本文介绍了在Python中使用重整形来重整数组的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我有一个如下所示的数组:
I have an array that looks like below:
array([[0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1],
[2, 2, 2, 2, 2, 2, 2, 2],
[3, 3, 3, 3, 3, 3, 3, 3],
[4, 4, 4, 4, 4, 4, 4, 4],
[5, 5, 5, 5, 5, 5, 5, 5],
[6, 6, 6, 6, 6, 6, 6, 6],
[7, 7, 7, 7, 7, 7, 7, 7]])
如何使用重塑将其分成4个卡盘,使其看起来像
How can I use reshape to divide it into 4 chucks, such that it looks like
array([[[0, 0, 0, 0],
[1, 1, 1, 1],
[2, 2, 2, 2],
[3, 3, 3, 3]],
[[0, 0, 0, 0],
[1, 1, 1, 1],
[2, 2, 2, 2],
[3, 3, 3, 3]],
[[4, 4, 4, 4],
[5, 5, 5, 5],
[6, 6, 6, 6],
[7, 7, 7, 7]],
[[4, 4, 4, 4],
[5, 5, 5, 5],
[6, 6, 6, 6],
[7, 7, 7, 7]]])
我在reshape(m,n,l)中尝试了m,n,l的不同整数组合,但都无效.
I tried different integer combinations of m, n, l in reshape(m,n,l), but none works.
推荐答案
对不起,我没有意识到这是3维结果,而不是4维结果.要获得3-d,必须再次重塑形状.额外的整形 将复制数据.
Sorry, I didn't realize it was a 3-d result, not a 4-d result. To get the 3-d one, you would have to reshape once more. And that extra reshape will copy the data.
您不能,也需要转置:
In [1]: a = np.arange(8)[:,None].repeat(8,axis=1)
In [2]: a
Out[2]:
array([[0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1],
[2, 2, 2, 2, 2, 2, 2, 2],
[3, 3, 3, 3, 3, 3, 3, 3],
[4, 4, 4, 4, 4, 4, 4, 4],
[5, 5, 5, 5, 5, 5, 5, 5],
[6, 6, 6, 6, 6, 6, 6, 6],
[7, 7, 7, 7, 7, 7, 7, 7]])
In [3]: b = a.reshape(2,4,2,4)
In [4]: b
Out[4]:
array([[[[0, 0, 0, 0],
[0, 0, 0, 0]],
...
[[7, 7, 7, 7],
[7, 7, 7, 7]]]])
In [5]: b.transpose(0,2,1,3)
Out[5]:
array([[[[0, 0, 0, 0],
[1, 1, 1, 1],
[2, 2, 2, 2],
[3, 3, 3, 3]],
[[0, 0, 0, 0],
[1, 1, 1, 1],
[2, 2, 2, 2],
[3, 3, 3, 3]]],
[[[4, 4, 4, 4],
[5, 5, 5, 5],
[6, 6, 6, 6],
[7, 7, 7, 7]],
[[4, 4, 4, 4],
[5, 5, 5, 5],
[6, 6, 6, 6],
[7, 7, 7, 7]]]])
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