# 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.

Getting started:

```>>> import scipy.io as sio
```

If you are using IPython, try tab completing on sio. You’ll find:

```sio.loadmat
sio.savemat
```

These are the high-level functions you will most likely use. You’ll also find:

```sio.matlab
```

This is the package from which loadmat and savemat are imported. Within sio.matlab, you will find the mio module - containing 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 / 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')
>>> print 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 3.2.3, 2010-05-30 02:13:40 UTC',
'__globals__': []}
>>> oct_a = mat_contents['a']
>>> print oct_a
[[[  1.   4.   7.  10.]
[  2.   5.   8.  11.]
[  3.   6.   9.  12.]]]
>>> print oct_a.shape
(1, 3, 4)
```

Now let’s try the other way round:

```>>> import numpy as np
>>> vect = np.arange(10)
>>> print vect.shape
(10,)
>>> sio.savemat('np_vector.mat', {'vect':vect})
/Users/mb312/usr/local/lib/python2.6/site-packages/scipy/io/matlab/mio.py:196: FutureWarning: Using oned_as default value ('column') This will change to 'row' in future versions
```

oned_as=oned_as)

Then back to Octave:

```octave:5> load np_vector.mat
octave:6> vect
vect =

0
1
2
3
4
5
6
7
8
9

octave:7> size(vect)
ans =

10    1
```

Note the deprecation warning. The oned_as keyword determines the way in which one-dimensional vectors are stored. In the future, this will default to row instead of column:

```>>> sio.savemat('np_vector.mat', {'vect':vect}, oned_as='row')
```

We can load this in Octave or MATLAB:

```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
```

### 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')
>>> print mat_contents
{'my_struct': array([[([[1.0]], [[2.0]])]],
dtype=[('field1', '|O8'), ('field2', '|O8')]), '__version__': '1.0', '__header__': 'MATLAB 5.0 MAT-file, written by Octave 3.2.3, 2010-05-30 02:00:26 UTC', '__globals__': []}
>>> oct_struct = mat_contents['my_struct']
>>> print oct_struct.shape
(1, 1)
>>> val = oct_struct[0,0]
>>> print val
([[1.0]], [[2.0]])
>>> print val['field1']
[[ 1.]]
>>> print val['field2']
[[ 2.]]
>>> print val.dtype
[('field1', '|O8'), ('field2', '|O8')]
```

In this version of Scipy (0.8.0), 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 arrarys - 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 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'
>>> print type(oct_struct)
<class 'scipy.io.matlab.mio5_params.mat_struct'>
>>> print 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 =
{
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)
>>> print arr
[(0.0, '') (0.0, '')]
>>> arr['f1'] = 0.5
>>> arr['f2'] = 'python'
>>> arr['f1'] = 99
>>> arr['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]
>>> print val
[[ 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 = 1
>>> obj_arr = 'a string'
>>> print obj_arr
[1 a string]
>>> 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 matrix A to a Matrix Market formatted file.

## Other¶

 save_as_module([file_name, data]) Save the dictionary “data” into a module and shelf named save.

## Wav sound files (scipy.io.wavfile)¶

 read(file) 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)

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