{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read lines from input and store each list of space separeted integers into left and right lists respectively.\n",
    "with open('input', 'r') as f:\n",
    "    data=f.readlines()\n",
    "\n",
    "data = [line.strip() for line in data]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "421"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horizontal = sum(row.count('XMAS') + row.count('SAMX') for row in data)\n",
    "horizontal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "463"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vertical = sum(\"\".join(col).count('XMAS') + \"\".join(col).count('SAMX') for col in zip(*data))\n",
    "vertical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1640"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def diagonals(matrix):\n",
    "    np_matrix = np.array(matrix)\n",
    "    n, m = np_matrix.shape\n",
    "    for offset in range(-n + 1, m):  # Diagonal offsets\n",
    "        yield np_matrix.diagonal(offset)\n",
    "        yield np.fliplr(np_matrix).diagonal(offset)\n",
    "\n",
    "matrix = np.array([list(line) for line in data])\n",
    "\n",
    "diagonal = sum(\"\".join(diag).count('XMAS') + \"\".join(diag).count('SAMX') for diag in diagonals(matrix))\n",
    "diagonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2524"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horizontal + vertical + diagonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern = np.array([\n",
    "    [\"M\",\".\",\"M\"],\n",
    "    [\".\",\"A\",\".\"],\n",
    "    [\"S\",\".\",\"S\"]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = pattern != '.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1873"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy.lib.stride_tricks import sliding_window_view\n",
    "    \n",
    "windows = sliding_window_view(matrix,pattern.shape)\n",
    "count = 0\n",
    "for i in range(4):\n",
    "    matches = (windows == np.rot90(pattern,k=i)) | ~mask  \n",
    "    count += np.sum(np.all(np.all(matches,axis=-1),axis=-1))\n",
    "\n",
    "count"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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