02_RegulaFalsi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /usr/local/Cellar/jupyterlab/2.2.9_1/libexec/lib/python3.9/site-packages (1.20.1)\n",
      "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 21.0.1 is available.\n",
      "You should consider upgrading via the '/usr/local/Cellar/jupyterlab/2.2.9_1/libexec/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: matplotlib in /usr/local/Cellar/jupyterlab/2.2.9_1/libexec/lib/python3.9/site-packages (3.3.4)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/Cellar/jupyterlab/2.2.9_1/libexec/lib/python3.9/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: numpy>=1.15 in /usr/local/Cellar/jupyterlab/2.2.9_1/libexec/lib/python3.9/site-packages (from matplotlib) (1.20.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /usr/local/Cellar/jupyterlab/2.2.9_1/libexec/lib/python3.9/site-packages (from matplotlib) (8.1.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/Cellar/jupyterlab/2.2.9_1/libexec/lib/python3.9/site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/Cellar/jupyterlab/2.2.9_1/libexec/lib/python3.9/site-packages (from matplotlib) (2.8.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/Cellar/jupyterlab/2.2.9_1/libexec/lib/python3.9/site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: six in /usr/local/Cellar/jupyterlab/2.2.9_1/libexec/lib/python3.9/site-packages (from cycler>=0.10->matplotlib) (1.15.0)\n",
      "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 21.0.1 is available.\n",
      "You should consider upgrading via the '/usr/local/Cellar/jupyterlab/2.2.9_1/libexec/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: scipy in /usr/local/Cellar/jupyterlab/2.2.9_1/libexec/lib/python3.9/site-packages (1.6.1)\n",
      "Requirement already satisfied: numpy>=1.16.5 in /usr/local/Cellar/jupyterlab/2.2.9_1/libexec/lib/python3.9/site-packages (from scipy) (1.20.1)\n",
      "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 21.0.1 is available.\n",
      "You should consider upgrading via the '/usr/local/Cellar/jupyterlab/2.2.9_1/libexec/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install numpy\n",
    "!{sys.executable} -m pip install matplotlib\n",
    "!{sys.executable} -m pip install scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definicja funkcji f(x):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fun(x):\n",
    "    #return(x**3 - 2*x**2 -3)\n",
    "    return (x**2 - 10*x +10)\n",
    "\n",
    "plt.figure()\n",
    "x=np.linspace(-1,5,num=100)\n",
    "y=fun(x)\n",
    "plt.plot(x,y)\n",
    "plt.grid()\n",
    "step=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear(x1,y1,x2,y2,x):\n",
    "    return y1+((y2-y1)/(x2-x1))*(x-x1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definicja funkcji regula falsi:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$x_{1}=a-\\frac{f(a)}{f(b)-f(a)} (b-a)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regulaFalsi(x0,x1,e):\n",
    "    plt.figure()\n",
    "    x=np.linspace(-20,5,num=100)\n",
    "    y=fun(x)\n",
    "    plt.plot(x,y)\n",
    "    plt.grid()\n",
    "    step = 1\n",
    "    print('\\n Iteracje metody Regula Falsi')\n",
    "    condition = True\n",
    "    plt.scatter(x0,fun(x0),facecolor='red')\n",
    "    plt.plot(x,linear(x1,fun(x1),x0,fun(x0),x))\n",
    "    while condition:\n",
    "\n",
    "        x2 = x0 - (x1-x0) * fun(x0)/(fun(x1) - fun(x0))\n",
    "        print('Iteracja-%d, x2 = %0.6f and f(x2) = \\\n",
    "        %0.6f'% (step, x2, fun(x2)))\n",
    "\n",
    "        plt.scatter(x2,fun(x2),facecolor='red')\n",
    "        plt.scatter(x1,fun(x1),facecolor='blue')\n",
    "        plt.plot(x,linear(x1,fun(x1),x2,fun(x2),x))\n",
    "        \n",
    "        if fun(x0) * fun(x2) < 0:\n",
    "            x1 = x2\n",
    "        else:\n",
    "            x0 = x2\n",
    "\n",
    "        \n",
    "        \n",
    "        step = step + 1\n",
    "        condition = abs(fun(x2)) > e\n",
    "\n",
    "    print('\\nMiejsce zerowe: %0.8f' % x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wywołanie funkcji regula falsi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Iteracje metody Regula Falsi\n",
      "Iteracja-1, x2 = -10.000000 and f(x2) =         210.000000\n",
      "Iteracja-2, x2 = 3.125000 and f(x2) =         -11.484375\n",
      "Iteracja-3, x2 = 2.444444 and f(x2) =         -8.469136\n",
      "Iteracja-4, x2 = 1.962025 and f(x2) =         -5.770710\n",
      "Iteracja-5, x2 = 1.642105 and f(x2) =         -3.724543\n",
      "Iteracja-6, x2 = 1.439220 and f(x2) =         -2.320847\n",
      "Iteracja-7, x2 = 1.314180 and f(x2) =         -1.414729\n",
      "Iteracja-8, x2 = 1.238468 and f(x2) =         -0.850880\n",
      "Iteracja-9, x2 = 1.193116 and f(x2) =         -0.507634\n",
      "Iteracja-10, x2 = 1.166124 and f(x2) =         -0.301395\n",
      "Iteracja-11, x2 = 1.150121 and f(x2) =         -0.178434\n",
      "Iteracja-12, x2 = 1.140655 and f(x2) =         -0.105458\n",
      "Iteracja-13, x2 = 1.135063 and f(x2) =         -0.062265\n",
      "Iteracja-14, x2 = 1.131763 and f(x2) =         -0.036741\n",
      "\n",
      "Miejsce zerowe: 1.13176283\n"
     ]
    },
    {
     "data": {
      "image/png": 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yp3VkT8q6haB39MOKiYJTh/norrv49P7JFGScbOyQLlpXfx0zJsaSml/OuM+3kFlc1dhVUhTlKtKkkz+A0Omo6dSJsOnTab5yJT4PTMJ6aA8e06bQY9Oz9A/PJqRVT0zacZwSUaQWHafVQQ3Rfr2QPcPRuPuiwUBlaQZfP/4oH91xHwc3rGnssC5K/1Z+fHdvD/LLaxj76WaSc8sbu0qKolwlmnzyP5shJJhmjzxCizWrCf7wAxxbtED/7VRazLibISyhQ7swdI7XUaDrT3JFDt57S4h27IRbl9aYAnzRazyoMeaw7OP3+eDWe1g24/PGDumCYiO8mTupFyaLlXGfb2F/RkljV0lRlKtAk0/+B/MPYpXWP5QJvR73oUMJm/EFzVeuwOe++9Ac3I7/Z1Pos/tleoZV4h00mArD9RyvFliPZdKuujmhbTtS3jIQg9YXk6WQgyt/5f0JE/n22Wcwm0yNFOGFRQe58+Pk3jjptUz4YitbTjTIbRWKolxDmnTyz6nIYULiBJ7PfJ6pO6ZyqOCvY+AYQkJo9tj/0XLNGoI/mIZzZDius1+lww/301+ziRYtYjA7jCXVHEJOZibRWV60DO9KeadQ9AY/rLKK3OR9fHjHRD6fPIXSgtxGivbvRfq68NODvQn0cGTiV9tZfvB0Y1dJUZRG1KSTv6ejJ1MHTCXcEM73R75n/OLxjFk4hun7ppNRlvGHZYVej/uwYYR9OeP3swHH/esJ/+oh+h99h86hTjh7j+a0tgfJeflEHodWPl2wdg9H6+IHaCgvSmfGlIf4aOL9nEja3jhB/40AD0fmPdCLdkHuPPhdEvN2nGrsKimK0kiadFdPB60DwyOG45DmQKeenViRvoLElEQ+3P0hH+7+kE5+nYiPimd4xHC8HL1+/5whNJRmj/0ffv+YQtmatRTPm4tmzn/x0eqoHDCOjID+ZGTVUFx6GK+D1QQ4tiG7VQ1Fp1IwFEJNdTa/vPUqBp0vMaMHMWD8bY34LfyRl4uB2ff1YPJ3u3jip33kV9Tw4IDmNODAqoqiXIWadPI/m6ejJ+Naj2Nc63FklWexJHUJiSmJvLrtVd7c/iZ9gvsQHxVPXGgcTjonAITBgPuI4biPGI4xPZ3i+fMp/mkBrQu/Jyo8mtzuE0gt68yJslT0xw/SXIRCay8OVR7GPcOC0ZzPzgU/sOeXlfi3i+Lmp5+9KgaTczbomHFnLP+ev5e3lh0lt7SGFxKi0WjUDkBR7IXdJP+zBbkGcV+H+7i3/b0cKzpGYkoiiamJrM9Yj7POmSHhQ4iPiqdHQA+0mtpkbQgPp9njj+P38MOUrV5N0bx56H98ngC9I6X9b+WkRzyZeUXI9L1EadzwCAkjyT0Zz8OVmMylZO7fzoe3TcTZz5vbX32l0QeTM+g0vDeuEz4uDsz8LZWCCiNv3xyDg67xd06KojQ8u0z+ZwghaO3dmtberXmkyyMk5SSRmJrIirQVLDqxCF8nX0ZEjCAhKoFon2iEELVnAyNH4j5yJMa0NIp+/BHDz7/gVTiTiqhYcjrexMlSJ/LyDhGSr8XfJ5pDITkYDuVjra6hLDeFzybdj4OTB9c/+X8Et2nbaPFrNILnE9rSzN2BN5YeoaC8hs/v6IqboxoOQlGaOrtO/mfTarR0D+xO98DuPNPjGTZkbGDxicXMPTqX7w5/R4R7BKOiRpEQmUCoeygAhogI/P/9b/weeYTyVasomvcjLj8/TYijB4W9byXdcTQnSk7iejiHQMdIclpLytPS0JZYqa7MYs5/nsGg86b7hNH0SBjTKHELIZg8oDl+rg48+dM+xn2+lVl3d6OZ+7UznIWiKJdOJf9zcNA6MDR8KEPDh1JSU8LK9JUkpiTyyZ5P+GTPJ8T4xZAQlcDwiOF4O3qjMRhwHzUK91GjqElNpfjH+Tgt+AH/4hKKWw3gdJtRnCoyIY/sI0Tnj2jtzYmyo7hkS4zmPDZ9O4Nts38hpHs7bvy/fzVKzDd1DcHXzYEHv0vihk82M+ue7rRoZl9PTFIUe9Kku3rWBw8HD8a2GstXI75ixU0reLTLo1SZq3ht22sMnjeYh1Y9xJKUJVSaKgFwiIzE/4l/02LDeoLfnkqwZyVtFz1B790f0Mo1iBKnOLLTdQQVexIe3JaqNsEYdH6YrCWkbl3H+7fcyZePP4axuvqKxzqglR9zJ/Wixmzhpk83syOt8IrXQVGUK0Ml/0sQ6BrIvR3uZcHoBcy/bj53tLuD48XHeXLjk8TNi+PpjU+zKXMTZqsZjcGAR0I84d/MImrJEkJuHkVU0lf0XvMEHcuy0HkMJr3IF990d8I8m2PpFInewQ8rRoozjvHxXXfzyX0PkHcy/YrG2CHEgwUP9sHHxcBtM7axdH/2Fd2+oihXhmr2uUxnLhQ/2uXR2gvFKYmsSF/B4pTFeDt6MzJyJPGR8bT3bY9DVCT+Tz6B36OPULZiJW5z59Js6eOUerUgp/NYMsxuOB7eR5CjC3mtoTrtJKLMQlVZJt/8+zEMBk/633srHeMGX5HYwnyc+enB3tz3zU4e+n4Xz45qy719I9W9AIrShKjkX0caoaFbQDe6BXTjmR7PsDFjI4mpicw7Oo/Zh2cT7h5OfGQ88VHxhLmH4XFdAh7XJVCTkkLx3Hl4//IpEZUWctqOJMO9G8bjx2mmNSGjXMgqT8ExT2I05rDq02msnz6b5gNjib//oQaP68zNYI/O2cMriYfJKKri+YRotOpeAEVpElTyr0cGrYHB4YMZHD6YUmMpq9JXkZiSyKd7P+WTvZ8Q4xvDqKhRjIgYgU9UFP5PP4XfY/9H2fLleMybR8iq+eT7d+R09GhO51Thaa7AJ9CNo44G3NJMGC2FHFm1hOTV2/BoHsjt/30Fna7hfkJHvZZPbuvCa0sOM2NTKhlFVXwwoRPOBvXPRlGudep/cQNxN7hzY8sbubHljZyuOM3S1KUkpiTyxvY3mLpjKj2DehIfGc/gsMF4jB6Nx+jR1CQn4z1vHv4Lp9HcaCC7TTwZlc1xLzAR6AnJAe44HS/FbCqnIPkAH94+EUdPd25/9SXcfPwaJA6NRvBcQjSh3s7899eD3DJ9KzPujFVdQRXlGqeS/xUQ4BLA3e3v5u72d5NclExiaiKJKYk8s+kZnHRODAwdSEJUAr2iehHwzDM0e+wxypYvx2fuPCI2fE9OUA8yWwzGMSUTP4diilqYMaZnIyuNVBad4ospD2EweDDskfto1bVng8QwsXcEwZ5OPPzDbq7/+Ddm3t2NNgGNe5eyoiiXr87JXwjhCGwAHGzrmy+l/I8QIhKYA/gAScAdUkqjEMIB+AboChQA46WUaXWtx7WihVcLHvF6hIc7P8zu3N0kpiSyPG05S1KX4O3ozfCI4cRHxRMzejQeY8ZQc/w4vvN+JHjhuxTjQ2bLkVhPueAudWgjteSVpKMrtFJTc5pf33oDg86HtvH90QWF13vdh0T78+PkXtw7awdjP93Cx7d1YUCrhjnjUBSlYdVHV88aYJCUsiPQCRghhOgJvAm8J6VsARQB99qWvxcospW/Z1vO7miEhq7+XXmh1wusHbeWaQOnEesfy4LjC7h9ye3E/xzPx3s+JruZnoBnn6HV+nW0eXoSXS2b6bftU4JOl2MsaIlLZRSB/lGYQoPQa70xmvPYu/BH9n4+i+9eegGr1XrhylyC9sEe/DKlD6Heztzz9Q6+3ZJWr+tXFOXKqPORv6x9OsqZh8PqbX8SGATcaiufBbwIfAqMsU0DzAc+EkII+eenrNgRg9bAoLBBDAobRJmxrPZCcWoin+/9nM/2fkZ7n/bER8UzYsQIIm64nuqjx/D/8UfCF35AniGcjOaDcNG44edeTLq/M07JlZjMJeQc3MUHt96Jo48HE19/Had6Gkwu0MOJHyf34pEfdvP8woOcyKvgufi26LTqthFFuVaI+si5QggttU07LYCPganAVtvRPUKIUGCplLK9EOIAMEJKmWGbdwLoIaXM/9M6JwGTAPz9/bvOmTPnsutXXl6Oq+u1N1RBsbmYpMokdpbvJMOUgUDQ2rE13Vy6EeMcg6NZg+POJJw2bsR0uoKMkP5k+IXiJrIoDa3EmpaHtboaqyxDCBf0Dq6EjRiMR2BQvdTPKiVzjxhZnm4mxlfL5I4OOOsbpyvotfob14WK2T7UJeaBAwcmSSljzzWvXpL/7ysTwhP4GXge+Louyf9ssbGxcufOnZddr3Xr1hEXF3fZn78anCg+QWJKIktSl5BZnomj1rH2QnHzBHoF9cJyvPa+gcLFS8lyiSY9tBtmbT66ZlWUFmciSqxYZAGgx6DzpvO4UfQdc1O91O37bSd5YeEBInxd+HJiLOE+LvWy3kvRFH7jS6Vitg91iVkIcd7kX6+9faSUxUKItUAvwFMIoZNSmoEQINO2WCYQCmQIIXSAB7UXfpW/0dyzOf/s8k8e7vwwe/L2kJiSyLK0ZSxNW4qXgxfDIoaRMPk6Ojz+GEHLluE7YwZVhVpOlfWlxi0GT/8STgkdTjkSozmXbd9/za45i2nWtSW3/OuZOtXt1h5hRPg68+B3u7j+49/49Pau9IzyqafIFUVpCHVupBVC+NmO+BFCOAFDgcPAWmCsbbGJwELb9CLbe2zz19hze/+lEkLQuVlnnuv5HGtvXsuHgz6kR2APfkn+hTuW3kHCsrF8G5XFkccm0vnrqfTrUEPfQwvxOFKMR3FnvNwisbYI/X0wucwdm3n/ljuZ/n//xFhVddn16t3cl1+m9MHLxcDtM7bx/baT9Ri1oij1rT6O/AOBWbZ2fw0wT0q5WAhxCJgjhHgF2A18aVv+S+BbIUQyUAjcUg91sEt6rZ640DjiQuOoMFWw+uRqFp9YzIz9M7BKKwuq25IwNoERU+4ibN0OCubNJyMZZEgsJmczMioH0osxGyspy0rh47vvQe/iyrgXnqFZeOQl1yfS14VfpvTh4e9388zP+zl6upTnEqLRqwvBinLVqY/ePvuAzucoTwG6n6O8Gri5rttV/shF78Lo5qMZ3Xw0eZV5fLTqI46II0zdOZV3xDv0COhB/GsT6FcZQqsFv5K9cjvpuZ3J8QvGIbSIqqIsKDdTU57Nt0/8C4Pek553j6Xb4JGXVA93Rz0z7+rGG0sP88XGVI7nlvPxrV3wcjE0UOSKolwOdUjWBPk5+zHQfSBzE+ay8PqF3NfhPk6VneK5355j2L4HeGdgOWVf/ZN+d3RgcFkSUUk5uFS3w927OdZm/ug17hhNOWyY/ikf3HoPCz5575K2r9UIno2P5u2bO7IzvYjRH2/icHZpA0WrKMrlUMM7NHFRHlE83Plh/tHpH+zN2/v7HcXL05bj4eDB8MeHk2COJnrlPk6tP0iKTwdqfBzJ9XbA6ZQRk6WQ1PWrmbZxLy5hPtz1yhvo9Bf3jN+xXUNo0cyVB77dyY2fbOadcR0Z1SGwgSNWFOViqCN/OyGEoFOzTjzb81lWj1vNx4M/pndQbxadWMSdKS9yb8dtJL3Wk26j/ehZfJDoPU64GNoiWkSgN/hhkRWUpB3lwzvu4uNJD1Ccl3NR2+0U6smv/+hLm0A3Hpq9i7eWHcFiVdf3FaWxqSN/O6TX6Okf0p/+If2pMFWw5uQaElMSmX7iWz4TVtre2ZabZQADtuSTsbWIdL+WVIRUYiysHUyuuiSTmQ//A73Bg7gpd9GhR9+/3V4zd0fmTOrJi4sO8sm6ExzMKuWDWzrj4XxxZxCKotQ/lfztnIveheuaX8d1za8jvyqfZanLSExJ5KXC7xBtBH16dmFcejle63M5ZmpGSbALpaShLbJirDnNinffZq12JlHDupNw1+TzbsdBp+X1G2PoEOzJfxYd4LqPNvHZ7V2JDlIjgypKY1DNPsrvfJ18uT36dn5I+IFfr/+VyR0nk27J5Z8eidw7Zg/JN+bR0SGT9ikOeDi2whpcO5icyZLP0aWLmTbhbma+8BQWi+W827i1RxhzbA+Jv/HT3/hld+Z5l1UUpeGo5K+cU4RHBA91eojEGxKZPWo2Y1vfzHKnE9zbeTNvTdiLtsUheqZb8DGFIMIjMeiaYbaWUnT0AB/edhcfPTiZiqLic667a7gXvz7cl5gQTx6du4cXFx3EaK7f0UcVRfl7qtlH+VtCCGL8Yojxi+Ff3f7F1qytJKYm8qF+DVUhlfQo9uGmwyHUlDbjVEt/ZMFpLDXV1BRm8PmDD6B3dGX4v/5Bq/Z/vBWkmZsjs+/rwetLjjDzt1T2Z5bw8a1dCPBQTwhTlCtBJX/louk1evqF9KNfSD8qTZWsPbWWxSmLedp7C4aqvYxNCaB9WXMyvYMp12QhSs0Yq3L49eWX0eu8aHPDAIaNvfN/69NqeOG6aLqEe/LE/H0kfLiRDyZ0pndz30aMUlHsg0r+ymVx1jsTHxVPfFQ8BVUFLE9bTmJIIt+2/Y2WWXDT0dZoZEuy/B3Q55kxmXPZ/+N8jvy0Fo8OoUx85qXf15UQE0SbADcmf7eL22ds4/FhrXlwQHM0msYZHlpR7IFq81fqzMfJh1vb3srs+Nkk3riEofFT+OH6Gt4ftJh8uZdAowuEhGPQ+WKyFpO/dxfTbpnIx/98kJrKSgBaNHNj4ZQ+JMQEMXX5Ue6dtYOiCmMjR6YoTZdK/kq9CnMP48GOD/Lr9b/y5U1z8LzjJj6+eT+bvdZhkMU4+Eeid2iGhWqqc07xyT338cHd93Iy5SguDjqm3dKJl69vz2/JBSR8uIldJ4saOyRFaZJUs4/SIIQQtPdtT3vf9jwe+zjbsreRmJLIlqOr6LFbT1RlVyq1ZVBuxlSZw49PP4te70HHCfHcEX8jHUM8mPL9LsZ9toWnRrbh3r6RCKGagRSlvqjkrzQ4nUZHn+A+9AnuQ1Wv51k3eB2JJxaTt20vfQ+0xeLtj67IgsmUw85vZrF39mK8ukay+OGneGL+Xl5JPMzWlELevjkGT2c1Oqii1AeV/JUryknnxMjIkYyMHElR3yKWpS1j9f6FBKwpwssQiSHPiNFSSO72XL7eeT8d/J3pct0/eXtNCqOm1fYGio3wbuwwFOWap9r8lUbj5ejFhDYTmHHzHB54awaud3ciuXMO+PtjMDTDIiuoyT5J5Rev8PDp+biaChg/fSsfr01Wg8MpSh2pI3/lqhDqFsoDnSYjOz7AocJDrNi7APPPyTiUu2KtMmIqz2XEofno9K4sT2zLb8n9eG98J/zd1U1hinI5VPJXripCCNr5tKPdoHZY4ixsz97G5h9mI447oC2xYDLmMuBUIfqsw7x3yJsPfv0XVSf8efttGDhQAhIp1QmtolyISv7KVUur0dIruDe9/tWbKnMV6/YkcvDb5RhyLZgs+fhl5fJGt6epHKLHbL0FqO0NJIRV7QAU5QLU/xDlmuCkc2Jk7Fj+Ne0Lbp/5NoZWzdHrm2GylqIvysIh6SvevfceQoOSOLMTUBTl/Oqc/IUQoUKItUKIQ0KIg0KIR2zl3kKIlUKI47ZXL1u5EEJ8IIRIFkLsE0J0qWsdFPvi7eTNwy+/xyOzv2R59g3oHZthxYy1LJdH+73NtDvu5auPXqbaXN3YVVWUq1Z9HPmbgcellNFAT2CKECIaeApYLaVsCay2vQcYCbS0/U0CPq2HOih2auXG23nkm5mUthmPxt0fDQZMNbkUbtzNFxMf4p0nJrM5ZR0W6/mfMaAo9qjObf5Symwg2zZdJoQ4DAQDY4A422KzgHXAk7byb6SUEtgqhPAUQgTa1qMol+BMd0+Bm5snj82YgWvHkzzV53XcCmswmvMgXbDz2W/Y4vMtDn2CGTTsFtp6t1V3Cyt2T9Tm4HpamRARwAagPXBSSulpKxdAkZTSUwixGHhDSrnJNm818KSUcuef1jWJ2jMD/P39u86ZM+ey61VeXo6rq+tlf/5aZC8xJyXVvoaElJORURtvcBsrn++roV3eOlqfzsNoKQJM6IQPVg8tRU7FWPq2oLNHd3z11+7w0fbyG59NxXxpBg4cmCSljD3XvHpL/kIIV2A98KqUcoEQovhM8rfNL5JSel1s8j9bbGys3LnzvLMvaN26dcTFxV32569F9hbzn+M1W6x8vPYEH6w5TqxIpc+pJMzGcqSsRCPc0Tg5YHbQcKpbGT373MjwiOF4OXo1XgCXwd5+Y1AxXyohxHmTf7109RRC6IGfgNlSygW24pwzzTlCiEAg11aeCYSe9fEQW5mi1BudVsMjQ1oysI0f/zfXmXctEUxo70jg+u+xlNVgrsxDVDkStdadnL0beNHwCQxpx4jWo4kLjcNJ59TYIShKg6qP3j4C+BI4LKV896xZi4CJtumJwMKzyu+09frpCZSo9n6locSEeJL4z37c1zeSOQermd/ydrq/OBXH0Ei0whWTKRdzTibNs1oQtszKoRenc8tH/Xh207NsztyM2Wpu7BAUpUHUR2+fPsAdwCAhxB7b3yjgDWCoEOI4MMT2HmAJkAIkA18AD9VDHRTlvBz1Wp5LiGbupF4A3Pb1LvL6T+b+WTMI7NMXvdYHk6UAsjPRGT0ZcLQjER/m8u20hxk5Zwhvbn+Tg/kHqc/rY4rS2Oqjt88mzn9XzeBzLC+BKXXdrqJcqu6R3ix7tB9vLj3C15vTWHs0l7duup9b/+nDbysXs+ubBZhMpcjCasqFJ60LOtJ1lQ/FK1bzaMdvcYyIZFTUKBIiEwh1D73wBhXlKqaGd1DsirNBx3/HtGdkh0CemL+P8dO3ckfPcJ4cOYI+QxPITDvBgpdfx1xRiaU8l1JRjkEfQsLJGAK2FLI64hM+b/Mx7QI6Eh8Zz4jIEXg7qiGmlWuPSv6KXeoZ5cOyR/vx9vJjfLU5lTVHcnn9xg70b9Wch7+cgbGqmhlP/wvT6TJMxlzIKiRX501rSz+GLXYi3+8gM9u8xls+b9E7qDfxUfEMDB2Is965sUNTlIuikr9it5wNOl64Lpr4mACemL+PO2du58YuwTwfH42XiyMPvf8RAN9NfYnCXWkYLfmQIcly8MVoCGHi9lh8rZmsbb2XZ09uwODgzKCwQSREJdAzsCc6jfrvpVy91L9Oxe51Dfcm8Z/9+HhtMp+uO8H6o3m8cF00ozsGIYTg9n+/AMDahXM58ONyjKZiRK6RAmEk311L89zBvLarkMoWuXxbvJbElES8Hb0ZETGChKgE2vu2V3cUK1cdlfwVhdoeQY8Pa018TCBP/rSfR+bs4addmbx6fXtCvWubcgaOGc/AMeNJPXKQxW++h6mqHFlaToWoItnHCY0ugttWRBLimkdSlyq+rfqR7498T7h7OKMiRxEfFU+4e3gjR6ootdSQzopyljYB7ix4sDf/uS6apLRChr63ns/Xn8Bksf6+TGSbdjz81Qwmz/gCJ9/Q2sHkqvOoOZVMjmseW0JccE9qy1tzu/LxsT60qvTgs72fkfBzArcm3srsw7PJr8pvxCgVRSV/RfkLrUZwd59IVj42gH4t/Xh96RGu+3ATSemFf1jO2dWNhz7+lEfnfINP+07oNB61g8mlpVMi09jZBpJPRzDgO19mr+nJK6YELCYjb2x/gyE/DmHyqsn8euJXKk2VjRSpYs9Us4+inEeQpxNf3BnLioOneXHRQW76dAsTuofyxPA2eLkY/rDsXc+/AsCy77/ieOJGjOZCRJaZMmGkOFxLri4C5yUW/q/CEc8BN7Chi56fijfxzKZncNI5MTB0IPFR8fQK6oVeo2+McBU7o5K/olzAsHYB9Gnhy/urjjHztzSWHTjNUyPbcHPXUDSaP17IHXHr3Yy49W6O7NrJymmfYaopRRZVUi6qqfRwoLhFKA4HnYlYdYJPoiIpG34jvwZkszxzFUtSl+Dl4MXwiOEkNE8gxjdGXShWGoxK/opyEVwcdDwbH81NXUN4/pcDPPnTfubuOMVLY9rTPtjjL8u36RJLm1kzKM3P59vnnsVYXIG5Mg/LyXKsOjf293TlZHYI3rPSSKg6yv0JN3G0byiLKrbwc/LPzDk6h1C30N8vFEd6RDZC1EpTptr8FeUStAlwZ94DvXj75o6kF1Ry3UebeO6X/RRXGs+5vLuvL1M++5yHv/sarxbRaIULRlMunEinquYIydGF7G/RjeVba+CxBTz8fTlLfF/k5Z4vEuQaxPR90xn9y2huWXwL3x76Vl0oVuqNOvJXlEskhGBs1xCGRvvz/qpjfLMlncR92Tw2rDW3dg9Dq/lrU41Op+OeV98CYNEXH5O2NgmjpQBtuoUiTQ1mXy3WZi05XOJJ6HuraGE8xus3jMAc/wgrqneRmJLIWzve4u2db9MjoAcJzRMwWA1/2Y6iXCyV/BXlMnk46fnPde0Y3y2U/yw8yPO/HGD21nReHN2OnlE+5/3c6PunwP2wZ8MaNn4xG6OxBPKrKRY1CBc9J7sEcup0HJ7rSgmf/TRxnYK5cfxkcnuFknhyKUtSl/DspmfRCz1r1q8hPiqe3sG91YVi5ZKo5K8oddQmwJ05k3qyZP9pXltymFumb2Vk+wCeHtmWMJ/zj/XTqf8gOvUfRH7GSea++ArG8nIs5XlUVVSgM7iQ3c6TKq9eHKryIPzlHwiWaUy4cTSTx87kkC6Xzzd+zpbsLSxNW4qXgxfDIoaREJVAR7+O6kKxckEq+StKPRBCEB8TyKA2zZi+IYXP1p9g9eFc7ukbyZSBzXFzPP9RuW9IGFNmTMdcXcNXzz5FZVYhppo8OFZMubaEynAHHJ1COWbsSNCK04TMug3/bm24Izqa2MnvsTlnK4kpiSxMXsjco3MJdg1mVOQoEqISiPKMuoLfgnItUclfUeqRk0HLI0NaMr5bKG8tP8Jn60/w485TPDq0FRO6haLTnr+Phc7RgfvfeQ+A+dOmkrX1EEZLProUSYGmmupm6VR6hrDddxTuZU6EzdtI6oKfaTf2JvqMfRxj7xdZfXI1iSmJfHngS77Y/wVtvdsSHxXPqMhR+Dn7XamvQbkGqOSvKA0gwMORd8d14q7eEbySeJjnfznArM1pPDWiDYPbNrtgs8zYR/4Nj8DWZQvZ8d0ijKYitKeN5AoT0l1LTVAwxwztOKwJJWzRXoK+uB7v3l0YOH4c1w36hPyaQpamLiUxNZG3d77Nu0nv0j2gOwlRCQwOG4yrwfUKfRPK1Uolf0VpQDEhnsyd1JMVh3J4Y+kR7vtmJz0ivXl6VFs6hXpe8PM9R4yh54gxZCYf4+fXpmKsLEOWlFNSWo3OwREZUU6B8CA55E4CC6sJfmIqPi4v4TX2JiaMHcud7e4kpSSFJSlLSExJ5LnfnuPlrS8TFxpHfGQ8fYP7oteqC8X2SCV/RWlgQgiGtwtgUJtmzNl+kmmrj3P9x78R3yGQx4e1IsrvwkfhwS1a8Y+ZX1BdUcGsp5+kKrcEU3UeHCqlSOdBRVQB1nw/kpwG4KYLInj+WgI++xKP/r1pNn4cU/o8yJROU9ibt5fElESWpy1nedpyPBw8GB4+nPioeDo164RGqFt/7IVK/opyhei1Gu7oFcENXUKYviGFGRtTWHbwNONiQ3lkcEsCPBwvuA5HFxce+KD2ITOfPf0vjGkFGM256I9pOK2ppDJYQzNZw4nwEI63HExITipBjzyPu5cWz7FjaXfTWDr1fJYnuj/BlqwtLE5ZzKITi5h3bB5BLkGMihpFfGQ8LbxaNPTXoTSyekn+QoiZQAKQK6VsbyvzBuYCEUAaME5KWSRqGzunAaOASuAuKeWu+qiHolwLXB10PDa0FXf0DOejNcf5fvtJFuzK4M5e4TwY1wJvl4u7eavN8ATi4uJYN+979i9cjdFcgP6UmWxhxuwFvt5aCk01pHaeQDOtM0GzE/H9+FPcBg7Aa/x4+vXuQ/+Q/lSaKmsvFKcmMvPATGbsn0Eb7zbER8YzMnIk/i7+DfyNKI2hvs7xvgZG/KnsKWC1lLIlsNr2HmAk0NL2Nwn4tJ7qoCjXFD83B/47pj1rHo8jPiaQGZtS6f/WWt5dcZSSKtNFrydu3K08PPtLxjz9Ag5OAViohMJ8Ck8cpZgsRNRJNBxnT4subBnwXw6dcuPEQ49xYugw8j/7DH1ROdc1v47PhnzG6ptX81T3p9AJHe8kvcPQ+UO5b/l9/Hz8Z8qMZQ34bShXWr0kfynlBqDwT8VjgFm26VnA9WeVfyNrbQU8hRCB9VEPRbkWhXo78+64Tqx4tD99W/jywZpk+r25hg9XH6es+uJ3Ai06deEfX89g0ief4uwdBoC5Mg/TgePkVGVS1Twbb3GIE546NvV6jAPNJ5A8cxHHBw0m4+F/Ur5xEz4O3tzW9jZ+SPiBxTcsZnLHyWRXZPPC5heImxvHY+seY/XJ1Rgt5x7LSLl2CCll/axIiAhg8VnNPsVSSk/btACKpJSeQojFwBtSyk22eauBJ6WUO/+0vknUnhng7+/fdc6cOZddt/Lyclxd7atrm73F3JTiTS+18Euyid25Flz0MDxCz9BwPU66P3YPvVDMFrOFY0t/xZhVjNmaD+gwaL0pC9fR2hJFUW4RlZpIXPW+hKWvIeDkJqS3O1V9+1LdqxdWj9rRSqWUpBvT2Vmxk6SKJMqt5ThpnOjs3JluLt2Icoi6YheKm9LvfLHqEvPAgQOTpJSx55p3RZK/7X2RlNLrYpP/2WJjY+XOneedfUHr1q0jLi7usj9/LbK3mJtivHtPFfPB6uOsPpKLh5Oee/tGMrF3BB5OtV0zLyXm5TOnc3TVVkyWfMCKTuNHjQ8EejVHn11BjtkDR5f2hFWn0mzXjzibinAbNAjP8eNw6dULoalN7iaria1ZW0lMTWTNyTVUmasIdAlkZORIEqISaOnVsoG+jVpN8Xe+kLrELIQ4b/JvyN4+OUKIQClltq1ZJ9dWngmEnrVciK1MUZSzdAz15Mu7urEvo5hpq47z7spjfLEhhbv6RHB3n0sb33/4PZMYfs8kDvy2gbWffYPRWIw2r5qCfDO46jC01tEsfQcnzGaSuz5EoJsDgQcXUXrv/RhCQ/C8+WY8b7wBva8v/UL60S+kH5WmStaeWsvilMXMOjiLmQdm0sqr1e93FAe4BDTQN6PUh4ZM/ouAicAbtteFZ5X/QwgxB+gBlEgpsxuwHopyTYsJqd0JHMgs4aM1yXy4JpkvN6XSL0jQpnP1RXURPaN9n/6079Ofwuws5v7nJapLS7CWFVG9t5JTBmcq23rRLiuN08X5ZIX0wC16AmGle6j48HPyPvgAt8GD8Ro/DueePXHWOxMfFU98VDwFVQUsT1tOYmoi7yW9x/tJ79PVvyvxUfEMDR+Kh8NfH3ijNK56afYRQvwAxAG+QA7wH+AXYB4QBqRT29Wz0Nb+/xG1vYMqgbv/rskHVLPP5bC3mO0p3mM5ZXy67gQL92Si02i4sUsw9/ePovlF3Cz2Z+bqGr594TlKTuZgkYWAHoPOi+IoAx2NLSjNP02BxReDUwdCnSvw3zUPl+yDGMLC8Lx5LJ433ojO54/DV58qPUViaiKJKYmklaah1+jpH9KfhKgE+oX0w0HrcNmx29PvfMZV3ewjpZxwnlmDz7GsBKbUx3YVxR618nfjvfGd6O1WyF6jH/N2ZjB35ymGRfvzwIDmdAnzuuh16RwduPutqQAs/PB90jfvw2jOw/kYpGlqqAwQhHv4oc/YTFoBpDYfi2/3BwjN2Uz1ex+S98GHuA0ZjNf48Th3747QaAh1D2Vyx8k8EPMAhwoOsThlMUtTl7L65Grc9G4MjRhKQlQCXf27qjuKG5G6w1dRrlF+zhpeGdWBRwa34pstaXyzJZ3lB3PoGu7F/f0iGRodcM6nip3PmIcfhYchadkSNn/3E0ZTEfosIznZFqzuAoc24YSln+B0/nZ2aVrhNPR9Ihyy8N0ym7Kld6MPD8Nr3Dg8brgBnbc3Qgja+bajnW87Ho99nG3Z20hMSWRZ6jIWHF+Av7P/788obu3duuG+KOWcVPJXlGucn5sDjw9rzeQBzZm38xQzf0tl8ne7CPdx5q7eEdwcG4qrw8X/V+86YhRdR4wi+8QxFrz6NjWVJciSCox7akhzcKSynQ8xmZXkly/kUGkQ2uh/ENxMT1D6GoxT3yHv/Wm4DR2K57hxOPfojhACnUZHn+A+9AnuQ5W5inWn1pGYksi3h77lq4Nf0cKzRe31g8h4Al3VbT9Xgkr+itJEuDjouLtPJHf2imD5wdPM2JjCf389xLsrjjGuWygTe0X87ZPF/iyweSumzJxOTUUl3z77DOWnCzBV56FPKuWErpCiNo50qXSgPHcdmVl6Mgyd8RgzknCRgmnDN5QuWYIhIgLPm2/G48Yb0HnVNkc56ZwYGTmSkZEjKaouqr1QnJLItF3TmLZr2u8XioeFD1MXihuQSv6K0sRoNYJRHQIZ1SGQ3SeL+Oq3NL7enMbM31IZ1LoZE3tH0LeFL5qLbBJycHHmvvffx2q18tPUN8jacxyjOReXAxpSNNWUhgpau0WjP3WYnMIt7LW0Qd/5OSICzAQeXYJx6lTy3n8ft2HDas8Gunf7/XkGXo5e3NLmFm5pcwunyk6xJGUJi1MW89KWl3ht22v0C+5HQlQCA0IH1OlCsfJXKvkrShPWOcyLzmFePDOqLbO3pfPD9pPcOXM7kb4u3NYjjJu7huLhfHHj+Ws0Gm5+8hkANs6fy56fV2A0F+CUbiZLWDB5CVyjwmmZVkF29U8kp0VwwnkYfuPHEmE8jHX115QmJmKIjMRz3Dg8rh/z+9kAQKhbKA90fIBJMZM4XHiYxSmLWZa6jLWn1uKqd2VI+BBCqkLoZ+2HVqNtkO/LntTbHb4NSXX1vHT2FrO9xQuXF3ON2cKS/dl8uyWdXSeLcdRrSIgJ4tYeYXQO9bzkB7+n7dtF4rufUVNdhJRVaIQHwslATXsfOmVGkl90jBK9GxZrB5w9Aon0qyBg38+IXRsRej1uw4fjNX4cTrGx59y2xWph++ntJKYksurkKipMFTRzbva/C8VerZv8w+qv6q6eiqJcGxx0Wm7oHMINnUM4mFXCd1tPsmhPJvOTMmgT4MaE7mFc3yn4os8GImK6MOXr6ZQXFvD9cy9SUViIpTIP/Y5yjukKKWjnRPeKIKqz95JXvoWDxdEcch9H2N13EVaSBCu/o3TxYgzNm+M17mY8xoxB6+n5+/q1Gi29gnrRK6gXz5mf45Pln5DqlMp3h7/j64Nf09yjee0dxVGjCHYNbqBvrWlSR/5NlL3FbG/xQv3FXF5jZuGeTH7YfpIDmaU46DSMbB/AuNhQekb5XPS1AQCz2cy8l18i71g6ZmsBZwaTK4rU0M65HU4ZNWQZUzHqWmKVbfHw86SFTzE+2+dh2bMdYTDgNmI4XuPG4dS161+O6s/EXFxdzIr0FSSmJLIrt/ZxIJ2bdSYhKoFh4cPwdPSs8/dytVBH/oqiNAhXBx239Qjnth7hHMgsYe6OU/yyJ5Nf9mQR7OnETV1DGNsl5KJ6Cul0Om7970sArJr1JYeX/4bRko9LspVMjYUqX/CMCqflKQcyyxZTWezHzpx26P3upvmUBwnJ+Y3yZXMoXfQrhhbNa+8bGDMGrccfe/14OnoyrvU4xrUeR2Z55u8Xil/e+jKvb3+dvsF9iY+KJy4kDkfdxQ9/YU9U8lcU5Xftgz1oH+zBs/FtWX7wNPOTMvhwzXE+WH2cbhFe3NA5hPgOgRfVLDRk4r0MmXgvRzZvZNVns6ipKUKXW0NVnpkDLmkYO/oRmxlKftUOSiwmju1vx1HRkYCb+hHlmoN+4w/kvPY6ue+8i/uIEXiOHwfnaKkIdg3m/pj7ua/DfRwpPEJiSiJLU5ey7tQ6XPQuDA4bTHxUPD0CeqgLxWdRyV9RlL9w1GsZ0ymYMZ2CySqu4pc9mSzYlckzP+/nxUUHiWvtx/WdgxnUphmO+r9PqG1696NN734UZWcz9z8vUVVajLW8CP3WKg4a8slv70rvsvaYcrLJtuygILMtp42tcQp7kFbDDASlr6VsyXxKFi7EJyiQwlMZeIwZ/ZezASEEbX3a0tanLf/X9f/YmbOTxJREVqavZNGJRfg5+TEicgTxUfFEe0c3+QvFF6La/Jsoe4vZ3uKFKx+zlJL9mSX8sjuLX/dlkVdWg6uDjqHR/lzXMZC+Lfww6C48Vo+ppoYf/vMChWnZtsHkDBh0nuS30BOjb4N7tuBk+X7wCqOqog0anR/h0Z5E6k9i+vkzDOnpCAcH3EeOxHPcOJw6d/rbRF5trmZDxgYSUxLZkLkBs9VMhHsECVEJjIoaRahb6Hk/ezVQbf6KojQqIQQxIZ7EhHjybHxbNp/IZ/HebJYeyObn3Zm4O+oYGh1AfEwAfVr44qA79xmB3sGBO994E4DETz7gxKbdGM15uB+BTI2Jo4ESv+YRtMgIJsu0iUpHLaeOtCfVHIFDzBN0vtMNvyPLKUv8mZJffsGhZUs8x4/HY/R1aN3d/7I9R50jwyKGMSxiGCU1Jb9fKP5oz0d8tOcjOvp1JCEqgeERw/FyvPhB8a516si/ibK3mO0tXrh6YjaarWw8nseS/adZceg0ZdVm3Bx0DGzTjOHtAohr7YfLBcYW2rNyKZtmzafGVAiY0AofLB4Cazs/uma3pyTvOLlkYTG0xmpuj97BhZZdfAi3HkOzdA7VBw4gHB1xHzkSr/HjcOzY8YLNOlnlWSxJXUJiSiLJxcnohI7ewb1JiEogLjQOJ51TPX5Ll08d+SuKclUy6DQMbuvP4Lb+GM0d+C05n+UHT7PiUA6L9mZh0Gno09yHIdH+DGnrj7/7X3vfdBo6kk5DR5Kbksz8V9+muqIIiivQba5hv0M++Z1d6VPUC3KKSK+Zh4N7c45sb8th6UNA9ydpfSt47l5MWeKvlPz8Mw6tW+M5fhwe112H1s3tnPUOcg3ivg73cW/7ezlWdIzElEQSUxPZkLEBZ50zg8MGkxCVQPfA7ug0TS9VqiP/JsreYra3eOHqj9lilexIK2TloRxWHsrhZGElAO2C3BnUphkD2zSjY4jnOYedri6v4Pvnn6c0OxeLLAYcMOg8yGnrQCfREt9cV9KKdmL1dsUqOlBdGYqzuwNtuvsRVrEf06I5VB86hHBywn3USLzGjcMxJuaCZwNWaSUpJ4nFKYtZmbaSMlMZPo4+jIwcSXxUPO182l3xC8UNdeSvkn8TZW8x21u8cG3FLKXkeG45qw7nsPZILknpRVgleDrr6dfSjwGt/OjX0vcvZwVWq5Vf3p7Kqd2HMVvzAS0GjQ/FoYKggBBa5bQkJ28f+bp8nL27UFYUidA4ERHjS6twM86bf6F0SSKyshKHNm3wGj8O9+uuQ+t64aee1Vhq2JixkcSURNZnrMdkNRHhHsGoqFEkRCYQ6n5lLhSr5K+S/yWxt5jtLV64tmMuqjCy4XgeG47ls/5YHvnlNQC0bOZK35a+9GnuS/cob9wd/3c/wZYF89n50xKM5gLAgk74YvQGa7QvPbI7U5GXSnr1IdxD2lFZ3haT0RNPf2eie/oSlL+LigVzqDl8uPZsIH4UXuPH49i+/UUdyZfUlLAqfRWJqYnsPL0TiSTGN4b4qHhGRI7A29G7ob4q1eavKErT4eVi+P0+AqtVcvh0KZuO57MpOZ/vt53kq9/S0AjoEOJJz0hvekR5EztqDDXevjT38eLXdz6muroITUEVuk1m9jitpKCzO70LBuJYWMOJokW4+PtgMXbit1/K0Ts0o9VNr9EyuArN6p8oTVxCyfyfcIhui9e4cbgnJPzt2YCHgwc3tbqJm1rdxOmK0yxNXcrilMW8vv113trxFr2CehEfFc+g0EE46y/+mQmNqWkf+VcVwecDqKwx4+zmCVo9aA22P9u0zvDXsr+dvshldQ5/LNfobGUGuAJ3GV7LR4WXw97ihaYbc7XJwu6TxWw5kc/mEwXszSjGZJFoBIS4ahjYPpTYCG/aesDaqW9QXpCPVZYihDN6nQtZMY7EWloQWOhPWt4OSh1L8PDvTkl+JBaLA4HNPYju4YPPyc2U/TiPmqNHEc7OeMSPwnPceJw6tL/ouh4vOv77heLTFadx0jkxKGwQCVEJ9AzsWS8Xiptcs48QYgQwDdACM6SUb5xv2ctO/tUlsOQJcrMzaObjCRYjWEy1r+YasJpq3/9l2vy/ZWiA70dozrHj+PMOxOHc88/eqWj0tdO6Py9r4GhyKq2j259nGw5/3Z7O4X/r+7382jkxbKqJ8O/YS8xVRgu7TxaxNbWQVXtOkFoqqDJZAAhwd6RzsBvR+xZgzcr+w2ByBVEaQrwDiM5sTX7pcU4V7cW7FKq9h1Dp2Q6nmhKiW2hoEReKcclPlC5ZiqyqwjE6Gs/fzwZcLqqOVmllV84uElMTWZ62nDJjGd6O3oyIGEFCVALtfS+ueelcmlTyF0JogWPAUCAD2AFMkFIeOtfyjdrmb7HtCH7/M/3p9ezyGjAb/2anctb02Z+xmP60vjPlZ7Z9vmnjVbCT+vMO62LOns69w7rkHeHvZ1Va1q1fbxeJ8Gz2kvzPtm7dOvr068/h7FJ2pRex62Qxu04WkVFUBcDAkp10Lk7HZM0HrOg1flT5gWjtQ7dTMViLcjhevgfngjwM+g7kBw5HgyBSk0309h9wTd1GiZ8fNRoNGmdn3BMS8Bw/Dqd27S66jkaLkY2ZtgvFp9ZjtBoJdw///RkE4e7hlxxzU2rz7w4kSylTAIQQc4AxwDmTf6PS6mxHwFd5O57V8r8djNnIlk3r6NU9tvYs5rw7mwtNG/96JvTn6TPrNlaCpfgcO8azd2w1IK0NELygv9DBZsezmvEupQlPd4GdzfmaB/9uvedYRqMDOx9Ppj7otZrf7zS+q09tWUF5DfsySthzqiV7M0soOZRE36wdmEzF6HJq0OZa2OO6hoKu7vQ63R8vT0gu34M+eyreNX5kBIwmpfu/8WqZTruDi4jI3U5Fx46ULFhA8bx5OFZX4anV4vHcc2juvvtv62fQGhgcNpjBYYMpM5bVXihOSeSzvZ/x6d5Pae/TnoTmtXcU+zr5NvwXdh6NdeQ/FhghpbzP9v4OoIeU8h9nLTMJmATg7+/fdc6cOZe9vfLyclwvomtXU3LVxiwtaKxmNFYTQloQ8sy0+ZzlGqv5L8sIaUJj/eNnzTWVOGoFQp69/NnbMp9nnZY/rdM2n/rfSUkEUuiwanS/v1o1ett07asU2j+V6/4w/+zP1pgkOkfn886vLdMihf4P6zl7mXN9VgrtVbuTupR/18Xb95BeboH0I1hqypCyHCFc0RmcyOzsTNeqcIIKw8kp2k1axTEsrmE4GbqgsYYghAVPj2J8nLPwTNuL0+Ej6IuKsOr1VHfuTNWQIZjDQqGwEDIzwWgEgwGCg8H73D1/is3FJFUksbNiJxmmDDRoaO3Ymm4u3YhxjsFBc+5nFNfl//LAgQOvumafCyb/s6munpfO3mKu93itlnM35Z2zic9YO/23zYN1OOsy1/yxuc92difNNQ2ykwLOf23oojo4XMpZ15nyv2k2POusa9OW7fQdMPDizqTOmmcSgrnXJZDv5IFFFnFmMLmcVjoi3JrRMrcj5UXJpJbu5qTeC+kQQ4hoiU6jw1iVh9PpJKJObibQWIDO1vvUMSgIrwP7cc/PR3Mmjzo7w/TpcNttf63P7Nnw7LNw8iTJXSNIfHgIS5zTyarIwknnRFxoHAlRCfQK6oVeo/998YcfXseHH8bx6qvnXu3fuRqbfTKBs++QCLGVKcrVQaOt/dNfvQ8CWb9uHXH9+/2pee3vdirGP15/Mhv/t8yZncwfyi/impO5GmpK/7gjPNc1Lmmpl5j7Avx2VsFfdiwO/9uZTPGEahNYJHoL3G5ZBRZYpu3GMRmF0ZyH1yFBpcbMpqAsDJ086XJqNJ1KijhamkSpaRkmpwiE4wAskSM4FDWQjQ6nKdRk0iIzmdgTR6j29uGkbzMyPHwpcHJDY7HiOms+7u264e7ijJuLMx4uTrguXIDzg5MQlbV3ObfYmcojD87m4c8/Z8+IaBJTElmevpylqUvxcvAi0jicX99MoCg9BoD0dJg0qTbkS90BnE9jJf8dQEshRCS1Sf8W4NZGqouiXLs0WtA4gf7qGITsvKzWC+xI/jx9jmtOFiPHjx6mZWTYxZ119Y6A1SvBWlOb6Rw0oIUR2iRGaHexzxjGhtLu1FiKcMwwocu0sMd9FSWxbnTP6k7nMgPHCveTUzKTQFcL0tATfXU/AgkhKgAio47jVZJPSYozhpNmIotycPQx4hVegfv87mh0/2tVMUotFf9yxSw9MEstZqnBIjVYDv8XrzQ3btXouVFo2eXkxganKnYa5hL8xBzaV+tJq/IH4qisrD0TuKaTv5TSLIT4B7Ac0AIzpZQHG6MuiqJcARoNaBzrfCaVWbGOln3iLv4D5zuknD2bmGefJebkLE63ac0vXXtSaaqAkkq81hhJcdzGhlhnuvqE0j+/O9mlR0gu3I9f2QZcWw/jlKUnJ4p641WeTgfjr7TQrqFS6Ckq9CC7wIvsfXo0naOwdg7D7OWIeeVyLDoNVp0WqRW1mVdb26FO4+r1+7WozmUmupVaKTPBLlcrm9xrKDbk/V7tkyfr8u39UaN15JZSLgGWNNb2FUWxY7fd9vshdAAwmdrB5Oa88AJFWTmYqvII2ORIrraKPe3SiAr1Ivb0LVgqcziUsx2N53Ei3MIpz/dmQ6t/sCXiHlofX0n7EytwevhOioqKKFu2HLYcxa1jRzx3luOemvq/awNnhIdD2o6/VC8iorapB+Ctt9f8Xh4WVn9fwbVzF4+iKEoDcnR14a5338FqtfLrO2+TtusQRnMuvnu1VGuMrApdjks7NzpmjMDHqudA5noKDbuITFmMRrblcJtRHGg3hiCDJx3GhxD15NOUL15E0dx5ZOsN5LRoiUdJCZ7FxTgaa2ovDr/66jnr8uqrtW38lZWgofbpaH+z+GVRyV9RFOUsGo2GMf9+AoBtP89n+/ylGM35uKRb0J2ysstrJZWdXens0I5O5pGkuG4juXQ/AUHr8AruxelUA8u/OICLh4Hofv2J/n48muP7KXrzLYr376fIywsnqwXP66/H/cYbOdeDL8+06z/7bO1reDiX1dvn76jkryiKch49bhhLjxvGcmr/Pn5992OqqwrQFlTjsdpMqtN2NvY8RBfnIAZU3E9JaQoHj3+PwduZll0GUlnRgh2LU0lakkZkJz86vDad5r6S0oWLKJ43j+xFv5Kzbj0eo0fjNX4cDi1b/mHbZ1qm1q2DtLT6j00lf0VRlAsI7RDDQ199TkVREXOef5HS/DwslfkEra2kQFfB950yaeXrSkfnm3HGyN6NqykVv9Aith96ly6cPFjIiV25eAe50GHAEFouuA3z/t0Uz5lL8dy5FH33HU5duuA57mbcR4xA49jwXYzPdcahKIqinIOLlxf3fjSNf373DWHR3dAKJ4ymPAJ2ZFK9PY8VYhlrQ7cQHN6foZF3U7X/NAdWTMXNYxnRvavRaGH9D8eY9fRmklI8cfr3S7RYv45mTzyBpaCA7Kee5nj/AZx+9TVqkpMbNBZ15K8oinKJtDodN//nPwBs+OZr9i7bgNGSj8dxif6EL7v81vBbJxc6aaIZbokju+gQB47MxNHHneheQ6iuacnBTZnsX5dBcGtPOgyIJ+LOO6neuZPieXMpmjOHom+/xalrVxxjYpADBtT74yNV8lcURamD/nfeRf877+LYls2s/PQrqmsK0OcYcVxhJc1lB1t6HSYmL4A+rndhkcXsXrmMKs1PtOoxABfvbqTur2LZ9AO4eDrQrl8Q0f95Hf/nnqPk558pmjcPp99+Q4gn6r3eKvkriqLUg1a9etOqV2+KMrP58eVXKC8uQJaXE7CqmkJDBd/HZtPK6kwbeR0+Bkf271rDofIVhLaLocXgAeRnebL911R2JqYR1cWPDgNuIuquu9i4dGmD1Fclf0VRlHrkFRzIpM8+xlhTw/z//pfclFOYavII/M1AjdaTZa1X4Regp7U5ls66UaQUJLHjp89w8fUmZsAwLJY2HE8qJHlnLj7BrhgCXZBSqmYfRVGUa4HBwYFbX3sNgBWffMyRjTsxWvLwOSTQHvFlV+B6dnZwJFq0Zaj7g5RZM9n16y+YdEba9I7DI6AnaQckpafqP/GDSv6KoigNbthDUxj2EOxbtYINs+ZRY8zHKdOMLsuHdPcktvc6RvvTzegWMQEnByu7tq/gQMVywtp3xD0kAikHqiN/RVGUa1XMkGHEDBlGbkoKC16bSmV5AZRUErjcRJFjOT90z6W1yYGWNQPo5ejNkbwtZOXvUkf+iqIoTUGzqCgmz/iU6ooK5r3wHwoyszFX5ROyoRyjzp1l7dYT5AMtjN3oY+ip2vwVRVGaEkcXF+58522sViuL33uH1J0HMZpy8d+jRavxISlsE/tbORMth6jkryiK0tRoNBpGP/5vAHb88jNbf1yM0ZyPa5oF/clmmMebMDic+xm/l0slf0VRlKtIt+tvoNv1N3By/34S3/0Is9lS74kfVPJXFEW5KoV16MCDX33OunXrGmT9amA3RVEUO6SSv6Ioih1SyV9RFMUO1Sn5CyFuFkIcFEJYhRCxf5r3tBAiWQhxVAgx/KzyEbayZCHEU3XZvqIoinJ56nrkfwC4EdhwdqEQIhq4BWgHjAA+EUJohRBa4GNgJBANTLAtqyiKolxBdertI6U8DJzr5oMxwBwpZQ2QKoRIBrrb5iVLKVNsn5tjW/ZQXeqhKIqiXJqG6uoZDGw9632GrQzg1J/Ke5xrBUKIScAkAH9//zp1dyovL2+w7lJXK3uL2d7iBRWzvWiomC+Y/IUQq4CAc8x6Vkq5sN5rZCOlnA5MB4iNjZVxcXGXva5169ZRl89fi+wtZnuLF1TM9qKhYr5g8pdSDrmM9WYCoWe9D7GV8Tfl55WUlJQvhEi/jHqc4Qvk1+Hz1yJ7i9ne4gUVs72oS8zh55vRUM0+i4DvhRDvAkFAS2A7IICWQohIapP+LcCtF1qZlNKvLpURQuyUUsZeeMmmw95itrd4QcVsLxoq5jolfyHEDcCHgB+QKITYI6UcLqU8KISYR+2FXDMwRUppsX3mH8ByQAvMlFIerFMEiqIoyiWra2+fn4GfzzPvVeDVc5QvAZbUZbuKoihK3djLHb7TG7sCjcDeYra3eEHFbC8aJGYhpWyI9SqKoihXMXs58lcURVHOopK/oiiKHWqyyV8IMVUIcUQIsU8I8bMQwvOseeccdO5ad76B9oQQEUKIKiHEHtvfZ41Zz/p0OYMLNiVCiBeFEJln/bajGrtODcUeB4UUQqQJIfbbftud9bnupvwkr5XA01JKsxDiTeBp4Mk/DToXBKwSQrQ60xX1GndmoL3PzzHvhJSy05WtzhVxzpib+O/8Z+9JKd9u7Eo0pLMGhRxK7bAwO4QQi6SU9jAu2EApZb3f2NZkj/yllCuklGbb263U3k0MZw06J6VMBc4edO6aJqU8LKU82tj1uJL+JuYm+zvbqe7YBoWUUhqBM4NCKpepySb/P7kHWGqbDuavg8sF/+UTTU+kEGK3EGK9EKJfY1fmCrCn3/kftubNmUIIr8auTAOxp9/zbBJYIYRIsg12WW+u6Wafixl0TgjxLLV3Gc++knVrKJc50F42ECalLBBCdAV+EUK0k1KWNlhF61FjDS54tfi7+IFPgZepTRIvA+9Qe7CjNA19pZSZQohmwEohxBEp5YYLfuoiXNPJ/0KDzgkh7gISgMHyfzc0/N2gc1e9yxloz/ZchRrbdJIQ4gTQCqjXC0gNpQEGF7ymXGz8QogvgMUNXJ3G0mR+z0shpcy0veYKIX6mtvmrXpJ/k232EUKMAJ4ARkspK8+atQi4RQjhYBtg7sygc02WEMLPdsEMIUQUtTGnNG6tGpxd/M5CiMCz3t5A7QXwpmgHtkEhhRAGai/mL2rkOjUoIYSLEMLtzDQwjHr8fa/pI/8L+AhwoPZUCWCrlHLy3w06d60730B7QH/gJSGECbACk6WUhY1Y1XpzOYMLNjFvCSE6UdvskwY80Ki1aSC2Xnv2NiikP/CzLX/pgO+llMvqa+VqeAdFURQ71GSbfRRFUZTzU8lfURTFDqnkryiKYodU8lcURbFDKvkriqLYIZX8FUVR7JBK/oqiKHbo/wEUYk0XCJIjwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "wynik = regulaFalsi(4, 5, 0.06)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zastosowanie funkcji root z pakietu numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.87298335 1.12701665]\n"
     ]
    }
   ],
   "source": [
    "coef=[1,-10,10]\n",
    "print(np.roots(coef))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}