File IO (:mod:`scipy.io`) ========================= .. sectionauthor:: Matthew Brett .. currentmodule:: scipy.io .. seealso:: `NumPy IO routines `__ MATLAB files ------------ .. autosummary:: loadmat savemat whosmat The basic functions ``````````````````` We'll start by importing :mod:`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): .. 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') >>> 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: .. 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 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). .. 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') >>> 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 the SciPy versions 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: .. sourcecode:: octave octave:13> size(my_struct) ans = 1 1 So, in MATLAB, the struct array must be at least 2-D, 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 "", line 1, in AttributeError: 'mat_struct' object has no attribute 'shape' >>> type(oct_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: .. sourcecode:: octave 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', '>> 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] >>> 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}) .. sourcecode:: octave octave:16> load np_cells.mat octave:17> obj_arr obj_arr = { [1,1] = 1 [2,1] = a string } IDL files --------- .. autosummary:: readsav Matrix Market files ------------------- .. autosummary:: mminfo mmread mmwrite Wav sound files (:mod:`scipy.io.wavfile`) ----------------------------------------- .. currentmodule:: scipy.io.wavfile .. autosummary:: read write Arff files (:mod:`scipy.io.arff`) --------------------------------- .. currentmodule:: scipy.io.arff .. autosummary:: loadarff Netcdf ------ .. currentmodule:: scipy.io .. autosummary:: netcdf_file Allows reading of NetCDF files (version of pupynere_ package) .. _pupynere: https://pypi.org/project/pupynere/ .. _octave: https://www.gnu.org/software/octave .. _matlab: https://www.mathworks.com/