DS3_AYARAYENIMA_21181032

DS_TK3_AYARAYENIMA_21181032.ipnyb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f0030272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAANVCAYAAAAgLDCxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABs00lEQVR4nO3dd3gU5d7G8XtT2PRQUyChhyYgJUg5Agm9SvOogAoiYEEUeBFFpImCoiKCigWkKAgeEY5HlE4QBSRREERAegATIy0hISQk2fePwMoSShZ3syHz/VzXXmaeeWbmt+vukjvPMzMmi8ViEQAAAADAcNxcXQAAAAAAwDUIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhABgUPPmzZPJZLI+PDw8FBYWpkceeUQnTpyw9uvfv78qVqzoukKvY8KECTKZTPnqW7FiRfXv399hx46KirK+bm5ubvL391fVqlX173//W1988YVycnKuWUOXLl1s2kwmk5566qk8fcePHy+TyaQnnnhCnTp1kr+/v7Kysmz6bN++XSaTSaGhoXm237Rpk0wmk2bMmPEPnykAoKgjEAKAwc2dO1dbtmzRmjVrNGjQIH322Wdq3ry50tLSJEljx47VsmXLXFxlXgMHDtSWLVtcdvzKlStry5Yt2rx5s5YvX67nn39e6enp+ve//62oqCglJyfbvU+LxaKnn35aL730kp5//nnNmjVLrVu3VmpqquLi4mz6xsTEyNfXV4mJidq7d2+edZIUHR19y88PAGAMHq4uAADgWrVr11ZkZKSk3ACRnZ2tSZMmafny5erbt6+qVKly031YLBZduHBB3t7ezi7XKiwsTGFhYQV2vKt5e3urSZMmNm0DBw7U3LlzNWDAAA0ePFhLlizJ9/6ysrI0YMAAffLJJ3r99dc1cuRISX+HupiYGJvjxcTEqFu3btqwYYM2bNigGjVq2KwrXbq0ateu/U+eIgDAABghBADYuBw6jh49KunaU0YvT3V8//33VbNmTZnNZs2fP1+SNHHiRDVu3FglS5ZUQECAGjRooDlz5shisdjs4/IUyq+//lr169eXt7e3atasqa+//lpS7pTWmjVrytfXV3fddVeeEbJrTRm9ePGiRo0apZCQEPn4+Ojuu+/Wtm3b8jzHv/76S08++aRq1aolPz8/BQUFqVWrVtq0adOtv3CXPPLII+rUqZP+85//WF/Dm7lw4YJ69eqlRYsWafbs2dYwKEn16tVTiRIlrKN+kpSTk6NNmzYpKipKLVu21IYNG6zrMjMztWXLFuu0VgAAboQRQgCAjQMHDkiSypQpc8N+y5cv16ZNmzRu3DiFhIQoKChIknTkyBE99thjKl++vCRp69atGjp0qE6cOKFx48bZ7OOXX37R6NGjNWbMGAUGBmrixInq2bOnRo8erXXr1mny5MkymUx67rnn1KVLFx0+fPiGo5CDBg3SggULNHLkSLVt21a//vqrevbsqXPnztn0O336tKTcc/VCQkKUmpqqZcuWKSoqSuvWrVNUVJRdr9nV7rnnHn3zzTfatGmTKlSocMO+586dU8eOHbV582YtWbJEvXr1slnv5uamFi1aaO3atcrKypKHh4d27NihM2fOqGXLlsrOztb48eOt/bdu3ar09HSmiwIA8oVACAAGl52draysLF24cEEbN27Uyy+/LH9/f91zzz033C41NVW7du1SiRIlbNrnzp1r/TknJ0dRUVGyWCx6++23NXbsWJtRq1OnTmnr1q0qV66cJKls2bKqV6+ePvroIx04cEA+Pj6Sckcku3fvrrVr16pr167XrGfv3r2aP3++hg8frqlTp0qS2rZtq+DgYPXt29emb/Xq1fXee+/ZvAbt27fXkSNHNGPGjH8cCC+HwD/++OOmfRcsWCBJ+vDDD/OEwcuio6P13//+V7GxsWratKliYmIUGhqqatWqKTs7W0lJSdq9e7fuuOMOzh8EANiFKaMAYHBNmjSRp6en/P391aVLF4WEhOjbb79VcHDwDbdr1apVnjAoSevXr1ebNm0UGBgod3d3eXp6aty4cTp16pSSkpJs+tarV88aBiWpZs2aknKv4nk5DF7ZfqMpmJenTV4d/u677z55eOT9++f777+vBg0ayMvLSx4eHvL09NS6deu0Z8+eGz7v/Lh6euyNNG/eXMWLF9fEiROto7NXu/I8wsv/bdmypaTc1yYoKMj6/GNiYhQcHGx9zQAAuBECIQAY3IIFCxQbG6vt27frjz/+0M6dO/Wvf/3rpttd63YH27ZtU7t27SRJH330kX744QfFxsZqzJgxkqT09HSb/iVLlrRZLlas2A3bL1y4cN16Tp06JUkKCQmxaffw8FCpUqVs2qZNm6YnnnhCjRs31tKlS7V161bFxsaqQ4cOeWq8FZeDa9myZW/at27dulq7dq3Onz+vli1b6vfff8/Tp06dOipdurQ2bNhgPX/wciCUpBYtWigmJkYZGRnasmULo4MAgHxjyigAGFzNmjWtVxm1x7UuWLJ48WJ5enrq66+/lpeXl7V9+fLl/6TEfLkc+hITE21GHbOysqxh8bJPP/1UUVFRmjVrlk371eca3qqvvvpKJpNJLVq0yFf/hg0bau3atWrbtq2io6O1fv16Va9e3breZDKpZcuWWrlypbZt26azZ8/aBMKWLVtqwoQJ2rJliy5cuEAgBADkGyOEAACHuXyDe3d3d2tbenq6PvnkE6cf+/J5fwsXLrRp//zzz/Pc1N1kMslsNtu07dy50yH3NZw7d66+/fZb9e7d23phnfxo0KCB1q1bp4yMDEVHR+e5t2B0dLTS0tL0+uuvKygoyGZKaMuWLXXq1CnNnDnT2hcAgPxghBAA4DCdO3fWtGnT1KdPHw0ePFinTp3SG2+8kSd8OUPNmjX14IMPavr06fL09FSbNm3066+/6o033lBAQIBN3y5dumjSpEkaP368WrZsqX379umll15SpUqV8oTH60lPT9fWrVutPx86dEjLly/X119/rZYtW+r999+3+znUq1dP69atU+vWra0jhZeD3+WQt2zZMt17770229WuXVulSpXSsmXLVK5cOUVERNh9bACAMREIAQAO06pVK3388cd67bXX1LVrV5UrV06DBg1SUFCQHn30Uacff86cOQoODta8efM0Y8YM1atXT0uXLtUDDzxg02/MmDE6f/685syZo6lTp6pWrVp6//33tWzZMpv7/d3IoUOH1LRpU0mSr6+vgoOD1aBBA/3nP/9Rz5495eZ2a5Nw7rzzTq1fv94mFNaqVUu1atVSSEiIEhMTbaaLSrkjns2bN9fy5cv/8RVSAQDGYrLYcyk0AAAAAECRwTmEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDKvL3IczJydEff/whf39/mUwmV5cDAAAAwEUsFovOnTunsmXL3vL9YouaIh8I//jjD4WHh7u6DAAAAACFxLFjxxQWFubqMgqFIh8I/f39JeX+Tw8ICHBxNQAAAABcJSUlReHh4daMAAMEwsvTRAMCAgiEAAAAADiV7ApMnAUAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADCoQhMIp0yZIpPJpGHDhlnbLBaLJkyYoLJly8rb21tRUVHavXu364oEAAAAgCKkUATC2NhYffjhh6pbt65N+9SpUzVt2jS98847io2NVUhIiNq2batz5865qFIAAAAAKDpcHghTU1PVt29fffTRRypRooS13WKxaPr06RozZox69uyp2rVra/78+Tp//rwWLVrkwooBAAAAoGhweSAcMmSIOnfurDZt2ti0Hz58WImJiWrXrp21zWw2q2XLltq8efN195eRkaGUlBSbBwAAAAAgLw9XHnzx4sX6+eefFRsbm2ddYmKiJCk4ONimPTg4WEePHr3uPqdMmaKJEyc6tlAAAAADMT1mcnUJKGIsH1hcXQKuw2UjhMeOHdMzzzyjTz/9VF5eXtftZzLZfiFZLJY8bVcaPXq0kpOTrY9jx445rGYAAAAAKEpcNkL4008/KSkpSQ0bNrS2ZWdn67vvvtM777yjffv2ScodKQwNDbX2SUpKyjNqeCWz2Syz2ey8wgEAAACgiHDZCGHr1q21a9cu7dixw/qIjIxU3759tWPHDlWuXFkhISFas2aNdZvMzExt3LhRzZo1c1XZAAAAAFBkuGyE0N/fX7Vr17Zp8/X1ValSpaztw4YN0+TJkxUREaGIiAhNnjxZPj4+6tOnjytKBgAAAIAixaUXlbmZUaNGKT09XU8++aTOnDmjxo0ba/Xq1fL393d1aQAAAABw2zNZLJYifcmflJQUBQYGKjk5WQEBAa4uBwAAoNDjKqNwtMJylVGyQV4uvw8hAAAAAMA1CIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgXBoIZ82apbp16yogIEABAQFq2rSpvv32W+v6/v37y2Qy2TyaNGniwooBAAAAoOjwcOXBw8LC9Oqrr6pq1aqSpPnz56tbt27avn277rjjDklShw4dNHfuXOs2xYoVc0mtAAAAAFDUuDQQdu3a1Wb5lVde0axZs7R161ZrIDSbzQoJCXFFeQAAAABQpBWacwizs7O1ePFipaWlqWnTptb2mJgYBQUFqVq1aho0aJCSkpJuuJ+MjAylpKTYPAAAAAAAebk8EO7atUt+fn4ym816/PHHtWzZMtWqVUuS1LFjRy1cuFDr16/Xm2++qdjYWLVq1UoZGRnX3d+UKVMUGBhofYSHhxfUUwEAAACA24rJYrFYXFlAZmam4uPjdfbsWS1dulSzZ8/Wxo0braHwSgkJCapQoYIWL16snj17XnN/GRkZNoExJSVF4eHhSk5OVkBAgNOeBwAAQFFheszk6hJQxFg+cGnksEpJSVFgYCDZ4AouPYdQyr1IzOWLykRGRio2NlZvv/22Pvjggzx9Q0NDVaFCBe3fv/+6+zObzTKbzU6rFwAAAACKCpdPGb2axWK57pTQU6dO6dixYwoNDS3gqgAAAACg6HHpCOELL7ygjh07Kjw8XOfOndPixYsVExOjlStXKjU1VRMmTFCvXr0UGhqqI0eO6IUXXlDp0qXVo0cPV5YNAAAAAEWCSwPhn3/+qYceekgJCQkKDAxU3bp1tXLlSrVt21bp6enatWuXFixYoLNnzyo0NFTR0dFasmSJ/P39XVk2AAAAABQJLg2Ec+bMue46b29vrVq1qgCrAQAAAABjKXTnEAIAAAAACgaBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGJRLA+GsWbNUt25dBQQEKCAgQE2bNtW3335rXW+xWDRhwgSVLVtW3t7eioqK0u7du11YMQAAAAAUHS4NhGFhYXr11VcVFxenuLg4tWrVSt26dbOGvqlTp2ratGl65513FBsbq5CQELVt21bnzp1zZdkAAAAAUCSYLBaLxdVFXKlkyZJ6/fXXNWDAAJUtW1bDhg3Tc889J0nKyMhQcHCwXnvtNT322GP52l9KSooCAwOVnJysgIAAZ5YOAABQJJgeM7m6BBQxlg8KR+QgG+RVaM4hzM7O1uLFi5WWlqamTZvq8OHDSkxMVLt27ax9zGazWrZsqc2bN193PxkZGUpJSbF5AAAAAADy8nB1Abt27VLTpk114cIF+fn5admyZapVq5Y19AUHB9v0Dw4O1tGjR6+7vylTpmjixIlOrRkAYGymDxk9geNYBheOkRMAxuTyEcLq1atrx44d2rp1q5544gn169dPv/32m3W9yWT7j67FYsnTdqXRo0crOTnZ+jh27JjTagcAAACA25nLRwiLFSumqlWrSpIiIyMVGxurt99+23reYGJiokJDQ639k5KS8owaXslsNstsNju3aAAAAAAoAlw+Qng1i8WijIwMVapUSSEhIVqzZo11XWZmpjZu3KhmzZq5sEIAAAAAKBpcOkL4wgsvqGPHjgoPD9e5c+e0ePFixcTEaOXKlTKZTBo2bJgmT56siIgIRUREaPLkyfLx8VGfPn1cWTYAAAAAFAkuDYR//vmnHnroISUkJCgwMFB169bVypUr1bZtW0nSqFGjlJ6erieffFJnzpxR48aNtXr1avn7+7uybAAAAAAoEgrdfQgdjXuNAAAcjauMwpEK41VGuQ8hHI37EBZehe4cQgAAAABAwSAQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAZFIAQAAAAAgyIQAgAAAIBBEQgBAAAAwKAIhAAAAABgUARCAAAAADAoAiEAAAAAGBSBEAAAAAAMikAIAAAAAAbl0kA4ZcoUNWrUSP7+/goKClL37t21b98+mz79+/eXyWSyeTRp0sRFFQMAAABA0eHSQLhx40YNGTJEW7du1Zo1a5SVlaV27dopLS3Npl+HDh2UkJBgfXzzzTcuqhgAAAAAig4PVx585cqVNstz585VUFCQfvrpJ7Vo0cLabjabFRISUtDlAQAAAECRVqjOIUxOTpYklSxZ0qY9JiZGQUFBqlatmgYNGqSkpKTr7iMjI0MpKSk2DwAAAABAXnYHwj///FMPPfSQypYtKw8PD7m7u9s8bpXFYtGIESN09913q3bt2tb2jh07auHChVq/fr3efPNNxcbGqlWrVsrIyLjmfqZMmaLAwEDrIzw8/JZrAgAAAICizGSxWCz2bNCxY0fFx8frqaeeUmhoqEwmk836bt263VIhQ4YM0YoVK/T9998rLCzsuv0SEhJUoUIFLV68WD179syzPiMjwyYspqSkKDw8XMnJyQoICLil2gAAuJLpQ9PNOwH5ZBls169iBcL0GO9xOJblg8LxPk9JSVFgYCDZ4Ap2n0P4/fffa9OmTapXr57Dihg6dKi++uorfffddzcMg5IUGhqqChUqaP/+/ddcbzabZTabHVYbAAAAABRVdgfC8PBw2TmoeF0Wi0VDhw7VsmXLFBMTo0qVKt10m1OnTunYsWMKDQ11SA0AAAAAYFR2n0M4ffp0Pf/88zpy5Mg/PviQIUP06aefatGiRfL391diYqISExOVnp4uSUpNTdXIkSO1ZcsWHTlyRDExMeratatKly6tHj16/OPjAwAAAICR2T1CeP/99+v8+fOqUqWKfHx85OnpabP+9OnT+d7XrFmzJElRUVE27XPnzlX//v3l7u6uXbt2acGCBTp79qxCQ0MVHR2tJUuWyN/f397SAQAAAABXsDsQTp8+3WEHv9nUU29vb61atcphxwMAAAAA/M3uQNivXz9n1AEAAAAAKGB2B8Irpaen6+LFizZtXL4VAAAAAG4Pdl9UJi0tTU899ZSCgoLk5+enEiVK2DwAAAAAALcHuwPhqFGjtH79er333nsym82aPXu2Jk6cqLJly2rBggXOqBEAAAAA4AR2Txn93//+pwULFigqKkoDBgxQ8+bNVbVqVVWoUEELFy5U3759nVEnAAAAAMDB7B4hPH36tPUG8gEBAdbbTNx999367rvvHFsdAAAAAMBp7A6ElStXtt6UvlatWvr8888l5Y4cFi9e3JG1AQAAAACcyO5A+Mgjj+iXX36RJI0ePdp6LuHw4cP17LPPOrxAAAAAAIBz2H0O4fDhw60/R0dHa+/evYqLi1OVKlV05513OrQ4AAAAAIDz/KP7EEpS+fLlVb58eUfUAgAAAAAoQLcUCLdt26aYmBglJSUpJyfHZt20adMcUhgAAAAAwLnsDoSTJ0/Wiy++qOrVqys4OFgmk8m67sqfAQAAAACFm92B8O2339bHH3+s/v37O6EcAAAAAEBBsfsqo25ubvrXv/7ljFoAAAAAAAXI7kA4fPhwvfvuu86oBQAAAABQgOyeMjpy5Eh17txZVapUUa1ateTp6Wmz/ssvv3RYcQAAAAAA57E7EA4dOlQbNmxQdHS0SpUqxYVkAAAAAOA2ZXcgXLBggZYuXarOnTs7ox4AAAAAQAGx+xzCkiVLqkqVKs6oBQAAAABQgOwOhBMmTND48eN1/vx5Z9QDAAAAACggdk8ZnTFjhg4ePKjg4GBVrFgxz0Vlfv75Z4cVBwAAAABwHrsDYffu3Z1QBgAAAACgoNkdCMePH++MOgAAAAAABczucwgBAAAAAEWD3SOE2dnZeuutt/T5558rPj5emZmZNutPnz7tsOIAAAAAAM5j9wjhxIkTNW3aNN13331KTk7WiBEj1LNnT7m5uWnChAlOKBEAAAAA4Ax2B8KFCxfqo48+0siRI+Xh4aHevXtr9uzZGjdunLZu3eqMGgEAAAAATmB3IExMTFSdOnUkSX5+fkpOTpYkdenSRStWrHBsdQAAAAAAp7E7EIaFhSkhIUGSVLVqVa1evVqSFBsbK7PZ7NjqAAAAAABOY3cg7NGjh9atWydJeuaZZzR27FhFRETo4Ycf1oABAxxeIAAAAADAOey+yuirr75q/fnee+9VWFiYNm/erKpVq+qee+5xaHEAAAAAAOexOxBerUmTJmrSpIkjagEAAAAAFCC7A+FXX311zXaTySQvLy9VrVpVlSpV+seFAQAAAACcy+5A2L17d5lMJlksFpv2y20mk0l33323li9frhIlSjisUAAAAACAY9l9UZk1a9aoUaNGWrNmjZKTk5WcnKw1a9borrvu0tdff63vvvtOp06d0siRI51RLwAAAADAQeweIXzmmWf04YcfqlmzZta21q1by8vLS4MHD9bu3bs1ffp0rjgKAAAAAIWc3SOEBw8eVEBAQJ72gIAAHTp0SJIUERGhkydP/vPqAAAAAABOY3cgbNiwoZ599ln99ddf1ra//vpLo0aNUqNGjSRJ+/fvV1hYmOOqBAAAAAA4nN1TRufMmaNu3bopLCxM4eHhMplMio+PV+XKlfXf//5XkpSamqqxY8c6vFgAAAAAgOPYHQirV6+uPXv2aNWqVfr9999lsVhUo0YNtW3bVm5uuQOO3bt3d3SdAAAAAAAHu6Ub05tMJnXo0EEdOnRwdD0AAAAAgAJyS4EwLS1NGzduVHx8vDIzM23WPf300w4pDAAAAADgXHYHwu3bt6tTp046f/680tLSVLJkSZ08eVI+Pj4KCgoiEAIAAADAbcLuq4wOHz5cXbt21enTp+Xt7a2tW7fq6NGjatiwod544w1n1AgAAAAAcAK7A+GOHTv0f//3f3J3d5e7u7syMjIUHh6uqVOn6oUXXnBGjQAAAAAAJ7A7EHp6espkMkmSgoODFR8fL0kKDAy0/gwAAAAAKPzsPoewfv36iouLU7Vq1RQdHa1x48bp5MmT+uSTT1SnTh1n1AgAAAAAcAK7RwgnT56s0NBQSdKkSZNUqlQpPfHEE0pKStKHH37o8AIBAAAAAM5h1wihxWJRmTJldMcdd0iSypQpo2+++cYphQEAAAAAnMuuEUKLxaKIiAgdP37cWfUAAAAAAAqIXYHQzc1NEREROnXqlLPqAQAAAAAUELvPIZw6daqeffZZ/frrr86oBwAAAABQQOy+yuiDDz6o8+fP684771SxYsXk7e1ts/706dMOKw4AAAAA4Dx2B8Lp06c7oQwAAAAAQEGzOxD269fPGXUAAAAAAAqY3ecQStLBgwf14osvqnfv3kpKSpIkrVy5Urt373ZocQAAAAAA57E7EG7cuFF16tTRjz/+qC+//FKpqamSpJ07d2r8+PEOLxAAAAAA4Bx2B8Lnn39eL7/8stasWaNixYpZ26Ojo7VlyxaHFgcAAAAAcB67A+GuXbvUo0ePPO1lypTh/oQAAAAAcBuxOxAWL15cCQkJedq3b9+ucuXKOaQoAAAAAIDz2R0I+/Tpo+eee06JiYkymUzKycnRDz/8oJEjR+rhhx92Ro0AAAAAACewOxC+8sorKl++vMqVK6fU1FTVqlVLLVq0ULNmzfTiiy86o0YAAAAAgBPYfR9CT09PLVy4UC+99JK2b9+unJwc1a9fXxEREc6oDwAAAADgJHYHwsuqVKmiypUrS5JMJpPDCgIAAAAAFIxbujH9nDlzVLt2bXl5ecnLy0u1a9fW7NmzHV0bAAAAAMCJ7B4hHDt2rN566y0NHTpUTZs2lSRt2bJFw4cP15EjR/Tyyy87vEgAAAAAgOPZHQhnzZqljz76SL1797a23XPPPapbt66GDh1KIAQAAACA24TdU0azs7MVGRmZp71hw4bKyspySFEAAAAAAOezOxA++OCDmjVrVp72Dz/8UH379nVIUQAAAAAA58vXlNERI0ZYfzaZTJo9e7ZWr16tJk2aSJK2bt2qY8eOcWN6AAAAALiN5CsQbt++3Wa5YcOGkqSDBw9KksqUKaMyZcpo9+7dDi4PAAAAAOAs+QqEGzZscHYdAAAAAIACdkv3IZSkAwcOaNWqVUpPT5ckWSwWhxUFAAAAAHA+uwPhqVOn1Lp1a1WrVk2dOnVSQkKCJGngwIH6v//7P4cXCAAAAABwDrsD4fDhw+Xp6an4+Hj5+PhY2++//36tXLnSocUBAAAAAJzH7hvTr169WqtWrVJYWJhNe0REhI4ePeqwwgAAAAAAzmX3CGFaWprNyOBlJ0+elNlsdkhRAAAAAADnszsQtmjRQgsWLLAum0wm5eTk6PXXX1d0dLRDiwMAAAAAOI/dU0Zff/11RUVFKS4uTpmZmRo1apR2796t06dP64cffnBGjQAAAAAAJ7B7hLBWrVrauXOn7rrrLrVt21ZpaWnq2bOntm/fLi8vL2fUCAAAAABwgnyPED7zzDN6++23JUkhISGaOHGizfoTJ04oOjpav//+u2MrBAAAAAA4Rb5HCBcsWKCXXnrpmusSEhIUHR2tkJAQuw4+ZcoUNWrUSP7+/goKClL37t21b98+mz4Wi0UTJkxQ2bJl5e3traioKO3evduu4wAAAAAA8sp3IPzqq6/02muv6d1337VpT0hIUFRUlMqUKaNvv/3WroNv3LhRQ4YM0datW7VmzRplZWWpXbt2SktLs/aZOnWqpk2bpnfeeUexsbEKCQlR27Ztde7cObuOBQAAAACwle8po82bN9fnn3+uXr16qWTJkurdu7cSExMVHR2tkiVLatWqVfL19bXr4FffyH7u3LkKCgrSTz/9pBYtWshisWj69OkaM2aMevbsKUmaP3++goODtWjRIj322GN2HQ8AAAAA8De7LirTuXNnffzxxxowYIDmzZun6OhoBQQEaNWqVfLz8/vHxSQnJ0uSSpYsKUk6fPiwEhMT1a5dO2sfs9msli1bavPmzdfcR0ZGhlJSUmweAAAAAIC87L7tRJ8+fXT27Fk9+uijatCggdasWaOAgIB/XIjFYtGIESN09913q3bt2pKkxMRESVJwcLBN3+DgYB09evSa+5kyZUqeC94AKFiRP0W6ugQUIXEN41xdAgAARVa+A2H9+vVlMpmsy56enjp79myem9H//PPPt1TIU089pZ07d+r777/Ps+7K40q54fHqtstGjx6tESNGWJdTUlIUHh5+SzUBAAAAQFGW70DYvXt3m+Vu3bo5rIihQ4fqq6++0nfffaewsDBr++WrliYmJio0NNTanpSUlGfU8DKz2Syz2eyw2gAAAACgqMp3IBw/frzDD26xWDR06FAtW7ZMMTExqlSpks36SpUqKSQkRGvWrFH9+vUlSZmZmdq4caNee+01h9cDAAAAAEZi9zmEjjRkyBAtWrRI//3vf+Xv7289ZzAwMFDe3t4ymUwaNmyYJk+erIiICEVERGjy5Mny8fFRnz59XFk6AAAAANz2XBoIZ82aJUmKioqyaZ87d6769+8vSRo1apTS09P15JNP6syZM2rcuLFWr14tf3//Aq4WAAAAAIoWlwZCi8Vy0z4mk0kTJkzQhAkTnF8QAAAAABiIXfchBAAAAAAUHXYHwgULFigjIyNPe2ZmphYsWOCQogAAAAAAzmd3IHzkkUeUnJycp/3cuXN65JFHHFIUAAAAAMD57A6E17sp/PHjxxUYGOiQogAAAAAAzpfvi8rUr19fJpNJJpNJrVu3lofH35tmZ2fr8OHD6tChg1OKBAAAAAA4Xr4DYffu3SVJO3bsUPv27eXn52ddV6xYMVWsWFG9evVyeIEAAAAAAOfIdyAcP368JKlixYq6//775eXl5bSiAAAAAADOZ/d9CPv16+eMOgAAAAAABczuQJidna233npLn3/+ueLj45WZmWmz/vTp0w4rDgAAAADgPHZfZXTixImaNm2a7rvvPiUnJ2vEiBHq2bOn3NzcNGHCBCeUCAAAAABwBrsD4cKFC/XRRx9p5MiR8vDwUO/evTV79myNGzdOW7dudUaNAAAAAAAnsDsQJiYmqk6dOpIkPz8/603qu3TpohUrVji2OgAAAACA09gdCMPCwpSQkCBJqlq1qlavXi1Jio2Nldlsdmx1AAAAAACnsTsQ9ujRQ+vWrZMkPfPMMxo7dqwiIiL08MMPa8CAAQ4vEAAAAADgHHZfZfTVV1+1/nzvvfcqPDxcP/zwg6pWrap77rnHocUBAAAAAJzH7kC4c+dO1a1b17rcuHFjNW7cWJK0fPlyde/e3WHFAQAAAACcx+4po+3bt9ehQ4fytC9dulR9+/Z1SFEAAAAAAOezOxA+8cQTat26tfXCMpK0ZMkSPfzww5o3b54jawMAAAAAOJHdU0bHjRunU6dOqU2bNtq0aZNWrlypgQMH6pNPPlGvXr2cUSMAAAAAwAnsDoSS9Pbbb+uhhx5SkyZNdOLECX322Wfq1q2bo2sDAAAAADhRvgLhV199laete/fu2rhxo3r37i2TyWTtw5VGAQAAAOD2kK9AeKMrh3788cf6+OOPJUkmk0nZ2dkOKQwAAAAA4Fz5CoQ5OTnOrgMAAAAAUMDsvsooAAAAAKBosPuiMjNmzLhmu8lkkpeXl6pWraoWLVrI3d39HxcHAAAAAHAeuwPhW2+9pb/++kvnz59XiRIlZLFYdPbsWfn4+MjPz09JSUmqXLmyNmzYoPDwcGfUDAAAAABwALunjE6ePFmNGjXS/v37derUKZ0+fVq///67GjdurLffflvx8fEKCQnR8OHDnVEvAAAAAMBB7B4hfPHFF7V06VJVqVLF2la1alW98cYb6tWrlw4dOqSpU6dyk3oAAAAAKOTsHiFMSEhQVlZWnvasrCwlJiZKksqWLatz58798+oAAAAAAE5jdyCMjo7WY489pu3bt1vbtm/frieeeEKtWrWSJO3atUuVKlVyXJUAAAAAAIezOxDOmTNHJUuWVMOGDWU2m2U2mxUZGamSJUtqzpw5kiQ/Pz+9+eabDi8WAAAAAOA4dp9DGBISojVr1mjv3r36/fffZbFYVKNGDVWvXt3aJzo62qFFAgAAAAAcz+5AeFmNGjVUo0YNR9YCAAAAAChAdgfC7OxszZs3T+vWrVNSUpJycnJs1q9fv95hxQEAAAAAnMfuQPjMM89o3rx56ty5s2rXri2TyeSMugAAAAAATmZ3IFy8eLE+//xzderUyRn1AAAAAAAKiN1XGS1WrJiqVq3qjFoAAAAAAAXI7kD4f//3f3r77bdlsVicUQ8AAAAAoIDYPWX0+++/14YNG/Ttt9/qjjvukKenp836L7/80mHFAQAAAACcx+5AWLx4cfXo0cMZtQAAAAAACpDdgXDu3LnOqAMAAAAAUMDsPocQAAAAAFA05GuEsEGDBlq3bp1KlCih+vXr3/Degz///LPDigMAAAAAOE++AmG3bt1kNpslSd27d3dmPQAAAACAApKvQDh+/HgNGDBAb7/9tsaPH+/smgAAAAAABSDf5xDOnz9f6enpzqwFAAAAAFCA8h0IuRE9AAAAABQtdl1l9EYXkwEAAAAA3F7sug9htWrVbhoKT58+/Y8KAgAAAAAUDLsC4cSJExUYGOisWgAAAAAABciuQPjAAw8oKCjIWbUAAAAAAApQvs8h5PxBAAAAAChauMooAAAAABhUvqeM5uTkOLMOAAAAAEABs+u2EwAAAACAooNACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAINyaSD87rvv1LVrV5UtW1Ymk0nLly+3Wd+/f3+ZTCabR5MmTVxTLAAAAAAUMS4NhGlpabrzzjv1zjvvXLdPhw4dlJCQYH188803BVghAAAAABRdHq48eMeOHdWxY8cb9jGbzQoJCSmgigAAAADAOAr9OYQxMTEKCgpStWrVNGjQICUlJd2wf0ZGhlJSUmweAAAAAIC8CnUg7NixoxYuXKj169frzTffVGxsrFq1aqWMjIzrbjNlyhQFBgZaH+Hh4QVYMQAAAADcPlw6ZfRm7r//fuvPtWvXVmRkpCpUqKAVK1aoZ8+e19xm9OjRGjFihHU5JSWFUAgAAAAA11CoA+HVQkNDVaFCBe3fv/+6fcxms8xmcwFWBQAAAAC3p0I9ZfRqp06d0rFjxxQaGurqUgAAAADgtufSEcLU1FQdOHDAunz48GHt2LFDJUuWVMmSJTVhwgT16tVLoaGhOnLkiF544QWVLl1aPXr0cGHVAAAAAFA0uDQQxsXFKTo62rp8+dy/fv36adasWdq1a5cWLFigs2fPKjQ0VNHR0VqyZIn8/f1dVTIAAAAAFBkuDYRRUVGyWCzXXb9q1aoCrAYAAAAAjOW2OocQAAAAAOA4BEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoFwaCL/77jt17dpVZcuWlclk0vLly23WWywWTZgwQWXLlpW3t7eioqK0e/du1xQLAAAAAEWMSwNhWlqa7rzzTr3zzjvXXD916lRNmzZN77zzjmJjYxUSEqK2bdvq3LlzBVwpAAAAABQ9Hq48eMeOHdWxY8drrrNYLJo+fbrGjBmjnj17SpLmz5+v4OBgLVq0SI899lhBlgoAAAAARU6hPYfw8OHDSkxMVLt27axtZrNZLVu21ObNm6+7XUZGhlJSUmweAAAAAIC8XDpCeCOJiYmSpODgYJv24OBgHT169LrbTZkyRRMnTnRqbf/E84eed3UJKGJerfyqq0sAAADAbarQjhBeZjKZbJYtFkuetiuNHj1aycnJ1sexY8ecXSIAAAAA3JYK7QhhSEiIpNyRwtDQUGt7UlJSnlHDK5nNZpnNZqfXBwAAAAC3u0I7QlipUiWFhIRozZo11rbMzExt3LhRzZo1c2FlAAAAAFA0uHSEMDU1VQcOHLAuHz58WDt27FDJkiVVvnx5DRs2TJMnT1ZERIQiIiI0efJk+fj4qE+fPi6sGgAAAACKBpcGwri4OEVHR1uXR4wYIUnq16+f5s2bp1GjRik9PV1PPvmkzpw5o8aNG2v16tXy9/d3VckAAAAAUGS4NBBGRUXJYrFcd73JZNKECRM0YcKEgisKAAAAAAyi0J5DCAAAAABwLgIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIARda8ewKffnYl/94P5mZmapatap++OEHB1RVsE7+76R2RO1wdRn5kpOZo52ddyptT5qrSwEAAHAIAiEKrRXPrtBrVV7TqhdX5Vm3etxqvVblNa14doULKvvbrBazFDs31qU1SNKHH36oChUq6F//+pe1zWQyWR/+/v6KjIzUl1/+8/BpZG7F3BTyUIhOzDzh6lIAAAAcgkCIQs0/1F97vt6jixcuWtuyMrL02/9+U0DZABdWVrjMnDlTAwcOzNM+d+5cJSQkKDY2Vnfeeaf+/e9/a8uWLS6osOgo2aGkUrenKv1wuqtLAQAA+Mc8XF0AcCMhd4To7LGz+n3V77qj2x2SpN9X/a6A0AAVDy9u0/fQxkPa/O5mnfz9pEzuJpWrX06tx7ZWiQolJEnZmdlaP3m99q3cpwvJF+Rbxlf1etdT0yeaSpK2zdmmXV/sUvKxZHkFeqlq66qKei5KxXyL3VLtOdk5WjlmpeK3xCvtrzQFlA1Q/b71FflI5HW3mdViliIfiVSjRxpZ2+Z2mauIthG6+5m7r7nNzz//rAMHDqhz58551hUvXlwhISEKCQnR+++/r8WLF+urr75S06ZNdeLECY0YMUKrV6+Wm5ub7r77br399tuqWLGiJKl///46e/as7r77br355pvKzMzUAw88oOnTp8vT01OSlJCQoIEDB2r9+vUKCQnRK6+8ohdeeEHZvbIV3CdYkvTnp3/q5P9OKvNEptwD3VW8eXGVe7qc3H3cr/l8Mo5n6Ni0Y0r7NU056TnyquSlckPKKaDx338A2NV1l0p3K60L8Rd0dsNZeQR6KHxkuHzv9NXRSUd1LvacipUtporjK8q3lq8kKetsluKnxit1R6qykrNkDjMr9JFQlexQ0rrffYP3yTvCW27F3HTyvydl8jSpTM8yKvtYWWsfj+Ie8q3rqzOrzsj7ce/r/r8EAAC4HRAIUejVubeOdn2xyxoId/5np+r8u46ObT1m0+9i+kU1erSRylQro4vpF/X99O+17IlleuTrR2RyMylufpz2r92vbjO7KaBsgM4lnFPKHynW7U1uJrUZ10aBYYFKPpas1eNXK+a1GLV7qd0t1W3Jscg/xF/dZnaTdwlvnfj5hFaNWSXfIF/V7Fzz1l+Qq3z33XeqVq2aAgJuPGLq6ekpDw8PXbx4UefPn1d0dLSaN2+u7777Th4eHnr55ZfVoUMH7dy5U8WK5YbgDRs2KDQ0VBs2bNCBAwd0//33q169eho0aJAk6eGHH9bJkycVExMjT09PjRgxQklJSSqlUn8f2E0q/2x5FQstpow/MhT/arw0Qyr/fPlr1pl9PluB/wpUuSfLyVTMpFNfn9KBEQdUe2ltFQv5O5z/+dmfKvdkOYUODFXSoiQdHn9Yfnf6qfQ9pRX2TJhOzDyhI+OPqNbntWQymZSTmSOfmj4K6Rcid193JX+frMPjD8scZpZvbV/rfk99fUrBfYNVY14Npe1M05GJR+R3p58Cmvz9+vre4atz28/Z/f8KAACgsGHKKAq9O7rfoeNxx5V8PFnJJ5J14qcT1nB4peodqqt6++oqWamkgmsFq+OUjvpr3186uf+kJOncH+dUsmJJhUWGKbBcoMIiw1TrnlrW7Rs90kgVmlZQ8fDiqtCsgpoPb6693+y95brdPd3VfFhzhdYNVfHw4rqj2x2q3av2P9rntRw5ckRly5a9YZ+MjAy9/PLLSklJUevWrbV48WK5ublp9uzZqlOnjmrWrKm5c+cqPj5eMTEx1u1KlCihd955RzVq1FCXLl3UuXNnrVu3TpK0d+9erV27Vh999JEaN26sBg0aaPbs2UpPt51KGdwnWP6R/jKXMyugUYDKPVFOp9ecvm6tPtV8VKZXGXlX9ZZXeS+Ve7KczOXMOrvxrE2/wGaBKtOrjLzKeyl0YKhy0nLkW8tXJdqUkFcFLwX3C9aFwxeUdSpLklQsqJhCHgqRT3UfmcPMCnogSAFNAnRm7Rnb40f4qOzgsvIq76VSXUrJp6aPUmJTbPoUCyqmzITMG77mAAAAtwNGCFHo+ZT0UZXoKtr15S7JIlWOriyfkj55+p05ekab3tqkP3b8ofQz6bLkWCRJKX+kqEz1Mqrdq7aW9Fuij9p8pMotKqtKqyqq1LySdfujW45qy6wtOnXglDJTM5WTlaOsjCxlns9UMZ9bmza6fdF27VyyU8l/JCvrQpayL2YruGbwrb0Q15Geni4vL69rruvdu7fc3d2Vnp6uwMBAvfHGG+rYsaOGDBmiAwcOyN/f36b/hQsXdPDgQevyHXfcIXf3v6d2hoaGateuXZKkffv2ycPDQw0aNLCur1q1qkqUKGGzz3Nx55TwcYIuHL6g7LRsWbItsmRYlJ2eLXfvvNNGs9OzlfBhgpK/T9bFvy7Kkm1RTkaOMhNtA5h3xN/TNT1K5X6VeVf9u82zZO601otnLsqztKcs2RYlzkvUmTVnlPlXpiyZFuVk5uSp4cr9SpJnaU9lnc6yaTOZTcq5kJOndgAAgNsNgRC3hbr/rqs1E9ZIktpOaHvNPksHL5V/qL86vNJBfsF+suRY9HHHj5V9MVuSFFI7RI/HPK5DGw/pyA9H9N+h/1WFf1VQj3d7KPlEsr549AvV61NPzYc3l3dxbx2PO65vn/9WOVm39ov/nhV7tP7l9Yp+IVrl6pdTMd9i+vGjH5XwS8J1tzG5mSSLbVt2VvYNj1O6dGlrSLvaW2+9pTZt2iggIEBBQUHW9pycHDVs2FALFy7Ms02ZMmWsP18+V9Ban8mknJzc18NiuarQS65sz0jI0P5n9ueeh/dEWXkEeCh1R6qOTjoqS9a1tz/x9gklb0lW2LAweYV7yWQ26dBzh/L0N3mYbOq6uk2Xf7z0v+/PT//Un4v+VPj/hcu7qrfcvN10/M3jyrlo+//XZh+X93P1/5PkbHkU5+sTAADc/viNBreFSi0qWYNdpRaV8qxPP5OuUwdOqf3L7RXeKFySdDzueJ5+Zn+zanapqZpdaqp6x+r6zyP/UfrZdCXuSlROdo5avdAqN5RJ2rvin03tPB57XOUalFODB/8eQTsbf/aG2/iU9FFqUqp1OeNchpKPJd9wm/r162vWrFmyWCzWYHRZSEiIqlatmmebBg0aaMmSJQoKCrrpuYfXU6NGDWVlZWn79u1q2LChJOnAgQM6e/as/OQnSTr/23lZsiwKGx5mfV3PrDlz3X1K0rnt51S6a2mViL50MaDz2cr8I1NqeEtlWqXuSFXxlsVVqlPu+Y2WHIsuxF+QV6Vrj67eSPrBdPlUzztKDQAAcLvhHELcFtzc3TRw1UANXDVQbu5537ZegV7yLuGtXxb/ojNHzujo5qNa/8p6mz6xH8fqt//9plMHT+n04dPa9+0++ZbxlVeAl4qXL66crBz9NP8nnY0/q1+X/artn23PV22pian687c/bR7pZ9NVokIJJexK0KHvDun04dP6btp3Sth5/dFBSarQtIJ2L9+tY7HH9Ne+v7Ti2RXXfL5Xio6OVlpamnbv3p2veiWpb9++Kl26tLp166ZNmzbp8OHD2rhxo5555hkdP543SF9LjRo11KZNGw0ePFjbtm3T9u3bNXjwYHl7e1tH58xhZilbSlqSpIzjGTq14pT++vKvG+7XK9xLZ9af0fl953X+9/M6PObwdUcj7WEOMyvlxxSl/pJ7y4j4yfG6eOrizTe8htQdqTYXmQEAALhdMUKI24bZ33zddSY3k+55+x6tfWmt5nSco5KVS6rNuDb6rM9n1j6ePp768cMfdebIGZncTAqtG6p/z/m3TG4mBdcKVqsxrfTjhz9q4xsbFd4oXC1HttSKkTe/8f222du0bfY2m7ZOr3VSvT719OeeP/XV019JJqlW11pq8GADHdp46Lr7avJ4E52NP6svBn4hs79ZzYc3V/LxG48QlipVSj179tTChQs1ZcqUm9YrST4+Pvruu+/03HPPqWfPnjp37pzKlSun1q1b2zViuGDBAj366KNq0aKFQkJCNGXKFO3evVtuxXJDrE91H4UND9Of8//UiXdOyL+Bv8oNKacj449cd59hI8J09KWj2jtgrzyKeyikX4iy0248bTY/QgeGKuOPDO0ful9uXm4q3aO0ikcVV3aqfftO3Zmq7NRslWhd4uadAQAACjmTxRF/ei/EUlJSFBgYqOTk5FueGudIzx963tUloIh5tfKr2rVrl9q0aXPNC8UUpOPHjys8PFwR70Uo4C7Xf96c4eBzB+VT3UehA0JdXYphxDWMc3UJeZg+NN28E5BPlsGF71cx02O8x+FYlg8Kx/u8sGWDwoARQqAIqFOnjqZOnaojR46oTp06BXbc9evXKzU1VXXq1FFCQoJGjRqlihUryr+B60KpM+Vk5sgnwkfBfRx7pVgAAABXIRACRUS/fv0K/JgXL17UCy+8oEOHDsnf31/NmjXTwoUL1etkrwKvpSC4FXNT6EBGBgEAQNFBIARwy9q3b6/27dvnXXGy4GsBAACA/bjKKAAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQyIe0U2la2Huh0k6luboUAAAAwGEIhEA+bJq2Sce3HdemaZtcXQoAAADgMARC4CYupFzQb//7TZL02/9+04WUCy6uCAAAAHAMAiFwE3Fz45RzMUeSlHMxR3Fz41xcEQAAAOAYBELgBi6kXFDc3DhV71hdklS9Q3XFzY1jlBAAAABFAoEQuIG4uXHKvpit2j1qS5Jq96yt7MxsRgkBAABQJBAIgRvYvXy36vetL+8S3pIk7xLeqv9gfe1evtvFlQEAAAD/nIerCwAKs39//G8VDy+upL1J1raWz7ZUvd71XFcUAAAA4CAEQuAGSlYqmafN3dP9mu0AAADA7YYpowAAAABgUARCAAAAADCoQh0IJ0yYIJPJZPMICQlxdVkAAAAAUCQU+nMI77jjDq1du9a67O7u7sJqYFT+If4qWaWk/EP8XV0KAAAA4DCFPhB6eHgwKgiX8y3tq0GrB7m6DAAAAMChCvWUUUnav3+/ypYtq0qVKumBBx7QoUOHbtg/IyNDKSkpNg8AAAAAQF6FOhA2btxYCxYs0KpVq/TRRx8pMTFRzZo106lTp667zZQpUxQYGGh9hIeHF2DFAAAAAHD7KNSBsGPHjurVq5fq1KmjNm3aaMWKFZKk+fPnX3eb0aNHKzk52fo4duxYQZULAAAAALeVQn8O4ZV8fX1Vp04d7d+//7p9zGazzGZzAVYFAAAAALenQj1CeLWMjAzt2bNHoaGhri4FAAAAAG57hToQjhw5Uhs3btThw4f1448/6t5771VKSor69evn6tIAAAAA4LZXqKeMHj9+XL1799bJkydVpkwZNWnSRFu3blWFChVcXRoAAAAA3PYKdSBcvHixq0sAAAAAgCKrUE8ZBQAAAAA4D4EQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAG4bc+bMUbt27Ry6z6ioKA0bNsyh+yxoX3/9terXr6+cnBy7tiMQAgAAAP9EjKQPJW26xrrvL62LKcB6rmXRpTo+lDTn0vJaSSdcWZT9MjIyNG7cOI0dO9baNmHCBNWrV8+m36ZNm1S8eHENHTpUFoulgKt0jS5dushkMmnRokV2bUcgBAAAAP4pX0kHJWVd0ZYl6YAkP5dUlFekpAcl3S8pWlIxSSsk/ezKouyzdOlS+fn5qXnz5tfts2LFCrVv317PPPOMZs6cKZPJVIAV/s1isSgrK+vmHR3okUce0cyZM+3ahkAIAAAA/FOllRv8Dl/RduRSW6mr+h6T9F9J8yTNl7RSUsoV67OVO7L4if4ezdt+xfqdkv4j6WNJCy/1vZiPGj0l+VyqKVRSC0kNJP0k6ewV/c5I+vbS/j+RtF7ShSvW/+/SMb+/4jnESrpyIG6/pC8lzc3dR58+fZSUlGRdHRMTI5PJpHXr1ikyMlI+Pj5q1qyZ9u3bd8OnsHjxYt1zzz3XXb9o0SL17NlTr776qiZOnGht/+2339SpUyeVLVtWkjR48GCdPHnSZtusrCw99dRTKl68uEqVKqUXX3zRZnTx008/VWRkpPz9/RUSEnLd57Rq1SpFRkbKbDZr06ZN6t+/v7p3725zrGHDhikqKsq6vHLlSt19993WY3fp0kUHDx60rj9y5IhMJpO+/PJLRUdHy8fHR3feeae2bNlis9977rlH27Zt06FDh274Ol6JQAgAAAA4QjVJv1+xvE9S9Wv0uyiprqQekjpfalutvwPVr5KOSmoj6T5JrST5X7G9SdK/JN0rKUq50z5/vMWaa1867pFLy+eVG/hKSeopqaOkdOVOL73SfuUmie6SmknaJWnvFeuzlTsi2UtSO+nw4cPq379/nsOPGTNGb775puLi4uTh4aEBAwbcsNxNmzYpMjLymuveffddPfLII5ozZ46efvppa3tCQoJatmypevXqKSYmRpKUlJSk++67z2b7+fPny8PDQz/++KNmzJiht956S7Nnz7auz8zM1KRJk/TLL79o+fLl131Oo0aN0pQpU7Rnzx7VrVv3hs/nsrS0NI0YMUKxsbFat26d3Nzc1KNHjzznA44ZM0YjR47Ujh07VK1aNfXu3dtmFLJChQoKCgrSpk3Xmr98bR757gkAAADg+qopd6Ts3KXlREmtJf1xVb/KVy23VO5I3BlJJSWlSgqUFKLc8Od/Vf86V/wcIKmRcs9fvPsWavaS5H3pmJL0m3JHO++6qr5Fyh1FLH6pzVdS00v1FZd0WrmhsOal9TVsa5wxZobuuusupaamys/v7zm0r7zyilq2bClJev7559W5c2dduHBBXl5eeUo9e/aszp49ax3lu9KePXv01FNPac6cOXrwwQdt1s2aNUsNGjTQ5MmTlZKSOxT77rvvqlatWvr9999VrVo1SVJ4eLjeeustmUwmVa9eXbt27dJbb72lQYMGSZJNWK1cubJmzLj2c3rppZfUtm3bPDXeSK9evWyW58yZo6CgIP3222+qXbu2tX3kyJHq3Dn3rwgTJ07UHXfcoQMHDqhGjb9f8HLlyunIkSP5PjYjhAAAAIAjeEkKV+4o4T5J5S+1XS1F0jpJnyl3SuVnl9ovh7Lqkk5J+lzSD5KOX7X9H8o99+/TS9tvkJSh/E0bvZm/Lu3/4ysen19R92VByg2DlwVLSpZ0eUDrpKRVyg2Sc2WdHhkfH29zuCtH0EJDQyXJZhrmldLT0yXpmmExLCxMDRo00NSpU5WQkGCz7qefftKGDRvk5+dnDZONGjWSJJtpmU2aNLE537Bp06bav3+/srOzJUnbt29Xt27dVKFCBfn7+1/3OV1vBPNGDh48qD59+qhy5coKCAhQpUqVrrnv/Lxe3t7eOn/+fL6PTSAEAAAAHKWGcgPhfl17uqiUe85ghnLP4et+6SH9HaZKS+qt3CmX2cqdrrnm0rpzyj2/r4Sktsqddvqvq7a3xwXlTgm9chSyvHKnel75uF+55x3mx0VJ3yj3nMXo3BqXLVsmKXfa5ZU8PT2tP18OY9e7bUKpUqVkMpl05syZPOv8/f21du1aa1D744+/h2VzcnLUtWtX7dixwzqVctOmTdq/f79atGiRr6eUlpamdu3ayc/PT59++qliY2Ov+5x8fX1tlt3c3PJc6fTiRdv03rVrV506dUofffSRfvzxR/3444/X3Hd+Xq/Tp0+rTJky+XpeEoEQAAAAcJww5Ya47Es/X+2Ccqde1pdUTrnBLvMa/YpJqqLc0NhauReruaDcEbwc5U7XDFbudM38Dwbl9atyR/oqXloupdypq/7KnbZ65cPziu2uHsRLutTHTbnP74Jyp52G5tZ4vVE/exQrVky1atXSb7/9ds31JUqU0Nq1a1WiRAlFRUXpxInce2o0aNBAu3fvVsWKFVWlShVJUpUqVVS1alWb8LZ161ab/W3dulURERFyd3fX3r17dfLkSb366qtq3ry5atSoke/nVKZMmTyjljt27LD+fOrUKe3Zs0cvvviiWrdurZo1a14z9ObHhQsXdPDgQdWvXz/f2xAIAQAAAEdxU+6FYO7TtX/TNl967FHuFMsTkrZc1Wencm9XcfbS45Byz/MzK/ecQYtyg1yKckcj9+SztovKDY+pkhIkfafcW040Um6Yk6Q7lDt6uU65IS9FuVNWY2Q7Apl2qe6zl2r9VbkXqJFyr2LqdkWNR6RJkybls8gba9++vb7//vvrrg8MDNTq1atVunRpRUVF6fjx4xoyZIhOnz6t3r1766effpIkrVu3TgMGDLBOB5WkY8eOacSIEdq3b58+++wzzZw5U88884wkqXz58ipWrJhmzpypQ4cO6auvvsr3c2rVqpXi4uK0YMEC7d+/X+PHj9evv/5qXV+iRAmVKlVKH374oQ4cOKD169drxIgRt/LyaOvWrTKbzWratGm+tyEQAgAAAI5U7NLjWkzKHfE7KekL5Yaqxlf18ZT0i3Jv27BMuQGu46VtS0tqcmn9f5Qbxhrls6445Z53uFi55x1mSuoiqd4VfXwldVNu6Pzm0jE2X3o+V54zGKHc+ywuU+55jrX19wVlvJV79dPDl7b/RXrjjTfyWeSNDRo0SN98842Sk5Ov2ycgIECrVq1ScHCwoqKilJ2drR9++EHZ2dnq0aOHpNwL2AQGBsrN7e849PDDDys9PV133XWXhgwZoqFDh2rw4MGSckf55s2bp//85z+qVauWXn311Xw/p/bt22vs2LEaNWqUGjVqpHPnzunhhx+2rndzc9PixYv1008/qXbt2ho+fLhef/31W3l59Nlnn6lv377y8fHJ9zYmy9UTWouYlJQUBQYGKjk5WQEBAa4uR88fet7VJaCIebXyq64uIY/In+w/mRq4nriGca4uIQ/Th665yTGKJsvgwvermOkx3uO4gcu3pWiW/00sHzjufX7fffepfv36Gj16tN3bFrZs4Eh//fWXatSoobi4OOtFafKDEUIAAAAAt43XX3/d5jYPyHX48GG99957doVBifsQAjCA5K3J8qvtJ3c/d1eXAgAA/qEKFSpo6NChri6j0Lnrrrt011133bzjVRghBFCkZSRk6MBTB3Tg/w64uhQAAIqGrrJruigKNwIhgCItcW6iJCl1R6oyk651XW8AAADjIhACKLIyEjJ06qtTkofk5ummxHmJri4JAACgUCEQAiiyEucmyt3PXSYPk/wa+unkspOMEgIAAFyBQAigSLo8Ohj8ULBMJpP86vnJzYdRQgAAgCsRCAEUSUmfJcndz11l/l1GkuRmdlNw32CdXHZSWclZLq4OAACgcOC2EwCKJP9G/gpoGiB3n79vNRH0QJAs2Ra5mflbGAAAgEQgBFBEFW9ePE+bu4+7yg4qW/DFAAAAFFL8mRwAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIo8ty83eTmzdcdAADA1bioDIAir/rs6ioWVMzVZQAAABQ6BEIARZ5XeS9XlwAAAFAoMYcKAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMCgCIQAAAAAYFIEQAAAAAAyKQAgAAAAABkUgBAAAAACDIhACAAAAgEERCAEAAADAoAiEAAAAAGBQBEIAAAAAMKjbIhC+9957qlSpkry8vNSwYUNt2rTJ1SUBAAAAwG2v0AfCJUuWaNiwYRozZoy2b9+u5s2bq2PHjoqPj3d1aQAAAABwWyv0gXDatGl69NFHNXDgQNWsWVPTp09XeHi4Zs2a5erSAAAAAOC25uHqAm4kMzNTP/30k55//nmb9nbt2mnz5s3X3CYjI0MZGRnW5eTkZElSSkqK8wq1Q8a5jJt3AuxQWN7bV8pOzXZ1CShCCuN7XOmuLgBFSaF8j2e6ugAUNYXlfX65DovF4uJKCo9CHQhPnjyp7OxsBQcH27QHBwcrMTHxmttMmTJFEydOzNMeHh7ulBoBV5uu6a4uAXCqQAW6ugTAqQKH8R5H0Rc4r3C9z8+dO6fAwMJVk6sU6kB4mclkslm2WCx52i4bPXq0RowYYV3OycnR6dOnVapUqetug8IlJSVF4eHhOnbsmAICAlxdDuAUvM9R1PEehxHwPr/9WCwWnTt3TmXLlnV1KYVGoQ6EpUuXlru7e57RwKSkpDyjhpeZzWaZzWabtuLFizurRDhRQEAAX64o8nifo6jjPQ4j4H1+e2Fk0FahvqhMsWLF1LBhQ61Zs8amfc2aNWrWrJmLqgIAAACAoqFQjxBK0ogRI/TQQw8pMjJSTZs21Ycffqj4+Hg9/vjjri4NAAAAAG5rhT4Q3n///Tp16pReeuklJSQkqHbt2vrmm29UoUIFV5cGJzGbzRo/fnyeqb9AUcL7HEUd73EYAe9zFAUmC9dcBQAAAABDKtTnEAIAAAAAnIdACAAAAAAGRSAEAAAAAIMiEMJhEhMT1bZtW/n6+t6W936MiorSsGHDXF0GABiWyWTS8uXLXV0GcEsmTJigevXq5WkLDg7mvY1CjUCI6+rfv7+6d++e7/5vvfWWEhIStGPHDv3+++/OK8xJvvzyS02aNMnVZeA20r9/f5lMJplMJnl6eio4OFht27bVxx9/rJycnHzvZ968ebflH1FQ8K5+z1WuXFkjR45UWlqaq0uzOnLkiEwmk3bs2OHqUmBQ1/r95YsvvpCXl5emTp3qtOOOHDlS69atsy7v2bNHEydO1AcffKCEhAR17NjRaccG/olCf9sJ3D4OHjyohg0bKiIi4pb3cfHiRXl6ejqwqvwrWbKkS46L21uHDh00d+5cZWdn688//9TKlSv1zDPP6IsvvtBXX30lDw++ZuFYl99zFy9e1KZNmzRw4EClpaVp1qxZri4NKJRmz56tIUOG6N1339XAgQMdvn+LxaLs7Gz5+fnJz8/P2n7w4EFJUrdu3WQymRx+XMBRGCFEvkRFRenpp5/WqFGjVLJkSYWEhGjChAnW9RUrVtTSpUu1YMECmUwm9e/fX5IUHx+vbt26yc/PTwEBAbrvvvv0559/Wre7PL3i448/VuXKlWU2m2WxWGQymfTBBx+oS5cu8vHxUc2aNbVlyxYdOHBAUVFR8vX1VdOmTa1ftlLuF2+3bt0UHBwsPz8/NWrUSGvXrrV5Hu+9954iIiLk5eWl4OBg3XvvvTbPkSmjsJfZbFZISIjKlSunBg0a6IUXXtB///tfffvtt5o3b54kadq0aapTp458fX0VHh6uJ598UqmpqZKkmJgYPfLII0pOTraO/Fz+bH366aeKjIyUv7+/QkJC1KdPHyUlJbnomaKwuPyeCw8PV58+fdS3b18tX75cFotFU6dOVeXKleXt7a0777xTX3zxhXW7mJgYmUwmrVu3TpGRkfLx8VGzZs20b98+m/2//PLLCgoKkr+/vwYOHKjnn38+zzS4uXPnqmbNmvLy8lKNGjX03nvvWddVqlRJklS/fn2ZTCZFRUVJkmJjY9W2bVuVLl1agYGBatmypX7++ec8z+/kyZPq0aOHfHx8FBERoa+++sq67lqj6cuXL7f5ZTs//xZUrFhRkydP1oABA+Tv76/y5cvrww8/vPmLj9vO1KlT9dRTT2nRokXWMLh582a1aNFC3t7eCg8P19NPP20zyn6z797Ln6VVq1YpMjJSZrNZmzZtspkyOmHCBHXt2lWS5ObmZn2P5udzYDKZNHv27Ot+DgBHIxAi3+bPny9fX1/9+OOPmjp1ql566SWtWbNGUu4XXIcOHXTfffcpISFBb7/9tiwWi7p3767Tp09r48aNWrNmjQ4ePKj777/fZr8HDhzQ559/rqVLl9pMMZo0aZIefvhh7dixQzVq1FCfPn302GOPafTo0YqLi5MkPfXUU9b+qamp6tSpk9auXavt27erffv26tq1q+Lj4yVJcXFxevrpp/XSSy9p3759WrlypVq0aOHkVw1G1KpVK91555368ssvJeX+MjBjxgz9+uuvmj9/vtavX69Ro0ZJkpo1a6bp06crICBACQkJSkhI0MiRIyVJmZmZmjRpkn755RctX75chw8ftv6xBbjM29tbFy9e1Isvvqi5c+dq1qxZ2r17t4YPH64HH3xQGzdutOk/ZswYvfnmm4qLi5OHh4cGDBhgXbdw4UK98soreu211/TTTz+pfPnyeUYeP/roI40ZM0avvPKK9uzZo8mTJ2vs2LGaP3++JGnbtm2SpLVr1yohIcH6OTh37pz69eunTZs2aevWrYqIiFCnTp107tw5m/1PnDhR9913n3bu3KlOnTqpb9++On36dL5fj5v9W3DZm2++qcjISG3fvl1PPvmknnjiCe3duzffx0Hh9/zzz2vSpEn6+uuv1atXL0nSrl271L59e/Xs2VM7d+7UkiVL9P3339v8PpHf795Ro0ZpypQp2rNnj+rWrWuzbuTIkZo7d64kWb/bpYL7HAB2sQDX0a9fP0u3bt0sFovF0rJlS8vdd99ts75Ro0aW5557zrrcrVs3S79+/azLq1evtri7u1vi4+Otbbt377ZIsmzbts1isVgs48ePt3h6elqSkpJs9i3J8uKLL1qXt2zZYpFkmTNnjrXts88+s3h5ed3wOdSqVcsyc+ZMi8VisSxdutQSEBBgSUlJuWbfli1bWp555pkb7g+40pWfkavdf//9lpo1a15z3eeff24pVaqUdXnu3LmWwMDAmx5v27ZtFkmWc+fO3Uq5KAKufs/9+OOPllKlSlnuvfdei5eXl2Xz5s02/R999FFL7969LRaLxbJhwwaLJMvatWut61esWGGRZElPT7dYLBZL48aNLUOGDLHZx7/+9S/LnXfeaV0ODw+3LFq0yKbPpEmTLE2bNrVYLBbL4cOHLZIs27dvv+FzycrKsvj7+1v+97//Wduu/u5PTU21mEwmy7fffmuxWK79WVm2bJnlZr/OXPlvgcVisVSoUMHy4IMPWpdzcnIsQUFBllmzZt1wP7g99OvXz1KsWDGLJMu6dets1j300EOWwYMH27Rt2rTJ4ubmZv0cXO3q797Ln6Xly5fb9Bs/frzNZyU/781b+RwAjsYIIfLt6r9+hYaG3nD62p49exQeHq7w8HBrW61atVS8eHHt2bPH2lahQgWVKVPmhscLDg6WJNWpU8em7cKFC0pJSZEkpaWladSoUdZj+Pn5ae/evda/Crdt21YVKlRQ5cqV9dBDD2nhwoU6f/68PS8BkG+WS1OfJWnDhg1q27atypUrJ39/fz388MM6derUTS8Esn37dnXr1k0VKlSQv7+/derd1SMdMJavv/5afn5+8vLyUtOmTdWiRQuNHDlSFy5cUNu2ba3nMfn5+WnBggU2U+sl2+/W0NBQSbJ+l+/bt0933XWXTf8rl//66y8dO3ZMjz76qM1xXn755TzHuVpSUpIef/xxVatWTYGBgQoMDFRqamqe9/OV9fn6+srf39+uqdI3+7fgWscxmUwKCQlhSnYRUrduXVWsWFHjxo2zGX376aefNG/ePJv3b/v27ZWTk6PDhw9Lyv93b2RkpN11FdTnALAHVztAvl19sReTyXTDKyle+Qvxjdp9fX1verzL/a/VdrmGZ599VqtWrdIbb7yhqlWrytvbW/fee68yMzMlSf7+/vr5558VExOj1atXa9y4cZowYYJiY2O5wiMcbs+ePapUqZKOHj2qTp066fHHH9ekSZNUsmRJff/993r00Ud18eLF626flpamdu3aqV27dvr0009VpkwZxcfHq3379tb3NIwpOjpas2bNkqenp8qWLStPT0/9+OOPkqQVK1aoXLlyNv3NZrPN8o2+R69su8xisVh/vtzvo48+UuPGjW36ubu737Du/v3766+//tL06dNVoUIFmc1mNW3aNM/7+Ub/1ri5udnUIynP5+hm/xbk5zi4/ZUrV05Lly5VdHS0OnTooJUrV8rf3185OTl67LHH9PTTT+fZpnz58nZ9917v95cbccTnAHA0AiGcplatWoqPj9exY8eso4S//fabkpOTVbNmTYcfb9OmTerfv7969OghKfc8kiNHjtj08fDwUJs2bdSmTRuNHz9exYsX1/r169WzZ0+H1wPjWr9+vXbt2qXhw4crLi5OWVlZevPNN+Xmljsp4/PPP7fpX6xYMWVnZ9u07d27VydPntSrr75q/fxcPncWxubr66uqVavatNWqVUtms1nx8fFq2bLlLe+7evXq2rZtmx566CFr25Xvu+DgYJUrV06HDh1S3759r7mPYsWKSVKe9/SmTZv03nvvqVOnTpKkY8eO6eTJk3bVV6ZMGZ07d05paWnWX8avvr1Ffv4tgDGUL19eGzduVHR0tNq1a6dVq1apQYMG2r17d57P0GW7du1y6nevIz4HgKMRCOE0bdq0Ud26ddW3b19Nnz5dWVlZevLJJ9WyZctbmmZxM1WrVtWXX36prl27ymQyaezYsTZ/Tfv666916NAhtWjRQiVKlNA333yjnJwcVa9e3eG1wDgyMjKUmJhoc9uJKVOmqEuXLnr44Ye1a9cuZWVlaebMmeratat++OEHvf/++zb7qFixolJTU7Vu3Trdeeed8vHxUfny5VWsWDHNnDlTjz/+uH799Vfuk4nr8vf318iRIzV8+HDl5OTo7rvvVkpKijZv3iw/Pz/169cvX/sZOnSoBg0apMjISDVr1kxLlizRzp07VblyZWufCRMm6Omnn1ZAQIA6duyojIwMxcXF6cyZMxoxYoSCgoLk7e2tlStXKiwsTF5eXgoMDFTVqlX1ySefKDIyUikpKXr22Wfl7e1t1/Ns3LixfHx89MILL2jo0KHatm2b9Wq+l93s3wIYS1hYmGJiYqyh8IMPPlDTpk01ZMgQDRo0SL6+vtqzZ4/WrFmjmTNnOv271xGfA8DROIcQTmMymbR8+XKVKFFCLVq0UJs2bVS5cmUtWbLEKcd76623VKJECTVr1kxdu3ZV+/bt1aBBA+v64sWL68svv1SrVq1Us2ZNvf/++/rss890xx13OKUeGMPKlSsVGhqqihUrqkOHDtqwYYNmzJih//73v3J3d1e9evU0bdo0vfbaa6pdu7YWLlyoKVOm2OyjWbNmevzxx3X//ferTJkymjp1qsqUKaN58+bpP//5j2rVqqVXX31Vb7zxhoueJW4HkyZN0rhx4zRlyhTVrFlT7du31//+9z/rbSDyo2/fvho9erRGjhypBg0aWK+u6OXlZe0zcOBAzZ49W/PmzVOdOnXUsmVLzZs3z3ocDw8PzZgxQx988IHKli2rbt26SZI+/vhjnTlzRvXr19dDDz2kp59+WkFBQXY9x5IlS+rTTz/VN998ozp16uizzz6zuQWSdPN/C2A85cqV08aNG3X27FkNGjRIGzdu1P79+9W8eXPVr19fY8eOtZ5P6+zvXkd8DgBHM1munowPAABwSdu2bRUSEqJPPvnE1aUAAJyAKaMAAECSdP78eb3//vtq37693N3d9dlnn2nt2rXWe84CAIoeRggBAIAkKT09XV27dtXPP/+sjIwMVa9eXS+++CIX3gKAIoxACAAAAAAGxUVlAAAAAMCgCIQAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACABwuQkTJqhevXoO36/JZNLy5csdvl8AAIoKAiEAwKlMJtMNH/3799fIkSO1bt06V5eah7OCKgAAhYWHqwsAABRtCQkJ1p+XLFmicePGad++fdY2b29v+fn5yc/PzxXlAQBgaIwQAgCcKiQkxPoIDAyUyWTK03b1SFz//v3VvXt3vfHGGwoNDVWpUqU0ZMgQXbx40donISFBnTt3lre3typVqqRFixapYsWKmj59+nVreemllxQcHKwdO3ZIkp577jlVq1ZNPj4+qly5ssaOHWs9xrx58zRx4kT98ssv1tHMefPmSZKmTZumOnXqyNfXV+Hh4XryySeVmppqPc68efNUvHhxrVq1SjVr1pSfn586dOhgE44BACgMGCEEABRKGzZsUGhoqDZs2KADBw7o/vvvV7169TRo0CBJ0sMPP6yTJ08qJiZGnp6eGjFihJKSkq65L4vFomHDhmn58uX6/vvvFRERIUny9/fXvHnzVLZsWe3atUuDBg2Sv7+/Ro0apfvvv1+//vqrVq5cqbVr10qSAgMDJUlubm6aMWOGKlasqMOHD+vJJ5/UqFGj9N5771mPef78eb3xxhv65JNP5ObmpgcffFAjR47UwoULnfmyAQBgFwIhAKBQKlGihN555x25u7urRo0a6ty5s9atW6dBgwZp7969Wrt2rWJjYxUZGSlJmj17tjXoXSkrK0sPP/yw4uLi9MMPPygsLMy67sUXX7T+XLFiRf3f//2flixZolGjRlmnsnp4eCgkJMRmn8OGDbP+XKlSJU2aNElPPPGETSC8ePGi3n//fVWpUkWS9NRTT+mll15yyGsDAICjEAgBAIXSHXfcIXd3d+tyaGiodu3aJUnat2+fPDw81KBBA+v6qlWrqkSJEnn2M3z4cJnNZm3dulWlS5e2WffFF19o+vTpOnDggFJTU5WVlaWAgICb1rZhwwZNnjxZv/32m1JSUpSVlaULFy4oLS1Nvr6+kiQfHx9rGLxc//VGMAEAcBXOIQQAFEqenp42yyaTSTk5OZJyp4Bey7Xa27ZtqxMnTmjVqlU27Vu3btUDDzygjh076uuvv9b27ds1ZswYZWZm3rCuo0ePqlOnTqpdu7aWLl2qn376Se+++64k2ZzjeK36r1c3AACuwgghAOC2U6NGDWVlZWn79u1q2LChJOnAgQM6e/Zsnr733HOPunbtqj59+sjd3V0PPPCAJOmHH35QhQoVNGbMGGvfo0eP2mxbrFgxZWdn27TFxcUpKytLb775ptzccv+u+vnnnzvy6QEAUGAYIQQA3HZq1KihNm3aaPDgwdq2bZu2b9+uwYMHy9vbWyaTKU//Hj166JNPPtEjjzyiL774QlLuFNP4+HgtXrxYBw8e1IwZM7Rs2TKb7S5fNGbHjh06efKkMjIyVKVKFWVlZWnmzJk6dOiQPvnkE73//vsF8rwBAHA0AiEA4La0YMECBQcHq0WLFurRo4f1CqFeXl7X7H/vvfdq/vz5euihh/Tll1+qW7duGj58uJ566inVq1dPmzdv1tixY2226dWrlzp06KDo6GiVKVNGn332merVq6dp06bptddeU+3atbVw4UJNmTKlIJ4yAAAOZ7JwQgMAoAg4fvy4wsPDtXbtWrVu3drV5QAAcFsgEAIAbkvr169Xamqq6tSpo4SEBI0aNUonTpzQ77//nueCLgAA4Nq4qAwA4LZ08eJFvfDCCzp06JD8/f3VrFkzLVy4kDAIAIAdGCEEAAAAAIPiojIAAAAAYFAEQgAAAAAwKAIhAAAAABgUgRAAAAAADIpACAAAAAAGRSAEAAAAAIMiEAIAAACAQREIAQAAAMCg/h9EJ/20BHxCQQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Label untuk setiap tingkat piramida\n",
    "labels = ['Informasi', 'Data', 'Pengetahuan', 'Kearifan']\n",
    "\n",
    "# Tinggi dan lebar setiap tingkat piramida\n",
    "heights = [10, 20, 30, 40]\n",
    "widths = [0.8] * len(labels)  # Lebar batang\n",
    "\n",
    "# Warna untuk setiap tingkat piramida\n",
    "colors = ['#77dd77', '#33cc33', '#009900', '#006600']\n",
    "\n",
    "# Plot batang untuk setiap tingkat piramida\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "for i in range(len(labels)):\n",
    "    ax.bar(i, heights[i], width=widths[i], color=colors[i], linewidth=2)\n",
    "\n",
    "# Set label sumbu\n",
    "ax.set_xlabel('Tingkatan')\n",
    "ax.set_ylabel('Tingkat Keberagaman')\n",
    "\n",
    "# Set label sumbu x sesuai dengan label piramida\n",
    "ax.set_xticks(range(len(labels)))\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "# Set title\n",
    "plt.title('Piramida DIKW')\n",
    "\n",
    "# Tambahkan panah\n",
    "arrow_x = [0.2, 0.8]\n",
    "arrow_y = [6, 2]\n",
    "for i in range(len(arrow_x)):\n",
    "    ax.annotate('', xy=(arrow_x[i], arrow_y[i]), xytext=(arrow_x[i], arrow_y[i] - 1),\n",
    "                 arrowprops=dict(arrowstyle='->', facecolor='black'))\n",
    "\n",
    "# Tambahkan teks\n",
    "ax.text(0, 7, 'Masa Lalu (Pengalaman)', color='black')\n",
    "ax.text(3, 2, 'Masa Depan (Kebaruan)', color='black')\n",
    "\n",
    "# Tampilkan plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb21a8e6",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (3551063839.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[9], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    Arah Waktu:\u001b[0m\n\u001b[1;37m         ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "Arah Waktu:\n",
    "\n",
    "Masa Lalu (Pengalaman): Data dan informasi berasal dari pengalaman masa lalu.\n",
    "    Contoh: data suhu di masa lalu digunakan untuk membuat ramalan cuaca.\n",
    "Masa Depan (Kebaruan): Pengetahuan dan kearifan digunakan untuk mengarahkan masa depan.\n",
    "    Contoh: pengetahuan tentang faktor yang mempengaruhi tinggi badan digunakan untuk merancang program latihan yang tepat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb5ba1c6",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1400583410.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[11], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    Data dan metadata memiliki peranan penting dalam berbagai bidang, namun dengan manfaat yang berbeda.\u001b[0m\n\u001b[1;37m         ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "Data dan metadata memiliki peranan penting dalam berbagai bidang, namun dengan manfaat yang berbeda.\n",
    "DATA\n",
    "Bahan baku: Digunakan untuk analisis, pelaporan, dan pengambilan keputusan.\n",
    "Sumber informasi: Memberikan wawasan tentang berbagai aspek.\n",
    "Membangun pengetahuan: Menjadi fondasi untuk pengembangan ilmu pengetahuan.\n",
    "\n",
    "METADATA\n",
    "Informasi tentang data: Menjelaskan asal-usul, struktur, dan makna data.\n",
    "Memudahkan pengelolaan data: Membantu menemukan, memahami, dan menggunakan data.\n",
    "Meningkatkan kualitas data: Memastikan data akurat, konsisten, dan terdokumentasi.\n",
    "\n",
    "Metadata penting dikarenakan:\n",
    "Mempermudah pencarian data: Metadata membantu pengguna menemukan data yang mereka butuhkan dengan cepat dan mudah.\n",
    "Meningkatkan pemahaman data: Metadata memberikan informasi tentang konteks dan makna data, sehingga pengguna dapat lebih memahami data tersebut.\n",
    "\n",
    "Tiga Jenis Metadata dan Penjelasannya:\n",
    "\n",
    "Metadata Deskriptif: Menjelaskan isi dan karakteristik data, seperti judul, penulis, tanggal publikasi, dan format data.\n",
    "Metadata Struktural: Menjelaskan struktur dan organisasi data, seperti skema database, nama kolom, dan hubungan antar tabel.\n",
    "Metadata Administratif: Menjelaskan informasi tentang pengelolaan data, seperti hak cipta, izin akses, dan riwayat versi data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33e2bf82",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (3087994205.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[12], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    Sebagai seorang Data Scientist, saya bekerja dengan berbagai macam data untuk memahami dan memecahkan masalah dalam berbagai bidang, seperti bisnis, keuangan, kesehatan, dan ilmu pengetahuan.\u001b[0m\n\u001b[1;37m            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "Sebagai seorang Data Scientist, saya bekerja dengan berbagai macam data untuk memahami dan memecahkan masalah dalam berbagai bidang, seperti bisnis, keuangan, kesehatan, dan ilmu pengetahuan.\n",
    "Tujuan utama ilmu data adalah untuk mengekstrak pengetahuan dan wawasan dari data yang dapat digunakan untuk membuat keputusan yang lebih baik.Data Scientist memainkan peran penting dalam membantu organisasi membuat keputusan yang lebih baik berdasarkan data\n",
    "\n",
    "Aspek fundamental dari data science:\n",
    "1. Matematika dan Statistik:\n",
    "Digunakan untuk memahami dan memodelkan data, termasuk regresi, klasifikasi, dan pembelajaran mesin.\n",
    "2. Pemrograman:\n",
    "Digunakan untuk menganalisis data, seperti Python, R, dan SQL.\n",
    "3. Komunikasi:\n",
    "Digunakan untuk menyampaikan hasil analisis data kepada stakeholders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "75988ac7",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (772624004.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[14], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    Reprodusibilitas Program Python dengan Virtual Environment\u001b[0m\n\u001b[1;37m                     ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "Reprodusibilitas Program Python dengan Virtual Environment\n",
    "Reprodusibilitas program adalah kemampuan untuk menjalankan program dengan hasil yang sama di berbagai lingkungan.\n",
    "Hal ini penting untuk memastikan bahwa program Anda dapat diandalkan dan dapat digunakan oleh orang lain.\n",
    "Virtual environment di Python adalah alat yang membantu Anda memastikan reprodusibilitas program dengan menyediakan\n",
    "lingkungan terisolasi yang berisi semua library dan package yang diperlukan untuk menjalankan program Anda.\n",
    "\n",
    "pip install venv\n",
    "python -m venv venv\n",
    "source venv/bin/activate\n",
    "pip install library_name\n",
    "python program.py\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "95223e5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Create the DataFrame with actual province names (replace \"...\" with names)\n",
    "data = pd.DataFrame({\n",
    "    \"Province\": [\"Aceh\", \"Sumatera Utara\", \"Sumatera Barat\", \"Papua Barat\", \"Papua\"],\n",
    "    \"EI10\": [63.42, 61.2, 61.06, 45.21, 42.45]\n",
    "})\n",
    "\n",
    "# Create the line plot\n",
    "plt.plot(data[\"EI10\"], data[\"Province\"])\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel(\"Education Index (EI)\")\n",
    "plt.ylabel(\"Province\")\n",
    "plt.title(\"Education Index per Province\")\n",
    "\n",
    "# Add annotations (optional)\n",
    "for i in range(len(data)):\n",
    "    plt.annotate(data[\"Province\"][i], (data[\"EI10\"][i], data[\"Province\"][i]))\n",
    "\n",
    "# Save the plot (optional)\n",
    "plt.savefig(\"education_index.png\")\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d0c9222",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}