{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "93d8c208-e950-4681-b4fe-1c78fc1b2a21",
   "metadata": {},
   "source": [
    "# Рабочая тетрадь No 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "362b66a9",
   "metadata": {},
   "source": [
    "1.3 Задание"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9a69252-6f4f-496f-bb22-6bbbed2cdcac",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 5 >= 2\n",
    "A = {1,3,7,8}\n",
    "B = {2,4,5,10,'apple'}\n",
    "C = A & B\n",
    "df = 'Антонова Антонина', 34, 'ж'\n",
    "z = 'type'\n",
    "D = [1, 'title', 2, 'content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "553ba58d-ca06-49fb-a652-cede2cc33559",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True | <class 'bool'> \n",
      " {8, 1, 3, 7} | <class 'set'> \n",
      " {2, 4, 5, 'apple', 10} | <class 'set'> \n",
      " set() | <class 'set'> \n",
      " ('Антонова Антонина', 34, 'ж') | <class 'tuple'> \n",
      " type | <class 'str'> \n",
      " [1, 'title', 2, 'content'] | <class 'list'>\n"
     ]
    }
   ],
   "source": [
    "print(x, '|', type(x), '\\n', A, '|', type(A), '\\n', B, '|', type(B),\n",
    "      '\\n', C, '|', type(C), '\\n', df, '|', type(df), '\\n',\n",
    "      z, '|', type(z),'\\n', D, '|', type(D),)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ee18350-697a-4d9a-a22a-4362b7870a7f",
   "metadata": {},
   "source": [
    "2.3 Задание"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "092547e5-a728-4b08-9640-8acc2537285c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "х принадлежит (5, +infinity)\n"
     ]
    }
   ],
   "source": [
    "x = 10\n",
    "\n",
    "if x < -5:\n",
    "    print(\"x принадлежит (-infinity, -5)\")\n",
    "elif x >= -5 and x <= 5:\n",
    "    print(\"x принадлежит [-5;5]\")\n",
    "else:\n",
    "    print(\"х принадлежит (5, +infinity)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b157c155-7cd1-485d-9dbd-63075743a3a3",
   "metadata": {},
   "source": [
    "Задание 3.3.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "638e3938-cdbe-48b5-a089-f47a1b7317f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "7\n",
      "4\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "x = 10\n",
    "while x > 0:\n",
    "    print(x)\n",
    "    x -= 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c64c1c7a-d9ec-491c-8b2f-4d0e3ac33d64",
   "metadata": {},
   "source": [
    "Задание 3.3.2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f6d05bd4-277e-4420-9368-3d3eb457e521",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Пол\n",
      "Возраст\n",
      "Группа крови\n"
     ]
    }
   ],
   "source": [
    "list_of_human_characters = [\"Пол\", \"Возраст\", \"Группа крови\"]\n",
    "\n",
    "for character in list_of_human_characters:\n",
    "    print(character)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f31280d8-374d-443e-8578-530748ea61aa",
   "metadata": {},
   "source": [
    "Задание 3.3.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a5413ba-1e73-473a-9940-326b75d2cb68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n"
     ]
    }
   ],
   "source": [
    "list_of_numbers = range(2, 16)\n",
    "\n",
    "print(list(list_of_numbers))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a8bf4ca-ec68-4224-b3cb-a57e7bfda81b",
   "metadata": {},
   "source": [
    "Задание 3.3.4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bc3e295f-1fa1-4822-96ec-87b128e97ec2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105\n",
      "80\n",
      "55\n",
      "30\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for i in range(105, 4, -25):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7cfb41f-d1dd-4e64-b524-6061eec4377a",
   "metadata": {},
   "source": [
    "Задание 3.3.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "071fff6c-ef63-40ea-bb59-b3c740cdd667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8, 1, 6, 3, 4, 5, 2, 7, 0, 9]\n"
     ]
    }
   ],
   "source": [
    "x = [0,1,2,3,4,5,6,7,8,9]\n",
    "x[::2] = x[::2][::-1]\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f07122f-4736-43ae-b784-9f809eb15867",
   "metadata": {},
   "source": [
    "4.3.1 Задание"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c0d364bc-976e-4e92-b23c-f0612d4d116e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from statistics import median\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a0e5ad2a-4f64-4dd1-a23e-f094834a1883",
   "metadata": {},
   "outputs": [],
   "source": [
    "count = 1000\n",
    "\n",
    "x_values = np.concatenate([np.random.random(count)])\n",
    "y_values = list(range(count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "90791a47-6edf-4d72-9ffc-2a859a9b5b2f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.48475484828462484\n",
      "0.4998818822942002\n"
     ]
    }
   ],
   "source": [
    "avg_value = sum(x_values) / len(x_values)\n",
    "median_value = median(x_values)\n",
    "\n",
    "print(avg_value)\n",
    "print(median_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "892aab6d-5ce0-4d66-b45a-e4ba218aff39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5U7YwKZL9E0KuDKVcoArZTAMV8x3Ockmlg5CgEwdHzwj9kADUQFvnJA6SSL+BKVgxlMed0gB8VYEG9ia79E77O4niTEH7gZiycKWZOTF56bn5fPZsvzUYw+AQ68IokqIqnYMyXRKSQ7KTvm6OOXf/0TO4XF8g3zU3F6G1K5mPTtEx94x7B2i8VJ8AtJFignMm7OqhGt6+PNeSddMNM59adKO/9Qz5PhSAlw/c2ZFxALTrxB2Hz8qi3mnMd6Wg3EsSxLowQq4XqjlL7mdOBWcR1/oUDn76tsLKQgxi3E0G5l1zc0GRHtpd7kLzkdvNRrl3eoKGwWBsdASTv/kz+PKuTSSFgkuAJCnDp8q8v7xrE4ZWDLRRC3SDZGlstJ0GINTYO3d5eIiyQUrwNjYqp5ww311tZffYNALcWKl5j6FPsJHiwuBSVKn3KSEh6xTM+/dRGNsUGznpPDhIKVlsmDng5oJbu5QVcN/hUy3P1ylq8Z4S+gZGAFKTb7g2Hpw4LVqInBDqJEd8LDghIOGVT7kfJ4ykPP9A6yFmyL64g/lyfWHxv03MghIoIQEUQ5+wakV/NBeSjRRhQh3ksRwyPqRwEY2NjmCB8CiYfcCtjVz8R2Ys92xd27b3B2v9ZKGjnc1G/Z1bu1yNT4rSUxQ949O3YcxIzrFFFVtRwTgq9a5THPEppjF38NyzdW120zrUWOL1mVlcP1jDdbU+MXdLET51Kqga+r7kwE7NDnLBBVmpOgoOReoKivicQ/uAWxu5/dxuV62QW8z8jcrB37Z+DRsX4eJ+9roqM6Buo+c0fZd7nEMOKtQY7TUVqaYxdfAMD9aS0tdCGhnXrcqMfWa2jsv1BRzatUkURJZ094q1tkJ8QpIDOzU7yAVnRXLzx/V8mK03OsXFrpkindRC+4B6ln6lku/JrUcjZE0fAHM9zm04Ntreg0EDOHJyerFGJKXLnVlvMW7LIug5TT+GnpVCjJbOaQC5AlepfDSU5pLis5ZogVQGVZGOYyHBzVXjUlW8XNejiclpUaEeVyEcC8qK5GgcAD6zzOcCCr33Iq7KUIYQ9Syp1CWh9Uj9/dG7NrJB05QeDObz+w6fClaFd6JYr+eEfqwvPaX1nAvfROZMz+I0U67d29hog4/IdmMZzWXLzeG2gTYkB09bPr8CqOQx6TxRAlqjkWVz8Z05soiOquKl+IR0c+xGKHBCtRNJcVSFrP3/qwn6aArcey/qquQEGnUopDZmCa3HVEUp9eAz1+yE+yaEnhP6XN6wj2bXJZ3KlUaWk7aXe6bQQZKiufgsFOlmoBa/C25T2/e/frDWNm8GobTK+oLGfYdPYff4VBvTI5WLb5viY6MjZJopVUDGIcXy4woGKfpo6n0BvIVSts+ZOhRS7hlaj6nCu8jBV2Y9RAx6TuhzrJCrVgxEUdoWgcQtYbQcw5FDFbFw7obcJjtloYQaktiQuNioTe3ef8Zq3BGj0RrYLKl2+0mKT8h9nlyB+hyWn5Q+mmPLpKwfexydFFq+AkNJym1oXlLnrejB1wn3TQg9J/Q5LW6GodnNDW7RuQLA5eUHWgWB+e+Qdho7Dh8oC2XlQF+bD5ZiXAwF0W3KCsn9TeOOmUv1Qi11TfvJmEwKLqMjBjksPyl99C17j5HXMLnznBumG0LLl3JrxmMQsgBDmTgS4Z374OsGX0/PZe8A8BaKcJ+XAS6bgdOEqcwFo536EKIPjskuogTLzGwdOzePLGYerB6qLfZONZkT5jDjYAciY+7PFcjEwGiT0kyKUEaHFDnqOaR53qH3VFYNSWq+vSRryM1gm5mtA7qxDqlMnJhMGXvsB4+fxZ7tt7Zl6cSiUwVpLnpO0wdABvZiWTOLgNMYQtwuuWiK7XFIzWcufnDk5PRig2mfq8LXOczG6qEaHvp5vrcBZ5l4+e/7FOa0TgqsSrXalLiIixxuoiLWSeo9pSjivpIciJwFSLVulM7vgxOnW5IdjCtw39NnWDbYEHLG9WLQk0Kf8tdSmnJZoLJ63IwhF2ZT+kxDjhWTMyUl5jMgix+Y/3b/xgn8L+/aJFronGCjDtKYIqYUWy+Hlp4jSCp1PfgO+th7xroligg4yYFYVuX7xOQ0vvLCubbP6/N6MS6SmnnXrWr9QkJfKbUbwH+Lhow6DeCXAQwBGAewDsCPANyttb7Q/P4DAL4AYB7Ar2mtjxe5fyo6VfmWglClMNDwF0vyjM3G3D0+hesHa7j4ztyijzOGIdBGkfgBhZHhQXHdQkiw+Q5SCROqQYqtl0NLz+Urlmqv5nspPuUUrb2IgPNacP0KF6/MLTLXxiQSxGD/0TOi73EHGPWOO1Wt7yKZZVMpNQLg3wP4sNZ6Vil1GMA3AHwYwJta6wNKqb0AVmut71dKfRjA4wA+CuBGAH8C4ENaazaNIyfLpoHbvk3iVugUJE06jEUSYl+U8P2vHqqRAVCOZZNjBKTG5uscZhpYbLn5htKYIWN6HxhWxJh75mbyXGrNOFzEsEHaWWg+SBkk3UY7LnNtrU8BCm2B26LrZx0T9Hbh2y/c2gDKZUMtq4nKAIBBpVQdDQ3/dQAPAPh48++/D+CbAO4H8CkAX9NaXwHwslLqB2gcAN8qOIYo+CbBdm2kXjPXJpXwj3PaEUdg5cOFS3Uy3TEUAKayVigBvm/HBrIY7Nh332CDdUXeb6gi0oYpwEq5fo414NOid49P4d7xKYwMh9sTdgJSrT102KamOt5+4Nm29epLTe30u5GmJ5t1bRf5dXLMyUJfaz2tlPrfAJwDMAvgGa31M0qp92qt32h+5w2l1HuaPxkB8IJ1idean3UUuYMnuRsfSEr8b+S06WZxTYybRev2kndJAJir5qViCz73Fefvz0W4JS0KA9JcVLlSGX3r0w4g2v7lsppshJQYqVuCUzyKNE6RpqbmwPBgTdQ7wfBHbdr/DJTCImEgpcC5RX4GJktoSXbOUkqtRkN7vwXADIAnlFKf437i+cyrdimlvgjgiwCwdu3a1CF6kTt4wnFlA/Gb0dYaQ60HuQbhEovB4K3ZOg7t2uRlHeQWIJe1QqWyxb5njnCryLt9fYbuc1y2T5VD7PvJne2RyqE0WOvHtvVrWtYLtf4UUKgXbid94ft2bMCeJ061uZJ2ffQmPPfS+bY9ah8Qvv1rj9U9XLetX4MjJ6ezKZAUirh3/isAL2utzwOAUuopAD8N4K+VUu9ravnvA/Dj5vdfA3CT9fv3o+EOaoPW+jEAjwENn36BMbaBCvikEmSFuLKBNMEvybix4xIG9QWNe8enMMzQE7i4sRlIdTWO0OZPOUBDwsA94IoQbvnem/tul1pAP+awNsiZ7RHLocQJLE7gAelu0dyJGNw4Qq67UCcuDf+63rZ+Tdv+iqFyL4IiQv8cgK1KqSE03DufAHACwEUAnwdwoPnvrze//zSAP1RK/TYagdwPAvh2gfsngXLppqboc5s0x4RxbgOO38WmJzCmprsxAXqzSDZ/jMZlB/QoYWBvEGP+pxBuSV1uuasrc0Di3nORU8ON4VCy39Pobz3jdUtRlqpvjuzc96EV/bj0zjw0GtbeZz920yLdd9kxFHetcHtQcuCa9ewSyVFuvJR7xKCIT/9FpdSTAL4DYA7AJBra+bsAHFZKfQGNg+HTze+faWb4fK/5/V8JZe6UAUpQphBkAeFNWmbObUgr9BWn+JpH+ErZQ75IQK5xuRvLJwwMzAaxzX/qHpSGFpuCupQyY0LuPRe5LZMU1wnXdtMn8MZGR3D7gWfbi6ms3PeL71z927zWi7EMW/CXFUOJUdYkltlqjxchR6V1Kgpl72itHwLwkPPxFTS0ft/3HwHwSJF7FkVuf2AoM0Ry3ZxmrouQhuaOI4b9UqpxxWg17pipewD+ql/399R1pZiYnG4pYhoerGHfjnLTezn3XtnZOymuEx8tiIFpu+mOMdaFBQCPv/iqqLkPt5/cv0mUGw6hPVjrV3j78tzi+gkRFOagcg+h5ypyyyjMojJDJNctkv3jaoU+xPhPQ2mevueRaFyxwtY9KH338GmKRkMrcrC7pF1/f2UO81YQb2a2jj1PxAfpUw/2TlsiKa4Tbn6pFNh+IojOQfJ9bj8B7YpCKO5gX5eKEQFoWTN29o6P0ZQiKASA62p9uK7WL24XmoKeE/pl+XFTr1vUvLQrKzk3iJ3p49IIG3Cbt0iKHSWEh2p9mK0vJGk2nDZ/aNemLAcwlaoXYqIMXTfmYO9GoZa7lo0mT9035OLwzVUqzxXXFAgIk7PFxB3se3Lzxx3MFKOpyZhzqTBm6wsAVHSBYAx6kmVzbNTfy7IIUjdnLlfE2CjNGrj/6Jm2LB5DI2yD0oSNjz31PW1bv6YtX9dkFtmjUgB2bpZpthyjJPcuOEgL2oC4+ZGwRPrgY2Hc8+QpbNr/TDRTpe/aFONlLPujj6nVhm+uUnmuQiyU3H6i/mbiDtRaSZ0/ILxOh1a0693Sa6ei5zT9MlBEk8sZY6A0DirI5n5ehuvLZSgEGsK91qdwyamE1gC+8sI5PPfS+eChGRqrzy/udshyUVZwLfVg9zJHZiD6Cq1XqfXpusL6VGsAFqDXD8WI+q7rGn0Rhlb0t13LHgfgt6pD+0lKH2GjKG+QW09T61eL74SykFJiHlJUQj8DirhoOOHVCdPevUfO9pATk9Pe3GMNtAl8GxJhJnWnxRzI0hz5Wp+KOghTycAkQiUlLZhar/uPnhFnbvlcYYO1fnxu61oc++4bQZpuyfxRPFRclXZIGUhRagorZr4N0AQV2yizt0cl9DOgiCaQkp0SK4SpUvKhWl/bPQwnfq50uNTKOiOEuHFIgpwxBzKlfa4Y6FvUOn3ZO6Fskbcvz7WNy9b2KEgPoVhXIPX9C5fqbMtJW8hR7/WPvjMNK+7dRtMdo8hQz89VaVN8NvaYQ+1HXRSxgA8eP9tSzQu0xoS60dujEvoFYBYwNT1STSA2OyVWIFOl5CsG+r2ZBbFBSmoTc8LIx7jp4sKlOtbtPVYKT4vv89hgvMvWCvhdJe6mBxr9mFOEjQ+xrsCUql9XyFHvNeSSiVFkKGEbqolx95Ov/ajbg6FIVS6H0PrrRm+PSugnIieDoA/UYpmemV3kEI9J+wPkzUWkmmPIdUIJFwUstkTk3AkG5ronXnkz2vUUa5pLUyS5+bcPTo4czL0eJVQ4SuGUdRZb9es7dGMPDpMFFKPIUOtWWqXNFRra9y1alcuBW38Tk9O4eKXdCiybCqQS+okoi0HQgNtUdkYFIHP3+NLwqGCZVHMMbWKKfvmerWtbNtPE5HSwReRsfb6tZZ3k+csITgPhTB8j7CWHjiQl0P5u0TiPT5j68skBOsi5bf0ab0cpCjc2q3J94JQMStiG5lRSaDg9M0ty5+TivOHI6Xzj60Rvj55M2cwBaqEaBkFu0iQNokNpcECr2Ry6pi8NzyfwJb5mg9Am9qVOHtq1qa2qcmx0BMODYcI7ioyKQ2r6Zggha8gIdd88ugIqJiXQpBsf2rUJAFoaz8fATVvet2NDcJxAYx1t2v8MK/DdEKS5Dpe+GDv20JxK0m8NHTKFHBQq1Fife+m8d3xDArdfUVSafiIoDW54qMbSEaeSgXFkTJJrSnPQJb5mA+od9CnV4oKS0Oju27EBu8enogO/0mB57o0Ucm9sW79m8d5AOyOlnUIaSwWQu4cDNc7Q2vXBFDpRwdJcVldoTkPrIsRpBOTjvPGNtahrtQgqoR8Jji2S4tkAeOErIQOjzNAbhwfJa953+FRQsLiQEs9R/kjgauZBLKVEyMXjQxkc6hKE/OLHvvuGN/7gE9hSKgCDolXcFEKCVKI4mOdwg6Vm36Rkz6SAW/NU8NRG2X713BxgMegJ947EnSK9jnGRAFdLuIHGQlq1YqAtU8M101PTOzk3Acfpb1w50qxfm6snVLHp+oD7PDeJqS6MzVjoJve9MdspXLhU91a0UuRzlEvEh5yEcjGIvb6Ze3ff+LJncsO3XxSAz21di+f33sGutVwuwNjxdWo9L3uhH1tSzoHasCbYRWnJ9mahTnLjEqEOJc6PKdEOfILFhct1Tr0zSuPzZCYCiGMsDMUxbHxk7fWl+z85jI2OiA8qIwBTqQBs5PKNxyLl+tOBrJ2yMDY6gp2bR1rWvAbw1RfOYd3eY7h4ZQ79Hi3FFN6Vva7KijVJsKzdOxOT017K41RTOKRhSUw2yi0gcYlQ5rc0Bc/lNqdoekM1AkVZMym4fmWqnaHB8z98Ew9OnCbpdjtR0RyT/sjR+YaoAEL37ISWSD3r6qEartTnvVXWXLA0dh3FzqevnaevraGNWDI9CSRd3DqJZSv0jbZKCY0UUzgk1KkUxW3r17QV8vSphmbsK8OOPZSkwlIqWFIPN1/BVawwsjeCJHBIcayXEeykxgtAFI8wB61LTRHzjjrtG7dBBXsBLNJNu7CDui5iLIeU+Ux1d+V0k3VqHcZg2Qr9UNDJzZOWaBASkq8Tr7zZsqk1gPFvv4o/fPFci/tjQQP9faqFq91GCssmJyxjBEvK4TZY628puMqhXdtChtIWqUM9JdiZYhmY34RgcrOPnJxOZhaVVJaWDZ92evuBZ71VxwZmnDHr0Z2LS+/MRc9nSuWx+V0ulBV0L4JlK/Q5oWkvuJiTWJLW5jMpqQ0xv6AXNX4XRRZekbJxQHa4cdeXCkrJ2IyQ+cAD34gipgpZK76OVG5jb7tna0oKo6/fry8m9NxL572/d7EUBQgQVlDs55fSW7h7MuXeEtdb7k5VuTtzlYFlK/Q5siY7YJJSGl4kP9jFgm7nE8nhny3iL5QcGqnX923o3eNTuHd8inVTfPZjN3kLgj77sZu89+GYLX1j8LGBhmiMY6uyi+ZmdytrJ3RIc8LNFPvFrJeYvgaccuRaij4Bn5tVtmg6biewbIU+pa26EfLcGynFpHz0ro0iLaiTXZS4TVpkHJS2C/BWlvHbP/7iq5jXGv1K4bMfu4n053PMltwYOLjKQKgq20XR3Oxu5HZLLGFWo7ZerHTdSPeeRDly3Z5l7h8uHbfsvrcxWLZCX+rioDTCYU8Hew5c0RYHBeDEK2+Krr8UAkJFxxHa0JyV9fDYRlFj7BCzJaVxS+Cm38YI4aJZN93I2pFYwubfvky5+oLGfYdP4d7xqZZ9UaSvgWp+J1Zol50tE0rHpai3O6XIGSxboQ/IJpnKCIyhs3YFoe90J+8PtLgtqM2wVPy5oZzr0AKWWEK+zSPdHBOT0+T1TR1FaoDP/NbcJ5YhsWispejvUyC1hLnD1BwEFHeSO37DAOvbPzGprZ1GbDputxS5ZS30JaAKqqR0BABt1lGpaiH4NkO3/LnS+3HdjOznkATXNBoZIRx1ge/a5nsUuOwjCdzitRSGxKLaZg5tNUcjE581k3KYUn0N3Cw4IC211ddIpawDM9YS65Yit+wrckPIUd3I0SDEVJhy1+xWFab0flw3Ixt2JSJAVwmHqAt81+YCgG72kT0GCewEAOo+nWBItJFCL5Kj6TnX9zZ2vVPr6eGxjTi0axNWW25WqvWiC29D+SdOYc+Tp1o+u3d8Cpv2P5NMy+JCWmVr5q1bmT09r+lTWt/FK3OYmJwWbWLOrHNT1Sject81Q+PsZC9dexy+Rs/2/9ugNDnXp8lxmkutHG6zuJvPjEHC5Q8AC1oHA7gxzWfsxihag0wL5a6R4hrI1cgk9F2Jxi/R3C9bVb4zTutFCr5npNKmpdeUImSJSQoOy1bkel7TN6fzaidwaxaDRAvgtKGx0TBvuQvfZqC0CADZuIU4GO3k3vGpdgGv0fb+DEIL2LwfSuM3gkZybep7I8ODbB2AhMvfvnYRq8vVQi9cqmNmth49dxLrx2cJlOUmNPeSBsnd1GkfUjl7UonhOoFQOmonMnt6XugDjY0/tKLd6JEuhpBZZ2++g8fPYufmkZbvfm7rWhHxknuAUK6G3IvYZUl0UV/Q0M16AxuxVcDU54ab3oX7eSpzYeggdq9RhCExtOmlc8fFVgDajUNlpVHvX+IO8n0nRO5nW06xzyhtYBODTsXGuPusHqp1hHSt5907BkU1IMqs85nhR05OZ5vcMgO8nOvFxVuzdRzatamUKmBKCLqVrDGuCNclZhfphFwuRbJoJPMi6YNMuRSV9Ww+ZWDlQF9UMaDEHSTNT3fHH0JqXYJvLdX6FBYAkvbEdxiW4Tblgt2XPYR1ZaBnhb47oVwFJ/e70EIoO0JfVsGOxPfo3q+sKuCYSlbJGHIcxKnPKs1wCfVBptIaNa6+Qx9iD2fOovjAA9/AZz92E5ufvtqzr6RWUWpdArWWfuOp7+ISIfTdJLuy0im5zLFOpWD3pND3TWitT7UFJEPNlosw/eWilS2rYCemFB5od7WkgBKkuQ826iDef/RM6ZXRsemi3LioADR3qMQeztwhNa81vvLCOaxa0e/tt2zy01PfVxGLyveMXMDeTdEuS1kzv6XG0gk307IW+tRio6L7w4M1rFo50ELCdfD41V6mOZn+ctPK5jZDYxeflDQsBbkPNurZLlyqs60ugeIaoDtftiuJcoVQ45K0/bMhfWf2vrl+sMZmZwHApXfmvS6jbevXtPSLPrRrU/S6LLuK1sDdj2W6TY0M6la7xEJCXyk1DOB3AfwDNKy5/wbAWQDjANYB+BGAu7XWF5rffwDAFwDMA/g1rfXxIvfnwG1OzvSdeuhnyN9TiGX681kQnNAOaR1lbIzYQpsyNZTcB5v02XwH+v6jZwprgNR8cbnbvvvFWA1Szn133c/M1lHrU143jYFGO3+Uj7WUOxxz+8991xuq9XkbvQBoOwzL5jnqBqWGQVFN/3cA/Dut9S8qpVYAGALwGwD+VGt9QCm1F8BeAPcrpT4M4DMANgC4EcCfKKU+pLWOK4sUghOUkgktg+mP4t4IaY6SIo7cm4ZalCsH+rx1BmVrKDkPthhhOT0zu1ivMTE5TQq+ItQRKeN6fWZ28Vr7j54hxwXQBHA+UFbw0IoB/N3sHElv7c5PqPuajdz+c+p6VEbRYK3PGzPx1aPkEsplWegSJAt9pdRPAviHAP4pAGit3wHwjlLqUwA+3vza7wP4JoD7AXwKwNe01lcAvKyU+gGAjwL4VuoYOHDm2aFdm4KnbCrTH9cazYeQ5jgxOR2kZy0j6EQtSgAd0VDKLDjzPRtXNGfeJZdK6Qv4x85JzLj6lFo8jA4eP8sK/RzV5a/PzOKerWvF9NYx7pHc/nPqehTIrBmq16IAkvXbKdeViyKa/k8BOA/g/1ZK3QbgJIBfB/BerfUbAKC1fkMp9Z7m90cAvGD9/rXmZ21QSn0RwBcBYO3atUmD47R5ySlL/X6o1ofVq1Yma+02JJrjweNnybV28cocbtl7zNseMVfQifp9LoFMcaSUTUTlPhuXrRSqDAba3QOpgkw6rnmtg+5KIP5A5vZNDL11jHskt/889ne+MfmYWqX9c5cKIy6FIkJ/AMBHAPyq1vpFpdTvoOHKoeCzrrzyTGv9GIDHAGDLli3xjGXg+9UC4VN22/o1Xq3m8twCKeRiN7pEc+QWsNEAuT7AZWjMuTQUanNcV+vrOBHV2GiD5Ms35wDf1Hx4sNY2rlyCzFzXR1sccldKql5dUG49Nyib4qqiDqDc/nNunqQ9nIvM31JhxKVQpCL3NQCvaa1fbP7/k2gcAn+tlHofADT//WPr+7Yd+H4Arxe4P4ux0RHs3DzSctJoAEdOTovK3KlslAVNC2vK925/blfnckE7sxBjef1tDA/VOkLRkApqc8T4zWPBkZRxGUhG0PkqcU1vYPf71HViMTY6ggXmYKfG9aW7b0vKlnGry3duHsGRk9NR68h3HeoA8o2/1q8WLVkpmRx3PTNP0jHFzp9kX3eaEZdCsqavtf7/lFKvKqVu1VqfBfAJAN9r/vN5AAea//568ydPA/hDpdRvoxHI/SCAbxcZfAi+frW+E9enDXMTRP2NolI2fVylBU+25pjAzAygsci1bvdlLiWNI4cZHoOQ2R1y38QE31KytratX0O27ivqroxBkaAsdx3ue/b4h4dqePvyHNuqMoSVA1etRZfyWnKNGCJG6b7uNCMuhaLZO78K4KvNzJ2/AvDLaFgPh5VSXwBwDsCnAUBrfUYpdRiNQ2EOwK+UlbljIDHRHpw43cLbbXOUUBpnn1LeMnnKzWI+l2QEuZpjDK9/v1JY0HpxXEV7spYNjkrAfZNlFZzZwkvivkkVZJL4D9dMJ3SIlBkULDNn3cAe/+0Hnm3be7P1edx3+NTidyn4BLAJ1Ma4OqnMqBkPK6d0X3ciHVOCQkJfaz0FYIvnT58gvv8IgEeK3DMGIV/hxOS0tyG24Sip9SkvJasR4m5TbyqXefVQLZiDTbWAk+aU+/r/drMARAJKm3LfuKQ5iQQh4bVt/Rpv4w6f+0aCUCA8JCjcWgzzu06k+NkC0pcoAJS3jqh5soPXMe/VVDVfri+QVp4PVGaUa+WEDr9+pbBzc3cydXxY1iybITZELjPmrdk6Dn76thbaXeUJRdsWwluzddT6W79U61d4+/IcK7hHhgdbmDPdZ6Dyi/uVYn2TvudXzbHG+knLgOv37fe9YORrTsL5aScmp3Hk5HTLelBAaZtVqiXb3xsbbWdZLQMua6ZP4BfxuYfAHSYhFlKu2jqFjVZi5YQOv3mtxbHETmBZ0zCEtCNu4/k4Sm7Ze4y934JuvFDbt8+VrwNhs29sdARPnDiH53/Y3jydSpezfwtc1filjaklyJUVZL9j6v3mciOEmDx9FocvuJvj2aUWXDesspAVsjqDz50C1XvYRohbKGcluSSzSFJUt5Riacta0wd47YjaUArtedfc9228M6/FfXG57AEbP/pb/8KU8N2Y5x8ZHiSD2rGQttyLbeeXM+PFBy6jhGOTDHHH7x6fwrpIjddnhbnolh+YE4Qjw4MYWjHQ5vZMXUs2zLuliuQMKIsQoK17qlGOhKY51DvBXVcUlkosbVlr+iFQufz3bF0rzshIhWEglECSChpCzmCcJA85pUClE3wklJ+d0xDtcVMWAXD1ADjxypusBWauBbRaoVz2jo2y22NySQwpWW1SSKlPXKWK643AVZIDjTkb/a1nyJiRNJbiBqKXciytp4V+bHDMdZekIlaQhVJBJYgtgOEES0grpoRjyMTtdLDShpTnPCTYNICvvnAOW26+ITjulIybotWekgODM1TNeskh1NyxSPeU3ZoztjeCj6fowqU69jxJZwbFzlM3ydQk6GmhD8RPqPn+xOQ09jxximy4TEHKdmgjlAoqQcxCDAkWiVacallI5yO3tmt+G+I5lwgn08ikjMOqSLWnb17t7DPzDrk04Vz8S76xcJ22bLx9eS6oXOw/esarjVM8RfV5P8VCyjrrpvIiQc8L/VSceOVNzFkCf9WKfvzCR0Za6GRt+FIqpaB400ciNKuYhRgSLBKtuExq2rK4TYxQ4MYtdfFxh1uRA6uIqy/kmjLvUEo3UUSoUWORCH6bA4fL1lm391ibkhXjniqyzsqsmyiKSugn4MGJ020cLRffmceRk69h5+b347mXzmN6ZnbRLZOi3dsoai66QibUzCKkpUu0YgmTaSrK5DaRFEGZMYSySHwoemAVcfWFrCyOp9+tVygq1KixSAW/1PKKsVJdypOi64wiE+y2BbAshX7Zga7HX3zV+/lsfSFr03ODIuZiipCRaOkhrbhMEzfFdSRdE75KzJUDfW3fMd9zK7qB4o3GubFTwlDi6pO4pmye/jL3EDWWPtVIfZb8Hmgc0lwbRKDdSqXcsrbbCCiW/ODbd3uePAVoLN67W+yby07od4LWlNtgZeXjpmpWKUJGall0ixogJSgduyZsjnVf6b3Bw2MbseXmG8QCMlaQSHldJK4+iWvKvEN37kz6ba5DwNekBJAJfHeN7Xv6TDDN07VSfU3lXerkIi5K377z1ezY8YeylVWDZZenzwm5GFA55pI87KWSjwukaStcPnvK93Jjz/ZbvZXPKdp17Pd964KrBXERW4sgSWOUVlnb82V+ZyMU2M/J1jo2OoJVK+Q6J1d9vm/HhmDNw9CKq3+XujYlOfqS64Rw4VIdD06c7hgj7rLT9HPko1Oa4YlX3sSRk+FJ6HY+bg7eFKmW3rWAVURXo9g1waWkFrUiY+MzoXUbqrL2aY+mPkSqWeaMoUhcVS5CSRCSOMuld1rHL3VhmuvGat+xlcGmMY2NsrwGy07o075ChQcnTouKX6hF7psYF93Ox3UPLN94fWPslGmZA1xXI/N3+zlizXSuKUlR4RcrSKhCqdVDNQytGGgbpz2ekFtLemDnKuyLoRZftXIgOk1ybHQE6wgqD3cXSA/fVKXGd/1avyJpWbhGSLmx7IQ+5bec15qlrrXBsfxxSOlUlAJOQIfcAb5MoqXe3s1FrCZumoBItWufv5nbsHZRmgQxgoRaclqHhXEuDT1X+m0MtXjqupNmN3GHbw4FiLo+FX+gxl2G12DZCX3zsn3t5VxQGyDWNDOY1xq7x6cW097KKDIq0giEon4oMwWyDMRq4s+9dB6P3rUxbiN73EcUdTaA0g5JqlDqrdl6UBjn0tCp1qGm9SgHqTuHohaPxWc/dpO4ebvv8M2pAFGHu4/6ZetPrcZ3zr3VkSreZRfIBfj2ci58m0ZChkUhJgiTEiALBSU5zSCXb7vboAJsnIkcE2yl3Edag1wXOQjHfKDmU6PRxckNaNtBXarVZqz2SBH7hQj/3PVNgaMWj8XDYxvxua1rFzX7fqXwua1r27iQqESNHIkgHNHg2Ki/jet3zr2FnZtHOpIUsew0fYMYbd01zV3TjAqGcpBoyikadkhAm45ZvtHG+rANz/xS8/VTpnOomlYK6h2/NVvHoV2bglQNOcGlWc7M1lHrU4sWiBvUrfWpNrdUivYYWnPUGpG6c8x4cq21h8c2soR3nDZfVAGSWApUG9fnXjovJmEsgmUr9GMYMSmeDvMZFRwC+OrB0KZIaaAcMunHRkdw4pU3owqGqHf15sUrLb7tpeTrl5rOKUKOe8ehorTcCGWm1Bc0hlYMeIO69QWdFBR1EVIKYgUo0O7O6WRciVO2isYvJIpc6iGaC8vSvQP4c8gpUH5aA6rEvV8pvHzgTvLa9qZwXTgPTpwmube5BSbJHX54bCMO7dokNhXNu1rtuANm6wttwcucpm7u3+WqGwi94yL521LYz29iRNR6mZ6ZJRWIt2brhbttcc8bEqA++Nw51HXuHZ/K3pmLE7pF51ZiKVBut+GhWik1ES6WraYPtGuDnMbOIcRyyaV/cemfvqtSDVwMpCl/salmRoMNHYBAXlM39XeUNhT73KFMKN/nRfK3pWPyPT/Hc0+BEjAx4J53dyb+JW5N5db6Q5YckJ7VI7EUuIysTiRVLGuh72J4sOZNl6K66hhwDc+BtE1BHSQa4YUtEWwpJqJUmIdMXXNv3+LPEeugKIIlzUvccYby2CmUWZRGPf/KgT4M1vpFLkuDty7Vo9JJKbjPaywRyrUZy78UisHlFHzb1q9h3Z9Fsnok+f9cRhb1t5zxop4S+vt2bGgjW6r1qRb2QB+4k9mAEgJceqFP8MfQJduwhfz1gzVcfGcu2hcvCX6HTF1JAY5vAUtS+7j885jmJQa5tKrcPthQIDnE8GljATS3f+q4Q3Ms5V9y1yxXCwHkEXwTk9M4cnK6ZY0pADs384e4dK1IDrqQNVB2vGjZ+vRtGK1k9/gUVq0cwOqh2qLP9+Cnb1vUHikfMncyh0D5CD/7sZuy+YVdP+DMbD3JF+8bq8kOUWhYNisH+rCb8bNKMjbcBSxN7Qvln5vmJVLkpOzI6YPlsqxM6mmMckAdsqnj5uZYGkeh1uyqFXSqdA7BRykMofTTmLUSSg/m4gadiBcte03f1UpmZusYrPW3cMqndoqSLELu5I9hZ+QgEbSArGsVNVapeRu6h28Bx6b2pfZwdTVb6joxwoXSAPcfPZM8txIXQUx2mu95ilg51Ds28aiDx89i9/hUNNUJ0ODI+dzWtVEV1DFIPehzVSUDMmugzOydZS/0JYs79J1YkiwXlImbyy+cyxcP0GOSCgnORUQ1k4lJ7QNkPVxd+A6tHHns1NgvXKovHigh95rPzRKqIKZSc130KX9iQBErh5rj4aGaOHDPWWtJFdQOKNdVqvAuKgNccHu/zHgR0ANCX7K4Q98pO1ujKHL44kOQCglqczx6VyPA6tMCqfFTtBGSHq4ufIdWjjx2aREgpUVTFtSjd20MFur4inxcUOnGlJUjyfah5vhKfR6zVh8CII3qZHpmtpDgcxvb2IePb+wKYUqJpS4DYrDshb7kZJfSrC7VCfYy+vUpvOu6AcxcqmdZoFINyS0mMnw4+54+QwaXY7UoUtN0erjaoA6tmdl6IYKvGDeLbwxluFlsuI1BDCTJCRR8ApDi56HGuWc73fHKPahiAs4Tk9Ne68e80+f33tFmIWkAR05OtyQBuEFmpbC4l0LtRpc6lr3QT/WPllFsE6slxLb4K1MLiXlH5r5uLMWFvRFjxk+NhcvC4jTLIjng5jcSgj+fC6EMN4vkWkWSE4B2Jej2A8+S37Wf217TFOz3GFvrcfD42WD2F0WDQKUE22t3KVWlp2LZC32JQKS+AyBLi7iUIqWY33SCHyf2YIkNLsdYUimHHKeRF80B5+oxDKgDsgjvkdTK8B02OQOTAH9I2dw6sa0fQwRo7vsJUZhwY+VSgn33r4T+EoNEEHLfyckFkmLCS39TZJyxh0WMYM4ZXC46FvN9AKWRpaUEsAHaatm2fk3bvN47PoV9T59ZdEe5rjRfU3HqsMlt3UpcblJFwPavU/NC9U6gYhV2pXvowJOshSLrpdskhssyT1+Sg+z7zr3jU9i0/5nFSSlKsWrALVwqL1pq9qeOM/T8RSER5p3uMjY2OsLyJBUBlV/95V2bWM6bsVE/X9BzL533CsiZZpN2M0cmJ/xHB+7EXz16ZxutMFV0RN23SGzD9/y2y00qKO2ceWpeqN4JPvprBeCerWsXny2UCy9ZC6nrpRPcOiEU1vSVUv0ATgCY1lr/nFLqBgDjANYB+BGAu7XWF5rffQDAFwDMA/g1rfXxovf3ITVNE2hsKrdrko2UE57LK6e0cqn5LTkcfJoF9/w5fJZ7ttMUzwCv/ZYJKnvDcNCbjR+riRWJq/isFs5dxGUCHTk5vegTn9e6LUAZum8qJM+fEoOgLBLKYrCrljlX7c7NI2Tr1JDbrIiy0glunRByuHd+HcD3Afxk8//3AvhTrfUBpdTe5v/fr5T6MIDPANgA4EYAf6KU+pDWWk4kIkSRNE0AbCl4ygnPxfeoCZea36HDgXL/5PRZUuYqRfHciZaSFFyXiMtBv+eJU4BCS5aR61Zx4T5/juyOkICMyQTyUYeXAfcQMVXudoaPW3Tlg5s1B8T1TvCNw90DR05Ok+vQvaebvVNEWclRBV4UhYS+Uur9AO4E8AiAf9b8+FMAPt78798H8E0A9zc//5rW+gqAl5VSPwDwUQDfKjIGHySCMKUxSuoJH8qIoEq5gbDmGDocKEFAcf9wY/KBiyk8PLYRW26+AfuPnlm0dFYOdN+jaITC7Qee9XLQ+0BZQGXxwIe0zZhMoAuZSNc4uAefK+CNoLU17KEV/bj4TuvzxTQnD1lsnFUfUmzKStHOHUBPQVFN/8sA/mcAP2F99l6t9RsAoLV+Qyn1nubnIwBesL73WvOzNiilvgjgiwCwdu3a6EFxLHpmk8YKfACLmkFsICaktVETLll4ocOBEgTzWrNmsnQRSjbUZatgJ5f7KAditSufoCjLXDe/tQ9MA3st2/UQ3Iou033gO/ioXHm3O1RqUDNksUn6Rr/ejKl1MqjaifTwEJKFvlLq5wD8WGt9Uin1cclPPJ9516nW+jEAjwHAli1boqRziEXv9gPPijIIXIw0zcYUzY7T2nJMOHc4cNWue7bfygoVCVLS35ZKylsKP737vEXNdU7omHn1fQdo1XRDSsz0zCxu2XusFMFGkZj54L6XGI3a9x6e33uH12Kz11gO2ohcKBL7yYUimv7tAHYopT4J4DoAP6mU+gqAv1ZKva+p5b8PwI+b338NwE3W798P4PUC9/cixKKX4juTuEvswg5qQm2tbF7rjgQzOc2CEyrSMaWmv1Gfd0rzmpicxtuX59o+71NAfx9N8etaQEXMdakC4ROMKcqLnS3i3sMeU+z7j9lTRbNefO8qtMaoPaA1uqKQlOU6kiJZ6GutHwDwAAA0Nf3/SWv9OaXUQQCfB3Cg+e+vN3/yNIA/VEr9NhqB3A8C+HbyyAlwfB63H3hWrN31K4UFrcXuEmMqpjblKAsSzUIyNkoYhMzVGKHYyYK0g8fPev33P3ldDft2bBBbQEXM9bIpGCjE8gABxfswGIQ4bmKarJvnCK0xag9wXb+WM8oozjoA4LBS6gsAzgH4NABorc8opQ4D+B6AOQC/UkbmDheglC5MAFjQGi8fuBNAaxYCFQC+cXhwyboyih44PmFgZ7RwjIgxQrETBWkGHBdPjAVUxFwv4muOEbTSe6euXyoN1rcLOd76VG1e0pbRtwe4DKDljCxCX2v9TTSydKC1/lsAnyC+9wgamT6lQRqgpRalAZXy6Lu+yRqgsFQ1B6m2HMrp5xghY4RijoK0om4pBSxmukgPy9RDtYivmYsThda2ubeL1PiEb46p/cBdi5rXe8enSGXOpGi695ccvEshqNoNLDsaBqqfrYvQpjDZERSRllmEqRtMirJ83DHactHepVKhKHEFTUxOiwSKhLfGVzxmum/FvuOUeSria+biRKk020XiE+4c+4KroWtJSdgMpG0ZKSyFoGo3sOyEfkImZhtMo3QutXOh4AbjYKficaloRRCjLYdy+ou4GWyENC/Dk04hVJAGoEVo5uLhSXU3FfU1U4KOErgAXQk9MTmNi1faA9upmm+o+bgPEpcVFWtLRbeDqt3AshP6UmpYoCHcr8wttAmZfTs2BMmhbhweZIWDr+OTBK4A4ShgiyDGlA+5zGyXSBFwmtfE5DTJ1w7IMqzuHZ/CweNnF69JHdqxllkRd1MZvmbq8KQqUCn2y9VDNTz08/G9BlKbj0tYQ+1YG3Xv3Jp7twnScmPZCX1pgMsmg/JNKMd9YgQMtTmpjk8SSJgIc8QIYkz5kEWT6hLxgdK89h89w/7OrvTl3k+R5i0UcpfWFx2Xr7CLq4Sm1tzQioGkOQ2lTQO8IKX2FQD0KUXWG5RRHV1WxXU3seyEvkRb6FeqRevxTR4lFN3f5gwEcf5qd2xFESNYuK5IBmUHq0NxGrvSN3TwGy08tnkL4BdWuUvrfRbPtvVrcPB4o9Xk8FANWjesWm7M0kro3IcWxyp7y95juH6wRnZRszOnfPvYWJ0+4VtG9txSzcgrgmUn9CXawoLWyUE232GRw/QzizyEXNkFvkDgbH0e9x0+hXvHp1p8v1yancFSSHMzm1Fy8BvBFOPTpbS+nZtH2ojEis6TPS73vvYB6BN+VAICJaxyH1rcoavBd1Gj9pYvVdr9TY7Dy22T6Btr7DWXGpad0Ad4Qi2gkRIX6oglFei5AkGcW8cEc3NX8PqsFZ8mFVrgnUhzG2Y2oI3XZ2ZFB3+KQKO0vudeOs/WKthw+XIkldkhl59bEc4lIPjmMnfqouTQlYzN3lu37D0W/E3Rw4trk5h6zaWIZSn0DXyLr9av8PbluUVtifLRdTp4wwnWGKre2HFzAiVU8Qh0jhd/344N2PPEKZIF08CuwqTcBKkCjdMkJYc/VfMR8hNLtErznf1HzwQTEFzkTl10rydNqOMEqUSgFz28JPG02GvaWCoB4WUt9IFGAMtM5OqmL9Q9wV0zsRvBG06wHjx+VnTvlHGHBApX8RjixY9d5CHyMeCqIBkequHty3MthwBFy2v/rshmK6pJSg7YmPiS+52JyWk29sEJK/fQcrnwY9+ZfT0uhdTAbmfog0SgF51raepxSj+IpRQQXrZC36fhXa4vkJvOFn6UGX/f4VMA4idJIvw4k1i6QFKCThLa55TNFLvIpbn1KdaYVAtPnaOLV+ZEKauSA9Y3FknzEZNNRsFNQOCQW0BJ3D06cO1OuFtD9SjAVbbdWCylgPCyFfrUS+bKuQ04DvoHnjqNE6+8SbZacxHDpGjG7RPCkgUiCWTFCJTYikf72pLAm42UTZErnhI7Ry4ZG5cZY0NywFJdnnZuHsHjL77qXbum+TiXZvylu28DAJH2nltASWIsVO9i9zplCsiQwC8S58idIVUE3W9jVBI4wc01RQZ4c322Po+vvnBO3NiY20AuxkYbTa59jQe4ZwqN261UtcduBIrZdKapdmyjbPfaMYHElM9zInaOhla060rU923s2d7ekNvArv2ggsVfuvs2tvn48FDNe+2hWmObu3NPrVsu5VIK4x66Ze8x3H7gWQDA83vvwOe2rm1b36lV6/b1czQW5w6eoo3jQ3uzk1i2mj5FoWyqDEO8LJw5GlMlG8pZjmFQDC2QkN+TEyipxWQG0iAYlTnFPXPZAbBcB1EMMRmVvcNRMIRcHJSiumKgP0p7l5DR+RCiDznxyptJlbq++3SqRWXRns7UOzHX7ga527IV+tQG0DpsJo6NNpp6hwqSbFAbPpSzLGVQlCyQkFDIrU3bwliSocFlTlHPvG39mtIDYLGHbFFiMuDqHLnZTxJueOq5KQqSmdk6+Tef8pFCRiehD/G5p9xKXQnK8o/nzmLyvZOy0q9jsGyFPrXI7c8pDdJwh/hAsWpSG95HPOXCzbM2izqlwxYnFHIW4VAVky5sgqyLV+bIzCmqOrYTAbDYQ7ZIaqBPS909PrVYEOeLsUivzWnoXPMgn/JBkdFxGWahtRDr8qNQpiswZ9yAoqMoQtOSA8vWp5/i3zY+TmoB9yuFe7aubfOr1voVLl6Za/Mv+oinKJjGGWZMwNX4Qy6NwOdTTjUxJZt8sNaPL919G14+cCee33sHeRDb1bHP771j8ftjo3xT61wYGx3Bo3dtxMjwIBTC/lvp931+Z0oQAO0xFslYbFDzqNGwcKl4goEdl6D828bF40IyH31EsCpW6VhK/nEOSyl4a2PZavqp/m2KPx9o0Dc8PLYRW26+oSWDoz6vFzVYW2OS+rmBznTekpqvEh96CsNoiqWRmyKAejaphuf+niqco/zOofVAxViKxjXemq3j0K5NQXecmddYF08oM6nWT/cdDrVQdJG7grgs5F67ubBsNX1OG+OIzbi0LXuybDIrF0ZQS090s2A7pdW62rQNzgKyQS3c1UM18toplkZO60T6bDl+z6UMh+DOt/S+XPaQEdZ7tt+Klw/cSWryduwgdDDY8M2TedKR4UGs8mQ8GcT69GMtMwplZADZyLl2c2LZavqA3z8nJTZzEbISXBiNzMv9M1jDqpUDbVoblcfcCc3AzjJw4bM29my/FXuePNWmvb19mS5USgmU5QiuxT4bhRhLLJQyHEOVIL1vSDmwrVCKOdXWumP6DYTmieLOkYzbh1TLzI7blZ0gkDswnAvLWuj7EONyMXCbSUgWqZlgnxm6b4e/MUW3zFZJUNbd/GOjI9j39Jm2wGx9QWcvqCoSXJM8m2Q+pW0aDagD3wTlY9L4qPu6n0voGkL1BLbWHbseU5IIzN9yIFR4GHK9Gvfu7vGpbAKaeifd5OHpOaGfolW4zSRCm8sNvkont1uageQg9OVohwKzSwGSZ7OD+5xWGPq9DU5g2oJAsvmpKvI+1VphK6FrAPj5sf+Wcz3u2X6rlzCv1q/IQyRGMPo0d1/WXMj1KiXBi0HMYdQJwa90jqayJWLLli36xIkT2a4nIX9yoYCWFm0+7bFT+bdlaAi37D0myjByU82od1lmSlrs84eerdavsGrFAGZm616t21AmU2sm1IYwx1ytY1wj7lh2bh5ZpAjxUWEAVzNzujF3tnXItWOk2FEfvWsjAH9qr3Rfm0QDyfeLvg9OVuS+lwul1Emt9Rb382Wr6VMbjspKAOQ5+EU1oFRhUJYfUroBXO2o0+6olOfnnm11k6nTCKFYrRDgGRdz5XyH2lUauJk/lMCZnpnF8GCtLaOmrLlz1zvl3rRBuV/2Hz3TQpwozYqyQblefShqtXIpurnvJcWyFPoh4UAVnmigLchGbYTUDV1EcBdJ6fQdNOaaPt+yD7kPv1ikPD8VcK41k8ZD/PxcQN7HuMi5iFLfk6TAzx6vgT0/7hzPzNZR61NYPVTDzCW+7WIRpK53SgD6Csw4IkUXlOuVsoqKxhtiBHmnUjmXpXsn5Hbg/m5MxbKEWBGXCOWqcN1PLnwaX61fAbpV6BmhsJrgqi/CQZIDqc+/af8zoq5bPowQWqHvfVDveaBPYdZJ8aXep4QFVQEYrPXhEpE27HMxhtZdWYHF1PWe4oblQNWOGHDupCLvgXoOypWYc3/1lHsnlO8uDbJ1Y2wcUos9fBqyr1DGLhFfKl1+bEif3x17qsCPDchT79n3rn0WijQYqdEgUdNQXveET5vm1p1UG09ZE6nrXep+MeA0fYlCVcRq5d6Lz0pzYy9V9k4GSEirgO7kzxap0kv1oceYmDYlQreFPNDeqDrkh/YJMMp1NTxYw5W5hTYt2heQl7yPohwyMf7fmdk6hgdruK7WR7o87EOFW3dc+iKAQnntqevddU2FQAl8an9wldkx4N4LAC+r6EfWXt81gQ8s04pcSSWcCereODyI12dmcfD42ewVealjo5BaiRjjK+x2ibgNtxJ1ZrYO6Ib7iXp+SnD6ONz37djQ9j4P7dqEHxEVxSEU5ZCJPTRmZutsZbh9PW7dcemLHB9Vag+BmPXO9ZcAwFY4c5xIRSqzbXAHpq9fsQbw5z98s+Xeu8en8OBEfMFoKpalpi/R5LvVs7KolZGijfgsBJ9P3yaOWwouHa+7ZEFjaMUAJn/zZ7y/oQSY0d597zzXM+7Zfivue+IU5gPBYcCfn86xZFJX5IKY9qHCrTtOmw5lMMX0EEjVbLlAekrGS0pCAGUZcAcmx2rq/v9XXziHLTffUOXpA/nz9A26kWNOIVfGB/d9Lnvn9Rm60Xg3g7cpgdtuzuvE5DSZGeZieLCGqYdaDy4qmGj8v6GCQGkQMrYHL5fX3qn3SgVZQ+4fn7sudl0VuX8MOpWnn+zeUUrdpJR6Tin1faXUGaXUrzc/v0Ep9cdKqf/Y/Pdq6zcPKKV+oJQ6q5TannrvHFgqtKeUqfngxOkoE5QzWTn/pSFfG1ox0Ja+6DPfyyapspFCodtNkquQq8OGr5qZct89PLYRz++9gyRJM9+zW16auXPnh2uZSblKzJrp1nvl3Jq+cdlwu3dNTE5Hryufm8bUDITu74JzVV0LefpzAO7TWn9HKfUTAE4qpf4YwD8F8Kda6wNKqb0A9gK4Xyn1YQCfAbABwI0A/kQp9SGtdRwRjhAhLTkn7WmRTBfK1PR1GeJM0NhiFqDVrSE5BDvtEksJXHczSJ8jJ5tz34WyzgAE54daJ6YHb+j63croot5LTMDX7J+YdTUxOU26acznOzePkF32XHJFruaiU/G0ZKGvtX4DwBvN//57pdT3AYwA+BSAjze/9vsAvgng/ubnX9NaXwHwslLqBwA+CuBbqWOgIBFOKQKFcpEUEYQhDhDp92OLWdzDQ3IIls337yJV0HQr80ha2ZyqIY+NNtp4GoWgX6mW/rKS+eHWT+h9l/1eU5UnMy5Jbr/kOW3sP3qGvZ5RqnzgyBV9aZydolzOEshVSq0DMArgRQDvbR4I0Fq/oZR6T/NrIwBesH72WvMz3/W+COCLALB27dro8UgWf8zEu5whwFXhfl2tr5AglAoK+/s5rpNCqVDEJWbTG8e0gVwqqaMSSHLLlYpvBG5gOrEZhWBeaxw5Ob0YAJTMjySdudPvm9tfZkwScBQrBsNDtcVrSq5Lafmhv/crRcZUTCOma5ZlUyn1LgBHANyrtf47RadQ+f7gnR+t9WMAHgMagdzYMUmFk2TiOWre2fo8ucGlpn4MF1BK39aVA33e4qQUSoVUl5j7DstgMlwKcN/h8FANV+rzLZWzWqNFUANyDTekzEjmJ9bCfXDidJurMSepYGh/xViRY6M0xYpBp/JWFrQWKzNm/nNSOnMoJPSVUjU0BP5XtdZPNT/+a6XU+5pa/vsA/Lj5+WsAbrJ+/n4Arxe5P4Wc/voU/n2goVHYtLfURHILlUszBNqFha/KD4B4k4cOwdTiMO4dluke6gbcd3j7gWdxyVmLs/V53Ds+hYPHz0bR7BapNLexcuCqdcoxXT44cdrrq855WPuCpDZig5shcjqKDpzC8GCNrOiOUaoodCN1PFnoq4ZK/38B+L7W+retPz0N4PMADjT//XXr8z9USv02GoHcDwL4dur9OfhItjjebgOfxpUSUa/1K7x9eW7R9AtNJLVQuRQu32I5cnKaNClzmJKpPvbQO1xK/Pu5wT1biPPdfa9FK819WjVX3PX4i6+Sf8txWHNBUoNYRS3kYjPXk1pX+3Zs8PYBMIclwCtVoft0Ok4GFNP0bwfwTwCcVkpNNT/7DTSE/WGl1BcAnAPwaQDQWp9RSh0G8D00Mn9+pazMHQD+CggG1Ik7PFQLLkwDk8988cpc2+nPTWSKBk2lkfnukdNP617LpHByNQYUg6HBUqoCzo1QrCW2D21onXBzHStgQqyVocNaIvA4pAQ3TbDbZ6EYxe/BidMthy2nlEkVHarOJqTFdyN1vEj2zr8HnXb6CeI3jwB4JPWeUhw8frbtZA618aM2xMqBvmBPU6BVK6f6gVITGatBcxpSJ7VmalGfeOXNFpcFJzxiuVGWIrixxhKHGfgOwqJpk7ECJkRXzLkwiwg8oN3tFLMeqEbrpjl7jHVlxhtSdHwWueSQzemKlmJZ0jCknJ7U396arePQrk3BylXbnIvh5nYX86Fdm4KbmNOQOqk1x9QYAFeFSCh7p1sUGSkIjVWSRx4TsC9itcWylHIC3+fCvHd8CvuePoN9OzYUEnjDg7UWmo3Y9cDt5YPHz5LW1fTMbFtLUBcxY5HIodQ4WREsS6GfcnqSC3CoxtIX+Mw532bxsUGmpqlxh1fMYimqTcfWGCxojR8R9Akhd9BSDfhK04N95r7BdbU+XFfrL7WZCSATMJJG8iOECxNokMBxv5cIvH07NrT8JtYtxe3/kCUc2n8xY5HIoaLWWwp6jmWTohHw/cZoMy61AYBF+gKbjZHKUnFzds3G8m0aCXMhdXgND9bEiyUH0yA1Dq6cXzKW2MK0biKkzdnr7eDxs9i5eQSrm7niBrP1BVyuL+CerY2alN3jU6VQXIyNhllaqTU8MjyIHx24c5GBlMuCMSRwPrgCT8IaG2u5c/s/ZAmbzCrq/ceMRUpdMTY64pUnZWFZavq+03Pb+jUizdr+jSQga2uolNno5uyG0kBDwo3zE4fMU24Msdo0pant3DzSRuDFmazStNilEPB1rSMq0H/j8CCZYXVdrV3Xmq3Pi4OLRRByD0mFWihAPa+1qPWoxF0Va7mHtGdJjIV6/zFj8dVtaN041A0dRDcs12Up9IH24gdJAYi7ALmArM89QyGWNz0k3MwY9x890yJwjGltf8eG5ICK0aa5zRVTcSi5J3dodCro6xPitT5FNnahDlZK4MQEF8uCVKiFAtQ5Wo/aVdwxcQ8gL1ePfZ1YHzzl2utmnGrZCn0bqZo15+eXZmT4FkRIS5L45cdGGzzorpZpu4di6HPtscWA21yhgFgopbNfKSxoHaTIKGMz+Q4S3zqqL+g2Ui0z1t1CmmUOnXZpSYUapXjY3y8SdHbn1TTC8XU18/2WO2xCMRYbvip+IN4H3418fAo9IfRTNWtqA2iNoPDkGjFz1AsxfnnquYzgswUhxQJoo1OkTxOT0y3Fc5QPf15rrB6qsZtKuplirAHqIKHm/K3Zehs3PoDoGo9uMi8axAg1W3jmtrR882r3cKYQowRItH7KbRP7fN3Ix6fQE0Kf06xDphnQvgEkGlxos5x45U0v056buRC6h++5DJ+6FNwBVQb2Hz3jbRbuE3wXLtWx58mrvVpdSDZTTDEOQB8kkg5VNmJ4Xu7ZujYqDlImYoUa9/3UA4FTaLjObrEaNaf153z/3cjHp9ATQn/b+jVeTXfVin488gt8Zyjfgpb4A90Gye7CDzHtSTYLZYnECHxXc+qEf1zaRs6gPq+x/+gZ77hCm2licjq6GIdLRZUEJw2kPC8jw4PZmReXQnFbEdcbp6i5mXT2tVI16rJTJ7uRj0+hJ4Q+VaE3PLQiaVKlVZaz9Xn88z86jQXLHUQV79iQbhZqoUoOJSCco72UiqIuXKp7uYxCm4krxomN5cQGJyV01/ZYi/jADXLRFOdAET+2ZI/5rlVEo87x/rlrA3Iq9zIP7J4Q+rn9ab4JpDb3xXfiKWNjNgu1UCWHksvrXmawyV7IlP+aawDuwozLWCnUJuHmODaWExuc9F2n1qfwrusGogqxpEIgJ01x6hhsFNl37h6THtxLSaN2IVk7nVC8ekLoUwG1YadIJgbuBH7ggW8ECapspFBCSJukSNPSXAuI+m7Rxs+SLIlan8Kuj96E8W+/2sabRMG8J24zUQeyAtqsHFuo+WiqYzddDpdBjBBIyVKTCHPfGHaPT+He8Sk2k6aoH9ueV6orlnut2He+FNxgNjqR5dMTQp+SxTkbKsQIfCCNEgIA1u09hn6l8NmP3YSHxzaS17A3zDohARwVqKSqK6XgKpXdlMwtN9/Q4p5Y7WlEYiARHj7NT6EROLXjJzE01THwHUgxgiZGCMRmqUkPFCqThvsNEK91xxLXpfaFiH3+TqITWT7LkobBBRVQi22owGEkIgofMjf3bL+VpC8FGgfMV144hwcnTjPfCo/NFQJc6mQRcIFRd3OPjY5g6qGfwZd3bcLI8CAuXKp7Bb6kP4K5nlvqf2jXppYDkxOsuRFLfxEjBLhDsNavcPHKXAv9iPS5QwKHele+d08dpKH3EnMtKTh68m6BmsOcWT49oel3Il2KyhByoQB8ZO31OHicbo82Nhpu+wY0mlxw2r7Bnu2ypjJcM5ci4CwXn3YlcQetWjEg3vAhza+TOdQ5ycNcUMHPVSv68c7cwqL1FKo7iKVcMNf00SxLte4Y4rocWCr05C46EZPoCU1/2/o1UZ/HwjSslkAD+PMfvhnU9CSCNkoDFzSVkRJExcJ3XRuudiXh4slppXVCuzKQHjATk9MY/a1nvMLWl3V1+4FnsXt8CisH+rB6qLaoDX951yYMD61oi5NISdGA8PwBDWWmCHlfWQcvRbDIafOdzp33kfLltGhc9ISmT6VsPv7iqy0NqlMhJQwzkOSMS1LWpL72g8f9TWV8ue+P3rUxe2BLEli2N7dko0s2ptR3nlO7Ct1Torm7Fcs2fM1F7LHPzNYxWOtv6ctAFRPGkKIBV+fPzbLyZV3FBh+p99KnFFuMxYHz2eeiJy+KMuNJFHpC0+d8yrEaScz1i1zD9mFS+OzHbkq6tsGFS3UxbXRRjI026GOp5zFdmG7Zewx9gsMstDFjfOe5/MWSe0qsqYPHz3oFvj1e+7shvzR1QK4eqomf28zfjw7ciUPNeIv5TQ7yPsqamNc62Xrg3g1HTw7Aax1woCyKImMsCz2h6XM+yRzpUCQx22ANV+YW2jJHpBwrtg/zwYnTix2pJNk7BlwnLxedIIDy5q47XZhCY5XwE6WW4xeB1C9tvmuT4dkxHs5/fuFSvYU+W9qdyWc5vH15DgBYLhsf3HdFpVMOD9XE1pb7XnI00uFSn4cHa1521J+77X3RGT1FsoC6wcnTE5p+yCdZ9AX7rq/QMLVdH+s9W9d6x3LxytyiduDTGh4e24gfPvpJ/OjAnfjho58UC3yqkxeFsoNYPq161YoBb24+p/CHNKlubCbpPY3W/PKBO7Fn+604cnK6xToI2Tn3HT61uDaoWhO3WYnpD2vD9I0uij3bb0Wtv33Ub83WseeJU2Jfv/1eFjI00uFcgDOzdUCjZW8+etdGPPfS+WjNu4i23sl4kkFPaPrmtL3v8KkowqzY6/t8niZjYnjwKlvklptvILnw3abiRXKHufz4n7huwNsLIPZdpBS3uJoi1bfATJVSrTUVM4G+AUDejC3fMwLtBUAp9+Ry4CmYNRzi87dBBb5zHIJjoyPe3hILGm3CW6qt55i/UFysvqAxtGKgpR8vFf9IKaaUvNtuVBD3hKYPNBbml+6+LTk7JeSzs33Wvk1rBJUxzYc8mtdsvdFUPJePj1p0C1pj344Nhd/Fur3HsHt8qlDWBhDeyD6lL/ROcmUi+fz0e544hT1Ptmuw29avib4nJxgkcfr6gsaqFQOLltPqoRpWDvS1tVwsW6OMyaaSCkOf9WxSQyVrTBIX86Wn+hAqpvRBA8GxllF/EELPCH2DlQNXH3n1UA07NzeakXABmJigILegbUHFBZd9SNHIuAWcutjsdwHQWRsxkKQE+sC9k1ybiWqc4vrHZ+vzeO6l8+Q9KaWBmqOR4UEcunuT6L28NVvH83vvwKFdm3C53sjHd9dp0UMwpPTEHB5S8jNbYNvWc4xyEUoguHF4sOXZLl6Za3NVSYopqXmSjNV2a1U9cjPCV/Dz9uU5jP/Fq4sbOKYMnTJTQ4E4I6g4LvxcLqiQ6RgTvLRb14UQe0BJybVcUO/EdcfY6YuxiHmW12dmSdoFKtAXIncD+OAmcPU9cOs0REzHQRKopAL00GiJ18QcNOZd+gLFsUFdanxvXrzSUgg5M1tHrU9h9VBNTIoXSknuRIJEDHpG6FMamwvfBIV8draQud6TFWDDbFBqs8c2FefgCo3UxsySClkbKQeULSypbBAbFA1DaiYFFZuQVKMaUM9dRBjb7yXU6CO0TlMzlFKzkqjYR+wYuCwcO5OJg28vvH15DrMeig+fr19y/bHREdyy95hXaXm9OdalQO7WM0I/VmOzwQWVfMUxgD8109WygeJNxQ2oBWX+KZJWFlN8lquCN3TI1OevZp6kWmUGsZp4rU8BCsHgqUEuYcytGaA8upGYrCQuHdNGjADkDt6YJAdXseBaWaYGuMn0baevdjfJ3XrGp1/E58j5QymBqIFFM5HyKRtf3qFdmwBgMfh24pU3veOi/KqSmEORtDLpBsgVhJIE4AD/c6ZkUoQOCtdPf/DTt+HgL94mjhfkCKLaVAsAcGjXpkUroYg/WoLcQeBY0jnOZ547ycEg9dkoWeHrq90tcree0fSLaGychsX1y5WYiT4t0yZuMxuCS+WUaLdF0spCLg4F4OUDdwavEwPOn2vD3jgHj9NdsrhNHKuJx5rpkrQ87pq+NbLnyVN44KnvtrgnUvzRFEIuyyKHSUrhHACShDA1ySGlb3YIlKxISQUtCz0j9MdGG83I7arWXR+9SexKoUxXaeCWgsR1YlI5fRWK+4+ewYyALbCI6R9yt2RsS9CGbevXeHvc2gixRoY2ccy7SXGThdwyoWt641Hz7RlEQJo/OvSMOQ8TIE0BMe8hFz8PtaZdbqMU+GQFNfZOk7sBPST0DROmEZzzWuPIyWlsufmG6DJ0GyGByE3qxOS0OEhIpXJeuFTHqhX93raM9r2LFIGEituKUi9TMHMWOlT6lSLfP9fZySDm3aR2NuL89qFrxmqD9vftrCuTGRZ6J1TSQ9HDxCBVAaH2ml2sJvWThw7i3OhGERaFnhH6qZs1BPNbt8IWuDqpVEWnWaASUKmcQKMPb8j8LrrIzfc6uXAlVpDLEmlDQcYrE/NuyqB34K4Zw51kYISnq7FLhWPZFBapAlCSwhqzp1OzmSTw7fkyGGxT0HGhr5T6WQC/A6AfwO9qrQ904r5lLmQ7Q4YS7q7pfl2tLyojZufmEbZJy6oVA1i1coBdUEUXeae1o9Dc9CuFnZtHSPdPjOksfTehTK6Ud0O6CBWw50m/dcXB9IngDk1OOJbddKjIOrLniaLv6GYTFIB21z1618ZCXoVc6KjQV0r1A/jfAfzXAF4D8BdKqae11t8r+95lL2TALzhuP/Cs18IICXxf/9h/c+oNL18O0PC7Tj1U3PQOoUztyEUoXrKgNZ576bxX4LuNz3OB0lK3rV+TnJJHuS20htdvrxQwoBTZQN70jwgJP+rvnXBF5FhHndjTKSjLq5ALnU7Z/CiAH2it/0pr/Q6ArwH4VCdunIuLJRYpWsdgrR9fuvu2trLsfTs2kL8p2rx8KSJEz3Dj8CD5fjXKyX/2pXCmsjO61xTPoQYOfvo28s921TcH6u/UMy4FgWWjW3s6hG4wvMag00J/BMCr1v+/1vysBUqpLyqlTiilTpw/fz7Ljbu1kLlmDT6BZhpbSItcDIo2L1+KMHO22kMfbDY3x11T5rhcrpSiG31sdISkE3ZhuJNCDe+5QzMkHH3PuNSwVA+nbtAlx6DTPn2fKtO20rXWjwF4DAC2bNmSTZp10jVhQJnKRmuP9WuW1bx8qYKLl3QjuEwhh6tBQvlgP5uEWwlAdPbOtYRu7OkQllKmjg+dFvqvAbB7/L0fwOsdHkNHEQpaxS7Ypb6gykKoxL/bWRE55oUqIHzXdQPe/HjJsy9FobjcsVTWJAWlO+gWUEoNAPgPAD4BYBrAXwD4x1rrM9RvtmzZok+cONGhEV4bWCrETRVakWNeqrmtkAtKqZNa6y1tn3dS6DcH8kkAX0YjZfP3tNaPcN+vhH6FChUqxIMS+h3P09dafwPANzp93woVKlSo0EMsmxUqVKhQoRL6FSpUqNBTqIR+hQoVKvQQKqFfoUKFCj2EjmfvxEIpdR7AKwk/fTeAv8k8nKWO6pl7A9Uz9waKPvPNWus17odLXuinQil1wpeutJxRPXNvoHrm3kBZz1y5dypUqFChh1AJ/QoVKlToISxnof9YtwfQBVTP3Buonrk3UMozL1uffoUKFSpUaMdy1vQrVKhQoYKDSuhXqFChQg/hmhf6SqmfVUqdVUr9QCm11/N3pZT6F82/f1cp9ZFujDMnBM98T/NZv6uU+nOlFN1b7xpB6Jmt7/3nSql5pdQvdnJ8ZUDyzEqpjyulppRSZ5RS/0+nx5gbgrV9vVLqqFLqVPOZf7kb48wJpdTvKaV+rJT6S+LveWWY1vqa/QcNeuYfAvgpACsAnALwYec7nwTwb9Ho2rUVwIvdHncHnvmnAaxu/vc/6oVntr73LBosrr/Y7XF3YJ6HAXwPwNrm/7+n2+PuwDP/BoD/tfnfawC8CWBFt8de8Ln/IYCPAPhL4u9ZZdi1rulLGq1/CsAf6AZeADCslHpfpweaEcFn1lr/udb6QvN/X0CjQ9m1DMk8A8CvAjgC4MedHFxJkDzzPwbwlNb6HABora/155Y8swbwE0opBeBdaAj9uc4OMy+01n+GxnNQyCrDrnWhL2m0LmrGfg0h9nm+gIaWcC0j+MxKqREAvwDgX3VwXGVCMs8fArBaKfVNpdRJpdQvdWx05UDyzP8SwH+KRpvV0wB+XWu90JnhdQ1ZZVjHm6hkhqTRuqgZ+zUE8fMopbahIfT/i1JHVD4kz/xlAPdrrecbSuA1D8kzDwDYjEb70UEA31JKvaC1/g9lD64kSJ55O4ApAHcA+ACAP1ZK/b9a678reWzdRFYZdq0LfUmj9eXWjF30PEqp/wzA7wL4R1rrv+3Q2MqC5Jm3APhaU+C/G8AnlVJzWuuJjowwP6Rr+2+01hcBXFRK/RmA29DoQ30tQvLMvwzggG44u3+glHoZwHoA3+7MELuCrDLsWnfv/AWADyqlblFKrQDwGQBPO995GsAvNSPgWwG8pbV+o9MDzYjgMyul1gJ4CsA/uYa1PhvBZ9Za36K1Xqe1XgfgSQD/wzUs8AHZ2v46gP9SKTWglBoC8DEA3+/wOHNC8szn0LBsoJR6L4BbAfxVR0fZeWSVYde0pq+1nlNK/Y8AjuNqo/UzSqn/rvn3f4VGJscnAfwAwCU0NIVrFsJn/k0A/wmA/6Op+c7pa5ihUPjMywqSZ9Zaf18p9e8AfBfAAoDf1Vp70/6uBQjn+X8B8K+VUqfRcHvcr7W+pimXlVKPA/g4gHcrpV4D8BCAGlCODKtoGCpUqFChh3Ctu3cqVKhQoUIEKqFfoUKFCj2ESuhXqFChQg+hEvoVKlSo0EOohH6FChUq9BAqoV+hQoUKPYRK6FeoUKFCD+H/B2/ekddpLh2UAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_values, y_values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5320fef5-cf42-4dd4-a09c-2705a93864c2",
   "metadata": {},
   "source": [
    "4.3.2 Задание"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d9b6bc90-8c75-4a0b-a3d9-739e12c1769b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7dc99ddf-fcbe-4a91-9ee3-f102f5c525c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def function(x):\n",
    "    return (np.sqrt(1 + np.e ** np.sqrt(x) + np.cos(x ** 2)) / abs(1 - np.sin(x) ** 3)) + np.log(2 * x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d1bf7069-4c70-4ca5-852c-5c0481c9ee23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(1, 11, 1)\n",
    "y = function(x)\n",
    "\n",
    "plt.grid()\n",
    "plt.plot(x, y)\n",
    "plt.scatter(x[:len(x)//2], y[:len(x)//2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3abb52c1",
   "metadata": {},
   "source": [
    "4.3.3 Задание"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b4763e0b-af25-4f52-b6f0-6f8461eeb686",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import trapz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0e8d1e51-9c2c-495d-bb97-0462401e6c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "def funtion2(x):\n",
    "    return abs(np.cos(x * np.e ** (np.cos(x) + np.log(x + 1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "55b31fe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(0, 11, 1)\n",
    "y = funtion2(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "898021c3-9fd5-4328-bbe1-d23c3cc004dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid()\n",
    "plt.plot(x, y)\n",
    "plt.fill_between(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7a18f9fa-0283-4c42-a7f4-9724ea606def",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.748183214657723\n"
     ]
    }
   ],
   "source": [
    "area = trapz(y)\n",
    "print(area)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "984e6926",
   "metadata": {},
   "source": [
    "4.3.4 Задание"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c5227063-0e66-4e3d-a867-65b7a939ad2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import trapz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "06b78197-20da-49e8-a625-059bfa571add",
   "metadata": {},
   "outputs": [],
   "source": [
    "apple_share = [132, 135, 145, 147, 124, 139, 149, 151, 141, 149, 148, 177]\n",
    "google_share = [1727, 1783, 2000, 2242, 2360, 2427, 2597, 2750, 2794, 2761, 2901, 2934]\n",
    "microsoft_share = [217, 239, 236, 242, 251, 247, 271, 284, 301, 289, 329, 330] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "afdf47d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(1, 13, 1)\n",
    "\n",
    "plt.grid()\n",
    "plt.plot(x, apple_share, label=\"apple\")\n",
    "plt.plot(x, microsoft_share, label=\"microsoft\")\n",
    "plt.plot(x, google_share, label=\"google\")\n",
    "plt.title(\"График цен акций\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b385688",
   "metadata": {},
   "source": [
    "4.3.5 Задание"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0e1778ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Чтобы выйти из программы введите exit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Введите (+, -, *, /, **, e, sin, cos):  exit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Программа завершена\n"
     ]
    }
   ],
   "source": [
    "import math as m\n",
    "\n",
    "print(\"Чтобы выйти из программы введите exit\")\n",
    "\n",
    "while True:\n",
    "    user_input = input(\"Введите (+, -, *, /, **, e, sin, cos): \")\n",
    "    if user_input == \"exit\":\n",
    "        break   \n",
    "    elif user_input in ['+', '-', '*', '/', '**', 'e', 'sin', 'cos']:\n",
    "        x = float(input(\"x = \"))\n",
    "        y = float(input(\"y = \"))\n",
    "        \n",
    "        if user_input == 'e':\n",
    "            print(\"e ** (x + y) = \", m.e**(x + y))\n",
    "        elif user_input == 'sin':\n",
    "            print(\"sin(x + y) =\", m.sin(x + y))\n",
    "        elif user_input == 'cos':\n",
    "            print(\"cos(x + y) =\", m.cos(x + y))\n",
    "        else:\n",
    "            formula = f\"{x} {user_input} {y}\"\n",
    "            print(formula, \"=\", eval(formula))\n",
    "    else:\n",
    "        print(\"Неверный знак операции!\")\n",
    "\n",
    "print(\"Программа завершена\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fda3145",
   "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": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "c8e7b8a5fb18722c706c80178f617ac0f7a16c4b5a7c63b4b737eecf7a54cfec"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}