SciPy

File IO (scipy.io)

See also

numpy-reference.routines.io (in numpy)

MATLAB files

loadmat(file_name[, mdict, appendmat]) Load MATLAB file
savemat(file_name, mdict[, appendmat, ...]) Save a dictionary of names and arrays into a MATLAB-style .mat file.
whosmat(file_name[, appendmat]) List variables inside a MATLAB file

The basic functions

We’ll start by importing scipy.io and calling it sio for convenience:

>>> 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
a =

   1   2   3   4   5   6   7   8   9  10  11  12

octave:2> a = reshape(a, [1 3 4])
a =

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]
>>> val
([[1.0]], [[2.0]])
>>> val['field1']
array([[ 1.]])
>>> val['field2']
array([[ 2.]])
>>> val.dtype
dtype([('field1', 'O'), ('field2', 'O')])

In versions of Scipy 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]

and:

octave:13> size(my_struct)
ans =

   1   1

So, in MATLAB, the struct array must be at least 2D, 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
1.0

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)
>>> arr
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] =

     2   3

}

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]
>>> val
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
}

IDL files

readsav(file_name[, idict, python_dict, ...]) Read an IDL .sav file

Matrix Market files

mminfo(source) Queries the contents of the Matrix Market file ‘filename’ to
mmread(source) Reads the contents of a Matrix Market file ‘filename’ into a matrix.
mmwrite(target, a[, comment, field, precision]) Writes the sparse or dense array a to a Matrix Market formatted file.

Wav sound files (scipy.io.wavfile)

read(filename[, mmap]) Return the sample rate (in samples/sec) and data from a WAV file
write(filename, rate, data) Write a numpy array as a WAV file

Arff files (scipy.io.arff)

Module to read ARFF files, which are the standard data format for WEKA.

ARFF is a text file format which support numerical, string and data values. The format can also represent missing data and sparse data.

See the WEKA website for more details about arff format and available datasets.

Examples

>>> from scipy.io import arff
>>> from cStringIO import StringIO
>>> content = """
... @relation foo
... @attribute width  numeric
... @attribute height numeric
... @attribute color  {red,green,blue,yellow,black}
... @data
... 5.0,3.25,blue
... 4.5,3.75,green
... 3.0,4.00,red
... """
>>> f = StringIO(content)
>>> data, meta = arff.loadarff(f)
>>> data
array([(5.0, 3.25, 'blue'), (4.5, 3.75, 'green'), (3.0, 4.0, 'red')],
      dtype=[('width', '<f8'), ('height', '<f8'), ('color', '|S6')])
>>> meta
Dataset: foo
        width's type is numeric
        height's type is numeric
        color's type is nominal, range is ('red', 'green', 'blue', 'yellow', 'black')
loadarff(f) Read an arff file.

Netcdf (scipy.io.netcdf)

netcdf_file(filename[, mode, mmap, version]) A file object for NetCDF data.

Allows reading of NetCDF files (version of pupynere package)