从使用Scipy.io加载的.mat文件中访问数组内容。loadmat——python

UPDATE: This is a long question that boils down to, can someone explain the numpy array class to me? I answered my own question below.

更新:这是一个很长的问题，可以有人向我解释numpy数组类吗?我在下面回答了我自己的问题。

I am working on a project to import data from matlab into a mysql database whose contents will be made available through a django website. I want to use Scipy.io.loadmat to get the information from matlab into a form I can use in python so that I can enter the data into the database with the django api.

我正在做一个项目，从matlab将数据导入到mysql数据库，该数据库的内容将通过django网站提供。我想用剪刀。io。loadmat可以将matlab中的信息转换为我可以在python中使用的表单，这样我就可以使用django api将数据输入数据库。

My problem is that I cannot work with the data imported by scipy.io.loadmat. It is loaded in the form of several nested arrays and some of the variable names seem to be lost.

我的问题是，我无法使用scipy.io.loadmat导入的数据。它以几个嵌套数组的形式加载，一些变量名似乎丢失了。

Here is the matlab code for a test structure that I have created for a trial:

下面是我为试验创建的一个测试结构的matlab代码:

sensors.time = [0:1:10].';
sensors.sensor1 = {};
sensors.sensor1.source_type = 'flight';                          
sensors.sensor1.source_name = 'flight-2';                       
sensors.sensor1.channels = {};
sensors.sensor1.channels.channel1.name = '1';                    
sensors.sensor1.channels.channel1.local_ori = 'lateral';         
sensors.sensor1.channels.channel1.vehicle_ori = 'axial';         
sensors.sensor1.channels.channel1.signals = {};
sensors.sensor1.channels.channel1.signals.signal1.filtered = 'N';
sensors.sensor1.channels.channel1.signals.signal1.filtered_description = 'none'; 
sensors.sensor1.channels.channel1.signals.signal1.data = sin(sensors.time)+0.1*rand(11,1); 

>> sensors
      time: [11x1 double]
      sensor1: [1x1 struct]
>> sensors.sensor1
      source_type: 'flight'
      source_name: 'flight-2'
      channels: [1x1 struct]
>> sensors.sensor1.channels
      channel1: [1x1 struct]
>> sensors.sensor1.channels.channel1
      name: '1'
      local_ori: 'lateral'
      vehicle_ori: 'axial'
      signals: [1x1 struct]
>> sensors.sensor1.channels.channel1.signals
      signal1: [1x1 struct]
>> sensors.sensor1.channels.channel1.signals.signal1
      filtered: 'N'
      filtered_description: 'none'
      data: [11x1 double]

I can easily visualize this structure as a python dictionary, so it does not seem like this should be such a complicated exercise.

我可以很容易地将这个结构形象化为python字典，因此看起来这不应该是一个如此复杂的练习。

Here is the python code I used to read the file in (eventually I want to read in multiple files):

这里是我用来读取文件的python代码(最终我想在多个文件中读取):

from scipy
import os, glob

path = 'C:\Users\c\Desktop\import'
for f in glob.glob( os.path.join(path, '*.mat')):
    matfile = scipy.io.loadmat(f, struct_as_record=True)

This is the resulting dictionary from loadmat:

这是由loadmat生成的字典:

>>> matfile
{'sensors': array([[ ([[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]],[[(array([u'flight'], 
      dtype='<U6'), array([u'flight-2'], 
      dtype='<U8'), array([[ ([[(array([u'1'], 
      dtype='<U1'), array([u'lateral'], 
      dtype='<U7'), array([u'axial'], 
      dtype='<U5'), array([[ ([[(array([u'N'], 
      dtype='<U1'), array([u'none'], 
      dtype='<U4'), array([[ 0.06273465],[ 0.84363597],[ 1.00035443],[ 0.22117587],[-0.68221775],[-0.87761299],[-0.24108487],[ 0.71871452],[ 1.04690773],[ 0.46512366],[-0.51651414]]))]],)]],
      dtype=[('signal1', '|O4')]))]],)]], 
      dtype=[('channel1', '|O4')]))]])]], 
      dtype=[('time', '|O4'), ('sensor1', '|O4')]), '__version__': '1.0', '__header__': 'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Tue Jun 07 18:38:32 2011', '__globals__': []}

The data is all there, but I don't know how to access these class objects. I would like to be able to loop over contents so that I can process, multiple sensors, then multiple channels for each sensor, etc.

数据都在那里，但我不知道如何访问这些类对象。我希望能够对内容进行循环，这样我就可以处理多个传感器，然后为每个传感器设置多个通道，等等。

Any explanations to help me simplify this data structure or suggested changes to make this easier would be greatly appreciated.

如果有任何解释可以帮助我简化这个数据结构，或者建议进行修改以使其更容易，我们将非常感激。

---

Update, based on Nick's suggestion here is the repr(matfile) and the dir(matfile)

更新，根据Nick的建议这里是repr(matfile)和dir(matfile)

>>> repr(matfile)
"{'sensors': array([[ ([[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]], [[(array([u'flight'], \n      dtype='<U6'), array([u'flight-2'], \n      dtype='<U8'), array([[ ([[(array([u'1'], \n      dtype='<U1'), array([u'lateral'], \n      dtype='<U7'), array([u'axial'], \n      dtype='<U5'), array([[ ([[(array([u'N'], \n      dtype='<U1'), array([u'none'], \n      dtype='<U4'), array([[ 0.0248629 ],\n       [ 0.88663486],\n       [ 0.93206871],\n       [ 0.22156497],\n       [-0.65819207],\n       [-0.95592508],\n       [-0.22584908],\n       [ 0.66569432],\n       [ 1.06956739],\n       [ 0.51103298],\n       [-0.53732649]]))]], [[(array([u'Y'], \n      dtype='<U1'), array([u'1. 5 Hz High Pass, 2. remove offset'], \n      dtype='<U35'), array([[ 0.        ],\n       [ 0.84147098],\n       [ 0.90929743],\n       [ 0.14112001],\n       [-0.7568025 ],\n       [-0.95892427],\n       [-0.2794155 ],\n       [ 0.6569866 ],\n       [ 0.98935825],\n       [ 0.41211849],\n       [-0.54402111]]))]])]], \n      dtype=[('signal1', '|O4'), ('signal2', '|O4')]))]],)]], \n      dtype=[('channel1', '|O4')]))]])]], \n      dtype=[('time', '|O4'), ('sensor1', '|O4')]), '__version__': '1.0', '__header__': 'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Wed Jun 08 10:58:19 2011', '__globals__': []}"

>>> dir(matfile)
['__class__', '__cmp__', '__contains__', '__delattr__', '__delitem__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__init__', '__iter__', '__le__', '__len__', '__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'clear', 'copy', 'fromkeys', 'get', 'has_key', 'items', 'iteritems', 'iterkeys', 'itervalues', 'keys', 'pop', 'popitem', 'setdefault', 'update', 'values', 'viewitems', 'viewkeys', 'viewvalues']

>>> dir(matfile['sensors'])
['T', '__abs__', '__add__', '__and__', '__array__', '__array_finalize__', '__array_interface__', '__array_prepare__', '__array_priority__', '__array_struct__', '__array_wrap__', '__class__', '__contains__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', '__delslice__', '__div__', '__divmod__', '__doc__', '__eq__', '__float__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getslice__', '__gt__', '__hash__', '__hex__', '__iadd__', '__iand__', '__idiv__', '__ifloordiv__', '__ilshift__', '__imod__', '__imul__', '__index__', '__init__', '__int__', '__invert__', '__ior__', '__ipow__', '__irshift__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__len__', '__long__', '__lshift__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__nonzero__', '__oct__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdiv__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__setitem__', '__setslice__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__xor__', 'all', 'any', 'argmax', 'argmin', 'argsort', 'astype', 'base', 'byteswap', 'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'ctypes', 'cumprod', 'cumsum', 'data', 'diagonal', 'dot', 'dtype', 'dump', 'dumps', 'fill', 'flags', 'flat', 'flatten', 'getfield', 'imag', 'item', 'itemset', 'itemsize', 'max', 'mean', 'min', 'nbytes', 'ndim', 'newbyteorder', 'nonzero', 'prod', 'ptp', 'put', 'ravel', 'real', 'repeat', 'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'shape', 'size', 'sort', 'squeeze', 'std', 'strides', 'sum', 'swapaxes', 'take', 'tofile', 'tolist', 'tostring', 'trace', 'transpose', 'var', 'view']

Obviously I need to learn a bit about objects and classes. How can I pull bits of the array and put them into variables. For example:

显然，我需要学习一些关于对象和类的知识。我怎样才能把数组的一些元素放到变量中。例如:

time = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
source_type = 'flight'
etc.   

2 个解决方案

#1

I've run into a similar issue with a fairly complex mat file at our company. I'm still getting my head wrapped around the scipy IO module, but here is what we found.

我在我们公司遇到了一个相当复杂的mat文件的类似问题。我还在思考scipy IO模块，但这里是我们发现的。

When you access matfile['sensors'] it returns a scipy.io.matlab.mio5_params.mat_struct object, which we can use to access the contents below. When you print it, it looks like a flat array, but you can still access the dict to get at the individual components. So you could run something like this to start accessing the components:

当您访问matfile['sensor ']时，它将返回一个scipy.io.matlab.mio5_params。mat_struct对象，我们可以使用它来访问下面的内容。当您打印它时，它看起来像一个平面数组，但是您仍然可以访问dict类型来获取单个组件。所以你可以运行这样的程序来开始访问组件:

from scipy.io import loadmat
matfile = loadmat('myfile.mat', squeeze_me=True, struct_as_record=False)
matfile['sensors'].sensor1.channels.channel1.name

In your case you want to be able to iterate over the elements in the structure, which you can do if you access the _fieldnames property of the mat_struct object. From there you can just loop over the field names and access them with getattr:

在这种情况下，您希望能够遍历结构中的元素，如果访问mat_struct对象的_fieldnames属性，就可以这样做。从那里你可以对字段名进行循环，并使用getattr访问它们:

for field in matfile['sensors']._fieldnames:
    # getattr will return the value for the given key
    print getattr(matfile['sensors'], field)

This is at least allowing us to access the deeply nested elements without having to alter our mat files.

这至少允许我们访问深度嵌套的元素，而不必修改mat文件。

#2

The solution I resorted to was to simplify the MATLAB structure. I eliminated nested structures. Each data set resides in a single file and I used python to loop over all the files of a particular type in the specified directory. (http://bogdan.org.ua/2007/08/12/python-iterate-and-read-all-files-in-a-directory-folder.html, if you would like to see an example of that.)

我采用的解决方案是简化MATLAB结构。我取消了嵌套结构。每个数据集都驻留在一个文件中，我使用python对指定目录中特定类型的所有文件进行循环。(http://bogdan.org.ua/2007/08/12/python-iterate-and-read-all-files-in directoryfolder.html，如果您想看一个例子的话。)

Importing the flat matlab structure results in a dictionary where the matlab variable names are the keys. Strings come in as arrays of shape (1,) --> [ string ], and numbers come in as arrays of shape (N, M) --> [[ numbers ]].

将平面的matlab结构导入字典，在字典中，matlab变量名是键。字符串以形状(1，)—> [string]的形式出现，数字以形状(N, M)—> [[number]的形式出现。

I still have to learn a bit more about the numpy arrays.

我还需要学习更多关于numpy数组的知识。