python 读取.mat文件

  • Z时代
  • 2024-01-10
  • 分类：综合

python

导入所需包

from scipy.io import loadmat

读取.mat文件

随便从下面文件里读取一个：

m = loadmat('H_BETA.mat')  # 读出来的 m 是一个dict（字典）数据结构

读出来的m内容：

m：
{'__header__': b'MATLAB 5.0 MAT-file, Platform: GLNXA64, Created on: Mon Aug  5 17:14:09 2019',
 '__version__': '1.0',
 '__globals__': [],
 'H_BETA': array([[ 0.68508148,  0.36764355,  0.73505849, ...,  0.27600164,
          0.67968929,  0.70506438],
        [ 0.74920812,  1.10949748,  0.47506305, ...,  0.32871445,
          0.61247345,  1.06948844],
        [ 0.83311522,  1.06321302,  0.97364609, ...,  0.85837753,
          0.96296771,  1.46095171],
        ...,
        [        nan,         nan,         nan, ...,         nan,
                 nan, -9.04648469],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,

In [29]: m.keys()
Out[29]: dict_keys(['__header__', '__version__', '__globals__', 'H_BETA'])

取出.mat里所需信息

.mat 文件里的数据结构是 dict ，所以取值要按照 key:value 的形式：

In [30]: m['H_BETA']
Out[30]:
array([[ 0.68508148,  0.36764355,  0.73505849, ...,  0.27600164,
         0.67968929,  0.70506438],
       [ 0.74920812,  1.10949748,  0.47506305, ...,  0.32871445,
         0.61247345,  1.06948844],
       [ 0.83311522,  1.06321302,  0.97364609, ...,  0.85837753,
         0.96296771,  1.46095171],
       ...,
       [        nan,         nan,         nan, ...,         nan,
                nan, -9.04648469],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])
In [31]: type(m['H_BETA'])
Out[31]: numpy.ndarray

预处理数据

上面读出来的数据是 ndarray 类型，为了方便数据的展示，我们可以将其转换为，pandas的DataFrame：

In [32]: import pandas as pd
In [33]: df = pd.DataFrame(m['H_BETA'])
In [34]: df.head()
Out[34]:
       0         1         2         3         4         5         6         7         8         9         10    
0  0.685081  0.367644  0.735058  0.085046  0.104332  0.560731  0.350219  0.758185  0.303823  0.114022  0.452877 
1  0.749208  1.109497  0.475063  0.896100  1.117772  0.611356  0.662669  0.603077  0.863930  0.756870  0.725808  
2  0.833115  1.063213  0.973646  0.935061  0.631670  0.916800  0.662993  0.543231  0.671558  1.027954  0.526402 
3  0.488906  0.932741  0.956622  0.573116  0.893764  0.987304  0.380807  1.211157  0.550213  0.898408  1.153289  
4  0.440694  0.503209  0.509693  0.477054  0.344717 -0.054662  1.124213  0.344906  0.612898  0.217625 -0.129715 
[5 rows x 2111 columns]

如此，数据就比较规整了，是保存成文件，还是做其他处理，就by yourself啦！

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