mat矩阵和npy矩阵实现互相转换(python和matlab)

import numpy as np
import scipy.io as io
mat_path = 'your_mat_save_path'
mat = np.zeros([4, 20])
io.savemat(mat_path, {'name': mat})

import numpy as np
from scipy import io
mat = io.loadmat('yourfile.mat')
# 如果报错:Please use HDF reader for matlab v7.3 files
# 改为下一种方式读取
import h5py
mat = h5py.File('yourfile.mat')
# mat文件里可能有多个cell，各对应着一个dataset
# 可以用keys方法查看cell的名字, 现在要用list(mat.keys()),
# 另外，读取要用data = mat.get('名字'), 然后可以再用Numpy转为array
print(mat.keys())
# 可以用values方法查看各个cell的信息
print(mat.values())
# 可以用shape查看维度信息
print(mat['your_dataset_name'].shape)
# 注意，这里看到的shape信息与你在matlab打开的不同
# 这里的矩阵是matlab打开时矩阵的转置
# 所以，我们需要将它转置回来
mat_t = np.transpose(mat['your_dataset_name'])
# mat_t 是numpy.ndarray格式
# 再将其存为npy格式文件
np.save('yourfile.npy', mat_t)

import numpy as np
a = np.mat('1, 2, 3;4, 5, 6')
print(a)
print(type(a))
print("=================================")
b = np.array([[1, 2, 3], [4, 5, 6]])
print(b)
print(type(b))

np.save('a.npy', a)
np.save('b.npy', b)

data1 = np.load('a.npy')
data2 = np.load('b.npy')
print(data1)
print(type(data1))
print("=================================")
print(data2)
print(type(data2))

import numpy as np
from scipy import io
a = np.mat('1, 2, 3;4, 5, 6')
print(a)
print(type(a))
print("=================================")
b = np.array([[1, 2, 3], [4, 5, 6]])
print(b)
print(type(b))
io.savemat('a.mat', {'matrix': a})
io.savemat('b.mat', {'array': b})

data1 = io.loadmat('a.mat')
print(data1)
print(type(data1))
print("=================================")
data2 = io.loadmat('b.mat')
print(data2)
print(type(data2))

print(data1['matrix'])
print(type(data1['matrix']))
print("=================================")
print(data2['array'])
print(type(data2['array']))