重新排列numpy数组中的元素以在2d中绘制3d数组，并保留分组

Numpy assumes C order. So when you reshape, if your array is not C contiguous, it generally makes a copy. It also means that you can rearrange the memory layout of an array by say transposing and then reshaping it.

You want to take an array of shape (nc1 * nc2, nx, ny) into one of shape (nx * nc1, ny * nc2). One approach is to split the array into 4D first. Adding an extra dimension will not change the memory layout:

data = data.reshape(nc1, nc2, nx, ny)

Now you can move the dimensions around to get the memory layout you need. This will change your strides without copying memory, since the array is still C contiguous in the last step:

data = data.transpose(2, 0, 3, 1) # nx, nc1, ny, nc2

请注意，我们感动nc1之后nx和nc2之后ny。在C顺序中，我们希望组是连续的。较晚的尺寸比较早的尺寸靠得很近。因此，闲逛[[1, 2], [3, 4]]产生[1, 2, 3, 4]，而闲逛[[1, 3], [2, 4]]产生[1, 3, 2, 4]。

现在您可以获得最终的数组。由于数据在重新排列尺寸后不再是C连续的，因此以下重新整形将使副本实际重新排列数据：

data = data.reshape(nx * nc1, ny * nc2)

您可以将其写成一行：

result = data.reshape(nc1, nc2, nx, ny).transpose(2, 0, 3, 1).reshape(nx * nc1, ny * nc2)

原则上，这是您所能达到的最高效率：第一次重塑会改变形状并大步前进；换位围绕形状的顺序交换并大步移动。在最后一次重塑期间仅发生一次复制操作。