{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aa9a2307-0fb5-4993-a0d9-35e6528a1c0f",
   "metadata": {},
   "source": [
    "# Puzzle 2 of AoC 2025\n",
    "Author: Victoria Ramírez López\n",
    "\n",
    "Date: Dec 4, 2025"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "051c8d59-04e3-4349-b029-32a76466583c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.signal import convolve2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "f40bd11f-5e02-4833-a0ea-17d6474f21c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read input file\n",
    "\n",
    "paper_roll_grid = open('paper_roll_grid.txt', mode ='r') # Open in 'read only' mode\n",
    "grid = paper_roll_grid.readlines()\n",
    "grid = [line.strip('\\n') for line in grid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "cd2ef290-5e35-4b12-b3f0-87018f2b30b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Part 1 answer:  1346\n",
      "Part 2 answer: 8493\n"
     ]
    }
   ],
   "source": [
    "# Find roll papers accessible by fork lift\n",
    "\n",
    "total_accessible_rolls = 1\n",
    "total_removed_rolls = 0\n",
    "\n",
    " # Turn grid to boolean matrix \n",
    "bool_grid = create_boolean_grid(grid)\n",
    "bool_grid_np = np.array(bool_grid) # Convert into numpy array\n",
    "\n",
    "# For part 1\n",
    "accessible_rolls = find_accessible_rolls(bool_grid_np)\n",
    "total_accessible_rolls = accessible_rolls.sum()\n",
    "print('Part 1 answer: ', total_accessible_rolls)\n",
    "\n",
    "# For part 2\n",
    "while total_accessible_rolls >= 1:\n",
    "    accessible_rolls = find_accessible_rolls(bool_grid_np)\n",
    "    total_accessible_rolls = accessible_rolls.sum()\n",
    "    bool_grid_np = remove_accessible_rolls(bool_grid_np,accessible_rolls)\n",
    "    total_removed_rolls = total_removed_rolls + total_accessible_rolls\n",
    "    \n",
    "print('Part 2 answer:', total_removed_rolls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f5966994-e969-4723-8c50-3a26e6bd424d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_accessible_rolls(bool_grid_np):\n",
    "    # This function finds the number of rolls that can be \n",
    "    # accessed by forklift\n",
    "    # input: grid = [[String]]\n",
    "    # output: sum_accessible_rolls = int\n",
    "\n",
    "    # Add padding\n",
    "    bool_grid_padded = np.pad(bool_grid_np, 1, mode='constant') \n",
    "\n",
    "    # Calculate how many rolls surround each roll\n",
    "    convolution = convolve2d(bool_grid_padded, \n",
    "                                    [[1,1,1],[1,0,1],[1,1,1]], mode='same')\n",
    "    convolution = convolution[1:-1,1:-1] # Remove padding\n",
    "\n",
    "    # Determine how many rolls are reachable\n",
    "    accessible_rolls = bool_grid_np & (convolution < 4)\n",
    "\n",
    "    return accessible_rolls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a62faf9d-2c86-474f-a403-7264129b6c42",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_accessible_rolls(bool_grid_np, accessible_rolls):\n",
    "    # This function removes the number of rolls that can be \n",
    "    # accessed by forklift\n",
    "    # input: accessible_rolls = numpy array\n",
    "    # output: updated_grid = numpy array\n",
    "    \n",
    "    updated_grid = np.logical_not(accessible_rolls) & bool_grid_np\n",
    "\n",
    "    return(updated_grid)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2b34b18d-6d8f-40fb-b619-502ec1b0a346",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_boolean_grid(grid):\n",
    "    # This function turns the original grid to a matrix where\n",
    "    # '@' is 1 and '.' is 0\n",
    "    # input: grid = [String]\n",
    "    # output: boolean_grid = [int]\n",
    "    \n",
    "    boolean_grid = []\n",
    "    \n",
    "    for row in grid:\n",
    "        boolean_row = [1 if x == '@' else 0 for x in row]\n",
    "        boolean_grid.append(boolean_row)\n",
    "\n",
    "    return boolean_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77ebe3d0-11bb-4553-ae88-666f704357c0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}