{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Основы работы с библиотекой `matplotlib`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "С помощью `matplotlib` можно строить различные графики. \n",
    "\n",
    "Построим простой график для визуализации данных в двух списках."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt # Импортируем модуль pyplot из библиотеки matplotlib\n",
    "# питоновская \"магическая\" строчка для того, чтобы графики отображались прямо в ipynb-файле\n",
    "% matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [-2, -0.5, 0, 2, 5, 8, 9, 10] # Создадим два списка\n",
    "Y = [4, 0.25, 0, 4, 25, 64, 81, 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим график."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x3ffef08>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как можно заметить, в списке `Y` сохранены элементы списка `X`, возведенные в квадрат. Однако наш график не похож на ветвь параболы, он угловатый. Это нормально, потому что в списках у нас всего по 8 элементов, то есть, всего 8 точек на графике соединяются линиями. Если бы точек было больше, график был бы более гладким. \n",
    "Воспользуемся функцией `linspace` из библиотеки `numpy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.        , -1.87878788, -1.75757576, -1.63636364, -1.51515152,\n",
       "       -1.39393939, -1.27272727, -1.15151515, -1.03030303, -0.90909091,\n",
       "       -0.78787879, -0.66666667, -0.54545455, -0.42424242, -0.3030303 ,\n",
       "       -0.18181818, -0.06060606,  0.06060606,  0.18181818,  0.3030303 ,\n",
       "        0.42424242,  0.54545455,  0.66666667,  0.78787879,  0.90909091,\n",
       "        1.03030303,  1.15151515,  1.27272727,  1.39393939,  1.51515152,\n",
       "        1.63636364,  1.75757576,  1.87878788,  2.        ,  2.12121212,\n",
       "        2.24242424,  2.36363636,  2.48484848,  2.60606061,  2.72727273,\n",
       "        2.84848485,  2.96969697,  3.09090909,  3.21212121,  3.33333333,\n",
       "        3.45454545,  3.57575758,  3.6969697 ,  3.81818182,  3.93939394,\n",
       "        4.06060606,  4.18181818,  4.3030303 ,  4.42424242,  4.54545455,\n",
       "        4.66666667,  4.78787879,  4.90909091,  5.03030303,  5.15151515,\n",
       "        5.27272727,  5.39393939,  5.51515152,  5.63636364,  5.75757576,\n",
       "        5.87878788,  6.        ,  6.12121212,  6.24242424,  6.36363636,\n",
       "        6.48484848,  6.60606061,  6.72727273,  6.84848485,  6.96969697,\n",
       "        7.09090909,  7.21212121,  7.33333333,  7.45454545,  7.57575758,\n",
       "        7.6969697 ,  7.81818182,  7.93939394,  8.06060606,  8.18181818,\n",
       "        8.3030303 ,  8.42424242,  8.54545455,  8.66666667,  8.78787879,\n",
       "        8.90909091,  9.03030303,  9.15151515,  9.27272727,  9.39393939,\n",
       "        9.51515152,  9.63636364,  9.75757576,  9.87878788, 10.        ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(-2, 10, 100) # 100 точек\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x569c2c8>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Так график больше похож на параболу. \n",
    "\n",
    "Изобразим ее полностью, определенную на участке от -10 до 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x5702dc8>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10, 10, 200)\n",
    "plt.plot(x, x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поменяем цвет линии по умолчанию на какой-нибудь другой."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x577be88>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'lightblue')\n",
    "\n",
    "plt.axhline(0, color='grey') # оси\n",
    "plt.axvline(0, color='grey')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Список цветов в Python см. [здесь](https://matplotlib.org/users/colors.html). \n",
    "\n",
    "Теперь изменим тип линии. По умолчанию используется сплошная линия, но ее можно заменить на пунктирную и т.п."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8366d08>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'red', linestyle = '--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x83d7d08>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ktMubjtzzpVs3f7GtWwcbN8ZOI5I869fDr38NPXvCCSfEThOdins+LV8OX/86jBoVO4lI8tx2G6xa5ZPVm8VOE11pf27Jt06d4Kyz/Ey+iGTXwIF+ArVnz9hJCoK6QoqIFCl1hSw0q1bBT38KK1bETiJS/JYtg/79/bv8m5plYvj8c5+BPQQYMyZ2GpHitmABzJrlk+TIv+nIPYauXeHCC73f+/z5sdOIFLfjj/cBwkpg0uv6UHGP5brrYIcdfOyLAjjvIVJ0tm71/uxbt5bs+DHbouIeS7t28Ktf+eBGjz8eO41I8ZkwAQYMgEceiZ2kIKm3TEybNsE3v+lH7vPn6+hDpK42bIDu3WHnneHFF6FRaR6nqrdMoWraFG66CV57DUaMiJ1GpHjccotfFHjzzSVb2GujvRJb377Qu7ePYPf++7HTiBS+d9+FG2+Ek06C730vdpqCpeIem5kffXz2mQ8LLCLbdu21Pj7T8OGxkxQ09XMvBD16+MQCZWWxk4gUtpkzvQvxlVfCnnvGTlPQdOReKCoL+9y56hopks7WrXDRRdChg+YlrgMV90Ly2GOw337+XUT+0/33w0svwe9/D9tvHztNwVOzTCHp3RtuvRV69YqdRKTwnHQSTJoEp54aO0lRUHEvJE2awJAhfjsEjUktUmnjRmjWDE47LXaSoqFmmUJUUQH77ANLl8ZOIhLf/Pmw++7wz3/GTlJUMi7uZtbYzOaY2aOp+13NbKaZLTGzSWbWLPOYJaZTJ79AY8gQnVwVadwYvvMd2Hvv2EmKSjaO3IcAC6vcHw7cEkLoBnwEnJ2FbZSWDh18LsjHH4cpU2KnEYlr7719/Jgdd4ydpKhkVNzNrDNwHDAmdd+Ao4D7U6uMBwZkso2SddFF3jQzZIhf4CRSaj7+GH72M78iVeot0yP3W4GfA1tT93cEPg4hbE7dXw50SveDZjbYzCrMrGLNmjUZxkigpk3hT3+CN9+EYcNipxHJv1/8Av78Z81Y1kANLu5mdjzwXghhVtXFaVZN22gcQhgVQigPIZSX6crM9I48Es44w4v7okWx04jkz0svwe23+6Q2Bx4YO01RyuTIvSfQz8zeBCbizTG3Am3MrLKLZWdgZUYJS90tt0CrVnDuuX6FnkjSbd7sr/dddoGhQ2OnKVoNLu4hhGtCCJ1DCF2AgcAzIYT/BqYDp6RWGwQ8nHHKUta+vV+RN2OGj6khknS33w5z5sBtt/lsZdIguejnfhVwmZktxdvgx+ZgG6XlrLPgzDOhc+fYSURya8UKb2vv0wdOOaX29aVGWblCNYTwLPBs6vYy4KBs/F5JadQI7r47dgqR3BsyxGcoGzFCV2hnSFeoFpMvvoDrr4epU2MnEcm+Bx+EBx7w1/gee8ROU/RU3ItJCD5w0jPPxE4ikn2LF0N5OVxxRewkiaAJsovNunU6ySTJtWmTJoqvB02QnSSVhX3uXJg9O24WkWyYMePLKSZV2LNGQ/4Woy1b4OST/R9hzhxo0SJ2IpGGGzrUB8qbN8+HvZas0JF7MWrc2HsTLFoEv/lN7DQimXn4YR8YTIU9q1Tci1Xv3t7//X//18d/Fyk2r70GGzZAy5bQrVvsNImj4l7MbroJdt4ZfvIT7yYpUizWr4e+feHEE2MnSSwV92LWpg2MGuUz1fzud7HTiNTdz38Oy5bBtdfGTpJYKu7F7rjj4Ec/ghtugBdfjJ1GpHZTp/r4MZdcAkccETtNYqmfexKsXQvf+hY0b+69Z1q1ip1IJL2PP4Z99/UuvbNmwXbbxU5U1NTPPelat4bx42HNGnjlldhpRGp28cU+s9Jdd6mw55j6HiXFkUfCW2/p6lUpXH/7mw+Ad/31PsyA5JSO3JNkhx18/JnRo/0oXqRQrFoFgwfDAQf4kL6ScyruSfP66z412ejRsZOIfGniRO/++Ne/aoiBPFGzTNLsuSe88ALst1/sJCJfuvRSGDAAunaNnaRk6Mg9ifbf3yc6eOstvwpQJJY5c3zMGFBhzzMduSfVli1wzDHQrJn3f1fPBInh4ovhvfdgwQIfE0nyRkfuSdW4sU8wPH8+XHZZ7DRSqh54wCeYUWHPOxX3JOvTB668Eu64A+67L3YaKSVz5sDmzdC+vc7/RKLinnS/+x0cfDD89Kfwxhux00gpWLAAvvtduOqq2ElKmop70jVtChMm+O2BA2Hjxrh5JNk+/RROOcWHwLj88thpSpqKeyno2hXGjvUTq2p/l1wJwS9UWrzY+7V37Bg7UUlTcS8VJ5/sR1IjRvgl4CLZNnKkf0ocOhS+//3YaUqeukKWkmHD4OWXfRRJkWx68UUfwrdvX7j66thpBBX30tKkCTz1FDTSBzbJovffh9NO82aYu+/W66tA6FkoNZX/eFOmwJlnwtatcfNIcdu0CU491Yfxvf9+aNcudiJJUXEvVW+/DYsWwUcfxU4ixWzDBv9EOHq0hvEtMJqJqVSF4EddzZrFTiLFKgQfw2jrVjXFRKKZmOSrzLywr1vn7aWzZsVOJMXkX/+CXr183BgV9oLU4GfFzHY1s+lmttDMXjWzIanl7cxsqpktSX1vm724knWffw4zZ0K/frByZew0UizWrIEPP9TY7AUsk7fczcDlIYS9gUOAC8ysB3A1MC2E0A2Ylrovhap9ez+5unatj7f92WexE0khq2zGHTDAP+211bFboWpwcQ8hrAohzE7d/gRYCHQC+gPjU6uNBwZkGlJy7FvfgnvugYoKOOusL/+BRaratAn694dx4/y+RnosaFlpLDOzLsD+wExg5xDCKvA3AKB9DT8z2MwqzKxijeb7jK9/f7jhBr9sXHNcSnUhwAUX+Ke8LVtip5E6yLi4m9nXgAeAS0II6+r6cyGEUSGE8hBCeVlZWaYxJBuuusrHBrnhBrj99thppJAMH+7dHa+5xkcYlYKX0RWqZtYUL+z3hBAeTC1ebWYdQgirzKwD8F6mISVPzHzsmVWrfJLtDh3gxBNjp5LYJk70on766fDb38ZOI3WUSW8ZA8YCC0MIN1d56BFgUOr2IODhhseTvGvSxP+ZDzrIJ/lQ+3tpe/ZZGDTIx2f/y1/U7bGIZHLk3hP4ITDPzF5OLbsWGAZMNrOzgbeBUzOLKHnXsiU8/rh/N4udRmJ56SU44QTYc0946CFo3jx2IqmHBhf3EMI/gZr+83s19PdKgagcI2TtWm+HHz4cunSJGkny6NVXfZrGsjKYOlVjxhQhfcaSbVu+HP7xD59oW0rHmjWw445e2DXpRlHSkL+ybfvsA6+/7tOmgXeDU//m5Nq40YelOPJInwu1iUpEsdKRu9SusrBPngyHHOKXnUvyfPCBj+w4cqTfV2EvairuUndt28Irr8Cxx/qAY5Is22/vn9T22it2EskCFXepux/8AO67D2bP9tsaCz4ZVq/22ZSaNfM5UDX/aSKouEv99OsHDzzgc7EedZQXBSleq1Z5+3r//rqmIWFU3KX++vWDRx7xmZyOPNKnWJPis3KlH6W/845Pnq5rGhJFxV0apndvv9DpzTfhiCO8y6QUjyVLoGdPWLEC/v53vwJVEkXFXRru+9+HJ5/0j/bHHAObN8dOJHUxe7YX9k8/henT4fDDYyeSHFBfJ8lMz57wzDNe4NV1rvA984xPtNGuHTz1FHzjG7ETSY7ov1EyV3XW+zvvhDZtvIBIYXngATjjDOjWzT9xdeoUO5HkkIq7ZM+WLT7md+vW3vtCJ+gKy8MP+xvxlCkaK6YEqLhL9jRu7B/1t271wv7hh17oNVxBPF984d1VO3WCUaP8uWnZMnYqyQOdUJXsatXKr3TctMlHFezbVxc7xTRwoF9wtnEjtGihwl5CVNwlN5o29aGCp0+H73zHh5CV/LvkErj+er/6VEqKirvkzjnn+Ew+n37qA4499FDsRKVh/Hi48Ua/fcQRfvQuJUfFXXLrsMOgogL23tvnY738cm8ikOzbsAHOPht+/GN4+mldd1DiVNwl9zp3hhkz4Gc/g5tv9oK/dGnsVMmyeLF/Oho3Dn7xC+/qqOsOSpqKu+RHixYwYgQ8+CAsWwb77+9j00hmQoCxY72L48qV8MQTMHSoCruouEuenXiijyh5ySXQvbsv27IlbqZitXIlHH+8n9soL4c5c7yHkggq7hLDbrv50aUZvPWWTw7xj3/ETlVc/vEP2Hdf7410220wbRrsumvsVFJA9NlN4vr8c7/AZvfdYycpDiH4m+Lee/uAX//3fxofRtLSkbvE1b27d5fs0sUL15lnetu8enp81ciR0KuXX2Xavr2Pqa/CLjVQcZfCsW6dtyNfeCHstx9MnRo7UWGonCHpa1/zQdk++SRuHikKKu5SOFq39rbjBx+Ezz7zMeL79YPXXoudLI45c+C44+DWW/3+mWf6vmndOm4uKQoq7lJYzLxHzauv+tRv06dDjx5+Yc6SJbHT5ceiRXDaaXDAAfD88z6UA2iUTakXFXcpTC1awFVXeUG/+GKYPNl71QwZEjtZ7sybB4MGwT77eH/1X/4S3njDm6lE6knFXQrbLrv4Va3LlsGll3o3SvATrtOnf9keXay2bvVCfswx8K1vwf33+zUAy5bB//yPmmCkwdQVUorDLrt4t79KU6bASSf5JN3HHhsvV6b+9S8fFrljRx/sa/BgTaQhWaHiLsXp2GPh3nt9rHLwi6IWLID//m8/Ci7UIW5D8KaX9u39zerww/0k6XHHFW5mKUpqlpHi1KIFnH76l2OobN3qs0CdcIIf5Z95JkyaBB9/HDfn++/DxIl+/gD8pOgOO/ikJpX3TzxRhV2yzkIO2izNrA9wG9AYGBNCGLat9cvLy0NFRUXWc0iJ2bjR+8ZPngyPPQYffOBT/B14oI9r3r8/9OyZ2wyrVnkPl+ef93MCs2f70XpZmfeCUZOLZJGZzQohlKd7LOtH7mbWGBgBHAv0AE43sx7Z3o7IVzRr5s0b48fD6tXwz3/C1Vf78ltvhQkTfL3Nm/1o+e9/9/uff+6F96OP6naCduNGWLgQ1q/3+08/Db17+9DGHTvCySfDH/4A223nJ0VnzvSir8IueZSLNveDgKUhhGUAZjYR6A8syMG2RNJr3NiP0iuP1Dds8BmhwAv/0qVfzu06bx4cdJDfbtbM54Bt2dKLM/iolZs3e6+dk06CF17wTwLTpsFRR/kk1GvW+O399oNDD/U+6s2b5/dvFqkiF8W9E/BOlfvLgYOrr2Rmg4HBALtVdm8TyZWWLb+cHLpTJy/olbp2hXvugXff9cK/fr1/bdjgbeJNmvibxU47+fp77eXr90h9ID3uOP8SKSC5KO7pLqP7ymfdEMIoYBR4m3sOcojUzU47wRln1H399u3rt75IBLnoLbMcqDqwdGdgZQ62IyIiNchFcX8J6GZmXc2sGTAQeCQH2xERkRpkvVkmhLDZzC4EnsS7Qo4LIbya7e2IiEjNcnKFagjhceDxXPxuERGpna5QFRFJIBV3EZEEUnEXEUkgFXcRkQTKycBh9Q5htgZ4q4E/vhPwfhbjZIty1Y9y1V+hZlOu+skk1+4hhLJ0DxREcc+EmVXUNCpaTMpVP8pVf4WaTbnqJ1e51CwjIpJAKu4iIgmUhOI+KnaAGihX/ShX/RVqNuWqn5zkKvo2dxER+aokHLmLiEg1Ku4iIglUFMXdzE41s1fNbKuZlVd77BozW2pmi82sdw0/39XMZprZEjOblBqKONsZJ5nZy6mvN83s5RrWe9PM5qXWy/ms4Gb2azNbUSVb3xrW65Pah0vN7Oo85PpfM1tkZq+Y2d/MrE0N6+Vlf9X295tZ89RzvDT1WuqSqyxVtrmrmU03s4Wp1/+QNOscaWZrqzy/1+c6V2q723xezP0htb9eMbMD8pCpe5X98LKZrTOzS6qtk7f9ZWbjzOw9M5tfZVk7M5uaqkVTzaxtDT87KLXOEjMb1KAAIYSC/wL2BroDzwLlVZb3AOYCzYGuwOtA4zQ/PxkYmLp9B3B+jvPeBFxfw2NvAjvlcd/9GriilnUap/bdHkCz1D7tkeNcxwBNUreHA8Nj7a+6/P3Az4A7UrcHApPy8Nx1AA5I3d4eeC1NriOBR/P1eqrr8wL0BZ7AZ2Y7BJiZ53yNgXfxi3yi7OtUPioAAAQiSURBVC/ge8ABwPwqy34PXJ26fXW61z3QDliW+t42dbttfbdfFEfuIYSFIYTFaR7qD0wMIXwRQngDWIpP0P1vZmbAUcD9qUXjgQG5ypra3mnAhFxtIwf+Pal5CGEjUDmpec6EEJ4KIWxO3X0Bn7Erlrr8/f3x1w74a6lX6rnOmRDCqhDC7NTtT4CF+BzFxaA/cFdwLwBtzKxDHrffC3g9hNDQK98zFkKYAXxYbXHV11FNtag3MDWE8GEI4SNgKtCnvtsviuK+Dekm467+4t8R+LhKIUm3TjZ9F1gdQlhSw+MBeMrMZqUmCc+HC1MfjcfV8DGwLvsxl87Cj/LSycf+qsvf/+91Uq+ltfhrKy9SzUD7AzPTPHyomc01syfMbJ88RarteYn9mhpIzQdYMfZXpZ1DCKvA37yB9mnWycq+y8lkHQ1hZk8Du6R56LoQwsM1/ViaZdX7dtZpwu66qGPG09n2UXvPEMJKM2sPTDWzRal3+AbbVi5gJDAU/5uH4k1GZ1X/FWl+NuM+snXZX2Z2HbAZuKeGX5P1/ZUuapplOXsd1ZeZfQ14ALgkhLCu2sOz8aaHT1PnUx4CuuUhVm3PS8z91QzoB1yT5uFY+6s+srLvCqa4hxCObsCP1WUy7vfxj4RNUkdcDZ6wu7aMZtYEOAk4cBu/Y2Xq+3tm9je8SSCjYlXXfWdmo4FH0zyUk0nN67C/BgHHA71CqrExze/I+v5Koy5/f+U6y1PPc2u++pE768ysKV7Y7wkhPFj98arFPoTwuJndbmY7hRByOkBWHZ6XnLym6uhYYHYIYXX1B2LtrypWm1mHEMKqVDPVe2nWWY6fG6jUGT/fWC/F3izzCDAw1ZOhK/4O/GLVFVJFYzpwSmrRIKCmTwKZOhpYFEJYnu5BM2tlZttX3sZPKs5Pt262VGvnPLGG7eV9UnMz6wNcBfQLIWyoYZ187a+6/P2P4K8d8NfSMzW9IWVLqk1/LLAwhHBzDevsUtn2b2YH4f/TH+Q4V12el0eAH6V6zRwCrK1sjsiDGj89x9hf1VR9HdVUi54EjjGztqlm1GNSy+onH2eNM/3Ci9Jy4AtgNfBklceuw3s6LAaOrbL8caBj6vYeeNFfCtwHNM9RzjuB86ot6wg8XiXH3NTXq3jzRK733d3APOCV1AurQ/Vcqft98d4Yr+cp11K8XfHl1Ncd1XPlc3+l+/uB/8HffABapF47S1OvpT3ysI8Oxz+Ov1JlP/UFzqt8nQEXpvbNXPzE9GF5yJX2eamWy4ARqf05jyq93HKcrSVerFtXWRZlf+FvMKuATan6dTZ+nmYasCT1vV1q3XJgTJWfPSv1WlsK/KQh29fwAyIiCVTszTIiIpKGiruISAKpuIuIJJCKu4hIAqm4i4gkkIq7iEgCqbiLiCTQ/wet/lvLjq+2FwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'red', linestyle = '-.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Список всех типов линий см. [здесь](https://matplotlib.org/gallery/lines_bars_and_markers/line_styles_reference.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x84458c8>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'red', linewidth = 3) # изменим толщину линии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x84c4048>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vWQ2Qvv2h+ojYCrwLeKDpXuqIiKuAtwAPAmTm7zJzudmuOrYJaEXEJuAK+ugqb5n5XeDn55R3Avuq+/uAiW5tr4hwP8ffAf/ZdBNtuqiLiveTiNgGjAKHmu2kls8DnwB+33QjNb0KWAK+XA0xPRARVzbdVLsycxH4DHAUOAH8MjO/3WxXtV2fmSdg9eAIuK5bL9w34R4R/1WNs537Z+eadf6R1aGBh5rrtCMXdVHxfhERrwC+DnwkM3/VdD+diIh3Aycz83DTvXTBJuANwBczcxT4DV387/+lUo1H7wRuArYAV0bE3zTb1cbVkysx9UJmvv2PPR4Ru4B3Azuy/ybvF3NR8YjYzGqwP5SZ0033U8OtwHsi4p3A5cBVEfHVzOzHMDkGHMvMM/+L2k8fhjvwduDHmbkEEBHTwJuBrzbaVT0vRsQNmXkiIm4ATnbrhfvmyP2PiYjbgE8C78nM3zbdTwdevqh4RFzG6kmixxruqW0REayO6x7JzM823U8dmbknM7dm5jZW348n+jTYycyfAi9ExEhV2gH8sMGWOnUUuCUirqj+ru2gD08Mn+MxYFd1fxfwaLdeuG+O3C/gn4E/Bx5ffc/5Xmb+fbMtXbyCLip+K/B+YD4ivl/VPlVdU1fN+hDwUHXw8DzwgYb7aVtmHoqI/cBTrA6/ztFHP0MQEQ8DbwWujYhjwL3A/cAjEXEXqx9ed3Rte/03giFJupAihmUkSWcz3CWpQIa7JBXIcJekAhnuklQgw12SCmS4S1KB/g86MqPDbJLWVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y) # построим график, состоящий только из точек (можно считать диаграммой рассеяния)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x8534048>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='red', marker = 'o') # меняем цвет точек и тип точек (тип маркера) одновременно"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x8592308>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='red', marker = '*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x85fd048>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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22ak6tg54dfWlQFfSQ9/ylpk/AH6zZPEOYF91fh9we7fWV0Tcl/hb4N+bHmKFLupLxXtJRGwGbgaONDtJLV8CPgmca3qQml4PtIGvVbuY7ouIvqaHWqnM/CXweeAkcBr4XWZ+r9mpars+M0/DwsYRMNCtB+6ZuEfEf1b72Zb+2bHoNv/Awq6BB5qbtCMX9aXivSIiXgN8C/hoZv6+6Xk6ERHbgZnMPNr0LF2wDhgGvpKZNwNn6eJ//y+Van/0DmALMAj0RcTfNDvV2rUq38S0GjLzXS93fUSMAduBrdl7B+8X86XiEfEqFsL+QGY+3PQ8NdwKvDci3g1cAVwVEd/IzF6MySngVGb+8X9R++nBuAPvAn6emW2AiHgYeDvwjUanquf5iNiQmacjYgMw060H7pkt95cTEbcBnwLem5l/aHqeDvwIGIqILRFxOQu/JHq04ZlWLCKChf26xzLzC03PU0dmfjozN2XmZhZej+/3aNjJzF8Bz0XEG6pFW4GpBkfq1Engloi4svq7tpUe/MXwEo8CY9X5MeCRbj1wz2y5X8A/A38OPLbwmvPDzPy7Zke6eAV9qfitwAeBn0bEj6tln6m+U1fN+jDwQLXxcAK4s+F5Viwzj0TEfuBJFna/PkUPfQxBRDwIvBO4LiJOAfcCnwUeioi7WPjhdUfX1td7ezAkSRdSxG4ZSdL5jLskFci4S1KBjLskFci4S1KBjLskFci4S1KB/h868t1ihIY+CgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='green', marker = '2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Список маркеров смотри [здесь](https://matplotlib.org/api/markers_api.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если бы все строки кода со `scatter()` были в одной ячейке, то графики бы просто накладывались друг на друга."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x99488c8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='red', marker = 'o')\n",
    "plt.scatter(X, Y, color ='blue', marker = '*')\n",
    "plt.scatter(X, Y, color ='green', marker = '2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если присмотреться, то на красных точках можно увидеть синие звездочки и зеленые треугольники. Чтобы такого не происходило (например, если вы создаете и сохраняете графики в цикле в пределах одной ячейки), нужно добавить строку с функцией `clf()`, которая очищает координатную плоскость для следующего графика (*clf* ‒ от *clear figure*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x9743cc8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='red', marker = 'o')\n",
    "plt.clf()\n",
    "plt.scatter(X, Y, color ='blue', marker = '*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На плоскости представлен только последний график с синими звездочками, красные точки от первого графика был стерты с помощью `clf()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При построении графиков стоит иметь в виду, что функция `plot()` всегда соединяет точки, причем последовательно, в том порядке, в котором они следуют в списках или массивах. Из-за этой особенности, допустив ошибку, связанную с заданием неверной области определения функции, можно получить некорректные графики. Построим для примера гиперболу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x9c0cd48>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-100, 100, 100)\n",
    "y = 1/x\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Полученный график не гипербола. Как известно, в точке $x=0$ график уходит на бесконечность, линия при $x=0$ отсутствует. Избавимся от нее, построив график \"по кусочкам\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x9af15c8>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD4CAYAAADhNOGaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAd9ElEQVR4nO3de5RedX3v8fcnCZnhEkhiQhIhkIAptyoBR/AsLEuRYGB5CCrV4Do19uBKz1EOXqoCi1O1FLpQW7RWWhsQRWsBxUOJiity1dVWkAmGS7glBCgxIRkSFNpAyOV7/vjtp7MzeZ657ed59uyZz2utvfbev733PN/seVY+89tXRQRmZjZ2jSu7ADMzK5eDwMxsjHMQmJmNcQ4CM7MxzkFgZjbGTSi7gOGYNm1azJkzp+wyzMwqZeXKlS9ExPS+7ZUMgjlz5tDd3V12GWZmlSLp2XrtPjRkZjbGOQjMzMY4B4GZ2RjnIDAzG+McBGZmY1xTgkDSdZI2S3qkwXJJ+pqktZIeknRibtkSSWuyYUkz6jEzs8FrVo/g28DCfpafCczLhqXA3wNImgp8HjgZOAn4vKQpTarJzMwGoSlBEBG/ALb2s8oi4DuR3AtMljQLeBdwe0RsjYgXgdvpP1DMRrRdu+Dzn4e77y67ErPBa9c5gkOA53Lz67O2Ru17kbRUUrek7p6enpYValbEuHFw2WVwzz1lV2I2eO0KAtVpi37a926MWBYRXRHRNX36XndIm40IEnR0wKuvll2J2eC1KwjWA7Nz84cCG/ppN6uszk7Yvr3sKswGr11BsBz4UHb10FuB30XERmAFcIakKdlJ4jOyNrPK6ux0j8CqpSkPnZN0A/B2YJqk9aQrgfYBiIhvALcBZwFrgW3AH2fLtkr6C+D+7EddFhH9nXQ2G/EcBFY1TQmCiDhvgOUBfKzBsuuA65pRh9lI4CCwqvGdxWZN5iCwqnEQmDWZrxqyqnEQmDWZewRWNQ4CsyZzEFjVOAjMmsz3EVjVOAjMmsw9AqsaB4FZkzkIrGocBGZN5iCwqnEQmDWZg8CqxkFg1mQOAqsaB4FZk9VuKIu6D1Q3G3kcBGZN1tmZxq+9Vm4dZoPlIDBrsloQ+F4CqwoHgVmT1YLA5wmsKhwEZk3mILCqcRCYNZmDwKqmKUEgaaGkJyStlXRxneVfkbQqG56U9Nvcsl25ZcubUY9ZmRwEVjWF31AmaTxwNbCA9DL6+yUtj4hHa+tExCdz6/8f4ITcj3glIuYXrcNspHAQWNU0o0dwErA2ItZFxGvAjcCiftY/D7ihCZ9rNiI5CKxqmhEEhwDP5ebXZ217kXQ4MBe4K9fcKalb0r2Szmn0IZKWZut19/T0NKFss9bo6EhjB4FVRTOCQHXaGt1TuRi4OSJ25doOi4gu4IPAVyUdWW/DiFgWEV0R0TV9+vRiFZu1kO8jsKppRhCsB2bn5g8FNjRYdzF9DgtFxIZsvA64hz3PH5hVjg8NWdU0IwjuB+ZJmitpIuk/+72u/pF0FDAF+GWubYqkjmx6GnAK8Gjfbc2qxEFgVVP4qqGI2CnpAmAFMB64LiJWS7oM6I6IWiicB9wYscejuI4B/kHSblIoXZm/2sisihwEVjWFgwAgIm4DbuvT9rk+81+os92/AW9sRg1mI4WDwKrGdxabNZmDwKrGQWDWZL581KrGQWDWZBMmpMFBYFXhIDBrgY4O30dg1eEgMGsBv7fYqsRBYNYCDgKrEgeBWQs4CKxKHARmLeAgsCpxEJi1gIPAqsRBYNYCDgKrEgeBWQs4CKxKHARmLdDZ6fsIrDocBGYt0NHhHoFVh4PArAV8aMiqxEFg1gIOAqsSB4FZCzgIrEqaEgSSFkp6QtJaSRfXWf5hST2SVmXDR3LLlkhakw1LmlGPWdkcBFYlhd9QJmk8cDWwgPQi+/slLa/zysmbIuKCPttOBT4PdAEBrMy2fbFoXWZlchBYlTSjR3ASsDYi1kXEa8CNwKJBbvsu4PaI2Jr95387sLAJNZmVqrMTdu6EHTvKrsRsYM0IgkOA53Lz67O2vt4n6SFJN0uaPcRtkbRUUrek7p6eniaUbdY606al8QsvlFuH2WA0IwhUpy36zP8ImBMRbwLuAK4fwrapMWJZRHRFRNf06dOHXaxZO8ycmcabNpVbh9lgNCMI1gOzc/OHAhvyK0TEloio3Wd5DfDmwW5rVkUzZqTx88+XW4fZYDQjCO4H5kmaK2kisBhYnl9B0qzc7NnAY9n0CuAMSVMkTQHOyNrMKq3WI3AQWBUUvmooInZKuoD0H/h44LqIWC3pMqA7IpYDF0o6G9gJbAU+nG27VdJfkMIE4LKI2Fq0JrOy1XoEPjRkVVA4CAAi4jbgtj5tn8tNXwJc0mDb64DrmlGH2Uix//5wwAHuEVg1+M5isxaZOdNBYNXgIDBrkZkzfWjIqsFBYNYiM2a4R2DV4CAwaxEfGrKqcBCYtciMGfDii35TmY18DgKzFqndS7B5c7l1mA3EQWDWIr6pzKrCQWDWIn7MhFWFg8CsRfzgOasKB4FZi7hHYFXhIDBrkY6O1Ct46qmyKzHrn4PArIXe9CZ48MGyqzDrn4PArIWOPx5Wr/YrK21kcxCYtdD8+fDaa/D442VXYtaYg8CshY4/Po19eMhGsqYEgaSFkp6QtFbSxXWWf0rSo9nL6++UdHhu2S5Jq7Jhed9tzarsqKPSSeNVq8quxKyxwi+mkTQeuBpYQHoH8f2SlkfEo7nVfg10RcQ2Sf8b+BLwgWzZKxExv2gdZiPRhAlw3HHuEdjI1owewUnA2ohYFxGvATcCi/IrRMTdEbEtm72X9JJ6szFh/vwUBBFlV2JWXzOC4BDgudz8+qytkfOBn+bmOyV1S7pX0jmNNpK0NFuvu6enp1jFZm104onQ0+P7CWzkakYQqE5b3b99JP0PoAv4cq75sIjoAj4IfFXSkfW2jYhlEdEVEV3Tp08vWrNZ25x1Vhov9xkwG6GaEQTrgdm5+UOBDX1XknQ6cClwdkT81xPaI2JDNl4H3AOc0ISazEaMuXPhjW+EW28tuxKz+poRBPcD8yTNlTQRWAzs8bePpBOAfyCFwOZc+xRJHdn0NOAUIH+S2WxUWLQI/uVfYMuWsisx21vhIIiIncAFwArgMeD7EbFa0mWSzs5W+zJwAPCDPpeJHgN0S3oQuBu4ss/VRmajwtlnw+7d8JOflF2J2d4UFbyUoaurK7q7u8suw2zQdu9Oh4iOOALuvrvsamyskrQyOye7B99ZbNYG48bBhRfCPffAr35VdjVme3IQmLXJ0qVw0EHw5S8PvK5ZOzkIzNpk0iT46Efhhz+EBx4ouxqzXg4Cszb67GfTm8vOP9+PpraRw0Fg1kaTJ8Pf/V16CN0VV5RdjVniIDBrs/e8B/7oj+DP/xxuuqnsasya8PRRMxu6a66Bp5+GJUugszPdcGZWFvcIzErQ0ZEeOXH88fDe98Lf/q2fTmrlcRCYlWTqVLjrrvRQugsvhHPOgd/8puyqbCxyEJiVaP/9U8/gqqtgxYr0RrPLL4ff/a7symwscRCYlWzcOPjkJ2H1aliwAP7sz2DOHPjTP4Unnyy7OhsLHARmI8SRR8Itt0B3N5xxBnzta6mHcNJJcOWVKSh8HsFawQ+dMxuhNm6EG25IQ+3r/vrXw6mnwtveBiefnN5z0NFRbp1WHY0eOucgMKuA9evTOYQ77oBf/AI2ZK9+mjABjj4afv/34ZhjUg/iyCPTU06nTAHVe3+gjVkOArNRIgKeey49xfTXv4YHH0yHjZ59ds9DRwceCLNnp+GQQ1JvYtas9IiLgw+G6dPTMHlyOk9ho1+jIPANZWYVI8Fhh6Xh3HN727dtg7Vr4amn0s1qzzwD//7vqTexahVs3pzei9DXuHEpDKZOTb2IyZPTU1Jrw4EHpgfmTZoEBxzQO+y/fxr226932Hff1EuxamnKr0zSQuBvgPHAtRFxZZ/lHcB3gDcDW4APRMQz2bJLgPOBXcCFEbGiGTWZjTX77QdvelMa6tm5E3p64Pnn0/iFF3rHW7em4be/TZeuPvdcGr/0Evznfw6tjgkTekOhs7N36OhIQ366owMmTkxDfnriRNhnn73HEyakcX6YMKF36DtfG8aP33O6Nl+bzs+PGzf2DqkVDgJJ44GrgQWkF9nfL2l5n1dOng+8GBFvkLQY+CLwAUnHkt5xfBzweuAOSb8XEbuK1mVme5owIR0amjVraNvt2gX/8R9pePnlFAwvv5zmt21L89u2wSuv9I5ffTWNX3kFtm/vHW/fnrZ94YXe+ddeS0NteseO8p/MOm5cb0AMNJ0f9zc9nEHae/qqq9KhvmZqRo/gJGBtRKwDkHQjsIg9X0K/CPhCNn0z8HVJytpvjIjtwNOS1mY/75dNqMvMmmD8+N7DRO0S0RsItaDYubO3rTbU2nbu3HN61649p2vLa9O7dvU/XRt27+5/Ot8W0f/07t31p3fs6H+d3bt7pyNSYDZbM4LgEOC53Px64ORG60TETkm/A16Xtd/bZ9u6WSdpKbAU4LDDDhtWoZdfDitXNk7c4Q59U7/vXwT1/nroO913qNdtzXdx8231hnwXeZ990vpjrbtr1SX1HiLaf/+yqxn9mhEE9f576XspUqN1BrNtaoxYBiyDdNXQUAqsef75dCItn7q1lO0vjftL9fxfBiP9Aqz8MdXacdf8Mdi+Q+2Ybf54bv54b2dn73Hg2jh/4rA27L9/7wnGffYpey+YWV/NCIL1wOzc/KHAhgbrrJc0ATgI2DrIbZvm619v1U/u1Tc4BtOlrDfU67L2bavXBa51mfPt9brS+WOxO3bseXx2+/Z0jPfll3uP49ba8tP1rkAZyMSJvVegTJqUrkipHXaYPDkNU6akK1imTesdpk/3dfFmrdKMILgfmCdpLvAb0snfD/ZZZzmwhHTs/1zgrogIScuBf5J0Felk8TzgV02oqTS1Q0JjwY4de54UzA+1k4i1E4m1k421E40vvdQ73rQpPVPnxRfTVSu7GlwqsM8+6Rr4WbPSNfGHHpqukT/sMJg7N91INW2aw8JsqAoHQXbM/wJgBeny0esiYrWky4DuiFgOfBP4bnYyeCspLMjW+z7pxPJO4GO+Yqg6aoeaJk1q3s+MSIGxdSts2ZKuLnnhhXQN/PPPp9DYuDEd4vv5z1Nw5B10ULq79thj0+MX5s+HE09MPQ0zq893Flulvfxyumlq3bo0PPkkPP44PPpoCo6ao49Oz+d5xzvg9NPTnbVmY43vLLZRadIkOO64NPTV05MewdDdDb/8Jdx8M1x7bTp0dPLJ6a7cxYubf022WdW4R2Bjxq5dKRh++lP453+GBx5I53Pe/W74xCfg7W/3+QUb3Rr1CMbIaU2zdC9FV1d68cvKlekw0kUXpd7Caaelxzvfd1/ZVZq1n4PAxqx58+Av/zKdY7j66nQC+q1vhT/5k6E/X8esyhwENuZ1dsJHPwpPPJFeD3nNNfDmN6d5s7HAQWCWmTQJ/uqv4M470z0Np5ziQ0U2NjgIzPp4xzvgX/813ZPwrnfBY4+VXZFZazkIzOp4wxvgrrvSYaOzzkqXopqNVg4CswYOPxx+9KP0fuALLii7GrPWcRCY9eMtb0mXm37/+3DrrWVXY9YaDgKzAXz2s+n1jx//ePlvzjJrBQeB2QAmToQrroBnn009A7PRxkFgNghnnZWeaPqlL438FxCZDZWDwGwQxo2Dz3wGHnoI7rij7GrMmstBYDZI552Xbjr7wQ/KrsSsuRwEZoPU0QELF6ZLSofzmk6zkapQEEiaKul2SWuy8ZQ668yX9EtJqyU9JOkDuWXflvS0pFXZML9IPWattmhReuHNryr9QlWzPRXtEVwM3BkR84A7s/m+tgEfiojjgIXAVyXlXxz4mYiYnw2rCtZj1lJnnpkeZ+17Cmw0KRoEi4Drs+nrgXP6rhART0bEmmx6A7AZmF7wc81KMXVqem/Bj39cdiVmzVM0CGZExEaAbNzvm2AlnQRMBJ7KNV+RHTL6iqSOfrZdKqlbUnePH/xiJTr1VFi92u8ssNFjwCCQdIekR+oMi4byQZJmAd8F/jgiaqfaLgGOBt4CTAUuarR9RCyLiK6I6Jo+3R0KK8/8+elegkceKbsSs+YY8OX1EXF6o2WSNkmaFREbs//oNzdY70DgJ8D/jYh7cz97Yza5XdK3gE8PqXqzEhx/fBqvWgUnn1xuLWbNUPTQ0HJgSTa9BNjrFJqkicAtwHci4gd9ls3KxiKdX/DfWDbizZkDBx4IDz5YdiVmzVE0CK4EFkhaAyzI5pHUJenabJ33A6cCH65zmej3JD0MPAxMAy4vWI9Zy0npIXQOAhstBjw01J+I2AK8s057N/CRbPofgX9ssP1pRT7frCzz58O3vpVuLBvn2zKt4vwVNhuG449PVw2tW1d2JWbFOQjMhiF/wtis6hwEZsNw1FFp/PTT5dZh1gwOArNhmDQJ9t0XNm0quxKz4hwEZsMgwcyZ6QF0ZlXnIDAbphkz3COw0cFBYDZM7hHYaOEgMBsmB4GNFg4Cs2GaMQO2bIEdO8quxKwYB4HZMM2cmZ5C6qeiW9U5CMyGaebMNPbhIas6B4HZMM2Ykca+csiqzkFgNkzuEdho4SAwG6Zaj8BBYFXnIDAbpv32Sy+o8aEhq7pCQSBpqqTbJa3JxlMarLcr91Ka5bn2uZLuy7a/KXubmVllzJjhHoFVX9EewcXAnRExD7gzm6/nlYiYnw1n59q/CHwl2/5F4PyC9Zi1lW8qs9GgaBAsAq7Ppq8nvXd4ULL3FJ8G3Dyc7c1GgpkzfWjIqq9oEMyIiI0A2fjgBut1SuqWdK+k2n/2rwN+GxE7s/n1wCGNPkjS0uxndPf4Dh4bIQ4+GDZvLrsKs2IGfGexpDuAmXUWXTqEzzksIjZIOgK4K3th/Ut11otGPyAilgHLALq6uhquZ9ZO++0Hr7xSdhVmxQwYBBFxeqNlkjZJmhURGyXNAur+bRQRG7LxOkn3ACcAPwQmS5qQ9QoOBTYM499gVprOTnj11fSoCansasyGp+ihoeXAkmx6CXBr3xUkTZHUkU1PA04BHo2IAO4Gzu1ve7ORrLMzhYAfPGdVVjQIrgQWSFoDLMjmkdQl6dpsnWOAbkkPkv7jvzIiHs2WXQR8StJa0jmDbxasx6ytOjvTePv2cuswK2LAQ0P9iYgtwDvrtHcDH8mm/w14Y4Pt1wEnFanBrEwdHWn86qvpPcZmVeQ7i80KqPUIXn213DrMinAQmBXgILDRwEFgVoCDwEYDB4FZAQ4CGw0cBGYFOAhsNHAQmBXgILDRwEFgVoCDwEYDB4FZAb6hzEYDB4FZAfkbysyqykFgVoAPDdlo4CAwK8BBYKOBg8CsAAeBjQYOArMCHAQ2GjgIzAqYMAHGj3cQWLU5CMwKqr2lzKyqHARmBTkIrOoKBYGkqZJul7QmG0+ps847JK3KDa9KOidb9m1JT+eWzS9Sj1kZOjocBFZtRXsEFwN3RsQ84M5sfg8RcXdEzI+I+cBpwDbgZ7lVPlNbHhGrCtZj1nadnb6z2KqtaBAsAq7Ppq8Hzhlg/XOBn0bEtoKfazZi+NCQVV3RIJgRERsBsvHBA6y/GLihT9sVkh6S9BVJHY02lLRUUrek7p6enmJVmzWRg8CqbsAgkHSHpEfqDIuG8kGSZpFeYr8i13wJcDTwFmAqcFGj7SNiWUR0RUTX9OnTh/LRZi3lILCqmzDQChFxeqNlkjZJmhURG7P/6Df386PeD9wSETtyP3tjNrld0reATw+ybrMRw0FgVVf00NByYEk2vQS4tZ91z6PPYaEsPJAk0vmFRwrWY9Z2DgKruqJBcCWwQNIaYEE2j6QuSdfWVpI0B5gN/LzP9t+T9DDwMDANuLxgPWZt5yCwqhvw0FB/ImIL8M467d3AR3LzzwCH1FnvtCKfbzYS+D4CqzrfWWxWkHsEVnUOArOCfEOZVZ2DwKwg9wis6hwEZgU5CKzqHARmBXV2wo4dsGtX2ZWYDY+DwKyg2lvKfJ7AqspBYFaQX1dpVecgMCuoI3tUooPAqspBYFaQewRWdQ4Cs4IcBFZ1DgKzgnyy2KrOQWBWkHsEVnUOArOCHARWdQ4Cs4IcBFZ1DgKzghwEVnUOArOCfB+BVV2hIJD0h5JWS9otqauf9RZKekLSWkkX59rnSrpP0hpJN0maWKQeszK4R2BVV7RH8AjwXuAXjVaQNB64GjgTOBY4T9Kx2eIvAl+JiHnAi8D5BesxazsHgVVdoSCIiMci4okBVjsJWBsR6yLiNeBGYFH2wvrTgJuz9a4nvcDerFIcBFZ17ThHcAjwXG5+fdb2OuC3EbGzT3tdkpZK6pbU3dPT07JizYZq333hfe+DI48suxKz4Rnw5fWS7gBm1ll0aUTcOojPUJ226Ke9rohYBiwD6OrqarieWbt1dMDNNw+8ntlINWAQRMTpBT9jPTA7N38osAF4AZgsaULWK6i1m5lZG7Xj0ND9wLzsCqGJwGJgeUQEcDdwbrbeEmAwPQwzM2uiopePvkfSeuC/AT+RtCJrf72k2wCyv/YvAFYAjwHfj4jV2Y+4CPiUpLWkcwbfLFKPmZkNndIf5tXS1dUV3d3dZZdhZlYpklZGxF73fPnOYjOzMc5BYGY2xjkIzMzGOAeBmdkYV8mTxZJ6gGeHufk00j0MI43rGhrXNTSua2hGa12HR8T0vo2VDIIiJHXXO2teNtc1NK5raFzX0Iy1unxoyMxsjHMQmJmNcWMxCJaVXUADrmtoXNfQuK6hGVN1jblzBGZmtqex2CMwM7McB4GZ2Rg3qoNA0h9KWi1pt6SuPssukbRW0hOS3pVrX5i1rZV0cRtqvEnSqmx4RtKqrH2OpFdyy77R6lr61PUFSb/Jff5ZuWV1912b6vqypMclPSTpFkmTs/ZS91dWQ1u/O/3UMVvS3ZIey77/H8/aG/5O21jbM5Iezj6/O2ubKul2SWuy8ZQ213RUbp+skvSSpE+Usb8kXSdps6RHcm1194+Sr2Xft4cknTjsD46IUTsAxwBHAfcAXbn2Y4EHgQ5gLvAUMD4bngKOACZm6xzbxnr/GvhcNj0HeKTEffcF4NN12uvuuzbWdQYwIZv+IvDFEbK/Sv3u9KllFnBiNj0JeDL7vdX9nba5tmeAaX3avgRcnE1fXPudlvh7fB44vIz9BZwKnJj/LjfaP8BZwE9Jb3t8K3DfcD93VPcIIuKxiHiizqJFwI0RsT0ingbWAidlw9qIWBcRrwE3Zuu2nCQB7wduaMfnFdBo37VFRPwset9zfS/pzXYjQWnfnb4iYmNEPJBNv0x6D0jD94GPAIuA67Pp64FzSqzlncBTETHcJxcUEhG/ALb2aW60fxYB34nkXtIbH2cN53NHdRD04xDgudz8+qytUXs7/AGwKSLW5NrmSvq1pJ9L+oM21ZF3QdblvC7XXS9zH/X1P0l/EdWUub9G0n75L5LmACcA92VN9X6n7RTAzyStlLQ0a5sRERshhRhwcAl11Sxmzz/Gyt5f0Hj/NO07V/kgkHSHpEfqDP39NaY6bdFPeztqPI89v4AbgcMi4gTgU8A/STqwaC1DqOvvgSOB+Vktf13brM6Pauo1yIPZX5IuBXYC38uaWr6/Biq7Tlup12ZLOgD4IfCJiHiJxr/TdjolIk4EzgQ+JunUEmqoS+lVumcDP8iaRsL+6k/TvnMDvrx+pIuI04ex2Xpgdm7+UGBDNt2ofdgGqlHSBOC9wJtz22wHtmfTKyU9Bfwe0LRXsw1230m6BvhxNtvfvmtLXZKWAO8G3hnZwdJ27K8BtHy/DIWkfUgh8L2I+H8AEbEptzz/O22biNiQjTdLuoV0SG2TpFkRsTE7tLG53XVlzgQeqO2nkbC/Mo32T9O+c5XvEQzTcmCxpA5Jc4F5wK+A+4F5kuZmfx0sztZttdOBxyNifa1B0nRJ47PpI7Ia17Whltrn5481vgeoXcXQaN+1q66FpHddnx0R23Ltpe4vyvvu7CU73/RN4LGIuCrX3uh32q669pc0qTZNOvH/CGk/LclWWwLc2s66cvbolZe9v3Ia7Z/lwIeyq4feCvyudghpyMo6O9+mM/DvIaXmdmATsCK37FLSVR5PAGfm2s8iXWXxFHBpm+r8NvC/+rS9D1hNuvrkAeC/t3nffRd4GHgo+8LNGmjftamutaTjoquy4RsjYX+V9d1pUMfbSIcIHsrtp7P6+522qa4jst/Pg9nv6tKs/XXAncCabDy1hH22H7AFOCjX1vb9RQqijcCO7P+u8xvtH9Khoauz79vD5K6MHOrgR0yYmY1xY/XQkJmZZRwEZmZjnIPAzGyMcxCYmY1xDgIzszHOQWBmNsY5CMzMxrj/D50TDd5l+ei5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = np.linspace(-100, -1, 100) # x < 0\n",
    "y1 = 1/x1\n",
    "\n",
    "x2 = np.linspace(1, 100, 100) # x > 0\n",
    "y2 = 1/x2\n",
    "\n",
    "plt.plot(x1, y1, 'blue')\n",
    "plt.plot(x2, y2, 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Далее построим целый рисунок (*figure*), состоящий сразу из нескольких графиков (подграфиков). Построим разные типы функций: $y=x^2$, $y=x^3$, $y=e^x$ и $y=|x|$. Сначала создадим соответствующие массивы значений:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-100, 100, 100)\n",
    "y = x ** 2\n",
    "z = x ** 3\n",
    "r = np.exp(x)\n",
    "m = abs(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим рисунок (*figure*):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А теперь будем добавлять в него графики, указывая их расположение. В функции `subplot()` указывается число. Первые две цифры ‒ это число графиков в строке и столбце (здесь 2 на 2, поэтому `22`). Последняя цифра ‒ это положение графика: левый верхний угол (`1`), правый верхний угол (`2`), левый нижний угол (`3`), правый нижний угол (`4`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'abs')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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45JNtbEzN4ZVb+tC3TVPLYvHJxBAV1oAxfaP5eGMaeYWlVoejOcictQdo1jCQm/u3sToUTbsoSin+/vVuvt5+lJmjunJ972hL4/HJxABwzxUdKCwp56MNenI9b7A/s4BVe45z++B2NAjytzocTbsor6/ax/vrUrn78vbcO6SD1eH4bmLoFtWEKzu34L2fU/W9GrzAnLUpBAf46XmRNI/zzg/7eWP1Psb1j+Gv13Vzi5tJ+WxiALj3yg6cKCjms8R0q0PR6uFYXhFfbDnCLQltaN5IX9CmeY43V+/jpeVJjO0bzQu/742fm/SN+XRiuKRDc/rEhDFn7QHK9DQZHuvdnw5QblPcc4X1p+CaVhtKKZ5ftodXVyZzU3xrXr3FddNd1IZPJwYR4b6rOnHoZCHL9DQZHim3sIT5Gw4zuk80bZu7bsoATaurkjIbj3yynX+vPcDtg9vx8s193CopgI8nBoBrukcS17IRs37Yj1LK6nC0i/T+ulQKS8qZ6qJZJzWtPnILS5j83q98lpjOg8Pj+PvYHm7TfGTP5xODn58wdWhH9h7LZ9WeTKvD0S5CQXEZ769LZXi3SLq0amx1OJp2QUnH8hn7zs9sSs3hlXF9eHB4Z7foaK6KzycGgNG9o2kb3pC3vt+nzxo8yIfrD5FbWMr9V+uzBc19KaX4ZFMaY9/5icKSchZOGczv+8dYHdYF6cQABPj7MfWqjmxPz+PH5Cyrw9FqobCkjP/+7wBDOregj4VXiGraheQVlvLAoq3836fb6dumKd/MuJz+7ZpZHVaNdGIw3RQfQ+umDXhjtT5r8AQLNhzm5OkSZuizBc1Nrdx9nBGv/cg3O47yyDWdmf/HwbRsbM3cRxdLJwZTUIAfU4d2ZMvhXNbuO2F1ONoFFJaU8a8fU7i8UwQJseFWh6Np50jLLuTeDzdxzwebCA8NYsm0y5h+dZzbjTy6kACrA3An4/q3YdYPKby2MpkhcRFu2zHk6+b/cpgTBSU8ODzO6lA07ay8wlJm/5jC3J8P4i/C/13bhSlDOnjkPcd1YrATFODHtKGd+MsXO1iTlMXQri2tDkmr5HRxGbN/TOGKOH22oLmHnNMlvL8ulbk/H6SguIwb+rbm0ZFdiAprYHVodaYTQyXjEmL4148pvLwiiau6tNBnDW7m/XWpZJ8u4eERna0ORfNxycfz+XD9IT7dnM6Z0nKu7RHJg8M70y2qidWh1ZtODJUE+vvxwLA4/vTJNr7beYxRvVx7r1WtenlnSvn3jykM79aSfm3df2SH5n1OFhSzbOcxPk9MZ8vhXIIC/BjTJ5p7rujgVdfS6MRQhRv6tWb2jym8sjKZEd0jCfDANkJvNGdtCqeKynh4RBerQ9F8hM2mSDqez//2ZbF6TyYbU7OxKYhr2Yi/XteNm+Jbe+XEjfVKDCKSCuQD5UCZUipBRMKBj4FYIBW4RSmVI0abzBvAdUAhMFkplWju507gcXO3zyil5tUnrvry9xMeuaYz/++jRD43Z+3UrJWZX8Tcn1IZ0yea7tGedaouIiMx6r4/8F+l1AsWh6RVQSnFsVNF7D2Wz64jeWxNy2XzoRxyzJt5dW3VmOlDOzGqVxRdWzX26mZmR5wxDFVK2Y/vnAmsVkq9ICIzzeePAaOAOPNnEDAbGGQmkqeABEABm0VkqVIqxwGx1dm1PVrRJyaM11cmM6ZPNCGB+uYvVnpz9T5Ky20e17cgIv7AO8AIIB3YaNbv3dZG5t1sNkWpzUZJmfFzprSc08XlFBSXknemlOzTpZwoKOb4qSKO5haRllPIoZOFFBSXnd1HhxahDOsWyeAOzbmsU3OP7ky+WM5oShoLXGU+ngeswUgMY4EPlHH12C8i0lREosx1VyqlsgFEZCUwEljohNhqTUR4bFRXJv5nA/PWpXLvlR2tDMenHcgqYOGvaUwc2JbYiFCrw7lYA4H9SqkDACKyCONY8MnEoJQi+3QJh7MLOZJ7hmN5RWQVFJNdUEJOYSn5RaUUFJdxpqScotJySsqND/Zym6JcKZQyvj1S6RpUxW+vldtqf4FqwyB/osJCaBPekAGx4XRoEUrXVk3oGtWYJiGBjvzTPUp9E4MCVoiIAv6tlJoDRCqljgIopY6KSMWYz9ZAmt226eay6pafR0SmAFMA2rZtW8/Qa3Zpxwiu6tKCd37Yz60D2tC0YZDTy9TO99LyJEIC/JgxzCOvW6iqfg+yX8HV9dpVbDZFSlYBW9Jy2ZGex95jp0g+XkDemXPvsx7k70fzRkGENQikSYNAIpuE0DDIn5BAf4IC/Aj0E/z9/AjwFwTONuFUbskRwE8EPzEmxwz09yPI34+gAD8aBPrTMNifRsEBhDUIJDw0iOaNgmkUrLtZq1Lfd+UypVSG+eG/UkT2XmDdqhrk1AWWn7/QSDxzABISElwyb8XMUV257o3/8ebq/Tw5ursritTsbEzN5tudx3hoeGdaNPbITr4a67cV9dpZjp8q4oe9mfyYnMUvB06ebZ9vFBxAt6jG/K53FB0iQoltHkpMeAOimjSgSYMAr26v90T1SgxKqQzzd6aIfIFx2nxcRKLMs4UooGIu63TAvhc3Bsgwl19Vafma+sTlSF1bNeHWAW348JdUbr+kHe09rynDY9lsime+2UOrJiHcM6S91eHUVXX13mucKCjmq20ZfLUtg8TDuQBEhYUwrFskA9uHE9+2GR0iQt3yvgNa1eqcGEQkFPBTSuWbj68B/g4sBe4EXjB/LzE3WQpMN9tYBwF5ZvJYDjwnIhUD068B/lzXuJzhoRGdWbo1g+eW7eE/dyRYHY7PWLLtCNvScnl5XB8aBnnsKf9GIE5E2gNHgPHARGtDqj+lFOsPnOTD9YdYufs4ZTZFt6gmPHJNZ0Z0b0XnyEb6LMCD1edoiwS+MP/5AcACpdR3IrIRWCwidwOHgXHm+sswhqruxxiueheAUipbRP6BcQAB/L2iI9pdtGwcwtShnXhpeRI/7z/BZZ0irA7J6xWWlPHPb5Po1TqMm/pV2eXkEZRSZSIyHViOMVx1rlJql8Vh1Vm5TfHNjqP8a00Ku4+eolnDQO66LJZxCW3oHOk9F3j5ujonBnOURZ8qlp8EhlWxXAHTqtnXXGBuXWNxhbsvb8+ijYf5+1e7+WbG5fqiNyebvSaFY6eKeGdSP49vglBKLcP4YuSxlFIs33Wcl1cksT+zgI4tQvnn73sxtm9rPZTbC3ns+bmrhQT68/jvunPvh5v5YP0h/nC5x7Z5u71DJ0/z77UHGNs3mv7t9ER5Vtt5JI+nv9rFxtQcOrYI5Z2J8Yzq2crjE7ZWPZ0YLsI13SMZ0rkFr61M5vo+UR5z0w1P8/RXuwn0E/5yXTerQ/Fpp4vLeGVFMu+vO0jThkE8d2MvbkmI0WfLPkD/hy+CiPC30d0pKivn+WUXGpmr1dWKXcf4fm8mDwyPI7KJTrxW2XDgJCPfWMt76w4ycVBbfnjkKiYOaquTgo/Q/+WL1KFFI+4d0pEvthxhXYq+05sjFZaU8fRXu+kS2Zi7LtNNdVYoK7fx4nd7Gf+fX/AXYfG9l/DMDb0Ia+C7VwH7Ip0Y6mD61Z1oE96Ax7/cSVFpudXheI3XV+3jSO4Znrmxp0fe9crTZZ4qYuJ/NjBrTQrj+sfwzYwrGKBvhuST9NFXByGB/jxzQy8OZJ1m1poUq8PxCjuP5PHf/x1g/IA2+sPIAlvTchn99k/sOJLHa7f24cWb+xCqp4vwWTox1NGVnVtwQ99oZq/ZT9KxfKvD8Whl5TZmfr6d8NBg/jxKdzi72rIdR7nl3+sJCvDji2mXcmO/GKtD0iymE0M9PHF9dxqHBPLop9soK7dZHY7HmvO/A+w8cop/jO1BWEPdlu1K7/98kKnzE+nVOowl0y6nayvPuteF5hw6MdRD80bBPD2mB9vS8/jvTwetDscj7c/M5/VV+7iuVyt9G1UXUkrx+qpk/vbVbq7tEcn8Pw4iPFTPHqwZdGKop+t7RzGyRyteXZFM8nHdpHQxSsttPLx4G42CA3h6TE+rw/EZSileXJ7E66v2Ma5/DO9MjNdXL2vn0ImhnkSEZ27sSeOQAB5evJWSMt2kVFuzfkhhe3oez9zQ01On1PY4SileXpHE7DUpTBrUln/+vre+NkE7j64RDhDRKJhnb+zFziOneH1VstXheIQth3N48/t9jO0bzXW6CcllZq1J4Z0fUpgwsC3P3NBTT2uhVUknBgcZ2bMVtyTEMPvHFH45cNLqcNxaQXEZD328lVZNQvj7WN2E5CoLNhzmpeVJ3NA3mmdv6KmnxdaqpRODAz01ugftwhvy4KKt5JwusToct/Xklzs5nF3IK7f00VfUusjqPcd5/MsdDO3SgpfG9dFnCtoF6cTgQKHBAbw1IZ6Tp4v5v0+3Ycw0rtn7bHM6n285wv1XxzG4Q3Orw/EJO4/kMX3BFnpEh/HOpHh9VblWI11DHKxXTBh/HtWNVXsymbP2gNXhuJWkY/n89csdDGofzv1Xd7I6HJ+QmV/EPR9solnDQN6dnODJd8LTXEgnBie467JYRvVsxYvLk3R/gym/qJT75m+mUXAgb03op0fCuEBJmY37Pkokt7CUOXck6GnitVrTR6cTiAgv3tybds0bMm1+Ihm5Z6wOyVI2m+Lhxds4dLKQtyf2o6WeTtslnv1mN5sP5fDizb3p2TrM6nA0D6ITg5M0Dglkzu39KS6zce+HmzlT4ruzsL6+eh8rdx/nr9d10/0KLvLVtgzmrT/EHy9vz+g+0VaHo3kYnRicqFPLxrx+a192ZuTxyCfbsNl8rzP6q20ZvLnauML2rstirQ7HJxw8cZo/f76D/u2a8diorlaHo3kgnRicbHj3SGaO7Mo3O47y8ookq8NxqU2p2fzpk20MiG3GMzfqcfOuUFpu44FFW/D3E96c0E+PQNLqRA9RcIEpQzqQetK4d0PrZg2YNKid1SE5XUpWAfd8sInosBD+fXsCwQF6Lh5XeH1VMtvT8/jXbfG0btrA6nA0D6UTgwuICH8f25NjeUU88eVOmocGMbKn904DcfxUEXe8+yt+Irx/10A9a6eLbD6Uzew1Kdya0Mar65fmfPo800UC/f14Z1I8fdo0ZcbCraxNzrI6JKc4WVDMpP9uILewhPfvGkhsRKjVIfmEMyXl/GnxNqKbNuCJ0d2tDkfzcDoxuFDDoADenzyQji0bMeXDTaxLOWF1SA6Vc7qE29/9lbTsQt6dPIBeMXqIpKu8vCKJ1JOFvHhzbxrpW3Jq9aQTg4uFNQzkw7sH0qZZQ/7w/kbW7feO5JB9uoRJ/93A/qwC5tyRoIelulDi4Rzm/nyQSYPacmnHCKvD0byATgwWiGgUzMIpg2kXHsrk9zeyavdxq0Oql+Onirj13+tJySrgP3ckcGXnFlaH5DNKy2385fMdtGoSwkw9NFVzEJ0YLBLRKJhFUwbTtVVj7v1oM4s3pVkdUp3szyzgplnryMg9w7w/DNRJwcXe/ekge4/l8/SYHjQO0TPVao6hE4OFmoUGseCewVzSoTmPfrqdV1cme9RFcOtTTnLzv9ZRXFbOoimX6OYjFzuSe4Y3Vu1jRPdIrunRyupwNC+iE4PFGgUHMHfyAG7uH8Obq/cxfWEip4vLrA7rgpRSfPTLIW5/dwPNQ4P4/L7LdEezBZ79ZjcKxVN6FJLmYHr4ghsICvDjpZt70zmyES98u5d9xwuYNSmeuMjGVod2nsKSMp5csotPN6dzVZcWvDmhH010E4bL/bz/BMt2HONPIzoT06yh1eFoXkafMbgJEWHKkI58ePcgcgpLGP32T3z0yyG3utnPjvQ8Rr/1E58lpjNjWBxz7xygk4IFyspt/P2r3bQJb8A9QzpYHY7mhXRicDOXdYpg2QNXMCA2nMe/3Mkdc43rAqxUVFrOqyuSuHHWzxQUlzH/7kE8PKKzvj2kRRZtTCPpeD5/GdWNkEA91YjmeDoxuKGWjUOYd9dA/jG2B4mHchjx2o+8tXofRaWunbpbKcX3e48z6o3/8eb3+xnTJ5oVD17JpZ30WHmr5BeV8trKZAa1D2dkT93hrDmH7mNwU35+wu2XxDKsWyT/+Ho3r6xMZsGvh5kxLI7fx8cQFODcnPfMvkIAACAASURBVL75UDYvL09m/YGTdGgRyod3D+SKOD0U1Wpz1h7g5OkS3vtdNz1breY0OjG4ueimDZh9W3/WpZzgn98l8efPd/DW6n1MviyWWxLa0LSh4yaoKyu3sXpvJnN/OsiGg9k0Dw3iqdHdmTSondMTkVazzFNF/Pd/BxndJ5reMU2tDkfzYm5ztIvISBFJEpH9IjLT6njczaUdI/hy6qW8d9cAYsIb8tyyvQx8djVT52/m6+0Z5BeV1mm/ZeU2NqZm84+vd3PJC99z74ebScsu5PHfdWPto0O567L2bp8UPvnkE3r06IGfnx+bNm0657Xnn3+eTp060aVLF5YvX352+XfffUeXLl3o1KkTL7zwwtnlBw8eZNCgQQA9ReRjEQkCEJFg8/l+EdkgIrEV24jIn83lSSJyrd1yh9bpt77fT2m5jUeu6VzfXWnaBbnFGYOI+APvACOAdGCjiCxVSu22NjL3IiIM7dKSoV1asufoKRZvSmPp1gyW7TiGv5/Qq3UY/do2pVurJrRr3pBWYSE0CQkkKMCPcqUoKCojK7+Y9Jwz7MvMZ1taLptSc8gvLiPQX7iqS0tu7h/DsK4tCfCgG7z07NmTzz//nHvvvfec5bt372bRokXs2rWLjIwMhg8fTnJyMgDTpk1j5cqVxMTEMGDAAMaMGUP37t157LHHeOihh5gwYcJOIAe4G5ht/s5RSnUSkfHAP4FbRaQ7MB7oAUQDq0Sk4pPbYXX68MlCFv56mPED29CuuZ6xVnMut0gMwEBgv1LqAICILALGAjoxVKNbVBOeGt2Dx3/Xnc2HclibnMWGgydZ9GsaZ2rRSS0CcS0bcX2fKC7rFMGVnVt47JQK3bp1q3L5kiVLGD9+PMHBwbRv355OnTrx66+/AtCpUyc6dDCGeo4fP54lS5bQrVs3vv/+exYsWMCECRMA5gF/w0gMY83HAJ8Cb4vRyD8WWKSUKgYOish+jPoMDqzTr69KJsBfuP/quLpsrmkXxV0SQ2vAfrKgdGBQ5ZVEZAowBaBt27auiczN+fsJA9uHM7B9OADlNsWRnDOknjxNZn4x+UWllJbb8BMhNDiAiEbBRDcNoUNEIxoEefdQxyNHjjB48OCzz2NiYjhy5AgAbdq0OWf5hg0bOHnyJE2bNiUg4OxhkY5RN8GujiqlykQkD2huLv/Frlj7bWqs01C7ej1xUFsGtg8nsknIhf9oTXMAd0kMVQ2vOO/KLqXUHGAOQEJCgvtc+eVG/P2Ets0b0ra5d10NO3z4cI4dO3be8meffZaxY8dWuU1VFweKCDabrcrl1VxMWLGwujpa3fKq2uKqLqAW9TohNpyE2PCqXtI0h3OXxJAOtLF7HgNkWBSL5oZWrVp10dvExMSQlvbbl/b09HSio6MBqlweERFBbm4uZWVn56qyr4cVdTRdRAKAMCCbC9ddXac1j+QuPYwbgTgRaW+OAhkPLLU4Js3DjRkzhkWLFlFcXMzBgwfZt28fAwcOZMCAAezbt4+DBw9SUlLCokWLGDNmjNG5P3Qon376acUu7gSWmI+Xms8Bbga+V8YpxlJgvDlqqT0QB/yKrtOaBxN3mYtHRK4DXgf8gblKqWdrWD8LOFTFSxGAu9wWTcdyPmfE0RRoi3EGXA4UAvvM11qZZQIcBk6Zj8OAWHP9E0BFO1UQ0BEIAT4HblNKFYtICPAh0A/jTGG8XcfyX4E/AGXAg0qpb83lF1WnzW2qq9fgPv9DcJ9Y3CUOcJ9YLhRHO6VUjVequk1icBQR2aSUSrA6DtCxuHMc4F6x1IY7xesusbhLHOA+sTgiDndpStI0TdPchE4MmqZp2jm8MTHMsToAOzqW87lLHOBesdSGO8XrLrG4SxzgPrHUOw6v62PQNM27ichk4I9KqcutjsVbeeMZg2YBEUkVkeFWx6FpWv3pxKBpmqadw6MTg4iME5FdImITkYRKr7lkKuRq4vqbiBwRka3mz3XVxDVBRD4TkSwROSgiM8x1lonIK3bbfCwic83Hk0XkZxF5S0TyRGSviAyzWzdaRJaKSLZZzut2f+8qEVksIh+ISL753iVU2va8eOz+piq3FZEPMa4j+EpECkTkUbvtUkVkh/k+bDKXhYvIShHZZ/5u5qT/w1wRyRSRnXbLqixbDG+a79N2EYl3Rky15Y51+yLq9bUX2s9FlDdTRFLM+rZbRG4892VZIiLlIlIiIv+xe2GyiBwwtzsoIpMcEU+l2Ly7XiulPPYH6AZ0AdYACXbLuwPbgGCgPZCCcZGRv/m4A8aFTNuA7k6I62/AI1Ust4+rA1AMPGXG0gE4AFyLcVFWJnA1MMlc3tjcx2SMC6keAgKBW4E8INx8/UdgFsYFWvEYF3DdZpZxzCzzOvO9eB74xdzOD9gMPFk5Hru/qaiqbc3XU4HhVfzNqUBEpWUvAjPNxzOBfzqpfgwx34OdNZVt/l3fYsx9NBjYoOt2ner12ZgcUN44jKnM/cx6fhqIsjsGTgCdgYlmPb8ECMW4iLGLuY8ooIcT/j9eXa8tq/gOeoPmYnyAnq508PwZ+LPd8+VmpbkE2IoxmVmCud475rKtZuW+0QFxVXcAnY0LY6bNM8AllV5/z3x8E8bsnCeAy+3WmYwx547YLfsVuB1jbp5yfksil2B8uL9vPl+FMRV0xXbdgTN28RyuIt737P6mVVVtaz5PpfaJIQmIMh9HAUlOrCOxlQ6gKssG/g1MqGo9i+v4mouo28urW89BsdRYr+1jcsJ7sRVj6vLJQFalvzcNWIyRGHKB3wMNnPh/8ep67dFNScD7wMgqllc1jXdrjKkOIoENdsv9MQ68vua+/i3GJGn1Nd08dZtrd0ppH1c7jG9Yq0QkV0Rygb+Y8QF8bcaWpJT6qdK+jyjzv2w6hPHNKhrIVkrl25WXwW/TQJ/CSBwVCoEQ8+9tB0RXxFJFPPDbtBGVt70QBawQkc1iTC8NEKmUOgpg/m5Zwz4cqbqyq6sz7qa6OF0Vf0312mFli8gdZlNNRX3syW/Tm+RVKjMDiFFKncY4u/h/wFER+UZEutY3lip4db12l9lVqyUiqzCaVir7q1JqidjdYtFcvyMwGvidiNwL3GO+pIAJwHaMD+QKpUqpiuk0Q6hmauSLiQvjxi7/MPf1D+AVjLl07KdoTgPygT8opT6rYj/PAnuA9iIyQSm10O611iIidsmhLcYEbRlAuIg0NpODAI2A/XbbVvf3pQEHlVJ1vRNMdfu9TCmVISItgZUisreO+3e2Wk397tACa6jb1W1WxbKLmua7rjFRu3pd57IrxdEO+A8wDFivlCoXka12ZVWeg7w5xtkxSqnlwHIRaQA8Y+7nivrEUwWvrtdunxiUUhc7BHIO8AlwEvgeo729FOMDsiFQYrduDJAhIoMwmqXaAbfbJYp6x2V2in1tPrWfovlX8/fVIrLMjKsb0MD8uQvog9Fm+6WIrFVKHTG3aQnMEJFZwA3mdsuUUidFZB3wvIg8gpEAuwH/Z27XBKP5qiq/AqdE5DHgTft4lFIba/GnHsdo3z6HUirD/J0pIl9g3N3suIhEKaWOikgURnOgq1RXtsunfq9D3QYnT/PtgHpd57IrCcX4AMsyy7sL44yhQhPgMhEJxDgGYoCFIhKJ0Sy6GqOuF3DuWbJDeHu99vSmpHOISCPgUuB6jE7UORjfpuMwPmj/aD4OwUiK44GlSqkNSqkewADgz2LMpFmfOKLsnt4IVIweODtFsxlXLsY3nYMYfQn/xWgf/ACYrpQ6YjYjvQu8JyIV2X+D+XecwDizuFkpddJ8bQJG+2MG8DRG89F+MaZ+7olRUc6jlCrHONPqWymesFr+2c8Dj5un/Y+Y70OoiDSueAxcY74X9lNY209t7QrVlb0UuMMcxTEYyKs4NXczlk3zXZt6XSmmOlPGvbFfAdZjfOnoBfxst8oGjKbTkxjHwBFgEcZn2p8w6n82cCUwtT6xVOYT9dpZnSOu+MGonEcBG0blWQUcNV/7K8boiCSMjqgTGB1Gx831y4BZVezzB+w6++oY14fADoxmq6XYdfZUimtUHfY9GfjpIta/Dkg2y/yri/8/HTA69LcBuyrKx0iGqzGmxl6NOaLKCeUvNOtHKUZCvLu6sjFOud8x36cd9a0DDqrb6RijyI5zbkdrlXXI2f9rZ9brOsZjSd32hXrt8VNimH0MXyuleprP1wGvKaU+Mb9h91ZKbau0zRqM0RWbzG84acq4j287jG8ovZVS7jCv+nlETwegaZqTeXRTkogsxPgg7yIi6SJyN8a4/7tFpCKbV31D4N9cDmwzO7a+AKa6a1LQNE1zBY8/Y9A0TdMcy6PPGDRN0zTHc/vhqtWJiIhQsbGxVoeheanNmzefULW4N66j6XqtOVNt67XHJobY2Fg2bdpkdRialxKRQ1Usm4sxFDrTbrBDOPAxxhDhVOAWpVSOOfDhDYyRM4XAZKVUYk3l6nqtOVNV9boquilJ02rvfc6fgmUmsFoZV4yvNp8DjMIYzx8HTMG4aljTPIJODJpPWpucRdKx/JpXtKOUWotx0ZS9scA88/E8jKtwK5Z/oAy/AE0rXSB2UY7mneHn/XqwnHZhZeU2lmw9Qn0HFenEoPmkBz/eyoe/pDpiV/WevExEpojIJhHZlJWVVWUhj3+xk3s/3MzBE6cdEbPmpV5blcwDi7by8/6TNa98AToxaD6prNxGgJ9Tq3+tJy9TSs1RSiUopRJatKi6X/DvN/QkwF+476PNFJU6fOofzQv8sDeTd35IYfyANlweF1HzBhegE4Pmk8psigC/qj67L9rxiiYiZ07K17ppA167tS97j+Xz1JJddQ5W807pOYU8+PFWukU14W9jetR7fzoxaD6pzKYI8HdI9XfZpHxDu7Rk+tBOfLwpjU82pdW8geYTSspsTFuwBZtNMXtSPCGB/vXep04Mmk8ympIu7oyhmilYXgBGiMg+YIT5HGAZxv0B9mPcD8AhM3w+NKIzl3RozhNLdrL32ClH7FLzcM8t28O2tFxeGteb2IhQh+zTY69j0LS6stkUNgUB/heXGJRSE6p5aVgV6ypgWh3CuyB/P+GNCX353Zs/MfWjRJbefzmNgvVh7Ku+3p7B++tSufvy9ozsWedBb+fRZwyazymzGX3ADupjcLmWjUN4e0I/DmUX8thn2+s9NFHzTClZBTz26Xb6tW3KYyMde/dSnRg0n1NmswE4qo/BEoM6NOeRa7rwzfajfLC+Vhezal7kTEk50+YnEhTgxzsT4wkKcGxd9twjQ9PqyNPPGCrcO6QDw7q25JlvdrM1LdfqcDQXemLJTpKO5/P6+H5EN23g8P3rxKD5nLJy70gMfn7CK7f0oWXjEKbNTyS3sKTmjTSPt3hTGp9uTuf+oZ24srNz5nnUiUHzOd7QlFShacMgZk2KJyu/mIcXb8Nm0/0N3mzP0VM88eVOLu3YnAeGd3ZaOZ5/ZGjaRfKWM4YKfdo05fHru/H93kz+tTbF6nA0J8kvKmXq/ETCGgTyxvh++Dux/urEoPmcs4nBC84YKtw+uB2j+0Tz8vIk1qfUb54czf0opZj52Q4OZxfy9sR4WjQOdmp53nNkaFotnW1K8pIzBgAR4fmbehEbEcr9C7eQmV9kdUiaA72/LpVvdhzl/67twsD24U4vTycGzeecHZV0kRe4ubtGwQHMntSfguJSZizcQlm5zeqQNAfYcjiH55btYXi3lky5ooNLytSJQfM5v/UxeF/179KqMc/c0ItfDmTz2qpkq8PR6inndAnT5icS2SSEV8b1xc9FZ7ned2RoWg28sSnJ3s39Y7g1oQ3v/JDCD3sza95Ac0s2m+LhxVs5UVDCrEnxhDUMdFnZOjFoPqe03Dubkuw9PbYH3aKa8NDiraTnFFodjlYHs39M4YekLJ4Y3Z3eMU1dWrZODJrPKbd5b1NShZBAf2ZNiqesXDF9wRZKynR/gydZl3KCV1YkMaZPNLcNauvy8r33yNC0alR0ynrzGQNA+4hQXrq5N1vTcnlu2R6rw9FqKfNUETMWbqV9RCjP39QLEdfXU7dIDCLSRkR+EJE9IrJLRB6wOibNe1WMSgr08sQAMKpXFHddFmsMd9xer/sEaS5QVm7j/oVbOF1cxuzb+hNq0ZTqbpEYgDLgT0qpbsBgYJqIdLc4Js1LVXQ++3txU5K9P4/qRnzbpjz22XYOZBVYHY52Aa+sTGbDwWyevbEnnSMbWxaHWxwZSqmjSqlE83E+sAdobW1UmrfytikxahIU4MfbE+MJ9Bemzk/kTEm51SFpVfh+73Fmr0lhwsA23BQfY2ksbpEY7IlILNAP2FDFa1NEZJOIbMrKynJ1aJqX8NYL3C4kumkDXru1L3uP5fPkkp1Wh6NVkp5TyEMfb6N7VBOeGt3D6nDcKzGISCPgM+BBpdR5N7RVSs1RSiUopRJatHDOdLOa9yut6Hz2kaakCld1acn0oZ34ZHM6izelWR2OZiouM266Y7MpZt8WT0igv9UhuU9iEJFAjKQwXyn1udXxaN6r3Etu1FMXD43ozKUdm/PElzvZc/S8716aBZ77Zg/b0vN4aVwf2jUPtTocwE0Sgxjjsd4F9iilXrU6Hs27lfnABW7V8fcT3hjfj7AGgUybn0h+UanVIfm0r7ZlMG/9If54eXtG9mxldThnuUViAC4DbgeuFpGt5s91Vgeleaffhqu6S/V3rRaNg3lrQj8OZRcy87MdKKVv7mOFlKwCZn623RgxNqqr1eGcw5pBspUopX4CfO/rm2aJ34ar+m6VG9ShOY9c04V/freXAeuaMfmy9laH5FPOlJQz9aNEuxFj7vUlxb2i0TQXqJgrKdDHOp8ru3dIB4Z3a8mzy/aw5XCO1eH4lCeW7CQ5M5/Xx/cjumkDq8M5j28fGZpPKq84Y/DBPgZ7fn7CK+P6EtkkhOkLtpBzusTqkHzC4o1pfLo5nfuvjuPKzu45ulInBs3nlPrYBW4XEtYwkFmT4snKL+bhxVux2XR/gzPtzjjFE0t2cnmnCB4YFmd1ONXSiUHzOeU+3vlcWe+YpjxxfTd+SMpi9o8pVofjtU4VlTJ1/maaNgzk9fF93bqPSx8Zms+pmF3VjY9Ll7ttcDtG94nmlRVJrE85aXU4XkcpxWOfbict5wxvT4wnolGw1SFdkE4Mms8ptSkC/cWS6YzdlYjw/E29iI0I5f6FW8jML7I6JK/y3s+pfLvzGI9e24UBseFWh1MjnRg0n1NuU259Gm+VRsEB/Ou2/pwuLmPGwi1nz6y0+kk8nMNzy/YwvFskU4Z0sDqcWtGJQfM5peU2nx+qWp3OkY155oae/HIgm1dXJlsdjsfLOV3C9PmJtAoL4ZVxfTzmLFUfHZrPKbcpn5wOo7Z+3z+G8QPaMGtNCt/vPW51OB7LZlM8tHgrJwpKmDUpnrCGgVaHVGs6MWg+p7Rc+cxNeurqb2N60D2qCQ99vI30nEKrw/FIs9bsZ01SFk+O7k7vmKZWh3NR9NGh+ZyycptP3NazPkIC/Zk1KR6bTTFtwRZKynR/w8VYl3KCV1cmM7ZvNJMGtbU6nIumE4Pmc3Tnc+3ERoTy0rjebEvL5blle6wOx2NknipixsKtdGjRiOdu7OUx/Qr2dGLQfI4xXFVX/doY2TOKP1zWnvfXpfLN9qNWh+P2ysptTF+4hdPFZcyaFE9osFvMU3rR9NGh+Zxym02fMVyEmaO6Et+2KY9+uo0DWQVWh+PWXl6RzK8Hs3nupp50jmxsdTh1phOD5nNKy5XD50kSkVQR2WHeS2STuSxcRFaKyD7zdzOHFuoiFVNDBwX4MXV+ImdKyq0OyS2t3nOcf/2YwoSBbbmxX4zV4dSLTgyazyl3XlPSUKVUX6VUgvl8JrBaKRUHrDafe6Topg14fXw/ko7n88SSnVaH43bSsgt5ePE2ekQ34anR3a0Op950YtB8Tmm5y5qSxgLzzMfzgBtcUaizXNm5BdOHduLTzeks3phmdThuo7isnGkLErEpxaxJ8YQE+lsdUr3pxKD5nLJy5YzhqgpYISKbRWSKuSxSKXUUwPzdsqoNRWSKiGwSkU1ZWVmOjsuhHhzemUs7NueJJTvZc/SU1eG4hWe/2cP29DxeurkP7ZqHWh2OQ+jEoPkcJw1XvUwpFQ+MAqaJyJDabqiUmqOUSlBKJbRo4Z43bqng7ye8Mb4fYQ0CmTo/kfyiUqtDstRX2zL4YP0h7rmiPSN7trI6HIfRiUHzOaU2m8P7GJRSGebvTOALYCBwXESiAMzfmQ4t1CItGgfz9sR4DmcXMvOzHSjlmzf32Z9ZwMzPttO/XTMeHdnV6nAcSicGzeeU2xw7KklEQkWkccVj4BpgJ7AUuNNc7U5gicMKtdjA9uH837Vd+GbHUd5fl2p1OC5XWFLG1PmbCQ705+2J/bzuuhjPvPpC0+rBCXMlRQJfmFe4BgALlFLfichGYLGI3A0cBsY5slCrTbmiA5tSs3lu2R76tmlKv7YeORr3oimlePzLnezLLGDeXQOJCmtgdUgO511pTtNqwdFzJSmlDiil+pg/PZRSz5rLTyqlhiml4szf2Q4r1A34+QmvjOtLZJMQps1PJOd0idUhucTiTWl8nniEGVfHMaSze/cJ1ZVODJrP0XMlOU5Yw0BmTYrnREEJDy3eis3m3f0NuzNO8eSSXVzeKYIZw+KsDsdpdGLQfI4zOp99We+YpjxxfTfWJGUx+8cUq8NxmlNFpUydv5mmDQN5fXxfr/5y4RZHh4jMFZFMEdGXVGpOV+6EKTF83W2D2zG6TzSvrEhiXcoJq8NxOKUUj36ynbScM7w9MZ6IRsFWh+RUbpEYgPeBkVYHofmGUn0HN4cTEV64qRftI0KZsXArmaeKrA7Joeb+nMp3u47x2MguDIgNtzocp3OLxKCUWgt4Vcec5r7Kym0E6Du4OVxocACzb+vP6eIy7l+4hbJy77i5z+ZDOTy/bA/XdI/knis6WB2OS3jU0eFJUwdo7qtMdz47TefIxjx7Y082HMzm1ZXJVodTb9mnS5i+IJGopiG8NK6PR950py48KjF40tQBmvty0lxJmumm+BgmDGzDrDUprN5z3Opw6sxmUzz08VZOFpQwa2J/whoEWh2Sy3hUYtA0RzCGq+qq70xPje5B96gmPLx4G2nZhVaHUyfv/LCfH5OzeHJ0d3rFhFkdjkvpo0PzOcZwVX3G4Ewhgf7Mvi0em00xbUEixWWedXOfn/ef4NVVydzQN5pJg9paHY7LuUViEJGFwHqgi4ikm1MIaJrDldsUSqE7n12gXfNQXhrXh+3peTz3zR6rw6m146eKeGDRFjq2aMSzN/bymX4Fe24xV5JSaoLVMWi+ocxmjJTRw1VdY2TPVtx9eXve/ekgCbHhjO4TbXVIF1RWbuP+BVs4XVzOwnviCQ12i49Il9NfmzSfUlZuTNmgL3BznZmjutK/XTNmfradlKwCq8O5oJdWJPFrajbP3dSTuMjGVodjGZ0YNJ9SZs7lo4eruk6gvx9vT+xHcKA/Uz9K5EyJe/Y3rNp9nH//eICJg9pyY78Yq8OxlE4Mmk+puOhKz5XkWlFhDXj91r4kZ+bzxBL3m/kmLbuQhxdvpUd0E568vrvV4VhOHx2aTyk3zxh0H4PrDencgvuHduLTzel8vPGw1eGcVVxWzrQFiShg9qT+hAT6Wx2S5XRi0HxKqU33MVjpgeGduaxTc55csotdGXlWhwPAM1/vYXt6Hi+P60Pb5g2tDsct6MSg+ZSKpiQ9XNUa/n7CG+P70bRhINPmJ3KqqNTSeJZuy+DDXw5xzxXtubZHK0tjcSf66NB8SpluSrJcRKNg3p4YT1rOGWZ+th2lrLm5z/7MAmZ+tp0Bsc14dGRXS2JwVzoxaD7lt+GquupbaUBsOI9e24VlO47x/rpUl5dfWFLG1PmbCQn0560J8XowQiX63dB8ir7AzX1MGdKB4d0iefabPSQeznFZuUopHv9iJ/syC3hjfF9ahYW4rGxPoROD5lP0BW7uQ0R4ZVwfopqGMH1+ItmnS1xS7qKNaXy+5Qgzro7jijg9S3NVdGLQfMpvZwy66ruDsIaBzJrYnxMFJTz48VZsNuf2N+zKyOOppbu4Ii6CGcPinFqWJ9NHh+ZT9BmD++kVE8YTo7uzNjmLd37Y77RyThWVMnV+IuENg3j91r766vcL0IlB8yll+joGt3TboLaM6RPNa6uSWbf/hMP3r5Ti0U+2k55zhrcn9qN5o2CHl+FNdGLQfIoeruqeRITnb+pFhxaNmLFoC5mnihy6/3d/Osh3u44xc2RXEmLDHbpvb6QTg+ZT9AVu7is0OIDZk+I5XVzO9IVbzv6v6mvzoWxe+HYv13SP5I9XtHfIPr2dPjo0n1Jozuyp58NxT3GRjXnupp78ejCbl1ck13t/2adLmL5gC9FNG/DSuD4+edOdutCJQfMpaTnG/YdbN2tgcSRadW7sF8OEgW35148prN5zvM77sdkUD368lZMFJcyaFE9Yg0AHRunddGLQfMrhk4VENAqikY/emctTPDW6Oz2im/DQx1tJyy6s0z7e/mE/a5OzeGpMd3q2DnNwhN5NJwbNpxzOLqRNuJ5B092FBPoza1I8Cpi2IJHisou7uc/P+0/w2qpkbugbzcSBbZ0TpBfTiUHzKYdOFtJOJwaP0K55KC/d3Ift6Xk8+82eWm93/FQRDyzaQqcWjXjupl66X6EOdGLQfEZJmY2jeWdoqxODxxjZsxV/vLw9H6w/xFfbMmpcv7TcxvQFiRSWlDP7tngaBukmw7rQiUHzGUdyz2BT0LZ5qNWhaBfhsVFd6d+uGTM/287+zIILrvvy8iQ2pubw/E296NSysYsi9D46MWg+49DJ0wD6jMHDBPr78fbEfgQH+jN1/mYKS8qqXG/FrmP8e+0BJg5qy9i+rV0cpXfRiUHzGRWjW9rpiX4fdwAABftJREFU2zd6nKiwBrwxvi/7Mgt4/Mud593cJy27kEc+2UbP1k148vruFkXpPdwmMYjISBFJEpH9IjLT6ng073PoZCHBAX600PPkeKQr4low4+o4Pk88wscb084uLyotZ+r8RBQwa2J/ffGiA7hFYhARf+AdYBTQHZggIjrtaw5TUFzGzyknade8IX4unkBPf+lxnBnD4ri8UwRPLt3Frow8AJ75Zjc7juTxyrg+tNVngw7hLl32A4H9SqkDACKyCBgL7L6YnRzJPcO3O446ITzNEykF5UpxuriM7/dmknw8n7cm9HNpDHZfekYA6cBGEVmqlLqouq0Z/P2E18f35Xdv/o+p8xO5tkcrPvrlMPcO6cA1PVpZHZ7XcJfE0BpIs3ueDgyqvJKITAGmALRte/5FK4dOnOaZixjvrPkGfz8hplkD3pnYj5E9o1xdvEO+9Gi/iWgUzNsT45k891fmrD3AFXERPHJtF6vD8irukhiqOrc/71ZOSqk5wByAhISE814f2D6c7X+7xvHRaR5JMJJCkL+flXdsq/FLT01feLTzDYgNZ/MTIygpt9E4OEBfxOZg7pIY0oE2ds9jgJqvZqkkwN+PJvqWjZp7qfFLT01feLSqhQT6645mJ3GXT9GNQJyItBeRIGA8sNTimDTNERzypUfTXMktEoNSqgyYDiwH9gCLlVK7rI1K0xxCf+nRPI5UvlDEU4hIFnCoipciAMffNLZudCznc5c44MKxtFNKtXBEISJyHfA64A/MVUo9e4F1q6vX4DnvnSu5SxzgPrHUu157bGKojohsUkolWB0H6FjcOQ5wr1hqw53idZdY3CUOcJ9YHBGHWzQlaZqmae5DJwZN0zTtHN6YGOZYHYAdHcv53CUOcK9YasOd4nWXWNwlDnCfWOodh9f1MWiapmn1441nDJqmaVo96MSgaZqmncOjE4OIjBORXSJiE5GESq/92ZzmOElErrVb7vQpkEXkbyJyRES2mj/X1RSXs1g95bOIpIrIDvN92GQuCxeRlSKyz/zdzEllzxWRTBHZabesyrLF8Kb5Pm0XkXhnxFRb7li33alem2VaVre9vl4rpTz2B+gGdAHWAAl2y7sD24BgoD2QgnFxkb/5uAMQZK7T3Qlx/Q14pIrlVcblxPfHJX9vDTGkAhGVlr0IzDQfzwT+6aSyhwDxwM6aygauA77FmNtoMLDBle9TFbG7Xd12l3ptlmlp3fb2eu3RZwxKqT1KqaQqXhoLLFJKFSulDgL7MaY/PjsFslKqBKiYAtlVqovLWaz+e6szFphnPp4H3OCMQpRSa4HsWpY9FvhAGX4BmoqIy+foruBhddvV9Rrcs257Tb326MRwAVVNddz6AsudYbp56jbX7pTSleVbUV5VFLBCRDab00sDRCqljsL/b+/eVRoIojCO/09lITYKQkoF30GwFUE7OysFfYyU1pZiYWlhKQYbUV/ATkXw0ivaWYrFWOxEdiW7huBcXL8fhA2byzk7OXCym2ECfjsdMZ+62DmM1TBS13YOdZ0qZlmr6zqXZbdrmdk5MOivmbrOueO6lw3Y5xjcCEear9uUF7AHbPv33gZ2gM2GvEKJHW+QBefck5lNA2dmdhc5/rCij1WOtf1H6ppEMctaXdfZNwbn3OIIL2ta6vhXlkAeNi8z2wdOhsgrhORLPjvnnvz21cyOKC4BvJhZxzn37E9rXyOmVBc7+ljlWNt/pK5TxfzS9rpu66WkHrBmZmNmNgPMAZdEWgL52zW8VaA/e6Aur1CSLvlsZuNmNtG/DyxRjEUP2PBP2wDqvh2HUBe7B6z7WRzzwFv/1DwzyWo7o7qGhLX9L+o61q/4gX6dX6XoiO/AC3BaeqxLMWvhHlgu7V8BHvxj3UB5HQA3wLX/YDo/5RVwjIIfb0PsWYrZIlfAbT8+MAVcAI9+Oxko/iHwDHz4Otmqi01xyr3rx+mG0kygFLccazunuo5xvA1xW1/XWhJDREQq2nopSURERqTGICIiFWoMIiJSocYgIiIVagwiIlKhxiAiIhVqDCIiUvEJ8u9kVZm2sc0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "plt.plot(x, y) # x^2\n",
    "plt.title('parabola')\n",
    "\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.plot(x, z) # x^3\n",
    "plt.title('hyperbola')\n",
    "\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.plot(x, r) # e^x\n",
    "plt.title('exponent')\n",
    "\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.plot(x, m) # |x|\n",
    "plt.title('abs')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Строка `plt.grid(True)` нужна для того, чтобы на графиках были добавлены линии разметки, привычные нам \"клеточки\", которые позволяют удобным образом определять координаты точек на графике."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7niFdWzNhcEf+9tVubvpBD9q1bBaM1bjeO2uy2ZdfxJybU3XrQdWJiEQCzwPjgRxgtZ2zW52NzF2Kyjzsyi1ky4HjrN93lBW7j5Cdb/XoMLxbAn+4cjBXjuhCS5cVBV/BiGwSMM5+PBdIwyoQk4A3jDEGWCkiCSLSyZ52iTEmH0BElgCXAvODEBsA917cj4+3fMvzSzN54PKBwVqNaxWVeXjms52cmdyG8/vpmUuqzs4CdhljdgOIyNtY+R1yBaKkvIK8E6XknyzjWHE5RWUeissrKPcYPF5DhTFgrHvK7NhXzr4VWRgDXmOo8BrKKryUlHspLvNwvNhDflEZeSdKOVBQTO6J0lPrSWgRTWqPtvz4nF5c0L8DXdsE9tagwdLQAmGAT0TEAC8bY14BkowxBwGMMQdFpPIbqAvge4u3HLutuvbvEZGZwEyApKQk0tLSTo0rLCz8zvOanN0lirnL99A/8ls6tHDmWH1dYw6URZll5J0oZ+ZA4YsvvqjTvE7FrFzFX86OqjpRIPO1obzGsO+4l93HvOw97mV/oZfcIi/H69q5wtYtfptjIqFFlNAyGlo3E/q1iuDsjtF0jouge6sI2jcXRAqhtJBdG7LY1fCXVCsNfZ8bWiDGGmMO2EVgiYhsP820/vZjmNO0f7/RKkCvAKSmpppx48adGpeWlobv85oMSClh3B/T+KIggecnptQ8QxDUNeZAyD1Rwu2fp3HJoCR+fFVqned3ImblOrXK2UDma30Ulnr4dOshPtn6LV/tPMyJEuv09oQW0fRNSmBk7zi6tmlOUqtY2sbF0LpFNHExUcRGRxATFUFURAQRERBhn/69fNlyxowdg2C1RUQIzaIiiImMcO1u2oa+zw0qEMaYA/bfXBH5N9am5yER6WRvPXQCKm/plgN085m9K3DAbh9XpT2tIXHVRlKrWGae24u/fraTW8bkk5rcNO69/OdPdlBe4WX2hAFOh6JCV3W57Aqbco4xd0UWH2w8SHF5BR3imzFhcEfGnJFIanIbuiQ0r9c1P62aCYlN7JhlvQuEiMQBEcaYE/bji4HfA4uAGcDj9t+F9iyLgDvs/ZWjgGN2EfkYeLTybCd7Ob+qb1x1cdt5vXhndTYP/XcrC2eNde2vgEDZvP8Y76zJ5kdje9IzMfh9yauwtRroIyI9gf3ADcBUZ0OyLjT7y6c7WLbrCHExkVw5ojNXp3RlZPc2YZ/bwdKQLYgk4N92JY4C3jLGfCQiq4EFInIrsA+YbE+/GJgI7AKKgFsAjDH5IvIw1ocO4PeVB6yDrUVMFLMn9Ofn76Tzz7XZXH9m4/aU2JiMMTy4aAttW8Rw14V9nA5HhTBjjEdE7gA+BiKBOcYY/zvnG0F2fhF/+GArH285RGLLZvx6Yn+mnNWd+CZ+nVMg1LtA2GcwDPPTfgS40E+7AWZVs6w5wJz6xtIQk4Z3Zt6qvTzxUQaXDOpIQosYJ8IIun+t28+avUd58pqhtG6uiaMaxhizGOtHn2O8XsPry7P448fbEYR7x/fl/87ppfdSD6Am1dWGPyLCQ1cM5lhxOU98lOF0OEFxrKicRxdvY0T3BK51qFdIpQIp93gJ019bxcPvb2XMGYl8du953HlhHy0OAebeKzQa0cDOrbhlTDJ/+3oP16R0CbsD1o99uI2C4nL+ceUQ3RerQt6arHx+8uY6TpZ6eOKaIVyX2k07mgySJr8FUenu8X3pktCc2f/aRKmnwulwAmZF5hHeXp3N/53Tk4Gd9VaiKrQtTN/P1FdX0bJZJP+ZNZbrz+yuxSGItEDY4ppF8chVg9mVW8gzn+10OpyAKCrzcN97G+nRrgU/v7Cv0+Eo1SCvL9vDz95OZ0T3BP4zayz9OsY7HVLY0wLhY1y/Dkwe2ZWXvthNenaB0+E02OMfbmdffhFPXDNU982qkPbyF5k89N+tXDIoiTduPStsTyZxGy0QVdx/+UCS4ptxzzvpFJWF7o2FvtyRxxsr9nLL2GRG632mVQh7fdkeHvtwO5cP68zzU1NoFqU/dhqLFogqWsVG86frhrHnyEkefn+b0+HUy+HCUn7xzw306dCS+y7t73Q4StXbf9bvP7Xl8JfrhhEVqV9ZjUnfbT/GnJHIbeeewfxv9vH+Rtf0IFArXq/hF//cQEFxOX+9YQSx0fprS4WmlbuP8P/e3cDoXm356w0jtDg4QN/xatx7cV9GdE9g9nub2J1X6HQ4tfbiF5mkZeRx/w8H6FlLKmRl5xfx0zfX0r1tC16enqo/dByiBaIa0ZERPD81hehI4bZ/rKWw1P3HI77ckcdTn2RwxbDOTB/dw+lwlKqXkvIKbvvHWiq8hr/NOJPWLfTKf6dogTiNzgnNeW5qCpl5hdz9Tjper99eyF1hd14hd7y1jr5J8Tx+zRA9N1yFrAcXbWHrweM8fcNw7VTSYVogajC2dyIPXDaQJVsP8diH7jxoffRkGbfOXUNUZASv3pRKixi9QF6Fpvc3HuDt1dn8dNwZXNA/yelwmjz9JqmFGWOS2XP4JK9+tYeOrZtz69k9nQ7plKIyDz+au5r9BcXM//EourUNjVsZKlXVwWPF/PpfmxjeLYF7xuuFnW6gBaIWRIQHLh/EoeOlPPz+VuJjo7gutVvNMwZZqcfaV7shu4AXpo1kZI/w6kNKNR3GGO57bxPlFYanrx9OtJ6x5Ar6X6ilyAjh6RuGc06fRO57byPvrc1xNJ6S8gp+8o+1fLXzMI9fM5RLB3d0NB6lGuLdtTl8uSOP2RP6k6zHHVxDC0QdxEZH8sqNqYw5ox2/eHcD/1i515E4Cks93Dp3NUsz8nj0qiGu2JpRqr4OF5byhw+2cWZyG27Us+9cRQtEHTWPieS1GWdyYf8O3P+fzTz50fZGPbvp22MlXP/yClbuzufP1w1j6qjwvQueahoeXbyNojIPj12t3dG7jRaIeoiNjuSl6SOZclY3XkjL5LY313K8pDzo612Tlc8Vz31N1uGT/O2mVK5O0Zv/qNC2Oiuff63bz4/P6UXvDto7q9togainqMgIHr1qCA9cNpDPt+dy2TNfs3bv0aCsy1Ph5fmlu7j+lZU0j4nkvdvHcH7/DkFZl1KNxes1PPTfLXRqHcsdF/R2OhzlhxaIBhARfnR2TxbcNpoKr2HyS8v5/X+3BnRrYvP+Y1zz4nL++HEGEwZ3ZNEdZ9O/o3ahoULfu+ty2Lz/OLMn9Ndrd1zKNQVCRC4VkQwR2SUis52Opy5G9mjLRz8/h6mjuvP68j2c/8c0Xvt6D8Vl9b8z3e68Qu55J53Ln/ua/QXFPDtlBM9OGUHr5trtQFMhIpNFZIuIeEUktcq4X9m5kiEil/i0+80jEekpIqtEZKeIvCMiMXZ7M/v5Lnt8cn3XURelFYanPslgeLcErhjWuT6LUI3AFWVbRCKB54HxQA6wWkQWGWO2OhtZ7cXHRvOHK60zih5bvJ2H39/Ks5/v5JqUrkwa3pkhXVrX2P1FUZmHtIw83l2bw9KMXGIiI5h5Ti9mXdCbVrFaGJqgzcDVwMu+jSIyELgBGAR0Bj4Vkcory6rLoyeAvxhj3haRl4BbgRftv0eNMb1F5AZ7uuvruY5aW7K3nEPHy3l2Sop2C+NirigQwFnALmPMbgAReRuYBIRMgag0tGsC82eOZnVWPq8v28MbK7J47es9JLZsxsgeCfRNiqdj61haNoti8/5ydn+9h335RWw9cJz07ALKKrx0iG/Gnef35sYfJNM+vpnTL0k5xBizDfD3BToJeNsYUwrsEZFdWDkEfvJIRLYBFwBT7WnmAg9iFYhJ9mOAd4HnxFphndZBHXL1WHE5i3eXc0H/DpzVUy/udDO3FIguQLbP8xxgVNWJRGQmMBMgKSmJtLS0U+MKCwu/89wNrusCE9s3Jz3Pw5YjHtL35PLJlkN856TYTVtpFgldW0ZwQbdIhraPoV+bCCIjDrJl7UGnQq+WG9/nJqgLsNLneY7dBv7zqB1QYIzx+Jn+VO4ZYzwicsyevq7r+J7q8nXJ3nKKPDCu7fGQ+iyF4me/oTG7pUD428b83sUFxphXgFcAUlNTzbhx406NS0tLw/e5m1zm87i8wsvhwlJOllawZvU3XDzubNq0iA6ZzWw3v8+hSEQ+BfxdBv8bY8zC6mbz02bwf0zRnGb60y2rruv4fmM1+XqeMZyx8HNuuuJCf7O5Vih+9hsas1sKRA7gezlwVyC0buVWS9GREXRq3RyAnLgI2sbpzdebMmPMRfWY7XT54q/9MJAgIlH2VoTv9JXLyhGRKKA1kF+PddSaiNArQW8AFArcUiBWA31EpCewH+vg2NTTzbB27drDIuLb10UiViKEknCIWftGaHyLgLdE5M9YB5D7AN9g/er/Xh4ZY4yILAWuBd4GZgALfZY1A1hhj//cnr5O66gpYM1XxzQsX40xrhiAicAOIBNr87qu869x+jVozDoE+L2+CuuXfClwCPjYZ9xv7FzJACb4tPvNI6AX1hf8LuCfQDO7PdZ+vsse36u+6wj3z1FTjFnshYQ8EVljjEmteUr30JhVUxWKn6OmGLNrLpRTSinlLuFUIF5xOoB60JhVUxWKn6MmF3PY7GJSSikVWOG0BaGUUiqAtEAopZTyKyQLRCB7uXSCm2LxJSJzRCRXRDb7tLUVkSV2L6BLRKSN3S4i8oz9GjaKSIpzkSs3C/V8dWM8lYKes06fp1vPc3sHAP2ANCDVp30gsAFoBvTEOk870h4ysc4Fj7GnGehQ7K6JxU9s5wIpwGaftieB2fbj2cAT9uOJwIdYF06NBlY5Hb8O7hxCOV/tOF0VT5XYgpqzIbkFYYzZZozJ8DPqVA+Uxpg9WBf/nIVPb7HGmDKsq0knNV7E3+GmWL7DGPMlVjcLviZh9f6J/fdKn/Y3jGUlVlcOnRonUhVKQjxfcWE8pwQ7Z0OyQJyGv15hu5ym3QluiqU2kowxBwHsv5X3Og2116HcJxTyFdwXT00ClrNu6Yvpexqpl0sn1Krn2hAQLq9DBUAY5yuEz2e9zq/DtQXCNE4vl04ItZ5rD4lIJ2PMQXtzNNduD7XXoYIojPMVQu+zHrCcDbddTIuAG8S6z25P/tcD5aneYsW6F+8N9rROcFMstVHZ2yd8vxfQm+wzI0YDxyo3a5WqpVDIV1wYT00Cl7NOH4Wv55H7gPVy6VD8romlSlzzgYNAuf3+3op1d7HPgJ3237b2tIJ1b+JMYBM+Z6fooIPvEOr56sZ4fOIKas5qVxtKKaX8CrddTEoppQJEC4RSSim/tEAopZTySwuEUkopv7RAKKWU8ksLhFJKKb+0QCillPJLC4RSSim/tEAopZTySwuEUkopv7RAKKWU8ksLhFJKKb+0QCillPJLC4RSSim/tEAopZTySwuEUkopv7RAKKWU8ksLhFJKKb+0QCillPJLC4RSSim/tEAopZTySwuEUkopv7RAKKWU8ksLhFJKKb+0QCillPJLC4RSSim/tEAopZTySwuEUkopv7RAKKWU8ksLhFJKKb+0QCillPJLC4RSSim/tEAopZTySwuEUkopv7RAKKWU8ksLhFJKKb+0QCillPJLC4RSSim/tEAopZTySwuEUkopv7RAKKWU8ksLhFJKKb+0QCillPJLC4RSSim/tEAopZTySwuEUkopv7RAKKWU8ksLhFJKKb+0QCillPJLC4RSSim/tEAopZTySwuEUkopv7RAKKWU8ksLhFJKKb+0QCillPJLC4RSSim/tEAopZTySwuEUkopv7RAKKVUNUTkZhH52uk4nKIFIoyJSJaIXOR0HEqp0KQFQimllF9aIKoQkc4i8p6I5InIHhG5y25fLCJP+Uz3jojMsR/fLCLLRORZETkmIttF5MIqy1wkIvkisktEfuwz7kERWSAib4jICRHZIiKpNcVT07wi8g+gO/BfESkUkV8G831TKpSJyGwRybTzaKuIXPXd0dXm9s0istueb4+ITHMg/OAxxuhgD1gFcy3wABAD9AJ2A5cAHYFc4AJgmt0eb893M+AB7gaigeuBY0Bbe/wXwAtALDAcyAMutMc9CJQAE4FI4DFgZU3x1DSvPT4LuMjp91UHHdw+AJOBznbOXQ+cBDqdLreBOOA40M9eRkqoKHAAABXISURBVCdgkNOvJaDvi9MBNPCfOsf+0t5ch3muBQyQaj8/C0i3h51AXpXpfwW8bj++GsgGDgNn+0xzM3AAEJ+2b4AbgW5ARWUxscc9Bvzdfvwg8KnPuIFAsf14FLDvNPFUO6/9XAuEDjrUY7C/DybVkNtxQAFwDdDc6ZiDMYT6Lqa/A5fWdmIRiQfuAlb5NG/GKhbDgT8CiSJSUDkAvwaS7Gnfx/qlnmGMqXpmw35jf3pse7F+kXQG8o0xJ6qM6+Lz/Fufx0VArIhEAT2AzqeJ53TzKqVqSURuEpF0nzwbDCTao/3mtjHmJNYWxU+AgyLygYj0b9zIgyukC4Qx5ksg37dNRM4QkY9EZK2IfFXlH/Yw8CTWbpnKZRQZYzz208NYv/YTjTEJ9hBvjJloj38E2AZ0EpEpVcLpIiLi87w71i+PA0Bbuzj5jttfi5eYDezxiaVqPDUxNU+iVNMmIj2AV4E7gHbGmASsH46V+VxdbmOM+dgYMx5r99J2ezlhI6QLRDVeAe40xowEfoG17x8RGQF0M8a8X3UGERklIluAN7B+HdwrIs1FJFJEBovImSJyLnALcJM9PCsivlsBHYC7RCRaRCYDA4DFxphsYDnwmIjEishQ4FZgXi1eyzfAcRG5r2o8tXwvDmEdt1BKVS8O68dUHoCI3IK1BVHJb26LSJKIXCEicUApUIj1AzNshFWBEJGWwBjgnyKSDryM9Ws/AvgLcK+/+Ywxq4wxg4AzsQ46pQB7sLYo/ob16+AN4A5jzH5799JrwOs+vyxWAX3seR4BrjXGHLHHTQGSsX51/Bv4nTFmSU2vxxhTAVyOdWDbN57WtXxLHgN+a282/6KW8yjVpBhjtgJPASuwflQNAZb5TFJdbkdgfaccwNqTcR5we+NFHnzy3V1roUdEkoH3jTGDRaQV1vGBTlWmaQ1kYlV4sM5IygeuMMasqTLtUuD/VW2vIYabgf8zxpxd39ehlFJuE1ZbEMaY48AeezMQsQwzxhwzxiQaY5KNMcnASuziICI9Kw/q2vsi+2Gd/aOUUk1aSBcIEZmPtVnYT0RyRORWrGsUbhWRDcAWrFPVTudsYIO9S+rfwO3GmMPBjFsppUJByO9iUkopFRwhvQWhlFIqeEL2gqrExESTnJx86vnJkyeJi4tzLqB6CIeY165de9gY097BkFQD2X2KXQbkGmMG221tgXewzr7LAq4zxhy1z9r7K1b3LkXAzcaYdTWtQ/PVGQ3OV6cv5a7vMHLkSONr6dKlJtSEUsxer9d4vd7vxQysMS74POhQ/wE4F+vU7s0+bU8Cs+3Hs4En7McTgQ+xLiIbDayqzTqq5uu/P/rMhJpQyldjjMk7UWI++/zz77TVNV91F5Oqle3fnqDnrxaz5ltPzROrkGL89EiAdXLHXPvxXOBKn/Y37O+blUCCiHSiDhasyea+L4vJ+PZEzROreikuq2D631bxysbSBi0nZHcxqcblqbBOZojUnxRNRZIx5iCAMeagiHSw27tgdQFTKcduO1h1ASIyE5gJkJSURFpaGgAxJV5iIgwzXv2K3/2gOc2jpOqsrlRYWHjqNbjda5tKyfjWw08HmQbFrAVC1YrH6wUgMjRyWQWPv0+A31MhjTGvYHV9Q2pqqhk3btypcQdPfsYf15SwOC+BZ24Yzne7OnKntLQ0fF+DWy1Yk81X+zdy5wW9GRlzsEEx6+9BVSser/UdEBECiawC4lDlriP7b67dnoPVhX2lrtgd19XFgHaR3HtxP/674QD/WLm3wcEqy7aDx7n/P5v5Qa92/Pyivg1enhYIVSundjFpfWgqFgEz7MczgIU+7TfZvRSMBo5V7oqqq5+edwbn92vPw+9vZUN2QcMjbuJOlJRz+7x1tG4ezTNTRhAZ0fBk1QKhauXULib9xISdanokeBwYLyI7gfH2c4DFWHc13IXVtXW9O6eLiBD+fN1wOsTHcvu8dRQUlTXodTRlxhhmv7eJfflFPDtlBO3jmwVkuXoMQtWKbkGEL2NM1XubVLqwaoN9quSsQK27TVwMz09LYfJLy7l3wQZevSmViAD88m1q5i7P4oNNB5k9oT+jerUL2HL196CqlcpjEFogVKAN75bAbyYO4LPtubz0ZabT4YSc9fuO8sjibVw0oAMzzwns7V+0QKha8VRU7mLSCqECb8aYZH44pBN/+jiDlbuP1DyDAuDoyTLueGs9HeJjeWry8IBvfWmBULWiWxAqmESEx68ZQo92cdw1fz15Jxp2gVdT4PUa7lmQTt6JUl6cnkLrFtEBX4cWCFUrlQepdQNCBUt8bDQvTk/heEk5P3t7PRVe7Wn6dF78IpOlGXncf9kAhnZNCMo6tECoWtGD1Kox9O/YiocnDWZ55hH+smSH0+G41vLMwzz1SQaXD+vM9NE9grYeVxQIEekmIktFZJuIbBGRnzkdk/quyl1MUa74xKhwNjm1G9elduW5pbtYmpFb8wxNTO7xEu6an05yYhyPXT0kqFehuyXdPcC9xpgBWD1EzhKRgQ7HpHxUHqTWK6lVY/j9pMH07xjPPe+ks7+g2OlwXMNT4eXO+espLC3nxWkjadksuFcquKJAGGMOGrtPeWPMCWAbVgdgyiX0ILVqTLHRkbw4fSTlFYZZ89ZR5vE6HZIr/HnJDlbtyeeRK4fQr2N80NfnigLhS0SSgRHAKmcjUb60N1fV2HomxvHENUNJzy7gsQ+3OR2O4z7ffogX0jK54cxuXDOya6Os01VXUotIS+A94OfGmON+xvvtPhhCqyveSqEU847dVjcIxSdPhkzMKvT9cGgnVmcl8/qyLM5MbsvEIXW69UTYyDlaxN3vbGBAp1Y8eMWgRluvawqEiERjFYd5xph/+ZvmdN0Hh0pXvL5CKeZNFTthxw5axceFTMwqPPx64gDSswv45bsbGdCpFT0TQ+u2nw1V5vEy6631eL2GF6elEBsd2WjrdsUOA/s+t68B24wxf3Y6HvV9egxCOSUmKoLnp6UQFSncPm8dJeUVTofUqB5dvI0N2QU8ee1Qkhu5OLqiQABjgRuBC0Qk3R4mOh2U+h+P10tUhITEjV1U+OmS0Jy/XD+cbQeP87uFW5wOp9G8v/EAf1+exY/G9mSCA7vXXLGLyRjzNf7vVKVcwlNhtB8m5ajz+3XgjvN789zSXaQmt2FyareaZwphmXmF3PfuRlK6JzB7Qn9HYnDLFoRyOY/XEK2nMCmH3T2+Lz/o1Y77F25m+7ffO48lbBSXVTBr3jpioiJ4bmoKMQ5doaoZr2rFU+HVLQjluMgI4a9ThhMfG83tb66jsNTjdEhBcf/CzWQcOsHTN4ygc0Jzx+LQAqFqxdqC0AKhnNchPpZnp4wg68hJZr+3EeseRuFjweps3l2bwx3n9+a8vu0djUULhKoVT4UhKkI/LsodRvdqxy8u6cf7Gw/yxoq9TocTMNsOHuf+hZsZ27sdP7+or9PhaIFQtVPu1V1Myl1+cu4ZXNC/A3/4YCvp2QVOh9NgJ0rKuX3eOlo3j+bp60e4It+0QKhaqdBdTMplIiKEP183jA7xscyat46CojKnQ6o3Ywyz39vEvvwinpuaQvv4Zk6HBGiBULWkp7k2TSKSJSKb7GuT1thtbUVkiYjstP+2cSq+hBYxPD8thdwTJdyzYAPeEL3J0N+XZ/HBpoP88pJ+nNWzrdPhnKIFQtWKx+vV01ybrvONMcONMan289nAZ8aYPsBn9nPHDO+WwG9/OJDPt+fy0peZToZSL+v2HeWRD7Zx0YAkZp7by+lwvkMzXtWKp8IQpbuYlGUSMNd+PBe40sFYALjpBz24bGgn/vRxBit3H3E6nFo7erKMO+ato2PrWJ6aPMx1PRW44kpq5X4eryEyIgJoWv3gKAzwiYgY4GW7w8wkY8xBsO7lIiId/M3Y2L0v/7CDYU1z4ba5q3hoTCwJzQL7+zfQMXuN4em1peQer+A3o2NZ/82ygC27UkNj1gKhasXj9RKtxyCaorHGmAN2EVgiIttrO6MTvS/3HHycK59fxtt7m/PmraOICuBu0UDH/PzSXWw8nMHDVw7mxiDdV7qhMesuJlUr5XqQukkyxhyw/+YC/wbOAg6JSCcA+69rbhzdv2MrHp40mJW783n6051Oh1Ot5ZmHeeqTDK4Y1pnpo7o7HU61tECoWqnQvpiaHBGJE5H4ysfAxcBmYBEww55sBrDQmQj9m5zajetTu/Hc0l0s3e6a2nVK7vES7pqfTs/EOB67eojrjjv4ckXGi8gcEckVkc1Ox6L881R49SB105MEfC0iG4BvgA+MMR8BjwPjRWQnMN5+7ioPTRrEgE6tuHtBOvsLip0O5xRPhZc756/nZKmHF6ePJK6Zu/fyu6JAAH8HLnU6CFU9j9cQpbuYmhRjzG5jzDB7GGSMecRuP2KMudAY08f+m+90rFXFRkfywrQUKioMs+ato8zjdTokAJ5asoNVe/J55KrB9E2KdzqcGrmiQBhjvgRc9yFT/6N9MalQ0zMxjieuHUp6dgGPfbjN6XD4bNshXkzLZMpZ3bg6pavT4dSKu7dvqmjs0+aCLZRiPl5YRL4UUdjGEzIxKzVxSCduGZvM68uySO3Rlh8Obfy7sgFk5xdxz4INDOzUit9dPsiRGOojpAqEE6fNBVMoxRyzeimdOybQsuWxkIlZKYBfTRhAenYB9723kQGd4unVvmWjrr/UU8Edb63DawwvTk8hNjqyUdffELrPQNWKdSW1flxU6Km8K1t0pHD7vHWUlDfuxZ6PfrCNDTnH+OO1w+jRLq5R191QmvGqVjxerx6kViGrS0Jz/nL9cLZ/e4IHFjbeyZL/3XCAuSv28n9n9+TSwR0bbb2B4ooCISLzgRVAPxHJEZFbnY5JfZf2xaRC3bh+Hbjj/N4sWJPDP9dkB319mXmFzH5vIyN7tOG+Cf2Dvr5gcMUxCGPMFKdjUKdnnebqit8TStXb3eP7snbvUe5fuJkhXVvTv2OroKynuKyC299cR7PoSJ6bOiJkLzINzahVo/NU6C4mFfoiI4RnpowgPjaan765jhMl5QFfhzGG+xduZkfuCZ6+fjidWjcP+DoaixYIVSseryFSdzGpMNA+vhnPTRnBvvwiZr+3CWMCe5OhBWuyeXdtDndd0Idz+7YP6LIbmxYIVSseryFadzGpMDGqVzt+cXE/Pth0kLnLswK23K0HjvPAwi2M7d2Ouy7sE7DlOkUzXtXIGEOFVw9Sq/By27m9uLB/Bx5ZvI307IIGL+94STm3z1tLQoto/nrDiLDo/VgLhKqRx77Prx6DUOEkIkJ46rphJLWKZda8dRQUldV7WcYYZr+3keyjxTw7JYXEls0CGKlztECoGnkq7AIRomdiKFWdhBYxPD81hbwTpdz9Tjpeb/2OR7y+LIvFm77ll5f046yebQMcpXM041WNPF6rJ0zdglDhaFi3BH572QCWZuTx4heZdZ5/7d6jPLp4G+MHJjHz3F5BiNA5WiBUjU5tQWiBUGHqxtE9uGxoJ576JIMVmUdqPd/Rk2Xc+dY6OraO5U/XDnP1zX/qQwuEqtFx+1zxFi6/uYlS9SUiPH7NUJIT47hz/npyT5TUOI/Xa7h7QTqHC8t4YVoKrVtEN0KkjUsLhKrR3iNFAHRv28LhSJQKnpbNonhx2kgKS8u5a/56PBWnv8nQC2m7SMvI4/7LBzK0a0IjRdm4tECoGu3LtwpEj3ZaIFR469cxnkeuHMLK3fn85dMd1U63fNdh/rxkB5OGd2b6qO6NGGHj0gKharQvv4iYqAiS4mOdDkWpoLtmZFduOLMbzy/NZOn23O+NP3S8hLveXk/PxDgevWpI2B138KUFQtVo75GTdGvTnAg9SK2aiAevGMTATq24e0E6OUeLTrV7KrzcOX89J0sreGn6SOLC/LicawqEiFwqIhkisktEZjsdj/qfvUeKQu5GJyq4wj1fY6MjeWFaChUVhllvrafMYx2P+NMnO/hmTz6PXj2YPknxDkcZfK4oECISCTwPTAAGAlNEZKCzUSmA3OMl7D1SpAeo1SlNJV+TE+N48tqhbMgu4NHF21if6+GlLzKZclZ3rhrR1enwGoVbto/OAnYZY3YDiMjbwCRga21m/tW/NvL55iKar14axBADr7i42LUxG6DCazhcWEqECNekNI2EULXSoHwNJROGdOJHY3syZ9keoiNgUOdW/O7ysKuF1XJLgegC+N7iKQcYVXUiEZkJzARISkoiLS0NAO+xcrrFeYmKKg1+pAHkiXB3zCIwsFUEY7tEcWTXetJ2QWFh4an3XTVZDcpXCK3P0Zg4w6Ge0RwtKuPqPuWsXPaV0yHVWoPfZ2OM4wMwGfibz/MbgWdPN8/IkSONr6VLl5pQEw4xA2uMCz5DOjTeoPkaOhqar644BoH1C6Sbz/OuwAGHYlFKnZ7maxMhVlFxOAiRKGAHcCGwH1gNTDXGbDnNPHnAXp+mROBwMOMMgnCIuYcxJrRvm6XqRPM1pDQoX11xDMIY4xGRO4CPgUhgzuk+bPY833mRIrLGGJMaxDADTmNWoUjzNXQ0NGZXFAgAY8xiYLHTcSilaqb52jS45RiEUkoplwmnAvGK0wHUg8asmqpQ/Bw1uZhdcZBaKaWU+4TTFoRSSqkA0gKhlFLKr5AsECIyWUS2iIhXRFKrjPuV3cNkhohc4tPumt4n3RSLLxGZIyK5IrLZp62tiCwRkZ323zZ2u4jIM/Zr2CgiKc5Frtws1PPVjfFUCnrO1uWya7cMwACgH5AGpPq0DwQ2AM2AnkAm1nnakfbjXkCMPc1Ah2J3TSx+YjsXSAE2+7Q9Ccy2H88GnrAfTwQ+BAQYDaxyOn4d3DmEcr7acboqniqxBTVnQ3ILwhizzRiT4WfUJOBtY0ypMWYPsAur58lTvU8aY8qAyt4nneCmWL7DGPMlkF+leRIw1348F7jSp/0NY1kJJIhIp8aJVIWSEM9XXBjPKcHO2ZAsEKfhr5fJLqdpd4KbYqmNJGPMQQD7bwe7PdReh3KfUMhXcF88NQlYzrrmSuqqRORToKOfUb8xxiysbjY/bQb/hdCp83urizHUhMvrUAEQxvkK4fNZr/PrcG2BMMZcVI/ZTtfLpFt6nwy1njAPiUgnY8xBe3O08i7uofY6VBCFcb5C6H3WA5az4baLaRFwg4g0E5GeQB/gG6zeJvuISE8RiQFusKd1gptiqY1FwAz78QxgoU/7TfaZEaOBY5WbtUrVUijkKy6MpyaBy1mnj8LX88j9VVjVsBQ4BHzsM+43WGccZAATfNonYnVRnIm12etk/K6JpUpc84GDQLn9/t4KtAM+A3baf9va0wrWfYkzgU34nJ2igw6+Q6jnqxvj8YkrqDmrXW0opZTyK9x2MSmllAoQLRBKKaX80gKhlFLKLy0QSiml/NICoZRSyi8tEEoppfzSAqGUUsqv/w8YiWEsgMNgHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(331)\n",
    "plt.plot(x, y) # x^2\n",
    "plt.title('parabola')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(333)\n",
    "plt.plot(x, z) # x^3\n",
    "plt.title('hyperbola')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(337)\n",
    "plt.plot(x, r) # e^x\n",
    "plt.title('exponent')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(339)\n",
    "plt.plot(x, m) # |x|\n",
    "plt.title('abs')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(441)\n",
    "plt.plot(x, y) # x^2\n",
    "plt.title('parabola')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(444)\n",
    "plt.plot(x, z) # x^3\n",
    "plt.title('hyperbola')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(4,4,13)\n",
    "plt.plot(x, r) # e^x\n",
    "plt.title('exponent')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(4,4,16)\n",
    "plt.plot(x, m) # |x|\n",
    "plt.title('abs')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сохраним график в файл."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.scatter(X, Y, color ='red', marker = '*')\n",
    "plt.savefig('MyScatter.png') # ищем файл в рабочей папке (рядом с текущим ipynb-файлом)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Добавление легенды для лучшего отображения информации"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Отображение легенды"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для отображения легенды на графике используется функция legend(). Чтобы самостоятельно указать текстовую метку для\n",
    "отображаемых данных нужно указать список значений."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.linspace(-10, 10 , 200)\n",
    "plt.plot(x, x**2)\n",
    "plt.plot(x, - x**2)\n",
    "plt.legend([\"парабола с положительным коэффициентом\", \"парабола с отрицательным коэффициентом\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также можно просто указать метки в функциях построения графиков (параметр label) и вызвать legend()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-10, 10 , 200)\n",
    "plt.plot(x, x**2, label = 'парабола с положительным коэффициентом')\n",
    "plt.plot(x, - x**2, label = 'парабола с отрицательным коэффициентом')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также можно в легенде можно вручную указать соответствие линий и меток."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-10, 10 , 200)\n",
    "fx, = plt.plot(x, x**2)\n",
    "gx, = plt.plot(x, - x**2)\n",
    "plt.legend((fx, gx), ['функция 2', 'функция 1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Расположение легенды"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "До этого легенда располагалась в разных местах графика. Чтобы задать расположение, используется параметр loc, ему можно присваивать как значения кода расположения, так и строковое описание:\n",
    "- 'best' = 0\n",
    "- 'upper right' = 1\n",
    "- 'upper left' = 2\n",
    "- 'lower left' = 3\n",
    "- 'lower right' = 4\n",
    "- 'right' = 5\n",
    "- 'center left' = 6\n",
    "- 'center right' = 7\n",
    "- 'lower center' = 8\n",
    "- 'upper center' = 9\n",
    "- 'center' = 10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.legend((fx, gx),['функция 2', 'функция 1'], loc = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.legend((fx, gx),['функция 2', 'функция 1'], loc ='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### У легенды можно задать ещё параметры:\n",
    "- fontsize (размер шрифта надписи)\n",
    "- frameon (отображение рамки True/False)\n",
    "- framealpha (прозрачность легенды)\n",
    "- facecolor (цвет заливки рамки)\n",
    "- edgecolor (цвет контура рамки)\n",
    "- title (текст заголовка)\n",
    "- title_fontsize (размер шрифта заголовка)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Легенда, у которой размер шрифта надписей = 16, голубая заливка и красный контур у рамки, заголовок \"Функции\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.legend((fx, gx),['функция 2', 'функция 1'], fontsize = 16, facecolor='aqua', edgecolor='r', title='Функции')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Построение диаграммы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Для вывода диаграммы используется функция bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "countries = [\"Россия\", \"США\", \"Индия\", \"Япония\", \"Китай\"]\n",
    "population = [146, 318, 1187, 127, 1336]\n",
    "with plt.xkcd():  #эффект рисования от руки\n",
    "    plt.bar(countries, population)\n",
    "    plt.xlabel(\"countries\")\n",
    "    plt.ylabel(\"population, mln\")\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Если изменим bar на barh, получим горизонтальную диаграмму:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.barh(countries, population)\n",
    "#оси x и y поменялись местами\n",
    "plt.ylabel(\"countries\")\n",
    "plt.xlabel(\"population, mln\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### В bar() и barh() можно задать дополнительные параметры, например:\n",
    "- color, т.е. цвет столбцов диаграммы\n",
    "- edgecolor, т.е. цвет границы столбцов\n",
    "- linewidth, т.е. ширина границы\n",
    "- tick_label, т.е. метки для столбца"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ниже пример диаграммы, у которой столбцы синего цвета, чёрная граница столбцов с шириной границы = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar(countries, population, color = \"b\", edgecolor = \"k\", linewidth = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Для построения круговых диаграмм в Matplotlib используется функция pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.pie(population, labels = countries)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Настройка уровня выдвижения секторов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explode = [0.05, 0.1, 0.08, 0.05, 0.1] \n",
    "plt.pie(population, labels = countries, explode=explode)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explode = [0.05 for i in range(len(population))]\n",
    "plt.pie(population, labels = countries, explode=explode)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Настройка цвета для определённого сектора и добавление тени \n",
    "Чтобы нужный сектор сделать определённого цвета, создаётся список со значениями цветов. Параметру colors присваивается этот список.\n",
    "При необходимости можно добавить тень, установив значение shadow=True:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['k', 'y','g','r','b']\n",
    "plt.pie(population, labels = countries, explode=explode, colors = colors, shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Построение в полярной системе координат\n",
    "За построение в определённой системе координат отвечает параметр projection.\n",
    "В matplotlib поддерживаются следующие проекции: \n",
    "- 'aitoff', \n",
    "- 'hammer', \n",
    "- 'lambert', \n",
    "- 'mollweide', \n",
    "- 'polar', \n",
    "- 'rectilinear'.\n",
    "\n",
    "При построении в полярной системе координат вместо projection = 'polar' можно писать polar=True."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ниже приведён пример графика функции 5*cos(3*φ + 4):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.subplot(projection = 'polar')             # задаём полярную систему координат\n",
    "\n",
    "phi = np.arange(0, 2*np.pi, 0.01)   # угол phi - массив от от 0 до 2*pi с шагом 0.01\n",
    "ro = 5*np.cos(3*phi + 4)            # уравнение функции\n",
    "plt.plot(phi, ro, linestyle = '--')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также в полярной системе кординат можно строить с помощью polar():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "phi = np.arange(0, 2*np.pi, 0.01)   # угол phi - массив от от 0 до 2*pi с шагом 0.01\n",
    "plt.polar(phi, 5*np.cos(3*phi + 4), linestyle = '--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "phi = np.arange(0, 2*np.pi, 0.01)\n",
    "plt.polar(phi, 5*np.cos(3*phi + 4), color = 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Изменение цветовой карты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "x = np.arange(0, 10, 0.1)\n",
    "y = x**2\n",
    "area = x**3\n",
    "colors = x \n",
    "print(plt.cm.cmaps_listed.keys())  #смотрим возможные значения для цветовой карты\n",
    "plt.scatter(x, y, c = colors, s = area, cmap = plt.cm.magma_r) #указываем название цветовой карты после plt.cm.\n",
    "plt.colorbar() # добавление цветовой панели\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(0, 10, 0.4)\n",
    "y = np.sin(x)\n",
    "area = x**3\n",
    "colors = x \n",
    "plt.scatter(x, y, c = colors, s = area, cmap = plt.cm.cividis)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы кратко обсудили лишь некоторые возможности библиотеки `matplotlib`. Кому интересно, стоит посмотреть документацию по `matplotlib`, а также заглянуть в галерею с примерами графиков, которые можно адаптировать под свои задачи и данные."
   ]
  }
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