import numpy as np
from scipy import io
import os

file_path = 'dataset/noise_FM'

# 批量處理mat數據,reshape為1000 x 1024，并存為.npy
def reshape_file(file_path):
    files = os.listdir(file_path)
    for file_origin in files:
        file = os.path.join(file_path, file_origin)
        data_mat = io.loadmat(file)
        data = data_mat['shiyu_pro'] #注意，通過io.loadmat加載的數據為字典格式
        data.reshape(1000, 1024)
        file_name = file_origin.split('.')[0]
        save_path = 'redata/noise_FM/' + file_name
        np.save(save_path, data)

# 批量加載.npy
def loadnpy(file_path):
    files = os.listdir(file_path)
    A[] = np.load(files)
    for file_origin in files:
        file = os.path.join(file_path, file_origin)
        data = np.load(file)
        A.append(data)

import numpy as np
from scipy import io
mat = io.loadmat('test.mat')

import h5py
mat = h5py.File('test.mat')

print(mat.keys())

print(mat.values())

print(mat['your_dataset_name'].shape)

mat_t = np.transpose(mat['your_dataset_name'])
mat_t 是numpy.ndarray格式

np.save('test.npy', mat_t)

import numpy as np
matrix = np.load('yourfile.npy')

import numpy as np
matrix = np.load('yourfile.npy')
f = h5py.File('yourfile.mat', 'w')
f.create_dataset('dataname', data=matrix)

from scipy import io
mat = np.load('rlt_gene_features.npy-layer-3-train.npy')
io.savemat('gene_features.mat', {'gene_features': mat})