hybrid-placement algo step by step implementation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "complete coding is done based on the paper [SEES](SEES.pdf)\n",
    "## Major Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we need to connect to a GUI event loop. This tells IPython where (and how) to display plots.\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we need \n",
    "\n",
    "N    - the total number of HN nodes\n",
    "\n",
    "Fs   - scalability factor which define as the maximum number of nodes to be accommodated in a single zone\n",
    "\n",
    "L    - length of area\n",
    "\n",
    "B    - cardinality of local base station(LBS)\n",
    "\n",
    "n    - number of heterogeneity level\n",
    "\n",
    "If N is the total number of nodes and n is the number of heterogeneity levels, then the network has n categories (types) of nodes: cate-1, cate-2, cate-3, ..., cate-n nodes with their cardinalities: N1t , N2t , N3t , ..., Nnt and energy levels: E1t , E2t , E3t , ..., Ent respectively, where:\n",
    "\n",
    "                            N1t > N2t > N3t > ⋯ > Nnt\n",
    "                            \n",
    "                            E1t < E2t < E3t < ⋯ < Ent\n",
    "                            \n",
    "The different levels of node energies are related according to the following formula, where the initial energy of cate-i\n",
    "nodes is alpha times more than that of cate-(i − 1):\n",
    "\n",
    "                            Eit = E1t × (1 + (i − 1) × aplha)\n",
    "                            \n",
    "where alpha is constant. The number of cate-i nodes in the network is given by:\n",
    "\n",
    "                            Nit = N × (beta − gamma1) × (beta − gamma2) × (beta − gamma3)×⋯× (beta − gammai)\n",
    "                            \n",
    "                            N1t + N2t + N3t + ⋯ + Nnt  = N\n",
    "                            \n",
    "alpha and betai are the primary and secondary parameters of heterogeneity in the Singh model respectively (Singh 2017), and\n",
    "are related according to the following formula:\n",
    "\n",
    "            (beta − gamma1) × (1 + (beta − gamma2) × (1 + (beta − gamma3)×⋯× (1 + (beta − gamman)))) = 1\n",
    "\n",
    "and gammai is given by:\n",
    "\n",
    "                            gammai = gammai−1 − 2 × theta\n",
    "\n",
    "where Θ(theta) is constant. The value of first secondary parameter gamma1 is predefined and must satisfy the following inequality:\n",
    "\n",
    "                            gamma1 / (2 × (n − 1)) > Θ\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter N( the total number of HN nodes)2000\n",
      "Enter Fs()20\n",
      "Enter L(length of area)200\n",
      "Enter B(LBS)12\n",
      "Enter n(number of heterogeneity level)2\n",
      "Enter alpha2\n",
      "Enter gamma0.4\n",
      "Enter theta0.025\n",
      "Enter E1(initial energy)0.5\n",
      "2000 20 200 12 2 2.0 0.4 0.025 0.5\n"
     ]
    }
   ],
   "source": [
    "#taking inputs\n",
    "N = int(input(\"Enter N( the total number of HN nodes)\"))\n",
    "Fs = int(input(\"Enter Fs()\"))\n",
    "L = int(input(\"Enter L(length of area)\"))\n",
    "B = int(input(\"Enter B(LBS)\"))\n",
    "n = int(input(\"Enter n(number of heterogeneity level)\"))\n",
    "\n",
    "#taking constants\n",
    "alpha = float(input(\"Enter alpha\"))#singh has taken this as 0.5\n",
    "#beta = input(\"Enter beta\")\n",
    "gamma = float(input(\"Enter gamma\"))#intial gamma will be given\n",
    "theta = float(input(\"Enter theta\"))#should validate a equation\n",
    "\n",
    "Einti = float(input(\"Enter E1(initial energy)\"))\n",
    "\n",
    "\n",
    "print(N,Fs,L,B,n,alpha,gamma,theta,Einti)\n",
    "#setting parameters are given based 4.2 section setting parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "proceed\n"
     ]
    }
   ],
   "source": [
    "#we need to validate theta and gamma values and find beta constant\n",
    "if((gamma /  (2*(n-1)) ) > theta):\n",
    "    print(\"proceed\")\n",
    "else:\n",
    "    print(\"error in input values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4, 0.35, 0.3]\n"
     ]
    }
   ],
   "source": [
    "#assume i need 6 level heterogenity\n",
    "#so now beta = gamma7\n",
    "#finding gamma values\n",
    "\n",
    "gammavalues = [gamma]\n",
    "#getting gamma values\n",
    "for i in range(1,n+1):\n",
    "    #gammai = gammai-1 - 2*theta\n",
    "    gammavalues.append(round(gammavalues[i-1] - 2*theta,3))\n",
    "    \n",
    "print(gammavalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4, 0.35]\n"
     ]
    }
   ],
   "source": [
    "#number of nodes in each level\n",
    "beta = gammavalues[len(gammavalues)-1]\n",
    "lastgamma = beta\n",
    "gammavalues[:] = gammavalues[:len(gammavalues)-1]\n",
    "\n",
    "print(gammavalues)\n",
    "\n",
    "#https://docs.sympy.org/latest/modules/solvers/solvers.html#systems-of-polynomial-equations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(x - 0.4)*(x + 0.65) - 1\n",
      "[1.00443569980765]\n"
     ]
    }
   ],
   "source": [
    "from sympy.solvers import solve\n",
    "from sympy import Symbol\n",
    "beta = Symbol('x',positive=True)\n",
    "#https://docs.sympy.org/latest/modules/solvers/solvers.html#systems-of-polynomial-equations\n",
    "f =1\n",
    "for i in range(n-1,-1,-1):\n",
    "    f = f*(beta-gammavalues[i])\n",
    "    f= f+1\n",
    "\n",
    "f = f-2\n",
    "print(f)\n",
    "beta = solve(f, beta)\n",
    "print(beta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it is given that \n",
    "Etot = N*[(beta-gamma1)*E1 + (beta-gamma1*(beta-gamma2)*E2)+.....+(beta-gamm1)(beta-gamma2)..*En]\n",
    "Energies are in Et\n",
    "gamma values are in gammavalues\n",
    "\n",
    "Singh as considered this\n",
    "\n",
    "For beta == gamma2, the model describes the homogenous network or\n",
    "1-level heterogeneity as the network contains only one type of\n",
    "nodes. The number of nodes in the network is 100 for\n",
    "MLHEED-1, which are of same type.\n",
    "\n",
    "For beta == gamma3, the model describes 2-level heterogeneity as the\n",
    "network contains two types of nodes. The number of type-1 and\n",
    "type-2 nodes are 60 and 40, respectively, for MLHEED-2.\n",
    "\n",
    "For beta == gamma4, the model describes 3-level heterogeneity as the\n",
    "network contains three types of nodes. The number of type-1,\n",
    "type-2, and type-3 nodes are 52, 30, and 18, respectively, for\n",
    "MLHEED-3.\n",
    "\n",
    "For beta == gamma5, the model describes 4-level heterogeneity as the\n",
    "network contains four types of nodes. The number of type-1,\n",
    "type-2, type-3, and type-4 nodes are 49, 26, 15, and 10, respectively,\n",
    "for MLHEED-4.\n",
    "\n",
    "For beta == gamma6, the model describes 5-level heterogeneity as the\n",
    "network contains five types of nodes. The number of type-1,\n",
    "type-2, type-3, type-4 and type-5 nodes are 47, 24, 14, 9 and 6,\n",
    "respectively, for MLHEED-5.\n",
    "\n",
    "For beta == gamma7, the model describes 6-level heterogeneity as the\n",
    "network contains six types of nodes. The number of type-1, type-\n",
    "2, type-3, type-4, type-5 and type-6 nodes are 46, 24, 13, 8, 5\n",
    "and 4, respectively, for MLHEED-6.\n",
    "\n",
    "\n",
    "why he did this?i don't know,i didn't read the paper..\n",
    "\n",
    "so proceed further i will accept it..\n",
    "\n",
    "let's take beta = gamma6 for 5 level heterogenity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0 [1.00443569980765] 0.4 [0.4, 0.35]\n",
      "2000 20 200 12 2 2.0 0.4 0.025 0.5\n"
     ]
    }
   ],
   "source": [
    "#we have taken beta as 0.8544 for building temporarly as mentioned in singh's paper\n",
    "#testing\n",
    "print(alpha,beta,gamma,gammavalues)\n",
    "print(N,Fs,L,B,n,alpha,gamma,theta,Einti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5, 1.5]\n",
      "[1208.87139961530, 791.128600384697]\n",
      "[1209, 791]\n",
      "Nodes in level -  1  =  1209  , Energy =  0.5\n",
      "Nodes in level -  2  =  791  , Energy =  1.5\n",
      "According to singh's \n",
      "total Energy in level - 0  =  604.5\n",
      "total Energy in level - 1  =  1186.5\n"
     ]
    }
   ],
   "source": [
    "\n",
    "Nt = [] #indicate cardinality of n categories\n",
    "Et = [] #indicate Energy of n categories\n",
    "EnergySinghTotal = []\n",
    "\n",
    "for i in range(1,n+1):\n",
    "    # we compute Nit and Eit and append them to Nt and Et,formulea are shown below\n",
    "    #(Einti * (1 + ((i − 1) * alpha)))\n",
    "    et = i-1\n",
    "    et = et*alpha\n",
    "    et = et+1\n",
    "    et = et*Einti\n",
    "    Et.append(et)\n",
    "    #Nit = N × (beta − gamma1) × (beta − gamma2) × (beta − gamma3)×⋯× (beta − gammai)\n",
    "    nt = N\n",
    "    for j in range(0,i):\n",
    "        nt = nt *(beta[0] - gammavalues[j])\n",
    "    Nt.append(nt)\n",
    "\n",
    "print(Et)\n",
    "print(Nt)\n",
    "\n",
    "import math\n",
    "Nt1 = [round(Nt[i],1) for i in range(len(Nt))]\n",
    "for i in range(len(Nt1)):\n",
    "    if (Nt1[i]-int(Nt1[i])) >= 0.5:\n",
    "        Nt1[i] = math.ceil(Nt1[i])\n",
    "    else:\n",
    "        Nt1[i] = math.floor(Nt1[i])\n",
    "        \n",
    "\n",
    "print(Nt1)\n",
    "\n",
    "Nt[:] = Nt1[:]\n",
    "for i in range(len(Nt1)):\n",
    "    print(\"Nodes in level - \",i+1,\" = \",Nt[i] ,\" , Energy = \",Et[i])\n",
    "    \n",
    "for i in range(len(Nt)):\n",
    "    EnergySinghTotal.append(Nt[i]*Et[i])\n",
    "#https://www.geeksforgeeks.org/precision-handling-python/\n",
    "print(\"According to singh's \")\n",
    "for i in range(len(Nt1)):\n",
    "    print(\"total Energy in level -\",i,\" = \",EnergySinghTotal[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum of nodes in all the level is equal to N \n"
     ]
    }
   ],
   "source": [
    "#checking\n",
    "if N == sum(Nt):\n",
    "    print(\"sum of nodes in all the level is equal to N \")\n",
    "else:\n",
    "    print(\"sum of nodes in all the level is not equal to N,Something went wrong \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1209, 791]\n",
      "[1200, 800]\n"
     ]
    }
   ],
   "source": [
    "#for sake of solving\n",
    "print(Nt)\n",
    "for i in range(len(Nt)):\n",
    "    Nt[i] = float(Nt[i]/100)\n",
    "    #print(Nt[i])\n",
    "    if (Nt[i] - int(Nt[i])) >= 0.5:\n",
    "        Nt[i] = math.ceil(Nt[i])*100\n",
    "    else:\n",
    "        Nt[i] = math.floor(Nt[i])*100\n",
    "        \n",
    "print(Nt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now starts Hybrid Placement Algorithm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### note\n",
    "\n",
    "First, it determines the number of zones Z, into\n",
    "which A will be divided based on two main parameters: the\n",
    "total number of HN nodes (N) in the network, and the scalability\n",
    "factor ( Fs ) which we define as the maximum number\n",
    "of nodes to be accommodated in a single zone. The value of\n",
    "Z is given by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0\n"
     ]
    }
   ],
   "source": [
    "Z = math.pow(math.ceil(math.sqrt(N)/math.sqrt(Fs)),2)\n",
    "print(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, it decides the number of HN nodes that will be deployed\n",
    "in each single zone. according to the following formula:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n"
     ]
    }
   ],
   "source": [
    "Nz = []\n",
    "for z in range(1,int(Z)+1):\n",
    "    if z==1:\n",
    "        Nz.append(math.ceil(N/Z))\n",
    "    else:\n",
    "        k = 0\n",
    "        for i in Nz:\n",
    "            k = k + i\n",
    "            \n",
    "        Nz.append(math.ceil((N - k)/(Z - z + 1)))\n",
    "        \n",
    "print(Nz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the working area A is divided into Z\n",
    "equal zones each L /√Z × L /√Z . Then each Nz heterogeneous\n",
    "nodes are randomly deployed in the respective zone in a\n",
    "uniform manner such that the vertical and horizontal coordinates\n",
    "of each node are randomly selected within the minimum\n",
    "and maximum limits of the zone dimensions. Here, we\n",
    "assume that the node sensing and communication ranges rs\n",
    "and rc are set to L/ √Z.√2 and 2. L /√Z.√2 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "#so 100 zones each with 20 nodes,for each node we need to find rsmax,rcmax\n",
    "\n",
    "rsmax = []#node sensing\n",
    "rcmax = []#communication ranges\n",
    "\n",
    "for i in range(0,int(Z)):\n",
    "    rsmax.append(  (L/math.sqrt(int(Z)  ))  *     math.sqrt(2)      )\n",
    "    rcmax.append( (L/math.sqrt(int(Z)))*math.sqrt(2)*2)\n",
    "\n",
    "print(rsmax,\"                \",rcmax)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note\n",
    "Third, it defines the value of R, the total number of EHs\n",
    "that should be deployed as relay nodes over all the zones\n",
    "as given by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121\n"
     ]
    }
   ],
   "source": [
    "R = int(math.pow( math.sqrt(int(Z))+1 , 2 ))\n",
    "print(R)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NETWORK FORMATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the working area A is divided into Z equal zones each L /√Z × L /√Z . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "D = int(L/math.sqrt(Z))\n",
    "print(D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use ticker to set the tick locations for the grid. The user can specify the input to MultipleLocator which will \"Set a tick on every integer that is multiple of base in the view interval.\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5TbNjx44bxhjbTvbycw2Vo7Zu3Tr27ds3722sGnv37s327dvnvY1VxcymMa9pzGsa85rGvKYxr2nMaxrzmqaqlhUqnvoFAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDsnFCpVdW1V3VtVNy1aO6+qrquqT84+nztbr6r6vaq6tao+UlXfeao2DwAArE0n+ojKHyV58TFrv5Lk/WOMy5K8f3Y6SV6S5LLZx84kb1r+NgEAgPXkhEJljPE3Se4/ZvllSd4y+/otSX540fofjwUfTHJOVW1Zic0CAADrQ40xTuyMVZcmec8Y4zmz0/84xjhn0fe/MMY4t6rek+QNY4wPzNbfn+S1Y4zrj/l5O5P8cpJzLrjggie/7W1vW4n/nnXh4MGD2bRp07y3saqY2TTmNY15TWNe05jXNOY1jXlNY17T7Nix44YxxraTvfzpK7mZmVpi7RE1NMbYlWRXkmzdunVs3779FGxlbdq7d2/Maxozm8a8pjGvacxrGvOaxrymMa9pzOvxtZxX/brn6FO6Zp/vna3fleTiRed7apK7l3E9AADAOrOcUHl3klfMvn5FknctWv+p2at/fU+SA2OM/cu4HgAAYJ05oad+VdVbk2xP8uSquivJryd5Q5K3V9Urk9yZ5MdnZ39vkpcmuTXJoSQ/s8J7BgAA1rgTCpUxxlXH+dYLlzjvSPLq5WwKAABY37wzPQAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABop8YY87vyqiuTXLlly5ZX7dmzZ277WG0OHjyYTZs2zXsbq4qZTWNe05jXNOY1jXlNY17TmNc05jXNjh07bhhjbDvZy881VI7aunXr2Ldv37y3sWrs3bs327dvn/c2VhUzm8a8pjGvacxrGvOaxrymMa9pzGuaqlpWqHjqFwAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtnH6yF6yqrUnetmjpGUn+lyTnJHlVks/N1n9tjPHek94hAACw7px0qIwx9iW5Ikmq6rQkn03yziQ/k+R3xxi/vSI7BAAA1p2VeurXC5N8aoxxxwr9PAAAYB1bqVB5eZK3Ljr9mqr6SFVdW1XnrtB1AAAA68SyQ6WqzkzyQ0n+02zpTUmemYWnhe1P8sbjXG5nVV1fVdcfOHBgudsAAADWkJV4ROUlST48xrgnScYY94wxHhxjHEnyB0met9SFxhi7xhjbxhjbNm/evALbAAAA1oqVCJWrsuhpX1W1ZdH3fiTJTStwHQAAwDpy0q/6lSRVtTHJDyT5uUXLv1VVVyQZSW4/5nsAAACPaVmhMsY4lOQbj1n7yWXtCAAAWPe8Mz0AANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANqpMcb8rrzqyiRXbtmy5VV79uyZ2z5Wm4MHD2bTpk3z3saqYmbTmNc05jWNeU1jXtOY1zTmNY15TbNjx44bxhjbTvbycw2Vo7Zu3Tr27ds3722sGnv37s327dvnvY1VxcymMa9pzGsa85rGvKYxr2nMaxrzmqaqlhUqnvoFAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgHaECAAC0I1QAAIB2hAoAANCOUAEAANoRKgAAQDtCBQAAaEeoAAAA7QgVAACgndOX+wOq6vYkX0zyYJLDY4xtVXVekrcluTTJ7Ul+YozxheVeFwAAsD6s1CMqO8YYV4wxts1O/0qS948xLkvy/tlpAACAE3Kqnvr1siRvmX39liQ/fIquBwAAWINWIlRGkr+oqhuqauds7cIxxv4kmX2+4NgLVdXOqrq+qq4/cODACmwDAABYK5Z9jEqS548x7q6qC5JcV1UfP5ELjTF2JdmVJFu3bh0rsA8AAGCNWPYjKmOMu2ef703yziTPS3JPVW1Jktnne5d7PQAAwPqxrFCpqidV1VlHv07yg0luSvLuJK+Yne0VSd61nOsBAADWl+U+9evCJO+sqqM/a88Y48+r6kNJ3l5Vr0xyZ5IfX+b1AAAA68iyQmWMcVuS71hi/fNJXricnw0AAKxf3pkeAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAOzXGmN+VV12Z5MotW7a8as+ePXPbx2pz8ODBbNq0ad7bWFXMbBrzmsa8pjGvacxrGvOaxrymMa9pduzYccMYY9vJXn6uoXLU1q1bx759++a9jVVj79692b59+7y3saqY2TTmNY15TWNe05jXNOY1jXlNY17TVNWyQsVTvwAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABoR6gAAADtCBUAAKAdoQIAALQjVAAAgHaECgAA0M5Jh0pVXVxVf1VVt1TVx6rqF2brv1FVn62qG2cfL1257QIAAOvB6cu47OEkvzTG+HBVnZXkhqq6bva93x1j/PbytwcAAKxHJx0qY4z9SfbPvv5iVd2S5KKV2hgAALB+rcgxKlV1aZLnJvm72dJrquojVXVtVZ17nMvsrKrrq+r6AwcOrMQ2AACANWLZoVJVm5K8I8kvjjEeSPKmJM9MckUWHnF541KXG2PsGmNsG2Ns27x583K3AQAArCHLCpWqOiMLkbJ7jPGnSTLGuGeM8eAY40iSP0jyvOVvEwAAWE+W86pfleTNSW4ZY/zOovUti872I0luOvntAQAA69FyXvXr+Ul+MslHq+rG2dqvJbmqqq5IMpLcnuTnlrVDAABg3VnOq359IEkt8a33nvx2AAAAvDM9AADQkFABAADaESoAAEA7QgUAAGhHqAAAAO0IFQAAoB2hAgAAtCNUAACAdoQKAADQjlABAADaESoAAEA7QgUAAGhHqAAAAO3UGGN+V151ZZIrt2zZ8qo9e/bMbR+rzcGDB7Np06Z5b2NVMbNpzGsa85rGvKYxr2nMaxrzmsa8ptmxY8cNY4xtJ3v5uYbKUVu3bh379u2b9zZWjb1792b79u3z3saqYmbTmNc05jWNeU1jXtOY1zTmNY15TVNVywoVT/0CAADaESoAAEA7QgUAAGhHqAAAAO0IFQAAoB2hAgAAtCNUAACAdoQKAADQjlABAADaWd2hsnt3cumlyYYNC5937573jgAAgBVw+rw3cNJ270527kwOHVo4fccdC6eT5Oqr57cvAABg2VbvIyrXXPP1SDnq0KGFdQAAYFVbvaFy553T1gEAgFVj9YbKJZdMWwcAAFaN1Rsqr399snHjw9c2blxYBwAAVrXVGypXX53s2pU87WlJ1cLnXbscSA8AAGvA6n3Vr2QhSoQJAACsOav3ERUAAGDNEioAAEA7QgUAAGhHqAAAAO0IFQAAoB2hAgAAtCNUAACAdoQKAADQjlABAADaESoAAEA7QgUAAGhHqAAAAO0IFQAAoB2hAgAAtCNUAACAdoQKAADQjlABAADaESoAAEA7qytUdu9OLr002bBh4fPu3fPeEQAAcAqcPu8NnLDdu5OdO5NDhxZO33HHwukkufrq+e0LAABYcavnEZVrrvl6pBx16NDCOgAAsKasnlC5885p6wAAwKq1ekLlkkumrQMAAKvW6gmV178+2bjx4WsbNy6sAwAAa8rqCZWrr0527Uqe9rSkauHzrl0OpAcAgDVo9bzqV7IQJcIEAADWvNXziAoAALButA+VI0dGjhwZp+Rn/9lH9+fuf/zyKfnZAADAyWsbKge/ejhv2vupPO9/e192/p83ZIyVjZUHvvJP+Z92fzi//9efWtGfCwAALF+7UDn41cP5d+/7RJ7/hr/Mb/75x3Pek87M+265J+/48Gcfcd4jR0Y+dPv9efAkHnH5+P4vJklu+uyBZe8ZAABYWe1C5dff9bH8u/d9Mv/s0vPyrlc/P3/+Cy/Itqedm9f9l4/l3ge+8tD5xhh53Xtuzo///n/L+2+5Z/L13Hz3QqDcvP+BHH7wyIrtHwAAWL5WofJPDx7JdTf/f/kfv/Op+cNXbMt3XHxONmyo/NaPfXu+evhIrvnPNz30FLDffd8n80f/z+1JTu5RkVtmj6h85Z+O5Lb7vrRi/w0AAMDytQqVG+74Qh74yuH8wOUXPGz9Gedvyi/94LNy3c335D0f2Z8//Nvb8nvv/2R+YttT880XbMrN+x+YfF03738gWzY/MUny0bs8/QsAADppFSp/+fF7c+ZpG/LPLzv/Ed975T/HFpo6AAAMp0lEQVR/Rr7j4nPy2nd8JP/r/31L/odv25L//Ue/Pd/6lLNz893TQuXwg0ey754v5iXP2ZJvOOO0fNRxKgAA0EqrUHnfLffku59xXjY94ZHvQ3nahspv/9i358EjI9/3rPPzu//qipy2oXL5lrNz94Gv5B8Pfe2Er+fT930pXzt8JM+56Oxc/pSz87G7hQoAAHTSJlQ+fd+XctvnvpQXfssFxz3PZReelQ+89vtz7U//s5x5+sLWL3/K2Uky6elfR897+VPOzrddtDkfu/uBk3rlMAAA4NRoEypHX7nrhc++8FHPd/5ZT8hpG+qh08/eMguVCU//unn/AznztA155vmb8pyLNufQ1x7Mpx1QDwAAbbQJlb/8+L151oWbcvF5G7++uHt3cumlyYYNC593737E5Z686Qm54KwnLPmIyu33fSkf+OR9j1i/+e4H8s0XbMoZp23Icy5aCB3vpwIAAH20CJUjI/n7T9+f7/+WRY+m7N6d7NyZ3HFHMsbC5507l4yVy49zQP3r3nNzfvYtH8qBL//Tw9Zv2f/Fh54y9s3nb8oTz9jggHoAAGikRah8+fDI4SMjL3r2ouNTrrkmOXTo4Wc8dGhh/RiXbzk7t957MF89/ODXz/q1w/nArffla4eP5L0f3f/Q+r1f/EruO/jVh54ydvppG/LsLWd7RAUAABppEirJuRvPyHMvOffri3feufSZl1i//Cln5/CRkU/ec/Chtb/95EKkPPGMDXnnP3z2ofWjb/R4+SxUkuQ5T1k4oP6IA+oBAKCFFqFy6PDIjq0XPOwg+VxyydJnXmL9aHQsPk7lfTffk7OeeHp+7gXPzN9/+v585v6FR2duOfqKX4tC5dsu2pyDXz2cO+4/5hEcAABgLlqEypGRfP+zj3lZ4te/Ptm48eFrGzcurB/jad/4pGw887SHjlN58MjIX3783uzYekF+7LuemiR5140Lj6rcfPcDueicb8jmjWc8dPnnXLQ5SRynAgAATbQIlSR5wbOOeTf6q69Odu1Knva0pGrh865dC+vHOG1DZes3nfXQIyo3fuYL+fyXvpYXXX5hLj5vY5739PPyp//w2Ywxcsv+B/LsLWc97PKXXbgpZ56+wXEqAADQRItQueSsDTn7iWc88htXX53cfnty5MjC5yUi5ajLt5ydW/Y/kDFGrrv53py+ofJ9s/j50edelNs+96V86PYv5FOfO/jQgfRHnXHahjz7m84SKgAA0ESLUFl8aMrJuvwpZ+eLXzmcu77w5bzvlnvy3c84L5u/YSF+XvJtW3Lm6Rvym3/+8RwZDz8+5ahvvWhzbvrsgYzhgHoAAJi3FqGyEo7Gx3s/uj+33nswL1r0Dvebv+GMvOjZF+SGO76QJI94RCVZOKD+ga8czp0OqAcAgLlbM6HyLd90djZU8gd/e1uSPCxUkuRHnrtwUP2Tzjwtl5y38RGX/7bZAfU3ffaRbxwJAAA8vnqFyu7dyaWXJhs2LHxe4l3oj+cbzjwtT3/yk3Lfwa/lW77prFx8TIx837POz7kbz8i3bDk7G5Z4rtmzLjwre1713fm+rec/4nsAAMDj6/R5b+Ahu3cnO3d+/d3o77hj4XTyqAfRL3b5UzbnU5/70iMeTUmSM0/fkP949Xdl45mnLXnZM0/fkP/+mU8+qa0DAAArq88jKtdc8/VIOerQoYX1E3T0OJUXHvueLDP/3TO/Md9x8TknvUUAAODxccoeUamqFyf590lOS/KHY4w3POoF7rxz2voSrnrexTn/rCfkCjECAACr2il5RKWqTkvyfyR5SZLLk1xVVZc/6oUuuWTa+hLO2Xhmfuy7npqqFXi9YwAAYG5O1VO/npfk1jHGbWOMryX5kyQve9RLvP71ycZjXo1r48aFdQAAYF05VaFyUZLPLDp912zt+K6+Otm1K3na05Kqhc+7dp3wgfQAAMDacaqOUVnquVcPe8v3qtqZ5JeTnJPkwaq68WHnvuOO5F//64UPjrU5yYEV+llPTnLfCv2szlZyZiup6/y7zmslreTs18O8VtIlSU78AETc50/T9c9j19l3nddKcn8/P9+6nAufqlC5K8nFi04/Ncndi88wxtiVZFeSVNXnxhjbTtFe1pyq2jXG2LlCP+v69TD7lZzZSuo6/67zWkkrOfv1MK+V5D5/Gvf503T989h19l3ntZLc389PVX1uOZc/VU/9+lCSy6rq6VV1ZpKXJ3n3o5z/H0/RPtaq/zLvDaxCZjaNeU1jXtO4z5/G7Wsa85rGvKYxr2mWdX9/Sh5RGWMcrqrXJPmvWXh54mvHGB97lIt4CG2CMYY/JBOZ2TTmNY15TeY+fwK3r2nMaxrzmsa8JlvW/f0pex+VMcZ7k7z3BM++61Ttg8dk9vNl/vNj9vNj9vNj9vNj9vNj9vOzrNnXGOOxzwUAAPA4OlXHqAAAAJy0uYdKVb24qvZV1a1V9Svz3s9aVlUXV9VfVdUtVfWxqvqF2fpvVNVnq+rG2cdL573Xtaiqbq+qj85mfP1s7byquq6qPjn7fO6897nWVNXWRbftG6vqgar6Rbf7U6Oqrq2qe6vqpkVrS97Oa8Hvze7/P1JV3zm/na9+x5n9v62qj8/m+86qOme2fmlVfXnR7f/357fzteE48z/u/UxV/erstr+vqv7FfHa9Nhxn9m9bNPfbj74Nhtv+ynqU3y1X5H5/rk/9qqrTknwiyQ9k4SWNP5TkqjHGzXPb1BpWVVuSbBljfLiqzkpyQ5IfTvITSQ6OMX57rhtc46rq9iTbxhj3LVr7rST3jzHeMAv1c8cYr53XHte62X3OZ5N8d5Kfidv9iquqFyQ5mOSPxxjPma0teTuf/dL2Pyd5aRb+n/z7McZ3z2vvq91xZv+DSf5y9iI3v5kks9lfmuQ9R8/H8h1n/r+RJe5nquryJG9N8rwkT0nyviTPGmM8+Lhueo1YavbHfP+NSQ6MMV7ntr+yHuV3y5/OCtzvz/sRlecluXWMcdsY42tJ/iTJy+a8pzVrjLF/jPHh2ddfTHJLkovmu6t172VJ3jL7+i1Z+MPNqfPCJJ8aY9wx742sVWOMv0ly/zHLx7udvywLv1iMMcYHk5wz+0uPk7DU7McYfzHGODw7+cEsvK8Zp8BxbvvH87IkfzLG+OoY49NJbs3C70SchEebfVVVFv5B9q2P66bWiUf53XJF7vfnHSoXJfnMotN3xS/Oj4vZvyg8N8nfzZZeM3sI7lpPPzplRpK/qKobqurom0VdOMbYnyz8YU9ywdx2tz68PA//y8rt/vFxvNu5vwMeXz+b5M8WnX56Vf1DVf11VX3vvDa1Dix1P+O2//j53iT3jDE+uWjNbf8UOOZ3yxW53593qNQSa16G7BSrqk1J3pHkF8cYDyR5U5JnJrkiyf4kb5zj9tay548xvjPJS5K8evZQNY+TWnjz2R9K8p9mS2738+fvgMdJVV2T5HCS3bOl/UkuGWM8N8m/SbKnqs6e1/7WsOPdz7jtP36uysP/gcpt/xRY4nfL4551ibXj3vbnHSp3Jbl40emnJrl7TntZF6rqjCzckHaPMf40ScYY94wxHhxjHEnyB/Hw8ykxxrh79vneJO/MwpzvOfqQ5+zzvfPb4Zr3kiQfHmPck7jdP86Odzv3d8DjoKpekeRfJrl6zA5MnT3l6POzr29I8qkkz5rfLtemR7mfcdt/HFTV6Ul+NMnbjq657a+8pX63zArd7887VD6U5LKqevrsXztfnuTdc97TmjV7nuabk9wyxvidReuLnxv4I0luOvayLE9VPWl2kFmq6klJfjALc353klfMzvaKJO+azw7XhYf9q5rb/ePqeLfzdyf5qdmrwHxPFg523T+PDa5VVfXiJK9N8kNjjEOL1s+fvbhEquoZSS5Lctt8drl2Pcr9zLuTvLyqnlBVT8/C/P/+8d7fOvCiJB8fY9x1dMFtf2Ud73fLrND9/il7Z/oTMXsVktck+a9JTkty7RjjY/Pc0xr3/CQ/meSjR1+mL8mvJbmqqq7IwkNvtyf5uflsb027MMk7F/485/Qke8YYf15VH0ry9qp6ZZI7k/z4HPe4ZlXVxiy8uuDi2/Zvud2vvKp6a5LtSZ5cVXcl+fUkb8jSt/P3ZuGVX25NcigLr8TGSTrO7H81yROSXDe7//ngGOPnk7wgyeuq6nCSB5P8/BjjRA8EZwnHmf/2pe5nxhgfq6q3J7k5C0/Je7VX/Dp5S81+jPHmPPK4xMRtf6Ud73fLFbnf9870AABAO/N+6hcAAMAjCBUAAKAdoQIAALQjVAAAgHaECgAA0I5QAQAA2hEqAABAO0IFAABo5/8HBbpVsmBb290AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#this is testing plot\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(30, 30))#defining size\n",
    "ax1 = fig.add_subplot(2,2,1)#adding a plot to figure\n",
    "\n",
    "\n",
    "spacing = D # This can be your user specified spacing. \n",
    "minorLocator = MultipleLocator(spacing)\n",
    "ax1.plot(9 * np.random.rand(10))#testing\n",
    "ax1.plot([1,2,3,4], [1,4,9,16], 'ro')#testing\n",
    "# Set minor tick locations.\n",
    "ax1.yaxis.set_minor_locator(minorLocator)\n",
    "ax1.xaxis.set_minor_locator(minorLocator)\n",
    "\n",
    "plt.axis([0, L, 0, L])#defining axix x and y\n",
    "# Set grid to use minor tick locations. \n",
    "\n",
    "ax1.grid(which = 'minor')#only major works fine\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to process each zone\n",
    "\n",
    "In each zone find Nzi random (x,y) with the zone limits and plot them\n",
    "\n",
    "distribute R EH node at the corners of each node so it becomes 121 it hope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(6, 6), (17, 0), (12, 8), (11, 8), (2, 2), (17, 3), (10, 4), (1, 17), (7, 10), (18, 16), (1, 8), (15, 18), (4, 8), (19, 4), (7, 3), (1, 17), (3, 16), (14, 9), (16, 7), (12, 3)]\n",
      "[[(12, 3), 0.5, 1], [(11, 8), 0.5, 2], [(15, 18), 0.5, 3], [(3, 16), 0.5, 4], [(14, 9), 0.5, 5], [(4, 8), 0.5, 6], [(12, 8), 0.5, 7], [(19, 4), 0.5, 8], [(16, 7), 0.5, 9], [(7, 10), 0.5, 10], [(6, 6), 0.5, 11], [(18, 16), 0.5, 12], [(7, 3), 1.5, 13], [(17, 3), 1.5, 14], [(10, 4), 1.5, 15], [(17, 0), 1.5, 16], [(2, 2), 1.5, 17], [(1, 8), 1.5, 18], [(1, 17), 1.5, 19], [(1, 17), 1.5, 20]]\n"
     ]
    }
   ],
   "source": [
    "#this is for one zone we need to do it for all the zones\n",
    "import random\n",
    "\n",
    "\n",
    "rangeX = (0, 20)\n",
    "rangeY = (0, 20)\n",
    "\n",
    "# deltas = set()\n",
    "# for x in range(1, 20):\n",
    "#     for y in range(1, 20):\n",
    "#         deltas.add((x,y))\n",
    "\n",
    "randPoints = []\n",
    "excluded = set()\n",
    "i = 0\n",
    "while i<D:\n",
    "    x = random.randrange(*rangeX)\n",
    "    y = random.randrange(*rangeY)\n",
    "    if (x,y) in excluded: continue\n",
    "    randPoints.append((x,y))\n",
    "    i += 1\n",
    "    excluded.update((x, y))\n",
    "print(randPoints)\n",
    "randomPoints = randPoints\n",
    "#https://stackoverflow.com/questions/19668463/generating-multiple-random-x-y-coordinates-excluding-duplicates\n",
    "\n",
    "#as i need to deploy a node in an x,y position with energy append to it,i am doing this\n",
    "#randomly selelcting a node\n",
    "#https://stackoverflow.com/questions/306400/how-to-randomly-select-an-item-from-a-list\n",
    "\n",
    "#no of nodes in each level\n",
    "nodesinlevel = []\n",
    "for i in range(len(Nt)):\n",
    "    nodesinlevel.append(int(Nt[i]/100))\n",
    "\n",
    "id = 1\n",
    "secure_random = random.SystemRandom()\n",
    "nodeswithenergy = []#(x,y),energy,id\n",
    "for j in range(len(nodesinlevel)):\n",
    "    for i in range(nodesinlevel[j]):\n",
    "        list1 = []\n",
    "        list1.append(secure_random.choice(randPoints))\n",
    "        randPoints.remove(list1[0])\n",
    "        list1.append(Et[j])\n",
    "        list1.append(id)\n",
    "        id = id+1\n",
    "        nodeswithenergy.append(list1)\n",
    "        \n",
    "print(nodeswithenergy)\n",
    "\n",
    "def getpoints(startx,starty,endx,endy,id):\n",
    "    rangeX = (startx,endx)\n",
    "    rangeY = (starty,endy)\n",
    "    randPoints = []\n",
    "    excluded = set()\n",
    "    i = 0\n",
    "    while i<D:\n",
    "        x = random.randrange(*rangeX)\n",
    "        y = random.randrange(*rangeY)\n",
    "        if (x,y) in excluded: continue\n",
    "        randPoints.append((x,y))\n",
    "        i += 1\n",
    "        excluded.update((x, y))\n",
    "        \n",
    "    secure_random = random.SystemRandom()\n",
    "    nodeswithenergy = []#(x,y),energy,id\n",
    "    for j in range(len(nodesinlevel)):\n",
    "        for i in range(nodesinlevel[j]):\n",
    "            list1 = []\n",
    "            list1.append(secure_random.choice(randPoints))\n",
    "            randPoints.remove(list1[0])\n",
    "            list1.append(Et[j])\n",
    "            list1.append(id)\n",
    "            id = id+1\n",
    "            nodeswithenergy.append(list1)\n",
    "    return(nodeswithenergy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[(11, 8), 0.5, 1], [(0, 11), 0.5, 2], [(16, 0), 0.5, 3], [(8, 16), 0.5, 4], [(0, 14), 0.5, 5], [(4, 13), 0.5, 6], [(2, 9), 0.5, 7], [(7, 2), 0.5, 8], [(14, 16), 0.5, 9], [(13, 15), 0.5, 10], [(7, 18), 0.5, 11], [(19, 10), 0.5, 12], [(18, 12), 1.5, 13], [(0, 4), 1.5, 14], [(1, 13), 1.5, 15], [(7, 16), 1.5, 16], [(12, 15), 1.5, 17], [(15, 4), 1.5, 18], [(11, 6), 1.5, 19], [(16, 11), 1.5, 20]]\n",
      "[[(36, 6), 0.5, 21], [(39, 11), 0.5, 22], [(29, 6), 0.5, 23], [(29, 9), 0.5, 24], [(24, 5), 0.5, 25], [(29, 0), 0.5, 26], [(39, 13), 0.5, 27], [(30, 6), 0.5, 28], [(21, 6), 0.5, 29], [(32, 8), 0.5, 30], [(25, 9), 0.5, 31], [(26, 3), 0.5, 32], [(33, 17), 1.5, 33], [(24, 7), 1.5, 34], [(20, 12), 1.5, 35], [(23, 7), 1.5, 36], [(31, 7), 1.5, 37], [(27, 6), 1.5, 38], [(29, 8), 1.5, 39], [(28, 6), 1.5, 40]]\n",
      "[[(40, 15), 0.5, 41], [(52, 13), 0.5, 42], [(45, 4), 0.5, 43], [(53, 1), 0.5, 44], [(41, 18), 0.5, 45], [(55, 3), 0.5, 46], [(51, 14), 0.5, 47], [(54, 5), 0.5, 48], [(58, 3), 0.5, 49], [(59, 11), 0.5, 50], [(43, 9), 0.5, 51], [(53, 6), 0.5, 52], [(52, 8), 1.5, 53], [(40, 7), 1.5, 54], [(53, 13), 1.5, 55], [(54, 9), 1.5, 56], [(57, 18), 1.5, 57], [(45, 2), 1.5, 58], [(59, 11), 1.5, 59], [(44, 9), 1.5, 60]]\n",
      "[[(76, 6), 0.5, 61], [(67, 11), 0.5, 62], [(79, 12), 0.5, 63], [(72, 10), 0.5, 64], [(61, 8), 0.5, 65], [(67, 13), 0.5, 66], [(79, 4), 0.5, 67], [(70, 11), 0.5, 68], [(74, 5), 0.5, 69], [(76, 16), 0.5, 70], [(73, 17), 0.5, 71], [(75, 18), 0.5, 72], [(63, 4), 1.5, 73], [(72, 5), 1.5, 74], [(77, 12), 1.5, 75], [(77, 5), 1.5, 76], [(78, 12), 1.5, 77], [(70, 7), 1.5, 78], [(64, 5), 1.5, 79], [(67, 16), 1.5, 80]]\n",
      "[[(80, 8), 0.5, 81], [(93, 4), 0.5, 82], [(87, 13), 0.5, 83], [(88, 5), 0.5, 84], [(89, 13), 0.5, 85], [(88, 9), 0.5, 86], [(84, 19), 0.5, 87], [(94, 1), 0.5, 88], [(92, 11), 0.5, 89], [(84, 9), 0.5, 90], [(83, 18), 0.5, 91], [(88, 4), 0.5, 92], [(82, 16), 1.5, 93], [(82, 4), 1.5, 94], [(94, 17), 1.5, 95], [(85, 5), 1.5, 96], [(97, 10), 1.5, 97], [(95, 10), 1.5, 98], [(89, 7), 1.5, 99], [(85, 17), 1.5, 100]]\n",
      "[[(103, 14), 0.5, 101], [(118, 11), 0.5, 102], [(100, 17), 0.5, 103], [(104, 6), 0.5, 104], [(101, 9), 0.5, 105], [(116, 0), 0.5, 106], [(103, 3), 0.5, 107], [(117, 1), 0.5, 108], [(104, 18), 0.5, 109], [(106, 9), 0.5, 110], [(103, 4), 0.5, 111], [(110, 8), 0.5, 112], [(117, 2), 1.5, 113], [(112, 6), 1.5, 114], [(112, 18), 1.5, 115], [(101, 2), 1.5, 116], [(115, 4), 1.5, 117], [(105, 0), 1.5, 118], [(106, 13), 1.5, 119], [(113, 6), 1.5, 120]]\n",
      "[[(127, 11), 0.5, 121], [(136, 14), 0.5, 122], [(127, 10), 0.5, 123], [(134, 16), 0.5, 124], [(125, 10), 0.5, 125], [(136, 0), 0.5, 126], [(131, 4), 0.5, 127], [(138, 5), 0.5, 128], [(128, 2), 0.5, 129], [(122, 3), 0.5, 130], [(129, 17), 0.5, 131], [(134, 6), 0.5, 132], [(120, 14), 1.5, 133], [(134, 16), 1.5, 134], [(126, 9), 1.5, 135], [(129, 3), 1.5, 136], [(137, 10), 1.5, 137], [(134, 1), 1.5, 138], [(126, 6), 1.5, 139], [(132, 3), 1.5, 140]]\n",
      "[[(151, 18), 0.5, 141], [(146, 7), 0.5, 142], [(158, 15), 0.5, 143], [(145, 15), 0.5, 144], [(149, 9), 0.5, 145], [(144, 2), 0.5, 146], [(141, 7), 0.5, 147], [(146, 1), 0.5, 148], [(156, 4), 0.5, 149], [(146, 0), 0.5, 150], [(153, 2), 0.5, 151], [(155, 16), 0.5, 152], [(151, 9), 1.5, 153], [(152, 11), 1.5, 154], [(144, 12), 1.5, 155], [(143, 2), 1.5, 156], [(158, 7), 1.5, 157], [(145, 9), 1.5, 158], [(154, 9), 1.5, 159], [(154, 19), 1.5, 160]]\n",
      "[[(167, 3), 0.5, 161], [(163, 17), 0.5, 162], [(161, 1), 0.5, 163], [(171, 15), 0.5, 164], [(173, 12), 0.5, 165], [(179, 13), 0.5, 166], [(179, 8), 0.5, 167], [(162, 2), 0.5, 168], [(170, 3), 0.5, 169], [(173, 11), 0.5, 170], [(170, 18), 0.5, 171], [(160, 11), 0.5, 172], [(173, 8), 1.5, 173], [(169, 9), 1.5, 174], [(160, 1), 1.5, 175], [(169, 19), 1.5, 176], [(167, 3), 1.5, 177], [(171, 1), 1.5, 178], [(175, 3), 1.5, 179], [(175, 19), 1.5, 180]]\n",
      "[[(199, 18), 0.5, 181], [(185, 17), 0.5, 182], [(192, 7), 0.5, 183], [(196, 18), 0.5, 184], [(183, 7), 0.5, 185], [(194, 14), 0.5, 186], [(180, 0), 0.5, 187], [(196, 18), 0.5, 188], [(190, 1), 0.5, 189], [(180, 3), 0.5, 190], [(196, 1), 0.5, 191], [(194, 18), 0.5, 192], [(199, 8), 1.5, 193], [(183, 19), 1.5, 194], [(182, 0), 1.5, 195], [(197, 7), 1.5, 196], [(189, 1), 1.5, 197], [(195, 5), 1.5, 198], [(194, 7), 1.5, 199], [(196, 10), 1.5, 200]]\n",
      "[[(14, 27), 0.5, 201], [(14, 29), 0.5, 202], [(15, 23), 0.5, 203], [(1, 37), 0.5, 204], [(6, 24), 0.5, 205], [(17, 34), 0.5, 206], [(7, 38), 0.5, 207], [(12, 28), 0.5, 208], [(15, 30), 0.5, 209], [(4, 26), 0.5, 210], [(0, 29), 0.5, 211], [(7, 20), 0.5, 212], [(0, 28), 1.5, 213], [(18, 35), 1.5, 214], [(11, 28), 1.5, 215], [(1, 30), 1.5, 216], [(11, 25), 1.5, 217], [(3, 24), 1.5, 218], [(0, 35), 1.5, 219], [(5, 39), 1.5, 220]]\n",
      "[[(20, 22), 0.5, 221], [(22, 34), 0.5, 222], [(22, 25), 0.5, 223], [(24, 35), 0.5, 224], [(20, 37), 0.5, 225], [(25, 20), 0.5, 226], [(22, 38), 0.5, 227], [(28, 27), 0.5, 228], [(22, 37), 0.5, 229], [(31, 24), 0.5, 230], [(37, 27), 0.5, 231], [(38, 25), 0.5, 232], [(27, 22), 1.5, 233], [(23, 39), 1.5, 234], [(35, 37), 1.5, 235], [(39, 28), 1.5, 236], [(21, 27), 1.5, 237], [(35, 22), 1.5, 238], [(27, 26), 1.5, 239], [(34, 21), 1.5, 240]]\n",
      "[[(47, 21), 0.5, 241], [(44, 32), 0.5, 242], [(53, 29), 0.5, 243], [(48, 35), 0.5, 244], [(41, 21), 0.5, 245], [(54, 34), 0.5, 246], [(45, 22), 0.5, 247], [(54, 32), 0.5, 248], [(58, 34), 0.5, 249], [(47, 32), 0.5, 250], [(55, 35), 0.5, 251], [(43, 26), 0.5, 252], [(50, 32), 1.5, 253], [(55, 28), 1.5, 254], [(54, 24), 1.5, 255], [(51, 27), 1.5, 256], [(56, 33), 1.5, 257], [(57, 23), 1.5, 258], [(43, 31), 1.5, 259], [(41, 39), 1.5, 260]]\n",
      "[[(71, 33), 0.5, 261], [(60, 23), 0.5, 262], [(69, 33), 0.5, 263], [(78, 28), 0.5, 264], [(77, 36), 0.5, 265], [(74, 37), 0.5, 266], [(60, 36), 0.5, 267], [(66, 26), 0.5, 268], [(60, 28), 0.5, 269], [(60, 39), 0.5, 270], [(69, 25), 0.5, 271], [(69, 29), 0.5, 272], [(72, 26), 1.5, 273], [(68, 23), 1.5, 274], [(66, 22), 1.5, 275], [(73, 28), 1.5, 276], [(77, 22), 1.5, 277], [(75, 39), 1.5, 278], [(61, 25), 1.5, 279], [(64, 20), 1.5, 280]]\n",
      "[[(97, 24), 0.5, 281], [(86, 38), 0.5, 282], [(80, 35), 0.5, 283], [(96, 34), 0.5, 284], [(92, 39), 0.5, 285], [(95, 20), 0.5, 286], [(98, 39), 0.5, 287], [(80, 34), 0.5, 288], [(86, 26), 0.5, 289], [(93, 22), 0.5, 290], [(83, 32), 0.5, 291], [(82, 38), 0.5, 292], [(99, 28), 1.5, 293], [(95, 28), 1.5, 294], [(84, 20), 1.5, 295], [(86, 26), 1.5, 296], [(86, 32), 1.5, 297], [(89, 36), 1.5, 298], [(83, 38), 1.5, 299], [(83, 21), 1.5, 300]]\n",
      "[[(104, 29), 0.5, 301], [(105, 33), 0.5, 302], [(103, 20), 0.5, 303], [(118, 22), 0.5, 304], [(117, 38), 0.5, 305], [(110, 39), 0.5, 306], [(109, 35), 0.5, 307], [(105, 26), 0.5, 308], [(118, 38), 0.5, 309], [(108, 38), 0.5, 310], [(116, 21), 0.5, 311], [(100, 27), 0.5, 312], [(105, 39), 1.5, 313], [(104, 39), 1.5, 314], [(118, 33), 1.5, 315], [(102, 26), 1.5, 316], [(111, 26), 1.5, 317], [(103, 32), 1.5, 318], [(107, 26), 1.5, 319], [(111, 27), 1.5, 320]]\n",
      "[[(125, 37), 0.5, 321], [(127, 29), 0.5, 322], [(121, 27), 0.5, 323], [(126, 25), 0.5, 324], [(138, 29), 0.5, 325], [(130, 35), 0.5, 326], [(122, 26), 0.5, 327], [(136, 39), 0.5, 328], [(135, 24), 0.5, 329], [(134, 22), 0.5, 330], [(131, 30), 0.5, 331], [(126, 25), 0.5, 332], [(124, 24), 1.5, 333], [(133, 28), 1.5, 334], [(132, 23), 1.5, 335], [(129, 28), 1.5, 336], [(123, 35), 1.5, 337], [(120, 30), 1.5, 338], [(120, 35), 1.5, 339], [(132, 38), 1.5, 340]]\n",
      "[[(145, 28), 0.5, 341], [(155, 37), 0.5, 342], [(150, 33), 0.5, 343], [(143, 21), 0.5, 344], [(157, 35), 0.5, 345], [(140, 35), 0.5, 346], [(157, 24), 0.5, 347], [(151, 38), 0.5, 348], [(158, 30), 0.5, 349], [(158, 36), 0.5, 350], [(156, 33), 0.5, 351], [(154, 29), 0.5, 352], [(146, 28), 1.5, 353], [(159, 38), 1.5, 354], [(153, 29), 1.5, 355], [(155, 25), 1.5, 356], [(155, 27), 1.5, 357], [(150, 37), 1.5, 358], [(151, 28), 1.5, 359], [(141, 24), 1.5, 360]]\n",
      "[[(179, 34), 0.5, 361], [(171, 27), 0.5, 362], [(164, 21), 0.5, 363], [(161, 25), 0.5, 364], [(174, 29), 0.5, 365], [(165, 31), 0.5, 366], [(175, 26), 0.5, 367], [(165, 30), 0.5, 368], [(160, 28), 0.5, 369], [(171, 35), 0.5, 370], [(179, 31), 0.5, 371], [(176, 32), 0.5, 372], [(176, 28), 1.5, 373], [(176, 24), 1.5, 374], [(171, 32), 1.5, 375], [(172, 21), 1.5, 376], [(170, 38), 1.5, 377], [(175, 36), 1.5, 378], [(178, 28), 1.5, 379], [(161, 25), 1.5, 380]]\n",
      "[[(198, 20), 0.5, 381], [(189, 29), 0.5, 382], [(185, 27), 0.5, 383], [(185, 28), 0.5, 384], [(182, 37), 0.5, 385], [(190, 27), 0.5, 386], [(184, 36), 0.5, 387], [(198, 33), 0.5, 388], [(196, 34), 0.5, 389], [(198, 24), 0.5, 390], [(190, 22), 0.5, 391], [(191, 29), 0.5, 392], [(180, 35), 1.5, 393], [(185, 26), 1.5, 394], [(195, 34), 1.5, 395], [(195, 35), 1.5, 396], [(198, 22), 1.5, 397], [(190, 30), 1.5, 398], [(191, 28), 1.5, 399], [(180, 34), 1.5, 400]]\n",
      "[[(4, 59), 0.5, 401], [(12, 48), 0.5, 402], [(4, 56), 0.5, 403], [(7, 52), 0.5, 404], [(2, 59), 0.5, 405], [(2, 46), 0.5, 406], [(19, 57), 0.5, 407], [(6, 48), 0.5, 408], [(9, 44), 0.5, 409], [(18, 55), 0.5, 410], [(12, 50), 0.5, 411], [(15, 42), 0.5, 412], [(3, 48), 1.5, 413], [(2, 42), 1.5, 414], [(1, 55), 1.5, 415], [(6, 44), 1.5, 416], [(18, 47), 1.5, 417], [(17, 46), 1.5, 418], [(1, 48), 1.5, 419], [(9, 52), 1.5, 420]]\n",
      "[[(39, 54), 0.5, 421], [(28, 58), 0.5, 422], [(38, 41), 0.5, 423], [(33, 41), 0.5, 424], [(21, 50), 0.5, 425], [(24, 56), 0.5, 426], [(26, 41), 0.5, 427], [(32, 49), 0.5, 428], [(36, 55), 0.5, 429], [(33, 40), 0.5, 430], [(32, 43), 0.5, 431], [(37, 46), 0.5, 432], [(39, 55), 1.5, 433], [(27, 43), 1.5, 434], [(31, 51), 1.5, 435], [(38, 42), 1.5, 436], [(30, 54), 1.5, 437], [(22, 49), 1.5, 438], [(35, 43), 1.5, 439], [(24, 54), 1.5, 440]]\n",
      "[[(42, 49), 0.5, 441], [(40, 59), 0.5, 442], [(49, 44), 0.5, 443], [(42, 58), 0.5, 444], [(49, 46), 0.5, 445], [(58, 58), 0.5, 446], [(52, 59), 0.5, 447], [(45, 52), 0.5, 448], [(48, 40), 0.5, 449], [(54, 55), 0.5, 450], [(42, 54), 0.5, 451], [(43, 50), 0.5, 452], [(54, 54), 1.5, 453], [(47, 57), 1.5, 454], [(58, 55), 1.5, 455], [(42, 54), 1.5, 456], [(44, 49), 1.5, 457], [(49, 59), 1.5, 458], [(48, 50), 1.5, 459], [(41, 45), 1.5, 460]]\n",
      "[[(64, 43), 0.5, 461], [(60, 46), 0.5, 462], [(60, 51), 0.5, 463], [(76, 55), 0.5, 464], [(72, 54), 0.5, 465], [(70, 58), 0.5, 466], [(74, 44), 0.5, 467], [(76, 41), 0.5, 468], [(71, 59), 0.5, 469], [(65, 54), 0.5, 470], [(67, 45), 0.5, 471], [(68, 52), 0.5, 472], [(66, 46), 1.5, 473], [(72, 40), 1.5, 474], [(64, 57), 1.5, 475], [(68, 49), 1.5, 476], [(63, 43), 1.5, 477], [(66, 51), 1.5, 478], [(79, 52), 1.5, 479], [(68, 44), 1.5, 480]]\n",
      "[[(90, 40), 0.5, 481], [(91, 58), 0.5, 482], [(81, 44), 0.5, 483], [(97, 44), 0.5, 484], [(96, 57), 0.5, 485], [(93, 51), 0.5, 486], [(85, 53), 0.5, 487], [(82, 41), 0.5, 488], [(81, 46), 0.5, 489], [(83, 58), 0.5, 490], [(87, 53), 0.5, 491], [(84, 41), 0.5, 492], [(90, 55), 1.5, 493], [(91, 42), 1.5, 494], [(87, 54), 1.5, 495], [(90, 48), 1.5, 496], [(87, 47), 1.5, 497], [(86, 47), 1.5, 498], [(99, 49), 1.5, 499], [(82, 57), 1.5, 500]]\n",
      "[[(101, 41), 0.5, 501], [(106, 41), 0.5, 502], [(103, 42), 0.5, 503], [(117, 56), 0.5, 504], [(100, 40), 0.5, 505], [(110, 41), 0.5, 506], [(114, 45), 0.5, 507], [(105, 54), 0.5, 508], [(113, 49), 0.5, 509], [(111, 59), 0.5, 510], [(119, 56), 0.5, 511], [(119, 49), 0.5, 512], [(116, 58), 1.5, 513], [(101, 45), 1.5, 514], [(118, 58), 1.5, 515], [(118, 58), 1.5, 516], [(115, 40), 1.5, 517], [(102, 52), 1.5, 518], [(101, 52), 1.5, 519], [(109, 50), 1.5, 520]]\n",
      "[[(134, 58), 0.5, 521], [(128, 44), 0.5, 522], [(123, 50), 0.5, 523], [(132, 40), 0.5, 524], [(123, 46), 0.5, 525], [(122, 58), 0.5, 526], [(138, 51), 0.5, 527], [(133, 53), 0.5, 528], [(129, 56), 0.5, 529], [(130, 41), 0.5, 530], [(122, 52), 0.5, 531], [(132, 49), 0.5, 532], [(139, 45), 1.5, 533], [(131, 47), 1.5, 534], [(123, 50), 1.5, 535], [(126, 53), 1.5, 536], [(135, 49), 1.5, 537], [(129, 53), 1.5, 538], [(130, 40), 1.5, 539], [(127, 44), 1.5, 540]]\n",
      "[[(143, 54), 0.5, 541], [(142, 53), 0.5, 542], [(157, 43), 0.5, 543], [(156, 44), 0.5, 544], [(156, 53), 0.5, 545], [(155, 44), 0.5, 546], [(157, 56), 0.5, 547], [(152, 44), 0.5, 548], [(152, 55), 0.5, 549], [(147, 43), 0.5, 550], [(141, 43), 0.5, 551], [(155, 50), 0.5, 552], [(158, 56), 1.5, 553], [(156, 51), 1.5, 554], [(140, 44), 1.5, 555], [(146, 56), 1.5, 556], [(149, 43), 1.5, 557], [(153, 42), 1.5, 558], [(148, 42), 1.5, 559], [(159, 51), 1.5, 560]]\n",
      "[[(177, 55), 0.5, 561], [(163, 57), 0.5, 562], [(174, 41), 0.5, 563], [(172, 54), 0.5, 564], [(167, 50), 0.5, 565], [(177, 43), 0.5, 566], [(174, 42), 0.5, 567], [(168, 41), 0.5, 568], [(162, 50), 0.5, 569], [(176, 56), 0.5, 570], [(175, 52), 0.5, 571], [(169, 40), 0.5, 572], [(174, 51), 1.5, 573], [(173, 44), 1.5, 574], [(179, 51), 1.5, 575], [(175, 59), 1.5, 576], [(166, 54), 1.5, 577], [(175, 40), 1.5, 578], [(173, 45), 1.5, 579], [(161, 52), 1.5, 580]]\n",
      "[[(184, 55), 0.5, 581], [(199, 46), 0.5, 582], [(197, 41), 0.5, 583], [(183, 42), 0.5, 584], [(194, 48), 0.5, 585], [(189, 53), 0.5, 586], [(197, 55), 0.5, 587], [(184, 41), 0.5, 588], [(188, 48), 0.5, 589], [(195, 48), 0.5, 590], [(187, 49), 0.5, 591], [(187, 53), 0.5, 592], [(198, 42), 1.5, 593], [(188, 57), 1.5, 594], [(197, 41), 1.5, 595], [(196, 44), 1.5, 596], [(196, 42), 1.5, 597], [(191, 55), 1.5, 598], [(193, 43), 1.5, 599], [(180, 42), 1.5, 600]]\n",
      "[[(2, 66), 0.5, 601], [(6, 65), 0.5, 602], [(13, 64), 0.5, 603], [(15, 61), 0.5, 604], [(8, 69), 0.5, 605], [(12, 69), 0.5, 606], [(10, 67), 0.5, 607], [(18, 78), 0.5, 608], [(2, 78), 0.5, 609], [(17, 61), 0.5, 610], [(11, 79), 0.5, 611], [(14, 66), 0.5, 612], [(13, 76), 1.5, 613], [(13, 74), 1.5, 614], [(5, 62), 1.5, 615], [(0, 69), 1.5, 616], [(5, 70), 1.5, 617], [(1, 74), 1.5, 618], [(7, 69), 1.5, 619], [(17, 61), 1.5, 620]]\n",
      "[[(23, 64), 0.5, 621], [(37, 65), 0.5, 622], [(20, 74), 0.5, 623], [(21, 71), 0.5, 624], [(25, 68), 0.5, 625], [(25, 68), 0.5, 626], [(20, 67), 0.5, 627], [(30, 76), 0.5, 628], [(36, 64), 0.5, 629], [(35, 73), 0.5, 630], [(38, 76), 0.5, 631], [(32, 74), 0.5, 632], [(31, 64), 1.5, 633], [(29, 71), 1.5, 634], [(38, 78), 1.5, 635], [(34, 64), 1.5, 636], [(35, 77), 1.5, 637], [(37, 64), 1.5, 638], [(20, 64), 1.5, 639], [(26, 61), 1.5, 640]]\n",
      "[[(47, 79), 0.5, 641], [(43, 66), 0.5, 642], [(54, 67), 0.5, 643], [(46, 74), 0.5, 644], [(52, 69), 0.5, 645], [(51, 64), 0.5, 646], [(46, 65), 0.5, 647], [(42, 77), 0.5, 648], [(48, 75), 0.5, 649], [(54, 61), 0.5, 650], [(50, 74), 0.5, 651], [(51, 70), 0.5, 652], [(49, 79), 1.5, 653], [(51, 73), 1.5, 654], [(52, 68), 1.5, 655], [(40, 66), 1.5, 656], [(41, 61), 1.5, 657], [(42, 68), 1.5, 658], [(49, 73), 1.5, 659], [(55, 69), 1.5, 660]]\n",
      "[[(66, 74), 0.5, 661], [(63, 64), 0.5, 662], [(75, 64), 0.5, 663], [(73, 65), 0.5, 664], [(65, 69), 0.5, 665], [(75, 75), 0.5, 666], [(74, 63), 0.5, 667], [(79, 69), 0.5, 668], [(68, 64), 0.5, 669], [(71, 71), 0.5, 670], [(64, 67), 0.5, 671], [(76, 73), 0.5, 672], [(61, 75), 1.5, 673], [(61, 62), 1.5, 674], [(69, 77), 1.5, 675], [(78, 67), 1.5, 676], [(72, 64), 1.5, 677], [(77, 71), 1.5, 678], [(65, 62), 1.5, 679], [(64, 71), 1.5, 680]]\n",
      "[[(90, 77), 0.5, 681], [(89, 74), 0.5, 682], [(83, 74), 0.5, 683], [(82, 75), 0.5, 684], [(96, 75), 0.5, 685], [(85, 78), 0.5, 686], [(85, 69), 0.5, 687], [(96, 67), 0.5, 688], [(92, 71), 0.5, 689], [(80, 78), 0.5, 690], [(98, 71), 0.5, 691], [(83, 61), 0.5, 692], [(83, 75), 1.5, 693], [(96, 69), 1.5, 694], [(90, 77), 1.5, 695], [(85, 63), 1.5, 696], [(82, 77), 1.5, 697], [(94, 73), 1.5, 698], [(94, 62), 1.5, 699], [(83, 69), 1.5, 700]]\n",
      "[[(112, 79), 0.5, 701], [(102, 76), 0.5, 702], [(116, 76), 0.5, 703], [(115, 66), 0.5, 704], [(106, 69), 0.5, 705], [(103, 70), 0.5, 706], [(115, 72), 0.5, 707], [(107, 62), 0.5, 708], [(113, 79), 0.5, 709], [(103, 74), 0.5, 710], [(117, 76), 0.5, 711], [(108, 73), 0.5, 712], [(119, 61), 1.5, 713], [(109, 62), 1.5, 714], [(105, 72), 1.5, 715], [(114, 70), 1.5, 716], [(107, 67), 1.5, 717], [(105, 71), 1.5, 718], [(119, 72), 1.5, 719], [(107, 63), 1.5, 720]]\n",
      "[[(134, 76), 0.5, 721], [(139, 69), 0.5, 722], [(120, 62), 0.5, 723], [(135, 75), 0.5, 724], [(123, 60), 0.5, 725], [(120, 66), 0.5, 726], [(120, 74), 0.5, 727], [(135, 62), 0.5, 728], [(132, 61), 0.5, 729], [(125, 69), 0.5, 730], [(133, 65), 0.5, 731], [(136, 76), 0.5, 732], [(128, 63), 1.5, 733], [(133, 71), 1.5, 734], [(139, 70), 1.5, 735], [(134, 60), 1.5, 736], [(134, 71), 1.5, 737], [(135, 68), 1.5, 738], [(120, 70), 1.5, 739], [(129, 64), 1.5, 740]]\n",
      "[[(141, 66), 0.5, 741], [(145, 66), 0.5, 742], [(155, 62), 0.5, 743], [(143, 73), 0.5, 744], [(155, 61), 0.5, 745], [(143, 63), 0.5, 746], [(155, 71), 0.5, 747], [(148, 79), 0.5, 748], [(145, 67), 0.5, 749], [(154, 65), 0.5, 750], [(148, 76), 0.5, 751], [(140, 71), 0.5, 752], [(158, 76), 1.5, 753], [(156, 69), 1.5, 754], [(145, 67), 1.5, 755], [(145, 78), 1.5, 756], [(155, 63), 1.5, 757], [(144, 76), 1.5, 758], [(153, 79), 1.5, 759], [(141, 77), 1.5, 760]]\n",
      "[[(173, 78), 0.5, 761], [(161, 79), 0.5, 762], [(172, 63), 0.5, 763], [(169, 62), 0.5, 764], [(170, 71), 0.5, 765], [(177, 75), 0.5, 766], [(172, 62), 0.5, 767], [(173, 60), 0.5, 768], [(165, 69), 0.5, 769], [(179, 73), 0.5, 770], [(174, 61), 0.5, 771], [(167, 73), 0.5, 772], [(160, 62), 1.5, 773], [(176, 79), 1.5, 774], [(161, 67), 1.5, 775], [(170, 63), 1.5, 776], [(160, 65), 1.5, 777], [(161, 78), 1.5, 778], [(171, 77), 1.5, 779], [(162, 75), 1.5, 780]]\n",
      "[[(195, 72), 0.5, 781], [(183, 65), 0.5, 782], [(196, 64), 0.5, 783], [(190, 60), 0.5, 784], [(183, 69), 0.5, 785], [(190, 74), 0.5, 786], [(190, 60), 0.5, 787], [(183, 72), 0.5, 788], [(184, 60), 0.5, 789], [(186, 76), 0.5, 790], [(195, 68), 0.5, 791], [(193, 69), 0.5, 792], [(197, 70), 1.5, 793], [(180, 79), 1.5, 794], [(188, 69), 1.5, 795], [(190, 79), 1.5, 796], [(188, 73), 1.5, 797], [(188, 61), 1.5, 798], [(198, 61), 1.5, 799], [(197, 66), 1.5, 800]]\n",
      "[[(6, 80), 0.5, 801], [(4, 86), 0.5, 802], [(5, 92), 0.5, 803], [(3, 96), 0.5, 804], [(0, 89), 0.5, 805], [(3, 88), 0.5, 806], [(10, 92), 0.5, 807], [(4, 92), 0.5, 808], [(4, 86), 0.5, 809], [(15, 89), 0.5, 810], [(9, 81), 0.5, 811], [(8, 93), 0.5, 812], [(7, 80), 1.5, 813], [(2, 92), 1.5, 814], [(11, 98), 1.5, 815], [(5, 95), 1.5, 816], [(16, 94), 1.5, 817], [(18, 88), 1.5, 818], [(11, 84), 1.5, 819], [(4, 81), 1.5, 820]]\n",
      "[[(24, 92), 0.5, 821], [(22, 99), 0.5, 822], [(27, 94), 0.5, 823], [(22, 90), 0.5, 824], [(22, 84), 0.5, 825], [(34, 86), 0.5, 826], [(39, 81), 0.5, 827], [(37, 84), 0.5, 828], [(21, 96), 0.5, 829], [(26, 96), 0.5, 830], [(38, 97), 0.5, 831], [(26, 85), 0.5, 832], [(32, 85), 1.5, 833], [(31, 96), 1.5, 834], [(20, 84), 1.5, 835], [(23, 98), 1.5, 836], [(26, 87), 1.5, 837], [(36, 86), 1.5, 838], [(30, 96), 1.5, 839], [(30, 97), 1.5, 840]]\n",
      "[[(59, 99), 0.5, 841], [(40, 90), 0.5, 842], [(41, 90), 0.5, 843], [(42, 86), 0.5, 844], [(51, 82), 0.5, 845], [(42, 80), 0.5, 846], [(41, 98), 0.5, 847], [(43, 80), 0.5, 848], [(46, 99), 0.5, 849], [(44, 91), 0.5, 850], [(44, 87), 0.5, 851], [(49, 97), 0.5, 852], [(50, 83), 1.5, 853], [(51, 80), 1.5, 854], [(52, 91), 1.5, 855], [(42, 89), 1.5, 856], [(46, 93), 1.5, 857], [(40, 85), 1.5, 858], [(44, 99), 1.5, 859], [(57, 85), 1.5, 860]]\n",
      "[[(71, 95), 0.5, 861], [(67, 92), 0.5, 862], [(69, 88), 0.5, 863], [(65, 82), 0.5, 864], [(71, 82), 0.5, 865], [(62, 89), 0.5, 866], [(79, 99), 0.5, 867], [(72, 98), 0.5, 868], [(61, 97), 0.5, 869], [(70, 83), 0.5, 870], [(69, 87), 0.5, 871], [(65, 89), 0.5, 872], [(79, 94), 1.5, 873], [(69, 83), 1.5, 874], [(63, 83), 1.5, 875], [(70, 80), 1.5, 876], [(69, 91), 1.5, 877], [(65, 84), 1.5, 878], [(70, 96), 1.5, 879], [(61, 98), 1.5, 880]]\n",
      "[[(87, 95), 0.5, 881], [(83, 83), 0.5, 882], [(87, 81), 0.5, 883], [(80, 84), 0.5, 884], [(99, 94), 0.5, 885], [(94, 90), 0.5, 886], [(83, 94), 0.5, 887], [(92, 85), 0.5, 888], [(97, 88), 0.5, 889], [(82, 95), 0.5, 890], [(97, 96), 0.5, 891], [(95, 98), 0.5, 892], [(87, 99), 1.5, 893], [(95, 92), 1.5, 894], [(96, 97), 1.5, 895], [(95, 95), 1.5, 896], [(89, 99), 1.5, 897], [(93, 89), 1.5, 898], [(87, 84), 1.5, 899], [(99, 97), 1.5, 900]]\n",
      "[[(100, 93), 0.5, 901], [(102, 89), 0.5, 902], [(110, 92), 0.5, 903], [(110, 95), 0.5, 904], [(116, 83), 0.5, 905], [(108, 93), 0.5, 906], [(107, 83), 0.5, 907], [(100, 92), 0.5, 908], [(102, 89), 0.5, 909], [(113, 99), 0.5, 910], [(102, 97), 0.5, 911], [(100, 99), 0.5, 912], [(102, 81), 1.5, 913], [(117, 82), 1.5, 914], [(117, 91), 1.5, 915], [(109, 83), 1.5, 916], [(106, 83), 1.5, 917], [(114, 86), 1.5, 918], [(119, 99), 1.5, 919], [(102, 80), 1.5, 920]]\n",
      "[[(132, 80), 0.5, 921], [(127, 86), 0.5, 922], [(131, 84), 0.5, 923], [(121, 93), 0.5, 924], [(132, 92), 0.5, 925], [(126, 93), 0.5, 926], [(125, 95), 0.5, 927], [(137, 96), 0.5, 928], [(139, 87), 0.5, 929], [(136, 83), 0.5, 930], [(121, 87), 0.5, 931], [(122, 96), 0.5, 932], [(133, 93), 1.5, 933], [(127, 87), 1.5, 934], [(130, 96), 1.5, 935], [(133, 98), 1.5, 936], [(120, 95), 1.5, 937], [(122, 81), 1.5, 938], [(133, 88), 1.5, 939], [(121, 84), 1.5, 940]]\n",
      "[[(153, 95), 0.5, 941], [(149, 86), 0.5, 942], [(156, 87), 0.5, 943], [(150, 82), 0.5, 944], [(142, 98), 0.5, 945], [(145, 93), 0.5, 946], [(151, 88), 0.5, 947], [(155, 82), 0.5, 948], [(144, 91), 0.5, 949], [(147, 81), 0.5, 950], [(146, 88), 0.5, 951], [(142, 98), 0.5, 952], [(141, 90), 1.5, 953], [(146, 88), 1.5, 954], [(143, 86), 1.5, 955], [(154, 95), 1.5, 956], [(159, 87), 1.5, 957], [(154, 81), 1.5, 958], [(148, 91), 1.5, 959], [(152, 98), 1.5, 960]]\n",
      "[[(177, 80), 0.5, 961], [(175, 99), 0.5, 962], [(171, 97), 0.5, 963], [(174, 91), 0.5, 964], [(165, 85), 0.5, 965], [(174, 98), 0.5, 966], [(168, 80), 0.5, 967], [(167, 90), 0.5, 968], [(160, 96), 0.5, 969], [(179, 87), 0.5, 970], [(179, 97), 0.5, 971], [(178, 95), 0.5, 972], [(179, 97), 1.5, 973], [(175, 97), 1.5, 974], [(165, 85), 1.5, 975], [(167, 93), 1.5, 976], [(170, 82), 1.5, 977], [(175, 89), 1.5, 978], [(174, 93), 1.5, 979], [(160, 87), 1.5, 980]]\n",
      "[[(195, 93), 0.5, 981], [(180, 89), 0.5, 982], [(192, 80), 0.5, 983], [(180, 84), 0.5, 984], [(190, 99), 0.5, 985], [(191, 84), 0.5, 986], [(191, 84), 0.5, 987], [(183, 87), 0.5, 988], [(190, 95), 0.5, 989], [(193, 84), 0.5, 990], [(180, 84), 0.5, 991], [(194, 98), 0.5, 992], [(182, 83), 1.5, 993], [(196, 86), 1.5, 994], [(195, 84), 1.5, 995], [(199, 87), 1.5, 996], [(188, 83), 1.5, 997], [(184, 82), 1.5, 998], [(187, 82), 1.5, 999], [(190, 96), 1.5, 1000]]\n",
      "[[(18, 110), 0.5, 1001], [(8, 113), 0.5, 1002], [(9, 109), 0.5, 1003], [(10, 111), 0.5, 1004], [(16, 113), 0.5, 1005], [(10, 116), 0.5, 1006], [(14, 115), 0.5, 1007], [(8, 118), 0.5, 1008], [(17, 119), 0.5, 1009], [(16, 102), 0.5, 1010], [(5, 103), 0.5, 1011], [(2, 117), 0.5, 1012], [(17, 104), 1.5, 1013], [(9, 115), 1.5, 1014], [(14, 103), 1.5, 1015], [(3, 105), 1.5, 1016], [(5, 113), 1.5, 1017], [(8, 118), 1.5, 1018], [(13, 113), 1.5, 1019], [(16, 114), 1.5, 1020]]\n",
      "[[(30, 119), 0.5, 1021], [(32, 112), 0.5, 1022], [(27, 104), 0.5, 1023], [(26, 101), 0.5, 1024], [(31, 101), 0.5, 1025], [(20, 109), 0.5, 1026], [(36, 111), 0.5, 1027], [(20, 118), 0.5, 1028], [(36, 107), 0.5, 1029], [(26, 110), 0.5, 1030], [(26, 111), 0.5, 1031], [(35, 113), 0.5, 1032], [(32, 109), 1.5, 1033], [(35, 115), 1.5, 1034], [(24, 113), 1.5, 1035], [(29, 103), 1.5, 1036], [(36, 116), 1.5, 1037], [(25, 117), 1.5, 1038], [(35, 110), 1.5, 1039], [(31, 108), 1.5, 1040]]\n",
      "[[(58, 110), 0.5, 1041], [(52, 114), 0.5, 1042], [(44, 108), 0.5, 1043], [(55, 110), 0.5, 1044], [(44, 106), 0.5, 1045], [(43, 117), 0.5, 1046], [(42, 101), 0.5, 1047], [(47, 117), 0.5, 1048], [(56, 119), 0.5, 1049], [(54, 116), 0.5, 1050], [(59, 101), 0.5, 1051], [(55, 109), 0.5, 1052], [(51, 117), 1.5, 1053], [(59, 107), 1.5, 1054], [(44, 114), 1.5, 1055], [(53, 101), 1.5, 1056], [(49, 116), 1.5, 1057], [(50, 114), 1.5, 1058], [(59, 100), 1.5, 1059], [(59, 111), 1.5, 1060]]\n",
      "[[(77, 102), 0.5, 1061], [(66, 113), 0.5, 1062], [(63, 108), 0.5, 1063], [(76, 100), 0.5, 1064], [(69, 109), 0.5, 1065], [(72, 100), 0.5, 1066], [(63, 113), 0.5, 1067], [(66, 114), 0.5, 1068], [(77, 117), 0.5, 1069], [(68, 114), 0.5, 1070], [(64, 102), 0.5, 1071], [(78, 102), 0.5, 1072], [(75, 110), 1.5, 1073], [(72, 114), 1.5, 1074], [(61, 115), 1.5, 1075], [(69, 113), 1.5, 1076], [(71, 100), 1.5, 1077], [(66, 100), 1.5, 1078], [(75, 107), 1.5, 1079], [(62, 106), 1.5, 1080]]\n",
      "[[(98, 115), 0.5, 1081], [(90, 109), 0.5, 1082], [(88, 119), 0.5, 1083], [(97, 103), 0.5, 1084], [(80, 119), 0.5, 1085], [(84, 103), 0.5, 1086], [(80, 107), 0.5, 1087], [(94, 101), 0.5, 1088], [(91, 110), 0.5, 1089], [(96, 118), 0.5, 1090], [(83, 108), 0.5, 1091], [(90, 117), 0.5, 1092], [(82, 106), 1.5, 1093], [(94, 109), 1.5, 1094], [(83, 102), 1.5, 1095], [(82, 102), 1.5, 1096], [(85, 108), 1.5, 1097], [(83, 119), 1.5, 1098], [(91, 102), 1.5, 1099], [(93, 103), 1.5, 1100]]\n",
      "[[(115, 104), 0.5, 1101], [(112, 101), 0.5, 1102], [(107, 117), 0.5, 1103], [(115, 116), 0.5, 1104], [(100, 100), 0.5, 1105], [(118, 117), 0.5, 1106], [(118, 117), 0.5, 1107], [(108, 100), 0.5, 1108], [(110, 114), 0.5, 1109], [(107, 115), 0.5, 1110], [(114, 112), 0.5, 1111], [(113, 118), 0.5, 1112], [(103, 104), 1.5, 1113], [(119, 108), 1.5, 1114], [(119, 103), 1.5, 1115], [(109, 103), 1.5, 1116], [(101, 118), 1.5, 1117], [(104, 102), 1.5, 1118], [(101, 118), 1.5, 1119], [(103, 109), 1.5, 1120]]\n",
      "[[(120, 101), 0.5, 1121], [(130, 108), 0.5, 1122], [(123, 119), 0.5, 1123], [(121, 116), 0.5, 1124], [(123, 100), 0.5, 1125], [(137, 107), 0.5, 1126], [(134, 105), 0.5, 1127], [(121, 100), 0.5, 1128], [(120, 116), 0.5, 1129], [(138, 111), 0.5, 1130], [(124, 115), 0.5, 1131], [(135, 108), 0.5, 1132], [(128, 102), 1.5, 1133], [(121, 112), 1.5, 1134], [(128, 105), 1.5, 1135], [(136, 110), 1.5, 1136], [(122, 101), 1.5, 1137], [(132, 110), 1.5, 1138], [(131, 108), 1.5, 1139], [(134, 119), 1.5, 1140]]\n",
      "[[(140, 105), 0.5, 1141], [(153, 102), 0.5, 1142], [(150, 115), 0.5, 1143], [(157, 109), 0.5, 1144], [(146, 113), 0.5, 1145], [(157, 104), 0.5, 1146], [(147, 103), 0.5, 1147], [(155, 111), 0.5, 1148], [(152, 117), 0.5, 1149], [(142, 103), 0.5, 1150], [(156, 114), 0.5, 1151], [(150, 105), 0.5, 1152], [(153, 102), 1.5, 1153], [(159, 103), 1.5, 1154], [(146, 107), 1.5, 1155], [(141, 111), 1.5, 1156], [(158, 104), 1.5, 1157], [(157, 119), 1.5, 1158], [(144, 110), 1.5, 1159], [(154, 107), 1.5, 1160]]\n",
      "[[(177, 106), 0.5, 1161], [(172, 115), 0.5, 1162], [(167, 118), 0.5, 1163], [(169, 100), 0.5, 1164], [(165, 100), 0.5, 1165], [(167, 105), 0.5, 1166], [(179, 114), 0.5, 1167], [(170, 103), 0.5, 1168], [(164, 102), 0.5, 1169], [(163, 105), 0.5, 1170], [(179, 115), 0.5, 1171], [(164, 104), 0.5, 1172], [(167, 102), 1.5, 1173], [(176, 111), 1.5, 1174], [(164, 113), 1.5, 1175], [(167, 113), 1.5, 1176], [(178, 109), 1.5, 1177], [(170, 102), 1.5, 1178], [(168, 111), 1.5, 1179], [(173, 110), 1.5, 1180]]\n",
      "[[(196, 103), 0.5, 1181], [(193, 100), 0.5, 1182], [(184, 104), 0.5, 1183], [(184, 109), 0.5, 1184], [(193, 101), 0.5, 1185], [(193, 110), 0.5, 1186], [(193, 114), 0.5, 1187], [(197, 110), 0.5, 1188], [(187, 111), 0.5, 1189], [(189, 110), 0.5, 1190], [(195, 104), 0.5, 1191], [(196, 107), 0.5, 1192], [(190, 115), 1.5, 1193], [(180, 118), 1.5, 1194], [(192, 113), 1.5, 1195], [(194, 101), 1.5, 1196], [(186, 110), 1.5, 1197], [(191, 119), 1.5, 1198], [(196, 115), 1.5, 1199], [(190, 118), 1.5, 1200]]\n",
      "[[(15, 139), 0.5, 1201], [(4, 120), 0.5, 1202], [(1, 137), 0.5, 1203], [(12, 122), 0.5, 1204], [(0, 132), 0.5, 1205], [(17, 125), 0.5, 1206], [(18, 132), 0.5, 1207], [(0, 133), 0.5, 1208], [(13, 135), 0.5, 1209], [(0, 131), 0.5, 1210], [(8, 133), 0.5, 1211], [(12, 123), 0.5, 1212], [(16, 124), 1.5, 1213], [(14, 124), 1.5, 1214], [(18, 126), 1.5, 1215], [(18, 130), 1.5, 1216], [(16, 134), 1.5, 1217], [(4, 122), 1.5, 1218], [(18, 122), 1.5, 1219], [(0, 120), 1.5, 1220]]\n",
      "[[(24, 126), 0.5, 1221], [(33, 127), 0.5, 1222], [(32, 131), 0.5, 1223], [(31, 125), 0.5, 1224], [(37, 136), 0.5, 1225], [(28, 129), 0.5, 1226], [(25, 121), 0.5, 1227], [(22, 131), 0.5, 1228], [(32, 136), 0.5, 1229], [(25, 120), 0.5, 1230], [(36, 124), 0.5, 1231], [(21, 139), 0.5, 1232], [(31, 123), 1.5, 1233], [(27, 128), 1.5, 1234], [(36, 133), 1.5, 1235], [(26, 139), 1.5, 1236], [(27, 129), 1.5, 1237], [(39, 128), 1.5, 1238], [(31, 136), 1.5, 1239], [(20, 127), 1.5, 1240]]\n",
      "[[(43, 123), 0.5, 1241], [(51, 124), 0.5, 1242], [(41, 127), 0.5, 1243], [(47, 135), 0.5, 1244], [(41, 131), 0.5, 1245], [(45, 122), 0.5, 1246], [(54, 126), 0.5, 1247], [(50, 123), 0.5, 1248], [(58, 129), 0.5, 1249], [(40, 121), 0.5, 1250], [(53, 138), 0.5, 1251], [(50, 121), 0.5, 1252], [(51, 139), 1.5, 1253], [(58, 128), 1.5, 1254], [(55, 125), 1.5, 1255], [(41, 133), 1.5, 1256], [(52, 130), 1.5, 1257], [(47, 133), 1.5, 1258], [(42, 122), 1.5, 1259], [(54, 135), 1.5, 1260]]\n",
      "[[(74, 129), 0.5, 1261], [(70, 138), 0.5, 1262], [(72, 122), 0.5, 1263], [(70, 137), 0.5, 1264], [(72, 121), 0.5, 1265], [(74, 128), 0.5, 1266], [(60, 123), 0.5, 1267], [(77, 123), 0.5, 1268], [(66, 123), 0.5, 1269], [(73, 121), 0.5, 1270], [(66, 139), 0.5, 1271], [(67, 136), 0.5, 1272], [(60, 137), 1.5, 1273], [(73, 121), 1.5, 1274], [(78, 124), 1.5, 1275], [(74, 128), 1.5, 1276], [(72, 129), 1.5, 1277], [(78, 130), 1.5, 1278], [(77, 132), 1.5, 1279], [(71, 134), 1.5, 1280]]\n",
      "[[(96, 130), 0.5, 1281], [(81, 128), 0.5, 1282], [(88, 134), 0.5, 1283], [(88, 131), 0.5, 1284], [(96, 127), 0.5, 1285], [(86, 120), 0.5, 1286], [(80, 136), 0.5, 1287], [(89, 131), 0.5, 1288], [(80, 134), 0.5, 1289], [(85, 135), 0.5, 1290], [(86, 126), 0.5, 1291], [(92, 124), 0.5, 1292], [(88, 135), 1.5, 1293], [(95, 135), 1.5, 1294], [(88, 124), 1.5, 1295], [(85, 135), 1.5, 1296], [(80, 130), 1.5, 1297], [(88, 127), 1.5, 1298], [(94, 134), 1.5, 1299], [(86, 138), 1.5, 1300]]\n",
      "[[(109, 128), 0.5, 1301], [(101, 127), 0.5, 1302], [(118, 138), 0.5, 1303], [(107, 123), 0.5, 1304], [(102, 137), 0.5, 1305], [(115, 122), 0.5, 1306], [(118, 136), 0.5, 1307], [(118, 135), 0.5, 1308], [(114, 135), 0.5, 1309], [(119, 136), 0.5, 1310], [(112, 122), 0.5, 1311], [(109, 132), 0.5, 1312], [(118, 139), 1.5, 1313], [(115, 124), 1.5, 1314], [(111, 130), 1.5, 1315], [(119, 136), 1.5, 1316], [(111, 134), 1.5, 1317], [(102, 128), 1.5, 1318], [(107, 127), 1.5, 1319], [(103, 125), 1.5, 1320]]\n",
      "[[(133, 122), 0.5, 1321], [(125, 134), 0.5, 1322], [(138, 124), 0.5, 1323], [(132, 124), 0.5, 1324], [(139, 126), 0.5, 1325], [(137, 135), 0.5, 1326], [(134, 122), 0.5, 1327], [(134, 125), 0.5, 1328], [(125, 138), 0.5, 1329], [(120, 132), 0.5, 1330], [(137, 120), 0.5, 1331], [(135, 127), 0.5, 1332], [(121, 137), 1.5, 1333], [(126, 121), 1.5, 1334], [(126, 121), 1.5, 1335], [(129, 136), 1.5, 1336], [(126, 129), 1.5, 1337], [(130, 130), 1.5, 1338], [(127, 138), 1.5, 1339], [(131, 122), 1.5, 1340]]\n",
      "[[(143, 122), 0.5, 1341], [(150, 123), 0.5, 1342], [(140, 120), 0.5, 1343], [(140, 122), 0.5, 1344], [(147, 133), 0.5, 1345], [(144, 122), 0.5, 1346], [(145, 136), 0.5, 1347], [(143, 135), 0.5, 1348], [(159, 122), 0.5, 1349], [(151, 121), 0.5, 1350], [(151, 132), 0.5, 1351], [(151, 134), 0.5, 1352], [(140, 126), 1.5, 1353], [(157, 132), 1.5, 1354], [(156, 127), 1.5, 1355], [(159, 120), 1.5, 1356], [(140, 132), 1.5, 1357], [(147, 138), 1.5, 1358], [(153, 133), 1.5, 1359], [(149, 137), 1.5, 1360]]\n",
      "[[(169, 129), 0.5, 1361], [(176, 129), 0.5, 1362], [(169, 133), 0.5, 1363], [(179, 126), 0.5, 1364], [(164, 122), 0.5, 1365], [(166, 126), 0.5, 1366], [(175, 121), 0.5, 1367], [(172, 136), 0.5, 1368], [(168, 135), 0.5, 1369], [(178, 134), 0.5, 1370], [(171, 120), 0.5, 1371], [(171, 135), 0.5, 1372], [(169, 128), 1.5, 1373], [(172, 134), 1.5, 1374], [(175, 122), 1.5, 1375], [(160, 128), 1.5, 1376], [(161, 135), 1.5, 1377], [(163, 127), 1.5, 1378], [(173, 123), 1.5, 1379], [(167, 133), 1.5, 1380]]\n",
      "[[(196, 133), 0.5, 1381], [(199, 131), 0.5, 1382], [(182, 131), 0.5, 1383], [(184, 122), 0.5, 1384], [(184, 125), 0.5, 1385], [(196, 120), 0.5, 1386], [(188, 128), 0.5, 1387], [(191, 139), 0.5, 1388], [(197, 133), 0.5, 1389], [(195, 132), 0.5, 1390], [(187, 125), 0.5, 1391], [(184, 135), 0.5, 1392], [(199, 128), 1.5, 1393], [(193, 128), 1.5, 1394], [(194, 132), 1.5, 1395], [(182, 127), 1.5, 1396], [(195, 130), 1.5, 1397], [(198, 123), 1.5, 1398], [(194, 132), 1.5, 1399], [(197, 131), 1.5, 1400]]\n",
      "[[(3, 140), 0.5, 1401], [(16, 145), 0.5, 1402], [(12, 155), 0.5, 1403], [(2, 150), 0.5, 1404], [(0, 154), 0.5, 1405], [(1, 151), 0.5, 1406], [(3, 158), 0.5, 1407], [(4, 152), 0.5, 1408], [(18, 144), 0.5, 1409], [(12, 159), 0.5, 1410], [(5, 159), 0.5, 1411], [(4, 153), 0.5, 1412], [(18, 155), 1.5, 1413], [(5, 151), 1.5, 1414], [(10, 151), 1.5, 1415], [(6, 158), 1.5, 1416], [(9, 152), 1.5, 1417], [(19, 153), 1.5, 1418], [(4, 152), 1.5, 1419], [(1, 147), 1.5, 1420]]\n",
      "[[(30, 158), 0.5, 1421], [(24, 145), 0.5, 1422], [(23, 146), 0.5, 1423], [(29, 148), 0.5, 1424], [(21, 140), 0.5, 1425], [(23, 142), 0.5, 1426], [(37, 159), 0.5, 1427], [(28, 159), 0.5, 1428], [(39, 156), 0.5, 1429], [(31, 146), 0.5, 1430], [(32, 141), 0.5, 1431], [(28, 146), 0.5, 1432], [(25, 151), 1.5, 1433], [(23, 159), 1.5, 1434], [(20, 145), 1.5, 1435], [(33, 152), 1.5, 1436], [(29, 155), 1.5, 1437], [(39, 150), 1.5, 1438], [(22, 156), 1.5, 1439], [(32, 151), 1.5, 1440]]\n",
      "[[(42, 152), 0.5, 1441], [(55, 151), 0.5, 1442], [(41, 152), 0.5, 1443], [(49, 151), 0.5, 1444], [(53, 153), 0.5, 1445], [(56, 152), 0.5, 1446], [(49, 158), 0.5, 1447], [(59, 142), 0.5, 1448], [(45, 152), 0.5, 1449], [(43, 140), 0.5, 1450], [(45, 151), 0.5, 1451], [(49, 143), 0.5, 1452], [(47, 142), 1.5, 1453], [(53, 142), 1.5, 1454], [(46, 157), 1.5, 1455], [(55, 149), 1.5, 1456], [(53, 153), 1.5, 1457], [(56, 154), 1.5, 1458], [(50, 155), 1.5, 1459], [(40, 150), 1.5, 1460]]\n",
      "[[(74, 142), 0.5, 1461], [(76, 148), 0.5, 1462], [(66, 148), 0.5, 1463], [(60, 157), 0.5, 1464], [(67, 147), 0.5, 1465], [(70, 144), 0.5, 1466], [(78, 158), 0.5, 1467], [(77, 153), 0.5, 1468], [(67, 152), 0.5, 1469], [(65, 156), 0.5, 1470], [(71, 149), 0.5, 1471], [(63, 158), 0.5, 1472], [(74, 158), 1.5, 1473], [(62, 153), 1.5, 1474], [(75, 147), 1.5, 1475], [(64, 142), 1.5, 1476], [(70, 147), 1.5, 1477], [(77, 151), 1.5, 1478], [(75, 156), 1.5, 1479], [(66, 140), 1.5, 1480]]\n",
      "[[(99, 153), 0.5, 1481], [(97, 146), 0.5, 1482], [(97, 159), 0.5, 1483], [(88, 159), 0.5, 1484], [(96, 144), 0.5, 1485], [(97, 151), 0.5, 1486], [(92, 141), 0.5, 1487], [(89, 142), 0.5, 1488], [(83, 142), 0.5, 1489], [(97, 157), 0.5, 1490], [(87, 149), 0.5, 1491], [(94, 152), 0.5, 1492], [(84, 152), 1.5, 1493], [(91, 158), 1.5, 1494], [(89, 145), 1.5, 1495], [(88, 144), 1.5, 1496], [(94, 148), 1.5, 1497], [(86, 151), 1.5, 1498], [(97, 146), 1.5, 1499], [(83, 150), 1.5, 1500]]\n",
      "[[(106, 144), 0.5, 1501], [(117, 141), 0.5, 1502], [(104, 158), 0.5, 1503], [(117, 144), 0.5, 1504], [(117, 153), 0.5, 1505], [(112, 155), 0.5, 1506], [(114, 142), 0.5, 1507], [(105, 142), 0.5, 1508], [(113, 150), 0.5, 1509], [(100, 152), 0.5, 1510], [(111, 140), 0.5, 1511], [(101, 143), 0.5, 1512], [(108, 157), 1.5, 1513], [(100, 148), 1.5, 1514], [(114, 155), 1.5, 1515], [(109, 144), 1.5, 1516], [(101, 141), 1.5, 1517], [(107, 145), 1.5, 1518], [(108, 159), 1.5, 1519], [(104, 141), 1.5, 1520]]\n",
      "[[(133, 152), 0.5, 1521], [(122, 155), 0.5, 1522], [(138, 159), 0.5, 1523], [(130, 149), 0.5, 1524], [(130, 153), 0.5, 1525], [(133, 155), 0.5, 1526], [(138, 143), 0.5, 1527], [(138, 142), 0.5, 1528], [(127, 146), 0.5, 1529], [(121, 142), 0.5, 1530], [(127, 151), 0.5, 1531], [(123, 147), 0.5, 1532], [(120, 154), 1.5, 1533], [(125, 144), 1.5, 1534], [(129, 152), 1.5, 1535], [(124, 147), 1.5, 1536], [(130, 140), 1.5, 1537], [(127, 142), 1.5, 1538], [(136, 142), 1.5, 1539], [(129, 147), 1.5, 1540]]\n",
      "[[(147, 158), 0.5, 1541], [(140, 147), 0.5, 1542], [(150, 147), 0.5, 1543], [(149, 145), 0.5, 1544], [(149, 154), 0.5, 1545], [(151, 150), 0.5, 1546], [(141, 148), 0.5, 1547], [(153, 150), 0.5, 1548], [(148, 155), 0.5, 1549], [(156, 150), 0.5, 1550], [(157, 155), 0.5, 1551], [(154, 156), 0.5, 1552], [(142, 141), 1.5, 1553], [(146, 158), 1.5, 1554], [(143, 145), 1.5, 1555], [(142, 149), 1.5, 1556], [(145, 153), 1.5, 1557], [(144, 148), 1.5, 1558], [(157, 143), 1.5, 1559], [(155, 153), 1.5, 1560]]\n",
      "[[(167, 158), 0.5, 1561], [(162, 153), 0.5, 1562], [(179, 142), 0.5, 1563], [(169, 155), 0.5, 1564], [(174, 159), 0.5, 1565], [(162, 152), 0.5, 1566], [(160, 144), 0.5, 1567], [(178, 151), 0.5, 1568], [(179, 145), 0.5, 1569], [(166, 154), 0.5, 1570], [(166, 154), 0.5, 1571], [(174, 152), 0.5, 1572], [(167, 143), 1.5, 1573], [(161, 140), 1.5, 1574], [(165, 155), 1.5, 1575], [(170, 157), 1.5, 1576], [(169, 148), 1.5, 1577], [(171, 150), 1.5, 1578], [(163, 159), 1.5, 1579], [(178, 144), 1.5, 1580]]\n",
      "[[(194, 140), 0.5, 1581], [(189, 148), 0.5, 1582], [(199, 145), 0.5, 1583], [(198, 146), 0.5, 1584], [(183, 142), 0.5, 1585], [(197, 141), 0.5, 1586], [(193, 147), 0.5, 1587], [(196, 140), 0.5, 1588], [(187, 151), 0.5, 1589], [(181, 157), 0.5, 1590], [(185, 151), 0.5, 1591], [(198, 145), 0.5, 1592], [(190, 157), 1.5, 1593], [(184, 147), 1.5, 1594], [(181, 146), 1.5, 1595], [(191, 143), 1.5, 1596], [(197, 142), 1.5, 1597], [(183, 151), 1.5, 1598], [(184, 144), 1.5, 1599], [(199, 143), 1.5, 1600]]\n",
      "[[(8, 168), 0.5, 1601], [(4, 160), 0.5, 1602], [(5, 176), 0.5, 1603], [(15, 161), 0.5, 1604], [(6, 167), 0.5, 1605], [(14, 173), 0.5, 1606], [(17, 163), 0.5, 1607], [(0, 172), 0.5, 1608], [(19, 176), 0.5, 1609], [(11, 165), 0.5, 1610], [(3, 179), 0.5, 1611], [(6, 172), 0.5, 1612], [(12, 175), 1.5, 1613], [(16, 165), 1.5, 1614], [(11, 173), 1.5, 1615], [(18, 160), 1.5, 1616], [(11, 167), 1.5, 1617], [(18, 171), 1.5, 1618], [(1, 175), 1.5, 1619], [(12, 174), 1.5, 1620]]\n",
      "[[(29, 172), 0.5, 1621], [(29, 164), 0.5, 1622], [(24, 174), 0.5, 1623], [(22, 160), 0.5, 1624], [(38, 165), 0.5, 1625], [(29, 160), 0.5, 1626], [(21, 171), 0.5, 1627], [(37, 160), 0.5, 1628], [(30, 171), 0.5, 1629], [(22, 168), 0.5, 1630], [(35, 169), 0.5, 1631], [(26, 162), 0.5, 1632], [(20, 168), 1.5, 1633], [(20, 172), 1.5, 1634], [(29, 169), 1.5, 1635], [(31, 178), 1.5, 1636], [(28, 169), 1.5, 1637], [(30, 176), 1.5, 1638], [(21, 160), 1.5, 1639], [(32, 163), 1.5, 1640]]\n",
      "[[(43, 175), 0.5, 1641], [(46, 163), 0.5, 1642], [(48, 166), 0.5, 1643], [(41, 178), 0.5, 1644], [(46, 164), 0.5, 1645], [(41, 171), 0.5, 1646], [(41, 172), 0.5, 1647], [(57, 177), 0.5, 1648], [(41, 175), 0.5, 1649], [(51, 177), 0.5, 1650], [(57, 168), 0.5, 1651], [(44, 177), 0.5, 1652], [(52, 166), 1.5, 1653], [(40, 178), 1.5, 1654], [(50, 164), 1.5, 1655], [(54, 170), 1.5, 1656], [(52, 168), 1.5, 1657], [(58, 178), 1.5, 1658], [(49, 160), 1.5, 1659], [(56, 173), 1.5, 1660]]\n",
      "[[(72, 169), 0.5, 1661], [(79, 162), 0.5, 1662], [(71, 160), 0.5, 1663], [(65, 176), 0.5, 1664], [(64, 175), 0.5, 1665], [(72, 172), 0.5, 1666], [(77, 160), 0.5, 1667], [(72, 175), 0.5, 1668], [(62, 164), 0.5, 1669], [(68, 170), 0.5, 1670], [(66, 169), 0.5, 1671], [(79, 166), 0.5, 1672], [(64, 163), 1.5, 1673], [(72, 176), 1.5, 1674], [(72, 177), 1.5, 1675], [(78, 174), 1.5, 1676], [(67, 166), 1.5, 1677], [(67, 170), 1.5, 1678], [(64, 163), 1.5, 1679], [(77, 176), 1.5, 1680]]\n",
      "[[(87, 162), 0.5, 1681], [(91, 167), 0.5, 1682], [(84, 173), 0.5, 1683], [(99, 172), 0.5, 1684], [(93, 165), 0.5, 1685], [(88, 164), 0.5, 1686], [(90, 165), 0.5, 1687], [(86, 172), 0.5, 1688], [(82, 166), 0.5, 1689], [(83, 179), 0.5, 1690], [(80, 165), 0.5, 1691], [(80, 173), 0.5, 1692], [(95, 172), 1.5, 1693], [(91, 174), 1.5, 1694], [(87, 170), 1.5, 1695], [(98, 162), 1.5, 1696], [(82, 176), 1.5, 1697], [(99, 175), 1.5, 1698], [(87, 177), 1.5, 1699], [(93, 166), 1.5, 1700]]\n",
      "[[(106, 169), 0.5, 1701], [(112, 178), 0.5, 1702], [(118, 174), 0.5, 1703], [(117, 161), 0.5, 1704], [(102, 163), 0.5, 1705], [(100, 162), 0.5, 1706], [(117, 173), 0.5, 1707], [(110, 161), 0.5, 1708], [(114, 162), 0.5, 1709], [(114, 165), 0.5, 1710], [(113, 163), 0.5, 1711], [(113, 163), 0.5, 1712], [(108, 177), 1.5, 1713], [(109, 177), 1.5, 1714], [(110, 172), 1.5, 1715], [(118, 161), 1.5, 1716], [(106, 177), 1.5, 1717], [(108, 172), 1.5, 1718], [(101, 170), 1.5, 1719], [(116, 175), 1.5, 1720]]\n",
      "[[(126, 171), 0.5, 1721], [(129, 173), 0.5, 1722], [(130, 173), 0.5, 1723], [(127, 165), 0.5, 1724], [(138, 179), 0.5, 1725], [(122, 164), 0.5, 1726], [(122, 169), 0.5, 1727], [(133, 175), 0.5, 1728], [(132, 165), 0.5, 1729], [(123, 174), 0.5, 1730], [(135, 168), 0.5, 1731], [(129, 161), 0.5, 1732], [(130, 170), 1.5, 1733], [(121, 162), 1.5, 1734], [(127, 166), 1.5, 1735], [(121, 179), 1.5, 1736], [(126, 175), 1.5, 1737], [(128, 163), 1.5, 1738], [(130, 165), 1.5, 1739], [(138, 165), 1.5, 1740]]\n",
      "[[(144, 163), 0.5, 1741], [(149, 170), 0.5, 1742], [(141, 178), 0.5, 1743], [(146, 171), 0.5, 1744], [(149, 175), 0.5, 1745], [(148, 163), 0.5, 1746], [(150, 178), 0.5, 1747], [(140, 162), 0.5, 1748], [(148, 169), 0.5, 1749], [(148, 173), 0.5, 1750], [(151, 179), 0.5, 1751], [(159, 168), 0.5, 1752], [(147, 165), 1.5, 1753], [(147, 160), 1.5, 1754], [(153, 176), 1.5, 1755], [(148, 163), 1.5, 1756], [(152, 167), 1.5, 1757], [(141, 164), 1.5, 1758], [(142, 166), 1.5, 1759], [(141, 161), 1.5, 1760]]\n",
      "[[(171, 174), 0.5, 1761], [(179, 169), 0.5, 1762], [(168, 177), 0.5, 1763], [(166, 164), 0.5, 1764], [(169, 177), 0.5, 1765], [(163, 164), 0.5, 1766], [(173, 176), 0.5, 1767], [(163, 176), 0.5, 1768], [(174, 167), 0.5, 1769], [(163, 163), 0.5, 1770], [(166, 163), 0.5, 1771], [(160, 176), 0.5, 1772], [(176, 175), 1.5, 1773], [(162, 175), 1.5, 1774], [(165, 164), 1.5, 1775], [(167, 171), 1.5, 1776], [(160, 178), 1.5, 1777], [(160, 160), 1.5, 1778], [(171, 163), 1.5, 1779], [(167, 165), 1.5, 1780]]\n",
      "[[(188, 171), 0.5, 1781], [(190, 174), 0.5, 1782], [(195, 175), 0.5, 1783], [(186, 163), 0.5, 1784], [(180, 168), 0.5, 1785], [(181, 172), 0.5, 1786], [(182, 167), 0.5, 1787], [(182, 165), 0.5, 1788], [(187, 165), 0.5, 1789], [(185, 163), 0.5, 1790], [(188, 177), 0.5, 1791], [(192, 178), 0.5, 1792], [(194, 172), 1.5, 1793], [(199, 177), 1.5, 1794], [(191, 161), 1.5, 1795], [(180, 173), 1.5, 1796], [(185, 174), 1.5, 1797], [(182, 168), 1.5, 1798], [(180, 165), 1.5, 1799], [(180, 173), 1.5, 1800]]\n",
      "[[(14, 197), 0.5, 1801], [(17, 184), 0.5, 1802], [(0, 196), 0.5, 1803], [(3, 182), 0.5, 1804], [(3, 182), 0.5, 1805], [(18, 196), 0.5, 1806], [(12, 195), 0.5, 1807], [(12, 188), 0.5, 1808], [(10, 192), 0.5, 1809], [(4, 191), 0.5, 1810], [(12, 191), 0.5, 1811], [(17, 197), 0.5, 1812], [(14, 180), 1.5, 1813], [(1, 180), 1.5, 1814], [(14, 192), 1.5, 1815], [(7, 196), 1.5, 1816], [(1, 188), 1.5, 1817], [(7, 187), 1.5, 1818], [(15, 180), 1.5, 1819], [(19, 180), 1.5, 1820]]\n",
      "[[(24, 192), 0.5, 1821], [(30, 190), 0.5, 1822], [(23, 196), 0.5, 1823], [(38, 188), 0.5, 1824], [(31, 182), 0.5, 1825], [(22, 194), 0.5, 1826], [(31, 189), 0.5, 1827], [(27, 187), 0.5, 1828], [(25, 187), 0.5, 1829], [(22, 180), 0.5, 1830], [(38, 181), 0.5, 1831], [(37, 185), 0.5, 1832], [(33, 193), 1.5, 1833], [(35, 191), 1.5, 1834], [(22, 199), 1.5, 1835], [(32, 187), 1.5, 1836], [(29, 184), 1.5, 1837], [(26, 188), 1.5, 1838], [(21, 194), 1.5, 1839], [(30, 199), 1.5, 1840]]\n",
      "[[(45, 198), 0.5, 1841], [(48, 187), 0.5, 1842], [(55, 182), 0.5, 1843], [(46, 183), 0.5, 1844], [(57, 188), 0.5, 1845], [(56, 192), 0.5, 1846], [(42, 188), 0.5, 1847], [(43, 190), 0.5, 1848], [(50, 197), 0.5, 1849], [(48, 191), 0.5, 1850], [(44, 188), 0.5, 1851], [(46, 196), 0.5, 1852], [(49, 183), 1.5, 1853], [(59, 182), 1.5, 1854], [(59, 186), 1.5, 1855], [(56, 189), 1.5, 1856], [(56, 196), 1.5, 1857], [(48, 182), 1.5, 1858], [(45, 189), 1.5, 1859], [(54, 185), 1.5, 1860]]\n",
      "[[(70, 196), 0.5, 1861], [(69, 182), 0.5, 1862], [(71, 189), 0.5, 1863], [(60, 198), 0.5, 1864], [(73, 198), 0.5, 1865], [(60, 188), 0.5, 1866], [(71, 195), 0.5, 1867], [(68, 182), 0.5, 1868], [(76, 192), 0.5, 1869], [(64, 193), 0.5, 1870], [(65, 184), 0.5, 1871], [(63, 193), 0.5, 1872], [(69, 182), 1.5, 1873], [(70, 184), 1.5, 1874], [(76, 194), 1.5, 1875], [(73, 197), 1.5, 1876], [(60, 191), 1.5, 1877], [(78, 196), 1.5, 1878], [(72, 193), 1.5, 1879], [(67, 186), 1.5, 1880]]\n",
      "[[(97, 181), 0.5, 1881], [(82, 197), 0.5, 1882], [(94, 193), 0.5, 1883], [(96, 182), 0.5, 1884], [(89, 196), 0.5, 1885], [(96, 194), 0.5, 1886], [(90, 186), 0.5, 1887], [(86, 193), 0.5, 1888], [(91, 183), 0.5, 1889], [(82, 191), 0.5, 1890], [(97, 184), 0.5, 1891], [(87, 197), 0.5, 1892], [(82, 198), 1.5, 1893], [(83, 196), 1.5, 1894], [(97, 189), 1.5, 1895], [(96, 198), 1.5, 1896], [(94, 186), 1.5, 1897], [(91, 189), 1.5, 1898], [(95, 183), 1.5, 1899], [(99, 187), 1.5, 1900]]\n",
      "[[(114, 187), 0.5, 1901], [(106, 198), 0.5, 1902], [(108, 194), 0.5, 1903], [(103, 189), 0.5, 1904], [(107, 186), 0.5, 1905], [(117, 189), 0.5, 1906], [(106, 184), 0.5, 1907], [(112, 196), 0.5, 1908], [(113, 194), 0.5, 1909], [(110, 183), 0.5, 1910], [(100, 186), 0.5, 1911], [(105, 183), 0.5, 1912], [(112, 181), 1.5, 1913], [(102, 192), 1.5, 1914], [(112, 182), 1.5, 1915], [(104, 186), 1.5, 1916], [(109, 196), 1.5, 1917], [(107, 191), 1.5, 1918], [(113, 186), 1.5, 1919], [(119, 197), 1.5, 1920]]\n",
      "[[(139, 181), 0.5, 1921], [(129, 183), 0.5, 1922], [(130, 188), 0.5, 1923], [(129, 193), 0.5, 1924], [(128, 186), 0.5, 1925], [(131, 191), 0.5, 1926], [(132, 191), 0.5, 1927], [(127, 197), 0.5, 1928], [(134, 182), 0.5, 1929], [(136, 190), 0.5, 1930], [(128, 195), 0.5, 1931], [(137, 190), 0.5, 1932], [(131, 190), 1.5, 1933], [(131, 189), 1.5, 1934], [(128, 195), 1.5, 1935], [(120, 198), 1.5, 1936], [(121, 189), 1.5, 1937], [(131, 191), 1.5, 1938], [(121, 180), 1.5, 1939], [(128, 197), 1.5, 1940]]\n",
      "[[(155, 196), 0.5, 1941], [(145, 184), 0.5, 1942], [(153, 187), 0.5, 1943], [(148, 190), 0.5, 1944], [(142, 185), 0.5, 1945], [(154, 195), 0.5, 1946], [(149, 199), 0.5, 1947], [(142, 198), 0.5, 1948], [(153, 185), 0.5, 1949], [(152, 194), 0.5, 1950], [(157, 190), 0.5, 1951], [(146, 190), 0.5, 1952], [(147, 194), 1.5, 1953], [(149, 187), 1.5, 1954], [(159, 184), 1.5, 1955], [(152, 188), 1.5, 1956], [(141, 195), 1.5, 1957], [(144, 195), 1.5, 1958], [(147, 182), 1.5, 1959], [(154, 184), 1.5, 1960]]\n",
      "[[(171, 192), 0.5, 1961], [(179, 193), 0.5, 1962], [(170, 198), 0.5, 1963], [(178, 197), 0.5, 1964], [(174, 192), 0.5, 1965], [(175, 196), 0.5, 1966], [(175, 198), 0.5, 1967], [(171, 198), 0.5, 1968], [(175, 188), 0.5, 1969], [(169, 199), 0.5, 1970], [(176, 192), 0.5, 1971], [(171, 182), 0.5, 1972], [(176, 182), 1.5, 1973], [(161, 199), 1.5, 1974], [(163, 185), 1.5, 1975], [(179, 188), 1.5, 1976], [(176, 184), 1.5, 1977], [(169, 192), 1.5, 1978], [(177, 194), 1.5, 1979], [(164, 187), 1.5, 1980]]\n",
      "[[(193, 194), 0.5, 1981], [(196, 187), 0.5, 1982], [(193, 181), 0.5, 1983], [(190, 189), 0.5, 1984], [(195, 192), 0.5, 1985], [(182, 198), 0.5, 1986], [(182, 196), 0.5, 1987], [(197, 195), 0.5, 1988], [(180, 185), 0.5, 1989], [(196, 185), 0.5, 1990], [(189, 192), 0.5, 1991], [(192, 188), 0.5, 1992], [(191, 198), 1.5, 1993], [(183, 187), 1.5, 1994], [(198, 199), 1.5, 1995], [(182, 183), 1.5, 1996], [(198, 187), 1.5, 1997], [(195, 180), 1.5, 1998], [(198, 184), 1.5, 1999], [(193, 197), 1.5, 2000]]\n"
     ]
    }
   ],
   "source": [
    "zonespoints = []\n",
    "for i in range(0,int(math.sqrt(int(Z)))):#for y axis\n",
    "    for j in range(0,int(math.sqrt(int(Z)))):#for x axis\n",
    "        zonespoints.append(getpoints(j*20,i*20,(j+1)*20,(i+1)*20,j*20+1+200*i))\n",
    "\n",
    "for i in zonespoints:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### now we got the points perfectly,so what remains is ploting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 3600x3600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = [[] for i in range(len(Et))]\n",
    "y = [[] for i in range(len(Et))]\n",
    "for i in zonespoints:\n",
    "    for j in i:\n",
    "        for k in range(len(Et)):\n",
    "            if Et[k] == j[1]:\n",
    "                x[k].append(j[0][0])\n",
    "                y[k].append(j[0][1])\n",
    "\n",
    "\n",
    "#print(x,y)\n",
    "fig = plt.figure(figsize=(50, 50))#defining size\n",
    "ax1 = fig.add_subplot(2,2,1)#adding a plot to figure\n",
    "\n",
    "\n",
    "spacing = D # This can be your user specified spacing. \n",
    "minorLocator = MultipleLocator(spacing)\n",
    "# jet = plt.get_cmap('jet')\n",
    "# colors = iter(jet(np.linspace(0,1,10)))\n",
    "colors = ['b','r','g','c','m','y','k','w']\n",
    "for i in range(0,len(x),1):\n",
    "    ax1.plot(x[i],y[i], 'o',color = colors[i])\n",
    "# Set minor tick locations.\n",
    "ax1.yaxis.set_minor_locator(minorLocator)\n",
    "ax1.xaxis.set_minor_locator(minorLocator)\n",
    "\n",
    "plt.axis([0, L, 0, L])#defining axix x and y\n",
    "# Set grid to use minor tick locations. \n",
    "\n",
    "ax1.grid(which = 'minor')#only major works fine\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0), (0, 20), (0, 40), (0, 60), (0, 80), (0, 100), (0, 120), (0, 140), (0, 160), (0, 180), (0, 200), (20, 0), (20, 20), (20, 40), (20, 60), (20, 80), (20, 100), (20, 120), (20, 140), (20, 160), (20, 180), (20, 200), (40, 0), (40, 20), (40, 40), (40, 60), (40, 80), (40, 100), (40, 120), (40, 140), (40, 160), (40, 180), (40, 200), (60, 0), (60, 20), (60, 40), (60, 60), (60, 80), (60, 100), (60, 120), (60, 140), (60, 160), (60, 180), (60, 200), (80, 0), (80, 20), (80, 40), (80, 60), (80, 80), (80, 100), (80, 120), (80, 140), (80, 160), (80, 180), (80, 200), (100, 0), (100, 20), (100, 40), (100, 60), (100, 80), (100, 100), (100, 120), (100, 140), (100, 160), (100, 180), (100, 200), (120, 0), (120, 20), (120, 40), (120, 60), (120, 80), (120, 100), (120, 120), (120, 140), (120, 160), (120, 180), (120, 200), (140, 0), (140, 20), (140, 40), (140, 60), (140, 80), (140, 100), (140, 120), (140, 140), (140, 160), (140, 180), (140, 200), (160, 0), (160, 20), (160, 40), (160, 60), (160, 80), (160, 100), (160, 120), (160, 140), (160, 160), (160, 180), (160, 200), (180, 0), (180, 20), (180, 40), (180, 60), (180, 80), (180, 100), (180, 120), (180, 140), (180, 160), (180, 180), (180, 200), (200, 0), (200, 20), (200, 40), (200, 60), (200, 80), (200, 100), (200, 120), (200, 140), (200, 160), (200, 180), (200, 200)]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200] [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200]\n"
     ]
    }
   ],
   "source": [
    "#for each corner place a EH node\n",
    "EH = []\n",
    "for i in range(0,L+1,20):\n",
    "    for j in range(0,L+1,20):\n",
    "        EH.append((i,j))\n",
    "print(EH)\n",
    "\n",
    "EHx = []\n",
    "EHy = []\n",
    "for j in EH:\n",
    "    EHx.append(j[0])\n",
    "    EHy.append(j[1])\n",
    "        \n",
    "print(EHx,EHy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x3600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = [[] for i in range(len(Et))]\n",
    "y = [[] for i in range(len(Et))]\n",
    "for i in zonespoints:\n",
    "    for j in i:\n",
    "        for k in range(len(Et)):\n",
    "            if Et[k] == j[1]:\n",
    "                x[k].append(j[0][0])\n",
    "                y[k].append(j[0][1])\n",
    "        \n",
    "#for each corner place a EH node\n",
    "EH = []\n",
    "for i in range(0,L+1,20):\n",
    "    for j in range(0,L+1,20):\n",
    "        EH.append((i,j))\n",
    "#print(EH)\n",
    "\n",
    "EHx = []\n",
    "EHy = []\n",
    "for j in EH:\n",
    "    EHx.append(j[0])\n",
    "    EHy.append(j[1])\n",
    "        \n",
    "#print(EHx,EHy)\n",
    "#print(x,y)\n",
    "fig = plt.figure(figsize=(50, 50))#defining size\n",
    "ax1 = fig.add_subplot(2,2,1)#adding a plot to figure\n",
    "\n",
    "\n",
    "spacing = D # This can be your user specified spacing. \n",
    "minorLocator = MultipleLocator(spacing)\n",
    "# jet = plt.get_cmap('jet')\n",
    "# colors = iter(jet(np.linspace(0,1,10)))\n",
    "colors = ['g','r','c','m','y','k','b']\n",
    "for i in range(0,len(x),1):\n",
    "    ax1.plot(x[i],y[i], 'o',color = colors[i])\n",
    "    \n",
    "ax1.plot(EHx,EHy, 'D',color = colors[len(colors)-1],markersize=12)\n",
    "# Set minor tick locations.\n",
    "ax1.yaxis.set_minor_locator(minorLocator)\n",
    "ax1.xaxis.set_minor_locator(minorLocator)\n",
    "\n",
    "plt.axis([0, L, 0, L])#defining axix x and y\n",
    "# Set grid to use minor tick locations. \n",
    "\n",
    "ax1.grid(which = 'minor')#only major works fine\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### At last placing of LBS\n",
    "\n",
    "for each bs in LBS\n",
    "    \n",
    "    place them in predefined location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# multi stage weighted election heuristic method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intro(may help me in understanding)\n",
    "SEES introduces a new election heuristic for choosing a set of\n",
    "nodes called ZAG (Zone Aggregation Group) in every zone.\n",
    "The task of ZAG is to collect data from other member nodes,\n",
    "perform the data aggregation and forward to the EH relay\n",
    "nodes. We call this heuristic as MSWE\n",
    "\n",
    "1) MSWE is a general solution that supports up to\n",
    "m different parameters ( p = p1, p2, p3,…, pm ) arranged in m\n",
    "different levels of importance ( g = g1, g2, g3,…, gm ).\n",
    "\n",
    "2) However,\n",
    "it selects ZAG in m consecutive stages (stage-1, stage-2,\n",
    "stage-3, ..., stage-m) based on a set of given weights of importance\n",
    "( w = w1, w2, w3,…, wm ). In each stage, one of the given\n",
    "parameters is taken as the dominant parameter (most important\n",
    "parameter) and is given the highest weight (in a stage-i,\n",
    "the dominant parameter is pi and is given w1 ).\n",
    "\n",
    "3) MSWE runs in\n",
    "two main procedures: (1) electing the ZAG member nodes, and\n",
    "(2) assigning some of ZAG member nodes as zone aggregators\n",
    "( ZAs ) for the current transmission round.\n",
    "\n",
    "SEES provides a general model to generate the set of\n",
    "weights w used in MSWE. The weight wi assigned to the\n",
    "parameter in the importance level gi is given according to\n",
    "the following formula:\n",
    "\n",
    "                            wi = 𝜇 × wi−1, for i = 2, 3,…,m and 0 < 𝜇 ≤ 1\n",
    "\n",
    "                            where w1 + w2 + w3 +...+ wm = 1\n",
    "\n",
    "𝜇 is the weighting factor defined in advance by the\n",
    "administrator based on some considerations (e.g., application\n",
    "nature and election parameters used)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the 𝜇 value0.8\n",
      "Enter m value4\n",
      "0.8 4\n"
     ]
    }
   ],
   "source": [
    "#from simulation parameters we get 𝜇 value\n",
    "𝜇 = float(input(\"Enter the 𝜇 value\"))\n",
    "m = int(input(\"Enter m value\"))# number of stages\n",
    "print(𝜇,m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                        w2 = 𝜇 × w1 = pow(𝜇,1) × w1\n",
    "                                        w3 = 𝜇 × w2 = 𝜇 × 𝜇 × w1 = pow(𝜇,2) × w1\n",
    "                                        w4 = 𝜇 × w3 = 𝜇 × 𝜇 × 𝜇 × w1 = pow(𝜇,3) × w1\n",
    "\n",
    "                                        wi = pow(𝜇,i−1) × w1\n",
    "                                        \n",
    "                                        w1 + w2 + w3 +⋯+ wm = 1\n",
    "                                        \n",
    "                                        w1 + 𝜇 × w1 + pow(𝜇,2) × w1 +⋯+ pow(𝜇,m−1) × w1 = 1\n",
    "                                        \n",
    "                                        w1 =  1/(1+𝜇+pow(𝜇,2)+....+pow(𝜇,m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9520000000000004 0.3387533875338753\n",
      "[0.3387533875338753, 0.27100271002710025, 0.2168021680216802, 0.17344173441734417]\n",
      "[0.34, 0.27, 0.22, 0.17]\n",
      "No Error in weights\n"
     ]
    }
   ],
   "source": [
    "#finding initial weight\n",
    "sum = 0\n",
    "for i in range(0,m):\n",
    "    sum =sum + math.pow(𝜇,i)\n",
    "    \n",
    "w1 = 1/sum\n",
    "\n",
    "print(sum,w1)\n",
    "\n",
    "weights = [w1]\n",
    "\n",
    "#for remaining weight\n",
    "\n",
    "for i in range(1,m):\n",
    "    weights.append(𝜇*weights[i-1])\n",
    "    \n",
    "print(weights)#these weigths are used in every stage of ZAG election without any change in their value\n",
    "\n",
    "\n",
    "weights[:] = [round(weights[i],2) for i in range(len(weights))]\n",
    "print(weights)\n",
    "#testing condition ,mah.fsum is used for computing sum of float values\n",
    "if (math.fsum(weights) == 1):\n",
    "    print(\"No Error in weights\")\n",
    "else:\n",
    "    print(\"Error in weights\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "there are actually 3 parameters that are to be considered\n",
    "\n",
    "    residual energy\n",
    "    centrality\n",
    "    distance to the nearest relay node\n",
    "    \n",
    "    "
   ]
  }
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