Python Numpy将子数组与4D数组合并，无法让swapax构建2D全局数组

Question

我有一个8x8的数组，分为2x2个块，所以我有16个子数组。这4个维度是（4,4,2,2）：第一个是块的行，第二个是块的列，第三个是子数组2x2的行索引，第四个是子数组2x2的列索引。你知道吗

全局数组的前2行是（2行8列）：

[3.28542331e+09 3.28542331e+09 0. 0. 0. 0. 0. 0]
[0. 0. 2.60113771e+10 2.60113771e+10 5.12629421e+10 5.12629421e+10 8.49990653e+10 8.49990653e+10]

我尝试从所有2x2块（总共16块）中获取8x8全局数组；我做到了：

arrayFullCross.swapaxes(0,2).reshape(8,8)

但这不管用。确实，第一行是正确的，但第二行不是。我得到的是：

reshape =  [[3.28542331e+09 3.28542331e+09 0.00000000e+00 0.00000000e+00
  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]
 [0.00000000e+00 0.00000000e+00 2.60113771e+10 2.60113771e+10
  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]
 ...

如您所见，值5.12629421e+10 5.12629421e+10和8.49990653e+10 8.49990653e+10不会出现在第二行。你知道吗

它们出现在第三行：

[0.00000000e+00 0.00000000e+00 5.12629421e+10 5.12629421e+10
  1.01028455e+11 1.01028455e+11 0.00000000e+00 0.00000000e+00]

相反，我想进入第二行：

 [[3.28542331e+09 3.28542331e+09 0.00000000e+00 0.00000000e+00
      0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]
     [0.00000000e+00 0.00000000e+00 2.60113771e+10 2.60113771e+10
      5.12629421e+10 5.12629421e+10 8.49990653e+10 8.49990653e+10]

如果有人能帮我从4D阵列建立一个2d8x8阵列。。。你知道吗

编辑1：在完整4D数组的打印下面（通过执行print 'arrayFullCross = ', arrayFullCross）：

arrayFullCross =  [[[[3.28542331e+09 3.28542331e+09]
   [8.97951610e+07 8.97951610e+07]]

  [[0.00000000e+00 0.00000000e+00]
   [0.00000000e+00 0.00000000e+00]]

  [[0.00000000e+00 0.00000000e+00]
   [0.00000000e+00 0.00000000e+00]]

  [[0.00000000e+00 0.00000000e+00]
   [0.00000000e+00 0.00000000e+00]]]


 [[[0.00000000e+00 0.00000000e+00]
   [0.00000000e+00 0.00000000e+00]]

  [[2.60113771e+10 2.60113771e+10]
   [7.10926896e+08 7.10926896e+08]]

  [[5.12629421e+10 5.12629421e+10]
   [1.40108708e+09 1.40108708e+09]]

  [[8.49990653e+10 8.49990653e+10]
   [2.32314196e+09 2.32314196e+09]]]


 [[[0.00000000e+00 0.00000000e+00]
   [0.00000000e+00 0.00000000e+00]]

  [[0.00000000e+00 0.00000000e+00]
   [0.00000000e+00 0.00000000e+00]]

  [[1.01028455e+11 1.01028455e+11]
   [2.76124733e+09 2.76124733e+09]]

  [[1.67515243e+11 1.67515243e+11]
   [4.57842318e+09 4.57842318e+09]]]


 [[[0.00000000e+00 0.00000000e+00]
   [0.00000000e+00 0.00000000e+00]]

  [[0.00000000e+00 0.00000000e+00]
   [0.00000000e+00 0.00000000e+00]]

  [[0.00000000e+00 0.00000000e+00]
   [0.00000000e+00 0.00000000e+00]]

  [[1.38878482e+11 1.38878482e+11]
   [3.79574089e+09 3.79574089e+09]]]]

编辑2好的，我要检查重塑是否完成得很好的方法是：

  print 'shape(arrayFull = ', np.shape(arrayFullCross)

  print 'here first line  , arrayFullCross column = 0 = ', arrayFullCross[0][0][0][0:2] 
  print 'here first line  , arrayFullCross column = 1 = ', arrayFullCross[0][1][0][0:2] 
  print 'here first line  , arrayFullCross column = 2 = ', arrayFullCross[0][2][0][0:2] 
  print 'here first line  , arrayFullCross column = 3 = ', arrayFullCross[0][3][0][0:2] 
  print ' '
  print 'here second line  , arrayFullCross column = 0 = ', arrayFullCross[1][0][0][0:2] 
  print 'here second line  , arrayFullCross column = 1 = ', arrayFullCross[1][1][0][0:2] 
  print 'here second line  , arrayFullCross column = 2 = ', arrayFullCross[1][2][0][0:2] 
  print 'here second line  , arrayFullCross column = 3 = ', arrayFullCross[1][3][0][0:2] 
  print ' '
  print 'test all  first line  , arrayFullCross column = 0,1,2,3 = ', arrayFullCross[0][0:4][0][0:2] 
  print ' '
  print 'here first line  , arrayFullCross column = 1 = ', arrayFullCross[0][1][0][0:2] 
  print 'here first line  , arrayFullCross column = 2 = ', arrayFullCross[0][2][0][0:2] 
  print 'here first line  , arrayFullCross column = 3 = ', arrayFullCross[0][3][0][0:2] 

它给出：

shape(arrayFull =  (4, 4, 2, 2)
here first line  , arrayFullCross column = 0 =  [3.28542331e+09 3.28542331e+09]
here first line  , arrayFullCross column = 1 =  [0. 0.]
here first line  , arrayFullCross column = 2 =  [0. 0.]
here first line  , arrayFullCross column = 3 =  [0. 0.]

here second line  , arrayFullCross column = 0 =  [0. 0.]
here second line  , arrayFullCross column = 1 =  [2.60113771e+10 2.60113771e+10]
here second line  , arrayFullCross column = 2 =  [5.12629421e+10 5.12629421e+10]
here second line  , arrayFullCross column = 3 =  [8.49990653e+10 8.49990653e+10]

但是我对沿着列索引（在arrayFullCross[i][j][k][l]中的第二个index j）打印第一行的方式有疑问。你知道吗

不幸的是，print 'reshape = ', arrayFullCross.swapaxes(2,0).reshape(8,8)的几乎解给出：

reshape =  [[3.28542331e+09 3.28542331e+09 0.00000000e+00 0.00000000e+00
  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]
 [0.00000000e+00 0.00000000e+00 2.60113771e+10 2.60113771e+10
  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]
 [0.00000000e+00 0.00000000e+00 5.12629421e+10 5.12629421e+10
  1.01028455e+11 1.01028455e+11 0.00000000e+00 0.00000000e+00]
 [0.00000000e+00 0.00000000e+00 8.49990653e+10 8.49990653e+10
  1.67515243e+11 1.67515243e+11 1.38878482e+11 1.38878482e+11]
 [8.97951610e+07 8.97951610e+07 0.00000000e+00 0.00000000e+00
  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]
 [0.00000000e+00 0.00000000e+00 7.10926896e+08 7.10926896e+08
  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]
 [0.00000000e+00 0.00000000e+00 1.40108708e+09 1.40108708e+09
  2.76124733e+09 2.76124733e+09 0.00000000e+00 0.00000000e+00]
 [0.00000000e+00 0.00000000e+00 2.32314196e+09 2.32314196e+09
  4.57842318e+09 4.57842318e+09 3.79574089e+09 3.79574089e+09]]

根据我的打印，第二行应该等于：

[0.00000000e+00 0.00000000e+00 2.60113771e+10 2.60113771e+10
      5.12629421e+10 5.12629421e+10 8.49990653e+10 8.49990653e+10]

可以多次使用交换盘吗？你知道吗

敬礼

Answer 1

我想我的评论还不够清楚。你知道吗

In [811]: arr = np.ones((4,4,2,2),int)
In [812]: arr.swapaxes(0,2).shape
Out[812]: (2, 4, 4, 2)

是的，这可以被重塑为（8，8），但肯定会有某种换位，因为一对维度是（2，4），另一对维度是（4，2）。你知道吗

如果您交换了轴以产生（2,4,2,4）或（4,2,4,2），我希望重塑将是正确的。你知道吗

哪个swap是正确的具体细节取决于您想要如何排列子块。希望你能追踪到？你知道吗

---

用漂亮的（2,2）块做一个简单的数组：

In [813]: arr = np.arange(4).reshape(2,2)
In [815]: arr1 =np.tile(arr[None,None,:,:],(4,4,1,1))
In [816]: arr1.shape
Out[816]: (4, 4, 2, 2)

In [817]: arr1
Out[817]: 
array([[[[0, 1],
         [2, 3]],

        [[0, 1],
         [2, 3]],
   ...

看看不同的掉期产生了什么：

In [822]: arr1.swapaxes(0,2).reshape(8,8)
Out[822]: 
array([[0, 1, 0, 1, 0, 1, 0, 1],
       [0, 1, 0, 1, 0, 1, 0, 1],
       [0, 1, 0, 1, 0, 1, 0, 1],
       [0, 1, 0, 1, 0, 1, 0, 1],
       [2, 3, 2, 3, 2, 3, 2, 3],
       [2, 3, 2, 3, 2, 3, 2, 3],
       [2, 3, 2, 3, 2, 3, 2, 3],
       [2, 3, 2, 3, 2, 3, 2, 3]])
In [823]: 
In [823]: arr1.swapaxes(1,3).reshape(8,8)
Out[823]: 
array([[0, 0, 0, 0, 2, 2, 2, 2],
       [1, 1, 1, 1, 3, 3, 3, 3],
       [0, 0, 0, 0, 2, 2, 2, 2],
       [1, 1, 1, 1, 3, 3, 3, 3],
       [0, 0, 0, 0, 2, 2, 2, 2],
       [1, 1, 1, 1, 3, 3, 3, 3],
       [0, 0, 0, 0, 2, 2, 2, 2],
       [1, 1, 1, 1, 3, 3, 3, 3]])
In [824]: arr1.swapaxes(1,2).reshape(8,8)
Out[824]: 
array([[0, 1, 0, 1, 0, 1, 0, 1],
       [2, 3, 2, 3, 2, 3, 2, 3],
       [0, 1, 0, 1, 0, 1, 0, 1],
       [2, 3, 2, 3, 2, 3, 2, 3],
       [0, 1, 0, 1, 0, 1, 0, 1],
       [2, 3, 2, 3, 2, 3, 2, 3],
       [0, 1, 0, 1, 0, 1, 0, 1],
       [2, 3, 2, 3, 2, 3, 2, 3]])

起作用的产生（4,2,4,2）形状：

In [825]: arr1.swapaxes(0,2).shape
Out[825]: (2, 4, 4, 2)
In [826]: arr1.swapaxes(1,3).shape
Out[826]: (4, 2, 2, 4)
In [827]: arr1.swapaxes(1,2).shape
Out[827]: (4, 2, 4, 2)

还有另一个交换

In [829]: arr1.swapaxes(0,3).shape
Out[829]: (2, 4, 2, 4)
In [830]: arr1.swapaxes(0,3).reshape(8,8)
Out[830]: 
array([[0, 0, 0, 0, 2, 2, 2, 2],
       [0, 0, 0, 0, 2, 2, 2, 2],
       [0, 0, 0, 0, 2, 2, 2, 2],
       [0, 0, 0, 0, 2, 2, 2, 2],
       [1, 1, 1, 1, 3, 3, 3, 3],
       [1, 1, 1, 1, 3, 3, 3, 3],
       [1, 1, 1, 1, 3, 3, 3, 3],
       [1, 1, 1, 1, 3, 3, 3, 3]])