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
}
Matrix Market files¶
mminfo(source) |
Return size and storage parameters from Matrix Market file-like ‘source’. |
mmread(source) |
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. |
Wav sound files (scipy.io.wavfile)¶
read(filename[, mmap]) |
Open 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.
Notes¶
The ARFF support in scipy.io provides file reading functionality only.
For more extensive ARFF functionality, see liac-arff.
See the WEKA website for more details about the ARFF format and available datasets.
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)