAAIDrpKQo4ocEhX8kOZPDlKKqcipENn+b6n1eT8VaFNPPR37Wg8sf1C9Hf8n38CKBRTTq5lF65NpH5G/noysAwHNkZUk9e0rJF3zPVKqUNGMGV4jFleNTDwAAAApefLw0fryCp05Xy+SzuXG2wnRMtyn4tSfl39pfj654VB9t+UiGjHxPcXfU3Xrv9vdUsWhFdzYHAOCSjBwp/fSTOZs2TSpXzpo+8C0M7AAAAFCwxo2Thg2TnE79da9tf6WoohbJeGaxXvwuXB82OZfv4VWKVdGH7T/UHbXvcEtdAAD+q7VrpTffNGdDh0rt21vTB76HgR0AAAAKzrhxOb+x/Aub09ArS8/pXIb03vU5mb/dX8OuH6aRLUcqLDDMtT0BALhMJ05I/fqZs6uukl5/3Zo+8E0M7AAAAFAw4uNzVtb9B2PXSKtqSqWatNDHd3ys+pH1XVQOAIAr53RK/ftLx47lZWFh0ty5UlCQdb3gexjYAQAAoGCMH5/zm8x/4GdIC0+3UdTANbLb2KEbAODZZs2SVq0yZx99JNWubU0f+C4+FQEAAODKpaRI06df1kPrrfxZ9tS0Ai4EAEDB69FDeu45yfb/m7T27p3/9FigIDCwAwAAwJWLiZHOnv33+13M2bNSbGzB9gEAwAUCAqTRo3MuOnHjjTmLy21/vcISUAA4JRYAAABXLHH5AUVcyRMkJxdUFQAAXK5NG6l1a4Z1cB1W2AEAAOCKJG1M0p43Tl7Zk4SHF0wZAADchGEdXImBHQAAAC5bSlyKvu71tZ6+a7qSLvfqeMWKSfXqFWgvAAAKQmKiZBhWt0BhxMAOAAAAlyX9SLpGPzZaffr00br6P2h6o8t8ov79pdDQAu0GAMCVysyUbrtN6tRJOn3a6jYobBjYAQAA4D9LOJSgVqNb6fUbXldKcIokaXxTyfFfTw+y26WHHir4ggAAXKHnn5d+/llaulRq1Ej6+murG6EwYWAHAACAS+ZwOvTepvfU8NOG+inyJ9OxHaWlWf2v+m9POHasVLduATYEAODKrVolvf123u3Dh6Uvv7SuDwofBnYAAAC4JHEn49RySks9sfYJpfulm45FOiO1uPti9Zvyq/Tuuzkr5/6J3S6NGyc98YTL+gIAcDmOHcvZreFC11wjjR5tTR8UTgzsAAAA8I+yHFl6beNravxJY206tCnf8S6nuyh+RLzuqntXTvDEE1JMjPTYYzKKFTPfuVgx6bHHco4//rjrywMA8B84nVK/ftKJE3lZkSLSnDlSYKB1vVD4+FtdAAAAAJ7rj2N/aOCSgfrt2G/5jpVLLKcXdr2g+xbcJ78QP/PBunWl995T+siR+mnqVPmnpenqm25ScJMmUliYm9oDAPDfjB0rrV1rzj7+WKpZ05o+KLwY2AEAACCfLEeWXv/udY36dpSyndmmYzbDpi4/dNGQA0PU/Nvm+Yd1FwoLU1KtWpIk45prpJAQV9YGAOCy/fST9Nxz5qxfP6l3b2v6oHBjYAcAAACTrce3asDiARddVVflZBUNXzJcjTMbq8nmJgosxflBAADvd+6c1LOnlH3Bd1S1akkffmhdJxRuDOwAAAAgKWdV3RvfvaFR345SljPLdMzutKvndz3V75t+Cg4KVoNvGiikOqvlAADezzCkhx6S9uzJywICcvatCw+3rhcKNwZ2AAAA0NbjWzVwyUD9evTXfMeqnqiqZxY/ozpH6kh2qd78eip6TVELWgIAUPCmT5dmzzZnb74pXX21NX0AiYEdAABAofZPq+r8DD/12NhD/b7pp0BHzqmvtSfUVskOJa2oCgBAgdu5UxoyxJy1a8eFzGE9BnYAAACF1Lbj2zRgyYCLrqqrlV1LwyYPy1lV9/8qP1NZ5e8v786KAAC41OrVUkpK3u2yZaWpUyW73bJKgCQGdgAAAIVOtjNbb373pl7+5uX8e9XZ7Hoo6CF1fL5j7qo6SSrVpZSqja7m7qoAALjUo49KNWpIAwZIp05JM2ZIkZFWtwIY2AEAABQqO07tUP/F/fXj4R/zHYsuHa1xpcYpoGeA5MzLw5uGK2p6lGx2mxubAgDgHu3bS3/8kbPa7pZbrG4D5GCRJwAAQCHgNJx6/8f3ddUnV+Ub1tltdj3T4hl9c903Ch4UbBrWBVUKUv0l9eUX6ufmxgAAuE+5cjmr7ABPwQo7AAAAH7c/ab8GLhmoDfs25DtWt1RdTes0TY3sjfTrtb/Kcd6Re8yviJ8aLG+goHJB7qwLAABQ6LHCDgAAwEcZhqEpv01RgwkN8g3rbLLpyeue1K/3/6qrI67W9o7blXEoI+8Odil6brSKNCzi5tYAALjO4sVSUpLVLYB/xwo7AAAAH3Ts/DHdv+x+Ldu5LN+xqsWraupdU9WqaisZTkMxPWKU/HOy6T41362pkh1KuqsuAAAut3mz1LWrVLGiNHu21Ly51Y2Av8cKOwAAAB/zeeznqj++/kWHdfc1uU9bH9yqVlVbSZL2Pr9Xp744ZbpP+SHlVeHRCm7pCgCAOyQlST17Sg6HtH+/1LKlNHGi1a2Av8cKOwAAAB+RmJaoR1Y+otnbZuc7Vq5IOX3W8TO1r9U+Nzs6+agOvHHAdL8St5dQzXE1ZbNxRVgAgG8wDOmBB3IGdX/y85OaNrWuE/BvGNgBAAD4gHV71mnAkgE6dO5QvmM96vfQR+0/UomQErlZ4oZE7Xxgp+l+YfXDFD0vWnZ/TsIAAPiOyZOl+fPN2ZgxUuPGltQBLgkDOwAAAC+Wnp2u59Y9p3d/eDffsRIhJTShwwTdU+8eU566K1UxXWJkZBu5WUBkgBosbyD/onw8BAD4jrg46dFHzdkdd+TPAE/DJzIAAAAv9cexP9RnUR9tP7E937EOtTpo4p0TVS68nCnPPpet7R23KzsxOzezB9vVYGkDBVcJdnlnAADcJT1d6tFDSkvLy8qXl6ZMkdj5AZ6OgR0AAICXcTgdemfzO/rfhv8p05FpOhYaEKpxbcdpcJPB+fahMxyGYnvFKjUu1ZTXnVpXRZsVdXlvAADcafhwaevWvNs2mzRzplSqlHWdgEvFwA4AAMCL7E/ar/6L++ub/d/kO9asQjPN6DxDtUrWuuhj9/5vr858ecaUVRlZRZHdI13SFQAAqyxdKn34oTl77jnp5put6QP8VwzsAAAAvIBhGJq1bZaGrBiicxnnTMf8bH4a2XKknm/5vPztF/94d3z28XxXhC3VuZSqvlTVVZUBALDEoUPSwIHm7PrrpZdesqQOcFkY2AEAAHi4M2ln9NCXD2l+zPx8x2qVqKUZnWeoWcVmf/v4c1vOacegHaYsrEGY6k6vK5udTXwAAL7D4ZD69JHOXLCgvFgxafZsyZ8JCLwIb1cAAAAPtmHvBvVd1FeHkw/nO/bA1Q9o7G1jFRYY9rePzziSoe2dtsuZ7szNAkoFqP7S+vIvwkdBAIBvee016Zu/7BoxcaJUtaoldYDLxqc0AAAAD5TpyNTI9SM1ZtMYGTJMxyLDIjWp4yTdUfuOf3wOR7pD2ztvV+aRvAtT2Pxtqvd5PYVUDXFJbwAArNSggRQRISUm5ty+7z6pWzdrOwGXg4EdAACAh9l5eqd6LeylX47+ku9YxzodNfHOiYoM++cLRRiGoZ3371TyT8mmvNZHtVS8VfGCrAsAgMfo1Em6+mqpd2/p1Clp3DirGwGXh4EdAACAhzAMQ5N+m6THVz2u1KxU07HQgFCNaztOg5sMls327/vOHRx7UMdnHDdl5YeUV/n7yxdoZwAAPE2lStL69dKJE1JoqNVtgMvDwA4AAMADnEk7o/uX3a+FcQvzHWtSrolm3z1bdUrVuaTnOr3itPaM2GPKit9cXDXfrVkgXQEA8HT+/lJ5vqOCF2NgBwAAYLG/u7CETTYNbz5co1qPUqBf4CU9V0pcimJ7xurCbe+CqwWr3oJ6sgfYC7I2AAAAXIRPbQAAABbJdGTqma+eUZvpbfIN68qHl9dX/b7Sm7e+ecnDuqzELG3vuF2Oc47czK+Inxosa6CAkgEF2h0AAKulpUmdO0s//2x1E6DgMbADAACwwM7TO9V8UnO9+f2b+a4C27luZ219cKtaV2t9yc9nOAzF9oxV2q60vNAmRc2OUli9sIKqDQCAxxg2TFq8WGreXBo7VnI6rW4EFBxOiQUAAHAjwzA0/Y/pGrJiiFKyUkzHQvxDNO72cbqvyX2XdGGJC+0duVeJqxNNWbXR1VTqzlJX3BkAAE/zxRfShAk5/5yVJT31lFS6tNSvn7W9gILCwA4AAMBNzmWc08NfPqxZ22blO3ZV2as0u8ts1S1V9z8/74nPT+jA6wdMWenupVX5mcqX3RUAAE914IA0aJA5u/FGqVcva/oArsDADgAAwA22HN6ingt7anfi7nzHnrr+Kb3a+lUF+Qf95+dNiUlR/IB4UxbWMEx1J9X9z6v0AADwdNnZUu/eUlJSXhYRIc2alXNlWMBX8HYGAABwIafh1Dub39Gz655VtjPbdKxMWBnN6DxDt9a49bKeOyspS9s7b5czJW/THv8If9VfVF9+YX5X1BsAAE/06qvSd9+Zs0mTpEqVrOkDuAoDOwAAABc5fv64BiwZoFW7VuU71rZGW03rNE1lipS5rOc2nIbi+sQpLeGCi0zYpei50QqpHnK5lQEA8FjffCONGmXOHnoo50qxgK9hYAcAAOACa3evVd9FfXU85bgpD7AH6PU2r2vo9UNlt9kv+/n3vbxPZ748Y8qqja6mEreVuOznBADAU50+LfXpY74SbP36OVeHBXwRAzsAAIAClOXI0v/W/09vbXor37EaETU0t+tcXVP+mit6jVNLTmn/K/tNWemupVX5aS4yAQDwPYaRc5GJQ4fyspAQae7cnP8GfBEDOwAAgAKyN3GveizsoZ8O/5TvWO8GvTW+w3gVDSp6Ra+REp+iuL5xpiy0XqjqTKnDRSYAAD5pwgRpyRJzNm6cVK+eJXUAt2BgBwAAUAAWxi7UoKWDdDbjrCkPCwjT+A7j1a9Rvyt+jexz2YrpHCNHsiM38yvmp/qL6su/CB/rAAC+Z+tW6cknzVmXLtJ991nTB3AXPtkBAABcgfTsdD215il9tOWjfMeuKnuV5nadq9ola1/x6xhOQ/H945Uan5oX2qToWdEKrRV6xc8PAICnMQzpwQeljIy8rHJlaeJEiUXl8HWXv9MxAABAIZdwOkHNJzW/6LDuiWZPaPOgzQUyrJOk/a/t16nFp0xZ1ZerqmSHkgXy/AAAeBqbTZo1S2rWLOe2n580e7YUEWFtL8AdWGEHAABwGeZsm6P7l9+v85nnTXlEcISmdpqqjnU6FthrnV5xWvte2GfKSt5VUlWer1JgrwEAgCeqVk3auFF68UWpSBGpRQurGwHuwcAOAADgP0jLStPjqx7XxF8n5jt2fcXrNbfrXFUuVnBXa03dlarYXrGSkZeF1AlR1PQo2eycDwQA8H0BAdJrr1ndAnAvBnYAAACXKP5UvO5ZcI+2ndiW79jTLZ7WqJtHKcAvoMBez5HqUEyXGDnOXnCRiXA/1V9cX/5F+RgHAADgq/ikBwAAcAmm/zFdD335kFKzUk15qdBSmtF5hm6veXuBvp5hGNr58E6lbE0x5XWn11VY3bACfS0AADzF2bOBKlYs0+oagOW46AQAAMA/SMlM0cAlA9V/cf98w7qWVVrq9wd+L/BhnSQd/eyojk87bsoqP1tZpTuVLvDXAgDAE3zzjV333XerVqyoJsP49/sDvoyBHQAAwN+IOxmnZp8109Tfp5pym2wa2XKk1vVbpwpFKxT46577+ZwSHkkwZcXbFFe1UdUK/LUAAPAEp05J994bqMxMf336aUN17x6o06etbgVYh1NiAQAALmL2ttm6f9n9Sskyn5JaJqyMZt49U7dUv8Ulr5t1OksxXWNkZOYtLQisEKjo2dGy+XGRCQCA7zEMaeBA6ejRvJ9z69fbdeKEVLKkhcUACzGwAwAAuEB6drqGrhqqj3/5ON+xNtXaaObdM1W2SFmXvLbhNBTXJ04Z+zNyM5u/TfUW1FNgZKBLXhMAAKt98IG0fLk5e/vtLEVF8bMPhRcDOwAAgP+3+8xudVvQTb8d+82U22TTC61e0MiWI+Vn93PZ6+9/db/OrDpjymq8U0PFri/mstcEAMBKv/8uDR9uzm644ZD692dpHQo3BnYAAACSFsUt0sAlA3U246wpLx1aWrPunqVba9zq0tc/s/qM9r20z5RF9ohUhUcKfo88AAA8QUqK1KOHlHnBRWEjI1P00EN/yGZrbV0xwAMwsAMAAIVapiNTz3z1jN794d18x26ofIPmdpnrkgtLXCh9f7pie8VKF1wRLzQqVLUn1pbNxr51AADf9Nhj0o4debf9/AwNG/aLwsKyrSsFeAgGdgAAoNA6cPaAun/eXT8c+iHfsadbPK1XW78qf7trPy45M5yK6Raj7DN5v5z4FfFTvS/qyb8IH9UAAL5p7lxp8mRz9sIL2apTJ9GaQoCHsVtdAAAAwAorE1bqqk+uyjesiwiO0LKey/TGLW+4fFgnSbuG7lLylmRTVmdSHYXVDXP5awMAYIU9e6QHHjBnbdpIw4axsg74E1/bAgCAQsXhdOjlb17Wq9++KuPCc1AlNS3fVPO7zVfV4lXd0uXYjGM6MuGIKav4REVF3hPpltcHAMDdsrKkXr2kc+fyslKlpOnTJTtLioBcDOwAAEChcSr1lHot7KW1e9bmO/bYtY9pzG1jFOgX6JYu57ed184Hdpqyoi2Kqvpb1d3y+gAAWOGFF6QffzRn06ZJ5ctLaWnWdAI8EQM7AABQKPx46Ed1W9BNB88dNOXhgeGa1HGSutXr5rYu2eeyFdMlRs40Z24WEBmgevPqyR7A8gIAgG/KzJTW/uU7syeekNq3t6QO4NH4RAgAAHyaYRgav2W8bpxyY75hXf3I+vr5/p/dOqwzDEM7Bu9QWsIFywjsUvTcaAVVCHJbDwAA3C0wUPr+e2no0JzbV10lvfGGtZ0AT8UKOwAA4LNSMlN0//L7NXvb7HzH+jTso487fKywQPde3OHI+CM6ueCkKas2upoibo5waw8AAKwQFCS98450yy1SzZo5twHkx8AOAAD4pB2ndqjL/C6KORljygP9AvXe7e/pgasfkM1mc2un5F+StevJXaasRIcSqjyislt7AABgNU6DBf4ZAzsAAOBzPo/9XAOXDNT5zPOmvHKxylrQbYGurXCt2ztlJWUppluMjMy8K9MGVQ5S1LQo2ezuHRwCAADAs7GHHQAA8BlZjiw9ufpJdVvQLd+wrm2Ntvrl/l8sGdYZhqEd9+5Q+t703Mzmb1P0vGgFlAxwex8AANwhOVmaNUsyjH+/LwAzBnYAAMAnHDt/TG2mt9G7P7xrym2y6aVWL+nLXl+qVGgpS7odfv+wTi06Zcqqv1Vdxa4rZkkfAADc4ZFHpD59pO7dpaQkq9sA3oVTYgEAgNfbdHCTus7vqqPnj5ryEiElNOvuWbq95u0WNZPO/XhOu5/abcpKdSqlik9UtKgRAACuN3OmNH16zj8vWCD99JO0caNUqZK1vQBvwcAOAAB4LcMwNH7LeD2x+gllO7NNx5qWb6oF3RaoSvEqFrWTss5kKeaeGBnZeecCBVcLVp3Jddx+wQsAANxl1y7poYfMWZ06UoUK1vQBvBEDOwAA4JVSs1L14PIHNWPrjHzH7m9yv95v976C/IMsaJbDMAzFD4hXxoGM3MwWaFP0/GgFRLBvHQDAN2VmSj17Sucv2Eo2MlKaNk2ysykXcMkY2AEAAK+zJ3GP7p53t/44/ocpD/IL0kftP9KgJoMsapbn4NiDOr3stCmrMbaGil5T1KJGAAC43vPPSz//bM6mT5fKlrWmD+CtGNgBAACvsjJhpXp/0VuJ6YmmvHKxylp4z0JdU/4ai5rlOfv9We15Zo8pK92ttCoM4VwgAIDvWrVKevttc/bUU1Lbttb0AbwZC1IBAIBXcBpOjfpmlDrM7pBvWNemWhv9cv8vHjGsyzyVqZjuMZIjLwupGaI6E9m3DgDgu44dk/r3N2fXXCONHm1NH8DbscIOAAB4vKT0JPVb1E/Ldi7Ld+zpFk/r1davyt9u/ccaw2kovm+8Mg9n5ma2IJuiF0TLv5j1/QAAcAWnU+rXTzpxIi8rUkSaM0cKDLSuF+DN+OQIAAA82vYT29V5XmftOrPLlBcJLKJpnabp7qi7LWqW34G3DujMqjOmrNZ7tRTeONyiRgAAuN7YsdLateZswgSpZk1r+gC+gIEdAADwWAtjF6r/4v5KyUox5XVL1dUX93yhqNJRFjXL7+z3Z7X3f3tNWWTPSJW7v5xFjQAAcL2ffpKee86c9esn9eljTR/AV7CHHQAA8DgOp0PPrXtOXRd0zTesuzvqbv00+CePGtZlnclSbM9Y8751tUNU+5Pa7FsHAPBZ585JPXtK2dl5Wc2a0ocfWtcJ8BUM7AAAgEdJTEvUnXPu1OvfvW7K7Ta73mjzhj7v9rnCgzznFFPDMBQ/MF4ZBzNyM1uQTfUW1JN/OCczAAB8V0CA+QqwAQHS3LlSuOf8mAa8ltcM7BYvXqwHHnhAV199tcqVK6fAwEAVL15czZs313vvvafMzMy/fezmzZt11113qXTp0goJCVF0dLRGjRql9PR0N/4JAADAv9l+YruaTmyqlbtWmvKI4Ait7L1ST9/wtMetWDv8wWGdXnralNV8t6aKNCxiUSMAANwjJEQaP1764gspIkJ64w3p6qutbgX4Bq/52vftt9/W999/r6CgIJUvX16NGjXS0aNHtXnzZm3evFkzZszQV199peLFi5seN2vWLPXv318Oh0MVKlRQpUqVtH37dr3wwgtatmyZvv76a4WGhlrzhwIAALn+br+6BpENtKj7ItUoUcOiZn8v+Zdk7R6+25SV6lJK5R8sb1EjAADcr3Nn6frrpchIq5sAvsNrVtgNHjxYGzZsUHJysvbs2aMtW7bo0KFD2rx5sypWrKhffvlFzz//vOkx+/bt06BBg+RwOPTWW2/p4MGD+vXXX5WQkKA6depoy5YtGjFihEV/IgAAIP3zfnX31LtHmwdt9shhXfa5bMV0j5GRaeRmwVWDVeezOh63ChAAAFcrW1aye82EAfB8XvN/pwEDBuimm25SQECAKb/uuuv0zjvvSMo5bfZCY8aMUUZGhm677TYNHz4898NzlSpVNHnyZEnSp59+quPHj7v+DwAAAPJJTEvUHXPuuOh+dW/e8qbmdpmrsMAwi9r9PcMwtPPBnUrfnbe9hs3fpuh50QooHvAPjwQAAAD+ndcM7P5J3bp1JUmpqam5mWEYWrRokSRp0KBB+R7TvHlz1a1bV1lZWVqyZIl7igIAgFx/7le3atcqU/7nfnUjWozw2JVqxyYf04k5J0xZtderqei1RS1qBACA661YIfXvLyUnW90E8H0+MbDbvHmzJKlJkya52YEDB3T06FFJUosWLS76uD/zH3/80cUNAQDAhRbFLdJ1n12n3Ynm/d8aRDbQz/f/rNtq3GZRs3+XEpOihEcTTFmJdiVU6clKFjUCAMD1jhzJGdZNny41aSL98ovVjQDf5jUXnfgrh8Oho0ePaunSpXrmmWcUFham11/PO50mISHng/SfF6m4mOrVq5vue6UOHTpkup38/187pKWl5TuV1xtdeFVdrrAL3g+4EO8HXOif3g9Ow6nXvn9Nr216Ld/jutbtqgm3T1BYYJjS0tJc3vNyOFIdiukWI2eaMzcLKBegqh9XVXoG7/2L4e8H/BXvCVyI94N3cDik3r0DdeqUnyRp1y7p9tsNxcWlK6wAd67g/YAL+er74VI/59oMwzD+/W6eY9y4cRo6dKgp69Spk0aNGqX69evnZgsWLNA999yjMmXK6NixYxd9rgkTJujhhx9W/fr1tW3btivu9nen7cyePZsr0QIACr00R5rGHRinH8+aV7bbZVff8n3VqXQnjz0F9k8hH4UocG1g7m3Dbijl5RQ5GjgsbAUAgGt9/nktzZwZbcqGDftZN9542KJGgPdKTU1Vr169dPbsWRUt+vfbqXjdKbEVKlRQixYtdO2116pMmTKSpA0bNmjOnDlyOPI+LP85fQ0MDLzo80g5q++kS59uAgCAy3M046ieTng637AuzC9MI6uPVOfIzh4/rAvYGGAa1klSRrcMhnUAAJ+2Y0eEZs+ua8ratNnPsA5wMa87JbZbt27q1q1b7u0ff/xRDzzwgF577TWdOXNGEyZMkCQFBwdLkjIzM//2uTIyMiRJISEhBdLt4MGDptvJycmKjo5W69at/3Fq6i3S09P17bffSpJatmyZ+78xCifeD7gQ7wdc6K/vh03HNum5pc8pMT3RdL+6Jetqwd0LVCOihhU1/5P03ena1nebnMo7FTa8Zbiu/exa2fw8e9BoNf5+wF/xnsCFeD94tqQk6fHHg+R05q31qV3bqblzIxUWVvD7zfJ+wIV89f1w7ty5S7qf1w3s/qpZs2ZasWKFqlevrk8//VTPPPOMqlSpooiICElSUlKSDMO46Lf2iYk5vzj8ed8rVbFiRdPtP/8lhISEFNhQ0FMEBwf73J8Jl4/3Ay7E+wF/MgxDE7dN1LNfPyun4TQd61ino2Z0nqGiQZ7/hZYzw6mY/jFyJl+wb12pANWfU19BRYIsbOZ9+PsBf8V7Ahfi/eBZDEMaOlTavz8vCwyU5s2zq1Qp1/974v2AC/nS+yErK+uS7ud1p8ReTPny5dW4cWM5nU798ccfkqRatWpJyllFd+TIkYs+bs+ePab7AgCAgpHpzNT7B97X0xuezjesG9lypBZ1X+QVwzpJ2vPsHp3/9bwpqzu9roLKM6wDAPiuyZOlefPM2dtvS40bW1IHKHR8YmAnSdnZ2ab/rly5ssqWLStJ+v777y/6mD/zZs2auaEhAACFw+Hkw3p+1/PakLjBlIcGhGpBtwV65eZXZLd5x0eQ0ytP69C75qvAV3qqkkq2K2lRIwAAXC8uTnrsMXN2553SI49Y0wcojLzj0/K/2LdvX+7KukaNGknKuWJr586dJUmTJk3K95hNmzYpPj5eAQEB6tixo/vKAgDgw3489KNunH6jElITTHnV4lW16d5N6hrd1aJm/13GsQzF9483ZeFNw1VtdDWLGgEA4Hrp6VKPHlJqal5WvnzOijsPvz4U4FO8YmD3yy+/6MUXX8w9hfVCq1atUrt27ZSdna327durRo28jauHDx+uwMBArVmzRmPGjJFhGJKk/fv3695775UkDR48OHclHgAAuHwz/pihVlNb6VjKMVN+U9WbtOW+LWpUtpFFzf47w2kovl+8sk7m7THiF+6n6DnRsgd6xccnAAAuy4gR0tatebdtNmnmTKlUKes6AYWRV3ziTE5O1iuvvKIaNWqoXLlyatq0qRo1aqSIiAi1a9dO8fHxatq0qaZNm2Z6XLVq1TRx4kTZ7XaNGDFClSpVUpMmTVSrVi3t2LFDV199tcaMGWPRnwoAAN/gcDo0Yu0I9VvcTxmODNOxB5s8qDV91qhUqHd9yj849qAS15qvalv749oKqeEbmx0DAHAxv/8uffCBOXv+eenmmy2pAxRqXjGwa9Sokd577z117NhRYWFhio+PV3x8vEJCQtSuXTtNmTJFmzZtUqmLjPz79eunjRs36o477lBaWppiY2NVvXp1vfTSS/ruu+8UFhZmwZ8IAADfcC7jnO6ae5fGbDJ/AeZv89eQSkP0zi3vKMAvwKJ2l+fclnPa+9xeU1amXxmV6VXGokYAALhH48bS7NlSeHjO7ebNpRdftLQSUGj5W13gUkREROixxx7TY3/d9fISNW/eXMuWLSvgVgAAFG67z+xWx7kdFXsy1pSXDi2toeWHKrpItEXNLl92crZie8bKyDZys5CaIar1IVeUBwAUDj17Ss2aSQ8/LH3yieTvFVMDwPd4xQo7AADgWdbvXa9rP7s237CuUZlG2th3o1cO6yQpYUiC0nen5962BdgUPTda/uH8tgIAKDyqV5dWrZKqVLG6CVB4MbADAAD/yfgt43XbjNt0Ju2MKe9ct7O+u/c7VS5W2aJmV+bYjGM6PuO4Kav+enWFXx1uUSMAAAAUVgzsAADAJclyZOnhLx/WkBVD5DAcpmMjW47U5/d8riKBRSxqd2VSd6Uq4eEEUxbRNkIVh1a0qBEAAK539qyUmmp1CwAXw/kdAADgX51OPa1uC7ppw74NpjzEP0RTO03VPfXusajZlXNmOhXXM06O83lDyIDIAEVNi5LNbrOwGQAArmMY0qBBUmysNHeu1LCh1Y0AXIgVdgAA4B/FnozVtZ9dm29YVyG8gjYO3OjVwzpJ2vu/vUr+OdmURU2PUmCZQIsaAQDgehMnSgsXSnFx0rXXSh99lDPEA+AZGNgBAIC/tWrXKl0/6XrtSdxjyptVaKYt923R1eWvtqhZwTiz5owOjjloyio9VUkl2pawqBEAAK4XEyM9/nje7YwM6auvrOsDID8GdgAAIB/DMPT+j++rw+wOOpdxznSsb8O++nrA1yoXXs6idgUj80Sm4vrFmbIiVxdRtdHVLGoEAIDrpaVJPXpI6XkXRVfFitKkSZKNnSAAj8EedgAAwCTLkaVHVz6qT375xJTbZNMbt7yh4c2Hy+bln+gNp6H4AfHKOp6Vm9nD7IqeEy17IN9nAgB817Bh0vbtebftdmn2bKkEi8sBj8LADgAA5DqTdkbdFnTT+r3rTXlYQJhmd5mtjnU6WtSsYB3+4LDOrDxjymqPr63QWqEWNQIAwPUWLZImTDBnL7wg3XijNX0A/D0GdgAAQJK08/RO3TH7DiWcSTDllYpW0rKey9SobCOLmhWs81vPa/eI3aYsslekyvQtY1EjAABc78CBnKvCXujGG6Xnn7emD4B/xsAOAABo3Z516rqgq5LSk0z5dRWv06Lui1S2SFlrihUwR5pDsT1jZWTmXQYvuFqwak+o7fWn+QIA8Heys6U+faTExLwsIkKaNUvyZyoAeCQ2aQEAoJD7+OeP1XZm23zDut4NemtD/w0+M6yTpN1P7VZqbGpe4CdFzYqSf1F+WwEA+K5XX5U2bjRnkydLlSpZ0wfAv2NgBwBAIZXtzNbjKx/XQ18+JIfhMB179eZXNaPzDAX7B1vUruCdWnZKR8YfMWVVX6yqYtcXs6gRAACu9+230qhR5uzhh6VOnSypA+AS8XUyAACF0Nn0s+qxsIdW7VplykP8QzSj8wx1ie5iUTPXyDiaoR337jBlxW4opirPVbGoEQAArnf6tNS7t+R05mUNGkhvv21dJwCXhoEdAACFzL6kfbpj9h2KORljyiuEV9DSnkvVpFwTi5q5huE0FN8/XlmnsnIzv2J+ipoZJZsf+9YBAHzX779LZy64KHpIiDR3bs5/A/BsnBILAEAh8sOhH9Tss2b5hnXXlL9GP933k88N6yTp0LhDSlybaMpqf1xbwVV853RfAAAupk0b6ddfpcaNc26PGydFR1vZCMClYoUdAACFxNztczVg8QBlODJMedforprWaZpCA0ItauY6yb8na8+ze0xZmf5lVKZHGYsaAQDgXnXqSD/8IM2eLQ0YYHUbAJeKFXYAAPg4wzA06ptR6rmwZ75h3XM3PKd5Xef55LDOkepQXM84GZlGbhZcI1i1PqhlYSsAANwvKEgaOFCysRME4DVYYQcAgA/LyM7Q4GWDNXPrTFMeYA/QxDsnqn/j/hY1c73dw3YrNT41L/CTomdFyz+cjz8AAADwbHxiBQDAR51KPaXO8zrruwPfmfISISX0xT1fqFXVVhY1c72Ti0/qyMdHTFm1V6qpaLOiFjUCAMD1Vq+WKleWoqKsbgLgSnFKLAAAPij+VLyafdYs37CuVola+mHQDz49rMs4kqEdg3eYsmKtiqny05UtagQAgOvt2yd17y5dc400aZJkGP/6EAAejIEdAAA+Zt2edbrus+u0J9F8sYWbqt6kHwb/oFolfXcPN8NpKK5fnLJPZ+dm/sX9FTUjSjY/Nu4BAPim7GypVy/p7FkpNVUaPFgaMsTqVgCuBAM7AAB8yGe/fqbbZ92usxlnTfnAxgO1us9qlQgpYVEz9zj4zkElrUsyZbU/ra3gSsHWFAIAwA1eeknavNmctWtnSRUABYSBHQAAPsBpOPXsV8/qvmX3KduZbTr2epvXNanjJAX6BVrUzj2Sf0vW3uf2mrKy95ZVZLdIixoBAOB6GzZIr71mzh57TLrzTmv6ACgYXHQCAAAvl5aVpv6L+2tB7AJTHuIfohmdZ6hLdBeLmrmPI9WhuN5xMrLyNuwJqRmimu/VtLAVAACudeqU1KePeb+6Ro2kN9+0rhOAgsHADgAAL3Yi5YTumnuXfjj0gykvE1ZGy3ouU9MKTS1q5l67R+xWalxq7m2bv01Rs6PkX4SPOgAA32QY0sCB0pELLooeGirNnSsFsxME4PX4FAsAgJeKPxWv9rPaa2+S+TTQeqXr6cteX6pK8SoWNXOv01+e1pGPjpiyqi9VVdGmRS1qBACA633wgbR8ef6sbl1r+gAoWAzsAADwQl/v+1qd53VWUnqSKb+1+q1a0G2BigUXs6aYm2WeyFT8vfGmrNgNxVT5mcoWNQIAwPV+/10aPtyc9eiRs+IOgG/gohMAAHiZ6X9M120zbss3rBt81WB92evLQjOsMwxD8ffGK+tEVm7mV9RPUTOjZPOzWdgMAADXSUnJGc5lZuZl1apJH38s2fjxB/gMBnYAAHgJwzD04oYX1X9xf2U5s0zH3mjzhj6981MF+AVY1M79jkw4ojNfnjFltcfXVnAVNu4BAPiuxx6TduzIu+3vL82ZIxUrHN/XAYUGp8QCAOAFMrIzNGjpIM3aNsuUB/kFaUbnGepWr5tFzayREpei3cN2m7LInpEq07uMRY0AAHC9uXOlyZPN2ahRUrNm1vQB4DoM7AAA8HCnU0+r87zO2nhgoykvFVpKS3ss1fWVrreomTWcGU7F9YqTM92ZmwVVDlKt8bUsbAUAgOs1aCDVqyfFxOTcvuUWacQIazsBcA1OiQUAwIPtSdyj5pOb5xvW1SlZRz8M+qHQDeskae/IvTr/+/m8wCZFzYhSQPHCczowAKBwqldP2rJFevBBqXRpafp0yc5v9YBP4v/aAAB4qJ8O/6TrPrtOO0/vNOWtqrTSpkGbVKNEDYuaWSdxQ6IOvn3QlFV+prKKtyxuTSEAANwsJESaMCFnlV25cla3AeAqDOwAAPBAS+KX6KapN+lk6klT3rdhX63pu0YlQkpY1Mw6WWeyFNc3TjLysvBrwlX1paqWdQIAwCqlS1vdAIArMbADAMDDvP/j++o8r7PSstNM+QstX9C0TtMU6BdoUTPrGIahnQ/uVObhzNzMHmpX1Kwo2QP5OAMAAADfwidcAAA8hNNw6snVT+rxVY/LuGAZmb/dX5M7TtbLN78sm81mYUPrHJ9+XCcXmFcb1ny3pkJrh1rUCAAA13I6pR49pFmz/v2+AHwPAzsAADxAWlaaui3opnd/eNeUhweGa0WvFRp41UCLmlkvbU+aEh5JMGUl7yqpcvexcQ8AwHe99540b57Up4/Uv790/vy/PwaA7/C3ugAAAIXdyZST6ji3o3449IMpr1i0olb0WqEGZRpY1Mx6zmyn4vrGyXHekZsFlg1UnYl1Cu1qQwCA7/v1V+npp/NuT58uRUZKY8ZY1wmAezGwAwDAQjtP71T7We21O3G3KW9UppG+7PWlKhStYFEzz3DgjQM6t+mcKas7ta4CSxe+ffwAAIVDcnLOqbBZWXlZjRrSyJHWdQLgfgzsAACwyKaDm9RxTkedTjttytvWaKsF3RYoPCjcomae4dxP57TvpX2mrMJjFVSibeG7Qi4AoPB49FEp4YKdIPz9pTlzpKJFresEwP3Yww4AAAssjF2o1tNa5xvWDb5qsJb1XFboh3WOFIfi+sRJeWfCKjQ6VNXfqG5dKQAAXGzWLGnaNHP22mtS06bW9AFgHQZ2AAC42fs/vq9uC7opw5Fhyke3Hq1P7/xUAX4BFjXzHLuG7VJaQlrubVuATVGzouQX4mdhKwAAXGf3bunBB81Z27bSsGHW9AFgLU6JBQDATZyGUyPWjtDYzWNNeYA9QFM7TVWvBr0sauZZTi07paOfHDVl1UZXU3jjwr3qEADguzIzc/atu/BKsJGROavt7CyzAQolBnYAALhBena6+i/ur/kx8015saBiWtR9kW6udrNFzTxL5vFM7Ri0w5QVv6m4Kj1ZyaJGAAC43vPPSz//bM5mzJDKlLGmDwDrMbADAMDFEtMS1WleJ327/1tTXrFoRa3svVL1I+tb1MyzGIah+EHxyjqZd1k8v2J+qjutrmx+NgubAQDgOqtXS2+/bc6GD5duu82aPgA8AwM7AABcaH/SfrWb1U5xp+JMecMyDbWi1wpVKFrBomae58gnR3TmyzOmrPaE2gquHGxRIwAAXOvYMalfP3PWtKn06qvW9AHgORjYAQDgIr8f+13tZ7XX0fPm/djaVGujhfcsVLHgYhY18zypO1K1+8ndpiyyV6TK9ORcIACA73rxRenEibzb4eHSnDlSYKB1nQB4BravBADABdbsXqMbp9yYb1jXp2Efrei9gmHdBZxZTsX2jpUzzZmbBVUKUq2PalnYCgAA1xs7VhowIO/2hAlSjRqW1QHgQVhhBwBAAZv2+zQNXjZY2c5sU/7sDc9qdOvRstnYj+1C+17ep/O/XHBZPJsUNSNKAcUDrCsFAIAbFCkiTZki3XqrtHmz1Lu31Y0AeAoGdgAAFBDDMPTaxtf0vw3/M+V2m10ftvtQDzV9yKJmnivpuyQdeP2AKas0opKKtypuTSEAACzQq1fOfwDgTwzsAAAoAA6nQ4+ufFQTfp5gykP8QzS361x1rNPRomaeK/tctuL7xkt5Z8KqSOMiqvZKNetKAQAAAB6AgR0AAFcoLStNvb7opcXxi015qdBSWt5zuZpVbGZNMQ+X8FiC0vel5962B9sVNStK9kC22AUA+KaDB6VKlaxuAcAb8IkYAIArcCbtjG6ZcUu+YV31iOradO8mhnV/48TnJ3R82nFTVn1MdYVFh1nUCAAA19q5U4qKkh54QEpNtboNAE/ntoFddna2Fi1a5K6XAwDA5fYn7VeLyS206eAmU35N+Wu06d5NqlWSq5xeTMbhDO18YKcpK3F7CVUYUsGiRgAAuFZGhtSjh5SSIn36qXTNNdK2bVa3AuDJXH5K7B9//KGpU6dq1qxZOnPmjLKzs//9QQAAeLg/jv2hdrPa6ej5o6b89pq3a0G3BSoSWMSiZp7NcBqKHxiv7DN5nwf8S/qrzuQ6XD0XAOCznnlG+u23vNvx8dKJE9b1AeD5XDKwO3PmjGbNmqUpU6bojz/+kJRz5Tw+iAMAfMH6vevVeV5nncs4Z8r7N+qviXdOVIBfgEXNPN/hDw8rcW2iKavzaR0FlQuyqBEAAK715ZfSuHHm7JlnpDZtLKkDwEsU2MDO6XRq5cqVmjJlipYvX66srCwZhiFJuuqqq9S1a1d17dq1oF4OAABLzN0+V/0W9VOWM8uUP3fDc3q19at8OfUPUmJStHvEblNWdmBZlb67tEWNAABwrSNHpAEDzFmzZtLLL1tSB4AXueKBXWxsrKZOnaqZM2fq+PHjuUM6m82m0aNHq0ePHqpWrdoVFwUAwGrvbH5Hw9YMM2U22fRh+w/1cNOHLWrlHZwZTsX1iZORYeRmwdWDVfO9mha2AgDAdRwOqW9f6dSpvKxoUWnOHCmAxfgA/sVlDezOnj2rOXPmaMqUKfr5558l5ZzyWqFCBfXq1UtjxoyRJD3++OMKDQ0tuLYAAFjAaTg1fM1wvfPDO6Y8yC9Ic7rMUeeozhY18x57X9ir87+fzwvsUtSMKPmHu3w7XQAALPHWW9L69ebs008l1rMAuBSX/CnZMAytXbtWU6ZM0ZIlS5SRkSHDMBQeHq67775bffr0UevWrWWz2XIHdgAAeLtMR6YGLhmo2dtmm/KI4Agt7blUN1S+waJm3iPpmyQdHHPQlFV5roqKNS9mUSMAAFxr82Zp5Ehzdu+9Uvfu1vQB4H0ueWBXpUoVHT58WIZhyN/fX7fffrv69u2rTp06KTg42JUdAQCwRHJGsrrM76K1e9aa8kpFK2lVn1WKLh1tUTPvkX02W3H94qS8M2EVfk24qrxQxbpSAAC4UFKS1LNnzimxf6pTR3r/fcsqAfBClzywO3TokGw2m4YMGaIXX3xRpUqVcmUvAAAsdSLlhNrPaq9fjv5iyhtENtDK3itVoWgFi5p5l31P7lPGgYzc2/YQu6JmRskeYLewFQAArmEY0gMPSPv352WBgdLcuVJYmHW9AHif//Rp2TAMjR8/Xh07dtT48eN1+vRpV/UCAMAyexL3qMXkFvmGdS2rtNS3A79lWHeJAr4L0Ok55s8KNcbWUGgd9rcFAPimSZOk+fPN2dtvS40bW1IHgBe75IHdb7/9pkcffVQlSpTQDz/8oEcffVTly5fXXXfdpc8//1wZGRn//iQAAHi4347+puaTmmvXmV2m/O6ou7W6z2oVDy5uTTEvYztlU8iEEFNWon0JlX+wvEWNAABwvR9+MN++807pkUes6QLAu13ywK5Ro0Z67733dOTIES1YsEDt2rWT0+nUsmXL1L17d5UpU0b33Xefvv76axfWBQDAddbvXa9WU1vpeMpxU/7g1Q9qftf5CvZnz9ZLYTgNhb4fKluKLTcLKBWgOpPqyGaz/cMjAQDwbhMnSpMnS6GhUvnyOf/Mjz4Al+M/byATEBCgLl26aPny5Tp48KDeeOMN1a1bV+fOndOkSZPUpk2b3Pv+/vvvBdkVAACXmR8zX7fPvF3Jmcmm/OWbXtb4DuPlZ/ezqJn3OfbRMflvNW+TW+ezOgoqG2RRIwAA3MNmkwYOlH79Vfr8c4mt3wFcriva8bls2bIaMWKEYmJitHnzZt13330qWrSopJz97m688UZVr15dw4cP148//lgghQEAKGgf/PiBenzeQ1nOrNzMbrPr4w4f64VWL7Aq7D84v+28Dr5w0JSVHVRWpe7iNxYAQOFRp450/fVWtwDgzQrsEm3NmjXTJ598oqNHj2rmzJm65ZZbZLPZtG/fPr3zzjtq3ry5qlSpUlAvBwDAFTMMQ8+ve16PrXpMhozcPMgvSJ93+1wPXPOAhe28jzPDqbg+cTIyLvjfsnqQao6raWErAAAAwPsU2MDuT8HBwerVq5fWrFmjvXv36uWXX1b16tVlGIYOHTpU0C8HAMBlyXZma/DSwXrtu9dMebGgYlrTd406R3W2qJn32vu/vUrZmpJ727AbqjGphvyL+P/DowAA8E4Oh/T++1J6utVNAPiiAh/YXahSpUoaOXKkEhIStGHDBvXr18+VLwcAwCVJy0pTl/ldNPn3yaa8fHh5bRy4US2rtLSomfdK3JCog2PNp8JmdM1QeLNwixoBAOBar70mPf54zqmv8fFWtwHga9z2lXerVq3UqlUrd70cAAAXlZSepI5zOmrjgY2mvE7JOlrdZ7WqFGf7hv8qKzFL8f3idcFZxcqula2MezKsKwUAgAt9/7300ks5//z779LVV0urV0s33GBlKwC+hHNUAACFxtHko7p91u3aenyrKb+2wrX6steXKhXKhREuR8KQBGUcyhvO2UPtSnsijU8ZAACflJgo9eolOZ15WdWqUpMmllUC4INcekosAACeIuF0glpMbpFvWNe2Rlut67eOYd1lOj77uE7MOWHKKr9RWc4Kzr95BAAA3sswpPvukw4cyMuCgqS5c6XQUOt6AfA9DOwAAD7v16O/6oYpN2hv0l5T3rN+Ty3tuVRFAotY1My7pR9I186Hd5qykneUVOSgSIsaAQDgWp9+Ki1caM7eeUdq0MCaPgB8FwM7AIBP27B3g26aepNOpJhXgT127WOaefdMBfoFWtTMuxlOQ/H94+U468jNAiIDVGdSHdlsNgubAQDgGjEx0hNPmLPOnaWHHrKkDgAfx8AOAOCzFsYu1O2zbldyZrIpH916tMbdPk52Gz8GL9fBdw4q6eskU1ZnUh0FRjIABQD4nrQ0qXt3KT09L6tYUfrsM4nvqQC4AttBAwB80ic/f6KHvnxIxgWXLrXb7Pq4w8e67+r7LGzm/c7/cV57nzOfXlzugXIqdQf7AAIAfNOTT+assPuT3S7Nni2VKGFdJwC+jYEdAMCnGIah0RtHa+SGkaY8yC9Is7vM1t1Rd1vUzDc40h2K7R0rIytvEBpSK0Q1x9a0sBUAAK7zxRfSxx+bsxdekG680Zo+AAoHBnYAAJ/hNJwaumqo3v/pfVMeHhiupT2X6qaqN1lTzIfsfXavUmNS8wI/KWpmlPzC/KwrBQCAixw4IA0aZM5uvFF6/nlr+gAoPBjYAQB8QpYjSwOXDNSsbbNMeWRYpFb1XqWryl1lUTPfcWbtGR0ad8iUVX2hqopeW9SiRgAAuI7DIfXuLSUl5WUREdKsWZI/v0kDcDF22wYAeL3UrFR1mtcp37CuekR1fX/v9wzrCkDWmSzFD4g3ZUWvK6rKz1W2qBEAAK5lt+cM7IKD87LJk6VKlazrBKDw4HsBAIBXS0pP0p1z7tR3B74z5Q0iG2h1n9UqF17Ooma+wzAM7XxwpzKPZOZm9jC7omZGye7Pd38AAN9ks0kPPijdcEPOFWJvuknq1MnqVgAKCwZ2AACvdez8MbWd2VZbj2815S0qtdCynssUERJhUTPfcnzmcZ1ccNKU1XqvlkJqhFjUCAAA96lfX9qyJWeABwDuwsAOAOCV9iTu0a0zbtWexD2mvH2t9lrQbYFCA0ItauZb0vamKWFIgikr1amUyt5b1qJGAAC4XygfKwC4GeexAAC8zrbj29Ricot8w7peDXppcffFDOsKiOEwFNc3To5kR24WUCZAtT+tLRvLDAAAPsgwrG4AADkY2AEAvMqmg5vUcmpLHTt/zJQ/0vQRzeg8QwF+ARY18z0H3jygc9+fM2V1p9RVYOlAixoBAOA6W7dKrVpJu3db3QQAGNgBALzIyoSVumX6LUpKTzLlL9/0st5v977sNn6sFZRzP5/Tvhf3mbLyQ8qrZLuS1hQCAMCFUlKkHj2kjRulq66SZs+2uhGAwo7fbAAAXmHOtjnqOLej0rLTTPkH7T7QC61e4BTNAuRIcSiud5yM7LzzgkKjQlXjrRoWtgIAwHWGDpXi4nL+OTlZ6tdPSkj458cAgCtx0QkAgMebsGWChqwYIkN5AyR/u7+mdZqmXg16WdjMN+1+arfSduYNRm0BNkXNjJJfqJ+FrQAAcI0FC6SJE83ZSy9JtWpZUgcAJDGwAwB4MMMw9NrG1/S/Df8z5SH+IVp4z0K1q9XOoma+69SyUzry8RFTVm1UNYU3CbeoEQAArrNvn3TffebsppukZ5+1og0A5GFgBwDwSIZhaPja4Rq7eawpLx5cXMt7LleLyi0saua7Mo9nasegHaasWKtiqvRUJYsaAQDgOllZUq9e0tmzeVmJEtLMmZIfi8oBWIyBHQDA4zicDt2/7H5N/n2yKS8TVkar+6xWo7KNLGrmuwzDUPygeGWdzMrN/Ir5KWp6lGx+7A8IAPA9L78sbd5szqZMkSpUsKYPAFyIgR0AwKNkZGeo9xe9tTBuoSmvUqyKvur3lWqWqGlRM9925JMjOvPlGVNWe3xtBVcOtqgRAACus3699Npr5uzRR6WOHa3pAwB/xcAOAOAxzmee193z7tbaPWtNeVSpKK3tu1YVivKVtyukxKdo95O7TVlkr0iV6VXGokYAALjOqVNSnz6SkXctKzVqJL31lnWdAOCvGNgBADxCYlqi2s9urx8O/WDKm5ZvqhW9V6hUaCmLmvk2Z6ZTcb3j5Exz5mZBlYJU6yMujQcA8D2GIQ0cKB09mpeFhkpz50rBLCoH4EEY2AEALHc0+ajazmyrbSe2mfKbq96sJT2WKDyIK5S6yr6X9+n8r+fzAptUd3pdBRQPsK4UAAAu8sEH0vLl5uz996W6da3pAwB/h4EdAMBSexP36tYZt2p3ovmUzI51Ompe13kK9ufrbldJ2pikA68fMGWVhldSxE0RFjUCAMB1EhOl//3PnHXvLt17rzV9AOCf2K0uAAAovGJOxKjF5Bb5hnV9G/bVwnsWMqxzoeyz2YrrGyddsH9PkcZFVG1UNetKAQDgQhER0tdfS7X+f9eHqlWlTz6RbFwMHYAHYmAHALDElsNb1HJqSx09f9SUP3rto5raaar87SwCd6WdQ3YqY39G7m17sF1Rs6JkD+SjAQDAdzVpIv36a86qujlzpGLFrG4EABfHb0MAALf7et/XunPOnTqfed6Uv9jqRb3Y6kXZ+KrbpY7PPq4Ts06YsupjqissOsyiRgAAuE+RItKkSVa3AIB/xsAOAOBWX+78Ul0XdFV6dropH9d2nB6/7nGLWhUeafvStPOhnaasRPsSqjCkgkWNAAAAAPwV570AANxm3vZ56jSvk2lYZ7fZNbnjZIZ1bmA4DMX3i5fjnCM3CygdoLqT67KqEQDgc7KypEOHrG4BAJeHgR0AwC0m/jJRPRf2VLYzOzcLsAdoXtd5GnjVQAubFR4H3jygsxvPmrI6k+sosEygRY0AAHCdkSOlBg2khQutbgIA/x0DOwCAy43dNFb3L79fxgWXJA3xD9HSnkvVNbqrhc0Kj3M/ndO+F/eZsvIPl1epO0pZUwgAABdau1Z6800pKUnq2lV68EEpLc3qVgBw6djDDgDgMoZh6MWvX9Sob0eZ8qJBRbW853LdWOVGi5oVLtnnsxXXO05Gdt7ANDQqVDXG1LCwFQAArnHihNSvnzn76SfJznIVAF6EgR0AwCWchlNDVw3V+z+9b8pLhZbS6j6r1aRcE4uaFT67h+5W2q68ZQW2AJuiZkfJL9TPwlYAABQ8p1Pq3186diwvCwuT5s6VgoKs6wUA/xUDOwBAgct2Zuu+Zfdp6u9TTXn58PL6qu9XiiodZU2xQujkopM6+tlRU1bttWoKbxxuUSMAAFxn3Dhp1Spz9tFHUu3altQBgMvGwA4AUKAysjPUd1lfLYwz7/BcPaK6vur7lapFVLOoWeGTcSRDOwbvMGXFWxdXpScrWdQIAADX+eUX6ZlnzFnv3vlPjwUAb8DADgBQYDKcGbpn0T1au3etKa9Xup7W9F2j8uHlLWpW+BhOQ/H945V9Ju+qvP4R/qo7ra5sdpuFzQAAKHjJyVKPHlJWVl5Wo4Y0frxk48ceAC/EwA4AUCBSHakavWe0YlJiTHnT8k21svdKlQwtaVGzwunQuENK/CrRlNWZWEfBFYMtagQAgOs88oi0a1febX9/ac4cqWhR6zoBwJVgYAcAuGJn0s7oxd0vKiE1wZS3qtJKy3ouU3gQ+6W50/k/zmvPs3tMWdl7y6p0l9IWNQIAwHVmzpSmTzdnr78uNW1qTR8AKAhc2BoAcEWOnT+mtnPa5hvWta/VXit7r2RY52aONIdie8XKyDRys+Aawar5Xk0LWwEA4BoJCdJDD5mztm2lJ5+0pg8AFBQGdgCAy3bg7AG1nNJSMafMp8F2je6qRd0XKSQgxKJmhdeeEXuUGpuaF/hJ0bOi5V+ERfUAAN+SmSn17CmdP5+XRUZK06ZJdn7TBeDl+GsMAHBZdp3ZpRun3KiEM+aVdX3q99GcLnMU6BdoUbPC6/SXp3X4w8OmrOpLVVW0GRv4AAB8z6FD0unT5mzGDKlMGWv6AEBBYmAHAPjPtp/Yrhun3KgDZw+Y8val2uvjdh/L385qLnfLOJqh+AHxpqxoi6Kq8mwVixoBAOBa1atLv/8ude+ec3v4cOm22yytBAAFht+oAAD/yc9HflbbmW11Ju2MKe8S2UV9yvWR3cZ3Qe5mOA3FD4hX1qms3MyvmJ+iZ0XL5mezsBkAAK5VrFjO1WDvuUe64w6r2wBAwWFgBwC4ZBv3b1SH2R2UnJlsyl9u+bIanWtkUSscGndIiWsSTVntj2sruEqwRY0AAHAfm026+26rWwBAwWIZBADgkqzZvUZtZ7bNN6x7//b3Nfy64Ra1QvJvydrzzB5TVqZ/GZXpwQY+AAAAgLdiYAcA+FdL4pfozjl3Ki07LTez2+ya3HGyHm32qIXNCjdHikNxveJkZBm5WXCNYNX6oJaFrQAAcI0tW6Tly61uAQDuwcAOAPCP5m6fqy7zuyjTkZmb+dv9NafLHA28aqCFzbDryV1KjU/NvW3ztyl6drT8w9nxAgDgW86dk3r0kO68U3riCSkjw+pGAOBaDOwAAH9r8m+T1WthLzkMR24W5Bekxd0X655691jYDCe/OKmjnx41ZVVHVVXRa4ta1AgAANcwDOmhh6Q9/78DxHvvSS1bSg7HPz8OALwZX8EDAC7qw58+1KMrzae7hgWEaWnPpWpdrbVFrSBJ6YfStWPwDlNW/Kbiqjy8skWNAABwnenTpdmzzVmPHpKfnzV9AMAdGNgBAPJ56/u39PRXT5uyokFFtbL3SjWv1NyiVpAkw2Eovm+8shOzczP/CH/VnVFXNj+bhc0AACh4O3dKQ4aYs3btpMcft6YPALgLAzsAQC7DMPTyNy/r5W9eNuUlQkpoTZ81urr81RY1w58OjDmgpK+TTFmdz+oouGKwNYUAAHCRjIyclXQpKXlZ2bLS1KmSnc2dAPg4BnYAAEk5w7oRa0fo7c1vm/IyYWX0Vb+vVD+yvkXN8KdzP53TvpH7TFm5+8up9N2lrSkEAIALPfOM9NtvebdtNmnGDCky0rpOAOAuXvG9hGEY+u677zR8+HBdd911Kl68uAIDA1W+fHl16dJFGzZs+MfHb968WXfddZdKly6tkJAQRUdHa9SoUUpPT3fTnwAAPJvTcGrIiiH5hnUVi1bUtwO/ZVjnAbKTsxXbK1ZGtpGbhdQJUc13alrYCgAA1/jyS2ncOHP29NPSLbdYUgcA3M4rBnbr16/XjTfeqLfffltbtmxRmTJlVL9+fSUnJ+uLL75Q69atNXLkyIs+dtasWbrxxhu1dOlSBQUFKSoqSrt27dILL7ygli1bKjU11c1/GgDwLA6nQ4OWDtKEnyeY8uoR1bVx4EbVLlnboma4UMKjCUrfnfdFky3Apug50fILY8dtAIBvOXJEGjDAnDVrJr3yiiV1AMASXjGwMwxDNWvW1Pjx43Xq1Cnt2LFDv/76q06fPq1nn31WkvTqq69q+fLlpsft27dPgwYNksPh0FtvvaWDBw/q119/VUJCgurUqaMtW7ZoxIgRVvyRAMAjZDmy1PuL3pr6+1RTXrdUXX074FtVLV7Vkl4wOz73uI5PO27Kqr9RXeFXhVvUCAAA13A4pL59pVOn8rKiRaU5c6SAAOt6AYC7ecXA7tprr1VcXJweeughRURE5OaBgYF67bXX1K5dO0nSxIkTTY8bM2aMMjIydNttt2n48OGy2XKunlelShVNnjxZkvTpp5/q+HHzL0EAUBikZ6er64Kumhczz5Q3LNNQ3wz4RhWKVrCoGS6UtidNO+/facoibotQxScqWtQIAICCk5Ii7dxZXH/8UUpbttj06qvS+vXm+3zyiVStmjX9AMAqXnHRiaJFi/7j8VtvvVUrV67Uzp15v9AYhqFFixZJkgYNGpTvMc2bN1fdunUVHx+vJUuW6P777y/Y0gDgwVKzUtV5Xmet2b3GlDct31Sr+qxSiZASFjXDhZxZTsX2jJUj2ZGbBZQKUN2pdWWz2yxsBgDAlYmPl8aPl6ZPD9bZs63+9n733ptzpVgAKGy8YoXdv/nz4hEhISG52YEDB3T06FFJUosWLS76uD/zH3/80cUNAcBznM88rw6zO+Qb1t1Q+QZ91e8rhnUeZO/IvUr+KdmU1Z1WV0HlgixqBADAlRs3TqpXT/rgA+ns2b//Aqp0aen9993XCwA8iVessPsnhmFowYIFksyDuYSEBElSUFCQypcvf9HHVq9e3XTfK3Xo0CHT7eTknF+y0tLSFOADGy5ceFVdrrAL3g/e6WzGWXVa0Ek/HjF/UXFzlZs1v/N8BTgDlJaW9p+fl/dDwTu77qwOvnnQlJV9tKxCbw69rH9H7sT7ARfi/YC/4j1RuH34oZ9GjAi8pPuePCmNH5+pRx5x/Pud4RP4+wEX8tX3w6V+lrcZhmG4uItLffrpp3rggQcUGBio2NhY1ahRQ5K0YMEC3XPPPSpTpoyOHTt20cdOmDBBDz/8sOrXr69t27ZdcZc/98j7q9mzZys0NPSKnx8ArsS57HN6effL2p2225RfU/Qajag6QoH2S/vwDNezJdlUZGgR2RPzFsI7qjt0/s3zkvd//wMAKKQOHSqixx5rLafz0rd1sNsNvf/+elWseN6FzQDAfVJTU9WrVy+dPXv2H7eA8+pTYn/99Vc9/vjjknKuEvvnsE7Km74GBv79L6BBQTmnFHn6SgUAuFJJWUkauWtkvmHddcWu09NVn2ZY50mcUsj7IaZhnRFsKHVYKsM6AIBXW7my2n8a1kmS02nTqlVVXVMIADyY154Su3fvXt1xxx1KT09Xr1699NRTT5mOBwcHS5IyMzP/9jkyMjIkmfe+uxIHD5pPXUpOTlZ0dLRat279rxfO8Abp6en69ttvJUktW7bM/d8YhRPvB+9xJPmIOszroP3p+035PVH36LMOn8nffuU/Cng/FJyj7x/VgV8PmLIaH9RQ6d6lLWr03/F+wIV4P+CveE8UTikpUv/+l/fveuPG6poypbzCwgq4FDwOfz/gQr76fjh37twl3c8rB3bHjh3TrbfeqqNHj6pDhw6aOnVqvtNRIyIiJElJSUkyDOOip6smJiaa7nulKlasaLr957+EkJCQAhsKeorg4GCf+zPh8vF+8FwHzh5Q27lttTvRvLJuYOOBmnjnRPnZ/Qr8NXk/XL7kX5J1cKT5y5/I3pGqNKjS32674Ol4P+BCvB/wV7wnCo9t26SzZy/vsWfP2rR3b4iaNi3YTvBs/P2AC/nS+yErK+uS7ud1p8SeOXNGt956q3bv3q1WrVppwYIFF72gQ61atSTlrKI7cuTIRZ9rz549pvsCgC/ZfWa3Wk5pmW9Y99A1D+mzjp+5ZFiHy5ednK3YHrEysvK2lg2uEaza42t77bAOAIA/JSf/+31c+XgA8DZeNbA7f/682rdvr+3bt6tp06ZatmzZ305YK1eurLJly0qSvv/++4ve58+8WbNmrikMABaJPxWvllNbav9Z82mwQ68bqo/afyS7zav++i8UEoYkKG1X3p6qNn+boudEy7+oVy6GBwDAJDzc2scDgLfxmt/YMjIydNddd+nHH39UvXr1tGrVKoX/w9/aNptNnTt3liRNmjQp3/FNmzYpPj5eAQEB6tixo8t6A4C7bTu+Ta2mttKRZPPq4udueE5jbxvLai0PdGzGMR2fcdyUVXu9moo29f79TwEASEyUxoy5/McXKybVq1dwfQDAG3jFwM7hcKhHjx5av369atSoobVr16pEiRL/+rjhw4crMDBQa9as0ZgxY2QYOacZ7d+/X/fee68kafDgwbkr8QDA2/129DfdPO1mnUg5YcpH3TxKo9uMZljngVITUpXwcIIpi2gboUpPVrKoEQAABeerr6QGDaTPP7/85+jfXwoNLbhOAOANvOI8m/nz52vx4sWSJLvdrm7dul30fuXKldOCBQtyb1erVk0TJ07UwIEDNWLECL333nuKjIzU9u3blZWVpauvvlpjruSrHgDwID8d/kltZ7ZVUnqSKR9z6xg91fypiz8IlnJmOhXbM1aO847cLKBMgKKmRclmZ7gKAPBeaWnSs89K7713Zc9jt0sPPVQwnQDAm3jFwC4jIyP3nxMSEpSQkHDR+1WpUiVf1q9fP9WsWVOvv/66Nm3apNjYWFWvXl09e/bU008/7TOXBQZQuG06uEm3z7xdyZnmHZk/aPeBHrn2EYta4d/seW6Pzv9y3pRFTY9SYJlAixoBAHDlfvtN6tNHio3Nf6xePSkm5tKfa+xYqW7dgusGAN7CKwZ2AwYM0IABAy778c2bN9eyZcsKrhAAeJBv9n2jDrM7KCUrJTezyaZP7vhE9119n4XN8E9OrzitQ2MPmbJKwyupxG3/vuUDAACeav78nGFdVpY5DwuTxo2TBg3KWXU3bJjkdP7989jt0jvvSI8/7tK6AOCxvGIPOwDAxX215yu1m9XONKyz2+yactcUhnUeLP1QuuL6xZmy8KbhqvZqNYsaAQBQMJo3zxnOXej666Xff5cGD5ZsNumJJ3JW2T32mFSsmGG6b7FiOXlMDMM6AIWbV6ywAwDktzJhpTrP66wMR962AX42P83oPEM9G/S0sBn+iTPbqbhecco+nZ2b+YX7KXpOtOyBfI8GAPBuFStKH38s9egh+ftLL70kPf10zj9fqG7dnJV2I0ema+rUn5SW5q+bbrpaTZoE5xv4AUBhxMAOALzQ0h1L1W1BN2U6MnMzf7u/5naZqy7RXSxshn+z/+X9OrvxrCmr81kdhdQIsagRAAAFq3t3aetWqXNn6Zpr/vm+YWFSrVpJkqRrrjEUwo9DAJDEwA4AvM7nsZ+r58KeynbmrdAK9AvU590+15117rSwGf7Nma/OaP/o/aas3APlFHlPpEWNAAC4PCtWSPv2SQ8/fPHjo0e7tQ4A+BwGdgDgRWZvm61+i/rJYThys2D/YC3qvki317zdwmb4NxnHMhTXJ066YKuesIZhqvluTetKAQDwH6WkSE89lXPaa0BAzv50V11ldSsA8D1slgMAXmLa79PU54s+pmFdiH+IlvdczrDOwxkOQ3F94pR1PO+SefYwu6LnRcsvxM/CZgAAXLoff8wZzn38cc7trKycK8KmpVnbCwB8EQM7APACn/36mQYuGSjjguVZRQKLaFWfVWpTvY2FzXApDrxxQEnrkkxZ7Qm1FVaXXbUBAJ4vKyvn4hEtWkgJCeZjhw5J27ZZUgsAfBqnxAKAh5uwZYIeXmHeIKZoUFGt6r1K11e63qJWuFRJG5O094W9pqzsgLIq27esRY0AALh0O3fmrKLbsiX/sVatpGnTpCpV3N8LAHwdK+wAwIO9/+P7+YZ1xYOL66u+XzGs8wKZpzIV2zNWcuZloXVDVevDWtaVAgDgEhiGNGGC1Lhx/mFdYKA0Zoy0fj3DOgBwFVbYAYCHGrtprJ5a+5QpKxlSUmv7rtVV5djd2dMZTkPx/eOVeTgzN7MH2xU9P1p+YexbBwDwXEePSoMGSStX5j/WoIE0c6bUsKH7ewFAYcIKOwDwQG9890a+YV3p0NLa0H8DwzovcfCdgzqz4owpq/l+TRVpUMSiRgAA/LsvvsgZyv11WGez5Vwd9qefGNYBgDuwwg4APMyob0bpha9fMGVlwspoff/1ii4dbVEr/Bdnfzirvc+a962L7BGpcoPLWdQIAIB/t3ix1KVL/rxSJWn6dOmmm9zdCAAKL1bYAYCHMAxDL254Md+wrlyRcvp6wNcM67xEVmKWYnvEysjOu6JvcI1g1f6ktmw2m4XNAAD4Zx06SNdea8769JG2bmVYBwDuxsAOADyAYRh6fv3zeuXbV0x5xaIV9c2Ab1S3VF2LmuG/MAxDO+7doYz9GbmZLdCmevPryb8oi9oBAJ4tICBnf7rQUCkiQpo3T5oxQype3OpmAFD48NsDAFjMMAyNWDtCb29+25RXLlZZG/pvUPWI6hY1w391aNwhnVp8ypTVeLuGwpuEW9QIAICLM4ycfen+qlYtaf78nKvDVqjg9loAgP/HCjsAsJBhGBq6emi+YV214tX0zYBvGNZ5kbObz2rPiD2mrFSnUqrwCL/tAAA8h9MpjRsnde2aM7S7mA4dGNYBgNVYYQcAFnEaTj264lGN/3m8Ka9ZoqbW91uvSsUqWdQM/1XmqUzF3vOXfeuqBavOlDrsWwcA8BiHDkkDBkjr1uXc/uQT6cEHLa0EAPgbrLADAAs4Dace/vLhfMO62iVr6+v+XzOs8yKG01B833hlHPrLvnUL6imgeICFzQAAyDNnjtSgQd6wTpKGDZN27rSuEwDg77HCDgDczGk49cCyB/TZb5+Z8qhSUVrff73KFilrUTNcjgNvHNCZVWdMWc1xNRV+NfvWAQCsl5goDRmSM7D7q7JlpeRk93cCAPw7VtgBgBs5nA4NWjoo37CufmR9fT3ga4Z1Xibx60TtHbnXlEX2jFT5B8tb1AgAgDzr1kkNG158WDdokPT779LVV7u9FgDgEjCwAwA3cTgdGrhkoKb+PtWUNyzTUOv7rVdkWKQ1xXBZMo5lKLZHrOTMy0LqhKj2J7XZtw4AYKm0NGnoUOmWW3L2rbtQ6dLS4sXSZ59J4SwGBwCPxSmxAOAG2c5s9V/cX7O3zTbljcs21ld9v1LJ0JIWNcPlMByG4nrGKet4Vm5mD7Gr3uf15B/Oj1YAgHV++03q00eKjc1/7I47cgZ1Zcq4vxcA4L9hhR0AuFi2M1t9F/XNN6xrUq6J1vVbx7DOC+17aZ+Svk4yZbUn1FaR+kWsKQQAKPQcDumNN6RmzfIP68LCpE8/lZYuZVgHAN6CZQAA4EJZjiz1/qK3FsQuMOXXlL9Ga/qsUURIhEXNcLlOrzqt/a/uN2Vl7y2rsv3ZfxAAYJ2ff5aefTZ/ft110owZUs2a7u8EALh8rLADABfJdGSqx8Ie+YZ1zSo009q+axnWeaH0g+mK6xNnysIahKnWB7UsagQAQI5mzXL2rfuTn5/0yivSxo0M6wDAG7HCDgBcINORqXsW3KMlO5aY8usqXqdVvVepWHAxi5rhcjmznIrtHqvs09m5mV8RP9X7vJ78Qv0sbAYAQI7XXpNWr845PXbGDKlpU6sbAQAuFwM7AChgGdkZ6ragm5btXGbKm1dqrpW9V6poUFGLmuFK7Hl2j85tPmfK6nxWR6G1Qy1qBAAorJKSpOLF8+fBwdLy5Tn71IXy4wkAvBqnxAJAAUrPTleX+V3yDeturHyjVvVexbDOS51cdFKHxh4yZeWHlFdk90iLGgEACqOUFOmhh6QGDaTExIvfp1o1hnUA4AsY2AFAAUnPTtfd8+7WlwlfmvJWVVppRe8VCg8Kt6gZrkTqzlTF9483ZeHXhKvmWDYEAgC4z08/SVddJX38sXTokDRkiNWNAACuxMAOAApAena6Os/rrJW7Vprym6verC97fakigUUsaoYr4UhxKKZLjBzJjtzMv7i/oudHyx7Ej1AAgOtlZUkvvyw1by4lJOTlc+ZIc+da1wsA4FrsYQcAVygtK02d5nXSmt1rTHmbam20tOdShQZwXoo3MgxDO+7foZTtKaY8amaUQqqFWNQKAFCY7Nwp9e2bs7rur1q2lK6/3v2dAADuwfIAALgCaVlpumvuXfmGdbdWv1XLei5jWOfFDn94WCdmnzBlVUZWUckOJS1qBAAoLAwj59TXq67KP6wLCJDeektav16qUsWafgAA12OFHQBcptSsVN019y59tecrU35bjdu0uPtihQSwCstbnd10Vruf3G3KItpGqOqLVa0pBAAoNI4dkwYNklasyH+sfn1p5kypUSP39wIAuBcr7ADgMqRmparjnI75hnVta7TVkh5LGNZ5sczjmYrpFiMj28jNgqoEKXpWtGx+NgubAQB83eLFOVeA/euwzmaThg2TtmxhWAcAhQUr7ADgP0rJTNGdc+7Uhn0bTPntNW/Xou6LFOwfbFEzXClntlOxPWKVeSQzN7MF2lTv83oKKBlgYTMAgC9LTpYef1yaMiX/sUqVpGnTpJtvdn8vAIB1WGEHAP9BSmaK7phzR75hXfta7RnW+YC9z+1V0tdJpqzWR7VU9Jqi1hQCABQK589LS5fmz/v0kbZuZVgHAIURAzsAuEQpmSnqMLuDvt73tSnvUKuDvrjnC4Z1Xu7kwpM6OOagKSt7b1mVH1zeokYAgMKiXDnp00/zbkdESPPmSTNmSMWLW1YLAGAhTokFgEtwPvO8OszuoG/3f2vK76h9hz7v9rmC/IMsaoaCkLojVfED401ZkauKqNaHtSxqBAAobO6+WxowQDp8OOfU2AoVrG4EALASAzsA+BfnM8+r/az22nhgoynvWKej5nedz7DOy2Wfz9b2u7fLkezIzfwj/FVvYT35hfhZ2AwA4GuczpwLRzRrdvHjEyZIgYGSnfOgAKDQ40cBAPyDvxvW3VXnLi3otoBhnZczDEM779up1NjUvNAmRc2KUkg1rvQLACg4hw5Jt90mtWgh/fTTxe8THMywDgCQgx8HAPA3/m5Y16luJ83vNl+BfoEWNUNBOfzBYZ2Ye8KUVXmhikq2K2lRIwCAL5ozR2rQQFq3TnI4ci4mkZJidSsAgCdjYAcAF/F3w7rOdTtrXtd5DOt8QNK3Sdo9bLcpK9GuhKq+UNWaQgAAn5OYKPXqlfOfpKS8PCFBev11y2oBALwAe9gBwF/807Bubte5DOt8QPrBdMV0i5GRbeRmwVWDFTUzSja7zcJmAABfsW5dzkUkDh3Kf2zQIOnpp91eCQDgRVhhBwAXYFjn+xxpDsXcHaOsE1m5mT3YrnoL6ymgRICFzQAAviAtTRo6VLrllvzDulKlpEWLpM8+k8LDrekHAPAOrLADgP/HsM73GYahnQ/tVPLPyaa89qe1Fd6E35wAAFfm99+l3r2l2Nj8xzp0kCZNksqUcXstAIAXYoUdAIhhXWFx+MPDOj7tuCmr+ERFle1b1qJGAABf4HBIb74pXXtt/mFdaKj0ySfSsmUM6wAAl44VdgAKPYZ1hUPSN0naNXSXKSt+c3FVH1PdokYAAF9w+rTUubO0cWP+Y82aSTNmSLVqub8XAMC7scIOQKHGsK5wSD+Qc5EJOfKyoMpBip4XLbs/PwoBAJeveHHJ6TRnfn7SK69I333HsA4AcHn4LQVAocWwrnBwpDm0/e7tyjppvshE/UX1FViaf8cAgCvj5ydNny4VKZJzu3ZtafNmaeRIyZ/zmQAAl4kfIQAKJYZ1hYNhGNr5wE6d/+W8Ka/zWR0uMgEAKDDVq0vvvy/9/LP01ltSWJjVjQAA3o4VdgAKnZTMFHWY3YFhXSFw+P3DOj7jLxeZeLKiyvRm128AwH+TmpozlPvr6a9/GjhQ+ugjhnUAgILBCjsAhcqfw7pv939ryhnW+Z7EDYnaNewvF5loU1zV3+QiEwCA/+ann6S+faWdOyXDkB5/3OpGAABfxwo7AIVGSmaK7phzh77Z/40p71S3E8M6H5O+P12x98SaLzJRJUjRc7nIBADg0mVn51w8onnznGGdJD39tBQba20vAIDv47cWAIVCalaq7pxzp77e97Upv6vOXZrXdR7DOh+Se5GJUxdcZCLErvqL6yuwFP+eAQCXJiFBuuEG6cUXJccFXwBlZEgffmhdLwBA4cDADoDP+3NYt2HfBlPesU5Hze82n2GdDzEMQzvu26Hzv/7lIhOT6ii8MReZAAD8O8OQPvlEatxY+vFH87GAgJyLSnzwgSXVAACFCHvYAfBpqVmp6jino9bvXW/K76x9pxZ0W8CwzsccePOATsw6YcoqDquoMj25yAQA4N8dOyYNHix9+WX+Y/XrSzNnSo0aub8XAKDwYYUdAJ+VlpWmu+bepXV715nyDrU6MKzzQaeWntLe5/aasohbIlT9DS4yAQD4d4sXSw0aXHxY9+ST0pYtDOsAAO7DCjsAPunPYd1Xe74y5R1qddDCexYqyD/IomZwhfPbziuud5xk5GUhNUMUPY+LTAAA/llycs5VX6dMyX+sUiVp6lSpdWu31wIAFHIM7AD4nLSsNHWa10lr96w15e1rtWdY54MyT2Zqe8ftcpzP2xHcr6if6i+tr4ASARY2AwB4utRUqUkTadeu/Md69865uETx4m6vBQAAp8QC8C3p2enqPK+z1uxeY8pvr3k7wzof5Mx0KqZLjNL3peeFdil6brTCosKsKwYA8AqhoVKXLuaseHFp7tyc/eoY1gEArMLADoDPyMjO0N3z7tbq3atN+e01b9ei7osU7B9sUTO4gmEYShiSoLMbz5ryGm/XUMl2JS1qBQDwNi+/nLc33S23SNu3S927W9sJAABOiQXgEzKyM9Rlfhet3LXSlLet0ZZhnY86/MFhHf3sqCkre29ZVXyiokWNAADeKCgoZzXd+vXSI49IdpY0AAA8AD+OAHi9TEemui3opi8TzJd1u7X6rQzrfNSZNWe0a6h5w6FiNxRT7fG1ZbPZLGoFAPBUhw7l7El36tTFj9evLz32GMM6AIDnYIUdAK+W5chS98+7a9nOZaa8TbU2WtJjiUICQixqBldJ3ZGqmHtiJGdeFlQ5SPUW1pM9iN+0AABm8+ZJDz4oJSVJGRnSggUS3+0AADwdv9kA8FpZjiz1WNhDi+MXm/Kbq96spT2XMqzzQVmJWdp25zY5zuZdEdYeZleDpQ0UGBloYTMAgKdJTMxZVdejR86wTpIWLpRmzLC0FgAAl4SBHQCvlOXIUq8veumLuC9MeasqrbSs5zKFBoRa1Ayu4sx2KvaeWKUlpJnyqJlRKtKoiEWtAACeaP16qWFDafbs/Md+/dX9fQAA+K8Y2AHwOtnObPVZ1Eefx35uym+sfKOW91qusMAwi5rBlXYP263ErxJNWbXR1VS6U2mLGgEAPE16uvTkk1KbNjn71l2oVClp0SJp3DhLqgEA8J+whx0Ar5LtzFa/Rf00P2a+KW9RqYVW9F6hIoGstPJFhz8+rMPvHzZlkT0jVfnZyhY1AgB4mt9/l/r0kWJi8h/r0EH67DOpbFm31wIA4LKwwg6A13A4HRqweIDmbJ9jyq+veL1W9l7JsM5HnVl9RgmPJJiy8KbhqjOpDleEBQDI4ZDefFO69tr8w7rQUOmTT6RlyxjWAQC8CyvsAHgFh9OhgUsGata2Waa8WYVmWtVnlcKDwi1qBlc6v+28YrrFSHnXmFBg+UDVX1xffiF+1hUDAHiEffukfv2kjRvzH2vWLOcCE7Vqub0WAABXjBV2ADye03Bq8LLBmrHVfFm3aytcq9V9VqtoUFGLmsGVMo5maFuHbXIk/+WKsMsaKKh8kIXNAACewOGQbrst/7DOz0965RXpu+8Y1gEAvBcDOwAezWk49cCyBzT196mm/Jry12h1n9UqFlzMmmJwKUeKQ9s7blfGwYy80C5Fz4lWeBNWUwIAcgZz77xjzmrXljZvlkaOlPw5lwgA4MUY2AHwWIZh6OEvH9Znv31mypuUa6I1fdaoeHBxa4rBpQyHobg+cUr+OdmU13ynpkrdWcqiVgAAT3THHdL99+f888MPS7/9JjVtam0nAAAKAt87AfBIhmHo0ZWP6pNfPjHljcs21tq+axUREmFRM7ja7qd369TiU6aswiMVVOGxChY1AgBYzeHIWVF3MWPHSl27Srfe6t5OAAC4EivsAHgcwzA0dPVQfbTlI1PeILKB1vZdqxIhJSxqBlc7/PFhHRp7yJSVaF9CNd6twRVhAaCQ+uknqUEDadOmix8vUoRhHQDA9zCwA+BRDMPQ8LXD9d6P75nyeqXraV2/dSoVyimRvurM6jNKeCTBlIU1ClP03GjZ/flxBQCFTXZ2zsUjmjeX4uKkvn2l5OR/fxwAAL6A34AAeAzDMPTsumc1dvNYU163VF2t67dOpcNKW9QMrnZ+23nFdIuR8i4Iq8DygWqwvIH8w9m9AQAKm4QE6YYbpBdfzDkdVpL27JGGDrW2FwAA7sLADoBHMAxDIzeM1Jvfv2nKa5esrfX91qtMkTIWNYOrZRzN0LYO2+RIzpvW2cPsarCsgYIrBlvYDADgboYhffKJ1Lix9OOP5mMBATlXgTUMS6oBAOBWLFsA4BFe+eYVjd442pTVLFFT6/utV7nwcha1gqs5Uhza3nG7Mg5m5IV2KXpOtMKbhFtXDADgdsePS4MHS8uX5z9Wr540c2bOIA8AgMKAFXYALPfqt6/qpW9eMmXVI6prQ/8NqlCUK4P6KsNhKK5PnJJ/Nm9IVPPdmip1J3sVAkBhsmSJVL/+xYd1Tz4p/fwzwzoAQOHCCjsAlnrzuzc1csNIU1a1eFVt6L9BFYtWtKgVXM0wDCU8nqBTi0+Z8gqPVFDFx/j3DgCFRXKy9Oij0qRJ+Y9VrChNmya1bu3+XgAAWI0VdgAsM3bTWD2z7hlTVrlYZW3ov0GVi1W2qBXc4eBbB3XkoyOmrESHEqrxbg2LGgEA3C0+PkLXXRd00WFdr17S1q0M6wAAhRcr7ABY4r0f3tNTa58yZRWLVtT6futVtXhVa0rBLY7NOKY9z+wxZUWuKqLoOdGy+/M9EgAUFnPm1NXevea/94sXlyZMkHr0sKYTAACegt+MALjd+C3j9cTqJ0xZ+fDyWt9vvWqUYIWVLzuz9ox23LvDlAVXDVaDFQ3kH853SABQmDzyyO8qVizvkq9t2kjbtjGsAwBAYmAHwM0+/eVTDVkxxJSVCSuj9f3Wq1bJWha1qCM5CwAAWSdJREFUgjsk/5asmLtjZGTn/XLmX9JfDVc1VFDZIAubAQCsULp0mt59N0tBQdK4cdKaNTn71gEAAE6JBeBGU36bogeWP2DKSoeW1vr+61WnVB2LWsEd0vamaVv7bXKcd+Rm9hC7GixvoNA6oRY2AwC42rFjUunSkp9f/mPduzt0yy1SpUru7wUAgCdjhR0At5i5daYGLR1kykqGlNS6fusUXTraolZwh8xTmdp6+1ZlHsvMC+1S9NxoFbuumHXFAAAuN2+eFBUlvfPOxY/bbAzrAAC4GAZ2AFxu7va56r+4vwzlnQoZERyhr/p9pQZlGljYDK7mSHVoe8ftStuZZsprj6+tUh1LWdQKAOBqSUlS7945+9ElJUnPPy/98YfVrQAA8B4M7AC41Oexn6vPF33kNJy5WbGgYlrbd60al21sXTG4nDPbqdiesTq3+ZwprzKyiso/UN6iVgAAV1u/XmrQQJo9Oy/LypL69pWys63rBQCAN2FgB8BllsQvUc+FPeUw8vYtCw8M1+o+q3V1+astbAZXMwxDCY8k6PTS06a87MCyqvpyVWtKAQBcKj1dGjYs52qvhw6Zj5UqJb3yiuTPDtoAAFwSfmQCcInlO5er24JuynbmfZUeFhCmlb1XqlnFZhY2gzvsH71fRz85aspKtCuh2p/Uls1ms6gVAMBV/vhD6tNH2r49/7EOHaTPPpPKlnV/LwAAvBUr7AAUuNW7VqvL/C7KcmblZqEBoVrRe4VaVG5hYTO4w8npJ7Vv5D5TFn5NuKLnR8sewI8dAPAlDof01ltS06b5h3WhodLHH0vLljGsAwDgv2KFHYACtW7POnWa10mZjrwrggb7B2tZz2VqWaWlhc3gDv4/+GvPW3tMWXCNYDX4soH8i/AjBwB8yb59Uv/+0rff5j927bXSjBlS7dpurwUAgE9gqQOAAvPNvm9055w7lZ6dnpsF+QVpSY8lal2ttYXN4A5+W/0U+naolHd9EQWUDlDDVQ0VGBloXTEAQIGbOVNq2DD/sM7PT3rpJen77xnWAQBwJVjuAKBAbDq4SR1md1BadlpuFugXqC+6f6HbatxmYTO4w/mfzyvstTDZsvP2p/Mr4qcGXzZQaM1QC5sBAFxhxw4pOdmc1aqVM8i79lprOgEA4EtYYQfgim05ukXtZrVTSlZKbuZv99eCbgvUvlZ7C5vBHVLiUrSj8w7Z0vOGdbZAm+ovrq+iTYta2AwA4CovvCBdfcEF3x96SPrtN4Z1AAAUFAZ2AK7I7tTdumv+XTqXcS4387P5aW6XuepYp6OFzeAO6fvT9cetfyj7dN7VgGWXoudGK6JNhHXFAAAuFRCQs5quWjVpxQpp/HgpLMzqVgAA+A5OiQVw2fal7dNLu19SsiPvnBi7za4ZnWeoS3QXC5vBHTKPZ+qPW/9Q5uFMU159fHWV7lzaolYAgIL0889S5cpSZGT+Y3XrSjt3Sv78RgEAQIFjhR2AyxJ3Kk4v7HrBNKyzyaYpd01RzwY9LWwGd8g+m62tt29VWkKaKU8bmKbS/RjWAYC3y86WRo2Srr9eGjxYMoyL349hHQAArsHADsB/tuPUDrWb107nHOdM+cQ7J6pfo34WtYK7OFId2nbnNp3//bwpT++Wrsy7Mv/mUQAAb5GQIN1wQ84+ddnZ0rJl0qRJVrcCAKBwYWAH4D/ZdWaXWk9vrRMpJ0z5+PbjNajJIItawV2cWU7F3BOjsxvPmvLIwZHK6JVhUSsAQEEwDOnTT6XGjaUffzQfe/pp6fz5iz4MAAC4AAM7AJdsX9I+tZ7WWkeSj5jyMa3H6KGmD1nUCu5iOA3FD4jXmS/PmPLIHpGq+m5VyXbxxwEAPN/x41LHjtIDD0ipqeZj9epJ69ZJRYpY0w0AgMKIgR2AS3Lw7EG1ntZaB88dNOUDyg/QkGuGWNQK7mIYhnY9vksnZptXVpa4vYTqTqsrmx/TOgDwVkuWSPXrS8uX5z82dGjOhScaN3Z7LQAACjW2iQXwr44kH1Hr6a21N2mvKe9drrc6RXayphTcxjAM7f3fXh3+8LApL9qiqOotrCd7oF1K+5sHAwA8VnJyzkDuYvvTVawoTZ0qtWnj9loAAEAM7AD8ixMpJ9RmehvtOrPLlD/b/Fk1S21mUSu40/5X9+vAawdMWVjDMDVY3kB+oX4WtQIAXInvv5f69ZP27Ml/rFcv6cMPpYgI9/cCAAA5OCUWwN86nXpat0y/RfGn4k350y2e1v9a/M+iVnCnA28d0L4X9pmy4BrBari6oQKKB1hTCgBwRcaOlVq2zD+sK15cmjNHmjWLYR0AAFZjYAfgohLTEnXrjFu17cQ2Uz70uqF6vc3rstnYs8zXHXr/kPY8bf5tLqhKkBqvb6ygskEWtQIAXKn69SWn05y1aSNt2yb16GFNJwAAYMbADkA+5zLO6fZZt/9fe/cdHmWVt3H8nklPIAkkEAiEjiAiVUThRTqKImXtSHHt4q7I2nYVxRXFVRYFXXHXiiAgNhQWpYNIEQuKtEgvUkIIIQnpyTzvH7MkGWZCJskkz8zk+7muXAvnOTP5ZTkck5tT9POJnx3ax102TtMGTSOsqwGO/eeY9o533AYd3ChYnVZ3UmiTUJOqAgB4wtVXS3/6k/3XISHS9OnS8uX2c+sAAIB34Aw7AA7O5p3V4LmD9f3R7x3a7+58t16/9nXCuhrgxAcntPv+3Q5tQXFB6rS6k8JahJlUFQDAk156SUpOlp5+WrrkErOrAQAA5yOwA1AkKz9L18+/XhuPbHRoH91htP5z/X9ktbAo198lzU9S4p2OZxYGxQap06pOCr8o3KSqAAAV8fHHUoMG9vPqzhceLn30UfXXBAAA3MNP3wAkSTkFORr+0XCtPbjWof2WS27Re8PeI6yrAZI/S9au0bukEucaBdYJVMeVHRVxSYR5hQEAyuXMGWn0aOmWW+z/m5ZmdkUAAKC8+AkcgPIK83Tjxzdqxf4VDu0j2o7QnBFzFGhlMa6/O7X4lHbeulMqLG4LiAxQh+UdVKtjLfMKAwCUy5o1UocO0ocf2n9/+LD05z+bWxMAACg/AjughssvzNctn96iJXuWOLRf1/o6fXTjRwoKCDKpMlSX08tPa8eNO2QUGEVt1girOiztoMjLIk2sDADgrpwc6dFH7be9Hjni+Oyrr6Tjx82pCwAAVAyBHVCDFdgKNHrhaH2R+IVD+8AWA/XpzZ8qOCDYnMJQbVLXpmr7sO0y8kqEdWFWdVjSQVFXRplYGQDAXb/+Kl1+uTRtmmQYjs8GD5a2b5caNjSnNgAAUDEEdkANZTNsuvPLO7VgxwKH9j7N+uiLW79QaGCoSZWhupz55oy2DdkmW07xoXWWEIvaL2qv6N7R5hUGAHBLYaE0darUrZu0bZvjs/Bw6c03pSVL7BdPAAAA38LBVEANZDNsum/xfZrz6xyH9p4JPbX4tsUKD+I2UH+XuipV267fJlt2ibAuyKL2n7dX3QF1TawMAOCOQ4ekMWOkdeucn11+uTRnjnTRRdVfFwAA8AxW2AE1jGEYGv/1eL3z8zsO7d3iu+mr279SrWAuGPB3p5eftq+sKxHWKUBq93E7xVwbY15hAIAyGYY0e7b9Yonzw7qAAOnZZ6X16wnrAADwdaywA2oQwzD02IrH9K8f/uXQ3qlBJy0btUyRIVww4O9SlqZo+/DtMnKLDzmyBFrU7uN2qje8nomVAQDc8fLL0l//6tzeurV9VV337tVfEwAA8DxW2AE1yDNrntG0TdMc2i6pd4lWjF6hOmF1TKoK1SVlSYr9gomSYV2QRZd8donqjSCsAwBfMHq0VOe8/2Q/8ID088+EdQAA+BMCO6CGeGHdC3r+2+cd2trEtNGqMasUGx5rUlWoLqcWndL2EY63wVqCLWq/sL1ih/LnDwC+Ij5e+s9/7L+Oi7NfKjFzphQRYW5dAADAs9gSC9QA0zZO08Q1Ex3aWtRpoVVjVimuVpxJVaG6JC9M1s6bd8ooKBHWhVjU/ov2irmGM+sAwNfcdJP0+uvSLbdI9VggDQCAXyKwA/zcG9+/oUdXPOrQ1iSqiVaPWa1GkY1MqgrV5eQnJ7Xztp1SYXGbNdSq9ovaq+5AboMFAG9UUCC9+KIUHCw98YTrPn/6U/XWBAAAqheBHeDH3tnyjv70teN39PG147VqzCo1jW5qUlWoLkkfJWnXqF2OYV2YVZf+91LV6ceZhQDgjfbutZ9T9913UmCgNGCA1LWr2VUBAIDqxhl2gJ+as3WO7l18r0Nb/Yj6WjVmlVrVbWVSVaguSXOTtOv288K6CKs6fN2BsA4AvJBhSG+/LXXqZA/rJPtKu1GjpOxsU0sDAAAmILAD/NDHOz7WHV/eIUPFZ5bFhMVo1ZhVahvb1sTKUB2OzzquXaN3SbbitoBaAerwdQdF9442rS4AgGtJSdLQodK990qZmY7PrFbpxAlz6gIAAOYhsAP8zJeJX+r2z2+XzShOa6JDo7V89HK1r9/exMpQHX5/7Xf99sffVCKrVUDtAHVY1kHRvaJNqwsA4NqiRdKll0r//a/zs4cfln76SWrevNrLAgAAJvOZwO7AgQN6++23dc8996hjx44KDAyUxWLR888/X+ZrN23apGHDhqlevXoKCwtTu3btNHnyZOXk5FRD5UD1WbZ3mW7+9GYV2AqK2moH19bS25eqS8MuJlaGqmYYhg5OPqi94/c6tAdEBqjjio6K6hFlUmUAAFcyMqS775aGDZOSkx2fNW4srVwpvfqqFBpqTn0AAMBcPnPpxIwZMzRjxoxyv27u3LkaO3asCgsL1ahRIyUkJGj79u165plntHjxYq1du1bh4eFVUDFQvdYcWKPhC4YrrzCvqC08KFxLRi5R98bdTawMVc0wDO17ZJ9+f/V3h/bAuoHqsLSDIrtFmlQZAMCVjRvtF0vs3+/87LbbpDfekOpw3CgAADWaz6ywi42N1ZAhQ/Tcc8/p66+/1g033FDmaw4ePKi77rpLhYWFevnll3XkyBFt2bJFe/bsUZs2bfTDDz/o8ccfr4bqgaq14fAGXT//euUUFK8aDQkI0aJbF6lX014mVoaqZhQa+u3u35zCuuCGweq8rjNhHQB4kfx8aeJEqVcv57AuOlqaN8/+QVgHAAB8ZoXdxIkTHX7/0UcflfmaqVOnKjc3V4MGDdJjjz1W1N60aVO999576tmzp9566y09/fTTiouL83jNQHX48diPunbetcrMLz6lOsgapIW3LFT/Fv1NrAxVzZZr065Ru5T8qeNeqtDmoeq4sqPCWoSZVBkAwJX33pNeeMG5vX9/6f33pYSE6q8JAAB4J59ZYVdehmFo4cKFkqS77rrL6XmPHj3Utm1b5efn68svv6zu8gCP2HpiqwbNGaT03PSitgBLgBbcuECDWw82sTJUtcLMQm0bts0prAtvF67O6zsT1gGAF7rrLumKK4p/HxIiTZ8uLV9OWAcAABz5bWB3+PBhHT9+XJLUs2dPl33OtW/evLna6gI8ZWfyTg2cM1CpOalFbVaLVR/+4UONuHiEiZWhquWfydfWq7cqdVmqQ3vtbrXVeV1nhcSHmFQZAOBCAgOlOXOkiAipUyf7DbDjx0tWv/2OHAAAVJTPbIktrz179kiSQkJCFB8f77JPixYtHPpW1u+/O54hlZGRIUnKzs5WUFCQRz6HmUreqssNu+bal7pPA+cNVHKW4+qqfw/+t4a1HKbs7Owqr4HxYI78k/lKHJqorF+zHNprX1VbbT5po4LwAhVkF5Ty6qrDeEBJjAeUVFPHQ26ufQXd+Ro1kpYssahTJ0PBwVI1/Cfb69TUMQHXGA8oifGAkvx1PLj787rfBnapqfaVJ9HR0bJYLC771Pnfib7n+lZWQil7GVavXu13N9GuW7fO7BJqrKTcJD219ymdyj/l0H5/4/tV/1h9LT+2vNprYjxUD0uyRRGTIhRwLMChPb9bvn5/8Hf9vun3Ul5ZvRgPKInxgJJqwng4ezZQb7/dQRkZwXr66e9UyrehWru2WsvyWjVhTMB9jAeUxHhASf40HrKyssruJD8O7M6lr8HBwaX2CfnfP3tWx2okwBNS8lL0zL5nnMK6O+Pv1DWx15hUFaqD9ahVEZMiZD3luG8q76o8ZT+U7cezOQD4jm3bYjRjRhedOmX/h9qvv26ua689YHJVAADAF/ntj3ihoaGSpLy8vFL75ObmSpLCwjxzOPuRI0ccfp+RkaF27dqpX79+ioyM9MjnMFNOTk5Rqn3VVVcV/X+M6pGUmaSr51+tpLwkh/a/X/V3PXbFY6W8quowHqpPxncZ2n3nbhWcdtzqWv/u+mo2vZks1lKWb1QjxgNKYjygpJowHnJzpWefDdRrrwXKMIrn5NmzL9UDD1ykNm0ME6vzPjVhTMB9jAeUxHhASf46HtLT08vuJD8O7M5tdz1z5owMw3C5LfbcVthzfSurcePGDr8/94cQFhbmsVDQW4SGhvrd1+TNUrJSdP0n12v36d0O7RN7TdQzfZ8xqapijIeqk/xFshJvS5Qtx+bQ3uSvTdR8SvNSt/ybifGAkhgPKMkfx8Ovv0qjRknbtjk/69vXori4UPnZl+xR/jgmUHGMB5TEeEBJ/jQe8vPz3ernt3dStW7dWpJ9Fd2xY8dc9tm/f79DX8Abnck5o0EfDtL2k9sd2h+58hE91/c5k6pCdTj65lHtuGGHU1jX4qUWavFiC68M6wCgpigslKZOlbp1cw7rwsKkN9+UliyRGjY0pz4AAODb/Dawa9KkiRo0aCBJ2rBhg8s+59q7d+9ebXUB5ZGRm6HBcwdry/EtDu0PdntQUwdOJbDxU4ZhaP9T+7Vn3B6pRFZnCbSo7Zy2avJ4E/OKAwDo0CGpf3/p8cel809f6dZN+uUX6f77VeqFEwAAAGXx28DOYrFoxIgRkqR3333X6fnGjRuVmJiooKAgDR06tLrLA8qUlZ+loR8N1Xe/f+fQfmenO/Xa4NcI6/yULd+mxD8m6vCUww7tAbUDdOlXl6rBqAYmVQYAMAxpzhypQwfpm28cnwUESJMmSRs2SBddZE59AADAf/htYCdJjz32mIKDg7V8+XJNnTpVhmE/8PfQoUO68847JUl333130Uo8wFvkFuRqxIIRWntwrUP7yEtH6q3r35LV4td/dWusgowCbbt+m5I+cLxYJLhBsDqt66S6A+uaVBkAICVFuvlmacwY6fyzolu1sgd1zz4rBQWZUh4AAPAzPvNT/4YNGxQbG1v08dFHH0mSXnzxRYf2kje1Nm/eXG+//basVqsef/xxJSQkqEuXLmrdurV+++03de3aVVOnTjXrSwJcyi/M182f3qzl+5Y7tP/h4j/og+EfKMAaYFJlqEq5J3L1S59flLos1aE9rE2YOm/qrNqdaptUGQBAsp9T9+mnzu333WffAssJKwAAwJN8JrDLz89XSkpK0Udubq4kKSsry6G9sLDQ4XVjxozRt99+qyFDhig7O1s7d+5UixYt9Oyzz2r9+vWKiIgw48sBXCqwFWjUwlFa9Nsih/ZrW1+r+TfMV6DVby92rtGydmfp5x4/6+yWsw7tkT0i1WVDF4U184/bkADAl/XpI/3lL8W/j4uT/vtf6d//lvh2EgAAeJrP/PTfp0+foi2t5dWjRw8tXrzYwxUBnmUzbLpr0V36eMfHDu39m/fXZzd/puCAYJMqQ1VK+y5N24ZsU0FKgUN77PBYXTzvYgWEsaISALzFCy9Iy5fbt8C+9ZZUr57ZFQEAAH/lM4Ed4M8Mw9C4JeM0e+tsh/b/a/J/+vLWLxUaGGpSZahKJz89qcQxibJl2xza48fFq/VrrWUJ4GIRAKhuBQXSwYP2UO58oaH2yybq1OEGWAAAULV8Zkss4K8Mw9Bflv1F//npPw7t3eK7acnIJYoIZp+NvzEMQ4deOKSdN+10CuuaT2mu1v8irAMAM+zbJ111ldS7t3T6tOs+desS1gEAgKpHYAeYbOLqiZq+ebpDW8e4jlo6aqkiQyLNKQpVxpZrU+KYRB2YeMCh3RJoUdtZbdX0b01l4SdBAKhWhiG9/bbUsaO0aZN07Jj0wAP2dgAAADMQ2AEmemHdC5qyfopD28WxF2v56OWqG1bXpKpQVfKS8/RL/1+U9GGSQ3tgdKA6LOugBmMbmFQZANRcJ09Kw4dL994rZWYWt3/8sbRwoWllAQCAGo4z7ACTvLrpVU1cM9GhrWWdllo5ZqXqR9Q3qSpUlcydmdo2ZJtyDuQ4tIe1CtOl/71U4W3CTaoMAGquRYuku++WkpOdn40fLw0eXP01AQAASAR2gCn+/eO/9Zflf3FoaxLVRKvHrlZ87XiTqkJVOb3stHbcvEOF6YUO7VG9o9T+s/YKigkyqTIAqJnOnpUmTJDeecf5WaNG0qxZ0oAB1V4WAABAEQI7oJp98MsHemDJAw5tDWs11Ooxq9UkqolJVaGqHH3jqPY8tEdyvFtCDe5soIvevEjWYE4mAIDqtGmTNGqUtH+/87NbbpFmzrRfLAEAAGAmflIEqtHHOz7WnYvudGirF15Pq8asUsu6LU2qClXBVmDTnj/v0Z4/nRfWWaQWU1uozTttCOsAoBrl50tPPy393/85h3VRUdLcudJHHxHWAQAA78AKO6CaLP5tsW7//HbZjOL0Jjo0WitGr9DF9S42sTJ4WkFagXbcskOpy1Id2q3hVrWb106xw2JNqgwAaqb9+6Wbb5Z++sn5Wb9+9i2wCQnVXhYAAECpCOyAarBi3wrd+MmNKrAVFLXVDq6tZaOWqWODjiZWBk/LTMzUjhE7lJWY5dAe0jhE7Re1V+3OtU2qDABqrogI6dAhx7aQEOnFF+2XS1hZ8AwAALwM354AVWzdoXUa9tEw5RXmFbWFBYZpycglurzR5SZWBk879eUpbbl8i1NYV/uy2uryfRfCOgAwSVyc4wUTHTtKP/5ov3iCsA4AAHgjVtgBVWjz75t13bzrlF2QXdQWEhCiRbctUq+mvUysDJ5k2Awd/PtBHXrukNOzejfWU9sP2iogPMCEygAA5wwbJt1zj/2Mur//3b7CDgAAwFsR2AFV5JcTv+iaudfobN7ZorZAa6A+vflTDWgxwMTK4En5Z/KVODpRKf9NcXxgkZo910xNn2wqi9ViTnEAUMOkpUmrV0sjRrh+/p//SBamZAAA4AMI7IAqsDN5pwbOGagzOWeK2qwWq+b9YZ6GXDTEvMLgUZk7M7V9+HZl78l2aA+IClC7ue0Uc12MSZUBQM2zdq00dqx09Ki0fr10xRXOfQjrAACAr+DUDsDD9p7eqwGzB+hU1qmiNossmjVslm665CYTK4MnJX+erC3dtziFdeHtwtX1h66EdQBQTXJzpUcftd/2eviwVFgojR4tZWaaXRkAAEDFEdgBHnQ47bD6z+6v42ePO7S/ed2bGt1xtElVwZOMQkP7J+7Xjht2qPBsocOz2Bti1eW7LgpvHW5SdQBQs/z6q9StmzRtmmQYxe1790rvvWdeXQAAAJXFlljAQ45nHFf/2f11OO2wQ/srg17RfZfdZ1JV8KT81Hztun2XTn992vGBRWr+QnM1+WsTWdhvBQBVrrBQevVV6amnpLw8x2dhYfYA7/77zakNAADAEwjsAA84lXVKA+YM0N7Tex3an+/7vCZcOcGkquBJZ7ee1fYbtitnX45De2B0oC6ef7FirmELLABUh0OH7GfVffON87Nu3aQ5c6Q2baq/LgAAAE9iSyxQSWdyzmjQnEHambzTof1v//c3PXXVUyZVBU8xDEPH3jqmn7r/5BTWRbSPUNcfuxLWAUA1MAx7GNehg3NYFxAgTZokbdhAWAcAAPwDK+yASsjIzdDguYP184mfHdofuvwhvdDvBZOqgqcUnC3Q7vt26+S8k07P6t1cT23ebaPAWkyjAFDVUlKkBx6QPvnE+VmrVtKHH0rdu1d/XQAAAFWFnzSBCsrOz9bQj4bqu9+/c2i/u/Pdmn7NdM4y83Fnt53Vjpt2KPs3x1tgFSC1eLGFEh5N4M8YAKrBvn1Sr17S8ePOz+6/X/rnP6WIiOqvCwAAoCoR2AEVkFuQqz98/AetPbjWoX3kpSP17yH/JsjxYYZh6MR7J7TnT3tky7E5PAtuFKx2H7VT9P9Fm1McANRAzZrZV9GVDOzi4qR335Wuu860sgAAAKoUZ9gB5VRgK9Btn92mpXuXOrSPaDtCHwz/QAHWAJMqQ2UVZhYqcWyifrv7N6ewru41dXXZz5cR1gFANQsIkGbPlmrXtv9++HBp2zbCOgAA4N9YYQeUQ6GtUGO/GKuFiQsd2q9pdY3m3zBfgVb+SvmqzB2Z2nHTDmXtynJ8YJWaP99cTZ5oIouVlZMAYIZmzaR//1vKzZXuuENiITsAAPB3pAuAmwzD0P3/vV/zts1zaO/TrI8+v/lzhQSGmFQZKuv4rOPaM26PbNnnbYFt+L8tsFdFm1MYANQg+/ZJM2dKU6dKVhd7QEaOrP6aAAAAzEJgB7jBMAw9vPRhvfPzOw7tVzS+QotuXaSwoDCTKkNlFGQUaM+f9yjpgySnZ3UG1tHFH16s4PrBJlQGAP4jM1PavTta2dmBqlPHoq5dHS+JMAzpnXekCRPsfRs3tv8aAACgJiOwA9wwcfVEvfb9aw5tnRt01te3f63aIbVNqgqVkfZdmnaN2qWcfTmOD6xSs783U9Mnm7IFFgAqITHRvmJu9uxQpaX1LmqPipLGjJHGjZPq1pXuuUdatKj4dX/7mzRwoNS+vQlFAwAAeAkCO6AMU76doinrpzi0tavXTstHL1d0aLQ5RaHCbAU2HZ5yWAefOygVOj4LbhCsi+dfrDp96phSGwD4i+nTpUcekWw2SXL8x4+0NOn116U33pDCwuyr6krKzZVWrCCwAwAANRuBHXABM76boadWP+XQ1qpuK60cvVKx4bEmVYWKyj6QrV2jdil9Y7rTszoD/rcFNo4tsABQGdOnu7el1WZzDuvi46VZs+wr7AAAAGoyAjugFO9seUcPL3vYoa1JVBOtGrNKDWs3NKcoVIhhGEqak6Q9f9qjwgzHZXWWYIta/KOFGo9vzBZYAKikxET7yrqKuOUW+xbaunU9WxMAAIAvIrADXJi3bZ7uXXyvQ1uDWg20aswqNYlqYlJVqIj81HztfmC3khckOz0LvyRc7ea2U62OtUyoDAD8z8yZ57bBls/VV0vz50sW/t0EAABAEoEd4GThroUas3CMDBlFbTFhMVo5eqVa1W1lYmUor9S1qUock6jcI7lOzxr9uZFavNRCAWEBJlQGAP4nM1OaPbtir/3uOykry/H2WAAAgJrManYBgDdZunepbvn0FhUaxdsmo0KitGL0Cl1S/xITK0N52PJs2vfXfdrab6tTWBcUF6RLv7pUrV9rTVgHAB60Y4f9QomKSEuTdu70bD0AAAC+jBV2wP98c/AbjVgwQvm2/KK2iKAIfX371+rcsLOJlaE8Mn7JUOIdicrcmun0LOb6GLV5t42C63GxBABUVkqK9M030po10urV0qOPVu79MjI8UxcAAIA/ILADJG3+fbOGzB+inIKcorbQwFAtvm2xrky40sTK4C5bnk2HphzS4RcOyygwHJ5Zw6xq9WorNby3oSwckAQAFZKWJn37rT2cW7NG2rpVMkpMt4mJlXv/2rUr93oAAAB/QmCHGu+XE7/omrnX6Gze2aK2IGuQPrv5M/Vt3tfEyuCuC62qq9Wlli6ee7Ei2nIwEgCUR2amtH598Qq6n3668IUSe/ZIUVEV2xYbFSVdwskTAAAARQjsUKPtSt6lQXMG6UzOmaI2q8Wq+TfM17WtrzWvMLjFlmfToRcO6fAU51V1CpCa/q2pmj7dVNZgjusEAHf985/SF19I338v5eeX2b3Irl3SmDHS66+X/3OOHSuFh5f/dQAAAP6KwA411r7T+zRgzgAlZyUXtVlk0QfDP9AN7W4wsTK440Kr6iIujVDbWW1Vuwv7qwCgvDZulDZsKLtfZKTUu7fUr5/Ut6906aXS7t3SG29ceCXe+axW6YEHKl4vAACAPyKwQ410JO2I+s/ur2MZxxza/z3k3xrVYZRJVcEdZa6qe7Kpmk5kVR0AnK+wUNqypXiL64wZUps2zv369ZMWLnRuDw+XevUqDug6d5YCz/tOsm1bado0acIE9+uaNs3+OgAAABQjsEONk3Q2SQPmDNChtEMO7a8MekX3dr3XpKrgDlbVAYD7bDZp27bigG7dOsfz5Vatch3Y9f3f8a0hIVKPHsUBXbduUrAbl2w//LD9fx955MIr7axW6ZVXpPHj3f6SAAAAagwCO9QoKVkpGjhnoHan7HZon9x3siZcWY7lAKhWhVmFOjT5kI7884jTqjpLoEVNnmyipk+xqg5AzWYY9ptazwV0a9dKKSml91+zRho3zrm9XTv7syuukEJDK1bLww9L11wjvfmm9MEHhtLSim/ojoqyn1n3wAOsrAMAACgNgR1qjPTcdF0z9xptO7nNof2Jnk/oqV5PmVQVypKyNEV7xu1RzoEcp2cRHf63qq4zq+oA1FwffSQtWmQP2U6ccP9169bZQz6LxbHdYpH69Kl8XW3b2rfdPv10jmbN+l7Z2YHq06erunQJVQQXdwMAAFwQgR1qhMy8TF037zr9eOxHh/YHuz2oF/u/KMv5P63AdLnHc7V3wl4lL0h2esaqOgAo9umn0mefude3Y8fiLa5XXeUc1lWFiAipdeszkqTLLjMUFlb1nxMAAMDXEdjB7+UW5GrEghFaf3i9Q/sdne7Qa4NfI6zzMobN0LG3jmn/X/erMK3Q6XmtrrXU5u02rKoDUCMkJRVvcX3sMal1a+c+ffuWHthdfHFxQNe7txQbW7X1AgAAwDMI7ODX8gvzdfOnN2vF/hUO7TdfcrPeuf4dWS2szvImZ7ed1e77dit9U7rTs4BaAWr+QnM1erCRLAGErAD8U0qK9M039oBuzRpp587iZx07lh7YndOyZXFA16eP1LBhlZcMAACAKkBgB79VaCvUmC/GaNFvixzar7/oen044kMFWANMqgznK8wq1MHnDur3ab87XSohSbEjYtXqtVYKbVzB088BwEulpUnfflsc0G3daj9XzpU1a6QHH3Ruv/hi6cMPpV69pCZNqrZeAAAAVA8CO/glm2HTvYvv1UfbP3Jo79+8vz6+6WMFBQSZVBnOl/L1/y6VOOh8qURI4xC1/ldrxQ5jDxcA/7FunfTVV/YA7scfJZvNvdetWWPvaz1vcbjFIt1+u+frBAAAgHkI7OB3DMPQhKUT9N4v7zm090zoqS9v/VKhgazS8gZZe7O07y/7lLI4xfmhVWo8vrGa/b2ZAmszTQHwL6+/br8owh0xMfbtrec+OHYVAACgZuAnYfidiasn6rXvX3No69Kwi5aMXKKI4AiTqsI5BWcLdPiFwzryyhEZec77vmp1raU2b7VR7S5cKgHA9+TlST/8YF8Nd9tt9jPlzte3b+mBXWSk/ey5vn3tZ9G1b++8og4AAAD+j8AOfmXKt1M0Zf0Uh7ZL6l2iZaOWKSo0yqSqINlXPp6cd1L7Ht+nvGN5Ts+5VAKALyoslLZsKb7Jdf16KTPT/iwqSvrzn51fU/KSiIgI+9lz5wK6zp2lAI5YBQAAqPEI7OA3Xtv8mp5a/ZRDW6u6rbRi9ArFhnMGmpkytmRoz0N7lL7B+fZXSap/e321fKmlQhqFVHNlAFA+Npu0bVvxJRHr1tkvjnBlzRrXgV3bttLLL0s9e0rduklBHKsKAACA8xDYwS+8u+VdjV863qGtSVQTrRqzSg1rNzSpKuQl5+nAxAM6/vZxycWth7U611Kr11op+v+iq702AHDX7t3SypX2kG7tWinFxdGbrnzzTemXRDz2mMfLBAAAgB8hsIPPm79tvu5ZfI9DW4NaDbRy9Eo1iWpiUlU1m63ApmNvHtPBZw6q4EyB0/Og2CA1n9JcDe9syPZXAF5v4kTpk0/c62u1Sl272re39u0rGS7+sQIAAAAoC4EdfNqXiV9q9MLRMkos34oJi9GK0SvUOqa1iZXVTIZhKGVRivb/db+yErOcOwRIjR5spGbPNlNQHfaAAfAOR47Yt6/27i01ber8vF+/Cwd2HTsWB3RXXWU/uw4AAACoDAI7+KwV+1bo5k9vVqFRWNQWGRKpZaOWqX399iZWVjOlb07Xvsf2Ke1b14c5RfeNVqvXWqlW+1rVXBkAODpxwr619dw5dHv32ttfe63sSyIk6eKLiwO63r2lWI5JBQAAgIcR2MEnrT+8XsMXDFdeYfFto+FB4fpq5FfqGt/VxMpqHutxq/aM2qPTn592+TykSYhavdJKsX+IlcXC9lcA1S8lxX6e3LmAbudO1/1Wr3Yd2F10kTRhgv2CiL59pQYNqrZeAAAAgMAOPufHYz/qunnXKSu/eMtlSECIFt26SD2b9DSxspol/1S+Qt8JVfDSYJ0ucA7rAmoFKOHxBCU8kqCA8AATKgRQU6WlSd9+WxzQbd3q3llyF7ok4pVXqqZWAAAAwBUCO/iU7Se36+oPr1Z6bnpRW6A1UJ/c9In6t+hvYmU1R2FWoX6f8bsO/+OwQtJDnDsESPH3xavZpGYKrh9c/QUCqPHuu09asMD9/nFxxVtc8/OlEBdTGwAAAFCdCOzgM/ak7NGA2QN0Ort4NZfVYtWHIz7U9W2uN7GymsFWYFPSh0k6MPGA8o7muewT+4dYtZjSQuFtwqu5OgA1SU6O9N139q2q8fHOz/v2vXBgFxMj9elTHNK1bWtfRQcAAAB4CwI7+IRDZw6p/+z+SspMcmh/5/p3dEv7W0yqqmYwbIZOLjipg38/qOzfsl32qdW9llq/0lpRPbgaEYDn5edLP/xQvMV1wwYpN1eaMUN66CHn/udfEhEZab8c4lxAd+mlztteAQAAAG9CYAevdzzjuPrP7q8j6Ucc2l+75jX9sfMfTarK/xk2Q6cWntKBSQeUtSPLZZ/C+ELljM7R5c9crvBwVtUB8IzCQunnn4sDum+/lTIznfutXu06sGvdWho5UurY0R7Sde4sBXCUJgAAAHwIgR282qmsUxo4Z6D2pe5zaH+x/4v6c3cXV/mh0gzDUMp/U3TwmYM6+8tZl32C6gUp/m/x+rXJr1KguP0VQKXYbNK2bfZwbs0a++UPaWllv27tWnu4d34YZ7FIc+dWSakAAABAtSCwg9dKy0nT1R9erR3JOxzan+r1lP76f381qSr/ZRiGUpen6sAzB5TxfYbLPgGRAUp4JEGNH26s/KB8/br812quEoA/uvtu6f333e8fGChdcYV9e2tOjhQRUXW1AQAAAGYgsINXyszL1HXzrtOW41sc2h+6/CFN7jvZpKr8V+qaVB185qDS1rte0hJQK0CNxjdSwiMJCqoTJEnKz86vzhIB+DDDkPbvt58lV6+e8/Pu3S8c2Fmt0mWX2QO6fv2knj0J6QAAAODfCOzgdXIKcjR8wXBtOLLBof2uznfp1WteZfulhxiGodRVqTr8wmGdWXvGZR9rmFWN/tRICY8nKDg2uHoLBODTkpPDtG1brD79NEjffCMdOSJNny6NH+/ct18/57ZOnYoDul69pCjutAEAAEANQmAHr5JfmK+bP7lZK/evdGi/tf2t+s+Q/8hq4Vq/yjJshk4tOqXDUw4r4wfXW18tIRbF3x+vJn9topAGIdVcIQBfdOJE8Rl0q1aFaP/+QU59Vq92Hdi1aiX17y+1bWsP6Hr3lmJiqqFoAAAAwEsR2MFrFNoKNXrhaC3evdihfWiboZo9fLYCrFzxVxm2ApuSFyTr0IuHSr311RJkUcO7G6rJk00U2ji0misE4EtSUuyXPqxZYw/idu0q+dT1P658803pl0SsXOnyJQAAAECNRGAHr2AzbLp38b1asGOBQ/uAFgO04MYFCgoIMqky31eYU6ikD5J0+KXDyjmQ47KPJdCiuLFxavZ0M4U2JagDcGFPPCG9/HL5XpOQYF89l54u1alTNXUBAAAA/oLADqYzDEMTlk7Qe7+859DeM6GnvrjlC4UGEiBVRMHZAh1/67iO/POI8o7nuexjDbOq4T0NlfBIgkKb8P8zgGKZmfbVcJGRzs9atiz79XXq5Kh9+1O69db6uvrqYLVoYV9JBwAAAKBsBHYw3cTVE/Xa9685tHVt2FVLRi5RRDDXAJZX7olcHZt5TEffOKqC0wUu+wREBqjRnxqp8fjGCq7PZRIApJwcadOm4i2u339vX0X38MPOfV1dEhETI/XpY3925ZU5OnRomSwWadCgQQoLq+rqAQAAAP9CYAdTvfjti5qyfopD2yX1LtGyUcsUFcqVgOVxdttZ/f7q70qamyQjz3DZJyg2SI0nNFb8uHgFRbPNGKjJ8vKkH34oDug2bpRycx37rF7tOrBr2VJq315q3twe0PXtK116qWT939F12dmGDh+u8i8BAAAA8FsEdjDN65tf15Orn3Roa1W3lVaMXqGYcK4HdIdhM3R62Wn9/urvSl2RWmq/kMYhSngsQQ3vbqiAcC7vAGqiwkJpy5bigG79evu21wv55hupoEAKPO+7BYtF+vVXtrgCAAAAVYXADqZ47+f39NDShxzaEiITtHL0SjWs3dCkqnxHYXahkj5M0u+v/q6sXa5vfJWksNZhavJEE8WNjpM12PWtjQD837Rp0uTJUlqa+68JCZEuu8x+G2xcnPNzwjoAAACg6hDYodot2L5A9yy+x6EtLiJOq8asUtPopiZV5RvykvJ0dOZRHZt5TPmn8kvtF90nWo3/0lgx18XIYuWnaqAmMAwpP18KdnEsZWRk2WFdUJDUvXvxFtcrrpBCuYsGAAAAMAWBHarV4t8Wa9TCUbIZtqK2umF1tXLMSrWOaW1iZd7LMAylb0rX0ZlHlfxJcqnn01kCLap/a301ntBYtbvUruYqAVQ3w5D27y/e4rpmjfT449KECc59XV0SYbXaV9CdC+h69pQiuOcHAAAA8AoEdqg2q/av0k2f3KQCW/HNpbWDa2vZqGVqX7+9iZV5p4KzBTo596SOzjyqzF9LP2gqsE6g4u+PV6MHGymkUUg1Vgiguh05UhzOrV5t/31Jq1e7DuxatJCaNZOio4sDul69pCju9gEAAAC8EoEdqsXGIxs19KOhyi0svoIwLDBMS0Yu0WXxl5lYmffJ3JmpY28e04kPTqgwo7DUfmGtwtR4QmM1GNtAARFcJAH4oxMn7OHcuYBu374L91+3rvRLInbtYosrAAAA4CsI7FDlthzfomvnXqus/OLLEYIDgvXFrV+oV9NeJlbmPWz5Np364pSOzTymM2vPXLBvdL9oNR7fWDFDOJ8O8FezZ0v/+Ic9ZCuP+vWlo0elpi6OAyWsAwAAAHwHgR2q1M7knRo0Z5DScotPOw+wBOjjGz/WoJaDTKzMO2Tvz9aJ90/o+LvHlXc8r9R+AVE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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import CubicSpline\n",
    "\n",
    "\n",
    "#Cubic Spline from data point t and a\n",
    "t = np.array( [ 1,  3, 5]) #t from output1\n",
    "a = np.array( [ 2.78, 36.30, 49.40]) #a (Area) from experiment\n",
    "f = CubicSpline(t, a, bc_type='natural') #Cubic Spline Interpolation\n",
    "\n",
    "#Cubic Spline equation\n",
    "for j in range(f.c.shape[1]):\n",
    "    a_j = f.c.item(3, j)\n",
    "    b_j = f.c.item(2, j)\n",
    "    c_j = f.c.item(1, j)\n",
    "    d_j = f.c.item(0, j)\n",
    "    t_low = t[j]\n",
    "    t_high = t[j+1]\n",
    "    \n",
    "    equation = f'S{j}({t_low}< t<={t_high}) = {a_j:.5f} + {b_j:.5f}(t-{t_low}) + {c_j:.5f}(t-{t_low})^2  + {d_j:.5f}(t-{t_low})^3'\n",
    "   \n",
    "    print(equation) #<< Cubic Spline polynomials\n",
    "    \n",
    "    \n",
    "#exact funtion\n",
    "def z(b):\n",
    "    return (49.2593*(2.71828**(2.128*b)))/(204.247+(2.71828**(2.128*b))) #<<Exact function in b terms\n",
    "\n",
    "#A(2) exact\n",
    "print('A(2) Exact={:.5f}'.format(z(2)))\n",
    "\n",
    "#plot graph exact funtion\n",
    "t_plot = np.linspace(t[0], t[-1], 100)\n",
    "\n",
    "a_exact = (49.2593*(2.71828**(2.128*t_plot)))/(204.247+(2.71828**(2.128*t_plot))) #<<Exact function in t_plot terms for plot graph\n",
    "\n",
    "#Euler's method\n",
    "def func( t_euler, a_euler ):\n",
    "    return a_euler*(2.128-(0.0432*a_euler)) #<<dA/dt in t_euler and a_euler terms \n",
    "     \n",
    "\n",
    "def euler( a0, a_euler, h, t_euler ):\n",
    "    temp = -0\n",
    " \n",
    "\n",
    "    while a0 < t_euler:\n",
    "        temp = a_euler\n",
    "        a_euler =a_euler + h * func(a0, a_euler)\n",
    "        a0 = a0 + h\n",
    "#A(2) Euler's method \n",
    "\n",
    "    print(\"A(2) Euler = \",\"%.5f\"% a_euler)\n",
    "    return a_euler\n",
    "\n",
    "a0 = 0 #<<<< t=0\n",
    "b0 = 0.24 #<< A(0)=0.24\n",
    "h = 1 #<<step size\n",
    " \n",
    "t_euler = 2 #<<approxination Euler A(2)\n",
    " \n",
    "a_euler = euler(a0, b0, h, t_euler)\n",
    "\n",
    "\n",
    "error = abs(z(2) - a_euler)\n",
    "#Error Euler's method \n",
    "print(\"Error Euler = \", \"%.5f\" % error)\n",
    "#A(2) Cubic spline\n",
    "print('A(2) Cubic Spline = {:.5f}'.format(f(2)))\n",
    "#Error Cubic spline\n",
    "print('Error Cubic Spline={:.5f}'.format(abs(z(2)-f(2))))\n",
    "\n",
    "\n",
    "plt.style.use('seaborn-poster')\n",
    "\n",
    "\n",
    "#Euler's method plot graph\n",
    "f1 = lambda w, s: s*(2.128-(0.0432*s)) #<<dA/dt in s and w terms \n",
    "h = 1 #<<step size\n",
    "w = np.arange(0, 5 + h, h) #<<t0 is 1 ,t2 is 5\n",
    "\n",
    "\n",
    "s0 = 0.24 #<< A(0)=0.24\n",
    "\n",
    "s = np.zeros(len(w))\n",
    "s[0] = s0\n",
    "\n",
    "for i in range(0, len(w) - 1):\n",
    "    s[i + 1] = s[i] + h*f1(w[i], s[i])\n",
    "w_1 = np.where((w >= 1) & (w <= 5))\n",
    "    \n",
    "t_new =np.linspace(t[0], t[-1], 100)\n",
    "a_new = f(t_new)\n",
    "#plot graph\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.grid()\n",
    "plt.plot(w[w_1], s[w_1], 'bo--', label='Euler')\n",
    "plt.plot(t_plot,a_exact, 'm',  label='Exact function')\n",
    "plt.plot(t_new, a_new, 'g', label='Cubic spline')\n",
    "plt.plot(t, a, 'ro',label='Data point')\n",
    "plt.title('Cubic Spline Interpolation and Euler\\'s method')\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('A')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20efb84b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'t' :  [1.0, 2.0, 3.0, 4.0, 5.0],\n",
      "'test' : [2.78, nan, 36.3, nan, 49.4],\n",
      "'Exact' : [1.94541, 12.64353, 36.62829, 47.31638, 49.01962],\n",
      "'Euler' :  [0.24, 0.74823, 2.31628, 7.01356, 19.8134],\n",
      "'Cubic Spline':  [2.78, 21.45438, 36.3, 44.76438, 49.4],\n",
      "'Error Euler':  [1.70541, 11.8953, 34.312, 40.30282, 29.20622],\n",
      "'Error Cubic Spline': [0.83459, 8.81085, 0.32829, 2.552, 0.38038]}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.interpolate import CubicSpline\n",
    "import re\n",
    "\n",
    "def func( t_euler, a_euler ):\n",
    "    return a_euler*(2.128-(0.0432*a_euler))  #<<dA/dt in t_euler and a_euler terms \n",
    "     \n",
    "\n",
    "def euler( a0, a_euler, h, t_euler ):\n",
    " \n",
    "    while a0 < t_euler:\n",
    "        temp = a_euler\n",
    "        a_euler =a_euler + h * func(a0, a_euler)\n",
    "        a0 = a0 + h\n",
    " \n",
    "\n",
    "    return a_euler\n",
    "\n",
    "a0 = 0 #<<t=0\n",
    "b0 = 0.24 #<< A(0)=0.24\n",
    "h = 1 #<<step size\n",
    " \n",
    "t_euler_values = np.arange(1, 5+h, h) #<<t0=1 and t2=5\n",
    "\n",
    "t_values = []\n",
    "euler_values = []\n",
    "error_euler = []\n",
    "s_values = []\n",
    "error_cs = []\n",
    "exact_values = []\n",
    "error_values=[]\n",
    "\n",
    "def z(b):\n",
    "    return (49.2593*(2.71828**(2.128*b)))/(204.247+(2.71828**(2.128*b)))  #<<Exact function in b terms\n",
    "\n",
    "t = np.array( [ 1,  3, 5]) #t from output1\n",
    "a = np.array( [ 2.78, 36.30, 49.40])#a (Area) from experiment\n",
    "cs = CubicSpline(t, a, bc_type='natural')#Cubic Spline Interpolation\n",
    "\n",
    "for i in np.arange(1, 5+h, h):  #<<t0=1 and t2=5 in step1\n",
    "    error = abs(z(i)-cs(i))\n",
    "    error_values.append(float(\"{:.5f}\".format(error)))\n",
    "\n",
    "for t in t_euler_values:\n",
    "    a_euler = euler(a0, b0, h, t)\n",
    "    error = abs(z(t) - a_euler)\n",
    "    s = np.round(cs(t), 5)\n",
    "    t_values.append(t)\n",
    "    euler_values.append(float(\"{:.5f}\".format(a_euler)))\n",
    "    error_euler.append(float(\"{:.5f}\".format(error)))\n",
    "    s_values.append(float(\"{:.5f}\".format(s)))\n",
    "\n",
    "\n",
    "t_values = [float(\"{:.5f}\".format(t)) for t in t_values]\n",
    "exact_values = [float(\"{:.5f}\".format(t)) for t in exact_values]\n",
    "euler_values = [float(\"{:.5f}\".format(t)) for t in euler_values]\n",
    "error_euler = [float(\"{:.5f}\".format(t)) for t in error_euler]\n",
    "s_values = [float(\"{:.5f}\".format(t)) for t in s_values]\n",
    "error_cs = [float(\"{:.5f}\".format(t)) for t in error_cs]\n",
    "\n",
    "print('{',end='')   \n",
    "print(\"'t' : \", t_values,end='')\n",
    "print(',')\n",
    "\n",
    "error_eulervalues=[]\n",
    "errorexactvalues=[]\n",
    "\n",
    "for t in t_euler_values:\n",
    "    exact_values.append(round(z(t),5))\n",
    "    \n",
    "a = [2.78,np.nan, 36.3,np.nan, 49.4] #a(Area) from experiment at t=[1,3,5]\n",
    "\n",
    "print(\"'test' :\",a,end='')\n",
    "print(',')\n",
    "print(\"'Exact' :\",exact_values,end='')\n",
    "print(',')\n",
    "print(\"'Euler' : \", euler_values,end='')\n",
    "print(',')\n",
    "print(\"'Cubic Spline': \", s_values,end='')\n",
    "print(',')\n",
    "\n",
    "\n",
    "for t_euler in t_euler_values:\n",
    "    t_values.append(round(t_euler, 5))\n",
    "    a_euler = euler(a0, b0, h, t_euler)\n",
    "    erroreuler = abs(z(t_euler) - a_euler)\n",
    "    error_eulervalues.append(round(erroreuler, 5))\n",
    "\n",
    "print(\"'Error Euler': \", error_eulervalues,end='')\n",
    "print(',')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for t_euler in t_euler_values:\n",
    "    s = np.round(cs(t_euler), 5)\n",
    "    s_values.append(float(\"{:.5f}\".format(s)))\n",
    "    error_cs.append(abs(z(t_euler)-s))\n",
    "\n",
    "s_values = [float(\"{:.5f}\".format(t)) for t in s_values]\n",
    "error_cs = [float(\"{:.5f}\".format(t)) for t in error_cs]\n",
    "\n",
    "print(\"'Error Cubic Spline':\", str(error_cs).replace(\"'\", \"\"),end='')\n",
    "print('}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "007258f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>test</th>\n",
       "      <th>Exact</th>\n",
       "      <th>Euler</th>\n",
       "      <th>Cubic Spline</th>\n",
       "      <th>Error Euler</th>\n",
       "      <th>Error Cubic Spline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.78</td>\n",
       "      <td>1.94541</td>\n",
       "      <td>0.74823</td>\n",
       "      <td>2.78000</td>\n",
       "      <td>1.19718</td>\n",
       "      <td>0.83459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.64353</td>\n",
       "      <td>2.31628</td>\n",
       "      <td>21.45438</td>\n",
       "      <td>10.32725</td>\n",
       "      <td>8.81085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>36.30</td>\n",
       "      <td>36.62829</td>\n",
       "      <td>7.01356</td>\n",
       "      <td>36.30000</td>\n",
       "      <td>29.61473</td>\n",
       "      <td>0.32829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.31638</td>\n",
       "      <td>19.81340</td>\n",
       "      <td>44.76438</td>\n",
       "      <td>27.50298</td>\n",
       "      <td>2.55200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>49.40</td>\n",
       "      <td>49.01962</td>\n",
       "      <td>45.01726</td>\n",
       "      <td>49.40000</td>\n",
       "      <td>4.00236</td>\n",
       "      <td>0.38038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     t   test     Exact     Euler  Cubic Spline  Error Euler  \\\n",
       "0  1.0   2.78   1.94541   0.74823       2.78000      1.19718   \n",
       "1  2.0    NaN  12.64353   2.31628      21.45438     10.32725   \n",
       "2  3.0  36.30  36.62829   7.01356      36.30000     29.61473   \n",
       "3  4.0    NaN  47.31638  19.81340      44.76438     27.50298   \n",
       "4  5.0  49.40  49.01962  45.01726      49.40000      4.00236   \n",
       "\n",
       "   Error Cubic Spline  \n",
       "0             0.83459  \n",
       "1             8.81085  \n",
       "2             0.32829  \n",
       "3             2.55200  \n",
       "4             0.38038  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "nan=np.nan\n",
    "# creating a DataFrame\n",
    "dict ={'t' :  [1.0, 2.0, 3.0, 4.0, 5.0],\n",
    "'test' : [2.78, nan, 36.3, nan, 49.4],\n",
    "'Exact' : [1.94541, 12.64353, 36.62829, 47.31638, 49.01962],\n",
    "'Euler' :  [0.74823, 2.31628, 7.01356, 19.8134, 45.01726],\n",
    "'Cubic Spline':  [2.78, 21.45438, 36.3, 44.76438, 49.4],\n",
    "'Error Euler':  [1.19718, 10.32725, 29.61473, 27.50298, 4.00236],\n",
    "'Error Cubic Spline': [0.83459, 8.81085, 0.32829, 2.552, 0.38038]} #The data from output 3 and the experimental results are summarized in the table.\n",
    "df = pd.DataFrame(dict)\n",
    "# displaying the DataFrame\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ac2146da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = [0, 20, 40, 60]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "t0=0 #<<t0 is 0\n",
    "h=20 #<<h is step size \n",
    "t_values = np.arange(t0, 60+h, h) #<<t3 is 60\n",
    "t = [round(i,5) for i in t_values.tolist()]\n",
    "\n",
    "\n",
    "print(\"t =\",t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e5768bf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S0(0< t<=20) = 72.00000 + -1.10333(t-0) + -0.00000(t-0)^2  + 0.00026(t-0)^3\n",
      "S1(20< t<=40) = 52.00000 + -0.79333(t-20) + 0.01550(t-20)^2  + -0.00029(t-20)^3\n",
      "S2(40< t<=60) = 40.00000 + -0.52333(t-40) + -0.00200(t-40)^2  + 0.00003(t-40)^3\n",
      "T(20) Exact=49.58527\n",
      "T(20) Euler =  46.47787\n",
      "Error Euler =  3.10740\n",
      "T(20) Cubic Spline = 52.00000\n",
      "Error Cubic Spline=2.41473\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import CubicSpline\n",
    "\n",
    "\n",
    "#Cubic Spline from data point t and y\n",
    "t = np.array([0,  20, 40,60]) #t from output1\n",
    "y = np.array( [72,52,40,29])  #T (temperature) from experiment\n",
    "f = CubicSpline(t, y, bc_type='natural') #Cubic Spline Interpolation\n",
    "\n",
    "#Cubic Spline equation\n",
    "for j in range(f.c.shape[1]):\n",
    "    a_j = f.c.item(3, j)\n",
    "    b_j = f.c.item(2, j)\n",
    "    c_j = f.c.item(1, j)\n",
    "    d_j = f.c.item(0, j)\n",
    "    t_low = t[j]\n",
    "    t_high = t[j+1]\n",
    "    \n",
    "    equation = f'S{j}({t_low}< t<={t_high}) = {a_j:.5f} + {b_j:.5f}(t-{t_low}) + {c_j:.5f}(t-{t_low})^2  + {d_j:.5f}(t-{t_low})^3'\n",
    "   \n",
    "    print(equation) #<< Cubic Spline polynomials\n",
    "    \n",
    "    \n",
    "#exact funtion\n",
    "def z(a):\n",
    "    return 47*np.exp(-0.0324*a)+25 #<<Exact function in a terms\n",
    "\n",
    "#T(20) exact\n",
    "print('T(20) Exact={:.5f}'.format(z(20)))\n",
    "\n",
    "#plot graph exact funtion\n",
    "t_plot = np.linspace(t[0], t[-1], 100)\n",
    "\n",
    "y_exact = 47*np.exp(-0.0324*t_plot)+25\n",
    "\n",
    "#Euler's method\n",
    "def func( t_euler, y_euler ):\n",
    "    return -0.0324*(y_euler-25) #<<dT/dt in t_euler and y_euler terms \n",
    "     \n",
    "\n",
    "def euler( a0, y_euler, h, t_euler ):\n",
    "    temp = -0\n",
    " \n",
    "\n",
    "    while a0 < t_euler:\n",
    "        temp = y_euler\n",
    "        y_euler =y_euler + h * func(a0, y_euler)\n",
    "        a0 = a0 + h\n",
    "\n",
    "        #T(20) Euler's method \n",
    "    print(\"T(20) Euler = \",\"%.5f\"% y_euler)\n",
    "    return y_euler\n",
    "\n",
    "a0 = 0 #<<t=0\n",
    "b0 = 72  #<< T(0)=72\n",
    "h = 10 #<<step size\n",
    " \n",
    "t_euler = 20 #<<approxination Euler T(20)\n",
    " \n",
    "y_euler = euler(a0, b0, h, t_euler)\n",
    "\n",
    "\n",
    "error = abs(z(20) - y_euler)\n",
    "#Error Euler's method \n",
    "print(\"Error Euler = \", \"%.5f\" % error)\n",
    "#T(20) Cubic spline\n",
    "print('T(20) Cubic Spline = {:.5f}'.format(f(20)))\n",
    "#Error Cubic spline\n",
    "print('Error Cubic Spline={:.5f}'.format(abs(z(20)-f(20))))\n",
    "\n",
    "\n",
    "plt.style.use('seaborn-poster')\n",
    "\n",
    "\n",
    "\n",
    "f1 = lambda w, s: -0.0324*(s-25) #<<dT/dt in s and w terms \n",
    "h = 10 #<<step size\n",
    "w = np.arange(0, 60 + h, h) #<<t0 is 0 ,t3 is 60\n",
    "\n",
    "\n",
    "s0 = 72 #<<T(0)=72\n",
    "\n",
    "s = np.zeros(len(w))\n",
    "s[0] = s0\n",
    "\n",
    "for i in range(0, len(w) - 1):\n",
    "    s[i + 1] = s[i] + h*f1(w[i], s[i])\n",
    "w_1 = np.where((w >= 0) & (w <= 60))\n",
    "    \n",
    "t_new =np.linspace(t[0], t[-1], 100)\n",
    "y_new = f(t_new)\n",
    "\n",
    "#plot graph\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.grid()\n",
    "plt.plot(w[w_1], s[w_1], 'bo--', label='Euler')\n",
    "plt.plot(t_plot,y_exact, 'm',  label='Exact function')\n",
    "plt.plot(t_new, y_new, 'g', label='Cubic spline')\n",
    "plt.plot(t, y, 'ro',label='Data point')\n",
    "plt.title('Cubic Spline Interpolation and Euler\\'s method')\n",
    "plt.xlabel('Time (minutes)')\n",
    "plt.ylabel('$Temperature ({^\\circ}C$)')\n",
    "plt.legend(loc='lower left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "690d24b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'t' :  [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0],\n",
      "'test' : [72, nan, 52, nan, 40, nan, 29],\n",
      "'Exact' : [72.0, 58.99276, 49.58527, 42.7813, 37.86033, 34.30124, 31.72712],\n",
      "'Euler' :  [72.0, 56.772, 46.47787, 39.51904, 34.81487, 31.63485, 29.48516],\n",
      "'Cubic Spline':  [72.0, 61.225, 52.0, 45.325, 40.0, 34.6, 29.0],\n",
      "'Error Euler':  [0.0, 2.22076, 3.1074, 3.26226, 3.04546, 2.66639, 2.24196],\n",
      "'Error Cubic Spline': [0.0, 2.23224, 2.41473, 2.5437, 2.13967, 0.29876, 2.72712]}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.interpolate import CubicSpline\n",
    "import re\n",
    "\n",
    "def func( t_euler, y_euler ):\n",
    "    return -0.0324*(y_euler-25) #<<dT/dt in t_euler and y_euler terms\n",
    "     \n",
    "\n",
    "def euler( a0, y_euler, h, t_euler ):\n",
    "    while a0 < t_euler:\n",
    "        temp = y_euler\n",
    "        y_euler =y_euler + h * func(a0, y_euler)\n",
    "        a0 = a0 + h\n",
    "\n",
    "    return y_euler\n",
    "\n",
    "a0 = 0 #<<t=0\n",
    "b0 = 72  #<< T(0)=72\n",
    "h = 10 #<<step size\n",
    " \n",
    "t_euler_values = np.arange(0, 60+h, h) #<<t0=0 and t3=60\n",
    "\n",
    "t_values = []\n",
    "euler_values = []\n",
    "error_euler = []\n",
    "s_values = []\n",
    "error_cs = []\n",
    "exact_values = []\n",
    "error_values=[]\n",
    "\n",
    "def z(a):\n",
    "    return 47*np.exp(-0.0324*a)+25 #<<Exact function in a terms\n",
    "\n",
    "t = np.array([0,  20, 40,60]) #t from output1\n",
    "y = np.array( [72,52,40,29])  #T (temperature) from experiment\n",
    "cs = CubicSpline(t, y, bc_type='natural') #Cubic Spline Interpolation\n",
    "\n",
    "for i in np.arange(0, 60+h, h):#<<t0=0 and t3=60 in step1\n",
    "    \n",
    "    error = abs(z(i)-cs(i))\n",
    "    error_values.append(float(\"{:.5f}\".format(error)))\n",
    "\n",
    "for t in t_euler_values:\n",
    "    y_euler = euler(a0, b0, h, t)\n",
    "    error = abs(z(t) - y_euler)\n",
    "    s = np.round(cs(t), 5)\n",
    "    t_values.append(t)\n",
    "    euler_values.append(float(\"{:.5f}\".format(y_euler)))\n",
    "    error_euler.append(float(\"{:.5f}\".format(error)))\n",
    "    s_values.append(float(\"{:.5f}\".format(s)))\n",
    "\n",
    "\n",
    "t_values = [float(\"{:.5f}\".format(t)) for t in t_values]\n",
    "exact_values = [float(\"{:.5f}\".format(t)) for t in exact_values]\n",
    "euler_values = [float(\"{:.5f}\".format(t)) for t in euler_values]\n",
    "error_euler = [float(\"{:.5f}\".format(t)) for t in error_euler]\n",
    "s_values = [float(\"{:.5f}\".format(t)) for t in s_values]\n",
    "error_cs = [float(\"{:.5f}\".format(t)) for t in error_cs]\n",
    "\n",
    "print('{',end='')   \n",
    "print(\"'t' : \", t_values,end='')\n",
    "print(',')\n",
    "\n",
    "error_eulervalues=[]\n",
    "errorexactvalues=[]\n",
    "\n",
    "for t in t_euler_values:\n",
    "    exact_values.append(round(z(t),5))\n",
    "    \n",
    "y =  [72,np.nan,52,np.nan,40,np.nan,29] #y((T) temperature) from experiment at t=[0,20,40,60]\n",
    "\n",
    "print(\"'test' :\",y,end='')\n",
    "print(',')\n",
    "print(\"'Exact' :\",exact_values,end='')\n",
    "print(',')\n",
    "print(\"'Euler' : \", euler_values,end='')\n",
    "print(',')\n",
    "print(\"'Cubic Spline': \", s_values,end='')\n",
    "print(',')\n",
    "\n",
    "\n",
    "for t_euler in t_euler_values:\n",
    "    t_values.append(round(t_euler, 5))\n",
    "    y_euler = euler(a0, b0, h, t_euler)\n",
    "    erroreuler = abs(z(t_euler) - y_euler)\n",
    "    error_eulervalues.append(round(erroreuler, 5))\n",
    "\n",
    "print(\"'Error Euler': \", error_eulervalues,end='')\n",
    "print(',')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for t_euler in t_euler_values:\n",
    "    s = np.round(cs(t_euler), 5)\n",
    "    s_values.append(float(\"{:.5f}\".format(s)))\n",
    "    error_cs.append(abs(z(t_euler)-s))\n",
    "\n",
    "s_values = [float(\"{:.5f}\".format(t)) for t in s_values]\n",
    "error_cs = [float(\"{:.5f}\".format(t)) for t in error_cs]\n",
    "\n",
    "print(\"'Error Cubic Spline':\", str(error_cs).replace(\"'\", \"\"),end='')\n",
    "print('}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9740dc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>test</th>\n",
       "      <th>Exact</th>\n",
       "      <th>Euler</th>\n",
       "      <th>Cubic Spline</th>\n",
       "      <th>Error Euler</th>\n",
       "      <th>Error Cubic Spline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.00000</td>\n",
       "      <td>72.00000</td>\n",
       "      <td>72.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.99276</td>\n",
       "      <td>56.77200</td>\n",
       "      <td>61.225</td>\n",
       "      <td>2.22076</td>\n",
       "      <td>2.23224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>49.58527</td>\n",
       "      <td>46.47787</td>\n",
       "      <td>52.000</td>\n",
       "      <td>3.10740</td>\n",
       "      <td>2.41473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.78130</td>\n",
       "      <td>39.51904</td>\n",
       "      <td>45.325</td>\n",
       "      <td>3.26226</td>\n",
       "      <td>2.54370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>37.86033</td>\n",
       "      <td>34.81487</td>\n",
       "      <td>40.000</td>\n",
       "      <td>3.04546</td>\n",
       "      <td>2.13967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.30124</td>\n",
       "      <td>31.63485</td>\n",
       "      <td>34.600</td>\n",
       "      <td>2.66639</td>\n",
       "      <td>0.29876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.72712</td>\n",
       "      <td>29.48516</td>\n",
       "      <td>29.000</td>\n",
       "      <td>2.24196</td>\n",
       "      <td>2.72712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      t  test     Exact     Euler  Cubic Spline  Error Euler  \\\n",
       "0   0.0  72.0  72.00000  72.00000        72.000      0.00000   \n",
       "1  10.0   NaN  58.99276  56.77200        61.225      2.22076   \n",
       "2  20.0  52.0  49.58527  46.47787        52.000      3.10740   \n",
       "3  30.0   NaN  42.78130  39.51904        45.325      3.26226   \n",
       "4  40.0  40.0  37.86033  34.81487        40.000      3.04546   \n",
       "5  50.0   NaN  34.30124  31.63485        34.600      2.66639   \n",
       "6  60.0  29.0  31.72712  29.48516        29.000      2.24196   \n",
       "\n",
       "   Error Cubic Spline  \n",
       "0             0.00000  \n",
       "1             2.23224  \n",
       "2             2.41473  \n",
       "3             2.54370  \n",
       "4             2.13967  \n",
       "5             0.29876  \n",
       "6             2.72712  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "nan=np.nan\n",
    "# creating a DataFrame\n",
    "dict ={'t' :  [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0],\n",
    "'test' : [72, nan, 52, nan, 40, nan, 29],\n",
    "'Exact' : [72.0, 58.99276, 49.58527, 42.7813, 37.86033, 34.30124, 31.72712],\n",
    "'Euler' :  [72.0, 56.772, 46.47787, 39.51904, 34.81487, 31.63485, 29.48516],\n",
    "'Cubic Spline':  [72.0, 61.225, 52.0, 45.325, 40.0, 34.6, 29.0],\n",
    "'Error Euler':  [0.0, 2.22076, 3.1074, 3.26226, 3.04546, 2.66639, 2.24196],\n",
    "'Error Cubic Spline': [0.0, 2.23224, 2.41473, 2.5437, 2.13967, 0.29876, 2.72712]} #The data from output 3 and the experimental results are summarized in the table.\n",
    "df = pd.DataFrame(dict)\n",
    "# displaying the DataFrame\n",
    "display(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ae7f03e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8284c39",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b60f6222",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28996d72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = [0, 20, 40, 60]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "t0=0 #<<t0 is 0\n",
    "h=20 #<<h is step size \n",
    "t_values = np.arange(t0, 60+h, h) #<<t3 is 60\n",
    "t = [round(i,5) for i in t_values.tolist()]\n",
    "\n",
    "\n",
    "print(\"t =\",t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c010350",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S0(0< t<=20) = 22.00000 + 0.52000(t-0) + -0.00000(t-0)^2  + 0.00045(t-0)^3\n",
      "S1(20< t<=40) = 36.00000 + 1.06000(t-20) + 0.02700(t-20)^2  + 0.00288(t-20)^3\n",
      "S2(40< t<=60) = 91.00000 + 5.59000(t-40) + 0.19950(t-40)^2  + -0.00332(t-40)^3\n",
      "y(20) Exact=49.31858\n",
      "y(20) Euler =  43.36236\n",
      "Error Euler =  5.95621\n",
      "y(20) Cubic Spline = 36.00000\n",
      "Error Cubic Spline=13.31858\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import CubicSpline\n",
    "\n",
    "\n",
    "#Cubic Spline from data point t and y\n",
    "t = np.array( [0, 20, 40, 60])#t from output1\n",
    "y = np.array( [22,36,91,256])  #population from experiment\n",
    "f = CubicSpline(t, y, bc_type='natural') #Cubic Spline Interpolation\n",
    "\n",
    "#Cubic Spline equation\n",
    "for j in range(f.c.shape[1]):\n",
    "    a_j = f.c.item(3, j)\n",
    "    b_j = f.c.item(2, j)\n",
    "    c_j = f.c.item(1, j)\n",
    "    d_j = f.c.item(0, j)\n",
    "    t_low = t[j]\n",
    "    t_high = t[j+1]\n",
    "    \n",
    "    equation = f'S{j}({t_low}< t<={t_high}) = {a_j:.5f} + {b_j:.5f}(t-{t_low}) + {c_j:.5f}(t-{t_low})^2  + {d_j:.5f}(t-{t_low})^3'\n",
    "   \n",
    "    print(equation) #<< Cubic Spline polynomials\n",
    "    \n",
    "    \n",
    "#exact funtion\n",
    "def z(a):\n",
    "    return 10000/(1+(453.54545*np.exp(-0.0405*a))) #<<Exact function in a terms\n",
    "\n",
    "#y(20) exact\n",
    "print('y(20) Exact={:.5f}'.format(z(20)))\n",
    "\n",
    "#plot graph exact funtion\n",
    "t_plot = np.linspace(t[0], t[-1], 100)\n",
    "\n",
    "y_exact = 10000/(1+(453.54545*np.exp(-0.0405*t_plot)))\n",
    "\n",
    "#Euler's method\n",
    "def func( t_euler, y_euler ):\n",
    "    return 0.0405*(1-(y_euler/10000))*y_euler #<<dy/dt in t_euler and y_euler terms \n",
    "     \n",
    "\n",
    "def euler( a0, y_euler, h, t_euler ):\n",
    "    temp = -0\n",
    " \n",
    "\n",
    "    while a0 < t_euler:\n",
    "        temp = y_euler\n",
    "        y_euler =y_euler + h * func(a0, y_euler)\n",
    "        a0 = a0 + h\n",
    "\n",
    "        #T(10) Euler's method \n",
    "    print(\"y(20) Euler = \",\"%.5f\"% y_euler)\n",
    "    return y_euler\n",
    "\n",
    "a0 = 0 #<<t=0\n",
    "b0 = 22  #<< T(0)=22\n",
    "h = 10 #<<step size\n",
    " \n",
    "t_euler = 20 #<<approxination Euler y(20)\n",
    " \n",
    "y_euler = euler(a0, b0, h, t_euler)\n",
    "\n",
    "\n",
    "error = abs(z(20) - y_euler)\n",
    "#Error Euler's method \n",
    "print(\"Error Euler = \", \"%.5f\" % error)\n",
    "#y(10) Cubic spline\n",
    "print('y(20) Cubic Spline = {:.5f}'.format(f(20)))\n",
    "#Error Cubic spline\n",
    "print('Error Cubic Spline={:.5f}'.format(abs(z(20)-f(20))))\n",
    "\n",
    "\n",
    "plt.style.use('seaborn-poster')\n",
    "\n",
    "\n",
    "\n",
    "f1 = lambda w, s: 0.0405*(1-(s/10000))*s #<<dy/dt in s and w terms \n",
    "h = 10 #<<step size\n",
    "w = np.arange(0, 60 + h, h) #<<t0 is 0 ,t3 is 60\n",
    "\n",
    "\n",
    "s0 = 22 #<<y(0)=22\n",
    "\n",
    "s = np.zeros(len(w))\n",
    "s[0] = s0\n",
    "\n",
    "for i in range(0, len(w) - 1):\n",
    "    s[i + 1] = s[i] + h*f1(w[i], s[i])\n",
    "w_1 = np.where((w >= 0) & (w <= 60))\n",
    "    \n",
    "t_new =np.linspace(t[0], t[-1], 100)\n",
    "y_new = f(t_new)\n",
    "\n",
    "#plot graph\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.grid()\n",
    "plt.plot(w[w_1], s[w_1], 'bo--', label='Euler')\n",
    "plt.plot(t_plot,y_exact, 'm',  label='Exact function')\n",
    "plt.plot(t_new, y_new, 'g', label='Cubic spline')\n",
    "plt.plot(t, y, 'ro',label='Data point')\n",
    "plt.title('Cubic Spline Interpolation and Euler\\'s method')\n",
    "plt.xlabel('Time (years)')\n",
    "plt.ylabel('Whooping Crane Population')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a4625da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'t' :  [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0],\n",
      "'test' : [22, nan, 36, nan, 91, nan, 256],\n",
      "'Exact' : [22.0, 32.94846, 49.31858, 73.76183, 110.18549, 164.29748, 244.3273],\n",
      "'Euler' :  [22.0, 30.8904, 43.36236, 60.84797, 85.34145, 119.60976, 167.4723],\n",
      "'Cubic Spline':  [22.0, 27.65, 36.0, 52.175, 91.0, 163.525, 256.0],\n",
      "'Error Euler':  [0.0, 2.05806, 5.95621, 12.91386, 24.84404, 44.68771, 76.855],\n",
      "'Error Cubic Spline': [0.0, 5.29846, 13.31858, 21.58683, 19.18549, 0.77248, 11.6727]}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.interpolate import CubicSpline\n",
    "import re\n",
    "\n",
    "def func( t_euler, y_euler ):\n",
    "    return 0.0405*(1-(y_euler/10000))*y_euler  #<<dy/dt in t_euler and y_euler terms\n",
    "     \n",
    "\n",
    "def euler( a0, y_euler, h, t_euler ):\n",
    "    while a0 < t_euler:\n",
    "        temp = y_euler\n",
    "        y_euler =y_euler + h * func(a0, y_euler)\n",
    "        a0 = a0 + h\n",
    "\n",
    "    return y_euler\n",
    "\n",
    "a0 = 0 #<<t=0\n",
    "b0 = 22  #<< y(0)=22\n",
    "h = 10 #<<step size\n",
    "\n",
    "t_euler_values = np.arange(0, 60+h, h) #<<t0=0 and t3=60\n",
    "\n",
    "t_values = []\n",
    "euler_values = []\n",
    "error_euler = []\n",
    "s_values = []\n",
    "error_cs = []\n",
    "exact_values = []\n",
    "error_values=[]\n",
    "\n",
    "def z(a):\n",
    "    return 10000/(1+(453.54545*np.exp(-0.0405*a)))\n",
    "\n",
    "t = np.array( [0, 20, 40, 60])#t from output1\n",
    "y = np.array( [22,36,91,256])  #opoulation from experiment\n",
    "cs = CubicSpline(t, y, bc_type='natural') #Cubic Spline Interpolation\n",
    "\n",
    "for i in np.arange(0, 60+h, h):#<<t0=0 and t3=60 in step1\n",
    "    \n",
    "    error = abs(z(i)-cs(i))\n",
    "    error_values.append(float(\"{:.5f}\".format(error)))\n",
    "\n",
    "for t in t_euler_values:\n",
    "    y_euler = euler(a0, b0, h, t)\n",
    "    error = abs(z(t) - y_euler)\n",
    "    s = np.round(cs(t), 5)\n",
    "    t_values.append(t)\n",
    "    euler_values.append(float(\"{:.5f}\".format(y_euler)))\n",
    "    error_euler.append(float(\"{:.5f}\".format(error)))\n",
    "    s_values.append(float(\"{:.5f}\".format(s)))\n",
    "\n",
    "\n",
    "t_values = [float(\"{:.5f}\".format(t)) for t in t_values]\n",
    "exact_values = [float(\"{:.5f}\".format(t)) for t in exact_values]\n",
    "euler_values = [float(\"{:.5f}\".format(t)) for t in euler_values]\n",
    "error_euler = [float(\"{:.5f}\".format(t)) for t in error_euler]\n",
    "s_values = [float(\"{:.5f}\".format(t)) for t in s_values]\n",
    "error_cs = [float(\"{:.5f}\".format(t)) for t in error_cs]\n",
    "\n",
    "print('{',end='')   \n",
    "print(\"'t' : \", t_values,end='')\n",
    "print(',')\n",
    "\n",
    "error_eulervalues=[]\n",
    "errorexactvalues=[]\n",
    "\n",
    "for t in t_euler_values:\n",
    "    exact_values.append(round(z(t),5))\n",
    "    \n",
    "y =  [22,np.nan,36,np.nan,91,np.nan,256] #y(t) population from experiment at t=[0,20,40,60]\n",
    "\n",
    "print(\"'test' :\",y,end='')\n",
    "print(',')\n",
    "print(\"'Exact' :\",exact_values,end='')\n",
    "print(',')\n",
    "print(\"'Euler' : \", euler_values,end='')\n",
    "print(',')\n",
    "print(\"'Cubic Spline': \", s_values,end='')\n",
    "print(',')\n",
    "\n",
    "\n",
    "for t_euler in t_euler_values:\n",
    "    t_values.append(round(t_euler, 5))\n",
    "    y_euler = euler(a0, b0, h, t_euler)\n",
    "    erroreuler = abs(z(t_euler) - y_euler)\n",
    "    error_eulervalues.append(round(erroreuler, 5))\n",
    "\n",
    "print(\"'Error Euler': \", error_eulervalues,end='')\n",
    "print(',')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for t_euler in t_euler_values:\n",
    "    s = np.round(cs(t_euler), 5)\n",
    "    s_values.append(float(\"{:.5f}\".format(s)))\n",
    "    error_cs.append(abs(z(t_euler)-s))\n",
    "\n",
    "s_values = [float(\"{:.5f}\".format(t)) for t in s_values]\n",
    "error_cs = [float(\"{:.5f}\".format(t)) for t in error_cs]\n",
    "\n",
    "print(\"'Error Cubic Spline':\", str(error_cs).replace(\"'\", \"\"),end='')\n",
    "print('}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc2fbc15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>test</th>\n",
       "      <th>Exact</th>\n",
       "      <th>Euler</th>\n",
       "      <th>Cubic Spline</th>\n",
       "      <th>Error Euler</th>\n",
       "      <th>Error Cubic Spline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>22.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.94846</td>\n",
       "      <td>30.89040</td>\n",
       "      <td>27.650</td>\n",
       "      <td>2.05806</td>\n",
       "      <td>5.29846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.31858</td>\n",
       "      <td>43.36236</td>\n",
       "      <td>36.000</td>\n",
       "      <td>5.95621</td>\n",
       "      <td>13.31858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.76183</td>\n",
       "      <td>60.84797</td>\n",
       "      <td>52.175</td>\n",
       "      <td>12.91386</td>\n",
       "      <td>21.58683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>110.18549</td>\n",
       "      <td>85.34145</td>\n",
       "      <td>91.000</td>\n",
       "      <td>24.84404</td>\n",
       "      <td>19.18549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164.29748</td>\n",
       "      <td>119.60976</td>\n",
       "      <td>163.525</td>\n",
       "      <td>44.68771</td>\n",
       "      <td>0.77248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>244.32730</td>\n",
       "      <td>167.47230</td>\n",
       "      <td>256.000</td>\n",
       "      <td>76.85500</td>\n",
       "      <td>11.67270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      t   test      Exact      Euler  Cubic Spline  Error Euler  \\\n",
       "0   0.0   22.0   22.00000   22.00000        22.000      0.00000   \n",
       "1  10.0    NaN   32.94846   30.89040        27.650      2.05806   \n",
       "2  20.0   36.0   49.31858   43.36236        36.000      5.95621   \n",
       "3  30.0    NaN   73.76183   60.84797        52.175     12.91386   \n",
       "4  40.0   91.0  110.18549   85.34145        91.000     24.84404   \n",
       "5  50.0    NaN  164.29748  119.60976       163.525     44.68771   \n",
       "6  60.0  256.0  244.32730  167.47230       256.000     76.85500   \n",
       "\n",
       "   Error Cubic Spline  \n",
       "0             0.00000  \n",
       "1             5.29846  \n",
       "2            13.31858  \n",
       "3            21.58683  \n",
       "4            19.18549  \n",
       "5             0.77248  \n",
       "6            11.67270  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "nan=np.nan\n",
    "# creating a DataFrame\n",
    "dict ={'t' :  [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0],\n",
    "'test' : [22, nan, 36, nan, 91, nan, 256],\n",
    "'Exact' : [22.0, 32.94846, 49.31858, 73.76183, 110.18549, 164.29748, 244.3273],\n",
    "'Euler' :  [22.0, 30.8904, 43.36236, 60.84797, 85.34145, 119.60976, 167.4723],\n",
    "'Cubic Spline':  [22.0, 27.65, 36.0, 52.175, 91.0, 163.525, 256.0],\n",
    "'Error Euler':  [0.0, 2.05806, 5.95621, 12.91386, 24.84404, 44.68771, 76.855],\n",
    "'Error Cubic Spline': [0.0, 5.29846, 13.31858, 21.58683, 19.18549, 0.77248, 11.6727]}#The data from output 3 and the experimental results are summarized in the table.\n",
    "df = pd.DataFrame(dict)\n",
    "# displaying the DataFrame\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cb5f193",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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