183 lines
3.7 KiB
Plaintext
183 lines
3.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"#read lines from input and store each list of space separeted integers into left and right lists respectively.\n",
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"with open('input', 'r') as f:\n",
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" data=f.readlines()\n",
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"\n",
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"data = [line.strip() for line in data]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"421"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"horizontal = sum(row.count('XMAS') + row.count('SAMX') for row in data)\n",
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"horizontal"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"463"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"vertical = sum(\"\".join(col).count('XMAS') + \"\".join(col).count('SAMX') for col in zip(*data))\n",
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"vertical"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1640"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"def diagonals(matrix):\n",
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" np_matrix = np.array(matrix)\n",
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" n, m = np_matrix.shape\n",
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" for offset in range(-n + 1, m): # Diagonal offsets\n",
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" yield np_matrix.diagonal(offset)\n",
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" yield np.fliplr(np_matrix).diagonal(offset)\n",
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"\n",
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"matrix = np.array([list(line) for line in data])\n",
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"\n",
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"diagonal = sum(\"\".join(diag).count('XMAS') + \"\".join(diag).count('SAMX') for diag in diagonals(matrix))\n",
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"diagonal"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2524"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"horizontal + vertical + diagonal"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"pattern = np.array([\n",
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" [\"M\",\".\",\"M\"],\n",
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" [\".\",\"A\",\".\"],\n",
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" [\"S\",\".\",\"S\"]\n",
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"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"mask = pattern != '.'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1873"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from numpy.lib.stride_tricks import sliding_window_view\n",
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" \n",
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"windows = sliding_window_view(matrix,pattern.shape)\n",
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"count = 0\n",
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"for i in range(4):\n",
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" matches = (windows == np.rot90(pattern,k=i)) | ~mask \n",
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" count += np.sum(np.all(np.all(matches,axis=-1),axis=-1))\n",
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"\n",
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"count"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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