Day_18_Settlers_of_The_North_Pole.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOGyFtu/UeY29KvDmuePYNK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/elichen/adventofcode/blob/main/Day_18_Settlers_of_The_North_Pole.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "qXwyrYYx9bbS"
      },
      "outputs": [],
      "source": [
        "data = \"\"\".#.#...|#.\n",
        ".....#|##|\n",
        ".|..|...#.\n",
        "..|#.....#\n",
        "#.#|||#|#|\n",
        "...#.||...\n",
        ".|....|...\n",
        "||...#|.#|\n",
        "|.||||..|.\n",
        "...#.|..|.\"\"\".split('\\n')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = [x.rstrip() for x in open(\"input.txt\").readlines()]"
      ],
      "metadata": {
        "id": "GISq9xM5_wlN"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def simulate_round(map_2d):\n",
        "    def count_adjacent_acres(x, y, acre_type):\n",
        "        count = 0\n",
        "        for dx in [-1, 0, 1]:\n",
        "            for dy in [-1, 0, 1]:\n",
        "                if dx == 0 and dy == 0:\n",
        "                    continue\n",
        "                if 0 <= x + dx < len(map_2d) and 0 <= y + dy < len(map_2d[0]):\n",
        "                    if map_2d[x + dx][y + dy] == acre_type:\n",
        "                        count += 1\n",
        "        return count\n",
        "\n",
        "    new_map = []\n",
        "    for i in range(len(map_2d)):\n",
        "        new_row = ''\n",
        "        for j in range(len(map_2d[i])):\n",
        "            acre = map_2d[i][j]\n",
        "            if acre == '.' and count_adjacent_acres(i, j, '|') >= 3:\n",
        "                new_row += '|'\n",
        "            elif acre == '|' and count_adjacent_acres(i, j, '#') >= 3:\n",
        "                new_row += '#'\n",
        "            elif acre == '#' and (count_adjacent_acres(i, j, '#') == 0 or count_adjacent_acres(i, j, '|') == 0):\n",
        "                new_row += '.'\n",
        "            else:\n",
        "                new_row += acre\n",
        "        new_map.append(new_row)\n",
        "    return new_map\n",
        "\n",
        "m = [row[:] for row in data]\n",
        "for _ in range(10):\n",
        "  m = simulate_round(m)\n",
        "  # for row in m:\n",
        "  #     print(row)\n",
        "  # print(\"\")"
      ],
      "metadata": {
        "id": "murzW_gL9gRn"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def count_acres(map_2d, acre_type):\n",
        "    return sum(row.count(acre_type) for row in map_2d)\n",
        "\n",
        "count_acres(m, '|') *  count_acres(m, '#')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVY6tYUa_jon",
        "outputId": "f44938aa-17bd-4919-aa05-9f121cce0063"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "606416"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "m = [row[:] for row in data]\n",
        "vs = []\n",
        "\n",
        "for _ in range(1000):\n",
        "  m = simulate_round(m)\n",
        "  r = count_acres(m, '|') *  count_acres(m, '#')\n",
        "  vs.append(r)\n",
        "\n",
        "plt.plot(vs)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "04ou7M77_zDQ",
        "outputId": "6eb64501-30bd-4327-cdd4-52aad813f346"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def find_indices(lst, value):\n",
        "    return [i for i, x in enumerate(lst) if x == value]\n",
        "\n",
        "find_indices(vs[-100:],215600)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1e_qcoGTAi9S",
        "outputId": "e8dddfee-d18a-4501-957f-b0bba396bd75"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[12, 40, 68, 96]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cycle = 28\n",
        "offset = 800 % cycle\n",
        "x = 1000000000-1\n",
        "vs[800 + (x%cycle)-(offset)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BEylrD41BJat",
        "outputId": "42496169-c211-4fc7-b596-4dc5c5339dc4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13250, 220748)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    }
  ]
}