scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
loaded as:
octave:21> load saved_struct
octave:22> a_dict
a_dict =
{
field2 = a string
field1 = 0.50000
}
Further up, you’ll remember this deprecation warning:
>>> mat_contents = sio.loadmat(’octave_a.mat’)
/home/mb312/usr/local/lib/python2.5/site-packages/scipy/io/Matlab/mio.py:84: FutureWarning: Using str
return MatFile5Reader(byte_stream, **kwargs)
The way that the reader returns struct arrays will soon change. Like this:
>>> mat_contents = sio.loadmat(’octave_struct.mat’, struct_as_record=True)
>>> oct_struct = mat_contents[’my_struct’]
>>> val = oct_struct[0,0]
>>> print val
([[1.0]], [[2.0]])
>>> print val.dtype
[(’field1’, ’|O8’), (’field2’, ’|O8’)]
You can also save structs back again to Matlab (or Octave in our case) like this:
>>> dt = [(’f1’, ’f8’), (’f2’, ’S10’)]
>>> arr = np.zeros((2,), dtype=dt)
>>> print arr
[(0.0, ’’) (0.0, ’’)]
>>> arr[0][’f1’] = 0.5
>>> arr[0][’f2’] = ’python’
>>> arr[1][’f1’] = 99
>>> arr[1][’f2’] = ’not perl’
>>> sio.savemat(’np_struct_arr.mat’, {’arr’: arr})
Matlab cell arrays
Cell arrays in Matlab are rather like python lists, in the sense that the elements in the arrays can contain any type of
Matlab object. In fact they are most similar to numpy object arrays, and that is how we load them into numpy.
octave:14> my_cells = {1, [2, 3]}
my_cells =
{
}
[1,1] = 1
[1,2] =
2 3
octave:15> save -6 octave_cells.mat my_cells
84 Chapter 1. SciPy Tutorial