loadmat(file_name, mdict=None, appendmat=True, **kwargs)¶
Load MATLAB file.
- file_name : str
Name of the mat file (do not need .mat extension if appendmat==True). Can also pass open file-like object.
- mdict : dict, optional
Dictionary in which to insert matfile variables.
- appendmat : bool, optional
True to append the .mat extension to the end of the given filename, if not already present.
- byte_order : str or None, optional
None by default, implying byte order guessed from mat file. Otherwise can be one of (‘native’, ‘=’, ‘little’, ‘<’, ‘BIG’, ‘>’).
- mat_dtype : bool, optional
If True, return arrays in same dtype as would be loaded into MATLAB (instead of the dtype with which they are saved).
- squeeze_me : bool, optional
Whether to squeeze unit matrix dimensions or not.
- chars_as_strings : bool, optional
Whether to convert char arrays to string arrays.
- matlab_compatible : bool, optional
Returns matrices as would be loaded by MATLAB (implies squeeze_me=False, chars_as_strings=False, mat_dtype=True, struct_as_record=True).
- struct_as_record : bool, optional
Whether to load MATLAB structs as numpy record arrays, or as old-style numpy arrays with dtype=object. Setting this flag to False replicates the behavior of scipy version 0.7.x (returning numpy object arrays). The default setting is True, because it allows easier round-trip load and save of MATLAB files.
- verify_compressed_data_integrity : bool, optional
Whether the length of compressed sequences in the MATLAB file should be checked, to ensure that they are not longer than we expect. It is advisable to enable this (the default) because overlong compressed sequences in MATLAB files generally indicate that the files have experienced some sort of corruption.
- variable_names : None or sequence
If None (the default) - read all variables in file. Otherwise variable_names should be a sequence of strings, giving names of the MATLAB variables to read from the file. The reader will skip any variable with a name not in this sequence, possibly saving some read processing.
- mat_dict : dict
dictionary with variable names as keys, and loaded matrices as values.
v4 (Level 1.0), v6 and v7 to 7.2 matfiles are supported.
You will need an HDF5 python library to read MATLAB 7.3 format mat files. Because scipy does not supply one, we do not implement the HDF5 / 7.3 interface here.
>>> from os.path import dirname, join as pjoin >>> import scipy.io as sio
Get the filename for an example .mat file from the tests/data directory.
>>> data_dir = pjoin(dirname(sio.__file__), 'matlab', 'tests', 'data') >>> mat_fname = pjoin(data_dir, 'testdouble_7.4_GLNX86.mat')
Load the .mat file contents.
>>> mat_contents = sio.loadmat(mat_fname)
The result is a dictionary, one key/value pair for each variable:
>>> sorted(mat_contents.keys()) ['__globals__', '__header__', '__version__', 'testdouble'] >>> mat_contents['testdouble'] array([[0. , 0.78539816, 1.57079633, 2.35619449, 3.14159265, 3.92699082, 4.71238898, 5.49778714, 6.28318531]])
By default SciPy reads MATLAB structs as structured NumPy arrays where the dtype fields are of type object and the names correspond to the MATLAB struct field names. This can be disabled by setting the optional argument struct_as_record=False.
Get the filename for an example .mat file that contains a MATLAB struct called teststruct and load the contents.
>>> matstruct_fname = pjoin(data_dir, 'teststruct_7.4_GLNX86.mat') >>> matstruct_contents = sio.loadmat(matstruct_fname) >>> teststruct = matstruct_contents['teststruct'] >>> teststruct.dtype dtype([('stringfield', 'O'), ('doublefield', 'O'), ('complexfield', 'O')])
The size of the structured array is the size of the MATLAB struct, not the number of elements in any particular field. The shape defaults to 2-D unless the optional argument squeeze_me=True, in which case all length 1 dimensions are removed.
>>> teststruct.size 1 >>> teststruct.shape (1, 1)
Get the ‘stringfield’ of the first element in the MATLAB struct.
>>> teststruct[0, 0]['stringfield'] array(['Rats live on no evil star.'], dtype='<U26')
Get the first element of the ‘doublefield’.
>>> teststruct['doublefield'][0, 0] array([[ 1.41421356, 2.71828183, 3.14159265]])
Load the MATLAB struct, squeezing out length 1 dimensions, and get the item from the ‘complexfield’.
>>> matstruct_squeezed = sio.loadmat(matstruct_fname, squeeze_me=True) >>> matstruct_squeezed['teststruct'].shape () >>> matstruct_squeezed['teststruct']['complexfield'].shape () >>> matstruct_squeezed['teststruct']['complexfield'].item() array([ 1.41421356+1.41421356j, 2.71828183+2.71828183j, 3.14159265+3.14159265j])