python保存大型 .mat 數(shù)據(jù)文件報(bào)錯(cuò)超出 IO 限制的操作

import h5py
def h5_data_write(train_data, train_label, test_data, test_label, shuffled_flag):
    print("h5py文件正在寫入磁盤...")
    
    save_path = "../save_test/" + "train_test_split_data_label_" + shuffled_flag + ".h5"
    with h5py.File(save_path, 'w') as f:
        f.create_dataset('train_data', data=train_data)
        f.create_dataset('train_label', data=train_label)
        f.create_dataset('test_data', data=test_data)
        f.create_dataset('test_label', data=test_label)
    print("h5py文件保存成功！")
def h5_data_read(filename):
    """
        keys() ： 獲取本文件夾下所有的文件及文件夾的名字
        f['key_name'] : 獲取對應(yīng)的對象
    """
    file = h5py.File(filename,'r')
    train_data = file['train_data'][:]
    train_label = file['train_label'][:]
    test_data = file['test_data'][:]
    test_label = file['test_label'][:]
    return train_data, train_label, test_data, test_label

import scipy.io as sio 
import matplotlib.pyplot as plt 
import numpy as np 
 
#matlab文件名 
matfn=u'E:/python/測試程序/162250671_162251656_1244.mat' 
data=sio.loadmat(matfn) 
 
plt.close('all') 
xi=data['xi'] 
yi=data['yi'] 
ui=data['ui'] 
vi=data['vi'] 
plt.figure(1) 
plt.quiver( xi[::5,::5],yi[::5,::5],ui[::5,::5],vi[::5,::5]) 
plt.figure(2) 
plt.contourf(xi,yi,ui) 
plt.show()  
sio.savemat('saveddata.mat', {'xi': xi,'yi': yi,'ui': ui,'vi': vi}) 

import scipy.io as sio
import numpy as np
 
###下面是講解python怎么讀取.mat文件以及怎么處理得到的結(jié)果###
load_fn = 'xxx.mat'
load_data = sio.loadmat(load_fn)
load_matrix = load_data['matrix'] #假設(shè)文件中存有字符變量是matrix，例如matlab中save(load_fn, 'matrix');當(dāng)然可以保存多個(gè)save(load_fn, 'matrix_x', 'matrix_y', ...);
load_matrix_row = load_matrix[0] #取了當(dāng)時(shí)matlab中matrix的第一行，python中數(shù)組行排列
 
###下面是講解python怎么保存.mat文件供matlab程序使用###
save_fn = 'xxx.mat'
save_array = np.array([1,2,3,4])
sio.savemat(save_fn, {'array': save_array}) #和上面的一樣，存在了array變量的第一行
 
save_array_x = np.array([1,2,3,4])
save_array_y = np.array([5,6,7,8])
sio.savemat(save_fn, {'array_x': save_array_x, 'array_x': save_array_x}) #同理，

# adapted from http://blog.csdn.net/rumswell/article/details/8545087
import scipy.io as sio  
#import matplotlib.pyplot as plt
from pylab import *
import numpy as np   
 
matfn='E:\Pythonrun\myuse\matdata.mat'   # the path of .mat data
data=sio.loadmat(matfn)  
xx=data['matdata']
figure(1)
plot(xx)
show()

from numpy import * 
def file2list(filename):  

    fr = open(filename)  
    array = fr.readlines() #以文件中的每行為一個(gè)元素，形成一個(gè)list列表  
    num = len(array)  
    returnMat = zeros((num,3))#初始化元素為0的，行號數(shù)個(gè)列表，其中每個(gè)元素仍是列表，元素?cái)?shù)是3，在此表示矩陣  
    index = 0   
 
    for line in array:  
        line = line.strip()#去掉一行后的回車符號  
        linelist = line.split(' ')#將一行根據(jù)分割符,劃分成多個(gè)元素的列表  
        returnMat[index,:] = linelist[0:3]#向矩陣賦值，注意這種賦值方式比較笨拙  
        index +=1  
    return returnMat
 
fname = 'E:\Pythonrun\myuse\num_data.txt'
data= file2list(fname)

import scipy.io as sio
import numpy
# matFile 讀取
matFile = 'matlabdata.mat'
datas = sio.loadmat(matFile)
# 加載 matFile 內(nèi)的數(shù)據(jù)
# 假設(shè) mat 內(nèi)保存的變量為 matlabdata
matlabdata = datas['matlabdata']
# matFile 寫入
save_matFile = 'save_matlabdata.mat'
save_matlabdata = np.array([1,2,3,4,5])
sio.savemat(save_matFile, {'array':save_matlabdata})

import h5py
with h5py.File('matlabdata.mat', 'r') as f:
    f.keys() # matlabdata.mat 中的變量名
datas = h5py.File('matlabdata.mat')['matlabdata'].value