有M✖️N 维度的矩阵，将它转化为MN✖️1的矩阵
import numpy as np from numpy import squeeze M, N = 4, 3 h_mat = np.random.randn(M, N) h_new = squeeze(h_mat.T.reshape(-1, 1)) print(h_new, h_mat, h_new.shape, h_mat.shape)
[-1.06950402 0.57459646 0.41926508 3.20023608 -0.8934199 -1.48817502 -1.46684445 1.04572083 -0.2769263 0.83589263 0.34593235 0.34324631] [[-1.06950402 -0.8934199 -0.2769263 ] [ 0.57459646 -1.48817502 0.83589263] [ 0.41926508 -1.46684445 0.34593235] [ 3.20023608 1.04572083 0.34324631]] (12,) (4, 3)
将虚数矩阵转化为实数矩阵
这里m为N*1维矩阵
from numpy import real, imag import numpy as np mat = np.array([1, 2, 3, -1, -2, -3]) + 1j * np.array([1, 1, 1, 2, 2, 2]) real_mat = squeeze(real(mat).reshape(-1, 1)) img_mat = squeeze(imag(mat).reshape(-1, 1)) all_mat = np.hstack((real_mat, img_mat)) print(mat, "n", all_mat, "n", all_mat.shape) vec_size = int(len(all_mat)/2) real_part = all_mat[:vec_size,] img_part = all_mat[vec_size:,] recon_mat = real_part + 1j * img_part np.array_equal(mat, recon_mat)
输出：
[ 1.+1.j 2.+1.j 3.+1.j -1.+2.j -2.+2.j -3.+2.j] [ 1. 2. 3. -1. -2. -3. 1. 1. 1. 2. 2. 2.] (12,) True
两个类似的，一个负号的区别
import numpy as np from numpy import real,imag mat = np.array([1, 2, 3, -1, -2, -3]) + 1j * np.array([1, 1, 1, 2, 2, 2]).reshape(-1,1) mat = mat * mat.T real_mat = real(mat) img_mat = imag(mat) up = np.hstack((real_mat, -img_mat)) down = np.hstack((img_mat, real_mat)) all_mat = np.vstack((up, down)) print(all_mat) #%% mat_size = int(all_mat.shape[0]/2) real_part = all_mat[:mat_size,:mat_size] img_part = all_mat[mat_size:,:mat_size] final_mat = real_part + 1j * img_part np.array_equal(final_mat, mat)
