정수가 더 큰 인접한 블록으로 그룹화되도록 numpy 배열에 정수를 무작위로 채 웁니다.

Question

저는 Python으로 에이전트 기반 모델을 만들려고합니다. 환경에 대한, 나는 MxN 크기의 배열을 사용하고있다. 각 픽셀은 토지 패치를 나타냅니다. 나는 창조 된 더 큰 블록이 연속적 이도록 토지의 각 패치를 소유자에게 할당하고 싶다. 이상적으로, 나는 더 큰 블록의 수를 지정할 수 있기를 원한다.

This is what I'm envisioning

것은 나는 무작위로 맵을 생성하려고 놀라 울 정도로 힘든 시간을 보내고 있습니다. 나는 정말 조잡한 해결책을 함께 해킹 할 수 있었지만 여전히 몇 가지 큰 결함이있다. 나는이 운명을 받아들이 기 전에 다른 사람들에게 아이디어를 구합니다.

Answer 1

샷을 제공하는 것을 거부 할 수 없으므로 여기에 scipy.ndimage.grey_dilation을 사용하는 시도가 상당히 빠르다. grey_dilation은 아래 코드에서 "구조 요소"- "성장 커널"을 사용하여 영역을 확장합니다. 나는 그들이 그 과정을 통해 제공 얼마나 제어에 관해서는 경험이 없지만 그들은 당신이 놀 수있는 일입니다

import numpy as np 
from scipy import ndimage 

growth_kernels = """ 
010 000 010 111 
111 111 010 111 
010 000 010 111 
""" 

growth_kernels = """ 
555555555 543212345 
444444444 543212345 
333333333 543212345 
222222222 543212345 
111101111 543202345 
222222222 543212345 
333333333 543212345 
444444444 543212345 
555555555 543212345 
""" 

def patches(shape, N, maxiter=100): 
    # load kernels 
    kernels = np.array([[[int(d) for d in s] for s in l.strip().split()] 
         for l in growth_kernels.split('\n') 
         if l.strip()], np.int) 
    nlev = np.max(kernels) + 1 
    # special case for binary kernels 
    if nlev == 2: 
     kernels = 2 - kernels 
     nlev = 3 
    kernels = -kernels.swapaxes(0, 1) * N 
    key, kex = kernels.shape[1:] 
    kernels[:, key//2, kex//2] = 0 
    # seed patches leave a gap between 0 and the first patch 
    out = np.zeros(shape, int) 
    out.ravel()[np.random.choice(out.size, N)] = np.arange((nlev-1)*N+1, nlev*N+1) 
    # shuffle labels after each iteration, so larger numbers do not get 
    # a systematic advantage 
    shuffle = np.arange((nlev+1)*N+1) 
    # also map negative labels to zero 
    shuffle[nlev*N+1:] = 0 
    shuffle_helper = shuffle[1:nlev*N+1].reshape(nlev, -1) 
    for j in range(maxiter): 
     # pick one of the kernels 
     k = np.random.randint(0, kernels.shape[0]) 
     # grow patches 
     out = ndimage.grey_dilation(
      out, kernels.shape[1:], structure=kernels[k], mode='constant') 
     # shuffle 
     shuffle_helper[...] = np.random.permutation(
      shuffle[(nlev-1)*N+1:nlev*N+1]) 
     out = shuffle[out] 
     if np.all(out): 
      break 
    return out % N 

res = patches((30, 80), 26) 
print(len(np.unique(res))) 
for line in res: 
    print(''.join(chr(j+65) for j in line)) 

샘플 출력 : 질문이 여기에 코드를 중심으로 suppsed된다

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WWWWWKKKKKKKKKKKKMMMLLLLLLLLLLJJJJJJJJJCCCCCCCCCCCCCSSSSSSSSSSSSSAAAAAAAAAAAAAAA 
WWWWWKKKKKKKKKKKKMMMLLLLLLLLLLJJJJJJJJJJJJJCCCCCCCCCSSSSSSSSSSSSSAAAAAAAAAAAAAAA 
WWWWWKKKKKKKKKKKKMMMLLLLLLLLLLLLLLJJJJJJJJJCCCCCCCCCSSSSSRRRRRRRRRAAAAAAAAAAAAAA 
WWWFFFFKKKKKKKKKKMMMLLLLLLLLLLLLLLJJJJJJJJJCCCCCCCCCSSSSSRRRRRRRRRAAAAAAAAAAAAAA 
WWWFFFFKKKKKMMMMMMMMMLLLLLLLLLLLLLJJJJJJJJJCCCCCCCCCSSSSSRRRRRRRRRAAAAAAAAAAAAAA 
WWWFFFFKKKKKMMMMMMMMMLLLLLLLLLLLLLJJJJJJJJJJJJJCSSSSSSSSSRRZZZZZZZZZZZGGGGGGGGGG 
WWWFFFFFFFFFMMMMMMMMTTTTTTLLLLLLLLJJJJJJJJJJJJJCSSSSSSSSSRRZZZZZZZZZZZGGGGGGGGGG 
WWWFFFFFFFFFMMMMMMMMTTTTTTLLLLLLLLJJJJJJJJJJJJJHSSSSSSSSSRRZZZZZZZZZZZGGGGGGGGGG 
FFFFFFNNNNNNMMMMMMMMTTTTTTLLLLLLLLJJJJJJJJJJJJJHSSSSSSSSSRRZZZZZZZZZZZGGGGGGGGGG 
FFFFFFNNNNNNMMMMMMMMTTTTTTLLLLLLLLJJJJJHHHHHHHHHSRRRRRRRRRRZZZZZZZZZZZGGGGGGGGGG 
FFFFFFNNNNNNMMMMMMMMTTTTTTLLLLLLLLLHHHHHHHHHHHHHSRRRRRRRRRRZZZZZZZZZZZGGGGGGGGGO 
FFFFFFNNNNNNMMMMMMMMTTTTTTTTTTTTHHHHHHHHHHHHHHHHDRRRRZZZZZZZZZZZZZZZZZGGGGGGGGGO 
NNNNNNNNNNNNMMMMMMMMTTTTTTTTTTTTHHHHHHHHDDDDDDDDDDRRRZZZZZZZZZZZZZZZZZGGGGGGGGGO 
NNNNNNNNNNNNNNNTTTTTTTTTTTTTTTTTHHHHHHHHDDDDDDDDDDUUUZZZZZZZZZZZZZZZZZGGGGGGGGGO 
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EEEEENNNNNNNNNNNTTTTTTTTTTTTTTTHHHHHHHHHDDDDDDDDDUUUUUUUUUUUUUUUUUZZZZGOOOOOOOOO 
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EEEEEEEEEEEENNNNTTTTTTTTTTTTTTTHHHHHHHHHDDDDDDDDDUUUUUUUUUUUUUUUUUXXXXGOOOOOOOOO 
EEEEEEEEEEEENNNNTTTTTTTTTTTTTTTHHDDDDDDDDDDDDDDDDUUUUUUUUUUUUUUUUUXXXXXOOOOOOOOO 
EEEEEEEEEEEENNNNVVVVVVVVVVVTTTTHHDDDDDDDDDDDDDDDDPPPPPBBUUUUUUUUUUXXXXXOOOOOOOOO 
EEEEEEEEEEEEVVVVVVVVVVVVVVVTTTTQQDDDDDDDDDDDDDDDDPPPPPBBUUUUUUUUUUXXXXXXXXXXXXXX 
EEEEEEEEEEEEVVVVVVVVVVVVVVVTTTTQQQQQQQQQQQQQQPPPPPPPPPBBUUUUUUUUUUXXXXXXXXXXXXXX 
EEEEEEEEEEEEVVVVVVVVVVVVVVVTTTTQQQQQQQQQQQQQQQQQPPPPPPBBUUUUUUUUUUXXXXXXXXXXXXXX 
EEEEEEEEEEVVVVVVVVVVVVVVVVVVVHHQQQQQQQQQQQQQQQQQPPPPPPBBUUUUUIIIIIIIIXXXXXXXXXXX 
EEEEEEEEEEVVVVVVVVVVVVVVVVVVVQQQQQQQQQQQQQQQQQQQPPPPPPBBBBBIIIIIIIIYYYYYXXXXXXXX 
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