>>> import scipy.io as sio
If you are using IPython, try tab-completing on sio. Among the many
options, you will find:
sio.loadmat
sio.savemat
sio.whosmat
These are the high-level functions you will most likely use when working
with MATLAB files. You’ll also find:
sio.matlab
This is the package from which loadmat, savemat, and whosmat
are imported. Within sio.matlab, you will find the mio module
This module contains the machinery that loadmat and savemat use.
From time to time you may find yourself re-using this machinery.
How do I start?#
You may have a .mat file that you want to read into SciPy. Or, you
want to pass some variables from SciPy / NumPy into MATLAB.
To save us using a MATLAB license, let’s start in Octave. Octave has
MATLAB-compatible save and load functions. Start Octave (octave at
the command line for me):
octave:1> a = 1:12
   1   2   3   4   5   6   7   8   9  10  11  12
octave:2> a = reshape(a, [1 3 4])
ans(:,:,1) =
   1   2   3
ans(:,:,2) =
   4   5   6
ans(:,:,3) =
   7   8   9
ans(:,:,4) =
   10   11   12
octave:3> save -6 octave_a.mat a % MATLAB 6 compatible
octave:4> ls octave_a.mat
octave_a.mat
Now, to Python:
>>> mat_contents = sio.loadmat('octave_a.mat')
>>> mat_contents
{'a': array([[[  1.,   4.,   7.,  10.],
        [  2.,   5.,   8.,  11.],
        [  3.,   6.,   9.,  12.]]]),
 '__version__': '1.0',
 '__header__': 'MATLAB 5.0 MAT-file, written by
 Octave 3.6.3, 2013-02-17 21:02:11 UTC',
 '__globals__': []}
>>> oct_a = mat_contents['a']
>>> oct_a
array([[[  1.,   4.,   7.,  10.],
        [  2.,   5.,   8.,  11.],
        [  3.,   6.,   9.,  12.]]])
>>> oct_a.shape
(1, 3, 4)
Now let’s try the other way round:
>>> import numpy as np
>>> vect = np.arange(10)
>>> vect.shape
(10,)
>>> sio.savemat('np_vector.mat', {'vect':vect})
Then back to Octave:
octave:8> load np_vector.mat
octave:9> vect
vect =
  0  1  2  3  4  5  6  7  8  9
octave:10> size(vect)
ans =
    1   10
If you want to inspect the contents of a MATLAB file without reading the
data into memory, use the whosmat command:
>>> sio.whosmat('octave_a.mat')
[('a', (1, 3, 4), 'double')]
whosmat returns a list of tuples, one for each array (or other object)
in the file. Each tuple contains the name, shape and data type of the
array.
MATLAB structs#
MATLAB structs are a little bit like Python dicts, except the field
names must be strings. Any MATLAB object can be a value of a field. As
for all objects in MATLAB, structs are, in fact, arrays of structs, where
a single struct is an array of shape (1, 1).
octave:11> my_struct = struct('field1', 1, 'field2', 2)
my_struct =
  field1 =  1
  field2 =  2
octave:12> save -6 octave_struct.mat my_struct
We can load this in Python:
>>> mat_contents = sio.loadmat('octave_struct.mat')
>>> mat_contents
{'my_struct': array([[([[1.0]], [[2.0]])]],
      dtype=[('field1', 'O'), ('field2', 'O')]), '__version__': '1.0', '__header__': 'MATLAB 5.0 MAT-file, written by Octave 3.6.3, 2013-02-17 21:23:14 UTC', '__globals__': []}
>>> oct_struct = mat_contents['my_struct']
>>> oct_struct.shape
(1, 1)
>>> val = oct_struct[0,0]
([[1.0]], [[2.0]])
>>> val['field1']
array([[ 1.]])
>>> val['field2']
array([[ 2.]])
>>> val.dtype
dtype([('field1', 'O'), ('field2', 'O')])
In the SciPy versions from 0.12.0, MATLAB structs come back as NumPy
structured arrays, with fields named for the struct fields. You can see
the field names in the dtype output above. Note also:
>>> val = oct_struct[0,0]
octave:13> size(my_struct)
ans =
So, in MATLAB, the struct array must be at least 2-D, and we replicate
that when we read into SciPy. If you want all length 1 dimensions
squeezed out, try this:
>>> mat_contents = sio.loadmat('octave_struct.mat', squeeze_me=True)
>>> oct_struct = mat_contents['my_struct']
>>> oct_struct.shape
Sometimes, it’s more convenient to load the MATLAB structs as Python
objects rather than NumPy structured arrays - it can make the access
syntax in Python a bit more similar to that in MATLAB.  In order to do
this, use the struct_as_record=False parameter setting to loadmat.
>>> mat_contents = sio.loadmat('octave_struct.mat', struct_as_record=False)
>>> oct_struct = mat_contents['my_struct']
>>> oct_struct[0,0].field1
array([[ 1.]])
struct_as_record=False works nicely with squeeze_me:
>>> mat_contents = sio.loadmat('octave_struct.mat', struct_as_record=False, squeeze_me=True)
>>> oct_struct = mat_contents['my_struct']
>>> oct_struct.shape # but no - it's a scalar
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'mat_struct' object has no attribute 'shape'
>>> type(oct_struct)
<class 'scipy.io.matlab.mio5_params.mat_struct'>
>>> oct_struct.field1
Saving struct arrays can be done in various ways. One simple method is
to use dicts:
>>> a_dict = {'field1': 0.5, 'field2': 'a string'}
>>> sio.savemat('saved_struct.mat', {'a_dict': a_dict})
loaded as:
octave:21> load saved_struct
octave:22> a_dict
a_dict =
  scalar structure containing the fields:
    field2 = a string
    field1 =  0.50000
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)
array([(0.0, ''), (0.0, '')],
      dtype=[('f1', '<f8'), ('f2', 'S10')])
>>> 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] =
octave:15> save -6 octave_cells.mat my_cells
Back to Python:
>>> mat_contents = sio.loadmat('octave_cells.mat')
>>> oct_cells = mat_contents['my_cells']
>>> print(oct_cells.dtype)
object
>>> val = oct_cells[0,0]
array([[ 1.]])
>>> print(val.dtype)
float64
Saving to a MATLAB cell array just involves making a NumPy object array:
>>> obj_arr = np.zeros((2,), dtype=np.object)
>>> obj_arr[0] = 1
>>> obj_arr[1] = 'a string'
>>> obj_arr
array([1, 'a string'], dtype=object)
>>> sio.savemat('np_cells.mat', {'obj_arr':obj_arr})
octave:16> load np_cells.mat
octave:17> obj_arr
obj_arr =
  [1,1] = 1
  [2,1] = a string
Reads the contents of a Matrix Market file-like 'source' into a matrix.
mmwrite(target, a[, comment, field, ...])
Writes the sparse or dense array a to Matrix Market file-like target.