您可以使用
np.reshape
进行重塑,然后再使用
np.transpose
重新排列维度,具体操作如下 -
H = data.reshape(N,Nt,N).transpose(0,2,1)
除了使用 np.transpose
,我们还可以使用 np.swapaxes
,因为基本上我们正在交换 axes 1,2
,像这样 -
H = data.reshape(N,Nt,N).swapaxes(1,2)
示例运行 -
In [300]: N = 2
...: Nt = 3
...: data = np.random.randint(0,9,(N*Nt,N))
...:
In [301]: data
Out[301]:
array([[3, 6],
[7, 4],
[8, 1],
[8, 7],
[4, 8],
[2, 3]])
In [302]: H = np.zeros((N,N,Nt),dtype=data.dtype)
...: for k in np.arange(N):
...: for l in np.arange(N):
...: for m in np.arange(Nt):
...: H[k,l,m] = data[m+Nt*k,l]
...:
In [303]: H
Out[303]:
array([[[3, 7, 8],
[6, 4, 1]],
[[8, 4, 2],
[7, 8, 3]]])
In [304]: data.reshape(N,Nt,N).transpose(0,2,1)
Out[304]:
array([[[3, 7, 8],
[6, 4, 1]],
[[8, 4, 2],
[7, 8, 3]]])
运行时测试 -
In [8]:
...: N = 10
...: Nt = 10*50
...: data = np.random.randint(0,9,(N*Nt,N))
...:
...: def original_app(data):
...: H = np.zeros((N,N,Nt),dtype=data.dtype)
...: for k in np.arange(N):
...: for l in np.arange(N):
...: for m in np.arange(Nt):
...: H[k,l,m] = data[m+Nt*k,l]
...: return H
...:
In [9]: np.allclose(original_app(data),data.reshape(N,Nt,N).transpose(0,2,1))
Out[9]: True
In [10]: %timeit original_app(data)
10 loops, best of 3: 56.1 ms per loop
In [11]: %timeit data.reshape(N,Nt,N).transpose(0,2,1)
1000000 loops, best of 3: 1.25 µs per loop
np.zeros
函数将默认创建一个浮点数数组。如果data
不是浮点数数组,则可能会产生不良后果。 - ali_m