Light Clean up
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Tobias Kessels committed
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@@ -1,164 +1,295 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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"cells": [
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{
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"data": {
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"text/plain": [
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"344"
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "xgruaa0X9m1U",
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"outputId": "374ce0d2-a69a-4e08-d15e-3c85b2710ad9"
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},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"344"
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]
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},
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"metadata": {},
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"execution_count": 2
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}
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],
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"source": [
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"# Load the larger test file and process it using the solution\n",
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"file_path = 'input'\n",
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"\n",
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"def process_large_file(file_path):\n",
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" with open(file_path, 'r') as file:\n",
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" lines = [line.rstrip('\\n') for line in file.readlines()]\n",
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"\n",
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" height = len(lines)\n",
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" width = len(lines[0]) if height > 0 else 0\n",
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"\n",
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" # Identify antennas and their frequencies\n",
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" freq_map = {}\n",
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" for y in range(height):\n",
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" for x in range(width):\n",
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" c = lines[y][x]\n",
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" if c != '.':\n",
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" if c not in freq_map:\n",
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" freq_map[c] = []\n",
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" freq_map[c].append((x, y))\n",
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"\n",
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" # A set to hold all unique antinode locations\n",
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" antinodes = set()\n",
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"\n",
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" # Candidate λ values based on derived equations\n",
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" lambdas = [2, -1, 1/3, 2/3]\n",
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"\n",
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" for freq, antennas in freq_map.items():\n",
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" n = len(antennas)\n",
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" if n < 2:\n",
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" continue\n",
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"\n",
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" for i in range(n):\n",
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" for j in range(i+1, n):\n",
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" x1, y1 = antennas[i]\n",
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" x2, y2 = antennas[j]\n",
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" dx = x2 - x1\n",
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" dy = y2 - y1\n",
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"\n",
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" for lam in lambdas:\n",
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" px = x1 + lam * dx\n",
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" py = y1 + lam * dy\n",
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"\n",
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" if abs(px - round(px)) < 1e-12 and abs(py - round(py)) < 1e-12:\n",
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" rx = round(px)\n",
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" ry = round(py)\n",
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"\n",
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" if 0 <= rx < width and 0 <= ry < height:\n",
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" antinodes.add((rx, ry))\n",
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"\n",
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" # Output the number of unique antinode locations\n",
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" return len(antinodes)\n",
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"\n",
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"process_large_file('input')\n"
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]
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},
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"execution_count": 1,
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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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"# Load the larger test file and process it using the solution\n",
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"file_path = 'input'\n",
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"\n",
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"def process_large_file(file_path):\n",
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" with open(file_path, 'r') as file:\n",
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" lines = [line.rstrip('\\n') for line in file.readlines()]\n",
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" \n",
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" height = len(lines)\n",
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" width = len(lines[0]) if height > 0 else 0\n",
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"\n",
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" # Identify antennas and their frequencies\n",
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" freq_map = {}\n",
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" for y in range(height):\n",
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" for x in range(width):\n",
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" c = lines[y][x]\n",
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" if c != '.':\n",
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" if c not in freq_map:\n",
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" freq_map[c] = []\n",
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" freq_map[c].append((x, y))\n",
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"\n",
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" # A set to hold all unique antinode locations\n",
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" antinodes = set()\n",
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"\n",
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" # Candidate λ values based on derived equations\n",
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" lambdas = [2, -1, 1/3, 2/3]\n",
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"\n",
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" for freq, antennas in freq_map.items():\n",
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" n = len(antennas)\n",
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" if n < 2:\n",
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" continue\n",
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"\n",
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" for i in range(n):\n",
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" for j in range(i+1, n):\n",
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" x1, y1 = antennas[i]\n",
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" x2, y2 = antennas[j]\n",
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" dx = x2 - x1\n",
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" dy = y2 - y1\n",
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"\n",
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" for lam in lambdas:\n",
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" px = x1 + lam * dx\n",
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" py = y1 + lam * dy\n",
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"\n",
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" if abs(px - round(px)) < 1e-12 and abs(py - round(py)) < 1e-12:\n",
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" rx = round(px)\n",
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" ry = round(py)\n",
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"\n",
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" if 0 <= rx < width and 0 <= ry < height:\n",
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" antinodes.add((rx, ry))\n",
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"\n",
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" # Output the number of unique antinode locations\n",
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" return len(antinodes)\n",
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"\n",
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"process_large_file(file_path)\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": 2,
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"metadata": {},
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"outputs": [
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},
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{
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"data": {
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"text/plain": [
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"1182"
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "RvPJM8km9m1W",
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"outputId": "77731738-0776-46da-e8e5-4faf9e334515"
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},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"1182"
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]
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},
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"metadata": {},
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"execution_count": 3
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}
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],
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"source": [
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"def process_part_two(file_path):\n",
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" with open(file_path, 'r') as file:\n",
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" lines = [line.rstrip('\\n') for line in file.readlines()]\n",
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"\n",
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" height = len(lines)\n",
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" width = len(lines[0]) if height > 0 else 0\n",
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"\n",
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" # Identify antennas and their frequencies\n",
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" freq_map = {}\n",
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" for y in range(height):\n",
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" for x in range(width):\n",
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" c = lines[y][x]\n",
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" if c != '.':\n",
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" if c not in freq_map:\n",
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" freq_map[c] = []\n",
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" freq_map[c].append((x, y))\n",
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"\n",
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" # A set to hold all unique antinode locations\n",
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" antinodes = set()\n",
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"\n",
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" # For each frequency group, consider all pairs of antennas\n",
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" for freq, antennas in freq_map.items():\n",
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" n = len(antennas)\n",
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" if n < 2:\n",
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" continue\n",
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"\n",
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" for i in range(n):\n",
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" for j in range(i + 1, n):\n",
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" x1, y1 = antennas[i]\n",
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" x2, y2 = antennas[j]\n",
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"\n",
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" # Calculate the collinearity condition\n",
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" for x in range(width):\n",
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" for y in range(height):\n",
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" if (x2 - x1) * (y - y1) == (y2 - y1) * (x - x1):\n",
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" antinodes.add((x, y))\n",
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"\n",
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" # Include the positions of all antennas as antinodes\n",
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" for freq, antennas in freq_map.items():\n",
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" for x, y in antennas:\n",
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" antinodes.add((x, y))\n",
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"\n",
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" # Return the number of unique antinode locations\n",
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" return len(antinodes)\n",
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"\n",
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"\n",
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"# Process the larger test file for part two\n",
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"process_part_two('input')\n"
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]
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},
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"execution_count": 2,
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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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"# prompt: visualize the map\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Load the larger test file and process it using the solution\n",
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"file_path = 'input'\n",
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"\n",
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"def process_large_file(file_path):\n",
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" with open(file_path, 'r') as file:\n",
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" lines = [line.rstrip('\\n') for line in file.readlines()]\n",
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"\n",
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" height = len(lines)\n",
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" width = len(lines[0]) if height > 0 else 0\n",
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"\n",
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" # Identify antennas and their frequencies\n",
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" freq_map = {}\n",
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" for y in range(height):\n",
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" for x in range(width):\n",
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" c = lines[y][x]\n",
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" if c != '.':\n",
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" if c not in freq_map:\n",
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" freq_map[c] = []\n",
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" freq_map[c].append((x, y))\n",
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"\n",
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" # A set to hold all unique antinode locations\n",
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" antinodes = set()\n",
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"\n",
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" # Candidate λ values based on derived equations\n",
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" lambdas = [2, -1, 1/3, 2/3]\n",
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"\n",
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" for freq, antennas in freq_map.items():\n",
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" n = len(antennas)\n",
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" if n < 2:\n",
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" continue\n",
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"\n",
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" for i in range(n):\n",
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" for j in range(i+1, n):\n",
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" x1, y1 = antennas[i]\n",
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" x2, y2 = antennas[j]\n",
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" dx = x2 - x1\n",
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" dy = y2 - y1\n",
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"\n",
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" for lam in lambdas:\n",
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" px = x1 + lam * dx\n",
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" py = y1 + lam * dy\n",
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"\n",
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" if abs(px - round(px)) < 1e-12 and abs(py - round(py)) < 1e-12:\n",
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" rx = round(px)\n",
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" ry = round(py)\n",
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"\n",
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" if 0 <= rx < width and 0 <= ry < height:\n",
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" antinodes.add((rx, ry))\n",
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" # The line below was previously commented out and caused the error\n",
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" # because the variable 'antinodes' was not defined.\n",
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" # It has been uncommented and restored to its original functionality.\n",
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" return antinodes, width, height, lines # Return antinodes and dimensions\n",
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"\n",
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"\n",
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"def visualize_map(antinodes, width, height, lines):\n",
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" # Create a 2D array to represent the map\n",
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" map_array = [['.' for _ in range(width)] for _ in range(height)]\n",
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"\n",
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" # Place antennas on the map\n",
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" for y in range(height):\n",
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" for x in range(width):\n",
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" map_array[y][x] = lines[y][x]\n",
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"\n",
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" # Mark antinodes on the map\n",
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" for x, y in antinodes:\n",
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" map_array[y][x] = '#'\n",
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"\n",
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" # Display the map using matplotlib\n",
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" plt.figure(figsize=(10, 10)) # Adjust figure size as needed\n",
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" plt.imshow([[ord(c) for c in row] for row in map_array], cmap='viridis')\n",
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" plt.colorbar()\n",
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" plt.show()\n",
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"\n",
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"\n",
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"# Call the function and get the results\n",
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"antinodes, width, height, lines = process_large_file('testinput')\n",
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"visualize_map(antinodes, width, height, lines)"
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],
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 821
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},
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"id": "TbsEuRwx-giw",
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"outputId": "2b2f61ab-c2b5-40e5-bddc-2c191f6529aa"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"<Figure size 1000x1000 with 2 Axes>"
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],
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||||
"image/png": "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 truncated
|
||||
},
|
||||
"metadata": {}
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [],
|
||||
"metadata": {
|
||||
"id": "yqLLsSep_F_j"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
}
|
||||
],
|
||||
"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": "3.12.8"
|
||||
},
|
||||
"colab": {
|
||||
"provenance": []
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def process_part_two(file_path):\n",
|
||||
" with open(file_path, 'r') as file:\n",
|
||||
" lines = [line.rstrip('\\n') for line in file.readlines()]\n",
|
||||
"\n",
|
||||
" height = len(lines)\n",
|
||||
" width = len(lines[0]) if height > 0 else 0\n",
|
||||
"\n",
|
||||
" # Identify antennas and their frequencies\n",
|
||||
" freq_map = {}\n",
|
||||
" for y in range(height):\n",
|
||||
" for x in range(width):\n",
|
||||
" c = lines[y][x]\n",
|
||||
" if c != '.':\n",
|
||||
" if c not in freq_map:\n",
|
||||
" freq_map[c] = []\n",
|
||||
" freq_map[c].append((x, y))\n",
|
||||
"\n",
|
||||
" # A set to hold all unique antinode locations\n",
|
||||
" antinodes = set()\n",
|
||||
"\n",
|
||||
" # For each frequency group, consider all pairs of antennas\n",
|
||||
" for freq, antennas in freq_map.items():\n",
|
||||
" n = len(antennas)\n",
|
||||
" if n < 2:\n",
|
||||
" continue\n",
|
||||
"\n",
|
||||
" for i in range(n):\n",
|
||||
" for j in range(i + 1, n):\n",
|
||||
" x1, y1 = antennas[i]\n",
|
||||
" x2, y2 = antennas[j]\n",
|
||||
"\n",
|
||||
" # Calculate the collinearity condition\n",
|
||||
" for x in range(width):\n",
|
||||
" for y in range(height):\n",
|
||||
" if (x2 - x1) * (y - y1) == (y2 - y1) * (x - x1):\n",
|
||||
" antinodes.add((x, y))\n",
|
||||
"\n",
|
||||
" # Include the positions of all antennas as antinodes\n",
|
||||
" for freq, antennas in freq_map.items():\n",
|
||||
" for x, y in antennas:\n",
|
||||
" antinodes.add((x, y))\n",
|
||||
"\n",
|
||||
" # Return the number of unique antinode locations\n",
|
||||
" return len(antinodes)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Process the larger test file for part two\n",
|
||||
"process_part_two('input')\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.12.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
Reference in new issue
Block a user