hestiwahyuningsih · December 4, 2025 18:31
diff --git a/matplotlib_scatter_plot.ipynb b/matplotlib_scatter_plot.ipynb
 {
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aeab4cc5-c2fe-46e5-a706-30c46082c976",
   "metadata": {},
   "source": [
    "MATPLOTLIB SCATTER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6eb1ce4a-5d90-4d0f-9c13-6023d048f2b7",
   "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": 5,
   "id": "3db8d5b6-2bc4-4714-85af-7a1bc22d397c",
   "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": 6,
   "id": "71e72bb3-ec92-4224-b646-1ffe65e53bef",
   "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": 7,
   "id": "9b60b76d-7c2c-4ef6-a790-8552b05b6965",
   "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": 8,
   "id": "150ec42c-f67c-4774-abdd-2471ef592386",
   "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": 9,
   "id": "edac5ebb-6c83-4439-b932-63447175d1e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAHYJJREFUeJzt3XuMlNX9P/DPArJrDWwEg7sGVGptdUWtaPGHmjb9eo8htU1sNdJ6SfyDYLy1RmkrlFRFtJrGS1FIak3w+kexYiONwVuMICrVSPD6lVairKs/dUHsotmdX87TLD92WRR05szuzOuVPC7POQfm5HGz895zm4ZSqVQKAIBMhuV6IQAA4QMAyM7IBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkNSIGmZ6ennj33Xdj1KhR0dDQUO3uAAA7IZ1ZumnTpthnn31i2LBhQyt8pOAxYcKEancDAPgK1q9fH+PHjx9a4SONePR2fvTo0dXuDgCwEzZu3FgMHvS+jw+p8NE71ZKCh/ABAEPLziyZsOAUAMhK+AAAshI+AICshA8AICvhAwDISvgAALISPgCArIQPACCrQXfIGABQGd09pVi17sPo2NQV40Y1xZSJY2L4sIbBP/Lx1FNPxbRp04oPjkmnmD344IPbfbDM7Nmzo7W1NXbfffc44YQT4o033ihnnwGAXbRszYY4bv5jcdailXHxfS8WX9N9Kh/04WPz5s1x+OGHx2233TZg/fXXXx8333xz3H777fHss8/GHnvsESeffHJ0dXWVo78AwC5KAWPG4tWxobPve3F7Z1dRnjuANJTSUMVX/csNDbFkyZI4/fTTi/v0T6URkV/+8pfxq1/9qijr7OyMvffeO/7yl7/EmWeeuVMfTNPc3Fz8PZ/tAgBff6oljXD0Dx690qRLS3NTPH3F/3ytKZhdef8u64LTdevWRXt7ezHV0it15Oijj44VK1YM+He2bNlSdHjbCwAoj7TGY0fBI0kjEKk+tculrOEjBY8kjXRsK9331vU3b968IqD0XunjeAGA8kiLS8vZria22s6aNasYoum91q9fX+0uAUDNGDeqqaztBl34aGlpKb6+9957fcrTfW9df42NjcXc0LYXAFAeaTtta3NTsbZjIKk81ad2QzJ8TJw4sQgZy5cv31qW1nCkXS9Tp04t50sBADshLSKdM62t+HP/ANJ7n+pznvexy+Hjk08+iRdffLG4eheZpj+//fbbxe6XSy65JK6++up46KGH4uWXX45f/OIXxQ6Y3h0xAEBep0xqjQXTJxe7WraV7lN5qh/UW22feOKJ+OEPf7hd+TnnnFNsp03/3Jw5c2LhwoXx8ccfx3HHHRd/+tOf4tvf/vZO/fu22gLA0DvhdFfev7/WOR+VIHwAwNBTtXM+AAC+jPABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZDUi78sBQG3o7inFqnUfRsemrhg3qimmTBwTw4c1VLtbQ4LwAQC7aNmaDTF36drY0Nm1tay1uSnmTGuLUya1ep5fwrQLAOxi8JixeHWf4JG0d3YV5ameLyZ8AMAuTLWkEY/SAHW9Zak+tWPHhA8A2ElpjUf/EY9tpciR6lM7dkz4AICdlBaXlrNdvRI+AGAnpV0t5WxXr4QPANhJaTtt2tWyow21qTzVp3bsmPABADspneORttMm/QNI732qd97HFxM+AGAXpHM8FkyfHC3NfadW0n0qd87Hl3PIGADsohQwTmxrccLpVyR8AMBXkKZWph4w1rP7Cky7AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAViPyvhwAbK+7pxSr1n0YHZu6YtyoppgycUwMH9bgUdWosoeP7u7u+N3vfheLFy+O9vb22GeffeLcc8+N3/72t9HQ4BsJgL6WrdkQc5eujQ2dXVvLWpubYs60tjhlUqvHVYPKHj7mz58fCxYsiLvuuisOOeSQeP755+O8886L5ubmuOiii8r9cgAM8eAxY/HqKPUrb+/sKsoXTJ8sgNSgsoePZ555Jn70ox/FaaedVtzvv//+ce+998aqVavK/VIADPGpljTi0T94JKksjZWn+hPbWkzB1JiyLzg95phjYvny5fH6668X9y+99FI8/fTTceqppw7YfsuWLbFx48Y+FwC1L63x2HaqZaAAkupTO2pL2Uc+rrzyyiJAHHTQQTF8+PBiDcg111wTZ5999oDt582bF3Pnzi13NwAY5NLi0nK2o45HPh544IG4++6745577onVq1cXaz/+8Ic/FF8HMmvWrOjs7Nx6rV+/vtxdAmAQSrtaytmOOh75uPzyy4vRjzPPPLO4P/TQQ+Pf//53McJxzjnnbNe+sbGxuACoL2k7bdrVkhaXDrTuI635aGn+77ZbakvZRz4+/fTTGDas7z+bpl96enrK/VIADGHpHI+0nTbpfxBD732qd95H7Sl7+Jg2bVqxxuPvf/97/Otf/4olS5bETTfdFD/+8Y/L/VIADHHpHI+0nTaNcGwr3dtmW7saSqXSQKNdX9mmTZviqquuKkJHR0dHccjYWWedFbNnz46RI0d+6d9Pi1XTmSBp/cfo0aPL2TUABiknnA59u/L+Xfbw8XUJHwAw9OzK+7cPlgMAshI+AICshA8AICvhAwDISvgAALISPgCArIQPACAr4QMAyEr4AACyEj4AgKyEDwAgK+EDAMhK+AAAshI+AICshA8AICvhAwDISvgAALISPgCArIQPACAr4QMAyEr4AACyGpH35QCotO6eUqxa92F0bOqKcaOaYsrEMTF8WIMHz6AhfADUkGVrNsTcpWtjQ2fX1rLW5qaYM60tTpnUWtW+QS/TLgA1FDxmLF7dJ3gk7Z1dRXmqh8FA+ACokamWNOJRGqCutyzVp3ZQbcIHQA1Iazz6j3hsK0WOVJ/aQbUJHwA1IC0uLWc7qCThA6AGpF0t5WwHlSR8ANSAtJ027WrZ0YbaVJ7qUzuoNuEDoAakczzSdtqkfwDpvU/1zvtgMBA+AGpEOsdjwfTJ0dLcd2ol3ady53wwWDhkDKCGpIBxYluLE04Z1IQPgBqTplamHjC22t2AHTLtAgBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWY3I+3IAQ0d3TylWrfswOjZ1xbhRTTFl4pgYPqyh2t2CIa8i4eOdd96JK664Ih555JH49NNP41vf+lbceeedcdRRR1Xi5QDKbtmaDTF36drY0Nm1tay1uSnmTGuLUya1euIwmKZdPvroozj22GNjt912K8LH2rVr48Ybb4w999yz3C8FULHgMWPx6j7BI2nv7CrKUz0wiEY+5s+fHxMmTChGOnpNnDix3C8DULGpljTiURqgLpWlSZdUf2JbiykYGCwjHw899FAxvXLGGWfEuHHj4ogjjohFixbtsP2WLVti48aNfS6AaklrPPqPePQPIKk+tQMGSfh46623YsGCBXHggQfGP/7xj5gxY0ZcdNFFcddddw3Yft68edHc3Lz1SqMmANWSFpeWsx2wvYZSqTTQ6OJXNnLkyGLk45lnntlalsLHc889FytWrBhw5CNdvdLIRwognZ2dMXr06HJ2DeBLrfjf/xtnLVr5pe3uveD/xNQDxnqisM37dxpE2Jn377KPfLS2tkZbW1ufsoMPPjjefvvtAds3NjYWndz2AqiWtJ027WrZ0YbaVJ7qUzvgqyl7+Eg7XV577bU+Za+//nrst99+5X4pgLJL53ik7bRJ/wDSe5/qnfcBgyh8XHrppbFy5cq49tpr480334x77rknFi5cGDNnziz3SwFURDrHY8H0ydHS3NSnPN2ncud8wCBb85E8/PDDMWvWrHjjjTeKbbaXXXZZXHDBBWWfMwKoJCecws7blffvioSPr0P4AIChp6oLTgEAvojwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAIHwAALXLyAcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZjcj7ckC96u4pxap1H0bHpq4YN6oppkwcE8OHNVS7W0AVCB9AxS1bsyHmLl0bGzq7tpa1NjfFnGltccqkVv8HoM6YdgEqHjxmLF7dJ3gk7Z1dRXmqB+qL8AFUdKoljXiUBqjrLUv1qR1QP4QPoGLSGo/+Ix7bSpEj1ad2QP0QPoCKSYtLy9kOqA3CB1AxaVdLOdsBtUH4ACombadNu1p2tKE2laf61A6oH8IHUDHpHI+0nTbpH0B671O98z6gvggfQEWlczwWTJ8cLc19p1bSfSp3zgfUH4eMARWXAsaJbS1OOAUKwgeQRZpamXrAWE8bMO0CAORlzQcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQ1Yi8LweUQ3dPKVat+zA6NnXFuFFNMWXimBg+rMHDBYaEio98XHfdddHQ0BCXXHJJpV8K6sKyNRviuPmPxVmLVsbF971YfE33qRwg6j18PPfcc3HHHXfEYYcdVsmXgbqRAsaMxatjQ2dXn/L2zq6iXAAB6jp8fPLJJ3H22WfHokWLYs8996zUy0BdTbXMXbo2SgPU9Zal+tQOoC7Dx8yZM+O0006LE0444QvbbdmyJTZu3NjnAraX1nj0H/HYVoocqT61A6i7Baf33XdfrF69uph2+TLz5s2LuXPnVqIbUFPS4tJytgOomZGP9evXx8UXXxx33313NDU1fWn7WbNmRWdn59Yr/X1ge2lXSznbAdTMyMcLL7wQHR0dMXny5K1l3d3d8dRTT8Wtt95aTLMMHz58a11jY2NxAV8sbadtbW4qFpcOtKojbbRtaf7vtluAuhr5OP744+Pll1+OF198cet11FFHFYtP05+3DR7AzkvneMyZ1lb8uf+JHr33qd55H0DdjXyMGjUqJk2a1Kdsjz32iLFjx25XDuyaUya1xoLpk4tdLdsuPk0jHil4pHqAwc4JpzDEpIBxYluLE06BIStL+HjiiSdyvAzUjTS1MvWAsdXuBsBX4oPlAICshA8AICvhAwDISvgAALISPgCArIQPACAr4QMAyEr4AACyEj4AgKyEDwAgK+EDAMhK+AAAshI+AICshA8AICvhAwDISvgAALISPgCArIQPACAr4QMAyEr4AACyEj4AgKyEDwAgqxF5Xw4Gn+6eUqxa92F0bOqKcaOaYsrEMTF8WEO1uwVQs4QP6tqyNRti7tK1saGza2tZa3NTzJnWFqdMaq1q3wBqlWkX6jp4zFi8uk/wSNo7u4ryVA9A+Qkf1O1USxrxKA1Q11uW6lM7AMpL+KAupTUe/Uc8tpUiR6pP7QAoL+GDupQWl5azHQA7T/igLqVdLeVsB8DOEz6oS2k7bdrVsqMNtak81ad2AJSX8EFdSud4pO20Sf8A0nuf6p33AVB+wgd1K53jsWD65Ghp7ju1ku5TuXM+ACrDIWPUtRQwTmxrccIpQEbCB3UvTa1MPWBs3T8HgFxMuwAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZjcj7ctS67p5SrFr3YXRs6opxo5piysQxMXxYQ7W7BUAth4958+bFX//613j11Vdj9913j2OOOSbmz58f3/nOd8r9Ugwyy9ZsiLlL18aGzq6tZa3NTTFnWlucMqm1qn0DoIanXZ588smYOXNmrFy5Mh599NH4/PPP46STTorNmzeX+6UYZMFjxuLVfYJH0t7ZVZSnegBIGkqlUqmSj+L999+PcePGFaHk+9///pe237hxYzQ3N0dnZ2eMHj3a/6UhMtVy3PzHtgsevdKkS0tzUzx9xf+YggGoUbvy/l3xBaepE8mYMWMGrN+yZUvR4W0vhpa0xmNHwSNJ6TbVp3YAUNHw0dPTE5dcckkce+yxMWnSpB2uEUlJqfeaMGGC/ytDTFpcWs52ANS2ioaPtPZjzZo1cd999+2wzaxZs4rRkd5r/fr1lewSFZB2tZSzHQC1rWJbbS+88MJ4+OGH46mnnorx48fvsF1jY2NxMXSl7bRpV0taXFr6gjUfqR0AlH3kI61fTcFjyZIl8dhjj8XEiRM95RqXzvFI22mT/id69N6neud9AFCR8JGmWhYvXhz33HNPjBo1Ktrb24vrP//5jydew9I5HgumTy5GOLaV7lO5cz4AqNhW24aGgU+zvPPOO+Pcc8/90r9vq+3Q5oRTgPq0cRe22pZ9zUeFjw1hkEtTK1MPGFvtbgAwiPlgOQAgK+EDAMhK+AAAshI+AICshA8AICvhAwDISvgAALISPgCArIQPACAr4QMAyEr4AACyEj4AgKyEDwAgK+EDAMhK+AAAshI+AICshA8AICvhAwDISvgAALISPgCArIQPACAr4QMAyGpE3pdjV3T3lGLVug+jY1NXjBvVFFMmjonhwxo8RACGNOFjkFq2ZkPMXbo2NnR2bS1rbW6KOdPa4pRJrVXtGwB8HaZdBmnwmLF4dZ/gkbR3dhXlqR4AhirhYxBOtaQRj9IAdb1lqT61A4ChSPgYZNIaj/4jHttKkSPVp3YAMBQJH4NMWlxaznYAMNgIH4NM2tVSznYAMNgIH4NM2k6bdrXsaENtKk/1qR0ADEXCxyCTzvFI22mT/gGk9z7VO+8DgKFK+BiE0jkeC6ZPjpbmvlMr6T6VO+cDgKHMIWODVAoYJ7a1OOEUgJojfAxiaWpl6gFjq90NACgr0y4AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAViOiTnT3lGLVug+jY1NXjBvVFFMmjonhwxqq3S0AqDsVCx+33XZb3HDDDdHe3h6HH3543HLLLTFlypSohmVrNsTcpWtjQ2fX1rLW5qaYM60tTpnUWpU+AUC9qsi0y/333x+XXXZZzJkzJ1avXl2Ej5NPPjk6OjqiGsFjxuLVfYJH0t7ZVZSnegBgiIePm266KS644II477zzoq2tLW6//fb4xje+EX/+858j91RLGvEoDVDXW5bqUzsAYIiGj88++yxeeOGFOOGEE/7/iwwbVtyvWLFiu/ZbtmyJjRs39rnKJa3x6D/isa0UOVJ9agcADNHw8cEHH0R3d3fsvffefcrTfVr/0d+8efOiubl56zVhwoSy9SUtLi1nOwCgBrbazpo1Kzo7O7de69evL9u/nXa1lLMdADAId7vstddeMXz48Hjvvff6lKf7lpaW7do3NjYWVyWk7bRpV0taXDrQqo600bal+b/bbgGAITryMXLkyDjyyCNj+fLlW8t6enqK+6lTp0ZO6RyPtJ026X+iR+99qnfeBwAM8WmXtM120aJFcdddd8Urr7wSM2bMiM2bNxe7X3JL53gsmD65GOHYVrpP5c75AIAaOGTsZz/7Wbz//vsxe/bsYpHpd7/73Vi2bNl2i1BzSQHjxLYWJ5wCwCDQUCqVBtUhF2mrbdr1khafjh49utrdAQDK/P5d9d0uAEB9ET4AgKyEDwAgK+EDAMhK+AAAshI+AICshA8AICvhAwAQPgCA2lWR49W/jt4DV9NJaQDA0ND7vr0zB6cPuvCxadOm4uuECROq3RUA4Cu8j6dj1ofUZ7v09PTEu+++G6NGjYqGht4Pvi9fKkuhZv369T43poI85zw8Z8+51vieHtrPOcWJFDz22WefGDZs2NAa+UgdHj9+fEVfIz1sH1pXeZ5zHp6z51xrfE8P3ef8ZSMevex2AQCyEj4AgKzqKnw0NjbGnDlziq94zkOd72fPudb4nq6f5zzoFpwCALWtrkY+AIDqEz4AgKyEDwAgK+EDAMiqbsLHbbfdFvvvv380NTXF0UcfHatWrap2l2rOvHnz4nvf+15xOu24cePi9NNPj9dee63a3ap51113XXEa8CWXXFLtrtScd955J6ZPnx5jx46N3XffPQ499NB4/vnnq92tmtLd3R1XXXVVTJw4sXjGBxxwQPz+97/fqc8H4Ys99dRTMW3atOLE0fQz4sEHH+xTn57x7Nmzo7W1tXj2J5xwQrzxxhuRQ12Ej/vvvz8uu+yyYmvR6tWr4/DDD4+TTz45Ojo6qt21mvLkk0/GzJkzY+XKlfHoo4/G559/HieddFJs3ry52l2rWc8991zccccdcdhhh1W7KzXno48+imOPPTZ22223eOSRR2Lt2rVx4403xp577lntrtWU+fPnx4IFC+LWW2+NV155pbi//vrr45Zbbql214a8zZs3F+936ZfvgaTnfPPNN8ftt98ezz77bOyxxx7Fe2NXV1flO1eqA1OmTCnNnDlz6313d3dpn332Kc2bN6+q/ap1HR0d6VeX0pNPPlntrtSkTZs2lQ488MDSo48+WvrBD35Quvjii6vdpZpyxRVXlI477rhqd6PmnXbaaaXzzz+/T9lPfvKT0tlnn121PtWiiCgtWbJk631PT0+ppaWldMMNN2wt+/jjj0uNjY2le++9t+L9qfmRj88++yxeeOGFYjhp28+PSfcrVqyoat9qXWdnZ/F1zJgx1e5KTUqjTKeddlqf723K56GHHoqjjjoqzjjjjGIa8YgjjohFixZ5xGV2zDHHxPLly+P1118v7l966aV4+umn49RTT/WsK2jdunXR3t7e5+dH+lyWtCwhx3vjoPtguXL74IMPijnFvffeu095un/11Ver1q9alz6dOK1BSMPWkyZNqnZ3as59991XTCGmaRcq46233iqmA9KU7a9//eviWV900UUxcuTIOOecczz2MrnyyiuLT1k96KCDYvjw4cXP62uuuSbOPvtsz7iCUvBIBnpv7K2rpJoPH1Tvt/I1a9YUv8FQXuljsC+++OJiXU1aQE3lAnQa+bj22muL+zTykb6n0/y48FE+DzzwQNx9991xzz33xCGHHBIvvvhi8YtLWiTpOdeump922WuvvYo0/d577/UpT/ctLS1V61ctu/DCC+Phhx+Oxx9/PMaPH1/t7tScNI2YFktPnjw5RowYUVxpsW9aOJb+nH5z5OtLOwDa2tr6lB188MHx9ttve7xldPnllxejH2eeeWaxm+jnP/95XHrppcXuOSqn9/2vWu+NNR8+0hDpkUceWcwpbvsbTbqfOnVqVftWa9KaphQ8lixZEo899lixdY7yO/744+Pll18ufkPsvdJv6GmYOv05hW2+vjRl2H+reFqXsN9++3m8ZfTpp58W6/C2lb6H089pKif9fE4hY9v3xjT9lXa95HhvrItplzRnm4bv0g/oKVOmxB//+MdiC9J5551X7a7V3FRLGjr929/+Vpz10TtvmBYxpT3klEd6tv3X0aQtcuksCutryif99p0WQ6Zpl5/+9KfF2UALFy4sLsonnUOR1njsu+++xbTLP//5z7jpppvi/PPP95i/pk8++STefPPNPotM0y8oaRNAet5peuvqq6+OAw88sAgj6byVNN2VzmiquFKduOWWW0r77rtvaeTIkcXW25UrV1a7SzUnfTsNdN15553V7lrNs9W2MpYuXVqaNGlSsf3woIMOKi1cuLBCr1S/Nm7cWGwTTz+fm5qaSt/85jdLv/nNb0pbtmypdteGvMcff3zAn8nnnHPO1u22V111VWnvvfcuvsePP/740muvvZalbw3pP5WPOAAAdbLmAwAYXIQPACAr4QMAyEr4AACyEj4AgKyEDwAgK+EDAMhK+AAAshI+AICshA8AICvhAwDISvgAACKn/wec60gmZUIodQAAAABJRU5ErkJggg==",
      "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,
   "id": "ed0e3fd1-df19-4052-a14e-796bbb49e921",
   "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": 12,
   "id": "e4f6715c-ae46-4fde-afd6-27e5f1dc5ba0",
   "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": 13,
   "id": "4b123819-5876-4d45-a01e-098673a74ff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A7h1o+WCPpTEe27d4bCtFjnQ+XQfg3oHwwR5Lg0vLeR3QGNw7GpfwwR5Ls1rKeR3QGNw7GpfwwR5L02nTrJadTahNx9P5dB2AewfCB3ssreORptMm2weQ9v103nofgHsHifBBWaR1PGadfVS0NHfuWkn76bh1PgD3Dtp5sRxlZYVTwL2jMbXtwovlrHBKWaWulXEj9lergHsHO6XbBQDISvgAALISPgCArIQPACAr4QMAyEr4AACyEj4AgKyEDwAgK+EDAMhK+AAAshI+AICshA8AICvhAwDISvgAALISPgCArIQPACAr4QMAyEr4AACyEj4AgKyEDwAgK+EDAMhK+AAAshI+AICshA8AICvhAwDISvgAALISPgCArIQPACAr4QMAyEr4AACyEj4AgKyEDwAgK+EDAMhK+AAAshI+AICshA8AICvhAwDISvgAALISPgCArIQPACAr4QMAyEr4AABqO3z8+Mc/jqampk7boYceWu6PAQBq1F5d8UsPP/zweOKJJ/79IXt1yccAADWoS1JBChstLS1d8asBgBrXJWM+XnnllRg8eHAcdNBBcdZZZ8Ubb7yx02s3b94cbW1tnTYAoH6VPXyMHTs27rrrrpgzZ07MmjUrVqxYEZ/97Gdj/fr1O7x+xowZ0dzc3LENHTq03EUCAKpIU6lUKnXlB6xbty4OPPDAuOmmm+L888/fYctH2tqllo8UQFpbW6Nv375dWTQAoEzS8zs1InyY53eXjwTdb7/94hOf+EQsX758h+d79uxZbABAY+jydT42bNgQr776agwaNKirPwoAaMTw8b3vfS/mz58fr7/+evzxj3+M0047Lbp37x5f+9rXyv1RAEANKnu3y5tvvlkEjXfffTcOOOCAOP7442PRokXF/wMAlD18PPjgg2oVANgp73YBALISPgCArLx0hYa3ZWspnl3xj1i7flMM6NMrxgzvF927NTV8vVC7fKepdsIHDW3O0lVxzWMvxqrWTR3HBjX3iqsnjYyTRpkeTu3xnaYW6HahoW/SF937fKfgkaxu3VQcT+ehlvhOUyuEDxq2WTq1eOzo3QLtx9L5dB3UAt9paonwQUNKYzy2b/HYVooc6Xy6DmqB7zS1RPigIaXBpeW8DirNd5paInzQkNKslnJeB5XmO00tET5oSGk6bZrVsrMJtel4Op+ug1rgO00tET5oSGkdjzSdNtk+gLTvp/PW+6BW+E5TS4QPGlZax2PW2UdFS3PnrpW0n45b54Na4ztNrWgqlUpVNZewra0tmpubo7W1Nfr27Vvp4tAArAZJvfGdptqf31Y4peGl5upxI/Zv+HqgfvhOU+10uwAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBktVfejwPKYcvWUjy74h+xdv2mGNCnV4wZ3i+6d2tSuWWmnqFrCB9QY+YsXRXXPPZirGrd1HFsUHOvuHrSyDhp1KCKlq2eqGfoOrpdoMYeiBfd+3yn4JGsbt1UHE/nUc9Q7YQPqKEugNTiUdrBufZj6Xy6DvUM1Uz4gBqRxnhs3+KxrRQ50vl0HeoZqpnwATUiDS4t53WoZ6gU4QNqRJrVUs7rUM9QKcIH1Ig0nTbNatnZhNp0PJ1P16GeoZoJH1Aj0joeaTptsn0Aad9P5633oZ6h2gkfUEPSOh6zzj4qWpo7d62k/XTcOh/qGWpBU6lUqqp5eW1tbdHc3Bytra3Rt2/fShcHqpKVN9UzVJtdeX5b4RRqUOpaGTdi/0oXo+6pZ+gaul0AgKy0fABZ6CoCujx8zJw5M2688cZYvXp1jB49Om699dYYM2ZMV30cUMW8pA3o8m6Xhx56KKZNmxZXX311PP/880X4mDhxYqxdu7YrPg6oYl6GB2QJHzfddFNccMEFce6558bIkSPjtttui3322Sd+/etfd8XHAVXKy/CALOHj/fffj8WLF8eECRP+/SHduhX7Cxcu/I/rN2/eXEzP2XYD6oOX4QFZwsc777wTW7ZsiYEDB3Y6nvbT+I/tzZgxo5gX3L4NHTq03EUCKsTL8ICqnGo7ffr0YkGS9m3lypWVLhJQJl6GB2SZ7dK/f//o3r17rFmzptPxtN/S0vIf1/fs2bPYgPp9Gd7q1k2xo6WUm/7/0vBehgeNpewtHz169Iijjz465s2b13Fs69atxf64cePK/XFAFfMyPCBbt0uaZnvHHXfE3XffHS+99FJcdNFFsXHjxmL2C9BYvAwPyLLI2Fe/+tV4++2346qrrioGmX7yk5+MOXPm/McgVKBxAsj/jWwpZr+kQahpLEjqakktI0Dj8VZbACDrW20rPtsFAGgswgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AADCBwBQv7pkefU9USqVOlZKAwBqQ/tzu/05XlPhY/369cXPoUOHVrooAMBuPMfTMus19W6XrVu3xltvvRV9+vSJpqamsqeyFGpWrlz5P9edRz1XO99n9VxvfKdru55TnEjBY/DgwdGtW7faavlIBR4yZEiXfkaqbOGj66nnPNSzeq43vtO1W8//q8WjndkuAEBWwgcAkFVDhY+ePXvG1VdfXfxEPdc632f1XG98pxunnqtuwCkAUN8aquUDAKg84QMAyEr4AACyEj4AgKwaJnzMnDkzPvaxj0WvXr1i7Nix8eyzz1a6SHVnxowZccwxxxSr0w4YMCBOPfXUWLZsWaWLVfd+9rOfFasBT506tdJFqTt///vf4+yzz479998/evfuHUcccUQ899xzlS5WXdmyZUtceeWVMXz48KKOR4wYEddee+2Hej8I/92CBQti0qRJxYqj6R7x8MMPdzqf6viqq66KQYMGFXU/YcKEeOWVVyKHhggfDz30UEybNq2YWvT888/H6NGjY+LEibF27dpKF62uzJ8/P6ZMmRKLFi2KuXPnxgcffBAnnnhibNy4sdJFq1t/+tOf4le/+lUceeSRlS5K3fnnP/8Zxx13XOy9997x+9//Pl588cX4+c9/Hh/96EcrXbS6cv3118esWbPiF7/4Rbz00kvF/g033BC33nprpYtW8zZu3Fg879If3zuS6vmWW26J2267LZ555pnYd999i2fjpk2bur5wpQYwZsyY0pQpUzr2t2zZUho8eHBpxowZFS1XvVu7dm3606U0f/78ShelLq1fv7508MEHl+bOnVv6/Oc/X7rkkksqXaS6cvnll5eOP/74Shej7p1yyiml8847r9Ox008/vXTWWWdVrEz1KCJKs2fP7tjfunVrqaWlpXTjjTd2HFu3bl2pZ8+epQceeKDLy1P3LR/vv/9+LF68uGhO2vb9MWl/4cKFFS1bvWttbS1+9uvXr9JFqUuplemUU07p9N2mfB599NH49Kc/HV/+8peLbsRPfepTcccdd6jiMjv22GNj3rx58be//a3Y/8tf/hJPP/10nHzyyeq6C61YsSJWr17d6f6R3suShiXkeDZW3Yvlyu2dd94p+hQHDhzY6Xjaf/nllytWrnqX3k6cxiCkZutRo0ZVujh158EHHyy6EFO3C13jtddeK7oDUpftD3/4w6Kuv/Od70SPHj1i8uTJqr1MfvCDHxRvWT300EOje/fuxf36Jz/5SZx11lnquAul4JHs6NnYfq4r1X34oHJ/lS9durT4C4bySq/BvuSSS4pxNWkANV0XoFPLx09/+tNiP7V8pO906h8XPsrnN7/5Tdx3331x//33x+GHHx5Lliwp/nBJgyTVc/2q+26X/v37F2l6zZo1nY6n/ZaWloqVq55dfPHF8fjjj8dTTz0VQ4YMqXRx6k7qRkyDpY866qjYa6+9ii0N9k0Dx9L/p78c2XNpBsDIkSM7HTvssMPijTfeUL1l9P3vf79o/TjzzDOL2UTf+MY34tJLLy1mz9F12p9/lXo21n34SE2kRx99dNGnuO1fNGl/3LhxFS1bvUljmlLwmD17djz55JPF1DnKb/z48fHXv/61+AuxfUt/oadm6vT/KWyz51KX4fZTxdO4hAMPPFD1ltF7771XjMPbVvoOp/s0XSfdn1PI2PbZmLq/0qyXHM/Ghuh2SX22qfku3aDHjBkTN998czEF6dxzz6100equqyU1nT7yyCPFWh/t/YZpEFOaQ055pLrdfhxNmiKX1qIwvqZ80l/faTBk6nb5yle+UqwNdPvttxcb5ZPWoUhjPIYNG1Z0u/z5z3+Om266Kc477zzVvIc2bNgQy5cv7zTINP2BkiYBpPpO3VvXXXddHHzwwUUYSeutpO6utEZTlys1iFtvvbU0bNiwUo8ePYqpt4sWLap0kepO+jrtaLvzzjsrXbS6Z6pt13jsscdKo0aNKqYfHnrooaXbb7+9iz6pcbW1tRXTxNP9uVevXqWDDjqo9KMf/ai0efPmShet5j311FM7vCdPnjy5Y7rtlVdeWRo4cGDxHR8/fnxp2bJlWcrWlP7T9REHAKBBxnwAANVF+AAAshI+AICshA8AICvhAwDISvgAALISPgCArIQPACAr4QMAyEr4AACyEj4AgKyEDwAgcvp/13nYsPXPJcwAAAAASUVORK5CYII=",
      "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": 14,
   "id": "4b19ed8d-996c-44aa-8d98-42ad96635f4c",
   "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": 15,
   "id": "0280badf-08be-4d91-a508-407ab5a2b840",
   "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": 16,
   "id": "6d5ae5df-c077-466a-85ab-0a0498a29373",
   "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": 18,
   "id": "5bb4266f-bc46-4899-a20f-d06fd3761de3",
   "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": 19,
   "id": "806f1545-015a-4fc4-aa53-1405db382695",
   "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": 20,
   "id": "b42e60d8-c281-427e-bbce-13e1523722f2",
   "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": 1,
   "id": "1e724703-c1d1-448c-b843-310f623715e5",
   "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": 2,
   "id": "52c514e3-296c-46cc-93d5-3cfdba00b963",
   "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": 4,
   "id": "82d0babe-fc30-4b8d-b25d-32cd933218a2",
   "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": 5,
   "id": "df55e666-5542-4843-94ea-169e553ab30e",
   "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": 6,
   "id": "40d13f89-4833-4761-b29f-94af7eb8a526",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GjhwZ+vXrl25jx44NjzzySDmLBNDmi+e0+1aGjfU7W+zfVL8z7RdWSkM9d11lDSpDhgwJc+bMCStWrAjLly8PH/rQh8JHP/rR8L//+7/lLBbAQXdDzHpobWitk6e4Lz6uG6h91HPX1rOcv3zSpEkttm+++ebUyrJkyZJwyimn7PP8xsbGdCvaunVrut+1a1e6lVLxeKU+Luq5HJzPHSOORXlt+xuhpsfvt2u6F1rcR/HxJS9sDmcMG9BBpah+6rn63jvacrxuhUIhixFfu3fvDv/2b/8WpkyZEp599tkwfPjwfZ4zc+bMMGvWrH32z58/P/Tp06eTSgoAtEdDQ0OYPHlyqK+vT0M/sg4qq1evTmNTdu7cGY444ogUOj784Q+3+tzWWlSGDh0atmzZcsA/9FDS3sKFC8P48eNDr169Snps1HNncz533Cf94gDaYkvKjac3hRuWdw+NTd327L97yhgtKuq5Iu3qoGthvH4PHDjwoIJKWbt+ohNPPDGsWrUqFfbf//3fU4vKokWLWm1RqampSbe9xcrrqDDRkcdGPXc253NpnXX8oDDgiMPSwNnmn/hiSGnc3S3EqFLXvzY9r0f3t4IL6rmrv3f0asOxyj49uXfv3uH4448Pp512Wpg9e3YYNWpU+Na3vlXuYgEcUAwfMyb9/kPV3jGkuB0fF1LaRz13bWUPKntrampq0b0DkLMLRhwV5l42OrWcNBe34/74OOqZQ1fWrp/rr78+TJw4MRxzzDFh27ZtaXzKk08+GX784x+Xs1gAbRLDyPjhdWl2z5Z1S9KYFN09paeeu6aytqhs3rw5fPKTn0zjVM4999ywbNmyFFLioB2ASuueKE5Bjve6e9QzVdCi8s///M/l/PUAQOayG6MCAFAkqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBUT1B54okn9vvYHXfc0d7yAAAcelC54IILwnXXXRd27dq1Z9+WLVvCpEmTwuc///m2Hg4AoLQtKgsWLAhjxowJa9euDT/84Q/DiBEjwtatW8OqVavaejgAgNIFlfe///0pkMRwMnr06HDJJZeEz3zmM+HJJ58Mxx57bFsPBwBQ2sG0zz//fFi+fHkYMmRI6NmzZ1i/fn1oaGg4lEMBAJQuqMyZMyeMHTs2jB8/PqxZsyYsXbo0PPvss2HkyJHh6aefbuvhAABKF1S+9a1vhQceeCDcdtttoba2NnUBxbDysY99LIwbN66thwMA2K+eoY1Wr14dBg4c2GJfr169wi233BIuuuiith4OAKB0LSp7h5TmzjnnnLYeDgBgv6xMCwBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKZbO7qRCWbngt/Tvex20AyCaozJ49O4wZMyb07ds3DBo0KFx88cVh/fr15SwSneTRNRvD2V99PEydtyxtx/u4HfcDQBZBZdGiReHKK68MS5YsCQsXLgy7du0KEyZMCDt27ChnsehgMYxMu29l2Fi/s8X+TfU7035hBYCinqGMHn300Rbb9957b2pZWbFiRfjgBz9YtnLRcWL3zqyH1obWOnnivm4hpMfHD68LPbrHLQC6srIGlb3V19en+wEDBrT6eGNjY7oVbd26Nd3Hlph4K6Xi8Up93K4ujkV5bfsboabH77druhda3Efx8SUvbA5nDGv9PKDtnM+dQz2r52qzq4OuhW05XrdCoZDFCMampqbwkY98JLz++uvhqaeeavU5M2fODLNmzdpn//z580OfPn06oZQAQHs1NDSEyZMnpwaKfv36VUZQmTZtWnjkkUdSSBkyZMhBt6gMHTo0bNmy5YB/6KGkvThuZvz48aFXr14lPXZXb1EpDqAttqTceHpTuGF599DY9FZXz91TxmhRKSHnc+dQz+q52uzqoGthvH4PHDjwoIJKFl0/V111VXj44YfD4sWL9xtSopqamnTbW6y8jgoTHXnsruis4weFAUcclgbONk/IMaQ07u6WxqjU9a9NzzNGpfScz51DPavnatOrxNfCthyrrLN+YmNODCkLFiwIjz/+eBg2bFg5i0MniOFjxqTh6d97D5UtbsfHhRQAyh5U4tTk++67L40xiWupbNq0Kd3eeOMNr04Vu2DEUWHuZaNTy0lzcTvuj48DQNm7fubOnZvux40b12L/PffcEy6//PIylYrOEMNInIIcZ/dsWbckjUnR3QNAVkElk3G8lEns3olTkH+0LqR73T0A7M13/QAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkq2e5C0Bp7G4qhKUbXgubt+0Mg/rWhjOGDQg9undTvSWmnjuHeqbaOKcrNKgsXrw43HLLLWHFihVh48aNYcGCBeHiiy8uZ5Eq0qNrNoZZD60NG+t37tl3VP/aMGPS8HDBiKPKWrZqop7VM3jv6GJdPzt27AijRo0Kt99+ezmLUfEXz2n3rWwRUqJN9TvT/vg46rlSOJ+pNs7pCg8qEydODDfddFO45JJLylmMim5KjC0phVYeK+6Lj8fnoZ5z53ym2jinu+AYlcbGxnQr2rp1a7rftWtXupVS8XilPm4pxTEpr21/I9T02P9z4uNLXticxqzkSD2r5yLnM9X0vhE5p/evLa9dt0KhkMXH7W7duh1wjMrMmTPDrFmz9tk/f/780KdPnw4uIQBQCg0NDWHy5Mmhvr4+9OvXr3qCSmstKkOHDg1btmw54B96KGlv4cKFYfz48aFXr14h17Q+dd6yAz7v7iljsm5RUc/qOXI+U03vG5Fzev/i9XvgwIEHFVQqquunpqYm3fYWT9SOOlk78tjtddbxg8KAIw5LA2dbS5txcnJd/9r0vNynKqtn9ex8ppreNyLn9P615XWz4FsFi+EjTkGO9o4hxe34eO4hJXfqWT2D947yKWtQ2b59e1i1alW6RRs2bEj/fvHFF8tZrIoS10mZe9no1HLSXNyO+62jop4rifOZauOcbr+ydv0sX748/PEf//Ge7enTp6f7KVOmhHvvvbeMJau8/xHGD6+zMq16rgrOZ6qNc7qCg8q4ceNCJmN5q6J7Yuy7jyx3MaqeelbP4L2jcxmjAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQaUVu5sKYemG19K/433chkrlfAYq+b0ji6By++23h+OOOy7U1taGM888MyxdurRsZXl0zcZw9lcfD1PnLUvb8T5ux/1QaZzPQKW/d5Q9qHzve98L06dPDzNmzAgrV64Mo0aNCueff37YvHlzp5clvgDT7lsZNtbvbLF/U/3OtF9YoZI4n4FqeO8oe1D5+te/Hj71qU+FK664IgwfPjx85zvfCX369Al33313p5YjNmnNemhtaK1hq7gvPq4biErgfAaq5b2jZyijN998M6xYsSJcf/31e/Z17949nHfeeeHpp5/e5/mNjY3pVrR169Z0v2vXrnRrj9j/9tr2N0JNj99v13QvtLiP4uNLXtgczhg2oF2/i7cUX7f2vn605HwuD+ezeq50SzvpWtiW9/xuhUKhbCNFX3nllfCHf/iH4b//+7/D2LFj9+z/7Gc/GxYtWhSeeeaZFs+fOXNmmDVr1j7HmT9/fmqFAQDy19DQECZPnhzq6+tDv3798m1RaavY8hLHszRvURk6dGiYMGHCAf/Qg0mRxUFDxfR44+lN4Ybl3UNjU7c9+++eMkaLSgnFVL1w4cIwfvz40KtXr1IeuktzPpeH81k9V7qlnXQtLPaIHIyyBpWBAweGHj16hFdffbXF/rhdV1e3z/NramrSbW/xAtfei9xZxw8KA444LA0Wat7EFF+Yxt3dQnx56vrXpuf16P7Wi0VplOI15C3O5/JyPqvnSnVWJ10L2/J+X9bBtL179w6nnXZaeOyxx/bsa2pqStvNu4I6Q6zwGZOGp3/vXfXF7fi4kEIlcD4D1fLeUfZZP7Er56677grz5s0L69atC9OmTQs7duxIs4A62wUjjgpzLxud0mJzcTvuj49DpXA+A9Xw3lH2MSp//ud/Hn7961+HL33pS2HTpk3h1FNPDY8++mgYPHhwWcoTX4Dxw+vSiOYt65akfjjdPVQq5zNQ6e8dZW9Ria666qrwq1/9Kk09jjN94uq05RRfiOIgoXivu4dK5nwGKvm9I4ugAgDQGkEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQrbIvod8ehUKhzV8X3Zava29oaEjH9q2+HUc9dw71rJ6rifO58uu6eN0uXserNqhs27Yt3Q8dOrTcRQEADuE63r9//7d9TrfCwcSZTDU1NYVXXnkl9O3bN3TrVtrvIIhpLwagl156KfTr16+kx0Y9dzbns3quJs7nyq/rGD1iSDn66KND9+7dq7dFJf5xQ4YM6dDfEV8YQaXjqefOoZ7VczVxPld2XR+oJaXIYFoAIFuCCgCQLUFlP2pqasKMGTPSPR1HPXcO9ayeq4nzuWvVdUUPpgUAqpsWFQAgW4IKAJAtQQUAyJagAgBkS1Bpxe233x6OO+64UFtbG84888ywdOnSzn9lqtzs2bPDmDFj0qrCgwYNChdffHFYv359uYtV1ebMmZNWcL7mmmvKXZSq9PLLL4fLLrssHHnkkeGwww4L733ve8Py5cvLXayqsnv37nDDDTeEYcOGpTp+97vfHW688caD+r4Y9m/x4sVh0qRJaZXY+B7xwAMPtHg81u+XvvSlcNRRR6V6P++888LPfvaz0FkElb1873vfC9OnT0/TsVauXBlGjRoVzj///LB58+ZOe1G6gkWLFoUrr7wyLFmyJCxcuDB98dWECRPCjh07yl20qrRs2bJwxx13hJEjR5a7KFXpt7/9bfjABz6QvrTtkUceCWvXrg233npr+IM/+INyF62qfPWrXw1z584N3/72t8O6devS9te+9rVw2223lbtoFW3Hjh3pWhc/pLcm1vE//dM/he985zvhmWeeCYcffni6Lu7cubNzChinJ/OWM844o3DllVfu2d69e3fh6KOPLsyePVs1daDNmzfHj0SFRYsWqecS27ZtW+GEE04oLFy4sHDOOecUrr76anVcYp/73OcKZ599tnrtYBdeeGFh6tSpLfZ97GMfK1x66aXqvkTi+/CCBQv2bDc1NRXq6uoKt9xyy559r7/+eqGmpqbw3e9+t9AZtKg08+abb4YVK1akZq3m3ycUt59++unOSY5dVH19fbofMGBAuYtSdWLL1YUXXtjivKa0fvCDH4TTTz89fPzjH09dme973/vCXXfdpZpL7P3vf3947LHHwvPPP5+2f/rTn4annnoqTJw4UV13kA0bNoRNmza1eP+I39ETh0V01nWxor+UsNS2bNmS+kAHDx7cYn/cfu6558pWrmoXvwU7jpuITecjRowod3Gqyv3335+6MGPXDx3nF7/4ReqSiN3GX/jCF1J9f/rTnw69e/cOU6ZMUfUl8vnPfz59m+9JJ50UevTokd6vb7755nDppZeq4w4SQ0rU2nWx+FhHE1TI4hP/mjVr0icjSid+LfvVV1+dxgDFgeF0bNiOLSpf+cpX0nZsUYnndOzTF1RK5/vf/37413/91zB//vxwyimnhFWrVqUPOXEQqHquXrp+mhk4cGBK6a+++mqLSorbdXV1nf3adAlXXXVVePjhh8MTTzwRhgwZUu7iVJXYjRkHgY8ePTr07Nkz3eIg5jgoLv47fhqlNOJsiOHDh7fYd/LJJ4cXX3xRFZfQddddl1pVPvGJT6RZVX/5l38ZPvOZz6RZhHSM4rWvnNdFQaWZ2Ex72mmnpT7Q5p+U4vbYsWM75QXpKuKYrRhSFixYEB5//PE03ZDSOvfcc8Pq1avTp87iLX7qj83k8d8xlFMasdty7+n1cRzFscceq4pLqKGhIY0bbC6ex/F9mo4R35tjIGl+XYzdb3H2T2ddF3X97CX2MccmxPiGfsYZZ4RvfvObaerWFVdc0SkvSFfq7onNtw8++GBaS6XY1xkHacV5+rRfrNe9x/zEaYVxnQ9jgUorfqqPAz1j18+f/dmfpbWX7rzzznSjdOJaH3FMyjHHHJO6fp599tnw9a9/PUydOlU1t8P27dvDCy+80GIAbfwwEyc3xLqO3Ws33XRTOOGEE1JwiWvZxO62uP5Vp+iUuUUV5rbbbiscc8wxhd69e6fpykuWLCl3kapOPPVau91zzz3lLlpVMz254zz00EOFESNGpGmbJ510UuHOO+/swN/WNW3dujVNr4/vz7W1tYV3vetdhS9+8YuFxsbGchetoj3xxBOtvh9PmTJlzxTlG264oTB48OB0fp977rmF9evXd1r5usX/dE4kAgBoG2NUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSpAlsaNG5e+YwTo2gQVACBbvusHyM7ll18e5s2b12Jf/EbX4447rmxlAspDUAGyU19fHyZOnBhGjBgRvvzlL6d973znO0OPHj3KXTSgk/Xs7F8IcCD9+/cPvXv3Dn369Al1dXUqDLowY1QAgGwJKgBAtgQVIEux62f37t3lLgZQZoIKkKU4w+eZZ54Jv/zlL8OWLVtCU1NTuYsElIGgAmTp2muvTbN8hg8fnmb8vPjii+UuElAGpicDANnSogIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAACFX/w8RMp1l1lS/kAAAAABJRU5ErkJggg==",
      "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": 7,
   "id": "7de2f306-d612-4c81-8cdb-fef4bf80bf29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6sK7ST0/u379/nHTSSXHmmWfGvHnzYuzYsXH77beXvVkAB5TCx03Tfvmlqn0MqU6n5ULK4VHnnq30oNJea2trm+4dgJxdOPq4WHDZuKLlZE9pOs1Py1FnDl2pXT/XX399TJ06NU444YTYsmVLMT7lySefjP/4j/8oc7MAOiSFkcmjmoqzezY9v6wYk6K7p/bUuWcqtUVl48aN8clPfrIYp3LeeefFihUripCSBu0AdLfuieopyOlZd486UwctKn/3d39X5q8HADKX3RgVAIAqQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQCgfoLKE088sd9ld9111+FuDwDAoQeVCy+8MD772c/Gzp07d8/btGlTTJs2La677rqOrg4AoLYtKosWLYrx48fHunXr4t///d9j9OjRsXnz5lizZk1HVwcAULug8qEPfagIJCmcjBs3Li655JL4zGc+E08++WSceOKJHV0dAEBtB9P+6Ec/ipUrV8awYcOib9++8eKLL0ZLS8uhrAoAoHZBZf78+TFx4sSYPHlyrF27NpYvXx7PPvtsjBkzJp5++umOrg4AoHZB5fbbb4/vfve7cccdd0RjY2PRBZTCyic+8YmYNGlSR1cHALBffaODnnvuuRgyZEibef369Ytbb701Lrrooo6uDgCgdi0q7UPKnj784Q93dHUAAPvlyrQAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqAAA2RJUAIBsCSoAQLYEFQAgW4IKAJAtQQUAyJagAgBkS1ABALIlqFCaXa2VWP7ym8XP6TlNA0A2QWXevHkxfvz4GDhwYAwdOjQuvvjiePHFF8vcJLrIo2vXxzm3PB4z71tRTKfnNJ3mA0AWQWXJkiVxxRVXxLJly2Lx4sWxc+fOmDJlSmzbtq3MzaKTpTAy6/7Vsb55e5v5G5q3F/OFFQCq+kaJHn300TbT3/72t4uWlVWrVsW5555b2nbReVL3ztyH1sW+OnnSvF4RxfLJo5qiT+80BUBPVmpQaa+5ubl4Hjx48D6X79ixo3hUbd68uXhOLTHpUUvV9dV6vT1dGovy5ta3o6HPL6cbelfaPCdp+bKXNsaEkfveD+g4+3PXUGd1rjc7O+lY2JH19apUKlmMYGxtbY2Pf/zj8dZbb8VTTz21z9fMmTMn5s6du9f8hQsXxoABA7pgKwGAw9XS0hLTp08vGigGDRrUPYLKrFmz4pFHHilCyrBhww66RWX48OGxadOmA/6hh5L20riZyZMnR79+/Wq67p7eolIdQFttSfniB1vjhpW9Y0fru10998wYr0WlhuzPXUOd1bne7OykY2E6fg8ZMuSggkoWXT9XXnllPPzww7F06dL9hpSkoaGheLSXitdZYaIz190TnX3S0Bh81BHFwNk9E3IKKTt29SrGqDQd3Vi8zhiV2rM/dw11Vud606/Gx8KOrKvUs35SY04KKYsWLYrHH388Ro4cWebm0AVS+Lhp2qji5/ZDZavTabmQAkDpQSWdmnz//fcXY0zStVQ2bNhQPN5++23vTh27cPRxseCycUXLyZ7SdJqflgNA6V0/CxYsKJ4nTZrUZv69994bn/rUp0raKrpCCiPpFOR0ds+m55cVY1J09wCQVVDJZBwvJUndO+kU5P/3fBTPunsAaM+9fgCAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCy1bfsDaA2drVWYvnLb8bGLdtj6MDGmDBycPTp3Ut5a0ydu4Y6U2/s0900qCxdujRuvfXWWLVqVaxfvz4WLVoUF198cZmb1C09unZ9zH1oXaxv3r573nFHN8ZN00bFhaOPK3Xb6ok6qzP47OhhXT/btm2LsWPHxp133lnmZnT7g+es+1e3CSnJhubtxfy0HHXuLuzP1Bv7dDcPKlOnTo2bb745LrnkkjI3o1s3JaaWlMo+llXnpeXpdahz7uzP1Bv7dA8co7Jjx47iUbV58+bieefOncWjlqrrq/V6aymNSXlz69vR0Gf/r0nLl720sRizkiN1Vucq+zP19LmR2Kf3ryPvXa9KpZLF1+1evXodcIzKnDlzYu7cuXvNX7hwYQwYMKCTtxAAqIWWlpaYPn16NDc3x6BBg+onqOyrRWX48OGxadOmA/6hh5L2Fi9eHJMnT45+/fpFrml95n0rDvi6e2aMz7pFRZ3VObE/U0+fG4l9ev/S8XvIkCEHFVS6VddPQ0ND8Wgv7aidtbN25roP19knDY3BRx1RDJzdV9pMJyc3Hd1YvC73U5XVWZ3tz9TT50Zin96/jrxvLvjWjaXwkU5BTtrHkOp0Wp57SMmdOqsz+OwoT6lBZevWrbFmzZrikbz88svFz6+++mqZm9WtpOukLLhsXNFysqc0nea7joo6dyf2Z+qNffrwldr1s3LlyvjIRz6ye3r27NnF84wZM+Lb3/52iVvW/f5HmDyqyZVp1bku2J+pN/bpbhxUJk2aFJmM5a2L7omJ7zum7M2oe+qszuCzo2sZowIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUFlH3a1VmL5y28WP6fnNA3dlf0Z6M6fHVkElTvvvDNGjBgRjY2NcdZZZ8Xy5ctL25ZH166Pc255PGbet6KYTs9pOs2H7sb+DHT3z47Sg8qDDz4Ys2fPjptuuilWr14dY8eOjQsuuCA2btzY5duS3oBZ96+O9c3b28zf0Ly9mC+s0J3Yn4F6+OwoPah87Wtfi09/+tNx+eWXx6hRo+Kb3/xmDBgwIO65554u3Y7UpDX3oXWxr4at6ry0XDcQ3YH9GaiXz46+UaJ33nknVq1aFddff/3ueb17947zzz8/nn766b1ev2PHjuJRtXnz5uJ5586dxeNwpP63N7e+HQ19fjnd0LvS5jlJy5e9tDEmjBx8WL+Ld1Xft8N9/2jL/lwO+7M6d3fLu+hY2JHP/F6VSqW0kaKvv/56/MZv/EZ8//vfj4kTJ+6ef+2118aSJUvimWeeafP6OXPmxNy5c/daz8KFC4tWGAAgfy0tLTF9+vRobm6OQYMG5dui0lGp5SWNZ9mzRWX48OExZcqUA/6hB5Miq4OGqunxix9sjRtW9o4drb12z79nxngtKjWUUvXixYtj8uTJ0a9fv1quukezP5fD/qzO3d3yLjoWVntEDkapQWXIkCHRp0+feOONN9rMT9NNTU17vb6hoaF4tJcOcId7kDv7pKEx+KgjisFCezYxpTdmx65ekd6epqMbi9f16f3um0Vt1OI95F3253LZn9W5uzq7i46FHfm8L3Uwbf/+/ePMM8+Mxx57bPe81tbWYnrPrqCukAp+07RRxc/tS1+dTsuFFLoD+zNQL58dpZ/1k7py7r777rjvvvvi+eefj1mzZsW2bduKs4C62oWjj4sFl40r0uKe0nSan5ZDd2F/Burhs6P0MSq///u/H//7v/8bN954Y2zYsCE+8IEPxKOPPhrHHntsKduT3oDJo5qKEc2bnl9W9MPp7qG7sj8D3f2zo/QWleTKK6+Mn/70p8Wpx+lMn3R12jKlN6I6SCg96+6hO7M/A935syOLoAIAsC+CCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFulX0L/cFQqlQ7fLrojt2tvaWkp1u2uvp1HnbuGOqtzPbE/d/9aV4/b1eN43QaVLVu2FM/Dhw8ve1MAgEM4jh999NG/8jW9KgcTZzLV2toar7/+egwcODB69artPQhS2ksB6LXXXotBgwbVdN2oc1ezP6tzPbE/d/9ap+iRQsrxxx8fvXv3rt8WlfTHDRs2rFN/R3pjBJXOp85dQ53VuZ7Yn7t3rQ/UklJlMC0AkC1BBQDIlqCyHw0NDXHTTTcVz3Qede4a6qzO9cT+3LNq3a0H0wIA9U2LCgCQLUEFAMiWoAIAZEtQAQCyJajsw5133hkjRoyIxsbGOOuss2L58uVd/87UuXnz5sX48eOLqwoPHTo0Lr744njxxRfL3qy6Nn/+/OIKzldffXXZm1KXfvazn8Vll10WxxxzTBxxxBHx/ve/P1auXFn2ZtWVXbt2xQ033BAjR44savy+970vvvjFLx7U/WLYv6VLl8a0adOKq8Smz4jvfve7bZan+t54441x3HHHFXU///zz48c//nF0FUGlnQcffDBmz55dnI61evXqGDt2bFxwwQWxcePGLntTeoIlS5bEFVdcEcuWLYvFixcXN76aMmVKbNu2rexNq0srVqyIu+66K8aMGVP2ptSl//u//4vf+q3fKm7a9sgjj8S6deviq1/9avz6r/962ZtWV2655ZZYsGBBfOMb34jnn3++mP7KV74Sd9xxR9mb1q1t27atONalL+n7kmr89a9/Pb75zW/GM888E0ceeWRxXNy+fXvXbGA6PZl3TZgwoXLFFVfsnt61a1fl+OOPr8ybN0+ZOtHGjRvTV6LKkiVL1LnGtmzZUjn55JMrixcvrnz4wx+uXHXVVWpcY5/73Ocq55xzjrp2so997GOVmTNntpn3iU98onLppZeqfY2kz+FFixbtnm5tba00NTVVbr311t3z3nrrrUpDQ0PlH/7hHypdQYvKHt55551YtWpV0ay15/2E0vTTTz/dNcmxh2pubi6eBw8eXPam1J3UcvWxj32szX5Nbf3bv/1bfPCDH4zf/d3fLboyzzjjjLj77ruVucY+9KEPxWOPPRY/+tGPiukf/OAH8dRTT8XUqVPVupO8/PLLsWHDhjafH+kePWlYRFcdF7v1TQlrbdOmTUUf6LHHHttmfpp+4YUXStuuepfugp3GTaSm89GjR5e9OXXlgQceKLowU9cPnecnP/lJ0SWRuo0///nPF/X+8z//8+jfv3/MmDFD6WvkuuuuK+7me+qpp0afPn2Kz+svfelLcemll6pxJ0khJdnXcbG6rLMJKmTxjX/t2rXFNyNqJ92W/aqrrirGAKWB4XRu2E4tKl/+8peL6dSikvbp1KcvqNTOd77znfj7v//7WLhwYZx++umxZs2a4ktOGgSqzvVL188ehgwZUqT0N954o02R0nRTU1NXvzc9wpVXXhkPP/xwPPHEEzFs2LCyN6eupG7MNAh83Lhx0bdv3+KRBjGnQXHp5/RtlNpIZ0OMGjWqzbzTTjstXn31VSWuoc9+9rNFq8of/MEfFGdV/dEf/VF85jOfKc4ipHNUj31lHhcFlT2kZtozzzyz6APd85tSmp44cWKXvCE9RRqzlULKokWL4vHHHy9ON6S2zjvvvHjuueeKb53VR/rWn5rJ088plFMbqduy/en1aRzFiSeeqMQ11NLSUowb3FPaj9PnNJ0jfTanQLLncTF1v6Wzf7rquKjrp53Ux5yaENMH+oQJE+K2224rTt26/PLLu+QN6UndPan59l//9V+La6lU+zrTIK10nj6HL9W1/ZifdFphus6HsUC1lb7Vp4Geqevn937v94prL33rW98qHtROutZHGpNywgknFF0/zz77bHzta1+LmTNnKvNh2Lp1a7z00kttBtCmLzPp5IZU69S9dvPNN8fJJ59cBJd0LZvU3Zauf9UluuTcom7mjjvuqJxwwgmV/v37F6crL1u2rOxNqjtp19vX49577y170+qa05M7z0MPPVQZPXp0cdrmqaeeWvnWt77Vib+tZ9q8eXNxen36fG5sbKy8973vrfzFX/xFZceOHWVvWrf2xBNP7PPzeMaMGbtPUb7hhhsqxx57bLF/n3feeZUXX3yxy7avV/pP10QiAICOMUYFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgCQLUEFAMiWoAJkadKkScU9RoCeTVABALLlXj9Adj71qU/Ffffd12ZeuqPriBEjStsmoByCCpCd5ubmmDp1aowePTq+8IUvFPPe8573RJ8+fcreNKCL9e3qXwhwIEcffXT0798/BgwYEE1NTQoGPZgxKgBAtgQVACBbggqQpdT1s2vXrrI3AyiZoAJkKZ3h88wzz8Qrr7wSmzZtitbW1rI3CSiBoAJk6ZprrinO8hk1alRxxs+rr75a9iYBJXB6MgCQLS0qAEC2BBUAIFuCCgCQLUEFAMiWoAIAZEtQAQCyJagAANkSVACAbAkqAEC2BBUAIFuCCgAQufr/3DSZGiQU3Z8AAAAASUVORK5CYII=",
      "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": 8,
   "id": "ee310d07-8f9f-449b-b2a3-915371bea3cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f4/nnn48hQ4bU+5oUfqZOnXpAewpmHTp0+MjHCwA0nR07dsTYsWOjqqoqOnbsWPpws3r16uIX/Pa3v41f//rX8aUvfSnuu++++NjHPtao4aY+5557bpx44olFyDncnptevXoVQ2kfVpyPkipTj9bIkSP3DtlReurcNNRZnXPjnG7ZdU6f3126dDmscNPgYak77rgjbr311vizP/uzmDFjRrz++uvxla98JQYNGhSPPfZYDB8+PJrSsGHDirk+B9OuXbti2V8qeGMFkMbcN+rc1JzP6pwb53TLrHND9tXgq6Xuvffe4pLt1FPTvn37Yt7LihUr4pJLLokRI0ZEU0vzfLp3797kvxcAaJ4a3HOzdu3aolto/zSVenH+8A//sMFzd1LPT600OTmFlc6dOxdDTZMnT45f/epX8U//9E/F8/fcc0/07t07TjnllNi5c2cx5+aZZ56J//iP/2jonwEAZKrB4Wb/YLP//JeGWLlyZXzhC1/Yuz5p0qTicfz48TF79uziHjbr16/f+/wHH3wQf/mXf1kEnjQZOA2F/ed//medfQAAR7cGh5tSSsNYh5rPnALOvtIVWWkBADgY3y0FAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAslLWcLNkyZK4+OKLo0ePHlFRURHz5s370NcsWrQohgwZEu3atYs+ffrE7Nmzm+RYc7SnuiaW/c9v4scv/qp4TOuoc0vlfCY3zumP7pgoo+3bt8fgwYPjqquuiksuueRDt1+3bl1cdNFF8dWvfjV+8IMfxMKFC+Oaa66J7t27x+jRo5vkmHMx/6UNMfUnP4sNVTv3tnXv1D5uvXhAXDCwe1mPLSfqrM7gveMo67m58MIL41vf+lb88R//8WFtP2vWrOjdu3fceeed0b9//5g4cWL8yZ/8Sdx9992Nfqy5feB+7bHVdYJNsrFqZ9GenkedWwrnM7lxTh9lc26WLVsW559/fp221GOT2jn8bs7UY1PfAFRtW3reENWRUeemoc7kxjmdwbBUQ23cuDG6detWpy2tb926Nd5///049thjD3jNrl27iqVW2jbZvXt3sZRS7f5Kvd9SWrHu7Xh72/vRrvXBt0nPL399cwzr3TmaI3VW51rOZ7x3HD3v0bsbsL+KmpqaZjGLNE0onjt3bowZM+ag2/z+7/9+TJgwISZPnry37amnnirm4ezYsaPecDNlypSYOnXqAe1z5syJDh06lPAvAAAaS/qcHzt2bFRVVUXHjh3z6bmprKyMTZs21WlL6+mPrC/YJCkITZo0qU7PTa9evWLUqFEfWpyPkioXLFgQI0eOjDZt2kRz/ZfuVY++8KHbPTz+zGbdc6PO6pw4n/HecfS8R2/9v5GXw9Giws3w4cOLnpp9pQKm9oNJl4ynZX+p4I0VQBpz30fq7D5do/PHjy0mD9fXZVeRQmSn9sV2rVulteZLndXZ+Yz3jqPnPbpNA/ZV1gnF27ZtixdffLFYai/1Tj+vX79+b6/LuHHj9m6fLgH/xS9+ETfeeGO88sor8b3vfS9++MMfxje+8Y2y/Q0tTQos6XLvZP/oUruenm/uwaa5U2d1Bu8d5VPWcLNy5co4/fTTiyVJw0fp51tuuaVY37Bhw96gk6TLwP/93/+96K1J98dJl4R///vfd4+bBkr3sZn55SFFD82+0npqd5+b0lDnpqHO5MY5feTKOiw1YsSIONR85vruPpxes2bNmkY+sqPj/zwjB1QWcxY2v7czuh7Xvphjo8dGnVsi5zO5cU4fmRY154bSSkFm+Kc/qayNTJ2bhjqTG+f0UXITPwCADyPcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBgDIinADAGRFuAEAsiLcAABZaRbh5oEHHoiTTz452rdvH2eddVasWLHioNvOnj07Kioq6izpdQAAzSLcPP744zFp0qS49dZbY/Xq1TF48OAYPXp0bN68+aCv6dixY2zYsGHv8sYbbzTpMQMAzVfZw81dd90V1157bUyYMCEGDBgQs2bNig4dOsTDDz980Nek3prKysq9S7du3Zr0mAGA5uuYcv7yDz74IFatWhWTJ0/e29aqVas4//zzY9myZQd93bZt2+Kkk06K6urqGDJkSNx+++1xyimn1Lvtrl27iqXW1q1bi8fdu3cXSynV7q/U+0Wdy8H5rM65cU637Do3ZH8VNTU1NVEmv/71r+OEE06IpUuXxvDhw/e233jjjbF48eJ4/vnnD3hNCj2vvfZaDBo0KKqqquK73/1uLFmyJF5++eXo2bPnAdtPmTIlpk6dekD7nDlzih4iAKD527FjR4wdO7b47E/TU5ptz81HkULQvkHonHPOif79+8eDDz4Y06ZNO2D71CuU5vTs23PTq1evGDVq1IcW56OkygULFsTIkSOjTZs2Jd036tzUnM/qnBvndMuuc+3Iy+Eoa7jp0qVLtG7dOjZt2lSnPa2nuTSHIxXu9NNPj9dff73e59u1a1cs9b2usQJIY+4bdW5qzmd1zo1zumXWuSH7KuuE4rZt28bQoUNj4cKFe9vSPJq0vm/vzKHs2bMn1q5dG927d2/EIwUAWoqyD0ulIaPx48fHGWecEcOGDYt77rkntm/fXlw9lYwbN66YlzN9+vRi/bbbbouzzz47+vTpE++++27MmDGjuBT8mmuuKfNfAgA0B2UPN5dddlm89dZbccstt8TGjRvjtNNOi/nz5++9vHv9+vXFFVS13nnnneLS8bTt8ccfX/T8pAnJ6TJyAICyh5tk4sSJxVKfRYsW1Vm/++67iwUAoFnexA8AoJSEGwAgK8INAJAV4QYAyIpwAwBkRbgBALIi3AAAWRFuAICsCDcAQFaEGwAgK8INAJAV4QYAyIpwAwBkRbgBALIi3AAAWRFuAICsCDcAQFaEGwAgK8INAJAV4QYAyIpwAwBkRbgBALIi3AAAWRFuAICsCDcAQFaEGwAgK8INAJAV4QYAyIpwAwBkRbgBALIi3AAAWRFuAICsCDcAQFaEGwAgK8INAJAV4QYAyIpwAwBkRbgBALIi3AAAWRFuAICsCDcAQFaEGwAgK8INAJAV4QYAyIpwAwBkRbgBALIi3AAAWRFuAICsCDcAQFaEGwAgK8JNieyprokV694ufk6PaR1aKucz0JLfO5pFuHnggQfi5JNPjvbt28dZZ50VK1asOOT2TzzxRPTr16/Y/tRTT42nnnoqymn+Sxvic995Jq569IViPT2m9dQOLY3zGWjp7x1lDzePP/54TJo0KW699dZYvXp1DB48OEaPHh2bN2+ud/ulS5fG5ZdfHldffXWsWbMmxowZUywvvfRSlEP6j/a1x1bHhqqdddo3Vu0s2gUcWhLnM5DDe0fZw81dd90V1157bUyYMCEGDBgQs2bNig4dOsTDDz9c7/b33ntvXHDBBXHDDTdE//79Y9q0aTFkyJC4//77m/zYU3fb1J/8LOrrdKttS88boqIlcD4Dubx3HBNl9MEHH8SqVati8uTJe9tatWoV559/fixbtqze16T21NOzr9TTM2/evHq337VrV7HUqqqqKh7ffvvt2L179xEd/6o33om3fvObvUU8promduyojmN2t4o91RVF21u/2R4LX/yfGHrS8Uf0u/id9N9tx44d8Zvf/CbatGmjNCXifC4P57Nat3Srmuiz8L333isea2pqmne42bJlS+zZsye6detWpz2tv/LKK/W+ZuPGjfVun9rrM3369Jg6deoB7b17947GMLaettF3NsqvgkbnfAaa23tHCjmdOnVqvuGmKaReoX17eqqrq4tem09+8pNRUfG/ibJUtm7dGr169Yo333wzOnbsWNJ9o85NzfmszrlxTrfsOqcemxRsevTo8aHbljXcdOnSJVq3bh2bNm2q057WKysr631Nam/I9u3atSuWfX3iE5+IxpT+Ywo3jU+dm4Y6q3NunNMtt84f1mPTLCYUt23bNoYOHRoLFy6s07OS1ocPH17va1L7vtsnCxYsOOj2AMDRpezDUmnIaPz48XHGGWfEsGHD4p577ont27cXV08l48aNixNOOKGYO5Ncf/31ce6558add94ZF110Ufzrv/5rrFy5Mh566KEy/yUAQHNQ9nBz2WWXxVtvvRW33HJLMSn4tNNOi/nz5++dNLx+/friCqpa55xzTsyZMyduvvnmuOmmm6Jv377FlVIDBw6MckvDX+l+PfsPg6HOLZHzWZ1z45w+eupcUXM411QBALQQZb+JHwBAKQk3AEBWhBsAICvCDQCQFeGmRB544IE4+eSTo3379nHWWWfFihUrSrVr/k+6HcCZZ54Zxx13XHTt2rX4NvhXX31VfRrRHXfcUdzJ+y/+4i/UuRH86le/ii9/+cvFHdOPPfbYOPXUU4tbW1A66St+vvnNbxZfuZNq/OlPf7r4wmXX0hyZJUuWxMUXX1zcLTi9R+z//Y6pvukq6O7duxd1T98Z+dprr0VTEW5K4PHHHy/u15MufVu9enUMHjy4+DLPzZs3l2L3/J/FixfHddddF8uXLy9u3Ji+cHDUqFHFfZEovRdeeCEefPDBGDRokPI2gnfeeSc++9nPFl/++vTTT8fPfvaz4v5dxx/vS3ZL6Tvf+U7MnDkz7r///vj5z39erP/d3/1d3HfffSX9PUeb7du3F5916R/29Uk1/vu///uYNWtWPP/88/Gxj32s+FzcuXNn0xxguhScIzNs2LCa6667bu/6nj17anr06FEzffp0pW1EmzdvTrcxqFm8eLE6l9h7771X07dv35oFCxbUnHvuuTXXX3+9GpfYX//1X9d87nOfU9dGdtFFF9VcddVVddouueSSmiuuuELtSyS9D8+dO3fvenV1dU1lZWXNjBkz9ra9++67Ne3atav5l3/5l5qmoOfmCH3wwQexatWqosutVrrpYFpftmzZke6eQ6iqqioeO3furE4llnrI0h3A9z2vKa1/+7d/K+7MfumllxbDrKeffnr8wz/8gzKXWLrxa/rKnv/+7/8u1v/rv/4rnnvuubjwwgvVupGsW7euuCnvvu8f6Tuh0pSNpvpcLPsdilu6LVu2FGO6tXdUrpXWX3nllbIdV+7Sd5CleSCpW7853J06J+krTdLwahqWovH84he/KIZL0pB2utt6qvfXv/714jv30lfSUBp/8zd/U3xLdb9+/Yovak7v19/+9rfjiiuuUOJGkoJNUt/nYu1zjU24ocX2LLz00kvFv8AonTfffLP4/rY0pylNjqdxA3rqubn99tuL9dRzk87pNEdBuCmdH/7wh/GDH/yg+NqeU045JV588cXiH0ZpIqw658uw1BHq0qVL8a+BTZs21WlP65WVlUe6e+oxceLEePLJJ+PZZ5+Nnj17qlEJpSHWNBF+yJAhccwxxxRLmsidJgamn9O/eimNdBXJgAED6rT179+/+D49SueGG24oem++9KUvFVejfeUrX4lvfOMbe7+MmdKr/ewr5+eicHOEUhfy0KFDizHdff9FltaHDx9+pLtnH2neWgo2c+fOjWeeeaa4tJPSOu+882Lt2rXFv25rl9S7kLrw088pyFMaaUh1/1sZpHkhJ510khKX0I4dO+p8+XKSzuP0Pk3jSO/NKcTs+7mYhgbTVVNN9bloWKoE0ph56t5MHwLDhg2Le+65p7hMbsKECaXYPfsMRaWu5R//+MfFvW5qx27TRLV0HwWOXKrr/nOY0iWc6T4s5jaVVuo9SJNd07DUn/7pnxb3xnrooYeKhdJJ92JJc2xOPPHEYlhqzZo1cdddd8VVV12lzEdg27Zt8frrr9eZRJz+AZQu8Ei1TkN/3/rWt6Jv375F2En3GkpDgen+ZE2iSa7JOgrcd999NSeeeGJN27Zti0vDly9fXu5Dyk46XetbHnnkkXIfWtZcCt54fvKTn9QMHDiwuES2X79+NQ899FAj/raj09atW4tbGaT35/bt29f83u/9Xs3f/u3f1uzatavch9aiPfvss/W+H48fP37v5eDf/OY3a7p161ac3+edd17Nq6++2mTHV5H+p2liFABA4zPnBgDIinADAGRFuAEAsiLcAABZEW4AgKwINwBAVoQbACArwg0AkBXhBsjGiBEjitu+A0c34QYAyIqvXwCycOWVV8ajjz5apy19md/JJ59ctmMCykO4AbJQVVUVF154YfHt5bfddlvR9qlPfSpat25d7kMDmtgxTf0LARpDp06dom3bttGhQ4eorKxUZDiKmXMDAGRFuAEAsiLcANlIw1J79uwp92EAZSbcANlIV0Y9//zz8ctf/jK2bNkS1dXV5T4koAyEGyAbf/VXf1VcHTVgwIDiSqn169eX+5CAMnApOACQFT03AEBWhBsAICvCDQCQFeEGAMiKcAMAZEW4AQCyItwAAFkRbgCArAg3AEBWhBsAICvCDQCQFeEGAIic/H+UDWKVDeALOgAAAABJRU5ErkJggg==",
      "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": 9,
   "id": "44b1c092-41a5-4803-908e-cb4df258531f",
   "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": 10,
   "id": "9711b92c-0fcf-4899-9081-69d8ff655baa",
   "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": "markdown",
   "id": "0edc5476-ad6d-4759-bef2-0164cfc291b8",
   "metadata": {},
   "source": [
    "Multiple series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7dd8799-f3e9-4ed9-8dc2-c59b33b47ae4",
   "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",
      "y3 = [4, 5, 7, 10] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# List as data source\n",
    "\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, '\\ny3 =', y3, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ecc8e1e-cfee-4aa8-b0b5-c0d59e954c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAHSBJREFUeJzt3XuMVtXdL/DfMJSbByaKwMALKvXYKuINFYOatr5S0RhOTU9sbbC1mvQPg/FC2wBthBgvI9pyjNaikNR6jnhLTrFqosa7xwiiokbjPZpKVBhsdQahoO/Mc7K2nSkzgII+s57LfD7JLrPXXjN7Zft0nu+s29NQKpVKAQCQyYBcNwIAED4AgOz0fAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVgOjynR2dsb7778fw4cPj4aGhko3BwDYBWnP0o0bN8a4ceNiwIABtRU+UvCYMGFCpZsBAHwFa9eujfHjx9dW+Eg9Hl2NHzFiRKWbAwDsgvb29qLzoOt9vKbCR9dQSwoewgcA1JZdmTJhwikAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFZVt8kYANA3Ojo7Yk3rmtiweUOMGjYqpoyeEo0DGqPqez6eeOKJmDlzZvHBMWkXs7vuumu7D5ZZsGBBjB07NoYOHRrTp0+PN998s5xtBgB200N/eyhm/N8Zcc4D58Tc/ze3+Dedp/KqDx+bNm2Kww47LK6//vodXr/qqqvi2muvjRtuuCGefvrp2GOPPWLGjBmxZcuWcrQXANhNKWDMeWxOrN+8vkd56+bWojx3AGkopa6Kr/rNDQ2xYsWKOO2004rz9KNSj8gvf/nL+NWvflWUtbW1xZgxY+LPf/5znHHGGbv0wTRNTU3F9/lsFwD4+kMtqYejd/Do0hANMWbYmLj/f97/tYZgduf9u6wTTt95551Yt25dMdTSJTXkmGOOiZUrV+7we7Zu3Vo0eNsDACiPNMdjZ8EjKUUp1m1eV9TLpazhIwWPJPV0bCudd13rraWlpQgoXUf6OF4AoDzS5NJy1quLpbbz588vumi6jrVr11a6SQBQN0YNG1XWelUXPpqbm4t/16/v2b2Tzruu9TZ48OBibGjbAwAoj7ScNs3pSHM7diSVNw9rLurVZPiYOHFiETIefvjh7rI0hyOtepk2bVo5bwUA7II0iXTe1HnF170DSNf53Klzs+73sdvh45NPPokXXnihOLommaav33333WL1y4UXXhiXXXZZ3H333fHSSy/Fz372s2IFTNeKGAAgr+n7To/F31sco4eN7lGeekRSebpe1UttH3vssTjhhBO2Kz/rrLOK5bTpxy1cuDCWLl0aH3/8cRx//PHxxz/+Mb71rW/t0s+31BYAam+H0915//5a+3z0BeEDAGpPxfb5AAD4MsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBth4+Ojo64+OKLY+LEiTF06NDYf//949JLL41SqVTuWwEANWhguX/gokWLYsmSJXHzzTfHwQcfHM8++2ycffbZ0dTUFOeff365bwcA9Pfw8dRTT8UPfvCDOPXUU4vz/fbbL2677bZYvXp1uW8FANSgsg+7HHvssfHwww/HG2+8UZy/+OKL8eSTT8Ypp5yyw/pbt26N9vb2HgcAUL/K3vMxb968IkAceOCB0djYWMwBufzyy2PWrFk7rN/S0hKXXHJJuZsBAPSXno8777wzli9fHrfeemusWbOmmPvxu9/9rvh3R+bPnx9tbW3dx9q1a8vdJACgijSUyrwMZcKECUXvx+zZs7vLLrvssrjlllvitdde+9LvT70maXJqCiIjRowoZ9MAgD6yO+/fZe/52Lx5cwwY0PPHpuGXzs7Oct8KAKhBZZ/zMXPmzGKOxz777FMstX3++edj8eLFcc4555T7VgBADSr7sMvGjRuLTcZWrFgRra2tMW7cuPjJT34SCxYsiEGDBn3p9xt2AYDaszvv32UPH1+X8AEAtaeicz4AAL6I8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkNXAvLcDgPrQ0dkRa1rXxIbNG2LUsFExZfSUaBzQWOlm1QThAwB200N/eyiuXH1lrN+8vrtszLAxMW/qvJi+73TP80sYdgGA3Qwecx6b0yN4JK2bW4vydJ0vJnwAwG4MtaQej1KUtrvWVbZo9aKiHjsnfADALkpzPHr3ePQOIOs2ryvqsXPCBwDsojS5tJz1+ivhAwB2UVrVUs56/ZXwAQC7KC2nTataGqJhh9dTefOw5qIeOyd8AMAuSvt4pOW0Se8A0nU+d+pc+318CeEDAHZD2sdj8fcWx+hho3uUpx6RVG6fjy9nkzEA2E0pYJww4QQ7nH5FwgcAfMUhmKObj/bsvgLDLgBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQDUfvh477334swzz4yRI0fG0KFD45BDDolnn322L24FANSYgeX+gR999FEcd9xxccIJJ8R9990Xo0aNijfffDP23HPPct8KAKhBZQ8fixYtigkTJsRNN93UXTZx4sRy3wYAqFFlH3a5++6746ijjorTTz89Ro8eHUcccUQsW7Zsp/W3bt0a7e3tPQ4AoH6VPXy8/fbbsWTJkjjggAPigQceiHPPPTfOP//8uPnmm3dYv6WlJZqamrqP1GsCANSvhlKpVCrnDxw0aFDR8/HUU091l6Xw8cwzz8TKlSt32PORji6p5yMFkLa2thgxYkQ5mwYA9JH0/p06EXbl/bvsPR9jx46NSZMm9Sg76KCD4t13391h/cGDBxeN3PYAAOpX2cNHWuny+uuv9yh74403Yt999y33rQCAGlT28HHRRRfFqlWr4oorroi33norbr311li6dGnMnj273LcCAGpQ2cPH0UcfHStWrIjbbrstJk+eHJdeemlcc801MWvWrHLfCgCoQWWfcJpzwgoAUB0qOuEUAOCLCB8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AADCBwBQv/R8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkNXAvLcDgO11dHbEmtY1sWHzhhg1bFRMGT0lGgc0elR1SvgAoKIe+ttDceXqK2P95vXdZWOGjYl5U+fF9H2nV7Rt9A3DLgBUNHjMeWxOj+CRtG5uLcrTdeqP8AFAxYZaUo9HKUrbXesqW7R6UVGP+iJ8AFARaY5H7x6P3gFk3eZ1RT3qi/ABQEWkyaXlrEftED4AqIi0qqWc9agdwgcAFZGW06ZVLQ3RsMPrqbx5WHNRj/oifABQEWkfj7ScNukdQLrO506da7+POiR8AFAxaR+Pxd9bHKOHje5RnnpEUrl9PuqTTcYAqKgUME6YcIIdTvsR4QOAqhiCObr56Eo3g0wMuwAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFYD894OKIeO//qveO3pB+KfH70XQ/f8jzjwmBnROLDK/+/c2RHxt6ciPlkf8d/GROx7bMSAxqhqtdjm4vXxaax56f/EhvZ3Y9SIfWLKIT+NxoGDKt0s6Nbnv62uvPLKmD9/flxwwQVxzTXX9PXtoO49/8DNMW7lJXFw/L27bP2DI+P9aQvjiBlnRVV65e6I++dGtL//77IR4yJOXhQx6X9EVarFNkfEQ0+2xJVvLI/1jQ3dZWOe/18x71uzYvrx8yvaNsgy7PLMM8/EjTfeGIceemhf3gb6VfA47KnzY1Tp38EjSeepPF2vyjfxO3/W8008af/g8/J0vdrUYpv/FTzmvLU81vf6zd46IIrydB3qOnx88sknMWvWrFi2bFnsueeefXUb6FdDLanHIxnw7z9qe5yPXXlJUa+qhi1S70GUdnDxX2X3z/u8XrWoxTb/a6gl9XgULWzo+QIp/et80RvLi3pQt+Fj9uzZceqpp8b06dO/sN7WrVujvb29xwFsL83xGBN/3y54dEnlzfH3ol7VSPMlevce9FCKaH/v83rVohbbHFHM8SiGWnoFj20DyLrGhqIe1OWcj9tvvz3WrFlTDLt8mZaWlrjkks//mgN2Lk0uLWe9LNJEzXLWy6EW2xxRTC4tZz2oqZ6PtWvXFpNLly9fHkOGDPnS+mkyaltbW/eRvh/YXlrVUs56WaQVIuWsl0MttjnN+xmxT1nrQU2Fj+eeey5aW1tjypQpMXDgwOJ4/PHH49prry2+7ujoOU46ePDgGDFiRI8D2F5aTrs+RkZnaSdTFUoR62JkUa9qpKWpaYVI7GSsKJWP+I/P61WLWmxzRLGcdkxHKRpKO36BpPLmjlJRD+oufJx44onx0ksvxQsvvNB9HHXUUcXk0/R1Y2P1r5GHapT28UjLaZPeAaTr/INpC6trv4+0J0Zamlro/Wb+r/OTr6yuvTNqsc3F62NQsZw26R1Aus7nfmuW/T6oz/AxfPjwmDx5co9jjz32iJEjRxZfA19d2sfjxWOvjQ0NI3uUtzaMLMqrcp+PtCfGj/53xIixPctT70Iqr8Y9M2qxzRHFPh6L//usGN3Zs3xMZxTl9vmgWjSUSjvpoyuj733ve3H44Yfv0iZjabVLU1NTMf/DEAzsmB1OM7HDKeyy3Xn/zhI+dofwAQC1Z3fev32wHACQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZDUw7+2g+nR0lmL1O/+I1o1bYvTwITF14l7ROKCh0s0CqFvCB/3a/S9/EJfc80p80Lalu2xs05BYOHNSnDx5bEXbBlCvDLvQr4PHubes6RE8knVtW4rydB2A8hM+6LdDLanHo7SDa11l6XqqB0B5CR/0S2mOR+8ej22lyJGup3oAlJfwQb+UJpeWsx4Au074oF9Kq1rKWQ+AXSd80C+l5bRpVcvOFtSm8nQ91QOgvIQP+qW0j0daTpv0DiBd5+m6/T4Ayk/4oN9K+3gsOXNKNDf1HFpJ56ncPh8AfcMmY/RrKWB8f1KzHU4BMhI+6PfS0Mq0/Uf2++cAkIthFwAgK+EDAMhK+AAAshI+AICshA8AICvhAwDISvgAALISPgCArIQPACAr4QMAyEr4AACyEj4AgKyEDwAgK+EDAMhK+AAAshI+AICshA8AICvhAwDISvgAALISPgCArIQPACCrgXlvR73r6CzF6nf+Ea0bt8To4UNi6sS9onFAQ6WbBUA9h4+Wlpb4y1/+Eq+99loMHTo0jj322Fi0aFF8+9vfLvetqDL3v/xBXHLPK/FB25busrFNQ2LhzElx8uSxFW0bAHU87PL444/H7NmzY9WqVfHggw/GZ599FieddFJs2rSp3LeiyoLHubes6RE8knVtW4rydB0AkoZSqVTqy0exYcOGGD16dBFKvvOd73xp/fb29mhqaoq2trYYMWKE/0o1MtRy/KJHtgseXdKgS3PTkHhy7n8aggGoU7vz/t3nE05TI5K99tprh9e3bt1aNHjbg9qS5njsLHgkKd2m66keAPRp+Ojs7IwLL7wwjjvuuJg8efJO54ikpNR1TJgwwX+VGpMml5azHgD1rU/DR5r78fLLL8ftt9++0zrz588veke6jrVr1/Zlk+gDaVVLOesBUN/6bKnteeedF/fee2888cQTMX78+J3WGzx4cHFQu9Jy2rSqJU0uLX3BnI9UDwDK3vOR5q+m4LFixYp45JFHYuLEiZ5ynUv7eKTltEnvHT26ztN1+30A0CfhIw213HLLLXHrrbfG8OHDY926dcXxz3/+0xOvY2kfjyVnTil6OLaVzlO5fT4A6LOltg0NO97N8qabboqf//znX/r9ltrWNjucAvRP7bux1Lbscz76eNsQqlwaWpm2/8hKNwOAKuaD5QCArIQPACAr4QMAyEr4AACyEj4AgKyEDwAgK+EDAMhK+AAAshI+AICshA8AICvhAwDISvgAALISPgCArIQPACAr4QMAyEr4AACyEj4AgKyEDwAgK+EDAMhK+AAAshI+AICshA8AIKuBeW/H7ujoLMXqd/4RrRu3xOjhQ2LqxL2icUCDhwhATRM+qtT9L38Ql9zzSnzQtqW7bGzTkFg4c1KcPHlsRdsGAF+HYZcqDR7n3rKmR/BI1rVtKcrTdQCoVcJHFQ61pB6P0g6udZWl66keANQi4aPKpDkevXs8tpUiR7qe6gFALRI+qkyaXFrOegBQbYSPKpNWtZSzHgBUG+GjyqTltGlVy84W1KbydD3VA4BaJHxUmbSPR1pOm/QOIF3n6br9PgCoVcJHFUr7eCw5c0o0N/UcWknnqdw+HwDUMpuMVakUML4/qdkOpwDUHeGjiqWhlWn7j6x0MwCgrAy7AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEBWwgcAkJXwAQBkJXwAAFkNjH6io7MUq9/5R7Ru3BKjhw+JqRP3isYBDZVuFgD0O30WPq6//vq4+uqrY926dXHYYYfFddddF1OnTo1KuP/lD+KSe16JD9q2dJeNbRoSC2dOipMnj61ImwCgv+qTYZc77rgj5syZEwsXLow1a9YU4WPGjBnR2toalQge596ypkfwSNa1bSnK03UAoMbDx+LFi+MXv/hFnH322TFp0qS44YYbYtiwYfGnP/0pcg+1pB6P0g6udZWl66keAFCj4ePTTz+N5557LqZPn/7vmwwYUJyvXLlyu/pbt26N9vb2Hke5pDkevXs8tpUiR7qe6gEANRo+Pvzww+jo6IgxY8b0KE/naf5Hby0tLdHU1NR9TJgwoWxtSZNLy1kPAKiDpbbz58+Ptra27mPt2rVl+9lpVUs56wEAVbjaZe+9947GxsZYv359j/J03tzcvF39wYMHF0dfSMtp06qWNLl0R7M60kLb5qbPl90CADXa8zFo0KA48sgj4+GHH+4u6+zsLM6nTZsWOaV9PNJy2qT3jh5d5+m6/T4AoMaHXdIy22XLlsXNN98cr776apx77rmxadOmYvVLbmkfjyVnTil6OLaVzlO5fT4AoA42Gfvxj38cGzZsiAULFhSTTA8//PC4//77t5uEmksKGN+f1GyHUwCoAg2lUqmqNrlIS23Tqpc0+XTEiBGVbg4AUOb374qvdgEA+hfhAwDISvgAALISPgCArIQPACAr4QMAyEr4AACyEj4AAOEDAKhffbK9+tfRteFq2ikNAKgNXe/bu7JxetWFj40bNxb/TpgwodJNAQC+wvt42ma9pj7bpbOzM95///0YPnx4NDR0ffB9+VJZCjVr1671uTF9yHPOw3P2nOuN13RtP+cUJ1LwGDduXAwYMKC2ej5Sg8ePH9+n90gP24fW9T3POQ/P2XOuN17Ttfucv6zHo4vVLgBAVsIHAJBVvwofgwcPjoULFxb/4jnXOq9nz7neeE33n+dcdRNOAYD61q96PgCAyhM+AICshA8AICvhAwDIqt+Ej+uvvz7222+/GDJkSBxzzDGxevXqSjep7rS0tMTRRx9d7E47evToOO200+L111+vdLPq3pVXXlnsBnzhhRdWuil157333oszzzwzRo4cGUOHDo1DDjkknn322Uo3q650dHTExRdfHBMnTiye8f777x+XXnrpLn0+CF/siSeeiJkzZxY7jqbfEXfddVeP6+kZL1iwIMaOHVs8++nTp8ebb74ZOfSL8HHHHXfEnDlziqVFa9asicMOOyxmzJgRra2tlW5aXXn88cdj9uzZsWrVqnjwwQfjs88+i5NOOik2bdpU6abVrWeeeSZuvPHGOPTQQyvdlLrz0UcfxXHHHRff+MY34r777otXXnklfv/738eee+5Z6abVlUWLFsWSJUviD3/4Q7z66qvF+VVXXRXXXXddpZtW8zZt2lS836U/vnckPedrr702brjhhnj66adjjz32KN4bt2zZ0veNK/UDU6dOLc2ePbv7vKOjozRu3LhSS0tLRdtV71pbW9OfLqXHH3+80k2pSxs3biwdcMABpQcffLD03e9+t3TBBRdUukl1Ze7cuaXjjz++0s2oe6eeemrpnHPO6VH2wx/+sDRr1qyKtakeRURpxYoV3eednZ2l5ubm0tVXX91d9vHHH5cGDx5cuu222/q8PXXf8/Hpp5/Gc889V3Qnbfv5Mel85cqVFW1bvWtrayv+3WuvvSrdlLqUeplOPfXUHq9tyufuu++Oo446Kk4//fRiGPGII46IZcuWecRlduyxx8bDDz8cb7zxRnH+4osvxpNPPhmnnHKKZ92H3nnnnVi3bl2P3x/pc1nStIQc741V98Fy5fbhhx8WY4pjxozpUZ7OX3vttYq1q96lTydOcxBSt/XkyZMr3Zy6c/vttxdDiGnYhb7x9ttvF8MBacj2N7/5TfGszz///Bg0aFCcddZZHnuZzJs3r/iU1QMPPDAaGxuL39eXX355zJo1yzPuQyl4JDt6b+y61pfqPnxQub/KX3755eIvGMorfQz2BRdcUMyrSROo6bsAnXo+rrjiiuI89Xyk13QaHxc+yufOO++M5cuXx6233hoHH3xwvPDCC8UfLmmSpOdcv+p+2GXvvfcu0vT69et7lKfz5ubmirWrnp133nlx7733xqOPPhrjx4+vdHPqThpGTJOlp0yZEgMHDiyONNk3TRxLX6e/HPn60gqASZMm9Sg76KCD4t133/V4y+jXv/510ftxxhlnFKuJfvrTn8ZFF11UrJ6j73S9/1XqvbHuw0fqIj3yyCOLMcVt/6JJ59OmTato2+pNmtOUgseKFSvikUceKZbOUX4nnnhivPTSS8VfiF1H+gs9dVOnr1PY5utLQ4a9l4qneQn77ruvx1tGmzdvLubhbSu9htPvafpO+v2cQsa2741p+Cutesnx3tgvhl3SmG3qvku/oKdOnRrXXHNNsQTp7LPPrnTT6m6oJXWd/vWvfy32+ugaN0yTmNIacsojPdve82jSErm0F4X5NeWT/vpOkyHTsMuPfvSjYm+gpUuXFgflk/ahSHM89tlnn2LY5fnnn4/FixfHOeec4zF/TZ988km89dZbPSaZpj9Q0iKA9LzT8NZll10WBxxwQBFG0n4rabgr7dHU50r9xHXXXVfaZ599SoMGDSqW3q5atarSTao76eW0o+Omm26qdNPqnqW2feOee+4pTZ48uVh+eOCBB5aWLl3aR3fqv9rb24tl4un385AhQ0rf/OY3S7/97W9LW7durXTTat6jjz66w9/JZ511Vvdy24svvrg0ZsyY4jV+4oknll5//fUsbWtI/9P3EQcAoJ/M+QAAqovwAQBkJXwAAFkJHwBAVsIHAJCV8AEAZCV8AABZCR8AQFbCBwCQlfABAGQlfAAAWQkfAEDk9P8B4NIwmvS11YYAAAAASUVORK5CYII=",
      "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": 8,
   "id": "b5096763-da4f-4070-a2f0-d8a86f637b7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ddkz3RUiE5KzPkW6p3Rwdgjl27Jj563/27NnSvXt3s2/WrFlSv379Kr3OP//5T8/3F198sTz66KMyf/58efzxx82+CRMmmN4OfajRo0fL8uXLT+v9qFGjhrz00ktewy39+vXzfK+9Khoe2rVrZ9qu4cE9vJKYmOhV81HWrl27TADSUJORkeF5LQAAwrbnQ2s8yvd4lA8geUV55jwnffXVV6a2okOHDp59+mHuHvKorAULFkjnzp3NsIgGAg0j+/bt8xzXnoay11AdO3Y87XVatGhxWp3Hpk2bpHfv3tKgQQPTw3L11Veb/WVf/1w2b94sUVFRnucCACDhHj60uNTJ85ykdRQul3ePTNl6jjVr1khmZqbccMMNZqjks88+kyeeeMKEmqrSno/y9Sg9evSQ+Ph4mTNnjmzYsEEWLVpkjlXl9atVq1bltgAAwk9YhQ+d1eLkeZXVpEkTMx113bp1nn06ZVWHKTzXrFvX1FK4ffnll1JUVOTZXr16tTRs2NAEDi0Mbdq0qezdu9frOs2bN/e6hlq7du0527djxw45cuSIKVa96qqrpFmzZp5iUzd3T0lJSckZX0d7VEpLS01tCwAAZxJW4UOn0yZVTzK1HRXR/cnVk815TtIhEq3D0KLTlStXytatW00xZ2TkL2//NddcI5MnTzY9Glos+te//tVr/QwNGzoEojUeOoyjNRnu3gm3QYMGySuvvCIzZ840wWbEiBHyn//855zt06EWDReTJk2Sr7/+WhYvXmyKT8vS4KO9M9rrcvjwYVMLUp7WofTt29fUj2jR6p49e0xhrdaBAAAQluFDi0iHtB9ivi8fQNzbWe2zfLLex7hx40yvgtZVaDFmly5d5Morr/Qc16m3OqtEz9FprlpMqlNq3X73u9/JI488Ig899JBZsVR7QnSqbVm333672acFqPra2jPSv3//c7ZNe110Fs0bb7whaWlppgfkmWee8TrnoosukpEjR8qQIUPMVF5tR0WmTp0qt9xyizz44IOmB+X+++83wzoAALhFuMoXGvhZYWGhJCQkSEFBgalBKEtnbehf040aNZK4uLhffQ2dTquzXsoWn2qPhwaPjIY/zdKwQdfI0CChs1TClVM/UwBA4H5+h/VUWzcNGDqdlhVOAQCwLyzDh9KhlXbJ7fzdDAAAwk7Yho9AoMWYAACEm7AqOAUAAP5H+AAAAFYFZfgIsAk6+A34WQJA+Amq8OFedKvsyp8Ibu6fZdkF1QAAoS2oCk71pmV6R1X30t+6CJeuuong7PHQ4KE/S/2Z6s8WABAegip8KL2jqyp/7xEEJw0e7p8pACA8BF340J6OlJQUSUxM9LrrK4KPDrXQ4wEA4SfowoebfmjxwQUAQPAJqoJTAAAQ/AgfAADAKsIHAAAI7vBRUlIiw4YNM7dIr1atmjRp0kRGjRrFYlIAAMA3Bac5OTkydepUmTVrllx++eWyceNGuffeeyUhIUEGDhzo9OUAAEC4h4/Vq1fLTTfdJL169TLbF198scybN0/Wr1/v9KUAAEAQcnzYpVOnTrJixQrZtWuX2d6yZYt88skncv3111d4fnFxsRQWFno9AABA6HK852PIkCEmQDRr1sysw6E1IGPGjJHMzMwKz8/OzpaRI0c63QwAABAuPR8LFy6UOXPmyNy5cyU3N9fUfjzzzDPma0WGDh0qBQUFnsf+/fudbhIAAAggES6H72memppqej8GDBjg2Td69GiZPXu27Nix45zP114TLU7VIBIfH+9k0wAAgI9U5fPb8Z4PvVNpZKT3y+rwS2lpqdOXAgAAQcjxmo/evXubGo8GDRqYqbafffaZjB8/Xvr16+f0pQAAQBByfNjl6NGjZpGxRYsWmdve16tXT+68804ZPny4xMTEnPP5DLsAABB8qvL57Xj4+K0IHwAABB+/1nwAAACcDeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYFW03csBABAaSkpLJDc/Vw4XHZa61etKm8Q2EhUZ5e9mBQXCBwAAVbR873IZu36sHCo65NmXVD1JhrQfIhkNM3g/z4FhFwAAqhg8Bn802Ct4qPyifLNfj+PsCB8AAFRhqEV7PFziOu2Ye1/O+hxzHs6M8AEAQCVpjUf5Ho/yASSvKM+chzMjfAAAUElaXOrkeeGK8AEAQCXprBYnzwtXhA8AACpJp9PqrJYIiajwuO5Prp5szsOZET4AAKgkXcdDp9Oq8gHEvZ3VPov1Ps6B8AEAQBXoOh7ju46XxOqJXvu1R0T3s87HubHIGAAAVaQBo1tqN1Y4/ZUIHwAA/MohmHbJ7XjvfgWGXQAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAAR/+Dhw4IDcddddUqdOHalWrZq0aNFCNm7c6ItLAQCAIBPt9Av+8MMP0rlzZ+nWrZu89957UrduXfnyyy+lVq1aTl8KAAAEIcfDR05OjqSmpsrMmTM9+xo1auT0ZQAAQJByfNhl8eLF0rZtW7n11lslMTFRWrduLTNmzDjj+cXFxVJYWOj1AAAAocvx8PH111/L1KlTpWnTpvL+++9L//79ZeDAgTJr1qwKz8/OzpaEhATPQ3tNAABA6IpwuVwuJ18wJibG9HysXr3as0/Dx4YNG2TNmjUV9nzow017PjSAFBQUSHx8vJNNAwAAPqKf39qJUJnPb8d7PlJSUiQtLc1rX/PmzWXfvn0Vnh8bG2saWfYBAABCl+PhQ2e67Ny502vfrl27pGHDhk5fCgAABCHHw8cjjzwia9eulaeeekp2794tc+fOlenTp8uAAQOcvhQAAAhCjoePdu3ayaJFi2TevHmSnp4uo0aNkgkTJkhmZqbTlwIAAEHI8YJTmwUrAAAgMPi14BQAAOBsCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAADCBwAACF30fAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwKtru5QAAOF1JaYnk5ufK4aLDUrd6XWmT2EaiIqN4q0IU4QMA4FfL9y6XsevHyqGiQ559SdWTZEj7IZLRMMOvbYNvMOwCAPBr8Bj80WCv4KHyi/LNfj2O0EP4AAD4bahFezxc4jrtmHtfzvoccx5CC+EDAOAXWuNRvsejfADJK8oz5yG0ED4AAH6hxaVOnofgQfgAAPiFzmpx8jwED8IHAMAvdDqtzmqJkIgKj+v+5OrJ5jyEFsIHAMAvdB0PnU6rygcQ93ZW+yzW+whBhA8AgN/oOh7ju46XxOqJXvu1R0T3s85HaGKRMQCAX2nA6JbajRVOwwjhAwAQEEMw7ZLb+bsZsIRhFwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFgVbfdyAJxQ8r//yY5178t/fzgg1WpdJM069JCo6AD/v3Npicje1SLHDomcnyTSsJNIZJQEtGBss/n9OCm5X7wuhwv3Sd34BtKmxd0SFR3j72YBHj7/12rs2LEydOhQGTRokEyYMMHXlwNC3mfvz5J6a0bK5XLEs+/QsjrybccR0rpHXwlI2xaLLM0SKfz2l33x9UR65oik/U4CUjC2WUSWf5ItY3fNkUNREZ59SZ89J0MuzZSMLkP92jbAyrDLhg0b5MUXX5QrrrjCl5cBwip4tFw9UOq6fgkeSrd1vx4PyA/xhX/y/hBXhQd/2q/HA00wtvnn4DF49xw5VO5f9vxIMfv1OBDS4ePYsWOSmZkpM2bMkFq1avnqMkBYDbVoj4eK/OWPWq/tlDUjzXkBNWyhvQfiquDgz/uWDvnpvEARjG3+eahFezxMCyO8f0FcP2/n7JpjzgNCNnwMGDBAevXqJRkZGWc9r7i4WAoLC70eAE6nNR5JcuS04OGm+5PliDkvYGi9RPneAy8ukcIDP50XKIKxzSKmxsMMtZQLHmUDSF5UhDkPCMmaj/nz50tubq4ZdjmX7OxsGTnyp7/mAJyZFpc6eZ4VWqjp5Hk2BGObRUxxqZPnAUHV87F//35TXDpnzhyJi4s75/lajFpQUOB56PMBnE5ntTh5nhU6Q8TJ82wIxjZr3U98A0fPA4IqfGzatEny8/OlTZs2Eh0dbR6rVq2SiRMnmu9LSrzHSWNjYyU+Pt7rAeB0Op32kNSRUtcZShVcInlSx5wXMHRqqs4QkTOMFen++It+Oi9QBGObRcx02qQSl0S4Kv4F0f3JJS5zHhBy4aN79+7yxRdfyObNmz2Ptm3bmuJT/T4qKvDnyAOBSNfx0Om0qnwAcW8f7DgisNb70DUxdGqqUf7D/OftnmMDa+2MYGyz+f2IMdNpVfkA4t7OujST9T4QmuGjZs2akp6e7vWoUaOG1KlTx3wP4NfTdTy2dJoohyPqeO3Pj6hj9gfkOh+6JsZtr4nEp3jv194F3R+Ia2YEY5tFzDoe4y/JlMRS7/1JpWL2s84HAkWEy3WGPjoHde3aVVq1alWpRcZ0tktCQoKp/2AIBqgYK5xawgqnQKVV5fPbSvioCsIHAADBpyqf39xYDgAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFZF270cEHhKSl2yfs/3kn/0hCTWjJP2jWpLVGSEv5sFACGL8IGwtnTrQRn57jY5WHDCsy8lIU5G9E6Tnukpfm0bAIQqhl0Q1sGj/+xcr+Ch8gpOmP16HADgPMIHwnaoRXs8XBUcc+/T43oeAMBZhA+EJa3xKN/jUZZGDj2u5wEAnEX4QFjS4lInzwMAVB7hA2FJZ7U4eR4AoPIIHwhLOp1WZ7WcaUKt7tfjeh4AwFmED4QlXcdDp9Oq8gHEva3HWe8DAJxH+EDY0nU8pt7VRpITvIdWdFv3s84HAPgGi4whrGnAuDYtmRVOAcAiwgfCng6tdGxSJ+zfBwCwhWEXAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYFW03csh1JWUumT9nu8l/+gJSawZJ+0b1ZaoyAh/NwsAEMrhIzs7W9566y3ZsWOHVKtWTTp16iQ5OTly2WWXOX0pBJilWw/KyHe3ycGCE559KQlxMqJ3mvRMT/Fr2wAAITzssmrVKhkwYICsXbtWli1bJqdOnZLrrrtOjh8/7vSlEGDBo//sXK/gofIKTpj9ehwAABXhcrlcvnwrDh8+LImJiSaU/N///d85zy8sLJSEhAQpKCiQ+Ph4fkpBMtTSJWflacHDTQddkhPi5JOsaxiCAYAQVZXPb58XnGojVO3atSs8XlxcbBpc9oHgojUeZwoeStOtHtfzAADwafgoLS2Vhx9+WDp37izp6elnrBHRpOR+pKam8lMJMlpc6uR5AIDQ5tPwobUfW7dulfnz55/xnKFDh5reEfdj//79vmwSfEBntTh5HgAgtPlsqu1DDz0kS5YskY8//ljq169/xvNiY2PNA8FLp9PqrBYtLnWdpeZDzwMAwPGeD61f1eCxaNEiWblypTRq1Ih3OcTpOh46nVaVX9HDva3HWe8DAOCT8KFDLbNnz5a5c+dKzZo1JS8vzzz++9//8o6HMF3HY+pdbUwPR1m6rftZ5wMA4LOpthERFa9mOXPmTLnnnnvO+Xym2gY3VjgFgPBUWIWpto7XfPh42RAEOB1a6dikjr+bAQAIYNxYDgAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFZF270cqqKk1CXr93wv+UdPSGLNOGnfqLZERUbwJgIAghrhI0At3XpQRr67TQ4WnPDsS0mIkxG906Rneopf2wYAwG/BsEuABo/+s3O9gofKKzhh9utxAACCFeEjAIdatMfDVcEx9z49rucBABCMCB8BRms8yvd4lKWRQ4/reQAABCPCR4DR4lInzwMAINAQPgKMzmpx8jwAAAIN4SPA6HRandVypgm1ul+P63kAAAQjwkeA0XU8dDqtKh9A3Nt6nPU+AADBivARgHQdj6l3tZHkBO+hFd3W/azzAQAIZiwyFqA0YFyblswKpwCAkEP4CGA6tNKxSR1/NwMAAEcx7AIAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsipYwUVLqkvV7vpf8oycksWactG9UW6IiI/zdLAAAwo7PwseUKVNk3LhxkpeXJy1btpRJkyZJ+/btxR+Wbj0oI9/dJgcLTnj2pSTEyYjeadIzPcUvbQIAIFz5ZNhlwYIFMnjwYBkxYoTk5uaa8NGjRw/Jz88XfwSP/rNzvYKHyis4YfbrcQAAEOThY/z48XL//ffLvffeK2lpaTJt2jSpXr26vPLKK2J7qEV7PFwVHHPv0+N6HgAACNLwcfLkSdm0aZNkZGT8cpHISLO9Zs2a084vLi6WwsJCr4dTtMajfI9HWRo59LieBwAAgjR8fPfdd1JSUiJJSUle+3Vb6z/Ky87OloSEBM8jNTXVsbZocamT5wEAgBCYajt06FApKCjwPPbv3+/Ya+usFifPAwAAATjb5cILL5SoqCg5dOiQ137dTk5OPu382NhY8/AFnU6rs1q0uLSiqg6daJuc8NO0WwAAEKQ9HzExMXLllVfKihUrPPtKS0vNdseOHcUmXcdDp9Oq8it6uLf1OOt9AAAQ5MMuOs12xowZMmvWLNm+fbv0799fjh8/bma/2KbreEy9q43p4ShLt3U/63wAABACi4zdfvvtcvjwYRk+fLgpMm3VqpUsXbr0tCJUWzRgXJuWzAqnAAAEgAiXyxVQi1zoVFud9aLFp/Hx8f5uDgAAcPjz2++zXQAAQHghfAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAAAgfAAAgNDlk+XVfwv3gqu6UhoAAAgO7s/tyiycHnDh4+jRo+Zramqqv5sCAAB+xee4LrMeVPd2KS0tlW+//VZq1qwpERHuG987l8o01Ozfv5/7xvgQ77MdvM+8z6GG3+ngfp81TmjwqFevnkRGRgZXz4c2uH79+j69hr7Z3LTO93if7eB95n0ONfxOB+/7fK4eDzdmuwAAAKsIHwAAwKqwCh+xsbEyYsQI8xW8z8GO32fe51DD73T4vM8BV3AKAABCW1j1fAAAAP8jfAAAAKsIHwAAwCrCBwAAsCpswseUKVPk4osvlri4OOnQoYOsX7/e300KOdnZ2dKuXTuzOm1iYqL06dNHdu7c6e9mhbyxY8ea1YAffvhhfzcl5Bw4cEDuuusuqVOnjlSrVk1atGghGzdu9HezQkpJSYkMGzZMGjVqZN7jJk2ayKhRoyp1fxCc3ccffyy9e/c2K47qvxFvv/2213F9j4cPHy4pKSnmvc/IyJAvv/xSbAiL8LFgwQIZPHiwmVqUm5srLVu2lB49ekh+fr6/mxZSVq1aJQMGDJC1a9fKsmXL5NSpU3LdddfJ8ePH/d20kLVhwwZ58cUX5YorrvB3U0LODz/8IJ07d5bzzjtP3nvvPdm2bZs8++yzUqtWLX83LaTk5OTI1KlTZfLkybJ9+3az/fTTT8ukSZP83bSgd/z4cfN5p398V0Tf54kTJ8q0adNk3bp1UqNGDfPZeOLECd83zhUG2rdv7xowYIBnu6SkxFWvXj1Xdna2X9sV6vLz8/VPF9eqVav83ZSQdPToUVfTpk1dy5Ytc1199dWuQYMG+btJISUrK8vVpUsXfzcj5PXq1cvVr18/r30333yzKzMz029tCkUi4lq0aJFnu7S01JWcnOwaN26cZ9+PP/7oio2Ndc2bN8/n7Qn5no+TJ0/Kpk2bTHdS2fvH6PaaNWv82rZQV1BQYL7Wrl3b300JSdrL1KtXL6/fbThn8eLF0rZtW7n11lvNMGLr1q1lxowZvMUO69Spk6xYsUJ27dpltrds2SKffPKJXH/99bzXPrRnzx7Jy8vz+vdD78uiZQk2PhsD7sZyTvvuu+/MmGJSUpLXft3esWOH39oV6vTuxFqDoN3W6enp/m5OyJk/f74ZQtRhF/jG119/bYYDdMj2H//4h3mvBw4cKDExMdK3b1/edocMGTLE3GW1WbNmEhUVZf69HjNmjGRmZvIe+5AGD1XRZ6P7mC+FfPiA//4q37p1q/kLBs7S22APGjTI1NVoATV8F6C15+Opp54y29rzob/TOj5O+HDOwoULZc6cOTJ37ly5/PLLZfPmzeYPFy2S5H0OXSE/7HLhhReaNH3o0CGv/bqdnJzst3aFsoceekiWLFkiH374odSvX9/fzQk5OoyoxdJt2rSR6Oho89BiXy0c0+/1L0f8djoDIC0tzWtf8+bNZd++fby9DnrsscdM78cdd9xhZhPdfffd8sgjj5jZc/Ad9+efvz4bQz58aBfplVdeacYUy/5Fo9sdO3b0a9tCjdY0afBYtGiRrFy50kydg/O6d+8uX3zxhfkL0f3Qv9C1m1q/17CN306HDMtPFde6hIYNG/L2OqioqMjU4ZWlv8P67zR8R/991pBR9rNRh7901ouNz8awGHbRMVvtvtN/oNu3by8TJkwwU5Duvfdefzct5IZatOv0nXfeMWt9uMcNtYhJ55DDGfrelq+j0SlyuhYF9TXO0b++tRhSh11uu+02szbQ9OnTzQPO0XUotMajQYMGZtjls88+k/Hjx0u/fv14m3+jY8eOye7du72KTPUPFJ0EoO+3Dm+NHj1amjZtasKIrreiw126RpPPucLEpEmTXA0aNHDFxMSYqbdr1671d5NCjv46VfSYOXOmv5sW8phq6xvvvvuuKz093Uw/bNasmWv69Ok+ulL4KiwsNNPE9d/nuLg4V+PGjV1PPPGEq7i42N9NC3offvhhhf8m9+3b1zPddtiwYa6kpCTzO969e3fXzp07rbQtQv/H9xEHAAAgTGo+AABAYCF8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAEJv+H9vEe5R7BLLcAAAAAElFTkSuQmCC",
      "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 = [\n",
    "    'linear',\n",
    "    'constant',\n",
    "    'quadratic'\n",
    "]\n",
    "\n",
    "plt.legend(labels)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66240558-e3a3-46b7-ad12-a4898d4e4481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "plt.scatter(x1, y1)\n",
    "plt.scatter(x2, y2)\n",
    "plt.scatter(x3, y3)\n",
    "\n",
    "labels = [\n",
    "    'linear',\n",
    "    'constant',\n",
    "    'quadratic'\n",
    "]\n",
    "\n",
    "plt.legend(labels)\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "04fbb826-8fc8-4e73-85bc-4387268fb2ea",
   "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 = [\n",
    "    'linear',\n",
    "    'constant',\n",
    "    'quadratic'\n",
    "]\n",
    "\n",
    "plt.legend(labels)\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "251a97aa-de77-4e17-b142-840ff670a4f3",
   "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', marker='s')\n",
    "plt.scatter(x2, y2, color='g', marker='v')\n",
    "plt.scatter(x3, y3, color='b', marker='^')\n",
    "\n",
    "labels = [\n",
    "    'linear',\n",
    "    'constant',\n",
    "    'quadratic'\n",
    "]\n",
    "\n",
    "plt.legend(labels)\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5f7e0e1-6dbd-4e52-afed-d2458ac487f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def poly(c, x):\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": 23,
   "id": "044ff6fb-2add-4ab1-8b39-1b5d924628c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "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": 25,
   "id": "9d1b3bed-5750-460a-b449-71487d674c4c",
   "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 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": 26,
   "id": "f6bb4f25-a40f-4678-8a11-4a69fdc2afef",
   "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": 27,
   "id": "9914e209-cf94-45e9-b8e1-0387695acce9",
   "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": 28,
   "id": "e3919ef8-03f1-4f40-9699-59f9373017cc",
   "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": 29,
   "id": "c6f391ac-5ad1-4160-83a3-3c3a29f0da4c",
   "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": 30,
   "id": "49f98cf9-645e-422d-8b84-a1dbb4b2b886",
   "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": 31,
   "id": "ee65dd90-4dc2-46c3-8f20-5406a6f6138d",
   "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": 32,
   "id": "2ecf8208-11e7-4dd1-b92c-e1cc162844da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sbYfmpVxyichjj5rqj/oTObWnQkOFOR9CrLwJALBcfJ9UU/3hbzjElRpv2qH9NDwkX3yxLVbebHewWL16tTVXAgDoMBxOhykpbaoqpFbapL6mHayhISJUJaX+sAkZACAotJRUS0q158K3p8KupaZoP4ZCAABBo+EhYWBXU/2hEzV1ToUOf9BT0XERLAAAQaUhgpLS6MFQCAAAsAzBAgAAWIZgAQAALMMcCwBAUHk8bjmwbYuUFJyQpLR0yRowSJxOVmruqAgWAICg2bF+rby7eJGUHD9adyypSze5aMpUyR05ijvfATEUAgAIWqhYtuChBqFC6Ws9rufR8RAsAABBGf7Qngp/3luyyLRDx0KwAABYzsyp8Omp8FV87Khph46FYAEAsJxO1LSyHSIHwQIAYDmt/rCyHSIHwQIAYDktKdXqD3+Su3Yz7dCxECwAANa/uThdpqTUn3GTp7KeRQdEsAAABIWuU3HV9Hsa9VxoT4UeZx2LjokFsgAAQaPhIWf4SFbejCIECwBA0IdFeg8awl2OEgyFAAAAyxAsAACAZRgKASKQ1+uV6upqcbtZDrk1XC6XxMTEiMPhCPq/DToGr9stZZ9ulOojRySme3fpfN654nCxI2trECyACFNVVSWHDh2SsrKycF9KROncubNkZmZKXFxcuC8FNle0YoUcfmiOVOfl1R2LyciQnvfMlJRLLgnrtUUCh1f/9AmhoqIiSU1NlcLCQklJSQnltwYinsfjkR07dpi/wLt3727eJPkr3D/9Fadh7MiRI6aHJzc3V5xORoHRfKg4cPsd+oPT8MSp3q6sxx6N2nBR1Mr3b3osgAiib5AaLnr37m3+AkfrdOrUSWJjY2Xv3r3mHiYkJHDr0OTwh/ZUNAoV5qTXhAs9n3zxxQyL+EFsByIQf3FzzyKJx+OVA9tPyNcb8syzvrYjM6ei3vBHIzq3KS/PtEPz6LEAAATNrs/zZc1LO6S0oLLuWGJavIy5Lldyhvaw1Z3XiZpWtotW9FgAAIIWKpY/ublBqFD6Wo/reTvR6g8r20UrggWAiPTFF1/ImDFjzHwJnXMyf/78cF8S6tHhDu2p8OfDl3fYalhES0q1+qN2omYjDoc5r+3QPIIFEIXcHq+s23VM/rHpgHnW15E2O/2SSy6R7Oxs2bhxozz88MMye/ZsWbRoUbgvDacc2lHQqKfCV8mJStPOLnSdCi0prXnhEy5OvdbzrGfhH3MsgCizfPMhue+NrXKosKLuWGZqgsyaNFAuG5wZtO87duxYGTJkiOlhePrpp02p7LRp00wgCNQLL7xgqjueffZZ83UGDRokmzZtkgULFsjUqf636kZolBZVWtouVEwp6WOPNl7HomdP1rFoJXosgCgLFb96/rMGoULlFVaY43o+mJYsWSKJiYmyfv16M3Rx//33yzvvvGPOTZw4UZKSkpp9aHiotW7dOrnwwgsbLHZ16aWXyvbt2+XEiRNB/W9A6ySmxFvaLtTh4sxVK+X0JUuk1yOPmGd9Ha3rVwSKHgsgSuhwh/ZUNDXooce0o1fPTxiYIS5ncJa+1h6LWbNmmY91oaqFCxfKqlWrZMKECaYXo7y8vNnP1XUoauXl5UmfPn0anO/Zs2fdufT09KBcP1ovMzfNVH/4Gw5JSo837exIhzsSR44I92VEJIIFECU+2XO8UU+Fb7jQ89ru/JyuQQsW9ekS2/n5NZUBWVlZQfmeCA+n02FKSrX6ozkXXKuroLJ/S0fDUAgQJfKLKyxt1xb1ex2ULkeuK4kGOhSSkZEhhw8fbvC1al/rOdiDrlNx2S8Hm54L354KPW63dSxgDXosgCjRIznB0nZWC2Qo5Pzzz5c//OEPcvLkybrjOlejf//+DIPYjIaHPt/rXlMlUlRp5lTo8Ac9FR0XwQKIEiP6dDHVHzpRs6l5FtohnZGaYNqFQyBDIddff73cd999cvPNN8uMGTNk8+bN8thjj8mf//znoF4j2kZDRFZ/5r1EC4ZCgCihEzK1pFT5jmrXvtbzwZq4aSXdYXHFihWyZ88eOffcc+W3v/2t/Pd//zelpoANsG06EEEqKirMm6lWRLR1h85wrWPREe4dEM2K2DYdQFM0PGhJqVZ/6ERNnVOhwx+R0FMBwP6YYwFEIQ0RwSopBRDdmGMBAAAsQ48FAEQQr8crlXsKxVNcJc7kOInvkyoOhrFgIwQLAIgQ5ZuPSsEbu8RdWFV3zJUaJ2mTcqTT4G5hvTagFkMhABAhoeLY89sahAqlr/W4ngfsgB4LAIiA4Q/tqfCn4I3dkjCwK8MiUcztcctn+Z/JkbIj0r1zdxnWY5i4nC5791g88cQTZhOhlJQU89Bldf/1r38F7+oAAGZOhW9PhS93YaVph+i0cu9KufSVS+Wmt2+SGWtmmGd9rcdtHSxOO+00mTt3rmzcuFE+/fRTueiii+Tqq6+WLVu2BO8KASDK6URNK9uhY1m5d6VMXz1dDpc13JgvvyzfHA91uAgoWEyaNEkuv/xyyc3NlX79+smDDz5odh38+OOPg3eFABDltPrDynah5nW7pXT9J1L45j/Ns76GdcMfcz+ZK94mdgCqPTbvk3mmne0nb7rdblm6dKmUlpaaIZHmVFZWmmVA6z8AoL3Lc0+ZMkXOPvtsiYmJkWuuuaZD31AtKdXqD39cqfGmnd0UrVghOy8eL/smT5aDv/udedbXehztp3MqfHsqfMNFXlmeaWfbYPHll1+aXor4+HiZNm2avPbaazJwYM3GRk2ZM2eO2TCo9tG7d+/2XjOA9tK/XvasEfny7zXPIfxrxgr6h02nTp3kN7/5jYwfP146Ol2nQktK9e9Pr7fhX6b6Wo+kTepru4mbGh4O3H6HVOflNTheffiwOU64aD+dqGllu7AEi/79+8umTZtk/fr18qtf/UomT54sW7dubbb9zJkzpbCwsO6xf//+9l4zgPbYukzk0cEiS64UeeXmmmd9rceDaOzYsSYI3HXXXdKlSxfJyMiQ2bNnt+lrJSYmmsnkt9xyi/k60eDb0q/lo8OvSbm7uMHxMnexOa7n7USHOw4/NEeTTxMna47peYZF2kerP6xsF5Zy07i4ODnzzDPNx7pd8YYNG+Sxxx6TJ598ssn22rOhDwA2oOHh5RtNB2kDRYdqjl/7V5GBVwXt2y9ZskSmT59u/jBZt26dGc4YPXq0TJgwQSZOnChr1qxp9nOzs7OjdqK4x+OWdxcvkpKyo3KwbId0SzhNOrmSpNxdIkcrvjXd3UVLFknO8JHiDEN5YVPKPt3YqKeiAa/XnNd2iSNHhPLSOpRhPYZJz849zUTNpuZZOMRhzmu7iFnHwuPxmHkUAGxOhzuWz2gcKgw95hBZfrfIWVeIBOnNScvVZ82aZT7WSeALFy6UVatWmWDx9NNPS3l5ebOfGxsbK9HqwLYtUnK8ZgEsffM4UtG457f42FHTrvegIWIH1UeOWNoOTdN1Ku4ecbep/tAQUT9c6Gs1Y8SMkK5nEVCw0GEN/avi9NNPl+LiYnnxxRdl9erV8vbbbwfvCgFYY+9akaKDfhp4RYoO1LTrMyZowaK+zMxMyc/PNx9nZWUF5Xt2BCUFJyxtFwox3btb2g7NG589XhaMXWCqQ+pP5NSeCg0Vej6UAgoW+gvgxhtvlEOHDpmJmPpLQkOF/rUBwOZKDlvbrg18ex0cDofp9VQMhTQvKS3d0nah0Pm8cyUmI8NM1GxynoXDITE9e5p2aD8ND+N6j7PFypsBBYtnnnkmeFcCILiSelrbzmIMhTQva8AgSerSrW44pCnJXbuZdnbhcLmk5z0zTfWHhogG4UJf61/U98w07WANDRHDM4ZLuLFXCBAtskeJpPSqmajZ5DwLR815bRcGgQ6FaDVaVVWVHD9+3AzNarWaOuecc6Sj0QmZF02ZKssWPNRsm3GTp9pm4matlEsuEXnsUVP9UX8ip/ZUaKgw59HhECyAaKFvOpfNO1UV4vAJF6fWP7hsbtAmblpNVwHeu3dv3euhQ4eaZ991HjqK3JGj5Krp98i7i5+UkuPH6o4nde0mF02eas7bkYaH5IsvrqkSOXLEzKnQ4Q96KjouggUQTbSUVEtKtTqk/kRO7anQUBHEUlOd6O3r9ddfb/PX++abbyTaOOPOlLiUX0hs1W4Rb6mII1Hikvua43amIYKS0uhBsACijYYHLSnV6g+dqKlzKnT4I0J6KqLVrs/zZfmTm83HrtjvVjAuKzxpjl/2y8GSM7RHGK8QqEGwAKKRhogglZTCeh6PV9a8tMNvmw9f3iF9vtddnDZb1hvRh2ABADZ3aEeBlBb4X4iw5ESlaZfV3z4lp5FMdwO1Q+lmJCJYAIDNlRZVWtoO/q3cu7LJxaZ0hctQLzYVidq8bToAIDQSU+ItbQf/oUKXx/bdilz34tDjeh7+ESwAwOYyc9MkMc1/aEhKjzft0L7hD+2paGozr9pj8z6ZZ9qheQQLALA5nZA55rpcv20uuDaXiZvtpHMqfHsqfMNFXlmeaYfmESwAIAJoKamWlPr2XGhPBaWm1tCJmla2i1ZM3gSACAoXWlJqqkSKKs2cCh3+oMTUGlr9YWW7aEWwAIAIoiGCktLg0JJSrf7QiZpNzbNwiMOc13ZoHkMhACKOLg9+9dVXS2ZmpiQmJpqNx1544YVwXxYinK5ToSWltSGivtrXM0bMYD2LFhAsgCiks9o35G2Qt3a/ZZ4jbZb72rVrZciQIfLKK6/IF198IT//+c/lxhtvlDfffDPcl4YIp+tULBi7QHp0brg8uvZU6HHWsWiZwxvirQCLiookNTVVCgsLJSUlJZTfGoh4FRUVsmfPHunTp48kJCRE1OI/Y8eONWFAr/vpp5+WuLg4mTZtmsyePduSr3/FFVdIz5495dlnnw3avbMDr9vNTqEhwMqbbX//Zo4FEIWL//iOH9cu/hPsv8iWLFki06dPl/Xr18u6detkypQpMnr0aJkwYYJMnDhR1qxZ0+znZmdny5YtW5o9r7/sBgwYIB1Z0YoVcvihOVKdl1d3LCYjQ3reM9NsTw5rh0WGZwznlrYBwQKIEi0t/qNjyLr4z7je44I2hqw9FrNmzTIf5+bmysKFC2XVqlUmWGgvRnl5ebOfGxsb2+y5l19+WTZs2CBPPvmkdORQceD2O0R8OpmrDx+uOf7Yo4QL2ALBAogSgSz+E6y/1DRY1KeTL/Pz883HWVlZbfqa7733nplj8dRTT8mgQYOkI9LhD+2p8A0VNSe9Ig6HOZ988cXicLFRFsKLyZtAlLDD4j++vQ4Oh0M8Ho/5WIdCkpKSmn00FRref/99mTRpkvz5z382kzc7qrJPNzYY/mjE6zXntR0QbvRYAFHC7ov/BDoUoiWnV155pcybN0+mTp3apu/p9Xilck+heIqrxJkcJ/F9UsXhbFhmaAfVR45Y2i7UmAgZXQgWQJSw++I/gQyF6PCHhorbb79d/vM//1PyTv01r5UmXbp0adXXKN98VAre2CXuwqq6Y67UOEmblCOdBncTO4np3t3SdqHEFuTRh6EQIEp0pMV/tLqkrKxM5syZY+Zp1D5++MMftjpUHHt+W4NQofS1HtfzdtL5vHNN9YfOpWiSw2HOazs7YQvy6MQ6FkAECdY6FhmdM0yo6MiL/9TeuzOyz5CCx75oFCrqc6XGS8aM4bYaFqmrClH1J3GeChtZNqsK0eGPS1+5tNkJw7U9ZMv/c3lEhFkI61gAaJqGBy0p1eoPnaipcyp0+CNafrlXfVvsN1Qod2GlmXuRkJMmdmFCw2OPNl7HomdPW65jYYcqJIQHcyyAKBTNi/94Sk+2rl2x//ARDhoetKTUVIkcOWLmVOjwhx1LTO1QhYTwIFgAiCqOzq17E3Yk2fPXo4aIxJEjxO7sXoWE4GHyJoCocqIyT8qqi6S5bZL0eGl1kRwt3x/ya+uIVUi+E4Vr6XGd28MW5B0PwQJAVCkrKZLPjq0yH/uGi9rXnx9bJSVFBWG5vo6iI1UhITAECwBRpXNyqhwo+1o+yn9dyt3FDc6VuYvNcT2flJYetmvsKNiCPDrZcxARAIKke58ccbqSTXg4WLZDuiWcJp1cSVLuLpGjFd+aagU9n9l/IP8GFoj2KqRoRLAAEFWO7S8VV8JY8ZS+YULEkYrGcyn0/OFdxZLVn14LK0RzFVI0YigEQFQpK6kSV1yuxCZO0tKPhicdyea4ni8tqgzXJQIRjR4LAFGlc1Kcedbw4IzNEU/1ARFvqYgjUZwxWeJw1Py9lZgSH+YrBSITPRYAIs727dtl3Lhx0rNnT7O0ed++feXee++VkydbXvyqe3aKJKbVhAYNEa7Y3uKKO8s814aKpPR4ycy1z6qbvktlb8jbIG/tfss862vATuixAKKQ1+2OiNUb/W2hfuONN8qwYcMkLS1N/v3vf8stt9wiHo9HHnroIb+f63Q6ZMx1ubL8yc3Ntrng2lzTzm7YKRSRgGABRBndzKrRfhMZGUHfb2Ls2LEyZMgQ08Pw9NNPmy3Op02bJrNnzw74a2kPhT5qZWdny+rVq2XNmjWt+vycoT3k+yPd8vGHJVIZm1p3PP5kofzHBUnmvN3U7hTqu+V9flm+Ob5g7IIOvYkcIgfBAogidTtk+iwMVX34cM3xIO+QqdudT58+XdavXy/r1q2TKVOmyOjRo2XChAkyceJEv8FAw8OWLVuaPLdz505Zvnx5q7dN1/vgmn+HjPKKFKSdKZVxKRJfVSRphbvEsdYrRVn22ylUd6T1DRVKj+mCU/M+mWfKOinjRLgRLIAoGv7QngrfUFFz0mu239bzuslVsIZFtMdi1qxZ5uPc3FxZuHChrFq1ygQL7cUoLy/3O/zha9SoUfLZZ59JZWWlTJ06Ve6///6A7oMOdqQX7GjYIAT3IVDsFIpIQrAAooSZU1Fv+KMRr9ec13bB2uRKg0V9mZmZkp+fbz7OysoK+Ou99NJLUlxcbOZY/P73v5dHHnlE7rrrLr+fU755S9jvQ6DYKRSRhGABRAmdqGllu7bw7XVwOBxmwqVqy1BI7969zfPAgQPF7XabXovf/va34vLT0+A+fjzs9yFQ7BSKSEKwAKKEVn9Y2c5qbRkKqU8Dipab6rO/YOHq0sXW98HfTqE6UbOpeRY6x0LPs1Mo7IBgAUQJLSnV6g+dqNnkPAuHQ2J69jTtwiGQoZAXXnjBBI2zzz5b4uPj5dNPP5WZM2fKdddd12IA6TR4kK3vg7+dQrX6Q0NE/XDBTqGwGxbIAqKETkTUktKaFz5rNJx6reftMmHRn5iYGJk3b56MGDHCzNu477775Ne//rXp9eio94GdQhEpHF5vU5E9eIqKiiQ1NVUKCwslJSUllN8aiHgVFRWyZ88e6dOnj1kPIpLWsbDbvYvU+6Clp+wUiibpKqx714qUHBZJ6imSPUrEwl1kW/v+HdBQyJw5c+TVV1+Vr776Sjp16mRKvfSvhv79+1txzQBCQN80tZTS6pU39W8Ub6VbvB6vOJwOccS7zOTMaLsPwcZOoWjS1mUiy2eIFB387lhKL5HL5okMvEpCKaBg8f7778ttt90mw4cPl+rqarnnnnvkkksuka1bt0piYmLwrhKApfTN08pSSk/5SakuqBRx1+sAdTkkJi1enJ38z3noSPcBCFuoePlGs1xaA0WHao5f+9eQhouAgoWubFff4sWLpUePHrJx40a58MILrb42ABHAhIpjFY1PuL3meExXsW24YFgBHWL4Y/mMxqHC0GMOkeV3i5x1haXDIkGrCtFxFtXFT/mWroinj/pjNAA6Bh3+OHmiwqxg2Rw9H5cQY7thETb0Qoewd23D4Y9GvCJFB2ra9Rlj76oQrRW/4447zDr/gwcP9jsvQyd71D5qF7QBEPl0ToWjZn2rZul5bWcntRt6HS473OSGXnoeiAglh61tF85goXMtNm/eLEuXLvXbTmvLtWej9rF///62fksANlNdUWlpOzts6KV0Qy9thyim//571oh8+feaZ7v+PCT1tLZduIZCtF78zTfflA8++EBOO+00v2118Rp9AOh4PF5Pq/460XZ2sfXY1kY9Fb7hIq8sz5R0Ds8YHtJrg03YqMKiRVpSqtemEzWbnGfhqDmv7ezYY6HjqRoqXnvtNXn33XdNPTiA6OV2OsTj9f+XnJ7XdnZxvOK4pRt/oYNWWPjOW6itsNDzduJ01QQew/f/s1OvL5sbsomb5pICHf54/vnn5cUXX5Tk5GTJy8szD3/r+wPouFyuOClzl/htU+YuNe3soktCF0s3/kI0VVhoeeTd9hsWGXhVTUlpSmbD49pTEeJS04CHQp544gnzPHbs2AbHn3vuOZkyZYq1VwbA9pwup5yUeCk5WSCdY5LF6XA16Kkoqy6Wk44E084uBnYdyIZeiJgKi1bT8KAlpUFceTMowSLEq38DsDmXu0Kc3hgTLgqqjkuM0yVOh9PMqaj2uMXhTBKn12XaiSQF5Rp27twpQ4cONTuaFhQUtHzNbOiFCKqwCIiGCBsEHvv8GQEgZDwerxzYfkK+3pBnnvV1W3irqyWh8rg4nNor0U3cjkQTMvRZX+vxhMoTpl0w6DbpP/nJT2TMmMB+mbKhV4hRYRFV2DYdiDK7Ps+XNS/tkFJdgvuUxLR4GXNdruQM7RHQ13LExEpMdbl0Kj8iFfFdRJzfzaVweKoloeKEOa/tdAhVdyLVDcB0F9K4uDiZNm2azJ49u83/Lffee6+cddZZcvHFF8vatWsDDhfjeo9jQ69go8Ii6tBjAURZqFj+5OYGoULpaz2u5wPhTOwsjtiacJFUekA6lx2WhPKj5jmp9GBNqIiNNe3UkiVLzL5C69evl/nz58v9998v77zzjjk3ceJESUpKavYxaNCgBt9bK9P+9re/yeOPP97uDb0u73u5edbXsBAVFlGJHgsgSuhwh/ZU+PPhyzukz/e6i7OV5aG6THdsRoZUnVr4zuWuFN+3Zj1fu5y39ljMmjXLfJybmysLFy6UVatWyYQJE0wvhr8Ks9jY7/YbOXbsmJkwrlVq/rZvRhjZcA+LgCosmlzHYq791rGwIYIFECUO7Sho1FPhq+REpWmX1T+91V+3NMEhx9Mc0rXIKzH11sGqdoocS3FIlwSH1L71a7CoLzMzU/Lza3pJsrKyWv09b7nlFrn++uvZ/NDOqLCIWgQLIEqUFlVa2q62UuxQ6SGpTqgJGAlVNeFCQ0VFXE0vRVVpniTHJTfqdVDak6H7DtUOhaxZs6bZ75WdnS1btmypGwZZtmyZPPLII3XXoV8nJiZGFi1aJDfddJN06J4AG5QUtogKi6hFsACiRGJKvKXtVFl1mVR7vqv4qA0T9Z30nDTtWhLIUMi6devE7f5ukaJ//OMfMm/ePDOBM5Cej4gTSRMhbbiHBUKDYAHYkNfjlco9heIprhJncpzE90kVRzuXxc7MTTPVH/6GQ5LS40271qofKtrbLpBAMGDAgAavP/30U3E6nX53Wu4wEyF95yzULjUdhhUWI20PC4QGwQKwmfLNR+XEsl3iKaqqO+ZMiZP0q3LEcWbbF5nSCZlaUqrVH8254NrcVk/cVDHOGEvboQNNhKzdw8KEIYfPtYdnDwuEhsMb4uU0i4qKJDU11WyhzmxuoHGoOPr8VvM7uLaSQpn/TR0iydfnyqHYArMBoK4HYdU6FtpToaEi0HUs9Lq+PvG13x6JWGes5KbnNvjvCYeKigrZs2dPu+5d2Oi23UuubLnd5DdtsfJiy8M3WVRYRKDWvn/zZwRgo+GPwy9tEZfX0ehNWF/rm/ixf+0QmdS+zbE0PGhJqakSKao0cyp0+COQnor615WZmCn7i2vKTZuSkfhduSmicCKkjfawQGgQLACbKNt5XGJOOhvvfHyKvjm7qh3iqW7/zooaIgIpKfUnJT5FekvvmuqQej0X2lOhoULP21akVFhE+kRIm+xhgdAgWAA28c26bZLainbuypNiNxoetKS0tkpE51R0juls756KSKqwYCIkIghLegM2UVza8s6cymPTXYY1RCTGJkpqfKp5tn2o0EmFvltk11ZY6Hk7qZ0IafjeVyZCwl4IFoBdOIulrLqoZqJmE/R4eXWJRouQX1p0VVhITYWFtrOT2qWmUzIbHtdeFruVmiKqMRQC2ERuZpKsXP+hjOox0YSIRlUhIrKt4DPp57o0jFfZARz8vHFPRQNekaIDNXMv7DYvgImQiAAEC8Am4np2k/j9O+QjqZRhXS+WzjHfTXoscxfL58dWSVJFiThcNpxcqDT8VJWIuE+KuGJF4pJ0fERsp+xY5FZYKCZCwuYIFoBNfNXbKV0Lt8hREfln2V+la0IX6eRKknJ3iRyrOC5nHHNLWsJxOWnHXFFeIFL4rYin3sRSZ6xI6mkinVq/kmdIdO4a2RUWgM0xxwKwiSOVx2TxBKf0/3aLjP/3l9L9m2/F+e1e86yv9fhro53isdscCw0VJ/Y0DBVKX+txPW8nvYbWzEtorq7XLDWdxVLTQBsRLACb6N65u3zS3yl/+qFTCpK9csbhnTJw35fmWV/r8S/PcIrT4bTX8If2VPij5+1UyUKFBRBUDIWgQwvGZl7BMqzHMOnZuads6J8vG3IdMmC/V9JLRE4kiWzr7dBVrWRwp24S54wT29A5Fb49Fb70vLaLr9k63QrffPONWZrbl+56+h//8R+tr7Boch2LuVRYAO1AsEBUbubVaXA3sRuX0yV3j7hbpq+ebkLE1uzv/sp3nOq2/8XZvxBHVfuDkcfjlgPbtkhJwQlJSkuXrAGDxNmWFSd1oqaV7QK0cuVKGTRoUN3rrl1bOX9CUWEBBAXBAlG1mZe7sNIc7/bTgbYMF+Ozx8uCsQtk7idz5XDZd1UJPTv3kBkj7pZRPUeZjbTaY8f6tfLu4kVSclynidZI6tJNLpoyVXJHBriFtVZ/tLLd2LFjZciQIWYDsKefflri4uJk2rRpMnv2bGkrDRIZGRlt/nwqLADr2WiwFrBu+CPvhX83ChXKvPaKOa/t7Gh8aZm8vf+APHvosMzLP2qel+8/YI63l4aKZQseahAqlL7W43o+IFpSqtUf/uh5bSciS5YskcTERFm/fr3Mnz9f7r//fnnnnXfMuYkTJ0pSUlKzj/o9E7Wuuuoq6dGjh1xwwQWybJnNVssEohQ9Fuhwyr4+KrHeOL+beel5bZd4Vvt2Cg3WUtMu8crw+scrTi01/aMXRVyN5xa0dvhDeyr8eW/JIskZPrL1wyIa1LSkVKs/mqPnTwU87bGYNWuW+Tg3N1cWLlwoq1atkgkTJphejPLy8ma/TGzsdwFGg8af/vQnGT16tDidTnnllVfkmmuukddff92EDQDhQ7BAh7Pznx9JV+naqnbfO+saiZylph0ia/4k8v3/adOXN3MqfHoqfBUfO2ra9R40pPVf2KxT0adV61hosKgvMzNT8vPzzcdZWVmt/pbdunWT6dOn170ePny4HDx4UB5++GGCBRBmBAt0OIWFx1oVLLSdregS0i0tNV16WLc3bdOX14maVrZrQMNDQmqLK2/W73Wo7T3yeDx1QyFr1qxp9ltkZ2fLli1bmj0/cuTIumEVAOFDsECHU5BWImUHi6STK7nJHTZ13w1dIrugh27oZSOtXUL61BtxoLT6w8p2jei9bkdJaSBDIU3ZtGmT6QEBEF4EC3Q4zol95POHP5RR3ZvfzGvT8Q8l8ebzxVZau4S0s21zrrWkVKs//A2HJHftZtqFQyBDIToJVKtKhg4dal6/+uqr8uyzz5pwAiC8qApBh9MzPVO+SNgpH+W/LuXu4gbntKdCj+t5bWcr2aNaXmo6saeIK75NX14nZGpJqT/jJk9t23oWYfDHP/5Rzj33XDME8o9//ENeeukl+fnPfx7uywKinsNb+ydciBQVFUlqaqoUFhZKSsp3uzfC/tzV1XLw/S+k4lixJHRNll7fHyKuGPt1erk9brl06YUy8sNe0u24U7rG19vMq/K4HO3ikU/GHJLl171vFqWyY1VIjfr/a9aEjYofvSh7XH3MqpO6HoRV61hoT4WGioDXsYggFRUVZg2Q9tw7IJoVtfL9237vCrClXa9+JJ6Pi6STM0k6mY6uUtn99jvi/I8UyfnhaLETjQp3Hz8h00cUiqta5MItfSW1rJMUppbLB4N2iztGZMExj2lnOy0tNd33IpF2LpCl4UFLSi1ZeRMAfBAs0KpQEbfeLeJIbHA8QV+vd8su+che4WLvWhl/9IAsKOskc7umy7vnfPdGnFHtlhn5J2R8WXlNFUafMWI7/paarqiw5FtoiAiopBQAWolggRaHP9xrC0VcSU2uYqkjaXrefVW1fYZFTlVXaHgYV1YunyXEyxGXS7q73TKsovK7norWVmGEg4YIO4YeAGiBTd4JYFf7Vn0mnWOaLyHUcKHntV2fS0eI3aorNEQMr6hsXxUGAKDVqAqBX3vXf2FpO9tUV6Rk1bQDAFiKYAG/yirLLW0XsmGEy+adeuEbLk691omQTFYEAMsRLOBXQkaVlFUX1y0s5UuPl1YXm3a2rK5I8VmrQnsy9LieBwBYjjkWYVJeWirrFi6V6hMVEpOeIOf/+v9Jp8SGVRd2cMHoLHnzyzwZnn5ms6tYbinKkytHt37VRFtUVwAAgoJgEQYr7v1fOaPiDOkX06/mwAmR/bPel28SvpFLHrhV7CQuPUviXH+S9YU/liFJ6Q0mcpa5S+TLkhOS4Fomcem/FVuiugIAQoqhkDCEigEnB5uVIOvT13pcz9tK9ii5Mne7JDj+Lu8dK5UPCg7KJ4X7zPPqYyXm+JW5XzMREgBgECxCPPyhPRWqqTUhlJ7XdnabCHllz/fk56f9UjJjF4nL+7p5/vlp08xxJkIiHHQo7pFHHpF+/fpJfHy82cTswQcf5B8DCDOGQkJo3cL/k34x/VtcE0LbXTTjF2K3iZBxy2fIONen3x3Xkk2trmAiZMTxerxSuadQPMVV4kyOk/g+qeJwNleea0+33367rFixwoSLs88+W44fP24eAMKLYBFC1UeKWnXHTTu7YSJkh1G++agUvLFL3IXfVfK4UuMkbVKOdBrcLWjfd+zYsTJkyBCzAZhub67bnk+bNk1mz54d8Nfatm2bPPHEE7J582bp378mrOvmYgDCr0MEi5LCIlk9/y8iJV6RJIeMvWuaJKXab+fUmM4nRapa2c6G3OKUTzwDJd/dV3p4EmSEOO25kVc9bo9XPtlzXPKLK6RHcoKM6NNFXBHwl3mwrltDxbHntzX+foVV5njXnw5oc7gwpceVbqn2eCTG6ZTEeFejIb8lS5bI9OnTZf369bJu3TqZMmWKjB49WiZMmCATJ06UNWvWNPv1s7OzZcuWLebjN954Q/r27StvvvmmXHbZZeZ7jx8/XubPny9dunTp0D8fXDP32e4/GwEHiw8++EAefvhh2bhxoxw6dEhee+01ueaaayRc3rhzrvR3DZFzYs4X6WTe/eTgHz+U7e4vZNKf7xY7Of+6c2X/c8VmoqbvL1ylvxx1a+/zf3au2M3yzYfkvje2yqHC7zbBykxNkFmTBsplg33WirCJSLzmlq577Jnp7Rr+0J4Kfwre2C0JA7sGPCxSWF4lBwsq5KTbU3cs1uWUXmkJktopru6Y9ljMmjXLfJybmysLFy6UVatWmWChvRjl5c0vtBYbG1v38e7du2Xv3r3yt7/9Tf7617+K2+2WO++8U370ox/Ju+++22F/Prhm7nMk/GwEPHmztLRUvve978njjz8u4aah4py4UU1WWOhxPW8nnQaMkz3l75uPfRecqn2t57Wd3X5gf/X8Zw1+YFVeYYU5ruftJhKvuTXXvebr/DZ/bZ1TUX/4oynuwkrTLtBQsfdYWYNQofS1Htfz9YNFfZmZmZKfX/PfpJMvzzzzzGYf2mNRy+PxSGVlpQkVY8aMMcMszzzzjLz33nuyffv2DvnzwTVznyPlZyPgYKHdlQ888ID84Ac/kHAPf2hPhb8KCz2v7ew0lPBeZopsK/qH6ZmoT1/rcT2v7ezUtaYpuKl1N2uP6XltZxeReM2tve7H39slzSyC2iKdqGllO3NdXq/pqfBHz9cG5/q9DrX/r2pIqP3dkpSU1Oxj0KBBDQJJTEyMqQipNWDAAPO8b9++DvfzwTVznyPpZyPocyz0rwp91CoqsuaNXudUmOGPFiostN2VD94ldqBjX0tLzpET6Z/ITPn/5OvCcVLt6SYxzqPSN3W1vBJ3g7xdco5cvee4nJ/TVexyzb4puD79UdXz2o5rDv69PlJSKVXV7jZ9fa3+sLKd0jkVvj0VvvS8tmtJIEMhOi+jurpadu3aJTk5OebY119/bZ7r92w05YtvC/iZDgF+d0TvfQ56sJgzZ47cd9991n9hnajZqZXtbEIn1Ki3PSPkHTlPRqR+JT2kQPKlv3ziuVo8p3oqatvZQWuvhWsO3b12t7HLQktKtfrD33CIKzXetGstnahpVTsdCmktnag5bNgwuemmm+TRRx81vR633XabmatRvxejKcdLW9cjw890+/C7I3rvc9D73GfOnCmFhYV1j/3791vzhZMc1rYLAZ2lW0tDxMeegbLMM8o814YK33bh1tpr4ZpDd69dTUz8bQ2dkKklpf6kTeob0MRNrf6wsl1rOZ1OUxnSrVs3ufDCC+WKK64wQyFLly5t8XO7JLauR4af6fbhd0f03ueg91joinj6sJqWlGr1R0sVFmNnThO70NIfnaWrE2qa+ptT/ysyUmtKhOyCa7bXve6eFC9xMW0v8tVSUi0pbbyORbwJFYGWmmpJqVZ/+BsO0fPabvXq1Y3Ovf7669JWvXr1kldeeSXgzxtyWhr/H4YAvzui9z7bZ5ZggHSdCi0p9VdhoefttJ6F1hNr6Y/yjUK1r/W8nerouWZ73evbxuVIGzss6mh4yJgxQrrdcrZ0+X/9zXPGjOFtWr9CQ72WlPqj55sK/+HCzzT3mZ8NmwWLkpIS2bRpk3moPXv2mI9bmokdDLpOxaaqtU1WWOhxu61jobSe+ImfDjMJsj59rcftWD/PNdvnXo/p18OS76PDHQk5adL5nB7muT3Lees6FdldO5ueifr0tR6vv46FXfAzzX3mZyN4HF7fP/dboN2Z48Y1Xmdh8uTJsnjx4hY/X6tCUlNTzXyLlJSUqFp5044rpAWCaw7/va6oqDBhXpev1qWx7aQ1K2+GU1P3jp/p0OA+d4z73Nr374CDRaguDEBjdg4Wdse9A0Lz/h2xcywAAID9ECyACFS7WiW4Z4DddIjdTYFooVuN6xoOBw8elO7du5vXdprHYEc62ltVVSVHjhwx907vGYDgIVgAEUTfGHV+he4srOECrde5c2c5/fTTzT0EEDwECyDC6F/c+gape2XoduFomcvlMpuW0bsDBB/BAohA+gapm3L57hYKAOFGnyAAALAMwQIAAFiGYAEAACJ3jkXtQp+6ghcAAIgMte/bLS3YHfJgUVxcbJ579+4d6m8NAAAseB/Xpb1ts1eIrhio9ffJycmWln5pktKwsn//fvYgCSLuc+hwr7nPHQk/z5F/nzUuaKjo1auX3/VgQt5joRdz2mmnBe3r641kc7Pg4z6HDvea+9yR8PMc2ffZX09FLSZvAgAAyxAsAACAZTpMsIiPj5dZs2aZZ3CfOwJ+prnPHQk/z9Fzn0M+eRMAAHRcHabHAgAAhB/BAgAAWIZgAQAALEOwAAAA0RksHn/8cTnjjDMkISFBRo4cKZ988onf9n/729/krLPOMu3PPvtseeutt0J2rZEskPv81FNPyZgxYyQ9Pd08xo8f3+K/CwK/z/UtXbrUrFp7zTXXcCuD8DOtCgoK5LbbbpPMzEwzu75fv378/gjCfX700Uelf//+0qlTJ7Na5J133ikVFRX8XPvxwQcfyKRJk8zql/p74PXXX5eWrF69WoYNG2Z+ls8880xZvHixBJU3QixdutQbFxfnffbZZ71btmzx3nLLLd60tDTv4cOHm2z/0UcfeV0ul3f+/PnerVu3eu+9915vbGys98svvwz5tUeSQO/z9ddf73388ce9n3/+uXfbtm3eKVOmeFNTU73ffvttyK+9I9/nWnv27PFmZWV5x4wZ47366qtDdr3RdK8rKyu95513nvfyyy/3fvjhh+aer1692rtp06aQX3tHvs8vvPCCNz4+3jzrPX777be9mZmZ3jvvvDPk1x5J3nrrLe8f/vAH76uvvqoVnd7XXnvNb/vdu3d7O3fu7J0+fbp5L/yf//kf8964fPnyoF1jxASLESNGeG+77ba6126329urVy/vnDlzmmx/7bXXeq+44ooGx0aOHOn95S9/GfRrjWSB3mdf1dXV3uTkZO+SJUuCeJXReZ/13o4aNcr79NNPeydPnkywCNK9fuKJJ7x9+/b1VlVVtf4fFAHfZ2170UUXNTimb36jR4/mbrZSa4LFXXfd5R00aFCDY9ddd5330ksv9QZLRAyFVFVVycaNG003e/09R/T1unXrmvwcPV6/vbr00kubbY+23WdfZWVlcvLkSenSpQu31MKfZ3X//fdLjx495Oabb+beBvFeL1u2TM4//3wzFNKzZ08ZPHiwPPTQQ+J2u7nvFt7nUaNGmc+pHS7ZvXu3GW66/PLLuc8WCsd7Ycg3IWuLo0ePmv+p9X/y+vT1V1991eTn5OXlNdlej8O6++xrxowZZuzP9wcZ7bvPH374oTzzzDOyadMmbmWQ77W+wb377rtyww03mDe6nTt3yq233moCs65oCGvu8/XXX28+74ILLjC7ZlZXV8u0adPknnvu4RZbqLn3Qt0Ftby83MxvsVpE9FggMsydO9dMLHzttdfM5C1YQ7cp/tnPfmYmynbr1o3bGmQej8f0DC1atEjOPfdcue666+QPf/iD/OUvf+HeW0gnFGpP0P/+7//KZ599Jq+++qr885//lD/+8Y/c5wgXET0W+svU5XLJ4cOHGxzX1xkZGU1+jh4PpD3adp9rPfLIIyZYrFy5UoYMGcLttPDnedeuXfLNN9+YmeD13/xUTEyMbN++XXJycrjnFtxrpZUgsbGx5vNqDRgwwPzlp13+cXFx3GsL7vN//dd/mcD8i1/8wrzWyr3S0lKZOnWqCXI6lIL2a+69ULdUD0ZvhYqIfzn9H1n/cli1alWDX6z6WsdCm6LH67dX77zzTrPt0bb7rObPn2/+yli+fLmcd9553EqLf561ZPrLL780wyC1j6uuukrGjRtnPtYyPVhzr9Xo0aPN8EdteFNff/21CRyECuvus87H8g0PtWGOLaysE5b3Qm8ElTJpadLixYtNyczUqVNNKVNeXp45/7Of/cx79913Nyg3jYmJ8T7yyCOmDHLWrFmUmwbhPs+dO9eUmP3973/3Hjp0qO5RXFxs/Q9BFN9nX1SFBO9e79u3z1Q2/frXv/Zu377d++abb3p79OjhfeCBB9rxL97xBXqf9Xey3uf/+7//MyWRK1as8Obk5JiKPjRPf7dqeb8+9C18wYIF5uO9e/ea83qP9V77lpv+/ve/N++FujwA5ab1aP3t6aefbt7ItLTp448/rjv3/e9/3/yyre/ll1/29uvXz7TXcpt//vOffv650Jb7nJ2dbX64fR/6SwPW/jzXR7AI7r1eu3atKU/XN0otPX3wwQdNuS+su88nT570zp4924SJhIQEb+/evb233nqr98SJE9xmP957770mf+fW3lt91nvt+znnnHOO+XfRn+fnnnvOG0xsmw4AAKJrjgUAAIgMBAsAAGAZggUAALAMwQIAAFiGYAEAACxDsAAAAJYhWAAAAMsQLAAAgGUIFgAAwDIECwAAYBmCBQAAsAzBAgAAiFX+fyZdkymxALsCAAAAAElFTkSuQmCC",
      "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": "markdown",
   "id": "e1049da4-a76c-482a-a287-65df36364b85",
   "metadata": {},
   "source": [
    "Marker customization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "da8b5d0e-e4a6-4f23-adb0-0b55ea7393d1",
   "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": 39,
   "id": "c8a72072-f362-4098-a8fb-e8f257a6d38a",
   "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": 40,
   "id": "c83618c5-a040-4238-ad9f-b4305129e44a",
   "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": 41,
   "id": "e0e8da20-8cfd-40eb-8f7e-f50d94c09ddb",
   "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": 42,
   "id": "d0552a9f-e95c-479c-9901-132436c2b716",
   "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, s=150)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "002e5599-e183-4fcc-b061-387a63ea4917",
   "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, 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": 45,
   "id": "355dcfba-5a0c-4ab6-9974-6f33717aed5e",
   "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, c=c, s=s)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e5279a47-b8d4-4d0e-b69b-48f08f893003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LrFYoWMGEqGyMcAQAQCyoUUPKEcTXujW9nXNOZuyRZxGOAACIBb17SwkJaQejTp0Yzh8r4WjUqFFq3LixChUqpNKlS6tjx47atGlTknWOHDmi3r17q0SJEipYsKA6deqk3bt3J1ln27ZtateunfLnz+9sZ+DAgTpB9SIAwOu6dJHq15dy5kz+frs9Pl4aOjSz98xzPBOOFi9e7ASfr7/+WvPmzdPx48fVpk0bHTp0KHGde++9Vx999JFmzpzprL9jxw5de+21ifefPHnSCUbHjh3TkiVLNG3aNE2dOlVDeaEAALwub17/cP6mTf3XAyPXAk1txYtL8+dLNWtm3T56hGdmyJ4zZ06S6xZqrOZn5cqVuvjii7Vv3z5NmjRJM2bMUIsWLZx1pkyZoho1ajiBqmnTppo7d642bNig+fPnq0yZMqpfv75Gjhyp++67T8OHD1eePHlO+7tHjx51loD9+/c7lxbObAmnwPbCvd1oEevlyw5lpHzexzGM8eNXpIi0cKH0zTfSjBn/nD6kXTupQwfJvuei/PPpeIQ+R0PZXpzPl9JsUdHtp59+UtWqVbV27VrVrl1bCxcuVMuWLfXHH3+oaNGiietVqlRJ/fr1c2qVrIboww8/1Jo1axLv37x5s84++2ytWrVKDRo0OO3vWGgaMWLEabdbCLOmOQAAEP0OHz6srl27OpUphQsXjo2aI7eEhAQn8FxwwQVOMDK7du1yan7cwchYDZHdF1jHrp96f+C+5AwePFj9+/dPUnNUoUIFp0kvrSc3PanWmgxbt26t3LlzK9bEevmyQxkpn/dxDL0t1o9fJMsYaPkJhifDkfU9Wrdunb788suI/634+HhnOZUdsEi9MCO57WgQ6+XLDmWkfN7HMfS2WD9+kShjKNvyTIfsgD59+mjWrFn67LPPVL58+cTby5Yt63S0/vPPP5Osb6PV7L7AOqeOXgtcD6wDAACyN8+EI+saZcHovffec/oXVa5cOcn9jRo1clLhggULEm+zof42dL9Zs2bOdbu0Pkp79uxJXMeq7qx5rCa99wEAgJea1awpzTpBf/DBB85cR4E+QkWKFFG+fPmcyx49ejj9g4oXL+4EnrvvvtsJRDZSzVg/IQtB3bt31+jRo51tDBkyxNl2ck1nAAAg+/FMOBo3bpxzeemllya53Ybr33LLLc6/n376aeXIkcOZ/NGG37dt21YvvfRS4ro5c+Z0muR69erlhKYCBQro5ptv1sMPP5zJpQEAANHKM+EomBkH8ubNqxdffNFZUmJD+2fPnh3mvQMAALHCM32OAAAAMgPhCAAAwIVwBAAA4EI4AgAAcCEcAQAAuBCOAAAAXAhHAAAALoQjAAAAF8IRAACAC+EIAADAi6cPAQAgVtgZsX7+WVq5UtqyxX+9bFmpYUOpZk0pF9/OWYqnHwCATHLsmDRpkvTss9KmTf7bcuaU4uKkEyf818uUke66S7r7bqlYMQ5NVqBZDQCATLBmjdSggdS7t/TDD//cfvLkP8HI7N4tjRghVasmzZrFockKhCMAACJs3jypaVN/bZE1odmSmoQE6X//k666SnrhBQ5PZiMcAQAQQevWSVdfLR0/7q8lCpYFJGPNa++8E7HdQzIIRwAARIgFohtv9F8Gwk6orD9Sz57Snj3h3jukhHAEZDM+n0/f/PqNbnrvJpUaXUrxj8SryONFdOnUSzVz/UwdP3k8q3cRiBnTp0vffRdajdGprAnuwAHpscfCuWdIDaPVgGxk54Gd6vR2Jy39daly5cilEwn+XqDHTh7Tl9u+1OKti1W6QGm9/a+3dclZl6Trbxw86O94asOUrZNpqVL+4clnnun/BQxkJ889J+XIkf5aowALVzbKzTpqI/IIR0A0279feu016auv/J+utWpJPXpI5cqlKxid/8r52nVwl3M9EIwCTvr8P21/O/ybWr3WSh93/VhtqrQJ+pft3Ln+L4I5c5L/Ijj7bKlPH//uFy4c8u4DnrNrl7R6dfi2Zz88liwJ3/aQMprVgDCxgGBzmITNq6/6Z4Wz3phvvy3NnOn/2VihgvTAAyH9FLWmtI5vddSuA7tOC0WnSvAlKCEhQR3f7Kjt+7enue2dO/2dTS+/XPr005R3a/NmacAAqXp1f5ACYp1N8BhONh+S1coi8ghHQAbYr0LLLvXqSXny+JuQTOPG0u23+yt80hqym6y33pJuvln66y//BqxO3VJH4HLUKOnBB4Pe3Ne/fq1l25fphC/1YBSQoASnqW3iyomprvf99/55Wz75xH89tX4VgeHLNodL27YMT0bs+/XX8G7Pmue2p/17BWFAOALSwWpBWrTw96UZP97f4dI9iZtN8DZlinThhdJ550lr14awcdtQ375pr/fEE0F/+r64/EWnj1EorJntpRUvpdhB20bOXHqp9Ntv6R+e/MYbIe0S4Cnp+mGUBdvE6QhHQIj+8x//uY+++MJ/3R2K3AK3f/utP0RNmBDkH5g921+9EszPSOuhGYRZP8xKszktOdb/6Lvd3yV7X69eoQcjN+ucbbVr/BJGrEpH18BUBc6/hsgjHAEheO89qXNn6ejRlEPRqQKnBrjzTn8tU5qsGiqYs05aFUyQVVIHjh1Qev155M/TbrPmwnffzfjw5CNHpCFD0r8NIJo1ahTe7dnniDVjI/IIR0CQtm2TunXLWNW2jdaymqQ0e10G8wes6sVqj4IQnzNe6ZUvd77Tbps4MTxnDbcP+xkzpN9/z/i2gGisObJa5nBNYZE3r9S8eXi2hdQRjoAgWc2PjUbLaJu/9bNOdRvNmgVXJWMbsXWDUL9sfeWIC/3tbv2UqpWolmzLX7A1Z2mx59SG/wOxxkLRPfeEZ1v2Y8Q+OwoVCs/2kDrCERAEG5VlI7IyGggs81jN0eefp7LSJZdI556bdq1Q7tzSLbcE9Xd7N+7tDNEPNRhdX+t6lchf4rT7wjllgRUj3EOegWhhgaZqVX+FcEbEx4c0QBUZRDgCgjBtWniakYxtZ/LkNH5uvvKK/9M0tYD0/PNSsWJB/c1/1fyXiucrrhwhvOWtA7eFqkizc07Z6D8gFllT2Ouv+9/WGWlee+EF/xRnyByEIyAI1gE5XM1Itp0vv0xjpYsukubPlypX/idRWRWLKVHCn9ZsqFeQ4nPFa8a1MyT7gLb/BWFQ80FqViG4ZruMykjHbiDa2bxnNnWZ/d4JpQYpEKZGjgy6khhhwulDgCCENE9REKymxEa8WVV5ii6+WPrxR2nhQn86swRRu7bUoYN/xskQtT2nrd67/j1dN/M6p1YocLoQt8D51iwYjWo1SpnBcl+ZMpnyp4Asc+21/reyDeqw6cnSmuDe3hf58kkvvfTPQBBkHsIREASbqDqcAsPYUw1HgZ+OLVv6lzC4utrV2tRnk8avGK/xK8cnGaZvwahzzc5OU9oFFS9Ic7fCxTJfUEOeraOTzR/w8sv+s9rmzy9ddZW/p3yghg2IYlYhvGGD9Oyz/mYyO/WOvZcCTfaBCfCt07Wdg3DgwPDPlYTgEI6AINgvOKvpCRf7QLRtZoVKRSs5tULDLx2uVTtXOQGpQJ4CqlGyhkoV+Pv8J0E0E9gkmBk903ggKLZqlcZKO3ZIrVv7v1ncpzi3qcjHjJHGjQupmRHIKgUK+E+NOGiQ//RDNhhhyxb/+8BqUG1GfXt/ZdXnA/wIR0AQ6tZNY4RZiOwM9eloGQsr64eU3j5FlkMWL874Plj/i8suk6pUSaPGyIKRBSHjTmSBzkp33OH/ZrEmR8ADrLbIQpAtiD50yAaCYOdIC+doNetO5GUdO0q1amV8eLLlnEceSWMla0qzGqPUesRbVZxNtc2JpwCEAeEICIKNFAnnaLVbb/X2024D5zI6PNkeZ00LTZqksaL1MUprzicLRevWSWvWpG9nAMCFcAQEwSZxu/rqjNceWU2LdT6+IPX+zp5Qv7709ttpT8eUUjC67jrp0UeDWPmnn4Lv3MSESQDCgHAEBMmG1NqEbhkZqWWPtSmKwjnaKytdc400d66/u08wTWyBIGUz/U6fHmSznI1KC1Yo6wJACuiQjWzB5/Ppm+3f6LVvX9P2A9ud28oVKqfudburafmmigsirZx5pvTGG//0+U1P95YJE/x9dWKJdai206s8/rh/0Niff/pDT+AptUubBdsu27WThg2TGjYM4Q/YcH2b7ymtmSJteE86q+S27dumSasm6Ye9P6hLfBfdMesOXXHuFepYvaNy5/x78k1kSweOHtD0tdO1eOti7T+6XwXzFFSTM5volvq3OLPOIzYRjhDz5v08TwPmDtDaPWsTJzk09u9xK8apTuk6erL1k84kiWlp3156/33phhv8Q/uD6YdkTXFWW2LBKFZnuS1cWHrsMX/wWbTIPzzZpiKyPFOypD8M2SnjLGCGzOYxeuqp1NexNPZ//xfyWTm3/LlF93xyj2b9MMs5MW98XLy61O2imetnasp3U1Qqfyndd8F96t+sf1ABGrHjyIkjenDBg858YH8d/8t5fdjEqXZpr4/BCwY7P66eavOUiuQtktW7izAjHCGmWU3RLR/c4tQcmUAwcv97/d71umL6FZrSYYpurn9zUBUZGzf6v7Pt7PQWfpILSYHbrcPxpElStdNPbh9zbFLLtm39S1jnPbA2TXvCLaCcWmVnwahmTWlUaDN6r9uzTpdOvdSZ58knn/PF54vzbzswe/jew3v173n/doL15A6TnS9GxL5Dxw6pzWtt9PX2rxNP2Bx4TQSuHzt5TFPXTNWSX5Zo8S2Lg54jDN7AOx0xa9GWRU4wsg8z+/JLSeD+Wz+8VQs3Lwxq23YCyI8/9o8w79dPatrU390lULlQr57Uq5e0YoX/PGrZIRhFlM1j9N57/hB0alOa3WdPslVfBem3w7+p9WutnWCU3GlUTjXt22ka9tmw9Ow5POiG/9zgNMMHglBK7LXzw/9+UPsZ7XUygRMExpJsG45efPFFnXXWWcqbN6+aNGmiZcuWZfUuIcyGLBwS8mMeWvhQSOvXqCE9+aS0dKl06JC/v42xCSOfey7I02Ig+MmV7CR31mb3zjv+artdu+zNHFIwMhNWTNCeQ3uCCkYBo5eM1u9//c7RinHLty/XRz98FPRrw9ZbtmOZPv7x44jvGzJPtgxHb731lvr3769hw4Zp1apVqlevntq2bas9e/Zk9a4hTNbvWa+vfvkqzV9+brbukl+XaO3uMJ9lFuFjVXPWgalTJ+mKK0IORcZ+4b+4/MWQXhvm+MnjTjMKYttLy19y+iOGImdcTj2/7PmI7RM8EI5uvvlmfR7O8yhkgbFjx6pnz576v//7P9WsWVPjx49X/vz5NXny5KzeNYTJm+veDPkDzthj3lj3Bschhllo3nlwZ7oe++q3r4a0vnWP2rrV34l/4kTJPmKsw/q+fen684gwC8wz1s1I0jcx2Nqj+f+d7zTXIjaE/O2xb98+tWrVSpUqVXLChYWlM9M1BCVrHDt2TCtXrtTgwYMTb8uRI4dTpqXWNnKKo0ePOkvA/v37ncvjx487SzgFthfu7UaLzCzf3gN7lS9HPp3QiZDD0W8Hf0v3PnIMo9+u/buc10ZyArendP8fh/8I6rVx4IA0dao/EG3blnwFmE2BYN2lrPN6Zg6E4zWasn1H9ymnL2eKxz8tO/ftVJHckR25FuvHL5JlDGV7cb7AMJ4Q7N27V6+99pqmTZumDRs2OMGiR48e6tChg3LbeQWi2I4dO5wwt2TJEjVr9s9JNwcNGqTFixfrm2++SbL+8OHDNWLEiNO2M2PGDKe2CQAARL/Dhw+ra9euTiVP4TSa5NMVjtysz86UKVP0yiuvqGDBgurWrZvuuusuVbXzLcRAOEqu5qhChQr67bff0nxy05Nq582bp9atW0d9yIz28j219Ck9+vmjIXW4DfQduP/C+zXogkHp+rscw+i3aucqXTbtsmTvsxqDybUn69Z1t+qvhL+S3GfD+M8rd57mdZ+X4rZtLis7X5zNaxXsGU9syociRfz9y6tXV8TxGk2ZfR1WeKaCM/FjqOyzY0u/LSocH97vhex2/CJZRvv+LlmyZFDhKEPzHO3cudMpgC05c+bUlVdeqbVr1zr9eEaPHq17771X0caeGNvX3bt3J7ndrpctW/a09ePj453lVHbAIvXCjOS2o0FmlK9rva564LMHUh3Cn5w4xalb/W4Z3j+OYfQ6v8L5qlisojMEO6XXhwWjU8ORuanBTSm+Nmz29LvvTt8+HT4stWwpffedlMzHUETwGk1e17pd9cLyF0Lqd2TN8VdVu0olCpZQZon14xeJMoayrRzpSXT/+c9/1L59e6ff0cyZM9WvXz+nRsaa2ebPn6+3335bDz/8sKJRnjx51KhRIy1YsCDxtoSEBOe6uyYJ3laxSEW1P7e982suWLZuu3PbqVLRShHdN2Qtm+n6nib3hPw4qxHoUrtLsvdt3y7dfnv6+w7ZTOJ//OHvg5SxunxkVK/GvULukG3r9zm/D09+DAk5HJ1xxhnOSC8LRjY30IoVK3TnnXcmqaK67LLLVLRoUUUrG8b/8ssvO2Fu48aN6tWrlw4dOuR0MEfsGNVylOJzxQc1q7Fz6ohc8XqsxWOZsm/IWnZerDpl6ihXXPCV52Naj1H+3Mn3M3zoIenIkYwFG5tN/cMPpYXBzUOKCDm3xLm6+/y7nVrkYNhnR6canXRhxQs5Jtk5HD399NNOLZFNoli/fv1k17FgtHnzZkWr66+/XmPGjNHQoUOdMqxZs0Zz5sxRGTu1OGJGrdK1NLvrbOXLlS/VL0G7L2+uvPq468fOFyZin4WcT7t9qqolqqZauxgI1ha0ezbqmew6v/8uTZ8e3Hn2gul/9MILGd8OMubptk+rW91uzr9TC0l2X5uz2+i1a17j3HvZPRx1797dmVXa6/r06aOtW7c6na2tE7bNko3Yc8lZl2jF7St0Xe3rnH4B9mWXO0duZ7F/222da3XW8p7LdelZl2b17iITlS1YVl/f9rUGNh+oonn9Nd32ujCBwNT0zKb66IaPnE76Kfn0U5siJDz7ZAHLTksTru0hfXLmyKlpHadpYvuJToA29llhr4/A/GkVCldwTlj9UdePlC93+ob+I3px4lnEvOolq2v6tdOdX4Nvr39b2/dvdzrinlnoTF1X6zqVKUiNYXZl/YhGtRql4ZcO17sb39X3e76XDkpDLh6iK6tdGVRNop3NxPp5hmtKFtvO+vVSgwbh2R7S3zfNagtva3ibPt/6uRZvXaz9R/erYJ6CanJmE7U9py0nIo5hhCNkG6ULlKbTJJJl/c1uqHODM+Bk9uzZ6t+sf9AjW6wHQTia1E7dJuEoHWzqcTsbtLEJe0uUCEtIshpoW5B9ZMtzqwFAuERidBkj1kL066/SbbdJ1m/U5kQw55wj9egh/fJL+A8QYh41RwCQAfZ9bB2pw3mmg1gdG2KVOl9/7W82/OsvqUABqU4dqXlzf5ZJl59+ki64wN8z3qrw8v3d/8cm7331Vemjj6SvvpKidGJiRCfCEQBkQMOG0vjx4XsKba6kFAYCnz65kp28beZMfxNS+fLSrbdKN97oTx1RwmrB3npLGj1aWr3af5u7xTIQKi3f3H+/1L59iBv/17/+CUansttsAqlOnaRvv83ck9jB02hWA4AMaNUqfN+5dtoRGzhbsGAaK77zjlS5svToo9LGjf6gtGyZfxbJatWk779XNNi1S7rySumGG/zZxB2IAkuA1ShddZXUpYs/zwTFThZuG06t05fdt3attGRJ+guCbIeaIwDIAMsobdpI8+f7Z7rOCDsfW5qnILEmIksQtrK7c1Lg35ZIWrTwt10VK5au/Th47KBm/zhbuw7ucqY1sFnj21Zpq9w5gz/9gnX1ufBCO5+l/3pa55oLPHeW+yzLLF5sp3tK44988IG/TTOtHvG2zvvv+6ungCAQjgAgg6wCZ17K56MNSs6cUo0aUufOaawYODVTSr22LWVYQJoyxU4HENI+bP5js3PS5ilrpujw8cPOUHU7Gav9Vyp/KfU6r5f6Nu2r4vmKp7odmy3cAqMFo1BH8tnu//CDdPXV0hdf+J+XFB04EFy1na1j6wJBolkNADKoUSPpgQfS37xmj7Pl9deT9sc5jTWfWQpLq4rKgtOECSHtw5fbvlT9CfU1YeUEJxiZBF9C4sl59x7eq0e/eFSNJjbSz7//nOq2RozwB5z0TnFgj7MWs2efTWPFM89Mu0rK2Dq2LhAkwhEAhMHw4VLXrqEHJOtnZLUj1q+6Xr0g2qqCHee/bVvQ+7B291q1fb2t05yW2klXT/pO6pd9v6jFqy2099DeZNfZs0caMya4zJKWYcOkQ4dSWaFbt+DDUffuGd8hZBuEIwAIAws4NnLcQpL9O9XmoMAHcA47mbe/MqhjxyD+SGCYejBCOM3TPXPu0dETR52aorRYQLJZ5q0WKTmTJoUnGJmDB6U330xlhUqV/KPz7IlMid1nPcLPOis8O4VsgXCE6HL4sDR5stS2rXTeeVK7dtIbb3CyKXiCfQ8PHeo/pYiFncB3tjWVWZ9gWwKhyfpKDx7sH2x2abCn9atVSypbNu317A/Z0K8gfP/b91q0ZZETeoJl605aPUmHjp1erTN7dvjCkT1/c+aksZJNZ2BDBo07kQb+bZNCvvxyeHYI2QYdshE9Vqzwj/vdu9f/qWifsHZpn7YVK/rP8Fm9elbvJZAmax6zUVc7d/o7Fa9a5X9Z2/d1lSr+PkoXXSTFx4f4ZFroseFsDz2UegKxTjt9+gS1yUmrJjknU02tOS051gT3n43/0U31bkq8zXbJyhoutj0b4p9mbZp9RtjIteef94/SM82a+ac26NDB/7wBIeAVg+hgJ5OyX3iBDgaBD/7ApXVEtZ/X330nlS6ddfsJhMCazK67zr+Ezb//LS1YIC1adHpAsg5P1idp5Ejp/POD2txPf/ykkwmhz0FgZ6j/6fefktxms15b5W84WahMk6XOa6/1LzZ5koWljz9Oo3c7kDKa1RAdrAenfaqmNArHbv/tN+mllzJ7z4DokieP/8vf2uSKFk16n52D47XXpCFDgt6c9TUKjEgL1bGTx5Jcj8QE1ExqjaxAOELWs0lRbE6WtMb9WkAaN46zcgLWHvfII/52O5t98r33pG++kTZt8o/gCkHpAqWdZrVQWb+jkvlLntbCVaRIeA+PnRUFyGyEI2Q9m7DO6uODYeOEw11vD3iVjUiz5mjr/W3NaOmoZrm2xrUh9zcyNrLtmurXJLnN/nw6dyPF1rKmTcOzLSAUhCNERzNBKOhHAIRNu6rtVK5QuZAeY6cUaXN2G1UpXuW0+65JmpcyxCqLrT81kNkIR4iOXqvWVyKtn5v2M7J589DDFICU31Y5cmrYJcNCeoasj9KDFz+Y7H3WqhfKdEwpsY8DG3tBOEJWIBwh69mnYN++wf2MTPOsnABC1bNhT/272b/TXC/u7/9evuplXVzp4mTXKVRIevzxjB8DG3T33HNUFCNrEI4QHWw+Eus7kdJMtxagrr8+zGOiAfjfXnEa3Xq0nrv8ORXN6x8BZyeddTejmQpFKuj9Lu/r1ga3pvrE9e4ttWgR3CzhybGPgbBPgQCEgHCE6GD9iGbN8tcgnVonX7iwf9K76dNTP00AgAwFpLub3K1dA3bp9WteV9sqbVW3TF01KNvA6Xg9u+tsbe67WVdXuzrNbdnb1AbQ2ST3ob5l7XdQmzbStGkM40fWYRJIRNfw5LFj/af0tnMG/P67v9PB5ZeHpxMDgLTfhrnidWPdG50lI+w3zWef+adcevppfy1SarN12CTW1pRmv4MeeIDmNGQtwhGij3Va6Nw5q/cCQAbZb5qnnvKfG/aZZ/wnkbUJrE3gDEGBGQluuslfcVyzJk87sh7hCAAQUQ0bSq++Ko0fL337rbRunX9qswIFpLp1pTp1/AEJiBaEIwBApsif338+WFuAaEbvVgAAABfCEQAAgAvhCAAAwIVwBAAA4EI4AgAAcCEcAQAAuBCOAAAAXAhHAAAALoQjAAAAF8IRAACAC+EIAADAhXAEAADgQjgCAABwIRwBAAC4EI4AAABccrmvAGnx+aTly6U5c6SVK6Xt26W4OKlKFalRI6lDB+ncc3keAQDe5Ymaoy1btqhHjx6qXLmy8uXLpypVqmjYsGE6duxYkvW+++47XXTRRcqbN68qVKig0aNHn7atmTNnqnr16s46derU0ezZszOxJN42d65Uv77UpIn08MPSrFn+gLRihfTOO9L990vVqkktW9qxyOq9BQAghsPR999/r4SEBE2YMEHr16/X008/rfHjx+uBBx5IXGf//v1q06aNKlWqpJUrV+rJJ5/U8OHDNXHixMR1lixZohtuuMEJWqtXr1bHjh2dZd26dVlUMm+wDHrHHVLbtlLgqTp5UkpI+Gcd9/XFi/21SE884a9pAgDASzzRrHb55Zc7S8DZZ5+tTZs2ady4cRozZoxz2/Tp052apMmTJytPnjyqVauW1qxZo7Fjx+r222931nn22Wed7QwcONC5PnLkSM2bN08vvPCCE7aSc/ToUWdxhzBz/PhxZwmnwPbCvd2MBqOuXaUFC6R8+UJ77IgR0u+/2/Psb3qLxvKFW6yXkfJ5H8fQ22L9+EWyjKFsL87n8+Zv+yFDhmjOnDlaYW06km666SYnuLz//vuJ63z22Wdq0aKFfv/9dxUrVkwVK1ZU//791a9fv8R1rHnOHvPtt98m+3es9mmEfcufYsaMGcqfP39EygYAAMLr8OHD6tq1q/bt26fChQt7v+boVD/99JOef/75xFojs2vXLqdPkluZMmUS77NwZJeB29zr2O0pGTx4sBOoAiyAWX8ma8JL68lNT6q1mqzWrVsrd+7cymrWn8j6D2UkPluNUYEC0qpVUvHi0VW+SIi2YxhulM/7OIbeFuvHL5JlDLT8BCNLw9H999+vJ6xjSio2btzodKAO2L59u9M01rlzZ/Xs2TPi+xgfH+8sp7IDFqkXZiS3HYrHHrNmRX9/ooywbUyYIA0dGl3li6RYLyPl8z6OobfF+vGLRBlD2VaWhqMBAwbolltuSXUd618UsGPHDl122WVq3rx5ko7WpmzZstq9e3eS2wLX7b7U1gncj3/s2OEfjRaORlcLV+PGSQ8+yDMMAIh+WRqOSpUq5SzBsBojC0aNGjXSlClTlCNH0oF2zZo104MPPuhUxwXSoVXLVatWzWlSC6yzYMGCJH2ObB27HUl9+WV4R5r973/WHMqzDACIfp4Yym/B6NJLL3U6VFs/o7179zr9hNx9hayTlY1Ss2H6Ntz/rbfeckanufsL9e3b1+nE/dRTTznTA1hna+vQ3adPnywqWfRavdqqIMO7TeY+AgB4gSc6ZFvtjnXCtqV8+fJJ7gsMtitSpIjmzp2r3r17O7VLJUuW1NChQxOH8RtrjrNRZjbSzeZIqlq1qjNSrXbt2plepmj3xx/h3Z51zLZtWudsAACimSfCkfVLSqtvkqlbt66++OKLVNexjty2IHXhrjWyDBvjfQcBADHCE81qyHx2frQTJ8K7zapVw7s9AAAigXCEZDVuHN4O2dasVrcuTzYAIPoRjpCs88+XKlYMz5OTK5d05ZVSmOfMBAAgIghHSP6FkcNG9/lrfDLKmudsWwAAeAHhCCmyGQ5q1pRy5kz/k2SPtf7vrVvzRAMAvIFwhBTlySO98YadQsVfk5Se5rRy5aQXX+RJBgB4B+EIqapTR5o/XypY0B92QqkxsimpPv/cZkLnSQYAeAfhCGmys6usXy+1bOm/nlpICtx3663St99KZ53FEwwA8BbCEYJitUCffCItXix16iQVKnT6OlZD1LOntHatZOcFZnQaAMCLPDFDNqKDjVy7+GL/YnMgbdki2ent7PZKlaQzzsjqPQQAIOMIR0gXC0SVK/sXAABiCc1qAAAALoQjAAAAF8IRAACAC+EIAADAhXAEAADgQjgCAABwIRwBAAC4EI4AAABcCEcAAAAuhCMAAAAXwhEAAIAL4QgAAMCFcAQAAOBCOAIAAHAhHAEAALgQjgAAAFwIRwAAAC6EIwAAABfCEQAAgAvhCAAAwIVwBAAA4EI4AgAAcCEcAQAAuBCOAAAAXAhHAAAALoQjAAAAF8IRAACASy73FcSeDXs3aM2uNTp8/LCKxBfRJWddotIFSmf1bgEAELUIRzHI5/Np5oaZeu6b5/TVL18luS9XjlzqXLOz7m16rxqf2TjL9hEAgGhFs1qMOX7yuLq/113Xv3O9vv7169PuP5FwwglOTV5povErxmfJPgIAEM08F46OHj2q+vXrKy4uTmvWrEly33fffaeLLrpIefPmVYUKFTR69OjTHj9z5kxVr17dWadOnTqaPXu2YqnG6PZZt2vG2hnO9ZO+k8muZwHJJ596fdxLr3/3eibvJQAA0c1z4WjQoEEqV67cabfv379fbdq0UaVKlbRy5Uo9+eSTGj58uCZOnJi4zpIlS3TDDTeoR48eWr16tTp27Ogs69atUyz4bMtnmrpmqhN8gnXHrDt04OiBiO4XAABe4qlw9Mknn2ju3LkaM2bMafdNnz5dx44d0+TJk1WrVi116dJF99xzj8aOHZu4zrPPPqvLL79cAwcOVI0aNTRy5Eg1bNhQL7zwgmLBC8tecPoUheKv439p+trpEdsnAAC8xjMdsnfv3q2ePXvq/fffV/78+U+7f+nSpbr44ouVJ0+exNvatm2rJ554Qn/88YeKFSvmrNO/f/8kj7N1bJupNePZ4q6hMsePH3eWcApsLz3b3Xt4r+b+OFe57b8cuYN+XJziNGnFJPWo10ORlpHyeUWsl5HyeR/H0Nti/fhFsoyhbC+XV/rS3HLLLbrzzjt13nnnacuWLaets2vXLlWuXDnJbWXKlEm8z8KRXQZuc69jt6dk1KhRGjFixGm3Ww1WciEtHObNm5eux02vk/4aoMzse5Xe8nlJrJeR8nkfx9DbYv34RaKMhw8f9kY4uv/++52andRs3LjRCSIHDhzQ4MGDldnsb7prm6zmyDp7W/+mwoULhz3V2ouhdevWyp07+Nof88XWL9T+jfbp+rs54nLo90G/O53cIykj5fOKWC8j5fM+jqG3xfrxi2QZAy0/UR+OBgwY4NQIpebss8/WwoULnSax+Pj4JPdZLdKNN96oadOmqWzZsk7Tm1vgut0XuExuncD9ybG/eerfNXbAIvXCTM+2SxUqpb8S/krX3yuWt1iS5shIi+RzFy1ivYyUz/s4ht4W68cvEmUMZVtZGo5KlSrlLGl57rnn9MgjjyRe37Fjh9NX6K233lKTJk2c25o1a6YHH3zQSZyBJ8CSZ7Vq1ZwmtcA6CxYsUL9+/RK3ZevY7V5Xq3QtVSxSUdv2bQvpcdaBu1PNThHbLwAAvMYTo9UqVqyo2rVrJy7nnnuuc3uVKlVUvnx5599du3Z1aj9smP769eud4GSj09xNYn379tWcOXP01FNP6fvvv3eG+q9YsUJ9+vSR11nT2N3n3+1chsLmPOrduHfE9gsAAK/xRDgKRpEiRZy+SZs3b1ajRo2cJruhQ4fq9ttvT1ynefPmmjFjhjP3Ub169fTOO+84I9UscMWCWxvc6jSR5YzLGdT6tl6bKm1Uv2z9iO8bAABe4YnRaqc666yznBFsp6pbt66++OKLVB/buXNnZ4lFxfMV16fdPtWl0y515i9KaYbsQDCqXrK63vrXW5m6jwAARLuYqTmCX6NyjbS0x1KnD5I5dVJIC0U2t1HH6h311a1fqWjeojx1AAB4veYIqatdurbW3LFG32z/Ri8tf0krdqzQoWOHVDRfUV15zpW647w7dFbRs3gaAQBIBuEoRtmcRU3LN3UWAAAQPJrVAAAAXAhHAAAALoQjAAAAF8IRAACAC+EIAADAhXAEAADgQjgCAABwIRwBAAC4EI4AAABcCEcAAAAuhCMAAAAXwhEAAIAL4QgAAMCFcAQAAEA4AgAASB41RwAAAC6EIwAAABfCEQAAgAvhCAAAwIVwBAAA4EI4AgAAcCEcAQAAuBCOAAAAXAhHAAAALoQjAAAAF8IRAACAC+EIAADAhXAEAABAOAIAAEgeNUcAAAAuhCMAAAAXwhEAAIAL4QgAAMCFcAQAAOBCOAIAAHAhHAEAALgQjgAAAFwIRwAAAC6EIwAAAK+Go48//lhNmjRRvnz5VKxYMXXs2DHJ/du2bVO7du2UP39+lS5dWgMHDtSJEyeSrLNo0SI1bNhQ8fHxOuecczR16tRMLgUAAIhmueQR//nPf9SzZ0899thjatGihRN61q1bl3j/yZMnnWBUtmxZLVmyRDt37tRNN92k3LlzO48xmzdvdta58847NX36dC1YsEC33XabzjjjDLVt2zYLSwcAAKKFJ8KRBaG+ffvqySefVI8ePRJvr1mzZuK/586dqw0bNmj+/PkqU6aM6tevr5EjR+q+++7T8OHDlSdPHo0fP16VK1fWU0895TymRo0a+vLLL/X000+nGI6OHj3qLAH79+93Lo8fP+4s4RTYXri3Gy1ivXzZoYyUz/s4ht4W68cvkmUMZXtxPp/PF9a/HgHLli1zmtMmT56s5557Trt27XLCj4Wl2rVrO+sMHTpUH374odasWZP4OKspOvvss7Vq1So1aNBAF198sdOk9swzzySuM2XKFPXr10/79u1L9m9bsBoxYsRpt8+YMcNpvgMAANHv8OHD6tq1q/N9X7hwYe/XHP33v/9NDCpjx47VWWed5dT+XHrppfrhhx9UvHhxJzBZjZFb4LrdF7hMbh2rDfrrr7+cvkynGjx4sPr375943datUKGC2rRpk+aTm55UO2/ePLVu3dppDow1sV6+7FBGyud9HENvi/XjF8kyBlp+gpGl4ej+++/XE088keo6GzduVEJCgvPvBx98UJ06dUqs8SlfvrxmzpypO+64I2L7aB23bTmVHbBIvTAjue1oEOvlyw5lpHzexzH0tlg/fpEoYyjbytJwNGDAAN1yyy2prmPNYta5+tQ+RhZY7D4boWasI7Y1v7nt3r078b7AZeA29zpWA5RcrREAAMh+sjQclSpVylnS0qhRIycMbdq0SRdeeGFitduWLVtUqVIl53qzZs306KOPas+ePc4wfmPVchZ8AqHK1pk9e3aSbds6djsAAIBn5jmygGPD74cNG+aMSrOQ1KtXL+e+zp07O5fWB8hCUPfu3fXtt9/q008/1ZAhQ9S7d+/EZjHbhvVfGjRokL7//nu99NJLevvtt3Xvvfdmafm0ebM0ZIh0883+66+9Zj3HsnafAADIpjwRjoyNTOvSpYsTfho3bqytW7dq4cKFzmSQJmfOnJo1a5ZzaTVB3bp1c+Y5evjhhxO3YcP4bSJJqy2qV6+e06n7lVdeybo5jmyCSgt5VapIjz8uffih//Y+faQzzpA++CBr9gsAgGzME6PVAh2pxowZ4ywpsSa2U5vNTmUj3FavXq2oYB3Jp0yRbDaFkyelvzueOw4ckK69VpozR2rdOiv3EgCAbMUzNUcx57vvpMmT/cEoOXa7LX37prwOAAAIO8JRVpk4UcqVRsWdhaKNG6WlSzNrrwAAyPYIR1ll1Sp/n6NgrF0b6b0BAAB/IxxllbRqjdxy5ozkngAAABfCUVax+ZqCDT1Nm0Z6bwAAwN8IR1k5Us09Oi05Fp6aN5f+PrkuAACIPMJRVrGZvUeOTD0Y2eSV48Zl5l4BAJDtEY6y0gMPSM8+KxUp4r/uPilejRrSl19Kdetm2e4BAJAdeWYSyJgUFyfdc4/Us6f07rvSjz/6b587V7rgAv/9AAAgUxGOokG+fNKNN9rZdCWb4btJE4IRAABZhGY1AAAAF8IRAACAC+EIAADAhXAEAADgQjgCAABwIRwBAAC4EI4AAABcCEcAAAAuhCMAAAAXZsgOkc/ncy7379+vcDt+/LgOHz7sbDu3+zxrMSLWy5cdykj5vI9j6G2xfvwiWcbA93bgezw1hKMQHThwwLmsUKFCeo4NAADI4u/xIoETvqcgzhdMhEKihIQE7dixQ4UKFVJcmE8Ma6nWQtcvv/yiwoULx9yzHuvlyw5lpHzexzH0tlg/fpEso8UdC0blypVTjhyp9yqi5ihE9oSWL19ekWQvhlh90WeH8mWHMlI+7+MYelusH79IlTGtGqMAOmQDAAC4EI4AAABcCEdRJD4+XsOGDXMuY1Gsly87lJHyeR/H0Nti/fhFSxnpkA0AAOBCzREAAIAL4QgAAMCFcAQAAOBCOAIAAHAhHGWyF198UWeddZby5s2rJk2aaNmyZamuP3PmTFWvXt1Zv06dOpo9e7ai0ahRo9S4cWNn5vDSpUurY8eO2rRpU6qPmTp1qjPLuHuxckar4cOHn7a/dmxi4fgZe12eWj5bevfu7cnj9/nnn+uqq65yZsO1fXv//fdPmy136NChOuOMM5QvXz61atVKP/74Y9jfw1lVRjs/1X333ee87goUKOCsc9NNNzkz/If7dZ5Vx/CWW245bV8vv/zymDmGJrn3pC1PPvlk1B/DUUF8Lxw5csT5jClRooQKFiyoTp06affu3aluN73v3VAQjjLRW2+9pf79+ztDFFetWqV69eqpbdu22rNnT7LrL1myRDfccIN69Oih1atXOy8sW9atW6dos3jxYucF/vXXX2vevHnOB3ObNm106NChVB9ns5/u3Lkzcdm6dauiWa1atZLs75dffpniul46fmb58uVJymbH0XTu3NmTx89ee/Yesy/C5IwePVrPPfecxo8fr2+++cYJEPZ+tA/rcL2Hs7KMduJO28eHHnrIuXz33XedL6arr746rK/zrDyGxsKQe1/feOONVLfppWNo3GWzZfLkyU7YsRAR7cdwcRDfC/fee68++ugj54ekrW/h/dprr011u+l574bMzq2GzHH++ef7evfunXj95MmTvnLlyvlGjRqV7PrXXXedr127dklua9Kkie+OO+7wRbs9e/bYOft8ixcvTnGdKVOm+IoUKeLzimHDhvnq1asX9PpePn6mb9++vipVqvgSEhI8f/zstfjee+8lXrcylS1b1vfkk08m3vbnn3/64uPjfW+88UbY3sNZWcbkLFu2zFlv69atYXudZ2X5br75Zl+HDh1C2o7Xj6GVt0WLFqmuE63HcM8p3wv2nsudO7dv5syZiets3LjRWWfp0qXJbiO9791QUXOUSY4dO6aVK1c61X8Bdp42u7506dJkH2O3u9c3lo5TWj+a7Nu3z7ksXrx4qusdPHhQlSpVck4y2KFDB61fv17RzKpurfr77LPP1o033qht27aluK6Xj5+9Xl9//XXdeuutqZ5g2WvHL2Dz5s3atWtXkuNj51yyJpaUjk963sPR+L6041m0aNGwvc6z2qJFi5wmm2rVqqlXr1763//+l+K6Xj+G1tz08ccfO7XRaYnGY7jvlO8FOxZWm+Q+Htb8V7FixRSPR3reu+lBOMokv/32m06ePKkyZcokud2u24FOjt0eyvrRIiEhQf369dMFF1yg2rVrp7iefZhZFfEHH3zgfBHb45o3b65ff/1V0cjefNbPZs6cORo3bpzzJr3oooucszzH0vEz1u/hzz//dPp0xMrxcwscg1COT3rew9HEmhysD5I19aZ2Ms9QX+dZyZrUXn31VS1YsEBPPPGE0yxzxRVXOMcpFo/htGnTnP47aTU7ReMxTEjme8Ge8zx58pwW1tP6XgysE+xj0iNX2LYE/M3amK1fTVpt3M2aNXOWAPtirVGjhiZMmKCRI0dG3fNpH7oBdevWdT6ArNbk7bffDuqXnJdMmjTJKa/98oyV45ed2a/z6667zunIal+WsfI679KlS+K/reO57W+VKlWc2qSWLVsq1tiPEasFSmvgQzQew95Bfi9EC2qOMknJkiWVM2fO03rh2/WyZcsm+xi7PZT1o0GfPn00a9YsffbZZypfvnxIj82dO7caNGign376SV5gv3bOPffcFPfXi8fPWKfq+fPn67bbbovZ4xc4BqEcn/S8h6MpGNlxtU6xqdUaped1Hk2sCcmOU0r76tVjaL744gunQ32o78toOIZ9UvhesOfcmjqtljqU78XAOsE+Jj0IR5nEqg4bNWrkVP+6qxntuvvXt5vd7l7f2IdbSutnJftFam+A9957TwsXLlTlypVD3oZVd69du9YZnukF1t/m559/TnF/vXT83KZMmeL04WjXrl3MHj97fdoHqfv47N+/3xn5ktLxSc97OFqCkfU/scBrw6XD/TqPJtaka32OUtpXLx5Dd22u7buNbPPKMfSl8b1g5bEfVe7jYQHQ+keldDzS895N784jk7z55ptOj/qpU6f6NmzY4Lv99tt9RYsW9e3atcu5v3v37r77778/cf2vvvrKlytXLt+YMWOcHvw2AsF69q9duzbqjlmvXr2ckUuLFi3y7dy5M3E5fPhw4jqnlm/EiBG+Tz/91Pfzzz/7Vq5c6evSpYsvb968vvXr1/ui0YABA5zybd682Tk2rVq18pUsWdIZgeH14+ceuVOxYkXffffdd9p9Xjt+Bw4c8K1evdpZ7KNu7Nixzr8DI7Uef/xx5/33wQcf+L777jtnFFDlypV9f/31V+I2bFTQ888/H/R7OJrKeOzYMd/VV1/tK1++vG/NmjVJ3pdHjx5NsYxpvc6jpXx237///W9nVJPt6/z5830NGzb0Va1a1XfkyJGYOIYB+/bt8+XPn983bty4ZLcRrcewVxDfC3feeafzmbNw4ULfihUrfM2aNXMWt2rVqvnefffdxOvBvHczinCUyewFbC+EPHnyOENKv/7668T7LrnkEmdoqtvbb7/tO/fcc531a9Wq5fv444990cje1MktNtw7pfL169cv8bkoU6aM78orr/StWrXKF62uv/563xlnnOHs75lnnulc/+mnn2Li+AVY2LHjtmnTptPu89rx++yzz5J9TQbKYEOCH3roIWff7cuyZcuWp5W7UqVKTqgN9j0cTWW0L8aU3pf2uJTKmNbrPFrKZ1+wbdq08ZUqVcr50WHl6Nmz52khx8vHMGDChAm+fPnyOUPWkxOtx1BBfC9YoLnrrrt8xYoVcwLgNddc4wSoU7fjfkww792Mivv7DwMAAIA+RwAAAEnRIRsAAMCFcAQAAOBCOAIAAHAhHAEAALgQjgAAAFwIRwAAAC6EIwAAABfCEQAAgAvhCAAAwIVwBAAA4EI4ApDt7d27V2XLltVjjz2W+FwsWbJEefLk0YIFC7L98wNkN5x4FgAkzZ49Wx07dnRCUbVq1VS/fn116NBBY8eO5fkBshnCEQD8rXfv3po/f77OO+88rV27VsuXL1d8fDzPD5DNEI4A4G9//fWXateurV9++UUrV65UnTp1eG6AbIg+RwDwt59//lk7duxQQkKCtmzZwvMCZFPUHAGApGPHjun88893+hpZn6NnnnnGaVorXbo0zw+QzRCOAEDSwIED9c477+jbb79VwYIFdckll6hIkSKaNWsWzw+QzdCsBiDbW7RokVNT9Nprr6lw4cLKkSOH8+8vvvhC48aNy/bPD5DdUHMEAADgQs0RAACAC+EIAADAhXAEAADgQjgCAABwIRwBAAC4EI4AAABcCEcAAAAuhCMAAAAXwhEAAIAL4QgAAMCFcAQAAKB//D/gIeykIu8TUQAAAABJRU5ErkJggg==",
      "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",
    "s = [50, 100, 200] * 7\n",
    "\n",
    "plt.scatter(x, y, c=c, s=s)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6a47e15a-d92c-4743-a791-20945fe6f4f3",
   "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 = ['#000', '#888', '#aaa', '#ccc', '#eee'] * 4\n",
    "\n",
    "plt.scatter(x, y, c=c, s=s)\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5563dcf3-55d2-4a28-ba9c-efa1f8aa050c",
   "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, c=c, s=s, marker='s')\n",
    "plt.grid()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a8471a2-cd76-41ab-b981-23794c6dd707",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
 }
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