scatterplot_examples.ipnyb

scatterplot_examples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [0, 1, 2, 3, 4, 5, 4, 3, 2, 1, 0]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [0, 1, 2, 1, 0, 1, 3, 5, 3, 1, 0]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [i for i in x]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [-i for i in x]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "N = 11\n",
    "x = [i for i in range (N)]\n",
    "y = [i for i in x]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "N = 11\n",
    "x = [i for i in range (N)]\n",
    "y = [-i for i in x]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "N = 11\n",
    "x = [i for i in range (N)]\n",
    "y = [-i if i < 6 else i for i in x]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "N = 11\n",
    "x = [i for i in range (N)]\n",
    "y = [(i-5)**2 for i in x]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "N = 11\n",
    "x = [i for i in range (N)]\n",
    "y = [-(i-5)**2 for i in x]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "N = 11\n",
    "x = [i for i in range (N)]\n",
    "y = [-i**2 if i < 5 else -16 for i in x]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "N = 11\n",
    "x = [i for i in range(N)]\n",
    "y = [(i-5)**3 for i in x]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g9LF37167RwISkokL/ZkYYKaeTQJMkfAxc/LJJyslJUWdnZ0x2zs7O+X1egf9mfT0dLnd7pgHgIFMXOjPxAAz8WwSYJKEj5m0tDTNnj1bW7ZsiW6LRCLasmWLiouLbZwMSA6mLfRnYoCZeDYJMIkRi+ZVVVVp4cKFmjNnjgoLC/Xoo4/q4MGDuv766+0eDUgKpi301x9g/3tnkDdB7wzqP5sUCPYO+r2Z/juwEulsEmASI2Lmxz/+sT799FOtWLFCgUBA3/72t9XQ0DDgS8EARi7F6UiY26+Hw6QA6z+btGhjsxxSTNAk6tkkwCRGrDNzvFhnBkAiMHWdGX5tBOwy3M9vI87MAEAyMOlsUj9TAwzjC2dmAACDMvX3diF5JM0KwACA+GOhP5iEmAEADMBCfzAJMQMAGICF/mASYgYAMAAL/cEkxAwAYAATf20Exi9iBgAwgIm/NgLjFzEDABiUab+3C+MXi+YBAIZk4kJ/GH+IGQDAVzLt93Zh/OEyEwAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjJZq9wAAAIy2cMRSU3u3unp6lZXhUqE/UylOh91jYYwQMwCApNLQ2qGa+jZ1BHuj23wel6rL81SW77NxMowVLjMBAJJGQ2uHFm1sjgkZSQoEe7VoY7MaWjtsmgxjiZgBACSFcMRSTX2brEH29W+rqW9TODLYETAZMQMASApN7d0Dzsh8mSWpI9irpvbu+A2FuCBmAABJoatn6JAZyXEwBzEDAEgKWRmuUT0O5iBmAABJodCfKZ/HpaFuwHbo6F1Nhf7MeI6FOCBmAABJIcXpUHV5niQNCJr+59Xleaw3k4SIGQBA0ijL96luQYG8nthLSV6PS3ULClhnJkmxaB4AIKmU5ft0aZ6XFYDHEWIGAJB0UpwOFZ822e4xECdcZgIAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYzdaYOfXUU+VwOGIeq1atijnmH//4hy688EK5XC7l5uZq9erVNk0LAAASUardA/ziF7/QTTfdFH2ekZER/edQKKTLLrtMJSUlWr9+vd555x3dcMMNmjRpkm6++WY7xgUAAAnG9pjJyMiQ1+sddN+mTZt06NAh/f73v1daWppmzpyplpYWPfLII8QMAACQlADfmVm1apUmT56sc845Rw899JCOHDkS3dfY2Kjvfve7SktLi24rLS3V7t279cUXXwz5mn19fQqFQjEPAACQnGw9M3PbbbepoKBAmZmZevPNN7Vs2TJ1dHTokUcekSQFAgH5/f6Yn8nOzo7uO+mkkwZ93draWtXU1Izt8AAAICGM+pmZpUuXDvhS7/8+du3aJUmqqqrSRRddpLPPPlu33HKLHn74Ya1du1Z9fX3HNcOyZcsUDAajj717947GXw0AACSgUT8zc8cdd+i66677ymOmT58+6PaioiIdOXJE//znP3XGGWfI6/Wqs7Mz5pj+50N9z0aS0tPTlZ6efmyDAwAAI416zEyZMkVTpkwZ0c+2tLTI6XQqKytLklRcXKx7771Xhw8f1gknnCBJ2rx5s84444whLzEBAIDxxbYvADc2NurRRx/V22+/rY8//libNm1SZWWlFixYEA2Vn/zkJ0pLS9ONN96od999V88++6x+/etfq6qqyq6xAQBAgrHtC8Dp6el65plndN9996mvr09+v1+VlZUxoeLxePS3v/1NFRUVmj17tk4++WStWLGC27IBAECUw7Isy+4hxlooFJLH41EwGJTb7bZ7HAAAMAzD/fy2fZ0ZAACA40HMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACj2fqLJgEAwFHhiKWm9m519fQqK8OlQn+mUpwOu8cyAjEDAIDNGlo7VFPfpo5gb3Sbz+NSdXmeyvJ9Nk5mBi4zAQBgo4bWDi3a2BwTMpIUCPZq0cZmNbR22DSZOYgZAABsEo5Yqqlv02BL8fdvq6lvUziS9Iv1HxdiBgAAmzS1dw84I/NllqSOYK+a2rvjN5SBiBkAAGzS1TN0yIzkuPGKmAEAwCZZGa5RPW68ImYAALBJoT9TPo9LQ92A7dDRu5oK/ZnxHMs4xAwAADZJcTpUXZ4nSQOCpv95dXke6818DWIGAAAbleX7VLegQF5P7KUkr8elugUFrDMzDCyaBwCAzcryfbo0z8sKwCNEzAAAkABSnA4VnzbZ7jGMxGUmAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYbcxiZuXKlTrvvPN04oknatKkSYMes2fPHs2dO1cnnniisrKydNddd+nIkSMxx7z22msqKChQenq6vvnNb2rDhg1jNTIAADDQmMXMoUOHdPXVV2vRokWD7g+Hw5o7d64OHTqkN998U0899ZQ2bNigFStWRI9pb2/X3LlzdfHFF6ulpUVLlizRT3/6U7388stjNTYAADCMw7Isayz/gA0bNmjJkiXav39/zPaXXnpJP/zhD7Vv3z5lZ2dLktavX6977rlHn376qdLS0nTPPffoL3/5i1pbW6M/N2/ePO3fv18NDQ3DniEUCsnj8SgYDMrtdo/K3wsAAIyt4X5+2/admcbGRp111lnRkJGk0tJShUIhvfvuu9FjSkpKYn6utLRUjY2NX/nafX19CoVCMQ8AAJCcbIuZQCAQEzKSos8DgcBXHhMKhfTf//53yNeura2Vx+OJPnJzc0d5egAAkCiOKWaWLl0qh8PxlY9du3aN1azDtmzZMgWDwehj7969do8EAADGSOqxHHzHHXfouuuu+8pjpk+fPqzX8nq9ampqitnW2dkZ3df/n/3bvnyM2+3WhAkThnzt9PR0paenD2sOAABgtmOKmSlTpmjKlCmj8gcXFxdr5cqV6urqUlZWliRp8+bNcrvdysvLix7z17/+NebnNm/erOLi4lGZAQAAmG/MvjOzZ88etbS0aM+ePQqHw2ppaVFLS4sOHDggSbrsssuUl5ena6+9Vm+//bZefvll/fznP1dFRUX0rMott9yijz/+WHfffbd27dqlxx9/XM8995wqKyvHamwAAGCYMbs1+7rrrtNTTz01YPurr76qiy66SJL0r3/9S4sWLdJrr72miRMnauHChVq1apVSU///hNFrr72myspKtbW1aerUqVq+fPnXXur6X9yaDQCAeYb7+T3m68wkAmIGAADzJPw6MwAAAKOBmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYLdXuAQAAgJnCEUtN7d3q6ulVVoZLhf5MpTgdcZ+DmAEAAMesobVDNfVt6gj2Rrf5PC5Vl+epLN8X11m4zAQAAI5JQ2uHFm1sjgkZSQoEe7VoY7MaWjviOg8xAwAAhi0csVRT3yZrkH3922rq2xSODHbE2CBmAADAsDW1dw84I/NllqSOYK+a2rvjNhMxAwAAhq2rZ+iQGclxo4GYAQAAw5aV4RrV40YDMQMAAIat0J8pn8eloW7AdujoXU2F/sy4zUTMAACAYUtxOlRdnidJA4Km/3l1eV5c15shZgAAwDEpy/epbkGBvJ7YS0lej0t1Cwrivs4Mi+YBAIBjVpbv06V5XlYABgAA5kpxOlR82mS7x+AyEwAAMBsxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADDauFgB2LIsSVIoFLJ5EgAAMFz9n9v9n+NDGRcx09PTI0nKzc21eRIAAHCsenp65PF4htzvsL4ud5JAJBLRvn37lJGRIYdj9H4BVigUUm5urvbu3Su32z1qr4tYvM/xw3sdH7zP8cH7HB9j+T5blqWenh7l5OTI6Rz6mzHj4syM0+nU1KlTx+z13W43/0OJA97n+OG9jg/e5/jgfY6PsXqfv+qMTD++AAwAAIxGzAAAAKMRM8chPT1d1dXVSk9Pt3uUpMb7HD+81/HB+xwfvM/xkQjv87j4AjAAAEhenJkBAABGI2YAAIDRiBkAAGA0YgYAABiNmDkO69at06mnniqXy6WioiI1NTXZPVJSqa2t1bnnnquMjAxlZWXpiiuu0O7du+0eK+mtWrVKDodDS5YssXuUpPPvf/9bCxYs0OTJkzVhwgSdddZZeuutt+weK+mEw2EtX75cfr9fEyZM0GmnnaZf/vKXX/v7ffDVXn/9dZWXlysnJ0cOh0MvvPBCzH7LsrRixQr5fD5NmDBBJSUl+uCDD+IyGzEzQs8++6yqqqpUXV2t5uZmzZo1S6Wlperq6rJ7tKSxdetWVVRUaNu2bdq8ebMOHz6syy67TAcPHrR7tKS1Y8cO/eY3v9HZZ59t9yhJ54svvtD555+vE044QS+99JLa2tr08MMP66STTrJ7tKTz4IMPqq6uTo899pjee+89Pfjgg1q9erXWrl1r92hGO3jwoGbNmqV169YNun/16tVas2aN1q9fr+3bt2vixIkqLS1Vb2/v2A9nYUQKCwutioqK6PNwOGzl5ORYtbW1Nk6V3Lq6uixJ1tatW+0eJSn19PRYp59+urV582bre9/7nnX77bfbPVJSueeee6wLLrjA7jHGhblz51o33HBDzLYrr7zSmj9/vk0TJR9J1vPPPx99HolELK/Xaz300EPRbfv377fS09OtP/zhD2M+D2dmRuDQoUPauXOnSkpKotucTqdKSkrU2Nho42TJLRgMSpIyMzNtniQ5VVRUaO7cuTH/vcbo+fOf/6w5c+bo6quvVlZWls455xz99re/tXuspHTeeedpy5Ytev/99yVJb7/9tt544w1dfvnlNk+WvNrb2xUIBGL+/eHxeFRUVBSXz8Vx8YsmR9tnn32mcDis7OzsmO3Z2dnatWuXTVMlt0gkoiVLluj8889Xfn6+3eMknWeeeUbNzc3asWOH3aMkrY8//lh1dXWqqqrSz372M+3YsUO33Xab0tLStHDhQrvHSypLly5VKBTSjBkzlJKSonA4rJUrV2r+/Pl2j5a0AoGAJA36udi/bywRMzBCRUWFWltb9cYbb9g9StLZu3evbr/9dm3evFkul8vucZJWJBLRnDlz9MADD0iSzjnnHLW2tmr9+vXEzCh77rnntGnTJj399NOaOXOmWlpatGTJEuXk5PBeJykuM43AySefrJSUFHV2dsZs7+zslNfrtWmq5LV48WK9+OKLevXVVzV16lS7x0k6O3fuVFdXlwoKCpSamqrU1FRt3bpVa9asUWpqqsLhsN0jJgWfz6e8vLyYbWeeeab27Nlj00TJ66677tLSpUs1b948nXXWWbr22mtVWVmp2tpau0dLWv2ffXZ9LhIzI5CWlqbZs2dry5Yt0W2RSERbtmxRcXGxjZMlF8uytHjxYj3//PN65ZVX5Pf77R4pKV1yySV655131NLSEn3MmTNH8+fPV0tLi1JSUuweMSmcf/75A5YWeP/993XKKafYNFHy+s9//iOnM/bjLSUlRZFIxKaJkp/f75fX6435XAyFQtq+fXtcPhe5zDRCVVVVWrhwoebMmaPCwkI9+uijOnjwoK6//nq7R0saFRUVevrpp/WnP/1JGRkZ0euuHo9HEyZMsHm65JGRkTHge0gTJ07U5MmT+X7SKKqsrNR5552nBx54QD/60Y/U1NSkJ554Qk888YTdoyWd8vJyrVy5UtOmTdPMmTP197//XY888ohuuOEGu0cz2oEDB/Thhx9Gn7e3t6ulpUWZmZmaNm2alixZovvvv1+nn366/H6/li9frpycHF1xxRVjP9yY3y+VxNauXWtNmzbNSktLswoLC61t27bZPVJSkTTo48knn7R7tKTHrdljo76+3srPz7fS09OtGTNmWE888YTdIyWlUChk3X777da0adMsl8tlTZ8+3br33nutvr4+u0cz2quvvjrov5MXLlxoWdbR27OXL19uZWdnW+np6dYll1xi7d69Oy6zOSyLJREBAIC5+M4MAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaP8Hh6Wbu9bJ2d4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "N = 11\n",
    "x = [i for i in range(N)]\n",
    "y = [-(i-5)**3 for i in x]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDhhBZSdO3dqzpw5cjgccjgc8ng8OnjwYLB/eHhYVVVVysrK0tSpU1VWVqb+/v6Q5+ju7lZpaaluuOEGZWdna/369bp48eLkbA0AAIgLYQWUadOm6emnn1Z7e7veeust3X333fqHf/gHnT59WpK0bt06NTQ0aN++fWpublZvb6+WL18eXH9sbEylpaUaHR3VkSNHtGfPHu3evVsbN26c3K0CAAAxzWZZlnU1T5CZmalnn31W9913n2666Sbt3btX9913nyTpnXfe0cyZM9XS0qKFCxfq4MGD+vrXv67e3l65XC5JUn19vR599FF9+OGHSk5O/lyv6ff75XQ65fP55HA4rqZ8AAAQIeEcv6/4GpSxsTG9+uqrOn/+vDwej9rb23XhwgUVFxcHx8yYMUP5+flqaWmRJLW0tGj27NnBcCJJJSUl8vv9wbMwExkZGZHf7w9ZAABA/Ao7oJw8eVJTp06V3W7XP//zP2v//v0qLCyU1+tVcnKyMjIyQsa7XC55vV5JktfrDQkn4/3jfZdTV1cnp9MZXPLy8sItGwAAxJCwA8pXvvIVnThxQq2traqsrFRFRYU6OjquRW1BtbW18vl8waWnp+eavh4AAIiupHBXSE5O1l//9V9LkubNm6djx47p3//93/WP//iPGh0d1dDQUMhZlP7+frndbkmS2+1WW1tbyPON3+UzPmYidrtddrs93FIBAECMuup5UAKBgEZGRjRv3jxNmTJFTU1Nwb7Ozk51d3fL4/FIkjwej06ePKmBgYHgmMbGRjkcDhUWFl5tKQAAIE6EdQaltrZW9957r/Lz83X27Fnt3btXv/3tb/X666/L6XRq9erVqqmpUWZmphwOh9asWSOPx6OFCxdKkhYvXqzCwkKtWrVKW7duldfr1eOPP66qqirOkAAAgKCwAsrAwIDuv/9+9fX1yel0as6cOXr99df193//95Kk5557TgkJCSorK9PIyIhKSkq0Y8eO4PqJiYk6cOCAKisr5fF4lJaWpoqKCj3xxBOTu1UAACCmXfU8KNHAPCgAAMSeiMyDAgAAcK0QUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgnLB+zRjmGQtYausa1MDZYWWnp2hBQaYSE2zRLgsArnvsn68OASWGHTrVp80NHerzDQfbcpwp2rS0UEtm5USxMgC4vrF/vnp8xROjDp3qU+XLx0M+/JLk9Q2r8uXjOnSqL0qVAcD1jf3z5CCgxKCxgKXNDR2yJugbb9vc0KGxwEQjAADXCvvnyUNAiUFtXYOXJPOPsyT1+YbV1jUYuaIAAOyfJxEBJQYNnL38h/9KxgEAJgf758lDQIlB2ekpkzoOADA52D9PHgJKDFpQkKkcZ4oud7OaTR9dLb6gIDOSZQHAdY/98+QhoMSgxASbNi0tlKRL/hGMP960tJD77QEgwtg/Tx4CSoxaMitHO1fOldsZeprQ7UzRzpVzuc8eAKKE/fPksFmWFXP3Ovn9fjmdTvl8PjkcjmiXE1XMVAgAZmL/fKlwjt/MJBvjEhNs8nwpK9plAAA+gf3z1eErHgAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABgnrIBSV1enO+64Q+np6crOztayZcvU2dkZMmZ4eFhVVVXKysrS1KlTVVZWpv7+/pAx3d3dKi0t1Q033KDs7GytX79eFy9evPqtAQAAcSGsgNLc3KyqqiodPXpUjY2NunDhghYvXqzz588Hx6xbt04NDQ3at2+fmpub1dvbq+XLlwf7x8bGVFpaqtHRUR05ckR79uzR7t27tXHjxsnbKgAAENNslmVZV7ryhx9+qOzsbDU3N+tv//Zv5fP5dNNNN2nv3r267777JEnvvPOOZs6cqZaWFi1cuFAHDx7U17/+dfX29srlckmS6uvr9eijj+rDDz9UcnLyZ76u3++X0+mUz+eTw+G40vIBAEAEhXP8vqprUHw+nyQpMzNTktTe3q4LFy6ouLg4OGbGjBnKz89XS0uLJKmlpUWzZ88OhhNJKikpkd/v1+nTpyd8nZGREfn9/pAFAADErysOKIFAQGvXrtWiRYs0a9YsSZLX61VycrIyMjJCxrpcLnm93uCYj4eT8f7xvonU1dXJ6XQGl7y8vCstGwAAxIArDihVVVU6deqUXn311cmsZ0K1tbXy+XzBpaen55q/JgAAiJ6kK1mpurpaBw4c0OHDhzVt2rRgu9vt1ujoqIaGhkLOovT398vtdgfHtLW1hTzf+F0+42M+yW63y263X0mpAAAgBoV1BsWyLFVXV2v//v164403VFBQENI/b948TZkyRU1NTcG2zs5OdXd3y+PxSJI8Ho9OnjypgYGB4JjGxkY5HA4VFhZezbYAAIA4EdYZlKqqKu3du1evvfaa0tPTg9eMOJ1Opaamyul0avXq1aqpqVFmZqYcDofWrFkjj8ejhQsXSpIWL16swsJCrVq1Slu3bpXX69Xjjz+uqqoqzpIAAABJYd5mbLPZJmx/6aWX9E//9E+SPpqo7eGHH9ZPf/pTjYyMqKSkRDt27Aj5+uZ//ud/VFlZqd/+9rdKS0tTRUWFnn76aSUlfb68xG3GAADEnnCO31c1D0q0EFAAAIg9EZsHBQAA4FogoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxwg4ohw8f1tKlS5WbmyubzaZf/OIXIf2WZWnjxo3KyclRamqqiouLdebMmZAxg4ODKi8vl8PhUEZGhlavXq1z585d1YYAAID4EXZAOX/+vG699VZt3759wv6tW7dq27Ztqq+vV2trq9LS0lRSUqLh4eHgmPLycp0+fVqNjY06cOCADh8+rAcffPDKtwIAAMQVm2VZ1hWvbLNp//79WrZsmaSPzp7k5ubq4Ycf1iOPPCJJ8vl8crlc2r17t1asWKG3335bhYWFOnbsmObPny9JOnTokL72ta/p/fffV25u7me+rt/vl9PplM/nk8PhuNLyAQBABIVz/J7Ua1C6urrk9XpVXFwcbHM6nSoqKlJLS4skqaWlRRkZGcFwIknFxcVKSEhQa2vrhM87MjIiv98fsgAAgPg1qQHF6/VKklwuV0i7y+UK9nm9XmVnZ4f0JyUlKTMzMzjmk+rq6uR0OoNLXl7eZJYNAAAMExN38dTW1srn8wWXnp6eaJcEAACuoUkNKG63W5LU398f0t7f3x/sc7vdGhgYCOm/ePGiBgcHg2M+yW63y+FwhCwAACB+TWpAKSgokNvtVlNTU7DN7/ertbVVHo9HkuTxeDQ0NKT29vbgmDfeeEOBQEBFRUWTWQ4AAIhRSeGucO7cOb333nvBx11dXTpx4oQyMzOVn5+vtWvX6qmnntL06dNVUFCgDRs2KDc3N3inz8yZM7VkyRI98MADqq+v14ULF1RdXa0VK1Z8rjt4AABA/As7oLz11lv66le/GnxcU1MjSaqoqNDu3bv1L//yLzp//rwefPBBDQ0N6a677tKhQ4eUkpISXOeVV15RdXW17rnnHiUkJKisrEzbtm2bhM1BLBgLWGrrGtTA2WFlp6doQUGmEhNs0S4LgMHYb1x/rmoelGhhHpTYdehUnzY3dKjP95eJ+3KcKdq0tFBLZuVEsTIApmK/ET+iNg8K8GkOnepT5cvHQ3YykuT1Davy5eM6dKovSpUBMBX7jesXAQURMRawtLmhQxOdrhtv29zQobFAzJ3QA3CNsN+4vhFQEBFtXYOX/A/o4yxJfb5htXUNRq4oAEZjv3F9I6AgIgbOXn4ncyXjAMQ/9hvXNwIKIiI7PeWzB4UxDkD8Y79xfSOgICIWFGQqx5miy90UaNNHV+UvKMiMZFkADMZ+4/pGQEFEJCbYtGlpoSRdsrMZf7xpaSHzGgAIYr9xfSOgIGKWzMrRzpVz5XaGno51O1O0c+Vc5jMAcAn2G9cvJmpDxDEjJIBwsd+ID+Ecv8Oe6h64WokJNnm+lBXtMgDEEPYb1x++4gEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABgnKdoFALFgLGCprWtQA2eHlZ2eogUFmUpMsEW7LOCK8HlGLCCgAJ/h0Kk+bW7oUJ9vONiW40zRpqWFWjIrJ4qVAeHj84xYwVc8wKc4dKpPlS8fD9mZS5LXN6zKl4/r0Km+KFUGhI/PM2IJAQW4jLGApc0NHbIm6Btv29zQobHARCMAs/B5RqwhoACX0dY1eMn/ND/OktTnG1Zb12DkigKuEJ9nxBoCCnAZA2cvvzO/knFANPF5RqwhoACXkZ2eMqnjgGji84xYQ0ABLmNBQaZynCm63M2XNn1098OCgsxIlgVcET7PiDUEFOAyEhNs2rS0UJIu2amPP960tNDY+SPGApZafv8nvXbiA7X8/k8xcfEjNV87sf55xvXHZllW1P41bd++Xc8++6y8Xq9uvfVWPf/881qwYMFnruf3++V0OuXz+eRwOCJQKa5nsThvBDVHBjUD4Qnn+B21gPKzn/1M999/v+rr61VUVKQf/ehH2rdvnzo7O5Wdnf2p6xJQEGmxNPPm+FwXn/yHPV7tzpVzjTsQUXNkxdLnGfElJgJKUVGR7rjjDr3wwguSpEAgoLy8PK1Zs0aPPfbYp65LQAEmNhawdNczb1z2dlKbJLczRW8+ercxByRqBq4f4Ry/o3INyujoqNrb21VcXPyXQhISVFxcrJaWlkvGj4yMyO/3hywALhWLc11QM4CJRCWg/PGPf9TY2JhcLldIu8vlktfrvWR8XV2dnE5ncMnLy4tUqUBMicW5LqgZwERi4i6e2tpa+Xy+4NLT0xPtkgAjxeJcF9QMYCJR+TXjL3zhC0pMTFR/f39Ie39/v9xu9yXj7Xa77HZ7pMoDYtb4XBde3/CEv7kyfm2ESXNdUDOAiUTlDEpycrLmzZunpqamYFsgEFBTU5M8Hk80SgLiQizOdUHNACYSta94ampq9OKLL2rPnj16++23VVlZqfPnz+vb3/52tEoC4sKSWTnauXKu3M7QrxfczhRjb32lZgCfFNWJ2l544YXgRG233Xabtm3bpqKios9cj9uMgc8Wi3NdUDMQ32JiHpSrQUABACD2GD8PCgAAwKchoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxonKrxlfrfHJb/1+f5QrAQAAn9f4cfvzTGIfkwHl7NmzkqS8vLwoVwIAAMJ19uxZOZ3OTx0Tk7/FEwgE1Nvbq/T0dNlsk/ujXH6/X3l5eerp6eF3fq4h3ufI4H2ODN7nyOB9jpxr9V5blqWzZ88qNzdXCQmffpVJTJ5BSUhI0LRp067pazgcDv4BRADvc2TwPkcG73Nk8D5HzrV4rz/rzMk4LpIFAADGIaAAAADjEFA+wW63a9OmTbLb7dEuJa7xPkcG73Nk8D5HBu9z5JjwXsfkRbIAACC+cQYFAAAYh4ACAACMQ0ABAADGIaAAAADjEFA+Zvv27fqrv/orpaSkqKioSG1tbdEuKa7U1dXpjjvuUHp6urKzs7Vs2TJ1dnZGu6y49/TTT8tms2nt2rXRLiUuffDBB1q5cqWysrKUmpqq2bNn66233op2WXFlbGxMGzZsUEFBgVJTU/WlL31JTz755Of6PRdc3uHDh7V06VLl5ubKZrPpF7/4RUi/ZVnauHGjcnJylJqaquLiYp05cyZi9RFQ/t/PfvYz1dTUaNOmTTp+/LhuvfVWlZSUaGBgINqlxY3m5mZVVVXp6NGjamxs1IULF7R48WKdP38+2qXFrWPHjunHP/6x5syZE+1S4tKf//xnLVq0SFOmTNHBgwfV0dGhH/zgB7rxxhujXVpceeaZZ7Rz50698MILevvtt/XMM89o69atev7556NdWkw7f/68br31Vm3fvn3C/q1bt2rbtm2qr69Xa2ur0tLSVFJSouHh4cgUaMGyLMtasGCBVVVVFXw8NjZm5ebmWnV1dVGsKr4NDAxYkqzm5uZolxKXzp49a02fPt1qbGy0/u7v/s566KGHol1S3Hn00Uetu+66K9plxL3S0lLrO9/5Tkjb8uXLrfLy8ihVFH8kWfv37w8+DgQCltvttp599tlg29DQkGW3262f/vSnEamJMyiSRkdH1d7eruLi4mBbQkKCiouL1dLSEsXK4pvP55MkZWZmRrmS+FRVVaXS0tKQzzUm1y9/+UvNnz9f3/jGN5Sdna3bb79dL774YrTLijt33nmnmpqa9O6770qS/vu//1tvvvmm7r333ihXFr+6urrk9XpD9h9Op1NFRUUROy7G5I8FTrY//vGPGhsbk8vlCml3uVx65513olRVfAsEAlq7dq0WLVqkWbNmRbucuPPqq6/q+PHjOnbsWLRLiWt/+MMftHPnTtXU1Ohf//VfdezYMX33u99VcnKyKioqol1e3Hjsscfk9/s1Y8YMJSYmamxsTN///vdVXl4e7dLiltfrlaQJj4vjfdcaAQVRUVVVpVOnTunNN9+Mdilxp6enRw899JAaGxuVkpIS7XLiWiAQ0Pz587VlyxZJ0u23365Tp06pvr6egDKJfv7zn+uVV17R3r17dcstt+jEiRNau3atcnNzeZ/jGF/xSPrCF76gxMRE9ff3h7T39/fL7XZHqar4VV1drQMHDug3v/mNpk2bFu1y4k57e7sGBgY0d+5cJSUlKSkpSc3Nzdq2bZuSkpI0NjYW7RLjRk5OjgoLC0PaZs6cqe7u7ihVFJ/Wr1+vxx57TCtWrNDs2bO1atUqrVu3TnV1ddEuLW6NH/uieVwkoEhKTk7WvHnz1NTUFGwLBAJqamqSx+OJYmXxxbIsVVdXa//+/XrjjTdUUFAQ7ZLi0j333KOTJ0/qxIkTwWX+/PkqLy/XiRMnlJiYGO0S48aiRYsuuVX+3Xff1c033xyliuLT//7v/yohIfRwlZiYqEAgEKWK4l9BQYHcbnfIcdHv96u1tTVix0W+4vl/NTU1qqio0Pz587VgwQL96Ec/0vnz5/Xtb3872qXFjaqqKu3du1evvfaa0tPTg99jOp1OpaamRrm6+JGenn7JdT1paWnKysriep9Jtm7dOt15553asmWLvvnNb6qtrU27du3Srl27ol1aXFm6dKm+//3vKz8/X7fccov+67/+Sz/84Q/1ne98J9qlxbRz587pvffeCz7u6urSiRMnlJmZqfz8fK1du1ZPPfWUpk+froKCAm3YsEG5ublatmxZZAqMyL1CMeL555+38vPzreTkZGvBggXW0aNHo11SXJE04fLSSy9Fu7S4x23G105DQ4M1a9Ysy263WzNmzLB27doV7ZLijt/vtx566CErPz/fSklJsb74xS9a//Zv/2aNjIxEu7SY9pvf/GbCfXJFRYVlWR/darxhwwbL5XJZdrvduueee6zOzs6I1WezLKbiAwAAZuEaFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACM83/tm60Ev7ejCwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "N = 11\n",
    "x = [i for i in range(N)]\n",
    "y = [(i-5)**4 for i in x]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "N = 11\n",
    "x = [i for i in range(N)]\n",
    "y = [-(i-5)**4 for i in x]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "N = 11\n",
    "x = [i for i in range(N)]\n",
    "y = [(i-5)**4 if i < 6 else i for i in x]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [0, 1, 2, 1, 0, 1, 3, 5, 3, 1, 0]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [0, 1, 2, 1, 0, 1, 3, 5, 3, 1, 0]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [0, 1, 2, 1, 0, 1, 3, 5, 3, 1, 0]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.grid()\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [0, 1, 2, 1, 0, 1, 3, 5, 3, 1, 0]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.grid()\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('x')\n",
    "plt.legend(['Series 1'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [0, 1, 2, 1, 0, 1, 3, 5, 3, 1, 0]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.grid()\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('x')\n",
    "plt.legend(['blue'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [0, 1, 2, 1, 0, 1, 3, 5, 3, 1, 0]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.grid()\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('x')\n",
    "plt.xlim([0, 7])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [0, 1, 2, 1, 0, 1, 3, 5, 3, 1, 0]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.grid()\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('x')\n",
    "plt.ylim([0, 3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [0, 1, 2, 1, 0, 1, 3, 5, 3, 1, 0]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.grid()\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('x')\n",
    "plt.xlim([0, 7])\n",
    "plt.ylim([0, 3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "y = [0, 1, 2, 1, 0, 1, 3, 5, 3, 1, 0]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.grid()\n",
    "plt.xlabel('t'); plt.ylabel('x')\n",
    "plt.xlim([0, 7]); plt.ylim([0, 3])\n",
    "plt.legend(['blue'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S1\n",
      "x1 = [0, 1, 2, 3, 4] \n",
      "y1 = [0, 1, 2, 3, 4] \n",
      "\n",
      "S2\n",
      "x2 = [4, 5, 6, 7] \n",
      "y2 = [4, 4, 4, 4] \n",
      "\n",
      "S3\n",
      "x3 = [7, 8, 9, 10] \n",
      "y1 = [4, 5, 7, 10] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Data\n",
    "x1 = [0, 1, 2, 3, 4]\n",
    "y1 = [0, 1, 2, 3, 4]\n",
    "\n",
    "x2 = [4, 5, 6, 7]\n",
    "y2 = [4, 4, 4, 4]\n",
    "\n",
    "x3 = [7, 8, 9, 10]\n",
    "y3 = [4, 5, 7, 10]\n",
    "\n",
    "print('S1\\nx1 =', x1, '\\ny1 =', y1, '\\n')\n",
    "print('S2\\nx2 =', x2, '\\ny2 =', y2, '\\n')\n",
    "print('S3\\nx3 =', x3, '\\ny1 =', y3, '\\n')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAf8ElEQVR4nO3df2yV9f338dfp6egPvu0JID1tR8HOrw5LEam1BDCb3p5ZDOl3ZnfcXIpjkOwPUgLYbaFsgYYolurW2+AYCMmUe4A/kg0HJkKgKt6EH0WONTSIaGygkbanip5TqK16znX/0bXz2FKpXOdznXP6fCTXH+c6F73eOUHO0+tXXZZlWQIAADAkxekBAADA2EJ8AAAAo4gPAABgFPEBAACMIj4AAIBRxAcAADCK+AAAAEYRHwAAwKhUpwf4pkgkoosXLyorK0sul8vpcQAAwDWwLEvd3d3Kz89XSsrIxzbiLj4uXryogoICp8cAAADfQVtbm6ZMmTLiNnEXH1lZWZL6h8/OznZ4GgAAcC1CoZAKCgoGv8dHEnfxMXCqJTs7m/gAACDBXMslE1xwCgAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYFTcPWQMAADERjgSlj/gV1dPlyZnTlZJToncKW7jc4z6yMebb76piooK5efny+Vy6eWXX45637IsrVu3Tnl5ecrIyJDP59P7779v17wAAOA7OHT+kMr/Ua6lB5Zq9f9braUHlqr8H+U6dP6Q8VlGHR9XrlzRrFmztHnz5mHff+KJJ7Rp0yZt3bpVJ06c0Pjx41VeXq7e3t7rHhYAAIzeofOHVP1GtTp7OqPWB3oCqn6j2niAuCzLsr7zH3a5tGfPHj3wwAOS+o965Ofn67e//a1+97vfSZKCwaC8Xq+ee+45PfTQQ9/6M0OhkDwej4LBIL/bBQCA6xSOhFX+j/Ih4THAJZe8mV7t/9/7r+sUzGi+v2294LS1tVUdHR3y+XyD6zwej+bMmaNjx44N+2f6+voUCoWiFgAAYA9/wH/V8JAkS5Y6ejrkD/iNzWRrfHR0dEiSvF5v1Hqv1zv43jfV1dXJ4/EMLgUFBXaOBADAmNbV02XrdnZw/FbbNWvWKBgMDi5tbW1OjwQAQNKYnDnZ1u3sYGt85ObmSpI6O6MP73R2dg6+901paWnKzs6OWgAAgD1KckrkzfTKJdew77vkUm5mrkpySozNZGt8FBYWKjc3V42NjYPrQqGQTpw4oblz59q5KwAAcA3cKW7VlNVI0pAAGXi9umy10ed9jDo+Ll++rObmZjU3N0vqv8i0ublZFy5ckMvl0qpVq/TYY49p7969On36tH71q18pPz9/8I4YAABglm+aTw13NygnMydqvTfTq4a7G+Sb5rvKn4yNUd9q+8Ybb+iee+4Zsn7x4sV67rnnZFmWamtrtW3bNn322We666679Ne//lW33HLLNf18brUFACA2YvmE09F8f1/Xcz5igfgAACDxOPacDwAAgG9DfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhle3yEw2GtXbtWhYWFysjI0E033aRHH31UlmXZvSsAAJCAUu3+gfX19dqyZYt27NihGTNm6K233tKSJUvk8Xi0YsUKu3cHAAASjO3xcfToUf30pz/VwoULJUk33nijnn/+eTU1Ndm9KwAAkIBsP+0yb948NTY26ty5c5Kkd955R0eOHNH9998/7PZ9fX0KhUJRCwAASF62H/moqalRKBTS9OnT5Xa7FQ6HtWHDBlVWVg67fV1dndavX2/3GAAAIE7ZfuTjpZde0q5du7R79275/X7t2LFDf/rTn7Rjx45ht1+zZo2CweDg0tbWZvdIAAAgjrgsm29DKSgoUE1NjaqqqgbXPfbYY9q5c6fOnj37rX8+FArJ4/EoGAwqOzvbztEAAECMjOb72/YjHz09PUpJif6xbrdbkUjE7l0BAIAEZPs1HxUVFdqwYYOmTp2qGTNm6O2331ZDQ4OWLl1q964AAEACsv20S3d3t9auXas9e/YoEAgoPz9fv/zlL7Vu3TqNGzfuW/88p10AAEg8o/n+tj0+rhfxAQBA4nH0mg8AAICREB8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYFSq0wMAAJCIwpGw/AG/unq6NDlzskpySuROcTs9VkIgPgAAGKVD5w9pY9NGdfZ0Dq7zZnpVU1Yj3zSfg5MlBk67AAAwCofOH1L1G9VR4SFJgZ6Aqt+o1qHzhxyaLHEQHwAAXKNwJKyNTRtlyRry3sC6+qZ6hSNh06MlFOIDAIBr5A/4hxzx+DpLljp6OuQP+A1OlXiIDwAArlFXT5et241VxAcAANdocuZkW7cbq4gPAACuUUlOibyZXrnkGvZ9l1zKzcxVSU6J4ckSC/EBAMA1cqe4VVNWI0lDAmTg9eqy1Tzv41sQHwAAjIJvmk8NdzcoJzMnar0306uGuxt4zsc14CFjAACMkm+aT/cU3MMTTr8j4gMAgO/AneLWnbl3Oj1GQuK0CwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIyKSXx89NFHWrRokSZNmqSMjAzNnDlTb731Vix2BQAAEkyq3T/w008/1fz583XPPffo1Vdf1eTJk/X+++9rwoQJdu8KAAAkINvjo76+XgUFBXr22WcH1xUWFtq9GwAAkKBsP+2yd+9elZaW6sEHH1ROTo5mz56t7du3X3X7vr4+hUKhqAUAACQv2+Pjww8/1JYtW3TzzTfrwIEDWrZsmVasWKEdO3YMu31dXZ08Hs/gUlBQYPdIAAAgjrgsy7Ls/IHjxo1TaWmpjh49OrhuxYoVOnnypI4dOzZk+76+PvX19Q2+DoVCKigoUDAYVHZ2tp2jAQCAGAmFQvJ4PNf0/W37kY+8vDwVFRVFrbv11lt14cKFYbdPS0tTdnZ21AIAAJKX7fExf/58vffee1Hrzp07p2nTptm9KwAAkIBsj49HHnlEx48f1+OPP64PPvhAu3fv1rZt21RVVWX3rgAAQAKyPT7uvPNO7dmzR88//7yKi4v16KOP6qmnnlJlZaXduwIAAAnI9gtOr9doLlgBAADxwdELTgEAAEZCfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUalODwAAQDgSlj/gV1dPlyZnTlZJToncKW6nx0KMEB8AAEcdOn9IG5s2qrOnc3CdN9OrmrIa+ab5HJwMscJpFwCAYw6dP6TqN6qjwkOSAj0BVb9RrUPnDzk0GWKJ+AAAOCIcCWtj00ZZsoa8N7Cuvqle4UjY9GiIMeIDAOAIf8A/5IjH11my1NHTIX/Ab3AqmEB8AAAc0dXTZet2SBzEBwDAEZMzJ9u6HRIH8QEAcERJTom8mV655Br2fZdcys3MVUlOieHJEGvEBwDAEe4Ut2rKaiRpSIAMvF5dtprnfSQh4gMA4BjfNJ8a7m5QTmZO1HpvplcNdzfwnI8kxUPGAACO8k3z6Z6Ce3jC6RhCfAAAHOdOcevO3DudHgOGcNoFAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMCrV6QEAjF74q6909sQBff7pR8qY8H1Nn1Mud2qc/+ccCUvnj0qXO6X/8krT5kkpbqenGlkiziwp/NUX8p/+u7pCFzQ5e6pKZj4sd+o4p8cCBsX8X6uNGzdqzZo1WrlypZ566qlY7w5Iem8f2KH8Y+s1Q58Mrus8OEkX59ZqdvliBycbwZm90v7VUujif9Zl50sL6qWi/3FurpEk4sySDh2p08Zzu9Tpdg2u8779f1RzS6V8d61xcDLgP2J62uXkyZN65plndNttt8VyN8CY8faBHZp1dIUmW59ErZ9sfaJZR1fo7QM7HJpsBGf2Si/9KvpLXJJC7f3rz+x1Zq6RJOLM6g+P6g92qfMb/7IHUqTqD3bp0JE6ZwYDviFm8XH58mVVVlZq+/btmjBhQqx2A4wZ4a++Uv6x9ZKkFFf0ewOv846tV/irrwxPNoJIuP/ogaxh3vz3uv01/dvFi0ScWf2nWjae29U/oSv6L4j179f153Yp/NUX5ocDviFm8VFVVaWFCxfK5/ONuF1fX59CoVDUAmCosycOyKtPhoTHgBSXlKtPdPbEAbODjeT80aFHD6JYUuij/u3iRSLOLMl/+u/9p1pcw/8FsVwudbhd8p/+u+HJgKFics3HCy+8IL/fr5MnT37rtnV1dVq/fn0sxgCSyueffmTrdkZc7rR3OxMScWZJXaELtm4HxJLtRz7a2tq0cuVK7dq1S+np6d+6/Zo1axQMBgeXtrY2u0cCkkLGhO/bup0R/+W1dzsTEnFmSZOzp9q6HRBLtsfHqVOnFAgEVFJSotTUVKWmpurw4cPatGmTUlNTFQ5HnydNS0tTdnZ21AJgqOlzytWpSYoMdymCpIgldWiSps8pNzvYSKbN679DRFc5VySXlP39/u3iRSLOLKlk5sPyhi25rOH/grgsS7lhSyUzHzY8GTCU7fFx77336vTp02pubh5cSktLVVlZqebmZrnd8X+PPBCP3Kmpuji3VpKGBMjA6/a5tfH1vI8Ud/+tqZKGfpn/+/WCjfH17IxEnFmSO3Wcam6plKQhATLwevUtlTzvA3HB9vjIyspScXFx1DJ+/HhNmjRJxcXFdu8OGFNmly/WO/M2qcs1KWp9wDVJ78zbFJ/P+Sj6H+nn/1fKzoten53fvz4en5mRiDNL8t21Rg3/XamcSPR6b0Rq+G+e84H44bKsqxyjs9Hdd9+t22+//ZoeMhYKheTxeBQMBjkFA1wFTzg1JBFnFk84hTNG8/1tJD5Gg/gAACDxjOb7m18sBwAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABiV6vQAgNPCEUtNrZcU6O5VTla6ygonyp3icnosAEhaxAfGtP0t7Vq/74zag72D6/I86aqtKNKC4jwHJwOA5MVpF4xZ+1vatWynPyo8JKkj2KtlO/3a39Lu0GQAkNyID4xJ4Yil9fvOyBrmvYF16/edUTgy3BYAgOtBfGBMamq9NOSIx9dZktqDvWpqvWRuKAAYI4gPjEmB7quHx3fZDgBw7YgPjEk5Wem2bgcAuHbEB8akssKJyvOk62o31LrUf9dLWeFEk2MBwJhAfGBMcqe4VFtRJElDAmTgdW1FEc/7AIAYID4wZi0oztOWRSXK9USfWsn1pGvLohKe8wEAMcJDxjCmLSjO00+KcnnCKQAYRHxgzHOnuDT3pklOjwEAYwanXQAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYler0AEgu4YilptZLCnT3KicrXWWFE+VOcTk9FgAgjtgeH3V1dfrnP/+ps2fPKiMjQ/PmzVN9fb1++MMf2r0rxJn9Le1av++M2oO9g+vyPOmqrSjSguI8BycDAMQT20+7HD58WFVVVTp+/LgOHjyoL7/8Uvfdd5+uXLli964QR/a3tGvZTn9UeEhSR7BXy3b6tb+l3aHJAADxxmVZlhXLHXR1dSknJ0eHDx/Wj370o2/dPhQKyePxKBgMKjs7O5ajwSbhiKW76l8bEh4DXJJyPek6svp/cQoGAJLUaL6/Y37BaTAYlCRNnDhx2Pf7+voUCoWiFiSWptZLVw0PSbIktQd71dR6ydxQAIC4FdP4iEQiWrVqlebPn6/i4uJht6mrq5PH4xlcCgoKYjkSYiDQffXw+C7bAQCSW0zjo6qqSi0tLXrhhReuus2aNWsUDAYHl7a2tliOhBjIyUq3dTsAQHKL2a22y5cv1yuvvKI333xTU6ZMuep2aWlpSktLi9UYMKCscKLyPOnqCPZquAuIBq75KCsc/tQbAGBssf3Ih2VZWr58ufbs2aPXXntNhYWFdu8Cccad4lJtRZGk/tD4uoHXtRVFXGwKAJAUg/ioqqrSzp07tXv3bmVlZamjo0MdHR36/PPP7d4V4siC4jxtWVSiXE/0qZVcT7q2LCrhOR8AgEG232rrcg3/f7fPPvusfv3rX3/rn+dW28TGE04BYGwazfe37dd8xPixIYhz7hSX5t40yekxAABxjF8sBwAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABiV6vQAuLpwxFJT6yUFunuVk5WussKJcqe4nB4LAIDrQnzEqf0t7Vq/74zag72D6/I86aqtKNKC4jwHJwMA4Ppw2iUO7W9p17Kd/qjwkKSOYK+W7fRrf0u7Q5MBAHD9iI84E45YWr/vjKxh3htYt37fGYUjw20BAED8Iz7iTFPrpSFHPL7OktQe7FVT6yVzQwEAYCPiI84Euq8eHt9lOwAA4g3xEWdystJt3Q4AgHhDfMSZssKJyvOk62o31LrUf9dLWeFEk2MBAGAb4iPOuFNcqq0okqQhATLwuraiiOd9AAASFvERhxYU52nLohLleqJPreR60rVlUQnP+QAAJDQeMhanFhTn6SdFuTzhFACQdIiPOOZOcWnuTZOcHgMAAFtx2gUAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUalOD2BKOGKpqfWSAt29yslKV1nhRLlTXE6PBQDAmBOz+Ni8ebOefPJJdXR0aNasWXr66adVVlYWq92NaH9Lu9bvO6P2YO/gujxPumorirSgOM+RmQAAGKtictrlxRdfVHV1tWpra+X3+zVr1iyVl5crEAjEYncj2t/SrmU7/VHhIUkdwV4t2+nX/pZ24zMBADCWxSQ+Ghoa9Jvf/EZLlixRUVGRtm7dqszMTP3tb3+Lxe6uKhyxtH7fGVnDvDewbv2+MwpHhtsCAADEgu3x8cUXX+jUqVPy+Xz/2UlKinw+n44dOzZk+76+PoVCoajFLk2tl4Yc8fg6S1J7sFdNrZds2ycAABiZ7fHx8ccfKxwOy+v1Rq33er3q6OgYsn1dXZ08Hs/gUlBQYNssge6rh8d32Q4AAFw/x2+1XbNmjYLB4ODS1tZm28/OyUq3dTsAAHD9bL/b5YYbbpDb7VZnZ2fU+s7OTuXm5g7ZPi0tTWlpaXaPIUkqK5yoPE+6OoK9w1734ZKU6+m/7RYAAJhh+5GPcePG6Y477lBjY+PgukgkosbGRs2dO9fu3Y3IneJSbUWRpP7Q+LqB17UVRTzvAwAAg2Jy2qW6ulrbt2/Xjh079O6772rZsmW6cuWKlixZEovdjWhBcZ62LCpRrif61EquJ11bFpXwnA8AAAyLyUPGfvGLX6irq0vr1q1TR0eHbr/9du3fv3/IRaimLCjO00+KcnnCKQAAccBlWVZcPeQiFArJ4/EoGAwqOzvb6XEAAMA1GM33t+N3uwAAgLGF+AAAAEYRHwAAwCjiAwAAGEV8AAAAo4gPAABgFPEBAACMIj4AAIBRxAcAADAqJo9Xvx4DD1wNhUIOTwIAAK7VwPf2tTw4Pe7io7u7W5JUUFDg8CQAAGC0uru75fF4Rtwm7n63SyQS0cWLF5WVlSWXy95f/BYKhVRQUKC2tjZ+b0wM8TmbwedsBp+zOXzWZsTqc7YsS93d3crPz1dKyshXdcTdkY+UlBRNmTIlpvvIzs7mL7YBfM5m8DmbwedsDp+1GbH4nL/tiMcALjgFAABGER8AAMCoMRUfaWlpqq2tVVpamtOjJDU+ZzP4nM3gczaHz9qMePic4+6CUwAAkNzG1JEPAADgPOIDAAAYRXwAAACjiA8AAGDUmImPzZs368Ybb1R6errmzJmjpqYmp0dKOnV1dbrzzjuVlZWlnJwcPfDAA3rvvfecHivpbdy4US6XS6tWrXJ6lKTz0UcfadGiRZo0aZIyMjI0c+ZMvfXWW06PlVTC4bDWrl2rwsJCZWRk6KabbtKjjz56Tb8fBCN78803VVFRofz8fLlcLr388stR71uWpXXr1ikvL08ZGRny+Xx6//33jcw2JuLjxRdfVHV1tWpra+X3+zVr1iyVl5crEAg4PVpSOXz4sKqqqnT8+HEdPHhQX375pe677z5duXLF6dGS1smTJ/XMM8/otttuc3qUpPPpp59q/vz5+t73vqdXX31VZ86c0Z///GdNmDDB6dGSSn19vbZs2aK//OUvevfdd1VfX68nnnhCTz/9tNOjJbwrV65o1qxZ2rx587DvP/HEE9q0aZO2bt2qEydOaPz48SovL1dvb2/sh7PGgLKyMquqqmrwdTgctvLz8626ujoHp0p+gUDAkmQdPnzY6VGSUnd3t3XzzTdbBw8etH784x9bK1eudHqkpLJ69WrrrrvucnqMpLdw4UJr6dKlUet+9rOfWZWVlQ5NlJwkWXv27Bl8HYlErNzcXOvJJ58cXPfZZ59ZaWlp1vPPPx/zeZL+yMcXX3yhU6dOyefzDa5LSUmRz+fTsWPHHJws+QWDQUnSxIkTHZ4kOVVVVWnhwoVRf7dhn71796q0tFQPPvigcnJyNHv2bG3fvt3psZLOvHnz1NjYqHPnzkmS3nnnHR05ckT333+/w5Mlt9bWVnV0dET9++HxeDRnzhwj341x94vl7Pbxxx8rHA7L6/VGrfd6vTp79qxDUyW/SCSiVatWaf78+SouLnZ6nKTzwgsvyO/36+TJk06PkrQ+/PBDbdmyRdXV1frDH/6gkydPasWKFRo3bpwWL17s9HhJo6amRqFQSNOnT5fb7VY4HNaGDRtUWVnp9GhJraOjQ5KG/W4ceC+Wkj4+4Iyqqiq1tLToyJEjTo+SdNra2rRy5UodPHhQ6enpTo+TtCKRiEpLS/X4449LkmbPnq2WlhZt3bqV+LDRSy+9pF27dmn37t2aMWOGmpubtWrVKuXn5/M5J7GkP+1yww03yO12q7OzM2p9Z2encnNzHZoquS1fvlyvvPKKXn/9dU2ZMsXpcZLOqVOnFAgEVFJSotTUVKWmpurw4cPatGmTUlNTFQ6HnR4xKeTl5amoqChq3a233qoLFy44NFFy+v3vf6+amho99NBDmjlzph5++GE98sgjqqurc3q0pDbw/efUd2PSx8e4ceN0xx13qLGxcXBdJBJRY2Oj5s6d6+BkyceyLC1fvlx79uzRa6+9psLCQqdHSkr33nuvTp8+rebm5sGltLRUlZWVam5ultvtdnrEpDB//vwht4qfO3dO06ZNc2ii5NTT06OUlOivIrfbrUgk4tBEY0NhYaFyc3OjvhtDoZBOnDhh5LtxTJx2qa6u1uLFi1VaWqqysjI99dRTunLlipYsWeL0aEmlqqpKu3fv1r/+9S9lZWUNnjf0eDzKyMhweLrkkZWVNeQ6mvHjx2vSpElcX2OjRx55RPPmzdPjjz+un//852pqatK2bdu0bds2p0dLKhUVFdqwYYOmTp2qGTNm6O2331ZDQ4OWLl3q9GgJ7/Lly/rggw8GX7e2tqq5uVkTJ07U1KlTtWrVKj322GO6+eabVVhYqLVr1yo/P18PPPBA7IeL+f00ceLpp5+2pk6dao0bN84qKyuzjh8/7vRISUfSsMuzzz7r9GhJj1ttY2Pfvn1WcXGxlZaWZk2fPt3atm2b0yMlnVAoZK1cudKaOnWqlZ6ebv3gBz+w/vjHP1p9fX1Oj5bwXn/99WH/TV68eLFlWf23265du9byer1WWlqade+991rvvfeekdlclsVj5AAAgDlJf80HAACIL8QHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMCo/w/g0jCalcYqWAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(x1, y1)\n",
    "plt.scatter(x2, y2)\n",
    "plt.scatter(x3, y3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(x1, y1)\n",
    "plt.scatter(x2, y2)\n",
    "plt.scatter(x3, y3)\n",
    "\n",
    "labels = ['linear', 'constant', 'quadratic']\n",
    "plt.legend(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ACwms8NGhr1ytk2Sd98SLTYq+ouZtt14UHh6uCRMm6NFHH9ULL7ygTz/9VNu2bdOyZcs0fPhwhYeHa8SIEdqzZ4/Wr1+v//mf/9E999zjvt7jYt5++20tWLBAu3fv1uHDh/XCCy/I5XK5w0unTp20fft2HTp0SCdOnJDL5VLnzp21c+dOrV69WgcOHNCkSZNUUFDQ6OfWqVMnbdq0SV988YVOnDjR6McDAAJPYIWPILu+GTDlPwvnBpD/LA+Z5ZP7fUyaNEn/+7//q8mTJ6tr16666667VFJSosjISK1evVonT55U79699dOf/lSDBg3SwoULGzx3mzZt9Nprr+mHP/yhunbtqmeeeUYvv/yyunXrJkkaP3687Ha70tLSFBcXpyNHjujXv/61fvKTn+iuu+7SjTfeqK+++koPPPBAo5/X73//ex06dEhXX3214uLiGv14AEDgsVmWVd9bP/ymrKxMMTExKi0tVXR0tMe6yspKFRUVKTU1VeHh4Y2e2+VyqaysTNFfbFTQ6olS2Zffroy+oiZ4+OBttoHG3efoaAUFXTjfXu73NJA5HA698847uvXWW+tcmwPvoc/m0GszfNXnC71+nyuw3mpbq2uO1PV27nAKAIAfBGb4kGqCRurN/q4CAICAE1jXfAAAAL8jfAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMJHCzd16lT17NnTZ/MPGDBA48aN89n8AICWJ3BvMoZG2bBhgwYOHKivv/5abdq0cY+/9tpr3AYZANAoARs+nC6nCksKdbziuOIi45QRnyF7AN5evbq6WqGhoZf8+NjYWC9WAwAIBAF52mXNkTXKfjVbI1eP1IR/TtDI1SOV/Wq21hxe47N9njlzRr/85S/VunVrJSUl6Y9//KPHKQubzaY33njD4zFt2rTR8uXL3csTJkzQtddeq8jISF111VWaNGmSHA6Hx2NmzZqlhIQERUVFadSoUaqsrPRYf++992ro0KGaMWOGkpOTdd1110mS/vKXvygzM1NRUVFKTEzUsGHDVFJSIkk6dOiQBg4cKElq27atbDab7r33Xkl1T7tUVVVp4sSJ6tatmyIiInTNNddo2bJll9k9AEBLEnDhY+OXGzV+43gdqzjmMV5SUaK8DXk+CyCPPPKINm7cqL///e967733tGHDBhUWFjZqjqioKC1fvlx79+7V/PnztXTpUv3pT39yr3/llVc0depUPfHEE9q5c6eSkpL09NNP15ln7dq12r9/v/Lz8/X2229LqvmUw+nTp+vDDz/UG2+8oUOHDrkDRkpKil599VVJ0v79+3X06FHNnz+/3hp/+ctfauXKlZo9e7b+/e9/689//rNat27dqOcJAGjZAuq0i9Pl1PyP5suSVWedJUs22TR7x2wNTBno1VMw5eXlWrZsmV588UUNGjRIkvT888/ryiuvbNQ8v/vd79x/79Spk8aPH6+VK1fq0UcflSTNmzdPo0aN0qhRoyRJjz/+uNasWVPn6EerVq30f//3fx6nW0aOHOn++1VXXaUFCxaod+/eKi8vV+vWrd2nV+Lj4z2u+fiuAwcO6JVXXtHq1avVp08fRUdH65prrmnUcwQAtHwBdeSjsKRQxyuPn3e9JUvFFcUqLGncEYmL+fTTT1VdXa0bb7zRPRYbG+s+5dFQf/3rX9WvXz8lJiaqdevW+t3vfqcjR4641+/bt89jH5LUt2/fOvN07969znUeu3btUk5Ojjp06KCoqCj94Ac/kCSP+S9m9+7dstvt7scCAFCfgAofJ7450aDtjlecP6D4is1mk2V5HpH57vUcW7du1fDhw3Xrrbfq7bff1gcffKDf/va3qq6ubvS+WrVq5bF85swZZWdnKzo6Wi+99JIKCgr0+uuvS1Kj5o+IiGh0LQCAwBNQ4aN9RPsGbRcXGefV/V599dUKCQnR9u3b3WNff/21Dhw48O0+4+J09OhR9/Inn3yiiooK9/KWLVvUsWNH/fa3v1VmZqY6d+6sw4cPe+yna9euHvuQpG3btl20vo8//lhfffWVZs2apZtvvlldunRxX2xaq/ZIidPpPO883bt3l8vl0saNGy+6TwBA4Aqo8JERn6G48DjZZKt3vU02JUYmKiM+w6v7bd26tUaNGqVHHnlE69at0549e3TvvfcqKOjb9v/whz/UwoUL9cEHH2jnzp367//+b4/7Z3Tu3FlHjhzRypUr9emnn2rBggXuoxO1HnroIT377LN67rnndODAAU2ZMkX//ve/L1pfhw4dFBoaqqeeekqfffaZ3nzzTU2fPt1jm44dO8pms+ntt9/W8ePHVV5eXmeeTp06acSIEfrVr36lf/zjHyoqKtKGDRv0yiuvNLZlAIAWLKDChz3Iroe6PyRJdQJI7fKEPhN8cr+PJ598UjfffLNycnKUlZWl/v3764YbbnCv/+Mf/6iUlBTdfPPNGjZsmMaPH6/IyEj3+jvuuEMPP/ywxo4dq549e2rLli2aNGmSxz7uuusuTZo0SY8++qhuuOEGHT58WGPGjLlobXFxcVq+fLn+9re/KS0tTbNmzdIf/vAHj22uuOIKTZs2TRMnTlRCQoLGjh1b71yLFy/Wf/3Xf2n8+PFKS0vT6NGjdebMmca0CgDQwtmscy808LOysjLFxMSotLRU0dHRHusqKytVVFSk1NRUhYeHN3pul8ulsrIy7Ti1Q3MK5ni83TYxMlET+kxQVsesy34ODTVgwAD17NlT8+bNM7ZPE2r7HB0d7XF0pz6X+z0NZA6HQ++8845uvfVW7jLrQ/TZHHpthq/6fKHX73MF1Ftta2V1yNKgDoO4wykAAH4QkOFDqjkF0zuxt7/LAAAg4ARs+GgKNmzY4O8SAAAwLqAuOAUAAP5H+AAAAEY1y/DRxN6gg8vA9xIAAk+zCh+1bwn67p0/0bzVfi95Wx0ABI5mdcGp3W5XmzZt3Lf+joyMlM1W/91K6+NyuVRdXa3KysqL3n8Cl64hfbYsSxUVFSopKVGbNm1kt/M2ZwAIFM0qfEhSYmKiJNX57JGGsCxL33zzjSIiIhoVWtA4jelzmzZt3N9TAEBgaHbhw2azKSkpSfHx8R6f+toQDodDmzZt0ve//30O8/tQQ/scEhLCEQ8ACEDNLnzUstvtjX7hstvtOnv2rMLDwwkfPkSfAQAXwoUPAADAKMIHAAAwivABAACM8nr4cDqdmjRpklJTUxUREaGrr75a06dP52ZSAABAkg8uOJ09e7YWL16s559/Xt26ddPOnTt13333KSYmRg8++KC3dwcAAJoZr4ePLVu26Mc//rFuu+02SVKnTp308ssva8eOHd7eFQAAaIa8Hj5uuukmLVmyRAcOHNC1116rDz/8UJs3b9bcuXPr3b6qqkpVVVXu5bKyMkk194po7H08LqZ2Pm/PC0/02Qz6bAZ9Nodem+GrPjdmPpvl5YsxXC6XfvOb32jOnDmy2+1yOp2aMWOGHnvssXq3nzp1qqZNm1ZnfMWKFYqMjPRmaQAAwEcqKio0bNgwlZaWKjo6+oLbej18rFy5Uo888oiefPJJdevWTbt379a4ceM0d+5cjRgxos729R35SElJ0YkTJy5afGM5HA7l5+dr8ODB3PzKh+izGfTZDPpsDr02w1d9LisrU/v27RsUPrx+2uWRRx7RxIkT9fOf/1yS1L17dx0+fFgzZ86sN3yEhYUpLCysznhISIjPfvh8OTe+RZ/NoM9m0Gdz6LUZ3u5zY+by+lttKyoq6nySqd1ul8vl8vauAABAM+T1Ix85OTmaMWOGOnTooG7duumDDz7Q3LlzNXLkSG/vCgAANENeDx9PPfWUJk2apAceeEAlJSVKTk7Wr3/9a02ePNnbuwIAAM2Q18NHVFSU5s2bp3nz5nl7agAA0ALw2S4AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAqGB/FwAAQHPkdDlVWFKo4xXHFRcZp4z4DNmD7P4uq1kgfAAA0EhrDq/RrB2zdKzimHssITJBE/tMVFbHLD9W1jxw2gUAgEZYc3iN8jbkeQQPSSqpKFHehjytObzGT5U1H4QPAAAayOlyataOWbJk1VlXOzZ7x2w5XU7TpTUrhA8AABqosKSwzhGP77JkqbiiWIUlhQaran4IHwAANNDxiuNe3S5QET4AAGiguMg4r24XqAgfAAA0UEZ8hhIiE2STrd71NtmUGJmojPgMw5U1L4QPAAAayB5k18Q+EyWpTgCpXZ7QZwL3+7gIwgcAAI2Q1TFLcwfMVXxkvMd4QmSC5g6Yy30+GoCbjAEA0EhZHbM0MGUgdzi9RIQPAAAugT3Irt6Jvf1dRrPEaRcAAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABG+SR8fPHFF7r77rvVrl07RUREqHv37tq5c6cvdgUAAJqZYG9P+PXXX6tfv34aOHCg3n33XcXFxemTTz5R27Ztvb0rAADQDHk9fMyePVspKSl67rnn3GOpqane3g0AAGimvB4+3nzzTWVnZ+vOO+/Uxo0bdcUVV+iBBx7Q6NGj692+qqpKVVVV7uWysjJJksPhkMPh8GpttfN5e154os9m0Gcz6LM59NoMX/W5MfPZLMuyvLnz8PBwSVJeXp7uvPNOFRQU6KGHHtIzzzyjESNG1Nl+6tSpmjZtWp3xFStWKDIy0pulAQAAH6moqNCwYcNUWlqq6OjoC27r9fARGhqqzMxMbdmyxT324IMPqqCgQFu3bq2zfX1HPlJSUnTixImLFt9YDodD+fn5Gjx4sEJCQrw6N75Fn82gz2bQZ3PotRm+6nNZWZnat2/foPDh9dMuSUlJSktL8xjr2rWrXn311Xq3DwsLU1hYWJ3xkJAQn/3w+XJufIs+m0GfzaDP5tBrM7zd58bM5fW32vbr10/79+/3GDtw4IA6duzo7V0BAIBmyOvh4+GHH9a2bdv0xBNP6ODBg1qxYoWWLFmi3Nxcb+8KAAA0Q14PH71799brr7+ul19+Wenp6Zo+fbrmzZun4cOHe3tXAACgGfL6NR+SdPvtt+v222/3xdQAAKCZ47NdAACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwCA3zldThUeK5QkFR4rlNPl9HNF8CXCBwDAr9YcXqPsV7OVuzZXkpS7NlfZr2ZrzeE1fq4MvkL4AAD4zZrDa5S3IU/HKo55jJdUlChvQx4BpIUifAAA/MLpcmrWjlmyZNVZVzs2e8dsTsG0QIQPAIBfFJYU1jni8V2WLBVXFKuwpNBgVTCB8AEA8IvjFce9uh2aD8IHAMAv4iLjvLodmg/CBwDALzLiM5QQmSCbbPWut8mmxMhEZcRnGK4Mvkb4AAD4hT3Irol9JkpSnQBSuzyhzwTZg+zGa4NvET4AAH6T1TFLcwfMVXxkvMd4QmSC5g6Yq6yOWX6qDL4U7O8CAACBLatjlgamDNTOL3eqeFexFg1apMzkTI54tGAc+QAA+J09yK6MhJprOzISMggeLRzhAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPoBmyHn2rD7etlqS9PG21XKePevnihrA5ZSK/il99P9qvrqc/q7o4lxO6fDWmr8f3to8apbkPFutgg+W6Z2NU1TwwTI5z1b7uyTAg8/Dx6xZs2Sz2TRu3Dhf7woICB+sfl4nHr9WXdaPkiR1WT9KJx6/Vh+sft7PlV3A3jeleenS87dLr46q+TovvWa8qaqtecWdNcsr7mz6NUtas3mmsl/I0Mh/zdOEQ69p5L/mKfuFDK3ZPNPfpQFuPg0fBQUF+vOf/6zrr7/el7sBAsYHq59Xjy0PKs76ymM8zvpKPbY82DQDyN43pVd+KZV96TledrRmvCm+mDfHmlUTPPIOvqRj5/xmLwmS8g6+RABBk+Gz8FFeXq7hw4dr6dKlatu2ra92AwQM59mzSt46TZIUZPNcV7uctHVa0zoF43JKqyZIsupZ+Z+xVROb1umM5lizak61zDrwUk2FNs8fEOs/y7MPvMQpGDQJwb6aODc3V7fddpuysrL0+OOPn3e7qqoqVVVVuZfLysokSQ6HQw6Hw6s11c7n7XnhiT77xsfbVqtL0Bk5FS6nJEdQuPSdr5LUTme0d+sqdfletp+qPMfhrVL5Sek7NdZR/pX02ftSx77m6rqQc2qur89NrmZJhf96Uafs4Qo73wY26Wu7tPPDF5Vx/T0mS2swfneY4as+N2Y+m2VZ9cX7y7Jy5UrNmDFDBQUFCg8P14ABA9SzZ0/NmzevzrZTp07VtGnT6oyvWLFCkZGR3i4NAAD4QEVFhYYNG6bS0lJFR0dfcFuvh4/PP/9cmZmZys/Pd1/rcaHwUd+Rj5SUFJ04ceKixTeWw+FQfn6+Bg8erJCQEK/OjW/RZ9/4eNtq90WmUs3/xPO7L9Dgjx5UiKvy2+0GLmtaRz5qL9i8kGF/azpHEc6p+Xx9blI1Syr811+Uu+fpi263KP2BJn3kg98dvuerPpeVlal9+/YNCh9eP+2ya9culZSUKCMjwz3mdDq1adMmLVy4UFVVVbLb7e51YWFhCgure6AwJCTEZz98vpwb36LP3pXWd4hOrG2lOOsrj2s+QlyVCnFVymVJJbZ2Sus7RPZgn51RbZyr+kmtY2su1Kz3GgqbFJ1cs12QvZ71fnCemmv73CRrlpTZ4261+XCuSoK+vcbju2yWpQRXzXb24Kb975LfHWZ4u8+NmcvrF5wOGjRIH330kXbv3u3+k5mZqeHDh2v37t0ewQNAw9mDg/Vl3ymSJNc5r+O1y0f7Tmk6wUOqeXEeMvs/C+e+IP5necisJvUi3ixrlmQPDtXEa4dLqgka31W7POHa4bIHhxqvDTiX18NHVFSU0tPTPf60atVK7dq1U3p6urd3BwSUXtkj9OFNC3Tc1s5jvMTWTh/etEC9skf4qbILSLtD+tkLUnSS53h0cs142h3+qetCmmPNkrL6P6a51wxXvMtzPMElzb1muLL6P+afwoBzNKH/IgFoiF7ZI+QcNFx7t66STjr18cBlSus7RIlN6YjHudLukLrcJh3eIpUfk1onSB1vanJHDzzU1vzZ+9K/v665xqOJnWqpT1b/xzTwe/+rwo/+ouNlRxQX3UEZ3e/hiAeaFCO/rTZs2GBiN0DAsAcHq8v3svXZO++oy/eym9aplvMJskupN/u7isYJstdcVPrvd2q+NvHgUcseHKrevUZdfEPAT/hsFwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABgV7O8CAH9zuiztKDqpktOVio8KV5/UWNmDbP4uCwBaLMIHAtqqPUc17a29Olpa6R5LignXlJw0DUlP8mNlANBycdoFAWvVnqMa82KhR/CQpOLSSo15sVCr9hz1U2UA0LIRPhCQnC5L097aK6uedbVj097aK6ervi0AAJeD8IGAtKPoZJ0jHt9lSTpaWqkdRSfNFQUAAYLwgYBUcvr8weNStgMANBzhAwEpPircq9sBABqO8IGA1Cc1Vkkx4TrfG2ptqnnXS5/UWJNlAUBAIHwgINmDbJqSkyZJdQJI7fKUnDTu9wEAPkD4QMAakp6kxXdnKDHG89RKYky4Ft+dwX0+AMBHuMkYAtqQ9CQNTkvkDqcAYBDhAwHPHmRT36vb+bsMAAgYnHYBAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+4FVOl6UdRSclSTuKTsrpsvxcEQCgqfF6+Jg5c6Z69+6tqKgoxcfHa+jQodq/f7+3d4MmaNWeo+o/e51GPl8gSRr5fIH6z16nVXuO+rkyAEBT4vXwsXHjRuXm5mrbtm3Kz8+Xw+HQLbfcojNnznh7V2hCVu05qjEvFupoaaXHeHFppca8WEgAAQC4BXt7wlWrVnksL1++XPHx8dq1a5e+//3ve3t3aAKcLkvT3tqr+k6wWJJskqa9tVeD0xJlD7IZrg4A0NR4PXycq7S0VJIUGxtb7/qqqipVVVW5l8vKyiRJDodDDofDq7XUzufteQPdjqKTOln+jcLsNcthQZbHV0k6Wf6Nth0sUZ/U+n8O0Hj8PJtBn82h12b4qs+Nmc9mWZbPrgh0uVy64447dOrUKW3evLnebaZOnapp06bVGV+xYoUiIyN9VRoAAPCiiooKDRs2TKWlpYqOjr7gtj4NH2PGjNG7776rzZs368orr6x3m/qOfKSkpOjEiRMXLb6xHA6H8vPzNXjwYIWEhHh17kC2o+ik+yJTqeaIx/RMlybtDFKV69vTLM+O6M2RDy/i59kM+mwOvTbDV30uKytT+/btGxQ+fHbaZezYsXr77be1adOm8wYPSQoLC1NYWFid8ZCQEJ/98Ply7kD0vWviFds6QsWllR7XfVS5bKpy2mSTlBgTru9dE881Hz7Az7MZ9Nkcem2Gt/vcmLm8/m4Xy7I0duxYvf7661q3bp1SU1O9vQs0MfYgm6bkpEmqubj0u2qXp+SkETwAAJJ8ED5yc3P14osvasWKFYqKilJxcbGKi4v1zTffeHtXaEKGpCdp8d0ZSowJ9xhPjAnX4rszNCQ9yU+VAQCaGq+fdlm8eLEkacCAAR7jzz33nO69915v7w5NyJD0JA1OS9S2gyU6sW+bnh3Rm1MtAIA6vB4+fHj9KpoBe5BNfVJj9c4+qU9qLMEDAFAHn+0CAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAo4L9XQDOz+mytKPopEpOVyo+Klx9UmNlD7L5uywAAC4L4aOJWrXnqKa9tVdHSyvdY0kx4ZqSk6Yh6Ul+rAwAgMvDaZcmaNWeoxrzYqFH8JCk4tJKjXmxUKv2HPVTZQAAXD7CRxPjdFma9tZeWfWsqx2b9tZeOV31bQEAQNNH+GhidhSdrHPE47ssSUdLK7Wj6KS5ogAA8CLCRxNTcvr8weNStgMAoKkhfDQx8VHhXt0OAICmhvDRxPRJjVVSTLjO94Zam2re9dInNdZkWQAAeA3ho4mxB9k0JSdNkuoEkNrlKTlp3O8DANBsET6aoCHpSVp8d4YSYzxPrSTGhGvx3Rnc5wMA0Kxxk7Emakh6kganJXKHUwBAi0P4aMLsQTb1vbqdv8sAAMCrOO0CAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjAqY8OF0WdpRdFKStKPopJwuy88VAQAQmHwWPhYtWqROnTopPDxcN954o3bs2OGrXV3Uqj1H1X/2Oo18vkCSNPL5AvWfvU6r9hz1W00AAAQqn4SPv/71r8rLy9OUKVNUWFioHj16KDs7WyUlJb7Y3QWt2nNUY14s1NHSSo/x4tJKjXmxkAACAIBhPgkfc+fO1ejRo3XfffcpLS1NzzzzjCIjI/Xss8/6Ynfn5XRZmvbWXtV3gqV2bNpbezkFAwCAQcHenrC6ulq7du3SY4895h4LCgpSVlaWtm7dWmf7qqoqVVVVuZfLysokSQ6HQw6H47Jq2VF0UifLv1GYvWY5LMjy+CpJJ8u/0baDJeqTGntZ+8K3ar9vl/v9w4XRZzPoszn02gxf9bkx89ksy/Lqf/u//PJLXXHFFdqyZYv69u3rHn/00Ue1ceNGbd++3WP7qVOnatq0aXXmWbFihSIjI71ZGgAA8JGKigoNGzZMpaWlio6OvuC2Xj/y0ViPPfaY8vLy3MtlZWVKSUnRLbfcctHiL2ZH0Un3RaZSzRGP6ZkuTdoZpCqXzT3+7IjeHPnwIofDofz8fA0ePFghISH+LqfFos9m0Gdz6LUZvupz7ZmLhvB6+Gjfvr3sdruOHTvmMX7s2DElJibW2T4sLExhYWF1xkNCQi67Kd+7Jl6xrSNUXFrpcd1HlcumKqdNNkmJMeH63jXxsgfZzjcNLpE3voe4OPpsBn02h16b4e0+N2Yur19wGhoaqhtuuEFr1651j7lcLq1du9bjNIwJ9iCbpuSkSZLOjRa1y1Ny0ggeAAAY5JN3u+Tl5Wnp0qV6/vnntW/fPo0ZM0ZnzpzRfffd54vdXdCQ9CQtvjtDiTHhHuOJMeFafHeGhqQnGa8JAIBA5pNrPu666y4dP35ckydPVnFxsXr27KlVq1YpISHBF7u7qCHpSRqclqhtB0t0Yt82PTuiN6daAADwE59dcDp27FiNHTvWV9M3mj3Ipj6psXpnn9QnNZbgAQCAnwTMZ7sAAICmgfABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMMpndzi9VJZV8/mzjflo3oZyOByqqKhQWVkZn5joQ/TZDPpsBn02h16b4as+175u176OX0iTCx+nT5+WJKWkpPi5EgAA0FinT59WTEzMBbexWQ2JKAa5XC59+eWXioqKks3m3c9fKSsrU0pKij7//HNFR0d7dW58iz6bQZ/NoM/m0GszfNVny7J0+vRpJScnKyjowld1NLkjH0FBQbryyit9uo/o6Gh+sA2gz2bQZzPoszn02gxf9PliRzxqccEpAAAwivABAACMCqjwERYWpilTpigsLMzfpbRo9NkM+mwGfTaHXpvRFPrc5C44BQAALVtAHfkAAAD+R/gAAABGET4AAIBRhA8AAGBUwISPRYsWqVOnTgoPD9eNN96oHTt2+LukFmfmzJnq3bu3oqKiFB8fr6FDh2r//v3+LqvFmzVrlmw2m8aNG+fvUlqcL774QnfffbfatWuniIgIde/eXTt37vR3WS2K0+nUpEmTlJqaqoiICF199dWaPn16gz4fBBe2adMm5eTkKDk5WTabTW+88YbHesuyNHnyZCUlJSkiIkJZWVn65JNPjNQWEOHjr3/9q/Ly8jRlyhQVFhaqR48eys7OVklJib9La1E2btyo3Nxcbdu2Tfn5+XI4HLrlllt05swZf5fWYhUUFOjPf/6zrr/+en+X0uJ8/fXX6tevn0JCQvTuu+9q7969+uMf/6i2bdv6u7QWZfbs2Vq8eLEWLlyoffv2afbs2ZozZ46eeuopf5fW7J05c0Y9evTQokWL6l0/Z84cLViwQM8884y2b9+uVq1aKTs7W5WVlb4vzgoAffr0sXJzc93LTqfTSk5OtmbOnOnHqlq+kpISS5K1ceNGf5fSIp0+fdrq3LmzlZ+fb/3gBz+wHnroIX+X1KJMmDDB6t+/v7/LaPFuu+02a+TIkR5jP/nJT6zhw4f7qaKWSZL1+uuvu5ddLpeVmJhoPfnkk+6xU6dOWWFhYdbLL7/s83pa/JGP6upq7dq1S1lZWe6xoKAgZWVlaevWrX6srOUrLS2VJMXGxvq5kpYpNzdXt912m8fPNrznzTffVGZmpu68807Fx8erV69eWrp0qb/LanFuuukmrV27VgcOHJAkffjhh9q8ebN+9KMf+bmylq2oqEjFxcUevz9iYmJ04403GnltbHIfLOdtJ06ckNPpVEJCgsd4QkKCPv74Yz9V1fK5XC6NGzdO/fr1U3p6ur/LaXFWrlypwsJCFRQU+LuUFuuzzz7T4sWLlZeXp9/85jcqKCjQgw8+qNDQUI0YMcLf5bUYEydOVFlZmbp06SK73S6n06kZM2Zo+PDh/i6tRSsuLpakel8ba9f5UosPH/CP3Nxc7dmzR5s3b/Z3KS3O559/roceekj5+fkKDw/3dzktlsvlUmZmpp544glJUq9evbRnzx4988wzhA8veuWVV/TSSy9pxYoV6tatm3bv3q1x48YpOTmZPrdgLf60S/v27WW323Xs2DGP8WPHjikxMdFPVbVsY8eO1dtvv63169fryiuv9Hc5Lc6uXbtUUlKijIwMBQcHKzg4WBs3btSCBQsUHBwsp9Pp7xJbhKSkJKWlpXmMde3aVUeOHPFTRS3TI488ookTJ+rnP/+5unfvrnvuuUcPP/ywZs6c6e/SWrTa1z9/vTa2+PARGhqqG264QWvXrnWPuVwurV27Vn379vVjZS2PZVkaO3asXn/9da1bt06pqan+LqlFGjRokD766CPt3r3b/SczM1PDhw/X7t27Zbfb/V1ii9CvX786bxU/cOCAOnbs6KeKWqaKigoFBXm+FNntdrlcLj9VFBhSU1OVmJjo8dpYVlam7du3G3ltDIjTLnl5eRoxYoQyMzPVp08fzZs3T2fOnNF9993n79JalNzcXK1YsUJ///vfFRUV5T5vGBMTo4iICD9X13JERUXVuY6mVatWateuHdfXeNHDDz+sm266SU888YR+9rOfaceOHVqyZImWLFni79JalJycHM2YMUMdOnRQt27d9MEHH2ju3LkaOXKkv0tr9srLy3Xw4EH3clFRkXbv3q3Y2Fh16NBB48aN0+OPP67OnTsrNTVVkyZNUnJysoYOHer74nz+fpom4qmnnrI6dOhghYaGWn369LG2bdvm75JaHEn1/nnuuef8XVqLx1ttfeOtt96y0tPTrbCwMKtLly7WkiVL/F1Si1NWVmY99NBDVocOHazw8HDrqquusn77299aVVVV/i6t2Vu/fn29v5NHjBhhWVbN220nTZpkJSQkWGFhYdagQYOs/fv3G6nNZlncRg4AAJjT4q/5AAAATQvhAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFH/H+9k0z3zgSNXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(x1, y1)\n",
    "plt.scatter(x2, y2)\n",
    "plt.scatter(x3, y3)\n",
    "\n",
    "labels = ['linear', 'constant', 'quadratic']\n",
    "plt.legend(labels)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(x1, y1, color ='r')\n",
    "plt.scatter(x2, y2, color = 'g')\n",
    "plt.scatter(x3, y3, color = 'b')\n",
    "\n",
    "labels = ['linear', 'constant', 'quadratic']\n",
    "plt.legend(labels)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(x1, y1, marker = 'v')\n",
    "plt.scatter(x2, y2, marker = 'o')\n",
    "plt.scatter(x3, y3, marker = 's')\n",
    "\n",
    "labels = ['linear', 'constant', 'quadratic']\n",
    "plt.legend(labels)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(x1, y1, c = 'b', marker = 's')\n",
    "plt.plot(x2, y2, c = 'g', marker = 'v')\n",
    "plt.plot(x3, y3, c = 'r', marker = '^')\n",
    "\n",
    "labels = ['linear', 'constant', 'quadratic']\n",
    "plt.legend(labels)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def poly(x, c):\n",
    "    y = []\n",
    "    for xx in x:\n",
    "        yy = 0 \n",
    "        for n, cc in enumerate(c):\n",
    "            yy += cc * xx*n\n",
    "        y.append(yy)\n",
    "    return y\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [*range(0, 11)]\n",
    "x = [0.1 * i for i in x]\n",
    "y0 = poly(x,[1])\n",
    "y1 = poly(x,[1, 1])\n",
    "y2 = poly(x,[1, 1, 1])\n",
    "y3 = poly(x,[1, 1, 1, 1])\n",
    "y4 = poly(x,[1, 1, 1, 1, 1])\n",
    "y5 = poly(x,[1, 1, 1, 1, 1, 1])\n",
    "y6 = poly(x,[1, 1, 1, 1, 1, 1, 1 ])\n",
    "l = 'upper left'\n",
    "plt.scatter(x, y0) ; plt.scatter (x, y1) \n",
    "plt.scatter(x, y2) ; plt.scatter (x, y3)\n",
    "plt.scatter(x, y4) ; plt.scatter (x, y5)\n",
    "plt.scatter(x, y6) \n",
    "plt.legend(['n=0', 'n=1', 'n=2', 'n=3', 'n=4', 'n=5', 'n=6'], loc = l)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uDqfvvQ/OnTuDdu6SkhJER0ejrKwM69atw6pVq7Br1y4AwPz582GxWPp95eTk9Pu5H3/8MUpLS/HNb34zaLF34cwHERGFVEt5A+w7quB2tHUfM1iNsBVkIio3IYSR9U243ahbvQYQfSRIQgCShLrVaxBzww1BWYLJy8tDUVERACArKwsbNmzA7t27MWfOHBQXF6OlpaXf742MjPQ6NnXqVBw5cgQulwuFhYVYtWpVwGPujckHERGFTEt5Axq3VHgddzva0LilAvG3ZWsuAbn4/mGvGQ8PQqCjthYX3z+M6PzJAT9/Xl6ex/vU1FTU19cDANLS0vz+vK1bt6KpqQn/+te/8Itf/AJPPPEEHnjggYDE2h8mH0REFBJCEbDvqPI5xr7jFMzj4iHJkkpRXVrH2bMBHeev3rMXkiRBURQAncsuvmo2Ro8ejePHj3scS09PBwCMGzcObrcbhYWF+NnPfgZDEAtnmXwQEVFIuKodHkstfXE7XHBVO2DOtKkT1ABEJCYGdFwgDWbZpSdFUdDe3g5FUZh8EBFR+FGafCce/o5Ty4hrJyIiJQUddXV9131IEiKSkzHi2omqx+bPssuLL76IyMhIXHXVVTCZTHj//ffx8MMPY9GiRZdMUoaKyQcREYWEHGMM6Di1SAYDklc8jNP33gdIkmcCInUuDyWveFjz/T4iIiLw+OOP46OPPoIQAqNHj8Y999yD+++/P+jnloToK20LHafTCavVCofDgdjY2FCHQ0REQSIUgdrHD/lcejFYTUh5cFJAaz5aW1tRXV2NjIwMmM3mQX9OKPp8hJqva+fP/ZszH0REFBKSLMFWkNnnbpcutoIxmio27Sn2xhsRc8MNuu1wGkpMPoiIKGSichMQf1t2H30+TLAVjNHcNtveJIMhKNtpwx2TDyIiCqmo3ASYx8XDVe2A0tQGOcYIU4ZVszMeNHRMPoiIKOQkWdLUdloKLj7bhYiIiFTF5IOIiIhUxeSDiIiIVMXkg4iIiFTF5IOIiIhUxeSDiIiIVMXkg4iIaJj7+OOPERMTA5vNpsr5mHwQERENkqIInD55Hh+9V4vTJ89DUTT1uLQBaW9vx/e//31Mnz5dtXOyyRgREdEgVB2tx/6tlbhgd3Ufi7aZMH1RFjInJAXlnDNmzEBeXh7MZjOKi4thNBqxdOlSrFy5ctCf+ctf/hJXXnklbrjhBhw4cCBwwfrAmQ8iIiI/VR2tR+nGco/EAwAu2F0o3ViOqqP1QTt3SUkJoqOjUVZWhnXr1mHVqlXYtWsXAGD+/PmwWCz9vnJycjw+a8+ePXj55ZfxzDPPBC3evnDmg4iIyA+KIrB/a6XPMW9vq0TG+ETIQXg+TV5eHoqKigAAWVlZ2LBhA3bv3o05c+aguLgYLS0t/X5vZGRk9783NjZiyZIl2LJlC2JjYwMepy9MPoiIiPxQU2n3mvHorfm8CzWVdqSNjQv4+fPy8jzep6amor6+c6YlLS1twJ9z11134Qc/+AGuv/76gMY3EFx2ISIi8sMFp+/Ew99x/uo5ewEAkiRBURQA/i277NmzB0888QQiIiIQERGBO++8Ew6HAxEREXj++eeDEnsXznwQERH5ITrWFNBxgeTPssvBgwfhdru73//tb3/D448/jgMHDvg1gzIYTD6IiIj8kJplQ7TN5HPpxRJnQmqWTb2gvuRP0pCdne3x/v3334csy8jNzQ10WF647EJEROQHWZYwfVGWzzHTFmYFpdg0XEhCCE11RHE6nbBarXA4HKpX3xIRUfhrbW1FdXU1MjIyYDabB/05ffX5sMSZMG1h8Pp8DJUQAu2tLXC73TAYDIg0R0GSBp4k+bp2/ty//V52+ec//4nf/e53OHz4MGpqavDqq6/i5ptv7v76kiVLUFJS4vE9c+fORWlpqb+nIiIi0qzMCUnIGJ/YufvF6UJ0bOdSi1ZnPFqbm9HUeBbujo7uY4aICMTEJ8Jssagai9/Jx4ULFzB+/Hj86Ec/wi233NLnmHnz5uHPf/5z93uTSf2iGyIiomCTZSko22kDrbW5Gfa6Gq/j7o4O2OtqYEOqqgmI38nH/PnzMX/+fJ9jTCYTUlJSBh0UERERBYYQAk2NZ32OaWo8C1N0tF9LMEMRlILTffv2ISkpCWPHjsVPfvITNDY29jvW5XLB6XR6vIiIiCgw2ltbPJZa+uLu6EB7a/9bdAMt4MnHvHnz8MILL2D37t14/PHH8dZbb2H+/Pkee4l7WrNmDaxWa/crPT090CEREQ0rQhForbLj4rF6tFbZIXT4pFUKnP7uv4MdFwgB7/Pxve99r/vfr7rqKuTl5SEzMxP79u3DDTfc4DX+4YcfxvLly7vfO51OJiBERIPUUt4A+44quB1t3ccMViNsBZmIyk0IYWQUKgaDIaDjAiHofT7GjBmDhIQEfPzxx31+3WQyITY21uNFRET+aylvQOOWCo/EAwDcjjY0bqlAS3lDiCKjUIo0R8EQ4XuuwRARgUhzlEoRqZB8fPHFF2hsbERqamqwT0VENGwJRcC+o8rnGPuOU1yCGYYkSUJMfKLPMTHxiaoVmwKDWHZpbm72mMWorq7GsWPHMHLkSIwcORKPPvoobr31VqSkpKCqqgoPPPAAvv71r2Pu3LkBDZyIiL7iqnZ4zXj05na44Kp2wJxpUyco0gyzxQIbUvXb5+P999/HzJkzu9931WssXrwYzz77LD744AOUlJTAbrdj1KhRuPHGG/HYY4+x1wcRURApTb4TD3/HUfgxWywwRUcPqcNpoPidfMyYMQO+OrL/4x//GFJARETkPznGGNBxFJ4kSYIxagQA4JNPPkFGRobXmIMHD+Ib3/hGUOPgU22JiMKAKcMKg9Xoc+nFYDXBlGFVMarwpyhunK44jmb7eVhscUjLzoEsq7drJBDefPNN5OTkdL+Pj48P+jmZfBARhQFJlmAryETjlop+x9gKxkDS6HNH9Kiy7AD2bN6E5nNf7SKyjEzArCWFyMqfGpRzzpgxA3l5eTCbzSguLobRaMTSpUuxcuXKQX9mfHy86l3Jg77bhYiI1BGVm4D427JhsHourRisJsTfls0+HwFUWXYA29ev9kg8AKD5XAO2r1+NyrIDQTt3SUkJoqOjUVZWhnXr1mHVqlXYtWsXgM5HoFgsln5fPWc4uixYsABJSUmYNm0atm/fHrS4e+LMBxFRGInKTYB5XDxc1Q4oTW2QY4wwZVg54xFAiuLGns2bfI7ZW7IJmZPyg7IEk5eXh6KiIgBAVlYWNmzYgN27d2POnDkoLi5GS0v/bdIjIyO7/91iseC//uu/cN1110GWZfz1r3/FzTffjNdeew0LFiwIeNw9MfkgIgozkixxO20Qna447jXj0VtTYwNOVxxHek5ewM+fl+f5mampqaivrwcApKWlDfhzEhISPDqMT5o0CWfOnMHvfve7oCcfXHYhIiLyQ7P9fEDH+avn7AXQuYNFURQAg1t26Sk/P7/fjuSBxJkPIiIiP1hscQEdF0j+LLv05dixY6p0JGfyQURE5Ie07BxYRib4XHqJiU9AWrbvWYZg8GfZpaSkBEajERMmTAAAvPLKK3j++edRXFwcrPC6MfkgIiLygywbMGtJIbavX93vmJmLC3XR7+Oxxx7Dp59+ioiICFx55ZXYunUrvvvd7wb9vJLw1a40BJxOJ6xWKxwOB59wS0REAdfa2orq6mpkZGTAbDYP+nP66vMRE5+AmYuD1+cj1HxdO3/u35z5ICIiGoSs/KnInJSv+w6nocDkg4iIaJBk2RCU7bThjskHERHRIAghNPGEWD1i8kFEROSn1uZmNDWehbujo/uYISICMfGJMFssIYxMH9hkjIiIyA+tzc2w19V4JB4A4O7ogL2uBq3NzSGKTD+YfBAREQ2QEAJNjWd9jmlqPAuNbSTVHCYfREREA9Te2uI149Gbu6MD7a39dxklJh9EREQD5na7AzpuuGLyQURENEAGw8B6eAx03HDF5IOIiGiAIs1RMET43ihqiIhApDlKpYj0ickHERHRAEmShJj4RJ9jYuITddPvQwiBJ554AldccQVMJhPS0tLw29/+NujnZZ8PIiIiP5gtFtiQiqbGs+hoa4eobQMuKjDERCI29zJd9fm49957sXPnTjzxxBO46qqrcO7cOZw7dy7o52XyQURE5CezxQKlugX2HVVQnO0AAAWA/S0nUJCJqNyEoJx3xowZyMvLg9lsRnFxMYxGI5YuXYqVK1f6/VkVFRV49tlnUV5ejrFjxwIAMjIyAhxx37jsQkRE5KeW8gace/Hf3YlHF7ejDY1bKtBS3tDPdw5dSUkJoqOjUVZWhnXr1mHVqlXYtWsXAGD+/PmwWCz9vnJycro/Z8eOHRgzZgxef/11ZGRk4Gtf+xp+/OMfc+aDiIhIa4QiYN9R5XOMfccpmMfFQ5IDX/uRl5eHoqIiAEBWVhY2bNiA3bt3Y86cOSguLkZLS/89RiIjI7v//dSpU/j000/x8ssv44UXXoDb7cb999+P7373u9izZ0/A4+6JyQcREZEfXNUOuB1tPse4HS64qh0wZ9oCfv68PM+n6KampqK+vh4AkJaWNuDPURQFLpcLL7zwAq644goAwHPPPYeJEyfi5MmT3UsxwcBlFyIiIj8oTb4TD3/H+avn7AXQuQNHURQA/i27pKamIiIiojvxAIDs7GwAwGeffRaU2Ltw5oOIqB9CEXBVO6A0tUGOMcKUYQ3KNDrpixxjDOi4QPJn2eW6665DR0cHqqqqkJmZCQD46KOPAACjR48OapxMPoiI+tBS3gD7jiqP6XWD1QhbEHcykD6YMqwwWI0+l14MVhNMGVYVo+rkz7LL7Nmzcc011+BHP/oRnnrqKSiKgmXLlmHOnDkesyHBwGUXIqJeWsob0LilwuvmosZOBtI+SZZgK8j0OcZWMEbzs2SyLGPHjh1ISEjA9ddfj29/+9vIzs7GSy+9FPRzc+aDiKiHUO9kIH2Iyk1A/G3ZfcyOmWArGBO02bF9+/Z5HXvttdcG/XmjRo3CX//618EHNEhMPoiIegj1TgbSj6jcBJjHxbMuaBCYfBAR9RDqnQykL5IsMQkdBNZ8EBH1oOWdDEThgskHEVEPXTsZfAnVTgaicMHkg4ioh3DZyUCkZUw+iIh66drJ0HsGxGA1If62bPb5CAJFcePz4x+g4p238PnxD6AobhXOqQT9HOFGCBGQz2HBKRFRH7iTQT2VZQewZ/MmNJ/7qn+KZWQCZi0pRFb+1ICfz2g0QpZlnDlzBomJiTAajZAk/n+9FCEEzp49C0mSvFq8+0sSgUpjAsTpdMJqtcLhcCA2NjbU4RARURBVlh3A9vWr+/36guUrgpKAtLW1oaamBhcvXgz4Z4czSZJw2WWXwWKxeH3Nn/s3Zz6IiCgkFMWNPZs3+Ryzt2QTMiflQ5YNAT230WjE5Zdfjo6ODrjdwV/iCReRkZEwGIb+/4LJBxERhcTpiuMeSy19aWpswOmK40jPyfM5bjC6lg+GuoRA/mPBKRERhUSz/XxAx5F+MPkgIqKQsNjiAjqO9IPJBxERhURadg4sI31vW46JT0Bado5KEZFamHwQEVFIyLIBs5YU+hwzc3FhwItNKfSYfBARUchk5U/FguUrvGZAYuITgrbNlkKPu12IiCiksvKnInNSfufuF/t5WGxxSMvO4YxHGGPyQUREISfLhqBspyVt4rILERERqYrJBxEREamKyQcRERGpiskHERERqYrJBxEREamKyQcRERGpiskHERERqYrJBxEREamKyQcRERGpiskHERERqYrJBxEREamKyQcRERGpig+WIyJVCEXAVe2A0tQGOcYIU4YVkiyFOqywpChuPiGWNI3JBxEFXUt5A+w7quB2tHUfM1iNsBVkIio3IYSRhZ/KsgPYs3kTms81dB+zjEzArCWFyMqfGsLIiL7CZRciCqqW8gY0bqnwSDwAwO1oQ+OWCrSUN/TzneSvyrID2L5+tUfiAQDN5xqwff1qVJYdCFFkRJ6YfBBR0AhFwL6jyucY+45TEIpQKaLwpShu7Nm8yeeYvSWboChulSIi6h+TDyIKGle1w2vGoze3wwVXtUOliMLX6YrjXjMevTU1NuB0xXGVIiLqH5MPIgoapcl34uHvOOpfs/18QMcRBROTDyIKGjnGGNBx1D+LLS6g44iCickHEQWNKcMKg9V3YmGwmmDKsKoUUfhKy86BZaTvnUMx8QlIy85RKSKi/jH5IKKgkWQJtoJMn2NsBWPY7yMAZNmAWUsKfY6ZubiQ/T6GOUUROH3yPD56rxanT56HEqJib7+Tj3/+858oKCjAqFGjIEkSXnvtNY+vCyHw61//GqmpqYiKisLs2bNRWVkZqHiJSGeichMQf1u21wyIwWpC/G3Z7PMRQFn5U7Fg+QqvGZCY+AQsWL6CfT6Guaqj9XhhxQG89uRR7HruBF578iheWHEAVUfrVY/F7yZjFy5cwPjx4/GjH/0It9xyi9fX161bh9///vcoKSlBRkYGfvWrX2Hu3Lk4ceIEzGZzQIImIn2Jyk2AeVw8O5yqICt/KjIn5bPDKXmoOlqP0o3lXscv2F0o3ViOeXfnInNCkmrxSEKIQc+5SJKEV199FTfffDOAzlmPUaNG4Wc/+xl+/vOfAwAcDgeSk5OxefNmfO9737vkZzqdTlitVjgcDsTGxg42NCIiIkLnUssLKw7ggt3V7xhLnAm3/3Yq5CH8hcCf+3dAaz6qq6tRW1uL2bNndx+zWq3Iz8/HwYMH+/wel8sFp9Pp8SIiIqLAqKm0+0w8AKD5vAs1lXZ1AkKAk4/a2loAQHJyssfx5OTk7q/1tmbNGlit1u5Xenp6IEMiIiIa1i44fSce/o4LhJDvdnn44YfhcDi6X59//nmoQyIiIgob0bGmgI4LhIAmHykpKQCAuro6j+N1dXXdX+vNZDIhNjbW40VERESBkZplQ7TNd2JhiTMhNcumTkAIcPKRkZGBlJQU7N69u/uY0+lEWVkZpkyZEshTERER0QDIsoTpi7J8jpm2MGtIxab+8nurbXNzMz7++OPu99XV1Th27BhGjhyJyy+/HPfddx9+85vfICsrq3ur7ahRo7p3xBAREZG6MickYd7dudi/tdKj+NQSZ8K0hVmqbrMFBpF8vP/++5g5c2b3++XLlwMAFi9ejM2bN+OBBx7AhQsXUFhYCLvdjmnTpqG0tJQ9PoiIiEIoc0ISMsYndu5+cboQHdu51KLmjEeXIfX5CAb2+SAiItKfkPX5ICIiIroUJh9ERESkKiYfREREpComH0RERKQqv3e7EBENF4ri5tNhqV+KIjSxc0SPmHwQEfWhsuwA9mzehOZzDd3HLCMTMGtJIbLyp4YwMtKCqqP1Xj0zom0mTF+kfs8MPeKyCxFRL5VlB7B9/WqPxAMAms81YPv61agsOxCiyEgLqo7Wo3RjudeTYi/YXSjdWI6qo/Uhikw/mHwQEfWgKG7s2bzJ55i9JZugKG6VIiItURSB/VsrfY55e1slFEVTLbQ0h8kHEVEPpyuOe8149NbU2IDTFcdVioi0pKbS7jXj0VvzeRdqKu3qBKRTTD6IiHpotp8P6DgKLxecvhMPf8cNV0w+iIh6sNjiAjqOwkt0rO9H0/s7brhi8kFE1ENadg4sIxN8jomJT0Bado5KEZGWpGbZEG3znVhY4jq33VL/mHwQEfUgywbMWlLoc8zMxYXs9zFMybKE6YuyfI6ZtjCL/T4ugckHEVEvWflTsWD5Cq8ZkJj4BCxYvoJ9Poa5zAlJmHd3rtcMiCXOhHl357LPxwBIQghN7Qfy55G8RMOVUARc1Q4oTW2QY4wwZVgh8W9aAccOp+QLO5x68uf+zQ6nRDrTUt4A+44quB1t3ccMViNsBZmIyvVdq0D+kWUD0nPyQh0GaZQsS0gby8LjweCyC5GOtJQ3oHFLhUfiAQBuRxsat1Sgpdx3fwoiIi1g8kGkE0IRsO+o8jnGvuMUBDsrEpHGMfkg0glXtcNrxqM3t8MFV7VDpYiIiAaHyQeRTihNvhMPf8cREYUKC06JdEKOMQZ0HJGWcOfI8MLkg0gnTBlWGKxGn0svBqsJpgyrilERDV3V0Xrs31rp8cC2aJsJ0xdlsWdGmOKyC5FOSLIEW0GmzzG2gjHs90G6UnW0HqUby72eFHvB7kLpxnJUHa0PUWQUTEw+iHQkKjcB8bdlw2D1XFoxWE2Ivy2bfT5IVxRFYP/WSp9j3t5WCYU7uMIOl12IdCYqNwHmcfHscEq6V1Np95rx6K35vAs1lXY28wozTD6IdEiSJZgzbaEOg2hILjh9Jx7+jiP94LILERGFRHSs70fT+zuO9IPJBxERhURqls3rybC9WeI6t91SeGHyQUSqUBQ3Pj/+ASreeQufH/8AiuIOdUgUYrIsYfqiLJ9jpi3MYr+PMMSaDyIKusqyA9izeROaz3314DvLyATMWlKIrPypIYyMQi1zQhLm3Z3r1efDEmfCtIXs8xGuJCGEpvYwOZ1OWK1WOBwOxMbGhjocIhqiyrID2L5+db9fX7B8BRMQYofTMODP/ZszH0QUNIrixp7Nm3yO2VuyCZmT8iHLBpWiIi2SZYnbaYcR1nwQUdCcrjjusdTSl6bGBpyuOK5SRESkBUw+iChomu3nAzqOiMIDkw8iChqLbWDT6AMdR0ThgckHEQVNWnYOLCN9P28mJj4Badk5KkVERFrA5IOIgkaWDZi1pNDnmJmLC1lsGmCKInD65Hl89F4tTp88zwezkeZwtwsRBVVW/lQsWL7Cq89HTHwCZi5mn49Aqzpa79UzI9pmwvRF7JlB2sE+H0SkCkVxd+5+sZ+HxRaHtOwczngEWNXRepRuLO/36/PuzmUCQkHDPh9EpDmybEB6Tl6owwhbiiKwf2ulzzFvb6tExvhENu+ikGPNBxFRGKiptHsstfSl+bwLNZV2dQIi8oHJBxFRGLjg9J14+DuOKJiYfBARhYHoWN+Ppvd3HFEwMfkgIgoDqVk2RNt8JxaWuM4HthGFGpMPIqIwIMsSpi/K8jlm2sIsFpuSJjD5ICIKE5kTkjDv7lyvGRBLnInbbElTuNWWiCiMZE5IQsb4xM7dL04XomM7l1o440FawuSDiCjMyLKEtLF8WB9pF5MPGvaEIuCqdkBpaoMcY4QpwwqJf0skIgoaJh80rLWUN8C+owpuR1v3MYPVCFtBJqJyfT+NlYiIBocFpzRstZQ3oHFLhUfiAQBuRxsat1Sgpbyhn+8kIqKhYPJBw5JQBOw7qnyOse84BcFHkRMRBRyXXWhYclU7vGY8enM7XHBVO2DOtKkTlB/4hFh1KIrgrhEKK8LtxsX3D6Pj7FlEJCZixLUTIRnU/93B5IOGJaXJd+Lh7zg1VZYdwJ7Nm9B87qtlIcvIBMxaUois/KkhjCy8VB2tx/6tlR4Pa4u2mTB9URb7ZZAuOXfuRN3qNeiore0+FpGSguQVDyP2xhtVjYXLLjQsyTHGgI5TS2XZAWxfv9oj8QCA5nMN2L5+NSrLDoQosvBSdbQepRvLvZ4Se8HuQunGclQdrQ9RZESD49y5E6fvvc8j8QCAjro6nL73Pjh37lQ1HiYfNCyZMqwwWH0nFgarCaYMq0oRXZqiuLFn8yafY/aWbIKiuFWKKDwpisD+rZU+x7y9rRIK64FIJ4TbjbrVawDRx8/sl8fqVq+BcKv3u4PJBw1LkizBVpDpc4ytYIym+n2crjjuNePRW1NjA05XHFcpovBUU2n3mvHorfm8CzWVdnUCIhqii+8f9prx8CAEOmprcfH9w6rFxOSDhq2o3ATE35btNQNisJoQf1u25vp8NNvPB3Qc9e2C03fi4e84olDrOHs2oOMCgQWnNKxF5SbAPC5eFx1OLbaBtcse6DjqW3Ss78fS+zuOKNQiEhMDOi4QmHzQsCfJkia30/aWlp0Dy8gEn0svMfEJSMvOUTGq8JOaZUO0zeRz6cUS17ntlkgPRlw7EREpKeioq+u77kOSEJGcjBHXTlQtJi67EOmELBswa0mhzzEzFxey38cQybKE6YuyfI6ZtjCL/T5INySDAckrHv7yTa+f2y/fJ694WNV+H0w+iHQkK38qFixfActIz3qUmPgELFi+gn0+AiRzQhLm3Z2LaJvn0oolzoR5d+eyzwfpTuyNNyLt6acQkZzscTwiORlpTz+lep8PSYi+5mBCx+l0wmq1wuFwIDY2NtThEGkSO5yqgx1OyRetdAv1RzBj9uf+zZoPIh2SZQPSc/JCHUbYk2UJaWNZwEvetNQt1B+SwYDo/MmhDoPLLkRERP7QWrdQPWLyQURENEBa7BaqR0w+iIiIBkiL3UL1iMkHERHRAGmxW6geseCUiFTBnSMUDrTYLVSPAp58rFy5Eo8++qjHsbFjx+Lf//53oE9FRDpRdbQe+7dWenQNjbaZMH1RFntmkK5osVuoHgVl2SUnJwc1NTXdr7fffjsYpyEiHag6Wo/SjeVe7cov2F0o3ViOqqP1IYqMyH9a7BaqR0FJPiIiIpCSktL9SkjQ1tNBiUgdiiKwf2ulzzFvb6uEomiq1yGRT1rrFqpHQan5qKysxKhRo2A2mzFlyhSsWbMGl19+eZ9jXS4XXK6v/kbkdDqDERIRhUBNpd3nA9oAoPm8CzWVdjbzGub01i009sYbEXPDDbqKWUsCnnzk5+dj8+bNGDt2LGpqavDoo49i+vTpKC8vR0xMjNf4NWvWeNWIEFF4uOD0nXj4O47CE7uFDj9Bf7aL3W7H6NGjsX79etx5551eX+9r5iM9PZ3PdiEKA6dPnsdrTx695Lib75/AmY9hqqtbqFfx5pf1E1zG0A9/nu0S9D4fNpsNV1xxBT7++OM+v24ymRAbG+vxIqLwkJpl83oybG+WuM5ttzT8sFvo8BX05KO5uRlVVVVITU0N9qmIBkVR3Pj8+AeoeOctfH78AygKf9EFiixLmL4oy+eYaQuz2O9jmGK30OEr4DUfP//5z1FQUIDRo0fjzJkzKCoqgsFgwPe///1An4poyCrLDmDP5k1oPtfQfcwyMgGzlhQiK39qCCMLH5kTkjDv7lyvPh+WOBOmLWSfj+GM3UKHr4AnH1988QW+//3vo7GxEYmJiZg2bRreffddJLLbG2lMZdkBbF+/2ut487kGbF+/GguWr2ACEiCZE5KQMT6RHU7JA7uFDl8BTz5eeumlQH8kUcApiht7Nm/yOWZvySZkTsqHLHPrXCDIssSiUvLAbqHDFx8sR8PS6YrjHkstfWlqbMDpiuMqRUQ0/LBb6PDF5IMCSigCrVV2XDxWj9YqO4RGO1c2288HdByRlgi3GxfKDsHx+t9xoeyQpneLsFvo8MSn2lLAtJQ3wL6jCm5HW/cxg9UIW0EmonK11WLfYhvY9P9AxxFphR4bdrFb6PDDmQ8KiJbyBjRuqfBIPADA7WhD45YKtJT7XuJQW1p2DiwjfSdEMfEJSMvOUSkioqHratjVe/tqR10dTt97H5w7d4Yoskvr6hZqvenbiM6fzMQjzDH5oCETioB9R5XPMfYdpzS1BCPLBsxaUuhzzMzFhZotNlUUgdMnz+Oj92px+uR5PpiN2LCLdIXLLjRkrmqH14xHb26HC65qB8yZNnWCGoCs/KlYsHyFV5+PmPgEzFys3T4fVUfrvXpmRNtMmL6IPTOGM38advF5JBRqTD5oyJQm34mHv+PUlJU/FZmT8jt3v9jPw2KLQ1p2jmZnPKqO1qN0Y7nX8Qt2F0o3lmPe3blMQIYpNuwiPWHyQUMmxxgDOk5tsmxAek5eqMO4JEUR2L+10ueYt7dVImN8Ipt3DUNs2EV6wpoPGjJThhUGq+/EwmA1wZRhVSmi8FRTafdYaulL83kXairt6gREmtLVsMurX0YXSUJESgobdpEmMPmgIZNkCbaCTJ9jbAVjIPFv40Nywek78fB3HIUXNuwiPWHyQQERlZuA+NuyvWZADFYT4m/L1lyfDz2KjvX9aHp/x1H4YcMu0gvWfFDAROUmwDwuHq5qB5SmNsgxRpgyrJzxCJDULBuibSafSy+WuM4HtlFgCLdbd42v2LCL9IDJBwWUJEua2k4bTmRZwvRFWX3udukybWEWi00DRI+dQrt0Newi0iouuxDpSOaEJMy7OxfRNs+lFUucidtsA0jPnUKJ9EASoq92eKHjdDphtVrhcDgQGxsb6nCINElRROfuF6cL0bGdSy2c8QgM4Xbj4xtm99+w68vHvH9995tcyiDqwZ/7N5ddiHRIliWkjeVD74KBnUKJgo/LLkREPbBTKFHwMfkgIuqBnUKJgo/JBxFRD+wUShR8TD6IiHpgp1Ci4GPBKQWUorh184TYLtw5og49NeyKvfFG4OmnvPt8JCfros8HUX/cihtH6o/g7MWzSByRiGuSroEhBL+jmXxQwFSWHcCezZvQfK6h+5hlZAJmLSlEVv7UEEbWv6qj9di/tdKja2i0zYTpi7LYMyOA9Niwi51CKdy8+embWHtoLeou1nUfSx6RjIcmP4TZo2erGgv7fFBAVJYdwPb1q/v9+oLlKzSXgFQdrffZLZRNuwKjq2EXev+q+XIJg88cIQq+Nz99E8v3LYeA559DCZ1/DtfPWD/kBMSf+zdrPmjIFMWNPZs3+Ryzt2QTFMWtUkSXpigC+7dW+hzz9rZKKIqmcnPdEW436lav8U48gO5jdavXQLi187NBFG7cihtrD631SjwAdB97/NDjcKv4O5rJBw3Z6YrjHkstfWlqbMDpiuMqRXRpNZV2nw9oA4Dm8y7UVNrVCShM+dOwi4iC40j9EY+llt4EBGov1uJI/RHVYmLNBw1Zs/18QMep4YLTd+Lh7zjqGxt2UTjTSvHmpZy9OLA/XwMdFwhMPmjILLaBtfke6Dg1RMeaLj3Ij3HUNzbsonClpeLNS0kcMbA/XwMdFwhcdqEhS8vOgWVkgs8xMfEJSMvOUSmiS0vNsnk9GbY3S1zntlsaPDbsonDUVbzZeymj/mI9lu9bjjc/fTNEkfXtmqRrkDwiubu4tDcJElJGpOCapGtUi4nJBw2ZLBswa0mhzzEzFxdqqt+HLEuYvijL55hpC7PY72OI2LCLwo0WizcvxSAb8NDkhwDAKwHpev/g5AdVXTJi8kEBkZU/FQuWr/CaAYmJT9DkNlsAyJyQhHl353rNgFjiTNxmG0CxN96ItKefQkRyssfxiORkbrMl3dFi8eZAzB49G+tnrEfSCM/fa8kjkgOyzdZfrPmggMnKn4rMSfm66nCaOSEJGeMTddfhVE/dQgE27KJLY/Fm8M0ePRsz02dq4joz+aCAkmUD0nPyQh2GX2RZQtpY7RTDXooeu4UCnUsw0fmTQx0GaRCLN9VjkA2YlDIp1GFw2YVIT7q6hfbundFRV4fT994H586dIYqMaHBYvDk8Mfkg0gl2C6Vww+LN4YvJB5FOsFsohRsWbw5frPnQMKEIuKodUJraIMcYYcqwQtJ4ISQFD7uF0kCxeDP4tFS8qUdMPjSqpbwB9h1VcDvauo8ZrEbYCjIRleu7oVcoKYrQ3c4RvWC3UBoIFm+qRyvFm3rE5EODWsob0Lilwuu429GGxi0ViL8tW5MJSNXReuzfWunxwLZomwnTF2WxZ0YAdHUL7air67vuQ5IQkZzMbqHDWH+PTe8q3tTakkBX8Wb9xfo+6z4kSEgekczizTDEmg+NEYqAfUeVzzH2HacgNPao96qj9SjdWO71pNgLdhdKN5aj6mh9iCILH+wWSr6weJP0hMmHxriqHR5LLX1xO1xwVTtUiujSFEVg/9ZKn2Pe3lYJRWMJkx6xWyj1h8WbpCdcdtEYpcl34uHvODXUVNq9Zjx6az7vQk2lXZPNvNgtlPqjl8JNgMWbpC9MPjRGjjEGdJwaLjh9Jx7+jlMTu4VSf/RUuAmweJP0hcsuGmPKsMJg9Z1YGKwmmDKsKkV0adGxvh9N7+84tbBbKPVHb103AXbeJH1h8qExkizBVpDpc4ytYIym+n2kZtm8ngzbmyWuc9utVrBbKPVHj4WbAIs3SV+YfGhQVG4C4m/LhhzrOQMiW42a3GYryxKmL8ryOWbawixN9ftgt1D1uRU33qt9D2+cegPv1b6nuZt3F70WbgIs3iT9YM2HRn1x4SPs+fxPiLpoRpTBghZ3M1ocrZh14S5kQVvJB9D5aPp5d+d69fmwxJkwbaH2+nywW6i69FQ/oefCTYDFm6QPTD40qLLsALavXw0AaO75hVZg+/rVWLB8BbLyp4YkNl8yJyQhY3yiLjqcsluoevTW+ErvhZsAizdJ+7jsojGK4saezZt8jtlbsgmKRqesZVlC2tg4XDEpBWlj4zSZeABfdQv1atbVRZIQkZLCbqFDpMf6CRZuEgUfkw+NOV1xHM3nGnyOaWpswOmK4ypFFJ7YLVQdeqyfYOEmUfAx+dCYZvv5gI5Tm3C7caHsEByv/x0Xyg5pereInruF6qV4U6/1EyzcJAou1nxojMU2sA6gAx2nJj027NJjt1A9FW/quX6ChZtEwSMJ0Vejg9BxOp2wWq1wOByIjY0NdTiqUxQ3/rTsTp9LLzHxCfjxhucga+iXYFfDLq++GV8uYWh9JkEv+ive7FoO0Nrfyt2KG3P/OveSTy0tvbWUN3UinfPn/s1lF42RZQNmLSn0OWbm4kJNJR5s2KUOPRZvsn6CSGMUN1C9H/jwfzv/GaLfF0w+NCgrfyom/59lkA0xHsdlQwwm/59lmttmy4Zd6tBj8SbA+gkizTixHXgqFyi5CfjrnZ3/fCq387jKWPOhQVVH6/HBPhMiY+6E0nEaEBcAKRpyRBo+2Cdj1Nh6TTXt0nvDLr08uVSvxZsA6ycoTClu4NMDQHMdYEkGRk8FtPozfWI7sO0OoPfMqbOm8/jCF4BxC1QLh8mHxiiKwP6tlQAASZJhiEz3GvP2tkpkjE/UTA8NPTfsYvGmetj4isLKie1A6YOA88xXx2JHAfMeV/UmPiCKuzPWPpZsO49JQOlDwJXfVi154rKLxtRU2j3ak/el+bwLNZV2dQIaAL027NLbk0vZ/IrCmkZqEQakaxahZ+IBfDWLEIJlDJ8+PeAdqwcBOE93jlMJkw+NueD0nXj4O04NemzYxeJNCmt6upEDmqpFuKRLziKgcxZBS9e8uf9asUGNCwAmHxoTHev70fT+jlOL3hp2sXiTwpaebuQAZxHUYEm+9Bh/xgUAaz40JjXLhmibyefSiyWu84FtWhN7440YMXMGPnjzJTTVfIaY1MuRN/t7iIg0hjo0LyzepAHRU0EhoLmiwkvSYC3CJWlwFuGSRk/trEdx1qDvay11fn20ejspmXxojCxLmL4oC6UbP/zyz16P6XUhAAmYtjBLM8WmPXkUbxoA1APJr73A4s0g0GXxph5v5HopKAT0eSP3ZxYhY7pqYfmkwVmES5INnT+32+4AIMHzZ+TLe8m8tar+XHDZRYMSzx5D7vFimFx2j+Mm13nkHi9G4tljIYnLFxZvkk9cCgg+PS4H6HkWoZ/fHZ2zCGmqziIMyLgFnTNfsamex2NHhWRGjDMfGtPVLTTpbC0Sz/4LdtvX4TLGwtTmhM3+MSSps1tozA03aKaA81LFmxIkPH7occxMn6mZpYGu4s3l+5ZDguQRuy6KN/U0i8ClAHXo8UbOWQR1jVvQ+XOrgd8dnPnQmJ7dQiUIxNkrkVJ/GHH2SkgQmuwWyuJNlelpFkGPOwP0OIMA6PNGzlkE9cmGziWsq77b+c8QJUmc+dAYPXYL1X3xZtr1OPLh/8NZ52dIjL0c11x1OwwR2iuSBaC/WQQ9runrcQYB0GRR4SVxFmHYYvKhMXrsFqrr4s0T22EofRCTet4g9z6hzaJCPS4H6PFGrscZBEC/N/KuWYQ+i3vXau/PYU9dswjkNyYfGjPi2onoSLBBbrD3uSamAFASbZrqFtpVvFl/sa6/v28hWYvFm5xFCD493sj1OIPQRa83cs4iDDus+dAYRQI2z5YhoTPR8PgaOm/km28wQNHQTluDbMBDqTcAQkASnr+sJdFZp/Jg6ixtFW/qsRZBj7MIelzT75pBAOAdt4ZnELqMWwDcVw4sfh249bnOf973oXYTjy4aqUUgdQyb5KPZ4cTrj6zD6/c+jtcfWYdmhzPUIfXpSP0R7Mxw4r9ukXEuxvNr52KA/7pFxs4Mh7aKNxU3Zh96AevrG5Dk9rxZJ7vdWF/fiNmH/p+2buR6LCrU4yxCjxu56HUjF1q+kX85gyB6FRQKPRQUAnBDxkFlHP7mnoKDyji4dfCr3q0IHKxqxN+OncbBqka4lb7+YqAtjHnwgrbs8swzz+B3v/sdamtrMX78ePzhD3/A5MmTg3U6n3bcvxZjDXm4OmIKEAXADZx57G2cdH+AgicfCklM/ekqyjw0VsZ7WRKyPxeIawbOW4CKdAniy+Zimire/PJGPhvAzIstOGI24azBgES3G9e0utB5W7moreUAHc8iCGdN586nXgQkSFpcDhi3AEenPI1RBx9FMhq7D9dhJGqmFGGCRm/kpcokPNb6NNLb/oUk2FEPGz5vHY9fKVdhXqiD86G0vAaP7jiBGkdr97FUqxlFBeMwLzfVx3eGDmNWh5ZiDko6vHXrVixfvhxFRUU4cuQIxo8fj7lz56K+vj4Yp/Npx/1rcbVxKqIMFo/jUQYLrjZOxY7716oeky+J5pHd/y5kCSdGy3gnR8aJ0XJ34tF7XMj1uEEbAExqdeFbFy5iUnfi4T0u5HQ6i3A05yEIIdD7LyuKAIQQOJrzoOZmEUrLa3DL3gRMaX0a32v7Jf6z7R58r+2XuK71adyyNwGl5TWhDtFLaXkNfrLlCE472/GuMg7blal4VxmHM852/GTLEU3GDHwVd8+bCwDUOlo1GzdjVofWYg5K8rF+/Xrcdddd+OEPf4hx48bhj3/8I0aMGIHnn38+GKfrV7PDibGGPACA1Otpq13vxxryNLUEM76lFckdHV61E10kIZDS0YHxLa19fj0U3NFJlx7kxzg1uNOnoA7xXjfxLooAahEPd/oUdQPzwa0I/PTIZfhJ+32ohWfyWYt4/LT9Pvz0yGWamvp1KwKP7jgBAUCB7HEj71oKeHTHCc3G3FvXMa3FDOgzbsasDi3GHPDko62tDYcPH8bs2V81aJJlGbNnz8bBgwe9xrtcLjidTo9XoOxb90eMiIjxSjy6SJKEEREx2LfujwE751B9Ul2NhxrPA0DfxZsAHmw8j0+qq1WPrT+H3FfijBjp80Z+RsTjkPtKdQPz4dCnDvy67XYA6HMWAQCK2m7HoU8dKkfWv0PV51DjaMU/lMmY5vq9xyzCNNfTKFUmo8bRikPV50IdareumPsjAMYcIHqMmzGrQ4sxBzz5aGhogNvtRnKvR6snJyej9svOnT2tWbMGVqu1+5Wenh64YJoHmMUNdJwK6oUNsy+2+CjebMDsiy2oF7bQBNiH+gvteLT9DgD938gfbb8d9RfaVY6sf/VNnTfx/mYRftJ+H/6hTEZ9k3ZmmHrG0nsWQenxR1mrMQdinBr0GDOgz7gZszq0GHPI+3w8/PDDWL58efd7p9MZuATEIgED2WBh0c6+VcPXrsOZt0di1oVzfRZvSgI4g3gYvnZdqEPtlhRj7r6RF0W+gFH4KnuuRTwebb8d/1AmY0mMOYRRekr6MpZ/KJOxy3UtJsv/7i4qPKRc2X0zT9JgzIEapwbGrB49xs2Y1aHFmAOefCQkJMBgMKCuzrO4sK6uDikpKV7jTSYTTCZToMMAAMx4YCnOPPY2ogyWPpdehBBocTdjxsNLg3L+wZicmYhHIn+M1e3rIInO4s0uXbMIv4+8E7/N1E630MkZI5FqNWOno+8buYCMVKsZkzO0UyTbFXOto7V7FqEnCUCKhmPur5kbYx46PcYM6DNuxqwOLcYc8GUXo9GIiRMnYvfu3d3HFEXB7t27MWWKusV7FmssTro/ANCZaPTU9f6k+wNYrLGqxuWLQZYw4+Yf4ac+igpn3PwjGGQNzdbIEooKOm/eotdygPjyR6yoYJxmY+6njRRjDgDGrB49xs2Y1aHFmCXR+64cAFu3bsXixYuxceNGTJ48GU899RS2bduGf//73161IL05nU5YrVY4HA7ExgYmKejq8zEi4quuXRc7mjTZ56NLaXkNHtv+IdKbe/QYsIzHrxZcxT3kAcSY1cGY1aPHuBmzOoIdsz/376AkHwCwYcOG7iZjV199NX7/+98jPz//kt8XjOQD6Nx2u2/dHzuLSy0SZjywVFMzHn1xKwKHqs+hvqkVSTGdU2Jayqb7wpjVwZjVoceYAX3GzZjVEcyYNZF8DFawkg8iIiIKHn/u39pv+E9ERERhhckHERERqYrJBxEREamKyQcRERGpiskHERERqYrJBxEREamKyQcRERGpiskHERERqYrJBxEREakq4E+1HaquhqtOpzPEkRAREdFAdd23B9I4XXPJR1NTEwAgPT09xJEQERGRv5qammC1Wn2O0dyzXRRFwZkzZxATEwNJCuwDepxOJ9LT0/H555/zuTFBxOusDl5ndfA6q4fXWh3Bus5CCDQ1NWHUqFGQZd9VHZqb+ZBlGZdddllQzxEbG8sfbBXwOquD11kdvM7q4bVWRzCu86VmPLqw4JSIiIhUxeSDiIiIVDWskg+TyYSioiKYTKZQhxLWeJ3VweusDl5n9fBaq0ML11lzBadEREQU3obVzAcRERGFHpMPIiIiUhWTDyIiIlIVkw8iIiJSVdglH8888wy+9rWvwWw2Iz8/H4cOHfI5/uWXX8aVV14Js9mMq666Cm+88YZKkeqbP9f5T3/6E6ZPn464uDjExcVh9uzZl/z/Qp38/Xnu8tJLL0GSJNx8883BDTBM+Hud7XY7li1bhtTUVJhMJlxxxRX83TEA/l7np556CmPHjkVUVBTS09Nx//33o7W1VaVo9emf//wnCgoKMGrUKEiShNdee+2S37Nv3z5cc801MJlM+PrXv47NmzcHPU6IMPLSSy8Jo9Eonn/+eXH8+HFx1113CZvNJurq6voc/8477wiDwSDWrVsnTpw4IX75y1+KyMhI8eGHH6ocub74e51/8IMfiGeeeUYcPXpUVFRUiCVLlgir1Sq++OILlSPXF3+vc5fq6mqRlpYmpk+fLr7zne+oE6yO+XudXS6XuPbaa8W3vvUt8fbbb4vq6mqxb98+cezYMZUj1xd/r/OLL74oTCaTePHFF0V1dbX4xz/+IVJTU8X999+vcuT68sYbb4hHHnlEvPLKKwKAePXVV32OP3XqlBgxYoRYvny5OHHihPjDH/4gDAaDKC0tDWqcYZV8TJ48WSxbtqz7vdvtFqNGjRJr1qzpc/zChQvFt7/9bY9j+fn54u677w5qnHrn73XuraOjQ8TExIiSkpJghRgWBnOdOzo6xNSpU0VxcbFYvHgxk48B8Pc6P/vss2LMmDGira1NrRDDgr/XedmyZWLWrFkex5YvXy6uu+66oMYZTgaSfDzwwAMiJyfH49iiRYvE3LlzgxiZEGGz7NLW1obDhw9j9uzZ3cdkWcbs2bNx8ODBPr/n4MGDHuMBYO7cuf2Op8Fd594uXryI9vZ2jBw5Mlhh6t5gr/OqVauQlJSEO++8U40wdW8w13n79u2YMmUKli1bhuTkZOTm5mL16tVwu91qha07g7nOU6dOxeHDh7uXZk6dOoU33ngD3/rWt1SJebgI1X1Qcw+WG6yGhga43W4kJyd7HE9OTsa///3vPr+ntra2z/G1tbVBi1PvBnOde3vwwQcxatQorx94+spgrvPbb7+N5557DseOHVMhwvAwmOt86tQp7NmzB//xH/+BN954Ax9//DF++tOfor29HUVFRWqErTuDuc4/+MEP0NDQgGnTpkEIgY6ODixduhQrVqxQI+Rho7/7oNPpREtLC6KiooJy3rCZ+SB9WLt2LV566SW8+uqrMJvNoQ4nbDQ1NeH222/Hn/70JyQkJIQ6nLCmKAqSkpKwadMmTJw4EYsWLcIjjzyCP/7xj6EOLazs27cPq1evxn//93/jyJEjeOWVV/D3v/8djz32WKhDowAIm5mPhIQEGAwG1NXVeRyvq6tDSkpKn9+TkpLi13ga3HXu8sQTT2Dt2rV48803kZeXF8wwdc/f61xVVYVPPvkEBQUF3ccURQEARERE4OTJk8jMzAxu0Do0mJ/n1NRUREZGwmAwdB/Lzs5GbW0t2traYDQagxqzHg3mOv/qV7/C7bffjh//+McAgKuuugoXLlxAYWEhHnnkEcgy/+4cCP3dB2NjY4M26wGE0cyH0WjExIkTsXv37u5jiqJg9+7dmDJlSp/fM2XKFI/xALBr165+x9PgrjMArFu3Do899hhKS0tx7bXXqhGqrvl7na+88kp8+OGHOHbsWPdrwYIFmDlzJo4dO4b09HQ1w9eNwfw8X3fddfj444+7kzsA+Oijj5CamsrEox+Duc4XL170SjC6Ej7BR5IFTMjug0EtZ1XZSy+9JEwmk9i8ebM4ceKEKCwsFDabTdTW1gohhLj99tvFQw891D3+nXfeEREREeKJJ54QFRUVoqioiFttB8Df67x27VphNBrF//7v/4qampruV1NTU6j+E3TB3+vcG3e7DIy/1/mzzz4TMTEx4p577hEnT54Ur7/+ukhKShK/+c1vQvWfoAv+XueioiIRExMj/ud//kecOnVK7Ny5U2RmZoqFCxeG6j9BF5qamsTRo0fF0aNHBQCxfv16cfToUfHpp58KIYR46KGHxO233949vmur7S9+8QtRUVEhnnnmGW61HYw//OEP4vLLLxdGo1FMnjxZvPvuu91f++Y3vykWL17sMX7btm3iiiuuEEajUeTk5Ii///3vKkesT/5c59GjRwsAXq+ioiL1A9cZf3+ee2LyMXD+XucDBw6I/Px8YTKZxJgxY8Rvf/tb0dHRoXLU+uPPdW5vbxcrV64UmZmZwmw2i/T0dPHTn/5UnD9/Xv3AdWTv3r19/r7turaLFy8W3/zmN72+5+qrrxZGo1GMGTNG/PnPfw56nJIQnL8iIiIi9YRNzQcRERHpA5MPIiIiUhWTDyIiIlIVkw8iIiJSFZMPIiIiUhWTDyIiIlIVkw8iIiJSFZMPIiIiUhWTDyIiIlIVkw8iIiJSFZMPIiIiUhWTDyIiIlLV/wf39cmb3/EwgAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [*range(0, 11)]\n",
    "x = [0.1 * i for i in x]\n",
    "y0 = poly(x,[1])\n",
    "y1 = poly(x,[1, 1])\n",
    "y2 = poly(x,[1, 1, 1])\n",
    "y3 = poly(x,[1, 1, 1, 1])\n",
    "y4 = poly(x,[1, 1, 1, 1, 1])\n",
    "y5 = poly(x,[1, 1, 1, 1, 1, 1])\n",
    "y6 = poly(x,[1, 1, 1, 1, 1, 1, 1 ])\n",
    "l = 'upper right'\n",
    "plt.scatter(x, y0) ; plt.scatter (x, y1) \n",
    "plt.scatter(x, y2) ; plt.scatter (x, y3)\n",
    "plt.scatter(x, y4) ; plt.scatter (x, y5)\n",
    "plt.scatter(x, y6) \n",
    "plt.legend(['n=0', 'n=1', 'n=2', 'n=3', 'n=4', 'n=5', 'n=6'], loc = l)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [*range(0, 11)]\n",
    "x = [0.1 * i for i in x]\n",
    "y0 = poly(x,[1])\n",
    "y1 = poly(x,[1, 1])\n",
    "y2 = poly(x,[1, 1, 1])\n",
    "y3 = poly(x,[1, 1, 1, 1])\n",
    "y4 = poly(x,[1, 1, 1, 1, 1])\n",
    "y5 = poly(x,[1, 1, 1, 1, 1, 1])\n",
    "y6 = poly(x,[1, 1, 1, 1, 1, 1, 1 ])\n",
    "l = 'lower right'\n",
    "plt.scatter(x, y0) ; plt.scatter (x, y1) \n",
    "plt.scatter(x, y2) ; plt.scatter (x, y3)\n",
    "plt.scatter(x, y4) ; plt.scatter (x, y5)\n",
    "plt.scatter(x, y6) \n",
    "plt.legend(['n=0', 'n=1', 'n=2', 'n=3', 'n=4', 'n=5', 'n=6'], loc = l)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [*range(0, 11)]\n",
    "x = [0.1 * i for i in x]\n",
    "y0 = poly(x,[1])\n",
    "y1 = poly(x,[1, 1])\n",
    "y2 = poly(x,[1, 1, 1])\n",
    "y3 = poly(x,[1, 1, 1, 1])\n",
    "y4 = poly(x,[1, 1, 1, 1, 1])\n",
    "y5 = poly(x,[1, 1, 1, 1, 1, 1])\n",
    "y6 = poly(x,[1, 1, 1, 1, 1, 1, 1 ])\n",
    "l = 'lower left'\n",
    "plt.scatter(x, y0) ; plt.scatter (x, y1) \n",
    "plt.scatter(x, y2) ; plt.scatter (x, y3)\n",
    "plt.scatter(x, y4) ; plt.scatter (x, y5)\n",
    "plt.scatter(x, y6) \n",
    "plt.legend(['n=0', 'n=1', 'n=2', 'n=3', 'n=4', 'n=5', 'n=6'], loc = l)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [*range(0, 11)]\n",
    "x = [0.1 * i for i in x]\n",
    "y0 = poly(x,[1])\n",
    "y1 = poly(x,[1, 1])\n",
    "y2 = poly(x,[1, 1, 1])\n",
    "y3 = poly(x,[1, 1, 1, 1])\n",
    "y4 = poly(x,[1, 1, 1, 1, 1])\n",
    "y5 = poly(x,[1, 1, 1, 1, 1, 1])\n",
    "y6 = poly(x,[1, 1, 1, 1, 1, 1, 1 ])\n",
    "l = 'center'\n",
    "plt.scatter(x, y0) ; plt.scatter (x, y1) \n",
    "plt.scatter(x, y2) ; plt.scatter (x, y3)\n",
    "plt.scatter(x, y4) ; plt.scatter (x, y5)\n",
    "plt.scatter(x, y6) \n",
    "plt.legend(['n=0', 'n=1', 'n=2', 'n=3', 'n=4', 'n=5', 'n=6'], loc = l)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [*range(0, 11)]\n",
    "x = [0.1 * i for i in x]\n",
    "y0 = poly(x,[1])\n",
    "y1 = poly(x,[1, 1])\n",
    "y2 = poly(x,[1, 1, 1])\n",
    "y3 = poly(x,[1, 1, 1, 1])\n",
    "y4 = poly(x,[1, 1, 1, 1, 1])\n",
    "y5 = poly(x,[1, 1, 1, 1, 1, 1])\n",
    "y6 = poly(x,[1, 1, 1, 1, 1, 1, 1 ])\n",
    "l = 'center right'\n",
    "plt.scatter(x, y0) ; plt.scatter (x, y1) \n",
    "plt.scatter(x, y2) ; plt.scatter (x, y3)\n",
    "plt.scatter(x, y4) ; plt.scatter (x, y5)\n",
    "plt.scatter(x, y6) \n",
    "plt.legend(['n=0', 'n=1', 'n=2', 'n=3', 'n=4', 'n=5', 'n=6'], loc = l)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [*range(0, 11)]\n",
    "x = [0.1 * i for i in x]\n",
    "y0 = poly(x,[1])\n",
    "y1 = poly(x,[1, 1])\n",
    "y2 = poly(x,[1, 1, 1])\n",
    "y3 = poly(x,[1, 1, 1, 1])\n",
    "y4 = poly(x,[1, 1, 1, 1, 1])\n",
    "y5 = poly(x,[1, 1, 1, 1, 1, 1])\n",
    "y6 = poly(x,[1, 1, 1, 1, 1, 1, 1 ])\n",
    "l = 'center left'\n",
    "plt.scatter(x, y0) ; plt.scatter (x, y1) \n",
    "plt.scatter(x, y2) ; plt.scatter (x, y3)\n",
    "plt.scatter(x, y4) ; plt.scatter (x, y5)\n",
    "plt.scatter(x, y6) \n",
    "plt.legend(['n=0', 'n=1', 'n=2', 'n=3', 'n=4', 'n=5', 'n=6'], loc = l)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [*range(0, 11)]\n",
    "x = [0.1 * i for i in x]\n",
    "y0 = poly(x,[1])\n",
    "y1 = poly(x,[1, 1])\n",
    "y2 = poly(x,[1, 1, 1])\n",
    "y3 = poly(x,[1, 1, 1, 1])\n",
    "y4 = poly(x,[1, 1, 1, 1, 1])\n",
    "y5 = poly(x,[1, 1, 1, 1, 1, 1])\n",
    "y6 = poly(x,[1, 1, 1, 1, 1, 1, 1 ])\n",
    "l = 'upper center'\n",
    "plt.scatter(x, y0) ; plt.scatter (x, y1) \n",
    "plt.scatter(x, y2) ; plt.scatter (x, y3)\n",
    "plt.scatter(x, y4) ; plt.scatter (x, y5)\n",
    "plt.scatter(x, y6) \n",
    "plt.legend(['n=0', 'n=1', 'n=2', 'n=3', 'n=4', 'n=5', 'n=6'], loc = l)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [*range(0, 11)]\n",
    "x = [0.1 * i for i in x]\n",
    "y0 = poly(x,[1])\n",
    "y1 = poly(x,[1, 1])\n",
    "y2 = poly(x,[1, 1, 1])\n",
    "y3 = poly(x,[1, 1, 1, 1])\n",
    "y4 = poly(x,[1, 1, 1, 1, 1])\n",
    "y5 = poly(x,[1, 1, 1, 1, 1, 1])\n",
    "y6 = poly(x,[1, 1, 1, 1, 1, 1, 1 ])\n",
    "l = 'lower center'\n",
    "plt.scatter(x, y0) ; plt.scatter (x, y1) \n",
    "plt.scatter(x, y2) ; plt.scatter (x, y3)\n",
    "plt.scatter(x, y4) ; plt.scatter (x, y5)\n",
    "plt.scatter(x, y6) \n",
    "plt.legend(['n=0', 'n=1', 'n=2', 'n=3', 'n=4', 'n=5', 'n=6'], loc = l)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 20\n",
    "x = [*range(0, N+1)]\n",
    "y = [(i-4)*(i-10)*(i-16) for i in x]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 20\n",
    "x = [*range(0, N+1)]\n",
    "y = [(i-4)*(i-10)*(i-16) for i in x]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 20\n",
    "x = [*range(N)]\n",
    "y = [(i-4)*(i-10)*(i-16) for i in x]\n",
    "\n",
    "c = ['r', 'b'] * 10\n",
    "\n",
    "plt.scatter(x,y, color = c)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 20\n",
    "x = [*range(N+1)]\n",
    "y = [(i-4)*(i-10)*(i-16) for i in x]\n",
    "\n",
    "c = ['r', 'g', 'b'] * 7\n",
    "\n",
    "plt.scatter(x,y, color = c)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 20\n",
    "x = [*range(N+1)]\n",
    "y = [(i-4)*(i-10)*(i-16) for i in x]\n",
    "\n",
    "c = ['r', 'g', 'b'] * 7\n",
    "\n",
    "plt.scatter(x,y, c = c, s = 150)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ScLH1AIiIiKhlqvQGHLh8AydyipFxrRQ6vQhvNwV6BqsxMNIPoX7uth6iXWE4IiIislPlOj0+2X0Ja1Mu49qtSsgEQCYIEAEIAKoMIgQAd3duj+fu74z+EX42HrF9YDgiIiKyQ8dzivDnr44g62YZRLF6m0EEDDUP7hABpFy4hj3nr2H60Ai8NKYbVC5y6w/YjjAcEVEdoigiX1uBE1eKka8thyAAwT5uiO7ojfaeKlsPj8jppVy8hifXHIROb8BvslC99HfafJZyGRcKbuGTaf0ZkBrBcERERuU6PTak5Rh/gNYnuqM3pg+JwLiYYLjIeU0HkbVl3yjDjLWHoNMbYDAhGEkZRGDPhWt4ddNJLH2kT+sM0AHwJxsRAQAOZ91Ewopf8eqmk7jYQDACgFO5xZi74RjGrdyL8/klVhwhEYmiiL9sOIbKZgSj//UBrD+Ug53pBZYdnANhOCIibDl+FY+sSkH2zTIA1XMUGlLzA/lsXgl+994epFy81voDJCIAwN4L17E/4wb0zU1Gd8gE4M0fz0I05ZycE2I4InJyqRev489fH4Yowqy/RPUGETq9AU+uOYj0PB5BIrKGf++7DLlMaHE/BrH6D5xjOcUWGJXjYTgismMGg4jM66U4l18CbbnO7NffqqjC8+uOAmj8aFGD7y9WXyo8Z90R6PSGZvRARKYyGETsPnetxUeNasgFAXvOF1qkL0fDCdlEdqiyyoC1KRlYm3IZuUXlAAAXmYDf9e6AxPvuQudAL5P6+WT3JRSUlDd77gJQfQTpzNUSfHc4B5MGhDW/IyJq1OXrpbit01usPxEijxw1gEeOiOxMuU6PJ1bvx+IfzxqDEVB9BOe/x6/iwff3YP+l6032o9Mb8HlqZouCUQ1BANbsvcz5C0St6HpppUX7M4hAvra86YZOiOGIqJXVnG6y1GmnZT+n40DGjXrXNtEbRFRWGTDjs0MoaeI02/GcIov9sBXvzF+4UnTbIv0RUV0tn2lUl1xojV7tH0+rEVmYKIrYn3EDG9NycDDjBvKLS7FkINDv7xoEeXtgUGQ7TOwfgthwXwhm/mC6VVGFr/ZnNXq0xyACpRVV+P7IFTwRF9Fgu+M5xRAEmLSAnKlOXilGYNf2luuQiIxCfC17fzS5TEBEew+L9ukoGI6ILOjMVS3mbjiG07layGUC9AYRqjuL0IoicPl6GbJv3sa6Q9noE+KNtyb2MXl+EADsOX/N5DkH/z2W22g4unLzNlxkAnR6y6QjmQDk3DT/yFFllQEKuWB2UCRyNoFqFXzdFbhZZv7FF/URRRG9OnpbpC9Hw3BEZCFf7s/Eq5tOGn/JN3RFSc32k7lajHlnN96c0BuPxIaY9B5NnSqrIQIovt14W0vPDhIgmHwUKrfoNj5LvYx1B7JRdFsHuUzAiK4BeHJoBIbcxSNPRPURBAFje3fA1weyLXLFmigC8T0CLTAyx8M5R0QW8NX+LLz8/UkYxIZD0W/pDSKqDNWr3X6blmPSa9p7mXZfM5kABKpdG+/LU2WxS4IBQC+KaO+lbLJdWuZNjFqxC5/8moGiOwFObxDxS3oBHv9kP95OTrfYmIgczdTBERb53MplAoZ39Ueon2VP1TkKhiOiFjqbp8Wrm0+2qI+XvjuOS4UN37KjxtBO7eHrrmiynUFEk0ejojt6W+RKtdp9+jT6/I3SSkxfcwC3K/XQ/+YwU80P/Pd+uYDvDpsWFomcTdcgL0wZFIaWrgMpCMDLY3tYZlAOiOGIqAVEUcQLG463uB+DCLz4bdP9KF1kmDmsU6Nt5DIBwT6uGN0rqNF2fcN84Kaw3F25g9QqRDUxufObg1korahqNJQJAFbuuMBlAYgaMO+B7gjxdW/RStkvP9AddwV4WnBUjoVzjoha4HDWTZy40vJF1PQGEQcv38TJK8VNTpB86p4oZF4vxVcHso2TvmvIBKC9pxJfzBgElUvjwcdD5YKJ/UPw5f4si9ynaWpcBGQyAfpG5ot/m5bT5NEqEcDFwlKcuVqCHsFqs8ciitUL2x3NuonzBbdQUWWAp8oF3YK80D/CF3cFmD4Bnqgt8lS54OunB2PSR6m4WnQbpl5TIaD68/Xs/Z3x5NDI1hyi3WM4ImqBjWk5dQJKc7nIBGxMy2kyHMlkAv7x+2iM7BGItXsvY+/F69AbRHTwdsUfBofj8YFh8PVoeu4PAMwc1gkb03JQVtn8VXdlAuDnocTUuPAm2167Zfq6StdLK8wah94g4puDWfj410vIvF4GQahew0VE9S+Fqjv7qG+oD/7vvrswihNRyY519HHDD3++Gwv+cwqbjuY2+XNIJgBergosfjgaD0R3sOJI7RPDEVELHLDA3bFrVBlEpGXeNKmtIAgY0S0QI7oFQhRFGEQ06xB7sI8bFjzUE3/d2PxTgwYReGtiH6hdm54LpXZ1afIquv+1bbq/Gtk3yvDs10dwJLvIuFCeKAJV9ZyaO5ZThKf+fQhjozvgjYej4e1m+vsQtSU+7kr887G+eGxgGNbszcC20wV15vIBQKCXCk8MiTDrDydnx3BE1EwGg4iMa6UW7fNcvvl3txcEAfIWTM58tH8ocm6U4d1fLpj3vqg+RL9ofC8M7xpg0mseignGqp0Xmzy11sHb1eT1Vy4U3MKjH6UaQ1dTUbXmvX86mYfzBSVY/0wcfNz5C4Ps1+Codhgc1Q63K/U4k6fFpcJSVOkN8HFXoGewN0J83biOmJk4IZuomXQGg8Wv9qqsss2d7ZPiu+LNh6PhqpCZdARKLhPg5eaCD/8Qi6mDmz6dVuPxQeGQy4Qmb4Pwp3uiTBpHSbkOf/hkP4pv68w+gqcXRVwsKMXTn6fBYOkdSWQDbko5+oX54pHYEDw2MAyje3VAqJ87g1EzMBwRNZNSblqQMIerBa8eM9djA8OgeX4YHokNgcql+keDXCbA5c5XzT2Y3JVyTIuLwI65w5u8Iu63Ovq4YeXj/SCXCXX+7Wp+fo+PCcaTQyJM6u+NrWdQUFLe7FObelHEgYwb+GJ/ZrNeT0SOiafViJpJEAR0DvDE2TzzT4U1pFuQba+kCvVzx5IJvfG3B7rjYMYNnLhSjIKScgACOvpUn+oaGOkHd2Xzf3TE9wzC9/83FB/uuoifTuYZ50h0CfDEjLuj8EhsCGQmhM7sG2X45kC2RVb6fjv5HCYNCG3yCj8icg4MR0QtMDDSD+cLbllsxdoBkX4WGFXLebspMLJHIEa20hVd0SHeWDmlH7TlOhSWVMBdKUeQ2tWsw/9f7s+CTBDqnYBqruLbOvx4Ig/j+3ZscV9EZP94Wo2oBR7tH2qxq9X0BhGP9jftHmuOQu2qQCd/T3TwNn/C6I6z9V+Z0xxymYDd569ZpC8isn8MR0Qt0KujNwZF+rV47pFcJmBYF38uUGiiiio9LhQ0fbsVU+kNIo5km7aMAhE5PoYjohZa+khvuJhwBVZDBFRP7l78cLQlh+XQim/rLHbUqMa1W+YtOklEjovhiKiFwtt5YPmjMYAAswOSgOqrtN6d3BfBPm6tMDrHJGuFS5NlzY63RORoGI6ILGBs7w744PF+UJm4ThBQfSrNTSnHx1P781YWZvJ1VxqXG7CUUD93i/ZHRPaL4YjIQsZEd4Dm+WEY2qkdgIZv51GzfVgXf2yfO6zVrghzZHKZgJ7NuCltQ1xkAmJCfSzWHxHZN17KT2RBoX7u+PeMQbhQUIINaTlIy7yJjHwtAD28VC7oHaBG/wg/TIwNQZS/p62Ha9ceiO6AI1lFFlnnqMogIqGneQtaEpHjYjgiagV3BXhh3pjuAACdToetW7cidd79UCh4k1NLmRgbimU/p6OihbdckQlAqK87ht7VzkIjIyJ7x3BETq2yyoAd6QXIvlEGlUKOe+5qj4j2HrYeFpnA212BufFd8MbWsy3qxyBW3zzX3HWWzlzV4sDFQvgA2HO+EPd262Dx28mQ4ym+rcO20/m4UVoJb3cFRnYPhJ8Hb3zc1jAckVMSRRGf78vECs053CzTQSYAolh9R/d7OrfHmxN6oyOvHmvzZtwdBc3pfBzOLGrWpf0CgD8MDse9XfxNfs25/BK8uPE4jmQXwdVFxJIBwMwvD8PXww1/G9sdD/UJNnsc5Pgqqwx488ez+HJ/JiqqDJAJ1cHcRSbgkdgQzH+wJ9yUvH1NW8EJ2eSU3t1+Aa9tPoWbZToA1T+kan61ply8jvEr9+Jq8W3bDZBMIpcJ+HT6APTqqEZzDto8FBOMBQ/1NLn9ufwS/P6DvTieUwygOlDXyNOW49mvj+CbA1nmD4QcWpXegGe+OIQ1KRnG08A1C+tXGUSsP5SNqZ/uR7lOb8NRkpTThqOVK1ciIiICrq6uGDRoEA4cOGDrIZGVXCgowYpt5xp8Xm8QcaO0Ev/YcsaKo6LmUrsqsO6ZOPxxaCQENHyVYA25IEApl2H+gz2w4tEYs06FvfTtcZRXGho9SvXq5pO4zgUlSWLT0VzsOFuIhr5tDCKQlnkTX+5nsG4rnDIcrVu3DklJSZg/fz4OHz6MPn36ICEhAQUFBbYeGlnBF/uymvyFqDeI+PFk3p070lNb56qQ45Xf9cAPz96NCf06Qimv/0ebl6sLZtwTiV/+MgxPDo2EzIxgdOaqFoezmj59pzeIWH8ox6zxk2NbuzfDpCOba/dmQLTwyu/UPE4552j58uV46qmn8OSTTwIAPvzwQ2zZsgWrV6/GSy+9VKttRUUFKir+91egVqsFUH0Fkk6ns+i4avqzdL9tkS1r3X+xEC6CAS5Nnt4XkZZxDSO7t2wdImfar4Bt6+3i745/jOuB+WO74kJBKS4W3kK5zgAPVxd0DfBERHsPYzA2d3wHLhbCVS7WWjpAJRNr/bdGWkYhdEPDWlRLW8Pv4+ap0OlxPr8YChMORRRoy5B7sxQBXqoWvae5nGXfmlOfIDpZTK2srIS7uzs2btyI8ePHG7dPmzYNRUVF2Lx5c632CxYswMKFC+v089VXX8HdnSvqEhER2YOysjI8/vjjKC4uhlrd+CKyTnfk6Nq1a9Dr9QgMrH00IDAwEGfP1r0keN68eUhKSjI+1mq1CA0NRXx8fJP/uObS6XTQaDQYNWqUw6+HY8ta53xzBDvOFUJvaPrvgu9mDUGXQK8WvZ8z7VfAcevdc74QM788XGubSiZiUX8DXj0kQ4Wh+oiUXCbgsf5hmPdAN7Pf4/K1Umw4lI1fz1/D5RulxjkqSpkMXYM8kdArCONjOsLH3fqXfjvqfm2IpeoVRRHD39qJ66WVTbZ1V8ix58X7oGz6sLZFOcu+rTnzYwqnC0fmUqlUUKnqHuJUKBSt9k3Umn23NbaoddKgSGw5VYjGbhMrE4Dojt7oGeJnsfd1pv0KOF6993brAF8PN+Rp685DqzAIqNDf+X7SA48NjjCr9uIyHV7/4RS+PXwFcplwJ7j/7/uzQi/iUHYJ0nJKsCz5IubGd8Gf7omyybpKjrZfm2KJeicNjMQ728+hsb/H5DIBv48Nh4eba4veqyUcfd+aU5vTTchu37495HI58vPza23Pz89HUBBvH+AM7r6rPYbe1a7BCZI1m1+6s8I1EVD9y+vlsY1/TwgCMKFfR7OONqbnlWDkil34/sgVAGj0iKYoApV6Axb/eBaTPk5F8W3HniPiKJ6IC0eg2rXR+y2qXV3w1L2RVh4ZNcTpwpFSqURsbCy2b99u3GYwGLB9+3bExcXZcGRkLTKZgI+m9jcu/FfzA0u48+WqkOOjqf0R14m3k6DaHuwTjCUToqGQC7XCdc330MN9O2Lxw71N7i/jWikmfpSCG6WVjR5VqM+RzCI8wbVx7IKvhxLrno5DmF/1PFX5ne+dmu+hAC8V1j0Thw7eXHi2rXDK02pJSUmYNm0a+vfvj4EDB+Kf//wnSktLjVevkePzVLlg7ZMDcSy7CN8czEbm9VK4KeQY3tUf4/t2hJer4x5appaZNCAMo3oEYf2hbBy6VAggH5MHhOGxQRHobMYRoyq9AX/++jBKK/QmzX/7Lb0o4sSVYrydnI6Xx/Yw+/VkXWHt3LEtaRh2phdg89FcFJZUwM9Did/17oCRPQKhaGD5CbINpwxHkyZNQmFhIV577TXk5eUhJiYGP/30U51J2uT4+oT6oE+oj62HQXbGz0OJmcM6QTckDFu3bsVLY7qZPVfjy/1ZOHnF9Ami9TGIwCe7M/D7viHoEWzZC0TI8uQyAfd3D8T9LVwehFqfU4YjAJg9ezZmz55t62EQkRMyGET8a/clCABaupaKTCZgbUoGlj7SxxJDc2oXC28h9eJ1nMothra0HPFqYIXmHHqF+uHeLv7wVDntr0ynwz1NRGRlR7JvIuemZe7dpzeI2Hw0F//4fTRPzTTT/kvX8bbmHA5k3DDegkYuGBA/EPgs9TJKf70MN4UckwaE4rn7O8PXw/pLKZB18ZNERGRlR7OLm3Wj3IZUVBlwLr/Ech06icoqA+ZvPolJH+/Docs3AFQfyauSzAGr+f/bOj0+T83EiLd3Ykc6bzXl6BiOiIis7EJBCWSCZdcoulBwy6L9ObrKKgOe+vch/HtfJgCYdLWgXhRRVKbDH9cexOajV1p5hGRLDEdERFZWoTNY/AajFTqDRftzdIt+OI1fzxfC3N0gonq9qefXHcXxnKLWGBq1AQxHRERW5unqAsHCR448XTmF1FSpF6/j832ZZgcjKQHAnHVHUVHFdaYcEcMREZGVdQtS15rXYgndO/BSflO9nZze4jlfehG4VFiKrSeuWmZQ1KYwHBERWdmACF+L9ufjrkD4ndWXqXHn80twKPOm2SuS10cmAJ+lZLa8I2pzeByWiMjKOgd6oU+IN05cKW7xL2mZADw+MAwyEw+F3KqowvdHrmDjoWzkacvh5arA73p3wOSBYQhUt/ymp6IoIvN6GS4W3kJllQFqNwW6d1DDr41c/r73wjUIAlp0Sq2GQQSO5RShrLIK7kr+OnUk3JtERDaQeN9dePrztBb3o3SRYWpcuElt0/NK8IdP96OwpMK4AGW+tgLvbj+PD3ZcxHuP90VCz+bdgPtCwS18nnoZ3x6+glsVVXWeD2/njqmDwzGxfyi83Wx3e56TuVrIBQFVFpoQL4rAmataxIb7WaQ/aht4Wo2IyAbiewbhgeggyFs4MfuVsT1MumHpjdJKPP6vfbhRWgmg9srcBhHQ6Q34vy8P40jWTbPev6JKj6U/nUX8il34Yn9WvcEIALKul+EfW85g+LIdSD6VZ9Z7WNKNWxUWn+91/ValRfsj22M4ojZPpzegQFuOfG05Kqt4uTI5jsUP98ZdAZ7NDkiPxIZgyqAwk9p+fSALN8sqG7zJbc3W93dcMPn9teU6TPpoH1btvAiDiEZvoCve+Sq6rcPTn6dheXK6xZczMIVMZvlfe3JLruhJbQJPq1GbVKAtx9cHspF8Og/n8kug01f/EHWRCegc6IlR3QMxeVCYSX8xE7VV3m4KrHtmMGZ+kYZ9l26Y9BqZUH2kZ8bdkXj5ge4mLwnw9f6sJuc36Q0ifjlbgGu3KtDeU9Vo2yq9AX9ccxAncorMuj9cTR5695cLULsp8Kd7osx4dcuFt3OHi0yw6NGj8HacDO9oGI6oTamo0uOdbefx0a5LECHW+WFeZRBx5moJ0vNK8P6OC5hxdyTmxneFq0JumwETtZCPuxJf/Wkwvtyfibc151BUpoNcJtQ5ClPzCz2yvQcWje+FIZ3am/U+V4vLTWonikBecXmT4eijXy/hUKZ5p+B+a/GPZ3F35/boFmT6MgQFJeVYdyAbaVk3YTCI6B6sxuMDwxDezsOk10d39LZoMHJVyBDZ3tNi/VHbwHBEbUaBthx/+HQ/zuffavIv0ZqfbZ/uycC2MwX44k+D0NGHR5HIPslkAqbGRWDSgDD8dCoPe84X4khWEa7fqoQgAGHt3BET6oOEnkEYFOnXrAUklQoZbleatmChq6LxU08FJeVYoTln9hjq89rmU1j/TJxJbT/ZfQmLt56t9YfT3ovX8dGuS/jj0Ei8PLZ7k6e47usaAKVchkp9y0/Ry2UCHujVgafVHBDDEbUJRWWVePSjVGTfvG3WIXqDCGTdKMPED1OwOfFu+Hs1/tcuUVumdJHhoT7BeKhPsMX7HtE1AD+dymt0XhAABPu4IqqJIyHrDmTDYIH5QnqDiAMZN3A+vwSdA70abfvl/kz8fcuZevsAgDV7M+AiF/C3B7o32o+3uwK/79cRG9Nymvy3aIreIJp8pSDZF07Ipjbh1U0nkX3jdrN+WOkNIvK1FXjpu+M2meBJZA+mD41o8vMlAHhySGSTayZtPpZrkUUUgeqjL1uaWGW6XKfHkh/PNtpGRPWRpavFt5t8z7nxXeCulKMlx3tkAjChX0f0DbPsgp7UNjAckc3tOleI/x6/Cn0Lgo3eIGL7mQIkn8634MiIHMeACD88O+KuBp8XBODeLv6YPjSi0X4qdHpcKrxlsXEZRBHHc4obbfPzqTxoy+tfIuC31h/MabJNgJcrlk7obVJ/9ZHLBHTwdsNrD/Zsdh/UtjEckc19svuSRc7Zy4TqvoiofknxXbHskd4I+82tRnzcFHh2RGd8Mq0/FPLGfy1cKSq32FEjoHoCeFNh60LBLbiY+DPigonBbUx0B7w5IRqCALPusyYTgCC1K9Y9M9imi1lS6+KcI7Kpa7cqsfv8NYv0ZRCBg5dvIudmGUJ8eWktUX0m9g/FI7EhOJZTjAJtOTxdXRAb7guVi2lXfFpirtFvNXXU2EUmM2kuogCYHKIAYNKAMET5e+L5dUeRc/N2o7cVqbmCcFxMRyx4sCe83RmMHBnDEdnUqStFFu/zWHYxwxFRIwRBQEyoT7Ne69sK90hr79H4hRQDIn1Nmo+oF4GBkebdxmNAhB+2JQ3DpiNXsDblMs7mldRpo5TLMCY6CE/ERSA2nHOMnAHDEdnUxWulxkXtLMFFJuB8QQmADpbpkIhqaeehRDsPJa6XWuaWGS4yAX2aCGpxUe0Q2c4dmTfKGvxZIQBwV8oxLsb8K/1cFXI8NjAMjw0MQ/FtHU7lFuNGSTn0mWn4dtYQdOvgA5cmTjeSY+HeJpuq0Bkga+G9paQEAajgLUaIWtWwLv4WW9unyiBiSKd2jbYRBAFvT4qBi0xW7/ygmk1LH+kDd2XL/ub3dlNgSKf2SOgZCADoGujFYOSEuMfJpjxVLhadw2AQq/skotYzZXB4i9cIquHvqcKIbgFNtusX5ov1M+PQNah6PSSZZCJ1iK8b/vVEf4ztzSPGZBn8LUI21SXQ06JXvugNIrp3aHwxOSJqmX5hPrinc3ukXLze4pCUFN/F5CMzMaE+2PrsPTiWU4zDmTdhEEV0C1JjSKd2Ta7NRGQOhiOyqZ4d1XCRC6jSWyYhyQSgT4iPRfoiovoJgoAlE3pj1PJduK3TN+sPHLkgYFCUHx4bEGr2e8eE+jR7QjmRKXhajWzKU6XAg72DLTJ/QS4TEN8zCO2auGEmEbVcsI8bPp0+AAp5/fOAGiOXCegU4IEPpvRr1n3iiFobwxHZ3FP3RFnkth8Gg4hn7o2ywIiIyBSDo9ph3TNxCPZxMykg1TQZ0S0AG54ZAh93yy8LQGQJDEdkcz2C1fi/4XehJX9AygRgxt2RvM8RkZXFhPpA8/wwJN53F9Su1TM15DLB+HmWywRjcOoS6ImVj/fDx1NjuYgitWlmzzmaNm0aZsyYgXvvvbc1xkNO6tn7O+N4ThH2XLhm9vwFmVC9kNtfErq2zuCIqFFuSjnmxnfF7BF3Yfe5azieU4QLhbdQWWWA2lWBHsFqDIjwQ+8Qb55GI7tgdjgqLi7GyJEjER4ejieffBLTpk1Dx44dW2Ns5ESULjJ8/ER//HXjcfznWC4EoMnbBdS0ie8RhBWTYuCqMO32B0TUOlQucozsEYiRPQJtPRSiFjH7tNqmTZtw5coVzJo1C+vWrUNERATGjBmDjRs3QqfTtcYYyUm4KuR4d3JfrJrSD0HergDqv09SzTZ/LxXeeSwGq/7QD25KBiMiIrKMZl3K7+/vj6SkJCQlJeHw4cNYs2YNpk6dCk9PT/zhD3/A//3f/6Fz586WHis5iTHRHRDfMwi7zhVg+5kCHM0uwtWi2xABBHm7om+YL+7rGoD7uvpz5VoiIrK4Fq1zdPXqVWg0Gmg0GsjlcjzwwAM4ceIEevTogaVLl+L555+31DjJychlAkZ0C8SIbjw8T0RE1mX2n906nQ7ffvstfve73yE8PBwbNmzAnDlzkJubi88++wzbtm3D+vXr8frrr7fGeImIiIhaldlHjjp06ACDwYDJkyfjwIEDiImJqdPmvvvug4+PjwWGR0RERGRdZoejFStWYOLEiXB1dW2wjY+PDzIyMlo0MCIiIiJbMDscTZ06tTXGQURERNQm8FIfIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgmGIyIiIiIJhiMiIiIiCYYjIiIiIgm7CEeXL1/GjBkzEBkZCTc3N3Tq1Anz589HZWVlrXbHjx/HPffcA1dXV4SGhmLp0qV1+tqwYQO6desGV1dXREdHY+vWrdYqg4iIiOyAXYSjs2fPwmAw4KOPPsKpU6ewYsUKfPjhh/jb3/5mbKPVahEfH4/w8HCkpaVh2bJlWLBgAT7++GNjm5SUFEyePBkzZszAkSNHMH78eIwfPx4nT560RVlERETUBrnYegCmGD16NEaPHm18HBUVhfT0dKxatQpvvfUWAODLL79EZWUlVq9eDaVSiZ49e+Lo0aNYvnw5nn76aQDAO++8g9GjR+OFF14AACxatAgajQbvv/8+Pvzww3rfu6KiAhUVFcbHWq0WAKDT6aDT6SxaZ01/lu63LWKtjsuZ6mWtjsuZ6nWWWs2pzy7CUX2Ki4vh5+dnfJyamop7770XSqXSuC0hIQFLlizBzZs34evri9TUVCQlJdXqJyEhAZs2bWrwfRYvXoyFCxfW2Z6cnAx3d/eWF1IPjUbTKv22RazVcTlTvazVcTlTvY5ea1lZmclt7TIcXbhwAe+9957xqBEA5OXlITIysla7wMBA43O+vr7Iy8szbpO2ycvLa/C95s2bVytQabVahIaGIj4+Hmq12hLlGOl0Omg0GowaNQoKhcKifbc1rNVxOVO9rNVxOVO9zlJrzZkfU9g0HL300ktYsmRJo23OnDmDbt26GR9fuXIFo0ePxsSJE/HUU0+19hChUqmgUqnqbFcoFK32TdSafbeGq8W38eu5Qtyu1CPE1x3DuvpDITdtOpu91doSzlQr4Fz1slbH5Uz1Onqt5tRm03A0d+5cTJ8+vdE2UVFRxv/Pzc3FfffdhyFDhtSaaA0AQUFByM/Pr7Wt5nFQUFCjbWqeJ/MUllTg5U0noDmdD1EEBAEQRcDPQ4k5Iztj6uBwCIJg62ESERGZxabhyN/fH/7+/ia1vXLlCu677z7ExsZizZo1kMlqH5mIi4vDyy+/DJ1OZ0yHGo0GXbt2ha+vr7HN9u3bMWfOHOPrNBoN4uLiLFOQE7l+qwK//2AvrhaXQxSrt9X890ZpJV7bfArXSiqQFN/VdoMkIiJqBru4lP/KlSsYPnw4wsLC8NZbb6GwsBB5eXm15go9/vjjUCqVmDFjBk6dOoV169bhnXfeqTVf6LnnnsNPP/2Et99+G2fPnsWCBQtw6NAhzJ492xZl2bWlP6fjanE59AaxwTbv/nIBp3NNP8dLRETUFthFONJoNLhw4QK2b9+OkJAQdOjQwfhVw9vbG8nJycjIyEBsbCzmzp2L1157zXgZPwAMGTIEX331FT7++GP06dMHGzduxKZNm9CrVy9blGW3ist0+P7wlUaDEQDIZQI+35dppVERERFZhl1crTZ9+vQm5yYBQO/evbF79+5G20ycOBETJ0600Mic07GcIlTqDU220xtE7DlfaIURERERWY5dHDmitkVnQjCqYUqIIiIiaksYjshsUf6eJrWTCUCXAK9WHg0REZFlMRyR2SLbe2BAhC9kTVylbxCBKYPDrDMoIiIiC2E4omb56+huEAQBDeUjuSAgJtQHI7sHNtCCiIiobWI4omYZEOGHj6fGwlUhhwAYQ5L8zuGkfuE+WPvkALiYuFI2ERFRW2EXV6tR23R/90Dsf/l+fH/4CrafyUdppR7hfu54dEAoBkX6cXVsIiKySwxH1CJqVwWmDYnAtCERth4KERGRRfCcBxEREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZGEi60HQK1PFEUUllTgVkUV3JRyBKldIQiCrYdFRETUJjEcOagqvQG/nC3ANwezcejyDWjLq4zPuSvliAn1waP9QzEmOggqF7kNR0pERNS2MBw5oCNZN5G07igyrpdBLgjQi2Kt58sq9dh36TpSLl7HG1tVWDaxD4Z18bfRaImIiNoWzjlyMGv2ZuDhVSnIvFEGAHWCUQ3Dnc3XblVg2uoDWPLTWYgNtCUiInImDEcO5PN9mVj439MQxf+Fn6bUtFu18yLeTj7XeoMjIiKyEwxHDiI9rwQL/3OqRX28v+MCUi5cs9CIiIiI7JPdhaOKigrExMRAEAQcPXq01nPHjx/HPffcA1dXV4SGhmLp0qV1Xr9hwwZ069YNrq6uiI6OxtatW6008tb10nfH0dKTYjIBeGHjcehNPexERETkgOwuHP31r39FcHBwne1arRbx8fEIDw9HWloali1bhgULFuDjjz82tklJScHkyZMxY8YMHDlyBOPHj8f48eNx8uRJa5ZgcSdyinEkq6jFocYgAleKbuOXswUWGhkREZH9satw9OOPPyI5ORlvvfVWnee+/PJLVFZWYvXq1ejZsycee+wxPPvss1i+fLmxzTvvvIPRo0fjhRdeQPfu3bFo0SL069cP77//vjXLsLhNR69ALrPMukVymYDvDudYpC8iIiJ7ZDeX8ufn5+Opp57Cpk2b4O7uXuf51NRU3HvvvVAqlcZtCQkJWLJkCW7evAlfX1+kpqYiKSmp1usSEhKwadOmBt+3oqICFRUVxsdarRYAoNPpoNPpWlhVbTX9mdvviazrcBEMsMxyRSJO5tyweG2/1dxa7ZEz1Qo4V72s1XE5U73OUqs59dlFOBJFEdOnT8fMmTPRv39/XL58uU6bvLw8REZG1toWGBhofM7X1xd5eXnGbdI2eXl5Db734sWLsXDhwjrbk5OT6w1plqDRaMxqP6Vj9ZfllFltLpa5tdozZ6oVcK56WavjcqZ6Hb3WsrIyk9vaNBy99NJLWLJkSaNtzpw5g+TkZJSUlGDevHlWGtn/zJs3r9bRJq1Wi9DQUMTHx0OtVlv0vXQ6HTQaDUaNGgWFQmHy62IXaVChN1h0LCkvjYDa1fQxmKu5tdojZ6oVcK56WavjcqZ6naXWmjM/prBpOJo7dy6mT5/eaJuoqCj88ssvSE1NhUqlqvVc//79MWXKFHz22WcICgpCfn5+redrHgcFBRn/W1+bmufro1Kp6rwvACgUilb7JjK3bxeFAtrKSou9v0wAvNxcoXBp/Slprfnv2NY4U62Ac9XLWh2XM9Xr6LWa9Xu1FcfRJH9/f/j7N33binfffRd///vfjY9zc3ORkJCAdevWYdCgQQCAuLg4vPzyy9DpdMZ/AI1Gg65du8LX19fYZvv27ZgzZ46xL41Gg7i4OAtWZX29Onpj97nCFl/KXyOyvSeUVghGREREbZFd/AYMCwtDr169jF9dunQBAHTq1AkhISEAgMcffxxKpRIzZszAqVOnsG7dOrzzzju1Tok999xz+Omnn/D222/j7NmzWLBgAQ4dOoTZs2fbpC5LGRTpB8EyF6tBLhMQ18nPMp0RERHZIbsIR6bw9vZGcnIyMjIyEBsbi7lz5+K1117D008/bWwzZMgQfPXVV/j444/Rp08fbNy4EZs2bUKvXr1sOPKWeyQ2xGJ96Q0iJg8Ms1h/RERE9sYurlb7rYiIiHpvktq7d2/s3r270ddOnDgREydObK2h2USg2hWPxIZgY1qOyfdUq49cJiAuqh16BntbbnBERER2xmGOHDm7l8f2gK+HEs1dC1IAoJTLsOSR3hYdFxERkb1hOHIQ3m4KfDptAJQuMrMDkiAAMpmAlVP6oqOPW+sMkIiIyE4wHDmQmFAfrHs6Dv5eKpMDklwQ4KVywerpAzCiW2DTLyAiInJwDEcOpk+oD7bPHY4/DA6HQi5AEKoDkJRM+N/XgzHB2PGX4RjWpeklFYiIiJyBXU7IpsZ5qlzw+rheSBrVBf85lovDmTdx/EoxSiuq4KaQo0ewGn1DfTEuJhgBaldbD5eIiKhNYThyYD7uSjwRF4En4iJsPRQiIiK7wdNqRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSDEdEREREEgxHRERERBIMR0REREQSdhWOtmzZgkGDBsHNzQ2+vr4YP358reezsrIwduxYuLu7IyAgAC+88AKqqqpqtdm5cyf69esHlUqFu+66C2vXrrVeAURERNTmudh6AKb69ttv8dRTT+GNN97AiBEjUFVVhZMnTxqf1+v1GDt2LIKCgpCSkoKrV6/iiSeegEKhwBtvvAEAyMjIwNixYzFz5kx8+eWX2L59O/70pz+hQ4cOSEhIsFVpRERE1IbYRTiqqqrCc889h2XLlmHGjBnG7T169DD+f3JyMk6fPo1t27YhMDAQMTExWLRoEV588UUsWLAASqUSH374ISIjI/H2228DALp37449e/ZgxYoVDEdEREQEwE7C0eHDh3HlyhXIZDL07dsXeXl5iImJwbJly9CrVy8AQGpqKqKjoxEYGGh8XUJCAmbNmoVTp06hb9++SE1NxciRI2v1nZCQgDlz5jT43hUVFaioqDA+1mq1AACdTgedTmfBKmHsz9L9tkWs1XE5U72s1XE5U73OUqs59dlFOLp06RIAYMGCBVi+fDkiIiLw9ttvY/jw4Th37hz8/PyQl5dXKxgBMD7Oy8sz/re+NlqtFrdv34abm1ud9168eDEWLlxYZ3tycjLc3d0tUt9vaTSaVum3LWKtjsuZ6mWtjsuZ6nX0WsvKykxua9Nw9NJLL2HJkiWNtjlz5gwMBgMA4OWXX8aECRMAAGvWrEFISAg2bNiAZ555ptXGOG/ePCQlJRkfa7VahIaGIj4+Hmq12qLvpdPpoNFoMGrUKCgUCov23dawVsflTPWyVsflTPU6S601Z35MYdNwNHfuXEyfPr3RNlFRUbh69SqA2nOMVCoVoqKikJWVBQAICgrCgQMHar02Pz/f+FzNf2u2Sduo1ep6jxrVvI9KpaqzXaFQtNo3UWv23dawVsflTPWyVsflTPU6eq3m1GbTcOTv7w9/f/8m28XGxkKlUiE9PR133303gOqke/nyZYSHhwMA4uLi8I9//AMFBQUICAgAUH2IUK1WG0NVXFwctm7dWqtvjUaDuLg4S5ZFREREdswu1jlSq9WYOXMm5s+fj+TkZKSnp2PWrFkAgIkTJwIA4uPj0aNHD0ydOhXHjh3Dzz//jFdeeQWJiYnGIz8zZ87EpUuX8Ne//hVnz57FBx98gPXr1+P555+3WW1ERETUttjFhGwAWLZsGVxcXDB16lTcvn0bgwYNwi+//AJfX18AgFwuxw8//IBZs2YhLi4OHh4emDZtGl5//XVjH5GRkdiyZQuef/55vPPOOwgJCcEnn3zCy/iJiIjIyG7CkUKhwFtvvYW33nqrwTbh4eF1Tpv91vDhw3HkyBFLD4+IiIgchN2EI0eWfaMM6w5mI+taCe73BL47nIOH+obBTSm39dCIiIicDsORDVXpDZj/n1P4an8WZAKglIu4fwDw2n9OYdHWc1j+aB/E9wyy9TCJiIicil1MyHZUf/v+BL7anwURgF4EDOL/niutqMLML9Kw+3yhzcZHRETkjBiObOTMVS3WH8qB2MDz4p2vhf89DVFsqBURERFZGsORjXx9IAtymdBoG1EELhTcwuGsm1YaFRERETEc2cjJK8XQG0w7InQ2r6SVR0NEREQ1GI5spKmjRrXaCqa3JSIiopZhOLKRARF+MDUf9Q3zbd3BEBERkRHDkY08PiiswcnYNeQCEBvui65BXlYZExERETEc2UyIrzvmjurS4PMyAVC6yPH38b2sOCoiIiJiOLKhxPvuwvwHe8BLVb0Wp4vkPFvnAE9smBmH7h3UthoeERGRU+IK2TYkCAKeHBqJyQPD8NPJPGQUFAPl5/HFjIHoH+kPgROxiYiIrI5HjtoAV4Uc4/t2xOwRnQEAMaG+DEZEREQ2wnBEREREJMFwRERERCTBcEREREQkwXBEREREJMFwRERERCTBcEREREQkwXBEREREJMFwRERERCTBcEREREQkwduHmEkURQCAVqu1eN86nQ5lZWXQarVQKBQW778tYa2Oy5nqZa2Oy5nqdZZaa35v1/webwzDkZlKSkoAAKGhoTYeCREREZmrpKQE3t7ejbYRRFMiFBkZDAbk5ubCy8vL4vc/02q1CA0NRXZ2NtRqtUX7bmtYq+NypnpZq+NypnqdpVZRFFFSUoLg4GDIZI3PKuKRIzPJZDKEhIS06nuo1WqH/gaVYq2Oy5nqZa2Oy5nqdYZamzpiVIMTsomIiIgkGI6IiIiIJBiO2hCVSoX58+dDpVLZeiitjrU6Lmeql7U6Lmeq15lqNRUnZBMRERFJ8MgRERERkQTDEREREZEEwxERERGRBMMRERERkQTDkZWtXLkSERERcHV1xaBBg3DgwIFG22/YsAHdunWDq6sroqOjsXXrViuNtPkWL16MAQMGwMvLCwEBARg/fjzS09Mbfc3atWshCEKtL1dXVyuNuGUWLFhQZ+zdunVr9DX2uF8BICIiok6tgiAgMTGx3vb2tF9//fVXPPjggwgODoYgCNi0aVOt50VRxGuvvYYOHTrAzc0NI0eOxPnz55vs19zPvLU0Vq9Op8OLL76I6OhoeHh4IDg4GE888QRyc3Mb7bM5nwVraGrfTp8+vc64R48e3WS/bXHfNlVrfZ9fQRCwbNmyBvtsq/u1NTEcWdG6deuQlJSE+fPn4/Dhw+jTpw8SEhJQUFBQb/uUlBRMnjwZM2bMwJEjRzB+/HiMHz8eJ0+etPLIzbNr1y4kJiZi37590Gg00Ol0iI+PR2lpaaOvU6vVuHr1qvErMzPTSiNuuZ49e9Ya+549expsa6/7FQAOHjxYq06NRgMAmDhxYoOvsZf9Wlpaij59+mDlypX1Pr906VK8++67+PDDD7F//354eHggISEB5eXlDfZp7mfemhqrt6ysDIcPH8arr76Kw4cP47vvvkN6ejoeeuihJvs157NgLU3tWwAYPXp0rXF//fXXjfbZVvdtU7VKa7x69SpWr14NQRAwYcKERvtti/u1VYlkNQMHDhQTExONj/V6vRgcHCwuXry43vaPPvqoOHbs2FrbBg0aJD7zzDOtOk5LKygoEAGIu3btarDNmjVrRG9vb+sNyoLmz58v9unTx+T2jrJfRVEUn3vuObFTp06iwWCo93l73a8AxO+//9742GAwiEFBQeKyZcuM24qKikSVSiV+/fXXDfZj7mfeVn5bb30OHDggAhAzMzMbbGPuZ8EW6qt12rRp4rhx48zqxx72rSn7ddy4ceKIESMabWMP+9XSeOTISiorK5GWloaRI0cat8lkMowcORKpqan1viY1NbVWewBISEhosH1bVVxcDADw8/NrtN2tW7cQHh6O0NBQjBs3DqdOnbLG8Czi/PnzCA4ORlRUFKZMmYKsrKwG2zrKfq2srMQXX3yBP/7xj43ehNme92uNjIwM5OXl1dpv3t7eGDRoUIP7rTmf+basuLgYgiDAx8en0XbmfBbakp07dyIgIABdu3bFrFmzcP369QbbOsq+zc/Px5YtWzBjxowm29rrfm0uhiMruXbtGvR6PQIDA2ttDwwMRF5eXr2vycvLM6t9W2QwGDBnzhwMHToUvXr1arBd165dsXr1amzevBlffPEFDAYDhgwZgpycHCuOtnkGDRqEtWvX4qeffsKqVauQkZGBe+65ByUlJfW2d4T9CgCbNm1CUVERpk+f3mAbe96vUjX7xpz91pzPfFtVXl6OF198EZMnT270xqTmfhbaitGjR+Pf//43tm/fjiVLlmDXrl0YM2YM9Hp9ve0dZd9+9tln8PLywsMPP9xoO3vdry3hYusBkGNLTEzEyZMnmzw/HRcXh7i4OOPjIUOGoHv37vjoo4+waNGi1h5mi4wZM8b4/71798agQYMQHh6O9evXm/QXmb369NNPMWbMGAQHBzfYxp73K1XT6XR49NFHIYoiVq1a1Whbe/0sPPbYY8b/j46ORu/evdGpUyfs3LkT999/vw1H1rpWr16NKVOmNHmRhL3u15bgkSMrad++PeRyOfLz82ttz8/PR1BQUL2vCQoKMqt9WzN79mz88MMP2LFjB0JCQsx6rUKhQN++fXHhwoVWGl3r8fHxQZcuXRocu73vVwDIzMzEtm3b8Kc//cms19nrfq3ZN+bst+Z85tuammCUmZkJjUbT6FGj+jT1WWiroqKi0L59+wbH7Qj7dvfu3UhPTzf7MwzY7341B8ORlSiVSsTGxmL79u3GbQaDAdu3b6/1l7VUXFxcrfYAoNFoGmzfVoiiiNmzZ+P777/HL7/8gsjISLP70Ov1OHHiBDp06NAKI2xdt27dwsWLFxscu73uV6k1a9YgICAAY8eONet19rpfIyMjERQUVGu/abVa7N+/v8H91pzPfFtSE4zOnz+Pbdu2oV27dmb30dRnoa3KycnB9evXGxy3ve9boPrIb2xsLPr06WP2a+11v5rF1jPCnck333wjqlQqce3ateLp06fFp59+WvTx8RHz8vJEURTFqVOnii+99JKx/d69e0UXFxfxrbfeEs+cOSPOnz9fVCgU4okTJ2xVgklmzZolent7izt37hSvXr1q/CorKzO2+W2tCxcuFH/++Wfx4sWLYlpamvjYY4+Jrq6u4qlTp2xRglnmzp0r7ty5U8zIyBD37t0rjhw5Umzfvr1YUFAgiqLj7Ncaer1eDAsLE1988cU6z9nzfi0pKRGPHDkiHjlyRAQgLl++XDxy5Ijx6qw333xT9PHxETdv3iweP35cHDdunBgZGSnevn3b2MeIESPE9957z/i4qc+8LTVWb2VlpfjQQw+JISEh4tGjR2t9jisqKox9/Lbepj4LttJYrSUlJeJf/vIXMTU1VczIyBC3bdsm9uvXT+zcubNYXl5u7MNe9m1T38eiKIrFxcWiu7u7uGrVqnr7sJf92poYjqzsvffeE8PCwkSlUikOHDhQ3Ldvn/G5YcOGidOmTavVfv369WKXLl1EpVIp9uzZU9yyZYuVR2w+APV+rVmzxtjmt7XOmTPH+O8SGBgoPvDAA+Lhw4etP/hmmDRpktihQwdRqVSKHTt2FCdNmiReuHDB+Lyj7NcaP//8swhATE9Pr/OcPe/XHTt21Pt9W1OPwWAQX331VTEwMFBUqVTi/fffX+ffIDw8XJw/f36tbY195m2psXozMjIa/Bzv2LHD2Mdv623qs2ArjdVaVlYmxsfHi/7+/qJCoRDDw8PFp556qk7IsZd929T3sSiK4kcffSS6ubmJRUVF9fZhL/u1NQmiKIqtemiKiIiIyI5wzhERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDEREREZEEwxERERGRBMMRERERkQTDERE5vcLCQgQFBeGNN94wbktJSYFSqcT27dttODIisgXeeJaICMDWrVsxfvx4pKSkoGvXroiJicG4ceOwfPlyWw+NiKyM4YiI6I7ExERs27YN/fv3x4kTJ3Dw4EGoVCpbD4uIrIzhiIjojtu3b6NXr17Izs5GWloaoqOjbT0kIrIBzjkiIrrj4sWLyM3NhcFgwOXLl209HCKyER45IiICUFlZiYEDByImJgZdu3bFP//5T5w4cQIBAQG2HhoRWRnDERERgBdeeAEbN27EsWPH4OnpiWHDhsHb2xs//PCDrYdGRFbG02pE5PR27tyJf/7zn/j888+hVqshk8nw+eefY/fu3Vi1apWth0dEVsYjR0REREQSPHJEREREJMFwRERERCTBcEREREQkwXBEREREJMFwRERERCTBcEREREQkwXBEREREJMFwRERERCTBcEREREQkwXBEREREJMFwRERERCTx/ypdtegqO2dBAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 20\n",
    "x = [*range(N)]\n",
    "y = [(i-4)*(i-10)*(i-16) for i in x]\n",
    "\n",
    "s = [50, 200] * 10\n",
    "\n",
    "plt.scatter(x,y, s = s)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 20\n",
    "x = [*range(N)]\n",
    "y = [(i-4)*(i-10)*(i-16) for i in x]\n",
    "\n",
    "c = ['r', 'b'] * 10\n",
    "s = [50, 200] * 10\n",
    "\n",
    "plt.scatter(x,y, s = s, c = c)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 20\n",
    "x = [*range(N+1)]\n",
    "y = [(i-4)*(i-10)*(i-16) for i in x]\n",
    "\n",
    "s = [50, 100, 200] * 7\n",
    "c = ['r', 'g', 'b'] * 7\n",
    "\n",
    "plt.scatter(x,y, s = s, c = c)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 20\n",
    "x = [*range(N)]\n",
    "y = [(i-4)*(i-10)*(i-16) for i in x]\n",
    "\n",
    "s = [50, 100, 150, 200, 250] * 4\n",
    "c = ['#000', '#888', '#aaa', '#ccc', '#eee'] * 4\n",
    "\n",
    "plt.scatter(x,y, s = s, c = c)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 20\n",
    "x = [*range(N)]\n",
    "y = [(i-4)*(i-10)*(i-16) for i in x]\n",
    "\n",
    "s = [50, 100, 150, 200, 250] * 4\n",
    "c = ['#f00', '#f88', '#faa', '#fcc', '#fee'] * 4\n",
    "\n",
    "plt.scatter(x,y, s = s, c = c)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}