funcion_numero_primos__1_.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "295b2851-3b84-4580-9f8e-ae80632bdcb1",
   "metadata": {},
   "source": [
    "# Función Número Primos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dcedf403-d041-4e8e-af8a-74db62c638bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#lOS PRIMOS: serán aquellos que al dividir con los números anteriore su residuo nunca da 0. {Modulo}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b549f814-f877-47fc-9ad7-125fa155ff3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Comando para sacar modulo \"%\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "730cf613-9a30-4f11-af25-43912e4dd3c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Se buscarán los números primos menores que 1000: Trabajo en clase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "27f87e1c-5aec-4bc5-98a1-cefeaeadd664",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da831e90-c254-4886-a097-8bc9a3361f86",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Para entender como funciona el codigo de contar los números primos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b4f878bf-f9f4-4f70-ac55-e355dc27dac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "N=150\n",
    "num = np.arange(2, N+1)\n",
    "num_prim =[num[0]]\n",
    "\n",
    "for i in np.arange(1, len(num)):\n",
    "    for j in np.arange(0, i):\n",
    "        cond= num[i]%num[j]\n",
    "        if cond == 0:\n",
    "            #print(num[i],'no es primo')\n",
    "            break\n",
    "            \n",
    "    if cond != 0:\n",
    "        #print(num[i],'es primo')\n",
    "        num_prim.append(num[i])\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "21b1aca6-fe63-479a-bb11-b6cc303c667e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(num_prim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "954ee676-d5d5-40ac-b9b8-1a59d40654cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Las funciones para graficar la cantidad de números primos (Cuantos primos hay hasta un número n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "3c0990ea-5b91-4be4-b532-4b96248ddf18",
   "metadata": {},
   "outputs": [],
   "source": [
    "def num_prim_func(N):\n",
    "    num = np.arange(2, N+1)\n",
    "    num_prim =[num[0]]\n",
    "\n",
    "    for i in np.arange(1, len(num)):\n",
    "        for j in np.arange(0, i):\n",
    "            cond= num[i]%num[j]\n",
    "            if cond == 0:\n",
    "                #print(num[i],'no es primo')\n",
    "                break\n",
    "            \n",
    "        if cond != 0:\n",
    "            #print(num[i],'es primo')\n",
    "            num_prim.append(num[i])\n",
    "            \n",
    "    return num_prim\n",
    "\n",
    "def counting_primes (N):\n",
    "    num_prim = num_prim_func(N)\n",
    "    return len(num_prim)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32bc0b9b-6b6e-4cdb-bc41-e90843019f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ejemplos de como funciona las funciones: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "62506e85-bcba-4e47-805b-729d8b3b5a76",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-101-e799b08cffa1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnum_prim_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1e4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-100-7d6c25b7794e>\u001b[0m in \u001b[0;36mnum_prim_func\u001b[1;34m(N)\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m             \u001b[0mcond\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mnum\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mcond\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m                 \u001b[1;31m#print(num[i],'no es primo')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "num_prim_func(int(1e4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ba05fafd-7e92-4d84-80b1-40b519cacd52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1229"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counting_primes(1e4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "527b840d-eddd-485b-986a-77b75d5dea11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Codigo para convertir los números primos en un arreglo que se pueda graficar (poder aplicar la función)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "82b3da03-297b-4980-89df-329dc8591ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "num=np.arange (2,1001)\n",
    "y = []\n",
    "\n",
    "for i in np.arange(0, len(num)):\n",
    "    y.append(counting_primes(num[i]))\n",
    "\n",
    "y=np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dee77d82-4a33-4211-971e-540559a4f3fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grafica y grafica logartimos para hacer la comparación ideal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "3aec2969-f1f7-4579-b175-139a82ca5032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20fd452e820>]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(num, y, '.') \n",
    "plt.plot(num, np.rint(num/np.log(num)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "542e5573-3529-4e17-b964-d933b1ee76f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Graficar con el logaritmo comparando con la de los numeros y sacar error. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "4faf5ac5-6211-469b-be6c-d72587e88c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "f=np.rint(num/np.log(num))\n",
    "error_p= np.abs(y-f)/y*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "054afc3e-e0c3-4c2a-8a2c-6d72b5512d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20fd48ed160>]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EpEs6p9nrAL7h7quDc6muMrNng/t+4O63p9+9Y4vEV8voI1URkS4Dnrm7e4W7rw6uNwCbgZLB6lhfxZdCJh7294m1+3i3ruW97oqIyAljUGruZjYDWAi8HjRdb2brzexeMxvdyz7LzKzMzMqqq6sH/NxHyjKxdG9q6+CGh9by+XteP9puIiIZLe1wN7MRwHLga+5eD/wUmA0sACqAO1Lt5+53u3upu5cWFxen242umXt7R+yKZu4i8n6WVribWQ6xYH/A3R8DcPdKd4+6eyfwC2BR+t3sXaTrK6qxi9ZoFIDDrR08/OaeoXxqEZETVjqrZQy4B9js7t9PaJ+UsNmngA0D715f+hG7jC+FbOvo7Lrvm8vf6rH9X91fxid+/MdBe/6GlnZW7a7RsW1E5ISSzmqZ84EvAG+Z2dqg7R+Aq81sAbG59C7gK2k8xzHFZ+7xcG9NCPdUnttcCcA/PbWJ6y+aw+jhuWk9/7ee2Mjja/axcNooLp43nusvnoPF/+KIiBwnAw53d/8jR779n+j3A+9O/0WSqzJJM/ejueePOykZNYwL5xUzq3hE0n2r9xzivpd38aXzZ3DmtJSfB+PuVB9u5Y2dNQCs2VPLmj21/PkHpzJ+ZP6AxiIiMljC/w3VbjP37uG+rbKBT931Mr986Z0eu/7jU5u4+I6VVDW0UNfU3tW+fFU5T67bz6fveoXL73yZt8rriHYml13+c+0+Ft26gn21zUnt1YdbezxPW0cnr+w4QE1j28DGmDCW5zZV0tbRSWfn0JWB3iqv44aH1vDqjoND9hwiMrTSKcucEOIzdxx2HWjkrX11Sfev3nOINXtq2XmgkfmTR6Z8jEW3rgDgoWXncPbMMdQmBP3avbV84iexGv13PjGfJadNYmJRPlvfPZzysZ7bVEV+ThazE94NPFK2l//znxtYcupEfvaFs9hX28z2qsOcN3ssOVmp/74eamyjbPchxo7I7Xr38PePrmfd3loAzpo+mu9+5nT+sKGC6y7qvRR08HArP3jubYqG5fCNP53Hpop6Tispwt279qltamPZr1fx95fO47nNlTy2eh8HDrdyuKWDhdNGkZcd6fH4dU3tLF9dzmklRbR1dHLe7LFdx/k5kdU2tVHf3EFudoSCvCxG5ucc7y6JDInQh7sRn7nDR374Yo+ae3NbbPVMbVM7n/3F0de+X3X3a3z+nGn87q2KlPd/57828dT6Ck6aWEjZrpqu9qULJjNvYiG3//dWfvDc2zz05h4WTB3FzHHDOWfWWDYEf3Dis/xlvypj4/56siLGqZNHcmXpVLIixkdOnUhhfjY5WRH+5enNPFJWDsDfXjyHHz2/HYDsiHHu7LG8vP0AV//iNaobWrmidCoTEkpBP16xjbV7a/nkgsk8uXY/K7ZUARDthJ+t3MGnzyzhqfUVXHPudLZXHaamqZ11e2u58uevJo13xZYqTv6/f2ByUT4Ti/I5Z9ZYblxyMgB/ef+brNp9qGvbf/+LD3LRvPFHfX2rGlrIzzl6oL74djWnlRRR1dDClNEFDM/NItrpZPfyRzBRa0eUlvZOioalfvzOTmfBPz7bdbswL5uVN15EQW4W+TlZXdv8cMU2zphSxMxxw3l9Zw1TRxcQicC0MQWYGcUj8sjNPj5vep9+q4KS0cM4fcqoPm3f2ek8t7mS5vYop04eScmoAnYdbGR8YR4AY0fkDWFv5XiyE2GVR2lpqZeVlQ1o34fe2MNNj73FKzddzHm3Pd/j/m8uOZnv/mFLul08qjduWcz4wnz21jRx5wvbeejNvSm3G1WQw7c/MZ+/e3hdr481LCeL8+eM47nNlWRHjI5u5ZdffrGUhtb2pMf45BmTmTN+BFeWTqWyvoWld76c9pj+7aoFVNS10NDSzsb99ew80EhFXQuPf/U8iobl8CfffSFp+4kj85kwMo+F00YT7XRmjhtO0bAcFs0cw4i8bPbVNvPxYJXS2OG5jB6ey8Unj2fPwSZOKxlJW9QZmZ/NP/9uM4X52TS0dPCx0ydhwFPrK5hUlM+Y4bnMGT+CLDO+ednJvL6zho5oLMynjx3O1x5ew/aqw8waN4JNFfVMGT2MklHDmD62gKyIsetAE6++k7rUNGFkHjWNbbRHj7zeowtyOJTwLi7uwycVU93QSn1zO0XDcsjNjvD9K8/o8dlNOlo7omypaKAgN4sxw3PJjkT4/YYKbn4stgLsi+dOB6Bk1DDyc7L41JklfOGeN1i3t5biwjyqG1oZNyKX9qhT13xkDOML86hq6Fk6BJgRvE5Fw3LIz8li6ugCou6cPXMMZ00fzTObKvnSeTN4adsBZhUP50BDK7nZEZrbotz5P9u55aPzee2dg4wZnkttUxvPbKrkgzPGEDGjvqWdR1eVYwbf/czpTCrKZ1bxCN6ta2HamIKu3/Wj/dGMv8OcNqaAGxbPJTc7Qk5WhPycCAW5R+apG/bV8do7Bxk/Mp/siHHG1FFMLspnT00T+w418+Pnt1Pb3M7CaaMoGTWMr144u9+LIBLf+R5PZrbK3UtT3hf2cH/kzb3cuHw9L990MeenCPcbFs/l31Zs69djnjRhBG9Xpi67dDdxZD4v33QxWUFJ4r6Xd/Kd/9rUr+cDuPaC2RTmZ7NyazV7DzVRUdfCklMn8oeN73Zt87cXz+Hrl87jUGMbtz+zlYq6Fp7fUkV+ToSW9p4fJA/LyeK2z3yAomE5LF+9j6r6Fl7fWdNjO4Alp07kkwsms7+2mea2aI9VP/+1bj9/8+CapH3OnTWWqxZNZffBJtaX13WtREp03uyxbNxfnxQwg+GSU8bz3OaqY253yqSRHDzcSkenU9PYxsj8bP7t6oXMKR7BS9sOUN/SzuGWDirqWli+urxrv9ysCFF3op3OWdNHs2r3IUpGDeP0KUU8vSH2bzJmeKxk9sLWKsYX5jFuRB7TxhbQ2elsebeBS0+dwJTRBcwpHsGemkamjC6g051HV5Xz7KZKmtqizBhbwPjCfD40dxzr99XhDlNGD+O+V3b1OqacrFgQ9uW/bun00eRmR7jklAnc8cxWmtujdDpdf0ABJhXlU1HXwhlTR3WV/d5Ls4qH0xF1Kuqa+dbH5zN6eC67DzaxfHU5k4ryueKsqTy6qpzXdx5M+uOb6M/OmkJ1Qysr3+75bXczmFw0rMfnY4lG5GXzJ3PGsfdQExv317Nw2ig27qtn2tgCZo0bzsSi2Dvj3KwI08cW8Ms/7mRUQS4NLe3sPtjE5FH5GMaS0yZy4bxiRuRls3r3IV595yDjC/Np7Yiyo7qRRTPHcKixjYlF+bz+Tg3ZWcb1F83h7FljB/TaZXa4l+3lxkfX89KNF/Gh773Q4/6vfHgWP38x+cPUTy8s4bE1+3p9zJ3/8lE27Kvnd29V8LOVO3qd7dz1uTNZcurEpFrz029V8NcPrO6x7b1fKmXu+MKuPn6gpIjdBxupD/6DvXnLJRQXHnmLvPtgIxOL8vn1q7tZu7eWZzZW8uKNF3X9kiXaUX2YxXesTGq75JQJXHfRbBZ2W+3T2el0dDqV9S1srz6Mu/PmrkN86bwZSaWd7jqinby07QDN7VG2vNuAAV+9aDZ52Vld21x+58usTQiH+ZNGsqmiHoCTJxZy6akT2bCvjpnjhvP8lipqm9p6zIzv+4sPcqipjcOtUfbWNNEe7eSt8jo+f850fvP6Hupb2mmPdrKjurFrn5suO5nbno69O7v2gtnkZBm1Te1cUTolqXxR39JOfnZWr7PDl7ZVs3F/PddeMLurrbE1Vp+vaWxjRF42HVHn6Q0V5GRFWLpgMtlZEX76PztYtfsQr+44QGNQBhwMM8YWsOtgEzPGFnBF6VTqmtv5s7OmcNKEQtydGx5aS3FhHntqmli9+xDTxxbw1QvncPqUIrZXHWbOhBGML+z5b9rSHiUrYjS1RsnJNnKyImyuqOf0KaNY+XY11Q2tLJw2ikfK9nLGlFE8u6mS9mgna/bUMqkon7LdhxhfmMeY4bm8W9/C9LHDuXT+BPKyI+wLJgfFhXm0tEdZuqCEXQcbeWXHQb7xpydRfbiVyvpWbnx0HY2tUTo6O1NOTFI5eWIhF8wr5gMlRTS3RWmPOm0dUX64YlvS52RjhudyzqwxnD1zLB2dzm/L9tLQ0sG+2mbmTSjklo+dQnbEKJ0xhp+v3EF9SzsPv7m36/8iwMxxw9l5IPY7FrHkY1elYhYL/mMtxU7l46dP4iefPbPf+8WeN4PD/dFV5fz9b9f1Gu5fPHc6v3p1d1Lbum9dypq9h7j9ma1s2FffY59dt32s63pjawdZEeP2/97KvtrmrlkbwKPXnkvpjDFJ+za1dfDAa3t4fWcNW96tp6U9yoHDbaz4xgXMLh7BjJt+l/QcG/bV0Rbt7HXJZV+0dXRy3m0rOHA4thrnklMm8MtrUv57D6n6lnaq6lvJy46wcX8ds4tH8OvXdlOQm83XLpnbVddO1NDSTkfUqW1up7kt2uuH3onao500tHTQ1tFJXnaE4XnZPLluPydNGNHnWvRQWL6qnBVbKrl60TTu+eNOvrnkZL5835u0dHSSnx2hor6FeRMK2fJuA2/cspiVW6u57AOTuO/lneyva+GUiYW8tO0Aly8s4YHXd3PPNR+ksbWDomE5ffrMIYzcncOtHUTMONzaQUNLB/e9spOyXYe463Nnsq3qMC3tUT5QUtRr2au5LcqBYJVaTWMb08cWMKqgf99faWhpZ09NE5OKhlFZ38Ipk0ay9d0GRg7LZlhOFo+v2ceF88bz5s4amtujfO7saTS0dJCXE+FwS0dskmOwuaKeyvoWfltWzuJTxnP6lFHsqWnk4nkTeHFbNZedNpFnNlUyYWQeJ08cSU1jG/k5WUkTu/7I6HBfvqqcb/x2HSv/94Vc8K//A0BBbmy1SveVM3Fv//Nl5GZHaI928vL2A/zwuW1JM87EcE/U3BbllR0HKNt9iHV7a/n5F86i8BirLQ4ebu16Owbw1/+xim1Vh3nu6xf0f7BH0R7tpCPqrN1by8kTC9P+cpYMrs5Opy3aiVns96i/4SOSytHCPfSrZSLBhCbxb9SEkfn8y6c/wA+f25ayDhx/W56TFeHCeeO5MFjl8b0/bGHK6IJen2tYbhaLT5nA4lMm9Ll/Y0fkJa1I+Onnz+rzvv2RkxUhJwvOnT2w2p0MrUjEyI/E3rkklrJEhkro3+sdWQrpCW1wWkkRv/jiWfzZWVMoyO3bf6Ybl5zMZ8+eNhTdFBF5T4U/3LsdOCzWGL/PuP2KM3jiuvP52OmTeu4sIpKhwl+WCdK9rSN55p5o7oRC7vzsmXzu7ANkR0L/90xE5JhCH+7xmXt79NhLkM6bPW6IeyMicmII/TS2a+aeEO4nwjfHRESOp9CHezzG2xO+PKBoF5H3u/CHe8qZ+/HqjYjIiWHIwt3MlpjZVjPbbmY3Dd3zxC7bkmbuSncReX8bknA3syzgTuAyYD6xU+/NH4rnitfcEw8opJm7iLzfDdXMfRGw3d3fcfc24CFg6VA8UfyYXf/01JEjMRbmh34RkIhIWoYqBUuAxIOalwNnJ25gZsuAZQDTpg38W6ELp43mM2dOobm9g/ao0x7t5G8unjvgxxMRyQRDFe6pCiNJRyhz97uBuyF24LCBPtGY4bncceUZA91dRCQjDVVZphyYmnB7CrB/iJ5LRES6GapwfxOYa2YzzSwXuAp4coieS0REuhmSsoy7d5jZ9cB/A1nAve6+cSieS0REehqyZSXu/nvg90P1+CIi0rvQf0NVRER6UriLiGQghbuISAZSuIuIZCBzH/D3hwavE2bVwO4B7j4OODCI3QkDjfn9QWN+f0hnzNPdvTjVHSdEuKfDzMrcvfR49+O9pDG/P2jM7w9DNWaVZUREMpDCXUQkA2VCuN99vDtwHGjM7w8a8/vDkIw59DV3ERHpKRNm7iIi0o3CXUQkA4U23N+rE3C/18xsqpm9YGabzWyjmd0QtI8xs2fNbFtwOTphn5uD12GrmX3k+PU+PWaWZWZrzOyp4HZGj9nMRpnZo2a2Jfj3Pvd9MOa/C36vN5jZg2aWn2ljNrN7zazKzDYktPV7jGZ2lpm9Fdz3I7N+nh3a3UP3Q+wwwjuAWUAusA6Yf7z7NUhjmwScGVwvBN4mdpLx7wE3Be03Ad8Nrs8Pxp8HzAxel6zjPY4Bjv3rwG+Ap4LbGT1m4H7gr4LrucCoTB4zsdNv7gSGBbcfAb6UaWMGPgycCWxIaOv3GIE3gHOJndnuaeCy/vQjrDP39+wE3O81d69w99XB9QZgM7H/FEuJhQHB5eXB9aXAQ+7e6u47ge3EXp9QMbMpwMeAXyY0Z+yYzWwksRC4B8Dd29y9lgwecyAbGGZm2UABsTO0ZdSY3f1FoKZbc7/GaGaTgJHu/qrHkv5XCfv0SVjDPdUJuEuOU1+GjJnNABYCrwMT3L0CYn8AgPHBZpnyWvwQuBHoTGjL5DHPAqqBfw9KUb80s+Fk8JjdfR9wO7AHqADq3P0ZMnjMCfo7xpLgevf2PgtruB/zBNxhZ2YjgOXA19y9/mibpmgL1WthZh8Hqtx9VV93SdEWqjETm8GeCfzU3RcCjcTervcm9GMO6sxLiZUfJgPDzezzR9slRVuoxtwHvY0x7bGHNdwz+gTcZpZDLNgfcPfHgubK4K0awWVV0J4Jr8X5wCfNbBexEtvFZvYfZPaYy4Fyd389uP0osbDP5DFfAux092p3bwceA84js8cc198xlgfXu7f3WVjDPWNPwB18In4PsNndv59w15PANcH1a4AnEtqvMrM8M5sJzCX2QUxouPvN7j7F3WcQ+7d83t0/T2aP+V1gr5nNC5oWA5vI4DETK8ecY2YFwe/5YmKfKWXymOP6NcagdNNgZucEr9UXE/bpm+P9yXIan0h/lNhKkh3ALce7P4M4rj8h9vZrPbA2+PkoMBZYAWwLLsck7HNL8DpspZ+fqJ9oP8CFHFktk9FjBhYAZcG/9X8Co98HY/5/wBZgA/BrYqtEMmrMwIPEPlNoJzYD//JAxgiUBq/TDuAnBEcU6OuPDj8gIpKBwlqWERGRo1C4i4hkIIW7iEgGUriLiGQghbuISAZSuIuIZCCFu4hIBvr/kW4SakVXuTsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(num[0:], error_p[0:])"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}