46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
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with open('day23-input', 'r') as file:
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data = [l.strip('\n') for l in file]
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import numpy as np
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import re
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numbers = [[int(s) for s in re.findall(r'-?\d+', d)] for d in data]
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arr = np.array(numbers, dtype=np.int64)
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def nanobots_in_range_of(index):
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delta_positions = arr[:,:3] - arr[index,:3]
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distances = np.abs(delta_positions).sum(axis=1)
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return (distances <= arr[index, 3]).sum()
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largest_rad_bot = arr[:,3].argmax()
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print(nanobots_in_range_of(largest_rad_bot)) # Part 1
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min_coords = arr[:,:3].min(axis=0)
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max_coords = arr[:,:3].max(axis=0)
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coords_range = max_coords - min_coords
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# This is an astronomical number of potential coordinates. We can evaluate them at a resolution of 200,000 with int32s for 11GiB
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def nanobots_ranging(x, y, z, downsample=1):
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delta_positions = (arr[:,:3]//downsample) - np.array((x,y,z))
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distances = np.abs(delta_positions).sum(axis=1)
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return (distances <= (arr[:,3]//downsample)).sum()
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def downsample_survey(start_coords, end_coords, ds_factor):
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points_ds = np.zeros(tuple(end_coords-start_coords), dtype=np.int32)
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for x in range(start_coords[0], end_coords[0]):
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for y in range(start_coords[1], end_coords[1]):
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for z in range(start_coords[2], end_coords[2]):
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points_ds[x-start_coords[0],y-start_coords[1],z-start_coords[2]] = nanobots_ranging(x, y, z, ds_factor)
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return points_ds
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ds_factor = 10000000
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mins_ds = min_coords//ds_factor
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maxs_ds = max_coords//ds_factor
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points_ds = downsample_survey(mins_ds, maxs_ds, ds_factor)
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hotspots = (np.argwhere(points_ds > points_ds.max()*0.9)+mins_ds)*ds_factor
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