### Table Of Contents

- File IO (`scipy.io`)
  - MATLAB files
    - The basic functions
    - How do I start?
    - MATLAB structs
    - MATLAB cell arrays
  - IDL files
  - Matrix Market files
  - Wav sound files (`scipy.io.wavfile`)
  - Arff files (`scipy.io.arff`)
    - Notes
  - Netcdf (`scipy.io.netcdf`)

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# 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) | 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)