scipy and preserving mat file (.mat matlab data file) structure

Question

After referring to scipy and numpy docs for a day and a half, I tried doing this -

dt = {\'names\':[u\'OSversInt\',u\'Desc\',u\'OSversStr\',\\
... u\'OSname\',u         


        

Answer 1

In Octave I created a cell with a structure object:

octave:14> y={x}
y = 
{
  [1,1] =

    scalar structure containing the fields:

      OSversInt =  15
      Desc = 
      OSverStr = 5.0.1
      OSname = Android

}
octave:15> save stack32723802.mat -V7 y

In numpy I load it as:

In [376]: L=loadmat('stack32723802.mat')
In [377]: L['y']
Out[377]: 
array([[ array([[([[15.0]], [], ['5.0.1'], ['Android'])]], 
      dtype=[('OSversInt', 'O'), ('Desc', 'O'), ('OSverStr', 'O'), ('OSname', 'O')])]], 
      dtype=object)

That's a 2d object array (1,1), with one item, which is also 2d (1,1), with the compound dtype.

In [390]: y=L['y']
In [391]: y[0,0]
Out[391]: 
array([[([[15.0]], [], ['5.0.1'], ['Android'])]], 
      dtype=[('OSversInt', 'O'), ('Desc', 'O'), ('OSverStr', 'O'), ('OSname', 'O')])

In [392]: y[0,0][0,0]
Out[392]: ([[15.0]], [], ['5.0.1'], ['Android'])

In [394]: y[0,0]['OSversInt']
Out[394]: array([[array([[ 15.]])]], dtype=object)

In [395]: y[0,0]['OSname']
Out[395]: 
array([[array(['Android'], 
      dtype='

The 4d 'actual outcome' is the result of np.array producing the highest dimensional array it can.

First create the inner structured array:

In [405]: dt=y.item().dtype
In [406]: item=([[15.0]], [], ['5.0.1'], ['Android'])
In [407]: array1 = np.array([[item]], dtype=dt)
In [408]: array1
Out[408]: 
array([[([[15.0]], [], ['5.0.1'], ['Android'])]], 
      dtype=[('OSversInt', 'O'), ('Desc', 'O'), ('OSverStr', 'O'), ('OSname', 'O')])

If I just wrap it in a 2d object array, I get a 4d array:

In [409]: np.array([[array1]], dtype=object)
Out[409]: array([[[[([[15.0]], [], ['5.0.1'], ['Android'])]]]], dtype=object)

But if instead I create an empty 2d object array, and insert this inner array, I get something that matches the loadmat result:

In [410]: z=np.empty((1,1),dtype=object)
In [411]: z[0,0]=np.array([[item]], dtype=dt)
In [412]: z
Out[412]: 
array([[ array([[([[15.0]], [], ['5.0.1'], ['Android'])]], 
      dtype=[('OSversInt', 'O'), ('Desc', 'O'), ('OSverStr', 'O'), ('OSname', 'O')])]], dtype=object)