A_Puzzle_A_Day_Solver.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "A-Puzzle-A-Day Solver",
      "provenance": [],
      "collapsed_sections": [
        "vIYX1FQDGBKR",
        "D9WpiLwdGQZF",
        "5DJmsz56HAav"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Yotty0404/A-Puzzle-A-Day-Solver/blob/main/A_Puzzle_A_Day_Solver.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vIYX1FQDGBKR"
      },
      "source": [
        "---\n",
        "# **Union-Find木**\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CDUCaWB_wND"
      },
      "source": [
        "from collections import defaultdict\n",
        "class UnionFind():\n",
        "    def __init__(self, n):\n",
        "        self.n = n\n",
        "        self.parents = [-1] * n\n",
        "\n",
        "    def find(self, x):\n",
        "        if self.parents[x] < 0:\n",
        "            return x\n",
        "        else:\n",
        "            self.parents[x] = self.find(self.parents[x])\n",
        "            return self.parents[x]\n",
        "\n",
        "    def union(self, x, y):\n",
        "        x = self.find(x)\n",
        "        y = self.find(y)\n",
        "\n",
        "        if x == y:\n",
        "            return\n",
        "\n",
        "        if self.parents[x] > self.parents[y]:\n",
        "            x, y = y, x\n",
        "\n",
        "        self.parents[x] += self.parents[y]\n",
        "        self.parents[y] = x\n",
        "\n",
        "    def size(self, x):\n",
        "        return -self.parents[self.find(x)]\n",
        "\n",
        "    def same(self, x, y):\n",
        "        return self.find(x) == self.find(y)\n",
        "\n",
        "    def members(self, x):\n",
        "        root = self.find(x)\n",
        "        return [i for i in range(self.n) if self.find(i) == root]\n",
        "\n",
        "    def roots(self):\n",
        "        return [i for i, x in enumerate(self.parents) if x < 0]\n",
        "\n",
        "    def group_count(self):\n",
        "        return len(self.roots())\n",
        "\n",
        "    def all_group_members(self):\n",
        "        group_members = defaultdict(list)\n",
        "        for member in range(self.n):\n",
        "            group_members[self.find(member)].append(member)\n",
        "        return group_members\n",
        "\n",
        "    def __str__(self):\n",
        "        return '\\n'.join(f'{r}: {m}' for r, m in self.all_group_members().items())"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D9WpiLwdGQZF"
      },
      "source": [
        "---\n",
        "# **連結成分のサイズを利用**\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Dr-cQ3l1f0a",
        "outputId": "8f1bd222-3761-4a2b-c048-adb83cf15b5b"
      },
      "source": [
        "# 連結成分のサイズを利用\n",
        "\n",
        "import numpy as np\n",
        "import datetime\n",
        "from itertools import product\n",
        "\n",
        "# ブロックを回転させる関数\n",
        "def rotate_block(block, num):\n",
        "    if num == 0:\n",
        "        return block\n",
        "    if num == 1:\n",
        "        return np.rot90(block,1)\n",
        "    if num == 2:\n",
        "        return np.rot90(block,2)\n",
        "    if num == 3:\n",
        "        return np.rot90(block,3)\n",
        "    if num == 4:\n",
        "        block = np.fliplr(block)\n",
        "        return block\n",
        "    if num == 5:\n",
        "        block = np.fliplr(block)\n",
        "        return np.rot90(block,1)\n",
        "    if num == 6:\n",
        "        block = np.fliplr(block)\n",
        "        return np.rot90(block,2)\n",
        "    if num == 7:\n",
        "        block = np.fliplr(block)\n",
        "        return np.rot90(block,3)\n",
        "\n",
        "# 重複なく回転パターンを洗い出す関数\n",
        "def get_rotate_num(block):\n",
        "    l = []\n",
        "    num_l = []\n",
        "    for i in range(8):\n",
        "        array1 = rotate_block(block,i)\n",
        "        flag = True\n",
        "        for array2 in l:\n",
        "            if array1.shape == array2.shape and np.allclose(array1,array2):\n",
        "                flag = False\n",
        "        \n",
        "        if flag:\n",
        "            l.append(array1)\n",
        "            num_l.append(i)\n",
        "    return num_l\n",
        "\n",
        "# 次のブロックの位置候補を返す関数\n",
        "def get_next_postion(block, position):\n",
        "    if position is None:\n",
        "        return [0,0]\n",
        "\n",
        "    max_r = 7-block.shape[0]\n",
        "    max_c = 7-block.shape[1]\n",
        "\n",
        "    position[1]+=1\n",
        "\n",
        "    if position[1] > max_c:\n",
        "        position[0]+=1\n",
        "        position[1]=0\n",
        "    \n",
        "    if position[0] > max_r:\n",
        "        return [-1,-1]\n",
        "    \n",
        "    return position\n",
        "\n",
        "# 指定された位置から探索して、ブロックの置く位置を探索する関数\n",
        "def get_position(board, block, position):\n",
        "    while 1:\n",
        "        position = get_next_postion(block, position)\n",
        "        if position[0] == -1:\n",
        "            return [-1,-1]\n",
        "    \n",
        "        r = position[0]\n",
        "        c = position[1]\n",
        "\n",
        "        temp = np.copy(board)\n",
        "        temp[r:r+block.shape[0],c:c+block.shape[1]] += block\n",
        "\n",
        "        if(judge_correct_board(temp)):\n",
        "            return [r,c]\n",
        "\n",
        "# 残るマスの連結成分のサイズが5の倍数かどうか判定する関数\n",
        "def judge_connected_component(board):\n",
        "    uf = UnionFind(49)\n",
        "\n",
        "    for i in range(7):\n",
        "        for j in range(7):\n",
        "            if i != 6 and board[i][j]==board[i+1][j]==0:\n",
        "                uf.union(i*7+j,(i+1)*7+j)\n",
        "            if j != 6 and board[i][j]==board[i][j+1]==0:\n",
        "                uf.union(i*7+j,i*7+j+1)\n",
        "\n",
        "    for i in range(7):\n",
        "        for j in range(7):\n",
        "            if board[i][j]==0 and uf.size(i*7+j)%5!=0:\n",
        "                return False\n",
        "\n",
        "    return True\n",
        "\n",
        "# ボードの状態を見て、継続可能か判定する関数\n",
        "def judge_correct_board(board):\n",
        "    if np.amax(board) > 1:\n",
        "        return False\n",
        "    if not judge_connected_component(board):\n",
        "        return False\n",
        "    return True\n",
        "\n",
        "# ボードの指定の位置にブロックを置いた時のボードの状態を返す関数\n",
        "def get_board_state(board, block, position):\n",
        "    r = position[0]\n",
        "    c = position[1]\n",
        "    board[r:r+block.shape[0],c:c+block.shape[1]] += block\n",
        "    return board\n",
        "\n",
        "# 現在のボードの状態を返す関数\n",
        "def get_board_state_by_all_block(initial_board, blocks, positions, block_num):\n",
        "    board = np.copy(initial_board)\n",
        "    for i in range(block_num+1):\n",
        "        board = get_board_state(board, blocks[i],positions[i])\n",
        "    return board\n",
        "\n",
        "# 指定したブロックの番号移行の位置を初期化する関数\n",
        "def clear_block_position(positions, block_num):\n",
        "    for i in range(block_num+1, 8):\n",
        "        positions[i] = None\n",
        "    return positions\n",
        "\n",
        "def main():\n",
        "    initial_board =np.array(\n",
        "                   [[0,0,0,0,0,0,1],\n",
        "                    [0,0,0,0,0,0,1],\n",
        "                    [0,0,0,0,0,0,0],\n",
        "                    [0,0,0,0,0,0,0],\n",
        "                    [0,0,0,0,0,0,0],\n",
        "                    [0,0,0,0,0,0,0],\n",
        "                    [0,0,0,1,1,1,1],\n",
        "                    ])\n",
        "    \n",
        "    # ブロックの種類\n",
        "    initial_blocks = [\n",
        "        np.array([\n",
        "            [1, 1, 1],\n",
        "            [1, 1, 1]]),\n",
        "        np.array([\n",
        "            [1, 0, 0],\n",
        "            [1, 0, 0],\n",
        "            [1, 1, 1]]),\n",
        "        np.array([\n",
        "            [1, 1, 0],\n",
        "            [0, 1, 0],\n",
        "            [0, 1, 1]]),\n",
        "        np.array([\n",
        "            [1, 0, 1],\n",
        "            [1, 1, 1]]),\n",
        "        np.array([\n",
        "            [0, 0, 0, 1],\n",
        "            [1, 1, 1, 1]]),\n",
        "        np.array([\n",
        "            [0, 0, 1, 1],\n",
        "            [1, 1, 1, 0]]),\n",
        "        np.array([\n",
        "            [0, 0, 1, 0],\n",
        "            [1, 1, 1, 1]]),\n",
        "        np.array([\n",
        "            [1, 1, 0],\n",
        "            [1, 1, 1]]),\n",
        "    ]\n",
        "\n",
        "    #--対象の日付-------------------------------\n",
        "    date = datetime.date(2000, 1, 1)\n",
        "    #-------------------------------------------\n",
        "\n",
        "    # 月の位置を取得\n",
        "    r = (date.month-1) // 6\n",
        "    c = (date.month-1) % 6\n",
        "    initial_board[r][c] = 1\n",
        "\n",
        "    # 日の位置を取得\n",
        "    r = (date.day-1) // 7 + 2\n",
        "    c = (date.day-1) % 7\n",
        "    initial_board[r][c] = 1\n",
        "\n",
        "    # 各ブロックの回転パターンを保持\n",
        "    l_num = []\n",
        "    for b in initial_blocks:\n",
        "        l_num.append(get_rotate_num(b))\n",
        "\n",
        "    start_time = datetime.datetime.now()\n",
        "    print(start_time)\n",
        "\n",
        "    cnt = 0\n",
        "    # 各ブロックの全組み合わせループ\n",
        "    for l in product(l_num[0], l_num[1], l_num[2], l_num[3], l_num[4], l_num[5], l_num[6], l_num[7]):\n",
        "        cnt += 1\n",
        "\n",
        "        #回転させたブロックを保持\n",
        "        blocks = []\n",
        "        for i in range(8):\n",
        "            blocks.append(rotate_block(initial_blocks[i], l[i]))\n",
        "        \n",
        "        # 各ブロックの位置を保持するリスト\n",
        "        positions = [None, None, None, None, None, None, None, None]\n",
        "        # 現在置く場所を判断しているブロックの番号を保持\n",
        "        block_num = 0\n",
        "\n",
        "        board = np.copy(initial_board)\n",
        "\n",
        "        while 1:\n",
        "            cnt+=1\n",
        "            position = get_position(board, blocks[block_num], positions[block_num])\n",
        "\n",
        "            if position[0] == -1:\n",
        "                block_num -= 1\n",
        "                positions = clear_block_position(positions, block_num)\n",
        "\n",
        "                # ブロックの番号の1つ前までのブロックを置いた状態のボードを取得\n",
        "                board = get_board_state_by_all_block(initial_board, blocks, positions, block_num-1)\n",
        "\n",
        "                if block_num == -1:\n",
        "                    break\n",
        "\n",
        "                continue\n",
        "            \n",
        "            positions[block_num] = position\n",
        "            board = get_board_state_by_all_block(initial_board, blocks, positions, block_num)\n",
        "\n",
        "            block_num+=1\n",
        "            if block_num == 8:\n",
        "                break\n",
        "\n",
        "        if block_num == 8:\n",
        "                break\n",
        "\n",
        "    end_time = datetime.datetime.now()\n",
        "    print(end_time)\n",
        "    t = end_time-start_time\n",
        "    print(f'探索時間：{t.seconds}秒')\n",
        "    print(f\"探索回数：{cnt}\")\n",
        "    for i in range(8):\n",
        "        print(positions[i])\n",
        "        print(blocks[i])\n",
        "        print()\n",
        "\n",
        "main()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-08-29 05:45:32.202310\n",
            "2021-08-29 06:07:15.349902\n",
            "探索時間：1303秒\n",
            "探索回数：2617745\n",
            "[4, 1]\n",
            "[[1 1 1]\n",
            " [1 1 1]]\n",
            "\n",
            "[4, 0]\n",
            "[[1 0 0]\n",
            " [1 0 0]\n",
            " [1 1 1]]\n",
            "\n",
            "[0, 4]\n",
            "[[1 1 0]\n",
            " [0 1 0]\n",
            " [0 1 1]]\n",
            "\n",
            "[3, 5]\n",
            "[[1 1]\n",
            " [0 1]\n",
            " [1 1]]\n",
            "\n",
            "[2, 0]\n",
            "[[0 0 0 1]\n",
            " [1 1 1 1]]\n",
            "\n",
            "[0, 1]\n",
            "[[1 1 1 0]\n",
            " [0 0 1 1]]\n",
            "\n",
            "[2, 4]\n",
            "[[1 0]\n",
            " [1 0]\n",
            " [1 1]\n",
            " [1 0]]\n",
            "\n",
            "[1, 0]\n",
            "[[1 1 1]\n",
            " [0 1 1]]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJJtQ_2zGxuz"
      },
      "source": [
        "---\n",
        "# **2進数を利用**\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-cw-XhR0D9IY",
        "outputId": "6e3e4f62-93e3-493f-d415-1e9da33098f4"
      },
      "source": [
        "# 2進数を利用\n",
        "\n",
        "import numpy as np\n",
        "import datetime\n",
        "import time\n",
        "import pandas as pd\n",
        "from itertools import product\n",
        "\n",
        "# ブロックを回転させる関数\n",
        "def rotate_block(block, num):\n",
        "    if num == 0:\n",
        "        return block\n",
        "    if num == 1:\n",
        "        return np.rot90(block,1)\n",
        "    if num == 2:\n",
        "        return np.rot90(block,2)\n",
        "    if num == 3:\n",
        "        return np.rot90(block,3)\n",
        "    if num == 4:\n",
        "        block = np.fliplr(block)\n",
        "        return block\n",
        "    if num == 5:\n",
        "        block = np.fliplr(block)\n",
        "        return np.rot90(block,1)\n",
        "    if num == 6:\n",
        "        block = np.fliplr(block)\n",
        "        return np.rot90(block,2)\n",
        "    if num == 7:\n",
        "        block = np.fliplr(block)\n",
        "        return np.rot90(block,3)\n",
        "\n",
        "# 重複なく回転パターンを洗い出す関数\n",
        "def get_rotate_num(block):\n",
        "    l = []\n",
        "    num_l = []\n",
        "    for i in range(8):\n",
        "        array1 = rotate_block(block,i)\n",
        "        flag = True\n",
        "        for array2 in l:\n",
        "            if array1.shape == array2.shape and np.allclose(array1,array2):\n",
        "                flag = False\n",
        "        \n",
        "        if flag:\n",
        "            l.append(array1)\n",
        "            num_l.append(i)\n",
        "    return num_l\n",
        "\n",
        "# 次のブロックの位置候補を返す関数\n",
        "def get_next_postion(block, position):\n",
        "    if position is None:\n",
        "        return [0,0]\n",
        "\n",
        "    max_r = 7-block.shape[0]\n",
        "    max_c = 7-block.shape[1]\n",
        "\n",
        "    position[1]+=1\n",
        "\n",
        "    if position[1] > max_c:\n",
        "        position[0]+=1\n",
        "        position[1]=0\n",
        "    \n",
        "    if position[0] > max_r:\n",
        "        return [-1,-1]\n",
        "    \n",
        "    return position\n",
        "\n",
        "# 指定された位置から探索して、ブロックの置く位置を探索する関数\n",
        "def get_position(board, block, position):\n",
        "    while 1:\n",
        "        position = get_next_postion(block, position)\n",
        "        if position[0] == -1:\n",
        "            return [-1,-1]\n",
        "    \n",
        "        r = position[0]\n",
        "        c = position[1]\n",
        "\n",
        "        temp = np.copy(board)\n",
        "        temp[r:r+block.shape[0],c:c+block.shape[1]] += block\n",
        "\n",
        "        if(judge_correct_board(temp)):\n",
        "            return [r,c]\n",
        "\n",
        "# ボードの指定の位置にブロックを置いた時のボードの状態を返す関数\n",
        "def get_board_state(board, block, position):\n",
        "    r = position[0]\n",
        "    c = position[1]\n",
        "    board[r:r+block.shape[0],c:c+block.shape[1]] += block\n",
        "    return board\n",
        "\n",
        "# ボードの情報を2進数に変換する関数\n",
        "def convert_to_binary_board(board):\n",
        "    rtn = \"\"\n",
        "    for row in board:\n",
        "        rtn += ''.join(map(str, row))\n",
        "\n",
        "    return rtn\n",
        "\n",
        "# 次のブロックステートを取得する関数\n",
        "def get_next_state(patterns_nums, pattern_states, block_No):\n",
        "    # そのブロックのパターン状態がブロックパターン数の場合、ステートをクリアしながらブロック番号を戻していく\n",
        "    while pattern_states[block_No] == patterns_nums[block_No]:\n",
        "        # 現在のブロック番号のステートを0に更新\n",
        "        pattern_states[block_No] = 0\n",
        "        # ブロック番号を1戻す\n",
        "        block_No -= 1\n",
        "        \n",
        "        # 探索終了\n",
        "        if block_No == -1:\n",
        "            return pattern_states, block_No\n",
        "\n",
        "    # ステート番号に1を足す\n",
        "    pattern_states[block_No] += 1\n",
        "\n",
        "    return pattern_states, block_No\n",
        "\n",
        "# ボードの状態を見て、継続可能か判定する関数\n",
        "def judge_correct_board(board):\n",
        "    if np.amax(board) > 1:\n",
        "        return False\n",
        "    return True\n",
        "\n",
        "# 残るマスの連結成分のサイズが5の倍数かどうか判定する関数\n",
        "def judge_connected_component(board_num):\n",
        "    uf = UnionFind(49)\n",
        "    board_binary = format(board_num, '049b')\n",
        "    for i in range(49):\n",
        "        # 右をチェック\n",
        "        if (i+1)%7 != 0 and board_binary[i] == board_binary[i+1] == '0':\n",
        "            uf.union(i,i+1)\n",
        "        # 下をチェック\n",
        "        if i<=41 != 0 and board_binary[i] == board_binary[i+7] == '0':\n",
        "            uf.union(i,i+7)\n",
        "\n",
        "    for i in range(49):\n",
        "        if board_binary[i] == '0' and uf.size(i)%5 != 0:\n",
        "            return False\n",
        "\n",
        "    return True\n",
        "\n",
        "# ボードの状態を見て、継続可能か判定する関数\n",
        "def judge_correct_board_for_binary(board_num, next_block_num):\n",
        "    # 重なるブロックがある場合 → 2進数ですべての桁が異ならない場合 → andを取って0にならない場合\n",
        "    if board_num & next_block_num != 0:\n",
        "        return False\n",
        "    if not judge_connected_component(board_num + next_block_num):\n",
        "        return False\n",
        "    return True\n",
        "        \n",
        "def main(date):\n",
        "    initial_board =np.array([[0,0,0,0,0,0,1],\n",
        "                 [0,0,0,0,0,0,1],\n",
        "                 [0,0,0,0,0,0,0],\n",
        "                 [0,0,0,0,0,0,0],\n",
        "                 [0,0,0,0,0,0,0],\n",
        "                 [0,0,0,0,0,0,0],\n",
        "                 [0,0,0,1,1,1,1],\n",
        "                 ])\n",
        "    \n",
        "    \n",
        "    # 各ブロックの回転パターンを保持\n",
        "    l_num = []\n",
        "    for b in initial_blocks:\n",
        "        l_num.append(get_rotate_num(b))\n",
        "\n",
        "    # 各ブロックの全配置パターンを取得\n",
        "    l_binarys = []\n",
        "    for i in range(8):\n",
        "        l_binary = []\n",
        "        rotates = l_num[i]\n",
        "\n",
        "        for rotate in rotates:\n",
        "            block = rotate_block(initial_blocks[i], rotate)\n",
        "            position = [0,0]\n",
        "            while position[0] != -1:\n",
        "                board = np.copy(initial_board)\n",
        "                get_board_state(board, block, position)\n",
        "\n",
        "                num = int(convert_to_binary_board(board), 2)\n",
        "                board = np.copy(initial_board)\n",
        "                # 置いたブロックのみが1になるように、初期ボード分を引く\n",
        "                l_binary.append(num - int(convert_to_binary_board(board), 2))\n",
        "\n",
        "                # 次の位置を取得\n",
        "                position = get_position(board, block, position)\n",
        "                \n",
        "        l_binarys.append(l_binary)\n",
        "\n",
        "    # ボードに日付を反映\n",
        "    # 月の位置を取得\n",
        "    r = (date.month-1) // 6\n",
        "    c = (date.month-1) % 6\n",
        "    initial_board[r][c] = 1\n",
        "\n",
        "    # 日の位置を取得\n",
        "    r = (date.day-1) // 7 + 2\n",
        "    c = (date.day-1) % 7\n",
        "    initial_board[r][c] = 1\n",
        "\n",
        "    initial_board_num = int(convert_to_binary_board(initial_board), 2)\n",
        "    complete_num = int('1111111111111111111111111111111111111111111111111',2)\n",
        "\n",
        "    # 各ブロックのパターン数を保持(index番号として1を引いておく)\n",
        "    patterns_nums = []\n",
        "    for i in range(8):\n",
        "        patterns_nums.append(len(l_binarys[i]) - 1)\n",
        "\n",
        "    pattern_states = [0]*8\n",
        "    block_No = 0\n",
        "    board_num = initial_board_num\n",
        "    ans_num = 0\n",
        "\n",
        "    start_time = datetime.datetime.now()\n",
        "    print(start_time)\n",
        "\n",
        "    cnt = 0\n",
        "\n",
        "    while 1:\n",
        "        # 全ての探索の終わり\n",
        "        if block_No == -1:\n",
        "            break\n",
        "\n",
        "        cnt += 1\n",
        "        next_block_num = l_binarys[block_No][pattern_states[block_No]]\n",
        "\n",
        "        if not judge_correct_board_for_binary(board_num, next_block_num):\n",
        "            pattern_states, block_No = get_next_state(patterns_nums, pattern_states, block_No)\n",
        "\n",
        "            # ボードの状態をクリア\n",
        "            board_num = initial_board_num\n",
        "            for i in range(block_No):\n",
        "                board_num += l_binarys[i][pattern_states[i]]\n",
        "            continue\n",
        "\n",
        "        # boardにブロックを足す\n",
        "        board_num += next_block_num\n",
        "\n",
        "        # ブロック番号が6だったら、最後のブロックの判定を行う\n",
        "        if block_No == 6:\n",
        "            # ボードの状態をビット反転させて、必要なブロックの形を取得\n",
        "            necessary_block = board_num ^ complete_num\n",
        "\n",
        "            # 必要なブロックの形がある場合\n",
        "            if necessary_block in l_binarys[7]:\n",
        "                # パターンステートに発見した番号を追加\n",
        "                pattern_states[7] = l_binarys[7].index(necessary_block)\n",
        "                ans_num += 1\n",
        "\n",
        "        # ブロック番号が6でない場合\n",
        "        else:\n",
        "            block_No += 1\n",
        "\n",
        "    end_time = datetime.datetime.now()\n",
        "    print(end_time)\n",
        "    t = end_time-start_time\n",
        "    print(f'探索時間：{t.seconds}秒')\n",
        "    print(f\"探索回数：{cnt}\")\n",
        "    print(f'正解パターン数：{ans_num}')\n",
        "    print()\n",
        "\n",
        "    return ans_num\n",
        "\n",
        "\n",
        "initial_board =np.array([[0,0,0,0,0,0,1],\n",
        "                 [0,0,0,0,0,0,1],\n",
        "                 [0,0,0,0,0,0,0],\n",
        "                 [0,0,0,0,0,0,0],\n",
        "                 [0,0,0,0,0,0,0],\n",
        "                 [0,0,0,0,0,0,0],\n",
        "                 [0,0,0,1,1,1,1],\n",
        "                 ])\n",
        "\n",
        "# ブロックの種類\n",
        "initial_blocks = [\n",
        "    np.array([\n",
        "        [1, 1, 1],\n",
        "        [1, 1, 1]]),\n",
        "    np.array([\n",
        "        [1, 0, 0],\n",
        "        [1, 0, 0],\n",
        "        [1, 1, 1]]),\n",
        "    np.array([\n",
        "        [1, 1, 0],\n",
        "        [0, 1, 0],\n",
        "        [0, 1, 1]]),\n",
        "    np.array([\n",
        "        [1, 0, 1],\n",
        "        [1, 1, 1]]),\n",
        "    np.array([\n",
        "        [0, 0, 0, 1],\n",
        "        [1, 1, 1, 1]]),\n",
        "    np.array([\n",
        "        [0, 0, 1, 1],\n",
        "        [1, 1, 1, 0]]),\n",
        "    np.array([\n",
        "        [0, 0, 1, 0],\n",
        "        [1, 1, 1, 1]]),\n",
        "    np.array([\n",
        "        [1, 1, 0],\n",
        "        [1, 1, 1]]),\n",
        "]\n",
        "\n",
        "cols = ['date', 'pattern_num']\n",
        "df = pd.DataFrame(index=[], columns=cols)\n",
        "date = datetime.date(2000, 1, 1)\n",
        "\n",
        "# 366日探索\n",
        "# for i in range(366):\n",
        "for i in range(1):\n",
        "    print(f'探索日付：{date.month}/{date.day}')\n",
        "    num = main(date)\n",
        "    df = df.append({'date': f'{date.month}/{date.day}', 'pattern_num': num}, ignore_index=True)\n",
        "    date += datetime.timedelta(days=1)\n",
        "\n",
        "# Excelに出力\n",
        "# df.to_excel('solve.xlsx', index=False)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "探索日付：1/1\n",
            "2021-08-30 13:03:47.048455\n",
            "2021-08-30 13:03:53.261819\n",
            "探索時間：6秒\n",
            "探索回数：1407146\n",
            "正解パターン数：64\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mnSW4MHG7k6"
      },
      "source": [
        "---\n",
        "# **パズルの描画**\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "-7aQ_zZkV19Y",
        "outputId": "5d3effb5-98f9-4cd6-a46b-d002167313a6"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import datetime\n",
        "from dateutil.relativedelta import relativedelta\n",
        "\n",
        "def draw_puzzle():\n",
        "    fig = plt.figure(figsize=(5,5))\n",
        "    ax = fig.add_subplot(111)\n",
        "    plt.xlim(0,7)\n",
        "    plt.ylim(0,7)\n",
        "\n",
        "    ax.set_aspect('equal', adjustable='box')\n",
        "    ax.grid()\n",
        "\n",
        "    dt = datetime.date(2000, 1, 1)\n",
        "\n",
        "    ax.axvspan(6,7,5/7,7/7, color = \"black\", alpha = 1)\n",
        "    ax.axvspan(3,7,0/7,1/7, color = \"black\", alpha = 1)\n",
        "\n",
        "    # 月の描画\n",
        "    for i in range(6):\n",
        "        nm = dt + relativedelta(months=i)\n",
        "        m = nm.strftime('%b')\n",
        "        ax.text(0.5+i, 6.5, m, fontsize=16,\n",
        "                            verticalalignment=\"center\",\n",
        "                            horizontalalignment=\"center\")\n",
        "    for i in range(6):\n",
        "        nm = dt + relativedelta(months=6+i)\n",
        "        m = nm.strftime('%b')\n",
        "        ax.text(0.5+i, 5.5, m, fontsize=16,\n",
        "                            verticalalignment=\"center\",\n",
        "                            horizontalalignment=\"center\")\n",
        "        \n",
        "    # 日の描画\n",
        "    for i in range(31):\n",
        "        x = 0.5 + i%7\n",
        "        y = 0.5 + abs(4-i//7)\n",
        "        ax.text(x, y, str(i+1), fontsize=16,\n",
        "                            verticalalignment=\"center\",\n",
        "                            horizontalalignment=\"center\")\n",
        "    \n",
        "    return ax\n",
        "\n",
        "def draw_blocks(ax, block_nums):\n",
        "    # 初期化\n",
        "    ax.axvspan(0,7,0,5, color = 'white', alpha = 1)\n",
        "    ax.axvspan(6,7,5/7,7/7, color = \"black\", alpha = 1)\n",
        "    ax.axvspan(3,7,0/7,1/7, color = \"black\", alpha = 1)\n",
        "\n",
        "    colors = ['orangered', 'gold', 'darkcyan', 'magenta', 'aqua', 'brown', 'greenyellow', 'mediumblue',]\n",
        "    for i in range(len(block_nums)):\n",
        "        binary = format(block_nums[i], '049b')\n",
        "        indexs = [i for i, x in enumerate(binary) if x == '1']\n",
        "        for index in indexs:\n",
        "            x = index%7\n",
        "            y = 6 - index//7\n",
        "            ax.axvspan(x,x+1,y/7,(y+1)/7, color = colors[i], alpha = 1)\n",
        "\n",
        "    plt.pause(0.1)\n",
        "\n",
        "ax = draw_puzzle()\n",
        "block_nums = [62053687492608, 70926016184320, 566362112, 169607168, 270549376, 101888, 144044680282112, 12400]\n",
        "draw_blocks(ax, block_nums)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5DJmsz56HAav"
      },
      "source": [
        "---\n",
        "# **ヒートマップ作成**\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 888
        },
        "id": "ruodfckNcaUj",
        "outputId": "977cc1ee-3650-4eb3-8467-9fbc379b646b"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "import calendar\n",
        "from dateutil.relativedelta import relativedelta\n",
        "\n",
        "# カラーマップを作成\n",
        "def gen_cmap_rgb(cols):\n",
        "    nmax = float(len(cols)-1)\n",
        "    cdict = {'red':[], 'green':[], 'blue':[]}\n",
        "    for n, c in enumerate(cols):\n",
        "        loc = n/nmax\n",
        "        cdict['red'  ].append((loc, c[0], c[0]))\n",
        "        cdict['green'].append((loc, c[1], c[1]))\n",
        "        cdict['blue' ].append((loc, c[2], c[2]))\n",
        "    return mpl.colors.LinearSegmentedColormap('cmap', cdict)\n",
        "\n",
        "def draw(fig, dt, l):\n",
        "    ax = fig.add_subplot(3,4,dt.month)\n",
        "    plt.xlim(0,7)\n",
        "    plt.ylim(0,7)\n",
        "    plt.subplots_adjust(wspace=0.2, hspace=-0.4)\n",
        "\n",
        "    ax.set_aspect('equal', adjustable='box')\n",
        "    ax.grid()\n",
        "\n",
        "    ax.axvspan(6,7,5/7,7/7, color = \"black\", alpha = 1)\n",
        "    ax.axvspan(3,7,0/7,1/7, color = \"black\", alpha = 1)\n",
        "\n",
        "    base_date = datetime.date(2000, 1, 1)\n",
        "\n",
        "    # 月の描画\n",
        "    for i in range(6):\n",
        "        nm = base_date + relativedelta(months=i)\n",
        "        m = nm.strftime('%b')\n",
        "        a = 1 if dt.month == nm.month else 0.2\n",
        "        ax.text(0.5+i, 6.5, m, fontsize=16,\n",
        "                            verticalalignment=\"center\",\n",
        "                            horizontalalignment=\"center\",\n",
        "                            alpha = a)\n",
        "    for i in range(6):\n",
        "        nm = base_date + relativedelta(months=6+i)\n",
        "        m = nm.strftime('%b')\n",
        "        a = 1 if dt.month == nm.month else 0.2\n",
        "        ax.text(0.5+i, 5.5, m, fontsize=16,\n",
        "                            verticalalignment=\"center\",\n",
        "                            horizontalalignment=\"center\",\n",
        "                            alpha = a)\n",
        "        \n",
        "    # 日の描画\n",
        "    for i in range(31):\n",
        "        a = 1 if i < calendar.monthrange(dt.year, dt.month)[1] else 0.2\n",
        "        x = 0.5 + i%7\n",
        "        y = 0.5 + abs(4-i//7)\n",
        "        ax.text(x, y, str(i+1), fontsize=16,\n",
        "                            verticalalignment=\"center\",\n",
        "                            horizontalalignment=\"center\",\n",
        "                            alpha = a)\n",
        "    \n",
        "    # 順位による色付け\n",
        "    cmap = gen_cmap_rgb([(0.2,0.2,1),(0.8,0.8,1),(1,1,1),(1,0.8,0.8),(1,0.2,0.2)])\n",
        "    rgb = cmap(np.arange(256))\n",
        "    for i in range(31):\n",
        "        if i >= len(l):\n",
        "            break\n",
        "        x = i%7\n",
        "        y = 4 - i//7\n",
        "        ax.axvspan(x,x+1,y/7,(y+1)/7, color = rgb[l[i]])\n",
        "\n",
        "fig = plt.figure(figsize=(20,20))\n",
        "\n",
        "# Excelから読み取り\n",
        "df = pd.read_excel('solve.xlsx')\n",
        "\n",
        "#日付を型変換\n",
        "df['date'] = '2000/' + df['date']\n",
        "df['date'] = pd.to_datetime(df['date'])\n",
        "\n",
        "# パターン数で順位付け、最大が255になるように調整\n",
        "df['rank'] = df['pattern_num'].rank(method='min',ascending=False)\n",
        "_max = df['rank'].max()\n",
        "df['rank'] = df['rank'] * 255 / _max\n",
        "df['rank'] = df['rank'].map(int)\n",
        "\n",
        "for i in range(1, 13):\n",
        "    dt = datetime.date(2000, i, 1)\n",
        "    l = list(df[df['date'].dt.month == i]['rank'])\n",
        "    draw(fig, dt, l)\n",
        "\n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 57
        },
        "id": "t8nonIKXwcrK",
        "outputId": "668a8e0f-c2a0-40c3-936f-51ad79f5d62a"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# カラーマップを作成\n",
        "def gen_cmap_rgb(cols):\n",
        "    nmax = float(len(cols)-1)\n",
        "    cdict = {'red':[], 'green':[], 'blue':[]}\n",
        "    for n, c in enumerate(cols):\n",
        "        loc = n/nmax\n",
        "        cdict['red'  ].append((loc, c[0], c[0]))\n",
        "        cdict['green'].append((loc, c[1], c[1]))\n",
        "        cdict['blue' ].append((loc, c[2], c[2]))\n",
        "    return mpl.colors.LinearSegmentedColormap('cmap', cdict)\n",
        "\n",
        "cmap = gen_cmap_rgb([(0.2,0.2,1),(0.8,0.8,1),(1,1,1),(1,0.8,0.8),(1,0.2,0.2)])\n",
        "\n",
        "gradient = np.linspace(0, 1, 256)\n",
        "gradient = np.vstack((gradient, gradient))\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.imshow(gradient, aspect=10, cmap=cmap)\n",
        "ax.set_axis_off()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAAoCAYAAABdEClxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAABNklEQVR4nO3aUWrCQBSG0ZnYJbn/FbilZPqQDkmnURsovwXPgWBuzIVUyocP1tZaASBjevUDALwT0QUIEl2AINEFCBJdgCDRBQj6ePTm9VpaKaVM03pcLuv1fj5Nx3M/T+7s5+TO+Pk826l1+3xr3Y4+j6/79/fXjs7P7oz3p3aOnvf0Tvn6qWM7eO1Ht5/H87M74/2pnaPn/cudZSllntd5WX7O87xdL2Wbf7Mzzv95Z/zb7t1zb6fPt9vuP/k733QBgkQXIEh0AYJEFyBIdAGCRBcgSHQBgkQXIEh0AYJEFyBIdAGCRBcgSHQBgkQXIEh0AYJEFyBIdAGCRBcgSHQBgkQXIEh0AYJEFyBIdAGCRBcgSHQBgmpr7dXPAPA2fNMFCBJdgCDRBQgSXYAg0QUIEl2AoE+H8O5L8lFPBQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}