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Advent-of-Code-Aggregator/2021/Timpy/day_1/solution.ipynb
Timo De Waele 976e095be9 Fixed booboo
2021-12-01 12:58:00 +01:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Imports"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from time import time\n",
"\n",
"from numpy.lib.stride_tricks import sliding_window_view"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load input"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"#INPUT_FILE = 'test.txt'\n",
"INPUT_FILE = 'input.txt'"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" 4868,\n",
" 4873,\n",
" 4877,\n",
" 4881,\n",
" 4888,\n",
" 4891,\n",
" 4896,\n",
" 4911,\n",
" 4910,\n",
" 4911,\n",
" 4906,\n",
" 4913,\n",
" 4915,\n",
" 4918,\n",
" 4926,\n",
" 4933,\n",
" 4968,\n",
" 4969,\n",
" 4970,\n",
" 4974,\n",
" ...]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"input_list = [int(height) for height in open(INPUT_FILE, 'r')]\n",
"input_list"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 188, 192, 193, ..., 10397, 10406, 10415], dtype=uint64)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"input_list_np = np.loadtxt(INPUT_FILE, dtype='uint64')\n",
"input_list_np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 1"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOLUTION: 1754, TIME: 0.3273ms\n"
]
}
],
"source": [
"start_t = time()\n",
"solution = sum([x > y for x, y in zip(input_list[1:], input_list[:-1])])\n",
"end_t = time()\n",
"print(f'SOLUTION: {solution}, TIME: {round((end_t - start_t) * 1000, 4)}ms')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOLUTION: 1754, TIME: 0.1428ms\n"
]
}
],
"source": [
"start_t = time()\n",
"solution = np.greater(input_list_np[1:], input_list_np[:-1]).sum()\n",
"end_t = time()\n",
"print(f'SOLUTION: {solution}, TIME: {round((end_t - start_t) * 1000, 4)}ms')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 2"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"WINDOW_SIZE = 3"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOLUTION: 1789, TIME: 1.07ms\n"
]
}
],
"source": [
"start_t = time()\n",
"windows_sum = [sum(input_list[i:i + WINDOW_SIZE]) for i in range(len(input_list) - WINDOW_SIZE + 1)]\n",
"solution = sum([x > y for x, y in zip(windows_sum[1:], windows_sum[:-1])])\n",
"end_t = time()\n",
"print(f'SOLUTION: {solution}, TIME: {round((end_t - start_t) * 1000, 4)}ms')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SOLUTION: 1789, TIME: 0.5288ms\n"
]
}
],
"source": [
"start_t = time()\n",
"windows_sum = np.sum(sliding_window_view(input_list_np, WINDOW_SIZE), axis=1)\n",
"solution = np.greater(windows_sum[1:], windows_sum[:-1]).sum()\n",
"end_t = time()\n",
"print(f'SOLUTION: {solution}, TIME: {round((end_t - start_t) * 1000, 4)}ms')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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