entityMap|0|type|LINK|mutability|MUTABLE|data|url|https://stackoverflow.com/a/56243305/901925|1|https://stackoverflow.com/questions/54660684/python-issue-reading-in-str-from-matlab-mat-file-using-h5py-and-numpy|blocks|key|fmcs|text|我对这个问题不太清楚。让我试着重现你的案例：|unstyled|depth|inlineStyleRanges|entityRanges|ah79t|In+[58]:+from+scipy.io+import+loadmat,+savemat+++++++++++++++++++++++++++++++
In+[59]:+A+=+np.empty((2,1),+object)+++++
In+[61]:+A[0,0]=np.arange(4).reshape(2,2)++++++++++++++++++++++++++++++++++++
In+[62]:+A[1,0]=np.arange(6).reshape(3,2)++++++++++++++++++++++++++++++++++++
In+[63]:+A+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Out[63]:+
array([[array([[0,+1],
+++++++[2,+3]])],
+++++++[array([[0,+1],
+++++++[2,+3],
+++++++[4,+5]])]],+dtype=object)
In+[64]:+B=A[[0,0],:]++++++++++++++++++++++++++++++++++++++++++++++++++++++++
In+[65]:+B+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Out[65]:+
array([[array([[0,+1],
+++++++[2,+3]])],
+++++++[array([[0,+1],
+++++++[2,+3]])]],+dtype=object)|code-block|syntax|javascript|57qph|正如我今天早些时候解释的那样，从匹配大小的数组创建对象数据类型数组需要特殊处理。np.array(...)尝试创建一个更高维数组。https://stackoverflow.com/a/56243305/901925|offset|length|style|CODE|2hhav|保存：|a4nl1|In+[66]:+savemat('foo.mat',+{'A':A,+'B':B})++++++++++++++++++++++++++++++++++|7caff|正在加载：|d5ep4|In+[74]:+loadmat('foo.mat')++++++++++++++++++++++++++++++++++++++++++++++++++
Out[74]:+
{'__header__':+b'MATLAB+5.0+MAT-file+Platform:+posix,+Created+on:+Tue+May+21+11:20:42+2019',
+'__version__':+'1.0',
+'__globals__':+[],
+'A':+array([[array([[0,+1],
++++++++[2,+3]])],
++++++++[array([[0,+1],
++++++++[2,+3],
++++++++[4,+5]])]],+dtype=object),
+'B':+array([[array([[0,+1],
++++++++[2,+3]])],
++++++++[array([[0,+1],
++++++++[2,+3]])]],+dtype=object)}
In+[75]:+_74['A'][1,0]+++++++++++++++++++++++++++++++++++++++++++++++++++++++
Out[75]:+
array([[0,+1],
+++++++[2,+3],
+++++++[4,+5]])|7fhmb|您的问题案例看起来像是一个包含数字的对象dtype数组：|bucpk|In+[89]:+C+=+np.arange(4).reshape(2,2).astype(object)++++++++++++++++++++++++
In+[90]:+C+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Out[90]:+
array([[0,+1],
+++++++[2,+3]],+dtype=object)
In+[91]:+savemat('foo1.mat',+{'C':+C})+++++++++++++++++++++++++++++++++++++++
In+[92]:+loadmat('foo1.mat')+++++++++++++++++++++++++++++++++++++++++++++++++
Out[92]:+
{'__header__':+b'MATLAB+5.0+MAT-file+Platform:+posix,+Created+on:+Tue+May+21+11:39:31+2019',
+'__version__':+'1.0',
+'__globals__':+[],
+'C':+array([[array([[0]]),+array([[1]])],
++++++++[array([[2]]),+array([[3]])]],+dtype=object)}|ebqn|显然，savemat已经将整数对象转换成与2dmatlab兼容的数组。在MATLAB中，任何东西，即使是标量，都至少是2d的。|3s2bp|===|3dh4k|在Octave中，对象dtype数组都会生成单元格，而2d数值数组会生成矩阵：|bo905|>>+load+foo.mat
>>+A
A+=
{
++[1,1]+=

++++0++1
++++2++3

++[2,1]+=

++++0++1
++++2++3
++++4++5

}
>>+B
B+=
{
++[1,1]+=

++++0++1
++++2++3

++[2,1]+=

++++0++1
++++2++3

}
>>+load+foo1.mat
>>+C
C+=
{
++[1,1]+=+0
++[2,1]+=+2
++[1,2]+=+1
++[2,2]+=+3
}|edaqk|Python:+Issue+reading+in+str+from+MATLAB+.mat+file+using+h5py+and+NumPy|elrm9|是相对较新的，这表明了倍频程HDF5和MATLAB之间的区别。|e28av^0|0|0|14|D|1T|17|0|0|0|0|0|0|0|0|3|7|0|0|0|0|0|1Z|1|0|0^^$0|$1|$2|3|4|5|6|$7|8]]|9|$2|3|4|5|6|$7|A]]]|B|@$C|D|E|F|2|G|H|1K|I|@]|J|@]|6|$]]|$C|K|E|L|2|M|H|1L|I|@]|J|@]|6|$N|O]]|$C|P|E|Q|2|G|H|1M|I|@$R|1N|S|1O|T|U]]|J|@$R|1P|S|1Q|C|1R]]|6|$]]|$C|V|E|W|2|G|H|1S|I|@]|J|@]|6|$]]|$C|X|E|Y|2|M|H|1T|I|@]|J|@]|6|$N|O]]|$C|Z|E|10|2|G|H|1U|I|@]|J|@]|6|$]]|$C|11|E|12|2|M|H|1V|I|@]|J|@]|6|$N|O]]|$C|13|E|14|2|G|H|1W|I|@]|J|@]|6|$]]|$C|15|E|16|2|M|H|1X|I|@]|J|@]|6|$N|O]]|$C|17|E|18|2|G|H|1Y|I|@$R|1Z|S|20|T|U]]|J|@]|6|$]]|$C|19|E|1A|2|G|H|21|I|@]|J|@]|6|$]]|$C|1B|E|1C|2|G|H|22|I|@]|J|@]|6|$]]|$C|1D|E|1E|2|M|H|23|I|@]|J|@]|6|$N|O]]|$C|1F|E|1G|2|G|H|24|I|@]|J|@$R|25|S|26|C|27]]|6|$]]|$C|1H|E|1I|2|G|H|28|I|@]|J|@]|6|$]]|$C|1J|E|-4|2|G|H|29|I|@]|J|@]|6|$]]]]

I'm not quite clear about the problem. Let me try to recreate your case:

<pre><code>In [58]: from scipy.io import loadmat, savemat 
In [59]: A = np.empty((2,1), object) 
In [61]: A[0,0]=np.arange(4).reshape(2,2) 
In [62]: A[1,0]=np.arange(6).reshape(3,2) 
In [63]: A 
Out[63]: 
array([[array([[0, 1],
 [2, 3]])],
 [array([[0, 1],
 [2, 3],
 [4, 5]])]], dtype=object)
In [64]: B=A[[0,0],:] 
In [65]: B 
Out[65]: 
array([[array([[0, 1],
 [2, 3]])],
 [array([[0, 1],
 [2, 3]])]], dtype=object)
</code></pre>

As I explained earlier today, creating an object dtype array from arrays of matching size requires special handling. <code>np.array(...)</code> tries to create a higher dimensional array. <a href="https://stackoverflow.com/a/56243305/901925">https://stackoverflow.com/a/56243305/901925</a>

Saving:

<pre><code>In [66]: savemat('foo.mat', {'A':A, 'B':B}) 
</code></pre>

Loading:

<pre><code>In [74]: loadmat('foo.mat') 
Out[74]: 
{'__header__': b'MATLAB 5.0 MAT-file Platform: posix, Created on: Tue May 21 11:20:42 2019',
 '__version__': '1.0',
 '__globals__': [],
 'A': array([[array([[0, 1],
 [2, 3]])],
 [array([[0, 1],
 [2, 3],
 [4, 5]])]], dtype=object),
 'B': array([[array([[0, 1],
 [2, 3]])],
 [array([[0, 1],
 [2, 3]])]], dtype=object)}
In [75]: _74['A'][1,0] 
Out[75]: 
array([[0, 1],
 [2, 3],
 [4, 5]])
</code></pre>

Your problem case looks like it's a object dtype array containing numbers:

<pre><code>In [89]: C = np.arange(4).reshape(2,2).astype(object) 
In [90]: C 
Out[90]: 
array([[0, 1],
 [2, 3]], dtype=object)
In [91]: savemat('foo1.mat', {'C': C}) 
In [92]: loadmat('foo1.mat') 
Out[92]: 
{'__header__': b'MATLAB 5.0 MAT-file Platform: posix, Created on: Tue May 21 11:39:31 2019',
 '__version__': '1.0',
 '__globals__': [],
 'C': array([[array([[0]]), array([[1]])],
 [array([[2]]), array([[3]])]], dtype=object)}
</code></pre>

Evidently <code>savemat</code> has converted the integer objects into 2d MATLAB compatible arrays. In MATLAB everything, even scalars, is at least 2d.

===

And in Octave, the object dtype arrays all produce cells, and the 2d numeric arrays produce matrices:

<pre><code>&gt;&gt; load foo.mat
&gt;&gt; A
A =
{
 [1,1] =

 0 1
 2 3

 [2,1] =

 0 1
 2 3
 4 5

}
&gt;&gt; B
B =
{
 [1,1] =

 0 1
 2 3

 [2,1] =

 0 1
 2 3

}
&gt;&gt; load foo1.mat
&gt;&gt; C
C =
{
 [1,1] = 0
 [2,1] = 2
 [1,2] = 1
 [2,2] = 3
}
</code></pre>

<a href="https://stackoverflow.com/questions/54660684/python-issue-reading-in-str-from-matlab-mat-file-using-h5py-and-numpy">Python: Issue reading in str from MATLAB .mat file using h5py and NumPy</a>

is a relatively recent SO that showed there's a difference between the Octave HDF5 and MATLAB.

I am trying to create .mat data files using python. The matlab code expects the data to have a certain format, where two-dimensional ndarrays of non-uniform sizes are stored as objects in a column vector. So, in my case, there would be k numpy arrays of shape (m_i, n) - with different m_i for each array - stored in a numpy array with <code>dtype=object</code> of shape (k, 1). I then add this object array to a dictionary and pass it to <code>scipy.io.savemat()</code>.

This works fine so long as the m_i are indeed different. If all k arrays happen to have the same number of rows m_i, the behaviour becomes strange. First of all, it requires very explicit assignment to a numpy array of dtype=object that has been initialised to the final size k, otherwise numpy simply creates a three-dimensional array. But even when I have the correct format in python and store it to a .mat file using <code>savemat</code>, there is some kind of problem in the translation to the matlab format.

When I reload the data from the .mat file using <code>scipy.io.loadmat</code>, I find that I still have an object array of shape (k, 1), which still has elements of shape (m, n). However, each element is no longer an int or a float but is instead a numpy array of shape (1, 1) that has to be further indexed to access the contained int or float. So an individual element of an object vector that was supposed to be a numpy array of shape (2, 4) would look something like this:

<pre><code>[array([[array([[0.82374894]]), array([[0.50730055]]),
 array([[0.36721625]]), array([[0.45036349]])],
 [array([[0.26119276]]), array([[0.16843872]]),
 array([[0.28649524]]), array([[0.64239569]])]], dtype=object)]
</code></pre>

This also poses a problem for the matlab code that I am trying to build my data files for. It runs fine for the arrays of objects that have different shapes but will break when there are arrays containing arrays of the same shape.

I know this is a rather obscure and possibly unavoidable issue but I figured I would see if anyone else has encountered it and found a fix. Thanks.

storing numpy object array of equal-size ndarrays to a .mat file using scipy.io.savemat

对象存储

Python

 我正在尝试使用python创建.mat数据文件。matlab代码期望数据具有一定的格式，其中非均匀大小的二维ndarray作为对象存储在列向量中。因此，在我的例子中，将有k个形状为( m_i，n)的numpy数组-每个数组具有不同的m_i-存储在形状为(k，1)的numpy数组中。然后，我将这个对象数组添加到一个字典...

问使用scipy.io.savemat将等大小ndarray的numpy对象数组存储到.mat文件中
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