Suguru_CP.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OptimizationExpert/Pyomo/blob/main/Suguru_CP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VvJL0bOSIm84"
      },
      "source": [
        "Developed by Alireza Soroudi\n",
        "alireza.soroudi@gmail.com\n",
        "\n",
        "Linkedin News letter : https://www.linkedin.com/newsletters/optimization-in-open-source-6874020019009859585/\n",
        "\n",
        "Git hub: https://github.com/OptimizationExpert/Pyomo\n",
        "\n",
        "Problem description:\n",
        "https://www.linkedin.com/pulse/suguru-puzzle-cp-alireza-soroudi-sgzke\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#  Solving Suguru (Tectonic) puzzles\n",
        "https://community.wolfram.com/groups/-/m/t/1077888\n"
      ],
      "metadata": {
        "id": "YUG7lD_SKTng"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_3yuwV8XtMF",
        "outputId": "4d64363c-46d2-4e80-836b-d296b40570cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: ortools in /usr/local/lib/python3.10/dist-packages (9.9.3963)\n",
            "Requirement already satisfied: absl-py>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ortools) (2.1.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.10/dist-packages (from ortools) (1.25.2)\n",
            "Requirement already satisfied: pandas>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ortools) (2.0.3)\n",
            "Requirement already satisfied: protobuf>=4.25.3 in /usr/local/lib/python3.10/dist-packages (from ortools) (5.26.1)\n",
            "Requirement already satisfied: immutabledict>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from ortools) (4.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->ortools) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->ortools) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->ortools) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=2.0.0->ortools) (1.16.0)\n",
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "!pip install ortools\n",
        "from ortools.sat.python import cp_model # CP-SAT solver\n",
        "import numpy as np\n",
        "from random import randint\n",
        "import random\n",
        "from math import ceil\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt # Data visualization\n",
        "from matplotlib.patches import Rectangle\n",
        "from google.colab import files\n",
        "\n",
        "import matplotlib.patches as patches\n",
        "\n",
        "from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tectons  = [\n",
        "    {(1, 1), (1, 2), (2, 1), (2, 2), (3, 1)},\n",
        "    {(1, 3), (1, 4), (1, 5), (2, 3), (2, 4)},\n",
        "    {(1, 6), (1, 7), (2, 5), (2, 6), (3, 5)},\n",
        "    {(1, 8), (2, 7), (2, 8), (3, 6), (3, 7)},\n",
        "    {(1, 9), (2, 9), (3, 9), (4, 8), (4, 9)},\n",
        "    {(3, 8)},\n",
        "    {(5, 8)},\n",
        "    {(5, 9), (6, 8), (6, 9), (7, 8), (7, 9)},\n",
        "    {(4, 6), (4, 7), (5, 6), (5, 7), (6, 7)},\n",
        "    {(5, 5), (6, 5), (6, 6), (7, 4), (7, 5)},\n",
        "    {(5, 2), (5, 3), (6, 3), (6, 4), (7, 3)},\n",
        "    {(3, 3), (3, 4), (4, 4), (4, 5), (5, 4)},\n",
        "    {(3, 2), (4, 1), (4, 2), (4, 3), (5, 1)},\n",
        "    {(6, 1), (6, 2), (7, 1), (7, 2)},\n",
        "    {(7, 6), (7, 7)}\n",
        "]\n",
        "\n",
        "given = {\n",
        "    (1, 1): 1,\n",
        "    (2, 2): 2,\n",
        "    (3, 1): 4,\n",
        "    (1, 4): 1,\n",
        "    (2, 8): 3,\n",
        "    (3, 3): 4,\n",
        "    (3, 9): 4,\n",
        "    (4, 4): 1,\n",
        "    (5, 1): 4,\n",
        "    (5, 3): 5,\n",
        "    (5, 5): 4,\n",
        "    (5, 7): 5,\n",
        "    (6, 8): 3,\n",
        "    (7, 5): 2,\n",
        "    (7, 9): 5\n",
        "}\n",
        "\n",
        "cells, loc2cells = {}, {}\n",
        "counter =0\n",
        "for x in range(1,10):\n",
        "  for y in range(1,8):\n",
        "    counter+=1\n",
        "    cells[counter]=(x,y)\n",
        "    loc2cells[x,y]= counter\n",
        "\n",
        "\n",
        "\n",
        "projects = {pr:[] for pr in range(1,1+len(tectons))}\n",
        "counter = 0\n",
        "for all_cels in tectons:\n",
        "  pr = tectons.index(all_cels)+1\n",
        "  for project in all_cels:\n",
        "    (y,x) = project\n",
        "    projects[pr]+= [loc2cells[x,y]]\n",
        "\n",
        "\n",
        "cell2proj = {cell:pr for pr,cells in projects.items() for cell in cells }"
      ],
      "metadata": {
        "id": "zp3BLJ8Jxjxh"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "AxZSoOv2-NEu"
      },
      "outputs": [],
      "source": [
        "KOLORS = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\",\n",
        "            \"#8c564b\", \"#e377c2\", \"#7f7f7f\", \"#bcbd22\", \"#17becf\",\n",
        "            \"#aec7e8\", \"#ffbb78\", \"#98df8a\", \"#ff9896\", \"#c5b0d5\",\n",
        "            \"#c49c94\", \"#f7b6d2\", \"#c7c7c7\", \"#dbdb8d\", \"#9edae5\",\n",
        "            \"#8c6d31\", \"#9c9ede\", \"#637939\", \"#e7cb94\", \"#b5cf6b\",\n",
        "            \"#cedb9c\", \"#c7c7c7\", \"#bd9e39\", \"#e7969c\", \"#7b4173\"]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,4))\n",
        "\n",
        "for pr,nodes in projects.items():\n",
        "  for node in nodes:\n",
        "    plt.scatter(cells[node][0],7- cells[node][1]+0.1, marker='s', s=600,  c= KOLORS[pr+5])\n",
        "\n",
        "for (y,x),v in given.items():\n",
        "    plt.text(x-0.1,7-y, s=str(v), fontsize=14, fontweight='bold', c='k')\n",
        "\n",
        "plt.axis('off')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "lqw1iTmuft6T",
        "outputId": "71fb8398-141e-4c7b-d55e-1c297d422330"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = cp_model.CpModel()\n",
        "solver = cp_model.CpSolver()\n",
        "U = {i:model.NewIntVar(1,len(projects[cell2proj[i]])  , f\"connection_{i}\") for i in cells.keys()}\n",
        "\n",
        "for project,list_V in projects.items():\n",
        "  expressions= [ U[c] for c in list_V]\n",
        "  model.AddAllDifferent(expressions)\n",
        "\n",
        "for cell1 in cells:\n",
        "  for cell2 in cells:\n",
        "    x1, y1= cells[cell1][0], cells[cell1][1]\n",
        "    x2, y2= cells[cell2][0], cells[cell2][1]\n",
        "    if cell1 !=cell2 and (x1-x2)**2 +(y1-y2)**2 <=2:\n",
        "        expressions= [ U[cell1], U[cell2]]\n",
        "        model.AddAllDifferent(expressions)\n",
        "\n",
        "for (y,x),v in given.items():\n",
        "  cell= loc2cells[x,y]\n",
        "  model.Add(U[cell] == v)\n",
        "\n",
        "status = solver.Solve(model)\n",
        "if status == cp_model.FEASIBLE:\n",
        "  print(\"feasible\")\n",
        "elif status == cp_model.OPTIMAL:\n",
        "  print(\"optimal\")\n",
        "elif status == cp_model.INFEASIBLE:\n",
        "  print(\"INFEASIBLE\")\n",
        "elif status == cp_model.UNKNOWN:\n",
        "  print(\"UNKNOWN\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1u74WJiO3-3W",
        "outputId": "e2763b08-5d73-4e87-8147-22c787f35f04"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "optimal\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,4))\n",
        "for (y,x),v in given.items():\n",
        "    plt.scatter(x,7-y+0.1, s=500, c='gold', alpha=0.7)\n",
        "    plt.text(x-0.1,7-y, s=str(v), fontsize=14, fontweight='bold', c='k')\n",
        "\n",
        "\n",
        "\n",
        "for pr,nodes in projects.items():\n",
        "  for node in nodes:\n",
        "    plt.scatter(cells[node][0],7- cells[node][1]+0.1, marker='s', s=700,  c= KOLORS[pr+5])\n",
        "    v = solver.value(U[node])\n",
        "    x,y = cells[node]\n",
        "    #plt.text(x-0.1,7-y, s=str(v), fontsize=14, fontweight='bold', c='k')\n",
        "\n",
        "\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "A8PQBlL4QJrj",
        "outputId": "ee4b9b53-4c5d-45c2-8823-660a2def4b5c"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,4))\n",
        "for (y,x),v in given.items():\n",
        "    plt.scatter(x,7-y+0.1, s=500, c='gold', alpha=0.7)\n",
        "    plt.text(x-0.1,7-y, s=str(v), fontsize=14, fontweight='bold', c='k')\n",
        "\n",
        "for pr,nodes in projects.items():\n",
        "  for node in nodes:\n",
        "    plt.scatter(cells[node][0],7- cells[node][1]+0.1, marker='s', s=700,  c= KOLORS[pr+5])\n",
        "plt.axis('off')\n",
        "\n",
        "counter= 0\n",
        "for pr,nodes in projects.items():\n",
        "  for node in nodes:\n",
        "    #plt.scatter(cells[node][0],7- cells[node][1]+0.1, marker='s', s=700,  c= KOLORS[pr+5])\n",
        "    v = solver.value(U[node])\n",
        "    x,y = cells[node]\n",
        "    if (y,x) not in given.items():\n",
        "      plt.text(x-0.1,7-y, s=str(v), fontsize=14, fontweight='bold', c='k')\n",
        "      #plt.savefig(f'/content/drive/My Drive/Colab Notebooks/img/rectangle_plot_{counter}.png')\n",
        "      counter+=1\n",
        "\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "KVj_xEHrn2k0",
        "outputId": "d01b0406-1fc9-4148-dd99-73e443ba9f05"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJdhHr-DIlur"
      },
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}