Star res 1 (r=3).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4249fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import random\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51b31031",
   "metadata": {},
   "outputs": [],
   "source": [
    "def nNumber(G,r,step):\n",
    "    n=len(G)\n",
    "    z=n\n",
    "    t=0\n",
    "    M_array=np.zeros(step)\n",
    "    M={random.randint(0,n-1)}\n",
    "    D={j for j in range(0,n)}\n",
    "    R=D-M;u=0\n",
    "    while u<step:\n",
    "        i=len(M)     \n",
    "        k=random.random()\n",
    "        pi_neg=(n-i)/((n-i)+(i*r))\n",
    "        pi_pos=(i*r)/((n-i)+(i*r))\n",
    "        pii=1-(pi_neg+pi_pos)\n",
    "        if k<(1-pii) and i!=0  :\n",
    "\n",
    "        \n",
    "            if k<pi_pos:  ###probabililty for a mutant to reproduce\n",
    "                ch=random.sample(M,1)\n",
    "                NE=[n for n in G.neighbors(ch[0])]\n",
    "                chh=random.sample(NE,1)\n",
    "                if chh[0]  in R:\n",
    "                    M.add(chh[0])\n",
    "                    R.remove(chh[0])\n",
    "                    t+=1\n",
    "\n",
    "            else:   ###of course when p <(1-pii) and >pi_pos so this is obvious this is for a mutant to die lonely\n",
    "                chre=random.sample(R,1)\n",
    "                NEre=[n for n in G.neighbors(chre[0])]\n",
    "                chhre=random.sample(NEre,1)\n",
    "                if chhre[0]  in M:\n",
    "                    R.add(chhre[0])\n",
    "                    M.remove(chhre[0])\n",
    "                    t+=1\n",
    "        M_array[u]=len(M)/n\n",
    "        u=u+1\n",
    "            \n",
    "    return(t,M_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a627c1c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def addedge(G):  ###for connecting degrees=1\n",
    "    a=nx.adjacency_matrix(G).toarray()\n",
    "    degree=np.sum(a,axis=0)\n",
    "    v=np.nonzero(degree==1)[0] ##identifying deg=1 vertices\n",
    "   \n",
    "    def chk(choice):\n",
    "        if choice[0]!=choice[1]:\n",
    "            return True\n",
    "        else:\n",
    "            return False\n",
    "        \n",
    "    \n",
    "    choice=np.random.choice(v,2)\n",
    "    while chk(choice)==False :\n",
    "        choice=np.random.choice(v,2)\n",
    "    \n",
    "    a[choice[0]][choice[1]]=1;a[choice[1]][choice[0]]\n",
    "\n",
    "    G_new=nx.from_numpy_matrix(a,create_using=nx.Graph())\n",
    "    \n",
    "    return G_new\n",
    "\n",
    "\n",
    "\n",
    "def check_stability(mm):\n",
    "    c=False\n",
    "    if mm[-1]==1 or mm[-1]==0:\n",
    "        c=True\n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a433d6ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3378/3078464903.py:28: DeprecationWarning: Sampling from a set deprecated\n",
      "since Python 3.9 and will be removed in a subsequent version.\n",
      "  chre=random.sample(R,1)\n",
      "/tmp/ipykernel_3378/3078464903.py:19: DeprecationWarning: Sampling from a set deprecated\n",
      "since Python 3.9 and will be removed in a subsequent version.\n",
      "  ch=random.sample(M,1)\n"
     ]
    }
   ],
   "source": [
    "t1=time.time()\n",
    "num=100\n",
    "step=1000000\n",
    "size=200\n",
    "r=3\n",
    "start=0;end=250\n",
    "no_graph=np.arange(start,end)\n",
    "fix_time_total=[]\n",
    "ext_time_total=[]\n",
    "#G_tot=[];\n",
    "#M=[]\n",
    "for j in (no_graph):\n",
    "    mm=[]\n",
    "    #mm=np.zeros(num*step)\n",
    "    fix_time=[];ext_time=[]\n",
    "\n",
    "    A=np.loadtxt('stargraph_r3_'+str(j)+'.txt')    \n",
    "    G=nx.from_numpy_array(A,create_using=nx.Graph())  \n",
    "\n",
    "    for h in range (num):\n",
    "        #print('h=',h)\n",
    "\n",
    "        t,m=nNumber(G,r,step)\n",
    "        \n",
    "        if check_stability(m)==False:\n",
    "            print('num=', h)\n",
    "            print('no=', j)\n",
    "        \n",
    "        #mm[0:step]=m\n",
    "        mm.append(m)\n",
    "        if m[-1]==1:\n",
    "            for tt in range (len(m)):\n",
    "                if m[tt]==m[-1]:\n",
    "                    fix_time.append(tt)\n",
    "                    break\n",
    "            \n",
    "        elif m[-1]==0:\n",
    "            for tt in range (len(m)):\n",
    "                if m[tt]==m[-1]:\n",
    "                    ext_time.append(tt)\n",
    "                    break\n",
    "    M_avg=mm[0]                      \n",
    "    for muts in mm[1:]:\n",
    "        M_avg=muts+M_avg\n",
    "    M_avg=M_avg/num\n",
    "    np.savetxt('M_avgstar'+str(j)+'.dat',M_avg)\n",
    "    np.savetxt('fixtimestar_r3_'+str(j)+'.dat',np.array(fix_time))\n",
    "    np.savetxt('exttimestar_r3_'+str(j)+'.dat',np.array(ext_time))\n",
    "    #M.append(mm)\n",
    "    \n",
    "#np.savetxt('fixation'+str(start)+'-'+str(end)+'.dat',fix_time_total)\n",
    "#np.savetxt('extinction'+str(start)+'-'+str(end)+'.dat',ext_time_total)\n",
    "t2=time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7b7b02f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "star_fix=[]\n",
    "for i in range (1000):\n",
    "    star_fix.append(np.loadtxt('fixtimestar_r3_'+str(i)+'.dat'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1dc39f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fs_mean=[]\n",
    "for f in star_fix:\n",
    "    fs_mean.append(np.sum(f)/len(f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a6b2096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(star_fix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aa25259f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'fixation time_r3')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mbERZzhn10VFd0g4+dJDjb3ZMoGlBxhjTE+n2ybSKSCHuu66ITMer2ZgcMvvh2bS+28rKGR19M1s+tSXuGFtzxhjTE+nWZG4CngFOEpHfAs8B385YqUxW+Av8FE4vpOjUom6PsT4ZY0xPpDutzDIRWQsswmsm+4qqHkqRzQxSZX9XRuOWxDMzh+vC6GjtOk2NMcYk0JP7ZCYAfiAPWCwiNqIsR7VVt3W7b9Upq3h9yeuEG+1GTWNMaukOYb4HuAf4BPAR9/hw0kxm0Eo0ACDW0eeO8tYXvIVR992xj6adTf1RLGPMIJRux/8iVZ2d0ZKYAWP05aPZEtySdCRZw/oGQvUh3vrCWxRML2DR9kX9WEJjzGCRbnPZayJiQWYIGfnBkUn3S74QafFGmrUd6r55zRgztKUbZO7DCzRbRWSDiLwhIhsyWTCTXbMfSv6dwpfvI9LkBZno/GfGGNNZus1l9wCfAd4g4VJXJtcEhif/aPgKfEQaXZCxkWbGmG6kW5PZrapPqOoOVd0VfaTKJCL3iEi1iGyMSfsXEXlXRNa7x8Ux+24Uke2uxnRhTPoCV3vaLiK3iZuhUUTyReRhl75SRKbE5LlKRLa5x1VpXqdJk6/AR7jJjTDrfvJmY8wQl26QeVNEficinxKRj0cfaeS7F7goQfqtqjrPPZ4CcH0+S4E5Ls+vRCT67+t24DpgpntEz3kNcERVZwC34lboFJEyvBtIzwQWAjeJSPJOBtMjvsKOmkzbwTbqXqnLcomMMQNRukGmEG8amSX0YAizqr4E1Kb5GpcAD6lqi6ruALYDC0VkHFCiqq+pqgL3Ax+LyXOf234UuMDVci4ElqlqraoeAZaRONiZXgqUBNj0yU3tz9eduy6LpTHGDFTp3vH/2T5+3RtE5EqgCviGCwQTgNj1fve6tDa33Tkd93OPK2NIROqAUbHpCfLEEZHr8GpJTJo06cSuagg5cO+BLrM2N25vpGhG91PSGGOGnqQ1GRH5tvv5H64vJO7Ry9e8HZgOzAP2Az+LvlyCYzVJem/zxCeq3qGqlapaWVFRkaTYJk6Cd3PVzFUsl+W0Hmrt//IYYwakVM1l0Sl4q4A1CR49pqoHVTWsqhHgTrw+E/BqGyfFHDoR2OfSJyZIj8sjIgFgBF7zXHfnMj0w+/ezec+K9/Q4X31VfQZKY4wZjJIGGVV90m02qup9sQ8g8QyKKbg+lqhLgejIsyeApW7E2FS8Dv5VqrofqBeRRa6/5Urg8Zg80ZFjlwHPu36bZ4ElIjLSdfgvcWmmB0ZfNpqSM0uY+6e5zH44/Xtxbc0ZY0xUuh3/N6aZFkdEHgReA04Wkb0icg3wk5ibOd8PfA1AVTcBjwCb8ZYVuF5Vo7MwfhG4C28wwNvA0y79bmCUiGwHvg58x52rFrgZWO0eP3RpphfKLyln9CdHM+7acUh+6ntiNn50IzWP1fRDyYwxA514X/y72Snyd8DFwCeBh2N2lQCzVXVhwoyDVGVlpVZVVWW7GAPepss3gR9qHvYCSfknyjn0WPzKD2UXlXH606dno3jGmH4mImtUtTLRvlSjy/bh9cd8lPg+mHpcDcQMPXN+PweA6suqKT6tmMJZhbzoezHuGA1bk5kxJkWQUdXXgddF5HeqarMgmjijLxvd7T4LMsYYSLNPxgKMSaX0/aVxzy3IGGOgZytjGtOtM/7nDE77y2n4R7iZgDotnNmwoYENH95A+LitqGnMUGJBxvQJ8QmjLh7VPiOzhpTQsRDLZTnVv69m0yc2UfuXWo6tOJblkhpj+lNa08qIyCzgW8Dk2Dyq+oEMlcsMVu5rS6QtQsueFgB2/NOO9hpO45uNjLxgJLXLagk3hKm41GZZMCaXpbuezO+BX+PdoW/tHaZb4z4/jj237PGmnXEBp2lrU/v+UF0IgA1LvDXvzj16Lm2H2iicXtjfRTXG9IN0g0xIVW/PaElMTpj2b9OoX11P2+E2Is1d17cLN8Z/R1lz5hqatjZxvp7fTyU0xvSndPtknhSRL4nIOBEpiz4yWjIzKIlPyBubR7g+nDDIVP+2mrpXO9aeia3lGGNyT7o1mej8YN+KSVNgWt8Wx+SCQEmAUF2IcEPXltXmnc2sO6fr2jOtNa0cuOcA464dR7AsCECoPkTb4TYKp1hTmjGDVbrryUzNdEFM7giUBQgdDrX3u6Rj9y272fuzvQRKA4z/wngAXv/A69RX1bc3pYWbvdpRsDSYiWIbYzIgreYyEQmKyD+KyKPucYOI2F+6Sah4XnGP87RVe/f7Rlo7mtiiSwYc33QcgPXnreeVka/0QQmNMf0l3T6Z24EFwK/cY4FLM6aLgqkFcc8rLk89TDnS5AUXDXWdKWD13NWArVNjzGCUbp/Me1X1jJjnz4vI65kokBn8AiXxH6vpP51Oze+TT/0fbvL6bxINFgCofqS6bwpnjOlX6dZkwiIyPfpERKZh98uYbgRGxAeZwMgAi1sXExzdfQtr7V+85X4iTRH23bmP6ofjg8rmKzb3fUGNMRmXbk3mW8ALIvIOIHh3/n82Y6Uyg5q/xN++fc7hcwgM9z5meaPzaKtuIzg62N4H01moLsSum3f1SzmNMZmX7uiy50RkJnAyXpB5U1VbMloyM2j5CjoqyNHhyABz/jiH6geradrexMH7D5I/OZ+WXfEfo9b9rSnPrxFtnyPNGDOwJW0uE5EPuJ8fBz4EzACmAx9yacZ0IeIFgPFfGh+XXjSjiCn/NIXhC4YDcOr9pzLpu5Pijmk90BFkiucnHqWmbbaMgDGDRaqazPuA54GPJNinwB/6vEQmJySbJmbClycwYvEIhs8bTuniUqb+69T2lTXr/tYxG8CoD4+iZW8LbTXxTWuR1gi+fJtA3JjBINXKmDe5zR+q6o7YfSJiN2iaXhERhs8bHvf8zLfPZNWpq9DWjlpK3ri8uPtmoqwmY8zgke7XwccSpD3alwUxQ1vhtEL8xf64tLxxeQkDSsveFg49fqi/imaMOQFJazIicgowBxjRqQ+mBChInMuY3okuAxCVNzZxkHl9yeu0HWxj0c5FFEy2j6ExA1mqmszJwIeBUrx+mejjPcC1GS2ZGXo63XkVG2SCFR2j1NoOen00K6asoHZZbb8VzxjTc6n6ZB4HHheRs1T1tX4qkxmiSi8o5ehzR9uf543Na98umFzQZQAAQN0rdZR90FadMGagSvdmzHUicj1e01l7+4Sqfi4jpTJD0tw/zaV1XyurTl4FgL/Az5zH5nB883GObzyeeO6yMDRsaKBodhG+gI04M2agSfev8gFgLHAh8CIwEbDZCk2fChQHKJpVFJdW8fEKpnx/CoUzEq8p07itkaozqnjr2rfa5z+LFW4M0/R2E/Xr62k7mniWAWNM5qRbk5mhqpeLyCWqep+I/A54NpMFM0PX/FfnE66PDxiTvjWJI88e6VKbifbPHLj3AA0bGqhcUxm3f9MnNlH7jNdvM3zhcBasXJDBkhtjOku3JhP9CnhUROYCI4ApqTKJyD0iUi0iG2PSykRkmYhscz9Hxuy7UUS2i8hWEbkwJn2BiLzh9t0m7pZyEckXkYdd+koRmRKT5yr3GttEJLqypxkERpw1grIl8f0sgREB5r88v8uxrTUdMwQ0rG3osj8aYADqV1nl25j+lm6QucMFg+8DTwCbgZ+kke9e4KJOad8BnlPVmcBz7jkiMhtYitfvcxHwKxGJ3jhxO3AdMNM9oue8BjiiqjOAW4Fb3LnKgJuAM4GFwE2xwcwMTr58HxO+MiEuLVqTSVfz7ua456p2Y6cxmZRWkFHVu1T1iKq+pKrTVHW0qv46jXwvAZ3HmF4C3Oe27wM+FpP+kKq2uNkFtgMLRWQcUKKqr6n3H+H+Tnmi53oUuMDVci4ElqlqraoeAZbRNdiZQWjEWSPinrcdig8yR18+yvHNx1kuy2l4owHi7+9kxeQV1L3iTV3zzo3v8KLvRTRigcaYTEl3+eUHRGREzPPJIvJcL19zjKruB3A/R7v0CcCemOP2urQJbrtzelweVQ0BdcCoJOfqQkSuE5EqEamqqUm+sJbJPl9h8o/s+vPW8+bVbwJQ/XB1wtma1527DoA9P/c+IuEGWxrJmExJt7nsZWCliFwsItfi1Qz+vY/Lkmjudk2S3ts88Ymqd6hqpapWVlSkXibYZFeqIANQv9rre9GQIv7ulwSILkkQOhrq9hhjzIlJdz2Z34jIJuAF4BAwX1UP9PI1D4rIOFXd75rCoksg7gVOijluIrDPpU9MkB6bZ6+IBPAGJNS69PM75Vney/KaAaS7IFN6filHlx+NS9tzy56Ex7afq8BH+Fi4y3Q2xpi+k25z2WeAe4Ar8TrznxKRM3r5mk8A0dFeVwGPx6QvdSPGpuJ18K9yTWr1IrLI9bdc2SlP9FyXAc+7fptngSUiMtJ1+C/BhlznhNgF0WIVL0i89kw657KajDGZk+59Mp8AzlXVauBBEfkjXof7vGSZRORBvBpFuYjsxRvx9WPgERG5BtgNXA6gqptE5BG8kWsh4HpVjTaWfxEvuBUCT7sHwN3AAyKyHa8Gs9Sdq1ZEbgZWu+N+qKo2yVUO8AW9wDDs9GGc9sRprJiywtvRw7770LFQR5CxmowxGZNuc9nHOj1fJSIL08j3qW52XdDN8T8CfpQgvQqYmyC9GRekEuy7B6/2ZXJJdLRYxFsKAGDyP08mfKxnnfdrz1rbHmTCddbxb0ympJrq/9uq+hMR+Q8Sf1f8x8wUy5jEomvOFEwtwJfn432h94EPtn91e4/O07i5sX1m59DREKH6EPVV9ZScWYK/yJ8itzEmXalqMpvdz6pMF8SYdBROKWTOH+ZQ+v5SgI7RY50W0Dzl3lPahzJ3Jzqr87YbtrHthm3t6cmWjjbG9EyqIHMF8GegVFV/0Q/lMSaliku7DjUvnBk/gWbR7KIux6Qr0hZp7/sxxpyYVH9JC0RkMvA5N1KrLPbRHwU0Jh0TbpjA+C+Ob39+IitmdjcQoOZPNRx/83ivz2vMUJQqyPwaeAY4BVjT6WFNaGbAEJ9Qfmk5ACXnlBAsDzLqw6Pa98+6YxaLmxcz645ZKWs5axetpeo9XT/emy7dxOpTVyfIYYzpTtIgo6q3qeqpwD1uzrKpMY9p/VRGY9ISHRRA2As6pz15GgCSL4y/djy+fB/jrx3P5O9NTnqe5rebaVgXP6OzTaRpTO+kO0HmFzNdEGNOlH+YF2TCxzuGJJ998GzOPnB23HHRUWUAxfM7buIc8+kx3Z470hTpdp8xpnvWu2lyRuHMQoIVQab+29T2tLzReQRLg3HH5U/I935OyidvbF57+sl3nUywPP5YgEhrJPHSz8aYlNK949+YAc9f6Oec6nNSHpc33gsseaPzCJR2/An48n1M+u4k3v762wDUvVbH4b8cpmlrEzWP2gzdxvSGBRkz5ARLg8x+eDYjzh3Bvt/si9sXGNnxJ7Hu7HX9XTRjco4FGTMkjf6kt4zR5O9NJm9sHuOv84Y/543JS5YNye9+6QBjTFfWJ2OGNF+ejwlfnNA+c0Dp+0qTHq8tysEHD/ZDyYzJDRZkjInhL/Iz/WfTkx6z5X9voe1wW9JjjDEeCzLGdBIdCp3MK+WvEAnZsGZjUrEgY0wnBdPSm5Im0mxBxphULMgY08mwOcPSOk5bbRYAY1KxIGNMJ/nj81n45kLG/8P4hPsnfXcSAJEWq8kYk4oFGWMSKDq5iFm3z2Lu4/ELsp7ywCkUzvCWFbAgY0xqdp+MMUmUf7Sc8/V89t21j/xx+Yz60CgO/s4bwqwt1lxmTCoWZIxJw/jPdzSd+fK9BgCryRiTmjWXGdND0bv+LcgYk5oFGWN6yGoyxqTPgowxPeTL8/5srE/GmNQsyBjTQ+3NZa1WkzEmFQsyxvTQQG0u23fnPpbLcloPtWa7KMa0syBjTA9Fg0x/N5dtvXYrK6au6Hb/vtu9tXFadrf0V5GMScmGMBvTQ9mqyey/a3/yA6IxL40lbxrfaiR/Uj7+gtSTgRpzIrJWkxGRnSLyhoisF5Eql1YmIstEZJv7OTLm+BtFZLuIbBWRC2PSF7jzbBeR20REXHq+iDzs0leKyJR+v0iTk3ozhDncGObFghepeSy9ZZx33ryT5bIc1eS1paYdTTTtaAJoP1Z8yaNMqCHEqpNXsekTm2jY0JBWeYzprWw3l71fVeepaqV7/h3gOVWdCTznniMis4GlwBzgIuBXIhL9CnY7cB0w0z0ucunXAEdUdQZwK3BLP1yPGQKiNZm2mtRryjS+1chyWU71Q9Voi/LO995J6zV2/mAnAJHG5IFs5bSVrJy20nviDu0uMEVCEVqrW4k0eQfWPlVL1RlVKQOZMSci20Gms0uA+9z2fcDHYtIfUtUWVd0BbAcWisg4oERVX1PvL+X+Tnmi53oUuCBayzHmRESDzI7v7YhLP7byGKvnrSZ8PNyeVvtMLQAHH/CmoomtZRz68yE2Xrqxy/nrVtSBO0WoPuSlvVKXslwa8YJF4+ZGDj15iJo/xNea3vqHt3h1zKuEG8Lx+UIWZEzmZLNPRoG/iogCv1HVO4AxqrofQFX3i8hod+wEILbHc69La3PbndOjefa4c4VEpA4YBRyKLYSIXIdXE2LSpEl9d3UmZ0le4u8qb3/zbY6/fpz6NfWULi7l0OOHqHm0U/NYzNe6jR/xAoyGtX35Z4C3rnurfTtcH6a5tZl1565LWqZQQ4jW/d6osi1/v6U9PVgRpPg9xZzxzBkcvM8LdJ1rYJHmCL7gQPu+aXJFNoPMOaq6zwWSZSLyZpJjE/1Va5L0ZHniE7zgdgdAZWWlfaUzKUVvxgSv9hCtnUT7ata/bz2z7pzFW9d2BAsNex+t1ndbaatti+vPibRGaD3YStW8KsZePZbWAx1DkMP14fbmraja/6ml7H+V8c73O5requZVEaoNdSlrW00bR5494pXB1VgSBRmG9+ANMKYHsvb1RVX3uZ/VwB+BhcBB1wSG+1ntDt8LnBSTfSKwz6VPTJAel0dEAsAIoDYT12KGltgmr9gAEG1GA+ICDHTcuBk66nW6H7jvQMe+lgh7frqHcF2Yd3/xblwQqPl9DYefPBx3rg0f3ED1o9Xs/tHu9rTmt5vTLn/boQRBxpgMyUqQEZFhIjI8ug0sATYCTwBXucOuAh53208AS92Isal4HfyrXNNavYgscv0tV3bKEz3XZcDzaj2cpo8d/O1BDj50kFfGvkLtU91/h4ldRbPtUFvcP3ZtVepeStznsvvHu9nx/R1d0jdfvrnXZX77m2/HPbcgYzIpW81lY4A/un74APA7VX1GRFYDj4jINcBu4HIAVd0kIo8Am4EQcL2qRnsvvwjcCxQCT7sHwN3AAyKyHa8Gs7Q/LswMLW994a3UBwEN6+KHCsd2tkdaI7Tsy+wNlHtv6+i67FyTaX6nmaKZRRl9fTN0iX2571BZWalVVVXZLoYZBJbL8hPKX/yeYhrWeoFn9sOz2XzFZsZfP559/7kvRc7MOF/Pz8rrmtwgImtibkWJY0NKjMmg7kaiRQMMwOYrvKavUR8aRfml5d2ea8ynx/Rt4YzpBxZkjOmF0589vUvaqEtGdUk75/A5aZ+zdHEpgVKvBbt4QTFFc+KbsPwjkk8BM+HLE5LuBxh+Zscwshm/mJF22YzpLQsyxvRCydkljL9+fFzarF/N6nJcoDj9bk9fka+95lNxWQWjLo4PWv7i5EGmcEYhAOO/NL7bY8ZePbZ9e9znx8XlMyYTLMgY0wuB4gCzfhkfVPzFfuY+OZeyD5X16pwigrZ5faTBkUEm//Pk+PMXxgSZBH+5BVMLOGvfWcy8bWa3r1FwUkHH+Yr8VFxWgQRsIgyTORZkjOkj/mF+yj9czul/Ph3fsO7/tBasW8B7t7yXWb/pWvOJjjqTPOlSCwqWB9u3AyO61pCGv3c4+ePyEb8w7SfTEr52/sT8+DKX+Akd63oTpzF9xYKMMSeg8GSvqan80vK4qWEW7VzEme+cmTBPwUkFDDtlGMFRwS77ojWZzrWL8/X8uL/WUR/qaEorWVRCxRUV5I/tCCCTvtUxRdLYazqayIKjvdcs/UAp4M1eYHOXmUyy9WSMOQELtywEvKauWHnleeAGis38z5kc+Z8jjP/CePbetpfASO/PruyiMkZ9ZFTcHf0Tvz6Rw385zMgPeqtcTP/ZdI5vOg5AqM6rcZScU8KsO2fRvLOZ8dePZ8zS5KPOxl45lgN3H+D0ZaeTPy6fs/ad1T7AAL9NkGkyy4KMMScgnYm9J3xpAhO+5I38Kruwo7/GP8zPnMfm8FLeS+1pJZUlnFd3Xvvzk77eMZtS6KgXZEZdPAp/gZ/5f5ufVhlLF5fG3QeTP66jxiMBaZ9XzZhMsOYyY7IoOvvxlB9MSXlsNMi010L6gPjFajImo6wmY0yWpXu3/Zj/PYb9v9lP2UXpjV6b9J1JKQOIBKR97RpjMsGCjDGDROdmr1Sm/d/EI8xiWU3GZJo1lxkzhFmfjMk0CzLGDGHiF9COpZuN6WsWZIwZwqL341htxmSKBRljhjI3U431y5hMsSBjzBDWXpOxIGMyxIKMMUNYdCocay4zmWJBxpghzGoyJtMsyBgzhLVPxGk3ZJoMsSBjzBDW3lxmNRmTIRZkjBnCbAizyTQLMsYMYVaTMZlmQcaYIcxqMibTLMgYM5TZzZgmwyzIGDOEWU3GZJpN9W/MEBbtk9n1g12UXlCK+AR/sZ/8SfloqxKqC9G0rQlfoY/GrY34i/2MfP9Imt5uYsTiERx+4jCF0wsZdsYwwvVh8sbl0bStibaaNgpnFDJszjAkTwjVhfAX+mne3UygNEBgZICWXS3g9wJdoCRA45ZGgmOCFE4pzPK7YvqSBRljhjBfnteYUfNoDTWP1qSVZ88te9I/f4EPBCJNkbTzjLlqDL48H8FRQZrebiJQGiBUFyLSGCFYESQwMkCoNoSGlEhrhEhThKJTivAX+zn85GHyJuSRNybPe23FO/5ICNQrj7/Ejy/Ph+R7ATU4Mkjz7maCo4K07m/FX+KncGYhbYfakIAQafTK7h/mJzDS/csUCJQEwA/NO5spmFJAsCxIpDUCYW9Wa1++DwkKkZYI/mF+wvXezUgte1uQgJA/MR9fkQ9tUfzD/YSOhAiWe+fQsKJtiviEYEWQcGOYQEkAjSiNbzZSMKkA/3A/kZaI9zsUaDvU5l1fsZ9IY4RIawRfgQ9tVQIjAu211egXi1jhxjDhhjB5o/PS/j2lK+eDjIhcBPwCr/X5LlX9cZaLZMyAMXLJSE5fdjqFUwvxFfg4vvk4odqQ988yAkWnFJE3No9QfYjWfa0EK4Ice+0YbYfaCB0J0bKvhWBFkKJZRTTvaMZf4idYFuTgbw8y4rwRaKsieYKv0PuHH64P0/hWI+IX8sfnEzoaomVvC8Xzi9l3+z4ADv/5METwAkMnviKf9w+4JaZ5T6D22Vq01aVV9cc7lyU+IBqv/Xg30Qr48n1EmpME8uix0dMU+bzgV+zHF/TRdqiN8k+UM/fRuX1eZFHN3bZYEfEDbwEfBPYCq4FPqermRMdXVlZqVVUuf0KNGTzCx8P4inyIuH4j1fbtSFuEcH0Y/3C/90824CPcFPZqKH4h3BSGiFdzCR0Ned/ow4oEhbbDbfiL/aDQuq+V0NGQ9+2/LUL4WBh/sat1CPiL/Wib0lrTSqAkQOiYF2wlKARGBAg3hr0aTEuEtuo2JF/aA6uGlEizV5uIHHe1iph1e/zD/YSPhdGQEm4I4y/xE2n2/vGLX7xAW+fVwCQotB5obS+3BLwakq/QR+vBVnx5PvIn5rcH/1BdiEhzhGFzhyF+oWl7E3nj80C9Go+/xI+/yE/oqHdc4fRChlcOp+zC9Jb27kxE1qhqZaJ9uV6TWQhsV9V3AETkIeASIGGQMcYMHP5h/rjn0QAD4Av68JXFj1vyF/oTbgdHBeOPmxizb2T8PtP3cn102QQgtgF5r0trJyLXiUiViFTV1KTXJm2MMSY9uR5kuvZwQVz7oKreoaqVqlpZUVHRT8UyxpihIdeDzF7gpJjnE4F9WSqLMcYMObkeZFYDM0VkqojkAUuBJ7JcJmOMGTJyuuNfVUMicgPwLN4gvntUdVOWi2WMMUNGTgcZAFV9Cngq2+UwxpihKNeby4wxxmSRBRljjDEZk9N3/PeUiNQAu07gFOXAoT4qzmAx1K55qF0v2DUPFSdyzZNVNeE9IBZk+pCIVHU3tUKuGmrXPNSuF+yah4pMXbM1lxljjMkYCzLGGGMyxoJM37oj2wXIgqF2zUPtesGueajIyDVbn4wxxpiMsZqMMcaYjLEgY4wxJmMsyPQBEblIRLaKyHYR+U62y9NXROQkEXlBRLaIyCYR+YpLLxORZSKyzf0cGZPnRvc+bBWRC7NX+t4TEb+IrBORP7vnOX29ACJSKiKPisib7vd9Vi5ft4h8zX2mN4rIgyJSkIvXKyL3iEi1iGyMSevxdYrIAhF5w+27TWJXkEtFVe1xAg+8iTffBqYBecDrwOxsl6uPrm0c8B63PRxvKevZwE+A77j07wC3uO3Z7vrzganuffFn+zp6cd1fB34H/Nk9z+nrdddyH/B5t50HlObqdeMtXLgDKHTPHwGuzsXrBRYD7wE2xqT1+DqBVcBZeGt0PQ38XbplsJrMiWtf4llVW4HoEs+DnqruV9W1brse2IL3B3oJ3j8l3M+Pue1LgIdUtUVVdwDb8d6fQUNEJgIfAu6KSc7Z6wUQkRK8f0Z3A6hqq6oeJbevOwAUikgAKMJbZyrnrldVXwJqOyX36DpFZBxQoqqvqRdx7o/Jk5IFmROXconnXCAiU4D5wEpgjKruBy8QAaPdYbnwXvw78G0gEpOWy9cLXi28Bvgv10x4l4gMI0evW1XfBX4K7Ab2A3Wq+ldy9HoT6Ol1TnDbndPTYkHmxKVc4nmwE5Fi4DHgq6p6LNmhCdIGzXshIh8GqlV1TbpZEqQNmuuNEcBrUrldVecDx/GaUbozqK/b9UFcgtckNB4YJiKfTpYlQdqgud4e6O46T+j6LcicuJxe4llEgngB5req+geXfNBVoXE/q136YH8vzgE+KiI78Zo9PyAi/03uXm/UXmCvqq50zx/FCzq5et3/C9ihqjWq2gb8ATib3L3eznp6nXvdduf0tFiQOXE5u8SzG0FyN7BFVX8es+sJ4Cq3fRXweEz6UhHJF5GpwEy8DsNBQVVvVNWJqjoF7/f4vKp+mhy93ihVPQDsEZGTXdIFwGZy97p3A4tEpMh9xi/A62/M1evtrEfX6ZrU6kVkkXu/rozJk1q2Rz/kwgO4GG/k1dvA97Jdnj68rnPxqsUbgPXucTEwCngO2OZ+lsXk+Z57H7bSgxEoA+0BnE/H6LKhcL3zgCr3u/4TMDKXrxv4AfAmsBF4AG9EVc5dL/AgXr9TG16N5JreXCdQ6d6rt4Ff4maLSedh08oYY4zJGGsuM8YYkzEWZIwxxmSMBRljjDEZY0HGGGNMxliQMcYYkzEWZMygJSLLRaSyH17nH93MxL/tZf57ReSyBOnnR2d67sG5en3NIjIldjbevuau5+xMnb+nunvfTf8KZLsAxmSDiARUNZTm4V/Cu2dgRybLlAPOBxqAV/vypO4GQFHVSMqDzYBjNRmTUe7b8xYRudOt3/FXESl0+9q/lYtIuZvOBRG5WkT+JCJPisgOEblBRL7uJm9cISJlMS/xaRF51a0LstDlH+bW0Vjt8lwSc97fi8iTwF8TlPXr7jwbReSrLu3XeBNIPiEiX0twbX8TkbXucbZLFxH5pYhsFpG/0DEBYXTtoTdF5GXg4zHp3ZW5UEQeEpENIvIwUNjN+7xARF4UkTUi8mzMtCELROR1EXkNuD7m+CIReSR6XhFZGfO7WCIir7lr+r14c9chIj9217RBRH7a+b0A/gH4moisF5HzRGSyiDznjn9ORCYlKHeFeGuarBWR34jILvdZiH5ufgWsBU4SkdtFpMp9jn4Qc46dInKLiKxyjxkxL7HYfT7esVpNlmT7jlR75PYDmAKEgHnu+SPAp932cqDSbZcDO9321XjTjA8HKoA64B/cvlvxJuqM5r/TbS/GrZkB/FvMa5TizcYwzJ13LzF3OMeUcwHwhjuuGNgEzHf7dgLlCfIUAQVueyZQ5bY/DizDW2toPHAUuAwowJvldibepIOP0DGrQHdl/jpwj0s/3b2XlZ3KEcSrPVS451fE5NkAvM9t/7+Y9+ibwG/c9tzoed3v4SVgmNv3f4B/Bsrw7gKP3sBdmuD9+BfgmzHPnwSuctufA/6UIM8vgRvd9kV4M0yU431uIsCimGPL3E+/+92fHvP7+Z7bvjLmPb0X+D3el+nZeEtyZP1vYqg9rCZj+sMOVV3vttfg/QNJ5QVVrVfVGrwg86RLf6NT/gehfd2MEhEpBZYA3xGR9Xj/jAqA6LfoZaraeX0N8KbQ+aOqHlfVBrxJE89LUcYgcKeIvIH3z2y2S18MPKiqYVXdBzzv0k/Bey+2qfdf8L9jztVdmRdHj1PVDXhBo7OT8QLFMpf/+8BEERmBFwxedMc90Ol6H3Ln3Rhz3kXuOl5x57oKmAwcA5qBu0Tk40BjivcGvEWufhfz2ucmOCa2HM8AR2L27VLVFTHPPykia4F1wBw63m9wnwP386yY9D+pakRVNwNj0iiz6WPWJ2P6Q0vMdpiOJp8QHU22BUnyRGKeR4j/3HaeFyk6NfknVHVr7A4RORNvGvtE0l9OtsPXgIPAGXjX0ZykXKnSuytzsjyxeTep6llxiV7ATfZ63aUvU9VPddnhNUdegDd56A3AB1KUq7NEZUn2vrf/rsSbsPGbwHtV9YiI3Ev8Z0a72Y79HPXmd2xOkNVkTDbtxGumAq85qTeuABCRc/EWn6oDngW+LO4/tIjMT+M8LwEfc30Vw4BLgb+lyDMC2K9eh/Rn8JpxoudaKiJ+1zfyfpf+JjBVRKa757H/yLsr80vA37u0uXhNZp1tBSpE5Cx3XFBE5qi3umWde2+Insd5GfikO342cJpLXwGcE+3XcO/HLNcvM0JVnwK+ijehZmf1eE2cUa/iBaToa7+cIE9sOZbgTcyZSAle0KkTkTHA33Xaf0XMz9e6OYfJAqvJmGz6KfCIiHyGjialnjoiIq/i/RP6nEu7GW+Fyw3un/ZO4MPJTqKqa9234+gU7nep6roUr/0r4DERuRx4gY5v3n/E+5b/Bl7fyovuNZpF5DrgLyJyCO8f7NwUZb4db8XK6EzYXaaYV9VW16l9m2siC7hzbQI+C9wjIo14gSy27Pe5867Day6rU9UaEbkaeFBE8t2x38cLII+LSAFejSBuEITzJPCoG7TwZeAf3Wt/C2/lzc8myPMD91pXuPdpv3ut4k7X+LqIrHPX9A7wSqfz5IvISrwvzl1qYSZ7bBZmY4YgEfEDQRf4puNN+T5LVVv7uRz5QFhVQ64mdruqzuvhOXbiDYY4lIEimhNkNRljhqYi4AXxVj4V4Iv9HWCcSXi1WR/QClybhTKYDLKajDHGmIyxjn9jjDEZY0HGGGNMxliQMcYYkzEWZIwxxmSMBRljjDEZ8/8BFvF9R7rreugAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fs_mean,'m')\n",
    "plt.xlabel('number of added edges to graph');plt.ylabel('fixation time_r3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fec652db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0249a12e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'fixation time-log_r3')"
      ]
     },
     "execution_count": 13,
     "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.plot(fs_mean,'m')\n",
    "#plt.xscale('log')\n",
    "#plt.yscale('log')\n",
    "plt.ylim(0,10000)\n",
    "plt.xlabel('number of added edges to graph-log');plt.ylabel('fixation time-log_r3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b412d6a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6253640b",
   "metadata": {},
   "outputs": [],
   "source": [
    "lastm_avg_star=[]\n",
    "for i in range (1000):\n",
    "    M_avg=np.loadtxt('M_avgstar'+str(i)+'.dat')\n",
    "    lastm_avg_star.append(M_avg[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fdb32abb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'fixation probablity_r3')"
      ]
     },
     "execution_count": 14,
     "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.plot(lastm_avg_star,'m')\n",
    "plt.ylim(0,1)\n",
    "plt.xlabel('number of added edges to graph');plt.ylabel('fixation probablity_r3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f55b6fa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('f10.dat',lastm_avg_star)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "501b4202",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "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": [
    "from numpy import arange\n",
    "from scipy.optimize import curve_fit\n",
    "#plt.plot(lastm_avg_star)\n",
    "popt,pcov=curve_fit(lambda x,c,a:c*x**(a),np.arange(0,1000)[5:],lastm_avg_star[5:])\n",
    "plt.plot(np.arange(0,1000)[5:],popt[0]*np.arange(0,1000)[5:]**(popt[1]),'r',)\n",
    "plt.ylim(0,1)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4172e9b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.03412152, -0.06593485])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98d8e48b",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}