File IO (:mod:`scipy.io`) ========================= .. sectionauthor:: Matthew Brett .. currentmodule:: scipy.io .. seealso:: :ref:`numpy-reference.routines.io` (in numpy) MATLAB files ------------ .. autosummary:: :toctree: generated/ loadmat savemat 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): .. sourcecode:: octave 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: .. sourcecode:: 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: .. sourcecode:: 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 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). .. sourcecode:: octave 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: .. sourcecode:: octave 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 "", line 1, in AttributeError: 'mat_struct' object has no attribute 'shape' >>> print type(oct_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: .. sourcecode:: octave 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[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. .. sourcecode:: octave 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[0] = 1 >>> obj_arr[1] = 'a string' >>> print obj_arr [1 a string] >>> sio.savemat('np_cells.mat', {'obj_arr':obj_arr}) .. sourcecode:: octave octave:16> load np_cells.mat octave:17> obj_arr obj_arr = { [1,1] = 1 [2,1] = a string } IDL files --------- .. autosummary:: :toctree: generated/ readsav Matrix Market files ------------------- .. autosummary:: :toctree: generated/ mminfo mmread mmwrite Other ----- .. autosummary:: :toctree: generated/ save_as_module Wav sound files (:mod:`scipy.io.wavfile`) ----------------------------------------- .. module:: scipy.io.wavfile .. autosummary:: :toctree: generated/ read write Arff files (:mod:`scipy.io.arff`) --------------------------------- .. automodule:: scipy.io.arff .. autosummary:: :toctree: generated/ loadarff Netcdf (:mod:`scipy.io.netcdf`) ------------------------------- .. module:: scipy.io.netcdf .. autosummary:: :toctree: generated/ netcdf_file Allows reading of NetCDF files (version of pupynere_ package) .. _pupynere: http://pypi.python.org/pypi/pupynere/ .. _octave: http://www.gnu.org/software/octave .. _matlab: http://www.mathworks.com/