beginner-s-a-star-algorithm-tutorial.ipynb

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   "source": [
    "Notebook from COREY LEHMANN; https://www.kaggle.com/code/clehmann10/beginner-s-a-star-algorithm-tutorial"
   ]
  },
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   "source": [
    "# 🎄 Overview\n",
    "\n",
    "At the outset, this competition looks just like a rubix cube solver, with the added twist of the wild cards.\n",
    "There's a few solutions using the a-star algorithm published so far, so let's give them a look.\n",
    "\n",
    "This solution was influenced by:\n",
    "* https://www.kaggle.com/code/whats2000/a-star-algorithm-polytope-permutation\n",
    "* https://www.kaggle.com/code/robikscube/santa-2023-polytope-permutation-first-look\n",
    "\n",
    "Reading other people's solutions is a great way to learn, so give them a look!\n",
    "\n",
    "I've sinced followed this up with a second notebook looking at the slightly more complex Iterative Deepning A-star algorithm. Check it out: https://www.kaggle.com/code/clehmann10/ida-star-algorithm-tutorial/edit/run/156214648\n",
    "\n",
    "Edit: Since first uploading this tutorial, I've made the following changes:\n",
    "- Improved the heuristic (the evaluation of whether a state is 'good') to include state adjacency\n",
    "- Fixed a bug where we were skipping over problems we could easily solve\n",
    "- Added a summary of the final calculated results and compared to sample solutions"
   ]
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   "source": [
    "***\n",
    "# ⚙️ Setup\n",
    "Let's get our python environment ready to go."
   ]
  },
  {
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     "text": [
      "/kaggle/input/santa-2023/sample_submission.csv\n",
      "/kaggle/input/santa-2023/puzzles.csv\n",
      "/kaggle/input/santa-2023/puzzle_info.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
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   "source": [
    "# Load Data\n",
    "INPUT_DIR = '/kaggle/input/santa-2023'\n",
    "puzzles_df = pd.read_csv(f'{INPUT_DIR}/puzzles.csv')\n",
    "puzzle_info_df = pd.read_csv(f'{INPUT_DIR}/puzzle_info.csv')\n",
    "ss_df = pd.read_csv(f'{INPUT_DIR}/sample_submission.csv')\n",
    "df_dict = {\n",
    "    \"Puzzles\": puzzles_df,\n",
    "    \"Puzzle Info\": puzzle_info_df,\n",
    "    \"Sample submission\": ss_df,\n",
    "}"
   ]
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    "***\n",
    "# 🔎 Examine the Data\n",
    "Now let's take a look at the data that we're working with.\n",
    "What we're looking for is:\n",
    "1. What data do I need to reformat/adapt before I even start looking at it?\n",
    "2. What does the data mean?\n",
    "3. Is there any data missing?\n",
    "4. What patterns and distributions are displayed in the raw data?"
   ]
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     "text": [
      "- Puzzles -\n",
      "Shape: (398, 5) - 398 Rows x 5 Columns\n",
      "Features: ['id' 'puzzle_type' 'solution_state' 'initial_state' 'num_wildcards']\n",
      "\n",
      "   id puzzle_type                                   solution_state  \\\n",
      "0   0  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "1   1  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "2   2  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "3   3  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "4   4  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "5   5  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "6   6  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "7   7  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "8   8  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "9   9  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "\n",
      "                                     initial_state  num_wildcards  \n",
      "0  D;E;D;A;E;B;A;B;C;A;C;A;D;C;D;F;F;F;E;E;B;F;B;C              0  \n",
      "1  D;E;C;B;B;E;F;A;F;D;B;F;F;E;B;D;A;A;C;D;C;E;A;C              0  \n",
      "2  E;F;C;C;F;A;D;D;B;B;A;F;E;B;C;A;A;B;D;F;E;E;C;D              0  \n",
      "3  A;C;E;C;F;D;E;D;A;A;F;A;B;D;B;F;E;D;B;F;B;C;C;E              0  \n",
      "4  E;D;E;D;A;E;F;B;A;C;F;D;F;D;C;A;F;B;C;C;B;E;B;A              0  \n",
      "5  A;F;E;E;A;A;C;F;D;B;C;F;E;C;B;D;D;B;E;A;B;D;F;C              0  \n",
      "6  F;C;B;E;E;A;F;F;D;A;C;D;B;E;C;E;D;A;B;C;B;D;F;A              0  \n",
      "7  E;A;D;B;F;A;D;F;E;D;B;D;E;F;E;B;B;C;A;A;C;C;C;F              0  \n",
      "8  D;A;E;B;F;C;F;E;A;D;A;F;E;A;B;C;C;D;D;B;E;B;F;C              0  \n",
      "9  D;E;C;C;F;A;F;F;B;B;C;F;A;A;E;D;C;B;A;E;D;D;E;B              0  \n",
      "──────────────────────────────────────────────────────────────────────\n",
      "- Puzzle Info -\n",
      "Shape: (26, 2) - 26 Rows x 2 Columns\n",
      "Features: ['puzzle_type' 'allowed_moves']\n",
      "\n",
      "     puzzle_type                                      allowed_moves\n",
      "0     cube_2/2/2  {'f0': [0, 1, 19, 17, 6, 4, 7, 5, 2, 9, 3, 11,...\n",
      "1     cube_3/3/3  {'f0': [0, 1, 2, 3, 4, 5, 44, 41, 38, 15, 12, ...\n",
      "2     cube_4/4/4  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...\n",
      "3     cube_5/5/5  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...\n",
      "4     cube_6/6/6  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...\n",
      "5     cube_7/7/7  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...\n",
      "6     cube_8/8/8  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...\n",
      "7     cube_9/9/9  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...\n",
      "8  cube_10/10/10  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...\n",
      "9  cube_19/19/19  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...\n",
      "──────────────────────────────────────────────────────────────────────\n",
      "- Sample submission -\n",
      "Shape: (398, 2) - 398 Rows x 2 Columns\n",
      "Features: ['id' 'moves']\n",
      "\n",
      "   id                                              moves\n",
      "0   0                                             r1.-f1\n",
      "1   1  f1.d0.-r0.-f1.-d0.-f1.d0.-r0.f0.-f1.-r0.f1.-d1...\n",
      "2   2  f1.d0.-d1.r0.-d1.-f0.f1.-r0.-f0.-r1.-f0.r0.-d0...\n",
      "3   3  -f0.-r0.-f0.-d0.-f0.f1.r0.-d1.-r0.-r1.-r0.-f1....\n",
      "4   4  d1.-f1.d1.r1.-f0.d1.-d0.-r1.d1.d1.-f1.d1.-d0.-...\n",
      "5   5  f0.r0.d0.f0.d0.-f1.r1.-r0.f0.r0.d0.f0.-r1.d0.f...\n",
      "6   6  f1.-r1.-d1.f1.r0.d0.f1.-r1.-f1.f0.r0.d0.f1.-r1...\n",
      "7   7  f1.-f0.-d1.-r0.-f0.-d1.-r0.-d1.-d1.-f1.d1.d1.-...\n",
      "8   8  -d0.-r0.f1.d1.-f0.-d0.-r0.f1.d0.-f1.-f0.-r1.-f...\n",
      "9   9  f0.d0.-f0.-d1.-r0.d0.-f0.-d1.-d0.r0.f1.d0.-f0....\n",
      "──────────────────────────────────────────────────────────────────────\n"
     ]
    }
   ],
   "source": [
    "# Print out an exerpt from each of the dataframes we've been given\n",
    "for df_key in df_dict.keys():\n",
    "    print(f\"- {df_key} -\\nShape: {df_dict[df_key].shape} - {df_dict[df_key].shape[0]} Rows x {df_dict[df_key].shape[1]} Columns\\nFeatures: {df_dict[df_key].columns.values}\\n\")\n",
    "    print(df_dict[df_key].head(10))\n",
    "    print('─'*70)"
   ]
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   "source": [
    "## 1. What data do I need to reformat/adapt before I even start looking at it?\n",
    "Some of these columns are just a pile of letters in a string. Let's separate these out into structures that are actually useful for us to look at like lists and dicts.\n",
    "\n",
    "While we're at it, let's add the 'reversed moves' into the set of allowed moves in the puzzle_info dataframe. Plus let's grab some of the useful numbers from the data."
   ]
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   "source": [
    "# Split up the initial state \n",
    "puzzles_df['initial_state_parsed'] = puzzles_df['initial_state'].apply(lambda x: x.split(\";\"))\n",
    "# Split up the solution state \n",
    "puzzles_df['solution_state_parsed'] = puzzles_df['solution_state'].apply(lambda x: x.split(\";\"))\n",
    "# Get total number of 'faces' to be solved\n",
    "puzzles_df['total_components'] = puzzles_df['solution_state_parsed'].apply(lambda x: len(x))\n",
    "# Get a list of 'colours'\n",
    "puzzles_df['unique_components'] = puzzles_df['solution_state_parsed'].apply(lambda x: np.unique(x))\n",
    "# How many 'colours' are there?\n",
    "puzzles_df['total_unique_components'] = puzzles_df['unique_components'].apply(lambda x: len(x))\n",
    "\n",
    "# Convert allowed moves from string to dict\n",
    "import json\n",
    "def get_allowed_moves(dict_string):\n",
    "    moves_dict = json.loads(dict_string.replace(\"'\", '\"'))\n",
    "    # add inverse moves\n",
    "    for move in list(moves_dict.keys()):\n",
    "        inverse_move = list(np.argsort(moves_dict[move]))\n",
    "        if inverse_move not in moves_dict.values():\n",
    "            moves_dict['-' + move] = inverse_move\n",
    "    return moves_dict\n",
    "puzzle_info_df['allowed_moves_dict'] = puzzle_info_df['allowed_moves'].apply(lambda x: get_allowed_moves(x))\n",
    "# Get total number of allowed moves\n",
    "puzzle_info_df['total_allowed_moves'] = puzzle_info_df['allowed_moves_dict'].apply(lambda x: len(x.keys()))\n",
    "\n",
    "# Split up sample submission into list\n",
    "ss_df['moves_parsed'] = ss_df['moves'].apply(lambda x: x.split('.'))\n",
    "# Count moves in sample submission solutions\n",
    "ss_df['total_moves'] = ss_df['moves_parsed'].apply(lambda x: len(x))"
   ]
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   "source": [
    "Great, now let's run the script again to look at what we've achieved."
   ]
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      "- Puzzles -\n",
      "Shape: (398, 10) - 398 Rows x 10 Columns\n",
      "Features: ['id' 'puzzle_type' 'solution_state' 'initial_state' 'num_wildcards'\n",
      " 'initial_state_parsed' 'solution_state_parsed' 'total_components'\n",
      " 'unique_components' 'total_unique_components']\n",
      "\n",
      "   id puzzle_type                                   solution_state  \\\n",
      "0   0  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "1   1  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "2   2  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "3   3  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "4   4  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "5   5  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "6   6  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "7   7  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "8   8  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "9   9  cube_2/2/2  A;A;A;A;B;B;B;B;C;C;C;C;D;D;D;D;E;E;E;E;F;F;F;F   \n",
      "\n",
      "                                     initial_state  num_wildcards  \\\n",
      "0  D;E;D;A;E;B;A;B;C;A;C;A;D;C;D;F;F;F;E;E;B;F;B;C              0   \n",
      "1  D;E;C;B;B;E;F;A;F;D;B;F;F;E;B;D;A;A;C;D;C;E;A;C              0   \n",
      "2  E;F;C;C;F;A;D;D;B;B;A;F;E;B;C;A;A;B;D;F;E;E;C;D              0   \n",
      "3  A;C;E;C;F;D;E;D;A;A;F;A;B;D;B;F;E;D;B;F;B;C;C;E              0   \n",
      "4  E;D;E;D;A;E;F;B;A;C;F;D;F;D;C;A;F;B;C;C;B;E;B;A              0   \n",
      "5  A;F;E;E;A;A;C;F;D;B;C;F;E;C;B;D;D;B;E;A;B;D;F;C              0   \n",
      "6  F;C;B;E;E;A;F;F;D;A;C;D;B;E;C;E;D;A;B;C;B;D;F;A              0   \n",
      "7  E;A;D;B;F;A;D;F;E;D;B;D;E;F;E;B;B;C;A;A;C;C;C;F              0   \n",
      "8  D;A;E;B;F;C;F;E;A;D;A;F;E;A;B;C;C;D;D;B;E;B;F;C              0   \n",
      "9  D;E;C;C;F;A;F;F;B;B;C;F;A;A;E;D;C;B;A;E;D;D;E;B              0   \n",
      "\n",
      "                                initial_state_parsed  \\\n",
      "0  [D, E, D, A, E, B, A, B, C, A, C, A, D, C, D, ...   \n",
      "1  [D, E, C, B, B, E, F, A, F, D, B, F, F, E, B, ...   \n",
      "2  [E, F, C, C, F, A, D, D, B, B, A, F, E, B, C, ...   \n",
      "3  [A, C, E, C, F, D, E, D, A, A, F, A, B, D, B, ...   \n",
      "4  [E, D, E, D, A, E, F, B, A, C, F, D, F, D, C, ...   \n",
      "5  [A, F, E, E, A, A, C, F, D, B, C, F, E, C, B, ...   \n",
      "6  [F, C, B, E, E, A, F, F, D, A, C, D, B, E, C, ...   \n",
      "7  [E, A, D, B, F, A, D, F, E, D, B, D, E, F, E, ...   \n",
      "8  [D, A, E, B, F, C, F, E, A, D, A, F, E, A, B, ...   \n",
      "9  [D, E, C, C, F, A, F, F, B, B, C, F, A, A, E, ...   \n",
      "\n",
      "                               solution_state_parsed  total_components  \\\n",
      "0  [A, A, A, A, B, B, B, B, C, C, C, C, D, D, D, ...                24   \n",
      "1  [A, A, A, A, B, B, B, B, C, C, C, C, D, D, D, ...                24   \n",
      "2  [A, A, A, A, B, B, B, B, C, C, C, C, D, D, D, ...                24   \n",
      "3  [A, A, A, A, B, B, B, B, C, C, C, C, D, D, D, ...                24   \n",
      "4  [A, A, A, A, B, B, B, B, C, C, C, C, D, D, D, ...                24   \n",
      "5  [A, A, A, A, B, B, B, B, C, C, C, C, D, D, D, ...                24   \n",
      "6  [A, A, A, A, B, B, B, B, C, C, C, C, D, D, D, ...                24   \n",
      "7  [A, A, A, A, B, B, B, B, C, C, C, C, D, D, D, ...                24   \n",
      "8  [A, A, A, A, B, B, B, B, C, C, C, C, D, D, D, ...                24   \n",
      "9  [A, A, A, A, B, B, B, B, C, C, C, C, D, D, D, ...                24   \n",
      "\n",
      "    unique_components  total_unique_components  \n",
      "0  [A, B, C, D, E, F]                        6  \n",
      "1  [A, B, C, D, E, F]                        6  \n",
      "2  [A, B, C, D, E, F]                        6  \n",
      "3  [A, B, C, D, E, F]                        6  \n",
      "4  [A, B, C, D, E, F]                        6  \n",
      "5  [A, B, C, D, E, F]                        6  \n",
      "6  [A, B, C, D, E, F]                        6  \n",
      "7  [A, B, C, D, E, F]                        6  \n",
      "8  [A, B, C, D, E, F]                        6  \n",
      "9  [A, B, C, D, E, F]                        6  \n",
      "──────────────────────────────────────────────────────────────────────\n",
      "- Puzzle Info -\n",
      "Shape: (26, 4) - 26 Rows x 4 Columns\n",
      "Features: ['puzzle_type' 'allowed_moves' 'allowed_moves_dict' 'total_allowed_moves']\n",
      "\n",
      "     puzzle_type                                      allowed_moves  \\\n",
      "0     cube_2/2/2  {'f0': [0, 1, 19, 17, 6, 4, 7, 5, 2, 9, 3, 11,...   \n",
      "1     cube_3/3/3  {'f0': [0, 1, 2, 3, 4, 5, 44, 41, 38, 15, 12, ...   \n",
      "2     cube_4/4/4  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...   \n",
      "3     cube_5/5/5  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...   \n",
      "4     cube_6/6/6  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...   \n",
      "5     cube_7/7/7  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...   \n",
      "6     cube_8/8/8  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...   \n",
      "7     cube_9/9/9  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...   \n",
      "8  cube_10/10/10  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...   \n",
      "9  cube_19/19/19  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...   \n",
      "\n",
      "                                  allowed_moves_dict  total_allowed_moves  \n",
      "0  {'f0': [0, 1, 19, 17, 6, 4, 7, 5, 2, 9, 3, 11,...                   12  \n",
      "1  {'f0': [0, 1, 2, 3, 4, 5, 44, 41, 38, 15, 12, ...                   18  \n",
      "2  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...                   24  \n",
      "3  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...                   30  \n",
      "4  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...                   36  \n",
      "5  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...                   42  \n",
      "6  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...                   48  \n",
      "7  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...                   54  \n",
      "8  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...                   60  \n",
      "9  {'f0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...                  114  \n",
      "──────────────────────────────────────────────────────────────────────\n",
      "- Sample submission -\n",
      "Shape: (398, 4) - 398 Rows x 4 Columns\n",
      "Features: ['id' 'moves' 'moves_parsed' 'total_moves']\n",
      "\n",
      "   id                                              moves  \\\n",
      "0   0                                             r1.-f1   \n",
      "1   1  f1.d0.-r0.-f1.-d0.-f1.d0.-r0.f0.-f1.-r0.f1.-d1...   \n",
      "2   2  f1.d0.-d1.r0.-d1.-f0.f1.-r0.-f0.-r1.-f0.r0.-d0...   \n",
      "3   3  -f0.-r0.-f0.-d0.-f0.f1.r0.-d1.-r0.-r1.-r0.-f1....   \n",
      "4   4  d1.-f1.d1.r1.-f0.d1.-d0.-r1.d1.d1.-f1.d1.-d0.-...   \n",
      "5   5  f0.r0.d0.f0.d0.-f1.r1.-r0.f0.r0.d0.f0.-r1.d0.f...   \n",
      "6   6  f1.-r1.-d1.f1.r0.d0.f1.-r1.-f1.f0.r0.d0.f1.-r1...   \n",
      "7   7  f1.-f0.-d1.-r0.-f0.-d1.-r0.-d1.-d1.-f1.d1.d1.-...   \n",
      "8   8  -d0.-r0.f1.d1.-f0.-d0.-r0.f1.d0.-f1.-f0.-r1.-f...   \n",
      "9   9  f0.d0.-f0.-d1.-r0.d0.-f0.-d1.-d0.r0.f1.d0.-f0....   \n",
      "\n",
      "                                        moves_parsed  total_moves  \n",
      "0                                          [r1, -f1]            2  \n",
      "1  [f1, d0, -r0, -f1, -d0, -f1, d0, -r0, f0, -f1,...           63  \n",
      "2  [f1, d0, -d1, r0, -d1, -f0, f1, -r0, -f0, -r1,...           62  \n",
      "3  [-f0, -r0, -f0, -d0, -f0, f1, r0, -d1, -r0, -r...           92  \n",
      "4  [d1, -f1, d1, r1, -f0, d1, -d0, -r1, d1, d1, -...           70  \n",
      "5  [f0, r0, d0, f0, d0, -f1, r1, -r0, f0, r0, d0,...           54  \n",
      "6  [f1, -r1, -d1, f1, r0, d0, f1, -r1, -f1, f0, r...           68  \n",
      "7  [f1, -f0, -d1, -r0, -f0, -d1, -r0, -d1, -d1, -...           83  \n",
      "8  [-d0, -r0, f1, d1, -f0, -d0, -r0, f1, d0, -f1,...           98  \n",
      "9  [f0, d0, -f0, -d1, -r0, d0, -f0, -d1, -d0, r0,...           76  \n",
      "──────────────────────────────────────────────────────────────────────\n"
     ]
    }
   ],
   "source": [
    "# Print out an exerpt from each of the dataframes we've been given\n",
    "for df_key in df_dict.keys():\n",
    "    print(f\"- {df_key} -\\nShape: {df_dict[df_key].shape} - {df_dict[df_key].shape[0]} Rows x {df_dict[df_key].shape[1]} Columns\\nFeatures: {df_dict[df_key].columns.values}\\n\")\n",
    "    print(df_dict[df_key].head(10))\n",
    "    print('─'*70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98461366",
   "metadata": {
    "papermill": {
     "duration": 0.009811,
     "end_time": "2023-12-27T11:08:43.757920",
     "exception": false,
     "start_time": "2023-12-27T11:08:43.748109",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Ok, now let's "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "455b5bfc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-27T11:08:43.779346Z",
     "iopub.status.busy": "2023-12-27T11:08:43.778920Z",
     "iopub.status.idle": "2023-12-27T11:08:44.402188Z",
     "shell.execute_reply": "2023-12-27T11:08:44.400846Z"
    },
    "papermill": {
     "duration": 0.636968,
     "end_time": "2023-12-27T11:08:44.404734",
     "exception": false,
     "start_time": "2023-12-27T11:08:43.767766",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "puzzles_df['puzzle_type'].value_counts().sort_values().plot(kind='barh', ax=ax)\n",
    "ax.set_title('Number of Puzzles by Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "81f60ff3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-27T11:08:44.429673Z",
     "iopub.status.busy": "2023-12-27T11:08:44.429271Z",
     "iopub.status.idle": "2023-12-27T11:08:44.595597Z",
     "shell.execute_reply": "2023-12-27T11:08:44.594349Z"
    },
    "papermill": {
     "duration": 0.182123,
     "end_time": "2023-12-27T11:08:44.598157",
     "exception": false,
     "start_time": "2023-12-27T11:08:44.416034",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30b62_row0_col4, #T_30b62_row0_col5, #T_30b62_row0_col6, #T_30b62_row0_col7, #T_30b62_row0_col8, #T_30b62_row0_col9, #T_30b62_row0_col10, #T_30b62_row0_col11, #T_30b62_row0_col12, #T_30b62_row0_col13, #T_30b62_row1_col6, #T_30b62_row1_col7, #T_30b62_row1_col8, #T_30b62_row1_col9, #T_30b62_row1_col10, #T_30b62_row1_col11, #T_30b62_row1_col12, #T_30b62_row1_col13, #T_30b62_row2_col1, #T_30b62_row2_col2, #T_30b62_row2_col3, #T_30b62_row2_col4, #T_30b62_row2_col5, #T_30b62_row2_col6, #T_30b62_row2_col7, #T_30b62_row2_col8, #T_30b62_row2_col9, #T_30b62_row2_col10, #T_30b62_row2_col11, #T_30b62_row2_col12, #T_30b62_row2_col13, #T_30b62_row3_col2, #T_30b62_row3_col4, #T_30b62_row3_col6, #T_30b62_row3_col8, #T_30b62_row3_col9, #T_30b62_row3_col10, #T_30b62_row3_col11, #T_30b62_row3_col12, #T_30b62_row3_col13, #T_30b62_row4_col2, #T_30b62_row4_col3, #T_30b62_row4_col4, #T_30b62_row4_col5, #T_30b62_row4_col6, #T_30b62_row4_col7, #T_30b62_row4_col8, #T_30b62_row4_col9, 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#T_30b62_row24_col10, #T_30b62_row24_col11, #T_30b62_row24_col12, #T_30b62_row24_col13, #T_30b62_row25_col0, #T_30b62_row25_col1, #T_30b62_row25_col2, #T_30b62_row25_col3, #T_30b62_row25_col5, #T_30b62_row25_col6, #T_30b62_row25_col7, #T_30b62_row25_col8, #T_30b62_row25_col9, #T_30b62_row25_col10, #T_30b62_row25_col11, #T_30b62_row25_col12, #T_30b62_row25_col13 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row1_col0 {\n",
       "  background-color: #71a8ce;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30b62_row1_col1, #T_30b62_row5_col1, #T_30b62_row17_col1 {\n",
       "  background-color: #c4cbe3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row1_col2 {\n",
       "  background-color: #d7d6e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row1_col3, #T_30b62_row8_col3, #T_30b62_row24_col3 {\n",
       "  background-color: #dbdaeb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row2_col0 {\n",
       "  background-color: #bfc9e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row3_col0 {\n",
       "  background-color: #c9cee4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row3_col1, #T_30b62_row9_col1, #T_30b62_row10_col1 {\n",
       "  background-color: #91b5d6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row3_col3 {\n",
       "  background-color: #9cb9d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row4_col0 {\n",
       "  background-color: #e7e3f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row4_col1 {\n",
       "  background-color: #1379b5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30b62_row5_col0 {\n",
       "  background-color: #ebe6f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row6_col0 {\n",
       "  background-color: #f0eaf4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row6_col1 {\n",
       "  background-color: #509ac6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_30b62_row6_col2, #T_30b62_row13_col2, #T_30b62_row21_col2 {\n",
       "  background-color: #eee9f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row7_col0 {\n",
       "  background-color: #f1ebf5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row7_col1, #T_30b62_row21_col1, #T_30b62_row24_col1 {\n",
       "  background-color: #e8e4f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row8_col0, #T_30b62_row9_col0, #T_30b62_row10_col0 {\n",
       "  background-color: #f4eef6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row11_col0 {\n",
       "  background-color: #f8f1f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row12_col0, #T_30b62_row13_col0, #T_30b62_row14_col0, #T_30b62_row15_col0 {\n",
       "  background-color: #f9f2f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row16_col0, #T_30b62_row17_col0, #T_30b62_row18_col0, #T_30b62_row19_col0, #T_30b62_row20_col0, #T_30b62_row21_col0 {\n",
       "  background-color: #fbf3f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row22_col0, #T_30b62_row23_col0 {\n",
       "  background-color: #fcf4fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row23_col4, #T_30b62_row25_col4 {\n",
       "  background-color: #b4c4df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row24_col0 {\n",
       "  background-color: #fef6fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_30b62_row24_col4 {\n",
       "  background-color: #2685bb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_30b62\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >num_wildcards</th>\n",
       "      <th id=\"T_30b62_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
       "      <th id=\"T_30b62_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_30b62_level0_col2\" class=\"col_heading level0 col2\" >4</th>\n",
       "      <th id=\"T_30b62_level0_col3\" class=\"col_heading level0 col3\" >6</th>\n",
       "      <th id=\"T_30b62_level0_col4\" class=\"col_heading level0 col4\" >8</th>\n",
       "      <th id=\"T_30b62_level0_col5\" class=\"col_heading level0 col5\" >10</th>\n",
       "      <th id=\"T_30b62_level0_col6\" class=\"col_heading level0 col6\" >12</th>\n",
       "      <th id=\"T_30b62_level0_col7\" class=\"col_heading level0 col7\" >16</th>\n",
       "      <th id=\"T_30b62_level0_col8\" class=\"col_heading level0 col8\" >18</th>\n",
       "      <th id=\"T_30b62_level0_col9\" class=\"col_heading level0 col9\" >34</th>\n",
       "      <th id=\"T_30b62_level0_col10\" class=\"col_heading level0 col10\" >38</th>\n",
       "      <th id=\"T_30b62_level0_col11\" class=\"col_heading level0 col11\" >42</th>\n",
       "      <th id=\"T_30b62_level0_col12\" class=\"col_heading level0 col12\" >54</th>\n",
       "      <th id=\"T_30b62_level0_col13\" class=\"col_heading level0 col13\" >176</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >puzzle_type</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row0\" class=\"row_heading level0 row0\" >cube_3/3/3</th>\n",
       "      <td id=\"T_30b62_row0_col0\" class=\"data row0 col0\" >99</td>\n",
       "      <td id=\"T_30b62_row0_col1\" class=\"data row0 col1\" >7</td>\n",
       "      <td id=\"T_30b62_row0_col2\" class=\"data row0 col2\" >9</td>\n",
       "      <td id=\"T_30b62_row0_col3\" class=\"data row0 col3\" >5</td>\n",
       "      <td id=\"T_30b62_row0_col4\" class=\"data row0 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row0_col5\" class=\"data row0 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row0_col6\" class=\"data row0 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row0_col7\" class=\"data row0 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row0_col8\" class=\"data row0 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row0_col9\" class=\"data row0 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row0_col10\" class=\"data row0 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row0_col11\" class=\"data row0 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row0_col12\" class=\"data row0 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row0_col13\" class=\"data row0 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row1\" class=\"row_heading level0 row1\" >cube_4/4/4</th>\n",
       "      <td id=\"T_30b62_row1_col0\" class=\"data row1 col0\" >50</td>\n",
       "      <td id=\"T_30b62_row1_col1\" class=\"data row1 col1\" >2</td>\n",
       "      <td id=\"T_30b62_row1_col2\" class=\"data row1 col2\" >2</td>\n",
       "      <td id=\"T_30b62_row1_col3\" class=\"data row1 col3\" >1</td>\n",
       "      <td id=\"T_30b62_row1_col4\" class=\"data row1 col4\" >3</td>\n",
       "      <td id=\"T_30b62_row1_col5\" class=\"data row1 col5\" >2</td>\n",
       "      <td id=\"T_30b62_row1_col6\" class=\"data row1 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row1_col7\" class=\"data row1 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row1_col8\" class=\"data row1 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row1_col9\" class=\"data row1 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row1_col10\" class=\"data row1 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row1_col11\" class=\"data row1 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row1_col12\" class=\"data row1 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row1_col13\" class=\"data row1 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row2\" class=\"row_heading level0 row2\" >cube_2/2/2</th>\n",
       "      <td id=\"T_30b62_row2_col0\" class=\"data row2 col0\" >30</td>\n",
       "      <td id=\"T_30b62_row2_col1\" class=\"data row2 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row2_col2\" class=\"data row2 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row2_col3\" class=\"data row2 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row2_col4\" class=\"data row2 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row2_col5\" class=\"data row2 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row2_col6\" class=\"data row2 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row2_col7\" class=\"data row2 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row2_col8\" class=\"data row2 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row2_col9\" class=\"data row2 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row2_col10\" class=\"data row2 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row2_col11\" class=\"data row2 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row2_col12\" class=\"data row2 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row2_col13\" class=\"data row2 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row3\" class=\"row_heading level0 row3\" >cube_5/5/5</th>\n",
       "      <td id=\"T_30b62_row3_col0\" class=\"data row3 col0\" >27</td>\n",
       "      <td id=\"T_30b62_row3_col1\" class=\"data row3 col1\" >3</td>\n",
       "      <td id=\"T_30b62_row3_col2\" class=\"data row3 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row3_col3\" class=\"data row3 col3\" >2</td>\n",
       "      <td id=\"T_30b62_row3_col4\" class=\"data row3 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row3_col5\" class=\"data row3 col5\" >2</td>\n",
       "      <td id=\"T_30b62_row3_col6\" class=\"data row3 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row3_col7\" class=\"data row3 col7\" >1</td>\n",
       "      <td id=\"T_30b62_row3_col8\" class=\"data row3 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row3_col9\" class=\"data row3 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row3_col10\" class=\"data row3 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row3_col11\" class=\"data row3 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row3_col12\" class=\"data row3 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row3_col13\" class=\"data row3 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row4\" class=\"row_heading level0 row4\" >wreath_6/6</th>\n",
       "      <td id=\"T_30b62_row4_col0\" class=\"data row4 col0\" >15</td>\n",
       "      <td id=\"T_30b62_row4_col1\" class=\"data row4 col1\" >5</td>\n",
       "      <td id=\"T_30b62_row4_col2\" class=\"data row4 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row4_col3\" class=\"data row4 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row4_col4\" class=\"data row4 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row4_col5\" class=\"data row4 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row4_col6\" class=\"data row4 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row4_col7\" class=\"data row4 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row4_col8\" class=\"data row4 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row4_col9\" class=\"data row4 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row4_col10\" class=\"data row4 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row4_col11\" class=\"data row4 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row4_col12\" class=\"data row4 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row4_col13\" class=\"data row4 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row5\" class=\"row_heading level0 row5\" >wreath_7/7</th>\n",
       "      <td id=\"T_30b62_row5_col0\" class=\"data row5 col0\" >13</td>\n",
       "      <td id=\"T_30b62_row5_col1\" class=\"data row5 col1\" >2</td>\n",
       "      <td id=\"T_30b62_row5_col2\" class=\"data row5 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row5_col3\" class=\"data row5 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row5_col4\" class=\"data row5 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row5_col5\" class=\"data row5 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row5_col6\" class=\"data row5 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row5_col7\" class=\"data row5 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row5_col8\" class=\"data row5 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row5_col9\" class=\"data row5 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row5_col10\" class=\"data row5 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row5_col11\" class=\"data row5 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row5_col12\" class=\"data row5 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row5_col13\" class=\"data row5 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row6\" class=\"row_heading level0 row6\" >globe_3/4</th>\n",
       "      <td id=\"T_30b62_row6_col0\" class=\"data row6 col0\" >10</td>\n",
       "      <td id=\"T_30b62_row6_col1\" class=\"data row6 col1\" >4</td>\n",
       "      <td id=\"T_30b62_row6_col2\" class=\"data row6 col2\" >1</td>\n",
       "      <td id=\"T_30b62_row6_col3\" class=\"data row6 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row6_col4\" class=\"data row6 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row6_col5\" class=\"data row6 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row6_col6\" class=\"data row6 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row6_col7\" class=\"data row6 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row6_col8\" class=\"data row6 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row6_col9\" class=\"data row6 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row6_col10\" class=\"data row6 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row6_col11\" class=\"data row6 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row6_col12\" class=\"data row6 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row6_col13\" class=\"data row6 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row7\" class=\"row_heading level0 row7\" >globe_1/8</th>\n",
       "      <td id=\"T_30b62_row7_col0\" class=\"data row7 col0\" >9</td>\n",
       "      <td id=\"T_30b62_row7_col1\" class=\"data row7 col1\" >1</td>\n",
       "      <td id=\"T_30b62_row7_col2\" class=\"data row7 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row7_col3\" class=\"data row7 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row7_col4\" class=\"data row7 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row7_col5\" class=\"data row7 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row7_col6\" class=\"data row7 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row7_col7\" class=\"data row7 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row7_col8\" class=\"data row7 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row7_col9\" class=\"data row7 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row7_col10\" class=\"data row7 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row7_col11\" class=\"data row7 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row7_col12\" class=\"data row7 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row7_col13\" class=\"data row7 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row8\" class=\"row_heading level0 row8\" >globe_3/33</th>\n",
       "      <td id=\"T_30b62_row8_col0\" class=\"data row8 col0\" >7</td>\n",
       "      <td id=\"T_30b62_row8_col1\" class=\"data row8 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row8_col2\" class=\"data row8 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row8_col3\" class=\"data row8 col3\" >1</td>\n",
       "      <td id=\"T_30b62_row8_col4\" class=\"data row8 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row8_col5\" class=\"data row8 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row8_col6\" class=\"data row8 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row8_col7\" class=\"data row8 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row8_col8\" class=\"data row8 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row8_col9\" class=\"data row8 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row8_col10\" class=\"data row8 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row8_col11\" class=\"data row8 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row8_col12\" class=\"data row8 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row8_col13\" class=\"data row8 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row9\" class=\"row_heading level0 row9\" >cube_6/6/6</th>\n",
       "      <td id=\"T_30b62_row9_col0\" class=\"data row9 col0\" >7</td>\n",
       "      <td id=\"T_30b62_row9_col1\" class=\"data row9 col1\" >3</td>\n",
       "      <td id=\"T_30b62_row9_col2\" class=\"data row9 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row9_col3\" class=\"data row9 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row9_col4\" class=\"data row9 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row9_col5\" class=\"data row9 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row9_col6\" class=\"data row9 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row9_col7\" class=\"data row9 col7\" >1</td>\n",
       "      <td id=\"T_30b62_row9_col8\" class=\"data row9 col8\" >1</td>\n",
       "      <td id=\"T_30b62_row9_col9\" class=\"data row9 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row9_col10\" class=\"data row9 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row9_col11\" class=\"data row9 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row9_col12\" class=\"data row9 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row9_col13\" class=\"data row9 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row10\" class=\"row_heading level0 row10\" >wreath_12/12</th>\n",
       "      <td id=\"T_30b62_row10_col0\" class=\"data row10 col0\" >7</td>\n",
       "      <td id=\"T_30b62_row10_col1\" class=\"data row10 col1\" >3</td>\n",
       "      <td id=\"T_30b62_row10_col2\" class=\"data row10 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row10_col3\" class=\"data row10 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row10_col4\" class=\"data row10 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row10_col5\" class=\"data row10 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row10_col6\" class=\"data row10 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row10_col7\" class=\"data row10 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row10_col8\" class=\"data row10 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row10_col9\" class=\"data row10 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row10_col10\" class=\"data row10 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row10_col11\" class=\"data row10 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row10_col12\" class=\"data row10 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row10_col13\" class=\"data row10 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row11\" class=\"row_heading level0 row11\" >wreath_21/21</th>\n",
       "      <td id=\"T_30b62_row11_col0\" class=\"data row11 col0\" >5</td>\n",
       "      <td id=\"T_30b62_row11_col1\" class=\"data row11 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row11_col2\" class=\"data row11 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row11_col3\" class=\"data row11 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row11_col4\" class=\"data row11 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row11_col5\" class=\"data row11 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row11_col6\" class=\"data row11 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row11_col7\" class=\"data row11 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row11_col8\" class=\"data row11 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row11_col9\" class=\"data row11 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row11_col10\" class=\"data row11 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row11_col11\" class=\"data row11 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row11_col12\" class=\"data row11 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row11_col13\" class=\"data row11 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row12\" class=\"row_heading level0 row12\" >globe_6/8</th>\n",
       "      <td id=\"T_30b62_row12_col0\" class=\"data row12 col0\" >4</td>\n",
       "      <td id=\"T_30b62_row12_col1\" class=\"data row12 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row12_col2\" class=\"data row12 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row12_col3\" class=\"data row12 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row12_col4\" class=\"data row12 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row12_col5\" class=\"data row12 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row12_col6\" class=\"data row12 col6\" >1</td>\n",
       "      <td id=\"T_30b62_row12_col7\" class=\"data row12 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row12_col8\" class=\"data row12 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row12_col9\" class=\"data row12 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row12_col10\" class=\"data row12 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row12_col11\" class=\"data row12 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row12_col12\" class=\"data row12 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row12_col13\" class=\"data row12 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row13\" class=\"row_heading level0 row13\" >globe_6/4</th>\n",
       "      <td id=\"T_30b62_row13_col0\" class=\"data row13 col0\" >4</td>\n",
       "      <td id=\"T_30b62_row13_col1\" class=\"data row13 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row13_col2\" class=\"data row13 col2\" >1</td>\n",
       "      <td id=\"T_30b62_row13_col3\" class=\"data row13 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row13_col4\" class=\"data row13 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row13_col5\" class=\"data row13 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row13_col6\" class=\"data row13 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row13_col7\" class=\"data row13 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row13_col8\" class=\"data row13 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row13_col9\" class=\"data row13 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row13_col10\" class=\"data row13 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row13_col11\" class=\"data row13 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row13_col12\" class=\"data row13 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row13_col13\" class=\"data row13 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row14\" class=\"row_heading level0 row14\" >cube_10/10/10</th>\n",
       "      <td id=\"T_30b62_row14_col0\" class=\"data row14 col0\" >4</td>\n",
       "      <td id=\"T_30b62_row14_col1\" class=\"data row14 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row14_col2\" class=\"data row14 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row14_col3\" class=\"data row14 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row14_col4\" class=\"data row14 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row14_col5\" class=\"data row14 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row14_col6\" class=\"data row14 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row14_col7\" class=\"data row14 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row14_col8\" class=\"data row14 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row14_col9\" class=\"data row14 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row14_col10\" class=\"data row14 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row14_col11\" class=\"data row14 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row14_col12\" class=\"data row14 col12\" >1</td>\n",
       "      <td id=\"T_30b62_row14_col13\" class=\"data row14 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row15\" class=\"row_heading level0 row15\" >cube_7/7/7</th>\n",
       "      <td id=\"T_30b62_row15_col0\" class=\"data row15 col0\" >4</td>\n",
       "      <td id=\"T_30b62_row15_col1\" class=\"data row15 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row15_col2\" class=\"data row15 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row15_col3\" class=\"data row15 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row15_col4\" class=\"data row15 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row15_col5\" class=\"data row15 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row15_col6\" class=\"data row15 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row15_col7\" class=\"data row15 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row15_col8\" class=\"data row15 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row15_col9\" class=\"data row15 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row15_col10\" class=\"data row15 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row15_col11\" class=\"data row15 col11\" >1</td>\n",
       "      <td id=\"T_30b62_row15_col12\" class=\"data row15 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row15_col13\" class=\"data row15 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row16\" class=\"row_heading level0 row16\" >cube_19/19/19</th>\n",
       "      <td id=\"T_30b62_row16_col0\" class=\"data row16 col0\" >3</td>\n",
       "      <td id=\"T_30b62_row16_col1\" class=\"data row16 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row16_col2\" class=\"data row16 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row16_col3\" class=\"data row16 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row16_col4\" class=\"data row16 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row16_col5\" class=\"data row16 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row16_col6\" class=\"data row16 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row16_col7\" class=\"data row16 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row16_col8\" class=\"data row16 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row16_col9\" class=\"data row16 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row16_col10\" class=\"data row16 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row16_col11\" class=\"data row16 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row16_col12\" class=\"data row16 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row16_col13\" class=\"data row16 col13\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row17\" class=\"row_heading level0 row17\" >globe_1/16</th>\n",
       "      <td id=\"T_30b62_row17_col0\" class=\"data row17 col0\" >3</td>\n",
       "      <td id=\"T_30b62_row17_col1\" class=\"data row17 col1\" >2</td>\n",
       "      <td id=\"T_30b62_row17_col2\" class=\"data row17 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row17_col3\" class=\"data row17 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row17_col4\" class=\"data row17 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row17_col5\" class=\"data row17 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row17_col6\" class=\"data row17 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row17_col7\" class=\"data row17 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row17_col8\" class=\"data row17 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row17_col9\" class=\"data row17 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row17_col10\" class=\"data row17 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row17_col11\" class=\"data row17 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row17_col12\" class=\"data row17 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row17_col13\" class=\"data row17 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row18\" class=\"row_heading level0 row18\" >cube_9/9/9</th>\n",
       "      <td id=\"T_30b62_row18_col0\" class=\"data row18 col0\" >3</td>\n",
       "      <td id=\"T_30b62_row18_col1\" class=\"data row18 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row18_col2\" class=\"data row18 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row18_col3\" class=\"data row18 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row18_col4\" class=\"data row18 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row18_col5\" class=\"data row18 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row18_col6\" class=\"data row18 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row18_col7\" class=\"data row18 col7\" >1</td>\n",
       "      <td id=\"T_30b62_row18_col8\" class=\"data row18 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row18_col9\" class=\"data row18 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row18_col10\" class=\"data row18 col10\" >1</td>\n",
       "      <td id=\"T_30b62_row18_col11\" class=\"data row18 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row18_col12\" class=\"data row18 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row18_col13\" class=\"data row18 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row19\" class=\"row_heading level0 row19\" >cube_8/8/8</th>\n",
       "      <td id=\"T_30b62_row19_col0\" class=\"data row19 col0\" >3</td>\n",
       "      <td id=\"T_30b62_row19_col1\" class=\"data row19 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row19_col2\" class=\"data row19 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row19_col3\" class=\"data row19 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row19_col4\" class=\"data row19 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row19_col5\" class=\"data row19 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row19_col6\" class=\"data row19 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row19_col7\" class=\"data row19 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row19_col8\" class=\"data row19 col8\" >1</td>\n",
       "      <td id=\"T_30b62_row19_col9\" class=\"data row19 col9\" >1</td>\n",
       "      <td id=\"T_30b62_row19_col10\" class=\"data row19 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row19_col11\" class=\"data row19 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row19_col12\" class=\"data row19 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row19_col13\" class=\"data row19 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row20\" class=\"row_heading level0 row20\" >cube_33/33/33</th>\n",
       "      <td id=\"T_30b62_row20_col0\" class=\"data row20 col0\" >3</td>\n",
       "      <td id=\"T_30b62_row20_col1\" class=\"data row20 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row20_col2\" class=\"data row20 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row20_col3\" class=\"data row20 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row20_col4\" class=\"data row20 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row20_col5\" class=\"data row20 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row20_col6\" class=\"data row20 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row20_col7\" class=\"data row20 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row20_col8\" class=\"data row20 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row20_col9\" class=\"data row20 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row20_col10\" class=\"data row20 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row20_col11\" class=\"data row20 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row20_col12\" class=\"data row20 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row20_col13\" class=\"data row20 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row21\" class=\"row_heading level0 row21\" >globe_2/6</th>\n",
       "      <td id=\"T_30b62_row21_col0\" class=\"data row21 col0\" >3</td>\n",
       "      <td id=\"T_30b62_row21_col1\" class=\"data row21 col1\" >1</td>\n",
       "      <td id=\"T_30b62_row21_col2\" class=\"data row21 col2\" >1</td>\n",
       "      <td id=\"T_30b62_row21_col3\" class=\"data row21 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row21_col4\" class=\"data row21 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row21_col5\" class=\"data row21 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row21_col6\" class=\"data row21 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row21_col7\" class=\"data row21 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row21_col8\" class=\"data row21 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row21_col9\" class=\"data row21 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row21_col10\" class=\"data row21 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row21_col11\" class=\"data row21 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row21_col12\" class=\"data row21 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row21_col13\" class=\"data row21 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row22\" class=\"row_heading level0 row22\" >globe_8/25</th>\n",
       "      <td id=\"T_30b62_row22_col0\" class=\"data row22 col0\" >2</td>\n",
       "      <td id=\"T_30b62_row22_col1\" class=\"data row22 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row22_col2\" class=\"data row22 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row22_col3\" class=\"data row22 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row22_col4\" class=\"data row22 col4\" >0</td>\n",
       "      <td id=\"T_30b62_row22_col5\" class=\"data row22 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row22_col6\" class=\"data row22 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row22_col7\" class=\"data row22 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row22_col8\" class=\"data row22 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row22_col9\" class=\"data row22 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row22_col10\" class=\"data row22 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row22_col11\" class=\"data row22 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row22_col12\" class=\"data row22 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row22_col13\" class=\"data row22 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row23\" class=\"row_heading level0 row23\" >wreath_33/33</th>\n",
       "      <td id=\"T_30b62_row23_col0\" class=\"data row23 col0\" >2</td>\n",
       "      <td id=\"T_30b62_row23_col1\" class=\"data row23 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row23_col2\" class=\"data row23 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row23_col3\" class=\"data row23 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row23_col4\" class=\"data row23 col4\" >1</td>\n",
       "      <td id=\"T_30b62_row23_col5\" class=\"data row23 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row23_col6\" class=\"data row23 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row23_col7\" class=\"data row23 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row23_col8\" class=\"data row23 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row23_col9\" class=\"data row23 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row23_col10\" class=\"data row23 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row23_col11\" class=\"data row23 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row23_col12\" class=\"data row23 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row23_col13\" class=\"data row23 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row24\" class=\"row_heading level0 row24\" >globe_6/10</th>\n",
       "      <td id=\"T_30b62_row24_col0\" class=\"data row24 col0\" >1</td>\n",
       "      <td id=\"T_30b62_row24_col1\" class=\"data row24 col1\" >1</td>\n",
       "      <td id=\"T_30b62_row24_col2\" class=\"data row24 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row24_col3\" class=\"data row24 col3\" >1</td>\n",
       "      <td id=\"T_30b62_row24_col4\" class=\"data row24 col4\" >2</td>\n",
       "      <td id=\"T_30b62_row24_col5\" class=\"data row24 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row24_col6\" class=\"data row24 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row24_col7\" class=\"data row24 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row24_col8\" class=\"data row24 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row24_col9\" class=\"data row24 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row24_col10\" class=\"data row24 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row24_col11\" class=\"data row24 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row24_col12\" class=\"data row24 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row24_col13\" class=\"data row24 col13\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30b62_level0_row25\" class=\"row_heading level0 row25\" >wreath_100/100</th>\n",
       "      <td id=\"T_30b62_row25_col0\" class=\"data row25 col0\" >0</td>\n",
       "      <td id=\"T_30b62_row25_col1\" class=\"data row25 col1\" >0</td>\n",
       "      <td id=\"T_30b62_row25_col2\" class=\"data row25 col2\" >0</td>\n",
       "      <td id=\"T_30b62_row25_col3\" class=\"data row25 col3\" >0</td>\n",
       "      <td id=\"T_30b62_row25_col4\" class=\"data row25 col4\" >1</td>\n",
       "      <td id=\"T_30b62_row25_col5\" class=\"data row25 col5\" >0</td>\n",
       "      <td id=\"T_30b62_row25_col6\" class=\"data row25 col6\" >0</td>\n",
       "      <td id=\"T_30b62_row25_col7\" class=\"data row25 col7\" >0</td>\n",
       "      <td id=\"T_30b62_row25_col8\" class=\"data row25 col8\" >0</td>\n",
       "      <td id=\"T_30b62_row25_col9\" class=\"data row25 col9\" >0</td>\n",
       "      <td id=\"T_30b62_row25_col10\" class=\"data row25 col10\" >0</td>\n",
       "      <td id=\"T_30b62_row25_col11\" class=\"data row25 col11\" >0</td>\n",
       "      <td id=\"T_30b62_row25_col12\" class=\"data row25 col12\" >0</td>\n",
       "      <td id=\"T_30b62_row25_col13\" class=\"data row25 col13\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7bd457323640>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nice plot borrowed from https://www.kaggle.com/code/robikscube/santa-2023-polytope-permutation-first-look\n",
    "puzzles_df.groupby(['puzzle_type'])['num_wildcards'].value_counts() \\\n",
    "    .unstack().fillna(0).astype('int').sort_values(0, ascending=False).style.background_gradient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "97a5f915",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-27T11:08:44.625105Z",
     "iopub.status.busy": "2023-12-27T11:08:44.624532Z",
     "iopub.status.idle": "2023-12-27T11:08:44.644551Z",
     "shell.execute_reply": "2023-12-27T11:08:44.643437Z"
    },
    "papermill": {
     "duration": 0.036377,
     "end_time": "2023-12-27T11:08:44.647058",
     "exception": false,
     "start_time": "2023-12-27T11:08:44.610681",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>puzzle_type</th>\n",
       "      <th>total_components</th>\n",
       "      <th>total_unique_components</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cube_2/2/2</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>cube_2/2/2</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>cube_3/3/3</td>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>cube_3/3/3</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>cube_4/4/4</td>\n",
       "      <td>96</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>cube_4/4/4</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>cube_5/5/5</td>\n",
       "      <td>150</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>cube_5/5/5</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>cube_6/6/6</td>\n",
       "      <td>216</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>cube_6/6/6</td>\n",
       "      <td>216</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>cube_7/7/7</td>\n",
       "      <td>294</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>cube_8/8/8</td>\n",
       "      <td>384</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>cube_9/9/9</td>\n",
       "      <td>486</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>cube_10/10/10</td>\n",
       "      <td>600</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>cube_19/19/19</td>\n",
       "      <td>2166</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>cube_33/33/33</td>\n",
       "      <td>6534</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>cube_33/33/33</td>\n",
       "      <td>6534</td>\n",
       "      <td>6534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>wreath_6/6</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>wreath_7/7</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>wreath_12/12</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>wreath_21/21</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>wreath_33/33</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>wreath_100/100</td>\n",
       "      <td>198</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>globe_1/8</td>\n",
       "      <td>32</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>globe_1/8</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>globe_1/16</td>\n",
       "      <td>64</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>globe_2/6</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>globe_3/4</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>globe_3/4</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>globe_6/4</td>\n",
       "      <td>56</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>globe_6/8</td>\n",
       "      <td>112</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>globe_6/10</td>\n",
       "      <td>140</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>globe_3/33</td>\n",
       "      <td>264</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>globe_3/33</td>\n",
       "      <td>264</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>globe_8/25</td>\n",
       "      <td>450</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        puzzle_type  total_components  total_unique_components\n",
       "0        cube_2/2/2                24                        6\n",
       "20       cube_2/2/2                24                       24\n",
       "30       cube_3/3/3                54                        6\n",
       "140      cube_3/3/3                54                       54\n",
       "150      cube_4/4/4                96                        6\n",
       "205      cube_4/4/4                96                       96\n",
       "210      cube_5/5/5               150                        6\n",
       "240      cube_5/5/5               150                      150\n",
       "245      cube_6/6/6               216                        6\n",
       "256      cube_6/6/6               216                      216\n",
       "257      cube_7/7/7               294                        6\n",
       "262      cube_8/8/8               384                        6\n",
       "267      cube_9/9/9               486                        6\n",
       "272   cube_10/10/10               600                        6\n",
       "277   cube_19/19/19              2166                        6\n",
       "281   cube_33/33/33              6534                        6\n",
       "283   cube_33/33/33              6534                     6534\n",
       "284      wreath_6/6                10                        3\n",
       "304      wreath_7/7                12                        3\n",
       "319    wreath_12/12                22                        3\n",
       "329    wreath_21/21                40                        3\n",
       "334    wreath_33/33                64                        3\n",
       "337  wreath_100/100               198                        3\n",
       "338       globe_1/8                32                       16\n",
       "343       globe_1/8                32                       32\n",
       "348      globe_1/16                64                       16\n",
       "353       globe_2/6                36                       12\n",
       "358       globe_3/4                32                        8\n",
       "368       globe_3/4                32                       32\n",
       "373       globe_6/4                56                       12\n",
       "378       globe_6/8               112                       24\n",
       "383      globe_6/10               140                       15\n",
       "388      globe_3/33               264                       22\n",
       "391      globe_3/33               264                      264\n",
       "396      globe_8/25               450                       30"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_puzzles_df = puzzles_df[['puzzle_type', 'total_components', 'total_unique_components']].drop_duplicates()\n",
    "reduced_puzzles_df.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "89536f38",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-27T11:08:44.675147Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                                                       205\n",
       "puzzle_type                                                       cube_4/4/4\n",
       "solution_state             N0;N1;N2;N3;N4;N5;N6;N7;N8;N9;N10;N11;N12;N13;...\n",
       "initial_state              N3;N33;N20;N0;N50;N85;N53;N72;N7;N22;N41;N66;N...\n",
       "num_wildcards                                                              0\n",
       "initial_state_parsed       [N3, N33, N20, N0, N50, N85, N53, N72, N7, N22...\n",
       "solution_state_parsed      [N0, N1, N2, N3, N4, N5, N6, N7, N8, N9, N10, ...\n",
       "total_components                                                          96\n",
       "unique_components          [N0, N1, N10, N11, N12, N13, N14, N15, N16, N1...\n",
       "total_unique_components                                                   96\n",
       "Name: 205, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigating some of these weird numbers\n",
    "puzzles_df.iloc[205]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c3d3c7c",
   "metadata": {
    "papermill": {
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     "start_time": "2023-12-27T11:08:44.701223",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Even within the same puzzle type there are actually different kinds of puzzles!\n",
    "\n",
    "Not only are the number of wildcards different, there are a few puzzles in there where every piece has to be uniquely placed, not the 'colours' correct. This is repeated for quite a few puzzles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0cb527b5",
   "metadata": {
    "execution": {
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     "start_time": "2023-12-27T11:08:44.727257",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>puzzle_type</th>\n",
       "      <th>total_allowed_moves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cube_2/2/2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cube_3/3/3</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cube_4/4/4</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cube_5/5/5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cube_6/6/6</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cube_7/7/7</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cube_8/8/8</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cube_9/9/9</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cube_10/10/10</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>cube_19/19/19</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cube_33/33/33</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>wreath_6/6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>wreath_7/7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>wreath_12/12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>wreath_21/21</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>wreath_33/33</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>wreath_100/100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>globe_1/8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>globe_1/16</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>globe_2/6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>globe_3/4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>globe_6/4</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>globe_6/8</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>globe_6/10</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>globe_3/33</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>globe_8/25</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       puzzle_type  total_allowed_moves\n",
       "0       cube_2/2/2                   12\n",
       "1       cube_3/3/3                   18\n",
       "2       cube_4/4/4                   24\n",
       "3       cube_5/5/5                   30\n",
       "4       cube_6/6/6                   36\n",
       "5       cube_7/7/7                   42\n",
       "6       cube_8/8/8                   48\n",
       "7       cube_9/9/9                   54\n",
       "8    cube_10/10/10                   60\n",
       "9    cube_19/19/19                  114\n",
       "10   cube_33/33/33                  198\n",
       "11      wreath_6/6                    4\n",
       "12      wreath_7/7                    4\n",
       "13    wreath_12/12                    4\n",
       "14    wreath_21/21                    4\n",
       "15    wreath_33/33                    4\n",
       "16  wreath_100/100                    4\n",
       "17       globe_1/8                   20\n",
       "18      globe_1/16                   36\n",
       "19       globe_2/6                   18\n",
       "20       globe_3/4                   16\n",
       "21       globe_6/4                   22\n",
       "22       globe_6/8                   30\n",
       "23      globe_6/10                   34\n",
       "24      globe_3/33                   74\n",
       "25      globe_8/25                   68"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "puzzle_info_df[['puzzle_type', 'total_allowed_moves']].head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2b7a7335",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9R3JzePXf1q1b0alTJ+zfvx9du3ZF165dsW/fPnTu3BlJSUn1ESMREVE1Fmn4r7xSZWaliuTlcKXq1VdfxbRp07Bw4cJqx2fOnIkHHnjAacERERHVxrqlgrvSWqliUkXycrhSdfLkSSQmJlY7Pn78eJw4ccIpQREREdXFuvmninOqyEU4nFQ1b94cqamp1Y6npqYiKCjIGTERERHVSapUubFSRa7B4eG/CRMmYOLEiTh37hzuueceAMDu3bvxzjvvYPr06U4PkIiI6EZU5UkVK1UkN4eTqtmzZ8PHxwdLlizBrFmzAAChoaGYO3cuXnzxRacHSEREVJPKm38CXP1H8nM4qVIoFJg2bRqmTZuGvLw8AICPj4/TAyMiIroR6/Cfm5Jzqsg13NQ+VVZMpoiISC7WieqcU0Wuwq6kqmfPnti2bRsCAgLQo0cPKBSKWtseOnTIacERERHVpmKiunX4j0kVycuupOqRRx6BRqMBAMTHx9dnPERERHaxplAqVqrIRdiVVL355ps1fk9ERCSXisvUcE4VuQaH96nKzMzEhQsXpPv79+/H1KlT8fHHHzs1MCIiohuxDv+puKM6uQiHk6onn3wSO3bsAADodDrExsZi//79eP311zF//nynB0hERFQTa1JVvvhPqlwRycXhpOrYsWPo06cPAODrr79GVFQU9uzZgy+//BKrV692dnxEREQ1sq7+U5ZnVUypSG4OJ1VGo1GatP7zzz/j4YcfBgB06NABly9fdm50REREtbCO9rmVr0i3cPiPZOZwUtW5c2esXLkS//vf/5CUlIShQ4cCAC5duoSmTZs6PUAiIqKaVAz/sVJFrsHhpOqdd97BP/7xD9x7770YPXo0unXrBgDYsGGDNCxIRERU3yqG/6QDRLJyeEf1e++9F1evXoXBYEBAQIB0fOLEifDy8nJqcERERLUqT6IUrFSRi3C4UlVUVISSkhIpoTp//jzee+89pKWlISgoyOkBEhER1cSaRFnnVHH1H8nN4aTqkUcewRdffAEAyM3NRXR0NJYsWYL4+Hh89NFHTg+QiIioJpbyJEraUkHGWIiAm0iqDh06hAEDBgAAvvnmGwQHB+P8+fP44osvsHz5cqcHSEREVBNRdfiPWRXJzOGkqrCwED4+PgCAn376CSNGjIBSqUTfvn1x/vx5pwdIRERUE2sOVbH6j1kVycvhpKpt27b4/vvvkZmZia1bt2LIkCEAgOzsbPj6+jo9QCIioppY51CVX0+ZlSqSncNJ1Zw5c/Dyyy+jVatWiI6ORkxMDICyqlWPHj2cHiAREVFNpH2qlBz+I9fg8JYKjz32GPr374/Lly9Le1QBwODBg/Hoo486NTgiIqLaSPtUcfiPXITDSRUAaLVaaLVam2Pc+JOIiG6n6hdUli8WIsDOpGrEiBFYvXo1fH19MWLEiBu2/fbbb50SGBER0Y3wMjXkauxKqvz8/KQlq35+fvUaEBERkT2qDf+xVEUysyupWrVqVY3fExERycXCShW5GIdX/xEREbkCzqkiV+NwUnXt2jVMmjQJnTp1QrNmzRAYGGhzc8SuXbvw0EMPITQ0FAqFAt9//73NeSEE5syZg5CQEHh6eiI2NhanT5+2aZOTk4OEhAT4+vrC398fiYmJyM/Pt2lz5MgRDBgwAB4eHggLC8OiRYuqxbJ+/Xp06NABHh4eiIqKwo8//uhwLEREdDuVD/9ZsyoimTm8+u+pp57CmTNnkJiYiODgYGmu1c0oKChAt27dMH78+BonwC9atAjLly/H559/joiICMyePRtxcXE4ceIEPDw8AAAJCQm4fPkykpKSYDQaMW7cOEycOBFr1qwBABgMBgwZMgSxsbFYuXIljh49ivHjx8Pf3x8TJ04EAOzZswejR4/GggUL8Kc//Qlr1qxBfHw8Dh06hC5dutgdCxER3T5VJ6qXHRO39LlEdEuEg7y9vUVqaqqjD6sTAPHdd99J9y0Wi9BqtWLx4sXSsdzcXKHRaMTatWuFEEKcOHFCABAHDhyQ2mzevFkoFApx8eJFIYQQH374oQgICBAlJSVSm5kzZ4rIyEjp/uOPPy6GDx9uE090dLR49tln7Y6lJsXFxUKv10u3zMxMAUDo9XpHfjRERFSDcav2i/CZG8U/dp4R4TM3ivCZG4XJbJE7LGqE9Hq9XZ/fDg//dejQAUVFRc7O7apJT0+HTqdDbGysdMzPzw/R0dFITk4GACQnJ8Pf3x+9e/eW2sTGxkKpVGLfvn1Sm4EDB0KtVktt4uLikJaWhuvXr0ttKr+OtY31deyJpSYLFiyAn5+fdAsLC7vZHwcREVVhEbar/wCuACR5OZxUffjhh3j99dexc+dOXLt2DQaDwebmLDqdDgAQHBxsczw4OFg6p9PpEBQUZHNepVIhMDDQpk1Nz1H5NWprU/l8XbHUZNasWdDr9dItMzOzjl4TEZG9rPlT5eE+plQkJ4fnVPn7+8NgMOD++++3OS7Kx7HNZrPTgmvoNBoNNBqN3GEQETVK1gSq8jx1FqpITg4nVQkJCXB3d8eaNWtueaL6jVgvg5OVlYWQkBDpeFZWFrp37y61yc7OtnmcyWRCTk6O9HitVousrCybNtb7dbWpfL6uWIiI6PYSNQ3/sVZFMnJ4+O/YsWNYtWoVnnjiCdx7770YNGiQzc1ZIiIioNVqsW3bNumYwWDAvn37EBMTAwCIiYlBbm4uUlJSpDbbt2+HxWJBdHS01GbXrl0wGo1Sm6SkJERGRiIgIEBqU/l1rG2sr2NPLEREdHtJq/+UimrHiOTgcFLVu3dvp80Nys/PR2pqKlJTUwGUTQhPTU1FRkYGFAoFpk6dirfeegsbNmzA0aNHMWbMGISGhiI+Ph4A0LFjRwwdOhQTJkzA/v37sXv3bkyePBmjRo1CaGgoAODJJ5+EWq1GYmIijh8/jq+++grLli3D9OnTpTimTJmCLVu2YMmSJfj9998xd+5cHDx4EJMnTwYAu2IhIqLbq+IyNTIHQmTl6LLCr7/+WnTq1EmsWrVKHDx4UPz22282N0fs2LFDoGxY3OY2duxYIUTZVgazZ88WwcHBQqPRiMGDB4u0tDSb57h27ZoYPXq08Pb2Fr6+vmLcuHEiLy/Pps1vv/0m+vfvLzQajWjRooVYuHBhjf1q3769UKvVonPnzmLTpk025+2JpS72LskkIqK6jf44WYTP3CjW7DsvbalQWGKSOyxqhOz9/FYI4VixVKmsXtxSKBScqG4Hg8EAPz8/6PV6+Pr6yh0OEVGDNvrjvUg+dw2LRnbFjP8cAQCcmB8HL7XD04WJbsjez2+Hf/PS09NvKTAiIiJnEDVcpsbCOVUkI4eTqvDw8PqIg4iIyCEW6TI1FcccHHwhcqqbqpGmpaXh/fffx8mTJwGUTRh/4YUXEBkZ6dTgiIiIalXTtf9kCoUIuInVf//5z3/QpUsXpKSkoFu3bujWrZt04eH//Oc/9REjERFRNdbhPwU3/yQX4XClasaMGZg1axbmz59vc/zNN9/EjBkzMHLkSKcFR0REVBtRQ6WKpSqSk8OVqsuXL2PMmDHVjv/5z3/G5cuXnRIUERFRXaz5k5uSO6qTa3A4qbr33nvxv//9r9rxX3/9FQMGDHBKUERERHWxiOqbf3L4j+Rk1/Dfhg0bpO8ffvhhzJw5EykpKejbty8AYO/evVi/fj3mzZtXP1ESERFVYU2gFJyoTi7Crs0/a9rws8Yn4+afN8TNP4mInOeRFbvxW2YuPhnTGxO+OAgASHkjFk29NTJHRo2NUzf/tFgsTguMiIjIKWoY/uPmnyQnh+dUERERuQKLNPxXsa0CJ6qTnBzeUqHqVgpVzZkz56aDISIispe0TxUUUKB8PhVzKpKRw0nVd999Z3PfaDQiPT0dKpUKbdq0YVJFRES3hTQjWFE+WV2wTkXycjipOnz4cLVjBoMBTz/9NB599FGnBEVERFSXypt/KqocI5KDU+ZU+fr6Yt68eZg9e7Yzno6IiKhOlQpVnFNFLsFpE9X1ej30er2zno6IiOiGrDsCKRSAtVbFShXJyeHhv+XLl9vcF0Lg8uXL+Ne//oVhw4Y5LTAiIqIbkTb/hAKQKlVE8nE4qVq6dKnNfaVSiebNm2Ps2LGYNWuW0wIjIiK6EetQn1JRsVeVHftZE9Ubh5Oq9PT0+oiDiIjIITar/zj8Ry7gludUnT9/HidOnOCu60REdFtV5FSKionqTKpIRnYnVZ999hneffddm2MTJ05E69atERUVhS5duiAzM9PpARIREdXEYjNRvQxX/5Gc7E6qPv74YwQEBEj3t2zZglWrVuGLL77AgQMH4O/vj3nz5tVLkERERNVIE9XLN/8EK1UkL7vnVJ0+fRq9e/eW7v/www945JFHkJCQAAB4++23MW7cOOdHSEREVANr/qRUVtr8U65giOBApaqoqAi+vr7S/T179mDgwIHS/datW0On0zk3OiIiolpI+1RJ/+HqP5KX3UlVeHg4UlJSAABXr17F8ePH0a9fP+m8TqeDn5+f8yMkIiKqgcU6/Gczp4pIPnYP/40dOxaTJk3C8ePHsX37dnTo0AG9evWSzu/ZswddunSplyCJiIiqEpXX/3FOFbkAu5OqGTNmoLCwEN9++y20Wi3Wr19vc3737t0YPXq00wMkIiKqiahUqbJu/slaFcnJ7qRKqVRi/vz5mD9/fo3nqyZZRERE9cmaVCkVFZUqC3MqkpHTLqhMREQkBwUqzaliUkUyYlJFREQNks3mn9IFlZlVkXyYVBERUYMkzamqVKtipYrkxKSKiIgaJGtVyqZSxaSKZHTTSVVpaSnS0tJgMpmcGQ8REZFdRI37VDGrIvk4nFQVFhYiMTERXl5e6Ny5MzIyMgAAL7zwAhYuXOj0AImIiGpSaZcqVqrIJTicVM2aNQu//fYbfvnlF3h4eEjHY2Nj8dVXXzk1OCIiotqIyhPVpVoVkXzs3qfK6vvvv8dXX32Fvn37SvuCAEDnzp1x9uxZpwZHRERUm5o2/2SliuTkcKXqypUrCAoKqna8oKDAJskiIiKqT9b8yXbzT2ZVJB+Hk6revXtj06ZN0n3rL/Knn36KmJgY50UGwGw2Y/bs2YiIiICnpyfatGmDv/71rzZXIRdCYM6cOQgJCYGnpydiY2Nx+vRpm+fJyclBQkICfH194e/vj8TEROTn59u0OXLkCAYMGAAPDw+EhYVh0aJF1eJZv349OnToAA8PD0RFReHHH390an+JiMh+0vBf5WPyhEIE4CaG/95++20MGzYMJ06cgMlkwrJly3DixAns2bMHO3fudGpw77zzDj766CN8/vnn6Ny5Mw4ePIhx48bBz88PL774IgBg0aJFWL58OT7//HNERERg9uzZiIuLw4kTJ6Q5XwkJCbh8+TKSkpJgNBoxbtw4TJw4EWvWrAEAGAwGDBkyBLGxsVi5ciWOHj2K8ePHw9/fHxMnTgRQdsHo0aNHY8GCBfjTn/6ENWvWID4+HocOHeKFpImIZGCpvPpPGv5jWkUyEjfhzJkz4plnnhF333236Nixo0hISBBHjhy5mae6oeHDh4vx48fbHBsxYoRISEgQQghhsViEVqsVixcvls7n5uYKjUYj1q5dK4QQ4sSJEwKAOHDggNRm8+bNQqFQiIsXLwohhPjwww9FQECAKCkpkdrMnDlTREZGSvcff/xxMXz4cJtYoqOjxbPPPltr/MXFxUKv10u3zMxMAUDo9XpHfxRERFRF1JtbRPjMjeJ0Vp7o/842ET5zo0g5nyN3WNQI6fV6uz6/b2qfqjZt2uCTTz7B/v37ceLECfz73/9GVFSUU5M9ALjnnnuwbds2nDp1CgDw22+/4ddff8WwYcMAAOnp6dDpdIiNjZUe4+fnh+joaCQnJwMAkpOT4e/vj969e0ttYmNjoVQqsW/fPqnNwIEDoVarpTZxcXFIS0vD9evXpTaVX8faxvo6NVmwYAH8/PykW1hY2K38OIiIqJKKOVUVq/9YqCI52TX8ZzAY7H5CX1/fmw6mqldffRUGgwEdOnSAm5sbzGYz/va3vyEhIQEAoNPpAADBwcE2jwsODpbO6XS6ahPrVSoVAgMDbdpERERUew7ruYCAAOh0uhu+Tk1mzZqF6dOnS/cNBgMTKyIiZ5GG/yr2qeKsKpKTXUmVv79/nSv7hBBQKBQwm81OCQwAvv76a3z55ZdYs2YNOnfujNTUVEydOhWhoaEYO3as016nvmg0Gmg0GrnDICJqlCo2/6y0ozpzKpKRXUnVjh076juOGr3yyit49dVXMWrUKABAVFQUzp8/jwULFmDs2LHQarUAgKysLISEhEiPy8rKQvfu3QEAWq0W2dnZNs9rMpmQk5MjPV6r1SIrK8umjfV+XW2s54mI6PayVN78s/wPf+ZUJCe7kqpBgwbVdxw1KiwshFJpO+3Lzc0NFosFABAREQGtVott27ZJSZTBYMC+ffvw/PPPAwBiYmKQm5uLlJQU9OrVCwCwfft2WCwWREdHS21ef/11GI1GuLu7AwCSkpIQGRmJgIAAqc22bdswdepUKZakpCSnbyNBRET2kTb/5GVqyEU4vKUCAFy/fh3//Oc/cfLkSQBAp06dMG7cOAQGBjo1uIceegh/+9vf0LJlS3Tu3BmHDx/Gu+++i/HjxwMo+8tk6tSpeOutt9CuXTtpS4XQ0FDEx8cDADp27IihQ4diwoQJWLlyJYxGIyZPnoxRo0YhNDQUAPDkk09i3rx5SExMxMyZM3Hs2DEsW7YMS5culWKZMmUKBg0ahCVLlmD48OFYt24dDh48iI8//tipfSYiIvtYL55c+YLK3PyTZOXossKdO3cKX19fERYWJh599FHx6KOPipYtWwpfX1+xc+fOm12tWCODwSCmTJkiWrZsKTw8PETr1q3F66+/brP1gcViEbNnzxbBwcFCo9GIwYMHi7S0NJvnuXbtmhg9erTw9vYWvr6+Yty4cSIvL8+mzW+//Sb69+8vNBqNaNGihVi4cGG1eL7++mvRvn17oVarRefOncWmTZsc6o+9SzKJiKhu7V//UYTP3CgycwrE4CW/iPCZG8WeM1flDosaIXs/vxVCOJbWR0VFISYmBh999BHc3NwAlO18/pe//AV79uzB0aNH6yH1axwMBgP8/Pyg1+udukqSiOhO1P6NzSg1WbD71fvx9Gf7cTo7H2smROOeNs3kDo0aGXs/vx3ep+rMmTN46aWXpIQKKJvnNH36dJw5c+bmoiUiInKQqHSZGmmBOkf/SEYOJ1U9e/aU5lJVdvLkSXTr1s0pQREREdXFOs6iVCgqNv+UMR4ihyeqv/jii5gyZQrOnDmDvn37AgD27t2LFStWYOHChThy5IjUtmvXrs6LlIiIqBJpnyqba//JFg6R40nV6NGjAQAzZsyo8ZxCoaiXjUCJiIgqqzz8Jx1jrYpk5HBSlZ6eXh9xEBEROcRSaUt1afNP5lQkI4eTqvDw8PqIg4iI6KYooIDSOvwnbyh0h7upzT8vXbqEX3/9FdnZ2dLu5lYvvviiUwIjIiKqTeXdgJSV5lRx80+Sk8NJ1erVq/Hss89CrVajadOmNhdaVigUTKqIiKjeVc6dFJVW/7FURXJyOKmaPXs25syZg1mzZlW7Lh8REdHtUDl3qrxPFSeqk5wczooKCwsxatQoJlRERCSbysN8la/9x9E/kpPDmVFiYiLWr19fH7EQERHZxWb4DxWTqphUkZwcHv5bsGAB/vSnP2HLli2IioqCu7u7zfl3333XacERERHVpPIwn0JZqVIlTzhEAG4yqdq6dSsiIyMBoNpEdSIiovpmW6mqvKM60yqSj8NJ1ZIlS/DZZ5/h6aefrodwiIiIHFO2+q8MUyqSk8NzqjQaDfr161cfsRAREdnFZqI6yi6qDHBOFcnL4aRqypQpeP/99+sjFiIiIrtUTp6UCgWH/8glODz8t3//fmzfvh0bN25E586dq01U//bbb50WHBERUU1s9qlSQNr8kykVycnhpMrf3x8jRoyoj1iIiIjsUq0iJVWqbn8sRFYOJ1WrVq2qjziIiIjsZrG5TE3lLRWYVZF8uC06ERE1PFU2/1SwUkUuwOFKFQB88803+Prrr5GRkYHS0lKbc4cOHXJKYERERLWpXJFSck4VuQiHK1XLly/HuHHjEBwcjMOHD6NPnz5o2rQpzp07h2HDhtVHjERERDZsNv/k6j9yEQ4nVR9++CE+/vhjvP/++1Cr1ZgxYwaSkpLw4osvQq/X10eMRERENmxW/6FiR3UiOTmcVGVkZOCee+4BAHh6eiIvLw8A8NRTT2Ht2rXOjY6IiKgGNpt/Krj5J7kGh5MqrVaLnJwcAEDLli2xd+9eAEB6ejrLrkREdFtUHf6zsvBziGTkcFJ1//33Y8OGDQCAcePGYdq0aXjggQfwxBNP4NFHH3V6gERERFVZJ6pb8ykFK1XkAhxe/ffxxx/DYrEAACZNmoSmTZtiz549ePjhh/Hss886PUAiIqJqypMna42KF1QmV+BwUqVUKqFUVhS4Ro0ahVGjRjk1KCIiohuxJk/WChVX/5ErsHv47+rVqzh//rzNsePHj2PcuHF4/PHHsWbNGqcHR0REVBPr3ClWqsiV2J1UvfDCC1i+fLl0Pzs7GwMGDMCBAwdQUlKCp59+Gv/617/qJUgiIqLKrAUppVSpspaqZAqICA4kVXv37sXDDz8s3f/iiy8QGBiI1NRU/PDDD3j77bexYsWKegmSiIioMil3Uth84bX/SFZ2J1U6nQ6tWrWS7m/fvh0jRoyASlU2Levhhx/G6dOnnR4gERFRVaLq8B+v/UcuwO6kytfXF7m5udL9/fv3Izo6WrqvUChQUlLi1OCIiIhqYk2eqm2pIFM8RIADSVXfvn2xfPlyWCwWfPPNN8jLy8P9998vnT916hTCwsLqJUgiIqLKqs2pKj/OzT9JTnZvqfDXv/4VgwcPxr///W+YTCa89tprCAgIkM6vW7cOgwYNqpcgiYiIKpM2/yy/z+E/cgV2J1Vdu3bFyZMnsXv3bmi1WpuhP6Bsv6pOnTo5PUAiIqKqKob/rJUqDv+R/Bza/LNZs2Z45JFHajw3fPhwpwRERERUF2nzT+tXafkf0yqSj8PX/rvdLl68iD//+c9o2rQpPD09ERUVhYMHD0rnhRCYM2cOQkJC4OnpidjY2GqrEHNycpCQkABfX1/4+/sjMTER+fn5Nm2OHDmCAQMGwMPDA2FhYVi0aFG1WNavX48OHTrAw8MDUVFR+PHHH+un00REdEPS3ClponrZV6ZUJCeXTqquX7+Ofv36wd3dHZs3b8aJEyewZMkSm7lcixYtwvLly7Fy5Urs27cPTZo0QVxcHIqLi6U2CQkJOH78OJKSkrBx40bs2rULEydOlM4bDAYMGTIE4eHhSElJweLFizF37lx8/PHHUps9e/Zg9OjRSExMxOHDhxEfH4/4+HgcO3bs9vwwiIhIUn2iOi+oTC5AuLCZM2eK/v3713reYrEIrVYrFi9eLB3Lzc0VGo1GrF27VgghxIkTJwQAceDAAanN5s2bhUKhEBcvXhRCCPHhhx+KgIAAUVJSYvPakZGR0v3HH39cDB8+3Ob1o6OjxbPPPmt3f/R6vQAg9Hq93Y8hIqLqTmcZRPjMjaLbvK1CCCH+8mWKCJ+5Uaz69ZzMkVFjZO/nt0tXqjZs2IDevXvj//7v/xAUFIQePXrgk08+kc6np6dDp9MhNjZWOubn54fo6GgkJycDAJKTk+Hv74/evXtLbWJjY6FUKrFv3z6pzcCBA6FWq6U2cXFxSEtLw/Xr16U2lV/H2sb6OjUpKSmBwWCwuRER0a2rMvrHa/+RS3BoorqVxWLBmTNnkJ2dDYvFYnNu4MCBTgkMAM6dO4ePPvoI06dPx2uvvYYDBw7gxRdfhFqtxtixY6HT6QAAwcHBNo8LDg6Wzul0OgQFBdmcV6lUCAwMtGkTERFR7Tms5wICAqDT6W74OjVZsGAB5s2bdxM9JyKiG5EmqpcP/1mHATn8R3JyOKnau3cvnnzySZw/f166TICVQqGA2Wx2WnAWiwW9e/fG22+/DQDo0aMHjh07hpUrV2Ls2LFOe536MmvWLEyfPl26bzAYuEEqEZETWGq5TA03/yQ5OTz899xzz6F37944duwYcnJycP36demWk5Pj1OBCQkKq7X3VsWNHZGRkAAC0Wi0AICsry6ZNVlaWdE6r1SI7O9vmvMlkQk5Ojk2bmp6j8mvU1sZ6viYajQa+vr42NyIiunXV96kikp/DSdXp06fx9ttvo2PHjvD394efn5/NzZn69euHtLQ0m2OnTp1CeHg4ACAiIgJarRbbtm2TzhsMBuzbtw8xMTEAgJiYGOTm5iIlJUVqs337dlgsFmkD05iYGOzatQtGo1Fqk5SUhMjISGmlYUxMjM3rWNtYX4eIiG6fWq/9x0IVycjhpCo6Ohpnzpypj1iqmTZtGvbu3Yu3334bZ86cwZo1a/Dxxx9j0qRJAMr+EU2dOhVvvfUWNmzYgKNHj2LMmDEIDQ1FfHw8gLLK1tChQzFhwgTs378fu3fvxuTJkzFq1CiEhoYCAJ588kmo1WokJibi+PHj+Oqrr7Bs2TKbobspU6Zgy5YtWLJkCX7//XfMnTsXBw8exOTJk2/Lz4KIiCpUu0xNleNEcnB4TtULL7yAl156CTqdDlFRUXB3d7c537VrV6cFd/fdd+O7777DrFmzMH/+fEREROC9995DQkKC1GbGjBkoKCjAxIkTkZubi/79+2PLli3w8PCQ2nz55ZeYPHkyBg8eDKVSiZEjR2L58uXSeT8/P/z000+YNGkSevXqhWbNmmHOnDk2e1ndc889WLNmDd544w289tpraNeuHb7//nt06dLFaf0lIiL7VK1Ugdf+IxegEFVnm9dBqaxe3FIoFBBCOH2iemNjMBjg5+cHvV7P+VVERLfg6AU9HvrgV4T4eSB51mC89PVv+M+hC3h1WAc8N6iN3OFRI2Pv57fDlar09PRbCoyIiOhWVRv+Y6WKXIDDSZV1kjgREZFcalv9xzlVJCe7kqoNGzZg2LBhcHd3x4YNG27Y9uGHH3ZKYERERLWpmjpx809yBXYlVfHx8dLO5NZVdTXhnCoiIrodpM0/pS0Vyr46OE2YyKnsSqoqX4qm6mVpiIiIbjdr7mStUHFOFbkCl76gMhERUc1sK1XWWVXMqUhOTKqIiKjBkSaql99npYpcAZMqIiJqcKy5E1f/kSthUkVERA2OxVLbRHWZAiICkyoiImqApEqV9JVzqkh+DidVhw4dwtGjR6X7P/zwA+Lj4/Haa6+htLTUqcERERHVpNrmn4oqJ4hk4HBS9eyzz+LUqVMAgHPnzmHUqFHw8vLC+vXrMWPGDKcHSEREVFXVy9RIm3/KFA8RcBNJ1alTp9C9e3cAwPr16zFw4ECsWbMGq1evxn/+8x9nx0dERFRNRaXK9riFlSqSkcNJlRBC2gD0559/xoMPPggACAsLw9WrV50bHRERUQ24+Se5IoeTqt69e+Ott97Cv/71L+zcuRPDhw8HAKSnpyM4ONjpARIREVVVdesETlQnV+BwUvXee+/h0KFDmDx5Ml5//XW0bdsWAPDNN9/gnnvucXqAREREVdU2UZ2VKpKTXdf+q6xr1642q/+sFi9eDDc3N6cERUREdCPVt1SwHmdWRfK5qX2qcnNz8emnn2LWrFnIyckBAJw4cQLZ2dlODY6IiKgm1gnpyvJPsYotFeSJhwi4iUrVkSNHMHjwYPj7++OPP/7AhAkTEBgYiG+//RYZGRn44osv6iNOIiKiCtK1/6zDf5xTRfJzuFI1ffp0jBs3DqdPn4aHh4d0/MEHH8SuXbucGhwREVFNpH2qrJepsR7npCqSkcNJ1YEDB/Dss89WO96iRQvodDqnBEVERHQj0kT18vtSpYo5FcnI4aRKo9HAYDBUO37q1Ck0b97cKUERERHdiJQ8VVn9Z2FSRTJyOKl6+OGHMX/+fBiNRgBlfx1kZGRg5syZGDlypNMDJCIiqkqaqF51+I+zqkhGDidVS5YsQX5+PoKCglBUVIRBgwahbdu28PHxwd/+9rf6iJGIiMhGtS0VuE8VuQCHV//5+fkhKSkJv/76K44cOYL8/Hz07NkTsbGx9REfERFRNdU2/4TiBq2Jbg+Hk6rMzEyEhYWhf//+6N+/f33EREREVIfy1X/l9yoqVSxVkXwcHv5r1aoVBg0ahE8++QTXr1+vj5iIiIhuyCJVqsq/lh9nSkVycjipOnjwIPr06YP58+cjJCQE8fHx+Oabb1BSUlIf8REREVVTdfgP3FKBXIDDSVWPHj2wePFiZGRkYPPmzWjevDkmTpyI4OBgjB8/vj5iJCIisiGqDv9VOU4kh5u69h9Q9tfBfffdh08++QQ///wzIiIi8PnnnzszNiIiohqJKsN/SlaqyAXcdFJ14cIFLFq0CN27d0efPn3g7e2NFStWODM2IiKiGlVsqcDNP8l1OLz67x//+AfWrFmD3bt3o0OHDkhISMAPP/yA8PDw+oiPiIioGusqP2V5aaBiQwVmVSQfh5Oqt956C6NHj8by5cvRrVu3+oiJiIjohiqu/WdbqeLwH8nJ4aQqIyOjYrUFERGRDKSJ6tLiP86pIvk5nFQpFArk5ubin//8J06ePAkA6NSpExITE+Hn5+f0AImIiKqqLXni6j+S003tU9WmTRssXboUOTk5yMnJwdKlS9GmTRscOnSoPmIkIiKyUe0yNRz+IxfgcKVq2rRpePjhh/HJJ59ApSp7uMlkwjPPPIOpU6di165dTg+SiIioMot1orq0o3r58J9cARHhJitVM2fOlBIqAFCpVJgxYwYOHjzo1OCqWrhwIRQKBaZOnSodKy4uxqRJk9C0aVN4e3tj5MiRyMrKsnlcRkYGhg8fDi8vLwQFBeGVV16ByWSyafPLL7+gZ8+e0Gg0aNu2LVavXl3t9VesWIFWrVrBw8MD0dHR2L9/f310k4iI6lCxpUL5V1aqyAU4nFT5+voiIyOj2vHMzEz4+Pg4JaiaHDhwAP/4xz/QtWtXm+PTpk3Df//7X6xfvx47d+7EpUuXMGLECOm82WzG8OHDUVpaij179uDzzz/H6tWrMWfOHKlNeno6hg8fjvvuuw+pqamYOnUqnnnmGWzdulVq89VXX2H69Ol48803cejQIXTr1g1xcXHIzs6utz4TEVEtqgz/WStWnFNFcnI4qXriiSeQmJiIr776CpmZmcjMzMS6devwzDPPYPTo0fURI/Lz85GQkIBPPvkEAQEB0nG9Xo9//vOfePfdd3H//fejV69eWLVqFfbs2YO9e/cCAH766SecOHEC//73v9G9e3cMGzYMf/3rX7FixQqUlpYCAFauXImIiAgsWbIEHTt2xOTJk/HYY49h6dKl0mu9++67mDBhAsaNG4dOnTph5cqV8PLywmeffVYvfSYiotpVv0wNV/+R/BxOqv7+979jxIgRGDNmDFq1aoVWrVrh6aefxmOPPYZ33nmnPmLEpEmTMHz4cMTGxtocT0lJgdFotDneoUMHtGzZEsnJyQCA5ORkREVFITg4WGoTFxcHg8GA48ePS22qPndcXJz0HKWlpUhJSbFpo1QqERsbK7WpSUlJCQwGg82NiIhunaXWierMqkg+Dk9UV6vVWLZsGRYsWICzZ88CANq0aQMvLy+nBwcA69atw6FDh3DgwIFq53Q6HdRqNfz9/W2OBwcHQ6fTSW0qJ1TW89ZzN2pjMBhQVFSE69evw2w219jm999/rzX2BQsWYN68efZ1lIiI7Fb12n/S8dsfCpHkpq/95+XlhaioKERFRdVbQpWZmYkpU6bgyy+/hIeHR728Rn2aNWsW9Hq9dMvMzJQ7JCKiRqHa8B83/yQXYHelavz48Xa1c+Yco5SUFGRnZ6Nnz57SMbPZjF27duGDDz7A1q1bUVpaitzcXJtqVVZWFrRaLQBAq9VWW6VnXR1YuU3VFYNZWVnw9fWFp6cn3Nzc4ObmVmMb63PURKPRQKPRON5xIiK6oaqVKmtyxZyK5GR3pWr16tXYsWMHcnNzcf369VpvzjR48GAcPXoUqamp0q13795ISEiQvnd3d8e2bdukx6SlpSEjIwMxMTEAgJiYGBw9etRmlV5SUhJ8fX3RqVMnqU3l57C2sT6HWq1Gr169bNpYLBZs27ZNakNERLdPxZYKnFNFrsPuStXzzz+PtWvXIj09HePGjcOf//xnBAYG1mds8PHxQZcuXWyONWnSBE2bNpWOJyYmYvr06QgMDISvry9eeOEFxMTEoG/fvgCAIUOGoFOnTnjqqaewaNEi6HQ6vPHGG5g0aZJURXruuefwwQcfYMaMGRg/fjy2b9+Or7/+Gps2bZJed/r06Rg7dix69+6NPn364L333kNBQQHGjRtXrz8DIiKqzpo8KctLA6xUkSuwu1K1YsUKXL58GTNmzMB///tfhIWF4fHHH8fWrVtl/ctg6dKl+NOf/oSRI0di4MCB0Gq1+Pbbb6Xzbm5u2LhxI9zc3BATE4M///nPGDNmDObPny+1iYiIwKZNm5CUlIRu3bphyZIl+PTTTxEXFye1eeKJJ/D3v/8dc+bMQffu3ZGamootW7ZUm7xORET1Txr+kypV0kZVRLJRiJvMiM6fP4/Vq1fjiy++gMlkwvHjx+Ht7e3s+BoVg8EAPz8/6PV6+Pr6yh0OEVGDtXp3Oub+9wSGdw3Biid74l/Jf2D2D8fxYJQWHyb0kjs8amTs/fy+6dV/SqUSCoUCQgiYzeabfRoiIiKHVb1MjXVSlcUiRzREZRxKqkpKSrB27Vo88MADaN++PY4ePYoPPvgAGRkZrFIREdFtYx1jUVo3/7Qe5/gfycjuiep/+ctfsG7dOoSFhWH8+PFYu3YtmjVrVp+xERER1chSnlVJWyrwgsrkAuxOqlauXImWLVuidevW2LlzJ3bu3Flju8qTxImIiOpTtWv/yRcKkf1J1ZgxYypWVxAREclI1HrtP5kCIoIDSdXq1avrMQwiIiL7VbtMTaUzRHK56dV/REREcrGwUkUuiEkVERE1ONWv/cc5VSQ/JlVERNTgVBv+47X/yAUwqSIioganWqXKuvknc6pG6egFPT785QyMZtfe3dWupKpnz564fv06AGD+/PkoLCys16CIiIhqs+/cNSzemgag0rX/ys8xp2qcHvrgVyzakoY1+zLkDuWG7EqqTp48iYKCAgDAvHnzkJ+fX69BERER1eaJj/dK3yvLP8U4/HdnOHnZIHcIN2TXlgrdu3fHuHHj0L9/fwgh8Pe//73Wy9LMmTPHqQESERHVznb1HzVuKjfXfqPtSqpWr16NN998Exs3boRCocDmzZuhUlV/qEKhYFJFRES3TbXVfyxUNTrFRrP0vUqpRE5BKQKbqGWMqHZ2JVWRkZFYt24dAECpVGLbtm0ICgqq18CIiIjqUm31H2dVNTo5BaXS99+kXMDqPX9g1rAOyM4rQc+WARjeNUTG6GzZvaO6lcXi2jPviYjozqGsMu7HSlXjcy2/IqnKLzEBABZs/h0A8E+kY0jnYXB3c43NDBxOqgDg7NmzeO+993Dy5EkAQKdOnTBlyhS0adPGqcERERHdSNUtFZhUNT7XCkpueD7l/HX0bd30NkVzYw6ndlu3bkWnTp2wf/9+dO3aFV27dsW+ffvQuXNnJCUl1UeMREREAKqv7rPWqZQc/mu0KleqarIjLfs2RVI3hytVr776KqZNm4aFCxdWOz5z5kw88MADTguOiIiostIqmz9K1/4DN/9srCrPqarJP3aeQ5CPBxL7R9ymiGrncKXq5MmTSExMrHZ8/PjxOHHihFOCIiIiqklRqbnG4wru/tloXasjqQKAv248gXNX5N9D0+Gkqnnz5khNTa12PDU1lSsCiYioXhVWSaqUiqo7qjOramyu5dc+pyquc7D0/dGL+tsRzg05PPw3YcIETJw4EefOncM999wDANi9ezfeeecdTJ8+3ekBEhERWVVNqiomqpd95UT1xudGw3+RWl8099Hg33szcOKSAY90b3EbI6vO4aRq9uzZ8PHxwZIlSzBr1iwAQGhoKObOnYsXX3zR6QESERFZFZaabO5XXGC3fPXfbY6H6t/VGyRVvh4qhPh5AACOX5L/EjYOJ1UKhQLTpk3DtGnTkJeXBwDw8fFxemBERERVVa1U5ReXJVm89l/jlXODLRW8NSp0CvUFABy/pIcQQlq8IIdb2i3Lx8eHCRUREd02VSeq55VvBsl56o1XQUnNixMAwMfDHe2DfeCmVOB6oRGX9cW3MbLqXGMLUiIiIjtUrVQVWJMqbv7ZaBnNtV/JxdtDBQ93N7Ru1gQAcCZb3hWATKqIiKjBqDqnynrZkorNP6mxMZlrf1d9PMpmMQX7ls2runqDlYK3A5MqIiJqMDin6s5jusE1h33Lk6pm3moATKqIiIjsVjWpqphTxeG/xkgIAWN5pWp415Bq57017gCAZt4aAMDVOi5pU99uKqmaPHkycnJynB0LERFRreb99zje2fK7zTFrpQq89l+jZK503aG/PtIFu165Dw90qtjw0zr818ynPKnKayCVqgsXLkjfr1mzBvn5ZZPBoqKikJmZ6fzIiIiIygkhsGr3H9WOtyqfoCyt/mNO1aiYKiVVapUSLZt6wUdTlkgpFYCX2g1ARaXqiszDf3bvU9WhQwc0bdoU/fr1Q3FxMTIzM9GyZUv88ccfMBqN9RkjERHd4YxVJis/GKWFu5sSU2PbA+Dqv8aq8so/VflqBGt1ylujkt73ijlVDWT4Lzc3F+vXr0evXr1gsVjw4IMPon379igpKcHWrVuRlZVVn3ESEdEdrMRkO5eqR1gAlo3qgYiqlarbHBfVr8or/9zdylIW7/KkysfDXTpXMaeqgQz/GY1G9OnTBy+99BI8PT1x+PBhrFq1Cm5ubvjss88QERGByMjI+oyViIjuUCUm2xVgnuXDPlZc/dc4GctX/ikUgFt5pco6Od1asQIqkqqcglJYLPL9Dtg9/Ofv74/u3bujX79+KC0tRVFREfr16weVSoWvvvoKLVq0wIEDB+ozViIiukNVTaq8qiZVkO/SJHL58ehl/O/0Fcx7uAvUqsa5mN9aqbIO/QGVK1UVKUzT8uE/s0Ugt8iIwCbq2xhlBbvfhYsXL+KNN96ARqOByWRCr169MGDAAJSWluLQoUNQKBTo379/fcZKRER3qBKj7fCfp7ttUiVt/nkHFar+8uUhrN2fiW9SLtTduIGqSKoq0pX2Qd4AgLZBFZfJc3dTwt+rrIIl5xCg3UlVs2bN8NBDD2HBggXw8vLCgQMH8MILL0ChUODll1+Gn58fBg0aVJ+xEhHRHapqpep6YZUFUuVJleVOyqrKyT2PqD5Zh/9UbhWVqujWTfHLy/di/iOdbdpK86pk3FbhpuuFfn5+ePzxx+Hu7o7t27cjPT0df/nLX5wZGxYsWIC7774bPj4+CAoKQnx8PNLS0mzaFBcXY9KkSWjatCm8vb0xcuTIapPmMzIyMHz4cHh5eSEoKAivvPIKTCbbSx388ssv6NmzJzQaDdq2bYvVq1dXi2fFihVo1aoVPDw8EB0djf379zu1v0REVLOqSVWPlv4296XNP29XQC5E2YhHPq2VKuskdatWzZpUO2ZdASjntgo3lVQdOXIEd911FwAgPDwc7u7u0Gq1eOKJJ5wa3M6dOzFp0iTs3bsXSUlJMBqNGDJkCAoKCqQ206ZNw3//+1+sX78eO3fuxKVLlzBixAjpvNlsxvDhw1FaWoo9e/bg888/x+rVqzFnzhypTXp6OoYPH4777rsPqampmDp1Kp555hls3bpVavPVV19h+vTpePPNN3Ho0CF069YNcXFxyM7OdmqfiYioutLypMrHQ4UNk/uhY4ivzfk7eaK6dVuBxsi6pYLKjszRJXZVFw1Idna2ACB27twphBAiNzdXuLu7i/Xr10ttTp48KQCI5ORkIYQQP/74o1AqlUKn00ltPvroI+Hr6ytKSkqEEELMmDFDdO7c2ea1nnjiCREXFyfd79Onj5g0aZJ032w2i9DQULFgwQK749fr9QKA0Ov1DvSaiIh2ncoW4TM3irilO2s8v/fsVRE+c6O47+87bm9gMgqfuVGEz9woVuw4LXco9eZwxnURPnOjuGfBtjrb/nE1X5zOyhP5xUanx2Hv53eDWi6g1+sBAIGBgQCAlJQUGI1GxMbGSm06dOiAli1bIjk5GQCQnJyMqKgoBAdXbGsfFxcHg8GA48ePS20qP4e1jfU5SktLkZKSYtNGqVQiNjZWalOTkpISGAwGmxsRETmuxFhWsdBUmaBuJVVr7rxCFZSNuFJlMlefU1Wb8KZN0DbIG000dm9s4HQNJqmyWCyYOnUq+vXrhy5dugAAdDod1Go1/P39bdoGBwdDp9NJbSonVNbz1nM3amMwGFBUVISrV6/CbDbX2Mb6HDVZsGAB/Pz8pFtYWJjjHSciImlOlaaWrQPutJzKVGmn8cY8p8pYw5YKrqzBJFWTJk3CsWPHsG7dOrlDsdusWbOg1+ulG6+RSER0c6w7qteaVJV/FXfInKpSm6SqYSQcN8N6QeWqk9JdlXw1MgdMnjwZGzduxK5du6QJ8gCg1WpRWlqK3Nxcm2pVVlYWtFqt1KbqKj3r6sDKbaquGMzKyoKvry88PT3h5uYGNze3GttYn6MmGo0GGo3G8Q4TEZGNikpVbcN/ZV/vjJSqYuI+0MgnqtewpYIrc+nUTwiByZMn47vvvsP27dsRERFhc75Xr15wd3fHtm3bpGNpaWnIyMhATEwMACAmJgZHjx61WaWXlJQEX19fdOrUSWpT+TmsbazPoVar0atXL5s2FosF27Ztk9oQEVH9sW7+Wfvw3511QeXKW0w05upcTZt/ujKXrlRNmjQJa9aswQ8//AAfHx9p/pKfnx88PT3h5+eHxMRETJ8+HYGBgfD19cULL7yAmJgY9O3bFwAwZMgQdOrUCU899RQWLVoEnU6HN954A5MmTZKqSM899xw++OADzJgxA+PHj8f27dvx9ddfY9OmTVIs06dPx9ixY9G7d2/06dMH7733HgoKCjBu3Ljb/4MhIrrD1DmnqvzrnbL5Z+VKVeWhwMbGOnfMvYFUqlw6qfroo48AAPfee6/N8VWrVuHpp58GACxduhRKpRIjR45ESUkJ4uLi8OGHH0pt3dzcsHHjRjz//POIiYlBkyZNMHbsWMyfP19qExERgU2bNmHatGlYtmwZ7rrrLnz66aeIi4uT2jzxxBO4cuUK5syZA51Oh+7du2PLli3VJq8TEZHzWZMIjTsrVYBtpcpazWmMjBZWqpzGnpKmh4cHVqxYgRUrVtTaJjw8HD/++OMNn+fee+/F4cOHb9hm8uTJmDx5cp0xERGRc9U5p+p2BuMCrBP3gYoNMhsjR7ZUcAUNI/UjIqI7Wp2r/+6wHdUrD/8ZG3GlqrbL1LiqhhElERHd0eqeU3VnXfuvxCaparyVKmn1H/epIiIico66d1Qv+3qHFKpsKlWmRpxUsVJFRETkZHUN/1mJO6RWZbv6r/H22cg5VURERM5l92VqGm9+YaPkTqlUNbDVfw0jSiIiuqPVtfrPeqmWOySnQqn5zlr911D2qWJSRURELs863KXm6j8AVVb/WRpvn6ULKjOpIiIico66L6h8527+aTQ14kqVtPqvYaQrDSNKIiK6o5XUuaN62dc7JKeyXf3XiCtVFdf+Y6WKiIjIKaQtFerYUf1OGf67Y/apkob/Gka60jCiJCKiO5rdO6rfroBkdqckVdbhP05UJyIiukXX8ksw9L1d+ONaIYDaK1W4w+ZU3SmXqZEqVZxTRUREdGs+/OUsftflSffrnFN1h2RVlS+o3Jj3qTJbuPknERGRU/yuM9jcr331X5k7I6W6c3ZUr7hMDZMqIiKim1ZqsuBwRq7Nsbo2/7xTsqo75dp/Ru6oTkREdGvMFoGVO8+isNRsc7yuieqWO2b4707ZUoET1YmIiG7Jih1n8G7SqWrHa91RHXfYZWoqD/814s0/uaUCERHRLSgsNeGz3ekAgJeHtMerwzpI53hB5TKl5sqVqsabVFXsqM5KFRERkcO+SbmA3EIjwpt64fl726J1sybSuboqFuIOqVVVXv3XmLdUqJio3jDSFZXcARAREVX2n5QLAICn72kFN6UCA9s3R/tgb9wV4FXrY+64StUdsvmntW8NZUsFJlVEROQyLuUW4bcLeigUwPCuIQAAD3c3bJ068IaPUyju3DlVjTmpMnH1HxERkX30hUabLQF+Oq4DAPQOD0CQj4d0XKFQSIlTTaQzd0hWZbP6r1EP/3H1HxERUZ2OXdSj51tJeP27YwDKPkC/Plg29BfXWevQc1VsU9V4E4zKSqtsqdBYd5Ln6j8iIiI7rNmfAbNF4KuDmbisL8KHv5zFicsG+Hio8HD3UIeey7r5ZyPNLaopqbKNQmOdrC5dULmBrP7jnCoiIpLF+WsF0vcxC7ZL1ab5j3S2Gfqzh/Uj907c/BMom1dV2x5eDZmpgVWqmFQREdFtcSY7H+tTMpFbYMSU2HZIrXIJGiGAp/qGI757C8ef/M66Sg1KTbY7zbvSvKrTWXm4K8ALnuqaLynkCGMDu6AykyoiIqp3GdcK8eiHu5FXbAIAfHUwEwDg6e6Gp2LCcSorD71aBmDSfW1vOCG9NtKO6q6TWzidyWzB3386hc3HLsNQ/nO0KpV5BeDOU1ewNOkUHugUjMVb0xDXORj/eKr3LT+vtE9VA1n9x6SKiIjqlcUiMHntIeQVm9CqqRcMxSbkFJQCAHqG++O1Bzve8mvcRB7m0nafuYo/rhVg9N0toSyfT7T992ys3HkWQNlquGFdQrD52GUYzUL2XdVnf38MGTmFSM3MBQBsPZ6FM9l5aBvkc0vPa50r5sY5VUREdKc6fkmPGd8cwdh7WqGZtxpHLujho1FhzYS+8FK74f3tZ5B0IgtjY1o55fUqf+QKIW6q2uUqhBD4y5eHoC8y4vfLeXh5SCT8vNyRdCILAPBojxb4a3wXeGtU6DQnC0azGUaTvCW6muay/fPXP7BgRNQtPa80UZ3Df0REdKfR6YuRdEKH+RtPwGgWePU/R1C+fyNG9QlDqL8nAGD2nzph9p86Oe11KydRQjTsytWV/BLoi4wAgH/tPY+1+zPw2oMdsf33bADA//W6C96aso9v6zXxjDJXqipfk/GuAE9cuF6Ebw9dwCtxkQhsopbOCSHw65mr6BbmD18P9zqft6FNVG8YURIRUYMw69sjmP3DcWnYxppQKRXA2Hta1dvr2lSq6u1Vbo9zVwps7pssAvM3nsC1glL4eKhwd0SgdM664k/uXdWv5pcN594X2RxrnumLLi18UWKyYM2+8wDK9tX6XWfAd4cv4ql/7ses/xy163mly9Rw+I+IiO4UV/JKcK2gBL+euSod+yihJy5cL0LqhVzc06bpDa/dd6sqV6bKNsJsGB/CNbEmVfdGNseqp+/G2z+exCf/SwcAPNQt1ObiwtbLt8i5+q/EZJYqa+8+3h0BTdRI7B+BaV/9hs+Tz+O5QW0w45vf8H3qJekxW4/rkFNQalPFKigx4XBGLi7lFuGBTsEIaKKG2cILKhMRUSN3KisPM745gj/3Dccj3UMRv2I3LuYWAQAimjXBjpfvva3x2Az/3dZXdr5zV/IBAK2beUOhUOD14Z0wJqYVCkpNaNvc26atu6qs33Ku/rtWXqVSKRXw8ywb0hseFYq/bjyJK3kl+MeuczYJFVBWfRvwznbMerAjHut1FxZvTcO/ks9L/eiTEoi1E/tWXPuPc6qIiKgxKjaa8eQne3E1vxSpmbloonaTEioAGNwh6LbHVLlS5aobgB7OuI6IZk3g76W+YbtzV8sqVa2bN5GOhQXWXOVzd4FK1dX8EgBAU2+1tFJRrVLiwSgt/r03A4u3ptX4uIJSM974/hje2nQCxcayZCqwiRo5BaXY/0cO+i7YJrXllgpERGQ3i0Xgan4JgnxvvJP4jVa2/XwiCx/sOINFj3VF++BbW8pearLgqwMZOH7JgLZB3th56gouXC/CghFRWLU7XZpDAwDPf3nI5rEPdg25pde+Gbar/277y9fpcMZ1PPrhHvh4qNCzZQDUKiUGtGuGlPPX4aVWIbCJO3qFB6BLCz8cSM8BYJtU1cY6LCbnnCprUtXMW2Nz/OFuLfDvvRkAgCZqN/QMD8D/Tl/F6D5huJJXCoWibH+rYqMFTZuo8c7IrhjcMQj/2nsec344jit5JdJzsVJFREQ39O+956F2U+Lxu8Mw97/H8UXyeaz8c08M7VJzUvJNygW8+cMxvD0iCo9U2XW82GjGa98dRXZeCd77+RQ+TOiFnIJSvLz+N/h7uePvj3WDUqnAmew8rNx5Ds/f2wZtmnvDUGzEF3v+gEKhwHOD2kCpAC5cL8LfNp3EluO6ajGM+ngvgLIl7v3bNsOOtCvSuY+f6oWAJmr0bBngxJ+SfVx9C4Vfyn9OecUm7DxV9r11i4TaVB3qq4k12ZA1qcorS7CrJlW9wwMQGeyDzOuFWD2+D6Ja+GHrcR2GdNJKu61fyStBlqEYkVofKUF8qm84Qv08kZVXjE1HLqOZtwZNNA0jXWkYURIRNTJpujy88f0xAEBhqQn/3lu2SmrR1jQ80EmL/BITPtl1Dv5e7mjdvAkyc4rw5objAIAp61JhMgus2Z8BXw8VxvWLwNGLemSX/2X/0/EsfPq/c1h3IBNnssvm5/QOD0R8j1BM/CIF564W4PglA96K74Jn/5UiVRr+vfc81Colzl8rBACo3ZQYExOOrSd08HJXoU1QE2w+poOPRoW3R0Th3sggTF5zCPvO5aBnuD8e6BQsW3Lj6pWqA3/kSN/fFeCJuwI8cfZKAUbdHQY3pQI6fTF2n72Ki9eLoFQoENsxGM19NDd4xjIVlSr5On2llkqVUqnA95P6ocRkloY8q/4x0NxHU62fCoUCsZ2CAQAJ0eH1FXa9YFLloBUrVmDx4sXQ6XTo1q0b3n//ffTp00fusIgaNaPZglKTpV7/WhVC4FRWPiKaNbH7wrQnLxtQWGpCr/DAuhtXsfFIxcTduf89IX1/7koBus37CQoAeSWmGh5Z5qX1v0nfV64Webq7ochoxlubTtq0n/vf41j68ylpSOXkZQNGfrTHps1lfTGAsipUp1A/vDykPQa0a47Xh5fteK5QKKTVWNYdrleP6+MSm23arP5zsanqRrMFh8uvc/j9pH6IauEHN6Wixp+byWyBUqGQ5ibVxboppskVhv98qs8V81S7OeUagA0FkyoHfPXVV5g+fTpWrlyJ6OhovPfee4iLi0NaWhqCgm7/xExXZTRboFIqpP9ZFJaaUFhqtvkrpsRkhrtSafM/jjPZefj20EWolAr8qVtojXNCDMVGnM7KR7tg7xo3jhNCIL/EBJ8q50pNFliEgId73f+4zRaBvGIj/DzdYbYIh/4HZy8hBK7kl6BZE02tz200W+xaRlz1511ZqckCd7eaz9X1nGZLxc+r2GiGQgFoVLX//K4XlOKPawXwUqtqTEyyDcV44/tjUCiA7mEB8PFQIU2Xh6v5JWgb5I2L14tgsgh0vcsPp7PycUlfhLZB3riSV4LNx3QwWwTujWyONs29YTJbEBbohV7hATiVlYddp6+iTXNvtAz0gqe7G/619w80Uaswuk9L/HrmKvaeu4ZHe7RAgJcaapUSu89chVkIdArxhaHIiPRrhcgrNuKXtCto3awJPNzdUGIyo+td/hh1dxh2nrqCTUcvw2QWaBnohf7tmuF6QSk+250OiwCeHdQaQzpp8bvOgBOXDCgymtGjZQAUKFsm7ufpjmKjGXcFeKHQaEZesRHvbz9j8/Px1qjwp64hWHcgE/nlyZT1ki6FpSYYzQJmi8CzA1vDUGzE94cv4Z42TdGyqRe+PpCJEpMFzw1qg7sjAjF13WG0C/LB/R2D8FC3ULyy/jfsOXsNV/JK4OOhwmO97sKq3X8AKJtUvmx0Dxy9oEfK+RxEan1xT5umNgls5d+fmi4XIndCBVRc+w8oG1L649p1WCwCSqUCXVv4IaCJGkIIm3/TecVGnMrKRwetD67klcBNqYCPhwqeajeo3ZTIKShFdvlxtZsSCkXZvlsqpQIlJjPyik1o7qNBmi4P/9p7HoFeavSJCMSW4zpcyy/Fk9EtEeyrQV6xCUVGM/y93NG1hZ/0b76mn5ujm1xa/x9hz+o/IQSOXtTjWn4pSkwWNNG4wcfDHQUlJhSUlMXo7qaEp7sbNO5lXz3VbvBQuUGtUsJkFig1W2CyWGA0lX2fkVMg/S419667stbYKYRwxUKpa4qOjsbdd9+NDz74AABgsVgQFhaGF154Aa+++mqdjzcYDPDz84Ner4evr6/T4jKaLbiUW4SLuUUwW4RU+ra+sda3WHqjK73jhmIj9EVGeKlVaKJ2g5dGBSEELELAZC7/Wv6cXmo3aFRu0BcZ4eGuhMkiYDRb4KYo+9A+eyUf/0m5gHNXC3BXgCcimjWBodiEk5cMKDVbEBnsA4Wi7C+X3zJz0dRbgxA/D3i4uyHEzwNJJ7JQWFp25XWlAvBwd4PWzwMRTZtAoQCy80pw7KIeFlF2PqqFH8xCoMRoQbHJjKJSC5Tl7SKDfRDkq8HhjFz4ebojv6Tsg2lAu+a4XliKy7nF8PFQoam3Gs28NTCZBXSGYuj0xbiSXwKzRaCJuuwvfosAWvh7olUzL1zWFyNLX4ye4QEwmQWuF5bCw90NgU3UsIiyicYms0Crpk2QU1iKwlITSowWBPlqEOzjgUv6IlzWF6Ow1Fz2AadRIdBbDU93N3ip3XA1vxR5xWXvx8XcIvRpFYgWAZ5QADhyUY8sQzGaqFVoEeAJN4UCeSUm/K4zoF2QN+4K8ML1wlJcLyhFYakZJotATkEpVEoFfD3d4euhgkVA+sD283SHEAKe5e+9ySJQbDTjWkEpruaXQKVUoG/rpriWX4qTOgOEANo0b4KCEjOKjGZ0aeELXw93KJUKnM7Kw6msfOn3ykvthiAfDXIKStEu2AcBXu44fskgVUKojFqlxM5X7kV+sQlhgV7QqJRIy8pDtqEETb3ViAz2ka7rdv5aIXadvoLx/SLg4e4mJQxA2XtaVGqudbjIYhE4nZ2P3MJSdAvzh4e7Gy7ri6B2U6JpI/kgLDaa0WH2lhrPKRRliZB1iMzdrWzpf26hESaLgFJRsUmptb2HquzfvzM9GKXFhwm9nPqcYz7bj13lc7RiOwZBrVLC3U2JYqMZQgA+Hu5llTsB/HGtAIfKK2b1Ydmo7tWG9xoLez+/mVTZqbS0FF5eXvjmm28QHx8vHR87dixyc3Pxww8/VHtMSUkJSkoqVi8YDAaEhYU5PamavOYQNh657LTnk1OfVoHw9VTh55PZtbaxLrkl16P19UBBianWYauwQE882SccRy/moqjUjPZaHwR4qXE2Ox/hTb2gUCiw4/dsBDRR497I5ki/UoAioxmj+7SEp9oNX+z5A6ryv6SPXtQjTZcHN6UCj3QPRXb5hNer+aXo0dIfRpMFRy/qEdhEjX5tm2H3matQq5TILzahQ4gPgnw8cPxSWZLeNsgbabo8PBUTjoxrhfD3coe/lxpr92Xg6EU9mvloMGFABEL8PHHysgG/pGXDU63Cg120MFoEvtjzB/64VoiOIT6IauEHtUqJbSez4eGuRHD5z0TlpsRlfRGaqFXw8VDBUGTCsCgtxvWLuM3vUuNksQjELt0pbZzZNsgbnu5uKCg1VduhvDIfDxXyik1llVVRveLTtPwPplKTBQpFWT3MZBFSVetqfgk83N0wsudd8FS7Yd+5awjx90SbZk2w/48c5BYacTG3CIPaN8echzohyOfGqzsdNeOb3/D1wQt2t9eolIjU+kClVCCvuKxC5e2hQhONCp7ubjCaLSg2WlBkNKNYullQarbAXamAyk0JdzcF3N2UULkp0NxbgwAvNRQK4N0nutt16ZmGiEmVk126dAktWrTAnj17EBMTIx2fMWMGdu7ciX379lV7zNy5czFv3rxqx52dVC3c/DtW7U5HiwBPm+EZa2G5coXZ+r21VO6ldkOAlxpFRjMKS00oKDFLf9UplQq4KRRlY/+A9A+sbCjDAjelAhqVEhYhYCmvZMV3b4F72jbFmex8ZBtK4KV2Q6TWB000Khy9oIfKTYHrhaWIauGHbEMJCkrLhkKu5pcg1N8TQztroXJT4lJuEUpMFpzNzse1gpKySplGhbtbBSDEzxOXcotw4I8ceLi7wUejgruq7IO2sNSMloFeOHg+BwUlJrQL9kFuYalU0k7NzEWovydC/T1RUGLC1fwSXMkrgbubElo/D2h9PRDi5wFfT3dk5hTC19MdCgVwJisf2eVDJ828NTh6UQ8fDxX8vdQoMZpxvbAUCijQzEcNswXIzClEMx8NfDxU0LgpceF6EfRFZUOK4U29oFQq0DnUFxeuF8FQZERhqRmFpWb4eKjg5+kOQ5ERQb4e2HvuGorKq06tmnqhvdYH+iIjsvTFECgbimkf7IPDGddhNFvg76VGYJOyypdSoUCInweKTWYYikzQFxnhpgS8NWV/ueYVlyU+haVmFJZ/6Hu6u8HP0x0h/h7IMhQj5fx1NPfWoEfLALi7KaSfuZ+nO85dKUBBqQmlJgtalg/FNfXWQAiBE5cNuF5ghL+XO9KvFiC/xAQvtRvujQySNgckcrYSkxmZOUXw83S3qdpdyy9BqdkClbIsISgsNSO30Ag/L3eE+nkgM6cIQb4aqN3K/n92vdCI/BKTVEl3ZTkFpfj1zFX4ebrjj6sFUCjKhv01KiWgUCC/2ASlouz//Wo3JR7orEWL8usvkv2YVDnZzSRVt6tSVWIyl4/3yz+vgYiIqLGxN6niRHU7NWvWDG5ubsjKst1XJCsrC1qttsbHaDQaaDT1P1/hRpOHiYiI6PZoGPu+uwC1Wo1evXph27aKbfMtFgu2bdtmU7kiIiKiOxMrVQ6YPn06xo4di969e6NPnz547733UFBQgHHjxskdGhEREcmMSZUDnnjiCVy5cgVz5syBTqdD9+7dsWXLFgQHB8sdGhEREcmME9Vvo/rap4qIiIjqj72f35xTRUREROQETKqIiIiInIBJFREREZETMKkiIiIicgImVUREREROwKSKiIiIyAmYVBERERE5AZMqIiIiIidgUkVERETkBLxMzW1k3bzeYDDIHAkRERHZy/q5XddFaJhU3UZ5eXkAgLCwMJkjISIiIkfl5eXBz8+v1vO89t9tZLFYcOnSJfj4+EChUDjteQ0GA8LCwpCZmdlorynIPjYO7GPD19j7B7CPjYUz+yiEQF5eHkJDQ6FU1j5zipWq20ipVOKuu+6qt+f39fVttP84rNjHxoF9bPgae/8A9rGxcFYfb1ShsuJEdSIiIiInYFJFRERE5ARMqhoBjUaDN998ExqNRu5Q6g372Diwjw1fY+8fwD42FnL0kRPViYiIiJyAlSoiIiIiJ2BSRUREROQETKqIiIiInIBJFREREZETMKlqBFasWIFWrVrBw8MD0dHR2L9/v9wh3bS5c+dCoVDY3Dp06CCdLy4uxqRJk9C0aVN4e3tj5MiRyMrKkjHiG9u1axceeughhIaGQqFQ4Pvvv7c5L4TAnDlzEBISAk9PT8TGxuL06dM2bXJycpCQkABfX1/4+/sjMTER+fn5t7EXN1ZXH59++ulq7+nQoUNt2rhyHxcsWIC7774bPj4+CAoKQnx8PNLS0mza2PN7mZGRgeHDh8PLywtBQUF45ZVXYDKZbmdXamVPH++9995q7+Nzzz1n08aV+/jRRx+ha9eu0kaQMTEx2Lx5s3S+ob+HQN19bOjvYVULFy6EQqHA1KlTpWOyv4+CGrR169YJtVotPvvsM3H8+HExYcIE4e/vL7KysuQO7aa8+eabonPnzuLy5cvS7cqVK9L55557ToSFhYlt27aJgwcPir59+4p77rlHxohv7McffxSvv/66+PbbbwUA8d1339mcX7hwofDz8xPff/+9+O2338TDDz8sIiIiRFFRkdRm6NCholu3bmLv3r3if//7n2jbtq0YPXr0be5J7erq49ixY8XQoUNt3tOcnBybNq7cx7i4OLFq1Spx7NgxkZqaKh588EHRsmVLkZ+fL7Wp6/fSZDKJLl26iNjYWHH48GHx448/imbNmolZs2bJ0aVq7OnjoEGDxIQJE2zeR71eL5139T5u2LBBbNq0SZw6dUqkpaWJ1157Tbi7u4tjx44JIRr+eyhE3X1s6O9hZfv37xetWrUSXbt2FVOmTJGOy/0+Mqlq4Pr06SMmTZok3TebzSI0NFQsWLBAxqhu3ptvvim6detW47nc3Fzh7u4u1q9fLx07efKkACCSk5NvU4Q3r2rCYbFYhFarFYsXL5aO5ebmCo1GI9auXSuEEOLEiRMCgDhw4IDUZvPmzUKhUIiLFy/ettjtVVtS9cgjj9T6mIbWx+zsbAFA7Ny5Uwhh3+/ljz/+KJRKpdDpdFKbjz76SPj6+oqSkpLb2wE7VO2jEGUfyJU/vKpqaH0UQoiAgADx6aefNsr30MraRyEaz3uYl5cn2rVrJ5KSkmz65ArvI4f/GrDS0lKkpKQgNjZWOqZUKhEbG4vk5GQZI7s1p0+fRmhoKFq3bo2EhARkZGQAAFJSUmA0Gm3626FDB7Rs2bJB9jc9PR06nc6mP35+foiOjpb6k5ycDH9/f/Tu3VtqExsbC6VSiX379t32mG/WL7/8gqCgIERGRuL555/HtWvXpHMNrY96vR4AEBgYCMC+38vk5GRERUUhODhYahMXFweDwYDjx4/fxujtU7WPVl9++SWaNWuGLl26YNasWSgsLJTONaQ+ms1mrFu3DgUFBYiJiWmU72HVPlo1hvdw0qRJGD58uM37BbjGv0VeULkBu3r1Ksxms80vBwAEBwfj999/lymqWxMdHY3Vq1cjMjISly9fxrx58zBgwAAcO3YMOp0OarUa/v7+No8JDg6GTqeTJ+BbYI25pvfPek6n0yEoKMjmvEqlQmBgYIPp89ChQzFixAhERETg7NmzeO211zBs2DAkJyfDzc2tQfXRYrFg6tSp6NevH7p06QIAdv1e6nS6Gt9n6zlXUlMfAeDJJ59EeHg4QkNDceTIEcycORNpaWn49ttvATSMPh49ehQxMTEoLi6Gt7c3vvvuO3Tq1AmpqamN5j2srY9A43gP161bh0OHDuHAgQPVzrnCv0UmVeRShg0bJn3ftWtXREdHIzw8HF9//TU8PT1ljIxu1qhRo6Tvo6Ki0LVrV7Rp0wa//PILBg8eLGNkjps0aRKOHTuGX3/9Ve5Q6k1tfZw4caL0fVRUFEJCQjB48GCcPXsWbdq0ud1h3pTIyEikpqZCr9fjm2++wdixY7Fz5065w3Kq2vrYqVOnBv8eZmZmYsqUKUhKSoKHh4fc4dSIw38NWLNmzeDm5lZtZUNWVha0Wq1MUTmXv78/2rdvjzNnzkCr1aK0tBS5ubk2bRpqf60x3+j902q1yM7OtjlvMpmQk5PTIPsMAK1bt0azZs1w5swZAA2nj5MnT8bGjRuxY8cO3HXXXdJxe34vtVptje+z9ZyrqK2PNYmOjgYAm/fR1fuoVqvRtm1b9OrVCwsWLEC3bt2wbNmyRvUe1tbHmjS09zAlJQXZ2dno2bMnVCoVVCoVdu7cieXLl0OlUiE4OFj295FJVQOmVqvRq1cvbNu2TTpmsViwbds2mzH0hiw/Px9nz55FSEgIevXqBXd3d5v+pqWlISMjo0H2NyIiAlqt1qY/BoMB+/btk/oTExOD3NxcpKSkSG22b98Oi8Ui/Q+xoblw4QKuXbuGkJAQAK7fRyEEJk+ejO+++w7bt29HRESEzXl7fi9jYmJw9OhRm+QxKSkJvr6+0tCMnOrqY01SU1MBwOZ9dOU+1sRisaCkpKRRvIe1sfaxJg3tPRw8eDCOHj2K1NRU6da7d28kJCRI38v+Pt7yVHeS1bp164RGoxGrV68WJ06cEBMnThT+/v42Kxsakpdeekn88ssvIj09XezevVvExsaKZs2aiezsbCFE2XLZli1biu3bt4uDBw+KmJgYERMTI3PUtcvLyxOHDx8Whw8fFgDEu+++Kw4fPizOnz8vhCjbUsHf31/88MMP4siRI+KRRx6pcUuFHj16iH379olff/1VtGvXzmW2GxDixn3My8sTL7/8skhOThbp6eni559/Fj179hTt2rUTxcXF0nO4ch+ff/554efnJ3755RebpeiFhYVSm7p+L63LuIcMGSJSU1PFli1bRPPmzV1mqXpdfTxz5oyYP3++OHjwoEhPTxc//PCDaN26tRg4cKD0HK7ex1dffVXs3LlTpKeniyNHjohXX31VKBQK8dNPPwkhGv57KMSN+9gY3sOaVF3RKPf7yKSqEXj//fdFy5YthVqtFn369BF79+6VO6Sb9sQTT4iQkBChVqtFixYtxBNPPCHOnDkjnS8qKhJ/+ctfREBAgPDy8hKPPvqouHz5sowR39iOHTsEgGq3sWPHCiHKtlWYPXu2CA4OFhqNRgwePFikpaXZPMe1a9fE6NGjhbe3t/D19RXjxo0TeXl5MvSmZjfqY2FhoRgyZIho3ry5cHd3F+Hh4WLChAnVkn5X7mNNfQMgVq1aJbWx5/fyjz/+EMOGDROenp6iWbNm4qWXXhJGo/E296ZmdfUxIyNDDBw4UAQGBgqNRiPatm0rXnnlFZs9joRw7T6OHz9ehIeHC7VaLZo3by4GDx4sJVRCNPz3UIgb97ExvIc1qZpUyf0+KoQQ4tbrXURERER3Ns6pIiIiInICJlVERERETsCkioiIiMgJmFQREREROQGTKiIiIiInYFJFRERE5ARMqoiIiIicgEkVERERkRMwqSKiO9Yff/wBhUIhXQOtobr33nsxdepUucMguuMxqSIiWT399NNQKBRQKBRQq9Vo27Yt5s+fD5PJJHdodvnll1+gUCjQuXNnmM1mm3P+/v5YvXq1PIER0W3HpIqIZDd06FBcvnwZp0+fxksvvYS5c+di8eLFcoflkHPnzuGLL76QOwynMZvNsFgscodB1KAwqSIi2Wk0Gmi1WoSHh+P5559HbGwsNmzYAKDmoa34+Hg8/fTTACoqRVVv1vOtWrWq8Xxtjh07hmHDhsHb2xvBwcF46qmncPXq1Tr78MILL+DNN99ESUlJjedrGmrMzc2FQqHAL7/8YtOXrVu3okePHvD09MT999+P7OxsbN68GR07doSvry+efPJJFBYW2jy/yWTC5MmT4efnh2bNmmH27NmofGnXkpISvPzyy2jRogWaNGmC6Oho6XUBYPXq1fD398eGDRvQqVMnaDQaZGRk1NlvIqrApIqIXI6npydKS0vtanvPPffg8uXL0m379u3w8PDAwIEDAQAHDhyQzl24cAF9+/bFgAEDanyu3Nxc3H///ejRowcOHjyILVu2ICsrC48//nidcUydOhUmkwnvv/++/R2txdy5c/HBBx9gz549yMzMxOOPP4733nsPa9aswaZNm/DTTz9Ve53PP/8cKpUK+/fvx7Jly/Duu+/i008/lc5PnjwZycnJWLduHY4cOYL/+7//w9ChQ3H69GmpTWFhId555x18+umnOH78OIKCgm65L0R3EpXcARARWQkhsG3bNmzduhUvvPCCXY9Rq9XQarUAgGvXruGZZ57B+PHjMX78eABA8+bNpbZTpkzB5cuXceDAgRqf64MPPkCPHj3w9ttvS8c+++wzhIWF4dSpU2jfvn2tcXh5eeHNN9/Ea6+9hgkTJsDPz8+u+Gvy1ltvoV+/fgCAxMREzJo1C2fPnkXr1q0BAI899hh27NiBmTNnSo8JCwvD0qVLoVAoEBkZiaNHj2Lp0qWYMGECMjIysGrVKmRkZCA0NBQA8PLLL2PLli1YtWqV1F+j0YgPP/wQ3bp1u+nYie5krFQRkew2btwIb29veHh4YNiwYXjiiScwd+5ch57DaDRi5MiRCA8Px7Jly6qd//jjj/HPf/4TGzZssEm0Kvvtt9+wY8cOeHt7S7cOHToAAM6ePVtnDImJiWjatCneeecdh2KvqmvXrtL3wcHB8PLykhIq67Hs7Gybx/Tt29dmWDMmJganT5+G2WzG0aNHYTab0b59e5u+7dy506ZfarXa5rWJyDGsVBGR7O677z589NFHUKvVCA0NhUpV8b8mpVJpMzcIKEugqnr++eeRmZmJ/fv32zweAHbs2IEXXngBa9euvWHSkJ+fj4ceeqjGpCgkJKTOfqhUKvztb3/D008/jcmTJ9ucUyrL/oat3Jea+gEA7u7u0vcKhcLmvvWYI5PI8/Pz4ebmhpSUFLi5udmc8/b2lr739PS84XwzIroxJlVEJLsmTZqgbdu2NZ5r3rw5Ll++LN03m804duwY7rvvPunYu+++i6+//hp79uxB06ZNbR5/5swZPPbYY3jttdcwYsSIG8bRs2dP/Oc//0GrVq2qJWb2+r//+z8sXrwY8+bNq9YPALh8+TJ69OgBAE7dH2vfvn029/fu3Yt27drBzc0NPXr0gNlsRnZ2dq3zyYjo1nH4j4hc2v33349NmzZh06ZN+P333/H8888jNzdXOv/zzz9jxowZWLx4MZo1awadTgedTge9Xo+ioiI89NBD6NGjByZOnCid0+l0Nb7WpEmTkJOTg9GjR+PAgQM4e/Ystm7dinHjxlXbg+pGFi5ciM8++wwFBQXSMU9PT/Tt2xcLFy7EyZMnsXPnTrzxxhs3/XOpKiMjA9OnT0daWhrWrl2L999/H1OmTAEAtG/fHgkJCRgzZgy+/fZbpKenY//+/ViwYAE2bdrktBiI7nRMqojIpY0fPx5jx47FmDFjMGjQILRu3dqmSvXrr7/CbDbjueeeQ0hIiHSbMmUKsrKy8Pvvv2Pbtm0IDQ21OV+T0NBQ7N69G2azGUOGDEFUVBSmTp0Kf39/afjOHvfffz/uv//+ahuYfvbZZzCZTOjVqxemTp2Kt9566+Z+KDUYM2YMioqK0KdPH0yaNAlTpkzBxIkTpfOrVq3CmDFj8NJLLyEyMhLx8fE4cOAAWrZs6bQYiO50ClF1sgIREREROYyVKiIiIiInYFJFRERE5ARMqoiIiIicgEkVERERkRMwqSIiIiJyAiZVRERERE7ApIqIiIjICZhUERERETkBkyoiIiIiJ2BSRUREROQETKqIiIiInOD/AQnCyQQbq5wmAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ss_df['total_moves'])\n",
    "ss_df.sort_values(by='total_moves', ascending=False).head(20)\n",
    "plt.xlabel(\"Puzzle Number\")\n",
    "plt.ylabel(\"# of Moves in Sample Submission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4177fa17",
   "metadata": {
    "papermill": {
     "duration": 0.014049,
     "end_time": "2023-12-27T11:08:45.110504",
     "exception": false,
     "start_time": "2023-12-27T11:08:45.096455",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "***\n",
    "# ⚒️ Operations\n",
    "Now we try implementing some of the basic operations we expect to need in simulating these puzzles. Specifically we need to:\n",
    "1. Get the set of possible moves\n",
    "2. Decide on a move\n",
    "3. Figure out the state of the puzzle after making the move.\n",
    "\n",
    "Part 3 is something we really want to get right from the start, so let's build that and test it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6ca2ae7a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-27T11:08:45.141687Z",
     "iopub.status.busy": "2023-12-27T11:08:45.140538Z",
     "iopub.status.idle": "2023-12-27T11:08:45.153559Z",
     "shell.execute_reply": "2023-12-27T11:08:45.152351Z"
    },
    "papermill": {
     "duration": 0.031222,
     "end_time": "2023-12-27T11:08:45.156076",
     "exception": false,
     "start_time": "2023-12-27T11:08:45.124854",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial State: ['D', 'E', 'C', 'B', 'B', 'E', 'F', 'A', 'F', 'D', 'B', 'F', 'F', 'E', 'B', 'D', 'A', 'A', 'C', 'D', 'C', 'E', 'A', 'C']\n",
      "Final State: ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'D', 'D', 'D', 'D', 'E', 'E', 'E', 'E', 'F', 'F', 'F', 'F']\n"
     ]
    }
   ],
   "source": [
    "def apply_move(state, move):\n",
    "    # The move vector tells us what index to sample the next state from\n",
    "    return [state[i] for i in move]\n",
    "\n",
    "def test_move_sequence(initial_state, move_sequence, move_dict):\n",
    "    new_state = initial_state\n",
    "    print(f\"Initial State: {initial_state}\")\n",
    "    for i, move_str in enumerate(move_sequence):\n",
    "        move = move_dict.get(move_str)\n",
    "        if move is None:\n",
    "            print(\"Requested move is not in puzzle's Move Dictionary\")\n",
    "            return\n",
    "        new_state = apply_move(new_state, move)\n",
    "#         print(f\"Move #{i}: {move_str}. New state: {new_state}\")\n",
    "    print(f\"Final State: {new_state}\")\n",
    "\n",
    "# Test using the sample submission solution for a certain puzzle number\n",
    "puzzle_number = 1\n",
    "move_sequence = ss_df.moves_parsed.iloc[puzzle_number]\n",
    "initial_state = puzzles_df.initial_state_parsed.iloc[puzzle_number]\n",
    "puzzle_type = puzzles_df.puzzle_type.iloc[puzzle_number]\n",
    "move_dict = puzzle_info_df.loc[puzzle_info_df['puzzle_type'] == puzzle_type]['allowed_moves_dict'].iloc[0]\n",
    "test_move_sequence(initial_state, move_sequence, move_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26be20ce",
   "metadata": {
    "papermill": {
     "duration": 0.014142,
     "end_time": "2023-12-27T11:08:45.184788",
     "exception": false,
     "start_time": "2023-12-27T11:08:45.170646",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The last line matches the solution so looks like everything's working correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe54133",
   "metadata": {
    "papermill": {
     "duration": 0.014072,
     "end_time": "2023-12-27T11:08:45.213572",
     "exception": false,
     "start_time": "2023-12-27T11:08:45.199500",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "***\n",
    "# ⭐ A-star Algorithm\n",
    "This is the algorithm we've chosen to try to decide what move to investigate next.\n",
    "\n",
    "Here's a great video by Sebatian Lague explaining how the algorithm works for moving along a 2D grid. We're doing the same thing, except the distance from the origin is simply the number of moves, and the distance to the end point is the number of components that are still mismatched. https://www.youtube.com/watch?v=-L-WgKMFuhE&t=110s\n",
    "\n",
    "The A-star algorithm is widely used for path-finding problems since when properly implemented it can quickly find the optimum solution to get from state A to state B. For solutions with a low number of required moves this will be the best option, but for the bigger problems like the 33x33x33 cube this simply won't work. In these cases we'll just use the answer from the sample submission instead.\n",
    "\n",
    "Here we've implemented the algorithm with 2 limiting factors: a timeout and maximum depth. This is because for the large puzzles this code could take days to run and still not find a solution. Better to cut it off short.\n",
    "\n",
    "Deciding the Heuristic (evaluate_score) is very important here. With the A-star algorithm if we choose a heuristic that is 'admissable', it will always find the optimum path. That won't be possible here due to the difficulty in finding such a heuristic and also due to timing constraints. We'll have to settle for an alternative heuristic.\n",
    "\n",
    "A lot of the solutions I've seen choose a heuristic where you count the number of squares which are different from the final state. This is quite a bad heuristic by itself since every move changes so many squares it jumps around a lot. Here I've tried to improve this by implementing a very basic check for adjacency. We add 1 point for every square that doesn't have a neighbour that's in the same state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e8d54cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-27T11:08:45.245027Z",
     "iopub.status.busy": "2023-12-27T11:08:45.244256Z",
     "iopub.status.idle": "2023-12-27T11:08:45.260962Z",
     "shell.execute_reply": "2023-12-27T11:08:45.259951Z"
    },
    "papermill": {
     "duration": 0.035164,
     "end_time": "2023-12-27T11:08:45.263445",
     "exception": false,
     "start_time": "2023-12-27T11:08:45.228281",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import heapq\n",
    "\n",
    "def evaluate_difference(current_state, final_state):\n",
    "    return (max(0, sum(s != g for s, g in zip(current_state, final_state))))\n",
    "\n",
    "def evaluate_score(current_state, final_state):\n",
    "    # Reward having the final position match, and also reward having 2 of the same state adjacent to each other\n",
    "    # This has to be fast since it's called so often\n",
    "    return (max(0, sum(s != g for s, g in zip(current_state, final_state))) + sum(s != g for s, g in zip(current_state[1:], current_state[:-1])) + 0.5*sum(s != g for s, g in zip(current_state[2:], current_state[:-2])))\n",
    "\n",
    "\n",
    "def astar(move_dict, initial_state, final_state, wildcards, max_depth=50, timeout = 180):\n",
    "    # Priority queue to store nodes with their f-values (g + h)\n",
    "    start_time = time.time()\n",
    "    open_set = []\n",
    "    node_counter = 1\n",
    "    \n",
    "    heapq.heappush(open_set, (0, initial_state, []))  # (priority, state, path, remaining wildcards)\n",
    "    # Set to keep track of visited nodes\n",
    "    closed_set = set()\n",
    "\n",
    "    while open_set:\n",
    "        # Check for timeout\n",
    "        if time.time() - start_time > timeout:\n",
    "            print(\"Timed out.\")\n",
    "            return None, node_counter\n",
    "        \n",
    "        # Get the node with the lowest f-value\n",
    "        current_f, current_state, current_path = heapq.heappop(open_set)\n",
    "        \n",
    "        if len(current_path) > max_depth:\n",
    "            print(\"Max depth exceeded.\")\n",
    "            return None, node_counter\n",
    "\n",
    "        if evaluate_difference(current_state, final_state) <= wildcards:\n",
    "            # We've achieved our goal. Return the move path.\n",
    "            return current_path, node_counter\n",
    "\n",
    "        closed_set.add(tuple(current_state))\n",
    "        \n",
    "        for move_str, move in move_dict.items():\n",
    "            new_state = apply_move(current_state, move)\n",
    "            if tuple(new_state) not in closed_set:\n",
    "                if len(current_path) < max_depth:\n",
    "                    heapq.heappush(open_set, (len(current_path) + 1 + evaluate_score(new_state, final_state), new_state, current_path + [move_str]))\n",
    "                    node_counter += 1\n",
    "    # If no solutions are found:\n",
    "    print(\"Open set completed. No solutions.\")\n",
    "    return None, node_counter\n",
    "\n",
    "def plot_final_path(move_dict, initial_state, final_state, path):\n",
    "    state_list = [initial_state]\n",
    "    for move in path:\n",
    "        state_list.append(apply_move(state_list[-1], move_dict[move]))\n",
    "    eval_list = [evaluate_score(state, final_state) for state in state_list]\n",
    "    plt.plot(eval_list)\n",
    "    plt.xlabel(\"Move Number\")\n",
    "    plt.ylabel(\"Heuristic Score\")\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13ff0f4b",
   "metadata": {
    "papermill": {
     "duration": 0.014067,
     "end_time": "2023-12-27T11:08:45.291835",
     "exception": false,
     "start_time": "2023-12-27T11:08:45.277768",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "I've added a plotting function (plot_final_path to see how our heuristic score changes as we progress through our solution. This is a good way to check whether our algorithm is working well. We expect this graph to *gradually* progress from the score at the initial state to the final state.\n",
    "\n",
    "There are two separate functions, evaluating our heuristic (evaluate_score) and also evaluating whether we have reached a 'solved' state (evaluate_difference)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "99fba1cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-27T11:08:45.322444Z",
     "iopub.status.busy": "2023-12-27T11:08:45.322048Z",
     "iopub.status.idle": "2023-12-27T11:08:48.582848Z",
     "shell.execute_reply": "2023-12-27T11:08:48.581708Z"
    },
    "papermill": {
     "duration": 3.280199,
     "end_time": "2023-12-27T11:08:48.586506",
     "exception": false,
     "start_time": "2023-12-27T11:08:45.306307",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing - Puzzle #4 solved in 14 moves. 106131 nodes. 0 wildcards. ['r0', '-f0', 'r1', '-f1', 'd0', 'r0', 'r0', '-f0', '-r0', 'f0', 'r0', '-f0', '-r0', 'f0'].\n"
     ]
    }
   ],
   "source": [
    "# Test using the sample submission solution for a certain puzzle number\n",
    "puzzle_number = 4\n",
    "move_sequence = ss_df.moves_parsed.iloc[puzzle_number]\n",
    "initial_state = puzzles_df.initial_state_parsed.iloc[puzzle_number]\n",
    "final_state = puzzles_df.solution_state_parsed.iloc[puzzle_number]\n",
    "puzzle_type = puzzles_df.puzzle_type.iloc[puzzle_number]\n",
    "wildcards = puzzles_df.num_wildcards.iloc[puzzle_number]\n",
    "move_dict = puzzle_info_df.loc[puzzle_info_df['puzzle_type'] == puzzle_type]['allowed_moves_dict'].iloc[0]\n",
    "\n",
    "solution_path, node_counter = astar(move_dict, initial_state, final_state, wildcards)\n",
    "plot_final_path(move_dict, initial_state, final_state, solution_path)\n",
    "\n",
    "print(f\"Testing - Puzzle #{puzzle_number} solved in {len(solution_path)} moves. {node_counter} nodes. {wildcards} wildcards. {solution_path}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d5828587",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-27T11:08:48.618954Z",
     "iopub.status.busy": "2023-12-27T11:08:48.618546Z",
     "iopub.status.idle": "2023-12-27T17:33:11.532365Z",
     "shell.execute_reply": "2023-12-27T17:33:11.530706Z"
    },
    "papermill": {
     "duration": 23062.965516,
     "end_time": "2023-12-27T17:33:11.567195",
     "exception": false,
     "start_time": "2023-12-27T11:08:48.601679",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Puzzle #0 solved in 2 moves. 0.00 seconds, 24 nodes. ['r1', '-f1']\n",
      "Puzzle #1 solved in 13 moves. 1.03 seconds, 38359 nodes. ['f1', '-d0', '-r0', 'f1', 'r0', 'f0', 'd1', '-r0', '-d0', 'f0', 'f0', 'd0', '-f0']\n",
      "Puzzle #2 solved in 14 moves. 2.59 seconds, 92753 nodes. ['-f1', 'f0', '-r1', '-r1', '-f0', '-r0', '-d0', '-f0', 'd0', 'f0', 'f0', '-d0', '-f0', 'd0']\n",
      "Puzzle #3 solved in 14 moves. 5.02 seconds, 176509 nodes. ['-f0', '-d0', '-f1', 'r0', '-d1', 'r0', 'f1', '-r1', '-d1', 'r1', '-f0', 'r1', 'f0', '-d1']\n",
      "Puzzle #4 solved in 14 moves. 2.80 seconds, 106131 nodes. ['r0', '-f0', 'r1', '-f1', 'd0', 'r0', 'r0', '-f0', '-r0', 'f0', 'r0', '-f0', '-r0', 'f0']\n",
      "Puzzle #5 solved in 12 moves. 2.95 seconds, 105516 nodes. ['-d1', '-r0', '-r0', 'f0', 'd1', 'r1', '-f1', '-r1', 'f0', '-d1', '-d1', '-f0']\n",
      "Puzzle #6 solved in 14 moves. 2.54 seconds, 90840 nodes. ['-r0', '-d1', 'r1', '-f0', '-d1', '-f0', 'r1', '-f1', '-d1', '-r0', '-f0', '-d0', 'f0', 'd0']\n",
      "Puzzle #7 solved in 13 moves. 5.82 seconds, 206673 nodes. ['-f0', 'd1', '-f0', 'r0', '-d0', 'r0', 'd0', '-r0', '-r0', '-d0', 'r0', 'd0', '-r0']\n",
      "Puzzle #8 solved in 14 moves. 5.96 seconds, 213640 nodes. ['-f0', 'd1', '-d0', '-d0', '-r1', 'd0', 'r1', '-f0', '-f0', 'r1', '-d1', 'f1', '-d1', '-f1']\n",
      "Puzzle #9 solved in 14 moves. 1.13 seconds, 42190 nodes. ['-d1', 'f1', '-d0', '-f0', 'd0', '-f0', '-r0', '-f0', 'd0', 'r0', 'd1', 'r0', '-d1', '-r0']\n",
      "Puzzle #10 solved in 16 moves. 1.14 seconds, 43293 nodes. ['f0', '-r0', '-d1', '-f0', '-r1', 'd1', 'f0', 'd1', 'd1', 'f0', '-d1', 'r1', '-f0', '-r0', 'd0', 'r0']\n",
      "Puzzle #11 solved in 13 moves. 0.54 seconds, 18066 nodes. ['-r1', 'r0', 'd1', '-f1', 'r1', 'f0', 'd0', '-r1', 'f1', 'd1', 'r1', '-d1', '-f1']\n",
      "Puzzle #12 solved in 16 moves. 3.20 seconds, 115607 nodes. ['-r0', 'd0', '-r1', 'f0', '-r0', 'd0', '-r0', 'd1', 'd1', '-f1', '-d1', '-r0', 'f1', 'r1', 'd0', '-r1']\n",
      "Puzzle #13 solved in 15 moves. 1.32 seconds, 50506 nodes. ['-r0', 'd0', '-f0', '-r0', '-d0', '-f0', 'd0', '-f1', '-d0', 'f1', 'r0', 'd1', 'f1', '-d1', '-f1']\n",
      "Puzzle #14 solved in 12 moves. 0.40 seconds, 12208 nodes. ['d1', 'r0', '-d1', '-r1', '-r1', 'd0', 'r1', '-d0', 'f1', 'r0', 'd0', '-r0']\n",
      "Puzzle #15 solved in 14 moves. 0.56 seconds, 23828 nodes. ['d0', '-r1', 'd0', '-r0', 'f0', 'd0', '-f0', '-d1', '-f0', '-f0', 'd1', '-f0', '-d1', 'f0']\n",
      "Puzzle #16 solved in 13 moves. 0.87 seconds, 31492 nodes. ['d0', '-f0', '-d1', '-d1', '-f1', 'r1', 'd0', 'r1', 'd1', '-f0', '-r0', '-d1', 'f0']\n",
      "Puzzle #17 solved in 14 moves. 0.06 seconds, 2577 nodes. ['-d1', 'd0', '-f1', '-d0', '-f1', '-r1', 'f1', '-r1', '-f1', 'r1', 'f1', '-r0', '-d1', 'r0']\n",
      "Puzzle #18 solved in 13 moves. 0.67 seconds, 23622 nodes. ['f1', 'd1', 'f0', '-d1', '-d1', '-r0', 'f1', 'r0', 'r0', 'd1', 'f1', '-d1', '-f1']\n",
      "Puzzle #19 solved in 14 moves. 7.01 seconds, 247156 nodes. ['-f1', 'd1', '-d0', '-r1', '-f1', 'r1', '-r0', '-d0', '-f0', '-d0', '-d0', 'f0', 'f0', 'd0']\n",
      "Puzzle #20 solved in 12 moves. 128.00 seconds, 4455595 nodes. ['-f0', 'r1', '-d0', 'd1', 'r0', '-f0', '-d0', '-f0', 'd0', '-f0', '-d0', 'f0']\n",
      "Timed out.\n",
      "Puzzle #21 failed after 184.60 seconds, 6569945 nodes. Using sample submission solution instead.\n",
      "Puzzle #22 solved in 11 moves. 26.80 seconds, 1106362 nodes. ['-f1', '-r1', '-f0', '-d1', '-f1', '-d0', 'r0', '-d0', '-d0', '-f0', '-d0']\n",
      "Puzzle #23 solved in 11 moves. 8.20 seconds, 284027 nodes. ['f0', 'f1', 'f1', '-r0', '-d0', '-d0', 'r0', '-d0', '-d0', 'r0', 'r0']\n",
      "Puzzle #24 solved in 11 moves. 12.08 seconds, 438910 nodes. ['d1', '-r0', '-d0', '-f0', 'd0', 'f0', '-d0', '-f0', '-f0', 'd0', 'd0']\n",
      "Puzzle #25 solved in 17 moves. 24.76 seconds, 881150 nodes. ['r0', 'd1', 'r1', 'd1', 'f1', '-d1', 'f1', 'r1', '-d1', '-f0', 'd1', '-r0', 'f1', 'f1', '-r1', '-f1', '-f1']\n",
      "Puzzle #26 solved in 13 moves. 6.01 seconds, 233034 nodes. ['-f1', 'd1', '-d0', 'r0', '-f0', '-d0', '-r0', '-r0', 'f0', '-d0', 'f0', 'd0', 'r0']\n",
      "Puzzle #27 solved in 15 moves. 20.19 seconds, 712912 nodes. ['f0', 'r1', 'f0', '-r1', 'f0', 'd1', '-r1', 'f0', 'r0', 'd0', '-r1', 'd1', '-f0', '-d1', 'f0']\n",
      "Puzzle #28 solved in 15 moves. 5.77 seconds, 222171 nodes. ['d1', '-r1', '-f0', '-f0', '-r1', '-f0', '-r1', 'f0', '-r0', 'f0', '-r0', '-f0', 'r1', '-d1', '-r1']\n",
      "Puzzle #29 solved in 16 moves. 8.65 seconds, 303736 nodes. ['-r0', '-d1', '-f1', 'd0', '-f1', '-r0', '-f1', '-r0', 'f0', 'd0', '-f0', 'r1', 'r1', 'f0', '-r1', '-r1']\n",
      "Timed out.\n",
      "Puzzle #30 failed after 183.52 seconds, 3948666 nodes. Using sample submission solution instead.\n",
      "Puzzle #31 solved in 38 moves. 21.98 seconds, 516453 nodes. ['f2', '-d1', '-r0', '-r1', '-d2', 'r1', 'f0', '-d0', 'f0', '-d0', '-d2', '-f0', 'd2', 'd2', '-d0', '-d0', 'r2', 'd0', '-r2', '-d0', 'r2', '-f0', 'd0', 'f0', 'd2', 'f0', '-d2', '-f0', 'd2', 'd2', 'f0', '-d2', '-d2', '-f0', 'd2', 'r2', '-d2', '-r2']\n",
      "Puzzle #32 solved in 30 moves. 147.65 seconds, 3215769 nodes. ['r1', '-r2', 'd1', 'r0', 'd2', 'r1', 'r2', '-f0', '-r2', 'd0', '-f0', '-f0', 'r2', '-f0', '-f0', 'f2', 'd2', '-f2', '-d2', '-d1', '-f2', '-r2', 'd1', 'd2', 'f2', 'r2', '-d2', '-r2', '-f2', 'r2']\n",
      "Puzzle #33 solved in 46 moves. 14.78 seconds, 353010 nodes. ['-r2', '-r2', '-f2', 'd2', 'd2', 'r1', 'f2', 'f2', '-f1', 'r0', 'd1', '-f1', 'd2', '-f0', 'd2', '-r0', 'd2', '-r0', '-d2', 'r0', '-d2', '-d2', 'f0', '-r2', '-f0', '-f0', 'r2', 'r2', 'f0', 'f2', '-d2', '-f2', 'd2', '-f0', '-d2', '-d2', 'f0', 'd2', '-f0', '-d2', 'f0', 'd2', '-f0', 'd2', 'f0', '-d2']\n",
      "Timed out.\n",
      "Puzzle #34 failed after 183.56 seconds, 4024551 nodes. Using sample submission solution instead.\n",
      "Puzzle #35 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #36 failed after 183.51 seconds, 4048424 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #37 failed after 183.57 seconds, 3989763 nodes. Using sample submission solution instead.\n",
      "Puzzle #38 solved in 39 moves. 179.13 seconds, 3941917 nodes. ['r2', '-d1', 'r2', 'f2', '-r2', '-d0', '-f0', '-f0', '-d0', '-r2', '-f2', 'r2', 'f2', 'd1', 'f2', '-d1', '-r0', 'd1', 'r0', '-d1', '-r0', '-d2', 'r0', 'd2', '-r0', '-r0', 'd2', '-f2', '-d2', 'f2', 'f2', 'd2', 'f2', '-d2', '-f2', '-f2', 'd2', 'f2', '-d2']\n",
      "Timed out.\n",
      "Puzzle #39 failed after 183.55 seconds, 3974785 nodes. Using sample submission solution instead.\n",
      "Puzzle #40 solved in 39 moves. 152.99 seconds, 3359677 nodes. ['-f1', '-r0', '-f0', '-f2', 'r0', 'd2', 'f2', '-d0', '-d2', '-f0', 'd0', 'f0', 'd2', '-f2', 'd2', 'f2', 'f0', '-d2', '-f0', '-r2', 'd2', '-r2', '-d2', '-d2', '-r2', 'd2', 'r2', '-d2', '-f0', 'd2', 'f0', '-d2', 'f0', 'd2', '-f0', 'd2', '-f0', '-d2', 'f0']\n",
      "Timed out.\n",
      "Puzzle #41 failed after 183.58 seconds, 4006031 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #42 failed after 183.74 seconds, 4036044 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #43 failed after 183.60 seconds, 3958209 nodes. Using sample submission solution instead.\n",
      "Puzzle #44 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #45 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #46 failed after 183.60 seconds, 4063523 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #47 failed after 183.63 seconds, 3956023 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #48 failed after 183.50 seconds, 4042191 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #49 failed after 183.55 seconds, 3977409 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #50 failed after 183.61 seconds, 4076485 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #51 failed after 183.68 seconds, 4000419 nodes. Using sample submission solution instead.\n",
      "Puzzle #52 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #53 failed after 186.20 seconds, 4040799 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #54 failed after 183.56 seconds, 4052966 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #55 failed after 183.55 seconds, 4054178 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #56 failed after 183.41 seconds, 4009710 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #57 failed after 183.47 seconds, 4062953 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #58 failed after 183.84 seconds, 3960408 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #59 failed after 183.56 seconds, 4046746 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #60 failed after 183.55 seconds, 4038386 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #61 failed after 183.57 seconds, 4038006 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #62 failed after 183.56 seconds, 4040742 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #63 failed after 183.57 seconds, 3968487 nodes. Using sample submission solution instead.\n",
      "Puzzle #64 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #65 solved in 38 moves. 180.66 seconds, 4037023 nodes. ['-f2', '-f0', 'f1', 'd0', '-d1', 'f0', 'd0', 'd2', 'r0', 'f1', '-d2', '-f1', 'd2', 'f0', 'f0', '-d2', '-f0', 'd2', 'f0', '-d2', 'r2', 'd0', 'f0', '-d0', 'f0', 'r1', 'f0', '-r1', '-f0', '-r1', 'f0', '-r1', '-d2', '-d2', '-r1', '-d2', '-d2', '-r1']\n",
      "Timed out.\n",
      "Puzzle #66 failed after 183.51 seconds, 4023204 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #67 failed after 183.49 seconds, 4016879 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #68 failed after 183.68 seconds, 4006451 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #69 failed after 183.49 seconds, 4041673 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #70 failed after 183.58 seconds, 4090173 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #71 failed after 183.49 seconds, 4026960 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #72 failed after 183.58 seconds, 4120506 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #73 failed after 183.45 seconds, 4059510 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #74 failed after 183.47 seconds, 4111286 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #75 failed after 183.44 seconds, 4051323 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #76 failed after 183.54 seconds, 4099251 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #77 failed after 183.37 seconds, 3984306 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #78 failed after 183.55 seconds, 4107005 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #79 failed after 183.46 seconds, 4023967 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #80 failed after 183.52 seconds, 4103217 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #81 failed after 185.93 seconds, 4060583 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #82 failed after 183.48 seconds, 4096673 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #83 failed after 183.55 seconds, 4120321 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #84 failed after 183.50 seconds, 4004230 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #85 failed after 183.69 seconds, 4007736 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #86 failed after 183.57 seconds, 4005521 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #87 failed after 183.69 seconds, 3993818 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #88 failed after 183.60 seconds, 3997545 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #89 failed after 183.72 seconds, 3896195 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #90 failed after 183.48 seconds, 3848463 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #91 failed after 183.58 seconds, 3974186 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #92 failed after 183.57 seconds, 3964484 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #93 failed after 183.62 seconds, 3997085 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #94 failed after 183.50 seconds, 3943026 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #95 failed after 183.66 seconds, 3995041 nodes. Using sample submission solution instead.\n",
      "Puzzle #96 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #97 failed after 183.60 seconds, 3970817 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #98 failed after 183.65 seconds, 4009945 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #99 failed after 183.43 seconds, 3980416 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #100 failed after 183.56 seconds, 3991569 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #101 failed after 183.61 seconds, 3933721 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #102 failed after 183.63 seconds, 4019130 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #103 failed after 183.48 seconds, 3950911 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #104 failed after 183.74 seconds, 4047536 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #105 failed after 183.71 seconds, 3927252 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #106 failed after 183.53 seconds, 3961025 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #107 failed after 183.63 seconds, 4012431 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #108 failed after 183.62 seconds, 3998653 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #109 failed after 184.60 seconds, 3948129 nodes. Using sample submission solution instead.\n",
      "Puzzle #110 solved in 39 moves. 28.99 seconds, 689410 nodes. ['-d1', '-f0', '-d0', '-d0', '-r1', '-r1', 'd0', '-r0', '-f2', '-d0', 'f2', 'f2', 'd0', 'f2', '-f1', '-r0', 'f1', 'f0', 'd0', '-f0', '-f0', '-d2', 'f0', 'd2', 'f0', '-r2', 'd2', '-f0', '-d2', '-f0', '-f0', 'd1', '-f0', '-d1', '-f0', 'd1', '-f0', '-d1', 'f0']\n",
      "Timed out.\n",
      "Puzzle #111 failed after 185.13 seconds, 4012649 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #112 failed after 183.54 seconds, 4066101 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #113 failed after 183.65 seconds, 4065074 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #114 failed after 183.45 seconds, 4076405 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #115 failed after 183.67 seconds, 4049858 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #116 failed after 183.58 seconds, 4009379 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #117 failed after 183.56 seconds, 4041254 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #118 failed after 183.52 seconds, 4010624 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #119 failed after 183.55 seconds, 4017041 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #120 failed after 183.72 seconds, 4067626 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #121 failed after 183.47 seconds, 3966356 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #122 failed after 183.69 seconds, 4056907 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #123 failed after 183.62 seconds, 4025195 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #124 failed after 184.45 seconds, 3988081 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #125 failed after 183.55 seconds, 4032416 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #126 failed after 184.18 seconds, 4011537 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #127 failed after 183.55 seconds, 4026078 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #128 failed after 183.59 seconds, 4097848 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #129 failed after 183.52 seconds, 4008077 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #130 failed after 183.46 seconds, 4040725 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #131 failed after 183.37 seconds, 3971089 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #132 failed after 183.41 seconds, 4049802 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #133 failed after 183.45 seconds, 4009752 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #134 failed after 183.62 seconds, 4024284 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #135 failed after 183.39 seconds, 4019432 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #136 failed after 183.52 seconds, 4125141 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #137 failed after 183.52 seconds, 3982254 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #138 failed after 183.50 seconds, 4036287 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #139 failed after 183.38 seconds, 3981983 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #140 failed after 183.69 seconds, 4005599 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #141 failed after 183.45 seconds, 3902400 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #142 failed after 183.47 seconds, 3983221 nodes. Using sample submission solution instead.\n",
      "Puzzle #143 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #144 failed after 183.25 seconds, 3961353 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #145 failed after 183.56 seconds, 3982697 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #146 failed after 183.32 seconds, 3853456 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #147 failed after 183.33 seconds, 3973321 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #148 failed after 183.63 seconds, 3998478 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #149 failed after 183.35 seconds, 3918427 nodes. Using sample submission solution instead.\n",
      "Puzzle #150 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #151 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #152 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #153 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #154 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #155 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #156 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #157 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #158 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #159 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #160 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #161 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #162 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #163 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #164 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #165 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #166 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #167 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #168 failed after 183.14 seconds, 2485562 nodes. Using sample submission solution instead.\n",
      "Puzzle #169 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #170 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #171 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #172 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #173 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #174 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #175 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #176 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #177 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #178 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #179 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #180 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #181 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #182 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #183 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #184 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #185 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #186 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #187 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #188 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #189 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #190 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #191 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #192 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #193 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #194 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #195 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #196 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #197 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #198 failed after 183.22 seconds, 2448932 nodes. Using sample submission solution instead.\n",
      "Puzzle #199 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #200 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #201 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #202 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #203 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #204 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #205 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #206 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #207 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #208 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #209 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #210 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #211 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #212 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #213 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #214 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #215 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #216 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #217 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #218 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #219 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #220 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #221 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #222 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #223 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #224 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #225 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #226 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #227 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #228 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #229 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #230 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #231 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #232 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #233 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #234 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #235 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #236 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #237 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #238 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #239 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #240 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #241 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #242 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #243 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #244 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #245 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #246 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #247 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #248 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #249 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #250 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #251 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #252 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #253 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #254 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #255 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #256 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #257 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #258 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #259 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #260 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #261 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #262 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #263 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #264 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #265 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #266 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #267 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #268 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #269 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #270 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #271 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #272 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #273 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #274 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #275 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #276 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #277 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #278 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #279 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #280 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #281 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #282 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #283 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #284 solved in 7 moves. 0.01 seconds, 571 nodes. ['-r', '-l', 'r', '-l', '-r', '-l', '-l']\n",
      "Puzzle #285 solved in 3 moves. 0.00 seconds, 11 nodes. ['-r', '-l', '-l']\n",
      "Puzzle #286 solved in 5 moves. 0.00 seconds, 198 nodes. ['-r', 'l', 'r', 'l', 'l']\n",
      "Puzzle #287 solved in 2 moves. 0.00 seconds, 8 nodes. ['-r', 'l']\n",
      "Puzzle #288 solved in 6 moves. 0.01 seconds, 763 nodes. ['-l', 'r', '-l', '-l', '-r', '-l']\n",
      "Puzzle #289 solved in 9 moves. 0.02 seconds, 1265 nodes. ['-l', 'r', 'r', 'l', 'l', '-r', '-l', '-l', 'r']\n",
      "Puzzle #290 solved in 10 moves. 0.08 seconds, 3886 nodes. ['-l', '-l', '-l', 'r', '-l', '-l', 'r', 'l', 'r', 'l']\n",
      "Puzzle #291 solved in 5 moves. 0.00 seconds, 143 nodes. ['r', 'l', '-r', 'l', 'l']\n",
      "Puzzle #292 solved in 8 moves. 0.03 seconds, 1508 nodes. ['-l', 'r', 'l', 'l', 'l', 'r', 'l', 'l']\n",
      "Puzzle #293 solved in 8 moves. 0.02 seconds, 1308 nodes. ['-l', '-r', '-l', '-r', 'l', 'l', 'r', 'l']\n",
      "Puzzle #294 solved in 10 moves. 0.06 seconds, 3183 nodes. ['r', 'l', '-r', '-l', 'r', 'l', 'l', 'r', '-l', '-l']\n",
      "Puzzle #295 solved in 9 moves. 0.05 seconds, 2547 nodes. ['-r', '-r', 'l', 'r', 'l', 'r', 'l', 'r', 'l']\n",
      "Puzzle #296 solved in 10 moves. 0.04 seconds, 2111 nodes. ['r', '-l', '-r', '-l', 'r', '-l', 'r', '-l', '-l', '-r']\n",
      "Puzzle #297 solved in 9 moves. 0.03 seconds, 1193 nodes. ['-l', '-l', '-r', 'l', 'l', '-r', '-l', '-l', 'r']\n",
      "Puzzle #298 solved in 7 moves. 0.02 seconds, 1028 nodes. ['-l', '-l', '-r', 'l', 'r', 'l', '-r']\n",
      "Puzzle #299 solved in 8 moves. 0.05 seconds, 2567 nodes. ['-l', '-l', '-l', '-r', '-l', 'r', '-l', '-r']\n",
      "Puzzle #300 solved in 12 moves. 0.11 seconds, 5365 nodes. ['l', '-r', '-l', '-l', '-l', '-r', 'l', 'r', '-l', '-l', '-l', '-r']\n",
      "Puzzle #301 solved in 5 moves. 0.00 seconds, 47 nodes. ['r', 'l', 'r', '-l', '-l']\n",
      "Puzzle #302 solved in 9 moves. 0.04 seconds, 1996 nodes. ['-r', 'l', 'r', 'l', '-r', 'l', 'r', '-l', '-l']\n",
      "Puzzle #303 solved in 11 moves. 0.11 seconds, 5331 nodes. ['-l', 'r', '-l', '-l', '-l', 'r', 'l', 'r', 'l', 'r', 'l']\n",
      "Puzzle #304 solved in 10 moves. 0.09 seconds, 4525 nodes. ['l', 'r', 'r', 'r', '-l', 'r', 'r', 'l', 'r', 'l']\n",
      "Puzzle #305 solved in 9 moves. 0.04 seconds, 2088 nodes. ['r', '-l', '-l', 'r', '-l', '-l', '-r', '-l', '-l']\n",
      "Puzzle #306 solved in 11 moves. 0.05 seconds, 2480 nodes. ['-l', '-l', '-r', '-l', '-l', 'r', 'l', 'l', '-r', '-r', '-r']\n",
      "Puzzle #307 solved in 11 moves. 0.06 seconds, 2921 nodes. ['-l', '-r', '-l', '-r', '-r', '-l', '-l', '-l', '-l', '-r', '-r']\n",
      "Puzzle #308 solved in 8 moves. 0.02 seconds, 713 nodes. ['r', 'l', '-r', 'l', 'l', '-r', 'l', 'r']\n",
      "Puzzle #309 solved in 7 moves. 0.00 seconds, 41 nodes. ['-r', '-l', '-r', '-l', '-l', 'r', '-l']\n",
      "Puzzle #310 solved in 4 moves. 0.00 seconds, 20 nodes. ['-r', 'l', '-r', '-r']\n",
      "Puzzle #311 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #312 solved in 12 moves. 0.08 seconds, 4041 nodes. ['l', 'r', '-l', 'r', '-l', '-l', '-l', 'r', 'r', 'r', 'l', 'l']\n",
      "Puzzle #313 solved in 13 moves. 0.13 seconds, 6384 nodes. ['-l', '-r', '-l', '-r', '-r', '-l', '-r', '-l', '-r', 'l', '-r', '-r', '-r']\n",
      "Puzzle #314 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #315 solved in 12 moves. 0.06 seconds, 3242 nodes. ['-l', '-r', '-r', '-l', 'r', '-l', '-r', '-r', 'l', '-r', '-r', '-r']\n",
      "Puzzle #316 solved in 8 moves. 0.02 seconds, 1052 nodes. ['r', 'l', 'r', 'l', '-r', '-r', '-l', '-l']\n",
      "Puzzle #317 solved in 8 moves. 0.02 seconds, 798 nodes. ['r', 'l', '-r', '-l', '-r', '-r', '-l', '-l']\n",
      "Puzzle #318 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #319 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #320 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #321 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #322 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #323 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #324 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #325 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #326 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #327 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #328 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #329 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #330 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #331 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #332 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #333 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #334 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #335 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #336 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #337 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #338 failed after 183.85 seconds, 5744958 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #339 failed after 184.30 seconds, 6008670 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #340 failed after 186.69 seconds, 5895445 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #341 failed after 184.12 seconds, 6043369 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #342 failed after 187.40 seconds, 5996851 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #343 failed after 183.81 seconds, 6083949 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #344 failed after 183.91 seconds, 6149067 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #345 failed after 183.84 seconds, 5956296 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #346 failed after 186.77 seconds, 6027092 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #347 failed after 184.04 seconds, 5886978 nodes. Using sample submission solution instead.\n",
      "Puzzle #348 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #349 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #350 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #351 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #352 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #353 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #354 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #355 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #356 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #357 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #358 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #359 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Timed out.\n",
      "Puzzle #360 failed after 184.02 seconds, 5853963 nodes. Using sample submission solution instead.\n",
      "Puzzle #361 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #362 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #363 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #364 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #365 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #366 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #367 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #368 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #369 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #370 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #371 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #372 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #373 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #374 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #375 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #376 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #377 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #378 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #379 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #380 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #381 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #382 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #383 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #384 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #385 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #386 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #387 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #388 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #389 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #390 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #391 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #392 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #393 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #394 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #395 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #396 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n",
      "Puzzle #397 failed after 0.00 seconds, 0 nodes. Using sample submission solution instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>moves</th>\n",
       "      <th>move_total</th>\n",
       "      <th>ss_moves</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>r1.-f1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>f1.-d0.-r0.f1.r0.f0.d1.-r0.-d0.f0.f0.d0.-f0</td>\n",
       "      <td>13</td>\n",
       "      <td>227</td>\n",
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       "      <td>1.033380</td>\n",
       "      <td>38359</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-f1.f0.-r1.-r1.-f0.-r0.-d0.-f0.d0.f0.f0.-d0.-f...</td>\n",
       "      <td>14</td>\n",
       "      <td>227</td>\n",
       "      <td>True</td>\n",
       "      <td>2.591525</td>\n",
       "      <td>92753</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-f0.-d0.-f1.r0.-d1.r0.f1.-r1.-d1.r1.-f0.r1.f0.-d1</td>\n",
       "      <td>14</td>\n",
       "      <td>341</td>\n",
       "      <td>True</td>\n",
       "      <td>5.024981</td>\n",
       "      <td>176509</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>r0.-f0.r1.-f1.d0.r0.r0.-f0.-r0.f0.r0.-f0.-r0.f0</td>\n",
       "      <td>14</td>\n",
       "      <td>252</td>\n",
       "      <td>True</td>\n",
       "      <td>2.799824</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>393</td>\n",
       "      <td>f19.f21.-f39.f20.f2.-f5.f7.-r3.f55.-f12.f65.-f...</td>\n",
       "      <td>30596</td>\n",
       "      <td>129256</td>\n",
       "      <td>False</td>\n",
       "      <td>0.001504</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>394</td>\n",
       "      <td>-f31.-f22.f16.-f17.-f13.-f24.-f14.f2.f21.f44.f...</td>\n",
       "      <td>28971</td>\n",
       "      <td>122720</td>\n",
       "      <td>False</td>\n",
       "      <td>0.001608</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>395</td>\n",
       "      <td>-r0.-f42.-f8.f16.-f49.f14.-f1.f56.f26.f35.f62....</td>\n",
       "      <td>28876</td>\n",
       "      <td>122608</td>\n",
       "      <td>False</td>\n",
       "      <td>0.001503</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>396</td>\n",
       "      <td>f25.-f29.f46.f49.-f8.f27.f26.-f20.f2.-f20.f6.f...</td>\n",
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       "    <tr>\n",
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       "      <td>397</td>\n",
       "      <td>-r1.f23.-f24.-r2.f30.f30.f38.-f19.f24.f19.f18....</td>\n",
       "      <td>21111</td>\n",
       "      <td>88693</td>\n",
       "      <td>False</td>\n",
       "      <td>0.001431</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                                              moves  move_total  \\\n",
       "0      0                                             r1.-f1           2   \n",
       "1      1        f1.-d0.-r0.f1.r0.f0.d1.-r0.-d0.f0.f0.d0.-f0          13   \n",
       "2      2  -f1.f0.-r1.-r1.-f0.-r0.-d0.-f0.d0.f0.f0.-d0.-f...          14   \n",
       "3      3  -f0.-d0.-f1.r0.-d1.r0.f1.-r1.-d1.r1.-f0.r1.f0.-d1          14   \n",
       "4      4    r0.-f0.r1.-f1.d0.r0.r0.-f0.-r0.f0.r0.-f0.-r0.f0          14   \n",
       "..   ...                                                ...         ...   \n",
       "393  393  f19.f21.-f39.f20.f2.-f5.f7.-r3.f55.-f12.f65.-f...       30596   \n",
       "394  394  -f31.-f22.f16.-f17.-f13.-f24.-f14.f2.f21.f44.f...       28971   \n",
       "395  395  -r0.-f42.-f8.f16.-f49.f14.-f1.f56.f26.f35.f62....       28876   \n",
       "396  396  f25.-f29.f46.f49.-f8.f27.f26.-f20.f2.-f20.f6.f...       26402   \n",
       "397  397  -r1.f23.-f24.-r2.f30.f30.f38.-f19.f24.f19.f18....       21111   \n",
       "\n",
       "     ss_moves  self_solved  time_taken  node_counter  \n",
       "0           6         True    0.002493            24  \n",
       "1         227         True    1.033380         38359  \n",
       "2         227         True    2.591525         92753  \n",
       "3         341         True    5.024981        176509  \n",
       "4         252         True    2.799824        106131  \n",
       "..        ...          ...         ...           ...  \n",
       "393    129256        False    0.001504             0  \n",
       "394    122720        False    0.001608             0  \n",
       "395    122608        False    0.001503             0  \n",
       "396    108876        False    0.001466             0  \n",
       "397     88693        False    0.001431             0  \n",
       "\n",
       "[398 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Puzzle solver automated\n",
    "def solve_puzzles(puzzles_df, puzzle_info_df):\n",
    "    solution_list = []\n",
    "    for puzzle_number in np.arange(len(puzzles_df)):\n",
    "        puzzle_start_time = time.time()\n",
    "        solution_dict = {'id': puzzle_number}\n",
    "        ss_moves = len(ss_df[ss_df['id'] == puzzle_number]['moves'].iloc[0])\n",
    "        solution_path = None\n",
    "        node_counter = 0\n",
    "        if ss_moves < 1500:\n",
    "            # Only try to solve the smaller puzzles, otherwise skip it to save time.\n",
    "            move_sequence = ss_df.moves_parsed.iloc[puzzle_number]\n",
    "            initial_state = puzzles_df.initial_state_parsed.iloc[puzzle_number]\n",
    "            final_state = puzzles_df.solution_state_parsed.iloc[puzzle_number]\n",
    "            puzzle_type = puzzles_df.puzzle_type.iloc[puzzle_number]\n",
    "            wildcards = puzzles_df.num_wildcards.iloc[puzzle_number]\n",
    "            move_dict = puzzle_info_df.loc[puzzle_info_df['puzzle_type'] == puzzle_type]['allowed_moves_dict'].iloc[0]\n",
    "\n",
    "            solution_path, node_counter = astar(move_dict, initial_state, final_state, wildcards, max_depth = ss_moves)\n",
    "        if solution_path is not None:\n",
    "            solution_moves = \".\".join(solution_path)\n",
    "            self_solved = True\n",
    "            print(f\"Puzzle #{puzzle_number} solved in {len(solution_path)} moves. {(time.time()-puzzle_start_time):.2f} seconds, {node_counter} nodes. {solution_path}\")\n",
    "        else:\n",
    "            # If no solution found, use the sample submission's result\n",
    "            solution_moves = ss_df[ss_df['id'] == puzzle_number]['moves'].iloc[0]\n",
    "            solution_path = ss_df[ss_df['id'] == puzzle_number]['moves_parsed'].iloc[0]\n",
    "            self_solved = False\n",
    "            print(f\"Puzzle #{puzzle_number} failed after {(time.time()-puzzle_start_time):.2f} seconds, {node_counter} nodes. Using sample submission solution instead.\")\n",
    "        solution_dict = {\n",
    "            'id': puzzle_number,\n",
    "            'moves': solution_moves,\n",
    "            'move_total': len(solution_path),\n",
    "            'ss_moves': ss_moves,\n",
    "            'self_solved': self_solved,\n",
    "            'time_taken': time.time()-puzzle_start_time,\n",
    "            'node_counter': node_counter,\n",
    "        }\n",
    "        solution_list.append(solution_dict)\n",
    "    solution_df = pd.DataFrame(solution_list)\n",
    "    return solution_df\n",
    "\n",
    "solution_df = solve_puzzles(puzzles_df, puzzle_info_df)\n",
    "solution_df.head(400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bcc38cc3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-27T17:33:11.635343Z",
     "iopub.status.busy": "2023-12-27T17:33:11.634972Z",
     "iopub.status.idle": "2023-12-27T17:33:11.643432Z",
     "shell.execute_reply": "2023-12-27T17:33:11.642305Z"
    },
    "papermill": {
     "duration": 0.045444,
     "end_time": "2023-12-27T17:33:11.646089",
     "exception": false,
     "start_time": "2023-12-27T17:33:11.600645",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total moves: 1204168\n",
      "Total sample solution moves: 4701608\n",
      "Improvement compared to sample solutions: 74.39%\n",
      "Total solved: 68\n",
      "Total time taken: 384.38 minutes\n"
     ]
    }
   ],
   "source": [
    "# Look over solution\n",
    "print(f\"Total moves: {solution_df['move_total'].sum()}\")\n",
    "print(f\"Total sample solution moves: {solution_df['ss_moves'].sum()}\")\n",
    "print(f\"Improvement compared to sample solutions: {100-100*solution_df['move_total'].sum()/solution_df['ss_moves'].sum():.2f}%\")\n",
    "print(f\"Total solved: {solution_df['self_solved'].sum()}\")\n",
    "print(f\"Total time taken: {solution_df['time_taken'].sum()/60:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fcb2890",
   "metadata": {
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     "duration": 0.032299,
     "end_time": "2023-12-27T17:33:11.710769",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "***\n",
    "# 📮 Final Submission\n",
    "Now we just need to convert this dict into a csv matching the sample submission and we're off!\n",
    "Thanks for following along, comment any questions you have and I'll try to answer!\n",
    "\n",
    "🎅 Merry christmas and happy new year! 🎅"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "675aa63c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-27T17:33:11.777746Z",
     "iopub.status.busy": "2023-12-27T17:33:11.777370Z",
     "iopub.status.idle": "2023-12-27T17:33:12.016939Z",
     "shell.execute_reply": "2023-12-27T17:33:12.015643Z"
    },
    "papermill": {
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     "exception": false,
     "start_time": "2023-12-27T17:33:11.743411",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      id                                              moves\n",
      "0      0                                             r1.-f1\n",
      "1      1        f1.-d0.-r0.f1.r0.f0.d1.-r0.-d0.f0.f0.d0.-f0\n",
      "2      2  -f1.f0.-r1.-r1.-f0.-r0.-d0.-f0.d0.f0.f0.-d0.-f...\n",
      "3      3  -f0.-d0.-f1.r0.-d1.r0.f1.-r1.-d1.r1.-f0.r1.f0.-d1\n",
      "4      4    r0.-f0.r1.-f1.d0.r0.r0.-f0.-r0.f0.r0.-f0.-r0.f0\n",
      "..   ...                                                ...\n",
      "393  393  f19.f21.-f39.f20.f2.-f5.f7.-r3.f55.-f12.f65.-f...\n",
      "394  394  -f31.-f22.f16.-f17.-f13.-f24.-f14.f2.f21.f44.f...\n",
      "395  395  -r0.-f42.-f8.f16.-f49.f14.-f1.f56.f26.f35.f62....\n",
      "396  396  f25.-f29.f46.f49.-f8.f27.f26.-f20.f2.-f20.f6.f...\n",
      "397  397  -r1.f23.-f24.-r2.f30.f30.f38.-f19.f24.f19.f18....\n",
      "\n",
      "[398 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# Compile into csv\n",
    "final_solution_df = solution_df[['id', 'moves']]\n",
    "final_solution_df.to_csv(\"submission.csv\", header=['id', 'moves'], index=False)\n",
    "print(final_solution_df)"
   ]
  }
 ],
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