Shikaku-puzzle-github.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eba21227",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random \n",
    "import pandas as pd\n",
    "from pyomo.environ import *\n",
    "from shapely.geometry import LineString\n",
    "import itertools\n",
    "import os\n",
    "from pyomo import environ as pe\n",
    "\n",
    "os.environ['NEOS_EMAIL'] = 'XXXXXX@gmail.com' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b97654f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96c30a0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: [0, 0, 6, 0, 0, 0, 0, 3, 0, 0],\n",
       " 1: [0, 4, 0, 0, 0, 0, 0, 0, 4, 0],\n",
       " 2: [4, 0, 3, 0, 0, 0, 0, 3, 0, 4],\n",
       " 3: [0, 0, 0, 2, 0, 0, 9, 0, 0, 0],\n",
       " 4: [0, 0, 0, 0, 2, 3, 0, 0, 0, 0],\n",
       " 5: [0, 0, 0, 0, 3, 4, 0, 0, 0, 0],\n",
       " 6: [0, 0, 0, 2, 0, 0, 6, 0, 0, 0],\n",
       " 7: [6, 0, 3, 0, 0, 0, 0, 4, 0, 6],\n",
       " 8: [0, 6, 0, 0, 0, 0, 0, 0, 6, 0],\n",
       " 9: [0, 0, 4, 0, 0, 0, 0, 3, 0, 0]}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Kolors  = [\n",
    "    \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\",\n",
    "    \"#8c564b\", \"#e377c2\", \"#7f7f7f\", \"#bcbd22\", \"#17becf\",\n",
    "    \"#aec7e8\", \"#ffbb78\", \"#98df8a\", \"#ff9896\", \"#c5b0d5\",\n",
    "    \"#c49c94\", \"#f7b6d2\", \"#c7c7c7\", \"#dbdb8d\", \"#9edae5\",\n",
    "    \"#393b79\", \"#e6550d\", \"#637939\", \"#d6616b\", \"#843c39\",\n",
    "    \"#b5cf6b\", \"#d62728\", \"#7b4173\", \"#ce6dbd\", \"#bd9e39\",\n",
    "    \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\",\n",
    "    \"#8c564b\", \"#e377c2\", \"#7f7f7f\", \"#bcbd22\", \"#17becf\"]\n",
    "random.shuffle(Kolors)\n",
    "\n",
    "\n",
    "\n",
    "# You can access these colors by index, e.g., matplotlib_colors[0] is \"b\" (blue).\n",
    "\n",
    "\n",
    "D = pd.read_csv(\"30x30-16.txt\")\n",
    "D = pd.read_csv(\"5x5-1.txt\")\n",
    "D = pd.read_csv(\"002_shikaku.txt\")\n",
    "D = pd.read_csv(\"003_shikaku.txt\")\n",
    "\n",
    "\n",
    "\n",
    "N = len(D)\n",
    "\n",
    "init_data = {}\n",
    "for r in range(N):\n",
    "    \n",
    "    #init_data[r] = [int(d) for d in D.iloc[r,0] if d!=' '] \n",
    "    my_list = [word.strip() for word in D.iloc[r,0].split(' ')]\n",
    "    L = []\n",
    "    for d in my_list:\n",
    "        if d=='-':\n",
    "            L.append(0)\n",
    "        elif d!='':\n",
    "            L.append(int(d))\n",
    "    init_data[r] = L\n",
    "    \n",
    "init_data \n",
    "    \n",
    "# [4, 4, 6, 8, 1, 2, 2, 8, 8, 8, 2, 2, 1, 2, 4, 2, 2, 8, 8]\n",
    "init_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7f0fc362",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 10\n"
     ]
    }
   ],
   "source": [
    "data = {}\n",
    "counter = 0\n",
    "Nr , Nc= len(init_data[r]), N\n",
    "print(Nr,Nc)\n",
    "for r in range(Nc):\n",
    "    for c in range(Nr):\n",
    "        s= init_data[r][c]\n",
    "        counter+=1\n",
    "        data[counter,'x'] =  c+1\n",
    "        data[counter,'y'] =  Nr-r\n",
    "        data[counter,'v'] =  int(s) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "812cd368",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 8, 12, 19, 21, 23, 28, 30, 34, 37, 45, 46, 55, 56, 64, 67, 71, 73, 78, 80, 82, 89, 93, 98] 24 100\n",
      "[6, 3, 4, 4, 4, 3, 3, 4, 2, 9, 2, 3, 3, 4, 2, 6, 6, 3, 4, 6, 6, 6, 4, 3]\n"
     ]
    }
   ],
   "source": [
    "def dist(D,i,j):\n",
    "    return ( np.sqrt((D[i,'x'] - D[j,'x'])**2 + (D[i,'y'] - D[j,'y'])**2))\n",
    "points = [c+1 for c in range(counter)]\n",
    "cpoints = [c for c in points if data[c,'v']>0]\n",
    "v_cpoints = [data[c,'v'] for c in points if data[c,'v']>0]\n",
    "\n",
    "print(cpoints, len(cpoints), sum(v_cpoints))\n",
    "connect = {}\n",
    "connect = { (c1,c2):dist(data,c1,c2) for c1 in points for c2 in points if c1!=c2}\n",
    "print(v_cpoints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "64db34a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw(L, LW=1):\n",
    "    if LW > 1:\n",
    "        LW =8\n",
    "        plt.plot([L[0][0],L[1][0]], [L[0][1],L[1][1]], lw=LW)\n",
    "    else:\n",
    "        plt.plot([L[0][0],L[1][0]], [L[0][1],L[1][1]], lw=2, c= 'k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae24a197",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AbstractModel()\n",
    "Np = len(points)\n",
    "model.i = RangeSet(Np)\n",
    "model.j = Set(initialize=model.i)\n",
    "model.c = Set(initialize = cpoints)\n",
    "\n",
    "model.U = Var(model.i,model.j,bounds=(0,1),initialize=0, within=Binary)\n",
    "model.flow = Var(model.c, model.i,model.j,bounds=(0,1),initialize=0, within=Reals)\n",
    "\n",
    "model.source = Var(model.c,bounds=(0,1),initialize=0, within=Reals)\n",
    "\n",
    "def rule_C1(model,i):\n",
    "    return sum( model.U[i,c] for c in model.c) == 1\n",
    "model.C1 = Constraint(model.i, rule=rule_C1)\n",
    "\n",
    "def rule_C2(model,c):\n",
    "    return sum( model.U[i,c] for i in model.i if data[i,'v']==0) ==  data[c,'v']-1\n",
    "model.C2 = Constraint(model.c, rule=rule_C2)\n",
    "\n",
    "def rule_C3(model,c,i):\n",
    "    if data[i,'v']==0 or i ==c:\n",
    "        if i==c:\n",
    "            return model.source[c] - model.U[i,c]/Np == sum( model.flow[c,i,j] for j in model.j if dist(data,i,j) ==1)-\\\n",
    "                                                    sum( model.flow[c,j,i] for j in model.j if dist(data,i,j) ==1)\n",
    "        elif data[i,'v']==0:\n",
    "             return                - model.U[i,c]/Np == sum( model.flow[c,i,j] for j in model.j if dist(data,i,j) ==1)-\\\n",
    "                                                    sum( model.flow[c,j,i] for j in model.j if dist(data,i,j) ==1)\n",
    "    else:\n",
    "        return Constraint.Skip \n",
    "model.C3 = Constraint(model.c,model.i, rule=rule_C3)\n",
    "\n",
    "def rule_C4A(model,c,i,j):\n",
    "    if  data[i,'v']==0 and data[j,'v']==0 and dist(data,i,j) ==1:\n",
    "        return model.flow[c,i,j] <= model.U[i,c] \n",
    "    else:\n",
    "        return Constraint.Skip \n",
    "model.C4A = Constraint(model.c,model.i,model.j, rule=rule_C4A)\n",
    "\n",
    "\n",
    "def rule_C4B(model,c,i,j):\n",
    "    if  data[i,'v']==0 and data[j,'v']==0 and dist(data,i,j) ==1:\n",
    "        return model.flow[c,i,j] <= model.U[j,c]\n",
    "    else:\n",
    "        return Constraint.Skip \n",
    "model.C4B = Constraint(model.c,model.i,model.j, rule=rule_C4B)\n",
    "\n",
    "\n",
    "def rule_OF(model):\n",
    "    return  sum(model.U[i,c] for i in model.i for c in model.c if (i,c) in connect) \n",
    "model.obj1 = Objective(rule=rule_OF, sense=minimize)\n",
    "instance = model.create_instance()  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "54d10feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = SolverFactory('cbc')\n",
    "opt.options = {'sec': 1600, 'threads': 4, 'ratio': 0.4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "15ebe5ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is feasible and optimal and nothing more\n",
      "OF=  76.0\n"
     ]
    }
   ],
   "source": [
    "for c1 in instance.i:\n",
    "    for c2 in instance.i:\n",
    "        if data[c1,'v']==0 and data[c2,'v']==0:\n",
    "                #pass\n",
    "                instance.U[c1,c2].fix(0)\n",
    "for c1 in instance.c:\n",
    "    instance.U[c1,c1].fix(1)\n",
    "for c1 in instance.c:\n",
    "    for c2 in instance.i:\n",
    "        if data[c2,'v']==0 and dist(data,c1,c2)>data[c1,'v']:\n",
    "                #pass \n",
    "                instance.U[c2,c1].fix(0)\n",
    "#\"\"\"\n",
    "for c in instance.c:\n",
    "    for (i,j) in connect:\n",
    "        if i!=c and j!=c and data[i,'v'] + data[j,'v'] >0: \n",
    "            instance.flow[c,i,j].fix(0)\n",
    "\n",
    "#\"\"\"                \n",
    "results = opt.solve(instance)\n",
    "\n",
    "from pyomo.opt import SolverStatus, TerminationCondition\n",
    "if (results.solver.status == SolverStatus.ok) and (results.solver.termination_condition == TerminationCondition.optimal):\n",
    "     print (\"this is feasible and optimal and nothing more\")\n",
    "elif results.solver.termination_condition == TerminationCondition.infeasible:\n",
    "     print (\"do something about it? or exit?\")\n",
    "else:\n",
    "     print ('ERROR ' + str(results.solver))\n",
    "print('OF= ', value(instance.obj1) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ff318940",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 6 [1, 2, 4, 5, 11]\n",
      "8 3 [6, 7]\n",
      "12 4 [13, 14, 22]\n",
      "19 4 [9, 10, 20]\n",
      "21 4 [31, 32, 41]\n",
      "23 3 [33, 43]\n",
      "28 3 [17, 18]\n",
      "30 4 [29, 40, 50]\n",
      "34 2 [24]\n",
      "37 9 [15, 16, 25, 26, 27, 38, 39, 49]\n",
      "45 2 [44]\n",
      "46 3 [35, 36]\n",
      "55 3 [53, 54]\n",
      "56 4 [47, 48, 57]\n",
      "64 2 [63]\n",
      "67 6 [58, 59, 60, 68, 69]\n",
      "71 6 [42, 51, 52, 61, 81]\n",
      "73 3 [62, 72]\n",
      "78 4 [66, 76, 77]\n",
      "80 6 [70, 79, 90, 99, 100]\n",
      "82 6 [65, 74, 75, 83, 84]\n",
      "89 6 [85, 86, 87, 88, 95]\n",
      "93 4 [91, 92, 94]\n",
      "98 3 [96, 97]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "plt.xticks(range(1,1+Nr))\n",
    "plt.yticks(range(2,2+Nc))\n",
    "plt.xlim(0,Nr+1)\n",
    "plt.ylim(0,Nc+1)\n",
    "node_size = 1200\n",
    "plt.axis('off')\n",
    "c_index = 0 \n",
    "counter =1 \n",
    "for i in instance.i:\n",
    "    plt.text(data[i,'x']-0.21,data[i,'y']-0.05, s=f\"{i}\" , fontsize=14,c ='w', fontweight='bold')\n",
    "    if data[i,'v']==0:\n",
    "        plt.scatter(data[i,'x'],data[i,'y'], s=node_size,marker ='s' , c='grey', alpha=0.2)\n",
    "    else:\n",
    "        plt.scatter(data[i,'x'],data[i,'y'], s=node_size,marker ='s' , c=Kolors[c_index])\n",
    "        plt.text(data[i,'x']+0.2,data[i,'y'], s=f\"  {data[i,'v']}\", fontsize=11,c ='r', fontweight='bold')\n",
    "        c_index+=1\n",
    "\n",
    "plt.savefig(f'shikaku base .png', format='png', dpi=400)\n",
    "\n",
    "c_index = 0 \n",
    "\n",
    "for c in instance.c:\n",
    "    a= [j for j in instance.j if (j,c) in connect and value(instance.U[j,c])>0]\n",
    "    print(c,data[c,'v'],a)\n",
    "    for j in a+[c]:\n",
    "        plt.scatter(data[j,'x'],data[j,'y'], s=node_size,marker ='s' , c=Kolors[c_index])\n",
    "    counter+=1\n",
    "    c_index+=1\n",
    "\n",
    "plt.savefig(f'shikaku {counter} .png', format='png', dpi=200)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "46cb4be3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "c_index = 0 \n",
    "for i in instance.i:\n",
    "    if data[i,'v']==0:\n",
    "        plt.scatter(data[i,'x'],data[i,'y'], s=600,marker ='s' , c='grey', alpha=0.5)\n",
    "    if data[i,'v']>0:\n",
    "        plt.text(data[i,'x']-0.15,data[i,'y']+0.3, s=f\"{i}-{data[i,'v']}\")\n",
    "        a= [j for j in instance.j if (j,i) in connect and value(instance.U[j,i])>0]\n",
    "        for j in [i]:\n",
    "            plt.scatter(data[j,'x'],data[j,'y'], s=600,marker ='s' , c=Kolors[c_index])\n",
    "        c_index+=1\n",
    "plt.xticks(range(1,1+Nr))\n",
    "plt.yticks(range(2,2+Nc))\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f193ff1",
   "metadata": {},
   "source": [
    "0  0 0 8 0 0\n",
    "1  0 0 0 0 0\n",
    "2  6 0 4 0 4\n",
    "3  0 0 0 0 0\n",
    "4  0 0 3 0 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9691cd39",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b7a2c5c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}