229 lines
8.1 KiB
Python
229 lines
8.1 KiB
Python
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import math
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import numpy as np
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import sys
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np.set_printoptions(threshold=sys.maxsize)
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def decode(x, N):
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index = 0
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output = np.zeros((N))
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while x > 0 and index < N:
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output[index] = x & 0b1
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x >>= 1
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index += 1
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return output
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def hamming_distance(a, b):
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return np.sum(np.logical_xor(a, b))
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def xor(x, bits):
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return np.sum(x[:bits]) % 2
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def compute_pseudopascal(N):
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dist = np.zeros((N, N))
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for j in range(0, N):
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dist[0][j] = math.comb(N - 1, j)
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dist[-1][j] = math.comb(N, j + 1) * (1 - (j % 2))
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for i in range(1, N):
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for j in range(0, i + 1):
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dist[i][j] = math.comb(i + 1, j + 1) * (1 - (j % 2))
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for k in range(i + 1, N):
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for j in reversed(range(0, k)):
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dist[i][j+1] = dist[i][j] + dist[i][j+1]
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return dist
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def compute_pyramids(N):
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num_orders = max(int(N / 2), 1)
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pyramids = np.zeros((num_orders, N, N)).astype(np.int32)
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# 1st order can be filled in as multiplication and forms the base case
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for i in range(0, N):
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for j in range(0, i + 1):
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pyramids[0][i][j] = (i - j + 1) * (j + 1)
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for order in range(1, num_orders):
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offset = order * 2
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# fill in the LHS and diagonal
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for i in range(0, N - offset):
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value = math.comb(2 * (order + 1) + i - 1, i)
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pyramids[order][i + offset][0] = value
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# mirror
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pyramids[order][i + offset][i + offset] = value
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# accumulate along the diagonals
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for i in range(1, N):
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value = pyramids[order][i][0]
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acc = value
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for j in range(1, N - i):
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value += acc
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pyramids[order][i + j][j] = value
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acc += pyramids[order - 1][i + j - 1][j - 1]
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return pyramids
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# 2
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# 4, 4,
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# 6, 8, 6
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# 8, 12, 12, 8
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# 10, 16, 18, 16, 10
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# 12, 20, 24, 24, 20, 12
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# 14, 24, 30, 32, 30, 24, 14
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# 16, 28, 36, 40, 40, 36, 28, 16
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# 1
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# 2, 2
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# 3, 4, 3
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# 4, 6, 6, 4
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# 5, 8, 9, 8, 5
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# 6, 10, 12, 12, 10, 6
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# 7, 12, 15, 16, 15, 12, 7
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# 6, 0, 6
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# 24, 12, 12, 24
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# 60, 48, 36, 48, 60
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# 120, 120, 96, 96, 120, 120
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# 210, 240, 210, 192, 210, 240, 210
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# 336, 420, 396, 360, 360, 396, 420, 336
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# 504, 672, 672, 624, 600, 624, 672, 672, 504
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# 1, 0, 1
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# 4, 2, 2, 4
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# 10, 8, 6, 8, 10
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# 20, 20, 16, 16, 20, 20
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# 35, 40, 35, 32, 35, 40, 35
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# 56, 70, 66, 60, 60, 66, 70, 56
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# 84, 112, 112, 104, 100, 104, 112, 112, 84
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#
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# 20, 0, 20, 0, 20,
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# 120, 40, 80, 80, 40, 120
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# 420, 240, 260, 320, 260, 240, 420
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# 1120, 840, 760, 880, 880, 760, 840, 1120
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# 1, 0, 1, 0, 1
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# 6, 2, 4, 4, 2, 6
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# 21, 12, 13, 16, 13, 12, 21
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# 56, 42, 38, 44, 44, 38, 42, 56
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# 70, 0, 70, 0, 70, 0, 70
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# 560, 140, 420, 280, 280, 420, 140, 560
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# 252, 0, 252, 0, 252, 0, 252, 0, 252
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# 2520, 504, 2016, 1008, 1512, 1512, 1008, 2016, 504, 2520
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# 1, 2, 3, 4,
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# 1, 3, 6, 10
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# 1, 4, 10, 20
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# 1, 5, 15, 35
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# 1, 6,
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# 1, 2, 1
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# 1, 3, 3, 1
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# 1, 4, 6, 4, 1
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# 1, 5, 10, 10, 5, 1
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# 1, 6, 15, 20, 15, 6, 1
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# 2, 6, 12, 20, 30, 42, 56
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# 6, 30, 90, 210, 420
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# 20, 140, 560,
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# 70
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# 1, 3, 6, 10, 15, 21, 28
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# 1, 5, 15, 35
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def main():
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last_incoherent_distances = None
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last_incoherent_bands = None
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last_incoherent_sub_bands = None
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for N in range(4, 5):
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# print(compute_pseudopascal(10))
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# print(compute_pyramids(10))
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points = []
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for i in range(0, 2 ** N):
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points.append(decode(i, N))
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bands = [[[] for _ in range(0, N + 1)] for _ in range(0, len(points))]
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for i in range(0, len(points)):
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a = points[i]
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for j in range(0, len(points)):
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if i == j:
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continue
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b = points[j]
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distance = hamming_distance(a, b)
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bands[i][distance].append(b)
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# for t in range(0, len(points)):
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for t in range(0, 1):
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incoherent_distances = np.zeros((N + 1, N + 1))
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incoherent_bands = np.zeros((N + 1, N + 1, N + 1)).astype(np.int32)
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incoherent_sub_bands = np.zeros((N + 1, N + 1, N + 1, N + 1)).astype(np.int32)
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for k in range(1, N + 1):
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# print(k, '================================')
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x_a = points[t]
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y_a = xor(x_a, k)
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total_bands = np.zeros((N + 1, N + 1)).astype(np.int32)
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for distance in range(0, N + 1):
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# print('distance', distance)
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band = bands[t][distance]
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for x_b in band:
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y_b = xor(x_b, k)
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if y_a != y_b:
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incoherent_distances[k][distance] += 1
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if len(band) < 2:
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continue
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for band_origin in range(0, len(band)):
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x_p = band[band_origin]
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y_p = xor(x_p, k)
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sub_bands = [[] for _ in range(0, N + 1)]
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for i in range(0, len(band)):
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if i == band_origin:
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continue
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x_q = band[i]
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y_q = xor(x_q, k)
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band_distance = hamming_distance(x_p, x_q)
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total_bands[distance][band_distance] += 1
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if y_p != y_q:
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incoherent_bands[k][distance][band_distance] += 1
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sub_bands[band_distance].append(x_q)
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# incoherent_sub_bands = np.zeros((N + 1, N + 1)).astype(np.int32)
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# total_sub_bands = np.zeros((N + 1, N + 1)).astype(np.int32)
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for band_distance in range(0, N + 1):
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sub_band = sub_bands[band_distance]
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if len(sub_band) < 2:
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continue
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for sub_band_origin in range(0, len(sub_band)):
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x_u = sub_band[sub_band_origin]
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y_u = xor(x_u, k)
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for i in range(0, len(sub_band)):
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if i == sub_band_origin:
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continue
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x_v = sub_band[i]
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y_v = xor(x_v, k)
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sub_band_distance = hamming_distance(x_v, x_u)
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# total_sub_bands[band_distance][sub_band_distance] += 1
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if y_u != y_v:
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incoherent_sub_bands[k][distance][band_distance][sub_band_distance] += 1
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# print(incoherent_sub_bands)
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# print(total_sub_bands)
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# print('==========================')
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if last_incoherent_sub_bands is not None:
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for distance in range(1, int(N / 2) + 1):
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for band_distance in range(0, N + 1):
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for sub_band_distance in range (0, N + 1):
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if band_distance >= N or sub_band_distance >= N or last_incoherent_sub_bands[1][distance][band_distance][sub_band_distance] == 0:
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value = incoherent_sub_bands[1][distance][band_distance][sub_band_distance]
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if value > 0:
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print(N, value, (distance, band_distance, sub_band_distance))
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last_incoherent_distances = incoherent_distances
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last_incoherent_bands = incoherent_bands
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last_incoherent_sub_bands = incoherent_sub_bands
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# print(bands)
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if __name__ == "__main__":
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main()
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