Untitled1 (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7d66cbda",
   "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": 6,
   "id": "df166918",
   "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": 7,
   "id": "afc96a6d",
   "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": 8,
   "id": "13958207",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_7356/3078464903.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_7356/3078464903.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=300000\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('Ggraph200_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_avgG200'+str(j)+'.dat',M_avg)\n",
    "    np.savetxt('fixtimeG200_r3_'+str(j)+'.dat',np.array(fix_time))\n",
    "    np.savetxt('exttimeG200_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": 9,
   "id": "b27194c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "treer3_fix=[]\n",
    "for i in range (1000):\n",
    "    treer3_fix.append(np.loadtxt('fixtimeG200_r3_'+str(i)+'.dat'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6e80fad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "f3_mean=[]\n",
    "for f in treer3_fix:\n",
    "    f3_mean.append(np.sum(f)/len(f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3a7639c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(treer3_fix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0b2d106d",
   "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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(f3_mean)\n",
    "plt.xlabel('number of added edges to graph');plt.ylabel('fixation time_r3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "53ae7953",
   "metadata": {},
   "outputs": [],
   "source": [
    "last3m_avg=[]\n",
    "for i in range (1000):\n",
    "    M_avg=np.loadtxt('M_avgG200'+str(i)+'.dat')\n",
    "    last3m_avg.append(M_avg[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "99a1e8b4",
   "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(last3m_avg)\n",
    "plt.ylim(0,1)\n",
    "plt.xlabel('number of added edges to graph');plt.ylabel('fixation probablity_r3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "07c54414",
   "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:],last3m_avg[5:])\n",
    "plt.plot(np.arange(0,1000)[5:],popt[0]*np.arange(0,1000)[5:]**(popt[1]),'b',)\n",
    "plt.ylim(0,1)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cf25f59",
   "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
}