Mischzeiten StoMo

Mischzeiten.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from scipy.stats import pearsonr\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "def swap_shuffle(arr):\n",
    "    a = np.copy(arr)\n",
    "    i = np.random.randint(low=0, high=a.size-1)\n",
    "    temp = a[i]\n",
    "    a[i] = a[i+1]\n",
    "    a[i+1] = temp\n",
    "    return a\n",
    "\"\"\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_to_random(arr):\n",
    "    a = np.copy(arr)\n",
    "    drawn = a[0]\n",
    "    deld = np.delete(a, 0)\n",
    "    i = np.random.randint(low=0, high=deld.size + 1)\n",
    "    a = np.insert(deld, i, drawn)\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(nd, n=1000):\n",
    "    decks = []\n",
    "    for k in range(n):\n",
    "        nd = top_to_random(nd)\n",
    "        decks.append(nd)\n",
    "    return decks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def acf(t, k=0):\n",
    "    r, pv = pearsonr(decks[k], decks[k+t])\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 52\n",
    "decks = run(np.arange(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "xvals = np.arange(0, 300)\n",
    "yvals = np.vectorize(acf)(xvals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L8HQ7/3MvrU7mdOk5Qn0sHd7PzcXETcnRPLbhOABxwV5Mj/LjwNkK/pNRSnZ5HTcmRzMnJgCAT48VohTce1kif/48g7zKeqIDO/YVFyPLntPlJIZ48cjXprJkfDAfpxby7sFcxvl1PROdyaSYGe3Pfy2M5S9fZPBBSh47s8oAOJJX1adtaq15enMmX2aV8qMrJ7AwIWjQ9kdcPKn5D5H2F3zbOlGsP5zPiie3sfVkMff++wDPbssi2NvCr1dN67F75bKkEIBOTTp9FextwdPt/Hne1WxiQpgP/p6dx21fNiHY/jguyIuV0yM4UVhDdrntGsArO8/YX993poIJoT72TxWnS89dVPnE0Pv4aAH/+3kGx/OrWZQYxLIJIZhMimtnRLD2znm9du+9fWEsrmYTP37rMD4WFxbEB5KSU8meU2U9dggA21AjT32ezt7T5Xx+vGgwd0sMgIT/EPGyuFBjXPAtqm5g6jhfXvn2fBKCvbjzH/vYd6aCp741iy0/uYyEkK4/creZFunHG2sW8q150UNe7sQQbyL83PGxuBDg6cptC2K4YnIY0YEe3LEolvdTbM0/R3OrOJhdwZzYABKM6wpnyiT8R6IjuZU88EYKT32eTk1jC9PG+fX7PUJ8LFw/K5IWq+a/FsWyODGYUyXn+Nbzu3nxP6e6/bnUvCoO55z/hNBWiejJA28c4q9fZPS7jKJ/pNlniPgYbf5aawqrGpgQ5sOyCSH8+eZZPL05g1vmx/SrJj9cH5WVUnxrXjTZZXX22uALd8ylvrkVF5MJk1L8a/dZXt+bA0BybAChPhY83cxS8x+h/vpFJp4WM011VsBWmbgYP1g+nvK6Jr6zJJ6j7Zp8/vpFJt+cG0XQBb29Mopq+NpfdxDk5YZSsCA+kJx23Uar6pr59UfHKattJMzXnVBfd66bOY5PjxVyPN+T+1YkXVQ5Rd9I+A8Rb3cXquqbueT3X1BZ38xSo+lmRpQ/L66e5+DS9ezBKyZ0eK6UsjcbPXrdVO5dnsj8xzcDkBwXgFKK2CAvzkj4jzhWq2bP6XK+MjWMUyXnOJxbSVJYz580uxMT5GnvHHBpUghr70wmws+Dr/5lB3/+PINff31ah/Xf2p+D1lBa20RCsBcTw3x472CevdPArS/uJr2ohglhPqTmV1Na28i2k8U0NFvJKK6lqq4Zvy7uOhaDQ5p9hsiKSaGYFORXNVDX1EqYb9cX1EajUB93/nHnPL45N4oY4wJvQrCX1PxHoBOFNVTVN7MwIYhfXDuZX103bVBmlzOZFCsmhTE5wpfbFsTw2t5sMo0bB8E2DMm6Q3m4udgiZnKEL9GBntQ0tlBV30xJbSPH8qv58VUT+ej+pex7+ApWTovgcO75TxQHcyoGXE7RPQn/ITI3NpCdD11ufx7uN7ZugFo+KZQ/3jjT3jQUF+xJTkU9za1WB5ds9PvsWCHPbsuyD7g2ELtP2XrmLEgIYk5MALcuGPyxmR+4PAk3s4lnt52iqcXKbzem8cAbhyitbeJX100FYGqkr70nWFZJLScKbCeKGVHnm6AubI46eFbCfyhJs88QCvdzJ9zXncLqhjFV8+9KXJAXrVZNbkU98cFeji7OqFXX1ML3/nUAq4bTJef4wzdnDOj9tqWXEBvkSaS/R+8rX6Qgbws3JUfx2t5spo3z5fnttgvAdyyK5Zb5McQGeTIjyt9+1/gNf99FXJDtRDAp/PxcwdMibY99LC7EBnuy/4yE/1CSmv8Qm2XckBU+xsO/LfCl3X9gThTW0NZz8mD2wMKvtLaRHZml9slZhtLdSxPQGn7zURrB3m6su3cxv/zqFAAWJwbjbXEhOvD8CehMWR0hPpYONyW29UJKCPEiOdbWlbRFPkkOGQn/IbYkKRgfiwsRfkNX8xoJ2sL/lIS/ndaa1LyqDndF9+Z4vm1E15vnRdsueg5g0LwNh/NptWqunx3Z+8oDFB3oyX8tjKXFqlk5PYLZMQGdZo/zcXflf2+exQ9X2IYBmXTB0BABXm4khXozLdKPObEB1De3cqKwBjE0pNlniN06P4avzRiHh9vAL7KNZIFebvi4u0jNv51Xdp3lkfXHeO72uXxlaniffuZ4QTW+7i5cOyOCN/blcDinkmUTQvr0s3/67CQbjhYQF+TFDXOieGNfDlPH+TIhbHjG37n/8iSyy+u4fWFst+usmhVJVV0zL/znVIf2/jZvf28RFhcz5XW24ccPnK246K6pomdS8x9iJpNyiu5qSinig70ccqNXq1Vzw9938tSm9GHfdndqG1v446cnATjWx2EQwDYF55RxvsyK9kcpOJTdtyk3W1qt/HPnGaxWTWZxLT947SAnCmu4c3HcxRT/ogR6ubH2znkk9XKy8fN05eMHlnHvZZ0HAvT3dMPDzcw443rZAbnoO2Qk/MWgiQtyTHfPD1LyOHC2gvdT8npfeZj8ZXOGfTKftD42XVitmhMFNUyO8MXH3ZVgbwsFfZxI5WB2JdUNLfzs6kl8dP8SogM9CPa28LWZ4y56H4ZSfLAXXpbuGx6UUkyP8uNYft9PnKJ/JPzFoIkP9iKvsp6G5oubuL6xpZWH1x0lp5chAL7MLOVorq0t/aUdp/nVh8cxmxRny+o4OwKGmMgqqWXtl6e5KTmK62aO43h+NTnlddQ1tfCjt1K6bRrLq6ynvrnV3kwT4OlKhdH80ZstJ4txMSnbNSZ3V9bdewnr7l18UQMBjhRTInw5VXqOuqahnQvbWUmbvxg0iaHeaA2nSs4xZZxv7z9wgT2nyvn3nmyiAjz5/mWJXa7T0mrlthf3ABDp70FeZT1Lk4K5Y1Ec97yyn+0Zpdwe1L+upiU1jWxPL8HH3YVlE0IGFJhaa3714XHcXcz89CuTeO9gLusP57P0iS3Mjwtk75ly/DxceeRrUzv9bNvF8rYx9P093aio69sF351ZZcyJCcDX3dbEOBYm1pkyzhet4WRhDbONkWTF4BmUmr9S6mql1EmlVKZS6qEuXrcopd40Xt+jlIobjO2KkWVKhK3G2tMcxN05ll9lHyo4o6iGnPI6mlps3fxaWq28uvssBVX19slHkkK9SQjx4hcrJ/HKt+dzxeRQYgI9+TAlv9/b/t3GNH789mHWvHqAm57b1eG1qvpm3tqXw8dHC/r0Xp8eK2R7egkPXjmBEB9Lh5Pg3jPlAHx8tLDLuQ+yjPmT2wbKC/B0pbIPNX+rVZNeWMPUyP6fcEeyKcY81McL+v/3JHo34PBXSpmBZ4BrgCnALUqpKRes9h2gQms9HngK+MNAtytGnrggL9xcTP3unpeaV8W1T+/gBWN0yB2ZpSx9YguX/2krxdUNPPhmCv/zfip//DTdPp/s/3x1Cq9+ZwFrliWilEIpxZ2L49h7ppwPUvL61VSQklPJZRND+N6liRzJraKsttH+2t+3ZvGzd4/w/X8f7HIynvZqGpp5ZP0xJkf4csciW4+XhQlB/PyaSTxxg+1mrZnR/hRWNzD/t593OqGcKq3Fz8OVIKPve0Afa/7Z5XXUN7cyOXxshX9UgAdebmYeXpfKW/tyHF2cMWcwav7zgUyt9SmtdRPwBrDqgnVWAS8bj98BLle9DSAuRh0Xs4mJYT6k9bOm9pkxxnurVeNqVhTX2MI3p7yen793lA1HbCF5MLuCvEpb+EcGdL5v4pb5MQR7u/HAGyn897tH+7TtqvpmTpWeY15cIEvG2+YyONnu5PVlZikuJtuf6rH8nvfrlV1nKapu5LfXT7PPb+tqNvHdSxO5MTmKf9w1j5fvmselE0KwuJh5+P1Uys81YbVqHnr3CP/ek01CiJd9yAx/Tzcq65p6vU+g7ZPWpIixNaWiUoqvz47E083MHz45ITd8DbLBCP9IoP1pOddY1uU6WusWoAqQ6XzGoEnhPqQV9K/mv+VEMZPCfbhyShjfWZIA2C4eL0oIYvOJYgDuWRrP6dJz7DOaTroarsDDzczb31vM0qRgdmWV9enmqlSjG+b0SD97eLb1zqmqayY1v8reXfJYXhUtrdYuLyo3tVh5eecZliYFd9k+rZRi+cRQ/D3dePnb83lxdTLl55pYdyiPU6W1vLHPNgKmn8f5bsEBnq40t2rONfV8Af1EYQ1KQVI3k7CPZo9fP50nb5xJ2bkmdp8qd3RxxpTBCP+uavAX/tf1ZR2UUmuUUvuVUvtLSkoGoWhiuE2O8LUNzZtewpOfney1509uRR1H86r42sxxvHBHMtcaQxGsmBTKikmhAEwI8+aGuVEAvLkvh2Bvt24vysYHe3HV1HBKaxvJrei9m+QRYxTJGVF+BHtbCPa28NSmdH72zmF2ny5Da7hqajjxwV4czavi9b3ZrHhyG+lFHU9wW04WU1zTyLeXxPe6TbD9noK83EgvrCGl3WQnV045P8dDgDHTWsW5ntv9TxTUEB/kNWZvJFw+KRQvNzMfHe3/9RzRvcEI/1yg/RRTUcCFR8m+jlLKBfADOp3GtdbPa62TtdbJISF9u6tRjCxfnRmBu6uJO/+xl798kcnre7N7XP/xj9KwuJhYNcvWH31ShA93LIrl9oWxLDfC/7KJoUwM8yHS3wOr7rrW396cGNt4Sr2NjWO1ajYcyWd8qLd9Sksvi5naxhbe2p/LxqMFWFxMzIr2Z+o4X1LzqvniRDGtVm2fvepUSS0/ffswOzJKcTObWJzY9w+0SWHepBfXcDinEm+LCyd+fTW3LTh/d6y/cXNgb909M0tqu52AfSxwdzWzKDGIfTLQ26AajPDfByQppeKVUm7AzcD6C9ZZD6w2Hn8T+EL3Z8ATMWqE+rhz5+J4tLZN/ffstqwua/9aax778DgfpxZy/+VJRAXYRnl0NZt4bNU04oK9GB/qzTO3zuG7yxJQSnFjclSfyjAxzAdPN3OPQwK3tFpZ++VpjuVX84Pl57uVfm3G+ZuiNh4tYHqkH27GCSCvsp4vs8pwM5t4/1A+ZbWN/Oitw7x9IJfX92YzNdK3X2PlTwjzIbOolkM5FcyI8uv0aaZt0LOKumaO51d3uBDdptWqyS6rG/MjqU6O8OVUSe1F30MiOhtw+Btt+PcBnwJpwFta62NKqceUUtcZq70EBCmlMoEfAZ26g4qx48dXTWDdvYv5440zKapuZFMXk3bvPV3O2i9Pc9uCGL67LKHb97p2RoR9esAbk20fMNuPBNkVF7OJmVH+HOxhaITff3yC33yUxtzYAFbNPH+J6sErktj18xUoBc2t2j4q641zo/H3dKWpxcrqxbE0tVp59MPjpORU4mY20WLVzOlnX/SkUG9qGltIzatmprGd9to+jfzsncOsfPo/PLwutdM6BVX1NLVaiRvj4T8p3BerhkyjO6wYuEHp56+13qi1nqC1TtRaP24s+6XWer3xuEFrfaPWerzWer7WuvsZn8Wo52o2MTsmgCXjg4nwc+ftA7kUV3fsJtnWd/uBy5M6jf7YnUh/D167ewFPfHNmr+vOjvEnraCa+qZWrFbNo+uP8T/vp2K1alqtmp1ZZSyID+TNNQsxmc5fknIxm4jw87DXpNsu3vp5uvKLayYT4mPhu5cm4mY28eHhfEJ8LPYJUmbHdA7wnrQfA+cbXYy8GWA0+xRV22r8m9KKOtV8zxpj5Mca4+OPVfaL8dLnf9DI8A5iyJhNiutnR7I9vYT5v93MrzccJ7+yng1H8jmeX02ApyshPv27E3Xx+OA+/cycmABarJojuZX8+fN0/rnzDK/uPsu0Rz9l9dq9ZJXUMi3Sr9sTT9vY8rPaBfpN86LZ+4vLCfa22EekXD4xhNsWxDA/PpBLEoP7tS9tQxpfOyOiy8HQ2vf8+eONM42TVmmHddrGUorr513No01ckBcWF1OHbrhiYGR4BzGk7roknrqmVmoaWnhpx2le2nEasJ0Y5hmTvw+Ftlr4D18/RHFNIzfOjeJcUwsbjxayI9MWoEk9XCS9KTkaL4ttdMn22so7Lz6Q/WcrWDEplKQwH9767qJ+l9Hf040d/72ccd3M9dB2YgrycuNrMyN4dP0xHl1/HB93V+bFBQJwtuwcFhfTmJ8syGxSTAr3YXtGCS9sP4WXxWVIpqR0JhL+YkiF+Fh41JjH9Zb50WzPKOXt/TkUVDV0mMJvsAV5W4jwc6dLLxZuAAAQeklEQVSgqoGlScE8fv103FxMbD1ZzJ3/2AfQYw+ZJUnBLEnqviZ/w5xIssvq+jzWfnfaLnR3Z/OPLyXEx4LFxcwzt83hp28f5k+fpfP6moUAnC6tIzbIs0PT1Vh134ok1ry6n8c3pgG2CW+cYb+HijT7iGGTHBfIj66cYJ9ZamL40N6U9Pztybz87fm8fNd83Fxsf+pzYgNo+7AxkO6R40N9eOa2OXi6DW39KTHE2z5Y26UTQlg5PcI+vaHWmuP5VWPy5q6uXDkljKdummVvcssqGTkXf1tarfzk7cMduhc/8kEqV/5pG+sPj8z7EyT8xbC7eV4MUyJ8+91G3l/To/y4dEJIh9qhr7srk8N9Cfa22HvTjCbtpzfMLq8jv6qBhQmBji7WsPn67Eievnk2AE9+ls6GI4MfrHVNLVT1cTTVNl9mlfHOgVxe3XUWgKLqBl7edZaM4lrW93GeiYpzTX0ayG+wSLOPGHYxQZ5sfGCpw7Z//+XjKakdvn+ywTQ31tb76MDZCizGp5lF/bixbCxo69n0ybFCPjlWyFdnDN6ENZ8dK+S+1w7RbLWy7t5L7F19e/P+IVvAb0svwWrV9ntMEkO8SMmxzT3R0/WtTceLuOeV/Vw2MYR/3jV/4DvSB1LzF07n6mkRPc4zO5KN83MnzNfCoewKdmaVEeJjsY//7yyUUtw87/ygAucaO4/g+klqAT9+63Cf33P/mXKmP/IpD7yRQmKoNx6uZp745ATfem4X+ZU9DxOSmlfFJ6mFhPlaKD/XxJObTvLhkXzcXEzcMj+G0tpG8qt6HhH2t8Z1jN2nyvpc5oGS8BdiFFFKMXWcHycKaziYXcH8+MAh6zE1kv3uG9P566225p8Lpw7VWvOnTem8ezCXouqeQ7fNhiMF1DS2EO7nzl9umc210yPYmVXGntPlbD15fpyx6oaOzUHF1Q3c8vxuAjxdee72ZFzNime2ZLHxaCEJwV72Xlk7Mkp48I1DfNhF+3/FuSb7PlitdDnXw1CQ8BdilJkQ5kNmcS25FfX2+xGcjVLKfqH7wgu/h3OrSC+yLTvUy/hObXZklrJsQghbfnIZ40O9Wb04jjBf2/0kJ40hszOLa5jz2CY2p52/Y/2vWzKpb27lX3cvYFa0Pxt+uJRnbp0DwOLEYCZF+ODmYuKh947yfko+z2zJ7LTtlBzbnejXTAunqdVK+TC1+0v4CzHKTAz3psWoHV7MdJljRWyQJ0rZpg1t75WdZ/BwNeNmNvU4xEebgqp6MotrWTr+fAeEaZF+7P755cyNDbAPUf5BSj4tVs1HxiQ8OeV1vL43m5vmRZNgNL1NDPfh2hkRbP7xpfzkKxOwuJj5261zuGdpAkmh3uRV1Heq2R/KrsCk4KqpthFdCyr79mlloCT8hRhlJoadD/ypThz+7q5mogI87HMfAxzPr+b9lDxuXxTL1EjfPtX8dxnThy4e3/HCuVKKKRG+HC+oxmrVfGRMKrTtpO2i7l++yEApxQ9XjO/0nokh3vZuwFdMCeMXKydzz9IEahpbOGPMB7Ezs5SdmaVsSy9hUriv/dpNQVXvQ5EPBuntI8QokxDihdmkCPZ2GxMTtQ9EQrA3W08W88L2U1i15ncfn8Db4mIfLPAfX54mvaiG6ADPbuc72H+2Ah93ly6nwZwyzpdXd5/lwyP5nCo9x4L4QPacLmdTWhHvHsxj9aI4Irq5Q/tC0437E47kVpEQ4s2tL+6xv/b7b0wn3LibvKCXi8ODRWr+Qowy7q5mJob59Lkb4lj2vUsTSQzx5vGNaTy9OYNFCUGsv+8Sgrwt3L0kHm+LC1c9tZ3Fv9/c7bzOB85UMCcmoMu7hdtGan3o3aMEeLry5E0zMSnbqLCtVs0NczsPyNedJKMX0ZHcKqrqz184/sXKSdw8P4ZgLwuuZiXhL4To3kt3JvPb66c7uhgOtygxiLV3zsPD1cy5plYeuCLJ3v4e6uvOX26ZQ6iPhYq6Zv6TcX5QvFaj3b2qvpn04hr7/RMXmhjuw/cvS6S+uZU1yxKJCvBkTkwAp0vPEejl1uWnhe64mE3EBnmSXX6ODGMmuLV3JrNmmW0+CZNJEebrLs0+Qoju9bWpwRkEerlx72WJ7DtbwYL4jnc7L0kK5suHVjD315vYdLyIr0wN56Udp3lqUzqXTgixX7xN7ib8AX5y1UQuSQy230m9fFIo+89WsCghqN9jC4X7uVNY3WDvjXTh0Bzj/D16va9gsEj4CyFGvR9entTta65mE8snhbI5rYhNx4v43cY0PNzMfHS0gJXTw/F1d2VuXPfhbzapDoP8XTkljP/36UmWTej/8CQRfu6k5lWRXlSDl5u505Sk8UFefJ7WefKjoSDhL4QY81YvjuPjo4Xc88p+4oO9eO/7i6ltbCE6sP+T4EwI8+HjB5YyoYs5GHoT5utOaW0TqXlVjA/z6fTJISnMmzf351B+rqnXGesGStr8hRBj3pyYAJ65bQ5fmRrGm99dSICX20UFf5vJEb6YL2I46QijR8+hnEomdXHySDRGmh2O6Sql5i+EcApXTgnjyilhDi1DmDHpTqtVMy2q893Z40POh//8+KEdrVVq/kIIMUzaX6if1sUNepH+Hni4moel5i/hL4QQw6Rtuk2zSTE5onP4m0yKxFAvModhohpp9hFCiGHi6+GCh6uZ2CBP3F27vuP4islh1De3DnlZJPyFEGKYKKWYHuXX493ZD14xYVjKIuEvhBDD6M01Cx1dBEDCXwghhtVImXxHLvgKIYQTkvAXQggnJOEvhBBOSMJfCCGckIS/EEI4IQl/IYRwQhL+QgjhhCT8hRDCCUn4CyGEE5LwF0IIJyThL4QQTkjCXwghnNCAwl8pFaiU2qSUyjC+B3SzXqtSKsX4Wj+QbQohhBi4gdb8HwI2a62TgM3G867Ua61nGV/XDXCbQgghBmig4b8KeNl4/DLw9QG+nxBCiGEw0PAP01oXABjfQ7tZz10ptV8ptVsp1e0JQim1xlhvf0lJyQCLJoQQoju9TuailPocCO/ipYf7sZ0YrXW+UioB+EIpdVRrnXXhSlrr54HnAZKTk3U/3l8IIUQ/9Br+WusruntNKVWklIrQWhcopSKA4m7eI9/4fkoptRWYDXQKfyGEEMNjoM0+64HVxuPVwAcXrqCUClBKWYzHwcAlwPEBblcIIcQADDT8fw9cqZTKAK40nqOUSlZKvWisMxnYr5Q6DGwBfq+1lvAXQggHGtAE7lrrMuDyLpbvB+42Hu8Epg9kO0IIIQaX3OErhBBOSMJfCCGckIS/EEI4IQl/IYRwQhL+QgjhhCT8hRDCCUn4CyGEE5LwF0IIJyThL4QQTkjCXwghnJCEvxBCOCEJfyGEcEIS/kII4YQk/IUQwglJ+AshhBOS8BdCCCck4S+EEE5Iwl8IIZyQhL8QQjghCX8hhHBCEv5CCOGEJPyFEMIJSfgLIYQTkvAXQggnJOEvhBBOSMJfCCGckIS/EEI4IQl/IYRwQhL+QgjhhCT8hRDCCUn4CyGEE5LwF0IIJyThL4QQTkjCXwghnJCEvxBCOCEJfyGEcEIDCn+l1I1KqWNKKatSKrmH9a5WSp1USmUqpR4ayDaFEEIM3EBr/qnAN4Dt3a2glDIDzwDXAFOAW5RSUwa4XSGEEAPgMpAf1lqnASilelptPpCptT5lrPsGsAo4PpBtCyGEuHjD0eYfCeS0e55rLBNCCOEgvdb8lVKfA+FdvPSw1vqDPmyjq48FupttrQHWAMTExPThrYUQQlyMXsNfa33FALeRC0S3ex4F5HezreeB5wGSk5O7PEEIIYQYuOFo9tkHJCml4pVSbsDNwPph2K4QQohuDLSr5/VKqVxgEfCRUupTY/k4pdRGAK11C3Af8CmQBryltT42sGILIYQYiIH29lkHrOtieT6wst3zjcDGgWxLCCHE4JE7fIUQwglJ+AshhBOS8BdCCCck4S+EEE5Iwl8IIZyQhL8QQjghCX8hhHBCEv5CCOGEJPyFEMIJSfgLIYQTkvAXQggnJOEvhBBOSMJfCCGckNJ6ZM6ZopQqAc4O4C2CgdJBKo6jjZV9GSv7AbIvI5XsC8RqrUN6W2nEhv9AKaX2a62THV2OwTBW9mWs7AfIvoxUsi99J80+QgjhhCT8hRDCCY3l8H/e0QUYRGNlX8bKfoDsy0gl+9JHY7bNXwghRPfGcs1fCCFEN8Zc+CulrlZKnVRKZSqlHnJ0efpLKXVGKXVUKZWilNpvLAtUSm1SSmUY3wMcXc6uKKXWKqWKlVKp7ZZ1WXZl87RxnI4opeY4ruSddbMvjyql8oxjk6KUWtnutZ8b+3JSKfUVx5S6a0qpaKXUFqVUmlLqmFLqAWP5qDo2PezHqDsuSil3pdRepdRhY19+ZSyPV0rtMY7Jm0opN2O5xXieabweN+BCaK3HzBdgBrKABMANOAxMcXS5+rkPZ4DgC5Y9ATxkPH4I+IOjy9lN2ZcBc4DU3soOrAQ+BhSwENjj6PL3YV8eBX7SxbpTjL81CxBv/A2aHb0P7coXAcwxHvsA6UaZR9Wx6WE/Rt1xMX633sZjV2CP8bt+C7jZWP4s8H3j8b3As8bjm4E3B1qGsVbznw9kaq1Paa2bgDeAVQ4u02BYBbxsPH4Z+LoDy9ItrfV2oPyCxd2VfRXwirbZDfgrpSKGp6S962ZfurMKeENr3ai1Pg1kYvtbHBG01gVa64PG4xogDYhklB2bHvajOyP2uBi/21rjqavxpYEVwDvG8guPSduxege4XCmlBlKGsRb+kUBOu+e59PzHMRJp4DOl1AGl1BpjWZjWugBs/wBAqMNK13/dlX20Hqv7jKaQte2a30bNvhjNBbOx1TRH7bG5YD9gFB4XpZRZKZUCFAObsH0yqdRatxirtC+vfV+M16uAoIFsf6yFf1dnwtHWnekSrfUc4BrgB0qpZY4u0BAZjcfq70AiMAsoAJ40lo+KfVFKeQPvAg9qrat7WrWLZSNmf7rYj1F5XLTWrVrrWUAUtk8kk7tazfg+6Psy1sI/F4hu9zwKyHdQWS6K1jrf+F4MrMP2R1HU9rHb+F7suBL2W3dlH3XHSmtdZPzDWoEXON+EMOL3RSnlii0w/621fs9YPOqOTVf7MZqPC4DWuhLYiq3N318p5WK81L689n0xXvej782SXRpr4b8PSDKumLthuzCy3sFl6jOllJdSyqftMXAVkIptH1Ybq60GPnBMCS9Kd2VfD9xh9CxZCFS1NUGMVBe0e1+P7diAbV9uNnpkxANJwN7hLl93jLbhl4A0rfWf2r00qo5Nd/sxGo+LUipEKeVvPPYArsB2DWML8E1jtQuPSdux+ibwhTau/l40R1/1HuwvbD0V0rG1nz3s6PL0s+wJ2HonHAaOtZUfW9veZiDD+B7o6LJ2U/7XsX3sbsZWU/lOd2XH9jH2GeM4HQWSHV3+PuzLq0ZZjxj/jBHt1n/Y2JeTwDWOLv8F+7IEWxPBESDF+Fo52o5ND/sx6o4LMAM4ZJQ5FfilsTwB2wkqE3gbsBjL3Y3nmcbrCQMtg9zhK4QQTmisNfsIIYToAwl/IYRwQhL+QgjhhCT8hRDCCUn4CyGEE5LwF0IIJyThL4QQTkjCXwghnND/B3EXgZI1tnx7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.ylim((-1.2, 1.2))\n",
    "plt.plot(xvals, yvals);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "235.47988794111393"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n*np.log(n)+np.euler_gamma*n"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}