example_sim_random_numbers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution pseudo random numbers generators tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QueueSim defines random number generators for some distributions. To test these generators, random numbers are generated and plotted as histograms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "# Plotting modules\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as formater\n",
    "import seaborn as sns\n",
    "\n",
    "# Generating pseudo random numbers\n",
    "import queuesim.random_dist as rnd\n",
    "\n",
    "# Recording statistics\n",
    "from queuesim.statistics import RecordDiscrete\n",
    "\n",
    "# Defining general plot style\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optional: Interactive diagram mode\n",
    "# Run \"pip install ipympl\" first\n",
    "# %matplotlib ipympl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper function for generating random numbers and showing the results of the tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_random_numbers(generator) -> RecordDiscrete:\n",
    "    statistics = RecordDiscrete()\n",
    "    for i in range(1_000_000):\n",
    "        statistics.record(generator())\n",
    "    return statistics\n",
    "\n",
    "\n",
    "def show_results(mean: float, sd: float, statistics: RecordDiscrete) -> None:\n",
    "    print(\"Defined mean = \", mean, sep=\"\")\n",
    "    print(\"Defined standard deviation = \", sd, sep=\"\")\n",
    "    print(\"Generated random numbers:\", statistics.count)\n",
    "    print(\"Actual mean = \", round(statistics.mean, 2), \" (relative deviation: \", round(abs(statistics.mean - mean) / mean * 100, 2), \"%)\", sep=\"\")\n",
    "    if sd > 0:\n",
    "        print(\"Actual standard deviation = \", round(statistics.sd, 2), \" (relative deviation: \", round(abs(statistics.sd - sd) / sd * 100, 2), \"%)\", sep=\"\")\n",
    "    else:\n",
    "        print(\"Actual standard deviation = \", round(statistics.sd, 2), \" (absolute deviation: \", round(abs(statistics.sd - sd), 2), \")\", sep=\"\")\n",
    "\n",
    "\n",
    "def plot_results(statistics: RecordDiscrete, name: str) -> None:\n",
    "    hist_y_sum = sum(statistics.histogram)\n",
    "    hist_y = statistics.histogram\n",
    "    hist_x = [i * statistics.histogram_stepwide for i in range(len(hist_y))]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(16, 9))\n",
    "    ax.bar(hist_x, hist_y, width=statistics.histogram_stepwide * 0.75)\n",
    "    ax.yaxis.set_major_formatter(formater.PercentFormatter(xmax=hist_y_sum, decimals=1))\n",
    "    ax.set_title(\"Frequency distribution of the generated pseudo random numbers (\" + name + \")\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deterministic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defined mean = 100\n",
      "Defined standard deviation = 0\n",
      "Generated random numbers: 1000000\n",
      "Actual mean = 100.0 (relative deviation: 0.0%)\n",
      "Actual standard deviation = 0.0 (absolute deviation: 0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7oAAAIZCAYAAACI+0OZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAA/JUlEQVR4nO3de5xd86H///ckMyJOUikmOKQe9LjW9XE0lQQRKbmLBt9KUqGuRVF1C0mkLjkuTcVdq0e1VItSCWkaWholQXF6iqIujXuaSYSQ22Qys35/eGT/RC4IMc6a5/Px8Hhk9l5r7c/en7WWec3ae6aqKIoiAAAAUBKtmnsAAAAA8GkSugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhd4CPZeuutM2DAgAwcOLDy34gRI5p7WJ+JJ598MnvvvXeS5Ne//nWuvfbaVS7/m9/8JjfddNMK73v/+nvvvXeefPLJjzWWV199NSeccEKSZObMmTn44IM/1vqra/To0dl7770zbty4lY7ntddeyy677PKZjOezduWVV+aPf/zjx15vl112yWuvvbYGRrR6+vfvn0ceeaS5h/G5e10+qtU5Zj+JX/3qV7nllls+s8f7uI466qi88MILq1zmiSeeyNlnn/2Rtrd0v3jyySdz4oknrvZ237/+8OHDc911132kx3+/ww8/PHPmzEny0Z7nqpx66ql58cUXV3t9YPVUN/cAgP87fvGLX2S99dZr7mE0q8GDB3/oMo8//ni23HLL1V5/Vd54441Mnz49SbLhhhvm5ptv/kTb+6huueWWTJkyJRtttNFKx1NmjzzySP7jP/6juYdBC/L666/njjvuyK233trcQ1mpn/70px+6zAsvvJCZM2d+rO3usMMOufzyy1d7ux9l/Q8zderUyr8/yvNclZNOOimnnHJKbrnlllRVVX2ibQEfndAFPrHtt98+PXv2zLPPPpuxY8dmnXXWyZgxY/L222+nsbExhxxySA488MAkyWWXXZa77rorX/ziF7Prrrvmqaeeyo033pjhw4dnyy23zBFHHJEky3w9c+bMnHvuuZkxY0YaGhrSr1+/fOc738lrr72Www47LN27d8/f/va3zJ07NyeffHL69u2bJUuW5Ic//GGmTJmS1q1bZ5dddsno0aMzYMCAjBo1KrvvvnuSZOTIkdlyyy1z6KGHLvOcfvWrX+UXv/hF2rVrl6222qpy+xVXXJG33norZ599dn71q1/l5ptvTk1NTdq0aZNzzz0306dPz3333ZepU6dm7bXXzpw5c/K///u/qaury9Zbb53NNtussv7Sx3n22WezePHifPvb386BBx6YRx55JOedd14mTpyYJJWvJ0yYkJEjR2bmzJk54ogjcs4552TAgAH561//moaGhlx44YV56KGH0rp16+y4444588wz065du+y99975xje+kYceeigzZsxInz59cvrppy83j88//3zOPffcvP3226mqqsrhhx+e/fffP0OGDElRFDnqqKMyevTo7LrrrkmSxsbG5cbT2NiYs88+O08++WTeeeednH766enVq1eS5Jprrsk999yTpqambLLJJhk9enQ23HDDZcbQ2NiYiy++OPfdd1/at2+fHXfcMS+++GJuvPHGvPvuuxkzZkyee+65NDQ0pEuXLjn99NNTXV2dHXbYIUcffXSmTp2aurq6DBs2LIcddliS966w//rXv05TU1M6dOiQUaNG5ctf/nKGDx+et99+O6+++mr22muvHHjggTn33HOzYMGC1NXVZZtttsmll16a2267LU899VQuvvjitG7dOt27d8/YsWPz6KOPprGxMdttt11GjhyZdu3a5bHHHst5552Xqqqq7LDDDmlqalrhMbP33nvn61//eh577LG8++67+fa3v50hQ4Zk/vz5OfPMM/Pyyy+nVatW+cpXvpJzzz03rVq1yn333ZdrrrkmDQ0NWXvttXPGGWdkl112WWaf/OA++sILL+Sss87KwoULs8UWW2TBggWVMfzxj3/MlVdemcbGxrRr1y5nnnlmdtxxx2XG+dprr2Xo0KH58pe/nNdffz033nhjfvvb3+aPf/xj6uvrs3DhwpxxxhnZZ599csUVV+T111/PrFmz8vrrr2e99dbLuHHjsuGGG67ydbnlllty4403plWrVtlggw0yatSobL755hk+fHjatGmTJ598MrNnz06fPn2y3nrr5U9/+lNmzZqV888/P126dFlmvI888kjGjRuXTp065fnnn8/ixYtz9tlnZ7fddlvlOWbvvfdO//79M2XKlLz99ts54YQT8j//8z/5+9//nurq6lxzzTWVfXVFx2ySVc7P+88Bxx57bEaMGJHFixenKIoceOCBGTp06HL7yE9+8pMMHDiwEkYrm69Vve4rO3d+0GuvvZZDDjkknTt3zrPPPpuiKHL22Wdn1113/dBzy2WXXZYFCxas8HXfbLPNcvnll+fdd9/NmWeemQsuuGCZx13ZfvH+c+Bjjz2WCy+8sHLfMccckx133HGZ7e6///4ZM2ZM1llnnSxYsCCnnXZaLrrooso59PHHH8/dd9+defPmpVu3bjnjjDNSXV2drbfeOg899FDlB7hLv/7hD3+YJDn00ENz7bXXZujQobnsssuyww47rHJ/bdeuXf7xj3/kX//6V7bYYotccskl+bd/+7d06tQp7du3z7333puvf/3rKzwnAGtAAfARbLXVVkX//v2L/fbbr/Lf7NmzK/fdcccdRVEURUNDQ9G3b9/iqaeeKoqiKN55552iT58+xV//+tfi7rvvLvr27Vu8++67xeLFi4sjjzyy+Na3vlUURVGcccYZxX//939XHu/9Xx9yyCHFvffeWxRFUSxatKg45JBDit/97nfFq6++Wmy11VbFfffdVxRFUUyePLnYa6+9iqIoil/84hfF0KFDi4ULFxaNjY3FSSedVNxxxx3F9ddfX5x44olFURTFu+++W+y2227F3Llzl3muTz/9dNGlS5eirq6uKIqiGDVqVNGjR4+iKIri8ssvL84555xiyZIlxVe+8pVi5syZRVEUxR133FHcfPPNy4398ssvL3r16lU0NDQss35RFEWPHj2K0aNHF0VRFP/617+K3XbbrXjuueeKhx9+uOjXr19lPO//+v3/fvXVV4udd965KIqiuOyyy4rvfve7xeLFi4vGxsZi+PDhxahRoyqPc+GFF1YeZ4cddiheeeWVZZ5zQ0ND0bNnz+Luu++uLLfHHnsU//M//1OZ4zfffHO5/eKD49lqq62KyZMnF0VRFPfcc0/Rs2fPyuvzve99r/I63HzzzcWRRx653PZ+/etfF0OHDi0WLVpU1NfXF4cffnhlHxk+fHhxww03FEVRFEuWLClOPfXU4tprr62M78YbbyyKoiiefPLJYvvtty8WLVpUPPLII8WQIUOKBQsWFEVRFA888EDRp0+fyjwdeuihlce+8MILi/HjxxdFURSLFy8u+vfvX3ku3/rWt4rf//73RVEUxRVXXFFceOGFRVNTU1EURfGjH/2oGD16dFFfX1907dq1mDZtWlEURXHXXXcVW221VfHqq68u9zx79OhRjBo1qmhqaipmzJhRfO1rXyueffbZ4o477igOP/zwynMcMWJE8dJLLxXTp08v+vfvX8yZM6coiqJ47rnnim7duhXz589fZp8qimX3sYEDBxa33nprURRF8dhjjxVbb7118fDDDxcvvPBC0bVr18p+MG3atKJbt27Fu+++u8w4l87po48+WhRFUbz22mvFIYccUixcuLAoiqKYOHFi0b9//8rj9uzZs7KNY445prjssstW+bpMmzat+PrXv17Zt26//faiT58+RVNTU3HGGWcUBx10ULF48eKirq6u2GqrrSrz//Of/7z49re/vdzr+vDDDxfbbrtt8fTTTxdFURTXXXddMXTo0Mp8r+wc06NHj+K//uu/iqIoit/97nfFNttsUzzzzDNFURTFcccdV1xzzTWV5VZ0zH7Y/Lz/HHDmmWcWP/nJT4qiKIq6urrie9/7XtHY2LjM82hqaiq+9rWvVfadVc3Xyl73olj5ufODls7znXfeWRRFUUyZMqXo1q1bsXjx4g89tzzxxBOrfN1vv/324uijj17uMVe1X7z/vDJs2LBi4sSJRVEUxTPPPFP84Ac/WG67Dz/8cLHNNtsUr732WuXrpeufccYZxTe+8Y1i/vz5RX19ffGtb32ruOmmm4qiWP689v6v3//vpc/zw/bXb37zm0V9fX2xePHiYv/99y9uu+22yrZ/+ctfFqeffvpyrwOw5riiC3xkq3rr8tKrfC+99FJeeeWVnHXWWZX7Fi1alKeffjovvPBC9tlnn7Rr1y5J8s1vfjO/+MUvVvmYCxYsyKOPPpq5c+fmsssuq9z27LPPZscdd0xNTU26d++eJNluu+3y9ttvJ0mmTZuWgQMHZu21106SXHrppUmSd955J1dddVXmzJmTyZMnZ6+99soXvvCFZR7zoYceSrdu3VJbW1sZ54MPPrjMMq1bt07v3r1z8MEHZ6+99kq3bt0yYMCAFT6HnXfeOdXVKz7dLv2M7YYbbpjdd989Dz30ULbeeutVviYr8uc//zknn3xyampqkiSHHHJIjj/++Mr9PXv2rDzO+uuvn7lz56ZTp06V+1966aXU19dn3333rSy377775oEHHvhYn7utqampXMHdZptt8uabbyZJ/vSnP+XJJ5/MAQcckCRpamrKwoULl1v//vvvz8CBA9OmTZsk7732N954Y5JkypQpefLJJ3PbbbcleW+/er+lz/ErX/lKFi9enAULFmTKlCl5+eWXl/ks89y5cyv7yX/+539Wbj/ttNMyderU/PSnP81LL72Uurq6Za6ALjVlypS8++67mTZtWpKkoaEh66+/fp577rlUV1dXrjL2799/lZ9NHDJkSKqqqrLRRhtljz32yNSpU7PPPvtk3LhxOeSQQ9K1a9cceuih2WyzzXLTTTelrq6ucpU6SaqqqvLKK6+sdPtvvfVW/vGPf2T//fevPNelb6l/+OGHs9tuu1X2gS5dumS99dbLU089ld12222Z7VRXV2fnnXdOkmyyySa56KKLctddd+Xll1/O3/72t8yfP7+ybOfOnSvH93bbbZe5c+eu8nV54IEH0rdv38p5ZdCgQRkzZkzl87s9evRITU1Namtrs84662SPPfZIknzpS1+qzOEH/fu//3u23XbbyhjuuOOOlb5G77d03+/UqVM22GCDbLPNNpXHmjt3bmW5FR2zrVu3XuX8vP8csM8+++SMM87IE088kS5dumTkyJFp1WrZX5ny1ltv5d13382mm26aZNXztbLXfVXnzr59+y73/Nddd93KOax79+5p3bp1/vGPf3zouWWpj/u6f9TjpU+fPjn33HNz3333pWvXrvn+97+/wu1tvPHG2WSTTVZ438CBA7POOuskSfbbb7/cf//9GTJkyCrHtyIftr/uscceWWuttZIkW2211TL7TadOnTJhwoSP/ZjA6hO6wKdi6TcRjY2N+cIXvrDM/9Bnz56d9u3b59JLL01RFJXbl37jlLz3TeH772toaEjyXhAVRZGbb745bdu2TZLMmTMnbdq0yVtvvZWamprKN4nv/+zTB8Ny9uzZaWpqSseOHdO7d+/ceeedueuuuzJ69OjlnssHx9K6desVPuexY8fmueeey7Rp0/LTn/40t912W6655pqVvjYr8v5vcIuiSHV19Upfi1X54Ftkm5qalllvaTgmyz+/Fa2/dDxLliz50Md+vw/O6fu3f+SRR1a+uVy8ePEy3wQu9cF5e//r09TUlMsuuyxf/vKXk7z3Q4v3P8bS57j0tqIo0tTUlIEDB+a0006rbKOuri7rrrtukmXn5vvf/34aGxvTp0+f7LXXXpkxY8Zyr9PSbZx11lmVH7DMnz8/9fX1K1x+ZT/g+OB9TU1NadWqVTp16pQ//OEPeeSRR/Lwww/n29/+dkaOHJmmpqZ06dKl8gObJJkxY0Y6duyYP/7xjyvcX97/OnzwMVf0vFY232uttVZlvb///e857rjjcthhh6Vbt2756le/mnPOOaey7NIfLC19/KIoVri/fdRxLI2GFb1mK7OiMXzw38nyx9X7H+v9+/EHreiYbWxsXOn8/OEPf1hmP+vRo0fuvvvuTJs2LQ899FCuuuqq3HzzzfnSl760zGMs3X+X/vuD3v86reg5r+rc+etf/7ry+f7tt98+xx577HLnuaamprRu3fpDzy1Lrex1X5lV7Rfvd/DBB6dHjx6ZOnVqHnjggVx55ZW58847l1tuVefZDz63FT3O4sWLVzne5MP311W9BkvnEvjsOOKAT9Xmm2+eNm3aVEJ3xowZ6d+/f5566qnstddemTx5cubOnZumpqaMHz++st4Xv/jFytWJOXPm5LHHHkuStGvXLjvvvHOuv/76JO/FzeDBg3PvvfeuchxdunTJxIkTs3jx4jQ1NeUHP/hBfve73yVJhg4dmhtuuCFFUSz3mcQk6dq1a6ZOnZp//etfSbLCKxNz5sxJ9+7d06FDhxx22GH53ve+l3/84x9J3vum6qMG4tJtv/HGG5k2bVrlSs0bb7yRN998M0VRLPPbflu3br3CbzL32GOP3HzzzWloaEhTU1NuuummdOvW7SONIXlv3mpqanLPPfckee83Ot99993p2rXrKtdb2Xg+aPfdd89tt92WefPmJXnvs9or+pxw9+7dc+edd2bx4sVZsmTJMq/97rvvnp///OcpiiKLFy/Osccem1/+8perfNxu3brld7/7Xerq6pK891uvP/h57KUefPDBHH/88enbt2+qqqryt7/9LY2NjZXnuXROd99999x0002VfWvUqFG55JJLstVWW6Uoitx///1JknvvvXeFMb/U0v3/jTfeyNSpU7PnnnvmV7/6Vc4888zsvvvuOe2007L77rvn+eefz2677ZapU6dWfnPr/fffn/322y/19fX54he/mL///e8piiILFiyovPugQ4cO+cpXvpLf/OY3Sd6L1Oeeey5JKtt79dVXk6Ty+e2ddtppla/no48+mu233z7f/va307lz59x7772V12hlVvW67L777pk0aVLlt9vefvvt6dChQzbbbLNVbnN1rOwc83Gt6Jhd1fx80CmnnJJJkyalX79+GT16dNq1a5cZM2Yss0yHDh3yhS98Ia+//nqS1ZuvVZ07Bw8enAkTJmTChAkZM2ZM5TX585//nOS9zxvX1NRkq622+sTnlpWdDz/q8XLwwQfnmWeeyaBBg3LeeeflnXfeydy5cz/WefZ3v/tdFi9enPr6+vz2t7/NnnvumSRZb731Kr9F+w9/+MOHjvuT7K+vvvpqtthii480XuDT4You8Klaa621cvXVV2fMmDH57//+7yxZsiQnnXRS5S2iw4YNy5AhQ9KmTZtl3mZ2yCGH5NRTT02vXr2y6aabpnPnzpX7xo4dm/POOy8DBgzI4sWL079//+y3336r/PMkBx98cF5//fUMGjQoRVGkc+fOOeSQQ5K895baddddd6V/mmfrrbfOaaedlkMPPTT/9m//tsIYXm+99XLsscfmsMMOy9prr53WrVvn/PPPT5LsueeeOe+88z7S61VfX59vfOMbaWhoyMiRI7P55ptXxn/AAQektrY2e+21V2X5LbfcMq1bt86BBx64zJ/6OfbYY3PRRRdl//33z5IlS7Ljjjtm1KhRH2kMyXtXsK6++uqcf/75ueKKK9LY2Jjjjz9+ubexftDKxvNBBx10UGbOnJn/9//+X6qqqrLxxhvnwgsvXG65QYMGZfr06dl///2zzjrrZNNNN61cjRoxYkTGjBmTAQMGpKGhIV27ds2RRx65yvHtscceOeqoo3L44Yenqqoq7dq1y5VXXrnC33x68skn5/jjj8+6666btm3b5qtf/Wrlrac9evTIRRddlIaGhhx33HG56KKL8o1vfCONjY3ZdtttM3z48NTU1OSqq67KD37wg1xyySXZdttts/766690bK+99loGDRqURYsWZeTIkdliiy2y0UYb5S9/+Uv69u2btm3b5t///d8zbNiwrLvuujn33HPz/e9/v3IV8Zprrsk666yT/fbbLw888ED23XffbLjhhtlll10qV5IuueSSnHnmmZUrhku/0f6P//iPjB49Ot/97nfT2NiYtddeOz/+8Y/Tvn37Vb6e/fv3zz333JO+ffumpqYmXbp0ydy5cys/wFiRVb0u3bp1y2GHHZZDDz00TU1NWW+99fKTn/xkjVz5WtU55uNY2TG7svn5oOOOOy4jRozILbfcktatW+frX//6Csey9KMDQ4YMWe35Wtm5c0WW/oBy7NixWXvttXPVVVeldevWn/jcsssuu+TSSy/N8ccfn6uuuqpy+0c9Xk499dT813/9Vy699NK0atUq3/3ud7Ppppumqampst1hw4atcgybbrppBg8enAULFmSfffbJN77xjSTv/TLCc889N1/4whfStWvXysdVkvfeYj5kyJBcffXVlds+yf76wAMP5Fvf+taHLgd8eqqKD3tvCcAaMnny5Nx0002Vz2B+Vl555ZUccsghmTx5ciWi+Hx48MEH8+abb2bgwIFJkvPPPz9t2rSpvPW4LJb+ttoddtihuYfC59Srr76ak046Kbfffvsa/5M0r732WuU3uPPpe+WVV3Lqqaf680LwGfPWZaBFueyyyzJ48OCcccYZIvdzaMstt8z48eOz3377pV+/fnnrrbdW+OdQoOw6deqU/fff/zP7W9msOZdeemnOP/98kQufMVd0AQAAKBVXdAEAACgVoQsAAECpCF0AAABKRegCAABQKqX+O7pvvTU/TU2f/9+1tf767fLmmyv/G4SUl7lvucx9y2XuWy5z33KZ+5bL3K85rVpV5Ytf/LeV3l/q0G1qKv5PhG6S/zPj5NNn7lsuc99ymfuWy9y3XOa+5TL3zcNblwEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACiVjxS68+bNS//+/fPaa68lSaZNm5YBAwZk3333zbhx4yrLPfPMMznggAPSq1evjBgxIkuWLFluW++8806OPvro9OnTJ0OHDs2sWbOSJI899lj69euXQYMGZfr06UmS+fPn5zvf+c4nfpIAAAC0HB8aun/7298yePDgvPTSS0mSRYsW5ayzzsrVV1+dSZMm5amnnsr999+fJDnttNMyatSo3H333SmKIrfeeuty27v00kuz66675ve//30OOuigjBkzJkly/fXX54ILLshRRx2Vm2++OUly3XXX5fDDD/+0nisAAAAtwIeG7q233prRo0enY8eOSZInnngim222WTp16pTq6uoMGDAgkydPzuuvv55FixZl5513TpIMGjQokydPXm57U6ZMyYABA5Ik/fv3z5///Oc0NDSkpqYm9fX1WbRoUWpqalJXV5d//vOf6dy586f4dAEAACi76g9bYOkV16Xq6upSW1tb+bpjx46ZOXPmcrfX1tZm5syZy23v/ctVV1enXbt2mTNnTo455piMHj06bdu2zcUXX5yrrroqxx133Go/sSRZf/12n2j9z1JtbfvmHgLNxNy3XOa+5TL3LZe5b1kWNzRmrZrWSf7/uX//bbQMjvvm8aGh+0FFUSx3W1VV1Upv/yhatWqVbbfdtvJW5xdffDFNTU3p0KFDTjjhhBRFkVNOOSWbb775xxrrm2/OS1PT8uP6vKmtbZ9Zs95t7mHQDMx9y2XuWy5z33KZ+5antrZ9BpwyYZnb7vrRQPtBC+K4X3Natapa5YXNj/1blzfccMPMnj278nVdXV06duy43O2zZs2qvN35/Tp27FhZbsmSJZk3b146dOiwzDJXXHFFjj/++Nxwww3Zd999c+SRR+bKK6/8uEMFAACgBfrYobvTTjtl+vTpefnll9PY2JiJEydmzz33zCabbJI2bdrk8ccfT5KMHz8+e+6553Lrd+/ePePHj0+STJo0Kbvuumtqamoq9z/66KPZdNNNs9FGG6WhoSHV1dVp1apV6uvrV/MpAgAA0JJ87Lcut2nTJhdeeGFOOOGE1NfXp3v37undu3eSZOzYsRk5cmTmz5+f7bbbLsOGDUuSXHbZZenYsWMGDx6ck046KcOHD0+/fv3Svn37jB07dpntX3vttfnRj36UJDnooINyyimnpCiKnHPOOZ/0uQIAANACVBUr+nBtSfiMLp935r7lMvctl7lvucx9y+Mzujju15xP/TO6AAAA8HkmdAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABK5ROF7oQJE9KvX7/069cvF110UZLkmWeeyQEHHJBevXplxIgRWbJkyXLrvfPOOzn66KPTp0+fDB06NLNmzUqSPPbYY+nXr18GDRqU6dOnJ0nmz5+f73znO59kmAAAALQgqx26CxcuzJgxY3LjjTdmwoQJeeyxxzJt2rScdtppGTVqVO6+++4URZFbb711uXUvvfTS7Lrrrvn973+fgw46KGPGjEmSXH/99bngggty1FFH5eabb06SXHfddTn88MNXd5gAAAC0MKsduo2NjWlqasrChQuzZMmSLFmyJNXV1Vm0aFF23nnnJMmgQYMyefLk5dadMmVKBgwYkCTp379//vznP6ehoSE1NTWpr6/PokWLUlNTk7q6uvzzn/9M586dV3eYAAAAtDDVq7tiu3btctJJJ6VPnz5Ze+2107lz59TU1KS2trayTG1tbWbOnLncunV1dZXlqqur065du8yZMyfHHHNMRo8enbZt2+biiy/OVVddleOOO251hwgAAEALtNqh++yzz+b222/Pn/70p7Rv3z6nnnpqpk6dutxyVVVVH2l7rVq1yrbbblt5q/OLL76YpqamdOjQISeccEKKosgpp5ySzTff/COPcf31233kZZtbbW375h4CzcTct1zmvuUy9y2XuSexH7Q05rt5rHboPvjgg+nSpUvWX3/9JO+9Tfm6667L7NmzK8vMmjUrHTt2XG7djh07Zvbs2dloo42yZMmSzJs3Lx06dFhmmSuuuCLDhw/PDTfckH333TedOnXKlVdemR/96EcfeYxvvjkvTU3F6j3Bz1BtbfvMmvVucw+DZmDuWy5z33KZ+5bL3Lc8Kwsc+0HL4bhfc1q1qlrlhc3V/ozuNttsk2nTpmXBggUpiiL33XdfOnfunDZt2uTxxx9PkowfPz577rnncut2794948ePT5JMmjQpu+66a2pqair3P/roo9l0002z0UYbpaGhIdXV1WnVqlXq6+tXd7gAAAC0EKt9RXf33XfP008/nUGDBqWmpiY77LBDjj766Oyzzz4ZOXJk5s+fn+222y7Dhg1Lklx22WXp2LFjBg8enJNOOinDhw9Pv3790r59+4wdO3aZbV977bWVK7cHHXRQTjnllBRFkXPOOecTPFUAAABagqqiKD7/7+1dTd66zOeduW+5zH3LZe5bLnPf8tTWts+AUyYsc9tdPxpoP2hBHPdrzhp76zIAAAB8HgldAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFL5RKF73333ZdCgQendu3fOP//8JMm0adMyYMCA7Lvvvhk3btwK13vjjTcydOjQ9O7dO8cee2zmz5+fJJk8eXL69OmTwYMHZ86cOZVlTz/99E8yTAAAAFqQ1Q7dV199NaNHj87VV1+du+66K08//XTuv//+nHXWWbn66qszadKkPPXUU7n//vuXW/ecc87JkCFDMnny5Gy//fa5+uqrkyQ//vGP88tf/jI9e/bMxIkTkyRXXXVVjj/++NUdJgAAAC3MaofuH/7wh/Tt2zcbbbRRampqMm7cuLRt2zabbbZZOnXqlOrq6gwYMCCTJ09eZr2GhoY8+uij6dWrV5Jk0KBBlWVqampSX1+fRYsWpaamJs8880xlmwAAAPBRVK/uii+//HJqampyxBFHZNasWenRo0e23HLL1NbWVpbp2LFjZs6cucx6b731Vtq1a5fq6vceura2trLMySefnO9+97vZcMMNM3bs2AwfPjw/+MEPVneIAAAAtECrHbqNjY157LHHcuONN2adddbJcccdl7Zt2y63XFVV1TJfF0Wx0mW6du2a3/72t0mSBx98MFtvvXXefvvtjBgxIm3bts2IESOywQYbfOQxrr9+u4/zlJpVbW375h4CzcTct1zmvuUy9y2XuSexH7Q05rt5rHbobrDBBunSpUvWW2+9JEnPnj0zefLktG7durJMXV1dOnbsuMx66623XubNm5fGxsa0bt06s2bNWm6Zpqam/PznP8/ll1+eESNG5Oijj86MGTNyww035Pvf//5HHuObb85LU9PyYf15U1vbPrNmvdvcw6AZmPuWy9y3XOa+5TL3Lc/KAsd+0HI47tecVq2qVnlhc7U/o9ujR488+OCDeeedd9LY2JgHHnggvXv3zvTp0/Pyyy+nsbExEydOzJ577rnMejU1Ndl1110zadKkJMn48eOXW+bOO+9Mz549s84666ShoSHV1dWpqqpKfX396g4XAACAFmK1r+jutNNOOfLIIzNkyJA0NDSkW7duGTx4cLbYYouccMIJqa+vT/fu3dO7d+8kyYgRI7L33nunZ8+eGT16dIYPH55rrrkmG2+8cS655JLKduvr63PnnXfm2muvTZIMGzYsZ555Ztq0abPMcgAAALAiVcWKPjRbEt66zOeduW+5zH3LZe5bLnPf8tTWts+AUyYsc9tdPxpoP2hBHPdrzhp76zIAAAB8HgldAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFIRugAAAJSK0AUAAKBUhC4AAAClInQBAAAoFaELAABAqQhdAAAASkXoAgAAUCpCFwAAgFL5xKF70UUXZfjw4UmSZ555JgcccEB69eqVESNGZMmSJcst/8477+Too49Onz59MnTo0MyaNStJ8thjj6Vfv34ZNGhQpk+fniSZP39+vvOd73zSIQIAANCCfKLQfeihh3LHHXdUvj7ttNMyatSo3H333SmKIrfeeuty61x66aXZdddd8/vf/z4HHXRQxowZkyS5/vrrc8EFF+Soo47KzTffnCS57rrrcvjhh3+SIQIAANDCrHbovv322xk3blzliuvrr7+eRYsWZeedd06SDBo0KJMnT15uvSlTpmTAgAFJkv79++fPf/5zGhoaUlNTk/r6+ixatCg1NTWpq6vLP//5z3Tu3Hl1hwgAAEALVL26K5599tk5+eSTM2PGjCRJXV1damtrK/fX1tZm5syZy633/uWqq6vTrl27zJkzJ8ccc0xGjx6dtm3b5uKLL85VV12V4447bnWHBwAAQAu1WqH7m9/8JhtvvHG6dOmS3/72t0mSoiiWW66qquojba9Vq1bZdtttK291fvHFF9PU1JQOHTrkhBNOSFEUOeWUU7L55pt/rHGuv367j7V8c6qtbd/cQ6CZmPuWy9y3XOa+5TL3JPaDlsZ8N4/VCt1JkyZl1qxZGThwYObOnZsFCxakqqoqs2fPriwza9asdOzYcbl1O3bsmNmzZ2ejjTbKkiVLMm/evHTo0GGZZa644ooMHz48N9xwQ/bdd9906tQpV155ZX70ox99rHG++ea8NDUtH+CfN7W17TNr1rvNPQyagblvucx9y2XuWy5z3/KsLHDsBy2H437NadWqapUXNlfrM7rXX399Jk6cmAkTJuTEE0/M3nvvnQsuuCBt2rTJ448/niQZP3589txzz+XW7d69e8aPH5/kvWDeddddU1NTU7n/0UcfzaabbpqNNtooDQ0Nqa6uTqtWrVJfX786QwUAAKCFWe3P6K7I2LFjM3LkyMyfPz/bbbddhg0bliS57LLL0rFjxwwePDgnnXRShg8fnn79+qV9+/YZO3bsMtu49tprK1duDzrooJxyyikpiiLnnHPOpzlUAAAASqqqWNGHa0vCW5f5vDP3LZe5b7nMfctl7lue2tr2GXDKhGVuu+tHA+0HLYjjfs1ZI29dBgAAgM8roQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKkIXAACAUhG6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoQsAAECpCF0AAABKRegCAABQKp8odK+88sr069cv/fr1y8UXX5wkmTZtWgYMGJB9990348aNW+F6b7zxRoYOHZrevXvn2GOPzfz585MkkydPTp8+fTJ48ODMmTOnsuzpp5/+SYYJAABAC7LaoTtt2rQ8+OCDueOOOzJ+/Pj8/e9/z8SJE3PWWWfl6quvzqRJk/LUU0/l/vvvX27dc845J0OGDMnkyZOz/fbb5+qrr06S/PjHP84vf/nL9OzZMxMnTkySXHXVVTn++ONXd5gAAAC0MKsdurW1tRk+fHjWWmut1NTU5Mtf/nJeeumlbLbZZunUqVOqq6szYMCATJ48eZn1Ghoa8uijj6ZXr15JkkGDBlWWqampSX19fRYtWpSampo888wzadu2bTbbbLNP8BQBAABoSVY7dLfccsvsvPPOSZKXXnopkyZNSlVVVWprayvLdOzYMTNnzlxmvbfeeivt2rVLdXV1kveCeekyJ598cr773e/m73//e/bbb79cffXVOfbYY1d3iAAAALRA1Z90A88//3yOOeaYnHHGGamurs706dOXub+qqmqZr4uiWG4bS5fp2rVrfvvb3yZJHnzwwWy99dZ5++23M2LEiLRt2zYjRozIBhts8JHHtv767T7u02k2tbXtm3sINBNz33KZ+5bL3Ldc5p7EftDSmO/m8YlC9/HHH8+JJ56Ys846K/369ctf/vKXzJ49u3J/XV1dOnbsuMw66623XubNm5fGxsa0bt06s2bNWm6Zpqam/PznP8/ll1+eESNG5Oijj86MGTNyww035Pvf//5HHt+bb85LU9PyYf15U1vbPrNmvdvcw6AZmPuWy9y3XOa+5TL3Lc/KAsd+0HI47tecVq2qVnlhc7Xfujxjxowcf/zxGTt2bPr165ck2WmnnTJ9+vS8/PLLaWxszMSJE7Pnnnsus15NTU123XXXTJo0KUkyfvz45Za5884707Nnz6yzzjppaGhIdXV1qqqqUl9fv7rDBQAAoIVY7Su61113Xerr63PhhRdWbjv44INz4YUX5oQTTkh9fX26d++e3r17J0lGjBiRvffeOz179szo0aMzfPjwXHPNNdl4441zySWXVLZRX1+fO++8M9dee22SZNiwYTnzzDPTpk2bZZYDAACAFakqVvSh2ZLw1mU+78x9y2XuWy5z33KZ+5antrZ9BpwyYZnb7vrRQPtBC+K4X3PW2FuXAQAA4PNI6AIAAFAqQhcAAIBSEboAAACUitAFAACgVIQuAAAApSJ0AQAAKBWhCwAAQKkIXQAAAEpF6AIAAFAqQhcAAIBSEboAAACUitAFAACgVIQuAAAApSJ0AQAAKBWhCwAAQKkIXQAAAEpF6AIAAFAqQhcAAIBSEboAAACUitAFAACgVIQuAAAApSJ0AQAAKBWhCwAAQKkIXQAAAEpF6AIAAFAqQhcAAIBSEboAAACUitAFAACgVIQuAAAApSJ0AQAAKBWhCwAAQKkIXQAAAEpF6AIAAFAqQhcAAIBSEboAAACUitAFAACgVIQuAAAApSJ0AQAAKBWhCwAAQKkIXQAAAEpF6AIAAFAqQhcAAIBSEboAAACUitAFAACgVIQuAAAApSJ0AQAAKBWhCwAAQKkIXQAAAEpF6AIAAFAqQhcAAIBSEboAAACUitAFAACgVIQuAAAApSJ0AQAAKBWhCwAAQKkIXQAAAEpF6AIAAFAqQhcAAIBSEboAAACUitAFAACgVIQuAAAApSJ0AQAAKBWhCwAAQKkIXQAAAEpF6AIAAFAqQhcAAIBSEboAAACUitAFAACgVIQuAAAApSJ0AQAAKBWhCwAAQKkIXQAAAEpF6AIAAFAqQhcAAIBSEboAAACUitAFAACgVIQuAAAApSJ0AQAAKBWhCwAAQKkIXQAAAEpF6AIAAFAqQhcAAIBSEboAAACUyhoJ3bvuuit9+/bNPvvsk5tuumm5+5955pkccMAB6dWrV0aMGJElS5YkSX7xi1+kX79+OeKII7J48eIkyRNPPJEf/vCHa2KYAAAAlNCnHrozZ87MuHHj8qtf/SoTJkzILbfckhdeeGGZZU477bSMGjUqd999d4qiyK233prkvdAdP358vvSlL+WBBx5Ikvz4xz/O0Ucf/WkPEwAAgJL61EN32rRp2W233dKhQ4ess8466dWrVyZPnly5//XXX8+iRYuy8847J0kGDRpUub+6ujoNDQ1ZtGhRampqcu+99+Y///M/s+66637awwQAAKCkqj/tDdbV1aW2trbydceOHfPEE0+s9P7a2trMnDkzSXL88cdn8ODB2WabbbLbbrvluOOOy9VXX73aY2nVqmq11/2s/V8aK58uc99ymfuWy9y3XOa+5en4xbbL3WY/aFnM95rxYa/rpx66RVEsd1tVVdVHun/gwIEZOHBgkuSWW25J//7988QTT+Taa6/N+uuvn7PPPjtt2y5/sliZL37x3z7u8JvN+uu3a+4h0EzMfctl7lsuc99ymfuW57qR+y53m/2gZTHfzeNTf+vyhhtumNmzZ1e+rqurS8eOHVd6/6xZs5a5P0kWLFiQe+65J/vtt18uvvjinHfeedl8881z5513ftrDBQAAoGQ+9dDt2rVrHnroocyZMycLFy7MPffckz333LNy/yabbJI2bdrk8ccfT5KMHz9+mfuT5Gc/+1kOPfTQtGrVKg0NDampqUlVVVXq6+s/7eECAABQMmvkiu7JJ5+cYcOGZf/990///v2z44475qijjsqTTz6ZJBk7dmwuuOCC9OnTJwsXLsywYcMq67/55pt5+umnK/F71FFHZejQofnTn/6UAQMGfNrDBQAAoGSqihV9aBYAAAD+j/rUr+gCAABAcxK6AAAAlIrQBQAAoFSELgAAAKUidAEAACgVoduM7rrrrvTt2zf77LNPbrrppuYeDmvYlVdemX79+qVfv365+OKLkyTTpk3LgAEDsu+++2bcuHHNPELWtIsuuijDhw9PkjzzzDM54IAD0qtXr4wYMSJLlixp5tGxJtx3330ZNGhQevfunfPPPz+J474lmTBhQuW8f9FFFyVx7JfZvHnz0r9//7z22mtJVn6s2wfK54Nzf8stt6R///4ZMGBAzjzzzCxevDiJuf/MFTSLf/3rX0WPHj2Kt956q5g/f34xYMCA4vnnn2/uYbGGTJ06tfjmN79Z1NfXF4sXLy6GDRtW3HXXXUX37t2LV155pWhoaCgOP/zwYsqUKc09VNaQadOmFV/72teKM844oyiKoujXr1/x17/+tSiKojjzzDOLm266qRlHx5rwyiuvFLvvvnsxY8aMYvHixcXgwYOLKVOmOO5biAULFhRf/epXizfffLNoaGgoDjzwwGLq1KmO/ZL63//936J///7FV77yleLVV18tFi5cuNJj3T5QLh+c+3/+85/FPvvsU7z77rtFU1NTcfrppxfXX399URTm/rPmim4zmTZtWnbbbbd06NAh66yzTnr16pXJkyc397BYQ2prazN8+PCstdZaqampyZe//OW89NJL2WyzzdKpU6dUV1dnwIAB9oGSevvttzNu3Lh85zvfSZK8/vrrWbRoUXbeeeckyaBBg8x9Cf3hD39I3759s9FGG6Wmpibjxo1L27ZtHfctRGNjY5qamrJw4cIsWbIkS5YsSXV1tWO/pG699daMHj06HTt2TJI88cQTKzzWnf/L54Nzv9Zaa+UHP/hB2rVrl6qqqmy11VZ54403zH0zqG7uAbRUdXV1qa2trXzdsWPHPPHEE804ItakLbfcsvLvl156KZMmTcohhxyy3D4wc+bM5hgea9jZZ5+dk08+OTNmzEiy/PFfW1tr7kvo5ZdfTk1NTY444ojMmjUrPXr0yJZbbum4byHatWuXk046KX369Mnaa6+dzp07p6amxrFfUmPGjFnm6xV9nzdz5kzn/xL64Nxvsskm2WSTTZIkc+bMyU033ZQLLrjA3DcDV3SbSVEUy91WVVXVDCPhs/T888/n8MMPzxlnnJEvfelLy91vHyif3/zmN9l4443TpUuXym2O/5ahsbExDz30UH74wx/m1ltvzZNPPln5/Nb7mftyevbZZ3P77bfnT3/6Ux588MG0atUqU6dOXW45819OKzvPO/+3HDNnzsyhhx6aAw44IF/72tfMfTNwRbeZbLjhhnnssccqX9fV1VXe8kA5Pf744znxxBNz1llnpV+/fvnLX/6S2bNnV+63D5TTpEmTMmvWrAwcODBz587NggULUlVVtczcz5o1y9yX0AYbbJAuXbpkvfXWS5L07NkzkydPTuvWrSvLOO7L68EHH0yXLl2y/vrrJ3nvbYrXXXedY7+F2HDDDVf4//gP3m4fKKcXX3wxRx11VL71rW/l8MMPT7L8PmHu1zxXdJtJ165d89BDD2XOnDlZuHBh7rnnnuy5557NPSzWkBkzZuT444/P2LFj069fvyTJTjvtlOnTp+fll19OY2NjJk6caB8ooeuvvz4TJ07MhAkTcuKJJ2bvvffOBRdckDZt2uTxxx9PkowfP97cl1CPHj3y4IMP5p133kljY2MeeOCB9O7d23HfQmyzzTaZNm1aFixYkKIoct9996Vz586O/RZiZf+P32STTewDJTdv3rwcccQROemkkyqRm8TcNwNXdJvJhhtumJNPPjnDhg1LQ0NDDjzwwOy4447NPSzWkOuuuy719fW58MILK7cdfPDBufDCC3PCCSekvr4+3bt3T+/evZtxlHyWxo4dm5EjR2b+/PnZbrvtMmzYsOYeEp+ynXbaKUceeWSGDBmShoaGdOvWLYMHD84WW2zhuG8Bdt999zz99NMZNGhQampqssMOO+Too4/OPvvs49hvAdq0abPS/8c7/5fbbbfdltmzZ+dnP/tZfvaznyVJ9t5775x00knm/jNWVazoDeMAAADwf5S3LgMAAFAqQhcAAIBSEboAAACUitAFAACgVIQuAAAApSJ0AQAAKBWhCwAAQKkIXQAAAErl/wOOOFPBWYO4GAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = 100\n",
    "\n",
    "generator = rnd.deterministic(mean, as_lambda=True)\n",
    "statistics = generate_random_numbers(generator)\n",
    "\n",
    "show_results(mean, 0, statistics)  # Deterministic: standard deviation = 0\n",
    "plot_results(statistics, \"one-point distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exponential distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defined mean = 100\n",
      "Defined standard deviation = 100\n",
      "Generated random numbers: 1000000\n",
      "Actual mean = 100.22 (relative deviation: 0.22%)\n",
      "Actual standard deviation = 100.36 (relative deviation: 0.36%)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = 100\n",
    "\n",
    "generator = rnd.exp(mean, as_lambda=True)\n",
    "statistics = generate_random_numbers(generator)\n",
    "\n",
    "show_results(mean, mean, statistics)  # Exponential distribution: mean = standard deviation\n",
    "plot_results(statistics, \"exponential distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log-normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defined mean = 100\n",
      "Defined standard deviation = 40\n",
      "Generated random numbers: 1000000\n",
      "Actual mean = 100.03 (relative deviation: 0.03%)\n",
      "Actual standard deviation = 40.09 (relative deviation: 0.23%)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA60AAAIZCAYAAABNidkIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAABDmklEQVR4nO3deXQUZf7+/auTDkkYAkoM0UFgHAfEDeHAVwmrgLJkMQioQIZFBVxQFBlkSQBFGAFBRFQQxwUZZBHZ5RcYWRQJLqDDojA4KsgmCSAQIOl00vfzB096aJIOIQb6Jnm/zuEcurb+VPVd1X2l7qpyGGOMAAAAAACwUFCgCwAAAAAAwB9CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGsRWgEAAAAA1iK0AgAAAACsRWgFypEbbrhBCQkJSkxM9P5LTk4OdFmXxLZt29S6dWtJ0pw5czRjxowip//www81e/bsQsedPX/r1q21bdu2C6pl7969evLJJyVJhw4dUteuXS9o/pIaNWqUWrdurcmTJ/utZ9++fWrQoMElqedSe+211/TJJ59c8HwNGjTQvn37LkJFJRMfH68vv/wy0GVYt12KqyT77O/xwQcfaN68eWV231q4cKEeeeSR80739ttva+jQoZKk5ORkpaWlFTl9SkqKtm/fXui4/PlLuk3XrVunKVOmSJJWr16tMWPGXPAy8m3fvl0jRowo8fwAiscZ6AIAXFozZ85U1apVA11GQHXr1u2802zevFm1a9cu8fxFOXDggH7++WdJUnR0tObOnfu7lldc8+bN07p163T11Vf7racs+/LLL/WXv/wl0GWgHNm/f78WLVqk+fPna//+/YEuxxpjx4497zRpaWl64IEHipy/pH802bZtm44fPy5JatOmjdq0aVOi5UjSLbfcotmzZ2vt2rVq1apViZcDoGiEVgCSznzxtmnTRjt37tTEiRNVsWJFjR07VseOHVNeXp569OihLl26SJKmTJmiZcuW6corr1SjRo20fft2zZo1S0OHDlXt2rX18MMPS5LP60OHDmn06NE6ePCg3G634uLi9Oijj2rfvn3q3bu3WrZsqS1btuj48eMaOHCgYmNjlZubq5deeknr1q1TcHCwGjRooFGjRikhIUEjRoxQs2bNJJ35i3zt2rXVq1cvn3X64IMPNHPmTFWqVEl16tTxDp86dap+++03jRw5Uh988IHmzp2rkJAQhYaGavTo0fr555+1Zs0abdiwQWFhYTp69Kj+/e9/Kz09XTfccINq1arlnT//fXbu3KmcnBw9+OCD6tKli7788ku98MILWr58uSR5Xy9ZskQpKSk6dOiQHn74YT3//PNKSEjQt99+K7fbrXHjxmnjxo0KDg5WvXr1NGzYMFWqVEmtW7fWvffeq40bN+rgwYPq0KGDnn322QKf4w8//KDRo0fr2LFjcjgceuihh9SxY0d1795dxhj17dtXo0aNUqNGjSRJeXl5BerJy8vTyJEjtW3bNp04cULPPvus2rVrJ0maNm2aVq1aJY/Ho+rVq2vUqFGKjo72qSEvL08TJkzQmjVrFBERoXr16unHH3/UrFmzlJmZqbFjx2rXrl1yu92KiYnRs88+K6fTqVtvvVX9+vXThg0blJ6erp49e6p3796Szpz5njNnjjwej6644gqNGDFC119/vYYOHapjx45p7969uvPOO9WlSxeNHj1ap0+fVnp6uurWratXXnlFCxYs0Pbt2zVhwgQFBwerZcuWmjhxor7++mvl5eXppptuUkpKiipVqqRNmzbphRdekMPh0K233iqPx1PoPtO6dWvddddd2rRpkzIzM/Xggw+qe/fuOnXqlIYNG6Y9e/YoKChIN998s0aPHq2goCCtWbNG06ZNk9vtVlhYmIYMGaIGDRr4tMlz2+h///tfDR8+XFlZWfrzn/+s06dPe2v45JNP9NprrykvL0+VKlXSsGHDVK9ePZ869+3bp6SkJF1//fXav3+/Zs2apYULF+qTTz6Ry+VSVlaWhgwZorvvvltTp07V/v37lZGRof3796tq1aqaPHmyoqOji9wu8+bN06xZsxQUFKSrrrpKI0aM0HXXXaehQ4cqNDRU27Zt0+HDh9WhQwdVrVpVa9euVUZGhsaMGaOYmBifer/88ktNnjxZNWrU0A8//KCcnByNHDlSjRs3LvIY07p1a8XHx2vdunU6duyYnnzySX3zzTf67rvv5HQ6NW3aNG9bLWyflVTk53P2MeCxxx5TcnKycnJyZIxRly5dlJSUVKCNvPnmm0pMTJTD4fAZXtS+vnXrVj333HNyu92qWbOmDhw4oKFDh+qOO+4o9nbKzMzU888/r507d8rhcKh58+Z65pln5HQ6Cxzru3fvrt69e2vdunU6efKkBg8erNTUVO3atUvVqlXT9OnTVbFiRS1YsEDz5s2T2+3W8ePH1bdvX3Xv3r3QfSN/HceMGaO0tDRFRkYqMjJSERERkqQePXooKSlJd911l1544QV98803CgkJ0bXXXqsXX3xRM2bMUHp6uv72t79pwoQJmjhxoqpUqaKffvpJ3bp106pVq5SUlKRbbrlFHo9HycnJ3s85JSVF9evX97tPJSYmau7cucrLy1NERIRq1aqllStX6s0339Svv/6q5557Tvv375cxRh07dlSfPn2K/J6SpAceeEDPPfccoRW4mAyAcqNOnTomPj7e3HPPPd5/hw8f9o5btGiRMcYYt9ttYmNjzfbt240xxpw4ccJ06NDBfPvtt2blypUmNjbWZGZmmpycHNOnTx/z17/+1RhjzJAhQ8w//vEP7/ud/bpHjx5m9erVxhhjsrOzTY8ePczHH39s9u7da+rUqWPWrFljjDEmNTXV3HnnncYYY2bOnGmSkpJMVlaWycvLM0899ZRZtGiReffdd82AAQOMMcZkZmaaxo0bm+PHj/us6/fff29iYmJMenq6McaYESNGmFatWhljjHn11VfN888/b3Jzc83NN99sDh06ZIwxZtGiRWbu3LkFan/11VdNu3btjNvt9pnfGGNatWplRo0aZYwx5tdffzWNGzc2u3btMl988YWJi4vz1nP267P/v3fvXlO/fn1jjDFTpkwxTzzxhMnJyTF5eXlm6NChZsSIEd73GTdunPd9br31VvPLL7/4rLPb7TZt2rQxK1eu9E7XvHlz880333g/4yNHjhRoF+fWU6dOHZOammqMMWbVqlWmTZs23u3z9NNPe7fD3LlzTZ8+fQosb86cOSYpKclkZ2cbl8tlHnroIW8bGTp0qHn//feNMcbk5uaav/3tb2bGjBne+mbNmmWMMWbbtm3mlltuMdnZ2ebLL7803bt3N6dPnzbGGLN+/XrToUMH7+fUq1cv73uPGzfOLF682BhjTE5OjomPj/euy1//+lfz//7f/zPGGDN16lQzbtw44/F4jDHGTJo0yYwaNcq4XC7TpEkTk5aWZowxZtmyZaZOnTpm7969BdazVatWZsSIEcbj8ZiDBw+aO+64w+zcudMsWrTIPPTQQ951TE5ONrt37zY///yziY+PN0ePHjXGGLNr1y7TtGlTc+rUKZ82ZYxvG0tMTDTz5883xhizadMmc8MNN5gvvvjC/Pe//zVNmjTxtoO0tDTTtGlTk5mZ6VNn/mf69ddfG2OM2bdvn+nRo4fJysoyxhizfPlyEx8f733fNm3aeJfxyCOPmClTphS5XdLS0sxdd93lbVsfffSR6dChg/F4PGbIkCHmvvvuMzk5OSY9Pd3UqVPH+/m/99575sEHHyywXb/44gtz4403mu+//94YY8zbb79tkpKSvJ+3v2NMq1atzN///ndjjDEff/yxqVu3rtmxY4cxxpjHH3/cTJs2zTtdYfvs+T6fs48Bw4YNM2+++aYxxpj09HTz9NNPm7y8PJ/18Hg85o477vC2neLs626327Ro0cKsW7fOGGPMxo0bvZ/3hWynZ5991rzwwgvG4/F498H8es8+1ue/njlzpjHGmDfffNM0aNDA/PrrryYvL8/ce++9ZunSpebkyZPm/vvv926bb7/91rsuH330kenXr1+B+t577z3Ts2dP43K5zKlTp8y9995rhgwZYoz537749ddfm/bt23v3wwkTJpjNmzd7P6etW7d6px82bJh32fnz57ftjz/+2BhjzGeffWZatmxpXC5XkfvU2f8/u/6kpCTzzjvvGGPOfO8lJCSY5cuXF/k9la9x48YFjskASg9nWoFypqjuwfln33bv3q1ffvlFw4cP947Lzs7W999/r//+97+6++67ValSJUln/sI8c+bMIt/z9OnT+vrrr3X8+HHvdUSnT5/Wzp07Va9ePYWEhKhly5aSpJtuuknHjh2TdKZ7WGJiosLCwiRJr7zyiiTpxIkTev3113X06FGlpqbqzjvvVOXKlX3ec+PGjWratKmioqK8dX7++ec+0wQHB6t9+/bq2rWr7rzzTjVt2lQJCQmFrkP9+vXldBZ+yMy/JjU6OlrNmjXTxo0bdcMNNxS5TQrz2WefaeDAgQoJCZF05mxE//79vePzu7BFR0crMjJSx48fV40aNbzjd+/eLZfLpbZt23qna9u2rdavX39B132FhIR4z6zWrVtXR44ckSStXbtW27ZtU+fOnSVJHo9HWVlZBeb/9NNPlZiYqNDQUElntv2sWbMknbmWbNu2bVqwYIGkM+3qbPnrePPNNysnJ0enT5/WunXrtGfPHp9rf48fP+5tJw0bNvQOHzx4sDZs2KC33npLu3fvVnp6us+ZyXzr1q1TZmam97o6t9utyMhI7dq1S06n03v2Lz4+3numpjDdu3eXw+HQ1VdfrebNm2vDhg26++67NXnyZPXo0UNNmjRRr169VKtWLc2ePVvp6enes8eS5HA49Msvv/hd/m+//ab//Oc/6tixo3dd87utf/HFF2rcuLG3DcTExKhq1aravn27Gjdu7LMcp9Op+vXrS5KqV6+u8ePHa9myZdqzZ4+2bNmiU6dOeae9/fbbvfv3TTfdpOPHjxe5XdavX6/Y2FjvcaVTp04aO3ast+tmq1atFBISoqioKFWsWFHNmzeXJNWsWdP7GZ7rj3/8o2688UZvDYsWLfK7jc6W3/Zr1Kihq666SnXr1vW+V353UKnwfTY4OLjIz+fsY8Ddd9+tIUOGaOvWrYqJiVFKSoqCgnxvE/Lbb78pMzNT1157bYE6/e3ru3btkiTv8bBx48Z+L1Moajt99tlnmjNnjhwOhypUqKCuXbtq5syZ6tevn6T/Hevz5e/vNWvWVJ06dbxnpK+99lodP35cf/jDHzR9+nR9+umn2r17t3bu3FnofnW2jRs3Kj4+XhUqVFCFChWUkJCg//znPz7T1KlTR8HBwbrvvvvUrFkztWvXrkBPgXzn1pyvcuXK3jOezZs3lzFGP/30U5G1Feb06dP65ptv9M4770iSIiIi1KlTJ3322We67bbb/H5P5atRo4Z+/vlnn2MygNJDaAXgVbFiRUlnundWrlxZS5Ys8Y47fPiwIiIi9Morr8gY4x2e/6NLOvMD7+xxbrdb0plwY4zR3LlzFR4eLkk6evSoQkND9dtvvykkJMT7g+/sbnTnhsTDhw/L4/GoWrVqat++vZYuXaply5Zp1KhRBdbl3FqCg4MLXeeJEydq165dSktL01tvvaUFCxZo2rRpfrdNYc7+sWqMkdPp9LstinJuN1SPx+MzX34IlAquX2Hz59eTm5t73vc+27mf6dnL79Onj7dLYE5Ojk8QyHfu53b29vF4PJoyZYquv/56SWf+AHH2e+SvY/4wY4w8Ho8SExM1ePBg7zLS09NVpUoVSb6fzTPPPKO8vDx16NBBd955pw4ePFhgO+UvY/jw4d4foadOnZLL5Sp0en9/rDh3nMfjUVBQkGrUqKF//etf+vLLL/XFF1/owQcfVEpKijwej2JiYrx/fJGkgwcPqlq1avrkk08KbS9nb4dz37Ow9fL3eVeoUME733fffafHH39cvXv3VtOmTfV///d/ev75573T5v+RKP/9jTGFtrfi1lGhQgW/28yfwmo49/9Swf3q7Pc6ux2fq7B9Ni8vz+/n869//cunnbVq1UorV65UWlqaNm7cqNdff11z585VzZo1fd4jv/2eG2j97evBwcEFtmf+sWvKlClas2aNpDNd0xs3bux3OxW2/LPbxbnHs7O3VWHb7ddff9UDDzyg+++/Xw0bNlT79u21du3aAtMVpbBjcP73zDfffKMvvvhCTz/9tM9lAWfzdww+d9saYxQSEnLBx+D876lzh+VvN3/fU/ny8vL8fs8A+P24ezCAAq677jqFhoZ6Q+vBgwcVHx+v7du3684771RqaqqOHz8uj8ejxYsXe+e78sorvXd7PHr0qDZt2iRJqlSpkurXr693331X0pmg0q1bN61evbrIOmJiYrR8+XLl5OTI4/Houeee08cffyxJSkpK0vvvvy9jTKF/mW/SpIk2bNigX3/9VZIKPVNz9OhRtWzZUldccYV69+6tp59+2nsmIDg4uNhhL3/ZBw4cUFpamveM14EDB3TkyBEZY3zuWhscHFzoD6jmzZtr7ty5crvd8ng8mj17tpo2bVqsGqQzn1tISIhWrVol6cydiVeuXKkmTZoUOZ+/es7VrFkzLViwQCdPnpR05kd0YdfVtmzZUkuXLlVOTo5yc3N9tn2zZs303nvvyRijnJwcPfbYY/rnP/9Z5Ps2bdpUH3/8sdLT0yWduXvzudcv5/v888/Vv39/xcbGyuFwaMuWLcrLy/OuZ/5n2qxZM82ePdvbtkaMGKGXX35ZderUkTFGn376qaQzdxYtLJjny2//Bw4c0IYNG9SiRQt98MEHGjZsmJo1a6bBgwerWbNm+uGHH9S4cWNt2LBBP/74o6QzZ6TvueceuVwuXXnllfruu+9kjNHp06e9vQKuuOIK3Xzzzfrwww8lnQmc+Wfj8pe3d+9eSfJe73zbbbcVuT2//vpr3XLLLXrwwQd1++23a/Xq1d5t5E9R26VZs2ZasWKFjh49Kkn66KOPdMUVV6hWrVpFLrMk/B1jLlRh+2xRn8+5Bg0apBUrViguLk6jRo1SpUqVdPDgQZ9prrjiClWuXLnQGzD529evv/56VahQQZ999pkkaevWrdq1a5ccDoeeeuopLVmyREuWLNFTTz1V5Prlt+/8/Wz+/PnnPQ4UZfv27apataoef/xxNW/e3BtYi2o3zZs31+LFi+VyueRyubRixYoC06xdu1a9e/dWgwYN9OSTT6pjx47auXOnpOIfg48dO+atZ82aNQoNDVWtWrX87lP+ll2pUiXddttt3rvGZ2ZmavHixcXabsYY7d+/X9ddd915pwVQMpxpBVBAhQoV9MYbb2js2LH6xz/+odzcXD311FPebpg9e/ZU9+7dFRoaqurVq3vn69Gjh/72t7+pXbt2uvbaa3X77bd7x02cOFEvvPCCEhISlJOTo/j4eN1zzz1F3v2xa9eu2r9/vzp16iRjjG6//Xb16NFD0pluq1WqVPH7uJgbbrhBgwcPVq9evfSHP/yh0GBbtWpVPfbYY+rdu7fCwsIUHBzsffRBixYt9MILLxRre7lcLt17771yu91KSUnx/nDp2rWrOnfurKioKN15553e6WvXrq3g4GB16dLF5/Ezjz32mMaPH6+OHTsqNzdX9erVu6BHKYSEhOiNN97QmDFjNHXqVOXl5al///4Fuoqey18957rvvvt06NAh3X///XI4HLrmmms0bty4AtN16tRJP//8szp27KiKFSvq2muv9Z5hT05O1tixY5WQkCC3260mTZqoT58+RdbXvHlz9e3bVw899JAcDocqVaqk1157rdCzHQMHDlT//v1VpUoVhYeH6//+7/+83TtbtWql8ePHy+126/HHH9f48eN17733Ki8vTzfeeKOGDh2qkJAQvf7663ruuef08ssv68Ybb1RkZKTf2vbt26dOnTopOztbKSkp+vOf/6yrr75aX331lWJjYxUeHq4//vGP6tmzp6pUqaLRo0frmWee8Z7dmzZtmipWrKh77rlH69evV9u2bRUdHa0GDRp4z/q8/PLLGjZsmPdM3p///GdJ0l/+8heNGjVKTzzxhPLy8hQWFqbp06d7b3bjT3x8vFatWqXY2FiFhIQoJiZGx48f9/4xojBFbZemTZuqd+/e6tWrlzwej6pWrao333yzwBmw0lDUMeZC+Ntn/X0+53r88ceVnJysefPmKTg4WHfddVehteR3zz/3hkX+9nWn06mpU6dq1KhRevnll/WnP/1JV111lc8Z1eJISUnRmDFjvPtZ8+bN9eijj17QMs7WtGlTLViwQO3bt1d4eLjq1aunqlWras+ePX7n6dq1q3755RfFx8f7/SNGixYt9Nlnnyk+Pl4VK1ZUlSpVvMfdu+66SwMHDjzv42giIyO1atUqvfLKKwoPD9fUqVPldDqL3KdiYmL05JNPKiQkRDfffLN3WRMnTtTo0aO1cOFC5eTkKCEhQZ06dTrvnZ+3bdummjVr6o9//GOR0wEoOYcprF8PABRTamqqZs+e7b1m8VL55Zdf1KNHD6WmpnoDEezw+eef68iRI0pMTJQkjRkzRqGhod7uvWVF69atNWXKFN16662BLgWW2rt3r5566il99NFHhf6RpTDjx4/Xww8/rKuuukoHDx5UYmKiPvnkkwLX7cMeQ4cOVfv27X3+OAmgdHGmFcBlZ8qUKZo/f76Sk5MJrBaqXbu23n77bb399tvKy8tT3bp19dxzzwW6LOCSq1Gjhjp27Ki5c+cW+/nO1atXV+/eveV0OmWM0ZgxYwisFtu2bZscDgeBFbjIONMKAAAAALAWN2ICAAAAAFiL0AoAAAAAsBahFQAAAABgrWKF1p49eyouLk6JiYlKTEzUli1bfManpaUpISFBbdu29Xlcwvjx4xUfH+9zx8gVK1Z4n4EFAAAAAEBRznv3YGOMfvrpJ61bt05OZ8HJs7OzNXz4cM2aNUvXXHONHnnkEX366adq0KCB1q9fr+XLl6tfv37auXOnrr/+ei1cuFDTp08vUbG//XZKHo/9942KjKykI0f8P+8OuFhoewgU2h4CgXaHQKHtIVDKctsLCnLoyiv/UOi484bWn376SQ6HQ3379tWRI0d0//33669//at3/NatW1WrVi3VqFFDkpSQkKDU1FQ1atRIeXl5ys3NVXZ2tkJCQjRnzhx16dKl0PBbHB6PuSxCq6TLpk6UPbQ9BAptD4FAu0Og0PYQKOWx7Z23e/CJEycUExOj119/Xe+9957mzp2rDRs2eMenp6crKirK+7patWo6dOiQ/vCHP6hz587q1KmTbr31VkVHR2vDhg1q3779xVkTAAAAAECZc95Tng0aNFCDBg0kSRUrVlSXLl306aefqmnTppLOdB8+l8PhkCT16dNHffr0kSRNnjxZffr00cqVK7Vw4ULVqFFDw4cPV1BQ8e8FFRlZqdjTBlpUVESgS0A5RdtDoND2EAi0OwQKbQ+BUh7b3nlD66ZNm+R2uxUTEyPpTEg9u3tvdHS0Dh8+7H2dnp6uatWq+Szj0KFD2rNnjwYOHKh27dpp6dKlGjt2rNLS0tSsWbNiF3vkyMnL4nR4VFSEMjIyA10GyiHaHgKFtodAoN0hUGh7CJSy3PaCghx+T1Ke9zRnZmamJkyYIJfLpZMnT2rRokW6++67veNvu+02/fzzz9qzZ4/y8vK0fPlytWjRwmcZr732mvr37y9JcrvdCgoKUlBQkFwu1+9ZLwAAAABAGXfeM62tWrXSli1b1LFjR3k8HnXv3l0NGjRQYmKiZsyYoejoaI0bN05PPvmkXC6XWrZs6XPd6q5duyRJtWvXlnTm8TmJiYmqXr26mjdvfpFWCwAAAABQFjhMYRelWoruwUDRaHsIFNoeAoF2h0Ch7SFQynLb+13dgwEAAAAACBRCKwAAAADAWoRWAAAAAIC1CK0AAAAAAGsRWgEAAAAA1iK0AgAAAACsRWgFAAAAAFiL0AoAAAAAsBahFQAAAABgLUIrAAAAAMBahFYAAAAAgLUIrQAAAAAAaxFaAQAAAADWIrQCAAAAAKzlDHQBwOUionK4wkJ9d5lsV64yT2QFqCIAAACg7CO0AsUUFupUwqAlPsOWTUpUpgoGWsIsAAAAUDoIrUApODfQ5odZAAAAAL8P17QCAAAAAKxFaAUAAAAAWIvQCgAAAACwFqEVAAAAAGAtQisAAAAAwFrcPRi4iHi2KwAAAPD7EFqBi6ioZ7sCAAAAOD+6BwMAAAAArEVoBQAAAABYi9AKAAAAALAW17QC5/B38yQAAAAAlx6hFTiHv5snAQAAALj06B4MAAAAALAWoRUAAAAAYC1CKwAAAADAWlzTCgSAv5s9ZZ7IClBFAAAAgJ0IrUAA+LvZU2aA6gEAAABsRfdgAAAAAIC1CK0AAAAAAGsRWgEAAAAA1iK0AgAAAACsRWgFAAAAAFiL0AoAAAAAsBahFQAAAABgLUIrAAAAAMBahFYAAAAAgLUIrQAAAAAAaxFaAQAAAADWcga6ACBQIiqHKyz0f7tAtitXmSeyAlgRAAAAgHMRWlFuhYU6lTBoiff1skmJygxgPQAAAAAKonswAAAAAMBahFYAAAAAgLUIrQAAAAAAa3FNK2AZbhAFAAAA/A+hFbAMN4gCAAAA/ofuwQAAAAAAaxFaAQAAAADWIrQCAAAAAKxFaAUAAAAAWIvQCgAAAACwFqEVAAAAAGAtQisAAAAAwFqEVgAAAACAtQitAAAAAABrEVoBAAAAANYitAIAAAAArEVoBQAAAABYi9AKAAAAALAWoRUAAAAAYC1CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGsRWgEAAAAA1iK0AgAAAACs5Qx0AQCKJ6JyuMJCfXfZbFeuMk9kBagiAAAA4OIjtAKXibBQpxIGLfEZtmxSojIDVA8AAABwKdA9GAAAAABgLUIrAAAAAMBahFYAAAAAgLUIrQAAAAAAaxFaAQAAAADW4u7BKNN4TAwAAABweSO0okzjMTEAAADA5Y3uwQAAAAAAaxFaAQAAAADWIrQCAAAAAKxFaAUAAAAAWIvQCgAAAACwFqEVAAAAAGAtQisAAAAAwFqEVgAAAACAtYodWsePH6+hQ4cWGH7gwAElJSWpffv2euyxx3Tq1ClJUmpqqjp06KBu3brp6NGj3mmfffbZUiodAAAAAFDWFSu0bty4UYsWLSp03PPPP6/u3bsrNTVVt9xyi9544w1J0vTp0/XPf/5Tbdq00fLlyyVJr7/+uvr3719KpQMAAAAAyrrzhtZjx45p8uTJevTRRwuMc7vd+vrrr9WuXTtJUqdOnZSamipJCgkJkcvlUnZ2tkJCQrRjxw6Fh4erVq1apbwKQPkWUTlcUVERioqKkCRFRUUoonJ4gKsCAAAASofzfBOMHDlSAwcO1MGDBwuM++2331SpUiU5nWcWExUVpUOHDkmSBg4cqCeeeELR0dGaOHGihg4dqueee650qwegsFCnEgYt8Rm2bFKiMgNUDwAAAFCaigytH374oa655hrFxMRo4cKFBcYbYwoMczgckqQmTZp45/n88891ww036NixY0pOTlZ4eLiSk5N11VVXXVCxkZGVLmj6QMo/6wU7+ft8SvK5XW7LAi4W2hwCgXaHQKHtIVDKY9srMrSuWLFCGRkZSkxM1PHjx3X69Gn9/e9/1/DhwyVJVatW1cmTJ5WXl6fg4GBlZGSoWrVqPsvweDx677339Oqrryo5OVn9+vXTwYMH9f777+uZZ565oGKPHDkpj6dgULZNVFSEMjI4z2UDfzt1RkZmoeP8DS+KzcsCLhWOewgE2h0ChbaHQCnLbS8oyOH3JGWRofXdd9/1/n/hwoX66quvvIFVOnPdaqNGjbRixQolJCRo8eLFatGihc8yli5dqjZt2qhixYpyu91yOp1yOBxyuVy/Z50AAAAAAOVAiZ7TmpycrNWrV0uSRo0apfnz5ys2NlabNm3S008/7Z3O5XJp6dKluu+++yRJPXv21LBhw/SPf/xD3bp1+/3VAwAAAADKtPPeiClfp06d1KlTJ0nS2LFjvcOrV6+uWbNmFTpPaGio3nnnHe/r22+/XR9//HFJawUAAAAAlDMlOtMKAAAAAMClQGgFAAAAAFiL0AoAAAAAsBahFQAAAABgLUIrAAAAAMBahFYAAAAAgLUIrQAAAAAAaxFaAQAAAADWIrQCAAAAAKxFaAUAAAAAWIvQCgAAAACwFqEVAAAAAGAtQisAAAAAwFqEVgAAAACAtQitAAAAAABrOQNdAFAaIiqHKyzUtzlnu3IDVA0AAACA0kJoRZkQFupUwqAlPsOWTUoMUDUAAAAASgvdgwEAAAAA1iK0AgAAAACsRWgFAAAAAFiL0AoAAAAAsBahFQAAAABgLUIrAAAAAMBahFYAAAAAgLV4TitQhkVUDldY6P9282xXrjJPZAWwIgAAAODCEFqBMiws1KmEQUu8r5dNSlRmAOsBAAAALhTdgwEAAAAA1iK0AgAAAACsRWgFAAAAAFiL0AoAAAAAsBahFQAAAABgLUIrAAAAAMBahFYAAAAAgLUIrQAAAAAAaxFaAQAAAADWIrQCAAAAAKxFaAUAAAAAWIvQCgAAAACwFqEVAAAAAGAtQisAAAAAwFqEVgAAAACAtQitAAAAAABrEVoBAAAAANYitAIAAAAArEVoBQAAAABYi9AKAAAAALAWoRUAAAAAYC1CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGs5A10AgEsvonK4wkJ9d/9sV64yT2QFqCIAAACgcIRWoBwKC3UqYdASn2HLJiUqM0D1AAAAAP7QPRgAAAAAYC1CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGsRWgEAAAAA1iK0AgAAAACsRWgFAAAAAFiL0AoAAAAAsBahFQAAAABgLUIrAAAAAMBahFYAAAAAgLWcgS4AKK6IyuEKC/VtstmuXGWeyApQRQAAAAAuNkIrLhthoU4lDFriM2zZpERlBqgeAAAAABcf3YMBAAAAANYitAIAAAAArEVoBQAAAABYi9AKAAAAALAWoRUAAAAAYC1CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGs5A10AAHtEVA5XWKjvYSHblavME1kBqggAAADlHaEVgFdYqFMJg5b4DFs2KVGZAaoHAAAAoHswAAAAAMBahFYAAAAAgLUIrQAAAAAAaxFaAQAAAADWIrQCAAAAAKxFaAUAAAAAWIvQCgAAAACwFqEVAAAAAGAtQisAAAAAwFqEVgAAAACAtQitAAAAAABrEVoBAAAAANYitAIAAAAArFWs0DplyhTFxsYqLi5O7777boHxO3bsUOfOndWuXTslJycrNzdXkjRz5kzFxcXp4YcfVk5OjiRp69ateumll0pxFQAAAAAAZdV5Q+tXX32lL774QkuXLtVHH32kWbNm6aeffvKZZvDgwRoxYoRWrlwpY4zmz58v6UxoXbx4sWrWrKn169dLkqZPn65+/fpdhFUBAAAAAJQ15w2tt99+u95//305nU4dOXJEeXl5qlixonf8/v37lZ2drfr160uSOnXqpNTUVEmS0+mU2+1Wdna2QkJCtHr1ajVs2FBVqlS5OGsDAAAAAChTitU9OCQkRK+++qri4uIUExOj6Oho77j09HRFRUV5X0dFRenQoUOSpP79+6tbt27yeDxq3Lix5syZox49epTyKgAAAAAAyipncSccMGCA+vbtq0cffVTz58/XAw88IEkyxhSY1uFwSJISExOVmJgoSZo3b57i4+O1detWzZgxQ5GRkRo5cqTCw8OLXWxkZKViTxtoUVERgS6h3CjJtvY3D8u6sGUBZ6OdIBBodwgU2h4CpTy2vfOG1h9//FE5OTm68cYbFR4errZt2+o///mPd3x0dLQOHz7sfZ2RkaFq1ar5LOP06dNatWqV3nrrLXXt2lVTp07VkiVLtHTpUm/4LY4jR07K4ykYkm0TFRWhjIzMQJdR5vjbQTMyMi945/U3D8vyvyygKBz3EAi0OwQKbQ+BUpbbXlCQw+9JyvN2D963b59SUlKUk5OjnJwc73Wp+apXr67Q0FBt3rxZkrR48WK1aNHCZxnvvPOOevXqpaCgILndboWEhMjhcMjlcv2e9QIAAAAAlHHnDa0tW7ZUy5Yt1bFjR3Xu3FkNGjRQXFyc+vbtq23btkmSJk6cqBdffFEdOnRQVlaWevbs6Z3/yJEj+v77771Btm/fvkpKStLatWuVkJBwkVYLAAAAAFAWFOua1gEDBmjAgAE+w9566y3v/+vWrasFCxYUOm9kZKTeeOMN7+vY2FjFxsaWpFYAAAAAQDlT7BsxASjfIiqHKyzU95CR7cpV5omsAFUEAACA8oDQCqBYwkKdShi0xGfYskmJKpu3AgAAAIAtivWcVgAAAAAAAoHQCgAAAACwFqEVAAAAAGAtQisAAAAAwFqEVgAAAACAtQitAAAAAABrEVoBAAAAANYitAIAAAAArEVoBQAAAABYi9AKAAAAALAWoRUAAAAAYC1CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGs5A10AcK6IyuEKC/Vtmtmu3ABVAwAAACCQCK2wTlioUwmDlvgMWzYpMUDVAAAAAAgkugcDAAAAAKxFaAUAAAAAWIvQCgAAAACwFqEVAAAAAGAtQisAAAAAwFqEVgAAAACAtQitAAAAAABrEVoBAAAAANYitAIAAAAArEVoBQAAAABYi9AKAAAAALAWoRUAAAAAYC1noAsAcHmLqByusFDfQ0m2K1eZJ7ICVBEAAADKEkIrgN8lLNSphEFLfIYtm5SozADVAwAAgLKF7sEAAAAAAGsRWgEAAAAA1iK0AgAAAACsRWgFAAAAAFiL0AoAAAAAsBahFQAAAABgLUIrAAAAAMBahFYAAAAAgLUIrQAAAAAAaxFaAQAAAADWIrQCAAAAAKxFaAUAAAAAWIvQCgAAAACwFqEVAAAAAGAtQisAAAAAwFqEVgAAAACAtQitAAAAAABrEVoBAAAAANYitAIAAAAArEVoBQAAAABYyxnoAgCUTRGVwxUW6nuIyXblKvNEVoAqAgAAwOWI0ArgoggLdSph0BKfYcsmJSozQPUAAADg8kT3YAAAAACAtQitAAAAAABrEVoBAAAAANYitAIAAAAArEVoBQAAAABYi9AKAAAAALAWoRUAAAAAYC1CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGsRWgEAAAAA1iK0AgAAAACsRWgFAAAAAFiL0AoAAAAAsBahFQAAAABgLWegC0D5FVE5XGGhvk0w25UboGoAAAAA2IjQioAJC3UqYdASn2HLJiUGqBoAAAAANqJ7MAAAAADAWoRWAAAAAIC1CK0AAAAAAGtxTSuAS87fTbgyT2QFqCIAAADYitAK4JLzdxOuzADVAwAAAHvRPRgAAAAAYC1CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGsRWgEAAAAA1iK0AgAAAACsRWgFAAAAAFiL0AoAAAAAsBahFQAAAABgLUIrAAAAAMBahFYAAAAAgLUIrQAAAAAAaxUrtL722muKi4tTXFycJkyYUGD8jh071LlzZ7Vr107JycnKzc2VJM2cOVNxcXF6+OGHlZOTI0naunWrXnrppVJcBQAAAABAWXXe0JqWlqbPP/9cixYt0uLFi/Xdd9/pX//6l880gwcP1ogRI7Ry5UoZYzR//nxJZ0Lr4sWLVbNmTa1fv16SNH36dPXr1+8irAoAAAAAoKw5b2iNiorS0KFDVaFCBYWEhOj666/XgQMHvOP379+v7Oxs1a9fX5LUqVMnpaamSpKcTqfcbreys7MVEhKi1atXq2HDhqpSpcrFWRsAAAAAQJly3tBau3ZtbyDdvXu3VqxYoZYtW3rHp6enKyoqyvs6KipKhw4dkiT1799f3bp1k8fjUePGjTVnzhz16NGjlFcBAAAAAFBWOYs74Q8//KBHHnlEQ4YM0Z/+9CfvcGNMgWkdDockKTExUYmJiZKkefPmKT4+Xlu3btWMGTMUGRmpkSNHKjw8vNjFRkZWKva0gRYVFRHoEsqNkmxrf/OwrAtbVkkUtSz2m8sbnx8CgXaHQKHtIVDKY9srVmjdvHmzBgwYoOHDhysuLs5nXHR0tA4fPux9nZGRoWrVqvlMc/r0aa1atUpvvfWWunbtqqlTp2rJkiVaunSpHnjggWIXe+TISXk8BUOybaKiIpSRkRnoMqxXWjtcRkbmBS/L3zws68KWVRJFLYv95vLFcQ+BQLtDoND2EChlue0FBTn8nqQ8b2g9ePCg+vfvr8mTJysmJqbA+OrVqys0NFSbN29Ww4YNtXjxYrVo0cJnmnfeeUe9evVSUFCQ3G63QkJC5HA45HK5SrhKAMqiiMrhCgv1PSxlu3KVeSIrQBUBAAAg0M4bWt9++225XC6NGzfOO6xr165as2aNBgwYoFtvvVUTJ05USkqKTp06pZtuukk9e/b0TnvkyBF9//33euKJJyRJffv2VVJSkq688kq9/vrrF2GVAFyuwkKdShi0xGfYskmJKpt/TwQAAEBxnDe0pqSkKCUlpcDwbt26ef9ft25dLViwoND5IyMj9cYbb3hfx8bGKjY2tiS1AgAAAADKmfPePRgAAAAAgEAhtAIAAAAArEVoBQAAAABYi9AKAAAAALAWoRUAAAAAYC1CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGsRWgEAAAAA1iK0AgAAAACsRWgFAAAAAFiL0AoAAAAAsBahFQAAAABgLUIrAAAAAMBahFYAAAAAgLUIrQAAAAAAazkDXQAAFEdE5XCFhf7vkJXtylXmiawAVgQAAIBLgdAK4LIQFupUwqAl3tfLJiUqM4D1AAAA4NKgezAAAAAAwFqEVgAAAACAtQitAAAAAABrEVoBAAAAANYitAIAAAAArEVoBQAAAABYi9AKAAAAALAWoRUAAAAAYC1CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGs5A10AyraIyuEKC/VtZtmuXGWeyApQRQAAAAAuJ4RWXFRhoU4lDFriM2zZpERlBqgeAAAAAJcXugcDAAAAAKxFaAUAAAAAWIvuwQAua1w3DQAAULYRWgFc1rhuGgAAoGyjezAAAAAAwFqEVgAAAACAtQitAAAAAABrEVoBAAAAANYitAIAAAAArEVoBQAAAABYi9AKAAAAALAWoRUAAAAAYC1CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGsRWgEAAAAA1iK0AgAAAACsRWgFAAAAAFiL0AoAAAAAsBahFQAAAABgLWegCwCAiyGicrjCQn0PcdmuXGWeyApQRQAAACgJQiuAMiks1KmEQUt8hi2blKjMANUDAACAkqF7MAAAAADAWoRWAAAAAIC1CK0AAAAAAGsRWgEAAAAA1iK0AgAAAACsRWgFAAAAAFiL0AoAAAAAsBahFQAAAABgLUIrAAAAAMBahFYAAAAAgLUIrQAAAAAAazkDXQAAXGoRlcMVFvq/w1+2K1eZJ7ICWBEAAAD8IbQCKHfCQp1KGLTE+3rZpERlBrAeAAAA+Ef3YAAAAACAtQitAAAAAABrEVoBAAAAANYitAIAAAAArEVoBQAAAABYi9AKAAAAALAWoRUAAAAAYC1CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGsRWgEAAAAA1iK0AgAAAACs5Qx0AQBgi4jK4QoL9T0sZrtylXkiK0AVAQAAgNAKAP+/sFCnEgYt8Rm2bFKiMgNUDwAAAAitKCX+zlABAAAAwO9BaEWp8HeGCgAAAAB+D27EBAAAAACwFqEVAAAAAGAtQisAAAAAwFqEVgAAAACAtQitAAAAAABrEVoBAAAAANYqdmg9efKk4uPjtW/fvgLjduzYoc6dO6tdu3ZKTk5Wbu6Z53POnDlTcXFxevjhh5WTkyNJ2rp1q1566aVSKh8ALr6IyuGKiorw+RdROTzQZQEAAJQLxQqtW7ZsUbdu3bR79+5Cxw8ePFgjRozQypUrZYzR/PnzJZ0JrYsXL1bNmjW1fv16SdL06dPVr1+/0qkeAC6B/OcQn/0vLJTHXAMAAFwKxQqt8+fP16hRo1StWrUC4/bv36/s7GzVr19fktSpUyelpqZKkpxOp9xut7KzsxUSEqLVq1erYcOGqlKlSumtAQAAAACgzCpWaB07dqwaNWpU6Lj09HRFRUV5X0dFRenQoUOSpP79+6tbt27yeDxq3Lix5syZox49epRC2QAAAACA8uB3928zxhQY5nA4JEmJiYlKTEyUJM2bN0/x8fHaunWrZsyYocjISI0cOVLh4cW/LiwystLvLfeSiYqKCHQJVivN7VOSZfmbh2Vd2LJKwtZ1CfR2KQvYHggE2h0ChbaHQCmPbe93h9bo6GgdPnzY+zojI6NAN+LTp09r1apVeuutt9S1a1dNnTpVS5Ys0dKlS/XAAw8U+72OHDkpj6dgSLZNVFSEMjIyA13GJXWhO09GRmap7XAlWZa/eVjWhS2rJGxdl5IuC2eUx+MeAo92h0Ch7SFQynLbCwpy+D1J+bsfeVO9enWFhoZq8+bNkqTFixerRYsWPtO888476tWrl4KCguR2uxUSEiKHwyGXy/V73x4AAAAAUIaVOLT27dtX27ZtkyRNnDhRL774ojp06KCsrCz17NnTO92RI0f0/fffe4Ns3759lZSUpLVr1yohIeF3lg8AAAAAKMsuqHvwmjVrvP9/6623vP+vW7euFixYUOg8kZGReuONN7yvY2NjFRsbe6F1AgAAAADKod/dPRgAAAAAgIuF0AoAAAAAsBahFQAAAABgLUIrAAAAAMBahFYAAAAAgLUIrQAAAAAAa13QI28AAL4iKocrLPR/h9JsV64yT2QFsCIAAICyhdAKAL9DWKhTCYOWeF8vm5SozADWAwAAUNbQPRgAAAAAYC1CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGsRWgEAAAAA1iK0AgAAAACsxSNvAOAiOPf5rRLPcAUAACgJQisAXATnPr9V4hmuAAAAJUH3YAAAAACAtQitAAAAAABrEVoBAAAAANYitAIAAAAArEVoBQAAAABYi9AKAAAAALAWj7wBgEuI57cCAABcGEIrLsi5P7j5sQ1cGJ7fCgAAcGEIrbgg5/7g5sc2AAAAgIuJa1oBAAAAANYitAIAAAAArEVoBQAAAABYi9AKAAAAALAWoRUAAAAAYC3uHgwAluCRUgAAAAURWgHAEjxSCgAAoCC6BwMAAAAArEVoBQAAAABYi9AKAAAAALAWoRUAAAAAYC1uxAQAljv3rsISdxYGAADlB6EVACx37l2FJe4sDAAAyg+6BwMAAAAArEVoBQAAAABYi9AKAAAAALAWoRUAAAAAYC1CKwAAAADAWtw9GAAuUzwKBwAAlAeEVgC4TPEoHAAAUB7QPRgAAAAAYC1CKwAAAADAWoRWAAAAAIC1CK0AAAAAAGsRWgEAAAAA1uLuwQBQBvE4HAAAUFYQWgGgDOJxOAAAoKygezAAAAAAwFqcaQWAcoRuwwAA4HJDaAWAcoRuwwAA4HJD92AAAAAAgLU404oC6D4IAAAAwBaEVhRA90EAAAAAtiC0AgDoYQEAAKxFaAUA0MMCAABYixsxAQAAAACsRWgFAAAAAFiL0AoAAAAAsBbXtAIAisRNmgAAQCARWgEAReImTQAAIJDoHgwAAAAAsBZnWgEAJXJut+GoqAi6DQMAgFJHaAUAlAjdhgEAwKVA92AAAAAAgLU40woAKHXndh2m2zAAACgpQisAoNSd23WYbsMAAKCk6B4MAAAAALAWZ1oBAJfMud2GJboOAwCAohFaAQCXjL87DoswCwAA/CC0AgACjsfnAAAAf7imFQAAAABgLc60lmP+ri0DAJvw+BwAAMo3Qms55vfaMgCwCI/PAQCgfCO0AgAuS9yJGACA8oHQCgC4LHEnYgAAygdCKwCgTOFOxAAAlC3cPRgAAAAAYC3OtAIAyg1/dyLm+lgAAOxFaAUAlBv+7kRMl2IAAOxFaAUAwA/OwAIAEHiEVgAA/CjqDCxdjQEAuDQIrQAAlABdjQEAuDQIreWAv7MBAIBLp6gzsJydBQDAv2KF1mXLlmnatGlyu93q3bu3kpKSfMbv2LFDKSkpOnnypBo1aqTnn39eTqdTM2fO1Pz583X11Vdr2rRpqlChgrZu3aqVK1dq8ODBF2WFUJC/swEAgEunqDOw/sbJT5jNX965wwnAAICy6Lyh9dChQ5o8ebIWLlyoChUqqGvXrrrjjjv0l7/8xTvN4MGDNWbMGNWvX1/Dhw/X/Pnz1b17d82cOVMrV67U3//+d61fv15t2rTR9OnT9eKLL17UlQIAoCzwG2alUgvAhQVdQi4AwCbnDa1paWlq3LixrrjiCklSu3btlJqaqieeeEKStH//fmVnZ6t+/fqSpE6dOunVV19V9+7d5XQ65Xa7lZ2drZCQEK1evVoNGzZUlSpVLtoKAQCAgi7kTK+/m01JF36m93xngItaFgAAUjFCa3p6uqKioryvq1Wrpq1bt/odHxUVpUOHDkmS+vfvr27duqlu3bpq3LixHn/8cb3xxhslLjYoyFHieS+1S11rpUphCj3nS9/lytXJk9mSpGpXhvuMy6/v3OH5Chvub57SXFZR85Tmsi7FupSHZRU1T2ku63LbLrYuq6h5SnNZl9t2sXVZRc1Tmssqal3CQp16eMwqn+Fvp7SVpEKHnypkHn/Di7Osc7/b8r/Xzh4eFRXhHSfJ73ehv+9Jf/MUtSxAurx+l6JsKattr6j1chhjTFEzT58+XVlZWRo4cKAk6cMPP9S2bds0evRoSdI333yjl156SXPmzJEk7dmzR4888ohSU1N9ljNv3jyFhobq2muv1YwZMxQZGamRI0cqPLzwL1YAAAAAAILON0F0dLQOHz7sfZ2enq5q1ar5HZ+RkeEzXpJOnz6tVatW6Z577tGECRP0wgsv6LrrrtPSpUtLYx0AAAAAAGXUeUNrkyZNtHHjRh09elRZWVlatWqVWrRo4R1fvXp1hYaGavPmzZKkxYsX+4yXpHfeeUe9evVSUFCQ3G63QkJC5HA45HK5Snl1AAAAAABlSbHOtA4cOFA9e/ZUx44dFR8fr3r16qlv377atm2bJGnixIl68cUX1aFDB2VlZalnz57e+Y8cOaLvv//eG2T79u2rpKQkrV27VgkJCRdptQAAAAAAZcF5r2kFAAAAACBQznumFQAAAACAQCG0AgAAAACsRWgFAAAAAFiL0AoAAAAAsBahFQAAAABgLUJrKVq2bJliY2N19913a/bs2YEuB2XUyZMnFR8fr3379kmS0tLSlJCQoLZt22ry5Mne6Xbs2KHOnTurXbt2Sk5OVm5ubqBKRhnw2muvKS4uTnFxcZowYYIk2h4ujSlTpig2NlZxcXF69913JdH2cOmMHz9eQ4cOleS/fR04cEBJSUlq3769HnvsMZ06dSqQJeMy17NnT8XFxSkxMVGJiYnasmWL34zh71hYJhmUil9//dW0atXK/Pbbb+bUqVMmISHB/PDDD4EuC2XMv//9bxMfH29uvvlms3fvXpOVlWVatmxpfvnlF+N2u81DDz1k1q1bZ4wxJi4uznz77bfGGGOGDRtmZs+eHcDKcTnbsGGDeeCBB4zL5TI5OTmmZ8+eZtmyZbQ9XHRffvml6dq1q3G73SYrK8u0atXK7Nixg7aHSyItLc3ccccdZsiQIcYY/+2rX79+Zvny5cYYY1577TUzYcKEgNSLy5/H4zFNmzY1brfbO8xfxijqN2BZxJnWUpKWlqbGjRvriiuuUMWKFdWuXTulpqYGuiyUMfPnz9eoUaNUrVo1SdLWrVtVq1Yt1ahRQ06nUwkJCUpNTdX+/fuVnZ2t+vXrS5I6depEe0SJRUVFaejQoapQoYJCQkJ0/fXXa/fu3bQ9XHS333673n//fTmdTh05ckR5eXk6ceIEbQ8X3bFjxzR58mQ9+uijkuS3fbndbn399ddq166dz3CgJH766Sc5HA717dtX99xzj/75z3/6zRj+fgOWVYTWUpKenq6oqCjv62rVqunQoUMBrAhl0dixY9WoUSPva3/t7tzhUVFRtEeUWO3atb0/1Hbv3q0VK1bI4XDQ9nBJhISE6NVXX1VcXJxiYmI47uGSGDlypAYOHKjKlStLKvh9m9++fvvtN1WqVElOp9NnOFASJ06cUExMjF5//XW99957mjt3rg4cOFCsY15Zzx6E1lJijCkwzOFwBKASlCf+2h3tERfDDz/8oIceekhDhgxRzZo1C4yn7eFiGTBggDZu3KiDBw9q9+7dBcbT9lCaPvzwQ11zzTWKiYnxDuP7FpdCgwYNNGHCBFWsWFFVq1ZVly5d9OqrrxaYrjy2PWegCygroqOjtWnTJu/r9PR0bxdO4GKJjo7W4cOHva/z2925wzMyMmiP+F02b96sAQMGaPjw4YqLi9NXX31F28NF9+OPPyonJ0c33nijwsPD1bZtW6Wmpio4ONg7DW0PpW3FihXKyMhQYmKijh8/rtOnT8vhcBTavqpWraqTJ08qLy9PwcHBtDv8Lps2bZLb7fb+wcQYo+rVqxfr+7asZw/OtJaSJk2aaOPGjTp69KiysrK0atUqtWjRItBloYy77bbb9PPPP2vPnj3Ky8vT8uXL1aJFC1WvXl2hoaHavHmzJGnx4sW0R5TYwYMH1b9/f02cOFFxcXGSaHu4NPbt26eUlBTl5OQoJydHq1evVteuXWl7uKjeffddLV++XEuWLNGAAQPUunVrvfjii4W2r5CQEDVq1EgrVqzwGQ6URGZmpiZMmCCXy6WTJ09q0aJFeumllwrNGP6+h8sqzrSWkujoaA0cOFA9e/aU2+1Wly5dVK9evUCXhTIuNDRU48aN05NPPimXy6WWLVuqffv2kqSJEycqJSVFp06d0k033aSePXsGuFpcrt5++225XC6NGzfOO6xr1660PVx0LVu21JYtW9SxY0cFBwerbdu2iouLU9WqVWl7uOT8ta9Ro0Zp6NChmjZtmq655hq9/PLLAa4Ul6tWrVp5j3kej0fdu3dXw4YN/WYMf9/DZZHDFNYhGgAAAAAAC9A9GAAAAABgLUIrAAAAAMBahFYAAAAAgLUIrQAAAAAAaxFaAQAAAADWIrQCAAAAAKxFaAUAAAAAWIvQCgAAAACw1v8HYNPmKZNiUiUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = 100\n",
    "sd = 40\n",
    "\n",
    "generator = rnd.log_normal(mean, sd, as_lambda=True)\n",
    "statistics = generate_random_numbers(generator)\n",
    "\n",
    "show_results(mean, sd, statistics)\n",
    "plot_results(statistics, \"log-normal distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gamma distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defined mean = 100\n",
      "Defined standard deviation = 40\n",
      "Generated random numbers: 1000000\n",
      "Actual mean = 100.06 (relative deviation: 0.06%)\n",
      "Actual standard deviation = 39.98 (relative deviation: 0.04%)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = 100\n",
    "sd = 40\n",
    "\n",
    "generator = rnd.gamma(mean, sd, as_lambda=True)\n",
    "statistics = generate_random_numbers(generator)\n",
    "\n",
    "show_results(mean, sd, statistics)\n",
    "plot_results(statistics, \"gamma distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trigangular distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defined mean = 66.66666666666667\n",
      "Defined standard deviation = 16.99673171197595\n",
      "Generated random numbers: 1000000\n",
      "Actual mean = 66.66 (relative deviation: 0.02%)\n",
      "Actual standard deviation = 17.0 (relative deviation: 0.01%)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "low = 20\n",
    "high = 100\n",
    "most_likely = 80\n",
    "\n",
    "mean = (low + most_likely + high) / 3\n",
    "sd = math.sqrt((high - low)**2 + (high - most_likely)**2 + (most_likely - low)**2) / 6\n",
    "\n",
    "generator = rnd.triangular(low, most_likely, high, as_lambda=True)\n",
    "statistics = generate_random_numbers(generator)\n",
    "\n",
    "show_results(mean, sd, statistics)\n",
    "plot_results(statistics, \"triangular distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uniform distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defined mean = 60.0\n",
      "Defined standard deviation = 23.094010767585033\n",
      "Generated random numbers: 1000000\n",
      "Actual mean = 59.99 (relative deviation: 0.02%)\n",
      "Actual standard deviation = 23.09 (relative deviation: 0.02%)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "low = 20\n",
    "high = 100\n",
    "mean = (low + high) / 2\n",
    "sd = (high - low) / math.sqrt(12)\n",
    "\n",
    "generator = rnd.uniform(low, high, as_lambda=True)\n",
    "statistics = generate_random_numbers(generator)\n",
    "\n",
    "show_results(mean, sd, statistics)\n",
    "plot_results(statistics, \"uniform distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Empirical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defined mean = 30.0\n",
      "Defined standard deviation = 10.458250331675945\n",
      "Generated random numbers: 1000000\n",
      "Actual mean = 30.0 (relative deviation: 0.01%)\n",
      "Actual standard deviation = 10.37 (relative deviation: 0.8%)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7QAAAIZCAYAAACWMnMqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAABJPUlEQVR4nO3de5yN9f7//+eaWatpRERrpj7CrdpOleKTZJBjZjAzxkZtw3ZIKImoLYfBbJWSlCiSdkkobDEz5DOU0sZQaG+n2LULOe2ZMc6HmVmz1vv7h5/1M2YwOY33rMf9dnO7zbWu9/ta7/f1uq5r5um61ozDGGMEAAAAAIBlgop7AAAAAAAAXAoCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFcNmqV6+u2NhYxcXF+f8lJCQU97Cuic2bN6t58+aSpM8++0zTpk27YPu///3vmj17dqHrzu7fvHlzbd68+XeNZffu3erfv78kKT09XZ06dfpd/S9VYmKimjdvrgkTJpx3PHv27FGdOnWuyXiutXfffVdfffXV7+5Xp04d7dmz5yqM6NLExMTou+++K+5hXHf7pagu5Zy9HJ9++qnmzp171d8nISFBaWlpBV6/3GvMldpf3333nWJiYi57O+fyer166qmnlJWVdcW3DeDKchb3AACUDDNmzFD58uWLexjFKj4+/qJtNmzYoKpVq15y/wvZt2+fduzYIUkKDw/XnDlzLmt7RTV37lytWLFCt91223nHU5J99913+sMf/lDcw0AA2bt3rxYuXKh58+Zd9fcaM2ZMoa9fy2tMcQgODlavXr00evRoTZo0qbiHA+ACCLQArqr77rtPLVq00Pbt2zV+/HiVKlVKY8aM0eHDh+X1etW1a1d17NhRkjRx4kQtWrRIt9xyi+rWrastW7Zo5syZGjp0qKpWraonn3xSkvItp6en66WXXtL+/fvl8XgUHR2tp59+Wnv27FGPHj3UpEkTbdy4UUeOHNGgQYPUpk0b5eXl6Y033tCKFSsUHBysOnXqKDExUbGxsRo5cqQaNWokSRoxYoSqVq2q7t2755vTp59+qhkzZqh06dKqVq2a//V33nlHhw4d0qhRo/Tpp59qzpw5crlcCgkJ0UsvvaQdO3bo66+/1urVq3XjjTfq4MGD+te//qWMjAxVr15dVapU8fc/8z7bt29Xbm6unnjiCXXs2FHfffedXn75ZS1evFiS/MvJyckaMWKE0tPT9eSTT2r06NGKjY3VP//5T3k8Ho0dO1Zr1qxRcHCw7r//fg0bNkylS5dW8+bN9cc//lFr1qzR/v371bp1a7344osF6vjzzz/rpZde0uHDh+VwONSzZ0+1a9dOnTt3ljFGvXv3VmJiourWrSvp9N2Nc8fj9Xo1atQobd68WUePHtWLL76oqKgoSdJ7772nZcuWyefzqWLFikpMTFR4eHi+MXi9Xo0bN05ff/21ypQpo/vvv1+//PKLZs6cqWPHjmnMmDH66aef5PF4FBERoRdffFFOp1O1atVSnz59tHr1amVkZKhbt27q0aOHpNN3zD/77DP5fD6VK1dOI0eO1N13362hQ4fq8OHD2r17t5o2baqOHTvqpZde0smTJ5WRkaEaNWro7bff1vz587VlyxaNGzdOwcHBatKkicaPH69169bJ6/Xqnnvu0YgRI1S6dGmtX79eL7/8shwOh2rVqiWfz1foOdO8eXM9+uijWr9+vY4dO6YnnnhCnTt31okTJzRs2DDt2rVLQUFBuvfee/XSSy8pKChIX3/9td577z15PB7deOONGjJkiOrUqZPvmDz3GP3Pf/6j4cOH69SpU7rrrrt08uRJ/xi++uorvfvuu/J6vSpdurSGDRum+++/P9849+zZoy5duujuu+/W3r17NXPmTC1YsEBfffWVcnJydOrUKQ0ZMkQtW7bUO++8o7179yozM1N79+5V+fLlNWHCBIWHh19wv8ydO1czZ85UUFCQbr31Vo0cOVJ33nmnhg4dqpCQEG3evFkHDhxQ69atVb58eX3zzTfKzMzUK6+8ooiIiHzj/e677zRhwgRVqlRJP//8s3JzczVq1CjVr1//gteY5s2bKyYmRitWrNDhw4fVv39//fDDD9q6daucTqfee+89/7Fa2Dkr6YL1Ofsa0LdvXyUkJCg3N1fGGHXs2FFdunQpcIy8//77iouLk8PhkCT98MMPGj9+vE6dOiWHw6H+/furWbNmWrBggZYtW6bs7Gzt3btXt99+u7p06aJZs2Zp586deuKJJ9SzZ08tWLBAX3zxhXw+n9LT0xUeHq6xY8cqPDxcXbt2VZcuXXTfffflq/fYsWPVs2dP/fOf/zzvNfXo0aMaNWqUsrKylJmZqYoVK+rtt99WhQoVCj32V61apddff12LFi2SJB09elQtWrTQV199pR9++EHvv/++cnNzdfDgQbVr104DBw7M1/9Svlfk5eXp5Zdf1g8//CCXy6U77rhDr732mm666SY99NBDSkxM1Pbt21WjRo1CxwzgOmAA4DJVq1bNxMTEmLZt2/r/HThwwL9u4cKFxhhjPB6PadOmjdmyZYsxxpijR4+a1q1bm3/+859m6dKlpk2bNubYsWMmNzfX9OrVy/z5z382xhgzZMgQ87e//c3/fmcvd+3a1SxfvtwYY0x2drbp2rWr+eKLL8zu3btNtWrVzNdff22MMSY1NdU0bdrUGGPMjBkzTJcuXcypU6eM1+s1zz33nFm4cKGZPn26GTBggDHGmGPHjpn69eubI0eO5Jvrjz/+aCIiIkxGRoYxxpiRI0eaZs2aGWOMmTRpkhk9erTJy8sz9957r0lPTzfGGLNw4UIzZ86cAmOfNGmSiYqKMh6PJ19/Y4xp1qyZSUxMNMYY89///tfUr1/f/PTTT2bt2rUmOjraP56zl8/+evfu3aZ27drGGGMmTpxonn32WZObm2u8Xq8ZOnSoGTlypP99xo4d63+fWrVqmd9++y3fnD0ej2nRooVZunSpv90jjzxifvjhB3+Ns7KyChwX546nWrVqJjU11RhjzLJly0yLFi38+2fgwIH+/TBnzhzTq1evAtv77LPPTJcuXUx2drbJyckxPXv29B8jQ4cONZ988okxxpi8vDzzl7/8xUybNs0/vpkzZxpjjNm8ebO57777THZ2tvnuu+9M586dzcmTJ40xxqxcudK0bt3aX6fu3bv733vs2LEmKSnJGGNMbm6uiYmJ8c/lz3/+s/m///s/Y4wx77zzjhk7dqzx+XzGGGPefPNNk5iYaHJyckyDBg1MWlqaMcaYRYsWmWrVqpndu3cXmGezZs3MyJEjjc/nM/v37zcPP/yw2b59u1m4cKHp2bOnf44JCQlm586dZseOHSYmJsYcPHjQGGPMTz/9ZBo2bGhOnDiR75gyJv8xFhcXZ+bNm2eMMWb9+vWmevXqZu3ateY///mPadCggf84SEtLMw0bNjTHjh3LN84zNV23bp0xxpg9e/aYrl27mlOnThljjFm8eLGJiYnxv2+LFi3823jqqafMxIkTL7hf0tLSzKOPPuo/tj7//HPTunVr4/P5zJAhQ8xjjz1mcnNzTUZGhqlWrZq//h9//LF54oknCuzXtWvXmpo1a5off/zRGGPMhx9+aLp06eKv9/muMc2aNTOvvvqqMcaYL774wtSoUcNs27bNGGPMM888Y9577z1/u8LO2YvV5+xrwLBhw8z7779vjDEmIyPDDBw40Hi93nzz8Pl85uGHH/YfO4cPHzaRkZH+5f/+97+mcePGZu/evebzzz83Dz74oNm3b5/xer2mTZs2pn///sbr9Zpt27aZWrVqGa/Xaz7//HNTu3Zt8+uvvxpjjHnjjTdM//79jTH///F9br3Pvsac75r68ccf++fj8/lMr169zIcffujfX5s2bSowt7Nfnz17tnnhhReMz+czf/7zn82OHTv8c6xZs6bJysrKd525lO8V69atM61atfKfs+PGjTMbNmzwb2Ps2LFm4sSJBsD1izu0AK6ICz1yfOau3c6dO/Xbb79p+PDh/nXZ2dn68ccf9Z///EctW7ZU6dKlJUl/+tOfNGPGjAu+58mTJ7Vu3TodOXJEEydO9L+2fft23X///XK5XGrSpIkk6Z577tHhw4clSWlpaYqLi9ONN94oSXr77bclnb4bMHnyZB08eFCpqalq2rSpbr755nzvuWbNGjVs2FBut9s/zlWrVuVrExwcrFatWqlTp05q2rSpGjZsqNjY2ELnULt2bTmdhV+Kz3w+LTw8XI0aNdKaNWtUvXr1C+6TwvzjH//QoEGD5HK5JEldu3ZVv379/OtbtGjhf58KFSroyJEjqlSpkn/9zp07lZOTo8jISH+7yMhIrVy58nd9LtblcvnvyNaoUcP/2bRvvvlGmzdvVocOHSRJPp9Pp06dKtD/22+/VVxcnEJCQiSd3vczZ86UJK1YsUKbN2/W/PnzJZ0+rs52Zo733nuvcnNzdfLkSa1YsUK7du3K9znAI0eO+I+TBx980P/64MGDtXr1an3wwQfauXOnMjIy8t3RPGPFihU6duyY/zOHHo9HFSpU0E8//SSn0+m/axgTE+O/a1qYzp07y+Fw6LbbbtMjjzyi1atXq2XLlpowYYK6du2qBg0aqHv37qpSpYpmz56tjIwM/11nSXI4HPrtt9/Ou/1Dhw7p3//+t9q1a+ef65lH4deuXav69ev7j4GIiAiVL19eW7ZsUf369fNtx+l0qnbt2pKkihUr+u+u7dq1Sxs3btSJEyf8bevVq+c/v++55x4dOXLkgvtl5cqVatOmjf+60r59e40ZM8b/+dpmzZrJ5XLJ7XarVKlSeuSRRyRJlStX9tfwXP/zP/+jmjVr+sewcOHC8+6js5059itVqqRbb73Vf7eucuXKOnLkiL9dYedscHDwBetz9jWgZcuWGjJkiDZt2qSIiAiNGDFCQUH5f93JoUOHdOzYMd1xxx2SpH/961/KzMzMd047HA79+9//liTVqlVLt99+uyTpjjvuUKNGjRQUFKRKlSr576RLUsOGDXXnnXdKkh5//HHFxcUV2A9n1/ts57umStL69es1ffp07dy5Uz///LMeeOCB8+5nh8Ohjh07auHChapVq5YWLFigwYMHy+FwaOrUqVqxYoUWL16sX375RcaYQq8ThbnQ94pGjRopODhYjz32mBo1aqSoqKh8TyNUrlxZ33//fZHeB0DxINACuOpKlSol6fQjozfffLOSk5P96w4cOKAyZcro7bffljHG//qZ8CWd/iHn7HUej0fS6eBjjNGcOXMUGhoqSTp48KBCQkJ06NAhuVwu/w+DZx7Nk1QgQB44cEA+n09hYWFq1aqVUlJStGjRIiUmJhaYy7ljCQ4OLnTO48eP108//aS0tDR98MEHmj9/vt57773z7pvCnP2DrDFGTqfzvPviQs59tNXn8+XrdyYgSgXnV1j/M+PJy8u76Huf7dyanr39Xr16qXPnzpKk3NzcfCHhjHPrdvb+8fl8mjhxou6++25Jp/9z4uz3ODPHM68ZY+Tz+RQXF6fBgwf7t5GRkaGyZctKyl+b559/Xl6vV61bt1bTpk21f//+AvvpzDaGDx/u/4+UEydOKCcnp9D25/uPjHPX+Xw+fwD58ssv9d1332nt2rV64oknNGLECPl8PkVEROQLEfv371dYWJi++uqrQo+Xs/fDue9Z2LzOV+8bbrjB32/r1q165pln1KNHDzVs2FAPPfSQRo8e7W97JuyceX9jTKHHW1HHccMNN5x3n51PYWM492up4Hl19nudfRyfq7Bz1uv1nrc+X375Zb7jrFmzZlq6dKnS0tK0Zs0aTZ48WXPmzFHlypXzvceZ4zcoKEher1d33323/v73v/vbpKenq3z58lq0aFGR99PZ1zKfz1fote3sel9om2euqTNmzNCmTZvUoUMHPfzww8rLyyu0rmfr0KGD2rVrp8cee0zHjh3Tww8/rJMnT+qPf/yjHn30UdWtW1cdOnQocGxLl/a94qabblJycrJ++OEHrV27VgMHDsz3sYQz+xnA9YszFMA1c+eddyokJMQfaPfv36+YmBht2bJFTZs2VWpqqo4cOSKfz6ekpCR/v1tuuUVbtmyRdPqHkPXr10uSSpcurdq1a2v69OmSToeY+Ph4LV++/ILjiIiI0OLFi5Wbmyufz6e//vWv+uKLLyRJXbp00SeffCJjTIHPDEpSgwYNtHr1av33v/+VpELv8Bw8eFBNmjRRuXLl1KNHDw0cONB/tyQ4OLjIQfDMtvft26e0tDT/nbJ9+/YpKytLxph8v103ODi40ID7yCOPaM6cOfJ4PPL5fJo9e7YaNmxYpDFIp+vmcrm0bNkySad/WF66dKkaNGhwwX7nG8+5GjVqpPnz5+v48eOSTn+WurDP8TZp0kQpKSnKzc1VXl5evn3fqFEjffzxxzLGKDc3V3379tWsWbMu+L4NGzbUF198oYyMDEmnf8v0uZ+XPmPVqlXq16+f2rRpI4fDoY0bN8rr9frneaamjRo10uzZs/3H1siRI/XWW2+pWrVqMsbo22+/lSQtX7680NB+xpnjf9++fVq9erUaN26sTz/9VMOGDVOjRo00ePBgNWrUSD///LPq16+v1atX65dffpF0+k5227ZtlZOTo1tuuUVbt26VMUYnT570P01Qrlw53Xvvvf4QtHXrVv3000+S5N/e7t27Jcn/+eoL3VmTpHXr1um+++7TE088oXr16mn58uX+fXQ+F9ovjRo10pIlS3Tw4EFJ0ueff65y5cqpSpUqF9zmpTjfNeb3KuycvVB9zvXCCy9oyZIlio6OVmJiokqXLq39+/fna1OuXDndfPPN2rt3r6TTd3h37dqldevWSZK2bdumqKgo/3FdVGvXrlV6erokac6cOWrWrFmR+57vmrpq1Sp1795d7dq1U4UKFZSWlnbRYyI8PFwPPPCARo0a5f8M8q5du3T8+HENHDhQzZs31/fff+9/r7NdyveKb775Rj169FCdOnXUv39/tWvXTtu3b/dvc/fu3brrrruKvC8AXHvcoQVwzdxwww2aMmWKxowZo7/97W/Ky8vTc88953+0s1u3burcubNCQkJUsWJFf7+uXbvqL3/5i6KionTHHXeoXr16/nXjx4/Xyy+/rNjYWOXm5iomJkZt27a94J/96NSpk/bu3av27dvLGKN69eqpa9eukk4/Clu2bNnz/jmK6tWra/DgwerevbtuuummQkNv+fLl1bdvX/Xo0UM33nijgoOD9corr0iSGjdurJdffrlI+ysnJ0d//OMf5fF4NGLECP/jgJ06dVKHDh3kdrvVtGlTf/uqVasqODhYHTt2zPcndPr27avXX39d7dq1U15enu6//36NHDmySGOQTt+RmjJlil555RW988478nq96tevX4HHT891vvGc67HHHlN6eroef/xxORwO3X777Ro7dmyBdu3bt9eOHTvUrl07lSpVSnfccYf/bktCQoLGjBmj2NhYeTweNWjQQL169brg+B555BH17t1bPXv2lMPhUOnSpfXuu+/mu7N7xqBBg9SvXz+VLVtWoaGheuihh/yPjDZr1kyvv/66PB6PnnnmGb3++uv64x//KK/Xq5o1a2ro0KFyuVyaPHmy/vrXv+qtt95SzZo1z/uLcaTTv3Cpffv2ys7O1ogRI3TXXXfptttu0/fff682bdooNDRU//M//6Nu3bqpbNmyeumll/T888/77wq+9957KlWqlNq2bauVK1cqMjJS4eHhqlOnjv8O1ltvvaVhw4b57wCe+aH9D3/4gxITE/Xss8/K6/Xqxhtv1NSpU1WmTJkL7s+YmBgtW7ZMbdq0kcvlUkREhI4cOeL/j4rCXGi/NGzYUD169FD37t3l8/lUvnx5vf/++1flbtmFrjG/x/nO2fPV51zPPPOMEhISNHfuXAUHB+vRRx8tdCxnHvnv3Lmzypcvr0mTJmncuHHKycmRMUbjxo3Ldw0tivDwcA0ePFiZmZn6wx/+oJdeeqnIfc93Ta1YsaLGjRunKVOmKDg4WP/7v/97wUfhz3jsscf03HPP+Z9qqV69upo2barWrVvr5ptvVuXKlfWHP/xBu3btyncH+lK+V3i9Xv3jH/9QTEyMSpUqpbJly+a7Rq9atcr/mDKA65PDXOzZDwAoBqmpqZo9e7b/M5LXym+//aauXbsqNTXVH5ZwfVi1apWysrL8n+175ZVXFBIS4n9kuKRo3ry5Jk6cqFq1ahX3UHCd2r17t5577jl9/vnnhf4HzO+1YMECLV26VO+///4VGF3J8d1332n27Nn82R7gOscjxwDw/5k4caLi4+M1ZMgQwux1qGrVqkpKSlLbtm0VHR2tQ4cO6emnny7uYQHXXKVKldSuXbsS/Xdgi5vX69Xf/vY3jRgxoriHAuAiuEMLAAAAALASd2gBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALBSkf8O7euvv65Dhw5p7Nix2rZtm0aMGKHjx4+rbt26Gj16tJzO/Js6evSo/vKXv2j37t0qX7683n77bbndbq1fv16JiYkKCQnRm2++qTvvvFMnTpzQCy+8oKlTp17SJA4dOiGf7/r/3VYVKpRWVtb5/x4fSi5qH7iofeCi9oGL2gcuah+4qP3VExTk0C233HTe9UUKtGvWrNHChQvVtGlTSdLgwYP1yiuvqHbt2ho+fLjmzZunzp075+vz9ttvq27dupo2bZqSkpI0ZswYvf3225o+fbpee+017d27V3PmzNGwYcP04YcfqmfPnpc8SZ/PWBFoJVkzTlx51D5wUfvARe0DF7UPXNQ+cFH74nHRR44PHz6sCRMm+P/W3969e5Wdna3atWtLktq3b6/U1NQC/VasWKHY2FhJUkxMjP7xj3/I4/HI5XIpJydH2dnZcrlcysjI0K+//qp69epdwWkBAAAAAEq6i96hHTVqlAYNGqT9+/dLkjIyMuR2u/3r3W630tPTC/Q7u53T6VTp0qV18OBBPfXUU0pMTFRoaKjGjRunyZMn65lnnrmsSVSoUPqy+l9LbneZ4h4Cigm1D1zUPnBR+8BF7QMXtQ9c1L54XDDQ/v3vf9ftt9+uiIgILViwQJJkTMFb6Q6Ho0hvFhQUpJo1a2revHmSpF9++UU+n0/lypVT//79ZYzRCy+8oDvvvPN3TSIr67gVt/jd7jLKzDxW3MNAMaD2gYvaBy5qH7iofeCi9oGL2l89QUGOC97AvGCgXbJkiTIzMxUXF6cjR47o5MmTcjgcOnDggL9NZmamwsLCCvQNCwvTgQMHdNtttykvL0/Hjx9XuXLl8rV55513NHToUH3yySeKjIxUpUqV9O677+rNN9/8ndMEAAAAAASaC36Gdvr06Vq8eLGSk5M1YMAANW/eXK+99ppCQkK0YcMGSVJSUpIaN25coG+TJk2UlJQk6XQwrlu3rlwul3/9unXrdMcdd+i2226Tx+OR0+lUUFCQcnJyruD0AAAAAAAlVZH/bM/Zxo8frxEjRujEiRO655571K1bN0nSxIkTFRYWpvj4eD333HMaOnSooqOjVaZMGY0fPz7fNqZNm+a/E/vYY4/phRdekDFGo0ePvswpAQAAAAACgcMU9qFYy/AZWlzvqH3govaBi9oHLmofuKh94KL2V8/FPkN70T/bAwAAAADA9YhACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVnMU9AAAlU5mbQ3VjSP5LTHZOno4dPVVMIwIAAEBJQ6AFcFXcGOJU7AvJ+V5b9GacjhXTeAAAAFDy8MgxAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYqUqCdOHGi2rRpo+joaE2fPl2SNGzYMEVGRiouLk5xcXH68ssvC/Tbtm2bOnTooKioKCUkJCgvL0+SNGPGDEVHR+vJJ59Ubm6uJGnTpk164403rtS8AAAAAAAl3EUD7ffff6+1a9cqJSVFn3/+uWbOnKlff/1VW7Zs0axZs5ScnKzk5GS1bNmyQN/Bgwdr5MiRWrp0qYwxmjdvnqTTgTYpKUmVK1fWypUrJUlTp05Vnz59rvD0AAAAAAAl1UUDbb169fTJJ5/I6XQqKytLXq9XISEh2rdvn0aOHKnY2FhNmjRJPp8vX7+9e/cqOztbtWvXliS1b99eqampkiSn0ymPx6Ps7Gy5XC4tX75cDz74oMqWLXvlZwgAAAAAKJGK9Mixy+XSpEmTFB0drYiICHm9XtWvX1+vvvqq5s2bp/Xr12v+/Pn5+mRkZMjtdvuX3W630tPTJUn9+vVTfHy8fD6f6tevr88++0xdu3a9gtMCAAAAAJR0zqI2HDBggHr37q2nn35aa9as0eTJk/3runbtqqSkJD3++OP+14wxBbbhcDgkyf+5W0maO3euYmJitGnTJk2bNk0VKlTQqFGjFBoaWuRJVKhQushti5vbXaa4h4BiQu1PC8T9EIhzxmnUPnBR+8BF7QMXtS8eFw20v/zyi3Jzc1WzZk2FhoYqMjJSS5YsUbly5RQVFSXpdHh1OvNvKjw8XAcOHPAvZ2ZmKiwsLF+bkydPatmyZfrggw/UqVMnvfPOO0pOTlZKSor+9Kc/FXkSWVnH5fMVDNDXG7e7jDIzjxX3MFAMArH257uoB+J+CLQ54zRqH7iofeCi9oGL2l89QUGOC97AvOgjx3v27NGIESOUm5ur3NxcLV++XA899JBeffVVHTlyRB6PR3Pnzi3wS6EqVqyokJAQbdiwQZKUlJSkxo0b52vz0UcfqXv37goKCpLH45HL5ZLD4VBOTs6lzBUAAAAAEEAueoe2SZMm2rhxo9q1a6fg4GBFRkbq2Wef1S233KL4+Hjl5eUpMjJSMTExkqTevXtrwIABqlWrlsaPH68RI0boxIkTuueee9StWzf/drOysvTjjz/q2Wef9ffr0qWLbrnllnyPMwMAAAAAUBiHKezDrpbhkWNc7wKx9m53GcW+kJzvtUVvxgXkfgi0OeM0ah+4qH3govaBi9pfPZf9yDEAAAAAANcjAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArFSnQTpw4UW3atFF0dLSmT58uSUpLS1NsbKwiIyM1YcKEQvvt27dPXbp0UatWrdS3b1+dOHFCkpSamqrWrVsrPj5eBw8e9Ld98cUXr8ScAAAAAAAB4KKB9vvvv9fatWuVkpKizz//XDNnztT27ds1fPhwTZkyRUuWLNGWLVv07bffFug7evRode7cWampqbrvvvs0ZcoUSdLUqVM1a9YstWjRQosXL5YkTZ48Wf369bvC0wMAAAAAlFQXDbT16tXTJ598IqfTqaysLHm9Xh09elRVqlRRpUqV5HQ6FRsbq9TU1Hz9PB6P1q1bp6ioKElS+/bt/W1cLpdycnKUnZ0tl8ulbdu2KTQ0VFWqVLkKUwQAAAAAlERFeuTY5XJp0qRJio6OVkREhDIyMuR2u/3rw8LClJ6enq/PoUOHVLp0aTmdTkmS2+32txk0aJCeffZZbd26VW3bttWUKVPUt2/fKzUnAAAAAEAAcBa14YABA9S7d289/fTT2rlzZ4H1Docj37Ix5rxtGjRooAULFkiSVq1aperVq+vw4cNKSEhQaGioEhISdOuttxZ5EhUqlC5y2+Lmdpcp7iGgmFD70wJxPwTinHEatQ9c1D5wUfvARe2Lx0UD7S+//KLc3FzVrFlToaGhioyMVGpqqoKDg/1tMjIyFBYWlq9f+fLldfz4cXm9XgUHByszM7NAG5/Pp48//liTJk1SQkKC+vTpo/379+uTTz7R888/X+RJZGUdl89XMEBfb9zuMsrMPFbcw0AxCMTan++iHoj7IdDmjNOofeCi9oGL2gcuan/1BAU5LngD86KPHO/Zs0cjRoxQbm6ucnNztXz5cnXq1Ek7duzQrl275PV6tXjxYjVu3DhfP5fLpbp162rJkiWSpKSkpAJtUlJS1KJFC5UqVUoej0dOp1MOh0M5OTmXMlcAAAAAQAC56B3aJk2aaOPGjWrXrp2Cg4MVGRmp6OholS9fXv3791dOTo6aNGmiVq1aSZISEhLUvHlztWjRQomJiRo6dKjee+893X777Xrrrbf8283JyVFKSoqmTZsmSerWrZuGDRumkJCQfO0AAAAAACiMwxT2YVfL8MgxrneBWHu3u4xiX0jO99qiN+MCcj8E2pxxGrUPXNQ+cFH7wEXtr57LfuQYAAAAAIDrEYEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEpFCrTvvvuuoqOjFR0drXHjxkmShg0bpsjISMXFxSkuLk5ffvllgX7btm1Thw4dFBUVpYSEBOXl5UmSZsyYoejoaD355JPKzc2VJG3atElvvPHGlZoXAAAAAKCEu2igTUtL06pVq7Rw4UIlJSVp69at+vLLL7VlyxbNmjVLycnJSk5OVsuWLQv0HTx4sEaOHKmlS5fKGKN58+ZJOh1ok5KSVLlyZa1cuVKSNHXqVPXp0+cKTw8AAAAAUFJdNNC63W4NHTpUN9xwg1wul+6++27t27dP+/bt08iRIxUbG6tJkybJ5/Pl67d3715lZ2erdu3akqT27dsrNTVVkuR0OuXxeJSdnS2Xy6Xly5frwQcfVNmyZa/8DAEAAAAAJdJFA23VqlX9oXTnzp1asmSJHnnkEdWvX1+vvvqq5s2bp/Xr12v+/Pn5+mVkZMjtdvuX3W630tPTJUn9+vVTfHy8fD6f6tevr88++0xdu3a9gtMCAAAAAJR0zqI2/Pnnn/XUU09pyJAhuuuuuzR58mT/uq5duyopKUmPP/64/zVjTIFtOBwOSfJ/7laS5s6dq5iYGG3atEnTpk1ThQoVNGrUKIWGhhZ5EhUqlC5y2+Lmdpcp7iGgmFD70wJxPwTinHEatQ9c1D5wUfvARe2LR5EC7YYNGzRgwAANHz5c0dHR+ve//62dO3cqKipK0unw6nTm31R4eLgOHDjgX87MzFRYWFi+NidPntSyZcv0wQcfqFOnTnrnnXeUnJyslJQU/elPfyryJLKyjsvnKxigrzdudxllZh4r7mGgGARi7c93UQ/E/RBoc8Zp1D5wUfvARe0DF7W/eoKCHBe8gXnRR47379+vfv36afz48YqOjpZ0OsC++uqrOnLkiDwej+bOnVvgl0JVrFhRISEh2rBhgyQpKSlJjRs3ztfmo48+Uvfu3RUUFCSPxyOXyyWHw6GcnJzfPVEAAAAAQGC56B3aDz/8UDk5ORo7dqz/tU6dOqlPnz6Kj49XXl6eIiMjFRMTI0nq3bu3BgwYoFq1amn8+PEaMWKETpw4oXvuuUfdunXzbyMrK0s//vijnn32WX+/Ll266JZbbsn3ODMAAAAAAIVxmMI+7GoZHjnG9S4Qa+92l1HsC8n5Xlv0ZlxA7odAmzNOo/aBi9oHLmofuKj91XPZjxwDAAAAAHA9ItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACs5CzuAQAlWZmbQ3VjyOnTzO0uo+ycPB07eqqYR4Vr4dzaS6L+AAAAVxiBFriKbgxxKvaFZP/yojfjdKwYx4Nr59zaS9QfAADgSuORYwAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASkUKtO+++66io6MVHR2tcePGSZLS0tIUGxuryMhITZgwodB++/btU5cuXdSqVSv17dtXJ06ckCSlpqaqdevWio+P18GDB/1tX3zxxSsxJwAAAABAALhooE1LS9OqVau0cOFCJSUlaevWrVq8eLGGDx+uKVOmaMmSJdqyZYu+/fbbAn1Hjx6tzp07KzU1Vffdd5+mTJkiSZo6dapmzZqlFi1aaPHixZKkyZMnq1+/fld4egAAAACAkuqigdbtdmvo0KG64YYb5HK5dPfdd2vnzp2qUqWKKlWqJKfTqdjYWKWmpubr5/F4tG7dOkVFRUmS2rdv72/jcrmUk5Oj7OxsuVwubdu2TaGhoapSpcpVmCIAAAAAoCS6aKCtWrWqateuLUnauXOnlixZIofDIbfb7W8TFham9PT0fP0OHTqk0qVLy+l0SjodjM+0GTRokJ599llt3bpVbdu21ZQpU9S3b98rNScAAAAAQABwFrXhzz//rKeeekpDhgyR0+nUjh078q13OBz5lo0xBbZxpk2DBg20YMECSdKqVatUvXp1HT58WAkJCQoNDVVCQoJuvfXWIk+iQoXSRW5b3NzuMsU9BBSzQD8GmH9gzz/QUO/ARe0DF7UPXNS+eBQp0G7YsEEDBgzQ8OHDFR0dre+//14HDhzwr8/IyFBYWFi+PuXLl9fx48fl9XoVHByszMzMAm18Pp8+/vhjTZo0SQkJCerTp4/279+vTz75RM8//3yRJ5GVdVw+X8EAfb1xu8soM/NYcQ8D11BhF7ZAOQbOd1Fn/oExf3DND2TUPnBR+8BF7a+eoCDHBW9gXvSR4/3796tfv34aP368oqOjJUkPPPCAduzYoV27dsnr9Wrx4sVq3Lhxvn4ul0t169bVkiVLJElJSUkF2qSkpKhFixYqVaqUPB6PnE6nHA6HcnJyfvdEAQAAAACB5aJ3aD/88EPl5ORo7Nix/tc6deqksWPHqn///srJyVGTJk3UqlUrSVJCQoKaN2+uFi1aKDExUUOHDtV7772n22+/XW+99ZZ/Gzk5OUpJSdG0adMkSd26ddOwYcMUEhKSrx0AAAAAAIW5aKAdMWKERowYUei6lJSUAq+NGTPG/3XFihU1c+bMQvuGhIToo48+8i/Xq1dPX3zxxUUHDAAAAACAVIRHjgEAAAAAuB4RaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFipyIH2+PHjiomJ0Z49eyRJw4YNU2RkpOLi4hQXF6cvv/yyQJ9t27apQ4cOioqKUkJCgvLy8iRJM2bMUHR0tJ588knl5uZKkjZt2qQ33njjSswJAAAAABAAihRoN27cqPj4eO3cudP/2pYtWzRr1iwlJycrOTlZLVu2LNBv8ODBGjlypJYuXSpjjObNmyfpdKBNSkpS5cqVtXLlSknS1KlT1adPnyswJQAAAABAIChSoJ03b54SExMVFhYmSTp58qT27dunkSNHKjY2VpMmTZLP58vXZ+/evcrOzlbt2rUlSe3bt1dqaqokyel0yuPxKDs7Wy6XS8uXL9eDDz6osmXLXsGpAQAAAABKMmdRGo0ZMybfclZWlurXr6+XXnpJpUqV0lNPPaX58+fr8ccf97fJyMiQ2+32L7vdbqWnp0uS+vXrp/j4eNWoUUP169fXM888oylTplzyJCpUKH3Jfa81t7tMcQ8BxSzQjwHmH9jzDzTUO3BR+8BF7QMXtS8eRQq056pUqZImT57sX+7atauSkpLyBVpjTIF+DodDkvyfu5WkuXPnKiYmRps2bdK0adNUoUIFjRo1SqGhoUUeT1bWcfl8Bd/veuN2l1Fm5rHiHgauocIubIFyDJzvos78A2P+4JofyKh94KL2gYvaXz1BQY4L3sC8pN9y/O9//1tLly71Lxtj5HTmz8bh4eE6cOCAfzkzM9P/yPIZJ0+e1LJly9S2bVuNGzdOL7/8su68806lpKRcyrAAAAAAAAHkkgKtMUavvvqqjhw5Io/Ho7lz5xb4pVAVK1ZUSEiINmzYIElKSkpS48aN87X56KOP1L17dwUFBcnj8cjlcsnhcCgnJ+cSpwMAAAAACBSXFGhr1KihPn36KD4+XtHR0apZs6ZiYmIkSb1799bmzZslSePHj9drr72m1q1b69SpU+rWrZt/G1lZWfrxxx/9Ibd3797q0qWLvvnmG8XGxl7uvAAAAAAAJdzv+gzt119/7f+6S5cu6tKlS4E2H3zwgf/rGjVqaP78+YVuq0KFCvl+EVSbNm3Upk2b3zMcAAAAAEAAu6Q7tAAAAAAAFDcCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVihxojx8/rpiYGO3Zs0eSlJaWptjYWEVGRmrChAmF9tm3b5+6dOmiVq1aqW/fvjpx4oQkKTU1Va1bt1Z8fLwOHjzob/viiy9e7nwAAAAAAAGiSIF248aNio+P186dOyVJ2dnZGj58uKZMmaIlS5Zoy5Yt+vbbbwv0Gz16tDp37qzU1FTdd999mjJliiRp6tSpmjVrllq0aKHFixdLkiZPnqx+/fpdoWkBAAAAAEq6IgXaefPmKTExUWFhYZKkTZs2qUqVKqpUqZKcTqdiY2OVmpqar4/H49G6desUFRUlSWrfvr2/jcvlUk5OjrKzs+VyubRt2zaFhoaqSpUqV3JuAAAAAIASrEiBdsyYMapbt65/OSMjQ263278cFham9PT0fH0OHTqk0qVLy+l0SpLcbre/zaBBg/Tss89q69atatu2raZMmaK+ffte9mQAAAAAAIHDeSmdjDEFXnM4HEVu06BBAy1YsECStGrVKlWvXl2HDx9WQkKCQkNDlZCQoFtvvbXI46lQofTvGX6xcrvLFPcQUMwC/Rhg/oE9/0BDvQMXtQ9c1D5wUfvicUmBNjw8XAcOHPAvZ2Rk+B9HPqN8+fI6fvy4vF6vgoODlZmZWaCNz+fTxx9/rEmTJikhIUF9+vTR/v379cknn+j5558v8niyso7L5ysYoK83bncZZWYeK+5h4Boq7MIWKMfA+S7qzD8w5g+u+YGM2gcuah+4qP3VExTkuOANzEv6sz0PPPCAduzYoV27dsnr9Wrx4sVq3LhxvjYul0t169bVkiVLJElJSUkF2qSkpKhFixYqVaqUPB6PnE6nHA6HcnJyLmVYAAAAAIAAckmBNiQkRGPHjlX//v3Vpk0b3XXXXWrVqpUkKSEhQcuXL5ckJSYmat68eWrTpo3Wr1+vgQMH+reRk5OjlJQUPfbYY5Kkbt26adiwYfrb3/6m+Pj4y5wWAAAAAKCk+12PHH/99df+ryMiIpSSklKgzZgxY/xfV6xYUTNnzix0WyEhIfroo4/8y/Xq1dMXX3zxe4YDAAAAAAhgl3SHFgAAAACA4kagBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArOYt7ACjZytwcqhtD8h9m2Tl5Onb0VDGNCMDVxnkPAACuFQItrqobQ5yKfSE532uL3ozTsWIaD4Crj/MeAABcKzxyDAAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEqXFWi7deum6OhoxcXFKS4uThs3bsy3Pi0tTbGxsYqMjNSECRP8r7/++uuKiYnR4MGD/a8tWbJEs2fPvpzhAAAAAAACiPNSOxpj9Ouvv2rFihVyOgtuJjs7W8OHD9fMmTN1++2366mnntK3336rOnXqaOXKlVq8eLH69Omj7du36+6779aCBQs0derUy5oMAAAAACBwXPId2l9//VUOh0O9e/dW27ZtNWvWrHzrN23apCpVqqhSpUpyOp2KjY1VamqqgoOD5fV6lZeXp+zsbLlcLn322Wfq2LFjocEYAAAAAIDCXHKgPXr0qCIiIjR58mR9/PHHmjNnjlavXu1fn5GRIbfb7V8OCwtTenq6brrpJnXo0EHt27dXrVq1FB4ertWrV6tVq1aXNxMAAAAAQEC55FuiderUUZ06dSRJpUqVUseOHfXtt9+qYcOGkk4/knwuh8MhSerVq5d69eolSZowYYJ69eqlpUuXasGCBapUqZKGDx+uoKCiZ+0KFUpf6jSuObe7THEP4boQyPshkOcuMf9Ann8gzj0Q54zTqH3govaBi9oXj0sOtOvXr5fH41FERISk0wH27EeGw8PDdeDAAf9yRkaGwsLC8m0jPT1du3bt0qBBgxQVFaWUlBSNGTNGaWlpatSoUZHHkpV1XD5fwQB9vXG7yygz81hxD+OaOt+JHSj7obD5B/LcJeYfCPMP5LmfLRCv+TiN2gcuah+4qP3VExTkuOANzEt+5PjYsWMaN26ccnJydPz4cS1cuFAtW7b0r3/ggQe0Y8cO7dq1S16vV4sXL1bjxo3zbePdd99Vv379JEkej0dBQUEKCgpSTk7OpQ4LAAAAABAgLvkObbNmzbRx40a1a9dOPp9PnTt3Vp06dRQXF6dp06YpPDxcY8eOVf/+/ZWTk6MmTZrk+5zsTz/9JEmqWrWqpNN/AiguLk4VK1bUI488cpnTAgAAAACUdJf1a4UHDhyogQMH5nstOTnZ/3VERIRSUlIK7VutWjW9/PLL/uUePXqoR48elzMcAAAAAEAAueRHjgEAAAAAKE4EWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArEWgBAAAAAFYi0AIAAAAArESgBQAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACsRKAFAAAAAFiJQAsAAAAAsBKBFgAAAABgJQItAAAAAMBKBFoAAAAAgJUItAAAAAAAKxFoAQAAAABWItACAAAAAKxEoAUAAAAAWIlACwAAAACwEoEWAAAAAGAlAi0AAAAAwEoEWgAAAACAlQi0AAAAAAArXVagXbRokdq0aaOWLVtq9uzZBdZv27ZNHTp0UFRUlBISEpSXlydJmjFjhqKjo/Xkk08qNzdXkrRp0ya98cYblzMcAAAAAEAAueRAm56ergkTJujTTz9VcnKy5s6dq//85z/52gwePFgjR47U0qVLZYzRvHnzJJ0OtElJSapcubJWrlwpSZo6dar69OlzGVMBAAAAAASSSw60aWlpql+/vsqVK6dSpUopKipKqamp/vV79+5Vdna2ateuLUlq3769f73T6ZTH41F2drZcLpeWL1+uBx98UGXLlr282QAAAAAAAobzUjtmZGTI7Xb7l8PCwrRp06bzrne73UpPT5ck9evXT/Hx8apRo4bq16+vZ555RlOmTLnUoSgoyHHJfa81m8Z6pYTdElrgtUDaD+fOP5DnLjH/QJl/IM/9bIE4Z5xG7QMXtQ9c1P7quNh+veRAa4wp8JrD4SjS+ri4OMXFxUmS5s6dq5iYGG3atEnTpk1ThQoVNGrUKIWGFvxh6HxuueWm3zv8YlOhQuniHsI19+GIyAKvBdJ+OHf+gTx3ifkHyvwDee5nC8Q54zRqH7iofeCi9sXjkh85Dg8P14EDB/zLGRkZCgsLO+/6zMzMfOsl6eTJk1q2bJnatm2rcePG6eWXX9add96plJSUSx0WAAAAACBAXHKgbdCggdasWaODBw/q1KlTWrZsmRo3buxfX7FiRYWEhGjDhg2SpKSkpHzrJemjjz5S9+7dFRQUJI/HI5fLJYfDoZycnEsdFgAAAAAgQFzWHdpBgwapW7duateunWJiYnT//ferd+/e2rx5syRp/Pjxeu2119S6dWudOnVK3bp18/fPysrSjz/+6A+5vXv3VpcuXfTNN98oNjb2MqcFAAAAACjpHKawD7sCAAAAAHCdu+Q7tAAAAAAAFCcCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQXgOLFi1SmzZt1LJlS82ePbu4h4Or7N1331V0dLSio6M1btw4SVJaWppiY2MVGRmpCRMmFPMIcbW9/vrrGjp0qCRp27Zt6tChg6KiopSQkKC8vLxiHh2uhq+//lrt27dXq1at9Morr0jivA8kycnJ/uv+66+/LolzvyQ7fvy4YmJitGfPHknnP9c5Bkqec2s/d+5cxcTEKDY2VsOGDVNubq4kan/NGVxV//3vf02zZs3MoUOHzIkTJ0xsbKz5+eefi3tYuEpWr15t/vSnP5mcnByTm5trunXrZhYtWmSaNGlifvvtN+PxeEzPnj3NihUrinuouErS0tLMww8/bIYMGWKMMSY6Otr885//NMYYM2zYMDN79uxiHB2uht9++800atTI7N+/3+Tm5pr4+HizYsUKzvsAcfLkSfPQQw+ZrKws4/F4TMeOHc3q1as590uof/3rXyYmJsbce++9Zvfu3ebUqVPnPdc5BkqWc2v/66+/mpYtW5pjx44Zn89nXnzxRTN9+nRjDLW/1rhDe5WlpaWpfv36KleunEqVKqWoqCilpqYW97Bwlbjdbg0dOlQ33HCDXC6X7r77bu3cuVNVqlRRpUqV5HQ6FRsbyzFQQh0+fFgTJkzQ008/LUnau3evsrOzVbt2bUlS+/btqX0J9OWXX6pNmza67bbb5HK5NGHCBIWGhnLeBwiv1yufz6dTp04pLy9PeXl5cjqdnPsl1Lx585SYmKiwsDBJ0qZNmwo917n+lzzn1v6GG27QX//6V5UuXVoOh0PVqlXTvn37qH0xcBb3AEq6jIwMud1u/3JYWJg2bdpUjCPC1VS1alX/1zt37tSSJUvUtWvXAsdAenp6cQwPV9moUaM0aNAg7d+/X1LB89/tdlP7EmjXrl1yuVx68sknlZmZqWbNmqlq1aqc9wGidOnSeu6559S6dWvdeOONqlevnlwuF+d+CTVmzJh8y4X9nJeens71vwQ6t/YVK1ZUxYoVJUkHDx7U7Nmz9dprr1H7YsAd2qvMGFPgNYfDUQwjwbX0888/q2fPnhoyZIgqV65cYD3HQMnz97//XbfffrsiIiL8r3H+Bwav16s1a9bojTfe0Lx587R582b/56vORu1Lpu3bt+vzzz/XN998o1WrVikoKEirV68u0I76l0znu85z/Q8c6enp6t69uzp06KCHH36Y2hcD7tBeZeHh4Vq/fr1/OSMjw/+oAkqmDRs2aMCAARo+fLiio6P1/fff68CBA/71HAMl05IlS5SZmam4uDgdOXJEJ0+elMPhyFf7zMxMal8C3XrrrYqIiFD58uUlSS1atFBqaqqCg4P9bTjvS65Vq1YpIiJCFSpUkHT68cIPP/yQcz9AhIeHF/o9/tzXOQZKpl9++UW9e/fWn//8Z/Xs2VNSwWOC2l993KG9yho0aKA1a9bo4MGDOnXqlJYtW6bGjRsX97Bwlezfv1/9+vXT+PHjFR0dLUl64IEHtGPHDu3atUter1eLFy/mGCiBpk+frsWLFys5OVkDBgxQ8+bN9dprrykkJEQbNmyQJCUlJVH7EqhZs2ZatWqVjh49Kq/Xq5UrV6pVq1ac9wGiRo0aSktL08mTJ2WM0ddff6169epx7geI832Pr1ixIsdACXf8+HE9+eSTeu655/xhVhK1Lwbcob3KwsPDNWjQIHXr1k0ej0cdO3bU/fffX9zDwlXy4YcfKicnR2PHjvW/1qlTJ40dO1b9+/dXTk6OmjRpolatWhXjKHEtjR8/XiNGjNCJEyd0zz33qFu3bsU9JFxhDzzwgHr16qXOnTvL4/GoYcOGio+P11133cV5HwAaNWqkH3/8Ue3bt5fL5VKtWrXUp08ftWzZknM/AISEhJz3ezzX/5Jt/vz5OnDggD766CN99NFHkqTmzZvrueeeo/bXmMMU9qA3AAAAAADXOR45BgAAAABYiUALAAAAALASgRYAAAAAYCUCLQAAAADASgRaAAAAAICVCLQAAAAAACsRaAEAAAAAViLQAgAAAACs9P8ASwxWO7SffM8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options={10: 5, 20: 15, 30: 25, 40: 15, 50: 5}\n",
    "generator = rnd.empirical(options, as_lambda=True)\n",
    "statistics = generate_random_numbers(generator)\n",
    "\n",
    "statRecord=RecordDiscrete()\n",
    "for key in options: \n",
    "    for i in range(0,options[key]): statRecord.record(key)\n",
    "\n",
    "show_results(statRecord.mean, statRecord.sd, statistics)\n",
    "plot_results(statistics, \"empirical values\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "50353b016531f30848d54ac32cae776bc182aa8d1967b29e76fe0c76333975c2"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}