用整数随机填充 numpy 数组，以便将整数分组为更大的连续块

Question

我正在尝试在 Python 中创建基于代理的模型。对于环境，我一直在使用 MxN 大小的 numpy 数组。每个像素代表一块土地。我想为每块土地分配一个所有者，以便创建的较大块是连续的。理想情况下，我希望能够指定较大块的数量。

This is what I'm envisioning

我在尝试生成随机地图时出奇地困难。我已经能够拼凑出一个非常粗糙的解决方案，但它仍然存在一些重大缺陷。我想我会在接受这种命运之前征求其他人的意见。

Answer 1

忍不住试一试，所以这里尝试使用 scipy.ndimage.grey_dilation，速度似乎相当快。 grey_dilation 在下面的代码中使用 "structure elements" -- "growth kernels" 扩展领土。我没有任何关于他们对流程的控制权的经验，但你可以玩它们：

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))

示例输出：

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

用整数随机填充 numpy 数组，以便将整数分组为更大的连续块

Randomly populate numpy array with integers such that integers are grouped into larger contiguous blocks

python

arrays

numpy

agent-based-modeling