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4/4.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read lines from input and store each list of space separeted integers into left and right lists respectively.\n",
+    "with open('4/input', 'r') as f:\n",
+    "    data=f.readlines()\n",
+    "\n",
+    "data = [line.strip() for line in data]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "421"
+      ]
+     },
+     "execution_count": 88,
+     "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": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "463"
+      ]
+     },
+     "execution_count": 89,
+     "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": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1640"
+      ]
+     },
+     "execution_count": 90,
+     "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": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2524"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "horizontal + vertical + diagonal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pattern = np.array([\n",
+    "    [\"M\",\".\",\"M\"],\n",
+    "    [\".\",\"A\",\".\"],\n",
+    "    [\"S\",\".\",\"S\"]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True, False,  True],\n",
+       "       [False,  True, False],\n",
+       "       [ True, False,  True]])"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask = pattern != '.'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1873"
+      ]
+     },
+     "execution_count": 129,
+     "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"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}