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December 5, 2024 15:20
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| import sys | |
| import numpy as np | |
| import time | |
| import scipy.ndimage as ndimage | |
| searchlines = [] | |
| with open(sys.argv[1], "r") as f_in: | |
| searchlines = [[ord(c) for c in line.strip()] for line in f_in] | |
| srch_matrix = np.array(searchlines, dtype='u8') | |
| def count_xmas(arr: np.array, deg=0): | |
| arr = np.rot90(arr, k=deg/90) | |
| # arr = np.rot90(arr, angle=deg, order=0, prefilter=False, axes=[1,0]) | |
| needle = np.array([ord(c) for c in "XMAS"], dtype="u8") | |
| conv_arr = [4,3,2,1] | |
| convsum = np.convolve(needle, conv_arr)[3] | |
| conv = np.array([np.convolve(l, conv_arr) for l in arr]) | |
| xmas_diag = np.array([[ord("X"), 0,0,0], [0, ord("M"), 0,0], [0,0, ord("A"),0], [0,0,0,ord("S")]]) | |
| diag_conv_m = np.array([[1, 0 , 0, 0], [0, 11, 0,0], [0, 0, 101,0], [0,0,0,1003]]) | |
| diag_conv = ndimage.convolve(xmas_diag, diag_conv_m, mode="constant") | |
| print(diag_conv) | |
| def diagonal(m, n): | |
| return np.array([arr[m+i, n+i] for i in range(min(arr.shape[0]-m, arr.shape[1]-n))]) | |
| def print_as_char(arr): | |
| print("\n".join(["".join([chr(i) if i else ' ' for i in r]) for r in arr])) | |
| diagonals = [diagonal(0, j) for j in range(arr.shape[1])] + [diagonal(i, 0) for i in range(1, arr.shape[0])] | |
| diagonal_convs = [np.convolve(d, conv_arr) for d in diagonals] | |
| arr_diag_conv = ndimage.convolve(arr, diag_conv, mode="constant") | |
| print(arr_diag_conv) | |
| # print_as_char(diagonals) | |
| d_xmas = sum(np.count_nonzero(c == convsum) for c in diagonal_convs) | |
| # d_xmas = np.count_nonzero(arr_diag_conv == diag_conv_m[1,1]) | |
| # print(f"Rotation {deg}") | |
| # print_as_char(arr) | |
| # print("------") | |
| return np.count_nonzero(conv == convsum) + d_xmas #+ np.count_nonzero(diagonal_convs == convsum) | |
| def count_x_mas(arr, deg=0): | |
| arr = np.rot90(arr, k=deg/90) | |
| x_mas = [['M', '\0', 'M'], ['\0', 'A', '\0'], ['S', '\0', 'S']] | |
| xmask = np.array([[ord(i) for i in j] for j in x_mas]) | |
| conv_matrix = np.array([[1, 0 ,3], [0, 11, 0], [100, 0, 101]]) | |
| conv_match = ndimage.convolve(xmask, conv_matrix, mode='constant')[1,1] | |
| conv_res = ndimage.convolve(arr, conv_matrix, mode='constant') | |
| # print(conv_res) | |
| return np.count_nonzero(conv_res == conv_match) | |
| # Correct answers with day4.in, md54sum 5fcbd00d8ba246818c056d95ab2b5060: | |
| #2336 | |
| #1831 | |
| start = time.time() | |
| print(sum([count_xmas(srch_matrix, deg=d) for d in [0, 90, 180, 270]])) | |
| c1 = time.time() | |
| print(sum([count_x_mas(srch_matrix, deg=d) for d in [0, 90, 180, 270]])) | |
| c2 = time.time() | |
| print(f"p1: {c1-start}, p2: {c2-c1}") |
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